glue																															
	en			English	(default)																										
	fr			Français	(note @acknowtranslate )																										
	nl			Nederlands	(note @acknowtranslate )																										
	de			Deutsch	(note @acknowtranslate )																										
	ru			Русский Язык	(note @acknowtranslate )																										
	es			Español	(note @acknowtranslate )																										
	pt			Português	(note @acknowtranslate )																										
	dk			Dansk	(note @acknowtranslate )																										
	no			Norsk	(note @acknowtranslate )																										
	zh			中国话																											
	sc			script/label codes	(see @names )																										
																															
	and		&	and			et			und			y			y			en			и								与	与
	per		/	per			par			pro			por			por			per			на			per			pr		每	每
	or		|	or			ou															или									
	ob		(																												
	cb		)																												
	colon		:																												
	ena			enabled			Activé			aktiv			activo			activo			Geactiveerd			включено			slået til			Tilkoblet			使得
	dis			disabled			Désactivé			inaktiv			inactivo			desactivo			Niet geactiveerd			выключено			slået fra			Frakoblet			不使得
	disposed		(d!)	(ignore- for debug)																											
	pwdl			Please Wait, Data Loading			Attendez SVP, chargement des données			Daten werden geladen			Por Favor Espere, Cargando Datos			Por favor aguarde, carregando dados			Wachten aub, laden van gegevens			Пожалуйста, подождите. Данные загружаются			Vent venligst, indlæser data			Venligst vent, laster data			
																															
	see		see			voir																									
	of		of			de																									
	divby		divided by			divisé par																									
	default		default			default																									
	adjin		adjust in			ajoutez dans																									
	paramof		parameter of			parametre de																									
	relations		Relationships			Relations																									
	interacs		Interactions		²°adju Interactions depend on model setup (see @flowchart, @iob), and on what is currently visible (see @layoutpanel, @complexitymenu).  ²	Interactions	²°adju Interactions dependent sur le setup du modèle (voir @flowchart, @iob), et sur ce qui est visible actuellement (voir @layoutpanel, @complexitymenu).  ²																								
	javacode		Code			Code																									
	moreinfo		Info on each option			Info sur chaque option																									
	affects		Also Affects			Influence aussi																									
	affectedby		Affected by			Influencé par																									
	modules		Modules			Modules																									
	panels		Panels			Panneaux																									
	panel		Panel			Panneau																									
	curves		Curves			Courbes																									
	curvelabel		Curve			Courbe																									
	scales		Scales/Units		more about @scale	Echelles/Units		plus au sujet de @scale																							
	controls		Arrow Controls		how to use @control	Flêche-Controles		comment utiliser @control																							
	menopts		Menus/Options			Menus/Options																									
	params		Adjustable Parameters			Paramètres Ajoutables																									
	iobinfo				£^interacs £^params																										
	panelinfo				£^interacs £^menopts																										
	graphinfo				£^interacs £^curves £^scales £^controls £^menopts																										
																															
units																															
	femto		f	femto																											
	pico		p	pico																											
	nano		n	nano																										纤	纤
	micro		u	micro																										微	微
	milli		m	milli																										毫	毫
	kilo		k	kilo																										千	千
	mega		M	mega			millions de			Millionen 			millones de 			miliões de			Miljoen 											兆	兆
	giga		G	giga			milliards de			Milliarden 			billones de			biliões de			Miljard			миллиарды			milliarder 			milliarder		京	京
	tera		T	tera																	Г									垓	垓
	peta		P	peta																										千垓	千垓
																															
	s		s	second			seconde			Sekunde			segundo			segundo			seconde		с	секунду			sekund			sekund			
	yr		yr	year		an	année		J	Jahr		a	año		ano	ano		jr	jaar		г	год		år	år		år	år		年	年
	inyear			in	(used for stabilisation in year X at level  Y)	en 	par l'année		in	im Jahr		en	en año		no	no ano		in	in jaar					i år	i år		i år	i år		在	在
	peryear				(not currently used)		par tonne en moins		/J	pro Jahr		/a	por año		/ano	por ano					/г	в год		/år	per år		/år	per år			
	atlevel			at	(used for stabilisation in year X at level  Y)																										
																															
	ton		t	ton			tonnes 			Tonnen			toneladas 			toneladas 			ton			тонны			tons			tonn		吨	公吨
	gram		g	gram																	гр									克	克
	joule		J	joule																											焦耳
	mol		mol	mols						Mol			mol			moles					моль/м2/ppm/г	моль на квадратный метр на часть на миллион в год			mol			mol			
	w		W	Watts						Watt									Watt		В	Ватт			Watt			Watt		瓦	瓦
																															
	metres		m	metres			mètres			Meter			metres			metros			meter		м	метры			meter			meter			米
	m2		m2	square metre			mètre carré						metro cuadrado			metro quadrado			vierkante meter		м2	квадратный метр			kvadratmeter			kvadratmeter			方米
	cm2		cm2	square centimetre			centimètre carré						cm-cuadrados			cm-quadrado			vierkante centimeter		см2	квадратные сантиметры			kvadratcentimeter 			kvadratcentimeter 			
	degc		C	degrees Celcius			degrés C			Grad Celsius			Grados Celcius			graus Celcius 			Centigraden			градусы Цельсия 			grader Celcius 			grader Celcius		度	摄度
	degcbase		C	degrees Celcius above baseline year			degrés C par rapport à l'année de référence			Grad Celsius im Vergleich zur Basistemperatur			grados Celcius comparados con el inicio						Centigraden ten opzichte van het referentiejaar											度	摄度 (比基础年)
	baseline				(not currently used)				>Basis	im Vergleich zur Basistemperatur		>inicio	comparados con el inicio			comparado com o inicio															
																															
	ppm		ppm	concentration, parts per million		ppm	ppm (concentration atmosphérique en CO2, en parts par million)		ppm	ppm (atmosphärische CO2-Konzentration, parts per million)		ppm	ppm (concentración atmosferica de CO2, partes por millon)		ppm	ppm (concentrações de CO2 atmosférico, partes por milhão)		ppm	ppm (CO2 concentratie in de atmosfeer, in deeltjes per miljoen)			(объемная) концентрация CO2 в атмосфере, части на миллион		ppm	ppmv (CO2-koncentrationen i atmosfæren, milliontedele rumfang)		ppm	ppmv (CO2-konsentrasjonen i atmosfæren, Andeler pr million)			ppm (大欺农宿, 百万分之)
	ppb		ppb	concentration, parts per billion		ppb	ppb (concentration atmosphérique, parts par milliard)											ppb	ppb (concentratie in de atmosfeer, deeltjes per miljard)												ppb (大欺农宿, 十亿分之)
	ppbppm		ppb or ppm	ppb or (for CO2) ppm		ppb ou ppm	ppb (parts par milliard, ou par million pour CO2)											ppb ou ppm	ppb (deeltjes per miljard, of per miljoen voor CO2)												ppb (或 ppm 对二氧化碳)
	ppt		ppt	concentration, parts per trillion		ppt	ppt (concentration atmosphérique, parts par billion)											ppt	ppt (concentratie in de atmosfeer, deeltjes per biljoen)												ppt (大欺农宿, 万亿分之)
																															
	tot		Tot	Total															Totaal		сум	сумма								总额	总额
	percent		%	percent		%	en pourcentage		%	Prozent		%	porciento		%	por cento		%	percent		%	проценты		%	procent		%	prosent		分数	百分之
	dy				(replaced by %/yr)	dy/y.dx	Affiche la variation relative par an		dy/y.dx	%-Änderung pro Jahr zeigen		dy/y.dx	Mostrar pocentaje de cambio por año					dy/y.dx	Toon(t) de relatieve variatie per jaar												
	frac		%	Fraction (%)	Combining the "stack" and "frac" options shows the relative proportion of the whole (scale is in %)	%	en pourcentage		%	Prozent		%	porciento		%	por cento		%	percent		%	проценты		%	procent		%	prosent		分数	分数(%)
	stack		stack	Stack curves	Stack the curves as coloured bands. This only makes sense for summable quantities, e.g. it's OK for regional emissions, but not for emissions per capita. Also, beware of using stack for quantities which may go negative, such as radiative forcing.																									堆数量	堆数量
																															
	varq		What	Choose data to plot	Choose emissions, @abate, or socioeconomic data from the @people	Quoi	Choisir la région à afficher		was	Regionendaten für Diagramm wählen		Que	escoger region a graficar					Wat	het te tonen gebied kiezen											哪资料	挑选资料
	perq		Per	Choose data to divide by	Choose emissions, @abate, or socioeconomic data from the @people<br><i>(°adju If you don't want any ratio, choose the blank item)</i>.	Par	Choisir la région à diviser par		gegen was	Regionendaten für Gegenüberstellung wählen		Por	Escoger region a dividir por					Door	het gebied kiezen ????											分哪资料	挑选分裂的资料
																															
	popn		Population	Population	from @people	Population	Population		Bevölkerung	Bevölkerung		Población	Población					Bevolking	Bevolking											人	人口
	person		pers	people	(used for millions of people)	pers	personnes		Mens	Menschen		pers	personas		pess	pessoas		mens	mensen		лю	людей			mennesker			mennesker		人	人口
	capita		cap	capita	(used for per capita)	hab	habitant		Pers	Kopf		pers	capita		pess	pessoa		pers	persoon		чел	человека			mennesker			mennesker		人	人口
	per&popn		per Capita		from @people																										
	co2emit		CO2 Emissions	CO2 Emissions	from @regshares	Emissions de CO2	Emissions de CO2		CO2-Emissionen	CO2-Emissionen		Emisiones de CO2	Emisiones de CO2					CO2 emissie	CO2 emissies											发出	二氧化碳发出
	abate		Abatement	CO2 Abatement	The difference between no-policy emissions (see @sresasbaseline) and mitigated emissions (see @mitigation, @regshares)	Réduction	Réduction du CO2		Reduktion	CO2-Reduktion		Reducción	Reducción de CO2					Reductie	CO2 reductie												
	energy		Energy	Energy	from @people	Energie	Energie		Energie	Energie		Energía	Energía					Energie	Energie												
	gdp		GDP	GDP dollars	from @people	PIB	PIB en dollars		BIP	BIP Dollars		GDP	GDP dolares					BNP	BNP in dollar												
	gnp		GNP	GNP dollars	from @people																										
	dollar		$	dollar (US)									dolares (EUA)																		美元
	whatper				°adju ²You can choose which quantity to plot from @varq, and  can also divide by a socioeconomic quantity from @perq (menus below). Current choice of curves is:²																										
	regscales				² °adju The timescale is only from 1900-2100, since human society changes too fast to predict further into the future. However the global science plots go from 1750-2300, you can use @linkx to make the scales the same (see also @scale).²																										
	siunits		SI units		Standard SI scalars are as follows:<br>  £~peta £`peta =1015<br>  £~tera £`tera =1012<br>  £~giga £`giga =109<br>  £~mega £`mega =106<br>  £~kilo £`kilo =103<br>  £~milli £`milli =10-3<br>  £~micro £`micro =10-6<br>  £~nano £`nano =10-9   <li>Note also: 1 ton = 106 grams.<p>See also @howmuchgtc																										
	howmuchgtc		How much is a GtC?		GtC is short for Gigatons of Carbon. Giga means a billion, so that's a billion tons. A ton is 1000kg, the weight of a cubic metre of water (or of about 16 typical adult people). Note that 1 gigaton is identical to 1 petagram (Pg), i.e. 1015grams.<br>  A ton of carbon is (roughly) the same as a ton of coal -which you can probably visualise. Oil and natural gas (methane) also contain hydrogen, but most of their total weight is still carbon.<br>  Burning 1 ton of carbon creates 3.67 tons of CO2 (3.67 is derived from 44/12 atomic mass units). As the global carbon cycle involves conversion between various forms of carbon (CO2, fossil fuel, plant biomass, bicarbonate ions in seawater, etc.), climate scientists find it more convenient to refer to tons of carbon than tons of CO2, but be careful which you are using when comparing figures.<br>  Incidentally, 3.67 tons of CO2 is the same amount as is currently found in the whole column of air above 646 square metres of the earth's surface. Or it's about the same amount as the carbon stored in a typical tree about 20m tall. The energy you get from burning one ton of carbon (as oil) could light 30 60Watt lightbulbs continuously for one year.<br>  Note, sometimes people refer instead to Gt CO2-equivalent (CO2-e) which includes other greenhouse gases scaled using "global warming potentials". Beware that the global warming potential for each gas depends on your time-horizon, since the gases have different lifetimes in the atmosphere!   <li>See also @co2eq																										
	unitbaseline		Units and baselines		The zero-point for reporting temperature rise is arbitrary ! Climate modellers usually start by assuming a steady-state in the preindustrial era (typically 1750), which thus defines the zero point. Those investigating historical data tend to use the 1960-1990 average as a baseline, to smooth out temporary fluctuations, while acknowledging that the best data comes from the recent past. Whereas people comparing future scenarios often want to to know the change from the present (typically 2000). So, be very careful to compare like with like! The same applies to sea-level rise.   <li>See also @baseyear<p>Most scientists report CO2 emissions in Gt Carbon, to allow for easy transformation to/from fossil fuel, bicarbonate ions in seawater, wood, soil, etc, whilst others report Gt CO2, including the weight of the oxygen atoms (Note 1 Gt= 1 billion tons = 1 Pg, and 1 Gt CO2 = (44/12)<li> Gt C). Also, sometimes people refer instead to Gt CO2-equivalent (CO2-e) which includes other greenhouse gases scaled using global warming potentials   <li>see also @howmuchgtc, @co2eq																										
	time		Time		Global climate / geochemical data runs from 1750 to 2300, whereas regional emissions / socioeconomic data runs from 1900 to 2100.<p>Currently JCM works in one-year timesteps (see also @loop).<p>²°adju You can link all the timescales using the @linkx option (in @layoutpanel).²<p>The simple 'time' class in jcm/mod contains the variables describing time, and methods to create standard arrays.<p>²°cogs This class may be replaced by an interface, with array methods moving to @module ²<p>²°cogs JCM has been run successfully to year 3000 or beyond, by changing time.gey, and recompiling (because java arrays have a fixed length). When 'parallel worlds' are implemented (see @modlist), it should be possible to change the timescale of the model dynamically, on creation of a new 'world'.²																										
																															
mitig																															
	mitigpanel		Emit	JCM Emissions options	<hr>General options controlling emissions and their mitigation. See @stabilisation, @aboutsres, @othgasemit, @distribution<br>  £^interacs £^menopts		Options des emissions en JCM																								
	emitmenu		Mitigation	Options for mitigating future emissions	Choose a future mitigation policy option. See also @stabilisation	Objectifs	Options d'intervention Fixer les options pour les émissions futures		Verminderung	Optionen zur Verminderung zukünftiger Emissionen		Mitigación	Opción para mitigar futuras emisiones		Emissões	Opções para futuras emissões		Doelstellingen	Interventie opties  de toekomstige emissie-opties vaststellen		Выбросы	Выбор сценария		Udledninger	Vælg hvordan fremtidige drivhusgasudledninger fastsættes		Utslipp	Velg hvordan fremtidige drivhusgassutslipp fastsettes			
	mitigation		Mitigation	Control emissions to reach a specific stabilisation target	This Module contains the calculations described by<li>@stabconcmethod,<li>@stabitmethod,<li>@stabemitmethod,   <li>The general principles are described in topics linked from @stabilisation<p>Mitigation module applies a policy feedback from the chosen target indicator (e.g. Concentration or Temperature) back to the Emissions. Thus it is at the heart of JCM's interacting modules , as shown by the @flowchart.		Limiter des emissions pour atteindre un certain niveau de stabilisation																								
	nopolicy			SRES no-climate-policy scenarios	See @sresmenu, @aboutsres, @philosophy		Scenarios sans politique de SRES																								
	constant			Constant emissions	This option may be useful to simplify investigation of different @distribution options, or to illustrate the @inertia in the climate system.<p>Note that this only affects CO2 emissions: To ensure that emissions of other gases are also constant, choose £`2000fix from the @othgasemit<p>As the emissions are not affected by anything else, this option may also be a useful basis for scripting custom scenarios. See @emitdeclinescript for an example.		Émissions constantes			konstante Emissionen			Emisiones constantes			Emissões constantes			Constante emissies			Постоянные выбросы			Konstante udledninger			Konstante utslipp			
																															
	stabemit			Stabilise CO2 emissions	This control fixes the stabilisation year and level for a CO2 emissions curve.<br>  For more explanation see:<li>@stabemitdoc<li>@stabemitmethod<li>@stabilisation		Stabiliser les émissions de CO2			CO2-Emissionen stabilisieren			Estabilizar emisiones de CO2			Estabilizar emissões de CO2			Stabiliseer de CO2 emissies			Стабилизация выбросов CO2			Stabiliser CO2-udledningerne			Stabiliser CO2-utslippene			
	integral			total 2000-2200:	see @stabemitmethod		total 2000-2200:			Total 2000-2200:			Total 2000-2200:			total 2000-2200:			totaal 2000-2200:			итог 2000-2200 гг.:			total 2000-2200:			total 2000-2200:			
	initgrow			initial growth rate:	see @stabemitmethod		taux de croissance initiale:			Wachstumsrate zu Beginn:			Tasa de crecimiento inicial:			taxa de crescimento inicial:			initiële groei/toename:			начальный темп роста:			startvækstrate for udledninger:			startvekstrate for utslipp:			
	integralopt		CI	Control cumulative emissions (integral)	see @stabemitmethod		Contrôler la somme cumulée des émissions		KE	kumulierte Emissionen kontrollieren (integral)		IA	Control de emisiones acumulativas (integral)		CI	Controlar as emissões cumulativas (integral)		CI	De gecummuleerde som van emissies controleren					CI	Vælg den akkumulerede CO2-udledning		CI	Velg det akkumulerte CO2-utslipp			
	stabconc			Stabilise CO2 concentration	This control fixes the stabilisation year and level for a target CO2 concentration curve.<br>  The formula for the curve is the same as that used for fixed IPCC scenarios (see @stabconcmenu).<br>  For more explanation see:<li>@stabconcdoc<li>@stabconcmethod<li>@atco2plot,<li>@stabilisation		Stabiliser la concentration en CO2			CO2-Konzentrationen stabilisieren			Estabilizar concentración de CO2			Estabilizar concentrações de CO2			Stabiliseer de CO2 concentratie			Стабилизация конценрации CO2			Stabiliser CO2-koncentrationen			Stabiliser CO2-konsentrasjonen			
	stabconcmenu		Stab-Scen	Menu of CO2 stabilisation scenarios	This menu of CO2  predefined stabilisation scenarios adjusts the parameter of @stabconc  to generate the same target concentration curves as in  IPCC S (="WG1") or WRE scenarios (see @wreopt).<p>For more explanation see<li>@stabconcdoc<li>@stabconcmethod<li>@stabilisation<br>  °cogs ²(Note: 400ppm and 500ppm were not in IPCC, but are calculated by the same method as the others)²		Choisir un scénario de stabilisation du CO2		Stabilisierung	CO2-Stabilisierungs-Szenario wählen		Estabilizar	Seleccionar escenario de estabilización de CO2		Estabelização	Selecionar cenário de estabelização de CO2		Stabilisering	Kies een CO2 stabiliseringsscenario		Стабил.	Выбор стабилизации CO2		Stabilisering	Vælg CO2 stabiliserings scenarie		Stabilisering	Velg CO2 stabiliserings scenarie			
																															
	wreopt		WRE	WRE delayed start for CO2 stabilisation.	This option, which derives from the proposal of Wigley Richels & Edmonds (Nature, 1995), delays the start of the @stabconc curve,  setting the initial emissions to follow a business as usual scenario (IS92A). Combining this option with scenarios from the @stabconcmenu will generate WRE stabilisation curves similar to those used in several IPCC reports.<br>  For further explanation see @wredelaystart,  @stabconcmethod		WRE  démarrage retardé pour la stabilisation de CO2		WRE	WRE verzögerter Start für CO2-Stabilisierung		WRE	WRE, comienzo atrasado para estabilizar el CO2		WRE	WRE começo atrasado para establização do CO2		WRE	WRE  vertraagde start voor de CO2 stabilisering					WRE	WRE Sen start på CO2 stabiliseringen		WRE	WRE Sen start på CO2 stabiliseringen			
	stabconcstartyear		Start Year for CO2 Stabilisation		scriptable parameter (no graphical control, but adjusted by @wreopt)																										
	stabrf			Stabilise Forcing	This control fixes the stabilisation year and level for a target Radiative Forcing curve.<br>  For more explanation see:<li>@stabrfdoc, @stabitmethod<li>@radforplot<li>@stabilisation		Stabiliser le forçage radiatif																								
																															
	co2eq		CO2 Eq	CO2 Equivalent Concentration	CO2 Equivalent Concentrations are calculated from the current radiative forcing by inverting the relationship between forcing and CO2 concentration - for more explanation see   @radfor<br>  ²°adju The CO2 Equivalent curves appear on @atco2plot when £`expert is chosen from the @complexitymenu  ²<br>  Note that the contribution of the other gases depends strongly on the option chosen from @othgasemit, and the scenario chosen from @sresmenu<br>  You can choose to stabilise £`co2eq using  £`stabrf -see @stabrfdoc<p>Although @art2 requires stabilisation of concentrations of greenhouse gases (note: plural),  there is not yet any consensus on the definition of CO2 equivalents for use in stabilisation scenarios. Which gases should be included (three alternatives are shown on @atco2plot)? Should we consider just the current forcing, or the integral of future impacts?<p>One problem is that the gases have very different  lifetimes  in the atmosphere -from a few weeks for tropospheric ozone and about ten years for CH4 (see @othgasplot), to about 100 years for CO2, and even to several  thousand years for SF6 (see @fgasplot). Moreover these lifetimes are changing due to various feedback processes. For example, the lifetime of CH4 is affected by OH (see @oghga), and the lifetime of CO2 depends on the response of ocean and biosphere sinks (see @carbonstoreplot).<p>Fixed Global Warming Potentials are not appropriate in the  context  of long-term stabilisation because:  <li> GWPs are defined in terms of emissions, not concentrations  <li> GWPs  only consider the integral of radiative forcing over a fixed time horizon (e.g. 100 years), which is an arbitrary policy decision.   <li> GWPs  should change over time, even disregarding evolution of the science, because of feedback effects on the lifetimes of gases (see above), which will vary between scenarios.  <li> GWPs  do not adequately represent the effects of short-lived unevenly distributed forcings from aerosols and ozone.<p>More-inclusive, longer-term replacements for the GWP concept are being investigated. The problem is similar to that of @attribution inspired by the Brazilian proposal, which also compares the climate impact from different sources of emissions (see also @attributeplot, @responsibility)																										
	6gas		6 Gas	Six Kyoto Gases	The 'six' gases controlled by the Kyoto protocol are CO2, N2O, CH4, SF6, PFCs, HFCs  <li>See also @co2eq	6 Gaz	Six Gaz de Kyoto																								
	allghg		All GHG	All Greenhouse Gases	This adds Tropospheric and Stratospheric Ozone, and CFCs to  @6gas. Beware that the distribution of ozone forcing is different to that of well-mixed greenhouse gases  <li>see also @oghga, @fgases, @co2eq	Tout GES	Tout Gas à Effet de Serre																								
	allghgaero		All Gas	All  Gases and Aerosols	This adds Sulphate and Carbon Aerosols to @allghg (so it includes all forcing from anthropogenic emissions). Beware that the distribution of aerosol forcings is very different to that of greenhouse gases, so one cannot say that positive forcing (from GHGs) in one region is cancelled by negative forcing (from aerosols) in another: the result is still climate change!  <li>see also @radforaerosol, @co2eq	Tout Gaz	Tout Gas à Effet de Serre plus Aerosols																								
																															
	stabtemp			Stabilise Temperature	This control fixes the stabilisation year and level for a target global average temperature curve.<br>  Note that the temperature is relative to the baseline defined by the @baseyear (blue arrow).<br>  For more explanation see:<li>@stabtempdoc<li>@stabitmethod<li>@stabtemp2c<li>@glotempplot<li>@stabilisation		Stabiliser la température			Temperatur stabilisieren			Estabilizar Temperatura			Estabilizar temperatura			Stabiliseer de temperatuur			Стабилизация температуры			Stabiliser temperaturen			Stabiliser temperaturen			
	stfuzzyopt		Fuzzy	Stabilise temperature by Fuzzy-Control	This option enables @stabtempfuzzy, as an experimental alternative to the default @stabitmethod																										
	stbyitopt	old	ItC	Stabilise temperature iteratively by guessing CO2 concentration			Stabiliser la température itérativement par prédiction de la concentration en CO2		ItC	Temp. schrittweise stabilisieren mit Abschätzung des CO2		ItC	Estabilizar iterativamente la temperatura infiriendo concentraciones de CO2					ItC	De temperatuur iteratief stabiliseren per voorspelling van de CO2 concentratie												
	fuzzymenu	old	Fuzzy	Fuzzy control climate-emissions feedback experiments											Fuzzy	Experiências de controlo fuzzy dos feedbacks clima-emissões								Fuzzy	Fuzzy kontrol, eksperimenter med feedback fra klima til udledninger		Fuzzy	Fuzzy kontroll, eksperimenter med feedback fra klima til utslipp			
	dampopt		Damp	Damp Oscillations	see @stabtempfuzzy	Amortir	Amortir les oscillations		Damp	Schwankungen dämpfen		Humedad	Oscilaciones de la humedad					Dempen	de schommelingen dempen												
	stabsea			Stabilise Sea Level Rise	This control fixes the stabilisation year and level for a target sea-level curve.<br>  For more explanation see:<li>@stabseadoc, @stabitmethod<li>@sealevelplot<li>@stabilisation		Stabiliser le niveau de la mer																								
	stabtempa	old		stabilise temperature at			stabiliser la température à			Temperatur stabilisieren bei			Estabilizar temperatura a			estabilizar a temperature em			de temperatuur stabiliseren op			Стабилизировать температуру на уровне			stabiliser temperaturen på			stabiliser temperaturen på			
	stabconca	old		stabilise concentration at			stabiliser la concentration à			Konzentration stabilisieren bei			Estabilizar concentración a			establizar a temperature em			de concentratie stabiliseren op			Стабилизировать концентрацию на уровне			stabiliser koncentrationen på			stabiliser konsentrasjonen på			
	stabemita	old		stabilisation at			stabilisation à			Stabilisierung bei			Estabilización a:			establização em			stabilisering op			стабилизация в:			stabilisation på			stabilisering på			
	reduceintensity		Reduce Emissions Intensity (tC/$GDP)	Emissions Intensity Change Per Year	This illustrates the long-term implications of a recent US proposal.<br>  It shows what happens, if the <b>ratio CO2 emissions per dollar GDP</b> (known as <i>emissions intensity</i>) is reduced by a prescribed percentage each year. The default rate is -2% per year (as US proposal). You can adjust this with the control (enabled from @emitmenu) which appears on a @distribplot of CO2 emissions. The same <i>intensity</i> reduction applies to all regions and all future years, but the regional GDP data changes according to the SRES scenario (see @sresmenu, @regiondatasource).		réduire l'intensité des émissions (tC/$PIB):			Emissionsintensität reduzieren (tC/$BIP):			reduce intensidad de emisiones (tC/$GDP):						de emissie-intensiteit verminderen (tC/$PIB):												
																															
	stabconnect		Connecting Scenarios to Current Emissions		The original IPCC "S" (or WG1) and "WRE" scenarios both started from 1990, whereas in this model the future starts at 2000. The total fossil CO2 emissions at 2000 were about 6.7Gt/yr, slightly higher than the original "S" scenarios at 2000, and slightly lower than projected under the IS92A scenario (7.1Gt/yr) which is used for the initial phase of WRE. Thus  It is sometimes stated that we must use the WRE scenarios because  we have lost too much time and 'S' are already history ? however the figures show that our current development path actually lies between the original WRE and 'S' pathways, so both sets need updating, but neither is more valid.<p><p>For both sets of scenarios, JCM starts from the present emissions (also scaling IS92A down by 6.7/7.1) in order to avoid a discontinuity which looks odd, and would have strange effects on the model. This correction may also have a small impact on the emissions peak, compared to that calculated with the original profiles (this problem was not apparent in IPCC, because they don't show you history and future on the same graphic!).<p>  ²Note: To get back to the original WG1 pathway from the present, would require rather abrupt reductions initially (this may partially explain why the economic mitigation costs shown in IPCC-TAR-SYR-Q7  seem rather high for the WG1 pathway. The choice of "discount rate" would also strongly influence any comparison of pathways, since discounting reduces later compared to earlier costs.)²<p>  The SRES scenarios (see @aboutsres) are not scaled, although they are also slightly higher than current emissions.																										
	stabtemp2c		Stabilisation below 2C: EU policy target		In 1996 the European Council of Ministers proposed a long-term climate policy target, that the global average temperature rise should not exceed a limit of  2C above the preindustrial level, and <i>therefore</i>, that the CO2 concentration should be stabilised at less than 550ppm. This policy has not been superceeded, however the climate science has evolved since 1996,  so further investigation of the implications may be valuable.<p>°adju You can explore this in JCM using the @stabtemp on @glotempplot (enable from @emitmenu). The default stabilisation level is already set to 2C, however the default @baseyear is 1990 (for consistency with IPCC) so you must first adjust this to preindustrial. ²Note that the preindustrial level is not precisely defined as there is some natural climate variability, try to find a mid-range level between 1750 and 1900.²<p> The resulting emissions and concentration pathways are rather sensitive to uncertainties in the climate model (@heatflux), and to assumptions about other gas emissions (@othgasemit).<br>  °adju Try experimenting with the @climodmenu and @othgasemit (combined with @sresmenu). Since the final temperature should remain constant, so the emissions and concentrations must change accordingly.<p>The @stabtemp2cscript does this for you automatically.<br>  You can see that there is a very wide range of uncertainty, regarding the implications for short term emissions reductions. This poses a challenge for policymakers, what do you think is a 'safe' pathway? On the other hand, choosing a temperature rather than a concentration target helps to reduce the uncertainty in the consequent climate change impacts. See also @uncertburden<p>  <hr>There is also a wide range of CO2 concentrations (see @atco2plot),  but the middle of this range lies between 450ppm and 500ppm. So it would seem that the EU figure of 550ppm was rather "optimistic". However we should recall that this policy was made based on science from IPCCSAR (Second Assessment Report), whereas JCM is based on the more recent IPCCTAR. One of the key differences is that the SAR used the old scenario IS92A, which projected much higher emissions of sulphate aerosols, whose large cooling effect offset some of the global warming due to greenhouse gases. You can see this, by picking IS92A from @sresmenu. The TAR also includes some additional forcings, which were not in the SAR. The climate model in the TAR was tuned to a range of 7 GCMs, however the average of these is not much different to the single model used in the SAR (which is also included in the @climodmenu).<p>Nevertheless, these scientific changes do not explain all of the discrepancy. We should also recall, that early research and policy on anthopogenic climate change focussed mainly on CO2, whilst the effect of other gases was considered later. Consequently, many policymakers think of other gases in terms of CO2 equivalents (although this is not well defined in the stabilisation context -see @co2eq). You can see curves for @co2eq on the @atco2plot (only at @expert complexity level) which are much closer to the EU's figure of 550ppm than those for CO2 alone. So it might seem reasonable to interpret this figure as referring to CO2 equivalent concentration.<p>Considering also convergence between regions (see @distribution), this would imply reducing EU emissions by about 75% by 2050.																										
	stabtemp2cscript	dem	98 Pathways to Stabilise below 2C: Demo-Script		This demonstration script shows how JCM  may be used to explore different pathways to keep the temperature rise below 2C (compared to preindustrial), which is the EU's long-term policy target. It illustrates the problem of  uncertainty in inverse stabilisation calculations.  <li>See @stabtemp2c for an introduction to this topic.  <li>These results were presented at the European Geophysical Society conference in Nice, April 2003. The inspiration was in response to a meeting of the "European Group on Further Action".<br>  £!stabtemp2cscript<br>  °cogs The script may take about five minutes to run on a typical PC. The model  should not be distrurbed during this process. Note that for every curve, the model must iterate several times (see @stabitmethod).<br>  <hr><p>For all the 98 curves, the @stabtemp is set to stabilise at 2C by 2150, and the @baseyear is set to 1850.<p>The 98 variants come from the combination of the 7 GCMs (parameterised as in the TAR -see @gcmfit) with 14 options for the emissions of non-CO2 gases.<br>  The greener curves correspond to cooler GCMs. The baseline is preindustrial, hence there is already divergence at 2000. <br>  °cogs ²Note: uncertainty in the carbon cycle / biogeochemical feedbacks is not considered²<p>The 14 other gas options are the combination of the options in @othgasemit and @sresmenu. The redder set of 49 curves have no mitigation of non-CO2 gases, whilst the other 49 assume that emissions of each gas (including aerosol and ozone precursors) are reduced by an equal proportion, compared to the SRES baseline in each year (see @sresscale).  <p>For a few variants (hottest GCMS, largest other  gas emissions) the iteration failed to find a pathway below 2C, these are shown in grey.<p>For comparison, you might like to check out @stabconc500script<br>  <hr><br>  It is acknowledged, that presenting such a wide range of pathways would not be particularly helpful for policymakers. However, we have to start by understanding why scientists may give apparently contradictory interpretations of a simple policy target, as a first step towards reducing the uncertainty range.<br>  Another JCM-script is under development, to see how this wide uncertainty range may be constrained by a probabilistic approach based on the fit to historical data. (see @probabilistic) <p>  <hr>See @scripting for an explanation of how to adapt the code.																										
	stabconc500script	dem	Stabilise CO2 concentration at 500ppm script		This script runs the same set of 98 scenarios as described in @stabtemp2cscript. See also @stabconcdoc, @scripting<br>  £!stabconc500script																										
	stabilisation		Stabilisation Scenarios: Overview		££art2short<li>See @art2 for further discussion.<br>  <hr>JCM can produce many types of stabilisation scenarios, based on target indicators at different stages of the cause-effect chain.<br>  The calculations are made in @mitigation<br>  <h4>Stabilise Emissions</h4>  (instructive, but not effective in the long-term)<li>@stabemitdoc<li>@stabemitmethod<br>  <h4>Stabilise CO2 Concentration</h4> (as IPCC stabilisation scenarios)<li>@stabconcdoc<li>@stabconcmethod<li>@wredelaystart<li>@stabipccsyrq6<br>  <h4>Stabilise Radiative Forcing</h4> (bringing together effects of many gases)<li>@stabrfdoc<li>@co2eq<li>@stabitmethod (also used for temperature and sea-level)<br>  <h4>Stabilise Temperature</h4> (closer to real climate change impacts)<li>@stabtempdoc<li>@stabtemp2c, @stabtemp2cscript<li>@stabtempfuzzy<br>  <h4>Stabilise Sea-level</h4> (barely possible, but interesting to try)<li>@stabseadoc<br>  <h4>General stabilisation topics</h4><li>@inverse<li>@stabpathways<li>@stabimpact<li>@othgasemit<li>@scalelanduse<li>@uncertburden<br>  <h4>Related topics</h4> (see also @stabrelated)<li>@philosophy<li>@distribution<li>@reduceintensity<li>@sresasbaseline<hr><h4>Recent Analysis</h4><li>@probwccc, @confpres, @moscow, @wccc2003																										
	emitcc		Emissions, Scenarios, Stabilisation, Distribution	Where are we going, where do we need to go?	<li>@stabilisation<br>²("Where do we need to go?")²<br>  The aim of the UN Climate Convention is to avoid dangerous climate change, by stabilising concentrations of greenhouse gases. You can investigate a range of levels and pathways to stabilise CO2 concentration, forcing, or temperature directly.   <li>@aboutsres<br>²("Where are we going?") ²<br>  There are many possible pathways of future development, and hence greenhouse gas emissions, even assuming no specific policy to avoid climate change. Explore the causes and consequences of the range of IPCC-SRES "baseline" scenarios.<br>  ³Related topics ³  <li>@philosophy (comparing above approaches)  <li>@distribution    <li>@othgasemit   <li>@effectofkyoto  <li>@equity  <li>@optimisation			<li>@stabilisation<br>²("Vers où nous devons diriger?")²<br> Le but eventuel  de Convention Cadre de l'ONU sur des Changements Cliamtiques est d'avertir changements climatiques dangereux, par stabiliser des concentrations des gaz à effet de serre. Vous pouvez explorer une gamme des niveaux et de routes pour stabiliser le concentration de CO2, le forçage radiatif, ou la temperature directement.   <li>@aboutsres<br>²("Vers où dirigeons nous actuellement?") ²<br>  Il y a beaucoup de sentiers possibles pour le developpement de l'avenir, et donc des emissions des gaz à effet de serre, même si nous faisons l'assumption qu'il ny aura aucune politique specifique pour eviter les changements climatiques. Explorer les causes et consequences de la gamme des scenarios de base 'SRES' .<br>  ³Thèmes liés: ³  <li>@philosophy (pour comparer les approches au dessus)  <li>@distribution    <li>@othgasemit   <li>@effectofkyoto  <li>@equity  <li>@optimisation																							
	art2short		.		Article 2 of the UN Climate Convention (UNFCCC ) states that its ultimate aim is:<br>  <i>"...to stabilise concentrations of greenhouse gases in the atmosphere at a level which will prevent dangerous anthropogenic interference with the climate system,"</i>																										
	art2		UNFCCC Article 2		££art2short<p>It continues: <i>"Such a level shall be achieved within a time-frame sufficient to allow  ecosystems to adapt naturally to climate change, to ensure that food production is not threatened, and to enable economic development to proceed in a sustainable manner"</i><br>  <hr>This is a laudable aim, however its interpretation raises many questions,  some of which may be explored with JCM  <li>What is "dangerous" climate change? Impacts are very unevenly distributed, so  dangerous for whom, where and when? See @impacts, @inertia, @equity  <li>Considering uncertainty in climate science, at what probability might dangerous  impacts be acceptable? See @uncertainty, @uncertburden  <li>"concentrations of greenhouse gases"  is plural  -how do we combine their effects? See @othgasemit, @co2eq, @stabrfdoc  (all gases)  <li>What is the easiest emissions pathway to reach this level - how fast should stabilisation occur? See @stabpathways, @wredelaystart  <li>Is concentration the best indicator for a stabilisation target?  See  @stabrfdoc, @stabtempdoc for alternatives<p>The global debate to balance these factors requires insight from complex models, however policy targets are likely to focus on a few simple indicators - for example see @stabtemp2c.<p>@stabitmethod describes the iteration mthod for many JCM  scenarios. This  illustates a more general point-  when designing an efficient iteration algorithm for inverse calculations, the correction-feedback process is more important than the initial guess. This also applies to the global iteration between scientists, policymakers and citizens, essential for interpreting Article 2. So we should not fear making bold guesses, but need to design better feedback in the global dialogue (see @dialogue, @concept)																										
	inverseintro		.		The emissions are adjusted to reach the target level. So the model is working backwards, from the effect, to the cause (see @inverse), as well as forwards to the climate impacts (see @stabimpact).																										
	inverse		Inverse Calculations		Stabilising concentration, Radiative Forcing, Temperature, or Sealevel  are all examples of <i>inverse</i> calculations, starting from a specified destination, and calculating backwards (from desired effect to required cause) to find a pathway to reach it.<br>  ²See<li>@stabconcdoc, @stabrfdoc, @stabtempdoc, @stabseadoc²<p>It should be emphasised that stabilisation scenarios are not <i>predictions</i>. They are useful for exploring <i>mitigation</i> policies, whereas the SRES scenarios may be more appropriate when exploring <i>adapation</i> policies.<p>Inverse calculations may also seem confusing, when considering the effect of scientific uncertainties. For example, when the target CO2 concentration is fixed, and  you adjust scientific uncertainty parameters affecting the ocean or biosphere carbon sinks,  the emissions change to keep the CO2 concentration on target. As there is also a biogeochemical feedback between the temperature and the carbon sinks, adjusting climate model parameters can also change the emissions.<p>So if you want to explore cause-effect relationships within the natural carbon-climate system, it is recommended to use either @stabemit (for low emissions) or @nopolicy (for high emissions).  <li>See also:  @philosophy, @flowchart																										
	stabimpact		Climate impact of stabilisation		The @glotempplot and @sealevelplot illustrate the climate impact of different stabilisation levels. Beware that regional, seasonal climate changes can be much greater than global average figures, as illustrated by the @regclimap.<p>Although there are many uncertainties in the carbon and climate models, lowering the stabilisation level always reduces the impact (see @uncertainty).<p>The temperature rise slows quite soon after CO2 stabilisation, but continues to increase slowly due to the gradual transfer of heat to the deep ocean (@oceantempplot, @rftemp).  The inertia is much more apparent in the sea-level, which continues to rise for centuries after CO2 stabilisation (see @inertia) .<br>  ££stabimpactdemo  <li>See also @impacts<br>  <hr><br>  You may also wish to consider the socioeconomic impact of emissions reductions<br>  (see @abate, @distribution, @regshares, @costs)																										
	stabimpactdemo		Stabilisation Impacts Demo		<i>JCM Demonstrations are  currently being rewritten </i>																										
	stabipccsyrq6		Stabilisation in IPCC-TAR SYR		IPCC-TAR Synthesis report Question 6 contained a well-known graphic of CO2 stabilisation profiles, based on the WRE scenarios   <li>See <a href="../doc/pic/syrspm6.jpg" target="pic"> IPCC Synthesis Report, figure SPM6</a>, also @ipcclinks.<p>You can recreate these scenarios in JCM by combining the @stabconcmenu and the @wreopt.<p>However, in order to calculate the impact on temperature and sea-level, an assumption must also be made about the contribution of the other greenhouse gases to radiative forcing.<br>  For this purpose IPCC-TAR SYR Q6 assumed that emissions of other gases are fixed according to SRES A1B scenario.<br>  To reproduce this, you should choose £`sresfix from the @othgasemit menu, and "A1B" from the @sresmenu.   <li>The @othgasemit offers some alternative assumptions, including mitigation of all greenhouse gases.  <li>Note also  @ipccothgas																										
	stabpathways		Different Pathways to Stabilisation		There are many possible pathways towards any given stabilisation level.<p>To satisfy the conditions of the second part of @art2, we have to find a pathway that avoids abrupt changes, balancing climatic and economic considerations. If we reduce emissions more earlier, we don't have to reduce so dramatically later. On the other hand, later reductions may be eased by new technologies, if we make an effort to develop these now. This is a question of <i>"intergenerational equity"</i> -what kind of legacy should we leave to future generations (see also @equity).  Moreover we mus consider the inertia in the socioeconomic system - it takes a long time to change some key factors which influence emissions ? such as planning of cities, and transport and energy infrastructure.<p>You can explore some variants by dragging the endpoint of the target stabilisation curve horizontally (move the 4-pointed  £`control) , thus changing the timing of stabilisation without changing the final level. This applies to all stabilisation controls in JCM. For @stabconc, you can also adjust  the start of the stabilisation curve  using the @wreopt.<p>The choice of pathway makes little difference to the long-term equilibrium climate impacts, but it does influence the rate of temperature rise, which affects the ability of ecosystems and society to adapt. It may also strongly affects the economic costs of mitigation   <li>See also @wredelaystart, @impacts, @ costsplot																										
	stabrelated		Topics Related to Stabilisation		Stabilisation scenarios may be combined with other emissions options (menus in the @mitigpanel):  <li>@distribution : Any reduction from the no-climate-policy baseline to approach a stabilisation target requires a global agreement to share the limited budget between countries. Investigate this controversial but critical question. Any mitigation scenario may also be combined with @kyotoopt, which applies the Kyoto Protocol targets for AnnexB countries up to 2013.  <li>@othgasemit : JCM has several options to link the emissions of other gases with those of CO2, noting that @art2 tells us to "stabilise concentrations of <i>gases</i>", not only CO2. Investigate how much difference this makes -noting that these options are also dependant on the @sresmenu.<br>  Note also @scalelanduse<br>  <hr>																										
	ipccothgas		Other gases in IPCC-TAR		Methane (CH4), Nitrous Oxide (N2O), Tropospheric Ozone (O3), HFCs and CFCs are important greenhouse gases.<br>  So the £~sres  specify emissions of CH4, N2O, HFCs, also of NOx, VOC, and CO which lead to the production of ozone, and of sulphate and carbon aerosols (watch @othgasplot, while adjusting @sresmenu).<p> In IPCC-TAR Synthesis report Q6 (see @ipccsyrq6) it was assumed that emissions of other gases are fixed according to SRES A1B no-policy scenario, for all the CO2 stabilisation levels  (see @stabilisation).<p>°adju You can reproduce  this in JCM by choosing £`sresfix from the @othgasemit menu.<p>In this case, the other gases contribute as much to the radiative forcing as 28% extra CO2 (since the CO2 radiative forcing is a logarithmic function of concentration, this applies to any level).<p>However this is not the default option in JCM, because it seems rather unrealistic, that we would make a big effort to stabilise CO2 concentrations, without also mitigating emissions of other gases.<p>²Note that with the IPCC assumption (SRES A1B fixed) 450ppm CO2 corresponds roughly to 550ppm CO2-equivalent (all gases together -see @co2eq), leading to a temperature rise of about 2oC (an upper limit proposed by the European Union). Whereas if you assume option (b) Equal % of SRES A1B as CO2, you can reach this temperature target stabilising CO2 at about 500ppm. See also @stabtemp2c ²																										
	stabemitdoc		Stabilise Emissions: Overview		Many people talk about reducing CO2 emissions to a "sustainable level".<br>  You can explore this concept, by choosing £`stabemit from the @emitmenu in the @mitigpanel. Then on a @distribplot you can see that total CO2 emissions follow a simple mathematical curve, starting  from the present level and trend, and eventually stabilising at a time and level determined by the 4-pointed yellow arrow control.<br>  ²°adju (@stabemit  works the same way as @stabconc on @atco2plot)²<p>For example it is often quoted, that we need to reduce global emissions by about 60% to stabilise the concentration of CO2 at current levels. This figure derived from the first IPCC report (1992) can be explained by considering that 3/5 of the fossil CO2 emissions stayed in the atmosphere whilst 2/5 was taken up by the ocean sink, while land-use sources and sinks rouhgly cancelled (these ratios are still approximately valid today -see @carbonstoreplot).<p>You can test whether this works, by moving the moving the yellow arrow to stabilise CO2 emissions at 2.4 GtC/yr in 2010. This makes the atmospheric CO2 concentration (black curve on carbon cycle plot) level off at about 375ppm, but only <i>instantaneously</i>. If you look further into the future, the concentration  starts to rise again, since the sinks reduce as the rate of atmospheric CO2 increase falls, the biosphere sink saturates, and the seawater becomes more acidic. If we really want to stabilise the atmospheric CO2 concentration, we have to keep reducing emissions for centuries, which is apparent when you choose instead the @stabconc option, and place the black arrow on @atco2plot at 375ppm in 2010.<p>²°adju Note, if you want to stabilise emissions at current levels, simply select £`constant from the @emitmenu²<p>At the "expert" @complexitymenu, you can also "fine-tune" the emissions stabilisation curve, by adjusting the @initgrow and @integral.<p>The calculations are made in @mitigation, for more explanation see @stabemitmethod.<br>  ££stabrelated																										
	stabemitmethod		Stabilise Emissions: How it Works		This formula simply fixes the global CO2 emissions according to a mathematical curve which is defined by:   <li>emissions in the start year (2000, or 2013 after @kyoto)   <li>@initgrow (adjustable parameter at @expert complexity level)   <li>target level in the stabilisation year (set by @stabemit)   <li>gradient in the stabilisation year (zero)<br>  These constraints are sufficient to define a unique cubic curve.<br>  If the @integralopt is selected (expert level), you can also adjust the @integral, to define a quartic curve.<br>  <hr><br>  The calculations are made in the @mitigation module.																										
	stabconcdoc		Stabilise CO2 concentration: Overview		The concept of stabilising CO2 concentration at a "safe" level is enshrined in Article 2 of climate convention (see @art2).<br>  £`stabconc is also the default option in JCM's @emitmenu (in @mitigpanel).<br>  £`stabconc sets a target CO2 concentration curve according to a simple mathematical formula<br>  ²°adju (Note: atmospheric CO2 concentration is the black curve shown on the @atco2plot, measured in ppm on the right hand scale).²<br>  You can adjust the stabilisation level and year, either by dragging the  black 4-pointed arrow (@stabconc) which appears on the @atco2plot.<br>  Alternatively you can use the @stabconcmenu which reproduces fixed IPCC scenarios (in which the stabilisation year is 2100 + (stabilisation level - 450) / 2<br>  Both methods may also be combined with the @wreopt.<br>  ££inverseintro<p>The mathematical formula,  based on that used for the original IPCC stabilisation scenarios, is the same for all these profiles, but they have different start and end points. For further explanation see @stabconcmethod.<br>  The calculation is made in @mitigation module.<br>  ££stabrelated  <li>See also @stabipccsyrq6<br>  ££stabconcdemo																										
	stabconcdemo		Stabilise Concentration Demo		<i>JCM Demonstrations are currently being rewritten</i>																										
	stabconcmethod		Stabilise Concentration: How it works		The calculations are made in the @mitigation module.<p>First, a target concentration curve is set, using the Pade formula defined by the IPCC Technical paper of Enting et al 1994 (see @sciref).<br>  This is a ratio of two quadratic polynomials, whose constants are defined by:  <li>initial concentration (c<sub>0</sub> at t<sub>0</sub>),   <li>initial concentration gradient (dc<sub>0</sub>/dt<sub>0</sub>)    <li>initial d2c<sub>0</sub>/dt<sup>2</sup><sub>0</sub> This constraint avoids a kink in the emissions curve. It has the same effect as the arbitrary parameter described in the Enting paper, but can be more easily generalised.   <li>final concentration (c<sub>s</sub> at t<sub>s</sub>) The stabilisation level and year are set by @stabconc or @stabconcmenu   <li>final concentration gradient  (dc<sub>s</sub>/dt<sub>s</sub>)  The final gradient is zero for CO2 stabilisation (although not for stabilisation of other indicators -see @stabitmethod)  <li>²°cogs Note: in the current JCM implementation an additional constraint has been added, that the final d2c<sub>s</sub>/dt<sup>2</sup><sub>s</sub> is also zero, thus defining a quintic curve. This helps with stabilisation of other indicators, it makes little difference for CO2²<p>The initial year, level and gradient  may be affected by the @wreopt -see @wredelaystart, and also by the @kyotoopt -see @kyoto. Note also @stabconnect<br>  <hr><br>  Having set the target curve, the emissions are then calculated in each timestep (year), as the change in concentration, plus the ocean and biosphere sinks. It is assumed that the sinks will change by the same amount as in the previous timestep (i.e. their second derivative is constant, during this part of the calculation). The model is then run to calculate the actual sinks and concentration as normal, including feedbacks (see @carboncycle). Any deviation from the target is corrected in the next step. This sink assumption works well in a "smoothly" changing model (if the target curve is plotted, you can barely see the difference from the actual curve).<br>  However it might not work in a GCM which includes rapdily changing natural climate variability, due to the large impact of climate-carbon feedbacks.<p>Total emissions are then shared between fossil fuels and land use change (see @scalelanduse)																										
	scalelanduse		Land-use change and fossil CO2 emissions		For all the stabilisation options, the calculated total CO2 emissions are distributed between fossil fuel and land-use change, using the same constant fractions as in the starting year. This is preferable to fixing land-use CO2 emissions according to SRES, which causes a rather jagged curve for the remaining fossil CO2 emissions. Alternative options may be added later.																										
	wredelaystart		Delaying the start of CO2 Stabilisation		IPCC considered two alternative sets of CO2 stabilisation pathways:<br>  the original formula from the IPCC 1994 technical paper, known as "S" or "WG1" scenarios, and a variant on this developed later by Wigley, Richels and Edmonds (1995), known as "WRE" scenarios, which can be enabled using the @wreopt.<br>  ²(note: this option cannot be combined with @kyotoopt)²<p>The WRE scenarios follow the IS92A "business as usual" pathway for an initial period of 10-30 years, before curving away to reach the stabilisation target. The delay is longer for higher levels, according to the formula:<br>  start year = 2002 + (stabilisation level - 350) / 23.0<br>  The starting level is scaled to be consistent with current emissions: see @stabconnect<p>As you can see, the WRE option allows higher emissions initially, but later they must drop more steeply.<br>  WRE suggested that it might be economically more efficient to delay initial emissions reduction, although they did not apply any economic optimisation model in developing these scenarios. On the other hand, they also stressed that although emissions reductions might be delayed, the effort to develop new technology and infrastructure, anticipating reductions later, should begin immediately.<p>² Note that, as WRE pointed out, the rate question is complicated by the short-term cooling effect of sulphate aerosols which are a by-product of burning coal. Considering this, the WRE pathway can actually lead to slightly cooler <i>global average</i> temperatures for the first few years! °adju (note you will only see this subtle effect if applying @othgasemit ²<br>  <hr><li>See also paper by Wigley Richels & Edmonds, Nature 1995, and @stabpathways																										
	stabrfdoc		Stabilise Radiative Forcing: Overview		When £`stabrf is chosen from the @emitmenu (in @mitigpanel), a four pointed arrow control appears on @radforplot which controls the  stabilisation level and year of a target radiative forcing curve, which includes the forcing from either @allghg or @allghgaero. Note that these curves are only shown at expert @complexitymenu.<br>  ²°adju (@stabrf works the same way as @stabconc on @atco2plot)²<br>  ££inverseintro<p>The calculations are made in the @mitigation as descibed in @stabitmethod.<p>Why might we want a target to stabilise radiative forcing? @art2 tells us to stabilise concentrations (plural), but not how to combine the effects of different gases. Stabilising radiative forcing is effectively the same as stabilising @co2eq, in a way that is simply defined (although the concept is not easily explained -see @radforintro).<br>  We could also include all gases by stabilising the Temperature, which is a more tangible indicator closer to climate impacts, however that would shift the large uncertainty regarding the @climsens onto the resulting  emissions pathway (see @stabtempdoc, @uncertburden). So stabilising Radiative Forcing may be a compromise, using an indicator in the middle of the cause-effect chain (see @flowchart).<p>² °adju  Making the radiative forcing constant also helps to illustrate how the surface temperature lags behind the forcing, due to the slow penetration of heat into the deep ocean. (see @rftemp, @glotempplot, @oceantempplot).²<br>  ££stabrelated																										
	stabitmethod		Stabilisation by Iteration: How it Works		Stabilisation of Radiative Forcing, Temperature or Sea-Level are all achieved using the same iterative method to find an emissions pathway which reaches the desired target. (²see @stabrfdoc, @stabtempdoc, @stabseadoc²)<br>  An iterative method is necessary because many factors combine in the radiative forcing,  some of them interacting by feedback processes, so it would be difficult to find a direct analytical solution to the inverse calculation.<p><p>Each time through the iteration loop, the CO2 concentration is set by two mathematical curves, before and after the stabilisation year (as defined below). The CO2 emissions are calculated from this, using the same assumptions about the carbon sinks as described in @stabconcmethod. The emissions of other gases and aerosols may then be scaled to CO2 emissions, depending on the option selected from @othgasemit. Now it has all the emissions, the model calculates everything forwards as usual, as far as the indicator that should be stabilised (forcing, temperature, sea-level). Three correction factors are then calculated to adjust the CO2 concentration curve, which are applied in the next iteration step. The iteration continues until the correction factors are smaller than a threshold (1% for the stabilisation level), or the maximum number of iterations has passed (currently 12).<p>The iteration must start with an intelligent guess of the CO2 concentration curve. For example, the radiative forcing is calculated from the required surface temperature increase using the climate sensitivity (assuming equilibrium and  ignoring heat exchange with the ocean), and 85% of this forcing is attributed to CO2. This is scaled by a the correction factor remembered from the last such calculation. The initial guess curves are displayed briefly on the model while it is iterating.  If you make a a large change to the parameters (such as changing the model from @climodmenu),  you will see that the initial guess may be far from the target, and the iteration takes longer time to find a better curve. On the other hand if you make a small change by dragging an arrow control, the initial guess is quite close due to the remembered correction factor, so the performance is good.<p>Up to the stabilisation year, the concentration curve is defined by a quintic curve: <p><nobr>y = ( a + b.x + c.x<sup>2</sup> + d.x<sup>3</sup> ) / ( 1 + e.x + f.x<sup>2</sup> ) </nobr><br>  The six parameters a-f are fixed by solving the following constraints:  <li>initial concentration (c<sub>0</sub> at t<sub>0</sub>),   <li>initial gradient (dc<sub>0</sub>/dt<sub>0</sub>)    <li>initial d2c<sub>0</sub>/dt<sup>2</sup><sub>0</sub>   <li>final concentration (c<sub>s</sub> at t<sub>s</sub>)   <li>final gradient  (dc<sub>s</sub>/dt<sub>s</sub>)    <li>final d2c<sub>s</sub>/dt<sup>2</sup><sub>s</sub> (= zero)<br>  This is a variant of the Padé formula as used in @stabconcmethod, and the initial values are fixed in the same way. The difference here is that the CO2 concentration gradient is not flat in the stabilisation year.<p>Between the stabilisation year and 2300 (the final year in JCM), the concentration is defined by a simpler quadratic curve fixed by three constraints: the initial level and gradient (same as final level/gradient above), and the final level.<p>The concentration and gradient in the stabilisation year, and the concentration in 2300, are the three factors which are corrected by the iteration loop (described above).<p>More complex formulae were tested, but did not produce better results. Providing extra degrees of freedom may fulfill the criteria better at the specified points, but cause strangely shaped curves between them.<br>  <hr>  <li>The calculations are part of the @mitigation, the curve algebra is in @mathcurve  <li>Note also the @stabtempfuzzy method																										
	stabtempdoc		Stabilise Temperature: Overview		If you choose £`stabtemp" from @emitmenu (in @mitigpanel), a four-pointed arrow appears on the @glotempplot, which  fixes the stabilisation year and level for a target global average temperature curve.  The temperature level  is relative to the baseline defined by the @baseyear.<br>  ²°adju (@stabtemp works the same way as @stabconc on @atco2plot)²<br>  ££inverseintro<p>Although @art2 refers to stabilising concentrations, it could be argued that a target for stabilising temperature would be closer to the real climate impacts which we are trying to avoid, and so reduces the uncertainty for the receivers of these impacts. On the other hand, this transfers the effect of large uncertainties in the @climsens and related factors onto the range of possible emissions pathways.    <li>@stabtemp2cscript illustrates the wide range of pathways  <li>@stabtemp2c discusses this issue further.   <li>See also @uncertburden  <li>Even disregarding uncertainties, there could be many possible ways to reach  a particular stabilisation level - see @stabpathways<p>The calculations are made  in the @mitigation as descibed by @stabitmethod.<br>  ² °cogs An alternative experimental method works by "fuzzy-control" correcting the emissions in each timestep according to the deviation from the target curve (°adju choose @stabfuzzyopt at experimental @complexitymenu).  This method  tends to produce oscillations.-see @stabtempfuzzy.  ²<p>  ££stabrelated<br>  Beware also, that regional temperature changes could be much higher than the global average-see @regclimap																										
	stabtempfuzzy		Stabilise Temperature by Fuzzy control		²Note: this method is only available at @experimental complexity level as it can produce some strange results! Use the @stfuzzyopt to enable it. The default method in JCM as described in @stabitmethod is much more reliable, although slower. ²<br>  This method first sets a target temperature curve, according to a formula similar to that used for @stabconcmethod (°adju this target curve is shown on the temperature plot). The emissions in each year are then adjusted slightly according to the deviation from the target temperature.<br>  If the other gas emissions are also mitigated proportionally to CO2 (see @othgasemit), oscillations arise due to the "destabilising" effect of sulphate aerosols (on a short timescale, the sulphate cooling effect is greater than the CO2 warming effect), whereas if these gases are fixed by SRES scenarios, the kinks in the scenarios cause corrective kinks in the CO2. However this formula works well, if the other gas emissions are constant at 2000 levels.   <li>see also @fuzzycontrol																										
	fuzzycontrol		Fuzzy control' or 'Geocybernetics'		The greek word cybernaut means 'helmsman', and the problem of steering ships is a useful analogy for climate policy. A big ship has much momentum so we cannot change course instantly, but we still need to find a strategy for responding to buffeting by wind and waves, changing weather and contradictory instructions, to steer a comfortable passage towards the destination.<p>Deliberate dynamic climate => emissions feedbacks may be used as a basis for a long-term policy formula which adjusts in response to changing climate science or observations. This aims to reduce the effect of uncertainties. If the climate warms more than expected, the global emissions budget should decrease, and vice-versa. Such an approach might also satisfy sceptics, who would not expect much warming.<p>In any year, the rate of change of emissions may be adjusted as a function of the recent rate of change of temperature or atmospheric CO2 (or other criteria). However, the slow response from emissions to impacts can lead to oscillations: this approach a bit like "steering a supertanker by eye down a narrow channel in the fog"!  A better formula would combine observation and prediction (a navigator should also use charts and instruments), calculating the rate emissions change based on the deviation from a scientifically modelled target temperature curve, and filtering misleading short-term effects.   <li>see also @stabtempfuzzy, @philosophy<br>  <hr>² (note some overlaps with philosophy page: needs reorganising!) ²																										
	stabseadoc		Stabilise Sea-Level Rise: Overview		When £`stabsea is chosen from the @emitmenu (in @mitigpanel), a four pointed arrow control appears on @sealevelplot which fixes the  stabilisation level and year of a target sea level curve.<br>  ²°adju (@stabsea works the same way as @stabconc on @atco2plot)²<br>  ££inverseintro<p>The calculations are made using the @stabitmethod, in the @mitigation<p>You will soon discover that this control does not succeed well, so it is provided for educational purposes, rather than as a serious policy option. It is almost impossible to stabilise the sealevel because it responds so slowly to surface temperature rise, due to the slow penetration of heat into the deep ocean and into the polar ice-caps (see also @inertia, @oceantempplot, @sealevel).<p>On the other hand, some low-lying countries have proposed that eventually we should think beyond stabilising temperature rise (see @stabtempdoc),  and try to reduce the long-term impacts of sea-level rise by continuing to reduce the temperature after it peaks.<br>  ££stabrelated																										
	uncertburden	scc	Shifting the Burden of Uncertainty		For any specified emissions pathway, there is a large range of possible climate change impacts, due to the combination of many uncertain factors in both the biogochemical cycles, and the climate system response and feedbacks. Thus, even if we had challenging long-term emissions reductions targets, those who have to adapt to climate change would have to cope with this uncertainty (some change is inevitable due to the large inertia in the system -see @inertia).<p>On the other hand, if we had a long-term target defined in terms of acceptable climate change impacts, and a commitment to keep on track towards this by adjusting the emissions as the science evolves, the uncertainty for those adapting to climate change would be reduced, although the uncertainty for those planning emissions reductions would be increased.<p>In a high-level policy debate it is likely that an indicator such as global average temperature would be used as a proxy for real impacts, as in the EU's long-term policy target (see @stabtemp2c). The resulting uncertainty in emissions pathways is illustrated graphically by the @stabtemp2cscript.<p>So, shifting the target to an indicator later in the cause effect chain (set at an equivalent level in the best-guess case) has the effect of shifting the burden of managing uncertainty away from the receivers of climate impacts, towards the controllers of emissions.  Since those most vulnerable to climate impacts tend to be in poorer countries, and those who produce most of the emissions are mainly in the rich countries, this has some equity implications (see @equity).<p>Economists have long acknowledged that uncertainty is expensive, because we have to choose  investments now, guessing which may produce the optimal return in an uncertain future. For this reason some have argued in favour of policy instruments  closer to the action of emissions reduction such as "emissions intensity" targets (see @reduceintensity) or carbon taxes.  On the other hand, uncertainty regarding adaptation will also be expensive, particularly if it involves large shifts in population, agriculture or infrastructure.  So an emissions intensity approach might seem efficient for the biggest polluters, but very inefficient from the point of view of those who suffer the consequences of climate change. The inverse applies to a climate-impacts target approach.   <li>See also @stabilisation, @uncertainty @moscow @wccc2003 @probwccc																										
	stabconcdemo	dem	Stabilise Concentration Demo		<i>JCM Demonstrations are being rewritten</i>																										
	stabconcimpactdemo	dem	Stabilise Concentration Impacts Demo		<i>JCM Demonstrations  are being rewritten</i>																										
	sresdemo	dem	SRES Demo		<i>JCM Demonstrations  are being rewritten</i>																										
																															
																															
carbon																															
	carboncycle		Carbon Cycle	Cycle of Carbon in the Atmospheric, Ocean and Biosphere.	This module calculates the change in atmospheric CO2 concentration, which is the sum of fossil and land-use emissions, minus ocean and biosphere sinks. The sinks respond dynamically to concentration and temperature.<br>  <hr>See also @atco2plot, @carbonstoreplot , @sinksdynamic, @sinksocean, @sinksbiosphere, @carbchem<br>  £§iobinfo ££carbonemissions ££carbonmodel  ££carbonfuture	Cycle du Carbone															Цикл углерода	Выбросы, Стоки и Концентрация CO2 в атмосфере									
	atco2plot		Atmospheric CO2	Carbon sources and sinks	£^apptag This plot shows how emissions and sinks of CO2 affect the concentration of CO2 in the atmosphere.   <li>² @carbonstoreplot shows more detail of where the carbon goes when it leaves the atmosphere, and contains controls for adjusting sink uncertainties. ²  <li>² By default (changed with @emitmenu) JCM will stabilise CO2 concentration - see @stabconcdoc, @stabilisation ²<br>  £§graphinfo ££sinksdynamic<br>  <hr>The calculations are made in @carboncycle using @carbonmodel. See also   <li>@sinksbiosphere,<li>@sinksocean,<li>@carbchem,<li>@carbonstoreplot  <li>²°adju If expert is selected from @complexitymenu, you can also see curves for @co2eq ²	CO2 Atmospherique	Sources et puits de carbone: GtC/an (concentration en CO2: ppm)		Kohlenstoffkreislauf	Kohlenstoffquellen und -senken: GtC /yr (+ CO2 concn ppm)		Ciclo del Carbono	Fuentes y sumideros de carbono: GtC /a (+ CO2 concn ppm)		Ciclo do Carbono	Fontes e sumidoros de carbono: GtC/ano (+ CO2 concentração ppm)		Koolstofcyclus	koolstof bronnen en putten: GtC/jaar (concentratie CO2: ppm)		CO2 в атмосфере	Глобальные выбросы и источники углерода, Гт С/г (+ конценрация CO2, ppm)		Kulstofkredsløbet:	Kulstofkilder og -dræn: GtC /år (+ CO2 konc. ppmv)		Karbonkretsløpet:	karbonkilder og -avgang: GtC /år (+ CO2 konc. ppmv)		大气中二氧化碳图像	
	atconcplot	old	Atmospheric CO2 Concentration			Concentration en CO2	Concentration atmosphérique en CO2 (ppm)		CO2-Konzentration	atmosphärische CO2-Konzentration: ppm		Concentración de CO2	Concentración atmosférica de CO2: ppm		Concentração de CO2	Concentração atmosférica de CO2: ppm		Concentratie CO2	Concentratie in de atmosfeer van CO2 (ppm)					CO2-koncentrationen i atmosfæren:	CO2-koncentrationen i atmosfæren :ppmv		CO2-konsentrasjonen i atmosfæren:	CO2-konsentrasjonen i atmosfæren :ppmv		大气中二氧化碳浓度图像	
	carbon		C	carbon			carbone			Kohlenstoff 			carbono			carbono			koolstof			углерода			kulstof			karbon		碳	碳
	co2		CO2	carbon dioxide (CO2)		CO2	dioxide de carbone		CO2	Kohlenstoffdioxid		CO2	dioxido de carbono		CO2	dioxido de carbono		CO2	koolstofdioxide			Диоксид углерода		CO2	kuldioxid		CO2	karbondioxid		二氧碳	二氧化碳
	co2fos		CO2fos	Fossil fuel CO2		CO2fos	CO2 des combustibles fossiles											CO2fos	CO2 van fossiele brandstoffen												
	co2luc		CO2luc	Land use change CO2		CO2luc	CO2 des changes des activités agricoles et forestières											CO2luc	CO2 van veranderingen in  land- en bosbouwactiviteiten / bodemgebruik												
																															
	totemit		TE	total CO2 emissions	Derived from either @mitigation or @sres	ET	émissions totales de CO2		TE	Total aller CO2-Emissionen		ET	Emisiones totales de CO2		ET	emissões totais de CO2		totemis	totale CO2 emissie			Суммарные выбросы CO2		TU	totale CO2 udledninger		TU	totale CO2 udledninger			总额total CO2 emissions
	fossilemit		FE	fossil fuel emissions	Same as total in @distribplot, but the timescale here is longer	EF	émissions issues des combustibles fossiles		FE	Emissionen fossiler Brennstoffe		EF	Emisiones de combustible fósil		EF	emissões de combustiveis fosseis		EF	emissies uit fossiele brandstoffen		ВИ	Выбросы ископаемых видов топлива		FB	CO2-udslip fra fossile brændsler (olie, kul og naturgas)		FB	CO2-utslipp fra fossile fyrings kilder (olje, kull og naturgass)			
	lucemit		LE	land-use change emissions	For SRES scenarios, this comes from @aboutsres (note, some scenarios have negative LUCF emissions, implying net regrowth / sequestration). For Mitigation scenarios, land-use is simply a constant fraction of the total.	ET	émissions des changes des activités agricoles et forestières		LE	Emissionen v. Landnutzungsänderungen		EC	Emisiones por cambio de uso de la tierra		EM	emissões das mudanças do solo		ET	emissies uit veranderende land- en bosbouwactiviteiten/ bodemgebruik			Выбросы в связи с изменением землепользования		SR	udledninger fra skovrydning mv		SR	Utslipp ifra skogsrydding mm			
	totsink		TS	total CO2 sinks	Sink curves show the net flux <i>out</i> of the atmosphere (see @carbonstoreplot for detail of sinks and carbon cycle parameters)	PT	puits total de CO2		TS	Total der CO2-Senken		ST	Sumidero total de CO2		ST	sumidoros totais de carbono		PT	totale CO2 putten			Суммарные стоки CO2		TO	totalt CO2 optag		TO	totalt CO2 optak			总额total CO2 sinks
	oceansink		OS	ocean sink	Includes deep ocean mixing, and carbonate chemistry feedback.	PO	puits océanique de CO2		OS	Ozean-Senke		SO	Sumidero en el oceano		SO	simudoro do oceano		PO	totale  (biologische) CO2 putten/ sinks in de oceaan		ОИ	Океанический сток		VO	Verdenshavenes optag af CO2		VO	Verdenshavenes opptak av CO2			
	landsink		LS	land plants sink	Includes 'CO2 fertilisation' of terrestrial biosphere	PV	puits de CO2 dans la végétation ('fertilisation par le CO2')		LS	Landpflanzen-Senke ('CO2-Düngung')		SP	Sumidero en las plantas terrestres ('Fertilización CO2')		SP	sumidoro das plantas terrestres ('fertilização de CO2')		PV	(biologische) CO2 putten/sinks in de vegetatie ('CO2 bemesting')		ЗР	Наземная растительность ("удобрение CO2")		PO	Planternes meroptag af CO2		PO	Plantenes meroptak av CO2			
	atco2data		AM	atmospheric CO2 -measured data	For comparison with the calculated curve	CM	CO2 atmosphérique - mesuré		AG	atmosphärisches CO2 - gemessen		AM	CO2 atmosférico -medido		AM	CO2 atmosférico - medido		CM	CO2 in de atmosfeer - gemeten		АИ	Наблюдаемая концентрация CO2 в атмосфере		AM	CO2-koncentrationen i atmosfæren (målt)		AM	CO2-konsentrasjonen i atmosfæren (målt)			
	atco2calc		AC	atmospheric CO2 -calculated	(concentration in ppm, right hand scale) The change in concentration is simply the sum of the emissions, minus the sinks. So, concentration rises when the brown curve is above the cyan curve, or vice versa.	CC	CO2 atmosphérique - calculé		AB	atmosphärisches CO2 - berechnet		AC	CO2 atmosférico -calculado		AC	CO2 atmosférico - calculado		CC	CO2 in de atmosfeer - berekend		AВ	Вычисленная концентрация CO2 в атмосфере		AB	CO2-koncentrationen i atmosfæren (beregnet)		AB	CO2-konsentrasjonen i atmosfæren (beregnet)			
	atco2		AC	Atmospheric CO2 Concentration	(in ppm, right hand scale)	CA	Concentration atmosphérique en CO2		AK	atmosphärische CO2-Konzentration		CA	Concentración atmosférica de CO2		CA	Concentração atmosférica do CO2		CA	CO2 concentratie in de atmosfeer			Концентрация CO2 в атмосфере		AK	CO2-koncentrationen i atmosfæren		AK	CO2-konsentrasjonen i atmosfæren			
	hdmbopt		HD	Historical Deforestation calculated by Mass Balance	Calculate LUCF emissions required to reach measured CO2 concentration	HD	Calculer l'histoire du déboisement d'après le bilan de biomasse		HA	historische Abholzung durch über Massenbilanz berechnen		DH	Calcular Deforestación Historica por el Balance de Masas		DH	Calcular a desflorestação histórica através do balanço da massa		HD	de geschiedenis van de ontbossing berekenen op basis van het biomassa bilan			Ретроспективный расчет обеслесения на основе баланса массы		HD	Beregn den historiske skovrydning som residual		HD	Beregn den historiske skogsrydning som residual			
	lucfemit1990			Landuse change CO2 emissions in 1990	Scales all historical LUCF emissions (dataset from Houghton et al)																										
	hlu	old	Hlu	High historical landuse (Houghton, not scaled)		Hlu	High historical landuse (Houghton, not scaled)		Hlu	Grosse historische Landnutzung (Houghton, nicht skaliert)		Hlu	Alto uso historico de la tierra (Houghton, sin escala)					Hlu	????historisch landgebruik (Houghton, niet op schaal???)												
																															
	carbonstoreplot		Carbon Storage	Anthropogenic Carbon stored in each box of Carbon Model	£^apptag This plot shows the contents of all the boxes in the carbon cycle model (ocean layers and biosphere). The curves are simply the contents of the cq array in the @carboncycle . These contain only "extra" anthropogenic carbon, excluding the contents in the preindustrial steady state. See also @atco2plot.<br>  ² °adju Note: It is easier to understand the effect of the @carboncycle parameters, when the model is working in forwards rather than @inverse mode: -see @sinksdynamic²<br>  £§graphinfo ££sinksbiosphere ££sinksocean ££carbchem	Graphe stockage de carbone	Carbone anthropogénique stocké dans chacune des boites du modèle de carbone		Diagramm Kohlenstoffspeicherung	v. Menschen produzierter Kohlenstoff, in Bereichen des Kohlenstoffmodells gespeichert		Gráfica de almacenamiento de Carbono	Carbono Antropogénico almacenado en cada Box Model de Carbono					Grafiek koolstofopslag	antropogeen koolstof opgeslagen in elk van de compartimenten van het koolstofmodel											碳储藏图像	
	newprod		newpg	New plant growth		nouvp												nieuwe													
	green			Green leaves		vert												groen													
	wood			Wood		bois												bos													
	soil			Soil (rapidly decaying soil)		terre																									
	humus			Humus (slowly decaying soil)		humus												hdiep													
	atmosphere		atmos	Atmospheric CO2		atmos	CO2 atmosphérique			atmosphärisches CO2			CO2 atmosférico					atmos	CO2 in de atmosfeer												
																															
	hsurf		High-hatitude surface ocean layer																												
	lsurf		Low-latitude surface ocean layer			bsurf																									
	hdeep		High-latitude deep ocean layers (well mixed)																												
	fertbeta			CO2 fertilisation factor beta:	(see @sinksbiosphere)		Facteur de fertilisation :			CO2-Düngung,Faktor beta:			factor beta de fertilización de CO2:			Factor beta de fertilização CO2:			Bemestingsfactor:			CO2 удобрение фактор			CO2-gødskningsfaktor beta:			CO2-gjødslingsfaktor beta:			
	resp_q10			Temperature-respiration feedback factor (Q10):	(see @sinksbiosphere)																										
	cupwell			Ocean upwelling rate:	(see @sinksocean)		Vitesse d'upwelling océanique:			Tiefenwasser-Auftriebsrate:			Tasa de afluencia de agua en el oceano:			taxa de upwelling dos ocenaos:			snelheid van upwelling in de oceaan:			Интенсивность океанского апвеллинга:			hav cirkulations-hastighed:			hav sirkulasjons-hastighet:			
	chighlat			Ocean high-lat mixing rate:	(see @sinksocean)		taux de mélange océanique aux hautes latitudes:			Ozean hohe Breiten, Durchmischungsrate:			Tasa de mezcla oceanica a altas latitudes:			taxa de mistura do oceano nas latitudes elevadas:			oceanische mengsnelheid op hoge breedtegraad:			ocean high-lat mixing rate:			lodret opblanding på høje breddegrader:			loddret oppblanding på høie breddegrader:			
	csidemix			Ocean horizontal mixing factor:	(see @sinksocean)		facteur de mélange horizontal océanique:			Ozean, horizontaler Durchmischungsfaktor:			Factor de mezcla horizontal oceanica:			Factor de mistura horizontal nos oceanos:			horizontale mengfactor in de oceaan:			Интенсивность горизонтального перемешивания океана:			horisontal opblandingsfaktor:			horisontal opblandingsfaktor:			
	ceddydiff			Ocean eddy diffusivity factor:	(see @sinksocean)		facteur de diffusion turbulente océanique:			turbulenter Transport im Ozean, Faktor:			Factor eddy diffusivity oceánica:			Factor de difusão do eddy nos oceanos:			turbulente difusiefactor in de oceaan:			Коэффициент турбулентной диффузии в океане:			effektiv diffusionskoefficient for kulstof (cm2/s):			effektiv diffusionskoeffisient for karbon (cm2/s):			
	asgasex			Air-sea gas exchange rate:	(see @sinksocean)		taux d'échange gazeux océan-atmosphère			Luft-Meer Gasaustauschrate:			Tasa de intercambio de gas aire-mar:			taxa de troca de gases entre o ar e o mar:			gasuitwisselingscoëfficient			Скорость газообмена воздух-вода:			atmosfære-hav gasudvekslingsrate:			atmosfære-hav gassutvekslingsrate:			
	chemfbopt		CF	Carbon Chemistry - Temperature Feedback	(see @carbchem, @flowchart)	CF	Chimie du carbone - rétrocontrôle sur la température		TF	Kohlenstoffchemie - Temperatur-Feedback		QC	Química del carbono - Feedback de temperatura		QC	Quimica do carbono - feedback da temperatura		CF	Koolstofchemie - retrocontrole op de temperatuur					CF	Feedback fra temperaturen på havets kulstofkemi		CF	Tilbakemelding fra temperaturen på havets karbonkjemi			
	carbchemmenu		CarbChem	Carbonate chemistry formulae	see @carbchem	Carbonate	Chimie des carbonates		ChemKohle	Chem. Formeln der Kohlenstoffverbindungen		CarbChem	Fórmula del carbonato químico		Carbonato	Formula química do carbonato		Koolstof	Koolstofchemie					CarbChem	Vælg Karbonat-kemiske formler		CarbChem	Velg Karbonat-kjemiske formler			
	realb		RealB	Real Chemistry inc Borate	Real Chemistry inc Borate (Ben iteration method) -see @carbchem																										
	realj		RealJ	Real Chemistry inc Borate	Real Chemistry inc Borate (Jesper iteration method) -see @carbchem																										
	hildaz0z1		Hilda	Original Hilda method	z0z1 Method used in original Hilda model  -see @carbchem																										
	cubicfit		Cubic fit		Better than Linear, but not so good as Real chemistry (note: Hilda options does better for low emissions scenarios, and this one for high emissions scenarios)																										
	linear		Linear	Linear: No effect of acidification	Many simple models assumed a linear response. However you can see that this significantly overestimates the ocean sink, compared to the formulae including the chemistry feedback. -see @carbchem																										
																															
	carbonemissions		Carbon Emissions		Historical fossil CO2 emissions are from CDIAC (add ref!) and land use change CO2 emissions data is from Houghton et al (add ref!), (unless you select the option to calculate historical land-use by mass-balance, fixing the atmospheric CO2 using the measured data from Mauna Loa).<br>  Future emissions are determined either by @mitigation module (applying a fixed fossil : landuse ratio) or by @sres module.																										
	carbonmodel		Carbon Cycle -How the Model Works		The carbon cycle is based on the Bern model, as used by IPCC. This was originally calibrated using chemical tracer and isotope data, and its predictions fall in the mid-range of model intercomparisons.<br>  ³Ocean sink: HILDA model ³<br>  (HILDA = High-Latitude Diffusion Advection)   <li>low-latitude (84% surface) divided into 36 layers   <li>depth-dependent vertical diffusion between layers   <li>high-latitude box, well-mixed   <li>horizontal advection between HL & LL   <li>slow upwelling loop (down in HL, up in LL)   <li>surface layer (HL and LL) exchanging with atmosphere   <li>non-linear carbonate chemistry with feedback from temperature<br>  ³Terrestrial Biosphere sink³<br>  4-box biosphere :   <li>green, wood, soil, humus boxes,   <li>linear fluxes between boxes and to atmosphere   <li>non-linear "CO2 fertilisation" factor 'beta'   <li>(note further development below)<br>  ³Calculation method³<br>  The entire system is solved using an efficient eigenvector calculation method with a ramp function for non-linear fluxes.<br>  <hr> See also<li> Joos et al 2001 (@references),<li>@eigenvec,<li>@compareipcc, IPCC-TAR WG1 Chapter 3																										
	carbonfuture	fut	Carbon Cycle -Future Development		<li>The simple 4-box biosphere is based on Bern model as used in IPCC-SAR. The Bern-CC biosphere as used for IPCC-TAR includes a more complex gridded dynamic vegetation model with many plant functional types, dependent on temperature an precipitation within each gridcell.  A java implementation of this was under development, and may be resumed.  <li>There is not yet any biology in the ocean sink model. The original assumption was that the biological pump is not climate or CO2 dependent, on the other hand it may be affected by circulation changes affecting nutrients, the range of this uncertainty should be illustrated.  <li>Note also @scalelanduse																										
	sinksdynamic		Carbon Sinks, Dynamic Response.		Both sinks increase in response to rising atmospheric CO2.<br>  It is easier to understand see this effect in a "forward" calculation, applying the "£~nopolicy" or the "£~stabemit" option (@emitmenu). Then, if you increase one of the sinks by adjusting the model parameters, the atmospheric CO2 falls slightly, and so the other sink drops. However, when you run the model in inverse mode, adjusting the sink parameters will cause the emissions to change, in order to continue to reach the target concentration or temperature curve (see also @inverse)².																										
	sinksbiosphere		Biosphere sink		The green/brown curves shows the amount of extra (anthropogenic) carbon taken up by the terrestrial biosphere (green plants, wood and soil) due to the "CO2 fertilisation" effect (photosynthetic carbon fixation is slightly more efficient at higher CO2 concentrations). You can adjust with with the @fertbeta control.<br>  Later, the biosphere sink begins to "saturate", as other factors such as water, sunlight and nutrients become more rate-limiting than CO2 for photosynthesis.<br> A simple temperature-respiration feedback has also been added, using a formula similar to that developed by Cox et al. You can adjust the 'q10' factor using the @resp_q10 control. The effect is to reduce the storage of carbon in the soil, especially in later years when temperatures are higher.																										
	sinksocean		Ocean mixing		The ocean has a very large capacity to store CO2 (due to chemical buffering - see below). However the mixing between surface water and deep water is very slow, so the uptake of anthropogenic CO2 is dependent on the mixing rate. This mixing is dominated by the vertical diffusion and horizontal advection.  The @ceddydiff (blue arrow) controls the rate at which CO2 mixes vertically in the bulk of the ocean.<p>  If you select "expert" from the @complexitymenu, you can see more controls, and can compare the relative importance of processes. The upwelling loop makes only a small difference. The effect of the gas-exchange rate is also small (unless you cut it altogether), since the mixed surface layer quickly catches up with the atmosphere.<p>  Note that the upwelling is more important in the heat-flux UDEB model (see @heatflux) which has no horizontal advection. There is some physical sense in this difference structure, since mixing depends on density gradients which depend on temperature, so this effect supresses mixing of heat in a way that does not affect CO2.																										
	carbchem		Carbonate Chemistry		CO2 reacts with alkaline seawater to form bicarbonate ions<br>  CO2 + H2O <=> HCO3- + H+<br>  This conversion reduces the partial pressure of CO2 in seawater, giving the ocean has a vast capacity to store CO2. Currently the ocean holds about 50 times more inorganic carbon than the atmosphere, 99% of it in the form of HCO3-.<br>  However adding CO2 to seawater makes it more acidic, which reduces this "buffer capacity". Increasing the temperature also affects the chemical equilibria and reduces the solubility of CO2 in seawater. Both these feedbacks (acidification and temperature) act to decrease the future ocean sink.<br>  If you select "linear" from the @carbchemmenu (expert level) you will switch off the acidification effect.<br>  If you disable the @chemfbopt you will switch off the feedback from temperature. You may observe that this removes the spikes in the historical ocean sink, which are caused by equivalent spikes in radiative forcing, especially volcanos.<br>  (see also @flowchart )																										
																															
oghga																															
	oghga		Other Gases/Aerosols	Other Greenhouse Gases and Aerosols	This module handles the emissions, atmospheric chemistry and radiative forcing for all gases except CO2<br>  £§iobinfo ££oghgahowwork ££oghgafuture	Autres gaz /aerosols																									
	othgasplot		Other Gases	Other gases influencing climate	£^apptag This shows the emissions, concentration or radiative forcing of CH4, N2O, and tropospheric Ozone (from emissions of CO, NOx, VOCs). Also incldued are emissions of SOx, and radiative forcing of F-gases and stratospheric ozone.<p>The emissions are prescribed by SRES scenarios, optionally mitigated proportionally to CO2 -see @othgasemit.<p>These gases are removed from the atmosphere mainly by oxidation. Their lifetimes vary widely and are affected by feedbacks involving OH radicals - see @atchem.<br>  £^interacs £^curves<br>  ²°adju The "other gas" curve in @radforplot shows the total forcing from all these greenhouse gases (excluding the aerosols). The @fgasplot shows the details of HFCs and CFCs. ²<br>  £^scales<br>  ²Note: Emissions and concentrations units are 1000 times smaller than for CO2, but the warming effect per molecule is much greater.²<br>  £^controls £^menopts ££atchem	Autres gaz	Autres gaz modifiant le climat		andere Gase	andere klimawirksame Gase		Otros Gases	Otros gases influyendo el climate					Andere gassen	Andere gassen die het klimaat veranderen/andere broeikasgassen ?		Другие парниковые газы	Выбросы, Концентрации и Радиационные воздействия другие парниковые газы								其它气体	其它力气候的气体
	othgasemit		Other gas	Options for Emissions of Non-CO2 gases	See the links above for the formula for each variant.<br>  These formulae scale the emissions of all gases other than CO2, including CH4, N2O, HFCs, PFCs,   SF6,  tropospheric ozone precursors (CO,VOC,NOx) and aerosols (SOx, Soot). Only the gases already controlled by the Montreal protocol (ClFCs, HClFCs) are excluded.<br>  ²°cogs Note: BC/OC aerosols are either scaled to CO, or to land-use change.  -see @radforaerosol ²<br>  <hr>The chosen formula apply to <b>all</b> stabilisation scenarios in JCM<br>  ²(see @stabemitdoc, @stabconcdoc, @stabrfdoc, @stabtempdoc, @stabseadoc)²<p>@sresscale is the default option for all JCM stabilisation scenarios.<br>  @sresfix (with scenario @A1B) was used in IPCC-SYR (see @ipccothgas)<br>  <hr>See also @othgasplot, @fgasplot, @radforplot																										
	sresfix			SRES fixed	This option simply sets the other gas emissions according to the no-policy SRES scenario chosen from @sresmenu, with no mitigation. This is the option used in IPCCTAR-SYR (see @ipccothgas).		Scénario d'émission (SRES) fixé			entspricht SRES			SRES fijado						Vast emissie scenario (SRES)												
	sresscale			Equal % of SRES as CO2	This option reduces the emissions of all gases in the same proportion, compared to a baseline SRES scenario  (choose from @sresmenu). The formula is:<p>(Em<sub>g,y</sub> / Es<sub>g,y</sub>) = (Em<sub>c,y</sub> / Es<sub>c,y</sub>)<p>Where: Em=mitigated emissions, Es=SRES emissions<p>c = CO2, g = any other gas, y = year		En % du scénario d'émission (SRES) de CO2			Equal % of SRES as CO2			Igual % del SRES como CO2						In % van het CO2 emissiescenario (SRES)												
	2000fix			2000 fixed	Other gas emissions set at 2000 level ? useful for comparing the effect of their different lifetimes (@othgasplot, @fgasplot)		niveau 2000			entspricht Jahr 2000			2000 fijado						Niveau 2000												
	2000scale			Equal % of 2000 as CO2	This option reduces the emissions of all gases in the same proportion, compared to their 2000 level. The formula is:<p>(Em<sub>g,y</sub> / E<sub>g,2000</sub>) = (Em<sub>c,y</sub> / E<sub>c,2000</sub>)<p>Where: Em=mitigated emissions, c = CO2, g = any other gas, y = year		En % du niveau de CO2 en 2000			Equal % of 2000 as CO2			Igual % del 2000 como CO2						In % van het CO2 niveau in 2000												
	sresrfopt	old	SRF	Fix non-CO2 RF according to SRES data		SRF	fixer le forçage radiatif des gaz autres que le CO2 en fonction des données des scénarios SRES		SRF	nicht CO2-Antrieb entsprechend SRES fixieren		SRF	Fijar CO2 no RF de acuerdo a datos SRES		SRF	Fixar o RF dos gases (excepto CO2) de acordo com os dados do SRES		SRF	de stralingsforcering van de niet CO2 gassen verankeren in functie van de gegevens van de SRES scenario's					SRF	Lad virkningen af ikke-CO2-gasser følge SRES data		SRF	La virkningen av ikke-CO2-gasser følge SRES data			
	oghgemit	old	Other gas	Options for Emissions of non-CO2 gases		Autres gaz	Options d'émission de gaz autres que CO2		andere Gase	Emissionen der nicht-CO2-Gase verändern		Otros gases	Opciones para emisiones de gases excluyendo CO2					Andere gassen	emissieopties voor niet-CO2 gassen		Другие газы	Выбросы Других парниковых газых									
	OHef		OHef	Hydroxy radical affects lifetime of CH4 and F-gases	See @atchem. (note, when this is disabled, the historical concentrations are not consistent with measurements)	OHef	Radical hydroxyle affecte les durées de vie du CH4 et des gaz fluorés		OHef	Hydroxylradikal beeinflusst Lebensdauer von Methan und Fluor-Gasen		OHef	Radical Hydroxi afecta tiempo de vida del CH4 y de gases-F					OHef	Hydroxyl radicaal bepaalt de levensduur van CH4 en andere gefluoreerde gassen												
	o3s1930		O3s1930	Stratospheric O3 RF baseline year 1930 (instead of 1970)		O3s1930	Forçage radiatif de O3 stratosphérique par rapport à 1930 (au lieu de 1970)		O3s1930	stratosphärisches Ozon, Basis 1930 (statt 1970)		O3s1930	Linea base RF O3 estratosférico año 1930 (en lugar de 1970)					O3s1930	Stralingsforcering van stratosferisch ozon ten opzichte van 1930 (i.p.v. 1970)												
	tarO3		TAR-O3	Use orignal IPCC-TAR Ozone formula	The original formula for calculating tropospheric ozone in IPCC TAR was corrected (by the same authors) to be more consistent with historical concentrations. Select this option for reproducing IPCC datatables made using the old formulae. - see @compareipcc																										
	oghgahowwork		Other gases: How it works		The formulae are identical to those used in the Bern CC Model, which was used to calculate the data given in IPCC-TAR-WG1 Ch6 and SRES appendix. The full set of equations are given in the appendix of Joos et al 2001 (@references).<p>The atmospheric chemistry formulae (See @atchem) are derived from the IPCC workshop of Prather et al. These include a simple parameterisation to calculate O3 and OH from CH4, NOx, CO and VOC, and the effect of OH on the lifetime of CH4 and of all the HFCs. The effect of N2O on it's own lifetime is also considered. The radiative forcing calculation includes the spectral overlap between CH4 and N2O.<p>The concentrations of the Montreal gases (CFCs, HCFCs) are prescribed by WMO data, and emissions are estimated by an inverse method.  See @fgases<p>For speed, the F-gas concentrations and radiative forcing are calculated using a ramp function over five year intervals, since the original emissions data is also in five-year intervals.<p>@othgasemit explains the formulae for scaling other gas emissions for mitigation scenarios. Note also @ipccothgas.																										
	atchem		Atmospheric Chemistry		The gases shown in the @othgasplot panel (calculated in @oghga module) are destroyed by chemical oxidation in the atmosphere, unlike CO2 which is absorbed into the ocean and biosphere sinks (see @carbonstoreplot).<p>Their atmospheric lifetimes vary widely. Tropospheric Ozone survives only a few days in the atmosphere, the lifetime of CH4 is just under a decade (depending on OH, see below), whilst the lifetime of N2O is just over a century (similar to CO2).<br>  ³CO, NOx, VOC, Ozone and Hydroxyl radicals³<br>  ² °adju Choosing "2000 fixed" from the @othgasemit shows the effect of different lifetimes (CH4 and ozone concentrations level off rapidly, N2O much more slowly). ²<p>Carbon Monoxide (CO) and Volatile Organic Carbons (VOC) are produced mainly by incomplete fossil fuel combustion (e.g. in cool engines or with insufficent oxygen), whilst NOx is produced mainly by the reaction of atmospheric nitrogen and oxygen in very hot engines. These gases have no direct influence on radiative forcing, but contribute to the formation of tropospheric ozone (O3), which is the an important greenhouse gas, the next after methane.<p>Note that we need ozone in the stratosphere where it protects us from ultraviolet radiation, but not so much in the troposphere where it causes adverse health impacts and photochemical smog.<p>CO, NOx, VOC and CH4 also affect the production of reactive hydroxy radicals (OH). These play a major role in atmospheric chemistry, including the oxidation of CH4 and the HFCs. Consequently CH4 has an effect on its own lifetime.<p>The atmospheric chemistry leading to formation and destruction of O3 and OH is very complicated, with dozens of transient species and reactions which are difficult to measure. The distribution of these gases is also highly variable over space and time. So this model uses some simple formulae taken from an IPCC workshop (Prather et al) (see @oghga module). Note that these may not be valid beyond the range of SRES scenarios for which they were calibrated.<br>  ³Minor radiative forcing effects³<br>  The model also accounts for the overlap in the radiative forcing of CH4 and N2O, and the small cooling effect of water vapour produced by the oxidation of methane in the stratosphere.<br>  ³Atmospheric Aerosols³<br>  The @othgasplot panel also shows Sulphate emissions (SOx), which lead to the production of sulphate aerosols -see @radforaerosol, @radforplot<p>Black carbon and organic carbon aerosols may also be calculated as a function of CO emissions (@oghga).<p>The oghga module predictions may be checked against data from IPCCTAR-WG1-SRESappx. See @radforplot, @compareipcc, note @tarO3 option.																										
	oghgafuture	fut	Other Gas Module: Future Development		<li>There could be strong biogeochemical climate feedback processes affecting natural methane emissions, for example the effect of melting Siberian permafrost. A simple way to explore this uncertainty should be added.  <li>Anthropogenic emissions of CH4 and N2O are mainly due to land-use factors, and this should be illustrated in the model.  <li>A related task is to incoporate emissions abatement cost functions for each gas, for use with mitigation scenarios, to replace simple scaling to SRES.  <li>There is also a large natural source of sulphate aerosols from marine phytoplankton, which may change as a function of ocean circulation. Although the potential feedback is poorly quantified, it should not be ignored.																										
	emit		Emiss	Emissions		Emiss	Emissions		Emissionen	Emissionen		Emisiones	Emisiones					Emiss	Emissies											发出	发出
	conc		Concn	Concentration		Concn	Concentration		Conzentration	Conzentration		Concentración	Concentración					Concn	Concentraties											农宿	大欺农宿
	rf		Rad-for	Radiative Forcing		For-Rad	Forçage radiatif											For-Rad	Stralingsforcering											放射力	放射力
	nitrogen		N	nitrogen		N	azote											N	stikstof											氮	氮
	sulphur		S	sulphur		S	soufre											S	zwavel											硫	硫
	cfc		CFC	CFCs (Montreal gases)	see @fgasplot for details	CFC	CFCs (protocole de Montréal)		CFC	CFCs (Montreal Protokoll)		CFC	CFCs (protocolo de montreal)		CFC	CFCs (protocolo de montreal)		CFC	CFCs (protocol van Montreal)			Фреоны (Монреальский протокол)		CFC	CFCer (Montreal protokol)		CFC	CFCer (Montreal protokol)		氯氟碳	氯氟碳的气 (Montreal 协定)
	hfc		HFC	HFCs, PFCs, SF6	see @fgasplot for details	HFC	HFCs, PFCs, SF6		HFC	HFCs, PFCs, SF6		HFC	HFCs, PFCs, SF6		HFC	HFCs, PFCs, SF6		HFC	HFCls, PFCs, SF6					HFC	HFCer, PFCer, SF6		HFC	HFCer, PFCer, SF6		氢氟碳	氢氟碳的气, 高氟碳的气, 六氟化硫
	strato3		O3S	stratospheric ozone		O3S	ozone stratosphérique		O3S	stratosphärisches Ozon		O3S	ozono estratosférico		O3E	ozono estratósferico		O3S	stratosferisch ozon			Стратосферный озон		O3S	stratosfærisk ozon		O3S	stratosfærisk ozon		同臭气	同温层的臭气
	ch4		CH4	Methane (CH4)		CH4	méthane		CH4	Methan		CH4	metano		CH4	metano		CH4	methaan			Метан		CH4	metan		CH4	metan		甲烷	甲烷
	n2o		N2O	Nitrous oxide (N2O)		N2O	oxyde nitroux		N2O	Lachgas		N2O	oxido nitroso		N2O	oxido de azoto		NOx	stikstofoxide			Закись азота		N2O	lattergas		N2O	lystgass		二氮氧	二氮化氧
	tropo3		O3T	Tropospheric Ozone (O3)		O3T	ozone troposphérique		O3T	troposphärisches Ozon		O3T	ozono troposférico		O3T	ozono tropósferico		O3T	troposferisch ozon			Тропосферный озон		O3T	troposfærisk ozon		O3T	troposfærisk ozon		对臭气	对流层的臭气
	tropo3du		O3T	Tropospheric Ozone (Dobson Units)		O3T	Ozone stratosphérique (unités Dobson)		O3t	Troposphärisches Ozon (Dobson Units)		O3t	Ozono Troposférico (Unidades Dobson)					O3t	stratosferisch ozon (Dobson eenheden)											对臭气	对流层的臭气(Dobson Units)
	co		CO	Carbon Monoxide (CO)		CO	Monoxide de carbone		CO	Kohlenstoffmonoxid		CO	Monoxido de Carbono					CO	koolstofmonoxide											一氧碳	一氧化碳
	voc		VOC	Volatile Organic Carbons (VOCs)		VOC	Carbures organiques volatiles		VOC	flüchtiger organischer Kohlenstoff		VOC	Carbonos Orgánicos Volatiles					VOC	vluchtige organische componenten											挥机碳	挥发有机的碳
	nox		NOx	NOx = NO, NO2, NO3 (in Tg N)		NOx	NO, NO2, NO3 (en Tg N)		NOx	NO, NO2, NO3 (in Tg N)		NOx	NO, NO2, NO3 (in Tg N)					NOx	NO, NO2, NO3 (in Tg N)											氧化氮	一氧化氮, 二氧化氮, 三氧化氮 (垓克氮)
	oh		OH	Hydroxyl Radical (OH)		OH	Radical hydroxyle		OH	Hydroxylradical		OH	Radical Hydroxylo					OH	hydroxylradicaal											氧氢基	氧氢基
	sox		SOx	Sulphate (SO2, SO3, in Tg S)		SOx	Sulfate (en Tg S)		SOx	Sulfat (in Tg S)		SOx	Sulfato (in Tg S)					SOx	sulfaat (in Tg S)											硫醃盐	硫醃盐 (垓克硫)
																															
fgas																															
	fgasplot		F/Cl-gases	F/Cl-gases (HFCs, ClFC etc.s)	£^apptag This plot shows the emissions, concentrations or radiative forcing from SF6, PFCs, HFCs, ClFCs, HClFCs, HClCs, ClCs. £^interacs £^curves<br>  <hr>The curve colours depend on the chemical formula of each gas:<li>Blue: Chlorine (Cl), Bromine (Br).<li>Red: Hydrogen (H)<li>Green: Fluorine (F),<li>Darker: more Carbon (or S, counts as 1.5 C)<p> The gases containing chloring/bromine destroy stratospheric ozone and were phased out since the Montreal Protocol. So you can see the transition in emissions from CFCs (blues) to HClFCs (pinks) to HFCs (browns) to PFCs and SF6 (green). The pattern is different for concentrations, due to the varying lifetimes (see @fgases).  <br>  ² °adju The negative forcing due to ozone depletion is also shown when @stack is disabled ²<br>  £^scales<br>  ²Note the concentrations are 1000 times smaller than on @othgasplot and 1000,000 times smaller than on @atco2plot.  However the warming per molecule is much greater! ²<br>  £^controls £^menopts ££fgases	gaz F/Cl	SF6, PFCs, HFCs, HClFCs, ClFCs, HClCs, ClCs		F/Cl-Gase	SF6, PFCs, HFCs, HClFCs, ClFCs, HClCs, ClCs		F/Cl-gases	SF6, PFCs, HFCs, HClFCs, ClFCs, HClCs, ClCs					gassen F/Cl	SF6, PFCs, HFCs, HClFCs, ClFCs, HClCs, ClCs		Фреоны	Выбросы, Концентрации и Радиационные воздействия Фреон								氟/氯气体图像	
	fgases		About F/Cl -gases		(See also @fgasplot panel, @oghga module)<br>  ³Source, lifetime, ozone³<br>  These gases (also known as freons, halocarbons etc.) are entirely anthropogenic (except CCl4). They were manufactured specifically because they are inert (unreactive) in the troposphere. The CFCs, HCFCs, HFCs etc. were developed mainly as propellants and refrigerants. SF6 is used mainly during the manufacture of silicon chips.<p>However, this inert nature gives them a long lifetime in the atmosphere, allowing enough time to reach the stratosphere where vertical mixing is very slow. There, they are split by intense u.v. radiation, and the chlorine atoms in CFCs and HCFCs are released, catalysing the destruction of the stratospheric ozone layer.<p>HCFCs were considered slightly better than CFCs, as the hydrogen atom makes them more vulnerable to reaction in the troposphere, so they have a shorter lifetime and fewer molecules make it to the stratosphere. Nevertheless HCFCs also destroy ozone, and production of both CFCs and HCFCs are now limited by the Montreal protocol.<p>The global aeverage radiative forcing from these gases is partially offset by the reduction of stratospheric ozone, which is also a greenhouse gas. Howevever, beware that the spatial and temporal distributions of these forcings are different.<br>  HCFCs are now being replaced by HFCs, which do not destroy ozone but are still powerful greenhouse gases. Some of the SRES scenarios (e.g. A1T) anticipate increasing production of these gases, although they are included in the Kyoto protocol "basket" of greenhouse gases.<p>The pure F gases CF4, C2F6 and SF6 (green on @fgasplot panel) are even more inert and have atmospheric lifetimes of thousands of years. CFC12 also has a lifetime of several centuries. You can see this, by switching from emissions to concentration. Emissions of CF4 and SF6 are quite modest, but their concentrations are relatively larger due to accumulation. SF6 also has a particularly high radiative forcing per molecule.<br>  ³Changing emissions and lifetime³<br>  The emissions show the transition in manufacture of these gases, from CFCs to HCFCs to HFCs (from blue to pink to brown on @fgasplot panel)<p>For the gases controlled by the Montreal protocol (CFCs, HCFCs), the concentrations are prescribed according to WMO data, the same for all scenarios.<p>The emissions of the other gases are dependent on the SRES scenario (SRES menu, top bar). They may also be scaled to the CO2 mitigation, depending on which option you choose from  @othgasemit<p>The atmospheric lifetime of each HFCs is also dependent on the concentration of reactive hydroxy radicals (OH), which is itself dependent on the concentration of CO, NOx, VOx and CH4. So adjusting emissions of these gases will also affect HFC concentrations slightly. These atmospheric chemistry interactions are explained in  @atchem.<br>  The total radiative forcing from CFCs and HFCs may be seen on @othgasplot and @radforplot  (expert level).<br>  ³Names³<br>  Common names for the freons use a numeric code:<br>  CFCxyz or HFCxyz has:   <li>x+1 C carbon atoms (if x=0 it's not shown)   <li>y-1 H hydrogen atoms   <li>z F fluorine atoms   <li>5 +2x -y -z Cl chlorine atoms.																										
	SF6		SF6	Sulphur Hexafluoride																											
	CF4		CF4	Carbon tetrafluoride																											
	C2F6																														
	CHF3		CHF3	HFC23																											
	C2H2F4		C2H2F4	HFC134a																											
	C2H4F2		C2H4F2	HFC152a																											
	C2HF5		C2HF5	HFC125																											
	CH2F2		CH2F2	HFC32																											
	C2H3F3		C2H3F3	HFC143a																											
	C3HF7		C3HF7	HFC227ea																											
	C3H3F5		C3H3F5	HFC245ca																											
	C5H2F10		C5H2F10	HFC4310mee																											
	CHF2Cl		CHF2Cl	HCFC22																											
	C2H3FCl2		C2H3FCl2	HCFC141b																											
	C2H3F2Cl		C2H3F2Cl	HCFC142b																											
	C2HF3Cl2		C2HF3Cl2	HCFC123																											
	C2H3Cl3		C2H3Cl3	CH3CCl3																											
	CFCl3		CFCl3	CFC11																											
	CF2Cl2		CF2Cl2	CFC12																											
	C2F3Cl3		C2F3Cl3	CFC11																											
	C2F4Cl2		C2F4Cl2	CFC114																											
	C2F5Cl		C2F5Cl	CFC115																											
	CCl4		CCl4	Carbon Tetrachloride																											
	CF2ClBr																														
	CF3Br																														
	eescl			Effective Equivalent Stratospheric Chlorine (effect on ozone).	This shows the effect of  stratospheric ozone depletion caused by ClFCs. Since ozone is a greenhouse gas, it gives a negative radiative forcing	EClS	Equivalent en chlore stratosphérique		EESCl	Effective Equivalent Stratospheric Chlorine		EESCl	Equivalente Efectivo de Cloro Estratosférico					EClS	Equivalenten stratosferisch ????											同层氯	同温层的氯 (Effective Equivalent Stratospheric Chlorine)
																															
radfor																															
	radfor		Radiative Forcing	Extra heat flux from greenhouse gases, aerosols, and solar variability	This module calculates the total radiative forcing, as the sum of that from CO2, all the other greenhouse gases and aerosols(from @oghga ), solar variability and volcanos. It also distributes the forcing between the 4 surface boxes of the @heatflux module<br>  £§iobinfo ££radforintro ££radforco2 ££radforothgas ££radforaerosol ££radforsolvol ££radfordistrib  ££radforipcc ££radforfuture	Forçage radiatif			:Strahlungsantrieb	:Strahlungsantrieb		:Radiación forzada	:Radiación forzada																		
	radforplot		Radiative Forcing	Extra heat flux from greenhouse gases, aerosols, and solar variability	£^apptag This plot shows the forcing due to greenhouse gases, aerosols (sulphate and smoke), and external forcing (solar variablility, volcanos).   <li> ²The concept of "radiative forcing" is introduced later.²  <li> ²°adju You also have the option to stabilise forcing -see @stabrfdoc, @co2eq²<br>  £§graphinfo ££radforintro ££stabilisationrf  ££radforco2 ££radforothgas ££radforaerosol ££radforsolvol ££radfordistrib ££rftemp ££radforipcc	Forçage radiatif	variation d'énergie reçue due aux gaz à effet de serre, aux aérosols et à la variabilité solaire (W/m2)		Strahlungsantrieb	Veränderung der Netto-Einstrahlung durch Treibhausgase, Aerosole und Sonnenaktivität: W /m2		Radiación Forzada	Cambio en el calentamiento debido a gases invernaderos, aerosoles & variabilidad solar W/m2		Radiative Forcing (Aquecimento)	Mudanças no aquecimento devido aos gases de efeito de estufa, aerosois e variabilidade solar W /m2		stralingsforcering	variatie van de ontvangen energie als gevol van broeikasgassen, aërosolen en van de  variabiliteit van de zonnestraling (W/m2)		Радиационные воздействия	Изменение радиационных воздействий, В/м2		Klimapåvirkningen:	Drivhusgasser, aerosoler og sol-variationer W /m2		Klimapåvirkningen:	Drivhusgasser, aerosoler og sol-variasjoner W /m2		放射力图像	
	rfco2d			RF for CO2 doubling	This is one of the parameters tuned to GCM predictions -see @gcmfit, @climodmenu. Its effect on temperature is not intuitive, because the temperature rise for CO2 doubling is already fixed by @climsens. That is divided by this parameter to get warming per forcing, which is applied to the forcing from all gases. So increasing this parameter effectively reduces the influence of other greenhouse gases, aerosols and solar variability, compared to CO2.		forçage radiatif pour un doublement de CO2:			Antriebsfaktor für CO2-Verdoppelung:			r.f. para duplicar CO2:			r.f. para o duplicar de CO2:			stralingsforcering bij een verdubbeling van de CO2:						klimapåvirkning for CO2-fordobling:			klimapåvirkning for CO2-fordobling:			
	sulfrf2000			Sulphate RF (dir+ind) in 2000	See @radforaerosol		Forçage radiatif des sulfates (dir+ind) en 2000:			Sulfat-Strahlungsantrieb (dir+ind) im 2000:			RF Sulfato (dir+ind) en 2000:			factor r.f. do enxofre:			Stralingsforcering van sulfaten (dir+ind) in 2000:			Фактор р.в. сульфата:			Sulfater. klimapåv. usikkerhedsfaktor:			Sulfater. klimapåv. usikkerhetsfaktor:			
	solarrf2000			Solar RF in 2000	See @radforsolvol		Forçage radiatif solaire en 2000:			Sonnen-Strahlungsantrieb im 2000:			RF Solar en 2000:			factor r.f. solar:			Stralingsforcering van de zon in 2000:			Фактор сольнейние р.в.:			Solen klimapåv. usikkerhedsfaktor:			Solen klimapåv. usikkerhetsfaktor:			
	sv		SV	Include solar and volcano forcing	See @radforsolvol	VS	Incluire les forçages solaire et volcanique		SV	Strahlungsantrieb von Sonne und Vulkanen einschliessen		SV	Incluir influencia solar y de volcanos					SV	Solaire en vulkanische forcering opnemen												
	futsolopt		FSV	Future solar variability		FVS	variabilité solaire future		FSV	zukünftige Sonnenvariabilität		VSF	Variabilidad solar futura		FSV	Variabilidade solar futura		FVS	toekomstige zonnevariabiliteit		FSV	Прогноз солнечной изменчивости		FSV	Fremtidige sol-variationer		FSV	Fremtidige sol-variasjoner			
	volcfac			Volcano Forcing Factor	See @radforsolvol																										
	unif		Unif	RF uniformly distributed between 4 climate boxes		Unif	Forçage radiatif distribué uniformément sur les 4 boites climatiques		Unif	Strahlungsantrieb gleich verteilt zwischen 4 Klimaboxen		Unif	RF uniformemente distribuido entre 4 climate boxes					Unif	Stralingsforcering uniform verdeeld over de 4 klimaatscompartimenten												
	bcocwig		BCOC-Wig	Use Wigley formula for Black/Organic Carbon Aerosols	See @radforaerosol	BCOC-Wig	Utiliser la formule de Wigley pour le carbone-suie et les aérosols carbonés organiques		BCOC-Wig	Wigley-formel für Russ und org. Kohlenstoffaerosole		BCOC-Wig	Usar fórmula Wigley para aerosoles de carbono Negro/Orgánico					BCOC-Wig	de Wigley formule gebruiken voor roetkoolstof en organische aërosolen												
	HadA		HadA	Use Hadley experiment aerosol forcing	Added for UNFCCC model intercomparison exercise, assessing the Brazilian proposal	HadA	Utiliser le forçage aérosols de l'expérience de Hadley		HadA	Aerosolantrieb v. Hadley-Experiment benutzen		HadA	Usar experimento Hadley de radiación forzada					HadA	de forcering van aërosolen "forcering" van het Hadley experiment gebruiken												
	4gas		4gas	Only 4 gases: CO2, CH4, N2O, sulphate aerosol	Option for UNFCCC model intercomparison exercise, assessing the Brazilian proposal -see @attribution	4gaz	Seulement 4 gaz : CO2, CH4, N2O, aérosol sulfaté		4Gase	nur 4 Gase: CO2, CH4, N2O, Sulfataerosol		4gas	Solo 4 gases: CO2, CH4, N2O, aerosoles sulfatos					4gaz	Alleen 4 gassen: CO2, CH4, N2O, zwavelhoudende aërosolen												
	radforintro		Influences on Radiative Forcing		<p>Radiative Forcing is a convenient concept for combining various influences on the heat balance of the earth. It refers to the change in instantaneous heating at the top of the troposphere, compared to the preindustrial climate. In response to radiative forcing, the earth's surface warms until the extra infra-red radiation it emits balances the extra incoming forcing.<p>² °glob For more explanation of the RF concept, see IPCCTAR WG1 Chapter six. ²																										
	radforco2		RF of CO2		RF For CO2 the radiative forcing is proportional to the logarithm of its concentration (this is due to the partial saturation of the absorption of infra-red by CO2, because its concentration is relatively high compared to other trace gases) .<p>CO2 rf = f x ln([CO2]/[CO2]prein)/ln(2)<p> where f= @rfco2d (parameter in @heatflux  included in @gcmfit ) <p> Consequently doubling the CO2 concentration has the same warming effect, regardless of the baseline.																										
	radforothgas		RF of other greenhouse gases		The other gas curve shows the sum of the radiative forcings from CH4, N2O, O3, CFCs and HFCs. Currently the radiative forcing from the other gases combined is almost as great as that from CO2. However methane and tropospheric ozone have much shorter atmospheric lifetimes than CO2, and so they become relatively less important in the longer term.  For more detail see @othgasplot, @fgasplot																										
	radforaerosol		Aerosol Radiative Forcing		Sulphate aerosols and white smoke reflect sunlight and there have a cooling effect (negative radiative forcing), whilst black smoke (soot) absorbs sunlight and has a warming effect.<p>Sulphate gases also combine with water to form droplets of sulphuric acid, which act as condensation nuclei for cloud droplets, which then reflect even more sunlight. This is known as the "indirect" effect.<p>Since these droplets are then removed from the atmosphere as acid rain, their atmospheric lifetime is very short, so these effects are greatest in industrial regions. In developed countries, the cooling effect of aerosols was greatest in 1970s-1980s and is now declining due to attempts to reduce acid rain by using of cleaner fuels or putting "scrubbers" on chimneys.<p>The magnitude of the sulphate aerosol radiative forcing (especially the indirect effect) is very uncertain, so an adjustable parameter is provided for you to experiment with this. Try to get a good fit between the calculated and measured temperatures on the @glotempplot.<p>The radiative forcing from carbon aerosols is even less certain. In 2000 the radiative forcing (W/m2) was assumed to be:<br>  <table><br>  <tr><td></td><td>Fossil Fuel</td><td>Biomass Burning</td></tr><br>  <tr><td>White (organic)</td><td>-0.1</td><td>-0.4</td></tr><br>  <tr><td>Black (soot)</td><td>+0.2</td><td>+0.2</td></tr><br>  </table><p>In the future, Bern-CC model simply scales these to CO emissions, whilst Wigley-Raper model assumes the fossil component scales with sulphate aerosols and the biomass part with "gross deforestation". By default the former is used, but the "BCOCWig" option (expert level) selects an approximation to the latter.   <li>See @oghga module for calculation formulae																										
	radforsolvol		Solar Variability and Volcanos		The direct impact of solar variability is rather small, however various mechanisms have been proposed which may amplify this. In @radforplot the curve from Hoyt and Schatten has been scaled to produce a forcing of 0.3W/m2 in 2000 compared to 1750 (as suggested by IPCCTAR-WG1-ch6).<p>As well as the regular eleven year sunspot cycle, there seems to be increased heating during the period 1910-1940, and a slight cooling thereafter. This helps to explain part of the shape of the historical temperature curve, but it cannot explain the particularly rapid warming since 1970 (See @glotempplot)<p>Since the magnitude of this effect is very uncertain, an adjustable parameter is provided for you to experiment with this.<p>In addition, the dust produced by major volcanic eruptions has a short term impact on global climate, the most recent example being Mt Pinatubo in 1991. These spikes are also visible in the carbon cycle, due to the feedback from temperature. However this forcing has no impact on the long term trends.																										
	radfordistrib		Different spatial distribution of forcing		It is assumed in @radforplot, that we can compare and sum the radiative forcing caused by various greenhouse gases, aerosols, and solar variability, which helps to illustrate the relative importance of various factors. However it must be emphasised that forcings with different regional distributions, cannot be considered to offset each other. For example, sulphate aerosols have a short lifetime so their cooling effect is felt mainly in industrialised areas of northern continents. Stratospheric ozone depletion is biased towards the polar regions. The effect of greenhouse gases may be greatest during winter nights, whilst the effect of increased solar activity may be greatest during summer days.<p>Some of these effects are considered by unevenly distributing the radiative forcing between the four surface boxes of the climate model (@heatflux). Try the @unif option to switch off this effect.<p>The distribution between the four surface boxes of the climate model is:   <li>Sulphate direct, black carbon aerosols: strong bias to north land   <li>Sulphate indirect, tropospheric ozone: weaker bias to north land   <li>Organic carbon aerosols (biomass burning): bias to south land   <li>Stratospheric ozone depletion: bias to south   <li>Other greenhouse gases, solar and volcano: evenly distributed.																										
	radforipcc		Correspondence with IPCC data		If you select expert complexity level, and then click the button "ipccdata" (top panel) you will see circles superimposed on this plot, which show the data published in the IPCCTAR-WG1-SRES appendix. These should be comparable with the model calculations, when "no-policy SRES scenarios" is also selected from the mitigation menu. See @compareipcc for more information.<p>² °cogs Note: Two sets of circles are given for CO2 R.F., derived from Bern-CC and W/R models. To match these data you should move the @rfco2d parameter to 3.71. For some GCM-fits this parameter is lower, but then is offset by a higher climate sensitivity (see above and @glotempplot). ²<p>²Another set of circles corresponds to the sum of BC+OC aerosol forcing using the Wigley formula (see above). ²<p>²The @tarO3 parameter (@othgasplot) should also be selected.²																										
	radforfuture	fut	RF Future Development		Aircraft contrails, dust and albedo changes are mentioned in IPCC Chapter 6, but not in the tabulated data for SRES scenarios, consequently they are not included here.<p>However the effect of aircraft contrails and Ozone from NOx could be shown, based on scenarios in the IPCC special report on aviation.<p>The most challenging development, would be to include the varying effect of unevenly distributed forcings (@radfordistrib) on the regional climate impacts (@regcli).																										
	sulfdir		SAD	sulphate aerosols direct		ASD	aérosols sulfatés (effets directs)		SAD	Sulfataerosole, direkt		ASD	aerosoles sulfatados directo		AED	aerosois de enxofre directos		ASD	zwavelhoudende aërosolen (directe effecten)		ACD	Аэрозоли Сульфата direct		SAD	sulfat aerosoler direkte		SAD	sulfat aerosoler direkte		硫直	硫醃盐悬浮微粒直接效应
	sulfind		SAI	sulphate aerosols indirect	The indirect effect is caused by the cloud-seeding properties of acidic sulphate aerosols, which form very good cloud condensation nuclei .	ASI	aérosols sulfatés (effets indirects)		SAI	Sulfataerosole, indirekt		ASI	aerosoles sulfatados indirecto		AEI	aerosois de enxofre indirectos		ASI	zwavelhoudende aërosolen (indirecte effecten)		ACI	Аэрозоли Сульфата indirect		SAI	sulfat aerosoler indirekte		SAI	sulfat aerosoler indirekte		硫间	硫醃盐悬浮微粒间接效应
	blackcarbon		BC	black carbon	Black smoke - mainly from incomplete combustion of fossil fuel - warming efect	BC	carbone suie'		SK	Schwarz Kohlenstoff		CN	carbono negro		CP	carbono preto		BC	roetkoolstof					BC	sod		BC	sot		黑碳	黑碳的悬浮微粒
	orgcarbon		OC	organic carbon	Light coloured smoke -mainly from biomass burning following deforestation - this has a temporary cooling effect.	CO	carbone organique		OK	organischer Kohlenstoff		CO	carbono organico		CO	carbono orgânico		CO	organische koolstof			Органический углерод		OC	organisk kulstof		OC	organisk karbon		有机碳	有机碳的悬浮微粒
	bioburn		BB	biomass burning		BB	biomasse brulée		BB	Biomasse-Verbrennung		CB	combustión de biomasa		CB	combustão da biomassa		VB	verbrande biomassa			Сжигание биомассы		BB	biomasse-afbrænding		BB	biomasse-forbgrenning			
	landalbedo		LUA	land-use albedo		AU	albédo continental		LA	Landnutzung, Albedo		AUT	albedo del uso de la tierra		AUS	albedo do uso do solo		AU	continentaal albedo					AFA	albedo-forandring fra ændret arealanvendelse		AFA	albedo-forandring fra endret arealanvendelse			
	contrails		ACO	aircraft contrails		CA	sillages d'avion		FKo	Flugzeug-Kondensstreifen		CoA	contrails aereo		RA	rasto dos aviões		CA	condensstrepen afkomstig van vliegtuigen					ACO	fly kondensstriber		ACO	fly kondensstriper			
	aerosol		AER	total aerosols: Sulphate and Carbon (smoke)	see @radforaerosol	AER	aérosols (sulfate et fumée)		AER	Total Aerosole (Sulfat und Rauch)		AER	aerosoles totales (sulfato and humo)		AET	aerosois totais (enxofre e fumo)		AER	aerosolen (zwavel en rook)			Общее содержание аэрозолей (сульфаты и дым)		AER	totalt aerosoler (sulfater og røg)		AER	sum aerosoler (sulfater og røyk)		总悬浮	总额悬浮微粒(硫羽碳)
	solvar		SOV	solar variability		VSO	variabilité solaire		SOV	Sonnenvariabilität		VSO	variabilidad solar		VSO	variabilidade solar		VSO	solaire variabiliteit		СOИ	Сольнечная Изменчивость		SOV	Varierende energiindfald fra solen		SOV	Varierende energitilførsel fra solen		太阳	太阳易变性
	volcano		VOL	volcanos		VOL	volcans		Vul	Vulkane		VOL	volcanes		VUL	vulcões		VOL	vulkanen			Вулканы		VUL	vulkaner		VUL	vulkaner		火山	火山的悬浮微粒
	natvar		NAT	natural forcing (solar and volcano)	see @radforsolvol	VAN	variabilité naturelle (solaire et volcanique)		NAT	nat. Variabilität (Sonne und Vulkane)		NAT	variabilidad natural (solar and volcanes)		VAN	variabilidade natural (solar e vulcões)		VAN	natuurlijke variabiliteit (zon en vulkanen)			Природная изменчивость (солнечная и вулканическая)		NAT	naturlige variationer (solen og vulkaner)		NAT	naturlige variasjoner (solen og vulkaner)		天然	天然的易变性(太阳与火山)
	otherghg		OGH	other greenhouse gases (CH4, N2O, O3, CFCs, HFCs)	see @othgasplot for details	AG	autres gaz à effet de serre (CH4, NOx, O3, CFCs, HFCs)		aTHG	andere Treibhausgase (CH4, N2O, O3, CFCs, HFCs)		OGI	otros gases invernaderos (CH4, N2O, O3, CFCs, HFCs)		OGE	outros gases de efeito de estufa (CH4, N2O, O3, CFCs, HFCs)		ABKG	andere broeikasgassen (CH4, NOx, O3, CFCs, HFCs)			Другие парниковые газы (CH4, N2O, O3, CFCs, HFCs)		ADG	andre drivhusgasser (CH4, N2O, O3, CFCs, HFCs)		ADG	andre drivhusgasser (CH4, N2O, O3, CFCs, HFCs)		其他气	其他的怎室气体 (甲烷,二氮化氧,臭气, 氯氟碳, 氢氟碳)
																															
	totrf		TOT	total radiative forcing		TOT	forçage radiatif total		TOT	Total Strahlungsantrieb		TOT	radiación forzada total		TOT	radiative forcing total		TOT	totale stralingsforcering		ИТO	Сумма радиационных воздействий		TOT	Samlet klimapåvirkning		TOT	Samlet klimapåvirkning		总额	总额放射力
	strath2o		H2Os	Stratospheric water from CH4		H2Os	Eau stratosphérique issue de CH4		H2Os	Stratosphärisches Wasser aus CH4		H2Os	Agua estratosférica del CH4					H2Os	stratosferisch water afkomstig van CH4											同温水	甲烷做的同温层的水
																															
temp																															
	heatflux		Heat Fluxes	UDEB climate module	This module calculates the global surface temperature change, considering the energy balance due to radiative forcing in four surface boxes (north, south, land, ocean), and the slow uptake of heat by a multilayer upwelling-diffusion ocean.<br>  £§iobinfo <hr>See also @glotempplot, @oceantempplot, @rftemp, @gcmfit ££udebmodel  ££climodfuture	Fluxes de Chaleur																									
	glotempplot		Temperature	Global annual average temperature change	£^apptag This plot shows the change in global average surafce temperature. Historical measured and proxy temperatures are shown for comparison.<p>² °clou Regional temperature changes can be much greater than the global averages shown here! -see @regclimap ²<p>² °emit You can also set a target temperature stabilisation curve, and calculate the emissions to attain it: see @stabtempdoc ²<p>² °glob °cogs See @udebmodel and @gcmfit for explanation of how the model works²<p>² °adju The ocean layer temperatures and ocean mixing parameters are shown in @oceantempplot  ²<br>  £§graphinfo<br>  ££rftemp  ££stabilisationtemp ££gcmfit	Température	variation de la température annuelle globale (C)		Temperatur	Änderung der globalen mittleren Jahrestemperatur (°C)		Temperatura	Cambio de la media anual de temperatura (C)		Temperatura global	Mudanças da temperatura média anual global (C)		Temperatuur	variatie van de jaarlijkse mondiale temperatuur (C)		Температура	Изменение глобальной поверхностной температуры, оC		Temperaturen:	Ændringer i den globale middeltemperatur (C)		Temperatur:	Endringer i den globale middeltemperatur (C)		全球平均温度图像	
	tempav		AV	global average	Model calculation	MG	moyenne globale		GM	globales Mittel		PG	promedio global		MG	média global		MG	mondiaal gemiddelde		ВТ	Вычисленная глобальная средняя температура		AV	globalt gennemsnit		AV	globalt gjennomsnitt		平均	全球平均温度改变(C )
	tempproxy		PT	proxy data (average)	Proxy data from tree rings, corals, sediments, from 1750-1990 (from Mann et al, see @dataref)	TP	données interprétées d'après 'proxies' (moyenne)		PD	Proxydaten (Mittel)		DP	datos proxy (promedio)		DP	dados proxy(média)		TP	geïnterpreteerde gegegevens op basis van 'proxies' (gemiddeld)					PT	proxy data (middel)		PT	proxy data (middel)			proxy data (平均)
	tempdata		MT	measured data (average)	Measured thermometer data from 1860-2001(compiled by Jones et al, CRU, see @dataref)	TM	données mesurées (moyenne)		MD	gemessene Daten (Mittel)		DM	datos medidos (promedio)		DM	dados medidos(média)		TM	gemeten gegevens (gemiddeld)		ИТ	Измеренное изменение температуры (среднее)		MT	Målt global opvarmning		MT	Målt global oppvarmning		测量	测量 (平均)
	temptrend		TR	measured trend(7-year average)	Trend of either measured or proxy data (7 year moving average)	TT	évolution mesurée (moyenne sur sept ans)		TR	gemessener Trend (7-Jahr Mittel)		TM	tendencia medida (promedio de 7-años)		TM	têndencia medida(média de 7 anos)		TT	gemeten evolutie (gemiddeld over zeven jaar)		TR	Измеренный тренд (7-летнее среднее)		TR	Trend (7-års løbende gennemsnit)		TR	Trend (7-års løpende gjennomsnitt)		趋势	测量趋势(7-年平均)
	tempnl		NL	northern land	(calculated) difference from average depends on @lotr, @kns, @klo and @radfordistrib. Nl responds most rapidly to radiative forcing, but also has most of the cooling from sulphate aerosols.	TN	continent hémisphère nord		LN	Land, Nord		TN	tierra del norte		TN	terra do norte		TN	continent noordelijke hemisfeer		СЗ	Северное полушарие: суша		NL	nordlige halvkugle, land		NL	nordlige halvkule, land		北地	北方陆地
	tempno		NO	northern ocean	as NL above, note that surface ocean temperature also depends on heat flux from below (see @oceantempplot)	ON	océan hémisphère nord		ON	Ozean, Nord		ON	oceano del norte		ON	oceano do norte		ON	oceaan noordelijke hemisfeer		СО	Северное полушарие: океан		NO	nordlige halvkugle, hav		NO	nordlige halvkule, hav		北海	北方海洋
	tempsl		SL	southern land	as NL above There is less land  in southern hemisphere, but also less aerosols	TS	continent hémisphère sud		LS	Land, Süd		TS	tierra del sur		TS	terra do sul		TS	continent zuidelijke hemisfeer		ЮЗ	Южное полушарие: суша		SL	sydlige halvkugle, land		SL	sydlige halvkule, land		南地	南方陆地
	tempso		SO	southern ocean	as NO above.	OS	océan hémisphère sud		OS	Ozean, Süd		OS	oceano del sur		OS	oceano do sul		OS	oceaan zuidelijke hemisfeer		ЮО	Южное полушарие: океан		SO	sydlige halvkugle, hav		SO	sydlige halvkule, hav		南海	南方海洋
	climsens			climate sensitivity (equilibrium)	This parameter is one of the most important uncertainties. It is defined as the global average temperature rise caused by a doubling of CO2 concentration. The sensitivity takes into account the fast, physical feedback processes in the climate system, such as changes in water vapour, clouds, and snow albedo.		sensibilité climatique (à l'équilibre):			Klimasensitivität (Gleichgewicht):			sensibilidad climatica (equilibrio):			sensitividade do clima (equilibrio):			klimaatssensibiliteit (bij evenwicht):			чувствительность климата (равновесие):			klimafølsomhed (i ligevægt):			klimafølsomhet (i likevekt):			气候敏感(平衡):
	lotr			land-ocean temp ratio (equilibrium)	This parameter defines the ratio of warming of land compared to ocean at <i>equilibrium</i>. It is used, together with @climsens, to calculate the internal model parameters which determine the heat fluxes to space.		ratio températures continentale/océanique (à l'équilibre):			Land-Ozean Temp.verhältnis (Gleichgewicht):			relación de temperatura tierra-oceano (equilibrio):			razão da temperatura terra-oceano (equilibrio):			ratio continentale/oceaan temperatuur (bij evenwicht):						land-hav temperaturforhold (i ligevægt):			land-hav temperaturforhold (i likevekt):			陆地海洋临时雇员比率(平衡):
	kns			north-south conductivity	Determines the rate of heat transfer between northern and southern surface boxes		conductivité nord-sud:			Nord-Süd-Wärmeleitung:			conductividad norte-sur:			condutividade norte-sul:			noord-zuid conductiviteit/geleidingsvermogen????:						nord-syd varmetransport:			nord-syd varmetransport:			南北面传导性:
	klo			land-ocean conductivity	Determines the rate of heat transfer between land and sea boxes		conductivité terre-mer:			Land-Ozean-Wärmeleitung:			conductividad tierra-oceano:			condutividade terra-oceano:			aarde-zee-conductiviteit/geleidingsvermogen????:						land-hav varmetransport:			land-hav varmetransport:			陆地海洋传导性:
	baseyear			baseline year (temperature = zero)	Temperature <i>change</i> is relative to temperature in a certain year. This parameter affects the plotted temperature curves (although the @heatflux calculations are always relative to 1750) . For the measured/proxy data, the baseline is averaged over five years (chosen year 2) to smooth odd peaks. This parameter also affects the @stabtempl, but note @heatflux  always calculates relative to 1750, and the temperatures on the @regclimap are always relative to 1961-90.<p>  °adju It is recommended to set the baseline to 1765, for understanding the effect of science model parameters, and to 1990 for comparison with IPCC predictions.		année de référence (température = zéro)			Basisjahr (Temperatur = Null)			año de inicio (temperatura = cero)						referentiejaar(temperatuur = nul)												
																															
	climodmenu		GCM-fit	Fit Parameters to Global Climate Model	This affects a set of parameters of @heatflux module (shown on @glotempplot, @oceantempplot and @radforplot), based on the table in IPCCTAR WG1apx9.1<br> See @gcmfit  for explanation.	MCG idéal	Paramètres idéaux du modèle climatique global		GCM	Alle Parameter ans globale Klimamodell anpassen		GCM-Ajustar	Ajustar Parametros a Modelo de Clima Global		Parâmetros	Configurar parâmetros ao modelo global climático		ideaal MCG	Ideale parameters van een globaal klimaatmodel					GCM-fit	Fit parametre til at efterligne resultater fra Globale klimamodeller		GCM-tilpassning	Tilpassnings parametre for å etterligne resultater fra Globale klimamodeller			
	gfdl			GFDL_R15_a																											
	csiro			CSIRO Mk2																											
	hadcm3			HadCM3																											
	hadcm2			HadCM2																											
	echam4			ECHAM4/OPYC																											
	csm			CSM 1.0																											
	doe			DOE PCM																											
	ipccsar			IPCC SAR																											
																															
	oceantempplot		Ocean Temperature	Temperature in each layer of the ocean	£^apptag This plot shows the change in temperature in each layer of the ocean, demonstrating the slow transfer of heat between surface and deep water.   <li>² @udebmodel explains more about the layers and factors affecting the mixing.²  <li>² °glob Deep ocean temperature is a major factor in sea-level rise -see @sealevelplot  ²  <li>² °adju Surface temperatures and parameters are shown in @glotempplot .<br>  £^interacs £^curves<br>  ² °cogs The curves simply correspond the temperature of each box in @heatflux .<li>Green-Cyan: South Ocean<li>Red-Pink: North Ocean<br> There are 70 boxes: 35 North, and 35 South, the upper layers are each 49m deep, the lower layers 196m deep. ²<br>  £^scales<br>  £^controls £^menopts<p>These parameters are usually tuned to a GCM chosen from the @climodmenu, as explained in @gcmfit ²<br>  <hr>²°cogs Note that the initial temperature profile is set such that the model starts in steady state in 1750 (i.e. if there were no forcing, there would be no temperature change). This profile is calculated analytically by fixing the surface temperature, and helps to reveal unrealistic combinations of parameters. ²<br>  ££gcmfit	Graphe température océanique	Température à chaque niveau de l'océan		Diagramm Meerestemperatur	Temp. in vers. Meerestiefen		Gráfica de Temperatura del Oceano	Temperatura en cada capa del oceano					Grafiek temperatuur van de oceaan	Temperatuur op elk niveau van de oceaan											海洋怎度图像	
	psi			polar sink temp ratio ('PSI')	The cold water which sinks around Greenland and Antarctica has a major influence on the deep ocean temperature. Since most of this cold salty water is formed near freezing ice, its temperature is always close to zero. This parameter determines the warming of this water, as a fraction of the warming of surface water elsewhere.		polar sink temperature ratio ('PSI '):			polare Senken, Temp.verhältnis ('PSI'):			relación temparatura sumidero polar ('PSI'):			razão do sumidoro da temperatura polar ('PSI'):			temperatuur ratio ('PSI ')polaire sink/biologische put:						relativ opvarmning af nedsynkende vand ved polerne:			relativ opvarmning av nedsynkende vann ved polene:			
	teddydiff			vertical eddy diffusivity	Affects the flux of heat from the surface to the deep ocean. Increasing this will cool the surface slightly, but warm the deep ocean, and consequently increase the sea-level rise due to thermal expansion.		diffusion verticale turbulente			vertikaler turbulenter Transport:			difusividad eddy vertical:			difusão vertical do eddy:			verticale turbulente diffusie			Коэффициент вертикальной турбулентной диффузии:			effektiv diffusionskoefficient for varme:			effektiv diffusionskoeffisient for varme:			
	tmixlay			mixed layer depth	Depth of the well-mixed surface ocean layer, above the thermocline.		profondeur de la couche de mélange			Tiefe der durchmischten Schicht:			profundidad capa mezclada:			profundidade da camada mista:			diepte van de menglaag			Глубина слоя перемешивания:			tykkelsen af det fuldt opblandede overfladelag:			tykkelsen av havets fullt opblandede overflatelag:			混合的层深度:
	seaice			sea-ice parameter	Introduces a difference between air and water temperatures in high latitudes.		paramètre de la glace de mer:			Meer-Eis Parameter:			parametro mar-hielo:			parametro mar-gelo:			parameter van zee ijs:						havis parameter:			havis parameter:			
	tufbopt		TU	Temperature -Upwelling Feedback	The rate of sinking of cold polar water (see @psi) may decrease as the surface temperature rises, due to stratification of the water column which weakens the thermohaline circulation.	TU	rétrocontrôle température-upwelling		TU	Temperatur -Feedback Tiefenwasserauftrieb		TU	Temperatura - Feedback de la afluencia de agua		TU	temperatura -Upwelling Feedback		TU	retrocontrole temperatuur-upwelling					TU	Feedback fra temperaturen til oceancirkulationen		TU	tilbakemelding fra temperaturen til havsirkulasjonen			
	tnoupwell			Temperature rise at which upwelling is reduced to zero	see @tufbopt		Hausse de la température pour laquelle l'upwelling est nul			Temp.anstieg, der Upwelling gegen Null reduziert			Aumento de temperatura donde afluencia de agua es reducida a cero						temperatuursstijging bij dewelke de opwelling van dieptewater nul is												
	uwbaserate			Upwelling Rate before temperature change	see @tufbopt		Vitesse d'upwelling avant le changement de température			Upwelling-Rate vor der Temperaturänderung			Tasa de afluencia de agua antes del cambio de temperatura						Snelheid van de opwelling van dieptewater voor de temperatuurverandering												
	uwredfrac			Proportion of upwelling affected by temperature feedback	see @tufbopt		Proportion de l'upwelling affecté par le feedback de la température			v. Temp. beeinflusster Anteil des Upwelling			Proporción de afluencia de agua afectada por el feedback de temperatura						Aandeel van de opwelling van dieptewater die beïnvloed wordt door de temperatuur feedback												
	udebmodel		UDEB Climate Model -How it Works		This is an efficient java implementation of the Wigley/Raper Upwelling-Diffusion Energy Balance (UDEB) model which was used to make many of the the smooth-curve plots in the IPCC-TAR WG1. Parameters are tuned to fit seven different GCM predictions, as described in IPCCTAR-WG1-Appx 9.1. The system of heat fluxes is resolved using an efficient eigenvector calculation method.<br>  ³Features of UDEB model³  <li>Four surface boxes: north & south, land & ocean   <li>In each box, the "lambda" values for calculating heat flux to space are derived from the prescribed equilibrium @climsens and @lotr parameters.   <li>Surface fluxes depend on @klo and @kns conductivities (these are fairly arbitrary, but have little impact on the global average temperature)   <li>Radiative forcing of aerosols and short-lived gases is unevenly distributed between boxes (e.g. most of the sulphate aerosol cooling is in the north-land box)   <li>Two 1-D Upwelling-Diffusion Oceans (north and south), connected only at surface:   <li>Fixed @teddydiff between layers (unlike carbon model).   <li>A high latitude downwelling "pipe" (rather than a separate box), for whose water temperature is a fixed fraction of the average temperature (@psi parameter).   <li>A @seaice parameter adjusts the water/air temperature ratio.   <li>The lag of the surface ocean warming also depends on the @tmixlay   <li>The rate of this downwelling/upwelling is reduced with temperature to account for changing thermohaline circulation (@tufbopt, @tnoupwell, @uwbaserate, @uwredfrac parameters).<br>  <hr>See also:<li>@eigenvec,<li>Raper et al 2001 and references therein (@references),<li>@compareipcc, IPCCTAR-WG1-Appx 9.1., IPCC technical paper (1997) describing an earlier version of this model.																										
	climodfuture	fut	Climate Module: Future Development		The next step from here, might be to investigate the possibility to develop an interactive java version of intermediate complexity models. The simplest of these is the Bern zonally averaged model with the 2.5D Ocean.																										
	rftemp		From radiative forcing to temperature		You can see how well the climate model predictions match the historical data, as you adjust the balance of radiative forcing from greenhouse gases, sulphate aerosols, and solar variability, which have different patterns over time (see @radforplot)<p>The surface temperature follows changes in radiative forcing fairly rapidly, with some delay due to exchange of heat with the deeper ocean. Note that short spikes in forcing (e.g. from volcanos) affect the land temperatures more than the ocean. The deep ocean, and hence sea-level rise, responds much more slowly. See also @inertia<p>You can also observe that some forcings are distributed unevenly between the four surface boxes (expert level). For example, most of the sulphate is in the northen land box.																										
	gcmfit		Fitting to GCM predictions		The credibility of a simple climate model depends on fitting the parameters to predictions from more sophisticated global climate models (GCMs). In IPCC-TAR seven GCMs were used for this purpose. This parameter fitting was carried out by Sarah Raper et al as described in IPCC-TAR WG1 Appx9.1 See @references, @compareipcc.<br>  °adju If you select the "expert" @complexitymenu level, you can see all the parameters changing together, as you choose different GCMs from @climodmenu. Surface flux (energy balance) parameters are shown in @glotempplot, ocean mixing parameters (affecting the inertia of the system) are in @oceantempplot. <br>  You can also adjust these parameters individually, to understand the effect of each one (see also @heatflux module)																										
sealev																															
	sealevel		Sea level Rise	Sea level rise (Thermal Expansion and Ice-Melting)	This module calculates sea-level rise considering thermal expansion of seawater (see @heatflux ), and ice melt from mountain glaciers, Greenland and Antarctica.  More information about each contributing factor  is given for each curve on @sealevelplot .<br>  £§iobinfo ££sealevelicemelt ££sealevelother ££sealevelfuture	Montée du niveau de la mer	Montée du niveau de la mer (Expansion thermique et fond de glace)																								
	sealevelplot		Sea level Rise	Global average sea level rise	£^apptag This plot shows the change in global average sea-level, due to thermal expansion (see  also @oceantempplot) and ice melting.<p>²°adju Note that sea-level continues to rise long after surface temperature stabilises, due to slow heat uptake into the deep ocean, and slow response of the ice-caps. (see @inertia)²<br>  £§graphinfo ££sealevelicemelt ££sealevelother	Montée du niveau de la mer	Moyenne globale de la montée du niveau de la mer (m)		Meeresspiegelanstieg	Anstieg im globalen Mittel (m)		Aumento del nivel del mar	Aumento global del nivel del mar (m)		Aumento do Nível dos Mares	Aumento médio global do nível dos mares (m)		Stijging van de zeespiegel	Mondiaal gemiddelde van de zeespiegelstijging (m)		Подъем уровня моря	Средний глобальный подъем уровня моря, м		Havstigningen:	Globale havstigninger, i middel (m)		Havstigning:	Globale havstigninger, i middel (m)		海平面的增加图像	
	totsl		TO	total		TO	total		TO	total		TO	total		TO	total		TO	totaal					TO	total		TO	total		总额	总额海平面的增加
	thermexp		TE	thermal expansion	Thermal expansion is calculated by the @heatflux  with a different expansion coefficient applied to the warming from each ocean layer. The physics of this expansion is relatively well understood. However it depends on the transfer of heat to the deep ocean, which varies in different ocean models.<p>°adju You can see this by changing the model in the "GCM-fit" menu (@climodmenu). For example, the ECHAM4 model predicts a much higher sea-level rise than the others, due to its high vertical mixing rate (paradoxically, this also lowers the surface temperature). See also @oceantempplot	DT	expansion thermique		TE	thermische Ausdehnung		ET	expansión termal		ET	expansão térmica		DT	thermische uitzetting					TE	termisk udvidelse		TE	termisk utvidelse			
	glaciers		GL	Mountain Glaciers	Glaciers outside polar regions. These are already receding fast in many regions and are a clear indicator of warming, they are also important to control water supply to major rivers.	GL	glaciers		GL	Gletscher		GL	glaciares		GL	glaciares		GL	gletsjers			Ледники		GL	gletchere		GL	isbre		冰川	冰川溶化
	greenland		GR	Greenland	Very uncertain! It is anticipated that ice-melting will exceed increased snowfall. In the longer term, IPCC-TAR predicts that the entire Greenland ice sheet may melt if there is a prolonged regional temperature rise of only 3C. This would lead to a sea-level rise of 5-6m.	GR	Groenland		GR	Grönland		GR	Groenlandia		GR	Gronelândia		GR	Groenland			Гренландия		GR	Grønland		GR	Grønland			
	antarctica		AN	Antarctica	Very uncertain! For moderate warming it is predicted that ice-melt at the periphery will be more than offset by increased snowfall in the centre of the continent, due to increased evaporation from the ocean, thus reducing sealevel. However the stability of the West Antarctic ice sheet (with similar volume to Greenland) is subject to much debate.	AN	Antarctique		AN	Antarktis		AN	Antarctica		AN	Antártica		AN	Antarctica			Антарктика		AN	Antarktis		AN	Antarktis			
	rfia		IA	Recovery from ice age	Constant background change	RP	poursuite de la déglaciation		IA	Erholung von Eiszeit		EH	recobrado de la edad de hielo		RG	recuperação da glaciação		RP	verderzetting van de deglaciatie					IA	indsvingning fra sidste istid		IA	innsvingning fra siste istid		冰期	冰期以后的篮板球
	freshwater		FW	freshwater reservoirs	Includes increase of water storage in dammed reservoirs, and decrease due to pumping from groundwater aquifers. Potentially a large contribution (see IPCCTARWG1ch11), however even the sign is uncertain, so it cannot be included yet.	RE	réservoirs d'eau douce		FW	Frischwasserreserven		AF	reservas de agua fresca		RA	reservas de água fresca		RE	zoetwater voorraden			Поверхностные воды		FW	tilførsel af ferskvand fra undergrunden		FW	tilførsel av ferskvann fra undergrunnen			
	sealevelicemelt		Ice Melting		The ice-melt components are very uncertain. The net effect of Antarctica and Greenland ice sheets depends on a balance between increasing snowfall due to increasing evaporation from a warmer ocean, and increasing melting of ice at the margins. The calculations in JCM are based on simple formulae fitted to data given in IPCC-TAR WG1 chapter 11. The  slow recovery from last ice age is a constant factor, consistent with observations. The moutain glaciers are divided into ten groups using method as for IPCC-SAR , but with parameters updated as for TAR.																										
	sealevelother		Sea-Level Other Issues		Note that local sea-level rise may be different from these global average values due to slow tectonic changes, and changes in atmospheric pressure and wind direction, especially in estuaries. Moreover, melting Antarctic ice raises sealevel more in the northern hemisphere, and vice versa for Greenland, as we shift the earth's centre of gravity.<p>There may also be a significant effect of changing terrestrial freshwater storage (lakes, dams, groundwater) partly due to anthropogenic activity. However, the uncertainty of this is so great (even regarding the sign), it is not yet included here.<p>See IPCCTAR WG1 Chapter 11 for more details.<p>You can check JCM predictions with the data from IPCC: see @compareipcc																										
	sealevelfuture	fut	Sealevel module: Future Development		The ice-melt calculation could be more sophisticated, and adjustable parameters added to illustrate uncertainty. It would be useful also to highlight areas vulnerable to sea-level rise on the regional climate map.																										
regcli																															
	regcli		Regional Climate	Regional climate from scaled GCM predictions	This module manipulates various gridded regional climate datasets from <a target="new" href="http://ipcc-ddc.cru.uea.ac.uk"> IPCC-Data-Distribution Centre (DDC) </a>. <br>  £§iobinfo<br>  <hr>@mapdata  loads the GCM datasets, which have been compressed to one byte per gridcell, for fast loading over the web. It also calculates statistics for each dataset.<p>@bord  loads the data for country/regional polygons, and calculates the averages within each polygon.<br>  ££regclipredict ££regclifuture	Climat regional	Climat regional derivé des données MCG								Mapa da temperatura	Mapa do aumento da temperatura (dimensionado com os dados do GCM)								Temperaturkort:	Kort over temperaturstigninger (skaleret fra GCM data)		Temperaturkart:	Kart over temperaturstigninger (skalert fra GCM data)			
	regclimap		Regional Climate Map	Regional climate change from GCM predictions, scaled to JCM warming	£^apptag This shows maps of various climatic variables (temperature, precipitation, wind etc.). You can see the <i>change </i>predicted by GCMs (scaled to the JCM global average temperature), or the baseline climatology (1961-1990), or both combined.<p>The data came from <a target="new" href="http://ipcc-ddc.cru.uea.ac.uk"> IPCC-Data-Distribution Centre (DDC) </a><br>  £§panelinfo £^mapstartlongitude ££regclimapuse ££regclipredict ££regclifuture	Carte climatique régionale	Distribution régionale du réchauffement (calibrée sur les MCG)		Temp.karte Regionen	Karte der Temp.erhöhung (von GCM-Daten)		Mapa Regional de Clima	Mapa de aumento de temperatura (escalado con los datos GCM)					regionale klimaatkaart	Regionale verdeling van de opwarming (geijkt op de GCM)		Региональный климат	Карта повышения температуры (scaled from GCM data)								地区性的气候地图	
	impacts		Climate Impacts and Adaptation		More documentation on this topic will be added later. Meanwhile, please explore the patterns in the @regclimap	Impactes et Adaptation aux changements climatiques																									
	regclimapuse		Using the Regional Climate Map		The colourscale loops (purple - blue- green - yellow -red - purple - blue etc.), in order to include extreme values without losing detail elsewhere.<br>  °adju When you move your mouse around the plot, the specific value (of temperature, precipitation etc.) is shown at the lower right. This corresponds to the specific latitude/longitude shown at lower left, and the date shown at top left. The average temperature for the country (or sub-region of large countries) is also shown.<br>  °adju It is very important to experiment with changing the months, to see the seasonal cycle.<br>  °adju You can rotate the map just by dragging it with your mouse																										
	regclipredict		Regional Climate Predictions		<p>The maps illustrate that regional seasonal climate changes can be much greater than the global average. Generally, the land warms more than the ocean. and warming is greatest at high latitudes in winter, but there are also some hotspots in the tropics during summer. Precipitation predictions vary greatly between GCMs, you should not place much trust in just one dataset.<p>(<i>add more here!</i>)																										
	regclifuture	fut	Regional Climate -Future Development		Much further work on regional impacts is anticipated. Some proposals are:  <li>Add more GCM datasets   <li>Blend different patterns for aerosols, greenhouse gases, aircraft contrails, albedo(?)   <li>Show patterns corresponding to El Nino, NAO, other oscillations.   <li>Calculate differences, ratios for any combination  <li>Calculate regional impact economic costs for specific regions   <li>Link to dynamic vegetation model (carbon / methane feedback?)   <li>Visualisation of specific local impacts evolving over time (e.g. flooding, water supply, vegetation changes)<p>The interpolation into country/region polygons will be developed further. It is anticipated that this may be used later to link regional climate calculations with socioeconomic data, to assess climate change impacts.<p>In the long term, rather than scaling GCM data to global averages from a simple model, we consider the possibility to develop an interactive version of intermediate complexity models.																										
	rotateopt		RO	Rotate Map	Rotates the map automatically <p> °adju To rotate the map manually, just drag it with the mouse																										
	mapstartlongitude		Map Start Longitude		£^info																										
	usereg		UseReg	Show average for each country / region	The average values (of temperature, precipitation etc.) are calculated for each polygon, corresponding to a specific country or region, and these are colored accordingly (on the same colorscale as the original data). The averages are calculated as required, for any datset, GCM change or baseline or combination. The name of each polygon and its average value will appear at the lower right, when you move your mouse over it (even if  this option is not selected). This option may be used with any set of regions (including all-nations, ocean regions etc.) -see @regions. ² Note that calculations may be slow with larger regions ²	UtiReg																								国家	显示每国家的平均
	land		Land	Land Only	Show land only (only for HadCM3 /HadCM2 grids)	Terre																								土地	只土地 (only Hadley grid)
	rescale		Color	Reset Colorscale (+/- 3 standard deviation)	Makes one cycle of the colorscale go from -3 to +3 standard deviations (of the whole scaled dataset). It doesn't change the underlying numbers, as shown when you move the mouse over the plot.																									颜色	定颜色尺度 (+/- 3 standard deviation)
	scaletojcm		Scale	Scale to JCM global average	This scales all map data (of any climate variable), by the global average temperature from JCM @heatflux  in a specific year (see @regcliyear control on @glotempplot), divided by the global average temperature from the GCM dataset. Temperatures are relative to 1961-90. This does not affect the baseline climate datasets.																										
	regcliyear			Year of temperature for scaling Regional map data	if @scaletojcm is enabled																										
	yearcycopt		YE	Cycle through years	automatic loop from 1750-2300, scaling the GCM data to JCM temperature.	AN	Faire avancer les années		YW	Jahre wechseln		CA	Ciclo a través de años		CA	Ciclo por anos		AN	De jaren doen opschuiven					VÅ	Vis år for år		VÅ	Vis år for år		年周	年周
	mapdata		Load GCM data		This class  loads the data from GCMs for use in @regcli, @regclimap																										
																															
	projection		View	Map Projection	Change the projection for viewing the map. (this does not affect the calculations.)																										
	grid		Grid	Lat/Long Grid Projection	A simple latitude-longitude grid. This overemphasises high-latitudes.																										
	coslat		Cos-Lat (eq-area)	Cosine Latitude (equal area)	A simple equal-area projection, made by scaling east-west distances by the cosine of the latitude.																										
	polar		Polar	Polar Projection	A simple polar projection (two hemispheres).																										
																															
	dataset		Data	Dataset (GCM prediction or baseline)	From this you can choose various GCM datasets   <li>The default is from Hadley Centre v3, SRES-A2, at 2.5x3.75 degrees   <li>Data from HadCM2 and some other older models is also available.   <li>The years correspond to the middle decade of a thirty year period (of the original GCM data, but this may be scaled to another year/scenario).   <li>The baseline data is the 1961-1990 climatology from CRU (land only). Note this has a high resolution, 0.5x0.5 degrees, so this data may take some time to download and plot   <li>The combination of the baseline plus the change (HadCM3), shows the actual climate under any scenario																									资料	挑选资料 (GCM prediction or baseline)
	had3_A2_20			HadCM3 2020s SRESA2																											
	had3_A2_50			HadCM3 2050s SRESA2																											
	had3_A2_80			HadCM3 2080s SRESA2																											
	basehad3			HadCM3 baseline 1961-90																											
	hhgsax80			HadCM2 2080s greenhouse+aerosol																											
	hhgsax20			HadCM2 2020s greenhouse+aerosol																											
	gggsa150			GFDL-R15 2050s greenhouse																											
	jjgsa180			CCSR/NIES 2080s greenhouse																											
	eegga180			ECHAM4 2080s greenhouse																											
	baseline			Baseline Climatology 1961-1990 av, 0.5 deg grid																											
	both1			Combined HadCM3 baseline plus HadCM3 change																											
	both2			Combined Climatology 0.5deg plus HadCM3 change																											
	quantity		Quantity	Choose Climate Variable	From this you can choose various climate variables. The available quantities depend on the dataset. If a chosen quantity (or combination) is not available, the map will just appear white.<p>The "mix" option combines tmax (red) /tmin (blue) /prec (green),  as a "summary" of the climate. This works best combined with the "baseline plus change" from the dataset menu. However this may plot slowly as the calculation combines six original datasets																									哪资料	挑选哪气候资料
	tmp			Average Temperature																											
	tmx			Maximum Temperature																											
	tmn			Minimum Temperature																											
	dtr			Day-Night Temperature range																											
	pre			Precipitation																											
	vap			Vapour Pressure (humidity)																											
	wnd			Wind speed																											
	cld			Cloud cover																											
	hum			Relative Humidity																											
	slp			Mean Sea Level Pressure																											
	rad			Incident Solar Radiation																											
	mix			Mixed Tmin, Tmax, and Precip																											
	both			Combined baseline plus HadCM3 change																											
																															
	month		Month	Choose Month		Mois	Choisir le mois		Monat	Monat wählen		Mes	Escoger Mes		Mês	Selecionar mês		Maand	De maand kiezen		Месяц	Выбор месяца		Måned	Vælg Måned		Måned	Velg Måned		月	挑选月
	monthcycopt		MO	Cycle through months	automatic loop<br>  Note, the automatic loops may be combined. They continue even while you simultaneously adjust controls on other plots. However, for this you need a fast computer!	MO	Faire avancer les mois		MW	Monate wechseln		CM	Ciclo a través de meses		CM	Ciclo por meses		MO	De maanden doen opschuiven					VM	Vis måned for måned		VM	Vis måned for måned		月周	月周
	jan		jan	January		jan	Janvier		jan	Januar		ene	Enero		jan	Janeiro		jan	Januari		Ян	Январь		jan	januar		jan	januar		一月	一月
	feb		feb	February		fév	Février		feb	Februar		feb	Febrero		fev	Fevereiro		fév	Februari		Фе	Февраль		feb	februar		feb	februar		二月	二月
	mar		mar	March		mar	Mars		mar	März		mar	Marzo		mar	Março		mar	Maart		Ма	Март		mar	marts		mar	mars		三月	三月
	apr		apr	April		avr	Avril		apr	April		abr	Abril		abr	Abril		avr	April		Ав	Апрель		apr	april		apr	april		四月	四月
	may		may	May		mai	Mai		may	Mai		may	Mayo		mai	Maio		mai	Mei		Ма	Май		may	maj		mai	mai		五月	五月
	jun		jun	June		juin	Juin		jun	Juni		jun	Junio		jun	Junho		juin	Juni		Юн	Июнь		jun	juni		jun	juni		六月	六月
	jul		jul	July		juil	Juillet		jul	Juli		jul	Julio		jul	Julho		juil	Juli		Юл	Июль		jul	juli		jul	juli		七月	七月
	aug		aug	August		août	Août		aug	August		ago	Agosto		ago	Agosto		août	Augustus		Ав	Август		aug	august		aug	august		八月	八月
	sep		sep	September		sep	Septembre		sep	September		sep	Septiembre		set	Setembro		sep	September		Се	Сентябр		sep	september		sep	september		九月	九月
	oct		oct	October		oct	Octobre		oct	Ocktober		oct	Octubre		out	Outubro		oct	October		Ок	Октябрь		oct	october		okt	oktober		十月	十月
	nov		nov	November		nov	Novembre		nov	November		nov	Noviembre		nov	Novembro		nov	November		Но	Ноябрь		nov	november		nov	november		十一月	十一月
	dec		dec	December		dec	Décembre		dec	Dezember		dic	Diciembre		dec	Decembro		dec	December		Де	Декабрь		dec	december		des	december		十二月	十二月
																															
reg																															
	regions		RegSet	Region Set	Choose a set of regions. These correspond to sets used in various models, datasets and intercomparison projects - the appropriate set depends on the question to be investigated. You can even consider all individual nations or even subregions (choose All-land). <p> This menu features in both @regclimap (see  @usereg ) and @regemitmap. <p> This menu is also available in @attributeplot. Currently attribution only works for JCM12 and SRES4 region sets,  however it is anticipated that all attribution, distribution and socioeconomic calculations may eventually be made with any set of regions. 																										
	bord		Country/Region borders		This module loads the polygon data from <a target='data' href='../data/poly.txt'> jcm/data/poly.txt</a> to make country/region borders in the maps. It is used by @regcli, @regclimap, @regemitmap. £§iobinfo																										
	reg		Reg Interface		This contains a list of the regions, used by @regshares, @costs, @distribplot, @costsplot. See @aboutregions, @regemitmap, @regions																										
	regintro				³ Regions: ³ ²(see also @aboutregions, @regemitmap, @regions )²<br>																										
	aboutregions		About JCM Regions		The 12 JCM regions in @regemitmap are used in all @distribplot plots.<br>  The six regions in warm reds, yellows, browns correspond to Annex B countries which agreed an emissions reduction target under the Kyoto protocol. The cool greens and blues are the developing countries. The regions are composed of countries sharing similar percapita emission histories as well as geographical proximity, and have no political significance.<p>²°adju The legend is given by the strip of coloured squares to the right of any regional plot. Move your mouse over each one to see its name pop up. ²<p>²°cogs Note @responsibility module (@attributeplot panel ) uses a different set of regions. Complete circle -ref regclimap²<p>²  Note, a curve for Denmark was added to @distribplot. This is because JCM was initially developed in  Copenhagen.<p>Later we might make an option, to show specific curves for any country, or to make regions from any combination of countries.²<br>  ££regiondatasource<br>  <hr>Future regional emissions may be mitigated according to various formulae -see @stabilisation, @distribution, @regshares module <p> Other region sets are also available -see @regions																										
	regemitmap		Emissions Regions Map	Show colours of curves in the emissions plots	£^apptag This shows the colours of curves in @distribplot and @costsplot .  ² °cogs This plot is now made with the same flexible framework as @regclimap (both extend @mapplot and get polygons from @bord). It is anticipated to develop this panel to show a variety of region sets, and to show maps of socioeconomic data<br>  ££aboutregions	Carte des régions d'émissions	Couleurs correspondant aux régions dans les graphes des émissions		Emissionenkarte Regionen	Farben entsprechen Regionen in Emissionsdiagrammen		Mapa de Emisiones Regionales	Colores corresponden a regiones en la gráfica de emisiones		Mapa das regiões das emissões	As cores correspondem a regiões no gráfico das emissões		Kaart van de emissiegebieden	Kleuren stemmen overeen met de gebieden in de emissiegrafieken		Карты выбросов по регионам	Цвета соответсвтуют регионам на графике выбросов		Kort over landegrupper:	Farver svarer til landegrupperne i udslipsplottet		Kart over landegrupper:	Farger svarer til gruppen av land i utslippsgrafen		发出地区的地图	
	distribplot		Distribution	Regional Distribution	£^apptag This plot shows CO2 emissions and socioeconomic data for twelve regions, from 1900 to 2100.   <li>See @aboutregions<p>Future Population, Energy, and GDP depend on SRES scenarios and illustrate some driving forces behind the scenarios.   <li>See @aboutsres<p>Future CO2 Emissions and Abatement depend on the combination of options from @emitmenu and @distribmenu in @mitigpanel.   <li> See @emitcc, @stabilisation,  @distribution<p>² °adju  Note: £§unspecified  ²<p>If @stabemit, @percapita, or @convpergdp are chosen, their parameter controls may appear on this plot. <p>The Kyoto protocol options also appear here   <li>See @kyoto<p>The historical emissions and their correlation with the socioeconomic data are also interesting   <li>See @regiondatasource @histemitobserv<br>  £§graphinfo	Distribution	Distribution par région											Verdeling	Verdeling per regio		Распределение	Региональные распределение									地区
	popplot	old	Population	Global Population (from SRES) billions		Population	population mondiale (scénarios SRES), en milliards		Bevölkerung	globale Bev. (SRES) in Milliarden		Población	Población global (de SRES) billones		População	População global em biliões (do SRES)		Bevolking	wereldbevolking (SRES scenario's), in miljarden		Население	Население мира (из ОДСВ), миллиарды		Verdens Befolkning	Global Befolkningsstørrelse (fra SRES-scenariet) i milliarder		Verdens Befolkning	Global Befolkningsstørrelse (fra SRES-scenariet) i milliarder			
	regemitplot	old	CO2 Emissions	Carbon dioxide emissions from fossil fuel, by region: GtC/yr		Émissions de CO2	Émissions de CO2 issues des combustibles fossiles, par région: GtC/an		CO2-Emissionen	CO2-Emissionen fossiler Brennstoffe pro Region: GtC/Jahr		Emisiones CO2	Emisiones de dioxido de carbono de combustible fósil, por region: GtC/a		Emissões de CO2	Emissões de dioxído de carbono de combustiveis fosseis, por região: GtC/ano		CO2 emissies	CO2 emissies uit fossiele brandstoffen, per regio: GtC/jaar					CO2-udledningen:	CO2-udledninger fra fossile brændsler, per landegruppe: GtC/år		CO2-utslipp:	CO2-utslipp fra fossile energi kilder, per gruppe av land: GtC/år		二氧化碳全球性发出编预算	
	percapplot	old	Per-capita CO2 Emissions	Fossil fuel CO2 emissions per capita: tC/pers/yr		Émissions de CO2 par personne	Émissions de CO2 issues de combustibles fossiles par personne: tC/pers/an		Pro-Kopf-CO2-Emissionen	CO2-Emissionen fossiler Brennstoffe pro Kopf: tC/Pers./Jahr		Emisiones per-capita de CO2	Emisiones de CO2 de combustible fósil per capita: tC/pers/a		Emissões de CO2 per-capita	Emissões de CO2 de combustiveis fosseis per capita: tC/pessoa/ano		CO2 emissies per persoon	CO2 emissies uit fossiele brandstoffen per persoon: tC/pers/jaar		Выбросы CO2 на душу населения	Региональные выбросы CO2 на душу населения, т С/чел./г		CO2 udslip per indbygger:	CO2-udledninger fra fossile brændsler per person: tC /pers/år		CO2 utslipp pr innbygger:	CO2-utslipp fra fossile energi kilder per person: tC /pers/år		每人的二氧化碳全球性发出编预算	
																															
																															
	JCM12			JCM 12 region set	See @aboutregions																										
	USA		USA	USA		US	Etats-Unis		US	USA		EUA	EUA		EUA	EUA		US	Verenigde Staten van Amerika		СШ	США		US	USA		US	USA		美国	美国
	CAZ		CAN	Canada, Australia, NewZealand		CA	Canada, Australie, Nouvelle-Zélande		KA	Kanada,Australien,Neuseeland		CA	Can,Aus,NZ		CA	Canada, Austrália, Nova Zelândia		CA	Canada, Australië, Nieuw-Zeeland		КА	Канада, Австралия, Новая Зеландия		KA	Kanada, Australien, NZ		KA	Kanada, Australia, NZ		加澳	加拿大,澳大利亚,新西兰
	JAP		JAP	Japan		JA	Japon		Ja	Japan		JA	Japon		JA	Japão		JA	Japan		ЯП	Япония		JA	Japan		JA	Japan		日本	日本
	EUW		WEU	West Europe (EU+)	EU15 + Norway, Switzerland	EO	Europe de l'Ouest (UE)		WE	W-Europe(EU)		EO	Europa del Oeste		EO	Europa Ocidental (UE)		EO	West-Europa (WE)		ЗЕ	Западная Европа (СЕ)		VE	Vesteuropa(EU)		VE	Vesteuropa(EU)		西欧	西欧洲
	EUE		EEU	East Europe	from Estonia to Bulgaria	EE	Europe de l'Est		EE	E-Europe		EE	Europe del Este		EL	Europa de Leste		EE	Oost-Europa		ВЕ	Восточная Европа		ØE	Østeuropa		ØE	Østeuropa		东欧	东欧洲
	RUB		RUB	Russia, Ukraine, Belarus		RU	Russie, Ukraine, Biélorussie		Ru	Russland,Ukr,Bel		RU	Rusia,Ucr,Bel		RU	Russia, Ucrância e Bielorussia		RU	Rusland, Oekraïne, Wit Rusland		РУ	Россия, Украина, Беларусь		RU	Rusland,Ukraine,Hviderusland		RU	Rusland,Ukraina,Hviterusland		俄乌	俄国,乌克兰,白俄罗斯
	MEC		MEC	Middle East, Central Asia	from Turkey to Pakistan including all the "-stans"	OA	Proche-Orient, Asie Centrale		MO	Mittlerer Osten,Zentralasien		ME	Med.Est,C.Asia		MO	Asia central e Médio Oriente		OA	Nabije Oosten, Centraal-Azië		ЦА	Средний Восток, Центральная Азия		ME	Mellemøsten,Centralasien		ME	Midtøsten,Sentralasia		中亚	中东中亚
	CHI		CHI	China		CH	Chine		Ch	China		CH	China		CH	China		CH	China		КИ	Китай		KI	Kina		KI	Kina		中国	中国
	AML		SCA	South / Central America		AS	Amérique du Sud et Centrale		SA	Mittel/S-Amerika		SA	Cent./S.America		AS	America do Sul e Central		AS	Zuid- en Centraal-Amerika		ЮА	Центральная и Южная Америка		SA	Central- & Sydamerika		SA	Sentral- & Sydamerika		南美	中/南美洲
	OAS		OAS	Other Asia	Bangladesh, S.E. Asia, Indonesia, Korea, Mongolia, Pacific Islands	AA	Autre pays d'Asie		RA	restl. Asien		OA	Otro Asia		OA	Outros países Asiáticos		AA	Andere landen van Azië		ДA	Остальная чась Азии		ØA	Øvrige Asien		ØA	Øvrige Asia		其亚	其它亚洲
	AFR		AFR	Africa		AF	Afrique		Af	Afrika		AF	Africa		AF	África		AF	Afrika		АФ	Африка		AF	Afrika		AF	Afrika		非洲	非洲
	IND		IND	India		IN	Inde		In	Indien		IN	India		IN	Índia		IN	India		ИН	Индия		IN	Indien		IN	India		印度	印度
																															
	WORLDTOT		WOT	World Total		TM	Total mondial		WT	Welttotal		TM	Total Mundial		TM	Total mundial		TM	Totaal wereld		ГCу	Глобальная сумма		VI	Verden ialt		VI	Verden totalt		世界	世界
	WORLDAV		WAV	World Average		MM	Moyenne mondiale		WM	Weltmittel		PM	Promedio Mundial		MM	Média mundial		MM	Gemiddeld wereld		ГCр	Глобальное среднее		GG	globalt gennemsnit		GG	globalt gjennomsnitt		界平	世界平均
	DEN		DK	Denmark		DK	Danemark		Dk	Dänemark		DK	Dinamarca		DI	Dinamarca		DK	Denemarken		ДА	Дания		DK	Danmark		DK	Danmark		丹麦	丹麦
	BUNKER		IAS	International Aircraft and Shipping	Bunker fuels	AC	Avions et cargos internationales		LS	Internat. Luft-/Schiffverkehr		TI	Transporte aereo y maritimo Internacional		TI	Transporte internacional marítimo e aéreo		AC	Vliegtuigen en internationale cargos		МТ	Международные воздушные и морские перевозки ("бункерное топливо")		IT	CO2-udslip fra international transport (fly- og skibstrafik)		IT	CO2-utslipp fra international transport (fly- og skipstrafikk)			
																															
	JCM50			JCM 50 region set	A base set of about 50 regions which may be combined to form any of the other (smaller) sets																										
	IMAGE			IMAGE-17	A set of regions used by RIVM's image model																										
	EDGAR			EDGAR	A set of regions used by RIVM's EDGAR database of historical emissions																										
	RICE			RICE94	A set of regions used by Nordhaus RICE94 model																										
	TGCIA			TCGIA	A set of regions defined as latitude/longitude boxes used by IPCC Task Group on Climate Impact Assessment																										
																															
	SRES4			SRES 4 region set																											
	OECD		OECD	OECD1990: Western Europe, USA, Japan, Canada, Australia, NewZealand																											
	REFREG		REF	Economies in transition: Former USSR and East Europe																											
	ASIA		ASIA	Asia (from Pakistan eastwards)																											
	ALM		ALM	Africa, Latin America, Middle East (to Iran)																											
																															
distrib																															
	people		People	Population/Economy/Energy	This module stores population, GDP, and secondary energy data, for the 12 regions (see @aboutregions). <br>  £§iobinfo  ££peopleintro ££peoplefuture	Peuple	Population/Economie/Energie																								
	peopleintro		People Module-How it Works		<p>Currently this module simply stores data, fixed for each scenario (see @aboutsres.  The socioeconomic data helps to reveal some of the driving forces behind the scenarios.<p>°adju You can view the socioeconomic data using the @varq menu on @distribplot , and also use it as a divisor to show quantites 'per capita', 'per dollar' etc., using the @perq menus on @distribplot, @attributeplot, @costsplot .<p>The socioeconomic data is also used for distributing regional CO2 emissions. -see @distribution																										
	regshares		Regional Shares	Regional Distribution of Emissions	This module calculates regional shares of future CO2 emissions according to various @distribution formulae.<br>  Each region's quota is the product of its share, multiplied by the global emissions from the @mitigation module<br>  This module also calculates regional emissions abatement, compared to SRES.<br>  £§iobinfo<br>  <hr>See also  @aboutregions, @distribution, @convergence, @effectofkyoto, @peoplefuture																										
	kyoto		Kyoto	Kyoto protocol	This module calculates the CO2 emissions (regional and total) during the period 2000-2013, if Kyoto option is enabled<br>  £§iobinfo ££kyotohowwork ££kyotofuture		Protocol de Kyoto																								
																															
	distribmenu		Distribn	Options for distribution of regional emissions	Choose distribution of future emissions between regions, see also @distribution	Distribution	Options pour la distribution régionale des émissions		Verteilung	Verteilung der regionalen Emissionen		Distribución	Opciones para distribuir emisiones regionales		Distribuição	Opções para distribuição de emissões regionais		Distribution	Opties voor de regionale verdeling van de emissies		Распределение	Выбор распределения выбросов по регионам		Fordeling	Valgmuligheder for fordelingen af udledninger mellem landegrupper		Fordeling	Valgmuligheter for fordelingen av utslipp mellom Grupper av land			
	percapita			Convergence to equal per-capita	Emissions allocations converge to reach equal per-capita levels in a target year. If you make a plot of emissions per capita, you can experiment with various adjustable paramaters, and see how the formula works.  <li>@convergence		Convergence du quota par personne			Annäherung an gleichmässiges Pro-Kopf-Einkommen			Convergencia a igual per-capita			Convergência para igualdade de emissões per capita			Convergentie van het quotum per persoon			Сближение к равным выбросам на душу населения			Konvergens mod ens udledninger per capita			Konvergens mot utslipp per enhet			
	grandfather			Initial distribn (grandfathering)	Equal percentage reductions from a baseline year:<br>  The more you polluted in the past, the more emissions quota you get in the  future! This is clearly inequitable, but was the starting point for "burden sharing" negotiations in the Kyoto process.		Répartition initiale des émissions ('héritage')			ursprüngliche Verteilung			Distribución inicial (grandfathering)			Distribuição inicial (grandfathering)			Initiële verdeling van de emisissies ('erfenis')			Исходное распределение (grandfathering)			Fordel proportionalt med start-fordelingen (grandfathering)			Fordel proporsjonalt med start-fordelingen (grandfathering)			
	sresdist			Distribn by baseline (SRES) projection	This option combines the SRES regional emissions distribution with total emissions fixed by mitigation scenarios.<p>Note, this option is provided only for <i>comparison</i> with other proposals. As you can see from changing between SRES scenarios, there is no single  "business as usual" projection. With such a variety of possible scenarios, it would be difficult to agree an emissions allocation formula based on deviation from a "baseline". Nevertheless, there is some convergence between richer and poorer countries in all scenarios.  <li>@aboutsres		Distribution par rapport au scénario de référence (SRES)			Verteilung nach Basis-Projektion (SRES)			Distribución por projección de partida (SRES)			Distribuição por projeção base (SRES)			Verdeling ten opzichte van het referentiescenario (SRES)			В соответствии с основным прогнозом ОДСВ МГЕИК			Fordeling som i reference-scenariet (SRES)			Fordeling som i referanse-scenariet (SRES)			
	unspecified			Unspecified regional distn	Since the future distribution is a controversial policy issue, the default option is "unspecified" (grey colour).You must choose another option to see meaningful curves for emissions and abatement.		Distribution régionale non spécifiée			nicht definierte regionale Verteilung			Distribución regional no especificada			Distribuição regional não especificada			niet gespecifieerde regionale verdeling			Не задано			Uspecificeret fordeling mellem landegrupper			Uspesifisertt fordeling mellom grupper av land			
	convpergdp			Convergence per GDP	This option uses the same @convergence formula as @percapita.<br>  °adju To see adjustable parameters for convergence year (and other expert options), make a @distribplot with @perq set to @gdp.<p>This is not a serious policy proposal, however it may be illustrative of proposals based on economic efficiency. It's interesting to compare it also with  @reduceintensity, which is effectively grandfathering of emissions per GDP.		Convergence par PIB			Annäherung pro BIP			Convergencia por GDP						convergentie per BPP												
	brazil			Brazilian Proposal (simple variant)	Allocation of emissions reductions based on attribution of responsibility for climate change.<p>JCM is now participating in the UNFCCC assessment of the scientific aspects of this proposal. For more about this see:   <li>@attribution, @attributeplot, and @responsibility<br>  ²Note: this distribution option is not working in the latest version, because the @responsibility module has been adapted to use the 4 SRES regions, as specified for the UNFCCC exercise, rather than than the 12 JCM regions shown in the distribution plot. Extension to JCM regions will be developed later -see @aboutregions²		proposition brésilienne (version simple)			Brazilian proposal (simple variant)			Propuesta de Brazil (variante simple)						Braziliaans voorstel (eenvoudige versie)												
																															
	kyotoopt		KP	Kyoto protocol	see @kyoto	KP	Protocole de Kyoto		KP	Kyoto Protokoll		PK	Protocolo de Kyoto		PQ	Protocolo de Quioto		KP	Protocol van Kyoto		КП	Киотский протокол		KP	Kyoto-protokollen		KP	Kyoto-protokollen		京都	
	incusaopt		US	Include USA in Kyoto protocol	see @kyoto	US	Inclure les Etats-Unis dans le protocole de Kyoto		US	USA ins Kyoto Protokoll einschliessen		EUA	Incluye EUA en el protocolo de Kyoto		EUA	Incluir os EUA no Protocolo de Quioto		US	De Verenigde Staten van Amerika opnemen binnen het protocol van Kyoto		СШ	США включены в Киотский протокол		US	USA deltagelse i Kyoto protokollen		US	USA deltagelse i Kyoto protokollen		美国	
	convyear			convergence year:	see @convergence		année de convergence:			Übereinstimmung Jahr:			Año de convergencia:			ano de convergência:			convergentie jaar:			год конвергенции:			Året for konvergensen:			Året for konvergensen:			
	convfac			convergence factor:	see @convfac		facteur de convergence:			Übereinstimmung Faktor:			Factor de convergencia:			factor de convergência:			convergentiefactor:			коэффициент конвергенции:			Konvergenfaktoren:			Konvergenfaktoren:			
	popcoy			population cutoff year:	see @convpopcoy		année de gel de la population:			Jahr von Bevölkerungsstopp:			año de disminución de la población:			population cutoff year:			bevolkingsbevriezingsjaar:			год ограничения населения:			Året for stop af befolkningstilvæksten:			Året for stopp av befolkningstilveksten:			
	expconvopt		EC	Exponential convergence formula	see @convfac	EC	Formule exponentielle de convergence		EK	Exponentielle Konvergenzformel		CE	Fórmula de convergencia exponencial		CE	Formúla da convergência exponencial		EC	exponentiële convergentieformule		ЭК	Экспонентная формула конвергенции		EC	Eksponentiel konvergensformel		EC	Eksponensiel konvergensformel			
	popcoyopt		PC	Freeze population used for convergence after cutoff year	see @convpopcoy	PC	Geler la population utilisée pour la convergence		BS	Bevölkerung nach Stopp einfrieren		CP	Congelar población usada para convergencia despues del año de disminución		PC	Manter a população constante usada para a convergência após ano cutoff		PC	De voor de convergentie gebruikte bevolking bevriezen		ОН	Определять население за конвергенция после год ограничения		PC	Stabiliseret befolkningstal anvendt til konvergens efter 'stop-året'		PC	Stabilisert befolkningstall anvendt til konvergens etter 'stop-året'			
																															
	regiondatasource		Origin of Regional Data		Historical emissions data comes from CDIAC (@dataref)  <li>See also @histemitobserv  <li>²°cogs Note the individual country data were summed for JCM regions (CDIACs regions are slightly different). ²  <li>² °cogs Historical emissions of other gases used in @responsibility module for @attributeplot are derived from RIVM Edgar-Hyde (link to be added) ²<p><p>Regional socioeconomic data (population, GDP, energy) and future SRES emissions data is derived from RIVM's implementation of the SRES scenarios using the IMAGE model (@dataref)  <li>see @people module  <li>²(Note SRES itself only reported data for 4 regions, whilst JCM has 12 and IMAGE has 17). These data are normalised to ensure consistency with totals tabulated in SRES. ²  <li>²°cogs The original data is compressed to one byte per number -see @loaddata²																										
	histemitobserv		Historical emissions -Interesting Observations		If you make a plot of Emissions ("what" menu) / Population ("per" menu), you can observe some well known historical events as sudden changes in the curves:   <li>Economic depressions in 1929 (Wall street crash) and 1979 (related to OPEC oil price rise)   <li>rapid economic growth in 1960s   <li>'great leap forward' in China 1950s   <li>burning oil wells in Middle East during 1991 Gulf war   <li>collapse of the USSR leading to closure of heavy industry (also in east europe)   <li>effect of weather variation on renewable energy supply (wind and hydro) in Denmark.																										
	distribution		Distribution of Future Emissions		££distribintro<br>  ££unspecified ££percapita ££grandfather ££sresdist ££unspecified ££convpergdp ££brazil ££distribdeaccat<br>  <hr>Note also @equity, @aboutregions, @stabilisation, @histemitobserv																										
	distribintro		Allocating shares for each region		Stabilisation of CO2 concentration, or any "mitigation" scenario, implies a global agreement to limit emissions. Such an agreement is unlikely to be achieved, without including a more equitable distribution of emissions, particularly between industrialised and developing countries (although note, this is only one of several @equity). Therefore, it is important to investigate the implications of various distribution options.<br>  The distribution of future emissions in stabilisation scenarios is a controversial issue which IPCC did not yet address, however it is often raised within the UNFCCC process (where it may be referred in phrases such as "differentiation of commitments", ?common but differentiated responsibilities", ?reducing per-capita inequity?, etc.).<br>  <!-- add direct links to unfccc docs?--><br>  You can explore some different approaches, by selecting an option from the @distribmenu (in @mitigpanel), and observing the effect on regional emissions in a @distribplot. Note that the distribution options define the shares of CO2 emissions allocated to each region. These shares are multiplied by the total emissions according to your chosen option from the @emitmenu. The calculations are made in @regshares.<br>  Any of the options below may also be applied following after the Kyoto protocol in 2013 -see @kyoto<br>  ²Note:  the list of distribution options in JCM is not intended to be comprehensive, other options will be added later -please suggest ideas²																										
	effectofkyoto		What is the Climate Effect of the Kyoto protocol?		Essentially the answer depends on   <li>what you compare it with (SRES or stabilisation or scenarios)   <li>what you think Kyoto might lead to later (in the UNFCCC process)<br>  See also  @kyotohowwork<p>You can experiment for yourself, by pressing the "Kyoto" button in the CO2 emissions plot. This will set the fossil fuel CO2 emissions according to the Kyoto targets for AnnexB countries, and according to the SRES scenarios for the other countries (change using the SRES menu).<p>As you can see, Kyoto reduces the emissions in 2012 compared with any of the SRES "no-policy" baseline scenarios. However it increases the emissions compared to most pathways leading to stabilisation   <li> @sres (where are we going?)  <li> @stabilisation  (where do we need to go?)<p>For example, for stabilisation of CO2 concentration at 450ppm, the emissions curve must drop more rapidly if starting after Kyoto 2013.<br>  Note, however, that Kyoto also reduces emissions compared to the WRE scenarios (see @pathways)  which initially follow a IS92A business-as-usual.<p>In all cases the immediate impact on the climate is very small, simply because the timescale of the Kyoto protocol is very short in the context of the slow, cumulative process of climate change<li>See @inertia<p>So the effectiveness of Kyoto really depends on whether it lays a good foundation for the climate convention process, which might lead to greater reductions later. Are the targets a step in the right direction? Can it help to build trust between diverse regional groups? Does it set good precedents in the rules for flexible mechanisms and accounting? Such questions require consideration of diplomacy as well as science.<p>The USA announced in spring 2001 that it does not intend to ratify the protocol. You can see what effect this might have by pressing the "US" button on the CO2 emissions plot (if "KP" button is enabled). This sets the US emissions according to the SRES scenario (choose from menu), as for the developing countries. In this case, only about 30% of the total emissions in 2012 would be limited by Kyoto quotas. Moreover, lack of US participation in the first step might discourage participation by other countries later.<br>  ³Per-capita emissions³<br>  The effect of the Kyoto protocol on the distribution of emissions may be illustrated by the a plot of emissions per capita<li> see @distribplot<p>You can see that the per-capita emissions targets for USA are about twice those of Europe and Japan, which are still about twice the world average. However the most obvious effect of Kyoto is the very large increase for Russia and Ukraine (brown colour), whose per-capita target is similar to that of USA (taking into account population changes). As it is unlikely that they will actually need to burn so much fossil fuel during this period, this surplus "hot air" quota will probably be sold to other countries.																										
	kyotohowwork		Kyoto in JCM: How it Works		Emissions targets for the six JCM regions corresponding to Annex B were calculated (externally), by summing the target emissions for each country, calculated using emissions data from CDIAC.<p>Note that Kyoto targets applied to a basket of six gases, and also included some emissions from land use change. Here the same percentage reduction targets are applied to fossil CO2 only.<br>  (i.e. for each country it has been assumed that ratio fossil CO2 emissions as a fraction of total greenhouse gas emissions will be the same during 2008-12 as it was in the baseline year 1990)<br>  One consequence of this, is that the total CO2 reduction for the six "Annex B" regions is slightly less than the 5.2% reduction in AnnexB total ghg emissions. Note also that EE region (orange) includes former Yugoslavia and Albania without targets, whose emissions are assumed to grow.<br>  The Kyoto targets for Annex B countries apply to the budget period 2008-2012, so a linear transition is shown between 2000 and 2007.<br>  For the non-AnnexB countries and also for USA, if not included (an adjustable option), emissions are set according to an SRES baseline, which varies according the chosen scenario (SRES menu at top). This data is derived from RIVM's IMAGE model (See @aboutregions)<br>  It should be noted, that these SRES projections seem rather high compared to current trends.<br>  <hr>for a general discussion see @effectofkyoto																										
	kyotofuture		Kyoto module: future development		<li>Targets should apply to all gases   <li>The effect of emissions trading and CDM could be shown   <li>Land-use change emissions should be included, limited as specified at COP7   <li>This approach should be extended to further commitment periods																										
	convergence		Convergence formula		The convergence formula defines a gradual transition from the current distribution of emissions, to an equal per-capita distribution in the "convergence year".<br>  Note this is not meant to be a prediction, but a possible framework for allocating tradeable emissions quotas under a global climate agreement. See @distribution<br>  ££convadju ££convmath<br>  ³Expert Convergence Options³ §²°adju Only available, if you choose expert version from the top menu²<br>  ££convfac ££convpopcoy																										
	convadju		Adjusting the convergence year		Select "convergence to equal-per-capita from the distribution menu. You can then adjust the convergence year by dragging the blue arrow on the per-capita emissions plot. You should see point where the lines converge move to the left or right accordingly. See also @distribplot																										
	convmath		Mathematical formula		The allocation for each country (or region) is simply calculated by<p>(allocation for country "c" in year "y") =<p> (global emissions budget in year "y") x  (share for country "c" in year "y").<p>The "share" for each country (i.e. the fraction of the global emissions budget)<br>  is calculated every year as follows:<p>For country "c" in year "y" the share "s" is given by:<p>s<sub>c, y+1</sub> = s<sub>c, y</sub> + f<sub>y</sub>. ( p<sub>c, y</sub> / p<sub>w, y</sub> -  s<sub>c,y</sub> )<p>where "p<sub>c, y</sub>" is the population of that country, "p<sub>w, y</sub>" is the world population <i>in that year</i>.<p>f<sub>y</sub> is a factor determining the rate of convergence.<p>A simple linear formula gives<p>f<sub>y</sub>  = 1 / ( y<sub>conv</sub> - y )<p>where "y<sub>conv</sub>" is the convergence year (which must be agreed in advance).<p>Alternatively, if you choose the "expert" version, you can experiment with  an exponential formula:<p>f<sub>y</sub> = e <sup>{q ( t -1 ) }</sup><p>where t = ( y - y<sub>start</sub> ) / ( y<sub>conv</sub> - y<sub>start</sub> )<p>and q is an arbitrary "convergence factor"<p>Some key features of this convergence formula (with either definition of f<sub>y</sub>) are:  <li>The sum of the shares in any year must always be equal to one.<br>  To stay within a fixed global emissions budget, you can only increase the share of one country, by decreasing the share of another.   <li>In the convergence year f=1, so shares are then exactly proportional to populations  <li>The formula for allocating shares between countries is independent of the formula for defining the global emissions budget  <li>The share in any year is only dependent on the population in the previous year, and on the share in the start year.<br>  A policy agreement based on this formula, would not need to specify any projections of future population changes.  <li>The same formula applies to all countries.<p>Other options for convergence criteria may also be considered, for example "energy demand" perhaps mixing population, economic activity, natural resources, local climate (feedback?), etc.																										
	convfac		Convergence factor		The linear convergence formula is used by default. This is simpler, but results in a sharp kink for some countries in the start year. So the exponential formula allows a smoother transition, although the penalty for higher emissions in earlier years, is a more rapid drop later.<p>°adju To choose the exponential convergence formula, click the button labelled "EC" at the top right of the plot. You should now see that the lines become more curved, and a new double-arrow (cyan) control appears. Dragging this control to the left or right changes the "convergence factor" as defined above. You can see the exact value of this factor in the pop-up info, the default value is 6.0.																										
	convpopcoy		The population cut-off year		It may be argued that the convergence @convmath provides no incentive for countries to restrain future population growth.<br>  Therefore the option is provided the option to freeze the population used for calculating convergence, in a certain year which we call the "population cutoff year". Specifically,  p<sub>c, y</sub> and p<sub>w, y</sub> in the formula above are fixed in and after the cutoff year (but not before that).<p>This cutoff year would have to be agreed in advance. On the plot it is shown by the position of the green upward-pointing arrow, which appears if you click the button labelled PC at the top right of the plot.<p>You may have observed that, when the population cutoff option is enabled, the per-capita emissions do not converge exactly in the convergence year, or appear to <i>diverge</i> thereafter. This is to be expected, since after the cutoff year, countries with growing populations have to divide their share among more people, and so get less for each person, whereas those with falling populations will have more for each person. Note that the per-capita emissions shown on the plot are calculated by taking the shares calculated using the "cutoff year" population as defined above, multiplying these by the global emissions budget, and then dividing by the actual (projected) population (<i>not by the "cutoff" population</i>).																										
	distribdeaccat		DEA-CCAT proposal		Another option which may be implemented later, is to reduce Annex-B emissions in steps (as Kyoto), and others follow with equivalent reductions compared to the  baseline after a fixed delay period.																										
	peoplefuture	fut	Socioeconomic Model- Future Work:		<li>Later it is anticipated to develop a simple dynamic socioeconomic model, including some feedbacks to/from the climate system.   <li>This should be tuneable to fit SRES, and thus extend/interpolate the scenarios.   <li>This will also require consideration of basic economic sectors, including energy, transport, agriculture etc, and must also capture the inherent inertia in changing infrastructure, technology, lifestyle and population patterns.  <li>We also need abatement costs for each gas, to find the most effective ways to reduce warming.  <li>These may be considered alongside climate change impacts  -see @costs, @impacts, @optimisation  <li>More regions are needed, to capture the diversity of socioeconomic, political and climatic patterns.  <li>A basic emissions trading module is important to show the effect of the Kyoto protocol and longer-term distribution options																										
																															
attrib																															
	responsibility		Responsibility	Attribution of Responsibility	££attribintro £§iobinfo ££resphowwork<p>²°cogs There are also notes within the java source code.²<br>  ²°cogs Note: This module is quite slow, because it effectively reruns the core parts of the @carboncycle and @heatflux  for each region.²	Responsibilité	Attribution de Responsibilité:																								
	attributeplot		Attribution	Attribution of Responsibility	£^apptag ££attribintro<p>  ² °adju Make sure "£~nopolicy" is selected from @emitmenu, as attribution under mitigation scenarios has not yet been developed²<br>  £^interacs £^curves<br>  ² °adju Note: the total RF, temperature and sea-level include negative forcing by aerosols, to see this you should switch off the "stacked" option.²<br>  £^scales £^controls £^menopts ££resphowwork	Attribution:	Attribution de Responsibilité:			Verantwortlichkeit 			Responsabilidad 					Attributie:	Attributie van de verantwoordelijkheid:												
	startyear		startyear	Beginning of Attribution period	Note that regional data is only available between 1890 and 2100.																										
	endyear		endyear	End of Attribution period																											
	sepbunker		bunk	Separate Bunker fuel emissions	Puts the aviation/shipping "bunker fuels" into a separate region (as specified for unfccc exercise).																										
	simplecarbon		simpC	Simple Carbon sinks calculation method	Applies a simpler formula for attributing carbon sinks (see responsibility.java code).																										
	differential		difRF	Differential Radiative Forcing attribution	Applies the differential attribution of radiative forcing (see @attribution JCM documentation, and Enting et al 98).																										
	incunatt		UnAtt	Include Unattributed Category	Include the 'Unattributed' Category in the plot. Unattributed includes the effect of emissions before and after the attribution period, of gases/forcings other than CO2, CH4 and N2O, and associated feedback processes.  By deselecting this option, and also selecting the @stack and @frac options, you can explore the relative (%) attribution from different regions, which is much less sensitive than the absolute attribution to certain scientific uncertainties and varying future scenarios.																										
	temp		Temperature	Temperature rise (Relative to baseline)		Température	Montée du Température											Temperatuur	Temperatuursstijging												
	slte		Thermal Exp	Sealevel rise (Thermal Expansion only)		Exp Therm	`Montée du niveau de la mer (Expansion Thermique seulement)											Exp Therm	`Zeespiegelstijging (alleen thermische uitzetting)												
	attribothghg		OthGhG	Other Greenhouse Gases (F-gases and Ozone, also includes effect of OH lifetime on CH4)		AutGaz	autres gaz à effet de serre (gaz de F/Cl, et Ozone)											ABKG	andere broeikasgassen ( F/Cl gas en ozon)											其他气	其他气 (臭气, 氯氟碳, 氢氟碳)
	othghg				(not currently used)																										
	unattr		Unattr	Unattributed (CO2, CH4, N2O before start or after end of attribution period)																											
	cumemitplot		Cumulative Emissions	Cumulative Emissions		Emissions cumulées	Emissions cumulées		kumulierte Emissionen	kumulierte Emissionen		Emisiones acumulativas	Emisiones acumulativas					Gecummuleerde emissies	Gecummuleerde emissies												
	acatco2		Atmospheric CO2	Accumulated Atmospheric CO2		CO2 atmosphérique	CO2 accumulé dans l'atmosphére		atmos. CO2	kumuliertes atmosphärisches CO2		CO2 atmosférico	CO2 atmosférico acumulado					CO2 in de atmosfeer	CO2 geaccumuleerd in de atmosfeer												
	attribintro		Attribution- for Brazilian proposal		This part of JCM attributes the regional responsibility for anthropogenic climate change. It was developed for the JCM contribution to the UNFCCC model intercomparison assessing the "Brazilian proposal" (see documentation in @attribution)																										
	resphowwork		Responsibility- How it Works		The methods section of the Documentation submitted for the UNFCCC assessment (@attribution) explains more about how these calculations are done, and discuss some scientific and methodological issues.<p>Potentially,  this approach could be applied not only to regions, but to any set of emissions from different sources -e.g. individual projects (CDM), time-periods (intergenerational equity), gases (to replace GWP), etc.<br>  ²°cogs Note: To fulfil the specifications of the UNFCCC assessment, this module works with only 4 regions, compared to the 12 in JCM (see @regshares, @aboutregions). Therefore it is not currently possible to apply the  Brazilian proposal as a @distribution option ²																										
	attribution		Attribution of Contributions to Climate Change	JCM contribution to UNFCCC Assessment of the Brazilian Proposal	<p>  @att_berlin_int<p>  See also @responsibility and @attributeplot<br>  ²Note: some new features have been added prior to the Berlin meeting, which have not yet been documented ²<br>  <hr><br>  The Documentation from Phase I/II intercomparison exercise (2002) has not yet been converted to the new format-   <li> <a href="http://www.chooseclimate.org/jcm24dec02/attribution.html" target='_new'><br>  Open the old version of JCM (Sept 2002), for consistency with the documentation and submitted results</a>  <li> <a href="http://www.chooseclimate.org/jcm24dec02/doc/fcccattrib/fcccattrib.html"> Old documentation only</a>																										
	att_berlin_int		JCM presentation for UNFCCC workshop in Berlin 8th Sept 2003		<center>UNFCCC workshop on scientific and technical aspects of the Brazilian Proposal<p>Berlin, 8-9 Sept 2003<p>Ben Matthews,<br>  matthews <p> @climate.be<p>UCL-ASTR, Belgium<p>Java Climate Model<br>  jcm.chooseclimate.org<br>  <hr><p><li>@att_berlin_new  <li>@att_berlin_absrel  <li>@att_berlin_sciasp  <li>@att_berlin_methasp  <li>@att_berlin_commsens<p>  <hr><br>  £!AttributeSetup																										
	att_berlin_new		What's New		(Since last workshop)  <li>More Regions  <li>Stabilisation - Concentration, Forcing, Temperature  <li>Climate-Carbon Feedbacks  <li>Presentation, Relative %,   <li>start probabilistic approach<p>  <hr>£!AttributeSetup <hr>@att_berlin_absrel																										
	att_berlin_absrel		Absolute or Relative (T or %)?		Sensitivity to uncertainties depends on what we care about:  <li>Absolute attribution (metric of future warming commitment, potential damages/adaptation fund?)  <li>Relative attribution (% for each Party as original proposal)<p>Choice affects design of Stage 3<p>  <hr>£!AttributeSetup <hr>@att_berlin_sciasp																										
	att_berlin_sciasp		Scientific Aspects		<li>Climate Model / Sensitivity, Sulphate / Solar forcing   <li>Response time / mixing rates  <li>Carbon cycle - mixing & Climate-Carbon Feedbacks<br>  ²(note "simple" method sink prop to concn)²  <li>LUCF emissions   <li>Other gas -OH lifetime ²depends on attribution method²  <li>Historical Emissions: how to constrain?<p>  <hr>£!AttributeSetup <hr>@att_berlin_methasp																										
	att_berlin_methasp		Methodological Aspects		<li>Timescale:  depends on indicator and data  <li>Indicator:  Forcing, Temperature, Sealevel<br>  - time response different (SL curves cross 30yr after T)<br>  - sensitivities of relative attribution similar  <li>Scenario:  Effect of Future Stabilisation level on Attributed Warming due to Historical Emissions<br>-² Depends on attribution method. Shape of scenario? ²<br>  -²(Future convergence: Percapita emissions doesn't converge percapita attribution! Integration?)²<p>  <hr>£!AttributeSetup <hr>@att_berlin_commsens																										
	att_berlin_commsens		Communicating Sensitivities		²(diversion from BP to Article2/Stabilisation, but about probabilistic <b>method</b>)²  <li>JCM Can stabilise Temperature, Forcing, Concentration.  <li>@wccc2003  <li>Relative Responsibility not so sensitive,<br>  but "Probabilistic" approach maybe useful for Absolute Responsibility.   <li>Communication of how-it-works / choices / sensitivities<br>  - interactive JCM shows cause-effect<br>  - general problem for IPCC-AR4 (more probabilities/dimensions)  <li>To solve Article 2 - need more citizen involvement - Article 6<br>  - same for value-judgements in methodological choices of BP<br>  - Recall "Policymaker Model" concept<br>  - Languages, web tool for Global Dialogue<p>  <hr>£!AttributeSetup <hr>@att_berlin_int																										
costs																															
	costs		Costs	Economic Costs (Experimental!)	This module may eventually help in comparison of regional climate impacts, and regional emissions abatement. Please note, that the current version is only experimental, as the cost functions are too simple. For more information see @costsplot<br>  £§iobinfo<br>  <hr>°cogs Note, the integrated discounted costs in 2000 can also be obtained (variable 'totcost'), for use in optimisation loop calculations (see @scripting, @optimisation).	Coûts (expérimental!):	Coûts economiques (expérimental!):																								
	costsplot		Costs (Experimental!)	Economic Costs (Experimental!)	£^apptag This plot illustrates the effect of varying JCM parameters on economic costs of emissions abatement and climate change impacts. Note that the current cost functions (derived from the RICE model) are very crude, and the author of JCM does <b>not</b> trust these formulae! It is envisaged that new functions will be added later.<p>  ² °adju Important! Abatement (and total) cost only makes sense, if you have chosen both a mitigation scenario from @emitmenu  <i>and</i> a distribution option from @distribmenu. In the default setup the future distribution is unspecified (because this is a controversial issue) -in this case, future regional emissions are zero, so the costs would look very high! ²<br>  £§graphinfo<br>  °adju Try adjusting the stabilisation level (@stabconcall or @stabtempdoc). Naturally, lowering this level increases costs in the short term, but reduces the total in the longer term - an issue of intergenerational equity (see @equity).  The balance in costs between the regions (best viewed per capita or per GDP) also depends on the @distribmenu.	Coûts (expérimental!):	Coûts economiques (expérimental!):											Kosten (experimenteel!):	Economische kosten (experimenteel!):												
	abcost		Abate-Cost	Abatement Cost	a nonlinear function (different for each region) of baseline emissions (from @sres) minus mitigated emissions (which depend on the future @stabilisation and @distribution options)	Coût de réduction	Coût de réduction		Reduktionskosten	Reduktionskosten		Costo por reducción	Costo por reducción					Reductiekost	Reductiekost												
	damcost		Damage-Cost	Damage Cost	a nonlinear function (different for each region)  of temperature from @heatflux .	Coût des dommages	Coût des dommages		Schaden	Kosten der Schäden		Costo-Daño	Costo del Daño					Schadekost	Schadekost												
	totcost		Total-Cost	Total Cost	The sum of : £§abcost plus £§damcost	Coût total	Coût total		Kosten Total	Kosten Total		Costo Total	Costo Total					Totale kost	Totale kost												
	disccost		Discounted Total-Cost	Discounted Total Cost	Integral of future total costs, discounted over time. Total costs = £§totcost																										
	abatepow			Abatement cost function power exponent			Exposant de la fonction de réduction des coûts			Reduktionskostenfunktion, Exponent			Costo de reducción Función potencia						exponent van de reductiefunctie van de kosten												
	abatelin			Abatement cost function linear multiplier			Facteur de la fonction linéaire de réduction des coûts			Reduktionskostenfunktion, linearer Multiplikator			Costo de reducción Función múltiplo linear						lineaire kostenreductiefunctiefactor												
	damagepow			Damage cost function power exponent			Exposant de la fonction des coûts des dommages			Schadenkostenfunktion, Exponent			Costo de reducción Función potencia						exponent van de schadekostfunctie												
	damagelin			Damage cost function linear multiplier			Facteur de la fonction linéaire des coûts des dommages			Schadenkostenfunktion, linearer Multiplikator			Costo de reducción Función múltiplo linear						lineaire schadekostenfunctiefactor												
	macgem		macgem	Use a different abatement cost formula from MacGEM model																											
	discountrate		Discount Rate	Rate of time-preference (fraction discounted per year).																											
scenario																															
	sres		SRES	SRES No-climate-policy Emissions Scenarios	This module holds the data for future emissions scenarios.  This module is currently passive, acting as a store for data loaded by @loaddata The linear interpolation routines are called by other modules as needed.<br>  £§iobinfo ££aboutsres <hr>See also @aboutsres, @aboutregions	SRES	Scénarios SRES sans politique volontariste de réduction			SRES Szenarien ohne Einbezug klimapolit. Massnahmen			SRES sin escenarios con política de clima			Cenários SRES sem politicas de intervenção no clima			SRES scenarios zonde vrijwillig reductiebeleid			Сценарий ОДСВ МГЕИК "Отсутствие политики по изменению климата"			Reference scenarie uden klima-politikker			Referanse scenarie uten klima-politikk			
	sresmenu		SRES	Select SRES Baseline Scenario	SRES will affect global CO2 emissions only if the £`nopolicy option is selected from the @emitmenu<br>  The choice of scenario also affects:<li>Socioeconomic data (@people)<li>The baseline for calculating @abate<li>The emissions of other gases (see @othgasemit)<li> The SRES option in @distribution.<br>  <hr>For more explanation,  see @aboutsres	SRES	Choisir un scénario SRES		SRES	SRES Basis-Szenario wählen		SRES	Seleccionar Escenario de Partida SRES		SRES	Selecionar cenário SRES		SRES	Kies een SRES scenario		ОДСВ	Выбор сценария ОДСВ МГЕИК		SRES	Vælg SRES-scenarie for de samlede udledninger		SRES	Velg SRES-scenarie for  samlede utslipp			
	A1B				See @sresA1																										
	A1T				See @sresA1																										
	A1F				See @sresA1																										
	A2				See @sresA2																										
	B1				See @sresB1																										
	B2				See @sresB2																										
	IS92A				This is an older mid-range scenario from the IPCC second assessment report (SAR). For comparison, IPCC-TAR also has some data for this scenario, which is incorporated in JCM.																										
	TGCIA450				This is an emissions scenario including all gases, leading to stabilisation of CO2 concentration at around 450ppmv (see @stabconcdoc). It was proposed by Swart et al (2001) to fill the gap at the low end of the range of GCM projections.																										
	aboutsres		IPCC SRES Scenarios		££sresintro ££srestable ££sresa1 ££sresb1 ££sresa2 ££sresb2 ££sresdriving ££sresclimate ££sresasbaseline ££sresdemo																										
	sresintro		SRES Introduction		The Intergovernmental Panel on Climate Change "Special Report on Emissions Scenarios" (SRES) explored pathways of future greenhouse gas emissions, derived from self-consistent sets of assumptions about energy use, population growth, economic development, and other factors. These considered a variety of possible "world-views", but explicitly exclude any global policy to reduce emissions to avoid climate change.<p>From SRES, six illustrative "marker" scenarios were selected for use in the climate projections of the IPCC Third Assessment Report. These are described below, followed by discussion of driving forces, climate impacts, and related topics.   <li>See also @sres																										
	srestable		Scenario overview		<table border=2> <tr><td></td><td align=center> Global integration </td> <td align=center> <font size=-1>Regionalism </font></td></tr><tr><td align=center rowspan=3> Economic emphasis </td> <td align=center>A1B<br><font color=red bold>B</font>alanced energy</a></td><td align=center rowspan=3>A2</td></tr><tr><td align=center> A1FI<br><font color=red bold>F</font>ossil-fuel <font color=red bold>I</font>ntensive</a></td> </tr><tr><td align=center>A1T<br>high-<font color=red bold>T</font>ech renewables </a></td></tr><tr><td align=center width=25%>Environ-mental emphasis </td> <td align=center width=50%>B1</td><td align=center width=25%>B2</td></tr></table>																										
	sresa1		A1 Scenario: Rapid convergent growth		The A1 scenarios all describe a future world  of very <b>rapid economic growth</b> and global population that peaks in  mid-century and declines thereafter, and the rapid introduction of new  and more efficient technologies.  Major underlying themes are convergence  among regions, capacity building, and increased cultural and social interactions,  with a substantial reduction in regional differences in per capita income.<p>   The difference between the A1FI, A1B, A1T and scenarios is mainly in the source of energy used to drive this expanding economy.  <li> A1FI: <b>Fossil-fuel Intensive</b>, coal, oil, and gas continue to dominate the energy supply for the forseeable future.  <li> A1B: <b>Balance</b> between fossil fuels and other energy sources  <li> A1T: emphasis on new <b>Technology</b> using renewable energy rather than fossil fuel.																										
	sresa2		A2 Scenario: Fragmented world		The A2 scenario describes a very heterogeneous world.<br>          The underlying theme is <b>self-reliance</b> and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously<b> increasing global population</b>. Economic<br>  development is primarily regionally oriented and per capita economic growth and technological change are more <b>fragmented</b> and <b>slower</b> than in other storylines.																										
	sresb1		B1 Scenario: Convergent with global environmental emphasis		The B1 storyline and scenario family describes a convergent world with the same global population that peaks in mid-century<br>  and declines thereafter, as in the A1 storyline, but with rapid changes in economic structures toward a service and information economy, with reductions in material intensity, and the introduction of clean and resource-efficient technologies. The emphasis is on global solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate initiatives.																										
	sresb2		B2 Scenario: Local sustainability		The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social, and environmental sustainability. It is a world with continuously increasing global population at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change than in the B1 and A1 storylines. While the scenario is also oriented toward environmental protection and social equity, it focuses on local and regional levels.																										
	sresdriving		SRES Driving Forces		The regional emissions/socioeconomic data shown in JCM regional plots helps to reveal some of the driving forces behind the SRES scenarios. First, choose "SRES no-climate-policy scenarios" from the "mitigation" menu (top panel), then choose CO2 emissions, population, GDP or energy from the "what" and/or "per" menus on a regional plot, and compare scenarios with the SRES menu (top panel).<p>For example, scenarios A1B, A1FI, A1T and B1 all have the same population, rising initially, but falling later this century, whilst for A2 and B2 the population, and consequent CO2 emissions, continue to rise in 2100. The implication is that global convergence of wealth in the A1/B1 group (look at the GDP data) helps to reduce population growth, and hence emissions.<p>The difference between A1B, A1T and A1FI emissions, is therefore mainly related to energy use.<br>  The energy data here refers to secondary energy, such as electricity, also including energy from traditional biomass sources (fuelwood) which is higher in developing countries.<br>  So the ratio energy/emissions tells you about the carbon "efficiency" of energy production, whilst GDP/energy might indicate more about the economic "efficiency" of its application.<p>See also: distribplot panel, people module																										
	sresclimate		SRES Effect on climate		Considering the temperature rise by 2100 (@glotempplot panel), the "hottest" scenario is A1FI, followed by A2, A1B, B2, A1T, and B1 is the "coolest". However, in the A1B and A1T  fossil CO2 emissions are falling by 2100, whereas in A2 and B2 they are still rising, implying that climate impacts would be greater during the following century.<p><p>The SRES also specifies other factors which influence climate change. For example, in scenarios A1FI, B1 and B2, CO2 emissions from land-use change drop below zero (implying net reforestation).<br>  °adju (You can see this on the @atco2plot : drag the y-axis near the origin, to move it up)<p><p>Scenario A2 also has relatively higher sulphate emissions than A1FI (this causes local cooling). In scenario B1, the emissions of methane and HFCs are also much lower. See @othgasplot, @radforplot<p><p>You can check the correspondence of the predictions from this model, with data published in IPCC-TAR-WG1 SRES appendix, shown as small circles superimposed on the plots (expert level). See @compareipcc<p><p>The 100-year time-frame of the SRES scenarios is too short to give a meaningful view of the effect on some slower climate processes, especially sea-level rise. In this model the scenarios are extended simply by assuming constant emissions after 2100. This is for transparency and is not intended as a prediction.  See @time, @inertia																										
	sresasbaseline		SRES as Baseline for Mitigation (Post-SRES)		SRES scenarios, which assume no climate policy, provide a useful set of baselines for comparison with stabilisation scenarios, and to calculate  the required mitigation effort.   <li>This is the principle behind the @abate curves in @distribplot, @costsplot.  <li>The regional distribution of emissions under SRES may also be used as an option for @distribution.   <li>The SRES projections for emissions other greenhouse gases may be combined with CO2 stabilisation scenarios (as in IPCC-TAR Synthesis report Q6). See  @othgasemit, @ipccothgas<p>°adju Note, for these reasons, the @sresmenu remains available, even when the CO2 emissions are determined by other options from @emitmenu.<p><p>However, it may be considered unrealistic to reduce greenhouse gas emissions, without changing other underlying socioeconomic projections which are also part of the SRES scenarios. Therefore IPCC has begun to review more complex mitigation scenarios, which still aim to reach specific CO2 stabilisation levels, but must also consider socioeconomic factors consistent with the original SRES storylines. The first stage of this "post-SRES" process is described in IPCCTAR WG3 Chap2.<p>These scenarios may help us to calculate the required mitigation effort, considering not only the difference in emissions, but also the interactive feedbacks with economic growth, population and technology development.<br>  <hr>  <li>Note also @whobau  <li>See also @aboutsres, @stabilisation																										
	sresdemo		SRES Demonstration		<H3><I>Sorry, Automatic Demonstration Scripts are NOT working </I></h3><br>  due to layout changes in latest version.<br>  A new layout-independent script system is under construction.<br>  If you really need the demos you can   <li><a href="../tech/download.html">download the September 02 Version</a><br>  </center><hr><br>  <input type="button" value="start SRES demo" onclick="myapplet.playfile('demo/sres.txt');" ><br>  <input type="button" value="stop" onclick="myapplet.stop();" ><p>  The demo wizard cycles through the six SRES scenarios, showing in turn:   <li>CO2 emissions (regional and global)   <li>Other gases / F-gases   <li>Radiative forcing / Temperature,    <li>Regional temperature / Sea-level   <li>Population / Per-capita emissions  <li>GDP percapita / Energy percapita<p>  Click the button to stop the demo and explore for yourself.																										
																															
base																															
	topmenu				@mainmenu+°home @back+°back @docsearch+°search @intro+°intro @emitcc+°emit @science+°glob @impacts+°clou @about+°about @applications+°teach @tech+°tech @mod+°mod @pan+°pan  @comp+°root																										
	mainmenu		JCM Home Menu		 <nobr>°intro @intro</nobr><br><nobr>°search @docsearch </nobr><br><nobr>°list @doclist  </nobr> ££mmccot  ££mmusejcm ££mmpartjcm <hr>  £§usehelpmode 	JCM Menu de Base																									
	mmccot		Cross-Cutting / Overview Topics 		<nobr>°emit @emitcc </nobr><br><nobr>°glob @science </nobr><br><nobr>°clou @impacts  </nobr>	Thèmes/ liaisons scientifiques 																									
	mmusejcm		Using JCM		<nobr>°about @about </nobr><br><nobr>°tech @tech </nobr><br><nobr>°teach @applications</nobr>	Utiliser JCM																									
	mmpartjcm		Parts of JCM (<i>as java code structure</i>)		<nobr>°pan @pan </nobr><br><nobr>°mod @mod </nobr><br><nobr>°root @comp </nobr>	Parties de JCM (<i>comme structure de code java</i>)																									
	mod		Science Modules	Science Modules	Modules contain the scientific calculations and parameters, the pages below describe "how it works".<br>  ² °adju If @helpmode is selected, clicking on a module in the @flowchart gets its documentation. ²<br>  ³ °glob Natural Science Modules³<br>  @carboncycle<br>@oghga<br>@radfor<br>@heatflux<br>@sealevel<br>@regcli<br>  ³ °emit Emissions /Socioeconomic Modules³<br>  @sres<br>@mitigation<br>@kyoto<br>@people<br>@regshares<br>@responsibility<br>@costs<br>  ³ °cogs Structural components in jcm/mod ³<br>  @loaddata<br>@mapdata<br>@bord<br>@mathcurve<br>@time<br>@reg<br>@module<br>@modlist<br>  ££aboutmodules	Modules Scientifiques																									
	pan		Panels	JCM graphs, maps, text-panels	The pages below describe the graphs or maps and adjustable controls.<br>  ² °adju Use the @plotmenu and @layoutmenu to change which panels are displayed. If @helpmode is selected, clicking on a panel gets its documentation. ²<br>  ³ °glob Global Science³<br>  @atco2plot<br>@carbonstoreplot<br>@othgasplot<br>@fgasplot<br>@radforplot<br>@glotempplot<br> @oceantempplot<br>@sealevelplot<br>°clou @regclimap<br>  ³ °emit Regional Emissions³<br>  @distribplot (emissions shares, per-capita, per GDP, etc.)<br>@attributeplot<br>@costsplot<br>@regemitmap  <br>  ³ °adju Control panels³<br>  @layoutpanel<br>@mitigpanel<br>@flowchart<br>@viewdata<br>@viewparams<br>@notepad<br>  ³ °cogs structural components ³<br>   @blankplot<br>@panlist<br>  ££aboutpanels	Panneaux (graphiques)																									
	usehelpmode				²°adju If @helpmode is enabled, you can quickly find relevant documentation, by clicking on plots, controls, menus, curves, or modules in the flowchart ²			²°adju Si @helpmode est activé, vous pouvez rapidement trouver de la documentation pertinente, par cliquer sur graphiques, contrôles, menus, courbes, ou modules dans l'organigramme ²																							
	applications		Applications of JCM	Use of JCM for Teaching, Research and in Policy Context	Learning and dialogue<br>@teaching  <p>Presentations<br>@confpres <br> @linkpres<br>   @moscow  <p>Brazilian Proposal<br>@attribution (<a href="http://unfccc.int/program/mis/brazil/index.html" target="_new">UNFCCC website</a>  ) <p>Investigate New Stabilisation Scenarios<br>@stabtempdoc<br>@stabrfdoc<br>@art2 <p>Integrated assessment / Uncertainty analysis<br>using new @scripting facility<br>e.g. @stabtemp2cscript <br> @probabilistic  <!-- add later impacts DDC and climnegIA --><br>  ££jcmmirror	Applications de JCM																									
	jcmmirror		JCM Mirror Sites		<li>JCM is part of UNEP's Climate Portal <a target="_new" href="http://climatechange.unep.net/jcm">climatechange.unep.net</a>  <li>Another copy is at of <a href="http://climate.unibe.ch/jcm" target="_new"> University of Bern Klima Umwelt Physik</a>   <li>New versions appear first on the homesite <a target="_new" href="http://jcm.chooseclimate.org">  jcm.chooseclimate.org </a><br>  See also @develop.	Sites Miroirs de JCM																									
	teaching		Using JCM for Teaching		JCM has already been used for student courses in universities in several countries, including:  <li>Climate-negotiation role-play in UCL, Louvain-la-Neuve (Belgium)  <li>Climate change distance-learning course in Open University (UK)  <li>Courses about the Carbon-Climate system in LLN, University of Bern (Switzerland), and University of East Anglia (UK)  <li>University of Waterloo FES, Canada  <li>Interest was expressed for use in university courses in US and Italy<br>  <hr><br>  The experience with students so far is very positive - they often learn to use JCM much faster than professors (!), and even those who are not experts on climatic topics ask sophisticated questions demostrating a thorough exploration of the issues, processes and uncertainties, which can inspire new developments in the model.<br>  More links/resources will be be added here soon<br>  <hr><br>  The new @scripting feature will help in tailoring for future courses (by recording demonstrations) and could also be used as a format for sharing results.<br>  The long-term vision is to connect groups of students across the web, to have a real debate between people in all corners of the world. See also @dialogue<br>  Do you have a suggestion of how this could be developed further? Please get in touch (@contact)	Usage de JCM pour l'Enseignement																									
																															
	about		Concept, History, Future	About JCM:  Vision and Development	@whatnew<br>@develop<br>@future<br>@concept<br>@dialogue<br>@philosophy<br>@contact<br>@acknow<br>@copyr	Concepte, Histoire, Futur	A propos de JCM: Vision et Développement																								
	philosophy		Different approaches to the climate science-policy problem		££approaches ££uncertcope ££whobau 	Différentes approches au problème de la science-politique du climat																									
	approaches		Prediction or Problem-solving?		Whilst on tour demonstrating this web model,<br>  the author has observed that the questions people ask<br>  vary depending on different philosophical approaches to the climate problem.<br>  This page is provided to help us see various points of view.<p><p>If the default setup of the model shows a stabilisation scenario,<br>  some people ask "but you seem to predict a rather low temperature increase, do you think it's realistic that emissions will just fall like that?, and are especially upzzled why adjusting the ocean mixing rate (for example) should change the emissions but not the temperature. On the other hand, if the default setup shows an SRES scenario, others might say "why do you want emissions to increase like that?, or even ?I tried to adjust the temperature, but it doesn't work, I thought the aim was to stabilise the climate".<p><p>Essentially, some are thinking "where are we going, is it a problem, should we deviate?", whilst others ponder "what is the best destination, and hence the course towards it?".<p>The former are looking  forwards from the present, from cause to effect, and tend to emphasise "baseline" <i>predictions</i>, whereas the latter are trying to look backwards from the future, from effect to cause, trying to find a <i>problem-solving</i> framework.<p><p>You may find the @flowchart helpful. Which way do you prefer the arrows to go?<p><p>Note that this JCM, like the IPCC, considers both approaches, reviewing both<br>  @sres and @stabilisation scenarios. However if you ask me simply 'so what's your prediction of climate change', I will reply - that's our choice -what do you want to do (and maybe later -what risks are acceptable to you)? Hence the name ?chooseclimate? (see also @concept). But to have a real choice, we have to reach a common understanding among all all citizens who share the same atmosphere.<p><p>In climate science, those who work with sophisticated but slow Global Circulation Models can only  make forward-looking <i>predictions</i> based on a small set of pre-defined scenarios,<br>  whereas  those who work with simple models (such as this one) are able to use them for "inverse" (backwards) calculations or within "Integrated Assessment" frameworks, eventually intending to seek the "optimum" solution.<p>The former might emphasise @uncertainty in the models, whilst the latter try to make policy-relevant analysis now, anticipating the  @inertia in the system.<p><p>Both approaches pose technical problems:<br>  Predictions from socioecnomic models tend to diverge wildly in the future, and so cannot be extended for long enough timescales to show the effect of slowly-responding processes such as sea-level rise.  Whereas inverse calculations can define a stable future, but tend to diverge more when calculating pathways away from the present (particularly if we choose a target later in the cause-effect chain -see @uncertburden).<p><p>Imagine on a ship, the lookout high in the "crows nest" has a better view than the navigator below decks, but the latter has a chart covering greater distances, including changing currents and underwater obstacles. So they may give contradictory advice to the helmsman who, knowing that the ship is heavy to steer, has to find the best compromise!<p><p>The problem-solving approach also assumes that it is possible to make an effective science-driven global agreement limiting greenhouse gas emissions, i.e. that the UN Climate Convention will achieve it's aim as expressed in Article 2 (see @stabilisation). Those who project current trends might be more pessimistic about this,<br>  and prefer to emphasise developing technology to enable us to reduce emissions later if necessary, or to adapt to climate change (see @stabpathways).<p>  In practice, we need a mixture of both prediction and problem-solving approaches, as discussed below.	Prédiction ou résolution du problème ?		Pendant qu’il était en train de démontrer son modèle interactif, l’auteur a observé que les questions que les gens posaient variaient selon différentes approches philosophiques au problème climatique.<br> Cette page a pour but de nous aider à voir les différents points de vue.<br>  Bien que l’installation par défaut du modèle montre un scénario de stabilisation, quelques personnes demandent quand même « mais vous semblez prédire une augmentation plutôt basse des températures ; pensez-vous qu’il soit réaliste que les émissions aient un tel comportement », ou « pourquoi l’ajustement de la vitesse de mélange des océans (par exemple) devraient changer les émissions mais pas la température? ». D’autre part, si l’installation par défaut montre un scénario SRES, d’autres personnes pourraient dire : « pourquoi voulez-vous que les émissions augmentent de cette manière ? ; j’ai essayé d’ajuster la température, mais cela n’a pas fonctionné ; j’ai pensé que le but était de stabiliser le climat ».<br> En général, certains se disent : « où allons-nous, est-ce un problème, devrions-nous dévier (changer notre comportement) ? », alors que d’autres considèrent : « quelle est la meilleure destination, et par conséquent le meilleur chemin pour y arriver ? ».<br>  Les premiers regardent vers l’avenir en partant du présent, c’est-à-dire de la cause à l’effet, et tentent de souligner les prédictions « dans les grandes lignes » (baseline), alors que les seconds cherchent à revenir en arrière depuis le futur, donc depuis l’effet vers la cause, essayant de trouver un ensemble de résolutions au problème.<br> Vous pouvez trouver  utile le @flowchart. De quelle manière préférez-vous bouger les flèches ?<br> Remarquez que le JCM (Java Climate Model), comme le GIEC (Groupe d’experts Intergouvernemental sur l’Evolution du Climat), considère les deux approches en passant en revue à la fois (voir @aboutsres et @stabilisation). Cependant, si vous me demandez simplement : « quelle est votre prédiction pour le changement climatique », je vous répondrai - c’est notre choix - qu’est-ce que vous voulez faire (et peut-être plus tard - quels risques semblent acceptables pour vous) ? Voici le nom «Chooseclimate»  (allez voir  @concept). Mais pour avoir un choix réaliste, nous devrions parvenir à un accord commun parmi tous les citoyens qui partagent la même atmosphère.<br> En climatologie, ceux qui travaillent avec les modèles globaux sophistiqués mais lents peuvent seulement faire des prédictions basées sur un petit ensemble de scénarios prédéfinis, alors que ceux qui travaillent avec de simples modèles (tels que celui-ci) sont capables de les utiliser pour faire des calculs « inverses » (backwards calculations) ou dans le cadre d’« évaluations intégrées », entendant chercher par la suite la solution optimale.<br> Les premiers pourraient souligner certaines @uncertainty dans les modèles, alors que les seconds essaient de faire aujourd’hui une analyse appropriée pour la politique, anticipant les @inertia dans le système.<br> Mais les deux approches posent des problèmes techniques : les prédictions des modèles socioéconomiques tendent à fortement diverger dans le futur, et ne peuvent alors pas être étendues pour des échelles de temps assez longues en vue de montrer l’effet de processus réagissant lentement tels que l’élévation du niveau de la mer.<br> Alors que les calculs inverses peuvent définir un futur stable, ils tentent cependant à diverger davantage en calculant des trajectoires de prévision au fur et à mesure que l’on s’éloigne du présent (particulièrement si nous choisissons une cible loin dans la chaîne de « causes à effets » (cfr. @uncertburden).<br> Imaginez-vous sur un bateau. La vue du haut du mât offre une meilleur vue que celle qu’a le navigateur situé sur le pont du bateau, mais ce dernier a une carte marine couvrant de plus grandes distances, incluant des courants variables et des obstacles sous-marins. Ainsi, ils peuvent fournir des conseils contradictoires à l’homme de barre qui, sachant que le bateau est difficilement manœuvrable, doit trouver le meilleur compromis.<br> L’approche de la résolution des problèmes suppose également qu’il est possible de faire un accord global efficace mené par la science limitant les émissions des gaz à effets de serre, c’est-à-dire que la convention sur le climat de l’ONU réalisera son but comme exprimé dans l’article 2 (voir @art2). Ceux qui projettent des tendances actuelles pourraient être plus pessimistes à ce sujet, et préfèrent souligner la technologie qui se développe pour nous permettre de réduire si nécessaire plus tard les émissions, ou de nous adapter au changement du climat (cfr. @stabpathways).<br> Dans la pratique, nous avons besoin à la fois des approches de prévision et de résolution des problèmes, comme discuté ci-dessous.																							
	uncertcope		Strategies for Coping with Uncertainty		In the IPCC TAR Synthesis Report, the answer to the first question<br>  regarding the application of science to the challenge posed by article 2 of the UN Climate Convention,<br>  emphasises that <br>  <i>"Climate change decision making is essentially a sequential process under general uncertainty"</i>.<br>  This implies that we should try to make some decisions now rather than wait for perfect knowledge, but be prepared to adapt them later as the science evolves.   <li>see @ipcclinks<p>Rather than either fixed stabilisation pathways or no-climate-policy "scenarios",<br>  we could investigate "strategies" incorporating deliberate climate-policy feedbacks, which are robust in response to unexpected changes, working by "geocybernetics" or @fuzzycontrol rather than either forward or "inverse" calculations. The structure of this model has been designed with this in mind.<p>This process may be aided by the development of "intermediate complexity" climate system models, that are sufficiently complex to reveal non-linear surprises and regional impacts, but fast enough to be used in an Integrated Assessment problem-solving framework.<p>However,  models are only useful if more people understand how they work, and the ultimate ?integrated assessment model? remains the global network of human heads. This web model is developed as a window into these processes, to enable more people to become involved. It may also enable feedback between people, to provide the @dialogue, which must accompany any decision-making process.<p>Generally, experts tend to assume that other fields are simpler than their own. Consequently, natural scientists emphasise possible non-linear climate surprises, and suggest that we change human behaviour to avoid such risks, whereas social scientists point out how difficult that is, and might be more optimistic about adaptation or technological solutions.<p>However there is a fundamental difference between uncertainty in natural science, and in social science. Regarding for example, the @heatflux sensitivity, we can try to find out more about it, but we can't change it (unless you consider ?climate engineering...?). This does not apply to discovering which of the SRES "worlds" we inhabit (see @aboutsres)! This is a philosophical question, the old debate between "fate" and "free-will". The traditional academic system encourages scientists (of all disciplines) to be rather fatalistic, which can frustrate policymakers and the public.   <li>See also @uncertainty, @uncertburden	Stratégies pour faire face à l’incertitude		Dans le troisième rapport de synthèse du GIEC (IPCC TAR), la réponse à la première question concernant l’application au défi posé par l’article 2 de la Convention du Climat de l’ONU, souligne que :<br> « La prise de décision au sujet du changement climatique est essentiellement un processus séquentiel sous incertitude générale ».<br> Ceci implique que nous devrions essayer de prendre quelques décisions maintenant plutôt que d’attendre d’avoir une connaissance parfaite, mais il s’agit également d’être préparé à adapter plus tard ces décisions à l’évolution de la science. (Cfr. @ipcclinks)<br> Plutôt que de rechercher des trajectoires à stabilisation fixées ou des « scénarios » sans politique sur le climat, nous pourrions rechercher des stratégies incorporant des feedbacks (rétroactions) délibérés sur la politique climatique, qui sont résistantes en réponse aux changements inattendus et travaillant par  « @fuzzycontrol » plutôt que par des calculs inverses. La structure de ce modèle a été conçue à cet effet.<br> Ce processus peut être facilité par le développement des modèles de système climatique « de complexité intermédiaire », ceux-ci sont suffisamment complexes pour indiquer des « surprises » non-linéaires et des impacts régionaux, mais assez rapides pour être employés dans un cadre intégré de résolution des problèmes d’évaluation.<br> Cependant, les modèles sont utiles seulement si plusieurs personnes comprennent comment ils fonctionnent, et le mieux modèle d’évaluation intégré demeure le réseau global des têtes humaines. Ce modèle interactif (web) est développé comme un fenêtre dans ces processus, pour permettre à plusieurs personnes de s’y impliquer. Cela peut aussi permettre un feedback entre plusieurs personnes pour fournir un cadre quantitatif à un @dialogue, lequel doit accompagner n’importe quel processus de décisions.<br> Généralement, les experts ont tendance à supposer que d’autres champs sont plus simples qu le leur. En conséquence, les scientifiques de la nature remarquent des « surprises » non-linéaires possibles dans le climat et proposent que nous changions notre comportement pour éviter de tels risques, tandis que les sociologues précisent à quel point cela est difficile, et pourraient être plus optimistes au sujet de l’adaptation ou des solutions technologiques.<br> Cependant, il y a une différence fondamentale entre l’incertitude en sciences naturelles, et en sciences sociales. Considérant par exemple la sensibilité du Module des Flux de Chaleur (voir @climsens), nous pouvons essayer d’en savoir plus à son sujet, mais nous ne pouvons pas le changer (sauf si vous prenez en compte la technologie concernant le climat … ?). Ceci ne nous aide pas à découvrir dans quel « monde SRES » nous habitons (cfr. @aboutsres ) ! C’est une question philosophique, c’est la vieille discussion entre le « destin » et le « volontaire ». Le système scolaire traditionnel encourage les scientifiques (de toutes les disciplines) à être plutôt fatalistes, ce qui peut frustrer les politiciens et le public.<br> (cfr. aussi @uncertainty, @uncertburden)																							
	whobau		Whose "business as usual"? -A note on terminology		We should be especially careful regarding the use of terminology which implicitly assumes a particular approach to the problem.<p>Regarding the distribution of future emissions, people emphasising the eventual "destination" tend to speak in terms of sharing <i>"rights"</i> to use the limited <i>"resource"</i> of the atmosphere in a sustainable way, whilst those emphasising current trends tend to speak of <i>"burden-sharing"</i> considering the <i>"mitigation effort"</i> to reduce emissions from a projected <i>"baseline"</i>.<p>Regarding impacts, perhaps we should remember that for many people, especially farmers or indigenous peoples depending on sustainable ecosystems, as well as for other species of life with which we share this planet, the business-as-usual <i>"baseline"</i> could be considered to be no anthropogenic climate change, the <i>"burden"</i> to be externally imposed climate impacts, and the <i>"effort"</i> to be adaptation!<p>²A related issue arises in economic studies, regarding the difference between "willingness to pay" to avoid change, and "willingness to accept" compensation for it. Regarding the latter, in the UNFCCC process small island states request compensation for lost territory, whilst OPEC request compensation for lost oil revenues!²  <li>See also @equity	Qui define le status quo ? Une remarque sur la terminologie		Nous devrions faire attention particulièrement à l’utilisation de la terminologie qui suppose implicitement une approche particulière au problème.<br> Concernant la distribution des émissions futures, les gens remarquant une certaine « trajectoire » ont tendance à parler en termes de « droits » de partage pour employer la ressource limitée de l’atmosphère d’une manière durable, alors que ceux qui remarquent des tendances actuelles tendent à parler d’un « partage du fardeau » en considérant l’« effort d’atténuation » pour la réduction des émissions en partant d’une « baseline » projetée.<br>  Concernant les impacts, nous devrions peut-être nous rappeler que pour beaucoup de gens, particulièrement les fermiers ou les peuples autochtones dépendant des écosystèmes (soutenables), comme pour d’autres espèces de vie avec lesquelles nous partageons cette planète ; la « baseline » du statu quo pourrait être considérée comme n’étant pas un changement climatique anthropique ; le fardeau pourrait être considéré comme représentant des impacts extérieurs au climat et l’effort d’être une adaptation.<br> Un problème relatif surgit dans des études économiques, concernant la différence entre la « bonne volonté de payer » pour éviter le changement, et la « bonne volonté d’accepter » la compensation pour le changement. Concernant ce dernier, dans le processus de l’UNFCCC, les petites îles demandent une compensation pour le territoire perdu, alors que l’OPEP requiert une compensation pour les revenus perdus de pétrole.<br> Cfr. @equity																							
	equity		Equity Issues		We all have to share the same atmosphere: Emissions from one place reach the other side of the world in about a month, although they may remain there influencing the climate for hundreds of years. So climate policy requires a long-term, global agreement. Yet such an agreement will not be implemented effectively unless it also seen to be equitable, for most people of the world, considering many different perspectives of justice. Much mistrust in the global climate negotiations has come about because participants have focussed on only one type of equity (most relevant to their situation), and failed to recognise the perspectives of other groups.<p>Several types of climate (in)equity may be explored using JCM, following the links below:<br>  <h4>Responsibility for climate change</h4> This question is being explored under the auspices of the 'Brazilian proposal'. See also:<li>@attributeplot,<li>@responsibility,<li>@attribution<br>  <h4>Distribution of future emissions quotas</h4>  Should the concept be to share the 'burden' of reducing emissions, or to share 'rights' to use the atmosphere?  See also:<li>@distribplot,<li>@distribution,<li>@convergence<br>  <h4>Distribution of climate change impacts</h4> This may be the biggest inequity, as the poorest (warmest and most vulnerable) countries are likely to suffer the greatest  impacts. However patterns of regional impacts are still very uncertain, and methods of comparing and aggregating them rather controversial. See also:<li>@regclimap,<li>@regcli,<li>@impacts<br>  <h4>Distributions over time (Intergenerational equity)</h4>  Both physical and human parts of the climate system have a large inertia due, for example, to slow mixing of heat and carbon in the ocean, and slow changes in infrastructure, technology and lifestyle. So actsions now affect the legacy we leave for our grandchildren and beyond. See also:  <li>@inertia,<li>@stabpathways<br>  <h4>Other issues</h4>  <li>Coping with uncertainty requires additional effort, but who has to bear this depends on the type of policy targets chosen: see @uncertburden  <li>We should beware of loaded terminology: see @whobau  <li>We also share this planet with many other species, who also deserve a stable future climate and habitat. Interactions between climate change and biodiversity are complex and not yet considered in JCM, but should not be forgotten.<br>  <hr><br>See also @equity	Principes d'Equité																									
	concept		Java Climate Model -the Concept		The @oldconcept page is out of date<br>  A new one will be added soon.  <li> see also @dialogue	Java Climate Model - la Concepte																									
	oldconcept		(Old) Java Climate Model -the Concept		<i>Abstract for IGBP Global Change Open Science Conference Amsterdam 1013 July 2001<br>  (which also included @oldfuture) </i><br>  <hr><br>  As we share a common atmosphere, safely controlling our global greenhouse experiment requires the cooperation and engagement of citizens worldwide. This requires better public understanding, at least of the scale of the problem, the slow time responses, and the relative importance of various scientific uncertainties and policy, technological and lifestyle choices. Yet even simple climate models illustrating these factors seem mysterious, even among climate policymakers.<p>  The Java Climate Model aims to help bridge that gap, by enabling anybody on the web to experiment with climate models and policy options. Parameters are adjusted simply by dragging graphical controls with a mouse in a web browser, causing an instant response in several linked plots (including regional and per-capita emissions, carbon cycle, radiative forcing, global temperature, sea-level, regional climate maps, etc.) Thus the human-carbon-climate system is presented in a dynamic, mechanical way, so it?s easy to see ?cause and effect? by ?playing? with parameters. This is not another "data visualiser" but a complete model, yet fast and compact (downloadable in a few seconds, then also working offline).<p>  To enhance credibility, the core calculation implements the same simple upwelling-diffusion carbon-cycle and climate models (fitted to AOGCM results), as used to make many of the smooth-curve plots in the recently published IPCC TAR (WG1). Although these U-D box models are conceptually simple, it has nevertheless been challenging to develop an instant response to moving controls. This was achieved using an efficient but exact eigenvector-matrix calculation method, originally developed by Jesper Gunderman for DEA-CCAT's earlier online web model. To check the accurate fit to the IPCC predictions, the SRES data tabulated in the report may be plotted alongside the model curves.<p>  All model calculations are within one timestep loop, therefore climate-carbon and other biogeochemical feedbacks are easily incorporated. Deliberate climate-emissions policy feedbacks or "geocybernetics" may also be investigated. Despite efforts to explain models, many policymakers don't trust predictions and prefer to respond after observed changes. Some problems with a responsive approach to climate control are illustrated by formulae adjusting emissions according to recent temperature trends. The slow response from emissions to impacts leads to oscillations, exacerbated by misleading effects of temporary aerosol cooling. Nevertheless specified targets may be approached this way.<p>  The Java model is also intended to enable feedback between people worldwide, and to broaden the discussion beyond english-speaking experts.<p>  Therefore the code structure is internationalised, making it easy to translate all the labels and pop-up information into any language (including Chinese) This may also be useful within the UNFCCC context.<p>  The model shown here is more an evolving ?proof of concepts? than a finished product,<br>  Therefore much further development is anticipated. Please read @future																										
	dialogue		A Quantitative Framework for Global Dialogue		Steering our global ship to identify and  avoid "dangerous anthropogenic interference in the climate system" (@art2) requires balancing many risk and value judgements. Computer models may help provide a quantitative framework to help resolve the effects of complex interacting processes. Yet these models remain a mysterious "black box" to all but a few experts, whilst to effectively implement any global agreement requires the active engagement of many citizens around the world. So we need some "democratisation" of climate science, as the ultimate  "integrated assessment model" will remain the global network of human heads.<p>  The Java Climate Model is designed to assist this dialogue, by enabling anybody to explore both mitigation policy options and scientific uncertainties simply by adjusting parameter controls with a mouse in a web browser. The instant response on linked plots helps to demonstrate cause and effect, and the sensitivity to various assumptions, risk and value judgements.  Moreover the code is  internationalised so the @labdoc can be translated into many languages, and the model is compact and fast, considering users with slower computers and connections. JCM has already been used for several @teaching and policy @applications. The eventual aim is to link people from around the world to debate our future climate  choices across the web -  @remotecontrol  describes some technical experiments towards this aim.<p>  See also @future, @localtolocal, @equity	Une cadre quantitative pour le dialogue mondiale																									
	localtolocal		From Local Emissions to Local Impacts		To engage people, we need to tell them how local emissions which they can influence, can change local impacts which affect them directly. Completing this loop, upscaling and downscaling via vast global natural and human systems (we have to assume some cooperation) is a challenge for any model, especially a fast interactive online tool. Currently JCM combines regional emissions and socio-economic data from the SRES scenarios (@aboutregions), with climate impact maps from a range of GCMs. (@regclimap), but this is still rather statistical and abstract. Eventually, plug-in modules should be developed which illustrate much more tangibly future climate impacts and specific actions  to reduce them.	Des emissions locaux aux impactes locaux																									
	future	fut	Future Development of JCM		Any chain is only as strong as its weakest link, this is especially true of integrated  assessment tools such as JCM.<br>  Therefore future development of JCM will focus, in the short term, on regional climate impacts, a socioeconomic module, and consideration of uncertainties.<br>   See:  <li>@regclifuture  <li>@peoplefuture  <li>@uncertfuture<br>  <hr><br>  More examples of integrated problem solving will also be made with the new @scripting facility, and with an @optimisation tool.<br>  To make it easier to compare scenarios and models, a @parallelworld module structure is also envisaged<br>  <hr><br>  Recalling that the ultimate aim of JCM is to help provide a @dialogue,<br>  continued effort will be made with @labdoctrans and applications for @teaching.  <li>Note also @oldfuture, @localtolocal<br>  <hr><br>  The core science modules are already based on IPCC-TAR. However some additions are considered in links below.  <li>@carbonfuture  <li>@oghgafuture  <li>@radforfuture  <li>@climodfuture  <li>@sealevelfuture<br>  <hr><br>  Future development of the graphical interface depends on the evolution of Java in web browsers  <li>@javafuture<p>  <hr>Can you help? Please get in touch (@contact)<p>  <!--<br>  There are many possibilities for the arrangement of plots and documentation  <li>@layoutfuture   <li>@docfuture<br>  Maybe a lighter approach can also help - people learn best from mistakes  <li>@gamefuture<br>  And recalling the original vision of chooseclimate:  <li>@referendum<br>  checkout longterm of jcmdoctodo<br>  -->																										
	oldfuture	fut	(Old) Future Development of JCM -summer 2001		<i>Note: This page dates from summer 2001!</i><p>  The model shown here is more an evolving 'proof of concept' than a finished product, and much further development is anticipated.<p>  Many more variants on policy proposals are envisaged. Modules will be developed in-situ during the evolving global climate negotiations over the next few months, in response to suggestions. Although the current focus is on details of 'flexible mechanisms', we need long-term global proposals in order to calculate the climate impact, and should consider defining 'strategies' rather than fixed 'scenarios', in order to be robust against uncertainties.<p>  The networking features of Java could enable distributed groups to interact with the same model over the internet, using shared parameters as a quantitative framework for science or policy discussion. Thus the java model encourages direct dialogue between different stakeholder groups, rather than via experts, and enables citizens to balance risks, values and equity from various viewpoints.<p>  The technique for coding 'remote control' might also be applied to construct educational demonstration sequences in response to 'frequently asked questions'.<p>  The regional climate impact map already shown, illustrates the importance of thinking beyond global average figures, and many more plots could be developed with more sophisticated scaling. Dynamic graphics illustrating local and sectoral impacts should also be developed.<p>  The rapid response of the core science modules suggests that more sophisticated calculations are possible -eventually moving towards intermediate complexity models. It is particularly important to include more biogeochemical feedbacks, especially when extending the model over longer timescales. Suggestions are welcome regarding feedback parameters or model structures.<p>  The flexible modular code structure (which will be partly "open source") encourages development of additional "plug-in" components. These may eventually be linked to larger '3rd generation Integrated Assessment models' which are also expected to share modules across the web.<p>  So there is much potential, and any ideas for cooperation would be most welcome.																										
	acknow		Acknowledgements		So many people have helped during development of JCM (see also @develop).<br>  ££acknowmain ££acknowtranslate ££acknowsupport ££acknowmodels																										
	acknowmain		Particular thanks are due to:		<li>Jesper Gunderman and Peter Laut (<nobr><a href="http://www.dea-ccat.dk" target="_new">DEA-CCAT Copenhagen</a> </nobr>), who provided the vital "break" inviting me to work with them in Copenhagen, and explaining their methods for solving the models.<p><li>Brian Lucas, Lawrence Hislop, Aake Bjorke, and many others in <nobr><a href="http://www.grida.no" target="_new"> UNEP/GRID Arendal</a></nobr>, for a great working experience and insight in design and communications.<p><li>Fortunat Joos (<nobr> <a href="http://www.climate.unibe.ch" target="_new"> KUP Bern</a> </nobr>), and many others in Bern, for sharing much insight into carbon/chemistry/climate models, also Jose Romero (BUWAL) for enthusiastic support of this project.<p><li>Jean-Pascal van Ypersele (<nobr><a href="http://www.climate.be" target="_new"> UCL Louvain-la-neuve</a></nobr>), for support and encouragement for the longer-term development of JCM, and building bridges between research, policymaking and education.<p><p><hr><br>  I should also thank colleagues in GCI for introducing me to climate policy, to friends in Edinburgh who helped me during spring 2001, and to many others who gave ideas and encouragement regarding the model. And not least, I thank friends in many places, who helped this "nomad with a laptop" to feel at home and to escape the computer occasionally to enjoy the real world!																										
	acknowtranslate		Acknowledgments for Translations		Much thanks to the following  who helped to translate the model labels (most recent first):  <li>Martine Vanderstraaten (Flemish/Nederlands)  <li>Theres Grau (Deutsch)  <li>Jose Corcho Alvarado (Español)  <li>Phillipe Rekacewicz & Gilles Delaygue (Français)  <li>Suraje Dessai (Português)  <li>Nikolai Denisov (Russian)  <li>Petter Neumann (Norsk)  <li>Jesper Gunderman (Dansk)<<hr> Some parts of french documentation were also translated by students at UCL <p>  Can you help with another language?  <li>See @labdoctrans, @labdoc, @contact																										
	acknowsupport		Financial Support for JCM		Financial support in developing JCM is acknowledged, from<p><li>Universite Catholique de Louvain & Climneg project (OSTC)  <li>Swiss Government (BUWAL)  <li>EnergiMiljoradet (Danish Council for Sustainable Energy)   <li>Danish Energy Agency  <li>UNEP-GRID (overheads in Arendal)																										
	acknowmodels		Original Models and Data		As JCM aims to reproduce IPCC predictions, it incorporates formulae from many research groups.<br>  Although "simple" carbon/climate models can be described with relatively few boxes/equations, the validity of such "conceptual" models depends on the careful tuning of the parameters, to capture the response of more complex GCMs, or to match historical measurements.<br>  This is not an easy task, and these models have taken years to develop.<p>  So, the effort that has gone into the original models, should also be acknowledged, particularly the Bern model used for the carbon-cycle / chemistry in JCM, and the Wigley-Raper model used for the temperature / sea-level.<p>  Many sources of data are also acknowledged, particularly from RIVM's Image model used for the socioeconomic data, from CDIAC for national emissions data, and from IPCC-DDC for regional climates.<p><li>See @references for more detail																										
	contact		Contact the author		<h2>Dr Ben Matthews</h2><br>  <img align=right src="http://www.chooseclimate.org/benglaciersmall.jpg" width=100 height=100><p>To contact me email <i><a href="mailto:ben2 <p> @chooseclimate.org">ben2 <p> @chooseclimate.org</a></i><br>  I am currently working at:<br>  <a href="http://www.astr.ucl.ac.be" target="_new">Institut d'Astronomie et de Géophysique</a><br>  Université Catholique de Louvain<br>   Belgium<p> (and during spring 2002 at:)<br>  <a href="http://www.climate.unibe.ch" target="_new">Klima und UmweltPhysik</a><br>  University of Bern<br>  Switzerland<p>  <hr><br>  How did theJava Climate Model develop?<p>@concept | @develop | @acknow | @future<br>  <hr><br>  Links to my other websites:<br>  <img align=right width=50% src="http://www.chooseclimate.org/benphd/figs/fig5-1.jpg">  <li><a href="http://www.chooseclimate.org/benphd/index.html" target="_new">The Rate of Air-Sea CO<sub>2</sub> Exchange:</a><br>Chemical Enhancement and Catalyis by Marine Microalgae<p><img src="http://www.chooseclimate.org/flying/littleplane.gif" width=60 height=30 border="none" align=right><img src="http://www.chooseclimate.org/flying/H2O.gif" width=25 height=30 border="none" align=right><img src="http://www.chooseclimate.org/flying/Nox.gif" width=25 height=30 border="none" align=right><img src="http://www.chooseclimate.org/flying/Co2.gif" width=25 height=30 border="none" align=right><img src="http://www.chooseclimate.org/flying/contrail2.gif" width=60 height=30 border="none" align=right><p><li><a href="http://www.chooseclimate.org/flying/index.html" target="_new"><br>  Flying off to a Warmer Climate: </a><br>  Map-calculator regarding aircraft emissions<p>  <img src="http://www.chooseclimate.org/climatetrain/pics/ctcover.gif" width=80 height=80 border="0"  align=right><p>  <li><a href="http://www.chooseclimate.org/climatetrain/index.html" target="_new">Climate Train to Kyoto</a>: full report																										
																															
intro																															
	intro		Introduction: What is JCM, How do I use it?		Climate change is influenced by complex interlinked processes. This interactive model lets you explore the system and how we can change it, simply by moving controls with your mouse and observing the effect instantly on plots ranging from emissions to impacts. The calculation methods are based on those used in the recent Intergovernmental Panel on Climate Change Third Assessment Report, implemented efficiently in the java language to work within your web browser.<br>  <hr><br>  So what do you want to explore?   <li>@howto<br>£~howto      <li>@emitcc<br>£~emitcc    <li>@science<br>£~science     <li>@pan<br>What is this plot / control / option / curve...?   <br>  ²£§usehelpmode²  <li>@autodemo<br>£~autodemo      <li>@about<br>Where did JCM come from, where is it going?<p>  <hr>Note also   <li>@layoutmenu<li>@languagemenu<li>@download<li>@whatnew<li>@copyr  <li><a href="../struc/opendoc.html">Opening introductory page</a>	Introduction: Qu'est-ce-que c'est JCM, comment peux-je l'utiliser?		Les changements climatiques sont influencés par processus complexes et interliés. Ce modèle interactif vous permettre d'explorer la système et les façons par lesquels nous pouvons le changer, simplement par ajouter des contrôles avec votre souris et observer l'effet tout de suite sur des graphiques qui vont des emissions jusqu'aux impactes. Les façons de calculation sont basés sur ceux qui etaient utilisés dans le Troisième Rapport d'Assessment de la Groupe d'Experts Intergouvernmentale sur l'Evolution du Climat, mais implementés d'un façon efficace dans la langue Java pour fonctionner dans votre web browser.<br> <hr><br> Alors qu'est-ce que vous voulez explorer? <li>@howto<br>£~howto      <li>@emitcc<br>£~emitcc    <li>@science<br>£~science     <li>@pan<br>Qu'est-ce que c'est ce graphique/contrôle/option...?<br>  ²£§usehelpmode²  <li>@autodemo<br>£~autodemo      <li>@about<br>D'où viens JCM, vers où il va?<br>  <hr>Noter aussi   <li>@layoutmenu<li>@languagemenu<li>@download<li>@whatnew<li>@copyr  <li><a href="../struc/opendoc.html">Opening introductory page</a>																							
	howto		Step-by-step introduction	How to use JCM plots, controls, options	A sequence of simple explanations of how to adjust parameters using the  controls and options, and change plots and menus.<br>  ²Note, this introduction starts at a basic level. If you want something more exciting/detailed try: @autodemo, @emitcc, @science, @pan ²<br>  ²°adju Note: To follow this tutorial, the @helpmode (top of model) must be  switched off, otherwise this page will be replaced by plot-specific documentation, whenever you click on the model. ²<br>  ££adjcontrol ££emittoimp ££morecomplex ££offtop	Introduction étape par étape.	Comment utiliser les controles, options, graphiques de JCM	Une sequence des etapes pour expliquer comment ajouter des paramêtres en utilisant des controles et options, et comment changer graphiques et menus.<br> ²Note: cette introduction commence d'un niveau de base. Si vous voulez quelque-chose plus interessant/detaillé essayez: @autodemo, @emitcc, @science, @pan²<br> ²°adju Note: Pour suivre ce tutorial, il faut que l'option @helpmode (au dessus du modèle) soit désactivé, sinon ce page sera remplacé par autre documentation specifique à chaque location où vous cliquez sur le modèle.²<br> ££adjcontrol ££emittoimp ££morecomplex ££offtop																							
	adjcontrol		Step 1: Adjusting controls		££howuco2tempdemo<br>  Click the button above for a simple demonstration showing the link between CO2 concentration (above) and temperature rise (below), both plotted as a function of time from 1750 to 2300 (i.e. we are now in the middle).<p>  The moving black arrow on the concentration plot adjusts the target CO2 stabilisation level and the year in which this is reached (a "mitigation" policy option)<p>  The red arrow on the temperature plot adjusts the climate sensitivity (a scientific uncertainty).<p>  You can get pop-up information about each arrow (parameter control), just by moving your mouse over it. You can also see the units by moving your mouse over the y-axis to the left of the plot.<p>  Now try adjusting the CO2 curve yourself, simply by dragging the black control with your mouse. Alternatively if you have a slow computer (or web browser), click on the control once, and click again elsewhere to move it in jumps. The control will pull the black curve with it: up and down changes the stabilisation level, left or right changes the year. The temperature responds accordingly.<p>  The red arrow pulls the temperature up and down, but in a more subtle way, as the climate sensitivity is not directly linked to the curve (it is the equilibrium temperature change corresponding to CO2 doubling, which depends on water vapour feedback processes).<br>  Moving this arrow horizontally has no effect.<p>  ²°cogs Note the temperature is relative to 1990, hence it appears to decrease in the past, when it increases in the future ²<br>  ²°adju Note also, that while you are dragging a control, information about its current value appears in red at the top of the plot.<br>  Otherwise, the x-y position of the mouse pointer is displayed. ²<br>  ²°adju The mouse will have no effect on the model if the demo is running -you must stop it first. ²	Etape 1: Ajustement des commandes		££howuco2tempdemo<br> Cliquez sur l’icône ci-dessus pour voir une démonstration simple du  lien existant entre la concentration en CO2 (ci-dessus) et l'augmentation de la température (ci-dessous). Les deux graphes sont tracés en fonction du temps, allant de 1750 à 2300 ans (nous sommes donc actuellement au milieu de cette période).<br> La flèche noire, mobile, visible sur le graphe des concentrations permet d’ajuster le niveau de CO2 au niveau de stabilisation recherché ainsi que l'année à laquelle vous désirez atteindre ce niveau. (politique de "réduction").<br> La flèche rouge sur le graphe des températures permet d’ajuster la sensibilité du climat (incertitude scientifique).<br> Vous pouvez obtenir des informations instantanées à propos de ces flèches (commande de paramètre), en positionnant simplement votre souris sur ces dernières. Il est également possible de voir l’échelle des ordonnées en pointant l’axe avec la souris.<br> Essayez maintenant d'ajuster la courbe de CO2 vous-même, en déplaçant simplement la flèche noire avec votre souris. Si vous avez un ordinateur lent (ou le web browser), cliquez une fois sur la flèche, puis cliquez ailleurs pour la déplacer en un coup. La courbe se déplacera avec la flèche : des mouvements verticaux modifient le niveau de stabilisation, tandis que des mouvements horizontaux modifient l'année. La température se modifie en fonction de ces changements.<br> La flèche rouge déplace la température verticalement, mais de manière plus subtile. En effet, la sensibilité du climat n'est pas directement liée à la courbe (c'est le changement de la température d'équilibre observé en doublant la quantité de CO2, changement qui dépend des procédés de rétroaction de vapeur d'eau). Déplacer cette flèche horizontalement n'a aucun effet.<br>   ²°cogs Notez que la température est relative à 1990, par conséquent elle semble diminuer dans le passé et augmenter dans le futur. ²<br> ²°adju Remarquez également que lorsque vous déplacez une flèche, ces coordonnées apparaissent en rouge dans le coin supérieur gauche du graphe. La position en tout point du graphe peut également être connue en pointant simplement, à l’aide de la souris, l’endroit désiré. ²<br> ²°adju La souris ne modifie en rien le modèle si la démo fonctionne - vous devez d’abord la stopper. ²																							
	emittoimp		Step 2: Emissions to Impacts		££howuemitseademo<br>  Click the button above to start the second demo. Now you can also see plots of emissions and sea-level rise, showing the full cause-effect sequence:<p>Emissions => Concentration => Temperature => Sea-level<p>  Try setting up the plots yourself: first select the 4-plots option from the "Layout" drop-down menu at the top of the model. By default, this places "Regional Climate Map" as the bottom left plot. To change this to Sea-level, use the "Plot" menu at its top right corner.<p>  ²°adju You can show as many copies as you like of each plot, which can have different  display options (scales, choice of curves) although they share the same underyling model options.²<p>  Since we choose to stabilise concentration as a policy option (this is the ultimate aim of the UN Climate Convention -see @art2, @stabconcdoc), the carbon cycle model calculates <i>backwards</i> from the target concentration curve to get the required emissions (this is known as an <i>"inverse"</i> calculation -see @inverse).<p>  You may have noticed that the climate sensitivity also slightly affects the required emissions: the warmer it is, the lower the emissions must be to achieve the specified concentration. This is due to the influence of temperature on the ocean carbonate chemistry - a positive climate => carbon feedback effect (see also @flowchart,  @carbchem)<p>  You should also notice that to keep the CO2 concentration level, the emissions must keep falling, due to the limited capacity of the ocean and biosphere sinks. Moreover,  even after stabilisation of atmospheric CO2, the sea-level continues to rise, due to the very slow uptake of heat by the deep ocean.<br>  Hence, it is not so easy to stop climate change!<p>  So how much do you think we should reduce emissions to avoid increasing the temperature? Perhaps it depends on the climate sensitivity (among other uncertainties)? Finding the balance requires both risk and value judgements as well as understanding of the science. See also @stabtempdoc	Etape 2: Des emissions aux impactes		££howuemitseademo<br> Cliquez sur le bouton ci-dessus pour commencer la deuxième démonstration. Maintenant vous pouvez également voir les graphiques des émissions et l’élévation du niveau de la mer, montrant l’ordre des causes à effets : <p> Émissions = > Concentration = > Température = > Niveau de la mer<br> Essayez d’établir les graphiques vous-mêmes : <p> Choisissez d'abord l'option 4-graphiques à partir du menu "disposition" situé au-dessus du modèle. Par défaut, celui-ci place "la carte régionale de climat" comme graphique dans le coin inférieur gauche. Pour changer celui-ci et obtenir le graphique représentant le niveau de la mer, employez le menu  "graphique" situé dans le coin supérieur droit.<br> ²°adju Vous pouvez montrer autant de copies que vous voulez de chaque graphique. Ceux-ci peuvent avoir différentes options d'affichage (échelles, choix des courbes) bien qu’en réalité ce sont les mêmes modèles et qu’ils présentent les mêmes options. ²<br>  Pendant que nous choisissons de stabiliser la concentration comme option  politique (c'est le but final de la convention de climat de l'ONU - voir 																							
	morecomplex		Step 3: More Curves and controls		££howulabelsdemo<br>  Now we increase the complexity level from "Simplest" to "Normal" using the menu at the top.<p>The overall concept of each plot is the same, but extra curves show how the total emissions, temperature, or sea-level is each a combination of various influences.<p>  There is also an "expert" level which introduces yet more curves and controls, to show the detail of how the model works.<p>  To see what the curves are, move your mouse over the coloured legend boxes, and see the pop-up info, demonstrated by the demo-wizard.<p>  You can also change the <i>language</i> of this pop-up info using the @languagemenu at the top.<br>  <hr><p>  The CO2 emissions are now divided into region shown by coloured bands. The colour legend is also shown by the Emissions Regions Map (select from a "Plot" menu). The six "warm" colours correspond to the industrialised countries with emissions targets in "annex B" of the Kyoto protocol. Note that the timescale of this plot is now only from 1900 to 2100, since there is insufficient regional data beyond this. <p>  The Atmospheric CO2 plot now shows CO2 sources and sinks as well as CO2 concentration. The concentration scale (ppm) has moved to the right hand axis, whilst the left axis shows GtC, for emissions and sinks (you can adjust these using controls on @carbonstorageplot)<p>  Historical data has now been added to the temperature plot, both direct measurements since 1860 (green), and also proxy data from tree rings and lake sediments (grey). The brown curve is calculated by the @heatflux module -how well does it match the data (the fit will be affected by uncertainty controls on @radforplot and @oceantempplot)?	Etape 3 : plus de courbes et plus de contrôle		££howulabelsdemo<br> Maintenant nous augmentons le niveau de difficulté du "plus simple" à "normal" en utilisant le menu du dessus.<br> Le concept général de chaque graphe est identique, mais les courbes supplémentaires montrent comment les émissions, la température, ou le niveau de la mer sont chacun une combinaison de diverses influences.<br> Il y a également un niveau "expert" qui présente plus de courbes et de commandes, ce qui permet de montrer en détail la façon dont le modèle fonctionne.<br> Pour voir ce à quoi correspondent les courbes, déplacez votre curseur de souris sur les boîtes colorées de la légende, et observez alors l'information instantanée, montrée par le « demo-wizard ».<br> Vous pouvez également changer la langue de cette information instantanée en utilisant @languagemenu au dessus. <hr><br> Les émissions de CO2 sont maintenant divisées en région montrée par les bandes colorées. La légende de couleur est également montrée par la carte de régions d'émissions (choisie à partir du menu des graphes). Les six couleurs chaudes correspondent aux pays industrialisés aux cibles d'émissions en "annexe B" du protocole de Kyoto. Notez que le calendrier de ce graphe est actuellement seulement de 1900 à 2100, puisque les données régionales sont insuffisantes au delà de ces dates.<br> Le graphe de CO2 atmosphérique montre maintenant les sources et les puits de CO2 aussi bien que la concentration en CO2. L’échelle de concentration (ppm) s'est déplacée vers l'axe de droite, tandis que l'axe de gauche montre la quantité de carbone en GtC, pour les émissions et pour les puits (vous pouvez ajuster ces commandes utilisées sur le @carbonstorageplot)<br> Des données historiques ont maintenant été ajoutées au graphe de la température, aussi bien des mesures directes effectuées depuis 1860 (vert) que des données de proximité des anneaux d'arbre et des sédiments de lac (gris). La courbe brune est calculée par le  @heatflux module - à quel point assortit-elle les données (l'ajustement sera-t-il affecté par des commandes d'incertitude sur les panneaux  @radforplot et @oceantempplot)?																							
	offtop		Step 4: Scenarios off the top!		££howuscalesdemo<br>  Now we see what might happen if there is no global climate policy (i.e. the UN Climate Convention process fails).<br>  The IPCC SRES scenarios explore various possible future worlds, excluding global policy to mitigate emissions (see @aboutsres)<p>  To see these, first select £~nopolicy from the @emitmenu at the top, then pick a scenario from the @sresmenu.<p>  As you can see, several of these scenarios take the emissions, CO2 and temperature way off the top of the plots. However you can change the scale, simply by dragging it up or down. If you click near the origin (usually 2000 for x-axis and zero for y-axis) you will shift the origin, otherwise, you will stretch or sqeeze the plot: try it. See also @scale.<p>  You could also use this to zoom in on the data: several historical events can be identified particularly in the  per-capita emissions plot.<br>  You can make such a plot  by choosing £`popn from the @perq on a @distribplot<br>  Note: SRES also changes the population<p>  Don't forget, you can always press reset (top left)!	Étape 4 : Scénarios supplémentaires!		££howuscalesdemo<br>Maintenant nous voyons ce qui pourrait se produire s'il n'y a aucune politique globale de climat (c.-à-d. si le processus de la convention du climat de l'ONU échoue). Les scénarios d'IPCC SRES explorent divers mondes futurs possibles, à l'exception de la politique globale pour atténuer les émissions (voir @aboutsres)<br> Pour voir ces derniers, sélectionnez d'abord  £~nopolicy de @emitmenu au dessus, choisissez ensuite un scénario du paramètre de @sresmenu.<br>  Comme vous pouvez le voir, plusieurs de ces scénarios comprennent les émissions, le CO2 et la température en plus des graphes ci-dessus. Cependant vous pouvez changer l’échelle, simplement en la tirant vers le haut ou vers le bas. Si vous cliquez près de l'origine (habituellement 2000 pour l'axe des abscisses et zéro pour l'axe des ordonnées) vous décalerez l'origine, sans quoi, vous allez étirer ou compresser le graphe : essayez. Voir également  @scale.<br> Vous pourriez également employer ceci pour voir les données de plus près : plusieurs événements historiques peuvent être identifiés en particulier dans les graphes d'émissions per-capita. Vous pouvez faire un tel graphe en choisissant £`popn à partir du @perq sur un @distribplot.<br>  Note: SRES change également la population<br>  N'oubliez pas, vous pouvez toujours appuyer sur reset (en haut à gauche) !																							
	howuco2tempdemo	dem	CO2 and Temperature Demo		£!howuco2tempdemo		Démonstration des modifications de CO2 et de la température																								
	howuemitseademo	dem	Emissions to Sea-level Demo		£!howuemitseademo		Démonstration des émissions et du niveau de la mer																								
	howulabelsdemo	dem	Labels Demo		£!howulabelsdemo		Démonstration des Étiquettes																								
	howuscalesdemo	dem	Scales Demo		£!howuscalesdemo		Démonstration des Echelles																								
	autodemo		Automatic Demonstration	Demonstrate key points and features.	Sorry, this has not yet been updated to the new scripting code - coming back soon!	Demonstration Automatique																									
																															
science																															
	science		Climate Processes, Timescales, Uncertainties	Core science models for global fluxes of gases and heat.	Cross-cutting global climate science topics in JCM  <li>@inertia<li>@uncertainty<li>@flowchart<li>@simplemodels<br>  <hr>For experts note also:  <li>@compareipcc<li>@eigenvec<li>@references<br>  <hr>Each part of the system is documented in detail in @pan and @mod<br>  £§usehelpmode<br>  ²Direct links to some subsections of core-science module documentation are below:  <li>@carbonmodel, @carbonemissions, @sinksdynamic, @sinksbiosphere, @sinksocean, @carbchem  <li>@oghgahowwork, @atchem, @fgases  <li>@radforintro, @radforfo2, @radforothgas, @radforaerosol, @radforsolvol, @radfordistrib, @rftemp, @co2eq  <li>@udebmodel, @gcmfit @sealevelicemelt, @sealevelother<br>  ²  <li>Note also @scale	Processus, Incertitudes et echelles de Temps dans la système climatique		Issues transversales de la science mondiale dans JCM <li>@inertia<li>@uncertainty<li>@flowchart<li>@simplemodels<br>  <hr>Pour experts notez aussi:  <li>@compareipcc<li>@eigenvec<li>@references<br>  <hr>Chaque partie de la systeme est aussi expliqué en detail dans @pan et @mod<br>  £§usehelpmode<br>  ²Liaisons directs au subsections de la documentation des core-science modules  sont en dessous:  <li>@carbonmodel, @carbonemissions, @sinksdynamic, @sinksbiosphere, @sinksocean, @carbchem  <li>@oghgahowwork, @atchem, @fgases  <li>@radforintro, @radforfo2, @radforothgas, @radforaerosol, @radforsolvol, @radfordistrib, @rftemp, @co2eq  <li>@udebmodel, @gcmfit @sealevelicemelt, @sealevelother<br>  ²  <li>Notez aussi @scale																							
	uncertainty		Uncertainty		££uncertintro ££uncertjcm ££uncertmixing  ££uncertexample ££uncertburden ££uncertcope ££probabilistic ££uncertfuture	Incertitudes																									
	uncertintro		Uncertainty: Introduction		It is well known that the weather is instrinsically chaotic. Although much of this variability cancels when considering long-term average climate change, nevertheless other uncertainties arise over such timescales, concerning slow ocean mixing and ice-melt processes, biogeochemical cycles and biospheric feedbacks, and also changes in human society.<p>So climate prediction will never be an exact science, and any decision based on climate predictions must be a risk judgement. Since the risks depend on a complex combination of uncertainties from many interacting processes, this leads to conflicting advice from "experts" each focusing on specific parts of the whole system.  People may then get the impression that everthing is so uncertain, so why bother to  draw any conclusions at all?<p>Actually, a clearer picture can emerge, so long as we think carefully about how the various uncertainties fit together. Are they intercorrelated or independent - is the impact of an uncertain factor damped by negative feedbacks with other processes, or amplified by positive feedbacks? Are we more concerned about the uncertainty in the <i>overall</i> climate impacts, or the uncertainty in the relative <i>change</i> in impacts due to a particular mitigation or adaptation policy measure? The net benefit of that particular measure may be clear, even if the overall uncertainty is higher.																										
	uncertmixing		Mixing Uncertainties		For static presentations, without such interactive exploration, uncertainties are often summarised with overall error-range statistics or bands on plots<br>  However it can be misleading to mix very different kinds of uncertainties in this way, for example:   <li>Calculating a probability estimate for sea-level rise by mixing uncertainties from low probability - high impact processes such as the collapse of the West Antarctic ice-sheet, with high probability lower impact processes, such as thermal expansion. The result would be an in-between number which does not give meaningful information about either type of risk (see @sealevelplot).  <li>Quoting an uncertainty range in in future temperature predictions derived by combining uncertainties in natural climate system processes (as predicted by various global climate models -see @climodmenu), with uncertainty regarding emissions scenarios (considering various future human "world-views", see @aboutsres).   <li>The latter combination may be useful when considering <i>local adaptation</i> policies, but is rather fatalistic in the context of <i>global mitigation</i> policy (the UNFCCC process) which aims to control emissions. -see also @philosophy																										
	uncertjcm		Exploring Uncertainty with JCM		How can we present complex interacting uncertainties, avoiding @uncertmixing?<br>  This interactive web model offers people a unique opportunity to experiment by adjusting parameters themselves. The instant response illustrates cause and effect  -how much difference does each parameter make, and how does this depend on the settings of other parameters? By viewing several plots together, you can also look for feedback processes which may be dampening or amplifying the effect.   <li>See also @flowchart<br>  Moreover, the options to stabilise greenhouse gas concentration and temperature (including emissions from all gases and aerosols) help to show the importance of scientific uncertainties in the context of inverse calculations (i.e. given a specific climate target, what are the range of possible pathways towards it?).    <li>See @stabilisation, @uncertburden, @stabtemp2c<p>Of course, the presentation of uncertainty in this model could always be improved  <li>See @uncertfuture																										
	uncertexample		Uncertainty - Examples		Discussion of specific uncertainties for each component of the system is now contained in the documentation for each module/plot. See:  <li>@atco2plot, @carbonstorageplot, @carboncycle  <li>@othgasplot, @fgasplot, @oghga  <li>@radforplot, @radfor  <li>@glotempplot, @heatflux  <li>@oceantempplot,  @sealevelplot, @sealevel  <li>@regclimap, @regcli<p>However, some factors are more uncertain than others. The table below makes some comparisons:<br>  <table border=2><tr>  <td>Component</td><td>Better understood</td><td>Less well understood</td></tr><tr>  <td>Emissions</td><td>CO2, F-gases</td><td>CH4, N2O, other gases (especially from soils)</td></tr><tr>  <td>Carbon Cycle</td><td>Ocean sink (physical and chemical)</td><td>Biosphere sink (climate feedback effects)</td></tr><tr>  <td>Atmospheric Chemistry</td><td>F-gases, CH4, N2O </td><td>Ozone and OH feedbacks</td></tr><tr>  <td>Radiative Forcing</td><td>Well-mixed greenhouse gases</td><td>Solar Variability and Aerosols</td></tr><tr>  <td>Temperature</td><td>Ocean warming (except surprise circulation changes)</td><td>Cloud processes and feedbacks ("climate sensitivity")</td></tr><tr>  <td>Sea-level</td><td>Thermal Expansion</td><td>Polar icecaps</td></tr><tr>  <td>Regional Climate</td><td>Average Temperature</td><td>Precipitation and Winds</td></tr><tr>  </tr></table><p>It is also important to note that many types of uncertainty <i>cannot</i> be represented in "deterministic" simple climate models such as this one (see @simplemodels). A long-term aim is to investigate the possibility to develop interactive versions of intermediate complexity models incorporating more non-linear feedbacks, and intrinsic and regional variability.<p>Emissions scenarios are not considered here, as we have some choice about what we emit -see @emitcc, @uncertburden, @philosophy<p>  ²Naturally there are many different views regarding relative ucnertainties, please tell me your opinions! @contact ²																										
	uncertfuture	fut	Uncertainty: Future Development		<li>An uncertainty range should be shown for some individual parameters. Suggestions for how best to portray this graphically are welcome! It may be necessary to adjust this range dynamically where uncertainties are not independent.   <li>The @scripting system offers potential for running ensemble calculations testing many parameter combinations. See  @probabilistic, @optimisation  <li>Noting that <i>"Climate change decision making is essentially a sequential process under general uncertainty"</i> (IPCCTAR Synthesis report Q1), how can we try to reach a particular goal such as that specified in @art2 ? One approach may be to investigate @fuzzycontrol strategies</i> incorporating deliberate climate-emissions feedbacks which are more robust against uncertainties. The structure of this model was designed to enable investigation of such feedbacks.																										
	probabilistic		Probabalistic Approach		££probintro ££probwccc ££probscript																										
	probintro		Probabilistic Approach - Introduction		Uncertainty in the carbon-climate system depends on a wide range of interacting parameters. We might be able to constrain the range of uncertainty in future climate impacts (or of emissions, in an inverse calculation), by assigning a probability to each combination of parameters using a score based on the model fit to historical data (or some other criteria), gradually building up a probability density function.  <p> Current development of JCM is directed towards a more complex Risk-Analysis framework, more information will be added here later. (see also @stabilisation or @optimisation). 																										
	probwccc		Probabilistic analysis of Stabilisation under Uncertainty		The analysis receently presented at WCCC in Moscow and subsequent conferences (autumn 2003) combined tens of thousands of variants (of carbon cycle, other gas emissions, forcing and climate model). For this purpose specific compiled java code was added to JCM, since the calculation was too intensive for an interpreted scripting system. This cannot be demonstrated online, however graphics of the results and principles of the calculation are illustrated in the presentations at @linkpres (see also @moscow, @wccc2003, @confpres). 																										
	probscript		Scripts to illustrate range of uncertainty 		You can use @scripting in JCM to explore uncertainty - <li>@carbonprobscript  <li>@stabprobscript<br> illustrate early exploration of probabilistic approach. <p> The following don't assign probabilities, but do explore an ensemble of parameter/model combinations:  <li>@stabtemp2cscript  <li>@stabconc500script   																										
	carbonprobscript	dem	Probabalistic CarbonCycle script		This script explores the range of possible CO2 concentrations produced by one emissions scenario SRES B1, considering various uncertainties in the @carboncyle.<p>The parameters varied are:  <li>@lucfemit1990 (1300, 1500, 1700, 1900, 2100, 2300 )  <li>@fertbeta (0.187, 0.237, 0.287, 0.337, 0.387 )  <li>@ceddydiff ( 0.5, 1.0, 1.5 )  <li>@chighlat ( 19, 38, 57 )  <li>@csidemix ( 0.0009, 0.0018, 0.0027 )   <li>@asgasex  ( 0.03, 0.06, 0.09 )<br>  There are thus 6x5x3x3x3x3 = 2430 combinations - so this script takes a long time!<br>  For each combination it calculates an error function, which is the standard deviation of the difference between the calculated and measured concentrations, between 1750 and 2000.<br>  If this error function is less than 2.5, it plots a CO2 concentration curve.<br>  The brightness of the curve depends on the error function, ranging from 1.5 = black, to 2.5 = white.<br>  £!carbonprobscript<br>  It takes some time to reach the first curve, so please be patient!<br>  After a while, you can see a spread of curves, with the blacker ones in the middle.<br>  Eventually it can be seen, that the range of CO2 concentrations is about 60ppm in 2100, and 100ppm in 2300.<p>Naturally, when the landuse emissions are higher, the sinks must be higher too. A close examination shows that this algorithm favours combinations with both high landuse emissions and high land sinks, which helps to match the early part of the curve. However beware that the emissions and concentration data for the earlier years are not so accurate, so this factor should also be considered, before drawing conclusions from such analysis.<p>Note also that the full effect of temperature-carbon feedbacks are not explored here<br>  <hr>See also @carbonprobscriptinv																										
	carbonprobscriptinv	dem	Probabalistic CarbonCycle Inverse script		This does the same as @carbonprobscript, except that the CO2 concentration is fixed to stabilise at 500ppm in 2125, and the range of CO2 total emissions pathways is plotted.<br>  £!carbonprobscriptinv<br>  See also @stabconc, @inverse																										
																															
	fcnormal		Normal sequence		The normal cause-effect sequence is:   <li>@mitigation or @sres   <li>(=>@regshares)   <li>=>@carboncycle & @oghga  <li>=>@radfor  <li>=>@heatflux  <li>=>@sealevel & @regcli<p>However there are some feedback processes which go the other way. For example, the carbonate-chemistry in the ocean is affected by the temperature, if the @chemfbopt button in the carbon cycle plot is enabled. Disabling this option removes the arrow from temperature to carbon, and increases the ocean carbon sink, also removing the "spikes" due to historical temperature variability.<br>  (see @carbchem, @carbonstoreplot)																										
	fcchanging		What is changing?		Red arrows on the flowchart indicate cause-effect flows that were changed by your last action (dragging a parameter, selection from menu, etc.). The module directly affected by the parameter is shown with red text. Yellow arrows show dependencies that didn't change.<p>For example, if @chemfbopt  is enabled, and you also choose "£~stabconc" from @emitmenu, then adjusting climate sensitivity will affect both carbon and emissions, so most of the arrows are red. If it is disabled, adjusting an uncertainty control on the temperature plot (e.g. climate sensitivity) has no effect on carbon or radiative forcing, so the arrows in the upper part are yellow.<p>Note the model only calculates the components that have changed, and are also needed by visible plots. So you may notice, that the more red arrows there are, the slower the response is.<br>  See also: @howfast, @struccode																										
	fcstabsres		Stabilisation or SRES		If you choose "£~stabconc" or "£~stabtemp" from @emitmenu, then there is a feedback from @carboncycle or @heatflux back to @mitigation, which attempts reach the target curve (controlled by the four-headed arrow). If you choose to "£~stabemit" , @carboncycle and @oghga are still controlled by @mitigation, but there is no feedback. If you choose "£~nopolicy", then mitigation does nothing, everything starts from @sres. See also  @philosophy.<br>  @sres affects several parts of the model ( depending on options in the @emitmenu, @distribmenu, and @othgasemit).   <li>Population, GDP and Energy (@people)   <li>Regional CO2 emissions and abatement (@regshares)   <li>Land use change CO2 emissions (@carboncycle)  <li> Other gases & aerosol emissions (@oghga)<p>Regional emissions are affected by @mitigation and @sres modules. @regshares module calculates the distribution, but doesn't affect the total.<p> Eventually, our aim is to complete the right hand side of the circle, to show how regional emissions policy may be affected by anticipation of regional climate impacts, and how regions may achieve a better result through global cooperation.																										
	eigenvec		Efficient Eigenvector Calculation method		The carbon and climate models both use about 40 ocean layers (x2 oceans for climate model). If using a simple integration method, the timestep would have to be quite small (e.g. 1 month) in order that the fluxes are much smaller than the box contents, and so the calculation over hundreds of years is rather slow. Therefore in order to get an instant response in the plots as the parameters are dragged by the mouse, a more efficient matrix-eigenvector method has been applied.  <li>See @carboncycle and @heatflux modules , also @howfast<br>  ££evmethod ££jama ££evmathprinc																										
	evmethod		Features of eigenvector method		An exact analytical solution   <li>The only approximation is that the change in non-linear fluxes is linear within one timestep.   <li>The timestep can be any size (depending on input data and plot resolution)   <li>The ramp function must be iterated when a non-linear flux is a function of box contents.   <li>Only matrix cells needed for data output or non-linear input are calculated.   <li>Usually more efficient than pulse response function especially with many timesteps.   <li>Unlike PRF, preserves knowledge of all box contents (needed for thermal expansion or depth profiles etc.)   <li>The eigenvectors only need to be recalculated when you change a parameter that alters the linear fluxes (such as internal mixing rates), not when the external emissions change.																										
	jama		Java Matrix Package		Eigenvectors, inverses, etc. are calculated using the convenient Java Matrix Package ("JAMA"), which was developed at MIT, and has simply been bolted onto Java Climate Model.<br>  See: <a href=http://math.nist.gov/javanumerics/jama/ target='new'> http://math.nist.gov/javanumerics/jama/  </a>																										
	evmathprinc		Mathematical Principle		<h4>Differential equation</h4><br>  Start with a simple differential equation:<p>dq/dt = -&lambda;q + x<p>If x changes linearly from x<sub>t</sub> to x<sub>t+dt</sub>, it can be shown that:<p>q<sub>t+dt</sub> = prop q<sub>t</sub> +  step x<sub>t</sub> + ramp (x<sub>t+dt</sub> - x<sub>t</sub>)<p>Where:<p>prop = e<sup>-&lambda;dt</sup><p>step = (1 - prop)/&lambda;<p>ramp = (dt - step)/(&lambda;.dt)<br>  <hr><h4>System of boxes (n equations)</h4><br>  A system of n boxes with diffusive fluxes between them, plus extra non-linear inputs, can be written as:<p>dQ/dt = R Q + X<p>Where Q is the vector of box contents, R is an nxn matrix for the fluxes, and X is the vector of extra inputs.<p>We can diagonalise R:<p>R = S diag(E) S<sup>-1</sup><p>Where S is the matrix of eigenvectors, and diag(E) the diagonal matrix of eigenvalues.<p>Premultiplying all by  S<sup>-1</sup> gives:<p>d/dt(S<sup>-1</sup>Q) = diag(E) (S<sup>-1</sup>Q) + S<sup>-1</sup>X<p>S<sup>-1</sup>Q and S<sup>-1</sup>X are just vectors, with each element of S<sup>-1</sup>Q multiplied by just one eigenvalue,<br>  so this is just like n simple differential equations as above, with diag(E) providing the &lambda; terms<p>Now, if we have a lot of equal timesteps, we only need to calculate the n prop, step and ramp functions once, at the beginning.<p>If we preserve the state information in the form S<sup>-1</sup>Q,<br>  then the diffusion for each timestep is given just by multiplying each element by it's prop function.<p>To add the step and ramp functions for extra inputs, we need to calculate S<sup>-1</sup>X,<br>  but if we have only y boxes with non-linear inputs<br>  (for example due to carbonate chemistry at the surface of an upwelling-diffusion ocean),<br>  we only have to multiply out y columns of the nxn matrix S<sup>-1</sup><br>  (which we can also premultiply by the step and ramp functions just once at the beginning).<p>To plot a result, we also need to convert S<sup>-1</sup>Q back to Q by premultiplying with S,<br>  but if we are plotting only z elements of Q (for example, the contents of the surface layer),<br>  we only have to multiply z rows of the nxn matrix S.<p>So if there are t timesteps, we have altogether<p> t.n.(1+2y+z) multiplications<p>(the 2 is for step + ramp functions)<p>(Note that if (1+2y+z)>n, it might be quicker preserving the state in Q rather than S<sup>-1</sup>Q).<br>  <hr><h4>Iteration</h4><br>  If the X are not just external inputs, but are also dependent on the contents of the box (as with the carbonate chemistry), then we have to iterate. A good first guess is usually that the change in X is the same as in the previous timestep.<br>  This can still be fast and accurate, providing the assumption that X changes linearly within one timestep is reasonable.<p>If X is a non-linear function of box contents, it helps to separate out an arbitrary linear part of X and include this within R, so that the remaining non-linear perturbation is as small as possible.<br>  <hr><h4>Compare with PRF</h4><br>  Note that (im)pulse response functions are derived from the same principle:<br>  an exact PRF for a linear system is derived from the n prop functions as above.<br>  The difference lies the way of adding it up:<p>  With PRFs, the fate of each input (X<sub>t</sub>) from every timestep must be calculated seperately for all subsequent timesteps.<br>  So if there are n boxes and t timesteps,<br>  then we have altogether n.t<sup>2</sup> /2 multiplications.<p>  So this method seems to be slower, so long as t > 8!<p>(note for comparison y and z must be 1, since PRF can only cope with input and output in one box).																										
	simplemodels		Simple Climate Models		JCM uses an efficient Java implementation of simple carbon and climate models, the same as those used to create many of the smooth-curve plots and quoted predictions in the recently published Intergovernmental Panel on Climate Change Third Assessment Report (IPCC-TAR)<br>  Note this does not mean that it contains the <i>same computer code</i>, only that we try to match the <i>same specification</i>, as described in published scientific papers and IPCC reports.<br>  You can check the good fit to published data from IPCC by superimposing the data from the WG1-SRES appendix -see @compareipcc<br>  ³Types of models³<br>  Note that this is a "simple" model which generates smooth curves, useful for comparing scenarios and understanding key processes. It cannot generate intrinsic climate variability, or regional climate variations, for which it is necessary to use much more sophisticated, but very slow "General Circulation Models" (GCMs).<p>Between these two extremes are also emerging "Earth System Models of Intermediate Complexity" (EMICS) which may be particularly useful for integrated assessment, for investigating non-linear "surprises", and for simulations over longer timescales (glacial interglacial cycles).<p>IPCC-TAR considered all these types of model -you can tell the difference by how smooth or "noisy" the plots are.<br>  ³JCM Model Components³<br>  The Carbon cycle, Atmospheric chemistry, and Radiative Forcing calculations are based on the Bern model. The temperature and sea-level calculations are based on the Wigley/Raper UDEB model parameterised to fit a range of GCM predictions according to appendix 9.1 of IPCC-TAR-WG1.<p>Both models incorporate upwelling-diffusion multi-layer oceans to account for the slow uptake of heat and carbon into the deep water. These systems are solved using an eigenvector method which is more efficient than direct integration and more flexible than pulse response functions.<p>The code also has a flexible modular structure, whose components are only calculated if they are both needed for output, and have changed due to parameter adjustments or feedbacks.<p>See also   <li>Details of how the calculations are made within the @mod documentation. ²°adju Note, to explore the details, it is recommended that you switch the version (top panel menu) to "expert" mode!²  <li>@howfast  <li>@struccode  <li>@aboutjava  <li>@references																										
	inertia		Timescales and Inertia		££timescaleintro ££timescalescript ££timescaleprocess  	Inertie et Echelles de Temps																									
	timescaleintro		Timescales Introduction		Some climate change processes, such as land surface warming in response to radiative forcing, or mixing of the atmosphere, occur within hours or months, whilst others, such as the transfer of heat or carbon to the deep ocean, or the melting of polar icecaps, take centuries or even millenia. Moreover, the deep ocean has a vast capacity to accumulate both heat and carbon.<p>Consequently, it takes hundreds of years to see the full climate impact of current emissions, and so the timescale in this model extends to 2300 although our planning horizon for specific mitigation or adaptation policy is much shorter (hence the regional scenarios data only extends to 2100).<br>  So we must bear in mind the great inertia of the system, in order to find effective policy to avoid dangerous climate change.<p>Also, by experimenting with the options in the emissions menu, you can see that stabilising emissions is not enough to stabilise concentration, stabilising concentration is not enough to stabilise temperature, and it is impossible to stabilise sea-level rise (on this timescale). This is mainly due to the slow accumulation of CO2 and heat in the deep ocean.   <li>See also @stabilisation	Introduction : Echelles de temps		Certains processus lié au changement climatique, tels que le réchauffement de la surface terrestre suite à un surplus radiatif, ou le mélange de l'atmosphère, se mesurent en heures ou en mois, tandis que d'autres, tel que le transfert de chaleur ou de carbone vers les profondeur océanique, ou la fonte des calottes glaciaires aux pôles, se mesurent en siècle ou en millénium.<br> En effet, les profondeurs de l’océan ont une surprenante capacité d'accumuler la chaleur et le carbone. C’est pourquoi, les changements climatiques liés aux émissions de carbone, ne s’observent qu’après des centaines d'années. C’est pourquoi l’échelle de temps pour ce modèle se prolonge jusqu’en 2300.<br> Cependant, notre échelle de temps relatif aux planifications politiques concernant les réduction ou l’adaptation, est largement plus courte (en effet les données régionales des scénarios se prolongent seulement jusqu’en 2100). Nous devons donc prendre en compte le mouvement d’inertie du système, afin de trouver la politique efficace pour éviter les changements climatiques de grandes importances.<br> En vous aventurant à travers les options du menu à propos des émissions, vous pourrez découvrir que la  stabilisation des émissions n'est pas suffisante pour stabiliser la concentration, de même que la stabilisation de la concentration n'assure pas la stabilisation de la température. Vous découvrirez également qu’il est impossible de stabiliser l'élévation du niveau des mers (avec une telle échelle de temps). Ceci résulte principalement de l'accumulation lente du CO2 et de la chaleur dans les profondeur océaniques.   <li>Voir également @stabilisation																							
	timescalescript	dem	TimeScale Plot Script		This script makes one plot showing CO2 Emissions, Concentration, Radiative Forcing, Temperature and Sealevel Rise, all on the same axes.<br>  It's similar to the timescales plot shown in IPCCTAR SYR (see @ipcclinks)<br>  £!timescalescript<br>  The units are arbitrary -see scaling factors in the code<br>  For discussion see @inertia<br>  This script builds on @blankplot, see also @scripting.	Graphique du scénario de l’échelle de temps.		La scripte nous montre un graphique insérant sur un même axe les émissions de CO2, sa concentration et sa puissance radiative, la température et l’augmentation du niveau des mers. Ce graphique est semblable au graphique exposée dans IPCCTAR SYR (voir @ipcclinks)<br>  £!timescalescript<br>  Les unités du graphiques sont arbitraire -voir les facteurs de graduation dans le code et pour plus d’explication voir @inertia.  Le graphique a été construit à partir de «  @blankplot » (voir également @scripting).																							
	timescaleprocess		Timescale of each process		The atmospheric CO2 concentration is the accumulation of past emissions, minus sinks into the ocean and land plants and soil. Although the processes controlling these sinks are complex so there is no simple "lifetime" for CO2, we can say that CO2 emissions from burning fossil fuels typically remain in the atmosphere for about 100 years. Actually, the atmosphere will always retain a small fraction (which increases the more we emit due to the acidification of seawater), until the fossil fuel may be recreated on geological timescales.   <li>See @atco2plot, @carbonstoreplot<br>  Most other greenhouse gases are eventually destroyed in the atmosphere, their lifetime ranging from about a decade for methane, to many millenia for some CFCs and SF6. As the atmosphere mixes globally within a few months, these gases can be considered to be almost uniformly distributed.<br>  Aerosols of sulphate or soot, by-products of fossil fuel combustion, biomass burning, are washed out of the atmosphere by rain and so survive at most few weeks. Tropospheric ozone also has a short lifetime as it is highly reactive. Consequently these are concentrated in more polluted regions.<br>  The radiative forcing combines the effect of all these gases and aerosols, plus solar variability. RF shows instantaneous heating power rather than accumulated heat, so it is measured in Watts (per m2).   <li>see @othgasplot, @radforplot<br>  The land surface responds quickly to changes in radiative forcing (both the land and the atmosphere have such a low heat capacity, that these are neglected in this simple energy-balance climate model). The ocean surface however, lags behind due to the slow exchange of heat with the deep ocean.<br>  If you choose the "expert" complexity level, you can compare the land and ocean temperature changes -notice how the oscillations due to solar variability (you can adjust this from the radiative forcing plot) affect the land more than the ocean.   <li>see @rftemp, @glotempplot<br>  The surface ocean is only mildly influenced by the warming of the deep ocean, hence if greenhouse gas concentrations are stabilised the temperature rises only slowly (note however, that dramatic changes in the thermohaline circulation, not included in this model, might alter this conclusion!).<br>  However the deep ocean warming determines the thermal expansion of seawater which is the largest contributor to the sea-level rise. You can see that this only begins to slow down, even centuries after the surface temperature has stabilised.<br>  Sea-level is also influenced by ice-melt. Some mountain glaciers may melt within a few decades, however it requires thousands of years to melt the polar icecaps. Indeed they are still responding to the warming at the end of the last ice-age.   <li>see @oceantempplot,  @sealevelplot<br>  Note the different timescales of the climate system are also discussed in IPCC Synthesis report Q5   <li>see @ipcclinks<br>  Note also that there are many more physical and biogeochemical feedback processes which are not yet included in this model, such as the response of permafrost, ocean phytoplankton, or the thermohaline circulation. Although these are generally slow, a combination of such feedback processes may lead to dramatic surprises on passing critical thresholds.<br>  <hr><li>For a more symbolic summary, see @calculuscc	Echelle de temps pour tous les processus		La concentration en CO2 dans l’atmosphère résulte, d’une part, de l'accumulation des émissions antérieures, et d’autre part de l’absorption minime de l'océan, des plantes et de la surface terrestre. Cependant les processus absorbant ces quantités minimes de CO2, sont très complexes et c’est pourquoi il n’existe aucune « durée de vie exacte » pour le CO2. <p> Malgré cela nous pouvons estimer la durée de vie des émissions de CO2 dans l’atmosphère provenant des combustibles fossiles. Celle-ci est d’environ 100 ans. En réalité l'atmosphère renferme, depuis la création des combustibles fossiles datée sur l’échelle géologique, une faible fraction de la quantité de CO2  émises (de plus suite à l'acidification des eaux des mers, plus nous émettons, plus cette fraction augmente). <p><li>voir @atco2plot, @carbonstoreplot<p> La plupart des autres gaz à effet de serre sont détruits dans l'atmosphère après avoir été émis. Leur durée de vie s'étend de quelques décennies, pour le méthane, à plusieurs millenium, pour certains CFCs et SF6. Comme l'atmosphère se mélange totalement en quelques mois, nous pouvons supposé que ces gaz y sont distribués uniformément.<br> En revanche les aérosols à base de sulfate ou provenant de la suie, les produits résultant de la combustion de combustible fossile et la biomasse carbonisée, sont extrait de l'atmosphère par la pluie. Ils ne survivent donc que quelques semaines.<br> L'ozone troposphérique a également une vie de courte durée car elle est très réactive. En conséquence celle-ci est fortement concentrée au-dessus des  régions les plus polluées. L’énergie radiative importante accentue l'effet de tous ces gaz et aérosols, ainsi que la variabilité solaire. RF (radiative forcing, énergie radiative) est mesurée en Watts (par m2) car elle décrit la puissance instantanée du réchauffement et non l’accumulation de chaleur. <p><li>voir  @othgasplot, @radforplot<br> La surface de terrestre répond rapidement face aux changements d’énergie radiative (la terre et l'atmosphère ont une capacité telle qu’ils ne retiennent que très peu de chaleur. C’est pourquoi nous ne représentons ni la terre, ni la l’atmosphère dans le modèle climatique simple représentant la balance énergétique). Par contre la surface océanique ne répond que bien après. Ceci est résulte de l'échange de chaleur relativement lent avec les profondeurs océaniques.<br> Si vous choisissez le niveau de complexité "expert", vous pouvez comparer les changements de température entre les continents et les océans -notez comment les oscillations, suite à la variabilité solaire (vous pouvez ajuster ceci à partir du graphique de l’énergie radiative) affectent davantage la terre et clairement moins l'océan. <p><li>voir @rftemp, @glotempplot<br>   La surface océanique n’est que modérément influencé par la chaleur présente dans les profondeurs des océans, par conséquent si les concentrations en gaz à effet de serre sont stabilisées, l’augmentation de la température ne se fera que très lentement (notez toutefois, que des changements dramatiques de la circulation thermohaline, non inclus dans ce modèle, pourraient changer cette conclusion !).<br> Cependant, c’est l’énergie thermique, reposant dans les fonds océaniques, qui détermine la dilatation thermique de l'eau de mer et c’est ce processus de dilatation qui contribue en majeure partie à l'élévation du niveau des mers.<br> Remarquez que ce phénomène de dilatation ne commence qu’à ralentir des siècles après que la température de surface ne se soit stabilisée. Le niveau des mers dépend également de la fonte des neiges et de la calotte glaciaire. Certains glaciers peuvent fondre en quelques décennies, par contre la fonte des calottes glaciaires polaires exige plus d’un millier d'années. En effet les calottes que nous retrouvons actuellement datent encore de la dernière période glaciaire. <p><li>voir @oceantempplot,  @sealevelplot<br> Remarquez que les différentes échelles de temps relatives au système climatique, sont également développées dans le ‘IPCC Synthesis rapport Q5’.<li>voir @ipcclinks<br> Constatez également qu'il existe encore une multitude de retroactions physiques et bio-géo-chimiques qui ne sont pas reprises dans ce modèle, tel que la réponse du permafrost, des phytoplanctons océaniques, ou de la circulation thermohaline. Bien que leurs réponses soient généralement très lentes, l'amoncellement de telles rétroactions peut mener à des ébahissements dramatiques qui peuvent dépasser les seuils critiques. <li>Pour un sommaire plus symbolique, voir @calculuscc.																							
	calculuscc		Calculus of climate change		²(or why it's so hard to find effective policy solutions)²<p>For the mathematically minded, we can say:  <li>&int; represents the time integral  <li>E = Emissions, S = Sinks,   <li>C =Concentration  <li>RF = Radiative Forcing  <li>T<sub>s</sub> = Surface temperature  <li>T<sub>d</sub> = Deep ocean temperature  <li>I = Ice Melt  <li>S = Sea-level rise<p>then to a first approximation:  <li>C = &int; (E - S) and S = f(C)  <li>RF = f (C) fast  <li>T<sub>s</sub> = f (RF ,T<sub>d</sub>) mainly RF  <li>T<sub>d</sub> = f (&int; T<sub>s</sub>) over centuries  <li>I = f (&int; T<sub>s</sub>) over millenia  <li>S = f (I, T<sub>d</sub>)<p>If we consider also that emissions reductions depend on cumulative policy actions,  <li>E = f (Pop, Lif, Tec) = f (&int;Pol)  <li>Where:  <li>Pop =Population  <li>Lif =Lifestyle  <li>Tec =Technology  <li>Pol =Policy<p>Then you can see that  we have a triple time-integral   <li>S = f (&int;&int;&int; Pol)<p>in going from climate policy to impacts such as sea-level rise (although not to temperature -see  note below).<br>  Hence it is so difficult to calculate in inverse mode (differentiate) to find the best policy to avoid dangerous impacts.<br>  Even with a double integral, a kink in the target curve implies an infinite jump in the policy!  <li>See also  @inertia, @inverse, @stabtempdoc<p>An alternative approach is to devise "fuzzy control" strateges for deliberate climate-policy feedback (as used for an experimental stabilise temperature  method in JCM - see @stabtempfuzzy), but such formulae tend to cause oscillations, which may not be so unrealistic!<br>  ³Policymake model?³<br>  The Brazilian government proposed a simple "policymaker model", based on a few differential equations, for the purpose of attributing responsibility for climate change. The first variant assumed that the surface temperature was the integral of the radiative forcing, which is incorrect as the surface has a much shorter "memory" than the deep ocean. However, the formula was improved in the second variant of the Brazilian proposal, whilst retaining some of the mathematical simplicity.<p>Also, the attribution of responsibility is found to be strongly dependent on the time period both for attributing emissions, and for calculating their future impact, raising issues of integenerational equity. For more on this topic, see: @attribution, @attribution																										
	compareipcc		Correspondence to IPCC-TAR predictions		@tarwg1data | @ipccdiff  |  @ipcclinks | @unitbaseline<br>  ££cipccintro ££tarwg1data ££ipccdiff  ££ipcclinks ££unitbaseline																										
	cipccintro		Check same input => same output		This web model uses an efficient java implementation of simple carbon and climate models with the same specification as used to generate many of the smooth-curve plots used within the IPCC-TAR report.<br>  Therefore, if we give it the same input (emissions or stabilsation scenarios), and use the same sets of model parameters (ocean mixing, climate sensitivity, etc.), we should get the same output (CO2 concentration or emissions, radiative forcing, temperature, sea-level) as IPCC. This can be checked, by comparing with specific plots and data from IPCC-TAR, as explained below.<br>  Experts can also check that the model behaves as they would expect, by experimenting with many possible combinations of parameters. For this purpose switch the version (top panel menu) to "expert" mode, and you will see more curves and controls.																										
	tarwg1data		IPCCTAR-WG1-SRES data		The SRES appendix at the end of IPCC-TAR WG1 report contains useful tables of data, giving IPCC predictions of CO2 concentration, radiative forcing, temperature and sea-level rise for the 6 SRES marker scenarios. To help you check the correspondence, this data may be superimposed as small circles on the plots.<br>  °adju To see this, you should do the following:   <li>Press the reset button (top), to get the default set of model parameters.   <li>Choose "SRES no-climate-policy scenarios" from the Mitigation menu (top)   <li>Select "expert" from the complexity menu (top)   <li>Press the "IPCC-data" button (top)   <li>Select a plot to check (CO2 Concentration, Radiative Forcing, Temperature, Sea-level)   <li>Select a scenario from the SRES menu (top)<br>  The circles should then appear, you should see a good fit to the curves generated by the model. (Of course, the data will not correspond, if you adjusted the model parameters since pressing the reset button!)<br>  °cogs There are also some important technical points to consider when making comparisons:   <li>The IPCC data for temperature and sealevel correspond to the average of the seven GCMs (see Climate Model). You can use the GCM-fit menu on the temperature plot to cycle through the range of models. HadCM3 is now the default option and is quite close to the average, however the difference between models varies between scenarios and is greater for sea-level.   <li>The concentration / radiative forcing data come from the Bern-CC Model and the temperature/ sealevel from the Wigley/Raper model, except that:   <li>The total RF is from the WR model   <li>CO2 RF is shown for both models. However, in the WR Model, the parameter "Radiative Forcing for CO2 doubling" varies with the GCM-fit, whereas in the Bern-CC model this was fixed at 3.71 W/m2. You must set this parameter, to check the CO2 RF data.   <li>You should select the @taro3 parameter (@othgasplot) to use the older formula for tropospheric ozone  <li>These two models used quite different formulae for RF from carbon aerosols. The default (Bern-CC) option scaled to CO emissions is consistent with IPCC Chapter 6 and SRES tables. However you should select the BCOCWig option when comparing temperature / sealevel / total rf. Data is shown for the total of BC+OC according to the WR. The BCOCWig option gives an approximate fit only.   <li>The circles are adjusted, if you move the temperature baseline year.   <li>The baseline year for sealevel rise is 1990 in the data and 1750 in JCM, so the circles are adjusted accordingly.   <li>Circles are not shown in the emissions plots, since the emissions are taken directly from SRES data tables.   <li>You can also choose the older scenario IS92A, but data is not available for all quantities. Also it had to be scaled down to match current emissions, and different models may make different assumptions about this.																										
	ipccdiff		Known differences JCM compared to TAR		³Mathematical Method³<br>  JCM uses an eigenvector calculation method rather than direct integration, combined with an iteration of the ramp-function for non-linear fluxes. This method finds the exact analytical solution, given the assumption that the non-linear fluxes change linearly within one timestep. The algorithm used by Bern-CC and WR models is different, but the difference should be almost negligible.   <li>See @eigenvec<p>  ³Terrestrial Biosphere sink³<br>  The carbon cycle model used here is an older version of the Bern model which uses the same "HILDA" ocean as the Bern-CC model, but a simpler 4-box terrestrial biosphere (as used in IPCC SAR). The Bern-CC (TAR) model incorporates a more sophisticated dynamic vegetation model including several plant types and many grid cells, each with different temperature and precipitation derived from GCM predictions. The temperature feedback tends to lower the biosphere sink (and hence increase the concentration) towards the end of this century. It is planned to develop a fast implemention of the dynamic vegetation model within JCM.<li>See @carboncycle module<br>  ³Climate-Carbon feedback³<br>  In this web model, the carbon cycle and climate are directly coupled to achieve the feedback from temperature to carbonate chemistry in the surface ocean. In the IPCC calculations, different models were used for the carbon cycle and temperature. Although both Bern-CC and WR model include some climate-carbon feedbacks, these two models were not directly coupled as they are here. This may result in slight differences, however direct coupling should be preferable.<br>  ³Sea-level rise due to ice-melt³<br>  The calculations here are based on simple formulae, parameterised to fit data in Chapter 11 of IPCC-TAR-WG1 (the glacier formula is adapted from that used in the SAR).																										
	ipcclinks		Links into IPCC-TAR:		³General links³  <li><a href="http://www.grida.no/ipcc_tar/index.htm" target="_new">IPCC-TAR Online at GRID Arendal</a>   <li><a href="http://www.ipcc.ch/"  target="_new">IPCC Homepage</a>  <li><a href="ref.html">JCM References Page</a><br>  ³Stabilisation scenarios³<br>  The formulae for defining the target curve towards a particular stabilisation level<br>  is derived from the original formulae in the IPCC technical paper of Enting et al 1994 (referred to as "S" "WG1" in IPCC-TAR-SYR). The "WRE "curves are a variant of this with a delayed start, following the IS92A business-as-usual scenario for the first few years.<p>  In JCM, stabilisation curves start from 2000 (current emissions), whereas the originals started from 1990. As current emissions lie significantly below the IS92A projections, these have been scaled down to fit the current level.<p>For more information see:  <li><a href="../emit/mitigation.html">Mitigation /Stabilisation scenarios</a>,   <li><a href="../emit/syrspm6.jpg" target="_new">IPCC-TAR-SYR Figure SPM6</a>  <li><a target="_new" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/figts-25.htm">WG1 TS Fig 25</a> WRE stabilisation curves & implied emissions<br>  ³Radiative forcing³<br>  The contributions of the minor greenhouse gases and aerosols were derived from figures and data in<li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/338.htm">Chapter 6 of TAR-WG1</a><p>Various contributions to RF  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/figts-9.htm">WG1 TS Fig 9</a>  <li>SYR main part Fig 2-2 (not yet available online)  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig6-6.htm">WG1 Ch6 Fig 6-6</a><p>Solar and volcano forcing history  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig6-8.htm">WG1 Ch6 Fig 6-8</a><p>Useful data table  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/251.htm">WG1 Ch6 Table 6-11</a><br>  ³Temperature projections³<br>  Explaining the climate model parameters  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/371.htm"> WG1 Ch9 Appendix 9.1</a><br>  <!--need similar from Ch3 for Carbon!--><p>SRES temperatures  <li>SYR SPM3 -SRES projections (not yet available online)  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/figts-22.htm">WG1 TS Fig 22</a>   <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig9-14.htm">WG1 Ch9 Fig 9-14</a>  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig9-15.htm">WG1 Ch9 Fig 9-15</a><p>WRE temperatures  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/figts-26.htm">WG1 TS Fig 26</a>  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig9-16.htm">WG1 Ch9 Fig 9-16</a>  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig9-17.htm">WG1 Ch9 Fig 9-17</a><br>  ³Sea level rise³<p>SRES sea-level  <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/figts-24.htm">WG1 TS Fig 22</a>   <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig11-12.htm">WG1 Ch11 Fig 11-12</a><p>Note various other plots and tables in   <li><a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/408.htm"> Chapter 11 of TAR-WG1</a>,<p>Note also <a target="fig" href="http://www.grida.no/adm/dev/ipcc_tar/wg1/fig11-9.htm">WG1 Ch 11 fig 11-9</a>  (uncertainty in different contributions to sea-level)<br>  ³Timescales of response³<br>  See Q5 of IPCC SYR regarding timescales of response, especially Figure SPM5.																										
	references		References		££sciref ££dataref																										
	sciref		Science models references		The two papers below (and references therein) describe the calculation methods used in IPCC-TAR, and also implemented in JCM.<p>  <h4>Bern-CC Model</h4><br>  ²(used for @carboncycle, @oghga modules in JCM )²  <li><b>Global Warming Feedbacks on Terrestrial Carbon Uptake under the IPCC emissions scenarios </b>,<br>  F.Joos, I.C.Prentice, S.Sitch, R.Meyer, G.Hooss, G.K.Plattner, S. Gerber, K.Hasselmann, Global Biogeochemical Cycles v15 no4, 891-907, 2001<p>  <h4>Wigley-Raper UDEB model</h4><br>  ²(used for @heatflux module in JCM)²  <li><b>Use of a upwelling-diffusion energy balance model to simulate and diagnose A/OGCM results</b>, S.C.B. Raper, J.M. Gregory, T.J. Osborn, Climate Dynamics v17, p601-13, 2001  <li>See also IPCCTAR-WG1-Ch9 Appendix 9.1<p>  <h4>Stabilisation scenarios</h4><br>  ²(used for @stabilisation scenarios in JCM)²  <li><b>Future Emissions and Concentrations of Carbon Dioxide, Key<br>  Atmosphere/Ocean/Land analyses</b>, Enting I.G., Wigley T.M.L., Heimann M., CSIRO Technical paper 1994<br>  <i><A href="http://www.dar.csiro.au/publications/Enting_2001a0.pdf" target=new>PDF online</a></i>																										
	dataref		Data References		<li><a href="http://cdiac.esd.ornl.gov/" target="_new">Carbon Dioxide Information Analysis Centre (CDIAC)</a> ²(source of historical CO2 Emissions for each country, combined to make JCM regional data)²  <li><a href="http://www.rivm.nl/image/" target="_new">RIVM IMAGE model</a> ²(source of regional socioeconomic projections under the IPCC-SRES scenarios)²  <li><a href="http://ipcc-ddc.cru.uea.ac.uk/" target="_new">IPCC-Data Distribution Centre</a> ²(source of  regional climate GCM data)²<p>See also @regiondatasource																										
	optimisation	fut	Optimisation		The concept of Optimisation is essentially to create a single objective function which one wants to maximise or minimise, given a set of adjustable parameters, and an efficient iterative algorithm to find the best solution.  There are several potential applications of this  it may be used for tuning the parameters of natural science models to fit historical data or predictions of more complex models (in which case the objective is to minimise the error - see @probabilistic, @uncertfuture). It may also be used for finding efficient future policy pathways in a 'cost-benefit' style of analysis (note, there are many interpretations of 'efficient' -see @uncertburden, @equity, @peoplefuture).<br>  Therefore it is proposed to add a general optimisation tool to JCM, to complement the @scripting facility. When there are many free parameters, the shape of the multidimensional objective function may become very complex, so it is important to combine an effective algorithm (possibly the simplex triangle method) with a fast model (see @howfast).  <li>Note that an iterative method is already used for JCM stabilisation scenarios (@stabitmethod). In this case, the future scenario is defined by a few parameters of a mathematical curve formula, rather than by discrete time intervals. This helps to reduce the dimensionality of the problem, and also ensures a smooth transition from current emissions.																										
																															
																															
gui																															
	layoutpanel		Setup	JCM General/Layout options	General options for JCM setup and panel layout £^interacs £^menopts	Fenêtre Supérieure	Options générales de pilotage de JCM (modèle climatique en language Java)		Hauptdiagramm	allgemeine Klimamodell-Optionen (GCM)		Top Panel	Opciones generales JCM		Painel de cima	Opções generais JCM		Bovenste venster	Algemene sturingsopties van het JCM (klimaatmodel in Java)					Top Panel	Generelle JCM options		Øvre Panel	Generelle JCM valg			
	plotmenu		Plot	Select a different panel		Graphe	Sélectionner un autre graphe		Graphik	Ein anderes Diagramm wählen		Gráfico	Seleccionar un panel diferente		Painel	Selecionar um painel diferente		Grafiek	Kies een andere grafiek		График	Выбор графика		Plot	Vælg hvilket plot der skal vises i denne figur		Graf	Velg hvilken graf som skal vises i denne figuren		图像	挑选其他的图像
	layoutmenu		Layout	Change layout of panels	Choose 1, 2, 4 or 9 panels, or 5, 12 or 16 small panels plus one big one in the middle (e.g. the @flowchart). In each case there is a default arrangement, but you can select others using the @plotmenu at the top of each panel.	Disposition	Changer la disposition des graphes		Ansicht	Anzahl sichtbare Diagramme ändern		Formato	Cambiar formato de gráficos		Formato	Mudar o formato dos gráficos		Schikking	Wijzig de schikking van de grafieken		Композиция	Выбор расположения графиков		Layout	Vælg antal figurer der skal vises		Layout	Velg antall figurer som skal vises		布局	图像的布局
	1plot			1 Plot			1 graphe			1 Diagramm			1 Graficar			1 Imagem			1 grafiek			1 График			1 Plot			1 Tegn graf			一图像
	2row			2 Row			2 lignes			2 Reihen			2 Linea			2 Images por linha			2 lijnen			2 Строка			2 under hinanden			2  Under hverandre			二列
	2col			2 Column			2 colonnes			2 Kolonnen			2 Columna			2 Imagens por coluna			2 kolommen			2 Столбец			2 på række			2 på rekke			二楹
	4plots			4 Plots			4 graphes			4 Diagramme			4 Gráficos			4 Imagens			4 grafieken			4 График			4 Plot			4 Tegn graf			四图像
	5+big			5small + 1big																											
	9plot			9 Plots			9 graphes			9 Diagramme			9 Gráficos			9 Imagens			9 grafieken			9 График			9 Plot			9 Tegn graf			九图像
	12+big			12small+1big																											十二图像与
	16+big			16small+1big																											十六图像与
	20+big			20small+1big																											
	25plot			25 Plots																											
	reset		Reset	Reset parameters and plots	resets the parameter values, graph setup etc. to the default values. Fixes most problems.	Reset	Initialiser paramètres et graphes		Reset	Reset parameters and plots		Reiniciar	Reiniciar parametros y gráficos		Recomeçar	Reinicializar parâmetros e graficos		Reset	Initialiseer parameters en grafiek		Сброс	Сброс параметров		Reset	Start helt forfra		Reset	Start helt forfra		重新	重新开始都图像
	helpmode		Help	Clicking on an object opens related web page	when this is enabled, clicking on any object calls the relevant documentation page here (which might explain why you are now reading this!)	Aide	Cliquer sur un objet ouvre la page Web concernée		Help	Clicking on an object opens related web page		Ayuda	Presionando un objeto se abren paginas web relacionadas		Ajuda	Carregar no objecto abre a pagina web relacionada		Aide	Klik een voorwerp aan en open de betreffende website		Помощь	Нажмите на интересующий вас объект для получения подсказки		Hjælp	Klik på kontrol-ikonerne for at få hjælp		Hjelp	Klikk på kontrol-ikonene for å få hjelp		帮	帮发现解释:指图像叫文件
	showcode		Code	Clicking on an object opens relevant java source code																											
	languagemenu		Language	Change language	Change the language of all labels and popup info, and some of the documentation. See @labdoc, @labdoctrans  <li>Many people helped with translations: see @acknowtranslate	Langue	Changer la langue		Sprache	Sprache ändern		Language	Cambiar lenguaje		Língua	Mudar língua		Taal	Verander de taal		Язык	Выбор языка		Sprog	Vælg sprog		Språk	Velg språk		语言	挑选语言
	complexitymenu		Complexity	Select basic, normal, or expert level	The choice of complexity level affects which visible curves, controls, menus, and options are displayed on @pan, and are included  in panel documentation.<br>  The complexity level does not affect the underlying scientific calculations in @mod. So even at  the simplest level, the complex parts of JCM are still calculated -you just can't see them.<br>  ²Note: some other options also affect which curves and controls are visible on a graph -see @whatper, @regionplot, @emit&or&conc&or&rf,  @mitigation.²	Niveau	Choisir le niveau : basique ou expert		Niveau	Basis- oder Expertenniveau wählen		Complejidad	Seleccionar nivel basico o experto		Nível	Selecionar nível básico ou avançado		Niveau	Kies het niveau: basis of expert		Уровень	Выбор уровня сложности		Detaljering	Vælg basis eller ekspert niveau		Detaljinnhold	Velg basis eller ekspert nivå		简福	挑选简还是福的水平
	verysimple			Simplest	This is to help beginners learning how to use JCM, showing just one curve and one or two key controls per plot. It is only implemented for a few panels, to show interactions between CO2 emissions, concentration, forcing, temperature and sealevel. Beware that any simplification hides important assumptions -you should not present results derived from consideration of this level alone!		Très simple			vereinfacht			Sencillo			Simples			Heel eenvoudig			Простейший			basalt niveau			Enkelt nivå			最简的
	basic			Normal	This is for general use. All curves and controls will be shown at normal level and above, unless their code specifies otherwise.		Normal			normal			Normal			Básico			Normaal			Базовый			Flere detaljer			Flere detaljer			正常
	expert			Expert	This is for scientists who know the climate system quite well and want to check out some more obscure parameters and subtle effects in JCM. It is not expected that everybody will understand expert level curves and controls - and expert guidance should be available if they are referred to during educational or policy applications.		Expert			Experte			Experto			Avançado			Expert			Экспертный			Ekspert niveau			Ekspert nivå			最福的
	experimental			Experimental	This is for new features of JCM under development, which may not be documented or may not yet work at all. Results from experimental features should not be quoted!		Expérimental			experimentell			Experimental			Experimental			Experimenteel			Экспериментальный			Ekperimentalt niveau			Ekperimentelt nivå			试验的
																															
	linkx		-x-	Link all timescales (x-axes)	Link all timescales (x-axes), so adjusting one will affect all	-x-	Lier toutes les échelles des temps (axes des x)		-x-	alle Zeiskalen (x-Achsen) verbinden		-x-	Enlazar todas las escalas de tiempo (eje-x)					-x-	alle tijdsschalen(x-assen) verbinden												
	showscales		¬¬¬	Show scales on graphs	Show scales on graphs (applies to all except central plot) This option switches off automatically for 9 or more plots, but you can reactivate it.	¬¬¬	Faire apparaître les échelles et légendes		¨¨¨	Skalen und Legenden zeigen		¬¬¬	Mostrar escalas y legenda					¬¬¬	de schalen en legendes tonen												
	showcontrols		[C]	Show controls, menus, options, legend	Disabling this option hides  anything with popup info, other than title and scales. These will reappear temporarily while your mouse moves over a plot.  This option is disabled automatically for 9 or more plots, but you can reactivate it.	[C]	Faire apparaître les contrôles		[C]	Kontrollfunktionen zeigen		[C]	Mostrar controles					[C]	de controles tonen												
	ipccdata		IPCCdata	Superimpose data circles from IPCCTAR WG1 SRES appendix	see @datapoints, @compareipcc	données IPCC	Superimpose data circles from IPCCTAR WG1 SRES appendix		IPCCDaten	Daten vom IPCC TAR WG1 SRES appendix überlagern		IPCCdata	Superimponer circulos de datos del appendice IPCCTAR WG1 SRES					IPCC gegevens	data circles van IPCCTAR WG1 SRES appendix op elkaar plaatsen												
	capim		png	capture and save "png" image (file in jcm/output)	see @capimage																										
	captab		tab	capture and save data table (file in jcm/output)	see @captable																										
																															
	flowchart		Cause-Effect Flowchart	Flowchart of JCM Module/Panel Interactions	This shows the chain of calculations in the model, and thus the interactions (cause-effect relationships) between the components of the system. The arrows  change as you adjust controls and menus on the plots.<br>  ²°adju The flowchart is available from the @layoutmenu and @plotmenu. If it is placed in the centre, arrows will also point to other panels.²<br>  ££fcnormal ££fcchanging ££fcstabsres	Organigramme	Diagramme de fonctionnement			Fliessdiagramm			Diagrama de flujo						Flowchart'											关联图像	关联图像
	blankplot		Blank Plot	Base for generating new plots by scripting	This makes an empty @graph, to which curves can be added by @scripting.<br>  Instructions are sent to blankplot in the same way as values are sent to a parameter. Each is split into sub-instructions using ~  <li>clear : removes all curves:  <li>title~titlename : set plot title (note: units added automatically when you set scale)  <li>scale~units~step~scalefactor : sets the yscale (whose max and min is set by the autoscale method as soon as there is at least one curve)  <li>curve~curvearray~curvename~true/false~curvecolor~scalefac<br>  Where:  <li>curvearray is the java identifier of the array of data to plot (found using java reflection see @ref),  <li>true/false specifies whether to copy the curvearray data, or just make  a reference to it. If false, the curve will move is the model is adjusted, if true it will be frozen (this is for comparing scenarios).  <li>curvecolor is either a color name from @colfont or a new color specified by red , green , blue   <li>scalefac is an optional scaling factor<p>  See some examples:<br>  £!stabconc500script<br>  £!timescalescript																										
																															
	gui		GUI components	JCM Graphical User Interface components	JCM Graphical-User-Interface general building blocks, used by @pan.<br>  ³ Adjustable objects³<br>  @control<br>@scale<br> @popob<br>@option<br>@menu   <br>  ³ Panels and layout³<br>   @jcmpanel<br> @graph<br>@regionplot<br>@mapplot<br>@textpan<br> @plotlayout<br>  ³ Other graph components³<br>  @legend<br>@title<br>@datapoints<br>  ³ Functional components³<br>  @jcmevent<br>@capimage<br> @colfont<br>@bufim<br>@complexitymenu<br>@jcmta<br>  <hr>² °cogs (Note: GUI may use @tls and @root but do not refer to specific science @mod - see also @struccode)²																										
	scale		Scales and Units		°adju You can adjust any scale by dragging the curve with your mouse. Dragging near the origin (usually 0 for vertical axis, 2000 for time axis) shifts the whole curve. Dragging elsewhere stretches or squeezes the scale.<p>°adju Move your mouse over the axis to see pop-up info about units and range<p>°adju You can hide all the scales using the @showscales option (in @layoutpanel). This option is selected automatically when many plots are shown (@layoutmenu).<p>For some flexible plots (descendents of @regionplot), an appropriate scale range is calculated automatically. When dividing two quantities, JCM also tries to cancel powers of ten, converting to standard SI units.<p> ²°adju If this gives you a strange quantity (e.g. "micro-person per joule per year"), it probably makes more sense to invert the ratio (e.g. megajoule per person per year). ²<br>  ££siunits ££unitbaseline <hr>See also @howmuchgtc ££time																										
	datapoints		Data-Circles		This superimposes circles on the graph, corresponding to external data. It is used for checking @compareipcc																										
	jcmpanel		jcmpanel		This holds the basic structure for a panel. Jcmpanel transfers coordinates, layout and events from mainapp, and extends iob.<br>  It does not extend the old 'heavyweight' java.awt panel, which is not sufficiently flexible for JCM, and would not allow for menus and other popup items to cross panels. Instead, it is a lightweight component more like those of Swing. However Swing is not used, due to its lack of support in common web browsers (note @javafuture).<br>  See also @jcmevent, @popob, @title. ££aboutpanels																										
	aboutpanels		Panels in JCM structure		Panels combine calculations and parameters from @mod with @gui, @tls and @root (°cogs see also @struccode)<p>You can now open as many instances of each panel as you like - they will all share underlying data and parameters from JCM £`mod, but each new panel will have its own layout parameters. So for example, you could have one @othgasplot or @attributeplot showing emissions, another showing forcing.																										
	mapplot		General map-plot		This general panel is extended by @regclimap and @regemitmap and plots the country polygon data from @bord. It also handles rotation.																										
	regionplot		Multi-Region Plot		This provides general functions for viewing and combining regional data, which are inherited by the following specific plots:   <li>@distribplot (for emissions distributions)   <li>@attributeplot (for "Brazilian proposal" calculating responsibility)   <li>@costsplot (experimental!)   <li>Note also @regemitmap, and @aboutregions<br>  °adju ³General features³  <li> The @varq and @perq menus allow you to divide one quantity by another, to show, for example, emissions per capita or abatement per GDP.  <li> Because there are many possible combinations, an appropriate vertical scale is chosen automatically - see @scale  <li>When quantities are not divided by any other quantity, they may be stacked (@stack option) such that the sum of the regions shows the world total. This may be combined with the @frac option  to show each region as a fraction of the world total (multiplied by 100 to give scale in %).																										
	graph		Graph		Graph contains the basic graph-plotting functions (the curves are actually plotted onto a @bufim and repainted as needed).<p>Each graph has a set of curves (each with a reference to an array of data in a module, a name, a colour, and a @complexitymenu level).<p>Graph is also the holder for all the extra-components that make up a graph   <li>@legend  <li>Axes (see @scale)  <li>draggable @control   <li>@datapoints<p>Graph is 'abstract', actual instances of graph are contained within specific @panels.<p>Currently, just line-curve and stacked curve plots are implemented. Later, other variants (e.g. scatterplots, surface plots) may be added.<p> Some extra functions are added by @regionplot																										
	textpan				General jcmpanel containing a java.awt.textarea. Textpan handles the problem, that textarea is a 'heavyweight' component, which doesn't mix easily with 'lightweight' components like @jcmpanel and @popob.																										
	jcmta				See @textpan																										
	popob		Pop-up Object		Popob is any object with pop-up information, that responds to mouse events. It is the superclass of @option, @menu, @control, @scale, @title, @legend.<br>  Popobs have a 'holder' which is a @jcmpanel. User-adjustable popobs are also the 'controller' of a @param, whose definition and effect is located elsewhere within the code (for example, within a @module or a @jcmpanel).																										
	legend		Curve Legend		This makes a curve legend composed of individual popobs. The complication is that  it may change, depending on @complexitymenu, @stack, @varq, @perq etc.<p>Move your mouse over each curve label  to see a longer description. If @helpmode  is enabled, clicking on each curve will  summon relevant documentation.																										
	title				Plot title. This also adds units and extra curve info to the basic name. Title is also used for modules in the @flowchart																										
	option		Option		Toggle button option (a few are just triggers). An option is the controller of a @param, but its holder is a @jcmpanel. If @helpmode  is enabled, clicking on the button will summon relevant documentation.																										
	menu		Menu		Drop-down menu.  An menu is the controller of a @param, but its holder is a @jcmpanel. If @helpmode  is enabled, clicking on the menu will summon relevant documentation.<br>  ²°cogs Menus may be large so they need to span several panels, everything that might be underneath should be repainted on removing the menu (complex when the menu changes the plot layout).²																										
	control		Arrow-Control		Draggable Arrow Controls: Each arrow controls one @param of a @module, and is positioned within a @graph whose curves it affects (via the underlying @module).<br>  Controls are a key feature of JCM, enabling people to understand cause-effect relationships by 'playing' with the climate system  almost as if it were a mechanical toy (see @concept).<p>A control may be positioned according to one or both axes of the graph, if its parameter shares the same units. Otherwise, it's position is arbitrary, and the graph limits correspond to the maximum and  minimum values of the parameter. The four-pointed arrows used for @stabilisation represent a specific point on a target curve  and so control two parameters -one for the x-axis, one for the y-axis.<p>°adju Normally, you adjust the value by dragging the control. The model is updated at every step, which may slow on older computers. In this case, you may find it easier to click once on the control (note that its colour becomes darker), move the mouse to the destination, and click again, the control will jump directly to the new position (if valid).<p>°adju Move your mouse over the control to see its value. If @helpmode  is enabled, clicking on it will also summon relevant documentation, including a textfield in which you can enter a specific precise value.																										
	plotlayout		plotlayout		This arranges the position of @pan. It also handles each @plotmenu which swaps individual plots. Specific layouts are listed in @panlist. Note, you can now have as many copies as you like of each panel, each with different setup (curves, scales etc.) -see @aboutpanels.																										
	panlist		panlist		This interface lists all available @pan for the plotmenus, and specifies the default setup for each @plotlayout.<br>  Panlist uses only strings to refer to each panel -consequently you can have as many copies of each panel as you like, including zero (panels that are not opened by the user, do not need to be initialised by the JVM -saving memory). See also ££aboutpanels.																										
	jcmevent				This transfers mouse events from the underlying Applet panel to a @jcmpanel or a @popob.																										
	capimage		Capture and Save '.png' Image		This tool captures and saves a snapshot image from JCM, and saves it as a '.png' image (.png is a replacement for .gif). Just press the button £`capim on a plot (to capture that plot) or in the @layoutpanel, to capture the whole model.<p>This tool only works if you are using Java version 1.4 or above (see @aboutjava). You must also start JCM as an application (see @startjcm) from the downloaded package (see @download), because applets in a web-browser don't have security permission to save files.<p>The file will be saved in a directory jcm/output, with the same name as the panel.   <li>See also @printing, @captable.<br>  ²(Note, this feature was added August 2002 to prepare the JCM submission for the intercomparison exercise on the Brazilian proposal)²																										
	captable		Capture and save a Table of Data		This tool saves a datatable corresponding to the curves of a specific panel (the same table as you see in @viewdata).  The file will be saved in the directory jcm/output, with the same name as the panel.<br>  This tool only works if you  have started JCM as an application, not as an applet in a web-browser (see @startjcm), because applets don't have security permission to save files.<br>  See also @printing, @capimage.																										
	printing		Printing		Many people ask, how can I print a copy of JCM graphics or documentation?<br>  <h4>Using  Ctrl-Alt-PrintScrn</h4><br>  One basic way, is to capture an image (bitmap) of the screen or window.<br>  In windows, you can do this by pressing Ctrl+Alt+PrintScrn (all together).<br>  Then paste as usual into another application (e.g. Word or Paint), and print from there.<p>  <h4>Capturing PNG image files</h4><br>  It is now easy to capture and save to disk a PNG image (or a data text file) from any plot, just by pressing one button.  <li>See @capimage<br>  ²Note: This is only possible if you download the package and launch startjcm.class as an application, using the latest version of Sun's Java (1.4).²<p>  <h4>Print documentation</h4><br>  The documentation is dependent on hyperlinks, it is not intended to be read linearly. However, if you want to print it, a file is provided which concatenates all the web pages together:<br>  <i>Sorry, this is currently being updated - please @contact </i><br>  <!--<li>See <a href="../jcmdocall.html" target="_new">jcmdocall.html</a>  (Large file, 460K!) --><p>  <h4>Present JCM itself</h4><br>  If you want to capture images for use within a presentation,<br>  have you considered downloading the JCM package, and showing people the real interactive model?<li>See @download<br>  ²Note: For experts, it's also possible to write scripts to set up JCM in a particular way (see @scripting)²<p>  <h4>Why can't I just print from the browser?</h4><br>  Java 1.1 does not include standard printing functions,<br>  and later versions are not available in most web browsers.<p>  As the dynamic response to adjustments is a core feature of JCM,<br>  which cannot be captured on static paper,<br>  developing printer support is not a high priority.<p>  It might be easier with the latest versions of java,<br>  for users who are willing to install large packages.																										
	bufim				An image buffer used by each @jcmpanel, so the main plot can quickly be repainted after moving/removing a @popob or its pop-up info, without recalculating all the curves.																										
	colfont				An interface with colors and fonts, implemented by each @jcmpanel																										
text																															
	viewparams		List of chosen parameters	Information about each parameter, option and menu choice	£^apptag This lists all the @param registered in JCM, showing the parameter name and value in the chosen language. Those you have changed are shown first, with their default value in brackets. The values should change, as you make adjustments elsewhere.<p> This list includes all @menu, @option, @control on currently visible panels, but also any other  parameters defining the current model setup, including some used only for scripting (see @scripting). Parameters which <i>only</i> affect setup of panels not currently visible will not be shown. The ?owner? (shown with an asterix) is the name of the object whose java code defines the direct effect of this parameter.<p>²°adju You can cut and paste the text to another application if you like. ²<br>   £§panelinfo	Liste des paramètres choisis	Informations sur chaque paramètre, option et menu		Liste deiner Auswahl	Imformationen über alle Parameter, Optionen und Menüs		Lista de parametros escogidos	Información sobre cada parametro, opción y menú de selección		Informação sobre parâmetros	Informação sobre cada parâmetro, opção e escolha de menu		Lijst van de gekozen parameters	Informatie over elke parameter, optie en menu		Список параметров	Информация о выбранных параметрах и установках		Listning af parameterværdier:	Information om parametre og valg		Listning av parameterverdier:	Informasjon om parametre og valg			
	viewdata		View Graph Data	Inspect curve data or copy into another application	£^apptag This panel enables you to see the data underlying any of the graphs - choose which plot from the menu (the graph must be visible elsewhere).<p>²°adju You can cut and paste from the table in the usual way. You can also choose the separator, in case you want it in a particular format for pasting into another application. ²<p>²°adju The data now refreshes when you move a parameter, beware that updating the text is rather slow on some computers/browsers. ²<p>²°adju Note, for a few data columns a scaling factor (e.g. x1000) has been applied on the visible graph. ²<br>   £§panelinfo<p>You can also save the table as a file-see @captab	Voir les données du graphe	Voir la courbe sous forme de données ou recopier les données vers une autre application		Daten zeigen	Daten einsehen oder kopieren für andere Anwendungen		Ver gráfico de datos	Inspeccionar curva con datos o copiar a otra aplicación		Inspecionar dados	Inspecionar dados da curva ou copiar para outra aplicação		Zie de gegevens van de grafiek	Zie kurve onder de vorm van gegevens of kopieer de gegevens naar een andere toepassing		Посмотреть данные	Посмотреть или скопировать данные, использованные для построения графика		Vis Graf Data	Vis kurvedata eller kopiér til en anden applikation		Vis Graf Data	Vis kurvedata eller kopiér til en annen applikasjon			
	whichpanel		Panel	Choose a graph from which to show data																											
	separator		Sep	Separator between data																											
	notepad		Notepad	Text panel for debugging or scripting	£^apptag Multipurpose Text Panel for debugging, writing/running scripts (see @scripting), updating doc, etc.<br>  You might want to open two notepads (one for input, one for output) <br>  £§panelinfo	Éditeur de texte	Fenêtre de texte pour commentaires ou corrections		Notizen	Textfeld für Notizen oder Fehlerbeseitigung		Libro de notas	Panel de Texto para notas o corregir errores		Notas	Paínel de texto para notas ou debugging		text Éditeur	Tekstvenster voor commentaar of correcties		Блокнот	Область для заметок и debugging		Notepad	Tekstpanel til noter eller fejlretning		Notepad	Tekstpanel til notater eller feilretting		笔记本	
	debug		Debug		Receive debugging info (erros and calculation info from the model) in this notepad. You can also get performance info -see @loop, and query state and interactions																										
	script		Script		Run the script in this notepad. You can use this both for sophisticated batch calculations, and for checking/adjusting one parameter at a time, in conjunction with debug. See @scripting																										
	stop		Stop		Stop the script!																										
	clear		Clear		Clear this panel (and any script it contains)	Effacer	Effacer cette fenêtre et le code enregistré		Löschen	Diagramm und aufgezeichneten Code löschen		Borrar	Borrar este panel y registrar codigos					Wegvegen	Dit venster en de opgenomen code wegvegen												
																															
	getdoc	old 	GetDoc	Get current documentation (as shown in web info window) for editing																											
	changedoc	old	SetDoc	Update doc/labels with contents of notepad.																											
	savedoc	old	SaveDoc	Save documentation (overwriting current files!) This will not work from a web browser, due to security restrictions																											
	choosevisiblepanel			Choose a visible panel to show data																											
																															
	autodoc		autodoc		Makes JCM documentation.<li>see @labdoc																										
	labinf		labinf		This class provides labels, pop-up info and documentation, interpreting a text file loaded for the specified @languagemenu.<li>see @labdoc																										
	labdoc		Labels and Documentation		££labdocintro ££labdoctrans  ££labdocmodel ££names  ££labdocfiles																										
	labdocintro		Lab/Doc -Introduction		All of JCM's plot labels, pop up information, and documentation are now stored in the same plain-text format,<br>  There are currently about 50,000 words of labels+documentation  (in english ), covering about 700 seperate items.<br>  Sections of text can be re-used in several different contexts, and hyperlinks are easy to write and check (there are about 1500).<br>  <hr><br>  °cogs The java code which handles labels and documentation can be found from @autodoc, @labinf, @txt, @showwebpage (all within @tls package).<br>  °adju The system can also be used without a web browser - see @startjcmswing<br>  °adju The documentation can be easily searched or indexed (see @docsearch, @doclist)																										
	labdoctrans		Translation		JCM's @labdoc system is designed to be easy to translate into any language.<br>  The graphical interface (plot labels and pop-up info) already has translations in 10 languages (change from @languagemenu).<br>  The documentation incorporates some translated items from the graphical interface (this avoids duplication), although the rest has not yet been translated.<br>  When a translation is not found the default (english) is used, so it's not necessary to translate everything, only key items and pages.<br>  Hyperlinks and subsections automatically use translations of other items, when found.  <li>Many people helped with translations - see @acknowtranslate.  <li>Why do this? See @dialogue, @concept, @teaching  <li>Only the @labdocfiles are translated, it's not necessary to compile java code.  <li>Please get in touch (@contact) if you can help translate to your language.<br>  ²(note, Java works with unicode so it can handle any language).²																										
	labdocmodel		Lab/Doc -Interaction with Model		The documentation for specific panels, modules, controls, menus, curves etc. is enhanced with information reflecting the current state of the model.<br>  The HTML menus, value-input boxes and checkboxes (to which the model responds via @scripting) are generated automatically.<br>  Which curves and controls are included within panel documentation also depends upon the current complexity level (see @complexitymenu), plot setup  (see @whatper, @emit&or&conc&or&rf) and model interactions (see  @flowchart).  <li>See code in @iob, @param, @jcmpanel, @graph<br>  ££usehelpmode																										
	names		Lab/Doc/Script Item-Code Names		JCM has a unique item-code name for each @iob (which includes panels, modules, curves, controls, menus, etc.).<br>  These item-code names are used to look up translatable @labdoc, and also for reference in @scripting.<br>  There are several ways to find the item-code name of an object or parameter:  <li>First, choose £`script-codes from the @languagemenu. Now item-codes are shown within the model interface and documentation, instead of the usual labels.  <li>Then view the @doclist webpage for a complete list of items with labels or documentation (including documentation sections without model objects).  <li>Or open the @viewparams panel (from a @plotmenu) to see a list of  all adjustable parameters (this includes a few @scriptparams)  <li>You can also look in the @labdocfiles : each hash symbol identifies a new item-code.<br>  Notes:<br>  ²Item-codes may not include spaces or other punctuation (numbers are ok).²<br>  ²Some names (particularly plot titles and axis units) are composed of multiple parts separated by '&' whose labels are looked up separately and joined together by @labinf   (see also @scale) ²<br>  ²°cogs In scripting, in case of ambiguity, the name of the object's <i>owner</i> (see @iob) should be prepended with a dot (e.g.  glotempplot.ymin, because every graph has a ymin parameter) ²																										
	labdocfiles		Lab/Doc Text Files		The labels/documentation are split into many separate files so that they are only loaded on demand, depending on the chosen language and topic. <hr>  All labels which may be used within the graphical interface, plus titles of documentation sections, are stored in files lab_XX.txt  (where XX is a language code).  Links to these files are below.<p>  <a href="../labdoc/lab_en.txt" target="txt">lab_en.txt</a> <br>  <a href="../labdoc/lab_fr.txt" target="txt">lab_fr.txt</a> (French) <br>  <a href="../labdoc/lab_nl.txt" target="txt">lab_nl.txt</a> (Dutch) <br>  <a href="../labdoc/lab_de.txt" target="txt">lab_de.txt</a> (German) <br>  <a href="../labdoc/lab_es.txt" target="txt">lab_es.txt</a> (Spanish)<br>  <a href="../labdoc/lab_es.txt" target="txt">lab_pt.txt</a> (Portuguese) <br>  <a href="../labdoc/lab_dk.txt" target="txt">lab_dk.txt</a> (Danish) <br>  <a href="../labdoc/lab_no.txt" target="txt">lab_no.txt</a> (Norwegian) <br>  <a href="../labdoc/lab_ru.txt" target="txt">lab_ru.txt</a> (Russian) <br>  <a href="../labdoc/lab_zh.txt" target="txt">lab_zh.txt</a> (Chinese)<hr>  The main text of documentation sections are further separated by topic, as well as language(currently there are only only a few pages translated, into French). <hr>All these files are  found in the jcm/labdoc directory in the recent JCM package (see @download).  <hr>  <li> Note that all files are in UTF-8 character format. <li>The <a href="../labdoc/syntax.txt" target="txt">syntax.txt</a> file explains the file format and rules for text-parsing (carried out by @autodoc)   <hr> The current syntax may soon be replaced by XML/HTML markup which would be easier to edit using a HTML composer -tell me if you would prefer this. <hr>  There is also a file containing all the labels and documentation as one big table (one row for each topic, one column for label/popup/documentation in each language). This is convenient for rearranging items and updating translations. It can be imported into a spreadsheet such as Excel, as if it were a ".csv" file, noting that the items are separated by TAB (not by comma), and the character format is UTF-8. A standalone routine built into @labinf converts between this form and the separate files as above. <li> <a href="../labdoc/jcmlabdoctab.txt" target="txt">jcmlabdoctab.txt</a> <hr>See also:   <li>@names  <li>@acknowtranslate   <!--<li>The raw text (source) for the currently visible webpage can be viewed and adjusted using @getdoc and @changedoc in a @notepad, but to save the changes you must use @startjcmswing<br> -->																										
																															
	scripting		Scripting		££scriptintro ££scriptlang ££scriptrun ££scriptcode ££names ££scriptparam ££scriptlist <hr> Note also @probwccc																										
	scriptintro		Scripting JCM: Introduction		JCM scripting code is designed to serve several functions.   <li>set the value of any paremeter you can adjust the mouse, and also many other public variables within JCM. This is useful for quick ad-hoc experiments and debugging, using a @notepad or System.input (see @scriptrun)  <li>run automatic demonstrations from web pages -explaining how to use JCM (see @howto), or illustrating specific points about the policy options or scientific uncertainties  <li>perform loops, mathematical operations etc., including creation of variables, like any simple computer language. This enables batch calculations and iterations in problem-solving frameworks, to be run without having to compile java or even to restart the model. For example, see @stabtemp2cscript  <li>connect multiple users to share the same model world across the web. The potential has already been demonstrated in earlier versions, but it is not yet implemented in the current version (see @remotecontrol).   <li>some other functions: make a new plot (see @blankplot), create and display a web page (see @showwebpage), load and save data, etc.  (under development).  <li>if you envisage another useful function, please tell me (see @contact)																										
	scriptlang		Why  a Scripting Language?		<li>Interpreted scripting languages, like Javascript, Perl, Basic, or Python, are usually easier to write, read, and adjust quickly  <li>Compiled languages like Java (see @aboutjava), C++, or Fortran usually run much faster, and are more reliable since the compilation process checks for errors.<p>  It makes sense, therefore, to use compiled Java for the computationally intensive, frequently used, core scientific modules and graph-plotting routines, while using an interpreted text script to direct specific applications such as batch-calculations or automatic demonstrations.<p>  Bridges between Java and several existing scripting-languages have already been built, however adding such packages to JCM would make it too large to load rapidly on the web. So a simple custom script interpreter (as described below) was developed for JCM.<p>  However parsing a script is not a trivial task, even with only a small set of functions, so for more complex applications it might be better to adapt an existing interpreter. Javascript might be a good choice as it is already built into most web browsers and has a (deceptively) similar syntax to Java. On the other hand, the script should also work when JCM is run as a standalone application: this is important because security restrictions prevent data files of model results from being saved from a web browser! 'Rhino' (www.mozilla.org/rhino) project may provide a solution to this, which will be investigated later. Another option could be 'Jython' (www.jython.org), which has also been demonstrated to work efficiently within applets.																										
	scriptrun		Running JCM Scripts		<li>You can test a simple JCM script using a @notepad (choose from any @plotmenu). Write the script there, or paste it in from a text file (examples are in jcm/script directory), and press the @script button. It may help to open another notepad and press the @debug button, to show any output from the script, as well as any routine progress info or error messages.  <li>JCM Scripts can also be called from webpages, using javascript instructions <script>jcm.playfile('script/scriptname.txt');</script> (to load an existing script file) or <script>jcm.playi('scriptstring');</script> to run a short script defined within the string.<br>  ²note, the wepage header must also include <script src=jcmjs.js></script>²  <li>Scripts can be included in documentation items using a simple tag (see <a href="../labels/syntax.txt" target="txt">syntax.txt</a> file) - this also puts buttons in the web page as follows £!dummy .  <li>It is envisaged that a method to run JCM with a script from the command-line will be developed (together with a method to save the resulting data)  <li>(for java experts!) It is also possible to send a short script to JCM using Java System.input, if you start  JCM from a tool  (e.g. a command-prompt console) which has a text input facility																										
	scriptcode		Scripting Code Syntax		The documentation page explaining the syntax of JCM scripting code can now be found in the jcm/script directory of the package (see @download). <a target='_new' href='../script/scriptcodesyntax.html'>Here is a link to scriptcodesyntax.html</a>																										
	scriptparam		Extra parameters		Some adjustable parameters in JCM may be used in scripting, which do not have graphical controls:  <li>backred, backgreen, backblue : set background color components (range 0-255) (in @mainapp)  <li>loopinfo : adds performance info to standard output (time spent calculating each module) (in @loop)  <li>scriptmode : sets one of three modes for response to adjustment of parameters (in @param)<br>  - demo : for automatic demonstrations, in which menus and control info pops up, as if the user passed a mouse over them<br>  - direct : model recalculates immediately, but no popup info (this is the default)<br>  - delay : no recalculation or replotting until next "run" instruction: this could be used to change several parameters at once more efficiently, or to recreate a snapshot of the model.																										
	player	dem	player		Runs a script  code: See @scripting																										
	recorder		recorder		This records JCM scripts, complementing @player. Recorder and Player may run in parallel threads for live connection between two JCMs across the web. Thsi feature was working in 2001, but has not yet been reinstated with new scripting code.																										
	scriptlist	dem	List of Demo Scripts		£!timescalescript<br>  £!emitdeclinescript<br>  £!backcoltempscript<br>  Stabilisation under Uncertainty<br>  £!stabtemp2cscript<br>  £!stabconc500script<br>  £!stabuncert25script<br>  Probabalistic Scripts (slow!)<br>  £!carbonprobscript<br>  £!carbonprobscriptinv<br>  How Use Demos<br>  £!howuco2tempdemo<br>  £!howuemitseademo<br>  £!howulabelsdemo<br>  £!howuscalesdemo<br>  Experimental<br>  £!testscript<br>  £!testscriptjs																										
	emitdeclinescript	dem	Emissions Decline 1% per year script		This script demonstrates how array values within JCM java code can be queried and reset dynamically<br>  However as you can see, the interpreter is rather slow (partly due to java reflection being slow)<br>  £!emitdeclinescript<br>  See also @scripting																										
	remotecontrol	fut	Remote Control / Snapshots		At Grid-Arendal in 2001 a system was developed whereby several users may share the same model by "remote control" across the internet, with a view towards developing JCM for @dialogue. This system worked very efficiently, because only the changing model @param needed to be sent across the internet, the model itself and the graphics are calculated locally. However synchronisation between people was found to be a challenge - how do you explain to a user who may be the other side of the world, why you are moving specific arrow-controls about on their screen? So an alternative approach was explored- to record a  demonstration or snapshot  of model events to illustrate a point, accompanied by textual explanation, which could be posted on the website for viewing by others later. This may have applications in organised group dialogues or @teaching.<p>It is hoped to revive the record/playback feature: see @scripting																										
	wizpanel	dem old	Remote Control	Link with others, Record & Playback		Télécommande	Liens, enregistrement et relecture		Remote Control	Link mit andern, aufzeichnen oder wiedergeben		Control remoto	Enlace con otros, record y Playback					Afstandsbediening	Verbanden, opname en nazicht												
	record	dem old	Record	Record events		Enregistrer	Enregistrer les simulations		Aufzeichnen	Handlungen aufzeichnen		Registro	Eventos registrados					Opnemen	De simulaties opnemen												
	play	dem old	Play	Playback recorded events		Jouer	Repasser des simulations enregistrées		Wiedergeben	aufgezeichnete Handlungen wiedergeben		Ejecutar	Playback eventos registrados					Spelen	De opgenomen simulaties overlopen												
	connect	dem old	Connect	Share events with other users across web		Partager	Partager les simulations avec d'autres utilisateurs sur le Web		Verbinden	Handlungen mit andern Web-Nutzern austauschen		Conectar	Compartir eventos con otros usuarios a través del web					Delen	De simulaties delen met andere gebruikers op het Web												
	newsession	dem old	New	Start a new shared web session		Nouveau	Commencer une nouvelle session Web partagée		Neu	neue Austausch-Websession starten		Nuevo	Comenzar una nueva sesión de compartición web					Nieuw	Een nieuwe gedeelde Web sessie beginnen												
	outcode	dem old	Code	Output instruction code to this panel		Code	Code d'instructions relatif à cette fenêtre		Code	Instruktions-Code zu Diagramm		Codigo	Codigo de instrucciones de salida a este panel					Code	Instructiecode bij dit venster												
	playtime	dem old	Time	Include timing in playing events		Temps	Inclure la notion de temps en jouant les simulations		Zeit	Zeitablauf in Wiedergabe integrieren		Tiempo	Incluir tiempo en eventos ejecutados					Tijd	De notie tijd opnemen bij het afspelen van de simulaties												
	doclist		JCM Documentation Index																												
	doclistinfo		About Index		²Doclist is generated automatically using currently selected language. It includes labels and popup-info as well as documentation pages.²<br>  ²°cogs (see @labdoc, @labinf for method)²																										
	docsearch		Search JCM Documentation																												
	anywords		Any-words																												
	onlytitles		Only-titles																												
	searchgo		Go																												
	searchresults		Search Results		²Search uses currently selected language. It may take a little time.²<br>  ²°cogs panel documentation pages only include curves and controls which are currently visible ²   <p>    ²°cogs (see @labdoc, @labinf for method)²																										
	searchawot	old			<p>  ²°adju try £`anywords to get more results, £`onlytitles to get less. ²  <p> 																										
	txt		txt		This class contains some methods for parsing text strings -it is mainly used by @labinf, @autodoc, @player																										
	sysin		sysin		To capture script instructions from System.input (for experts!)																										
	fileio		fileio		This class contains some methods for loading and saving text files (loading either from the web or locally).																										
	showwebpage		showwebpage		This class makes a web page appear in a certain frame, and stores a text string ("info") which may then be collected by javascript within that page. It is used by @autodoc and @player. See also @helpmode.																										
	sort		sort		Quicksort tool - sorting the labels/doc sections makes the lookup faster. Also used for search-doc and list-doc																										
																															
struc																															
	tech		Technical Tips for using JCM	Downloading, Printing, Scripting, Java-Code	@copyr<br>@printing<br>@download<br>@labdoc<br>@scripting <br>  @startjcm<br> @aboutjava<br>@struccode<br>@howfast    <li>Note also <a href="../struc/javaproblem.html" target="_new">problems with java</a>																										
	comp		Structural components	JCM Root Structure, GUI Components, Text/Script tools	<nobr>°gui @gui </nobr><br><nobr> °tool @tls </nobr><br><nobr>°root @root </nobr><br>  <hr><br>  See also @struccode,<br>°mod @mod,<br>°pan @pan																										
	struccode		JCM Code Structure		JCM code is divided into several java Packages:<br>  <table border=1><tr><td colspan=3 align=center><b>jcm/</b> <i>@root</i><br> (iob param loop mainapp) </td></tr><br>  <tr><td align=center><b>jcm/mod</b> <i>@mod</i></td><td align=center><b>jcm/gui</b> <i>@gui</i></td><td align=center><b>jcm/tls</b> <i>@tls </i></td></tr><br>  <tr><td>climate calculations</td><td>layout, plots, controls, events</td><td> files, labels, scripts, webdoc</td></tr><br>  <tr><td colspan=3 align=center><b>jcm/pan</b> <i>@pan</i><br>fill plots with module data, connect parameters with controls</td></tr></table><br>  Most components descend from the same @iob, which provides the link between them. This also holds flags specifying what needs to be recalculated (a key element of JCM's efficiency -see @howfast). The calculation itself is handled by @loop.<p>  @mod and @tls are entirely independent of the graphical interface, and may be compiled and run without the gui and pan packages being present.<br>  This independence may be useful for batch calculations which loop through the model many times without plotting graphics at every step, for example for probability analysis or optimisation problems. In this case, a text instruction script with file input/output could be used to control the model.<p>  @gui include general panels and graphs, menus, buttons, draggable controls, pop-up labels, also  the layout manager, event handler, etc. These do not refer to specific @mod so they might be reused for other applications<p>  Calculations in @mod are combined with graphics plotting routines in @gui to make the visible @pan.<br>  Each adjustable control combines both a @param (usually defined in a module) and a @popob (part of the graphical interface).<p>  <hr><br>  Note: JCM documentation has the same structure as the model. Each page has a link to the source code and also to the page about  the java class which it <i>extends</i>.<br>  If @helpmode is selected,  clicking on any part of the model interface will summon the relevant documentation.<br>  <hr>(See also @aboutjava)<br>  ³All code in one file³ This may be useful for printing.<li><a href="../code/jcmcodeall.txt" target="new">jcmcodeall.txt</a> (500K!)<br>  If you want to study the code, you should download the package<li>see @download<br>  ³makedata³ need to add links here																										
	howfast		How does JCM work so fast?		A great effort has been made, to keep JCM very efficient and compact,<br>  both considering users with slower computers and web connections,<br>  and because the instant "mechanical" response to adjustments helps to demonstrate cause-effect interactions. <p>Some key features that make this possible are described by the links below.<p><li>@eigenvec<p>Efficient eigenvector prop/step/ramp functions for the core carbon and climate models<p><li>@iob<p>Interaction between Modules and Panels<br>  Only run modules / panels which are both <b>needed</b> for screen output, and have been <b>changed</b> by the user since the last calculation.<p><li>@loaddata<p>Historical / Scenario / Climate-pattern data is compressed to one-byte per number, thus minimising download time.<p>  ³Model and graphics in one tool³<br>  There are many "visualisers" written in java,<br>  which simply plot data created using model code in another language (typically fortran),<br>  transferred by intermediate text files.<br>  This is intrinsically very inefficient:<br>  saving and loading data files may take longer than the science calculations,<br>  especially if working across the web.<p>  Whereas in JCM, the model and graphics are both written in the same language,<br>  and so interact much more smoothly and efficiently.<br>  Moreover, by giving people the tool (the model) rather than the product (the data),<br>  the scope is much wider, and the package size is kept to a minimum.																										
	root		Root components	JCM Root and Startup	Core java classes binding JCM together and handling interactions   <li>see also @struccode<br>  ³ Core building blocks³<br>  @iob<br>@mainapp<br>@loop<br>@param<br>  ³ JCM Startup ³<br>  @openapp<br>@startup<br>@startjcm<br>@testmod																										
	loop				Loop controls the main calculation / drawing loop.<br>  It calls methods of every @iob, in several stages (precalc, calc, postcalc, plot, postplot etc) to enable feedbacks between iobs.<p>Before running the loop, modloop also calls the checkinteractions method of @iob and thus work out which modules are both needed and changed. See also @flowchart.<p>The science modules are always calculated in the same order specified by @modlist, for every timestep (see @time).  This makes it easy to add both biogeochemical feedbacks (for example the effect of temperature on @carbchem) and also climate-emissions feedbacks (see @mitigation module).<p> The graphical components are updated after the model calculations, from the back to the front.<p>²°adju Enabling the loopinfo parameter will provide information (shown in a @notepad or java console) about which iobs were calculated and how many milliseconds were spent on each one. ²																										
	modlist		modlist		This interface lists all @mod.  It is implemented by all @mod and all @pan, helping them refer to each other. It also defines the sequence for model calculations in @loop.<p>The current order is:   <li>@loaddata<li> =&gt @sres<li> =&gt @kyoto<li> =&gt @mitigation<li> =&gt @people<li> =&gt @regshares<li> =&gt @carboncycle<li> =&gt @oghga<li> =&gt @radfor<li> =&gt @heatflux<li> =&gt @sealevel<li> =&gt @bord<li> =&gt @regcli<li> =&gt @costs<li> =&gt @responsibility<p>Modlist is implemented by all modules, so they can easily refer to each other. However interface variables are always "static", which means there is only one copy of each module. It is anticipated to replace this with a @parallelworld structure, to allow comparison of different  model setups.<p> See also @struccode																										
	module		Module		££aboutmodules<br>  Modules calculation methods are called by @loop in the following sequence:   <li>precalc(): calculations just before main time loop   <li>calcstep(step): calculate one timestep: most of the work is done here.   <li>postcalc(): calculations after main timeloop (e.g. for normalising etc.)<br>  For each stage, for each timestep, all modules are called in the order specified by  the @modlist interface, which also enables modules to refer to each other easily.<br>  See also @flowchart, @iob.																										
	loaddata		Load historical and scenario data		This module fills arrays of historical data (historical emissions, concentrations, temperatures) and predefined future scenarios (see @aboutsres). £§iobinfo<p>The original data is found in the java files  <li><a target='data' href='../makedata/histdata.java'>histdata</a>Historical emissions, concentrations and temperatures.  <li><a target='data' href='../makedata/histresp.java'>histresp</a> Historical regional emissions data used for the @responsibility calculations (UNFCCC assessment of Brazilian proposal)   <li><a target='data' href='../makedata/sresdata.java'>sresdata</a> Future SRES scenarios (global emissions and IPCC predictions)  <li><a target='data' href='../makedata/sresreg.java'>sresreg</a> Future SRES scenarios (regional socioeconomic data)<p> This data  is compressed by the routines in <a href='../makedata/savedata.java'>savedata</a>, to make the binary file jcm/data/data.dat For each sequence of data, the maximum and minumum and range are found. Each data item is then converted to one byte, by subtracting the minimum, dividing by the range, and multiplying by 256. So the precision is 0.4% of the range, or one pixel of a typical plot.<p>When the model starts up, "loaddata" simply loads and uncompresses this datafile into specific modules.<p> See also @histemitobserv and @rftemp																										
	aboutmodules		About Modules		Modules contain the core scientific calculations of JCM.  They are owners of most adjustable @param, and also arrays of output data which may be referenced by visible graphs.<p>Modules may use @tls and @root but do not refer to @gui or @pan (°cogs see @struccode)																										
	parallelworld	fut	Parallel Worlds		Currently JCM has only one copy of each of the @mod , because the @modlist effectively makes them all 'static'.<br>  ²°cogs Note: 'static' has a specific meaning in java -see Sun's Java Tutorial (link from @aboutjava). ²<br>  This makes it easy for modules to interact with each other (see @iob, @flowchart), however it is restrictive if we wish to compare different scenarios or different implementations of climatic components. So it is proposed that modules should no longer be static, instead  referring to each other via a root 'world' object, of which there may be more than one instance. For efficiency different 'worlds' could share certain modules: for example, the same emissions scenario combined with different climate modules, or vice versa (although in such cases  careful consideration of feedback effects would be needed).<p>Note that it is already possible to have multiple copies of each panel, with different layout options (see @jcmpanel). This was slightly easier to implement because there are no direct interactions between panels.																										
	mathcurve		Mathematical Curve Formulae		This class simply contains mathematical formuale to generate various kinds of curves for @stabilisation. It is used by @mitigation module. The curves are mainly variants of the Padé formulae used for the original IPCC 'S' scenarios (Enting et al 1994).																										
	param		Parameter		A parameter contains the setting of each adjustable parameter, as well as its default setting (used to reset the model).<p>There are several types - true/false, numerical values, choice of strings, choice of iobs.<p>A parameter has both a  "controller" (a graphical @popob) used to adjust it, and an "owner" (e.g. a science module or layout setting) whose code defines its direct effect.<p>Changing a parameter triggers the whole model to recalculate as needed. Thus parameters enable interaction between @mod and @gui.<p>If @helpmode is selected, you can get documentation about any parameter by clicking on its "controller" (arrow, menu or option).<p>A Parameter can also write html code describing itself and containing burttons/menus/textfields to adjust it, for use within documentation.<p>You can also adjust any parameter by @scripting, and see all parameter values using @viewparams.<p> Parameters may later be extended to associate probability density functions -see @uncertainty.																										
	iob		Iob	Interaction Object	Iob (Interaction Object) handles the interactions between JCM components (see also @flowchart, @struccode)<br>  Iob is the java superclass of all @mod, @pan, @param and @gui (menus, options and controls).<br>  Each iob stores a list of all the other iobs which are it affects, or is affected by. These interactions are declared in the individual modules and panels.<br>  This enables panels to interact with modules, via controls and parameters, without the module code making any reference to the graphical interface. Thus, the science and graphics components can be developed independently, yet they can still interact seamlessly.<p>  Each iob also stores flags, to say whether it is needed (for visible output) or has been changed, due to adjustments by the user.<br>  If an iob is needed, all those which it is affected by must also be needed. If an iob has changed, all those which it affects must also be changed. When one iob changes or becomes needed, a recursive loop checks through all the iobs, to set these flags accordingly.<p>  Only those iobs which are both needed and changed are run during the main @loop calculations. This makes a key contribution to the speed and efficiency of JCM. You can observe that the model responds faster when you adjust controls affecting only the end of cause-effect sequence (e.g. climate model parameters, with feedbacks disabled). The response is also faster if you view less plots (e.g. if only regional emissions are visible, the carbon-climate modules are not run).<p>  Each iob also has an "owner" which contains methods specifying effects of adjustment (used by @param and @popob).<br>  Each iob also has a unique name (see @names) and contains methods to also provide textual information, for documentation, scripting or debugging (see @labdoc, @scripting).																										
	openapp				loads main model, first testing for browser compatability																										
	startup				handles startup and reset sequence																										
	mainapp				Mainapp extends Applet to provide the interface with the web-browser.<p>Mainapp handles applet parameters and methods called from javascript, and each @jcmpanel gets its graphics context and events from the underlying Panel.<p>It also triggers @startup to initialise JCM and @plotlayout to respond to repainting and resizing, although the main drawing routines are called by @loop.<p>(note: you can use @startjcm to create a mainapp window without any web browser).																										
	testmod		Test of model without gui		A simple applet which uses jcm/mod to calculate the sealevel rise, without any reference to the graphical interface (it should still work if jcm/gui and jcm/pan are not compiled)																										
	startjcm		Starting JCM as an application		You don't need a web browser to run JCM, but you do need a "java virtual machine" (JVM) installed on your computer (see @jvm)<br>  Most operating systems have a JVM already installed. Recent versions of MS Windows already include a very fast JVM which can be opened using "wjview.exe" (although microsoft doesn't advertise this any more).<br>  However to use some newer features in JCM (such as image capture) you will need the latest JVM from Sun (java.sun.com).<p>If you installed a recent version of Sun's JVM you may find that clicking on the jcm.jar file in the JCM root directory is sufficient to open the model.<p>If not you need the following commands, which could be run from the command line, or put into a desktop shortcut for convenience:<br>  <pre>  <li><i>Using MS Jview:</i> C: <p> Windows <p> System32 <p> wjview.exe /cp %path1% <p> jcm.jar jcm.startjcm %path2%/jcm   <li><i>Or using Sun's JVM:</i> java -cp %path1% <p> jcm.jar jcm.startjcm %path2%/jcm   <li><i>or try: </i>C: <p> java <p> jdk <p> bin <p> java.exe -cp %path1% <p> jcm.jar jcm.startjcm %path2%/jcm<br>  <i>changing c: <p> ... to wherever java.exe is located on your system </i><br>  %path1% and %path2% are variants of the path to the JCM root directory (the directory containing jcm.jar)<br>  (e.g. on my system:)<br>  %path1% is c: <p> java <p> jcm<br>  %path2% is /java/jcm (note initial / replaces c:)<br>  %path2%/jcm should not be necessary if the command is typed from the same directory as jcm.jar<br>  </pre><br>  @contact me for more information .  <li>Note also @startjcmswing																										
	startjcmswing		JCM as a standalone application, with documentation		If you have Java v1.4 you can now use startjcmswing to run JCM standalone, including the html documentation (see @labdoc)<br>  Just click on jcm.jar from the @download. See @startjcm for more instructions (replacing startjcm with startjcmswing).<br>  From this, a @notepad can also be used to edit and save the documentation.																										
	tls		Text/Script Tools	JCM Text/Script Tools	Tools for handling  labels, scripts and documentation, file input/output, text parsing.<br>  ³ Labels /  Documentation / Scripts / Demos³<br>  @labinf<br>@autodoc<br>@sort<br>@showwebpage<br>@player<br> @recorder<p>  ³ General tools³<br>  @ref<br>@txt<br> @fileio<br>@sysin <br>  <hr>² °cogs (Note: Tools may use @root but do not refer to @gui or science @mod or @pan - see also @struccode)²																										
	ref		ref		This class contains some methods using Java "Reflection" to help parts of JCM find each other (see jdk-api java.lang.reflect).																										
																															
java	javafuture	fut	Future of Java in Web Browsers		The future development of the graphical interface of JCM depends on the evolution of Java Virtual Machines in web browsers (see @aboutjava). Currently most people are using Internet Explorer as their web browser. This contains Microsoft's Java Virtual Machine, which works very fast, but is based on the old 1.1 specifcation of Java. So JCM currently supports Microsoft's JVM as the 'lowest common demoninator', however to do this many wheels were reinvented, to implement components/tools which already exist in later versions of Java  (e.g. lighweight GUI components and event handling, text parsing, collections, java2D graphics etc.). So future development of JCM would be much easier if the latest version of Java were included in all web browsers. The legal case between Sun and Microsoft specifies that Microsoft must drop it's old JVM at the beginning of 2004, however whether another JVM will be included with IE thereafter is still disputed.<p>Meanwhile, beyond the current version of JCM (July 2003) it is anticipated that new features (especially those mainly for research rather than educational purposes) will be developed using the latest features of java (1.4/1.5), and only retrofitted to support old web browsers if this still seems necessary next year.<br>  As JCM becomes used more for new research and for organised student courses, the issue of supporting casual web surfers becomes a lower priority (as serious users can install the latest  version of java).  However a web-browser version (with limited features) is still needed to catch peoples interest initially.																										
	aboutjava		About Java		Java is an computer language,<br>  developed by <a href="http://java.sun.com"  target="new">Sun microsystems</a>.<br>  It has an object-oriented structure similar to C++.<br>  ££jvm ££whyjava <br>  <hr>See also<li>@javafuture<li>@writejava																										
	jvm		The Java Virtual Machine		Java is semi-compiled, operating via a "java virtual machine" (JVM).<p>  The original source code is written as plain text files (ending in .java).<br>  This must be compiled to java bytecode class files (ending in .class).<br>  These class files are then interpreted by the JVM.<p>  Note many class files may also be packaged together in a "java archive" (.jar) file, which is compressed like zip to make it easy to send across the web.<p>  The class files are the same for any type of computer,<br>  but the java virtual machine (JVM) is different and must be set up locally,<br>  as it converts java bytecode to raw machine code, and interacts with the operating system.<p>  The JVM may be built into a web browser for running applets.<br>  Internet Explorer and Netscape provide their own JVMs,<br>  which differ between versions and platforms.<br>  It's also possible to use a JVM without a web browser (e.g. Sun JRE).<p>  In theory, this JVM arrangement means you can "write once run anywhere",<br>  yet the calculations run much faster than directly interpreted languages such as basic or javascript.<p>  The JVM also incorporates security checks which restrict what an applet loaded across the web can do -for instance it can't load or save files locally, only on the server from which it came.<br>  This makes it "safe" for applications in web browsers. Also, java was designed to make it easy to communicate across the web, with many networking and "internationalisation" features.<p>see also<li>@startjcm   <li><a href="../struc/javaproblem.html" target="_new">Problems with Java in your browser?</a>																										
	whyjava		Why Use Java for this model?		Java is currently the only computer language which can both make big calculations efficiently, and draw vector graphics in a web browser (without plugins).<br>  Hence it is the only way to make a climate model working fast on the web<br>  (note interactive tools in other languages, require that the server sends a new graphic every time you change something, which is intrinsically much slower).<p>  The modular "object oriented" structure is good for making an interactive graphical-user interface where the flow may jump about, and where components may be reused in different ways. However java has a steep learning curve, you have to construct a basic framework, before you can develop anything interesting.<p> See also:   <li>@struccode  <li>@writejava																										
	writejava		Writing Java		££getjdk ££simpleapplets ££javasyntax ££compiletips																										
	getjdk		Get the JDK		JVMs are built into most web browsers, however to compile your own code you also need the Java Development Kit (JDK or SDK) from Sun microsystems<br>  (Java was invented by Sun, which is still the best source for the basic tools,<br>  although the specification is open so others can also develop them)<p><p>Go to<br>  <a href="http://java.sun.com/" target="new">http://java.sun.com/</a><br>  and to products and APIs (top-left menu), then pick a version to download.<p>  This is a dilemma: the basic web browsers (NS4.6+, IE4or5) use a variant of Java 1.1 (with some features missing), but Sun is now promoting Java 1.4 (or even 1.5). JCM is still based on 1.1 for this reason. If you start with JDK 1.1, you won't be confused by many new features that won't work in many web browsers. However, the JDK doc and tools are better in later packages.  <li>See also @javafuture<p>  Also download the Java Tutorial from Sun<br>  which you can find under the docs and training section (top-left menu)<br>  This is a good introduction to the language,<br>  but has much more detail than you need at the beginning!<p>  Note as well as the main tutorial package (also big!),<br>  you are advised to download the old archive "creating a user-interface AWT only",<br>  since the newer "Swing" classes aren't available in most web browsers.<p>  Unpack and setup the package on your computer according the instructions included<p>  Then you can, in theory, start compiling code by typing instructions<br>  at the DOS prompt (in Windows) or a terminal window (Unix).<br>  The JDK documentation explains this,<p>  However you could soon get bored typing in commands this way.<p>  To make it easier, you can set up windows explorer to compile java files, just by right-clicking with the mouse. This involves some tricks using DOS batch routines,<br>  which I got from a small package called JDEX.<p>  Alternatively, use a code-editing tool. I like the <a href="http://mathsrv.ku-eichstaett.de/MGF/homes/grothmann/je/" target="_new">"Java Editor"</a> by Réné Grothmann.  It's simple but flexible to use, and written in Java 1.1 so you can use with wjview (see @startjcm).																										
	simpleapplets		Simple Java Applets		An interactive, object-oriented code has no simple beginning and end, but you must start somewhere.<p>  The best way to learn is by trial and error,<br>  frequently checking the API documentation that comes with the JDK<br>  which describes each class in detail.<p>  An applet is a special class that appears in a web browser.<br>  Here is the simplest possible:<br>  <pre><br>  import java.applet.Applet;  import java.awt.<li>; <br>  public class Myapplet extends Applet {<br>  public void paint(Graphics g) { g.drawString("Hello",4,20); }<br>  }<br>  </pre><p>  The first line imports the necessary java packages (awt=abstract window toolkit).<br>  The next specifies you are adding to the existing Applet class.<br>  The paint method will be called by the browser which provides the graphics context "g".<br>  All applets must have either a paint or an init method, which must be declared "public".<p>  Copy the four lines above into a text file called Myapplet.java.<br>  Then compile this file, which should produce Myapplet.class<br>  Then make a very simple web page (put it in the same directory).<br>  <pre><p>  &lthtml&gt&ltbody&gt<br>  Below is my applet&ltp&gt<br>  &ltapplet width=100% height40% code="Myapplet.class"&gt&lt/applet&gt<br>  &lt/body&gt&lt/html&gt<p>  </pre><p>  Then just open that web page, in internet explorer.<br>  You should see it says "Hello" inside the grey rectangle.<p>  The example below is more fun, it makes a wavy sea with floating balls.<p>  <pre><p>  import java.applet.Applet;  import java.awt.<li>; <br>  public class Wave extends Applet {<p>  boolean loop; int i,x,y,w,h;<p>  public void paint(Graphics g) {<br>  w=this.size().width; h=this.size().height;<br>  i=0; while(loop) {<br>  i++; if (i&gt1256) i=0;<br>  for(x=0; x&ltw; x++) {<br>  y=(int)((0.5<li>h + (0.4<li>h)<li>Math.sin((double)(x+i)/50.0) + (0.05<li>h)<li>Math.sin((double)(x+i<li>4)/10.0) );<br>  g.setColor(Color.cyan); g.drawLine(x,0,x,y);<br>  g.setColor(Color.blue); g.drawLine(x,y,x,h);<br>  if (((x+(int)(1.5<li>i))%80)==0) {g.setColor(Color.yellow);g.fillOval(x-10,y-8,12,12);}<br>  if (((x+(int)(2.0<li>i))%150)==0) {g.setColor(Color.red);g.fillOval(x-20,y-16,20,20);}<br>  }}}<p>  public void start(){loop=true;}<br>  public void stop(){loop=false;}<br>  }<p>  </pre><p>  Now you need this web page to run it:<p>  <pre><p>  &lthtml&gt&ltbody&gt<br>  &ltapplet width=100% height40% code="Wave.class"&gt&lt/applet&gt<br>  &lt/body&gt&lt/html&gt<p>  </pre><p>  Once you see what it does when you run it,<br>  you might be able to understand how the code works,<br>  by reading my notes on java syntax appended below.<p>  Notes specific to code above:<p><li>start() and stop() are called when you open and close the page (applies to any applet)  <li>while(loop){} will keep looping so long as loop is true  <li>%80 calculates the remainder after dividing by 80  <li>the g.methods are part of java.awt.Graphics but it should be fairly obvious what they do  <li>note the type conversions (double) (int) etc.<p>  Changing the applet size will change the wave speed too, since the java is calculating as fast as it can. Math.sin is a rather slow function, so you could make this more efficient by storing an array of y values before the main loop.																										
	compiletips		Compiling Tips		This keeps changing with new versions of JDK, JCM etc. Please email ben@chooseclimate.org																										
	compiletipsold		Compiling Tips		Internet Explorer's JVM starts up the first time it meets an applet, and then stays open as long as Explorer does (note your desktop is also a copy of Explorer!). This JVM doesn't check whether you have changed a class file. So to force the JVM to start again after you changed a java class file, use CTRL-F5 immediately after refreshing or reopening the web page. <p> You can instead use the java appletviewer supplied with JDK, which always refreshes. However beware it doesn't understand percentage width/height in the html applet tag! <p> If the compiler reports an error, you won't get any class file, instead a text file describing the errors. <p> But if it succeeds, that doesn't mean you won't sometimes get errors, or "runtime exceptions" which may stop the applet from working. These will usually be reported in "Standard Output". In a web browser, the standard output is the Java Console, which you can find in the view menu in internet explorer (or tools in netscape). If it's not there, you need to enable it first by changing the web browser settings ("java console" or "java logging" under advanced options). The java console also catches anything you write to System.out, e.g. <br> System.out.println("Got to here OK"); <br> (you can put this into your code for debugging)																										
	javasyntax		Java Syntax		Please see the simple introduction page found in the  jcm/code directory of the package (<a target='_new' href='../code/simplejavasyntax.html'>Here is a link to simplejavasyntax.html'</a>																										
																															
pack																															
	whatnew		What's New in JCM?		³Latest Version March 2004³ <li>Documentation reorganised: seperate topic-language files for loading on demand over web, big table for updating / reorganising - See @labdoc. Also some documentation in French.  <li>Probabilistic Approach - Stabilisation under Uncertainty - see @applications <li> Attribution for 12 regions, and relative shares - see @attribution <li>Climate Map regions and projections -see @regclimap <li> Carbon Cycle -see @sinksbiosphere, @TGCIA450<hr> See also  <li>@new2003 <li>@new2002  <li>@develop  <li>@future																										
	new2003		JCM: New in 2003		Version of July 8th 2003:  <li>Documentation:  now about 50,000 words, hundreds of topics, many ways to explore. Metaformat including content generated by the model - See @labdoc  <li>Scripting: new facility can be used for both batch-calculations and automatic demonstrations -  See @scripting  <li>Stabilisation Scenarios: - More general iteration algorithm- see @stabrfdoc, @stabtempdoc, @stabitmethod  <li>Teaching and policy @applications  <li>Swing variant for documentation without webbrowser  <li>More dynamic internal structure and interactions.<br>  																										
	new2002		JCM: New in 2002		<h3>Oct-December 2002</h3><br>  Regional Climate Map:  <li> Several GCM datasets, inc new HadCM3 data  <li> Several climate variables, plus combinations  <li> Baseline climatology, & baseline plus change  <li> Averages for countries/regions - may plot as polygons  <li> Rotate by dragging, other display options<p>Other changes:  <li> New layouts: ring of small plots + big in centre (flowchart or another).  <li> Carbon-flux / heat-flux parameters move to carbon-storage / ocean temperature plots.  <li> CO2 equivalent curves ("expert")<p>  <h3>August-Sept 2002</h3>  <li>Attribution of responsibility (JCM contribution to UNFCCC assessment of the "Brazilian proposal")   <li>Multi-part labels, cancelling units  <li>Capture image and data-table functions (only as application with Java1.4)<p>  <h3>June-July 2002</h3>  <li>Spanish and German labels  <li>Developing an efficient java variant of the dynamic vegetation model used in Bern-CC (not yet included in JCM!)<p>  <h3>April-May 2002</h3>  <li>Comprehensive documentation -now over 70 webpages with 40000 words  <li>Carbon-storage and ocean-temperature plots  <li>Upwelling feedback on sealevel fixed  <li>New code structure, separate model and GUI, more adaptable /reliable<p>  <h3>February-March 2002</h3>  <li>Atmospheric chemistry calculations as Bern-CC model  <li>Other gas and F-gas plots  <li>Other gas emissions options  <li>Improved stabilise temperature formula<p>  <h3>January 2002</h3>  <li>Flexible regional data plot  <li>Cause-effect flowchart  <li>Energy, GDP, Abatement  <li>More distribution options																										
	develop		JCM: History of Development		<h3>Early development: Spring 2000</h3><br>  The idea was born out of frustration at the lack of connection between climate science and policy, as observed from personal experience both investigating CO2 fluxes in the laboratory, and attending UN climate conventions. (See <a href="author.html">About the Author</a>)<p>  The first stage was a simple java applet, demonstrating the flexible<br>  "contraction and convergence" policy framework, whose concept was developed with <a href="http://www.gci.org.uk">Global Commons Institute</a> in summer 1996.<p>  <a href="http://www.chooseclimate.org">ChooseClimate website</a> was launched in April 2000 to provide an independent "home" for JCM to consider a wider range of options and scientific issues,<br>  also initially persuing the concept of a global climate referendum.<br>  This work was carried out at home, in Norwich.<p>  <h3>Core Science: Copenhagen, Winter 2000 - 2001</h3><br>  The next stage developed with <nobr><a href="http://www.dea-ccat.dk" target="_new">DEA-CCAT Copenhagen</a> </nobr>, together with Jesper Gunderman of Danish Energy Agency, and Peter Laut of Danish Technical University .<p>  Firstly a version was developed for display in the "Experimentarium" in Copenhagen, supported by EnergiMiljoradet. The interface was improved and the code internationalised, to support Danish and Swedish versions.<p>  After COP6, the core carbon and climate science modules were developed to match the predictions of IPCC-TAR, using an effecient eigenvector calculation method originally developed by DEA-CCAT for an earlier web model.<p>  <h3>"Nomad with laptop" -  Spring 2001</h3><br>  After a visit to Arendal Norway, JCM travelled to Scotland, persuing a project proposal with Edinburgh university, also working a few weeks at the Centre for Ecology and Hydrology. The sea-level and radiative forcing components were improved, and the timescale extended for longterm impacts.<br>  Then followed a "European tour" of many conferences and institutes during summer 2001 (see @confpres)<p>  <h3>Synthesis questions: UNEP-GRID, Autumn 2001</h3><br>  Development continued at <nobr><a href="http://www.grida.no" target="_new"> UNEP/GRID Arendal</a></nobr>, Norway, from mid-August to the end of 2001, with an interval around COP7, Marrakech.<p>  Here the emphasis was on improving the design of the user interface and documentation, focussed around introductory questions inspired by the new IPCC Synthesis report. The stabilisation scenarios were implemented to complement the SRES. Following a  "peer-review"  process, a version was launched on the new UNEP.net climate portal at COP7.<p><p>A scripting code was developed for automatic demonstrations of key points. This was also adapted for "remote control" across the web, exploring the model's potential as a framework for global dialogue. Further translations were also made, to  Russian, French, Norwegian.<p>  <h3>Biogeochemical cycles: Bern, February-June 2002</h3><br>  In <nobr> <a href="http://www.climate.unibe.ch" target="_new"> Klima und Umwelt Physik, University of Bern</a> </nobr>, working with Fortunat Joos, and supported by Swiss BUWAL. Here the emphasis was on the atmospheric chemistry and radiative forcing of all greenhouse gases, and also on the terrestrial biosphere component of the carbon cycle, to match the latest version of Bern model.<br>  A better stabilise-temperature method was also developed. The source code was also restructured and made open in downloadable packages. Translations were added for German and Spanish.<p>  <h3>Integrated Assessment: UCL-ASTR, Louvain-la-Nueve, since July 2002</h3><br>  Current development is at <nobr><a href="http://www.climate.be" target="_new">Institut d'Astronomie et de Géophysique, University Catholique de Louvain </a></nobr>, Belgium, working with Jean-Pascal van Ypersele.<br>  During the last year, JCM has participated in the UNFCCC Assessment of the Brazilian proposal (see @attribution), has been presented at other IPCC and UNFCCC workshops, and EGS in Nice (see @confpres). JCM has also been used for @teaching, with several courses. The @regclimap were improved and many variants of @stabilisation developed, addressing the @stabtemp2c question using a new @scripting code. Dutch translation was added, and all the @labdoc restructured.<p>  <hr> See also<li>@confpres,<li>@whatnew,<li>@future,<li>@acknow																										
	copyr		Conditions of Use		³JCM "Copyright"³ In the interests of global understanding, the Java Climate Model is made freely available and the source code is also open.<br>  However, users should respect the following points (in which the term "JCM" refers to the complete package, model, code and documentation).<br>  <hr>The copyright and ownership of JCM remains with the author Ben Matthews. The author cannot accept responsibility for any adverse impacts of using the JCM.<br>  The user:   <li>May store the JCM locally for use offline, but please check regularly for updates, and tell the author if you use it frequently.   <li>May use the JCM for educational or research purposes, but not for profit-making activities (note, non-profit applies to the activity, not to the organisation).   <li>May not publish, redistribute or sell the JCM or adaptations thereof in any form (please consult the author, if you wish to use this within an educational package).   <li>May consult the code and experiment with minor modifications. Please coordinate with the author, regarding major adaptions. Any adaptations should remain open source and not-for-profit, as above, and the scientific integrity of the model should remain paramount.   <li>see also @contact																										
	download		Download Package		<li>Before downloading, please read @copyr  <li>Please check regularly for updates. If you use JCM frequently and wish to be informed about new developments, @contact.<br> ££pack24mar04 ££pack8jul03 ££packchange ££packnotes ££pack5apr03 ££pack24dec02 ££pack1dec02 ££pack1sep02 ££pack7jul02																										
	packchange		Recent changes in packages		<li>@pack24mar04 Documentation reorganised: separate topic-language files with big table for restructuring. Some french documentation. Probabilistic Approach, Attribution for 12 regions, Climate Map regions and projections, Carbon Cycle additions <li> @pack8jul03 New documentation 50000 words, searchable, translatable, context-related and interactive, also works in Swing. New scripting code for IA scripts and demos. Cloneable, adaptable, more efficient Panels.<p><li>²(@pack5apr03 New stabilisation scenario options, new internal interactions structure. Note, this version was not finished - a transient phase!)²<p><li>@pack24dec02 More regional climate datasets and options. New layout options. Fixed 1Dec bug for older JVMs. Documentation update. Note no auto-demo scripts in this version<p><li>@pack1dec02 Improved regional climate impact maps: eight variables, baseline climatology, combination, borders and and averages for each country/region. Also some efficiency improvements. Added Dutch labels. Won't work in older JVMs.<p><li>@pack1sep02 for Brazilian proposal calculations: added responsibility module, attribution plot, documentation of submitted results. Also multi-part labels with cancelling units, and capture image and data-table functions (with JDK1.4 and startjcm).<p><li>@pack7jul02  added German labels  <li>17th June: Added Spanish labels, Mitigation module now resetting OK.<p>  <hr>  <li>see also @whatnew																										
	packnotes		Notes about packages		<li>The same java code works on any system (see <a href="java.html">about java</a>)  <li><b>.zip</b> packages require an unzip tool (e.g. try <a href="http://zipcentral.iscool.net/" target="_new">zipcentral</a>).  <li>² old <b>.exe</b> packages are self-extracting, for windows only.²<p>To start the model, open "index.html" in the root jcm directory<p>(or "open.html" in "basic" packages)<br>  ²°cogs (For experts) If you have Java 1.4 installed, you can open /jcm/startjcm.class as an application, rather than an applet, which adds the facility to capture images and datatables from plots.<li>See @startjcm²<br>  <!-- Note the concatenated files for printing are not included in packages: These are   <li><a href="../jcmdocall.html" target="_new"> jcmdocall.html</a>  <li><a href="../../code/jcmcodeall.txt" target="_new"> jcmcodeall.txt</a> --><p>  ³File structure³<br>  The file structure is essential for the interaction between the model, the data, and the  documentation. Be sure to enable "preserve directory structure" in your unzip program.<li>See also: @struccode<p> Individual compiled java classes are not included in the package, as they are already inside the jar file, and can be generated from the source code. They should be placed below the root directory, with structure according to packages (e.g. jcm/jcm/mod/carboncycle.class)<p>for hints on compiling JCM  <li>@contact   <li>@aboutjava<br>  <!--<li> see: <a href="compile.html">Compiling info</a> or better, --><p> The Jama Matrix Package (@jama) and the classes for javascript-java interaction (Netscape) are also included.																										
	pack24mar04		24th Mar 2004		Latest version <p>Complete JCM package: Model, core data, labels/documentation (inc table), java source code   <li><a href="http://www.chooseclimate.org/jcm/pack/24mar04/jcm24mar04.zip">jcm24mar04.zip</a> (1150 KB) <li> You may also want to download extra regional climate datasets as in @pack24dec02 package (they have not changed) <p>Smaller package, as above but without java source code, labdoc table, and precipitation data <li><a href="http://www.chooseclimate.org/jcm/pack/24mar04/jcm24mar04basic.zip">jcm24mar04basic.zip</a> (670 KB) 																										
	pack8jul03		8th July 2003		Complete JCM package: Model, labels, core data, documentation & java source code   <li><a href="http://www.chooseclimate.org/jcm/pack/8jul03/jcm8jul03.zip">jcm8jul03.zip</a> (993KB) Complete Package  <li> You may also want to download extra regional climate datasets as in @pack24dec02 package (they have not changed)  <p>Smaller package, as above but without java source code and precipitation data<li><a href="http://www.chooseclimate.org/jcm/pack/8jul03/jcm8jul03nocode.zip">jcm8jul03nocode.zip</a> (635KB) 																										
	pack5apr03		5th April 2003		<br>(with new scripting code for batch calculations and demonstrations, new stabilisation scenarios, and new internal interactions framework)<br>  <P class=note>Note: this version is not yet finished, it is for people already familiar with JCM, to consider further developments. It may have bugs, and the documentation has not yet been updated.<p><b>Please see readme.txt file inside package.</B><br>  </P><p><p>Complete JCM package: Model, labels, core data, documentation & java source code   <li><a href="http://www.chooseclimate.org/jcm/pack/5apr03/jcm_complete_5apr03.zip">jcm_complete_5apr03.zip</a> (approx 900KB)<p><li> You may also want to download the regional climate datasets as in @pack24dec02 package (they have not changed)																										
	pack24dec02		24th December 2002		<br>(with improved regional impacts)<p><p>Basic JCM package: model, labels, core data   <li><a href="http://www.chooseclimate.org/jcm/pack/24dec/jcm_basic_24dec02.zip">jcm_basic_24dec02.zip</a> (340KB)<p><p>Complete JCM package: Basic + documentation & java source code   <li><a href="http://www.chooseclimate.org/jcm/pack/24dec/jcm_complete_24dec02.zip">jcm_complete_24dec02.zip</a> (870KB)<p><p>Regional Climate datasets (much bigger than the model - but much smaller than the original). Note, you can run JCM OK <i>without</i> these datasets, they are provided to show more GCM patterns, baseline climatology, and climate variables. This zipfile must be unpacked into the directory ..../jcm/data  <li><a href="http://www.chooseclimate.org/jcm/pack/24dec/jcmdata_24dec02.zip">jcmdata_24dec02.zip</a> (6.5MB)																										
	pack1dec02		1st December 2002		<p>Standard JCM package: including data (except baseline below) documentation & demos   <li><a href="http://www.chooseclimate.org/jcm/pack/1dec/jcm_standard_1dec02.zip">jcm_standard_1dec02.zip</a> (860KB)<p><p>Complete JCM package: Standard + java source code   <li><a href="http://www.chooseclimate.org/jcm/pack/1dec/jcm_complete_1dec02.zip">jcm_complete_1dec02.zip</a> (1.04MB)<p><p>Baseline climatology datasets (much bigger than the model - but much smaller than the original)  <li><a href="http://www.chooseclimate.org/jcm/pack/1dec/baselinedata.zip">baselindata.zip</a> (3.5MB)<br>  Note, these datafiles must be unpacked and put in the directory ..../jcm/data																										
	pack1sep02		1st September 2002		<p>Minimal: Model, data, english labels, no maps<p><li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_min_1sep02.zip"> jcm_min_1sep02.zip</a> (K)  <li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_min_1sep02.exe"> jcm_min_1sep02.exe</a> (K)<p><p>Basic: Model, data, all labels, maps<p><li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_basic_1sep02.zip"> jcm_basic_1sep02.zip</a> (K)  <li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_basic_1sep02.exe"> jcm_basic_1sep02.exe</a> (K)<p><p>Standard: Basic + documentation & demos<p><li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_standard_1sep02.zip"> jcm_standard_1sep02.zip</a> (K)  <li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_standard_1sep02.exe"> jcm_standard_1sep02.exe</a> (K)<p><p>Complete: Standard + java source code<p><li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_complete_1sep02.zip"> jcm_complete_1sep02.zip</a> (K)  <li><a href="http://www.chooseclimate.org/jcm/pack/1sep/jcm_complete_1sep02.exe"> jcm_complete_1sep02.exe</a> (K)<p>  pack7jul0																										
	pack7jul02		7th July 2002		<p>Minimal: Model, data, english labels, no maps<p><li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_min_7jul02.zip"> jcm_min_7jul02.zip</a> (196K)  <li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_min_7jul02.exe"> jcm_min_7jul02.exe</a> (234K)<p><p>Basic: Model, data, all labels, maps<p><li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_basic_7jul02.zip"> jcm_basic_7jul02.zip</a> (275K)  <li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_basic_7jul02.exe"> jcm_basic_7jul02.exe</a> (313K)<p><p>Standard: Basic + documentation & demos<p><li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_standard_7jul02.zip"> jcm_standard_7jul02.zip</a> (471K)  <li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_standard_7jul02.exe"> jcm_standard_7jul02.exe</a> (509K)<p><p>Complete: Standard + java source code<p><li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_complete_7jul02.zip"> jcm_complete_7jul02.zip</a> (646K)  <li><a href="http://www.chooseclimate.org/jcm/pack/7jul/jcm_complete_7jul02.exe"> jcm_complete_7jul02.exe</a> (684K)																										
																															
conf																															
	confpres		JCM at conferences/workshops		JCM has been demonstrated in many corners of Europe. ££linkpres ££conf2004 ££conf2003 ££conf2002 ££conf2001 <hr> See also<li> @develop @applications																										
	linkpres		Presentations/Abstracts/Papers		<a href="http://www.chooseclimate.org/confpres/confpres.html" target="_new">Links to files of recent presentations, abstracts, posters etc. on chooseclimate website</a>																										
	conf2004		JCM presentations 2004		²coming soon!²																										
	conf2003		JCM presentations 2003		<li>IPCC workshop on Article 2, Geneva (Feb 2003) <li>European Geophysical Society, Nice (April 2003) <li>UNFCCC workshop on Article 6, Mons Belgium (May 2003)<li>UNFCCC workshop on Attribution (Brazilian Proposal), Berlin (Sept 2003) (see also @att_berlin_int) <li>European Strategy Meeting, Firenze Italy (Sept 2003) <li>World Climate Change Conference Moscow (Oct 2003) (see also @moscow @wccc2003) <li>"Policy beyond Kyoto", Ghent Belgium (Nov 2003) <li>UNFCCC COP9 Milano (Dec 2003) <li>ICTP Trieste (Dec 2003)																										
	conf2002		JCM presentations 2002		<li>Proclim Swiss Climate Day, Bern  (Apr 2002) <li>IPCC Plenary in Geneva (Apr 2002)  <li>UNFCCC workshop on Attribution (Brazilian Proposal), Hadley Centre UK (Sept 2002) (see also @attribution)																										
	conf2001		JCM presentations 2001		<li>Earth System Processes conference, Edinburgh (June 2001)  <li>IGBP Open Science conference, Amsterdam (July 2001) <li>UNFCCC COP7 Marrakech (Nov 2001 - special event),  also COP6.5 Bonn (July 2001)  <li>Edinburgh Centre for Human Ecology  <li>Tyndall Centre, Univ East Anglia   <li>Reading University  <li>Potsdam Institute for Climate Impact Research  <li>Danish Centre for Earth System Science Copenhagen <li>CICERO Oslo																										
	moscow		Stabilisation under Uncertainty		@moscow_stab<br>  @moscow_carb<br>  @moscow_ogas<br>  @moscow_clim<br>  @moscow_prob																										
	moscow_stab		Stabilisation		Inverse calculation to stabilise  <li>CO2 concentration   <li>Radiative Forcing   <li>Global Temperature   <li>(Sea-level -difficult)<br>  <hr><br>  Adjust level and rate.<br>  Iterating to find solution<br>  Mitigating all gases and aerosols<br>  <hr><br>  Systematically calculate probabilistic analysis<p>  <hr><br>  @moscow_carb																										
	moscow_carb		81 Carbon cycle variants		<li>3 Land-use-change emissions (Houghton, scaled),   <li>3 CO2 fertilisation of photosynthesis ("beta"),    <li>3 Temperature-soil respiration feedback ("q10"),  <li>3 Ocean mixing rate (eddy diffusivity of Bern-Hilda model)<br>  <hr><br>  @moscow_ogas																										
	moscow_ogas		6 Ratios of emissions of different gases		Emissions of all gases   <li>CH4, N2O, HFCs,   <li>Sulphate / Carbon Aerosols   <li>Ozone precursors<br>  all reduced by same proportion as CO2 with respect to one of six SRES baseline scenarios<br>  ²note: atmospheric chemistry feedbacks included, but not varied²<br>  <hr><br>  @moscow_clim																										
	moscow_clim		84 Forcing/Climate Model variants		<li>3 Solar variability radiative forcing   <li>4 Sulphate aerosol radiative forcing  <li>7 GCM parameterisations climate sensitivity, ocean mixing/upwelling, surface fluxes (W-R UDEB model tuned as IPCC TAR appx 9.1)<br>  ²note: for sea-level rise, should add uncertainty in Ice-melt parameters²<br>  <hr><br>  @moscow_prob																										
	moscow_prob		Probability from fit to historical data		Relative probability of each set of parameters derived from inverse of "error" (model - data)   <li>Measured global temperatures (CRU + proxies)  <li>Measured CO2 concentration (Mauna Loa + others)<br>  Reject low-probability variants (kept 468 / 6804)<br>  <hr><br>  Ensures coherent combinations of parameters, e.g. :  <li>More sensitive climate models with higher sulphate forcing  <li>High historical landuse emissions with higher fertilisation factor<br>  <hr><br>  Still 2808 curves per plot (including 6 SRES per set)So show 10% cumulative frequency bands (using probabilities)<br>  <hr><br>  @moscow_oth																										
	moscow_oth		Other features of JCM...		<li>9 Languages - for global dialogue  <li>Regional emissions, Regional climates,   <li>Attribution (Brazilian proposal)																										
	wccc2003		Shifting the Burden of Uncertainty		(prepared for WCCC2003 Moscow)  Note also @probwccc, @moscow <hr> <li>Stabilisation to achieve Article 2  <li>Uncertainty depends on choice of Indicator   <li>8000+ variants, constrained to about 800 by fit to historical temperature/CO2 record.  <li>Made by scripting of same (interactive) Java Climate Model<br>  <img src="file://c:/ben/climate/conf/moscow/750scen15plotbw2.png" width=900 height=900><p>  The plots show (from top to bottom)   <li>CO2 emissions (GtC, including land use change),   <li>CO2 Concentration (ppm),   <li>Radiative Forcing (Wm-2, total including all gases, aerosols, and natural forcing),   <li>Temperature (C), and   <li>Sea-level rise (m).<p>Three sets of scenarios (from left to right) stabilise:  <li>CO2 Concentration at 475ppm,   <li>Radiative Forcing at 3.4 Wm-2, and   <li>Temperature at 2C above preindustrial (the EU's proposed limit for future warming).<p>  ²The average temperature is the same for each set, but the uncertainty ranges, shown by up to 780 variants, are very different. Note that the distributions are skewed, for example, on the  ST-Emissions plot, most of the curves are below the middle of the range (the mean in 2100 is at 3.2 GtC, with sd= 1.9).²<p>  ² Seven sets of parameters for tuning the climate model to GCMs (as in IPCC-TAR)were combined with fifteen sets varying sulphate and historical solar forcing. From these the 26 combinations resulting in temperatures most consistent with the historical record were kept. Five sets of parameters were used to vary the carbon-cycle (Bern model), consistent with historical concentrations. The six SRES scenarios were used to set the emissions of other gases (including aerosol and ozone precursors) relative to CO2, such that all are reduced by the same proportion of the baseline in each year. An iterative method was applied to find emissions pathways leading to stabilisation of forcing or temperature.²<br>  ²Note: only six GCM fits are used for sea-level rise, for consistency with more-recently available GCM data  ²<br>  ²A few variants with emissions higher than SRES were excluded. ²																										
