This is part of the labels / documentation for <a href='http://jcm.chooseclimate.org'>Java Climate Model</a><hr/>

#regcli		§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>. 
  £§iobinfo
  <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.

@bord  loads the data for country/regional polygons, and calculates the averages within each polygon.
  ££regclipredict ££regclifuture

#regclimap		§£^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.

The data came from <a target="new" href="http://ipcc-ddc.cru.uea.ac.uk"> IPCC-Data-Distribution Centre (DDC) </a>
  £§panelinfo £^mapstartlongitude ££regclimapuse ££regclipredict ££regclifuture

#impacts		§More documentation on this topic will be added later. Meanwhile, please explore the patterns in the @regclimap

#regclimapuse		§The colourscale loops (purple - blue- green - yellow -red - purple - blue etc.), in order to include extreme values without losing detail elsewhere.
  °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.
  °adju It is very important to experiment with changing the months, to see the seasonal cycle.
  °adju You can rotate the map just by dragging it with your mouse

#regclipredict		§

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.

(<i>add more here!</i>)

#regclifuture	¨fut		§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)

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.

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		§Rotates the map automatically 

 °adju To rotate the map manually, just drag it with the mouse

#mapstartlongitude		§£^info

#usereg		§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

#land		§Show land only (only for HadCM3 /HadCM2 grids)

#rescale		§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.

#scaletojcm		§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		§if @scaletojcm is enabled

#yearcycopt		§automatic loop from 1750-2300, scaling the GCM data to JCM temperature.

#mapdata		§This class  loads the data from GCMs for use in @regcli, @regclimap

#projection		§Change the projection for viewing the map. (this does not affect the calculations.)

#grid		§A simple latitude-longitude grid. This overemphasises high-latitudes.

#coslat		§A simple equal-area projection, made by scaling east-west distances by the cosine of the latitude.

#polar		§A simple polar projection (two hemispheres).

#dataset		§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

#quantity		§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.

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

#monthcycopt		§automatic loop
  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!