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

#baseline		¨oldJCM4		§This baseline data is the 1961-1990 climatology from CRU (land only), at a much higher resolution than GCMs, 0.5x0.5 degrees.  

€€cogs Since these datafiles are large (even after compression), and the resolution is not very useful since the @map no longer shows gridded data, they are **not** currently included in the package gcmdata.jar. You could @contact to get the datafile.

€€adju This baseline data can be viewed using @jcm4.

€€cogs It is anticipated to put back this data, with a high-resolution view,  in @JCM6

#regcli		¨oldJCM4 addJCM5		§This module imports gridded datasets of regional seasonal  changes of various  climatic variables (temperature, precipitation, wind etc.), taken from the IPCC Data-Distribution Centre (DDC). 
€€cogs @mapdata loads the data, which is then interpolated to jcm @regionsets.

€€cogs Changes are always relative to 1961-90 baseline. 

€€cogs The @scaletojcm option can be used to connect the GCM datasets to the rest of JCM (@glotemp) but this is **switched off by default** (to avoid confusion).

=== Regional Climate Maps ===

Unlike most @modules, this one does not produce any @qtset, instead it provides only a source of data to make @maps
€€adju To create such maps, click on the popup which appears beside this module in @jcmtree, or use the @mapsmenu @Menu. 
€€adju You can make more than one of these maps, each will access the same data but may use a different @colorscale, @regionset or @projection. See @mapplot for more information.
  €€adju It is very important to experiment with changing the @month, to see the seasonal cycle.
  €€adju When you move your mouse around the plot, the regional-average value (of temperature, precipitation etc.) is shown, as well as the region name and the latitude/ longitude.  

=== Regional Climate: Observations ===

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.

See also @regclifuture

#regclimap		¨oldJCM4		§£>regcli, @maps

#impacts		¨oldJCM4		§This is a placeholder module for experiments with regional impact functions, which will not be ready before @JCM6
Meanwhile look at @regcli

#regclimapuse		¨oldJCM4		§£>regcli, @maps

#regclipredict		¨oldJCM4		§£>regcli

#regclifuture		¨update		§Old JCM4 doc, to be updated....

Much further work on regional impacts is anticipated. Some proposals are:  
<ul><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)
</ul>
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		¨oldJCM4		§Rotates the map automatically 

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

#mapstartlongitude		¨oldJCM4		§A parameter for rotating a @mapplot

#usereg		¨oldJCM4		§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. %% Note that calculations may be slow with larger regions %%

#land		¨oldJCM4		§Show land only (only for HadCM3 /HadCM2 grids)

#rescale		¨oldJCMHELLO		§Makes one cycle of the colorscale go from -2 to +4 standard deviations of all the data in the map (scaled if @scaletojcm is selected). 
This doesn't change the underlying numbers, as shown when you move the mouse over the plot.

#scaletojcm		¨oldJCM4		§This option connects the maps in @regcli to the rest of JCM, to  estimate regional climate change for any scenario, as defined in @jcm.mod.obj. 

If this option is enabled, the regional climate map data (of any climate variable) is multiplied by the ratio of global average temperature rise from JCM  (see @glotemp) in the  @regcliyear, divided by the global annual average temperature  rise from the chosen @dataset.

#regcliyear		¨oldJCM4		§Applies if @scaletojcm is selected

Note that:<ul>
<li>The baseline year for calculating temperature rise is 1961-1990 in both cases (i.e. unaffected by @baseyear parameter). 
<li>Thus, for a year earlier than 1975, most regions show some net cooling.
<li> The year-range of the source @dataset is not changed.
</ul>
For example, if you want to see the effect of a low stabilisation scenario in 2150, but you want to use GCM data generated from a higher SRES scenario, it may be more appropriate to use the 2050s dataset in order to minimise the difference in average temperature. 
On the other hand beware that the pattern of  transient changes is rather different from those at equilibrium.
To resolve this problem, in @JCM6 it is anticipated to provide a wider range of GCM datasets from AR4, and to add an option to automatically select the closest available dataset (scenario / year) to match the chosen JCM scenario.
</ul>

#yearcycopt		¨oldJCM4		§automatic loop from 1750-2300, scaling the GCM data to JCM temperature.

#mapdata		¨oldJCM4 addJCM5		§This class imports the data from GCMs for use in @regcli and @regseacli

The data, from <a target="new" href="http://ipcc-ddc.cru.uea.ac.uk"> IPCC-Data-Distribution Centre (DDC) </a>  is stored in the package **gcmdata.jar**. It has been compressed to one byte per gridcell, for fast loading over the web. 

This class unpacks that data, and also calculates statistics for each dataset. 

€€cogs Note that interpolation to statistics for individual regions is done by code in @region. Thus, it is possible to change the region-set without reloading the data.

#projection		¨oldJCM4		§Change the projection for viewing the map. (this does not affect the calculations.)
@coslat
@grid
@polar
Note code in @mapprojection

#grid		¨oldJCM4 addJCM5		§old code to fix - please ignore

#coslat		¨oldJCM4		§A simple equal-area projection, made by scaling east-west distances by the cosine of the latitude.

#polar		¨oldJCM4		§A simple polar projection (two hemispheres).

#dataset		¨oldJCM4		§From this parameter you can choose various GCM datasets for @regcli

About the data options:<ul>
<li>HadCM3 is the GCM from the Hadley Centre v3
<li>The resolution for HadCM3 is 2.5x3.75 degrees
<li>Data from HadCM2 and some other older models is also available.   
<li> SRESA2 is a rather high scenario, but favoured by GCM modellers, see @aboutsres 
<li>The years (2080s etc.) indicate the middle decade of a thirty year period over which the original GCM data has been averaged.
<li>This data will soon be replaced by new AR4 data
</ul>€€cogs Note also: <ul>
<li>If @scaletojcm is enabled, the scaling year is fixed by @regcliyear and the scenario by parameters in  @jcm.mod.obj
 <li>If the chosen @quantity  is not available for this dataset, the map will just appear white.
<li>This parameter acts independently of the one used for @gcmfit 
</ul>

#both1		¨oldJCM4		§This combination of the baseline plus the change (projected by HadCM3), for SRES A2 (possibly scaled -see @scaletojcm).

#both2		¨oldJCM4		§Not currently available (see @baseline)

#quantity		¨oldJCM4		§From this you can choose various climate variables. The available quantities depend on the dataset. If a chosen quantity (or combination) is not available for the chosen @dataset, the map will just appear white.

#month		¨oldJCM4		§Parameter to the change the month for viewing maps of @regcli.
@regseacli offers another way to explore the variation between months.

#monthcycopt		¨oldJCM4		§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!