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

#sresdata		§An old container module for loading static SRES data - to be removed soon.

#jcm.mod.scen		§This package stores data from published scenarios, and interpolations/extrapolations thereof. 

Earlier versions of JCM relied heavily on IPCC-SRES scenarios, especially for future socioeconomic and baseline emissions trends. These are now long out-of-date, and being replaced by the new IPCC Shared Socioeconomic Pathways (SSPs). 

Although the functions of SRES in JCM have recently (2015-16) been replaced by SSPs, much of the older documentation  does not (yet) reflect this. 

The older SRES data remains available here for reference. 

Gradually JCM is developing away from reliance on scenario data, replacing this with @jcmbotupscen .

#sresimgdata		§Old SRES data from the IMAGE model, which was used because it has a better regional resolution and higher internal consistency (between scenarios) than the published IPCC tables. 

For more info see @data, @aboutsres,  @dataref .

#sspchooser		¨[oldkey=ssp_chooser]		§This module (developed 2015-16) contains plots and parameters relating to the new IPCC @SSPs. 

This data is used for various purposes in JCM, interpolating or extrapolating gaps not covered by @JCMbotupscen, and replacing the former role of @sresimgdata.
 
Currently this includes scaling emissions of some other gases and future land use change, relative to fossil CO2, but it is intended to phase out such scaling. So, parameter choices here may affect various other modules - likely to change (and hence poorly documented).  

You can also use this module to explore the SSP scenarios. As this data has many dimensions, you have to select some with parameters,  and plot a range of others - i.e. compare regions, gases, @RCPs, SSPs or models (IAMs). 
 
Note that the data was derived from an early release of the SSPs and may change with an update.

#ssps		§The new Shared Socioeconomic Pathways were created - by a consortium of integrated assessment model (IAM) groups -  to provide a set of scenarios to  span the full range of "challenge space" relevant to both Mitigation and Adaptation policies.  

For example, scenarios with faster economic growth may have higher emissions (challenge for mitigation) but lower vulnerability (challenge for adaptaton). The scenarios explore, to some extent, consequences of globalisation. 
 
These share some characteristics with the older @SRES, but unlike the latter SSPs were not used directly to drive physical climate models, as the   @RCPs filled that role for IPCC AR5 and AR6.  

Rather, the SSPs are grouped based on sets of common internally-consistent assumptions about changing demographics, economy, energy, and landuse change, and starting from these the IAMs may attempt to find pathways leading to any one of the RCPs, which relate closer to climate policy goals.   
Thus scenario data is provided, from each IAM, for a set of possible SSP-RCP combinations (not all are possible, nor were all modeled by all groups).  

As there is already much documentation online explaing the differences between SSPs, it will not (yet?) be repeated here.

#rcps		§The set of RCP scenarios was created to drive the Global Climate Models (GCMs) in IPCC AR5 and later reports. 

They are therefore designed, not to represent likely or desirable futures, but simply to span the full range of possible atmospheric concentration pathways, so that the climate models can explore the space, including nonlinear and extreme impacts, and also including (unlike @SRES) low peak+decline stabilisation policy pathways. 

Rather than being derived from bottom up socioeconomic models (as were @SRES), the RCPs were defined simply by levels of @radfor (related to 
@co2eq) in the centre of the cause-effect chain, in order to give a headstart to the (slow) climate modeling process.  
The names of the RCPs reflect the level of forcing (in W/m2). The lowest RCP is a peak+decline pathway. 
 
These results from complex 3D physical models (GCMs) may then be used, inter alia, to calibrate simpler models (such as this one) and other Integrated Assessment  Models (IAMs) used to explore related socioeconomic Mitigation and Adaptation challenges, in conjunction with the new @SSPs .
  
As there is already much documentation online explaining the concept and data of different RCPs, it will not (yet?) be repeated here.

#applyto		§The choices (SSPs, RCPs, gases, IAMs, regions) selected by the other parameters, are applied to one of four plots (1,2,3,4) selected here. This enables you to compare several scenarios / quantities at once.

#compare		§What quantity to plot

#iam_model		§Choose which Integrated Assessment Model data is used. 
%%Note - Not all models ran all combinations%%

#ssp		§Choose one of the @SSPs

#rcp		§Choose one of the @RCPs

#gas		§Choose to plot emissions of which gas

#sresbase		¨[oldkey=sres_base]		§A module managing the data and choices related to the IPCC @SRES scenarios. These scenarios are now rather outdated, and explicitly assumed no climate policy, however they provide a well known reference. 

In JCM, SRES-derived scenarios are used to project @popgdp, and as a baseline for @costs comparisons. They are also used in some scaling methods in @shares and @othgasemit.  

We cannot just use the raw data from SRES as there is a substantial gap between these old scenarios (based on data from around 1995) and current trends of emissions and socioeconomic data. So this module also extrapolates short-term national trends to provide a smoother transition, and downscales from the large regions of the original models.

#sspdata		¨[oldkey=ssp_data]		§A module which contains the SSP data, loaded just once on initialisation. There are no plots or parameters.