The launch version of the Climate Collaboratorium uses a single climate model, C-LEARN.
C-LEARN is a Web-based version of C-ROADS, a simulation of Sustainability Institute and Ventana Systems that is part of the Climate Interactive effort. In the Collaboratorium, C-LEARN is the primary climate model. It takes emission and land use targets as inputs and provides outputs such as atmospheric concentrations
The model's inputs are:
- Targets for greenhouse gas emission reduction
- Targets for reductions in deforestation and increases in aforestation
Its outputs are:
- Atmospheric concentration of carbon dioxide (CO2)
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- Increase in global mean temperature (GMT)
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Overview
- above baseline year
This writeup about Name C-LEARN contains three primary sections Brief description
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- overview
High level description of the model; information about its developers and their institutional affiliation; the model's history and how it can be accessed; documentation and key publications.
- C-LEARN attributes
The model's geographic scope and resolution; its start date, end date, and time step; its data sources; its approach for dealing with uncertainty; and its overall structure.
- C-LEARN modules
- Regional CO2 emissions
- Other greenhouse gases
- Land use
- Carbon cycle
- Climate
- Sea level rise
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Model attributes
Model type For example, General circulation model (GCM) or Integrated assessment model (IAM) or other (followed by acronym)
Geographic scope Extent of geographic coverage
Geographic resolution Level of geographic disaggregation
Start date Earliest date when model runs can begin
End date Latest date when model runs can end
Time step Number of years between discrete
Approach for addressing risk/uncertainty Brief phrase describing how model deals with risk/uncertainty e.g. stochastic modeling approach, estimations of uncertainty based on variances in input values for discrete variables etc.
Key modules and linkages between them
Note: Include only if multiple modules, and if so, include next major header, “Variables and key assumptions,” for each
Model diagram Insert model diagram if one is available
Note: Format image file as: !imagefile.gif/jpg!
Variables and key assumptions
Input variables
Key assumptions Explain in words and include equations if available
Output variables
Note: As noted above, if there are multiple modules, include section like this for each, with module name at start of header, followed by a colon.
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