C-ROADS Technical Reference


Purpose and Intended Use🔗

C-ROADS stands for Climate-Rapid Overview and Decision Support. It is a rigorous – but rapid and user-friendly – computer simulation of the climate system and its impacts including temperature and sea level rise. C-ROADS is designed to improve understanding of the long-term implications of greenhouse gas emissions and land use decisions.

The climate is a dynamically complex system characterized by feedbacks, time delays, and nonlinear cause-and-effect relationships. Research shows that people misunderstand climate dynamics (Brehmer, 1989; Sterman, 2008); that it is difficult to make decisions in such complex systems (Brehmer, 1989; Kleinmuntz and Thomas,1987; Sterman, 1989); and that computer simulations can help improve decision-making (Morecroft and Sterman, Eds., 1994; Sterman, 2000). Our conversations with stakeholders, such as negotiators tasked with reaching global climate agreements or leaders working to influence those agreements, suggest that even within very high-level policy-making discussions, the ability to understand the aggregate effects of national, regional, or sectoral mitigation commitments on atmospheric CO2 level and temperature is limited by the scarcity of simple, real-time decision-support tools. The C-ROADS simulator is a tool intended to close this gap.

Thus, the purpose of C-ROADS is to improve public and decision-maker understanding of the long-term implications of international emissions and sequestration futures with a rapid-iteration, interactive tool as a path to effective action that stabilizes the climate. We created C-ROADS to provide a transparent, accessible, real-time decision-support tool that encapsulates the insights of more complex models. The C-ROADS simulator allows for fast-turnaround, hands-on use by decision-makers. It emphasizes:

  • Transparency: equations are available, easily auditable, and presented graphically.
  • Understanding: model behavior can be traced through the chain of causality to origins; we don’t say “because the model says so.”
  • Flexibility: the model supports a wide variety of user-specified scenarios at varying levels of complexity.
  • Consistency: the simulator is consistent with historic data, the structure and insights from larger models, and the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5).
  • Accessibility: the model runs with a user-friendly graphical interface on a laptop computer, in real time.
  • Robustness: the model captures uncertainty around the climate outcomes associated with emissions decisions.

C-ROADS is not a substitute for larger integrated assessment models (IAMs) or detailed climate models, such as General Circulation Models (GCMs). Those complex and disaggregated models offer spatial resolution and more details on climate impacts and economic considerations, at a cost of run time, computer power, and opacity. C-ROADS captures some of the key insights from more complex models and makes them available for rapid policy experimentation. Simple models such as C-ROADS complement more disaggregated models, allowing users to gain rapid insights. In turn, larger disaggregated models generate the insights and data used to calibrate and improve the performance of simple models.

C-ROADS is designed to be used interactively with groups as a basis for scientifically rigorous conversations about addressing climate change. It is not intended as a tool for prediction or projections. It is suitable for decision-makers in government, business, and civil society; or for anyone who is curious about the choices of our world.


The C-ROADS simulator was constructed according to the principles of System Dynamics (SD), which is a methodology for the creation of simulation models that help people improve their understanding of complex situations and how they evolve over time. The method was developed by Jay Forrester at the Massachusetts Institute of Technology in the 1950’s and described in his book Industrial Dynamics (Forrester, 1961). SD was the methodology used to create the World3 simulation model that provided the basis for the book The Limits To Growth (Meadows et al., 1972). System dynamics has been described more recently by John Sterman in Business Dynamics (Sterman, 2000).

System dynamics computer simulations, including the C-ROADS simulator, consist of linked sets of differential equations that describe a dynamic system in terms of accumulations (stocks) and changes to those stocks (inflows and outflows). Feedback, delays, and non-linear responses are all included in the simulation. System dynamics simulations help users understand the observed behavior of systems and anticipate future behavior under a variety of scenarios. The C-ROADS simulator is the product of many years of effort, beginning as the graduate research of Tom Fiddaman (Fiddaman, 1997), under the direction of John Sterman, and continued by Tom Fiddaman at Ventana Systems and Lori Siegel, Andrew Jones, and Elizabeth Sawin for Climate Interactive. The simulation model is based on the biogeophysical and integrated assessment literature and includes representations of the carbon cycle, other GHGs, radiative forcing, global mean surface temperature, and sea level change. Consistent with the principles articulated by, e.g., Socolow and Lam, 2007, the simulation is grounded in the established literature yet remains simple enough to run quickly on a laptop computer. Fossil fuel carbon dioxide emissions scenarios for individual nations or groups of nations are aggregated into total fossil fuel CO2 emissions. These combine with additional uptake and/or release of CO2 from land use decisions to form the input to the carbon cycle sector of the model. CO2 concentrations thus determined combine with the influence on net radiative forcing of other well-mixed GHGs (CH4, N2O, PFCs, SF6, and HFCs) via their explicit cycles, to determine the global temperature change, which in turn determines sea level rise.

The model uses country-level historical data through the most recent available data. These data include:

  • CO2 emissions from fossil fuels (FF), 1850-2021: Global Carbon Budget – Friedlingstein et al., 2022;
  • land areas, 1850-2015: Land Use Harmonization (LUH2) data prepared for the Climate Research Program Coupled Model Intercomparison Project (CMIP6);
  • CO2 emissions from changes in land use, 1850-2015: Houghton and Nassikas, 2017;
  • population - World Population Prospects 2022 (UN, 2022);
  • GDP, 1960-2021 (World Bank, 2022);
  • Other well-mixed GHGs, 1850-2018: PRIMAP 2.2-hist (Gütschow et al, 2021), extend through 2018. PRIMAP2.2-hist only provides the total HFC for each country so the allocation of each HFC type is determined from data provided by the European Commission Joint Research Centre (JRC)/PBL Netherlands Environmental Assessment Agency. Baseline CO2 and other well-mixed gas emissions, population, and GDP default projections are all calibrated to be consistent with the IPCC’s SSP2 Baseline scenario in terms of rates yet accounting for divergences in recent years’ data from the IPCC projections. Users may change the assumptions driving GDP.

The core carbon cycle and climate sector of the model is based on Dr. Tom Fiddaman’s MIT dissertation (Fiddaman, 1997). The model structure draws heavily from Goudriaan and Ketner (1984) and Oeschger and Siegenthaler et al. (1975). The sea level rise sector is based on Rahmstorf, 2007. Temperature feedbacks to the carbon cycle are included, as are the temperature feedbacks to the economy. Model users determine the path of net GHG emissions (CO2 from FF and land use, CH4, N2O, PFCs, SF6, HFCs, and CO2 sequestration from afforestation) at the country or regional level, through 2100. The model calculates the path of atmospheric CO2 and other GHG concentrations, global mean surface temperature, sea level rise, and ocean pH changes resulting from these emissions. The user can choose the level of regional aggregation. Users may choose to provide emissions inputs for one, three, or six different blocs of countries, depending on the purpose of the session. Outputs may be viewed for any of these aggregation levels. Other key variables, such as per capita emissions, and carbon intensity of the economy (e.g., tonnes C per dollar of real GDP), and cumulative emissions, are also displayed. Users can specify the year to stop increasing emissions, the year to start decreasing emissions, and the rate of emissions reductions.

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