The Joint Global Change Research Institute (JGCRI) is the primary development institution for the Global Change Assessment Model (GCAM), a comprehensive integrated modeling tool for exploring energy, water, land, and climate interactions over timescales of decades to a century.
GCAM and its predecessor models have been in use for over 35 years. GCAM is now a freely available community model and documented online (See below). The team at JGCRI is comprised of economists, engineers, energy experts, forest ecologists, agricultural scientists, and Earth system scientists who develop the model and apply it to a range of science and policy questions and work closely with Earth system and ecosystem modelers.
GCAM is a global integrated model that represents the behavior of, and complex interactions between, five systems: energy , water, agriculture and land use, economy, and climate. GCAM has been under development for over 35 years. Work began in 1980 on the Edmonds-Reilly, and subsequently Edmonds-Reilly-Barnes (ERB), model (Edmonds and Reilly 1982, 1983a,b, 1985). The model was renamed MiniCAM in the mid-1990s and GCAM in the mid-2000s, around which time the model was also re-written in an object-oriented C++ framework (Kim et al. 2006). The first coupling to a carbon cycle model was published in Edmonds, et al. (1984). The first use of the model in conjunction with a Monte Carlo uncertainty analysis was published in Reilly et al. (1987).
Throughout its lifetime GCAM has evolved to address an expanding set of science and assessment questions. The original question to which the model was applied was the magnitude of mid-21st-century global emissions of fossil fuel CO2. Over time GCAM has expanded its scope to include a wider set of energy producing, transforming, and using technologies; emissions of non-CO2 greenhouse and air pollutant gases; agriculture and land use; water supplies and demands; and physical Earth systems. It is increasingly being used in multi-model, multi-scale analysis, in which it is coupled to other models with different foci and often greater resolution in key sectors. For example, it has been coupled to a state of the art Earth system model (Collins, et al., 2015). GCAM has been used to produce scenarios for national and international assessments ranging from the very first IPCC scenarios through the present Shared Socioeconomic Pathways (Ebi, et al., 2014, Calvin et al. 2017). Hundreds of papers have been published in peer-reviewed journals using GCAM and the model continues to be an important tool for scientific inquiry. GCAM is also a community model being used by researchers across the globe, creating a shared global research enterprise. GCAM can be run on Windows, Linux, Macintosh, and high-performance computing systems.
The role of integrated modeling tools such as GCAM is to bring multiple human and physical Earth systems together to shed light on system interactions and provide scientific insights that would not otherwise be available from the pursuit of traditional disciplinary scientific research alone. Such integrated assessment models (IAMs) are constructed to explore these interactions in a single computational platform with a sufficiently low computational requirement to allow for broad explorations of scenarios and uncertainties. IA models include both human and physical Earth systems. Components of an IA model are designed to capture the behavior of human and physical systems, but they do not necessarily include the most detailed process-scale representations of its constituent components. On the other hand, IA model components aim to provide a faithful representation of the best current scientific understanding of underlying behavior.
Models such as GCAM do not provide predictions of the future. They are used to provide conditional “forecasts” of the future. GCAM takes in external scenario assumptions for key drivers such as population, economic activity, technology costs, and policies, and produces a model scenario that illustrates the implications of the scenarios assumptions, for example, on commodity prices, energy use, land use, water use, emissions, and concentrations.
Conditional forecasts with a small set of scenario assumptions is the most common way that a model such as GCAM is used to explore scientific and assessment questions. However, another class of question that GCAM has taken up is the systematic representation of uncertainty. As early as the 1980s, GCAM was used to map the implications of uncertain key input assumptions and parameters into implied distributions of outputs such as greenhouse gas emissions, energy use, energy prices, and trade patterns. A range of techniques has been employed using GCAM to explore the potential range of future outcomes including structured scenario analysis, sensitivity analysis, and Monte Carlo simulations. Exploring and understanding the role of uncertainty in shaping events remains an important research use of GCAM.
Note that IAMs can generally be divided into two categories: high-resolution and highly aggregated. GCAM is a higher resolution model which, by including economic and Earth system components, focuses on understanding physical and economic details of human and Earth system interactions. A second type of IAM is more highly aggregated and focused on the conduct of global, century-scale, cost-benefit analysis (examples include DICE, FUND, and PAGE). GCAM does not contain the components necessary to conduct cost-benefit analysis.
Calvin, K., et al. (2017). “The SSP4: A world of deepening inequality.” Global Environmental Change-Human and Policy Dimensions 42: 284-296.
Collins WD, AP Craig, JE Truesdale, A Di Vittorio, AD Jones, B Bond-Lamberty, KV Calvin, JA Edmonds, SH Kim, AM Thomson, PL Patel, Y Zhou, J Mao, X Shi, PE Thornton, LM Chini, and GC Hurtt. 2015. “The integrated Earth System Model Version 1: formulation and functionality.” Biogeosciences 8:2203-2219. doi:10.5194/gmd-8-2203-2015
Ebi, K., et al. (2014). “A new scenario framework for climate change research: background, process, and future directions.” Climatic Change 122(3): 363-372.
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