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Thematic Guide to Integrated Assessment Modeling

General Circulation Models (GCMs)

  1. Introduction-What is a GCM?
  2. Climate complexity and model simplifications
  3. Validation of GCMs
  4. How are GCMs different than IAMs?
  5. References

Introduction-What is a GCM?

General Circulation Models (GCMs) are mathematical models that attempt to simulate the Earth's climate system. These models lie at the upper end of the climate model hierarchy which range in complexity from simple zero-dimensional atmospheric models that calculate a single globally-averaged quantity such as temperature, to fully-coupled three-dimensional atmosphere-ocean-land cover models that can predict changes over time of many variables such as temperature, humidity, winds, sea ice cover, and soil moisture. The mathematical expressions that comprise a GCM can be loosely broken down into three separate, but coupled categories: (1) the dynamics of the climate system that describe the large-scale movement of air masses and transport of energy and momentum; (2) the physics of the climate system such as radiation transmission through the atmosphere, thermodynamics, and evaporation; and (3) other factors such as air-sea interaction, topography, and vegetation parameters. These expressions are based on known physical laws such as the conservation of energy and mass, as well as empirical relations based on observed characteristics and trends, such as formulas that relate temperature and humidity to cloud formation.

Climate complexity and model simplifications

The natural climate system is composed of a tremendous number of variables that are constantly changing and interacting with one another. This maze of variables and interrelationships among variables results in a system that is difficult, at best, to simulate with even the most sophisticated computer models. The challenge in modeling the climate system arise in the areas of mathematical complexity of the governing equations, the temporal and spatial scales at which climatic variables are acting (and interacting), and in the perceived importance of particular variables and climatic processes. The complexity of the mathematical equations is often reduced by making assumptions about the climate system that allow certain components of the equations to be simplified or neglected altogether. The wide range of spatial and temporal scales at which climatic variables and processes are important require additional simplifications in order to devise a GCM that can execute in a reasonable amount of time. A typical model has a resolution of 4.5 degrees latitude by 7.5 degrees longitude, resulting in a grid cell size of 500x640 km at 40 degrees N latitude. Even the most finely spaced models have resolutions on the order of 200 km. Such a large horizontal scale causes uncertainty in the model results when considering that geophysical features such as clouds, topography, and land cover change on a much smaller scale. Similar uncertainties arise in the vertical structure of the atmosphere and oceans; the most complex models include up to 20 layers, extending to a height of about 30-40 km above the surface. As an example of the computational intensity of an atmospheric GCM alone (i.e. one that is not coupled to an ocean or land surface model), a model having a 4.5x7.5 degree latitude/longitude resolution with 9 vertical layers running a year-long climate simulation at 30 minute time increments requires approximately 10 hours to execute on a Cray X-MP supercomputer.

Validation of GCMs

With the inherent limitations of climate models and GCMs noted above, one may ponder how realistic the model results are, and if they can be validated to a reasonable degree where the results can be employed in the social and economic decision-making arena. There are three primary mechanisms to validating model components and their results. The first is to see if the model can reproduce current climate conditions on Earth. Using observations of various meteorological parameters and atmospheric characteristics as comparisons, the models can be run to see if they can reproduce large-scale climate features such as seasonal changes in temperature, precipitation, and atmospheric wind patterns. A second method is to isolate certain pieces of the model, such as the soil moisture or cloud model component, and run sensitivity studies to see how well they correspond to actual observations and measurements. An important point to note is that empirical parameterizations that do not account for the underlying physical processes may reproduce observed conditions of certain climatic variables, but may not be good models when considering feedbacks and complex interrelationships between one or more variables. A third method of model validation involves testing the model against long-term paleoclimate records of Earth, and even with observed conditions of the nearby terrestrial planets, Mars and Venus.

Most climate models have predicted that the globally-averaged temperature of the Earth should have experienced a 0.5-1.0 degree Celsius warming over the past century based on trends in atmospheric carbon dioxide concentrations and other radiatively important constituents. The fact that the mean temperature has risen less than this amount (~0.3-0.6 degrees Celsius) imply uncertainty in our knowledge of the climate system; some potential sources of uncertainty include feedbacks among changing variables, regional impacts on factors that influence temperature, and uncertainty as to the heat capacity of ocean waters.

How are GCMs different than IAMs?

Numerous examples exist that indicate humans can have a significant impact upon their environment, but how do these environmental concerns impact society? To address the issue of greenhouse gas emissions and its potential impact on society and world economics, interdisciplinary teams of researchers have combined efforts to develop integrated assessment models (IAMs) that address not only the physical science issues involved in climate change, but also the forecasted "human dimension" impacts of climate change on populations and economies. These models generally include both physical and social science models that consider population, industry, political, and economic variables that affect greenhouse gas emission scenarios in addition to the physical climate system. GCMs, however, focus on the physical climate system alone as described in the previous sections. Many IAMs do include some form of climate modeling scheme in their routines, such as zero-dimensional or 2-dimensional energy balance models, but due to computing time limitations it is currently infeasible to integrate a full 3-dimensional GCM with a human dimensions model to create an IAM. Until computers become fast enough to significantly reduce computation times, IAMs will not be able to configure a full GCM into its model structure, and must rely on simpler forms of climate models to forecast changes in climate based on future scenarios of greenhouse gas emissions and other significant variables.


Committee on Environment and Natural Resources Research. 1995. Our Changing Planet: The FY 1996 U.S. Global Change Research Program. A report by the Subcommittee on Global Change Research, Committee on Environment and Natural Resources Research of the National Science and Technology Council. Washington, D.C.

Schneider, S. H. 1993. Introduction to climate modeling. Chapter 1 in Climate System Modeling, K. E. Trenberth, ed. Cambridge: Cambridge University Press.



Parson, E.A. and K. Fisher-Vanden, Searching for Integrated Assessment: A Preliminary Investigation of Methods, Models, and Projects in the Integrated Assessment of Global Climatic Change. Consortium for International Earth Science Information Network (CIESIN). University Center, Mich. 1995.

Suggested Citation

Consortium for International Earth Science Information Network (CIESIN). 1995. Thematic Guide to Integrated Assessment Modeling of Climate Change [online]. University Center, Mich.


This work, including access to the data and technical assistance, is provided by CIESIN, with funding from the National Aeronautics and Space Administration under Contract NAS5-32632 for the Development and Operation of the Socioeconomic Data and Applications Center (SEDAC).

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