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Thematic Guide to Integrated Assessment Modeling
General Circulation Models (GCMs)
- Introduction-What is a GCM?
- Climate complexity and model simplifications
- Validation of GCMs
- How are GCMs different than IAMs?
- References
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.
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.
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.
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.
Sources
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.
CIESIN URL: http://sedac.ciesin.org/mva/iamcc.tg/TGHP.html
Acknowledgement
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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