CAESAR

General Circulation Models (GCMs)

The big picture understanding of climate change science starts with the building block of that science, The General Circulation Model (GMC). It is a statistical model to explain, describe and predict global weather patterns under various conditions, including increased greenhouse gasses in the atmosphere. When building a General Circulation Model (GCM) climate scientists assume climate to be the product of a variety of inter-related factors.

Starting in the 1960s scientists began producing a general 'ABC' model of climate and weather. They gathered time series data on atmospheric, biosphere and cryospheric conditions around the world as a way of building the first climate models.

This basic 'ABC' General Circulation Model for weather forecasting runs along the following lines:

Global Climate = (A) Atmosphere + (B) Biosphere + (C) Cryosphere + (G) Geosphere + (O) Oceans).

Global weather patterns are a function of atmospheric, biosphere, cryospheric, geospheric and oceanic factors. The way this example model is written includes geospheric and oceanic factors (G + O), that were added to GCMs after the initial atmospheric weather based models were up and running. The simplicity of the model is found in its common sense design. For example, anyone who would want to know the cause of the past two summers of hurricane activity in the south eastern United States would want an explanation that covered all of those factors.

Climate modeling begins with these basic assumptions. However, the meat and potatoes of the work involves countless hours of theory, concept building and data gathering in order to flesh out the general factors addressed in the equation. In trying to explain the degree of sophistication of the newer models, consider how the Community Climate System Model of the National Atmospheric Research Center explains its model. "To recreate a single day of the world's climate, the model must perform 700 billion calculations."

Over the past four decades General Circulation models have gone through two updates. The first GCMs mentioned above closely resembled their weather cousins, using data similar to what weather forecasts were using around the world. Time and budgetary constraints slowed progress in GCM research. For example the early GCMs lacked any ocean concepts and data. As weather data became more reliable and available, especially from satellites, scientists were able to refine their initial models. These refinements spurred the second wave of climate research, moving the GCMs to what is known as coupled models design. These models furthered the science of global weather forecasting by coupling models about ocean activity such as currents and water temperature with models about atmospheric, biosphere, cryospheric and geospheric activity.

Models from the Canadian Climate Centre illustrate the general findings of the first round of ocean coupling models around the year 2000. First they show patterns of increased surface air temperatures at the Polar Regions. Two separate versions of the general model predictions diverged with respect to temperature increases at the lower latitudes. The first version of their model predicted asymmetrical north/south warming patterns with the Asian continent and Saharan Africa experiencing, on average, higher temperatures. The second version of their model predicts more of a symmetrical north/south warming pattern.

By April of 2005, the Canadian Climate Center was demonstrating its third version of a Coupled Global Climate Model, which went back and improved the design and data for atmospheric variables. Results from their second version are online in very well done animated maps showing changes in surface air temperature, precipitation and soil moisture through the year 2099. One generalized result they highlight is "that surface air temperature changes accelerate with time and that there is more warming over land and polar regions than over the oceans."