Climate models have undergone a transition from physical to mathematical. Early studies of atmospheric and oceanic circulation patterns examined simple physical models. Sometimes, a container filled with fluid was placed on a hotplate that represented the warming of the tropics whereas cooler areas of the container represented the poles. In more elaborate physical models, a heated tank was mounted on a turntable to mimic Earth’s rotation, which is related to the Coriolis force. These efforts became known as general circulation models (GCMs).

Physical GCMs, however, were limited in their abilities to account for the intricacies of atmospheric convection or ocean currents, and climatologists soon turned to mathematical GCMs. Contemporary mathematical models depict Earth’s climate in its entirety, and the acronym GCM also now stands for global climate model.

Digital computers, which were developed near the end of World War II, are well suited to the repetitive nature of the calculations in GCMs. The first large-scale digital computer, named ENIAC for electronic numerical integrator and computer, was used to calculate trajectories of artillery shells and parameters for the first hydrogen bomb before it was applied to climate models.

As digital computers became more sophisticated, GCMS also improved. GCMs divide the world into boxes having a certain breadth and width (grid size) and a certain height determined by the number of vertical levels. They also treat the passage of time in a stepwise fashion whereby they calculate conditions only at discrete moments separated by a certain time interval (time step). Generally, the accuracy of such models improves with a smaller grid size, more vertical levels, or a shorter time step, but these require additional computation time.

Contemporary GCMs continue to balance the finite nature of computer resources against the need for simulating climate over long periods with a grid size that contains adequate spatial detail and a time step that provides acceptably smooth transitions. One approach is to run different parts of a model simultaneously on separate computer systems linked by high-speed networks. Another is to feed the results from a coarse-scale model that covers a large area such as a North America to a fine-scale model that covers with a smaller grid and shorter time step a small region such as California. [1] Such combined models expend only about 6 seconds of computer time per simulated day [2] and can thus calculate long-term climate reconstructions for a particular region in a more reasonable amount of time.

This is an excerpt from the book Global Climate Change: Convergence of Disciplines by Dr. Arnold J. Bloom and taken from UCVerse of the University of California.

©2010 Sinauer Associates and UC Regents