Chapter Six - Climate Models

Summary

“Models try to squeeze the blooming, buzzing, confusion into a miniature Joseph Cornell box, and then, if it more or less fits, assume that the box is the world itself.”

—South African writer and financial engineer Emanuel Derman

A wide range of models are used in climate science to study the climate system and its behavior across multiple temporal and spatial scales. Climate models range from simple energy balance models to exceedingly complex Earth system models. Global climate models (GCMs) create a coarse-grained simulation of the Earth's climate system using computers. GCM development has followed a pathway mostly driven by scientific curiosity, although efforts have been constrained by computational limitations. GCMs were originally designed as a tool to help understand how the climate system works, integrating community knowledge so that the implications of collective knowledge can be explored. GCMs are used by researchers to represent aspects of climate that are difficult to observe, experiment with theories by enabling otherwise infeasible calculations, understand a complex system of equations that would otherwise be impenetrable, and explore the climate system to identify unexpected outcomes. As such, GCMs have emerged as an important tool in climate research.

While GCMs continue to be used by scientists to increase understanding about how the climate system works, their outputs play a central role in developing international, national, and local policies. In the context of the IPCC and the UNFCCC, climate models are used to assess the causes of recent climate change, predict future climate states, provide guidance for emissions reduction policies, support local adaptation policies, and to provide inputs for Integrated Assessment Models to assess the social cost of carbon.

There is considerable uncertainty and disagreement about the extent to which climate model simulations provide accurate information about the world. Scientists lack the data they need to comprehensively test a model's performance, and scientists disagree as to which metrics should be used for climate model evaluation. Conclusions about model reliability are made more difficult insofar as the climate system is nonlinear, complex, and not well understood theoretically.

Initially, climate research programs aimed at the reduction of the uncertainties in climate models. However, since the mid-1990s, it has been increasingly recognized that more research does not necessarily reduce the overall uncertainties regarding future climate change.