IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis

8.3 Evaluation of Contemporary Climate as Simulated by Coupled Global Models

Due to nonlinearities in the processes governing climate, the climate system response to perturbations depends to some extent on its basic state (Spelman and Manabe, 1984). Consequently, for models to predict future climatic conditions reliably, they must simulate the current climatic state with some as yet unknown degree of fidelity. Poor model skill in simulating present climate could indicate that certain physical or dynamical processes have been misrepresented. The better a model simulates the complex spatial patterns and seasonal and diurnal cycles of present climate, the more confidence there is that all the important processes have been adequately represented. Thus, when new models are constructed, considerable effort is devoted to evaluating their ability to simulate today’s climate (e.g., Collins et al., 2006; Delworth et al., 2006).

Some of the assessment of model performance presented here is based on the 20th-century simulations that constitute a part of the MMD archived at PCMDI. In these simulations, modelling groups initiated the models from pre-industrial (circa 1860) ‘control’ simulations and then imposed the natural and anthropogenic forcing thought to be important for simulating the climate of the last 140 years or so. The 23 models considered here (see Table 8.1) are those relied on in Chapters 9 and 10 to investigate historical and future climate changes. Some figures in this section are based on results from a subset of the models because the data set is incomplete.

In order to identify errors that are systematic across models, the mean of fields available in the MMD, referred to here as the ‘multi-model mean field’, will often be shown. The multi-model mean field results are augmented by results from individual models available as Supplementary Material (see Figures S8.1 to S8.15). The multi-model averaging serves to filter out biases of individual models and only retains errors that are generally pervasive. There is some evidence that the multi-model mean field is often in better agreement with observations than any of the fields simulated by the individual models (see Section, which supports continued reliance on a diversity of modelling approaches in projecting future climate change and provides some further interest in evaluating the multi-model mean results.

Faced with the rich variety of climate characteristics that could potentially be evaluated here, this section focuses on those elements that can critically affect societies and natural ecosystems and that are most likely to respond to changes in radiative forcing.