Polar and Topographic Amplifications of Intermodel Spread of Surface Temperature in Climate Models

Abstract

There are two complementary ways to understand the model bias in climate simulations. One is via the observation-model difference, which is widely used in model evaluation as well as in model tuning during the development phase of the models (e.g., Flato et al., 2013). The other is based on the dispersion among different model results, which is also called intermodel spread or model spread. The small intermodel spread usually implies small model bias, but not necessarily so because of the existence of structural bias in the models. While the structural bias must come from incomplete knowledge in correctly representing relevant processes in climate models, the origin of intermodel spread is more complicated. Indeed, except that the diversity in representing/ parameterizing the model processes may cause the intermodel spread, the intrinsically chaotic nature of coupled climate system may also lead to the intermodel spread. Therefore, the intermodel spread should not simply be