A global perspective on CMIP5 climate model biases

Abstract

The use of models for the prediction of future climate conditions is commonplace. This study investigates regional sea surface temperature biases across 22 climate models and finds that they are linked to the large circulation system in the Atlantic Ocean. Improvements to climate models will need to consider the impact of remote biases on regional processes. The Intergovernmental Panel on Climate Change’s Fifth Assessment Report largely depends on simulations, predictions and projections by climate models1. Most models, however, have deficiencies and biases that raise large uncertainties in their products. Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of special regions and aspects of the climate system2,3,4. Here we show that biases or errors in special regions can be linked with others at far away locations. We find in 22 climate models that regional sea surface temperature (SST) biases are commonly linked with the Atlantic meridional overturning circulation (AMOC), which is characterized by the northward flow in the upper ocean and returning southward flow in the deep ocean. A simulated weak AMOC is associated with cold biases in the entire Northern Hemisphere with an atmospheric pattern that resembles the Northern Hemisphere annular mode. The AMOC weakening is also associated with a strengthening of Antarctic Bottom Water formation and warm SST biases in the Southern Ocean. It is also shown that cold biases in the tropical North Atlantic and West African/Indian monsoon regions during the warm season in the Northern Hemisphere have interhemispheric links with warm SST biases in the tropical southeastern Pacific and Atlantic, respectively. The results suggest that improving the simulation of regional processes may not suffice for overall better model performance, as the effects of remote biases may override them.

Cite this article

Wang, C., Zhang, L., Lee, SK. et al. A global perspective on CMIP5 climate model biases. Nature Clim Change 4, 201–205 (2014). https://doi.org/10.1038/nclimate2118

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