A global perspective on CMIP5 climate model biases
by Chunzai Wang, Liping Zhang, Sang-Ki Lee, Lixin Wu, and Carlos R. Mechoso
A recent paper published in Nature Climate Change by PhOD researchers, in collaboration with researchers at the Ocean University of China and at the University of California, found a common pattern of global SST biases in 22 climate models. The global SST biases for different regions are commonly linked with a weak AMOC simulated by these models. The paper suggests that an improvement of the simulated AMOC in climate models is needed for better climate predictions and projections.
The Intergovernmental Panel on Climate Change’s Fifth Assessment Report largely depends on simulations, predictions and projections by climate models . 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 system.
The study by Wang et al. (2014) shows that biases or errors in special regions can be linked with others at far away locations. It is found 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.
Global SST bias and its relationship with the AMOC. a, The annual-mean SST bias averaged in 22 climate models. The SST bias is calculated by the SST difference between the model SST and extended reconstructed SST. The dots denote where at least 18 of 22 models (82%) have the same sign in the SST bias. The rectangles represent the focused regions. b,c, Spatial maps of SST bias and the AMOC for the first inter-model SVD mode (accounting for 45% of total covariance). d, Their corresponding coeffcients. The x axis in d represents different models. The coeffcients have been normalized by their own standard deviations.
Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C. R. Mechoso, 2014: A global perspective on CMIP5 climate model biases. Nature Clim.Change, 4, 201-205. [PDF]