A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the northern hemisphere

Abstract

Abstract. Global Climate Models are a keystone of modern climate research. In many applications relevant for decision making, and particularly when deriving future projections with the delta-change method, they are assumed to be perfect. However, these models have not been originally developed to reproduce the regional-scale climate, which is where information is needed in practice. To overcome this dilemma, two general efforts have been made since their introduction in the late 1960ies. First, the models themselves have been steadily improved in terms of physical and chemical processes, parametrization schemes, resolution and complexity, giving rise to the term Earth System Model. Second, the global models' output has been refined at the regional scale using Limited Area Models or statistical methods in what is known as dynamical or statistical downscaling. Both approaches, however, are in principle unable to correct errors resulting from a wrong representation of the large-scale circulation in the global model. Also, dynamical downscaling has a high computational demand and thus cannot be applied to all available global models in practice. On this background, there is an ongoing debate in the downscaling community on whether to thrive away from the model democracy paradigm towards a careful selection strategy based on the global models' capacity to reproduce key aspects of the observed climate. The present study attempts to be useful for such a selection by providing a performance assessment of the historical global model experiments from CMIP5 and 6 based on recurring regional atmospheric circulation patterns (Lamb, 1972). The latest model generation is found to perform better on average, which can be partly explained by a moderately strong statistical relationship between performance and horizontal resolution in the atmosphere. A few models rank favourably over almost the entire northern hemisphere extratropics, but the better models tend to be less complex than others. Model selection should therefore not solely rely on model performance but also on model complexity and a discussion is needed on how to combine these two criteria. Internal model variability only has a small influence on the model ranks. Reanalysis uncertainty is an issue in Greenland and the surrounding seas, the southwestern United States and the Gobi desert, but is otherwise negligible.