Understanding the systematic air temperature biases in a coupled climate system model through a process-based decomposition method
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A quantitative attribution analysis is performed on the systematic atmospheric temperature biases in a coupled climate system model (flexible global ocean–atmosphere–land system model, spectral version 2) in reference to the European Center for Medium-Range Weather Forecasts, Re-analysis Interim data during 1979–2005. By adopting the coupled surface–atmosphere climate feedback response analysis method, the model temperature biases are related to model biases in representing the radiative processes including water vapor, ozone, clouds and surface albedo, and the non-radiative processes including surface heat fluxes and other dynamic processes. The results show that the temperature biases due to biases in radiative and non-radiative processes tend to compensate one another. In general, the radiative biases tend to dominate in the summer hemisphere, whereas the non-radiative biases dominate in the winter hemisphere. The temperature biases associated with radiative processes due to biases in ozone and water vapor content are the main contributors to the total temperature bias in the tropical and summer stratosphere. The overestimated surface albedo in both polar regions always results in significant cold biases in the atmosphere above in the summer season. Apart from these radiative biases, the zonal-mean patterns of the temperature biases in both boreal winter and summer are largely determined by model biases in non-radiative processes. In particular, the stronger non-radiative process biases in the northern winter hemisphere are responsible for the relatively larger ‘cold pole’ bias in the northern winter polar stratosphere.
KeywordsModel air temperature bias Process-based decomposition CFRAM FGOALS-s2
This work was jointly supported by the National Natural Science Foundation of China (91437105), a Chinese Academy of Sciences project (XDA11010402) and the China Meteorological Administration Special Public Welfare Research Fund (GYHY201406001). M. Cai is supported in part by research grant from the National Science Foundation (AGS-1354834).
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