Attributing analysis on the model bias in surface temperature in the climate system model FGOALS-s2 through a process-based decomposition method
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This study uses the coupled atmosphere-surface climate feedback-response analysis method (CFRAM) to analyze the surface temperature biases in the Flexible Global Ocean-Atmosphere-Land System model, spectral version 2 (FGOALS-s2) in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics, which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.
Key wordsattribution model bias surface temperature FGOALS-s2 CFRAM
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- Kimoto, M., 2005: Simulated change of the East Asian circulation under global warming scenario. Geophys. Res. Lett., 32, doi: 10.1029/2005GL023383.Google Scholar
- Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM), NCAR Tech. Note TN-461+STR, 174 pp.Google Scholar
- Park, T. W., Y. Deng, M. Cai, J. H. Jeong, and R. Zhou, 2013: A dissection of the surface temperature biases in the Community Earth System Model. Climate Dyn., doi: 10.1007/s00382-013-2029-9.Google Scholar
- Randall, D. A., and Coauthors, 2007: Climate Models and Their Evaluation. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.Google Scholar
- Solomon, S., and Coauthors, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.Google Scholar
- Sun, H. C., G. Q. Zhou, and Q. C. Zeng, 2012: Assessments of the climate system model (CAS-ESM-C) using IAP AGCM4 as its atmospheric component. Chinese J. Atmos. Sci., 36, 215–233. (in Chinese)Google Scholar
- Zhou, T. J., and Coauthors, 2005: The climate system model FGOALS-s using LASG/IAP spectral AGCM SAMIL as its atmospheric component. Acta Meteorologica Sinica, 63, 702–715.Google Scholar