A dissection of the surface temperature biases in the Community Earth System Model
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Based upon the climate feedback-responses analysis method, a quantitative attribution analysis is conducted for the annual-mean surface temperature biases in the Community Earth System Model version 1 (CESM1). Surface temperature biases are decomposed into partial temperature biases associated with model biases in albedo, water vapor, cloud, sensible/latent heat flux, surface dynamics, and atmospheric dynamics. A globally-averaged cold bias of −1.22 K in CESM1 is largely attributable to albedo bias that accounts for approximately −0.80 K. Over land, albedo bias contributes −1.20 K to the averaged cold bias of −1.45 K. The cold bias over ocean, on the other hand, results from multiple factors including albedo, cloud, oceanic dynamics, and atmospheric dynamics. Bias in the model representation of oceanic dynamics is the primary cause of cold (warm) biases in the Northern (Southern) Hemisphere oceans while surface latent heat flux over oceans always acts to compensate for the overall temperature biases. Albedo bias resulted from the model’s simulation of snow cover and sea ice is the main contributor to temperature biases over high-latitude lands and the Arctic and Antarctic region. Longwave effect of water vapor is responsible for an overall warm (cold) bias in the subtropics (tropics) due to an overestimate (underestimate) of specific humidity in the region. Cloud forcing of temperature biases exhibits large regional variations and the model bias in the simulated ocean mixed layer depth is a key contributor to the partial sea surface temperature biases associated with oceanic dynamics. On a global scale, biases in the model representation of radiative processes account more for surface temperature biases compared to non-radiative, dynamical processes.
KeywordsCESM1 surface temperature bias Temperature decomposition Radiative and nonradiative processes Climate feedback-responses analysis method
The ERA-Interim data used in this study were provided by the European Centre for Medium-Range Weather forecast (ECMFW). The Georgia Tech authors (Deng and Park) and FSU author (Cai) were supported by DOE Office of Science Regional and Global Climate Modeling (RGCM) program DE-SC0005596 and Grant DE-SC0004974, respectively. Deng is also supported by NSF grant AGS 114760. Jee-Hoon Jeong is supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2013-3066.
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