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.
- Cess RD, Potter GL, Blanchet JP, Boer GJ, Delgenio AD, Deque M, Dymnikov V, Galin V, Gates WL, Ghan SJ, Kiehl JT, Lacis AA, Letreut H, Li ZX, Liang XZ, Mcavaney BJ, Meleshko VP, Mitchell JFB, Morcrette JJ, Randall DA, Rikus L, Roeckner E, Royer JF, Schlese U, Sheinin DA, Slingo A, Sokolov AP, Taylor KE, Washington WM, Wetherald RT, Yagai I, Zhang MH (1990) Intercomparison and interpretation of climate feedback processes in 19 atmospheric general-circulation models. J Geophys Res Atmos 95(D10):16601–16615. doi:10.1029/JD095ID10P16601 CrossRefGoogle Scholar
- Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thepaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J Roy Meteor Soc 137(656):553–597. doi:10.1002/QJ.828 CrossRefGoogle Scholar
- Deng Y, Park T-W, Cai M (2013) Radiative and dynamical forcing of the surface and atmospheric temperature anomalies associated with the Northern annular mode. J Clim 26. doi:10.1175/JCLI-D-12-00431.1
- Kay JE, Hillman BR, Klein SA, Zhang Y, Medeiros B, Pincus R, Gettelman A, Eaton B, Boyle J, Marchand R, Ackerman TP (2012) Exposing global cloud biases in the community atmosphere model (CAM) using satellite observations and their corresponding instrument simulators. J Clim 25(15):5190–5207. doi:10.1175/JCLI-D-11-00469.1 CrossRefGoogle Scholar
- Pincus R, Barker HW, Morcrette JJ (2003) A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J Geophys Res Atmos 108(D13). doi:10.1029/2002JD003322
- Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Cilmate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) 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, pp 589–662Google Scholar
- Solomon S, Qin D, Manning M, Marquis M, Averyt K, Tignor MMB, Miller HL Jr, Chen Z (2007) Climate change 2007: the physical science basis. Cambridge University Press, CambridgeGoogle Scholar
- Trenberth KE, Jones PD, Ambenje P, Bojariu R, Easterling D, Klein Tank A, Parker D, Rahimzadeh F, Renwick JA, Rusticucci M, Soden B, Zhai P (2007) Observations: surface and atmospheric climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (ed) 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, pp 235–336Google Scholar
- Zhang MH, Lin WY, Klein SA, Bacmeister JT, Bony S, Cederwall RT, Del Genio AD, Hack JJ, Loeb NG, Lohmann U, Minnis P, Musat I, Pincus R, Stier P, Suarez MJ, Webb MJ, Wu JB, Xie SC, Yao MS, Zhang JH (2005) Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J Geophys Res Atmos 110(D15):D15S02. doi:10.1029/2004JD005021 Google Scholar