Abstract
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.
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Bao, Q., G. X. Wu, Y. M. Liu, J. Yang, Z. Z. Wang, and T. J. Zhou, 2010: An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci., 27, 1131–1142, doi: 10.1007/s00376-010-9177-1.
Bao, Q., and Coauthors, 2013: The flexible global ocean-atmosphere-land system model, spectral version 2: FGOALSs2. Adv. Atmos. Sci., 30, 561–576, doi: 10.1007/s00376-012-2113-9.
Cai, M., and J. H. Lu, 2009: A new framework for isolating individual feedback processes in coupled general circulation climate models. Part II: Method demonstrations and comparisons. Climate Dyn., 32, 887–900.
Cai, M., and K. K. Tung, 2012: Robustness of dynamical feedbacks from radiative forcing: 2% solar versus 2×CO2 experiments in an idealized GCM. J. Atmos. Sci., 69, 2256–2271.
Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16 601–16 615.
Chapman, W. L., and J. E. Walsh, 2007: Simulations of Arctic temperature and pressure by global coupled models. J. Climate, 20, 609–632.
Collins, W. D., and Coauthors, 2006: The community climate system model version 3 (CCSM3). J. Climate, 19, 2122–2143.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.
Deng, Y., T. W. Park, and M. Cai, 2013: Radiative and dynamical forcing of the surface and atmospheric temperature anomalies associated with the northern annular mode. J. Climate, 26, 5124–5138.
Dessler, A. E., Z. Zhang, and P. Yang, 2008: Water-vapor climate feedback inferred from climate fluctuations, 2003–2008. Geophys. Res. Lett., 35, L20704, doi: 10.1029/2008GL035333.
Fu, Q., and K. N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 2139–2156.
Fu, Q., and K. N. Liou, 1993: Parameterization of the radiative properties of cirrus clouds. J. Atmos. Sci., 50, 2008–2025.
Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441–475.
Huang, W. Y., B. Wang, L. J. Li, and Y. Q. Yu, 2014: Improvements in LICOM2. Part II: Arctic Circulation. J. Atmos. Ocea. Tech., 31, 233–245.
Kharin, V. V., F. W. Zwiers, X. B. Zhang, and G. C. Hegerl, 2007: Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Climate, 20, 1419–1444.
Kimoto, M., 2005: Simulated change of the East Asian circulation under global warming scenario. Geophys. Res. Lett., 32, doi: 10.1029/2005GL023383.
Li, G. Q., S. P. Harrison, P. J. Bartlein, K. Izumi, and I. C. Prentice, 2013a: Precipitation scaling with temperature in warm and cold climates: An analysis of CMIP5 simulations. Geophys. Res. Lett., 40, 4018–4024, doi: 10.1002/grl.50730.
Li, L. J., and Coauthors, 2013b: The flexible global ocean-atmosphere-land system model, grid-point Version 2: FGOALS-g2. Adv. Atmos. Sci., 30, 543–560, doi: 10.1007/s00376-012-2140-6.
Lin, P. F., Y. Q. Yu, and H. L. Liu, 2013a: Long-term stability and oceanic mean state simulated by the coupled model FGOALS-s2. Adv. Atmos. Sci., 30, 175–192, doi: 10.1007/s00376-012-2042-7.
Lin, P. F., Y. Q. Yu, and H. L. Liu, 2013b: Oceanic climatology in the coupled model FGOALS-g2: Improvements and biases. Adv. Atmos. Sci., 30, 819–840, doi: 10.1007/s00376-012-2137-1.
Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012: The baseline evaluation of LASG/IAP climate system ocean model (LICOM) version 2. Acta Meteorologica Sinica, 26, 318–329.
Lu, J. H., and M. Cai, 2009: A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: Formulation. Climate Dyn., 32, 873–885.
Lu, J. H., and M. Cai, 2010: Quantifying contributions to polar warming amplification in an idealized coupled general circulation model. Climate Dyn., 34, 669–687.
Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM), NCAR Tech. Note TN-461+STR, 174 pp.
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.
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.
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.
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)
Taylor, P. C., M. Cai, A. Hu, J. Meehl, W. Washington, and G. J. Zhang, 2013: A decomposition of feedback contributions to polar warming amplification. J. Climate, 26, 7023–7043.
Wetherald, R. T., and S. Manabe, 1988: Cloud feedback processes in a general circulation model. J. Atmos. Sci., 45, 1397–1416.
Wu, G. X., H. Liu, Y. C. Zhao, and W. P. Li, 1996: A nine-layer atmospheric general circulation model and its performance. Adv. Atmos. Sci., 13, 1–18.
Xu, S. M., and Coauthors, 2013: Simulation of sea ice in FGOALS-g2: Climatology and late 20th century changes. Adv. Atmos. Sci., 30, 658–673, doi: 10.1007/s00376-013-2158-4.
Zhang, L. X., and T. J. Zhou, 2014: An assessment of improvements in global monsoon precipitation simulation in FGOALS-s2. Adv. Atmos. Sci., 31, 165–178, doi: 10.1007/s00376-013-2164-6.
Zhou, T. J., and R. C. Yu, 2006: Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J. Climate, 19, 5843–5858.
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.
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Yang, Y., Ren, R., Cai, M. et al. Attributing analysis on the model bias in surface temperature in the climate system model FGOALS-s2 through a process-based decomposition method. Adv. Atmos. Sci. 32, 457–469 (2015). https://doi.org/10.1007/s00376-014-4061-z
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DOI: https://doi.org/10.1007/s00376-014-4061-z