Advances in Atmospheric Sciences

, Volume 32, Issue 4, pp 457–469 | Cite as

Attributing analysis on the model bias in surface temperature in the climate system model FGOALS-s2 through a process-based decomposition method

Article

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.

Key words

attribution model bias surface temperature FGOALS-s2 CFRAM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. Chapman, W. L., and J. E. Walsh, 2007: Simulations of Arctic temperature and pressure by global coupled models. J. Climate, 20, 609–632.CrossRefGoogle Scholar
  7. Collins, W. D., and Coauthors, 2006: The community climate system model version 3 (CCSM3). J. Climate, 19, 2122–2143.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. 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.CrossRefGoogle Scholar
  12. Fu, Q., and K. N. Liou, 1993: Parameterization of the radiative properties of cirrus clouds. J. Atmos. Sci., 50, 2008–2025.CrossRefGoogle Scholar
  13. Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441–475.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. Kimoto, M., 2005: Simulated change of the East Asian circulation under global warming scenario. Geophys. Res. Lett., 32, doi: 10.1029/2005GL023383.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM), NCAR Tech. Note TN-461+STR, 174 pp.Google Scholar
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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.CrossRefGoogle Scholar
  30. Wetherald, R. T., and S. Manabe, 1988: Cloud feedback processes in a general circulation model. J. Atmos. Sci., 45, 1397–1416.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Earth, Ocean, and Atmospheric ScienceFlorida State UniversityTallahasseeUSA

Personalised recommendations