Robust Seasonality of Arctic Warming Processes in Two Different Versions of the MIROC GCM

Masakazu Yoshimori Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan

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Ayako Abe-Ouchi Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, and Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

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Masahiro Watanabe Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan

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Akira Oka Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan

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Tomoo Ogura National Institute for Environmental Studies, Tsukuba, Japan

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Abstract

It is one of the most robust projected responses of climate models to the increase of atmospheric CO2 concentration that the Arctic experiences a rapid warming with a magnitude larger than the rest of the world. While many processes are proposed as important, the relative contribution of individual processes to the Arctic warming is not often investigated systematically. Feedbacks are quantified in two different versions of an atmosphere–ocean GCM under idealized transient experiments based on an energy balance analysis that extends from the surface to the top of the atmosphere. The emphasis is placed on the largest warming from late autumn to early winter (October–December) and the difference from other seasons. It is confirmed that dominating processes vary with season. In autumn, the largest contribution to the Arctic surface warming is made by a reduction of ocean heat storage and cloud radiative feedback. In the annual mean, on the other hand, it is the albedo feedback that contributes the most, with increasing ocean heat uptake to the deeper layers working as a negative feedback. While the qualitative results are robust between the two models, they differ quantitatively, indicating the need for further constraint on each process. Ocean heat uptake, lower tropospheric stability, and low-level cloud response probably require special attention.

Corresponding author address: Masakazu Yoshimori, Faculty of Environmental Earth Science, Hokkaido University, Kita 10, Nishi 5, Kita-ku, Sapporo 060-0810, Japan. E-mail: myoshimo@ees.hokudai.ac.jp

Abstract

It is one of the most robust projected responses of climate models to the increase of atmospheric CO2 concentration that the Arctic experiences a rapid warming with a magnitude larger than the rest of the world. While many processes are proposed as important, the relative contribution of individual processes to the Arctic warming is not often investigated systematically. Feedbacks are quantified in two different versions of an atmosphere–ocean GCM under idealized transient experiments based on an energy balance analysis that extends from the surface to the top of the atmosphere. The emphasis is placed on the largest warming from late autumn to early winter (October–December) and the difference from other seasons. It is confirmed that dominating processes vary with season. In autumn, the largest contribution to the Arctic surface warming is made by a reduction of ocean heat storage and cloud radiative feedback. In the annual mean, on the other hand, it is the albedo feedback that contributes the most, with increasing ocean heat uptake to the deeper layers working as a negative feedback. While the qualitative results are robust between the two models, they differ quantitatively, indicating the need for further constraint on each process. Ocean heat uptake, lower tropospheric stability, and low-level cloud response probably require special attention.

Corresponding author address: Masakazu Yoshimori, Faculty of Environmental Earth Science, Hokkaido University, Kita 10, Nishi 5, Kita-ku, Sapporo 060-0810, Japan. E-mail: myoshimo@ees.hokudai.ac.jp
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  • Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor, 2012: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate models. Geophys. Res. Lett., 39, L09712, doi:10.1029/2012GL051607.

    • Search Google Scholar
    • Export Citation
  • Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19, 34453482, doi:10.1175/JCLI3819.1.

    • Search Google Scholar
    • Export Citation
  • 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, 887900, doi:10.1007/s00382-008-0424-4.

    • Search Google Scholar
    • Export Citation
  • Chan, W.-L., A. Abe-Ouchi, and R. Ohgaito, 2011: Simulating the mid-Pliocene climate with the MIROC general circulation model: Experimental design and initial results. Geosci. Model Dev., 4, 10351049, doi:10.5194/gmd-4-1035-2011.

    • Search Google Scholar
    • Export Citation
  • Crook, J. A., P. M. Forster, and N. Stuber, 2011: Spatial patterns of modeled climate feedback and contributions to temperature response and polar amplification. J. Climate, 24, 35753592, doi:10.1175/2011JCLI3863.1.

    • Search Google Scholar
    • Export Citation
  • Cuzzone, J., and S. Vavrus, 2011: The relationships between Arctic sea ice and cloud-related variables in the ERA-Interim reanalysis and CCSM3. Environ. Res. Lett., 6, 014016, doi:10.1088/1748-9326/6/1/014016.

    • Search Google Scholar
    • Export Citation
  • Eastman, R., and S. G. Warren, 2010: Interannual variations of Arctic cloud types in relation to sea ice. J. Climate, 23, 42164232, doi:10.1175/2010JCLI3492.1.

    • Search Google Scholar
    • Export Citation
  • Graversen, R. G., and M. H. Wang, 2009: Polar amplification in a coupled climate model with locked albedo. Climate Dyn., 33, 629643, doi:10.1007/s00382-009-0535-6.

    • Search Google Scholar
    • Export Citation
  • Hall, A., 2004: The role of surface albedo feedback in climate. J. Climate, 17, 15501568, doi:10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hall, A., and S. Manabe, 1999: The role of water vapor feedback in unperturbed climate variability and global warming. J. Climate, 12, 23272346, doi:10.1175/1520-0442(1999)012<2327:TROWVF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hasumi, H., and S. Emori, Eds., 2004: K-1 coupled GCM (MIROC) description. Center for Climate System Research Tech. Rep., 34 pp.

  • Hwang, Y.-T., D. M. W. Frierson, and J. E. Kay, 2011: Coupling between Arctic feedbacks and changes in poleward energy transport. Geophys. Res. Lett., 38, L17704, doi:10.1029/2011GL048546.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and A. Gettelman, 2009: Cloud influence on and response to seasonal Arctic sea ice loss. J. Geophys. Res., 114, D18204, doi:10.1029/2009JD011773.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., M. M. Holland, C. M. Bitz, E. Blanchard-Wrigglesworth, A. Gettelman, A. Conley, and D. Bailey, 2012: The influence of local feedbacks and northward heat transport on the equilibrium Arctic climate response to increased greenhouse gas forcing. J. Climate, 25, 54335450, doi:10.1175/JCLI-D-11-00622.1.

    • Search Google Scholar
    • Export Citation
  • Komuro, Y., 2014: The impact of surface mixing on the Arctic river water distribution and stratification in a global ice–ocean model. J. Climate, 27, 4359–4370, doi:10.1175/JCLI-D-13-00090.1.

    • Search Google Scholar
    • Export Citation
  • Komuro, Y., and Coauthors, 2012: Sea-ice in twentieth-century simulations by new MIROC coupled models: A comparison between models with high resolution and with ice thickness distribution. J. Meteor. Soc. Japan, 90A, 213232, doi:10.2151/jmsj.2012-A11.

    • Search Google Scholar
    • Export Citation
  • Laîné, A., M. Kageyama, P. Braconnot, and R. Alkama, 2009: Impact of greenhouse gas concentration changes on surface energetics in IPSL-CM4: Regional warming patterns, land–sea warming ratios, and glacial–interglacial differences. J. Climate, 22, 46214635, doi:10.1175/2009JCLI2771.1.

    • Search Google Scholar
    • Export Citation
  • Langen, P. L., R. G. Graversen, and T. Mauritsen, 2012: Separation of contributions from radiative feedbacks to polar amplification on an aquaplanet. J. Climate, 25, 30103024, doi:10.1175/JCLI-D-11-00246.1.

    • Search Google Scholar
    • Export Citation
  • Lu, J. H., and M. Cai, 2009a: Seasonality of polar surface warming amplification in climate simulations. Geophys. Res. Lett., 36, L16704, doi:10.1029/2009GL040133.

    • Search Google Scholar
    • Export Citation
  • Lu, J. H., and M. Cai, 2009b: A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: Formulation. Climate Dyn., 32, 873885, doi:10.1007/s00382-008-0425-3.

    • Search Google Scholar
    • Export Citation
  • Lu, J. H., and M. Cai, 2010: Quantifying contributions to polar warming amplification in an idealized coupled general circulation model. Climate Dyn., 34, 669687, doi:10.1007/s00382-009-0673-x.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., and R. T. Wetherald, 1975: Effects of doubling CO2 concentration on climate of a general circulation model. J. Atmos. Sci., 32, 315, doi:10.1175/1520-0469(1975)032<0003:TEODTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., and R. J. Stouffer, 1980: Sensitivity of a global climate model to an increase of CO2 concentration in the atmosphere. J. Geophys. Res., 85, 55295554, doi:10.1029/JC085iC10p05529.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, K. E. Taylor, T. Delworth, R. J. Stouffer, M. Latif, B. McAveney, and J. F. B. Mitchell, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, doi:10.1175/BAMS-88-9-1383.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. de Boer, G. Feingold, J. Harrington, M. D. Shupe, and K. Sulia, 2012: Resilience of persistent Arctic mixed-phase clouds. Nat. Geosci.,5, 1117, doi:10.1038/ngeo1332.

  • Ogura, T., S. Emori, M. J. Webb, Y. Tsushima, T. Yokohata, A. Abe-Ouchi, and M. Kimoto, 2008: Towards understanding cloud response in atmospheric GCMs: The use of tendency diagnostics. J. Meteor. Soc. Japan, 86, 6979, doi:10.2151/jmsj.86.69.

    • Search Google Scholar
    • Export Citation
  • Ohmura, A., 1984: On the cause of ‘Fram’ type seasonal change in diurnal amplitude of air temperature in polar regions. J. Climatol., 4, 325338, doi:10.1002/joc.3370040309.

    • Search Google Scholar
    • Export Citation
  • Palm, S. P., S. T. Strey, J. Spinhirne, and T. Markus, 2010: Influence of Arctic sea ice extent on polar cloud fraction and vertical structure and implications for regional climate. J. Geophys. Res., 115, D21209, doi:10.1029/2010JD013900.

    • Search Google Scholar
    • Export Citation
  • Perovich, D. K., and C. Polashenski, 2012: Albedo evolution of seasonal Arctic sea ice. Geophys. Res. Lett., 39, L08501, doi:10.1029/2012GL051432.

    • Search Google Scholar
    • Export Citation
  • Perovich, D. K., T. C. Grenfell, B. Light, and P. V. Hobbs, 2002: Seasonal evolution of the albedo of multiyear Arctic sea ice. J. Geophys. Res., 107, 8044, doi:10.1029/2000JC000438.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., M. S. Lian, and R. D. Cess, 1979: Increased atmospheric CO2: Zonal and seasonal estimates of the effect on the radiation energy balance and surface temperature. J. Geophys. Res., 84, 49494958, doi:10.1029/JC084iC08p04949.

    • Search Google Scholar
    • Export Citation
  • Schneider, E. K., B. P. Kirtman, and R. S. Lindzen, 1999: Tropospheric water vapor and climate sensitivity. J. Atmos. Sci., 56, 16491658, doi:10.1175/1520-0469(1999)056<1649:TWVACS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schweiger, A. J., R. W. Lindsay, S. Vavrus, and J. A. Francis, 2008: Relationships between Arctic sea ice and clouds during autumn. J. Climate, 21, 47994810, doi:10.1175/2008JCLI2156.1.

    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010a: The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464, 13341337, doi:10.1038/nature09051.

    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010b: Increasing fall-winter energy loss from the Arctic Ocean and its role in Arctic temperature amplification. Geophys. Res. Lett., 37, L16707, doi:10.1029/2010GL044136.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., A. P. Barrett, J. C. Stroeve, D. N. Kindig, and M. M. Holland, 2009: The emergence of surface-based Arctic amplification. Cryosphere, 3, 1119, doi:10.5194/tc-3-11-2009.

    • Search Google Scholar
    • Export Citation
  • Shiogama, H., M. Watanabe, T. Ogura, T. Yokohata, and M. Kimoto, 2014: Multi-parameter multi-physics ensemble (MPMPE): A new approach exploring the uncertainties of climate sensitivity. Atmos. Sci. Lett., 15, 97–102, doi:10.1002/asl2.472.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008: Quantifying climate feedbacks using radiative kernels. J. Climate, 21, 35043520, doi:10.1175/2007JCLI2110.1.

    • Search Google Scholar
    • Export Citation
  • Steele, M., R. Morley, and W. Ermold, 2001: PHC: A global ocean hydrography with a high-quality Arctic Ocean. J. Climate, 14, 20792087, doi:10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stein, U., and P. Alpert, 1993: Factor separation in numerical simulations. J. Atmos. Sci., 50, 21072115, doi:10.1175/1520-0469(1993)050<2107:FSINS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, P. C., R. G. Ellingson, and M. Cai, 2011a: Geographical distribution of climate feedbacks in the NCAR CCSM3.0. J. Climate, 24, 27372753, doi:10.1175/2010JCLI3788.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, P. C., R. G. Ellingson, and M. Cai, 2011b: Seasonal variations of climate feedbacks in the NCAR CCSM3. J. Climate, 24, 34333444, doi:10.1175/2011JCLI3862.1.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1175/JCLI-D-12-00696.1.

    • Search Google Scholar
    • Export Citation
  • Vavrus, S., 2004: The impact of cloud feedbacks on Arctic climate under greenhouse forcing. J. Climate, 17, 603615, doi:10.1175/1520-0442(2004)017<0603:TIOCFO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dyn.,41, 3339–3362, doi:10.1007/s00382-013-1725-9.

  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335, doi:10.1175/2010JCLI3679.1.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., and Coauthors, 2012: Using a multiphysics ensemble for exploring diversity in cloud–shortwave feedback in GCMs. J. Climate, 25, 54165431, doi:10.1175/JCLI-D-11-00564.1.

    • Search Google Scholar
    • Export Citation
  • Wu, D. L., and J. N. Lee, 2012: Arctic low cloud changes as observed by MISR and CALIOP: Implication for the enhanced autumnal warming and sea ice loss. J. Geophys. Res., 117, D07107, doi:10.1029/2011JD017050.

    • Search Google Scholar
    • Export Citation
  • Yoshimori, M., T. Yokohata, and A. Abe-Ouchi, 2009: A comparison of climate feedback strength between CO2 doubling and LGM experiments. J. Climate, 22, 33743395, doi:10.1175/2009JCLI2801.1.

    • Search Google Scholar
    • Export Citation
  • Yoshimori, M., M. Watanabe, A. Abe-Ouchi, H. Shiogama, and T. Ogura, 2014: Relative contribution of feedback processes to Arctic amplification of temperature change in MIROC GCM. Climate Dyn., 42, 16131630, doi:10.1007/s00382-013-1875-9.

    • Search Google Scholar
    • Export Citation
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