Editorial Type:
Article
Article Type:
Research Article

Individual Feedback Contributions to the Seasonality of Surface Warming

Sergio A. Sejas Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida

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Ming Cai Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida

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Aixue Hu Climate Change Research, Climate and Global Dynamics, National Center for Atmospheric Research,* Boulder, Colorado

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Gerald A. Meehl Climate Change Research, Climate and Global Dynamics, National Center for Atmospheric Research,* Boulder, Colorado

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Warren Washington Climate Change Research, Climate and Global Dynamics, National Center for Atmospheric Research,* Boulder, Colorado

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Patrick C. Taylor NASA Langley Research Center, Hampton, Virginia

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Print Publication:
15 Jul 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00658.1
Page(s):
5653–5669
Restricted access

Abstract

Using the climate feedback response analysis method, the authors examine the individual contributions of the CO2 radiative forcing and climate feedbacks to the magnitude, spatial pattern, and seasonality of the transient surface warming response in a 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4).

The CO2 forcing and water vapor feedback warm the surface everywhere throughout the year. The tropical warming is predominantly caused by the CO2 forcing and water vapor feedback, while the evaporation feedback reduces the warming. Most feedbacks exhibit noticeable seasonal variations; however, their net effect has little seasonal variation due to compensating effects, which keeps the tropical warming relatively invariant all year long. The polar warming has a pronounced seasonal cycle, with maximum warming in fall/winter and minimum warming in summer. In summer, the large cancelations between the shortwave and longwave cloud feedbacks and between the surface albedo feedback warming and the cooling from the ocean heat storage/dynamics feedback lead to a warming minimum. In polar winter, surface albedo and shortwave cloud feedbacks are nearly absent due to a lack of insolation. However, the ocean heat storage feedback relays the polar warming due to the surface albedo feedback from summer to winter, and the longwave cloud feedback warms the polar surface. Therefore, the seasonal variations in the cloud feedback, surface albedo feedback, and ocean heat storage/dynamics feedback, directly caused by the strong annual cycle of insolation, contribute primarily to the large seasonal variation of polar warming. Furthermore, the CO2 forcing and water vapor and atmospheric dynamics feedbacks add to the maximum polar warming in fall/winter.

Corresponding author address: Sergio Sejas, Department of Earth, Ocean, and Atmospheric Science, Florida State University, Mail Code 4520, P.O. Box 3064520, Tallahassee, FL 32306-4520. E-mail: sas07t@my.fsu.edu

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Abstract

Using the climate feedback response analysis method, the authors examine the individual contributions of the CO2 radiative forcing and climate feedbacks to the magnitude, spatial pattern, and seasonality of the transient surface warming response in a 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4).

The CO2 forcing and water vapor feedback warm the surface everywhere throughout the year. The tropical warming is predominantly caused by the CO2 forcing and water vapor feedback, while the evaporation feedback reduces the warming. Most feedbacks exhibit noticeable seasonal variations; however, their net effect has little seasonal variation due to compensating effects, which keeps the tropical warming relatively invariant all year long. The polar warming has a pronounced seasonal cycle, with maximum warming in fall/winter and minimum warming in summer. In summer, the large cancelations between the shortwave and longwave cloud feedbacks and between the surface albedo feedback warming and the cooling from the ocean heat storage/dynamics feedback lead to a warming minimum. In polar winter, surface albedo and shortwave cloud feedbacks are nearly absent due to a lack of insolation. However, the ocean heat storage feedback relays the polar warming due to the surface albedo feedback from summer to winter, and the longwave cloud feedback warms the polar surface. Therefore, the seasonal variations in the cloud feedback, surface albedo feedback, and ocean heat storage/dynamics feedback, directly caused by the strong annual cycle of insolation, contribute primarily to the large seasonal variation of polar warming. Furthermore, the CO2 forcing and water vapor and atmospheric dynamics feedbacks add to the maximum polar warming in fall/winter.

Corresponding author address: Sergio Sejas, Department of Earth, Ocean, and Atmospheric Science, Florida State University, Mail Code 4520, P.O. Box 3064520, Tallahassee, FL 32306-4520. E-mail: sas07t@my.fsu.edu

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

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  • Alexeev, V. A., P. L. Langen, and J. R. Bates, 2005: Polar amplification of surface warming on an aquaplanet in “ghost forcing” experiments without sea ice feedbacks. Climate Dyn., 24, 655–666, doi:10.1007/s00382-005-0018-3.

    • Search Google Scholar
    • Export Citation

  • Bintanja, R., and E. C. van der Linden, 2013: The changing seasonal climate in the Arctic. Sci. Rep., 3, 1556, doi:10.1038/srep01556.

  • Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate feedback processes? J. Climate, 19, 3445–3482, doi:10.1175/JCLI3819.1.

    • Search Google Scholar
    • Export Citation

  • Cai, M., 2006: Dynamical greenhouse-plus feedback and polar warming amplification. Part I: A dry radiative-transportive climate model. Climate Dyn., 26, 661–675, doi:10.1007/s00382-005-0104-6.

    • Search Google Scholar
    • Export Citation

  • Cai, M., and J. Lu, 2007: Dynamical greenhouse-plus feedback and polar warming amplification. Part II: Meridional and vertical asymmetries of the global warming. Climate Dyn., 29, 375–391, doi:10.1007/s00382-007-0238-9.

    • Search Google Scholar
    • Export Citation

  • Cai, M., and J. 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, doi:10.1007/s00382-008-0424-4.

    • Search Google Scholar
    • Export Citation

  • 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, doi:10.1175/JAS-D-11-0117.1.

    • Search Google Scholar
    • Export Citation

  • Chapman, W. L., and J. E. Walsh, 2007: Simulations of Arctic temperature and pressure by global coupled models. J. Climate, 20, 609–632, doi:10.1175/JCLI4026.1.

    • Search Google Scholar
    • Export Citation

  • Colman, R., 2003: Seasonal contributions to climate feedbacks. Climate Dyn., 20, 825–841, doi:10.1007/s00382-002-0301-5.

  • 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, doi:10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Fu, Q., and K. N. Liou, 1993: Parameterization of the radiative properties of cirrus clouds. J. Atmos. Sci., 50, 2008–2025, doi:10.1175/1520-0469(1993)050<2008:POTRPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 4973–4991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation

  • Hall, A., 2004: The role of surface albedo feedback in climate. J. Climate, 17, 1550–1568, doi:10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Hansen, J., A. Lacis, D. Rind, G. Russell, P. Stone, I. Fung, R. Ruedy, and J. Lerner, 1984: Climate sensitivity: Analysis of feedback mechanisms. Climate Processes and Climate Sensitivity, Geophys. Monogr., Vol. 29, Amer. Geophys. Union, 130–163.

  • Hartmann, D. L., 1994: Climate sensitivity and feedback mechanisms. Global Physical Climatology, D. L. Hartmann, Ed., International Geophysics Series, Vol. 56, Academic Press, 229–253.

  • Holland, M. M., and C. M. Bitz, 2003: Polar amplifications of climate change in coupled models. Climate Dyn., 21, 221–232, doi:10.1007/s00382-003-0332-6.

    • Search Google Scholar
    • Export Citation

  • Ingram, W. J., C. A. Wilson, and J. F. B. Mitchell, 1989: Modeling climate change: An assessment of sea ice and surface albedo feedbacks. J. Geophys. Res., 94, 8609–8622, doi:10.1029/JD094iD06p08609.

    • Search Google Scholar
    • Export Citation

  • Kato, S., and Coauthors, 2011: Detection of atmospheric changes in spatially and temporally averaged infrared spectra observed from space. J. Climate, 24, 6392–6407, doi:10.1175/JCLI-D-10-05005.1.

    • Search Google Scholar
    • Export Citation

  • Li, C., D. Notz, S. Tietsche, and J. Marotzke, 2013: The transient versus the equilibrium response of sea ice to global warming. J. Climate, 26, 5624–5636, doi:10.1175/JCLI-D-12-00492.1.

    • Search Google Scholar
    • Export Citation

  • Lu, J., 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., and M. Cai, 2009b: A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: Formulation. Climate Dyn., 32, 873–885, doi:10.1007/s00382-008-0425-3.

    • Search Google Scholar
    • Export Citation

  • Lu, J., and M. Cai, 2010: Quantifying contributions to polar warming amplification in an idealized coupled general circulation model. Climate Dyn., 34, 669–687, doi:10.1007/s00382-009-0673-x.

    • Search Google Scholar
    • Export Citation

  • Manabe, S., and R. T. Wetherald, 1975: The effects of doubling the CO2 concentration on the climate of a general circulation model. J. Atmos. Sci., 32, 3–15, 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, 5529–5554, doi:10.1029/JC085iC10p05529.

    • Search Google Scholar
    • Export Citation

  • Manabe, S., and R. T. Wetherald, 1980: On the distribution of climate change resulting from an increase in CO2 content of the atmosphere. J. Atmos. Sci., 37, 99–118, doi:10.1175/1520-0469(1980)037<0099:OTDOCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Meehl, G. A., and Coauthors, 2007: Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–846.

    • Search Google Scholar
    • Export Citation

  • Pincus, R., H. W. Barker, and J.-J. Morcette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res., 108, 4376, doi:10.1029/2002JD003322.

    • Search Google Scholar
    • Export Citation

  • Pithan, F., and T. Mauritsen, 2014: Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci., 7, 181–184, doi:10.1038/ngeo2071.

    • Search Google Scholar
    • Export Citation

  • Räisänen, P., H. W. Barker, M. F. Khairoutdinov, J. Li, and D. A. Randall, 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130, 2047–2067, doi:10.1256/qj.03.99.

    • 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, 4949–4958, doi:10.1029/JC084iC08p04949.

    • Search Google Scholar
    • Export Citation

  • Screen, J. A., and I. Simmonds, 2010: The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464, 1334–1337, doi:10.1038/nature09051.

    • Search Google Scholar
    • Export Citation

  • Shell, K. M., J. T. Kiehl, and C. A. Shields, 2008: Using the radiative kernel technique to calculate climate feedbacks in NCAR’s Community Atmospheric Model. J. Climate, 21, 2269–2282, doi:10.1175/2007JCLI2044.1.

    • Search Google Scholar
    • Export Citation

  • Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, 19, 3354–3360, doi:10.1175/JCLI3799.1.

    • 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, 3504–3520, doi:10.1175/2007JCLI2110.1.

    • Search Google Scholar
    • Export Citation

  • Solomon, S., and Coauthors, 2007: Technical summary. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 19–91.

    • Search Google Scholar
    • Export Citation

  • Song, X., G. J. Zhang, and M. Cai, 2014: Quantifying contributions of climate feedbacks to tropospheric warming in the NCAR CCSM3.0. Climate Dyn., 42, 901–917, doi:10.1007/s00382-013-1805-x.

    • Search Google Scholar
    • Export Citation

  • Taylor, K. E., and S. J. Ghan, 1992: An analysis of cloud liquid water feedback and global climate sensitivity in a general circulation model. J. Climate, 5, 907–919, doi:10.1175/1520-0442(1992)005<0907:AAOCLW>2.0.CO;2.

    • 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, 2737–2753, 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, 3433–3444, 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, 603–615, doi:10.1175/1520-0442(2004)017<0603:TIOCFO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wetherald, R. T., and S. Manabe, 1988: Cloud feedback processes in a general circulation model. J. Atmos. Sci., 45, 1397–1416, doi:10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Winton, M., 2006: Amplified Arctic climate change: What does surface albedo feedback have to do with it? Geophys. Res. Lett., 33, L03701, doi:10.1029/2005GL025244.

    • Search Google Scholar
    • Export Citation
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