Evaluating Emergent Constraints on Equilibrium Climate Sensitivity

Peter M. Caldwell Lawrence Livermore National Laboratory, Livermore, California

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Mark D. Zelinka Lawrence Livermore National Laboratory, Livermore, California

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Stephen A. Klein Lawrence Livermore National Laboratory, Livermore, California

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Abstract

Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. This study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physical explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. The fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0631.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter M. Caldwell, caldwell19@llnl.gov

Abstract

Emergent constraints are quantities that are observable from current measurements and have skill predicting future climate. This study explores 19 previously proposed emergent constraints related to equilibrium climate sensitivity (ECS; the global-average equilibrium surface temperature response to CO2 doubling). Several constraints are shown to be closely related, emphasizing the importance for careful understanding of proposed constraints. A new method is presented for decomposing correlation between an emergent constraint and ECS into terms related to physical processes and geographical regions. Using this decomposition, one can determine whether the processes and regions explaining correlation with ECS correspond to the physical explanation offered for the constraint. Shortwave cloud feedback is generally found to be the dominant contributor to correlations with ECS because it is the largest source of intermodel spread in ECS. In all cases, correlation results from interaction between a variety of terms, reflecting the complex nature of ECS and the fact that feedback terms and forcing are themselves correlated with each other. For 4 of the 19 constraints, the originally proposed explanation for correlation is borne out by our analysis. These four constraints all predict relatively high climate sensitivity. The credibility of six other constraints is called into question owing to correlation with ECS coming mainly from unexpected sources and/or lack of robustness to changes in ensembles. Another six constraints lack a testable explanation and hence cannot be confirmed. The fact that this study casts doubt upon more constraints than it confirms highlights the need for caution when identifying emergent constraints from small ensembles.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-17-0631.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter M. Caldwell, caldwell19@llnl.gov

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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, https://doi.org/10.1029/2012GL051607.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andrews, T., J. M. Gregory, and M. J. Webb, 2015: The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J. Climate, 28, 16301648, https://doi.org/10.1175/JCLI-D-14-00545.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Armour, K. C., C. M. Bitz, and G. H. Roe, 2013: Time-varying climate sensitivity from regional feedbacks. J. Climate, 26, 45184534, https://doi.org/10.1175/JCLI-D-12-00544.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., and J.-L. Dufresne, 2005: Marine boundary layer clouds at the heart of cloud feedback uncertainties in climate models. J. Geophys. Res., 32, L20806, https://doi.org/10.1029/2005GL023851.

    • 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, https://doi.org/10.1175/JCLI3819.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and P. N. Blossey, 2014: Low cloud reduction in a greenhouse-warmed climate: Results from Lagrangian LES of a subtropical marine cloudiness transition. J. Adv. Model. Earth Syst., 6, 91114, https://doi.org/10.1002/2013MS000250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brient, F., and T. Schneider, 2016: Constraints on climate sensitivity from space-based measurements of low-cloud reflection. J. Climate, 29, 58215835, https://doi.org/10.1175/JCLI-D-15-0897.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brient, F., T. Schneider, Z. Tan, S. Bony, X. Qu, and A. Hall, 2016: Shallowness of tropical low clouds as a predictor of climate models’ response to warming. Climate Dyn., 47, 433449, https://doi.org/10.1007/s00382-015-2846-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caldwell, P. M., C. S. Bretherton, M. D. Zelinka, S. A. Klein, B. D. Santer, and B. M. Sanderson, 2014: Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett., 41, 18031808, https://doi.org/10.1002/2014GL059205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caldwell, P. M., M. D. Zelinka, K. E. Taylor, and K. Marvel, 2016: Quantifying the sources of intermodel spread in equilibrium climate sensitivity. J. Climate, 29, 513524, https://doi.org/10.1175/JCLI-D-15-0352.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charney, J. G., and Coauthors, 1979: Carbon Dioxide and Climate: A Scientific Assessment. National Academies Press, 34 pp., https://doi.org/10.17226/12181.

    • Search Google Scholar
    • Export Citation
  • Collins, M., and Coauthors, 2013: Long-term climate change: Projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1029–1136.

  • Covey, C., and Coauthors, 2000: The seasonal cycle in coupled ocean–atmosphere general circulation models. Climate Dyn., 16, 775787, https://doi.org/10.1007/s003820000081.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cox, P. M., D. Pearson, B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494, 341344, https://doi.org/10.1038/nature11882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cox, P. M., C. Huntingford, and M. S. Williamson, 2018: Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature, 553, 319322, https://doi.org/10.1038/nature25450.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dufresne, J.-L., and S. Bony, 2008: An assessment of the primary sources of spread of global warming estimates from coupled atmosphere–ocean models. J. Climate, 21, 51355144, https://doi.org/10.1175/2008JCLI2239.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., and K. Trenberth, 2012: A less cloudy future: The role of subtropical subsidence in climate sensitivity. Science, 338, 792794, https://doi.org/10.1126/science.1227465.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., B. M. Sanderson, and K. E. Trenberth, 2015: Recent progress in constraining climate sensitivity with model ensembles. Curr. Climate Change Rep., 1, 268275, https://doi.org/10.1007/s40641-015-0021-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Forster, P. M., and K. E. Taylor, 2006: Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J. Climate, 19, 61816194, https://doi.org/10.1175/JCLI3974.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972.

    • Search Google Scholar
    • Export Citation
  • Gordon, N. D., and S. A. Klein, 2014: Low-cloud optical depth feedback in climate models. J. Geophys. Res. Atmos., 119, 60526065, https://doi.org/10.1002/2013JD021052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregory, J., and Coauthors, 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, https://doi.org/10.1029/2003GL018747.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grise, K. M., L. M. Polvani, and J. T. Fasullo, 2015: Reexamining the relationship between climate sensitivity and the Southern Hemisphere radiation budget in CMIP models. J. Climate, 28, 92989312, https://doi.org/10.1175/JCLI-D-15-0031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441475, https://doi.org/10.1146/annurev.energy.25.1.441.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and K. M. Shell, 2012: Using relative humidity as a state variable in climate feedback analysis. J. Climate, 25, 25782582, https://doi.org/10.1175/JCLI-D-11-00721.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirota, N., and Y. N. Takayabu, 2012: Inter-model differences of future precipitation changes in CMIP3 and MIROC5 climate models. J. Meteor. Soc. Japan, 90A, 307316, https://doi.org/10.2151/jmsj.2012-A16.

    • Search Google Scholar
    • Export Citation
  • Huber, M., I. Mahlstein, M. Wild, J. Fasullo, and R. Knutti, 2011: Constraints on climate sensitivity from radiation patterns in climate models. J. Climate, 24, 10341052, https://doi.org/10.1175/2010JCLI3403.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hwang, Y.-T., and D. M. W. Frierson, 2013: Link between the double-intertropical convergence zone problem and cloud biases over the Southern Ocean. Proc. Natl. Acad. Sci. USA, 110, 49354940, https://doi.org/10.1073/pnas.1213302110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kamae, Y., H. Shiogama, M. Watanabe, T. Ogura, T. Yokohata, and M. Kimoto, 2016: Lower-tropospheric mixing as a constraint on cloud feedback in a multiparameter multiphysics ensemble. J. Climate, 29, 62596275, https://doi.org/10.1175/JCLI-D-16-0042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., C. Wall, V. Yettella, B. Medeiros, C. Hannay, P. Caldwell, and C. Bitz, 2016: Global climate impacts of fixing the Southern Ocean shortwave radiation bias in the Community Earth System Model (CESM). J. Climate, 29, 46174636, https://doi.org/10.1175/JCLI-D-15-0358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and A. Hall, 2015: Emergent constraints for cloud feedbacks. Curr. Climate Change Rep., 1, 276287, https://doi.org/10.1007/s40641-015-0027-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., Y. Zhang, M. D. Zelinka, R. Pincus, J. Boyle, and P. J. Gleckler, 2013: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J. Geophys. Res. Atmos., 118, 13291342, https://doi.org/10.1002/jgrd.50141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klocke, D., R. Pincus, and J. Quaas, 2011: On constraining estimates of climate sensitivity with present-day observations through model weighting. J. Climate, 24, 60926099, https://doi.org/10.1175/2011JCLI4193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., 2010: The end of model democracy? Climatic Change, 102, 395404, https://doi.org/10.1007/s10584-010-9800-2.

  • Knutti, R., and G. C. Hegerl, 2008: The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nat. Geosci., 1, 735743, https://doi.org/10.1038/ngeo337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., G. A. Meehl, M. R. Allen, and D. A. Stainforth, 2006: Constraining climate sensitivity from the seasonal cycle in surface temperature. J. Climate, 19, 42244233, https://doi.org/10.1175/JCLI3865.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett., 40, 11941199, https://doi.org/10.1002/grl.50256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., M. A. A. Rugenstein, and G. C. Hegerl, 2017: Beyond equilibrium climate sensitivity. Nat. Geosci., 10, 727736, https://doi.org/10.1038/ngeo3017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lipat, B. R., G. Tselioudis, K. M. Grise, and L. M. Polvani, 2017: CMIP5 models’ shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent. Geophys. Res. Lett., 44, 57395748, https://doi.org/10.1002/2017GL073151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masson, D., and R. Knutti, 2013: Predictor screening, calibration, and observational constraints in climate model ensembles: An illustration using climate sensitivity. J. Climate, 26, 887898, https://doi.org/10.1175/JCLI-D-11-00540.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCoy, D. T., D. L. Hartmann, M. D. Zelinka, P. Ceppi, and D. P. Grosvenor, 2015: Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models. J. Geophys. Res. Atmos., 120, 95399554, https://doi.org/10.1002/2015JD023603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCoy, D. T., I. Tan, D. L. Hartmann, M. D. Zelinka, and T. Storelvmo, 2016: On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs. J. Adv. Model. Earth Syst., 8, 650668, https://doi.org/10.1002/2015MS000589.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pennell, C., and T. Reichler, 2011: On the effective number of climate models. J. Climate, 24, 23582367, https://doi.org/10.1175/2010JCLI3814.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., A. Hall, S. A. Klein, and P. M. Caldwell, 2013: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dyn., 42, 26032626, https://doi.org/10.1007/s00382-013-1945-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., A. Hall, A. M. DeAngelis, M. D. Zelinka, S. A. Klein, H. Su, B. Tian, and C. Zhai, 2018: On the emergent constraints of climate sensitivity. J. Climate, 31, 863875, https://doi.org/10.1175/JCLI-D-17-0482.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

  • Ringer, M. A., T. Andrews, and M. J. Webb, 2014: Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere–ocean climate change experiments. Geophys. Res. Lett., 41, 40354042, https://doi.org/10.1002/2014GL060347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rose, B. E. J., K. C. Armour, D. S. Battisti, N. Feldl, and D. D. B. Koll, 2014: The dependence of transient climate sensitivity and radiative feedbacks on the spatial pattern of ocean heat uptake. Geophys. Res. Lett., 41, 10711078, https://doi.org/10.1002/2013GL058955.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanderson, B. M., 2011: A multimodel study of parametric uncertainty in predictions of climate response to rising greenhouse gas concentrations. J. Climate, 24, 13621377, https://doi.org/10.1175/2010JCLI3498.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanderson, B. M., R. Knutti, and P. Caldwell, 2015: A representative democracy to reduce interdependency in a multimodel ensemble. J. Climate, 28, 51715194, https://doi.org/10.1175/JCLI-D-14-00362.1.

    • Crossref
    • 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. Hydrometeor., 21, 22692282, https://doi.org/10.1175/2007JCLI2044.1.

    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., S. Bony, and J.-L. Dufresne, 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505, 3742, https://doi.org/10.1038/nature12829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, J., T. DelSole, M. Fennessy, J. Kinter, and D. Paolino, 2006: Climate model fidelity and projections of climate change. Geophys. Res. Lett., 33, L07702, https://doi.org/10.1029/2005GL025579.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siler, N., S. Po-Chedley, and C. S. Bretherton, 2018: Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds. Climate Dyn., 50, 12091220, https://doi.org/10.1007/s00382-017-3673-2.

    • Crossref
    • 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, 33543360, https://doi.org/10.1175/JCLI3799.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B. J., A. J. Broccoli, and R. S. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17, 36613665, https://doi.org/10.1175/1520-0442(2004)017<3661:OTUOCF>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1175/2007JCLI2110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, H., J. H. Jiang, C. Zhai, T. J. Shen, J. D. Neelin, G. L. Stephens, and Y. L. Yung, 2014: Weakening and strengthening structures in the Hadley circulation change under global warming and implications for cloud response and climate sensitivity. J. Geophys. Res. Atmos., 119, 57875805, https://doi.org/10.1002/2014JD021642.

    • Crossref
    • 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, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, B., 2015: Spread of model climate sensitivity linked to double-intertropical convergence zone bias. Geophys. Res. Lett., 42, 41334141, https://doi.org/10.1002/2015GL064119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J. Climate, 23, 440454, https://doi.org/10.1175/2009JCLI3152.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Volodin, E. M., 2008: Relation between temperature sensitivity to doubled carbon dioxide and the distribution of clouds in current climate models. Izv. Atmos. Ocean. Phys., 44, 288299, https://doi.org/10.1134/S0001433808030043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waugh, D. W., and V. Eyring, 2008: Quantitative performance metrics for stratospheric-resolving chemistry–climate models. Atmos. Chem. Phys., 8, 56995713, https://doi.org/10.5194/acp-8-5699-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webb, M. J., and Coauthors, 2015: The impact of parametrized convection on cloud feedback. Philos. Trans. Roy. Soc. London, 373A, 20140414, https://doi.org/10.1098/rsta.2014.0414.

    • Search Google Scholar
    • Export Citation
  • Williams, K. D., W. J. Ingram, and J. M. Gregory, 2008: Time variation of effective climate sensitivity in GCMs. J. Climate, 21, 50765090, https://doi.org/10.1175/2008JCLI2371.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winton, M., K. Takahashi, and I. M. Held, 2010: Importance of ocean heat uptake efficacy to transient climate change. J. Climate, 23, 23332344, https://doi.org/10.1175/2009JCLI3139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, C., J. H. Jiang, and H. Su, 2015: Long-term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity. Geophys. Res. Lett., 42, 87298737, https://doi.org/10.1002/2015GL065911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., 2014: An investigation of the connections among convection, clouds, and climate sensitivity in a global climate model. J. Climate, 27, 18451862, https://doi.org/10.1175/JCLI-D-13-00145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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