CMIP5 Scientific Gaps and Recommendations for CMIP6

R. J. Stouffer NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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V. Eyring Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

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G. A. Meehl National Center for Atmospheric Research, Boulder, Colorado

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S. Bony Laboratoire de Meteorologie Dynamique, IPSL, CNRS, Paris, France

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C. Senior Met Office Hadley Centre, Exeter, United Kingdom

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B. Stevens Max Planck Institute for Meteorology, Hamburg, Germany

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K. E. Taylor Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California

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Abstract

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6:

  1. How does the Earth system respond to changes in forcing?

  2. What are the origins and consequences of systematic model biases?

  3. How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios?

CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. These biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.

CORRESPONDING AUTHOR E-MAIL: Ronald J. Stouffer, ronald.stouffer@noaa.gov

Abstract

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6:

  1. How does the Earth system respond to changes in forcing?

  2. What are the origins and consequences of systematic model biases?

  3. How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios?

CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. These biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.

CORRESPONDING AUTHOR E-MAIL: Ronald J. Stouffer, ronald.stouffer@noaa.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, doi:10.1029/2012GL051607.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arora, V. K., and Coauthors, 2013: Carbon–concentration and carbon–climate feedbacks in CMIP5 Earth system models. J. Climate, 26, 52895314, doi:10.1175/JCLI-D-12-00494.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balmaseda, M. A., K. E. Trenberth, and E. Kallen, 2013: Distinctive climate signals in reanalysis of global ocean heat content. Geophys. Res. Lett., 40, 17541759, doi:10.1002/grl.50382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 10231043, doi:10.1175/2011BAMS2856.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boucher, O., and Coauthors, 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 571–657.

  • Braconnot, P., S. P. Harrison, M. Kageyama, P. J. Bartlein, V. Masson-Delmotte, A. Abe-Ouchi, B. Otto-Bliesner, and Y. Zhao, 2012: Evaluation of climate models using palaeoclimatic data. Nat. Climate Change, 2, 417424, doi:10.1038/nclimate1456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Branstator, G., and H. Y. Teng, 2012: Potential impact of initialization on decadal predictions as assessed for CMIP5 models. Geophys. Res. Lett., 39, L12703, doi:10.1029/2012GL051974.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheruy, F., J. L. Dufresne, F. Hourdin, and A. Ducharne, 2014: Role of clouds and land-atmosphere coupling in midlatitude continental summer warm biases and climate change amplification in CMIP5 simulations. Geophys. Res. Lett., 41, 64936500, doi:10.1002/2014GL061145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chung, E.-S., and B. J. Soden, 2015: An assessment of direct radiative forcing, radiative adjustments, and radiative feedbacks in coupled ocean–atmosphere models. J. Climate, 28, 41524170, doi:10.1175/JCLI-D-14-00436.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ciais, P., and Coauthors, 2013: Carbon and other biogeochemical cycles. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 465–570.

  • 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.

  • 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, doi:10.1038/nature11882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crueger, T., and B. Stevens, 2015: The effect of atmospheric radiative heating by clouds on the Madden-Julian Oscillation. J. Adv. Model. Earth Syst., 7, 854864, doi:10.1002/2015MS000434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., and Coauthors, 2013: Initialized near-term regional climate change prediction. Nat. Commun., 4, 1715, doi:10.1038/ncomms2704.

  • Eyring, V., and Coauthors, 2013: Long-term ozone changes and associated climate impacts in CMIP5 simulations. J. Geophys. Res. Atmos., 118, 50295060, doi:10.1002/jgrd.50316.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., and Coauthors, 2016a: ESMValTool (v1.0)—A community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP. Geosci. Model Dev., 9, 17471802, doi:10.5194/gmd-9-1747-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016b: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, doi:10.5194/gmd-9-1937-2016.

    • 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.

  • Friedlingstein, P., M. Meinshausen, V. K. Arora, C. D. Jones, A. Anav, S. K. Liddicoat, and R. Knutti, 2014: Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Climate, 27, 511526, doi:10.1175/JCLI-D-12-00579.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frolicher, T. L., J. L. Sarmiento, D. J. Paynter, J. P. Dunne, J. P. Krasting, and M. Winton, 2015: Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J. Climate, 28, 862886, doi:10.1175/JCLI-D-14-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP—The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970, doi:10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 2955, doi:10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghan, S. J., 2013: Technical note: Estimating aerosol effects on cloud radiative forcing. Atmos. Chem. Phys., 13, 99719974, doi:10.5194/acp-13-9971-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., C. Doutriaux, P. J. Durack, K. E. Taylor, Y. Zhang, D. N. Williams, E. Mason, and J. Servonnat, 2016: A more powerful reality test for climate models. Eos, Trans. Amer. Geophys. Union, 97, doi:10.1029/2016EO051663.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goddard, L., and Coauthors, 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dyn., 40, 245272, doi:10.1007/s00382-012-1481-2.

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

    • 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, doi:10.1029/2005GL025127.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, J., and Coauthors, 2005: Efficacy of climate forcings. J. Geophys. Res., 110, D18104, doi:10.1029/2005JD005776.

  • Hawkins, E., and R. Sutton, 2009: Decadal predictability of the Atlantic Ocean in a Coupled GCM: Forecast skill and optimal perturbations using linear inverse modeling. J. Climate, 22, 39603978, doi:10.1175/2009JCLI2720.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2011: The potential to narrow uncertainty in projections of regional precipitation change. Climate Dyn., 37, 407418, doi:10.1007/s00382-010-0810-6.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hibbard, K. A., G. A. Meehl, P. M. Cox, and P. Friedlingstein, 2007: A strategy for climate change stabilization experiments. Eos, Trans. Amer. Geophys. Union, 88, 217221, doi:10.1029/2007EO200002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hourdin, F., A. Găinuşă-Bogdan, P. Braconnot, J.-L. Dufresne, A.-K. Traore, and C. Rio, 2015: Air moisture control on ocean surface temperature, hidden key to the warm bias enigma. Geophys Res Lett., 42, 10 88510 893, doi:10.1002/2015GL066764.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D., and Coauthors, 2014: Process-oriented MJO simulation diagnostic: Moisture sensitivity of simulated convection. J. Climate, 27, 53795395, doi:10.1175/JCLI-D-13-00497.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B., and Coauthors, 2013: Near-term climate change: Projections and predictability. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 953–1028.

  • Klein, S. A., X. Jiang, J. Boyle, S. Malyshev, and S. Xie, 2006: Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach. Geophys. Res. Lett., 33, L18805, doi:10.1029/2006GL027567.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., G. Abramowitz, M. Collins, V. Eyring, P. J. Gleckler, B. Hewitson, and L. Mearns, 2010: Good practice guidance paper on assessing and combining multi model climate projections. Meeting report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, T. Stocker et al., Eds., Rep. 0165-0009, IPCC Working Group I Technical Support Unit, 1–15.

  • Kriegler, E., B. C. O’Neill, S. Hallegatte, T. Kram, R. J. Lempert, R. H. Moss, and T. Wilbanks, 2012: The need for and use of socio-economic scenarios for climate change analysis: A new approach based on shared socio-economic pathways. Global Environ. Change, 22, 807822, doi:10.1016/j.gloenvcha.2012.05.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamarque, J.-F., and Coauthors, 2013: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics. Geosci. Model Dev., 6, 179206, doi:10.5194/gmd-6-179-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, G., and S. P. Xie, 2012: Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys. Res. Lett., 39, L22703, doi:10.1029/2012GL053777.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and H. Teng, 2014: CMIP5 multi-model hindcasts for the mid-1970s shift and early 2000s hiatus and predictions for 2016–2035. Geophys Res Lett., 41, 17111716, doi:10.1002/2014GL059256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., G. J. Boer, C. Covey, M. Latif, and R. J. Stouffer, 1997: Intercomparison makes for a better climate model. Eos, Trans. Amer. Geophys. Union, 78, 445451, doi:10.1029/97EO00276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., G. J. Boer, C. Covey, M. Latif, and R. J. Stouffer, 2000: The Coupled Model Intercomparison Project (CMIP). Bull. Amer. Meteor. Soc., 81, 313318, doi:10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, B. McAvaney, M. Latif, and R. J. Stouffer, 2005: Overview of the Coupled Model Intercomparison Project. Bull. Amer. Meteor. Soc., 86, 8993, doi:10.1175/BAMS-86-1-89.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., K. E. Taylor, T. Delworth, R. J. Stouffer, M. Latif, B. McAvaney, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2009: Decadal prediction: Can it be skillful? Bull. Amer. Meteor. Soc., 90, 14671485, doi:10.1175/2009BAMS2778.1.

  • Meehl, G. A., R. Moss, K. E. Taylor, V. Eyring, R. J. Stouffer, S. Bony, and B. Stevens, 2014: Climate model intercomparisons: Preparing for the next phase. Eos, Trans. Amer. Geophys. Union, 95, 77, doi:10.1002/2014EO090001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moss, R. H., and Coauthors, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747756, doi:10.1038/nature08823.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, B., and S. I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128134, doi:10.1002/2013GL058055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myhre, G., and Coauthors, 2013: Anthropogenic and natural radiative forcing. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 659–740.

  • Oueslati, B., and G. Bellon, 2015: The double ITCZ bias in CMIP5 models: Interaction between SST, large-scale circulation and precipitation. Climate Dyn., 44, 585607, doi:10.1007/s00382-015-2468-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pongratz, J., C. H. Reick, R. A. Houghton, and J. I. House, 2014: Terminology as a key uncertainty in net land use and land cover change carbon flux estimates. Earth Syst. Dyn., 5, 177195, doi:10.5194/esd-5-177-2014.

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

  • Rauser, F., P. Gleckler, and J. Marotzke, 2015: Rethinking the default construction of multimodel climate ensembles. Bull. Amer. Meteor. Soc., 96, 911919, doi:10.1175/BAMS-D-13-00181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riahi, K., and Coauthors, 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Change, doi:10.1016/j.gloenvcha.2016.05.009, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russell, J. L., K. W. Dixon, A. Gnanadesikan, R. J. Stouffer, and J. R. Toggweiler, 2006a: The Southern Hemisphere westerlies in a warming world: Propping open the door to the deep ocean. J. Climate, 19, 63826390, doi:10.1175/JCLI3984.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russell, J. L., R. J. Stouffer, and K. W. Dixon, 2006b: Intercomparison of the Southern Ocean circulations in IPCC coupled model control simulations. J. Climate, 19, 45604575, doi:10.1175/JCLI3869.1; Corrigendum, 20, 4287, doi:10.1175/JCLI4326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanderson, B. M., R. Knutti, and P. Caldwell, 2015: Addressing interdependency in a multimodel ensemble by interpolation of model properties. J. Climate, 28, 51505170, doi:10.1175/JCLI-D-14-00361.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santer, B. D., K. E. Taylor, T. M. L. Wigley, J. E. Penner, P. D. Jones, and U. Cubasch, 1995: Towards the detection and attribution of an anthropogenic effect on climate. Climate Dyn., 12, 77100, doi:10.1007/BF00223722.

    • Crossref
    • 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, doi:10.1038/nature12829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., S. Bony, O. Boucher, C. Bretherton, P. M. Forster, J. M. Gregory, and B. Stevens, 2015: Adjustments in the forcing-feedback framework for understanding climate change. Bull. Amer. Meteor. Soc., 96, 217228, doi:10.1175/BAMS-D-13-00167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, D. M., and Coauthors, 2013: Real-time multi-model decadal climate predictions. Climate Dyn., 41, 28752888, doi:10.1007/s00382-012-1600-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., 2015: Rethinking the lower bound on aerosol radiative forcing. J. Climate, 28, 47944819, doi:10.1175/JCLI-D-14-00656.1.

  • Stevens, B., and S. Bony, 2013: What are climate models missing? Science, 340, 10531054, doi:10.1126/science.1237554.

  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., and R. Knutti, 2007: The use of the multi-model ensemble in probabilistic climate projections. Philos Trans. Roy. Soc. London, 365A, 20532075, doi:10.1098/rsta.2007.2076.

    • Search Google Scholar
    • Export Citation
  • Teixeira, J., D. Waliser, R. Ferraro, P. Gleckler, T. Lee, and G. Potter, 2014: Satellite observations for CMIP5: The genesis of Obs4MIPs. Bull. Amer. Meteor. Soc., 95, 13291334, doi:10.1175/BAMS-D-12-00204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wenzel, S., P. M. Cox, V. Eyring, and P. Friedlingstein, 2014: Emergent constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system models. J. Geophys. Res. Biogeosci., 119, 794807, doi:10.1002/2013JG002591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, D. N., and Coauthors, 2015: A global repository for planet-sized experiments and observations. Bull. Amer. Meteor. Soc., 97, 803816, doi:10.1175/BAMS-D-15-00132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williamson, D., and Coauthors, 2013: The Aqua-Planet Experiment (APE): Response to changed meridional SST profile. J. Meteor. Soc. Japan, 91A, 5789, doi:10.2151/jmsj.2013-A03.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williamson, D., A. T. Blaker, C. Hampton, and J. Salter, 2015: Identifying and removing structural biases in climate models with history matching. Climate Dyn., 45, 12991324, doi:10.1007/s00382-014-2378-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yu, W., M. Doutriaux, G. Seze, H. LeTreut, and M. Desbois, 1996: A methodology study of the validation of clouds in GCMs using ISCCP satellite observations. Climate Dyn., 12, 389401, doi:10.1007/BF00211685.

    • Crossref
    • Search Google Scholar
    • Export Citation
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