Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics

Ming Zhao NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and University Corporation for Atmospheric Research, Boulder, Colorado

Search for other papers by Ming Zhao in
Current site
Google Scholar
PubMed
Close
,
J.-C. Golaz NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by J.-C. Golaz in
Current site
Google Scholar
PubMed
Close
,
I. M. Held NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by I. M. Held in
Current site
Google Scholar
PubMed
Close
,
V. Ramaswamy NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by V. Ramaswamy in
Current site
Google Scholar
PubMed
Close
,
S.-J. Lin NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by S.-J. Lin in
Current site
Google Scholar
PubMed
Close
,
Y. Ming NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by Y. Ming in
Current site
Google Scholar
PubMed
Close
,
P. Ginoux NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by P. Ginoux in
Current site
Google Scholar
PubMed
Close
,
B. Wyman NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by B. Wyman in
Current site
Google Scholar
PubMed
Close
,
L. J. Donner NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by L. J. Donner in
Current site
Google Scholar
PubMed
Close
,
D. Paynter NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by D. Paynter in
Current site
Google Scholar
PubMed
Close
, and
H. Guo NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, and University Corporation for Atmospheric Research, Boulder, Colorado

Search for other papers by H. Guo in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the intermodel spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, the authors use a developmental version of the next-generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the model ensemble from phase 5 of CMIP (CMIP5). The authors demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model’s convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere–land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear observational constraint that favors one version of the authors’ model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections.

Corresponding author address: Dr. Ming Zhao, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, 201 Forrestal Road, Princeton, NJ 08540-6649. E-mail: ming.zhao@noaa.gov

Abstract

Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the intermodel spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, the authors use a developmental version of the next-generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the model ensemble from phase 5 of CMIP (CMIP5). The authors demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model’s convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere–land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear observational constraint that favors one version of the authors’ model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections.

Corresponding author address: Dr. Ming Zhao, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, 201 Forrestal Road, Princeton, NJ 08540-6649. E-mail: ming.zhao@noaa.gov
Save
  • Anderson, J. L., and Coauthors, 2004: The new GFDL global atmosphere and land model AM2–LM2: Evaluation with prescribed SST simulations. J. Climate, 17, 46414673, doi:10.1175/JCLI-3223.1.

    • Search Google Scholar
    • Export Citation
  • Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, part I. J. Atmos. Sci., 31, 674701, doi:10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., M. Kohler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. Rodwell, F. Vitart, and G. Balsamo, 2008: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor. Soc., 134, 13371351, doi:10.1002/qj.289.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., and Harshvardhan, 1987: Thermodynamic constraint on the cloud liquid water feedback in climate models. J. Geophys. Res., 92, 84838485, doi:10.1029/JD092iD07p08483.

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

    • Search Google Scholar
    • Export Citation
  • Bony, S., J.-L. Dufresne, H. L. Treut, J.-J. Morcrette, and C. Senior, 2004: On dynamic and thermodynamic components of cloud changes. Climate Dyn., 22, 7186, doi:10.1007/s00382-003-0369-6.

    • 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
  • Bony, S., and Coauthors, 2015: Clouds, circulation and climate sensitivity. Nat. Geosci., 8, 261268, doi:10.1038/ngeo2398.

  • Bretherton, C. S., 2015: Insights into low-latitude cloud feedbacks from high-resolution models. Philos. Trans. Roy. Soc. London, A373, 20140415, doi:10.1098/rsta.2014.0415.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., J. R. McCaa, and H. Grenier, 2004: A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: Description and 1D results. Mon. Wea. Rev., 132, 864882, doi:10.1175/1520-0493(2004)132<0864:ANPFSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov, 2005: An energy-balance analysis of deep convective self-aggregation above uniform SST. J. Atmos. Sci., 62, 42734292, doi:10.1175/JAS3614.1.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., P. N. Blossey, and C. Jones, 2013: Mechanisms of marine low cloud sensitivity to idealized climate perturbations: A single-LES exploration extending the CGILS cases. J. Adv. Model. Earth Syst., 5, 316337, doi:10.1002/jame.20019.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., P. N. Blossey, and C. Stan, 2014: Cloud feedbacks on greenhouse warming in the superparameterized climate model SP-CCSM4. J. Adv. Model. Earth Syst., 6, 11851204, doi:10.1002/2014MS000355.

    • Search Google Scholar
    • Export Citation
  • Brient, F., and S. Bony, 2012: How may low-cloud radiative properties simulated in the current climate influence low-cloud feedbacks under global warming? Geophys. Res. Lett., 39, L20807, doi:10.1029/2012GL053265.

    • Search Google Scholar
    • Export Citation
  • Brient, F., and S. Bony, 2013: Interpretation of the positive low-cloud feedback predicted by a climate model under global warming. Climate Dyn., 40, 24152431, doi:10.1007/s00382-011-1279-7.

    • Search Google Scholar
    • Export Citation
  • Brient, F., T. Schneider, Z. Tan, and S. Bony, 2015: Shallowness of tropical low clouds as a predictor of climate models’ response to warming. Climate Dyn., doi:10.1007/s00382-015-2846-0.

    • Search Google Scholar
    • Export Citation
  • Cess, R., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16 60116 615, doi:10.1029/JD095iD10p16601.

    • Search Google Scholar
    • Export Citation
  • Cess, R., and Coauthors, 1996: Cloud feedback in atmospheric general circulation model: An update. J. Geophys. Res., 101, 12 79112 794, doi:10.1029/96JD00822.

    • Search Google Scholar
    • Export Citation
  • de Roode, S., and P. Duynkerke, 2000: Analogies between mass-flux and Reynolds-averaged equations. J. Atmos. Sci., 57, 15851598, doi:10.1175/1520-0469(2000)057<1585:ABMFAR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Climate, 24, 34843519, doi:10.1175/2011JCLI3955.1.

    • Search Google Scholar
    • Export Citation
  • Donohoe, A., K. C. Armour, A. G. Pendergrass, and D. S. Battisti, 2014: Shortwave and longwave radiative contributions to global warming under increasing CO2. Proc. Natl. Acad. Sci. USA, 111, 16 70016 705, doi:10.1073/pnas.1412190111.

    • 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, doi:10.1175/2008JCLI2239.1.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1991: A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 23132335, doi:10.1175/1520-0469(1991)048<2313:ASFRCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., and M. Zivkovic-Rothman, 1999: Development and evaluation of a convection scheme for use in climate models. J. Atmos. Sci., 56, 17661782, doi:10.1175/1520-0469(1999)056<1766:DAEOAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., A. Wing, and E. Vincent, 2013: Radiative-convective instability. J. Adv. Model. Earth Syst., 6, 7590, doi:10.1002/2013MS000270.

    • 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, 741866. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter09_FINAL.pdf.]

    • Search Google Scholar
    • Export Citation
  • Golaz, J.-C., L. Horowitz, and H. Levy II, 2013: Cloud tuning in a coupled climate model: Impact on 20th century warming. Geophys. Res. Lett., 40, 22462251, doi:10.1002/grl.50232.

    • Search Google Scholar
    • Export Citation
  • Gregory, D., and P. R. Rowntree, 1990: A mass flux scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon. Wea. Rev., 118, 14831506, doi:10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D., and K. Larson, 2002: An important constraint on tropical cloud–climate feedback. Geophys. Res. Lett., 29, 1951, doi:10.1029/2002GL015835.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., M. Zhao, and B. Wyman, 2007: Dynamic radiative–convective equilibria using GCM column physics. J. Atmos. Sci., 64, 228238, doi:10.1175/JAS3825.11.

    • Search Google Scholar
    • Export Citation
  • Joshi, M. M., M. J. Webb, A. C. Maycock, and M. Collins, 2010: Stratospheric water vapour and high climate sensitivity in a version of the HadSM3 climate model. Atmos. Chem. Phys., 10, 71617167, doi:10.5194/acp-10-7161-2010.

    • Search Google Scholar
    • Export Citation
  • Lindzen, R., M.-D. Chou, and A. Hou, 2001: Does the earth have an adaptive infrared iris. Bull. Amer. Meteor. Soc., 82, 417432, doi:10.1175/1520-0477(2001)082<0417:DTEHAA>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mauritsen, T., and B. Stevens, 2015: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nat. Geosci., 8, 346351, doi:10.1038/ngeo2414.

    • Search Google Scholar
    • Export Citation
  • Medeiros, B., B. Stevens, I. Held, M. Zhao, D. Williamson, J. Olson, and C. Bretherton, 2008: Aquaplanets, climate sensitivity, and low clouds. J. Climate, 21, 49744991, doi:10.1175/2008JCLI1995.1.

    • Search Google Scholar
    • Export Citation
  • Mitchell, J., C. Senior, and W. Ingram, 1989: CO2 and climate: A missing feedback? Nature, 341, 132134, doi:10.1038/341132a0.

  • Moorthi, S., and M. Suarez, 1992: Relaxed Arakawa Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002, doi:10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Muller, C., and I. Held, 2012: Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. J. Atmos. Sci., 69, 25512565, doi:10.1175/JAS-D-11-0257.1.

    • Search Google Scholar
    • Export Citation
  • Murphy, J. M., D. M. H. Sexton, D. N. Barnett, G. S. Jones, M. J. Webb, M. Collins, and D. A. Stainforth, 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430, 768772, doi:10.1038/nature02771.

    • Search Google Scholar
    • Export Citation
  • Nuijens, L., B. Stevens, and A. Siebesma, 2009: The environment of precipitating shallow cumulus convection. J. Atmos. Sci., 66, 19621979, doi:10.1175/2008JAS2841.1.

    • 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, 589662. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf.]

    • Search Google Scholar
    • Export Citation
  • Rayner, R., D. Parker, E. Horton, C. Folland, L. Alexander, and D. Rowel, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Rieck, M., L. Nuijens, and B. Stevens, 2012: Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere. J. Atmos. Sci., 69, 25382550, doi:10.1175/JAS-D-11-0203.1.

    • Search Google Scholar
    • Export Citation
  • Ringer, M., and Coauthors., 2006: Global mean cloud feedbacks in idealized climate change experiments. Geophys. Res. Lett., 33, L07718, doi:10.1029/2005GL025370.

    • Search Google Scholar
    • Export Citation
  • Ringer, M., T. Andrews, and M. Webb, 2014: Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere–ocean climate change experiments. Geophys. Res. Lett., 41, 40354042, doi:10.1002/2014GL060347.

    • Search Google Scholar
    • Export Citation
  • Roeckner, E., U. Schlese, J. Biercamp, and P. Loewe, 1987: Cloud optical depth feedbacks and climate modelling. Nature, 329, 138140, doi:10.1038/329138a0.

    • Search Google Scholar
    • Export Citation
  • Sanderson, B. M., C. Piani, W. J. Ingram, D. A. Stone, and M. R. Allen, 2008: Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Climate Dyn., 30, 175190, doi:10.1007/s00382-007-0280-7.

    • Search Google Scholar
    • Export Citation
  • Sanderson, B. M., K. M. Shell, and W. Ingram, 2010: Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs. Climate Dyn., 35, 12191236, doi:10.1007/s00382-009-0661-1.

    • Search Google Scholar
    • Export Citation
  • Senior, C. A., and J. F. B. Mitchell, 1993: Carbon dioxide and climate: The impact of cloud parameterization. J. Climate, 6, 393418, doi:10.1175/1520-0442(1993)006<0393:CDACTI>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Siebesma, A., P. Soares, and J. Teixeira, 2007: A combined eddy-diffusivity mass-flux approach for the convective boundary layer. J. Atmos. Sci., 64, 12301248, doi:10.1175/JAS3888.1.

    • Search Google Scholar
    • Export Citation
  • Smith, G. L., K. J. Priestley, N. G. Loeb, B. A. Wielicki, T. P. Charlock, P. Minnis, D. R. Doelling, and D. A. Rutan, 2011: Clouds and Earth Radiant Energy System (CERES), a review: Past, present and future. Adv. Space Res., 48, 254263, doi:10.1016/j.asr.2011.03.009.

    • Search Google Scholar
    • Export Citation
  • Soden, B., and I. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, 19, 33543360, doi:10.1175/JCLI3799.1.

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

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

    • Search Google Scholar
    • Export Citation
  • Somerville, R., and L. A. Remer, 1984: Cloud optical thickness feedbacks in the CO2 climate problem. J. Geophys. Res., 89, 96689672, doi:10.1029/JD089iD06p09668.

    • Search Google Scholar
    • Export Citation
  • Song, X., and G. J. Zhang, 2011: Microphysics parameterization for convective clouds in a global climate model: Description and single-column model test. J. Geophys. Res., 116, D02201, doi:10.1029/2010JD014833.

    • Search Google Scholar
    • Export Citation
  • Stainforth, D. A., and Coauthors, 2005: Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433, 403406, doi:10.1038/nature03301.

    • Search Google Scholar
    • Export Citation
  • Stephens, G., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, doi:10.1175/JCLI-3243.1.

  • Stephens, G., and T. D. Ellis, 2008: Controls of global-mean precipitation increases in global warming GCM experiments. J. Climate, 21, 61416155, doi:10.1175/2008JCLI2144.1.

    • Search Google Scholar
    • Export Citation
  • Stephens, G., and Coauthors, 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.

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

  • Stevens, B., and Coauthors, 2013: The atmospheric component of the MPI-M earth system model: ECHAM6. J. Adv. Model. Earth Syst., 5, 146172, doi:10.1002/jame.20015.

    • Search Google Scholar
    • Export Citation
  • Suzuki, K., J.-C. Golaz, and G. Stephens, 2013: Evaluating cloud tuning in a climate model with satellite observations. Geophys. Res. Lett., 40, 44644468, doi:10.1002/grl.50874.

    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 17791800, doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tobin, I., S. Bony, and R. Roca, 2012: Observational evidence for relationships between the degree of aggregation of deep convection, water vapor, surface fluxes, and radiation. J. Climate, 25, 68856904, doi:10.1175/JCLI-D-11-00258.1.

    • 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, 33393362, doi:10.1007/s00382-013-1725-9.

    • Search Google Scholar
    • Export Citation
  • Walsh, K., and Coauthors, 2015: Hurricanes and climate: The U.S. CLIVAR Working Group on Hurricanes. Bull. Amer. Meteor. Soc., 96, 9971017, doi:10.1175/BAMS-D-13-00242.1.

    • Search Google Scholar
    • Export Citation
  • Webb, M., and Coauthors, 2006: On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Climate Dyn., 27, 1738, doi:10.1007/s00382-006-0111-2.

    • Search Google Scholar
    • Export Citation
  • Webb, M., F. H. Lambert, and J. Gregory, 2013: Origins of differences in climate sensitivity, forcing and feedback in climate models. Climate Dyn., 40, 677707, doi:10.1007/s00382-012-1336-x.

    • Search Google Scholar
    • Export Citation
  • Webb, M., and Coauthors, 2015: The impact of parametrized convection on cloud feedback. Philos. Trans. Roy. Soc. London, A373, 20140414, doi:10.1098/rsta.2014.0414.

    • Search Google Scholar
    • Export Citation
  • Wetherald, R. T., and S. Manabe, 1988: Cloud feedback processes in a general circulation model. J. Atmos. Sci., 45, 13971415, doi:10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wyant, M. C., C. S. Bretherton, J. T. Bacmeister, J. T. Kiehl, I. M. Held, M. Zhao, S. A. Klein, and B. J. Soden, 2006: A comparison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity. Climate Dyn., 27, 261279, doi:10.1007/s00382-006-0138-4.

    • Search Google Scholar
    • Export Citation
  • Wyant, M. C., C. S. Bretherton, and P. Blossey, 2009: Subtropical low cloud response to a warmer climate in a superparameterized climate model. Part I: Regime sorting and physical mechanisms. J. Adv. Model. Earth Syst., 1, 7, doi:10.3894/JAMES.2009.1.7.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M., K. Klein, K. Taylor, T. Andrews, M. Webb, J. Gregory, and P. Forster, 2013: Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. J. Climate, 26, 50075027, doi:10.1175/JCLI-D-12-00555.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate centre general circulation model. Atmos.–Ocean, 33, 407446, doi:10.1080/07055900.1995.9649539.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., J. Hack, J. Kiehl, and R. Cess, 1994: Diagnostic study of climate feedback processes in atmospheric general circulation models. J. Geophys. Res., 99, 55255537, doi:10.1029/93JD03523.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., and Coauthors, 2013: CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models. J. Adv. Model. Earth Syst., 5, 826842, doi:10.1002/2013MS000246.

    • 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, doi:10.1175/JCLI-D-13-00145.1.

    • Search Google Scholar
    • Export Citation
  • Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 66536678, doi:10.1175/2009JCLI3049.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 4617 1038 63
PDF Downloads 1359 308 15