GCM intercomparison of global cloud regimes: present-day evaluation and climate change response

The radiative feedback from clouds remains the largest source of variation in climate sensitivity amongst general circulation models (GCMs). A cloud clustering methodology is applied to six contemporary GCMs in order to provide a detailed intercomparison and evaluation of the simulated cloud regimes. By analysing GCMs in the context of cloud regimes, processes related to particular cloud types are more likely to be evaluated. In this paper, the mean properties of the global cloud regimes are evaluated, and the cloud response to climate change is analysed in the cloud-regime framework. Most of the GCMs are able to simulate the principal cloud regimes, however none of the models analysed have a good representation of trade cumulus in the tropics. The models also share a difficulty in simulating those regimes with cloud tops at mid-levels, with only ECHAM5 producing a regime of tropical cumulus congestus. Optically thick, high top cloud in the extra-tropics, typically associated with the passage of frontal systems, is simulated considerably too frequently in the ECHAM5 model. This appears to be a result of the cloud type persisting in the model after the meteorological conditions associated with frontal systems have ceased. The simulation of stratocumulus in the MIROC GCMs is too extensive, resulting in the tropics being too reflective. Most of the global-mean cloud response to doubled CO2 in the GCMs is found to be a result of changes in the cloud radiative properties of the regimes, rather than changes in the relative frequency of occurrence (RFO) of the regimes. Most of the variance in the global cloud response between the GCMs arises from differences in the radiative response of frontal cloud in the extra-tropics and from stratocumulus cloud in the tropics. This variance is largely the result of excessively high RFOs of specific regimes in particular GCMs. It is shown here that evaluation and subsequent improvement in the simulation of the present-day regime properties has the potential to reduce the variance of the global cloud response, and hence climate sensitivity, amongst GCMs. For the ensemble of models considered in this study, the use of observations of the mean present-day cloud regimes suggests a potential reduction in the range of climate sensitivity of almost a third.

[1]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[2]  S. Manabe,et al.  Cloud Feedback Processes in a General Circulation Model , 1988 .

[3]  John F. B. Mitchell,et al.  Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models , 1990 .

[4]  W. Rossow,et al.  ISCCP Cloud Data Products , 1991 .

[5]  John F. B. Mitchell,et al.  Carbon Dioxide and Climate. The Impact of Cloud Parameterization , 1993 .

[6]  S. Klein,et al.  The Seasonal Cycle of Low Stratiform Clouds , 1993 .

[7]  S. Klein,et al.  On the Relationships among Low-Cloud Structure, Sea Surface Temperature, and Atmospheric Circulation in the Summertime Northeast Pacific , 1995 .

[8]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[9]  George C. Craig,et al.  Sensitivity of Tropical Convection to Sea Surface Temperature in the Absence of Large-Scale Flow , 1999 .

[10]  S. Klein,et al.  Validation and Sensitivities of Frontal Clouds Simulated by the ECMWF Model , 1999 .

[11]  Taneil Uttal,et al.  Variability of Cloud Vertical Structure during ASTEX Observed from a Combination of Rawinsonde, Radar, Ceilometer, and Satellite , 1999 .

[12]  Adrian Lock,et al.  A New Boundary Layer Mixing Scheme. Part II: Tests in Climate and Mesoscale Models , 2000 .

[13]  G. Tselioudis,et al.  Cloud and Radiation Variations Associated with Northern Midlatitude Low and High Sea Level Pressure Regimes , 2000 .

[14]  V. Pope,et al.  The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3 , 2000 .

[15]  Christopher P. Weaver,et al.  Improved Techniques for Evaluating GCM Cloudiness Applied to the NCAR CCM3 , 2001 .

[16]  M. Collins,et al.  Projections of future climate change , 2002 .

[17]  S. Bony,et al.  Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models , 2001 .

[18]  John F. B. Mitchell,et al.  Transient Climate Change in the Hadley Centre Models: The Role of Physical Processes , 2001 .

[19]  G. Tselioudis,et al.  Evaluation of midlatitude cloud properties in a weather and a climate model: Dependence on dynamic regime and spatial resolution , 2002 .

[20]  Luca Bonaventura,et al.  The atmospheric general circulation model ECHAM 5. PART I: Model description , 2003 .

[21]  G. Tselioudis,et al.  Objective identification of cloud regimes in the Tropical Western Pacific , 2003 .

[22]  M. Ringer,et al.  Evaluating the cloud response to climate change and current climate variability , 2003 .

[23]  G. Boer,et al.  Climate sensitivity and response , 2003 .

[24]  W. Rossow,et al.  International satellite cloud climatology project (ISCCP): cd documentation , 2003 .

[25]  A. Lacis,et al.  Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data , 2004 .

[26]  S. Bony,et al.  On dynamic and thermodynamic components of cloud changes , 2004 .

[27]  M. Ringer,et al.  Evaluating climate model simulations of tropical cloud , 2004 .

[28]  Mark A. R Inger,et al.  Evaluating climate model simulations of tropical cloud , 2004 .

[29]  William B. Rossow,et al.  Tropical climate described as a distribution of weather states indicated by distinct mesoscale cloud property mixtures , 2005 .

[30]  K. Williams,et al.  Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes , 2005 .

[31]  S. Bony,et al.  Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models , 2005 .

[32]  M. Winton Simple Optical Models for Diagnosing Surface-Atmosphere Shortwave Interactions. , 2005 .

[33]  Nicolas Clerbaux,et al.  Can desert dust explain the outgoing longwave radiation anomaly over the Sahara during July 2003 , 2005 .

[34]  S. Bony,et al.  Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements , 2005 .

[35]  George Tselioudis,et al.  The Radiative, Cloud, and Thermodynamic Properties of the Major Tropical Western Pacific Cloud Regimes , 2005 .

[36]  I. Musat,et al.  Evaluation of a component of the cloud response to climate change in an intercomparison of climate models , 2006 .

[37]  G. Martin,et al.  The Physical Properties of the Atmosphere in the New Hadley Centre Global Environmental Model (HadGEM1). Part I: Model Description and Global Climatology , 2006 .

[38]  Michel Crucifix,et al.  The new hadley centre climate model (HadGEM1) : Evaluation of coupled simulations , 2006 .

[39]  I. Musat,et al.  On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles , 2006 .

[40]  Johannes Quaas,et al.  Global mean cloud feedbacks in idealized climate change experiments , 2006 .

[41]  B. Soden,et al.  An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models , 2006 .

[42]  Deliang Chen,et al.  Projections of Future Anthropogenic Climate Change , 2008 .