A multi-diagnostic approach to cloud evaluation

Abstract. Most studies evaluating cloud in general circulation models present new diagnostic techniques or observational datasets, or apply a limited set of existing diagnostics to a number of models. In this study, we use a range of diagnostic techniques and observational datasets to provide a thorough evaluation of cloud, such as might be carried out during a model development process. The methodology is illustrated by analysing two configurations of the Met Office Unified Model – the currently operational configuration at the time of undertaking the study (Global Atmosphere 6, GA6), and the configuration which will underpin the United Kingdom's Earth System Model for CMIP6 (Coupled Model Intercomparison Project 6; GA7). By undertaking a more comprehensive analysis which includes compositing techniques, comparing against a set of quite different observational instruments and evaluating the model across a range of timescales, the risks of drawing the wrong conclusions due to compensating model errors are minimized and a more accurate overall picture of model performance can be drawn. Overall the two configurations analysed perform well, especially in terms of cloud amount. GA6 has excessive thin cirrus which is removed in GA7. The primary remaining errors in both configurations are the in-cloud albedos which are too high in most Northern Hemisphere cloud types and sub-tropical stratocumulus, whilst the stratocumulus on the cold-air side of Southern Hemisphere cyclones has in-cloud albedos which are too low.

[1]  Martyn P. Chipperfield,et al.  Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model , 2010 .

[2]  P. Earnshaw,et al.  The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations , 2011, Geoscientific Model Development.

[3]  S. Bony,et al.  The ‘too few, too bright’ tropical low‐cloud problem in CMIP5 models , 2012 .

[4]  P. Field,et al.  Snow Size Distribution Parameterization for Midlatitude and Tropical Ice Clouds , 2007 .

[5]  W. Rossow,et al.  Advances in understanding clouds from ISCCP , 1999 .

[6]  W. Gates AMIP: The Atmospheric Model Intercomparison Project. , 1992 .

[7]  E. O'connor,et al.  The CloudSat mission and the A-train: a new dimension of space-based observations of clouds and precipitation , 2002 .

[8]  M. Khairoutdinov,et al.  A New Cloud Physics Parameterization in a Large-Eddy Simulation Model of Marine Stratocumulus , 2000 .

[9]  The Role of Shear in the Morning Transition Boundary Layer , 2008 .

[10]  S. Bony,et al.  Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model , 2008 .

[11]  Michael J. Reeder,et al.  The three‐dimensional distribution of clouds around Southern Hemisphere extratropical cyclones , 2011 .

[12]  C. Bretherton,et al.  On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability , 2006 .

[13]  K. Williams,et al.  Initial Tendencies of Cloud Regimes in the Met Office Unified Model , 2008 .

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

[15]  S. Bony,et al.  The GCM‐Oriented CALIPSO Cloud Product (CALIPSO‐GOCCP) , 2010 .

[16]  G. Tselioudis,et al.  GCM intercomparison of global cloud regimes: present-day evaluation and climate change response , 2007 .

[17]  Charles Doutriaux,et al.  Performance metrics for climate models , 2008 .

[18]  Gerald G. Mace,et al.  The CloudSat radar‐lidar geometrical profile product (RL‐GeoProf): Updates, improvements, and selected results , 2014 .

[19]  Tsuyoshi Koshiro,et al.  Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2 Models , 2014 .

[20]  C. Jones,et al.  The HadGEM2 family of Met Office Unified Model climate configurations , 2011 .

[21]  John M. Haynes,et al.  COSP: Satellite simulation software for model assessment , 2011 .

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

[23]  S. Klein,et al.  Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator , 2012 .

[24]  G. Tselioudis,et al.  Global Weather States and Their Properties from Passive and Active Satellite Cloud Retrievals , 2013 .

[25]  G. Mace,et al.  Evaluation of the Hydrometeor Layers in the East and West Pacific within ISCCP Cloud-Top Pressure–Optical Depth Bins Using Merged CloudSat and CALIPSO Data , 2013 .

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

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

[28]  Franco Marenco,et al.  A self‐consistent scattering model for cirrus. II: The high and low frequencies , 2014 .

[29]  Masahiro Watanabe,et al.  The Transpose-AMIP II Experiment and Its Application to the Understanding of Southern Ocean Cloud Biases in Climate Models , 2012 .

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

[31]  I. Boutle,et al.  Spatial variability of liquid cloud and rain: observations and microphysical effects , 2014 .

[32]  P. Field,et al.  Mixed‐phase clouds in a turbulent environment. Part 2: Analytic treatment , 2014 .

[33]  R. Marchand,et al.  Hydrometeor Detection Using Cloudsat—An Earth-Orbiting 94-GHz Cloud Radar , 2008 .

[34]  J. Norris,et al.  On the Relationships between Subtropical Clouds and Meteorology in Observations and CMIP3 and CMIP5 Models , 2015 .

[35]  H. Chepfer,et al.  Comparison of Two Different Cloud Climatologies Derived from CALIOP-Attenuated Backscattered Measurements (Level 1): The CALIPSO-ST and the CALIPSO-GOCCP , 2013 .

[36]  M. Webb,et al.  A quantitative performance assessment of cloud regimes in climate models , 2009 .

[37]  P. Field,et al.  Precipitation and Cloud Structure in Midlatitude Cyclones , 2007 .

[38]  O. Boucher,et al.  Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2‐ES and the role of ammonium nitrate , 2011 .

[39]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[40]  K. Taylor,et al.  Evaluating the present‐day simulation of clouds, precipitation, and radiation in climate models , 2008 .

[41]  A. Bodas‐Salcedo,et al.  Large contribution of supercooled liquid clouds to the solar radiation budget of the Southern Ocean , 2016 .

[42]  David R. Doelling,et al.  Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget , 2009 .

[43]  H. Treut,et al.  THE CALIPSO MISSION: A Global 3D View of Aerosols and Clouds , 2010 .

[44]  Marion Mittermaier,et al.  A critical assessment of surface cloud observations and their use for verifying cloud forecasts , 2012 .

[45]  S. Ghan,et al.  A parameterization of aerosol activation: 2. Multiple aerosol types , 2000 .

[46]  W. Collins,et al.  Evaluation of climate models , 2013 .

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

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

[49]  John M. Haynes,et al.  A Multipurpose Radar Simulation Package: QuickBeam , 2007 .

[50]  Yunyan Zhang,et al.  Factors Controlling the Vertical Extent of Fair-Weather Shallow Cumulus Clouds over Land: Investigation of Diurnal-Cycle Observations Collected at the ARM Southern Great Plains Site , 2013 .