Evaluating aerosol//cloud//radiation process parameterizations with single-column models and Second Aerosol Characterization Experiment (ACE-2) cloudy column observations

[1] The Second Aerosol Characterization Experiment (ACE-2) data set along with ECMWF reanalysis meteorological fields provided the basis for the single column model (SCM) simulations, performed as part of the PACE (Parameterization of the Aerosol Indirect Climatic Effect) project. Six different SCMs were used to simulate ACE-2 case studies of clean and polluted cloudy boundary layers, with the objective being to identify limitations of the aerosol/cloud/radiation interaction schemes within the range of uncertainty in in situ, reanalysis and satellite retrieved data. The exercise proceeds in three steps. First, SCMs are configured with the same fine vertical resolution as the ACE-2 in situ data base to evaluate the numerical schemes for prediction of aerosol activation, radiative transfer and precipitation formation. Second, the same test is performed at the coarser vertical resolution of GCMs to evaluate its impact on the performance of the parameterizations. Finally, SCMs are run for a 24–48 hr period to examine predictions of boundary layer clouds when initialized with large-scale meteorological fields. Several schemes were tested for the prediction of cloud droplet number concentration (N). Physically based activation schemes using vertical velocity show noticeable discrepancies compared to empirical schemes due to biases in the diagnosed cloud base vertical velocity. Prognostic schemes exhibit a larger variability than the diagnostic ones, due to a coupling between aerosol activation and drizzle scavenging in the calculation of N. When SCMs are initialized at a fine vertical resolution with locally observed vertical profiles of liquid water, predicted optical properties are comparable to observations. Predictions however degrade at coarser vertical resolution and are more sensitive to the mean liquid water path than to its spatial heterogeneity. Predicted precipitation fluxes are severely underestimated and improve when accounting for sub-grid liquid water variability. Results from the 24–48 hr runs suggest that most models have problems in simulating boundary layer cloud morphology, since the large-scale initialization fields do not accurately reproduce observed meteorological conditions. As a result, models significantly overestimate optical properties. Improved cloud morphologies were obtained for models with subgrid inversions and subgrid cloud thickness schemes. This may be a result of representing subgrid scale effects though we do not rule out the possibility that better large-forcing data may also improve cloud morphology predictions.

[1]  A. Slingo,et al.  Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model , 1996 .

[2]  J. Putaud,et al.  Aerosol activation in marine stratocumulus clouds: 2. Köhler and parcel theory closure studies , 2003 .

[3]  R. Smith A scheme for predicting layer clouds and their water content in a general circulation model , 1990 .

[4]  Steven J. Ghan,et al.  A parameterization of aerosol activation: 1. Single aerosol type , 1998 .

[5]  W. Cotton,et al.  Autoconversion rate bias in stratiform boundary layer cloud parameterizations , 2002 .

[6]  M. Claussen,et al.  The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate , 1996 .

[7]  Olivier Boucher,et al.  The sulfate‐CCN‐cloud albedo effect , 1995 .

[8]  Zhaoxin Li,et al.  Sensitivity of an atmospheric general circulation model to prescribed SST changes: feedback effects associated with the simulation of cloud optical properties , 1991 .

[9]  D. Lilly,et al.  Dynamics and chemistry of marine stratocumulus - DYCOMS II , 2003 .

[10]  H. Treut,et al.  Precipitation and radiation modeling in a general circulation model: Introduction of cloud microphysical processes , 1995 .

[11]  Steven J. Ghan,et al.  Impact of aerosol size representation on modeling aerosol‐cloud interactions , 2002 .

[12]  J. Katzfey,et al.  A Scheme for Calculation of the Liquid Fraction in Mixed-Phase Stratiform Clouds in Large-Scale Models , 2000 .

[13]  George A. Isaac,et al.  Physical and chemical observations in marine stratus during the 1993 North Atlantic Regional Experiment: Factors controlling cloud droplet number concentrations , 1996 .

[14]  Robert F. Cahalan Bounded cascade clouds: albedo and effective thickness , 1994 .

[15]  C. Bretherton,et al.  The Atlantic Stratocumulus Transition Experiment - ASTEX , 1995 .

[16]  Damian R. Wilson,et al.  A microphysically based precipitation scheme for the UK meteorological office unified model , 1999 .

[17]  Audrey B. Wolf,et al.  Intercomparison and evaluation of cumulus parametrizations under summertime midlatitude continental conditions , 2001 .

[18]  P. Rowntree,et al.  A Mass Flux Convection Scheme with Representation of Cloud Ensemble Characteristics and Stability-Dependent Closure , 1990 .

[19]  M. Wendisch,et al.  Microphysics of clouds: Model vs measurements , 1997 .

[20]  M. Yao,et al.  Efficient Cumulus Parameterization for Long-Term Climate Studies: The GISS Scheme , 1993 .

[21]  Stephen B. Fels,et al.  The simplified exchange method revisited: An accurate, rapid method for computation of infrared cooling rates and fluxes , 1991 .

[22]  Olivier Boucher,et al.  History of sulfate aerosol radiative forcings , 2002 .

[23]  A. Tompkins A Prognostic Parameterization for the Subgrid-Scale Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to Diagnose Cloud Cover , 2002 .

[24]  J. Penner,et al.  Correction to “Prediction of the number of cloud droplets in the ECHAM GCM” by Ulrike Lohmann et al. , 1999 .

[25]  L. Ruby Leung,et al.  Prediction of cloud droplet number in a general , 1997 .

[26]  U. Lohmann,et al.  The sulfate-CCN-cloud albedo effect: a sensitivity study with two general circulation models , 1996 .

[27]  L. Schüller,et al.  Radiative Properties of Boundary Layer Clouds: Droplet Effective Radius versus Number Concentration , 2000 .

[28]  Henning Rodhe,et al.  The second Aerosol Characterization Experiment (ACE-2) , 2000 .

[29]  J. Hansen,et al.  A parameterization for the absorption of solar radiation in the earth's atmosphere , 1974 .

[30]  B. Albrecht Aerosols, Cloud Microphysics, and Fractional Cloudiness , 1989, Science.

[31]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[32]  D. W. Johnson,et al.  The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds , 1994 .

[33]  B. Briegleb Delta‐Eddington approximation for solar radiation in the NCAR community climate model , 1992 .

[34]  William R. Cotton,et al.  A Numerical Investigation of Several Factors Contributing to the Observed Variable Intensity of Deep Convection over South Florida , 1980 .

[35]  Frank McGovern,et al.  The 2nd Aerosol Characterization Experiment (ACE-2): general overview and main results , 2000 .

[36]  D. Rind,et al.  Improved surface and boundary layer models for the Goddard Institute for Space Studies general circulation model , 1997 .

[37]  J. Hansen,et al.  Light scattering in planetary atmospheres , 1974 .

[38]  J. Louis A parametric model of vertical eddy fluxes in the atmosphere , 1979 .

[39]  U. Lohmann Possible Aerosol Effects on Ice Clouds via Contact Nucleation , 2002 .

[40]  Sally A. McFarlane,et al.  Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites , 1999 .

[41]  Anthony D. Del Genio,et al.  A Prognostic Cloud Water Parameterization for Global Climate Models , 1996 .

[42]  S. Klein,et al.  Unresolved spatial variability and microphysical process rates in large‐scale models , 2000 .

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

[44]  Leon D. Rotstayn,et al.  A physically based scheme for the treatment of stratiform clouds and precipitation in large‐scale models. I: Description and evaluation of the microphysical processes , 1997 .

[45]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[46]  Harshvardhan,et al.  Influence of anthropogenic aerosol on cloud optical depth and albedo shown by satellite measurements and chemical transport modeling , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[47]  A. Lock The Numerical Representation of Entrainment in Parameterizations of Boundary Layer Turbulent Mixing , 2001 .

[48]  L. Schüller,et al.  Retrieval of microphysical, geometrical, and radiative properties of marine stratocumulus from remote sensing , 2003 .

[49]  G. Verver,et al.  The 2nd Aerosol Characterization Experiment (ACE-2): meteorological and chemical context , 2000 .

[50]  Harshvardhan,et al.  Aerosol Influence on Cloud Microphysics Examined by Satellite Measurements and Chemical Transport Modeling , 2002 .

[51]  Jacques Pelon,et al.  An overview of the ACE2 CLOUDYCOLUMN closure experiment , 2000 .

[52]  K. D. Beheng A parameterization of warm cloud microphysical conversion processes , 1994 .

[53]  J. Morcrette Radiation and cloud radiative properties in the European Centre for Medium Range Weather Forecasts forecasting system , 1991 .

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

[55]  George Tselioudis,et al.  GCM Simulations of the Aerosol Indirect Effect: Sensitivity to Cloud Parameterization and Aerosol Burden , 2002 .

[56]  S. Twomey The Influence of Pollution on the Shortwave Albedo of Clouds , 1977 .

[57]  J. Kristjánsson,et al.  Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model , 1989 .

[58]  Andrew S. Jones,et al.  Indirect sulphate aerosol forcing in a climate model with an interactive sulphur cycle , 2001 .

[59]  Leon D. Rotstayn,et al.  On the “tuning” of autoconversion parameterizations in climate models , 2000 .

[60]  J. Brenguier,et al.  Microphysical properties of stratocumulus clouds during ACE‐2 , 2000 .

[61]  C. C. Chuang,et al.  A parameterization of cloud droplet nucleation , 1993 .

[62]  J. Descloitres,et al.  Cloud optical thickness and albedo retrievals from bidirectional reflectance measurements of POLDER instruments during ACE‐2 , 2000 .

[63]  Joyce E. Penner,et al.  An assessment of the radiative effects of anthropogenic sulfate , 1997 .

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

[65]  Hanna Pawlowska,et al.  An observational study of drizzle formation in stratocumulus clouds for general circulation model (GCM) parameterizations , 2003 .

[66]  Ulrike Lohmann,et al.  Erratum: ``Prediction of the number of cloud droplets in the ECHAM GCM'' , 1999 .

[67]  Hanna Pawlowska,et al.  Cloud microphysical and radiative properties for parameterization and satellite monitoring of the indirect effect of aerosol on climate , 2003 .

[68]  Juergen Fischer,et al.  Generating cloudmasks in spatial high-resolution observations of clouds using texture and radiance information , 2002 .

[69]  J. Hansen,et al.  Radiative forcing and climate response , 1997 .

[70]  Thomas M. Smith,et al.  A High-Resolution Global Sea Surface Temperature Climatology , 1995 .

[71]  A. Slingo A GCM Parameterization for the Shortwave Radiative Properties of Water Clouds , 1989 .

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

[73]  S. Ghan,et al.  A Comparison of Three Different Modeling Strategies for Evaluating Cloud and Radiation Parameterizations , 1999 .

[74]  J. Brenguier,et al.  Aerosol activation in marine stratocumulus clouds: 1. Measurement validation for a closure study , 2003 .