A new WRF-Chem treatment for studying regional scale impacts of cloud-aerosol interactions in parameterized cumuli

A new treatment of cloud-aerosol interactions within parameterized shallow and deep convection has been implemented in WRF-Chem that can be used to better understand the aerosol lifecycle over regional to synoptic scales. The modifications to the model to represent cloud-aerosol interactions include treatment of the cloud dropletnumber mixing ratio; key cloud microphysical and macrophysical parameters (including the updraft fractional area, updraft and downdraft mass fluxes, and entrainment) averaged over the population of shallow clouds, or a single deep convective cloud; and vertical transport, activation/resuspension, aqueous chemistry, and wet removal of aerosol and trace gases in warm clouds. Thesechanges have been implemented in both the WRF-Chem chemistry packages as well as the Kain-Fritsch cumulus parameterization that has been modified to better represent shallow convective clouds. Preliminary testing of the modified WRF-Chem has been completed using observations from the Cumulus Humilis Aerosol Processing Study (CHAPS) as well as a high-resolution simulation that does not include parameterized convection. The simulation results are used to investigate the impact of cloud-aerosol interactions on the regional scale transport of black carbon (BC), organic aerosol (OA), and sulfate aerosol. Based on the simulations presented here, changes in the column integrated BC can be as large as -50%more » when cloud-aerosol interactions are considered (due largely to wet removal), or as large as +35% for sulfate in non-precipitating conditions due to the sulfate production in the parameterized clouds. The modifications to WRF-Chem version 3.2.1 are found to account for changes in the cloud drop number concentration (CDNC) and changes in the chemical composition of cloud-drop residuals in a way that is consistent with observations collected during CHAPS. Efforts are currently underway to port the changes described here to WRF-Chem version 3.5, and it is anticipated that they will be included in a future public release of WRF-Chem.« less

[1]  Evgueni I. Kassianov,et al.  Temporal Variability of Fair-Weather Cumulus Statistics at the ACRF SGP Site , 2008 .

[2]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[3]  B. Stevens,et al.  Untangling aerosol effects on clouds and precipitation in a buffered system , 2009, Nature.

[4]  A. Robinson,et al.  A volatility basis set model for summertime secondary organic aerosols over the eastern United States in 2006 , 2012 .

[5]  Siyu Chen,et al.  Uncertainty in modeling dust mass balance and radiative forcing from size parameterization , 2013 .

[6]  J. Barnard,et al.  Observations of the first aerosol indirect effect in shallow cumuli , 2011 .

[7]  G. Grell,et al.  Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology‐chemistry‐aerosol model , 2006 .

[8]  S. Freitas,et al.  Inclusion of biomass burning in WRF-Chem: impact of wildfires on weather forecasts , 2010 .

[9]  Evgueni I. Kassianov,et al.  Evaluation of a Modified Scheme for Shallow Convection: Implementation of CuP and Case Studies , 2013 .

[10]  Jerome D. Fast,et al.  Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) , 2008 .

[11]  P. Palmer,et al.  Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature) , 2006 .

[12]  P. Rasch,et al.  Manipulating marine stratocumulus cloud amount and albedo: a process-modelling study of aerosol-cloud-precipitation interactions in response to injection of cloud condensation nuclei , 2011 .

[13]  L. Ruby Leung,et al.  Heavy pollution suppresses light rain in China: Observations and modeling , 2009 .

[14]  S. Ghan,et al.  A parameterization of aerosol activation 3. Sectional representation , 2002 .

[15]  Witold F. Krajewski,et al.  The effects of aerosols on intense convective precipitation in the northeastern United States , 2009 .

[16]  J. Lamarque,et al.  Impact of Mexico City emissions on regional air quality from MOZART-4 simulations , 2010 .

[17]  Xiaoliang Song,et al.  Investigation of aerosol indirect effects using a cumulus microphysics parameterization in a regional climate model , 2014 .

[18]  Hitoshi Matsui,et al.  Development and validation of a black carbon mixing state resolved three‐dimensional model: Aging processes and radiative impact , 2013 .

[19]  Yuan Wang,et al.  Aerosol impacts on clouds and precipitation in eastern China: Results from bin and bulk microphysics , 2012 .

[20]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[21]  S. K. Akagi,et al.  The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning , 2010 .

[22]  D. Edwards,et al.  CO source contribution analysis for California during ARCTAS-CARB , 2011 .

[23]  R. Stull,et al.  Parameterization of Joint Frequency Distributions of Potential Temperature and Water-Vapor Mixing Ratio in the Daytime Convective Boundary Layer , 2004 .

[24]  N. McFarlane,et al.  Sensitivity of Climate Simulations to the Parameterization of Cumulus Convection in the Canadian Climate Centre General Circulation Model , 1995, Data, Models and Analysis.

[25]  Y. Qian,et al.  Downscaling aerosols and the impact of neglected subgrid processes on direct aerosol radiative forcing for a representative global climate model grid spacing , 2011 .

[26]  Rohit Mathur,et al.  Evaluation of several PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study: PM2.5 FORECAST MODEL EVALUATION , 2007 .

[27]  A. Nenes,et al.  Parameterization of cloud droplet formation in large‐scale models: Including effects of entrainment , 2007 .

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

[29]  G. Grell.  Prognostic evaluation of assumptions used by cumulus parameterizations , 1993 .

[30]  S. Freitas,et al.  A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling , 2013 .

[31]  Allen L. Robinson,et al.  Effects of gas particle partitioning and aging of primary emissions on urban and regional organic aerosol concentrations , 2008 .

[32]  Georg A. Grell,et al.  Fully coupled “online” chemistry within the WRF model , 2005 .

[33]  Zavisa Janjic,et al.  The Step-Mountain Coordinate: Physical Package , 1990 .

[34]  W. Collins,et al.  Description of the NCAR Community Atmosphere Model (CAM 3.0) , 2004 .

[35]  P.-L. Ma,et al.  Assessing the CAM5 Physics Suite in the WRF-Chem Model : Implementation, Evaluation, and Resolution Sensitivity , 2013 .

[36]  Larry K. Berg,et al.  Overview of the Cumulus Humilis Aerosol Processing Study , 2007 .

[37]  Steven J. Ghan,et al.  Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources , 2008 .

[38]  D. Koch,et al.  Clouds and sulfate are anticorrelated: A new diagnostic for global sulfur models , 2003 .

[39]  G. Grell,et al.  A generalized approach to parameterizing convection combining ensemble and data assimilation techniques , 2002 .

[40]  Larry K. Berg,et al.  A simple parameterization coupling the convective daytime boundary layer and fair-weather cumuli , 2005 .

[41]  L. Ruby Leung,et al.  Radiative impact of mineral dust on monsoon precipitation variability over West Africa , 2010 .

[42]  A. Hodzic,et al.  Modeling organic aerosols in a megacity: comparison of simple and complex representations of the volatility basis set approach , 2010 .

[43]  J. Curry,et al.  A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description , 2005 .

[44]  G. Thompson,et al.  Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes , 2009 .

[45]  S. Ghan,et al.  Sensitivity of remote aerosol distributions to representation of cloud–aerosol interactions in a global climate model , 2013 .

[46]  J. Lamarque,et al.  Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4) , 2009 .

[47]  Spyros N. Pandis,et al.  Evaluation of the volatility basis-set approach for the simulation of organic aerosol formation in the Mexico City metropolitan area , 2009 .

[48]  Roland B. Stull,et al.  A Fair-Weather Cumulus Cloud Classification Scheme for Mixed-Layer Studies , 1985 .

[49]  S. Madronich,et al.  Modeling organic aerosols in a megacity: Potential contribution of semi-volatile and intermediate volatility primary organic compounds to secondary organic aerosol formation , 2010 .

[50]  Steven J. Ghan,et al.  Impact of cloud-borne aerosol representation on aerosol direct and indirect effects , 2006 .

[51]  Steven J. Ghan,et al.  Impact on modeled cloud characteristics due to simplified treatment of uniform cloud condensation nuclei during NEAQS 2004 , 2007 .

[52]  A. Laskin,et al.  Modeling aerosols and their interactions with shallow cumuli during the 2007 CHAPS field study , 2013 .