Analysis of the WRF-Chem contributions to AQMEII phase2 with respect to aerosol radiative feedbacks on meteorology and pollutant distributions

Abstract As a contribution to phase2 of the Air Quality Model Evaluation International Initiative (AQMEII), eight different simulations for the year 2010 were performed with WRF-Chem for the European domain. The four simulations using RADM2 gas-phase chemistry and the MADE/SORGAM aerosol module are analyzed in this paper. The simulations included different degrees of aerosol–meteorology feedback, ranging from no aerosol effects at all to the inclusion of the aerosol direct radiative effect as well as aerosol cloud interactions and the aerosol indirect effect. In addition, a modification of the RADM2 gas phase chemistry solver was tested. The yearly simulations allow characterizing the average impact of the consideration of feedback effects on meteorology and pollutant concentrations and an analysis of the seasonality. Pronounced feedback effects were found for the summer 2010 Russian wildfire episode, where the direct aerosol effect lowered the seasonal mean solar radiation by 20 W m−3 and seasonal mean temperature by 0.25°. This might be considered as a lower limit as it must be taken into account that aerosol concentrations were generally underestimated by up to 50%. The high aerosol concentrations from the wildfires resulted in a 10%–30% decreased precipitation over Russia when aerosol cloud interactions were taken into account. The most pronounced and persistent feedback due to the indirect aerosol effect was found for regions with very low aerosol concentrations like the Atlantic and Northern Europe. The low aerosol concentrations in this area result in very low cloud droplet numbers between 5 and 100 droplets cm−1 and a 50–70% lower cloud liquid water path. This leads to an increase in the downward solar radiation by almost 50%. Over Northern Scandinavia, this results in almost one degree higher mean temperatures during summer. In winter, the decreased liquid water path resulted in increased long-wave cooling and a decrease of the mean temperature by almost the same amount. Precipitation over the Atlantic Ocean was found to be enhanced by up to 30% when aerosol cloud interactions were taken into account. The inclusion of aerosol cloud interactions can reduce the bias or improve correlations of simulated precipitation for some episodes and regions. However, the domain and time averaged performance statistics do not indicate a general improvement when aerosol feedbacks are taken into account. Except for conditions with either very low or very high aerosol concentrations, the impact of aerosol feedbacks on pollutant distributions was found to be smaller than the effect of the choice of the chemistry module or wet deposition implementation.

[1]  Michael D. Moran,et al.  Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII , 2012 .

[2]  S. Twomey Pollution and the Planetary Albedo , 1974 .

[3]  M. Jacobson Development and application of a new air pollution modeling system-part I: Gas-phase simulations , 1997 .

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

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

[6]  Georg A. Grell,et al.  Integrated modeling for forecasting weather and air quality: A call for fully coupled approaches , 2011 .

[7]  Gabriele Curci,et al.  Sensitivity analysis of the microphysics scheme in WRF-Chem contributions to AQMEII phase 2 , 2015 .

[8]  Jean-Noël Thépaut,et al.  The MACC reanalysis: an 8 yr data set of atmospheric composition , 2012 .

[9]  Gabriele Curci,et al.  Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone , 2015 .

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

[11]  Gabriele Curci,et al.  Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate matter , 2015 .

[12]  Leonard K. Peters,et al.  A new lumped structure photochemical mechanism for large‐scale applications , 1999 .

[13]  W. Collins,et al.  Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models , 2008 .

[14]  New Directions: Understanding interactions of air quality and climate change at regional scales , 2012 .

[15]  Yang Zhang,et al.  Online coupled regional meteorology chemistry models in Europe: current status and prospects , 2013 .

[16]  C. Kottmeier,et al.  Regional scale effects of the aerosol cloud interaction simulated with an online coupled comprehensive chemistry model , 2011 .

[17]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[18]  Stefan Emeis,et al.  Application of a multiscale, coupled MM5/chemistry model to the complex terrain of the VOTALP valley campaign , 2000 .

[19]  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 .

[20]  Spyros N. Pandis,et al.  Optimizing model performance: variable size resolution in cloud chemistry modeling , 2001 .

[21]  Oliver Wild,et al.  Fast-J: Accurate Simulation of In- and Below-Cloud Photolysis in Tropospheric Chemical Models , 2000 .

[22]  G. Grell,et al.  WRF-Chem model sensitivity to chemical mechanisms choice in reconstructing aerosol optical properties , 2015 .

[23]  Jaakko Kukkonen,et al.  An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting , 2009 .

[24]  Steven E. Peckham,et al.  Online versus offline air quality modeling on cloud‐resolving scales , 2004 .

[25]  S. Madronich,et al.  Influence of the choice of gas-phase mechanism on predictions of key gaseous pollutants during the AQMEII phase-2 intercomparison , 2015 .

[26]  Gabriele Curci,et al.  Sensitivity of feedback effects in CBMZ/MOSAIC chemical mechanism , 2015 .

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

[28]  Michael D. Moran,et al.  Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII , 2012 .

[29]  Yang Zhang,et al.  Online-coupled meteorology and chemistry models: history, current status, and outlook , 2008 .

[30]  C. Hogrefe,et al.  Feedbacks between air pollution and weather, part 2: Effects on chemistry , 2015 .

[31]  C. Walcek,et al.  A Theoretical Method for Computing Vertical Distributions of Acidity and Sulfate Production within Cumulus Clouds , 1986 .

[32]  Efisio Solazzo,et al.  ENSEMBLE and AMET: Two systems and approaches to a harmonized, simplified and efficient facility for air quality models development and evaluation , 2012 .

[33]  F. Kirchner,et al.  A new mechanism for regional atmospheric chemistry modeling , 1997 .

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

[35]  P. Hess,et al.  How does climate change contribute to surface ozone change over the United States , 2006 .

[36]  P. Jones,et al.  A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .

[37]  M. Memmesheimer,et al.  Modal aerosol dynamics model for Europe: development and first applications , 1998 .

[38]  Bent Hansen Sass,et al.  Online coupled chemical weather forecasting based on HIRLAM - overview and prospective of Enviro-HIRLAM , 2008 .

[39]  Stefano Galmarini,et al.  Air Quality Model Evaluation International Initiative (AQMEII): Advancing the State of the Science in Regional Photochemical Modeling and Its Applications , 2011 .

[40]  Christina Mitsakou,et al.  Impact of natural aerosols on atmospheric radiation and consequent feedbacks with the meteorological and photochemical state of the atmosphere , 2014 .

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

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

[43]  A.J.H. Visschedijk,et al.  TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling , 2014 .

[44]  Michael D. Moran,et al.  Comparing emission inventories and model-ready emission datasets between Europe and North America for the AQMEII project , 2012 .

[45]  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 .

[46]  J. Coakley,et al.  Climate Forcing by Anthropogenic Aerosols , 1992, Science.

[47]  Jeremy P. Rishel,et al.  An evaluation of the wind erosion module in DUSTRAN , 2008 .

[48]  William I. Gustafson,et al.  Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem , 2011 .

[49]  R. Sokhi,et al.  Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII Phase-2 , 2015 .

[50]  Gabriele Curci,et al.  Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2 , 2015 .

[51]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[52]  I. J. Ackermann,et al.  Modeling the formation of secondary organic aerosol within a comprehensive air quality model system , 2001 .

[53]  M. Andreae,et al.  Emission of trace gases and aerosols from biomass burning , 2001 .

[54]  T. Stanelle,et al.  The comprehensive model system COSMO-ART – Radiative impact of aerosol on the state of the atmosphere on the regional scale , 2009 .

[55]  Stefano Galmarini,et al.  Web-based system for decision support in case of emergency: ensemble modelling of long-range atmospheric dispersion of radionuclides , 2004, Environ. Model. Softw..

[56]  C. Hogrefe,et al.  Feedbacks between air pollution and weather, Part 1: Effects on weather , 2015 .

[57]  Zev Levin,et al.  An integrated modeling study on the effects of mineral dust and sea salt particles on clouds and precipitation , 2010 .

[58]  J. Dudhia,et al.  Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model , 2012 .

[59]  U. Blahak,et al.  Saharan Dust Event Impacts on Cloud Formation and Radiation over Western Europe , 2011 .

[60]  Xindi Bian,et al.  MIRAGE: Model description and evaluation of aerosols and trace gases , 2004 .

[61]  W. Stockwell,et al.  The second generation regional acid deposition model chemical mechanism for regional air quality modeling , 1990 .

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

[63]  J. Dudhia,et al.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes , 2006 .

[64]  Paul A. Makar,et al.  Analysis of the emission inventories and model-ready emission datasets of Europe and North America for phase 2 of the AQMEII project , 2015 .

[65]  Peter Suppan,et al.  Effect of aerosol-radiation feedback on regional air quality – A case study with WRF/Chem , 2012 .

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