Development towards a global operational aerosol consensus: basic climatological characteristics of the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME)

Abstract. Here we present the first steps in developing a global multi-model aerosol forecasting ensemble intended for eventual operational and basic research use. Drawing from members of the International Cooperative for Aerosol Prediction (ICAP) latest generation of quasi-operational aerosol models, 5-day aerosol optical thickness (AOT) forecasts are analyzed for December 2011 through November 2012 from four institutions: European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), NASA Goddard Space Flight Center (GSFC), and Naval Research Lab/Fleet Numerical Meteorology and Oceanography Center (NRL/FNMOC). For dust, we also include the National Oceanic and Atmospheric Administration-National Geospatial Advisory Committee (NOAA NGAC) product in our analysis. The Barcelona Supercomputing Centre and UK Met Office dust products have also recently become members of ICAP, but have insufficient data to be included in this analysis period. A simple consensus ensemble of member and mean AOT fields for modal species (e.g., fine and coarse mode, and a separate dust ensemble) is used to create the ICAP Multi-Model Ensemble (ICAP-MME). The ICAP-MME is run daily at 00:00 UTC for 6-hourly forecasts out to 120 h. Basing metrics on comparisons to 21 regionally representative Aerosol Robotic Network (AERONET) sites, all models generally captured the basic aerosol features of the globe. However, there is an overall AOT low bias among models, particularly for high AOT events. Biomass burning regions have the most diversity in seasonal average AOT. The Southern Ocean, though low in AOT, nevertheless also has high diversity. With regard to root mean square error (RMSE), as expected the ICAP-MME placed first over all models worldwide, and was typically first or second in ranking against all models at individual sites. These results are encouraging; furthermore, as more global operational aerosol models come online, we expect their inclusion in a robust operational multi-model ensemble will provide valuable aerosol forecasting guidance.

[1]  B. White,et al.  Soil Transport by Winds on Mars , 1979 .

[2]  T. Eck,et al.  Spectral discrimination of coarse and fine mode optical depth , 2003 .

[3]  J. Reid,et al.  International Operational Aerosol Observability Workshop , 2011 .

[4]  J. Gröbner,et al.  Ground-based aerosol optical depth trends at three high-altitude sites in Switzerland and southern Germany from 1995 to 2010 , 2012 .

[5]  Donald Dabdub,et al.  Potential significance of photoexcited NO2 on global air quality with the NMMB/BSC chemical transport model , 2012 .

[6]  C. Jones,et al.  Development and evaluation of an Earth-System model - HadGEM2 , 2011 .

[7]  T. Hamill.  Interpretation of Rank Histograms for Verifying Ensemble Forecasts , 2001 .

[8]  M. Razinger,et al.  Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation , 2009 .

[9]  Sara Basart,et al.  Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 2: Experimental campaigns in Northern Africa , 2011, Atmospheric Chemistry and Physics.

[10]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..

[11]  清孝 柴田,et al.  大気大循環モデルMRI/JMA98と結合した全球対流圏エーロゾル化学輸送モデル MASINGAR , 2003 .

[12]  Christopher J. Merchant,et al.  Saharan dust in nighttime thermal imagery: Detection and reduction of related biases in retrieved sea surface temperature , 2006 .

[13]  E. Nilsson,et al.  Laboratory simulations and parameterization of the primary marine aerosol production , 2003 .

[14]  S. Woodward,et al.  Modeling the atmospheric life cycle and radiative impact of mineral dust in the Hadley Centre climate model , 2001 .

[15]  H. Tsujino,et al.  2012-A 02 A New Global Climate Model of the Meteorological Research Institute : MRI-CGCM 3 — Model Description and Basic Performance — , 2012 .

[16]  M. Heimann,et al.  Impact of vegetation and preferential source areas on global dust aerosol: Results from a model study , 2002 .

[17]  Arlindo da Silva,et al.  An adaptive buddy check for observational quality control , 2013 .

[18]  M. Razinger,et al.  Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power , 2011 .

[19]  J. Baldasano,et al.  WMO/GEO Expert Meeting On An International Sand And Dust Storm Warning System , 2009 .

[20]  Alexander Smirnov,et al.  Cloud-Screening and Quality Control Algorithms for the AERONET Database , 2000 .

[21]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[22]  Z. Janjic,et al.  Corrigendum to "Modeling and evaluation of the global sea-salt aerosol distribution: sensitivity to size-resolved and sea-surface temperature dependent emission schemes" published in Atmos. Chem. Phys., 13, 11735–11755, 2013 , 2013 .

[23]  Jaakko Kukkonen,et al.  A review of operational, regional-scale, chemical weather forecasting models in Europe , 2012 .

[24]  Charles R. Sampson,et al.  Experiments with a Simple Tropical Cyclone Intensity Consensus , 2008 .

[25]  J. Kain,et al.  Sensitivity of Several Performance Measures to Displacement Error, Bias, and Event Frequency , 2006 .

[26]  J. Baldasano,et al.  Interactive dust‐radiation modeling: A step to improve weather forecasts , 2006 .

[27]  Ratko Vasic,et al.  A Class of Conservative Fourth-Order Advection Schemes and Impact of Enhanced Formal Accuracy on Extended-Range Forecasts , 2011 .

[28]  Zhaokai Meng,et al.  Single-scattering properties of tri-axial ellipsoidal mineral dust aerosols: A database for application to radiative transfer calculations , 2010 .

[29]  Becky Alexander,et al.  Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations , 2010 .

[30]  R. Buizza,et al.  A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems , 2005 .

[31]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[32]  D. Westphal,et al.  Operational aerosol and dust storm forecasting , 2009 .

[33]  Wei Li,et al.  Radiative feedback of dust aerosols on the East Asian dust storms , 2010 .

[34]  Ramesh P. Singh,et al.  Optical Properties of Fine/Coarse Mode Aerosol Mixtures , 2010 .

[35]  Sara Basart,et al.  Operational Dust Prediction , 2014 .

[36]  K. Lau,et al.  Accumulation of aerosols over the Indo-Gangetic plains and southern slopes of the Himalayas: distribution, properties and radiative effects during the 2009 pre-monsoon season , 2011 .

[37]  Ricardo Todling,et al.  The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0 , 2008 .

[38]  Alexander Smirnov,et al.  Comparison of aerosol optical depth from four solar radiometers during the fall 1997 ARM intensive observation period , 1999 .

[39]  Angela Benedetti,et al.  Background error statistics for aerosols , 2007 .

[40]  W. MacNee,et al.  Particulate air pollution and acute health effects , 1995, The Lancet.

[41]  Soo Chin Liew,et al.  Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program , 2013 .

[42]  K. Rajeev,et al.  Spatial distribution of the Southeast Asian smoke plume over the Indian Ocean and its radiative heating in the atmosphere during the major fire event of 2006 , 2009 .

[43]  T. Sekiyama,et al.  MASINGAR, a global tropospheric aerosol chemical transport model coupled with MRI/JMA98 GCM: Model description , 2003 .

[44]  M. Chin,et al.  Sources and distributions of dust aerosols simulated with the GOCART model , 2001 .

[45]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[46]  E. Lorenz.  Atmospheric Predictability as Revealed by Naturally Occurring Analogues , 1969 .

[47]  J. Christensen.  The Danish Eulerian Hemispheric Model : A three-dimensional air pollution model used for the Arctic , 1997 .

[48]  Cecelia DeLuca,et al.  The architecture of the Earth System Modeling Framework , 2003, Computing in Science & Engineering.

[49]  M. Deeter,et al.  Wintertime pollution over the Eastern Indo-Gangetic Plains as observed from MOPITT, CALIPSO and tropospheric ozone residual data , 2010 .

[50]  David B. Stephenson,et al.  Simple Uncertainty Frameworks for Selecting Weighting Schemes and Interpreting Multimodel Ensemble Climate Change Experiments , 2013 .

[51]  Owen B. Toon,et al.  Simulations of microphysical, radiative, and dynamical processes in a continental-scale forest fire smoke plume , 1991 .

[52]  S. Gong,et al.  A parameterization of sea‐salt aerosol source function for sub‐ and super‐micron particles , 2003 .

[53]  M. Smith,et al.  The sea spray generation function , 1998 .

[54]  Hong Wang,et al.  Sensitivity studies of aerosol data assimilation and direct radiative feedbacks in modeling dust aerosols , 2013 .

[55]  Prabha R. Nair,et al.  Wintertime aerosol characteristics over the Indo‐Gangetic Plain (IGP): Impacts of local boundary layer processes and long‐range transport , 2007 .

[56]  Ulrich Pöschl,et al.  Atmospheric aerosols: composition, transformation, climate and health effects. , 2005, Angewandte Chemie.

[57]  M. H. Smith,et al.  Marine aerosol concentrations and estimated fluxes over the sea , 1993 .

[58]  Jean-Jacques Morcrette,et al.  Sea‐salt and dust aerosols in the ECMWF IFS model , 2008 .

[59]  A. Robins,et al.  Air pollution modeling and its application XX , 2010 .

[60]  Dale A. Gillette,et al.  A wind tunnel simulation of the erosion of soil: Effect of soil texture, sandblasting, wind speed, and soil consolidation on dust production , 1978 .

[61]  Giacomo R. DiTullio,et al.  A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month , 1999 .

[62]  G. Bergametti,et al.  Parametrization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas , 1999 .

[63]  Watson W. Gregg,et al.  Direct Insertion of MODIS Radiances in a Global Aerosol Transport Model , 2007 .

[64]  Sundar A. Christopher,et al.  Assessment of the Met Office dust forecast model using observations from the GERBILS campaign , 2011 .

[65]  Jeffrey S. Reid,et al.  An analysis of clear sky and contextual biases using an operational over ocean MODIS aerosol product , 2009 .

[66]  Soo Chin Liew,et al.  Tropical cirrus cloud contamination in sun photometer data , 2011 .

[67]  J. Knaff,et al.  On the Decay of Tropical Cyclone Winds Crossing Narrow Landmasses , 2006 .

[68]  Zavisa Janjic,et al.  Scientific documentation of the NCEP nonhydrostatic multiscale model on the B grid (NMMB). Part 1 Dynamics , 2012 .

[69]  Michael Schulz,et al.  Sea-salt aerosol source functions and emissions , 2004 .

[70]  K. Lau,et al.  Impact of assimilated and interactive aerosol on tropical cyclogenesis , 2014, Geophysical research letters.

[71]  Jeffrey S. Reid,et al.  Impact of modeled versus satellite measured tropical precipitation on regional smoke optical thickness in an aerosol transport model , 2009 .

[72]  Dick Dee,et al.  Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology , 1999 .

[73]  B. Marticorena,et al.  Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme , 1995 .

[74]  T. Hogan,et al.  The Description of the Navy Operational Global Atmospheric Prediction System's Spectral Forecast Model , 1991 .

[75]  P. Koepke,et al.  Optical Properties of Aerosols and Clouds: The Software Package OPAC , 1998 .

[76]  Zhaoyan Liu,et al.  Two contrasting dust‐dominant periods over India observed from MODIS and CALIPSO data , 2009 .

[77]  V. M. Karyampudi,et al.  Analysis and Numerical Simulations of the Saharan Air Layer and Its Effect on Easterly Wave Disturbances , 1988 .

[78]  Masaru Chiba,et al.  Global Simulation of Dust Aerosol with a Chemical Transport Model, MASINGAR( ADEC-Aeolian Dust Experiment on Climate Impact-) , 2005 .

[79]  T. Reichler,et al.  How Well Do Coupled Models Simulate Today's Climate? , 2008 .

[80]  M. Chin,et al.  Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006 , 2009 .

[81]  D. Dee,et al.  Variational bias correction of satellite radiance data in the ERA‐Interim reanalysis , 2009 .

[82]  Marcin L. Witek,et al.  Global sea‐salt modeling: Results and validation against multicampaign shipboard measurements , 2007 .

[83]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[84]  D. N. Walters,et al.  Impacts of increasing the aerosol complexity in the Met Office global numerical weather prediction model , 2014 .

[85]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[86]  Reto Knutti,et al.  Risks of Model Weighting in Multimodel Climate Projections , 2010 .

[87]  Damien A. Fordham,et al.  Strengthening forecasts of climate change impacts with multi‐model ensemble averaged projections using MAGICC/SCENGEN 5.3 , 2012 .

[88]  Bernard Aumont,et al.  Modeling the atmospheric dust cycle: 2. Simulation of Saharan dust sources , 1997 .

[89]  M. Chin,et al.  Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth , 2010 .

[90]  Thomas M. Smith,et al.  Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation , 1994 .

[91]  Lorraine Remer,et al.  Machine Learning and Bias Correction of MODIS Aerosol Optical Depth , 2009, IEEE Geoscience and Remote Sensing Letters.

[92]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[93]  Oreste Reale,et al.  Impact of Interactive Aerosol on the African Easterly Jet in the NASA GEOS-5 Global Forecasting System , 2011 .

[94]  J. Reid,et al.  Patterns of fire activity over Indonesia and Malaysia from polar and geostationary satellite observations , 2013 .

[95]  J. Reid,et al.  An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals , 2010 .

[96]  N. Mahowald,et al.  Ocean temperature forcing by aerosols across the Atlantic tropical cyclone development region , 2008 .

[97]  S. Meenu,et al.  Observational evidence for the radiative impact of Indonesian smoke in modulating the sea surface temperature of the equatorial Indian Ocean , 2008 .

[98]  T. Eck,et al.  Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols , 1999 .

[99]  G. Kallos,et al.  A model for prediction of desert dust cycle in the atmosphere , 2001 .

[100]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[101]  Mian Chin,et al.  Sources of carbonaceous aerosols over the United States and implications for natural visibility , 2003 .

[102]  Tami C. Bond,et al.  Emissions of primary aerosol and precursor gases in the years 2000 and 1750 prescribed data-sets for AeroCom , 2006 .

[103]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[104]  E. Lorenz,et al.  The predictability of a flow which possesses many scales of motion , 1969 .

[105]  Michael D. King,et al.  Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[106]  Mark DeMaria,et al.  On the Decay of Tropical Cyclone Winds after Landfall in the New England Area , 2001 .

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

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

[109]  Z. Janjic,et al.  Modeling and evaluation of the global sea-salt aerosol distribution: sensitivity to emission schemes and resolution effects at coastal/orographic sites , 2013 .

[110]  Sundar A. Christopher,et al.  Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[111]  T. Eck,et al.  Coarse mode optical information retrievable using ultraviolet to short-wave infrared Sun photometry : Application to United Arab Emirates Unified Aerosol Experiment data , 2008 .

[112]  Ulrich Poeschl,et al.  Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects , 2006 .

[113]  David S. Lee,et al.  Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application , 2010 .

[114]  Brent N. Holben,et al.  An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation , 2010 .

[115]  S. Carn,et al.  Tracking volcanic sulfur dioxide clouds for aviation hazard mitigation , 2009 .

[116]  A. Stohl,et al.  Around the world in 17 days - hemispheric-scale transport of forest fire smoke from Russia in May 2003 , 2004 .

[117]  T. N. Krishnamurti,et al.  Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. , 1999, Science.

[118]  V. M. Karyampudi,et al.  Synoptic-Scale Influence of the Saharan Air Layer on Tropical Cyclogenesis over the Eastern Atlantic , 2002 .

[119]  Alexander Smirnov,et al.  Influence of observed diurnal cycles of aerosol optical depth on aerosol direct radiative effect , 2013 .

[120]  Jeffrey S. Reid,et al.  A system for operational aerosol optical depth data assimilation over global oceans , 2008 .

[121]  Andrew A. Lacis,et al.  Modeling of particle size distribution and its influence on the radiative properties of mineral dust aerosol , 1996 .

[122]  F. Sanders,et al.  Skill In Forecasting Daily Temperature and Precipitation: Some Experimental Results , 1973 .

[123]  M. Tippett,et al.  Is unequal weighting significantly better than equal weighting for multi‐model forecasting? , 2013 .

[124]  E. Lorenz.  A study of the predictability of a 28-variable atmospheric model , 1965 .

[125]  Mark R. Schoeberl,et al.  Transport of smoke from Canadian forest fires to the surface near Washington, D.C.: Injection height, entrainment, and optical properties , 2004 .

[126]  Steven Pawson,et al.  Goddard Earth Observing System chemistry‐climate model simulations of stratospheric ozone‐temperature coupling between 1950 and 2005 , 2008 .

[127]  Reto Knutti,et al.  Challenges in Combining Projections from Multiple Climate Models , 2010 .

[128]  Lance M. Leslie,et al.  Reduction of Tropical Cyclone Position Errors Using an Optimal Combination of Independent Forecasts , 1990 .

[129]  M. Schulz,et al.  Influence of the source formulation on modeling the atmospheric global distribution of sea salt aerosol , 2001 .

[130]  W. Collins,et al.  An AeroCom Initial Assessment - Optical Properties in Aerosol Component Modules of Global Models , 2005 .

[131]  C. Velden,et al.  ARTICLES: The Impact of the Saharan Air Layer on Atlantic Tropical Cyclone Activity. , 2004 .

[132]  R. Miller,et al.  Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 1: Model description, annual simulations and evaluation , 2011 .

[133]  D. Tanré,et al.  ALGORITHM FOR REMOTE SENSING OF TROPOSPHERIC AEROSOL OVER DARK TARGETS FROM MODIS : Collections 005 and 051 : Revision 2 ; Feb 2009 Product ID : MOD 04 / MYD 04 , 2009 .

[134]  James S. Goerss,et al.  Tropical Cyclone Track Forecasts Using an Ensemble of Dynamical Models , 2000 .

[135]  Michael Schulz,et al.  Estimates of global multicomponent aerosol optical depth and direct radiative perturbation in the Laboratoire de Météorologie Dynamique general circulation model , 2005 .

[136]  J. McGregor,et al.  Investigating the haze transport from 1997 biomass burning in Southeast Asia: its impact upon Singapore , 2001 .

[137]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[138]  R. Koster,et al.  The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4. Volume 38; Technical Report Series on Global Modeling and Data Assimilation , 2015 .

[139]  Bodo Ahrens,et al.  On the Weighting of Multimodel Ensembles in Seasonal and Short-Range Weather Forecasting , 2009 .

[140]  Johannes W. Kaiser,et al.  Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System : Forward modeling , 2009 .

[141]  D. E. Spiel,et al.  A Model of Marine Aerosol Generation Via Whitecaps and Wave Disruption , 1986 .

[142]  Teruyuki Nakajima,et al.  Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements , 2002 .

[143]  Jeffrey S. Reid,et al.  MODIS aerosol product analysis for data assimilation: Assessment of over‐ocean level 2 aerosol optical thickness retrievals , 2006 .

[144]  Menas Kafatos,et al.  Influences of Winter Haze on Fog/Low Cloud Over the Indo-Gangetic Plains , 2007 .

[145]  Doug M. Smith,et al.  Anthropogenic aerosol forcing of Atlantic tropical storms , 2013 .