Baro, R.

Bianconi, R.

Brunner, D.

Curci, G.

Forkel, R.

Hirtl, M.

Honzak, L.

Jimenez-Guerrero, P.

Manders, A.

Neal, L.

Palacios-Pena, L.

Savage, N.

Schaap, M.

Tuccella, P.

Van der Gon, H.

Werhahn, J.

Zabkar, R.

KITopen

HOW GOOD ARE AEROSOL-CLOUD INTERACTIONS IN ONLINE COUPLED MODELS? R. Baró*, D. Brunner, L. Palacios-Peña, R. Bianconi, G. Curci, M. Hirtl, L. Honzak, R. Forkel, A. Manders, L. Neal, N. Savage, M. Schaap, P. Tuccella, H. Van der Gon, J. Werhahn, R Žabkar and P. Jiménez-Guerrero**Regional Campus of International Excellence “Campus Mare Nostrum”, University of Murciarocio.baro@um.es10th International Conference on Air Quality - Science and ApplicationSpecial acknowledgment to  the people involved in  the European Space Agency  Cloud  Climate Change Iniciative:  Martin  Stengel,  Oliver  Sus,  Rainer HollmannWHY  STUDY  AEROSOL-­CLOUD  INTERACTIONS?• Atmospheric aerosols affect air quality and influence the Earth’sclimate through the aerosol effects and feedbacks• Nowadays is one of the most important topics in climate science(AR5, IPCC 2013) Aerosol–cloud interactionsACIAerosol–radiation interactionsARIMOTIVATION• Account for these feedbackà Fully couped modelACIs constitute one of the most important uncertainties in anthropogenic climate perturbationsAerosol–cloud interactionsACIAerosol–radiation interactionsARI(AR5, IPCC 2013) MOTIVATION• Atmospheric aerosols affect air quality and influence the Earth’sclimate through the aerosol effects and feedbacks• Nowadays is one of the most important topics in climate scienceIn order to build confidence in air quality-­climate interaction studies, anevaluation of integrated meteorology-­atmospheric chemistry models is neededMOTIVATIONIn order to build confidence in air quality-­climate interaction studies, anevaluation of integrated meteorology-­atmospheric chemistry models is neededModel output Observational dataTo  study the improvements of  modelling the aerosol  interactions+MOTIVATIONMOTIVATIONModel outputAir  Quality Model Evaluation International  Initiative• Joint effort of different european andamerican groups• Focus on online coupled meteorology-­chemistry models.• Assess how well coupled regional AQmodels simulate aerosols feedbacks• Years 2006 and 2010AQMEII-­Phase 2Satellite data+MOTIVATIONSatellite data+• Phase 1 (2010-2013) data sets from 2007-2009• Phase 2 (2014-2016) with sensors:1. AVHRR/MODIS/(A)ATSR data from 1982-20142. MERIS/AATSR time series from 2002 to 2012,extended by OLCI/SLSTR on-board Sentinel-3.ESA  CLOUD  CCI  ProjectEuropean SpaceAgency,  Climate Change InitiativeModel outputAir  Quality Model Evaluation International  InitiativeAQMEII-­Phase 2• Joint effort of different european andamerican groups• Focus on online coupled meteorology-­chemistry models.• Assess how well coupled regional AQmodels simulate aerosols feedbacks• Years 2006 and 2010OBJECTIVESTo  assess whether the inclusion of  ACIs in  regional-­scale,  integratedmodels improves the simulation of  the climate-­chemistry-­cloud-­radiationsystem over EuropeMAIN  OBJECTIVEOBJECTIVESü Test the Cloud CCI preliminar data and provide feedbacks to the CCIpeopleü To study the relationship between Aerosol Optical Depth (AOD) andseveral cloud variables in online coupled modelsTo  assess whether the inclusion of  ACIs in  regional-­scale,  integratedmodels improves the simulation of  the climate-­chemistry-­cloud-­radiationsystem over EuropeMAIN  OBJECTIVEMETHODOLOGY• One year simulations for 2010 conducted with several different modelsunder the umbrella of AQMEII-2.• 5 simulations with ACIs and one withoutModel Microphysics Gas  Phase  SW  radiationLW  radiationAerosol Aerosol  feedbacks1WRFChemLin RADM2RRTM Goddard MADESORGAM YesMorrison2RADM2  integ13 RACMRRTMG RRTMGMADE  VBS4* RADM2MADE  SORGAM No5RACMOLOTOS-­EUROSTiedtke,  Tompkins  NeggersCB-­IV RRTM RRTM ISORROPIA  II  2  bins Yes6 METUMUKCAWilson  &  Ballard RAQ Edwards-­Slingo ClassicNo  FeedbacksMETHODOLOGYModel Microphysics Gas  Phase  SW  radiationLW  radiationAerosol Aerosol  feedbacks1WRFChemLin RADM2RRTM Goddard MADESORGAM YesMorrison2RADM2  integ13 RACMRRTMG RRTMGMADE  VBS4* RADM2MADE  SORGAM No5RACMOLOTOS-­EUROSTiedtke,  Tompkins  NeggersCB-­IV RRTM RRTM ISORROPIA  II  2  bins Yes6 METUMUKCAWilson  &  Ballard RAQ Edwards-­Slingo ClassicCloud  Fraction à CFRCloud  Optical Depth à CODCloud  Liquid Water Pathà CWPCloud  Ice  Water Path à CIPSTUDIED  VARIABLESNo  Feedbacks• One year simulations for 2010 conducted with several different modelsunder the umbrella of AQMEII-2.• 5 simulations with ACIs and one withoutMETHODOLOGY• Satelite data  :  AVHRR  NOAA-­19    (equator  crossing  time  of  1:30  to  2:00PM-­ local  solar  time)• Mean  BIASerror (MBE)  • Model  Ensemble  vs  NoFeedback model• Correlation  over  timeModel EvaluationAOD  vs  Cloud  variables  relationshipRESULTSMODEL  EVALUATION  using preliminar  Cloud  cci  data.  Comparison of  the NoFeedback simulation BIAS  and  theENSEMBLE   (w  Feedbacks)  BIASRESULTSBIAS  ENSEMBLE  w  FeedbacksBIAS  NoFeedbacksCLOUD FRACTION -CFR• The same BIAS response, negative BIAS over the Sea and positive BIASover land• The inclusion of the ACIs imply a lower positive BIAS over land andnegative BIAS is slightly higherWinter  monthsRESULTSBIAS  ENSEMBLE  w  FeedbacksBIAS  NoFeedbacksCLOUD FRACTION -CFR• The inclusion of the ACIs imply a lower positive BIAS over land andnegative BIAS is slightly higher• The same BIAS response, negative BIAS over the Noth Sea and NorthAfrica and positive BIAS over the Mediterranean Sea and land.Summer monthsRESULTSBIAS  ENSEMBLE  w  FeedbacksCLOUD OPTICAL DEPTH- COD• The Ensemble Mean understimates the Cloud Optical Depth duringall 2010• Higher understimation is found during winter monthsRESULTSBIAS  ENSEMBLE  w  FeedbacksBIAS  NoFeedbacksCLOUD LIQUID WATER PATH- CWP• The inclusion of the ACIs imply a lower positive BIAS over AtlanticSea and negative BIAS is slightly higher in Center Europe.Winter  monthsRESULTSBIAS  ENSEMBLE  w  FeedbacksBIAS  NoFeedbacksCLOUD LIQUID WATER PATH- CWP• The inclusion of the ACIs imply a lower positive BIAS over AtlanticSea and negative BIASis slightly higherSummer monthsRESULTSBIAS  ENSEMBLE  w  FeedbacksBIAS  NoFeedbacksCLOUD LIQUID ICE PATH- CIP• For NoFeedback case and the Ensemble Mean CIP is understimated,with same valuesWinter  monthsRESULTSBIAS  ENSEMBLE  w  FeedbacksBIAS  NoFeedbacksCLOUD LIQUID ICE PATH- CIP• For NoFeedback case and the Ensemble Mean CIP is understimated,with same valuesWinter  monthsAOD  vs  Cloud  VariablesEnsemble Model Mean  during July and  AugustRussian and  Portugal  FiresRESULTSTemporal  CorrelationsRESULTSSlighlty negative correlated, -­0.2/-­0.3AOD  vs  CDNC AOD  vs  CLWPAOD  vs  COD AOD  vs  CFRCloud  Droplet Number concentration Cloud  Liquid Water PathCloud  Optical Depth Cloud  FractionRESULTSPositive correlated: extinctiondue to Biomass Burning aerosolis mostly absorbingThese quantities refer to different volumesof air. (CCN most relevant to ACI arelocated at the cloud base altitude, theAOD entire vertical column)We expected a positive correlation withthe CCN….We think that..Any suggestions ?AOD  vs  CRFAOD  vs  CCNCloud  Radiative ForcingCloud  Condensation NucleiCONCLUSIONSü Cloud Fraction, negative BIAS over the Sea and positive BIAS over land is found.ü The Ensemble mean BIAS tends to underestimate the cloud optical depth over theentire domain and year, being higher during winter months.ü For the CWP inclusion of the ACIs imply a lower positive BIAS over The Atlantic Seaand negative BIAS is slightly incresased.ü CIWP is underestimated for both cases and there is no change when taking intoaccount the ACIs.ü In general it is observed that the inclusion of the ACIs imply a lower positive BIASand negative BIAS is slightly higherCONCLUSIONSü Cloud Fraction, negative BIAS over the Sea and positive BIAS over land is found.ü The Ensemble mean BIAS tends to underestimate the cloud optical depth over theentire domain and year, being higher during winter months.ü For the CWP inclusion of the ACIs imply a lower positive BIAS over The Atlantic Seaand negative BIAS is slightly incresased.ü CIWP is underestimated for both cases and there is no change when taking intoaccount the ACIs.ü In general it is observed that the inclusion of the ACIs imply a lower positive BIASand negative BIAS is slightly higherü We will investigate the anticorrelation found between AOD-­CCN simulated by all themodels.ü We provided feedback to the Cloud CCI people and are in contact for the finaldataset that we will test again in order to see if there are improvements.Acknowledgements:AQMEII2, EuMetChem COST Action ES1004, the Joint Research Center Ispra and the Institute for Environmentand Sustainability for its ENSEMBLE system, and CGL2013-48491-R/CGL2014-59677-R (through EuropeanRegional Development Fund –FEDER).THANK YOU FOR YOUR ATENTIONSpecial acknowledgment to  CCI  Cloud  people:  Martin  Stengel,  Oliver  Sus,  Rainer HollmannContact:  rocio.baro@um.es

How good are aerosol-cloud interactions in online coupled models?

https://publikationen.bibliothek.kit.edu/1000062189/3945533

How good are aerosol-cloud interactions in online coupled models?

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