Introduction 1. Advantages and difficulties related to the use of optical data 2. Aerosol retrieval and comparison methodology 3. Results of the comparison model/observations during: - The pollution episode of 26 March The August 2003 heat wave episode Conclusion and Perspectives * Hodzic Alma*, Vautard R., Chepfer H., Goloub P., Menut L., Chazette P., Deuzé J.L., Apituley A., Couvert P.. CHIMERE Workshop, Paris March 2005 Aerosol model validation using optical measurements Evolution of AOT over Europe during the 2003 summer heat wave as seen from CHIMERE simulations and POLDER-2 data Laboratoire de Météorologie Dynamique - École Polytechnique - Paris (*)

Why using optical data for model evaluation ? Advantages/Difficulties Surface measurements (AIRPARIF network) + + Continuous measurements of PM 10 and PM 2.5 => spatial distribution - -Lack of information on the vertical mixing. Remote sensing + + Quasi-continuous measurements of the aerosol vertical distribution at great number of sites (Lidar and Sun-photometer data) + + Wide spatial coverage of satellite data - -No one-to-one correspondence between the measured signal and model outputs (aerosol concentrations): AOT / backscattering signal is proportional to the aerosol load Rarely used for the validation of aerosol models at urban scale. Airborne measurements (ESQUIF, ESCOMPTE) + + Aerosol chemical composition and size distribution - -Short data series. Evaluation of aerosol models : CHIMERE Workshop, Paris March 2005

Available optical measurements CHIMERE Workshop, Paris March 2005 Ground-based measurements SIRTA Data Base : Backscattering lidar LNA (532nm) EARLINET Data Base : European aerosol lidar network AERONET Data Base : Global Sun-photometer network Aerosol optical properties (AOT, Albedo, refractive index) Satellite measurements (King et al., 1999) POLDER remote sensing on board the ADEOS satellite - - Radiometer that measures spectral, directional and polarized radiance over land and oceans. - - Retrieval of AOT at 865nm for accumulation mode (large or non spherical particules not detected bc of their low polarization). (Deuzé et al., 2001) - -7 months of data : April – October satellite overpass time around 11:00 UTC

Aerosol retrieval from model simulations Approach “Model to Observation” CHIMERE Workshop, Paris March Accumulation Mode: (0.16 – 2.5 µm): > 88% - - Nucleation Mode: (< 0.16 µm) : ~ 4% - - Coarse Mode: (>2.5 µm) : ~ 8% Contribution of aerosol mode to optical efficiency: ( ( Hodzic et al., 2004, JGR) CHIMERE (Gas / Aerosols) Chemical speciation Mass distribution Aerosol Optical Properties m, SSA, AOT Lidar Profiles Pr 2, PBL MIE code Sun-photometer AERONET POLDER Lidar data SIRTA Direct comparison of observed and simulated backscattering lidar profiles to avoid new hypothesis in observations.

Comparison with lidar data at Palaiseau Pollution episode of 26 March 2003 LIDAR 532nm – 2003/03/26 – ln(Pr2) CHIMERE 532nm – 2003/03/26 – ln(Pr2) Dust PBL RL 11 GMT 14 GMT   CHIMERE LIDAR Variability (Hodzic et al., 2004, JGR) Integrated optical thickness at 532nm Ground concentrations of PM 10

POLDER derived AOT at 865 nm due to Aerosols Accumulation Mode Example of comparison with satellite data Monthly mean AOT over Europe from POLDER data (Hodzic et al., 2005, submitted to ACP) CHIMERE Workshop, Paris March 2005 Summer heat wave 4-13 August 2003

POLDER derived AOT at 865 nm due to Aerosols Accumulation Mode Evolution of AOT during the August 2003 heat wave episode

Evolution of AOT from POLDER and CHIMERE 05 August August 2003

Systematic comparisons model/observations CHIMERE Workshop, Paris March 2005 Mean AOTs over EuropeCorrelaton model/obs. over Europe Uncertainties in aerosol retrievals from both satellite and model data - Off-set in POLDER data? - Aerosol parameterization used in the model? Major discrepancies model/observations: - - General model overestimation - - Underestimation of peak values on 5-6 August

The origin of discrepancies: model systematic bias CHIMERE Workshop, Paris March 2005 Comparison with AERONET-derived AOTs Results: - - POLDER underestimates AERONET data - - Good agreement CHIMERE/AERONET except on August Model overestimation due to negative off-set in POLDER data 1:1 2:1 1:2

AOT peaks : Advection of smoke particles from Portugal forest fires CHIMERE Workshop, Paris March August 2003 Passive tracer run with CHIMERE

AOT peaks : Advection of smoke particles from Portugal forest fires Passive tracer runs with CHIMERE CHIMERE Workshop, Paris March 2005

Conclusion and Perspectives General model/observation comparison results : Remote sensing (lidar and sun-photometer) provide useful routine measurements of the vertical aerosol distribution that can be easily used for the evaluation of mesoscale aerosol models. Ability of the model to reproduce with reasonable skill both the observed optical thickness and the vertical backscatter lidar profiles. Comparison allows identifying missing processes and emission sources in model simulations. Reveals difficulties of comparing simulated and POLDER-derived AOTs due to uncertainties in satellite and model retrievals of aerosol optical properties. CHIMERE Workshop, Paris March 2005

Conclusion and Perspectives Comparison results during the heat wave episode : Model reproduces main spatial structures during the heat wave episode. Model simulates generally higher AOTs than POLDER due to negative bias in POLDER retrievals identified by comparison with AERONET ground-based measurements. AOTs peaks due to smoke particles advected from Portuguese forest fires are missed in model simulations. Necessity to include emissions and high-altitude transport of smoke from Portuguese wildfires to explain the observed AOT peaks over Europe. Future work: Introduction of forest fire real emissions and evaluation of their impact on AOT Take into account the transport of thin layers Comparison with MODIS- and GLAS-derived aerosol optical properties CHIMERE Workshop, Paris March 2005