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Satellite Monitoring of the First Aerosol Indirect Effect: Retrieval of the Droplet Concentration of Water Clouds.

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  1. Satellite Monitoring of the First Aerosol Indirect Effect: Retrieval of the Droplet Concentration of Water Clouds Reinout Boers*, Juan R. Acarreta’ and John L. Gras+*KNMI, PO Box 201, 3730AE De Bilt, The Netherlands‘DEIMOS-Space. Ronda de Poniente 19, 28760 Tres Cantos, Madrid, Spain +CSIRO Atmospheric Research, PO Box 1, Aspendale, VIC 3195, AustraliaPRESENTED BY: ROB ROEBELING, KNMI AMS Cloud Conference Madison 2006

  2. Cloud optical depth OD CDNC A OD Warm cloud CDNC / size The first Indirect Aerosol Effect: Linking Surface Aerosol to Cloud Albedo Cloud albedo CDNC CCN F A CCN CCN Mass Earth Radiation Budget Aerosol mass emission Aerosol mass Surface AMS Cloud Conference Madison 2006

  3. Over the Southern Ocean light and temperature dependent DMS emission from the ocean surface is the primary source of nss-sulphates acting as CCN in the marine atmosphere Seasonal Cycle in CCN, N, albedo Low CCN High CCN Sea salt spray Sea salt spray DMS Winter Ocean Surface Summer AMS Cloud Conference Madison 2006

  4. Cape Grim Australia Tasmania Modis box (2x2˚) MODIS Terra L2 data July 2000 – July 2004 AMS Cloud Conference Madison 2006

  5. Cape Grim Baseline Air Pollution Station CCN observations since 1976 AMS Cloud Conference Madison 2006

  6. Retrieval Procedure MODIS radiance (0.6/0.8 mm) MODIS radiance (2.2 mm) Optical depth  Effective radius reff Atmospheric model (single layered cloud) =A1N1/3h5/3, reff=A2N-1/3h2/3 Droplet concentration N Cloud depth h AMS Cloud Conference Madison 2006

  7. MODIS data Error bars: Std. of the monthly mean as calculated from the daily averages AMS Cloud Conference Madison 2006

  8. Comparison CCN and CDNC CCN obtained at Cape Grim only on days and periods when the wind is in baseline sector. Baseline wind direction sector is defined between 190 – 280 degrees (no offshore wind or continental influence) Droplet concentration and cloud depth: Error based on uncertainties in adiabaticity (0.3 < Fr < 0.9), cloud model, microphysical model 0.84 < k1 < 0.90 (<r3> = k1 reff3) CCN-concentration: Error: Standard deviation of geometric mean plus 10% in calibration uncertainty AMS Cloud Conference Madison 2006

  9. N retrieval (MODIS), CCN observations (Cape Grim) AMS Cloud Conference Madison 2006

  10. Comparison of N and CCN AMS Cloud Conference Madison 2006

  11. Main differences between N and CCN observations Results are not expected to be the same!! They should merely see the same process. N obtained from MODIS retrievals based on 1x1 km2 pixels, averaged over 2x2 degree grid,  < 20, h < 700 m, at 250 km distance from Cape Grim AMS Cloud Conference Madison 2006

  12. Linking Albedo Calculations to Droplet Concentration / Cloud Depth Retrievals Question: Are albedo variations for a fixed solar geometry mostly due to variations in droplet concentration or in cloud depth? Cloud albedo is in principal a function of 1) cloud depth 2) cloud droplet concentration 3) solar zenith angle • For fixed sun angle: calculate cloud albedo as a function of monthly averaged cloud optical thickness and cloud droplet effective radius • Assumptions: Standard water vapor, standard ozone (no seasonal variation), two-stream model suffices AMS Cloud Conference Madison 2006

  13. Cloud albedo calculations based on monthly averaged optical thickness and effective radii Error bars: Std. of the monthly mean as calculated from the daily averages AMS Cloud Conference Madison 2006

  14. Comparison of Cloud Albedo with N, strong correlation AMS Cloud Conference Madison 2006

  15. Retrieval product: cloud thickness, cannot be verified against local observations AMS Cloud Conference Madison 2006

  16. Cloud albedo versus cloud depth, weak correlation AMS Cloud Conference Madison 2006

  17. First results from METEOSAT/SEVIRI • The atmospheric model is tested to derive CDNC and Cloud Depth from with SEVIRI retrieved t and reff • At the CloudNET site of Cabauw, the Netherlands SEVIRI Cloud Depth retrievals are compared to Lidar and Radar Cloud Depth observations. • Droplet concentration validation will follow when the CCN counter is installed in Cabauw or using microwave and radar/lidar based droplet concentration retrievals. CLOUDNET: http://www.met.rdg.ac.uk/radar/cloudnet/ AMS Cloud Conference Madison 2006

  18. First results from METEOSAT/SEVIRI AMS Cloud Conference Madison 2006

  19. Conclusions Visible / near IR retrievals of optical depth and effective radius are highly suitable to retrieve cloud droplet concentrations and cloud depth Computed errors in the N, h retrievals are primarily caused by uncertainties in the value of the factor linking the third moment of the droplet size distribution and the effective radius (the famous factor k1, <r3>=k1reff3 ) , and by our lack of knowledge of the departure of the cloud from adiabaticity (Fr) Additionally, the error is dependent upon the choice of near - IR-channel used to retrieve reff Over the Southern Ocean, seasonal variations in ground observations of CCN and MODIS retrievals of droplet concentration are coherent over time periods of four years AMS Cloud Conference Madison 2006

  20. Conclusions (2) Our results demonstrate that long term local monitoring of the variations in the droplet concentration over time is now achievable with current satellite instrumentation Our results demonstrate that albedo variability at fixed sun angle is mostly due to variations in droplet concentration, but not to variations in cloud depth (thus validation of the first indirect effect) Our results should be extended to validation of cloud depth. This validation is currently underway at Cabauw, the Netherlands (with very good results!!)…….. AMS Cloud Conference Madison 2006

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