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AERONET in the context of aerosol remote sensing from space and aerosol global modeling

AERONET in the context of aerosol remote sensing from space and aerosol global modeling. Stefan Kinne MPI-Meteorology, Hamburg Germany. Overview. AERONET statistics – a sampler AERONET and satellite data Validation of Satellite Data ( a ) Evaluation or Regional Representation ( )

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AERONET in the context of aerosol remote sensing from space and aerosol global modeling

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  1. AERONET in the context of aerosol remote sensing from space andaerosol global modeling Stefan Kinne MPI-Meteorology, Hamburg Germany

  2. Overview • AERONET statistics – a sampler • AERONET and satellite data • Validation of Satellite Data (a) • Evaluation or Regional Representation (\) • AERONET and global modeling • Evaluation of Model Simulation (a) • Data on composition and vertical profile (\) • state in global modeling and AeroCom

  3. AERONET statisticsmonthly average properties • a sampler for three sites: • GSFC (near Washington DC) ‘urban’ • Mongu (Zambia) ‘biomass’ [Jul-Nov] • Cape Verde (west of Sahara) ‘dust’ • measured properties (aot, alfa) • derived properties (absorption, size) • value-added properties (forcing, lidar ratio) • locally – aerosol can be defined well

  4. urban absorption: 10*aot*(1-w0) lidar ratio: for spheres atoo small for non-spheres ToA Forcing: clr-sky [W/m2]

  5. biomass 70% PDF value 30% PDF value absorption: 10*aot*(1-w0) lidar ratio: for spheres atoo small for non-spheres ToA Forcing: clr-sky [W/m2]

  6. dust absorption: 10*aot*(1-w0) lidar ratio: for spheres atoo small for non-spheres ToA Forcing: clr-sky [W/m2]

  7. the consistency among data allows combination for global assessment this example: seasonal avg. for aerosol absorption [t * (1-w0)] interesting… … AERONET indicates moreabsorption by aerosol over Europe than over east- US US Europe

  8. AERONET and other aerosol data-sets • Aeronet data can ‘help’ (a) • Aeronet data can ‘learn’ (\) • examples are introduced next

  9. AERONET a satellite data satellite aot data(aerosol optical thickness or depth) • what is available ? what is best? Satellite Advantage Disadvantage --------------------------------------------------------------------------------------------------------- AVHRR historic record calibration, not over land TOMS historic record 50km pixel, height or absorption needed MODIS small pixel failure over deserts MISR altitude info temporally spare POLDER short record, land: less sensitive to large sizes SEAWIFS not over land, no IR channels GOES or high temporal lack of detail with broad MSG resolution bands, land limitations

  10. aot - global yearly averages with all data available a normalized by model to offset sampling biases a AERONET has lower aots than satellite retrievals a clear-sky bias?

  11. comparisons or annual pattern Mo: MODIS composites: Mi: MISR 12:Mo,Mi To: TOMS 13:Mo,To Av: AVHRR Po: POLDER Ae:Aeronet • difficult to depict a best global retrieval • composite needed • a MODIS (ocean) MISR (land) combination seems promising ……but differences to AERONET still exist

  12. local comparisons to AERONET • choice : MI / MO still … generally larger than AERONET, particular in urban regions …but seasonal data show biomass burning aots are too low

  13. seasonal comparisons at AERONET

  14. first impressions • MODIS best choice over the oceans … but too low in dust outflow regions (high aot a clouds) • MISR most complete land cover … while biased high over oceans • MODIS (ocean) / MISR (land) combination the ‘best’ satellite product is generally larger than AERONET … but too low during biomass burning open issues: • are AERONET aot smaller due to a clear-sky bias? • what can be said about the quality of retrievals of low aot in remote regions (of no AERONET sites?) • is it ‘fair’ to compare point data with regional data?

  15. satellite data a AERONET • use spatial information of satellite data • to relate local measurement detail to • coarse gridded data-sets • coarse resolution data in global modeling • how ? • compare averages for different scales • agreement … indicates a ‘useful’ site • bias: ‘useful’ site after a bias adjustment • highly variable (season/years) : leave off comparison … unless secondary data exist

  16. “scaling” • Comparison of • 300*300km data • 100*100km data • 10*10km data • GSFC (urban) • 20% above the regional average • Mongu (biomass) • good match for the biomass season (Jul-Nov) bat the bottom are AERONET-MODIS comparisons (2001) note: MODIS statis- tics are very poor! MODIS AERONET

  17. needed scaling activities • for different spatial domains a data-base of simultaneous satellite retrievals over AERONET sites is needed • satellite requirements: • small (~1km) pixel retrievals at regional coverage • sufficient data (for seasonal /annual dependence) • coverage of all AERONET sites (incl. desert sites) MODIS and MISR data are a start … although their smallest pixels size at 10.0 and 17.6 km is too large to represent ‘truly’ local characteristics

  18. AERONET a global modeling • pick 20 sites (well spread globally) • compare • aerosol optical depth • sub-micron sized aerosol optical depth • aerosol mass (note AERONET: wet mass, Models: dry mass) • sub-micron sized mass • refractive index, imaginary part • … • identify large disagreements • identify poor concepts • eliminate poor concepts (or poor models)

  19. white rings: AERONET aot smaller than model average color-coded compositional fractions as predicted by global models at selected AERONET sites black rings: AERONET aot larger than model average

  20. aerosol optical depth comparisons

  21. beyond annual aot averages • differences among (15) models and to AERONET are better understood on a monthly basis • test seasonality major ‘enhancements’ • MARCH dust (Africa and Asia) • JUNE urban aerosol (NH) • SEPTEMBER biomass burning (SH) • DECEMBER biomass burning (trop. Africa) • identify monthly outliers in modeling

  22. aerosol optical depth in March

  23. aerosol optical depth in June

  24. aerosol optical depth in September

  25. aerosol optical depth in December

  26. aerosol optical depth comparisons for selected month

  27. anthropogenic = smaller sizes = SU + OC + BC

  28. optical depth comparisons for selected month for submicron size aerosol

  29. AERONET over- estimates expected in humid conditions aerosol mass comparisons (Models: dry, AERONET: wet)

  30. aerosol mass comparisons for selected months (Models: dry, Aeronet: wet)

  31. Anthropogenic = smaller sizes ? = SU + OC + BC ? mass comparisons for submicron size aerosol (Models: dry, AERONET: wet)

  32. aerosol refractive index imaginary part (absorption) (Models: dry, AERONET: wet)

  33. modeling and AeroCom • AeroCom http://nansen.ipsl.jussieu.fr/Aerocom • validate against data! • surface concentrations (IMPROVE, EMEP, GAW) • surface remote sensing (AERONET, EARLINET) • remote sensing from space (MODIS, MISR) • 14 groups participate so far • A: ‘best as you can’ – simulation • B: yr 2000 simulation with prescribed emissions • C: yr 2000 simulation with pre-industrial emissions – to address anthropogenic ‘forcing’

  34. global modeling a AERONET • Aeronet • component combined totals • vertical integrated column properties • Modeling • detail on compositional mixture • detail on vertical distribution …still modeling though compositional is the dominant component captured? detail a is the seasonality captured? is the anthropogenic fraction correct?

  35. 3 examples of AERONET sites dominated by a particular typedustbiomass (carbon)urban (sulfate) Aerosol modules agree better on total aot than in terms of aot composition AERONET can help identify unusual model behavior

  36. Summary • large differences in component aerosol modules in global modeling • annual averages hide larger seasonal differences • global averages hide larger regional difference • component totals hide larger diff. among compo. • integrated properties (e.g. forcing) hide larger differences on a sub-level basis (e.g. mass) • AERONET provide constraints to models • for aot (and locally even for component aots) • for other aerosol properties (e.g. size, mass, absorption) only at lower certainty

  37. Message • anthropogenic impact of aerosol on climate needs to be better quantified (reduce uncertainties) • uncertainties in aerosol forcing (the end product in modeling) do not represent ‘actual’ uncertainties • model differences at intermediate processing steps and on different scales are much larger • AERONET can provide needed constraints … …especially in conjunction with space data and value-added modeling (e.g. inversions)

  38. Outlook • tighter and stronger links (via selective sampling) between AERONET and remote sensing from space are needed • to extend detail on size and composition to remote sensing from space • to identify sites with regional representation needed for evaluations in global modeling • complementary information on vertical distribution is highly desirable ... and we keep our fingers crossed for the A-train

  39. extras

  40. AERONET statistics absorption = 10* aot*(1-w0) statistics for 1998-2001

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