1. Using satellite observations to investigate natural aerosol loading Colette L. Heald David A. Ridley, Kateryna Lapina UPMC Paris March 22, 2011

2. DUST FROM NORTH AFRICA: IMPACTING AQ AND THE BIOSPHERE DOWN-WIND More than half of dust emitted globally from N. Africa TOMS: June 13-21, 2001 summer winter/spring Miami (1989-1997) [Prospero et al., 1999] [Prospero et al., 1981] French Guiana (1978-1979)

3. SATELLITE CONSTRAINTS ON DUST SOURCE & TRANSPORT Dave Ridley GEOS-Chem overestimates observed AOD in source region, underestimates summertime export

4. REALISTIC APPORTIONMENT OF SUB-MICRON DUST MASS Shifted mass to larger sub-micron sizes (less optically efficient). Reduces AOD in better agreement with satellite & AERONET observations [Haywood et al., 2003] Observed Saharan dust size distribution Simulated decrease in AOD OLD NEW

5. DUST TRANSPORT FROM NORTH AFRICA CALIOPGEOS-Chem CALIOPGEOS-Chem WINTER SUMMER Annual Mean AOD Good model simulation of dust transport and removal in winter/spring. Underestimate in dust in the SAL in summertime.

6. DEPOSITION OF AFRICAN DUST & PHOSPHOROUS IN THE AMAZON We estimate 13 Tg/yr transported to Amazon annually. This is ~10-25% of the P supply for the Amazon. Otherwise from fires and biogenic particles [Mahowald et al., 2005]. Impact of greening of the Sahel on productivity of the Amazon? [Ridley et al., in prep] ANNUAL

7. ANNUAL MEAN AOD OVER THE REMOTE OCEANS MODISGEOS-Chem % DifferenceGEOS-Chem Sea Salt GEOS-Chem underestimates (~30%) marine AOD observed by MODIS. Likely fine aerosol [Jaegle et al., 2010], but does not match simulated sea salt. Gong [2003] sea salt scheme updated to include SST dependence & validated against observations [Jaeglé et al., 2010] Kateryna Lapina

8. COMPARISON WITH MARTIME AEROSOL NETWORK (MAN) Model AOD (over remote regions) is also ~13% low compared to MAN

9. OTHER POSSIBLE FINE MARINE PARTICLE SOURCES [O’Dowd et al., 2004] GEOS-Chem simulation of sulfate relatively unbiased  not the problem. BUT under biologically active conditions, OA dominates sub-micron aerosol mass. SeaWIFS Comparison of simulated sulfate to recent cruise observations (AMS) Observed aerosol composition at Mace Head

10. IS THE OCEAN AN IMPORTANT SOURCE OF OA? Previous estimates range from 2.3 to 75 TgC/yr No marine OAWith marine OA Observations from 5 ship cruises show that marine OA from 2 schemes [Spracklen et al., 2008; Langmann et al., 2008] of ~8 TgC/yr are more than sufficient to reproduce sub-micron OA. Makes very little contribution to AOD (0.003). [Lapina et al., ACPD, 2011] OA Emissions

11. ORGANIC AEROSOL MAKES UP AN IMPORTANT FRACTION OF OBSERVED AEROSOL Globally makes up 25-75% of total fine aerosol at the surface (ignoring soot here) [Zhang et al., 2007] Sulfate Organics

12. CHALLENGES IN MODELING THE RIGHT LEVELS OF OA SOA measured/modeled = 4-100! [Volkamer et al., 2006] Models do get it right sometimes (even more puzzling?) but is it for the right reason? ITCT-2K4 IMPEX AMAZE-08 AMMA Egbert

13. WHY DON’T MODELS GET IT RIGHT…. Terpenes (gas-phase) PBAP Hydrocarbons (gas-phase & particulate) Uncertain Formation (Missing sources? Poorly understood processes?) Continuing Oxidation/Partitioning in the Atmosphere 10,000’s of (unidentified?) compounds with variable properties

14. CAN SATELLITE OBSERVATIONS SHED ANY LIGHT ON THE BUDGET OF OA? SURFACE REFLECTANCE Bottom-up calculations suggest that SOA source may be anywhere from 140-910 TgC/yr [Goldstein and Galbally, 2007]. Organic aerosol Sulfate Dust Sea Salt Nitrate SATELLITE AOD Assumptions: Optical Properties Size Distributions Aerosol Distributions AEROSOL SPECIATED MASS CONCENTRATIONS Soot

15. IF ONLY AEROSOL IN THE ATMOSPHERE WAS OA, WHAT LOADING IS IMPLIED BY SATELLITE AOD? Calculate the “hypothetical” AOD implied by a constant 1  g/sm 3 profile over the land, and see how we need to scale this locally to make up ENTIRE AOD reported by MISR. Inverted OA loading is 3.5 TgC over land. Assume a 6 days lifetime = 215 TgC/yr  extrapolate to include outflow ~430 TgC/yr. (middle of Goldstein & Galbally range) Inverted total MISR AOD: Surface OA concentrations

16. Estimate that ~150 TgC/yr source is required to close the MISR-GEOS-Chem* discrepancy. DJFJJA MISR GEOS-Chem* MISR- GEOS-Chem* *excluding OA A MORE REALISTIC POSSIBILITY: REMOVE CONTRIBUTIONS FROM DUST, BC, INORGANICS (assuming all the negative bias in the model is ONLY OA)

17. UNCERTAINTY ANALYSIS (boring but important!) Assumed optical properties based on GADS database and log-normal size distribution recently evaluated by Drury et al. [2010] Uncertainty on estimated OA source = 80% Estimated uncertainty on OA budget due to:Uncertainty on OA optical properties * Except over high RH regions Aerosol optical properties Size parameters Refractive indices Aerosol water uptake (growth factor) Relative humidity (assuming 5% uncertainty in GEOS-5 fields) 50% 20% 10% 6%* Conversion from burden to source Aerosol lifetime (including effects of vertical profile and export fraction) 50% Global budget of “other” aerosols simulated in GEOS-Chem 25% MISR AOD measurements10% Total Error (added in quadrature)80%

18. This is more than THREE TIMES what is currently included in global models…. BUT at the low end of Goldstein & Gallbally [2007] range. HAVE WE REDUCED THE UNCERTAINTY ON THE OA BUDGET? 910 47 Existing GEOS-Chem sources 140 Our satellite top-down estimate 150 Range estimated by: Goldstein and Galbally [2007] All units in TgCyr -1 [Heald et al., 2010]

19. Acknowledgments: Easan Drury, Sonia Kreidenweis, Dominick Spracklen, Steve Arnold, James Allan, Hugh Coe and Gordon McFiggans, Soeren Zorn, Frank Drewnick, Tim Bates, Lelia Hawkins, Lynn Russell, Sasha Smirnov, Colin O’Dowd, Andy Hind and MISR, MODIS & CALIOP retrieval teams