1. US Aerosols : Observation from Space, Climate Interactions Daniel J. Jacob and funding from NASA, EPRI, EPA with Easan E. Drury (now at NREL), Loretta J. Mickley, Eric M. Leibensperger, Amos Tai

2. Aerosol observation from space by solar backscatter Pollution off U.S. east coastDust off West Africa California fire plumes Easy to do qualitatively for thick plumes over ocean… I   I   I (  )exp[-AOD] …but difficult quantitatively! Fundamental quantity is aerosol optical depth (AOD) aerosol scattering, absorption Measured top-of-atmosphere reflectance = f (AOD, aerosol properties, surface reflectance, air scattering, gas absorption, Sun-satellite geometry)

3. Aerosol optical depths (AODs) measured from space Jan 2001 – Oct 2002 operational data MODIS (c004) return time 2x/day; nadir view known positive bias over land MISR 9-day return time; multi-angle view better but much sparser van Donkelaar et al. [2006] 550 nm AODs

4. OPERATIONAL MODIS RETRIEVAL ALGORITHM (c4, c5) Observed top-of-atmosphere (TOA) reflectance 2.13  m (transparent atmosphere) IR surface reflectance Assumed Vis/IR surface reflectance ratios Vis surface reflectance Vis aerosol reflectance Assumed aerosol optical properties AOD 0.47  m 0.65  m radiative transfer model (RTM) fit remove gas exinction

5. IMPROVED MODIS RETRIEVAL ALGORITHM Observed top-of-atmosphere (TOA) reflectance 2.13  m (transparent atmosphere) IR surface reflectance Local Vis/IR surface reflectance ratios Vis surface reflectance Vis aerosol reflectance Local aerosol optical properties for observed scenes AOD 0.47  m 0.65  m GEOS-Chem CTM aerosol simulation observed statistics for low-aerosol scenes LIDORT RTM fit remove gas exinction Drury et al. [JGR 2008]

6. APPLICATION TO ICARTT AIRCRAFT MISSION PERIOD (Jul-Aug 2004) EASTERN U.S. EPA AQS/IMPROVE surface networks: mass concentrations NASA AERONET surface network: AODs NASA, NOAA, DOE aircraft: speciated mass concentrations, microphysical & optical properties MODIS satellite instrument: top-of-atmosphere reflectance GEOS-Chem aerosol optical properties Vis/IR surface reflectance ratios Drury et al. [JGR, submitted] NASA DC-8 AOD retrieval

7. DERIVING THE Vis/IR SURFACE REFLECTANCE RATIO Plot Vis vs. 2.13  m nadir-scaled top-of-atmosphere reflectances for individual locations over season (Jul-Aug 2004): lower envelope defines surface reflectance ratio 2.13  m TOA reflectance 0.65  m TOA reflectance surf reflectance ratio = 0.56 MODIS operational retrievals underestimate Vis surface reflectances over arid regions and boreal forests, overestimates over grasslands Drury et al. [JGR 2008] 1 o x1.25 o grid squares

8. MEAN AEROSOL VERTICAL PROFILES DURING ICARTT Most of the mass is in boundary layer below 3 km: mostly sulfate, organic (high dust observations in boundary layer are probably incorrect) Model overestimates sulfate in boundary layer – aqueous-phase chemistry? Dust, organic dominate above 3 km Drury et al. [JGR, submitted] NASA DC-8 and NOAA WP-3D GEOS-Chem model

9. AEROSOL OPTICAL PROPERTIES IN ICARTT Single-scattering albedo AERONET standard model assumption (GADs) improved fit (this work) Drury et al. [JGR, submitted] Size distributions External mixture is better assumption Narrow sulfate and OC size distributions relative to GADS (  2.2  1.6); decreases 180 o backscatter observed

10. AEOSOL OPTICAL DEPTHS (0.47  m), JUL-AUG 2004 GEOS-Chem model MODIS (this work) MODIS (c004) MODIS (c005) c004 and c005 are the MODIS operational data; AERONET data are in circles Drury et al. [JGR, submitted]

11. AEOSOL OPTICAL DEPTHS (0.47  m), JUL-AUG 2004 GEOS-Chem model MODIS (this work) MODIS (c004) MODIS (c005) c004 and c005 are the MODIS operational data; AERONET data are in circles Beyond improving on the operational products, our MODIS retrieval enables quantitative comparison to model results (consistent aerosol optical properties) Results indicate model underestimate in Southeast US – organic aerosol Drury et al. [JGR, submitted]

12. INFERRING PM 2.5 FROM MODIS AODs Infer PM 2.5 from AOD by Match to EPA data is generally within 30% (requires >1-week averaging) Remaining bias in source regions due to overestimate of aqueous-phase sulfate production? Drury et al. [JGR, submitted] MODIS PM 2.5 (this work) EPA AQS surface network data Jul-Aug 2004 MODIS PM 2.5 (this work) EPA AQS PM 2.5

13. REGIONAL CLIMATE FORCING BY U.S. AEROSOLS today US sulfur emissions are decreasing: what will be the regional climate impacts? Present-day sulfate radiative forcingU.S. SO 2 anthropogenic emissions Liao et al., 2004 W m -2

14. CALCULATING THE CLIMATE RESPONSE FROM SHUTTING DOWN U.S. AEROSOL Mickley et al. (in prep.) Consider two scenarios: Control: aerosol optical depths fixed at 1990s levels. Sensitivity: U.S. aerosol optical depths set to zero (radiative forcing of about +2 W m -2 over US) Conduct ensemble of 3 simulations for each scenario. GISS GCM

15. Removal of anthropogenic aerosols over US causes 0.5-1 o C regional warming in eastern US Mean 2010-2025 temperature difference: No-US-aerosol case – Control White areas signify no significant difference. Results from an ensemble of 3 for each case. Annual mean surface temperature change in Control. Warming due to 2010-2025 trend in greenhouse gases. Additional warming due to zeroing of aerosols over the US. Mickley et al., in prep.

16. Regional surface temperature response to aerosol removal is a robust and persistent effect Temperature ( o C) No-US-aerosols case Control, with US aerosols Annual mean temperature trends over Eastern US Mickley et al., in prep. Ensemble of 3 for each case

17. EFFECT OF FUTURE CLIMATE CHANGE ON US AIR QUALITY Northeast Midwest California Texas Southeast 2000-2050 change of 8-h daily max ozone in summer, keeping anthropogenic emissions constant ppb Models show consistent increase of ozone, mainly driven by temperature Results from six coupled GCM-CTM simulations Weaver et al. [BAMS, in press] …but model results for aerosols show no such consistency, including in sign. How can we progress?

18. AEROSOL CORRELATION WITH METEOROLOGICAL VARIABLES Multilinear regression model fit to 1998-2008 deseasonalized EPA/AQS data for PM2.5 (total and speciated) R 2 fit mostly precipitation mostly temperature and stagnation Tai et al. [in prep.]

19. TEMPERATURE COEFFICIENTS FOR SPECIATED PM 2.5 Positive correlation of nitrate with temperature in California appears driven by ammonia emissions Tai et al. [in prep.] NH 4 + EC OC SO 4 2- NO 3 - Total

20. WIND VECTOR COEFFICIENTS FOR SPECIATED PM2.5 Tai et al. [in prep.] PM has relatively localized sources and short lifetime; climate-driven changes in regional circulation could have large impact