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US Aerosols : Observation from Space, Climate Interactions

Explore aerosol observation from space, focusing on solar backscatter and its interactions with climate. Learn about aerosol optical depth, measurements, operational data analysis, retrieval algorithms, aerosol simulation, and more.

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US Aerosols : Observation from Space, Climate Interactions

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

  2. Aerosol observation from space by solar backscatter Easy to do qualitatively for thick plumes over ocean… California fire plumes Pollution off U.S. east coast Dust off West Africa …but difficult quantitatively! Fundamental quantity is aerosol optical depth (AOD) Il () Measured top-of-atmosphere reflectance = f (AOD, aerosol properties, surface reflectance, air scattering, gas absorption, Sun-satellite geometry) aerosol scattering, absorption Il (0)=Il()exp[-AOD]

  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 550 nm AODs MISR 9-day return time; multi-angle view better but much sparser van Donkelaar et al. [2006]

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

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

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

  7. surf reflectance ratio = 0.56 0.65 mm TOA reflectance 2.13 mm TOA reflectance DERIVING THE Vis/IR SURFACE REFLECTANCE RATIO Plot Vis vs. 2.13 mm nadir-scaled top-of-atmosphere reflectances for individual locations over season (Jul-Aug 2004): lower envelope defines surface reflectance ratio 1ox1.25o grid squares MODIS operational retrievals underestimate Vis surface reflectances over arid regions and boreal forests, overestimates over grasslands Drury et al. [JGR 2008]

  8. GEOS-Chem model MEAN AEROSOL VERTICAL PROFILES DURING ICARTT NASA DC-8 and NOAA WP-3D • 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]

  9. Single-scattering albedo Size distributions AEROSOL OPTICAL PROPERTIES IN ICARTT observed standard model assumption (GADs) AERONET improved fit (this work) • External mixture is better assumption • Narrow sulfate and OC size distributions relative to GADS (s 2.2 g1.6); decreases 180o backscatter Drury et al. [JGR , submitted]

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

  11. GEOS-Chem model MODIS (this work) MODIS (c004) MODIS (c005) AEOSOL OPTICAL DEPTHS (0.47 mm), JUL-AUG 2004 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. EPA AQS surface network data Jul-Aug 2004 MODIS PM2.5 (this work) INFERRING PM2.5 FROM MODIS AODs Infer PM2.5 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? MODIS PM2.5 (this work) EPA AQS PM2.5 Drury et al. [JGR , submitted]

  13. Liao et al., 2004 today W m-2 REGIONAL CLIMATE FORCING BY U.S. AEROSOLS Present-day sulfate radiative forcing U.S. SO2 anthropogenic emissions US sulfur emissions are decreasing: what will be the regional climate impacts?

  14. CALCULATING THE CLIMATE RESPONSE FROM SHUTTING DOWN U.S. AEROSOL GISS GCM 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. Mickley et al. (in prep.)

  15. Removal of anthropogenic aerosols over US causes 0.5-1o C regional warming in eastern US Additional warming due to zeroing of aerosols over the US. Warming due to 2010-2025 trend in greenhouse gases. Annual mean surface temperature change in Control. 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. Mickley et al., in prep.

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

  17. 2000-2050 change of 8-h daily max ozone in summer, keeping anthropogenic emissions constant ppb Northeast Midwest California Texas Southeast EFFECT OF FUTURE CLIMATE CHANGE ON US AIR QUALITY 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) mostly precipitation mostly temperature and stagnation R2 fit Tai et al. [in prep.]

  19. TEMPERATURE COEFFICIENTS FOR SPECIATED PM2.5 Total NH4+ NO3- SO42- OC EC Positive correlation of nitrate with temperature in California appears driven by ammonia emissions Tai et al. [in prep.]

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

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