U.S. aerosols: observation from space, interactions with climate Daniel J. Jacob with Easan E. Drury, Loretta J. Mickley, Eric M. Leibensperger, Amos Tai and funding from NASA, EPRI, EPA

SATELLITE OBSERVATIONS OF TROPOSPHERIC COMPOSITION: a revolution over the past decade The NASA “A-Train” Integrated observing system Satellites Models aircraft, ships, sondes, lidars Surface sites Principal tropospheric species measured from space: Ozone , NO2, formaldehyde, BrO, glyoxal CO, CO2, methane Aerosols, SO2

AEROSOL CHARACTERISTICS number AEROSOL CHARACTERISTICS area Typical size distribution (Seinfeld and Pandis, 1998) volume PM2.5 (EPA std.) Chemical composition of PM2.5 (NARSTO, 2004) sulfate (coal combustion) nitrate (fossil fuel combustion) ammonium (agriculture) black carbon (combustion) organic carbon (combustion, vegetation) soil other

HOW TO OBSERVE AEROSOLS FROM SPACE? Solar occultation (SAGE, POAM…) Active system (CALIPSO…) Solar back-scatter (MODIS, MISR…) laser pulse EARTH Surface Surface Pros: high S/N, vertical profiling Cons: sparse sampling, cloud interference, low horizontal resolution Pro: vertical profiling Con: sparse sampling Pro: horiz. resolution Con: daytime and column only

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]

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]

MODIS OPERATIONAL RETRIEVAL OVER LAND Use top-of-atmosphere (TOA) reflectance at 2.13 mm (transparent atmosphere) to derive surface reflectance Assume fixed 0.47/2.13 and 0.65/2.13 surface reflectance ratios to derive atmospheric reflectances at 0.47 and 0.65 mm by subtraction Assume generic aerosol optical properties to convert atmospheric reflectance to AOD MISR does along-track multi-angle viewing of same aerosol column – better constraints but sparser data TOA reflectance 0.47 mm 0.65 mm 2.13 mm SURFACE Y. Kaufman, L. Remer, and MODIS Science Team

IMPROVING THE MODIS AEROSOL RETRIEVAL USING ICARTT AIRCRAFT DATA OVER US (Jul-Aug 2004) fit AODs synthetic TOA reflectance = f(AOD,…) MODIS satellite instrument: TOA reflectance NASA, NOAA, DOE aircraft: speciated mass concentrations, microphysical & optical properties GEOS-Chem model evaluate NASA DC-8 MODIS local surface reflectance and ratio EPA AQS/IMPROVE surface networks: mass concentrations NASA AERONET surface network: AODs EASTERN U.S. Drury et al. [JGR 2008, in prep]

IMPROVING THE SURFACE REFLECTANCE CORRECTION FOR MODIS AEROSOL RETRIEVALS 2.13 mm 0.65 mm Measured top-of-atmosphere (TOA) reflectances (ICARTT period) 0.65/2.13 surface reflectance ratio Measured 0.65 vs. 2.13 TOA reflectances: take lower envelope for given location to derive surface reflectance ratio Fresno, CA ICARTT period Derive aerosol reflectance at 0.65 mm (same procedure for 0.47 mm) Drury et al. [JGR 2008]

CONVERTING TOA AEROSOL REFLECTANCES TO AODs Standard MODIS algorithm assumes generic aerosol optical properties Better way is to use local info for given scene from a global 3-D aerosol model Use GEOS-Chem model driven by NASA/GEOS assimilated meteorological data with 2ox2.5o resolution Model simulates mass concentrations of different aerosol types Size distributions and optical properties for different aerosol types are assumed (test with ICARTT data) Calculate TOA reflectances from model fields to compare with MODIS Consistency of aerosol optical properties enables subsequent model evaluation with observed MODIS AODs

PREVIOUS MODEL EVALUATION: sulfate-nitrate-ammonium Annual mean concentrations at IMPROVE sites (2001) – CASTNET for NH4+ r = 0.96 bias = +10% r = 0.60 bias = +30% r =0.94 bias = +10% Sulfate is 100% in aerosol; Ammonia NH3(g) neutralizes sulfate to form (NH4)2SO4; Excess NH3(g) if present can combine with HNO3(g) to form NH4NO3 as function of T, RH Park et al. [AE 2006]

PREVIOUS MODEL EVALUATION: carbonaceous aerosol Annual mean concentrations at IMPROVE sites (2001) Elemental carbon (EC) Organic carbon (OC) r = 0.75 bias = -15% r = 0.70 bias = +20% Primary sources: fossil fuel, biofuel, wildfires Also large growing-season biogenic source of secondary organic aerosol (SOA) volatile organic compounds (VOCs) oxidation, multi-step SOA Park et al. [AE 2006]

PREVIOUS MODEL EVALUATION: mineral dust Annual mean concentrations at IMPROVE sites (2001) GEOS-Chem Asian dust Saharan dust Local Fairlie et al. [AE 2007]

AEROSOL VERTICAL PROFILES IN ICARTT bulk filter (Dibb, UNH) IMPROVE (<2.5 mm) NASA DC-8 PILS (Weber, GIT) Sulfate model overestimate: excessive cloud processing? Unresolved issue with aircraft dust observations at low altitude Easan Dury, in prep.

ORGANIC AEROSOL IN ICARTT PILS water-soluble organic carbon (WSOC) on NOAA P-3 IMPROVE measurements of organic carbon Fu et al. (AE, 2009) Standard reversible SOA (Pankow/Seinfeld): Dicarbonyl SOA (Liggio/Fu):

MEAN AEROSOL VERTICAL PROFILES IN ICARTT Bulk of mass is in boundary layer below 3 km: mostly sulfate, organic Dust, organic dominate above 3 km Easan Drury, in prep.

AEROSOL OPTICAL PROPERTIES IN ICARTT Single-scattering albedo = fraction of aerosol extinction due to scattering standard model assumption (GADs) improved fit (this work) AERONET Easan Drury, in prep.

MEAN AEROSOL OPTICAL DEPTHS DURING ICARTT Model results compared to observations from AERONET network (circles) Model w/ GADs size distributions Model w/improved size distributions r = 0.89 bias = -21% r = 0.89 bias = -7% Main improvement was to reduce the geometric standard deviation in the log-normal size distributions for sulfate and OC from 2.2 to 1.6 Easan Drury, in prep.

AEOSOL OPTICAL DEPTHS (0.47 mm), JUL-AUG 2004 c004 and c005 are the MODIS operational data; AERONET data are in circles GEOS-Chem model MODIS (this work) MODIS (c004) MODIS (c005) 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., in prep.

Can we use AODs measured from space as proxy for PM2.5? Infer PM2.5 from AOD by MODIS PM2.5 (this work) EPA AQS surface network data Drury et al. [in prep] Sulfate observations in Pittsburgh (Wittig et al. 2004) Bias in source regions could be partly due to PM2.5 diurnal cycle: MODIS

RADIATIVE FORCING OF CLIMATE BY AEROSOLS Globally averaged radiative forcing due to CO2 is +1.7 Wm-2 Liao et al. , 2004 Over eastern US, radiative forcing due to sulfate aerosols is -2 Wm-2 IPCC (2007) US sulfur emissions are decreasing rapidly: what are the impacts on the regional climate? today

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.)

Removal of anthropogenic aerosols over US leads to a 0 Removal of anthropogenic aerosols over US leads to a 0.5-1o C warming in annual mean surface temperature. Warming due to 2010-2025 trend in greenhouse gases. Additional warming due to zeroing of aerosols over the US. The 2010-2025 trend in greenhouse gases leads to warming surface temperatures in both the Control (left panel) and the No-US-aerosol model simulation. As expected, temperatures for both cases rise most rapidly at high northern latitudes, as much as 1.5oC. For the No-US-aerosol case, we find additional warming over the eastern US, where the aerosol optical depths in the Control are greatest (right panel). This additional warming (+0.5 C, annual mean) is about the same in magnitude as the warming in this region due to the 2010-2025 trend in greenhouse gases. In effect, the removal of the US anthropogenic aerosols doubles the warming experienced over the eastern US in this time frame. 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.

Regional surface temperature response to aerosol removal is a persistent effect Temperature (oC) No-US-aerosols case Control, with US aerosols Annual mean temperature trends over Eastern US The calculated surface temperature response in the No-US-aerosol case persists over time. The plots shows the surface temperature trend for 2010 to 2040 averaged over the Northeast US for the two simulations: the Control (green) and the No-US-aerosol case (blue). The solid curves show the trends in annual mean temperatures, the dashed curves show the 9-year running means. Mickley et al., in prep.

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 Northeast Midwest California Texas Southeast 2000-2050 change of 8-h daily max ozone in summer, keeping anthropogenic emissions constant ppb Weaver et al. [BAMS, submitted] …but model results for aerosols show no such consistency, including in sign. How can we progress?

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 R2 fit Tai et al. [in prep.]

TEMPERATURE COEFFICIENTS FOR SPECIATED PM2.5 Positive association of nitrate with temperature in California could be driven by ammonia emissions Tai et al. [in prep.]

WIND VECTOR COEFFICIENTS FOR SPECIATED PM2.5 Tai et al. [in prep.]