Synthesis of satellite (MODIS), aircraft (ICARTT), and surface (IMPROVE, EPA‐AQS, AERONET) aerosol observations over eastern North America to improve MODIS aerosol retrievals and constrain surface aerosol concentrations and sources

Easan Drury,Bruce E Anderson,Jun Wang,Rodney Weber,Antony D Clarke,Daniel J Jacob,Jack Dibb,Cameron Mcnaughton,Yohei Shinozuka,Robert J D Spurr

doi:10.1029/2009jd012629

Abstract

We use an ensemble of satellite (MODIS), aircraft, and ground‐based aerosol observations during the ICARTT field campaign over eastern North America in summer 2004 to (1) examine the consistency between different aerosol measurements, (2) evaluate a new retrieval of aerosol optical depths (AODs) and inferred surface aerosol concentrations (PM2.5) from the MODIS satellite instrument, and (3) apply this collective information to improve our understanding of aerosol sources. The GEOS‐Chem global chemical transport model (CTM) provides a transfer platform between the different data sets, allowing us to evaluate the consistency between different aerosol parameters observed at different times and locations. We use an improved MODIS AOD retrieval based on locally derived visible surface reflectances and aerosol properties calculated from GEOS‐Chem. Use of GEOS‐Chem aerosol optical properties in the MODIS retrieval not only results in an improved AOD product but also allows quantitative evaluation of model aerosol mass from the comparison of simulated and observed AODs. The aircraft measurements show narrower aerosol size distributions than those usually assumed in models, and this has important implications for AOD retrievals. Our MODIS AOD retrieval compares well to the ground‐based AERONET data (R = 0.84, slope = 1.02), significantly improving on the MODIS c005 operational product. Inference of surface PM2.5 from our MODIS AOD retrieval shows good correlation to the EPA‐AQS data (R = 0.78) but a high regression slope (slope = 1.48). The high slope is seen in all AOD‐inferred PM2.5 concentrations (AERONET: slope = 2.04; MODIS c005: slope = 1.51) and could reflect a clear‐sky bias in the AOD observations. The ensemble of MODIS, aircraft, and surface data are consistent in pointing to a model overestimate of sulfate in the mid‐Atlantic and an underestimate of organic and dust aerosol in the southeastern United States. The sulfate overestimate could reflect an excessive contribution from aqueous‐phase production in clouds, while the organic carbon underestimate could possibly be resolved by a new secondary pathway involving dicarbonyls.

Highlights

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[1] We use an ensemble of satellite (MODIS), aircraft, and ground‐based aerosol observations during the ICARTT field campaign over eastern North America in summer 2004 to (1) examine the consistency between different aerosol measurements, (2) evaluate a new retrieval of aerosol optical depths (AODs) and inferred surface aerosol concentrations (PM2.5) from the MODIS satellite instrument, and (3) apply this collective information to improve our understanding of aerosol sources
We use an improved MODIS AOD retrieval based on locally derived visible surface reflectances and aerosol properties calculated from GEOS‐Chem

Summary

Introduction

[2] Measuring atmospheric aerosol concentrations is of considerable interest for a wide range of environmental issues ranging from public health to climate change. We use a new algorithm to retrieve aerosol optical depths (AODs) from the MODIS satellite instrument [Drury et al, 2008] and apply it to North America for the summer 2004 period of the ICARTT aircraft field campaigns. Aircraft, and ground‐based aerosol observations over this period, in combination with a global three‐dimensional chemical transport model (GEOS‐Chem CTM), to test the consistency of this integrated aerosol observing system and improve our understanding of U. We combine the aircraft data with aerosol observations from surface networks during the ICARTT period including speciated aerosol mass concentrations from the IMPROVE network [Malm et al, 1994], AODs and single scattering albedos (SSAs) from the AERONET network [Dubovik et al, 2000], and mass concentrations of particulate matter of less than 2.5 mm diameter (PM2.5) from the U.S Environmental Protection Agency’s Air Quality System (EPA‐AQS). GEOS‐Chem is driven by our best prior understanding of regional aerosol sources and processes, and we will see how comparison to the ensemble of MODIS and other observations can improve this prior understanding

GEOS‐Chem Aerosol Simulation

Methodology

Findings

Conclusions