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Lorraine A. Remer, Yoram J. Kaufman, Zev Levin, and Steven Ghan

Abstract

The new generation of satellite sensors such as the moderate resolution imaging spectroradiometer (MODIS) will be able to detect and characterize global aerosols with an unprecedented accuracy. The question remains whether this accuracy will be sufficient to narrow the uncertainties in estimates of aerosol radiative forcing at the top of the atmosphere. The discussion is narrowed to cloud-free direct forcing. Satellite remote sensing detects aerosol with the least amount of relative error when aerosol loading is high. Satellites are less effective when aerosol loading is low. The monthly mean results of two global aerosol transport models are used to simulate the spatial distribution of smoke aerosol in the Southern Hemisphere during the tropical biomass burning season. This spatial distribution allows us to determine that 87%–94% of the smoke aerosol forcing at the top of the atmosphere occurs in grid squares with sufficient signal-to-noise ratio to be detectable from space. The uncertainty of quantifying the smoke aerosol forcing in the Southern Hemisphere depends on the uncertainty introduced by errors in estimating the background aerosol, errors resulting from uncertainties in surface properties, and errors resulting from uncertainties in assumptions of aerosol properties. These three errors combine to give overall uncertainties of 1.2 to 2.2 W m−2 (16%–60%) in determining the Southern Hemisphere smoke aerosol forcing at the top of the atmosphere. Residual cloud contamination uncertainty is not included in these estimates. Strategies that use the satellite data to derive flux directly or use the data in conjunction with ground-based remote sensing and aerosol transport models can reduce these uncertainties.

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Andréa Dde Almeida Castanho, Paulo Artaxo, J. Vanderlei Martins, Peter V. Hobbs, Lorraine Remer, Marcia Yamasoe, and Peter R. Colarco

Abstract

The Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment was carried out off the central East Coast of the United States in July 2001. During CLAMS, aerosol particle mass was measured at two ground stations and on the University of Washington’s Convair 580 research aircraft. Physical and chemical characteristics of the aerosols were identified and quantified. Three main aerosol regimes were identified in the region and are discussed in this work: local pollution/sea salt background, long-range transported dust, and long-range transported pollution. The major component measured in the fine mode of the aerosol on the ground at Wallops Island, Virginia, was sulfate, estimated as NH4HSO4, which accounted for 55% ± 9% on average of the fine particle mass (FPM) during the experiment period. Black carbon concentrations accounted for 3% ± 1% of FPM; soil dust was also present, representing on average 6% ± 8% of FPM. The difference between the sum of the masses of the measured compounds and the total fine particle mass was 36% ± 10% of FPM, which is attributed primarily to nitrates and organic carbon that were not measured. Aerosol chemical composition in the atmospheric column is also discussed and compared with ground-based measurements. Aerosol dust concentration reached 40% of FPM during an incursion of Saharan dust between 24 and 26 July. Sulfate aerosol reached 70% of FPM during the transport of regional pollution on 17 July. Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness, coupled with air parcel back trajectories, supported the conclusion of episodes of long-range transport of dust from the Sahara Desert and pollutants from the continental United States.

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Clark Weaver, Arlindo da Silva, Mian Chin, Paul Ginoux, Oleg Dubovik, Dave Flittner, Aahmad Zia, Lorraine Remer, Brent Holben, and Watson Gregg

Abstract

In this paper results are presented from a simple offline assimilation system that uses radiances from the Moderate Resolution Imaging Spectroradiometer (MODIS) channels that sense atmospheric aerosols over land and ocean. The MODIS information is directly inserted into the Goddard Chemistry and Aerosol Radiation Transport model (GOCART), which simulates the following five aerosol types: dust, sea salt, black carbon, organic carbon, and sulfate. The goal is to produce three-dimensional fields of these aerosol types for radiative forcing calculations.

Products from this assimilation system are compared with ground-based measurements of aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Insertion of MODIS radiances draws the GOCART model closer to the AERONET AOD. However, there are still uncertainties with surface reflectivity over moderately bright surfaces and with the amount of absorbing aerosol.

Also described is the assimilation cycle. The forward model takes the aerosol information from the GOCART model and calculates radiances based on optical parameters of the aerosol type, satellite viewing angle, and the particle growth from relative humidity. Because the GOCART model is driven by previously assimilated meteorology, these forward model radiances can be directly compared with the observed MODIS level-2 radiances. The offline assimilation system simply adjusts the aerosol loading in the GOCART model so that the observed minus forward model radiances agree. Minimal change is made to the GOCART aerosol vertical distribution, size distribution, and the ratio of the five different aerosol types. The loading in the GOCART model is updated with new MODIS observations every 6 h. Since the previously assimilated meteorology provides surface wind speed, radiance sensitivity to wind speed over rough ocean is taken into account. Over land the dark target approach, also used by the MODIS–atmosphere group retrieval, is used. If the underlying land surface is deemed dark enough, the surface reflectances at the 0.47- and 0.66-μm wavelengths are constant multiples of the observed 2.13-μm reflectance. Over ocean the assimilation AOD compares well with AERONET, over land less so. The results herein are also compared with AERONET-retrieved single-scattering albedo. This research is part of an ongoing effort at NASA Goddard to integrate aerosols into the Goddard Modeling and Assimilation Office (GMAO) products.

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Alexander Ignatov, Patrick Minnis, Norman Loeb, Bruce Wielicki, Walter Miller, Sunny Sun-Mack, Didier Tanré, Lorraine Remer, Istvan Laszlo, and Erika Geier

Abstract

Understanding the impact of aerosols on the earth’s radiation budget and the long-term climate record requires consistent measurements of aerosol properties and radiative fluxes. The Clouds and the Earth’s Radiant Energy System (CERES) Science Team combines satellite-based retrievals of aerosols, clouds, and radiative fluxes into Single Scanner Footprint (SSF) datasets from the Terra and Aqua satellites. Over ocean, two aerosol products are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) using different sampling and aerosol algorithms. The primary, or M, product is taken from the standard multispectral aerosol product developed by the MODIS aerosol group while a simpler, secondary [Advanced Very High Resolution Radiometer (AVHRR) like], or A, product is derived by the CERES Science Team using a different cloud clearing method and a single-channel aerosol algorithm. Two aerosol optical depths (AOD), τ A1 and τ A2, are derived from MODIS bands 1 (0.644 μm) and 6 (1.632 μm) resembling the AVHRR/3 channels 1 and 3A, respectively. On Aqua the retrievals are made in band 7 (2.119 μm) because of poor quality data from band 6. The respective Ångström exponents can be derived from the values of τ. The A product serves as a backup for the M product. More importantly, the overlap of these aerosol products is essential for placing the 20+ year heritage AVHRR aerosol record in the context of more advanced aerosol sensors and algorithms such as that used for the M product.

This study documents the M and A products, highlighting their CERES SSF specifics. Based on 2 weeks of global Terra data, coincident M and A AODs are found to be strongly correlated in both bands. However, both domains in which the M and A aerosols are available, and the respective τ/α statistics significantly differ because of discrepancies in sampling due to differences in cloud and sun-glint screening. In both aerosol products, correlation is observed between the retrieved aerosol parameters (τ/α) and ambient cloud amount, with the dependence in the M product being more pronounced than in the A product.

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Toshi Matsui, Charles Ichoku, Cynthia Randles, Tianle Yuan, Arlindo M. da Silva, Peter Colarco, Dongchul Kim, Robert Levy, Andrew Sayer, Mian Chin, David Giles, Brent Holben, Ellsworth Welton, Thomas Eck, and Lorraine Remer
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Jassim Al-Saadi, James Szykman, R. Bradley Pierce, Chieko Kittaka, Doreen Neil, D. Allen Chu, Lorraine Remer, Liam Gumley, Elaine Prins, Lewis Weinstock, Clinton MacDonald, Richard Wayland, Fred Dimmick, and Jack Fishman

Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.

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Anne K. Smith, Mary Barth, William R. Boos, Elie Bou-Zeid, Yoshio Kawatani, Sukyoung Lee, David Mechem, Lorraine Remer, Christopher Rozoff, Susan van den Heever, Zhuo Wang, Louis Wicker, and Ping Yang
Open access
Theodore L. Anderson, Robert J. Charlson, Nicolas Bellouin, Olivier Boucher, Mian Chin, Sundar A. Christopher, Jim Haywood, Yoram J. Kaufman, Stefan Kinne, John A. Ogren, Lorraine A. Remer, Toshihiko Takemura, Didier Tanré, Omar Torres, Charles R. Trepte, Bruce A. Wielicki, David M. Winker, and Hongbin Yu

This document outlines a practical strategy for achieving an observationally based quantification of direct climate forcing by anthropogenic aerosols. The strategy involves a four-step program for shifting the current assumption-laden estimates to an increasingly empirical basis using satellite observations coordinated with suborbital remote and in situ measurements and with chemical transport models. Conceptually, the problem is framed as a need for complete global mapping of four parameters: clear-sky aerosol optical depth f f, radiative efficiency per unit optical depth δ, fine-mode fraction of optical depth f f, and the anthropogenic fraction of the fine mode f af . The first three parameters can be retrieved from satellites, but correlative, suborbital measurements are required for quantifying the aerosol properties that control E, for validating the retrieval of f f, and for partitioning fine-mode δ between natural and anthropogenic components. The satellite focus is on the “A-Train,” a constellation of six spacecraft that will fly in formation from about 2005 to 2008. Key satellite instruments for this report are the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth's Radiant Energy System (CERES) radiometers on Aqua, the Ozone Monitoring Instrument (OMI) radiometer on Aura, the Polarization and Directionality of Earth's Reflectances (POLDER) polarimeter on the Polarization and Anistropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL), and the Cloud and Aerosol Lider with Orthogonal Polarization (CALIOP) lidar on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). This strategy is offered as an initial framework—subject to improvement over time—for scientists around the world to participate in the A-Train opportunity. It is a specific implementation of the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) program, presented earlier in this journal, which identified the integration of diverse data as the central challenge to progress in quantifying global-scale aerosol effects. By designing a strategy around this need for integration, we develop recommendations for both satellite data interpretation and correlative suborbital activities that represent, in many respects, departures from current practice.

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P. Jeremy Werdell, Michael J. Behrenfeld, Paula S. Bontempi, Emmanuel Boss, Brian Cairns, Gary T. Davis, Bryan A. Franz, Ulrik B. Gliese, Eric T. Gorman, Otto Hasekamp, Kirk D. Knobelspiesse, Antonio Mannino, J. Vanderlei Martins, Charles R. McClain, Gerhard Meister, and Lorraine A. Remer

Abstract

The Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission represents the National Aeronautics and Space Administration’s (NASA) next investment in satellite ocean color and the study of Earth’s ocean–atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth’s changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food-web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave-infrared wavelengths, with a ground sample distance of 1 km at nadir. This payload is complemented by two multiangle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022 to early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean–atmosphere–land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.

Open access