Editorial Type:
Article
Article Type:
Research Article

Natural and Anthropogenic Aerosol Trends from Satellite and Surface Observations and Model Simulations over the North Atlantic Ocean from 2002 to 2012

Andrew R. Jongeward Earth System Science Interdisciplinary Center, and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

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Zhanqing Li Earth System Science Interdisciplinary Center, and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland, and State Key Laboratory of Earth Surface Processes and Resource Ecology, and College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

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Hao He Earth System Science Interdisciplinary Center, and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

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Xiaoxiong Xiong Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 Nov 2016
Collections:
Aerosol-Cloud-Precipitation-Climate Interaction
DOI:
https://doi.org/10.1175/JAS-D-15-0308.1
Page(s):
4469–4485
Restricted access

Abstract

Aerosols contribute to Earth’s radiative budget both directly and indirectly, and large uncertainties remain in quantifying aerosol effects on climate. Variability in aerosol distribution and properties, as might result from changing emissions and transport processes, must be characterized. In this study, variations in aerosol loading across the eastern seaboard of the United States and the North Atlantic Ocean during 2002 to 2012 are analyzed to examine the impacts of anthropogenic emission control measures using monthly mean data from MODIS, AERONET, and IMPROVE observations and Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulation. MODIS observes a statistically significant negative trend in aerosol optical depth (AOD) over the midlatitudes (−0.030 decade−1). Correlation analyses with surface AOD from AERONET sites in the upwind region combined with trend analysis from GOCART component AOD confirm that the observed decrease in the midlatitudes is chiefly associated with anthropogenic aerosols that exhibit significant negative trends from the eastern U.S. coast extending over the western North Atlantic. Additional analysis of IMPROVE surface PM2.5 observations demonstrates statistically significant negative trends in the anthropogenic components with decreasing mass concentrations over the eastern United States. Finally, a seasonal analysis of observational datasets is performed. The negative trend seen by MODIS is strongest during spring (MAM) and summer (JJA) months. This is supported by AERONET seasonal trends and is identified from IMPROVE seasonal trends as resulting from ammonium sulfate decreases during these seasons.

Corresponding author address: Z. Li, GCESS and ESPRE, Beijing Normal University, Jingshi Science and Technology Bldg. B, College Road South, Haidian District, Beijing 100875, China. E-mail: zhanqingli@msn.com

This article is included in the Aerosol-Cloud-Precipitation-Climate Interaction Special Collection.

Abstract

Aerosols contribute to Earth’s radiative budget both directly and indirectly, and large uncertainties remain in quantifying aerosol effects on climate. Variability in aerosol distribution and properties, as might result from changing emissions and transport processes, must be characterized. In this study, variations in aerosol loading across the eastern seaboard of the United States and the North Atlantic Ocean during 2002 to 2012 are analyzed to examine the impacts of anthropogenic emission control measures using monthly mean data from MODIS, AERONET, and IMPROVE observations and Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulation. MODIS observes a statistically significant negative trend in aerosol optical depth (AOD) over the midlatitudes (−0.030 decade−1). Correlation analyses with surface AOD from AERONET sites in the upwind region combined with trend analysis from GOCART component AOD confirm that the observed decrease in the midlatitudes is chiefly associated with anthropogenic aerosols that exhibit significant negative trends from the eastern U.S. coast extending over the western North Atlantic. Additional analysis of IMPROVE surface PM2.5 observations demonstrates statistically significant negative trends in the anthropogenic components with decreasing mass concentrations over the eastern United States. Finally, a seasonal analysis of observational datasets is performed. The negative trend seen by MODIS is strongest during spring (MAM) and summer (JJA) months. This is supported by AERONET seasonal trends and is identified from IMPROVE seasonal trends as resulting from ammonium sulfate decreases during these seasons.

Corresponding author address: Z. Li, GCESS and ESPRE, Beijing Normal University, Jingshi Science and Technology Bldg. B, College Road South, Haidian District, Beijing 100875, China. E-mail: zhanqingli@msn.com

This article is included in the Aerosol-Cloud-Precipitation-Climate Interaction Special Collection.

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  • Alston, E. J., I. N. Sokolik, and O. V. Kalashnikova, 2012: Characterization of atmospheric aerosol in the US Southeast from ground- and space-based measurements over the past decade. Atmos. Meas. Tech., 5, 1667–1682, doi:10.5194/amt-5-1667-2012.

    • Search Google Scholar
    • Export Citation

  • Chin, M., R. B. Rood, S.-J. Lin, J.-F. Muller, and A. M. Thompson, 2000: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties. J. Geophys. Res., 105, 24 671–24 687, doi:10.1029/2000JD900384.

    • Search Google Scholar
    • Export Citation

  • Chin, M., and Coauthors, 2002: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements. J. Atmos. Sci., 59, 461–483, doi:10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Chin, M., T. Diehl, P. Ginoux, and W. Malm, 2007: Intercontinental transport of pollution and dust aerosols: Implications for regional air quality. Atmos. Chem. Phys., 7, 5501–5517, doi:10.5194/acp-7-5501-2007.

    • Search Google Scholar
    • Export Citation

  • Chin, M., and Coauthors, 2014: Multi-decadal aerosol variations from 1980 to 2009: A perspective from observations and a global model. Atmos. Chem. Phys., 14, 3657–3690, doi:10.5194/acp-14-3657-2014.

    • Search Google Scholar
    • Export Citation

  • de Meij, A., A. Pozzer, and J. Lelieveld, 2012: Trend analysis in aerosol optical depths and pollutant emission estimates between 2000 and 2009. Atmos. Environ., 51, 75–85, doi:10.1016/j.atmosenv.2012.01.059.

    • Search Google Scholar
    • Export Citation

  • Diehl, T., A. Heil, M. Chin, X. Pan, D. Streets, M. Schultz, and S. Kinne, 2012: Anthropogenic, biomass burning, and volcanic emissions of black carbon, organic carbon, and SO2 from 1980 to 2010 for hindcast model experiments. Atmos. Chem. Phys. Discuss., 12, 24 895–24 954, doi:10.5194/acpd-12-24895-2012.

    • Search Google Scholar
    • Export Citation

  • Engel-Cox, J. A., C. H. Hollman, B. W. Coutant, and R. M. Hoff, 2004: Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmos. Environ., 38, 2495–2509, doi:10.1016/j.atmosenv.2004.01.039.

    • Search Google Scholar
    • Export Citation

  • Fischer-Bruns, I., J. Feichter, S. Kloster, and A. Schneidereit, 2010: How present aerosol pollution from North America impacts North Atlantic climate. Tellus, 62A, 579–589, doi:10.1111/j.1600-0870.2010.00446.x.

    • Search Google Scholar
    • Export Citation

  • Gan, C.-M., J. Pleim, R. Mathur, C. Hogrefe, C. N. Long, J. Xing, S. Rossel, and C. Wei, 2014: Assessment of the effect of air pollution controls on trends in shortwave radiation over the United States from 1995 through 2010 from multiple observation networks. Atmos. Chem. Phys., 14, 1701–1715, doi:10.5194/acp-14-1701-2014.

    • Search Google Scholar
    • Export Citation

  • Ginoux, P., M. Chin, I. Tegen, J. M. Prospero, B. Holben, O. Dubovik, and S.-J. Lin, 2001: Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res., 106, 20 255–20 273, doi:10.1029/2000JD000053.

    • Search Google Scholar
    • Export Citation

  • Green, M., S. Kondragunta, P. Clren, and C. Xu, 2009: Comparison of GOES and MODIS Aerosol Optical Depth (AOD) to Aerosol Robotic Network (AERONET) AOD and IMPROVE PM2.5 mass at Bondville, Illinois. J. Air Waste Manage. Assoc., 59, 1082–1091, doi:10.3155/1047-3289.59.9.1082.

    • Search Google Scholar
    • Export Citation

  • Guenther, A., and Coauthors, 1995: A global model of natural volatile organic compound emissions. J. Geophys. Res., 100, 8873–8892, doi:10.1029/94JD02950.

    • Search Google Scholar
    • Export Citation

  • Hand, J. L., and Coauthors, 2011: IMPROVE (Interagency Monitoring of Protected Visual Environments): Spatial and seasonal patterns and temporal variability of haze and its constituents in the United States. Cooperative Institute for Research in the Atmosphere Rep. V, 507 pp. [Available online at http://vista.cira.colostate.edu/Improve/spatial-and-seasonal-patterns-and-temporal-variability-of-haze-and-its-constituents-in-the-united-states-report-v-june-2011/.]

  • Hand, J. L., B. A. Schichtel, M. Pitchford, W. C. Malm, and N. H. Frand, 2012: Seasonal composition of remote and urban fine particulate matter in the United States. J. Geophys. Res., 117, D05209, doi:10.1029/2011JD017122.

    • Search Google Scholar
    • Export Citation

  • Heidinger, A. K., W. C. Straka III, C. C. Molling, J. T. Sullivan, and X. Wu, 2010: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record. Int. J. Remote Sens., 31, 6493–6517, doi:10.1080/01431161.2010.496472.

    • Search Google Scholar
    • Export Citation

  • Hidy, G. M., P. K. Mueller, and E. Y. Tong, 1978: Spatial and temporal distributions of airborne sulfate in parts of the United States. Atmos. Environ., 12, 735–752, doi:10.1016/0004-6981(78)90255-X.

    • Search Google Scholar
    • Export Citation

  • Hoff, R. M., and S. A. Christopher, 2009: Remote sensing of particulate pollution from space: Have we reached the promised land? J. Air Waste Manag. Assoc., 59, 646–675.

    • Search Google Scholar
    • Export Citation

  • Holben, B. N., and Coauthors, 1998: AERONET—A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 66, 1–16, doi:10.1016/S0034-4257(98)00031-5.

    • Search Google Scholar
    • Export Citation

  • Holben, B. N., R. F. Eck, I. Slutsker, A. Smirnov, A. Sinyuk, J. Schafer, D. Giles, and O. Dubovik, 2006: AERONET’s Version 2.0 quality assurance criteria. Remote Sensing of the Atmosphere and Clouds, S.-C. Tsay et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 6408), 64080Q, doi:10.1117/12.706524.

  • Hsu, N. C., R. Gautam, A. M. Sayer, C. Bettenhausen, C. Li, M. J. Jeong, S.-C. Tsay, and B. N. Holben, 2012: Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010. Atmos. Chem. Phys., 12, 8037–8053, doi:10.5194/acp-12-8037-2012.

    • Search Google Scholar
    • Export Citation

  • Hubanks, P., S. Platnick, M. King, and B. Ridgway, 2015: MODIS Atmosphere L3 gridded product algorithm theoretical basis document (ATBD) & users guide. MODIS Algorithm Theoretical Basis Doc. ATBD-MOD-30, 119 pp. [Available online at http://modis-atmos.gsfc.nasa.gov/MOD08_M3/atbd.html.]

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., doi:10.1017/CBO9781107415324.

  • Kaufman, Y. J., D. Tanre, and O. Boucher, 2002: A satellite view of aerosols in the climate system. Nature, 419, 215–223, doi:10.1038/nature01091.

    • Search Google Scholar
    • Export Citation

  • Kim, D., and Coauthors, 2014: Sources, sinks, and transatlantic transport of North African dust aerosol: A multimodel analysis and comparison with remote sensing data. J. Geophys. Res. Atmos., 119, 6259–6277, doi:10.1002/2013JD021099.

    • Search Google Scholar
    • Export Citation

  • Koren, I., L. A. Remer, and K. Longo, 2007: Reversal of trend of biomass burning in the Amazon. Geophys. Res. Lett., 34, L20404, doi:10.1029/2007GL031530.

    • Search Google Scholar
    • Export Citation

  • Lana, A., and Coauthors, 2011: An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean. Global Biogeochem. Cycles, 25, GB1004, doi:10.1029/2010GB003850.

    • Search Google Scholar
    • Export Citation

  • Levy, R. C., L. A. Remer, D. Tanré, S. Mattoo, and Y. J. Kaufman, 2009: Algorithm for remote sensing of tropospheric aerosol over dark targets from MODIS: Collections 005 and 051: Revision 2. NASA Algorithm Theoretical Basis Doc., 96 pp. [Available online at http://modis-atmos.gsfc.nasa.gov/_docs/ATBD_MOD04_C005_rev2.pdf.]

  • Levy, R. C., L. A. Remer, R. G. Kleidman, S. Mattoo, C. Ichoku, R. Kahn, and T. F. Eck, 2010: Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmos. Chem. Phys., 10, 10 399–10 420, doi:10.5194/acp-10-10399-2010.

    • Search Google Scholar
    • Export Citation

  • Levy, R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Hsu, 2013: The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech., 6, 2989–3034, doi:10.5194/amt-6-2989-2013.

    • Search Google Scholar
    • Export Citation

  • Li, J., B. E. Carlson, O. Dubovik, and A. A. Lacis, 2014: Recent trends in aerosol optical properties derived from AERONET measurements. Atmos. Chem. Phys., 14, 12 271–12 289, doi:10.5194/acp-14-12271-2014.

    • Search Google Scholar
    • Export Citation

  • Li, Z., 1998: Influence of absorbing aerosols on the inference of solar surface radiation budget and cloud absorption. J. Climate, 11, 5–17, doi:10.1175/1520-0442(1998)011<0005:IOAAOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Li, Z., and Coauthors, 2009: Uncertainties in satellite remote sensing of aerosols and impact on monitoring its long-term trend: A review and perspective. Ann. Geophys., 27, 2755–2770, doi:10.5194/angeo-27-2755-2009.

    • Search Google Scholar
    • Export Citation

  • Malm, W. C., J. F. Sisler, D. Huffman, R. A. Eldred, and T. A. Cahill, 1994: Spatial and seasonal trends in particle concentration and optical extinction in the United States. J. Geophys. Res., 99, 1347–1370, doi:10.1029/93JD02916.

    • Search Google Scholar
    • Export Citation

  • Mi, W., Z. Li, X. Xia, B. Holben, R. Levy, F. Zhao, H. Chen, and M. Cribb, 2007: Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two Aerosol Robotic Network stations in China. J. Geophys. Res., 112, D22S08, doi:10.1029/2007JD008474.

    • Search Google Scholar
    • Export Citation

  • Mishchenko, M. I., and I. V. Geogdzhayev, 2007: Satellite remote sensing reveals regional tropospheric aerosol trends. Opt. Express, 15, 7423–7438, doi:10.1364/OE.15.007423.

    • Search Google Scholar
    • Export Citation

  • Mishchenko, M. I., I. V. Geogdzhayev, W. B. Rossow, B. Carins, B. E. Carlson, A. A. Lacis, L. Liu, and L. D. Travis, 2007: Long-term satellite record reveals likely recent aerosol trend. Science, 315, 1543, doi:10.1126/science.1136709.

    • Search Google Scholar
    • Export Citation

  • Mishchenko, M. I., L. Liu, I. V. Geogdzhayev, J. Li, B. E. Carlson, A. A. Lacis, B. Cairns, and L. D. Travis, 2012: Aerosol retrievals from channel-1 and -2 AVHRR radiances: Long-term trends updated and revisited. J. Quant. Spectrosc. Radiat. Transfer, 113, 1974–1980, doi:10.1016/j.jqsrt.2012.05.006.

    • Search Google Scholar
    • Export Citation

  • Myhre, G., and Coauthors, 2013: Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmos. Chem. Phys., 13, 1853–1877, doi:10.5194/acp-13-1853-2013.

    • Search Google Scholar
    • Export Citation

  • Remer, L. A., and Coauthors, 2008: Global aerosol climatology from the MODIS satellite sensors. J. Geophys. Res., 113, D14S07, doi:10.1029/2007JD009661.

    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation

  • Shindell, D. T., A. Voulgarakis, G. Faluvegi, and G. Milly, 2012: Precipitation response to regional radiative forcing. Atmos. Chem. Phys., 12, 6969–6982, doi:10.5194/acp-12-6969-2012.

    • Search Google Scholar
    • Export Citation

  • Smirnov, A., B. N. Holben, T. F. Eck, O. Dubovik, and I. Slutsker, 2003: Effect of wind speed on columnar aerosol optical properties at Midway Island. J. Geophys. Res., 108, 4802, doi:10.1029/2002JD002701.

    • Search Google Scholar
    • Export Citation

  • U.S. EPA, 2004a: Air quality criteria for particulate matter. Vol. 1, U.S. Environmental Protection Agency Final Rep. EPA 600/P-99/002aF, 900 pp. [Available online at https://cfpub2.epa.gov/ncea/risk/recordisplay.cfm?deid=87903.]

  • U.S. EPA, 2004b: Air quality criteria for particulate matter. Vol. 2, U.S. Environmental Protection Agency Final Rep. EPA 600/P-99/002bF, 1148 pp. [Available online at https://cfpub2.epa.gov/ncea/risk/recordisplay.cfm?deid=87903.]

  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 496 pp., doi:10.1017/CBO9780511612336.

  • Wang, J., and S. A. Christopher, 2003: Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophys. Res. Lett., 30, 2095, doi:10.1029/2003GL018174.

    • Search Google Scholar
    • Export Citation

  • Wild, M., A. Ohmura, and K. Makowski, 2007: Impact of global dimming and brightening on global warming. Geophys. Res. Lett., 34, L04702, doi:10.1029/2006GL028031.

    • Search Google Scholar
    • Export Citation

  • Xiong, X., J. Sun, X. Xie, W. L. Barnes, and V. V. Salomonson, 2010: On-orbit calibration and performance of Aqua MODIS reflective solar bands. IEEE Trans. Geosci. Remote Sens., 48, 535–546, doi:10.1109/TGRS.2009.2024307.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., and J. S. Reid, 2010: A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products. Atmos. Chem. Phys., 10, 10 949–10 963, doi:10.5194/acp-10-10949-2010.

    • Search Google Scholar
    • Export Citation

  • Zhao, T. X.-P., O. Dubovik, A. Smirnov, B. Holben, J. Sapper, C. Pietras, K. Voss, and R. Frouin, 2004: Regional evaluation of an Advanced Very High Resolution Radiometer (AVHRR) two-channel aerosol retrieval algorithm. J. Geophys. Res., 109, D02204, doi:10.1029/2003JD003817.

    • Search Google Scholar
    • Export Citation

  • Zhao, T. X.-P., I. Laszlo, W. Guo, A. Heidinger, C. Cao, A. Jelenak, D. Tarpley, and J. Sullivan, 2008: Study of long-term trend in aerosol optical thickness observed from operational AVHRR satellite instrument. J. Geophys. Res., 113, D07201, doi:10.1029/2007JD009061.

    • Search Google Scholar
    • Export Citation

  • Zhao, T. X.-P., P. K. Chan, and A. K. Heidinger, 2013: A global survey of the effect of cloud contamination on the aerosol optical thickness and its long-term trend derived from operational AVHRR satellite observations. J. Geophys. Res. Atmos., 118, 2849–2857, doi:10.1002/jgrd.50278.

    • Search Google Scholar
    • Export Citation
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