• Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. J. Geophys. Res., 103D , 3214132157.

    • Search Google Scholar
    • Export Citation
  • Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill, 2008: Cloud detection with MODIS. Part II: Validation. J. Atmos. Oceanic Technol., 25 , 10731086.

    • Search Google Scholar
    • Export Citation
  • Beesley, J. A., and R. E. Moritz, 1999: Toward an explanation of the annual cycle of cloudiness over the Arctic Ocean. J. Climate, 12 , 395415.

    • Search Google Scholar
    • Export Citation
  • Birch, C. E., I. M. Brooks, M. Tjernstrom, S. F. Milton, P. Earnshaw, S. Soderberg, and P. O. G. Persson, 2009: The performance of a global and mesoscale model over the central Arctic Ocean during late summer. J. Geophys. Res., 114 , D13104. doi:10.1029/2008JD010790.

    • Search Google Scholar
    • Export Citation
  • Cavalieri, D., M. Thorsten, and J. Comiso, cited. 2004: AMSR-E/Aqua daily L3 25-km brightness temperature and SIC polar grids V002, July 2006 to December 2008. National Snow and Ice Data Center. [Available online at http://nsidc.org/data/ae_si25.html].

    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., C. L. Parkinson, R. Gersten, and L. Stock, 2008: Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett., 35 , L01703. doi:10.1029/2007GL031972.

    • Search Google Scholar
    • Export Citation
  • Curry, J. A., W. B. Rossow, D. Randall, and J. L. Schramm, 1996: Overview of Arctic cloud and radiation characteristics. J. Climate, 9 , 17311764.

    • Search Google Scholar
    • Export Citation
  • Deser, C., and H. Teng, 2008: Evolution of Arctic sea ice concentration trends and the role of atmospheric circulation forcing, 1979-2007. Geophys. Res. Lett., 35 , L02504. doi:10.1029/2007GL032023.

    • Search Google Scholar
    • Export Citation
  • Eloranta, E. W., J. P. Garcia, I. A. Razenkov, T. Uttal, and M. Shupe, 2008: Cloud fraction statistics derived from 2-years of high spectral resolution lidar data acquired at Eureka, Canada. Proc. 24th Int. Laser Radar Conf., Boulder, CO, NCAR and NOAA, 555–558.

    • Search Google Scholar
    • Export Citation
  • Evan, A. T., Y. Liu, and B. Maddux, 2008: Global cloudiness. Bull. Amer. Meteor. Soc., 89 , (Suppl.). S23S26.

  • Frey, R. A., S. A. Ackerman, Y. Liu, K. I. Strabala, H. Zhang, J. R. Key, and X. Wang, 2008: Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. J. Atmos. Oceanic Technol., 25 , 10571072.

    • Search Google Scholar
    • Export Citation
  • Gao, B. C., W. Han, S. C. Tsay, and N. F. Larsen, 1998: Cloud detection over the Arctic region using airborne imaging spectrometer data during the daytime. J. Appl. Meteor., 37 , 14211429.

    • Search Google Scholar
    • Export Citation
  • Hanssen, A. W., and W. J. A. Kuipers, 1965: On the relationship between the frequency of rain and various meteorological parameters. Meded. Verh., 81 , 215.

    • Search Google Scholar
    • Export Citation
  • Holland, M. M., and C. M. Bitz, 2003: Polar amplification of climate change in coupled models. Climate Dyn., 21 , 221232.

  • Holz, R. E., S. A. Ackerman, F. W. Nagle, R. Frey, S. Dutcher, R. E. Kuehn, M. A. Vaughan, and B. Baum, 2008: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP. J. Geophys. Res., 113D , D00A19. doi:10.1029/2008JD009837.

    • Search Google Scholar
    • Export Citation
  • Inoue, J., J. Liu, J. O. Pinto, and J. A. Curry, 2006: Intercomparison of Arctic regional climate models: Modeling clouds and radiation for SHEBA in May 1998. J. Climate, 19 , 41674178.

    • Search Google Scholar
    • Export Citation
  • Intrieri, J. M., C. W. Fairall, M. D. Shupe, P. O. G. Persson, E. L. Andreas, P. S. Guest, and R. E. Moritz, 2002: An annual cycle of Arctic surface cloud forcing at SHEBA. J. Geophys. Res., 107 , 8039. doi:10.1029/2000JC000439.

    • Search Google Scholar
    • Export Citation
  • Jin, X., J. M. Hanesiak, and D. G. Barber, 2007: Time series of daily averaged cloud fractions over landfast first-year sea ice from multiple data sources. J. Appl. Meteor. Climatol., 46 , 18181827.

    • Search Google Scholar
    • Export Citation
  • Kato, S., N. G. Loeb, P. Minnis, J. A. Francis, T. P. Charlock, D. A. Rutan, E. E. Clothiaux, and S. Sun-Mack, 2006: Seasonal and interannual variations of top-of-atmosphere irradiance and cloud cover over polar regions derived from the CERES data set. Geophys. Res. Lett., 33 , L19804. doi:10.1029/2006GL026685.

    • Search Google Scholar
    • Export Citation
  • Key, J., and R. G. Barry, 1989: Cloud cover analysis with Arctic AVHRR data. 1. Cloud detection. J. Geophys. Res., 94D , 1852118535.

  • King, M. D., and Coauthors, 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Trans. Geosci. Remote Sens., 41 , 442458.

    • Search Google Scholar
    • Export Citation
  • Lindsay, R. W., J. Zhang, A. Schweiger, M. Steele, and H. Stern, 2009: Arctic sea ice retreat in 2007 follows thinning trend. J. Climate, 22 , 165176.

    • Search Google Scholar
    • Export Citation
  • Liu, J. P., J. A. Curry, and Y. Y. Hu, 2004a: Recent Arctic sea ice variability: Connections to the Arctic Oscillation and the ENSO. Geophys. Res. Lett., 31 , L09211. doi:10.1029/2004GL019858.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., J. R. Key, R. A. Frey, S. A. Ackerman, and W. P. Menzel, 2004b: Nighttime polar cloud detection with MODIS. Remote Sens. Environ., 92 , 181194.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., J. R. Key, J. A. Francis, and X. Wang, 2007: Possible causes of decreasing cloud cover in the Arctic winter, 1982-2000. Geophys. Res. Lett., 34 , L14705. doi:10.1029/2007GL030042.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., J. R. Key, and X. Wang, 2008: The influence of changes in cloud cover on recent surface temperature trends in the Arctic. J. Climate, 21 , 705715.

    • Search Google Scholar
    • Export Citation
  • Lubin, D., and E. Morrow, 1998: Evaluation of an AVHRR cloud detection and classification method over the central Arctic Ocean. J. Appl. Meteor., 37 , 166183.

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., cited. 2007: Level-2 radar-lidar GEOPROF product version 1.0, process description and interface control document. Jet Propulsion Laboratory. [Available online at http://www.CloudSat.cira.colostate.edu/dataICDlist.php?go=list&path=/2B-GEOPROF-LIDAR].

    • Search Google Scholar
    • Export Citation
  • Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. Stephens, C. Trepte, and D. Winker, 2009: A description of hydrometeor layer occurrence statistics derived from the first year of merged CloudSat and CALIPSO data. J. Geophys. Res., 114 , D00A26. doi:10.1029/2007JD009755.

    • Search Google Scholar
    • Export Citation
  • Paluch, I. R., D. H. Lenschow, and Q. Wang, 1997: Arctic boundary layer in the fall season over open and frozen sea. J. Geophys. Res., 102D , 2595525971.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., M. King, S. Ackerman, W. Menzel, B. Baum, J. Riedi, and R. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41 , 459473.

    • Search Google Scholar
    • Export Citation
  • Quinn, P. K., G. Shaw, E. Andrews, E. G. Dutton, T. Ruoho-Airola, and S. L. Gong, 2007: Arctic haze: Current trends and knowledge gaps. Tellus, 59B , 99114.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

  • Schweiger, A. J., 2004: Changes in seasonal cloud cover over the Arctic seas from satellite and surface observations. Geophys. Res. Lett., 31 , L12207. doi:10.1029/2004GL020067.

    • Search Google Scholar
    • Export Citation
  • Schweiger, A. J., and J. R. Key, 1994: Arctic Ocean radiative fluxes and cloud forcing estimated from the ISCCP C2 cloud dataset, 1983–1990. J. Appl. Meteor., 33 , 948963.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. A. Francis, 2006: The Arctic amplification debate. Climatic Change, 76 , 241264.

  • Serreze, M. C., M. M. Holland, and J. Stroeve, 2007: Perspectives on the Arctic’s shrinking sea-ice cover. Science, 315 , 15331536.

  • Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, 2007: Summary for policymakers. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 1–18.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the a-train—A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83 , 17711790.

    • Search Google Scholar
    • Export Citation
  • Tjernstrom, M., J. Sedlar, and M. D. Shupe, 2008: How well do regional climate models reproduce radiation and clouds in the Arctic? An evaluation of ARCMIP simulations. J. Appl. Meteor. Climatol., 47 , 24052422.

    • Search Google Scholar
    • Export Citation
  • Vavrus, S., 2004: The impact of cloud feedbacks on Arctic climate under greenhouse forcing. J. Climate, 17 , 603615.

  • Vavrus, S., and D. Waliser, 2008: An improved parameterization for simulating Arctic cloud amount in the CCSM3 climate model. J. Climate, 21 , 56735687.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., V. M. Kattsov, W. L. Chapman, V. Govorkova, and T. Pavlova, 2002: Comparison of Arctic climate simulations by uncoupled and coupled global models. J. Climate, 15 , 14291446.

    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., S. J. Vavrus, and W. L. Chapman, 2005: Workshop on modeling of the Arctic atmosphere. Bull. Amer. Meteor. Soc., 86 , 845852.

    • Search Google Scholar
    • Export Citation
  • Wang, X. J., and J. R. Key, 2003: Recent trends in Arctic surface, cloud, and radiation properties from space. Science, 299 , 17251728.

    • Search Google Scholar
    • Export Citation
  • Wang, X. J., and J. R. Key, 2005a: Arctic surface, cloud, and radiation properties based on the AVHRR Polar Pathfinder dataset. Part I: Spatial and temporal characteristics. J. Climate, 18 , 25582574.

    • Search Google Scholar
    • Export Citation
  • Wang, X. J., and J. R. Key, 2005b: Arctic surface, cloud, and radiation properties based on the AVHRR Polar Pathfinder dataset. Part II: Recent trends. J. Climate, 18 , 25752593.

    • Search Google Scholar
    • Export Citation
  • Winker, D. M., J. Pelon, and M. P. McCormick, 2003: The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds. Lidar Remote Sensing for Industry and Environment Monitoring III, U. N. Singh, T. Itabe, and Z. Liu, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 4893), 1–11.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., and J. E. Walsh, 2006: Toward a seasonally ice-covered Arctic Ocean: Scenarios from the IPCC AR4 model simulations. J. Climate, 19 , 17301747.

    • Search Google Scholar
    • Export Citation
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Errors in Cloud Detection over the Arctic Using a Satellite Imager and Implications for Observing Feedback Mechanisms

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  • 1 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin
  • | 2 Cooperative Institute for Meteorological Satellite Studies, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin
  • | 3 Center for Satellite Applications and Research, NOAA/NESDIS, Madison, Wisconsin
  • | 4 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin
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Abstract

Arctic sea ice extent has decreased dramatically over the last 30 years, and this trend is expected to continue through the twenty-first century. Changes in sea ice extent impact cloud cover, which in turn influences the surface energy budget. Understanding cloud feedback mechanisms requires an accurate determination of cloud cover over the polar regions, which must be obtained from satellite-based measurements. The accuracy of cloud detection using observations from space varies with surface type, complicating any assessment of climate trends as well as the understanding of ice–albedo and cloud–radiative feedback mechanisms. To explore the implications of this dependence on measurement capability, cloud amounts from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with those from the CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) satellites in both daytime and nighttime during the time period from July 2006 to December 2008. MODIS is an imager that makes observations in the solar and infrared spectrum. The active sensors of CloudSat and CALIPSO, a radar and lidar, respectively, provide vertical cloud structures along a narrow curtain.

Results clearly indicate that MODIS cloud mask products perform better over open water than over ice. Regional changes in cloud amount from CloudSat/CALIPSO and MODIS are categorized as a function of independent measurements of sea ice concentration (SIC) from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). As SIC increases from 10% to 90%, the mean cloud amounts from MODIS and CloudSat–CALIPSO both decrease; water that is more open is associated with increased cloud amount. However, this dependency on SIC is much stronger for MODIS than for CloudSat–CALIPSO, and is likely due to a low bias in MODIS cloud amount. The implications of this on the surface radiative energy budget using historical satellite measurements are discussed. The quantified ice–water difference in MODIS cloud detection can be used to adjust estimated trends in cloud amount in the presence of changing sea ice cover from an independent dataset. It was found that cloud amount trends in the Arctic might be in error by up to 2.7% per decade. The impact of these errors on the surface net cloud radiative effect (“forcing”) of the Arctic can be significant, as high as 8.5%.

Corresponding author address: Yinghui Liu, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, 1225 West Dayton Street, Madison, WI 53706. Email: yinghuil@ssec.wisc.edu

Abstract

Arctic sea ice extent has decreased dramatically over the last 30 years, and this trend is expected to continue through the twenty-first century. Changes in sea ice extent impact cloud cover, which in turn influences the surface energy budget. Understanding cloud feedback mechanisms requires an accurate determination of cloud cover over the polar regions, which must be obtained from satellite-based measurements. The accuracy of cloud detection using observations from space varies with surface type, complicating any assessment of climate trends as well as the understanding of ice–albedo and cloud–radiative feedback mechanisms. To explore the implications of this dependence on measurement capability, cloud amounts from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with those from the CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) satellites in both daytime and nighttime during the time period from July 2006 to December 2008. MODIS is an imager that makes observations in the solar and infrared spectrum. The active sensors of CloudSat and CALIPSO, a radar and lidar, respectively, provide vertical cloud structures along a narrow curtain.

Results clearly indicate that MODIS cloud mask products perform better over open water than over ice. Regional changes in cloud amount from CloudSat/CALIPSO and MODIS are categorized as a function of independent measurements of sea ice concentration (SIC) from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). As SIC increases from 10% to 90%, the mean cloud amounts from MODIS and CloudSat–CALIPSO both decrease; water that is more open is associated with increased cloud amount. However, this dependency on SIC is much stronger for MODIS than for CloudSat–CALIPSO, and is likely due to a low bias in MODIS cloud amount. The implications of this on the surface radiative energy budget using historical satellite measurements are discussed. The quantified ice–water difference in MODIS cloud detection can be used to adjust estimated trends in cloud amount in the presence of changing sea ice cover from an independent dataset. It was found that cloud amount trends in the Arctic might be in error by up to 2.7% per decade. The impact of these errors on the surface net cloud radiative effect (“forcing”) of the Arctic can be significant, as high as 8.5%.

Corresponding author address: Yinghui Liu, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, 1225 West Dayton Street, Madison, WI 53706. Email: yinghuil@ssec.wisc.edu

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