Quantifying the Dependence of Satellite Cloud Retrievals on Instrument Uncertainty

Yolanda L. Shea NASA Langley Research Center, Hampton, Virginia

Search for other papers by Yolanda L. Shea in
Current site
Google Scholar
PubMed
Close
,
Bruce A. Wielicki NASA Langley Research Center, Hampton, Virginia

Search for other papers by Bruce A. Wielicki in
Current site
Google Scholar
PubMed
Close
,
Sunny Sun-Mack Science Systems and Applications, Inc., Hampton, Virginia

Search for other papers by Sunny Sun-Mack in
Current site
Google Scholar
PubMed
Close
, and
Patrick Minnis NASA Langley Research Center, Hampton, Virginia

Search for other papers by Patrick Minnis in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Cloud response to Earth’s changing climate is one of the largest sources of uncertainty among global climate model (GCM) projections. Two of the largest sources of uncertainty are the spread in equilibrium climate sensitivity (ECS) and uncertainty in radiative forcing due to uncertainty in the aerosol indirect effect. Satellite instruments with sufficient accuracy and on-orbit stability to detect climate change–scale trends in cloud properties will improve confidence in the understanding of the relationship between observed climate change and cloud property trends, thus providing information to better constrain ECS and radiative forcing. This study applies a climate change uncertainty framework to quantify the impact of measurement uncertainty on trend detection times for cloud fraction, effective temperature, optical thickness, and water cloud effective radius. Although GCMs generally agree that the total cloud feedback is positive, disagreement remains on its magnitude. With the climate uncertainty framework, it is demonstrated how stringent measurement uncertainty requirements for reflected solar and infrared satellite measurements enable improved constraint of SW and LW cloud feedbacks and the ECS by significantly reducing trend uncertainties for cloud fraction, optical thickness, and effective temperature. The authors also demonstrate improved constraint on uncertainty in the aerosol indirect effect by reducing water cloud effective radius trend uncertainty.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 3 August 2017 to correct several acronyms when first introduced and to fix a minor typographical error, all in section 1.

Publisher’s Note: This article was revised on 25 January 2018 to include the entry for the linear regression coefficients for cloud fraction in Table 4 that was missing when originally published.

Corresponding author: Yolanda L. Shea, yolanda.shea@nasa.gov

Abstract

Cloud response to Earth’s changing climate is one of the largest sources of uncertainty among global climate model (GCM) projections. Two of the largest sources of uncertainty are the spread in equilibrium climate sensitivity (ECS) and uncertainty in radiative forcing due to uncertainty in the aerosol indirect effect. Satellite instruments with sufficient accuracy and on-orbit stability to detect climate change–scale trends in cloud properties will improve confidence in the understanding of the relationship between observed climate change and cloud property trends, thus providing information to better constrain ECS and radiative forcing. This study applies a climate change uncertainty framework to quantify the impact of measurement uncertainty on trend detection times for cloud fraction, effective temperature, optical thickness, and water cloud effective radius. Although GCMs generally agree that the total cloud feedback is positive, disagreement remains on its magnitude. With the climate uncertainty framework, it is demonstrated how stringent measurement uncertainty requirements for reflected solar and infrared satellite measurements enable improved constraint of SW and LW cloud feedbacks and the ECS by significantly reducing trend uncertainties for cloud fraction, optical thickness, and effective temperature. The authors also demonstrate improved constraint on uncertainty in the aerosol indirect effect by reducing water cloud effective radius trend uncertainty.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 3 August 2017 to correct several acronyms when first introduced and to fix a minor typographical error, all in section 1.

Publisher’s Note: This article was revised on 25 January 2018 to include the entry for the linear regression coefficients for cloud fraction in Table 4 that was missing when originally published.

Corresponding author: Yolanda L. Shea, yolanda.shea@nasa.gov
Save
  • Anderson, J., J. Dykema, R. Goody, H. Hu, and D. Kirk-Davidoff, 2004: Absolute, spectrally-resolved, thermal radiance: A benchmark for climate monitoring from space. J. Quant. Spectrosc. Radiat. Transf., 85, 367383, doi:10.1016/S0022-4073(03)00232-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19, 34453482, doi:10.1175/JCLI3819.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caldwell, P. M., M. D. Zelinka, K. E. Taylor, and K. Marvel, 2016: Quantifying the sources of intermodel spread in equilibrium climate sensitivity. J. Climate, 29, 513524, doi:10.1175/JCLI-D-15-0352.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, C., F. J. De Luccia, X. Xiong, R. Wolfe, and F. Weng, 2014: Early on-orbit performance of the Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite. IEEE Trans. Geosci. Remote Sens., 52, 11421156, doi:10.1109/TGRS.2013.2247768.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CERES Science Team, 2016: CERES SSF1deg, edition 4A, subset monthly. NASA Atmospheric Science Data Center (ASDC), Hampton, VA, accessed June 2016, doi:10.5067/AQUA/CERES/SSF1DEGMONTH_L3.004A.

    • Crossref
    • Export Citation
  • Chen, T., W. B. Rossow, and Y. Zhang, 2000: Radiative effects of cloud-type variations. J. Climate, 13, 264286, doi:10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, M., and Coauthors, 2013: Long-term climate change: Projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1029–1136, doi:10.1017/CBO9781107415324.024.

    • Crossref
    • Export Citation
  • Cubasch, U., D. Wuebbles, D. Chen, M. Facchini, D. Frame, N. Mahowald, and J.-G. Winther, 2013: Introduction. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 119–158, doi:10.1017/CBO9781107415324.007.

    • Crossref
    • Export Citation
  • Dessler, A., and N. Loeb, 2013: Impact of dataset choice on calculations of the short-term cloud feedback. J. Geophys. Res. Atmos., 118, 28212826, doi:10.1002/jgrd.50199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolinar, E. K., X. Dong, B. Xi, J. H. Jiang, and H. Su, 2015: Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Climate Dyn., 44, 22292247, doi:10.1007/s00382-014-2158-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowell, M., and Coauthors, 2013: Strategy towards an architecture for climate monitoring from space. CEOS/CGMS/WMO, 39 pp.

  • Feldman, D. R., C. A. Algieri, W. D. Collins, Y. L. Roberts, and P. A. Pilewskie, 2011: Simulation studies for the detection of changes in broadband albedo and shortwave nadir reflectance spectra under a climate change scenario. J. Geophys. Res., 116, D24103, doi:10.1029/2011JD016407.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866, doi:10.1017/CBO9781107415324.020.

    • Crossref
    • Export Citation
  • Fox, N., A. Kaiser-Weiss, W. Schmutz, K. Thome, D. Young, B. Wielicki, R. Winkler, and E. Woolliams, 2011: Accurate radiometry from space: An essential tool for climate studies. Philos. Trans. Roy. Soc. London, 369A, 40284063, doi:10.1098/rsta.2011.0246.

    • Search Google Scholar
    • Export Citation
  • Goldberg, M., and Coauthors, 2011: The Global Space-Based Inter-Calibration System. Bull. Amer. Meteor. Soc., 92, 467475, doi:10.1175/2010BAMS2967.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goody, R., J. Anderson, T. Karl, R. Balstad Miller, G. North, J. Simpson, G. Stephens, and W. Washington, 2002: Why we should monitor the climate. Bull. Amer. Meteor. Soc., 83, 873878, doi:10.1175/1520-0477(2002)083<0873:WWSMTC>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1535 pp.

  • Jin, Z., and M. Sun, 2016: An initial study on climate change fingerprinting using the reflected solar spectra. J. Climate, 29, 27812796, doi:10.1175/JCLI-D-15-0297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., F. G. Rose, X. Liu, B. A. Wielicki, and M. G. Mlynczak, 2014: Retrieval of atmospheric and cloud property anomalies and their trend from temporally and spatially averaged infrared spectra observed from space. J. Climate, 27, 44034420, doi:10.1175/JCLI-D-13-00566.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1109/TGRS.2002.808226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, T. E., S. D. Miller, F. J. Turk, C. Schueler, R. Julian, S. Deyo, P. Dills, and S. Wang, 2006: The NPOESS VIIRS day/night visible sensor. Bull. Amer. Meteor. Soc., 87, 191199, doi:10.1175/BAMS-87-2-191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leroy, S., J. Anderson, J. Dykema, and R. Goody, 2008a: Testing climate models using thermal infrared spectra. J. Climate, 21, 18631875, doi:10.1175/2007JCLI2061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leroy, S., J. Anderson, and G. Ohring, 2008b: Climate signal detection times and constraints on climate benchmark accuracy requirements. J. Climate, 21, 841846, doi:10.1175/2007JCLI1946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Coauthors, 2017: Spectrally dependent CLARREO infrared spectrometer calibration requirement for climate change detection. J. Climate, doi:10.1175/JCLI-D-16-0704.1, in press.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., and Coauthors, 2007: Multi-instrument comparison of top-of-atmosphere reflected solar radiation. J. Climate, 20, 575591, doi:10.1175/JCLI4018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766, doi:10.1175/2008JCLI2637.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., J. M. Lyman, G. C. Johnson, R. P. Allan, D. R. Doelling, T. Wong, B. J. Soden, and G. L. Stephens, 2012: Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat. Geosci., 5, 110113, doi:10.1038/ngeo1375.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loveland, T., B. Reed, J. Brown, D. Ohlen, Z. Zhu, L. Yang, and J. Merchant, 2000: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens., 21, 13031330, doi:10.1080/014311600210191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lukashin, C., B. A. Wielicki, D. F. Young, K. Thome, Z. Jin, and W. Sun, 2013: Uncertainty estimates for imager reference inter-calibration with CLARREO reflected solar spectrometer. IEEE Trans. Geosci. Remote Sens., 51, 14251436, doi:10.1109/TGRS.2012.2233480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyapustin, A., and Coauthors, 2014: Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech., 7, 43534365, doi:10.5194/amt-7-4353-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marvel, K., M. Zelinka, S. A. Klein, C. Bonfils, P. Caldwell, C. Doutriaux, B. D. Santer, and K. E. Taylor, 2015: External influences on modeled and observed cloud trends. J. Climate, 28, 48204840, doi:10.1175/JCLI-D-14-00734.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2008: Cloud detection in nonpolar regions for CERES using TRMM VIRS and Terra and Aqua MODIS data. IEEE Trans. Geosci. Remote Sens., 46, 38573884, 10.1109/TGRS.2008.2001351.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2011: CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part I: Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 43744400, doi:10.1109/TGRS.2011.2144601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moeller, C., D. Tobin, and G. Quinn, 2013: S-NPP VIIRS thermal band spectral radiance performance through 18 months of operation on-orbit, Earth Observing Systems XVIII, J. J. Butler, X. Xiong, and X. Gu, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 8866), 88661N, doi:10.1117/12.2023389.

    • Crossref
    • Export Citation
  • Myhre, G., and Coauthors, 2013: Anthropogenic and natural radiative forcing. Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 659–740, doi:10.1017/CBO9781107415324.018.

    • Crossref
    • Export Citation
  • National Research Council, 2007: Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. National Academies Press, 428 pp.

  • National Research Council, 2015: Continuity of NASA Earth Observations from Space: A Value Framework. National Academies Press, 118 pp.

  • Nolin, A., R. L. Armstrong, and J. Maslanik, 1998: Near-real-time SSM/I-SSMIS EASE-Grid daily global ice concentration and snow extent (updated daily). National Snow and Ice Data Center, accessed 9 November 2015. [Available online at http://nsidc.org/data/nise.]

  • Ohring, G., B. Wielicki, R. Spencer, B. Emery, and R. Datla, 2005: Satellite instrument calibration for measuring global climate change: Report of a workshop. Bull. Amer. Meteor. Soc., 86, 13031313, doi:10.1175/BAMS-86-9-1303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phojanamongkolkij, N., S. Kato, B. A. Wielicki, P. C. Taylor, and M. G. Mlynczak, 2014: A comparison of climate signal trend detection uncertainty analysis methods. J. Climate, 27, 33633376, doi:10.1175/JCLI-D-13-00400.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2017: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, doi:10.1109/TGRS.2016.2610522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, Y., P. Pilewskie, D. Feldman, B. Kindel, and W. Collins, 2014: Temporal variability of observed and simulated hyperspectral reflectance. J. Geophys. Res. Atmos., 119, 10 26210 280, doi:10.1002/2014JD021566.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roithmayr, C. M., C. Lukashin, P. W. Speth, G. Kopp, K. Thome, B. Wielicki, and D. F. Young, 2014: CLARREO approach for reference intercalibration of reflected solar sensors: On-orbit data matching and sampling. IEEE Trans. Geosci. Remote Sens., 52, 67626774, doi:10.1109/TGRS.2014.2302397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roman, J., R. Knuteson, and S. Ackerman, 2014: Time-to-detect trends in precipitable water vapor with varying measurement error. J. Climate, 27, 82598275, doi:10.1175/JCLI-D-13-00736.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slingo, A., 1989: A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos. Sci., 46, 14191427, doi:10.1175/1520-0469(1989)046<1419:AGPFTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B. J., and G. A. Vecchi, 2011: The vertical distribution of cloud feedback in coupled ocean–atmosphere models. Geophys. Res. Lett., 38, L12704, doi:10.1029/2011GL047632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008: Quantifying climate feedbacks using radiative kernels. J. Climate, 21, 35043520, doi:10.1175/2007JCLI2110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, doi:10.1175/JCLI-3243.1.

  • Stephens, G. L., S.-C. Tsay, P. W. Stackhouse Jr., and P. J. Flatau, 1990: The relevance of the microphysical and radiative properties of cirrus clouds to climate and climatic feedback. J. Atmos. Sci., 47, 17421754, doi:10.1175/1520-0469(1990)047<1742:TROTMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, W., J. Corbett, Z. Eitzen, and L. Liang, 2015: Next-generation angular distribution models for top-of-atmosphere radiative flux calculation from CERES instruments: Validation. Atmos. Meas. Tech., 8, 32973313, doi:10.5194/amt-8-3297-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and Coauthors, 2013: Challenges of a sustained climate observing system. Climate Science for Serving Society, A. Ghassem and J. W. Hurrell, Eds., Springer, 13–50.

    • Crossref
    • Export Citation
  • Trepte, Q., P. Minnis, and R. F. Arduini, 2003: Daytime and nighttime polar cloud and snow identification using MODIS data. Optical Remote Sensing of the Atmosphere and Clouds III, H.-L. Huang, D. Lu, and Y. Sasano, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 4891), 449–459, doi:10.1117/12.467306.

    • Crossref
    • Export Citation
  • Twomey, S., 1977: The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci., 34, 11491152, doi:10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vial, J., J.-L. Dufresne, and S. Bony, 2013: On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Climate Dyn., 41, 33393362, doi:10.1007/s00382-013-1725-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weatherhead, E. C., and Coauthors, 1998: Factors affecting the detection of trends: Statistical considerations and applications to environmental data. J. Geophys. Res., 103, 17 14917 161, doi:10.1029/98JD00995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webb, M. J., F. H. Lambert, and J. M. Gregory, 2013: Origins of differences in climate sensitivity, forcing and feedback in climate models. Climate Dyn., 40, 677707, doi:10.1007/s00382-012-1336-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wenny, B. N., A. Wu, S. Madhavan, Z. Wang, Y. Li, N. Chen, K.-F. Chiang, and X. Xiong, 2012: MODIS TEB calibration approach in collection 6. Sensors, Systems, and Next-Generation Satellites XVI, R. Meynart, S. P. Neeck, and H. Shimoda, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 8533), 85331M, doi:10.1117/12.974231.

    • Crossref
    • Export Citation
  • Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee III, G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth’s radiant energy system (CERES): An Earth observing system experiment. Bull. Amer. Meteor. Soc., 77, 853868, doi:10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., and Coauthors, 2013: Achieving climate change absolute accuracy in orbit. Bull. Amer. Meteor. Soc., 94, 15191539, doi:10.1175/BAMS-D-12-00149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, A., X. Xiong, Z. Jin, C. Lukashin, B. N. Wenny, and J. J. Butler, 2015: Sensitivity of intercalibration uncertainty of the CLARREO reflected solar spectrometer features. IEEE Trans. Geosci. Remote Sens., 53, 47414751, doi:10.1109/TGRS.2015.2409030.

    • Crossref
    • 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, 535546, doi:10.1109/TGRS.2009.2024307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012a: Computing and partitioning cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernels. J. Climate, 25, 37153735, doi:10.1175/JCLI-D-11-00248.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012b: Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution to changes in cloud amount, altitude, and optical depth. J. Climate, 25, 37363754, doi:10.1175/JCLI-D-11-00249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., S. A. Klein, K. E. Taylor, T. Andrews, M. J. Webb, J. M. Gregory, and P. M. Forster, 2013: Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. J. Climate, 26, 50075027, doi:10.1175/JCLI-D-12-00555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1052 630 41
PDF Downloads 348 55 6