Examining Cloud Macrophysical Changes over the Pacific for 2007–17 Using CALIPSO, CloudSat, and MODIS Observations

Seung-Hee Ham aScience Systems and Applications, Inc., Hampton, Virginia

Search for other papers by Seung-Hee Ham in
Current site
Google Scholar
PubMed
Close
,
Seiji Kato bNASA Langley Research Center, Hampton, Virginia

Search for other papers by Seiji Kato in
Current site
Google Scholar
PubMed
Close
,
Fred G. Rose aScience Systems and Applications, Inc., Hampton, Virginia

Search for other papers by Fred G. Rose in
Current site
Google Scholar
PubMed
Close
,
Norman G. Loeb bNASA Langley Research Center, Hampton, Virginia

Search for other papers by Norman G. Loeb in
Current site
Google Scholar
PubMed
Close
,
Kuan-Man Xu bNASA Langley Research Center, Hampton, Virginia

Search for other papers by Kuan-Man Xu in
Current site
Google Scholar
PubMed
Close
,
Tyler Thorsen bNASA Langley Research Center, Hampton, Virginia

Search for other papers by Tyler Thorsen in
Current site
Google Scholar
PubMed
Close
,
Michael G. Bosilovich cGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Michael G. Bosilovich in
Current site
Google Scholar
PubMed
Close
,
Sunny Sun-Mack aScience Systems and Applications, Inc., Hampton, Virginia

Search for other papers by Sunny Sun-Mack in
Current site
Google Scholar
PubMed
Close
,
Yan Chen aScience Systems and Applications, Inc., Hampton, Virginia

Search for other papers by Yan Chen in
Current site
Google Scholar
PubMed
Close
, and
Walter F. Miller aScience Systems and Applications, Inc., Hampton, Virginia

Search for other papers by Walter F. Miller in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Cloud macrophysical changes over the Pacific Ocean from 2007 to 2017 are examined by combining CALIPSO and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño–Southern Oscillation (ENSO) events. For example, midcloud (cloud tops at 3–10 km) and high cloud (cloud tops at 10–18 km) amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, relative to CALCS, MODIS shows stronger anticorrelation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, relative to CALCS, MODIS shows smaller impacts of mid- and high clouds on the low troposphere (<3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid- and high clouds.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Seung-Hee Ham, seung-hee.ham@nasa.gov

Abstract

Cloud macrophysical changes over the Pacific Ocean from 2007 to 2017 are examined by combining CALIPSO and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño–Southern Oscillation (ENSO) events. For example, midcloud (cloud tops at 3–10 km) and high cloud (cloud tops at 10–18 km) amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, relative to CALCS, MODIS shows stronger anticorrelation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, relative to CALCS, MODIS shows smaller impacts of mid- and high clouds on the low troposphere (<3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid- and high clouds.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Seung-Hee Ham, seung-hee.ham@nasa.gov

Supplementary Materials

    • Supplemental Materials (PDF 588.05 KB)
Save
  • Angell, J. K., 1981: Comparison of variations in atmospheric quantities with sea surface temperature variations in the equatorial eastern Pacific. Mon. Wea. Rev., 109, 230243, https://doi.org/10.1175/1520-0493(1981)109<0230:COVIAQ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, https://doi.org/10.1029/2006JC003798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, W. L., T. S. Pagano, and V. V. Salomonson, 1998: Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Trans. Geosci. Remote Sens., 36, 10881100, https://doi.org/10.1109/36.700993.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berry, E., G. G. Mace, and A. Gettleman, 2020: Using A-Train observations to evaluate east Pacific cloud occurrence and radiative effects in the Community Atmosphere Model. J. Climate, 33, 61876203, https://doi.org/10.1175/JCLI-D-19-0870.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bond, N. A., M. F. Cronin, H. Freeland, and N. Mantua, 2015: Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett., 42, 34143420, https://doi.org/10.1002/2015GL063306.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., and Coauthors, 2015: MERRA-2: Initial evaluation of the climate. NASA Tech. Rep. Series on Global Modelling and Data Assimilation NASA/TM–2015-104606, Vol. 43, 145 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf.

  • CALIPSO Team, 2018: CALIPSO low laser energy technical advisory for data users. NASA Doc., 7 pp., https://www-calipso.larc.nasa.gov/resources/calipso_users_guide/advisory/advisory_2018-10-10-CALIPSO_Laser_Energy_Technical_Advisory_Ver03.pdf.

  • Cesana, G., and H. Chepfer, 2012: How well do climate models simulate cloud vertical structure? A comparison between CALIPSO-GOCCP satellite observations and CMIP5 models. Geophys. Res. Lett., 39, L20803, https://doi.org/10.1029/2012GL053153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chepfer, H., H. Brogniez, and V. Noel, 2019: Diurnal variations of cloud and relative humidity profiles across the tropics. Sci. Rep., 9, 16045, https://doi.org/10.1038/s41598-019-52437-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiriaco, M., and Coauthors, 2007: Comparison of CALIPSO-like, LaRC, and MODIS retrievals of ice-cloud properties over SIRTA in France and Florida during CRYSTAL-FACE. J. Appl. Meteor. Climatol., 46, 249272, https://doi.org/10.1175/JAM2435.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Lorenzo, E., and N. Mantua, 2016: Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Climate Change, 6, 10421047, https://doi.org/10.1038/nclimate3082.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernández, N. C., R. R. García, R. G. Herrera, D. G. Puyol, L. G. Presa, E. H. Martín, and P. R. Rodríguez, 2004: Analysis of the ENSO signal in tropospheric and stratospheric temperatures observed by MSU, 1979–2000. J. Climate, 17, 39343946, https://doi.org/10.1175/1520-0442(2004)017<3934:AOTESI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ham, S.-H., B.-J. Sohn, S. Kato, and M. Satoh, 2013: Vertical structure of ice cloud layers from CloudSat and CALIPSO measurements and comparison to NICAM simulations. J. Geophys. Res., 118, 99309947, https://doi.org/10.1002/jgrd.50582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ham, S.-H., and Coauthors, 2017: Cloud occurrences and cloud radiative effects (CREs) from CERES-CALIPSO-CloudSat-MODIS (CCCM) and CloudSat radar-lidar (RL) products. J. Geophys. Res., 122, 88528884, https://doi.org/10.1002/2017JD026725.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., 2015: Pacific sea surface temperature and the winter of 2014. Geophys. Res. Lett., 42, 18941902, https://doi.org/10.1002/2015GL063083.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hearty, T. J., and Coauthors, 2014: Estimating sampling biases and measurement uncertainties of AIRS/AMSU-A temperature and water vapor observations using MERRA reanalysis. J. Geophys. Res., 119, 27252741, https://doi.org/10.1002/2013JD021205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Illingworth, A. J., and Coauthors, 2015: The EarthCARE Satellite: The next step forward in global measurements of clouds, aerosols, precipitation, and radiation. Bull. Amer. Meteor. Soc., 96, 13111332, https://doi.org/10.1175/BAMS-D-12-00227.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J. Climate, 22, 615632, https://doi.org/10.1175/2008JCLI2309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., S. Sun-Mack, W. F. Miller, F. G. Rose, Y. Chen, P. Minnis, and B. A. Wielicki, 2010: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. J. Geophys. Res., 115, D00H28, https://doi.org/10.1029/2009JD012277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and Coauthors, 2011: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties. J. Geophys. Res., 116, D19209, https://doi.org/10.1029/2011JD016050.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and Coauthors, 2019: Radiative heating rates computed with clouds derived from satellite-based passive and active sensors and their effects on generation of available potential energy. J. Geophys. Res., 124, 17201740, https://doi.org/10.1029/2018JD028878.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., A. Hall, and J. R. Norris, 2017: Low-cloud feedbacks from cloud-controlling factors: A review. Surv. Geophys., 38, 13071329, https://doi.org/10.1007/s10712-017-9433-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Niño events: Cold tongue El Niño and warm pool El Niño. J. Climate, 22, 14991515, https://doi.org/10.1175/2008JCLI2624.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, A., and M. P. Hoerling, 2003: The nature and causes for the delayed atmospheric response to El Niño. J. Climate, 16, 13911403, https://doi.org/10.1175/1520-0442-16.9.1391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., J. Huang, K. Stamnes, T. Wang, Q. Lv, and H. Jin, 2015: A global survey of cloud overlap based on CALIPSO and CloudSat measurements. Atmos. Chem. Phys., 15, 519536, https://doi.org/10.5194/acp-15-519-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., and Coauthors, 2009: The CALIPSO lidar cloud and aerosol discrimination: Version 2 algorithm and initial assessment of performance. J. Atmos. Oceanic Technol., 26, 11981213, https://doi.org/10.1175/2009JTECHA1229.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., and Coauthors, 2019: Discriminating between clouds and aerosols in the CALIOP version 4.1 data products. Atmos. Meas. Tech., 12, 703734, https://doi.org/10.5194/amt-12-703-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., N. Manalo-Smith, S. Kato, W. F. Miller, S. K. Gupta, P. Minnis, and B. A. Wielicki, 2003: Angular distribution models for top-of-atmosphere radiative flux estimation from the Clouds and the Earth’s Radiant Energy System instrument on the Tropical Rainfall Measuring Mission satellite. Part I: Methodology. J. Appl. Meteor., 42, 240265, https://doi.org/10.1175/1520-0450(2003)042<0240:ADMFTO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and F. J. Wrenn, 2013: Evaluation of the hydrometeor layers in the east and west Pacific within ISCCP cloud-top pressure–optical depth bins using merged CloudSat and CALIPSO data. J. Climate, 26, 94299444, https://doi.org/10.1175/JCLI-D-12-00207.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and Q. Zhang, 2014: The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results. J. Geophys. Res., 119, 94419462, https://doi.org/10.1002/2013JD021374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., R. Marchand, Q. Zhang, and G. L. Stephens, 2007: Global hydrometeor occurrence as observed by CloudSat: Initial observations from summer 2006. Geophys. Res. Lett., 34, L09808, https://doi.org/10.1029/2006GL029017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. L. 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, https://doi.org/10.1029/2007JD009755.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchand, R., and G. G. Mace, 2018: Level 2 GEOPROF product process description and interface control document. NASA Doc., 27 pp., http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2B-GEOPROF_PDICD.P1_R05.rev0__0.pdf.

  • Marchand, R., G. G. Mace, T. Ackerman, and G. L. Stephens, 2008: Hydrometeor detection using CloudSat—An Earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519533, https://doi.org/10.1175/2007JTECHA1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCoy, D. T., R. Eastman, D. L. Hartmann, and R. Wood, 2017: The change in low cloud cover in a warmed climate inferred from AIRS, MODIS, and ERA-Interim. J. Climate, 30, 36093620, https://doi.org/10.1175/JCLI-D-15-0734.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., 1989: Viewing zenith angle dependence of cloudiness determined from coincident GEOS East and GOES West data. J. Geophys. Res., 94, 23032320, https://doi.org/10.1029/JD094iD02p02303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2010: CERES Edition 3 cloud retrievals. 13th Conf. on Atmospheric Radiation, Portland, OR, Amer. Meteor. Soc., 5.4, https://ams.confex.com/ams/pdfpapers/171366.pdf.

  • Minnis, P., and Coauthors, 2011a: 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, https://doi.org/10.1109/TGRS.2011.2144601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2011b: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part II: Examples of average results and comparisons with other data. IEEE Trans. Geosci. Remote Sens., 49, 44014430, https://doi.org/10.1109/TGRS.2011.2144602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., K. Bedka, T. Qing, C. R. Yost, S. T. Bedka, B. A. Scarino, K. Khlopenkov, and M. M. Khaiyer, 2016: A consistent long-term cloud and clear-sky radiation property dataset from the Advanced Very High Resolution Radiometer (AVHRR). NOAA Climate Algorithm Theoretical Basis Doc., 159 pp., https://www.ncdc.noaa.gov/sites/default/files/cdr-documentation/CDRP-ATBD-0826%20AVHRR%20Cloud%20Properties%20-%20NASA%20C-ATBD%20(01B-30b)%20(DSR-1051).pdf.

  • Minnis, P., and Coauthors, 2021: CERES MODIS cloud product retrievals for Edition 4—Part I: Algorithm changes. IEEE Trans. Geosci. Remote Sens., 59, 27442780, https://doi.org/10.1109/TGRS.2020.3008866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myers, T. A., and J. R. Norris, 2016: Reducing the uncertainty in subtropical cloud feedback. Geophys. Res. Lett., 43, 21442148, https://doi.org/10.1002/2015GL067416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nayak, M., and Coauthors, 2012: CloudSat anomaly recovery and operational lessons learned. SpaceOps 2012, Stockholm, Sweden, AIAA, https://doi.org/10.2514/6.2012-1295798.

    • Crossref
    • Export Citation
  • Platnick, S. E., 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, https://doi.org/10.1109/TGRS.2016.2610522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., A. Hall, S. A. Klein, and P. M. Caldwell, 2014: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dyn., 42, 26032626, https://doi.org/10.1007/s00382-013-1945-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., A. Hall, S. A. Klein, and A. M. DeAngelis, 2015: Positive tropical marine low-cloud cover feedback inferred from cloud controlling factors. Geophys. Res. Lett., 42, 77677775, https://doi.org/10.1002/2015GL065627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randel, W. J., F. Wu, and D. J. Gaffen, 2000: Interannual variability of the tropical tropopause derived from radiosonde data and NCEP reanalysis. J. Geophys. Res., 105, 15 50915 523, https://doi.org/10.1029/2000JD900155.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., C. S. Draper, Q. Liu, M. Girotto, S. P. P. Mahanama, R. D. Koster, and G. J. M. de Lannoy, 2017: Assessment of MERRA-2 land surface hydrology estimates. J. Climate, 30, 29372960, https://doi.org/10.1175/JCLI-D-16-0720.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and Y. Zhang, 2010: Evaluation of a statistical model of cloud vertical structure using combined CloudSat and CALIPSO cloud layer profiles. J. Climate, 23, 66416653, https://doi.org/10.1175/2010JCLI3734.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salomonson, V. V., W. L. Barnes, P. W. Maymon, H. E. Montgomery, and H. Ostrow, 1989: MODIS: Advanced facility instrument for studies of the earth as a system. IEEE Trans. Geosci. Remote Sens., 27, 145153, https://doi.org/10.1109/36.20292.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scott, R. C., T. A. Myers, J. R. Norris, M. D. Zelinka, S. A. Klein, M. Sun, and D. R. Doelling, 2020: Observed sensitivity of low-cloud radiative effects to meteorological perturbations over the global oceans. J. Climate, 33, 77177734, https://doi.org/10.1175/JCLI-D-19-1028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, T. H. M., J. Delanoë, and R. J. Hoan, 2011: Comparison among four different retrieval methods for ice-cloud properties using data from CloudSat, CALIPSO, and MODIS. J. Appl. Meteor. Climatol., 50, 19521969, https://doi.org/10.1175/2011JAMC2646.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train. Bull. Amer. Meteor. Soc., 83, 17711790, https://doi.org/10.1175/BAMS-83-12-1771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, 2018: CloudSat and CALIPSO within the A-Train: Ten years of actively observing the Earth system. Bull. Amer. Meteor. Soc., 99, 569581, https://doi.org/10.1175/BAMS-D-16-0324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stubenrauch, C. J., and Coauthors, 2013: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel. Bull. Amer. Meteor. Soc., 94, 10311049, https://doi.org/10.1175/BAMS-D-12-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Su, H., D. Neelin, and J. E. Meyerson, 2005: Mechanisms for lagged atmospheric response to ENSO SST forcing. J. Climate, 18, 41954215, https://doi.org/10.1175/JCLI3514.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., 1988: Parametrization of condensation and associated clouds in models for weather prediction and general circulation simulation. Physically-Based Modelling and Simulation of Climate and Climate Change, M. E. Schlesinger, Ed., Kluwer, 433–461.

  • Teixeira, J., 2001: Cloud fraction and relative humidity in a prognostic cloud fraction scheme. Mon. Wea. Rev., 129, 17501753, https://doi.org/10.1175/1520-0493(2001)129<1750:CFARHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 30403061, https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and L. Smith, 2006: The vertical structure of temperature in the tropics: Different flavors of El Niño. J. Climate, 19, 49564973, https://doi.org/10.1175/JCLI3891.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. M. Caron, D. P. Stepaniak, and S. Worley, 2002: Evolution of El Niño-Southern Oscillation and global atmospheric surface temperatures. J. Geophys. Res., 107, 4065, https://doi.org/10.1029/2000JD000298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tseng, Y.-H., R. Ding, and X. Huang, 2017: The warm blob in the northeast Pacific—The bridge leading to the 2015/16 El Niño. Environ. Res. Lett., 12, 054019, https://doi.org/10.1088/1748-9326/aa67c3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaughan, M., and Coauthors, 2009: Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. J. Atmos. Oceanic Technol., 26, 20342050, https://doi.org/10.1175/2009JTECHA1228.1.

    • Crossref
    • 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. Proc. SPIE, 4893, https://doi.org/10.1117/12.466539.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winker, D. M., B. H. Hunt, and M. J. McGill, 2007: Initial performance assessment of CALIOP. Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winker, D. M., M. A. Vaughan, A. Omar, Y.-X. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 23102323, https://doi.org/10.1175/2009JTECHA1281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., and C. S. Bretherton, 2006: On the relationship between stratiform low cloud cover and lower-tropospheric stability. J. Climate, 19, 64256432, https://doi.org/10.1175/JCLI3988.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, F., J. Li, W. Tian, J. Feng, and Y. Huo, 2012: Signals of El Niño Modoki in the tropical tropopause layer and stratosphere. Atmos. Chem. Phys., 12, 52595273, https://doi.org/10.5194/acp-12-5259-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., and Coauthors, 2009: El Niño in a changing climate. Nature, 461, 511514, https://doi.org/10.1038/nature08316.

  • Yost, C. R., P. Minnis, S. Sun-Mack, Y. Chen, and W. L. Smith Jr., 2021: CERES MODIS cloud product retrievals for Edition 4—Part II: Comparisons to CloudSat and CALIPSO. IEEE Trans. Geosci. Remote Sens., 59, 36953724, https://doi.org/10.1109/TGRS.2020.3015155.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yue, Q., B. H. Kahn, E. J. Fetzer, S. Wong, R. Frey, and K. G. Meyer, 2017: On the response of MODIS cloud coverage to global mean surface air temperature. J. Geophys. Res., 122, 966979, https://doi.org/10.1002/2016JD025174.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 240 0 0
Full Text Views 2887 2340 740
PDF Downloads 479 97 4