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Climate Dynamics

, Volume 48, Issue 1–2, pp 447–457 | Cite as

An analysis of high cloud variability: imprints from the El Niño–Southern Oscillation

  • King-Fai LiEmail author
  • Hui Su
  • Sze-Ning Mak
  • Tiffany M. Chang
  • Jonathan H. Jiang
  • Joel R. Norris
  • Yuk L. Yung
Article

Abstract

Using data from the International Satellite Cloud Climatology Project (ISCCP), we examine how near-global (60°N–60°S) high cloud fraction varies over time in the past three decades. Our focus is on identifying dominant modes of variability and associated spatial patterns, and how they are related to sea surface temperature. By performing the principal component analysis, we find that the first two principal modes of high cloud distribution show strong imprints of the two types of El Niño–Southern Oscillation (ENSO)—the canonical ENSO and the ENSO Modoki. Comparisons between ISCCP data and 14 models from the Atmospheric Model Intercomparison Project Phase 5 (AMIP5) show that models simulate the spatial pattern and the temporal variations of high cloud fraction associated with the canonical ENSO very well but the magnitudes of the canonical ENSO vary among the models. Furthermore, the multi-model mean of the second principal mode in the AMIP5 simulations appears to capture the temporal behavior of the second mode but individual AMIP5 models show large discrepancies in capturing observed temporal variations. A new metric, defined by the relative variances of the first two principal components, suggests that most of the AMIP5 models overestimate the second principal mode of high clouds.

Keywords

Sea surface temperature Cloud fraction Interannual variability Principal component analysis 

Notes

Acknowledgments

TMC was supported by the 2013 Summer Undergraduate Research Fellowships at the California Institute of Technology. SNM was supported by the 2014 Summer Undergraduate Research Exchange Program at the Chinese University of Hong Kong. We thank Katie Antilla, Run-Lie Shia, and Edmund K.-M. Chang for their technical support and invaluable comments. We also thank Qiong Zhang, Sally Newman, and Mimi Gerstell for reading the manuscript. HS and JHJ acknowledge funding support from NASA Energy and Water Cycle Study (NEWS) program. They performed the work at Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. KFL was supported partly by the Jack Eddy Fellowship managed by the University Corporation for Atmospheric Research and party by the NASA Grant NNX14AR40G. YLY was supported by UHOUST.130027 subcontract from the University of Houston to Caltech. The monthly SST data and monthly ENSO index were provided by NOAA/OAR/ESRL, Boulder, Colorado, from their website (http://esrl.noaa.gov/psd/). The monthly ENSO Modoki index was provided by the Japan Agency for Marine-Earth Science and Technology (http://www.jamstec.go.jp/frcgc/research/d1/iod/modoki_home.html.en). The AMIP5 models were obtained from the CMIP5 model archive (http://cmip-pcmdi.llnl.gov/cmip5/).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Applied MathematicsUniversity of WashingtonSeattleUSA
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  3. 3.Department of PhysicsThe Chinese University of Hong KongShatinHong Kong
  4. 4.Division of Applied MathematicsBrown UniversityProvidenceUSA
  5. 5.Scripps Institution of OceanographyUniversity of California, San DiegoLa JollaUSA
  6. 6.Divisions of Geological and Planetary SciencesCalifornia Institute of TechnologyPasadenaUSA

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