DOI: 10.1175/JCLI-D-14-00537.1
Scopus记录号: 2-s2.0-84942844424
论文题名: An intercomparison of the spatiotemporal variability of satellite- and ground-based cloud datasets using spectral analysis techniques
作者: Li J. ; Carlson B.E. ; Rossow W.B. ; Lacis A.A. ; Zhang Y.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 14 起始页码: 5716
结束页码: 5736
语种: 英语
Scopus关键词: Atmospheric pressure
; Budget control
; Climate change
; Climatology
; Clouds
; Earth (planet)
; Spectrum analysis
; Cloud retrieval
; Interannual variability
; Principal components analysis
; Seasonal variability
; Spectral analysis/models/distribution
; Principal component analysis
; annual variation
; cloud cover
; comparative study
; data set
; El Nino-Southern Oscillation
; principal component analysis
; satellite data
; seasonal variation
; spatiotemporal analysis
; spectral analysis
英文摘要: Because of the importance of clouds in modulating Earth's energy budget, it is critical to understand their variability in space and time for climate and modeling studies. This study examines the consistency of the spatiotemporal variability of cloud amount (CA) and cloud-top pressure (CTP) represented by five 7-yr satellite datasets from the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project, and total cloud fraction observation from the Extended Edited Cloud Reports Archive (EECRA). Two spectral analysis techniques, namely combined maximum covariance analysis (CMCA) and combined principal component analysis (CPCA), are used to extract the dominant modes of variability from the combined datasets, and the resulting spatial patterns are compared in parallel. The results indicate that the datasets achieve overall excellent agreement on both seasonal and interannual scales of variability, with the correlations between the spatial patterns mostly above 0.6 and often above 0.8. For seasonal variability, the largest differences are found in the Northern Hemisphere high latitudes and near the South African coast forCAand in the Sahel region for CTP, where some differences in the phase and strength of the seasonal cycle are found. On interannual scales, global cloud variability is mostly associated with major climate modes, including El Niño-Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IODM), and the datasets also agree reasonably well. The good agreement across the datasets supportsthe conclusion that they are describing cloud variations with these climate modes. © 2015 American Meteorological Society. 资助项目: NASA, National Aeronautics and Space Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50643
Appears in Collections: 气候变化事实与影响
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作者单位: NASA Goddard Institute for Space Studies, Department of Applied Physics and Applied Math, Columbia University, New York, NY, United States; NASA Goddard Institute for Space Studies, New York, NY, United States; Cooperative Remote Sensing Science and Technology Center, City College of New York, New York, NY, United States
Recommended Citation:
Li J.,Carlson B.E.,Rossow W.B.,et al. An intercomparison of the spatiotemporal variability of satellite- and ground-based cloud datasets using spectral analysis techniques[J]. Journal of Climate,2015-01-01,28(14)