DOI: 10.1175/JCLI-D-15-0257.1
Scopus记录号: 2-s2.0-84962278687
论文题名: Observation-based longwave cloud radiative kernels derived from the A-Train
作者: Yue Q. ; Kahn B.H. ; Fetzer E.J. ; Schreier M. ; Wong S. ; Chen X. ; Huang X.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期: 6 起始页码: 2023
结束页码: 2040
语种: 英语
Scopus关键词: Atmospheric radiation
; Climatology
; Feedback
; Image reconstruction
; Radiative transfer
; Satellite imagery
; Climate variability
; Cloud properties
; Cloud radiative effects
; Cloud radiative forcing
; Moderate resolution imaging spectroradiometer
; Radiative forcings
; Radiative transfer model
; Satellite observations
; Radiometers
; broadband data
; climate feedback
; cloud microphysics
; cloud radiative forcing
; longwave radiation
; MODIS
; satellite data
英文摘要: The authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu-Liou radiative transfer model are shown. Good agreement between observation- and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clear-sky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability. © 2016 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/50010
Appears in Collections: 气候变化事实与影响
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作者单位: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, MI, United States
Recommended Citation:
Yue Q.,Kahn B.H.,Fetzer E.J.,et al. Observation-based longwave cloud radiative kernels derived from the A-Train[J]. Journal of Climate,2016-01-01,29(6)