HR: 17:45h
AN: GC34A-07    [Abstracts]
TI: Applying AIRS Hyperspectral Infra-Red Data to Cloud and Water Vapor Studies of Climate
AU: * Li, K
EM: kfl@gps.caltech.edu
AF: Division of Geological and Planetary Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, United States
AU: Huang, X
EM: xianglei@umich.edu
AF: Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109, United States
AU: Tian, B
EM: btian@jpl.nasa.gov
AF: Science Division, Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Waliser, D E
EM: duane.waliser@jpl.nasa.gov
AF: Science Division, Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Shia, R
EM: rls@gps.caltech.edu
AF: Division of Geological and Planetary Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, United States
AU: Yung, Y L
EM: yly@gps.caltech.edu
AF: Division of Geological and Planetary Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, United States
AB: Advantages of using spectrally resolved radiance in climate studies were initially pointed out by Iacono and Clough (1996) and Haskins et al. (1997). In a recent paper by Huang and Yung (2005), an overview of the spatial variability of (time-space averaged) spectra in different climate zones derived from a limited amount of AIRS data was discussed. In their studies, the EOFs were performed on spatial-temporal averages of AIRS spectra. Since atmospheric processes, specifically with regards to the hydrological cycle ( e.g., cloudiness), are non-linear, it is important to consider the impact of this averaging on the results. Understanding the impact of this averaging has implications for interpreting satellite data and developing the spatial and temporal sampling requirements of new satellite missions intended to characterize the role of atmospheric radiation and clouds on climate. As a test of these ideas, a subset of the AIRS data is subjected to the EOF analysis before time-space averaging and compared to an analysis of the same data that have first undergone time-space averaging. While the sum of the variances percentages of the first two modes in both cases is similar, their partitioning is quite different. In the EOFs without time-space averaging, the first mode is dominated by variability in the window region while the second the variability is located in the water vapor bands. However, in the EOFs with time-space averaging, the first and second modes do not clearly distinguish between these two impacts/processes, since the averaging process tends to make all locations cloudy. In addition, the spectral variations associated with the third EOF mode - which exhibits an influence from ozone and CO₂ - is virtually averaged away in the case with time- space averaging. This inconsistency between the two results is important for understanding the variability of the atmospheric hydrological cycle, as well as considering measurement and model-diagnostic strategies, particularly those associated with clouds and their impact on climate.
DE: 0321 Cloud/radiation interaction
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting