Applying AIRS Hyperspectral Infra-red Applying AIRS Hyperspectral Infra-red Data to Cloud and Greenhouse Gas Data to Cloud and Greenhouse Gas
Studies of ClimateStudies of Climate
King-Fai Li, Run-Lie Shia and Yung L YungKing-Fai Li, Run-Lie Shia and Yung L YungDivision of Geological and Planetary Sciences, CaltechDivision of Geological and Planetary Sciences, Caltech
Xianglei HuangXianglei HuangDepartment of Atmospheric, Oceanic, and Space Sciences, Department of Atmospheric, Oceanic, and Space Sciences,
University of MichiganUniversity of Michigan, Ann Arbor, Ann Arbor
Baijun Tian and Duane E WaliserBaijun Tian and Duane E WaliserScience Division, Jet Propulsion LaboratoryScience Division, Jet Propulsion Laboratory
AGU 2007GC34A-07
ReferenceStephens, GL. "Cloud feedbacks in the climate system: A critical review." Journal of climate, 18(2), 2005:237-273.
IPCC, 2001
Origins: Hanel, R. A., Salomons, V., et al., 1972:
Nimbus 4 Infrared Spectroscopy Experiment .1. Calibrated Thermal Emission-Spectra. J. Geophys. Res., 77, 2629-2641.
Haskins, R., R. Goody, and L. Chen, 1999: Radiance covariance and climate models. J. Climate, 12, 1409-1422.
Recent work: Huang, X., and Y. L. Yung. (2005). “Spatial and
spectral variability of the outgoing thermal IR spectra from AIRS: A case study of July 2003.” J. Geophys. Res. 110, D12102.
Empirical Orthogonal Functions Empirical Orthogonal Functions (EOFs)(EOFs)
Haskins et al. and Huang et al. approach: Given a set of spectra
Do time averaging
Empirical orthogonal functions expansion
, , , ttS S t N x x
, ,S t x
, m mm
S S f g x x
,S N xxx
EOFsExpansion coeff.
Huang and Yung (2004), JGR, 110, D12102
Variance ~ 97.0%
Variance ~ 2.2%
1-16 July, 2003
Cloud mixing upon time averaging
Cloud processes are non-linear Sequence of time and spatial averaging is
important
Empirical Orthogonal Functions (EOFs)Empirical Orthogonal Functions (EOFs)RevisitedRevisited
Proposed approach: Given a set of spectra
Empirical orthogonal functions expansion
Do time averaging over the expansion coefficients
,,, , tt
S t N xxx
, ,S t x
, , ,m mm
S t S f t g x x
EOFsExpansion coeff.
, m mm
S S f g x x
Pacific Cross Section1-30 July, 2005
• — • — 66% quartile boundary
Cloud top temperaturevariability
Spectral statistics in 1-30 July 2005
Without time average
15-day average
Variance ~91.7%
Variance ~84.2%
New EOFapproach
Old EOFapproach
Less clear-cloudysky contrasts for the old approach
Variance ~6.8%
Variance ~13.8%
New EOFapproach
Old EOFapproach
Future workFuture work
Radiative transfer model will be used to identify the spectral features of different types of clouds
Climate model must be capable of simulating the cloud variations, both spatially and temporally
SummarySummary
Cloud distributions contributes most of the uncertainties in current climate modeling
IR spectra can be used to study empirically the cloud effect on climate change
The sequence of spatial and temporal averaging are important in isolating spectral features of different atmospheric species and clouds