Applying AIRS Hyperspectral Infra-red Data to Cloud and Greenhouse Gas Studies of Climate King-Fai Li, Run-Lie Shia and Yung L Yung Division of Geological.

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