1. 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 and Planetary Sciences, Caltech Xianglei Huang Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor Baijun Tian and Duane E Waliser Science Division, Jet Propulsion Laboratory AGU 2007 GC34A-07

2. Reference Stephens, GL. "Cloud feedbacks in the climate system: A critical review." Journal of climate, 18(2), 2005:237-273. IPCC, 2001

3. 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.

4. Empirical Orthogonal Functions (EOFs)  Haskins et al. and Huang et al. approach:  Given a set of spectra  Do time averaging  Empirical orthogonal functions expansion EOFs Expansion coeff.

5. Huang and Yung (2004), JGR, 110, D12102 Variance ~ 97.0% Variance ~ 2.2% 1-16 July, 2003

6. Cloud mixing upon time averaging  Cloud processes are non-linear  Sequence of time and spatial averaging is important

7. Empirical Orthogonal Functions (EOFs) Revisited  Proposed approach:  Given a set of spectra  Empirical orthogonal functions expansion  Do time averaging over the expansion coefficients EOFsExpansion coeff.

8. Pacific Cross Section 1-30 July, 2005

9. — — 66% quartile boundary Cloud top temperature variability Spectral statistics in 1-30 July 2005 Without time average 15-day average

10. Variance ~91.7% Variance ~84.2% New EOF approach Old EOF approach Less clear-cloudy sky contrasts for the old approach

11. Variance ~6.8% Variance ~13.8% New EOF approach Old EOF approach

12. Future 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

13. Summary  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