1. Climate Signal Detection from Multiple Satellite Measurements Yibo Jiang, Hartmut H. Aumann Jet Propulsion Laboratory, Californian Institute of Technology, Pasadena, CA Yuk Yung, Fai Li Division of Geological and Planetary Sciences, Californian Institute of Technology, Pasadena, CA Abstract. Clouds play an important role in the climate change. Many satellites have been observing the clouds over decades, but have not detected any trends in cloud cover (Wylie, DP, 2005). The Principle Component Analysis (PCA) provides a very sensitive tool to the analysis of the cloud signals from the enormous amount of data sets. In this study, we have analyzed measurements from the IRIS spectrometer (Prabhakara, 1988), AIRS (Chahine, 2006) and IASI infrared interferometer (Chalon, 2001). IRIS flew on Nimbus 4 satellite between April 1970 and January 1971, AIRS is on Aqua satellite since May 2, 2002. Results I.The global averaged brightness temperature in 2007 (AIRS) is warmer by ~3K as compared to that in 1970 (IRIS), which appear to show the cloud coverage has decreased by 2% per decade. This trend is consistent with Rossow & Schiffer (1999). II.AIRS ozone channels show a maximum 5K warmer than IRIS in October around south polar region. This indicates the ozone depletion in 2007 as compared to that in 1970. III. AIRS is consistent warmer in the higher northern latitude region (>50N) was observed in all months than IRIS. IV.The warming and cooling of ~±1K over 37-year period in CO 2 channels (650-720 cm-1) need further analysis. Data & Method. In this study, we used both clear and cloud data from AIRS and IRIS. The total radiometric uncertainties for AIRS and IRIS are ±0.2K and ±0.3K. The year 2007 AIRS data was used to compare with the year 1970IRIS data, both of these two years have similar Oceanic Nino Index (ONI) (NOAA Climate Prediction Center). The high resolution AIRS data were first smoothed to match the IRIS resolution. The 3σ outliers in both data sets are filtered out. The locations of the spectrum are then random selected and uniform distributed globally. Both day time and night time spectrum are used in this study. Figure 1. Standard deviation (left) of Brightness temperature and the data distribution at channels 901 cm -1 in the night over the ocean show the consistent of both data sets. Figure 2. Monthly averaged brightness temperature differences between AIRS and IRIS at channels 901 cm -1 in the night over the ocean. The thick red line is the daytime average and the black thick line shows the nighttime average. Figure 4. Monthly mean brightness temperature (left), standard deviation (middle) and the difference between AIRS and IRIS (right) in October for AIRS (top panel) and IRIS (bottom panel) from 650 cm -1 to 1135 cm -1. References I.Harries et al., Increases in greenhouse forcing infered from the outgoing longwave radiation spectra of the Earth in 1970 and 1997, Nature, 410,355-357 (2001). II.Rossow and Schiffer, Advances in Understanding Clouds From ISCCP, Bul. Of Amer. Meteo. Soc., 80, 2261-2287 (1999). Figure 3. Monthly averaged brightness temperature differences between AIRS and IRIS at channels 901 cm -1 in the nighttime (top) and daytime (bottom) over the ocean in October.