In this study, we use CALIPSO and CloudSat data to evaluate the MODIS-derived cloud and investigate how active-sensor derived clouds vary with ENSO phase. Preliminary results show that the difference between the CALIPSO/CloudSat- and MODIS-derived mean cloud fraction for high-, mid-, and low-level clouds exposed to space are (MODIS CALIPSO/CloudSat), -0.05, 0.06, and -0.08 over the ascending branch, -0.02, 0.05, and -0.09 over the northern hemisphere descending branch, and 0.00, 0.08, and -0.13 over the southern hemisphere descending branch. This result agrees with earlier studies showing larger mid-level cloud amounts derived from passive sensors compared with that derived from active sensors. The cloud fraction of high-, mid-, and low-level clouds that are not exposed to space are, 0.15, 0.09, and 0.20, over the ascending branch, 0.05, 0.04, and 0.12, over the northern hemisphere descending branch, and 0.05, 0.04, and 0.14 over the southern hemisphere descending branch. These account for 24%, 63%, and 52% of the total high-, mid-, and low-level clouds over the ascending branch, 16%, 45%, and 35% over the northern hemisphere descending branch, and 17%, 45%, and 29% over the southern hemisphere descending branch.
Despite missing overlapping clouds in MODIS-derived cloud data, when deseasonalized cloud fraction anomalies are computed, the correlation coefficient of high-, mid-, and low-level cloud fraction anomalies derived from MODIS and CALIPSO/CloudSat are, respectively, 0.49, 0.45, and 0.87 over the ascending branch, 0.72, 0.69, and 0.74 over the northern hemisphere descending branch, and 0.42, 0.37, and 0.87 over the southern hemisphere descending branch. The standard deviation of deseasonalized cloud fraction anomalies derived from MODIS is, however, smaller than the standard deviation of cloud fraction anomalies derived from CALIPSO/CloudSat for all cloud types over all branches.
The differences in the cloud fraction in turn affect surface irradiances computed with MODIS-derived cloud properties. In this study, we will further investigate how the cloud fraction difference affects surface irradiance computations.
Reference Loeb, N. G., D. Rutan, S. Kato, and W. Wang, 2014: Observing interannual variations in Hadley circulation atmospheric diabatic heating and circulation strength, submitted to J. Climate.
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