An Intercomparison of the Spatiotemporal Variability of Satellite- and Ground-Based Cloud Datasets Using Spectral Analysis Techniques

Abstract

ecause of the importance of clouds in modulating Earth?s energy budget, it is critical to understand their variability in space and time for climate and modeling studies. This study examines the consistency of the spatiotemporal variability of cloud amount (CA) and cloud-top pressure (CTP) represented by five 7-yr satellite datasets from the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project, and total cloud fraction observation from the Extended Edited Cloud Reports Archive (EECRA). Two spectral analysis techniques, namely combined maximum covariance analysis (CMCA) and combined principal component analysis (CPCA), are used to extract the dominant modes of variability from the combined datasets, and the resulting spatial patterns are compared in parallel. The results indicate that the datasets achieve overall excellent agreement on both seasonal and interannual scales of variability, with the correlations between the spatial patterns mostly above 0.6 and often above 0.8. For seasonal variability, the largest differences are found in the Northern Hemisphere high latitudes and near the South African coast for CA and in the Sahel region for CTP, where some differences in the phase and strength of the seasonal cycle are found. On interannual scales, global cloud variability is mostly associated with major climate modes, including El Niño?Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IODM), and the datasets also agree reasonably well. The good agreement across the datasets supports the conclusion that they are describing cloud variations with these climate modes.