Singular Vector Decomposition (SVD) of satellite datasets: relation between cloud properties and climate indices

Abstract. We describe a technique using singular vector decomposition (SVD), that can identify the spatial patterns that best describe the temporal variability of a global satellite dataset. These patterns, and their temporal evolution, are then correlated with established climate indices. We apply this technique to datasets of cloud properties over three decades, derived from five visible/IR imagers ((A)ATSR, SLSTR-A/-B and MODIS and jointly from the IR and microwave sounders on MetOp (IASI, MHS,AMSU-A), but it can be more generically used to extract the pattern of variability of any regular gridded dataset such as different parameters from satellite products and models. The leading singular vector for these three independent global data sets, on both cloud fraction and cloud-top height, from these polar orbiting satellites covering different time periods, is found to be strongly correlated with the ENSO index. The SVD approach could potentially offer a new tool for using global satellite observations in assessing global climate model (GCM) performance.