Preprints
https://doi.org/10.5194/amt-2023-232
https://doi.org/10.5194/amt-2023-232
22 Nov 2023
 | 22 Nov 2023
Status: this preprint is currently under review for the journal AMT.

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

Elisa Carboni, Gareth E. Thomas, Richard Siddans, and Brian Kerridge

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.

Elisa Carboni, Gareth E. Thomas, Richard Siddans, and Brian Kerridge

Status: open (extended)

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  • RC1: 'Comment on amt-2023-232', Anonymous Referee #1, 14 Dec 2023 reply
Elisa Carboni, Gareth E. Thomas, Richard Siddans, and Brian Kerridge
Elisa Carboni, Gareth E. Thomas, Richard Siddans, and Brian Kerridge

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Short summary
We analyzed different satellite datasets of cloud properties with a new approach to quantify and interpret their interannual variability based on singular vector decomposition (SVD). The spatial pattern and its temporal evolution are strikingly similar for all the satellite datasets and follow the El Nino Southern Oscillation. The SVD approach reported here has potential for application to satellite data sets and to evaluate consistency between models and observations.