Analysis of the interannual variability in satellite gravity solutions: detection of climate modes fingerprints in water mass displacements across continents and oceans

• Published in: Climate Dynamics
• Main Authors: Pfeffer, Julia, Cazenave, Anny, Barnoud, Anne
• Other Authors: H2020 European Research Council
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2021
• Subjects: Atmospheric Science; Arctic; Pacific
• Online Access: http://dx.doi.org/10.1007/s00382-021-05953-z; https://link.springer.com/content/pdf/10.1007/s00382-021-05953-z.pdf; https://link.springer.com/article/10.1007/s00382-021-05953-z/fulltext.html

Abstract This study analyzes the interannual variability of the water mass transport measured by satellite gravity missions in regard to eight major climate modes known to influence the Earth’s climate from regional to global scales. Using sparsity promoting techniques (i.e., LASSO), we automatically select the most relevant predictors of the climate variability among the eight candidates considered. The El Niño–Southern Oscillation, Southern Annular Mode and Arctic Oscillation are shown to account for a large part the interannual variability of the water mass transport observed in extratropical ocean basins (up to 40%) and shallow seas (up to 70%). A combination of three Pacific and one Atlantic modes is needed to account for most (up to 60%) of the interannual variability of the terrestrial water storage observed in the North Amazon, Parana and Zambezi basins. With our technique, the impact of climate modes on water mass changes can be tracked across distinct water reservoirs (oceans, continents and ice-covered regions) and we show that a combination of climate modes is necessary to explain at best the natural variability in water mass transport. The climate modes predictions based on LASSO inversions can be used to reduce the inter-annual variability in satellite gravity measurements and detect processes unrelated with the natural variability of climate but with similar spatio-temporal signatures. However, significant residuals in the satellite gravity measurements remain unexplained at inter-annual time scales and more complex models solving the water mass balance should be employed to better predict the variability of water mass distributions.