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Global mean sea level (GMSL) is considered one of the leading indicators of global climate change as it reflects changes taking place in different components of the climate system. Present-day sea level rise and its acceleration, currently estimated by high-precision satellite altimetry measurements, are primarily driven by anthropogenic global warming, more specifically by ocean warming-induced thermal expansion, and ice mass loss from glaciers, Greenland and Antarctica. Since the early 1990s, sea level is measured by high-precision satellite altimeters, that allow the monitoring of sea level change from global to regional scales. In addition, various observing systems from space (e.g., GRACE and GRACE-FO) and in situ (e.g., Argo floats) are used to monitor the components of the sea level variability. Ocean model simulations have revealed that besides the atmospherically-forced variability(AFV) of sea level, a strong low-frequency chaotic intrinsic variability (CIV) spontaneously emerges from the ocean. Recent studies have disentangled the imprints of AFV and CIV on the inter annual variability and on the trends of regional sea level. Results indicate that very low-frequency chaotic ocean variability may hinder the unambiguous attribution of regional sea level trends to the atmospheric forcing over 38% of the global ocean area. Another study showed that the chaotic part of the inter annual (1993-2015) sea level variability exceeds 20%over 48%of the global ocean area; these fractional areas are 48% and 26% for steric and manometric sea level, respectively. However, the frequency distribution and the spatial structure of the chaotic variability have not been studied yet. The first goal of this project is to quantify, for the first time as a function of frequency (temporal scales from 10 days to 36 years) and within each oceanic region, the chaotic and atmospherically-forced variability of sea level observations (satellite data from the ESA CCI project, Argo floats and GRACE and GRACE-FO). The second goal is to adapt and extend an existing filtering method to attenuate the imprint of chaotic variability on observational fields of sea level (steric and manometric) components. This study will also help identify the mechanisms that are revealed by the regional patterns of chaotic sea level variability.

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