OST/ST2019: Attenuating the ocean chaotic variability in altimetric observations: from band-pass filtering to machine learning
The variability of sea-level anomalies (SLA) observed by satellite altimeters combines two components: a forced component paced by the atmospheric variability, and an intrinsic and chaotic component emerging from the ocean itself. This paper presents a Machine Learning algorithm, trained on ensemble simulation outputs, to estimate the forced variability from SLA observations. Our algorithm estimates the forced model variability with a 2 cm RMS error and a temporal correlation of 0.9, a skill similar to that of a bandpass filter proposed recently, but with the additional ability to adapt its cutoff scales locally. The algorithm is applied to global altimetric observations, and shown to increase the variance of winter SLA explained by the variance of the North Atlantic Oscillation (NAO) index significantly.
