Benoit Presse

Supervisors and co-supervisors : Salle Close (LOPS), Pierre Tandeo (IMT-Atlantique) and Guillaume Maze (LOPS)

Start Date : Octerber 2023

Funding : ANR - REPLICA

Summary :
Understanding the role of ocean-atmosphere interactions is crucial in determining the ocean’s variability. However, a part of this variability is not driven by the atmosphere but spontaneously and randomly generated by the ocean (intrinsic variability) through non-linear processes. Intrinsic variability occurs on multiple scales of time and space and may complicate the detection and attribution of climate change signals. Hence, quantifying the relative importance of atmospherically- forced and intrinsic variability is necessary to understand the mechanisms of climate change in the ocean-atmosphere system.
This project aims to describe and predict the random part of the ocean’s variability using machine learning methods. The intrinsic variability estimation cannot be performed from observations or a single model experiment alone: an ensemble simulation approach is required. The probabilistic information (e.g. Probability Density Function) is estimated based on a 50-members ensemble simulation (OCCIPUT experiment). The aim of this work is to estimate this PDF using a reduced dataset. Probabilistic information will be used to generate an artificial member. Two methods will be combined : analog forecasting and generative models.