Impact of Sampling Strategies on the Monitoring of Climate Regime Shifts with a Learning Data Assimilation Method

In oceanography, the acquisition and processing of observations are crucial to improve the understanding of complex oceanic processes. Considering an idealized model of the North Atlantic ocean circulation, we propose to implement a variational data assimilation method optimized by deep learning to reconstruct abrupt changes in ocean circulation, representing Dansgaard-Oeschger climate events. We show that this assimilation method leads to improved reconstruction performances, particularly at low sampling frequencies. Focusing on this difficult latter case, four sampling strategies are studied more specifically. Our experiments highlight that clusters of three consecutive observations regularly sampled leads to a better monitoring of the ocean circulation regime shifts. These results pave the way for further research in optimal ocean observation.