Deep learning network to project future Arctic ocean waves (Proposals Track)

Merce Casas Prat (Environment and Climate Change Canada); Lluis Castrejon (Mila, Université de Montréal, Facebook AI Research); Shady Moahmmed (University of Ottawa)

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Climate Science & Modeling Computer Vision & Remote Sensing


The Arctic Ocean is warming at an alarming rate and will likely become ice-free in summer by mid-century. This will be accompanied by higher ocean surface waves, which pose a risk to coastal communities and marine operations. In order to develop climate change adaptation strategies, it is imperative to robustly assess the future changes in the Arctic ocean wave climate. This requires a large ensemble of regional ocean wave projections to properly capture the range of climate modeling uncertainty in the Arctic region. This has been proven challenging, as ocean wave information is typically not provided by climate models, ocean wave numerical modeling is computationally expensive, and most global wave climate ensembles exclude the Arctic region. Here we present a framework to develop a deep learning network based on CNN and LSTM which could be potentially used to obtain such a large ensemble of Arctic wave projections with an affordable cost.