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Sea surface height observations provided by satellite altimetry since 1993show a rising rate (3.4 mm/year) for global mean sea level. While on average,sea level has risen 10 cm over the last 30 years, there is considerableregional variation in the sea level change. Through this work, we predict sealevel trends 30 years into the future at a 2-degree spatial resolution andinvestigate the future patterns of the sea level change. We show the potentialof machine learning (ML) in this challenging application of long-term sea levelforecasting over the global ocean. Our approach incorporates sea level datafrom both altimeter observations and climate model simulations. We develop asupervised learning framework using fully connected neural networks (FCNNs)that can predict the sea level trend based on climate model projections.Alongside this, our method provides uncertainty estimates associated with theML prediction. We also show the effectiveness of partitioning our spatialdataset and learning a dedicated ML model for each segmented region. We comparetwo partitioning strategies: one achieved using domain knowledge, and the otheremploying spectral clustering. Our results demonstrate that segmenting thespatial dataset with spectral clustering improves the ML predictions.

Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data