Multi-decadal Sea Level Prediction using Neural Networks and Spectral
  Clustering on Climate Model Large Ensembles and Satellite Altimeter Data
Saumya Sinha John Fasullo R. Steven Nerem Claire Monteleoni
Sea surface height observations provided by satellite altimetry since 1993
show 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 considerable
regional variation in the sea level change. Through this work, we predict sea
level trends 30 years into the future at a 2-degree spatial resolution and
investigate the future patterns of the sea level change. We show the potential
of machine learning (ML) in this challenging application of long-term sea level
forecasting over the global ocean. Our approach incorporates sea level data
from both altimeter observations and climate model simulations. We develop a
supervised 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 the
ML prediction. We also show the effectiveness of partitioning our spatial
dataset and learning a dedicated ML model for each segmented region. We compare
two partitioning strategies: one achieved using domain knowledge, and the other
employing spectral clustering. Our results demonstrate that segmenting the
spatial dataset with spectral clustering improves the ML predictions.