Sea level Projections with Machine Learning using Altimetry and Climate
  Model ensembles
Saumya Sinha John Fasullo R. Steven Nerem Claire Monteleoni
Satellite altimeter observations retrieved since 1993 show that the global
mean sea level is rising at an unprecedented rate (3.4mm/year). With almost
three decades of observations, we can now investigate the contributions of
anthropogenic climate-change signals such as greenhouse gases, aerosols, and
biomass burning in this rising sea level. We use machine learning (ML) to
investigate future patterns of sea level change. To understand the extent of
contributions from the climate-change signals, and to help in forecasting sea
level change in the future, we turn to climate model simulations. This work
presents a machine learning framework that exploits both satellite observations
and climate model simulations to generate sea level rise projections at a
2-degree resolution spatial grid, 30 years into the future. We train fully
connected neural networks (FCNNs) to predict altimeter values through a
non-linear fusion of the climate model hindcasts (for 1993-2019). The learned
FCNNs are then applied to future climate model projections to predict future
sea level patterns. We propose segmenting our spatial dataset into meaningful
clusters and show that clustering helps to improve predictions of our ML model.