Machine-Learning Based Reconstructions of Past Regional Sea Level Variability From Proxy Data

Abstract

The analysis of past regional climate-related sea level variations has important implications for diagnosing changes in future sea level driven by climate fluctuations. As the climate changes, there is a need for new explanatory variables of within-region climate factors and for more complex methods able to identify nonlinear relationships, such as machine learning algorithms. This study demonstrates the application of a new machine learning-based methodology to reconstruct historical sea level tide gauge records from proxy data (i.e., upper-ocean temperature estimates in open ocean regions), which provide a reasonably good dynamical representation of coastal sea level variations linked to slow and persistent natural processes like internal climate variability. The learning performance of our method was evaluated against observations of multiple stations and across a variety of model reconstructions, as shown and evidenced by the results.