Abstract

All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales). In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). Our findings also demonstrate the utility of machine learning to estimate the possible tendency of near-future regional sea levels. When compared to actual sea-level records, our models perform particularly well in the coastal areas most influenced by internal climate variability. Yet, the models are widely applicable to evaluate the patterns of rising and falling sea levels across many places around the globe. Thus, our approach is a promising tool to model and anticipate sea level changes in the coming (1–3) years, which is crucial for near-term decision making and strategic planning about coastal protection measures.

Highlights

  • All ocean basins have been experiencing significant warming and rising sea levels in recent decades

  • Short-term variations in regional coastal sea levels depend on a combination of nearshore and offshore processes, including fast-paced changes like those associated to high tides or storms and interannual to multi-year changes driven by relatively large temperature oscillations in large open ocean areas—just to name a ­few[1]

  • To model sea level variations in the coastal regions from upper-ocean temperature changes in the open ocean, we first calculate the median estimate within each ocean region (Fig. 2) for each variable over the period 1993–2018

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Summary

Introduction

All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales) In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). As demonstrated in the case studies, the large temperature fluctuations (by short-term climate changes and ocean circulation) are dominating the patterns of sea level variability changes for most of the regions

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