Regionalizing the Sea-level Budget Using a Neural Network Approach

<p><span>Understanding the drivers of present-day sea-level change is vital for improving sea-level projections and for adaptation and mitigation plans against sea-level rise. Sea-level budget (SLB) studies focus on attributing the observed sea-level change to its different drivers (steric and barystatic changes). While the global mean SLB is closed, explaining the drivers of sea-level change on a finer spatial scale leads to large discrepancies. Recent studies have shown that closing the regional budget on a regular 1x1˚ grid is not possible due to limitations of the observations itself, but also due to the spatial patterns and variability of the underlying processes. Consequently, the regional budget has been mainly analyzed on a basin-wide scale. </span></p><p><span> </span><span>In this study we use Self-Organizing Maps (SOM), an unsupervised learning neural network, to extract regions of coherent sea-level variability based on 27 years of satellite altimetry data. The SOM clusters have a higher level of spatial detail compared to entire ocean basins, while being large enough to allow for a consistent sea-level budget analysis. The clusters also show how sea-level variability is interconnected among different ocean regions (for example, due to large-scale climate patterns). We perform the clustering analysis on the Atlantic and Indo-Pacific Oceans separately, obtaining a total of 18 clusters. Preliminary results show that we can close the sea-level budget from 1993-2017 in 67% of the clusters. The regions with discrepancies highlight important regional processes that are affecting sea-level change and have not thus far been included in the sea-level budget. In this way, using neural networks provides new insight into regional sea-level variability and its drivers. </span></p>