Host: Jacob Steinberg

This project will explore and identify patterns of regional sea level variability at seasonal and longer timescales. Leveraging state-of-the-art global climate models, we will partition the ocean into regions of 'like' variability with the aim of connecting any coastal location to a local region-of-influence. In taking this approach, we will gain understanding of the physical processes responsible for coastal sea level change (including long term sea level rise) and their spatio-temporal extent. This analysis will be carried out using readily available, simple clustering (machine learning) tools that evaluate patterns of sea level covariance. Results will highlight regions of sea level co-variability across timescales and provide a basis for understanding underlying physical mechanisms. As the nature of sea level variability changes, especially at the coast, this analysis will importantly provide a means to link coastal sea level to larger scale changes in ocean mass, heat content, and circulation. Initial project goals include: evaluate sea level variability at seasonal timescales, develop clustering data analysis skills/tools, and apply toolkit in disentangling complex patterns of climate variability. Depending on the interests and skillset of the intern, project direction will change and may focus on software toolkit development, model-observation comparison, or clustering approaches.