  Given multi-model ensemble climate projections, the goal is to accurately and
reliably predict future sea-level rise while lowering the uncertainty. This
problem is important because sea-level rise affects millions of people in
coastal communities and beyond due to climate change's impacts on polar ice
sheets and the ocean. This problem is challenging due to spatial variability
and unknowns such as possible tipping points (e.g., collapse of Greenland or
West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost
thawing), future policy decisions, and human actions. Most existing climate
modeling approaches use the same set of weights globally, during either
regression or deep learning to combine different climate projections. Such
approaches are inadequate when different regions require different weighting
schemes for accurate and reliable sea-level rise predictions. This paper
proposes a zonal regression model which addresses spatial variability and model
inter-dependency. Experimental results show more reliable predictions using the
weights learned via this approach on a regional scale.
