# Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach

arXiv: https://arxiv.org/abs/2310.15179 · HTML: https://mlsanity.com/paper/2310.15179/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Towards a Tighter Bound on Possible-Rendezvous Areas: Preliminary Results](https://mlsanity.com/paper/2206.12756/) (2022)
- [Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation](https://mlsanity.com/paper/2301.05739/) (2023)

## Built on by (most cited first, 2 of 2)

- [Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning](https://mlsanity.com/paper/2502.14840/) (2025)
- [Towards Physics-Guided Foundation Models](https://mlsanity.com/paper/2502.15013/) (2025)

## Abstract (arXiv)

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.