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

• Main Authors: Ghosh, Subhankar, An, Shuai, Sharma, Arun, Gupta, Jayant, Shekhar, Shashi, Subramanian, Aneesh
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Atmospheric and Oceanic Physics physics.ao-ph; Artificial Intelligence cs.AI; Machine Learning cs.LG; Dynamical Systems math.DS; Other Statistics stat.OT; FOS Physical sciences; FOS Computer and information sciences; FOS Mathematics; J.2; I.2.m; I.2.6; I.2.1; I.2; Antarctic; Greenland; Antarc*; Ice; Ice Shelf; permafrost
• Online Access: https://dx.doi.org/10.48550/arxiv.2310.15179; https://arxiv.org/abs/2310.15179

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 ... : 6 pages, 5 figures, I-GUIDE 2023 conference ...