Document Type
Paper Presentation
Start Date
5-10-2023 10:30 AM
End Date
5-10-2023 11:45 AM
Event Website
https://iguide.illinois.edu/forum-2023/
Abstract
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.
DOI
10.5703/1288284317665
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Atmospheric Sciences Commons, Climate Commons, Databases and Information Systems Commons, Data Science Commons, Dynamical Systems Commons, Geographic Information Sciences Commons, Geological Engineering Commons, Geology Commons, Geophysics and Seismology Commons, Glaciology Commons, Human Geography Commons, Hydrology Commons, Numerical Analysis and Scientific Computing Commons, Ocean Engineering Commons, Oceanography Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Other Computer Sciences Commons, Other Earth Sciences Commons, Other Environmental Sciences Commons, Other Geography Commons, Other Mathematics Commons, Other Physical Sciences and Mathematics Commons, Physical and Environmental Geography Commons, Remote Sensing Commons, Spatial Science Commons, Urban Studies and Planning Commons, Water Resource Management Commons
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach
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.
https://docs.lib.purdue.edu/iguide/2023/presentations/3