A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models...

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Bibliographic Details
Main Authors: Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.
Format: Article
Language:English
Published: American Geophysical Union 2024
Subjects:
General Earth and Planetary Sciences
Environmental Chemistry
Global and Planetary Change
Online Access:https://hdl.handle.net/1721.1/154212

Internet

https://hdl.handle.net/1721.1/154212

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