Using Machine Learning to Cut the Cost of Dynamical Downscaling

Using Machine Learning to Cut the Cost of Dynamical Downscaling

Abstract Global climate models (GCMs) are commonly downscaled to understand future local climate change. The high computational cost of regional climate models (RCMs) limits how many GCMs can be dynamically downscaled, restricting uncertainty assessment. While statistical downscaling is cheaper, its...

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Bibliographic Details
Main Authors: Sanaa Hobeichi, Nidhi Nishant, Yawen Shao, Gab Abramowitz, Andy Pitman, Steve Sherwood, Craig Bishop, Samuel Green
Format: Article
Language:English
Published: Wiley 2023-03-01
Series:Earth's Future
Subjects:
hybrid downscaling
machine learning
spatial transitivity
multilayer perceptron
subgrid variability
Online Access:https://doi.org/10.1029/2022EF003291

Internet

https://doi.org/10.1029/2022EF003291

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