On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates

Autor: Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Geophysical Research Letters, Vol 51, Iss 23, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 1944-8007
0094-8276
DOI: 10.1029/2024GL112492
Popis: Abstract While deep‐learning downscaling algorithms can generate fine‐scale climate projections cost‐effectively, it is unclear how effectively they extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM) over New Zealand. Both approaches emulate future changes in annual mean precipitation well, when trained on historical data, though training on a future climate improves performance. For extreme precipitation (99.5th percentile), RCM simulations predict a robust end‐of‐century increase with future warming (∼5.8%/°C on average from five simulations). When trained on a future climate, GANs capture 97% of the warming‐driven increase in extreme precipitation compared to 65% in a deterministic baseline. Even GANs trained historically capture 77% of this increase. Overall, GANs offer better generalization for downscaling extremes, which is important in applications relying on historical data.
Databáze: Directory of Open Access Journals
Externí odkaz: