On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913599384977408 |
|---|---|
| author | Rampal, Neelesh Gibson, Peter B. Sherwood, Steven Abramowitz, Gab |
| author_facet | Rampal, Neelesh Gibson, Peter B. Sherwood, Steven Abramowitz, Gab |
| contents | While deep-learning downscaling algorithms can generate fine-scale climate projections cost-effectively, it is still unclear how well they will 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). Both approaches emulate future changes in annual mean precipitation well, even 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13934 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates Rampal, Neelesh Gibson, Peter B. Sherwood, Steven Abramowitz, Gab Atmospheric and Oceanic Physics While deep-learning downscaling algorithms can generate fine-scale climate projections cost-effectively, it is still unclear how well they will 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). Both approaches emulate future changes in annual mean precipitation well, even 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. |
| title | On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2409.13934 |