| Title: |
Potential for machine learning emulators to augment regional climate simulations in provision of local climate change information |
| Authors: |
Kendon, Elizabeth J; Addison, Henry J; Doury; Somot; Watson, Peter A G; Booth; Coppola; Gutierrez; Murphy; Scullion |
| Source: |
Kendon, E J, Addison, H J, Doury, Somot, Watson, P A G, Booth, Coppola, Gutierrez, Murphy & Scullion 2025, 'Potential for machine learning emulators to augment regional climate simulations in provision of local climate change information', Bulletin of the American Meteorological Society, vol. 106, no. 6, pp. E1175-E1203. https://doi.org/10.1175/BAMS-D-24-0114.1 |
| Publication Year: |
2025 |
| Collection: |
University of Bristol: Bristol Reserach |
| Description: |
High-resolution regional climate simulations provide detailed information on future climate change to support decision making. Ensembles of simulations, including at km-scale resolution, are becoming available from international coordinated initiatives, but these do not effectively sample the full range of uncertainties. Machine learning (ML) has already been used for statistical downscaling, but has the potential to augment high-resolution simulations, via emulators, enabling rapid production of local climate information at a fraction of the cost. Here we explore skill in ML-based emulators sampling a range of architectures and identify remaining scientific issues that need to be addressed before such emulators can be considered ready for application in climate services. This includes the ability to capture extremes, produce coherent multivariate predictions, account for memory in the climate system, and robustly downscale other (out-of-sample) global climate models. Climate expertise needs to be integrated into the development and evaluation of ML emulators, and here we provide recommendations on validation methods. If skillful, ML emulation has implications for how we coordinate and perform regional climate simulations. We should focus on running at the highest resolution and greatest earth system complexity affordable, to give the best representation of processes at the local scale, for subsequent training of ML emulators. Emphasis should be on sampling the full range of conditions, including high end scenarios. Overall, ML has promise to augment our production of regional-to-local climate projection information over the next 5-10 years and as a climate community we need to come together to address the relevant scientific issues. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/hdl/https://hdl.handle.net/1983/7aca346e-9c99-4545-97d3-32dbe41b0259 |
| DOI: |
10.1175/BAMS-D-24-0114.1 |
| Availability: |
https://hdl.handle.net/1983/7aca346e-9c99-4545-97d3-32dbe41b0259; https://research-information.bris.ac.uk/en/publications/7aca346e-9c99-4545-97d3-32dbe41b0259; https://doi.org/10.1175/BAMS-D-24-0114.1 |
| Rights: |
info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.8D8F2AF3 |
| Database: |
BASE |