ClimateBench: A benchmark for data-driven climate projections
- 1University of Oxford, Physics, Atmospheric, Oceanic and Planetary Physics, Oxford, United Kingdom of Great Britain – England, Scotland, Wales (duncan.watson-parris@physics.ox.ac.uk)
- 2North Carolina Institute for Climate Studies, North Carolina State University, Asheville, NC 28801, USA
- 3Norwegian Meteorological Institute, Oslo, Norway
- 4Climatic Research Unit, School of Environmental Sciences, Norwich, UK
- 5Image Processing Laboratory, Universitat de València, València, Spain
- 6Department of Statistics, University of Oxford, Oxford, UK
- 7Department of Meteorology, Stockholm University, Stockholm, Sweden
- 8Institute of Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland
- 9Institute for Meteorology, Universität Leipzig, Leipzig, Germany
- 10Fraunhofer ITWM, Kaiserslautern, Germany
- 11Department of Electronic and Electrical Engineering, University College London, London, UK
- 1212Laboratory of Atmospheric Processes and their Impacts, School of Architecture, Civil & Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- 13School of Geosciences, University of Edinburgh, Edinburgh, UK
Exploration of future emissions scenarios mostly relies on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Such approaches are unable to reliably predict climate variables which respond non-linearly to emissions or forcing (such as precipitation) and must rely on heavily simplified representations of e.g., aerosol, neglecting important spatial dependencies.
Here we present ClimateBench - a benchmark dataset based on a suite of CMIP, AerChemMIP and DAMIP simulations performed by NorESM2, and a set of baseline machine learning models that emulate its response to a variety of forcers. These surrogate models can skilfully predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and spatially resolved aerosol. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, consistency and mathematical tractability.
We hope that by defining a clear baseline with appropriate metrics and providing a variety of baseline models we can bring the power of modern machine learning techniques to bear on the important problem of efficiently and robustly sampling future climates.
How to cite: Watson-Parris, D., Rao, Y., Olivié, D., Seland, Ø., Nowack, P., Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J., Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard, L., and Roesch, C.: ClimateBench: A benchmark for data-driven climate projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3961, https://doi.org/10.5194/egusphere-egu22-3961, 2022.
