Machine Learning Emulation of Precipitation from km-scale Regional Climate Simulations using a Diffusion Model
Henry Addison, Elizabeth Kendon, Suman Ravuri, Laurence Aitchison, P.A. Watson
Abstract
Dynamical downscaling of climate simulations to local scales is valuable for understanding climate change impacts and planning adaptation measures, but is very computationally expensive. We present CPMGEM (Convection-Permitting Model Generative EMulator) [1]: a novel application of a generative machine learning model, a diffusion model, that skilfully emulates precipitation simulations by Met Office’s UK convection-permitting model (CPM). This achieves similar results to dynamical downscaling at a fraction of the computational cost. This emulator enables stochastic generation of high-resolution (8.8km) daily-mean precipitation samples, fine enough for use in applications such as flood modelling, conditioned on coarse (60km) weather states from a general circulation model (GCM).We trained the emulator to produce output over England and Wales, using Met Office simulations from the United Kingdom Climate Projections (UKCP) Local product, covering 1980-2080. The output precipitation has a similarly realistic spatial structure and intensity distribution to the CPM simulations. Our generative emulator outputs a well-calibrated spread of predictions and reproduces the small-scale structure and frequency of extreme intensities better than a deterministic model. We also find evidence that the emulator captures the main features of the CPM-simulated 21st century climate change but exhibits some error in the magnitude.Potential applications include producing high-resolution precipitation predictions for large-ensemble climate simulations and predictions using inputs from different GCMs and scenarios to better sample uncertainty. We will also discuss extending the emulator to predict sub-daily precipitation by downscaling temporally as well as spatially.[1] Addison, H., Kendon, E. J., Ravuri, S., Aitchison, L. and Watson, P.A.G., 2024. Machine learning emulation of precipitation from km-scale regional climate simulations using a diffusion model. arXiv preprint arXiv:2407.14158