Improving global CMIP6 Earth system model precipitation output with generative adversarial networks for unpaired image-to-image translation

Abstract

Numerical Earth system models (ESMs) are our primary tool for projecting future climate scenarios. Their simulation output is used by impact models that assess the effect of anthropogenic global warming, e.g., on flood events, vegetation changes or crop yields. Precipitation, an atmospheric variable with arguably one of the largest socio-economic impacts, involves various processes on a wide range of spatial-temporal scales. However, these cannot be completely resolved in ESMs due to the limited discretization of the numerical model. This can lead to biases in the ESM output that need to be corrected in a post-processing step prior to feeding ESM output into impact models, which are calibrated with observations [1]. While established post-processing methods successfully improve the modelled temporal statistics for each grid cell individually, unrealistic spatial features that require a larger spatial context are not addressed.Here, we apply a cycle-consistent generative adversarial network (CycleGAN) [2] that is physically constrained to the precipitation output from Coupled Model Intercomparison Project phase 6 (CMIP6)  ESMs to correct both temporal distributions and spatial patterns. The CycleGAN can be naturally trained on daily ESM and reanalysis fields that are unpaired due to the deviating trajectories of the ESM and observation-based ground truth. We evaluate our method against a state-of-the-art bias adjustment framework (ISIMIP3BASD) [3] and find that it outperforms it in correcting spatial patterns and achieves comparable results on temporal distributions. We further discuss the representation of extreme events and suitable metrics for quantifying the realisticness of unpaired precipitation fields. [1] Cannon, A.J., et al. "Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?." Journal of Climate 28.17 (2015): 6938-6959. [2] Zhu, J.-Y., et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.[3] Lange, S. "Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)." Geoscientific Model Development 12.7 (2019): 3055-3070.