Conditioned Emulation of Global Climate Models With Generative Adversarial Networks

Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we demonstrate the capabilities of a Generative Adversarial Network (GAN) emulating a climate model. Our GAN is trained to produce spatio-temporal samples of precipitation and temperature conditioned on spatial monthly averages. We condition our GAN on several climate scenarios and task the generator with producing new realizations. These realizations are then evaluated under several climate metrics (SDII, dryness measures, etc.). Our trained GANs can generate realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events.