2020/11/23 by Alexandra Puchko, Puchko, Alexandra, Robert Link +7 · 1 citation
Computer Science · Earth and Planetary Sciences · Environmental Science · #Climate variability and models #Computational Physics and Python Applications #FOS: Computer and information sciences #Meteorological Phenomena and Simulations #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2011.11705
openalex publication_date 2020/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally intensive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint probability distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.