High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia

作者：Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, Angelina Bushenkova · 发表于：Geoscientific model development · 年份：2024 · DOI：10.5194/gmd-17-229-2024 · 被引用次数：41 · 研究领域：Climate variability and models、Meteorological Phenomena and Simulations、Cryospheric studies and observations

Abstract. Deep learning (DL) methods have recently garnered attention from the climate change community for being an innovative approach to downscaling climate variables from Earth system and global climate models (ESGCMs) with horizontal resolutions still too coarse to represent regional- to local-scale phenomena. In the context of the Coupled Model Intercomparison Project phase 6 (CMIP6), ESGCM simulations were conducted for the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) at resolutions ranging from 0.70 to 3.75∘. Here, four convolutional neural network (CNN) architectures were evaluated for their ability to downscale, to a resolution of 0.1∘, seven CMIP6 ESGCMs over the Iberian Peninsula – a known climate change hotspot, due to its increased vulnerability to projected future warming and drying conditions. The study is divided into three stages: (1) evaluating the performance of the four CNN architectures in predicting mean, minimum, and maximum temperatures, as well as daily precipitation, trained using ERA5 data and compared with the Iberia01 observational dataset; (2) downscaling the CMIP6 ESGCMs using the trained CNN architectures and further evaluating the ensemble against Iberia01; and (3) constructing a multi-model ensemble of CNN-based downscaled projections for temperature and precipitation over the Iberian Peninsula at 0.1∘ resolution throughout the 21st century under four Shared Socioeconomic Pathway (SSP) scenarios. Upon...