FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

arXiv:2307.10052v2 Announce Type: replace-cross
Abstract: Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modelling or more advanced machine learning techniques, data-driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state-of-the-art Earth system models. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data-driven emulator that …

abstract advanced arxiv balance bayesian climate climate models complexity computational data data-driven earth energy key machine machine learning machine learning techniques modelling research resources series stat.ap stat.ml surface type