Abstract
Many different emission pathways exist that are compatible with the
Paris climate agreement, and many more are possible that miss that
target. While some of the most complex Earth System Models have
simulated a small selection of Shared Socioeconomic Pathways, it is
impractical to use these expensive models to fully explore the space of
possibilities. Such explorations therefore mostly rely on
one-dimensional impulse response models, or simple pattern scaling
approaches to approximate the physical climate response to a given
scenario. Here we present ClimateBench - a benchmarking framework based
on a suite of CMIP, AerChemMIP and DAMIP simulations performed by a full
complexity Earth System Model, and a set of baseline machine learning
models that emulate its response to a variety of forcers. These
emulators can predict annual mean global distributions of temperature,
diurnal temperature range and precipitation (including extreme
precipitation) given a wide range of emissions and concentrations of
carbon dioxide, methane and aerosols, allowing them to efficiently probe
previously unexplored scenarios. We discuss the accuracy and
interpretability of these emulators and consider their robustness to
physical constraints such as total energy conservation. Future
opportunities incorporating such physical constraints directly in the
machine learning models and using the emulators for detection and
attribution studies are also discussed. This opens a wide range of
opportunities to improve prediction, consistency and mathematical
tractability. We hope that by laying out the principles of climate model
emulation with clear examples and metrics we encourage others to tackle
this important and demanding challenge.