Stochastic Emulators of Spatially Resolved Extreme Temperatures of Earth
System Models
Abstract
Prediction of extreme events under climate change is challenging but
essential for risk management of natural disasters. Although earth
system models (ESMs) are arguably our best tool to predict climate
extremes, their high computational cost restricts the application to
project only a few future scenarios. Emulators, or reduced-complexity
models, serve as a complement to ESMs that achieve a fast prediction of
the local response to various climate change scenarios. Here we propose
a data-driven framework to emulate the full statistics of spatially
resolved climate extremes. The variable of interest is the near-surface
daily maximum temperature. The spatial patterns of temperature
variations are assumed to be independent of time and extracted using
Empirical Orthogonal Functions (EOFs). The time dependence is encoded
through the coefficients of leading EOFs which are decomposed into
long-term seasonal variations and daily fluctuations. The former are
assumed to be functions of the global mean temperature, while the latter
are modelled as Gaussian stochastic processes with temporal correlation
conditioned on the season. The emulator is trained and tested using the
simulation data in CMIP6. By generating multiple realizations, the
emulator shows significant performance in predicting the temporal
evolution of the probability distribution of local daily maximum
temperature. Furthermore, the uncertainty of the emulated statistics is
quantified to account for the internal variability. The emulation
accuracy in testing scenarios remains consistent with the training
datasets. The performance of the emulator suggests that the proposed
framework can be generalized to other climate extremes and more
complicated scenarios of climate change.