# Computationally-Efficient Climate Predictions using Multi-Fidelity Surrogate Modelling. (arXiv:2109.07468v1 [physics.ao-ph])

Accurately modelling the Earth’s climate has widespread applications ranging
from forecasting local weather to understanding global climate change.
Low-fidelity simulations of climate phenomena are readily available, but
high-fidelity simulations are expensive to obtain. We therefore investigate the
potential of Gaussian process-based multi-fidelity surrogate modelling as a way
to produce high-fidelity climate predictions at low cost. Specifically, our
model combines the predictions of a low-fidelity Global Climate Model (GCM) and
those of a high-fidelity Regional Climate Model (RCM) to produce high-fidelity
temperature predictions for a mountainous region on the coastline of Peru. We
are able to produce high-fidelity temperature predictions at significantly
lower computational cost compared to the high-fidelity model alone: our
predictions have an average error of $15.62^circtext{C}^2$ yet our approach
only evaluates the high-fidelity model on 6% of the region of interest.