  Bayesian optimisation is a powerful method for optimising black-box
functions, popular in settings where the true function is expensive to evaluate
and no gradient information is available. Bayesian optimisation can improve
responses to many optimisation problems within climate change for which
simulator models are unavailable or expensive to sample from. While there have
been several feasibility demonstrations of Bayesian optimisation in
climate-related applications, there has been no unifying review of applications
and benchmarks. We provide such a review here, to encourage the use of Bayesian
optimisation in important and well-suited application domains. We identify four
main application domains: material discovery, wind farm layout, optimal
renewable control and environmental monitoring. For each domain we identify a
public benchmark or data set that is easy to use and evaluate systems against,
while being representative of real-world problems. Due to the lack of a
suitable benchmark for environmental monitoring, we propose LAQN-BO, based on
air pollution data. Our contributions are: a) identifying a representative
range of benchmarks, providing example code where necessary; b) introducing a
new benchmark, LAQN-BO; and c) promoting a wider use of climate change
applications among Bayesian optimisation practitioners.
