  Accurately forecasting the weather is a key requirement for climate change
mitigation. Data-driven methods offer the ability to make more accurate
forecasts, but lack interpretability and can be expensive to train and deploy
if models are not carefully developed. Here, we make use of two historical
climate data sets and tools from machine learning, to accurately predict
temperature fields. Furthermore, we are able to use low fidelity models that
are cheap to train and evaluate, to selectively avoid expensive high fidelity
function evaluations, as well as uncover seasonal variations in predictive
power. This allows for an adaptive training strategy for computationally
efficient geophysical emulation.
