Earth’s climate is one of the fundamental boundary conditions on many Earth surface processes. For this reason, global climate models (GCMs) are often a critical part of Earth science research. However, they remain highly computationally expensive to run, and often access to a super-computer is needed to run a GCM in a reasonable amount of time. This motivates the question: is it possible to reasonably predict climate without an expensive GCM?
Continue reading “Using machine learning and simple features to predict climate – part 1”
Data & Earth Scientist