Articles | Volume 16, issue 15
Development and technical paper
10 Aug 2023
Development and technical paper |  | 10 Aug 2023

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik

Model code and software

raghul-parthipan/l96_rnn: v1.1.0 (v1.1.0) Raghul Parthipan

Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.