Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 379:2194 (2021) ARTN 20200083

Authors:

Matthew Chantry, Hannah Christensen, Peter Dueben, Tim Palmer

Abstract:

In September 2019, a workshop was held to highlight the growing area of applying machine learning techniques to improve weather and climate prediction. In this introductory piece, we outline the motivations, opportunities and challenges ahead in this exciting avenue of research. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Scale‐aware space‐time stochastic parameterization of subgrid‐scale velocity enhancement of sea surface fluxes

Journal of Advances in Modeling Earth Systems American Geophysical Union (AGU) (2021)

Authors:

Julie Bessac, Hannah M Christensen, Kota Endo, Adam H Monahan, Nils Weitzel

Impact of stochastic physics and model resolution on the simulation of tropical cyclones in climate GCMs

Journal of Climate American Meteorological Society 34:11 (2021) 4315-4341

Authors:

Pl Vidale, K Hodges, B Vanniere, P Davini, M Roberts, Kristian Strommen, A Weisheimer, E Plesca, S Corti

Abstract:

The role of model resolution in simulating geophysical vortices with the characteristics of realistic Tropical Cyclones (TCs) is well established. The push for increasing resolution continues, with General Circulation Models (GCMs) starting to use sub-10km grid spacing. In the same context it has been suggested that the use of Stochastic Physics (SP) may act as a surrogate for high resolution, providing some of the benefits at a fraction of the cost. Either technique can reduce model uncertainty, and enhance reliability, by providing a more dynamic environment for initial synoptic disturbances to be spawned and to grow into TCs. We present results from a systematic comparison of the role of model resolution and SP in the simulation of TCs, using EC-Earth simulations from project Climate-SPHINX, in large ensemble mode, spanning five different resolutions. All tropical cyclonic systems, including TCs, were tracked explicitly. As in previous studies, the number of simulated TCs increases with the use of higher resolution, but SP further enhances TC frequencies by ≈ 30%, in a strikingly similar way. The use of SP is beneficial for removing systematic climate biases, albeit not consistently so for interannual variability; conversely, the use of SP improves the simulation of the seasonal cycle of TC frequency. An investigation of the mechanisms behind this response indicates that SP generates both higher TC (and TC seed) genesis rates, and more suitable environmental conditions, enabling a more efficient transition of TC seeds into TCs. These results were confirmed by the use of equivalent simulations with the HadGEM3-GC31 GCM.

OpenEnsemble 1.0: a boon for the research community

Geoscientific Model Development Discussions Copernicus Publications (2020)

Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

Journal of Advances in Modeling Earth Systems American Geophysical Union (AGU) 12:12 (2020)

Authors:

Peter Ukkonen, Robert Pincus, Robin J Hogan, Kristian Pagh Nielsen, Eigil Kaas