Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
This work addresses uncertainty in climate modeling by introducing a machine learning-based stochastic parameterization, though it is incremental as it builds on existing GAN methods applied to a specific domain.
The authors tackled the problem of stochastic parameterization for unresolved sub-grid processes by developing a generative adversarial network (GAN) framework, achieving better performance than a baseline parameterization at weather and climate timescales in the Lorenz '96 model.
Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and sub-grid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate timescales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system. We also find that in general those models which produce skillful forecasts are also associated with the best climate simulations.