Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events

By P. O’Gorman, J. Dwyer. 2018

341 citations recorded by the source corpus.

The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high‐resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML‐based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and nonnegativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large‐scale environment.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “This work is being pursued in diverse disciplines (e.g., earth systems [214], climate science [75, 135, 186], turbulence modeling [28, 179, 273], material discovery [40, 202, 226], quantum chemistry [222, 228], biological sciences [285], and hydrology [275]), and it is being performed in the…”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “Already, ML-based parameterizations have shown success in geology [49, 104], atmospheric science [38, 39, 98, 195], and hydrology [28].”
Can deep learning beat numerical weather prediction? (2021, background): “…and extreme climate events: Vandal et al. [102] ﬁnd that complex DL models have more problems in capturing extremes than classical statistical downscaling models, whereas O’Gorman et al. [103] state that their model captures extremes quite well without the need for special training on these cases.”
Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives (2020, methodology): “This approach involves fitting a statistical model to the output of relatively expensive physical models (e.g., CRMs) that more faithfully represent the subgrid processes (Brenowitz & Bretherton, 2018; Gentine et al., 2018; O'Gorman & Dwyer, 2018; Rasp et al., 2018; Schneider et al., 2017).”
Challenges and design choices for global weather and climate models based on machine learning (2018, background): “, 2018) and random forest decision trees (O’Gorman and Dwyer, 2018).”