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Keyword: machine learning parameterization ×Clear all

4 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

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

   O'Gorman and Dwyer · Journal of Advances in Modeling Earth Systems · 2018

   The authors trained a random forest to mimic a conventional convection scheme and tested it in a climate model, looking at climate statistics, extremes and climate change.

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   1. Unchecked“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.”
   2. UncheckedIn idealised tests, an ML convection scheme trained only on a control climate misses warming, but captures it when trained on both climates.“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.”
   3. Unchecked“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.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

   Yuval and O'Gorman · Nature Communications · 2020

   The authors trained a random forest on output from a 3D high-resolution atmospheric model to represent subgrid processes, and used it in a coarser version of the model for stable climate simulations.

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   1. UncheckedA random-forest parameterization of small-scale processes, put into an atmospheric model, gave stable coarse-resolution runs matching the high-resolution climate.“Integrating this parameterization into the atmospheric model leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation.”