Emulating state-of-the-art gravity wave parameterizations

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Don’t miss Y. Qiang Sun’s paper on the use of machine learning to emulate a physics-based atmospheric gravity wave parameterizations in a state-of-the-art climate model, in review in JAMES. It’s also up on the arxiv.

This is a tour-de-force study, where Qiang emulated orographic, frontal, and convective gravity wave parameterizations in WACCM, which has perhaps the most sophisticated treatment of gravity waves on the market! He first had to overcome set imbalance to make training possible, and then probed uncertainty in the emulators, showing that many of the current approaches provided equivalent information. Finally, he showed that while the machine learning based emulators couldn’t generalize out-of-set to climate perturbations, with just a bit of data from the new climate, transfer learning can fix a scheme. This suggests that a small amount of data from high resolution simulations in warm climates might be sufficient for the purposes of data-driven parameterizations trained on today’s climate.