Data imbalance, uncertainty quantification, and transfer learning in data‐driven parameterizations: Lessons from the emulation of gravity wave momentum transport in WACCM

Scientists increasingly use machine learning methods, especially neural networks (NNs), to improve weather and climate models. However, it can be challenging for an NN to learn rare, large-amplitude events because they are infrequent in training data. In addition, NNs need to express their confidence (certainty) about a prediction and work effectively across different climates, for example, warmer climates due to increased CO2. Traditional NNs often struggle with these challenges. Here, we share insights from emulating known physics (gravity waves) with NNs in a state-of-the-art climate model. We propose specific strategies for effectively learning rare events, quantifying the uncertainty of NN predictions, and making reliable predictions across various climates. For instance, one strategy to address the learning of rare events involves inflating the impact of infrequent events in the training data. We also demonstrate that several methods could be useful in determining the uncertainty of the predictions. Furthermore, we show that NNs trained on simulations of the historical period do not perform as well in warmer climates. We then improve NN performance by employing transfer learning using limited new data from warmer climates. This study provides lessons for developing robust and generalizable approaches for using NNs to improve models in the future.

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Recommended citation: Sun, Y. Qiang, Hamid A. Pahlavan, Ashesh Chattopadhyay, Pedram Hassanzadeh, Sandro W. Lubis, M. Joan Alexander, Edwin P. Gerber, Aditi Sheshadri, and Yifei Guan. “Data imbalance, uncertainty quantification, and transfer learning in data‐driven parameterizations: Lessons from the emulation of gravity wave momentum transport in WACCM.” Journal of Advances in Modeling Earth Systems 16, no. 7 (2024): e2023MS004145.