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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models

Yixuan Sun · Elizabeth Cucuzzella · Steven Brus · Sri Hari Krishna Narayanan · Balu Nadiga · Luke Van Roekel · Jan Hückelheim · Sandeep Madireddy


Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generate perturbed parameter ensemble data and generate surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.

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