Improving the quasi-biennial oscillation via a surrogate-accelerated multi-objective optimization
| Title: | Improving the quasi-biennial oscillation via a surrogate-accelerated multi-objective optimization |
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| Authors: | Damiano, Luis, Hannah, Walter M., Chen, Chih-Chieh, Benedict, James J., Sargsyan, Khachik, Debusschere, Bert, Eldred, Michael S. |
| Publication Year: | 2025 |
| Collection: | Physics (Other) Statistics |
| Subject Terms: | Physics - Atmospheric and Oceanic Physics, Physics - Fluid Dynamics, Statistics - Applications |
| More Details: | Simulating the QBO remains a formidable challenge partly due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM to yield a more realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency of the underlying stochastic process from complex observations, we extract a concise set of interpretable and physically meaningful quantities of interest capturing key attributes, such as oscillation amplitude and period. Building on this, we train a probabilistic surrogate model. Leveraging the Karhunen-Loeve decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, thereby capturing the cross-correlations among multiple physics quantities evaluated at different stratospheric pressure levels, and enabling rapid surrogate-based inference at a fraction of the computational cost of inference reliant only on full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution that simultaneously satisfies both objectives. To navigate this challenge, we quantify the bi-criteria trade-off and generate a representative set of Pareto optimal physics parameter values that balance the conflicting objectives. This integrated workflow not only improves the fidelity of QBO simulations but also advances toward a practical framework for tuning modes of variability and quasi-periodic phenomena, offering a versatile template for uncertainty quantification in complex geophysical models. Comment: Submitted to JAMES |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2503.13498 |
| Accession Number: | edsarx.2503.13498 |
| Database: | arXiv |
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