Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.20580 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911700162183168 |
|---|---|
| author | Hillier, Adeline Sleeman, Jennifer Brett, Jay Tang, Caroline Millison, Jenelle Gnanadesikan, Anand |
| author_facet | Hillier, Adeline Sleeman, Jennifer Brett, Jay Tang, Caroline Millison, Jenelle Gnanadesikan, Anand |
| contents | This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulations. Focusing on multivariate time series describing global ocean transport, we demonstrate the surrogate's ability to forecast tip events across thousands of time steps. The data involve up to 21 non-stationary time series in addition to static covariates describing free parameters and initial conditions. Modifications to the architecture and objective function yield a surrogate that anticipates the timing of Atlantic and Pacific collapses to high fidelity and captures the stochastic uncertainty in transition timing across ensemble predictions. The learned surrogate achieves a 465x computational speedup over the numerical simulator while maintaining differentiability with respect to parameters and initial conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20580 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics Hillier, Adeline Sleeman, Jennifer Brett, Jay Tang, Caroline Millison, Jenelle Gnanadesikan, Anand Machine Learning This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulations. Focusing on multivariate time series describing global ocean transport, we demonstrate the surrogate's ability to forecast tip events across thousands of time steps. The data involve up to 21 non-stationary time series in addition to static covariates describing free parameters and initial conditions. Modifications to the architecture and objective function yield a surrogate that anticipates the timing of Atlantic and Pacific collapses to high fidelity and captures the stochastic uncertainty in transition timing across ensemble predictions. The learned surrogate achieves a 465x computational speedup over the numerical simulator while maintaining differentiability with respect to parameters and initial conditions. |
| title | Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.20580 |