arXiv:2605.20580v1 Announce Type: new Abstract: 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 t
The increasing computational power and advanced AI models like Temporal Fusion Transformers allow for more sophisticated emulation of complex Earth systems.
This development can significantly accelerate climate modeling and prediction, enabling better anticipation of critical environmental tipping points with reduced computational cost.
The ability to use AI surrogates to forecast climate tipping events across thousands of time steps marks a shift towards more efficient and faster climate science, potentially informing policy and adaptation strategies sooner.
- · Climate scientists
- · Environmental policy makers
- · AI research & development
- · Renewable energy sector
- · High-emissions industries (long-term pressure)
- · Traditional climate modeling reliance on purely physical simulations
More accurate and timely predictions of climate tipping points become possible.
Improved predictive capabilities could lead to more robust climate mitigation and adaptation strategies being developed and implemented.
These tools might inform the reallocation of resources towards preventing or preparing for specific climate-induced disruptions, potentially influencing global capital flows and risk assessments.
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Read at arXiv cs.LG