Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
arXiv:2606.18857v1 Announce Type: new Abstract: Machine-learning emulators are increasingly used for weather prediction and have the potential to extend skill on subseasonal-to-seasonal timescales by learning dynamically important sources of predictability. A key challenge is whether the models can exploit predictability anchors, such as stratospheric variability, that influence tropospheric circulation beyond short lead times. We test how architectural inductive bias affects emulation of sudden stratospheric warming (SSW) dynamics using paired idealised Isca simulations that differ only in an
The increasing sophistication of machine learning models and the urgency of climate modeling drive research into improving climate prediction capabilities.
Improving weather and climate prediction through AI can significantly impact agriculture, disaster preparedness, energy grids, and various economic sectors.
This research contributes to understanding how specific AI architectures can better simulate complex atmospheric phenomena, potentially leading to more accurate subseasonal-to-seasonal forecasts.
- · Climate scientists
- · AI researchers (weather/climate)
- · Agriculture sector
- · Energy sector
- · Traditional climate modeling approaches (potentially disintermediated)
Machine learning models gain enhanced ability to predict stratospheric warming events and their tropospheric impacts.
Improved long-range weather forecasts allow for better resource allocation and risk management in climate-sensitive industries.
The demonstrated utility of AI in complex physical systems accelerates its adoption in other scientific domains, potentially requiring increased computational resources.
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Read at arXiv cs.LG