Climate analysis & attribution
Characterisation of Atlantic meridional overturning hysteresis using Langevin dynamics
In one sentence. A machine learning fit of a six-parameter Langevin model reproduces AMOC collapse trajectories from intermediate-complexity climate models under freshwater forcing.
Abstract
Abstract. Using a machine learning technique, collapse trajectories of the Atlantic Meridional Overturning Circulation from climate models of intermediate complexity are fitted to a simple model based on the Langevin equation. A total of six parameters are sufficient to quantitatively describe the collapses seen in these simulations under a freshwater forcing. Reversing the freshwater forcing results in asymmetric behaviour that is less well captured and would require a more complicated model.
Labels
- Topic
- Climate analysis & attribution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels), Physics-informed / physics-constrained
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.5194/esd-12-69-2021 OpenAlex:W3121534514
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.