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Published on: June 8, 2015
Enhanced regime predictability in atmospheric low-order models due to stochastic forcing
1College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK f.kwasniok@exeter.ac.uk.
Summary
Noise can surprisingly improve weather regime predictability in models. Too much noise reduces predictability, but an optimal level enhances atmospheric regime persistence and forecasting accuracy.
Area of Science:
- Atmospheric science
- Climate modeling
- Nonlinear dynamics
Background:
- Atmospheric models often incorporate stochastic forcing to represent unresolved processes.
- Understanding the impact of noise on climate regime predictability is crucial for improving forecasts.
- Identifying persistent atmospheric states (regimes) is key to coarse-grained predictability analysis.
Purpose of the Study:
- To investigate the relationship between stochastic forcing (noise) and the predictability of atmospheric regimes in low-order models.
- To determine if noise can enhance or degrade regime predictability.
- To explore the underlying mechanisms and implications for weather and climate prediction.
Main Methods:
- Utilized hidden Markov model (HMM) analysis to identify atmospheric regimes as metastable states.
- Employed low-order models, including the Lorenz '63 model and a barotropic flow model.
- Systematically varied the level of stochastic forcing and analyzed its effect on regime persistence and predictability.
Main Results:
- Observed a coherence resonance-like effect where regime predictability increases with intermediate noise levels.
- Found that increased noise enhances the persistence of atmospheric regimes.
- Noted that this enhanced predictability is specific to the coarse-grained regime dynamics, not individual trajectories.
Conclusions:
- Stochastic forcing can non-monotonically affect regime predictability, with an optimal noise level enhancing it.
- The phenomenon of enhanced regime predictability is linked to increased regime persistence.
- Findings suggest potential improvements for weather and climate modeling by optimizing noise parameterization for regime-based predictions.
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