Fixed points, stable manifolds, weather regimes, and their predictability.

Area of Science:

• Atmospheric Science
• Climate Dynamics
• Nonlinear Dynamics

Background:

• Atmospheric models often exhibit multiple coexisting "weather regimes."
• Transitions between these regimes are complex and not fully understood.
• Low-frequency variability plays a crucial role in atmospheric dynamics.

Purpose of the Study:

• To investigate the relationship between low-frequency variability and model fixed points in phase space.
• To identify the role of stable manifolds in separating different weather regimes.
• To understand the predictability of transitions between regimes.

Main Methods:

• Utilized a simple, one-layer atmospheric model.
• Focused on identifying stable manifolds associated with fixed points.
• Employed "bred vectors" and singular vectors to track manifolds.
• Verified findings using ensemble forecasts from initial state "clouds."

Main Results:

• Stable manifolds were identified as separatrices between weather regimes.
• Bred vectors and singular vectors effectively tracked these manifolds.
• Trajectory divergence in ensemble forecasts confirmed links between predictability and manifold geometry.
• Demonstrated connections between low predictability zones and regime transitions.

Conclusions:

• Stable manifolds are key to understanding regime separation in atmospheric models.
• Predictability measures can effectively characterize manifold geometry and regime transitions.
• This framework enhances the understanding of weather regime dynamics and predictability.