Dynamical proxies of North Atlantic predictability and extremes

Area of Science:

• Atmospheric dynamics
• Dynamical systems theory
• Chaos theory

Background:

• Atmospheric flows exhibit chaotic dynamics and recurring patterns, suggesting an underlying attractor.
• Estimating the attractor's average dimension (D) is challenging and doesn't capture transient motions like extreme weather.
• Transient motions, crucial for understanding weather extremes, require methods beyond average attractor properties.

Purpose of the Study:

• To classify transient atmospheric motions using instantaneous properties of the atmospheric attractor.
• To link extreme values of instantaneous properties to specific atmospheric patterns and extreme weather events.
• To evaluate the utility of instantaneous attractor properties in operational weather forecasting.

Main Methods:

• Application of recent developments in dynamical systems theory.
• Classification of atmospheric motions via instantaneous dimension and stability of the attractor.
• Correlation analysis between instantaneous properties and ensemble weather forecast data.

Main Results:

• Instantaneous dimension and stability uniquely determine transient atmospheric motions.
• Extreme values of instantaneous properties correspond to extreme weather occurrences.
• Significant correlation found between instantaneous properties and sea-level pressure field spread in long-range forecasts.

Conclusions:

• Instantaneous properties of the atmospheric attractor offer a novel way to classify and understand transient motions.
• These properties provide a more informative approach to predicting extreme weather events than average dimension.
• Instantaneous properties can efficiently evaluate and enhance operational weather forecasts, especially for lead times exceeding two weeks.