Using unstable periodic orbits to understand blocking behavior in a low order land-atmosphere model.

Area of Science:

• Atmospheric dynamics and climate modeling.
• Dynamical systems theory and chaos.
• Computational physics and spectral methods.

Background:

• Reduced-order models are crucial for understanding complex climate system dynamics.
• Unstable Periodic Orbits (UPOs) offer a framework for analyzing chaotic attractors.
• Identifying distinct climate regimes and transitions is key to climate prediction.

Purpose of the Study:

• To cluster the chaotic attractor of a coupled atmosphere-land spectral model using numerically derived UPOs.
• To investigate the origin of these clusters using continuation software.
• To link identified climate regimes and transitions to atmospheric blocking events.

Main Methods:

• Utilizing Unstable Periodic Orbits (UPOs) to identify regimes and transitions within a reduced-order spectral model.
• Clustering the chaotic attractor based on a set of numerically derived UPOs.
• Applying continuation software to trace the origins of UPO clusters.
• Introducing and applying the concept of cumulative shadowing to quantify UPO-trajectory interactions.

Main Results:

• The chaotic attractor was successfully clustered using UPOs.
• The origin of these clusters was investigated via continuation software.
• Cumulative shadowing was used to identify UPO sets corresponding to different cluster life cycles.
• Identified regions and transitions on the attractor are linked to atmospheric blocks.

Conclusions:

• UPOs provide a powerful tool for characterizing complex dynamics in climate models.
• The concept of cumulative shadowing enhances the understanding of UPO influence on model trajectories.
• This approach successfully links model dynamics to observable atmospheric phenomena like blocking events.
• The study offers new insights into the life cycles of atmospheric blocks through the lens of dynamical systems theory.