A new dynamical systems perspective on atmospheric predictability: Eastern Mediterranean weather regimes as a case...

Area of Science:

• Atmospheric science
• Dynamical systems theory
• Climate modeling

Background:

• The atmosphere exhibits chaotic behavior with recurring large-scale patterns.
• Dynamical systems theory offers tools to analyze complex systems.
• Understanding atmospheric predictability is crucial for weather forecasting and climate science.

Purpose of the Study:

• To apply dynamical systems theory to assess atmospheric predictability in the eastern Mediterranean.
• To evaluate the predictability of different synoptic classifications (SCs).
• To explore new methods for climate model evaluation.

Main Methods:

• Utilized local dimension and persistence metrics from dynamical systems theory.
• Analyzed daily sea-level pressure fields from reanalysis and CMIP5 models.
• Grouped atmospheric configurations into synoptic classifications (SCs).

Main Results:

• Local dimension and persistence effectively characterize atmospheric predictability.
• These metrics provide qualitative insights into the predictability of various SCs.
• The approach demonstrated potential for evaluating climate model performance.

Conclusions:

• Local dimension and persistence are valuable for assessing atmospheric predictability.
• These metrics, combined with SC transitional probabilities, can complement operational forecasts.
• The methodology offers a novel perspective for climate model evaluation and can be applied globally.