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This study introduces Floquet Multipliers to assess Earth system stability without data pre-processing. This method predicts catastrophic shifts in systems like glaciers and rainforests, improving climate and ecosystem stability analysis.

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Area of Science:

  • Earth System Science
  • Dynamical Systems Theory
  • Ecological Stability

Background:

  • Earth systems often exhibit multiple stable states, risking abrupt shifts.
  • Current stability assessments require stationary data, involving complex pre-processing.
  • Existing methods struggle with non-stationary, noisy, or diverse datasets.

Purpose of the Study:

  • To develop a novel method for quantifying the stability of periodically forced systems.
  • To enable stability analysis of Earth system components without data pre-processing.
  • To assess the stability of real-world systems such as glaciers and rainforests.

Main Methods:

  • Application of Floquet Multipliers to analyze periodically forced systems.
  • Utilizing diverse time-series data without requiring trend or seasonality removal.
  • Testing the method on synthetic data, vegetation models, and real-world datasets.

Main Results:

  • Successfully predicted glacier surge onset using surface velocity data.
  • Identified spatially explicit destabilization patterns in the Amazon rainforest.
  • Demonstrated robustness to varying noise levels and data merging.

Conclusions:

  • Floquet Multipliers offer a robust, pre-processing-free approach to stability assessment.
  • The method is applicable to a wide range of spatio-temporal systems, including climate and ecosystems.
  • Enables early detection of potential catastrophic shifts in Earth systems.