Predicting Instabilities in Transient Landforms and Interconnected Ecosystems

Many parts of the Earth system are thought to have multiple stable equilibrium states, with the potential for rapid and sometimes catastrophic shifts between them. The most common frameworks for analyzing stability changes, however, require stationary (trend- and seasonality-free) data, which necessitates error-prone data pre-processing. Here we propose a novel method of quantifying system stability based on eigenvalue tracking and Floquet Multipliers, which can be applied directly to diverse data without first removing trend and seasonality, and is robust to changing noise levels, as can be caused by merging signals from different sensors. We first demonstrate this approach with synthetic data and further show how glacier surge onset can be predicted from observed surface velocity time series. We then show that our method can be extended to analyze spatio-temporal data and illustrate this flexibility with remotely sensed Amazon rainforest vegetation productivity, highlighting the spatial patterns of whole-ecosystem destabilization. Our work applies critical slowing down theory to glacier dynamics for the first time, and provides a novel and flexible method to quantify the stability or resilience of a wide range of spatiotemporal systems, including climate subsystems, ecosystems, and transient landforms.