Estimating the resilience of non-stationary systems
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Taylor Smith1,2, Andreas Morr3,4, Christof Schötz5,4
1Institute of Geosciences, Universität Potsdam, Potsdam, Germany. tasmith@uni-potsdam.de.
Abstract:
A wide body of work has applied the concept of critical slowing down to estimate the stability of different Earth system components. Most of them - such as global vegetation - are inherently non-stationary, for example due to strong seasonal forcing that complicates the estimation of their resilience to external perturbations. Here, we introduce a method to account for non-stationarity in estimating the resilience of diverse synthetic and real-world systems via a regression-based formulation of the Langevin equation. Our method does not require extensive data pre-processing, is robust to gaps in the data record, and does not require regular time sampling. We further show that our method can incorporate time-varying data uncertainties, derive uncertainty bounds for stability estimates, and can be natively extended to examine spatial systems. Our method is a drop-in replacement for widely-used autocorrelation-based resilience estimates, and can be applied across diverse Earth system components.
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