Uncertain ability of AI-based reconstruction methods to recover system dynamics for resilience assessment

Determine whether deep learning–based reconstruction methods for observational climate and ecosystem time series can recover the underlying system dynamics required for resilience assessment, ensuring that the reconstructed data preserve the dynamical properties essential for accurate estimation of resilience from time-series indicators.

Background

The paper shows that widely used resilience indicators based on variance and lag-one autocorrelation can be strongly affected by data gaps and outliers, undermining indicator agreement and potentially biasing resilience estimates in real-world applications. Traditional gap-filling methods, such as temporal resampling and linear interpolation, can distort higher-order statistics and autocorrelation structure, which are crucial for resilience inference.

The authors highlight emerging AI-based reconstruction approaches as promising for addressing data gaps but explicitly note that it is not yet established whether these methods can recover the true underlying system dynamics in a way that supports reliable resilience estimation. This uncertainty motivates the need for rigorous evaluation of AI reconstruction methods with respect to preserving resilience-relevant dynamical properties.