Preprints
https://doi.org/10.5194/esd-2022-41
https://doi.org/10.5194/esd-2022-41
 
31 Aug 2022
31 Aug 2022
Status: this preprint is currently under review for the journal ESD.

Reliability of Resilience Estimation based on Multi-Instrument Time Series

Taylor Smith1, Ruxandra-Maria Zotta2, Chris A. Boulton3, Timothy M. Lenton3, Wouter Dorigo2, and Niklas Boers3,4,5,6 Taylor Smith et al.
  • 1Institute of Geosciences, Universität Potsdam, Germany
  • 2Department of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria
  • 3Global Systems Institute, University of Exeter, Exeter, UK
  • 4Earth System Modelling, School of Engineering & Design, Technical University of Munich, Germany
  • 5Potsdam Institute for Climate Impact Research, Germany
  • 6Department of Mathematics, University of Exeter, UK

Abstract. Many widely-used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study, and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process, rather than actual changes in the dynamical properties of the system, is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as the Vegetation Optical Depth and the Normalized Difference Vegetation Index from different satellite sources. We find that varying satellite noise levels and data aggregation schemes can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor noise levels. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.

Taylor Smith et al.

Status: open (until 12 Oct 2022)

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Taylor Smith et al.

Taylor Smith et al.

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Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well-captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
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