Reliability of resilience estimation based on multi-instrument time series

Smith, Taylor; Zotta, Ruxandra-Maria; Boulton, Chris A.; Lenton, Timothy M.; Dorigo, Wouter; Boers, Niklas

doi:https://doi.org/10.5194/esd-14-173-2023

Articles | Volume 14, issue 1

https://doi.org/10.5194/esd-14-173-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-14-173-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 1

Research article

|

14 Feb 2023

Research article |

| 14 Feb 2023

Reliability of resilience estimation based on multi-instrument time series

Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers

Supplement

https://doi.org/10.5194/esd-14-173-2023-supplement

Data sets

The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA L. Moesinger, W. Dorigo, R. De Jeu, R. Van der Schalie, T. Scanlon, I. Teubner, and M. Forkel https://doi.org/10.5281/zenodo.2575599

MCD12C1 MODIS/Terra+ Aqua Land Cover Type Yearly L3 Global 0.05 Deg CMG V006 M. Friedl and D. Sulla-Menashe https://doi.org/10.5067/MODIS/MCD12Q1.061

MOD13C1 MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05Deg CMG V006 K. Didan https://doi.org/10.5067/MODIS/MOD13C1.006

Model code and software

Reliability of Resilience Estimation based on Multi-Instrument Time Series T. Smith and N. Boers https://doi.org/10.5281/zenodo.7009414

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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.