Articles | Volume 14, issue 1
https://doi.org/10.5194/esd-14-173-2023
https://doi.org/10.5194/esd-14-173-2023
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

Viewed

Total article views: 4,410 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,682 650 78 4,410 158 50 79
  • HTML: 3,682
  • PDF: 650
  • XML: 78
  • Total: 4,410
  • Supplement: 158
  • BibTeX: 50
  • EndNote: 79
Views and downloads (calculated since 31 Aug 2022)
Cumulative views and downloads (calculated since 31 Aug 2022)

Viewed (geographical distribution)

Total article views: 4,410 (including HTML, PDF, and XML) Thereof 4,323 with geography defined and 87 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Altmetrics
Final-revised paper
Preprint