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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Feb 2023
Research article |
| 14 Feb 2023
| 14 Feb 2023
Reliability of resilience estimation based on multi-instrument time series
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
Ruxandra-Maria Zotta
Department of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria
Chris A. Boulton
Global Systems Institute, University of Exeter, Exeter, UK
Timothy M. Lenton
Global Systems Institute, University of Exeter, Exeter, UK
Wouter Dorigo
Department of Geodesy and Geo-Information, Vienna University of Technology, Vienna, Austria
Niklas Boers
Global Systems Institute, University of Exeter, Exeter, UK
Earth System Modelling, School of Engineering and Design, Technical University of Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Department of Mathematics, University of Exeter, Exeter, UK
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Cited
13 citations as recorded by crossref.
- Tipping point detection and early warnings in climate, ecological, and human systems V. Dakos et al. 10.5194/esd-15-1117-2024
- VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring R. Zotta et al. 10.5194/essd-16-4573-2024
- Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook S. Bathiany et al. 10.1007/s10712-024-09833-z
- Reply to: Little evidence that Amazonian rainforests are approaching a tipping point C. Boulton et al. 10.1038/s41558-023-01854-7
- Reliability of vegetation resilience estimates depends on biomass density T. Smith & N. Boers 10.1038/s41559-023-02194-7
- Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation M. Ben-Yami et al. 10.1038/s41467-023-44046-9
- Remotely sensing potential climate change tipping points across scales T. Lenton et al. 10.1038/s41467-023-44609-w
- Critical slowing down of the Amazon forest after increased drought occurrence J. Van Passel et al. 10.1073/pnas.2316924121
- Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model D. Nian et al. 10.1016/j.scitotenv.2024.174378
- Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America Y. Zhang et al. 10.1038/s41559-024-02551-0
- Assessment of terrestrial ecosystem sensitivity to climate change in arid, semi-arid, sub-humid, and humid regions using EVI, LAI, and SIF products J. Chen et al. 10.1016/j.ecolind.2023.111511
- Uncertainties too large to predict tipping times of major Earth system components from historical data M. Ben-Yami et al. 10.1126/sciadv.adl4841
- Reliability of resilience estimation based on multi-instrument time series T. Smith et al. 10.5194/esd-14-173-2023
12 citations as recorded by crossref.
- Tipping point detection and early warnings in climate, ecological, and human systems V. Dakos et al. 10.5194/esd-15-1117-2024
- VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring R. Zotta et al. 10.5194/essd-16-4573-2024
- Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook S. Bathiany et al. 10.1007/s10712-024-09833-z
- Reply to: Little evidence that Amazonian rainforests are approaching a tipping point C. Boulton et al. 10.1038/s41558-023-01854-7
- Reliability of vegetation resilience estimates depends on biomass density T. Smith & N. Boers 10.1038/s41559-023-02194-7
- Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation M. Ben-Yami et al. 10.1038/s41467-023-44046-9
- Remotely sensing potential climate change tipping points across scales T. Lenton et al. 10.1038/s41467-023-44609-w
- Critical slowing down of the Amazon forest after increased drought occurrence J. Van Passel et al. 10.1073/pnas.2316924121
- Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model D. Nian et al. 10.1016/j.scitotenv.2024.174378
- Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America Y. Zhang et al. 10.1038/s41559-024-02551-0
- Assessment of terrestrial ecosystem sensitivity to climate change in arid, semi-arid, sub-humid, and humid regions using EVI, LAI, and SIF products J. Chen et al. 10.1016/j.ecolind.2023.111511
- Uncertainties too large to predict tipping times of major Earth system components from historical data M. Ben-Yami et al. 10.1126/sciadv.adl4841
1 citations as recorded by crossref.
Latest update: 12 Nov 2024
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.
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as...
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