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Anomaly Detection in Paleoclimate Records Using Permutation Entropy
Joshua Garland1, Tyler R Jones2, Michael Neuder3
1Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
Permutation entropy effectively detects anomalies in paleoclimate data. Reanalyzing ice core data with advanced instruments confirmed that older equipment introduced noise, validating this technique as a forensic tool for data quality assessment.
Area of Science:
- Paleoclimatology
- Data Science
- Geophysics
Background:
- Paleoclimate records, such as water-isotope data from ice cores, are crucial for understanding past climate changes.
- Identifying anomalies like noise and outliers is essential for accurate climate reconstructions.
- Previous analysis of polar ice core data indicated an abrupt change in signal complexity, suspected to be instrument-related noise.
Purpose of the Study:
- To validate the hypothesis that an older laboratory instrument introduced noise into paleoclimate records.
- To demonstrate the utility of permutation entropy as a forensic tool for identifying data anomalies in paleoclimate records.
- To showcase the application of permutation entropy in detecting non-climatic artifacts in ice core data.
Main Methods:
- Application of weighted and unweighted permutation entropy techniques to water-isotope records from a deep polar ice core.
- Reanalysis of a specific ice core section using a more advanced laboratory instrument.
- Comparative analysis of permutation entropy traces from data acquired with different instruments.
Main Results:
- Permutation entropy analysis of reanalyzed ice core data, using an advanced instrument, revealed the absence of anomalous noise levels previously detected.
- The study confirmed that the abrupt change in signal complexity observed in earlier analyses was indeed attributable to noise from an older laboratory instrument.
- Permutation entropy successfully identified anomalies in other data sections unrelated to climate, stemming from fieldwork, laboratory processing, or data handling.
Conclusions:
- Permutation entropy is a robust method for detecting and characterizing noise and other artifacts in paleoclimate data.
- The technique serves as a valuable forensic tool, pinpointing data sections that require targeted reanalysis.
- Permutation entropy can guide subsequent data analysis and improve the reliability of paleoclimate reconstructions.
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