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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
PubMed
Summary
This summary is machine-generated.

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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:

Keywords:
anomaly detectionice corepaleoclimatepermutation entropy

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  • 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.