Preprints
https://doi.org/10.5194/npg-2021-7
https://doi.org/10.5194/npg-2021-7

  05 Mar 2021

05 Mar 2021

Review status: a revised version of this preprint was accepted for the journal NPG and is expected to appear here in due course.

Comparing estimation techniques for timescale-dependent scaling of climate variability in paleoclimate time series

Raphaël Hébert1, Kira Rehfeld2, and Thomas Laepple1,3 Raphaël Hébert et al.
  • 1Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Telegrafenberg A45, 14473 Potsdam, Germany
  • 2Institut für Umweltphysik, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 229, 69120 Heidelberg, Germany
  • 3University of Bremen, MARUM – Center for Marine Environmental Sciences and Faculty of Geosciences, 28334 Bremen, Germany

Abstract. Characterizing the variability across timescales is important to understand the underlying dynamics of the Earth system. It remains challenging to do so from paleoclimate archives since they are more than often irregular and traditional methods to produce timescale-dependent estimates of variability such as the classical periodogram and the multitaper spectrum generally require regular time sampling. We have compared those traditional methods using interpolation with interpolation-free methods, namely the Lomb-Scargle periodogram and the first-order Haar structure function. The ability of those methods to produce timescale-dependent estimates of variability when applied to irregular data was evaluated in a comparative framework using surrogate paleo-proxy data generated with realistic sampling. The metric we chose to compare them is the scaling exponent, i.e. the linear slope in log-transformed coordinates, since it summarizes the behaviour of the variability across timescales. We found that for scaling estimates in irregular timeseries, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescale which are particularly affected by the irregularity. In addition, our results suggest that the Haar structure function is the safer choice of interpolation-free method since the Lomb-Scargle periodogram is unreliable when the underlying process generating the timeseries is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a paleoclimate timeseries, and that it is also possible that this changes as a function of timescale, it is a desirable characteristic for the method to handle both stationary and non-stationary cases alike.

Raphaël Hébert et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-7', Anonymous Referee #1, 01 Apr 2021
    • AC1: 'Reply on RC1', Raphael Hébert, 03 May 2021
  • RC2: 'Comment on npg-2021-7', Anonymous Referee #2, 01 May 2021
    • AC2: 'Reply on RC2', Raphael Hébert, 03 May 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-7', Anonymous Referee #1, 01 Apr 2021
    • AC1: 'Reply on RC1', Raphael Hébert, 03 May 2021
  • RC2: 'Comment on npg-2021-7', Anonymous Referee #2, 01 May 2021
    • AC2: 'Reply on RC2', Raphael Hébert, 03 May 2021

Raphaël Hébert et al.

Model code and software

RScaling Raphaël Hébert https://bitbucket.org/RphlHbrt/rscaling/src/master/

Raphaël Hébert et al.

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Short summary
Paleoclimate proxy data is essential to broaden our understanding of climate variability. There remains however challenges for traditional methods of variability analysis to be applied to such data which is usually irregular. We perform a comparative analysis of different methods of scaling analysis, which provide variability estimates as a function of timescales, applied to irregular paleoclimate proxy data.