EGU2020-1054, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using hierarchical dynamic time warping to synchronize age-uncertain (proxy) time series

Yuval Burstyn1,2 and Asaf Gazit
Yuval Burstyn and Asaf Gazit
  • 1Hebrew University of Jerusalem, Institute of Earth Sciences, Geology, Jerusalem, Israel (
  • 2Geological Survey of Israel, Jerusalem, Israel

Climate- and environmental-proxy time series obtained from different archives, such as speleothems, allowed for major leaps in the understanding of past climate and environmental dynamics. However, age uncertainties that arise from the applied dating techniques and from the proxy sampling methodologies, respectively, are often neglected. These age uncertainties are important when leads and lags between different proxy time series are examined or if the relationship to climate-forcing is investigated. This is most pronounced when examining data that detail events of sub-centennial down sub-annual resolution, where noise is not smoothed by a low resolution sampling (e.g. conventional dental drill), or in records karst systems where the noise is inherently high (e.g. water-limited environments).

We explore the use of dynamic time warping with a hierarchical aggregation layer (or HDTW) on multiple trajectories to generate an indexing table for the input samples. We hypothesize that this aggregation process results a temporally aligned references table (of the original trajectories) and allows for an analytical space to investigate and distinguish between local and non-local phenomena. We aim to compare sample derived features, such as peaks in trace element, organic fluorescence analyses and potentially δ18O (not tested here), on the derived analytical space, for the purpose of enabling a robust and simplified approach to multi-sample age modelling.

We show HDTW compatibility to existing peak-counting methodologies applied on laser-ablation trace element analysis and confocal fluoresce laser microscopy. As a case study, we use HDTW on three published micron-scale elemental measurements of samples from Mediterranean climates with strong dry summer – wet winter seasonality - two from south-western Australia (Nagra et al., 2017) and one from the Soreq Cave in the Eastern Mediterranean (Orland et al., 2014). The HDTW continuous space for these samples yields results that are within the published age constraints, without the need to stack multiple traverses and manually account for double or missing peaks.

HDTW is an important new tool for locating and identifying local and non-local phenomena in micron scale measurements (e.g. parallel laser ablation trace element traverses) by automatically aligning several coeval time axes of similar proxies. In the future HDTW could be applied for regional scale investigation (e.g. a coeval speleothems from a single cave or the same region, multiple cores from a single lake) allowing the unbiased fine-tuning between different environmental archives registering similar forcing mechanisms.

Nagra, G., Treble, P.C., Andersen, M.S., Bajo, P., Hellstrom, J.C., Baker, A., 2017. Dating stalagmites in Mediterranean climates using annual trace element cycles. Sci. Rep. 7, 621.

Orland, I.J., Burstyn, Y., Bar-Matthews, M., Kozdon, R., Ayalon, A., Matthews, A., Valley, J.W., 2014. Seasonal climate signals (1990–2008) in a modern Soreq Cave stalagmite as revealed by high-resolution geochemical analysis. Chem. Geol. 363, 322–333.

How to cite: Burstyn, Y. and Gazit, A.: Using hierarchical dynamic time warping to synchronize age-uncertain (proxy) time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1054,, 2019


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  • CC1: Comment on EGU2020-1054, Dana Riechelmann, 04 May 2020

    Hi Yuval,

    are age uncertainties are taken into account during the HDTW?



  • AC1: Comment on EGU2020-1054, Yuval Burstyn, 04 May 2020

    Accuracy and error - specifically for this demonstration we used speleothems with tightly constrained ages (in the case of flowstone we tested, it's has an earthquake marker, the sample we "borrowed" from Pauline is exactly 80 years old).
    We wanted to fine tune the sub-annual cycles, not the overall age model. When we did age modelling by peak counting, we usually implemented some way to test the sensitivity of a) sampling window b) original age uncertainties (say U/Th error or model evelope).
    That said, the HDTW aggregation/sync doesn't care about "age", it simply matches features (depth-time warping). To put it slightly more accurately - this is a "tool" to be implemented in your "software". For the demo we used it for accurate peak counting (without having to think too much on manually locating hidden or double peaks). We hope to use it for other implementations, but that'll take some more time and work (and collaborations).

    • CC2: Reply to AC1, Dana Riechelmann, 04 May 2020

      Thank you very much for the answer.

    • CC3: Reply to AC1, Mark Turner, 04 May 2020

      Do I understand that you used DTW within an already well-constrained age model? I have had poor experience of using DTW to generate long age models (~100 ka) by matching palaeoclimate records with e.g. Greenland d18O - as soon as it starts to go off track, that's it.

  • AC2: Comment on EGU2020-1054, Yuval Burstyn, 04 May 2020

    We show here well costrained results. The age model is just one implementation of it. Our "addition" is the hierarchical part, which, while not new, aggregates top-wise (as in Vaughan & Gabrys) without letting you examine local features, we needed that last option for a yet-unpublished sample (coming soon, we hope).

    You are probably familiar with the DTW to SPECMAP, insolation, GINP etc., we started out from the micro-scale, so while we are aware of the potential applications, we haven't tested it yet.

displays version 3 – uploaded on 04 May 2020, no comments
Corrected references.
displays version 2 – uploaded on 04 May 2020
  • CC1: Comment on EGU2020-1054, Mark Turner, 04 May 2020

    Could you give the reference for Vaughana and Gabrys 2016?

    • AC1: Reply to CC1, Yuval Burstyn, 04 May 2020



      It's -

      Vaughan, N., Gabrys, B., 2016. Comparing and Combining Time Series Trajectories Using Dynamic Time Warping. Procedia Comput. Sci. 96, 465–474.

      Thank you for spotting that mistake, I'll revise.

displays version 1 – uploaded on 04 May 2020, no comments