Hostname: page-component-76fb5796d-wq484 Total loading time: 0 Render date: 2024-04-26T09:26:08.766Z Has data issue: false hasContentIssue false
  • English
  • Français

SCALING THE 14C-EXCURSION SIGNAL IN MULTIPLE TREE-RING SERIES WITH DYNAMIC TIME WARPING

Published online by Cambridge University Press:  22 April 2022

Irina Panyushkina Open the ORCID record for Irina Panyushkina [Opens in a new window] ,
Valerie Livina ,
Mihály Molnár ,
Tamas Varga  and
A J Timothy Jull
Irina Panyushkina*
Affiliation:
Laboratory of Tree-Ring Research, University of Arizona, Tucson, AZ 85721, USA
Valerie Livina
Affiliation:
Data Science Department, National Physical Laboratory, Teddington, TW11 0LW, UK
Mihály Molnár
Affiliation:
Isotope Climatology and Environmental Research Centre (ICER), Institute for Nuclear Research, Debrecen 4026, Hungary
Tamas Varga
Affiliation:
Isotope Climatology and Environmental Research Centre (ICER), Institute for Nuclear Research, Debrecen 4026, Hungary
A J Timothy Jull
Affiliation:
Isotope Climatology and Environmental Research Centre (ICER), Institute for Nuclear Research, Debrecen 4026, Hungary Department of Geosciences and Department of Physics, University of Arizona, Tucson, AZ 85721, USA
*
*Corresponding author: Email: ipanyush@arizona.edu
Rights & Permissions [Opens in a new window]

Abstract

A signal of rapid changes in 14C production is logged in annual series of 14C derived from tree rings, which can be associated with diverse effects of cosmic-ray fluxes, including solar burst and supernova events. These 14C signatures may vary in time and space. The intensity and structure of the 14C signal is multifaced, which complicates understanding of the forcing and attribution of the underlying astrophysical events. It was suggested that Δ14C in 1052/53 CE and 1054/55 CE signatures at a 4‰–6‰ range over two years could be caused by the Crab Nebula supernova (SN1054) or/and solar perturbation. The temporal incoherence of the signals in published 14C series is investigated with dynamic time warping (DTW), novel approach for matching time-behavioral patterns in multiple 14C datasets. DTW analysis of four 14C signatures from tree rings of California, Finland and England suggests that 14C spikes between 1052 CE and 1055 CE can be caused by a single event. The flickering fingerprint may result from cross-dating inconformity. Cross-checking of tree-ring records from distant locations is impossible sometimes due to large difference in environmental conditions limiting tree growth. The methodology helps to align the signals and can be applied to other 14C datasets.

Keywords

14C excursionsDTW methodSEPsupernovatree rings
Type
Conference Paper
Information
Radiocarbon , Volume 64 , Issue 6: 3rd Radiocarbon in the Environment Conference Gliwice, Poland, July 5–9, 2021 , December 2022 , pp. 1587 - 1595
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press for the Arizona Board of Regents on behalf of the University of Arizona

INTRODUCTION

The annual rate of 14C production in the lower stratosphere-upper troposphere reflects the changes in cosmic-ray flux over time due to changes in the intensity of galactic cosmic rays and solar energetic particles, and solar-geomagnetic shielding (Burr Reference Burr, Elias and Mock2007). Variations of the 14C production rate are present in time series that can be derived from various terrestrial archives. Yet, annual series of 14C from tree rings are considered superior tracers of the 14C signal in time and space, since trees continuously use carbon dioxide gas from the air and ground water during photosynthesis, and distribution of the carbon isotopes has been almost completely mixed and homogenized over the globe (Poluianov et al. Reference Poluianov, Kovaltsov, Mishev and Usoskin2016; Wu et al. Reference Wu, Usoskin, Krivova, Kovaltsov, Baroni, Bard and Solanki2018).

So far, three confirmed solar energetic particle events (SEP 774 CE, 993 CE, 660 BCE, and 7176 BCE), one supernova (SN1056) and proposed solar-proton event (5259 BCE) have been confirmed with annual 14C series from numerous geographical locations and different AMS radiocarbon laboratories (Jull et al. Reference Jull, Panyushkina, Lange, Kukarskih, Clark, Myglan, Salzer, Burr and Leavitt2014; Fogtmann-Schulz et al. Reference Fogtmann-Schulz, Østbø, Nielsen, Olsen, Karoff and Knudsen2017; Büntgen et al. 2018; Terrasi et al. Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020; Sakurai et al. Reference Sakurai, Tokanai and Miyake2020; Brehm et al. Reference Brehm, Christl and Adolphi2021a; Paleari et al. Reference Paleari, Mekhaldi and Adolphi2022). Four other proposed 14C excursions (1279 CE, 813 BCE, 5480 BCE, and 5410 BCE) need to be corroborated with data from multiple locations or need to be confirmed (Miyake et al. Reference Miyake, Jull, Panyushkina, Wacker, Salzer, Baisan, Lange, Cruz, Masuda and Nakamura2017, Reference Miyake, Panyushkina, Jull, Helama, Kanzawa, Moriya, Nicolussi, Oinonen, Salzer, Tokanai, Takeyama and Wacker2021; Jull et al. Reference Jull, Panyushkina, Miyake, Masuda, Nakamura, Lange, Cruz, Baisan, Janovics, Varga and Molnár2018; Brehm et al. Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b).

It is known that manifestations of the same astrophysical event in carbon-14 may occur with delays in timing and variation in intensity across different parts of the globe. Büntgen et al. (2018) clearly demonstrated the meridional decline of 11-year mean atmospheric 14C concentrations across the northern and southern hemispheres in a case study of SEP 774 CE and 993 CE. Studies of possible supernova events have been hampered by inconclusive patterns in the 14C signal response due to smaller effects than in the SEP cases and temporal discord in the variation of Δ14C (Dee et al. Reference Dee, Pope, Miles, Manning and Miyake2016; Eastoe et al. Reference Eastoe, Tucek and Touchan2019; Brehm et al. Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b). Yet, the increase of production rate in the 14C signatures around supernova appearances cannot be explained by the Schwabe cycle alone (Terrasi et al. Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020; Brehm et al. Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b). It seems that the SN-signal in the rapid change in 14C production is less comprehensible and different from highly coherent SEP-signal in Δ14C series forced by solar proton radiation.

The γ-ray burst produced by a supernova explosion arrives to the Earth at the same time as the visible light, which is documented by historical archives describing the appearance of a guest or new star (Pavlov et al. Reference Pavlov, Blinov, Vasilyev, Vdovina, Volkov, Konstantinov and Ostryakov2013). The mean interval between supernova events in the Milky Way galaxy is not that long and is estimated at 40 years ± 10 yr (Tammann et al. Reference Tammann, Löffler and Schröder1994). Cosmic rays accelerated at supernova remnant shocks change the chemistry of the atmosphere through ionization and dissociation of O3 and N2 which lead to the formation of nitrogen oxide compounds (Thomas and Melott Reference Thomas and Melott2006). Damon et al. (Reference Damon, Kaimei, Kocharov, Mikheeva and Peristykh1995) searched for the effects of γ-ray bursts produced by supernovae in 14C records and determined an upper limit of 6‰ in Δ 14C for a large SN event within our Galaxy (Damon et al. Reference Damon, Kaimei, Kocharov, Mikheeva and Peristykh1995). Back then the error of 14C measurements produced by beta-counting systems used to be high around 4‰ (Cook and van der Plicht Reference Cook, van der Plicht, Elias and Mock2013). The precision of modern AMS 14C measurements has significantly improved the errors and reduced the estimate for the background concentration to 1.3–2‰ (Wacker et al. Reference Wacker, Bonani, Friedrich, Hajdas, Kromer, Nemec, Ruff, Suter and Synal2010). The AMS records and the finer temporally resolved series of 14C from tree rings may assist in improving the attribution of SN signal in 14C series.

A recent investigation of the effect of the Crab Nebula supernova (SN1054) in the annual rate of 14C production found a Δ 14C excursion at 1054/55 CE of 4‰ at tree-ring archives from Sierra Nevada Mountains and Finnish Lapland (Terrasi et al. Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020). However, other studies of tree rings from Central England and another location in the Sierra Nevada Mountains (California) report much higher 14C ranges for this time interval (up to 6‰) but in 1053 CE, one year prior to the SN1054 visible light arrival to the Earth atmosphere, and attribute it to a possible SEP event (Eastoe et al. Reference Eastoe, Tucek and Touchan2019; Brehm et al. Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b). Brehm et al (Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b) suggested this designation for a 6‰ change from 1052 CE to 1053 CE (1 yr) or a ca. 8‰ variation from 1052 CE to 1055 CE (3 yr). This research points to the interval 1052–1055 CE that may have two separate 14C spikes causes by two different cosmic events. Interestingly, some tree-ring series did not show any significant rise in Δ 14C at that time (see Güttler et al. Reference Güttler, Wacker, Kromer, Friedrich and Synal2013) and others record a slight intensification of 14C production (Menjo et al. Reference Menjo, Miyahara, Kuwana, Masuda, Muraki and Nakamura2005). Dee et al. (Reference Dee, Pope, Miles, Manning and Miyake2016) examined the Δ14C response in tree-ring series covering the timing of four historical supernovae and found no significant increase in Δ14C tree-ring series around 1054 CE. It appears that the SN signature in annual 14C series is more difficult to identify than SEP signatures.

In this paper, we employ a novel technique of dynamic time warping (DTW) to 14C annual series in order to investigate the temporal coherence of the published 14C series for 1053(52)–55CE signature from four tree-ring archives at three locations (Finland, California, and England) from published literature (Brehm et al. Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b; Eastoe et al. Reference Eastoe, Tucek and Touchan2019; Terrasi et al. Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020). The DTW algorithm performs matching of similar patterns in datasets that may not be coeval. The method allows stretching or compressing sub-sections of the time axis at different scales to minimize and quantify the dissimilarity between the compared time series, as discussed by Izakian et al. (Reference Izakian, Pedrycz and Jamal2015).

METHODS

We evaluate the temporal coherence of five 14C series with 1052 CE/1054 CE signatures developed independently and published by Eastoe et al. (Reference Eastoe, Tucek and Touchan2019), Terrasi et al. (Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020), and Brehm et al. (Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b) (Figure 1a). The tree rings are originated from four locations (Figure 1b). At one location we measured 14C in earlywood (EW) and latewood (LW) of the rings separately. Supplementary material includes the original 14C datasets used in this study. All details concerning the tree-ring sampling, 14C measurements and Δ14C calculation can be found in the relevant publications. The records of three tree species are positioned in the coordinate box 36°N, 68°N and 118°W, 28°E, with an elevational gradient between 191 m asl and 1890 m asl (Figure 1b). The wood is sourced from living trees (sequoia), buried subfossils (pine) and historical timber (oak). The tree-ring records of Sequoiadendron giganteum come from two nearby sites in the Sierra Nevada Mountains, California (36.56°N, 118.75°W, 1890 m asl and 36.75°N, 118.97°W, 1850 m asl), Pinus sylvestris from Finnish Lapland (68.31°N, 28.09°E, 191 m asl) and English oak from the UK historical buildings near Hertfordshire and Greater Manchester with unknown harvesting locations in the area (51.75°N, 0.34°W for STA-C34 specimen and 53.41°N, 2.15°W for STK-C10 specimen). Annually resolved 14C data in addition to one location with sub-annually earlywood and latewood series of sequoia were examined for the period 1037–1067 CE (30 yr) with DTW.

Figure 1 14C dataset used in the study. Left: annual series of Δ 14C attributed to possible SN1054 or SEP events. Color code of the curves: red—Full Ring Sequoia (Seq) from Eastoe et al. (Reference Eastoe, Tucek and Touchan2019); blue—EW Seq, brown—LW Seq and magenta—Finnish Pine from Terrasi et al. (Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020); green—English oak from Brehm et al. (Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b). Shaded area is the signature interval 1052–1055 CE. Right: map of the tree-ring data locations. (See online version for color figures.)

DTW is an approach of machine learning (ML) and artificial intelligence (AI) for data analysis that is gaining popularity with increasing computational power, although many of the ML and AI techniques were developed in the 1960s–1980s. DTW was introduced by Sakoe and Chiba (Reference Sakoe and Chiba1978) for spoken-word recognition, where a warping time function is used for optimal dynamic programming. Paliwal et al. (Reference Paliwal, Agarwal and Sinha1982) proposed an improvement of the method with removal of the warping function slope constraints. Recent improvements introduced weights for uncertainty accounting (see Zuo and Yan Reference Zuo and Yan2018). DTW is a generalization of a point-wise comparison of records based on Euclidean distance. Instead of “rigid” comparison of correlation, DTW allows “elastic” matching of patterns by means of minimizing the differences between time series with added time intervals, thus allowing variability in time by means of DTW alignment.

We use the Matlab built-in function for implementation of DTW (https://uk.mathworks.com/help/signal/ref/dtw.html) that performs the pairwise transformation and synchronization of time series as described in Aribas-Gil and Muller (Reference Arribas-Gil and Mueller2014). In our context, the pairs of time series are the tree-ring 14C series to be compared. Our goal is to achieve the alignment of the time series with optimized matching of the entire sequence (Aribas-Gil and Muller Reference Arribas-Gil and Mueller2014). Given two time series $$A = \left( {{a_1},\; \ldots ,\;{a_n}} \right)$$ and $$B = \left( {{b_1},\; \ldots ,\;{b_m}} \right)$$ , we construct an nxm matrix of Euclidean distances of all pairs of (i,j) between the elements of A and B. The warping path W is a set the matrix elements $$W = \left( {{w_1},\; \ldots ,\;{w_k}} \right),\max \left( {m,n} \right) \le K \le m + n - 1$$ , satisfying the boundary condition and conditions of continuity and monotonicity required of a path (and there are multiple paths of this kind). The optimal path is obtained as

$$DTW\left( {A,B} \right) = argmi{n_{W = {w_1},\; \ldots ,\;{w_K}}}\sqrt {\mathop \sum \limits_{k \,=\,1}^K {{({a_i} - {b_j})}^2}} $$

where A and B are two 14C series, a and b refer to the 14C values, w represents time weights that account for the optimized alignment, k is the index of the alignment pairs. The DTW algorithm measures similarity between time series where oscillations vary in speed and power. In principle, this method can evaluate time-series variance over a period of several decades to several thousand years. We hypothesize that the cosmic excursion effect could be detected in Δ14C series from years with extreme variance such as year-to-year spikes. The DTW approach aligns the selected 14C series that register similar signal within different time-response window. Analyzing the dynamics of time series of interest with shape-based DTW distance determines a close relationship between the time series exhibiting a similar patterns in 14C production rate presented in tree rings at different years.

RESULTS

The studied signature in the annual 14C series appears as a rapid increase of Δ14C, the peak is sustained for the next one to two years and is followed by a 2-yr decay (Figure 1a). The range of Δ14C in the signature varies from –10.59‰ and –7.83‰ (1052–53 CE) and –2.9‰ and –0.91‰ (1054–55 CE). The standard deviation (1σ) for the sequoias and Finnish pine series is typical between 2‰ and 2.09‰. The English oak series has a much smaller measurement error of 1.13‰–1.6‰. The increment of Δ14C at the first-year increase is between 7.5‰ and 3.8‰, which is a significant change. However, the peak signature occurs at different year: 1054 CE or 1055 CE. Brehm et al. (Reference Brehm, Bayliss, Christl, Synal, Adolphi, Beer, Kromer, Muscheler, Solanki, Usoskin, Bleicher, Bollhalder, Tyers and Wacker2021b) named the signature “1052 CE event” observed in the English oak and Full-Ring Sequoia (Eastoe et al. Reference Eastoe, Tucek and Touchan2019). Its attribution to a possible SEP occurred during the Oort solar minimum (1021–1060 CE) has yet not been confirmed, while Terrasi et al. (Reference Terrasi, Marzaioli, Passariello, Porzio, Capano, Helama, Oinonen, Nöjd, Uusitalo, Jull, Panyushkina, Baisan, Molnar, Kovaltsov, Poluianov and Usoskin2020) attributed a similar-structured signature with 1-yr shift from three 14C series possibly to γ-rays from the Crab Nebula supernova (SN1054), an unusually weak solar minimum or a SEP.

We applied DTW to compare the 1054 CE event versa the 1052 CE event in pairing 14C tree-ring time series over the 30-yr interval: 1037–1067 CE. Firstly, the English oak 14C series was paired with four other 14C sets. Figure 2 shows the original series and globally aligned-signal series with the calculated Euclidean distance. Second, we aligned the 14C series of sequoia from California (EW, LW, and Full Ring). Figure 3 shows the best aligned series from this test. Finally, the sequoia and Finnish pine 14C series were processed in the same way (not shown). DTW is often used as a distance measure (not a norm, as the triangular inequality is not fulfilled) in cauterization of time series sets (Kate Reference Kate2016). The DTW analysis suggests that the signatures of 1054 CE event and 1052 CE event are matching and manifest the same pattern in the studied time series, but they do not coincide in time. Moreover, the 14C time series from Finnish pine has the closest relation to all series, while the English oak and LW Sequoia are most remote time series (Table 1). Instead of a “rigid” comparison, DTW allows “elastic” matching of signals with minimization of the differences between time series via added time intervals, thus allowing the alignment of variance. Sequoia 14C series of EW and LW from the same ring demarcate two different Δ14C patterns that can be aligned by DTW (Figure 3). The difference in the 14C variance of these series possibly results from the different time scales. Early wood is mostly responsive to May–June moisture driving by the snowmelt, while the late wood formation occurs in July–August extremely dry season (Hughes and Brown Reference Hughes and Brown1992).

Figure 2 DTW-matching of 1054 CE event/1052 CE-event signal between the English oak (blue line) and other (red line) studied locations and AMS faculties: Isotope Climatology and Environmental Research Centre, Debrecen, Hungary (ICER), Center for Isotopic Research on the Cultural and Environmental heritage University of Naples, Italy (CIRCE), NSF-Arizona AMS facility, University of Arizona, USA (Arizona), Laboratory of Ion Beam Physics, Swiss Federal Institute of Technology in Zürich, Switzerland (ETHZ). In each of four blocks of the figure, the top panels display the original series to be compared for the interval 1038–1068 CE. The low panels show the best global alignment of the signal between 14C annual series with added time points. (See online version for color figures.)

Figure 3 DTW-matching of 1054CE event/1052 CE event signal between Sequoia series from two locations in California and two AMS labs. The top panel displays the original series, and the bottom plot denotes the best global alignment of the signal in the paired 14C annual series with added time points. (See online version for color figures.)

Table 1 Euclidean distance between DTW paired 14C series. Seq refers to Sequoia.

Analysis of the lagged cross-correlation for the 14C series suggests that there is a two-year lag between the English oak and California EW-LW sequoia series (Figure 4, right plot). Perhaps, this two-year offset is caused by cross-dating nonconformity between these two tree-ring chronologies. It is surprising that the 1052 CE event signal showed up in other California sequoia record since its chronology matched well with the EW and LW sequoia records with the 1054 CE signal. DTW analysis demonstrates that 14C variance amplification at both 1052 CE and 1054 CE is caused by the impact of a single event. The inconsistent fingerprint of the 1054 CE event in time across the space may result from cross-dating nonconformity, regional (local) flux of old carbon at the northern site (Finland) due to intensified permafrost melt and ancient CO2 release when the tree grew during the Medieval Warm Epoch (Feng et al. Reference Feng, Vonk, van Dongen, Gustafsson, Semiletov, Dudarev, Wang, Montluçon, Wacker and Eglinton2013; Wild et al. Reference Wild, Andersson, Bröder, Vonk, Hugelius and McClelland2019; Zhang et al. Reference Zhang, Bianchi, Hanna, Shields, Izon, Hutchings, Ping, Kanevskiy, Haghipour and Eglinton2021) and/or large latitude gradient (e.g., Burr Reference Burr, Elias and Mock2007; Büntgen et al. 2018; Uusitalo et al. Reference Uusitalo, Arppe and Hackman2018). Clearly, the 1052–1054 CE signature is a unique increase in 14C production rate and warrants further investigation.

Figure 4 Cross-correlation of considered time series at different lags. Left panel: cross-correlation between Finnish pine and EW Sequoia (blue line) and between Finnish pine and LW Sequoia (red line). Right panel: cross-correlation between English oak and EW Sequoia (blue line) and between English oak and LW Sequoia (red line). The sharp peaks denote the lags at which the time series have the highest cross-correlations confirming the DTW analysis and the shift in the timing of the 14C events. (See online version for color figures.)

CONCLUSIONS

Our first-time application of a DTW approach to 14C annual datasets has shown that it is possible to scale and match similar patterns in the Δ14C variance with the signal placed chronologically apart and originated by an astrophysical impact (γ-ray or solar protons). DTW analysis effectively measures the distance with respect of signal separated in time. Temporal separation of the signal can be attributed to complex cross-dating of various wood sources and continues progress in the development of multimillennial tree-ring chronologies. Another source of transient errors can arise from instrumental bias and occur during ring sampling, cellulose processing, and digitization of datasets.

Whatever the reason for the chronological divergence of signals, DTW can identify divergencies of the discording signals. Our DTW analyses demonstrates an effective way of determining the distance between individual 14C series (points in space) with focusing on the similarity of their temporal (not chronological) behavior. In this case study, we cannot attribute the observed 14C signature between 1052/53–1056 CE to either SN1054 or a possible SEP event. Moreover, this signature and the offset in the 14C production rate should be revisited and investigated with larger spatial datasets.

ACKNOWLEDGMENTS

This research was supported in part by U.S. NASA Grant # 80NSSC21K1426 and the European Union and the State of Hungary co-financed by the European Regional Development Fund in the project of GINOP-2.3.4-15-2020-00007 “INTERACT”. The ICER lab was co-financed by the European Regional Development Fund project # GINOP-2.3.2-15-2016-00009 ICER.

CONFLICT OF INTEREST

Dr. Jull has disclosed an outside interest in Hungarian and Czech Academies of Sciences to the University of Arizona. Conflicts of interest resulting from this interest are being managed by the University of Arizona in accordance with its policies.

Supplementary Material

To view supplementary material for this article, please visit https://doi.org/10.1017/RDC.2022.25

Footnotes

Selected Papers from the 3rd Radiocarbon in the Environment Conference, Gliwice, Poland, 5–9 July 2021

References

REFERENCES

Arribas-Gil, A, Mueller, H. 2014. Pairwise dynamic time warping for event data. Computational Statistics and Data Analysis 69:255–268.CrossRefGoogle Scholar
Brehm, N, Christl, M, Adolphi, F et al. 2021a. Tree rings reveal two strong solar proton events in 7176 and 5259 BCE. Preprint doi: 10.21203/rs.3.rs-753272/v1.CrossRefGoogle Scholar
Brehm, N, Bayliss, A, Christl, M, Synal, H-A, Adolphi, F, Beer, J, Kromer, B, Muscheler, R, Solanki, SK, Usoskin, I, Bleicher, N, Bollhalder, S, Tyers, C, Wacker, L. 2021b. Eleven-year solar cycles over the last millennium revealed by radiocarbon in tree rings. Nature Geoscience 14:10–15. doi: 10.1038/s41561-020-00674-0.CrossRefGoogle Scholar
Büntgen U and others. 2018. Tree rings reveal globally coherent signature of cosmogenic radiocarbon events in 774 and 993 CE. Nature Communication 9:3605. doi: 10.1038/s41467-018-06036-0.CrossRefGoogle Scholar
Burr, GS. 2007. Radiocarbon dating: causes of temporal variations. In: Elias, SA, Mock, CJ editors. Encyclopedia of Quaternary science. Oxford: Elsevier. p. 2931–2941. doi: 10.1016/B0-44-452747-8/00044-2.CrossRefGoogle Scholar
Cook, GT, van der Plicht, J. 2013. Radiocarbon dating—conventional method. In: Elias, SA, Mock, CJ, editors. Encyclopedia of Quaternary science. 2nd edition. 305–315. Amsterdam: Elsevier. doi: 10.1016/B978-0-444-53643-3.00048-0.CrossRefGoogle Scholar
Damon, PE, Kaimei, D, Kocharov, GE, Mikheeva, IB, Peristykh, AN. 1995. Radiocarbon production by the gamma-ray component of supernova explosions. Radiocarbon 37(2):599–604.CrossRefGoogle Scholar
Dee, MW, Pope, B, Miles, D, Manning, S, Miyake, F. 2016. Supernovae and single-year anomalies in the atmospheric radiocarbon record. Radiocarbon 59(2):293–302.CrossRefGoogle Scholar
Eastoe, CJ, Tucek, CS, Touchan, R. 2019. Δ14C and δ13C in annual tree-ring samples from Sequoiadendron Giganteum, AD 998–1510: solar cycles and climate. Radiocarbon 61(3):661–680. doi: 10.1017/RDC.2019.27.CrossRefGoogle Scholar
Feng, X, Vonk, JA, van Dongen, BE, Gustafsson, O, Semiletov, IP, Dudarev, OV, Wang, Z, Montluçon, DB, Wacker, L, Eglinton, TI. 2013. Differential mobilization of terrestrial carbon pools in Eurasian Arctic river basins. PNAS 110(35):14168–14173. doi: 10.1073/pnas.1307031110.CrossRefGoogle ScholarPubMed
Fogtmann-Schulz, A, Østbø, SM, Nielsen, SGB, Olsen, J, Karoff, C, Knudsen, MF. 2017. Cosmic-ray event in 994 CE recorded in radiocarbon from Danish Oak. Geophysical Research Letters 44(16):8621–8628. doi: 10.1002/2017GL074208.CrossRefGoogle Scholar
Güttler, D, Wacker, L, Kromer, B, Friedrich, M, Synal, H-A. 2013. Evidence of 11-year solar cycles in tree rings from 1010 to 1110 AD—progress on high precision AMS measurements. Nuclear Instruments and Methods in Physics Research B 294:459–463.CrossRefGoogle Scholar
Hughes, MK, Brown, PM. 1992. Drought frequency in central California since 101 B.C. recorded in giant sequoia tree rings. Climate Dynamics 6(3–4):161–167.CrossRefGoogle Scholar
Izakian, H, Pedrycz, W, Jamal, I. 2015. Fuzzy clustering of time series data using dynamic time warping distance. Engineering Appl. Artificial Intelligence 39:235–244.CrossRefGoogle Scholar
Jull, AJT, Panyushkina, IP, Lange, TE, Kukarskih, VV, Clark, KJ, Myglan, VS, Salzer, M, Burr, GS, Leavitt, SL. 2014. Excursions in the 14C record at AD 774–775 from tree rings from Russia and America. Geophysical Research Letters 41(8):3004–3010. doi: 10.1002/2014GL059874.CrossRefGoogle Scholar
Jull, AJT, Panyushkina, IP, Miyake, F, Masuda, K, Nakamura, T, Lange, TE, Cruz, RJ, Baisan, C, Janovics, R, Varga, T, Molnár, M. 2018. More rapid carbon-14 excursions in the tree-ring record: A record of different kind of solar activity at about 800 BC? Radiocarbon 60(4):1237–1248. doi: 10.1017/RDC.2018.53.CrossRefGoogle Scholar
Kate, R. 2016. Using dynamic time warping distances as features for improved time series classification. Data Mining and Knowledge Discovery 30(2):283–312.CrossRefGoogle Scholar
Miyake, F, Jull, AJT, Panyushkina, IP, Wacker, L, Salzer, M, Baisan, C, Lange, T, Cruz, R, Masuda, K, Nakamura, T. 2017. Large 14C excursion in 5480 BC indicates an abnormal sun in the mid-Holocene. PNAS Physical Sciences—Earth, Atmospheric, and Planetary Sciences 114 (5):881–884. doi: 10.1073/pnas.1613144114.Google Scholar
Miyake, F, Panyushkina, IP, Jull, AJT, Helama, S, Kanzawa, K, Moriya, T, Nicolussi, K, Oinonen, M, Salzer, M, Tokanai, F, Takeyama, M, Wacker, L. 2021. A single-year cosmic ray event of 5410 BCE registered in 14C of tree rings. Geophysical Research Letters 48 (11):e2021GL093419. doi: 10.1029/2021GL093419.CrossRefGoogle ScholarPubMed
Menjo, H, Miyahara, H, Kuwana, K, Masuda, K, Muraki, Y, Nakamura, T. 2005. Possibility of the detection of past supernova explosion by radiocarbon measurement. International Cosmic Ray Conference 2:357.Google Scholar
Paleari, CI, Mekhaldi, F, Adolphi, F et al. 2022. Cosmogenic radionuclides reveal an extreme solar particle storm near a solar minimum 9125 years BP. Nature Communicaton 13(214). doi: 10.1038/s41467-021-27891-4.CrossRefGoogle Scholar
Paliwal, K, Agarwal, A, Sinha, S. 1982. A modification over Sakoe and Chibas dynamic time warping algorithm for isolated word recognition. Signal Processing 4(4):329–333.CrossRefGoogle Scholar
Pavlov, AK, Blinov, AV, Vasilyev, GI, Vdovina, MA, Volkov, PA, Konstantinov, AN, Ostryakov, VM. 2013. Gamma-ray bursts and the production of cosmogenic radionuclides in the Earth’s Atmosphere. Astronomy Letters 39(9):571–577.CrossRefGoogle Scholar
Poluianov, SV, Kovaltsov, GA, Mishev, AL, Usoskin, IG. 2016. Production of cosmogenic isotopes 7Be, 10Be, 14C, 22Na, and 36Cl in the atmosphere: altitudinal profiles of yield functions. J. Geophys Res Atmos 121:8125–8136. doi: 10.1002/2016JD025034.CrossRefGoogle Scholar
Sakoe, H, Chiba, S. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing ASSP-26:1.Google Scholar
Sakurai, H, Tokanai, F, Miyake, F et al. 2020. Prolonged production of 14C during the ∼660 BCE solar proton event from Japanese tree rings. Scientific Reports 10:660. doi: 10.1038/s41598-019-57273-2.CrossRefGoogle ScholarPubMed
Tammann, GA, Löffler, W, Schröder, A. 1994. The galactic supernova rate. Astrophysical Journal Supplement Series 92:487–493.CrossRefGoogle Scholar
Terrasi, F, Marzaioli, F, Passariello, I, Porzio, G, Capano, M, Helama, S, Oinonen, M, Nöjd, P, Uusitalo, J, Jull, AJT, Panyushkina, IP, Baisan, C, Molnar, M, Kovaltsov, G, Poluianov, S, Usoskin, I. 2020. Can the 14C anomaly in 1054 CE be due to SN1054? Radiocarbon. doi: 10.1017/RDC.2020.58.CrossRefGoogle Scholar
Thomas, BC, Melott, AL. 2006. Gamma-ray bursts and terrestrial planetary atmospheres. New Journal of Physics 8(7):120. doi: 10.1088/1367-2630/8/7/120.CrossRefGoogle Scholar
Uusitalo, J, Arppe, L, Hackman, T et al. 2018. Solar superstorm of AD 774 recorded subannually by Arctic tree rings. Nature Communication 9:3495. doi: 10.1038/s41467-018-05883-1.CrossRefGoogle ScholarPubMed
Wacker, L, Bonani, G, Friedrich, M, Hajdas, I, Kromer, B, Nemec, M, Ruff, M, Suter, M, Synal, H-A, Vockenhuber. 2010. MICADAS: routine and high-precision radiocarbon dating. Radiocarbon 52(2–3):252–262. doi: 10.1017/S0033822200045288.CrossRefGoogle Scholar
Wild, B, Andersson, A, Bröder, L, Vonk, J, Hugelius, G, McClelland, JW, et al. 2019. Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. PNAS 116(21):10280–10285. doi: 10.1073/pnas.1811797116.CrossRefGoogle ScholarPubMed
Wu, C-J, Usoskin, I, Krivova, N, Kovaltsov, G, Baroni, M, Bard, E, Solanki, S. 2018. Solar activity over nine millennia: A consistent multi-proxy reconstruction. Physics, Astronomy & Astrophysics: A&A 615, A93. doi: 10.1051/0004-6361/201731892.CrossRefGoogle Scholar
Zhang, X, Bianchi, TS, Hanna, AJM, Shields, MR, Izon, G, Hutchings, JA, Ping, C-L, Kanevskiy, M, Haghipour, N, Eglinton, TI. 2021. Recent warming fuels Increased organic carbon export from Arctic permafrost. AGU Advances 2(2):e2021AV000396. doi: 10.1029/2021AV000396.CrossRefGoogle Scholar
Zuo, L, Yan, L. 2018. A weighted DTW approach for similarity matching over uncertain time series. Journal of Computing and Information Technology 26(3):179–190.Google Scholar
Figure 0

Figure 1 14C dataset used in the study. Left: annual series of Δ 14C attributed to possible SN1054 or SEP events. Color code of the curves: red—Full Ring Sequoia (Seq) from Eastoe et al. (2019); blue—EW Seq, brown—LW Seq and magenta—Finnish Pine from Terrasi et al. (2020); green—English oak from Brehm et al. (2021b). Shaded area is the signature interval 1052–1055 CE. Right: map of the tree-ring data locations. (See online version for color figures.)

Figure 1

Figure 2 DTW-matching of 1054 CE event/1052 CE-event signal between the English oak (blue line) and other (red line) studied locations and AMS faculties: Isotope Climatology and Environmental Research Centre, Debrecen, Hungary (ICER), Center for Isotopic Research on the Cultural and Environmental heritage University of Naples, Italy (CIRCE), NSF-Arizona AMS facility, University of Arizona, USA (Arizona), Laboratory of Ion Beam Physics, Swiss Federal Institute of Technology in Zürich, Switzerland (ETHZ). In each of four blocks of the figure, the top panels display the original series to be compared for the interval 1038–1068 CE. The low panels show the best global alignment of the signal between 14C annual series with added time points. (See online version for color figures.)

Figure 2

Figure 3 DTW-matching of 1054CE event/1052 CE event signal between Sequoia series from two locations in California and two AMS labs. The top panel displays the original series, and the bottom plot denotes the best global alignment of the signal in the paired 14C annual series with added time points. (See online version for color figures.)

Figure 3

Table 1 Euclidean distance between DTW paired 14C series. Seq refers to Sequoia.

Figure 4

Figure 4 Cross-correlation of considered time series at different lags. Left panel: cross-correlation between Finnish pine and EW Sequoia (blue line) and between Finnish pine and LW Sequoia (red line). Right panel: cross-correlation between English oak and EW Sequoia (blue line) and between English oak and LW Sequoia (red line). The sharp peaks denote the lags at which the time series have the highest cross-correlations confirming the DTW analysis and the shift in the timing of the 14C events. (See online version for color figures.)

Supplementary material: File

Panyushkina et al. supplementary material

Panyushkina et al. supplementary material

Download Panyushkina et al. supplementary material(File)
File 13.8 KB