the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Feb 2024
The Paleochrono-1.1 probabilistic model to derive optimized and consistent chronologies for several paleoclimatic sites
Abstract. Past climate and environmental changes can be reconstructed using paleoclimate archives such as ice cores, lake and marine sediment cores, speleothems, tree rings and corals. The dating of these natural archives is crucial for deciphering the temporal sequence of events and rates of change during past climate changes. It is also essential to provide quantified estimates of the absolute and relative errors associated with the inferred chronologies. However, this task is complex since it involves combining different dating approaches at different paleoclimatic sites and often on different types of archives. Here we present Paleochrono-1.1, a new probabilistic model to derive a common and optimised chronology for several paleoclimatic sites with potentially different types of archives. Paleochrono-1.1 is based on the inversion of an archiving model: a varying deposition rate (also named growth rate, sedimentation rate or accumulation rate) and also, for ice cores, a lock-in-depth of air (since, in the absence of significant surface melt, the air is trapped in the ice at about 50–120 m below the surface) and a thinning function (since glacier ice undergoes flow). Paleochrono-1.1 integrates several types of chronological information: prior knowledge of the archiving process, independently dated horizons, depth intervals of known duration, undated stratigraphic links between records, and, for ice cores, Δdepth observations (depth differences between events recorded synchronously in the gas and solid phases of a certain core). The optimization is formulated as a least-squares problem, assuming that all probability densities are near-Gaussian and that the model is nearly linear in the vicinity of the best solution. Paleochrono-1.1 is the successor of IceChrono, which produces common and optimized chronologies for ice-cores. Paleochrono1.1 outperforms IceChrono in terms of computational efficiency, ease of use, and accuracy. We demonstrate the ability of Paleochrono-1.1 in a new ice-core–speleothem dating experiment, which combines the Antarctic Ice Core Chronology 2023 dating experiment, based on records from five polar ice cores, with data from two speleothems dated using uranium/thorium radiometric techniques from Hulu Cave (China). We analyse the performance of Paleochrono-1.1 in terms of computing time and memory usage in various dating experiments. Paleochrono-1.1 is freely available under the MIT open-source license.
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RC1: 'Comment on egusphere-2023-2911', Anonymous Referee #1, 14 Mar 2024
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Review of the manuscript “The Paleochrono-1.1 probabilistic model to derive optimized and consistent chronologies for several paleoclimatic sites” by Parrenin et al., submitted to Geoscientific Model Development
General comment:
This is an interesting, well-written paper presenting a new probabilistic model (Paleochrono-1.1) to derive a common and optimised chronology for several paleoclimatic sites with potentially different types of archives. This is an important modelling question, which definitely fits within the scope of GMD. The new model builds on a previous model (IceChrono), but has substantial advantages compared with its predecessor (computational efficiency, ease of use, accuracy), which are clearly outlined and discussed in the paper, and can thus be considered as a substantial advance in terms of age modelling (in particular to combine different types of information from several archives).
All methods and assumptions appear valid and are clearly outlined, and the detailed description enables the reader to reproduce the results. The results of the example presented in the paper nicely demonstrate the general potential of the model. All conclusions are supported by the results.
The authors mention several aspects of the model (e.g., that it can also be used to construct age models for individual sites, the risk of choosing incorrect values for error bars and correlation lengths, etc.). The paper (and in particular the non-expert readers, who should use the model later) would strongly benefit from a more detailed explanation how to choose these values and their effect on the modelling results. Thus, I recommend to either present more examples or to calculate the same example with different choices of the parameters (see below for further details).
In summary, I highly recommend publication in GMD, but I am convinced that the paleoclimate community would benefit from a more detailed illustration based on several examples. Below, I also list some other minor comments that may be useful to further improve the paper.
Detailed comments:
It may be good to modify the title to better illustrate the potential of the model to the general reader. The current title – in my opinion – does not illustrate that the model derives a common, combined age model from several sites using stratigraphic links, etc.
Line 49: I would not consider speleothems as an archive with a continuous deposition process. We meanwhile know that many speleothems show various hiatuses (ranging from a few years to tens or hundreds of ka), and their growth thus rather needs to be considered as episodic. Later, it becomes obvious that the authors are aware of this, but it may be useful to state this major difference to, e.g., ice cores right at the beginning.
Line 58 ff.: Maybe mention the various, very sophisticated methods used in dendrochronology here.
Line 127: “Paleochrono-1.1 does not integrate information regarding hiatuses, …” Even if this is true, as mentioned further down, hiatuses can be included: “If there is a known hiatus in the archive, the sections before and after the hiatus should be considered as two different sites in Paleochrono-1.1.” This means that hiatuses will not be detected by the model, but they can be included. This should be clarified and may be very important for archives like speleothems (see above).
Line 134: “… simple archives, with one unique depth age relationship …” This is not clear to me. What does unique mean in this context. Please clarify.
Line 151 ff.: “Uncertainties on the prior estimates and on the observations are assumed to be Gaussian …” This may be problematic for old (i.e., > 200 ka) U-series ages, where the errors become asymmetric. This should be included later, when, e.g., non-Gaussian uncertainties of 14C ages are mentioned.
Line 308 ff.: “We assume also a correlation length of 1,000 yr for the deposition rate of both speleothems, assuming higher frequency variations are absent.” This is OK for this paper, but it may be noteworthy that the Asian speleothem d18O records often show a correlation on the precession time scale. Would this have an effect of the results?
Line 317 ff.: “We assign a constant uncertainty (1σ) of 100 yr to these synchronisation horizons. 100 yr is a rough estimate of the synchronisation error during DO transitions.” This is not clear to me. What is the effect of this uncertainty in the end? What would happen if the chosen value was too small? It may be interesting to demonstrate and discuss the effect for different values. Alternatively, more information should be provided to assist the readers how to choose this value.
Line 331 ff.: “Figure 4 shows that Paleochrono-1.1 is able to reconstruct a variable deposition rate from the chronological information, in particular the dated horizons along the MSL speleothem. It is also able to estimate an uncertainty on this posterior reconstruction, which will depend mainly on the uncertainty of the U/Th dated horizons, the depth resolution of the U/Th dates and the assumed growth-rate variation that affects interpolation uncertainty.” It is clear to me that the main scope of the paper is to demonstrate that the model can generate a common, combined model for different archives. However, since the model can also be used to calculate individual age models for single archives (e.g., speleothems) and will probably also be used for this purpose, it may be good to present the results for this as well. If so, it would be interesting how the results compare with other published age models (for speleothems, see, for instance, Comas-Bru et al., 2020).
Line 381 ff.: “… whereas the NGRIP ice core provides very accurate relative ages (i.e.,
durations) from counting of annual layers across intervals.” Even if the relative accuracy of such layer counted chronologies is very high, the counting uncertainty sums up to considerably (e.g., for GICC05). How is this included in the model?
Line 407 ff.: “Additionally, the models for combining all the information may grow to be so comprehensive that most users will not be able to maintain an overview of the data employed, and operating the model will entail sometimes implicit and important choices, e.g. on how to estimate the error bars of the prior and of the observations, how to set the correlation lengths for the prior, etc.” I completely agree with that and would like to encourage the authors to demonstrate the effect of, e.g., incorrect choices for error bars and correlation lengths of the prior. This would not only be helpful to avoid such mistakes, but also improve the applicability of the model.
Line 427 ff.: “In conclusion, optimal chronologies are practical for users who want to use the best possible common chronology, but it absolutely does not replace the need to compare and improve the chronologies of individual sites. The compromises involved in the modelling entail a risk that wrong chronological information or insufficiently quantified uncertainties will influence the resulting time scale negatively in a non-transparent way.” This is a very important point, and - again – I think, it would be very useful to better demonstrate the mentioned effects in the paper. One way would be to (i) construct an individual age model for one of the speleothems (and compare with one of the published models in SISAL), then (ii) construct the common model, and then (iii) construct a (iii) model using wrong chronological information or insufficiently quantified uncertainties to demonstrate the effect.
Conclusions: This is mainly a repetition of the previous sections and could be shortened.
Comas-Bru, L., Rehfeld, K., Roesch, C., Amirnezhad-Mozhdehi, S., Harrison, S.P., Atsawawanunt, K., Ahmad, S.M., Ait Brahim, Y., Baker, A., Bosomworth, M., Breitenbach, S.F.M., Burstyn, Y., Columbu, A., Deininger, M., Demény, A., Dixon, B., Fohlmeister, J., Hatvani, I.G., Hu, J., Kaushal, N., Kern, Z., Labuhn, I., Lechleiter, F.A., Lorrey, A., Martrat, B., Novello, V.F., Oster, J., Pérez-Mejías, C., Scholz, D., Scroxton, N., Sinha, N., Ward, B.M., Warken, S., Zhang, H. and SISAL Working Group Members (2020) SISALv2: A comprehensive speleothem isotope database with multiple age-depth models. Earth System Science Data 12, 2579–2606.
Citation: https://doi.org/10.5194/egusphere-2023-2911-RC1
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