Response to a comment on my YDIH review paper

The following article has been accepted in Earth-Science Reviews.  © 2021. This author manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

The published version is available at https://authors.elsevier.com/a/1eGLv2weQlVhI

Response to a comment by Jorgeson, Breslawski and Fisher on “The Younger Dryas impact hypothesis: Review of the impact evidence” by Sweatman

Martin B. Sweatman

Institute of Materials and Processes, School of Engineering, King’s Buildings, University of Edinburgh, EH9 3FB, UK.

 

ABSTRACT

In their comment, Jorgeson, Breslawski and Fisher challenge comments made in the earlier review paper “The Younger Dryas impact hypothesis: Review of the impact evidence” by Sweatman. All these comments pertain to prior work by Jorgeson, Breslawski and Fisher concerning the synchroneity of radiocarbon measurements taken directly from the Younger Dryas boundary “Radiocarbon simulation fails to support the temporal synchroneity requirement of the Younger Dryas impact hypothesis”. Here, it is explained how Jorgeson, Breslawski and Fisher’s earlier work and recent comments display an unsupportable confidence in modelling the radiocarbon data from the Younger Dryas boundary.

 

Review of the prior work by JBF

The most important aspect of the earlier work by Jorgeson, Breslawski and Fisher (JBF) (Jorgeson et al., 2020), is a key assumption which forms the basis of their argument. Essentially, JBF assume that radiocarbon data for any event taken from its boundary layer will have a similar distribution, regardless of the type or scale of event, once radiocarbon calibration and measurement uncertainties are taken into account. They then compare the distribution of radiocarbon dates obtained directly from the Younger Dryas Boundary (YDB) and the boundary Layer of the Laacher See volcanic event (the LSB) with Monte Carlo simulations modelling these events that take into account radiocarbon measurement and calibration uncertainty, and find that radiocarbon dates from the YDB have much greater dispersion than they expect. Even when allowing for some additional sources of uncertainty in YDB dates, such as the “old wood” effect at Arlington Canyon (AC), Santa Rosa Island, they find that the residual age distribution pertaining to the YDB is much greater than for the LSB. On this basis, they conclude that, since the LSB is known to relate to a singular event and its radiocarbon dates are reasonably consistent with MC simulation, the radiocarbon dates relating to the YDB are unlikely to represent a synchronous event. This, they claim, calls into question the Younger Dryas impact hypothesis (Firestone et al., 2007) more generally.

A simple, but extreme, counter-example serves to highlight their error. For example, consider the flap of the wings of a butterfly, and the impact of a 10 mile-wide asteroid. Even if these two events occurred independently, say, 13,000 years ago, it is not clear that they would result in a similar distribution of radiocarbon dates at their respective boundary layers. The butterfly’s wings might dislodge a seed, which could then be deposited and radiocarbon dated, but would otherwise not perturb the environment in any significant way. The asteroid impact, on the other hand, would alter the environment catastrophically through a hierarchy of interlinked events and processes, many of which could lead to an increase in the distribution of radiocarbon dates relating to the event. Ancient forests might be felled, tsunamis, earthquakes and landslides might mix and redeposit soils, and old sources of carbon might be redistributed. Even if some of these catastrophic processes might be modelled, there will always remain some doubt about the suitability and completeness of such models. If even one of these catastrophic processes is not modelled adequately, the distribution of radiocarbon dates from the asteroid impact boundary layer is likely to show greater variance than those relating to the flap of the butterfly’s wings.

Nevertheless, JBF contend that their work brings the Younger Dryas impact hypothesis into doubt. Implicit in their view is the assumption that their modelling accounts adequately for all sources of uncertainty in the YDB radiocarbon measurements. Of course, this is unknown, and therefore their conclusion is not supported. An alternative explanation for the greater variance in the YDB radiocarbon data than they expect is that their modelling of uncertainty is inadequate.

To see how their modelling might be inadequate, let’s consider the radiocarbon data they use for the YDB, displayed in Figure 3 of Jorgeson et al. (2020). Immediately, by inspection, we can see that this data appears reasonably consistent with a synchronous event in the range 10,735 – 10,835 Cal yr BP (indicated by the vertical grey band in their Figure 3), as determined by Kennett et al. (2015), except for some of the 12 AC data points, a measurement from Big Eddy, Missouri, (at 11,900 ± 80 ¹⁴C yr BP) and a measurement from Murray Springs, Arizona, (at 10,260 ± 140 ¹⁴C yr BP). Any modelling of this data set that does not adequately account for this anomalous data will likely lead to the conclusion that it is inconsistent with a synchronous event. The problem for such studies, as explained above, is that we do not know all the processes that lead to this data, and therefore it cannot ever be known whether such modelling is adequate.

Kennett et al. (2015) accounted for the anomalous AC data used by JBF in terms of the “old wood” effect, given that there is evidence for an ancient pine forest on Santa Rosa island (where AC is situated), with trees that can be older than 1000 years. Using a Bayesian statistical approach together with an “old wood” model, they obtained an estimated age range of 12,695 to 12,925 Cal yr BP, for all the AC data, consistent with a YDB age.

Regarding their “old wood” model for the AC data, JBF’s modelling draws random age offsets for each AC data point from an exponential distribution with median age of 100 years (corresponding to a time constant l = 0.01). Most offsets are, therefore, younger than 100 years, but 37% are older with an exponentially decaying distribution. They therefore assume a very specific old wood model, with a specific distribution and a specific decay constant. In fact, the true “old wood” offset distribution for samples at Arlington Canyon is unknown, and it is not clear whether any exponential distribution at all, with any value of l, is adequate.

JBF further account for the anomalous Murray Springs data point in terms of “soil organic matter”, or SOM. In this case, it is well-known that radiocarbon measurements on SOM can be anomalously young. Moreover, in an alternative scenario, JBF exclude the anomalous Big Eddy data point on the grounds that it might be anomalously-old for an unknown reason. Indeed, Table 1 below shows all the radiocarbon measurements in the YDB-bearing stratum obtained by Hajic et al. (2007) from charcoal samples at Big Eddy measured by AMS (accelerator mass spectrometry). The two data points used by JBF in their study, which are located exactly at the YDB according to Kennet et al. (2015), are highlighted in red. The anomalous ‘Big Eddy’ data point in Figure 3 does, therefore, appear to be anomalously-old relative to others from the stratum.

depth(m)	median	upper	lower
-0.41	13060	12890	13430
-0.43	12020	11620	12390
-0.46	13765	13570	13960
-0.48	12755	12400	12880
-0.53	13090	12940	13220
-0.62	12900	12820	13050

Table 1: radiocarbon data (AMS on charcoal only) from the YDB-bearing stratum at Big Eddy (Hajik et al., 2007). Radiocarbon ages are calibrated and in calendar years BP, while upper and lower limits correspond to a 2s (95%) confidence interval. The red data corresponds to the YDB (Kennet et al., 2015).

As already indicated, JBF’s conclusions are based on comparison of the actual YDB data set to data produced by a Monte Carlo simulation based on their modelling. Therefore, if any of their models are inadequate or incomplete, they will obtain a negative result. This means that if any of their modelled scenarios do not simultaneously account accurately for the old wood effect at Arlington Canyon, or for the SOM effect at Murray Springs, or for the anomalous data point at Big Eddy, they will likely obtain a negative result. In fact, they do not consider any scenario that allows simultaneously for all three effects. The scenarios they do consider are described below.

Their ‘Base Case’ (simulation “C2”) allows for the “old wood” effect at AC (in addition to a lab-based source of error, which is unimportant here), but only using the exponential offset distribution with time constants l = 0.01 or 0.04, and it is not known whether this is an adequate model. They also consider an alternative scenario (“alternative 1”) which includes additional anomalous radiocarbon data from Murray Springs and Big Eddy. Clearly, this will not improve the level of agreement. They further consider an alternative scenario (“alternative 2”) that excludes the anomalous data points from Big Eddy and Murray Springs included in the main data set. But, clearly, if their “old wood” model for AC is inadequate, they will still likely obtain a negative result, especially as the AC data dominates the data set (12 out of 30, or 40% of samples). Finally, they consider another alternative scenario (“alternative 3”) in which the Arlington Canyon data is excluded, but the anomalous data points from Big Eddy and Murray Springs are retained. It appears these data points alone are sufficient to cause a negative result, as expected.

However, they do not consider a combined scenario where the Big Eddy and Murray Springs SOM data points and the AC data are excluded. Given that they acknowledge problems with this data through modelling the various scenarios described above, it is curious as to why they did not consider a scenario that simultaneously eliminates all the problematic data points identified. If they had, they might have obtained reasonable agreement with the YDB data. Therefore, their conclusion that the YDB likely does not represent a synchronous event is not supported.

However, to defend their position, JBF point to the Laacher See radiocarbon data set they used which is already consistent with their MC simulations without modelling further scenarios to account for anomalies. Implicit in this argument is that the Laacher See and YD impact events are expected to be equally disperse in terms of their radiocarbon distributions. In other words, once radiocarbon measurement and calibration errors are taken into account, they expect all events, regardless of type or size, to produce similar levels of residual dispersion in radiocarbon data taken from their respective boundaries. However, this view is not substantiated, and for the reasons given above we can expect it to be false. The problem here is that it cannot be known whether any model adequately accounts for all the dispersion in the residual data.

Finally, their work does not explain the physical evidence for the YD impact event at numerous YDB sites found, and confirmed, on multiple continents as reported in dozens of papers (Sweatman, 2021). The most parsimonious explanation, therefore, for their negative result is that their modelling is inadequate, and they are over-confident in their ability to model the dispersion of radiocarbon measurements relating to global-scale catastrophic events.

 

Response to specific comments by JBF

Having reviewed JBF’s earlier work at greater length, we now have sufficient context to deal with the specific comments in Jorgeson et al. (2021) relating to comments in Sweatman (2021).

Their first comment reinforces the criticism above. They again contend that, once measurement and calibration uncertainties are taken into account, the residual dispersion in radiocarbon data for any event, regardless of type and scale, will be similar. This is the key argument in their earlier paper, it is an unsubstantiated assumption, and we can expect it to be false for the reasons given above. Moreover, in their comment they claim their earlier work was misrepresented by Sweatman (2021) since “We did not compare the Laacher See radiocarbon record to the YDIH radiocarbon record”. But this misinterprets the criticism in Sweatman (2021) which is referring to the residual radiocarbon records, which they do compare. Indeed, this is the central idea of their paper (Jorgeson et al., 2020). They use the residual LSB radiocarbon data set as a benchmark for evaluating the residual YDB data set.

Their second comment relates to their simple “old wood” model. They describe the exponential model with l = 0.01 as “slightly too conservative”. In their earlier paper they also discussed results based on using l = 0.04, which produces an even smaller offset average. Clearly, this value is far too conservative. In their comment, they further discuss exponential models with longer time constants. However, this debate about exponential time constants for “old wood” models is irrelevant. In fact, the exact “old wood” model for AC is unknown, nor is it known whether any exponential distribution with any value of l is adequate. In their comment they conclude “As such, old wood effects do not sufficiently explain dispersion in the observed YDIH radiocarbon record”, and further state that “They cannot, as Sweatman seems to argue, have both the massive old wood effects needed to create the dispersion seen in the YDIH dataset and the smaller old wood effects needed to shift the measurements into the age range proposed for the YDIH.” But neither statement can be substantiated because they did not explore all possible forms of “old wood” model. They only discuss simple exponential forms. In any case, it may well be the case that no “old wood” model, of whatever form, will ever be adequate for modelling the dispersion in the YDB data if the anomalous data points at Murray Springs and Big Eddy are also retained in the data set or not modelled adequately. This view is supported by the negative result for their ‘alternative 3’ model in their earlier paper (Jorgeson et al., 2020).

Their third comment relates to the inclusion of new data from Lake Hind in their YDB radiocarbon data set. We can see by inspection of Figure 3 in Jorgeson et al. (2020) that the single Lake Hind data point is probably the next most anomalous data point after the anomalous AC, Big Eddy and Murray Springs data points. A new data point for the Lake Hind YDB, consistent with a YDB age at 10,850 ± 35 ¹⁴C yr BP, was generated by Teller et al. (2020), but this was published only a few weeks before JBF published their earlier paper (Jorgeson et al., 2020), and therefore not included in their analysis. In their comment, JBF re-model the YDB data set by replacing the ‘old’ Lake Hind data point with this new data point, finding that it makes no practical difference. But this is probably because of their inadequate modelling of the data for AC, Big Eddy and Murray Springs. If these data points are excluded from the YDB dataset, or modelled adequately, then they might find that the new Lake Hind data point makes a more significant difference.

 

Acknowledgments

I thank James Powell for reviewing this manuscript.

 

References

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