Mixing/dispersion effects during CFA result in coarser resolution after sampling [Breton et al., 2012].

These effects are minimized in the BU core analysis and completely absent from the discretely sampled GISP2-H data as shown in Figure 2.

The effects of dispersion and resolution loss on the Zoe and D4 cores can be demonstrated by (1) applying a Gaussian filter to the fine- resolution BU data of the form exp(-28.3t 2 ) and (2) decimating the filtered BU data by a factor of 16 (21 days) to match the sampling rate of the Zoe data from 1937 to 1951.

The coefficient corresponds to a low-pass frequency filter for f c = 3 year -1 (4 months), the point at which the contribution from adjacent samples falls off from 1 at t = 0 to exp(-π).

The filtered/decimated BU data and their power spectrum are presented in Figures 5a and 5b, respectively.

Filtering the BU data to the Zoe resolution simulates how mixing in the CFA fluid stream smoothes and removes the high-frequency peaks as shown in Figure 5a.

The resulting power spectrum of the filtered BU data in Figure 5b strongly resembles that of Zoe shown in Figure 3a.


The first observed SPEs were the relativistic high-energy (>4 GeV) events detected by cosmic ray ionization chambers in 1942, 1946, and 1949 [Forbush, 1946; 1950].

Figure 6 shows all of the high-energy solar cosmic ray ground-level events observed by ionization chambers and/or muon telescopes since the beginning of the continuous cosmic ray measurement era (1936 to present).

Such events are expected to ionize the atmosphere down to low altitudes [Atri et al., 2010; Usoskin et al., 2011].

As can be seen in Figure 6, the events in the 1940s were extremely large over a short time period compared to events during the contemporary spacecraft era (1965 to present).


In an examination of the BU core in Figure 2d, the large-amplitude impulsive nitrate increases (more than 10 standard deviations above the pre-event background) in the core are time associated with the first known very high-energy solar cosmic ray events (28 February and 7 March 1942 (combined); 25 July 1946, 19 November 1949).

This ultrafine-resolution CFA method resulted in a FWHM of 9 days (0.6 cm water equivalent) for the impulsive nitrate increases coincident with these high-energy relativistic solar cosmic ray events.

The probability that during a 14-year period (i.e. 168 months) you would purely by chance select three events that would correspond this closely to the month of known high-energy solar cosmic ray events (February/March, 1942, July 1946, and November 1949) is very small ~(3/168) 3 , or about 1 in 175,000.


Nitrate increases are also observable in the GISP2-H core but not as distinctly as in the higher-resolution BU core.

None of these events is uniquely discernible in the Zoe or D4 cores shown in Figure 2.

Figure 7 presents the BU (nitrate and conductivity) and the Zoe (multi-species) data on the same time scale.

This is an interval when the annual cycles for these two cores (BU and Zoe) are in good synchronization, based on the surface and 1947 Hekla eruption tie points, the Meese et al. [ 1994] depth-age scale, and interpolation using species that display annual cycles; the data are plotted directly as acquired without any adjustments.

Despite differences in resolution, note that these impulsive nitrate increases found in both the BU and GISP2-H cores do not correspond in time to indicators of biomass burning such as anomalous increases in ammonium or black carbon in the Zoe and D4 multi-species data such as shown in Figure 7.


In the BU core analysis, Kepko et al. [ 2009] also identified small nitrate increases that correspond to the time of strong polar cap blackouts recorded by vertical ionospheric soundings that were identified as probable solar proton events by Švestka [1966].

Solar proton events undoubtedly occurred during the solar maximum years of the 17th and 18th solar cycles; however, the only known high-energy solar cosmic ray events are those recorded by the Forbush ionization chambers.

The only short-duration impulsive nitrate events that are time associated to high-energy solar cosmic ray events are the increases in 1942, 1946, and 1949.

This is why we have concentrated on these well-documented relativistic solar cosmic ray events to show that the lack of temporal resolution in the D4 and Zoe cores do not permit the identification of these short-lived impulsive nitrate events.


Wolff et al. [ 2012] did not find a nitrate increase in the ice cores they examined corresponding to the 1859 Carrington event.

Subsequently, their analysis of this single, incompletely characterized event has been used to discredit impulsive nitrate events as possibly indicative of large fluence SPEs [e.g., Schrijver et al., 2012; Cliver et al., 2014].


After examining the data provided by Wolff et al. that overlap the GISP2-H and BU data for the well-documented, large, high-energy relativistic solar cosmic ray events in the 1940s, we conclude that their data resolution was inadequate to differentiate any of the impulsive nitrate events in the test period of 1937-1951.

The limitations on significant data resolution mean that the Zoe and D4 data sets cannot confidently detect or represent changes at frequencies greater than ~3 year -1 , or on time scales of less than approximately 3-5 months.

The 36 year -1 (~10 day) average resolution that is obtainable, as indicated by the BU core, corresponds to a depth resolution < 1 cm water equivalent at Summit.

These findings also imply that ice core analysis of nitrate at a resolution such as Zoe and D4 probably could not discern the Carrington event as well.


We note that the GISP2-H core power spectrum up to its sampling interval frequency appears to be similar to the BU core power spectrum as shown in Figures 3c and 3d.

The fact that the BU data resolution makes it possible to discern impulsive nitrate events means that there is valuable information down to time scales of order 10 days in Summit ice core data that is currently not being exploited.


These results have relevance and impact beyond the issue of SPE resolvability in the polar ice core nitrate record.

This study shows that the ice sheet at Summit contains sub-seasonal information in at least some species at the approximate time scale of snowfall events that can be extracted relatively easily for comparison with current and historical climatological records and applied to studying interactions between different species.

This will allow glaciologists and atmospheric scientists to better understand sources in the atmosphere and the transport mechanisms of impurities to the ice sheet, as well as post-depositional processes.


We suggest additional fine-resolution, multiple species analysis that include nitrate and potentially interfering and "fixing" impurities (i.e., ammonium, black carbon, vanillic acid, dust, and sea salt) with replicate sampling at times of other potentially large-amplitude solar events in the last two millennia (e.g., 14 C and 10 Be events) such as events documented circa 774-775 A.D. and 993-994 A.D. [Miyake et al., 2013; Melott and Thomas, 2012; Usoskin et al., 2013].

A similar recommendation was made by Wolff et al. [ 2008].


We stress that astrophysical ionization events are not expected to produce any of the chemical species attributable to volcanoes or wildfires, other than nitrate.


This is important, as there are indications of much more energetic events on stars similar to the Sun [Nogami et al., 2014] which are potentially very dangerous if they occur on the Sun as well.


The medieval events were very high in fluence or had extreme spectral hardness since 14 C is dependent on high-energy proton fluence [Thomas et al., 2013], and ice core nitrate studies may be important in our understanding of these events by effectively giving information on the total energy deposition in the atmosphere, and thus a crude approximation to the spectrum and total energy deposition.


Soft solar events with enough fluence to account for the observed 14 C in 774-775 A.D. and 993-994 A.D. can severely deplete the ozone layer and are potentially catastrophic [Thomas et al., 2013].


Smart et al. ( 2014) suggested that the detection of nitrate spikes in polar ice cores from solar energetic particle (SEP) events could be achieved if an analytical system with sufficiently high resolution was used.

Here we show that the spikes they associate with SEP events are not reliably recorded in cores from the same location, even when the resolution is clearly adequate.

We explain the processes that limit the effective resolution of ice cores.

Liquid conductivity data suggest that the observed spikes are associated with sodium or another nonacidic cation, making it likely that they result from deposition of sea salt or similar aerosol that has scavenged nitrate, rather than from a primary input of nitrate in the troposphere.

We consider that there is no evidence at present to support the identification of any spikes in nitrate as representing SEP events.

Although such events undoubtedly create nitrate in the atmosphere, we see no plausible route to using nitrate spikes to document the statistics of such events.


Large solar energetic particle (SEP) events have the potential to severely disrupt satellite, communications, and electronic systems.

There is therefore strong motivation to establish a proxy that could document the statistics of occurrence of SEP events of different magnitudes and in particular the recurrence frequency of the largest events.

There has long been a controversy as to whether spikes in the concentration of nitrate in polar ice cores can be used as such a proxy [1][2][3][4][5].

The perspective of the proponents, coming from the space physics community, has been that SEP events will produce NOx in the atmosphere, some of which will be deposited in ice.

They have then attempted to align measured spikes with known SEP event dates to establish a link.

The perspective of the opponents, coming from the atmospheric chemistry and ice core community, has been that any signal would be too small and broad to be detected as a spike, and that there are other causes of such spikes, unrelated to SEPs.


In an attempt to answer the questions posed by the controversy, and to reach both scientific communities, we presented a study [6] in which numerous high-resolution ice core profiles of nitrate from Greenland and Antarctic ice cores were compiled and compared for a 40 year period surrounding the well-known Carrington space weather event of 1859.

We showed that a peak corresponding to that event was not present in most cores and that most nitrate peaks in Greenland during the 40 year period were due to the transit of biomass burning plumes over the ice core site (simultaneously depositing ammonium, formate, and to a lesser extent nitrate [7], along with specific fire tracers).

Subsequently, a new paper [8] has been published, essentially as a critique of our paper [6].

This has three main conclusions: 1. The Carrington Event was a poor choice of test case because it is not clear whether it had the characteristics that the authors would expect to lead to a significant and sharp nitrate enhancement.

The resolution typically used to discern nitrate spikes in ice cores is insufficient to discover the kind of events the authors have in mind.

3. Analysis at higher resolution and measuring multiple chemical species may allow nitrate spikes caused by SEPs to be isolated, reinstating the possibility to assess their occurrence using the ice core record.


In this comment we would like to discuss each of these points in turn.

Before doing so, we think it would be helpful to summarize two issues on which we think that the authors of the commented paper [8] and ourselves can now agree, which may not have been so obvious in earlier discussions.

SEPs deposit most of their energy and therefore produce most NOx in the middle stratosphere or above.

A source of NOx at these altitudes would take months to years to reach ground level, would be broad and diffuse over time, and would be diluted by other sources.

The signature of such a source could never be a sharp spike deposited almost immediately after the event.

It seems therefore to now be a common ground that only hard spectrum events that can deposit energy into the troposphere could possibly produce the kind of spikes being described, and only nitrate produced at low altitudes should be seen as spikes.

It also is agreed that there are other causes of sharp nitrate spikes in polar ice, and in particular that some nitrate spikes, previously attributed to SEPs, are in fact caused by biomass burning plumes.

This is critical because it instantly establishes that inventories of nitrate spikes without additional chemical information cannot be used to establish the statistics of SEP events, and this was actually the main message intended in our earlier paper [6].

We now discuss the three main issues we have identified from the paper on which we are commenting [8].


The Role of the Carrington Event We acknowledge that this event may not have deposited energy at low altitudes and that a nitrate spike of the kind the authors now propose would therefore not be expected.

We emphasize that our earlier paper [6] used the 40 years around the Carrington Event as an example period, in which we demonstrated that most peaks previously claimed to be SEP-related actually have a different origin.

Our conclusion about the use of nitrate to identify SEP events did not rely on the Event itself or alone.

However, the apparent coincidence of timing of the largest integrated nitrate peak in a 400 year period, and the Carrington Event, had previously been used as a major statistical underpinning of the hypothesis that nitrate spikes were indeed caused by SEPs [9].

Once that coincidence is removed (because the spike has the signature of a biomass burning event), so that there is no evidence for any spike associated with the Carrington Event, the idea that any nitrate spikes above background are due to SEPs reverts to being speculation.

We agree that it is worthwhile to assess whether other events, known to have a hard spectrum, might show a signature, but the idea cannot be considered to have any prior support.

In the reply to this comment [10], it is claimed that there is a nitrate peak in the GISP2-H core associated with the February 1956 SEP event.

However, given the rapidly changing snow accumulation rate deduced in this part of the core [8, Figure 1] (14 samples in 1955 but 35 in 1956), the dating of the nitrate peak must be considered uncertain within several months, and the evidence that the nitrate peak is related to the SEP is very weak.

As an additional point, our earlier work does establish that events such as the Carrington, which was associated with a huge geomagnetic storm, cannot be logged through nitrate.


Ice Core Resolution Smart et al. [ 8] carried out power spectral analysis to assess the resolution of different ice core records, and on this basis they suggest that many of them were inadequate to detect the kind of signals that SEPs might cause.

First, we comment that the resolution of most of the analytical systems in use has been characterized directly by applying a rapid change in concentration at the melter and observing the character of the signal at the detector.

This is an empirical and direct way to discover what kind of smoothing is caused by mixing of water on the melt head, in the tubes leading to the detector, and in the detector itself.

It therefore includes all the sources of dispersion that would occur for a real sample.

The resolutions quoted in Table 1 of our earlier paper [6] were generally derived in this way.

It is certainly likely that the system used on the Boston University (BU) core, analyzing only one component, has a higher resolution than the multi-component systems used at Zoe and D4.

However, this analysis ignores many issues concerning the way in which signals are recorded in ice cores, which inherently limit the resolution that is useful and reliable.

Chemicals can be deposited either by wet or dry deposition.

If they are dry deposited, they may give a very thin layer between snowfalls.

If they are deposited by wet deposition, then they may give a signal that is initially the width of a snowfall (typically millimeters to centimeters).

While there are numerous snowfall events each year at Summit (84 events were recorded in 2001), they are far from uniformly distributed through the year [11].

This has two consequences: first, it is impossible to accurately attach a calendar date to a layer in an ice core, because the snowfall quantity varies strongly from month to month.

Based on [11], linear interpolation of the quantity of new snow with time within a year would lead to an error of up to about 2 months at some parts of the year.

Second, there are months when there are only one or two snowfall events, so that the effective resolution, even in the deposited fresh snow, is 1–2/month.

With no a priori way of knowing which months this applies to, the reliable resolution (if one is going to assess event frequencies) in the deposited snow in central Greenland is of order 1 month.

However, after snowfall, snow is redistributed through wind, leading to mixing of different snowfall layers, and to inhomogeneous deposition across sastrugi, where some parts of the surface may contain a thick layer, and the layer may be completely lost from other parts of the surface.

Recent model studies suggest that drifting snow occurs on 50 or more days per year in central Greenland [12].

Finally, nitrate that is deposited as nitric acid is known to be mobile in the snow pack, so that even sharp peaks become smoothed through vapor redistribution.

Nitrate that is deposited as aerosol will not be subject to this process but suffers from the issue that the deposition rate may be controlled by atmospheric concentrations of the countercation [5][13] rather than by concentrations of nitrate itself.

For example, an influx of marine air containing high concentrations of sea salt will scavenge acidic nitrate from the atmosphere, leading to deposition of a nitrate spike even in the absence of any primary input of nitrate.

Of course, despite these issues, an event lasting only a few days can be detected if it is large enough.

Indeed, the numerous biomass burning events that have been detected on the basis of markers such as ammonium, formate [14][15], and vanillic acid clearly derive from events that would only have passed over the ice core site for days, but many give clear nitrate signals [6].

However, a method that would rely on very narrow signals being reliably present in an ice core at such high resolution cannot be successful.

This is illustrated in Smart et al. ’s [8] own paper.

The BU core shows perhaps five peaks that could be identified as sharp nitrate spikes in the period 1937–1951.

These spikes appear to be 1–2 cm across and therefore should clearly show up in the GISP2-H core (which has a resolution of 1.5 cm from discretely cut samples, which equates to about 0.03 years at this age).

We have (Figure 1) binned the data from the BU core into 0.03 year sections to mimic what the H core should show if the same peaks were recorded.

We bin rather than smooth [10], because discrete samples are indeed bins of the ice section.

Some years have more than 30 samples in the H core; however, changing our bins to 0.04 or even 0.05 years does not affect the result.

Assuming the dating of both cores is correct, it is immediately obvious that the largest peak in BU (in late 1946), which should show a peak of 250–340 ppb after binning (depending on the position of the bin boundaries), is not seen in the H core.

Other significant sharp spikes in BU are also not seen at the expected depth in the H core; although one can find peaks in the H core at ages near to those in BU in some cases (e.g., at the end of 1949), they actually occur in the wrong part of the seasonal nitrate curve.

The attempts to find plausible peak matches [10, section 3.3] require such a flexible attitude to dating, seasonality and peak size and shape that it is hard to imagine a situation in which an apparent match would not be found.

In fact, the GISP2-H core also shows one very clear spike, but it is in a year when BU shows no spike.

Even if these spikes could be attributed to SEPs, they are not reliably recorded in adjacent cores.

We note that the very high resolution Law Dome (Antarctica) ice core was sampled discretely at about 12–20 samples/year (decreasing with depth) and also showed no significant nitrate enhancement, let alone a sharp spike, after individual SEP events [4], although a small, broad, enhancement of nitrate concentrations was found between 3 and 15 months after the SEP date, when averaged over the event population.

We note also that we see no clear nitrate signal in the Law Dome DSS core around the 10Be peak associated with an event in 775 A.D. [16], recently identified as most likely a hard spectrum event 25–50 times as strong as the February 1956 event [17].


A Signature of SEP Events It has been suggested that replicate sampling of several cores, with multiple chemical species, and at very high resolution, will allow SEP signals to be isolated [8].

In principle, deposition of acidic nitrate might, at least in the preindustrial era, be indicative of SEP or other events of atmospheric origin, distinguishable from biomass burning events (with associated ammonium) or deposition of nitrate as a consequence of scavenging by sea salt or dust.

This criterion (of nitrate peaks with no measured countercation) was used to isolate candidate peaks in surface snow in a recent paper [13].

However, if an additional requirement is that the peak preserved in ice at depth must be very narrow, such as those discussed [8], then we would predict that they must have been deposited as aerosol and will therefore not have an acidic signature that might be a fingerprint of an SEP event.

No cation information exists for the BU or GISP2-H core data.

However, we can infer the nature of the cation from the liquid conductivity, which was also presented [18][8].

The largest nitrate peak in the BU core, in 1946, rises about 400 ppb (6.5 μeq L⁻¹) above the background.

Using well-documented ionic conductances, we would expect such a peak to be accompanied by a liquid conductivity increase of almost 3 μS cm⁻¹, exactly what would be expected in the case where Na+ is the countercation (and similar for ammonium or calcium).

A similar calculation confirms that none of the sharp peaks shown in the BU core between 1937 and 1951 are acidic.

Finally, we need to consider whether it is plausible that the events discussed [8] could arise from a hard SEP event acting in the lower atmosphere.

Modeling studies are needed to assess this.

Newly accepted work [19] using the Whole Atmosphere Community Climate Model found that very large hypothetical SEP events, 2–3 orders of magnitude larger than those observed in recent decades, produced, as expected, NOy enhancements above the troposphere but insufficient NOx in the lowest 15 km of the atmosphere to produce spikes in Greenland ice cores.

Earlier work [20] would also not support the kind of enhancements in nitrate deposition flux that would be needed to observe spikes for events such as those in the 1940s and 1950s.

We understand that other modeling studies are in progress, and we await their results with interest, emphasizing that to be relevant to the production of very sharp spikes, such models will need to focus on production in the troposphere or lowest stratosphere.


Conclusion Smart et al. [ 8] showed that very sharp nitrate peaks may be detected in a system with very high resolution.

However, there is no evidence that these arise from SEPs.

Liquid conductivity data confirm that many sharp peaks are not acidic, making it likely that they are a by-product of influx of sea salt, biomass burning, or dust aerosol, rather than a consequence of a significant influx of nitrate.

There seems no a priori way to distinguish SEP signals from other causes of nitrate enhancement, and we again emphasize that the evidence that was used previously, regarding the coincidence in date of SEP events and nitrate spikes, is no longer valid.

In any case, we have shown clearly that such spikes are not reliably deposited in adjacent cores, for reasons we have discussed, which makes the use of such sharp features to log the power and frequency of SEP events impractical.

We do not doubt that the largest SEPs will cause an enhancement of nitrate in the middle atmosphere and in extreme cases the troposphere.

As ice core scientists, we would very much like to be able to use a proxy such as that envisaged, but we cannot see a plausible route for identifying and using nitrate deposition in snow to diagnose past SEPs.

The use of 10Be [17], although at much lower time resolution, seems a much more promising path.


Wolff et al. [ 2016] comment on Smart et al. [ 2014] and in doing so concentrate on issues other than the main point.

They do not dispute our central assertion, the inadequate resolution of nearly all extant ice cores for detection of impulsive nitrate events (spikes) from any source, including past solar proton events (SPEs).


We explain why comparing two short-length cores from other researchers and analyzed by different methods is insufficient for disputing subannual reproducibility, and call for a multiple, fine-resolution, replicate core study to resolve this issue.

While acknowledging the creation of nitrate by SPEs and the existence of ice core nitrate spikes detected by others, they present several weak arguments, such as alleged scavenging of nitrate by some unnamed and unmeasured aerosol, and why no enhanced nitrate signal for documenting SPE statistics should be distinguishable in the ice.


These are not derived from the main points in our Smart et al. [ 2014] paper.

We address these briefly and show that ionization from the February 1956 SPE was sufficient to produce a winter, likely acidic, nitrate spike at Summit, Greenland.


While noting some convergence of interpretation, we show why their claim that nitrate spikes cannot be used for deriving SPE statistics is unproven and why rejection of fine resolution core studies as unreliable is premature.


To understand the complexities regarding the Comment by Wolff et al. [ 2016] and this Reply, some background material is necessary.

Table 1 in the McCracken et al. [ 2001] paper is a tabulation of 72 short duration, “impulsive nitrate events” or spikes that those authors proposed as representing very large fluence solar proton events (SPEs) with the solar particles ionizing the atmosphere and forming oxides of nitrogen, leading to enhanced nitrate deposition in polar ice.

Wolff et al. [ 2012] chose to concentrate on one event associated with the white light solar flare observed on 1 September 1859 [Carrington, 1860] and noted that they were unable to find a corresponding nitrate increase in several “properly dated” ice cores.

Based on the analysis of this one event and their assertion that “all large spikes” in their data were “due to biomass burning plumes,” they concluded that nitrates in ice cores cannot be used to derive SPE statistics.


Smart et al. [ 2014] noted in the data supplied by Wolff and coworkers that no nitrate spikes corresponding to known high-energy ground level solar cosmic ray events observed by cosmic ray muon detectors could be found.

Smart et al. [ 2014] attributed this to insufficient resolution of ice core data primarily acquired to study long-term climatological phenomena.

Using standard statistical analysis software, Smart et al. [ 2014] noted a substantial difference between the resolutions reported in Table 1 of Wolff et al. [ 2012] and the statistically significant data resolutions in their Zoe and D4 ice cores (closest in location to the GISP2-H ice core), contradicting their claims of “comparable resolution” to the GISP2-H data, which contained significant nitrate spikes.

Smart et al. [ 2014] concluded that only ice cores with statistically significant, fine resolution of less than 2 months are capable of resolving short duration nitrate spikes regardless of their source.


This resolution issue has implications regarding inferences drawn previously about a host of short-term phenomena in coarsely resolved ice cores, including the claimed associations between biomass burning, sea salt, and nitrate spikes that cannot be proven with analyses that only delineate broad summer/winter variations.

It needs to be addressed.


We are now in the “comment stage.” Wolff et al. [ 2016] maintain that the entire nitrate spike concept for deriving SPE statistics is inconsistent with well-regarded standard models for transport through the atmosphere and preservation in ice.

These are the key points in the argument Wolff et al. [ 2016] present, but they are not in response to the key point in Smart et al. [ 2014].


The basic thesis of our paper [Smart et al., 2014] that the data in Wolff et al. [ 2012] lack the resolution to identify 0.5–2 month duration impulsive nitrate events has not been challenged in a substantive way.

Nevertheless, Wolff et al. [ 2016] continue to maintain that their ice core data are “high resolution” and “The resolution typically used to discern nitrate spikes.

” We strongly dispute these assertions.

In their Comment, Wolff et al. [ 2016] focus primarily on side issues to dispute our paper and to repeat previous arguments [e.g., Legrand and Delmas, 1986; Wolff et al., 2012; Duderstadt et al., 2014] against the feasibility of observing very large, hard spectrum SPEs as spikes in the polar nitrate record.


For example, they list our side point that the Carrington SPE was not well documented and is not a good test as our first main conclusion.

Our conclusion was not an attempt at “reinstating” SPE-produced nitrate spikes because the concept has never been disproved, despite Wolff et al. ’s [2016] continuing efforts.

Although it appears that the two sides in this debate have drawn incrementally closer, as our understanding of the physical and chemical processes involved has improved, there is still considerable disagreement, and we welcome this opportunity to respond to these additional arguments to clarify the issues.


Wolff et al. ’s [2016] assumption, coming from the atmospheric chemistry and ice core community, that both sides agree SPEs deliver most of their energy to the middle stratosphere and above, is only partly correct.

Coming from the space physics, cosmic ray, and atmospheric physics communities and with expertise in polar glaciology, we emphatically disagree that this is the only locale for energy deposition to generate nitrate spikes and that this generalization applies to all SPEs.

Standard energy deposition models have routinely omitted primary particles with energies above 300 MeV and have neglected air shower cascades that deposit additional energy in the lower stratosphere (from which rapid deposition can occur and which Wolff et al. [ 2016] continue to ignore), as well as the troposphere.

We contend it is the high-fluence, high-energy events that can produce an impulsive nitrate enhancement in ice cores.

We discuss this issue below.


Wolff et al. [ 2016] now assert that “many” nitrate spikes in Greenland cores are “likely” caused from scavenging by “sea salt, biomass burning, or dust aerosol,” but this is very different from their previous claims [i.e., Wolff et al., 2012] and has not been proven.

First, we find it remarkable that they assert that most nitrate spikes must have this association using ice core analyses that have not resolved a single impulsive nitrate spike.

Given their limited 3–5 month resolution, the Wolff et al. [ 2012] data essentially only reflect the annual cycle where nitrates, biomass burning, and a number of additional indicators vary in phase and tend to peak in summer.

This summer maximum does not apply to impulsive nitrate enhancements, which are of shorter duration and can occur at any time of year, including in winter when biomass burning interference is minimal.

For example, Figure 1 in Smart et al. [ 2014] shows the winter nitrate spike in Greenland associated with the 23 February 1956 SPE.

Second, Wolff et al. [ 2008] conducted a coastal study at Halley, Antarctica, which reflects a marine environment with associated high sea-salt and nitrate concentrations in surface snow samples, not ice cores.

They specifically state that in central Greenland, which is remote from the oceans, sea salt “…will rarely be at the concentrations that could cause the effects described…” Third, there are exceptions even with the coarse-resolution data.


The strong statement in Wolff et al. ’s [2012] abstract that “in the 40 years surrounding 1859…where other chemistry was measured, all large spikes have the unequivocal signal…of biomass burning plumes” is simply wrong.

Their supporting Figure 4 selectively covers only 20 of the 40 years in their study.

When the full 40 year interval for the D4 core is shown (Figure 1), two of the five highest summer nitrate peaks display no associated biomass burning signal.

Similar results also can be observed in the Zoe core.

Given these issues, nitrate spike source conclusions must be considered speculative.


We also agree that impulsive nitrate spikes produced by SPEs are difficult to isolate from nitrate produced by other sources without additional chemical information, which is why we support multispecies analyses in our call for fine-resolution, follow-on studies.

However, the concluding statement at the end of Wolff et al. ’s [2012] abstract and Wolff et al. ’s [2016] main message continues to be “Nitrate spikes cannot be used to derive the statistics of (SPEs).” Given that Wolff et al. [ 2012] did not resolve impulsive nitrate events in any of their ice cores used to dispute an association with SPEs, the subsequent effect of their absolute conclusion on the glaciology and space physics communities compelled us to take issue with their publication.


Wolff et al. [ 2016] argue that (1) prior studies show no evidence for measurable SPE influence on nitrate deposition, (2) without a Carrington signature there is no longer any smoking gun in the data, and (3) large events such as the Carrington “cannot be logged through nitrate.

” We disagree.

First, prior studies of possible SPE impacts have been very incomplete, and second, there are other major SPE/nitrate spike candidates in the ice core sequence that have not been properly studied.

We address both issues here.


Most past numerical simulations involving analytic range-energy calculations [e.g., Duderstadt et al. [ 2014], and references therein] used an incomplete model that is more appropriate for soft spectrum SPEs such as the August 1972 and November 2000 events.

These calculations use atmospheric inputs covering a limited energy range and ignore elementary particle interaction cascades in the atmosphere.

These studies contradict experimental data—balloon-measured ionization—which show substantial SPE-induced ionization in the lower stratosphere and troposphere, revealing an SPE ionization peak at about 17 km, the bottom of the stratosphere at midlatitudes [Nicoll and Harrison, 2014].

Most studies that have included a more complete air shower simulation [e.g., Usoskin et al., 2011, 2013] have not looked specifically at nitrate deposition.

The models and simulations have ignored high-fluence, hard spectrum SPEs that can produce enough ionization in the troposphere and lower stratosphere to be deposited as nitrate spikes in the snow in just weeks to months, particularly if they occur at the right time of year (i.e., winter).


Calisto et al. [ 2013] modeled a scaled-up, Carrington-like, February 1956 SPE at the end of August and predicted up to a 50% increase in total nitric acid deposition for the next month over the South Pole.

In asserting that once the Carrington Event is removed from consideration, “…the idea that any nitrate spikes above background are due to (SPEs) reverts to speculation.

” Wolff et al. [ 2016] ignore the 23 Feb 1956 SPE, which also has a well-resolved, associated nitrate peak in a Greenland ice core [Smart et al. 2014, Figure 1].


The 1956 SPE is the largest amplitude (a 46-fold increase in 15 min data) and the largest fluence (above 200 MeV) ground level event (GLE) in the cosmic ray monitoring history [Kovaltsov et al., 2014], and it had a hard spectrum [Pfotzer, 1958; Smart and Shea, 1990; Belov et al., 2005].

This event was worldwide in character, observed at the geomagnetic equator, and had estimated maximum proton energies >50 GeV [Sarabhai et al., 1956].

In contrast, even though low-latitude aurorae coincident with an extreme geomagnetic storm were associated with it, there is no spectral information on the Carrington event.


Using greatly improved simulations that follow the development of the proton-induced air shower and its ionizing effect on the atmosphere, we found consistency with the recent experiments showing substantial excess ionization down to 10 km and demonstrate sufficient nitrate deposition from the 1956 SPE.

We discuss our results in section 4.


SPE candidates deserving consideration in addition to the February 1956 event include the impulsive nitrate spikes observed in the fine resolution Boston University (BU) core that correspond to the very hard spectrum solar cosmic ray ground level events observed by muon detectors in 1942, 1946, and 1949.

Other candidates include the probable medieval SPEs of 775 A.D. and 994 A.D. [Miyake et al., 2012, 2013; Melott and Thomas, 2012; Thomas et al., 2013; Ding et al., 2015].

Wolff et al. [ 2016] assert that the 1937–1951 nitrate spikes in the BU core are not reliably reproduced in the GISP2-H core.


In section 3.3 we show why their claim cannot be substantiated without fine-resolution, replicate core studies.


The 775 A.D. event may have been 25–50 times stronger than the February 1956 SPE [Usoskin et al., 2013; Thomas et al., 2013] and should be detectable at Summit if it occurred in winter.


There is also evidence suggesting that the 775 A.D. probable SPE may have been several somewhat smaller events occurring in Northern Hemisphere summer 776 A.D. [Ding et al., 2015], which would not be detectable at Summit or in Antarctic ice cores with resolutions corresponding to fewer than 10 samples per year.


The problem is that almost no replicate ice core nitrate profiles exist with the fine temporal/depth resolution required to detect such events.


This is why the inadequate resolution of the ice cores presented by Wolff et al. [ 2012] is a major issue in the SPE/nitrate deposition debate and why we are calling for a dedicated campaign of replicate, fine resolution, ice core analyses.


We do not dispute Wolff et al. ’s [2016] general method of applying a rapid concentration change at the melter for characterizing smoothing in their system or that this method can be used to derive a resolution that is attainable, in principle, by an ideal Continuous Flow Analysis (CFA) system.

However, this method is valid “only for systems with negligibly small dispersion” [Breton et al., 2012], and it is indisputable that there was a huge loss of resolution, whatever the cause, somewhere between the value Wolff et al. [ 2012] derived by this method (and reported in their Table 1) and the final statistical resolution that they actually obtained from the Zoe and D4 cores.


The resolution of any time series is generally characterized as the ability to distinguish between two adjacent peaks and, in the case of discrete sampling of an ice core, is only limited by the sample size, sampling rate, and postdepositional processes, whereas what Wolff et al. [ 2012, 2016] are calling resolution corresponds much more closely with their sampling rate, which is already twice the best attainable (Nyquist frequency) resolution in discretely sampled data.

With CFA, resolution is additionally limited by mixing issues on the melthead and significant dispersion effects in the meltwater stream, due to nonuniform velocity profiles of a nonideal (real) fluid, causing smoothing of the resulting signal [Breton et al., 2012].


In the case of the ice cores listed in Table 1 of Wolff et al. [ 2012], it is clear that significant dispersion effects combined with multispecies analysis and other issues inherent with all CFA logging systems [Rasmussen et al., 2005] considerably limited their final, effective resolution.


Using the data provided by Wolff and McConnell (personal communication, 2012), we determined their time resolution [Smart et al., 2014] and demonstrated that there is no significant signal in Zoe and D4 for periods smaller than about 4 months.


Our determination of the time resolution, using both power spectral analysis and examination of the full width at half maximum of peaks in each of the four data sets, showed the Zoe and D4 resolutions are far more coarse (by factors of at least ~7–13) than those reported in Table 1 of Wolff et al. [ 2012].


Summer peaks, which are sharp in other data (i.e., GISP2-H and BU), are spread out over months in Zoe and D4 [Smart et al., 2014, Figures 4 and 5], and nitrate spikes are completely indiscernible.


Asserting in Table 1 of Wolff et al. [ 2012] resolutions of ~0.05 years (0.6 months) for Zoe and ~0.025 years (0.3 months) for D4, both sampled by CFA, versus 0.067 years (0.8 months) for the discretely sampled GISP2-H core without any additional qualification, thereby implying that their ice core resolutions were finer, is misleading, regardless of whether “all the sources of dispersion” have been accounted for or how these numbers were derived.


These results show that either the “empirical and direct way” Wolff et al. [ 2012] used to characterize their resolutions in Table 1 and elsewhere needs substantial revision or the term “sampling rate” and the corresponding numbers should be stated instead.


Wolff et al. [ 2016] claim that we ignore the inherent limits on resolution, but the fact is that this issue has not been investigated with fine-resolution, multispecies ice core studies, as our analysis demonstrated.

We agree that it is difficult to attach precise calendar dates at subannual resolution because of snowfall variability.

Nevertheless, precise dating is not required for deriving useful SPE statistics, and subannual dating estimates can be improved by applying what we know about the distribution of other species and snowfall within a year (e.g., more snow accumulation in summer than winter at Summit, Greenland [Dibb and Fahnestock, 2004]).

Wolff et al. [ 2016] acknowledge that a resolution of order 1 month (significantly finer than the ~4 months in their data) is probably attainable after controlling for postdepositional processing, and we assert that this should be sufficient for deriving the statistics of large high-energy, hard spectrum SPEs.


Wolff et al. [ 2016] attempt to compare the BU and GISP2-H cores by rebinning the BU data to mimic the coarser resolution of GISP2-H—and note that they do not exactly correspond.

Their choice of 1.5 cm samples or 0.03 year sections is not appropriate.

The correct rebinning should be closer to 0.04 years.

We do agree that natural variability occurring at the time of deposition and afterward makes matching of fine-resolution nitrate spikes deposited over ~0.5–3.5 months challenging but, we assert, not impossible as discussed below.


The issue of reproducibility of ice cores has long been a legitimate concern.

This was a major reason for the duplicate drilling programs of GISP2 (American) and Greenland Ice Core Project-GRIP (European) at Summit, which showed similarities and differences in two parallel ice cores 28 km apart over long time intervals.

Significant variability between pits and cores only a few meters apart at annual, as well as sub-annual, time scales (e.g. [Laird, 1987] and others) that is primarily related to post-depositional processes is well known.

It is possible that some of the misdating scenarios detailed by Wolff et al. [ 2016], occurred with the GISP2-H core during this interval and simple seasonal adjustments would improve peak matching between cores.

The comparison is also complicated by the fact that the BU core had a number of breaks and missing data in this time interval.


We do not accept Wolff et al. ’s [2016] assertion that such an effort “cannot be successful,” and we take strong issue with the comparison of GISP2-H data and binned BU data to show a lack of correlation and to support their asserted futility of finding repeatable SPE nitrate spikes.

Just as two deep ice cores 28 km apart could be synchronized over periods in excess of 30,000 years, with careful analysis, it should be possible to extract information at the subannual scale from 4 to 5, relatively shallow ice cores a few meters apart covering a few thousand years.

While a casual observer with little or no glaciological experience (with nitrate in particular) might be inclined to agree that there appears to be no matching nitrate spikes between the two cores presented in the figure of Wolff et al. [ 2016], careful, informed analysis suggests otherwise.


We note that the two cores were dated using different methodologies.

In the case of GISP2-H, after selecting the yearly fiducial marks based on volcanic tie points and winter nitrate minima (supplemented with annual accumulation information), samples were linearly interpolated to assign subannual dates.

The BU dating included an additional step where an annual accumulation cycle (based on surface measurements) was assumed (more snow in summer and less in winter) and dating was interpolated nonlinearly between yearly fiducial marks.

This different approach introduced more apparent sample offset between the two cores than would exist had they been analyzed by the same method and is partially responsible for the misalignment of nitrate spikes.


Working backward through the Wolff et al. [ 2016] figure and analyzing their alleged lack of matches, we find approximately six major BU and/or GISP2-H spikes:


First, the BU spike near the end of summer 1949 could correspond to the smaller spike on the right side of the GISP2H 1949 summer nitrate maximum with their different magnitudes resulting from post depositional processes.

Natural variability complicates the business of matching peaks, but these issues can be potentially resolved.


Second, the lack of a match between the winter nitrate peak identified as late 1946 in the BU core alleged by Wolff et al. [ 2016] is predicated on “Assuming that the dating of both cores is correct…” We assert that in fact the dating around this time, interval of at least one of the cores is incorrect, and this is the same peak as that dated to early 1947 in GISP2-H. Both peaks clearly occurred in winter, and the year breaks were chosen, in part, on the basis of the minimum nitrate values in those intervals, which are essentially the same on both sides of the GISP2H peak.

Another set of analyses might determine that the actual year fiducial mark lies on the right side, at which point the two peaks would line up and match.


Third, the GISP2-H spike on the right side of the summer 1944 nitrate maximum (which Wolff et al. [ 2016] claim occurs in a year when BU has no spike) could correspond to the right side of the slightly offset BU 1944 summer high, which has a break just to the left of this peak.

The missing data between the summer maxima and this peak preclude a proper interpretation.


Fourth to sixth, the BU spikes in 1937, 1939, and 1942 all have potential matches with GISP2-H spikes that may be more subdued, due to post depositional processes, or possibly offset (in the case of 1939).

Again, these potential matches may not be substantiated but neither are they dismissible without additional analysis.


Despite assertions that our peak matching exercise is too flexible (possibly allowing spurious correlations of spikes), our point is that two cores analyzed with different methodologies are not enough for disputing or resolving such ambiguities.

Similarly, arguments that subseasonal resolution at depth in Summit ice cores is inherently unachievable, based on surface studies alone, are also speculative until this issue has been addressed with dedicated, follow-on efforts involving statistical analysis of more than two replicate cores.

The actual useful resolution (probably in the 0.5 to 2 month range) in ice cores at Summit, Greenland, has not been determined, thus reinforcing our call for a study involving multiple ice cores.

Fine-resolution, multispecies analysis of 4 to 5 overlapping cores from the same site should be sufficient to characterize the subannual variance and reproducibility of each impurity (such as nitrate) and would establish limits on inherent depth and time resolutions.

Wolff et al. ’s resistance to this is puzzling.


For example, if the winter nitrate spike in the GISP2-H core is from the 23 February 1956 SPE, it should appear in several (though possibly not all) replicate cores, and methods of superposed epoch analysis, used routinely in such studies, would allow for a more complete characterization of such spikes, including their dating.

A feasibility study covering a reasonable initial test time interval could be accomplished before funding a dedicated campaign to analyze the last several kyr.


The Law Dome ice core [Palmer et al., 2001] cited by Wolff et al. [ 2016] as a discretely sampled, very fine resolution core to dispute identification of nitrate spikes from SPEs is a poor example because this core (with at best 2 month resolution for 1840–1880) (like Halley) was drilled at a coastal site and reflects primarily a maritime climate.

Palmer et al. [ 2001] and Roberts et al. [ 2015] both stated this caveat clearly in their papers.

The fact that statistical evidence for SPEs was found in this core with these resolution and location limitations is remarkable.


Wolff et al. ’s [2016] comments concerning the BU core and the lack of acidic nitrate spikes in the 1940s, especially the two GLEs in 1942 may have some validity, if confirmed with additional fine-resolution studies.

However, the 1946 GLE they use as their example occurred in July (Northern Hemisphere summer) and thus may not be a good candidate for observing in the Greenland ice core record for the reasons stated above, though, it may be observable as a winter peak in Antarctica at some suitable site.

There is also ambiguity in the interpretation of the 1949 results, and the possibility that the GISP2-H conductivity is more compatible with a nitric acid source for the nitrate spike associated with the November 1949 GLE.


Wolff et al. [ 2016] still neglect conductivity associated with the GISP2-H 1956 winter nitrate spike in Figure 1 of Smart et al. [ 2014].

Applying their same conductivity methodology to the 1956 nitrate peak suggests opposite results.

The integrated 1956 peak averages a nitrate concentration of 33.9 ppb (μg/l) or 0.547 μequiv/l above the local background.

Assuming no significant change in sulfate (i.e., no significant volcanic activity was reported during this period), and using standard molar conductivity tables [Haynes, 2015], the conductivity rise is expected to be about 0.23 μS/cm if the nitrate peak is acidic and 0.066–0.079 μS/cm if it is due to a nitrate salt.


The conductivity values colocated with nitrates in Figure 1 in Smart et al. [ 2014] were unfortunately mislabeled and are high by a factor of 100.

The increase above background of the GISP2-H 1956 conductivity peak is actually 0.21 μS/cm.

This is only 11% less than predicted for nitric acid, but a very large 159% to 212% more than predicted if the 1956 spike was formed by a nitrate salt intrusion, which Wolff et al. [ 2008] state is rare at Summit.


In principle, the 1956 peak could result from some combination of sea salt, biomass burning, or dust aerosol scavenged nitrates, as invoked by Wolff et al. [ 2016], but this is increasingly improbable as it requires multiple strong winter sources and ignores the most straightforward explanation, i.e., nitric acid.

This is strong circumstantial evidence against Wolff et al. ’s [2016] thesis that nitrate spikes can only be caused by salts or other aerosol.


Wolff et al. ’s [2016] mention that they await more detailed modeling studies, which are in progress.

Such a study of the 23 February 1956 SPE has recently been conducted where the amount of nitrate available from this SPE has been computed using full spectrum modeling involving high-energy showers [Melott et al., 2016].

These results indicate that nitrate production in the stratosphere is roughly 2–10 times greater than estimated by past limited energy range, analytic models.

Summing the February 1956 SPE ionization from the surface to approximately 45 km at Summit produces the nitrate deposition found in the integrated winter 1956 GISP2-H peak.

Therefore, this event can account for the measured nitrate with timely (~2 months) transport downward to the surface via mechanisms involving the polar vortex, denitrification, and polar stratospheric cloud assisted nitric acid deposition.

These results lend strong support to the conclusion that the 1956 winter nitrate spike in GISP2-H data is mostly acidic and likely due to the February 1956 SPE.


Wolff et al. [ 2016] assert that our SPE/nitrate spike evidence is “very weak” by questioning the GISP2-H dating based on a sample number ratio of 2.5 between 1956 and 1955.

However, our conclusion is also based on the likely acidic composition of this spike, and the new results mentioned above show that 1956 class SPEs can quantitatively explain spikes of this size and does not rely on dating alone.

Additionally, snow accumulation can vary significantly from one year to the next at any given point on the ice sheet.

Sample numbers from the nearby Zoe core dated as 1939 and 1941 differ from 1940 by factors of 2.2 and 2.3, respectively.

The GISP2-H core section was dated using the Hekla (1947), and Askja (1961), Iceland volcanic eruptions as tie points, together with counting and interpolating annual nitrate cycles to determine 1956 as the most probable year for the spike in question.

Wolff et al. [ 2016] consider the dating “uncertain within several months,” but it is difficult to justify shifting the well-defined, midwinter, nitrate low (where the spike is found) from February by more than about 1 month.


Finally, Wolff et al. [ 2016] cite model studies by Duderstadt et al. [ 2015] as evidence opposing nitrate spikes produced by large SPEs.

Duderstadt et al. now use essentially the same full air shower approach that we have been advocating for some time [e.g., Melott et al., 2010].

They calculated percentage increases in SPE-produced nitrate relative to all NOy species in the atmospheric reservoir and concluded that they were too small to account for Greenland nitrate spikes.


This approach is still incorrect for comparing with surface nitrate concentrations, which correlate more with nitric acid at Halley, Antarctica, because the atmospheric reservoir consists largely of organic nitrates [Jones et al., 2011], which have a long residence time compared to HNO3 [Ridley et al., 2000].

Using a percentage enhancement dilutes the expected effect.


Instead, calculations of the absolute amount of additional, SPE-produced, inorganic, nitric acid available for prompt deposition are consistent with the observed nitrate increase [Melott et al., 2016].


We agree that the McCracken et al. [ 2001] calibration must be revised before nitrate spikes in ice cores can be used for identifying extremely large SPEs.

This is very different from Wolff et al. ’s assertion that it cannot be done.


As our understanding of the physics and chemistry by which SPEs can deposit nitrate in the snow has evolved, it has become apparent that most events do not have the spectral hardness and/or sufficient fluence to produce an observable spike, and we agree with the opponents on this point.


Nevertheless, the standard atmospheric production model used by Duderstadt et al. [ 2014] and others has been clearly inadequate.

It totally ignored the particle cascade in the atmosphere that results in energy deposition in the lower atmosphere.


Consequently, we emphatically dispute the extrapolations by Wolff et al. [ 2012, 2016], Duderstadt et al. [ 2014], Duderstadt et al. [ 2015], and others that no SPEs can be found in polar ice core nitrates and that these records cannot be used to obtain statistics of past, very large SPEs.


On the contrary, recent work, showing direct experimental and model evidence of substantial lower stratosphere/troposphere ionization from SPEs (e.g., Usoskin et al. [ 2011, 2013], Nicoll and Harrison [2014], and Melott et al. [ 2016]) indicates exactly the opposite may be the case.


Thus, the SPE/polar ice nitrate hypothesis is still very much alive.


Wolff et al. [ 2016] advocate the alternate use of cosmogenic isotopes such as 10Be to study past SPEs.

We agree that these provide useful information.

However, they omit recently published work [McCracken and Beer, 2015] that has identified all of the same events in the 1940–1956 time interval previously identified as impulsive nitrate events in Figures 1 and 7 of Smart et al. [ 2014].

The 10Be and 14C records are annual; the 10Be increases appear 12–15 months after the high-energy SPEs.

Including fine-resolution nitrate signatures together with the 10Be and 14C records would enhance the overall database of historic high-energy SPEs and might refine the dating and enable estimates of the spectral shape and fluence of past events.


In dismissing nitrates, while acknowledging their production by SPEs in the middle atmosphere and troposphere, Wolff et al. ’s [2016] conclusion that “…we cannot see a plausible route to identifying and using their deposition in snow to diagnose past (SPEs)” is unproven, and their call for excluding nitrates from 14C and 10Be studies, based on inadequate analyses and incomplete models, is premature.

We reiterate and stand by our main point: “Low time resolution analysis of polar ice cores cannot detect impulsive nitrate events.”


To date, validation of most EWS tools has been suboptimal.

Validation of the NEWS quoted within the Royal College of Physicians' NEWS2 report [2] is based on a single centre on the south coast of England [4].

The demographic tested by this validation may not be applicable to other hospitals, particularly those in urban areas.

Although much of the perceived benefit of EWS comes from a systematised approach, rigorous validation of these scoring systems prior to widespread use is Commented [A1]: This statement should have some supporting evidence referenced.


essential to optimise the benefit for patients and reduce the workload for staff.

Most validation of NEWS has been in acute admissions and medical in-patients, settings in which it appears to perform well; there is less validity in elective surgical settings and non-medical specialities.

These findings have encouraged researchers to develop specialty-specific scores and, within these subpopulations, the predictive value of a generic EWS suffers in comparison [5, 6].

The potential inference is that one standardised EWS may not apply with accuracy to all types of patients and all relevant critical illness events.


Validation of EWS models have generally focussed on discrimination (the ability to differentiate between patients who will and will not suffer an adverse event) rather than calibration (the degree of agreement between model predictions and the actual outcomes) [3, 7].

Calibration is most important when models are used in different populations and is often not reported for EWS, despite their use in varying population groups.

Without confirmation of acceptable discrimination and calibration, the predictive accuracy of a model cannot be stated firmly [8].

Early warning scores developed using statistical analysis of routinely collected data representative of clinical practice appear to provide both improved discrimination and calibration, with potential for better and more efficient care than is possible [9] using consensus-based scores, such as NEWS-2.


The linked study by Chiu at al. in this issue of Anaesthesia may represent the next step towards future EWS, namely population-specific predictions developed using statistical analyses of large populations derived from electronic health records (EHR) [10].

Cardiac surgery in the UK has many good risk prediction tools, but these all aim to predict mortality pre-operatively (EuroSCORE-2 [11]) or in the first 24 h of ICU admission (ICNARC ARCtIC [12]).

This new study analysed data from 13,631 patients who were discharged to the ward from ICU, extracted from an electronic vital signs charting system from four UK cardiac surgery centres.

The centres varied in case-mix and workload.

Logistic regression was used to model the relationship between bedside observations and subsequent adverse event (death, cardiac arrest or ICU admission) within 24 h. The performance of the resulting prediction score was compared with that of NEWS scores.

Model validation on a subset of the dataset held out from model derivation ('internal validation') suggested that the logistic EWS achieved greater discrimination and better calibration than the NEWS.

However, the improvement was only modest: for a score threshold of 5 during the previous 6 h, sensitivity increased from 48% to 52%, with no change in the specificity of 92%.


Importantly, Chiu et al. did not validate their score using external validation in a dataset separate from that used to develop the score.

External validation of a model is important, because we expect worse performance in a new dataset than in a selected (hence representative) subset of the development dataset.

Given that NEWS was not derived from the dataset analysed by Chiu et al., the performance of their EWS is expected to be somewhat better.

It remains possible that the performance of NEWS and the logistic EWS would be similar in an external dataset.


The event rates in the dataset analysed by Chiu et al. were low and re-admission to ICU was the most frequent event within the composite outcome and therefore dominates the model predictions.

Re-admission to ICU is sensitive to the varying cultures, behaviours and resources between different hospitals using EWS.

For example, one in 25 patients in Papworth are re-admitted to ICU, compared with 1 in 45 in Wolverhampton.

Differences in case-mix will not fully account for this variation.

Further, the logistic EWS predicts only subsequent re-admission to ICU, rather than all clinically meaningful deteriorations requiring intervention.


Using EHRs to collect large volumes of data from multiple sites represents the future in the development of EWS.

As more UK hospitals adopt EHRs and standardise data definitions, the sample size and generalisability of EWS based on very large, combined datasets will increase.

Linking physiological data to robust clinical outcomes stored elsewhere in the EHR is likely to improve the granularity and accuracy of predictions.

To do this on a large scale requires national health and care data repositories.

An early example of this is the National Institute of Health Research Critical Care Health Informatics Collaborative [13], which combines ICU data from seven UK centres (including physiological observations, drugs, interventions and outcomes at hourly intervals) that can be linked to Office for National Statistics and NHS Digital records. Were large numbers of UK hospitals to be included, then the opportunities for healthcare research would be myriad.


The NHS has advocated a 'bottom-up' approach to information technology solutions - that NHS trusts should choose their own EHR systems.

To reap the potential research benefit of EHRs, these systems must communicate at the 'back end', even if the 'front end' between different software suppliers looks different.

Ideally, any repository would use a federated model, where centres classify and store their EHR data in a standardised way, with access to a central 'port' allowing retrieval of the required data from local sites.


Machine-learning techniques may improve predictions when based on very large, detailed clinical databases.

Applications of machine-learning are beginning to appear [14].

A sample size of 13,000 patients, as in the data analysed by Chiu et al., is at the lower end of when machine-learning is useful compared with standard regression modelling.

Machine-learning avoids strong parametric Commented [A2]: Needs reference.


Commented [A3R2]: This is data from the Chiu et al paper from which the editorial is about.


Commented [A4]: NHS Digital is the name of a body that deals with digital records of the NHS and therefore needs a capital letter.


assumptions, though this may be at the expense of being able to succinctly present or explain the patterns behind underlying predictions [15].

It is vital that predictions based on machine-learning are validated before they are implemented in clinical practice.


Chiu et al. should be applauded for developing an EWS driven by data, rather than using an expert- opinion based approach.

However, before logistic EWS models can replace current, consensus-based scores such as NEWS2, clinically important performance improvements, based on robust external validation must be demonstrated.

This will require large datasets, representative of the clinical setting within which the EWS will be used, which in turn requires the infrastructure to store and retrieve relevant data.

This will only be possible with investment and a collaborative approach at a regional and national scale from healthcare providers, academic institutions and industry.

It is critical that with the development of increasingly sophisticated EWS models, we retain an end-user experience that is simple and standardised.

Accurate model predictions are of no use with an inadequate response from the clinical team.

To this end, widespread dissemination, training and behavioural interventions are vital to realise the potential benefits of future specialty-specific EWS.


We thank Oglesby et al. for their interesting editorial [1] accompanying our recent paper [2].

We value their interpretation that a single standardised early warning score (EWS) may not be applicable to all types of patients and are grateful for their qualified support of the concept of future population-specific EWS.

We are also in agreement that evidence-based models are preferable to consensus-based scores and that external validation is imperative for all EWS.


We have a major issue with their criticism that we failed to externally validate our score in a dataset separate from that used to develop the score.

Multiple external validation techniques were undertaken and have been available in electronic format in the online supplement since 3 July 2019 [2].

Each centre independently collected four separate datasets.

Although these datasets were ultimately merged into one combined database, we retained the ability to use separate databases for external validation purposes.

Multiple predictive performance and validation techniques were utilised and these are summarised in the supporting information which accompanies the online version of our paper (Appendix 3: Table A3.4.) [ 3].

Debray et al. [ 4] define external validation as using new participant-level data, external to those used for model development, to examine whether the model’s predictions are reliable in individuals from potential population(s) for clinical use.

Following that definition, we purposely held back from using some centres to fit/develop the model so that we could then use them to externally validate a model fit for other centres.

We even reported all possible combinations of doing this, which were a sensible cut of the dataset to avoid accusations of potential bias.


Given the revised evidence-based physiological weightings, novel sub-division of oxygen therapy and potential clinical utility of our app [5] in discriminating between ‘hard’ and ‘soft’ escalation threshold NEWS scores (≥ 5 or ≥ 7), we believe ‘Improving EWS – more data, better validation, a step in the right direction’ may have been a more accurate choice of editorial title.


We thank Mackay et al. [ 1] for their comments on our editorial [2], which accompanied the article by Chiu et al. [ 3] and agree with the definition of external validation that they provide.

In broad terms, internal validation is concerned with reproducibility of a prediction model, whereas external validation is concerned with the transportability of model predictions to other settings and populations.

Randomly selected patients whose data are not included in the training data used to develop the prediction model and used for validation are similar to, and subject to, the same sampling processes as patients in the development dataset.

Internal validation based on such data will address overfitting due to selection of predictor variables and parametrisation of their associations with the outcome, but does not address transportability to different settings.


We applaud Mackay et al. for conducting a range of validation exercises; given the importance of validation we encourage authors and editors to include results of such exercises in the main text of published papers rather than in supplementary material.

From our reading of the manuscript and supplementary material, it appears that no population from a different source was used to externally validate their model.

The populations used for validation were subsets of data from the same four hospitals used in model development (some excluded from the development data set), either: (1) using three hospitals other than Papworth; (2) using data from all four hospitals in 2017; and (3) using the temporally ‘last 10%’ of data from each patient.

These discussions illustrate that there is a continuum within the dichotomy ‘internal validation’ and ‘external validation’ [4].


Siltstone, argillaceous siltstone, silty carbonate, siliciclastic mudstone, shale, and chert are common lithofacies in the upper Devonian-Middle Mississippian strata in basins of the North American midcontinent that accumulated in epeiric seas of the southern Laurentian craton (Fig. 1, 2; Lowe 1976; Lane and De Keyser 1980; Gutschick and Sandberg 1983; Over 1990; Schwartzapfel 1990; Turner et al. 2015; Schieber 2016; Kondas et al. 2018; Godwin et al. 2019; Mazzullo et al. 2019; Miller et al. 2019b; Milad et al. 2020; Price et al. 2020).

These strata are commonly organic-rich, and have attracted interest primarily as petroleum source rocks and unconventional reservoirs (Barrows and Cluff 1984; Smith and Bustin 1998; Smith and Bustin 2000; Slatt and O'Brien 2011; Angulo and Buatois 2012; Higley 2013; Wang and Philp 2019), but they also provide insights on climate, tectonics, and carbon sequestration of the time.


The range of explanations for deposition of the siliciclastic fraction includes: 1) suspension settling (Kirkland et al. 1992; Smith and Bustin 1998), 2) a combination of suspension settling, density currents, and gravity flows (e.g., Wright and Friedrichs 2006; Loucks and Ruppel 2007; Macquaker et al. 2010; Milad et al. 2020; Price et al. 2020), and 3) wind- or tide-induced subaqueous currents that remobilized muds across ancient epeiric seas (Schieber 2016).

Although all accept that these strata ultimately accumulated in marine environments, the provenance, generation, and transport of the significant volume of uniformly fine-grained siliciclastic material remain enigmatic and unresolved.


To better constrain the origin and transport of the siliciclastic fraction in these units, this work integrates new data from Upper Devonian and Lower-Middle Mississippian siltstone, mudrock, and argillaceous carbon- ate units across Oklahoma with compilations of coeval systems of the greater midcontinent region (Fig. 2).

We use these data to test the hypothesis of eolian delivery to the marine environment for the vast volume of this material.

An eolian-dust origin explains otherwise perplexing attributes of these units and has significant implications for interpretation of organic productivity in these and analogous systems.


The Anadarko Basin was initially part of the Oklahoma Basin (Johnson et al. 1988) which originated in the Early Cambrian in response to thermal subsidence associated with development of the Southern Oklahoma Aulacogen (Wickham 1978; Dalziel 1991; Meert and Torsvik 2003).

Cambro-Ordovician carbonate strata accumulated in the axis of the failed rift (Perry 1989) as a broad epicontinental shelf developed on the flanks (Johnson et al. 1989).

Carbonate and, subordinately, siliciclastic mudstone sedimentation continued through the Devono-Mississippian (e.g., Hunton Limestone, Woodford Shale, Sycamore Limestone, Caney Shale), a time of relative tectonic quiescence in this part of the Anadarko Basin and greater midcontinent region, before the tectonic disruptions of the Ouachita and Ancestral Rocky Mountains orogenies (Feinstein 1981; Johnson et al. 1988, 1989).


Closure of the Rheic Ocean and associated contractional tectonics beginning in the latest Mississippian (Serpukhovian)-Early Pennsylvanian (Nance et al. 2010) ultimately produced the thin-skinned Ouachita fold-and-thrust belt and the associated Arkoma basin (Fig. 1).

By Pennsylvanian time, a series of NW-SE-oriented basement-cored uplifts developed, termed the Ancestral Rocky Mountains, including the Wichita- Amarillo uplift and yoked Anadarko Basin of southwestern Oklahoma and northern Texas (Kluth and Coney 1981).

The present configuration of the Anadarko Basin is a product of this compressional inversion of the earlier aulacogen along basement-involved reverse faults, and development of accommodation space along the mountain front (Johnson et al. 1989).


Beyond Oklahoma, Late Devonian orogenic events affected both the eastern and western margins of the craton, in the form of the Acadian- Neoacadian and Antler orogenies, respectively.

The Acadian and Neo- acadian orogenies produced a series of westward-prograding clastic wedges, the Catskill Delta complex during the Middle-Late Devonian and the Price-Pocono-Borden-Grainger delta complex during the Early- Middle Mississippian (Ettensohn and Woodrow 1985; Ettensohn 2004, 2008, 2022), whereas the Antler clastic wedge prograded eastward (Sandberg and Gutschick 1980; Johnson and Pendergast 1981) (Fig. 3).

The Oklahoma Basin remained isolated and far removed from each of these clastic wedge complexes,~1800 km from the Antler and~1500 km from the Appalachian, throughout the Late Devonian to Middle Mississippian (Fig. 3).

Note that we use North American stage names for the Mississippian, for consistency with the literature.


The eastern deltaic complexes are well exposed in outcrops and boreholes throughout the Appalachian Valley and Ridge and Appalachian Plateau provinces, and exhibit both marine and continental facies for nearly 1000 km along orogenic strike (Fig. 1).

The Catskill Delta complex developed during the Middle to Late Devonian, transporting siliciclastics from the Acadian Orogen to marginal marine environments along the Appalachian foreland (Fig. 3).

During the Famennian, this complex comprised an estimated four main delta systems, fed by low-gradient coastal rivers emanating from alluvial systems in the highlands-akin in scale to the Eocene and modern Texas-Louisiana coastal plain, or the northern Sumatran coast (Sevon 1985; Woodrow et al. 1985; Boswell and Donaldson 1988).

Organic-rich black shales characterize the basinal facies of the Catskill Delta complex in the Appalachian foreland basin (Ettensohn and Woodrow 1985; Boswell and Donaldson 1988; Ettensohn 2008; Ettensohn et al. 2019).


Clastic-wedge deposition continued into the Early-Middle Mississippi- an (Kinderhookian-Osagean) in response to Neoacadian tectonism, such that the Appalachian foreland overfilled and deltaic deposition of the Borden and Grainger formations (Figs. 2, 3) prograded into the eastern and southeastern Illinois Basin (Ettensohn et al. 2022).


Active denudation ceased in the Middle to Late Mississippian (Meramecian-middle Chesterian), resulting in extensive carbonate deposition in the Appalachian foreland (Greenbrier and Newman Limestones, Fig. 2; Ettensohn et al. 2022).


Toward the eastern Appalachian foreland basin, carbonate strata intertongue with continental redbeds and eolianites throughout West Virginia, Virginia, and Maryland (Wynn and Read 2008; Ettensohn et al. 2022).


Early development of the Mauch Chunk Delta began in eastern Pennsylvania in the Meramecian (Wynn and Read 2008; Ettensohn et al. 2022) (Fig. 3).


In the extensive cratonic region between the Acadian (east) and Antler (west) orogenic systems, broad epeiric seas, intracratonic basins, and emergent arches prevailed through the Late Devonian-Early Mississippian, FIG.

1.-Major basins (gray shading), domes, and arches (red lines and text) of the U.S. for the Devono-Mississippian.

Abbreviations are as follows: AnB, Anadarko Basin; AntB, Antler Basin, ArB, Arkoma Basin; BWB, Black Warrior Basin; FCB, Forest City Basin; FtWB, Fort Worth Basin; IB, Illinois Basin; MB, Michigan Basin; N. ApB, Northern Appalachian Basin; S. ApB, southern Appalachian Basin; TB, Tobosa Basin; WB, Williston Basin; OD, Ozark Dome.

Numbers within circles correspond to locations of stratigraphic columns in Figure 2.

Yellow polygon outlines the Appalachian Valley and Ridge Province (V&R).

The edge of the brown polygon shows the western limit of the Appalachian Plateau Province (A.P.).

Modified from Adams (1965), Gutschick and Sandberg (1983), Smith and Bustin (2000), Loucks and Ruppel (2007), Soeder et al. ( 2014), Ettensohn et al. ( 2019) and Thomas et al. ( 2020).

Downloaded from http://pubs.geoscienceworld.org/sepm/jsedres/article-pdf/92/12/1186/5763983/i1938-3681-92-12-1186.pdf by McGill University user with organic-rich mudstones, including the Chattanooga Shale, Ohio Shale, New Albany Shale, Antrim Shale, and Woodford Shale accumu- lating east of the Transcontinental Arch and correlative mudstones such as the Bakken Shale and Duvernay Shale accumulating west of the Transcontinental Arch (Figs. 2, 3).


Carbonate-dominant systems with variably silty and cherty components prevailed through the Early-Middle Mississippian (e.g., the North Hill Limestone, Keokuk Limestone, St. Joe Group, Boone Group, Osage Limestone of northern Oklahoma, and Madison Limestone; Figs. 2, 3; Gutschick and Sandberg 1983, and others).


By the Middle to Late Mississippian (Meramecian-late Chesterian), fine siliciclastic contents increased, with deposition of silty carbonates, marine siltstones, and silty organic-rich mudstones (e.g., Blue River Group, Warsaw Shale, Barnett Shale, Meramec and Sycamore formations, Caney Shale and Madison Limestone; Figs. 2, 3).


The midcontinent region ranged from southern subtropical latitudes (~158 S-308 S) in the Late Devonian to near-tropical latitudes (~158 S) by the Early-Middle Mississippian (e.g., Boucot et al. 2013; Domeier and Torsvik 2014; Scotese and Wright 2018) (Fig. 3).

Paleoclimatic reconstructions document arid conditions in lowland regions throughout the midcontinent within these time intervals (Boucot et al. 2013) (Fig. 3).


Additionally, a number of instances of ice-contact deposits document evidence for suspected icesheets and/or alpine and even tidewater glaciation within the central Appalachian orogenic system during the latest Devonian (approximately lower to middle Siphonodella praesulcata conodont zonation; LE, Retispora lepidophyta-Indotriradites explanatus Glaciation in the central Appalachians marks the apex of the late Devonian icehouse.

Glaciation in Gondwanaland began in the late Famennian (approximately Palmatolepis postera conodont zonation; VCo, Diducites versabilis-Grandispora cornuta miospore zonation) and extended to the latest Famennian (Upper Siphonodella praesulcata conodont zonation; LN, Retispora lepidophyta-Verrucosisporites nitidus miospore zonation) (Fig. 2) (Caputo et al. 2008; Isaacson et al. 2008; Lakin et al. 2016).


Additionally, minor evidence for Gondwanan glaciation exists for the Early and Middle Mississippian (Kinderhookian-Osagean and Meramecian-Chesterian) (Caputo et al. 2008; compilation in Soreghan et al. 2019).


In Table 1, we summarize the facies attributes and inferred depositional environments for Upper Devonian and Lower-Middle Mississippian strata of Oklahoma (Figs. 2, 4, 5).

These units are well exposed and have been widely characterized in the Arbuckle Mountains (southern Oklahoma), the Ozark Plateau (northeastern Oklahoma), and the subsurface; correlatives occur throughout the greater midcontinent as demonstrated through hundreds of thousands of well penetrations (Craig et al. 1978; See Supplemental File 1).


To place the study units (Fig. 4) in a broader context (Figs. 2, 3), we used the paleogeographic maps of Blakey (2013), North American Key Time Slices Ó2013 Colorado Plateau Geosystems, Inc, as base maps, and appended these with major climate belts following Boucot et al. ( 2013) and Scotese and Wright (2018).


The Correlation of Stratigraphic Units of North America (COSUNA) project charts (Ballard et al. 1983; Hills and Kottlowski 1983; Patchen et al. 1984a, 1984b; Bergstrom and Morey 1985; Hintze 1985; Shaver 1985; Adler 1986; Mankin 1987; Kent et al. 1988) provided the initial framework for age constraints and correlations, but all unit ages were updated with the most recent available data (Cohen et al. 2013) (See Supplemental File 2 for references).


To assess the nature of and possible transport modes for the siliciclastic material in the target strata, we collected samples for particle-size analysis from seven outcrops and ten cores distributed throughout Oklahoma (Fig. 4; See Supplemental Files 2 and 3).

While carbonate grains and iron oxides, as well as grain coatings, may constitute a minor fraction of the original detrital material, we specifically target the silicate mineral fraction (SMF) as a proxy for detrital grain size.

Therefore, all organic, carbonate, and iron oxide components were removed using a sequential procedure following standard methods (cf.

Sur et al. 2010a, 2010b; Jiang and Liu 2011; Sweet et al. 2013; Foster et al. 2014), as outlined briefly here.


Samples were gently crushed with a ceramic mortar and pestle to pea- size gravel to accelerate chemical reactions, then rinsed with distilled water and sieved at 250 lm to remove any fines generated during crushing.

Samples were subsequently treated with 2N HCl (minimum 24 hours) for carbonate removal.

To remove organic matter and oxidize pyrite, samples were combusted at 500 8C for~12-20 hours.

This does not affect the grain size of silicate minerals, since silicates do not begin to sinter until temperatures exceed 500 8C (Schomburg 1991; Rickard et al. 2016).

Samples were then treated with sodium citrate-bicarbonate-dithionite (CBD) (Sur et al. 2010a; Rea and Janecek 1981) rinses until all iron oxides were removed.


Smear slides of residues were inspected using a polarizing reflected-light microscope under 103, 203, and 403 objectives to assess sample composition and ensure disaggregation, and check for the presence of remnant authigenic phases such as silicified microfossils (Fig. 6).

If present, silicified microfossils were physically removed by sieving following visual inspection to determine appropriate sieve size needed to remove microfossils, microfossil fragments, and/or aggregates while retaining detrital material (See Supplemental Files 2 and 3 for additional information).

Samples exhibiting a predominance of authigenic material were eliminated from analysis.

Furthermore, both visual inspection and previous studies on these strata indicate no to minimal instances of quartz overgrowths (Shelley et al. 2019; Price et al. 2020; Milad et al. 2020; Duarte et al. 2021; Supplemental Files 3, 4), lending further confidence to the fidelity of subsequent particle-size analyses.


Particle sizes of fully disaggregated samples were measured using a Malvern Mastersizer 3000 Laser Particle Size Analyzer (LPSA).

Of 103 samples processed in this way, 82 disaggregated sufficiently to enable analysis by LPSA.


To address compositional characteristics of the silicate material in the Sycamore and Woodford Shale, nineteen~5 g samples from the Arbuckle Uplift transect (Figs. 2, 4) were fragmented to gravel size and submerged in a 1N HCl for 24 hours to eliminate carbonate before whole-rock and trace- element geochemical analysis (acquired by ALS Geochemical).

Remnant CaO values and high loss-on-ignition (LOI) necessitated application of the correction by Taylor and McClennan (1985) to interpret the silicate fraction.

Data were analyzed (and biplots were created) using principal- components analysis (PCA) in R.


To assess provenance of the siliciclastic material, three 10-20 kg samples from each of the Arbuckle Uplift units (Figs. 2, 4; Woodford Shale, Sycamore Limestone, and Caney Shale) were processed for zircon separation and analyzed for detrital-zircon geochronology at the University of Arizona (UA) LaserChron Center.

Analyses were conducted using the Element 2 laser ablation-single collector-inductively coupled plasma- mass spectrometer (LA-SC-ICP-MS) following the analytical and data processing methods described in Pullen et al. ( 2018) and Gehrels and Pecha (2014).

Insufficient yield from the Woodford Shale sample (despite denotes the western limit of the subaqueous delta prograding from the Appalachians (Borden and Grainger Formations) (Ettensohn et al. 2022).


E) Middle Mississippian (~340 Ma, Meramecian) paleogeography of Laurentia.

F

Middle Mississippian, Meramecian, facies and tectonic paleogeographic map.

Both Parts D and F show the midcontinent region within the subtropical arid climate belt, and far removed from shorelines.

Widespread shallow water is present across the craton, including the Appalachian Basin, such as the Newman and Greenbrier limestones (Fig. 2) (Ettensohn et al. 2022).

Stippled clastics in beige in the Appalachian Basin region of West Virginia, Virginia, Pennsylvania, denote late Meramecian coastal sands, silts, and eolian environments (Wynn and Read 2008; Ettensohn et al. 2022).

Stipple and mudstone pattern sketched in Oklahoma (outlined in red), denote the deposition of the Meramec, Sycamore, and lower Caney Shale formations.

Paleogeographic maps A, C, E are from Blakey (2013), North American Key Time Slices Ó2013 Colorado Plateau Geosystems, Inc., and were used as base maps for Parts B, D, F. Paleoclimate boundaries are derived from Boucot et al. ( 2013) and Scotese and Wright (2018).

See Supplemental File 2 for all references pertaining to construction of facies maps and tectonic maps.

Downloaded from http://pubs.geoscienceworld.org/sepm/jsedres/article-pdf/92/12/1186/5763983/i1938-3681-92-12-1186.pdf by McGill University user .

20 kg of processed material) precluded analysis.


Best ages from the Sycamore Limestone and Caney Shale are reported as 206 Pb/ 207 Pb for ages greater than 900 Ma and 206 Pb/ 238 U for ages less than 900 Ma.

These ages are displayed as normalized probability density plots using an Excel macro, provided by the UA LaserChron Center (Gehrels 2010).

Analyses with . 10% discordance and 5% reverse discordance were eliminated before analysis.


To address climatic attributes and potential atmospheric transport pathways, we reanalyzed results of a total of 12 coupled general- circulation-model (GCM) experiments of global climate from intervals spanning the Middle Devonian to the Late Mississippian.

The simulations were performed with the HadCM3BL-M2.1aD atmosphere-ocean-vege- tation model (Valdes et al. 2017) and are described in detail in Valdes et al. ( 2021).

The model uses the same horizontal grid with a resolution of 3.758 3 2.58 in the atmosphere and ocean, with 19 and 20 unequally spaced vertical levels, respectively.

The paleogeographies underlying the model simulations were derived from the PALEOMAP paleogeographic atlas (Scotese and Wright 2018).

Current geographical coordinates of Oklahoma and the Appalachian clastic wedges were rotated using the PALEOMAP rotation model in the pyGplates software (Müller et al. 2018) to place these features within the appropriate paleogeographical positions.

Locations for the Appalachian clastic wedges are from Ettensohn et al. ( 2019).


Besides the model geography, the simulations differ only by a linearly increasing solar constant (0.8% per 100 Myr) and varying concentrations of atmospheric carbon dioxide.

CO 2 levels for the selected simulations were derived from the Foster et al. ( 2017) curve and range from a peak of 1377 ppmv in the Middle Devonian to a minimum of 233 ppmv in the Middle


-The paleoenvironmental conditions of the Oklahoma and greater North American mid-continent region changed from predominantly subtropical and arid conditions during the Famennian to a rather monsoonal system exhibiting seasonal wind reversals and summer precipitation during the Early to Middle Mississippian (Kinderhookian through Meramecian).

These seasonal wind reversals suggest that the greater midcontinent region was influenced by trade winds during Southern Hemisphere winter months (June through August) (Fig. 10).

The simulated surface wind patterns are dominated by an interplay between the prevailing southeasterly trade winds, the land-sea thermal gradient, and local orography.

As such, Acadian and Caledonian orogens tend to, in general, guide the flow of air around the landmasses, and absolute wind speeds are reduced due to the presence of these orogenic belts.


Summer winds in the Late Devonian (Fig. 10A) are shown to have come from the south and diverge across the Laurentian epeiric seas with a westerly jet flowing along the northern front of the Acadian orogen.

During Late Devonian winter months, a more southeasterly trade-wind circulation developed across Laurentia (Fig. 10B).

Westerlies along the southern front of the Acadian orogen show reduced wind values topping over the mountain range with acceleration on the lee side of the orogen.


The Osagean (Fig. 10C, D) and Meramecian (Fig. 10E, F) surface-wind patterns are very similar.

With the progressive northward movement of Laurentia, the mid-continent region came under the influence of cross- equatorial, northwesterly winds bringing moist air and summer precipita- tion toward emergent landmasses (Fig. 10E, F).

Winter precipitation was very low over the adjacent subtropical land masses and limited to tropical regions northwards of 108 S throughout the Devonian to Mississippian (Supplemental File 6).

Surface windflow patterns during the Osagean and Meramecian winter months (JJA) flowed westward along the southern front of the orogen, topped over the range, and swept across the Laurentian epeiric seas in a west to northwestern direction (Fig. 10E, F; Supplemental File 6).


Interpretation.-Model simulations reveal that the Laurentian land- mass and associated orogens strongly influenced surface wind patterns.

In fact, the presence of a larger landmass at subtropical latitudes during the Early Mississippian promoted seasonal wind reversals and a southward migration of the Intertropical Convergence Zone (ITCZ).

According to the model data, a northwestward transport of continental dust toward the midcontinent seems physically plausible, although such a transport would have been possible only during the austral winter months.

These months are characterized by northwestward, offshore winds across the potential source regions of the Acadian-Neoacadian clastic wedges.


These wind patterns may be analogous to present winds along the Andean orogen, where westerlies from the South Pacific anticyclone are deflected northward creating a southerly jet along the Andean Pacific front.

Air masses top over the orogen and descend as warm dry air off the lee side of the mountain range, known as Zonda winds (Puliafito et al. 2015; Zou and Xi 2021) which are similar in process to Foehn winds off New Zealand's Alps, Chinook winds off the North American Rocky Mountains, and Santa Ana winds over the mountains of southern California (McGowan et al. 1996; Hugenholtz et al. 2007;Álvarez et al. 2021).


Even though coastal and continental summer precipitation increased towards the Early Mississippian, winter months around the orogenic highlands were still dry in the simulations and a stronger easterly wind component may have transported material to the Oklahoma region of Laurentia.

A comparable analog for the Early-Middle Mississippian model results, which sits at the same latitude, might be present-day northwestern Australia, where easterlies transport dust westward towards the Indian Ocean during the winter months following the northward migration of the ITCZ (Christensen et al. 2017; Keep et al. 2018).


exhibit much higher values of Al more typical of source material that is highly chemically weathered.

Rather, both units-especially the Sycamore Limestone-exhibit a signature more similar to loess (Fig. 8B).

The inflection in Zr and TiO 2 with respect to Al 2 O 3 (Zr/Ti O 2 , Zr/Al 2 O 3 , TiO 2 / Al 2 O 3 ) in the basal Sycamore Limestone (Fig. 8C) is interpreted to reflect a possible excursion in provenance.

These compositional data are indepen- dent of grain size, as evinced by the weak relationship (r 2 , 0.07) between grain size and these elements (Supplemental File 4), as well as the negligible grain-size variation within each of the Sycamore and Woodford formations in general (Fig. 7).


Delivery of siliciclastic material to epeiric seas occurs by either deltaic or eolian transport.

Various processes can then redistribute this material, e.g., hyperpycnal flows, sediment gravity flows induced by slope failure or wave and/or current agitation, or contour currents (Stow et al. 2001; Walsh and Nittrouer 2009; Macquaker et al. 2010; Schieber 2016).

Assuming an average wave base of 50 m, Schieber (2016) posited a maximum transport distance of 100 km for remobilized sediment, or up to 400 km for an extreme case of a 200 m wave base.

Acknowledging the vast distribution of siliciclastic mud across the Devonian Laurentian sea, Schieber (2016) advocated transport induced by bottom currents, but these arguments demand lateral transport for thousands of kilometers in low-gradient epeiric seas with wave bases ,, 200 m. While we acknowledge these arguments as one hypothesis, we propose that eolian delivery is an additional and simpler explanation that avoids the need to call upon unknown and unusually powerful storms or tides, as well as flocculation, which is inconsistent with the minimal clay-mineral content of the study units.


The genesis and initial mobilization of the large volumes of ubiquitously fine siliciclastic material characterizing the study units remain unresolved, in large part owing to the absence of proximal feeder system(s).

For the Devono-Mississippian Bakken Formation of the Williston Basin, for example, Egenhoff and Fishman (2013) inferred the presence of a deltaic fan that is either not preserved or not exposed.

Similar inferences of deltaic feeder systems are applied to Devono-Mississippian mudstone units of the midcontinent (Price et al. 2020) yet without documentation of any remnants of the subaerial (delta plain) or proximal (subaqueous delta) depositional systems.


It is highly unlikely that researchers have overlooked such delta systems, given the great density of subsurface data points across the midcontinent region (e.g., . 100,000 wells in Kansas and Oklahoma; Newell et al. 1987; Oklahoma Corporation Commission 2021).

The coeval deltas nearest to the midcontinent region during Late Devonian time are the Catskill (Appalachian foreland) and Antler (Antler foreland) systems, and, for the early Mississippian, the Price-Pocono-Grainger-Borden (Appala- chians) deltas, that are separated from the midcontinent study area bỹ 1,000-1,500 km (Fig. 3) (Lane and De Keyser 1980; Ausich and Meyer 1990; Gutschick and Sandberg 1991; Richardson and Ausich 2005; Ettensohn 2004, 2008; Ettensohn et al. 2019, 2022).


In contrast to the Upper Devonian, Lower-Middle Mississippian (Kinderhookian-Meramecian) strata in northeastern Oklahoma are cast as a shallow-water carbonate ramp that yielded to the sediment-starved (during Kinderhookian-Osagean) Ouachita trough (southeast) and proto- Anadarko Basin (southwest; Lane and De Keyser 1980; Gutschick and Sandberg 1983; Godwin and Puckette 2019; Miller et al. 2019b).

Yet the occurrence of partial Bouma sequences inferred from the Sycamore Limestone (southern Oklahoma) (Schwartzapfel 1990; Miller and Cullen 2018; Milad et al. 2020), as well as clinoform geometries inferred from A.J. MCGLANNAN ET AL.

University user subsurface data, led Price et al. ( 2020) to similarly infer a nearby fluvio- deltaic source despite the ubiquitous absence of evidence for proximal feeder systems.

The Transcontinental Arch and the Ozark Dome were the most proximal exposed regions during this time but consist of correlative carbonates with limited exposures of Cambro-Ordovician sandstones and localized occurrences of upper Devonian sandstone-insufficient in volume, grain size, and provenance to supply the enormous amounts of exclusively fine-grained material to surrounding regions (Hills and Kottlowski 1983; Bergstrom and Morey 1985; Adler 1986).


If any of these Devono-Mississippian systems were deltaic, they are uniformly ''headless''-missing all but the distal-most ''prodelta'' fines, even where the shelf margins are preserved.

In other words, there were no deltaic systems located nearby to transport the siliciclastic material basinward. And if the siliciclastic units represent prodelta fines, they exhibit remarkably uniform grain sizes that fail to fine with distance from the inferred deltaic feeders, and exhibit an unusual dearth of true clay (minerals or grain sizes; Figs. 7, 8).


In contrast to the prevalence of fluvio-deltaic inferences for the study units, few have mentioned eolian processes (e.g., Miller and Cullen 2018; Shelley et al. 2019), whilst others make a connection to the origin of chert in correlative units such as the Arkansas Novaculite, the Caballos Novaculite, and cherts of the Osage-Meramecian strata (Banks 1970; Lowe 1976; Cecil 2004, 2018b).


Schieber (2016) explicitly dismissed an eolian origin for these mudrocks based on the presumed need for large erg systems to supply the fines via saltation abrasion of sand; yet eolian abrasion produces very little silt (Swet et al. 2019, 2020; Adams and Soreghan 2020).