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Impossible as it seems, the cosmogenic-nuclide job market looks like the job market in the rest of the US

June 30, 2021

Cosmogenic-nuclide geochemistry is not generally noted for being anything like real life. It can’t even be explained to most folks without a 20-minute briefing on nuclear physics, and it is rare to find any normal person off the street who is even willing to get through the entire 20 minutes. The business model for the field involves taking bright undergraduates with many skills and great potential for success…and using the lure of exotic fieldwork in places like Antarctica and Greenland to systematically divert them into successively more arcane graduate programs, eventually leading to an unlikely chance of employment in a vanishingly small number of poorly-salaried positions. This is precisely the opposite of the economic model for prominent fields otherwise suitable for bright undergraduates, such as law, medicine, and finance, in which enduring an arduous training program yields guaranteed employment at a high salary. This may only prove that cosmogenic-nuclide geochemists are just people who failed the marshmallow test, but for present purposes highlights the deep divide between this field and the rest of the global economy. We are fishing in different oceans.

The purpose of this blog posting, therefore, is to highlight an unusual convergence between the two. The big economic story right now as the US emerges from the COVID-19 pandemic is that it is almost impossible to hire workers. Depending on who you are, this is either (i) Joe Biden’s fault for rewarding slackers with rich unemployment benefits, or (ii) a welcome rebalancing of the relationship beween capital and labor. What is bizarre is that this also appears to be a problem in the alternative economy of cosmogenic-nuclide geochemists. Recently, the U.S. National Science Foundation provided some funding to support the ICE-D database project, which has been the subject of quite a number of blog entries in recent years and, basically, is supposed to be producing a so-called transparent-middle-layer database to facilitate regional- and global-scale research using cosmogenic-nuclide data. Part of this funding is for postdoctoral support, and since late May we have been advertising for this position. Sure, maybe the ICE-D website itself is not the greatest way to reach a broad audience, but it’s also been posted with AGU, and the Twitter announcement got 8000 views, which isn’t huge by global Twitter standards, but is probably well in excess of the total number of geochemists in the US.

However, so far this advertisement has only generated ONE APPLICATION that passed an initial screening for qualifications in some reasonably related field. One is an extremely small number for even a postdoc position in a fairly obscure field, and it’s not enough to conclude that we have made a reasonable effort to attract a representative pool of applicants, so we are continuing to advertise. But this is very strange. From the perspective of the rest of the US economy, we might expect it to be hard to fill this position. But this is the cosmogenic-nuclide geochemistry economy! It should have no relation. So, why is this? I can think of a few possibilities.

One: cosmogenic-nuclide geochemists are kicking back and collecting unemployment. Frankly, this seems unlikely, and we did make sure to set the salary above the total of California unemployment and the Federal supplement. But if this is the problem, it’ll be fixed in a couple of months when the supplement expires.

Two: everyone has a job already. This seems very possible, as NSF has been aggressively giving out supplemental funding to extend existing PhD and postdoctoral salaries so as to bridge over the disruption to nearly all field and lab work during the 2020-21 academic year. In addition, I haven’t attempted to look at this systematically, but my impression is that a large number of postdoc positions have been advertised recently on Earth science newsgroups. These together would imply a pipeline stoppage in which all postdocs have jobs, but no PhD students will need to become postdocs until next year. If this is the explanation, we might expect to have to wait 6 months or so before seeing a decent number of applications. Not ideal.

Three: international travel is still closed. This position is open to non-US citizens, and under normal circumstances I would expect to see a lot of international applications. Let’s face it, places outside the US, in particular Europe and the UK, have produced a lot more early-career cosmogenic-nuclide geochemists than the US in recent years. However, right now it is extremely unclear whether or not, and under what conditions, a European Ph.D. would be able to enter the US right now to actually take the job. I don’t know the answer to this myself, but I agree it doesn’t look great in the near term. Solving this problem, again, might take 6 months to a year.

Four: there are structural obstacles to folks moving right now. Mainly, “structural obstacles” in this context means “I have kids and there is no school or child care right now.” Just considering the demographics, this doesn’t seem super likely, but it could be a show-stopper for individuals. By the way, if this describes you, this might be fixable. Contact me.

Five: Berkeley is impossibly expensive. Berkeley is in the heart of one of the most expensive metropolitan areas in the world. Although BGC aims to have postdoctoral salaries be equivalent to those specified by NSF in their postdoctoral programs (currently $64k), these are set nationally and are low compared to the cost of living here. This is a serious problem and I don’t have a good way to fix it, except to move somewhere else myself, which may be the only long-term solution but is tough to implement immediately. However, if you would have applied for this job if not for the cost-of-living situation, please contact me and let me know. If we are going to figure out some way to raise salaries, it’ll be because of emails that say “I am a great scientist but I can’t work at BGC because Berkeley is too expensive.”

Six: the software part of the job description is off-putting. I’ll give this one some attention because I’m a bit worried about this: I know of several candidates who would be qualified for this job and have applied for other recent postdocs related to cosmogenic-nuclide geochemistry, but have not applied for this one, and I don’t know why. Also, this is one of the possibilities that I can do something about fixing. Basically, the point of the ICE-D project is to build computational infrastructure to make it possible to look at large data sets of cosmogenic-nuclide data to do things like paleoclimate analysis, slip reconstructions on complex fault systems, ice sheet model validation, etc. Doing this requires some knowledge of both cosmogenic-nuclide geochemistry and software development. People who are good at both of these things are rare, of course, but the job description for this position includes both. It’s asking for someone who (i) at least cares about cosmogenic-nuclide geochemistry and how it’s applied to important Earth science problems, and also (ii) is at least interested in software design. It may be that the job description has too much of (ii) in it, and otherwise qualified cosmogenic-nuclide geochemists are suffering from impostor syndrome about the software part.

Unlike US immigration policy and apartment rents in Berkeley, this one is fixable. I’ll start by admitting how many programming classes I’ve ever taken, which is zero. Whoever you are, you have probably had more training in this than I have. I am a terrible impostor when it comes to software.

The next point is that software development is not that hard. The most important thing from the perspective of this project is having an idea about what you want to do with large amounts of cosmogenic-nuclide data, and a willingness to learn what you need to learn to implement this idea. Once you have this, it is not that hard to learn enough to implement what you want to do in ways like we have already set up with the ICE-D project. Also, there are actual professional software developers involved with this project. You don’t have to do it yourself. In other words, we don’t expect to find a candidate for this who already knows a lot about cosmogenic-nuclide geochemistry AND software development. This would be very unlikely. What we want is a candidate who would like to become this person in future.

Finally, to get back to the point of the beginning of this blog entry, the really unusual aspect of this postdoctoral position, which seems kind of attractive to me, is that it’s a fairly rare opportunity to break out of the death spiral of scientific overspecialization. The problem that this project is aiming to fix — how to do something with lots of data that together are too complicated for an Excel spreadsheet — is, uh, not exactly unique to geochronology. If you spend a couple of years working on this, no one is going to tell you you can’t describe yourself as a ‘data scientist’ in future job applications, which is likely to lead to many options that are not generally open to ‘cosmogenic-nuclide geochemist.’ So far, one postdoc has worked on the ICE-D project. They left early to work as a data scientist for a cybersecurity startup. If you still want to be a professor at a small college regardless, can it hurt to be able to teach “data science” as well? Possibly not.

To summarize: if you are reading this and you considered applying for this job but didn’t do so for one of the reasons listed above, or some other reason, please contact me and let me know why. That would be extremely helpful and could make it possible to fix the problem. If you didn’t know about it before, take a look at the description and circulate it. Thanks much.

Antarctic deglaciation in one figure

January 25, 2021

This post is not so much about how one generates cosmogenic-nuclide exposure-age data, but what one does with it. Especially when you have a lot of it. The example focuses on exposure-age data from Antarctica that record the LGM-to-present shrinkage of the Antarctic ice sheets.

First, the background. Right from the beginning of exposure-dating, one of its most useful applications has been figuring out what happened to the Antarctic ice sheets. Even though only a tiny amount of Antarctica is actually ice-free at the moment, the areas that are ice-free are thoroughly stocked with glacial deposits that were deposited when the ice sheet was thicker or more extensive. Unfortunately, it is really hard to date these deposits, because they are just piles of rock, with almost nothing organic that can be radiocarbon-dated. Enter exposure dating. Cosmogenic-nuclide exposure dating is nearly perfect for Antarctic glacial deposits, because most of the time the rock debris in these deposits is generated by subglacial erosion of fresh rock that hasn’t been exposed to the cosmic-ray flux, so when it gets deposited at a retreating ice margin, its subsequent nuclide concentration is directly proportional to how long it has been exposed. Let’s say you visit a peak sticking out of the ice that looks like this:

This is a place called Mt. Darling, in Marie Byrd Land in West Antarctica. Upon the glacially-scoured bedrock of this summit you find things that look like this:

These are “erratic” clasts that don’t match the bedrock, so must have been transported by somewhere else when the ice sheet was thicker, and deposited here by ice thinning. If you measure the exposure age of a bunch of erratics at different elevations, you get this:

Exposure ages are older at higher elevations and younger at lower elevations, indicating that this peak has been gradually exposed by a thinning ice sheet during LGM-to-present deglaciation between, in this case, 10,000 years ago and the present. This is a classic application of exposure dating, first implemented in West Antarctica circa 2000 in a couple of studies by Robert Ackert and John Stone (this example is from the Stone study).

The usefulness of this technique in measuring how fast the Antarctic ice sheets shrank after the LGM, and therefore in figuring out the Antarctic contribution to global sea level change in the past 25,000 years, immediately inspired everyone else working in Antarctica to get busy and generate more of this sort of data. Now, 20 years later, we have a lot of it. Usefully, these data are all compiled in one place in the ICE-D:ANTARCTICA database.  At the moment, there are 163 sites in the database at which some LGM-to-present (that is, 0 to 25,000 years before present) exposure-age data exist, and they are all over Antarctica, at least in all the areas where there is some rock outcrop available:

So, the challenge for this blog posting is to use these data to generate some kind of overall picture that shows the overall response of the Antarctic ice sheet to LGM-to-present deglaciation. And the further challenge is to do it with an automated algorithm. It is way too much work to individually curate 163 data sets, and if it is written as a script then it can be repeatedly run in future to assimilate new data. The main problem that the hypothetical no-humans-involved algorithm needs to deal with is that the data sets from most of the 163 sites are much messier than the example from Mt. Darling shown above. For that example, you just draw a line through all the data and you have the thinning history at that site during the period represented by the data. Done. However, consider this site:

These data are from a place called Mt. Rea, also in Marie Byrd Land. At some elevations, there is not just one exposure age, but a range of exposure ages. The reason for this is that a lot of these samples were not deposited during the LGM-to-present, most recent deglaciation. They were deposited in some previous interglaciation prior to the LGM, exposed for a while, covered by LGM ice but not disturbed, and then exposed again. Thus, their exposure ages are older than the age of the most recent deglaciation. This problem is very common in Antarctica because the thicker LGM ice sheet was commonly frozen-based, so did not erode or remove pre-existing glacial deposits.

The usual means of dealing with this is to assume that the population of exposure-dated samples at a site consists of some mixture of (i) samples that have only been exposed since the most recent deglaciation, and (ii) samples that were exposed in prior interglaciations and covered at the LGM. We want to consider (i) and ignore (ii). This assumption implies that the youngest sample at any particular elevation should give the correct age of the most recent deglaciation, and other samples at that elevation must overestimate the age. In addition, the basic geometry of the situation — that ice cannot uncover a lower elevation until and unless it has already uncovered all higher elevations — leads to the basic concept that the true deglaciation history inferred from a data set like this one is the series of line segments with positive slope (that is, increasing age with increasing elevation) that most closely bounds the left-hand (i.e., younger) side of the data set. It is fairly straightforward to turn these rules into a MATLAB function that will generate an allowable deglaciation history for a typical set of messy exposure-age data from some site. Here is the result for this data set:

This algorithm is extremely simple, but most of the time comes up with about the same interpretation that a knowledgeable human would, although it has a tendency to take outliers on the young side slightly more seriously than the human. So, let’s apply this to all 163 data sets. Of course, it is only possible to apply this algorithm to a data set that contains at least two samples for which the higher sample is older, so this step excludes 64 sites with either single measurements or small numbers of data that can’t be fit with a positive slope, leaving 99 sites. Here are the results:In each of the panels in this plot, the x-axis is exposure age from 0 to 25,000 years. The y-axis is always elevation, but the limits vary with the elevation range of the data set. The dots are the measured exposure ages, and the red line is what the pruning algorithm comes up with. Readers who actually know something about the details of some of these sites may notice that this algorithm is not quite doing exactly what they would do — the main difference is that it is ignoring information from some papers to the effect that samples at the same elevation at a particular site may not reflect the same amount of ice thickening. But what we see here is that the majority of LGM-to-present data exposure-age data sets from Antarctica do, in fact, indicate LGM-to-present thinning of the ice sheet.

The next step is to put all these deglaciation histories on the same page:

In this plot they are shown with actual elevations. This plot gets us pretty close to the goal of representing all the exposure-age data pertaining to Antarctic deglaciation on a single figure, and it is interesting. What is most interesting is that it shows very clearly that rapid ice sheet thinning is very common in the middle Holocene, between about 4-8 ka, and very rare otherwise. This is not necessarily expected. It’s also neat because it highlights the fact that when you have a large enough amount of data, even if you think that either the data from some sites are garbage or the pruning algorithm is delivering garbage results sometimes (both of which are probably true), it is not necessary to rely on any one particular record to have high confidence in the overall conclusion that rapid thinning is much more common in the mid-Holocene than at any other time. If we wanted to strictly disprove the hypothesis that a couple of sites experienced rapid thinning at younger or older times, then we would have to think in more detail about the details of the few anomalous sites. And it is possible that the pruning algorithm is biased slightly young by accepting a few young outliers that humans might have rejected. But I will argue that the broad conclusion from these data that nearly all recorded rapid thinning events took place in the mid-Holocene is very error-tolerant. It has low dependence on the pruning algorithm performance or on any one or a few individual sites.

Another thing that is interesting about this plot is that the period during which rapid thinning took place is basically the same at all elevations. Ice sheet thinning forced by grounding line retreat, for example, should be evident first at low-elevation sites near the grounding line and then later at higher-elevation inland sites. There is no evidence of that in this plot. Everything seems to basically go off at once.

Another way to show this is to move these results from time-elevation space to time-thinning rate space, as follows:

What happened here is that, remember, each deglaciation history generated by the pruning algorithm is a series of line segments. This plot just shows the time extent of each line segment plotted against its slope, which is the deglaciation rate in meters per year. For example, suppose a thinning history has a line segment whose endpoints are 100 m elevation at 5 ka and 200 m elevation at 6 ka. 100 m of elevation change in 1000 years is 0.1 m/yr, so in the second plot this would be represented as a line that extends from x = 5 ka to x = 6 ka, and has a y-coordinate of 0.1 m/yr. This plot has several weird pathologies, mostly focused on the fact that because of the basic properties of averaging, only short line segments can have steep slopes…the longer the period of averaging, which is just determined by what data were collected, the lower the slope is likely to be. However, it is very effective at highlighting the basic  thing that we learn from these data, which is that nearly all rapid ice sheet thinning events in Antarctica during the LGM-to-present deglaciation took place during a relatively short period between about 4 and 8 ka. Even if you ignore the extremely high thinning rates shown by a handful of short lines, the cloud of longer lines near the bottom of the plot shows the same effect. Thinning rates were relatively slow basically everywhere until well into the Holocene, and all the action happens in a few thousand years in the middle Holocene.

Although of course rapid mid-Holocene thinning has been observed and discussed at length in a lot of studies focusing on one or a few of the data sets that are summarized here, it is not very clear from first principles why this should be true. Because nearly all Antarctic glaciers are marine-terminating, the simplest glaciological explanation for accelerated ice sheet thinning in Antarctica has to do with nonlinear grounding line retreat. Sea level rise or ice thinning results in the grounding line of a marine-based part of the ice sheet becoming ungrounded, which triggers rapid thinning and retreat until the grounding line can stabilize again at some upstream position. This process should be driven by global sea level rise associated with melting of the Northern Hemisphere ice sheets. However, global sea level rise starts about 19,000 years ago, is fastest 14-15 ka, and is basically complete by 7-8 ka. It appears that nearly all of the Antarctic outlet glaciers that we have exposure-age data for waited until sea level rise was almost over before responding to sea level rise. Weird.

The basic concept that probably resolves most of this apparent discrepancy, as mostly inferred from ice cores and implemented in ice sheet models of the last deglaciation, probably has to do with the fact that climate warming after the LGM greatly increased accumulation rates throughout Antarctica, This seems to have somewhat balanced the tendency toward grounding line retreat and sustained a relatively large ice sheet until the mid-Holocene. And in addition, there are important biases in the exposure age data set, specifically that many of the sites where we have exposure-age data were completely covered during the LGM, so they can only preserve a record of deglaciation for the time period after they first became exposed. But even so, sites that were not covered during the LGM show slow thinning until well into the Holocene. And nearly all ice sheet models have a very strong tendency to initiate rapid thinning around the beginning of the Holocene, which is still a couple to a few thousand years before it is recorded by exposure-age data.

Regardless, this is a decent attempt at the goal of showing nearly all of the exposure-age data set that is relevant to LGM-to-present deglaciation in one figure. It highlights that even if you have a lot of messy data sets, considering them all together leads to what I think is a fairly error-tolerant conclusion, that doesn’t critically depend on any of the data sets or assumptions individually, that most of the action in Antarctica during the last deglaciation took place during a short period in the middle Holocene.

An end to the Cl-36 monopoly

October 28, 2020

For the last several years, folks interested in computing exposure ages from beryllium-10 or aluminum-26 measurements have enjoyed a proliferation of online exposure age calculators enabling them to do this. This seems like a good thing, because it’s useful to be able to make sure that whatever conclusion you are trying to come to doesn’t depend on the specific calculation method you chose — it should be true for any reasonably legit method and choice of parameters — and, in addition, competition keeps prices down. In fact, this works so well that all the online exposure age calculators are free.

On the other hand, folks interested in chlorine-36 exposure dating have, alas, not enjoyed the same diversity of choices. So far, the only option for Cl-36 exposure-dating has been the ‘CRONUSCalc‘ service, which works great but has several practical disadvantages for some users. For one, it has a fixed production rate calibration and no means of experimenting with alternative calibrations, which could be either a feature or a bug depending on your attitude. Second, it was designed for precision and not speed, and it is quite slow, returning results asynchronously via email instead of immediately via the web browser. And, personally, I find the input format maddening because all the measurements are at the beginning and all the uncertainties are at the end. This is weird. Of course, none of these issues are catastrophic, show-stopping, or really even more than mildly annoying, but, more importantly, if there is only one option for calculating Cl-36 exposure ages, it is hard to tell how sensitive your conclusions are to whatever assumptions the CRONUSCalc developers made in putting together the rather complex Cl-36 code. And also, monopolies are financially precarious for customers. What if CRONUSCalc suddenly requested your credit card info? You would have to pay up. No choice. Not good. 

So, the purpose of this post is to describe progress in ending the Cl-36 monopoly. First, the group responsible for CREp has focused on the calibration issue, and is working on a Cl-36 adaptation of CREp that, like the existing version for Be-10 and Al-26, will have several calibration options. This is not done yet but I am reliably informed that it is fairly close. Second, I finally finished a draft version of a Cl-36 exposure age calculator that, like the existing version 3 of the online exposure age calculators, is designed to be much faster than CRONUSCalc so that it can serve as a back end for dynamic exposure age calculations used by the ICE-D online databases. The point of this blog entry is to briefly explain what is in it and, less briefly, discuss the messy subject of production rate calibration for Cl-36.

The overall summary is that now there are two choices. You can wait until the end to decide whether they are good choices, but they are choices. 

So, the problem with Cl-36 exposure age calculations is that there are too many ways to produce cosmogenic Cl-36. In most commonly used nuclide-mineral systems, e.g., Be-10, Al-26, He-3, C-14, and Ne-21, nearly all production is by spallation on one or two target nuclei, with a small amount of production by muons. For exposure-dating, you need to do some kind of a muon calculation, but it doesn’t have to be very precise because it only accounts for a few percent of surface production. So, in general, you are just scaling a single production reaction on one or two elemental targets. Mostly these nuclides are measured in quartz, so the target chemistry isn’t even variable. That is pretty straightforward. Cl-36, on the other hand, is produced by (i) spallation on four different target nuclei (K, Ca, Fe, Ti) whose concentrations are quite variable in rocks and minerals commonly used for Cl-36 measurements, (ii) muons, and (iii) thermal neutron capture on Cl, which is, basically, a mess, and requires a calculation of the entire thermal and epithermal neutron field surrounding the sample. Thus, the first problem in extending the existing online exposure age calculator to Cl-36 is that we have to compute production due to four different spallation reactions, which all have slightly different scaling, instead of one, and also add an algorithm to calculate production rates via the thermal neutron pathway. Muons are basically the same as before, with the exception that you have to account for variable target composition in computing Cl-36 yields from muon reactions. However, this is all feasible, and for this application I have just implemented the thermal neutron and muon calculations from an extremely clear 2009 paper by Vasily Alfimov. So that is done, although there are a few minor issues left having to do with calibration of muon reaction yields from K and Ca. 

The real problem that arises from the diversity of pathways for Cl-36 production is not that it is hard to code — that is a pain but is manageable. The problem is that it is hard to calibrate. For Be-10 in quartz, for example, it is straightforward to find a lot of different calibration sites where the same reaction — high-energy neutron spallation on Si and O — is happening in the same mineral. Thus, you are fitting a scaling algorithm to data that are geographically variable, but are always the result of the same reaction. You can apply a large data set of equivalent measurements to evaluate reproducibility and scatter around scaling model predictions. For Cl-36, on the other hand, target rocks or minerals for Cl-36 measurements at different calibration sites not only have different ages and locations, they have different chemical composition, so not only is the production rate from each pathway geographically variable in a slightly different way, the relative contribution of each pathway is variable between samples. 

The result of this is a bit of a mess. Results from two sites are not comparable without applying transformations not only for geographic scaling but also for compositional variability. Of course, increasing complexity brings not only increased messiness but also increased opportunities for complex methods to reason your way out of the mess, and anyone who has ever had a class in linear algebra will immediately grasp that the problem is, in principle, solvable by (i) collecting a lot of calibration data with the widest possible diversity of chemical composition and production mechanisms, and (ii) solving a large system of equations to get calibration parameters for the five production rates. In fact, that would be really cool. If you have no idea what I am talking about, however, that’s OK, because the application of this approach to large sets of Cl-36 calibration data by different research groups, mostly in the 1990’s and 2000’s, yielded thoroughly inconsistent and largely incomprehensible results. Theoretically, two applications of this idea to different calibration data sets should at least yield the same reference production rate for at least some of the pathways, but in reality different attempts to do this yielded estimates of the major production parameters (e.g., the reference production rate for spallation on K or Ca) that differed by almost a factor of two. That is, even though two calibration attempts might yield a good fit to large data sets consisting of samples with mixed production pathways, if you want to use those results to compute an exposure age from a sample with only one of those pathways, your exposure ages will differ by a factor of two. Figuring out why is extremely difficult, because this calibration approach yields parameter estimates that are highly correlated with each other, such that a mis-estimate of, say, the production rate from K spallation might actually arise from a mistake in handling production from Cl. Essentially, although this approach is mathematically very attractive and should work, so far history has shown that it does not work very well. 

The alternative approach to production rate calibration is to cast aside the samples with multiple production pathways and focus on collecting a series of calibration data sets, each consisting of samples where Cl-36 production is dominated by one pathway. For example, Cl-36 production in K-feldspar separates with low native Cl concentrations is nearly all by K spallation, and likewise production in plagioclase separates is nearly all by Ca spallation. This approach was mostly first used in a series of papers dating back to the 1990’s by John Stone, and was also what was adopted for production rate calibration for the CRONUSCalc code. Although it is mathematically much more boring and has the significant disadvantage that the need to obtain calibration samples with very specific chemistry greatly reduces the size of each calibration data set and therefore makes it hard to quantify fit to scaling models, it has the advantage that it actually works. The calibration of each pathway is independent of the others, so it is not necessary to, e.g., have a complete understanding of thermal neutron production to date samples where production is mostly by K spallation. 

I am using this approach. The pathway-specific calibration data sets used for CRONUSCalc, however, were very small, so I am using larger sets of literature calibration data from more sites to try to reduce the dependence on any one site. Of course, because the whole point of the version 3 online exposure age calculators is to be part of the overall ICE-D database ecosystem, the entire development project is somewhat elaborate and  looks something like this:

  1. Figure out an extended input format for Cl-36 data that looks like the existing He-Be-C-Ne-Al input format and includes major and trace element compositions (and doesn’t drive me nuts like the CRONUSCalc input format). That’s here
  2. Finish the Cl-36 online exposure age calculator part. Done, mostly. 
  3. Incorporate Cl-36 calibration data in the ICE-D:CALIBRATION database of production rate calibration data. Fix up the web server to display the data in the format from (1) above. Done, and now this page provides access to the pathway-specific calibration data sets used for CRONUSCalc and also the expanded ones that I am using. There are still a lot of Cl-36 calibration data not yet entered, but there are enough to get started. 
  4. Modify the other ICE-D databases to include Cl-36 data, spit it out in the appropriate format for online exposure age calculator input, and compute Cl-36 exposure ages.  That’s done for ICE-D:ALPINE (here is an example), but not the others. 
  5. Do the production rate calibration well enough that the exposure ages aren’t obviously wrong. 

Those parts of the project are more or less complete, and here are the results of the calibration in (5), for St and LSDn scaling. As in other literature having to do with production rate calibration, the procedure for making these plots is to (i) fit the various production parameters to the calibration data, (ii) use the fitted parameters to compute the exposure ages of the calibration data, and then (iii) compare those to the true ages of the sites. We’d like to see the differences from unity to be about the same scale as the individual uncertainties, and also not systematically correlated with scaling parameters like latitude and elevation. 

The data are color-coded by dominant production pathway, and the calibration data sets used are this one for K, this one for Ca, this for Fe, and this for neutron capture on Cl. Of course these are moving targets so they might be different by the time you read this. As Cl-36 calibration results go, these are not bad. Scatter for K-spallation data is only 7%, comparable to fitting scatter for Be-10 and Al-26. It’s larger (10%) for Cl-dominated production, which is not surprising — the only reason it is that good is that the Cl-dominated data set is small so far —  but it is also much larger (13%) for Ca-spallation data, which is more surprising. It looks like maybe some work on Ca-spallation calibration data is needed. But even though this is not a huge calibration data set, I think this result is good enough to make calculated exposure ages believable, and even reasonably accurate for simple target minerals like K-feldspar. 

So that’s good. It basically works. Now you have a choice of two options, at least until the CREp version is finished. Also, progress can happen on including Cl-36 data in the ICE-D databases. However, there are still a few things that are not done:

  1. There are a couple of small things relating to muon production  that need to be fixed up. These are not very important for surface exposure age calculations. 
  2. A couple of minor decisions on how to handle arcane aspects of input data still need to be made. Like, if there is an LOI value included as part of an XRF analysis, do you assume that is water? CaCO3? Ignore it? No idea. 
  3. It is generally not obvious how to compute uncertainties for Cl-36 exposure ages. The approach at present uses estimates of scaling uncertainty for each production pathway and the same linearization as is used in the existing exposure age calculators, but there seems to be an undiagnosed bug that causes it to break for some target chemistries. Needs a bit of work. 
  4. More calibration data from existing literature could be added. 
  5. It could be faster. It’s fast enough that it doesn’t take too long for the ICE-D web pages to load, but it’s slower than the He-Be-C-Ne-Al version. This might just be because of the plotting overhead for the production-proportion plots, but it could be improved a bit. 
  6. Of course, there is not really any documentation. 
  7. Multiple nuclide plots. Cl-36/Be-10, Cl-36/Ne-21, etc. Really interesting. Possible to implement and potentially rather useful, but the banana is shaped differently for every Cl-36 production pathway, and figuring out how to get all that into one figure is kind of daunting. 
  8. Production rate calibration. The current He-Be-C-Ne-Al online exposure age calculators have a production rate calibration interface that allows you to enter arbitrary calibration data for one nuclide, fit the scaling methods to those data, and then proceed to compute exposure ages in an internally consistent way. It is going to be hard to replicate this for Cl-36, mainly because if one is allowed to enter completely arbitrary Cl-36 calibration data, many data sets that one could enter simply would not work. For example, if the input data were dominated by K production and you used the resulting calibration to compute exposure ages for Ca-dominated samples, you would probably get the right answer only by accident. It is possible to envision a method by which you could enter calibration data for one pathway at a time and use default values for the others — this would address quite a lot of useful applications while making it a little harder to screw up completely — but this approach would also require quite a lot of error checking to make sure the calibration data set had the advertised properties, and the unknown-age samples had similar production pathways to the calibration data. In any case, I am not sure what the most sensible way to do this is. 

 

 

 

 

Version 3 erosion rate calculator benchmarked, finally

October 10, 2020

This is another post about the very slow upgrade of the online exposure age and erosion rate calculators to the latest version 3. As alluded to in the previous post, erosion rate calculations somehow always seem like a lower priority, and that part of the upgrade was a bit delayed. However, the version 3 erosion rate calculator is now more or less complete (although a couple of interesting bugs having to do with very high erosion rates survived until a couple of weeks ago and have just now been fixed), and seems to be mostly in use.

However, one problem with the version 3 erosion rate calculator that hasn’t really been addressed is that even though I personally think it probably mostly works, no one really has any idea if this is true. For one thing, the documentation is not particularly good and could be improved, and for another, at least as far as I know, there hasn’t been any systematic testing against either the previous version or other methods. If there has been, no one has told me about it.

Fixing the documentation is a lot of work and not much fun. On the other hand, systematic testing is not nearly as difficult and could be fun. So that part of the problem is fixable. Here goes.

The test data set is 3105 measurements on fluvial sediment from the OCTOPUS database, nearly all of which are Be-10 data but also including a few Al-26 measurements.  As noted in the associated paper, it is possible to obtain all these data in a reasonably efficient way using the WFS server that drives the OCTOPUS website. So the procedure here is just to write a script to parse the WFS results and put them in the correct input format for the version 3 online calculator. The OCTOPUS database actually has more than 3105 entries, but a number of them have minor obstacles to simple data processing such as missing mean basin elevations or unknown Be-10 or Al-26 standardizations, and for purposes of this exercise it’s easiest to just discard any data that would require extra work to figure out. That leaves 3105 measurements from basins all over the world, which is still a good amount of data.

The data extraction script requires a few additional decisions. One, the OCTOPUS database mostly includes mean elevations for source watersheds, but does not include effective elevations. So we’ll use the mean elevations. Second, we also need a latitude and longitude for an erosion rate calculation — OCTOPUS stores the basin boundaries but not any digested values — so the script just calculates the location of the basin centroid and uses that as the sample location. We’ll also assume zero topographic shielding and a density of 2.7 g/cm3 for the conversion from mass units to linear units. This is the same data digestion script that drives the ICE-D X OCTOPUS web server. All of these things matter if you actually want to know the real erosion rate, of course — and I will get back to this a bit more at the end of this post — but they don’t matter if you just need a realistic data set for comparing different calculation methods.

The main difference between version 2 (here ‘version 2’ will refer to the last update thereof, which is 2.3) and version 3 of the online erosion rate calculator is that version 3 abandons several scaling methods (“De,” “Du,” and “Li”) and adds one (“LSDn”). So the scaling methods common to both versions are the non-time-dependent “St” method based on Lal (1991) and Stone (2000), and the time-dependent “Lm” method that basically just adds paleomagnetic variation to the Lal scaling, although several details of the Lm method have changed in version 3, so the v2 and v3 Lm implementations are not exactly the same. Thus, task one is to compare the results of the version 2 and 3 erosion rate calculations for the simpler “St” method. Here is the result.

Obviously, the axes here are the erosion rate from the v2 code and the ratio of erosion rates calculated using the v3 and v2 code. Happily, they are quite similar; all the v3 results are within a couple of percent of the v2 results. Some small differences are expected and have to do with small changes between v2 and v3, including (i) a different elevation vs. air pressure conversion, which probably accounts for most of the random scatter, and (ii) different handling of muon-produced Be-10, which accounts for the small systematic decrease in the v3/v2 ratio with increasing erosion rate. But, in general, this comparison reveals no systematic problems and counts as a pass.

Task 2 is to compare the results of the v2 and v3 code for time-dependent “Lm” scaling. This is a bit more complicated. Frankly, it’s also a lot less relevant because the time-dependent scaling methods are not widely used for erosion rate applications. The issue of which scaling method to use is relatively minor by comparison with all the other assumptions that go into a steady-state erosion rate calculation, so most researchers don’t worry about it and use the non-time-dependent method. However, erosion rates calculated with time-dependent scaling have some interesting properties that are potentially useful in figuring out whether things are working as they should, so here are the results:

Again, the y-axis in both plots is the ratio of erosion rates calculated using the v3 and v2 implementations of Lm scaling, the x-axes are apparent erosion rate on the left and elevation on the right, and the data are color-coded by latitude, as shown in the legend. This turns out to be kind of a strange plot. At low latitude, there are some fairly large differences between the results of the v2 and v3 code, and there is a systematic difference between v3 and v2 results that varies with latitude. At high latitude, v3 results are a bit lower; at low latitude they are a bit higher with a difference that depends on elevation. These differences appear to be mostly the result of a change in how the v3 implementation of Lm scaling relates geomagnetic latitude to cutoff rigidity. This, in turn, affects how the Lal scaling polynomials, which were originally written as a function of geomagnetic latitude, map to cutoff rigidity values in paleomagnetic field reconstructions. The implementation in version 3 is supposed to be more consistent with how the cutoff rigidity maps in the field reconstructions were calculated. Another change between version 2 and 3 is that the paleomagnetic field reconstruction is different, so that also contributes to the differences and most likely accounts for a lot of the nonsystematic scatter, especially at erosion rates in the hundreds-of-meters-per-million-years range where the Holocene magnetic field reconstruction is important. The point, however, is that these differences appear to be more or less consistent with what we expect from the changes in the code — so far there is no evidence that anything is seriously wrong with version 3.

That pretty much completes the main point of this exercise, which was to establish, by comparison with version 2, that there doesn’t seem to be anything seriously wrong with version 3. This is good, because version 3 is much faster. However, having set up the whole test data set and calculation framework, there is some other interesting stuff we can do with the results.

The first interesting thing is that the calculations apply both time-dependent and non-time-dependent scaling methods to the same data set. This is a neat opportunity to see what the effect of time-dependent scaling in erosion rate calculations is. Here is the effect:

This is another complicated figure that looks like the last one, but is actually showing something different — the previous figure was comparing two different versions of Lm scaling, whereas this one is comparing the results of a time-dependent method (Lm on the left, LSDn on the right) with the non-time-dependent St scaling.

The main thing that this shows is that using a time-dependent scaling method makes a difference. Basically, the measured nuclide concentration in a sample with a higher erosion rate is integrating production over a shorter time than that in a sample with a lower erosion rate. In other words, samples with different erosion rates are sampling different parts of the paleomagnetic field record. Because the present magnetic field seems to have been anomalously strong by comparison with the last couple of million years, samples with lower erosion rates have, on average, experienced periods of lower magnetic field strength and therefore higher production rate. For samples with erosion rates around 100 m/Myr, most of the measured nuclide concentration was produced during the past 20,000 years, which also happens to be the approximate age range of most of the production rate calibration data that are used to calibrate all the scaling methods. Thus, calculated erosion rates in this range are about the same for time-dependent and non-time-dependent scaling. Samples with lower erosion rates reflect production during longer-ago periods of weaker magnetic field strength and higher production rates, so an erosion rate computed with time-dependent scaling will be higher than one computed with non-time-dependent scaling. These plots also show that this effect is most pronounced for samples at lower latitudes where magnetic field changes have a larger effect on the production rate. The final thing that is evident in these plots is the difference between Lm and LSDn scaling. The elevation dependence of the production rate is the same for both St and Lm scaling, so differences are closely correlated with latitude and erosion rate; this is evident in the minimal scatter around the central trend for each color group in the left-hand plot. On the other hand, LSDn scaling has a different production rate – elevation relationship, so the fact that the samples in each group are all at different elevations leads to a lot more scatter. But overall, the relationship is basically the same.

Although, as noted, time-dependent scaling methods mostly aren’t used for erosion rate studies, it’s worth thinking a bit about whether or not this is a potential problem. The main thing that most erosion rate studies are trying to do is look for correlations between erosion rates and something else — basin slope, relief, whatever. Thus, if you created spurious variations in calculated erosion rates by using a non-time-dependent instead of a time-dependent method, you might incorrectly attribute that variation to some characteristics of the basins. For basins that are reasonably close together, the data in the plot above shows that this isn’t likely to be a problem — the difference in scaling method is essentially just applying a monotonic stretch to the erosion rates, so it might increase the size of variations, but not create variations that weren’t there already.  However, for basins that span a wide range of location and elevation, or for global compilations, this might be something to pay attention to.

The second interesting side project we can do with this data set is to compare the results of erosion rates calculated using the version 3 calculator with the erosion rates that were calculated as part of the OCTOPUS project and are reported in the OCTOPUS database. These calculation methods are very different, and, in addition, here is where all the simplifying assumptions we made in generating the test data set become important. For the version 3 calculations, as described above, we approximated each basin by a central point and a mean elevation. Choosing a single location for the basin doesn’t make a ton of difference, but using the mean elevation is nearly always wrong, and will mostly underestimate the true average production rate in the basin, because the production rate – elevation relationship is nonlinear. Although the OCTOPUS calculations use St scaling, the approximations for muon production are different, and, most importantly, they use the much more elaborate ‘CAIRN‘ erosion rate calculation, which considers all the pixels in the basin as sediment sources with different production rates and obtains the erosion rate by numerically solving a complete forward model calculation of the nuclide concentration. In principle, this method is much better because it is much more realistic, and generally much less dumb, than approximating the basin by a single point. On the other hand, it also incorporates at least one thing — an adjustment to production rates for topographic shielding effects — that we now know to be incorrect. Of course, the point of recalculating all the OCTOPUS data with the v3 calculator code wasn’t to generate more accurate erosion rate estimates than the CAIRN estimates — it was just to take advantage of an easily available data set for benchmarking — but quite a lot of the erosion rate literature does use single-point-approximations for basin-scale erosion rate calculations, so it’s interesting to look at the differences (even though the CAIRN paper already did this, the v3 code wasn’t available at that point). Anyway, here are the differences.

Here I have split out Be-10 (red) and Al-26 (blue), which highlights that there aren’t a whole lot of Al-26 data. Overall, this is not bad. There doesn’t appear to be any systematic offset with erosion rate, so here it is as a single histogram:

There is a small systematic offset (4% to the low side) that appears to be the result of using different muon calculations and slightly different production rate calibrations,  a decent amount of scatter (the standard deviation is 6%), and some weird outliers. We expect quite a lot of scatter because of the single-point approximation — the difference between the results of full-basin and single-point calculations will vary with basin elevation, relief, and hypsometry, which of course are different for all basins. I think we expect the scatter to be skewed to the low side in these results, because the effective elevation is nearly always higher than the mean elevation, and this effect does appear in the histogram. As regards the handful of weird outliers, I did not try to figure them out individually.

Regardless, the fact that the version 3 results are more or less in agreement with the OCTOPUS results also generally seems to indicate that nothing is seriously wrong. The version 3 code may be safe to use.

 

 

Can we switch yet?

October 6, 2020

No, not switch governments — switch exposure-age calculators. Sorry. This post is about the really excessively long transition from the not-so-recent to the most-recent version of the online exposure age and erosion rate calculators.

It is true that this subject is less important than the news of the day, which is that the entire top of the US military command structure, including the Joint Chiefs of Staff, is now in preventative quarantine. Leaving that aside for the moment, though, the background to this post, detailed here, is that various iterations of the so-called “version 2” of the online exposure age calculators were in use from the middle of 2007, shortly after the whole thing was invented in the first place, until approximately 2017. In 2017, I developed an updated “version 3” that had more features, fewer scaling methods, and was much faster. However, adoption was rather slow, most likely because (i) people like what they’re used to, and (ii) while the version 2 calculators came with an actual, Good-Housekeeping-Seal-Of-Approval peer-reviewed paper describing them, as well as ridiculously comprehensive documentation, version 3 has no paper and unimpressive documentation. The no-paper part seems to lead to difficulty in figuring out how to cite the version 3 calculators in a paper and, perhaps, a diffuse, creeping sense of doubt about whether or not they are really legit or authorized. Frankly, the quite-poor-by-comparison-to-version-2 documentation doesn’t help. This could be improved.

Regardless, the version 3 calculators are better. They are faster, replace scaling methods known to be inaccurate with a more up-to-date one that is known to be more accurate, and compute ages for many different nuclide/mineral systems instead of just Be-10 and Al-26. They are much better. The point of this post is to determine whether the user community is, in fact, figuring this out and switching.

The following shows two things. In the top panel, total manual usage of the online exposure age calculators during the past 5 years. “Manual” in this case means that users actually cut-and-pasted data into the online entry forms. The majority of the total calculation load on the version 3 online calculators is not from manual entry, but instead from the automated, dynamic calculations associated with serving web pages for the ICE-D databases, and these are not included here. Manual usage of the online exposure age calculators has been remarkably stable over the past several years around an average of about 8500 exposure-age requests monthly.

The bottom panel shows the fraction of those requests that use version 3 rather than version 2. It took almost three years and the switchover was remarkably gradual, but at this point it appears that nearly everyone has switched. As version 3 of the exposure age calculators has now been fairly well debugged and is pretty much known to work, it is probably time to take version 2 off the front page and move it to the archives. While it certainly deserves some wall space next to a hunk of Libyan Desert Glass in any future museum of exposure dating, I think we are done with it.

That’s just the exposure-age calculations, though. The online erosion rate calculators get about 15% of the usage of the exposure age calculators, so weren’t a priority in developing version 3, and this part of version 3 was somewhat delayed. As with the exposure-age calculations, however, the erosion-rate parts of version 3 are generally better and very, very much faster than the equivalent bits of version 2. On the other hand, they are even less well documented than the exposure-age calculations and the details are a bit murky. Total usage of the online erosion rate calculators, as with exposure-dating, has been a bit patchy but fairly stable around an average of 1500 calculations/month. Again, this doesn’t include automated use in the back end of the ICE-D X OCTOPUS web server, but that is fairly minor.

Regardless, most users of the erosion rate calculators now seem to have switched as well. The pattern is interesting: there appears to have been a trial period in late 2017 that resulted in users recoiling in horror and going back to version 2, followed by a rapid, abrupt, and globally synchronous rediscovery near the end of 2018. This is odd, but the result is the same as for exposure-age calculation — the new version now gets about 90% of the traffic. However, the version 3 erosion rate code has not been as well tested as the exposure-age code, and there are still a couple of known bugs having to do with integrating muon production for extremely fast erosion rates in the range of km/Myr. Thus, we are probably not quite ready to take version 2 off the front page for erosion rates.

Summary: nearly everyone has gotten over their doubts about the documentation and switched. Good.

Ne-21 production rate calibration demystified. Or not.

August 27, 2020

Neon-21 is now ‘trending’ on this website, because this is the second post about it this year. The purpose of this one is to try to unscramble the tedious and obscure subject of production rate calibration for cosmogenic neon-21 in quartz. Eventually, at the end, it describes an update to the part of the online exposure age calculator dealing with Ne-21, which doesn’t fix everything, but is a legitimate conceptual improvement and results in only small differences between Ne-21 exposure ages calculated before the update (i.e., yesterday) and after (today).

Even though Ne-21 production in quartz considered by itself  is nearly all just by neutron spallation and isn’t any more complicated than production of Be-10, Al-26, or He-3, it is a messy nuclide-mineral system to calibrate because measurement of cosmogenic Ne-21 has a terrible signal-to-noise ratio compared to these other nuclides.

The reason for this is simply that there is a lot of non-cosmogenic Ne-21 out there, both present as a trace isotope in all sources of natural neon (like the atmosphere) and also produced in minerals indirectly from decay of natural uranium and thorium. It is easily possible to identify and correct for atmospheric neon based on its isotope ratio, but even after doing this there is nearly always some extra non-cosmogenic Ne-21 in quartz. Ne-21 is not radioactive, so the only way to get rid of it is to heat the quartz up to high temperature. Most quartz in typical rocks at the Earth’s surface has been below the neon retention temperature for tens or hundreds of millions of years, and contains quite a lot of non-cosmogenic (“nucleogenic”) Ne-21 produced from U and Th decay, so can contain nucleogenic Ne-21 equivalent to tens or hundreds of thousands of years of surface exposure. Suppose you go to a site where the production rate of cosmogenic Ne-21 is about 20 atoms/g/yr, pick up a rock, and observe 2 Matoms/g of Ne-21 in the quartz. This rock could really have a 100,000 year exposure age, or it could have arrived at the surface yesterday with a lot of nucleogenic Ne-21. Hard to tell.

This property of Ne-21 is a serious problem for production rate calibration because of how calibration normally works. Normally, we find a rock surface that we already know the exposure age of, usually by radiocarbon dating of related deposits. We measure the amount of, say, cosmogenic beryllium-10, divide the number of atoms per gram of Be-10 by the age, and obtain the Be-10 production rate in atoms per gram per year. This is quite straightforward if you can find rock surfaces that have been exposed for a known amount of time and also have not been disturbed or eroded since they were emplaced. Unfortunately, you can generally only do this for sites that are relatively young. Most good production rate calibration sites have exposure ages less than about 20,000 years, and the reason for this is that rock surfaces in most environments simply do not survive in pristine condition for a lot longer than this. Older surfaces get eroded, weathered, flaked, spalled, covered, uncovered, and generally messed up, which makes them hard to use for production rate calibration. If the surfaces that are good for production rate calibration have short exposure durations, they also have low concentrations of cosmogenic Ne-21. For nearly all production rate calibration sites that we have used for Be-10 and Al-26 calibration, concentrations of cosmogenic Ne-21 in quartz are lower than concentrations of non-cosmogenic Ne-21. Even if you measure the total amount of Ne-21 very precisely, and also you could estimate the amount of non-cosmogenic Ne-21 very precisely (which is usually impossible for a variety of reasons), estimating cosmogenic Ne-21 would still involve subtracting one big number from another big number to yield a small number. This results in a small number with a big uncertainty.

So, a surface that is good for direct production rate calibration has to be young enough that it is still pristine, but if this is true it is also too young to make a decent measurement of the cosmogenic Ne-21 concentration. This is extremely inconvenient. Most attempts to estimate the Ne-21 production rate, therefore, have had to use much more complicated strategies, mostly based on the idea of finding surfaces that have very high concentrations of cosmogenic Ne-21 so any correction for nucleogenic Ne-21 is negligible, but also have some kind of back story that enables you to make a believable argument about what the long-term exposure history has been, so you can assume an exposure history and back-calculate the production rate. One example, which I will focus on here because it is more polite to highlight the weaknesses of my own work than of others’, is a 2009 paper about rock surfaces in the Antarctic Dry Valleys. This involves a handful of samples from slowly eroding rock surfaces that are believed to have been ice-free and slowly weathering for millions of years, and also have Be-10 and Al-26 concentrations that are consistent with production-decay-erosion steady state. In other words, their Be-10 and Al-26 concentrations lie on the steady erosion line in a 2-nuclide diagram:

So, we assume that these samples have also reached production-erosion equilibrium for Ne-21. Then if the cosmogenic Ne-21 concentration is N21, the production rate is P21, the erosion rate inferred from the Be-10 and Al-26 concentrations is E, and we ignore muon production for the moment, P21 = N21*E/L, where L is the attenuation depth constant for spallogenic production (Lambda in most places). Basically, we are asking, OK, what Ne-21 production rate is needed to make it so that these data are also on the steady erosion line in Al-26/Ne-21 and Be-10/Ne-21 diagrams?

The advantage of this approach is that these surfaces are eroding very slowly, so cosmogenic Ne-21 concentrations are about two orders of magnitude higher than nucleogenic concentrations. It is easy to accurately measure the cosmogenic Ne-21 concentration. The disadvantage is that the back story involves a lot of assumptions. As noted, we have  to assume that the surfaces really are at erosional steady state. That might be true, but we also rely on accurately knowing the Al-26 and Be-10 production rates, so that we get the right erosion rate to plug into this formula. To some extent the dependence on the Be-10 production rate can be mitigated if you compute a ratio of Ne-21 to Be-10 production rates, and this paper eventually concluded that the Ne-21/Be-10 production ratio is 4.08. This more or less agrees with the conclusions of four other papers that used similarly devious and complex strategies, particularly including a very creative one by Florian Kober and Vasily Alfimov, as well as an additional paper describing an early attempt to determine the Ne-21 production rate directly from a young surface by Samuel Niedermann. Since approximately 2009, therefore, folks have mostly assumed that the Ne-21/Be-10 production ratio is close to 4, and computed Ne-21 production rates by applying this ratio to their best estimate of the Be-10 production rate.

This is what the Ne-21 production rate calibration in version 3 of the online exposure age calculators, at least as of last week, is based on. I think that’s the only online exposure age calculator that accepts Ne-21 input at all. This assumption mostly seems to work pretty well, but to get to the main point of this post, if you look at this carefully, it is not really clear that it should work at all, or if it is a good idea. For example, here are some problems.

The main problem is that the production ratio estimate from the Dry Valleys data described above is based on an impressive number of assumptions that were fine at the time, but are now known to be obsolete or incorrect. One: the erosion rates calculated from the Be-10 and Al-26 concentrations are based on obsolete production rate calibration data that have since been discovered to be inaccurate. Basically, the Be-10/Al-26 production rates were too high, which means the estimates of the erosion rates are also too high. Two: these calculations also used a Be-10 half-life that we now know is wrong. Because the erosion rates are so low, the half-life update affects the erosion rate estimate. Three: AMS standardization of Be-10 measurements had not fully been sorted out when that paper was written, which is related to the production rate and half-life revisions. Four: that paper assumes that the quartz samples had zero nucleogenic Ne-21. Later investigation has shown that they have a fairly large amount. Five: that paper concluded that muon production of Ne-21 was fairly high. It is now known from other data to be lower. Six: recent interlaboratory comparisons show that the Ne-21 concentrations in that paper need to be adusted.

This is a huge list of things that we know are wrong and need to be updated.

Disappointingly, another recent paper (also by me) addressed items (4-6) above and concluded that they had a negligible effect on the estimate of the Ne-21/Be-10 production ratio. However, it didn’t address items (1-3), which is kind of misleading, so this conclusion might be right but possibly shouldn’t have been included in the paper.

And then there is one more thing. The ‘LSDn’ production rate scaling method of Lifton and Sato (various references), which is now in common use and believed to be the most accurate available, predicts that the Ne-21/Be-10 production ratio should be variable with elevation, and this was not taken into account at all in the 2009-2011 studies. And, finally, adding insult to injury, the 2014 implementation of the LSDn scaling method did not include any estimates of the cross-sections for Ne-21 production reactions, so the online exposure age calculator has been using a generic neutron flux scaling, which predicts a ratio-elevation relationship that is not even the correct one.

So, this is about ten reasons that the currently accepted production rate calibration that is in the online exposure age calculators for Ne-21 is probably wrong. On the other hand, the other papers that all basically agree on the 21/10 production ratio are subject to a different set of obsolete inputs and assumptions, and the fact that they all basically agree may well indicate that the various inaccuracies all cancel each other out. However, it seems like a bad idea to rely on this sort of magical thinking. So the current overall credibility of the Ne-21 production rate calibration that we have mostly been using is not ideal.

Fortunately, and in sharp contrast with the arc of American history at this precise moment, there is hope. Just as we were faced with the unpleasant and possibly impossible task of trying to recalculate all the old estimates of the 21/10 production ratio with new input parameters, a new paper by Cassie Fenton and others has saved us from despair. Almost. I’ll get to the “almost” part at the end.

Here’s how. Remember the root problem with Ne-21 production rate calibration is that we can’t do a direct calibration on surfaces of known age if all we have are young surfaces with high concentrations of nucleogenic Ne-21. So, instead of doing all this fancy stuff with elaborate back stories and steady-erosion assumptions, wouldn’t it be better to just find an old but pristine rock surface that doesn’t have any nucleogenic Ne-21 in the quartz? It is hard to argue with that reasoning. The surface that Cassie and colleagues have located is a quartz-bearing basaltic lava flow in Arizona with an unprintable name having the abbreviation “SP.” When you add some other words like “Cosmogenic” and, maybe, “Intercalibration” and “Experiment” – I am not sure – you get a project called “SPICE,” which should not be confused with either the Spice Girls or the SPICE core. But the point is that they have located a 72,000-year-old quartz-bearing lava flow in the middle of a desert where erosion and surface disturbance rates are very low. And because the amount of nucleogenic Ne-21 scales with the cooling age of the rock, if you want the minimum possible amount of non-cosmogenic Ne-21, you want a rock whose cooling age is no older than the exposure age. Like a lava flow. So we have a site that is a lot older than most calibration sites and has a lot less nucleogenic Ne-21 than most other rocks. Perfect.

This is important because it means we might not have to unscramble the mess of old production ratio estimates from ten years ago. We can forget about them and move on to a better plan. This implies that from the online exposure age calculator perspective, we should do the following:

  1. Fix the LSDn scaling code to use specific reaction cross sections for Si –> Ne-21. Also fix it to use up-to-date muon-related parameters from here.
  2. Calibrate the various production rate scaling methods using the SPICE data.
  3. Figure out if the results agree with what we were using before. Have we been doing it wrong?

We can obtain reaction cross-sections from a 2015 revision of the LSDn scaling code provided by Nat Lifton, that originate with estimates by Bob Reedy described here.  Here is what they look like:

This shows the energy dependence of the main production reactions for Ne-21 and Be-10, with the X-axis in MeV. The threshold energy and peak cross-section energy for Ne-21 production is a little bit higher than for Be-10, so the Ne-21/Be-10 production ratio should go up a little bit as elevation increases. This is actually the opposite of what has been in the online exposure age calculator until now: so far, the Ne-21 scaling has just used a total-neutron-flux scaling (e.g., the ‘Sf’ method in CRONUSCalc), which has no threshold energy, so will predict a higher 21/10 ratio at low elevation.

Having fixed that, we can now take the SPICE Ne-21 data, which are here, and paste them into the production rate calibration input page here (note that this posting is slightly asynchronous with the server update, so this may not look exactly like this until such time as I update the web servers to use the new scaling code). This is the result:

Now, compare this with what we were doing before, which was computing the Be-10 production rate and multiplying by 4. Start with ‘St’ and ‘Lm’ scaling. For these scaling methods, the fitting parameter is a dimensional spallogenic production rate (atoms/g/yr) at some reference condition. If we were to take the existing default calibrated reference production rates for Be-10 for St and Lm scaling (4.09 and 4.21 atoms/g/yr, which derive from the CRONUS-Earth ‘primary’ calibration data set here) and apply the 21/10 production ratio of about 4 that is expected from the 2009 studies, we would get 16.36 and 16.84, which are within 3-4% of the results of calibrating with the SPICE data. So that’s good. The other way to say that is that if we calibrate Ne-21 using SPICE data and Be-10 using the existing default calibration, we predict Ne-21/Be-10 production ratios of (16.896/4.09 = 4.13) and (16.243/4.21 = 3.86) for ‘St’ and ‘Lm’ scaling, which are both within 3% of the previously accepted value of 4, or basically the same given the expected precision of the various measurements. Of course this only includes spallogenic production, but both nuclides have minimal muon production so that has a negligible effect on the ratio. Even though our best estimate of the reference Be-10 production rate now is very different from the 2009 estimate — it is about 10-15% lower — these results are indistinguishable from what we thought the ratio should be at the time. So this result (which, by the way, just duplicates a conclusion that Cassie already came to in the SPICE paper) is good. We were not totally wrong before.

One interesting aspect of this result (which is also thoroughly discussed in the SPICE paper, although with slightly different conclusions) is that the apparent production ratios for the two scaling methods are different. This is unexpected because the ratio primarily results from the mineral chemistry and reaction cross-sections, so the true ratio should be close to independent of the scaling method.  On the other hand, the way the calculation is done is expected to lead to a difference here because the SPICE site is much older than the calibration sites used to estimate the Be-10 production rate, so the two calibration data sets are averaging over different time periods. Because most of the sites involved are at low enough latitude that magnetic field variations have an effect on production rates, the older site is sampling a period of lower average magnetic field strength, and therefore higher production rates, than the younger sites. The ‘Lm’ scaling method accounts for this, and the non-time-dependent ‘St’ method doesn’t. This means that St scaling with this particular combination of sites should overestimate the 21/10 production ratio by a few percent.

For LSDn scaling, the calibrated parameter is a nondimensional correction factor, not a reference production rate, and in addition the production ratio is predicted to vary with elevation, so we need to compute both Be-10 and Ne-21 production rates for a range of elevations instead of just dividing the fitted reference production rates. After calibrating Ne-21 production with the SPICE data set and leaving the Be-10 production rate calibration as the default, here is the predicted 21/10 production ratio for LSDn scaling as a function of elevation for polar latitudes (like Antarctica, where we care about this because there are a lot of Ne-21 data):

Basically, it is exactly what we expected it to be based on the complex and sketchy indirect calibration scheme from 2009: at the elevation of the Dry Valleys (about 1500 m) it predicts P21/P10 = 4.05. This figure also shows what I said above, that the ratio is basically the same whether or not you ignore muons. Again, this seems good. Either we got it right the first time, or we made so many offsetting errors that we still got it right by accident. It’s a few percent higher than what we get for the also-paleomagnetically-corrected Lm scaling, but this is not necessarily significant because LSDn scaling treats each production reaction separately, whereas St and Lm treat them all the same, so the two estimates are not really comparable.

So, this seems like a complete solution for the online exposure age calculator. Disregard the messy and in-need-of-revision attempts to measure the production ratio from ten years ago. Update the various background parameters. Calibrate the Ne-21 production rate directly from the SPICE data. Done.

 

 

OK, careful readers will immediately notice that the previous paragraph ended in “Done,” but this already quite tedious posting hasn’t actually ended. This is because there is a small problem with this reasoning. Here is the problem. The SPICE calibration study didn’t just measure Ne-21 in the quartz from the SP flow…it also measured Be-10. Thus, no matter what the scaling method is and regardless of whether or not we have messed up anything to do with scaling and production rate calibration, the measured ratio of Ne-21 and Be-10 concentrations should be equal to the ratio of the production rates (or almost equal…a small correction for radioactive decay of Be-10 is needed). The production ratio computed from the measured concentrations in this study is…4.35 +/- 0.23. Which is 5-10% larger than we think it should be from both the old calibration studies and the paragraphs above that compare Ne-21 and Be-10 production rates calibrated using separate data sets. Theoretically, measuring both nuclides in the same quartz at the same site should be a better estimate of the production ratio than comparing production rates calibrated from different data sets, so this is weird.

On closer examination, what is happening here is that the Be-10 concentrations measured here are lower than we expect from other production rate calibrations. In other words, as noted at length in the SPICE paper, production rates calibrated using the SPICE Be-10 data alone are 5-10% lower than those calibrated using the rest of the global data set, depending on which scaling method is used. This is not just an artifact of the fact that most of the global calibration data are much younger, because it’s true for both paleomagnetically corrected and uncorrected scaling methods. And, in addition, Be-10 production rates that are as low as predicted by the SPICE data are inconsistent with many Be-10 data from Antarctica that are from surfaces with near-zero erosion rates and are close to production-decay saturation. If production rates were as low as predicted by the SPICE Be-10 data, predicted saturation concentrations would be 10% lower than the measured concentrations in these samples, which is impossible. To see an example of this calculation, take the Be-10 calibration data from here, put it in the production rate calibration page here, and then use the result to calculate Be-10 exposure ages for samples from this site.

So, the reason the apparent P21/P10 ratio at this site is a little anomalous is actually not because of the Ne-21 data, but because the Be-10 data are anomalous with respect to the rest of the world. There could be a couple of reasons for this.

Option 1 is the elaborate one. In this option, we solve the mismatch between these Be-10 data and others by arguing that there has been unrecognized erosion at the site. 18 cm of erosion in 72 ka would make the Be-10 data agree with other calibration data. But then we have too much Ne-21, so we need to solve that by arguing that 5-10% of the Ne-21 is actually nucleogenic and not cosmogenic. This is rather unlikely, as nucleogenic production of 10% of the observed excess Ne-21 in 72 ka would require something like 50 ppm U and Th in the quartz, which is about two orders of magnitude more than typically observed. Theoretically one could appeal to trapped magmatic neon with some strange isotope composition, but overall that is not a very good explanation.

Option 2 is that the Ar-Ar age is inaccurate and the flow is really a bit younger. However, quite a lot of work has gone into the Ar-Ar dating and there is no reason to suspect this. This option also requires that 10% of the Ne-21 is noncosmogenic, as above.

Option 3 is the simple one and involves concluding that the Be-10 measurements are systematically a little too low, and the Ar-Ar age and the Ne-21 measurements are correct. The argument against this option is that there is no reason to think that any measurement errors were made in this study. The Be-10 measurements should be totally fine. On the other hand, a ~10% difference is not too far out of the range of normal measurement scatter. So this would tend to indicate that we should calibrate the Be-10 production rate using a large data set from many sites (of which the SPICE data are a moderate, although not extreme, outlier), and the Ne-21 production rate using the SPICE data (because there are no other equivalent sites, so they are the entire global data set for Ne-21). This is probably the least unsatisfying option.

Summary: the online exposure age calculator is now updated to use this approach going forward, even though this is still a bit unsatisying, because the concentration ratio measured at any one site should agree with the production ratio estimated from large calibration data sets, and the fact that it doesn’t means that we are missing something. Even though this is not perfectly satisfying, remember, the screwup we are arguing about now is only a 5% effect. That’s pretty good. So, we will be happy that this approach agrees with all other estimates of the P21/P10 ratio, and we will temporarily ignore the fact that it doesn’t quite agree with the measured concentration ratios. Because this site turns out to be so transformatively useful for Ne-21 production rate calibration, though, it would probably also be a good idea to take some steps to try to unscramble this, including (i) sending some SPICE quartz to additional labs to deal with the possibility of measurement error for both Be-10 and Ne-21, (ii) maybe replicating the Ar-Ar age a couple more times, and (iii) looking for a shielded sample of the SP flow to see if there is any noncosmogenic Ne-21. Unfortunately, a quick Google Maps search did not disclose any road cuts or borrow pits in the flow, so the shielded sample might be tough.

Noncosmogenic helium-3 in pyroxene and Antarctic exposure dating

August 22, 2020

This posting falls into the category of something that is a really interesting scientific result if you care about cosmogenic-nuclide arcana, but is probably too obscure to actually write a paper about. Specifically, it covers the topic of noncosmogenic helium-3 in pyroxene from the Ferrar Dolerite in Antarctica, which is obscure enough that even among the already highly select group of readers of this blog, only a few folks will be select enough to have any idea what I am talking about.

The context — there is always a bunch of context that wastes a lot of space at the beginning of these postings  — is just the inconvenient property of stable cosmogenic nuclides that, because they are stable, there are always small concentrations of these nuclides in many minerals that are really hard to get rid of. Radionuclides such as Be-10 and Al-26, of course, eventually disappear due to radioactive decay, so minerals that have been in the subsurface for a few million years have negligible concentrations of these nuclides and arrive at the surface as the proverbial clean slate. Mostly this isn’t true for the stable noble gases He-3 and Ne-21, because the only way to get rid of them is to heat the mineral to fairly high temperature, and typical surface rocks haven’t been heated to high enough temperatures for tens to hundreds of millions of years, often not since the rock was formed. So nearly all rocks contain significant concentrations of noncosmogenic He-3 and Ne-21, either trapped at the time of rock formation or subsequently produced by nuclear reactions induced in various ways by radioactive decay of naturally occurring U and Th.

This may or may not be a problem, depending on the application. The next post, if I ever get it done, will explain why this is a big problem for production rate calibration for Ne-21. For He-3, depending on the rock type and the mineral being analysed, it is often possible to accurately quantify and correct for noncosmogenic He-3 by crushing to extract magmatic helium independently of cosmogenic helium, and/or measuring U and Th concentrations in the mineral, as described in detail in a paper by PH Blard. From the perspective of the present post, however, this type of approach commonly does not work in the application of exposure-dating using He-3 in pyroxene. This application is particularly important in Antarctica because a common target for exposure-dating of Antarctic bedrock and glacial deposits is pyroxene extracted from the Ferrar Dolerite, a mafic intrusive rock that is extremely widespread in the Transantarctic Mountains and therefore covers a major fraction of the total ice-free area on the continent. Fine-grained facies of the Ferrar Dolerite are also one of the most durable and weathering-resistant rocks on Earth, so the effect of millions of years of weathering in many long-exposed ice-free areas in Antarctica has been to destroy pretty much everything else and produce large areas where nearly every surface clast is Ferrar.

This is not an exaggeration. Fresh clasts of Ferrar start off greenish-gray, and develop a reddish-chocolatey weathering rind over time. In this photo of a moraine sequence at Otway Massif, in the southern Transantarctic Mountains, the entire landscape is this color. Essentially every object in this photo, with the exception of glacial geologist Gordon Bromley and some distant snowfields, is Ferrar dolerite. The example that Gordon has triumphantly slain has an exposure age of 2.3 Ma.

Thus, Ferrar dolerite is everywhere, pyroxene extraction from this rock is fairly straightforward, and He-3 measurement in the pyroxene is also fairly simple, so we would like to be able to use it widely for exposure-dating applications throughout the Transantarctic Mountains. To do this, we need an estimate of the noncosmogenic He-3 concentration. However, it’s not possible to estimate this in Ferrar pyroxene internally from a single sample. Without getting too far into the details, the rock has a cooling age of 183 Ma and reasonable U and Th concentrations, so pyroxenes have very high concentrations of He-4 that is due to U/Th decay and not to trapping of magmatic gas, and in any case the pyroxene generally doesn’t yield any significant amount of gas when crushed. In this situation the methods described by Blard and others don’t work.

So, what do we do now? The other strategy to determining the noncosmogenic He-3 concentration in a given mineral from a given lithology is just to obtain a deeply buried sample of the lithology that hasn’t been exposed to the surface cosmic-ray flux. Measure the amount of He-3 in the shielded sample and subtract this amount from total He-3 measured in surface samples to arrive at an estimate of cosmogenic He-3 in the surface sample.

This is easy when you can obtain a subsurface sample of the same lithology. A good example is a recent paper by Gunnar Speth and others dealing with He-3 exposure dating of glacially transported boulders in the Oregon Cascades: they figured out the source lithologies of the boulders and obtained shielded samples from quarries and road cuts in those units. No problem.

Unfortunately, in Antarctica, problem. Road cuts, quarries, mines, and boreholes are absent in the Transantarctic Mountains. However, in the past 20 years or so, a number of people have struggled to overcome this inconvenience and find the most shielded samples of Ferrar Dolerite that they could.

Robert Ackert searched the Dry Valleys for samples from the deepest crevices beneath the largest overhanging cliffs that he could. The lowest He-3 concentrations in these samples, described in his dissertation, are in the range of 5-9 Matoms/g. This is not a huge amount, but is equivalent to 30,000-60,000 years of exposure at sea level, so is significant for exposure dating.

Helen Margerison argued in this paper that the Ferrar Dolerite was basically the same as other dolerites stranded in Tasmania by Gondwana breakup, and obtained some samples of deep drill core from the Tasmanian example.  Result: 6.8 Matoms/g.

Mike Kaplan found two clasts of Ferrar Dolerite that appear to have very recently melted out of ablating blue ice at Mt. Achernar, and reported in this paper that pyroxene in these samples had 5 Matoms/g He-3.

Of course, with the exception of the Tasmanian borehole sample, we don’t really know that any of these samples have an exposure age of exactly zero — and it is quite likely that they don’t — so they are all upper limits on the noncosmogenic He-3 concentration. The Kaplan and Ackert samples have lower concentrations than the Tasmanian sample, which does indicate that Antarctic and Tasmanian dolerites are not exactly the same from this perspective, but they only show that the true noncosmogenic concentration must be less than about 5 Matoms/g. We still can’t prove it’s greater than zero.

So, lacking a drill rig or explosives, how can we do better than “somewhere between 0 and 5?” An interesting way to accomplish this relies on a study by Julia Collins, Shaun Eaves, and others that attempted to make cosmogenic Be-10 measurements on pyroxene instead of the usual quartz. There are many reasons this is difficult, and the production rate of Be-10 in pyroxene is not super well established, so this approach is not immediately very useful for exposure dating, but one result of this study was a set of Be-10 measurements in pyroxene from samples of Ferrar dolerite bedrock and erratics at a site near Mackay Glacier.

Subsequently, thanks to the greatly appreciated assistance of the authors of this paper in supplying splits of the samples, we measured He-3 concentrations in some of these samples at BGC. These data can be found in a summary table here, and they’re also in the ICE-D:ANTARCTICA database. This is what they look like.

We didn’t find any samples that have less He-3 than the previous record holders with around 5 Matoms/g. But the important thing here is that for samples that have experienced a single period of exposure — as we expect for these samples that have Be-10 ages that record Holocene deglaciation — cosmogenic Be-10 and He-3 concentrations have to be linearly related. The slope of the line is equal to the He-3/Be-10 production ratio, and the intercept  is equal to the noncosmogenic He-3 concentration in the pyroxene. These data are, in fact, linearly related as we expect, so they enable us to estimate the noncosmogenic He-3 concentration without actually finding a true zero-exposure-age sample. Drill rig not needed. Very useful.

The estimate from linear regression of these data is 3.3 +/- 1.1 Matoms/g, which is consistent with the maximum limiting estimates from Antarctic samples noted above. In addition, we recently measured a He-3 concentration of 2.9 +/- 0.7 Matoms/g (actually the mean of three not-super-precise measurements) from an erratic clast collected by Jamey Stutz near the ice margin at Evans Heights, adjacent to David Glacier. So that sample may be the hotly pursued true zero-age sample, or something close to it. Here is the regression above together with all of the estimates from attempts to find shielded samples shown as horizontal lines:

Blueish lines are from Ackert (5-9 Matoms/g); green, Margerison (6.8); pink, Kaplan (5); gray, Stutz (3).

All the measurements from Antarctica (although not the one from Tasmania) are consistent with the 3.3 +/- 1.1 Matoms/g estimate. If we accept that the Tasmanian and Antarctic dolerites are not equivalent from this perspective, this all seems to make sense. On the other hand, it also brings up some aspects of helium systematics in the Ferrar that are still unexplained, and tend to cast a bit of doubt on this whole story. Even though the Ferrar is a single magmatic system that was emplaced over a geologically fairly short period of time, it consists of many different intrusions, and each intrusion includes various petrographic facies with distinct crystallization histories. Suppose all the noncosmogenic He-3 we observe in Ferrar pyroxene is magmatic. It sounds somewhat plausible that the He-4/He-3 ratio of magmatic gases could be uniform and constant throughout the magma system, but it would be very surprising if the He-3 concentration in pyroxene that presumably crystallized at different times, places, and rates was also uniform and constant. However, what we observe in the regression plot above is that samples of Ferrar pyroxene from a set of glacial erratics that were presumably drawn from many different levels of the Ferrar in the Mackay Glacier area appear to have a fairly constant noncosmogenic He-3 concentration. If the noncosmogenic He-3 concentration was much more variable than the typical uncertainties in these measurements, we wouldn’t see anything resembling a linear relationship.

This leads us to believe that maybe the noncosmogenic He-3 isn’t magmatic, but was produced post-emplacement by the reaction Li-6(n,alpha)He-3 induced by neutrons derived from decay of U and Th in the rock. Helen Margerison did this calculation with a few trial measurements and came up with something of at least the right order of magnitude. For He-3 from this source to be constant throughout the Ferrar would require (i) a fairly constant Li concentration in pyroxene throughout the Ferrar, and (ii) a fairly constant neutron flux, which in turn requires fairly constant U and Th concentrations in the whole rock. These conditions seem more plausible, and could potentially be verified by measurements. Although the high variability of He-4 concentrations in Ferrar pyroxene (an order of magnitude or more) implies that U and Th concentrations in pyroxenes are also highly variable, the thermal neutron flux is controlled by the bulk composition of the whole rock and not the pyroxenes. Thus, the hypothesis that noncosmogenic He-3 in Ferrar pyroxene is mostly nucleogenic, mostly not magmatic, and fairly constant throughout the unit at least can’t be disproved by available data at the moment. However, it would probably be a good idea to try a little harder to disprove it by more systematic measurements of U, Th, and Li in pyroxene and whole rock. We could also measure Be-10 and He-3 on a lot more Holocene erratics; there are plenty near major glaciers throughout the TAM.

To summarize, we can’t disprove the hypothesis that noncosmogenic He-3 in Ferrar pyroxene is always 3.3 +/- 1.1 Matoms/g. In fact, so far it seems like a pretty good hypothesis. If we assume that it is pretty good, what does this do for us in the exposure-dating application? At sea level in Antarctica, this amount of He-3 is equivalent to 22,000 +/- 7000 years of exposure, so this is still not ideal for exposure-dating of LGM-to-present glacial deposits at low elevation. Even if we measure the total amount of He-3 with perfect precision, we are still stuck with a 7 ka uncertainty after we subtract this estimate of noncosmogenic He-3. On the other hand, at 2000 m elevation, it only equates to 3800 +/- 1200 years of exposure, so in principle this allows us to exposure-date LGM-to-present deposits at high-elevation sites with reasonable precision. For example, this implies that LGM-age deposits near the top of Beardmore Glacier are near 12 ka, rather then just less than 20 ka as calculated in the original paper without a correction. This seems like progress.

 

Where is the production rate?

June 10, 2020

Based on recent emails, a common complaint about version 3 of the online exposure age calculator is that when you calculate an exposure age, the results page does not include the nuclide production rate at the sample location.

This was a feature of the version 2 exposure age calculator that apparently was much more widely used than I thought.

Some users then proceed to further confusion about whether or not the production rate calibration input page is intended to be used for this purpose. Of course this is not the case — production rate calibration is something quite different.

The main reason that the sample production rate has been removed from the results page in version 3 is that for a variety of reasons, it is now fairly clear that time-dependent production rate scaling methods that take account of magnetic field changes are more accurate than scaling methods that assume that the production rate is unchanged. In practice, there’s not much difference for many common applications, mainly exposure-dating glacial deposits at relatively high latitude where the unknown-age sites are similar in age to calibration data, but in general it is almost certainly better to use the time-dependent methods. With a time-dependent scaling method, of course, there is really no point in telling you what the production rate is at any one time. So, no production rate.

The purpose of this post is to point out that even though there is no box on the results that says “production rate,” once you have calculated an exposure age, it is quite easy to determine the average production rate during the period of exposure, which is probably what you are looking for if you are sending me emails asking about where the production rate is.

In the case of Be-10, you have already measured the nuclide concentration N_{10} (atoms/g). You have just calculated the exposure age t (yr). You know the decay constant for Be-10 (\lambda_{10} = 4.99 x 10-7 /yr). These things are related to the Be-10 production rate P_{10} (atoms/g/yr) by:

 

\frac{P_{10}}{\lambda_{10}}\left[ 1 - \exp{\left(-\lambda_{10}t \right)} \right]

 

This is easily solved for P_{10}.

This is even simpler for stable nuclides:

 

N = tP

 

As noted above, for stable nuclides and for short exposure times relative to the half-life of a radionuclide (that is, most exposure-dating applications), this will give you the mean production rate during the period of exposure.

Of course it is the mean production rate in the sample — thickness and shielding corrections are included — not at a theoretical unshielded flat surface.

For long exposure times with radionuclides, what you get is a decay-weighted effective mean production rate, which is something like:

 

\frac{1}{t} \int_{0}^{t}P(\tau)\exp{\left(-\lambda \tau \right)} d\tau

 

where t is zero now and positive for times in the past, and \tau is a variable of integration.

This is additionally simplified because zero erosion is assumed: if you had included an erosion rate, you would either have to make the equation slightly more complex to include both radioactive decay and erosion. However, if you just wanted the production rate, it would be easiest to just do the calculation with zero erosion and use this equation.

Again of course, for a time-dependent scaling method the mean production rate is not the same as the production rate in the sample right now, except by accident. For non-time-dependent scaling the present production rate and the mean production rate during the period of exposure are the same.

OK, done. The point is that if not having the local production rate reported is a problem, it is a pretty easy problem to overcome.

 

 

 

 

Some don’t like it hot: accounting for Ne diffusion during exposure in hot deserts

April 22, 2020

Another guest author! Michal Ben-Israel is a Ph.D. from the Hebrew University of Jerusalem, spending her time attempting to apply cosmogenic Ne-21 to quantify rates of surface processes in the deep geological past, to variable degrees of success.


It has been pointed out before that cosmogenic Ne-21 is, simply put, a pain in the neck. As a stable cosmogenic nuclide, Ne-21 has undeniable potential, especially when it comes to exposure dating past 10^7 yr. Unfortunately, cosmogenic Ne-21 comes with an impressive list of issues that need to be considered when applying it over such timescales. Issues such as interferences from non-cosmogenic Ne-21 trapped in the crystal lattice (of atmospheric, nucleogenic, or other sources), inherited cosmogenic Ne-21, post-burial muon produced Ne-21, not to mention the overall scarcity of preserved sediments/surfaces that are old enough to justify getting into this whole Ne-21 mess. 

In defense of cosmogenic Ne-21, it has been shown to be useful in dating exposure of very slow eroding surfaces that have been exposed beyond the reach of radioactive cosmogenic nuclides, like in Antarctica or the Atacama. These two cold deserts constitute the perfect case-study for exposure dating with cosmogenic Ne-21: uninterruptedly exposed, slowly eroding, and with plenty of quartz to sample. But these are not the only locations that check those Ne-21 boxes, parts of Australia, the southwest US, and the Levant all make good candidates. Except, that when we consider hot deserts, we find ourselves faced with one more disadvantage of Ne-21: diffusion. 

Thermally activated diffusion of Ne from quartz is yet another thorn in the side of the application of cosmogenic Ne-21 over geological timescales. Neon, being a noble gas, is inert and so it readily diffuses out of the quartz lattice, which like all diffusive processes, depends on temperature and time. Diffusion can be useful, and a few experimental studies into diffusion kinetics of Ne in quartz led the way to some cool applications of diffusion, such as paleothermometry. However, when it comes to extended periods of surface exposure, diffusion is a foe. 

This thought experiment started with a thought provoking discussion with Marissa Tremblay (see referee comment – RC3) wherein she wondered about possible diffusion in the reported Miocene chert pebbles, sampled from the Negev Desert. While diffusion from these pebbles is most likely insignificant, the question arose – what would happen to exposure dating in uninterruptedly exposed, slow eroding, quartz covered surfaces in hot deserts.

The first thing to note about hot deserts is that they get very very hot. Air temperatures in the Sahara, for example, can reach 56ºC (~134ºF for the metrically challenged). Surface temperatures and particularly temperatures of exposed dark rocks such as chert or patina covered quartz/quartzolite can be significantly hotter. In fact, Mcfadden et al. proposed solar heating as a mechanism for physical weathering of rocks in arid landscapes, leading to preferential vertical cracking in exposed boulders and pebbles. So it would be reasonable to assume that a dark rock in a hot desert during mid-day in the summertime would reach temperatures of up to 80ºC (this might be on the high end of the scale but is probably still within reason). The question I’ll examine next is, would such temperatures affect surface exposure dating and by how much?

On the left are fragments of what used to be a chert cobble from the hyperarid Eilat area. On the right is a well-developed desert pavement composed of dark fragments of cherts and quartzolites located in the northern Negev Desert.

For the diffusion calculations, we can assume that only during Apr-Oct and only between 10 am to 4 pm, rock temperatures get sufficiently hot. We can simplify these calculations by assuming that during [(6/12)*(6/24)] of the time each year, dark rocks at the surface reach a steady temperature of 80°C. One more assumption we need to make is regarding the effective mineral radius. In the presented calculations, I used a 250 and 850 micrometers diameter (the commonly used grain-size sampling range for cosmogenic nuclide analysis), and 1 cm mineral diameter (a typical size for desert pavement fragment). [sidenote: mineral radius is one of the variables in the diffusion equation, which might have the most uncertainty, as mineral radii can range between tens of microns to a few centimeters. It is also important to note that mineral diameter isn’t necessarily equal to grain size, but that is a whole other question that I am not sure I know how to answer, and I definitely don’t want to get into here.]

Now we can calculate the time-integrated diffusion over 100yr intervals for a total span of 10 Myr. For this imaginary scenario, I scaled production for a site in the Negev Desert, where some very slowly eroding surfaces have been reported. This calculation could have also been normalized to production rate or calculated for SLHL production rates, but I chose this site for no other reason other than I wanted to use data for an existing location.

This figure shows how diffusion affects cosmogenic Ne-21 calculated exposure ages. On the left, we can see how the concentration of cosmogenic Ne-21 deviates (dashed lines) from the cosmogenic Ne-21 produced (continuous line), depending on grain size radius. On the right is the actual exposure age versus the calculated exposure age, with the continuous line representing no diffusion and the dashed lines representing diffusion depending on grain size.

What we learn from this figure is that for sand size range, diffusion becomes significant after ~2-3 Myr of exposure, and beyond 5 Myr, the difference between apparent exposure time and calculated exposure time gets problematic. So right around the time that exposure can no longer be quantified using Al-26 and Be-10, and Ne-21 gets useful, is when cosmogenic Ne-21 starts to falter and we need to start considering the effects of diffusion. To sum it all up, I guess we can add diffusion during exposure (in hot deserts) to the long list of nuisances that come with cosmogenic Ne-21 exposure dating. 

At the base if this text hangs the question, is cosmogenic Ne-21 worth bothering with for exposure dating? There is now even more evidence to suggest the answer is – no, but I don’t think the answer is this clear-cut. At this point in time, Ne-21 remains the ONLY available cosmogenic nuclide that could be applied to quantify rates of surface processes throughout the geological record, at least until another stable or slow decaying nuclide comes along (I’m looking at you, Mn-53…)

Computational infrastructure for cosmogenic-nuclide geochemistry

February 25, 2020

The point of this post is just to provide a link to an otherwise obscure document that is, nevertheless, interesting if you are (i) a cosmogenic-nuclide geochronologist, which is unusual already, and also (ii) a cosmogenic-nuclide geochronologist interested in data management and computational infrastructure, which is, to say the least, far beyond unusual. However, I am sure that if any such people do exist, they are probably readers of this blog, so this seemed like a good place to put it.

The document is here.

What it is is the project description section of a proposal submitted by myself and Ben Laabs (NDSU) to the NSF ‘Geoinformatics’ program in August, 2019.

Basically, the main idea of the proposal is to provide some support for the ICE-D database projects previously featured in this blog. Although presumably this proposal has now been reviewed, its funding status is unknown at the moment.

In any case, I think this document is interesting for several reasons, as follows:

  1. It contains the best and probably only intelligible description of what the ICE-D database project is supposed to be doing, and why it is set up the way it is. The ICE-D websites are otherwise nearly entirely without documentation. It’s not going to give you the details about exactly what is happening in the back end (and I apologize in advance for Figure 2), but it is a good overview of the purpose and context of the project that is not otherwise written down anywhere else.
  2. It contains some ideas on why the ICE-D project infrastructure is, potentially, a pretty good way to think about computational and data management infrastructure for geochemistry and geochronology.
  3. It contains some decent ideas on what science applications we could use it for in the future.
  4. It introduces the phrase “transparent middle layer” to geochronology. Although this makes the whole thing sound like a terrible Silicon Valley venture-capital pitch — synergizing the enterprise cloud data ecosystem with a transparent middle layer — and I may regret it later, I think it is a good way to think about how geochronology data management should work if it is going to be useful for anything.
  5. It also contains a compact account of the historical development of the bits of computational infrastructure used for cosmogenic-nuclide geochemistry, in particular the various online exposure age calculators. Mainly because the production rate calculations needed to get from a nuclide concentration to an exposure age are such a mess, cosmogenic-nuclide geochemists were quite early adopters of cloud computing in the form of the online exposure age calculators. I think this is interesting.

On the other hand, it is a proposal, so there are also a lot of things in there that are just proposed and may never happen, and in addition it wildly overuses the word “synoptic.” But that part is really not too bad. In any case, if you plan to be involved in Earth science applications of cosmogenic-nuclide geochemistry in future, it is a potentially useful read.