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Shadow New Zealand and the importance of the atmospheric pressure distribution

October 25, 2013

The world map on the front page of the online exposure age calculators reflects information about samples that calculator users have entered. If you use the calculator to compute an exposure age or erosion rate, the code generates a dot at the location of the sample. The idea is to generate a representation of how often and where the calculator is used, and the map is a visually appealing and compact way to do that. The fact that the only record of each sample is a dot that is tens of kilometers wide at map scale is intended to reassure users that no one is stealing their hard-earned scientific data for nefarious purposes, although in the modern era of continual surveillance by Google and Facebook, this concern seems quaint. The point of this post is that there are some interesting clusters of dots on this map. For example, there is a strange cluster of sample locations in the northwest Pacific:

At least, this cluster appears strange until one realizes that it is a mirror image across the Equator of a cluster of sample locations in New Zealand. Basically, these dots represent calculator users who have mistakenly entered data for sites in New Zealand with positive instead of negative latitude values. That is, for example, they wanted to enter:

Hypothetical-NZ-sample -43.5761 170.6084 1025 std 0.8 2.65 0.992 0 110165 1900 07KNSTD 0 0 KNSTD

but mistakenly entered:

Hypothetical-NZ-sample 43.5761 170.6084 1025 std 0.8 2.65 0.992 0 110165 1900 07KNSTD 0 0 KNSTD

The most likely scenario here is that latitudes in a published data table were given as positive values of south latitude, rather than negative values of north latitude, and a reader was cutting-and-pasting from data table to spreadsheet without focusing on this. However,  it’s important because if you used the first input line to calculate an exposure age, for example, using the calibration data set of Putnam et al., 2010 and the “Li” scaling scheme, you would infer an exposure age of 12076 years (in the middle of the Younger Dryas). If you used the (incorrect) second line, you’d get 12531 years (early in the YD and probably indistinguishable from the end of the Antarctic Cold Reversal). So this might turn out to be important.

Here’s another example, an odd cluster of samples in the southern Indian Ocean (interestingly, these are pretty close to Kerguelen Island, which would be a legitimately very interesting place to study, but they are not actually on land there):


In this case, samples in Patagonia were entered incorrectly in east longitude instead of correctly in west longitude — i.e., a minus sign was omitted. Again with a hypothetical Patagonia sample, correctly entered:

fake-Patagonia -50.0843 -73.1635 285 std 1.2 2.65 0.9998 0 81167 1630 07KNSTD 0 0 KNSTD

With the production rate calibration data set of Kaplan et al. (2011) and the Li scaling scheme, this is 14734 years old. Without the minus sign to signal W longitude:

fake-Patagonia -50.0843 73.1635 285 std 1.2 2.65 0.9998 0 81167 1630 07KNSTD 0 0 KNSTD

13945 years.

Why the difference? Two reasons in this case. First, the online calculator uses a geographically variable elevation/atmospheric pressure relationship that reflects the observed pressure field. Thus, the production rate at a given elevation is predicted to be slightly different depending on where you are. Second, the paleomagnetically corrected scaling schemes (Lifton is one) include non-dipole magnetic field variability, also predicting geographic variability in production rates. Thus, sure it is funny that there is a shadow New Zealand in the north Pacific, but the point is that this highlights the importance of things like the air pressure distribution on production rates and therefore on exposure ages.

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