December 1986 – Irony

A guest post by John Goetz

In my post December 1986, I presented a histogram showing the GISS estimate of December 1986 minus the actual for GHCN stations in Europe and Russia. As noted, GISS under-estimated December 1986 for this region by a greater than 2 to 1 margin. The result was, when GISS combined multiple records for a single station, the stations with a cold estimate for December 1986 had their records artificially cooled pre-1987. By cooling the older record and leaving the current record unchanged, an enhanced warming trend was introduced.

I promised I would show other regions of the world in future posts. Therefore, in this post I present Africa, which essentially shows polar-opposite results from Europe / Russia.

In Africa, GISS tends to over-estimate December 1986 when combining records. Because the temperature is over-estimated, older records must be warmed slightly before they are combined with the present record. By introducing artificial warming in a past record, the overall trend through the present is cooled.

Following is a histogram showing the GISS estimate of December 1986 minus the actual for GHCN stations in Africa.

africa.GIF

The implication is that the GISS algorithm introduces a cooling trend to most African records.

As can be seen in the next plot, however, the number of stations reporting temperature data in Africa drops off rather sharply before 1950. This means any warming of past records likely does not go very far back in time.

africastations.GIF

We need to peek backwards some and see how many of the “warmed” station records actually exist before 1950:

  1950 1940 1930 1920
Warmed 50 10 8 5
Cooled 31 13 13 10
No Change 52 22 19 15

As can be seen from the table above, prior to 1950 the “cooled” stations tend to outnumber the “warmed” stations. In other words, from roughly 1950 to 1986, GISS artificially warms the African records, and prior to 1950 it artificially cools the records. Granted, we are not talking about a lot of stations here, but it does give one whiplash from all of the double-takes.

As was pointed out in several comments to December 1986, the average bias for that month, while negative, was not particularly large. Furthermore, the value would end up being divided by 36 or 48 in order to yield the adjustment amount. See here and here.

The same is of course true of Africa. The implication in both cases is that the net adjustment ends up being so small that we won’t see it at the global or perhaps even zonal level. This might indeed be true. Whether the trend is enhanced or not does not necessarily mean the trend is not there. At the macroscopic level the adjustment may not matter at all.

Nevertheless, I find it rather amusing / interesting / ironic that as I go back in time and look at the average bias adjustment of African stations, the cooled stations not only outnumber the warmed stations, but they far outweigh them when averaging the adjustment. This comes in spite of the fact that most of the records get the warming bias.

Here is what I mean:

africabias.GIF

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40 thoughts on “December 1986 – Irony

  1. “As can be seen in the next plot, however, the number of stations reporting temperature data in Africa drops off rather sharply before 1950. This means any warming of past records likely does not go very far back in time.”
    Did you apply the same analysis when you looked at the Europe/Russia records? I’m wondering why you pick up this point here when considering a cooling trend but not, apparently, when considering a warming trend?
    Reply: Yes, I did last year and again this year, but I have not yet tied those loose ends into this latest thread. It is on my to do list. Here is a teaser graphic I generated last year and posted on CA.
    As for this post, I happened to notice the bias plot for Africa as I was writing the above post and looking back through older analyses. I thought “this is odd”, which caused me to look more closely, and ultimately changed the conclusion I was originally drawing (and planning to post).

  2. On CA, the contention was that GISS was cooling the past (esp. the 1930s). Steve Goddard also pointed out that GISS “adjusted” the recent ten-year slope by six degrees (angle, not temperature!).
    This all seems in line with that. I want to see the raw data and then ech adjustment shown and explained. The way NOAA did it for USHCN1.
    Unfortunately, since that explanation became one of the most quoted passages (and graphs) by skeptics, NOAA has (most wisely) stopped providing the information in accessible form!
    So far as those “adjustments” go, “Everything that is suppoed to be UP is DOWN! And everything that is supposed to be DOWN is UP!” to quote Al Gore.

  3. What’s this I hear about a new sunspot cluster. Is it real? Or am I misinformed? (I can’t find it on the web.)

  4. ” Evan Jones (11:44:35) :
    What’s this I hear about a new sunspot cluster. Is it real? Or am I misinformed? (I can’t find it on the web.)”
    It’s here
    http://sidc.oma.be/LatestSWData/LatestSWData.php
    It has a Catania number – the observer at Catania saw it and drew it this morning
    http://web.ct.astro.it/sun/draw.jpg
    but not yet a NOAA number.
    It’s so tiny that the observers at Mt. Wilson
    http://www.astro.ucla.edu/~obs/cur_drw.html
    Locarno and Uccle among others, did not see it.
    http://www.specola.ch/e/drawings.html
    http://sidc.oma.be/images/last_ORBdrawing.jpg
    In the magnetograms
    http://gong.nso.edu/Daily_Images/
    it has a Cycle 24 signature (negative (black) polarity leading in the northern hemisphere), and would be consistent with Cycle 24 producing none or few spots, and then only very weak and short-lived.

  5. Also very important about this spot is the low solar latitude (about 15 degrees north).
    That’s unusually low for early, new cycle spots.

  6. From solarcycle24:
    “My understanding is latitude trumps polarity, which would make this sc23. But first it has to become a spot we can see. Plage areas don’t count. And it has to exist as a spot for some minimum time.”

  7. @John-X (12:44:15) :
    Also very important about this spot is the low solar latitude (about 15 degrees north).
    That’s unusually low for early, new cycle spots.

    Is the polarity certain to be SC24 type? I was also puzzled by the low latitude and assumed it was an SC23 group, which would be interesting of course.
    Either way it seems odd.

  8. “a gargantuan house of cards rested on models, assumptions, and values that were, for the most part, baseless. It was hard for the man on the street to know this, of course, because thoroughly-conflicted insiders, clueless academics, corrupt politicians, toothless regulators and various industry shills were running around claiming that they knew what was going on” . As it happens, he was writing about Wall St, but his remarks might be more widely applicable, don’t you think?

  9. John, the task of epic proportions and a significant result.
    … .. it artificially cools the records… .. (U$ 30 billion)
    Leif Svalgaard …. Please HELP …..
    Evan Jones…good question (about the whales; we talk later. civility above all.)

  10. John Goetz,
    Thanks for your response to my question above. You’ll understand, I’m sure, that I’m trying to get an idea of what net effect any of this may have had on the GISS record. It’s interesting to consider the effects (both cooling trend and warming trend) at the micro level, but it remains somewhat academic without knowing whether or not this has had a significant effect upon the record as a whole.
    Personally, I am not alarmed by the fact that record blending will give rise to some systemic fudging. I would be alarmed by any evidence of human bias in such a process, or evidence of the fact that such a process undermines the effective reliability of the ‘end product’ record. Without some evidence of the former, or some figures to judge the latter, this seems to me to be interesting but (currently) inconsequential. Perhaps you’re heading towards some figures to quantify net effect, at which point it will be very interesting to look at your conclusions.

  11. Re: Evan Jones (10:56:42)
    Evan,
    You said, “The way NOAA did it for USHCN1.” I’m a newcomer to CA and this blog. Can you please direct me to the archives here or at CA (or other sources) that contain NCDC’s explanation of their adjustments of USHCN surface station data sets?
    Thanks

  12. Evan Jones…good question (about the whales; we talk later. civility above all.)
    To be clear, whales are very intelligent, and thus I regard them as especially worthy of protection, regardless of whether they are endangered or not.
    It is interesting to note the juxtaposition of the current “War on Terror”/oil put side-by-side with the 1830-1860 “War on Piracy”/whale oil.

  13. <iI would be alarmed by any evidence of human bias in such a process, or evidence of the fact that such a process undermines the effective reliability of the ‘end product’ record.
    I don’t know what you mean by ‘human bias’. All bias results from humans. ‘Noise’ is used to describe non-human sources of error in measurements. There is definitely bias here.
    And that bias will definitely affect the ‘end product’ record. However, John cannot say by how much. So all we can say we with certainty is that some of the reported temperature trend is due to human bias.

  14. hmccard: Just posted the links. (Hope they don’t get caught in the spam filter.)
    Reply: They did–I sealed up my nostrils, closed my eyes, reached in, and dug them out.~charles the moderator

  15. Philip_B, let me answer for Steven Talbot. I believe he was referring to intentional versus unintentional bias.
    Potential unintentional biases which in some way can all be considered human biases, but not what he was referring to:
    Observation bias
    Instrumental bias
    Improper analytic procedure bias (what this post is about)

  16. There can also be unconscious bias where you’re actively creating a bias because it agrees with your preconceptions. So you wind up correcting for biases that don’t support your preconceptions while ignoring those that do. It’s intentional but not consciously so.

  17. jeez – thanks 🙂
    In order to introduce any intentional bias or observational bias to this, it seems to me that the scientist(s) would have had to have calculated the effects of alternative methods of blending the records, and then chosen the one which best favoured the intent, or was in best accord with their observational bias. I find that quite implausible, and am more inclined to think that they simply chose a method which may have arbitrarily thrown up some systemic error.
    At the moment we cannot say whether bias of any kind is either positive or negative in terms of global trend. It will be interesting to see whether a time comes when we can, and then whether it is of any consequence.

  18. Steven Talbot,
    I wholeheartedly reject the idea that there is any form of intentional bias in the results or most of the records. I think there might be some intentional bias in some of the records, but that is a topic for a future post as I continue gathering evidence.
    I use postings like this to do nothing more than think out loud about some “micro-level” step in a much larger process. I do keep the broader process in the back of my mind, but right now it is too hard for me to put my arms around the full set of implications.
    I posted the following comment on CA in response to someone asking if the adjustments “CAN”T be anything but insignificant”. It is essentially an outline of the questions I ask myself and periodically find time to investigate:
    I am not yet convinced of the significance one way or the other.
    1. The distribution of stations worldwide is nowhere near uniform. This is true now and back through the historical record. The United States has far more representation in the record than any other country or region on the planet, yet occupies only about 3% of the surface, as we are sometimes gently reminded. The gridded temperatures covering the US are going to therefore be more accurate than elsewhere. How much more is not yet known to me.
    2. The number of stations participating in the effort to measure global temperature was greatest from roughly 1950 to 1990. Prior to 1950 the number grew from very few stations in 1880 to a moderate number in the late 1940s, before jumping dramatically. Since 1990 the number has dropped dramatically, such that now the number of stations participating is roughly equal to that in the very early part of the 1900s. There’s progress for you! Of course, that does not mean the same few stations reporting now were reporting in 1900. Some have come and some have gone.
    3. Even the few stations that have been reporting that long have gone through changes that affect the fidelity of their records. We have seen that the Burlington, Vermont station has not always been located at their international airport because, well, airplanes did not exist when the station first began reporting. Like a present-day yuppie, the station migrated to the burbs over time, having originated in downtown close to the lake. Along the way it spent time in a couple back yards, on a city roof, and at the local university. Hopefully not partying.
    So, we start with rather mediocre spatial and temporal representation. To that we add:
    4. Of the records we do have, most are missing a number of monthly data points – some more than others. There are many reasons data might be missing, but one thing we have learned is that GHCN will drop an entire month’s worth of data if even one day is missing or suspicious. This happens over and over and is not a rare occurrence. This missing day or days could be estimated using a variety of techniques, or better yet, some of the days can be recovered if someone actually looked at the record and spotted the typical transcription error. You know, when June 10 is 24C and June 12 is 25C, but June 11 is -23C. That type of error will result in the entire month being discarded. Whaddya thing the real June 11 temperature was???
    5. So rather than fixing a transcription error here or there or estimating the missing day or two, GHCN drops the month and leaves it up to GISS to estimate the month. We have seen just how robust that estimation is. (Imagine for a second you are watching Lewis Black tell you this).
    6. Now, GHCN very, very often passes along multiple records meant to represent a single station. Many times these records are consistent, indicating they are essentially pages from the same book. Sometimes they are not, which could mean they came from some other nearby location, were collected by a different piece of equipment – who knows. GISS says “what the heck, I don’t have a lot of records to begin with, why don’t I just splice these bad boys together and make one long record.”
    You can imagine what might happen when two records from different books are spliced. You get sausage. But interestingly enough, as we have seen, even when they are from the same book you still get sausage, just a milder variety.
    7. Keeping to the theme of not throwing away a good record no matter how crappy it looks after we’ve ground it up, we come to the point where we need to adjust urban stations because we know their temperature trends are artificially inflated by the urban heat island effect. So we find the rural stations that are within 500km, or wait, maybe sometimes 1000km (the distance from Indianapolis, IN to New York, NY), munge their trends together, and decide that MUST be the real trend of the urban station. So we go in and change the urban stations record so that its trend matches the munged trend, whipping the station back into line.
    From that point comes the gridding part, which I have yet to explore in my spare time. I’ve also not bothered to mention the other machinations that go on with the record, such as the TOBS adjustment and infilling. I’ve ignored mentioning how the temperature is collected and whether or not station standards are consistently met or not met.
    Nevertheless, from this we have pronouncements that the earth is 0.7 degrees warmer now than it was 100 years ago, and that Armageddon is upon us and we need to make some serious policy decisions based on the data. And equally robust simulations.
    So no, I can’t conclude that the adjustments “CAN’T be anything but insignificant”. To me, what is done with the historical record is nothing more that putting out a fire with an ice pick.

  19. John Goetz (18:43:02)
    ‘So no, I can’t conclude that the adjustments “CAN’T be anything but insignificant”. To me, what is done with the historical record is nothing’….
    I think you ran out space.
    Reply: Or your browser did, because I see it all in my browser.

  20. John Goetz: It was better than the post: clear.
    http://www.surfacestations.org : We know what is inside those boxes. (called meteorological stations).
    Armageddon: hmmmmm
    Evan?
    The dinosaurs were extinct:
    a – Because an asteroid (meteor or comet) collided with Earth?
    b – Because they did not know what was an asteroid (meteor or comet)?

  21. I’m certainly potentially interested in this material, if only I could fathom out what those wretched acronyms (the bane of modern language) GHCN and GISS actually meant.

  22. Looks like the different methodologies at most any given time are greater than the supposed temp increase in the last 100 years! How do we really know that global mean temp has increased at all? Not that I think there has been no increase.
    Bear in mind that those are Farenheit measurements. The overall correction is around 0.3°C. That’s half the increase.
    But even so, note that FILNET is supposed to be neutral and SHAP should definitely be ‘way negative. And it’s ironic that the MMTS adjustment is positive considering that the switchover created massive CRN4 violations.
    McKitrick and Michaels (2007) estimate that around half of the global increase of the last century is spurious.

  23. I’m certainly potentially interested in this material, if only I could fathom out what those wretched acronyms (the bane of modern language) GHCN and GISS actually meant.
    Check out the GLOSSARY tab at the top of the page. All the acronyms are there.

  24. Fernando: My prejudice in favor of the whales is just that. Prejudice.
    On the whole, I think the environmental movement is quite misguided (e.g., the Polar bear nonsense, and much other equally bad nonsense such as spotted owls, snail darters, and peregrine falcons). This bothers me because I think a non-misguided environmental movement is necessary. Unfortunately sensible conservationism has been turned into religious idiocy and is just plain wrong about nearly all their “facts”.

  25. Pingback: STAY WARM, WORLD… Roger Carr « Stay Warm, World…

  26. Reply: Or your browser did, because I see it all in my browser.
    Thanks, problem adjusted.

  27. Evan, re: your most recent post on a “sane” environmental movement.
    I wholeheartedly concur.

  28. I have the same space problems with John Goetz’s posting too. It end at “nothing” with no punctuation or anything. I assume it continues a little more at least, but I haven’t a clue what it says.

  29. In case readers here do not visit this topic over on CA, I had made a comment there which has attracted some discussion. I present a summation of some points from it here in case others have anything to add. I’m not sure of the overlap of readership between Watt’s Up, CA, and Climate Skeptic. I do make some corrections due to my errors in math from trying to crunch too many numbers at once and to improve the grammatic flow of the points I brought up.
    “I went and checked GISTEMP, and they do explain how they divide up the Earth for their statistical averaging on this page. But one thing snagged at my mind a bit. Step three of the process involves dividing the planet into 8000 grid boxes. This would create boxes of roughly 63,759 square kilometers within which an average temperature anomaly is computed and then supplied to the global computation. It is how the average temperature is computed that bothers me.
    That average temperature anomaly is computed from the temperature stations within that grid box, and also any within a 1200 kilometers radius. To give a graphic perspective so people can visualize this, let’s say my grid box is centered in St. Louis, Missouri. My local grid average is determined by not only the stations within my grid (roughly within 125 kilometers of me), but also from stations in Pittsburgh, Atlanta, Dallas, and Minneapolis, to name just a few. Basically, the radius of effect means that my one grid box’s temperature anomaly is not determined from the only 63,759 square kilometers within it, but from the 4.52 million square kilometers around it, an area 71 times as large.
    Based upon this computation design, the United States should be represented by roughly two grid boxes if you are looking at total area involved in determining the average temperature anomaly compared to total area of the US (9,826,630 square kilometers total area of the United States divided by the 4,521,600 square kilometers derived from the 1200 kilometer radius of effect). But the US grid sample is still based upon the 63,759 square kilometer grid box determination, so the total US grid boxes are 154. That would seem to me to heavily weight the US sample.
    Why the 1200 kilometer area of effect? Doesn’t this mean that certain stations get counted multiple times? Based upon my math above, it would suggest that many stations in the United States get counted over 77 times. Does the temperature in St. Louis really effect the temperature in Atlanta, and vice versa? Surely, we could just use the temperatures provided by the stations within the grid boxes to determine that grid box’s average anomaly and work out a good global average from that.
    It does bother me that temperatures from regions separated by natural geologic barriers that would disrupt weather patterns (as an example, mountain ranges) are used to determine one anomaly statistic. I can see Hansen’s problem due to coverage zones for his 8000 grid boxes. There are probably several areas in the South Pacific where he would be lucky to get one reliable temperature reporting station within a 1200 km radius. Even so, if he would admit this, and explain his rationale for his process, I’m sure it would create more understanding. I also think that it makes no sense to have stations count more than once in the total computation.
    …Note, my 63,759 square kilometers figure is derived from using the entire Earth’s surface, both land and ocean. If you only use the land surface area (148,940,000 square kilometers), our grid box shrinks to 18,618 square kilometers. The 1200 kilometer radius circle would hold 243 of those boxes. I gave Hansen the benefit of the doubt that he used the entire planet surface when computing his grid. The source for the surface area data of the Earth is wikipedia.”

  30. Evan Jones and Charles “the Moderator”,
    Thanks, I am aware of the referenced NCDC sources. Unfortunately, the explanations by Karl, et al, have not enabled me to understand some of the significant differences that I have observed in the USHCN/NCDC surface station temperature data sets.
    I am puzzled by the residual difference between TAVE and TMED, i.e., del TAVE = TAVE – (TMAX + TMIN)/2. A graph of del TAVE for a specific station displays distinct step-wise seasonal and annual trend patterns. The seasonal pattern for DJF includes the prior-year-December anomaly that John Goetz discusses in this post related to GISS data sets. The intervals between the step-wise changes vary from a few years to several decades. For example, Tucumcari 4NE, NM (299156), a graphical display of the 1900:2005 data set show step-wise changes in del TAVE occurred in 1914 and 1982.
    Anthony’s 6/30 post on highlighted Tucumcari 4NE. His 7/1 update included a hyperlink to a report, Weather Observations at the Agricultural Science Center at Tucumcari 1905–2002 (http://tucumcarisc.nmsu.edu/documents/rr751.pdf). I posted comments noting that the TMAX, TMIN and TMEAN data contained in the report were quite different in comparison to the corresponding the NCDC data set for Tucumcari 4NE that I downloaded from http://cdiac.ornl.gov. I also compared the NMSU/ASC data set with a data set that I downloaded from http://www.wrcc.dri.edu/summary/Climsmco.html. The NMSU/ASC and WRCC data set were quite similar.
    Recently, I have compared NCDC and WRCC data sets for several other surface stations. In all cases, I observed similar step-wise trend patterns. I now believe that I understand the analytical linkages between RCC and NCDC surface station temperature data sets. The analytical details are beyond the scope of this message, except to state that one of the key relationships is:
    Adj WRCC TAVE = WRCC TAVE – NCDC
    = Adj WRCC TMED + WRCC del TAVE – del NCDC TAVE
    This leads to my postulation that:
    “NCDC causes adjustment to be made to WRCC TMAX and/or WRCC TMIN on a seasonal basis which, in effect, adjusts WRCC TMED. NCDC then causes an adjustment to Adj WRCC TAVE to be made by subtracting del NCDC TAVE from Adj WRCC TMED which, depending on its sign, compensates for some or increases the adjustments reflected in Adj WRCC TMED.”
    I realize this postulation may appear to be quite a reach and a “picture might be better that 1000 words”. However, I haven’t learned how to export Excel graphs to WordPress!!
    Although I believe that I now understand the analytical linkages between RCC and NCDC surface station temperature data sets, I have not insights about NCDC’s reasoning as regards the timing or magnitude of the step-wise adjustments.

  31. Bobby Lane:
    a “sane” environmental movement.
    Yes, it’s like having the Boy Who Cried Wolf running the fire dept.

  32. hmccard:
    It would seem risible, prima facie, that SHAP adjustments could possibly be a positive trend in the first place. This goes counter to what is obvious.

  33. Evan,
    Thanks, I haven’t found anything in the station records that correlate with the step-wise transitions that I observe. Therefore, I don’t think they are SHAP-related. Also, I don’t observe positive trends. Instead, the step-wise transitions are followed by intervals that vary from a few years to several decades with essential zero-slope and small variances.
    Although graphs would be better, perhaps the following tables will be helpful:
    Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
    WRCC TMAX (°F)
    (+ indicates WRCC>NCDC value)
    Interval DJF MAM JJA SON
    1900:1905 1.14 0.63 1.28 1.98
    1906:1910 0.05 0.00 1.01 0.97
    1911:1940 0.01 0.07 0.38 1.00
    1941:1960 0.52 0.56 1.17 1.55
    1961:1989 0.49 0.61 0.33 0.57
    1990:2005 -0.18 -0.04 -0.35 -0.11
    Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
    WRCC TMIN (°F)
    (+ indicates WRCC>NCDC value)
    Interval DJF MAM JJA SON
    1900:1905 2.26 2.12 0.61 3.39
    1906:1910 0.16 2.33 0.00 2.33
    1911:1940 -0.29 2.26 0.00 2.26
    1941:1960 -1.02 0.96 0.00 0.96
    1961:1989 0.53 0.96 0.00 0.96
    1990:2005 1.11 1.54 0.00 1.54
    Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
    NCDC del TAVE (°F)
    (+ indicates WRCC>NCDC value)
    Interval DJF MAM JJA SON
    1900:1905 -0.99 0.69 -0.40 0.23
    1906:1910 -0.98 0.70 -0.39 0.23
    1911:1940 -0.99 0.70 -0.23 0.23
    1941:1960 -0.74 0.02 -0.32 0.49
    1961:1989 0.02 0.02 0.03 0.03
    1990:2005 0.00 0.00 0.00 0.00
    Ft. Collins, CO (053005) NCDC Seasonal Adjustments of
    Net Adjustment (°F)
    (+ indicates WRCC>NCDC value)
    Interval DJF MAM JJA SON
    1900:1905 1.56 -0.04 0.98 1.42
    1906:1910 0.15 0.02 0.01 0.01
    1911:1940 0.00 0.00 0.00 0.00
    1941:1960 1.48 -0.03 0.27 1.40
    1961:1989 0.08 0.04 0.02 0.03
    1990:2005 0.00 0.00 0.00 0.00
    Note: Net Adjustment equals (Adj WRCC TMAX + Adj WRCC TMIN)/2 – NCDC del TAVE.)
    The varinaces in the intervals between the step-wise for WRCC TMAX, WRCC TMIN and Net Adjustments are less than 0.3 and usually less than 0.1; the variance for NCDC del TAVE is less than 0.0001.
    The above tables show quite clearly the dynamics and significance of the adjustments that have been made to the Ft. Collins, CO data set. I believe the asymmetries and trend patterns are remarkable but you will certainly draw your own conclusions. Some might argue that since the Net Adjustment has remained essentially constant since 1933, the bias introduce by NCDC’s adjustments haven’t changed. However, the fact that NCDC TMIN was lower than WRCC TMIN by more than 1.1 °F for the latest interval, 1990:2005, whereas, NCDC TMAX was only slightly above zero than WRCC TMAX and NCDC del TAV was equal to zero is quite interesting when compared to the preceding interval. I imagine those same adjustments are being made currently.
    The only possible cause of the observed step-wise adjustments that I have imagined is the splicing of data from other surface stations but I can find no record of that having occured.

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