We already called out Willis Eschenbach for his impudently wrong claim that last summer in Australia was “nothing at all unusual.” Evidently he wants you to believe that the hottest summer on record, the hottest month on record, the hottest day on record, and the longest national scale heatwave on record, all add up to “nothing at all unusual.” Not too bright.
Perhaps he felt stung by having his foolishness exposed so plainly. He should. But instead of admitting how wrong he was, or just keeping his mouth shut and laying low until the “heat” is off, he decided to try to smear the Bureau of Meteorology’s ACORN temperature data. How? By pointing out that out of about four million days’ of station data, on 917 of those days “the minimum temperature for the day was HIGHER than the maximum temperature for the day … oooogh. Not pretty, no.” Can you feel the glee of his “gotcha!” moment? Can you smell how sure, how absolutely certain, he is that this means the data are screwed up and the folks who maintain it aren’t doing a righteous job? As Willis says,
The issue is that the authors and curators of the dataset have abdicated their responsibilities. They have had a year to fix this most simple of all the possible problems, and near as I can tell, they’ve done nothing about it. They’re not paying attention, so we don’t know whether their data is valid or not. Bad Australians, no Vegemite for them …
I must confess … this kind of shabby, “phone it in” climate science is getting kinda old …
What’s really shabby — and is way beyond “old” — is Willis Eschenbach’s eagerness to criticize what he doesn’t understand.
Here’s a clue for you, Willis: if, out of four million days’ data, there were none in which the minimum temperature for the day was higher than the maximum temperature for the day, then I would know that the “authors and curators” weren’t doing a righteous job. Gosh, Willis, you might even have figured this out for yourself if you were sincerely interested in understanding the data, rather than motivated solely by the desire to discredit it in hopes of distracting attention from your impudent blunder.
Alas, Willis, I strongly suspect that simply giving you a clue won’t be sufficient. I think we’ll have to explain it to you.
The Bureau of Meteorology (BOM) explains their methodology here:
Air temperature is measured in a shaded enclosure (most often a Stevenson Screen) at a height of approximately 1.2 m above the ground. Maximum and minimum temperatures for the previous 24 hours are nominally recorded at 9 am local clock time. Minimum temperature is recorded against the day of observation, and the maximum temperature against the previous day.
So: every morning at 9AM they record the minimum temperature for that day and the maximum temperature for the previous day. They’ll record the maximum temperature for that day at 9AM the next morning. Truly, undeniably, the day’s maximum temperature cannot be lower than the day’s minimum temperature. Can’t happen. Not possible.
But that doesn’t mean that the day’s maximum temperature measurement can’t be lower than the day’s minimum temperature measurement. Every measurement deviates from the true value, and when you read a thermometer and estimate its reading to the nearest 0.1 deg.C, your estimate won’t be perfect. Instead the measured value x for a day’s high temperature will be equal to the true value X plus some random fluctuation
Likewise the measured value y for a day’s low temperature will be equal to the true value Y plus some random fluctuation
When we compute the measured difference x-y, it will be the difference between the true high and low temperatures, plus the difference between the random fluctuations
or to put it another way,
where is the true difference, d is the measured difference, and is the random fluctuation in the difference.
The true difference can’t be negative. Just can’t be. But the random fluctuation sure can. In fact, it’s inevitable that a lot of the random fluctuations will be negative. If none of them are, then we know there’s a problem with the data.
But the true difference sure can be zero. Recall that the daily low is the minimum temperature from 9AM yesterday to 9AM today, while the daily high is the maximum temperature from 9AM today to 9AM tomorrow. You bet that the true daily high can be no higher than (although it can’t be lower than) the daily low — the true difference can be zero. In fact, it happens surprisingly often.
If the true difference is zero (which happens often), and the random fluctuation in the difference measurement is negative (which happens about half the time), then the measured difference will be negative. It not only can happen, it must happen — or somebody has messed up the data. You can also get a negative measured difference when the true difference is positive but quite small, and the measurement fluctuation is both negative and larger than the true difference. It not only can happen, it must happen — or somebody has messed up the data.
When that happens, you do not get to remove the data, or “fix” the data. Removing all the negative-difference data would introduce a bias into the temperature time series. It would also introduce a plain old mistake into the distribution of difference estimates — as though on zero-true-difference days the measurement fluctuation can’t be negative, which is absolute nonsense.
If we put Willis Eschenbach in charge of the data, he might decide to go Willy-nilly removing all those data values which don’t fit his ignorant expectation. Then, the data really would be screwed up by incompetence.
Of course, we wouldn’t expect the measured differences to be very negative, or for them to crop up very often unless it was routine for the true daily difference to be very small. I surveyed the ACORN data which recorded both a daily high and a daily low temperature, to find those days for which the measured high temperature was lower than the measured low. I found 954 such days out of 3,404,808. That’s not a lot (only 0.03%).
More to the point, the vast majority of the negative differences were very small. Here’s the count for each difference value:
The most frequent negative difference is 0.1 deg.C, the smallest possible for data recorded to the nearest 0.1 deg.C, which is what we would expect. Out of 954 days with negative difference, 182 (19%) were only 0.1 deg.C.
We can get a clue about the distribution of negative differences by plotting the counts on a logarithmic scale:
There’s a nearly linear relationship between the difference and the count of days which exhibit that difference. This is hardly unexpected, given the behavior of random fluctuations.
There is a very small number of large negative differences. The largest of all is 4.8 deg.C, which is not impossible given random fluctuations (measurement error), but I suspect is more likely simply a mistake. In fact, I would estimate that there may be somewhere in the neighborhood of 20 days for which the high temperature estimate is far enough below the low temperature estimate that it may indicate the existence of mistaken data values.
That’s about 20 out of 3,404,808. That’s 0.0006%. That’s a pretty damn low error rate.
That’s because the folks at the BOM have done such an excellent job in constructing the ACORN temperature data. They worked very hard at it, and unlike Willis Eschenbach, they know what they’re doing.
But since his real purpose is to distract attention from his collossal blunder, I would be remiss if I let him get away with it. Therefore I remind you all of the origin of this brouhaha, which is not the quality of the ACORN data, it’s the fact that Willis Eschenbach and most of the contributors to the WUWT blog are so deep in denial, they will actually claim that Australia’s most recent summer was “nothing at all unusual.”
That’s the kind of ridiculous claim they have to resort to, to avoid the truly disturbing truth: climate change has already made such events so much more likely that Australians had better get used to suffering through them, not just once in a thousand years or once in a lifetime, but often enough to threaten their peace and prosperity. The era of the “angry summer” is here to stay.
Worst of all, if climate change continues — and it will — then angry summers will become even more common.