Guest Post by Willis Eschenbach
My theory is that the BEST folks must have eaten at a Hollywood Chinese restaurant. You can tell because when you eat there, an hour later you find you’re hungry for stardom.
Now that the BEST folks have demanded and received their fifteen minutes of fame before their results have gone through peer review, now that they have succeeded in deceiving many people into thinking that Muller is a skeptic and that somehow BEST has ‘proven the skeptics wrong’, now that they’ve returned to the wilds of their natural scientific habitat far from the reach of National Geographic photographers and people asking real questions, I thought I might take a look at the data itself. Media whores are always predictable and boring, but data always contains surprises. It can be downloaded from the bottom of this page, but please note that they do not show the actual results on that page, they show smoothed results. Here’s their actual un-smoothed monthly data:
Figure 1. BEST global surface temperature estimates. Gray bars show what BEST says are the 95% confidence intervals (95%CI) for each datapoint.
I don’t know about you, but Figure 1 immediately made me think of the repeated claim by Michael Mann that the temperatures of the 1990s were the warmest in a thousand years.
WHAT I FIND IN THE BEST DATA
Uncertainty
I agree with William Briggs and Doug Keenan that “the uncertainty bands are too narrow”. Please read the two authors to see why.
I thought of Mann’s claim because, even with BEST’s narrow uncertainty figures, their results show we know very little about relative temperatures over the last two centuries. For example, we certainly cannot say that the current temperatures are greater than anything before about 1945. The uncertainty bands overlap, and so we simply don’t know if e.g. 2010 was warmer than 1910. Seems likely, to be sure … but we do not have the evidence to back that up.
And that, of course, means that Mann’s claims of ‘warmest in a mill-yun years’ or whatever he has ramped it up to by now are not sustainable. We can’t tell, using actual thermometer records, if we’re warmer than a mere century ago. How can a few trees and clamshells tell us more than dozens of thermometers?
Disagreement with satellite observations
The BEST folks say that there is no urban heat island (UHI) effect detectable in their analysis. Their actual claim is that “urban warming does not unduly bias estimates of recent global temperature change”. Here’s a comment from NASA, which indicates that, well, there might be a bias. Emphasis mine.
The compact city of Providence, R.I., for example, has surface temperatures that are about 12.2 °C (21.9 °F) warmer than the surrounding countryside, while similarly-sized but spread-out Buffalo, N.Y., produces a heat island of only about 7.2 °C (12.9 °F), according to satellite data. SOURCE
A 22°F (12°C) UHI warming in Providence, and BEST says no UHI effect … and that’s just a couple cities.
If there were no UHI, then (per the generally accepted theories) the atmosphere should be warming more than the ground. If there is UHI, on the other hand, the ground station records would have an upwards bias and might even indicate more warming than the atmosphere.
After a number of adjustments, the two satellite records, from RSS and UAH, are pretty similar. Figure 2 shows their records for global land-only lower tropospheric temperatures:
Figure 2. UAH and RSS satellite temperature records. Anomaly period 1979-1984 = 0.
Since they are so close, I have averaged them together in Figure 3 to avoid disputes. You can substitute either one if you wish. Figure three shows a three-year centered Gaussian average of the data. The final 1.5 years are truncated to avoid end effects.
Remember what we would expect to find if all of the ground records were correct. They’d all lie on or near the same line, and the satellite temperatures would be rising faster than the ground temperatures. Here are the actual results, showing BEST, satellite, GISS, CRUTEM, and GHCN land temperatures:
Figure 3. BEST, average satellite, and other estimates of the global land temperature over the satellite era. Anomaly period 1979-1984 = 0.
In Figure 3, we find the opposite of what we expected. The land temperatures are rising faster than the atmospheric temperatures, contrary to theory. In addition, the BEST data is the worst of the lot in this regard.
Disagreement with other ground-based records.
The disagreement between the four ground-based results also begs for an explanation. Note that the records diverge at the rate of about 0.2°C in thirty years, which is 0.7° per century. Since this is the approximate amount of the last century’s warming, this is by no means a trivial difference.
My conclusion? We still have not resolved the UHI issue, in any of the land datasets. I’m happy to discuss other alternative explanations for what we find in Figure 3. I just can’t think of too many. With the ground records, nobody has looked at the other guys’ analysis and algorithms harshly, aggressively, and critically. They’ve all taken their own paths, and they haven’t disputed much with each other. The satellite data algorithms, on the other hand, has been examined minutely by two very competitive groups, UAH and RSS, in a strongly adversarial scientific manner. As is common in science, the two groups have each found errors in the other’s work, and when corrected the two records agree quite well. It’s possible they’re both wrong, but that doesn’t seem likely. If the ground-based folks did that, we might get better agreement. But as with the climate models and modelers, they’re all far too well-mannered to critically examine each other’s work in any serious fashion. Because heck, if they did that to the other guy, he might return the favor and point out flaws in their work, don’t want that kind of ugliness to intrude on their genteel, collegiate relationship, can’t we just be friends and not look too deeply? …
w.
PS—I remind folks again that the hype about BEST showing skeptics are wrong is just that. Most folks knew already that the world has been generally warming for hundreds of years, and BEST’s results in that regard were no surprise. BEST showed nothing about whether humans are affecting the climate, nor could it have done so. There are still large unresolved issues in the land temperature record which BEST has not clarified or solved. The jury is out on the BEST results, and it is only in part because they haven’t even gone through peer review.
PPS—
Oh, yeah, one more thing. At the top of the BEST dataset there’s a note that says:
Estimated 1950-1980 absolute temperature: 7.11 +/- 0.50
Seven degrees C? The GISS folks don’t even give an average, they just say it’s globally about 14°C.
The HadCRUT data gives a global temperature about the same, 13.9°C, using a gridded absolute temperature dataset. Finally, the Kiehl/Trenberth global budget gives a black-body radiation value of 390 W/m2, which converts to 14.8°. So I figured that was kind of settled, that the earth’s average temperature (an elusive concept to be sure) was around fourteen or fifteen degrees C.
Now, without a single word of comment that I can find, BEST says it’s only 7.1 degrees … say what? Anyone have an explanation for that? I know that the BEST figure is just the land. But if the globe is at say 14° to make it easy, and the land is at 7°, that means that on average the ocean is at 17°.
And I’m just not buying that on a global average the ocean is ten degrees C, or 18 degrees F, warmer than the land. It sets off my bad number detector.
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william says:
October 25, 2011 at 8:20 am
It was a handshake deal between ostensibly honorable men. When one man turns out to be … let me take mpaul’s suggestion and describe Muller as “less than honorable” in that situation, court is often an expensive and ultimately futile exercise.
Plus the optics are bad, it would make Anthony look like a sore loser when in fact he is the injured party.
w.
I don’t think they are going to sentence the poor to more expensive energy. What they want to do is redistribute some of the higher energy taxes to the poor to help pay for the higher energy costs as well as help their collective net worth move closer to the rich (as the collective net worth of the rich decreases).
They also want to do this on a global level so that at some point in the future, all nations have roughly the same per-capita wealth and standard of living.
What I see are technocrats using science to achieve socialistic goals.
With regard to UHI, I thought BEST compared rural stations to all stations and deduced that the difference was the UHI. As this difference showed no trend, this meant UHI did not skew the overall figures.
In fact, this approach simply implies that the UHI trend for all stations is similar to the UHI trend for rural stations. Any habitation has a UHI effect and, as the effect is logarithmic in relation to population, a one horse town which becomes 2 horses and a goat might show a similar UHI increase as an expanding city.
The UHI effect is a serious source of bias and, for me, the BEST methodology does not trump previous work in this field.
Willis,
In line with your goal of using more humor, I think we are missing information buried in the acronym for the the Berkeley project. Like you, I grew up in the mountains of northern California and so take an earthier analysis of what the output from BEST is. It is, afterall, the BEST P. And so my mind wandered back to long treks along Interstate 80 in an open air Volkswagen consuming copious quantities of coffee and caffeinated beverages and having to wait for 50 or 60 miles for the next rest stop. The output was similar to what you had to analyze, but more satisfying. BEST P indeed.
Paul
kramer says:
October 25, 2011 at 9:47 am
Seriously? Your claim is that some poor shlub living on a dollar a day and sleeping on a back street in Bombay, whose energy costs have been jacked by the first-world scientists insisting that the World Bank not fund inexpensive coal fired power plants in India, is going to get a “tax redistribution” to “help pay for the higher energy costs”???
I run out of question marks to question that astounding claim … kramer, you truly haven’t thought this one through any further than the first-world scientists have.
w.
@Willis
You write: “So rather than you advising me to be all calm and peaceful about this, how about I advise you to be less calm and peaceful, and to stand up and denounce bad science wherever you find it?”
YES!! YES!!!
There is a growing portion of sceptics or at most “semi-sceptics” I would call it, that are so focussed on appearing “correct” that they dont really ever make a difference at all.
It IS “dangerous” to point out anything significant, but if we play to safe, we dont play at all.
BRAVO Willis.
How much champagne would it take to make you look at this article, and leave a comment?
“http://hidethedecline.eu/pages/posts/ruti-global-land-temperatures-1880-2010-part-1-244.php”
This is the kind of article I have asked no one to publish, because I cant PROOVE that all I do is correct. But truth is, its the best real alternative to landtemperatures there is, SADLY SADLY, and this does mean that im even near perfect. it just means that the rest appears not honest.
K.R. Frank
@Willis
“So rather than you advising me to be all calm and peaceful about this, how about I advise you to be less calm and peaceful, and to stand up and denounce bad science wherever you find it?”
I like that spirit!
((Sorry if some of this is already in a comment, I had some internet problems))
K.R.Frank
Frank Lansner says:
October 25, 2011 at 10:46 am
I’m a cheap date …
I looked at the site. I see a couple of issues. You are putting together what you call the RUTI, the “Rural Unadjusted Temperature Index”. The implications are:
1. You can unambiguously divide the world’s temperature stations into two groups, “Rural” and “Not-rural”.
2. Adjustments are bad.
I’m not sure I’d agree (in general) with either of those. First, what criteria are you using for “Rural”? BEST uses MODIS data, GISS uses night-lights, GHCN uses a similarity algorithm to adjust anomalous sites, Roy Spencer uses log(population), and Ross McKitrick uses economic development … which one are you using, why are you using it, and where is the dividing line?
Second, adjustments, while they are to be decried, may be important or even necessary. For example, the MMTS electronic temperature sensors tend to read a bit higher (from memory) than the Stephenson Screen thermometers. When the changeover is made at a particular station, an erroneous trend is introduced, where it looks like it’s warming but it isn’t. It seems to be your contention that such an erroneous trend should not be corrected for.
HTH,
w.
Lets look at the bright side here. We have an accurate temperature reading with error bars, something that I have never seen before. There are valid arguments that these bounds are too narrow, but any small child can see the shape (to quote my two year old, “Twiangle”). The uncertainty growth also makes perfect intuitive sense and can be easily understood by laymen. This then raises the obvious question: If this is the temperature record 200 years ago, how can we be certain about what it will be 200 years from now? Is that flat line in the hockey stick just a giant “we don’t know” (answer= yes)?
That gets people asking questions. When people ask questions, they typically drift at least to center if not to the skeptical camp.
Hi Willis, that was a fast responce time!!
You write. “1. You can unambiguously divide the world’s temperature stations into two groups, “Rural” and “Not-rural”.
This is true.
In the general RUTI introduction
http://hidethedecline.eu/pages/posts/ruti-global-land-temperatures-1880-2010-part-1-244.php
I write:
“RUTI is not all rural nor all unadjusted. However, RUTI is a temperature index aiming to use still more rural data (less use of city and airport data), still more unadjusted data when available and reasonable.”
The method I mostly use to determine rural status, is google maps. I have checked a thousand stations this way. The relative growth of population around a station that has large UHI effect, and therefore stations outside urban areas on google maps are preferred. This means that stations named after a large sity may be used if stations is well outside city, allthough declared “Urban” by GHCN.
Adjustments: There are situations where I accpet adjustments done (Albany, Beyrouth etcetc.).
What I do is the HARD WORK. I track areas of similar trend, and it takes forever. But this way it has been possible to get a rather good idea if a station is indeed outlier or not. That is, if it looks correct to adjust a station.
Example DE BILT.
I CLAIM BOLDLY that i show very very solidly that the area NW Europe DOES support the DE BILT station and therefore adjustment is NOT acceptable nor included in RUTI:
http://hidethedecline.eu/pages/ruti/europe/nw-europe-and-de-bilt.php
Example DARWIN
No adjustment justified either:
http://hidethedecline.eu/pages/ruti/australia.php
Example of very limited public availability of East Chinese rural data… But when held together for the whole region, suddenly you see clearly what was hidden. “Grafitty data” from China:
http://hidethedecline.eu/media/ARUTI/Asia/China/fig22.jpg
Etc etc.
What I have done is hard work, and to get the overall result has taken all evenings weekend of 6 months… 🙂 And I show openly what I do and am ready to change what is concluded wrongly.
Thankyou Willis for commenting 😉
K.R: Frank
Wops, Willis, I forgot to mention: I use GHCN Unadjusted as primary data source, although rather limited in years publicly available, these data are often i complance with original data i have found.
I also use NORDKLIM, and more where I have reason to believe we have mostly origina data.
K.R. Frank
Willis… May I ask what baseline adjustments you used for your Fig 3?
Muller seems to admit UHI is a problem without admitting it! Check out what he says.
http://notalotofpeopleknowthat.wordpress.com/2011/10/23/mullers-problem-with-uhi/
Willis,
You should have looked to see whether your chart looks like BEST’s comparing the different records. It doesn’t.
http://berkeleyearth.org/analysis.php
The issue with GISS is shown here:
http://rankexploits.com/musings/2010/the-great-gistemp-mystery/
I do think that a little critical thought would have told you that the GISS graph was wrong. We all know that Hadley is the outlier not GISS.
So lets consider it this way: BEST, GISS and NOAA are all very similar when properly compared using land-only and Hadley is noticeably lower in recent years. Why? Well simply because CAM is too restrictive for many sites particularly in the Arctic (etc).
Anyways the only disagreement is between Hadley and BEST and “supposedly” the satellites with BEST but if you look on the STAR analysis publication website you will see that their satellite record is coming, with more accurate satellite combining and what looks to be a significantly higher TLT trend when they actually do the product based on the relationship between TMT and TLT. I wouldn’t hold my breath that RSS and UAH are the end all product of the sat work.
kramer says:
October 25, 2011 at 9:47 am
They also want to do this [redistribute wealth] on a global level so that at some point in the future, all nations have roughly the same per-capita wealth and standard of living.
What I see are technocrats using science to achieve socialistic goals.
Therefore, kramer, the obvious and proven Communistic outcome once again is going to be a trend among all nations and people toward the holy “equality” of equal impoverishment and slavery, except of course in the case of the Socialist technocrats themselves, who will necessariy thereby own all the wealth themselves since they have the power to redistrubute it.
And, as usual, the totality of wealth will progressively decrease, because once redistribution of “wealth” starts and then continues toward “equalizing” it, eventually no one has any incentive to produce it – congenital mental and physical slaves, and Saints, excepted.
So that, finally, the “poor” = the rest of us, will in fact be lucky if the noble Communist Party technocrats can manage “to sentence the poor to more expensive energy” even if they wanted to, because there simply won’t be very much of it to go around, and lucky if we poor have any means at all left to pay for it, as the “wealth” keeps getting progressively redistributed, and thus lucky to even keep on existing.
kramer, that’s the “science” your technocrats are really enacting, and which you apparently support. So is it any wonder that their CO2 = CAGW “science” is as equally bad, especially in the sense of being no more than an evil unscientific propagandistic subterfuge used to subserve evil ends?
kramer, when the noble socialist technocrats come to own “the means of production”, they own you, and you finally become disposable!
Willis
I’m not sure I agree with you calling ’em “media whores”.
I have been told some whores are quite nice, if misunderstood folk. Couldn’t vouch for it myself, though.
And a lot of snakes are quite harmless, if slithery.
But, with these little quibbles, I think this is another one of your stonking, unmissable pieces. When you’ve finished the autobiography, please can you put together an anthology of your climate writings?
Pretty please?
This entire CON is MORE of the same, PRETENSE that MYSTERIOUSLY ADJUSTED DATA are REAL DATA.
MYSTERIOUSLY ADJUSTED DATA are DATA YOU CAN’T GET EVERY FACT ABOUT.
This is as OLD as the 5-LEG COW joke.
“So: we have a cow: it has 4 legs, and a tail. But we need to have another leg for this joke to work, so let’s say, the cow’s tail, is a leg.”
“So: NOW we have a 5 legged cow – RIGHT?”
W.R.O.N.G.
You have a 4 legged cow.
You just CALLED it a 5 legged cow.
GISS, NOAA, CRU, etc data are A.D.J.U.S.T.E.D. to MAKE INSTRUMENTAL RECORDS look WARMER than they TELL.
This UNALTERABLE REALITY is why Phil Jones’ statement in 2005, “the scientific world would come down on me in no uncertain terms ***IF I SAID the WORLD had COOLED since 1998.***
*** OK IT HAS *** but it’s (and here’s the truly telling part of this for those of you wondering just what the ultimate question about all this, is: watch carefully) Only seven years of data (every year FROM ’98 to ’05)
*** AND it I.S.N’T. S.T.A.T.I.S.T.I.C.A.L.L.Y. S.I.G.N.I.F.I.C.A.N.T. ***
THAT STATEMENT of non statistical significance to the COOLING since ’98 is what is TRULY TELLING here: Note, that in the email he said it and no one else said a word about “Hey Phil, bud, you’re wrong: the world is still warming.”
And the reason Phil stated no statistically significant cooling is because: WATCH CAREFULLY KIDS who are just learning of this: HE was REFERRING to the RAW DATA being COLLECTED WORLDWIDE, which SHOWED PRECISELY THAT: A little COOLING, but APPARENT STASIS: not EVEN COOLING to the STATISTICALLY SIGNIFICANT LEVEL.
The DATA have been SYSTEMATICALLY WASHED to MAKE it APPEAR WARM. The EARTH has not warmed for MANY YEARS. The CLAIM is a FALSE ONE.
Look up BBC PHIL JONES INTERVIEW 2010. Jones was not discharged from his job on agreement he ADMIT he was WRONG about SOMETHING to the B.B.C. that he had CLAIMED, the BBC had MIS-REPORTED.
He was CAUGHT in CLIMATE GATE preparing to RUIN the CAREER of a BBC REPORTER.
Look at the qustion PUT to HIM by the BBC:” DO you AGREE the WORLD has NOT WARMED since 1998?”
HIS ANSWER: Y.E.S.
He then dessiminated on various probabilities saying there isn’t a 99 or 98 percent chance but rather a lesser one but that it is indeed warming. But HE was FORCED to ADMIT what HE had ALREADY SAID, IN 2005, WHILE NOT UNDER DURESS, and that NOT ONE SOUL CORRECTED HIM ABOUT: The WORLD stopped WARMING in the MID ’90s.
Actually I mis-stated the question by the BBC: the question was “Do you agree the world has not warmed since 1995.”
This ENTIRE SCAM has been NOTHING: from the VERY BEGINNING: but a DATA WASHING scam.
The fact it’s based on physics a child can debunk is beside the point. This has been a LEGAL issue from the beginning. The fact
THERE IS NO TROPOSPHERIC HOT SPOT shows you how ACCURATE claims are, of a class of gases HEATING the earth to a measurable degree, when the VASTLY OVERWHELMING one: water – is a phase change REFRIGERANT that covers 70 percent of the earth.
Why is it that MANDATORY INSTRUMENTAL DETECTION of this voodoo, fails to detect ANY greenhouse gas correlation, over and over? HuH?
LoL. Why doesn’t the INFRA RED ASTRONOMY FIELD even show everyone the RISING INFRA RED visible through the proper filters, that correlates to manmade gas ratios in the atmosphere rising?
Because it HAS not been WARMING for YEARS.
What turned off the Atlantic storm system? MORE HEAT?
Or LESS than when there are MORE, and more VIOLENT storms?
The entire laughable charade that ANY of this voodoo is based in reality is shocking to anyone with a background in instrumentation etc.
Claims of “Wayul, WE ALL AGREE it’s WARMER” go hand in hand with “We all agree there’s a greenhouse gas effect. All our instruments must be broke.”
Pfft.
Well done Willis !
Rob Honeycutt says:
October 25, 2011 at 11:49 am
As noted below the graph, they are all baselined to their 1979-1984 average, This makes it easy to see the divergence.
w.
I’ve just taken a look at the data in the BEST data.txt file for my local site (leb2w – 136146). I am a bit disappointed at what I found. I was expecting both the raw data and their adjusted data. What I found was neither. The temperature data supplied appears to be seasonal normalized. That is that temperatures supplied for each month are not the actual monthly average temperature. What it appears to be is the annual average temperature plus the monthly anomaly from the average of that month throughout the data set. When comparing it with the raw data available from the USHCN, it has some strange hiccups added. The BEST monthly data differs from a simple arithmetic monthly normalization in a very regular manner:
(in degrees Fahrenheit) January -2.9, February -3.3, March +1.5, April +2.8, May -1.2, June -1.4, July +0.8, August +1.4, September -1.4, October +0.3, November +2.5, and December +0.7.
Those values are consistent across the entire leb2w data set plus or minus less than 0.5 degrees Fahrenheit.
While that is interesting, I as surprised that this was the output from all their effort. I am wondering now if what this indicates is that after seasonal normalization, my local leb2w site does not need any adjustments. If so, why do you suppose USHCN shows quite significant adjustments for Time of Observation and homogenization? And why seasonal normalized? Oh well, I guess this is suppose to be the preliminary data.
Question Willis,
first I’m not sure that the measurement from satellites of the troposphere over land is directly comparable with the land surface temps. horizontal advection comes to mind.
But lets say it is.
what you show is a .3C difference between the temperature anomaly of the troposphere over land and the land surface measurement.
So it would see to a first order that this makes sense. The UHI effect in the land record doesn’t
exceed .3C.
In fact, since we know SST pretty well in the modern era and we know that the land warms faster than the water.. we can also bound the UHI effect by looking at the difference between SST and the land.. again.. just a first order.. rough estimate of the magnitude of the effect.
In fact .3C comes pretty close to what Ross argues..
So, perhaps we can start by getting folks to agree to this.
1. There was a LIA
2. Global temperature makes “sense”, that is we say it was cooler in the LIA
3. Its warmer now then it was then.
4. UHI? a rough order guess is that it is less than .3C
Any takers
Thanks, Willis.
Willis, is there any reason you used a 5 year baseline rather than the standard 30 year baseline that all these datasets normally use (and that UAH just switched over to)?
The quote of the week that bears repeating, from the thread on unadjusted data: (warning protect keyboard from airborne liquids)
George Turner says:
October 24, 2011 at 1:29 pm
“If you follow some of the adjustments they make to the temperature record, we should worry less about the present getting warmer than the alarming rate at which the past keeps getting cooler. If present trends continue, millions of extra people in the 1940′s are going to freeze to death.”