From the “we told you so” department comes this paper out of China that quantifies many of the very problems with the US and global surface temperature record we have been discussing for years: the adjustments add more warming than the global warming signal itself
A paper just published in Theoretical and Applied Climatology finds that the data homogenization techniques commonly used to adjust temperature records for moving stations and the urban heat island effect [UHI] can result in a “significant” exaggeration of warming trends in the homogenized record.
The effect of homogenization is clear and quite pronounced. What they found in China is based on how NOAA treats homogenization of the surface temperature record.
According to the authors:
“Our analysis shows that “data homogenization for [temperature] stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature.”
Basically what they are saying here is that the heat sink effect of all the concrete and asphalt surrounding the station swamps the diurnal variation of the station, and when it is moved away, the true diurnal variation returns, and then the homogenization methodology falsely adjusts the signal in a way that increases the trend.
You can see the heat sink swamping of the diurnal signal in the worst stations, Class 5, nearest urban centers in the graphs below. Compare urban, semi-urban, and rural for Class 5 stations, the effect of the larger UHI heat sink on the Tmax and Tmin is evident.
In Zhang et al, they study what happens when a station is moved from an urban to rural environment. An analogy in the USA would be what happened to the signal of those rooftop stations in the center of the city, such as in Columbia, SC when the station was moved to a a more rural setting.
U.S. Weather Bureau Office, Columbia SC. Circa 1915 (courtesy of the NOAA photo library)Here is the current USHCN station at the University of South Carolina:The Zhang et al paper studies a move of Huairou station in Beijing from 1960 to 2008, and the resultant increases in trend that result from the adjustments from homgenization being applied, resulting in a greater trend. They find:
The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax.
The figures 4 and 5 from the paper are telling for the effect on trend:
Huairou station and reference data for original (dotted lines) and adjusted (solid lines) data series during 1960–2008. The solid straight lines denote linear trends
Now here is the really interesting part, they propose a mechanism for the increase in trend, via the adjustments, and illustrate it.
They conclude:
The larger effects of relocations, homogenization, and urbanization on Tmin data series than on Tmax data series in a larger extent explain the “asymmetry” in daytime and nighttime SAT trends at Huairou station, and the urban effect is also a major contributor to the DTR decline as implied in the “asymmetry” changes of the annual mean Tmin and Tmax for the homogeneityadjusted data at the station.
In my draft paper of 2012 (now nearing completion with all of the feedback/criticisms we received dealt with, thank you. It is a complete rework. ), we pointed out how much adjustments, including homogenization, added to the trend of the USCHN network in the USA. This map from the draft paper pretty much says it all: the adjusted data trend is about twice as warm as the trend of stations (compliant thermometers) that have had the least impact of siting, UHI, and moves:
The Zhang et al paper is open access, an well worth reading. Let’s hope Petersen, Karl, and Menne at NCDC (whose papers are cited as references in this new paper) read it, for they are quite stubborn in insisting that their methodology solves all the ills of the dodgy surface temperature record, when it fact it creates more unrecognized problems in addition to the ones it solves.
The paper:
Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality Theoretical and Applied Climatology February 2014, Volume 115, Issue 3-4, pp 365-373,
Lei Zhang, Guo-Yu Ren, Yu-Yu Ren, Ai-Ying Zhang, Zi-Ying Chu, Ya-Qing Zhou
Abstract
Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are −0.004°C/10 year and −0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change
Download the PDF (531 KB) Open Access
h/t to The Hockey Schtick
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UPDATE 1/30/14: Credit where it is due, Steve McIntyre found and graphed the physical response to station moves three years ago with this comment at Climate Audit.
Here’s another way to think about the effect.
Let’s suppose that you have a station originally in a smallish city which increases in population and that the station moves in two discrete steps to the suburbs. Let’s suppose that there is a real urbanization effect and that the “natural” landscape is uniform. When the station moves to a more remote suburb, there will be a downward step change. E.g. the following:
The Menne algorithm removes the downward steps, but, in terms of estimating “natural” temperature, the unsliced series would be a better index than concatenating the sliced segments.
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REPLY: no, not until our paper is published, we’ve been hijacked in the past by both NOAA and BEST for sharing this data ahead of publication, and I won’t do so again – Anthony
Absolutely understand! 🙂 Thank you.
REPLY — Fear not. I did not devote a man-year’s worth of effort (not to mention the long, hard work on the part of all involved) just so the results can be disappeared or chucked in some obscure, difficult-to-access archive. We shall shout them from the rooftops, be assured. Besides, scientific method demands that they be made completely and easily available for audit, replication, and falsification. ~ Evan
Effect of UHI on temporal trends does not depend on size of settlement, but on rate of urbanization
That the rate is critical to the question is a given.
Yet it is possible that even a constant urban environment can effect trend. This appears certainly true for microsite, which is the gold speck in our findings. More study required. (I’m on it.)
[Snip. Invalid email address. ~ mod.]
The adjustments have always shown over the years they caused most of the warming and it makes a change to reflect this once in a while. I still find significant evidence we are little /no warmer than global temperatures during 1930s and 1940s. The Arctic suggests this and especially the Met Office are now saying how they missed the warming data in it. Therefore if the Arctic is the most important and no different now to back then, why should global temperatures be warmer now? Either polar temperatures warm and cool at higher rates proportional to global temperatures or they don’t. The Met Office suggests Arctic temperatures warm at higher rate proportional to global temperatures so how can global temperatures be warmer now than previous similar compared Arctic temperatures around the 1930s and 1940s? This only highlights a bias in warming caused by data changes.
Far more reliable DMI and satellite data disagrees with the Met Office and GISS Arctic nonsense. The changes to both HADCRUT and GISS are far too big to only be caused by limited uncovered Arctic temperatures. I have seen warming bias for decades with adjustments and I couldn’t trust them as far as I could throw any one of them involved..
Maybe the alarmist have been getting annoyed with skeptics because they personally know that they have warmed the recent period deliberately and are angry when skeptics point out its natural, when they cant say they know it was partly them. The downside to this global temperatures would have to be cooler than the 1930s and 1940s just to be equal with that data period in future. Data sets cant be comparable in the decades before the satellite data, with so many ongoing changes. The upside is they can only squeeze so much warming out without it becoming too obvious. The period of data changing is now becoming too obvious, so its only a matter of time. Carry this on any more and true scientists will only trust satellite data in future.
The main issue for me is not so much how data has been changed recently with Arctic, even though shows bad science with not being backed up. Satellites are keeping recent surface temperatures more honest, but still playing their warming bias games. The main problem is the alienation between recent decades and periods before satellite, Cant trust any of these being good enough precision data for good comparisons now.
See the UK’s average household energy bills. That is a tax and a wicked one too.
“Steep rise in winter deaths” 26 November 2013
http://www.bbc.co.uk/news/health-25100497
RichardLH the other problem in the UK is that if all the electable parties have climate change on the brain then getting elected is not an issue.
‘the data homogenization techniques commonly used to adjust temperature records for moving stations and the urban heat island effect [UHI] can result in a “significant” exaggeration of warming trends in the homogenized record.’
Yes that is the idea , its not an accident if it produces the ‘desired’ result on a constant bases .
And for some it proves how well they work.
Matt G says:
January 30, 2014 at 12:05 pm
The gold speck in DMI (going all the way back to 1958) is that it shows considerable Arctic warming during the winter months, but the melt season trend remains as flat as Bambi-Meets-Godzilla.
Gail Combs:
I always suspected that AGWers drank gin-and-buttermilk martinis instead of kool-aid!
On a more serious note, Six’s max-min-registering thermometer has long been the standard instrument used by Met services in English-speaking countries. The TOBS error is a misnomer; it should be called “time-of-reading” error. Inasmuch as temperature at time of reading is always recorded alongside, the possible error introduced when one of the registered extrema coincides with YESTERDAY’S temperature at time reading can be readily fixed by simple clerical changes. Instead, Karl introduces a misguided blanket “correction” based upon empirical comparisons with hourly instantantanous temperatures, which have precious little relationship to diurnal extrema. Small wonder that no other Met service accepts his nonsense.
Reblogged this on Power To The People and commented:
If NOAA adjusting Temperature Data To “over estimate rising trends” how can President Obama claim “Climate Change is a fact” ?
Just as “instantantanous” has little relationship to correct spelling!
1sky1 says: @ur momisugly January 30, 2014 at 1:17 pm
… Six’s max-min-registering thermometer has long been the standard instrument used by Met services in English-speaking countries. The TOBS error is a misnomer; it should be called “time-of-reading” error. Inasmuch as temperature at time of reading is always recorded alongside, the possible error introduced when one of the registered extrema coincides with YESTERDAY’S….
>>>>>>>>>>>>>>>>>>>>>>>>>
AHHH That is what I thought. In other words it is a 24 hour shift in one reading. Given the data is to the nearest degree in many cases and all the data for the year got recorded – BFD
It is not only the increase in area and population density but also in energy consumption.
“In Tokyo, a study showed a 1-2°C increase in air temperatures due to AC usage during weekdays (Ohashi et al. 2007).”
http://www.nasa.gov/pdf/505252main_demunck.pdf
Gail Combs:
IIRC, all the temperatures written into B-92 forms are in TENTHS of a degree F. They are rounded to full degrees only while computing monthly average Tmax and Tmin, which are then reported again to the nearest tenth. Good enough for government work!
BTW, the gin-and-buttermilk martinis are stirred–not shaken. It helps explain the “missing heat.”
evanmjones says:
January 30, 2014 at 12:49 pm
Matt G says:
January 30, 2014 at 12:05 pm
The gold speck in DMI (going all the way back to 1958) is that it shows considerable Arctic warming during the winter months, but the melt season trend remains as flat as Bambi-Meets-Godzilla.
—————————————————————-
DMI does show warming since the 1970s during Winter and it is fair to say summer temperatures change at a much smaller rates due to latent heat and very limited solar warming. What the DMI doesn’t show is how this warming was compared to the 1930s and 1940s and how that compares to 1950s-1970s being cooler than that using Arctic instrumental data.
DMII during winter has shown warming since 1970s, mainly very large yearly changes with very random peak and troughs related to AMO, AO and NAO.
One of the warmest winters was during 1976 with the well known PDO change at the time.
http://ocean.dmi.dk/arctic/plots/meanTarchive/meanT_1975.png
http://ocean.dmi.dk/arctic/plots/meanTarchive/meanT_1976.png
Still have difficulty beating then recently.
http://ocean.dmi.dk/arctic/plots/meanTarchive/meanT_2013.png
Currently not as warm either.
http://ocean.dmi.dk/arctic/plots/meanTarchive/meanT_2014.png
HADCRUT and especially GISS have shown major warming even in the summer.
Evanmjones at 10:58 am. To explain my method, I used the GHCN data till 2010 and a simple statistical tool. Divide the earth into a number of latitude regions, twelve or so. Estimate with a conventional method for each region the regional temperature time series. Compute for each station the correlation (pmc) between its time series and the regional series over the period the station has data. The lower this correlation the more the station is an outlier. On the basis of this statistic you can compare stations disappearing and remaining. Comparing for example, from all stations included 1991, I found a 27 sigma difference regarding this statistic. Even among the younger stations you will find the effect. Succes with your approach.
How does / did the NOAA adjust for the urban heat island effect?
Could it be that the editor deletes sentences between unequal signs? I try it again: Comparing for example, from all stations included before 1970, those dropped during 1970-1991 and dropped (or not yet dropped) after 1991, I found a 27 sigma difference…
What I forgot to mention was with DMI temperatures showing huge changes every year, a warm or cool winter will have very little difference to anomaly global data. Make the Arctic much warmer in summer where the change is much smaller, then this will affect the global anomaly data a lot more. The HADCRUT and GISS are doing this, hence why too much warming from there data products are too large to be explained only by missing Arctic data.
Mindert Eiting says:
January 30, 2014 at 2:36 pm
Good question, Mindert. In HTML, such as WordPress uses, the < and > signs are reserved to indicate HTML instructions. They are not printed, but the instruction inside them is executed (like bold, italic, whatever).
So the excel style signs will not print at all … but you can use what other computer languages use, which is != …
w.
PS—If you want them to print, like I did above, use an ampersand (&) followed by either gt; or lt; for greater or less than. You need to include the semicolon.
[Willis: The mods request you reprint this paragraph into the “TEST” section at WUWT for future use by other writers. Mod]
Of course it does. There are more “outliers” in the cold part of the temperature. Look at the polar vortex scenario. You can have temps 40F below normal. For instance, I live in Ohio, and just guessing at the average winter high is mid 30. That would make a -10 reading (no wind chill) like Monday night 40 off. But the average summer high is mid 80s. Can you imagine what would happen in the news if there was a 125 degree reading in July?! Think that would get dropped off the record?
I don’t care either except when they are incorrectly referred to as a “scientist” as Roger has been repeatedly mentioned as one. These are factual errors that I notice and I believe the correction is meaningful. When I don’t make the comments, the urban legends live on.
Poptech says:
January 30, 2014 at 7:50 pm
Again I say, it is meaningless what someone is called. A woman or man either is or isn’t a scientist depending on what they do, not on their biographical details. In my opinion Michael Mann, for example, is not a scientist at all. Why? Because he’s not doing science.
To do science, you have to play by the rules. One of them is transparency. You’ve seen a sign outside a restaurant saying “No shoes, no shirt, no service”? For me, the rule for science is equally simple—”No data, no code, no science.” Science can’t work without perfect transparency. Michael Mann doesn’t provide data or code, so he’s not a scientist. And when Roger Tallbloke as Editor didn’t demand the same of Nicola Scafetta, he was not acting as a scientist either.
Now, I suppose I could take on the case of Michael Mann as my cause celebre. I can’t tell you how many times I’ve seen him called a “scientist”, a “distinguished scientist”, an “eminent scientist” in the public press, when he’s nothing of the sort. And if I took on that quest, I suppose I could wander around every thread I could find, like this one on temperature adjustments, and spend my time posting things like
…
…
w.
There is no single trend as such. Or rather an OLS trend line will tell you nothing about what s going to happen in the future.
Right. What it tells us is what has happened during the study period. That is what are attempting to examine.
Nick and Andy: WIthout definitive metadata, we can’t know whether to correct or not correct for these breakpoints.
a) Let’s suppose that a breakpoint is caused by maintenance of the shelter protecting the thermometer. Let’s suppose, for example, that accumulated dust and grime on the surface of a station shelter cause an undetectable upward bias in the temperature trend. After a decade or two, the shelter is cleaned, causing temperatures to suddenly be cooler and creating an obvious breakpoint. Since cleaning restored the original observing conditions, it would be inappropriate to correct this breakpoint.
b) Let’s suppose that a breakpoint is caused by moving a station and that the trend at both the previous and new location is not biased by any artifacts. It would be appropriate to correct this breakpoint.
When a breakpoint is cause by NEW observing conditions, it should be corrected. When a breakpoint is caused by RESTORING the original observing conditions, correcting the breakpoint biases the trend. When BEST splits a temperature record at a breakpoint, the result is similar to correcting the breakpoint.