We have talked about finding Waldo for a while. Here are a few interesting graphics showing the Adjusters in Peru. Here are 4 stations where temperatures in the mid-20th century have been lowered by around 3 deg C. These are all reverse UHI effects.
For all of the above graphics, I’ve plotted original monthly data without taking an annual average. The annual variation in monthly temperatures is only a few degrees. Given this very placid temperature variation, how can Hansen simply assume that something happened that threw the measurements off by 3 deg C?
In order to make adjustments of this magnitude, Hansen should have written to the Peruvian meteorological service and asked them for an explanation or interpretation of any results that didn’t appear to make sense. If he were unsatisfied with the explanation, maybe he could decide not to use the data. In order to make adjustments of this magnitude, he should have to prepare a detailed technical report on Peruvian station histories showing how the problems originated.
As I’ve noted before, I increasingly wonder whether it makes any sense for academic scientists like Hansen (or Jones) to be reporting temperature statistics. They don’t appear to have the faintest interest in the gritty details. So turn the calculations over to professional data analysts.
Climate Audit 
96 Comments
Quote Roget’s Thesaurus “ADJUST, graduate, equalize, mix, regularize, make conform”, do I need go on.
Opening should be… We -haven’t- talked about (yes?)
I didn’t think the ROW received adjustments? What is the criteria they use to decide which stations get adjusted?
Without documentation, how can they even keep it straight?
Its getting harder to chalk this all up to “reasonable” adjustments given the lack of documentation and the apparent one-sided direction of the adjustments.
Well, it does always seem to be linear, with a slope of the same sign in these cases. That con’t be too hard to remember. Just take a piece of butt hay and lay it over the data.
These are the most significant adjustments we have seen. The level of these adjustments has not been translated to the rest of South America have they?
RE 3. In the ROW stations are adjusted like so.
IF the population in 1980 was less than 10,000, then the station does not get adjusted.
ELSE the station gets adjusted.
Look for all the stations within 1000km of these stations. the rural one are the stations
which control the adjustment.
Now, one thing that is not clear. In the USA hansen appears to argue that he uses only UNLIT
stations. And he shows how well his adjustments work for dim and bright.
However, its never exactly clear whether for the rest of the world he only uses RURAL stations.
Steve says:
Since the international desk of HPC of the NWS has deep contacts with South America and regularly trains meteorologists it would have been a nice exercise to contact this fellow. See the following.
Maybe he’s making them colder so when we do cool off it doesn’t look like we’re any colder than it was before. Just trying to cover his bases.
Here’s any interesting read:
Click to access Solar_Arch_NY_Mar2_08.pdf
Hi,
Steve let me ask why is there so much data loss after 1985 or so? You would think that with time the data would get better not worse. He maybe it is a “fill in the blanks” temperature quiz. Sorry for the bad humor. LOL
So do the Hadley Crew play with the same stations under the same rules as the Team?
I realize that most of the sites that have been discussed on CA have some element of uniqueness that makes them worthy of discussion, such as what appears to be illogical adjustments. But these adjustments seem to be so counter-intuitive, it really makes me want to better understand the effect of adjustments worldwide. Is their a simple/quick way of plotting the average adjustment across all sites worldwide? One would expect to see a slight downward slope as UHI has become more of a factor. I would even go a step further to suggest it should be relatively flat up to the 1950’s or so before the downslope really begins to show. I suspect that is not what the data will show but I readily admit, I may be wrong.
If fresh and salt water cover > 70% of the Earth’s surface and the temperature of the air/water and air/earth boundary is the measurement of concern, the adjustments to the air/earth boundary would be of less concern than any adjustments to the SST. It would be interesting to see a breakdown of the average corrections vs time applied to each interface.
What Schlew (#12) said. I asked this before. I ask it again.
Surely it is as simple as overlaying the 20-Century GISS/World-adjusted graph over 20-Century NOAA-raw (with only outliers, TOBS applied).
Could we please, please see that (preferably with a data link)? For us nonscientists who have a VERY serious need to look at the overall bottom line.
And yes, it also seems to me that the adjustment is exactly in reverse. And that we need not only to take UHI into consideration, but CRN site violation as well.
Also, am I correct that a Peru station has a MUCH greater effect, station-for-station than any of the US because of the gridding issue?
I wonder what the “Most Impoirtant” weather station in the world is, in its impact on the World record. And what its CRN rating is.
“Here’s any interesting read:”
What is most interesting about it is that it was apparently written next month. I am always wary of downloading documents from the future.
One thing is certain, though, and that is the the sun will brighten as it ages. We don’t know if this brightening comes in steps, or gradually, or what.
Hear, hear. This is getting worse and worse. And no response from NASA? Shamefuul.
THREE DEGREES! That’s incredible.
RE:15 Didn’t download it. Came from another blog so I copied and pasted the link.
The info wasn’t really much different from what I seen before, but his conclusion that we should ramp up our CO2 emmissions. That was different than what I’ve seen. Thought that was kinda interesting.
On a another note, I think Steve and the gang should find all the good stations and develop a code on there own. Show them boys over in NASA how you do things right.
BTW, there is an International Conference on Climate Change coming up March 2-4 in New York City.
Conference Goals
The goals of the 2008 International Conference on Climate Change are:
* to bring together the world’s leading scientists, economists, and policy experts to explain the often-neglected “other side” of the climate change debate;
* to sponsor presentations and papers that make genuine contributions to the global debate over climate change;
* to share the results of the conference with policymakers, civic and business leaders, and the interested public as an antidote to the one-sided and alarmist bias that pervades much of the current public policy debate; and
* to set the groundwork for future conferences and publications that can turn the debate toward sound science and economics, and away from hype and political manipulation.
Peru isn’t the only area of the ROW with special warming adjustments. New Zealand shows essentially no warming in the raw data — but the adjustments create warming. The following graphs show four stations in New Zealand (blue unadjusted, red adjusted) from the NOAA GHCN database. Hokitika is listed as a rural station — and it receives the most extreme “homogeneity” adjustment. See the NZ summary at http://www.appinsys.com/GlobalWarming/RS_NewZealand.htm
It is becoming increasingly difficult to give Hansen the benefit of the doubt.
The fact that the adjustments have different slopes leads me to believe that someone looked at the raw data before “adjusting” it. Cuzco and Pucallpa have an adjustment of 1 degree in 20 years whereas Haunacayo and Puerto Muldon have adjustments of 1 degree in 10 years.
It is also interesting how the adjustments were made linearly with respect to time so that the data would “look” OK to the average observer.
#19
What the … ?
#19, when added to the Peru stations plus the recently-cited U.S. stations could be seen as an attempt to create a positive slope to a temperature graph regardless of what the very recent data shows.
Here is my question: Is it possible to plot this entire data set without adjustment?
…
What Mike Smith says.
For NZ. AND THE WHOLE WORLD. I want the bottom line.
I am tired of being accused of cherry picking. Is not everyone else around here tired of being accused of cherry picking? I want the whole tree.
And if the whole tree isn’t available, then I want to know that, too. THAT is what a policy maker would require.
I’m sure that there are problems with the raw data and do not believe that merely using raw data is any sort of panacea. Whether HAnsen’s adjustments in (say) Peru make sense is a different issue. I’m certainly not sold on HAnsen’s particular method for making the adjustments./
Basic data is sacrosanct. Fiddling basic data is a serious issue and should only be done for valid reasons in exceptional cases. Unless transparent complete documentation is provided, it is unacceptable. …..
Yes, I’m sure there are. I’d want the outliers removed and TOBS adjusted (if a summer or winter were missed, I obviously wouldn’t want to take that year’s average as a correct measure) But to heck with the rest; I’ll even do without UHI adjustments (which I bet are being lowballed anyway).
Gosh, yes. And I’m sure it’s valid to remove outliers and take TOBS into account. But whatever GISS is doing to the data can’t be that.
But I really want the same comparison that is being made for these individual stations to be done in ONE graph for the ENTIRE world for the last century.
Or just link me to the raw and adjusted numbers so I can break out the old Excel and do the comparison myself.
And if the data is not available in comprehensive form, I want to know that, too. (Unavailability alone would speak volumes.)
I very much support what is going on here, especially these station data comparisons. But I need a unified, world view. And I bet I am not alone, here.
Alan # 19. Living in New Zealand we are being constantly harrassed about global warming, carbon footprints and sssuuuussssstainability which is the current buzword, though how long we can sustain it I do not not know!!
Wellington was attacked once before by Steve I believe and I have long believed New Zealand would be an excellent place to do a real good study of what has occured re; raw and adjusted temperatures. Being a small polulace it would be a great place to start people taking about the real issues and we have an election this year which would help things along. Carbon taxes are a real issue here and this country may well face a carbon trading defecit of $2 billion and we think we are a clean green country.
Any information about corresponding recent corrections to sea temperatures?
Several years ago I saw an article about how sea temperatures were corrected
to match coastal trends. If I remember correctly the sea temperatures were
corrected upwards to match land based measurements.
The source could have been Warwick Hughes but I haven’t been able to find the original article.
In the absence of any explanation, the so-called “adjustments” look somewhat damning.
I’m wondering if Hansen has made “adjustments” in the opposite direction.
That seems pretty obvious. The two questions are:
a.) How did he get there from here? On what grounds?
b.) Is this a ubiquitous, worldwide problem and not just a few islolated stations? AGW critics will surely claim the latter unless a bottom line is provided. (That’s why I very badly want the GISS 20-century world data for comparison with the NOAA semi-raw and adjusted versions.)
Does Dr. Hansen (et al) claim that these adjustments have meaning on a per site basis? Or are they algorithmically designed to just be give an “accurate” global mean when they are all aggregated. That would not be very intuitive, but could explain all these strange examples.
Lars,
It is quite difficult to figure out what has been done with SSTs, but here are some picks from the literature:
Folland et al [1] assesses accuracy of SST bias corrections through simulations of LAT (Land surface-air temperature) using the HadAM3 atmospheric model:
Then, Brohan et al [2] tells that SST data is better than marine air temperature:
and better than LAT
And indeed, Brohan’s Figure 12. tells that SST data is much much more accurate than land data, especially in the pre-bucket adjustment era.
[1] Global temperature change and its uncertainties since 1861
[2] Uncertainty estimates in regional and global observed temperature changes: a new dataset from 1850
Re SST adjustments
I posted this a while back:
Climate Audit – by Steve McIntyre » More Phil Jones Correspondence Phil. AUS 24/03/2006 23:31 . You might be able to find this and link to it.
I noted then that Phil Jones/CRU were having problems reconciling SST with Land temps for Australia and New Zealand and gave the actual emails. I also noted that this was about the time the IPCC was going to press with the statements ‘the science is settled’ ‘Consensus reigns’ etc. Bloody hell, the science was in such poor condition that no respectable scientist would sign off on it. Look at this Peru data in hindsight and wonder in disbelief what it must to to global calculations.
Re # 21 bender
The word you seek is Foxtrot Uncle Charlie Kilo
Huancayo – why so sparse data? A bit of Googling shows that this one belongs to Instituto Geofisico del Peru, they even do some cosmic ray measurements there, so why something so mundane as temperature should be missing and/or leaky
I’ve recently been playing with the GISS data and comparing it to the satellite data. If you take the GISS global monthly data from http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt from 1979 to present and plot it against the satellite data from, for example, http://www.remss.com/pub/msu/monthly_time_series/RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_1.txt, you get a very nice match. If you re-range the GISS data to use a zero point of the 1979-1998 mean (to match the satellite data) and overlay the two trends, they match quite closely. It’s almost as though the GISS people realize that they can’t fudge the data from 1979-present since they would have to explain any differences from satellite data, but any data prior to 1979 can be tweaked as much as they like since there is no other significant data set to check them against.
As Evan Jones and Mike Smith said, does anyone know where I can find the “uncorrected” global monthly GISS data set to compare it to their corrected data set for the years prior to 1979?
Artificial temperature adjustments in Germany:
Link to post http://www.oekologismus.de/?p=726 for german temperature adjustments.
What I think would be useful is a fully transparent qaudit of “adjustments” for GISS or any other series.
A running record (and possibly a running accumulation) of the “adjustments” applied, weighted by their influence on the final global series.
I have seen the US aggregate adjustment, which is clearly a signficant source of “warming” in this component of the global series. The NZ adjustments, the Peruvian adjustments could/should be added to the mix to see how much accumulated adjustment is contributing, rather than raw data. Are there sea surface adjustments available as well (the famous bucket adjustments).
The outcome should be two series that are always referenced when citing “average global temperatures” in time series. The adjusted series plus the series of adjustments.
I am not saying that these adjustments are necessarily wrong, but at present it is clear there are extensive adjustments hidden to anyone trying to interpret the data. Surely that can’t lead to good science.
I prefer ‘modify’ and modifications in contrast to ‘adjust’ and adjustments. But whichever is used in this case, it means ‘altered’.
Re 41. The bucket adjustment actually raises SSTs up to 1941. There’s a thread with a nice graph somewhere on this blog.
Aha…
http://www.climateaudit.org/index.php?p=226&phpMyAdmin=274c45c8cc4ct3b44e627
http://www.climateaudit.org/index.php?p=226&phpMyAdmin=274c45c8cc4ct3b44e627
JF
ATTN: EVAN JONES
GO TO THE LATE JOHN DALY’S WEBSITE AT: htpp://www.john-daly.com, SCROLL DOWN AND CLICK ON “STATION TEMPERATURE DATA”.
I GUESS I HAVE TO SHOUT TO GET EVERYBODIES ATTENTION!
After a career as mechant seaman where he developed a keen interest in weather and climate, he settled in NZ and began his studies of climate as a hobby. As far as I can tell, his articles are as good if not better than most scientific papers, most of which are poorly written.
Be sure to read the two obituaries.
re 36. stevemc has decribed the raw source data and given locations. you
can also scrape giss web site.
RE 39 Tomislav! Wie geht es? I continue in EE:
I wonder if the Wetterzentrale “Klimadaten”
Berlin-Tempelhof are the raw values since 1701??
If so, the period 1756-1785 was only 0.47 C colder
than dito 1987-2007, 1993-1995 not counted as no values
on Nasa-Giss at the moment. The year of 1756 is with 11.5 C
probably the warmest ever, especially UHIE included So we
have in that case a clear cooling trend in the Berlin area
since late 18th century…My hypothesis is that if white=cool
and black=warm, planet Earth is a zebra, but perhaps it’s
more like a chess-board…
A good scientist is supposed to be a “professional data analyst.” How else do you understand the science that you are working on? It says a lot about these people that you have to make this statement.
OT: This lurker will attempt to insert here a word of thanks and encouragement to all who are dedicated to finding truth. It seems that to the degree that politicians, lawyers, status-seekers, money-grubbers and popular media get involved in an issue, the truth becomes less knowable to the general public. Even peer-reviewed articles supporting whatever politically-correct position spontaneously appear, much as fruit flies do when the bananas are left on the kitchen counter too long. I consider us fortunate that there are a few who have the training, ability, and inclination to seek truth and communicate their findings.
Thanks, and keep up the good work!
#45. Maintaining monthly temperature statistics is a pretty lowbrow activity for a supposedly big scientist. Keynes didn’t spend part of every month making a Consumer Price Index – that’s what I mean. Temperature stats is essentially an accounting job – it needs proper audit trails, footnotes, documentation of adjustments,…
Would it be a big programming job to do the global (or NH/SH) mean anomaly over land using the raw, unadjusted data, then re-do it using the adjusted data, with stations selected to replicate the GISS or CRU products as closely as possible? I’d like to see how much of the 20th C trend emerges as a result of the adjustments.
Just a quick correction… Hansen isn’t a REAL academic “scientist”. He’s a government “scientist” that happens to have an academic affiliation. Don’t lump all the academics in with folks like him! 🙂
Good scientists insist on critical review of their work, insist on openness and reproducibility, and are generally quite reluctant to assume that what they have discovered is all that there is to be known about their area.
Hansen has demonstrated none of the above.
Bruce
Steve: Hansen has provided far more information on his results than Phil Jones, an academic scientist. Also please try to understand the precise nature of my suggestion and don’t assume that I’m saying something different than I am. The first CPI statistics were done by academics, but the routine chore of making a monthly CPI has long since gone to proper statistical services, as it’s an accounting job. It’s nothing to do with the scientist being an “academic” or not. I don’t think that it would ba a good idea for an eminent climate scientist at (say) Georgia Tech or Stanford or wherever to maintain a monthly CPI in his part time and then publish the results in little academic papers. It’s the sort of thing that should be done by government scientists. And much of the responsibility here really belong to Karl and NOAA, as well as Hansen. No economic statistical service responsible for collecting international statistics would have failed to update Canadian copper production (say) since 1989 as NOAA/NASA have done with so many Canadian temperature records.
#48. CRU / Parker is essentially a calculation using unadjusted data. They pretty much just use things like Phoenix Airport as is and simply assert that UHI doesn’t matter, based on Jones et al 1990. So that’s one guide to the results of unadjusted data – though it really goes from one extreme to the other.
I think that the result that is emerging from this – and I’ll post separately on this – is that these large negative UHI adjustments in South American, New Zealand (and now German) cities are a type of reductio ad absurdum of HAnsen’s UHI arjustment methodology. There’s nothing wrong with trying to do the adjustment, but surely any method that deduces that there was 3 deg C urban cooling in South American cities requires proof beyond arm-waving. IT doesn’t mean that they should give up, but that they have to roll up their shirtsleeves, collect metadata, skip a few conferences and do the work that they were paid to do.
ONe thing that is interesting is that both Pucallba and Cuzco appear to have discontinuities around approximately 1975 that probably should be corrected for but which aren’t fixed by the current adjustment method. From what I have found from various sites, there does seem to be some justification for certain data adjustments; however, it seems to me that they should be done on a more site-specific basis with an approach/method that is best-suited to the specific cause of error at that site.
Bob North
=====
#48 Ross McKitrick:
It would be a simple job to run the ROW data through OpenTemp.
I propose that it should be run a few ways:
1. All stations, raw data
2. All stations, adjusted data
3. Rural stations, adjusted data
We need to define “raw” carefully. If the goal is to investigate the effect of the urban adjustments, then the raw data should probably include TOBS.
If the urban adjustments work as they should, then the results should be similar for all three runs.
I am thoroughly confused (not an uncommon state, I assure you).
What possible rationale could justify linear adjustments like these?
Something that has always bothered me about the GISTEMP algorithm is their choice to adjust urban stations to match their rural neighbours. It seems to me that if the urban stations are suspect they should be excluded. This should have little effect on coverage since urban stations without rural neighbours are discarded already.
I think a comparison should be made between the adjusted urban trends and the surrounding rural trends. I believe we agree that rural stations are preferred, so they should be considered a closer approximation of the truth.
In these particular cases, I quickly browsed the surrounding rural stations. They seem to all show a strong warming trend consistent with the adjusted urban versions.
I have a couple of questions:
1. If an urban adjustment is a good thing when it removes an artificial urban warming bias, is it not also a good thing when it removes an artificial urban cooling bias?
2. Do you have an automated way of finding these odd stations? If so, are there examples of large adjustments in the other direction?
The reason for this is, of course, that the urban versions were adjusted to match the neighboring rural stations. So the match is hardly unexpected.
There are some large adjustments in the opposite direction. Hansen et al 1999 illustrates two of the largest such adjustments – Phoenix Airport and Tokyo Airport. He did not illustrate the large opposite adjustments in Peru or other places, which might well have raised questions about his methods on an earlier occasion. The effect of his illustration and discussion is very much to leave the impression that a standard UHI adjustment is what this adjustment does.
There’s nothing wrong with removing an “artificial urban cooling” if that can be demonstrated. Is there some sort of park effect in the Peruvian cities? I very much doubt it. My guess is that the “rural” Peru locations are nothing of the sort and have undergone their own UHI effect which outstripped the cities – think of Oke’s log(population) rule of thumb here. Hansen has not demonstrated an artificial urban cooling effect requiring adjustment – where are the pictures? Yeah, the effect may be more intense in the rapidly urbanizing towns turning into cities, but that’s a different phenomenon and would not support what Hansen did in the least.
1. One possible explanation is that the sign of the adjustment is reversed. It’s a common programming error, quickly caught when it results in an unwanted result, easily missed when it provides the desired result.
2. Adjusting the trend of an urban reading to match the trend of a rural reading is equivalent to using the rural trend over a larger area. This isn’t doesn’t result in an overall error if the rural trend is correct and the urban trend is incorrect.
3. Averaging an urban trend with a rural trend, which I fear is happening, simply averages the errors earlier rather than later. This disguises but does not eliminate the error.
#55 Steve McIntyre:
That is of course a possibility. I looked at the five closest rural neighbours to Pucallpa, and they do all have nearby towns. In my opinion, a couple of factors make “rural” UHI an unlikely culprit:
1. The rural trends are present before 1980 (when their rural designations were applied, I believe);
2. The pattern of *urban* heat island appearing in *only* “rural” stations seems unlikely;
A few caveats:
I am *not* saying that the adjustments are justified or that they should not be investigated.
I do *not* have an explanation for the difference between urban and rural.
I still think it’s best to discard urban sites and remove the question of adjustments.
As an aside, looking at the Google Earth photos of those little Peruvian towns makes me want to travel.
John V,
I plan on taking my son hiking there when he get a little older. Peru is nice– If you can afford the Hiram Bingham train take it. If you are into arranged group travel I have heard from friends that Mountain Travel Sobek does an excellent job. We also plan on mountain biking the death road (Yungas) in Boliva and since I recall that you are into biking that may be something to add to your todo list.
conard:
I’ve read about the Death Road in Bolivia. That’s a long way down.
My daughter is too young right now, but we do hope to eventually take her on mountain bike trips. She’s keen to ride with me in the mountains. The Rockies are an hour from home so we’ll get her started there.
I could go on but we better get back on-topic…
The problem is that very few of the so called rural stations, actually are.
The rise in temperatures at many so-called rural sites where there is a growing population and/or technological presence does not necessarily manifest itself linearly. The simple act of laying pavement on or adjacent to an instrumented site creates fountains of surface driven convection in the lowest few meters which can easily rise above the ~1.5 meter height of the recording instruments. Further “urbanization” may not have nearly the effect of this initial pulse of heat. Anyone who has traveled down a long, level, paved road on a hot summer day has seen the plumes of rising hot air, visual aberrations created by the altered refractive index of the air heated by the road surface.
Also, let no one forget the original NYC Central Park UHI adjustment in USHCNv1 (presumably by Tom Karl et al at NCDC), in which large portions of the 1960-1990 raw data were revised downward by more than 3°C! (This may have prompted the invention/rationalization of the “cool parks” explanation).
Isn’t it about time that some group gets organized to investigate the Fanciful Retrospective Adjustments to Urban Data?
Regarding adjusters: I prefer to use the term “Climopractors”
The whole point behind an audit is to find and correct the things that are wrong. No audit dwells on the things that are right.
Steve: I disagree with this. Most financial audits endorse the company’s financial statements. However auditors do go through a process of asking questions and examining things before they sign off. I regard many of the points in these posts as “audit questions” – and do not exclude the possibility of some of them be resolved.
As a lurker, I am always reluctant to post. The chances of me contributing something of value to the conversation are close to non-existent.
But, looking at all the adjustments that have been posted, it strikes me that almost all of the adjustments have resulted in a cooling of the past. It seems that UHI adjustments would cool the present (or heat the past to preserve the correct trend). Are the adjustments being shown here anomalies, or am I missing something?
Re Hansen’s method(s?) for making adjustments.
Perhaps one of the RC lurckers would like to cite the reference about where we can find his documentation.
Steve: Hansen et al 1999; HAnsen et al 2001 contain descriptions. Hansen (unwillingly) archived code in Sep 2007 after the Y2K embarrassment.
#64 BillBodell:
Steve McIntyre and/or Anthony Watts would be most qualified to answer this. My suspicion is that the adjustments being highlighted here are anomalies. This gets back to Ross McKitrick’s comment in #48. It would be a very useful audit activity to look at all of the urban adjustments to see if there are patterns. Is the distribution of adjustment trends bimodal? Is there regional distribution? Is there some correlation with economic factors?
SteveMc, it would be very interesting to see a histogram and/or map of urban adjustments over 25, 50, 75, and 100 years (for example). I realize this could be a big job, so I’m not asking that you do it. If your collated versions of the data are available, I can try to perform the analysis myself. A link would be appreciated. Thanks.
Dr. McKitrick, you are singing my song! I would just love to know that.
It would come as a surprise if that data (at least the GISS adjusted version) were not already packaged and available. As for the semi-raw data, well I am trying to check.
Mr. Pierce, thanks for the shout-out. I think I dug up the deeper link
http://www.john-daly.com/ges/surftmp/surftemp.htm
and I am trying to run down the data (as opposed to the mere graph). Maybe I can follow a trail to unadjusted world data.
Re #68 Patrick I know of one case in the US where the slope was flattened slightly. The station is Woodville, Mississippi. Between August 2007 and February 2008 the pre-2000 temperatures were raised by about 0.5C, as shown here .
Woodville appears to be a code 1 station, based on Steve M’s map.
Perhaps this was part of the correction of the Y2K problem.
http://www.climateaudit.org/?p=2793
I suspect you already know that USCHN has that information available at their web sites (and it has been reproduced here at CA a few times) for US stations and that the adjustments do make up most of the long term trend.
ToB is undoutably real, but I’d trust the ToB adjustment a lot more if it was based on an analysis of ToB on the temperature anomaly trend from actual temperature data.
The reality is we have an estimated adjustment to monthly data (with a significant error and with no reference to actual ToB) which is then rolled into annual averages (and we know annual ToB is smaller than monthly ToB) which then becomes the anomaly.
The ToB adjustment in the US data is half the warming in the last 40 or so years.
Someone needs to do an analysis of sites where ToB changes versus those where it doesn’t and see how large the bias in the anomaly actually is.
Does ther TOBS adjustment consist of:
1.) measuring only the non-missing part of a year to their corresponding dates and weighting accordingly? Or,
2.) “Bumping the rest of the year” based on what data is not mising?
I know I’d trust method 1 a heck of a lot more than method 2, seeing as how it would be based on actual data only. But the term “adjustment” seems to imply method 2. Which is it?
Mr. Pierce! Dude! Thanks!
GISS Global anomalies:
sources: GHCN 1880-1/2008 (meteorological stations only) using elimination of outliers and homogeneity adjustment
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
I am taking that to mean it’s the GISS adjusted data we need. It is only up to 2007, but I am assuming the adjustments we have seen are applied to this series.
I am going to hack that apart and get it up on Excel.
(It shows c. a 0.8C warming for the 20th century.)
Then on to The semi-raw data we need. Then we can compare.
There’s a lot of talk about adjustments here; here’s one point I’d like to address: When you make an adjustment, you need to take uncertainty into account. That adjustment is never perfect, however sound the adjustment method. How are these uncertainties dealt with in global temperature records ? Folland and Brohan papers talk about bucket adjustment uncertainties, that’s a good start.
The following figure is based on data available here :
( http://signals.auditblogs.com/files/2008/02/bias_u.png )
And it looks like combination of Folland Fig1 a SST bias-corrections, urbanisation (based on Jones90) and changes in thermometer exposures on LAT. I can’t make any sense of Brohan’s way of dealing with uncertainties (*). Brohan says that
Which seems to be not true (the figure above uses data uploaded yesterday). In addition, why bias uncertainties are zero over the normal period? And then, not zero outside the normal period? If bias error is random constant, it shouldn’t matter at all with the anomaly method. If it changes slowly, why it happens to be zero over the normal period, by definition ? And why it is not zero in the archived data?? Why I get MBHx flashbacks when I read Brohan’s paper?
* tried many times, for example in here , but the error propagation rules and Brohan’s online data just doesn’t match. I’d appreciate a lot if somebody can explain where I go wrong. Annual data with uncertainties are here , with this data format, and smoothed data is
here. Smooth is non-causal 21-tap binomial, with end-points values padded, Coeffs
9.5367431640625e-007
1.9073486328125e-005
0.000181198120117188
0.00108718872070313
0.00462055206298828
0.0147857666015625
0.0369644165039063
0.0739288330078125
0.120134353637695
0.160179138183594
0.176197052001953
0.160179138183594
0.120134353637695
0.0739288330078125
0.0369644165039063
0.0147857666015625
0.00462055206298828
0.00108718872070313
0.000181198120117188
1.9073486328125e-005
9.5367431640625e-007
These can be obtained in Matlab by F=diag(fliplr(pascal(21)));F=F/sum(F) , or by reverse-engineering from Brohan’s data, works both ways 🙂
Open/Closed Mind just posted about Puerto Maldon. I post my comment here as he doesn’t like anything that doesn’t agree with his POV:
Have you seen a picture of the weather station site? Find one and then tell me the weather station is “Urban”. By the way, as you only allow posts which support you point of view I’ll post a copy on Climate Audit.
PS Why not try doing a global Raw v Adjusted data comparison, and the see which regions have the largest adjustment?
http://tamino.wordpress.com/2008/02/25/one-of-these-things-is-not-like-the-others/#comment-13303
PS to CA. Puerto Maldon weather station appears to be at a virtually unused airstrip a long way from the town of Puerto Maldon.
PPS #74. I was going to say “lightly used” as opposed to “virtually un-used”, but posted accidentally first.
For North Africa (Morocco, Algeria, Tunisia, Lybia), 25% of the 110 stations are NOT adjusted (no homogeinised temperature).
google earth padre aldamiz (airport)
SPTU PUERTO MALDONADO/PADRE ALDAMIZ PERU
http://www.fallingrain.com/icao/SPTU.html
http://weather.gladstonefamily.net/site/SPTU Interesting site, although their info about this one is wrong. Looks like a duplication of effort for worldwide weather station details.
According to some of the co-ordinates on the web for this location, the weather station could be at lots of locations in the jungle.
Evan, John Daly gave the best explanation and analysis of ToB.
BTW, ToB isn’t a missing data problem. It’s a bias toward recording yesterday’s lower temperature or yesterday’s higher temperature instead of today’s actual temperature, because the last 24 hours min/max temperature aren’t taken at midnight.
ToB can only occur on the day when ToB changes, which for most sites will only happen rarely if ever. However, if there is a trend of change in ToB over time at all sites, then this will introduce a trend in the averages (anomalies). I understand such a trend has occured.
The issue is not whether a ToB adjustment is needed. The issue is whether the ToB adjustment in fact reflects the time of observation bias on the long term term anomaly. As I said, it’s a large proportion of the claimed warming over the last 40 years.
MarkR,
Does it have any Russian jets parked next to the sensor?
MarkW. I think it’s more likely to be drug runners at this particular airport.
Got this posted at Tamino, let’s see how long it stays up.
Tony Edwards // February 26, 2008 at 1:26 pm
As far as I can see, this whole business of adjusting the data is fraught with potential error. As an engineer and machinist, I know that what you measure is what you measure, subject to the tolerance of your equipment . You don’t measure the diameter of something and then “adjust” the number for any reason before you machine the part. Similarly, the temperature that was taken would originally only have been in error by the built in error of the thermometer. Trying to hindcast for microclimate, site faults, UHI, etc. can never make the answer anything other than more dubious. Throw into the mixture a failure to show the exact operations that have been applied and the general public should have extreme doubts as to the accuracy of the final anomalies that get published.
My experience with Tamino is you can get stuff posted as long as you don’t:
– include links to sites that Tamino does not approve of (this includes CA).
– say anything that could even vaguely be construed as an insult or jibe directed at alarmists.
The second point would be understandable if he applied to same standards to alarmist posters who overtly insult and denigrate skeptics.
I don’t bother posting there anymore because I get extremely annoyed when a reasonable argument is deleted for obscure reasons.
RE 56. the sign of the adjustment is not changed. A collection of rural sites is made surrounding
the urban site. This collection is used to create an average trend. The urban trend is subtracted
and a bias is created. this bias can be positive or negative. The assumption is that some Urban locations can exhibit a cooling trend. From everything I have seen you only get cooling trends in TMAX, when the conctrete acts as a heat sink. las vegas is a good example. However, TMIN gets
hit hard in these cases. I know of no independent study showing a cooling in TMEAN which is what Hansen adjusts
I looked at the Brno-Turany airport station. The comparison of Giss pictures says, that the past until cca 1990 (arrow) has been “cooled” and since that year the homogenized/adjusted is the same as raw. The station is at the airport (No. 6) and from one study in Czech I’ve found that the urban UHI doesn’t really influence it.
Just for fun I added a graph of population development in Brno.
ANEXO 17G
METAR, SPECI E TAF DISPONÍVEIS NO BANCO OPMET
Click to access ica_105-1.pdf
Page 159 SPTU PUERTO MALDONADO/PADRE ALDAMIZ
Brazilian military document includes list of weather stations
A presente Instrução tem por finalidade estabelecer normas e
procedimentos para confecção de mensagens meteorológicas e
divulgação e/ou interc’mbio de informações meteorológicas,
objetivando atender às operações aéreas de modo mais rápido e
eficiente.
Raven, lets see how he deals with this one. I really cannot understand why some of these people have get so shrill. In fact that, as much as anything else is why I am extremely dubious that there is anything to be worried about. This is my latest effort, hope it’s not too OT, Steve, but I just felt that it was too much.
*
Tony Edwards // February 26, 2008 at 7:08 pm
[Response: I guess you really are that naive. That’s what McIntyre repeatedly insists is his purpose — but all he ever DOES is try to embarrass the surface record. Far more often than he insists his purpose is noble, he makes some suggestion, some innuendo, some implication, of either gross incompetence or deliberate deception or both by modern climate scientists. It’s his bread and butter.
Actions speak louder than words.]
Really, why does someone posting under a pseudonym have to be so exceptionally rude about someone who, so I believe, makes no money from his weblog, but is looking for answers which might get us closer to an accurate notion of what is happening to the world climate and why. Surely a serious scientist should be pleased that people are concerned enough about accuracy. As to Heretic’s suggestion that the operations “have been explained” why is it that the actual codes, operating procedures and algorithms haven’t, so far as I can ascertain, been put into the public domain?
Incidentally, the described procedure of adjusting urban areas to match up to adjacent (1000km is adjacent?) doesn’t seem to be a sensible way to get rid of any UHI. All it does is put spurious information, if you can call it that, into the mix. A better way, surely, would be to take the stations out of towns and site them in reliable locations.
Sometimes, when you are in a hole, the best thing to do is stop digging.
Leave a Comment
#86. The GISS source code was put online only in Sept 2007. Previously Hansen had refused to provide it in response to a request. Other than the Y2K embarrassment and the pressure from CA, I doubt that he ever would have made it available. So Tamino should not pretend that it’s always been available.
It is the only code that is available. No USHCN adjustment codes are available. No GHCN codes are available. No CRU codes are available. It took a couple of years and multiple FOI requests to even get a list of CRU stations. CRU station data as used is not available.
Tamino’s response to you is highly misleading.
Yes, that’s about what I thought, only more forcefully.
Steve – I am impressed by the level headedness of your response at #87 given some of the rhetoric at Tamino’s site (which can provide very helpful information btw). Please strive to keep it at this level of civil discussion and do your best to avoid the jabs at those with a different opinion regarding the various adjustments to the recorded instrument data. The civil tone is appreciated.
Regards,
Bob North
Re #79
No but it does have a vintage Antonov An-2 bush plane parked on the premises.
An-2
Re # 70 Philip_B
Re TOBS again
Is is, or is it not correct, that if an observation is made every day (as opposed to missing a few days) then either the Tmax or the Tmin will be as recorded (one will always be correct) but that if one of them is incorrect then so is Tmean. By using daily Tmean, you increase the chances that an adjustment is needed. Would it not be simpler to assign a temp taken on the wrong days because of observation time, to the wrong day and carry on with the knowledge that some errors will arise but that they should cancel? This is because irrespective of observation time, the thermometers will record a correct Tmax and T min every day, with the arror being usually small because it is only the error of assigning a temp to a day early or a day late.
If the above is correct, on any given day, half the data at least will not need a TOBS correction at all. Yet, the current method seems to TOBS everything. Is it Irish?
An interesting post here http://temperaturewatch.blogspot.com/2008/03/dodgy-peruvian-temperature-adjustments.html on Puerto Maldonado.
Interesting indeed. Nothing in the article establishes a basis for making the following claim in the concluding paragraph:
I took a look at population growth in Puerto Maldonado and found that there has been a lot of growth. The official consensus shows that in 1981 the population was 12,693. The population in 2005 was 51,349. It is now estimated to be56,917. Yet all of the adjustments took place before 1981. A more extensive write up is on my blog, http://temperaturewatch.blogspot.com/2008/03/dodgy-peruvian-temperature-adjustments.html
Oops, just noticed that Steve mentioned my post in #92. Thanks Steve.
#63
“I disagree with this. Most financial audits endorse the company’s financial statements. However auditors do go through a process of asking questions and examining things before they sign off. I regard many of the points in these posts as “audit questions” – and do not exclude the possibility of some of them be resolved.”
That is my oppinion as well.
Also, here in Canada we do not appear too much in the media since our country is usually overshadowed by the United States south of the border.
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