NOTE: An update to the compendium has been posted. Now has bookmarks. Please download again.
I have a new paper out with Joe D’Aleo.
First I want to say that without E.M. Smith, aka “Chiefio” and his astounding work with GISS process analysis, this paper would be far less interesting and insightful. We owe him a huge debt of gratitude. I ask WUWT readers to visit his blog “Musings from the Chiefio” and click the widget in the right sidebar that says “buy me a beer”. Trust me when I say he can really use a few hits in the tip jar more than he needs beer.
The report is over 100 pages, so if you are on a slow connection, it may take awhile.
For the Full Report in PDF Form, please click here or the image above.
As many readers know, there have been a number of interesting analysis posts on surface data that have been on various blogs in the past couple of months. But, they’ve been widely scattered. This document was created to pull that collective body of work together.
Of course there will be those who say “but it is not peer reviewed” as some scientific papers are. But the sections in it have been reviewed by thousands before being combined into this new document. We welcome constructive feedback on this compendium.
Oh and I should mention, the word “robust” only appears once, on page 89, and it’s use is somewhat in jest.
The short read: The surface record is a mess.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
I just can’t stop ROTFLMAO at everything that’s been going on lately. I’m not posting much these days, Everyone and their grandmother are jumping in on exposing the fraud, and all the fraud being exposed is just completely overwhelming me. Thanks, it takes a lot of pressure off me to do the job, although I do enjoy it so.
Did you here? Everyone is turning the Obama State of Confusion Address into a drinking game. Every time he says all the typical blather, somebody gets to drink. How drunk are we going to get every time BO says “Green Jobs” and “Climate Change”? Everyone is jut going to give a big belly laugh every time BO Lies this time.
Nick Stokes (23:21:59) :
“It doesn’t matter”.
I think you’ll find it does. Quite soon.
Quite a comprehensive study. The section on GHCN adjustments is eye opening, Dawin 0 was just the tip of the iceberg.
A small note – in the summary for the Argo bouy section a quotation mark appears out of place in the last paragraph p59. (unless I am looking at an older version)
Interesting reading indeed and thanks to all contributors. Apart from poor device siting, if this doesn’t represent unidirectional manipulation of temperature data I don’t know what does.
“Konrad (23:11:16) :
O/T
I just missed out on getting in to see Lord Monckton in Sydney. The venue was filled to capacity and a large number of people had to be turned away with Lord Monckton’s apologies. Interesting times…”
Indeed. And due to other commitments (Today, the 27th, for me means I qualify for Australian citizeship – Been a hard road getting here) I was also unable to attend however, I colleague of mine is there for Lord Monkton’s presentation to night in Sydney. We’ll have an interetsing smoko tomorrow.
When my finances are healthier I’ll certainly contribute to the tip jars of Messers Smith and Watts.
Well done people.
Nick Stokes,
I really take exception to your over-written post. Rhetoric that appeals to sensibilities raises the suspicion that you are attempting to obscure the sense of your thoughts.
While such a style may be essential to writers of romantic fiction and certain types of politician, it has no place in discussing matters of scientific accuracy, because it obscures more than clarifies.
If you have no natural ability to write concisely, please could you repost using bullet-points to state your thoughts?
“Nick Stokes (23:21:59) :
[…]
After 1997, it was decided to continue to update the archive. But it wasn’t possible to continue to regularly update monthly all the sites that had provided batches of historic data to the original collection. That’s a different kind of operation. They could only, on a regular basis, maintain a smaller number”
yeah, because as we all know, all things climate science are so UNDERFOUNDED they couldn’t pay a grad student… because the darn supercomputer time is so expensive… puuulease Mr. Politician, can i have a million more????
“Of course there will be those who say “but it is not peer reviewed” as some scientific papers are.”
So what? Neither are many of the ‘peer reviewed’ reports documented in the IPCC reports.
Great work.
small quibble – page 33, i doubt that the population increased from 1.5B to 6.7 B in 2010…
To all who have given thanks and compliments, my humble gratitude.
Per “authorship”: There are sufficient references to “source” to suit me. A collection of poems is authored by the editor and collector. My “poems” are suitably designated. (Besides, with a name like “Smith” it’s hard to get worked up about attribution… Just try “Mr. Smith, your table is ready!” in a restaurant… 1/4 of the place will rush the table 😉 So I leave such choices to Anthony and Joseph. And for folks who’ve spent months wondering what “E.M.” is, you know now that I’m “Michael” when informal; E. Michael Smith when being more formal. E.M.Smith when something will be typed a hundred times 😉
D. King (22:08:44) : It is mind-numbing the level of deception that went
into this. I don’t know how you guys stayed sane.
How does that line go? “I think you may be jumping to conclusions from facts not yet in evidence.” 😉
But yes, at times it was ‘a challenge’ not to throw things at the TV… Especially when some ‘talking head’ would be saying one thing and the data were saying another…
Your methodical and relentless unraveling of this
should get you a medal. […]
Good work gentlemen.
Medal enough… “Public Review”.
When you are working without a safety net, in full public view, all hands dealt face up, that is “Public Review”. It’s a bit more draining than “peer review”, but a lot more honest and a lot more effective. I think it is the way of the future. (And, oddly, the way Science was done in the past… the circle turns…)
Per the ‘relentless’ part: Well, I must admit that the last 2 weeks or so I’ve taken a rest. Then again, I think it was a suitable time. Today I finally got all my “stuff” onto a newer bigger box. Only 10 years out of date 8-0 now! Honestly. Has Windows 2000 Pro on it. Still need to find “Office” somewhere (it has “viewer” versions). I’ve now got a 40 GB 2nd disk in it with Linux dual boot and the GIStemp code; so I have room to do more analysis runs. (I’d filled up the old machine 10 GB disk – GIStemp does not take much CPU, but it’s a real disk hog…)
Already found one thing. The temperature data for Madagascar end at an odd time. While the other places get killed off about 1990, Madagascar struggles on to about 2005 or 2006 IIRC, then dies. I’m pondering using it as the “poster child posting” for the rebuttal to the “it was a batch in 1990” claim… (Wonder if the anomaly maps still show Madagascar … )
So having taken a week to play with new hardware and get the software all installed on a new home and get everything working again, I guess it’s time to start back in. So maybe not completely ‘relentless’… just mostly 😉
E. Michael Smith
Nick Stokes,
You talk of trends and the replacement of stations.
So if you’re just looking at trends and discarding stations, how do you calculate an average global temperature, or compare one year to another, based on trends? Do you say that since the cooler stations that were dropped were last seen trending with slope M that you can assume the trend (which is probably fractal noise imposed on a complex quasi-periodic signal) can be straight lined to infinity? Do you say that two stations that have different temperatures that were last seen with the same trend must always have the same trend, and do you adjust a rural grid point between two remaining urban stations with the historic lower temperatures of the missing point or do you make the missing point track the average of the two urban points? Can you just wait till two temperature trends happen to cross and then lock things in forever?
In short, how can you claim to be measuring temperatures in N places if you not actually taking the measurements, just extrapolating to make it look like you’re taking more measurements than you actually are? If such a technique is accepted then why did we ever bother with taking so many temperature measurements in the first place?
Finally, we’ve seen what kind of computer code and comments accompany such calculations. Can you provide us with source code so we can see if the original temperature offsets are being carried in or whether it just calculates a weighted average of remaining stations?
Patrick Davis (23:57:38)
If you are using the term “smoko” then I’m guessing the citizenship ceromony is a mere formality 🙂
I have sent this to the senators that I have email addresses for here in Australia, I suggest everyone does the same in their own countries if we saturate them with this information then they will not have much choice but to do something about it.
Scott
Isn’t it all Garbage In -> Gorebage Out ?
Great paper, Anthony, Joe, and E.M.
My poor high school reunion web site has had more hits on the Illinois and Wisconsin USHCN charts than it ever got from my classmates.
http://www.rockyhigh66.org/stuff/USHCN_revisions.htm
http://www.rockyhigh66.org/stuff/USHCN_revisions_wisconsin.htm
well you can bin the case 2 study in your compendium, members of the NZ CSC have admitted that the NIWA adjustments, which were a result of station site changes, are justified. And other stations where the data was not adjusted because there were no reasons for adjustment, show the same warming trend.
George Turner (00:53:27) :
So if you’re just looking at trends and discarding stations, how do you calculate an average global temperature, or compare one year to another, based on trends?
Well, first, you don’t usually calculate an average global temperature, for good reason. You calculate an anomaly. That’s what all those famous temp plots show. For each station, the anomaly is basically the difference, for each month say, between the current value and the mean value for some reference period (1951-1980 for GISS). That’s what the Climate Anomaly Method returns – GISS uses a slightly different method which returns a gridpoint anomaly, but anyway, it’s a local value.
Then in any month, you average over a region (globe, NH etc) the anomalies in your current dataset. It’s a weighted average to account for area etc. It’s probably an average of grid values, but these in turn are got by averaging underlying station values.
Then the trend comes out as the trend of these monthly averages (annually averaged if necessary).
The point of the anomaly method is that you don’t need to worry a lot about whether one years stations include a few that are warmer or cooler relative to another year, because you are looking at deviation from station means. GISS explains this here. Scroll down to “Anomalies and Absolute Temperatures”, and follow the link to SAT.
supercritical (00:10:31) :
I didn’t use bullet points – I discussed three quotes from the paper, as blockquotes. These didn’t show quite as prominently as I expected, but that’s the structure of what I wrote.
In Australia today, climate change minister Penny W(R)ong announced Australia’s emission cut commitments. 5%, inline with every other country present at Copenhagen.
And as usual, Lord Monckton’s arrival in Sydney today in MSM media wasn’t covered, but apparently we had our warmest night, last night, in 4 years. Don’t know about that, it was certainly humid ~87%.
“Konrad (00:57:48) :
If you are using the term “smoko” then I’m guessing the citizenship ceromony is a mere formality :)”
I’ve been downunder for quite sometime now and “antipodean speak” has rubbed off a bit 😉
Patrick Davis (23:57:38)
Congrats on becoming a citizen of the lucky country. You can look forward to a lifetime of smoko’s with a hot cuppa and a natter with some mates. I assume you’ll use your new vote wisely at the next election!
Nick Stokes (23:21:59) : A big compendium of nonsense here. I’ll try to make a start.
Thought you ‘tried to make a start” back on the other posting where we already hashed this over.
“More than 6000 stations were active in the mid- 1970s. 1500 or less are in use today.”
This just propagates a misunderstanding of what GHCN is. 6000 stations were not active (for GHCN) in the 1970’s. GHCN was a historical climatology project of the 1990’s. V1 came out in 1992, V2 in 1997. As part of that, they collected a large number of archives, sifted through them, and over time put historic data from a very large number in their archives.
Those station were, in fact, active in 1970. 5997 of them in that year. The exact date the data get into GHCN is not particularly important. And, BTW, data neatly archived but unavailable is functionally useless. (Like that warehouse scene in Raiders of the Lost Ark…) I’d hope you are not asserting that GHCN is only usable as an archival location…
After 1997, it was decided to continue to update the archive. But it wasn’t possible to continue to regularly update monthly all the sites that had provided batches of historic data to the original collection. That’s a different kind of operation. They could only, on a regular basis, maintain a smaller number. This notion of a vast swag of sites being discontinued about 1992 is very misleading. 1992 is about when regular reporting started.
So you are saying that the data set is 1/2 obsolete archive and 1/4 usable data (and 1/4 misc who knows what… like Madagascar that gets sort of updated sometimes… maybe… until 2005 or so). OK, fine with me. Means that ALL the work based on it is based on a horridly botched data set design. Sure you want to “go there”? Broken by design? Obsolete archive?
“It is only when data from the more southerly, warmer locations is used in the interpolation to the vacant grid boxes that an artificial warming is introduced”
A constantly repeated, way-off meme.
Nope. An accurate statement of what the data say.
Firstly, there’s little quantification of such a drift.
Try looking at the data. I did. It’s easy to see and well characterized:
http://chiefio.wordpress.com/2009/11/07/gistemp-ghcn-selection-bias-measured-0-6-c/
http://chiefio.wordpress.com/2009/08/13/gistemp-quartiles-of-age-bolus-of-heat/
(I’m especially fond of the “Bonus Round” top 10% table at the very bottom. The more stabilized the thermometer set, the less drift of the average temperature. In that set of ‘over 100 years in the same place’, “Global Warming” is effectively non-existent. I’d love to know how the globe can be warming when the best longest lived thermometers are not, but only the new ones at tropical airports are…)
The first of those links looks, in particular, at the impact of leaving out of GHCN the USHCN stations. GIStemp provided a convenient vehicle to do this since it uses both, but neatly dropped the USHCN stations on the ground from May 2005 to November 2009 (when they finally put in the USHCN.v2 data). So we can MEASURE the impact. And it is 0.6 C for those stations. That is the warming bias in the base data from those locations being left out of GHCN.
BTW, this also illustrates another silly thing you keep asserting. Those stations that are in the USHCN and were dropped from the GHCN were not due to some archival unavailability of the data or similar lack of reporting. NOAA / NCDC produce both data sets. They would have to move the data all the way from their right pocket into their left… It was a decision not an unfortunate accident of reporting circumstances. So asserting otherwise is, at best, disingenuous.
The second of those links lets you see directly how much the different groups of records carry a warming signal. All the warming is in short lived records. I have a whole series of “by latitude” reports as well. They clearly show the migration of the average thermometer location toward the equator.
Though I must grant you that the “southernly” reference is a bit broad. Yes, most thermometers drift south, but in Australia we found them drifting north… An early look here:
http://chiefio.wordpress.com/2009/08/17/thermometer-years-by-latitude-warm-globe/
just shows the southern drift. It was later in the detailed ‘by country’ and ‘by continent’ looks that I saw the more subtile patterns:
“Most” of them can be reached through this link:
http://chiefio.wordpress.com/2009/11/03/ghcn-the-global-analysis/
though the full list is probably here:
http://chiefio.wordpress.com/category/ncdc-ghcn-issues/
And here is that Australia trend:
http://chiefio.wordpress.com/2009/10/29/ghcn-pacific-basin-lies-statistics-and-australia/
Now, for all the folks who look at these (the results of lots of hours of computer time, full of charts of numbers) please remember that Nick thinks theses are “little quantification”…
But the main thing is, all the GMST calcs are done with anomaly data. Station temps measured with respect to their own mean over a period, or at most, at their own supplemented with some nearby station data. It doesn’t matter if stations are replaced with other stations of higher mean.
And this, frankly, is bull pucky. Station temps are run through a meat grinder of processes long before the “anomaly map” is calculated in STEP3. We have UHI “corrections” that go the wrong way in about 1/4 of the cases. We have lots and lots of “in-fill” and “homogenizing” and who knows what, then, at the very end, the station data is compared to an average of a bunch of other stations to compute an anomaly, NOT just to itself. I posted the code comments on the other thread (I’ll not put all of them here, too, folks who care can go see what the code says it does here:)
http://wattsupwiththat.com/2010/01/22/american-thinker-on-cru-giss-and-climategate
down near the very bottom (at least, right now).
What could matter is if stations are replaced by others with a higher warming trend.
Say, like Airports?
http://chiefio.wordpress.com/2009/12/08/ncdc-ghcn-airports-by-year-by-latitude/
Where we find a persistent increase in the percentage of thermometers are what are now airports over time. Like, oh, 92% in the USA. Good luck finding a ‘rural reference station’ in that lot…
And that’s where this argument gets really silly. The stations with higher warming trend are at higher latitudes. Shifting stations away from the poles (to whatever extent it may have happened) would have a cooling trend, not warming.
Bald faced assertion with NOTHING in the way of data to back it up. All hypothesis, no cattle.
So: No, that’s just where you are ‘sucking your own exhaust’ a bit too much. If you look at the actual DATA from Canada, you find it cooling. It’s only when you compare it to thermometers from different places over time that the “north” is warming. Same thing in New Zealand. No warming if you use the stable set. The warming only comes in because one very southernly island is in the baseline (AND used to fill in grid boxes… I’ve run the code…) but taken out recently (so grid boxes must look elsewhere for ‘in fill’ and elsewhere is airports closer to the equator…) IIRC, Campbell Island about 68 S. Oh, and in Canada they use ONE thermometer in “The Garden Spot of the Arctic” to get that warming trend north of 65 N.
“Interestingly, the very same stations that have been deleted from the world climate network were retained for computing the average-temperature base periods”
Misunderstanding of how anomalies are actually calculated underlie a lot of the argument about station shifts.
Yes, they do. And almost universally from the “warmers” side where folks assert anomalies are calculated in some nice neat “self to self” same station way when they are not. The code averages baskets of thermometers together (and different baskets at different time intervals) and compares a station to the baskets. Read The Code. An excerpt from comments in the other thread:
from:
http://chiefio.wordpress.com/2009/03/07/gistemp-step345_tosbbxgrid/
C**** The spatial averaging is done as follows:
C**** Stations within RCRIT km of the grid point P contribute
C**** to the mean at P with weight 1.- d/1200, (d = distance
C**** between station and grid point in km). To remove the station
C**** bias, station data are shifted before combining them with the
C**** current mean. The shift is such that the means over the time
C**** period they have in common remains unchanged (individually
C**** for each month). If that common period is less than 20(NCRIT)
C**** years, the station is disregarded. To decrease that chance,
C**** stations are combined successively in order of the length of
C**** their time record. A final shift then reverses the mean shift
C**** OR (to get anomalies) causes the 1951-1980 mean to become
C**** zero for each month.
C****
C**** Regional means are computed similarly except that the weight
C**** of a grid box with valid data is set to its area.
C**** Separate programs were written to combine regional data in
C**** the same way, but using the weights saved on unit 11.
So not exactly like you’ve been asserting. LOTS of weighting going on.
They do not calculate a global average and then subtract it. The basic method is the Climate Anomaly Method, which NOAA uses. Each station has an anomaly calculated with respect to its own average.
Flat out WRONG. The data from NOAA arrive as temperatures at GIStemp, not anomalies. An error you made in the other thread too.
In GIStemp Station data is carried AS station data through STEP2 (they do produce a couple of “zonal averages” along the way, but the temp data are carried forward) THEN that process noted above is applied. Notice that a basket of stations is averaged based on a scaling factor and then compared. But only after adjusting their mean and some other changes.
Gistemp uses the same method, but applied to grid points (Sec 4,2), rather than individual stations. Again, this is very little affect by any general drift in stations – the grid points don’t move.
BTW, many of those “grid boxes” have exactly NO stations in them and many have exactly ONE. Good luck with that whole “it’s a grid so individual station bias won’t matter” thing… ( 8000 boxes, 1500 stations… do the math…)
The anomalies are calculate in STEP3 (STEP4_5 just blends in a pre-fab sea anomaly map from HadCRUT). So GIStemp carries temperature data to the end, then makes an anomaly map out of it after most of the damage was already done to the temperature data. And does NOT do it by comparing that thermometer data to an earlier self.
Frankly, it is blatantly obvious that it can’t. The “record” is largely made up of disjoint segments of too few years to be usable if they did. Only 10% of it is over 100 years and a hugh chunk of thermometers are less than 25 years. And with all of 1500 stations surviving, and many of THEM short lived, they would be hard pressed to find anything against which to compare. From an analysis of the “best” thermometers representing the top quartile ( a bit over 3000 thermometers and about 1/2 the total data in the data set) we have a report that shows not many survive into the present DECADE (and we know more of them die off during that decade…):
This is a set of monthly averages of the temperature data, then the annual average, and finally the thermometer count. I’ve deleted most decades so you can focus on what matters:
That middle chunk with about 3000 is the “baseline”. Our present decade has 304 survivors.
That’s right. 304 for the whole world. The rest (1200) are all fairly short lived records and mostly at warm low latitude and low altitude locations.
So unless you want to say that you are somehow comparing those other 1200 to an average bucket, you have to accept that they are not being compared to much at all. They just are not long enough lived.
So, you pick it: Compared to a composite bucket (as the code claims) or not compared to anything at all and we’re just wasting our time talking about ‘anomalies’…
I apologize for the length and detail of this reply, but I have gone through all the code and all the data and when folks just want to hand wave that away with “the anomaly will save us!”, well, lets just say they really need to look at what is really DONE and not what they would like to imagine is done. We’ve had enough imagination applied to the data already…
Oh, and BTW, I did a benchmark on the anomaly. It DOES change when you leave out the thermometers GHCN left out. This is a crude benchmark in that the anomaly report is for the whole N. Hemisphere while the data are only changed in the USA. In theory, this means a 25 X uplift is needed to adjust for the area dilution ( 50% / 2% = 25 ). The anomalies change by 1/100, 2/100. Heck even some 4/100 C. Scaled for the small number of grid boxes of the hemisphere that are shifted, that implies about a 1/4 C to 1 C shift in the anomaly in those boxes…
http://chiefio.wordpress.com/2009/11/12/gistemp-witness-this-fully-armed-and-operational-anomaly-station/
So you can take your theoreticals and smoke ’em. I’ve run a benchmark with the actual GIStemp code on real data and the anomaly map changes. By a very significant amount. Now we’re just haggling over the price…
George Turner (00:53:27) :
Forgot your last question. The Gistemp source code is here.
I didn’t say much about your discussion of local spatial averaging, because I’m not sure what you are saying it is. There is some local averaging with homogenisation in GISS (GHCN is different), but it doesn’t have the sort of effects you speak of. It’s still pretty much the gridding of individual station values. And spatial averaging as in USHCN’s Filnet have small effect on the total region averaging..
Mr Watts/Dr D’Aleo
To say that your paper represents a ‘smoking gun’ in the refutation of AGW would be akin to saying that Omaha beach was recaptured by one man and his dog………..
Many congratulations on synthesising a coherent, wide-ranging, global argument base which should, as a matter of principle, priority and moral rectitude, be urgent reading material for all Public Politicians, functionaries and policy makers globally.
It is not for me to make judgements on your paper, but I suspect that this synthesis will act as an urgent spur to global efforts both to return climate science to its rightful place within the pantheon of subjects for critical analysis, but also to start to return meteorological measurement to its needed place within the warning systems, prediction mechanisms and public planning inputs that has been so shamefully abused in the past 20 years.
I would strongly suggest that you email a copy to the three leaders of the UK political parties, whose email addresses, publicly accessible on the internet, are:
browng@parliament.uk; camerond@parliament.uk; and cleggn@parliament.uk.
All three are in urgent need of education in this arena and it may be helpful in the weeks and months ahead for they and their colleagues to be insightfully stimulated at HOC to change their somewhat unscientific, incoherent and inaccurate positions expessed lucidly, if not with due attention to scientific fact, in the months and years recently past……
YF
Rhys Jaggar
Anthony, Joe D’Aleo and E.M. Smith,
To paraphrase Churchill: “Clucking Bell!”.
Hope this gets the wider coverage it deserves; how refreshing to have real data, clearly presented and free from hyperbole.
Keep up the good work; I’m not entirely sure how you chaps are managing the work-rate at the moment – have you given up sleep entirely?
[Yes. ~dbs]
Cheers
Mark
Mark Fawcett (02:59:52) : Anthony, Joe D’Aleo and E.M. Smith,
To paraphrase Churchill: “Clucking Bell!”.
I always loved Churchill… My Mum lived in England during that time. Left at the end of WWII (with Dad & kid). I was raised with stories of the English Bulldog. (Guess where some of my ‘persistence’ comes from 😉 Though, thankfully, my Dad persuaded her to swap from cold baths to warm baths as we kids were not going to sea when we grew up… (Grandad was in HM merchant marine and his brother moved to Australia after a few voyages..)
Hope this gets the wider coverage it deserves; how refreshing to have real data, clearly presented and free from hyperbole.
My belief is that you ought to ask the data what they have to say, politely, then shut up and listen… You hear more truth that way…
Keep up the good work; I’m not entirely sure how you chaps are managing the work-rate at the moment – have you given up sleep entirely?
Well, I’m in California. It’s now 3:25 AM…
[Yes. ~dbs]
You too, eh? …
(Do the math. An hour of time is worth what? Coffee per pound is much less, tea even better. Both is best ;-0 But I do sleep sometimes. Today it will likely be from 4 AM until 15 after… Unless I do a new posting… )
I’ve been following the global warming/climate change controversy for many years now, having been always skeptical of the “science is settled” claims. My hat is off to so many that have worked so diligently to cut through the lies, manipulations and misinformation.
One would think that the evidence accruing to the skeptical camp is becoming overwhelming and that the long nightmare of environmental propaganda is ending.
I hope that the skeptics keep the pressure on and keep documenting the fraud that went into the claims of a network of Luddites and misanthropes. We must keep hammering the nails into the coffin of this beast so that it is laid to rest for good.
I’m a longtime reader of this website as well as many of the linked sites on the WUWT blogroll. I’ve never submitted a post on this site, but am compelled to do so this morning to congratulate all involved on this magnificent piece of work. It’s a BLOCKBUSTER.
I’d like to state at the outset that that I consider myself a luke warmer, have am convinced that the Hockey Stick has basically been proven to be an outright fraud, that Climategate shows much of current climate science to lack any credibility, and acknowledge that there does appear to have been a lot of tampering with surface temp record, always aimed at getting the same (warming) result. The case against the warmistas is strong, which is why I’m concerned about how weak some of this paper appears to be. There is enough strong evidence against the CRU crew and their fellow travellerswithout diluting it with some pretty underwhelming points. I have so far only skimmed down to page 20 or so, but already have a few questions and (possibly mistaken) assertions on the content, specifically:
Page 12: “Number of stations by category”. In this chart, whilst it is true that the greatest number of stations were cut from the rural category, as there were many more rural stations in the first place, the actual share of rural stations stayed pretty constant, so rural areas should be as well represented in any final averages as they originally were, maybe even a bit more. It looks to me that in 1985, there were around 2600 each of urban and suburban stations, and maybe 6800 rural, so shares of approx. 22%, 22%, and 56% respectively. In 2000 it looks like about 800 Urban, 1200 suburban, and 3000 rural, giving 16%, 24% and 60%. This is very rough eyeballing, but I think those numbers are about right.
Page 17, Statement accompanying map of Russia, that the majority of stations added since 2003 have been in the warm bits (I assume white is cold, green warm), is true, but it is equally obvious that the that the majority of stations dropped since 2003 were also in the green. I think that it will be the relative shares of stations in the warm and cold bits that will matter rather than the total numbers, and I’m not convinced that has changed much.
Page 18, Graph of Canada’s Temps and total station count. Is the temp. axis missing a leading 0, or does the simple average really show a drop of about 8 degrees C since 1865? If so, I think that the only thing the simple average shows is that it is useless for any purpose, unless it is actually being proposed that a real change of this size has taken place without anyone really noticing!
Page 19, “The dropout in Europe as a whole was almost 65%. In the Nordic countries it was 50%”. I’m reading this as only 35% of the original stations remain in Europe as a whole, with 50% remaining in the Nordic countries. This sentence partially contradicts the shift to the Mediterranean asserted, although it does’t undercut the statement about a shift to lower altitudes.
Perhaps I’m missing something, but that stuff really doesn’t seem very strong to me at all.