This new paper by Dr. Ross McKitrick of the University of Guelph is a comprehensive review of the GHCN surface and sea temperature data set. Unlike many papers (such as the phytoplankton paper in Nature, complete code is made available right from the start, and the data is freely available.
There is a lot here that goes hand in hand with what we have been saying on WUWT and other climate science blogs for months, and this is just a preview of the entire paper.This graph below caught my eye, because it tells one part of the GHCN the story well.
1.2.3. Growing bias toward lower latitudes
The decline in sample has not been spatially uniform. GHCN has progressively lost more and more high latitude sites (e.g. towards the poles) in favour of lower-latitude sites. Other things being equal, this implies less and less data are drawn from remote, cold regions and more from inhabited, warmer regions. As shown in Figure 1-7, mean laititude declined as more stations were added during the 20th century.
Here’s another interesting paragraph:
2.4. Conclusion re. dependence on GHCN
All three major gridded global temperature anomaly products rely exclusively or nearly exclusively on the GHCN archive. Several conclusions follow.
- They are not independent as regards their input data.
- Only if their data processing methods are fundamentally independent can the three series be considered to have any independence at all. Section 4 will show that the data processing methods do not appear to change the end results by much, given the input data.
- Problems with GHCN, such as sampling discontinuities and contamination from urbanization and other forms of land use change, will therefore affect CRU, GISS, and NOAA. Decreasing quality of GHCN data over time implies decreasing quality of CRU, GISS and NOAA data products, and increased reliance on estimated adjustments to rectify climate observations.
From the summary: The quality of data over land, namely the raw temperature data in GHCN, depends on the validity of adjustments for known problems due to urbanization and land-use change. The adequacy of these adjustments has been tested in three different ways, with two of the three finding evidence that they do not suffice to remove warming biases.
The overall conclusion of this report is that there are serious quality problems in the surface temperature data sets that call into question whether the global temperature history, especially over land, can be considered both continuous and precise. Users should be aware of these limitations, especially in policy sensitive applications.
Read the entire preview paper here (PDF), it is well worth your time.
h/t to E.M. Smith
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Ah, thank you, Nick. So, according to that, a shift towards zero…well, I can hardly call it a good state of affairs, considering that it’s caused by stations dropping out of the record, but it does mean the data sources are getting more evenly distributed over the globe. That might or might not cause an overall warming trend; a lot of US stations were cut out (and the US has been warming faster than the globe on average: http://data.giss.nasa.gov/gistemp/graphs/Fig.D.lrg.gif), so…would that give a cooling bias, overall? Or warming, because the US is cooler than places closer to the equator (then again, Australian stations got cut, as well, and that hardly counts as a cool and clement place).
PolyisTCOandbanned: In response to your question #4, 30 degrees is the average latitude (set the integral of sine theta from 90 degrees to x degrees equal to the integral of sine theta from x degrees to 0 degrees, and solve. If I made a mistake in the setup, please, let me know). According to what Nick Stokes posted, though, that may be irrelevant; if the southern hemisphere latitudes are given negative values, then 0 degrees should be what you’d expect for a perfect global distribution.
Sorry, meant cosine in that last comment.
[snip – policy me@meme.com is not a valid email address ~mod]
It’s a self-evident non-valid one. And funnily so.
BillD: all I’ve tried to do is pull together the strands of work done by others on blogs and in journal articles, and put together a readable story with it. This isn’t what I’d call original research. It’s more of a survey. Based on this info people might want to go on and do some original research, such as trying to quantify the effect of the changing sample distribution/locations on the global average. That kind of work should go to journals.
Ross,
“Steve, you are right, it’s a change file. The chimney brush uses a delta series constructed such that v2.mean is computed on its spatial basis, and v2.mean.adj is computed only using the gridcells with adjusted data, rather than filling the remaining cells with unadjusted data.”
Thanks, Thanks go to Zeke and a guy named zoro80 for figuring it out.
Also, be careful about merging them blindly. Working with R I found some irregularities in the .adj file.
Take a dataline in v2.mean
999123450001 1980 NA NA NA 12 13 14 62 23 23 24 25 67
Thats a line for a GHCNID 99912345000 DUPLICATE 1. year, then months.
A change file, should then have only lines that either MATCH this line or are absent
Like so:
999123450001 1980 NA NA NA 12 13 14 12 23 23 24 25 26
And you can see that I changed some values for that GHCNID/Dup.
BUT, v2.mean.adj has Lines that DONT appear in v2mean?? weird. are these
new records? how do you adjust something that didnt exist?
SECOND. there are duplicate lines in v2.mean.adj. Same ghcnid/dup, same year, but
MULTIPLE occurances.
My guess is the programmer didnt check for this kind of error. But when you work in R and merge based on matching indices these goofs become readily apparent.
have chad double check or write me or zeke.
Geoff:
“WRT time of day, do you think that daily temperature max-mins are those recorded electronically these days – or is there curve fitting and rejection of transients? If the latter, it would be hard to splice mercury records to thermistor/thermocouple records as they were successively replaced. Have you ever sen an instrumental overlap comparisson or any code to cope with the transition?”
Electronic AFAIK. WRT splicing. I’d have to look around but typically there are studies done to estimate the adjustment required at a splice. Rememeber a splice,
even DONE POORLY doesnt hit your trend very much, and depnds upon the year the splice was done.
WRT the energy calculations. Im working on a different study right now, but I’ll return to the airport one. You could just wing it or back of the envelop.
Depending on the altitude a 777 GULPS 100Klbs of fuel per hour
at MAX throttle. ( two engines at 50K)
Go figure 3 minutes for takeoff. The busiest airport has fewer than 3000 movements per day.
The number 30 busiest airport would only have 1000 movements per day.
So I’d start by bounding the problem from above.
5K lbs of fuel burned for every takeoff and landing ( thats a HIGH estimate)
Work from that. None of the top 30 airports are in ghcn. so you can bound the movements to less than 1000 per day.
more later gotta run
A new version of the report is posted at my home page with a few corrections: Fig 1-7 mean latitude is now mean absolute latitude (graph looks similar overall, but without spikes); Fig 1-10 re-done using ghcn.adj as a change file, only using grid cells where both raw and adjusted obs are available. Accompanying text revised as needed; revised codes in appendix. Thanks for the corrections. BTW some of you might be interested in our new ASL paper, also on my homepage.
Right, I just saw news about the new paper of McKitrick, McIntyre and Herman at Pielke Pere’s site. The climate models’ projections don’t look very good.
========================
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At 6:37 PM on 5 August, Dr. McKitrick had written:
“A new version of the report is posted at my home page with a few corrections: Fig 1-7 mean latitude is now mean absolute latitude (graph looks similar overall, but without spikes); Fig 1-10 re-done using ghcn.adj as a change file, only using grid cells where both raw and adjusted obs are available. Accompanying text revised as needed; revised codes in appendix. Thanks for the corrections. BTW some of you might be interested in our new ASL paper, also on my homepage.”
The exchanges in this thread present the most interesting example of open-forum peer review that I have yet seen. Everything is transparent, and even queries from the “unqualified” are posted and receive response.
What’s that old saw? “The greatest fool may ask more questions than the wisest man could answer.”
Dr. McKitrick’s readiness to read and respond to pertinent interrogatories and comments demonstrates that in a fashion far more flexible and immediate than the formal mechanism of academic peer review, manuscripts can be refined to improve their quality, and as Mr. Watts maintains these exchanges in a Web archive of sorts, all of this is available for later review by anyone interested in the subject under consideration.
This beats the hell out of an editor designating a small cadre of peer reviewers who frequently use their participation in the vetting process to snipe at an academic rival from behind a screen of anonymity.
I would ask that Dr. McKitrick please provide an active link to his revised version of this paper, as a look into his personal Web site does not readily lead to this element.
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I barely understand half of what you guys are talking about, partly because I have developed a temperaturegraph phobia.
But thanks to all of you for investigating and back-engineering this issue with a calm, reasonable and professional conduct. I believe you are the true scientists. Because you don’t need an approved title to be right.
Ross;
Where has the Sociometric paper now been accepted? At the same journal, or “elsewhere”?
Rich, the current version of the paper is here. I have migrated to http://rossmckitrick.weebly.com/ so the most updated version will be there.
Brian H., “elsewhere.” I’ll post up details when I have final acceptance.