Yeah, wouldn’t it be nice if NOAA actually disclosed what they did to make the graph. That might even be considered scientific.

It would certainly be worth determining what procedure was used.
It also seems to be the case that GISS uses a different procedure to adjust the raw data for anomalies.

The problem is they didn’t compare to 70 good stations. They compared to 70 data adjusted stations with the data strongly compromised and influenced by the surrounding network. They didn’t compare apples to apples. They compared one apple to itself.

NOAA does not own the 1228 or so surface stations or have the staff to control them and do physical upgrades and resite them to meet the ideal standards.
It appears to me that they are using software to mak
e adjustments so that temperature trends can be derived from them, even though the absolute temperatures they are measuring are affected by the equipment and the siting.

By using their procedure for handling the data, they show that they can make the average US temperature trends for all the stations equivalent to the 70 good stations, and that the problems Anthony mentioned, do not really matter for the purpose of finding US average temperature trends. The graphs appear convincing to me.

No one has presented a cogent argument that says NOAA is wrong about this important point ,as far as I can glean, but there is so much chaff on this page, I can’t be sure that I didn’t miss something.

“Anthony — The Talking Points memo at http://www.ncdc.noaa.gov/oa/about/response-v2.pdf now (7/1) clearly lists your study, with you as author, in its references. It’s still dated 6/9, however.”

Changing information without changing the issue date or version number is standard Government policy lately. The USDA uses it on farmers all the time and then states the farmers are spreading “disinformation”

Making copies of the original memos and putting them up on blogs has become standard self defense among those of us fighting the National Animal Identification System and other USDA/FDA idiocy. Seems overworked farmers are going to be expected to record and tell the US gov’t in minute detail everything they do on their farms or face million dollar fines but the government can’t do reasonable quality control over something as simple as a thermometer despite their fancy procedures and lofty PHds!

The US govenrment and their tame puppet scientists lost all credibility with me long ago. To bad Thomas C. Peterson: NOAA National Climatic Data Center, Asheville, North Carolina
and other government officials are not subject to the same type of million dollar fines they are aiming at farmers. http://www.opednews.com/articles/A-solemn-walk-through-HR-8-by-Linn-Cohen-Cole-090314-67.html

Perhaps we should find an honest congressman to sponsor a bill that subjects government officials to the same standards and fines they wish to subject farmers to….. No wait we will never be able to find an honest Congressman so scratch that idea.

Analysis will be done using raw data (and TOBS). There are also angles that John V never considered.

Patience, please.

P.S., it is a scandal in and of itself if NOAA does not make raw data available.

“So here’s a public challenge: Does your latest data show that their is any systematic departure between the best stations (as determined by you) and the overall record?”

The talking points memo analysis appears to ignore other factors that may influence the trend comparisons, such as population, altitude, longitude and latitude of the “good” stations. A multiple regression of population, altitude, latitud, longitude and CRN (quality) rating against anomaly temperature trends for 1920-2005 showed significant trend differences in an analysis done early on (1) by Kenneth Fritsch. Similarly, an analysis of covariance “…with the variables altitude, longitude and elevation as covariates, but with CRN rating and population as categorical” done at the same time by RomanM also showed significant trend differences (2).

Unfortunately, updating of these results may be difficult because unadjusted station data is no longer available in the new USHCN data sets. In addition, the publicly available USHCN data sets apparently use information from “not good” stations to adjust the temperatures of the “good” stations, so trend information may be all mixed up (3). Although these analysis may not be definitive, I think they do show that it is important not to ignore other factors when making trend comparisons.

(1) http://www.climateaudit.org/?p=3169#comment-267760 and http://www.climateaudit.org/?p=3169#comment-268119

(2) http://www.climateaudit.org/?p=3169#comment-270488 and http://www.math.unb.ca/~roman/graphs/trendout.pdf

(3) see discussion at http://www.climateaudit.org/?p=6370

“So here’s a public challenge: Does your latest data show that their is any systematic departure between the best stations (as determined by you) and the overall record?”

A trend difference between “good” and “bad” stations has already been identified early on, as follows:

The methodology used to “compare” the “good” to the “bad” stations in the talking points memo appears to be very similar to that used by John V at climateaudit.org identified and posted at http://www.opentemp.org/_results/ very early on when there were less than 20 “good” rural stations identified at surfacestations.org (rural stations only were used in an attempt to avoid urban heat index or UHI contamination). Similarly to the claim in your comments and in the talking points memo now, the claim then was that there did not appear to be much of a difference when comparing trends between the “good” and the “bad” stations.

However, in this comment (http://www.climateaudit.org/?p=3169#comment-267760) Kenneth Fritsch did a multiple regression of population, altitude, latitud, longitude and CRN (quality) rating against anomaly temperature trends for 1920-2005. In this comment (http://www.climateaudit.org/?p=3169#comment-268119), he found that the trend in degrees C per Century increased significantly as the station rating declined (i.e. the trend is greatest for the lowest quality stations – see graph).

On that same thread, commenter RomanM in this comment (http://www.climateaudit.org/?p=3169#comment-270488) did an analysis of covariance “…with the variables altitude, longitude and elevation as covariates, but with CRN rating and population as categorical” and also obtained similar results of a clear increase in trend as the quality of the station decreased. His results can be found at http://www.math.unb.ca/~roman/graphs/trendout.pdf and in the comment referenced above.

Please refer to the referenced comments for more exact language. I may have unintentionally phrased things incorrectly in trying to summarize the analysis. Updating of these results may be difficult because unadjusted station data is no longer available in the new USHCN data sets. The publicly available USHCN data sets apparently use information from “not good” stations to adjust the temperatures of the “good” stations, so trend information may be all mixed up. Unfortunately, the computer code that shows exactly how these adjustments are done has apparently not been released yet either (see http://www.climateaudit.org/?p=6370 for more information and more comments). In closing, RomanM says in this comment (http://www.climateaudit.org/?p=6370#comment-347325), that his analysis “…should not be looked at as definitive in any particular way, but rather more as an example of what should be done in assessing the effect of station quality on trends given the presence of other factors.” In short, the talking points memo analysis appears to ignore other factors that may influence the trend comparisons.

Gina Becker (04:58:10) :

I wish we could find money to run a television advertising campaign, showing the broad public…

1. Photos of all the poorly sited stations, including “rural” ones which are supposedly free from heat islands

2. Urban heat island growth around stations

3. Visuals and explanations on how the gradual MMTS changeover corresponds with temperature rise

4. And the point: this is the USA, which has the most extensive, longest, best temperature record. Think what the rest of the world relies on.

I’ll second this. Too bad such a campaign couldn’t have run prior to the passage of the Waxman – Markey bill, but perhaps it’s not too late.

It’s in preparation. Patience required. All of these issues will be directly addressed.

#B^1

#P^U

Perhaps Peterson might be more effective in advancing science if he focused on his real job instead of drafting BS memos and running throught the rain.

No, it just means they’re spreading the error around and blurring the differences.”

You must be kidding: that would only work if they used cooling and warming adjustments.

Above you say, “5. … For all I know, they could be comparing homogenized data from CRN1 and 2 (best stations) to homogenized data from CRN 345 (the worst stations), which of course would show nearly no difference.”

Doesn’t that imply that the homogenisation process works?

The key point in the study is the graph showing that the 70 stations classified (by you) as “good” or “best” show almost exactly the same temperature trends as the set of all stations put together.

You have validated their work!!

Now it is true that this list of 70 station dates from “early June” (i.e., a few weeks ago). And its also true that you’ve added another 20 stations or so, and perhaps changed some ratings. But you don’t indicate that this makes any difference at all to the bottom line, or to the important work you’ve done to validate the excellent work performed at NOAA and NCDC.

So here’s a public challenge: Does your latest data show that their is any systematic departure between the best stations (as determined by you) and the overall record?

If the answer is yes, you will have something interesting to write about.”

I agree with the above: if the best 70 stations (selected from Mr. Watts surfacestations.org site) show no significant difference from the whole network, where’s the problem?

And what’s the response to the public challenge issued above?

“Anthony,”

snip

Looks to me like your knee jerk resulted in a load of strawmen, nonsequitor and red herring. Who are you?

You seem upset that some talking points were developed by NCDC (anonymously) based on your incomplete survey of the HCN. Yet, last month you published your Heartland Institute paper with the very damming conclusion that both the U.S. and global surface temperature records were unreliable. If it is so critical to do an analysis based on a complete survey of the HCN, why then did you publish your paper last month before your survey was complete, and, more importantly, before you did any analysis of the observations themselves? How can you sure that an analysis of the data will support your conclusions?

You also stated in your Heartland publication that “Since these MMTS/Nimbus electronic thermometers have been gradually phased in since their inception in the mid-1980s, the bias trend that likely results from the thermometers being closer to buildings, asphalt, etc. would be gradual, and likely not noticed in the data.” If that is true, why do Menne et al. state in the abstract of their forthcoming BAMS paper that “ The largest biases in the HCN are shown to be associated with changes to the time of observation and with the widespread changeover from liquid-in-glass thermometers to the maximum minimum temperature system (MMTS).”? Plus, the impact of the MMTS has been studied by others (Hubbard and Lin, 2006; Quayle et al. 1991). Have you ignored those studies for some reason?

REPLY: Simply put, they used old data, never contacted me to ask for current data, listed no author, no citation of my publication, and showed no data or methods to arrive at the graph. Let me ask you: can you get a paper published with such techniques?

Finding all of the best sites is the critical issue of the survey. Menne et al have not looked at all of the site biases. They know they can’t spot all these, and know they can’t assign a magnitude, so they don’t try. I published my census report 1) because I had an interest and offer 2) to help build interest in finding the last few best stations. We picked all the low hanging fruit already. A full paper with data analysis follows when I’m confident we’ve gotten all of the best CRN1/2 sites. – Anthony