Dr. Roger Pielke Sr. on two recent "game changing" climate papers

Pielke Pere, he knows where.
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We shouldn’t forget that the results are corroborated by other evidence, such as
1) The continual upward revisions of recent warming on GISSTEMP (accompanied by downward revisions of the pre-1940 data)
2) The Steirou and Koutsoyiannis paper on weather station data homogenization, which showed up to half of global warming is artificial.
3) The Steig paper on Antarctic warming in generating greater warming than is justified by the raw data.
4) Issues of bias within the Australian and New Zealand temperature records.

Given the timely coincidence with Watts et al 2012, Mr Muller is just making a clown of himself.
Running kind of a family business is not easy nowadays, but this is a desparate business case and bad timing too.

An analysis team led by Anthony Watts has shown that 70% of the USHCN temperature
stations are ranked in NOAA classification 4 or 5, indicating a temperature uncertainties greater than 2C or 5C, respectively. First sentence from Muller 2011?
I have seen some other statements from NOAA that equates station classification to degrees of uncertainty. Implicit in these discussions seems to be that as a station’s classification moves from 1 to 5, the standard deviation of the temperatures and their anomalies is expected to increase, but the mean bias is expected (assumed?) to be and remain zero.
It is true that BEST, by removing absolute temperature from the analysis and only looking trends within and between stations makes an effort to make the assumption moot.[1] However, the assumption of mean bias = 0 for all classification might be at the heart of NOAA adjustments that Watts-2012 addresses.
IF one can argue that if the mean bias of class 1 stations is zero and so is the mean bias of class 5’s, then these can be homogenized to reduce overall uncertainty.
Look at the physics of the thermometers related to the classification scheme to assume contamination that increases the uncertainty. It strains credulity to believe that the contamination is equally likely to be cooler contamination as it is hotter. My hypothesis is that the mean bias from the class of cat 5 stations is > (hotter) than cat 1 as sources for heating contamination are more common than cooling. Mean Bias of cat 5 stations cannot be assumed to be zero and cannot be assumed to be equal to cat 1 stations. When you remove the assumption that mean bias is not zero, then the homogenization become much less tenable. Mixing cat 5 with cat 1 will raise temperatures.
So what? Trends are all that matter, right. The problem is that a cat 5 station today was probably not installed as a cat 5. Another hypothesis: the vast majority of stations start out as cat M and become today as cat N, where M <= N and mean_bias(M) <= mean_bias(N). The bias grows with time — UHI (Macro and micro) growth over time.
Two hypothesies to be tested using Leroy-2010 methodologies.
[1] In my opinion, BEST does finesse the mean bias well, but their scalpel and suture commits an even larger bias by removing low frequency data content from the data processing and is therefore no more trustworthy than the NOAA adjustments.

Well I hope you know this means you and Dr. Pielke are off Mikey Mann’s Christmas card list.

Some people are claiming that time of observation changes in rural stations may have messed up Anthony’s results, but if TOBS was a problem in Anthony’s current paper wouldn’t it have messed up his last one as well?

From Pielke Sr’s post toward the end:
My Comment: There statement that there is “the tendency to cancel random errors” is shown in the Watts et al 2012 and McNider et al 2012 papers to be incorrect. This means their claim that “the large number of stations also greatly facilitates temporal homogenization since a given station may have several “near-neighbors” for “buddy-checks.” is erroneously averaging together sites with a warm bias.
Hasn’t this been a major point that the temperature records show warming with certainty?

In my humble opinion, climate science has no better scientist than Pielke, Sr. When genuine science replaces activist/clownish science, climate science will look just like Pielke Sr’s work.
Pielke Sr’s endorsement of Watts’ work (and of other papers) is the gold standard at this time.

In the Fall, Watts, Nielsen‐Gammon, Jones, Niyogi, Christy, and Pielke Sr paper (2011), in Figure 4, the trend in the raw data is about 0.2°C per decade. The trend in the data corrected for differences in the time of observation is 0.3°C per decade. (The rest of the homogenization does not change the mean temperature much.)
Thus the difference between the trends in the raw data and the one in the homogenized (adjusted) the manuscript Watts et al. (2012) found are most likely due to forgetting to correct for the time of observation bias (TOB). This is an important issue, which is why McIntre is having some doubts about the manuscript.
As http://rabett.blogspot.de/2012/07/bunny-bait.html points out: “There is practically no time of observation bias in urban-based stations which have taken their measurements punctually always at the same time, while in the rural stations the times of observation have changed.”
Thus the differences in the trends for the different station quality classes are likely due to forgetting to correct for the TOB. Another likely problem with the analysis of Watts et al. (2012) is that the classification of the stations was performed at the end of the study period. Stations that were poor at the end, but average in the beginning will show an artificially stronger trend. Similarly stations that were good at the end, but average at the beginning will slow a weaker trend (or even negative trend). This selection bias may well explain the differences found in the trends for the various quality classes. The very least, these are issues, which a rigorous scientific paper would discus.
For now, I will not study this manuscript any further, expecting that it will never be submitted. If it is, I am happy to review it more closely, knowing how much Anthony Watts likes blog review.

I wonder whether Muller is loathing the day he decided to get into climate, and to engage with the critical community 🙂 Everything could be nice and cushy… now he has to read blog posts and deal with the whole enchilada that trails it.

@Bill Parsons , July 31, 2012 at 1:18 pm
Yep, them there thunderstorms is knowed as a negative feedback.
🙂

I noticed in the Fox article there is a picture at the top:
http://www.foxnews.com/scitech/2012/07/30/weather-station-temp-claims-are-overheated-report-claims/?intcmp=features
of a form “WS FORM B-91” for a station in Bedford MA for the month of Nov 2008. The MIn\Max temperatures (and precipitation) are hand recorded with a precision of a single digit Fahrenheit (i.e. no decimal places).
My questions:
Is this the normal way they obtain some (or all) of their data sets? If not, what is this data used for?
How can you claim precision to several decimal places using this raw data?
How much does human error (or possibly bias) affect the data that is recorded?

outgrowth and cause of the same imaginary result. That be Hot Air, I take it. Polly Ticks – which should be a four letter word. I came across this a while back – which should be a cause to understand the evils of filthy lucre. http://fabiusmaximus.wordpress.com/2010/06/27/18115/

There is a common and gross misunderstanding that keeps cropping up in objection. Namely that since Watts didn’t do things My Way therefore the paper is invalid. This is simply not how it works.
Based on prior argumentation, further use of the same argumentation can be leveraged to show that the original assumptions are absurd. This is the class that the current paper is in. It need not satisfy any and every itch.
The other case is that, based on prior argumentation, use of reality can be leveraged to show that the original assumptions are absurd. This has been the legacy of AGW science thus far. And, so far as I am aware, an unbroken batting average on the matter.
Either manner of demonstration is suitable to refute any given theory. Examples of both have been continually produced. And yet we still have the establishment credo that having any theory that is ‘morally right’, even if it produces errors twice that of a random walk, is better than having those theories that are ‘morally wrong’ which are better than a random walk; and so have any predictive power at all. Or even that absurd answers are preferential to admitting ignorance in any matter.
We are long past the point that we are dealing with science. But the latest beating of the dead horse is still necessary for moving religious fanatics off their cocksure pedestals of what ‘decent humans do’. And for that it is simply dandy to show that the argumentation is absurd by at least one of use of the argumentation or use of reality.

vvenema says:
July 31, 2012 at 1:06 pm
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Hand flapping about TOB adjustment in the hope that it will remove this thorn from the side of the consensus is not going to work. In fact it may have the opposite effect than what you intend. You are just attracting attention to Tom Karl’s pet rat TOBy.
There are two types of TOB adjustment that may be valid. The first is time zone adjustment, to account for the true sidereal position of the sun over a station. The second is a one time only step adjustment to individual station data for a change between evening or morning reading of max/min mercury thermometers. The second type of adjustment cannot be validly made from a desk in a distant city. It can only be valid if it is made on an individual station basis with direct reference to individual station paper records. Which method do you think Toms rat TOBy has been using? Yes, that’s right, from a desk in a distant city, not on an individual station basis and with no supporting metadata.
To make a valid TOB adjustment you would need to know whether an individual station was making evening or morning readings of a mercury thermometer, if and when the reading time for that station changed and when the station changed to an MMTS sensor. To achieve this for USHCN stations would require a project similar in scale to Anthony’s surface station project.
Anthony has shown that you cannot adjust for station site issues from behind a desk with any amount data smearing. NOAA thought you could. NOAA also thinks you can adjust for TOB from behind a desk with no supporting individual station metadata. So by all means make a fuss about TOB adjustment invalidating Anthony’s work. Lets drag TOBy squeaking into the disinfecting sunlight.

Some researches here in NW Italy (performed by the University of Turin), as well in France, have shown a clear difference in seasonal trends: in the last decades, there’s been a NEGATIVE trend in summer maxima, outbalanced by a more strongly POSITIVE trend in winter temperatures, so you obtain a “mean” annual positive trend.
IMHO, this is only a measurement of “thermal pollution” in urban areas, where most of the stations are sited.
Time ago, in a 2009 paper by Jones (yes, THAT Jones) & others (sorry, I don’t have the references ready at hand….) something similar was studied on a worldwide base, showing a “stronger” positive trend in winter then in summer.
I think a seasonally-based analysis of data could lead to some interesting conclusion…

First these government employees were telling us all, that magical mathematics from a mystical dimension had cyfered doomsday from even more magical treemomiturs which were utterly unaware they are utterly bound by Liebig’s law.
When even their own colleagues were snickering behind their backs they were still claiming the magical hockey stick making from calibration data scrawls, were a ‘hole new brayunch uh.. Mayuth.’
Then everybody started asking why no mandatory tropospheric hot spot had ever occurred.
Then everybody started asking why no mandatory rise of atmospheric infrared was occurring vis-a-vis the infrared astronomy field.
Then everybody started asking why no mandatory rise of atmospheric infrared was occurring when nitrogen cooled infrared detectors were set outside in Hockey Central U.S. Midwest for fourteen years. There was less atmospheric infrared after the 14 year test than before.
Then everybody started asking why no mandatory rise in activity of gas – the atmosphere – gases are mandatory: more active when heated –
was occurring. If a gas system has more heat added it by definition: go look it up: becomes more turbulent.
When people caught onto this these government employees simply point to Al Gore and claim to be above the law: ‘I don’t operate by the rule of law, I operate by the law of doing what I want to whom and being unfired while doing it.
This is crime
It always was crime,
It’s going to be crime when some of these people are finally indicted and sued out of existence.

poor and well sighted stations

Can you cite who sighted the sites and desited which was what?
😉
From the ludicrous denial of UHI biasing–accelerating with time– to the insistence on Large Number magical elimination of systematic bias, the NOAA and co-conspirators have done their best to reduce the temperature record to malleable garbage.
Hold on to this fundamental: The cleanest records (i.e., of the continuously isolated rural stations) show a decadal warming of 0.034°C. All others are systematically warm-biased.

Doubting Rich says:
July 31, 2012 at 5:52 pm
….When I taught meteorology my students would do a short calculation on temperature changes caused by mixing in turbulence. … So how come what my students were learning was not the obvious basics, taken into account by every climate researcher?
_____________________________
Because “Climate Scientists” know squat all about meteorology! They are all “Specialists” with degrees in physics or tree ring reading or some such. It is like the old “Check with your Doctor before going on a diet” crap when a MD had never been required to take a course in nutrition in his life! (This has changed more recently)

Brian H says:
August 1, 2012 at 8:35 am: “…poor and well sighted stations…”
Can you cite who sighted the sites and desited which was what?
The poor sites were identified by observers who cited that they sighted large cardboard placards reading, “Will provide observations for food”…

Manfred: “shows almost identical raw data trends for tmin, tmax, tmean for category 1+2 stations. Tmin and tmax are not effected by TOBS.”
Tmin and Tmax are affected by the TOB. Tmin and Tmax are used to compute Tmean: Tmean = (Tmax-Tmin)/2. Thus Tmean could not be affected without at least Tmin or Tmax being affected.
Jeremy says: “Metadata in it’s proper use is only just over a decade old, crowd-sourcing for new metadata is entirely new in the field of science and Anthony is quite correct to use these tools to re-examine the temperature record. This is new data and work that should be welcomed with open arms.”
Yes, the surface stations project has made an valuable contribution and added to the amount of meta data available. The new data is very welcome; I hope Anthony Watts will give it out. It sounds as if he wants to wait with that as long as possible because has “a bad experience”. He had the bad experience of NOAA wanting to defend their reputation and publish their analysis that showed that the quality of the data is sufficient to compute trends of the annual mean temperature after careful processing.
Gunga Din says: “I’m just a layman but one thing that is clear to me at this point is that any adjustments to the raw numbers, whether for “TOBs” or something else, must be done on a site by site basis. Study the individual site first before changing the number an observer wrote down or a sensor recorded. “One size fits all” adjustments add error, not accuracy.”
Yes, the TOB correction needs to be performed for every station separately. It depends on the diurnal cycle and on the day to day variability at that station. Fortunately, this is known for ever station with daily data and one can thus develop a correction function for all stations in a region using a few stations with hourly measurements. More details can be found in DeGaetano (2000).
Making a TOB correction is easy. Everyone can nowadays perform sub-daily temperature measurements and simulate the influence of the observation time on the monthly mean temperature. The first study I know on this topic is 150 years old, well before man-made climate change. It is not be best problem to search for a conspiracy.
A short introduction on the TOB problem and its correction can be found on my blog.
I am no expert on this topic, but still I hope it will be useful.
http://variable-variability.blogspot.com/2012/08/a-short-introduction-to-time-of.html
DeGaetano, A.T. A serially complete simulated observation time metadata file for US Daily Historical Climatology Network stations. Bull. Am. Meteorol. Soc., Vol. 81, no 1, 2000.