Warming in the USHCN is mainly an artifact of adjustments

Dr. Spencer, will these people do bank accounts?
😉

It’s well-remarked-on that El Ninos seem to be followed by warmer average temperatures. I speculated that these events were being used as an opportunity to tweak upwards. This article’s analysis looks to support that idea. Under the cover of a sudden change, “the team” jumps into action, adjusting upwards, ever upwards. The “taxing the plebs into the dirt” part comes later — er, now, actually.

Jimbo says: April 13, 2012 at 3:07 pm
“The past isn’t what it used to be.”
It never was.
Climate Science has a great future behind it.

Given that it is almost April 15th, perhaps we should send a copy of this to the IRS. They are the people who really appreciate creative number juggling. A.P. Ershov’s logic would seem appropriate.
“Finding errors in a computer program is like finding mushrooms in the forest having found one look for others in the same place” ~ A. P Ershov

It is a shame that when the global warming alarmist-modelers find their predictions do not match observed data they choose the wrong approach. Choice 1: Models in error wrt observations, continue refining and correcting models and Choice 2: Models in error wrt to observations, go back and adjust (falsify) the observations until they are in better agreement with model predictions.
Choice 1 is honest and advances knowledge of physical processes to be included in models, and thus, advances science. This is good for everyone.
Choice 2 is dishonest and freezes improvements in knowledge of physical processes and the models used for prediction. In addition it corrupts historical data and precludes the opportunity for future scientists to compare their theories of physical processes and modeling with real historical data. It is a sin to lie and it is a sin to add corruption of historical data to the previous lies. Also it is a sin to rob future scientists of a chance to advance our knowledge using historical observations and data. Everyone here loses, and it is good for no one.
Do not think it helps the AGW crooks who seek power and money over over truth and honesty. They may gain temporary advantages in positions, power, and money. But, the truth will come out and attempts to cover up crimes always fail. But in the long run they will be exposed as crooks and fools. Morever, they will forever be damned by honest scientists who honor and need historical data to test theories and hypotheses. The crooked AGW tinkers will have their legacy become recognized and more and more evil over time.
And finally, the damage they do to scientists’ credibility will make billions of people suffer forever after.

USHCN is useless either through gross professional incompetence or malfeasance. Zero out the budgets or eliminate the agencies involved as a budget cost-saving measure.

Documenting this is important. Sadly however, getting it into the published record may not happen.

Hold on, I thought Fall et al 2011 (http://www.landsurface.org/publications/J108.pdf) concluded that the basic trends in the USHCN records were reliable?

NASA says 7C – 9C UHI in urban areas of the northeast.

Is there any explanation for why these results seem to disagree with previous studies, including the BEST study and the study done by Anthony Watts? If I recall, those studies found little difference between the trends for adjusted and unadjusted and for quality rural and bad urban sites. Are these results consistent or conflicting with the satellite data for this area?

Roy, would you please note the the time period used to define the baseline 30 year average against which these data are compared (see David A Evans above). I have looked but I cannot find it (perhaps I wasn’t looking in the right place?).
This may be quite important in interpreting your findings on pop density (as a proxy for UHI) if most of the changes in pop density at monitoring sites occurred prior to or early on in the monitoring period (1973-2012)…

What, pray tell, is a legitimate “Station Location Quality” Adjustment, that is (a) not a UHI effect, and (b) is a net positive 0.20 C over 60 years? By this, it means that an average station’s reading today must be RAISED by 0.20 to make it functionally equivalent to the same station 60 years before.
As I understand the history, Anthony Watts, started his sleigh ride by investigating a change from white-wash to white latex paint that would require a 1.00 negative adjustment, not a positive one. Encroaching parking lots? That’s another negative adjustment.
Oh, I know! There are fewer incinerators today than 60 years ago. /sarc
Let’s see that list of all positive and negative site location adjustments that are possible. There number and gross sizes should amount to some staggering statistical error bars.

Nick Stokes says:
April 13, 2012 at 5:48 pm
I did a TempLS run using monthly GHCN unadjusted data, ConUS. This data is as it says, unadjusted – as reported by the met stations. I got a trend 1973-2011 (actually Jan 2012) of 0.161 C/decade. A bit less than CRUTem 3, but not nothing.

You found a trend in a compound metric.
Now do the same but use humidity to calculate the atmospheric enthalpy and then from that the average kilo Joules per kilogram of atmosphere. You may be surprised at what you find as (contrary to the AGW hypothesis) global humidity has been dropping . You will also then be using the correct metric for measuring heat content in a gas. Atmospheric temperature alone is meaningless average global temperature is like an average telephone number.

Display note: Pleazze, do not, evah, plot lines or dots or labels in yellow. Really. It’s display screen invisibility ink.

[Nick Stokes says:
April 13, 2012 at 5:48 pm
I did a TempLS run using monthly GHCN unadjusted data, ConUS. This data is as it says, unadjusted – as reported by the met stations. I got a trend 1973-2011 (actually Jan 2012) of 0.161 C/decade. A bit less than CRUTem 3, but not nothing.]
Do you consider an increased temperature of 1.61C/century by 2073 as CAGW?

In case you have forgetten, I’d like to remind you that we’re doomed.

I’m sorry, I know this will seem trollish, but every time I see the good ‘ol average = [(Tmax+Tmin)/2]; I can’t help but to think who ever thought that up wouldn’t have done very well on the TV show “Are You Smarter Than a 5th Grader”.

Apologies fo being O/T [snip . . please repost on Tips & Notes . . kbmod]

Constant adjustment of data does not inspire confidence in the accuracy of the data collected .I would expect that very early data would be less reliable than the present data but it is the present data that is being adjusted and it does not seem that we ever get it right and no more adjustments are needed.All these adjustments to try to make average temperatures more accurate make the local temperature changes unclear the uhi effect is part of these local changes and if the local temperature is measured consistently we should see the way local temperatures are trending.The local weather stations were not meant to measure global or regional temperature only local temperature.

One of the claims against AGW scientists is that they have apparently started with a preposition (AGW) and massaged the data to fit.
We all know Dr Spencer’s position on AGW – he’s written books setting it out. How is it he escapes a similar accusation from “sceptics”?
If Dr Spencer is claiming that climate scientists have “fudged the data” he should be having a field day in the journals deconstructing the hypothesis. why is he not doing so? Are “sceptics” going to run with the conspiracy theory racket they seem to be reliant upon and claim he is being thwarted by vested interests?
I’m sure my own skepticism will be unpoplpular here but I suspect Spencer, profiting from contrarian books as he does, also appears to have a vested interest in maintaining his stance, despite the science.
And yet the satellite dataset Spencer maintains, clearly shows the trend he seems to want to deny in the surface record, despite the polynomial fit he applies “for entertainment puposes” but was faithfully reproduced as scientifically based by a contrarian journalist recently.
I think that is there are true sceptics here, perhaps Spencer deserves some of their attention and scrutiny.
[this sounds very like “trolling” so if you don’t wish to be considered a troll perhaps you could include some proof of your suspicions . . kbmod]

I think we should not welcome something just because it provides pleasant results. Dr. Spencer’s way of doing population density adjustments is suspicious at best and in my opinion wrong. Urban heat island does not introduce trend changes, it introduces temperature shift which changes with many factors generally summed up as station siting quality which may (and does) change over time. In Dr. Spencer’s work, station siting is approximated by population density and there are even no changes to it considered. If that was true, then assuming there was no warming, badly sited stations would not produce a different data trend, they would just produce higher average temperatures with the same (zero) trend. Yet the adjustment is done assuming there are no changes to station siting and yet done by manipulating station trends. Dr. Spencer also didn’t care to explain his method in sufficient detail and based on the result I assume he sucessfully decreased trends even for lowest population stations – no wonder he got no warming as a result.
USHCN adjustments are suspicious, but not because they differ in a strange way from Dr. Spencer’s data.

Well, they have to adjust everything now ; because they have found Antarctic Urban Heat Centres are twice what they thought they were….
http://www.businessweek.com/news/2012-04-13/penguin-count-doubles-as-satellite-spies-on-birds-poop-stains

[snip . . please post with content, thank you . . kbmod]

Nick Stokes says:
We have to figure out where we’re going with adding carbon to the atmosphere because if we keep doing it, it’s going to get hot. We’ve burnt about a tenth of readily available C. And it will take a long time to get an agreement to get it under control, so we’d better start.

No sir, Goodridge already proved that CO2 has no effect on temperatures by demonstating that rural stations do not show any warming over the last 100 years despite of global CO2 increase. http://wattsupwiththat.com/2012/03/30/spencer-shows-compelling-evidence-of-uhi-in-crutem3-data/
It is also known from the 19th century that plants need CO2 to grow and the more CO2 in the atmosphere, the more plants grow (classic chemical cinetics) So the most sensible thing to do is to pump CO2 into the atmosphere..
However, if you want to live in a cave with no light and no running water, be my friend and go. Don´t expect me to join, thanks. But please, don´t try to impose a global dictatorship over this.

Nick Stokes says:
April 14, 2012 at 1:12 am
Andrew says: April 13, 2012 at 11:34 pm
“but do I take it this figure represents the “consensus” estimate of global climate sensitivity?”
None of these figures relate to anything global at all. They are about ConUS.
_________________________
Exactly.
You don’t realize it, but you just gave your whole game away.
Since almost all (and quite possibly all) of the “Global Warming” touted in datasets is from ConUS adjusted datasets.
The cooler the southern hemisphere and other locales- the more adjustments to the ConUS records.

Nick Stokes says:
April 14, 2012 at 2:33 am
“Greenhouse effect. Putting CO2 in the air impedes outgoing IR. Heat accumulates, temperature rises. Flux balance then restored (until more CO2 accumulates).”
I thought the it was the postitive feedback of a warmer atmosphere holding more water vapor that was the major factor of global warming. Are you now not including this feedback or did you just forget to mention it?

RoHa says:
April 13, 2012 at 7:12 pm
In case you have forgetten, I’d like to remind you that we’re doomed.
____________________________________
You are correct but it is doom via greedy politicians and the Regulating Class and not doom via climate.

Without reading the replies yet, I predict this will anger the warmers alot.
.013C/decade just doesn’t cut it for proper alarmism.

OK, that screwed up again. Rats. For some reason the wordpress interface is actually getting worse than it was three or four months ago.
I will quickly summarize my partial post in case it went through instead of away. I’m having trouble with the final two figures and conclusion that average neighboring station distances are the same for the two datasets. The reason is that the continental US has an area of km$latex^2$. This works out to square km per station in the first case, and square km per station in the second. Taking the square root and multiplying by 2 in both cases leads to a crude estimate of the (root mean) average distance between stations of 330 km and 160 km, respectively. This makes sense — there are four times as many stations in the second set so the distance between stations is half as great.
What this means is that a lot more stations in the second set have neighbors within 200 km, simply because the average distance between stations is less than this cutoff and most stations indeed have multiple neighbors within the cutoff. This is reflected in the numbers — on average every station in the first set has four neighbors within the cutoff (two pairs), while every station in the second set has twenty neighbors inside the cutoff (ten pairs).
And here’s the rub. This means that one packs 20 stations into a circle of radius 200 km and ends up with the same mean distance between them that you end up with packing 4 into the same circle. This is at least a bit odd. I do realize that the stations are hardly randomly distributed (far from it) and that in all likelihood they were selected with a minimum distance criterion, so that they are spatially antibunched on the short end of the length scale, while on the other hand humans live in a highly bunched environment along artery roads an in or near urban centers, so that they are bunched on the long end of the length scale, but one would still expect something like a shorter mean distance between the stations in the denser set instead of identical means.
I think that this means that one cannot fairly compare the two spatial autocorrelation “corrections” (where I actually think that trying to compute such a correction is such a horrendous abuse of statistics as to beggar the imagination, by the way — I can see why one has to apply some corrections to site data if it is known that the data is recorded differently between sites, although this correction comes at the expense of increasing the error bars on the final result (that never seem to get plotted, why is that one wonders) but one is never justified in trying to “correct” one site’s temperature on the basis of temperature measurements from neighboring sites.
Once one has the best guess for a site’s max/min/mean temperature, there is only one reasonable way to transform that data into a continentally averaged temperature and that is to perform a numerical integral of the temperature field over the area. There is no possible justification for smoothing the data on any length scale before doing this integral — the whole point of doing the integral is that it is the only unbiased way of doing the smoothing, given that one has no theoretical basis for cutting off any interpolating polynomial representation at some spatial length that is longer than the distance between sites.
I sometimes wonder if anybody who works in climate science has actually taken a course in numerical methods and learned about numerical quadrature and the problems attendant thereupon (or ODE solution methods, or if they actually understand statistics, or…)
What is really needed in this field is a double blind experiment. After all, an unbiased observer, performing a double blind analysis of the data, would not know what is being computed and would not know what direction “up” and “down” were for the variable. They would simply be given a pile of data presumably sampled from a wide range of sites around the US and asked to turn it into an average and trend. It might represent average exam scores of students in high school, it might represent a normalized estimate of the prevalence of drug abuse, it might represent average wind speed, it might represent average income per household (all on suitably obfuscated scales). Note that some of these might have the same kinds of problems that temperature has (only bigger) — the exam itself might have changed over the time studied, or high school students might be getting more intelligent (Flynn effect), or high school exam scores could be confounded by prevalence of drug abuse. However, “correcting” for these things using some sort of prior knowledge is dangerous because one of the things one might wish to do is infer the effect of an exam change, Flynn, drugs from the result, so “correcting” for them a priori simply makes the result useless, a self-fulfilling or obscuring prophecy.
Fortunately, for climate science, there are a number of them that potentially exist. I offer one of them up for consideration:
Suppose one takes all of the station data and inverts it with respect to its (station) mean, and then passes all of the data through the same “correction” process. Note that this is a pure symmetry operation; if one is looking for a temperature anomaly inverting with respect to the station means had better produce a perfectly symmetric downward trend in temperatures!
That is, if one has the UHI effect and all other corrections right, then an inversion of the data must lead to a perfectly symmetric inversion of the trend. This seems like an absolutely trivial test that can be performed with almost no additional programming effort to any of the algorithms used to transform temperature data into anomaly. If inversion of the data is not symmetric, the algorithm is biased. It is as simple as that. Symmetry is a necessary condition of an unbiased algorithm.
There are several other ways to effectively “double blind” temperature analysis. For example, give the data to a professional statistician but lie about what it represents. Tell them that it is data representing the average count of golliwogs per square hectare, that you suspect that it is biased by golliwogs being kept in urban zoos so that those numbers may be up a bit and you want to correct for that in the spatial average as you really want only the wild golliwog count, and that some of your golliwog counters did a better job than others (but you aren’t sure who is who). Let them take it away, crank up SAS or R and come back in six weeks with a Grand Average Golliwog Population of the US curve. I’ll bet that given exactly the same data their curve would look nothing like any of the “official” temperature curves.
I would be very, very interested in how CRU or GISS would respond to a demonstration that their algorithms do not possess inversion symmetry, if it were to be clearly demonstrated that this is the case. Personally, I think that would be the end of the game. How is it possible to argue that they should not possess this symmetry? If there isn’t every bit as much cooling apparent from inverted data as there is warming now, how exactly could we observe cooling in the future? Inversion symmetry must be an absolute constraint of any average temperature estimate, a sign that a UHI correction (or any other corrections applied) are plausible.
rgb

Robertvdl says:
April 14, 2012 at 4:46 am
///////////////////////////////////////////////
I wish that I could understand the Dutch site. It is probably very interesting.
As I see matters there is a considerable problem with DWLWIR and the oceans which has not been fully nor properly thought through. .
First, as you note, due to its wave length, DWLWIR is fully absorbed within the first 10 microns of the ocean. However, of more significance, 20% of all DWLWIR is absorbed within 1 micron and 60% within 4 microns. If DWLWIR has the intensity suggested by the K & T energy diagram. it would mean that from an optical physics persepctive there would be so much energy being absorbed in the first couple of microns that it would lead to rampant evaporation. There is a problem here since if there was such eavporation, the energy would not enter the ocean but would end up in the atmosphere (as a consequence of the evaporation and latent heat change). We are not seeing rampant evaporation and this therefore suggests that DWLWIR is not of the order of magnitude claimed, or lacks sensible energy, or is merely nothing more than a signal incapable of performing sensible work, or that much of the DWLWIR is either blocked from reaching the ocean, or is simply reflected by the ocean.
Second, water is essentially an LWIR block (this follows from the fact that LWIR is fully absorbed within 10 microns and 60% of all LWIR within 4 microns). If there is a very thin veil of sea swept mist/spume/spray of just a couple of microns thickness, lying immediately above the oceans, then this very thin veil would be sufficient to effectively block DWLWIR from penetrating the oceans below. For much of the time over much of the oceans there will inevitably be such a thin veil of wind swept mist/spray/spume.
Third, the K & T energy diagram shows solar irradiance being reflected from the surface. This is predominantly from the oceans reflecting solar rays at low angles of incidence. Why does the diagram not show any reflection of DWLWIR? Since GHGs radiate in all directions, it follows that some DWLWIR must be bombarding the oceans at a low angle of incidence? Why is this not reflectsd? Can DWLWIR not be reflected? Can the oceans not reflect DWLWIR? Not sufficient consideration has been given to the reflection of DWLWIR.
Fourth. at the very top micron level of the ocean, the energy flow is upward. The top micron layer is colder than the 4 to 8 micron layer. It follows from this and from the first two points that it would appear that there is no effective mechanism whereby DWLWIR could warm the bulk ocean. It would appear that given that the energy flow is upwards, heat cannot run against it and thus any energy absorbed in the first few microns could not find its way downwards into the bulk ocean and thereby cannot heat the bulk ocean.
The upshot of the above is that ocean heat is almost certainly driven simply by solar irradiance and an increase in ocean heat is a factor of an increase in solar energy, possibly (I would say probably) due to a reduction in cloudiness.

vanmjones says:
April 14, 2012 at 6:22 am
Gail: Actually, you can get more precise readings from crude data — by “oversampling”. If you take hundreds of readings that are accurate to within a degree C and average them, you wind up with an accuracy finer than a degree C….
________________________________
I am well aware of that. If I take a sample and mix well, divide into ten samples and do my chemical analysis on each of the ten samples I can come up with a better estimate of the true value. (BTDT) However that is not applicable here because as I said the sample size is ONE.
You are not doing several readings with calibrated thermometers at the same time at the same place. Instead you are doing a bunch of number juggling but the sample size is STILL ONE, therefore the estimate of the true value of the temperature for that specific location at that specific time has the error bars for a sample size of one. You can not get better accuracy or precision into that specific record by comparing it to the reading from the next town over any more than I could by combining the measurements of a widget from the machine next to the first machine. Heck by combining widgets from several cavities in just one machine I would INCREASE the error not decrease it.
We know darn well the Atlantic Ocean modifies the temperatures along the Eastern Seaboard such that you can see it.
Raw 1856 to current Atlantic Multidecadal Oscillation
href=”http://data.giss.nasa.gov/cgi-bin/gistemp/gistemp_station.py?id=425723080040&data_set=1&num_neighbors=1″>Norfolk City VA
Elisabeth City NC
Wilmington NC
The same holds true for the West Coast and the Pacific. The location of the jets determine the location of the Polar Express, Arctic air that gets sucked down over the USA. Therefore you can not just group a bunch of temperature data together to get a larger sample size.

More good work Dr. Spencer, thank you!

Anyone who thinks a minus 0.05 degree C adjustment for UHI is adequate need only consider the previous post concerning peak temperatures at airports. Recall as well that even in the early 1950s commercial jets were uncommon.
It isn’t the average correction that matters — that SHOULD be rather small if there are enough rural sites that don’t need it. The size of the correction as a function of urban density, land use change, and so on should, in most cases, increase with urban density and many sitings, and as Roy pointed out, the real correction need not just be “urban” and may not have a uniform sign — land use change can net cool a site by e.g. planting trees and grass and importing irrigation water from rivers far away in a site that properly speaking should be and once was desert.
However, the real test of any such correction is the inversion test I mentioned above (which is one of a family of symmetries one can test for). One can take the original data, subtract your UHI correction, form the now “corrected” sample means, and invert around this. What happens to the temperatures now? One can apply the UHI with the opposite sign (and then invert). The point is that in the end, all of the algorithms must possess an inversion symmetry of some sort or another or they are biased.
rgb

Gail, you bring up a very important point in this debate. On any given day, all temperature readings have immediate natural drivers that are well known and completely unrelated to AGW (except for jet engines, black top, brick walls, BBQ’s, burn barrels, and overturned aluminum boats). A hot day is hot because of local atmospheric conditions which can be traced back to pressure systems and jetstream interplay. Those can be traced back to systems that are created over oceans and then meander their way to land. The creation of those conditions are brought about by the coreolis affect, Earth’s tilt, land masses, and oceans swirling the pattern of the atmosphere this way and that in a somewhat choreographs dance. A cold day is likewise. Logically, you cannot therfore deduce an AGW signal by gridding up the resulting temperature readings, adding adjustments, data averaging, and plotting.
Straw cannot be spun into gold. Natural temperature readings cannot be spun into another signal of a different nature.

Gail, can I just write my comments on a word processor then paste?

Budgenator says:
April 13, 2012 at 9:59 pm
I’m sorry, I know this will seem trollish, but every time I see the good ‘ol average = [(Tmax+Tmin)/2]; I can’t help but to think who ever thought that up wouldn’t have done very well on the TV show “Are You Smarter Than a 5th Grader”.

Budgenator – unfortunately you are so right.
Firstly, atmospheric temperature is NOT a measure of atmospheric heat content.
Note that Nick Stokes did not respond to my proposal that he used the humidity metrics to calculate the enthalpy of the atmosphere at the time of the measurements. This is because the AGW hypothesis fails totally if the correct metric of KiloJoules per atmospheric Kilogram is used.
The AGW hypothesis is that CO2 traps HEAT —- CO2 cannot trap TEMPERATURE so stop measuring temperature and start measuring heat content!!!.
The heat content in KJ/Kg at each hourly observation should be calculated from the atmospheric enthalpy and temperature at the time of observation and then a graph of the atmospheric heat content would show whether there was any ‘heat being trapped’ or not. A cool humid misty morning at 60F that turns into a low humidity 100F afternoon may actually show a totally level heat content profile. These buffoons would have generated a totally meaningless ‘average temperature of 80F’ then compound the unreality by averaging all those averages.
We appear to be joining the climate ‘scientists’ associated useful idiots and trolls in their deliberate use of incorrect metrics. This must stop.
If they can only think in temperatures then use the ocean temperature – ideally before they have adjusted and overwritten the raw data there as well.

OK, I have found a trend line for UAH data set that shows warming of approximately .5 degrees C over the last 30 years.
From listening to Lord Monckton and I recall posts from Anthony Watts, the question of global warming is not so much “if,” but “how much.” So the question is, say the earth warmed another 1 degrees C over the next 60 years, how bad/good is that?
My view is the models are proving themselves to not be worth much. It’s not a surprise, as I think of Climate science as nascent, and perhaps well beyond humans and their tools to really understand for who knows how long. The truth is in the “muck and mire.”
Is it equally futile to attempt to understand what different increases in temperature would mean? What would a 1 degree C increase mean, for instance. I suppose it is a lot easier if you say “Temperatures are going to increase 5 degrees C,” because then major systems would be impacted, and we can all make the case about nasty humans changing mother gaiai earth’s system.
I feel I’m wasting my time trying to understand this stuff. I wish our leaders would take a step back, and re-focus the discussion to something meaningful. I’m tired of hearing how global warming causes an increase in toe fungus.

@Evan Jones
Think about the difference between measuring the temperature 20 times a day at one station v.s. once per day at 20 nearby stations. Each reading has a 0.5 degree precision. Which is more accurate? Now think of measuring one station once. The precision of the measurements remains 0.5 degrees no matter how many times they are read.
What you can say about the multiple readings is that the Mean is known with more confidence. It says nothing about the accuracy. They might all be quite inaccurate. Multiple readings don’t improve precision or accuracy as they remain properties of the measurement system and directly give the error bars that have to be placed around a more or less well know Mean.
You can quickly see the meaningfulness of saying the ConUS temp went up 0.161 plus minus 0.5 degrees.

Chuck Nolan says:
April 14, 2012 at 8:40 am
Gail, can I just write my comments on a word processor then paste?
_______________________
Yes, At least I do it with Ubunto Libre office and firefox or opera. Makes editing much easier. you can use WUWT test to see if the HTML markup is OK.

Crispin in Johannesburg says: @ April 14, 2012 at 2:05 pm
Think about the difference between measuring the temperature 20 times a day at one station v.s. once per day at 20 nearby stations. Each reading has a 0.5 degree precision. Which is more accurate? Now think of measuring one station once. The precision of the measurements remains 0.5 degrees no matter how many times they are read….
What you can say about the multiple readings is that the Mean is known with more confidence. It says nothing about the accuracy…..
_____________________________________
Actually it is WORSE. I look at it the same why I would for sampling a batch or continuous process in industry.
Your best accuracy/precision (tightest distribution) is multiple samples from several different points in a well mixed batch, with testing preformed by the same TRAINED tech with the same CALIBRATED equipment. This gives you the best shot of getting something close to the “true value”
As soon as you add another tech the distribution is not going to be as tight (larger standard deviation), add different equipment and again the standard deviation increases. Add different batches, different mix equipment, different plants and different raw material lots and the distribution becomes wider and wider. I really do not care how many samples you take that standard deviation is going to reflect the differences in equipment and technicians as added error. At this point coming up with the “true value” of say the average mgs of codeine per gram of liquid in cough syrups on the shelf in the USA, by just using batch test records from selected manufacturers gets a lot harder and the standard deviation wider. This is what I see as the equivalent to what climate scientists are trying to do with temperature.
The 95% confidence interval is when 95% of the data is within 1.96 standard deviations of the mean Normally plus minus 2 standard deviations are used. (At least in chemistry)

Nick Stokes says:
April 14, 2012 at 5:07 pm
Ian W says: April 14, 2012 at 9:10 am
“Note that Nick Stokes did not respond to my proposal that he used the humidity metrics to calculate the enthalpy of the atmosphere at the time of the measurements.”
No, because it is OT. This post is about temperature metrics. Why not suggest to Roy and Anthony that they use humidity? Then we could compare.

It is NOT off topic – and if you bothered to read the posts that I made I said:
“We appear to be joining the climate ‘scientists’ associated useful idiots and trolls in their deliberate use of incorrect metrics. This must stop.
Atmospheric temperature metrics do not measure the amount of heat ‘trapped’ by the ‘green house effect’. So everyone can discuss temperature metrics as much as they like – they are the incorrect metric.
I am sorry if you don’t understand the concept of enthalpy – you should really learn what it means. then you might not be parading your ignorance quite as loudly.
.

Let us not forget that any large sample of adjustments for instrument error and external factors should average out to zero, meaning about half up and half down, by similar spreads. No such thing is happening; the adjustments display clear patterns, always in the direction of propping up AGW.
There is only one plausible cause for this. The adjusters want it that way.

Removing the rate of growth of the population density in the US temperature data is a rather complicated way of demosntrating a corellation between temperature trends and US population.
The corellation does not prove causation.
Looking at another measure of temperature where changes in population are very unlikely to influence the trend might indicate if the trend is real or just a matter of local causation from population changes.
Is the UAH LT record affected by the US population density increase, or would it make any sense to correct the UAH record with the population metric Roy Spencer has used ?

Philip Bradley says:
April 15, 2012 at 1:24 am
“Nick Stokes says:
April 14, 2012 at 5:57 am
Yes, water vapor feedback amplifies the temperature increase due to any forcing.”
The rather obvious problem with the +ve WV feedback is that the oceans are an unlimited source of WV. A +ve WV feedback would immediately cause runaway warming. Which means even a small net +ve WV feedback is impossible (at current temperatures).
Such a brilliant, succinct statement of something I’ve been trying to articulate, you leave me stunned. People who postulate positive feedback for a stable system simply don’t understand what the word means. And in this case there is coupled positive feedback — the warmer the oceans the more CO_2 and/or methane they release to complete a vicious cycle of self-amplifying runaway positive feedback.
However, the conclusion is still not precisely correct, because WV feedback could be positive but overall feedback could still be negative. Also, because the Earth’s climate is a self-organized chaotic system, altering WV concentrations could move stable attractors up or down or sideways (and probably does). Still, this argument places a strong burden of proof on those who assert positive feedback to explain in detail why there isn’t an upward trail to Venus-like conditions with the oceans boiled away precisely like Hansen has been idiotic enough to assert as his worst-case doomsday scenario! Oh, wait, this is what they think will happen.
Which is why they should not be taken seriously by anyone with common sense. It’s all well and good to make B-grade science fiction movies that show the Earth’s climate self-organizing overnight into a giant refrigerator that cause glaciers to instantly form or into a hothouse that boils away the oceans, all because Humans Did It with their silly little thing called “civilization”. Sensible people look at the actual data from the paleontological climate record over the last billion or so years as recorded in life forms and other proxies, and recognize that first of all, the Earth itself does have the capacity for catastrophic climate change all by itself on hundred-million-year timescales due to forces or forcings over which we have not the slightest shred of control.
Second that the “catastrophes” visible in that record are without exception in one of two catagories — cold phase/ice age, a disaster beyond our imagining — and atmospheric climate catastrophes brought about by things like the Siberian Traps or Supervolcanos, million year long extrusions of magma dumping enormous amounts of aerosols and dust into the air, or asteroid collisions ditto. Even the latter have not managed to kick the Earth into an ocean-boiling state, with many times as much CO_2 and other GHGs in the atmosphere, and the only thing that humans can do that might trigger a similar event is start a global thermonuclear war or go into space and push an asteroid down ourselves deliberately as an act of war.
The probability of global thermonuclear war is at its lowest point since the invention of thermonuclear weapons — at the moment, it is utterly implausible even if the North Koreans or Iranis or Pakistanis lob a bomb at South Korea or the US, at Israel, at India. It would also be much more likely to trigger a nuclear winter, not runaway heating. Yellowstone could, naturally, decide to wake up and spew magma for 50,000 years at any moment, but there is damn all we can do about it if it decides to do so. Similarly, if a rogue asteroid appears all we’ll be able to do — if we see it in time — is draft Bruce Willis, send up a thermonuclear armed space shuttle to try to deflect it (oh wait, we don’t HAVE a space shuttle any more, damn) and pray.
Beyond that, we are in the only visible warm phase in the climate record. It gets as much as 2 or 3 K warmer, depending on parameters we do not understand, possibly things like the actual positions of the continents and oceanic currents, however, those warmer temperatures appeared to become completely and systematically unstable over a stretch that ran from 4 to 1 million years ago, and are now almost completely stable in an ice age. Our current warm phase is part of a transient, obviously geologically unstable excursion and is very likely approaching its end, although we cannot really predict this without knowing why the Earth warms up in this way in the first place (or why it went cold in the first place) and we haven’t a clue about either one.
We are obsessing over the wrong catastrophe. There is essentially zero chance of a still hotter stable state, and if there were — and it were truly stable, the premature end of the current ice age — it would be the best thing possible for the human race and all species of animal life! If you want to see extinction events galore, bring back the glaciers. Humanity came of age in the Holocene, and the advent of Fimbulwinter once again, ice giants and all, would condemn countless species to a massive die-off as the temperate zones they now inhabit become intemperate and frost reaches towards the equator.
Stability analysis is such an obvious step in climate science, and yet all we have there is not “science”, it is the mad ravings of Hansen.
rgb

Has Dr Spencer commented on the fact that his own data set (UAH) gives a statistically significant warming of the US at 0.22C/decade (1979 – present), and is thus quite similar to the CRUtem (0.198C) and USHCN (0.245C) trends, and quite dissimilar from his populaiton density trend (0.013C/decade)?
http://vortex.nsstc.uah.edu/public/msu/t2lt/uahncdc.lt – (UAH decadal trend for ‘US48’ is given bottom right hand corner of this data page)

Allan McRae:
I’ve seen lots of temperature graphs. I think knowing the earth’s temperature is tricky business, and applaud Anthony Watt’s efforts here. I admire that and many other things Anthony does.
So, yes I looked at your graph. Does the PDO change the Earth’s temperature? Could be. How is it meant to transport more heat into space? I have no clue.
Regards,
Ed

rgb:

People who postulate positive feedback for a stable system simply don’t understand what the word means. And in this case there is coupled positive feedback — the warmer the oceans the more CO_2 and/or methane they release to complete a vicious cycle of self-amplifying runaway positive feedback.

That doesn’t happen here. Positive feedback systems don’t run away if they are stabilized by a dissipative nonlinearity, such as Stefan-Boltzman sigma T^4 factor here.
Simple example of this is the Van der Pol oscillator. This linear equation runs away:
x”(t) – x'(t) + x(t) = 0, x(0) = 1, x'(0) = 0.
This equation is stable and gives sinusoidal oscillations:
x”(t) + (x(t)^2-1) x'(t) + x(t) = 0.
Just for clarity, this equation is not stable:
x”(t) – x'(t) + x(t) + x(t)^3 = 0.
(So just having a nonlinearity in the system doesn’t imply it will be stable.)

I find it very curious that Dr. Spencer hasn’t even attempted to square-the-circle between his claims here and his UAH satellite data. It appears he cannot.

@Willis Eschenbach says: April 20, 2012 at 1:29 pm,
It’s been a week, Willis. That question has been asked by many over the past seven days. Do you have an opinion as to why the blaring discrepancy? It seems reasonable that Dr. Spencer would anticipate being challenged on that obvious point and have a ready answer.

@Willis Eschenbach says: April 20, 2012 at 5:20 pm
The question has been asked several times and under multiple original posts on Dr. Spencer’s own site, Willis. There are relatively few responses to wade through in those threads. This in addition to questions posed at multiple related sites across the blogosphere. The chances of Dr. Spencer not having seen some of these comments are slim. He’ll be compelled to respond eventually. I’m just surprised he apparently wasn’t armed with ready response.

Given that there are major changes in population in the US on the county level and lower between 1970 and 2010 (major parts of the midwest west of the mississippi had changes of -50%, the mountain west had major increases, etc,) the idea of using population density from a single year is risible.