Nick Stokes is raising dust over at WUWT about my USHCN data.
My USHCN calculations are very simple. I average all of the USHCN monthly data per year. The average USHCN final data diverges massively from the measured (raw) temperatures.
Nick claims that the biggest part of the adjustments is TOB (Time of Observation Bias.) That is nonsense. Most of the adjustments are the magical ones they do on top of TOB.
The hockey stick in their adjustments since 2000 is due to simply making up data. Almost half of their final temperatures are now fake.
Nick wants you to believe that area weighting the data is very important. It isn’t. The USHCN stations are relatively evenly spaced by design, though my method does tend to weight populated regions more heavily than rural regions. Overall, area weighting has a minimal effect. Every good engineer understands that you don’t obscure your code with optimizations until you have established basic principles and understand what the underlying data is telling you.
Nick also claims that I am comparing two different sets of stations. This is complete BS. USHCN fabricates missing data for almost half their stations. That is an utterly unacceptable practice.
All US warming over the past century is due to USHCN adjustments.
I believe Mosher told the truth with this comment on WWUT; “There is no “issue” with the practice. Everyone who works with the data understands that it is an INDEX and not a temperature.” ~http://wattsupwiththat.com/2015/08/11/an-analysis-of-best-data-for-the-question-is-earth-warming-or-cooling/#comment-2005505
My understanding of Mosher’s declaration is an admission that the adjusted temperatures ARE NOT REAL. I believe he is telling the truth. Of course this means that any effort by anyone to use the adjusted temperature record to compare temperatures, or to claim one year is warmer than another, is futile. Those who insist on doing so are either ignorant or lying.
I suggest you read this item
especially its Appendix B.
It is about estimates of mean global temperature (MGT) and explains;
So, we considered MGT according to two interpretations of what it could be; viz.
(i) MGT is a physical parameter that – at least in principle – can be measured;
(ii) MGT is a ‘statistic’; i.e. an indicator derived from physical measurements.
These two understandings derive from alternative considerations of the nature of MGT.
If the MGT is assumed to be the mean temperature of the volume of air near the Earth’s surface over a period of time, then MGT is a physical parameter indicated by the thermometers (mostly) at weather stations that is calculated using the method of mixtures (assuming unity volume, specific heat, density etc). We determined that if MGT is considered as a physical parameter that is measured, then the data sets of MGT are functions of their construction. Attributing AGW – or anything else – to a change that is a function of the construction of MGT is inadmissable.
If the thermometers (mostly) at weather stations are each considered to indicate the air temperature at each measurement site and time, then MGT is a statistic that is computed as being an average of the total number of thermometer indications. But if MGT is considered to be a statistic then it can be computed in several ways to provide a variety of results, each of different use to climatologists. (In such a way, the MGT is similar in nature to a Retail Price Index, which is a statistic that can be computed in different ways to provide a variety of results, each of which has proved useful to economists.) If MGT is considered to be a statistic of this type, then MGT is a form of average. In which case, the word ‘mean’ in ‘mean global temperature’ is a misnomer, because although there are many types of average, a set of measurements can only have one mean. Importantly, if MGT is considered to be an indicative statistic then the differences between the values and trends of the data sets from different teams indicate that the teams are monitoring different climate effects. But if the teams are each monitoring different climate effects then each should provide a unique title for their data set that is indicative of what is being monitored. Also, each team should state explicitly what its data set of MGT purports to be monitoring.
Thus, we determined that – whichever way MGT is considered – MGT is not an appropriate metric for use in attribution studies.”
In summation, Mosher could not have been more wrong when he asserted “There is no “issue” “.
Further to your view:
The following explanation cannot affect the Unite States Historic Climatology Network temperature, because this temperature product is not affected by sea surface temperatures, however, it might explain the introduction of the term “temperature index” for other temperature products:
The response from Steven Mosher was a response to the following quote:
“I take exception to the practice of conflating Sea Surface Temperatures (SST) with land air temperatures. There are several issues with this practice. While a weak excuse is, there is a strong correlation between SST and nighttime air temperatures, that is hardly justified with modern instrumentation. ”
For temperature products based on both sea surface temperatures and close to the surface air temperatures, the following will be valid: The heat capacity of the oceans is about 1000 times the heat capacity of the atmosphere. This means that an amount of energy, which would be sufficient to warm the atmosphere by 1 K , would only be sufficient to warm the oceans by 0.001 K. This further means that any amount of warming of the atmosphere can be explained by a minuscule change in the temperature of the oceans.
The moment you start combining a varying number of Sea Surface Temperatures, at continuously varying locations, with a various number of close to the surface air temperatures, without taking into account heat capacities, the definition of the measurand, (global average temperature), becomes incomprehensible.
I guess this is the reason behind this ad hoc change of definition from global mean temperature to some kind of a temperature INDEX. As the definition becomes poorly defined, or incomprehensible, the consequence is that the output from the temperature product becomes less informative, the empirical content diminish, the temperature product becomes harder to falsify. Hence, the product becomes less scientific, or maybe even impossible to use in scientific work.
I guess they have not thought about the consequences of attempting such a change of definitions.
I didn’t read the WUWT comments; did you (I hope) address the issue over there ?
I addressed it. Please see my above reply to Dan W.
Nick Stokes is one of those people that cannot ever admit that he is wrong.
If someone points out his error, he simply ignores the comment, and rambles on with AGW rhetoric.
Im’ma thinkin Nick is swatting at flies and not paying attention to the gorilla in the room.
Whether it is Best, NOAA, GISS or Hadcrut; they are all full of adjustments. Most not justified. But they do generate similar results. That would be called collusion.
And then you have the satellite temperatures. Confirmed by radiosonde data and unadjusted rural temperature records. One hell of a gorilla.
“Whether it is Best, NOAA, GISS or Hadcrut; they are all full of adjustments. Most not justified. But they do generate similar results. That would be called collusion.”
There is no need for “collusion” because confirmation bias is sufficient for all ‘adjustments’ to be conducted towards a target value which they all ‘know’ each datum ‘should’ be. Such confirmation bias provides a complete explanation of the correlation between USHCN temperature ‘adjustments’ and atmospheric CO2 which Steven Goddard reports at
As it said in the item I linked above (i.e. http://www.publications.parliament.uk/pa/cm200910/cmselect/cmsctech/memo/climatedata/uc0102.htm )
It should also be noted that there is no possible calibration for the estimates of MGT.
The data sets keep changing for unknown (and unpublished) reasons although there is no obvious reason to change a datum for MGT that is for decades in the past. It seems that – in the absence of any possibility of calibration – the compilers of the data sets adjust their data in attempts to agree with each other. Furthermore, they seem to adjust their recent data (i.e. since 1979) to agree with the truly global measurements of MGT obtained using measurements obtained using microwave sounding units(MSU) mounted on orbital satelites since 1979. This adjustment to agree with the MSU data may contribute to the fact that the Jones et al., GISS and GHCN data sets each show no statistically significant rise in MGT since 1995 (i.e. for the last 15 years). However, the Jones et al., GISS and GHCN data sets keep lowering their MGT values for temperatures decades ago. ”
You quote the phrase “in attempts to agree with each other.”
What this phrase describes is not actually confirmation bias. Confirmation bias is when apparent (but spurious) conditions of nature or measurements are trusted in, believed, and reported because they confirm what one was expecting to find.
What is described with the words “in attempts to agree with each other” is actually collusion to fabricate measurement data to agree with a consciously pre-determined conclusion. That is not merely bias, but scientific fraud. I’m frankly disappointed that you would need to have this explained to you. However, that is a bona fide example of confirmation bias on your part! Your seeing something, but concluding it’s something else, because “something else” is what you expected.
Richard T. Fowler:
There was no need for you to be “disappointed” because frankly you are wrong.
I was addressing the true and accurate meaning of confirmation bias which is stated in many places; e.g. this example
In psychology and cognitive science, confirmation bias (or confirmatory bias) is a tendency to search for or interpret information in a way that confirms one’s preconceptions, leading to statistical errors.
Confirmation bias is a type of cognitive bias and represents an error of inductive inference toward confirmation of the hypothesis under study.
Confirmation bias is a phenomenon wherein decision makers have been shown to actively seek out and assign more weight to evidence that confirms their hypothesis, and ignore or underweigh evidence that could disconfirm their hypothesis.”
As such, it can be thought of as a form of selection bias in collecting evidence.”
As I said,
“There is no need for “collusion” because confirmation bias is sufficient for all ‘adjustments’ to be conducted towards a target value which they all ‘know’ each datum ‘should’ be.”
Of course, it does not concur with the misunderstanding of confirmation bias which you espouse.
If you agree that collusion is not a part of the definition of confirmation bias, then we’re fundamentally in agreement with what confirmation bias is, and your attempt to prove that my definition is wrong is unfortunately beside the point.
There is no way on God’s green Earth that if I did a study that supported my predetermined (“predetermined” does not equal “preconceived”, the latter of which could include subconscious conceptions) conclusion which I vocally advocated prior to the study, and a friend of mine did the same, and we both altered our data “in attempts to agree with each other” (yes, I did notice that you didn’t address that quote in your response), THERE IS NO WAY on God’s green Earth that you would attribute that to confirmation bias. Because after all, it’s me, and I’m “wrong”.
But if it’s them, your reaction is different. Ask yourself why that is. It’s apparently some kind of bias.
Richard T. Fowler:
You claimed an untrue definition of confirmation bias in attempt to assert I was wrong about the lack of need for collusion to obtain the observed nature of the ‘adjustments’.
And you went so far as to write this to me;
“What is described with the words “in attempts to agree with each other” is actually collusion to fabricate measurement data to agree with a consciously pre-determined conclusion. That is not merely bias, but scientific fraud. I’m frankly disappointed that you would need to have this explained to you. However, that is a bona fide example of confirmation bias on your part!”
ALL OF THAT IS PLAIN WRONG!
A simple apology would have been an appropriate response to my having pointed out that your offensive remarks are untrue and result from your misunderstanding of confirmation bias.
Post Script to Richard T. Fowler.
Reading your posts I suspect you did not read the link so you are unaware that the words I quoted (i.e. “in attempts to agree with each other.”) are my own and, therefore, I know what they are are intended to – and do – mean.
Isn’t this classic. When someone like me professes to correct the likes of you, that’s ‘offensive’. But you can do it to me all you want to, apparently.
I’ve been expressing my regret and surprise that you understand less than I had expected you to. I haven’t been a regular reader of WUWT for a long time, so admittedly I was basing my expectation on old data. I figured surely you would have come around by now.
But, regardless, I wasn’t just trying to make you feel bad. I was trying to make you understand why you are wrong about this being a simple case of incompetence.
My main points have been repeatedly ignored by you, as you try to refocus the discussion toward the matter that we agree on, that is, that collusion is not a part of confirmation bias, while you repeatedly claim that I don’t understand this. (How ridiculous!)
I gave you a definition of what I understand to be confirmation bias. Materially, it’s the same as yours. (I.e. the differences are immaterial to the question at hand, which is whether there is collusion or not.)
You seem not to understand that the two definitions are materially the same. I have concluded that this myopia is unintentional, i.e. the result of confirmation bias under both of our definitions. Certainly, in your case, it does not suggest collusion with data fraudsters, alterers, and fabricators.
But, regarding them, legally they are colluding or conspiring. There is no need to prove that one knew exactly what the other was doing, or why; legally, it is still criminal conspiracy or collusion, no matter how much we might want it to be mere incompetence.
Collusion is a legal term, not merely a condition of nature.
Regarding the fact that you authored the quote … well, if you agree that they did this, then you agree that they colluded. Whether you realize it or not, that is what that means.
This is not really a debate about what exactly your belief is or was, since that belief is readily apparent. It is a debate about what was in their minds as far as their intentions.
If they were attempting to make the two data sets agree with each other, then they were colluding. It’s really as simple as that.
What were they colluding to do? To fabricate false data to give the impression that “independent” data sets agree with each other. And there is the greatest point of contention between us, whether you will admit it or not. You apparently disagree that data were fabricated. But even though I gave you a chance to correct the record and agree with that proposition, you have doubled down and continue to attribute the disagreement to an alleged misunderstanding of confirmation bias on my part. There is no misunderstanding, unless we both misunderstand; our two definitions are fundamentally in agreement with each other.
In fact, if I accept your exact definition exclusively, it doesn’t change any of my statements about the matter of collusion.
That reminds me of a quote from computer scientist Donald Knuth: “premature optimization is the root of all evil.”
I particularly like nick’s new hampshire graph with a whole degree warming suddenly applied a few years ago. Their actual temps were obviously not cooperating.
The reality with the temperature adjustments(particularly due to the satellite measurements) is that the warmest may be admitting that they have gone as as they can fudging the computer models to provide the appearance of legitimacy. So instead of forcing the models to match reality(20 year pause) which would require reducing the efffect of increasing CO2 on temperature, they have inverted the argument by fudging the temperature record to match the models.
The media continues to spout the ridiculous propaganda strewn by the politicians and church of climatology. If even someone simple as I can see the grand fabric of misdirection, I fear that John Q Public has not the time nor inclination to delve into the staggering morass of half truth and intentionally confusing argument that keeps it alive. The child will never see the King to remark on his state of dress. The King is surrounded constantly by powerful sycophants and an army of willing conspirators including to a distressing degree our supposed watchdogs. So deep as to utterly prevent it.
The motivations of these people, power, money, power, influence, power are all too familiar and their methods all too effective.
Keep at it you brave soldiers of the enlightenment. Truth should be spoken. The key is to get the worm to turn. This “adjustment ” fiasco may be enough to do it, as it is simple and easy to explain enough to get the lap dogs to turn on their masters. But only if you can get enough sunlight on it before they corrupt the satellite data as well.