I’ve noticed there’s a lot of frenetic tweeting and re-tweeting of this “sound bite” sized statement from this Climate Central piece by Andrew Freedman.
July was the fourth-warmest such month on record globally, and the 329th consecutive month with a global-average surface temperature above the 20th-century average, according to an analysis released Wednesday by the National Climatic Data Center (NCDC).
It should be noted that Climate Central is funded for the sole purpose of spreading worrisome climate missives. Yes it was a hot July in the USA too, approximately as hot as July 1936 comparing within the USHCN, No debate there. It is also possibly slightly cooler if you compare to the new state of the art Climate Reference Network.
But, those comparisons aside, here’s what Climate Central’s Andrew Freedman and NOAA/NCDC won’t show you when discussing the surface temperature record:
Since I know some people (and you know who you are) won’t believe the graph above created by taking the final adjusted USHCN data used for public statements and subtracting the raw data straight from the weather station observers to show the magnitude of adjustments. So, I’ll put up the NCDC graph, that they provided here:
http://www.ncdc.noaa.gov/img/climate/research/ushcn/ts.ushcn_anom25_diffs_urb-raw_pg.gif
But they no longer update it, nor provide an equivalent for USHCN2 (as shown above), because well, it just doesn’t look so good.
As discussed in: Warming in the USHCN is mainly an artifact of adjustments on April,13th of this year, this graph shows that when you compare the US surface temperature record to an hourly dataset (ISH ) that doesn’t require a cartload of adjustments in the first place, and applies a population growth factor (as a proxy for UHI) all of the sudden, the trend doesn’t look so hot. The graph was prepared by Dr. Roy Spencer.
There’s quite an offset in 2012, about 0.7°C between Dr. Spencer’s ISH PDAT and USHCN/CRU. It should be noted that CRU uses the USHCN data in their data, so it isn’t any surprise to find no divergence between those.
Similar, but not all, of the adjustments are applied to the GHCN, used to derive the global surface temperature average. That data is also managed by NCDC.
Now of course many will argue that the adjustments are necessary to correct the data, which has all sorts of problems with inhomogenity, time of observation, siting, missing data, etc. But, none of that negates this statement: July was also the 329th consecutive month of positive upwards adjustment to the U.S. temperature record by NOAA/NCDC
In fact, since the positive adjustments clearly go back to about 1940, it would be accurate to say that: July was also the 864th consecutive month of positive upwards adjustment to the U.S. temperature record by NOAA/NCDC.
Dr Spencer concluded in his essay Warming in the USHCN is mainly an artifact of adjustments :
And I must admit that those adjustments constituting virtually all of the warming signal in the last 40 years is disconcerting. When “global warming” only shows up after the data are adjusted, one can understand why so many people are suspicious of the adjustments.
To counter all the Twitter madness out there over that “329th consecutive month of above normal temperature”, I suggest that WUWT readers tweet back to the same people that it is also the 329th or 864th consecutive month (your choice) of upwards adjustments to the U.S. temperature record.
Here’s the shortlink to make it easy for you:
KR:
You begin your fallacious rant at August 23, 2012 at 1:40 pm saying
SAY WHAT!?
I stuck rigidly to the subject and concluded my reply to you by directly quoting from my post which you questioned and saying nothing you had presented changed that in any way.
The remainder of my answer to you directly addressed the relevance of the paper you (not me) cited.
Your only response to my explanations of faults with that paper is to make the mistaken (or deliberately untrue) assertion
As I have repeatedly explained to you, that is not true. Indeed, in my post you dispute I wrote an explanation of how changing some measurement sites altered both the obtained average and its statistical significance. But in your response you make the daft assertion
That is risible!
The “entire set has to be changed” for “continuity” to be lost? Did you think before writing that? It says you are claiming that if all except one measurement site closed then the data would still be contiguous so the results would be comparable to the data obtained using many sites. OK. Which one do you want to choose because using only that one would save a lot of bother?
And you again misrepresent what I have said. Your repetition of the falsehood can only be egregious because I directly refuted it to you at August 23, 2012 at 3:26 am saying
I am not surprised that you have run away.
Richard
PS I shall be absent and unable to reply for some days after this. (My absence will probably good for my blood pressure because I will not be able read any more of your twaddle).
Dear Richard,
This is a good time to give a short overview of the discussion so far.
But let me first say that i fully think that your approach is less then constructive. KR aptly summerized that as:
“…claims that amount to “There’s uncertainty, therefore we know nothing, therefore don’t believe what anyone says about the data”.
However, i do not want to start a new sideline in the discussion about that, but instead will try to focus on the actual science: the corrections of the raw data and the associated errors.
The main post of Anthony made a very simple statement:
“July was also the 329th consecutive month of positive upwards adjustment to the U.S. temperature record by NOAA/NCDC”
Although not explicitly stated, the message of the post is that the correction is the only real source of the rising temperature, and that there is no true signal in the data.
I commented that the statement was meaningless unless the reasons behind the adjustments and their validity were taken into account. To give an extreme example, if a new thermometer would be introduced that gives a consistent reading one degree higher than the old model, than a correction of one degree down is needed for meaningful comparisons. It is not at all a problem that the correction is of the same order (or larger) as the signal itself, as long as the correction (and associated errors!) are calculated correctly. I also provided sources to the original literature that explains how and why the corrections were done.
Anthony just defended his headline, arguing it remains true, irrespective of the accuracy of the corrections. That is true, but not informative. You made a (much) stronger statement. I quote:
“The effect(s) of sampling error are not known, and there is no way they can be known, so there is no known way to model them correctly.”
This changes the topic slightly, the original post concerns the positive corrections in the data, and those are largely due to the correction of time of observation bias (TOBS), but your (false) statement is about sampling errors.
I provided a references about TOBS (Karl 1986) and KR mentioned Vose et all. 2003. Anybody questioning the validity of the TOBS correction should point out the flaws in the methods detailed in these papers. That has not happened.
But as said, you brought up the problem of sampling errors and make the (false) claim they can not be estimated. I quoted Shen at al 2012 that shows the sampling errors can be, -and have been- estimated. KR mentioned Weithmann 2011.
Your called the Shen et all. paper “pseudoscientific nonsense” and based that very strong statement on a (distorted) representation of the last 9 lines of the paper.
If you read the paper you will see that Shen et all. *do* estimate the errors (hence your claim they are unknown is false). They quantify the errors using the fact that the data is correlated, and find that especially in the older measurements the variances are “non-trivial” (and that is something else than unknow!!) but in other periods are nearly zero. The authors subsequently evaluate the effect of the error estimates on the overall trend, and conclude that in spite of the fact that they were sometimes of “non-trivial” size, they they were not large enough to alter the trend of the contiguous U.S. surface air temperature. If you want to defend the position that the error estimates in Shen at al 2012 are not correctly computed, you need to demonstrate that in a more rigorous way than just pointing to a (in your eyes) logical inconsistency in the formulation of their end conclusion.
The other point you assert is this:
“each datum (e.g. annual or monthly) for average global temperature is an individual datum. It is NOT part of a data set. This is because the measurements used to provide each datum are a unique data set.”
This is not true, a series of measurements of april temperatures from a particular gridbox are all correlated over time because they are at about the the same geographic location. This correlation holds over distances in the order of a thousand kilometers as demonstrated by Hansen & Lebedeff (1987). You dismiss this argument by boldly stating that Hansen & Lebedeff’s results are also wrong, just as you say that Shen at all. 2012 are wrong. You give two arguments: one is that not all groups use these results (an argument that in no way invalidates the reasoning in the paper itself). The other argument is that H&L87 use a comparison with global climate models to validate their results, and that this proves their results are invalid since describe local effects and GCM are global. Apart from the fact that the logic here is questionable to say the least, you ignore the fact that this method is only one of the ways used in H&L87 to validate their result. But we can argue forever. I am willing to do that if you insist, but i want to make a more general point
So to sustain your position you have consistently argued that *all* the papers that were cited as evidence against it were faulty, and there authors badly informed about measurement techniques or worse, that the papers were pseudoscience.
There is a point where such a strategy becomes untenable. If you can only maintain your position by arguing everybody except yourself is misguided (or a fraud), without backing your statements up with peer reviewed papers, you have to start to question your own position.
Peter.
Peter Roessingh:
I apologise that I have been unable to reply to your post at August 25, 2012 at 12:56 pm until now. If you are a regular reader of WUWT then you will know I often have such absences.
For the record, I absolutely refute your following unjustifiable and completely untrue assertions aimed at me.
Untruth #1
You say I am claiming:
“There’s uncertainty, therefore we know nothing, therefore don’t believe what anyone says about the data”.
There is no possible excuse for that lie because I have twice refuted it saying;
Untruth #2
You say:
NO! ABSOLUTELY NOT!
The papers you cite make no assessment of the effects of the samples not being random. Indeed, they cannot do that because there is no known way to make such an assessment.
“Correcting” wrong data prior to processing it may make the results of the processing more or less accurate, and there is no way to determine which it does. Hence, as I said, the “corrections” cannot be justified. Hence, as I explained, the adoption of any “methods” for the “corrections” is a “flawed” procedure.
Untruth #3
You say :
I quoted Shen at al 2012 that shows the sampling errors can be, -and have been- estimated. KR mentioned Weithmann 2011.
Effects of changing non-random samples cannot be assessed by any known method. A claim to make such an assessment is NOT an “estimate”: it is a guess with no possibility of validation.
Untruth #4
You say to me:
“Your called the Shen et all. paper “pseudoscientific nonsense” and based that very strong statement on a (distorted) representation of the last 9 lines of the paper.
If you read the paper you will see that Shen et all. *do* estimate the errors (hence your claim they are unknown is false).”
Indeed, they do claim to assess effects of non-random samples. Any such claim is pseudoscientific nonsense because there is no known way to do it. Correlations certainly do not.
Untruth #5
You say to me:
“You dismiss this argument by boldly stating that Hansen & Lebedeff’s results are also wrong”
NO! I explained how and why they are wrong. Also, I pointed out that no other team which provides estimates of global temperature accepts the silly arguments of Hansen & Lebedeff because they, too, know those arguments are wrong.
Untruth #6
You say to me:
“There is a point where such a strategy becomes untenable. If you can only maintain your position by arguing everybody except yourself is misguided (or a fraud), without backing your statements up with peer reviewed papers, you have to start to question your own position.”
That is offensive in the extreme.
At August 20, 2012 at 5:23 am (n.b it was addressed to you) I cited my earlier post at August 19, 2012 at 2:32 pm which explained how the Team used nefarious method to prevent publication of a paper with me as Lead Author that addressed these issues. I linked to the UK Parliamentary Record where an explanation of the problems is spelled out.
Richard
Richard,
You wrote:
“Indeed, they [Shen at al 2012] do claim to assess effects of non-random samples. Any such claim is pseudoscientific nonsense because there is no known way to do it.”
So we have at least established that Shen at al 2012 provide a method to assess effects of non-random samples, and that you claim that their method is nonsense.
As proof for this claim you provide a linked to the UK Parliamentary Record and a reference to an draft of a paper with the title: “A call for revision of Mean Global Temperature (MGT) data sets”
I failed to find any argument in your sources that relate to the methods used by Shen at al 2012. Can you cite the lines in your document that are relevant, and explain in more detail what is wrong with the method of Shen at al? If I misunderstood you, can you point me to another source to back up your strong claim that Shen et al are wrong and write ‘pseudoscientific nonsense” ?
Peter.