Deltoid

John Quiggin has another post on the right wing attack on science, this time describing the Australian front. Chris Mooney has great article in the The American Prospect about James Inhofe’s part in the attack on science.

And Iain Murray is at it again. He has a post where he refers to graph on the left, saying that it is one of the most important elements in the debate, and writing:

“The fact that the ten hottest years happened since 1991 may well be an artifact of the collapse in the number of weather monitoring stations contributing to the global temperature calculations following the fall of communism (see graph).”

As I’ve said before, I’m reluctant to comment on global warming because many others are better informed on the matter, but in the case of Murray’s graph, helps me. Even though I’m not an expert, it took me all of ten seconds to think of way to test to see if the increase was an artifact of the change in the weather stations reporting. All you have to is produce another graph of average temperature just using the weather stations that have data for the whole period. If this graph shows a similar increase, then Murray’s suggestion is proven false. If it doesn’t show an increase, then Murray’s suggestion is proven true. And if you have the data to produce this graph, then you have the data to produce the graph that tests his suggestion.

There are three possibilities:

1. Murray didn’t think of this really obvious test. In this case he isn’t competent to write about global warming.
2. The test was done and Murray knows that it showed that his suggestion was false. In this case it would not be honest for him to present his suggestion the way he did.
3. The test was done and Murray knows that it showed that his suggestion was true. If this was this case, why wouldn’t he say so?

Update: In comments, Christopher Enckell provides the source of the graph Murray showed: a paper by Ross McKitrick. McKitrick writes:

Figure 3 shows the total number of stations in the GHCN and the raw (arithmetic) average of temperatures for those stations. Notice that at the same time as the number of stations takes a dive (around 1990) the average temperature (red bars) jumps. This is due, at least in part, to the disproportionate loss of stations in remote and rural locations, as opposed to places like airports and urban areas where it gets warmer over time because of the build-up of the urban environment. This poses a problem for users of the data. Someone has to come up with an algorithm for deciding how much of the change in average temperature post-1990 is due to an actual change in the climate and how much is due to the change in the sample. When we hear over and over about records being set after 1990 in observed global temperatures this might mean the climate has changed, or it means an inadequate adjustment is being used, and there is no formal way to decide between these.

I’m stunned. As I wrote above, it took me ten seconds to think of way to test if the increase was due to a change in the sample and McKitrick writes that “there is no formal way to decide”. It would appear that my possibility 1 applies to both Murray and McKitrick.

[This correspondence started with an email from McKitrick commenting on this post. I’ve edited it to remove most of the quoted text from previous emails. Further discussion is here.] (more…)

The graph above, which Iain Murray claimed showed that

“The fact that the ten hottest years happened since 1991 may well be an artifact of the collapse in the number of weather monitoring stations contributing to the global temperature calculations following the fall of communism (see graph)”

comes from this paper by Ross McKitrick. McKitrick recently was in the news for publishing a controversial paper that claimed that an “audit” of the commonly accepted reconstruction of temperatures over the past 1000 years was incorrect, so I thought it would be interesting to “audit” McKitrick’s graph.

I should first caution readers that I am not an expert in this area—I’m a computer scientist, not a climatologist. In other words, I’m no better qualified to comment on this than McKitrick. McKitrick writes:

“The main problem in the debate over what the Global Temperature is doing is that there is no such thing as a Global Temperature. Temperature is a continuous field, not a scalar, and there is no physics to guide reducing this field to a scalar, by averaging or any other method. Consequently the common practice of climate measurement is an ad hoc approximation of a non-existent quantity.”

This is untrue. Average temperature has a real, physical meaning. For example, if I have one kg of water at 20 degrees and another at 30 degrees, then their average temperature is 25 degrees. This is the temperature I would get if I mixed the water.

McKitrick then reproduces this graph (figure 2) (from GISS), describing it as “NASA’s version of this simulacrum”. He claims that a decreases in the number of weather stations is “problematic”, writing:

“In the early 1990s, the collapse of the Soviet Union and the budget cuts in many OECD economies led to a sudden sharp drop in the number of active weather stations.”

However, the graph he reproduces that shows the drop gives a different reason:

“The reasons why the number of stations in GHCN drop off in recent years are because some of GHCN’s source datasets are retroactive data compilations (e.g. World Weather Records) and other data were created or exchanged years ago.”

I looked at the GHCN data and while the number of weather stations in the former Soviet Union did drop from about 270 to 100, but the total number fell from 5000 to 2700 so the decrease there was only a small factor in the overall decrease.

McKitrick next refers to his figure at the top of this post:

“Figure 3 shows the total number of stations in the GHCN and the raw (arithmetic) average of temperatures for those stations. Notice that at the same time as the number of stations takes a dive (around 1990) the average temperature (red bars) jumps. This is due, at least in part, to the disproportionate loss of stations in remote and rural locations, as opposed to places like airports and urban areas where it gets warmer over time because of the build-up of the urban environment.”

I downloaded the raw GHCN temperature data from here, and tried to reproduce McKittrick’s graph by plotting the number of stations and the average temperature of all stations for each year. If you want to check my work, the program I wrote to do the calculations can be downloaded here. The graph above is reasonably similar to McKitrick’s graph. The biggest difference is that the right-hand vertical scale in McKittrick’s graph is clearly incorrect. The number peaked at 6,000, not 14,000 as his figure 3 indicates. (He actually has the correct number in his figure 2, which was copied from another paper.) Just taking the average of all the station temperatures is a rather poor way to estimate the global average temperature, since regions with a large number of stations will count for far too much in the global average. However, even this crude way of computing the average shows significant warming in the 90s. McKitrick’s graph is also rather misleading since the GISS graph above is not calculated this way—the stations are weighted so that regions get the correct weighting.

To test McKittrick’s claim that the warming in 90’s might have been caused by the decline in the number of stations, all I had to do was just consider the stations that has measurements for every year from 1980 to 2000. The average temperature of those stations is shown as the green line in the graph above, while the average of all stations is in red. The blue line is the average temperature shown in the GISS graph. Note that all three lines show significant warming in the 90s. Whether you analyse the data in a crude way or a sophisticated way you still see warming. It is true that after correcting for the change in the number of stations, the warming is less, but it actually agrees better with the average temperature shown in the GISS graph. If you look at Hansen et al’s paper

that describes how the GISS graph was constructed, you will find that of course they noticed and accounted for the change in the number of stations:

“Sampling studies discussed below indicate that the decline in number of stations is unimportant in regions of dense coverage, although the estimated global temperature change can be affected by a few hundredths of a degree.”

McKitrick does not acknowledge this or cite this paper.

The outcome of my analysis was just as I expected—if correcting for the change in the number of stations had removed the warming trend, Murray and McKitrick would already have told us about it.

In an email, McKitrick claimed that there were two problems with my test:

First, there was a change post-1990 in the quality of data in stations still operating, as well as the number of stations. Especially in the former Soviet countries after 1990, the rate of missing monthly records rose dramatically. So you need a subset of stations operating continuously and with reasonably continuous quality control.

However, the Soviet stations are only a small percentage of the total, so don’t make much difference. And of course, if you look at Hansen et al you find that they have extensive checks on the data quality.

McKitrick continued:

Second, if in this subset you observe an upward trend comparable to the conventional global average, in order to prove that this validates the global average you have to argue that the subset is a randomly chosen, representative sample of the whole Earth. Of course if this were true the temperature people would only use the continuously-available subset for their data products. It isn’t, which is why they don’t. It would leave you with a sample biased towards US and European cities, so it is not representative of the world as a whole. The large loss in the number of stations operating (50% in a few years) was not random in a geophysical sense, it was triggered by economic events, in which stations were closed in part if they were relatively costly to operate or if the country experienced a sudden loss of public sector resources. One can conjecture what the effect of that discontinuity was, but to test the conjecture, at some point you have to guess at what the unavailable data would have said if they were available. Because of that, I cannot see how one can devise a formal test of the representativeness of the subsample.

Now this is just wrong. You don’t need a random sample to estimate the temperature across the Earth’s surface. Temperatures tend to be quite similar at places that are close to each other. You just need to space your stations over the Earth’s surface and you have a representative sample. So you can actually estimate what the temperature would have been in the missing stations and you can actually test to see how representative the sample is and in fact Hansen et al wrote:

Sampling studies discussed below indicate that the decline in number of stations is unimportant in regions of dense coverage, although the estimated global temperature change can be affected by a few hundredths of a degree.

McKitrick, however, did not cite this paper.

McKitrick concludes:

None of this means that those researchers with access to the raw data can’t propose and implement such tests as you propose (I wish they would).

Gee, McKitrick implies that researchers hadn’t done such tests, when, as we have already seen, they had done such tests. When I challenged him on this, he contradicted himself:

I do not claim that adjustments are not being made, only that there is no formal test of their adequacy.

Presumably he talks of “formal” tests so he doesn’t have to count the tests that have actually been done. (Our entire email exchange is here.)

Chris Mooney notes that McKitrick defended Inhofe’s claim that “manmade global warming is the greatest hoax ever perpetrated”

I’m not the only one who has found problems with McKitrick’s writings on climate. Robert Grumbine has some comments on another McKitrick paper:

He was fooling around with correlating per capita income with the observed temperature changes. He concluded that the warming was a figment of climatologists imaginations, as there was a correlation between money and warming. ‘Obviously’ this had to be due to wealth creating the warming in the dataset, rather than any climate change—his conclusion.

Along the way he:

1. selected a subset of temperature records
   1. without using a random method
   2. without paying attention to spatial distribution
   3. without ensuring that the records were far enough apart to be independant—ok, I shouldn’t say ‘he’ did it, because he didn’t. He blindly took a selection that his student made and which was—to my eyes—distributed quite peculiarly.
2. Treated the records as being independant (I know William knows this, but for some other folks: Surface temperature records are correlated across fairly substantial distances—a few hundred km. This is what makes paleoreconstructions possible, and what makes it possible to initialize global numerical weather prediction models with so few observations.)
3. Ignored that we do expect, and have reason to expect that the warming will be higher in higher latitudes
4. Ignored that the wealthy countries are at higher latitudes

Hence my calling it fooling around rather than work or study. He was, he said, submitting that pile of tripe* to a journal.
*pile of tripe being my term, not his.

and

His main conclusion was regarding climate change—namely that there isn’t any. His secondary conclusion was that climate people studying climate data were idiots. Neither of those is a statement of economics, so my knowledge of economics is irrelevant (though, in matter of fact, it is far greater than his knowledge of climate; this says little, as his displayed level doesn’t challenge a bright jr. high student.).

Grumbine’s correspondence with McKitrick is here

Last week I wrote about Paul Georgia’s review of Essex and McKitrick’s Taken by Storm. Based on their book, Georgia made multiple incorrect statements about the physics of temperature. Of course, it might have just been that Georgia misunderstood their book. Fortunately Essex and McKitrick have a briefing on their book, and while Georgia mangles the physics even worse than them, they do indeed claim that there is no physical basis to average temperature. They present two graphs of temperature trends that purport to show that you can get either a cooling trend or a warming trend depending on how you compute the average. McKitrick recently was in the news for publishing a controversial paper that claimed that an audit of the commonly accepted reconstruction of temperatures over the past 1000 years was incorrect, so it only seems fair to audit Essex and McKitrick’s graphs. As we will see, both of their graphs are wrong, and their results go away when the errors are corrected.

In their briefing, Essex and McKitrick claim that physics provides no basis for defining average temperature and:

“In the absence of physical guidance, any rule for averaging temperature is as good as any other. The folks who do the averaging happen to use the arithmetic mean over the field with specific sets of weights, rather than, say, the geometric mean or any other. But this is mere convention.”

Physics does, in fact, provide a basis for defining average temperature. Just connect the two systems that you want to average by a conductor. Heat will flow from the hotter system to the colder one until the temperatures are equalized. The final temperature is the average. That average will be a weighted arithmetic mean of the original temperatures. Which is why the folks doing the averaging use weighted arithmetic means rather than the geometric mean.

Of course, even if they were right and there were other equally valid ways to calulate the average temperature, they still need to show that it actually makes a difference, so Essex and McKitrick present an example the purports to show that whether you use the arithmetic or some other mean can affect whether or not you find a warming trend. They constructed the graph on the left by taking monthly observations from ten weather stations and averaging them with the arithmetic mean. They found an overall warming trend of +0.17 degree Celsius per decade.

They next present a graph where they

“treat each month as a vector of 10 observed temperatures, and define the aggregate as the norm of the vector (with temperatures in Kelvins). This is a perfectly standard way in algebra to take the magnitude of a multidimensional array. Converted to an average it implies a root mean square rule.”

Note that nobody, but nobody, averages temperatures this way. Anyway, when they calculated the trend they found an overall cooling trend of +0.17 degree Celsius per decade.

They triumphantly conclude:

“The same data can’t imply global warming and cooling can they? No they can’t. The data don’t imply global anything. That interpretation is forced on the data by a choice of statistical cookery. The data themselves only refer to an underlying temperature field that is not reducible to a single measure in a way that has physical meaning. You can invent a statistic to summarize the field in some way, but your statistic is not a physical rule and has no claim to primacy over any other rule.”

I looked at their graphs and something seemed wrong to me. Their root mean square average gives almost the same answer as the arithmetic mean. For example, it gives the mean of 0 and 20 degrees Celsius as 10.2 instead of 10 degrees. It didn’t make sense to me that it could make as big a difference to the trend as what they found.

McKitrick kindly sent me a spreadsheet containing the data they used and I almost immediately saw where they had gone wrong. You see, some stations had missing values, months where no temperature had been recorded. When calculating the root mean square they treated the missing values as if they were measurements of 0 degrees. This is incorrect, since the temperature was not actually zero degrees. Because the overall average temperature was positive this meant that the root mean square was biased downwards when there were missing observations. And since there were more missing values in the second half of the time series, this produced a spurious cooling trend.

When calculating the arithmetic mean they treated missing values differently. If only eight stations had observations in a given month, they just used the average of those stations. This isn’t as obviously wrong as the other method they used, but the stations in colder climates were more likely to have missing observations, so this biased the average upwards and produced a spurious warming trend.

I filled in the missing values by using the observation for that station from the same month in the previous year and recalculated the trends. Now both mean and root mean square averaging produced the same trend of -0.03, which is basically flat. When analysed correctly, their data shows neither warming or cooling, regardless of which average is used. The different trends they found were not because of the different averaging methods, but because of inconsistent treatment of missing data.

I also calculated the trend with their root mean square average and ignoring missing values, and with the arithmetic mean and replacing missing values with zero (spreadsheet is here). As the table below shows, the averaging method made almost no difference, but treating missing values incorrectly does.

Trend
Missing values	Mean	Root Mean Square
Ignored	0.16	0.15
Treated as 0 degrees	-0.15	-0.17
Previous year used	-0.03	-0.03

I emailed McKitrick to point out that arithmetic mean and root mean square did not give different results. He replied:

Thanks for pointing this out. It implies there are now 4 averages to choose from, depending on the formula used and how missing data are treated, and there are no laws of nature to guide the choice. The underlying point is that there are an infinite number of averages to choose from, quite apart from the practical problem of missing data.

Incredible isn’t it? He still doesn’t understand basic thermodynamics. And he seems to think that are no laws of nature to guide us in estimating the missing values so that it is just as valid to treat them as zero as any other method, even for places where the temperature never gets that low.

Mann, Bradley and Hughes have published some corrections to the supplementary information for the famous hockey stick graph showing the temperature record of the last 1000 years. They say that the errors do not affect their published results. This could explain why McKitrick and McIntyre could not reproduce their results, but McKitrick is continuing to insist that Mann’s graph is wrong.

McKitrick has also published some errata. Unlike Mann’s error McKitrick’s error affects his results:

Figure 3 in the Cooler Heads Briefing on TBS contains an error. Tim Lambert of Australia has pointed out that missing data were handled differently between Figures 2 and 3, and when this is fixed the example no longer illustrates the intended point. The point (that the trend can change if the averaging rule is changed) is shown in this Revised Spreadsheet. Our thanks to Tim Lambert for pointing out the error.

(The post where I pointed out the error is here.)

I looked at his revised spreadsheet. This time he has dealt with missing values consistently and it does indeed show a warming trend when the usual arithmetic mean is used and a cooling trend when their unusual root-mean square is used. So how did he manage this? After all, as I showed in my earlier post, the root-mean square in Kelvins gives almost he same answer as the regular average. Well, McKitrick invented his own temperature scale. McKitrick modestly did not give it a name, but I am dubbing it the McKitrick scale in honour of its creator. To help you gain familiarity with this new scale, the form below lets you convert between degrees McKitrick and the old-fashioned degrees Celsius and degrees Fahrenheit. Just type a number into any of the boxes and press “Enter”.

°M     °F     °C

Anyway, in his revised spreadsheet McKitrick takes the root-mean-square average of temperatures measured in degrees McKitrick. This way of averaging temperatures gives some rather odd results. For example, the RMS average of -10°M and -10°M is not -10°M as you might expect, but +10°M. Needless to say no-one actually uses RMS averages of temperatures in the McKitrick or any other scale, and no-one in their right mind would use them.

So revising their original example to use degrees McKitrick means the trend is different for different averaging methods? Well, no. If you take their original example and use the root-mean-square-in-degrees-McKitrick average, you still get the same trend. In the revised spreadsheet McKitrick has also changed the set of weather stations used. Even then it makes little difference to the size of trend—it changes an insignificant warming trend to an insignificant cooling trend.

To summarize: even if you use a weird root-mean-square-in-degrees-McKitrick average it makes little difference to the size of any warming or cooling trend you might see.

I’ve had a closer look at the “bombshell” paper that Patrick Michaels described like this:

After four years of one of the most rigorous peer reviews ever, Canadian Ross McKitrick and another of us (Michaels) published a paper searching for “economic” signals in the temperature record. … The research showed that somewhere around one-half of the warming in the U.N. surface record was explained by economic factors, which can be changes in land use, quality of instrumentation, or upkeep of records.

There seems to be some problems with their work. To understand them you need to understand the two different ways of measuring angles.

This angle is one degree.	This angle is one radian.

Can you spot the difference?

If you do calculations and get degrees and radians mixed up, you get the wrong answer. Which is what McKitrick did. His analysis included a variable cosablat, which was supposed to be the cosine of absolute latitude. Trouble is, the software he used expects angles to be measured in radians, his data has latitude in degrees, and he didn’t convert from degrees to radians. Consequently, every single number he calculates is wrong. I corrected the error and reran his regressions. The sizes of the “economic” signals were greatly reduced. They no longer “explain” half of the surface warming trend. Removing the effects of the economic variables now just reduces the warming trend for his sample from 0.27 degrees/decade to 0.18 degrees/decade, which is very close to the warming trend for the whole globe.

Even this overstates his results—McKitrick did not calculate statistical significance correctly—his analysis incorrectly assumes that each observation comes from a different country. His “economic signals” may not even be statistically significant.

Somehow these errors were not detected during the “four years of one of the most rigorous peer reviews ever”. Nor did peer review by Climate Research detect the problems. Unfortunately, this is not the first time that the peer review process at Climate Research has failed. Last year, several of the editors resigned after another defective paper slipped through peer review. Oddly enough, that paper also attempted to cast doubt on anthropogenic global warming.

If you’re new here: In previous postings on Ross McKitrick I have shown how he messed up an analysis of the number of weather stations, showed he knew almost nothing about climate, flunked basic thermodynamics, couldn’t handle missing values correctly and invented his own temperature scale.

Update: John Quiggin confirms my findings.

This article (scroll to page 13) by Clare Goodess has more on the problems with the review process at Climate Research that led to the resignation of half of the editors. Chris de Freitas, the same editor that published the previous improperly reviewed papers, also published the McKitrick and Michaels effort.

Update 28/8: A poster on the Internet Infidels forums demonstrates how easy it is to verify that McKitrick got it wrong.

McKitrick has added a correction his page describing his paper that purports to find economic signals that I posted on here. McKitrick admits to mixing up degrees and radians but claims:

There was a small error in the calculation of regression coefficients in our paper. Our conclusions were not affected by this problem

As I noted in my post, correcting the error halves the size of the economic signal in the warming trend, reducing it from 0.16 (out of 0.27) to 0.09. McKitrick’s correction states:

Outside the dry/cold regions the measured temperature change is significantly (previous: primarily ) influenced by economic and social variables.

That’s quite a difference, so how can he say that their conclusions were not affected? Well, all the conclusion says is that there were socioeconomic effects, without mentioning their size. The size of the effects, which change substantially, are only mentioned in the body. And the “bombshell” nature of the paper touted by Michaels et al in their TCS article depends on socioeconomic effects being the primary cause of the warming trend, something that McKitrick has now retracted.

McKitrick has also failed to correct or even acknowledge another serious problem in his paper—he has not corrected his standard errors for clustering. This is required because his socioeconomic variables are all the same for the stations in the same country. This means he will find some variables to be statistically significant when they are not really so.

Nor has McKitrick explained why he decided to take the cosine of the absolute latitude in the first place. Calculating it correctly makes no difference to the model, while calculating in incorrectly makes the model fit worse. There does not seem to be any theoretical or empirical justification for this change to his model. As John Quiggin observes:

a trawl back through the files makes it pretty clear that this error was not exactly an innocent mistake. It seems pretty clear that McKitrick tried some regressions with (absolute) latitude as the explanatory variable, didn’t like the results he got and switched to the cosine (note that, if you were starting here, you wouldn’t need to take the absolute value, since cosine is a symmetric function). Because of the degrees-radians mistake, this variable came out insignificant, as desired, and McKitrick didn’t do the checks that would have revealed the error. Asymmetric error-checking is a standard problem with cherry picking, as illustrated by the work of John Lott.

In this column, Richard Muller claims that McKitrick and McIntyre have shown that the hockey stick graph is an “artifact of poor mathematics”. If you have been following the global warming debate this claim should look familiar, because McKitrick and McIntyre made the same claim last year as well. So what’s new? Well, last year they claimed that the hockey stick was the product “collation errors, unjustifiable truncations of extrapolation of source data, obsolete data, geographical location errors, incorrect calculations of principal components, and other quality control defects.” Now they are saying that the hockey stick is the product of improper normalization of the data. This is an improvement on their previous claims, since it seems that it will be reasonably simple to test. William Connolley has looked at the data and thinks M&M are probably wrong:

But (having read their paper) I now think I understand what they think the problem is (aside: they complain about data issues with some series but I think this is beside the point: the main point they are talking about is below), and I think that they are probably wrong, based on reading MBH’s Fortran (aside: Fortran is a terrible language for doing this stuff, they should use a vector language like IDL). But anyway:

Lets for the moment assume for simplicity that these series run from 1000 (AD) to 1980. MBH want to calibrate them against the instrumental record so they standardise them to 1902–1980. 1902–1980 is the “training period”.

What M&M are saying (and Muller is repeating) is (and I quote): the data

“were first scaled to the 1902-1980 mean and standard deviation, then the PCs were computed using singular value decomposition (SVD) on the transformed data…”

they complain that this means that:

“For stationary series in which the 1902–1980 mean is the same as the 1400–1980 mean, the MBH98 method approximately zero-centers the series. But for those series where the 1902–1980 mean shifts (up or down) away from the 1400–1980 mean, the variance of the shifted series will be inflated.”

This is a plausible idea: if you take 2 series, statistically identical, but when one trends up at the end where the other happens to be flat, and you compute the SD of just the end bit, and then scale the series to this SD, then you would indeed inflate the variance of the up trending series artificially. But hold on a minute… this is odd… why would you scale the series to the SD? You would expect to scale the series by the SD. Which would, in fact, reduce the variance of upwards trending series. And also, you might well think, shouldn’t you take out a linear trend over 1902–1980 before computing the SD?

So we need to look at MBH’s software, not M&M’s description of it. MBH’s software is here, and you can of course read it yourself… Fortran is so easy to read…

What they do is (search down over the reading in data till you get to 9999 continue):

1. remove the 1902-1980 mean
2. calc the SD over this period
3. divide the whole series by this SD, point by point

At this point, the new data are in the situation I described above: datasets that trend upwards at the end have had their variance reduced not increased. But there is more…

4. remove the linear trend from the new 1902-1980 series
5. compute the SD again for 1902-1980 of the detrended data
6. divide the whole series by this SD.

This was exactly what I was expecting to see: remove the linear trend before computing the SD.

Then the SVD type stuff begins. So… what does that all mean? It certainly looks a bit odd, because steps 1–3 appear redundant. The scaling done in 4–6 is all you need. Is the scaling of 1–3 harmful? Not obviously.

Perhaps someone would care to go through and check this. If I haven’t made a mistake then I think M&M’s complaints are unjustified and Nature correct to reject their article.

My previous experience with McKitrick gives me no confidence in his work. David Appell is also sceptical of this latest attack on the hockey stick.

There seems to be some confusion about McKitrick’s latest attempt to refute global warming. For instance, Andrew Sullivan thinks that McKitrick’s famous degrees-radians screw up is part of this latest attempt. However, McKitrick claims to have refuted global warming in several different ways and the degrees-radians screw up was a in a different paper to his latest one. I decided to draw up a table to help folks sort them out.

Authors	Summary	Consequences if he is right	Status
Essex and McKitrick	There is no physical basis to average temperature.	No global warming because there is no such thing as global temperature.	Failed—the whole field of thermodynamics has not been thrown out.
McKitrick and McIntyre version 1	The hockey stick graph was the product of “collation errors, unjustifiable truncations of extrapolation of source data, obsolete data, geographical location errors, incorrect calculations of principal components, and other quality control defects.”	The global warming we are seeing might be natural.	Mann et al publish a correction to the supplementary information for the hockey stick graph. They say that the errors do not affect their published results.
McKitrick and Michaels	Surface temperature record is contaminated by economic influences.	No evidence that there is global warming going on	Results go away after errors such as confusing degrees with radians are corrected.
McKitrick and McIntyre version 2	hockey stick is the product of improper normalization of the data.	The global warming we are seeing might be natural.	Jury is still out, but it does not look promising for McKitrick

Meanwhile, James Annan agrees with Connolley’s concerns about M&Mv2:

Having had a quick glance at this and their papers, I think I agree with you. In fact it appears that we can add not knowing the difference between multiplication and division, to the already impressive list of blunders that M&M have made. They even seem to talk about adding the mean to the time series rather than subtracting it too. I might check this more carefully over the next few days if no-one else beats me to it.

Brad DeLong also seems to agree.

But Connolley argues—I think correctly—that McKitrick and McIntyre are simply confused: the normalizations diminish the influence of series that show a recent uptrend.

Excellent news. Some climate scientists have started a blog called RealClimate, something sorely needed to correct the disinformation put about by Tech Central Station and the like. I hope they can do for climate science what The Panda’s Thumb does for evolution.

One of the first posts is by Rasmus Benestad on the McKitrick-Michaels paper that got degrees and radians mixed up. Years ago, when McKitrick was first working on the paper Robert Grumbine observed that McKitrick had

Treated the records as being independant (I know William knows this, but for some other folks: Surface temperature records are correlated across fairly substantial distances—a few hundred km. This is what makes paleoreconstructions possible, and what makes it possible to initialize global numerical weather prediction models with so few observations.)

Unfortunately, even in the published version McKitrick still treated the records as independent, and Benestad shows that their model is invalid.

And check out my McKitrick guide if you’re having trouble keeping all your McKitrick studies straight. This one is the one that purported to show that the surface temperature record was contaminated by economic influences, not the one that purported to destroy the hockey stick. (Michael Mann is a contributor to RealClimate, so we might see something there as well.)

Update:Chris Mooney is also pleased.

Chris Mooney reports on the latest attack on the hockey stick. Joe Barton, chair of the Committee on Energy and Commerce has sent out a set of letters, supposedly “requesting information regarding global warming studies”. However, if you look at the letters, you will find that the only study he is interested is Mann, Bradley and Hughes from way back in 1998 (the “hockey stick” study); and the questions are loaded ones of the form: “Can you explain why you made all the errors detailed in Mcintyre and McKitrick’s Energy and Environment paper?”

It is probably just a coincidence that Joe Barton has received $574,000 in campaign contributions from the oil and gas industry, more than any other congressman.

Update: Reaction from:

• Atrios: “The appropriate response to this is ‘Bite me, Congessman’.”

• teece: “This is the kind of tactic you would have expected in Soviet Russia.”

• Kevin Drum: “Joe Barton is harassing scientists who have the temerity to publish results he finds inconvenient”

• Josh Rosena: “This is an anti-climate science Congressman trying to get material for a smear against Mann.”

• john m. lynch: “The interference continues.”

• Paul from Wizbang: “I’m guessing the creators of the global warming hockey stick are –shall we say– pucked.”

• Steve Verdon: “there seems to be a pattern with regards to climate scientists and their willingness to share data”

• Mark Trodden: “Dear Congressman Barton, … I am extremely concerned by the tone and implications of these letters and consider them a thinly-veiled attempt to intimidate honest scientists into avoiding work that might lead to an opinion different from the current administration on topics that are politically sensitive.”

• de Selby: “I expect industry whore congressmen to create false controversies. When they abuse their power at the expense of individual citizens, I call it McCarthyism”

• David Appell: “This is unprecedented, as far as I know, and has the air of a scientific witch-hunt.”

• PZ Meyers: “Joe Barton is an arrogant pissant”

• James Annan: “I suspect that a witch-hunt like this could have serious repercussions for scientific research in the USA”

• HangLeft: “it fits the pattern we’ve come to know and expect from the Republicans: when facts get in the way of their bankrupt ideology, they cover up those facts and intimidate the messenger.”

• Coturnix: “With all the misuse of science by the current Administration I still never expected the Lysenko-style persecution of scientists whose data do not support the party line. Yet, this day has come. The USA has its Lysenko, and his name is Joe Barton.”

• Will: “Barton is known for being a staunch opponent of the Kyoto Protocol and referring to climate change provisions as “odorous” measures that would be kept out of energy legislation.”

• William Connolley: “the TAR was quite cautious in its use of the MBH record (which was entirely appropriate, it being fairly new then). So attacking Mann (or, being more charitable, attacking MBH98) is pointless, from a scientific standpoint. But then, this isn’t about science, its about $.”

• Kevin Vranes: “the letters are primarily meant to embarrass and harass and the hearings, if they ever happen, could be seen as an abuse of power.”

• Sylvia S Tognetti: “This is more Funk from the Swamp emanating from the Hill that arises from the Foggy Bottom, and is not worthy of a serious response.”

• back40: “the usual suspects are in full shriek mode claiming abuse of power and political motivations. It’s not abuse, it’s congress doing its job for a change.”

My previous post also got some reactions:

• Hans Erren calls my post the start of an “ad hom and smear campaign”. Oh, I think the ad-hom-and-smear campaign started long ago.

• Steve Verdon: “Notice that Lambert is his usual dishonest self and not pointing out that environmental groups fund Real Climate.” Yes, because it must cost like $100 per year to run the site.

Update: Steve McIntyre has his own roundup. He claims:

Many posters do not distinguish between the PC codes for tree ring which are on Mann’s FTP site and the code for the rest of the calculation, which Mann has refused to provide. We are obviously aware of the code on the site, since we published an article discussing it and specifically cited the URL. I’ve made this distinction on several occasions in very specific terms, but people like Tim Lambert seem unable to fathom the distinction.

This is, of course, untrue. I have never said that Mann has released all of his code. He has, however, released the data, the algorithm, and some of the code. Perhaps McIntyre is unable to fathom the distinction between “code” and “algorithm”.

McIntyre continues:

Also one more time, Wahl and Ammann have not replicated anything that we had not already done.

Wahl and Ammann don’t seem to think so:

Ammann and Eugene Wahl of Alfred University have analyzed the Mann-Bradley-Hughes (MBH) climate field reconstruction and reproduced the MBH results using their own computer code. They found the MBH method is robust even when numerous modifications are employed. … Ammann and Wahl conclude that the highly publicized criticisms of the MBH graph are unfounded.

The Wall Street Journal has a reputation for publishing excellent news pages and mendacious editorial pages. Now, an investigation by Environmental Science and Technology on an WSJ front page article on McIntyre and McKitrick makes you wonder if the editorial pages are influencing the news reporting. You should read the whole thing, but here are a few extracts:

But the harshest critic of the whole issue is former Wall Street Journal page-one editor, Frank Allen. He now directs the Institutes for Journalism & Natural Resources in Missoula, Mont. When asked to read the front-page article, he described it to ES&T as a “public disservice” littered with “snide comments” and “unsupported assumptions”. He says he does not understand how the story got past the editors.

“It was a strange story ’cause it had this bizarre undertone of being investigative but it didn’t investigate,” says Allen. “And this piece—what I thought was bothersome about it—it purported to be authoritative, and it’s just full of holes.” …

Tom Crowley, a professor of earth systems science at Duke University, says he tried to put the brakes on the story when contacted by Regalado. “I did go into a long explanation for why McIntyre’s work isn’t great shakes, as some people would like to believe. That didn’t come out in the article, but that doesn’t mean that what he wrote wasn’t edited by the higher-ups.”

The resulting bias in the article, he says, confirmed his suspicions that the Wall Street Journal slants their news on climate change. “They acted like I suspected,” he says. “And on their op-ed page their writers get free shots at global warming.” Crowley’s name did not appear in the article. …

But what began as an interview, Mahlman explains, quickly evolved into a spirited debate. Whenever he pointed out the importance of Mann’s work, Regalado would try to shift the discussion back to McIntyre and McKitrick. “I told him that as far as I know they’re quacks. That kinda riled him.”

Mahlman says he also pointed out that numerous other studies have confirmed Mann’s original results. “Then he started to get squirmy because I was saying that [even] if we didn’t have the hockey stick and the paleorecord, we have an absolutely reliable record over the last 100 years or so, and it’s warming like crazy.” We didn’t have thermometers 1000 years ago, but we do now, Mahlman says.

In the end, Mahlman was not mentioned in the article.

Regalado emailed me and I offered to talk to him about McKitrick but he never got back to me. Given how he ignored Crowley and Mahlman I guess it wouldn’t have mattered anyway.

And here’s von Storch on McIntyre and McKitrick’s paper:

We sent in a comment that the glitch [McIntyre] detected in Mann’s paper is correct, but it doesn’t matter. It’s a minor thing.

Hat tip: Dave Roberts via Bob

Eli Rabbett has encountered Essex and McKitrick’s briefing about their book Taken by Storm (which I criticised here) and is not impressed:

with so many dubious claims that one hardly knows where to begin.

Eli Rabett continues to try to puzzle out the weird statements about temperature in Taken by Storm:

Reading the several versions of Essex and McKitrick anyone familiar with thermodynamics (heat engines, blackbodies, chemical reactions, etc.) will start to scratch their heads. One peculiar statement after another appears dealing with temperature and other basic stuff. It turns out that Essex is using a rather special definition of temperature for a non-equilibrium radiation field. If you want to read about it look up “How hot is radiation”, C. Essex, D.C. Kennedy and R.S. Berry, Am. J. Phys. 71 (2003) 969 .

Read his post for the details of Essex’s definition. Rabett concludes:

So, pretty clearly Essex is talking about non-equilibrium thermodynamics, and probably playing telephone with McKitrick

Another round of telephone gives you Louis Hissink, who citing Essex and McKitrick as an authority, last year wrote:

If we now examine the ranking of sportsmen and have the class best sportsman, we could place Ian Thorpe as a swimmer, Mark Waugh as cricketer, and Dick Johnson as race-car driver, and we could then associate as best = Ian Thorpe=Mark Waugh= Dick Johnson. This is an entirely permissable equivalence and has nothing to do with quantities. It is a subjective ranking and equivalence. Temperature is the same type of category. Heat content is not. (I am using Australian sportsmen as examples). So mathematically A) above is a nonesense if 1 Deg C is regarded as a quantity - but not if it is regarded as a category of subjective value, say similar to the sports category of “Best”. This nonesense comes about from the logical fallacy that if my cat has four legs, and my dog has four legs, then my cat is a dog. Therefore temperature is not a measure of heat content. Temperature is therefore not a quantity, it is a class category, conveniently described as a number. It is a means by which we rank hotness. It cannot be mathematically processed. However heat units, or in the modern jargon, energy units, can be mathematically processed. Unfortunately we have specified temperature as a numerical ranking, and this has unfortunately resulted in those in the social sciences assuming that as it is a number, we can do maths on it. (It goes without saying that temperatures can be manipulated mathematically but it is a meaningless procedure). There might an argument that air is air, and that it’s specific heat is so and so, and we can count temperatures of air and make a meaningful estimate of it’s temperature as an average. No, because it’s specific heat is dependent on its composition, since casual inspection of both Hydrogen and Carbon Dioxide, two components of air, shows that these two gases have extremely different specific heats. If you wish to compute the temperature average of air at two localities, you must first of all demonstrate that both samples of air are compositionally identical, but it is irrelevant because temperature is not a quantity - it is a category of subjective hotness.

Update: Hissink responds:.

I suspect the confusion in Tim’s audience also arises by the use of numbers to rank objects in terms of hotness. You could use the Roman system of numbers, eg, IVXIII, to rank objects in terms of temperature, only to discover this is not a terribly useful way of doing things. However assigning numbers to a ranking system does not necessarily mean that manipulating those numbers has any intrinsic meaning.

Umm, what number is IVXIII?

Eli Rabett dissects Essex and McKitrick’s incompetence with averages:

Unfortunately, either Essex or McKitrick or both do not understand zero and negative numbers. You know where my money is.

Read his post to see why.

Mind you, Steve McIntyre isn’t convinced that there is anything wrong with their argument because “Chris Essex is an accomplished thermodynamicist” and

my impression was that your counter-argument was mostly just belligerence. While it’s possible that they made a mistake. I very much doubt whether Essex made a trivial mistake and your argument seemed to be assuming that it was trivial.

Oddly enough, Mann, Bradley and Hughes are accomplished scientists but this hasn’t stopped McIntyre from arguing that they made trivial mistakes.

Eli Rabett has scored Essex and McKitrick’s briefing for Taken By Storm at Global Warming Skeptic Bingo. Alas, they don’t win. I reckon their book will do better. For example, they get another box at bingo with this passage (from page 134 of their book):

There are enemies of T-Rex who think that the satellite average is the true one and the surgface average is so much crap. The Knights of the White Boxes respond that the satellite averages are very silly and no one should pay any attention to them. The Defenders of the Satellites went before the Grand Council of the American Meteorological Society and were given golden medallions for their work. Then they rode forth and smote the Knights of the White Boxes. Then all the people cried out in confusion and the High Priests of the National Academy of Sciences inquired of the oracle. It issued a report declaring that everyone is right and we should all just get along.