Jeff Id emailed me today, to ask if I wanted to post this with the caveat “it’s very technical, but I think you’ll like it”. Indeed I do, because it represents a significant step forward in the puzzle that is the Steig et all paper published in Nature this year ( Nature, Jan 22, 2009) that claims to have reversed the previously accepted idea that Antarctica is cooling. From the “consensus” point of view, it is very important for “the Team” to make Antarctica start warming. But then there’s that pesky problem of all that above normal ice in Antarctica. Plus, there’s other problems such as buried weather stations which will tend to read warmer when covered with snow. And, the majority of the weather stations (and thus data points) are in the Antarctic peninsula, which weights the results. The Antarctic peninsula could even be classified under a different climate zone given it’s separation from the mainlaind and strong maritime influence.
A central prerequisite point to this is that Steig flatly refused to provide all of the code needed to fully replicate his work in MatLab and RegEM, and has so far refused requests for it. So without the code, replication would be difficult, and without replication, there could be no significant challenge to the validity of the Steig et al paper.
Steig’s claim that there has been “published code” is only partially true, and what has been published by him is only akin to a set of spark plugs and a manual on using a spark plug wrench when given the task of rebuilding an entire V-8 engine.
In a previous Air Vent post, Jeff C points out the percentage of code provided by Steig:
“Here is an excellent flow chart done by JeffC on the methods used in the satellite reconstruction. If you see the little rectangle which says RegEM at the bottom right of the screen, that’s the part of the code which was released, the thousands of lines I and others have written for the rest of the little blocks had to be guessed at, some of it still isn’t figured out yet.”
With that, I give you Jeff and Ryan’s post below. – Anthony
Posted by Jeff Id on May 20, 2009
I was going to hold off on this post because Dr. Weinstein’s post is getting a lot of attention right now it has been picked up on several blogs and even translated into different languages but this is too good not to post.
Ryan has done something amazing here, no joking. He’s recalibrated the satellite data used in Steig’s Antarctic paper correcting offsets and trends, determined a reasonable number of PC’s for the reconstruction and actually calculated a reasonable trend for the Antarctic with proper cooling and warming distributions – He basically fixed Steig et al. by addressing the very concern I had that AVHRR vs surface station temperature(SST) trends and AVHRR station vs SST correlation were not well related in the Steig paper.
Not only that he demonstrated with a substantial blow the ‘robustness’ of the Steig/Mann method at the same time.
If you’ve followed this discussion whatsoever you’ve got to read this post.
RegEM for this post was originally transported to R by Steve McIntyre, certain versions used are truncated PC by Steve M as well as modified code by Ryan.
Ryan O – Guest post on the Air Vent
I’m certain that all of the discussion about the Steig paper will eventually become stale unless we begin drawing some concrete conclusions. Does the Steig reconstruction accurately (or even semi-accurately) reflect the 50-year temperature history of Antarctica?
Probably not – and this time, I would like to present proof.
I: SATELLITE CALIBRATION
As some of you may recall, one of the things I had been working on for awhile was attempting to properly calibrate the AVHRR data to the ground data. In doing so, I noted some major problems with NOAA-11 and NOAA-14. I also noted a minor linear decay of NOAA-7, while NOAA-9 just had a simple offset.
But before I was willing to say that there were actually real problems with how Comiso strung the satellites together, I wanted to verify that there was published literature that confirmed the issues I had noted. Some references:
(NOAA-11)
Click to access i1520-0469-59-3-262.pdf
(Drift)
(Ground/Satellite Temperature Comparisons)
Click to access p26_cihlar_rse60.pdf
The references generally confirmed what I had noted by comparing the satellite data to the ground station data: NOAA-7 had a temperature decrease with time, NOAA-9 was fairly linear, and NOAA-11 had a major unexplained offset in 1993.
Let us see what this means in terms of differences in trends.
The satellite trend (using only common points between the AVHRR data and the ground data) is double that of the ground trend. While zero is still within the 95% confidence intervals, remember that there are 6 different satellites. So even though the confidence intervals overlap zero, the individual offsets may not.
In order to check the individual offsets, I performed running Wilcoxon and t-tests on the difference between the satellites and ground data using a +/-12 month range. Each point is normalized to the 95% confidence interval. If any point exceeds +/- 1.0, then there is a statistically significant difference between the two data sets.
Note that there are two distinct peaks well beyond the confidence intervals and that both lines spend much greater than 5% of the time outside the limits. There is, without a doubt, a statistically significant difference between the satellite data and the ground data.
As a sidebar, the Wilcoxon test is a non-parametric test. It does not require correction for autocorrelation of the residuals when calculating confidence intervals. The fact that it differs from the t-test results indicates that the residuals are not normally distributed and/or the residuals are not free from correlation. This is why it is important to correct for autocorrelation when using tests that rely on assumptions of normality and uncorrelated residuals. Alternatively, you could simply use non-parametric tests, and though they often have less statistical power, I’ve found the Wilcoxon test to be pretty good for most temperature analyses.
Here’s what the difference plot looks like with the satellite periods shown:
The downward trend during NOAA-7 is apparent, as is the strange drop in NOAA-11. NOAA-14 is visibly too high, and NOAA-16 and -17 display some strange upward spikes. Overall, though, NOAA-16 and -17 do not show a statistically significant difference from the ground data, so no correction was applied to them.
After having confirmed that other researchers had noted similar issues, I felt comfortable in performing a calibration of the AVHRR data to the ground data. The calculated offsets and the resulting Wilcoxon and t-test plot are next:
To make sure that I did not “over-modify” the data, I ran a Steig (3 PC, regpar=3, 42 ground stations) reconstruction. The resulting trend was 0.1079 deg C/decade and the trend maps looked nearly identical to the Steig reconstructions. Therefore, the satellite offsets – while they do produce a greater trend when not corrected – do not seem to have a major impact on the Steig result. This should not be surprising, as most of the temperature rise in Antarctica occurs between 1957 and 1970.
II: PCA
One of the items that we’ve spent a lot of time doing sensitivity analysis is the PCA of the AVHRR data. Between Jeff Id, Jeff C, and myself, we’ve performed somewhere north of 200 reconstructions using different methods and different numbers of retained PCs. Based on that, I believe that we have a pretty good feel for the ranges of values that the reconstructions produce, and we all feel that the 3 PC, regpar=3 solution does not accurately reproduce Antarctic temperatures. Unfortunately, our opinions count for very little. We must have a solid basis for concluding that Steig’s choices were less than optimal – not just opinions.
How many PCs to retain for an analysis has been the subject of much debate in many fields. I will quickly summarize some of the major stopping rules:
1. Kaiser-Guttman: Include all PCs with eigenvalues greater than the average eigenvalue. In this case, this would require retention of 73 PCs.
2. Scree Analysis: Plot the eigenvalues from largest to smallest and take all PCs where the slope of the line visibly ticks up. This is subjective, and in this case it would require the retention of 25 – 50 PCs.
3. Minimum explained variance: Retain PCs until some preset amount of variance has been explained. This preset amount is arbitrary, and different people have selected anywhere from 80-95%. This would justify including as few as 14 PCs and as many as 100.
4. Broken stick analysis: Retain PCs that exceed the theoretical scree plot of random, uncorrelated noise. This yields precisely 11 PCs.
5. Bootstrapped eigenvalue and eigenvalue/eigenvector: Through iterative random sampling of either the PCA matrix or the original data matrix, retain PCs that are statistically different from PCs containing only noise. I have not yet done this for the AVHRR data, though the bootstrap analysis typically yields about the same number (or a slightly greater number) of significant PCs as broken stick.
The first 3 rules are widely criticized for being either subjective or retaining too many PCs. In the Jackson article below, a comparison is made showing that 1, 2, and 3 will select “significant” PCs out of matrices populated entirely with uncorrelated noise. There is no reason to retain noise, and the more PCs you retain, the more difficult and cumbersome the analysis becomes.
The last 2 rules have statistical justification. And, not surprisingly, they are much more effective at distinguishing truly significant PCs from noise. The broken stick analysis typically yields the fewest number of significant PCs, but is normally very comparable to the more robust bootstrap method.
Note that all of these rules would indicate retaining far more than simply 3 PCs. I have included some references:
Click to access North_et_al_1982_EOF_error_MWR.pdf
I have not yet had time to modify a bootstrapping algorithm I found (it was written for a much older version of R), but when I finish that, I will show the bootstrap results. For now, I will simply present the broken stick analysis results.
Fig. 7: Broken Stick Analysis on AVHRR data.
The broken stick analysis finds 11 significant PCs. PCs 12 and 13 are also very close, and I suspect the bootstrap test will find that they are significant. I chose to retain 13 PCs for the reconstruction to follow.
Without presenting plots for the moment, retaining more than 11 PCs does not end up affecting the results much at all. The trend does drop slightly, but this is due to better resolution on the Peninsula warming. The rest of the continent does not change if additional PCs are added. The only thing that changes is the time it takes to do the reconstruction.
Remember that the purpose of the PCA on the AVHRR data is not to perform factor analysis. The purpose is simply to reduce the size of the data to something that can be computed. The penalty for retaining “too many” – in this case – is simply computational time or the inability for RegEM to converge. The penalty for retaining too few, on the other hand, is a faulty analysis.
I do not see how the choice of 3 PCs can be justified on either practical or theoretical grounds. On the practical side, RegEM works just fine with as many as 25 PCs. On the theoretical side, none of the stopping criteria yield anything close to 3. Not only that, but these are empirical functions. They have no direct physical meaning. Despite claims in Steig et al. to the contrary, they do not relate to physical processes in Antarctica – at least not directly. Therefore, there is no justification for excluding PCs that show significance simply because the other ones “look” like physical processes. This latter bit is a whole other discussion that’s probably post worthy at some point, but I’ll leave it there for now.
III: RegEM
We’ve also spent a great deal of time on RegEM. Steig & Co. used a regpar setting of 3. Was that the “right” setting? They do not present any justification, but that does not necessarily mean the choice is wrong. Fortunately, there is a way to decide.
RegEM works by approximating the actual data with a certain number of principal components and estimating a covariance from which missing data is predicted. Each iteration improves the prediction. In this case (unlike the AVHRR data), selecting too many can be detrimental to the analysis as it can result in over-fitting, spurious correlations between stations and PCs that only represent noise, and retention of the initial infill of zeros. On the other hand, just like the AVHRR data, too few will result in throwing away important information about station and PC covariance.
Figuring out how many PCs (i.e., what regpar setting to use) is a bit trickier because most of the data is missing. Like RegEM itself, this problem needs to be approached iteratively.
The first step was to substitute AVHRR data for station data, calculate the PCs, and perform the broken stick analysis. This yielded 4 or 5 significant PCs. After that, I performed reconstructions with steadily increasing numbers of PCs and performed a broken stick analysis on each one. Once the regpar setting is high enough to begin including insignificant PCs, the broken stick analysis yields the same result every time. The extra PCs show up in the analysis as noise. I first did this using all the AWS and manned stations (minus the open ocean stations).
Fig. 8: Broken stick analysis on manned and AWS stations, regpar = 8.
Fig. 9: Broken stick analysis on manned and AWS stations, regpar=12.
I ran this all the way up to regpar=20 and the broken stick analysis indicates that 9 PCs are required to properly describe the station covariance. Hence the appropriate regpar setting is 9 if all the manned and AWS stations are used. It is certainly not 3, which is what Steig used for the AWS recon.
I also performed this for the 42 manned stations Steig selected for the main reconstruction. That analysis yielded a regpar setting of 6 – again, not 3.
The conclusion, then, is similar to the AVHRR PC analysis. The selection of regpar=3 does not appear to be justifiable. Additional PCs are necessary to properly describe the covariance.
IV: THE RECONSTRUCTION
So what happens if the satellite offsets are properly accounted for, the correct number of PCs are retained, and the right regpar settings are used? I present the following panel:
Fig. 10: (Left side) Reconstruction trends with the post-1982 PCs spliced back in (Steig’s method).
(Right side) Reconstruction trends using just the model frame.
RegEM PTTLS does not return the entire best-fit solution (the model frame, or surface). It only returns what the best-fit solution says the missing points are. It retains the original points. When imputing small amounts of data, this is fine. When imputing large amounts of data, it can be argued that the surface is what is important.
RegEM IPCA returns the surface (along with the spliced solution). This allows you to see the entire solution. In my opinion, in this particular case, the reconstruction should be based on the solution, not a partial solution with data tacked on the end. That is akin to doing a linear regression, throwing away the last half of the regression, adding the data back in, and then doing another linear regression on the result to get the trend. The discontinuity between the model and the data causes errors in the computed trend.
Regardless, the verification statistics are computed vs. the model – not the spliced data – and though Steig did not do this for his paper, we can do it ourselves. (I will do this in a later post.) Besides, the trends between the model and the spliced reconstructions are not that different.
Overall trends are 0.071 deg C/decade for the spliced reconstruction and 0.060 deg C/decade for the model frame. This is comparable to Jeff’s reconstructions using just the ground data, and as you can see, the temperature distribution of the model frame is closer to that of the ground stations. This is another indication that the satellites and the ground stations are not measuring exactly the same thing. It is close, but not exact, and splicing PCs derived solely from satellite data on a reconstruction where the only actual temperatures come from ground data is conceptually suspect.
When I ran the same settings in RegEM PTTLS – which only returns a spliced version – I got 0.077 deg C/decade, which checks nicely with RegEM IPCA.
I also did 11 PC, 15 PC, and 20 PC reconstructions. Trends were 0.081, 0.071, and 0.069 for the spliced and 0.072, 0.059, and 0.055 for the model. The reason for the reduction in trend was simply better resolution (less smearing) of the Peninsula warming.
Additionally, I ran reconstructions using just Steig’s station selection. With 13 PCs, this yielded a spliced trend of 0.080 and a model trend of 0.065. I then did one after removing the open-ocean stations, which yielded 0.080 and 0.064.
Note how when the PCs and regpar are properly selected, the inclusion and exclusion of individual stations does not significantly affect the result. The answers are nearly identical whether 98 AWS/manned stations are used, or only 37 manned stations are used. One might be tempted to call this “robust”.
V: THE COUP DE GRACE
Let us assume for a moment that the reconstruction presented above represents the real 50-year temperature history of Antarctica. Whether this is true is immaterial. We will assume it to be true for the moment. If Steig’s method has validity, then, if we substitute the above reconstruction for the raw ground and AVHRR data, his method should return a result that looks similar to the above reconstruction.
Let’s see if that happens.
For the substitution, I took the ground station model frame (which does not have any actual ground data spliced back in) and removed the same exact points that are missing from the real data.
I then took the post-1982 model frame (so the one with the lowest trend) and substituted that for the AVHRR data.
I set the number of PCs equal to 3.
I set regpar equal to 3 in PTTLS.
I let it rip.
Fig. 11: Steig-style reconstruction using data from the 13 PC, regpar=9 reconstruction.
Look familiar?
Overall trend: 0.102 deg C/decade.
Remember that the input data had a trend of 0.060 deg C/decade, showed cooling on the Ross and Weddel ice shelves, showed cooling near the pole, and showed a maximum trend in the Peninsula.
If “robust” means the same answer pops out of a fancy computer algorithm regardless of what the input data is, then I guess Antarctic warming is, indeed, “robust”.
———————————————
Code for the above post is HERE.
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I’ll wade in too. We should be very, very, VERY careful and thorough and ALWAYS default to respect before ever even hinting at charging fraud.
It’s just slimy to do otherwise. Irresponsible, too.
We owe it to ourselves to behave in the same manner we would have others behave in. I know that I would be enraged to the point of distraction if someone were to smugly claim fraud on my part because they cursorily read a critical article. That’s the way you poison wells and burn bridges.
There are a lot of men and women of good will who have been duped or have inadvertently come to the wrong conclusions. We need to acknowledge this (there but for the grace of Dog go I…), and leave the door open to them to come back into the fold. It’s hard enough to do as is.
Mark
What is happening at the Southern Hemisphere?
In New Zealand, winter has arrived skipping Autumn this year.
http://www.iceagenow.com/2009_Other_Parts_of_the_World.htm
And heavy hail covering a surfer resort and low temperatures have convinced some people that a new ice age is due.
http://mickysmuses.blogspot.com/2009/05/global-warming-hits-my-home-town.html
Anthony is celebrated as the big “Star” promoting “climate common sense”.
Off Topic but I just thought I would bring this story to everyones attention. This one crossed my home page today and the claims in item number 2 caught my eye. I am very skeptical about “super pollen”. Also the longer season item in 1. is suspect. I noticed they didn’t comment on how great the cold spring this year will be to reduce allergies.
http://healthandfitness.sympatico.msn.ca/HealthyLiving/ContentPosting?newsitemid=547102&feedname=RODALE-WOMENSHEALTH&show=False&number=0&showbyline=True&subtitle=&detect=&abc=abc&date=False
I’ve spent a lot of time over at CA, and so, as a result, I did not find this post to be “too technical” at all–if fact, quite the opposite: very cogent and well-presented, IMO. My thanks, also, for all your time, effort, and ingenuity. Let’s face it: It’s the efforts of people like you who enable the rest of us to see through the darkness, at least to some extent.
“”” John Silver (06:26:57) :
Lucy Skywalker (04:08:46) :
………….
“We maybe should establish an alternative Nobel prize here… though really, I’d rather the real Nobel Prize got freed from its corruption by work like this.”
Surely you are not mistaken the peace prize (always a bad joke from Oslo, Norway) with the real prices? (Royal Swedish Academy of Science, Stockholm, Sweden) “””
You mean you believe there’s a difference ? I think history shows that both are pretty much popularity contests.
Just take the case of Albert Einstien for example; the vast majority of people know of Einstein only because of E=Mc^2 and his special and general theories of relativity (as well as his Jewishness of course), yet none of that work won him a Nobel prize despite its impact on the course of Science.
He actually got his Nobel prize for his work on the Photo-Electric effect; about which the general public knows virtually nothing.
So far as I know, even today there is NO classical physical explanation for the PE effect; it can only be expained via Quantum mechanics which Einstein himself virtually rejected.
Even our latest Wunderkind; Energy Secrtetary Steven Chu was awarded his Physics Nobel for work on Optical Trapping; something which was invented (discovered if you will) fifteen years before Chu did his work, by Arthur Ashkin at Bell Labs, Holmdel. Chu was taught by Ashkin; yet neither the teaher nor any of Chu’s many co-workers who worked on the project was ever recognised.
It’s all about who knows whom, and who is the better politician; not who does the best science.
With regard to the present essay; and figs (1) and (2), I would love to see the signal filter that turns the black chicken scratchings into the blue graph (with forgiveness for the pixel quantization noise).
Does anyone really believe that the blue is representative of the black ?
George
“That’s because you do not understand what these results mean. That’s OK, of course. Nobody here believes you would answer any other way, even if you did understand.
Flanagan(X) = { for all x, Flanagan(X) = [snip] }”
is there life on mars?
Well I see I made a misteak and should have put: E=mc^2
And I had some exchanges with Steig before all the fat hit the shin; so I think it’s a little bit rash to be throwing around words like science fraud. Having Mann’s name associated with that work gets me blinking.
I believe that both Steig et al, and the current essay suffer from the same problem.
That is the attempt to use statistical mathematics to rectify a more fundamental problem; namely a failure to observe the rules for sampled Data Systems. No amount of Statistics; or Central Limit theorems, can buy you a reprieve from aliassing noise due to Nyquist violation.
Why attempt to create information where none exists.
George
This is great work. I have been following it every day a the Air Vent and at Climate Audit. I will be shocked if it gets anywhere in the MSM.
A coworker sent me a copy of a report a month ago and I finally got around to reading it. It is from SRI Consulting Business Intellegence, “The US Consumer and Global Warming”. It is scary:
Quote—-: In early 2008, a nonprofit environmental advocacy organization, ecoAmerica, lined up a half dozen partners to launch a research effort, the American Climate Values Survey (ACVS), to measure the impact that a year of global warming messaging had had on U.S. attitudes toward global warming and related issues.
The ACVS showed that only 18% of U.S. consumers strongly agreed that global warming is happening, is harmful, and is caused by humans.
Researchers at SRIC-BI use cultural gender to measure the extent to which consumers view an issue, product, or attitude as stereotypically feminine (or tender) versus masculine (or tough). Findings showed that U.S. consumers perceive concern for global warming in culturally feminine/tender terms, whereas they perceive denying global warming as a problem in strictly masculine/tough terms. One reason for the lack of masculine associations with global warming concerns and solutions may be that the problem does not allow for brute-force solutions typical of masculine problem-solving strategies. Further contributing to the divide, current messaging themes surrounding global warming often appear in feminine terms, focusing on the nurturing issue of protecting children and future generations from the harm of warming.
In addition to revealing masculine denial of the problem, the ACVS findings revealed a type of consumer who primarily denies global warming’s existence on intellectual or scientific grounds. Global warming “contrarians” are outspoken, informed consumers who instill public doubt about the science of global warming through blogs, newspaper articles, and face-to-face discussions. Because of their outsize influence, contrarians became a key messaging target in 2008, and ecoAmerica is launching a major media-watch group to monitor and counter claims made by contrarians. ————–End quote
I have begun to feel that we “contrarians” have more imagination than most. The AGW propagandists are using untruthful pictures of fuzzy animals and pretty landscapes and claiming they are all going to be destroyed by CO2. But they get the pictures up. When I hear that power plants are not going to be built, I don’t need to see pictures of the destroyed industry, unemployment, suicides, or burned out houses where the poor died while trying to keep warm with a fire. I can see it all in my mind. I doubt that the average person does. They worry more about a perfectly safe polar bear warming himself in the sun on an ice flow than they do about the unemployed people down the street.
To get back on topic this report by Jeff Id needs a picture. Perhaps one of a frozen penguin chick with a caption about how cold it still is in Antarctica:
http://tinyurl.com/qxyxo5
Here is what the AGW propagandists do:
http://news.nationalgeographic.com/news/bigphotos/51400691.html
The pictures that appear here at WattsUpWithThat of “how not to measure temperature, number xx” are great. When policy topics come up such as biofuels, besides the charts and graphs think about adding a picture of fertilizer runoff from marginal cropland or a starving child from somewhere where the crops have failed and imports are too expensive.
@Leif
Thanks for the clarification. I withdraw the implication of fraud. It’s still bad science, though.
I used to be good at doing math in my head. One day our 6th grade science class was asked to calculate the distance light travels in a year. I correctly calculated the answer but received no credit because I didn’t show my work.
Steig’s methods of creating data where none exist are dubious. Still, he should be asked to show all his work. His paper should have been rejected outright and this also calls into question the peer-review process.
George E. Smith (10:21:17) :
Ryan’s work as I see it is basically a better representation of the spatial distribution of ground data. Infilling between stations based on covariance with the satellite data. By releasing the RegEM TTLS requirement that the satellite grid data is fixed in the reconstruction it becomes a more appropriate blend of the two sets allowing the acutal measured temperatures to direct the reconstruction. This is why it and other higher order RegEM matches well with the area weighted reconstructions which included only surface station data.
As far as fraud, Rayn and I have no intention of calling Steig et al. fraud and nobody else I know of who’s worked on this paper would call it fraud. I don’t mind calling it incorrect though and do have suspicions about choices made during publication – see the link in my comments above.
I think Mann was chosen as a coauthor for his work with RegEM on the M08 hockeystick.
” David Ball (23:08:49) :
Last night I awoke to find 2 feet of ice in my bed. Both of them belonged to my wife. 8^]”
Has to make it to finalist for QOTW.
DaveE.
From the post- “So without the code, replication would be difficult, and without replication, there could be no significant challenge to the validity of the Steig et al paper.”
I respectfully disagree. Without replication, there is no validity. period. It is totally against the scientific process to call a finding valid if it cannot be fully explained and recreated independent of the original scientist. Anyone remember cold fusion?
“Malcolm (02:17:46) :
As suspected it is the Steig methodology that is flawed.
Well done to everyone for a great effort.
Anthony should consider giving you guys a medal for this work.”
They’ll get their medals in due course, just not from Oh, Bummer.
DaveE.
“George E. Smith (10:21:17) :
Why attempt to create information where none exists.”
Jim Hansen, (Henson?)/GISS & Phil Jones/HADCRU have been doing it for years, why stop now?
DaveE.
doesn’t this sound familiar ?
“…Schön was, in effect, doing science backwards: working out what his conclusions should be, and then using his computer to produce the appropriate graphs…”
http://www.telegraph.co.uk/scienceandtechnology/5345963/The-scientific-fraudster-who-dazzled-the-world-of-physics.html
“Leon Brozyna (04:07:47) :
Perhaps Steig et al should take their books and consider taking part in a reality show. Something along the lines of FOX Broadcasting’s Hell’s Kitchen. Perhaps they could compete against Mann et al.”
This was Mann et all! All that changed was the name on the cover.
DaveE.
“I think Mann was chosen as a coauthor for his work with RegEM on the M08 hockeystick.”
Is not that a very good reason to be suspicious, having in mind that Mann has a pretty good track record of performing scientific frauds, such as Hockey Stick reconstruction? In Steig’s chooses I would be much more reluctant to team up with proven fraudster.
Moreover, your assertion that Mann was probably invited because of his statistical skills in constructing the Hockey Stick is even more worrying and suggests that he was employed to help making a new fraud, by using the same fraudulent statistical tricks, abundantly documented by McIntyre and McKitrick. As far as I could see Steve McIntyre considered Steig et al methodology similar to Mann’s in MBH 98 and 99.
George E. Smith (10:09:06) :
Does anyone really believe that the blue is representative of the black ?
No. The blue is simply a linear model. No one believes that a linear model is an accurate model for near-surface temperatures. It is used because it is simple and provides a gross comparison between different analyses. Because it is a poor model of the underlying physical process, the confidence limits associated with it are quite large.
Your comments about the Nyquist sampling theorem are not quite apt in this case. There is no attempt – either in Steig or in the above post – to reconstruct the high-frequency portion of the temperature signal. The attempt is to reconstruct the low-frequency portion, the wavelength of which is determined by the interval over which you want to determine the trend. Given that Steig was not concerned with trends of less than about 15 years, there is no need to sample at high rates.
Will be aliasing and interpolation errors? Absolutely. A portion of that concern is eliminated by monthly averages, which is essentially a low-pass filter to remove the diurnal and higher frequency components. A portion of that concern is eliminated by the use of anomalies rather than absolute temperatures to (partially) remove the annual and higher frequency components. A portion of that is the use of linear trends, which are insensitive to wavelengths much shorter than the period you are trending.
The purpose of statistics, when used properly, is to place bounds on those errors to avoid conclusions that over-reach the data.
Gary P (10:35:33) : —–
That was a sad article you quoted and doomed to failure actually. It tries to continue the “deniers are dumb cavemen with clubs”meme, but then is forced to acknowledge that “Global warming “contrarians” are outspoken, informed consumers”. Because their analysis is not internally consistent, I suspect that they will have trouble formulating any kind of coherent response more effective the equivalent of the play ground taunt “your just a doodyhead”.
A better analysis on their part would have relied more on the yin/yang argument of passivity/receptiveness vs drive/action. But if they had used that then they would have to admit that AGW types tend to be herd followers and ‘contrarians’ tend to be questioners. Probalby not so good for the ol’ PR campaign.
Re: highaltitude (23:10:11) :
Accurate measurements are far from simple, and when as is the case with the satellite data, each instrument has its own idiosyncracies and each satellite has its own orbital history, the data has to be calibrated. If you took chemistry in college or high school, you will remember converting your measurements to STP conditions in order to insure comparable results. There is no difference here. Even a simple measurement like the length of a board can be complex if the required accuracy is even moderately high, e,g, you want your corners square. As far as reading the thermometer goes, that may seen simple enough, but when there are many thermometers scattered over thousands of kilometers, and hundreds of meters difference in altitude, each reading could be accurate to within the instrument limits. But you want compare them continentally, you want to extract global trends in those readings. That is another matter, and satellites don’t USE thermometers, so you need to achieve comparison between disparate TYPES of instruments. It isn’t pseudo science at all, though it does look as Steig et al. might have stopped work to soon.
Ryan O and Jeff Id, fraud or not, the results of Steig et al. paper are not robust and are biased. For a paper that made the cover of Nature, and was realyed by the MSM the world over, we all are entitled to a bit more explanation from both authors and journal. The questions are 1) Who are the Nature reviewers who bought Steig results at face value without doing what you guys did i.e. replicate. The same reviewers failed to correct mistakes that Steve McIntyre found 2) How long would it take and what chances of success would a proper Nature comment about Steig’s paper have? Would the comment receive the same media coverage? 3) Bias or fraud, when are scientists who systematically are involved in controversial papers -i.e. Mann and Team- going to be brought to a peer body and have to explain their actions and face consequences? Your work shows it is time to make these people accountable once for all.
Manfred (12:24:16),
Very good link, thanks for posting. I’ve been following Schön’s and Hwang’s deception for a few years now, and from what I can see, the problem is getting worse. The current withholding of taxpayer funded data and methodology by many climate scientists is unacceptable. It invites fraud.
It wouldn’t be surprising in the least if Schön’s name was replaced with Michael Mann’s, their stories are so similar. It is outrageous that the IPCC political scientists have so uncritically accepted Mann’s fraudulent hockey stick chart — simply because Mann was saying what they wanted to hear. That’s not science, it’s advocacy of a predetermined result.
Ron de Haan (09:41:47) :
What is happening at the Southern Hemisphere?
In New Zealand, winter has arrived skipping Autumn this year.
http://www.iceagenow.com/2009_Other_Parts_of_the_World.htm
It certainly has. We’re getting strong southerlies straight from the Antarctic and it doesn’t feel like its warming much to me.
Not only has it been very cold, but also very wet. Dunedin airport has doubled its previous rainfall record for May, with a week to go before month’s end and the official start of winter. Snow has fallen in the higher parts of town for the third time this year; the start of the week saw some schools closed along with two of the three main routes into town. The main Dunedin-Christchurch highway was closed by flooding and the power companies are spilling water in record volumes; the hydro lakes are already over-full. Queensland (Australia) has also seen extensive flooding.
This is what might be expected in July and, even then, not every year. The ski-fields could have opened more than a month early if staffing had been available.
The farming community believe that its going to be a very hard winter. Good job we all understand that its only weather.
Re: “Leif Svalgaard (07:35:48): It is only fraud if there was fraudulent intent, and that has not been shown to be the case.”
Intent has so many different meanings and applications that it can be considered ambiguous unless used in context that provides specific meaning. See for example:
http://en.wikipedia.org/wiki/Fraud
This link provides several varying meanings for intent as used in the context of civil, academic and criminal rules.
http://article.nationalreview.com/?q=NjZkNGZiODM0YjNmODNkMWFhZDU5Mzk2ZDMwNGRmMDQ
This article by Andrew McCarthy (prosecutor of 1993 World Trade Towers bombers) discusses the differences between “general intent” and “specific intent” in a criminal law context concerning what is torture and the requirements to prove it for water boarding.
Steig et al could commit fraud in numerous ways, including falsification of data, conducting research using empirical data that is deceptively organized to produce false outcomes or opinions that are intended to mislead the public, fail to disclose conflicts of interest that can bias the research, refuse to disclose data and analysis in order to prevent detection of deceptive work, or violate laws, rules and procedures that are intended to protect public from fraudulent or misleading research by providing transparency.
Academic fraud, intellectual dishonesty, and scientific misconduct are forms of fraud that should discredit research and those responsible. Intent in those contexts requires proof that the actors knew what they were doing, and that the outcome was what a reasonable person who performed the acts would expect. If the outcome of the misconduct misleads those who rely upon the research, intent to commit fraud is proved.