Guest Post by Willis Eschenbach
In my last post, I talked about the “secret sauce”, as I described it, in the Neukom et al. study “Inter-hemispheric temperature variability over the past millennium”. By the “secret sauce” I mean the method which is able to turn the raw proxy data, which has no particular shape or trend, into a consensus-approved hockeystick … but in truth, I fear I can’t reveal all of the secret sauce, because as is far too common in climate science, they have not released their computer code. However, they did provide some clues, along with pretty pictures.
Figure 1. The overview graphic from Neukom2014. Click to embiggen.
So what did they do, and how did they do it? Well, don your masks, respirators, coveralls, and hip boots, because folks, we’re about to go wading in some murky waters …
From my last post, Figure 2 shows the mean of the proxies used by Neukom, and the final result of cooking those proxies with their secret sauce:
Figure 2. Raw proxy data average and final result from the Neukom2014 study. Note the hockeystick shape of the result.
Let me start with an overview of the whole process of proxy reconstruction, as practiced by far too many paleoclimatologists. It is fatally flawed, in my opinion, by their proxy selection methods.
What they do first is to find a whole bunch of proxies. Proxies are things like tree ring widths, or the thickness of layers of sediment, or the amounts of the isotope oxygen-18 in ice cores—in short, a proxy might be anything and everything which might possibly be related to temperature. The Neukom proxies, for example, include things like rainfall and streamflow … not sure how those might be related to temperature in any given location, but never mind. It’s all grist for the proxy mill.
Then comes the malfeasance. They compare the recent century or so of all of the proxies to some temperature measurement located near the proxy, like say the temperature of their gridcell in the GISS temperature dataset. If there is no significant correlation between the proxy and the gridcell temperature where the proxy is located, the record is discarded as not being a temperature proxy. However, if there is a statistically significant correlation between the proxy and the gridcell temperature, then the proxy is judged to be a valid temperature proxy, and is used in the analysis.
Do you see the huge problem with this procedure?
The practitioners of this arcane art don’t see the problem. They say this procedure is totally justified. How else, they argue, will we be able to tell if something actually IS a proxy for the temperature or not? Here is Esper on the subject:
However as we mentioned earlier on the subject of biological growth populations, this does not mean that one could not improve a chronology by reducing the number of series used if the purpose of removing samples is to enhance a desired signal. The ability to pick and choose which samples to use is an advantage unique to dendroclimatology.
“An advantage unique to dendroclimatology”? Why hasn’t this brilliant insight been more widely adopted?
To show why this procedure is totally illegitimate, all we have to do is to replace the word “proxies” in a couple of the paragraphs above with the words “random data”, and repeat the statements. Here we go:
They compare the recent century or so of all of the random data
proxiesto some temperature measurement located near the random dataproxy, like say the temperature of their gridcell in the GISS temperature dataset. If there is no significant correlation between the random dataproxyand the gridcell temperature, the random dataproxyis discarded. However, if there is a statistically significant correlation between the random dataproxyand the gridcell temperature, then the random dataproxyis judged to be a valid temperature proxy, and is used in the analysis.
Now you see the first part of the problem. The selection procedure will give its blessing to random data just as readily as to a real temperature proxy. That’s the reason why this practice is “unique to dendroclimatology”, no one else is daft enough to use it … and sadly, this illegitimate procedure has become the go-to standard of the industry in proxy paleoclimate studies from the original Hockeystick all the way to Neukom2014.
The name for this logical error is “post-hoc proxy selection”. This means that you have selected your proxies, not based on some inherent physical or chemical properties that tie them to temperature, but on how well they match the data you are trying to predict …
The use of post hoc proxy selection in Neukom2014 is enough in itself to totally disqualify the study … but wait, it gets worse. I guess that comparing a proxy with the temperature record of the actual gridcell where it is physically located was too hard a test, and as a result they couldn’t find enough proxies random data that would pass that test. So here is the test that they ended up using, from their Supplementary Information:
We consider the “local” correlation of each record as the highest absolute correlation of a proxy with all grid cells within a radius of 1000 km and for all the three lags (0, 1 or -1 years). A proxy record is included in the predictor set if this local correlation is significant (p<0.05).
“Local” means within a thousand kilometers? Dear heavens, how many problems and misconceptions can they pack into a single statement? Like I said, hip boots are necessary for this kind of work.
First question, of course, is “how many gridcells are within 1,000 kilometres of a given proxy”? And this reveals a truly bizarre problem with their procedure. They are using GISS data on a regular 2° x 2° grid. At the Equator, there are no less than 68 to 78 of those gridcells whose centers are within 1,000 km of a given point, depending on the point’s location within the gridcell … so they are comparing their proxy to ABOUT 70 GRIDCELL VALUES!!! Talk about a data dredge, that about takes the cake … but not quite, because they’ve outdone themselves.
The situation on the Equator doesn’t take the cake once we consider say a proxy which is an ice core from the South Pole … because there are no less than 900 2° x 2° gridcells within 1000 kilometres of the South Pole. I’ve heard of tilting the playing field in your favor, but that’s nonsense.
I note that they may be a bit uneasy about this procedure themselves. I say this because they dodge the worst of the bullet on other grounds, saying:
The predictors for the reconstructions are selected based on their local correlations with the target grid. We use the domain covering 55°S-10°N and all longitudes for the proxy screening. High latitude regions of the grid are excluded from the correlation analysis because south of 55°S, the instrumental data are not reliable at the grid-point level over large parts of the 20th century due to very sparse data coverage (Hansen et al., 2010). We include the regions between 0°N and 10°N because the equatorial regions have a strong influence on SH temperature variability.
Sketchy … and of course that doesn’t solve the problem:
Proxies from Antarctica, which are outside the domain used for proxy screening, are included, if they correlate significantly with at least 10% of the grid-area used for screening (latitude weighted).
It’s not at all clear what that means. How do you check correlation with 10% of a huge area? Which 10%? I don’t even know how you’d exhaustively search that area. I mean, do you divide the area into ten squares? Does the 10% have to be rectangular? And why 10%?
In any case, the underlying issue of checking different proxies against different numbers of gridcells is not solved by their kludge. At 50°S, there are no less than one hundred gridcells within the search radius. This has the odd effect that the nearer to the poles that a proxy is located, the greater the odds that it will be crowned with the title of temperature proxy … truly strange.
And it gets stranger. In the GISS temperature data, each gridcell’s temperature is some kind of average of the temperature stations in that gridcell. But what if there is no temperature station in that gridcell? Well, they assign it a temperature as a weighted average of the other local gridcells. And how big is “local” for GISS? Well … 1,200 kilometres.
This means that when the proxy is compared to all the local gridcells, in many cases a large number of the gridcell “temperatures” will be nothing but slightly differing averages of what’s going on within 1,200 kilometres.
Not strange enough for you? Bizarrely, they then go on to say (emphasis mine):
An alternative reconstruction using the full un-screened proxy network yields very similar results (Supplementary Figure 20, see section 3.2.2), demonstrating that the screening procedure has only a limited effect on the reconstruction outcome.
Say what? On any sane planet, the fact that such a huge change in the procedure has “only a limited effect” on your results should lead a scientist to re-examine very carefully whatever they are doing. To me, the meaning of this phrase is “our procedures are so successful at hockeystick mining that they can get the same results using random data” … how is that not a huge concern?
Returning to the question of the number of gridcells, here’s the problem with looking at through that many gridcells to find the highest correlation. The math is simple—the more times or places you look for something, the more likely you are to find an unusual but purely random result.
For example, if you flip a coin five times, the odds of all five flips coming up heads are 1/2 * 1/2 * 1/2 * 1/2 * 1/2. This is 1/32, or about .03. This is below the normal 0.05 significance threshold usually used in climate science.
So if that happened the first time you flipped a coin five times, five heads in a row, you’d be justified in saying that the coin might be weighted.
But suppose you repeated the whole process a dozen times, with each sample consisting of flipping the same coin five times. If we come up with five heads at some point in that process, should we still think the coin might be loaded?
Well … no. Because in a dozen sets of five flips, the odds of five heads coming up somewhere in there are about 30% … so if it happens, it’s not unusual.
So in that context, consider the value of testing either random data or a proxy against a hundred gridcell temperatures, not forgetting about checking three times per gridcell to include the lags, and then accepting the proxy if the correlation of any one of those is above 0.05 … egads. This procedure is guaranteed to drive the number of false positives through the roof.
Next, they say:
Both the proxy and instrumental data are linearly detrended over the 1911-1990 overlap period prior to the correlation analyses.
While this sounds reasonable, they haven’t thought it all the way through. Unfortunately, procedure this leads to a subtle error. Let me illustrate it using the GISS data for the southern hemisphere, since this is the mean of the various gridcells they are using to screen their data:
Figure 3. GISS land-ocean temperature index (LOTI) for the southern hemisphere.
Now, they are detrending it for a good reason, which is to keep the long-term trend from influencing the analysis. If you don’t do that, you end up doing what is also known as “mining for hockeysticks”, because the trend of the recent data will dominate the selection process. So they are trying to solve a real problem, but look what happens when we do linear detrending:
Figure 4. Linearly detrended GISS land-ocean temperature index (LOTI) for the southern hemisphere.
All that this does is change the shape of the long-term trend. It does not remove the trend, it rises steadily after 1910. So they are still mining for hockeysticks.
The proper way to do this detrending is to use some kind of smoothing filter on the data to remove the slow swings in the data. Here’s a loess smooth, you can use others, the particular choice is not critical for these purposes:
Figure 5. Loess smooth of GISS land-ocean temperature index (LOTI) for the southern hemisphere.
And once we subtract that loess smooth (gold line) from the GISS LOTI data, here’s what we get:
Figure 6. GISS land-ocean temperature index (LOTI) for the southern hemisphere, after detrending using a loess smooth.
As you can see, that would put all of the proxies and data on a level playing field. Bear in mind, however, that improving the details of the actual method of post-hoc proxy selection is just putting lipstick on a pig … it’s still post-hoc proxy selection.
And since they haven’t done that, they are definitely mining for hockeysticks. No wonder that their proxy selection process is so meaningless.
From there, the process is generally pretty standard. They “calibrate” each proxy using a linear model to determine the best fit of the proxy to the temperature data from whichever of the 70 gridcells that the proxy got the best correlation with. Then they use some other portion of the data (1880-1910) to “validate” the calibration parameters, that is to say, they check how well their formula works to replicate the early portion of the data.
However, in Neukom2014 they introduced an interesting wrinkle. In their words:
For most of these choices, objective “best” solutions are largely missing in literature. The main limitation is that the real-world performance of different approaches and parameters can only be verified over the instrumental period, which is short and contains a strong trend, complicating quality assessments. We assess the influence of these methodological choices by varying methodological parameters in the ensemble and quantifying their effect on the reconstruction results. Obviously, the range within which these parameters are varied in the ensemble is also subjective, but we argue that the ranges chosen herein are within reasonable thresholds, based our own experience and the literature. Given the limited possibilities to identify the “best” ensemble members, we treat all reconstruction results equally and consider the ensemble mean our best estimate.
OK, fair enough. I kind of like this idea, but you’d have to be very careful with it. It’s like a “Monte Carlo” analysis. For each step in their analysis, they generate a variety of results by varying the parameters up and down. That explores the parameter space of the model to a greater extent. In theory this might be a useful procedure … but the devil is in the details, and there are a couple of them that are not pretty. One difficulty involves the uncertainty estimates for the “ensemble mean”, the average of the whole group of results that they’ve gotten by varying the parameters of the analysis.
Now, the standard formula for the errors in calculating the mean has been known for a long time. the error of the mean is the standard deviation of the results, divided by the square root of the number of data points.
However, they don’t use that formula. Instead, they say that the error is the quadratic sum (the square root of the sum of the squares) of the standard deviation of the data and the “residual standard deviation”. I can’t make heads or tails out of this procedure. Why doesn’t the number of data points enter into the calculation of the standard error? Is this some formula I’m unaware of?
And what is the “residual standard error”? It’s not explained, but I think the “residual standard error” is the standard deviation of the residuals in the model for each proxy. This is a measure of how well or how poorly the individual proxy matched up with the actual temperature it was calibrated against.
So they are saying that the overall error can be calculated as the quadratic sum of the year-by-year average of the residual errors of all proxies contributing to that year and the standard deviation of the 3,000 results for that year … gotta confess, I’m not feeling it. I don’t understand even in theory how you’d calculate the expected error from this procedure, but I’m pretty sure that’s not it. In any case, I’d love to see the theoretical derivation of that result.
I mentioned that the devil is in the details. The second kinda troublesome detail about their Monte Carlo method is that at the end of the day, their method does almost nothing.
Here’s why. Let me take one of the “methodological parameters” that they are actually varying, viz:
Sampling the weight that each proxy gets in the PC analysis by increasing its variance by a factor of 0.67-1.5 (after scaling all proxies to mean zero and unit standard deviation over their common period).
OK, in the standard analysis, the variance is not adjusted at all. This is the equivalent of a variance factor of 1. Now, they are varying it above and below 1, from 2/3 to 3/2, in order to explore the possible outcomes. This gives a whole range of possible outcomes, they collected 3,000 of them
The problem is that at the end of the day, they average out all of the results to get their final answer … and of course, that ends them back where they started. They have varied the parameter up and down from the actual value used, but the average of all of that is just the actual value …
Unless, of course, they vary the parameter more in one direction than the other. This, of course, has the effect of simply increasing or decreasing the parameter. Because at the end of the day, in a linear model if you vary a parameter and average the results, all you end up with is what you’d get if you had simply used the average of the random parameters chosen.
Dang details, always messing up a good story …
Anyhow, that’s at least some of the oddities and the problems with what they’ve done. Other than that it is just more of the usual paleoclimate handwaving, addition and distraction. Here’s one of my favorite lines:
To determine the extent to which reconstructed temperature patterns are independently identified by climate models, we investigate inter-hemispheric temperature coherence from a 24-member multi-model ensemble
Yes siree, that’s the first thing I’d reach for in their situation, a 24-model climate circus, that’s the ticket …
If nothing else, this study could serve as the poster child for the need to provide computer code. Without it, despite their detailed description, we don’t know what was actually done … and given the fact that bugs infest computer code, they may not even have done what they think they’ve done.
Conclusions? My main conclusion is that almost the entire string of paleoclimate reconstructions, from the Hockeystick up to this one, are fatally flawed through their use of post-hoc proxy selection. This is exacerbated by the bizarre means of selection. In addition their error results seem doubtful. They are saying that they know the average temperature of the southern hemisphere in the year 1000 to within a 95% confidence interval of plus or minus a quarter of a degree C?? Really? … c’mon, guys. Surely you can’t expect us to believe that …
Anyhow, that’s their secret sauce … post-hoc proxy selection.
My best wishes to all,
w.
CODA: With post-hoc proxy selection, you are choosing your explanatory variables on the basis of how well they match up with what you are trying to predict. This is generally called “data snooping”, and in real sciences it is regarded as a huge no-no. I don’t know how it got so widespread in climate science, but here we are … so given that post-hoc selection is clearly the wrong way to go, what would be the proper way to do a proxy temperature reconstruction?
First, you have to establish the size and nature of the link between the proxy and the temperature. For example, suppose your experiments show that the magnesium/calcium ratio in a particular kind of seashell varies up and down with temperature. What you do then is you get every freaking record of that kind of seashell that you can lay your hands on, from as many drill cores in as many parts of the ocean as you can find.
And then? Well, first you have to look at each and every one of them, and decide what the rules of the game are going to be. Are you going to use the proxies that are heteroskedastic (change in variance with time)? Are you going to use the proxies with missing data, and if so, how much missing data is acceptable? Are you going to restrict them to some minimum length? Are you only allowing proxies from a given geographical area? You need to specify exactly which proxies qualify and which don’t.
Then once you’ve made your proxy selection rules, you have to find each and every proxy that qualifies under those rules. Then you have to USE THEM ALL and see what the result looks like.
You can’t start by comparing the seashell records to the temperature that they are supposed to predict and throw out the proxies that don’t match the temperature, that’s a joke, it’s extreme data snooping. Instead, you have to make the rules in advance as to what kind of proxies you’re going to use, and then use every proxy that fits those rules. That’s the proper way to go about it.
PS–The Usual Request. If you disagree, quote what you disagree with. Otherwise, no one really knows what the heck you’re talking about.
Rud,
Izen’s paper reference regarding “temps” is very off in his implied point. The writers mostly apply mositure budgets and “solar irradiance” variability which is mostly a product of cloud patterns. Not much new there. The idea that tree rings are akin to thermometers developed from an idea that annually varying temps or growing seasons might play a noticeable role in tree rings applied only to trees under a lot of cold stress, that is, timberline species. That is why Sierra bristlecones and Siberian taiga tress show up.
I have no knowledge of any North Hemisphere dendro study using anything other than timberline trees as possible temps indicators. There appears to have grown a heresy of sorts in Australia and New Zealand science circles that have dropped the timberline business. I have no knowledge how they ground that, even Mann could talk about mysterious “teleconnections” for his ideas. No one has challenged the Aussie and Kiwi treemometer assertions because…where is the money in that?? A simple comparative study of the types of trees used in NH studies and Aussie/NZ studies would be fruitful on the point.
“I fear I can’t reveal all of the secret sauce, because as is far too common in climate science, they have not released their computer code.”
Looking at the shape of the blade at Fig. 2, I would guess some twist on the modern instrumental temperature records are smeared in. The code might also be adulterated with Law Dome CO2 concentration findings.
Note in fig. 1, color-wise, the most intense warming, and even some of the cooling, is located far from the proxy sites. E.g. the north Indian Ocean. How does that happen? Isn’t it convenient the most extreme temps can’t be corroborated with surface stations?
Isn’t GISS the data set that Hansen “corrected”?
A month or so ago I looked at what is currently posted and then used “TheWayBackMachine” (http://archive.org/web/web.php) to look at older versions. The oldest I found was from 2012. I didn’t do anything that could be called an analysis but even so it was obvious that many, many changes had been made. Even the first numbers from January 1880 were different.
I wonder what kind of stick would be formed using the old numbers? (Especially if data before 2012 could be found.)
Dr. Unfrozen Caveman raises an important issue on oncology research.
Another is that the oncologists did not use prospective “new drug” vs. “old drug” vs. NO (oncology) drug double-blinded studies.
What happened in the 1970s-80s was an unaccounted factor: intensive care. Intensive care increased the lifespans of millions of patients suffering from myriad diseases. In the cancer chemotherapeutic patients, it got them through immunodeficiency crises. New penicillinase-proof penicillin-based drugs and cephalosporins–Thank You! chemists and chemical engineers at Eli Lilly et al– mechanical ventilation –Thank you! real-scientist mechanical engineers at Bird and Siemens. Oxygen delivery and C02 expungement, good job!
Battling DIC (disseminated intravascular coagulation) was a matter of combining a lot of ordinary-people blood-donations, scientists’ studies on fractionation, and engineers figuring out how to do it, e.g. platelet concentration, and coagulation-factor concentration.
Anyway, we got millions of people through crises. Did the oncologists ever try to measure our inputs’ effects? No. They ascribed the improved longetiveties to new oncology drugs. All they had to do was to use old anti-tumor drugs and old anti-tumor drugs in a controlled fashion using new intensive care medicine, head to head. This didn’t happen.
“Historic control” studies were completely unscientific, putting up old oncology drugs and old supportive care longevity numbers, vs. new drug and new supportive care longevity numbers, and ascribing new longer-term longevities only to the new oncology drugs.
Is it Nixon’s fault for promoting the “War on Cancer” which provided billions of dollars to do research whose funds attracted tens of thousands of second, third and fourth-rate scientific minds to the new federal gravy train?
The federal gravy train is what it is. The Continental Railroad, and Northern Capitalists’ desire to substitute low-cost semi-slave European laborers for lower-cost slave African laborers may have been the start of this. The new crop was abused. Read “Sod and Stubble”. Read about how the New England textile mills were made the model for public education. Read about how Common Core is not a “Federal” program, but it only “works” if it is implemented nationwide. Read deeply about “International Baccalaureate Programme” to find it is a UN scheme.
Why aren’t Barack Obama’s daughters, or Bill Gates’ children, or even Arne Duncan’s children in Common Core schools? They have decided that Common Core is good for remedial students, which characterization does not describe their kids. But CC is good for “stupid kids”. Alas, the “stupid kids” will reject it.
I visited a “post-modern” school. Its perimeter was enwrapped by chainlink topped by razor wire. No joke. It was estensibly designed to prevent after-school vandelism. But the attending students, what were they supposed to think? Vandals excluded, or we are imprisoned?
Gosh Gee-Willikers thank that these people are in charge of trivial matters like the future of the global economy and the survival of democracy! and not the quality standard of your car’s brake linings, or the PORV in your boiler, or the surge sensor in your house’s consumer unit. Because that level of wilfully ignorant incompetence would be dangerous if it were exercised on matters of importance….
Yes yes /sarc on
Follow the Money says:
April 4, 2014 at 3:15 pm
Thanks, Money. Actually, the colors don’t show temperature. They show correlation between gridcell values and the SH average … although, that said, I can’t replicate that part of the study. They show very high correlations (up to almost 1) that seem extremely unlikely given that they have (at least in theory) detrended the data before calculating the correlation. What I get looks like this:
I don’t find correlation much above 0.3 anywhere … not sure why, possibly my error.
w.
You would think that after the hockey statistical methods had been debunked, there might be some stoppage to the usage of such methods… I guess that’s asking for a lot from today’s climate scientists. Learning is not their specialty it seems.
“They are saying that they know the average temperature of the southern hemisphere in the year 1000 to within a 95% confidence interval of plus or minus a quarter of a degree C?? Really? … c’mon, guys. Surely you can’t expect us to believe that …”
This – from when I first started reading about the topic, I found it hard to believe the error analysis wouldn’t swamp any trends people were searching for. I find it impossible to believe we know the average temperature of the southern hemisphere in the year 1935 to within plus or minus 1 degree.
Willis, what is that high horse your pedestal is supported?
There’s logic!
I don’t know why you posted this as I do not hate Willis nor have I ever implied any such thing.
Oops, I screwed up. The proper tests in oncology-drugs vs. new oncology drugs was old oncology drugs with old intensive care, new oncology drugs with old intensive care, old oncology drugs with new intensive care, new oncology drugs with new intensive care. What the oncology profession decided to do was old oncology drugs with old intensive care vs. new oncology drugs with new intensive care and decide that marginally prolonged lives were due to new oncology drugs.
I went to Berkeley a bit before Micheal Mann. I didn’t have a father who was a tenured Associate Professor at UMass. I didn’t have the chance to study math under my dad, in Amherst, MA, and take DiffEQ and linear algebra at Amherst College or UMass. Michael Mann had advantages I
couldn’t have dreamed of.
Here is the interesting thing: Michel Mann, Mass native, pater was a UMass tenured Asso. Prof, didn’t get into Harvard or MIT. Why not? Then, at Berkeley, Mann graduated with “Honors”. Not High Honors or Highest Honors. I graduated with High Honors.
Living in Amherst, Dr. Mann was able to concurrently-enroll, pass out of lower-division physics and math, then graduate from Berkeley in three years, or two. It took him five years.
Here’s the thing. In my field, I was an immediate set up for MIT, Harvard, Stanford, Caltech.
Michael Mann didn’t make it. Yale physics, not really happening. Zero Nobel Laureates. 4 NAS Physics-Section members (now down to two). Who told Mr. Mann, “You’re not good enough for Harvard, MIT, Stanford, Berkeley,or Caltech.” Or even physics-legendary, albeit faded Cornell, Columbia or Chicago.
If you’re a high student at Berkeley, you can go to Harvard, MIT, Stanford, Caltech or Berkeley. Or Cambridge. Mr. Mann had the opportunity to study and excel in high school and college. Mr. Mann’s Massachusetts teachers thought he wasn’t good enough for undergrad study at MIT or Harvard. His Berkeley physics and math teachers decided he wasn’t worthy of studying at graduate level under first rate minds.
I don’t want my international policies determined by second-rate minds. The UN is populated by second-rate minds. They want second-rate science minds to give them cover. I’m not interested in subjecting myself to that.
Except Neukom is most likely pronounced “noykom”.
At 3:06 am on April 4, 2014, Kon Dealer said, “Is it because they are incompetant [sic], stupid, or mates of the authors (pal review)- or all 3?”
Richard Lindzen hinted that it’s door number one: incompetent. Listen to 3 minutes of his remarks before the UK Parliament House of Commons Energy and Climate Change Committee on January 28, 2014 (video should start playing at 2:49:10):
http://youtu.be/6GzNATrGH7I?t=2h49m10s
I think this study and you all underestimate the quality of the proxy temperature record. Many independent studies have been done using corals, ocean and lake sediments, cave deposits, ice cores, boreholes, glaciers, and documentary evidence such as paintings of glaciers. You can dismiss the value of such information but the methods used are fully transparent and not at all as described above. The website for the National Academy of Science and others give very detailed information on how they reach their conclusions.
Izen at 4 April 0906 answers Michael Moon’s queries “How does rainfall correlate to temperature? How do tree ring widths or “latewood density” correlate to temperature?” by referring to a particular site. I have read it; it is an interesting site.
Read it and you find :
“Absorbed photosynthetically active radiation (APAR) is estimated from global solar radiation, derived if necessary from an established empirical relationship based on average maximum and minimum temperatures. The utilized portion of APAR (APARU) is obtained by reducing APAR by an amount determined by a series of modifiers derived from constraints that cause partial to complete stomatal closure: (a) subfreezing temperatures; (b) high daytime atmospheric vapour pressure deficit (VPD); (c) depletion of soil water reserves.”
Then you find that the incoming radiation – which is what the trees use and what the researchers are interested in, is obtained using temperature where actual measurement of APAR is not available.
The relevance of temperature is this. If it is sub-freezing trees do not grow (this puts a ‘modifier’ of ‘0’ into their equations). If they haven’t got the APAR they have to use temperature records (av max and min) as a proxy for APAR. It is radiation and water (plus CO2 of course) that allows the trees to grow. Negative temperatures can stop this.
It is difficult to get the reverse logic, “if the tree grows well, the temperature is X”.
Poptech says:
April 4, 2014 at 10:07 pm
Aussiebear, it’s all a mystery to me why poptech thinks I’m so important, but clearly he does—he’s devoted a whole page on his site to attacking me personally. I don’t have a page devoted to him … but he has a post devoted to me. You do the math.
He doesn’t attack my science, you might note … he attacks me instead. It’s the sure sign of a failed critic, when the attacks on the science stop and the personal attacks start.
The part he doesn’t seem to get is that I thrive on controversy. I think it’s great. As far as I’m concerned, all publicity is good publicity. Why?
Because it makes people curious about my work. Folks read what poptech says, and it makes them wonder—what is it about this Willis guy that has poptech’s knickers in such a twist? So they come over here, read my work, and like you, end up mystified … and the important part is, they end up here.
All the posts like poptech’s out there do nothing but send traffic to my own posts. And there are lots of them, I find people discussing my work all over the climate blogosphere. Some are dissing it, and some are praising it. Heck, someone came to my defense over at poptech’s place, how sweet is that?
If he were smart, he’d take down his post and stop driving people to read my work … but if he were that smart, he wouldn’t have put it up in the first place.
One small correction. He says:
I am very good at a lot of things I was never trained at. I wrote my first computer program a half century ago, something I doubt that poptech (or many people) can say.
As to whether I list any languages on my CV, he’s simply wrong about that. My CV is linked to in a variety of my posts, it’s here (Word doc).There is a section called “Computer Languages” where I list the following:
Actually, I can program Mathematica in three different languages, it’s an amazing system. And I used to write in other languages as well, Lisp, Logo, 68000 Assembly Language, Alcom, Fortran, languages live and dead … haven’t messed with any of them in decades, so I didn’t list them. In any case, it appears that poptech is so opposed to me that it is affecting his reading ability, leading him to make provably false statements about me …
In addition, I have held a variety of jobs that required me to write a variety of programs of various levels of complexity … I’ll put my programming skills up against anyone. How does poptech think that I have done the literally hundreds of very complex analyses that I’ve published here if I’m not a computer programmer?
Anyhow, Aussiebear, I’m totally in mystery, just as you are, as to why poptech has engaged in this vendetta against me. I can’t remember doing something wrong to him, and if I did, he should tell me. If it’s my bad, I’m happy to apologize to him.
But I don’t mind his attacks. Having him yapping and trying to bite my ankles makes folks aware of just who he is … and when he shows up here to try his luck with my ankles, every time his reputation slips a bit more. I mean, I wouldn’t have noticed his provable and foolish misrepresentations about me if he hadn’t shown up here to lose ground … but now everyone knows he can’t even read a CV.
But that’s all minor stuff, and at the end of the day, Aussie, I wish poptech nothing but the best. Life is too short to engage in vendettas like he’s doing, it is hard on a man’s spirit. So from a larger point of view I can only desire and intend that he be free of his bitter obsession, and putting that wasted energy energy in some positive direction.
But from a selfish point of view, he’s driving folks to read my work, so I guess either one is ok with me.
Best to you, and best to poptech as well,
w.
matayaya says:
April 4, 2014 at 11:34 pm
Thanks, matayaya. I fear you misunderstand my point. I am not attacking the proxies. The proxies are just what they are. Some are good for one thing and some for another. Some make perfect sense, and some are just voodoo. I personally think that there may be good information in some of them … although it’s hard to dig out. And while I’m far from the expert that Steve McIntyre is, I’ve made my own contributions to the field, and written extensively about many of the different kinds of proxies.
My concern here, however, is not with the proxies but with the procedures. Using post-hoc selection is a fatally flawed procedure, regardless of whether the proxies are good or not. Their linear detrending procedure has problems. Their procedure for testing has problems. That’s what this post is about.
Finally, let me suggest Feynmann to you, where he said “Science is the belief in the fallibility of experts”. You sound far too trusting that they know what they are doing. Far too often, particularly in climate science, they don’t have a clue. As I pointed out in this post, the field of climate science contains as much shonky, bogus science as it does real science. It’s the only field, for example, where they not only practice but atually defend post-hoc selection of explanatory variables … and there’s no one but you to tell the true from the false. If you’re looking at four studies, the odds are very good that one or two of them are wrong, even badly wrong … and if you don’t know which ones the wrong ones are, you’re not likely to get far in the field.
It’s like the old joke about poker. If you look around the table and you don’t know who the sucker is … it’s you.
Similarly, if you look around at four climate studies and you can’t recognize the bogus one …
My best to you, stay skeptical.
w.
The fact that the reconstruction matches pre-1979 GISS.. (Fig 1c)
PROVES that it is a fantasy. !!
Izen, were you a reviewer of the Neukim paper?
Max Hugoson says:April 4, 2014 at 10:31 am
Bingo!! Until we stop talking temperature and start measuring enthalpy, it is a meaningless discussion.
Willis, thanks once again for a great dissection.
Willis, your CV is weird. My read on it is you are an unmitigated outlier. Why do you insist on being off the statistical charts? And how did you cook those Alaskan salmon? Did you grill them over alderwood, or did you use fir? I’m just asking because one of my kids, and my best friend from high school, want an adventure, and they want me to lead it. You know these Cali kids, they want something exciting. I made the mistake of taking them to Baja. Once you’ve been there, you can’t really go back. Salmon fishing in Alaska, true also
it won’t be long before rgb is termed a “denier”.
Oh, it happens all the time when I post on any forum outside of WUWT. It’s the quickest way to end debate and “win”, after all. First I’m labelled a denier. Then I present evidence, usually straight from e.g. W4T but I have a pile of it bookmarked. Then I’m accused of cherrypicking dates in my presentation of evidence, usually accompanied by some robust cherrypicking straight back at me. These days, I end up presenting evidence straight out of AR5, chapter 9.
The really silly thing is that I’m not, actually “a denier”. I not only acknowledge that there is a GHE, I try to educate people about how it works (usually to no point). My primary beef with climate science is that the claim that the GCMs are accurate and have predictive skill is not borne out by the usual sorts of comparisons and tests we would put any new theory through. The best of them are starting to look “better” as they up computational resolution and include more dynamics but CMIP5 is fully of models that really pretty much suck and it isn’t surprising that they suck. What is surprising is that AR4 and AR5 use them as if they don’t suck, as if they are equally likely to be accurate predictors as the better models. What is surprising is that there is no empirical component to determining what the best models are (as in, which ones DO the best job of predicting the actual temperature).
What is tragic about the whole discussion is that the side that is so very quick to label somebody a “denier” and hence avoid having to confront any of the uncertainties or inconsistencies in the picture of catastrophe that is being sold, hard, around the world is completely unwilling to acknowledge that the climate system is enormously complex and may not do at all what one expects from a naive argument, especially one bolstered by infinitely adjustable model based proxy based determinations of past climates that are custom tuned to accentuate present warming and that deliberately neglect confounding effects such as UHI corruption of the land surface record that would reduce the apparent severity of the recent warming (which is none too severe even without removing the UHI effect). They do not with to acknowledge that anybody could disagree with them, have reasons for disagreeing with them, and not be either in the pay of special interests or Evil Incarnate.
rgb
they could make an easy test , split temperature data in two, and apply selection methods on each periods and then compare the results with temperature record.
That helps and will undoubtedly screen out more proxies included merely by chance but it does not address the more fundamental weakness of their approach. It seems to me that you first need a compelling rationale for testing against temp 1000KM away and with leads and lags. Assume that you had a temp record at the location and it did not correlate, but those further away did. On what basis would you ignore the in situ record?
“We Live in Cold Times” should be compulsory viewing for Neukom and supporters.