Comparing IPCC Projections to Individual Measurement Systems.
Recently, the subject of using only one set of measurements to perform a hypothesis test arose. As many are aware, I prefer to average over instruments. But, I’m willing to consider each set individually. So, today I did that.
My main results are: Looking at the data 12 possible ways, I get 9 results that say “reject the IPCC best estimate” to a confidence of 95%.
So, in today’s post, I’ll explain my results. But first, I will explain why I prefer to use merged data when comparing IPCC projections to data.
Why I use an “ensemble average” of multiple GMST data sets.
When testing data, it is obviously possible to take two approaches: a) Select a data set and use only that data set or b) Use a collection of all data thought reliable by practitioners. I prefer the second approach. As I see it, the advantages and disadvantages of each approach are as follows:
- Pick one GMST data set, ignore evidence from other data sets.
Advantages: Least effort. Readers will notice that back in January, when I first began using data, I would use only one data set: GISSTemp. This choice was dictated by pure laziness. I was interested in getting up to speed and gaining familiarity with available data sets and the literature.However, while some analytical laziness is excusable in a blog post, I always planned to include more data sets as they became available.
Disadvantages: There are two main disadvantages of using only one data set. These are: one may be suspected of cherry picking and one increases β error. The difficulties with the first can be largely minimized by explaining one’s data choice prior to performing an analysis.
The second disadvantage cannot be eliminated. Selecting one data set when 5 are available increase β error. Period. There are some valid reasons for discounting available data. For example, if some data are known to be of poor quality or in error, one can justify leaving it out of an analysis. So, for example, had NASA GISS failed to correct the Y2K error recently discovered in their data, this might be good reason to leave it out of the analysis. However, if reasons are valid, the reasons can be stated up directly, and should be.
- Use all the standard data sets thought reliable.
Disadvantages: Slightly more work.
Advantages: a) Appear more trustworthy, b) reduce β error, c) reduces uncertainty in the mean results.
I may be wrong, but in my opinion, the averaging over multiple data sets is better than relying on only one.
However, since this topic has been discussed in blog comments, I will now take the liberty to elaborate a further on these two issues, as both are important in the context of the “blog climate wars” we all enjoy. 
What is the problem with raising suspicious of cherry picking? Of course no one cherry picks.
Nevertheless, should a blogger with a particularly point-of-view accidentally select a temperature record that happens to be the outlier that gives the result that blogger is known to prefer, using that particular one data set fosters suspicions of cherry picking.
I believe AGW to be true, but since I am willing to pro-actively test projections against data during what appears to be a “stall” in warming, much of my audience consists of skeptics. Clearly, they are not going to be convinced this stall is meaningless if I restrict my analysis to using GISSTemp, the data set that shows the least recent cooling. Rather, what will happen is this: They will decide I simply pick data to suit my pre-conceived notions.
I know that trust and distrust are feelings that last. So, for this reason, I prefer to include a variety of respected data sets in my analysis and report on as many results as possible. That way, when the temperatures do warm, and my updated plots and trends show the renewed warming, I think my audience will trust my plots are not simply attempts to present a tendentious argument in favor of a theory I believe to be true.
What is the problem with elevates β error? Oddly enough, the possibility that I might be accused of cherry picking is minor compared to the real difficulty which is that using one data set inherently introduces more scatter due to instrument variability. This elevates β error, without reducing α error. I discussed β error previously and explained that if a null hypothesis is actually wrong it can take many, many years of data to disprove even a false hypotheses to a chosen, high level of statistical confidence.
Since I know many of my readers are aware of β error. Many are aware that using test with high β errors is a well know trick to claim something is proven, when in fact, all one has done is failed to disprove using very little data. Since my readers know this trick, know that I know it, and know that any competent statistician is aware of this issue, I prefer to use methods that minimize β error, while holding the α error at a specified value.
This results in a hypothesis tests that, on average, do not increase the rate of rejecting IPCC projections when they are correct (i.e. α error), but have some chance of rejecting it when it is, in fact, false (i.e. β error.)
(By convention, a “failed to falsify” result should be accompanied by an estimate of the β error, or statistical power. I haven’t seen these discussed in the ‘climate blog wars’, but I do intend to extend my spreadsheet to include these at some point. Reporting both α and β errors are important if people are to draw inferences about statistical results.)
Current comparison between IPCC projections and five data sets.
My readers already know I computed the trend in Global Mean Surface Temperature (GMST) four data sets using data from Jan 2001 through Jan. 2008. I compared that result to the IPCC projections, and found the IPCC projections…. erhmmm… not so good? ( That is: a hypothesis test using Cochrane-Orcut, and confidence intervals computed using a “t” distribution, indicated that the IPCC projections should be rejected to the 5% confidence intervals when compared to the data.)
But now it’s March! So, February data are in. Also, due to interest in this exercise, other bloggers are now performing variations on this analysis. So, naturally, I am extending my analysis. I think the variety of results will give various people more information to consider when forming their opinions about the predictive ability of IPCC projections.
To extend the analysis, I have decided to show results of hypotheses test to determine whether the IPCC best estimate for the trend in GMST during the next three century, published in the AR4, is consistent with observation for the GMST measured after the projections were made.
I will use to basic analytical to test the hypothesis, both using two-sided 95% confidence intervals (i.e. &alpha=5%). The two methods are:
a) Cochrane-Orcutt (CO) , with two-sided confidence intervals, calculated assuming the uncertainty in the mean is t-distributed and
b) Ordinary Least Squares ( OLS )with the number of degrees for freedom adjusted using Neff/N = (1-ρ)/(1+ρ), where ρ is the correlation of the residuals for the OLS fit.
In addition, I will test:
- The “average” temperature for each month, computed by averaging the temperature from each of 5 reliable data sources.1. This gives a one trend based on an average. Done this way, the uncertainty intervals on the mean trend include the uncertainty due to weather noise; however, uncertainty due to measurement error, which arises due to lack of precision from each data source is mimized.
- The temperatures from each source individually. This results in 5 trends. Because the lack of precision due to each instrument, these will have the largest uncertainty intervals. Making conclusions based on these maximize β error. That is: we increase our risk of failing to reject the IPCC projections when they are wrong.
- The average of the trends for each source, calculated , with the uncertainty intervals calculated as if the residuals for each instrument at a given time are uncorrelated from each other. This is incorrect, but the this uncertainty band would enclose the uncertainties in the slope computed using the five sources. It is illustrative for this reason.
Methods 1 & 2 have identical α (alpha) errors. So, I consider the method with the minimum β error superior, as it is gives the least, overall, number of errors. This is why I average over all instruments. Method 3 is deficient as method to test the IPCC hypothesis, and merely gives some sense of the uncertainty due to measurement noise without regard to ‘weather noise’.
Results
After applying this test, I find that using the method I prefer (averaging first, then fitting the trend), I the best estimate by the IPCC is rejected to a confidence of 95%. It is too high to be consistent with the weather data we have experienced.
| Best Fit Trend | Reject 2.0 C/century to confidence of 95%? (α=5%) | |||
| Method | C-O C/century <m> | OLS( C/century) | C-O | OLS |
| Average all, then fit trend. | -1.1 ±2.2 | -0.3 ± 2.2 C/century | IPCC Projection Rejected | IPCC Projection Rejected |
| Fit trend to each, then average. | -0.9 ± 1.6 | -0.3 + 1.4 | See note. | See note. |
| Individual Instruments | ||||
| GISS | -0.4± 2.2 | 0.2 ± 2.3 | IPCC Projection Rejected | Fail to reject |
| HadCrut | -1.6 ± 1.8 | -1.0 ± 1.9 | IPCC Projection Rejected | IPCC Projection Rejected |
| NOOA | -0.3 ± 1.7 | 0.0 ± 1.7 | IPCC Projection Rejected | IPCC Projection Rejected |
| RSS | -1.4 ± 2.1 | -0.6 ± 2.2 | IPCC Projection Rejected | IPCC Projection Rejected |
| UHA | -0.8 ± 2.9 | 0.0 ±2.9 | Fail to reject | Fail to reject |
| Note: 1 ‘Method 3′, that is taking the average of the 5 individual trends results in ‘reject/reject’ for the IPCC 2C/century trend. However, as I noted, that is meaningless, as the uncertainty intervals only include the variation due to measurement uncertainty and fail to properly include weather. Note: 2: Estimates using OLS are given for comparison only. When data exhibit ‘red noise’, the C-O results are more accurate than OLS.) |
||||
Examining the table, we see that the IPCC projections of 2C/century are “rejected” to the 95% confidence level using most the methods I tested. If we average the data, and then test, the trend is rejected to the 95% confidence level using both C-O and OLS. (Note however, that when the two methods disagree, C-O is more accurate.) Using each individual instrument, it is rejected under 7 out of 10 possible test methods. The ambiguous result “fail to reject” arises in 3 out of 10. However, due to the small sample time, we know that β error is large– so, “fail to reject” is best interpreted as “not enough data to tell for sure”, rather than “IPCC projections are likely correct”.
Below, I have graphically illustrated the main result and illustrated it below.
Larger Image: GMST vs Time March 25, 2008
“Average” results are those obtained by applying Cochrane-Orcutt to the “averaged” temperature as my standard for determining the trend. The ±95 uncertainty intervals are also calculated using Cochrane-Orcutt; I assume the uncertainty in the mean is t-distributed. (These give very slightly larger uncertainty intervals than the corrected OLS. So, it reduces the rate at which I reject the IPCC trends.)
The best fit for each instrument is illustrated; as are all the data. Currently, GISS gives the least negative trend; HadCrut gives the most negative trend. Other instruments provide intermediate results with UHA MSU giving results closest to the mean off individual instruments.
The IPCC central tendency is illustrated: it lies outside the uncertainty intervals which corresponds to rejecting the hypothesis that the IPCC projections are correct.
So, can this change?
I suspect that the current trend will break, as all trends do. Warming is hardly excluded by the current data. As I have said repeatedly, warming is not rejected by this data. In fact, pre-existing 30 year trends are not excluded by the current data.
So, given the past trend, and the strength of the theory underlying the theory of AGW, warming is likely to resume. When this occurs, the central tendencies for all data sets should turn positive.
But what this data indicates is that if and when warming resumes, it will likely occur at a rate that is lower than projected by the IPCC. So, while the trends will turn up they are unlikely to reach the 2C/century of warming.
I’d also like to note another feature of these test. Let us supposed, the “true” underlying tendency turns out to be 1 C/century. How will these hypothesis test “look” over time?
Oddly enough, due to β error, we are quite likely to see a number of “failed to rejects” increase and decrease over time. The reality is that, though I have not calculated β error, we are in the period of time when β error is anticipated to be large. So, until there is sufficient time for β error to drop below 50%, we will tend to see more periods of “failed to reject” than periods of “reject” even if the IPCC projections are wrong.
Because of the effect of high β (beta) error, careful scientists rarely interpret “failed to reject” as confirming a hypothesis that has not been supported by very large amounts of historical data and sound theory with very few approximation or assumptions. While the theory of AGW is well supported, it is not clear to me that the specific quantitative predictions by the IPCC are, by extension, supported with equal strength.
My understanding is: The consensus states that AGW is proven. But the magnitude is still being debated. One of the ways to test the various hypotheses regarding the magnitude is to do data comparisons. This comparison to observation suggests the IPCC’s estimates are high.
Footnotes:
1. Data from GISS Land Ocean, UHA MSU, NOAA, RSS, and HadCrut
Updates
3/27/2008: I inserted a link to a relevant post comparing to IPCC projections to data.
3/27/2008: I uploaded a figure about beta error. The figure is supplied by reader martin ringo.
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116 Responses to “Comparing IPCC Projections to Individual Measurement Systems.”
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Niche Modeling » May 3rd, 2008 at 3:04 am
[...] changes in ocean currents. This is why temperatures have not been increasing as noted here and here. So it is natural to ask, if natural cycles are masking global warming, why haven’t them [...]
Geoff Larsen May 3rd, 2008 at 3:47 am
JM, 2513
Whats to discuss? You are simply wrong as many have pointed out to you with the reasons why. This is simple high school stuff.
If you take say GISSTEMP & HADCRU anomaly time series which are drawn on bases a & b (I think the base lines differ by .1C) and average them, you come up with a new “composite” anomaly time series with a new base line(a + b)/2.
It doesn’t matter what the base of any anomaly time series is, they are all valid. You convert one time series to the other by adding or subtracting the difference between the base lines. It doesn’t matter whether you average first then adjust, or adjust first then average, as long as you make the correct adjustments you get exactly the same “composite” anomaly time series. If you are doing trend analysis, as Lucia is doing, there’s not even a need to adjust, as trends don’t change when you change the base line.
JM, 2522
“Water does not freeze at -136.5C, it freezes at 0C”
You have created a new temperature scale with the freezing point midway between 0 & 273, lets call it, in your honour, the JM scale.
So water freezes at: -
0 C
136.5 JM
273 K
32 F
2519, dover_beach
Point taken
JM May 3rd, 2008 at 4:11 am
Geoff: “as long as you make the correct adjustments ”
Well I can agree with that, but Lucia has been very insistent that she makes *no* adjustments.
JM May 3rd, 2008 at 4:18 am
Geoff: “as trends don’t change when you change the base line.”
They do if you’re trending an average of 5 figures off *different*, unadjusted baselines
Graeme Bird May 3rd, 2008 at 5:15 am
“I also think that the majority of sceptics would agree that ~1 deg/century is a reasonable estimate of the effect of CO2 (e.g. Pat Micheals).”
I think its too high. I think Pats letting the nutballs push him around too much. He doesn’t have the data to back that up. It may seem like a reasonable compromise to take Pats point of view. But if you don’t have the evidence then its just pure speculation and way up on the high side.
I shouldn’t be surprised if it made that difference over many thousands of years however. Because I’d expect our planets temperature to be highly serendipitous, and so if the extra CO2 could, for example, slow the advance of glaciers, then perhaps it could make a bit of a difference over the longer term. What we have to get away from is this light-and-air model of whats going on. And we ought to get away from air temperature as our metric. Its clearly accumulating and decumulating joules in the oceans that are going to tell us which direction we are headed in. And we don’t want to think of warming as some instantaneous light and air show, but rather a strata and heat budgets deal. Strata and heat budgets. Not gasses and light.
Arthur Smith May 3rd, 2008 at 9:37 am
JM - while Graeme is totally wrong in his assessment of the impact of CO2, he is absolutely right in his critique of your argument. JM, you say “[trends change] if you’re trending an average of 5 figures off *different*, unadjusted baselines” - no they don’t. Do the math yourself. Here’s an example with 3 figures instead of 5:
x(t) = m1 t + b1
y(t) = m2 t + b2
z(t) = m3 t + b3
If we adjust to a common baseline, say b1, we add (b1-b2) to y, and (b1 - b3) to z. So “adjusted” would be:
x’(t) = x(t) = m1 t + b1
y’(t) = m2 t + b1
z’(t) = m3 t + b1
Ok, let’s average.
Unadjusted: (x+y+z)/3 = (m1 + m2 + m3)/3 t + (b1 + b2 + b3)/3
Adjusted: (x’+y’+z’)/3 = (m1 + m2 + m3)/3 t + b1
The only difference is a constant - b1 vs. (b1 + b2 + b3)/3, which has no effect on the slope which is the value we’re trying to get: (m1+m2+m3)/3 is the same for both.
If you believe something different, show your math, because you’re making absolutely no sense with your arguments so far.
lucia May 3rd, 2008 at 10:21 am
Arthur–
Let me know what you conclude about the Troposphere. This is the lower troposphere, but yes, it is true it’s not literally the surface. So, that can be an issue. One of the reasons I show results individually and collectively is so readers can get a real idea of the type of spread we are getting depending on ‘instruments.’
Obviously, I could easily average the troposphere measurements and the surface measurements. Then we could examine each of the two averages separately. I don’t know if the version of the spreadsheets you have shows any of the “peaking” I’ve done at cross-correlations in the deviations between a particular ‘instrument’ and the mean. If it does, you may be as puzzled as I am about which instrument correlate with which. (Or, you may know something I don’t know and understand why things correlate as they do.)
JM May 3rd, 2008 at 10:54 am
Arthur
I agree with everything you say (including not worrying about Graeme)
Except it doesn’t apply. Arithmatic only works if there is only one zero. There can be only one.
Lucia’s input dataset - unadjusted - has five.
Her subsequent processing has no validity unless she reduces those five to one by applying corrections *before* any subsequent calculations occur (which is a point that Geoff made)
Lucia. You have insisted throughout this thread that adjustments are not necessary. If you still believe that, could you please run your spreadsheet on both an adjusted and unadjusted set of input data and post the results here.
I’d appreciate it if you could also post the actual Excel files themselves rather than just GIFs.
That would be very nice.
Thank you
JM May 3rd, 2008 at 11:05 am
” I could easily average the troposphere measurements and the surface measurements.”
Lucia, my understanding is that this is an issue of some controversy.
I’m not sure, and I can’t give you a reference, but I also understand that the standard practice is to apply a factor of 1.2 to troposphere measurements.
You’ll have to conduct your own research there.
Graeme Bird (10 comments.) May 3rd, 2008 at 3:47 pm
“JM - while Graeme is totally wrong in his assessment of the impact of CO2…”
Lets see some backup for that Arthur. We could all use some good news. Given that our starting point is the brutal ice age we are in. One which appears to be getting worse over time.
lucia May 3rd, 2008 at 3:56 pm
Graeme–
As much as I hate cold weather, and complain through out Chicago lands wretched winters, we are not currently in an ice age. During the Wisconsin glaciation, my house was buried under glaciers. I’m only 30 miles south of Wisconsin. It’s a cool spring, but my tulips are blooming nicely.
Graeme Bird (10 comments.) May 3rd, 2008 at 4:51 pm
Lets get the terminology right. We have been in an ice age for 39 million years. We have glaciations and interglacial periods within this ice age. Not only are we in an ice age but we are in a particularly nasty phase of this ice age. And have been so since North and South America fused. The interglacials don’t last very long. The glacial periods last a very long time and are very nasty for terrestrial life. Not only this but the longer term trend has been for cooling for the last 55 million years and there is no indication that matters have turned around.
After splitting the protagonists up into climate rationalists and climate alarmists still we find that most people are not starting the argument from the fact that we are in a truly brutal ice age. And in not doing so they are seriously at fault. This must be the starting point to our understanding of what industrial-CO2 release is all about. The sort of silliness that JM gets up to distracts people from the only rational starting point to this issue.
lucia May 3rd, 2008 at 5:06 pm
Ahhh– I see what you mean. Yes. I’m used to people calling the glaciations “ice ages”, and the interglacial otherwise.
Graeme Bird (10 comments.) May 3rd, 2008 at 5:25 pm
Right. It may seem like a language-nazi point to be making. But from that starting point industrial-CO2 becomes the best dumb luck the human race ever stumbled upon if it “works” at all. And this warming panic becomes the biggest case of wrong-way-Corrigan behaviour yet seen in all allegedly scientific discourse. JM and others will see us all splitting hairs in some argument cul de sac when the real issue is whether or not industrial-CO2 is so marvellous that it can warm things up a little bit, even as it makes nature more robust and agriculture more productive.
Some arguments have a middle ground to them. But not this one. This one the alarmists are idiotic in every constituent part to their argument and we must try hard not to compromise with them.
Arthur Smith May 3rd, 2008 at 8:37 pm
Graeme - read Jim Hansen’s latest. If we exceed 450 ppm CO2, we’re likely to be out of the whole “ice age” thing altogether before long. No more Greenland ice, no Antarctic ice. Oh, and sea levels hundreds of feet higher. If you think the planet would be better that way, go ahead and make your case… In any case, there’s no doubt in the science on the fact that additional CO2 brings warming.
JM - as I mentioned in my first post on this thread, Lucia *has* posted her spreadsheet on another thread - here’s the link directly:
http://rankexploits.com/musing.....solar2.xls
JM May 3rd, 2008 at 9:22 pm
Arthur
Thank you for the link, I’ll have a look
And ignore Graeme, if you don’t you’ll out why soon enough
JM May 3rd, 2008 at 10:16 pm
Lucia
I’ve just had a look at your Excel spreadsheet, and I’d like to give you some advice. Take it as you will.
You’re averaging the raw input anomalies. You should not do that, and you should ignore anyone who makes an argument from calculus.
You are not doing calculus, you are doing numerical methods.
Numerical methods are arithmetic approximations to calculus, but they are not calculus - they rely on arithmetic operations (+-*/) which do *not* work if you have five zeros in your data.
Please try rerunning your spreadsheet on *corrected* anomalies. I think you’ll find you get quite different results.
Another thing. Compare your March and April trendlines. They seem to have moved quite a bit for the addition of one months data, which looks unusually sensitive to me. Particularly as that data has 2 positive anomalies, 2 negative and one at zero. Something’s wrong there.
I also think it would be a good idea to plot the ordinary least squares fit in addition to your other method. Least squares is something we are all familiar with.
Graeme Bird (10 comments.) May 3rd, 2008 at 10:32 pm
“Graeme - read Jim Hansen’s latest. If we exceed 450 ppm CO2, we’re likely to be out of the whole “ice age” thing altogether before long.”
Hansen doesn’t have any evidence for this. What is your evidence? 450ppm won’t have an effect even close to that. This is all make-believe. One of you is going to have to make a case. Don’t throw it back onto me when none of you has shown that CO2 can do anything like this globally. If Hansen was right then we would be fine since we would have a convenient thermostat. If Hansen was right we ought to just throw one big street party. But Hansen isn’t right and so lets have some evidence. Hansen thinks a doubling of CO2 will give you a 6 degree increase in temperature. I’m not popping any champagne bottles over that since he doesn’t have any evidence for this. Annan thinks a doubling of CO2 will give you a 3 degrees increase. He has no evidence for this either. But notice between the two of them they cannot get their story straight.
Start from the fact that we are in a brutal and pulverising ice age. Never lose that context. But make your case.
Graeme Bird (10 comments.) May 3rd, 2008 at 11:00 pm
Hansen works on the basis that this is just a light and air show. That any energy generation or capture by the earth as a whole is just neither here nor there. And that the oceans are a fudge factor or a delay factor rather than the real deal.
We cannot be thinking in this way. For one thing when models that make these assumptions are used to focus on the preindustrial past they never work. Goddard tried to train its computer model on the snowball earth and the computer could not account for it. So Goddard being a refuge for bad scientists, rejected the empirical evidence and reaffirmed their computer model. But a model which was based around strata and heat budgets and not just air and light would easily have been able to mimic the earth freezing over, accumulating heat, melting and freezing over again. I call Goddards problem the curse of the lone paradigm. And whats bad about this controversy is that both climate rationalists and climate alarmists are suffering from it. So we have to keep pushing things back to strata and heat budgets and away from air and light.
Graeme Bird (10 comments.) May 4th, 2008 at 12:06 am
“In any case, there’s no doubt in the science on the fact that additional CO2 brings warming.”
This is not true at all. But its worthwhile to see how such an assumption comes about. It comes about because the climate rationalists are better scientists than the climate alarmists. So what happens is this. Immediately the climate rationalists admit that their best estimate is that CO2 is likely to have some warming of some sort. “Thats our best guess…” say the climate rationalists. The CO2 might have a cooling effect. It might have no effect. Or it might have a warming effect. But since it appears most likely that the effect would be a warming effect the climate rationalists immediately admit this. HENCE THE ASSUMPTION IS IMMEDIATELY bipartisan. But the fact is the assumption has not been proven.
So we cannot just lock this assumption in and move on. Yes for sure. My assumption also is that C02 would have some sort of minor warming effect. Thats why the Pat Michaels assumption is so seductive. But the fact is none of this has been proven. And a slight cooling effect cannot be ruled out.
Nature laughs at science-sociology and takes no account of the structure of our debates. A like situation happened back in the 60’s and 70’s. There was an argument between the Steady State and Big Bang folks. The Steady State people admitted that their version of their theory wasn’t as good as the theory that the Big Bang people were spruiking. They did so not because the Big Bang theory was any good. But simply because their own theory was being shown to be bogus and because they were very good scientists. Fred Hoyle and that crowd. From there the Big Bang theory was held to be bipartisan and we are stuck with it though it looks sillier every year.
So neither side of this debate can yet lock in the idea that CO2 has a global warming effect, as reasonable as this assumption seems. Because it has not been proven and its not time to put it in cement. The only thing we know for sure is that this CO2-warming is either so slow-acting or weak that its not yet shown up clearly in the data. And that therefore if its effect is warming it cannot-not be a good thing. It must be a good thing. Its a positive externality that must never be subject to taxation.
Boris May 4th, 2008 at 11:38 pm
If this “brutal and pulverizing ice age” produced our current civilization, we should try not to end it.
In fact, I think this means I’m for more brutal and pulverizing things in general.
Graeme Bird (10 comments.) May 5th, 2008 at 12:33 am
Well it probably helped produce our species via subjecting us to pulsing holocausts and cutting us off from eachother, so allowing evolution to work by subjecting all the struggling clans to a like stress in a context where the gene pool could be altered by partial isolation. It probably helped produce our species also by destroying virtually all of our food supply and forcing us to the coast where we had to spend a lot of time in the water. Freezing our butts off and trading shell-fish for animal skins.
So you could say it produced our SPECIES. But you wouldn’t say it produced our civilisation. Its the interglacial that did that. And we don’t need another holocaust in my view.
Can we vote on that? I’m saying we don’t need another holocaust. The environmentalists want a holocaust and I’m against it. Do we get to vote?
Boris May 5th, 2008 at 12:52 am
Shouldn’t you be fighting the jihadists somewhere? Please?
lucia May 5th, 2008 at 6:35 am
Graeme–
I don’t think Holocaust is precisely the correct word for species going extinct or adapting to environmental pressures.
I think there are risks for people both if it gets too hot or too cold. I sincerely doubt even the most alarmist of warmers want an ice age or even to return to the little ice age. I also doubt even the most stone-cold denialist wants the temperatures to rise 2C in a decade.
For the most part, the disagreements are over how much or little warming GHG’s may cause, how fast it would occur, can it be predicted etc. Ramping this up to “holocaust” is unwise and unhelpful.
Graeme Bird May 5th, 2008 at 12:54 pm
Now lucia. Seperate the SCIENCE SENTIMENT from the SCIENCE EVIDENCE.
Do that and you will know that there is absolutely no chance at all that its going to get too warm. There is no evidence for the likelihood of catastrophic warming. You know when you are pounded with this manufactured consensus day after day, world without end, it can make you lose the context. But this planet in its current form displays a one-way bias towards catastrophic cooling, no likelihood of damaging warming, and no seriously bad warming problem for many tens of millions of years. There hasn’t been a warming catastrophe on this planet (if ever) for at least 55 million years.
If you doubt it try producing the evidence. Find me some sort of evidence for the likelihood of catastrophic warming. And for the idea that a little bit of human-induced warming is a BAD thing during a brutal and pulverising ice age. And also for the idea that if human action weren’t in the equation that the temperature would be in some sort of static equilibrium.
You ought never lose sight of the fact that this argument begins with us in a brutal, nasty, ice age which is entirely inimical to terrestrial life, but as it turns out is pretty kind to life in the sea. Where is the evidence even that this industrial-CO2 release will stave off the oscillation down to a cold, dry world where hominid numbers are thinned right down?
We have to keep circling back to the relevant context. Which is not a leftist context but a scientific context.
JM May 21st, 2008 at 4:16 am
Lucia, sorry for coming back to this after a break, but I’ve had to be concerned with a more important personal matter for the last week or so.
I’ve got a couple of questions about the spreadsheet implementation of your model (the one you posted here and Arthur pointed me to)
Sheet: ‘Raw_Temperature’
——————————
Raw_Temperature:B9 contains 0.05 which is described as amplitude, and subsequently used in column J ‘Solar’.
Q1. Is this a representation of the sunspot cycle and variation?
Q2. If so, does it represent variation in the Solar Constant or TSI? Because if it does, it’s too large. TSI variation over the sunspot cycle is 0.001 not 0.05 (ie. 0.1% not 5%). (The solar constant at the earth varies by more (7%) but that’s an annual cycle not 11 years.)
Q3. Or is it a temperature variation with an amplitude of 0.05C? I have some vague memory of a comment on your blog that the solar cycle causes temperature variation at the earth of 0.1C, but I couldn’t find it today. If so, there are two problems:
- your model assumes no thermal inertia, even though it is considerable. A simple COS curve seems to be far too simple a model for solar forcing, no matter what it’s magnitude
- it’s still too large IMHO, because it would imply that the much larger annual variation of 7% in the solar constant should induce an annual cycle between January and July of 7C which would swamp all other monthly anomalies, something that is just not present in the data.
Q4. Given all the above can be corrected (or you can convince me that it’s valid), shouldn’t you use the solar corrected model as the basis for an analysis of underlying trends?
I’d also comment that the lack of this correction for reduction in solar forcing is to pull down the trend towards the end of the your 7 year period - almost monotonically. Your inclusion of a reduction in solar forcing in your main graph is almost entirely responsible for your cooling effect.
Implementation of Cochrane-Orcutt
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Sheet: ‘Regress_All’
I have some comments and questions about the implementation of Cochrane-Orcutt here.
Firstly, in column I, you do a simple average of the input anomalies across all series. I’ve commented on this before - it’s not valid as they have different bases.
Q5. Have you noted that a regression on your average has an r2 of only 0.0038? Isn’t that rather small to be drawing conclusions from?
Next you have a circular reference here (which Excel detects as an ‘Inconsistent Formula”)
The remaining residuals in column L are calculated as
=m_co*G8+b_co-H8
But m_co and b_co are calculated as LINEST() of columns G and H in row 113 and therefore feedback into the calculation above.
You may not have noticed this because your sheet appears to stop Excel complaining about this by what appears to be a manual cut and paste of the rho value from J5 to H5. Excel can no longer detect the circularity.
Q6. Have you checked that your calculations are unaffected by this circularity?
Q7. What is your convergence criteria? Why do you iterate only once?
You comment here (http://rankexploits.com/musings/2008/correcting-for-serial-autocorrelation-cochrane-orcutt/) that OC is iterative and should be carried out until convergence occurs in the error term. Specifically Pold and Pnew.
So you’re looking at the correlation of residuals at each iterative step, but there’s no evidence of convergence there. In J5 and L5 you correlate the new and old residuals and get two values 0.568 and -0.054. Markedly different. Was that your convergence criteria? Shouldn’t you have done it one more time to make sure?
(I tried to check this by cutting and pasting and doing another iteration, but I may have done it wrong - it appeared to me to be diverging rather than converging)
Q8. Have you backtested this model against other 7 year periods?
lucia May 21st, 2008 at 5:39 am
JM–
Many of your current questions reflect that you a) have not read the previous blog posts and b) don’t know how CO is done. It is an iterative process, as has been discussed both here, in the links I posted when I first posted the CO etc.
The discussion of the comparison to past 7 year periods will be discussed this week. The comparison will be made to periods known to be unaffected by major stratospheric volcanic eruptions.
JM May 21st, 2008 at 8:42 am
Lucia
“Many of your current questions reflect that you a) have not read the previous blog posts”
I have read them. I am either making specific criticisms of some of those choices, or more pertitently, questioning your implementation rather than your methods.
“… CO … is an iterative process”
I agree. Why doesn’t your spreadsheet implement it iteratively then?
(And what is your justification for 0.1C of solar forcing over the cycle?)
lucia May 21st, 2008 at 9:04 am
JM–
I iterate manually. It converges instantly.
(And what is your justification for 0.1C of solar forcing over the cycle?)
Had you read the post on the solar cycle, you would know that this is a computation done at JohnV’s request based on a precise quote from the IPCC AR4. He requested that we do a back of the envelop, speculative calculation to see how much difference the solar cycle could possibly make. I did this to show him the answer if we used is value.
You will also find that various people, including me, think that magnitude of effect is dubious. So, even though it has an 11 year periodicity — outside the 84 month band– it does not explain the falsification.
Had you read the posts, you would be aware of this.
JM May 21st, 2008 at 9:14 am
Lucia: “I iterate manually.”
Do you? How? Your spreadsheet doesn’t, it calculates one error term and then does a plot. If you iterate outside the spreadsheet until convergence then you’re not plotting or publishing those results.
“It converges instantly.”
Hmmmm. Do you mean without iteration, with one iteration, with two, or what?
“you would know that this is a computation done at JohnV’s request based on a precise quote from the IPCC AR4.”
Sorry, I don’t recall seeing it. Could you give me a specific pointer to the comment with the calculation (and also the AR4 quote), perhaps it will satisfy my curiosity.