IPCC Projections Overpredict Recent Warming.
It’s true. Every climate blogger knows it. Global Mean Surface Temperature have gone a bit flat. But is the recent flat trend statistically significant? Well, as my readers know, I took up Roger Jr.’s suggestion and set out to compare IPCC projections to data collected after the projections were made.
Over the weekend, I applied the Cochrane-Orcutt method to monthly GMST data, as reported by four separate groups (GISS, Hadley, UAH and RSS).
This analysis technique permitted me to estimate the best fit line that fits the monthly data and also includes 95% confidence intervals for the slope. I have now compared the trend line that best fits data collected after 2001 to IPCC projections for trends after 2001.
What’s important in the graph?
The short term IPCC projection is show with a red line which I superimposed on the IPCC chart. They predict that during the first 3 decades after 2000, the mean trend will be 2C/century; I indicated that with the straight line. (I extrapolate beyond 2030, so you can see the IPCC expect the trend to increase with thime.)
The shaded area on the IPPC graph shows the IPCC uncertainty intervals around their projected trend.
So, we should expect that if the IPCC trend is correct data trends will fall inside the shaded areas of the graph.
I estimate the empirical trend using data, show with a solid purple line. Not it is distinctly negative with a slope of -1.1 C/century.
But, more importantly, the IPCC projections for the mean trend, as indicated by the red line do not fall inside the 95% confidence intervals for the data. Those confidence intervals are bounded by the two purple dashed lines.
So, both the central tendency of the IPCC projections and the uncertainty intervals the IPCC applied to their projection fall outside the 95% confidence bands for the recent trend based on the data collected after the IPCC projections were made.
So, now, in answer to Roger Jr’s question posed in January:
What behavior of the climate system could hypothetically be observed over the next 1, 5, 10 years that would be inconsistent with the current consensus on climate change?
The current data appears at least somewhat inconsistent with the near term projections by the IPCC. The central tendency of the IPCC projection, m=2.0C/century, falls outside the 95% uncertainty intervals for trends estimed based on data collected since 2001. Moreover, the full uncertainty interval for trends projected by the IPCC fall outside the empirical uncertainty intervals.
Obvious follow on questions
When readers see this graph, I suspect these questions will come to mind:
- What does this mean in terms of IPCC projections: It appears that IPCC projections for the near term trend are high. I don’t know why they are high, but there have been no recent major volcanic eruptions. The climate modelers and NASA say that solar activity can no longer have a significant impact on the trend. “It’s in the pipeline” cannot explain a slowdown in the trend. The effect of thermal mass is to cause the temperature to rise more slowly initially, and then rise more rapidly later as the ocean begins to warm.
- Do I think AGW is ‘over’? Absolutely not.
The confidence interval for this data set are -3.3 C/century < m < + 1.1 C/century. The confidence intervals when I compute the trend with data from 1979 is +1.0 C/century < m < + 2.1 C/century. The newer data is not inconsistent with the longer term trend. (If it were, this would be surprising. I included the newer data when calculating the longer trend!)
Nevertheless, based on data collected after the IPCC projections were first published, the IPCC projections appear to be on the high side. (That said: I would strongly prefer to defer full judgment on the consensus prediction. For many reasons, I prefer to use annual average data rather t han monthly data.).
It has always been a claim of skeptics that long term trends may exist in climate. If they do, any tests based on short series of data are problematic, because it is not possible to detect the true autocorrelation in temperatures from the data set. This applies equally to proving AGW exists and to falsifying it.
- Could different results be obtained with fancier statistical methods? Sure. Possibly someone will perform them.
The main purpose of applying Cochrane-Orcutt or any method to deal with serial autocorrelation, is to get a better estimate of uncertainty intervals. When serial autocorrelation exists, but is not corrected, the serial autocorrelation makes the uncertainty intervals appear to much too small.
It is also worth nothing that even though Cochrane-Orcutt widens the uncertainty intervals significantly, they may still be to small. The reason is that the uncertainty intervals I posted do not account for the uncertainty in the estimate for the serial autocorrelation on the uncertainty in the trend, “m”.
- Did I do lots of thorough fancy checks on this fit? No. I just assumed for the purpose of testing the IPCC model that the data should fit a linear trend and that the scatter around the trend is ‘noise.’ I recognized that the residuals for monthly data are serially autocorrelated and applied Cochrane-Orcutt to the data.
I didn’t check whether the residuals are normally distributed or do any additional checks. The residuals don’t need to be normally distributed to obtain a trendline that minimizes the sum of the square of the residual. However, the distribution of the residuals does matter if we are estimating uncertainty intervals on “m”.
That said, I’m not sure it’s worth a great deal of effort to do a whole lot of checks, at this point. I think it’s better to recognize the uncertainty in the empirical trend is quite large. It’s possible that if the uncertainty in the correlation coefficient were included in the estimate of the confidence intervals, they might widen. If they widened sufficiently, the IPCC projections might end up falling inside the uncertainty band for the data.
(It’s also worth understanding that temperature is not literally expected to vary linearly. However, the near term predictions by the IPCC are nearly linear. So, for the purpose of testing that projection, I assumed the near term trend is linear.)
What next?
Well, now that I think I have a handle on how to do this, I’ll be updating the evaluation rather regularly. We should expect that over the next few years, the confidence intervals on the trend will narrow. I suspect the trend will rise. After all the 30 year trend is positive, and radiative physics do argue strongly for some warming.
Still, as an empiricist, I do like to compare data to projections. So, I plan to do so.
Related posts
For new readers, here are links you might like to read:
- How I applied Cochrane Orcutt:http://rankexploits.com/musings/2008/correcting-for-serial-autocorrelation-cochrane-orcutt/>Correcting for Serial Autocorrelation.
- Why I start comparison in 2001: What Are The IPCC Projections? And How Not to Cherry Pick.
- My initial response to Roger Jr.’s hypothetical question. http://rankexploits.com/musings/2008/what-weather-would-falsify-the-current-consensus-on-climate-change/
References:
Updates:
- March 10: I adding a paragraph to better explain the graph. Later– modified graph for clarity and typos.
- March 11: Link to spreadsheet: GMST data after 2001
- March 11: Images for discussion in comments. (Click to enlarge)
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77 Responses to “IPCC Projections Overpredict Recent Warming.”
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lucia March 16th, 2008 at 6:49 pm
Boris:
We are all in violent agreement that the IPCC projections shows means and does not include weather noise. This method is meant to test the validity of hypothesis that predict a the underlying trend– a mean.
If we start comparisons after the IPCC made it’s projections, their central tendency and standard error bands fall outside the 95% confidence intervals of climate trends that are consistent with the noisy data. So, the data include weather noise, the IPCC graphic doesn’t. And the technique is supposed to explain whether that noise-less graphic is consistent with the noisy data.
As far as I can tell, this is precisely the graphic or sort of graphic that is suitable for testing.
If you think there is a different graphic, suggest it. Then we can discuss it.
lucia March 16th, 2008 at 6:52 pm
MarkB–
If you read the document, you will find that they working group found projections for all scenarios were similar for the first few decades. They diverge afterwards.
In any case, the issue of predicting a thunderstorm and testing with 1 days weather is irrelevant. The IPCC graphs was predicting climate trends, not weather. Also, the test method uses a collection of data and the method results in confidence intervals.
Boris March 16th, 2008 at 7:23 pm
Okay, but you seem to make a big deal that the IPCC’s confidence interval falls outside of the 95% error bars for the trend you calculated (emphasis mine throughtout):
And again:
And in another post:
(emphasis mine throughout)
I don’t think such comparisons are warranted.
If the IPCC put error bars based on internal variability, then the two ranges would overlap.
lucia March 16th, 2008 at 8:57 pm
No Boris. That would be an inappropriate comparison. The correct comparison is to compare the climate trends that are consistent with the weather to the IPCC climate trends with no internal variability. That’s the way this test is done. By definition, climate trends do not include internal varibiability aka “climate noise”. So, the full confidence intervals for climate trends does not contain the additional variablity due to noise by definition.
There is also a specific test to compare two sets of weather data fit the same climate trend. But, in that case, we need two sets of weather data. By definition, the IPCC does not create weather data, the predict climate trends.
Frank O'Dwyer March 17th, 2008 at 12:40 am
Lucia,
“The IPCC shaded areas are their range of projections for the possible climate trends compatible with the different climate sensitivity. Yes. That’s how I interpret them.”
But you also interpret them as an uncertainty range on their projection. But on short timescales, they clearly are not that. They can’t be. Do this:
Take a piece of paper and hide the graph prior to 2001. Take another piece of paper and slide it from the right until you are only including a single month or a single year’s worth of projections. Now, look at that. Does what you are looking at seem to you like the error bars that anyone in their right mind would put on an estimate of the central trend of one month’s or one year’s weather? Yet that is how you are using them - on longer timescales certainly, but still short enough to be a problem. I don’t think that sliding the paper out to 2008 makes that problem go away, though if you slide it out far enough then it should become insignificant.
Nor do I think the error bars in your analysis compensate for this. For example, please don’t tell me that if you did the analysis for one month or a year, *your* error bars would become so wide as to include the narrow bars between the pieces of paper - i.e. no harm, no foul. I am sure they would. The point I am making is that the IPCC error bars are in fact not present and these narrow bars are for a different thing, so your comparison (of the uncertainty ranges only) is not like for like in the first place.
“My purple lines are the 95% confidence intervals for climate trends that would be compatible with the weather we have had.”
Well, that’s another thing. The data you are measuring includes not just noise but the effects of physical phenomena that we know something about. Does your analysis assume that internal variability is uniformly distributed? If so then don’t we already know this assumption is wrong on these timescales? We know for example that summer is warmer than winter, and I think your data includes a little more winter data than summer data (one more winters than summers). We also know that a La Nina is recently cooling the data and hence the short-term trend (an effect that shouldn’t alter the long term trend). We could expect that both of these will bias your observed trend downward. We can also expect that had the IPCC really been attempting to provide error bars for the short term, they would have been wider to allow for these effects (which are well known and no surprise to anybody).
Raven March 17th, 2008 at 1:28 am
Frank,
Lucia may need to correct me but my understanding is Lucia is looking at the observed data and working backwards and figuring out what underlying linear trends are plausible given the actual data. This means she only really cares about it is the uncertainty in the underlying linear trends predicted by the models. Adding ‘weather error bars’ to the IPCC plot does not change anything when dealing with trends rather than an individual temperature measurement.
That said, Atmoz has a post where he superimposes a cyclic ENSO effect onto the IPCC trend and illustrates that a linear trend + large cyclic ENSO is consistent with the observed data. This is a reasonable premise, however, he is also implying that the simple linear model presented in the IPCC reports is wrong because it did not include a large cyclic ENSO effect. You cannot argue that a large cyclic ENSO effect is the same as ‘random weather noise’ because it is not random.
You argument wrt seasons is a red herring - all of these discussions are based on monthly anomolies which removes daily and seasonal variations from the picture.
Dr. Gerhard Loebert March 17th, 2008 at 1:47 am
A Compilation of the Arguments that Irrefutably Prove that Climate Change is driven by Solar Activity and not by CO2 Emission
Dr. Gerhard Löbert, Otterweg 48, 85598 Baldham, Germany. March 6, 2008.
Physicist. Recipient of The Needle of Honor of German Aeronautics.
Program Manager “CCV, F 104G” (see Internet).
Program Manager “Lampyridae, MRMF” (see Internet)
Conveyor of a super-Einsteinian theory of gravitation that explains, among many other post-Einstein-effects, the Sun-Earth-Connection and the true cause of the global climate changes.
I. Climatological facts
As the glaciological and tree ring evidence shows, climate change is a natural phenomenon that has occurred many times in the past, both with the magnitude as well as with the time rate of temperature change that have occurred in the recent decades. The following facts prove that the recent global warming is not man-made but is a natural phenomenon.
1. In the temperature trace of the past 10 000 years based on glaciological evidence, the recent decades have not displayed any anomalous behaviour. In two-thirds of these 10 000 years, the mean temperature was even higher than today. Shortly before the last ice age the temperature in Greenland even increased by 15 degrees C in only 20 years. All of this without any man-made CO2 emission!
2. There is no direct connection between CO2 emission and climate warming. This is shown by the fact that these two physical quantities have displayed an entirely different temporal behaviour in the past 150 years. Whereas the mean global temperature varied in a quasi-periodic manner, with a mean period of 70 years, the CO2 concentration has been increasing exponentially since the 1950’s. The sea level has been rising and the glaciers have been shortening practically linearly from 1850 onwards. Neither time trace showed any reaction to the sudden increase of hydrocarbon burning from the 1950’s onwards.
3. The hypothesis that the global warming of the past decades is man-made is based on the results of calculations with climate models in which the main influence on climate is not included. The most important climate driver (besides solar luminosity) comes from the interplay of solar activity, interplanetary magnetic field strength, cosmic radiation intensity, and cloud cover of the Earth atmosphere. As is shown in Section II, this phenomenon is generated by the action of galactic vacuum density waves on the core of the Sun.
4. The extremely close correlation between the changes in the mean global temperature and the small changes in the rotational velocity of the Earth in the past 150 years (see Fig. 2.2 of http://www.fao.org/DOCREP/005/Y2787E/y2787e03.htm), which has been ignored by the mainstream climatologists, leaves little room for a human influence on climate. This close correlation results from the action of galactic vacuum density waves on the Sun and on the Earth (see Section II). Note that temperature lags rotation by 6 years.
5. From the steady decrease of the rotational velocity of the Earth that set in in Dec. 2003, it can reliably be concluded that the mean Earth temperature will decrease again in 2010 for the duration of three decades as it did from 1872 to 1913 and from 1942 to 1972.
6. The RSS AMSU satellite measurements show that the global temperature has not increased since 2001 despite the enormous worldwide CO2 emissions. Since 2006 it has been decreasing again.
II. Physical explanation for the strong correlation between fluctuations of the rotational velocity and changes of the mean surface temperature of the Earth
Despite its great successes, the gravitational theory of the great physicist Albert Einstein, General Relativity, (which is of a purely geometric nature and is totally incompatible with the highly successful quantum theory) must be discarded because this theory is completely irreconcilable with the extremely large energy density of the vacuum that has been accurately measured in the Casimir experiment.
Seaon Theory, a new theory of gravitation based on quantum mechanics that was developed eight decades after General Relativity, not only covers the well-known Einstein-effects but also shows up half a dozen post-Einstein effects that occur in nature. From a humanitarian standpoint, the most important super-Einsteinian physical phenomenon is the generation of small-amplitude longitudinal gravitational waves by the motion of the supermassive bodies located at the center of our galaxy, their transmission throughout the Galaxy, and the action of these waves on the Sun, the Earth and the other celestial bodies through which they pass. These vacuum density waves, which carry with them small changes in the electromagnetic properties of the vacuum, occur in an extremely large period range from minutes to millennia.
On the Sun, these vacuum waves modulate the intensity of the thermonuclear energy conversion process within the core, and this has its effect on all physical quantities of the Sun (this is called solar activity). This in turn has its influences on the Earth and the other planets. In particular, the solar wind and the solar magnetic field strength are modulated which results in large changes in the intensity of the cosmic radiation reaching the Earth. Cosmic rays produce condensation nuclei so that the cloud cover of the atmosphere and the Earth albedo also change.
On the Earth, the steady stream of vacuum density waves produces parts-per-billion changes in a large number of geophysical quantities. The most important quantities are the radius, circumference, rotational velocity, gravitational acceleration, VLBI baseline lengths, and axis orientation angles of the Earth, as well as the orbital elements of all low-earth-orbit satellites. All of these fluctuations have been measured.
Irrefutable evidence for the existence of this new, super-Einsteinian wave type is provided by the extremely close correlation between changes of the mean temperature and fluctuations of the mean rotational velocity of the Earth. (see the figure referred to in Section I.4). Einsteinian theory cannot explain this amazing correlation between two physical quantities that seem to be completely unrelated.
While the rotational velocity of the Earth and the thermonuclear energy conversion process on the Sun react simultaneously to the passage of a vacuum density wave, a time span of 6 years is needed for the energy to be transported from the core of the Sun to the Earth’s atmosphere and for the latter’s reaction time.
As can be seen, super-Einsteinian gravitation reveals the true cause of climate change.
Frank O'Dwyer (19 comments.) March 17th, 2008 at 1:51 am
Raven,
“You argument wrt seasons is a red herring - all of these discussions are based on monthly anomolies which removes daily and seasonal variations from the picture.”
Right you are. (I was just about to post to retract that anyway, I forgot these were anomalies - that’s what I get for writing on the train.)
However I think that the point about ENSO is still valid on these time scales (especially as we actually know it’s been operating in these timescales).
lucia March 17th, 2008 at 4:35 am
You are correctly describing a procedure to estimate the error bars on the weather. I am using a statistical technique that tests the the uncertainty intervals for the climate.
Yes. In the limit of only less than 3 months data, my uncertainty intervals become infinite. With 3 months data, they are very, very, very wide. That’s the way the uncertainty bars for the trend work. This is because they are determined using a formula that contains the (N-2) in the denominator, with N = the number of data points in the fit.
In contrast, had the IPCC provided uncertainty intervals for the weather noise (which they did not) the uncertainty intervals for the weather noise remain more or less constant at all time intervals.
The anomaly are defined to deal with the summer/winter variablity. January is normalized to January etc. That’s what I understood when I read the literature and because I wanted to confirm it when trying another analysis, I actually emailed both Hansen and Kennedy about the monthly anomoly issue.
Honestly Frank, you are just trying to throw things out there.
Boris March 17th, 2008 at 4:47 am
1. Since you’ve chosen a graphic that shows only 1 SD, I’d say you don’t even have the full error bars for climate trends shown.
2. I realize how you are doing the test, but it’s an apples to oranges test and is pretty much meaningless. The climate trend is for 30 years for a reason–to eliminate weather noise.
3. Your comparison to IPCC error bars for the climate signal is misleading.
Boris March 17th, 2008 at 5:03 am
For instance, when you say the IPCC “overpredicts recent warming” how do you justify this given that the IPCC does not make a prediction for the recent warming? How can someone overpredict something they have not made a prediction for?
It must be frustrating not to be able to falsify the projections after several years, but that’s just the nature of climate.
Frank O'Dwyer March 17th, 2008 at 5:46 am
Lucia,
“You are correctly describing a procedure to estimate the error bars on the weather. I am using a statistical technique that tests the the uncertainty intervals for the climate.”
No, I am describing how any projection of climate trend for such a short interval would look, because the climate trend would be drowned in the weather noise. Are you seriously suggesting that if you asked the IPCC, or anyone, for a projection of the data trend for just two months then slice of the graph I have just described (with those very narrow error bars) is what they would hand you?
If not then why do you think it is appropriate to compare to those error bars for any short timescale.
“Yes. In the limit of only less than 3 months data, my uncertainty intervals become infinite. With 3 months data, they are very, very, very wide.”
Exactly. And the uncertainty intervals of the IPCC projections for climate trend would look a lot wider on that timescale also, for precisely the same reason. The fact that they do not should tell you that they are not confidence intervals on the projected trend on short timescales.
“The anomaly are defined to deal with the summer/winter variablity”
Gack, yes I know, I already retracted that. Sorry about that. I realised that you were probably using anomalies after I’d posted, and indeed you are. Apologies for any confusion caused.
However I still think you may be neglecting components such as ENSO which on larger timescales don’t matter so much but in this case we can surmise are having an effect. It’s a bit like trying to test the hypothesis that a car will go faster when someone steps on the accelerator. No amount of stats technique will recover from failing to take account of the fact that we saw somebody step on the brakes.
lucia March 17th, 2008 at 8:29 am
Frank: If we assume a linear model applies, the underlying trend, and individual temperature data, related this way:
Ti= m ti + ei
“T” are the temperature, “m” is the underlying, aka climate, aka mean trend. the “ei” is the measurement noise. t is the temperature. The “i” subscripts denote a data triple. (Temperature, time, weather noise.)
The weather noise has a standard deviation– known as “weather noise”.
Based on any set of “Ti” and “ti” linear regression method estimates the true value of “m” and provides a best fit sample value for “m”. I get -1.1 C/century. That’s a “mean”. It doesnt not contain “weather noise”. If T had no weather noise, and was dictated only by climate and followed this trend, temperture would vary as show in by the solid purple line in my graph. That would be a “climate” prediction. It would not be a weather prediction.
Because this is statitics, based on a set of Ti and ti, I can also estimate the both the standard deviation in ei, associated with weather noise, (which we call se) and the standard error in the slope: sm.
The standard error in “m” the climate trend, sm tells us how close to the true “m” the best fit “m” is likely to be. Both the sm and se are provided by Linest, and t their meanings are explained in the “HELP” file in excel and undergraduate books on statistics.
sm have to do with climate. We obtain the confidence intervals for “m” from this. If we later wish to find uncertainties for individual temperature we look at ei.
If one wishes to test climate projections, one uses sm, and
The IPCC graphs contains “m” and “sm” type information only. It does not include “ei” data or “Ti” or “ti” data. That is: It does not contain any information about weather variability at all.
My uncertainty intervals test “m” information with “sm” type information. It is designed to test projections of “m” and sm tupe information, stripped of all weather noise.
You keep trying to explain that the IPPC chart doesn’t describe weather data. That’s right. It doesn’t. I have said repeatedly it doesn’t; I never thought it contained that data.
Had the IPCC chart included weather information (se) I would have had to subtract that out to do apply my hypothesis test.
As for ENSO: Enso introduced serial autocorrelation in the temperatures. This technique is designed to remove that from the estimate, and pump up the uncertainty intervals. Had I not corrected for the serial autocorrelation, my estimate on uncertainty in m (sm) would have been less htan 1/2 the value I used. It is because of Enso, that I corrected for serial autocorrelation.)
That this may be an outlier is true. Things that happen 5% ofthe time, do happen 5% of the time. If that is your point, we agree. This could be an outlier. If so, the falsification will reverse. But that doesn’t make this analysis wrong. It simply means that things that happen 5% of the time do happen, and it just so happened immediately after the IPCC made its projections.
There is really nothing I can do about coincidences. They happen.
Frank O'Dwyer March 17th, 2008 at 9:49 am
Lucia,
“The IPCC graphs contains “m” and “sm” type information only.”
This is the key point I think. I say it doesn’t contain sm or even ’sm-type’ information for your purposes, at least not yet. This is what I’ve been saying all along. And yes I’m talking about sm relative to the climate trend, as you are.
The proof, again, is if you look on some really short timescales, i.e. imagine the IPCC projection stopped in March 01 or Jan 02. If it really included sm type information, then sm would be much larger on those scales. (Not as wide as yours - for example never infinite as it would be constrained by physics - but wider than shown on the graph). Instead, eyeballing the graph, it looks like it starts out as small as 1/10 or even 1/100 of a degree. Does that mean that the IPCC is really saying that it or anyone else is able to project the climate trend in a few months or a year’s data to that accuracy - as in ‘what will the observed climate trend look like for the next few months’? No. Is the IPCC really betting that the 1 month or 1 year trend that you will see will be X + or - 0.01 degrees with a 95% CI? No. Nor is there any reason to take those error bars as representative of the IPCC’s CI around its projection on the ~7 years scale that you’re so far forced to use. They have simply not stated this CI.
Put it another way, forget about the IPCC graph. Look at your own analysis. You are trying to project the trend based on stats alone and look at the width of the error range that you have. What makes you think that anyone else trying to project the climate trend on the same timescale wouldn’t have similarly wide error bars? What makes you think that they can ignore the weather on this scale any more than you can? You’re both trying to project the same thing!
As Boris and I have pointed out, if you allowed the IPCC the same luxury of stating a real 95% CI with representative error bars for their projection, then your ranges would overlap.
“You keep trying to explain that the IPPC chart doesn’t describe weather data.”
No I’m saying that the confidence bars on that graph up there do not describe the 95% CI for the projection of the climate trend on short timescales. They could only do that on long timescales, i.e. in hindsight when the natural variability uncertainty has been averaged out.
lucia March 17th, 2008 at 10:46 am
Frank: You are still wrong.
My trends identify the trends consistent with a string of data colleted after the IPCC made their projections for “m”. Be cause I can only get 1 data point a month, my uncertainty intervals are infinite at time zero.
But the IPCC didn’t “project” using data starting after they made their projections. This specific limitation simply doesn’t apply to their uncertainty estimate.
The IPCC trends are supposedly, the result of processing ensembles of climate models of various sorts, looking at past data, and a variety of things. That means there is no reason their uncertainty in their projected “m” must be large at time =0. They
First: Are you suggesting the IPCC report can be read to imply that a trend of 0C/century fell within the “very likely limits”? Or some other trend?
Second: It’s impossible to discover the 95% confidence limits based on the IPCC document. Based on the curves appearance the intervals on the chart, the +1 sigma limit is larger than the -1sigma limit. Additional informaiotn is not provided. We know the distribution is not normal. So, it is not even possible to do as you suggest.
All we can do is test the limits they communicated to the public. If the wish to explain the fuller limits (possibly explaining that their projections really included the possibility of nearly indescernable warming) I suggest they clarify this.
If almost now warming is consistent with that document, I suspect policy makers would like to know this!
Third: For normally distributed randome variables intervals can overlap and still have their means. In fact, it happens all the time. I have a graph in my next post.
What I have shown is range of climate trends communicated to the public, falls outside the region supported by the data.
Frank O'Dwyer (19 comments.) March 17th, 2008 at 12:53 pm
Lucia,
“First: Are you suggesting the IPCC report can be read to imply that a trend of 0C/century fell within the “very likely limits”?”
Yes. Obviously so for the ultra short-term (a few months). How could it not? The observed trend could kick off in almost any direction. Any prediction on that scale would include almost any slope.
For the term of 7 years on your chart the CI might include 0C/century also. Certainly it seems likely that the CI for their slope would at least overlap your CI for your slope. How much they’d overlap is important but it is also not clear.
For the long term 0C/century is ruled out by their projection and the error bars on there would start to be appropriate to use.
“Second: It’s impossible to discover the 95% confidence limits based on the IPCC document.”
Probably true, but irrelevant. The fact you don’t have it doesn’t mean you don’t need it.
lucia March 17th, 2008 at 1:53 pm
Frank– No. The fact that I don’t need it means I don’t need it.
The unalterable fact is: Their predictions and related uncertainty, as communicated to the public falls outside the support given by later data. The graph is their climate trend projections as published. If you think they blundered and should have created a different illustration, you’ll need to communicate that with them.
The trend in the weather could. And, if I computed a trend based on month 0 and month 1, the unceratinty in my estimate of the climate trends consistent with the estimate would be infinite. So, I would be unable to falsify the IPCC trend no matter what data I got. This is true even though the IPCC data for time zero appears to collapse to a point.
I’m hoping to have time to make a graphic to illustate it, but in the meant time, I’m afraid I’m not going to discuss this particular issue, because it’s clear we aren’t getting anywhere.
Mark Bahner (3 comments.) March 18th, 2008 at 8:20 am
Hi Lucia-
You advise me, “If you read the document, you will find that they working group found projections for all scenarios were similar for the first few decades.”
I have read “the” document (the Fourth Assessment Report). In fact, I’ve read both documents. The document to which Ian Castles was referring was the Third Assessment Report, not the Fourth. This can be seen by the fact that Ian quoted Ross Garnaut as writing, “Recent rises in global temperatures [have been] at the upper end of what was predicted [by the IPCC] in 2001.″
Since the reference is to what was “predicted”(sic!) in 2001, it must be a reference to the Third Assessment Report, not the Fourth (i.e., not the one you’re referring to when you urge me to “read the document”).
I was merely pointing out that the Third Assessment Report made no predictions at all. Instead, they made pseudo-scientific projections. It you read any of the books of the “Limits to Growth” series, you’ll see the same type of pseudo-scientific nonsense masquerading as science.
As far as my analogy about thunderstorms or snowstorms, you misunderstood my point. (Perhaps I should have used something other than a weather forecast for my analogy. My apologies if the analogy created confusion, rather than providing explanation.)
My point was that the “predictions” (actually, “projections”) in the Third Assessment Report could be described as “conditional forecasts” (although “pseudoscientific rubbish” would be more accurate). A conditional forecast can only be falsified if the condition(s) upon which the forecast was made come to pass. If the conditions upon which the forecast was made do not come to pass, then the forecast can’t be shown to be wrong. To give an example unrelated to weather, suppose you were wearing blue near a bull pasture at 9 am last Sunday. And suppose I told you at 8:59 am that, if you were wearing red, the bull would charge you, because bulls always charge someone dressed in red on Sunday March 16, 2008 at 9:00 am. Well, you’d never know whether my conditional forecast was false, if you couldn’t get dressed in red by 9:00 am.
This problem, and another, occurs in coming-few-decades “prediction” of the Fourth Assessment Report. On page 7 of the Summary for Policymakers, the IPCC writes:
“For the next two decades a warming of about 0.2°C per decade is projected for a range of SRES emissions scenarios.”
The first problem is that there is no definition of “about” in that sentence. If the exact level over the next 20 years is 0.1°C per decade, is that “about” 0.2°C? What if the exact level of warming over the next two decades is 0.06°C per decade? Is that “about 0.2°C per decade”?
There is also the problem that the temperature rise is projected “for a range of SRES emissions scenarios,” but does anyone know whether that “range of SRES scenarios” actually encloses the likely climate forcings over the next few decades? For example, as I’ve pointed out, the methane atmospheric concentrations in the IPCC TAR (and AR4) are likely higher than will actually occur in the next 20-30 years. Black carbon emission projections in the TAR were likely higher than will actually occur. (On the other hand, on the cooling side, projected SO2 emissions were also higher than will probably occur.)
So I appreciate what you’re trying to do, Lucia. But I think you’re dreaming if you think that a significant portion of the “climate change community” is going to accept that the IPCC “projections” were shown to be false…regardless of present or future data.
The whole point of what the IPCC has been doing for more than a decade has been to avoid making falsifiable predictions.
Mark
lucia March 18th, 2008 at 9:01 am
MarkB.
Ok. Sorry, I did think you were discussing the fourth report. Yes, if I understand, the fourth report is based on models run earlier and reported earler. The results of those models had been published, so they are “frozen”. It’s for that reason, the data stops long before 2007 when it was published.
I’ve looked through the TAR, and I did already comment that it’s difficult to dig up precise projections. I’ve been looking a bit, and I plan to post something at sometime, but the way blog discusion go. . . Well. . .
This is why I dredged up the graphic in the AR4. It at least shows some uncertainty intervals. I can’t find any discussions of their estimate of overall uncertainty using words.
I have no illusions about modifying the behavior of the IPCC. I like fiddling with numbers, I’m currently interested in this and I started a blog. I could have used my knitting blog, but I think my readers would have been bored. So, I started this one.
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Wolfgang Flamme March 19th, 2008 at 12:13 am
@JohnV
The mean of trends simply does not account for individual differences in their significance and error bars. Thus I suggest you redo your analysis.
Peter Gallagher (4 comments.) March 19th, 2008 at 5:24 am
I think Ian Castles has made an important point (at least in the Australian policy context). Our national enquiry into a response to ‘climate change’ is recommending drastic precautionary measures that are not proportionate to any threat that they have evaluated.
They claim both to be unable to do this evaluation and to accept (as I.C. points out) that the consensus trends are at the top end of an alarming range of forecasts. This is the worst way to make public policy: to adopt a dreadful (but at best contentious) threat uncritically as the justification for a dreadful (but certainly dreadful) attack on the well being of ordinary citizens.
I am not a statistician, and do not know what we should expect from the removal of autocorrelation errors. I note that Ian Castles, who is a statistician, does not endorse the conclusion, although he seems friendly toward it.
I am too. Lucia’s argument seems to plausibly fit the facts (of a halt and apparent fall in average global temperature anomalies). Could we have more responses from statisticians?
Roger W. Cohen March 19th, 2008 at 5:57 am
I am interested in what can be said with statistical validity about the entire record of IPCC projections, going back to the 1990 report. This would help address the objection: “7 years is too short,” no matter how valid the statistics. Certainly 20 years is sufficiently long to say something about the record of model projections, especially since they are the basis for end of century predictions and policy proposals based on them. So I did a rough calculation for all four IPCC report projections. Each calculation was similar to Lucia’s for the 2001 case. Differences were (1) I used only HadCRUT3 data, not the average of four data sets; (2) I treated month-to-month correlations per Lucia, assuming her 2001-2008 value (0.78) of the 1-month autocorrelation coefficient for residuals for all four epochs (one should really do it the way she did); and (3)I added the recently released Hadley February 2008 datum. For the IPCC projections I used Roger Pielke Jr.’s entries in his January 18 blog post. The result is:
1. Projected 2. Regressed 3. Approx. 4. Standard 5. Statistic
Central Slope Degrees Deviation ( 1. – 2. )/4.
IPCC Tendency HadCRUT3 of of Slope 2.
Deg./Decade) (Deg/Decade) Freedom (Deg./Decade)
1990 0.315 0.20 53 0.035 3.2
1995 0.17 0.14 38 0.071 0.4
2001 0.20 - 0.16 20 0.105 3.4
2007 0.20 - 5.34 2 2.47 2.2
A few remarks:
1. The table results are essentially Lucia’s analysis for 2001 (but for Hadley only) extended to the other three IPCC projections.
2. Of the four attempts, 1995 does the best, in agreement with visual inspection. The other three lie outside the usual range of statistical uncertainty.
3. The 2001 numbers appear to be very close to Lucia’s values. They should differ slightly because I used only Hadley data vs. her average of four data sets, and I included February 2008.
4. One might be tempted to throw out 2007 because of the small number of DOFs, but that effect is included in the large standard deviation. And it so happens that the r2 for the trend is pretty good (0.76) and correlations for this epoch are small. In any case, as will be seen below, it does not make much difference.
Now the question is, what do these values mean for the class of models? Can one make a statement about the collective accuracy of the models projections over nearly 20 years of records? I take the 4 IPCC projections as independent attempts to project the historical temperature trend in different epochs. This is not strictly true since the four epochs overlap to varying degrees. I use the four values of the statistic in the fifth column of the table to define a best estimate for the mean value of the difference between models and historical actuals, in units of the standard deviation of the historical actuals. Those four values also allow an estimate the standard error of the mean for the four attempts. This gives a mean of 2.3 standard deviations above the set of historical trend lines, with a standard error of the mean of 0.60. Then the statistic for the group of four “measurements” is 2.3 / sqrt ( 1 + 0.6^2) = 2.0. By this reasoning, the class of four attempts by IPCC models to project future climate falls outside statistical uncertainty; p = 0.98 that they are too high. This number drops only slightly to 0.97 if you throw out 2007.
I’ll welcome any comments on the validity of this approach or suggestions for how to deal with the model class problem. Even better I’d be happy if the pros did it.
Roger W. Cohen March 19th, 2008 at 6:02 am
I am interested in what can be said with statistical validity about the entire record of IPCC projections, going back to the 1990 report. This would help address the objection: “7 years is too short,” no matter how valid the statistics. Certainly 20 years is sufficiently long to say something about the record of model projections, especially since they are the basis for end of century predictions and policy proposals based on them. So I did a rough calculation for all four IPCC report projections. Each calculation was similar to Lucia’s for the 2001 case. Differences were (1) I used only HadCRUT3 data, not the average of four data sets; (2) I treated month-to-month correlations per Lucia, assuming her 2001-2008 value (0.78) of the 1-month autocorrelation coefficient for residuals for all four epochs (one should really do it the way she did); and (3)I added the recently released Hadley February 2008 datum. For the IPCC projections I used Roger Pielke Jr.’s entries in his January 18 blog post. The result is:
1. Projected 2. Regressed 3. Approx. 4. Standard 5. Statistic
Central Slope Degrees Deviation ( 1. – 2. )/4.
IPCC Tendency HadCRUT3 of of Slope 2.
Deg./Decade) (Deg/Decade) Freedom (Deg./Decade)
1990 0.315 0.20 53 0.035 3.2
1995 0.17 0.14 38 0.071 0.4
2001 0.20 - 0.16 20 0.105 3.4
2007 0.20 - 5.34 2 2.47 2.2
A few remarks:
1. The table results are essentially Lucia’s analysis for 2001 (but for Hadley only) extended to the other three IPCC projections.
2. Of the four attempts, 1995 does the best, in agreement with visual inspection. The other three lie outside the usual range of statistical uncertainty.
3. The 2001 numbers appear to be very close to Lucia’s values. They should differ slightly because I used only Hadley data vs. her average of four data sets, and I included February 2008.
4. One might be tempted to throw out 2007 because of the small number of DOFs, but that effect is included in the large standard deviation. And it so happens that the r2 for the trend is pretty good (0.76) and correlations for this epoch are small. In any case, as will be seen below, it does not make much difference.
Now the question is, what do these values mean for the class of models? Can one make a statement about the collective accuracy of the models projections over nearly 20 years of records? I take the 4 IPCC projections as independent attempts to project the historical temperature trend in different epochs. This is not strictly true since the four epochs overlap to varying degrees. I use the four values of the statistic in the fifth column of the table to define a best estimate for the mean value of the difference between models and historical actuals, in units of the standard deviation of the historical actuals. Those four values also allow an estimate the standard error of the mean for the four attempts. This gives a mean of 2.3 standard deviations above the set of historical trend lines, with a standard error of the mean of 0.60. Then the statistic for the group of four “measurements” is 2.3 / sqrt ( 1 + 0.6^2) = 2.0. By this reasoning, the class of four attempts by IPCC models to project future climate falls outside statistical uncertainty; p = 0.98 that they are too high. This number drops only slightly to 0.97 if you throw out 2007.
I’ll welcome any comments on the validity of this approach or suggestions for how to deal with the model class problem. Even better I’d be happy if the pros did it. Am sending this to Roger Jr. and Matt Briggs.
Roger W. Cohen March 19th, 2008 at 6:44 am
The table in 1198 came through as gibberish, but you can recover correct numbers from each horizontal sequence as follows:
Year IPCC Report: e.g., 1990
1. Projected Central Tendency Slope(Deg/Decade): e.g., 0.315
2. Regressed Historical Slope (Deg/Dec): e.g., 0.20
3. Approx. Degrees of Freedom: e.g., 53
4. Standard Deviation of Regressed Slope (Deg/Dec):e.g., 0.035
5. Statistic ( 1 - 2 )/ 4: e.g., 3.2
Mark Bahner (3 comments.) March 19th, 2008 at 6:36 pm
Hi Lucia,
You write, “I’ve looked through the TAR, and I did already comment that it’s difficult to dig up precise projections.”
That’s putting mildly! I’ve asked a fairly large number of climate scientists to label these assertions as, “true,” “false,” or “don’t know” regarding the projections in the IPCC TAR:
1) The IPCC thinks that there is an approximately 50/50 chance that the warming will be less than 3.6 degrees Celsius.
2) The IPCC thinks that there is an approximately 50/50 chance that the warming will be less than 3.1 degrees Celsius.
3) The IPCC thinks that there is less than a 10 percent chance that the warming will be less than 1.4 degrees Celsius.
4) The IPCC thinks that there is less than a 10 percent chance that the warming will be more than 5.8 degrees Celsius.
5) The IPCC thinks that there is more than a 50/50 chance that the warming will be less than 1.4 degrees Celsius,
6) The IPCC thinks that there is more than a 50/50 chance that the warming will be more than 5.8 degrees Celsius.
7) The IPCC thinks that there is more than a 99 percent chance that the warming will be less than 1.4 degrees Celsius.
Only John Nielsen-Gammon had the honesty to (correctly) label all the assertions as “don’t know.”
That is, a reader can read everything in the IPCC TAR, and still not know whether the IPCC thinks that there is more than a 99 percent chance that the warming will be LESS than 1.4 deg C, or a more than 99 percent chance that the warming will be MORE than 5.8 deg Celsius.
In other words, the “projections” in the IPCC TAR are completely invalid, as a matter of science. The make no falsifiable predictions.
And the AR4 is no different. The answer would still be “don’t know,” because the IPCC AR4 makes no assessment of the probability of occurrence of any of their scenarios.
I have also offered to debate any climate scientist, in any forum, this assertion: “The IPCC Third Assessment Report’s (TAR’s) projections for methane atmospheric concentrations, carbon dioxide emissions and atmospheric concentrations, and resultant temperature increases constitute the greatest fraud in the history of environmental science.”
None has ever done so, because they’d lose.
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