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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Ian Castles March 10th, 2008 at 2:49 pm
Lucia, Your conclusion that “The current data appears at least somewhat inconsistent with the near term projections by the IPCC” appears to me to be at odds with the recent statement by Australia’s Professor Ross Garnaut (which I’ve quoted on another thread) that “Recent rises in global temperatures [have been] at the upper end of what was predicted [by the IPCC] in 2001.″ Do other readers agree? In particular, do the “real” statisticians agree that you’ve correctly applied the Cochrane-Orcutt technique to ascertain the trend that best fits the monthly data since 2001? (Incidentally, you’ve misspelled ‘Orcutt’ in your chart).
lucia March 10th, 2008 at 2:54 pm
Ian–
The other two people did different analyses, but tell me I did C) correctly. I’ll fix the spelling!
I don’t see how anyone can think rate of change in global temperatures since 2001 is at the uppe end of what was predicted by the IPCC in 2001. If we don’t correct for serial autocorrelation, it’s flat. If we correct, it’s negative. If we look at annual data, it’s flat. How can this be at the upper end?
I guess I’d have to read more of what Prof. Ross said though.
Roger Pielke. Jr. March 10th, 2008 at 3:16 pm
Ian-
This reference you cite comes from Rahmstorf et al. 2007 and is cited on p. 21 of the Garnaut Interim Report. Rahmstorf et al. compare 2001 IPCC predictions with data since 1990. Two things make the analysis here different:
1. The inclusion of more recent data than used by Rahmstorf et al.
2. The 2007 AR4 predictions are a bit higher than those of 2001
Thus the statements here are not at odds with those in the Garnaut Interim Report, and if anything that report and its reliance on Rahmstorf et al. 2007 are now out of date.
Ian Castles March 10th, 2008 at 4:45 pm
Thanks Roger. I think we may be at cross purposes here. The 2001 ‘predictions’ for each of the six illustrative SRES scenarios were shown decade by decade in the tables in Appendix II of the WGI contribution to the TAR. These showed an acceleration of the projected temperature increase in the 2000-2010 decade compared with the (standardised) increase of 0.16 C for the 1990-2000 decade. At the Garnaut Review’s third public forum on 14 November 2007 (”Climate Change: What is the Science Telling Us? Is there a Need to Develop New Emissions Scenarios?”), at which I was a panelist, there was extensive discussion of the most recent report of the Global Carbon Project (Canadell et al in PNAS, 2007) and the claim in that paper that emissions since 2000 have been rising faster than in any of the SRES scenarios. This work was post-Rahmstorf et al. The Garnaut Interim Report modifies the Canadell et al conclusion somewhat (see the last complete para. on p. 15) - I suspect as a result of my comments at the forum and subsequently - but Ross has retained his view that post-TAR developments, including the observed increase in global mean temperatures, have strengthened the case for urgent action.
The statement I quoted on this blog was made by Garnaut to ‘The Age’ before the Interim Report was released (but I agree that he may have been relying on Rahmstorf et al as interpreted by Graeme Pearman, and may not have known of the observed decline in global temperatures in recent months).
I agree with you that if anything the Garnaut Interim Report may be out of date on this point, because of the more recent observational evidence as interpreted in Lucia’s analysis. That is why I am keen to learn of the verdict of expert statisticians on that analysis, and on what it does (and doesn’t) mean. I hope that some experts make submissions to the Review on this important matter.
Ian Castles March 10th, 2008 at 8:02 pm
It’s also worth noting that Lucia’s conclusion has an unambiguous meaning. If I have correctly understood her analysis, she has shown that, using the Cochrane-Orcutt technique to correct for serial autocorrelation, the mean estimate of the rate of change of global temperatures between January 2001 and January 2008 which provides the best fit to the observations during that period, and using the average of the four series as published by the respective sources, is minus 1.1 C/century. One can debate the reasons for this result, whether or not the number of observations is sufficient, et cetera, but at least the conclusion has a clearly defined meaning.
By way of contrast, the statement in the WGI contribution to AR4 that “Six additional years of observations since the TAR (Chapter 3) show that temperatures are continuing to warm near the surface of the planet” (Chapter 9, p. 683) is ambiguous. The ordinary meaning of this sentence, as constructed, is that the observational record shows that temperatures CONTINUED to rise DURING the six additional years following the TAR. But it appears from the full context that the meaning that the lead authors had in mind was that during these six years the world was warmer on average than in various earlier periods such as the first 50 years of the instrumental record or the first decade of the twentieth century or some other period. This says nothing about the trend DURING the six-year period concerned. It’s surprising that the lead authors of a chapter in a scientific report, and the various government and expert reviewers, were satisfied with such a fuzzy statement.
John V March 10th, 2008 at 8:53 pm
Hmmm, my numbers don’t quite match yours. I hope you don’t mind a question or two to track down the discrepancies:
Using monthly data, I get the following global trends from Jan 2001 to Feb 2008:
GISS: +0.83 C/century
HadC: -0.55 C/century
RSS: +0.41 C/century
UAH: -0.07 C/century
AVERAGE: +0.16 C/century
However, when I compute the trends from Jan 2002 to Feb 2008 I get:
GISS: -0.29 C/century
HadC: -1.67 C/century
RSS: -0.91 C/century
UAH: -1.71 C/century
AVERAGE: -1.14 C/century
From your article I’m not sure if you intended to start in Jan 2001 or Jan 2002. I believe your previous articles stated that Jan 2001 would be the start date. But it looks like you may have started in Jan 2002. Is that correct?
It’s amazing the difference a single year can make.
John V March 10th, 2008 at 8:59 pm
…or maybe I’m way off base and Cochrane-Orcutt can actually change the slope by that much.
lucia March 11th, 2008 at 4:20 am
John,
I’ll post figures of the least squares and Cochrane-Orcutt today. I have been known to make mistakes in my life, but looking at my spread sheet, my calculations do start in 2001. Feb 2008 data aren’t fully in yet, so I don’t have Feb 2008 in the calculation.
lucia March 11th, 2008 at 5:52 am
JohnV,
Yes. I do think it’s important to start in 2001. Picking and choosing years gives the analyst free reign to pick whatever the heck they want and screws up the basis for stating confidence intervals. (When there isn’t much data, it also screws up the trend– but generally shifting them within the confidence intervals.
Here are thumbnails of the charts. You can click to see larger images.
As you can see, we do get different trends for different data.
I added the spreadsheet above so you can check my numbers. (I downloaded the data from Watt’s site– as noted in my earlier post. Obviously, if something got corrupted in between, that would cause problems. But, I do start from Jan 2001.)
On your observation of the choice of year: YES. Right now, switching by a year makes a difference in conclusions. So, when Feb and March data come in, it may turn out that, while this “falsifies” in terms of hypothesis test, the later data show it just happened to be the 2σ event. (These happen– 5% of the time!)
Nevertheless, as Roger pointed out, for something to be called science, it must be at least hypothetically possible to falsify. Otherwise, it’s pseudo-sciece. Clearly, the IPCC does make projections that are at least hypothetically falsifiable, and I think it’s important to show how one would falsify and demonstrate what happens.
John V March 11th, 2008 at 6:28 am
lucia, thanks for the extra details.
I was exhausted when writing last night and hope it didn’t come across as adversarial. The negative trend caught my attention because I’ve never seen that before.
lucia March 11th, 2008 at 6:37 am
JohnV,
You didn’t come off as adversarial. The negative trend stunned me when I saw it too!
Anyway, you and I both discussed checking the correlation in the residuals in the past. So, I happen to know you are very interested in this, as am I.
In all honestly, I’d love you to check my numbers. I think the issue of “how much warming” is important. You should also note that this result doesn’t say AGW is falsified; the upper uncertainty intervals clearly do include 0C/century.
The result says 2 C/century lies outside the bounds. So, if it’s correct, it gives us information to bound estimates and also to gauge how well the IPCC project and/or draws estimates their own uncertainty interval.
FWIW….. I’m waiting for February to come in. I have GISS, RSS and MSU data; I’m waiting for Hadley. I notice the numbers announced by the various agencies are susceptible to change the first few weeks after they are announced.
Still, I’ll be posting as soon as Hadley is in. I do know, based on “fiddling” that the “uptick” on the normal Temperature vs. Time plot looks small. It’s bitter on the Cochrane-Orcutt chart, because that takes into account the issue of correlation. Still, the new number is not going to reverse the negative value. That requires a change in the weather.
I’m also trying to learn some other methods– ARMA etc.
John V March 11th, 2008 at 7:25 am
I’ve been able to confirm that we were working on the same numbers. (A small step, but definitely important). I will try to find time to understand C-O so I can check the rest. Today does not look good though…
BTW, now that we’ve cleared up the coincidence of OLS trends from Jan2002 matching C-O trends from Jan2001 I’m pretty confident that your calculations are correct. The spreadsheet *looks* right.
lucia March 11th, 2008 at 7:40 am
JohnV,
I found NOAA numbers, and I’m adding that to the average.
Also, I need to learn the other techniques. I helpful anonumous tutor says I should look at various other methods too.
BarryW March 11th, 2008 at 8:57 am
Lucia,
Could you post your graphics in a larger size? Some of us don’t have the resolving power to see the details.
lucia March 11th, 2008 at 9:38 am
BarryW–
I make them smaller to save bandwidth. (I guess I’m used to blogging about knitting where the majority of my visitors always had the slowest connections.)
The full size graphics are also in the spreadsheet. So, if you download that, you can open in Excel and see the graphic. The link is in the “update” section.
lucia March 11th, 2008 at 9:44 am
Oh— Also, the graphic in THIS post started out in the IPCC document. It’s already blown up compared to their document. I added lines so we could have some hope of reading it.
Unfortunately, the IPCC isn’t to good about providing the sorts of numbers that make it easy to falsify of validate easily. To do a full t-test, I would need to actually read the values at the top and bottom of their uncertainty intervals to estimate what they are saying the standard error in their prediction is.
lucia March 11th, 2008 at 9:53 am
oh… heh. I just realized, you may mean the thumbnails in the update. Click on those, they’ll open to new windows with larger images.
steven mosher March 11th, 2008 at 11:21 am
Hey JohnV,
Do you think we can agree on a common format for these monthly anomalies. Some of the GISS stuff I have is
in a table ( year by month). The RSS and UAH stuff is in a table of year/month vectors. I prefer the hadcru approach,
a vector… ALSO, I just found out that Openoffice supports R extensions in its spreadsheet. FWIW
What do you think? Your Opentemp would be a good place to host it, being Open and all.
Something Like
Year Month UAH RSS GISS NOAA HADCRU
1850 1 xx xx xx xx xx
….
2008 2 xx
Not sure how you want to code missing data for those records that only go back to 1979 or 1880.
Anyway it would give folks who want to load it into a spread sheet and play around a good resource.
Or Lucia could do it.
Also Lucia, I’m unclear why you mean by Averaging these difference temperature indices, since they measure different
things. Not saying its wrong, but just asking for clarity
lucia March 11th, 2008 at 11:38 am
stevemoscher–
It would be nice if these were all in similar formats. But, they aren’t. Still, I find as long as all I’m looking for is monthly anomaly data, I find it and getting it into the spreadsheet isn’t anywhere the time sink that learning new ways to process data is. (Of course, getting the real temperatures is not so easy. OTOH, it’s really easy to find the little on line lectures telling us we shouldn’t want it. And that if we think we want it, we should realize we shouldn’t want it. No…. it’s not what we should want.
)
As for archiving: I’m not an appropriate person to be an archivist. It doesn’t suit my personality. . . Take my word for this! I’d be wretched.
The fact is, these data also sometimes sift. UAH found an error earlier this year, SteveMcYntire found errors in the NASA processing which caused them to fix an error. Who ever the archivist is, they will need to keep track of this stuff, and communicate it. I’m just not the person to do it.
What I’m doing currently is getting the data from an archive, slapping it into my spreadsheet, and after doing the analysis, uploading the current version of the spreadsheet to my wordpress logs. That way, if I screw up, at least precisely what I used is traceable. (I wasn’t even in this habit back in January. . .) I’m trying to remember to state where I got the data in the blog post when I report the data. But really, that’s about all.
lucia March 11th, 2008 at 1:09 pm
For those wondering, I hunted down the NOAA monthly values and added them to my analysis. The 95% confidence bands for the trend, with NOAA added to GISS (Land/Ocean), Hadcrut, UAH and RSS are:
-3.0 C/century < m < 1.1 C/century.
The best estimate based on this data is -0.9C/ century. So, NOAA to the average, the IPCC projections are inconsistent with this data.
steven mosher March 11th, 2008 at 4:24 pm
Lucia,
Dont sell yourself short. you are the pefect person to do this work for the rest of us. and we would
all be ever so grateful…
Aint working huh? rats
steven mosher March 11th, 2008 at 4:28 pm
How are you averaging? NOAA, GISS and HADcru are not independent measures, and UAH RSS measure something else..
Chillguy33 (1 comments.) March 11th, 2008 at 9:25 pm
Is there any connection between IPCC and Oil For Food?
The efforts appear to operate with remarkable similarity. Is it the same folks?
TIA
PaulM March 12th, 2008 at 5:59 am
steven mosher,
Anthony Watts has done that at least back to 1979, see his very useful file 4metrics_temp_anomalies.txt which is in almost exactly the format you suggest. Let’s hope he or somebody else keeps it up to date as the new data comes in. Of course you are right that just averaging GISS HADCRU UAH RSS is a bit crude, but I think that’s what Lucia is doing and it seems as good as anything else.
Lucia,
I’m afraid I have doubts that these numbers are significant. If we just look at the simple least-squares trend I get -0.11 C / century from Jan 2001 - Jan 2008 (which I think agrees with the b in your spreadsheet). But if I start in Feb 2001, I get -0.28, and starting in March 2001 gives -.40. So the trends are all over the place, if you change your start by just one data point. But they are all less than +2 C/century, so maybe your conclusion still holds. Expert statistician needed - have you asked Briggs?
lucia March 12th, 2008 at 6:45 am
PaulM-
The error bands stated in the blog post are:
-3.3 C/century < m < + 1.1 C/century
The values you give (-1.1 C/century, -0.28 C/century and -0.4 C/century) all fall in the stated uncertainty intervals which were calculated using this method.
I think what you are seeing is precisely what those calculated uncertainty bands mean: We expect the value of “m” to vary “all over the place”– but within [-3.3 C/century, 1.1 C/century] based on this specific data.
So, yes, if what I’m claiming is true, I’d expect the various different choices to result in precisely the sort of scatter you are finding.
We could ask Briggs. He knows more than I do about this stuff. But, no, I haven’t asked him.
The Huge Swing 07-08. Does that affect the results? | The Blackboard March 12th, 2008 at 8:05 am
[...] Comments: IPCC Projections … [...]
aaron (4 comments.) March 12th, 2008 at 10:12 am
Chillguy33, yeah. They are both run by the UN.
Roger (1 comments.) March 12th, 2008 at 7:25 pm
Not surprised the Garnaut report is out of date already. It was never going to contadict from Stern.
If Garnaut was relying on an interpretaion by Graeme Pearman it would certainly be one of “dangerous warming”, of which Graeme has been an advocate for many years.
Ah well, it’s only taxpayer’s money.
At least the new PM has said it’s one of many inputs to climate policy. Hopefully the issues raised here will also also be considered before $zillions are committed to little or no effect.
Some of you may be interested in gustofhotair.blogspot.com which does statistical stuff with local data
Ian Castles March 12th, 2008 at 8:26 pm
Thanks Roger. I need to make clear that my comment about Garnaut relying on an interpretation of Pearman was based on the following statement by the Garnaut Review itself:
‘This section draws largely on the research of the Intergovernmental Panel on Climate Change (IPCC) as interpreted and presented by Dr. Graeme Pearman’ ( Issues Paper 3, p. 2. The section is headed “The Global Impacrs of Climate Change”).
lucia March 13th, 2008 at 6:13 am
Ian and Roger–
I plan to check that particular issue when I have time. It seems to me that there is lots of rhetoric on both sides, often not supported by any number of analysis. (Certainly, politicians rarely give citations when interviewed. I don’t actually expect them to do so.)
One of the difficulties is tracing down precisely what was ‘predicted’ in some of these documents. I’ve quickly examined the TAR, and didn’t find projections stated quite as clearly as in AR4. (I may find them yet, but so far… just not quite a clear.)
Some of the earlier documents are “unfalsifiable” in the scientific sense because they only predict things one could never even hope compare against empirical data.
For example, there is no really good empirical data to compare predictions of steady state climate sensitivity as a function of doubling of CO2. Schwartz (2007) gave it a good shot looking at post 1880. Hansen gave it a shot looking at glacial vs. interglacial. But to get really good data, we need to double the C02 and let the planet sit there for a while. Obviously, this experiment can’t be done! All we have for that is models.
Ian Castles March 13th, 2008 at 7:45 am
Lucia, I think that you’ll find the TAR ‘predictions’ you’re looking for, decade by decade, in Appendix II (”SRES Tables”) of the contribution of Working Group I (”Climate Change 2001: The Scientific Basis”). These Tables show that, since 2000, emissions have increased more rapidly than in most of the SRES scenarios. This leads in turn to a faster-than-projected rise in atmospheric concentrations, in forcings and in the model average surface temperature (see Table II.4).
One way of looking at this is to imagine that, when representatives of the world’s governments met in Shanghai to approve the WGI TAR in January 2001, they were so seized with the urgency of the climate change emergency that they agreed to implement drastic immediate reductions in emissions of greenhouse gases. What would the consequence have been?
As the SPM of the WGI contribution to AR$ explains, “Even if the concentrations of all greenhouse gases and aerosols had been kept constant at year 2000 levels, a further warming of about 0.1 C per decade would be expected’ (p. 18, second sentence of emphasised paragraph).
If this had happened, the slashing of emissions would by now be being hailed as a great success. According to your analysis, the UPPER LIMIT of the increase in temperature since January 2001 has been at the rate of about 1.0 C/century - i.e. 0.1 C per decade. But this has been achieved in the face of an ACCELERATION of the growth of emissions, not a drastic reduction!
Raniers or Maraschino? Accusations of Cherry Picking and Climate Change. | The Blackboard March 14th, 2008 at 10:06 am
[...] SteveUK– I can’t fix your… IPCC Projections … [...]
William (2 comments.) March 15th, 2008 at 11:38 am
I think you’ve misunderstood the IPCCs pic. It doesn’t include natural variability uncertainty: http://scienceblogs.com/stoat/....._front.php
lucia March 15th, 2008 at 11:54 am
William,
My analysis assumes their error bars don’t include natural variability.
steven mosher March 15th, 2008 at 12:12 pm
Lucia,
What has to happen in 2008 for the IPCC to get back on track?
what has to happen in 2009? in 2010?. there is clearly an envelope of futures
that puts the IPPC back in line with their projections. How probable is that?
I think I have a way of looking at this…empirically. lazy saturday
lucia March 15th, 2008 at 1:07 pm
SteveM,
Interesting questions. It would take some fiddling to do active speculating! Maybe monday. But I may address the issue both William and Boris brought up: Weather noise, and how that relates to this. I think that may be more important than trying to figure out what hypothetical weather needs to happen to get IPCC back on track in by the end of the decade.
steven mosher March 16th, 2008 at 8:45 am
Lucia, I looked at the weather noise in a rather odd stupid way. I made every possible 30 year time series
from GISS 1880-2007 (t1-t30,t2-t31, etc etc ) I figure every 30 year sequence has got some climate trend
and some weather noise. Then I made a big spaggetti graph, grafting every 30 year sequence onto 2001.
A huge spread in data. ennormous variability over a 30 year span ( using an observationalist perspective)
Take away. The IPCC projections are based on GCM. GCM underpredict the variability of weather on short time
scales, and have not been tested on longer time scales.
Raven March 16th, 2008 at 12:15 pm
Lucia, Steven,
Here is a model output that specifically includes ‘weather noise’:
http://en.wikipedia.org/wiki/I.....bution.png
From the link:
This is from a Meehl et al., 2004 paper which is referenced numerous times in AR4. This should allow you quantify the weather noise built into the IPCC projections.
lucia March 16th, 2008 at 12:28 pm
Raven,
If the bands are interpreted to include weather, then the IPCC did an even worse job at predicting the central tendency.
I will be discussing the weather vs. climate issue either on Monday or Tuesday. My hubby is mostly monopolozing the computer doing our taxes and our inlaws taxes today. And yesterday we were having a big get-together.
The weather/climate issue William discussed is a non-issues as the standard statistical techniques distinguish between year to year variability and trends. That’s actually the whole point of using this technique.
Boris March 16th, 2008 at 12:39 pm
I have a few more questions:
1. If using the TAR estimates, why did you decide to use a graph from AR4? It’s not a big deal, but I am curious.
2. Where do you get the 2C century number for IPCC TAR’s projection? Does this number have error bars or a shaded area like the graph shown? If so, is it based on the ensemble mean? (The shaded area in the graph shown is based on the ensemble mean.)
3. Does the IPCC discuss “weather noise” in relation to their projection?
4. Does “during the first 3 decades after 2000″ actually start in 2001? I assume so, but there’s a bit of ambiguity there.
Frank O'Dwyer (19 comments.) March 16th, 2008 at 1:17 pm
Lucia,
“The weather/climate issue William discussed is a non-issues”
His point re your (mis)interpretation of the shaded area is an issue. The shaded area in the graph represents uncertainty in the climate sensitivity of the models. You are interpreting them as a confidence interval around the projected trend itself, but they aren’t and they don’t purport to be that. If I have understood correctly, they represent confidence about the model prediction, not in the model prediction. If I’m right then you would need to include this further uncertainty in order to compare like to like. And if you did, it seems plausible that the top of your error range would overlap the bottom of the IPCC range.
“IPCC projections
overpredictoverlap recent warming” is not such a snappy headline but you could still in principle falsify the projections with more data, and it would still be an interesting exercise. However I don’t know how you would go about quantifying the additional uncertainty in the prediction.lucia March 16th, 2008 at 2:09 pm
Boris,
I use the AR4 because that’s the most recent one. Also, the AR4 itself, though published in 2007, calls what they are doing projecting the climate after 2000. (As you can see, the graphic includes data through 2000.)
The reason for the lag is the projections were not done fresh during 2007 and were published ain a previous document. The AR4 itself includes a graph with the 2C/century number and reiterates it in text several places.
As far as I am aware, the IPCC does not specifically discuss weather noise, but if you know differently, let me know. My understanding of their document is they discuss climate, as that is the issue of interest. In a previous post, I discussed what weather, measured using annual average data is consistent with the climate trends they project. In this post, I discuss the climate trends that are consistent with the weather measured at monthly intervals that we actually experienced.
Yes, the 3 decades are the first three of this century, and as you can see on the graph, that is the trend for this century, not the last decade of 1900. The whole thing is encapsulated in the graphic. As you can see, the graphic includes data up to and including 2000; the projections are for climate that occurs after.
For what it’s worth, I picked the start date for analysis before I obtained the monthly data and before I learned how to do Cochrane-Orcut. And obviously, if the temperature trends turn up, the falsification won’t “stick”. But right now, that’s what the projected climate is insistent with the recent string of weather we actually experienced.
lucia March 16th, 2008 at 2:26 pm
Frank:
William says this:
I agree with William natural variability (weather noise) is not included in the IPCC projection. I agree with him the error bars are not supposed to constraint the year-to-year variation.
I agree with William that if the uncertainty intervals included natural variability, the uncertainty intervals would be larger.
So, William is confirming what I believed when I did my analysis: The projections do not include natural variablity. My analysis assumed this.
My analysis is specifically designed to detect which underlying trends (climate) are consistent with the weather we actually experienced. This technique is used in calibration, quality control and all sorts of other areas. It’s taught to undergraduates in engineering and science curricula, though the most painful lessons are generally driven home when doing laboratory experiments.
It is not clear to me why William thinks this makes my analysis incorrect. However, I’ll elaborate more, since this seems to be an area some have difficulty with.
As for your comment, could you explain whatever you think is the difference in ” confidence about the model prediction” and “confidence in the model prediction”?
I can’t image what the difference might mean, quantitatively.
I’m also a bit bemused that William says “Its not absolutely clear what the error bars are to me,”.
If a climate scientist at RealClimate doesn’t know what those uncertainty intervals mean, what was the public supposed to take away from that graphic? (That said: The text of AR4 says what they mean. I know because I read it. They apply to uncertainties in predicting climate.
)
Frank O'Dwyer (19 comments.) March 16th, 2008 at 3:00 pm
Lucia,
“As for your comment, could you explain whatever you think is the difference in ” confidence about the model prediction” and “confidence in the model prediction”?”
William put it like this:
We have a family of curves going into the future, of the nature of dT=CO2*f*t, for various values of f, where t is time. IPCC is graphing that. Missing from that is the weather noise, which corresponds to the throw of the dice for each year
In other words, there is some uncertainty about what the models actually predict in the first place - or maybe this is better stated there is a range of curves that could be used for prediction, and it is uncertain as to which one is the best one to pick if you want to make a prediction. It is this range of different model climate sensitivities that the shaded area represents. The mean is chosen as being the prediction likely to be closest to obs, but (since the climate sensitivities aren’t certain) it could be that a more accurate prediction lies in the shaded area. If that’s correct then the 95% confidence interval (shaded area) you’re using is just about the climate sensitivies.
The problem is that nothing about the process described above says what the error in the prediction (the difference between prediction and obs) is likely to be. It is just the process for making the prediction in the first place. (My understanding of it, anyway. I could be wrong.)
In any case, if I am right, then your use of the central trend in the IPCC graph should still be OK but you’re using the wrong error bars around their predicted trend, because the error bars you need aren’t stated at all in that graph.
lucia March 16th, 2008 at 3:39 pm
Frank-
My purple lines are the 95% confidence intervals for climate trends that would be compatible with the weather we have had.
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.
Their range of projections for climate trends are not compatible with the weather we have had.
The IPPC trends are not weather predictions– they are climate predictions. So, of course they don’t tells us how well they predict weather. They tell us what the underlying trend– as expected given a range of climate sensitivies– is supposed to be.
So, in other words I have interpreted them exactly as you say I should.
Boris March 16th, 2008 at 5:00 pm
Lucia, the text from the figure you’ve used says:
So the IPCC is clear on what the shaded area means, but this graphic is not the most suitable for your comparison.
Also, since it is a mean, the internal variability should be, to a large extent, averaged out.
Mark Bahner (3 comments.) March 16th, 2008 at 6:42 pm
I don’t agree that the IPCC made any predictions in 2001. They had a bunch of scenarios, and the temperature rises associated with those scenarios. But none of the scenarios matches the trends in the various climate forcing agents. For example, all the scenarios have higher atmospheric methane concentrations than have been observed, and most or all had higher black carbon emissions than have (likely) occurred.
However, on the cooling side, the most scenarios overestimate the SO2 emissions that have (likely) occurred.
If I say that there’s going to be a thunderstorm if the high temperature tomorrow hits 90 deg F, and a foot of snow if the high temperature doesn’t get above 30 deg F, it’s not possible to say how accurate those “projections” were, if the high temperature is 70 deg F tomorrow.
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.