Re the GISTEMP Land-Ocean Index graph: I should think that an 8-year RUNNING MEAN would give an astonishingly-good fit to the data; one that will be statistically-sound as a regression. But would we welcome the eventual outcome of its prediction?
Gavin and Stefan - nice discussion. I saw Tierney’s post yesterday and thought - “why is anybody trying to do a comparison of such a short time period? It’s meaningless!”
That said, I do have a question that’s come up in my mind after some discussions with perhaps more knowledgeable “skeptics” than Tierney and Pielke Jr. As you put it, “the climate system has enormous amounts of variability on day-to-day, month-to-month, year-to-year and decade-to-decade periods.” and this seems to derive from the chaotic forces that determine weather itself.
So the question is - do we have a good handle on the natural decay times for random perturbations on the climate system? Pinatubo provided one example of a delta-function perturbation, and it seemed like the response decayed over roughly a year. But there are certainly longer-term responses as well: the deep ocean we know has 1000-year response times, the large icesheets presumably also on about that timescale. What about in between? Wouldn’t a natural climate system response time of several decades make it almost impossible to get meaningful averages out of climate system variables, if they could be significantly influenced by random fluctuations accumulated over such long time periods? Any good references on this issue?
[Response: It’s clear from the data that there is no one time scale for the climate system response to forcings. There are short term responses to the seasonal cycle for instance, longer responses to Pinatubo, and even longer decadal responses to GHG forcing - which could in fact be longer once you factor in vegetation or ice sheet impacts. Thus for any averaging period, one needs to be cognizant of the slower components for which that period won’t average over the noise. The deep ocean instrumental records are particular problematic in this respect. Glaciers are a little different because they are integrated metrics and have some averaging already built in. - gavin]
In the category of comparing “long-term climate change to short-term weather variability,” Boston Globe columnist Jeff Jacoby has offered something that may be new. In a Jan. 6 column headlined “Br-r-r! Where did global warming go?” he used a list of recent instances of unexpected coldness to suggest that the planet may in fact be cooling, not warming. It’s interesting that he admitted that his data are anecdotal, but that he persisted at the level of global climate generalization anyway. (Have your cake and eat it too?) He wrote that “all of these may be short-lived weather anomalies, mere blips in the path of the global climatic warming that Al Gore and a host of alarmists proclaim the deadliest threat we face. But what if the frigid conditions that have caused so much distress in recent months signal an impending era of global cooling?”
Oh do come on.
I have seen that the hadley center has said that the global average temperatures since 2001 and 2007 are statistically indistinguishable. each year has a temperature that falls within the error bars of all the years 2001 - 2007. That being the case you can say NOTHING about any trend in this data set except that the data indicates no change in temperature over that period. Whether you think that is evidence that global warming has halted is debatable but you can’t bring any statistical analysis to bear to show that the temperature change in the period was other than zero.
You are working hard with a lot of statistical analysis to see structure in the 2001-2007 period that you are just not justified in seeing. We know nothing about any trend in this period because we haven’t measured a trend. The measurements show the temp has been constant and no amount of ‘analysis’ on your part will change that.
Congratulations on an excellent post. I thought I would draw attention to a stark example of a national newspaper in the UK repeatedly claiming that global warming has stopped, using exactly the flawed reasoning that you have highlighted - a columinist for ‘The Sunday Telegraph’ has made this claim nine times in the last six months - most recently last Sunday (see halfway down: http://www.telegraph.co.uk/news/main.jhtml?xml=/news/2008/01/06/nbook106.xml). I have written a number of times to the newspaper to correct the misleading impression he is creating - sometimes they publish the letter, mostly they don’t. But Booker hasn’t stopped repeating his misleading claim. I’m now planning to take the case to the UK Press Complaints Commission on the grounds that it represents a persistent breach of its code on misleading and inaccurate reporting.
Thanks for this post. There are a few clarifications that you ought to probably point out, and I’d ask that you present my comments in full, rather than selectively edit them.
1. IPCC 2007 issuing a “prediction” starting in 2000, is not really a prediction, since it starts at a time before the prediction is made. As you know, rigorous forecast evaluation begins at the date a prediction is made. Thus, what I prepared and John Tierney showed are simply examples of how a forecast verification is done. In addition, the figure that you point to in IPCC AR4 Chapter 1 really wouldn’t qualify as a rigorous forecast verification (except perhaps for 1990).
2. One way to look at a comparison of models and short-term is as you have and suggest that it is “misguided” because models are based on the longer-term. My view is the opposite — modelers are doing us a disservice by neglecting the short-term. If multi-year and decadal variability is so great as to obscure a long-term trend, then it would be nice to see that reflected in the uncertainty estimates of the models for what to expect over the next 10 years. You run very close to suggesting that climate predictions simply cannot be verified except on a multi-decadal time scale, which I think overstates the case at best, and moves modeling outside the realm of falsifiability, and thus away from the scientific method. Hindcast checks are great, but science aways works better with falsifiable hypotheses on timescales that allow for feedback into the research process.
There are perhaps good reasons for this like IPCC treatment of aerosols and a subsequent volcanic eruption (but forecast failures always tell us why they were wrong, which is part of their value). IPCC 1995 dramatically lowered its predictions (and as I’ll post up on our blog soon, and had a much more accurate prediction through 2007). But both IPCC 1990 and IPCC 1995 cannot be consistent with observations, since 1995 cut its decadal average trend by 50%.
4. Finally, the IPCC has made many predictions in 1990, 1995, 2001, and 2007. To suggest that comparing the evolution of predicted variables with observations is misguided is itself a strange dodge. There are good reasons to compare models with data, and in fact, your deconstruction of Douglass et al. paper does exactly that. Over the long run, if your believe your predictions to be correct, then a comparison with actual data will eventually prove you to be correct. That there is large variability in the shorter terms simply means a bit larger uncertainty bounds on such predictions. But to avoid forecast verification altogether is a strange position to take.
Thanks again.
[Response: Roger, How can you read the post above and claim that we’re avoiding forecast verification altogether? This is just perverse. I spend almost all of my time comparing observations with predicted variables, so I don’t see what you are getting at in point 4 at all. The point being made is that each comparison has varying degrees of usefulness. 8 year trends in the global mean temperatures are not very useful. 20 year trends are more so. Better still are superposed averages of the response to volcanoes or ENSO variability etc. In each case, models predict a mean response and an estimate of the noise.
Dealing with your individual points though, IPCC may have been published in 2007, but the model runs reported were made in 2004 in most cases, similarly, Hansen’s paper was published in 1988, but the model runs were started in 1985, thus forecast validation can usefully be made from when the runs started, rather than when they were published. Secondly, the pictures you and John showed were not correct if you wanted to do a short term forecast simulation. The line you took from IPCC was not the envelope, or even a fair distribution of the simulations over that short period. You would find that the actual simulations would have a substantially greater error bar (as in the distribution of 8 year trends we mentioned above). Instead you used the IPCC estimate of the long term trend (which has a much smaller uncertainty). This data is easily available, and if you want to do forecast validation properly you should use it. The response you’ve got from other scientists is precisely because they are aware of this.
I’m not really sure what was being forecast in 1992, and I’ll have to look it up before responding.
You talk about the absence of short-term validation as being unscientific. This is simply ridiculous. It is easy to see that unpredictable weather noise dominates short term variability. It is well known that this is unpredictable more than a short time ahead. Claiming that the forced climate response must be larger than the weather noise for climate prediction on all time scales is just silly. There are examples where it is - for instance in the response to Pinatubo (for which validated climate model predictions were made ahead of time - Hansen et al 1992) - but this is not in general going to be true. A bigger point is that ‘predictions’ from climate models do not just mean predicting what is going to happen next year or the next decade. They also predict variables and relationships between variables that haven’t yet been measured or analysed - that is just as valid a falsifiability criteria. They can test hypotheses for climate changes in the past and so on. The statistics of the weather make short term climate prediction very difficult - particularly for climate models that are not run with any kind of initialization for observations - this has been said over and over. Why is this hard to understand? - gavin]
Gavin- A further comment. Based on your analysis is it fair to conclude that linking the 2007 NH sea ice melt to long-term climate change is equally as misguided as comparing an 8-year record of global temperatures to long-term climate change?
[Response: The long term decline in Arctic sea ice is by far the more powerful test. But ‘equally misguided’ is incorrect. The issue is how unusual an event is (and by all analyses the 2007 melt was huge). That makes it extremely unlikely to be on it’s own just another fluctuation of the noise - the same was true of the 2003 European heat wave. In such cases it is sometimes possible to estimate how much more likely a similar event has become because of climate change, and if that is a substantial increase, it might make sense to apportion some causality. But it depends very much on the case at hand, how unusual it was and how it ties into expectations. - gavin]
Bob Ward should take the UK’s Met Office Hadley Center to the courts as they have made exactly the same claim as the telegraph Newspaper. What’s source for the goose is source for the gander as I think they say.
I offer this brief off-topic introduction. I have been a regular at the accuweather blog for awhile now, and I’ve come here because I welcome the more disciplined moderation policy here. I’m weary of seeing the same denier lies repeated relentlessly with no apparent intervention. The mere fact that several of the more egregious deniers there complain of being “censored” here raises my appreciation for this site.
This particular piece is an excellent example of the kind of reasoned, analytic, and scientific information about AGW that I seek. Regarding Corneliussen’s comment (number 3) above, I do hope that nobody takes Jacoby too seriously. While there is, of course, some statistical likelihood that we are entering a period of cooling, I would remind us that a) even a stuck (analog) clock is right twice a day and b) even if we do enter a period of cooling, my take on our current understanding (which may eventually prove incorrect) is that the anthropogenic warming signal will make such cooling less pronounced than it would otherwise be.
In short, even a period of cooling will not, of itself, invalidate the AGW hypothesis whatsoever. Even in that scenario, it seems to me that the question is whether or not the measured temperature is or is not warmer than it would be without the measured dramatic increase in anthropogenic atmospheric CO2.
I am curious why you chose an 8 year running average. Those who suggest that global warming has “stopped” or that the data does not suggest recent global warming usually say “since 2000″ or “since 2001″. These would indicate 7 year or 6 year averages, yet you choise a 8 year average for illustration.
Why?
[Response: Because Tierney&Pielke’s plot runs from 2000 to 2007 (8 data points), and since we responded to Tierney, we chose the same interval as them. For shorter intervals the problem obviously gets worse and worse. The extreme case is two-year trends. That is the red lines in the plot. They go downward very often. Yet nobody in their right mind would describe this as “global warming stopped in 1981, then again in 1983, then again in 1988 and again in 1990″. Or claim that these short coolings cast any doubt on global warming. -stefan]
Fig.1.1. of the 4AR reference given clearly shows that since 2001 we have stable global temperatures (2001 to 2005, now we know this to be true also for 2006 and 2007); this is an observational situation that was never found before since 1990. So ignoring trends, and focusing on year to year variability, what would be the correct conclusion to draw?
[Response: Making statements about statistically significant changes in variability is even harder than statements about trends. Thus, I would simply note it and not draw any particular conclusion. - gavin]
For what it’s worth, the blue 8-year trend lines all seem to converge into a positive linear trend between 1995 and 2005, suggesting a consistent increase in temps. Obviously it isn’t worth much since a decade isn’t really long enough to look for climate trends, but I don’t understand what Tierney and Pielke Jr’s logic is. Even a rudimentary understanding of trends and stats renders their point moot.
I’ve been staring at the Hadley data lately, and they are definitely reporting cooling for the last two to three years. It seems this is far more southern hemisphere than northern, and far more ocean than land.
First, what meaning can be derived from the difference between land and ocean anomalies? Is it accurate to produce a global average which gives the ocean temperature more weight than land? In other words, is an “area average” a good way to determine the actual global trend?
Second, have the instruments being used to determine sea surface temperature been satisfactorily validated? If I recall correctly, there have been some problems correctly interpreting readings from the instruments which were launched several years ago.
Or is this more related to the difference in methods Gavin mentioned?
Gavin - thanks for an informative post. Does the 2007 value you plot include December? I noticed from GISTEMP that Dec. ‘07 was cooler than other months - can this be attributed to the strong ongoing La Nina in the Pacific? Thanks, David
[Response: Yes it does include Dec 07. And indeed it does seem to be related to the La Nina. - gavin]
You ask “How can you read the post above and claim that we’re avoiding forecast verification altogether?”
Well, my first clue is when you misrepresented why I did, and what John Tierney reported. You characterized the effort as “attempts to validate (or falsify) IPCC projections of global temperature change over the period 2000-2007.” No such claims were made by me, and I don’t think by Tierney.
In my post I was careful to note the following: “I assume that many climate scientists will say that there is no significance to what has happened since 2000, and perhaps emphasize that predictions of global temperature are more certain in the longer term than shorter term.”
And John Tierney wrote: “you can’t draw any firm conclusions about the IPCC’s projections — a few years does not a trend make”
So why misrepresent what we said? Models of open systems cannot in principle be “validated” (see Oreskes et al 1994).
I simply compared IPCC predictions with observations as an example of how to do a verification, which is standard practice in the atmospheric sciences, but much less so in the climate modeling community (and yes, I think this is indeed the case). Instead of telling your readers all of the reasons that a verification exercise is “misguided” you might have instead constructively pointed to the relevant forecasts with proper uncertainty bars (please do post up the link), or better yet, simply shown how an analysis comparing 2000-2007 with relevant predictions would have been done to your satisfaction.
Given that you point to the IPCC AR4 Figure 1.1 in positive fashion, I remain confused about your complaint about what I did — I don’t recall you complaining about IPCC efforts in verification previously.
How about this: We agree that rigorous forecast verification is important. There also does not a clear agreement among researchers as to (a) what variables are most important to verify (b) Over what times scales, (c) what actual constitutes the relevant forecasts, and (d) what actually constitutes the relevant observational verification databases. Then this is a subject to work through collegially, rather than try to discredit, dismiss, or suppress.
Thanks.
PS. As I stated on my blg. If discussing forecast verification in the context of climate model predictions is to be a sign of “skepticism,” then climate science is in bad shape. For the record I accept the consensus of IPCC WG I.
[Response: Roger, I’m flummoxed. You keep bringing in things that have not been said, and rebuttals to arguments that have not been made. All we have done is point out statistical issues in two things you compared. Oreskes paper is a case in point - I have no desire to argue about the semantics of verification vs evaluation vs validation - none of that is relevant to the overriding principle that you have to compare like with like. In a collegial spirit, I suggest you download the model data directly from PCMDI and really look at what you can learn from it. You might get a better appreciation for the problems here. Verification is not misguided. Your attempt at verification was. - gavin]
I see now that in reporting in item 3 above on the Boston Globe’s Jeff Jacoby’s contribution in the category of misconstruing weather as climate, I probably should have bee explicit: my own opinion, for what it’s worth, is that Jacoby’s contribution is preposterous.
You indicated that the GISS product extrapolates over the arctic region. Extrapolate means to infer or estimate by extending or projecting known information. How is this done and can you explain why is it valid? Is this simply using widely spaced and limited data from within the arctic itself and extrapolating to cover the entire region, or extrapolating somehow from the perimeter of the arctic?
[Response: This is explained in the GISTEMP documentation, but in areas with no SST information (like most of the Arctic), the information from met stations is extrapolated over a radius of 1200 km. That fills in some of the Arctic. A validation for that kind of approach would be a match to the Arctic buoy program - but I haven’t looked into that specifically. - gavin]
“In fact, IPCC 1990 dramatically over-forecast trends, as show by the IPCC figure that you reference and that I provide here:”
Roger,
Your blog post neglects to even mention the “good reasons” for “forecasts” to be off–namely the Mt. Pinatubo eruption. Don’t you think you owe it to those who read your blog to point out that:
A. The IPCC projections do not include volcanic eruptions and
B. Your graphs starts immediately before a volcanic eruption that had a large effect on global temperatures.
I’m afraid burying it (and IMO downplaying it) in your comments does not draw an accurate picture.
Jon P writes ” Can you provide graphs of the other data sets? I want to see them do just as well” I agree. And this, of course, brings up the perennial question. Which of the various data sets of average annual global temperature anomaly is closest to the truth? When we get some good scientific analysis which brings an understanding of that question, we will have advanced the yardsticks a very long way.
Is there an element of “cherry picking” in this article? Would an illustration using HadCRU or RSS with a moving average of either 5, 6, 7, or 9 years been as helpful to you in making your point?
[Response: The distribution of trends will be very similar - most of this variability is due to real weather, not instrumental noise. But I’ll check and report back. - gavin]
As a layman who is trying to understand this issue, I’ve been struggling with how to assess the forecast skill of the GCMs. I think this is an extremely important issue and I wish I could find more information regarding tests that would confirm or falsify the forecasting skill of the GCMs. I agree with your point that short-term deviations from a trend are not necessarily significant and do not necessarily indicate that the GCM’s are unreliable. However, I think that Roger Pielke, Jr. has a point when he suggests that accurate short-term forecasts are used to show how reliable the GCMs are, but inaccurate short-term forecasts are attributed to random noise in the actual data. This seems like a “head we win, tails you loose” verification method.
As another example, I have read here how the GCM’s accurately forecast the effect of the Pinatubo erruption. If by accurate, you mean that the GCM’s predicted that dumping massive amounts of aersols into the atmosphere would cause cooling, I am not impressed. The mental model that exists in my head would be just as “accurate”. If, however, by “accurate” you mean that the GCM’s made reasonably close predictions of the extent of the cooling, then it seems you are playing the “head I win, tails you loose” game suggested by Roger Pielke’s comment.
[Response: Huh? When did we say that short term predictions were good if they agreed with the models? Any test of a model has to be accompanied by an analysis of the uncertainties - and if a test happens to be a good match, but the uncertainties indicate that was just a piece of luck, then it doesn’t count. Pinatubo is different because the forcing in that case was very strong, and so it dominated the short term noise (at least in some metrics like the global mean temperature). Look at Hansen et al 2007 for more discussion of this. Pinatubo is more interesting validation than just for temperatures too though. Models get the LW and SW TOA radiation changes, they get the changes in water vapour, they get the impact on the winter NAO. None of those things were programmed in ahead of time. - gavin]
Walt, you said you’d been “staring at the data” and that Hadley was “definitely reporting ….” How much statistics do you know? Did you do any math? People are very good at detecting trends simply by looking at images — and very often see what is not actually there.
This worked to detect large predators — imagining a leopard has low cost compared to not seeing a leopard — but the same talent leads to gross error — imagining a recession is expensive compared to not noticing a recession.
Looking doesn’t suffice for definitely reporting. Math may.
How did you arrive at your conclusion?
However, if someone says “the last 6 or 7 years is suggestive that global warming has stopped” it is not good form to refute by showing a spread of 8 year trends. It just inserts another issue.
Why 8 for the refutation rather than the 6 or 7 year record advanced in the original assertion?
[Response: Because that was what was used by Tierney. The spreads for 6 or 7 year trends are even higher. - gavin]
The problem here is that you dont have weather variability you have VOLCANO variability. If you factor out the volcano effects, ( I recall tamino doing something similiar on his site) and then look at the 8 year trend you will get a different picture. That might be an interesting excercise. Tamino??
Some interesting comments here, and would just like to add my own observations, well not my “own” but reports here in Scandinavia. Spitzbergen is having extremely mild conditions, and when I say mild, well. Last week is was the warmest place in Norway about +8C, and heavy preciptation at their main weather station, 43mm in one day, and no it was not snow, it was rain. There is still no winter sea ice south of the islands, neither was there any for the past 2 winters. Further south on the mainland of Norway, the high plateaus are having huge snow deposits and its only January, something like 10-12 meters already on the Jolsterdals glacier. Because of the milder winter “weather”, records are being broken all the time with precipitation, most likely the record of over 5000mm is in danger.
Thanks but this is a pretty lame response: “In a collegial spirit, I suggest you download the model data directly from PCMDI and really look at what you can learn from it.” You are the climate scientist no? If you are unwilling to explain what is substantively wrong is my efforts to provide an example of forecast verification, then so be it.
I am quite confident in my conclusions from this exercise as summarized from my blog Prometheus, and nothing that you say here contradicts those conclusions whatsoever:
1) Nothing really can now be said on the skill of 2007 IPCC predictions.
2) By contrast IPCC dramatically over-predicted temperature increases in its 1990 report.
For 1995, 2001 (and some interesting surprises) please tune in next week.
Gavin, if you do decide to provide substantive critiques of the two conclusions above please do share them, as I still have absolutely no idea what your complaint about this exercise actually is, other than the fact that it took place.
[Response: You are again changing the subject. Who ever claimed that the 2007 IPCC projections had been shown to be skillful? I need to look at the 1990/1992 reports in more detail to comment on point two (as I said above). If you don’t ‘get’ what my complaint was after all this, I am surprised, but I will repeat it concisely: 1) You need to compare like with like. 2) Long-term trends have different statistical properties than short term variability. 3) Any verification attempt needs to take that into account. - gavin]
Roger Pielke Jr is capable of doing good work, but too often he just likes to be provocative and will be disingenuous to do it. His latest critique of the IPCC is another unfortunate example of the later.
Roger took a quote from a comment of mine on RealClimate about Environmental Defense’s website. I wrote that I liked the Q&A section where readers could submit questions about climate change. Roger on his blog dishonestly claimed that Dr Judith Curry made my comment and claimed Dr Curry was endorsing Environmental Defense’s politics. Roger then blocked my comments when I tried to correct his mistaken claims.
Its a bit of a stretch for him to complain that RC is selectively editing his comments.
Please explain how you accounted for short term variability in your over effort at verification here (other than to say they don’t mater when looking at trends):
My approach to verification is identical to yours in the Hansen post that you link to. And indeed in my first blog post on this I was careful to make the same qualification about short term trends as you do in this current post, which I will repeat since you haven’t acknowledged it:
“I assume that many climate scientists will say that there is no significance to what has happened since 2000, and perhaps emphasize that predictions of global temperature are more certain in the longer term than shorter term.”
So what is it that you are complaining about again?
[Response: If you try and step back from simply trying to be contrary, I suggest focusing on the the main principle that you have to compare like with like. In the post I did on the 1988 projections, I compared long term trends with long term trends. It works there because in both the model output and observational data have uncertainties in the long term trends that were small compared to the signal. This is not true for 8 year trends. The figures you produced show the long term trend (and it’s uncertainty) and the short term variability. That is not an appropriate comparison. Either put in the full envelope of model output over the same period, or just plot the trends and their uncertainty for the 8 year period. - gavin]
[Response: Let me try to explain with a simple example. Imagine you want to check the prediction that Colorado gets warmer during spring. The prediction is for a roughly sinusoidal seasonal temperature cycle, based on solar zenith angle. Would you test this prediction against a piece of observational data from 10-17 April? Of course not! Random weather variability means that a cooling from 10-17 April does not falsify the seasonal cycle, nor would a warming verify it in any way, because the time period is just too short. The variance of weather is larger than the seasonal warming from 10-17 April. Do exactly the same exercise for a two-month period rather than 8 days, and it will make sense. The variance of weather is then still the same, but the seasonal warming over this longer period is much larger, so now you get a sensible signal/noise ratio. -stefan]
p.s. If you’re still not getting it, try reading our popular post “Doubts about the advent of spring“.
Not sure I follow your train of thought, but I can handle that last question: the Hadley graphs clearly come down for the past two years, and I have seen the results from number crunchers which validate that result.
The color coded maps clearly show two large cooling regions, both over water.
The graphs for northern and southern hemisphere are broken out; the northern hemisphere goes up continuously while the southern hemisphere goes down in the last two years.
Mr. O’Sullivan (31): I’m not sure why RC lets off-topic hearsay comments with personal attacks through like this but for the record you are welcome to post a comment on our site at any time. I have no idea what you are talking about regarding Judy, but she is a top scholar who I have an awful lot of respect for, even though we don’t always agree on everything.
Great post Gavin and Stefan. This whole line of attack by skeptics/doubters/deniers is silly, since they won’t admit they’re wrong once we get some record hot years in the near future. I have blogged on this:
A pdf / frequency distribution of the eight year trends from your graph might be interesting too. The centre of the distribution should be ~0.2/decade.
Gavin, I think you’re copying me! My own version of the signal-to-noise issue is here, my display of year-end results from NASA GISS is here, and while I’m waiting for HadCRU and NCDC to post their year-end data I looked at northern-hemisphere land data from NASA here.
I guess it’s often true, great minds think alike.
Re: #17 (Jon Pemberton) and #22 (Jim Cripwell)
Jon, it looks like my blog isn’t the only place you’re contributing a drive-by pot-shot. As I responded there, I haven’t posted the year-end results from HadCRU or NCDC because they haven’t been put in the online data files yet.
My last comment on this thread. As the focus of your criticism, you have selected from a series of posts leading to one focused on a much longer time period starting in 1990 an example that I provided to show what verification looks like using IPCC AR4 predictions from 2000 (hence 8 years). You ignore the longer term view that I have provided.
Not only that, you pretend as if I did not explicitly acknowledge in my first post that nothing can be said about verification over 8 years, for exactly the reasons that you describe here. Instead, you suggest that I have implied otherwise.
If you can do a better verification of historical IPCC predictions, then by all means show it. There are many forecasts that have been made since 1990 and the more people engaged in this the better.
More generally, you guys at RC may indeed be the smartest guys in the room, but sometimes constructive efforts are more appropriate than simply criticism of why everyone else doesn’t meet your standards. I’ll continue the exercise next week, with verifications for IPCC 1995 and 2001 and then I’ll place all four assessments into comparative context. I have no doubt that you won’t like any of it, but even so, your comments are welcomed, but especially constructive comments.
Thanks for the exchange today, and providing a forum for discussion here at RC.
[Response: I still don’t know why you are reacting so negatively to this post. We spent considerable effort to outline the issues involved in forecast verification very much in the spirit of constructive engagement, and yes, pointing out issues with your figure as used on Tierney’s blog. Your responses here and the additional commentary on your blog this morning have certainly not added to the spirit of collegiality. That is a shame. - gavin]
I see over at Roger Pielke. Jr.’s blog he has now posted the IPCC forecasts from 1990. It appears from his graphic that the projection based on a 1.5 degree climate sensitivity for 2x CO2 has been the most accurate. I would further note that Steve McIntyre discusses today (1-11-2008) Wigley 1987 published in Climate Monitor. McIntryre notes in passing a discussion by Wigley of climate sensitivity. Wigley states, “If one accounts for the ocean damping effect using either a PD or UD model, and, if one assumes that greenhouse gas forcing is dominant on the century time scale, then the climate sensitivity required to match model predictions is only about 0.4 deg C/wm-2. This corresponds to a temperature change of less than 2 deg C for a CO2 doubling. Is it possible that GCMs are this far out? The answer to this question must be yes.”
So it appears from Pielke’s graph from 1990 on that the actual data is consistent with a CO2 doubling sensitivity of about 1.5C and from Wigley’s comments based on observations prior to 1987 a climate sensitivity of less than 2C for 2x CO2. So looking at these “long-term” trends isn’t it reasonable to question the output of GCM that place CO2 climate sensitivity well above 2C?
I realize this may be a complex question to answer so if you could direct me to link with an explanation that would be helpful to me.
[Response: I am looking at the 1992 projections as we speak. First off, these are not GCM estimates, but from a simple box model - and so ‘weather’ variability is outside their scope. Nonetheless, it’s worth looking into. The first question is the forcing that was applied. From figure Ax.1, it appears that the increase in forcings for all the IS92 scenarios are in the range of 0.5 W/m2 per decade to 2010 (slightly higher maybe, but it’s difficult to read off the graph). In the real world, forcings (as estimated by GISS and not including volcanic effects) increased at 0.36 W/m2 per decade (1990 to 2003). Thus the projections are likely biased high not due to the climate sensitivity but due to the overestimated forcings growth. The temperature trends over that period in the GISS record is 0.24 +/- 0.04 degC/dec. The best estimate ‘2.5 deg C’ sensitivity model has a trend of 0.25 degC/dec in figure Ax.2 (2.8 degrees in 110 years) which is somewhat less than shown in RP’s graph for some unknown reason (actually, none of his model output lines seem to match up with the figure - puzzling). Once you include an adjustment for the too-large forcings (by ~40%) the mid-range model outputs line up pretty well. Pinatubo complicates things, but I don’t see any big problem here. - gavin]
[[That being the case you can say NOTHING about any trend in this data set [i.e., 2001-2007] except that the data indicates no change in temperature over that period. Whether you think that is evidence that global warming has halted is debatable but you can’t bring any statistical analysis to bear to show that the temperature change in the period was other than zero.]]
Well, no, because a sample size of N = 7 isn’t enough to be statistically meaningful. But if you do a linear regression, the trend is up. Try it yourself!
I think that roger is trying to wind you all up here RC. It looks to me as if you have wasted enough time answering his queries and deliberate attempts at obfuscation.
I was caught off guard by the 2007 NASA hemispheric and global temperature anomalies which just came out. It seems that December of 2007 was much warmer than NOAA and others had figured.
Re #40: Gavin, now why did you exclude the volcanic forcing when figuring the GISS “actual” forcing increases? Is there no “average natural aerosol forcing” that is assumed in the projections? It seems plausible anyway that when volcanic forcing is added then the forcing increase due just to the emmissions scenario is going to be higher than you have shown. It may in fact be close to what is shown by the 1992 report? Roger states that we are running on the high end of the emmissions scenario. Please advise.
[Response: Because I was comparing the scenarios which didn’t have volcanoes either. But as I said, Pinatubo complicates things. Given that those projections were done with a simple energy balance model though, it would be trivial to add Pinatubo in as an extra forcing and see what difference it made. As to whether we are on the high end of the scenarios, I don’t think that is correct (but I haven’t looked carefully). CO2 is increasing faster, but CH4 has stabilised, and CFCs are falling faster, aerosol changes are potentially important but not very well characterised. This will become clearer in a few years time. - gavin]
Cite please? For a two or three year trend to be significant it has to be huge.
Gavin, Tamino, could y’all do what William Connolley did in his exercise (stoat, link above) and indicate on your similar images which of the short-term trend lines are significant and which aren’t? It helps make the point that we can’t _see_ which is which on a picture.
What I would like to see is a 29 year graph (1979 - 2007) with all the data sets GISS, UAH, RSS, and HADCRUT3v as a comparison.
Love to have an explanation of the differences between the data sets (what average warming trend they are showing and why they ‘might’ be different) and if there are discrepancies between the actual data and any modeling for the same period.
I am assuming that this data exists for the requested time period.
As you can all tell I am a layman, but find this all very interesting and believe all temperature measurement data in one “post” would be the most beneficial to many people.
btw, Tamino, thank you. I can understand your reaction from what I have read on your blog at times
No deniers, no alarmists, just science. Cue the Coke commercial music…..
Ref 42 Christian writes “Well, no, because a sample size of N = 7 isn’t enough to be statistically meaningful. But if you do a linear regression, the trend is up. Try it yourself!” I have and you are quite right when you start any time in the 1970’s. Which means current temperatures are significantly higher than they were in the 1970’s. However, if you try any other sort of least squares regression fit, e.g. polynomial, then the NASA/GISS data still shows increasing temperatures, but the other data sets show that temperatures have stabilized, if not actually peaked. I used CurveExpert 1.3 for the analysis; shareware.
I’m relatively new here, but I watched PBS NOVA’s “Dimming the Sun” documentary last night, for about the third time. I find it very powerful, and scientifically accurate. Regarding modeling, are the effects of pollution, aerosols, and aircraft contrails accounted for in climate models? In the comment sections of USA Today’s articles on global warming, there are many skeptics that say NASA’s models just don’t reflect reality. If that’s right, it might have to do with the “global dimming” effect.
Dr. Hansen says if we didn’t have that pollution in the atmosphere, global temperatures would be about 2 F. higher than they are now.
How many here have seen that documentary, and what do you think of it?
[Response: We discussed it when it was first broadcast in the UK and later on in the US. - gavin]
Jon, have you used the “Start here” link at the top of the page?
Or read anything at http://www.globalwarmingart.com/ yet?
Have you read the discussion on the page there along with this, which is at least half of what you’re wishing for I think:
If you will give us some idea what you’re starting from, where your questions arise, where you’ve looked, what you believe or know so far, it will help us (most like me are just fellow readers here) point to answers.
Gavin- On #40 above. You want to use scenario IS92e or IS92f, rather than IS92a that you found in Figure Ax.2 (which says something about temperature change under different assumptions of climate sensitivity for IS92a). As I explained in my blog post on this, the proper figure to use is Figure Ax.3 to determine these values.
You write “Once you include an adjustment for the too-large forcings” — sorry but in conducting a verification you are not allowed to go back and change inputs/assumptions that were made at the time based on what you learn afterwards, that is cheating. Predicting what happened after 1990 from the perspective of 2007 is easy;-)
[Response: The IS92 scenarios do not diverge significantly until after 2010 - so assessing model response to 2007 is independent of exactly which scenario is used. I don’t see that your figure uses the data from fig Ax.3 - that figure has all models on the same trajectory until 2000 and only a small amount of divergence in the years following. That is nothing like what you have used. And finally, when it comes to projection verification, ideally you would only do it (as I did for Hansen et al 1988) if the scenarios matched up with reality - that gives a test of the model. If the scenarios are off, then the model response is also off and the model-data comparison not particularly useful. If your claim is that the IS92 scenarios were high, I’m fine with that. But don’t confuse that with model verification. - gavin]
Gavin- Good. With this exercise I am not interested in model verification and never have claimed to be, and as I’ve stated all along my interest is in forecast verification. You can find out more about the various scenarios in that same report beginning at p. 69, Figure A3.1 for instance shows how dramatically the scenarios diverge quite early. If you spend a bit more time with it you’ll also see that my 1990 IPCC prediction matches just about exactly with the used by IPCC AR4 in their figure TS.26, so if I’m wrong, so too is IPCC. More next Monday.
[Response: The only thing that matters in those simple box models is the net forcing, which is in fig Ax.1 - which clearly shows that the different scenarios have not significantly diverged by 2010. It’s not clear to me what the FAR range in fig 1.1 of AR4 represents and it isn’t clearly stated. Plus they reference it to IPCC 1990, not the 1992 supplement. I invite anyone who knows what’s plotted there to let me know. - gavin]
It will be interesting to see if this temperature slowdown/fall since 1999 (depending on the dataset used) continues in the future and become statistically significant.
It is my observation that skeptics are only doing what the other side has been doing for years. That is every record high temperature recorded in the United States is promoted in the media via climate scientists as “proof” that humans are causing global warming. In Montana, recent bad fire seasons are promoted in the media via climate scientists as “proof” that humans are causing global warming. It is refreshing to read that RealClimate has taken a stand against temperature chasing, drought chasing, and fire season chasing.
Thank you, I looked at the links but they are not current.
What I am exactly looking for is a comparison of all satellite and GISS temp data compared on the same graph through 2007.
Beliefs… Warming? yes Global? not convinced, CO2 as main forcing? not convinced.
I read RC, Open Mind, Climate Audit, Accuweather, Lubus Motl, Eli Rabbet, Anthony Watts, and Pielke sites/blogs. And probably some random others.
I also like looking at sea ice extents from Artcic and Antartic.
I see the “forts” with “high walls” being bulit between various camps and usually one piece of data posted on and then the “fur flies”. Recent example, GISS data reveals 1998 and 2007 are tied as 2nd warmest year, but RSS data differs (as noted on Lubos’s site).
Looking for a post that discusses and shows all data on one graph. I selected 1979 as the starting point as that is when satellite data became available, correct me if my assumption on this is wrong.
Another example, Eli has running post on Artic Sea ice extent, but not Antartic. Antartic melting appears to be behind last years rate. Maybe a long term comparison of both of these.
I can read the data and understand it, but I am in no position to validate it. For that I have to rely on others and it is pretty tough doing so when you are between the “forts”..
>forecast
>prediction
These are used to plan picnics and political decisions.
>scenario
>model
Scenarios can be run for the deep past, recent past, and near future. The better a model’s range of outcomes matches the known real climate, the more interesting its outcomes when it’s run through into the near future.
Because when I read Dr. Pielke write
> that is cheating
> my interest is in forecast verification
That’s saying the work done with an original Cray supercomputer wasn’t so good, so redoing that work today is cheating.
Cheating? It’s showing politicians how much better work can be done now — and that runs can better match what did happen, so they may better match what _will_ happen.
That will scare those who want to say nobody knows enough to decide.
Remember, the original Cray supercomputer could not have run Windows 95, it didn’t have enough memory. Your doorstop Win95 machine is more powerful than the Cray was then.
Gavin describes how one can improve a model, and run it again starting at some past point, and if the model is better, the range of outcomes when it’s run on into the future may also be better.
That’s not cheating, for science, it’s how it’s done.
20 years ago Dr. Hansen was saying it’d take about til now to have an idea whether the models then were useful, because the climate signal would take that long to emerge from the noisy background.
I’m sure he was referring to statistical and measurement noise, not to political noise. Over 20 years, the statistical noise decreases.
Political scientists might study whether statistical noise is inverse to political noise, on issues like this.
Has anyone tried to calculate the heat content of the entire atmospheric and oceanic system? Wouldn’t that be a better metric to verify instead of surface based averages of temperatures? Wouldn’t this take ENSO and lags because of ocean storage out of the equation?
[Response: You find this analysis in the IPCC report. The heat content change is completely dominated by the change in ocean heat content, because of the large heat capacity of water this is the only component of the climate system that stores a significant amount - see Fig. 5.4 in chapter 5. So this tells you how much ocean heat storage has delayed surface warming, i.e. what portion of the anthropogenic forcing is soaked up by the ocean rather than being balanced by radiation back into space (the latter implies a surface warming, so the portion going into the ocean does not lead to immediate surface warming). - stefan]
I think climate scientists in the UK have given up correcting the misrepresentationa and misuses of global temperature data in the media. I can understand why - it is a seemingly endless task. Unfortunately, some newspaper editorial teams are apparently not up to the task of spotting arguments based on dodgy statistics and are publishing them, misleading millions of people in the process. I am glad that RC has not given up the task of challenging attempts to mislead the public about climate change science.
One only has to read the comments for the “Global warming has stopped” article to see how pervasive the denialist take on this issue is with the public - or at least with those who chose to comment. Even though the essential flaws in Dr Whitehouse’s opinion piece were glaring even to someone without deep knowledge of the subject such as myself, pointing them out merely ended up lost within a plethora of unsubstantiated claims of biased science, misrepresentations and repetitions of repeatedly debunked denialist myths. The attempts to inform by some commenters probably changed no-one’s mind.
Has anyone with the in depth knowledge and solid scientific arguments taken Dr Whitehouse to task? I hope some of you do - he ought to have the education and intelligence to be engaged by what real science tells us, but I wouldn’t count on it. I think too much of the media are primarily about entertainment and his career is a media career. Controversy, even when it has little sound basis, attracts readers/viewers and Dr Whitehouse’s career in media is probably strengthened by the kind of writing in his “Has Global Warming Stopped?” article.
I for one am pleased when this Blog does take people like Dr Whitehouse to task. Letting them and the organisations get away with it uncriticised leaves the public free to believe what they say is true ie ill-informed on an issue of critical importance.
“… Dr Doug Smith said: “Occurrences of El Nino, for example, have a significant effect on shorter-term predictions. By including such internal variability, we have shown a substantial improvement in predictions of surface temperature.” Dr Smith continues: “Observed relative cooling in the Southern Ocean and tropical Pacific over the last couple of years was correctly predicted by the new system, giving us greater confidence in the model’s performance”
A decade,or less, does not a climate era make. What would we call this period- the tiny little ice age? Climate eras have lasted centuries and millenia, while a dominant forcing(GHGs)governs which is the case at present.
It should be a given that the more data available, the more accurately projections regarding future scenarios can be made. To reduce this to an absurdity, who ever rolled a single die and came with a 3.5?( the expected average over a large number of throws.)
At the Tierney Lab site on the right just under the heading About Tierney Lab, it states “John Tierney always wanted to be a scientist,but …..”.
Commentary to this point (Post # 62.) seems to indicate that my suggestion (in Post # 1) of using a running mean (average) was misinterpreted, especially since other readers employed the same terminology to quite different measures. A true “running mean” of eight values begins with the first data point and is an arithmetic average of the first 8 values. This becomes the first graph point. The next graph point simply drops the first data point and increments by one. It forms a nicely smoothed, continuous regression line. It’s a much better way to present data such as those plotted in the GISTEMP index, and, I believe, is a better portrayal of reality than the “pick-up-sticks” jumble of “8-year trends”.
Seems to me you’re asking others to do an impossible amount of work for you — to get and chart for you exactly what you want, all of what you want, and nothing but what you want. It’s certainly doable. But the person behind you in line will have slightly different demands.
You can take the chart I pointed you to, look up the next year’s numbers and chart them.
You can charm the person who maintains the site with intelligent questions and suggestions and perhaps get the charts updated a bit sooner than they’d otherwise find time to do it. I’ve _often_ found that to be true. Sometimes I find I’m the only person all year to thank a scientist for making the time to put such charts up online, they may get lots of pointers and lots of mentions but theys till perk up when someone emails a simple thank-you along with a question about how to find out a bit more, say, to extend such a chart.
Bystanders like you and I could keep a whole lot of experts very busy, if they tried to respond to all such requests (and many people do start off by insisting that they know exactly what they need to find, to get their certain and final understanding of what puzzles them, or irrefutable proof of some claim they heard, or the like).
That’s why people refer you to the data sources and programs that let you do your own charting. If you understand the statistics you can do your own error ranges. If you don’t, charts won’t help much.
My suggestion is to stay away from anything that looks like a “fort” to you, and hang around in the trenches with the people who are doing the actual digging, or watch them as I and so many others do.
The better you inform yourself, first, the better questions you can ask, and I tell you, I feel really happy if I manage to get a “Good question!” response from one of the climate scientists here once a year myself. They’re making a gift of their time to the rest of us.
Last thought, I’ve heard this as ‘Rule One of Database Management’ — one data set, many pointers. One reason very few people make the kind of effort you find at globalwarmingart is that there is a vast collection of data sets, some easier to find than other, some very significantly revised when errors are found in work later on.
People who make copies and then write based on their copies may be doing so based on outdated information. Look for people who give you references rather than put answers together for you — the references will lead you forward in time to better info.
Oh, Jon, you mentioned Eli’s notes on sea ice and said you wished he had something. Did you click his link? The comparison of the poles that you wished for is at the source Eli gives. The charts there that are pulled automagically from the databases are working now; the hand-edited one will be updated in a week or two to fill out the 2007 year, I just asked (nicely) about that yesterday myself (grin).
“… Like most attempts to characterise a complicated system by a single number, a scale throws away a great deal of the subtlety. … The scale responds with a number that means something or other. If only we knew what…. Over time, certainly, the scale will measure the cumulative effect of too much or too little food. But from day to day, the scale gives results that seem contradictory and confusing. We must seek the meaning hidden among the numbers and learn to sift the wisdom from the weight.”
…
“… The right way to think about a trend chart is to keep it simple. The trend line can do one of three things:
* Go up
* Go down
* Stay about the same
That’s it. The moving average guarantees the trend line you plot will obviously behave in one of these ways; the short term fluctuations are averaged out and have little impact on the trend.”
He addresses the reason that the moving average you ask for may not give a clear picture, on the same page:
“… The familiar moving average trend line is drawn as before. The dashed line is the best fit to all 90 days …. But, obviously, it misses the point. … Short term straight line trend lines … provide accurate estimates ….
Excel workbooks are provided at the site to try out the different methods of charting. See also everything else, wonderful site.
It is now raining in mid-winter where before it would be snow. I need no other proof or to be told weather is different than climate. Again, in mid-winter, it is now rain, and if it does snow, it is a wet snow and melts a few days later. before, there would be snow on the ground from Thanksgiving until Easter, now, it is raining in January.I am old enought to know the pattern has changed by what I have experienced and what I am experiencing now.
Re: #31 my comment and #34 Roger Pielke Jr’s reply:
My tone was unduly harsh. Roger Pielke Jr does like to provoke discussion. To do this he will make controversial statements on his blog. Its common in some academic circles, but its likely to be misunderstood in a public forum like a blog. I did not like see my comment used in a way I thought was inappropriate.
The misquote did occur, and I submitted comments and several where not admitted. After the posts moved on, one my milder comments made it through. After that I had all but one of my comments admitted on other posts.
I do not think that Roger Pielke Jr does not respect Dr Curry, but I do think he was trying to stir things up.
You take a data set of 30 years and say that the initial warming observed of less than 20 years is a long term trend and can be relied upon but you say the past 7 years of statistically indistinguishable temperature data is short term climatic fluctuation and can be ignored (note this short term ‘fluctuation’ isn’t fluctuating).
There are no errors bars in the graph - put them in and you can draw your ‘trend lines’ with much more latitude. And because the recent observed stasis is 7 years you, ignoring errors, chose an 8 year grouping which is bound to drag the stasis back towards the rising section of the graph because you are giving it less weight than the data in the centre of the graph.
YOU are the denialists - denying data. Finding ways to prove it isn’t what it is and making it conform to your worldview.
First of, let me qualify my post by saying that I come here as a believer, a questioner, and a skeptic. By that I mean that, I am a believer in that I find the evidence for long-term global warming, since at least the 1880s, overwhelming and apparently irrefutable. Secondly, basic physics/thermodynamics dictate that increasing concentrations of greenhouse gasses will have an affect on the overall climate. However, I am a questioner in that I am not as fully convinced of the magnitude of the impact of anthropogenic versus non-anthropogenic causes of the warming trend but readily concede that the current warming trend is at least in part due, if not substantially, due to anthropogenic releases of CO2 and other GHGs. Finally, I am a skeptic that has some experience in much simpler modeling(mostly ground water contaminant fate & transport)in that I have much less confidence in our current ability to accurately model future climatethan is often portrayed in the popular press or even on websites such as this. In other words, I believe we can with near certaintude say that if global temperatures continue to rise, there will be a rise in sea level due to the melting of glaciers and thermal expansion of the oceans, but that projections of drought, hyper-intensive storms, mass extinctions, and other calamities, etc. are somewhat less certain.
As an educated layman, my take on this is Much Ado about Nothing. I have read and re-read Tierney and Pielke’s posts and what I get out of both of them is that they are saying you can read whatever you want out of the recent (2001-2006) Global temperature estimates. ehy are very clear in stating that the recent Global temperatures neither prove or disprove the overall AGW model. What Pielke did say is that the most recent number will provide “cherry-pickers” with ammunition to quibble about this, that, or the other thing. Tierney correctly noted that there is a wide range of variance in the estimated global temperature anomalies and that, depending on where you fall on the sociological spectrum (denier, questioner/ skeptic, advocate, disciple), will help dictate which estimate you will rely on most. What both Tierny and Pielke seem to be asking for is continued and further refinement of the models as we gather additional climate data. In other words, don’t become defensive and just say short-term perturbations don’t affect the validity of the “MODEL”, continue to try to make the model account for the short-term perturbations. The models are nothing more than our attempts to account for all the variables that do drive climate change.
Gavin, a man with the patience of a saint. My question has to do with how well behaved the psuedo-climate system, as defined by GCM runs is. I work in FEA engineering, and it is quite common for such systems to contain bifurcations, whereby a large collection of runs will demonstrate two (or more) general solutions, superimposed of course with shortterm noise. Have any of the climate models shown such behavior? I.E. do you see situtaions where some fraction of the runs (with the same parameters and forcings, but perturbed initial conditions) show more than a single solution trend?
Another way to see that data is take the GISTEMP data, compute 8-year regressions (via SLOPE), and put that series into as scatter plot, whihc gives one line that graphs the slopes of the blue lines. The only times the slopes go below zero are those around the volcanoes.
Here is the 12 month moving average for NASA GISS, Hadley/CRU, UAH, and RSS temperature analyses from January 1979 to October 2007. NASA GISS and Hadley/CRU are land-ocean instrument data, while UAH and RSS are lower troposphere satellite data.
Here is the raw data for each analysis, including the linear fit. With both of the instrumental analyses, the slope is 0.17 degrees per decade. In the UAH satellite analysis, it is 0.14 degrees per decade. In the RSS satellite analysis, it is 0.18 degrees per decade.
In all cases, the anomalies are adjusted up or down so as to give the linear regression for each analysis a y intercept of zero.
In short, the anomalies are where you’d expect them to be, given the warming signal, plus natural variability and the fact that each analysis uses different methods.
Looks like we’re about to have a big volcanic eruption in Ecuador. So temperature will go down by 0.2 K for a couple of years and we’ll have to put up with two more years of deniers saying, “See, global warming stopped!”
Ref 52. If you want to see the sort of thing I am talking about, I am afraid you need to go to Yahoo Climate Skeptics and download the graphs I uploaded under the title “Rctner”. I fully realize that there a pseudo-infinite number of such graphs, and these three are merely examples.
Thanks much for the crystal clear diagram and discussion. Contrary to some readers who are growing weary of it, I’m delighted to see RC giving the other side “enough rope” like this.
Your main point (which you have now repeated 60 zillion times) is irrefutable, as is your observation that the exercise in question violates the simple principle that LIKE SHOULD BE COMPARED WITH LIKE. (You haven’t tried shouting yet, I suppose, but I doubt that would prove any more effective.) In the face of an argument which could hardly be framed more crisply, your dissenter has no more interesting stratagem than feigning deafness. Like a World Wrestling Federation spectacle, this classic thread has been a grossly unfair fight, and a lot of fun!
I understand that between the 1940s and 1970s, global mean temperature did not change much and this has been ascribed to increased particulates arising from industrialisation. So if you take a 30 year average between say 1945 and 1975 and then again between 1975 and 2005, the latter average should be significantly higher. What is the proper explanation for this? What changed in the 70s? Surely particulate emissions on a world scale did not decrease in the 1970s though it might have, in some industrialised countries? What about increasing use of coal by China and India say, and the consequent increase in particulates in the recent past? Would this not be expected to have a dimming effect and a reduction in global temperatures?
[Response: Not all aerosols are the same. Some, such as black carbon released by coal burning, actually have a surface warming impact. Sulphate aerosols and secondarily nitrate aeresols, which do have a surface cooling impact, increased substantially in burden from the 1940s through the 1970s, decreasingly markedly with the passage of the Clean Air Acts of the 1970s and 1980s. The various issues you raise have been discussed many times before here and in links provided. Start here, here, and here. -mike]
It should be equally obvious that the 90s had to get much warmer than the 80s because of a much higher energy input at TOA.
But the explanation is that there was one type of quite stable weather between 1994 and 2000, and a totally different type of weather between 2002 and 2005 (and probably longer), capable of reducing the radiation by 5 W/m2?
What is this assumption based on?
[Response: The ISCCP data is great, but can’t be relied on for trends due to difficulties in tying different satellite records together. The implication that albedo has suddenly increased to Pinatubo levels without anyone else noticing or a rapid decrease in temperatures is …. surprising, to say the least. - gavin]
cce says in #79: “In short, the anomalies are where you’d expect them to be, given the warming signal, plus natural variability and the fact that each analysis uses different methods.”
Of course, the denialists will say it’s clear the warming stopped in 2000…and in 1998 and in 1995 and in 1991 and in 1987…” At least Pielke and Douglass et al. are sufficiently sophisticated to realize that the only way to attack the anthropogenic hypothesis is to simply deny that warming is occurring. Unfortunately, since all the science and the evidence support a warming trend, they can’t get beyond misusing statistics and saying “No it isn’t.”
1. Pielke is right that so long as the four major sources of “global” temperature disagree what one sees is largely a matter of which record one looks at.
This is quite troubling since the members of each pair, the two surface records, and the two satellite records, essentially have the same raw data. The differences between members of the pairs is thus a difference in after the fact “adjustments”. Surely those can be ironed out and agreement reached on the “better” methods?
The increased divergence of GISStemp and HadleyCRU, and between UAH and RSS, suggests that rather than being reconciled the differences are growing.
Very troublesome.
[Response: Not really. The trends for the most part are similar given the underlying uncertainty, and there are defendable reasons for the various choices made in the different analyses. Reasonable people can disagree on what is ‘best’ in these cases, and so rather than being troubled, one should simply acknowledge that there are some systematic uncertainties in any large scale climate metric. That uncertainty feeds into assessments of attribution and the like, but is small enough not to be decisive. Of course, assessing the reason for any divergence is important and can lead to the discovery of errors (such as with the UAH MSU record a couple of years ago), but mostly it is due to the different choices made - gavin]
2. For assessing “global climate change” the absolute trend lines are apt. However, for assessing man caused global warming the “neutral” trend line would not be zero. Since the Little Ice Age we have naturally warmed. Should not this be taken into account? In other words a trend of some figure, say .6 C per century, should be regarded as “neutral” for purpose of assessing a man caused trend.
[Response: There is no ‘neutral trend’ that can simply be regarded as ‘natural’ - volcanic and solar forcing changes are not any different to GHG forcing when it comes to attribution and modeling. If instead you propose that the 20th Century trends are simply some long term intrinsic change then you need to show what is going on. No such theory exists. The trends can however be explained quite adequately from the time-series of forcings (including natural and human caused effects). - gavin]
In 68 Hank writes “Jon Pemberton Seems to me you’re asking others to do an impossible amount of work for you — to get and chart for you exactly what you want, all of what you want, and nothing but what you want. It’s certainly doable. But the person behind you in line will have slightly different demands.”
I think you are being a little unkind to Jon. In my computer I have complete sets of temperature data from NASA/GISS, HAD/CRU and RSS/MSU. If anyone can direct me to a site which has similar data for NCDC/NOAA I would be grateful. I have done some simple trend analysis on all four sets of data, and I am convinced that the HAD/CRU, NCDC/NOAA and RSS/MSU are highly correlated and give very similar results. The NASA/GISS data set is different. Notice I say “different”. I have no idea which set is closest to the truth. I have searched, written emails, etc. but I cannot find any study which has compared and contrasted the different data sets, so I have no idea which is “best”. Gavin has, however, used the NASA/GISS set, and seems to claim that any analysis using the other data sets would give the same result. My simple minded analyses indicates this may not be true. So I think it is perfectly legitimate to ask that Gavin either shows that the NASA/GISS data set is the best one available, or he shows that using the other three data sets produces the same answer.
I do not agree. Dr. Pielke and others have a point when comparing data and model output. At least they have a strong point in the public discussion. One can clearly argue that tackling climate change would be easier if recent years would have shown significant warming. Correct, this does not invalidate the models and the time series is too short and error margins are too big to have a scientific argument against them, however, it is disturbing. The sceptics on the other hand have to come up with their own models and theories and support it by data to have a plain scientific battelfield. Criticism is easy. As long as is there is nothing better out there, we better stick with what we have.
In 1980 I became the national training director for a carburetor company. I would travel around America training mechanics on how to fine tune using a chassis dynamometer and an exhaust gas analyzer. As part of the demonstration I would disable the then mistrusted emissions systems so mechanics could see that the systems were in fact eliminating large quantities of CO, NOX, and hydrocarbons from the exhaust gas - especially when simulating climbing hills at high speeds (needles pegged). When I would enable the emissions systems the exhaust gas, even under very heavy loads, would be mostly CO2 and H2O vapor - plant food is what I told them.
So the change the emissions systems made in dramatically reducing aerosols makes perfect sense to me.
In the mid 1980s there was a widespread resurgence in the use of wood stoves for home heating. Lots of towns, even small ones, had a store that specialized in selling them. Did those make their presence known?
[Response: I don’t see any support in Loeb’s data for any significant shift in TOA SW absorption in recent years. And on slide 18, he estimates that it would take 15-20 years to be able to detect such a shift, and slide 19 shows no big changes in either ISCCP or CERES. As you should know, ocean heat content data post-2003 are the subject of a lot of investigation right now because of the shifts to the ARGO network and various problems that there have been. Whether OHC peaked in 2003 is therefore very uncertain, but SST records are rising less rapidly than SAT records there is likely an increasing offset in air-sea temperatures which implies that the oceans are still warming (and that OHC is increasing). As in the rest of this post, there is short term variability in all these metrics, and only significant long term trends count. - gavin]
re 83: I started thinking, what if aerosol forcing is now growing fast enough to counterbalance GHG forcing by increasing CO2? Then GW would be temporarily stopped. We know CO2 emmisions have increased rapidly in the past few years prinicapally due to rapid growth in developing economies. These economies tend to burn coal dirtily. A second factor is that depletion of higher grade coal is forcing more and more consumption of lower grade product. Could these twin trends mean that the aerosol load might be rapidly increasing? Perhaps fast enough to counteract GHG forcing? A global temperature metric wouldn’t be the best way to detect such a change. Perhaps some other globally measured metrics could shed some light on this question?
If this is indeed hapenning, it could make the job of obtaining consensus for mitigation more difficult.
Jim Cripwell, glad you’re volunteering, but I suggest you post pointers to the source data (and note the date when you downloaded the copies you’re using), and describe what you’re doing to compare them. It’s too easy to get the wrong file or outdated copies, elsewise.
The data indicates the standard deviation on the yearly data since 1975 is 0.1 deg C. Any data with +/- 0.2 deg C (two standard deviations) would be in the 95% confidence interval. Instead Dr. Pielke has shown dashed lines only one fourth that interval on his chart.
Isn’t this standard deviation calculated from the temperature data alone, not from the IPCC models? So wouldn’t the temperature noise variation be used for the confidence intervals in a forecast verification (not model verification, as Dr. Pielke points out)? Wouldn’t any forecast have the same confidence intervals applied to it, regardless of how the forecast was arrived at?
Is it possible the confidence levels shown on the Pielke chart, are based on a plot of some kind of longer term average, and seem to be overlaid on annual temperature data? Somehow, the large scatter in the data compared to the confidence intervals, seems inconsistent?
(Disclosure… I am just a chemical engineer, who worked for an oil company once upon a time, and have no experience in climate studies.)
[Response: The error bars on the forecast for the IPCC models is the uncertainty on the long term trend, not the envelope or distribution for annual anomalies. I think that this is misleading when comparing to real annual data, and is at the heart of my statements above. - gavin]
Now Gavin and Stefan, changes in upper ocean heat content is a really interesting question, and relates directly to the subject at hand. As you are aware, Roger Pielke Sr. (and others) have been pointing out the need to assess upper ocean heat content changes. Although there have been some well publicized problems with changing from XBTs to the Argo floats, the error bars in assessing global changes in ocean heat content are decreasing dramatically. The conclusion that the upper ocean has warmed over the last 40 years is certainly robust, but the shorter term changes are also interesting, since they are a direct proxy to the current radiative imbalance at the top of the atmosphere. After the Willis and Lyman papers showing short-term cooling, then their correction, we are still left with the more robust conclusion that the upper ocean heat content has been essentially flat the last few years. This is really more informative than a two-dimensional surface temperature analysis, since the ocean heat content goes directly to the question of unrealized “heating in the pipeline”, and also to what the current sum of all the forcing and feedbacks add up to, and how this sum changes on annual and multidecadal scales. What is also interesting is how well the SST changes map over upper ocean heat content, and how quickly these SST changes seem to be realized in the atmospheric volume (ie El Nino). It has been pointed out that the so-called “time constant” to equilibriate to a change in forcing via ocean mixing processes has a direct bearing on climate sensitivity to changes in a forcing.
The first, ncdc/noaa site only gives data for that month. I have searched the site you quoted, but cannot find anything similar to the other three sites. If you can find it, I would be grateful. As I have noted, I downloaded shareware CurveExpert 1.3, and simply plug in different time scales for all four data sets, call for a specific type of analysis, and look at the results. I do this for my own education; I am sure you dont want to read any non-peer reviewed results.
In 86 Gavin writes “That uncertainty feeds into assessments of attribution and the like, but is small enough not to be decisive.” I wish I shared your optimism. I think the uncertainly is large enough to be decisive, but have nothing to offer in the way of a reference. Just the results of my playing around with simple analyses. On what do you base your opinion?
[Response: The fact that the model match to obs doesn’t depend on whether you use GISTEMP, NOAA or HADCRU. - gavin]
Thanks for the quick response, Gavin. Couldn’t Dr. Pielke fix his chart, by simply drawing 95% confidence interval lines, 0.2 deg above and below the IPCC forecast, and then compare with the annual anomaly data? That interval would reflect where the annual data should fall.
The forecast looks pretty good, if that is the expected range of variation in the actual temperature anomaly data.
I realize this may not be entirely correct, because the standard deviation probably wasn’t calculated from just one of the temperature measurement systems, but then the chart wouldn’t as misleading as it currently is.
The inconsistency between Loeb’s slide 19 and ISCCP’s own web page is strange, I agree. Which one should be the more reliable?
I find it hard to believe we were able to send man to the moon 40 years ago but now we are unable to measure the temperature of the oceans. There must be some system still in operation which was also in operation in the 90s. Replacing all floats without an overlap would be too stupid.
So why would that system be correct pre-2003 and wrong post-2003?
” 2001-2007] except that the data indicates no change in temperature over that period”
And Superman might sell Mr Desjardins his fortress of solitude at the North Pole for a good price!
I don’t think its possible, just yet, to have a single trend based on a couple of years without some deviation from the over all long term path, because the measuring techniques resolution are not at the quantum level. In addition, here is what I don’t see: world wide maximas and minimas anomalies, say from sea level to Bogota’s height above sea level.
In the Arctic, just the other day, the temperature at surface was about -27 C (….no its warm, should be -35 C), yet aloft at about 900 meters it was -15.4 C. If this station was 900 meters high, the surface record would be different. These thermal heat sources aloft, may be found anywhere in the world, but are particularly strong in the Arctic. If we pick one single Upper air level, we will either miss a cooling or a warming above or below. The idea that adiabatic lapse rates are constant and therefore we can pick a single representative height, will equally mislead.
But if we search a GW trend, we should find that every year the maximas (1 to 3000 ASL) are pushing upwards relentlessly. Then again I don’t think satellites can find profile maximas, and the world wide radiosonde network is too sparsely located, although its data is state of the art.
Lacking resolution, we can rely on other measurements: very long term temperature trends, Polar ice melt, world wide glaciers retreating, deep sea temperatures for the most part and other bench mark measurements, until temperature resolution deficiencies have been eliminated, by increasing present network densities, or by finding a different way to measure the weighted temperature of the entire atmosphere, we do that for other planets. As a complement to present techniques, I suggest using the sun as a fixed disk of reference,,, It works!
Its a little off topic, but in the sunday comics there was a funny strip about interpreting statistics in research. It reminded me of the current discussion. The strip even uses the +/- symbol. Its funny.
I would say that every modern weather event bears the fingerprints of global warming. The problem is that it is hard to find “fingerprints” on a thunderstorm or hurricane. However, it is easy to find the fingerprints of global warming on the weather prediction models. Those models use sea surface temperatures and atmospheric temperatures that reflect the full impact of global warming. The success of these weather models proves that global warming affects our daily weather.
Climate we can plan for, engineer for, and survive. The problem is always the weather, ( E.g., a rain event, a snow event, a drought, ice melt, a heat wave, a cyclone, a typhoon.) Global warming brings us increasingly intense weather. That means the weather problems become more intense.
Last week there were tornadoes across the US in January. In our recent climate that would have been a very rare event. Global warming supplied heat to make it a less rare event. Sudden Arctic Sea ice melt was a rare event. Global warming has supplied the extra heat necessary to make it less rare. At some point you have to say, ”Global warming has given us a new climate. We are having weather that for all practical purposes did not occur in the old climate.” We may not be sure what else glob
11 January 2008 at 10:27 AM
Re the GISTEMP Land-Ocean Index graph: I should think that an 8-year RUNNING MEAN would give an astonishingly-good fit to the data; one that will be statistically-sound as a regression. But would we welcome the eventual outcome of its prediction?
11 January 2008 at 10:34 AM
Gavin and Stefan - nice discussion. I saw Tierney’s post yesterday and thought - “why is anybody trying to do a comparison of such a short time period? It’s meaningless!”
That said, I do have a question that’s come up in my mind after some discussions with perhaps more knowledgeable “skeptics” than Tierney and Pielke Jr. As you put it, “the climate system has enormous amounts of variability on day-to-day, month-to-month, year-to-year and decade-to-decade periods.” and this seems to derive from the chaotic forces that determine weather itself.
So the question is - do we have a good handle on the natural decay times for random perturbations on the climate system? Pinatubo provided one example of a delta-function perturbation, and it seemed like the response decayed over roughly a year. But there are certainly longer-term responses as well: the deep ocean we know has 1000-year response times, the large icesheets presumably also on about that timescale. What about in between? Wouldn’t a natural climate system response time of several decades make it almost impossible to get meaningful averages out of climate system variables, if they could be significantly influenced by random fluctuations accumulated over such long time periods? Any good references on this issue?
[Response: It’s clear from the data that there is no one time scale for the climate system response to forcings. There are short term responses to the seasonal cycle for instance, longer responses to Pinatubo, and even longer decadal responses to GHG forcing - which could in fact be longer once you factor in vegetation or ice sheet impacts. Thus for any averaging period, one needs to be cognizant of the slower components for which that period won’t average over the noise. The deep ocean instrumental records are particular problematic in this respect. Glaciers are a little different because they are integrated metrics and have some averaging already built in. - gavin]
11 January 2008 at 10:37 AM
In the category of comparing “long-term climate change to short-term weather variability,” Boston Globe columnist Jeff Jacoby has offered something that may be new. In a Jan. 6 column headlined “Br-r-r! Where did global warming go?” he used a list of recent instances of unexpected coldness to suggest that the planet may in fact be cooling, not warming. It’s interesting that he admitted that his data are anecdotal, but that he persisted at the level of global climate generalization anyway. (Have your cake and eat it too?) He wrote that “all of these may be short-lived weather anomalies, mere blips in the path of the global climatic warming that Al Gore and a host of alarmists proclaim the deadliest threat we face. But what if the frigid conditions that have caused so much distress in recent months signal an impending era of global cooling?”
11 January 2008 at 10:40 AM
Oh do come on.
I have seen that the hadley center has said that the global average temperatures since 2001 and 2007 are statistically indistinguishable. each year has a temperature that falls within the error bars of all the years 2001 - 2007. That being the case you can say NOTHING about any trend in this data set except that the data indicates no change in temperature over that period. Whether you think that is evidence that global warming has halted is debatable but you can’t bring any statistical analysis to bear to show that the temperature change in the period was other than zero.
You are working hard with a lot of statistical analysis to see structure in the 2001-2007 period that you are just not justified in seeing. We know nothing about any trend in this period because we haven’t measured a trend. The measurements show the temp has been constant and no amount of ‘analysis’ on your part will change that.
11 January 2008 at 10:48 AM
Congratulations on an excellent post. I thought I would draw attention to a stark example of a national newspaper in the UK repeatedly claiming that global warming has stopped, using exactly the flawed reasoning that you have highlighted - a columinist for ‘The Sunday Telegraph’ has made this claim nine times in the last six months - most recently last Sunday (see halfway down: http://www.telegraph.co.uk/news/main.jhtml?xml=/news/2008/01/06/nbook106.xml). I have written a number of times to the newspaper to correct the misleading impression he is creating - sometimes they publish the letter, mostly they don’t. But Booker hasn’t stopped repeating his misleading claim. I’m now planning to take the case to the UK Press Complaints Commission on the grounds that it represents a persistent breach of its code on misleading and inaccurate reporting.
11 January 2008 at 10:53 AM
Gavin-
Thanks for this post. There are a few clarifications that you ought to probably point out, and I’d ask that you present my comments in full, rather than selectively edit them.
1. IPCC 2007 issuing a “prediction” starting in 2000, is not really a prediction, since it starts at a time before the prediction is made. As you know, rigorous forecast evaluation begins at the date a prediction is made. Thus, what I prepared and John Tierney showed are simply examples of how a forecast verification is done. In addition, the figure that you point to in IPCC AR4 Chapter 1 really wouldn’t qualify as a rigorous forecast verification (except perhaps for 1990).
2. One way to look at a comparison of models and short-term is as you have and suggest that it is “misguided” because models are based on the longer-term. My view is the opposite — modelers are doing us a disservice by neglecting the short-term. If multi-year and decadal variability is so great as to obscure a long-term trend, then it would be nice to see that reflected in the uncertainty estimates of the models for what to expect over the next 10 years. You run very close to suggesting that climate predictions simply cannot be verified except on a multi-decadal time scale, which I think overstates the case at best, and moves modeling outside the realm of falsifiability, and thus away from the scientific method. Hindcast checks are great, but science aways works better with falsifiable hypotheses on timescales that allow for feedback into the research process.
3. You are incorrect when you assert that “Each of these analyses show that the longer term temperature trends are indeed what is expected.” In fact, IPCC 1990 dramatically over-forecast trends, as show by the IPCC figure that you reference and that I provide here:
http://sciencepolicy.colorado.edu/prometheus/archives/climate_change/001317verification_of_1990.html
There are perhaps good reasons for this like IPCC treatment of aerosols and a subsequent volcanic eruption (but forecast failures always tell us why they were wrong, which is part of their value). IPCC 1995 dramatically lowered its predictions (and as I’ll post up on our blog soon, and had a much more accurate prediction through 2007). But both IPCC 1990 and IPCC 1995 cannot be consistent with observations, since 1995 cut its decadal average trend by 50%.
4. Finally, the IPCC has made many predictions in 1990, 1995, 2001, and 2007. To suggest that comparing the evolution of predicted variables with observations is misguided is itself a strange dodge. There are good reasons to compare models with data, and in fact, your deconstruction of Douglass et al. paper does exactly that. Over the long run, if your believe your predictions to be correct, then a comparison with actual data will eventually prove you to be correct. That there is large variability in the shorter terms simply means a bit larger uncertainty bounds on such predictions. But to avoid forecast verification altogether is a strange position to take.
Thanks again.
[Response: Roger, How can you read the post above and claim that we’re avoiding forecast verification altogether? This is just perverse. I spend almost all of my time comparing observations with predicted variables, so I don’t see what you are getting at in point 4 at all. The point being made is that each comparison has varying degrees of usefulness. 8 year trends in the global mean temperatures are not very useful. 20 year trends are more so. Better still are superposed averages of the response to volcanoes or ENSO variability etc. In each case, models predict a mean response and an estimate of the noise.
Dealing with your individual points though, IPCC may have been published in 2007, but the model runs reported were made in 2004 in most cases, similarly, Hansen’s paper was published in 1988, but the model runs were started in 1985, thus forecast validation can usefully be made from when the runs started, rather than when they were published. Secondly, the pictures you and John showed were not correct if you wanted to do a short term forecast simulation. The line you took from IPCC was not the envelope, or even a fair distribution of the simulations over that short period. You would find that the actual simulations would have a substantially greater error bar (as in the distribution of 8 year trends we mentioned above). Instead you used the IPCC estimate of the long term trend (which has a much smaller uncertainty). This data is easily available, and if you want to do forecast validation properly you should use it. The response you’ve got from other scientists is precisely because they are aware of this.
I’m not really sure what was being forecast in 1992, and I’ll have to look it up before responding.
You talk about the absence of short-term validation as being unscientific. This is simply ridiculous. It is easy to see that unpredictable weather noise dominates short term variability. It is well known that this is unpredictable more than a short time ahead. Claiming that the forced climate response must be larger than the weather noise for climate prediction on all time scales is just silly. There are examples where it is - for instance in the response to Pinatubo (for which validated climate model predictions were made ahead of time - Hansen et al 1992) - but this is not in general going to be true. A bigger point is that ‘predictions’ from climate models do not just mean predicting what is going to happen next year or the next decade. They also predict variables and relationships between variables that haven’t yet been measured or analysed - that is just as valid a falsifiability criteria. They can test hypotheses for climate changes in the past and so on. The statistics of the weather make short term climate prediction very difficult - particularly for climate models that are not run with any kind of initialization for observations - this has been said over and over. Why is this hard to understand? - gavin]
11 January 2008 at 11:01 AM
Gavin- A further comment. Based on your analysis is it fair to conclude that linking the 2007 NH sea ice melt to long-term climate change is equally as misguided as comparing an 8-year record of global temperatures to long-term climate change?
[Response: The long term decline in Arctic sea ice is by far the more powerful test. But ‘equally misguided’ is incorrect. The issue is how unusual an event is (and by all analyses the 2007 melt was huge). That makes it extremely unlikely to be on it’s own just another fluctuation of the noise - the same was true of the 2003 European heat wave. In such cases it is sometimes possible to estimate how much more likely a similar event has become because of climate change, and if that is a substantial increase, it might make sense to apportion some causality. But it depends very much on the case at hand, how unusual it was and how it ties into expectations. - gavin]
11 January 2008 at 11:18 AM
Bob Ward should take the UK’s Met Office Hadley Center to the courts as they have made exactly the same claim as the telegraph Newspaper. What’s source for the goose is source for the gander as I think they say.
11 January 2008 at 11:23 AM
I offer this brief off-topic introduction. I have been a regular at the accuweather blog for awhile now, and I’ve come here because I welcome the more disciplined moderation policy here. I’m weary of seeing the same denier lies repeated relentlessly with no apparent intervention. The mere fact that several of the more egregious deniers there complain of being “censored” here raises my appreciation for this site.
This particular piece is an excellent example of the kind of reasoned, analytic, and scientific information about AGW that I seek. Regarding Corneliussen’s comment (number 3) above, I do hope that nobody takes Jacoby too seriously. While there is, of course, some statistical likelihood that we are entering a period of cooling, I would remind us that a) even a stuck (analog) clock is right twice a day and b) even if we do enter a period of cooling, my take on our current understanding (which may eventually prove incorrect) is that the anthropogenic warming signal will make such cooling less pronounced than it would otherwise be.
In short, even a period of cooling will not, of itself, invalidate the AGW hypothesis whatsoever. Even in that scenario, it seems to me that the question is whether or not the measured temperature is or is not warmer than it would be without the measured dramatic increase in anthropogenic atmospheric CO2.
11 January 2008 at 11:27 AM
I am curious why you chose an 8 year running average. Those who suggest that global warming has “stopped” or that the data does not suggest recent global warming usually say “since 2000″ or “since 2001″. These would indicate 7 year or 6 year averages, yet you choise a 8 year average for illustration.
Why?
[Response: Because Tierney&Pielke’s plot runs from 2000 to 2007 (8 data points), and since we responded to Tierney, we chose the same interval as them. For shorter intervals the problem obviously gets worse and worse. The extreme case is two-year trends. That is the red lines in the plot. They go downward very often. Yet nobody in their right mind would describe this as “global warming stopped in 1981, then again in 1983, then again in 1988 and again in 1990″. Or claim that these short coolings cast any doubt on global warming. -stefan]
11 January 2008 at 11:28 AM
Fig.1.1. of the 4AR reference given clearly shows that since 2001 we have stable global temperatures (2001 to 2005, now we know this to be true also for 2006 and 2007); this is an observational situation that was never found before since 1990. So ignoring trends, and focusing on year to year variability, what would be the correct conclusion to draw?
[Response: Making statements about statistically significant changes in variability is even harder than statements about trends. Thus, I would simply note it and not draw any particular conclusion. - gavin]
11 January 2008 at 11:30 AM
For what it’s worth, the blue 8-year trend lines all seem to converge into a positive linear trend between 1995 and 2005, suggesting a consistent increase in temps. Obviously it isn’t worth much since a decade isn’t really long enough to look for climate trends, but I don’t understand what Tierney and Pielke Jr’s logic is. Even a rudimentary understanding of trends and stats renders their point moot.
11 January 2008 at 11:39 AM
I’ve been staring at the Hadley data lately, and they are definitely reporting cooling for the last two to three years. It seems this is far more southern hemisphere than northern, and far more ocean than land.
First, what meaning can be derived from the difference between land and ocean anomalies? Is it accurate to produce a global average which gives the ocean temperature more weight than land? In other words, is an “area average” a good way to determine the actual global trend?
Second, have the instruments being used to determine sea surface temperature been satisfactorily validated? If I recall correctly, there have been some problems correctly interpreting readings from the instruments which were launched several years ago.
Or is this more related to the difference in methods Gavin mentioned?
11 January 2008 at 11:52 AM
Gavin - thanks for an informative post. Does the 2007 value you plot include December? I noticed from GISTEMP that Dec. ‘07 was cooler than other months - can this be attributed to the strong ongoing La Nina in the Pacific? Thanks, David
[Response: Yes it does include Dec 07. And indeed it does seem to be related to the La Nina. - gavin]
11 January 2008 at 12:08 PM
Gavin (7)-
Thanks, a few replies:
You ask “How can you read the post above and claim that we’re avoiding forecast verification altogether?”
Well, my first clue is when you misrepresented why I did, and what John Tierney reported. You characterized the effort as “attempts to validate (or falsify) IPCC projections of global temperature change over the period 2000-2007.” No such claims were made by me, and I don’t think by Tierney.
In my post I was careful to note the following: “I assume that many climate scientists will say that there is no significance to what has happened since 2000, and perhaps emphasize that predictions of global temperature are more certain in the longer term than shorter term.”
And John Tierney wrote: “you can’t draw any firm conclusions about the IPCC’s projections — a few years does not a trend make”
So why misrepresent what we said? Models of open systems cannot in principle be “validated” (see Oreskes et al 1994).
I simply compared IPCC predictions with observations as an example of how to do a verification, which is standard practice in the atmospheric sciences, but much less so in the climate modeling community (and yes, I think this is indeed the case). Instead of telling your readers all of the reasons that a verification exercise is “misguided” you might have instead constructively pointed to the relevant forecasts with proper uncertainty bars (please do post up the link), or better yet, simply shown how an analysis comparing 2000-2007 with relevant predictions would have been done to your satisfaction.
Given that you point to the IPCC AR4 Figure 1.1 in positive fashion, I remain confused about your complaint about what I did — I don’t recall you complaining about IPCC efforts in verification previously.
How about this: We agree that rigorous forecast verification is important. There also does not a clear agreement among researchers as to (a) what variables are most important to verify (b) Over what times scales, (c) what actual constitutes the relevant forecasts, and (d) what actually constitutes the relevant observational verification databases. Then this is a subject to work through collegially, rather than try to discredit, dismiss, or suppress.
Thanks.
PS. As I stated on my blg. If discussing forecast verification in the context of climate model predictions is to be a sign of “skepticism,” then climate science is in bad shape. For the record I accept the consensus of IPCC WG I.
[Response: Roger, I’m flummoxed. You keep bringing in things that have not been said, and rebuttals to arguments that have not been made. All we have done is point out statistical issues in two things you compared. Oreskes paper is a case in point - I have no desire to argue about the semantics of verification vs evaluation vs validation - none of that is relevant to the overriding principle that you have to compare like with like. In a collegial spirit, I suggest you download the model data directly from PCMDI and really look at what you can learn from it. You might get a better appreciation for the problems here. Verification is not misguided. Your attempt at verification was. - gavin]
11 January 2008 at 12:16 PM
John Lederer,
http://scienceblogs.com/stoat/2007/05/the_significance_of_5_year_tre.php#
11 January 2008 at 12:21 PM
Gavin,
You stated “The red line is the annual global-mean GISTEMP temperature record (though any other data set would do just as well),…
Can you provide graphs of the other data sets? I want to see them do just as well..
Thanks
Jon P
11 January 2008 at 12:29 PM
If you replace 8-year trend lines with n-year, at what value of n do they start to faithfully reflect the underlying trend?
11 January 2008 at 12:30 PM
I see now that in reporting in item 3 above on the Boston Globe’s Jeff Jacoby’s contribution in the category of misconstruing weather as climate, I probably should have bee explicit: my own opinion, for what it’s worth, is that Jacoby’s contribution is preposterous.
11 January 2008 at 12:30 PM
Gavin,
You indicated that the GISS product extrapolates over the arctic region. Extrapolate means to infer or estimate by extending or projecting known information. How is this done and can you explain why is it valid? Is this simply using widely spaced and limited data from within the arctic itself and extrapolating to cover the entire region, or extrapolating somehow from the perimeter of the arctic?
[Response: This is explained in the GISTEMP documentation, but in areas with no SST information (like most of the Arctic), the information from met stations is extrapolated over a radius of 1200 km. That fills in some of the Arctic. A validation for that kind of approach would be a match to the Arctic buoy program - but I haven’t looked into that specifically. - gavin]
11 January 2008 at 12:40 PM
“In fact, IPCC 1990 dramatically over-forecast trends, as show by the IPCC figure that you reference and that I provide here:”
Roger,
Your blog post neglects to even mention the “good reasons” for “forecasts” to be off–namely the Mt. Pinatubo eruption. Don’t you think you owe it to those who read your blog to point out that:
A. The IPCC projections do not include volcanic eruptions and
B. Your graphs starts immediately before a volcanic eruption that had a large effect on global temperatures.
I’m afraid burying it (and IMO downplaying it) in your comments does not draw an accurate picture.
11 January 2008 at 12:42 PM
Jon P writes ” Can you provide graphs of the other data sets? I want to see them do just as well” I agree. And this, of course, brings up the perennial question. Which of the various data sets of average annual global temperature anomaly is closest to the truth? When we get some good scientific analysis which brings an understanding of that question, we will have advanced the yardsticks a very long way.
11 January 2008 at 12:43 PM
Is there an element of “cherry picking” in this article? Would an illustration using HadCRU or RSS with a moving average of either 5, 6, 7, or 9 years been as helpful to you in making your point?
[Response: The distribution of trends will be very similar - most of this variability is due to real weather, not instrumental noise. But I’ll check and report back. - gavin]
11 January 2008 at 12:47 PM
As a layman who is trying to understand this issue, I’ve been struggling with how to assess the forecast skill of the GCMs. I think this is an extremely important issue and I wish I could find more information regarding tests that would confirm or falsify the forecasting skill of the GCMs. I agree with your point that short-term deviations from a trend are not necessarily significant and do not necessarily indicate that the GCM’s are unreliable. However, I think that Roger Pielke, Jr. has a point when he suggests that accurate short-term forecasts are used to show how reliable the GCMs are, but inaccurate short-term forecasts are attributed to random noise in the actual data. This seems like a “head we win, tails you loose” verification method.
As another example, I have read here how the GCM’s accurately forecast the effect of the Pinatubo erruption. If by accurate, you mean that the GCM’s predicted that dumping massive amounts of aersols into the atmosphere would cause cooling, I am not impressed. The mental model that exists in my head would be just as “accurate”. If, however, by “accurate” you mean that the GCM’s made reasonably close predictions of the extent of the cooling, then it seems you are playing the “head I win, tails you loose” game suggested by Roger Pielke’s comment.
[Response: Huh? When did we say that short term predictions were good if they agreed with the models? Any test of a model has to be accompanied by an analysis of the uncertainties - and if a test happens to be a good match, but the uncertainties indicate that was just a piece of luck, then it doesn’t count. Pinatubo is different because the forcing in that case was very strong, and so it dominated the short term noise (at least in some metrics like the global mean temperature). Look at Hansen et al 2007 for more discussion of this. Pinatubo is more interesting validation than just for temperatures too though. Models get the LW and SW TOA radiation changes, they get the changes in water vapour, they get the impact on the winter NAO. None of those things were programmed in ahead of time. - gavin]
11 January 2008 at 12:47 PM
Walt, you said you’d been “staring at the data” and that Hadley was “definitely reporting ….” How much statistics do you know? Did you do any math? People are very good at detecting trends simply by looking at images — and very often see what is not actually there.
This worked to detect large predators — imagining a leopard has low cost compared to not seeing a leopard — but the same talent leads to gross error — imagining a recession is expensive compared to not noticing a recession.
Looking doesn’t suffice for definitely reporting. Math may.
How did you arrive at your conclusion?
11 January 2008 at 1:05 PM
Hank Roberts:
I understand the problem with short term trends.
However, if someone says “the last 6 or 7 years is suggestive that global warming has stopped” it is not good form to refute by showing a spread of 8 year trends. It just inserts another issue.
Why 8 for the refutation rather than the 6 or 7 year record advanced in the original assertion?
[Response: Because that was what was used by Tierney. The spreads for 6 or 7 year trends are even higher. - gavin]
11 January 2008 at 1:20 PM
The problem here is that you dont have weather variability you have VOLCANO variability. If you factor out the volcano effects, ( I recall tamino doing something similiar on his site) and then look at the 8 year trend you will get a different picture. That might be an interesting excercise. Tamino??
11 January 2008 at 1:24 PM
Some interesting comments here, and would just like to add my own observations, well not my “own” but reports here in Scandinavia. Spitzbergen is having extremely mild conditions, and when I say mild, well. Last week is was the warmest place in Norway about +8C, and heavy preciptation at their main weather station, 43mm in one day, and no it was not snow, it was rain. There is still no winter sea ice south of the islands, neither was there any for the past 2 winters. Further south on the mainland of Norway, the high plateaus are having huge snow deposits and its only January, something like 10-12 meters already on the Jolsterdals glacier. Because of the milder winter “weather”, records are being broken all the time with precipitation, most likely the record of over 5000mm is in danger.
11 January 2008 at 1:42 PM
Gavin-
Thanks but this is a pretty lame response: “In a collegial spirit, I suggest you download the model data directly from PCMDI and really look at what you can learn from it.” You are the climate scientist no? If you are unwilling to explain what is substantively wrong is my efforts to provide an example of forecast verification, then so be it.
I am quite confident in my conclusions from this exercise as summarized from my blog Prometheus, and nothing that you say here contradicts those conclusions whatsoever:
1) Nothing really can now be said on the skill of 2007 IPCC predictions.
2) By contrast IPCC dramatically over-predicted temperature increases in its 1990 report.
For 1995, 2001 (and some interesting surprises) please tune in next week.
Gavin, if you do decide to provide substantive critiques of the two conclusions above please do share them, as I still have absolutely no idea what your complaint about this exercise actually is, other than the fact that it took place.
[Response: You are again changing the subject. Who ever claimed that the 2007 IPCC projections had been shown to be skillful? I need to look at the 1990/1992 reports in more detail to comment on point two (as I said above). If you don’t ‘get’ what my complaint was after all this, I am surprised, but I will repeat it concisely: 1) You need to compare like with like. 2) Long-term trends have different statistical properties than short term variability. 3) Any verification attempt needs to take that into account. - gavin]
11 January 2008 at 1:52 PM
A quick question about the 1997/1998 El Nino and the annual mean growth in atmospheric CO2 concentrations that occurred in 1998. See:
http://www.esrl.noaa.gov/gmd/ccgg/trends/
Is there a connection that’s obvious from the models, or other studies?
(Also, what’s the html tag for the tilde? That’d be useful to post in a handy location, given how often El Nino/La Nina events come up.)
11 January 2008 at 1:54 PM
Roger Pielke Jr is capable of doing good work, but too often he just likes to be provocative and will be disingenuous to do it. His latest critique of the IPCC is another unfortunate example of the later.
Roger took a quote from a comment of mine on RealClimate about Environmental Defense’s website. I wrote that I liked the Q&A section where readers could submit questions about climate change. Roger on his blog dishonestly claimed that Dr Judith Curry made my comment and claimed Dr Curry was endorsing Environmental Defense’s politics. Roger then blocked my comments when I tried to correct his mistaken claims.
Its a bit of a stretch for him to complain that RC is selectively editing his comments.
11 January 2008 at 2:01 PM
Gavin-
Please explain how you accounted for short term variability in your over effort at verification here (other than to say they don’t mater when looking at trends):
http://www.realclimate.org/index.php/archives/2007/05/hansens-1988-projections/
My approach to verification is identical to yours in the Hansen post that you link to. And indeed in my first blog post on this I was careful to make the same qualification about short term trends as you do in this current post, which I will repeat since you haven’t acknowledged it:
“I assume that many climate scientists will say that there is no significance to what has happened since 2000, and perhaps emphasize that predictions of global temperature are more certain in the longer term than shorter term.”
So what is it that you are complaining about again?
[Response: If you try and step back from simply trying to be contrary, I suggest focusing on the the main principle that you have to compare like with like. In the post I did on the 1988 projections, I compared long term trends with long term trends. It works there because in both the model output and observational data have uncertainties in the long term trends that were small compared to the signal. This is not true for 8 year trends. The figures you produced show the long term trend (and it’s uncertainty) and the short term variability. That is not an appropriate comparison. Either put in the full envelope of model output over the same period, or just plot the trends and their uncertainty for the 8 year period. - gavin]
[Response: Let me try to explain with a simple example. Imagine you want to check the prediction that Colorado gets warmer during spring. The prediction is for a roughly sinusoidal seasonal temperature cycle, based on solar zenith angle. Would you test this prediction against a piece of observational data from 10-17 April? Of course not! Random weather variability means that a cooling from 10-17 April does not falsify the seasonal cycle, nor would a warming verify it in any way, because the time period is just too short. The variance of weather is larger than the seasonal warming from 10-17 April. Do exactly the same exercise for a two-month period rather than 8 days, and it will make sense. The variance of weather is then still the same, but the seasonal warming over this longer period is much larger, so now you get a sensible signal/noise ratio. -stefan]
p.s. If you’re still not getting it, try reading our popular post “Doubts about the advent of spring“.
11 January 2008 at 2:06 PM
Re: #25
Hank,
Not sure I follow your train of thought, but I can handle that last question: the Hadley graphs clearly come down for the past two years, and I have seen the results from number crunchers which validate that result.
The color coded maps clearly show two large cooling regions, both over water.
http://www.metoffice.gov.uk/research/hadleycentre/obsdata/HadCRUT3.html
The graphs for northern and southern hemisphere are broken out; the northern hemisphere goes up continuously while the southern hemisphere goes down in the last two years.
http://www.metoffice.gov.uk/research/hadleycentre/obsdata/HadCRUGNS.html
The graphs for land and sea are broken out. Land goes up sharply through the most recent year; sea temps trend down noticably over the last two years.
http://www.metoffice.gov.uk/research/hadleycentre/CR_data/Monthly/NMAT_SST_LSAT_plot.gif
11 January 2008 at 2:08 PM
Mr. O’Sullivan (31): I’m not sure why RC lets off-topic hearsay comments with personal attacks through like this but for the record you are welcome to post a comment on our site at any time. I have no idea what you are talking about regarding Judy, but she is a top scholar who I have an awful lot of respect for, even though we don’t always agree on everything.
11 January 2008 at 2:14 PM
As promised, the distribution of 8 year trends in the different data sets:
Data ____ Mean (degC/dec) ___ standard deviation (degC/dec)
UAH MSU-LT: __ 0.13 ____ 0.25
RSS MSU-LT: __ 0.18 ____ 0.24
HADCRUT3v: ___ 0.18 ____ 0.16
In no case is the uncertainty low enough for the 8 year trend to be useful for testing models or projections.
11 January 2008 at 2:30 PM
Great post Gavin and Stefan. This whole line of attack by skeptics/doubters/deniers is silly, since they won’t admit they’re wrong once we get some record hot years in the near future. I have blogged on this:
http://climateprogress.org/2008/01/07/no-warming-since-1998-get-real-deniers/
11 January 2008 at 2:31 PM
A pdf / frequency distribution of the eight year trends from your graph might be interesting too. The centre of the distribution should be ~0.2/decade.
11 January 2008 at 2:35 PM
Gavin, I think you’re copying me! My own version of the signal-to-noise issue is here, my display of year-end results from NASA GISS is here, and while I’m waiting for HadCRU and NCDC to post their year-end data I looked at northern-hemisphere land data from NASA here.
I guess it’s often true, great minds think alike.
Re: #17 (Jon Pemberton) and #22 (Jim Cripwell)
Jon, it looks like my blog isn’t the only place you’re contributing a drive-by pot-shot. As I responded there, I haven’t posted the year-end results from HadCRU or NCDC because they haven’t been put in the online data files yet.
11 January 2008 at 2:47 PM
Gavin-
My last comment on this thread. As the focus of your criticism, you have selected from a series of posts leading to one focused on a much longer time period starting in 1990 an example that I provided to show what verification looks like using IPCC AR4 predictions from 2000 (hence 8 years). You ignore the longer term view that I have provided.
Not only that, you pretend as if I did not explicitly acknowledge in my first post that nothing can be said about verification over 8 years, for exactly the reasons that you describe here. Instead, you suggest that I have implied otherwise.
If you can do a better verification of historical IPCC predictions, then by all means show it. There are many forecasts that have been made since 1990 and the more people engaged in this the better.
More generally, you guys at RC may indeed be the smartest guys in the room, but sometimes constructive efforts are more appropriate than simply criticism of why everyone else doesn’t meet your standards. I’ll continue the exercise next week, with verifications for IPCC 1995 and 2001 and then I’ll place all four assessments into comparative context. I have no doubt that you won’t like any of it, but even so, your comments are welcomed, but especially constructive comments.
Thanks for the exchange today, and providing a forum for discussion here at RC.
[Response: I still don’t know why you are reacting so negatively to this post. We spent considerable effort to outline the issues involved in forecast verification very much in the spirit of constructive engagement, and yes, pointing out issues with your figure as used on Tierney’s blog. Your responses here and the additional commentary on your blog this morning have certainly not added to the spirit of collegiality. That is a shame. - gavin]
11 January 2008 at 3:24 PM
I see over at Roger Pielke. Jr.’s blog he has now posted the IPCC forecasts from 1990. It appears from his graphic that the projection based on a 1.5 degree climate sensitivity for 2x CO2 has been the most accurate. I would further note that Steve McIntyre discusses today (1-11-2008) Wigley 1987 published in Climate Monitor. McIntryre notes in passing a discussion by Wigley of climate sensitivity. Wigley states, “If one accounts for the ocean damping effect using either a PD or UD model, and, if one assumes that greenhouse gas forcing is dominant on the century time scale, then the climate sensitivity required to match model predictions is only about 0.4 deg C/wm-2. This corresponds to a temperature change of less than 2 deg C for a CO2 doubling. Is it possible that GCMs are this far out? The answer to this question must be yes.”
So it appears from Pielke’s graph from 1990 on that the actual data is consistent with a CO2 doubling sensitivity of about 1.5C and from Wigley’s comments based on observations prior to 1987 a climate sensitivity of less than 2C for 2x CO2. So looking at these “long-term” trends isn’t it reasonable to question the output of GCM that place CO2 climate sensitivity well above 2C?
I realize this may be a complex question to answer so if you could direct me to link with an explanation that would be helpful to me.
[Response: I am looking at the 1992 projections as we speak. First off, these are not GCM estimates, but from a simple box model - and so ‘weather’ variability is outside their scope. Nonetheless, it’s worth looking into. The first question is the forcing that was applied. From figure Ax.1, it appears that the increase in forcings for all the IS92 scenarios are in the range of 0.5 W/m2 per decade to 2010 (slightly higher maybe, but it’s difficult to read off the graph). In the real world, forcings (as estimated by GISS and not including volcanic effects) increased at 0.36 W/m2 per decade (1990 to 2003). Thus the projections are likely biased high not due to the climate sensitivity but due to the overestimated forcings growth. The temperature trends over that period in the GISS record is 0.24 +/- 0.04 degC/dec. The best estimate ‘2.5 deg C’ sensitivity model has a trend of 0.25 degC/dec in figure Ax.2 (2.8 degrees in 110 years) which is somewhat less than shown in RP’s graph for some unknown reason (actually, none of his model output lines seem to match up with the figure - puzzling). Once you include an adjustment for the too-large forcings (by ~40%) the mid-range model outputs line up pretty well. Pinatubo complicates things, but I don’t see any big problem here. - gavin]
11 January 2008 at 3:31 PM
Jon Pemberton has commented on my blog that he’s not taking pot-shots, just asking a question. I have no reason not to believe him. So, my apologies.
11 January 2008 at 3:32 PM
Christian Desjardins asserts:
[[That being the case you can say NOTHING about any trend in this data set [i.e., 2001-2007] except that the data indicates no change in temperature over that period. Whether you think that is evidence that global warming has halted is debatable but you can’t bring any statistical analysis to bear to show that the temperature change in the period was other than zero.]]
Well, no, because a sample size of N = 7 isn’t enough to be statistically meaningful. But if you do a linear regression, the trend is up. Try it yourself!
11 January 2008 at 3:35 PM
I think that roger is trying to wind you all up here RC. It looks to me as if you have wasted enough time answering his queries and deliberate attempts at obfuscation.
You have a great deal of patience.
11 January 2008 at 4:17 PM
I second that pete best (#43).
11 January 2008 at 4:30 PM
re 29 (Gavin):
Gavin: “Who ever claimed that the 2007 IPCC projections had been shown to be skillful?”
What is your opinion on the “skill” of the 2007 IPCC projections?
[Response: I expect that they will be skillful. But this can’t yet be determined. - gavin]
11 January 2008 at 4:37 PM
I was caught off guard by the 2007 NASA hemispheric and global temperature anomalies which just came out. It seems that December of 2007 was much warmer than NOAA and others had figured.
11 January 2008 at 4:56 PM
Re #40: Gavin, now why did you exclude the volcanic forcing when figuring the GISS “actual” forcing increases? Is there no “average natural aerosol forcing” that is assumed in the projections? It seems plausible anyway that when volcanic forcing is added then the forcing increase due just to the emmissions scenario is going to be higher than you have shown. It may in fact be close to what is shown by the 1992 report? Roger states that we are running on the high end of the emmissions scenario. Please advise.
[Response: Because I was comparing the scenarios which didn’t have volcanoes either. But as I said, Pinatubo complicates things. Given that those projections were done with a simple energy balance model though, it would be trivial to add Pinatubo in as an extra forcing and see what difference it made. As to whether we are on the high end of the scenarios, I don’t think that is correct (but I haven’t looked carefully). CO2 is increasing faster, but CH4 has stabilised, and CFCs are falling faster, aerosol changes are potentially important but not very well characterised. This will become clearer in a few years time. - gavin]
11 January 2008 at 4:56 PM
> I have seen the results from number crunchers
Cite please? For a two or three year trend to be significant it has to be huge.
Gavin, Tamino, could y’all do what William Connolley did in his exercise (stoat, link above) and indicate on your similar images which of the short-term trend lines are significant and which aren’t? It helps make the point that we can’t _see_ which is which on a picture.
11 January 2008 at 4:57 PM
What I would like to see is a 29 year graph (1979 - 2007) with all the data sets GISS, UAH, RSS, and HADCRUT3v as a comparison.
Love to have an explanation of the differences between the data sets (what average warming trend they are showing and why they ‘might’ be different) and if there are discrepancies between the actual data and any modeling for the same period.
I am assuming that this data exists for the requested time period.
As you can all tell I am a layman, but find this all very interesting and believe all temperature measurement data in one “post” would be the most beneficial to many people.
btw, Tamino, thank you. I can understand your reaction from what I have read on your blog at times
No deniers, no alarmists, just science. Cue the Coke commercial music…..
Jon P
11 January 2008 at 5:09 PM
Ref 42 Christian writes “Well, no, because a sample size of N = 7 isn’t enough to be statistically meaningful. But if you do a linear regression, the trend is up. Try it yourself!” I have and you are quite right when you start any time in the 1970’s. Which means current temperatures are significantly higher than they were in the 1970’s. However, if you try any other sort of least squares regression fit, e.g. polynomial, then the NASA/GISS data still shows increasing temperatures, but the other data sets show that temperatures have stabilized, if not actually peaked. I used CurveExpert 1.3 for the analysis; shareware.
11 January 2008 at 5:16 PM
I’m relatively new here, but I watched PBS NOVA’s “Dimming the Sun” documentary last night, for about the third time. I find it very powerful, and scientifically accurate. Regarding modeling, are the effects of pollution, aerosols, and aircraft contrails accounted for in climate models? In the comment sections of USA Today’s articles on global warming, there are many skeptics that say NASA’s models just don’t reflect reality. If that’s right, it might have to do with the “global dimming” effect.
Dr. Hansen says if we didn’t have that pollution in the atmosphere, global temperatures would be about 2 F. higher than they are now.
How many here have seen that documentary, and what do you think of it?
[Response: We discussed it when it was first broadcast in the UK and later on in the US. - gavin]
11 January 2008 at 5:27 PM
#50. Jim Cripwell, I don’t understand your argument. Can you plot your analysis ona graph and post it on the net somewhere so we can see it?
11 January 2008 at 5:33 PM
Jon, have you used the “Start here” link at the top of the page?
Or read anything at http://www.globalwarmingart.com/ yet?
Have you read the discussion on the page there along with this, which is at least half of what you’re wishing for I think:
http://www.globalwarmingart.com/images/7/7e/Satellite_Temperatures.png
If you will give us some idea what you’re starting from, where your questions arise, where you’ve looked, what you believe or know so far, it will help us (most like me are just fellow readers here) point to answers.
11 January 2008 at 5:49 PM
Gavin- On #40 above. You want to use scenario IS92e or IS92f, rather than IS92a that you found in Figure Ax.2 (which says something about temperature change under different assumptions of climate sensitivity for IS92a). As I explained in my blog post on this, the proper figure to use is Figure Ax.3 to determine these values.
You write “Once you include an adjustment for the too-large forcings” — sorry but in conducting a verification you are not allowed to go back and change inputs/assumptions that were made at the time based on what you learn afterwards, that is cheating. Predicting what happened after 1990 from the perspective of 2007 is easy;-)
[Response: The IS92 scenarios do not diverge significantly until after 2010 - so assessing model response to 2007 is independent of exactly which scenario is used. I don’t see that your figure uses the data from fig Ax.3 - that figure has all models on the same trajectory until 2000 and only a small amount of divergence in the years following. That is nothing like what you have used. And finally, when it comes to projection verification, ideally you would only do it (as I did for Hansen et al 1988) if the scenarios matched up with reality - that gives a test of the model. If the scenarios are off, then the model response is also off and the model-data comparison not particularly useful. If your claim is that the IS92 scenarios were high, I’m fine with that. But don’t confuse that with model verification. - gavin]
11 January 2008 at 6:06 PM
Gavin- Good. With this exercise I am not interested in model verification and never have claimed to be, and as I’ve stated all along my interest is in forecast verification. You can find out more about the various scenarios in that same report beginning at p. 69, Figure A3.1 for instance shows how dramatically the scenarios diverge quite early. If you spend a bit more time with it you’ll also see that my 1990 IPCC prediction matches just about exactly with the used by IPCC AR4 in their figure TS.26, so if I’m wrong, so too is IPCC. More next Monday.
[Response: The only thing that matters in those simple box models is the net forcing, which is in fig Ax.1 - which clearly shows that the different scenarios have not significantly diverged by 2010. It’s not clear to me what the FAR range in fig 1.1 of AR4 represents and it isn’t clearly stated. Plus they reference it to IPCC 1990, not the 1992 supplement. I invite anyone who knows what’s plotted there to let me know. - gavin]
11 January 2008 at 6:17 PM
It will be interesting to see if this temperature slowdown/fall since 1999 (depending on the dataset used) continues in the future and become statistically significant.
It is my observation that skeptics are only doing what the other side has been doing for years. That is every record high temperature recorded in the United States is promoted in the media via climate scientists as “proof” that humans are causing global warming. In Montana, recent bad fire seasons are promoted in the media via climate scientists as “proof” that humans are causing global warming. It is refreshing to read that RealClimate has taken a stand against temperature chasing, drought chasing, and fire season chasing.
[Response: As we always have. - gavin]
11 January 2008 at 6:20 PM
Hank,
Thank you, I looked at the links but they are not current.
What I am exactly looking for is a comparison of all satellite and GISS temp data compared on the same graph through 2007.
Beliefs… Warming? yes Global? not convinced, CO2 as main forcing? not convinced.
I read RC, Open Mind, Climate Audit, Accuweather, Lubus Motl, Eli Rabbet, Anthony Watts, and Pielke sites/blogs. And probably some random others.
I also like looking at sea ice extents from Artcic and Antartic.
I see the “forts” with “high walls” being bulit between various camps and usually one piece of data posted on and then the “fur flies”. Recent example, GISS data reveals 1998 and 2007 are tied as 2nd warmest year, but RSS data differs (as noted on Lubos’s site).
Looking for a post that discusses and shows all data on one graph. I selected 1979 as the starting point as that is when satellite data became available, correct me if my assumption on this is wrong.
Another example, Eli has running post on Artic Sea ice extent, but not Antartic. Antartic melting appears to be behind last years rate. Maybe a long term comparison of both of these.
I can read the data and understand it, but I am in no position to validate it. For that I have to rely on others and it is pretty tough doing so when you are between the “forts”..
Jon P
11 January 2008 at 6:40 PM
Jon Pemeberton (57) — Have you read The Discovery of Global Warming, linked in the Science section of the sidebar?
11 January 2008 at 6:49 PM
>forecast
>prediction
These are used to plan picnics and political decisions.
>scenario
>model
Scenarios can be run for the deep past, recent past, and near future. The better a model’s range of outcomes matches the known real climate, the more interesting its outcomes when it’s run through into the near future.
Because when I read Dr. Pielke write
> that is cheating
> my interest is in forecast verification
That’s saying the work done with an original Cray supercomputer wasn’t so good, so redoing that work today is cheating.
Cheating? It’s showing politicians how much better work can be done now — and that runs can better match what did happen, so they may better match what _will_ happen.
That will scare those who want to say nobody knows enough to decide.
Remember, the original Cray supercomputer could not have run Windows 95, it didn’t have enough memory. Your doorstop Win95 machine is more powerful than the Cray was then.
Gavin describes how one can improve a model, and run it again starting at some past point, and if the model is better, the range of outcomes when it’s run on into the future may also be better.
That’s not cheating, for science, it’s how it’s done.
20 years ago Dr. Hansen was saying it’d take about til now to have an idea whether the models then were useful, because the climate signal would take that long to emerge from the noisy background.
I’m sure he was referring to statistical and measurement noise, not to political noise. Over 20 years, the statistical noise decreases.
Political scientists might study whether statistical noise is inverse to political noise, on issues like this.
11 January 2008 at 6:51 PM
Has anyone tried to calculate the heat content of the entire atmospheric and oceanic system? Wouldn’t that be a better metric to verify instead of surface based averages of temperatures? Wouldn’t this take ENSO and lags because of ocean storage out of the equation?
[Response: You find this analysis in the IPCC report. The heat content change is completely dominated by the change in ocean heat content, because of the large heat capacity of water this is the only component of the climate system that stores a significant amount - see Fig. 5.4 in chapter 5. So this tells you how much ocean heat storage has delayed surface warming, i.e. what portion of the anthropogenic forcing is soaked up by the ocean rather than being balanced by radiation back into space (the latter implies a surface warming, so the portion going into the ocean does not lead to immediate surface warming). - stefan]
11 January 2008 at 7:12 PM
RE#8 Try not to misrepresent the UK Met Office. Here is a link to its most recent media release on trends in global average temperature: http://www.metoffice.gov.uk/corporate/pressoffice/2008/pr20080103.html.
I think climate scientists in the UK have given up correcting the misrepresentationa and misuses of global temperature data in the media. I can understand why - it is a seemingly endless task. Unfortunately, some newspaper editorial teams are apparently not up to the task of spotting arguments based on dodgy statistics and are publishing them, misleading millions of people in the process. I am glad that RC has not given up the task of challenging attempts to mislead the public about climate change science.
11 January 2008 at 7:25 PM
VirgilM — are you the VirgilM from CA? You know about Triana, right?
11 January 2008 at 8:51 PM
One only has to read the comments for the “Global warming has stopped” article to see how pervasive the denialist take on this issue is with the public - or at least with those who chose to comment. Even though the essential flaws in Dr Whitehouse’s opinion piece were glaring even to someone without deep knowledge of the subject such as myself, pointing them out merely ended up lost within a plethora of unsubstantiated claims of biased science, misrepresentations and repetitions of repeatedly debunked denialist myths. The attempts to inform by some commenters probably changed no-one’s mind.
Has anyone with the in depth knowledge and solid scientific arguments taken Dr Whitehouse to task? I hope some of you do - he ought to have the education and intelligence to be engaged by what real science tells us, but I wouldn’t count on it. I think too much of the media are primarily about entertainment and his career is a media career. Controversy, even when it has little sound basis, attracts readers/viewers and Dr Whitehouse’s career in media is probably strengthened by the kind of writing in his “Has Global Warming Stopped?” article.
I for one am pleased when this Blog does take people like Dr Whitehouse to task. Letting them and the organisations get away with it uncriticised leaves the public free to believe what they say is true ie ill-informed on an issue of critical importance.
11 January 2008 at 8:51 PM
Skip the trailing period to get Bob Ward’s link to work:
http://www.metoffice.gov.uk/corporate/pressoffice/2008/pr20080103.html
See also (would Dr. Pielke say this is ‘cheating’ by improving a model and then showing that it better matches what happened recently?)
http://www.metoffice.gov.uk/corporate/pressoffice/2007/pr20070810.html
“… Dr Doug Smith said: “Occurrences of El Nino, for example, have a significant effect on shorter-term predictions. By including such internal variability, we have shown a substantial improvement in predictions of surface temperature.” Dr Smith continues: “Observed relative cooling in the Southern Ocean and tropical Pacific over the last couple of years was correctly predicted by the new system, giving us greater confidence in the model’s performance”
11 January 2008 at 8:59 PM
The December, 2007 RSS anomaly for the lower atmosphere is -0.046C.
The December, 1979 RSS anomaly is +0.022C
Cherrypicking for sure, but a change in temperatures of -0.068C over 28 years should be taken into account I imagine.
11 January 2008 at 9:21 PM
A decade,or less, does not a climate era make. What would we call this period- the tiny little ice age? Climate eras have lasted centuries and millenia, while a dominant forcing(GHGs)governs which is the case at present.
It should be a given that the more data available, the more accurately projections regarding future scenarios can be made. To reduce this to an absurdity, who ever rolled a single die and came with a 3.5?( the expected average over a large number of throws.)
At the Tierney Lab site on the right just under the heading About Tierney Lab, it states “John Tierney always wanted to be a scientist,but …..”.
That says a lot.Beware of wannabes.
11 January 2008 at 9:36 PM
Commentary to this point (Post # 62.) seems to indicate that my suggestion (in Post # 1) of using a running mean (average) was misinterpreted, especially since other readers employed the same terminology to quite different measures. A true “running mean” of eight values begins with the first data point and is an arithmetic average of the first 8 values. This becomes the first graph point. The next graph point simply drops the first data point and increments by one. It forms a nicely smoothed, continuous regression line. It’s a much better way to present data such as those plotted in the GISTEMP index, and, I believe, is a better portrayal of reality than the “pick-up-sticks” jumble of “8-year trends”.
11 January 2008 at 10:25 PM
Jon Pemberton –
Seems to me you’re asking others to do an impossible amount of work for you — to get and chart for you exactly what you want, all of what you want, and nothing but what you want. It’s certainly doable. But the person behind you in line will have slightly different demands.
You can take the chart I pointed you to, look up the next year’s numbers and chart them.
You can charm the person who maintains the site with intelligent questions and suggestions and perhaps get the charts updated a bit sooner than they’d otherwise find time to do it. I’ve _often_ found that to be true. Sometimes I find I’m the only person all year to thank a scientist for making the time to put such charts up online, they may get lots of pointers and lots of mentions but theys till perk up when someone emails a simple thank-you along with a question about how to find out a bit more, say, to extend such a chart.
Bystanders like you and I could keep a whole lot of experts very busy, if they tried to respond to all such requests (and many people do start off by insisting that they know exactly what they need to find, to get their certain and final understanding of what puzzles them, or irrefutable proof of some claim they heard, or the like).
That’s why people refer you to the data sources and programs that let you do your own charting. If you understand the statistics you can do your own error ranges. If you don’t, charts won’t help much.
My suggestion is to stay away from anything that looks like a “fort” to you, and hang around in the trenches with the people who are doing the actual digging, or watch them as I and so many others do.
The better you inform yourself, first, the better questions you can ask, and I tell you, I feel really happy if I manage to get a “Good question!” response from one of the climate scientists here once a year myself. They’re making a gift of their time to the rest of us.
Last thought, I’ve heard this as ‘Rule One of Database Management’ — one data set, many pointers. One reason very few people make the kind of effort you find at globalwarmingart is that there is a vast collection of data sets, some easier to find than other, some very significantly revised when errors are found in work later on.
People who make copies and then write based on their copies may be doing so based on outdated information. Look for people who give you references rather than put answers together for you — the references will lead you forward in time to better info.
11 January 2008 at 10:27 PM
Re: #48
Hank,
You misunderstood my post. Also, did you see my followup, with links to the references I made?
My point was not: “Hey, isn’t it statistically significant that Hadley shows two consecutive years of cooling?” My point was: “Is Hadley right?”
NASA-GISS seems to think it has kept on warming.
I’m just trying to understand the Hadley data at face value.
11 January 2008 at 10:29 PM
Oh, Jon, you mentioned Eli’s notes on sea ice and said you wished he had something. Did you click his link? The comparison of the poles that you wished for is at the source Eli gives. The charts there that are pulled automagically from the databases are working now; the hand-edited one will be updated in a week or two to fill out the 2007 year, I just asked (nicely) about that yesterday myself (grin).
11 January 2008 at 10:51 PM
Charles, a moving average gives you a different look than a trend line.
There’s an excellent treatment of this here, in a rather famous website: http://www.fourmilab.ch/hackdiet/e4/signalnoise.html
“… Like most attempts to characterise a complicated system by a single number, a scale throws away a great deal of the subtlety. … The scale responds with a number that means something or other. If only we knew what…. Over time, certainly, the scale will measure the cumulative effect of too much or too little food. But from day to day, the scale gives results that seem contradictory and confusing. We must seek the meaning hidden among the numbers and learn to sift the wisdom from the weight.”
…
“… The right way to think about a trend chart is to keep it simple. The trend line can do one of three things:
* Go up
* Go down
* Stay about the same
That’s it. The moving average guarantees the trend line you plot will obviously behave in one of these ways; the short term fluctuations are averaged out and have little impact on the trend.”
He addresses the reason that the moving average you ask for may not give a clear picture, on the same page:
http://www.fourmilab.ch/hackdiet/e4/figures/figure737.png
“… The familiar moving average trend line is drawn as before. The dashed line is the best fit to all 90 days …. But, obviously, it misses the point. … Short term straight line trend lines … provide accurate estimates ….
Excel workbooks are provided at the site to try out the different methods of charting. See also everything else, wonderful site.
11 January 2008 at 11:11 PM
It is now raining in mid-winter where before it would be snow. I need no other proof or to be told weather is different than climate. Again, in mid-winter, it is now rain, and if it does snow, it is a wet snow and melts a few days later. before, there would be snow on the ground from Thanksgiving until Easter, now, it is raining in January.I am old enought to know the pattern has changed by what I have experienced and what I am experiencing now.
11 January 2008 at 11:35 PM
Re: #31 my comment and #34 Roger Pielke Jr’s reply:
My tone was unduly harsh. Roger Pielke Jr does like to provoke discussion. To do this he will make controversial statements on his blog. Its common in some academic circles, but its likely to be misunderstood in a public forum like a blog. I did not like see my comment used in a way I thought was inappropriate.
The misquote did occur, and I submitted comments and several where not admitted. After the posts moved on, one my milder comments made it through. After that I had all but one of my comments admitted on other posts.
I do not think that Roger Pielke Jr does not respect Dr Curry, but I do think he was trying to stir things up.
This is the post:
http://sciencepolicy.colorado.edu/prometheus/archives/climate_change/000904hurricanes_and_globa.html
12 January 2008 at 12:08 AM
I am not convinced by much of this.
You take a data set of 30 years and say that the initial warming observed of less than 20 years is a long term trend and can be relied upon but you say the past 7 years of statistically indistinguishable temperature data is short term climatic fluctuation and can be ignored (note this short term ‘fluctuation’ isn’t fluctuating).
There are no errors bars in the graph - put them in and you can draw your ‘trend lines’ with much more latitude. And because the recent observed stasis is 7 years you, ignoring errors, chose an 8 year grouping which is bound to drag the stasis back towards the rising section of the graph because you are giving it less weight than the data in the centre of the graph.
YOU are the denialists - denying data. Finding ways to prove it isn’t what it is and making it conform to your worldview.
12 January 2008 at 12:21 AM
I made a chart from the updated NASA-GISS anomaly data and I think it came out fairly well:
http://bp1.blogger.com/_hb0jssUZaPY/R4hNSaRKHzI/AAAAAAAAABM/Gabp4uz77ag/s1600-h/anom.gif
I plot gross annual anomalies (a score of 1200 would be a mean anomaly of +1*C per month) along with 5, 10 and 30 year running means.
I’d appreciate any feedback as to method and conclusions.
12 January 2008 at 12:42 AM
First of, let me qualify my post by saying that I come here as a believer, a questioner, and a skeptic. By that I mean that, I am a believer in that I find the evidence for long-term global warming, since at least the 1880s, overwhelming and apparently irrefutable. Secondly, basic physics/thermodynamics dictate that increasing concentrations of greenhouse gasses will have an affect on the overall climate. However, I am a questioner in that I am not as fully convinced of the magnitude of the impact of anthropogenic versus non-anthropogenic causes of the warming trend but readily concede that the current warming trend is at least in part due, if not substantially, due to anthropogenic releases of CO2 and other GHGs. Finally, I am a skeptic that has some experience in much simpler modeling(mostly ground water contaminant fate & transport)in that I have much less confidence in our current ability to accurately model future climatethan is often portrayed in the popular press or even on websites such as this. In other words, I believe we can with near certaintude say that if global temperatures continue to rise, there will be a rise in sea level due to the melting of glaciers and thermal expansion of the oceans, but that projections of drought, hyper-intensive storms, mass extinctions, and other calamities, etc. are somewhat less certain.
As an educated layman, my take on this is Much Ado about Nothing. I have read and re-read Tierney and Pielke’s posts and what I get out of both of them is that they are saying you can read whatever you want out of the recent (2001-2006) Global temperature estimates. ehy are very clear in stating that the recent Global temperatures neither prove or disprove the overall AGW model. What Pielke did say is that the most recent number will provide “cherry-pickers” with ammunition to quibble about this, that, or the other thing. Tierney correctly noted that there is a wide range of variance in the estimated global temperature anomalies and that, depending on where you fall on the sociological spectrum (denier, questioner/ skeptic, advocate, disciple), will help dictate which estimate you will rely on most. What both Tierny and Pielke seem to be asking for is continued and further refinement of the models as we gather additional climate data. In other words, don’t become defensive and just say short-term perturbations don’t affect the validity of the “MODEL”, continue to try to make the model account for the short-term perturbations. The models are nothing more than our attempts to account for all the variables that do drive climate change.
Bob North
Bob North
12 January 2008 at 12:50 AM
Gavin, a man with the patience of a saint. My question has to do with how well behaved the psuedo-climate system, as defined by GCM runs is. I work in FEA engineering, and it is quite common for such systems to contain bifurcations, whereby a large collection of runs will demonstrate two (or more) general solutions, superimposed of course with shortterm noise. Have any of the climate models shown such behavior? I.E. do you see situtaions where some fraction of the runs (with the same parameters and forcings, but perturbed initial conditions) show more than a single solution trend?
12 January 2008 at 1:17 AM
Another way to see that data is take the GISTEMP data, compute 8-year regressions (via SLOPE), and put that series into as scatter plot, whihc gives one line that graphs the slopes of the blue lines. The only times the slopes go below zero are those around the volcanoes.
12 January 2008 at 1:46 AM
Re: 57
Here is the 12 month moving average for NASA GISS, Hadley/CRU, UAH, and RSS temperature analyses from January 1979 to October 2007. NASA GISS and Hadley/CRU are land-ocean instrument data, while UAH and RSS are lower troposphere satellite data.
http://cce.890m.com/temp-compare.jpg
Here is the raw data for each analysis, including the linear fit. With both of the instrumental analyses, the slope is 0.17 degrees per decade. In the UAH satellite analysis, it is 0.14 degrees per decade. In the RSS satellite analysis, it is 0.18 degrees per decade.
In all cases, the anomalies are adjusted up or down so as to give the linear regression for each analysis a y intercept of zero.
http://cce.890m.com/giss.jpg
http://cce.890m.com/hadcrutv.jpg
http://cce.890m.com/uah.jpg
http://cce.890m.com/rss.jpg
In short, the anomalies are where you’d expect them to be, given the warming signal, plus natural variability and the fact that each analysis uses different methods.
12 January 2008 at 6:53 AM
Looks like we’re about to have a big volcanic eruption in Ecuador. So temperature will go down by 0.2 K for a couple of years and we’ll have to put up with two more years of deniers saying, “See, global warming stopped!”
12 January 2008 at 7:12 AM
Ref 52. If you want to see the sort of thing I am talking about, I am afraid you need to go to Yahoo Climate Skeptics and download the graphs I uploaded under the title “Rctner”. I fully realize that there a pseudo-infinite number of such graphs, and these three are merely examples.
12 January 2008 at 7:27 AM
Thanks much for the crystal clear diagram and discussion. Contrary to some readers who are growing weary of it, I’m delighted to see RC giving the other side “enough rope” like this.
Your main point (which you have now repeated 60 zillion times) is irrefutable, as is your observation that the exercise in question violates the simple principle that LIKE SHOULD BE COMPARED WITH LIKE. (You haven’t tried shouting yet, I suppose, but I doubt that would prove any more effective.) In the face of an argument which could hardly be framed more crisply, your dissenter has no more interesting stratagem than feigning deafness. Like a World Wrestling Federation spectacle, this classic thread has been a grossly unfair fight, and a lot of fun!
12 January 2008 at 9:01 AM
I understand that between the 1940s and 1970s, global mean temperature did not change much and this has been ascribed to increased particulates arising from industrialisation. So if you take a 30 year average between say 1945 and 1975 and then again between 1975 and 2005, the latter average should be significantly higher. What is the proper explanation for this? What changed in the 70s? Surely particulate emissions on a world scale did not decrease in the 1970s though it might have, in some industrialised countries? What about increasing use of coal by China and India say, and the consequent increase in particulates in the recent past? Would this not be expected to have a dimming effect and a reduction in global temperatures?
[Response: Not all aerosols are the same. Some, such as black carbon released by coal burning, actually have a surface warming impact. Sulphate aerosols and secondarily nitrate aeresols, which do have a surface cooling impact, increased substantially in burden from the 1940s through the 1970s, decreasingly markedly with the passage of the Clean Air Acts of the 1970s and 1980s. The various issues you raise have been discussed many times before here and in links provided. Start here, here, and here. -mike]
12 January 2008 at 9:28 AM
“These comparisons are flawed since they basically compare long term climate change to short term weather variability”
What is short term weather variability?
Isn’t it obvious that the warming had to stop after a 5 W/m2 drop in the energy input to the climate system in 2002.
http://isccp.giss.nasa.gov/zFD/an9090_TOTnet_toa.gif
It should be equally obvious that the 90s had to get much warmer than the 80s because of a much higher energy input at TOA.
But the explanation is that there was one type of quite stable weather between 1994 and 2000, and a totally different type of weather between 2002 and 2005 (and probably longer), capable of reducing the radiation by 5 W/m2?
What is this assumption based on?
[Response: The ISCCP data is great, but can’t be relied on for trends due to difficulties in tying different satellite records together. The implication that albedo has suddenly increased to Pinatubo levels without anyone else noticing or a rapid decrease in temperatures is …. surprising, to say the least. - gavin]
12 January 2008 at 9:35 AM
cce says in #79: “In short, the anomalies are where you’d expect them to be, given the warming signal, plus natural variability and the fact that each analysis uses different methods.”
Of course, the denialists will say it’s clear the warming stopped in 2000…and in 1998 and in 1995 and in 1991 and in 1987…” At least Pielke and Douglass et al. are sufficiently sophisticated to realize that the only way to attack the anthropogenic hypothesis is to simply deny that warming is occurring. Unfortunately, since all the science and the evidence support a warming trend, they can’t get beyond misusing statistics and saying “No it isn’t.”
12 January 2008 at 9:53 AM
A couple of small comments:
1. Pielke is right that so long as the four major sources of “global” temperature disagree what one sees is largely a matter of which record one looks at.
This is quite troubling since the members of each pair, the two surface records, and the two satellite records, essentially have the same raw data. The differences between members of the pairs is thus a difference in after the fact “adjustments”. Surely those can be ironed out and agreement reached on the “better” methods?
The increased divergence of GISStemp and HadleyCRU, and between UAH and RSS, suggests that rather than being reconciled the differences are growing.
Very troublesome.
[Response: Not really. The trends for the most part are similar given the underlying uncertainty, and there are defendable reasons for the various choices made in the different analyses. Reasonable people can disagree on what is ‘best’ in these cases, and so rather than being troubled, one should simply acknowledge that there are some systematic uncertainties in any large scale climate metric. That uncertainty feeds into assessments of attribution and the like, but is small enough not to be decisive. Of course, assessing the reason for any divergence is important and can lead to the discovery of errors (such as with the UAH MSU record a couple of years ago), but mostly it is due to the different choices made - gavin]
2. For assessing “global climate change” the absolute trend lines are apt. However, for assessing man caused global warming the “neutral” trend line would not be zero. Since the Little Ice Age we have naturally warmed. Should not this be taken into account? In other words a trend of some figure, say .6 C per century, should be regarded as “neutral” for purpose of assessing a man caused trend.
[Response: There is no ‘neutral trend’ that can simply be regarded as ‘natural’ - volcanic and solar forcing changes are not any different to GHG forcing when it comes to attribution and modeling. If instead you propose that the 20th Century trends are simply some long term intrinsic change then you need to show what is going on. No such theory exists. The trends can however be explained quite adequately from the time-series of forcings (including natural and human caused effects). - gavin]
12 January 2008 at 9:56 AM
In 68 Hank writes “Jon Pemberton Seems to me you’re asking others to do an impossible amount of work for you — to get and chart for you exactly what you want, all of what you want, and nothing but what you want. It’s certainly doable. But the person behind you in line will have slightly different demands.”
I think you are being a little unkind to Jon. In my computer I have complete sets of temperature data from NASA/GISS, HAD/CRU and RSS/MSU. If anyone can direct me to a site which has similar data for NCDC/NOAA I would be grateful. I have done some simple trend analysis on all four sets of data, and I am convinced that the HAD/CRU, NCDC/NOAA and RSS/MSU are highly correlated and give very similar results. The NASA/GISS data set is different. Notice I say “different”. I have no idea which set is closest to the truth. I have searched, written emails, etc. but I cannot find any study which has compared and contrasted the different data sets, so I have no idea which is “best”. Gavin has, however, used the NASA/GISS set, and seems to claim that any analysis using the other data sets would give the same result. My simple minded analyses indicates this may not be true. So I think it is perfectly legitimate to ask that Gavin either shows that the NASA/GISS data set is the best one available, or he shows that using the other three data sets produces the same answer.
12 January 2008 at 10:12 AM
I do not agree. Dr. Pielke and others have a point when comparing data and model output. At least they have a strong point in the public discussion. One can clearly argue that tackling climate change would be easier if recent years would have shown significant warming. Correct, this does not invalidate the models and the time series is too short and error margins are too big to have a scientific argument against them, however, it is disturbing. The sceptics on the other hand have to come up with their own models and theories and support it by data to have a plain scientific battelfield. Criticism is easy. As long as is there is nothing better out there, we better stick with what we have.
12 January 2008 at 10:23 AM
In 1980 I became the national training director for a carburetor company. I would travel around America training mechanics on how to fine tune using a chassis dynamometer and an exhaust gas analyzer. As part of the demonstration I would disable the then mistrusted emissions systems so mechanics could see that the systems were in fact eliminating large quantities of CO, NOX, and hydrocarbons from the exhaust gas - especially when simulating climbing hills at high speeds (needles pegged). When I would enable the emissions systems the exhaust gas, even under very heavy loads, would be mostly CO2 and H2O vapor - plant food is what I told them.
So the change the emissions systems made in dramatically reducing aerosols makes perfect sense to me.
In the mid 1980s there was a widespread resurgence in the use of wood stoves for home heating. Lots of towns, even small ones, had a store that specialized in selling them. Did those make their presence known?
12 January 2008 at 11:10 AM
#84
Gavin,
The ISCCP and ERBS data are in very good agreement, are they also wrong?
http://lasp.colorado.edu/sorce/news/2006ScienceMeeting/presentations/Day01_Wed/S2_01_Loeb.pdf page 21
“without anyone else noticing” ?
So it’s not true that the ocean heat content peaked in 2003 either, after a hugh increase since 1994?
www.iges.org/c20c/workshops/200703/ppt/Ingleby.ppt page 29
[edit]
[Response: I don’t see any support in Loeb’s data for any significant shift in TOA SW absorption in recent years. And on slide 18, he estimates that it would take 15-20 years to be able to detect such a shift, and slide 19 shows no big changes in either ISCCP or CERES. As you should know, ocean heat content data post-2003 are the subject of a lot of investigation right now because of the shifts to the ARGO network and various problems that there have been. Whether OHC peaked in 2003 is therefore very uncertain, but SST records are rising less rapidly than SAT records there is likely an increasing offset in air-sea temperatures which implies that the oceans are still warming (and that OHC is increasing). As in the rest of this post, there is short term variability in all these metrics, and only significant long term trends count. - gavin]
12 January 2008 at 11:10 AM
re 83: I started thinking, what if aerosol forcing is now growing fast enough to counterbalance GHG forcing by increasing CO2? Then GW would be temporarily stopped. We know CO2 emmisions have increased rapidly in the past few years prinicapally due to rapid growth in developing economies. These economies tend to burn coal dirtily. A second factor is that depletion of higher grade coal is forcing more and more consumption of lower grade product. Could these twin trends mean that the aerosol load might be rapidly increasing? Perhaps fast enough to counteract GHG forcing? A global temperature metric wouldn’t be the best way to detect such a change. Perhaps some other globally measured metrics could shed some light on this question?
If this is indeed hapenning, it could make the job of obtaining consensus for mitigation more difficult.
12 January 2008 at 11:20 AM
Jim Cripwell, glad you’re volunteering, but I suggest you post pointers to the source data (and note the date when you downloaded the copies you’re using), and describe what you’re doing to compare them. It’s too easy to get the wrong file or outdated copies, elsewise.
12 January 2008 at 11:43 AM
Jim Cripwell, was it NASA or NOAA data that you’re looking for?
NOAA is here:
http://www.ncdc.noaa.gov/oa/climate/climateinventories.html
“digital holdings … contain almost 300 terabytes of information, and grow almost 80 terabytes each year”
12 January 2008 at 12:17 PM
I am a newbie here, and would like to pose a question that may be seem a bit stupid to some.
I read the statistical work at this site, and it claims that a standard deviation for the annual average temperature series is 0.1 deg C.
http://tamino.wordpress.com/2007/12/16/wiggles/
The data indicates the standard deviation on the yearly data since 1975 is 0.1 deg C. Any data with +/- 0.2 deg C (two standard deviations) would be in the 95% confidence interval. Instead Dr. Pielke has shown dashed lines only one fourth that interval on his chart.
Isn’t this standard deviation calculated from the temperature data alone, not from the IPCC models? So wouldn’t the temperature noise variation be used for the confidence intervals in a forecast verification (not model verification, as Dr. Pielke points out)? Wouldn’t any forecast have the same confidence intervals applied to it, regardless of how the forecast was arrived at?
Is it possible the confidence levels shown on the Pielke chart, are based on a plot of some kind of longer term average, and seem to be overlaid on annual temperature data? Somehow, the large scatter in the data compared to the confidence intervals, seems inconsistent?
(Disclosure… I am just a chemical engineer, who worked for an oil company once upon a time, and have no experience in climate studies.)
[Response: The error bars on the forecast for the IPCC models is the uncertainty on the long term trend, not the envelope or distribution for annual anomalies. I think that this is misleading when comparing to real annual data, and is at the heart of my statements above. - gavin]
12 January 2008 at 12:28 PM
Now Gavin and Stefan, changes in upper ocean heat content is a really interesting question, and relates directly to the subject at hand. As you are aware, Roger Pielke Sr. (and others) have been pointing out the need to assess upper ocean heat content changes. Although there have been some well publicized problems with changing from XBTs to the Argo floats, the error bars in assessing global changes in ocean heat content are decreasing dramatically. The conclusion that the upper ocean has warmed over the last 40 years is certainly robust, but the shorter term changes are also interesting, since they are a direct proxy to the current radiative imbalance at the top of the atmosphere. After the Willis and Lyman papers showing short-term cooling, then their correction, we are still left with the more robust conclusion that the upper ocean heat content has been essentially flat the last few years. This is really more informative than a two-dimensional surface temperature analysis, since the ocean heat content goes directly to the question of unrealized “heating in the pipeline”, and also to what the current sum of all the forcing and feedbacks add up to, and how this sum changes on annual and multidecadal scales. What is also interesting is how well the SST changes map over upper ocean heat content, and how quickly these SST changes seem to be realized in the atmospheric volume (ie El Nino). It has been pointed out that the so-called “time constant” to equilibriate to a change in forcing via ocean mixing processes has a direct bearing on climate sensitivity to changes in a forcing.
12 January 2008 at 12:37 PM
Ref 92 and 93. Each month I download the following sites.
http://lwf.ncdc.noaa.gov/oa/climate/research/2007/dec/global.html#Temp
http://www.cru.uea.ac.uk/cru/data/temperature/hadcrut3gl.txt
http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt
ftp://ftp.ssmi.com/msu/monthly_time_series/rss_monthly_msu_amsu_channel_tlt_anomalies_land_and_ocean_v03_0.txt
The first, ncdc/noaa site only gives data for that month. I have searched the site you quoted, but cannot find anything similar to the other three sites. If you can find it, I would be grateful. As I have noted, I downloaded shareware CurveExpert 1.3, and simply plug in different time scales for all four data sets, call for a specific type of analysis, and look at the results. I do this for my own education; I am sure you dont want to read any non-peer reviewed results.
12 January 2008 at 12:46 PM
In 86 Gavin writes “That uncertainty feeds into assessments of attribution and the like, but is small enough not to be decisive.” I wish I shared your optimism. I think the uncertainly is large enough to be decisive, but have nothing to offer in the way of a reference. Just the results of my playing around with simple analyses. On what do you base your opinion?
[Response: The fact that the model match to obs doesn’t depend on whether you use GISTEMP, NOAA or HADCRU. - gavin]
12 January 2008 at 12:56 PM
Thanks for the quick response, Gavin. Couldn’t Dr. Pielke fix his chart, by simply drawing 95% confidence interval lines, 0.2 deg above and below the IPCC forecast, and then compare with the annual anomaly data? That interval would reflect where the annual data should fall.
The forecast looks pretty good, if that is the expected range of variation in the actual temperature anomaly data.
I realize this may not be entirely correct, because the standard deviation probably wasn’t calculated from just one of the temperature measurement systems, but then the chart wouldn’t as misleading as it currently is.
[Response: I would suggest asking him. - gavin]
12 January 2008 at 1:01 PM
#90
The inconsistency between Loeb’s slide 19 and ISCCP’s own web page is strange, I agree. Which one should be the more reliable?
I find it hard to believe we were able to send man to the moon 40 years ago but now we are unable to measure the temperature of the oceans. There must be some system still in operation which was also in operation in the 90s. Replacing all floats without an overlap would be too stupid.
So why would that system be correct pre-2003 and wrong post-2003?
12 January 2008 at 1:43 PM
#42 Barton,
” 2001-2007] except that the data indicates no change in temperature over that period”
And Superman might sell Mr Desjardins his fortress of solitude at the North Pole for a good price!
I don’t think its possible, just yet, to have a single trend based on a couple of years without some deviation from the over all long term path, because the measuring techniques resolution are not at the quantum level. In addition, here is what I don’t see: world wide maximas and minimas anomalies, say from sea level to Bogota’s height above sea level.
In the Arctic, just the other day, the temperature at surface was about -27 C (….no its warm, should be -35 C), yet aloft at about 900 meters it was -15.4 C. If this station was 900 meters high, the surface record would be different. These thermal heat sources aloft, may be found anywhere in the world, but are particularly strong in the Arctic. If we pick one single Upper air level, we will either miss a cooling or a warming above or below. The idea that adiabatic lapse rates are constant and therefore we can pick a single representative height, will equally mislead.
But if we search a GW trend, we should find that every year the maximas (1 to 3000 ASL) are pushing upwards relentlessly. Then again I don’t think satellites can find profile maximas, and the world wide radiosonde network is too sparsely located, although its data is state of the art.
Lacking resolution, we can rely on other measurements: very long term temperature trends, Polar ice melt, world wide glaciers retreating, deep sea temperatures for the most part and other bench mark measurements, until temperature resolution deficiencies have been eliminated, by increasing present network densities, or by finding a different way to measure the weighted temperature of the entire atmosphere, we do that for other planets. As a complement to present techniques, I suggest using the sun as a fixed disk of reference,,, It works!
12 January 2008 at 1:47 PM
Its a little off topic, but in the sunday comics there was a funny strip about interpreting statistics in research. It reminded me of the current discussion. The strip even uses the +/- symbol. Its funny.
http://www.comics.com/comics/getfuzzy/archive/getfuzzy-20080106.html
12 January 2008 at 2:09 PM
I would say that every modern weather event bears the fingerprints of global warming. The problem is that it is hard to find “fingerprints” on a thunderstorm or hurricane. However, it is easy to find the fingerprints of global warming on the weather prediction models. Those models use sea surface temperatures and atmospheric temperatures that reflect the full impact of global warming. The success of these weather models proves that global warming affects our daily weather.
Climate we can plan for, engineer for, and survive. The problem is always the weather, ( E.g., a rain event, a snow event, a drought, ice melt, a heat wave, a cyclone, a typhoon.) Global warming brings us increasingly intense weather. That means the weather problems become more intense.
Last week there were tornadoes across the US in January. In our recent climate that would have been a very rare event. Global warming supplied heat to make it a less rare event. Sudden Arctic Sea ice melt was a rare event. Global warming has supplied the extra heat necessary to make it less rare. At some point you have to say, ”Global warming has given us a new climate. We are having weather that for all practical purposes did not occur in the old climate.” We may not be sure what else glob