Tamino Misses The Point And Attempts To Distract His Readers
By Bob Tisdale
The obvious intent of my recent post “17-Year And 30-Year Trends In Sea Surface Temperature Anomalies: The Differences Between Observed And IPCC AR4 Climate Models” was to illustrate the divergence between the IPCC AR4 projected Sea Surface Temperature trends and the trends of the observations as presented by the Hadley Centre’s HADISST Sea Surface Temperature dataset. Tamino has written a response with his post “Tisdale Fumbles, Pielke Cheers.” Obviously he missed the point of the post. Since he does not address this divergence, his post is simply a distraction. That fact is blatantly obvious. Everyone reading his post will realize this, though it is doubtful his faithful followers will call his attention to it. Tamino resorts to smoke and mirrors once again. But let’s look at a few of the points he tries to make.
Tamino objects to this statement that is included on all of the graphs in the “17-year and 30-year trends post”:
The Models Do Not Produce Multidecadal Variations In Sea Surface Temperature Anomalies Comparable To Those Observed, Because They Are Not Initialized To Do So. This, As It Should Be, Is Also Evident In Trends.
The reason I included that statement was because I have illustrated and discussed the lack of multidecadal variability in the IPCC AR4 models in earlier posts and I wanted to draw the readers’ attention to the difference between the trends of the model mean and the observed trends. It’s really that simple.
Tamino makes the following statement toward the end of the post:
“There are definitely problems with the models. For one thing, they don’t reproduce the rapid warming of sea surface temperature from 1915 to 1945 as strongly as the observed data indicate. But overall they’re not bad, and the amount of natural variability they show is realistic.”
But the fact that “For one thing, they don’t reproduce the rapid warming of sea surface temperature from 1915 to 1945 as strongly as the observed data indicate” means the Sea Surface Temperatures of the models also don’t flatten from 1945 to 1975 as the observations do, and it’s those two portions of the multidecadal variations in sea surface temperatures that are known to be missing in the models. That’s what’s being referred to on each of the graphs in red. The models capture the rise in temperature from 1975 to 2000, but they do not capture the rise and flattening from 1910 to 1975.
Tamino presents a comparison of 30-year trends for HADISST, the model mean, and the 9 runs of the GISS Model ER, which I’ve reproduced here as Figure 1. He then writes:
Note that the individual model runs show much more variability than the multi-model mean. In fact they show variability comparable to that shown by the observed data.
I’ve highlighted a portion of his graph in Figure 1 that he obviously overlooked. Look closely at the significant rise in trends of the HADISST data in the early 20th century, and then the equally impressive decline in trends. Do any of the GISS model runs produce the “Multidecadal Variations In Sea Surface Temperature Anomalies Comparable To Those Observed” during the early part of the 20thcentury? No. So thank you for confirming one of my points, Tamino. It also contradicts your nonsensical statement, “In fact they show variability comparable to that shown by the observed data.”
Figure 1
Tamino also goes into a detailed discussion of how the model mean can obscure any multidecadal variations in the individual model runs. But note that he doesn’t use the actual model runs. He uses “Artificial Models”. Refer to Figure 2. Artificial models?
Figure 2
Why doesn’t Tamino use the real models instead of artificial ones? Because then Tamino would have to show you that the majority of the models do not have multidecadal variations in trend that are similar in timing, frequency, and magnitude of the observation-based SST data. Refer to Animation 1.
Animation 1
I could have provided that animation in my post, but I elected not to present it because it added no value to the post.
CLOSING
As I noted earlier, Tamino’s post is simply a distraction from my post “17-Year And 30-Year Trends In Sea Surface Temperature Anomalies: The Differences Between Observed And IPCC AR4 Climate Models”, which showed the divergence between the trends of the IPCC AR4 model mean for global Sea Surface Temperatures and the observed Sea Surface Temperature trends.
Tamino makes a few statements in his post that I will be happy to agree with:
There are definitely problems with the models.
And:
Certainly the models need more work.
Thanks for the opportunity to call attention to my post once again, Tamino.
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D. J. Hawkins says: “I believe he’s refering to Figure 1. At around 1970, all the individual model runs displayed appear to be well below the graphed multi-model mean.”
Figure 1 is Tamino’s graph, not mine. The models he has shown are only the GISS-ER models. I believe the model mean in Tamino’s graph is of all 30+ AR4 models . So the GISS models represent only about a third of the models in the model mean.
Jack Greer says: “Bingo. But if you’re trying to get BT to admit the process by which he analyzes data is, to put it kindly, faulty … which it is … Good luck with that.”
Jack Greer, refer to the post that’s the basis for this discussion:
http://bobtisdale.wordpress.com/2011/11/19/17-year-and-30-year-trends-in-sea-surface-temperature-anomalies-the-differences-between-observed-and-ipcc-ar4-climate-models/
Under the heading of ABOUT THE GRAPHS IN THIS POST, I discussed what they presented and in doing so, I explained how they were created. If you’re having trouble with that explanation, please advise.
There are definitely problems with the models.
And:
Certainly the models need more work.
Oh, geez –
it’s a shame we are just finding this out.
/sarc
🙂
But there is no match between individual models and the natural variability either. They don’t work either singly or averaged together.
George E. Smith; says: November 20, 2011 at 10:57 am
“Isn’t the WHOLE IDEA of modelling, to reproduce the OBSERVED DATA; nothing else matters !”
No, the whole idea of modelling is to figure out what may happen in the future.
Models are based on physics. They can only be expected to reproduce observed data insofar as that data does reflect the physics. Two things happen:
1. The data is noisy. I plotted here three different measures of SST vs the model mean. The difference between the model mean and the observations is comparable to the difference amongst the observations.
2. There are events that we know will occur, and have some idea how often, but don’t know when. Volcanoes are an obvious example. The various oscillations are another. A physical model may reproduce these, but not be specific about the phase. The physics doesn’t tell you that. So when you average over several models, this event information gets lost.
Anthony,
Do you agree the method of analysis used by Bob is faulty, or don’t you?
It’s not okay to simply say “pay no attention to my analysis, here’s the valid point I was trying to convey”.
… and I’ve already expressed the types of posts on your blog that I find valuable.
REPLY: I agree with his analysis and conclusion, you don’t so we are worlds apart – Anthony
Jack Greer says:
November 20, 2011 at 11:59 am
NyqOnly says:
November 20, 2011 at 10:57 am
… This is a simple, logical, demonstration that EVEN IF the models were perfect, the technique you used would not show a good match between a multi-model mean and the natural variability.
It is a good point. It would be interesting to see you address it and it is odd that you currently seem to not understand it.
Bingo. But if you’re trying to get BT to admit the process by which he analyzes data is, to put it kindly, faulty … which it is … Good luck with that.
_________________________________
So if you can not get a useful “multi-model mean” then why do the climate scientists use it?
More importantly if you can not get a useful “multi-model mean” (about 50 or so trends) Then how the heck can you take thousands of temperature readings from all over the world put them through a sausage machine (called a computer program) and come up with any useful meaning?
Either you can get a useful mean or you can not get a useful mean.
Using multiple model runs averaged together to create a model mean, then finding the trend of that mean and projecting it into the future as the basis for a prediction of future climate is nonsense.
It makes as much sense as going into a casino, recording all the winning numbers on a roulette table, averaging them together, finding the trend in the mean of all the winning numbers and then projecting that into the future to predict that on August 4th 2035 at 3:14 pm the winning number on that roulette wheel will be 14 Red.
Just what are these guys smoking?
Larry
kadaka (KD Knoebel) says: “Uh-oh Bob, I watched your Animation and kept track. While apparently showing Ensemble Members 0 to 31, the numbers 18 and 30 are missing. Hope there’s a good explanation or Tami’s Troupe will pounce on you for cherrypicking!”
Thanks for picking up on that, kadaka. I forgot to explain that in the post. The source model data from KNMI for ensemble members 18 and 30 are each missing data. Ensemble Member 18 is missing data in 1918 and, of course, it prevents EXCEL from calculating the trends for 30 years before and after.
http://i39.tinypic.com/2ij2c78.jpg
Ensemble Member 30 is missing data in 1928, and likewise, it prevents EXCEL from calculating the trends for 30 years before and after.
http://i42.tinypic.com/qsjnk4.jpg
Ruined by teh blinking. Again. Thanks.
REPLY: Then don’t look at it and don’t comment about it. The animation stays, get over it, Anthony
“Just what are these guys smoking?”
Who knows, but whatever it is they want to pay for it with tax dollars.
Santer’s PR release says.
They find that tropospheric temperature records must be at least 17 years long to discriminate between internal climate noise and the signal of human-caused changes in the chemical composition of the atmosphere.
Ignoring the unscientific ‘at least’, Santer is saying the models predict that the AGW will always show in 17 years of tropospheric temperature data.
Therefore, 17 years without significant warming falsifies the models, which is what Bob showed in the SST record.
Whether, the models can reproduce natural variability, which Santer calls ‘internal climate noise’ is secondary, and IMO not a significant issue.
Tamino makes the following statement toward the end of the post: “…overall they’re not bad,..”
Right. Not bad. Meaningless.
“…the amount of natural variability they show is realistic…”
They go up and down. Real data go up and down. So the models are realistic.
Jack Greer says, “Do you agree the method of analysis used by Bob is faulty, or don’t you?”
There’s nothing wrong with the analysis, Jack. It’s your perception of it that’s faulty. I provided you with a link in an earlier comment that explained what was presented. Sorry if you can’t grasp it.
@Bob Tisdale says:
November 20, 2011 at 12:16 pm
Tamino is saying the method of averaging model runs and then commenting on the ability of model run averages to demonstrate natural multidecadal variability, initialized to do so or not, indicates a lack of understanding of how natural variability in the context of models s/b analyzed. You could alter your post to neutralize that valid point.
Bob,
You write “Tamino resorts to smoke and mirrors once again.”
For those readers who have not come across this character before, might I refer them to a couple of examples of his work.
In July 2011, Tamino attempted a hatchet job on a study of Australian Sea Levels. This study indicated that sea level rises had “consistent trend of weak deceleration” in the period 1940 to 2000.
http://manicbeancounter.wordpress.com/2011/08/01/tamino-on-australian-sea-levels/
In July 2010, Tamino attempted a hatchet job on AW Montford’s “The Hockey Stick Illusion” at Real Climate blog. http://www.realclimate.org/index.php/archives/2010/07/the-montford-delusion/
Steve McIntyre replies included a re-posting of a piece from two years earlier, where he had answered most of Tamino’s points. http://climateaudit.org/2010/07/25/repost-of-tamino-and-the-magic-flute/.
One discredited tree-ring analysis that Tamino tried to defend was the Gaspé series. This I analysed on my own blog. http://manicbeancounter.wordpress.com/2010/07/24/tamino-v-montford-on-the-gaspe-series/
Jorge: The models really are not too bad. They virtually reproduce the data almost to a high degree, if not somewhat. I think you are picky. Some of the peaks on the whole nearly on average are close to the original data, virtually congruent with the observational data points, virtually to a large, if not significant degree, notwithstanding the precision of the artificial models.
[/sarc]
The model runs provide a mean for each year and the variance around the mean. The various temperature series all have global means and tiny variances around the mean.
If the the 90% confidence levels of the models and various ‘global’ temperature series do not overlap we can state that the models do not match reality at the 1% level.
The models clearly fail.
Jorge: forgot to put (/sarc).
Bob,
As you well know models will probably never get the timing right. that’s the initialization problem.
Further if modelers did ‘fiddle” with the initialization states to get the timing correct people would howl.
one way forward is to look at
1. the distribution of all 17 and 30 years trends in ALL the model runs.
2. the distribution of all 17 and 30 year trends in observations.
that will give some insight as to whether or not models have similar variability.
or look at amplitudes.
But as long as you focus on the timing issue you really cant make the best argument.
what you are showing is a logical consequence of the starting conditions imposed
on the test
Jack Greer says:
November 20, 2011 at 1:29 pm
“Tamino is saying the method of averaging model runs and then commenting on the ability of model run averages to demonstrate natural multidecadal variability, initialized to do so or not, indicates a lack of understanding of how natural variability in the context of models s/b analyzed.”
The IPCC is constantly publishing ensemble means and bases its forecasts on them. You say they’re invalid? Tell the IPCC!
“…He’s not under wraps and hasn’t been for sometime, though why he keeps the moniker presently is a curiousity…”
Because he wants to be envisioned as Tamino from Mozart’s “the Magic Flute” – a handsome prince who goes through a number of trials and triumphs over all.
steven mosher says:
November 20, 2011 at 1:42 pm
“As you well know models will probably never get the timing right. that’s the initialization problem.”
Why don’t the IPCC climate scientists discard the init state that lead to a wrong timing, and select the init states that lead to a more correct timing? Would that not improve the forecast?
I tell you why they don’t do it: It would make them vulnerable. The “ensemble mean” is a smokescreen.
manicbeancounter says: November 20, 2011 at 1:29 pm
Bob… writes “Tamino resorts to smoke and mirrors once again.” For those readers who have not come across this character before, might I refer them to a couple of examples of his work.
Haha. Here’s some more. IIRC I felt honoured that Tamino thought it worth his while to respond twice to my 2009 piece Circling the Arctic. Unfortunately Tamino’s pages before March 2010 have vanished. But I used ammo from him as good evidence – when put in context. He had his use, even if it was not what he planned.
Steve Mosher says: “But as long as you focus on the timing issue you really cant make the best argument.”
Steve, this post is a result of a distraction created by Tamino. I really had no reason to respond to Tamino, other than to call attention to the “17-year and 30-year trend post” once again. We’ve discussed initialization and the reasons for the models’ inabilities to recreate the multidecadal variations of the instrument temperature record many other times. There was no reason for me to discuss it in the “17-year and 30-year trend post”.
Regards