Tamino Misses The Point And Attempts To Distract His Readers

UPDATE (November 20, 2011): Refer to note at the end of the post about the missing graphs for Ensemble Members 18 and 30 in Animation 1.

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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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UPDATE (November 20, 2011): In the cross post of this at WattsUpWithThat, Shades of Foster Grant, blogger kadaka (KD Knoebel) in his November 20, 2011 at 11:17 amcomment noted the missing Ensemble Members 18 and 30 in Animation 1. I forgot to explain that in the post. The source model data from KNMI for the individual ensemble members numbered 18 and 30 are each missing data a couple of months of 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

And Ensemble Member 30 is missing data in 1928. Likewise, it prevents EXCEL from calculating the trends for 30 years before and after.

http://i42.tinypic.com/qsjnk4.jpg

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in CAGW Proponent Arguments, Climate Model Problems, Model-Data Comparison SST. Bookmark the permalink.

23 Responses to Tamino Misses The Point And Attempts To Distract His Readers

  1. Pingback: Tamino Disparages Bob Tisdale’s Analysis Of Model Predicted Trends In SSTs | Climate Science: Roger Pielke Sr.

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  4. Nyq Only says:

    “Why doesn’t Tamino use the real models instead of artificial ones?”
    Gosh – I thought he explained EXACTLY why in his post. Perhaps you missed it. He used artifical models that INTENTIONALLY matched the natural variability to demonstrate that when you average those models the result does NOT match the natural variability. 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.

  5. Bob Tisdale says:

    Nyq Only says: “Gosh – I thought he explained EXACTLY why in his post. Perhaps you missed it.”

    I did not miss it. It’s a distraction from the actual models and a distraction from my post. Nothing more, nothing less.

  6. Rosco says:

    I cannot believe there is any argument over models versus real data. By defending the veracity of models when data shows they do a poor job at best these people forfeit the right to call themselves scientific.

    Why can’t everyone come to some form of agreement over data and its veracity – oh I forgot – there is an agenda to support – I used to believe in scientific integrity but climate scientists have completely destroyed any faith I used to have.

  7. Camburn says:

    Bob:
    Figure one shows the various model runs as I understand it.

    The time period from 1960-1985 approx has the model runs looking substantially lower than the indicated mean.
    The time period from 1920-1960 has the indicated mean higher than most of the model runs.

    Is the model mean a measure of the change in trend? Or is it indicative of the actual change in trend?

    I am relying on visual without knowing the methodology of averageing etc.

    Thank you in advance.

  8. Bob Tisdale says:

    Camburn says: 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.

  9. This is the way I see it….

    The IPCC uses the multi model mean as it’s forecast for what will happen over the next 90 years. It does *not* use individual model runs. Now, if the basis of the forecast is the multi-model mean, and it is shown that over, say 80 years of hindcasting, the model mean is useless at predicting what actually happened, of what value can it be given in forecasting the *next* 90 years.

    The fact that the multi model mean has less variability than individual model runs is to be expected, but does not distract from Bob’s point. Tamino seems to be attempting to excuse the model mean forecast by trying to explain *why* it is lousy when used to hindcast. OK, but so what? It’s still lousy.

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  12. Bob
    is it really so hard to understand?
    “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. ”

    The model are not initialized to do any in phase reproduction of natural climate variability. If you want to make statements on ‘ timing, frequency, and magnitude’ you must take or model runs that are set up to do so (assimilation techniques etc) or you must do a statistical analysis of the individual runs.
    Your animation admits that you failed at analysing what you claimed you were analysing and it gives already a visual impression that the models are actually not so bad at all. Now make a real analysis of the ensemble runs or find it in the literature.

  13. Bob Tisdale says:

    Georg Hoffmann says: “is it really so hard to understand?”

    Not difficult at all, Georg. You obviously also missed the point of the post. Read the opening paragraph above.

    You continued, “The model are not initialized to do any in phase reproduction of natural climate variability.”

    I believe I stated this in each of the graphs of the original post, which is what Tamino took exception to. You must have missed that as well.

    With respect to the rest of your comment, here’s another way of looking at the differences between the observed rise in Sea Surface Temperature anomalies and the multi-model mean of the models presented in AR4. It’s really easy to comprehend. The multi-model mean represents the anthropogenic and natural forcings-driven variations in Sea Surface Temperatures, without the noise of the individual ensemble members and without the noise of the individual models. In effect, it’s the IPCC’s best guess hindcast/projection for the period of 1900 to 2005. The following two graphs compare the Observed Sea Surface Temperatures (average of ERSST.v3b, HADISST, HADSST2, and HADSST3) and the forcings-driven Multi-Model Mean during the first warming period of the 20th century…

    …and during the warming period from 1975 to 2005:

    The observed trends for the early warming period (0.134 deg C per decade) and the late warming period (0.141 deg C per decade) are quite similar, indicating that the forcings have little influence on the two warming-period trends of the observations mean SST data. But the model mean trend for the Global Sea Surface Temperature anomalies during the early warming period (0.035 deg C per decade) is only 28% of the trend of the modeled late warming period (0.124 deg C per decade), and that’s because the multi-model mean is only driven by the forcings. The following graph summarizes it pretty well:

    You concluded your comment with, “Now make a real analysis of the ensemble runs or find it in the literature.”

    Feel free to research and present anything you want, Georg, in an attempt to dispute the conclusions of the original post. But keep in mind, the graphs you’re complaining about are really only the “back story” of the tale presented by the data. My post was about the trends of the last 17 and 30 years of the data, as presented in Table 1 above.

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