Tisdale on the "17 year itch" – Yes, there is a Santer clause

Guest post by Bob Tisdale

What Do Observed Sea Surface Temperature Anomalies and Climate Models Have In Common Over The Past 17 Years?

One word answer: NOTHING!!!!

OVERVIEW

In this post, we’ll compare satellite-based sea surface temperature anomalies (Reynolds OI.v2) for the past 17 years to the multi-model ensemble mean of the climate models that were prepared for the 2007 4th Assessment Report (AR4) of the Intergovernmental Panel on Climate Change. We’ve already showed how poorly the models simulate the warming rates of the global oceans on an individual ocean basis for the entire 30-year term of the Reynolds OI.v2 sea surface temperature data. Refer to the posts here and here, and more recently here. So the failings of the models come as no surprise. But this post does present something that will come as a surprise to many of you.

The choice of 17 years is based on the Santer et al (2011) paper, Separating Signal and Noise in Atmospheric Temperature Change: The Importance of Timescale. In the abstract, Santer et al (2011) conclude with:

Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature.

Since sea surface temperature anomalies are not as variable as lower troposphere temperature (TLT) anomalies, we’ll assume that 17 years would also be an acceptable timescale to present sea surface temperature anomaly trends on a hemispheric, or greater, basis. This was the foundation for an earlier post that compared models and the same sea surface temperature dataset. And we’ll also divide the oceans into their individual basins to illustrate why I’ve presented, as one combined dataset, the Indian and Pacific Oceans from pole to pole.

While the failings of the models might come as no revelation, something else might—but first a note to build the suspense. Combined, the Indian and Pacific Oceans from pole to pole (90S-90N, 20E-70W) represent about 75% of the surface area of the global oceans. See Figure 1. It’s a map of the global oceans that’s been divided into two sections: the “Indian & Pacific Ocean Plus” and “Atlantic Ocean Plus” where the “Plus” is used to note that the datasets have been extended to the South and North Poles.

Figure 1

Why are we dividing the ocean into those two subsets? Here comes the surprise.

The sea surface temperature anomalies for the combined Indian and Pacific Oceans from pole to pole show basically no warming for the past 17 years. None, nada, zip. See Figure 2. The cooling of the entire Pacific Ocean is strong enough since 1995 and the Pacific is so large that we can merge its data with the still-warming Indian Ocean data and wind up showing the combined dataset has not warmed for 17 years. Again, the Indian and Pacific Oceans represent 75% of the surface of the global oceans and together they have not warmed in 17 years.

Figure 2

Also illustrated in Figure 2 is the multi-model ensemble mean for the IPCC’s climate model simulations of the sea surface temperature anomalies for that portion of the global oceans. The model data continued to climb contentedly skyward, projecting a blistering warming rate in sea surface temperatures for the “Indian and Pacific Oceans Plus” dataset of about 0.151 deg C per decade. That monumental divergence between models and observations for such a large part of the globe is a significant problem for the hypothesis of anthropogenic global warming—and for the alarmist proponents who believe in that hypothesis—a hypothesis that makes its presence known only in climate models, not in observational data. Anthropogenic Greenhouse Gases are supposed to force sea surface temperature to warm. The model mean of the climate model simulations of sea surface temperatures presented in this post show the response of the models to that forcing, yet the satellite-based sea surface temperature data for 75% of the global oceans show that they are not reacting to the anthropogenic forcing—not at all. One might think the modelers ought to reevaluate the assumptions they’ve made to divine the effects of greenhouse gases on sea surface temperatures, especially when they consider that 70% of the surface of the Earth is covered by ocean. Their assumptions just aren’t working.

FOR THOSE THINKING THE “ATLANTIC OCEAN PLUS” WILL COME TO THE RESCUE

If you’re for some reason hoping the data for the rest of the global oceans, the “Atlantic Ocean Plus” data, will make up the difference, you’re about to be disappointed. As illustrated in Figure 3, the models are showing a warming rate that’s about 50% higher than what has been observed. That’s not too good. Then when you consider the blatantly obvious model failings for the “Indian & Pacific Ocean Plus” subset, you wonder how the climate-model based anthropogenic global warming charade can continue. Yet it does.

Figure 3

A FEW PRELIMINARY NOTES FOR NEWCOMERS TO MODEL-DATA PRESENTATIONS

The Reynolds OI.v2 sea surface temperature anomaly data is available for download from the NOAA NOMADS website and from the KNMI Climate Explorer. NOAA uses the bases years of 1971-2000 for anomalies. But we’re looking at the period of January 1995 to March 2012 and that extends outside of those base years. The base years are not adjustable at the NOAA NOMADS site, but they are adjustable at the KNMI Climate Explorer. I used the data through the KNMI Climate Explorer so that I could change the base years for anomalies to 1995-2011. This helped to reduce the strong seasonal signal that appears in the data of some ocean basins. The North Pacific (0-65N, 100E-90W) sea surface temperature anomaly data from NOAA, for example, has a very strong seasonal component, as shown in Figure 4. Using the base years of 1995-2011, also illustrated, the seasonal component is drastically reduced. And as shown, the trends are basically the same, so minimizing the additional seasonal component makes no difference to the model-data comparisons in this post. (And yes, the sea surface temperature anomalies of the North Pacific have been cooling for the past 17 years.)

Figure 4

The multi-model mean sea surface temperature dataset is identified as TOS (ocean surface temperature) at the KNMI Climate Explorer and is available through its Monthly CMIP3+ scenario runs webpage. If you were to scroll up to Figure 2, you’ll note that there are major year-to-year variations in sea surface temperature anomalies that don’t appear in the multi-model mean data. Those observed major variations are caused by El Niño events (the upward spikes) and La Niña events (the downward ones). There are a few things to keep in mind about the model-mean data and the resulting curves. They represent the average of the climate model simulations at the CMIP3 archive, which was used in the IPCC’s AR4. There are a couple dozen climate models in the archive and some of the models include multiple simulations. For example, GISS presented 9 simulations (ensemble members) for its Model-ER and 5 ensemble members for its Model-EH. Some of the climate models attempted to model the El Niño-Southern Oscillation; others didn’t. The models that tried to simulate ENSO did a poor job and none of them could match the observed frequencies and magnitudes of El Niño and La Niña events. And since each model simulation has a different frequency and magnitude for their ENSO signals, they are smoothed out when the models are averaged. But that’s a good thing. That leaves a signal that is supposed to represent the forced component of the models, which is why we use the multi-model mean.

The reasons I’m presenting the multi-model mean were discussed in more detail in an earlier post Part 2 – Do Observations and Climate Models Confirm Or Contradict The Hypothesis of Anthropogenic Global Warming?, under the heading of CLARIFICATION ON THE USE OF THE MODEL MEAN. Please refer to that discussion.

MODEL-DATA COMPARISONS FOR THE INDIAN AND PACIFIC OCEANS

As shown in Figure 2, there has been no warming of the “Indian & Pacific Ocean Plus” sea surface temperature anomalies since 1995. That doesn’t mean that one of the individual ocean basins has not warmed. See Figure 5. The Indian Ocean (60S-30N, 20E-120E) sea surface temperature anomalies have warmed, except it’s at a rate that’s about 42% of what was simulated by the IPCC’s climate models. And as noted earlier, the North Pacific data shows that it has cooled. So has the South Pacific (60S-0, 120E-70W). Refer to Figures 6 and 7. Think about that for a moment. Not only has the largest ocean on this planet not warmed in agreement with the models, it’s actually cooled over the past 17 years.

Figure 5

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Figure 6

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Figure 7

THE OTHER OCEAN BASIN THAT’S COOLING

The Southern Ocean (90S-60S) is the ocean “basin” that surrounds Antarctica. It has cooled over the 30-year term of the Reynolds OI.v2 dataset. See the graph here from this post. Since January 1995, the rate at which it’s cooling is even stronger. The difference between the rate that it’s cooling and the rate the climate models say it should be warming is 0.14 deg C/decade.

Figure 8

LET’S NOT FORGET THE OTHER OCEAN BASINS THAT WARMED

At the other end of the planet, the Arctic Ocean (65N-90N) has warmed over the past 17 years at a rate that’s about 2.5 times faster than the model simulations. See Figure 9. Surprisingly, we often hear from climate alarmists that the Arctic is warming faster than projected by climate models, with all of the dire consequences of that warming thrown in heighten the risks they perceive. But the doomsayers are actually heralding yet another failing of the climate models. The observations are the target the models are shooting for, and in the Arctic, the models have missed the planet the target’s nailed to.

Figure 9

In the North Atlantic (0-70N, 80W-0), the observations are warming at a rate that’s about 65% of the rate simulated by the models, Figure 10. And as shown in Figure 11, in the South Atlantic (60S-0, 70W-20E) over the past 17 years, the models are doing remarkably well. There, the trend is only about 31% too high. So we’ll give the modelers a “B-” for one basin.

Figure 10

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Figure 11

AND HOW WELL DO THE MODELS SIMULATE HEMISPHERIC AND GLOBAL SEA SURFACE TEMPERATURES?

In the Northern Hemisphere, Figure 12, according to the models, the sea surface temperatures should be warming about 3.4 times faster than has been observed for the past 17 years. The model performance in the Southern Hemisphere is even worse, Figure 13. There, the models show a warming rate that is about 8.5 times higher than the actual warming rate. In total, for the global oceans, the models have projected a warming that’s 5 times higher than the rate the oceans have actually warmed. The model trend isn’t 50% higher, not twice as high, not three times. The models are off by a factor of 5. Written another way, global sea surface temperatures have warmed at a rate over the past 17 years that’s only 20% of the rate projected by the multi-model mean of the climate models presented to the CMIP3 archive for use by the Intergovernmental Panel on Climate Change in its 4thAssessment Report published in 2007.

Figure 12

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Figure 13

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Figure 14

CLOSING

For more than a year, in posts here at Climate Observations and in cross posts at WattsUpWithThat, we have presented and discussed numerous ways in which the climate models show no skill at being able to simulate the warming, or lack thereof, of global surface temperatures. Keep in mind global surface temperature is the metric most commonly used to define global warming.

This post was primarily intended to show that 75% of the surface area of the global oceans, the Indian and Pacific Oceans from pole to pole, has not warmed in 17 years. This lack of warming opposes the continued rise in anthropogenic greenhouse gases—which only appear to make the sea surface temperatures warm consistently in climate models. There’s nothing alarming about the rate at which sea surface temperature anomalies have warmed. In fact, the 30 rise in sea surface temperatures can be explained by natural factors. So the only thing that should be sounding any alarms is the lack of skill shown by the climate models.

IF YOU’D LIKE TO LEARN MORE ABOUT THE FAILINGS OF THE IPCC’s CLIMATE MODELS

As illustrated and discussed in If the IPCC was Selling Manmade Global Warming as a Product, Would the FTC Stop their deceptive Ads?, the IPCC’s climate models cannot simulate the rates at which surface temperatures warmed and cooled since 1901 on a global basis, so their failings illustrated in this post are not abnormal.

Additionally, the IPCC claims that only the rise in anthropogenic greenhouse gases can explain the warming over the past 30 years. Satellite-based sea surface temperature disagrees with the IPCC’s claims. Most, if not all, of the 30-year rise in satellite-based global sea surface temperature is shown to be the result of a natural process called the El Niño-Southern Oscillation, or ENSO. This is discussed in detail in If the IPCC was Selling Manmade Global Warming as a Product, Would the FTC Stop their deceptive Ads?, which is available in pdf and Kindle editions. A copy of the introduction, table of contents, and closing in pdf form can be found here.

SOURCE

The modeled and observed sea surface temperature data presented in this post are available through the KNMI Climate Explorer:

http://climexp.knmi.nl/selectfield_obs.cgi?someone@somewhere

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Chris Nelli

Well, if you look at the RSS dataset, there’s been zero trend for 16 years (1997-2012). And the 17 year trend is insignificant, or if real, something mankind can live with. Seriously, Santer’s paper already shows that Catastrophic AGW is a fraud.

Oops. I just noticed the typo in the second to last sentence of the closing. It should read “30-year rise”, not “30 rise”.
In fact, the 30-year rise in sea surface temperatures can be explained by natural factors.
I’ll fix the cross post over at my blog.
Sorry.

Jimmy Haigh

“Yes, there is a Santer clause’. I like it.
Better watch out that he doesn’t try to “beat the crap out of you”.

James Hansen, Gavin Schmidt, & John Cook go on a duck hunt together. As they are sitting out in their rapidly warming boat on the rapidly warming lake a skein of mallards comes flapping by. In the same motion Hansen & Schmidt both fire, Hansen’s shot neatly breezing by three feet (0.9144 metres) in front of the lead duck, Schmidt’s wafting a similar distance (±0.0000002m) behind the laggardliest. John Cook leaps to his feet, nearly capsizing the boat, and shouts, “Nailed ’em dead centre!”

Finally! A breakdown of the global story by regions, showing how the “global” warming is not global but a mathematical construct of relative warming and cooling by regions.
Note how many parts are overwarmed in the models: that means that the moderate warming of the result comes from large errors in warming expectations being compensated for by large coolings elsewhere.
The failings in parts and a “success” overall should be very disheartening for those who think the science is settled and certain. For a “success” to be claimed by Hansen et al, it means that they consider their model probabilistic at a global scale, not deterministic at any scale. Which puts huge error bars on what they do, greater than they claim. It certainly puts the boots to any claim about specific weather events being tied to global warming. If your model can’t get an ocean right, it can’t account for a specific hurricane or summer of tornadoes.
How about the same analysis for the land/station data?
The combined results will show where the “success” comes from, i.e. where the errors on both sides are that nullify each other enough to give the warmists their “predicted” global results.
This is great! It shows that climate models fit the real world like badly made clothes: they fit where they touch.

Mike

Can someone turn the heat back on, the pool is getting cold. Or is this the missing heat that Dr. Trenberth frets about ?

MrX

In Computer Science, there is a universal law called “garbage in, garbage out”, or GIGO. It doesn’t matter what the computer model or software is. You don’t need to know a thing about climate science or whatever field the model is trying to represent. If the input data is fictional, then so will the output. IOW, fictional data is garbage data. So their models MUST give garbage output. The AGW proponents are trying to tell us that the computer can produce correct results with wrong data. I’m with Charles Babbage on this one.
“On two occasions I have been asked, ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.”
– Charles Babbage

JohnBUK

Bugger! There was I waiting for the kettle to boil for a cup of tea and now you tell me I’ll have to wait even longer. It’s worse than we thought.
Never mind, here in the UK the BBC and Grauniad are concentrating on the “wettest month since time began” or something like that.

You have done another fine job here. The major problem with all this foolishness is faith. Those supporting AGW believe as strongly in it is the faithful of any religious faith believe in their god. Those opposed the reverse. Therefore AGW, yes or no, is more related to faith and politics then to reality. This leaves us atheists allied with all kinds of crazies. Politics and religion make strange bedfellows for sure. I can remember attending geoscience conferences in the late 80’s and pleading with the model obsessed to either fully calibrate or give it up. They did neither. Today it is all falling apart on a technical merit basis. Still widely accepted on an “article of faith” basis.
I suspect AGW will become the creation science of the future sadly much time and energy on both sides will be spent for little or no gain.

LazyTeenager

Since sea surface temperature anomalies are not as variable as lower troposphere temperature (TLT) anomalies, we’ll assume that 17 years would also be an acceptable timescale
————
Maybe, maybe not. Having ENSO in there with 3 year durations might make this tricky. Not all of the models handle ENSO well.

Robbie

Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion. As it should be done the right and scientific way. Where are your papers Mr. Tisdale? Or are you just complaining in some blogs?

phlogiston

The Tyger
William Blake
Tyger! Tyger! burning bright
In the forests of the night,
What immortal hand or eye
Could frame thy fearful symmetry?
In what distant deeps or skies
Burnt the fire of thine eyes?
On what wings dare he aspire?
What the hand dare seize the fire?
And what shoulder, and what art,
Could twist the sinews of thy heart?
And when thy heart began to beat,
What dread hand? and what dread feet?
What the hammer? what the chain?
In what furnace was thy brain?
What the anvil? what dread grasp
Dare its deadly terrors clasp?
When the stars threw down their spears,
And watered heaven with their tears,
Did he smile his work to see?
Did he who made the Lamb make thee?
Tyger! Tyger! burning bright
In the forests of the night,
What immortal hand or eye,
Dare frame thy fearful symmetry?

crosspatch

Figure 3 looks to me like all the observed warming happened up to 2004. Since 2004, there doesn’t look to be any warming whatsoever.

George E. Smith

Say Bob,
How’s your supply of salt doing; I figured you must be running low with all the open wounds to rub it in ?
A lot of nice data , and I’m glad you take the time to show it to us Bob. I would say it strengthen’s my own personal belief that any LWIR radiant energy returned from the atmosphere to the surface as a consequence of CO2 (GHG) capture, mostly results in prompt evaporation from the ocean surface, which returns that energy to the atmosphere, and does not store it in the ocean depths.
Thanks for the presentation.
George

timetochooseagain

Yes, the trends are different, but are they ~significantly~ different? I would very much like to see that question answered by anyone with statistical expertise. Otherwise, we can’t be sure that the different trends aren’t just bad luck (actually we can’t be sure, just have the probability to the contrary arbitrarily small).

Jimbo

If you’re for some reason hoping the data for the rest of the global oceans, the “Atlantic Ocean Plus” data, will make up the difference, you’re about to be disappointed. As illustrated in Figure 3, the models are showing a warming rate that’s about 50% higher than what has been observed. That’s not too good. Then when you consider the blatantly obvious model failings for the “Indian & Pacific Ocean Plus” subset, you wonder how the climate-model based anthropogenic global warming charade can continue. Yet it does.

[my bold]
The answers are several:
Money, scientific glamor, damage to reputations, the end of the IPCC and I hate to say it but living in denial because there is a consensus and the models must be right and observations wrong. This is it in a nutshell. When are they going to let go and move on to some other scare???

Jim Petrie

Since water expands when it warms, most of the ocean’s warmth must be in the top 10 metres or so. Currents like the gulf stream can carry water down but how can this warm water stay down?
Do the Argo people seriously think that a significant portion of the ocean’s heat lies 1 km or more below the surface?
Jim Petrie

Skeptikal

The models are right, it’s the planet that’s wrong. Will someone please tell the planet to get on board and follow the models predictions.

Two oopses in the post.
I didn’t color code the title block of Figure 3. Here’s a copy of the corrected Figure 3:
http://bobtisdale.files.wordpress.com/2012/04/figure-3-revised.png
Sorry again.

richardscourtney

Robbie:
At April 29, 2012 at 1:32 pm you say;
“Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion. As it should be done the right and scientific way. Where are your papers Mr. Tisdale? Or are you just complaining in some blogs?”
Yes, Robbie, your post is “laughable”.
First the alarmists claimed in “peer-reviewed magazines” that 10-years of global temperature stasis could occur withgout refuting AGW. As the 10-years approached they change 10-years to 15-years, When 15-years of global temperature stasis passed then Santer said 17-years would be needed.
Tisdale’s article says;
“Since sea surface temperature anomalies are not as variable as lower troposphere temperature (TLT) anomalies, we’ll assume that 17 years would also be an acceptable timescale to present sea surface temperature anomaly trends on a hemispheric, or greater, basis.”
Do you have a problem with that assumption? You do not state any.
And Tisdale’s article provides clear data that 17-years has elapsed of stasis in sea surface temperature rise. You state no fault in this observation that he clearly obtained in “the right and scientific way” and which he has clearly presented here in “the right and scientific way”.
Also, Tisdale has published in “peer-reviewed magazines” although it is not clear what – if any – relervance that has to the above article and/or its contents..
Please return when you have something to say. Until then, please stop wasting space in this thread.
Richard

DaveS

Robbie says:
April 29, 2012 at 1:32 pm
Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion. As it should be done the right and scientific way. Where are your papers Mr. Tisdale? Or are you just complaining in some blogs?
My, what an original contribution. If you have some valid criticism of this post, why not raise it here, and I’m sure Mr Tisdale will be glad to respond.
Quite why anyone believes that ‘peer-reviewed magazines’ represent the only ‘right and scientific way’ to discuss technical matters is a mystery to me, particularly in a field in which pal-review has so debased peer-review.

MB

Fact: Human CO2 is tiny compared to the natural CO2. It is even tiny compared to the natural variation of the natural CO2.
Why anybody thinks that human CO2 is a significant or dominant factor in the planetary temperature is utterly baffling.
Observation: The variance of the temperature “signals” in nature are much stronger than the model.
That tells us instantly that the model results are garbage. Anyone who models dynamic systems for a living would tell you instantly, within an instant, that the model results are incorrect. If you rescale the model parameters to yield the observed variance of temperature then you will end up with warming of 100’s to 1000’s of C.
I suspect that there has been a lot of “tuning” to get the result to look acceptable. The model likely “blows up” when sensible estimates of the parameters are used. Would be nice to look over the source code and see what values were used. How viscous was the water in the model? I betcha it was like treacle.

RACookPE1978

DaveS says:
April 29, 2012 at 2:34 pm – (As he Responds to “Robbie” earlier)
Who is correct? A person who tried to write about continental drift in 1923 – when it would be rejected by every “scientific” journal in the world … but when the continents were moving despite every so-called “scientific” expert in geology and physics?
Was Copernicus wrong when he wrote about circular orbits … despite being afraid of criticism from the “scientific” consensus of globes and spheres that Ptolemy argued for, and that the worldwide scientific press supported? Was Tycho “wrong” when he proposed a combined system of geo-centric and helio-centric orbits?
Name a single time in history when the so-called “scientific consensus” of worldwide so-called scientific thought was correct.
Name a single time in history when the global “scientific societies” were correct in their predictions and assumptions.

View from the Solent

Shouldn’t that be “Yes, Virginia, there is a Santer clause”?

timetochooseagain says:
April 29, 2012 at 1:53 pm (Edit)
Yes, the trends are different, but are they ~significantly~ different? I would very much like to see that question answered by anyone with statistical expertise. Otherwise, we can’t be sure that the different trends aren’t just bad luck (actually we can’t be sure, just have the probability to the contrary arbitrarily small).
##############
that is a question that bob won’t address. The problem is he really cant address it on a regional level. the 17 year figure is derived from variations in the GLOBAL metrics. It cant be applied to regional series. Well, you can try to apply it, but it’s really misleading and innumerate.
The 17 year figure comes from a characterization of the SNR in the global number. We already know that models are having a hard time getting regions correct. Region A is a bit too hot, Region B is a bit too cold. That is well known. Its so well known that its on the front burner of research. basically climate modelers have said : we dont get regional predictions done very well, we need to work on that. Thats in the IPCC” along comes Bob. He finds what was already known. He applies a figure derived from SNR in a global metric to the regional scale. bad analysis.
The models do a fair job at global metrics. that is, they are better than a naive forecast.
They admittedly have less skill at a regional level. Addressing that issue is a grand challenge.
This is just standard. We have a system. Its complex in space and time.
I know mathematically that I cant predict behavior of every molecule at every time. So, folks
aim at getting gross systems metrics right, KNOWING that at shorter time scales and smaller
spatial scales the answer will be less accurate. Globally, for example, you predict a 1 meter rise in sea level. and you KNOW that means some places will be more than this average and other places will be less. Ideally, you like to get improved regional skill. that may be computationally impossible. in the end place xyz may see 0 rise, and place xyz2 may see a 2 meter rise.
is a global prediction of 1 meter on average wrong? yes and no. depends what you want to use the prediction FOR.
Bottomline. The models need improvement. And don’t try to apply the SNR derived from a global metric to regional scale statistical problems. Knowing you have a problem and working to fix it, trumps misusing math and not knowing what you are doing.

Logicophilosophicus

Presumably this has huge implications for the supposed positive feedback via evaporation.

LazyTeenager says: “Maybe, maybe not. Having ENSO in there with 3 year durations might make this tricky. Not all of the models handle ENSO well.”
Please identify papers that claim “models handle ENSO well.” There are no models that handle ENSO well. Also, the three-year ENSO event during this time period was a La Nina that lasted from 1998 to 2001. What point are you trying to make or are you just attempting to confuse yourself?

commieBob

Robbie says:
April 29, 2012 at 1:32 pm
Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion. As it should be done the right and scientific way. Where are your papers Mr. Tisdale? Or are you just complaining in some blogs?

He’s not complaining Robbie. He’s analyzing the data. You, on the other hand, are just being a troll. Why don’t you exercise your brain muscles and try to make a logical argument. People would think better of you if you could actually find something wrong with the article.

Kev-in-UK

LazyTeenager says:
April 29, 2012 at 1:29 pm
”Maybe, maybe not. Having ENSO in there with 3 year durations might make this tricky. Not all of the models handle ENSO well.”
Just wondering, can you point to one(just one will do!) model that does handle ENSO?
and, failing that – can you point to a single climate model that handles anything to a reasonable degree of accuracy, say +/- 25%? and preferably from a forecast as well as hindcast perspective?
I must confess, I haven’t noticed or observed one, but then again, I do tend to brush over the models as they seem to be demonstrably inadequate within the climate science meme.

Interstellar Bill

The only ‘warming signal’ of any kind in the last four decades is in an ‘adjusted’ record of surface temperatures, many collected from obviously overheated stations. Naturalists cherry-pick anecdotes of earlier springs or minor range shifts, but nothing beyond natural ecological variability. Otherwise, all they have is the so-called global average temperature,a kindergarten-level statistic with no physical significance whatsoever.
Satellite signals of atmospheric temperature or thermal IR output show NO change.
Every Warmista prediction in the 20th century has proven wildly wrong.
Yet so extensive is the climate brainwashing that even skeptics will admit when pressed
‘Well, yes there’s been warming…”
JUST SAY NO.
Since CO2 went over 350 there has been NO warming.

JPeden

Robbie says:
April 29, 2012 at 1:32 pm
Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion.
But still, Robbie, who you gonna’ believe? Santer’s peer-reviewers widdle Warming Elves or you own lying eyes?

Kev-in-UK

Steven Mosher says:
April 29, 2012 at 2:55 pm
Thats a bit simplistic, don’t you think, Steven?
As you are so fond of saying, ‘models’ are an essential part of our lives – but they are no good if they are not realistic or plain wrong!
Trying to model a spatially large system – i.e. a global system is largely futile without billions of data points over an adequately temporal frame. I doubt they could even model a regional area with the current data available.
I don’t have a problem with the models being ‘wrong’ or if you prefer, ‘less than accurate’ – that’s fine – but at least the provideers should state the fact openly and advise that they are not used for important policy decisions. That’s called honesty!!
In ISO 9001 – if you cannot calibrate and verify your equipment one is supposed to set it aside with a big label saying ‘DO NOT USE’ – and this remains UNTIL it’s fixed. I honestly believe climate models need to be in this category. When climate science models reach a degree of VERIFIED quality assurance, then we can start to take them more seriously – but ’til then – the ‘DO NOT USE’ sign must be prominently displayed!!

@Mosher:
“The models do a fair job at global metrics. that is, they are better than a naive forecast.”
=====================================
My naive forecast is that the globe will warm at approx. the same rate this century as it did last century. Let’s say .7C / 10 = .07C per decade. The IPCC predicts .2C per decade. Who did better then? The climate model or the naive forecast?
Possibly you can explain what you mean by “naive forecast” and why the IPCC model ensemble is better? If there is a reasonable basis for your claim, that’s fine… But you have the habit of making pronouncements Ex Cathedra here. It’s a bad habit both of Warmists and some who identify as more sceptical.

Robbie says: “Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion. As it should be done the right and scientific way. Where are your papers Mr. Tisdale? Or are you just complaining in some blogs?”
I’m not complaining, Robbie. Nor did I take issue (in this post) with Santer et al’s findings. I simply prepared a blog post that illustrates the sea surface temperature anomalies of 75% of the global ocean surface have not warmed in 17 years. And I used Santer et al simply as a reference for the 17-year time span. It appears you didn’t read the post, but simply commented on your own misunderstandings about what was presented in it. By the way, the not-peer-reviewed argument is worn out and tired—and has no bearing on this post. Find something new to use.

richardscourtney

Steven Mosher:
In your post at April 29, 2012 at 2:55 pm you say;
“The 17 year figure comes from a characterization of the SNR in the global number. We already know that models are having a hard time getting regions correct. Region A is a bit too hot, Region B is a bit too cold. That is well known. Its so well known that its on the front burner of research. basically climate modelers have said : we dont get regional predictions done very well, we need to work on that. Thats in the IPCC” along comes Bob. He finds what was already known. He applies a figure derived from SNR in a global metric to the regional scale. bad analysis. “
“The models do a fair job at global metrics. that is, they are better than a naive forecast.”
“Bottomline. The models need improvement. And don’t try to apply the SNR derived from a global metric to regional scale statistical problems. Knowing you have a problem and working to fix it, trumps misusing math and not knowing what you are doing.”
NO! The “bottomline” is that the models are fudged to emulate past global warming, they are known to NOT emulate the climate system of the real Earth and, therefore, they should be rejected.
I have repeatedly explained this on WUWT but it seems I need to repeat it again. The explanation is as follows.
None of the models – not one of them – could match the change in mean global temperature over the past century if it did not utilise a unique value of assumed cooling from aerosols. So, inputting actual values of the cooling effect (such as the determination by Penner et al.) would make every climate model provide a mismatch of the global warming it hindcasts and the observed global warming for the twentieth century.
This mismatch would occur because all the global climate models and energy balance models are known to provide indications which are based on
1.
the assumed degree of forcings resulting from human activity that produce warming
and
2.
the assumed degree of anthropogenic aerosol cooling input to each model as a ‘fiddle factor’ to obtain agreement between past average global temperature and the model’s indications of average global temperature.
More than a decade ago I published a peer-reviewed paper that showed the UK’s Hadley Centre general circulation model (GCM) could not model climate and only obtained agreement between past average global temperature and the model’s indications of average global temperature by forcing the agreement with an input of assumed anthropogenic aerosol cooling.
And my paper demonstrated that the assumption of aerosol effects being responsible for the model’s failure was incorrect.
(ref. Courtney RS An assessment of validation experiments conducted on computer models of global climate using the general circulation model of the UK’s Hadley Centre Energy & Environment, Volume 10, Number 5, pp. 491-502, September 1999).
More recently, in 2007, Kiehle published a paper that assessed 9 GCMs and two energy balance models.
(ref. Kiehl JT,Twentieth century climate model response and climate sensitivity. GRL vol.. 34, L22710, doi:10.1029/2007GL031383, 2007).
Kiehl found the same as my paper except that each model he assessed used a different aerosol ‘fix’ from every other model.
He says in his paper:
”One curious aspect of this result is that it is also well known [Houghton et al., 2001] that the same models that agree in simulating the anomaly in surface air temperature differ significantly in their predicted climate sensitivity. The cited range in climate sensitivity from a wide collection of models is usually 1.5 to 4.5 deg C for a doubling of CO2, where most global climate models used for climate change studies vary by at least a factor of two in equilibrium sensitivity.
The question is:
if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy. Kerr [2007] and S. E. Schwartz et al. (Quantifying climate change–too rosy a picture?, available at http://www.nature.com/reports/climatechange, 2007)
recently pointed out the importance of understanding the answer to this question. Indeed, Kerr [2007] referred to the present work and the current paper provides the ‘‘widely circulated analysis’’ referred to by Kerr [2007]. This report investigates the most probable explanation for such an agreement. It uses published results from a wide variety of model simulations to understand this apparent paradox between model climate responses for the 20th century, but diverse climate model sensitivity.”
And, importantly, Kiehl’s paper says:
”These results explain to a large degree why models with such diverse climate sensitivities can all simulate the global anomaly in surface temperature. The magnitude of applied anthropogenic total forcing compensates for the model sensitivity.”
And the “magnitude of applied anthropogenic total forcing” is fixed in each model by the input value of aerosol forcing.
Thanks to Bill Illis, Kiehl’s Figure 2 can be seen at
http://img36.imageshack.us/img36/8167/kiehl2007figure2.png ]
Please note that the Figure is for 9 GCMs and 2 energy balance models, and its title is:
”Figure 2. Total anthropogenic forcing (Wm2) versus aerosol forcing (Wm2) from nine fully coupled climate models and two energy balance models used to simulate the 20th century.”
It shows that
(a) each model uses a different value for “Total anthropogenic forcing” that is in the range 0.80 W/m^-2 to 2.02 W/m^-2
but
(b) each model is forced to agree with the rate of past warming by using a different value for “Aerosol forcing” that is in the range -1.42 W/m^-2 to -0.60 W/m^-2.
In other words the models use values of “Total anthropogenic forcing” that differ by a factor of more than 2.5 and they are ‘adjusted’ by using values of assumed “Aerosol forcing” that differ by a factor of 2.4.
So, each of the models only gets an approximation to past global warming because this is fixed as an INPUT to the model (by choice of “aerosol forcing” value) and this approximation is NOT AN OUTPUT of the model.
And the fact that each model requires a different ‘aerosol adjustment’ proves that at most only one – and probably none – of the models emulates the climate system of the real Earth (there is only one Earth).
Richard

richardscourtney says: “Also, Tisdale has published in ‘peer-reviewed magazines’…”
It’s been about 40 years since I was published in any type of technical magazine and that was a rebuttal, not a peer-reviewed paper.

Philip Bradley

The problem that Santer was addressing in his paper is that the Forcings model/theory of climate predicts a uniform warming with increasing forcings. This is the core prediction of the theory. Santer’s 17 years is supposedly the longest period natural variability can mask the warming in the surface temperature. Although, according the Forcings theory, the warming is still occuring. Hence Trenbeth’s ‘missing heat’.
There are 3 possible conclusions.
1. The Forcings model/theory of climate is wrong.
2. Santer is wrong in his 17 years conclusion.
3. The actual forcings are wrong, and the greenhouse effect of CO2 is much smaller than claimed.

StuartMcL

Steven Mosher says:
April 29, 2012 at 2:55 pm
The models do a fair job at global metrics. that is, they are better than a naive forecast.
They admittedly have less skill at a regional level. Addressing that issue is a grand challenge.
==================================================================
I do a fair job at darts on a global basis. My throws average out at bulls.
I admittedly have less skill at a regional level. When I throw at the triple 20, I am just as likely to hit 12,5,20,1 or 18.

richardscourtney

Bob Tisdale:
re. your post at April 29, 2012 at 3:27 pm.
I did not intend to misrepresent you, and I sincerely and abjectly apologise for any offence my post caused to you. It was a genuine misunderstanding on my part.
Richard

Steven Mosher says: “that is a question that bob won’t address. The problem is he really cant address it on a regional level. the 17 year figure is derived from variations in the GLOBAL metrics. It cant be applied to regional series. Well, you can try to apply it, but it’s really misleading and innumerate.”
I have no need to address it. 75% of the surface area of the global oceans is not regional. That’s what this post was about, the flatness of the sea surface temperature anomalies of the “Indian and Pacific Ocean Plus” SST dataset for the past 17 years, as stated in the closing. And as I noted toward the beginning, the rest of the presentation was for information purposes. Did you read the post, Steven, or just look at the pictures?
Also, if memory serves me well, the Santer et al paper was a statistical analysis of signal-to-noise ratio of the climate models. ENSO is the greatest source of noise in the sea surface (and other surface) temperature records and we know climate models do not model ENSO well—those that try do it poorly. So the source of model noise Santer et al evaluated in their paper really has no relevance in a discussion of sea surface temperature data trends or any other global surface temperature metric, for that matter. I can remove the model data from Figure 2 above with a click of a mouse. And it will not change the fact that the sea surface temperature anomalies for 75% of the surface area of the global oceans have not warmed in 17 years.

Doug Proctor says: “Finally! A breakdown of the global story by regions, showing how the “global” warming is not global but a mathematical construct of relative warming and cooling by regions. ”
Finally, Doug? I’ve been posting regional (basin) sea surface temperature anomalies at my blog for almost 4 years.

Berényi Péter

Robbie says:
April 29, 2012 at 1:32 pm
Haha. Laughable! At least Santer is publishing in peer-reviewed magazines and thus contributing in the scientific discussion. As it should be done the right and scientific way.

Eh, you are the kind of person insisting an aquatic panmnesia denier can only make a valid point by publishing in a peer reviewed journal on that particular topic.
However, in cases when the majority of peers or at least the influential ones are gone astray in a branch of science, this requirement itself is laughable.
This way it is quite impossible to exclude certain methods applied in fields like Homeopathy, Ufology, Torah Code Research, Phrenology, Reflexology, Eugenics, Astrology, Psychoanalysis, Iridology, Graphology, Alchemy, Lysenkoism, Metoposcopy, Physiognomy, Creation Science or Computational cAGW Theory from the inventory of scientific endeavor, is not it? If so, we are left with nothing but a bunch of New Age crap and traditional science is finished for good.

richardscourtney: No apology was necessary. I just wanted to clear up that misunderstanding.
Regards

Steve Mosher: A postscript: Scroll up to the last model-data comparison. That’s a graph of global sea surface temperature anomalies. The trend of the model projection is 5 times higher than observed.

jorgekafkazar

Jimmy Haigh says: ” “Yes, there is a Santer clause’. I like it.”
I don’t like it. A curious novice looking here for information would have no idea who “Santer” is (lucky them). Nor would he/she know what the significance of the “17 year itch” is wihout first reading the paper. The “Tisdale on” part is superfluous; the post is attributed to Bob Tisdale right below the title. Titles are vital. They are the epitome of Willis Eschenbach’s ‘elevator speech’ concept, and, unless you wish to preach only at the choir, should be designed to draw the new reader in, not make him scratch his head and wander off in confusion.

Steven Mosher says: “We already know that models are having a hard time getting regions correct. Region A is a bit too hot, Region B is a bit too cold. That is well known. Its so well known that its on the front burner of research. basically climate modelers have said : we dont get regional predictions done very well, we need to work on that.”
Yet the IPCC paints the models as being able to perform well on a regional basis. The IPCC in AR4 actually writes that the models do a good job on continental and sub-continental basis. See page 32 (of 84) of Chapter 9 or page 694 of the entire report. It’s under the heading of “9.4.2 Continental and Sub-continental Surface Temperature Change”. It reads:
“The ability of models to simulate many features of the observed temperature changes and variability at continental and sub-continental scales and the detection of anthropogenic effects on each of six continents provides stronger evidence of human infl uence on climate than was available to the TAR. A comparison between a large ensemble of 20th-century simulations of regional temperature changes made with the MMD at PCMDI (using the same simulations for which the global mean temperatures are plotted in Figure 9.5) shows that the spread of the multi-model ensembles encompasses the observed changes in regional temperature changes in almost all sub-continental regions (Figure 9.12; see also FAQ 9.2, Figure 1 and related figures in Chapter 11).”
That’s what the public has been told—those who bothered to read the report.

pouncer

I generally agree with, and respect, Steven Mosher’s approach to the temperature record. His analogy comparing the various CRU and BEST datasets to a “market index” is, for me, a highly helpful analogy.
The challenge here is that knowing the overall climate — or market index — is doing one thing or another gives me (at least) no basis upon which to invest. If 75% of the companies making up the W-3000 index are all stagnant, one sixth of the remaining are making money, and 1/12th are going bust — is this a good time to re-allocate my entire portfolio and a good basis upon which to do so?
Why not simply do what Hansen calls “business as usual”? Some winners, some losers, some rising tides raising “all boats” — in one basin or another — and some tides leaving some boats stranded on the sand bar.

Eugene WR Gallun

HE SHINES AND SMELLS LIKE ROTTEN MACKEREL IN THE MOONLIGHT. John Randolph
Steven Mosher says: “We already know that models are having a hard time getting regions correct. Region A is a bit too hot, Region B is a bit to cold. That is well known. Its so well known that its on the front burner of research. Basically climate modelers have said: we dont get regional predictions done very well, we need to work on that.”
Now this is rather interesting. A climate “scientist” publicly admitting doubt about models? The only reason any of these people will admit any type of problem is because RIGHT OUT IN PLAIN SIGHT THEIR ASSES ARE BEING HELD TO THE FRONT BURNER by real scientists.
Eugene WR Gallun
[Moderator’s Note: Eugene, I suspect you are new to this, don’t know about Mosher, and are about to get pounded. Don’t take it personally. What doesn’t kill you makes you stronger. -REP]

Eugene WR Gallun

Also should have added that Mosher covers up the big errors by admitting small ones. The real take off the papers is that the models don’t get either the parts nor the whole right. Total fail.
Eugene WR Gallun

Eugene WR Gallun

To REP
HaHa — I am a conservative poet who hung out in the sub-arts for many years. I know all about sticks and stones. And lies. No need to print this. Thanks for the concern.
[REP says: Eugene, poets are always anarchists… just gotta see it. Mosher is an anarchist. Just gotta see that, too. Uhhh.. not sure what you mean by “sub-arts”. -REP]