I discovered this climate model failure a while ago, but haven’t published a post about it because, if I were to compare the modeled and observed sea ice area for each hemisphere, I would need to make too many approximations and assumptions. The reasons: The NSIDC sea ice area data through the KNMI Climate Explorer is presented in millions of square kilometers, while the CMIP5-archived model outputs there are presented in the fraction of sea ice area—assumedly a fraction of the ocean area for the input coordinates.
I decided to take a simpler approach with this post—to show whether the models simulate a gain or loss in each hemisphere.
That is, we know the oceans have been losing sea ice in the Arctic since November 1978, but gaining it around Antarctica. See Figure 1.
Figure 1
Then there are the oodles of climate models stored in the CMIP5 archive. They’re the models being used by the IPCC for the upcoming 5th Assessment Report. Would you like to guess whether they show the Northern and Southern Hemispheres should have gained or lost sea ice area over the same time period?
The multi-model ensemble mean of their outputs indicate, if sea ice area were dependent on the increased emissions of manmade greenhouse gases, the Southern Ocean surrounding Antarctica should have lost sea ice from November 1978 to May 2013. See Figure 2.
Figure 2
Well at least the models were right about the sea ice loss in the Northern Hemisphere. Too bad for the modelers that our planet also has a Southern Hemsiphere.
We could have guessed the models simulated a loss of sea ice around Antarctica based on their simulation of the sea surface temperatures in the Southern Ocean. As illustrated in the most recent model-data comparison of sea surface temperatures, here, sea surface temperatures in the Southern Ocean have cooled, Figure 3, while the models say they should have warmed.
Figure 3
STANDARD BLURB ABOUT THE USE OF THE MODEL MEAN
We’ve published numerous posts that include model-data comparisons. If history repeats itself, proponents of manmade global warming will complain in comments that I’ve only presented the model mean in the above graphs and not the full ensemble. In an effort to suppress their need to complain once again, I’ve borrowed parts of the discussion from the post Blog Memo to John Hockenberry Regarding PBS Report “Climate of Doubt”.
The model mean provides the best representation of the manmade greenhouse gas-driven scenario—not the individual model runs, which contain noise created by the models. For this, I’ll provide two references:
The first is a comment made by Gavin Schmidt (climatologist and climate modeler at the NASA Goddard Institute for Space Studies—GISS). He is one of the contributors to the website RealClimate. The following quotes are from the thread of the RealClimate post Decadal predictions. At comment 49, dated 30 Sep 2009 at 6:18 AM, a blogger posed this question:
If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?
Gavin Schmidt replied with a general discussion of models:
Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).
To paraphrase Gavin Schmidt, we’re not interested in the random component (noise) inherent in the individual simulations; we’re interested in the forced component, which represents the modeler’s best guess of the effects of manmade greenhouse gases on the variable being simulated.
The quote by Gavin Schmidt is supported by a similar statement from the National Center for Atmospheric Research (NCAR). I’ve quoted the following in numerous blog posts and in my recently published ebook. Sometime over the past few months, NCAR elected to remove that educational webpage from its website. Luckily the Wayback Machine has a copy. NCAR wrote on that FAQ webpage that had been part of an introductory discussion about climate models (my boldface):
Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.
In summary, we are definitely not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.
CLOSING
Just add sea ice onto the growing list of variables that are simulated poorly by the IPCC’s climate models. Over the past few months, we’ve illustrated and discussed that the climate models stored in the CMIP5 archive for the upcoming 5th Assessment Report (AR5) cannot simulate observed:
Satellite-Era Sea Surface Temperatures
Global Surface Temperatures (Land+Ocean) Since 1880
And in an upcoming post, we’ll illustrate how poorly the models simulate daily maximum and minimum temperatures and the difference between them, the diurnal temperature range. I should be publishing that post within the next week.
Model-Data Comparison: Hemispheric Sea Ice Area
Posted on June 15, 2013 by Bob Tisdale
Thanks Bob for yet another model data comparison, putting the finger on another issue where it pains!
The models need a general overhaul before using any of the data even for estimations, and indeed as rgbatduke said a great deal of those should find themselves in the history archive as erroneous tries failing to model the climate.
A question to the modellers:
To my limited model understanding I think models (GCM) use the CO2 forcing in the same way they use the cloud forcing as a backradiation from cloud atmospheric level – where a certain parameter (+3.7 W/m2 by CO2 doubling is used)
Is this done this way?
See the excellent post by rgbatduke where he shreds current GCMs and “climate scientists” for their abject failures to model the real world
An eloquent devastating critique of current climate models which are so far biased hot as to give meaningless results.
Tisdale has clearly shown a major failure of climate models to predict antarctic ice.
Back to the drawing boards.
Nicola Scafetta’s empirical natural oscillation dominated projections appear to be predicting temperatures since 2000 far better than the current menagerie of IPCC endorsed “Global Climate Models”.
Let’s use what works fairly well while the GCM mess has been sifted, weighed and found wanting.
Restore “climate science” to the world of “hard science” based on physics with grants to the best to improve performance measured against hard reality.
not lemming driven politically motivated prognostications fed by billion dollar troughs.
This might be a good time to address the following point:
When we look at an ensemble of outcomes, i.e. Scenarios, we see the variability dependent on specific situations that arise, the various situations representing either the noise or the potential variation in important parameters. The observations we receive represent one, specific situation, which involves both fundamental, unchanging aspects, i.e. radiative forcings of various kinds, and specific instances of the variables. What we see may not be the mean, though, but one of the recognized low potential Scenarios.
In other words, when we see the observations from 1979 to 2013 match the lowest IPCC Scenario, close to “C”, we see that observations come closer to the 5% chance, but that does not mean that the mean is incorrrect. What happened is 100% by occurrence, but was recognized as 5% by procedure. We could also have had the top 5%, i.e. Scenario A+, without the mean being incorrect. Each 5% would simply indicate that the variables, not the fundamentals, conspired to produce what they did. Again, the results do not invalidate the mean.
The question we must answer is, what caused the situational outcome as observed, the variables, incorrect fundamentals or a combination of both? Going forward, moreover, we need to understand the basis of the prior Scenarios: is there enough variability in noise and variables to take us from where we are in 2013 to the endpoint of Scenario A in 2100?
This is something I have spokien to a number of times: why do we continue to show the history from 1979 or so on the projections from the AR series from the same date? Should not the AR projections always restart at the end of the current observational data?
The only way forward I can see is that the Scenarios have the variability to go from the present 2013 to the endpoint of Scenario A or C in 2100. This must be the position of the climatologists with the IPCC. I don’t believe it is true, but it is the only way I see to justify Scenario A at this time: somehow we must be able, within the IPCC mathematics, to jump 3C in the next 87 years. As well, the melting of continental glaciers must be able to increase within the IPCC math to crank sea level rise to about 20 mm/yr towards the end of the century. If the variability of the IPCC processes of climate change do not have that ability – as the science is said to be much more deter inistic, much less probabilistic (which is why they can claim that CO2 is the primary driver of heating) that such a rapid end-result change indicates, then nobody can place observations on the AR4 or 5 Scenarios graph. Some of the Scenarios are simply impossible to occur by 2100.
Your opinion would be appreciated.
See the IPCC’s graphic on Arctic sea ice extent anomaly going back to 1974 up to 1990. I wonder whether it will be in the latest IPCC report due out later?
http://stevengoddard.files.wordpress.com/2013/06/screenhunter_170-jun-15-11-10.jpg
http://stevengoddard.wordpress.com/2013/06/15/ignoring-inconvenient-arctic-data/
“In summary, we are definitely not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.”
This comparison is unfair, you are comparing an average response against an individual run of a unique physical instance, apples to oranges.
The correct way to do it is to spawn scores of replica Earth systems and measure their responses to the very same carbon dioxide emission scenario, then take the ensemble average and compare that to ensemble average of computational climate model runs. We are not interested, after all, in the climate system’s internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions, are we?
There, you have it. Admittedly, it may take some time to realize this program and it costs money to build many identical specimens of Earth, but hey, this is the way science is done.
Until it is done this way, properly, computational climate models can’t be falsified. And, if they are not falsified, they must be true, right?
Wrong. In traditional scientific practice the logical status of a theory is indeterminate until it survived several actual falsification attempts. If the theory is designed in a way that no such procedure can be carried out ever, it lays outside the realm of science, belongs to metaphysics, not physics. That’s the current epistemological status of computational climate models.
Pamela Gray says:
June 15, 2013 at 8:19 am
” Very busy and non-random” brain activity.
Experimenter bias.
Keith Gordon: The trouble is that whenever NH seesm to be going the wrong way for the team the tend to “freeze” the graphs… take a look at DMI. They are usually very timely when the scenario goes the teams way (down). Polar Ice melt seems to be stalled, in fact tending to up again and guess what its now 4 days since the graph has been looked at. They are probably wondering what to do LOL
[ab initio — Bob Tisdale did not ask me to (likely would prefer I not!) do this]
SUPPORT OUR BOB!
While being kicked by a donkey is no insult, it still hurts.
If you have time, read the following and let Mr. Tisdale know what YOU think of his competence and integrity.
http://bobtisdale.wordpress.com/2013/06/15/i-dont-like-being-called-a-liar-fabricator-or-data-manipulator/#comment-11773
AAack! I messed up the above link! (it lands on my comment, oh, brother)
Please, just SCROLL BACK UP THE PAGE on Bob’s site (linked above) and read HIS post.
Well done (again!), Mr. Tisdale. Your graphs (esp. figure 1, here — superCOOL!) are “worth a thousand words.”
Janice Moore says: “ab initio”
Hmmm. I only recall seeing that term in legal documents.
Thank you for the kind words and the support.
Regards
This would be an interesting thing to check in the models: relationship of atmospheric CO2 to SST:
http://climategrog.wordpress.com/?attachment_id=233
http://climategrog.wordpress.com/?attachment_id=223
Now accepting that their individual models “internal noise” variations look different we’d need to look at individual models.
Now my guess is that they’ve hardwired the CO2 level to human emission “senarios” and their “random” internal variations will be uncorrelated to SST.
Now if they have not got the relationship between the two primary variables that they are shouting about at least a little bit right, all bets are off.
Bob, while SSTs in the Southern Ocean seem to be heading downwards, what about heat content? I can’t find any newer update on Southern Ocean OHC on your site than this one which is only up to December 2011. It would be great if you could prepare a chart which includes the newest data!
http://bobtisdale.wordpress.com/2012/01/26/october-to-december-2011-nodc-ocean-heat-content-anomalies-0-700meters-update-and-comments/
With the increased sea ice extent in Antarctica, how long should it take for that extra cooling to circulate and cool larger sections of the global oceans?
Bob, I keep referring people to your excellent work here at WUWT. It is really the best analysis around. The quality of the comments today is also top notch.
Go team!
Espen: I stopped providing updates for the NODC OHC data when the NODC introduced the 0-2000m data in that nonsensical pentadal form that adds over 35% to the trend of the annual data. It’s a contrived dataset.
And for those reading this thread who are new to discussions of ocean heat content data, see the post here:
http://bobtisdale.wordpress.com/2013/03/11/is-ocean-heat-content-data-all-its-stacked-up-to-be/
But here’s the Southern Ocean OHC data (90S-60S) for the depths of 0-700 meters. Keep in mind that there are very few readings in the Southern Ocean before the ARGO floats were deployed there, which wasn’t until 2003/04. Also keep in mind the ARGO-era data has to be adjusted to show warming:
http://oi44.tinypic.com/awxpjp.jpg
You really should learn how to use the KNMI Climate Explorer:
http://climexp.knmi.nl/start.cgi?id=someone@somewhere
When is the Antarctic terror melt going to slow down? It’s worse than I thought!
Indeed. Averaging multiple runs of multiple models necessarily averages the choice of parameters (selection and settings), which is inherently meaningless. Especially since the point of having different models is partly (mostly?) to distinguish the value of making various assumptions (preferably as few as possible). One can understand rgb’s agony contemplating the conceptual mess which averaging them creates (but is ignored).
Bob, I recently tried to find the satellite era Antarctic Sea Ice minimum extent/area data, in order to compute a trend as we are endlessly bombarded with the trend in the Arctic sea ice minimum.
I was unable to locate the data or any source that had computed or graphed the trend.
The closest I could find was this page that shows the 1979 to 2000 average and individual years thereafter. Although note 1999/2000 should be included already in the pre-2000 average.
http://earthobservatory.nasa.gov/Features/WorldOfChange/sea_ice_south.php
Post 2000 minimums are roughly 350,000 sq km higher than pre-2000 average. So there is a significant trend, but it would be nice to see it graphed up, if you have the data.
regards
Thanks for the link to P. Gosselin’s excellent site, Jimbo. Great article.
******************************************************************************
Well, looks like A-th-y is taking a well-deserved Father’s Day break. Please forgive this being TOTALLY OFF TOPIC, but I wanted to pay tribute to the dads of WUWT somewhere! You are appreciated!
!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!*!
Whether you’re a biological or adoptive or step or foster or father-figure dad, if you have ever been a dad to someone in some way, this is for you. [Note: When I use the terms “dad” and “mom” below I mean genuine, loving, parents, not emotionally or physically abusive or absent parents, regardless of their biological contributions. Some biological parents do not deserve to be called, not ever, “Mom” or “Dad.” Forgiving does not mean pretending.]
From: Your kid(s), whoever they are
To: You
A Dad Is There When It Matters
A mom is “always there” (sometimes, moms are “there” waaay too much!),
but a dad is there
when it matters.
Some dads are gone a lot. They may be in the military or have to travel for their job or just have to work long hours. They may miss the school play or the ball game or the birthday party. But you had all the glitter or fabric (or duct tape) you needed for that costume or the money for that mitt or those shoes or that bicycle because of all those long hours.
Dads don’t usually talk a lot. They may have a lot on their mind, or they may be worried about money or work, or they may just be tired. Some dads just have a hard time saying what’s on their mind, even more, what’s in their heart.
But, when something needs to be said, they say it. In the June/July “Reminisce” magazine, one long-ago bride remembered that just before she walked down the aisle on her dad’s arm on her wedding day, he whispered, “It’s not too late. You can still change your mind.” Another bride in that same magazine remembered her dad calling a halt to the nonsense of a very long “you may kiss the bride” interlude: “Is that really necessary!?” Dads don’t put up with a lot of nonsense – and that’s good. Think how many of us would be maimed or dead if dad hadn’t said: “Quit horsing around!” or “Get down from there, NOW!” Dads are good at saying important things like: “Stand on your own two feet and look them in the eye;” or “When you’re kicked by a jack-ass, consider the source;” or “Keep pedaling! … Keep pedaling! Look up! Keep pedaling!” (which of course applies to much more than just learning how to ride a bike).
Some of them never say, “I love you,” but you could see it … if you took the time to watch them.
Of course, most kids don’t. When we have all the time in the world to learn more about our dads, we are too busy living our own lives. We may think they didn’t care much, but, really, I think we just didn’t notice much of what they did that proved they cared, cared very much, indeed.
In the movie “Fiddler on the Roof,” when his second daughter, Hodl, is sitting in a tiny station, waiting for the train to Siberia, Tevye is there. His customers, for once, will have to wait. He doesn’t talk a lot, but he is there. As the train pulls away for the distant East, Tevye asks God simply to, “See that she dresses warmly.” When your dad told you to: “Wear a helmet. WEAR a HELMET!” or “Get the oil changed in your car for once!” or “Eat something. Do you eat? What do you eat? You look like you don’t eat anything;” or “Stop hanging around with those jerks. You’ll end up in jail or worse;” or “Do your homework – now!” or “So help me, if I catch you doing that again, I’ll pound the living daylights out of you;” he was really saying, “I love you. I care.”
Dads are fun! Most of them know how to play. They don’t take things too seriously (unless it really is serious). Moms tend to get way too serious. We may have nearly drowned in the middle of it, but we had fun! Dads know how to live. What is life without adventure?
If you know where to look, it’s easy to see a dad’s love. All a kid has to do is think about all the nice things dad could have had if he hadn’t been spending his money on cotton candy and tickets to the carnival and team uniforms and summer camp. And you thought doing that made him happy. Well, it did. Because you mattered more than anything in the world to him. He just wanted you to be happy. His new shoes could wait.
Because he loved you.
Real dads care and, if it is humanly possible,
real dads are there –
when it matters.
HAPPY FATHER’S DAY!
How much should we trust models of any type?
this comes down to the level of understanding of the subject matter by the creators of the models.
looking at the difference between the various peer reviewed model outputs (tropospheric temps), it is obvious that the level of understanding is nil. the range is greater than the overall change that is supposed to eventuate. this is a negative understanding ie proof of creator bias as the determining factor, not the input variables. the bias is an easy one to determine too, each model heads up.
the mean shows no understanding, and should be treated as such.
the world leading scientific body on the matter willing to use such crap as their ‘selling’ point is an indication of how poor the level of education and critical thinking has become.
Am I getting these things right?
!) A paper says an ice shelf melts mainly at the bottom due to the water beneath it, not at the top due to air temperature?
2) Another paper says that the Antarctic ice sheets that are melting are melting due to the arrival of warmer ocean currents beneath them?
3) Switching to Arctic ice am I remembering correctly about reading something here about a dramatic Arctic ice loss of many years ago that then researchers were blaming on a shift in the Gulf Stream sending measurably warmer water into the Arctic regions?
If i got the things above correct then what is most probably causing the today’s loss of sea ice in the Arctic? Certainly not warmer air temperatures? A shift in currents? if the currents have shifted would another place be getting colder?
At the age of 65 I must say — expiring minds want to know!
Eugene WR Gallun.
We are suppose to be interested in the ice because of the ability of ice to reflect the warming sunlight, correct? “Albedo” is all but a magic word for many Alarmists. However ice has no ability to reflect sunlight at night, and therefore things get more complex than simply saying, “There is less ice at the poles, so less sun is reflected, so it will be warmer.”
The way the word “albedo” is flung around makes it unclear whether it refers to the potential an object has to reflect light, or the actual light reflected. For example, a paper will say that freshly fallen snow has a high albedo of .9 (90%) Does that mean it is only .9 when the sun is shining, or does it have the same albedo when the sun has set and it is reflecting zip-zero?
For albedo to be a meaningful word, it seems that it should sink to near zero for all objects, whether white or black, after the sun goes down. However perhaps the albedo of freshly fallen snow remains at .9, because it is still reflecting 90%, even if it is only 90% of incoming starlight.
If the latter is the case, then we need a new word. (If it already exists, I don’t think laymen use it.) I propose the word be “calbedo,” named after me, so I can become famous, and rich enough to hire Bob to do some graphs I’m curious about.
I’d like to see the globe divided into stripes of latitude, such as the area between the pole and 80 degrees, and the area between 80 degrees and 70 degrees, and so forth. The farther you got from the pole and closer you got to the equator, the higher the sun would be in the sky, and the greater the power the ice would have. The ice would have more power because the sunbeams were more intense, and so would be the reflections. In the same way the ice in June would have more power than the ice in September.
The word “calbedo” would emphasis the power of the Antarctic ice, which extends quite far from the south pole. They have ice that, if you flipped the globe, would be like having ice clear south to the north coast of Scotland on the first day of summer.
The word “calbedo” would also emphasis how little power the ice at the north pole has, by the time the records are set and Alarmists are freaking out, in September. By then the sun is about to set on the pole and getting quite low at the arctic circle. (Also another weird factor is coming into play up north in September: Once the sun is sitting near the horizon, open water starts to have a higher albedo than ice does.)
Philip Bradley says: “Bob, I recently tried to find the satellite era Antarctic Sea Ice minimum extent/area data, in order to compute a trend as we are endlessly bombarded with the trend in the Arctic sea ice minimum.”
The KNMI Climate Explorer has the NSIDC area and extent data on the “Monthly climate indices” webpage:
http://climexp.knmi.nl/selectindex.cgi?id=someone@somewhere
Regards
Philip Bradley says: “Bob, I recently tried to find the satellite era Antarctic Sea Ice minimum extent/area data, in order to compute a trend as we are endlessly bombarded with the trend in the Arctic sea ice minimum.”
Rather than producing an equally meaningless “trend” for the other end of the planet from one day per year series ignoring 364/365th of the available data, maybe we should be using ALL the daily data to show the complementarity of the poles.
http://climategrog.wordpress.com/?attachment_id=206
or if we are interested in change we should be looking at the change directly not trying to guess it by looking at area time series:
http://climategrog.files.wordpress.com/2013/03/ddt_arctic_ice.png
Fighting fire with fire can sometimes be effective. Fighting stupidity with more stupidity tends to legitimise the former.
There’s much enlightenment to be found in ice cover data if we use ALL of it. Using just one day per year is egregious cherry picking at its worst. This plot has some links you may find useful.
http://climategrog.wordpress.com/?attachment_id=226
spectral analysis reveals some interesting patterns too
http://climategrog.wordpress.com/?attachment_id=216
There’s lots of interesting information in that, if only could get away from the obsession with unrepresentative hype like the annual minima, we might actually learn something about what is happening.