Guest Post by Bob Tisdale
In his post Schmidt and Sherwood on climate models, Andrew Montford of BishopHill commented on the new paper by Schmidt and Sherwood A practical philosophy of complex climate modelling (preprint). I haven’t yet studied the Schmidt and Sherwood paper in any detail, but in scanning it, a few things stood out. Those of you who have studied the paper will surely have additional comments.
DO CLIMATE MODELS SIMULATE GLOBAL SURFACE TEMPERATURES BETTER THAN A LINEAR TREND?
The abstract of Schmidt and Sherwood reads (my boldface):
We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project (CMIP). We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naïve predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian (in an informal sense), and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work.
The boldfaced sentence caught my attention. A straight line based on a linear trend should be considered a more naïve method of prediction. A linear trend is a statistical model and it is definitely a whole lot simpler than all of those climate models used by the IPCC. So I thought it would be interesting to see if, when and by how much the CMIP5 climate models simulated global surface temperatures better than a simple straight line…a linear trend line based on global surface temperature data.
Do climate models simulate global surface temperatures better than a linear trend? Over the long-term, of course they do, because many of the models are tuned to reproduce global surface temperature anomalies. But the models do not always simulate surface temperatures better than a straight line, and currently, due to the slowdown in surface warming, the models perform no better than a trend line.
Figure 1 compares the modeled and observed annual December-to-November (Meteorological Annual Mean) global surface temperature anomalies. The data (off-green curve) are represented by the GISS Land-Ocean Temperature Index. The models (red curve) are represented by the multi-model ensemble mean of the models stored in the CMIP5 archive. The models are forced with historic forcings through 2005 (later for some models) and the worst-case scenario (RCP8.5) from then to 2014. Also shown is the linear trend (blue line) as determined from the data by EXCEL. The data and models are referenced to the full term (1881 to 2013) so not to skew the results.
Figure 1
Over the past decade or so, the difference between the models and the data and the difference between the trend and the data appear to be of similar magnitude but of opposite signs. So let’s look at those differences, where the data are subtracted from both the model outputs and the values of the linear trend. See Figure 2. I’ve smoothed the differences with 5-year running-mean filters to remove much of the volatility associated with ENSO and volcanic eruptions.
Figure 2
Not surprisingly, in recent years, the difference between the models and the data and the difference between the trend line and the data are in fact of similar magnitudes. In other words, recently, a straight line (a linear trend) performs about as well at modeling global surface temperatures as the average of the multimillion dollar climate models used by the IPCC for their 5th Assessment Report. From about 1950 to the early 1980s, the models perform better than the straight line. Now notice the period between 1881 and 1950. A linear trend line, once again, performs about as well at simulating global surface temperatures as the average of the dozens of multimillion dollar climate models.
Obviously, the differences between the trend line and the data are caused by the multidecadal variability in the data. On the other hand, differences between the models and the data are caused by poor modeling of global surface temperatures.
For those interested, Figure 3 presents the results shown in Figure 2 but without the smoothing.
Figure 3
SCHMIDT AND SHERWOOD ON SWANSON (2013)
The other thing that caught my eye was the comment by Schmidt and Sherwood about the findings of Swanson (2013) “Emerging Selection Bias in Large-scale Climate Change Simulations.” The preprint version of the paper is here. In the Introduction, Swanson writes (my boldface):
Here we suggest the possibility that a selection bias based upon warming rate is emerging in the enterprise of large-scale climate change simulation. Instead of involving a choice of whether to keep or discard an observation based upon a prior expectation, we hypothesize that this selection bias involves the ‘survival’ of climate models from generation to generation, based upon their warming rate. One plausible explanation suggests this bias originates in the desirable goal to more accurately capture the most spectacular observed manifestation of recent warming, namely the ongoing Arctic amplification of warming and accompanying collapse in Arctic sea ice. However, fidelity to the observed Arctic warming is not equivalent to fidelity in capturing the overall pattern of climate warming. As a result, the current generation (CMIP5) model ensemble mean performs worse at capturing the observed latitudinal structure of warming than the earlier generation (CMIP3) model ensemble. This is despite a marked reduction in the inter-ensemble spread going from CMIP3 to CMIP5, which by itself indicates higher confidence in the consensus solution. In other words, CMIP5 simulations viewed in aggregate appear to provide a more precise, but less accurate picture of actual climate warming compared to CMIP3.
In other words, the current generation of climate models (CMIP5) agrees better among themselves than the prior generation (CMIP3), i.e., there is less of a spread between climate model outputs, because they are converging on the same results. Overall, however, the CMIP5 models perform worse than the CMIP3 models at simulating global temperatures. “[M]ore precise, but less accurate.” Swanson blamed this on the modelers trying to better simulate the warming in the Arctic.
Back to Schmidt and Sherwood: The last paragraph under the heading of Climate model development in Schmidt and Sherwood reads (my boldface):
Arctic sea ice trends provide an instructive example. The hindcast estimates of recent trends were much improved in CMIP5 compared to CMIP3 (Stroeve et al 2012). This is very likely because the observation/model mismatch in trends in CMIP3 (Stroeve et al 2007) lead developers to re-examine the physics and code related to Arctic sea ice to identify missing processes or numerical problems (for instance, as described in Schmidt et al (2014b)). An alternate suggestion that model groups specifically tuned for trends in Arctic sea ice at the expense of global mean temperatures (Swanson 2013) is not in accord with the practice of any of the modelling groups with which we are familiar, and would be unlikely to work as discussed above.
Note that Schmidt and Sherwood did not dispute the fact that the CMIP5 models performed worse than the earlier generation CMIP3 models at simulating global surface temperatures outside of the Arctic over recent decades. Schmidt and Sherwood simply commented on the practices of modeling groups. Regardless of the practices, in recent decades, the CMIP5 models perform better (but still bad) in the Arctic but worse outside the Arctic than the earlier generation models.
As a result, the CMIP3 models perform better at simulating global surface temperatures over the past 3+ decades than their newer generation counterparts. Refer to Figure 4. That fact stands out quite plainly in a satellite-era sea surface temperature model-data comparison.
Figure 4
CLOSING
Those are the things that caught my eye in the new Schmidt and Sherwood paper. What caught yours?
Wow, what a bunch of horse ….. Whats is more naive a model that uses linear math and does a reasonable job of prediction, or the alternative?
Schmidt and Sherwood reads:
We stress that adequacy or utility of climate models is best assessed via their skill against more naïve predictions.
My educated prediction model says; “We do not know. (Yet.)” It is perfect in predicting so far. Why is the ‘nul’ result so unacceptable?
run your linear model back to the LIA. run it back 1 million years.
run it out 10000 years.
linear model is non physical.
Not so, and that is a poorly defined test. Linear models typically arise as first order Taylor series expansions of more complicated functions. They typically have limited range for accuracy, but are essential for a wide range of applications.
But, that is missing the point entirely, anyway. If you think a linear approximation is bad, isn’t a more complicated expression, which requires vast resources to obtain yet performs no better, actually worse?
Try it with CAM5.1. Is it physical? Yes, but it is wrong. They use a dry water.
What a dopey remark. Is the purpose of a model to guide policy makers in the coming decades or is to inform policy makers of what will happen 10000 or 1 million years from now?
Considering life as we know lives in quite a narrow temp. band (compared to conditions in the rest of the U) you could run a linear trend back a couple billion with some success. 😉
They should have titled this, A Practical Joke of Climate Modeling,
Ha ha what excrement from “Nobel” Winners.
What caught my eye? All those angels dancing on the head of a pin.
It’s becoming apparent that Warmunists artificially overweight Arctic warming (just 6% of Earth’s total land area) in an effort to make CMIP5 models “work”—too bad Antarctic temps trends are being so uncooperative…..
It’s also becoming apparent that Arctic temps are closely correlated to 30-year AMO and PDO warm/cool cycles and, again, it’s too bad for the Warmunists that the PDO entered its 30-yr cool cycle in 2005 and the AMO’s current warm cycle is winding down, and will switch to a 30-yr cool cycle in the early 2020’s.
To complicate matters for the Warmunists, the sun is in its weakest solar cycle since 1906 with the next solar cycle (starting around 2020) expected by some astrophysicists to be the weakest since the Maunder Grand Solar Minimum ended in 1715…
Since various natural cycles are overwhelming CO2’s tiny forcing effect, it’s imperative for Warmunists to marginalized the importance of linear trends because their models and catastrophic predictions are now so wildly off the mark.
As the CAGW hypothesis crashes and burns, it’s funny watching the progression of their arguments: first they said “The Pause” wasn’t happening, then it was called “cherry picking” the data, and now they admit “The Pause” is real, but suddenly, linear trends have become completely irrelevant; how convenient….
SAMURAI,
lol, Antarctic temperature trends are being the most “cooperative” since The Hiatus got into full swing:
https://drive.google.com/file/d/0B1C2T0pQeiaSNDlDUTIxXzVsdlk
Remind me again what happens when warm stuff from one place mixes with cool stuff from somewhere else and vice versa?
Hey, don’t leave out ENSO!
https://drive.google.com/file/d/0B1C2T0pQeiaSbU9sdjVvYzlMb28
Though admittedly I left PDO out of that one. I suppose I should be happy that a Deniofascist at least comprehends that oceans do have the ability to cool things off from time to time.
Keep making our excuses for us and you might just get hired on.
The thing about 95% confidence intervals is that one expects predictions to fall outside the envelope 5% of the time. My second plot above shows actuals about 5 pixels away from tucking back inside the CMIP5 95% CI.
And we still are. Ocean heat content continues to accumulate unabated.
And it still is called cherry picking becuase that is exactly what it is. The last 20 years have at least two other precedents during the instrumental record:
https://drive.google.com/file/d/0B1C2T0pQeiaSbE1Rb2xobGlVdkU
No. Of limited use only, over short periods of time. Non-linear dynamics become too important to ignore over the long-term. None of this is a sea change in talking points, or any big news whatsoever to folk who understand the actual arguments.
Anthony
The term “Deniofascist” above deserves the same treatment you have meted out in the past for the use of the term “denier”.
Mr. Brandon Gates,
That is as ugly a term as I have seen used in this debate. Your comments until now have been thought provoking and often of value as without the other side of the debate being articulated, this becomes just an echo chamber. By using such language you reduce it to name calling and cast yourself as a purveyor of hate rather than science. I suggest you apologize and move on.
Brandon Gates: retract and apologize for the use of that term, or you’ll find your comments moderated.
“DenioFascists”…. Cute, Brandon, real cute.., but inaccurate.
In the spirit of the holiday season, I’d prefer to be called a LukeWarmoLibertarian…
BTW, contrary to conventional teachings, fascism is actually a leftist ideology, with: hindered private property rights, overwhelming state control via onerous tax codes, debilitating government rules, regulations, mandates and massive public works/military spending…. i.e the National Socialist Party–better known as the Nazi Party….
Regarding the “Inconvenient Truth” of Antarctic temp trends, I was referring to this rather embarrassing reality the Warmunists like to ignore:
ftp://ftp.ssmi.com/msu/graphics/tlt/plots/rss_ts_channel_tlt_southern%20polar_land_and_sea_v03_3.png
OHC trends are also below CAGW projections, so you’re really not helping “The Cause” by bringing it up. OHC for the top 2000 meters of oceans has only increased 0.09C since 1955 (Levitus et al 2012)….Oh, the humanity!!
I would also suggest not bring up ENSO to support “The Cause” as there were 6 El Niño events from 1983~98 (including the 97/98 Super El Niño) which accounts for a large portion of 1979~1998 warming spike. Since 1998, there has only been 2 El Niños plus the El Nada we’re currently in, which may well turn into an actual La Niña next year, which will put observed global temps outside the 95% confidence interval by the end of 2015….
Moreover, during 30-yr PDO cool cycles, El Ninos become weaker and less frequent, and La Niñas become colder and more frequent… Oh, my…
There is also a 100% direct correlation between PDO warm/cool cycles and global temps trends, i.e. during PDO warm cycles, global temp trends tend to increase and fall during PDO cool cycles– as we’re already seeing since the PDO entered its cool cycle in 2005.
CAGW has about 5 years of relevance, after which it will be snickered at and eye-rolled into oblivion..
Here B Gates manifests his preoccupation with junk science and shows frustration at the fact that no one here takes him seriously. Some people get hooked on junk food, some on junk science. Thus B. Gates.
Anthony, I apologize for my use of an offensive term and retract it. I would feel better about my retraction and apology if terms like “warmusnist” and “climastrologist” were met with the same level of warning to those who use it.
SAMURAI,
I like that, but as I appear to be on thin ice with the name calling I’ll stick with “climate contrarian” as a less loaded term when I feel the need for a convenient label.
Not the first time that particular comparision has been made in this forum.
A ten-degree latitude band does not cover the whole of Antarctica. 85S-60S is the coverage of the plot I provided to you, courtesy of UAH: http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc_lt_5.6.txt
I suggest you don’t forget data covering 15 degrees of latitude, then bizarrely turn around and tell us “Warmunists” that we like to ignore inconvenient truths. That is a bit … what’s the word … ah: inconsistent.
If you say so. The key point being it doesn’t appear to be slowing down, which means absorbed energy isn’t leaving through the atmosphere. For some mysterious reason. It’s a puzzler.
Perchance you haven’t compared that to what the 0-100m layer has done? And you do realize sea water has 4x the heat capacity of air, do you not?
AMO explains a good portion of the runup from 1980-2000. I don’t bring these things up to support any cause but good science, which is interested in isolating and reporting as many known factors as possible. Very clearly not every wiggle, dip, or multi-decadal trend in surface temperatures are caused by GHGs.
It’s beginning to look like you ascribe all warming in the instrumental record to internal variability.
I consistently find the highest correlations between AMO and ENSO for multi-decadal and multi-annual variations respectively. That doesn’t mean PDO isn’t a player. I don’t have a precise comparison handy at the moment.
You’re behind the curve my new friend as (C)AGW/CC been snickered and eyerolled into oblivion in this forum for years. I do appreciate your thick skin, however. I’m glad to see someone here who is willing to take as good as they give, as I enjoy a spirited banter. Cheers and Happy Holidays.
davidmhoffer,
I agree.
I appreciate your recognition of the better aspects of most of my posting activity here. If I survive my gaffe, I will remember that at least some readers here recognize my attempts to stay on point. That fact alone reduces the angst I was feeling last night a great deal.
Just to be clear, I don’t hate anyone here or on the contrarian side of the debate. I read a lot of arguments from your side of the fence which I think are silly and nonsensical and am not shy about showing my derision for such, but that doesn’t qualify as hate in my book. Clearly I pushed it too far for your tastes, and as Mr. Watts agrees, I understand there’s a line not to be crossed, apologize for crossing it, and will endeavor to not push that button in the future.
Well I hunkered down and actually read the d*mn thing. OK, more than skimmed, but not quite “read”. Here are my quick notes:
1. This is a marketing document. Its clear purpose is to argue that climate models are “real science”.
2. This isn’t a science paper. It has no data, no tables, no charts, no comparison to observations, no quantifiable information what so ever.
3. The paper states that the models are wrong. Oh, not in so many words, but it goes to great lengths explaining how one of the big problems with the models is that any adjustment to make one part better, makes another part or parts worse. That is a certain sign that the model is wrong. Further, the inability to improve one part without making another part worse is a direct admission that they don’t know why the model is wrong (else they could avoid that).
4. Now the humdinger. I read this three times to see if it actually said what I thought it did on first read. I’m just going to quote it without further comment.
When comparing satellite records to simulations, it is often necessary to embed code to simulate what a satellite would directly measure according to the model… A similar situation holds for paleo-climate proxies
such as water isotopes
OK, I lied, I’m going to comment. Satellites don’t produce “records”. They produce observational data, as do proxies. So they are basically saying that not only are the models wrong, they require additional adjustments depending on which observational data they are being used for. One adjustment for satellites (which by their own admission must make other parts of the model worse) and a different adjustment for proxy data (which again, by their own admission must make other parts of the model worse).
This is a snow job in my opinion, its only purpose being to create a facade of credibility where none would otherwise exist. The failure of a paper of this nature to produce a single comparison to observational data says as much as one really needs to know.
Well noted. Straight to the heart of the issue..
Nick Stokes said
“Swedes taller than Greeks? Maybe. But no use checking one Swede and one Greek. Average 1000 heights of each and if there is a tallness signal there, you may be able to quantify it. Nothing new here.”
That will tell you the averages between the two groups but will tell you nothing about the height of and one random selected Greek against on randomly selected Swede, only what may be likely, not what is. The same is true 50 years out Greek could possible have caught up to the Swedes by then or there may be no Swedes or Greeks by then, you simple cannot model future events, the model will tell you nothing only what likely, not what will be.
I do not understand how intelligent people cannot understand this simple truth! It also begs the question why the human race at this point and time is wasting so much tax payer money of what if games, for that is all that models trying to predict the future are!
The real question is what does the average height of 2000 men (1000 Swedes and 1000 Greeks) actually tell you. Same withe the average temperature over the whole year – how can you use that to make any statement about climate. For instance, a Mediterranean Climate is characterized by hot dry summers and warm wet winters. A single temperature for the whole year will tell you nothing. Same with Antarctica. A single temperature will not tell you that you have a bi-modal distribution of temperatures, one for summer and one for winter.
In Statistics there is a concept of a sufficient statistic. It tells you when a single statistic gives you the same information as the individual values.
A multi model mean from 1880 !? The first computer, Eniac, wasn’t turned on until 1947 and the first climate model was in the 60’s. Anyone can hindcast by curve fitting.
So sorry, but ‘no’.
Check “Konrad Zuse”, e.g. here: http://en.wikipedia.org/wiki/Konrad_Zuse
Oh these FOOLS! Perpetuating the MYTH that “global” land or sea temperature records have ANY validity, prior to WWII or prior to Nibus I, or 1982 or the like.
Complete nonsense. These “averages” and numbers are complete HOGWASH and any decent ENGINEER can go through the history of the measurements, the stations, the stupidity of “average temperatures” and make MINCEMEAT of it.
Of course decent engineers are oft times in the food line, or retiring to get out of the rat race. While the boys with the grants keep spewing GARBAGE and no one has the courage to call them on it. BOB those charts are worthless. Those alledged “global temps” have no meaning.
Aside from the “hoist on the petard” if they DID! WHAT WOULD THAT HOIST ON THEIR OWN PETARD BE???
I’ll let the active mind figure that one out.
“Lottery managers make very sure that there is no signal there”, Nick Stokes at 4:36. Are you implying the lotteries are manipulated? Just like the UN is manipulating info? Tell me it isn’t so.
The linear term “explains” the lion’s share of the little variance seen over the industrial age : http://cosy.com/Science/CO2vTkelvin.jpg .
To paraphrase Howard Hayden : If it were science , there would be 1 model instead of N .
What the hell does skill mean anyway ? Reduced unexplained variance ?
“Climate Science” seems to come up with terms I’ve never seen in any other quantitative field . I have yet to see a definition of a “forcing” in any fundamental physical terms . It seems to be some sort of derivative guesstimated from data , not defined in terms of spectra or other observables .
“Climate Science” seems to come up with terms I’ve never seen in any other quantitative field .
= = = = = = = = =
A strong iceberg forcing made the Titanic downwell to the bottom of the sea–like a photon falling down a well in the sky.
You probably thought it sunk! ☺
That must be what they mean by a ‘tipping’ point. 😉
🙂 I guess the iceberg had more skill than Captain Smith .
I think the concept “mean of the models” is rather meaningless.
The fact is that there exist many models; some of them perform badly, and some of them perform better when compared to reality. The way to deal with that is to reject the bad model and use the good ones.
We can learn something from analyzing both the bad models and the good models and then try to improve the good ones further.
The means of a heap of different models does not teach us much.
/Jan
In investment, there is the same concept – a fund of funds – a mutual fund where the portfolio manager constructs a portfolio from other mutual funds. The idea is that a fund of funds is less volatile than the individual funds. However, if each fund under-performs relative to the S&P 500, the fund of funds also under performs.
The fundamental error in the mean of models is the assumption that what you are looking at is signal plus noise and that the noise is drawn from a distribution that has a zero mean. There is no a priori reason why the noise in the different processes should even come from identical distributions or that the noise in a single process should even come from a single distribution, let alone that all these distributions should have a zero mean or the sum of the means should be zero.
“Once put together, a climate model typically has a handful of loosely-constrained parameters that can in practice be used to calibrate a few key emergent properties of the resulting simulations.”
Gavin Schmidt/ Steven Sherwood
It’s laughable that the Chief Scientist of the UK Met Office can say about their computer models:
“So they are not in a sense tuned to give the right answer, what they are representing is how weather, winds blow, rain forms and so forth, absolutely freely based on the fundamental laws of physics.”
The parameterisations are essentially simple empirical formulas trying to characterise aspects that are not understood well enough to model properly. So claiming that models incorporating parameterisations are ‘based on the laws of physics’ is being economical with the truth.
Tuning a model based on partial understanding can improve its ability to reproduce past history but without necessarily any benefit to its accuracy as a model of the physics of the situation. My spreadsheet table can reproduce past history with complete accuracy but its ability to predict future data is zero.
Big trouble in a perfect “modeled”
world.
trafamadore
December 29, 2014 at 3:04 pm
My second comment, is: using GISTEMP Global at Wood for Trees, linear trends from 1970-2000, 1980-2000, 1970-2010, and 1980-2010 hit the middle of the temperature bracket from the last 3 months.
Interesting. What happened to the great pause?
= = = = = = = = = =
You sound just like Leif Savalgaard, celebrating the restoration of “global warming” on the basis of one maladjusted data set:
http://wattsupwiththat.com/2014/11/14/claim-warmest-oceans-ever-recorded/#comment-1788597
The kind of questions you would be asking as a skeptic are…
“What happened to Snowfalls Are Just A Thing of The Past?”
or
“Why have predictions of milder winters been replaced with predictions of colder winters?”
or
“Why, in an allegedly warming world, is Antarctic ice at record extent?”
or
“Why, in an allegedly warming world, is snowfall in the northern hemisphere breaking new records”?
or
“Why, given the claims of “Hottest Year Evah,” did ice on the Great Lakes break records for extent and duration”?
or
“Why did Niagara falls freeze during the warmest year on the continuously revised GIS books?”
or
“Why does the Ministry of Climate Truth continuously adjust the records of the past?”
But since you are evidently a true believer, you will just have to accept whatever garbage the authorities serve up today. It will be a different today that you have to contend with tomorrow, so don’t forget to update your beliefs on a regular basis:
http://wattsupwiththat.com/2014/06/29/noaas-temperature-control-knob-for-the-past-the-present-and-maybe-the-future-july-1936-now-hottest-month-again/
That’s what Schmidt and Sherwood said:
>>We stress that adequacy or utility of climate models is best assessed via their skill against more naïve predictions.<<
Common sense stresses that adequacy or utility of climate models is best assessed via comparison with reality.
+10
The recent release of CO2 data from the satellite must have come as a rude shock.
Are they running their business from the ER, from underneath the oxygen tent, now?
So, how many of the climate “models” predicted the ‘pause’?
Oh, that’s right, NONE OF THEM.
And how many of these climate models have successfully “back predicted” the climate from , say, 1400 to 1800 in just , say, Europe?
Oh, that’s right, NONE OF THEM.
Well then , that being the case, why don’t we just take the average of these WRONG climate models, and presto!!!! we arrive at the correct results!
If a physical model is wrong, there is no statistical massaging one can employ that will tease out the “correct” result.
As I have maintained and will continue to maintain unless PROVEN wrong the climate system has a tremendous amount of noise in it, it is non linear and subject to thresholds. These factors make it next to impossible to get a strong correlation with factors that influence the climate despite the fact that they do
influence the climate.
This is why thus far not one climate forecast for future conditions has been correct on a consistent basis. I take that back not even one seasonal forecast in advance for climate conditions has been correct on a consistent basis much less the climate.
What I have come across in this field are people that try to justify what they say and using any means to show they are right while everyone else is wrong. The truth is they are also wrong and have yet to show otherwise. It is getting old and most of the material is the same arguments with just a different spin.
My argument is if solar parameters reach extreme enough values and stay at those values for a sufficient amount of time they will over come the noise in the climate system and exert an influence on the climate in general terms.
I am also of the opinion that given solar changes will never result in the same climatic out come due to the beginning initial state of the earth in regards to present climate, land /ocean arrangements ,random terrestrial or extra terrestrial events and so on. The best that can be done is to forecast a general climatic trend.
This is why when I hear the climate will do this or that because of this or that to a point of exactness I just shake my head. Another annoying point is so many try to relate the climate to one particular item which will rule the vast climatic system which is ridiculous with the exception of the sun. Which we know that if it is variable enough it will exert an influence on the climate, The argument here however is, is it variable enough. I SAY YES.
In conclusion I think this field is in a state of complete disarray and needs to be approached in an entirely different manner. A more humble approach for lack of a better word.
About 15 years ago Munk argued that global sea level data over-stated rising sea level because the gauges happened to be located where response to warming oceans was largest. In other words, this global data set unwittingly contained a systematic bias. This led me to speculate in a book I was writing that many global data sets, despite their size and care in preparation, may contain unknown biases. I had in mind the uneven distribution of surface recording stations and what effect it might have on surface temperatures.
Now, in this paper we get a hint that the bias in climate model predictions is a “bandwagon” effect, the same one Fischoff showed to be present in many series of measurements of universal constants. The bandwagon is a serious systematic bias in that it prevents truly independent thinking. If anyone has failed to notice, a powerful bandwagon drives climate science. So in the presence of a powerful enough bandwagon effect, is a “97% consensus” on any topic unexpected? If a bandwagon effect produces a bias in climate models, might it not also produce a bias in surface temperature compilations, through subjective decisions about which data to include, and which corrections to apply?
About 15 years ago Munk wrote in Science Magazine that global sea level rise, as measured with hydrographs, appeared an over-estimate because hydrographs happened to be located where temperature would most affect sea level. I was writing a book at the time and this prompted me to speculate that other global data sets may contain unrecognized biases. I was thinking specifically of the hodge-podge of data that goes into estimating surface temperatures.
In this paper we see the possibility of a bias in climate modeling which is none other than the “bandwagon effect” that Fischoff identified at work in series of precision measurements of physical constants. The bandwagon is a serious bias in science because it prevents truly independent thinking. In the presence of a powerful bandwagon effect is a “97% consensus” unexpected? More to the point, if a bandwagon effect influences modeling, should we not also think a similar effect influences the collection and processing of surface temperature records, or any other data offered as proof of catastrophic warming?
When you think about it,people tend not to live in places where it is undesirable to live. The “undesirability is usually some aspect of weather”. So, collecting temperatures at places where people live creates a strong geographic oversampling.
I agree, it may be time to get rid of the current climate modeling efforts as being unproductive. A wholesale cleanout of those that cannot be open and honest, those that obscure rather then enlighten, those that hide behind semantics and ignore reality. Climate science is currently a “self-licking” ice cream cone.
Climate science is currently administered by humans. As long as that be the case I don’t see a lot of overall change. When change occurs it will not be because of a moral overhaul of the system. It will be because someone was right and their model has predictive power. Real science trumps all. Climate science is in such rough shape simply because we don’t understand the climate, so anything goes. Any field with such a lack of fundamental understanding and over the top funding is going to be in similar shape.
Thanks, Bob.
My attention was also caught by:
“One plausible explanation suggests this bias originates in the desirable goal to more accurately capture the most spectacular observed manifestation of recent warming, namely the ongoing Arctic amplification of warming and accompanying collapse in Arctic sea ice.”
The “ongoing Arctic amplification of warming” is theory, at best.
The “accompanying collapse in Arctic sea ice” is science-fiction, at its worst.
Re Figure 1. You don’t need to look at the differences, just calculate the R^2 = goodness of fit. Against Hadcrut4.3 with the simplest of regressions (lineair on 1861-2013, annual)
R^2(line) = 0.6904
R^2(CMIP5) = 0.835
R^2(perfect) = 1
Sorry to spoil your conclusion – it’s wrong. The projection is clearly better. The log(co2) is also pretty good, it has an R^2 of 0.7917.
The authors: “Here we suggest the possibility that a selection bias based upon warming rate is emerging in the enterprise of large-scale climate change simulation. Instead of involving a choice of whether to keep or discard an observation based upon a prior expectation, we hypothesize that this selection bias involves the ‘survival’ of climate models from generation to generation, based upon their warming rate.”
However ANY research chooses to “keep or discard” “an observation” “based upon” any reason whatsoever other than that each data point EXISTS through observation/measurement – this is simply preposterous. They are now arguing with themselves over how best to cherry pick the observations – which to include and which to exclude.
I am sorry, but if the use ANY expectations to pre-judge the INDIVIDUAL data points with the intention of selecting some IN and some OUT – holy mother of god and they call themselves objective scientists…
WTFF? “We will only let in the data that, one way or another is within what WE expect to see.” Nothing but cherry picking and trying to justify one cherry picking filter versus another.
The single observations ARE the observations. They must become the data values. Excluding some is not science but office politics… They wouldn’t want to actually let in any values that make them appear to not be part of the team, or that might weaken the message.