![CMIP5-90-models-global-Tsfc-vs-obs-thru-2013[1]](https://wattsupwiththat.files.wordpress.com/2014/06/cmip5-90-models-global-tsfc-vs-obs-thru-20131.png?quality=75)
Alex Sen Gupta, a biological scientist, has written an article (reviewed by John Cook) which attempts to defend the validity of climate models which can’t predict the climate.
According to Gupta;
To understand what’s happening, it is critical to realise that the climate changes for a number of reasons in addition to CO₂. These include solar variations, volcanic eruptions and human aerosol emissions.
The influence of all these “climate drivers” are included in modern climate models. On top of this, our climate also changes as a result of natural and largely random fluctuations – like the El Nino Southern Oscillation, ENSO and the Interdecadal Pacific Oscillation, [IPO] – that can redistribute heat to the deep ocean (thereby masking surface warming).
Such fluctuations are unpredictable beyond a few months (or possibly years), being triggered by atmospheric and oceanic weather systems. So while models do generate fluctuations like ENSO and IPO, in centennial scale simulations they don’t (and wouldn’t be expected to) occur at the same time as they do in observations.
The problem with this claim is that, as Gupta says, the climate models are supposed to take these random fluctuations into account. Climate models are supposed to accommodate randomness, by providing a range of predicted values – the range is produced by plugging in different values for the random elements which cannot be predicted. However, observations are right on the lower border of that range. The divergence between climate models and predictions is now so great, that climate models are on the brink of being incontrovertibly falsified.
As Judith Curry recently said, If the pause continues for 20 years (a period for which none of the climate models showed a pause in the presence of greenhouse warming), the climate models will have failed a fundamental test of empirical adequacy.
This is important, because it strikes at the heart of the claim that climate models can detect human influence on climate change. If climate models cannot model climate, if the models cannot be reconciled with observations, how can the models possibly be useful for attributing the causes climate change? If scientists defending the models claim the discrepancy is because of random fluctuations in the climate, which have pushed the models to the brink of falsification, doesn’t this demonstrate that, at the very least, the models very likely underestimate the amount of randomness in the climate? Is it possible that the entire 20th century warming might be one large random fluctuation?
Nature is certainly capable of producing large, rapid climate fluctuations, such as the Younger Dryas, an abrupt return to ice age conditions which occurred 12,500 years ago. You can’t use climate models which demonstrably underestimate the randomness of climate change, to calculate how much of the observed 20th century warming is not random.
If current mainstream climate models cannot predict the climate, then scientists have to consider the possibility that other models, with different assumptions, can do a better job. It is no accident that Monckton, Soon, Legates and Brigg’s paper on an irreducibly simple climate model, which does a better job of hind casting climate than mainstream models, has received over 10,000 downloads. As every scientific revolution in history has demonstrated, being right is ultimately more important than being mainstream, even if it sometimes takes a few years to win acceptance.
For now, mainstream climate scientists are mostly hiding in the fringes of their estimates. When they acknowledge it at all, they claim that the anomaly, the pause, is a low probability event which is still consistent with climate models. Hans Von Storch, one of the giants of German climate research, a few years ago claimed that 98% of climate models cannot be reconciled with reality – which still, for now, leaves 2% possibility that climate scientists are right.
Is the world really preparing to spend billions, trillions of dollars, on a 2% bet?
I saw this, and immediately noticed that it resembles climate model spaghetti:
https://www.pinterest.com/pin/1407443606165081/
The article (it is not a scientific paper ) is a critique of Maurice Newman’s scepticism concerning models .
Gupta concludes
“Verdict
Mr Newman’s implication that discrepancies resulting from the recent climate fluctuation somehow invalidates climate models is incorrect.
Climate models have been thoroughly and critically tested against observations and are able to simulate with fair accuracy the component of climate change caused by human emissions of greenhouse gases and aerosols as well as natural factors like solar variations and volcanic eruptions.
However, long-term climate simulations do not and likely never will reproduce the timing of shorter-term random fluctuations, like the recent slowdown in surface temperatures. In the long run, this fluctuation, like many before, will just be noise on a gradually increasing temperature signal.
That the discrepancy is a “well-kept secret” is demonstrably false given the large number of scientific papers discussing and trying to explain exactly this issue”
and Cook’s review says:
“Review
This is a sound analysis that effectively explains the appropriate way to assess the reliability of models. Scientists can glean much scientific insight from comparing observations to model predictions, especially when there are discrepancies between the two. In contrast, the critique of models employed by Maurice Newman does not increase scientific understanding. – John Cook”
I think that we would all agree with Cook that :
Scientists can glean much scientific insight from comparing observations to model predictions,
What some might like to see is an analysis of why some models fare better in this comparison than others and , using techniques derived from evolutionary theory , evolve a better model by a “survival of the fittest”strategy
Perhaps this has been done already?
‘Climate models have been thoroughly and critically tested against observations and are able to simulate with fair accuracy ‘ just how big is the barn to which these doors of ‘fair accuracy’ are attached too?
‘However, long-term climate simulations do not and likely never will reproduce the timing of shorter-term random fluctuations,’ of course by not defying what ‘long term’ actual means the author gives themselves full wiggle room to claim any period can be called ‘short term’ and therefore not significant. An approach which came about , like the missing heat , due to the failure of the models in the first place.
‘like the recent slowdown in surface temperatures’ actual has this supposed to be ‘science’ then to the standards acceptable to science , there is no slow down its ‘stopped’ or ‘paused’ .
The notions of changing or unchanging, direction and magnitude are all well defined in science, so there should be no issue with the idea of ‘no change’ has a status.
But then this not ‘science’ so BS wording is to be expected .
‘That the discrepancy is a “well-kept secret” is demonstrably false given the large number of scientific papers discussing and trying to explain exactly this issue’
Well you could show the author some e-mails that show much effort had gone into keeping this information from the public and that there was efforts to get journal editors sacked for punishing papers that went against the ‘consensus’ But I do not think this author is into ‘evdainced ‘ in any real sense , there merely out to show their loyalty to ‘the cause ‘ with a side order of grant hunting .
Claim: Data does not prove that climate models are wrong
I agree with this claim and also with the claim that the data does not prove that climate models are right.
I accept the null hypothesis and declare that we don’t know what causes climate change, at least not in a quantitatively verifiable sense that supports a scientific theory of climate.
We have half a theory of climate, which is why we get what Pat Michael has demonstrated:
http://www.cato.org/publications/commentary/when-will-climate-scientists-say-they-were-wrong
We have got cargo cult science, which Richard Feynman explained:
http://neurotheory.columbia.edu/~ken/cargo_cult.html
The problem in climatology is the huge gap between what the scientists know to be certain and uncertain and what the promoters declare to be certain.
The scientists have said that we accept humans as being the cause because we cannot discover a natural cause. This is the argumentum ad ignorantiam, the appeal to ignorance.
In actuality ignorance is the motive for accepting the null hypothesis that humans are not responsible for global warming. The uncertainty in the data is the main justification for a scientist to remain skeptical towards catastrophic anthropogenic global warming (CAGW).
What are the two models that standout at the bottom of the graph? What differentiates them from the others?
Jean Parisot
You ask
Nobody knows, but the explanation is probably pure chance.
Similarly, it is probably pure chance that one model prediction (shown in red) was that the temperature dip of 1993-1997 would not occur.
Richard
Probably pure chance but it wouldn’t harm to check the input data to see what made them closer to reality? Different sensitivity, some negative feedback mechanism etc.? We all agree that the average of all the (wrong) models is meaningless.
Does any have a couple of spare grad students dig into model entrails and describe the similarities and differences?
Jean Parisot and Chris Schoneveld
It would be an error to reject all except the two models that seem to be ‘right’. Either
(a) assess each and every model to determine the effects of all their different assumptions and algorithms
or
(b) reject each and every model.
I again remind of the following.
The selection of models that seem to fit after the event is an example of the Texas Sharpshooter Fallacy .
Such post hoc selection indicates nothing about ability to forecast the future but it is tempting to think it does, and fr-a-udsters use the temptation to mislead their victims.
I again explain how they do this.
A set of, say 4, different investment plans is generated.
Each investment plan is sent to, say 4000, random people.
At a later date one (or more) of the plans has provided a very good return.
Those who were sent the ‘successful’ plan are now sent a report of its ‘success’ together with another investment plan. These new investment plans are another 4 different investment plans so 4 groups each of 1000 people each obtains one of these second plans.
Again, at a later date one (or more) of the second plans has provided a very good return.
Those who were sent the ‘successful’ second plan are now sent a report of its ‘success’ together with a third investment plan. These new investment plans are another 4 different investment plans so 4 groups each of 250 people each obtain one of them.
Yet again, at a later date one (or more) of the third plans has provided a very good return.
Those who were sent the ‘successful’ third plan are now sent a report of its ‘success’ together with an offer to invest $10,000 in the next investment plan which uses the astonishingly accurate prediction method that has apparently been successful three times without fail.
If 100 of the 250 targeted people invest then the fr-a-udsters gain an income of $1,000,000.
This is, in fact, the same ploy as is used when the ‘best’ climate models are selected after the event.
Richard
Mods.
I have had a post vanish. I would be grateful for your telling me if it does not appear in the ‘bin’ so I can do a repost. Thanking you in anticipation.
Richard
[Found. .mod]
How much did “they” pay Gupta to look like an idiot?
Not so many will go cheerfully to the stake for money. There are motivations far more powerful.
==============
My 97.7% satellite data trumps your 97% ‘climate scientist’ consensus.
For climate science it is not the evidence that will matter, it will be the cultural significance of the myths it postulates.
There is historical empirical evidence that King Akhenatan existed and brought the concept of monotheism to the kingdom of Egypt but was forgotten after his death. Moses who also introduced the idea of monotheism while in Egypt has no historical evidence to verify his existence. Culturally King Akhenatan is non-existent, Moses is of course famous and renowned. One is a figure of history but not memory, the other of memory but not history.
Models 100% wrong, 95% wrong is not remembered so unimportant, the myths left behind are important and drive cultural direction. One myth is that science hand in hand with government is never wrong. Another is that humanity is separate from nature and inherently a blight upon the land, redemption only available through paying tribute to the government and corrective government discipline. And there is the golden nugget that science has the omniscient capability to predict global climate and ecology centuries into the future.
It is unfortunate that science is being used to create a cultural mythology but there it is. I am involved in this subject matter not because of fears about climate, but fears over the undermining subjugation of science. Once the rational underpinnings of society start to become undermined where do we go from there?
the point is when were they rpoven to be right? and how??
Truly amazing pretzel logic.
We’re right because we’re wrong because we’re right.
Might want to check out “Do global temperature trends over the last decade falsify climate
predictions?” published in “State of the Climate 2008” (dated August 2009) by the American Meteorological Society.
They experimented with state-of-the-art models and concluded this: “Near-zero and even negative trends are common for intervals of a decade or less in the simulations, due to the model’s internal climate variability. The simulations rule out (at the 95% level) zero trends for intervals of 15 yr or more, suggesting that an observed absence of warming of this duration is needed to create a discrepancy with the expected present-day warming rate.”
By 2008, the temperature plateau had already lasted for a decade, though that was something admitted only in scientific papers, not in press releases or interviews meant for general consumption. So, they thought they were safe by concluding that at least some simulations could account for a 10-year plateau.
I conclude that it never entered their wildest nightmares that zero trends would continue into 2015.
http://www1.ncdc.noaa.gov/pub/data/cmb/bams-sotc/climate-assessment-2008-lo-rez.pdf
h/t Real Science
They are blatantly stalling for time. “We’re not wrong, yet” is what they’re saying. They were never right.
It’s easy to see from the 90 models graph that real temperature anomolies are running about 50% lower than the model averages (0.3 deg C). If this deviation was due to natural variation then at some point in the future we should expect real anomolies to be 0.3 deg C above the model average. Now is after about 30 years elapse from the start of the graph. So in 30 years time when a 60 year cycle (where have i heard that from) has been completed it would not be unreasonable for the real temperature to be at least 0.3 deg C above the model average which by then will be about 1.2 deg C ie 1.5 deg C.
So do I believe that global average anaomly will rise by 1.2 deg C in the next 30 years? NO.
DATA NOT ONLY PROVES THE MODELS WRONG BUR THE WHOLE CONCEPT OF AGW THEORY.
Here is the evidence. Why don’t you refute each point with data ,not theory to prove I am wrong. You will not do it because there is no supportive data. I would hardly call all these blunders SELECT EVIDENCE.
AGW theory has predicted thus far every single basic atmospheric process wrong.
In addition past historical climatic data shows the climate change that has taken place over the past 150 years is nothing special or unprecedented, and has been exceeded many times over in similar periods of time in the historical climatic record. I have yet to see data showing otherwise.
Data has also shown CO2 has always been a lagging indicator not a leading indicator. It does not lead the temperature change. If it does I have yet to see data confirming this.
SOME ATMOSPHERIC PROCESSES AND OTHER MAJOR WRONG CALLS.
GREATER ZONAL ATMOSPHERIC CIRCULATION -WRONG
TROPICAL HOT SPOT – WRONG
EL NINO MORE OF -WRONG
GLOBAL TEMPERATURE TREND TO RISE- WRONG
LESSENING OF OLR EARTH VIA SPACE -WRONG? I have a study showing this to be so.
LESS ANTARCTIC SEA ICE-WRONG
GREATER /MORE DROUGHTS -WRONG
MORE HURRICANES/SEVERE WX- WRONG
STRATOSPHERIC COOLING- ?? because lack of major volcanic activity and less ozone due to low solar activity can account for this. In addition water vapor concentrations decreasing.
WATER VAPOR IN ATMOSPHERE INCREASING- WRONG- all of the latest data shows water vapor to be on the decrease.
AEROSOL IMPACT- WRONG- May be less then a cooling agent then expected, meaning CO2 is less then a warming agent then expected.
OCEAN HEAT CONTENT TO RISE- WRONG – this has leveled off post 2005 or so. Levels now much below model projections.
Those are the major ones but there are more. Yet AGW theory lives on.
Maybe it is me , but I was taught when you can not back up a theory with data and through observation that it is time to move on and look into another theory. Apparently this does not resonate when it comes to AGW theory , and this theory keeps living on to see yet another day.
Maybe once the global temperature trend shows a more definitive down trend which is right around the corner (according to my studies ) this nonsense will come to an end. Time will tell.
Greenhouse score card showing more blunders
http://www.warwickhughes.com/hoyt/scorecard.htm
Past historical data showing no correlation.
http://wattsupwiththat.com/2012/04/11/does-co2-correlate-with-temperature-history-a-look-at-multiple-timescales-in-the-context-of-the-shakun-et-al-paper/
Current data not agreeing with what AGW calls for.
http://patriotpost.us/opinion/34748
For a complete discussion of the uselessness of the IPCC’s modeling approach to forecasting climate see Section 1 at
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
Here are the conclusions
“In summary the temperature projections of the IPCC – Met office models and all the impact studies which derive from them have no solid foundation in empirical science being derived from inherently useless and specifically structurally flawed models. They provide no basis for the discussion of future climate trends and represent an enormous waste of time and money. As a foundation for Governmental climate and energy policy their forecasts are already seen to be grossly in error and are therefore worse than useless. A new forecasting paradigm needs to be used.”
The biggest mistake of the modeler establishment was to ignore the longer term cycles and to project forward several decades of data linearly when we are obviously approaching, at or just past a peak in a millennial cycle. This is more than scientific inadequacy – it is a lack of basic common sense. It is like taking the temperature trend from say Jan – June and projecting it forward linearly for ten years or so. The modelers approach is analogous to looking at a pointillist painting from 6 inches – they simply can’t see the wood for the trees or the pattern for the dots. ( In a recent paper Mann has finally after much manipulation managed to discover the 60 +/- year cycle which any schoolboy can see by looking at Fig 15 at the linked post above).
The same post also provides estimates of the timing and amplitude of the coming cooling based on the 60 and especially the millennial quasi- periodicity so obvious in the temperature data and using the neutron count and 10 Be data as the most useful proxy for solar “activity”
Climate models epitomize the Ludic Fallacy employing Recency Bias.
From the linked article: “The IPCC climate models are further incorrectly structured because they are based on three irrational and false assumptions. First, that CO2 is the main climate driver. Second, that in calculating climate sensitivity, the GHE due to water vapour should be added to that of CO2 as a positive feed back effect. Third, that the GHE of water vapour is always positive. As to the last point, the feedbacks cannot be always positive otherwise we wouldn’t be here to talk about it.” Bingo! 🙂
Gamecock’s Climate Model
inc iyear
temp(iyear) = temp(iyear-1) + .02
We are only one step away from the ultimate Orwellian situation where we are told, by “authority”, that observed data is not true and that the only truth exists in computer model output.
So now Cook is an authority on ensemble climate models?
They teach cartooning different at his school.
Is it just me? All of these comparison graphs seem to be playing ‘Pin The Tail On The Donkey’ by normalizing the starting point of all plots at a single point. I’d really like to see what temperature the models show at the starting date of the graph. I suspect that would show somewhat wider spread from observed values.
http://i142.photobucket.com/albums/r103/HocusLocus_photos/OldMissPringle.jpg
This defiance to correlating climate models to measured data is an example of money driving the car, being able to sell your services. If the people developing these climate models depended on sales income from people buying their models, and the people buying them needed accurate predictions, the model developers would correlate the models to match past results. But since the model developers livelihood depends on the continued spending by governments and others to study the changing climate, they need to demonstrate an urgency for climate science spending. The models must make a case for that continued spending. And models that match the past in showing a negligible change in the climate are not going to raise any urgency on the need to study global warming.
I think what is really needed is a book or webpage or something that addresses the economics of global warming. The science stuff has really very little to do with “the debate”. So much of what is happening in this climate debate is just climate scientists trying to make money. The attempted shut down of skeptics isn’t because they think skeptics are wrong, its because skeptics are exposing their scam, or at least ruining their sales pitch. Climate models aren’t correlated to be correct, they are correlated to maximize funding for climate science (comment above). When there is a need for your services or your opinion you are raised in importance in our society, your time is worth more money, you make more money, so there is an economic driver for people who make a living studying and talking about climate to create and nurture an increased sense of urgency in studying the climate. The IPCC demonstrates this all the time.
Only the Magratheans have developed a computer model that can accurately predict earth’s climate. Unfortunately they only made one such computer and the Magrathean Exclusion Principle means no other computer can accurately reproduce its predictions. The difference between the original and any other attempt to model it is the result of natural variability which can be measured but not predicted, The natural variability in question is the result of Desired Objective Fulfillment Syndrome. This occurs when the desired result of 97% of scientists (sic) conflicts with the natural order of the Sceptic Realists. Om!!!!
Well this is right in line with the first rule of climate ‘science’ which is ‘when models and reality differ in value it is reality which is in error ‘ so I cannot see anything wrong here.
Expect if your actual doing science , value honesty and consider your integrity to be important .
None of which are features of climate ‘science’
NASA: “Variations in Earth’s magnetic field and atmospheric circulation can affect the deposition of radioisotopes far more than actual solar activity. ”
http://www.vukcevic.talktalk.net/SLR-MD
http://www.vukcevic.talktalk.net/SLR-MD.gif
All computational climate models included in the 5th phase Coupled Model Intercomparison Project (CMIP5) are already falsified, there is no need to falsify them further.
None of them reproduces the observed inter-hemispheric symmetry in reflected shortwave radiation, nor do they exhibit the remarkable inter-annual stability in this quantity. On top of that their annual cycle of albedo changes do not even resemble reality.
Reviews of Geophysics Volume 53, Issue 1, March 2015, Pages 141–163
First published: 2 March 2015
DOI: 10.1002/2014RG000449
Review Article: The albedo of Earth
Graeme L. Stephens, Denis O’Brien, Peter J. Webster, Peter Pilewski6, Seiji Kato and Jui-lin Li
Until representation of the amount of energy absorbed by the climate system gets realistic, computational climate models are good for nothing.