From the University of York:
A new study led by a University of York scientist addresses an important question in climate science: how accurate are climate model projections?
Climate models are used to estimate future global warming, and their accuracy can be checked against the actual global warming observed so far. Most comparisons suggest that the world is warming a little more slowly than the model projections indicate. Scientists have wondered whether this difference is meaningful, or just a chance fluctuation.
Dr Kevin Cowtan, of the Department of Chemistry at York, led an international study into this question and its findings are published in Geophysical Research Letters. The research team found that the way global temperatures were calculated in the models failed to reflect real-world measurements. The climate models use air temperature for the whole globe, whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.
Dr Cowtan said: “When comparing models with observations, you need to compare apples with apples.”
The team determined the effect of this mismatch in 36 different climate models. They calculated the temperature of each model earth in the same way as in the real world. A third of the difference between the models and reality disappeared, along with all of the difference before the last decade. Any remaining differences may be explained by the recent temporary fluctuation in the rate of global warming.
Dr Cowtan added: “Recent studies suggest that the so-called ‘hiatus’ in warming is in part due to challenges in assembling the data. I think that the divergence between models and observations may turn out to be equally fragile.”
Dr Cowtan’s primary field of research is X-ray crystallography and he is based in the York Structural Biology Laboratory in the University’s Department of Chemistry. His interest in climate science has developed from an interest in science communication. This is his second major climate science paper. For this project, he led a diverse team of international researchers, including some of the world’s top climate scientists.
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So help me out here if the different in data types (air temp vs some other temp) are responsible for the divergence, How can they be ok when the models and data generally agree between 75 and 95?
* “difference in data types…”
Jaye…it’s like Taylor said
First they cooled the past…by making up (adjusting) temperatures cooler….to make it look like warming was going faster…accelerating
Then they fed that fake warming trend into the climate models….
…when the climate models extended that trend…..it went off the chart
They will never be able to model climate (temperatures) as long as they are using their own fake temp data
Furthermore, they do err in trying to model chaotic phenomena with traditional, non-chaotic math.
Because the models are ‘cooked to hind cast this period. The thing is, if the correction he’s talking about gets made to the 1975-1995 peri on, it will then likely be to low to match actual a – like the preverbial thread, this thing just keeps unraveling…
Roger that, I used to do a fair amount of bayesian pattern recognition. We were very diligent about separating the training data from the test data. So the hindcast is all about using training data as test data?
“Most comparisons suggest that the world is warming a little more slowly than the model projections indicate. ”
A blatant lie. Biased much? It’s far from “a little..”
Which is why every time one of these charts is published, a bright red vertical line needs to be drawn showing the year in which the model was made. Otherwise, people look at these charts and think the models work fairly well for some period of time – when in fact it’s simply “cooked to hind cast”. Even Anthony fails to do insert such a line when publishing the charts.
aneipris…
āMost comparisons suggest that the world is warming a little more slowly than the model projections indicate. ā
A blatant lie. Biased much? Itās far from āa little..ā
Yes, it sticks out like a sore thumb. They have no conscience. Once you start lying, it gets easier as time passes.
Patrick B, for CMIP5 your bright red line is January 2006. Now you can draw as many as you want, yourself. Essay Models all the way Down in ebook Blowing Smoke, which even posts a more complex version of the above image (actual CMIP5 model tracks, since as RGBatDuke is fond of pointing out, averaging them is statistical nonsense)— WITH the bright red line you request.
I wonder if a simple neural net program, trained on (proper) historical temperatures would do better than these models? (Of course the interval of time over which it is trained would make a significant difference.)
The climate models use air temperature for the whole globe, whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.
=============
The training was done with a combination of air and sea surface temperature readings. This means that the training was faulty, because it should have been done with air temperature for the whole globe.
So what Dr Cowtan et al have truly discovered is that the models were incorrectly trained, which would explain why their predictions have turned out to be so bad.
Yes, and even making the (implicit) observation “that the models were incorrectly trained” doesn’t say enough. They didn’t train or rely upon themselves.
I understood that the climate models do disparate projections for the troposphere, stratosphere, and the surface, and, as bad as they are for the surface, they are worse in the troposphere. I saw a vertical presentations of the models vs. the observations for this once, perhaps in a Christopher Monckton post.
As far as the surface air vs. the surface water T, well were not the SSTs just modified to match.
…modified to match closer to the models. Also, where is the evidence that the relationship between the SST and the air T 2 m above has changed? I thought we were now having record SST anyway?
There are really just two possibilities.
1) The science is “settled” and they should stand by their 25 year old predictions (which have been falsified by subsequent observations).
2) They can update their models to reduce the discrepancy that exists between the old models and observations. However, in this case the science is clearly not “settled”, and they need to begin to seriously think about why they’ve been so blatantly wrong, and perhaps even listen to the skeptics occasionally.
Instead, all to often, we get these weasel treatments: changing their predictions while simultaneously arguing that they’ve been right all along.
This is a two edged sword for warmistas. If the model temperature growth is too high based on the way it’s calculated, that affects the long term estimates, and reduces that scary 1100 scenario. So to get the models fixed, CAGW is eliminated, right? Will be interesting to see how this is received.
As a side note, I hope this puts to rest their refusal to pay attention to anyone but a ‘climate scientist’ – this guy is clearly not one, but seems to be accepted anyway
Taylor
Oops meant 2100 scenario
I wager that his acceptance is a result of finding a way to raise recent temperatures.
I think Dr. Cowtan may be juggling his apples a bit. GISS and HADCRUT use “a combination of air and sea surface temperature readings.” However, satellite and balloon data do not. They measure as much of the troposphere as they can, which makes them very much like the model output.
If the serious divergence between data and models didn’t actually exist (as claimed by Dr. Cowtan), then there wouldn’t be any need for the on-going data manipulation by NCEI, GISS, and UKMO. For example, the Karl, er al., 2015 Science paper released with much cheering from the CAGW crowd.
the problem is that the climate models use land and sea temeratures for their training, so they should match the land and see temps in their predictions – if their predictions have any skill.
so in this Dr Cowtan is incorrect. The training was done with apples and the comparison is to apples, but the models themselves describe oranges.
in point of fact the training and comparison should have been done using air temps all along, because the models describe air temps. but that wasn’t done because the data isn’t available prior to 1970 or so.
It is my understanding that SST is slightly warmer then the air above it as a global mean.
So, according to Dr. Cowtan the climate modelers do not know how to compare “apples to apples”…. What way you look at it there’s the same result – incompetence!
Where’s the link?
http://onlinelibrary.wiley.com/doi/10.1002/2015GL064888/abstract
Dr Cowtan added: āRecent studies suggest that the so-called āhiatusā in warming is in part due to challenges in assembling the data. I think that the divergence between models and observations may turn out to be equally fragile.ā
Yes, and that also means that any prior agreement was even more fragile, does it not?
Every global surface temperature dataset should be using air temperatures, not ocean temperatures. However, there is no doubt while using the satellite data, which shows a much larger divergence between observational data and model output. Satellite data and model output data can be compared almost perfectly directly.
Loo crazy makes the main point. Cowpat has ignored satellite data.
iPad autocorrect has made two interferences with what I typed. Apologies to both parties wrongly named. Does anyone know how to turn the darned thing off?
And… the lower stratosphere wv .Which collapsed after 97 . Concurrently with the “hiatus” , that of course ,did not really occur.
–
Eos, Vol. 95, No. 27, 8 July 2014
Another Drop in Water Vapor
PAGES 245ā246
–
See page 2 .Fig 1
Well, auto correct leads to some interesting sentences, and the world need a bit of humor, so I say, carry on.
Yes, the obvious answer is that we have a pristine satellite record of the atmosphere, and the models are worse then ever compared to the observations, however, bypassing that for a moment, let us examine what they did, and claim.
I take it that the IPCC has a modeled SST but did not use it, but instead used a modeled 2m air surface in their projections. Now the say they are using it, the previously unused modeled SST, to compare to the claimed SST observations, instead of a modeled 2m air surface in their projections. They claim that the models assume the 2m air T above the oceans would rise faster then the SST.
1. What are the physics of this claim based on?
2, How do we know that the 2m air surface above the oceans did not change at exactly the same rate as the SST, and therefore the models of the air T are now as wacked as ever?
3.. I do not think we have any accurate 2m air surface T record above the oceans.
4. Why did they make this obvious bonehead comparison, if they had a modeled SST to use all the while?
5. Could it be that the wanted the scariest projections possible?
6. Could it be that traditional physics assumes that the oceans drive the atmosphere, are on average warmer then the surface air temperature, and there is still no evidence that this is not true, and there is still no evidence that the 2m air temperature is not as far below the models as ever?
7. How much have the SSTs been adjusted?
we already have enough apples to oranges problems in the temperature record … land measurements are of the AIR above the ground … sea surface measurements are of the water NOT the air above the water … its a mess and this “study” is just an a** covering exercise …
So the lead scientist in the “study” is what ? a X-ray crystallography professor … riiiiiight … he’s qualified (according to the warmists no)
Either there’s something odd about this new bit of research, or there’s been something odd about modeling for quite a while.
Why have models been producing results which can not be compared to observations?
Why have people been comparing observations to model results?
How did modelers know that their results were reasonable, if they could not be compared to other data?
Here’s the abstract. Co-Authors are familiar–Zeke H, Michael Mann, ..
Also familiar was the fast track to publication–The draft was first received June 10, revision July 20, published July 29, in plenty of time for the run-up to December in Paris. One month. I normally wait 3 months to get peer reviews finished, a month or so revising, and then wait 6-18 months before it appears in the journal.
Robust comparison of climate models with observations using blended land air and ocean sea surface temperaturesā
Authors
Kevin Cowtan,
Zeke Hausfather,
Ed Hawkins,
Peter Jacobs,
Michael E. Mann,
Sonya K. Miller,
Byron A. Steinman,
Martin B. Stolpe,
Robert G. Way
Accepted manuscript online: 29 July 2015Full publication history
DOI: 10.1002/2015GL064888View/save citation
Cited by: 0 articles Check for new citations
Article has an altmetric score of 43
ā This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/2015GL064888
Abstract
The level of agreement between climate model simulations and observed surface temperature change is a topic of scientific and policy concern. While the Earth system continues to accumulate energy due to anthropogenic and other radiative forcings, estimates of recent surface temperature evolution fall at the lower end of climate model projections. Global mean temperatures from climate model simulations are typically calculated using surface air temperatures, while the corresponding observations are based on a blend of air and sea surface temperatures. This work quantifies a systematic bias in model-observation comparisons arising from differential warming rates between sea surface temperatures and surface air temperatures over oceans. A further bias arises from the treatment of temperatures in regions where the sea ice boundary has changed. Applying the methodology of the HadCRUT4 record to climate model temperature fields accounts for 38% of the discrepancy in trend between models and observations over the period 1975-2014.
“Mann” – do they want anyone to take it seriously?
Article has an altmetric score of 43
How is the Altmetric score calculated?
ļ»æWhile the most important part of an Altmetric report is the qualitative data, it’s also useful to put attention in context and see how some research outputs are doing relative to others. The Altmetric score for a research output provides an indicator of the amount of attention that it has received. The score is a weighted count
The score is derived from an automated algorithm, and represents a weighted count of the amount of attention we’ve picked up for a research output. Why is it weighted? To reflect the relative reach of each type of source. It’s easy to imagine that the average newspaper story is more likely to bring attention to the research output than the average tweet. This is reflected in the default weightings:
http://support.altmetric.com/knowledgebase/articles/83337-how-is-the-altmetric-score-calculated
From the table of scores if they wrote a wiki page and each of the 9 authors put it on facebook, each tweet once, got 20 friends to tweet it and 2 blogs wrote about it, that would be – 45 points.
So much for impact score.
It’s easy to make a prediction fit the data if you then change the data to fit the prediction.
So the only test that matters in real science is whether the model predicted the data as it was defined when the prediction was made.
Otherwise … it would be like betting on a horse race and then changing your bet after it was run. Does anyone seriously think anyone would change their bet so that they were more likely to lose?
+1
If changing the data doesn’t work, they can always try changing the prediction and hope nobody notices.
Or have so many predictions that there’s bound to be at least one that is remotely suitable.
Curious why you didn’t use any of the actual graphics from Cowtan et al. They show something dramatically different from Christy’s graphic that you used. Seems a little misleading, don’t you think?
You can plot for yourself all the data. Go to KNMI explorer for the CMIP5 archive. Go get UAH and RSS for satellite, and so on. Cowtan et. al did NOT use the full CMIP5 archive. They cherry picked stuff that was less divergent from observation, yet another ‘cheating tell’ like yhe push formrapid publication. Their figure is by definition a misleading representation of the publicly available CMIP5 archive. Christy’s figure gives the whole truth, and nothing but the truth, cause it uses themwhole archive– except an average of model runs (an IPCC favorite, so fair to to do in this circumstance) is still statistical nonsense. A point made in a previous comment.
Why does the UAH and RSS plot in the graph above not look anything like these:
http://woodfortrees.org/plot/uah
http://woodfortrees.org/plot/rss
I somehow entered only part of my name before the comment posted.
Why does the UAH and RSS plot in the graph above not look anything like these:
http://woodfortrees.org/graph/rss
http://woodfortrees.org/graph/uah
Me cholas is inadvertently comparing oranges with segments. The famous Christmas and Soencer graph uses annual data, while the woodforcarbonsinks graph uses monthly data.
ristvan, that doesn’t answer my question.
Why post the Christy graph, which tells you nothing about the Cowtan paper? Why not post the actual graphs from the Cowtan paper?
Zeke, Mann… I don’t know why you bother….
This is quite humorous. It seems that whenever they are eager to make a point in favor of AGW (in spite of it’s obvious problems) they invite Mann and the other ‘usual suspects’ – in disregard of their real reputations. Is ‘climate science’ even interested in integrity and veracity?
Are they saying that observations are wrong because they don’t use the same data as the models?
Hi katherine009. Nope.
They are saying model-data comparisons are normally biased because they usually include the modeled air temperature over the oceans, while the data include sea surface temperatures for the oceans. The bias results because the models show a higher warming rate for the marine air than the sea surface. Their results show, if you replace the marine air temperature outputs of the models with the modeled sea surface temperatures, then the model-data difference decreases. Nothing new. They just quantified it using the UKMO HADCRUT data.
So, in other words, he has less knowledge and understanding of climatology than a sophomore, just a lot more ego. He sounds like what he’s really doing is selling ad space in a glossy, rhetorically speaking. “let’s cobble together a thesis, stick some 50 cent names on it, get a buddy to peer it, and walla, more grant funds….”
The big difference between climate models and models running algorithms for the stock exchange ,is that the models working on the stock exchange sometimes come up with the right answers. Climate models predict wrongly forever with zero accountability. Hedge funds fail if their model is not fit for purpose, climastrologists just ask the suckers for more money.
How did “voila” become walla?
Is the models predict anomalies associated with a decrease in solar activity?
http://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/gif_files/time_pres_HGT_ANOM_ALL_NH_2015.gif
What two satellite datasets are these? What region of the earth and what level of the atmosphere are they for? Is there an averaging among multiple years? I don’t see these curves peaking strongly at 1998 the way the UAH and RSS global TLT datasets do.
yes, what data – adjusted data is working better now? for model belief? for Paris?
well, the hiatus is back in print
Can’t even get this bit right and yet can anybody trust them?
http://www.woodfortrees.org/plot/uah-land/from:1979/plot/rss-land/from:1979
Interesting that the UAH trend is lower than RSS, although a bit steeper. I presume this is still 5.6 and not version 6?
http://www.woodfortrees.org/plot/uah-land/from:1979/plot/rss-land/from:1979/plot/uah/from:1979/trend/plot/rss/from:1979/trend
Lets presume you are not a troll, just a newby deserving a response. The two sat data sets are UAH and RSS. Google is your friend. Same satellite raw MSU signals in the various channels, different processing algorithms. Temp results since 1979 for lower and upper troposphere and stratosphere. (Not surface air temps, per se. But for anomalies, no biggie.) UAH new beta v6.0 corrects a simpler algorithm ‘field of view’ issue (the Earth is curved…), and is now much closer to RSS. The problem and the solution for v6.0 can be read on Roy Spencer’s blog. The peer reviewed technical paper version on the v5.6 to v6.0 UAH is in the works, but obviously not receiving the stunningly expedited Cowtan treatment–else I would also provide that reference.
Yes, I just posted a similar question Mr. Klipstein.
These do not look at all like RSS and UAH that I know.
They are 5 year running averages. Original post at Dr. Spencer’s blog.
I don’t see an option to reply to Bart in his comment below, so I am putting my reply here. The graph at the top of this article is not showing any normal 5 year running mean of satellite datasets since the satellite curve is plotted from 1979 to 2015. The satellite datasets start with 1979.
Dr Cowtan said: āWhen comparing models with observations, you need to compare apples with apples.ā
You do need to compare apples to apples which is why Dr. Cowtan knew to compare the apples to orangutans. (and please send more grant money)
you need to compare apples with apples
==========
during training of the models you have to train apples to apples as well. The models represent air temps, but were trained using surface and ocean temps.
In effect you have trained a horse to act like a monkey, and wonder why it it is so bad at climbing trees.
š
The satellites do not show any warming. The problem is it difficult to fiddle with the satellite data.
http://realclimatescience.com/wp-content/uploads/2015/07/ScreenHunter_10030-Jul.-30-09.22.gif
The cult of CAGW is going to keep to their agenda until there is in your face global cooling.
There an interesting race that few are aware of. What will occur first?
1) The first public announcement that the solar cycle has been interrupted or
2) The first observations of unequivocal global cooling.
http://wattsupwiththat.files.wordpress.com/2012/09/davis-and-taylor-wuwt-submission.pdf
Greenland ice temperature, last 11,000 years determined from ice core analysis, Richard Alleyās paper. William: As this graph indicates the Greenland Ice data shows that have been 9 warming and cooling periods in the last 11,000 years.
The past warming and cooling cycles correlate with solar cycle changes. i.e. The past cyclic warming and cooling periods have a physical cause which is physically capable of causing cyclic warming and cooling in both hemispheres. Big surprise cyclic changes to the sun causes cyclic changes to the earth’s climate.
Who would have thought up that wild idea?
P.S. There are periods of millions of years in the paleo record when atmospheric CO2 was high and planetary temperature was low and vice versa. There is not even correlation.
The so called without ‘feedback’ calculation of forcing for a doubling of atmospheric CO2, did not take into account the fact that CO2 increases the lap rate (another big surprise hot air rises and is replaced with falling colder air which offset greenhouse warming. Again who would come up with that wild idea?) which reduces the warming for a doubling of atmospheric CO2 without amplification from 1.5C to 0.1C to 0.3C.
The same calculation did not include the fact there is an overlap of water’s absorption spectrum and CO2’s absorption spectrum. As there is a great deal of water vapour in the lower atmosphere, particularly in the tropics (70% of the planet is covered in water) that also reduces the warming due to a doubling of atmospheric CO2 to roughly 0.1C to 0.3C.
http://www.climate4you.com/images/GISP2%20TemperatureSince10700%20BP%20with%20CO2%20from%20EPICA%20DomeC.gif
The first public announcement that the solar cycle has been interrupted
The solar cycle has not been ‘interrupted’ [whatever that means], so no such announcement will be forthcoming.
Exactly. Instead of whining about the mismatch, why not just use the state-of-the-art temperature record that measures precisely what the models are built predict — satellite…
Of course, the measurements that align with the models show cooling.
Do these scientists “work” to be this naive and ignorant?
You leave out a third option, the most frustrating and terrible one, and the one most likely to occur:
Since Earth’s climate operates on a different time-scale than our own brief little moments in the sun, there will be no definitive, inarguable global cooling in our lifetime, and perhaps much longer, just as there is no definitive, inarguable global warming. It is a ridiculous show of hubris to think that our puny temperature sampling networks, and rudimentary conception of how climate functions, allow us to define and speak authoritatively about “global temperature” at all.
This is terrible because it means there will probably BE NO MOMENT when CAGW believers have to admit they’ve been wrong, and the skeptics get to finally feel that warm glow of vindication.
It will take some unforeseen speech from a famous person that sets off the light bulb in enough people’s heads (like Gore with his sanctimony at just the right moment), or the rise of a different imminent and unstoppable manmade disaster, to get the idea of CAGW to go away. Even then it will persist in the dark corners of the world, like Ebola, biding its time, waiting to leap back into the light…
It leaves this layman shaking his head in astonished wonderment.
You’d think that, before engaging in all the decades of study, the expenditure of billions of dollars and the claims of impending doom, the climate boffins would have agreed to a definition of terms.
These clowns have permanently damaged the credibility of “climate science.” I don’t believe a word out of themā and I’m amazed when anybody else does.
Worst than that… They have permanently damaged the credibility of science in general. They keep harping about how certain scientists (and skeptics) are nothing but shills for the oil companies when in actuality, the vast majority of scientists today are funded by the government. And many (take for example the current crop of “Climate Scientists”) have become shills for the current executive branch, regardless of which party. President Eisenhower warned about this is his farewell address:
“
the climate boffins would have agreed to a definition of terms
====================
Climate Science hasn’t even defined “climate change”.
1. climate change – refers only to human caused.
2. climate change – refers to both human caused and naturally caused.
3. Climate change should only refer to naturally occurring change – which includes naturally occurring global warming (or cooling) . If one wishes to refer to some perceived (goodness knows there is no empirical evidence of any of this) additional climate change being potentially caused by additional warming, or AGW, then that would be referred to as ACC.
Climate is a range of parameters, not just temperature, each of which is highly variable.
Materially, climate change is not climate change until the change is outside the bounds of natural variation when viewed and measured over at least a multi-centennial scale, if not a millennial scale.
Given what we know of the past, the idea that climate should be viewed as something to be assessed over a 30 year period is farcical in the extreme.
Presently, we have yet to observe any real climate change
The models are way off and it will become worse going forward.
my suggestion….stop making up, adjusting, fudging, and lying about surface temps
You’ll never get it as long as you are using fake data.
BTW…here’s the paper
http://onlinelibrary.wiley.com/doi/10.1002/2015GL064888/abstract
I find it interesting that, as shown in Figure 2, the adjustments required to “correct” for the difference between air and air+water temperature measurements followed a accelerating downward trend. How would a “correction” to a temperature measurement result in a smooth trend over time with the largest deviations occurring today? Did the air and water measurements agree more closely in 1900, or was there simply more land? /sarc on Perhaps this plot is an indication of the rapidly rising sea levels with the fraction of land decreasing rapidly with time.
exactly….they give it away
“Applying the methodology of the HadCRUT4 record to climate model temperature fields accounts for 38% of the discrepancy in trend between models and observations over the period 1975-2014.”
HadCRUT faked the past….cooled it…to show a faster trend
…when they feed those fake numbers in it….they got a fake trend
“Youāll never get it as long as you are using fake data.”
That’s wrong.
The only way they can get it is using fake data.
Otherwise, with real data, they’re done.
” The research team found that the way global temperatures were calculated in the models failed to reflect real-world measurements. The climate models use air temperature for the whole globe, whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.”
Dr Cowtan must be kidding, right? Is he saying that these scientists do not know how to correctly verify their own projections? If I would have pulled this in my Freshman Physics lab, the instructor would have failed me on the spot.
Dr Cowtanās
interest in climate science has developed from , the fact there is ton of grant cash and some fame in climate ‘science’ while it crystallography there is no cash and no one wants to know what our doing , but let us call it ‘an interest in science communication.’ instead
It seems to me that water acts as a buffer for air temperature, and since water is approximately 2/3 of the earth’s surface, it would naturally close the gap between the hypothetical models and the real world observations. A no-brainer paper.
Brian – they’ve found an easy way out.
Thanks for clearing sights – Hans
There is a logical contradiction there: the models don’t even agree between themselves, so how can they possibly all agree with reality?
bingo
+1
Which 36 modeling projections did he choose to evaluate, and WHY those 36.
The ones where projected CO2 matches the observed CO2. Then you could evaluate whether calculated temperatures, humidity, cloudiness, rain and so on for each grid cell match the corresponding observations.
Hi Jake,
The 36 models used were all CMIP5 models that included the requisite fields for the analysis (e.g. “the surface air temperature (ātasā in CMIP5 nomenclature), sea surface temperature (ātosā), sea ice concentration (āsicā), and the proportion of ocean in each grid cell (āsftotfā)”)
This is a remarkably straightforward paper. Instead of comparing simulated 2-meter surface air temperature from models to observations (which combine air temperature over land and sea surface temperatures over oceans), compare the blended air temperatures over land and sea surface temperatures over oceans from the models to the observations. Otherwise you end up with a bias because models have air temperature over oceans warming a bit faster than sea surface temperature.
You haven’t solved the problems of off-setting model parameter errors and of non-unique solutions. You’re not modeling climate, you’re just mimicking a climate observable; an observable (air temperature) that in any case is very poorly constrained. Studies such as yours can never be anything more than tendentious.
Pat Frank writes: “Falsifiable because if the prediction is wrong, the physical theory is refuted.”
…
(refrence: http://wattsupwiththat.com/2015/05/20/do-climate-projections-have-any-physical-meaning/ )
…
Impressive!!!
…
Weather forecasting models are based on the physical theory that hot air rises. So according to Mr. Frank, when the weather prediction fails (which happens whenever you plan a picnic) , it proves that hot air doesn’t rise.
Otherwise you end up with a bias because models have air temperature over oceans warming a bit faster than sea surface temperature.
==============
which would make the models run hot. which they are. however, the problem is worse than this. you are only addressing how well the predictions match reality.
You cannot properly train the models by comparing 2 meter surface temps to combination of surface and ocean temps. faulty training leads to faulty results.
This is the elephant in the room. The training was faulty in exactly the same way the scoring of the results was faulty, because they both use the same methodology.
However, faulty training is much more serious than faulty marking, because it means the models are inherently wrong.
Not only wrong as usual, Joel, but you’ve managed an especially fatuous comment. Well done (for you).
In any case, weather modeling mentioned nowhere in my analysis.
You’re right to focus on hot air, though. It rises vigorously whenever you operate a keyboard.
I apologize to you Mr Frank, it seems whenever someone uses direct quotes from the things you write, it exposes how incoherent your writings are.
..
PS, I quoted you from your article on “climate models”
…
Keep posting, the appropriate saying is , “like shooting ducks in a barrel”
It’s remarkable how nearly all the biases discovered by climate scientists make observed temperature trends warmer. Perhaps there is some secret, fossil-fuel-funded effort to systematically interfere with all climate-related data gathering and analysis, which climate scientists must heroically struggle against.
I can’t think of any other reasonable explanation.
Bogus Zeke
http://www.nature.com/ngeo/journal/v7/n3/fig_tab/ngeo2098_F1.html
more excuses
It’s not the quote, Joel, it’s your inevitably irrelevant mindlessness. You never lose an opportunity to be simultaneously outspoken and vacuous.
Well, a new day, and I am back to wondering if Joel Jackson and I inhabit the same planet.
Zeke says……compare the blended air temperatures over land and sea surface temperatures over oceans from the models to the observations…’
————————————————————————————————————————
So really, they never used the modeled SST, they had them, but instead ran modeled 2m air T and compared them to SSTs?
“Otherwise you end up with a bias because models have air temperature over oceans warming a bit faster than sea surface temperature”
==========================================
Are they suppose to? If not is this physics issue the models got wrong? Why would the relationship between SST and 2 m air T above the sea surface change?
Maybe I read too much into this; but maybe Lord Monckton’s push of difference between model and data is getting some traction and this is an attempt to deflect that line of questioning.
Maybe I’ve just been exposed to a little to much ‘Climate Science’ over the years, but something about this paper makes me think it’s just another excuse to ‘adjust’ the temperature data again.
Oh ya, that would be it. ^Āæ^
the pause is
GW theory says that the Polar region will warm faster than the rest of the world due to amplifications in the region, in fact this map from NOAA http://polar.ncep.noaa.gov/sst/ophi/color_anomaly_NPS_ophi0.png seems to indicate that the polar region is about 5C above the average but the temperature graph from DMI shows the situation to be Business as usual with the High following the Bell Curve http://ocean.dmi.dk/arctic/plots/meanTarchive/meanT_2015.png
The PAWS BUOY system http://psc.apl.washington.edu/northpole/index.html, a series of 6 small Buoys collecting local meteorological data in the Polar Ice indicate something different. 5 of 6 have temp readings of 0.7, 2.2, 4.9, 4.4, 0.52 for an average of about 2.5C which agrees with the DMI temp graph. One buoy however, Buoy 553800 indicates a temperature of 17.4C or 63F and site about 1/2 way between Greenland and the Pole. Adding this measurement into the mix skews the average to 5C and matches the NOAA map. Could all the red on the NOAA map be a product of a broken thermometer?
Oh it’s not broken, it’s telling them exactly what they want it to.
The polar regions should warm more due to less water% in the air and therefore more CO2 gases in theory have a greater influence.
We know the CO2 theory is contradicted with atmospheric behavior above deserts. They should show any influence CO2 has, but very little warming in the tropics (high humidity) and sub tropic zones where all the world deserts are based.
The NOAA map has anomaly on average nowhere near 5 c and more like 2-3 c, but has a biased continually cooled past of which the exaggerated anomalies arrive. There is no indication in the chart that the bad buoy has been used.
This below is the best data source on the planet we have for polar temperatures by far. DMI data is good for unbiased source covering regions the satellite below doesn’t.
http://climate4you.com/images/MSU%20UAH%20ArcticAndAntarctic%20MonthlyTempSince1979%20With37monthRunningAverage.gif
Only goes to 85 N and 85 S , but that is only 0.48% of the planets surface from both poles missing.
No, its the 5 thermometers that show lower readings that are faulty, obviously.
First we had Heidi the Decline, then Heidi the Heat, Heidi the Pause and now, Heidi the Gap.
Soon to be followed by Heidi the Oceans and shortly thereafter by Heidi the Satellites.
Did you mean “robust assumptions and maybe – possiblies”?
Any remaining differences may be explained by the recent temporary fluctuation in the rate of global warming.
I believe this is called “natural variability.”
Here lies the “Catch 22” of climate science. Their mistakes are “natural variability.”
How and why can it be assumed that anything is “temporary”?
This betrays a bias that is hard to deny.
ta, that was the bit that narks me..temporary? 18+yrs?
when one yr or less of someplace being a tad warm is a global tragedy?
sheesh
Do not the UAH and RSS satellite datasets record global air temperature? Cowtan says the models compute air temperature, thus so long as one is comparing satellite temps with modeled temps, is it not apples and apples? And is there not a divergence?
I’m not getting something here.
Satellites do not measure air temperature. They measure microwave brightness. The “data” you are looking at when referencing UAH or RSS is not “temperatures” but model output.
Satellites do not measure air temperature? Then mercury (or any other) thermometers do not measure temperature, speedometers do not measure speed, scales do not measure weight, etc, etc. But that doesn’t mean they are unreliable.
The UAH and RSS are converted to temperatures and this data is shown, so they are temperatures. Like saying the surface data is not how much the expansion/contraction of liquid was so it’s not temperature.
Satellites measure brightness AT THE SENSOR.
Then a physics model is applied. The physics model is a radiative transfer model. radiative transfer physics is the SAME PHYSICS that tells you C02 will warm the planet
in other words.
IF you accept UAH temperature, THEN you have to accept the physics model that gets you
from BRIGHTNESS to TEMPERATURE.
BUT, that same physics, radiative transfer, is the physics that tells you C02 will warm the planet
When skeptics discover this, they mumble.
True. But irrelevant. Because those modeled temperature outputs were VALIDATED (explicitly in the UAH case) by many radiosonde temperature measurements at many latitudes, for example along the entire North American West Coast from southern Mexico to northern Alaska. Do try to keep up with model validation stuff. Cowtan in an effort to minimize the pause exposes a basic consensus climate science goof. Christy uses massive amounts of weather balloon data to make sure his translation ( really not a model) of MSU channel signals into temperatures is correct.
The difference is stark.
The skeptics may mumble, Steve, because they’re embarrassed to tell you the obvious — that a satellite sensor is not a physical analogy of the terrestrial climate. You ought to listen to them.
Wow, Frank, your response to Steve was outspoken and vacuous.
…
Ever hear about “glass houses?”
So, Mr. Mosher, those rejecting UAH and RSS data are REJECTING the physics that tells you CO2 will warm the planet. It’s a two-way street. If you accept the science, you must accept both the satellite data and CO2 warming, but you can do the latter and arrive at only about 2 degree maximum warming. To that warming, one must add the effect of natural variation. Since no one has demonstrated the ability to predict the natural variation, the final result is, we have no idea if the future will be cooler, warmer, or the same, but it seems likely that, because of Man, it will be two degrees warmer than it would be otherwise.
You can’t make policy decisions based on that.
Steven Mosher; I most disagree. The Satellites are a man made artifact manufacture for the purpose of measurement of temperature. Yes it does this through first measuring radiance. The conversion to temperature is not unlike that of metric to English standard.Or the Specific gravity of water and choc milk when calculating weight to volume. No model just math.
Now CO2 it of course is an object to be measured and recorded. Model are used to project its effect on the atmosphere-climate, this is not the case with the Satellite date.
michael
Thermometers do not measure temperature either, then.
Liquid in glass ones measure the expansion and contraction of a fluid, and bimetal ones measure the relative expansion coefficients of different metals.
And those modern gizmos measure some modern gizmo dealio stuff *sorry about the technical jargon here š *
All then use some method of converting this into a meaningful form: A NUMBER! Which our brains then interpret as being related to how hot or cold it is.
Sophistry:
noun
-The use of clever but false arguments, especially with the intention of deceiving.
“The Seven Basic Types of Temperature Sensors
Thu, 2000-12-28 09:17
Temperature is defined as the energy level of matter which can be evidenced by some change in that matter. Temperature sensors come in a wide variety and have one thing in common: they all measure temperature by sensing some change in a physical characteristic.
The seven basic types of temperature sensors to be discussed here are thermocouples, resistive temperature devices (RTDs, thermistors), infrared radiators, bimetallic devices, liquid expansion devices, molecular change-of-state and silicon diodes.
Thermocouples
Thermocouples are voltage devices that indicate temperature by measuring a change in voltage. As temperature goes up, the output voltage of the thermocouple rises – not necessarily linearly.
Often the thermocouple is located inside a metal or ceramic shield that protects it from exposure to a variety of environments. Metal-sheathed thermocouples also are available with many types of outer coatings, such as Teflon, for trouble-free use in acids and strong caustic solutions.
Resistive Temperature Devices
Resistive temperature devices also are electrical. Rather than using a voltage as the thermocouple does, they take advantage of another characteristic of matter which changes with temperature – its resistance. The two types of resistive devices we deal with at OMEGA Engineering, Inc., in Stamford, Conn., are metallic, resistive temperature devices (RTDs) and thermistors.
In general, RTDs are more linear than are thermocouples. They increase in a positive direction, with resistance going up as temperature rises. On the other hand, the thermistor has an entirely different type of construction. It is an extremely nonlinear semiconductive device that will decrease in resistance as temperature rises.
Infrared Sensors
Infrared sensors are noncontacting sensors. As an example, if you hold up a typical infrared sensor to the front of your desk without contact, the sensor will tell you the temperature of the desk by virtue of its radiation – probably 68Ā°F at normal room temperature.
In a noncontacting measurement of ice water, it will measure slightly under 0Ā°C because of evaporation, which slightly lowers the expected temperature reading.
Bimetallic Devices
Bimetallic devices take advantage of the expansion of metals when they are heated. In these devices, two metals are bonded together and mechanically linked to a pointer. When heated, one side of the bimetallic strip will expand more than the other. And when geared properly to a pointer, the temperature is indicated.
Advantages of bimetallic devices are portability and independence from a power supply. However, they are not usually quite as accurate as are electrical devices, and you cannot easily record the temperature value as with electrical devices like thermocouples or RTDs; but portability is a definite advantage for the right application.
Thermometers
Thermometers are well-known liquid expansion devices. Generally speaking, they come in two main classifications: the mercury type and the organic, usually red, liquid type. The distinction between the two is notable, because mercury devices have certain limitations when it comes to how they can be safely transported or shipped.
For example, mercury is considered an environmental contaminant, so breakage can be hazardous. Be sure to check the current restrictions for air transportation of mercury products before shipping.
Change-of-state Sensors
Change-of-state temperature sensors measure just that – a change in the state of a material brought about by a change in temperature, as in a change from ice to water and then to steam. Commercially available devices of this type are in the form of labels, pellets, crayons, or lacquers.
For example, labels may be used on steam traps. When the trap needs adjustment, it becomes hot; then, the white dot on the label will indicate the temperature rise by turning black. The dot remains black, even if the temperature returns to normal.
Change-of-state labels indicate temperature in Ā°F and Ā°C. With these types of devices, the white dot turns black when exceeding the temperature shown; and it is a nonreversible sensor which remains black once it changes color. Temperature labels are useful when you need confirmation that temperature did not exceed a certain level, perhaps for engineering or legal reasons during shipment. Because change-of-state devices are nonelectrical like the bimetallic strip, they have an advantage in certain applications. Some forms of this family of sensors (lacquer, crayons) do not change color; the marks made by them simply disappear. The pellet version becomes visually deformed or melts away completely.
Limitations include a relatively slow response time. Therefore, if you have a temperature spike going up and then down very quickly, there may be no visible response. Accuracy also is not as high as with most of the other devices more commonly used in industry. However, within their realm of application where you need a nonreversing indication that does not require electrical power, they are very practical.
Other labels which are reversible operate on quite a different principle using a liquid crystal display. The display changes from black color to a tint of brown or blue or green, depending on the temperature achieved.
For example, a typical label is all black when below the temperatures that are sensed. As the temperature rises, a color will appear at, say, the 33Ā°F spot – first as blue, then green, and finally brown as it passes through the designated temperature. In any particular liquid crystal device, you usually will see two color spots adjacent to each other – the blue one slightly below the temperature indicator, and the brown one slightly above. This lets you estimate the temperature as being, say, between 85Ā° and 90Ā°F.
Although it is not perfectly precise, it does have the advantages of being a small, rugged, nonelectrical indicator that continuously updates temperature.
Silicon Diode
The silicon diode sensor is a device that has been developed specifically for the cryogenic temperature range. Essentially, they are linear devices where the conductivity of the diode increases linearly in the low cryogenic regions.
Whatever sensor you select, it will not likely be operating by itself. Since most sensor choices overlap in temperature range and accuracy, selection of the sensor will depend on how it will be integrated into a system.”
http://www.wwdmag.com/water/seven-basic-types-temperature-sensors
Mosher writes “BUT, that same physics, radiative transfer, is the physics that tells you C02 will warm the planet”
Ouch Mosher. You sound as if you understand the instantaneous radiative transfer solution has anything to do with atmospheric physics leading to warming. Radiative transfer is merely one part of a very complex whole.
“Everything Should Be Made as Simple as Possible, But Not Simpler” – einstein.
So your basis for believing CO2 forced warming is a fail, Steve.
But Mosh conveniently fails to mention that the satellite data is tested and tuned against weather balloon temperature data which use more conventional forms of meteorological thermometers, so the imaged brightness which is measured by the satellite sensor is properly control calibrated by tuning it with real and measured radiosonde data..
Calibrated against independent PRT and radiosonde data, which you always neglect to include in your posts.
It is the Cowtan Way.
Or in real world, the way to kowtow.
This sure has that smell of desperation, so the models are right, once we adjust the created data to suit?
Maybe civilization is completely collapsed and I just have not noticed yet.
Do climate scientists actually believe modelers know enough about the climate and its feedbacks to model it accurately? That is pure arrogance. They have already admitted they don’t know how to model “clouds,” and they can’t possibly know much about other feedbacks either. So, if the models were to get it right, it would be random chance. It would be like a broken clock getting the time right twice a day. They have to know that. But apparently, the “cause” is more important than the truth to these people.
Modelers proceed, Louis Hunt, as though the models were perfect representations of the physics of the climate. The only errors then are caused by the parameter unknowns.
This assumption of perfect models is directly implicit in the way modelers represent physical error. It’s always the variation in projections produced by “perturbed physics.” In these studies, the parameters are varied through their uncertainty range. The only way these studies fully represent error is if the model itself is physically complete.
In practice, climate modelers don’t know anything about physical error analysis, and as a consequence have no idea how to evaluate the reliability of their own models.
“Modelers proceed, Louis Hunt, as though the models were perfect representations of the physics of the climate.”
…
No, most modeler follow what Gavin Schmidt says… http://www.columbia.edu/cu/alliance/EDF-2012-documents/Reading_Schmidt_2.pdf
Gavin’s article does not mention model accuracy, reliability, error, or uncertainty. It’s irrelevant to my point.
The RSS / UAH sensors, match the weather balloons, so model – meets observation, meets verification. The climate models predict about three times the warming of what is observed in the troposphere, so J.J. meet failed model.
Steven M, pay attention also. We do not in general object to models for study. We object to models that fail to meet the observations, being used for public policy, We object that the molded mean of many models, all running wrong in one direction, to warm, are being used for CAGW harm projections.
Mosher, STOP making straw man arguments. Stop lumping all skeptics into one basket.
Thanks for the link, Joel. In that article, titled “Wrong but useful,” Gavin Schmidt explains that climate scientists like him don’t actually believe that models can model the climate accurately at this time. That’s good to know. I’m glad they’re not as arrogant as I thought. His reasons are similar to mine: “interactions among the various components ā like low-level ozone, aerosols (airborne particles) and clouds ā can get hideously complicated.” Obviously, something that is “hideously complicated” cannot be well understood. And something that is not well understood cannot be accurately modeled. Schmidt admits that when he says, “All climate models are wrong, but some of them are useful…” But what exactly can “wrong” climate models be useful for? They “might,” he explains, “have a vitally important part to play in breaking through some of the log jams now hampering policymakers.” In other words, they might be useful politically to help persuade policymakers to get on board with the program. Climate models that are “wrong” obviously cannot be used to establish scientific truth or provide factual information. But they can be useful as propaganda.
Schmidt wrote the article back in 2009. It appears that using “wrong” climate models to break through the “log jams” hampering policymakers hasn’t worked as well as he had hoped. So now they’re busy working feverishly to adjust temperature observations to more closely match model forecasts and thereby give them more weight. There’s another meeting of the policymakers coming soon, and so it doesn’t matter that the models are wrong, or that they are corrupting temperature data with their adjustments. It only matters that they persuade policymakers to break the “log jams” and support the cause.
Actually real computer modellers do know enough to know that before you make any assumption about CO2 you have to model the entire natural CO2 system, prove that it matched the data before the industrial era. You have to then show that the projected characteristics no longer match after the CO2 of industrialisation.
A proper QA department then queries the assumption that the change is due to the industrial CO2 and checks that the CO2 distribution is consistent with man made CO2 production and not a coincidental natural global increase or worse still a local phenomenon. They also check that all the stations were properly annually certified accurate and no adjustments made to the data.
This is the case for the computer modelling of products for the low end of the commercial market.For life critical the demands are far more stringent.
Please do not dismiss all computer modelling because of the behaviour of the dregs.
The average guy on the street is not buying this stuff, there are so many great scientists of the past that used phrases like” “if you can’t explain it to a …. 6 year old …. your mother …. some guy at a bar ….” then you really don’t know what your talking about. At some point it does begin to sound like a mish-mosh of doubletalk. The only people that are buying into this crap are the pseudo intellectuals like John Kerry and George Clooney, and the hundreds of severely compromised folks that graduate from Oberlin each year.
And you forgot to mention celebrities whose wealth and fame are the result of their ability to make the entirely fictitious appear real.
“Challenges in assembling the data”… Does anyone else find this disturbing? Sounds like a euphemism for “we haven’t yet mastered the art of manipulating the data to suit our agenda”.
” . . . whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.”
He neglected to mention that the real-world data used by scientists has been heavily adjusted, homogenized and in-filled, all done to fit a political narrative.
The other paper mentioned in this article is the one Cowtan and Way published to try and bring back the warming. They did this by infilling the polar regions, where there is little actual data, with what they felt the data should be.
wrong. Cowtan and Way treated the artic in just the same way that Skeptics Odonnel, jeff id and Steve Mcintyre treated antartica when they debunked Steigs paper.
There reconstruction of the artic passed all validation tests, out of sample tests, and tests against independent data from arctic bouys, and reanalysis AND data from the new AIRS satellite
SM, they krigged. Krigging was invented as statistical method to extrapolate mineral ore bodies from limited drill core information. A fact you do or should know. There are ‘uniformity’ assumptions behind the method, which you also do or should know. (Uniformity means, in layman speak, no abrupt discontinuities within the ore body, although are expected at its edges.) Those underlying assumptions are met in Arctic winter and early speing, when everything is snow covered aomething. They are not in late spring, summer, and fall, when the mix of open water, sea ice, and tundra is anything but ‘uniform’. Cowtan’s methodolgy is suspect to anyone with deep knowledge of statistical methods, or the ability to look them up as needed. Essay Unsettling Science explained this ‘little detail’. You might learn something by reading it.
Oh, and that essay also finishes with a figure you yourself provided to CE that overlays Berkeley Earth on the other datasets including CW. The pause exists! Well, until Karl further fudged the data.
I thought krigging was invented so my girlfriend could strengthen the muscles in her…oh, never mind.
Kegel menicolas… Kegel!
A little off topic…but….that wing they’ve found that is suspected to be from that Malaysian plane that disappeared, washed up on shore in an area 4100 miles from where the COMPUTER MODELS forecast it to have been.
Ahhhh, the wonderful world of modeling.
after tonight’s adjustments the models will correctly predict the debris to be found on Reunion.
+1
No doubt all the carbon in the plane caused it to crash!
Be careful with this. Oceanic currents make it very likely that an object would drift from the west coast of Australia across the Indian Ocean towards Reunion in this time frame.
The phenomenon has nothing to do with modeling. It’s a simple matter of the prevailing ocean currents which have been well-known to mariners for centuries.
Of course that never happens to the Argo floats.
So the models did not incorporate currents which have been known for centuries? Still sounds like faulty modelling.
Wing of jet found is a “little” off topic?
And CAGW is a “little” fib, told for no particular reason.
What can we expect from a bunch of activist scientists, objectivity?
If they can just figure out why all this warming hasn’t been detectable, they will have their cake well-frosted.
Ironic that we agree that the past global temps have been misrepresented. This looks like an attempt to muddy the waters some more and nullify the sceptic perspective on it.
Primary field of X-ray Crystallography eh, I suppose that is better than psychology for climate science but it still heavily discounts the phrase “I think….”
“real-world data used by scientists are a combination of air and sea surface temperature readings”
The satellite data is the lower troposphere air temperature regardless of whether it’s over land or sea.
“The research team found that the way global temperatures were calculated in the models failed to reflect real-world measurements. ”
And yet they had a 95% Confidence level in AR5. Will an Adjustment to AR5 be forthcoming?
“The climate models use air temperature for the whole globe, whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.”
This is ridiculous. First of all, satellite temperatures are air temperatures, and they show even less warming.
Secondly, the “real-world” data is not even data — GISS is a model of the data with adjustments and fabricated, er, infilled readings.
Third, the adjustment to surface temperatures are generally to the land stations, so this study is basically saying the climate models match the “data” temperature models. Of course they do! That tells us a lot about the state of climate science but not much about the science of climate.
I can’t help but wonder what the chances of getting a huge grant for X-ray crystallography research are compared to a huge grant for climate change research. Maybe wildlife isn’t the only thing that migrates due to climate change.
According to the University of York, the Cowtan, et al. (2015) study was unfunded. Of course, all of the time donated by the authors, along with equipment and office space, etc., might effectively be “double-dipping” into extraneous grants.
Or maybe they all did this on their summer vacation.
If you can’t predict the past how can we rely on you to predict the future. Time to stop this scam
A genuine expert can always foretell a thing that is 500 years away easier than he can a thing that’s only 500 seconds off.
– A Connecticut Yankee in King Arthur’s Court
“Dr Cowtanās primary field of research is X-ray crystallography…” Going by the usual shrieks of AGW-ers doesn’t that disqualify him from pronouncing on climate matters as he “isn’t a climate expert”.
he is publsihed in climate science as are his co authors.
Being published is the standard for qualification to opine, Mr. Mosher?
Maybe some day, being correct will be the standard for what makes someone an expert.
Actual railway engineers, and people who can not keep their hands of others or the failed politicians are amongst the many people with zero qualifications in the area , or in science at all, whose unquestioning support for ‘the cause ‘ has earned them the right to be consider ‘experts’ in climate ‘science’
Although to be fair given the standard of science seen in climate ‘science’ is not far away from the standard seen from a dead raccoon, then this may actual make sense, after all if all what matters its your ability to produce BS than it is true ‘anyone’ can be a expert .
This is really basic.
When you compare the models output with observations you have to compare the same thing.
Note I have lodged this complaint many times against people on both sides who do model/observation
comparisons.
Lets start with observations:
Observations consist of SAT ( surface air temperature taken over land ) and SST ( ocean temperatures taken just beneath the surface)
Note; this is WHY we call global “averages” indexes. because SAT and SST have been mashed together.
Now you want to compare the model output to this. What the vast majority of people do is they go to the model output and they select t2m or the temperature at 2 meters OF THE AIR!
But this is NOT what the observations are. The observations are the air temp over land and SST ( not MAT)
over ocean.
So fundamentally Cowtan is doing it the right way. using modelled SST and Modelled SAT and comparing that to OBSERVED SST and OBSERVED SAT
Which demonstrates- One. More. Time. -that the models are running hot and the only culprit is the CO2 fudge factor. I predict they eventually have to reduce the fudge factor to the point that it will be another few 1000’s of years before the models rise above the natural noise.
Steven Mosher July 30, 2015 at 2:22 pm
This is really basic.
When you compare the models output with observations you have to compare the same thing.
==============
agreed. the exact same rules apply when training. both the training and evaluation of the prediction were flawed, since they did not compare the same thing.
but of the two flaws, the training flaw is the more serious, because it means the models must be inherently wrong. not simply that they were evaluated incorrectly, but that their fundamental training was wrong.
Where is the global data set for surface 2m air T above the oceans?
Steve M, the post says, “The climate models use air temperature for the whole globe, whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.”
So are you saying they reran the models using modeled SST?
Here is the Hadc 3 SST anomalies
http://woodfortrees.org/graph/hadsst3gl/from:1974/to:2014
http://c3headlines.typepad.com/.a/6a010536b58035970c017ee88df70e970d-pi
The model projections describe the takeoff flight path of a 747 packed with climateers, AR5 was the last chance to abort, red lights are flashing on the control panel alarms are sounding and the captain is reassuring the passengers that everything is under control.
Just love those ‘Anomally’ graphs where no one states the value of zero.
Can anyone tell us what it is.
The value of zero is define as how much this article is worth.
Apologies all around to the authors who are present, but I gots to calls ’em like I sees ’em.
Why don’t we want nicer weather and more abundant crops again?
Again?
This is very confusing, because crop yields have been on a steady march upwards for a very long time now.
Both as an absolute number, and in terms of Calories per person on Earth, yields per unit area, and about any other metric one may care to choose.
And hey, the weather is fine!
Are you sore because you moved to a desert or something, and are disappointed to find out there are long droughts in deserts?
Oh, I know…maybe you are a snowman, and anxiously await the return of full on ice age conditions?
Please, do tell.
Cryptic comments make me so dang curious!
The models diverge because the physics that underpins them is wrong. Until scientists acknowledge that we have an atmospheric effect and not a Greenhouse Effect, there is no hope for any of them!
correct. greenhouses warm by limiting convection. CO2 theory says the surface will warm due to increased radiation, which has nothing to do with greenhouses.
+1 on the “Dog ate my model” list.
Neville July 30, 2015 at 3:22 pm
The versions are 5.6.
Dr Cowtan added:
āRecent studies suggest that the so-called āhiatusā in warming is in part due to challenges in assembling the data. I think that the divergence between models and observations may turn out to be equally fragile.ā
____
the challenges in assembling data for DrCowtans team could lessen with experience in spread sheets.
but
‘the so-called divergence between models and observations may turn out to be equally fragile’ –
as long as real world resists to equal models.
– watch DrCowtans teams next paper ‘work on ‘observations”.
Hans
“āRecent studies suggest that the so-called āhiatusā in warming is in part due to challenges in assembling the data.”
Gavin and Tom are doing all they can to address that challenge. š
The entire AGW argument is based on assumption, and when we assume…
https://youtu.be/KEP1acj29-Y
[Fake email address. ~mod.]
Steven Mosher July 30, 2015 at 2:31 pm
Nothing new, surface measure liquid expansion or contraction, just the conversion is more complex with brightness and needs a model to do it. The point earlier is once the conversion has occurred it is not data from brightness or liquid expansion, it is data that corresponds to temperatures. (therefore it is now temperature data) The satellite has been measured to be more accurate than the thermometer, matches with balloon data and covers far more coverage of the planet then surface temperatures ever will even if they were 50 x more.
Its going to be HILARIOUS watching the antics of the climate bletheren and salesmen when the current drop in solar activity starts to kick in.
Even a small downward trend in REAL temperatures will cause massive panic amongst them. (Even more than now)
Popcorn time , for sure š
Robert Way left a stinker of a comment on my blog about the fact that other’s model vs temp comparisons aren’t done right and if it is models do agree with observations.
I suppose if you crank models down a third, crank sea temps up a third and then have a 1/3 difference in your CI, models are magically in agreement!
Eureka! Good science finally!
Well, after reading this article and all the comments, I am saddened that this new paper has not ended all disagreement and settled the entire matter of global warming, like it was intended and supposed to do.
I am going to hold out the hope that the NEXT paper which attempts to explain and reconcile everything will finally do the job.
It seems we are ever so close to having everyone converge on the same point of view and reading of the “facts”.
*rolls the eyes*
So close!
So close, and yet…
*
“Any remaining differences may be explained by the recent temporary fluctuation in the rate of global warming.”
In other words, any remaining differences between the models and actual observations, may be explained by the actual observations being different than the models?
Which remaining differences are only temporary because the models say so.
Zeke,
‘compare
from the models to the observations. Otherwise you end up with a bias because models have air temperature over oceans warming a bit faster than sea surface temperature.’
____
compare, what for?
You’re already biased showing models leading reality, CO2 ahead of temperature.
____
compare FROM MODELS
TO OBSERVATIONS ?
____
from Hollywood Matrix
to pay the rent in LA ?
We can make the models look better by adding even more bad sea temperature data to the real world record.
If, over the past 10-15 years, we have not known “how to assemble the data” of the past 10-15 years, how can we have been correctly “assembling the data” of the past 100-150 years?
Cowtan et al have made an astounding admission. That the wrong data set has been used to evaluate the models.
This is important because it established that the wrong data set was used to train the models. As since the models were trained incorrectly, the cannot be expected to perform correctly.
The models report atmospheric temperatures. As Dr Cowtan says:”you need to compare apples with applesā. This applies to model training as well as model evaluation.
As such, if Cowtan et al are correct, then the model training must also be faulty. And with faulty training, no model can hope to deliver the correct answer, except by accident.
This then explains the divergence. FAULTY TRAINING. This is a HUGE ISSUE.
Yes, I think you may be right Mr. Berple.
Either that or this whole thing is a bunch of made up gobbledygook and means nothing, and the models are just wrong because they fail to model the atmosphere accurately, have way to grainy of a resolution, use faulty reasoning in their conception, overestimate some feedbacks, fail to account for others, leave clouds and solar variability out, and all the other stuff that has been discussed up to now.
Or what you said…
Either way.
Even if the models are 100% correct on all the other possible issues. You cannot train a dog to be a duck and expect it to fly.
Dear colleagues:
Predictions are products of the scientific method of investigation. Projections are products of a pseudoscientific method of investigation. Thus, “prediction” and “projection” should not be used as synonyms.
The only thing that they can project in the pseudo-chaotic climate system is their own egos. !
“Dr Cowtanās primary field of research is X-ray crystallography …”
But he felt he needed to get a piece of the climate band wagon and after all, it is not as if any relevant qualifications are necessary.
I’m guessing Dr. Cowtan has figured out that a scientist, and especially an “X-ray crystallographer”, can’t a research grant to study squirrels in the park (or their crystals) unless that scientist somehow ties it to AGW. Man’s gotta’ EAT!
Wow, what a crock of cow dung. The alarmists models predict too far over the observed temperatures to be some chance fluctuation. The models are simply wrong because they’re wrong about the CO2/water vapour positive feedback relationship, the lack of a mid tropospheric hotspot proves that.
The bottom line is there’s no need to panic according to the evidence and that is not the reality the globalists looking to use CO2 as a means of economic world governance want to promote.
sabretruthtiger:
That’s incorrect. The alarmist’s models project. They don’t predict.
Terry Oldberg:
Thatās incorrect. The alarmistās models predict.
Richard
Debate over usage of “predict” and “project” dates back to a post to the blog of “Nature” by Kevin Trenberth circa 2008. In the post, Trenberth insisted that the general circulation models did not make predictions. Instead, he asserted, they made projections.
Green and Armstrong reacted by polling professional climatologists on their use of the two terms. As I recall from reading Green and Armstrong’s subsequent journal article, most climatologists used “prediction” in reference to the outputs from the models.
Green and Armstrong concluded from this evidence that the models made “predictions.” Thereupon they compared the values of features of the models to those of “scientific” models. They found that the match was poor. Thus, they concluded that the models were not “scientific.” Later, I reached the same conclusion but by a different argument.
In separate articles, Vincent Gray and I demonstrated that climatologists were repeatedly guilty of applications of the equivocation fallacy. According to Gray this practice had been so successful in deceiving people as to have led to the “triumph of doublespeak.” “Doublespeak” is a synonym for “equivocation.”
Applications of the equivocation fallacy are prevented when a monosemic language is used in making arguments. In a paper ( http://wmbriggs.com/post/7923/ ) I present a list of terms which, if used, would halt applications of this fallacy. The suggested terminology follows Trenberth’s suggestion of distinguishing between “predict” and “project.” Any set of terms that distinguishes between models having underlying statistical populations and models lacking underlying statistical populations would have the same effect. The models of the alarmists are of the latter type. They are unscientific and otherwise unsuited to the task of regulating Earth’s climate but are being applied to this task as a result of the phenomenon that Vincent Gray calls the “triumph of doublespeak.”
Terry, do you, or do you not utilize the projections made by weather forecasting models when planning outdoor activities such as picnics, travel, etc.?
What’s the relevancy?
[Comment deleted. commenter using fake identity, deleted per WUWT policy āmod]
Terry Oldberg:
I repeat, the alarmistās models predict.
Equivocation about the matter is silly. But I have met it before.
Long ago, in 2000, I gave a presentation on climate model performance at the US Congress, Washington, DC.
There were questions after my presentation and one of the questioners asserted that the IPCC doesn’t provide predictions. The assertion is only true if one accepts that the IPCC reporting climate model predictions is not a provision of predictions.
So, I replied saying,
“Sir, there is much you say that I agree, but not all.
For example, you say the IPCC doesn’t provide predictions.
The IPCC says it is going to warm.
I call that a prediction.”
The questioner was not sufficiently stupid for him to dispute the fact that climate models predict warming.
Richard
richardscourtney:
The argument that you have repeated remains an application of the fallacy of argument by assertion.
Predictions, projections, shoot ’em all; let Gaia assert ’em out.
===================
What say?
Terry Oldberg:
The climate models predict warming: they all do.
That is NOT “argument by assertion”: it is a statement of empirical observation.
You took the trouble to repeat your silly and untrue assertion that climate models don’t predict but you forgot to provide the long-awaited explanation of what you mean by an “event”.
Please correct your oversight.
Richard
richardscourtney:
Let’s try this. An argument with a true conclusion aka syllogism has three lines. The form is:
Major premise
Minor premise
Conclusion
The argument that “climate models predict” lacks a major premise and a minor premise. As this argument is not of the form of a syllogism there is not a logical reason for belief in the truth of the conclusion of this argument, namely that “climate models predict.”
Terry Oldberg:
No your “try” did not work because it was meaningless gobbledygook.
Try this.
You need to accept the reality that – as every body knows and can see – the models predict and their predictions have proven to be wrong to date.
Also, you have still failed to provide the long-awaited explanation of what you mean by an āeventā.
Richard
richardscourtney:
In the English vernacular, “predict” has a number of different meanings. For use in scientific research one needs a single, precise meaning that is drawn from the field of probability theory and statistics.
A model that “predicts” under this scientific use of the term leaves telltale signs that include: a sample space, frequencies, relative frequencies, relative frequency values, probabilities, probability values, sampling units, unit events and validation or invalidation of the model in a test of it. In AR4 the report of Working Group I exhibits none of these signs. The signs that it exhibits are possessed by the entities that Dr. Trenberth calls “projections.” Projections can neither be validated nor invalidated. However they can be “evaluated.” AR4 exhibits “evaluation” but not “validation” of its general circulation models. It is validation of a model that makes of it a scientific theory. Thus none of the general circulation models are scientific.
In its early assessment reports, the IPCC claimed its general circulation models to be “validated.” In the paper entitled “Spinning the Climate” Dr. Vincent Gray reports informing IPCC management that: a) the models were not validated and b) were insusceptible to being validated. The management responded by replacing the word “validate” with the similar-sounding word “evaluate.” People with weak to nonexistent grasps of probability theory and statistics such as yourself failed to note the difference. Some, including yourself, persistently exhibited their ignorance by insisting upon calling a projection a “prediction.”
To call a projection a prediction has the downside of creating arguments of the type that is called an “equivocation.” It is an argument that looks to a person as ignorant as yourself to be a syllogism. However, while the conclusion of a syllogism is true, the conclusion of an equivocation is false or unproved.
A model that āpredictsā under this scientific use of the term leaves telltale signs that include: a sample space, frequencies, relative frequencies, relative frequency values, probabilities, probability values, sampling units, unit events and validation or invalidation of the model in a test of it.
Not at all. A prediction made by application of known [or assumed] physical laws [most scientific predictions] does not need any of those ‘signs’. Some typical examples:
The prediction of the return of Halley’s comet
The prediction of the position of the the planet Neptune
The prediction of the deflection of light by mass in General Relativity
The prediction of the size of Solar cycle 24
The prediction of the total solar eclipse in 2017
The notion of an ‘event’ does not enter in those scientific predictions; there is no ‘sample space’, etc.
You seem to have no idea about what scientific predictions are.
lsvalgaard:
Thank you for giving me the opportunity to clarify.
As you use the word “prediction” it may or may not be the product of a conditional prediction aka “predictive inference.” A predictive inference has the properties of being falsifiable and conveying information to the user of the associated model. Absent a predictive inference, there is an absence of falsifiability and information.
The entities that Dr. Trenberth calls “projections” are not the product of a predictive inference. Thus the associated model lacks falsifiability and conveys no information. To call them “predictions” is to make of “prediction” a word with dual meanings. In one of these a “prediction” is the product of a model that is non-falsifiable and conveys no information. In the other it is the product of a model that is falsifiable and conveys information. When this dual meaning “prediction” is used in making an argument, this argument is an example of an equivocation. One cannot draw a logically draw a conclusion from an equivocation. To draw one is the “equivocation fallacy.” Applications of this fallacy are common in making climatological arguments ( http://wmbriggs.com/post/7923/ ).
Applications of the equivocation fallacy can be avoided through maintenance of the distinction made by Trenberth between a “prediction” and a “projection.” Maintenance of it has no downside unless one’s purpose is deception.
As you use the word āpredictionā it may or may not be the product of a conditional prediction aka āpredictive inference.ā A predictive inference has the properties of being falsifiable
All my examples [and almost all scientific predictions] are eminently falsifiable [otherwise they would not be scientific].
Again: you have no idea what you are talking about. I know you think you have, but you are as wrong as one can be. What Trenberth may have said or meant is irrelevant. The climate models are supposed [and claimed] to be based on the physics of the situation, not on statistics. This is not a word-game, but hard-nosed physical science. And it is not about ‘my use of the word “prediction”‘, it is about the generally accepted use of that word in science.
lsvalgaard:
In my message to you I asserted that your use of “prediction” makes no distinction between whether this “prediction” is or is not the product of a “conditional prediction” aka “predictive inference.” Thus this usage sets up an application of the equivocation fallacy. Though this was at the heart of my argument you ignored it thus reaching the false conclusion that “you have no idea what you are talking about.”
Perhaps you have read the late Ed Jaynes’s book “Probability theory: the logic of science.” I stand with Jaynes. To eliminate probability theory and statistics from science is to divorce it from logic. If you disagree with me and with Jaynes this could be a fruitful topic for discussion.
To eliminate probability theory and statistics from science is to divorce it from logic
Most scientific predictions based on physical models are not about probability [although the may predict a probability] and certainly not about statistics, and even less [if possible] about logic. The examples I gave illustrate that abundantly. From experiment we derive physical laws [with very few exceptions] or relationships expressible as mathematical equations. From some initial conditions we predict events [future or past]. If the prediction fails, the laws or the relationship or the assumed initial conditions are falsified. If the prediction is successful, we gain confidence in what went into it. As simple as that. No verbal or philosophical gymnastics needed. And we never, ever do ‘projections’. There is little need to discuss any of this with non-scientists who have never made a scientific prediction.
You appear to overlook the fact that circumstances arise in practice in which the values of probabilities are limited to 0 and 1. This produces the classical logic. If the equations that you reference express logical relationships then they conform to the classical logic or the more general probabilistic logic. Mathematical relations conform to the classical logic.
The classical logic applies to situations for which information needed for a deductive conclusion from an argument is not missing. In the environment in which a scientific researcher usually works, information is missing. Thus, the classical logic is inapplicable.
Global warming climatology is one of the many fields of research for which information is missing. Thus, mathematical reasoning is of limited usefulness for it.
Global warming climatologists proceed as though information were not missing. This is expressed by their expectation of success from an approach in which solution of coupled differential equations produces a set of projections. This has led them into the costly blunder of creating models that convey no information to a policy maker and using scare tactics to induce politicians to use these models in policy making.
The only way to respond to such a question is āI doā or āI donātā
What about “F*ck off you crap strawman builder?”
That’s now four ways and counting
philincalifornia:
Its better if we avoid swearing at each other.
Terry Oldberg:
In contrast to the meaningless twaddle you spout, the word ‘prediction’ has a clear and unequivocal meaning. Clearly, you do not know the meaning of ‘prediction’ and it is obvious that use of a dictionary is beyond you so I will help.
All dictionaries agree on the meaning of ‘prediction’ and this is the definition in the OED:
Climate models PREDICT (i.e. make forecasts).
Richard
Terry Oldberg:
PS You have still failed to provide the long-awaited explanation of what you mean by an āeventā.
Richard
Terry Oldberg is correct. His discussion of the meaning of prediction in science is exactly right. In particular, here, where he distinguishes between prediction and projection.
Prediction implies falsifiability. Unfalsifiable statements about the future, no matter how precise, are not predictions. The reason is that such statements convey no causal information. I.e., a guess can be arbitrarily precise, but its falsification by subsequent observation does not increase our knowledge content. Guesses are not deductions from a set of logically rigorous statements about physical causality.
Climate model projections are not physically unique and have no physical meaning, as I noted here. No physical meaning means their output is not a rigorously specific deductive inference regarding the future behavior of the terrestrial climate. No deductive inference is identical with no prediction.
While it is true that climate models include hard physics, the centrally pertinent question is whether that physics represents a complete, or even adequate, physical model of the terrestrial climate. The magnitude of cloud errors alone produced by advanced climate models clearly indicates that the physics is either wrong or incomplete. See also more CMIP5 error. Model errors are so large that their projections must quickly diverge from the future trajectory of the physically real climate. The divergence reflects physical error, not dynamical chaos. Uncertainty due to error becomes so large so quickly, that the projections lose any physical meaning and therefore have no predictive power. In that event, whatever the climate is eventually observed to do can neither verify nor falsifiy the model.
Terry Oldberg’s distinction between prediction and projection in terms of falsifiability is exactly the standard of science. So, insistent statements to the contrary notwithstanding, he appears to know exactly what he’s talking about. And from my own experience of his posts, that seems always the case.
Prediction implies falsifiability. Unfalsifiable statements about the future, no matter how precise, are not predictions
Climate model predictions are eminently falsifiable [one might argue that they have already been falsified].
Terry and you, it seems, have no idea what you are talking about. Have you ever made a scientific prediction?
I’ll answer your question to Mr. Frank for myself. Over a period of 13 years I held the lead role in the design and management of a succession of scientific studies on behalf of the electric utilities of the U.S. I specialized in building falsifiable predictive models. This work resulted in some of the first applications of information theory in the construction of a model. While the IPCC general circulation models convey no information to a policy maker these models conveyed the maximum possible information to him or her.
applications of information theory in the construction of a model
You don’t build scientific models on that basis, but on the physics and the engineering constraints of the subject matter. Otherwise you are just making curve fitting, with limited predictive capability.
Actually, what we do using information theory is build models that are statistically validated and reflect all of the available information but no more. Contrary to your assumption, curve fitting is not involved. Among the well known products of this method of construction for a model are thermodynamics and the modern theory of telecommunications. When you sit down to watch your HDTV you are the beneficiary of this method of construction for a model.
Leif, with respect to Terry Oldberg’s categories and your example of Halley’s Comet:
Sample space: the solar system
Frequency: comet periodicity
Relative frequency: periodicity with respect to long times
Relative frequency values: periodicity magnitudes over time
Probabilities: likelihood of continued observed and/or predicted periodicities
Probability values: specification of likelihood magnitudes of periodicity variation over time
Sampling units: dimensional units (time, distance, orbital ellipticity, etc.)
Unit events: specified instances of future appearance.
Your other examples can be similarly parsed.
Terry uses generalized terms that take some thought to understand in the specific context of any field of science. Anyone in a serious conversation owes it to him to make the effort to figure out what he’s saying.
Your other examples can be similarly parsed.
Nonsense.
Leif, climate models are not falsifiable in the scientific sense. I included a link to that in my previous post.
The fact that model projections do not conform to the observed trajectory of the evolving climate merely tells us that their guesses are wrong. That’s naĆÆve falsifiability, not scientific falsifiability.
Do you understand the difference between naĆÆve and scientific falsifiability, Lief?
If you do, then you’ll know my point stands.
If you don’t then you’ll argue on.
Arguing on will demonstrate no understanding that merely being shown wrong is not necessarily identical with scientific falsifiability.
Every prediction is qualified. Often we don’t know ALL of the physics and will have to parameterize our unknowns, often the physics is only approximately right [e.g. Newtonian gravity], always we never know all of the initial conditions with enough precision, etc. None of that matters: we make predictions based on what we know, surmise, and guess. In the absurd limit you and Terry are advocating, there can be NO predictions ever. As a working scientist I have no problems with calling a spade a spade and a prediction a prediction. As I said, you guys have no idea.
You have no problem with applying the equivocation fallacy evidently.
equivocation fallacy
Does not apply in this context. What I described is how science works, whether you understand it or not.
To the contrary, the equivocation fallacy applies to any situation in which a term changes meaning in the midst of an argument. Under your version of reality, “scientific” changes meaning at the whim of a “scientist.”
You contradict yourself, Leif. You wrote, “Every prediction is qualified.” followed by , “In the absurd limit you and Terry are advocating, there can be NO predictions ever.”
My prior post included a linked analysis that was all about qualifying climate model projections in terms of their physical uncertainty.
Likewise, here’s what Terry wrote, “your use of āpredictionā makes no distinction between whether this āpredictionā is or is not the product of a āconditional predictionā aka āpredictive inference.ā”
Both Terry and I are clearly talking about qualified predictions. We all know there can be no prediction in science without some qualification concerning its physical uncertainty bound. That concept is in obvious evidence in our posts.
But you’ve ignored it.
At best, you’re not doing either Terry or I the courtesy of a careful reading. At worst, you can’t brook contradiction. Whatever the source, the content of your riposte does not rise above a straw man argument.
qualifying climate model projections
Projections by definition are not predictions and are not science, but a statement of belief. By limiting yourself to projections you divorce yourself from discussing scientific predictions. ‘Projections’ is the straw man. Climate models are concerned with predictions, not projections, e.g. https://www.e-education.psu.edu/earth501/content/p5_p10.html
Now, part of the model may be projections, e.g. of future CO2 emissions, but that is OK as we here can play a ‘what-if’ game. With the assumed [projected] input the model predicts a falsifiable outcome.
As I said, you guys don’t know what you are talking about.
There is a disconnect between you and the government of the United States. You say the models make predictions. The government says the models make projections ( http://www.epa.gov/climatechange/science/future.html ). Who is right and why?
The models make predictions based on projections of future emissions. Try to understand the difference. Future emission is a free parameter and cannot be predicted and the models do not concern themselves with that, but take the future emissions as input. As simple as that.
lsvalgaard:
Your argument has a shortcoming that is independent of the magnitudes of future CO2 emissions. It is that your “predictions” are not the product of a predictive inference. “Predictions” matching this description lack falsifiability and convey no information to a policy maker about the outcomes from his/her policy decisions. If you are skeptical about my allegations I’d be pleased to provide a detailed argument for your review.
It is that your āpredictionsā are not the product of a predictive inference.
They are the product of solving the equations of motions of the atmosphere for given scenarios. That is good enough for me [and ought to be good enough for you too]. No ‘inference’ involved, just plain old physics.
lsvalgaard:
I disagree. A model is a procedure for making inferences. The builder of a model is repeatedly faced with selection of the inferences that will be made from among the many candidates. Builders of global warming models make this selection ineptly with the result that their models are non-falsifiable and convey no information to the users of them. They are unscientific and worthless for the intended purpose.
The dictionary definition of ‘projection’ is
“an estimate or forecast of a future situation or trend based on a study of present ones”
This is predicated on the belief that the present one and its trend are good predictors of the future [that is certainly often the case: I live in California and based on the present situation I can with confidence project that it will not rain tomorrow], but it is not a scientific prediction as it does not follow from solution of the equations with appropriate input that govern the evolution of the weather.
To the contrary, you cannot project with confidence that it will not rain tomorrow because “confidence” is a statistical concept but “project” is not.
Confidence can be measured by how much one would wager on the outcome. I would wager quite a lot based on experience. No statistics needed (the future is not part of the sample space).
Though that is not the “confidence” of statistics it sounds as though you are “confident” of something. Are you confident of the outcome of an event? This can’t be the case as you have dismissed the import of probability and statistics. What is it that you are confident of if this is not the outcome of an event?
if this is not the outcome of an event?
Since you have not defined what an ‘event’ is [see your discussion with Richard] it is unclear what you mean. To perhaps clarity I would say that the event is the outcome.
lsvalgaard
Thank you for revealing your thinking. To think as you do that “the event is the outcome” is a mistake that sometimes arises among people who are confused about probability theory and statistics. Actually rather than being an event an outcome is a description of an event aka state of nature. Folks who think an event is an outcome are prone to confusing an IPCC-style “evaluation” with a statistical “validation” for though there are not the events that are required for validation it seems to these folks as though there are. Thinking that a pseudoscientific theory has been validated these folks mistake it for a scientific theory.
outcome is a description of an event
Nonsense, the ‘outcome’ is what actually happens. If I flip a coin and get ‘heads’, the outcome of the flip is ‘heads’. The ‘event’ [if you wish to mislead] is that a flip takes place, the outcome is ‘heads’ [what actually happens].
You stated (incorrectly) that “the event is the outcome.” Do you mean to revise this statement?
Since you have not defined ‘event’ I sought to clarify what you might have meant [as it comes across]. I have already explained what I think. I’ll repeat it here for your convenience:
the āoutcomeā is what actually happens. If I flip a coin and get āheadsā, the outcome of the flip is āheadsā. The āeventā [if you wish to mislead] is that a flip takes place, the outcome is āheadsā [what actually happens].
lsvalgaard:
Actually, the definition of “event” in probability theory was defined before my birth by mathematicians thus needing no definition by me. Thus Mr. Courtney’s persistent demand for me to define the term is nonsensical and cranky.
Regarding your contention that an outcome is an event is this still your contention? Is ‘heads’ an example of an event? Is ‘tails’? Or is it the coin flip?
For this particular case, the event is the flip, and it can have two outcomes, ‘heads’ and ‘tails’.
Remember that Science is not statistics and probability theory. Those are handy tools that can be used [and misused] as the situation calls for. In your case it seems that if you are a hammer everything looks like a nail.
But you have gotten in so deep now that you have lost sight of the issue and have stooped to irrelevancies.
You may benefit from studying this paper by Frisch:
http://citations.springer.com/item?doi=10.1007/s13194-015-0110-4
http://adsabs.harvard.edu/abs/2014AGUFMGC43G..04F
“Model tuning is unavoidable in climate models. This raises the question whether data used in tuning or calibration can also be used in evaluating a model’s performance or skill. In the philosophical literature this question is discussed as the problem of old evidence: is a model more highly confirmed by novel evidence predicted by the model or is evidence that is accommodated by the model during model construction equally as confirmatory of the model? In this paper I present several conditions under which a weak predictivism holdsāconditions under which predictive success is more highly confirmatory of a model’s empirical performance than mere accommodationāand argue that these conditions are met in the case of climate modeling. In particular, I argue that predictive success can be evidence that a model has certain ‘good-making’ features that are ‘epistemically opaque’āthat is, the presence of which is difficult to detect otherwise. I also propose a Bayesian formulation of the predictivist thesis.”
lsvalgaard:
Thus, an outcome is not an event though you claimed the opposite a few hours ago. Interestingly, for a statistical ignoramous to think an outcome is an event can lead him/her to thinking that an “evaluation” of a model is a “validation” of this model though validation is impossible because the underlying statistical population does not exist. Isn’t this what we have in modern global warming climatology: an error in thinking among people with a dim grasp of statistical ideas that has gotten so out of hand as to be about to cost us trillions of dollars in expenditures for replacing fossil fuels by renewables?
an outcome is an event
I explained to you that the event was the flip, the outcome was either heads or tails. Try to grasp that.
You should study:
http://adsabs.harvard.edu/abs/2014AGUFMGC43G..04F
āModel tuning is unavoidable in climate models. This raises the question whether data used in tuning or calibration can also be used in evaluating a modelās performance or skill. In the philosophical literature this question is discussed as the problem of old evidence: is a model more highly confirmed by novel evidence predicted by the model or is evidence that is accommodated by the model during model construction equally as confirmatory of the model? In this paper I present several conditions under which a weak predictivism holdsāconditions under which predictive success is more highly confirmatory of a modelās empirical performance than mere accommodationāand argue that these conditions are met in the case of climate modeling. In particular, I argue that predictive success can be evidence that a model has certain āgood-makingā features that are āepistemically opaqueāāthat is, the presence of which is difficult to detect otherwise. I also propose a Bayesian formulation of the predictivist thesis.ā
In English, “an outcome is an event” implies the equivalence of an outcome to an event. Perhaps you meant to say “an event has an outcome.”
lsvalgaard
I am glad to see that you have reversed your position and now agree with me that the event is the coin flip while heads and tails are the outcomes. Mr. Courtney appears to think heads and tails are the events. This is why, I believe, he harangues me to supply āmyā definition of āeventā and why I continue to refer him to the literature. Now that we agree on the respective roles of events and outcomes, please cease joining Mr. Courtney in his harangue.
Regarding a possible role for statistical ideas in global warming climatology, climatologists already exhibit fondness for statistical ideas when these take the form of parameterized models and Bayesian parameter estimation. This fondness has led them to create models that make non-falsifiable claims and convey no information to their users. Models with these characteristics should be described as making āprojectionsā according to Dr. Trenberth but should be described as making āpredictionsā according to Mr. Courtney and yourself. This choice of language makes of āpredictionā a polysemic term. When used in making a climatological argument it makes of this argument an equivocation. People, thinking it to be a syllogism, draw conclusions from this argument. However unlike a syllogism, an equivocation does not have a true conclusion. By this mechanism people draw conclusions from climatological arguments that are false or unproved thinking they are true. You can help us to avoid this phenomenon by heeding Dr. Trenberthās advice.
Perhaps your use of ‘projection’ is of the nature described here;
http://www.urbandictionary.com/define.php?term=Projection
” An unconscious self-defence mechanism characterised by a person unconsciously attributing their own issues onto someone or something else as a form of delusion and denial”
etc
Leif, you wrote, “With the assumed [projected] input the model predicts a falsifiable outcome.” (your bold).
You’re wrong, Leif.
If you actually do think a 3Ā±15 C climate model air temperature expectation value is falsifiable, or amounts to a physical prediction, then you, in demonstrated fact, don’t know what you’re talking about.
If you actually do think a 3Ā±15 C climate model air temperature expectation value is falsifiable
Your error estimate is junk, but apart from that, such a model is certainly falsifiable in principle, even if not in actuality, and thus qualifies as [very poor] science. Your belief, on the other hand, that it is not, just betrays your bias.
That claims made by a model are “falsifiable” implies that the associated propositions have truth-values. What are these propositions?
What are these propositions
You are thrashing around. The result of the prediction is what it is. It will have an uncertainty, and if the observed data are too far outside the uncertainty, the model is falsified. As easy as that.
“The result of the prediction is what it is” is circular and unresponsive.
No, it is a statement of fact. If you feel it is not responsive to your comments, perhaps you should review and revise those offending comments.
Leif, you wrote, “Projections by definition are not predictions and are not science, but a statement of belief. ”
Terry’s entire point, e.g., here right at the start, and mine, e.g., here, right at the start, has been that projections are not predictions.
The fact that you finally elaborate this idea in your post just means you’ve come to agree with us without having to directly admit it.
You wrote, “I live in California and based on the present situation I can with confidence project that it will not rain tomorrow]” It’s presently raining in Alturas, CA, with a 50% chance of extending into tomorrow. Oh, well, Leif.
You link to Penn State as evidence that climate models make predictions. But that’s just an argument from authority. Perusal of the site shows that climate models, in fact, do not make predictions in the scientific sense, and in fact are incapable of making such predictions.
For example, Penn State Figures 1 and 3 show that climate models will project the same air temperature for a large number of different climate energy states, and different temperatures for the same climate energy state. The Figures they present as predictions — a diagnosis with which you agreed — instead show that climate models are incapable of producing unique solutions to the problem of the climate energy state.
When their physical error is propagated through their air temperature projections, the uncertainty limits are huge. That means climate models are not even capable of producing usefully constrained solutions to the problem of the climate energy state. I have demonstrated that fact, here; an argument you have not yet seen fit to dispute.
Figures 1 and 3 at the Penn State site also show that the writers of that essay think that model precision is a measure of accuracy. It’s not. This mistake on their part yet again demonstrates the banal truth that climate modelers are not scientists. They have no idea of the meaning of prediction in the scientific sense.
The fact that you think such pictures constitute predictions makes me wonder whether you apply critical thinking outside your own discipline.
In short, Leif, you’re wrong. Climate models don’t make predictions in the scientific sense. They are incapable of making predictions in the scientific sense. Terry Oldberg has been correct all along.
Obviously I meant rain where I live.
But to the main point: climate models attempt to predict based on physics, not on curve fitting to the current trend, and are thus predictions, not projections. That they are not any good is another matter.
About your +/-15 C: that is completely unsupported, no climate model asserts that.
Scientific is what scientists [like me] say it is. That your bias makes you believe otherwise is your problem and can simply be dismissed [herewith done].
That “Scientific is what scientists [like me] say it is” is an oxymoron.
An oxymoron is juxtaposing elements that appear to be contradictory. What are those elements here. Please be as specific as you can in order not to appear moronic.
āScientific is what scientists [like me] say it isā makes “scientific” polysemic thus being a perfect vehicle for applications of the equivocation fallacy.
Leif, you wrote, “Your error estimate is junk,….
Your unsupported word is worthless, Leif. Let’s see you demonstrate your point.
And the Wichita Lineman is still on the line by the time you get to Phoenix rain will be just fine.
=================
We’re in the midst of a serious conversation. If you would omit the doggerel for the time being I would appreciate same.
While I agree that Kim’s comment is noisy, useless, and unnecessary chatter. I disagree that your comments are in any way serious. As I said you don’t know what you are talking about. As Mark Twain said: “it is not what you know that gets you in trouble, it is what you know that ain’t”
Leif, on the contrary, climate models are curve fit to past observables as a way to choose their parameter sets. Then they are extrapolated to future climate using the parameters derived from the fits.
This curve fitting approach is called model tuning. It’s very well known that various climate models are able to reproduce past observables, despite factor of 2-3 differences in so-called climate sensitivity, because their chosen parameter sets have off-setting errors.
So, by your definition, namely that, “climate models attempt to predict based on physics, not on curve fitting to the current trend, and are thus predictions, not projections.“, climate models produce projections not predictions.
climate models are curve fit to past observables as a way to choose their parameter sets
This is not a correct statement. Some of the microphysics are parameterized, but the resulting relationships are used in concert with solving the equations, time-step for time-step. This is no different from using measured values for macroscopic physical constants [such as Enthalpy of vaporization] instead of calculating them from first principles.
A projection is using the present state and trends [and nothing else] to extrapolate into the future. It is important that you understand the difference.
Your definition of “projection” differs from the IPCC’s and from Trenberth’s. In both cases, a “projection” is the result of the computations that are made by a general circulation model.
Especially noting the chatter is temporally disorganized, which makes the line hard to follow. Who is serious about that static?
Heh, seriously static, like this serious convocation.
===================
‘Tain’t worth beans for policy, and there goes the pea under the thimble. Isn’t that what you’re betting on?
Mebbe you aren’t very serious.
===========
Pat Frank:
A person presenting an argument has a responsibility to provide it to the target audience by explaining it in language the audience can comprehend.
A recipient of the argument has no responsibility to translate it into comprehensible language and any such translation may include errors.
Hence, although I usually agree with you, I strongly disagree when you write
NO!
Terry uses āgeneralised termsā that he cannot ā at least, he refuses to ā explain when asked to define them. In other words, Terry only writes meaningless nonsense because the meanings of his words are not known by their recipients.
Richard
richardscourtney
My guess is that the words are meaningless to you because you are ignorant on topics that one needs to master to perform competently on the theoretical side of scientific research. I climbed the learning curve on these topics in earning between 9 years and 10 years worth of university credits and three degrees then working over a period of 20 years in scientific institutions. Based on competency attained in this work I rose to a level at which I designed and managed a long sequence of scientific studies. By listening to experts I learned how to design a study and to build the associated model in a manner that was logically flawless.
So far as I can determine you are close to square 1 in attaining mastery over most of the topics that one needs to master to achieve competency. If I try to steer you in the right direction I find that you respond only with complaints about the quality of the instruction I’ve tried to supply. Thus, I’ve abandoned attempts at tutoring you and left you to your own devices.
Leif and Pat:
I write in hope of helping by clarifying the issue of āpredictionā and āprojectionā.
A prediction is a forecast.
It is a statement concerning what a future event(s) will be.
Scientific predictions were made by scientists long, long before scientists started to use much statistical analysis.
A scientific prediction is a forecast that can be falsified by comparison with later reality. So, for example, an assertion that a horse will win the Derby is a scientific prediction. It does not matter how or why that forecast was obtained and/or used: it is a scientific prediction because it can be falsified by comparison with later reality.
The condition of falsifiability being the requirement of scientific predictions is uncomfortable for many scientists because it means that some very unscientific activities (e.g. astrology) make scientific predictions. However, falsifiability being THE requirement is why predictive skill is important for scientific predictions.
Importantly, merely being scientific does not mean a prediction is useful .
Pure chance provides a probability of some predictions being right. But a series of predictions made using a particular method can be assessed to determine if the method makes forecasts which have higher statistical probability of being right than would be provided by pure chance. And this probability is the predictive skill of the method.
A projection is an extrapolation from existing trend(s).
It is an assumption that existing trend(s) will continue for some amount of future time.
Projections are probably the most used method for obtaining predictions. For example, every sportsman running to catch a ball is projecting the trajectory of the ball as a method for predicting where he will be able to catch the ball. This method has a high predictive skill. Indeed, its predictive skill is so high that the military uses it to compute where to shoot moving targets.
Climate models use projections to make predictions with no demonstrated predictive skill.
A climate model is provided with a future scenario such as projection of atmospheric CO2 concentration then calculates a prediction of the future climate if that projection were to be correct. The prediction can be compared to future reality.
Please note that the use of a projection to obtain the prediction does NOT convert the prediction into a projection. Similarly, an aim at an enemy aircraft projects when the aircraft will be when a shell reaches altitude, and the aim is a prediction of where to send the shell whether or not the aircraft alters course.
Richard
richardscourtney:
An assertion that a horse will win the Derby is insusceptible to being falsified unless it makes a predictive inference. My guess is that you lack experience with falsification thus failing to understand that this is true.
A model is falsified when it is tested on “out of sample” data and it is found that the predicted values of the probabilities of the outcomes fail to match the observed relative frequencies. For a model that makes projections, this cannot happen as the underlying statistical population does not exist.
When tested in this way and not falsified, a model is said to be “validated.” Thus, models that make projections cannot be validated. However, they can be “evaluated.” “Evaluation” is a term that was introduced into IPCC assessment reports after Vincent Gray informed IPCC management that models which the IPCC was claiming in its assessment reports to be “validated” were neither validated nor even susceptible to validation. It kinda sounds as though a model has been “validated” when the IPCC says it has been “evaluated” as the two words are similar sounding. This may have been IPCC management’s intent. Validation is a requirement for a model to be “scientific” and while the models do not satisfy this requirement the tricky wording makes it sound as though they do.
Predict this
Project that;
All the fire
Is in the fat.
It seems that sometime during the night Terry has edged over to sabretruthtiger’s original point. Leif’s still dancing pinheadedly.
The models are crap for policy guidance; can we get on with that understanding?
===============
By the time we get to Paris, she’ll be dying.
=====================
Terry Oldberg:
I suggest you don’t take out a bet with an organisation fronting for the mob if you think an assertion that a horse will win the Derby is not a prediction which can be falsified. They would give you the education most people want to.
And your words are meaningless because you refuse to say what you mean when you use them. Furthermore, when Lief did ascribe a very sensible meaning to the word “event” then you claimed he was wrong!
It is long, long past the time when you should have defined what you mean when you use the word “event”.
Richard
Brian G Valentine
To provide one’s CV does not make one guilty of argument from authority.
Leif, you wrote that, my observation that, “climate models are curve fit to past observables as a way to choose their parameter sets” “is not a correct statement.”
The following examples are far from exhaustive.
From Eisenman, I., N. et al., On the reliability of simulated Arctic sea ice in global climate models Geophys. Res. Lett., 2007. 34(10) L10501 “A frequently used approach in GCM sea ice components is to tune the parameters associated with the ice surface albedo.”
From Hargreaves and Annan Using ensemble prediction methods to examine regional climate variation under global warming scenarios Ocean Modelling, 2006. 11(1-2), 174-192. ““Recent developments in parameter estimation have now opened up the possibility of performing ensemble integrations of models which have been objectively tuned to climate observations, and which therefore have the potential to generate more meaningful probabilistic estimates of future climate.”
From Kiehl, J.T. Twentieth century climate model response and climate sensitivity Geophys. Res. Lett., 2007. 34(22), L22710. ““Note that the range in total anthropogenic forcing is slightly over a factor of 2, which is the same order as the uncertainty in climate sensitivity. 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.”
From Knutti, R., et al., Challenges in Combining Projections from Multiple Climate Models J. Climate, 2010. 23(10), 2739-2758 ““One of the difficulties [of evaluating model predictions] is that the observations often have been used in the modeling process before, to derive parameterizations, or to tune earlier versions of models. Therefore, there is a risk of double-counting information, overconfidence, or circular logic if model evaluation and weighting is done on the same datasets that were used to develop the models.”
From Lauer and Hamilton Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data J. Climate, 2013. 26(11), 3823-3845 “There is generally only very modest improvement in the simulated cloud climatology in CMIP5 compared with CMIP3. The better performance of the models in reproducing observed annual mean SCF and LCF therefore suggests that this good agreement is mainly a result of careful model tuning rather than an accurate fundamental representation of cloud processes in the models.”
Climate model parameter sets are tuned so that model output matches observations. They achieve that goal using parameter sets with offsetting errors.
Tuned models with internal offsetting errors are then used to project future climate. The method exactly matches your definition of curve fitting.
Their method is not at all like, “using measured values for macroscopic physical constants [such as Enthalpy of vaporization] instead of calculating them from first principles..
It’s important that you know this.
Richard Courtney, falsification of a declarative statement by observation is necessary but not sufficient to be scientific. The falsification criterion in science necessarily extends all the way back to the theory.
Scientific statements about future observables — predictions, in other words — are necesarily deduced from a causally specific physical theory by way of an unbroken chain of analytical logic; typically stated in mathematical language so as to be completely unabmbiguous.
Falsification of deductive predictions not only disprove the prediction itself, but also disprove the causal theory from which the prediction was derived. Disproof of theory is a consequence of the unbroken chain of logic between the theory and the prediction. Inference travels down the chain into a prediction, falsification travels back up (as does verification).
Therefore, a statement such as “a [certain] horse will win the Derby” is not a scientific prediction because it was not derived from an analytically causal and falsifiable theory of horse racing. That is, the statement is not a rigorously deductive inference. It is just a declaration of belief. Its falsification by way of the victory of another horse does not travel back up an unbroken chain of rigorous logic to falsify any analytically causal theory.
So, the criterion for a scientific statement is two-fold: the prediction itself must be falsifiable, as you suggested, which requires that the prediction be very constrained (tightly bounded or, ideally, unique) and the prediction must have been deduced from an analytical theory of causality by way of an unbroken chain of rigorous logic.
The large uncertainty limits manifested when error is propagated through climate model air temperature projections shows that such projections do not meet the criterion of falsification. The reason is that any concivable change in the true physical air temperature will fall well within the uncertainty bars. Falsification is become impossible. So, the models are incapable of predictions because their outputs are not sufficiently constrained.
This is apart from the problem that models are adjusted to observables, which method produces parameter sets with offsetting errors. Their projections are hardly more than extrapolations from semi-empirical curve fitting.
Pat Frank:
Sorry, but you are making one of the same mistakes as Terry Oldberg promotes when you say
NO!
1. There may not exist any theory but only an hypothesis.
2. The hypothesis may have no āunbroken chain of analytical logicā but merely represent a deduction from observations.
3. And the use of mathematics is only one of many tools that scientists may or may not choose to use.
Often a prediction is made as a test of the hypothesis when there is insufficient data to provide a theory. Lief provided an example of this when he cited the prediction that Halleyās Comet would return in a specific year. A comet did appear in that year and no statistical analysis was required to discern that this was a correct prediction although there was no evidence that it was the same comet that āreturnedā. This apparent āreappearanceā was supporting evidence for the hypothesis that comets were satellites of the Sun that had much more elliptical orbits than planets. Accumulation of subsequent evidence enabled construction of a theory of comets.
Most seminal science has no āScientific statements about future observables ā predictions, in other words ā are necessarily deduced from a causally specific physical theory by way of an unbroken chain of analytical logic; typically stated in mathematical language so as to be completely unambiguous.ā
Almost all seminal science uses predictions based on observations of past behaviour to explain a system. (This is exactly the same method as predicting the winner of a horse race on the basis of the past records of the racers’ performances.) Subsequent scientific studies then attempt to generate theories which explain the predictive skill of ascribing the observed system behaviours to future system b ehaviours. The method operates as follows:
A scientist discovers e.g. a new species.
1. He/she names it (e.g. he/she calls it a gazelle) and describes it (e.g. a gazelle has a leg in each corner).
2. He/she observes that gazelles leap. (n.b. the muscles, ligaments etc. that enable gazelles to leap are not known, do not need to be discovered, and do not need to be theorised and/or modelled to observe that gazelles leap. The observation is evidence.)
3. Gazelles are observed to always leap when a predator is near. (This observation is also evidence.)
4. From (3) it can be deduced that gazelles leap in response to the presence of a predator and this deduction is a scientific prediction of future actions of gazelles.
5. n.b. The gazelleās internal body structure and central nervous system do not need to be studied, known, theorised or modeled for the conclusion in (4) that āgazelles leap when a predator is nearā to be a valid scientific conclusion. Indeed, study of a gazelleās internal body structure and central nervous system may never reveal that, and such a model may take decades to construct following achievement of the conclusion from the evidence because theories need to be developed for each part of the gazelleās gazelleās internal body structure and central nervous system before such a model can be constructed.
This has direct relevance to the present discussion of āpredictionsā and āprojectionsā of climate models. In the illustration of typical seminal science the prediction of future gazelle behaviour has no ācausally specific physical theory by way of an unbroken chain of analytical logicā. It is prediction based on projection of observed past system behaviour.
The global climate system is more complex than the central nervous system of a gazelle and an incomplete model of a gazelleās central nervous system could be expected to provide incorrect indications of gazelle behaviour. For the same reasons, the numerical climate models can be expected to provide incorrect indications of gazelle behaviour.
Mathematics is one tool used by science and some of the best science uses almost none of it (e.g. Darwin C āOn the Origen of Speciesā, 1859)
Richard
Ouch! I wrote:
For the same reasons, the numerical climate models can be expected to provide incorrect indications of gazelle behaviour.
I intended to write:
For the same reasons, the numerical climate models can be expected to provide incorrect indications of climate behaviour.
Sorry.
Richard
richardscourney:
Your arguments continue to suffer from the shortcoming of incorporating polysemic terms such as “predict” and “science” into arguments. It would be well if you were to switch to monosemic terms.
Terry Oldberg:
It is very annoying that you repeatedly ask me to use a dictionary for you when you use words in ways that are unique to you, which you change over time, and that you refuse to define.
I still await your definition of what you mean by an āeventā.
You say you donāt know the clear and unambiguous words āpredictā and āscienceā.
Please see my above post which informed you that all dictionaries state a prediction is a forecast and quoted the OED as example. To predict is to make a forecast.
The OED also says
Please learn to use a dictionary for yourself so avoiding the need for me to repeatedly do it for you.
And you need to use an English dictionary because my posts are written in English while yours are written in gobbledegook.
Richard
richardscourtney:
Your post is loaded with inaccuracies as usual. This habit of yours makes attempts at logical discourse with you uniformly unproductive.
I have never asked you to use a dictionary. I have repeatedly referred you to the literature of probability theory for a definition of “event”; this word is given an unambiguous definition there. Contrary to your innuendo I do not use “event” in an unusual way.
In the language of global warming climatology, “predict” and “science” are among sever al terms that are polysemic. That they are polysemic and used in making climatological arguments yields frequent applications of the equivocation fallacy. I draw this conclusion from the argument that I make in the peer-reviewed article at http://wmbriggs.com/post/7923/. If you don’t like this conclusion you could try composing a refutation and seek peer-reviewed publication for it.
You may be unaware of the fact that many of the words in the English vernacular are polysemic. There are those of us who wish to avoid leading people to draw logically illicit conclusions from equivocations when we make arguments. We can head off the possibility of doing so by making equivocations impossible. This can be accomplished by replacing the polysemic words of the dictionary by monosemic ones. For example we can give “predict” one of is two definitions and “project” the other. Over the past few days, you have battled like a tiger to preserve your ability to give “predict” two meanings. I guess you must like the result.
Richard Courtney it is almost a truism in science that a hypothesis rises to the level of theory when it is strongly and repeatedly verified by observation and/or experiment.
This rising to the level of theory does not of necessity include a modification of the structure of the hypothesis. The rising only requires repeated successful verification. Therefore, a given hypothesis merits the label “hypothesis” in science only if it is falsifiable by virtue of the predictions deduced from it.
This means a scientific hypothesis has the same structure as a scientific theory. That structure includes internal logical consistency, rigor of expression, and capable of deductive inferences that are connected to the hypothesis by an unbroken chain of causal logic.
In your number 2, how is it possible to deduce from observations absent a chain of logic? Does not “deduction” itself require a reasoned process? How does reasoning proceed successfully absent unbroken logic?
Your description of the progress concerning Halley’s comet and gazelles reflects the use of observation to generate limited-scope semi-empirical analytical models. This is one of the ways science proceeds to full theories. There’s nothing wrong with that. Nevertheless, such models must be internally self-consistent, i.e., logically coherent, and must make predictions according to the rules of logical deduction. That is, they operate by the same rules as does a full theory.
Contradictory observations must have the capacity to falsify your semi-empirical models, as such falsification is the only way these models can be improved. The same is true of full theories. Causal theories are improved by falsification of less correct prior versions.
Just to add, Darwin began the Origin of Species with a long discussion of the effects of breeding upon pigeons. The causal validity of this discussion required an implied physical and systematic relation between external morphology and internal heritable trait. The mathematics describing this relation was necessarily latent in Darwin’s elaboration of his theory; necessary, that is, to justify Darwin’s hypothesis as a part of science. The fact that the mathematical elaboration became explicit only later does not mean it was not intrinsic then.
And we must further agree that the ruthlessness of science is that if such an analytical theory of evolution had later proved impossible by virtue of disconfirming evidence, Darwin’s hypothesis would have been disproved. That is, even Darwin’s hypothesis as expressed using words, was sufficiently monosemous to be logically connected to its deduced implications — its predictions — so as to be falsifiable by disconfirmatory evidence. If no heritable traits (genes) had been found, Evolutionary Theory would have been disproven.
Those internal structural traits are what made Darwin’s work a hypothesis in the scientific sense. Since then, it has been repeatedly verified and strongly elaborated and has risen to the status of Evolutionary Theory. In so doing, Darwin’s Evolutionary Hypothesis has not changed its logical structure.
Leif, you also wrote, About your +/-15 C: that is completely unsupported, no climate model asserts that.”
Now, I’m really worried. What is it you suppose that Ā±15 C indicates? Really interested in your answer to that.
It is your value. You explain how you got that, and what you think it means.
Leif, I’ve explained that Ā±15 C in detail here, here, and especially here (2.9 MB pdf).
Now it’s your turn. You wrote my analysis is “junk” and that “no climate model asserts that [Ā±15 C].”
So, what’s it mean, that Ā±15 C? In your view.
Richard Courtney, I understand your point. Nevertheless, when the conversation is serious, I don’t mind taking the additional step of parsing unfamiliar terms to understand what someone means to communicate.
As you can see here, it was not so difficult to convert Terry Oldberg’s general criteria into more specific scientific delineations. Terry Oldberg has not corrected my indications, and I’ll take his continued silence on this as an assent. So, I don’t agree that his terms are meaningless, although sometimes they take an effort. But that sort of effort is not unusual during professional conversations between differently trained persons.
On the other hand, Terry Oldberg’s response to you was not very helpful either.
I do believe that speculations on another’s professional failings don’t bring much light to a debate.
Pat Frank:
You are arguing for being ālogically coherentā while being inconsistent.
You said
But that is NOT true because scientific predictions are NOT necessarily deduced from a causally specific physical theory. So I replied saying
And I explained it with the gazelle illustration.
I also pointed out that Halleyā comet prediction was made as a test of an hypothesis that later developed into a theory. There must be very many similar predictions made as tests of hypotheses which were rejected because they failed the test(s) and, therefore, did not become theories.
You now say to me
That is very, very different. And, contrary to your earlier claim, it does NOT reject as being unscientific a prediction of a horse winning the Derby. As I said, it āis exactly the same method as predicting the winner of a horse race on the basis of the past records of the racersā performancesā.
You are supporting one of Oldbergās untrue assertions and ā as he does ā you are claiming you said other than you did when shown to be wrong. This is not good. (Perhaps you need to also adopt his practice of using words he refuses to define and alters at will).
In order to remind onlookers of why this issue is important, I repeat the conclusion of my post you have answered.
This has direct relevance to the present discussion of āpredictionsā and āprojectionsā of climate models. In the illustration of typical seminal science the prediction of future gazelle behaviour has no ācausally specific physical theory by way of an unbroken chain of analytical logicā. It is prediction based on projection of observed past system behaviour.
The global climate system is more complex than the central nervous system of a gazelle and an incomplete model of a gazelleās central nervous system could be expected to provide incorrect indications of gazelle behaviour. For the same reasons, the numerical climate models can be expected to provide incorrect indications of climate behaviour.
Richard
Richard, this, your statement, “contrary to your earlier claim, it does NOT reject as being unscientific a prediction of a horse winning the Derby. As I said, it āis exactly the same method as predicting the winner of a horse race on the basis of the past records of the racersā performancesā. ” says that scientific predictions can be theory-free inductive inferences.
But already in the 18th century, David Hume showed such inferences are free of any predictive content.
Sorry to say, Richard, your descriptions of science are foreign, and inconsistent with how I know science to proceed (predictive deduction from falsifiable theory).
We’ll have to agree to disagree. I don’t see any reason to continue arguing.
My best wishes to you . . .
Pat Frank:
I accept that we must agree to disagree.
Your citation as truth of the well-known error of David Hume does not convince me. Contrary to the sophistry of Hume, the premise that the future will resemble the past is rationally founded for the immediate future.
The future will not continue to resemble the past for ever. But if the premise that the future will resemble the past were rationally unfounded then it would be irrational to get out of bed in the morning.
Others can evaluate our views for themselves. I am content that I have described how and why most scientific predictions are made and that I have provided clear examples which support my view.
Richard
Terry Oldberg
My post to you was NOT “loaded with inaccuracies”.
As is my “habit”, my post was clear, accurate, factual, referenced and quoted the reference.
As is your “habit”, your reply is complete bollocks, and it is yet another failure to state what you mean by an “event”.
Richard
So what they are saying is what they were told back in the sixties. Scientists are clueless about doing things in the real world and should leave real world thing to engineers. Lets face it they never were interested in anything but theories and as long as they are elegant and plausible to their peers do not give a stuff about how well they stand up in reality as they are quite happy to change the measurements of reality to fit the theory if it is popular with their cronies.
Engineers test by how well the theory fits reality without constant adjustments made even after claiming the science was beyond question. Either the adjustments are criminal deception or the original claim was at best the sort of over sellling that in the banking world has given rise to thousands of damages claims.
Too bad no legal companies are wiling to stretch their wings and take on the climate scientists and their universities for damages and demand compensation for all the subsidies justified by climate change.
Climatologists relentlessly apply the equivocation fallacy in making arguments (http://wmbriggs.com/post/7923/ ). In this way they draw logically illicit conclusions from these arguments. This is provable in court.
“This is provable in court”
…
Except that courts deal with issues of law, they don’t deal with issues of science.
Actually, the courts do deal with scientific issues. They deal with them when the admissability of scientific testimony arises as an issue. To address this issue a court needs a definition of “scientific.” For the federal courts and most of the state courts of the U.S. this definition is supplied by the Daubert standard.
We agree that admissibility and validity are separate issues. If a plaintiff were to sue a defendant for fraud over a climatological issue the judge or jury would have to judge the validity of the scientific testimony as well as the admisssibility. I don’t know of a barrier to filing such a lawsuit. Do you?
[Comment deleted. commenter using fake identity, deleted per WUWT policy āmod]
Joel D. Jackson (July 31 at 11:09 am):
You’ve defended your hypothetical client from a straw man argument. A plaintiff could not prove a defendant knew the “science” to be false as “science” lacks a truth-value. A plaintiff could possibly prove a defendant knowingly made a deceptive argument for profit thus being guilty of fraud. The deceptive argument that I have in mind is an application of the equivocation fallacy that conflates “prediction” with “projection.”
Joel,
The courts have repeatedly ruled on the validity of scientific issues, as in this case:
http://ncse.com/creationism/legal/intelligent-design-trial-kitzmiller-v-dover
Hang on…
But this means that the new atmosphere-only method heats faster than the combined atmosphere sea surface method. But last week they told us that the missing heat was goign into the sea, and the sea was heating more. So which is it? Is the sea heating more or less? Can they keep their stories straight?
Friends:
The article reports
Ah, yes, “challenges in assembling the data”. Obviously it must be that! sarc off/
The fact is that divergence exists and the models predicted it would not.
The divergence indicates that the models lack predictive ability.
That is reality, and what Cowtan thinks or what I think does not change it.
Richard
In making arguments would be well if you were to make a distinction between “project” and “predict” as failure to make this distinction is the basis for applications of the equivocation fallacy ( http://wmbriggs.com/post/7923/ ) that are widespread among climatologists . When this distinction is made, it is accurate to state that the general circulation models make “projections” and inaccurate to state that they make “predictions.” Models that make projections convey no information to a policy maker about the outcomes from his/her policy decisions thus being unsuitable for making policy on CO2 emissions. The claims that are made by these models are not falsifiable thus being unscientific.
Terry Oldberg:
The models predict.
It would be well if you were to recognise this reality instead of trying to obfuscate the matter.
Richard
PS
I still await your clear statement of whatever it is that you think you mean by an “event”.
The argument that “the models predict” is an application of the fallacy of argument by assertion.
I predict that the model projection causes psychological projection in politicians who feel that they ‘must do something’ to alleviate a projected future. They then take money to accomplish that.
The model projection is an anomaly. The political projection is a forcing.
Thank you. That makes more sense than an IPCC assessment report.
(Comment deleted. commenter using fake identity, deleted per WUWT policy āmod)
In coming to grips with the shortcomings of modern global warming climatology, the example of a hurricane is of limited value because a hurricane is outside of our control. When we succeed at bringing a system under control this is because we have information about the outcomes for this system conditional upon whatever actions are taken in attempting control. The measure of this information is the mutual information and not the skill. For a projection-making model, the mutual information is nil. For a prediction-making model, the mutual information is not nil.
That’s a misrepresentation of what I said!
Terry Oldberg:
Climate models predict; i.e. they make forecasts.
To date all the predictions of climate models have proven to be wrong.
As lsvalgaard explains to you above in this thread
And your posts make no sense so it would be difficult to “misrepresent” them.
Richard
PS You have still failed to say what you mean by an “event”.
Terry Oldberg,
Please respond to the (repeated) question from Richard: it will do us all good to read your answer(s). Thank you in anticipation.
PS: There is no escape from the truth here at WUWT and you will find that there is always someone who can help you, or, simply “blow you out of the water”. This is a friendly word of advice and is politely given with respect and civility.
Regards,
WL
Warren Latham:
To comply with your request I’d have to tutor you in elementary probability theory and statistics without compensation. While I don’t need the money, I’ve found that a climate blog provides a hostile environment for teaching due to the tendency of a significant subset of bloggers to think and act emotionally rather than logically. Thus, I’ve dropped back into a position of limiting my participation to making and refuting arguments.
My summary from this paper is: The models should make projections for the land only (without oceans) and compare it with the measurements.
My summary is that we should be embracing UAH and RSS datasets as the only truly valid global thermometers.
Heh, hammered for short timespan of operation, but ironically the satellite series now have the longest series of unadulterated original data.
We shall see, won’t we, one fine day?
==================
I searched for a working username and password for hours..finally i found..
4-Ever Models Account – http://www2.4evermodels.com/track/MjAwNDU1OjM6NjE/
Well. I was wondering. Why do the models need training? No one trains F=ma.
Newton already trained it.
My view of the debate thus far:
Frank and Oldberg continue to attempt disambiguation of the language of the debate thus heading off applications of the equivocation fallacy. Svaalgard and Courtney continue to attempt ambiguation of this language thus enabling applications of this fallacy. Svaalgard and Courtney exhibit aversion to addressing the issue of why one would wish to enable applications of the equivocation fallacy. Frank and Oldberg exhibit eagerness to address the same issue.
Every time I run across this issue it is a diversion from the point that models are not mature enough for policy action. What amuses me is that the diversion is rarely deliberate; oh, the banality of the innocent.
================
Kim, I’ve been on your point for years. I’ve been trying to publish a paper on exactly the unreliability of models for more than 2 years now, in the face of the ‘agreement review’ process that controls climate science. Manuscripts are acceptable when they agree with the consensus, and rejected when they do not. In my experience, even if you get by peer review, an editor will find another reason to reject.
But it’s not a diversion to speak of other things. Other topics are legitimate, despite that you’re right that the central issue is the adamantine policy adherence to completely unreliable climate models.
kim
It is true that models are not mature enough for policy action. However, most arguments for the proposition that this is true draw conclusions from equivocations thus being logically improper. Thus, rather than being a diversion ridding arguments of application of this fallacy is essential.
It seems we all agree,
It’s a noisome way to be.
====================
Predict
“say or estimate that (a specified thing) will happen in the future or will be a consequence of something”
Projection
“1.an estimate or forecast of a future situation based on a study of present trends:”
Climate models are definitely a prediction as they have never been based on a study of present trends. The present trends have always been ignored by the alarmists and always been about the future consequences.
Matt G:
Your definition of “predict” is not completely accurate. To say that a specified thing will happen in the future is a “prediction.” To say that a specified thing will be a consequence of something is a “predictive inference.”
Example:
Prediction: Rain in the next 24 hours.
Predictive inference: Given cloudy, rain in the next 24 hours.
In the absense of a “predictive inference,”rain in the next 24 hours” is not an example of a “prediction” but rather is an example of a “projection.”
If there is a predictive inference, the observation of “cloudy” conveys the information to us that it will rain in the next 24 hours. If there is not a predictive inference, the observation of “cloudy” conveys no such information to us. Thus, it is not the prediction that conveys information to us but rather is the associated predictive inference. That this is so can be proved with the help of information theory.
To generalize from this example, one should not conflate “prediction” with “projection” as it is the predictive inference that is associated with a prediction but not a projection that conveys information to us. To treat “prediction” and “projection” as synonyms is to make the error of conflating a situation that conveys information to us with one that does not.
Global warming climatologists have made this error. The projections of their models convey no information to a policy maker but seem to convey such information because global warming climatologists plus true believers in their utterances use “prediction” and “projection” as synonyms. For control of the climate a policy maker needs information about the outcomes of events and though he has no information it seems to policy makers and true believers as though this information is available. Thus, though the climate is uncontrollable policy makers persist in attempts at controlling it.
Again, you are completely wrong.
Let this be a teachable moment for you:
A projection is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A prediction is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations]. Thus is not an inference but a real-world expectation.
This closes the debate.
.
lsvalgaard:
Your high regard for the cogency of your own argument is misplaced. My example is chosen for simplicity. Though it incorporates no natural laws there is no barrier to incorporation of them into a model that makes a predictive inference, contrary to your assumption. Colleagues of mine and I have successfully built models having this characteristic.
As a model that makes no predictive inference conveys no information to a policy maker, it is a disastrous error to build a model that makes no predictive inference and to use it in making policy. Thus, a semantic distinction should be made between the output of a model that makes a predictive inference and and the output of a model that does not. The specifics of the terminology that makes this distinction are unimportant.
The terminology suggested by Dr. Trenberth is available for use for this purpose and in widespread use. The “projections” described by Trenberth as the outputs from currently available climate models are not products of a predictive inference. Thus, they convey no information to a policy maker and are unsuitable for use in making policy. If we reserve the term “prediction” for use in reference to the outputs from a predictive inference it then becomes impossible for it to be falsely concluded that modern climate models are suitable for use in making policy by application of the equivocation fallacy.
If we reserve the term āpredictionā for use in reference to the outputs from a predictive inference it then becomes impossible for it to be falsely concluded that modern climate models are suitable for use in making policy by application of the equivocation fallacy
That is just meaningless gobbledygook..’Inference’ is ‘an educated guess’ and in no way is applicable to the result of physics-based models. The reason, modern climate models are not suitable for policy is simply that they don’t work. Not that they are ‘projections’.
lsvalgaard:
“That is just meaningless gobbledygook” is the conclusion of an argument. What are the major and minor premises to this argument and why are these premises true?
This is not an argument, but simply a statement of fact. Take it to heart.
lsvalgaard:
You claim “that is just meaningless gobbledygookāis a “fact” but are unable to make an argument in support of this conclusion let alone prove it. Do you take the audience for this debate to be made up of fools?
not generally, but there are certainly a couple around…
lsvalgaard:
You are unwilling or unable to defend your conclusion. I rest my case.
We all wish that you would indeed do that. RIP.
Terry Oldberg August 6, 2015 at 11:33 pm
Isvalgaard is exactly right, you state “Given cloudy, rain in the next 24 hours.” This is a projection not a prediction as confirmed in most English dictionaries. It is only a projection forecasting future based on now or recently, sorry you are wrong.
Weather models are based on projections to give accurate forecasts, if they were based on predictions they would be wrong all the time like climate models. Weather models increasingly become useless further from point of observation because they become a prediction and can’t rely on current, very recent observations.
The prediction of their climate models only gives information on the future and ignores information/no information based on recent/current knowledge.
Good grief, is this still going on?
Hi Doc
This is a big one, it is facing us; If it pops-up it could be interesting.
http://sdo.gsfc.nasa.gov/assets/img/latest/latest_1024_HMIIC.jpg
And it sits on a Hale boundary, so is a good candidate for flaring activity.
Leif, how are model expectation values āpredictionsā when they have no knowable physical meaning?
Itās very clear that model outputs that conspicuously lack physically valid error bars are misrepresentations. It is quite obvious that models are being extended well beyond their resolution limit. What you call āpredictionsā are knowledge claims where no knowledge exists. Such claims cannot ipso facto be predictions.
I see now that you have admitted that models are ā[possibly calibrated using observations].ā Thank-you for conceding my point that models are tuned, a fact that you previously denied.
But in doing so you go on to misrepresent models fitted to observations by utilizing offsetting errors as being ācalibrated.ā
From Kiehl, 2007, again, ā All of these [climate model] simulations show very good agreement between the simulated anomaly in global mean surface temperature and the observational record. ā¦ Note that the range in total anthropogenic forcing is slightly over a factor of 2 [in climate models], which is the same order as the uncertainty in climate sensitivity. 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.ā
Models: tuned to observables by offsetting parameter errors. Thatās what you call ācalibrated,ā Leif. Application of inverted errors is not calibration. Your argument engages a tendentious abuse of language.
Under the actual circumstances of physically insupportable knowledge claims and indulgence of model false precision, there are no grounds whatever to call their expectation values āpredictionsā in any scientific sense of that word.
Also, I note that you have avoided addressing your erroneous thinking regarding that Ā±15 C projection uncertainty. The content of your error was pretty clear in the way you expressed yourself, Leif. I.e., āno climate model asserts that.ā I just hoped youād show the intellectual courage to correct it yourself.
There are so many things wrong with your comment that it is hard to know where to begin.
Let me repeat the clear definitions:
A projection is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A prediction is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations].
Now, a prediction can be wrong as climate models apparently are. That does not affect the definitions.
Your notion of ‘tuning’ is nonsense. If the models were tuned to match the observations the models would always be correct. Calibrating responses is normal practice in science.
Your +/-15 degrees is absurd on its face. For one, it lacks a time horizon: is the error that large after the first time-step of five minutes?
Leif, your definition of projection is merely you being insistently self-serving.
Standard IPCC usage is that modeled future climate states are “projections,” your insistence notwithstanding.
Likewise, the IPCC AR5 WG1:
Chapter 11, “Near-term Climate Change: Projections and Predictability”
Chapter 12, “Long-term Climate Change: Projections, Commitments and Irreversibility”
It seems, Leif, you’re wrong again. Fortunately for you, I knew more to deal with your wrongness than you with my rightness.
To qualify for prediction in science, a deduced state must include the threat of theory falsification. That means the prediction is constrained to be within observational bounds. Any output of a physical model that is so vague as to be unconstrained by any possible observation does not qualify to be called a prediction.
Climate models fall under that latter condition. They do not produce deductions that are constrained within observational bounds. Their outputs are not predictions.
Your comment that, “Your notion of ātuningā is nonsense. If the models were tuned to match the observations the models would always be correct.” is so wrong you must have written it with your mind turned off.
There is no reason to think that a physical model tuned to past obsrvations will invariably produce correct predictions of future states. A good example of this problem can be found in the quasi-thermodynamic linear free energy relationships (LFERs) used in physical organic chemistry to understand reactivity or solute behavior in non-aqueous solvents. LFERs are tuned using observables, but have only very limited success in extrapolation beyond their verification bounds. The main problem is that solvent behavior is too complex for current physical theory.
I discussed the problem of climate models tuned to observations, here where it’s explained why a climate model that accurately reproduces observations is not “correct.” See also here, where the meaning of uncertainty from propagated error is discussed, as applied to climate model projections.
The reason they do not predict, Leif, is that climate models are not capable of producing unique solutions to the problem of the climate energy state. The falsifiability criterion, remember? Because of that, there is no way to know that the underlying physics is correct, even if the observables trend is reproduced.
That, of course, and the fact that propagated error makes the projection uncertainty grow so much faster than the magnitude of the model expectation value that no possible observation could ever falsify the model.
Finally, your comment that, “Your +/-15 degrees is absurd on its face. For one, it lacks a time horizon: is the error that large after the first time-step of five minutes?” merely shows that you’ve never bothered to read any of my analyses, here for example, and especially here (2.9 MB pdf) before posting your negative comments.
I assume here (hope, really) that you actually do understand the meaning both of propagated error and of the resulting uncertainty bars as an ignorance width.
Your entire discussion in this thread lacks care, Leif. Substantively, it’s been no deeper than Joel Jackson’s empty contrarianism.
Pat Frank:
I’ll add that the lack of the threat of theory falsification is associated with the absence of identification of the statistical population underlying this theory. Though this threat is eliminated, the theorist can create the illusion of falsifiability through applications of the equivocation fallacy wherein polysemic terms that include “predict,” “model” and “science” are used in making arguments. Though these arguments appear to believers in the scrupulousness of their “scientists” to be syllogisms they are examples of equivocations. While the conclusion from a syllogism is true, the conclusion from an equivocation is false or unproved. Thus, a logical conclusion cannot be drawn from an equivocation. The deception is complete when the theorist draws a conclusion from an equivocation. Those who have believed in the scrupulousness of their “scientists” have been screwed!
By your comment you admit that you didn’t learn anything.
The standard definitions in science are:
A projection is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A prediction is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations].
A model can use a projected variable as input. That is called a ‘scenario’. Based on the projected values, the model now predicts the future evolution. That prediction may fail [often does], but that does not alter the meaning of the words.
I noticed, by the way, that in the WG1 Report, “The Physical Science Basis” of the IPCC 5AR, neither Chapter 9, “Evaluation of Climate Models nor Chapter 11 “Near-term Climate Change: Projections and Predictability” discuss the failed predictability showed by perfect model tests, except for the North Atlantic (out to 10 years).
That is, the very chapters that purport to evaluate climate models are silent about their predictive failure. The same analytical lacuna is found in the AR4 as well; discussed here.
Terry Oldberg, agreed. The equivocation fallacy you describe so well is unfortunately very common in social debate. The real tragedy here is that it’s become both deliberately used and mindlessly allowed among otherwise science professionals.
Leif, I see by your doctrinaire insistence that you’re unwilling to grapple with the substance of prediction in science.
Your definition of prediction is not how climate modelers themselves describe what they do. It further does not describe what climate models do, because within the models much physical description is replaced by parametrizations. This replacement is particularly relevant as regards the climatological effect of GHGs, because the magnitude of the GHG effect falls below the energy flux magnitudes of the parametrized sub-systems.
The extent of parametrization in climate models also negates your argument that models make predictions, based upon your own criterion that prediction is “computation from the physics of the phenomenon.” There it is, Leif. Your own definition of prediction destroys your own argument about climate models.
Your description of prediction further does not include the necessity of falsification. It’s not a prediction if it cannot be falsified, Leif. Climate models cannot be falsified because the propagated uncertainty is always far larger than their expectation values. No conceivable observation can falsify model outputs when they are accompanied by propagated uncertainties that extend beyond physical bounds.
You can turn your blind eye to that as much as you like, but you can’t avoid being wrong. One might observe, in your continued avoidance of this point, that you tacitly “admit that you didnāt learn anything.” Or maybe can’t. Or maybe won’t. None of that saves you from being wrong.
substance of prediction in science
Your desperate demonstrations of your ignorance about science are so typical of bias-driven people.
So one more time:
The standard definitions in science are:
A projection is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A prediction is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations].
Even NOAA agrees:
https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-prediction
lsvalgaard:
By binding definitions to the words “predict” and “project” that differ from the definitions that were bound to them by Dr. Frank and me, you have altered the terms of our debate. To get this debate back on track toward reaching a logically valid conclusion, let’s employ the made-up words “prediction-a” and “prediction-b.” That they are made-up will allow us to bind whatever meanings to the two words are necessary for the purpose of drawing a logically valid conclusion from our argument.
I’ll stipulate that prediction-a and prediction-b are both monosemic. Thus, use of them will free our arguments from the danger of drawing logically illicit conclusions from equivocations. I’ll further stipulate that prediction-a’s are the products of modern day climate models.
It is easy to prove that prediction-a’s have the properties of: a) lacking falsifiability and b) conveying no information to a policy maker about the outcomes from his/her policy decision. I’ll stipulate that prediction-b’s have the opposing properties of: a) falsifiability and b) conveying information to a policy maker about the outcomes from his/her policy decision.
We have discovered that prediction-b’s have the properties that are needed for regulation of the climate, that prediction-a’s have none of these properties and that prediction-a’s are the product of modern day climate models.
Let “prediction-c” be a made-up word that is polysemic and takes on the meanings of both prediction-a and prediction-b. If the word “prediction-c” is used in making an argument and changes meaning in the midst of the argument this argument is an “equivocation” by the definition of this term. As an equivocation is not a syllogism it is logically illicit to draw a conclusion from it. By drawing such a conclusion one can prove a falsehood, for example that prediction-a’s have the properties that are needed for regulation of the climate.
Thus, a scrupulous person would be attracted to using the monosemic terms prediction-a and prediction-b in preference to the polysemic term prediction-c in making an argument. An unscrupulous person would be attracted to using prediction-c.
Leif, nothing on your reference site supports your definition of prediction.
In fact, it’s self-description of climate models as “Special numerical models,” in contrast to physical models flies right in the face of your insistence. That is, you’re contradicted by your own source.
There is no “desperation” in my comments, which have been clear, consistent, and explicit throughout. The content of the thread does make it obvious, however, that you’re either completely unwilling or completely unable to thoughtfully engage the subject of prediction in science. You’ve offered no considered view. You’ve shown no recognition of the impact of propagated error, or of its resulting uncertainty, on predictive status. Instead, you just rote-repeat the same inadequate conception.
one last time:
The standard definitions in science are:
A projection is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A prediction is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations].
Even NOAA agrees:
https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-prediction
lsvalgaard:
Do you mean to say that we should change the meanings of “prediction” and “projection” in the midst of our argument?
You should simply use generally accepted meanings employed by scientists:
A projection is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A prediction is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations].
Now, in a certain sense it doesn’t matter which words you use. You could also define as follows:
A drageef is a forecast based on the current observed state and the current observed trend, i.e no input from physics, just statistics. Thus is an inference based on extrapolation..
A putlihoot is a forecast computed from the physics of the phenomenon, using the current [or the past for that matter] state as input [but not the trend] and solving for the time evolution of the governing equations [possibly calibrated using observations].
If those were the words in general use, you’d do fine. If not, you are a bit in trouble [as with your current usage]. What is important is not what word you use, but what the concepts behind the words are.
lsvalgaard:
I gather that under your definition of “prediction” each GCM makes predictions. However, no GCM makes a conditional prediction aka predictive inference. That a prediction is “conditional” implies that each of its outcome probabilities is conditional.
Information theory establishes that a model conveys no information to us in advance of observing the outcomes of events unless its outcome probabilities are conditional. This information is called the “mutual information.”
A non-nil level of mutual information is required for regulation of the climate. Currently, the EPA cannot regulate the climate because the mutual information from each of its models is nil.
Thus, these models are useless for the purpose of making policy. Nonetheless they are being used in making policy. This regulatory absurdity is a consequence from defining “prediction” as you’d like us to define it.
Information theory establishes that a model conveys no information to us in advance of observing the outcomes of events unless its outcome probabilities are conditional.
Nonsense. The result of predicting the position of Mars from the physical theory of gravity is not ‘conditional’ and is not an ‘inference’.
lsvalgaard:
You are correct in stating that “The result of predicting the position of Mars from the physical theory of gravity is not āconditionalā and is not an āinferenceā.” However the correctness of this statement does not support your conclusion that I wrote “nonsense.”
An “inference” is an extrapolation from an observed state of a system to an unobserved state of the same system. Conventionally the unobserved state is called the “condition” while the observed state is called the “outcome.” An inference is “predictive” when the condition precedes the outcome.
When the outcome probabilities are conditional a kind of inference is made. It is called a “predictive” inference. It is through the use of a predictive inference that one can estimate the position of Mars on Jan. 1, 2026.
Though there is a predictive inference by which one can estimate the position of Mars, there is not a predictive inference by which one can predict the outcomes of events for Earth’s climate. The basis for my claim is an eight year search for the statistical population underlying each GCM in which I’ve found nothing resembling a statistical population. If present a statistical population would provide the means for assigning values to the conditional probabilities of the outcomes of the events. In the place of a statistical population I’ve found applications of the equivocation fallacy that create the illusion of a statistical population.
Though there is a predictive inference by which one can estimate the position of Mars
No, the prediction is not an ‘inference’, but the result of applying physical laws. Similarly, any other application of physical laws [e.g. climate models] are also not inferences.
Leif, your mode of argument is no more than oracular declamation.
You have repeatedly avoided discussing anything. That includes your notable avoidance of the subject of physical error propagation and predictive uncertainty, indicating either that you know nothing of them or that you do not wish to admit your mistake.
You denied the falsification criterion of prediction, ludicrously dismissing 350 years of scientific practice. And thereby implying no important distinction between scientific deduction and the loopy theorizing of the liberal arts.
The impression given is that you’d rather be insistently fatuous than ever admit a mistake. Which in your case have been plenty and obvious, and include self-contradiction. Which never seems to bother you.
You’re a scientist to be emulated, Leif, no doubt about it.
Terry, there appears no point in continuing the debate here. Evidently, Leif’s tactic is to stubbornly insist so as to avoid admitting his mistakes, and to endure with methodological vacuity until everyone leaves. Victory!
Pat Frank:
I agree on the merits of continuing the debate. It was a rare treat to have a person who was versed on general systems theory, information theory, probability theory, statistics, philosophy of science and related topics as an ally!
to endure with methodological vacuity until everyone leaves. Victory!
On the contrary, it is about educating you. To the extent that you don’t seem to learn, my effort here is a failure.
On the contrary, it is about educating you.. The sad of it, Leif, is you evidently believe that.
Terry, you were right about any scientific prediction being both an inference and conditional.
Scientific inferences as deduced from valid theories are logically coherent, quantitative, and tightly bounded. These traits separate scientific inferences from all other sorts.
The tightly bounded criterion enforces the unique solution necessary to impose the threat of observational falsification.
Scientific inferences, for all that, are conditioned by the known uncertainties. Known uncertainties produce the bounds around a quantitative prediction. They condition the prediction in terms of our state of knowledge.
The accuracy of Leifās Mars orbital prediction, for example, is conditioned by the level of systematic uncertainty in the gravitational constant.
The inference of Mars orbital position from Newtonian mechanics would also be conditioned by the uncertainties in the mass and orbital parameters of Mars. Not to mention the small relativistic error.
Even though these uncertainties are small, they necessarily condition any predicted position of Mars, and may become sources of significant uncertainty over very long prediction time-lines.
Orbital prediction, especially over long times, will also be conditioned by fluctuations in the general gravitational background around Mars, exerted by the rest of the solar system. These are again small and often unpredictable, but one might be able to estimate some general average time-wise deviation due to them. But nevertheless, these again condition any statements about the future orbital position of Mars.
These uncertainties might be called the known unknowns, and such things necessarily condition our scientific knowledge statements. They will put uncertainty bounds, albeit tight bounds, around any prediction from theory.
So, once again, you were right and Leif wrong. There was no need for you to later qualify your statement.
The accuracy of Leifās Mars orbital prediction, for example, is conditioned by the level of systematic uncertainty in the gravitational constant.
Not at all, that uncertainty does not enter at all. End-of-education.
Pat Frank:
My understandings on various issues are the result of a background in information theoretically optimal model building. By building a model in this way, the builder of it can ensure that the maximum possible information about the outcomes of unobserved events will flow to the users of the model. Maximization of this information (the “mutual information”) ensures the best possible result if the model is used in an attempt at controlling a system.
When a model is built in this way, the many inferences that are made by the model are selected by optimization. The alternative is to select them by intuitive rules of thumb aka heuristics. There are many possible rules of thumb and they select many different inferences. Thus, the method using rules of thumb violates the law of non-contradiction thus creating David Hume’s “problem of induction.” Optimization satisfies the law of non-contradiction, solves the problem of induction and yields the best possible result from attempts at control.
Though the technology for building a model by optimization is 52 years old it caught on among relatively few scientists. Consequently, virtually all model builders select the inferences that will be made by their models by rules of thumb. One result is variability in the quality among the models that are constructed.
One of the more disastrous possibilities is for a model to be constructed that conveys no information to its users but seems to them to convey information. Thus, they believe they can control a system when in fact it is uncontrollable. This has been the fate thus far for global warming climatology. That this has happened has been obscured by frequent applications of the equivocation fallacy on the part of global warming climatologists. These applications exploit the fact that in the climatological literature many words and word-pairs that are descriptive of methodology are polysemic. Among the words, as you are well aware, is “predict.”
Thanks for a great discussion all.