Bringing Skillful Observation Back To Science

Guest post by Steve Goddard

File:GodfreyKneller-IsaacNewton-1689.jpg

Wikipedia Image: Issac Newton

Archimedes had his eureka moment while sitting in the bathtub.  Newton made a great discovery sitting under an apple tree.  Szilárd discovered nuclear fission while sitting at a red light.

There was a time when observation was considered an important part of science. Climate science has gone the opposite direction, with key players rejecting observation when reality disagrees with computer models and statistics.  Well known examples include making the MWP disappear, and claiming that temperatures continue to rise according to IPCC projections – in spite of all evidence to the contrary.

Here is a simple exercise to demonstrate how absurd this has become.  Suppose you are in a geography class and are asked to measure the height of one of the hills in the Appalachian Plateau Cross Section below.

Image from Dr. Robert Whisonant, Department of Geology, Radford University

How would you go about doing it?  You would visually identify the lowest point in the adjacent valley, the highest point on the hill, and subtract the difference.  Dividing that by the horizontal distance between those two points would give you the average slope.  However, some in the climate science community would argue that is “cherry picking” the data.

They might argue that the average slope across the plateau is zero, therefore there are no hills.

Or they might argue that the average slope across the entire graph is negative, so the cross section represents only a downwards slope. Both interpretations are ridiculous.  One could just as easily say that there are no mountains on earth, because the average slope of the earth’s surface is flat.

Now lets apply the same logic to the graph of Northern Hemisphere snow cover.

It is abundantly clear that there are “peaks” on the left and right side of the graph, and that there is a “valley” in the middle.  It is abundantly clear that there is a “hill” from 1989-2010.  Can we infer that snow cover will continue to increase?  Of course not.  But it is ridiculous to claim that snow extent has not risen since 1989, based on the logic that the linear trend from 1967-2010 is neutral.  It is an abuse of statistics, defies the scientific method, and is a perversion of what science is supposed to be.

Tamino objects to the graph below because it has “less than 90% confidence” using his self-concocted “cherry picking” analysis.

So what is wrong with his analysis?  Firstly, 85% would be a pretty good number for betting.  A good gambler would bet on 55%.  Secondly, the confidence number is used for predicting future trends.  There is 100% confidence that the trend from 1989-2010 is upwards.  He is simply attempting to obfuscate the obvious fact that the climate models were wrong.

Science is for everyone, not just the elite who collect government grant money.  I’m tired of my children’s science education being controlled by people with a political agenda.

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422 thoughts on “Bringing Skillful Observation Back To Science

  1. Wholly agree that selecting a short segment of a long, squiggly line and declaring that whatever trend that segment shows will extend forever into the future is a sin.

    But I am not too clear about Szilard ‘discovering nuclear fission.’ I thought that was Hahn. Do you mean, Szilard’s insight into the explosive implications of uncontrolled fission?

  2. AS we build this bridge to reality (climate changes are natural cycles), there seems to be more and more creatures lurking under the bridge in threads of late – hhhmmmmm…

    >>>

    Just an antidotal observation, but in surfing the net for articles on recent AGW articles (Climategate) originating from the “old” media sources and allowing comments, there seems to be a trend developing.

    The comments are becoming more skeptical / cynical by a significant margin. I place it a close 70% (anti) vs. 30% (pro) AGW. That must be somewhat frustrating for article creators who used to see their musings well received.

    I’d be interested in what other thinks about this ?

  3. How interesting. The 1967-2010 graph looks just like this one:

    the last snow on a peak north of my town from 1902 to 1983 (the readings stopped on Aug 3rd, so I don’t know if the snow ever melted that El Nino year).

    Snow cover varies, and has been doing so quite well, all by itself, long before AGW came along and threw dirt on it.

  4. Suppose you are in a geography class and are asked to measure the height of one of the hills
    The question is meaningless unless you first have specified the height above what?.

  5. Some people are not climatologists and others have no clue about statistics.
    However, some climatologists complain if non-climatologists talk about climate.
    But why are those climatologists all the time dealing with statistics? And even try to be creative in a rather stupid way?
    Wasn’t McIntyre and the Hockey Stick story not lesson enough for them? Ridiculous.

  6. “So what is wrong with his analysis? Firstly, 85% would be a pretty good number for betting. A good gambler would bet on 55%. Secondly, the confidence number is used for predicting future trends. There is 100% confidence that the trend from 1989-2010 is upwards. He is simply attempting to obfuscate the obvious fact that the climate models were wrong.

    Science is for everyone, not just the elite who collect government grant money. I’m tired of my children’s science education being controlled by people with a political agenda.”

    Science is for everyone. Snowboarding is for everyone too, but wear a helmet and start on the bunny slopes.

    A confidence interval is about distinguishing a random distribution from a pattern. By convention, you need to be 95% confident in your trend in order to reject the null hypothesis. 90% is on the bleeding edge of acceptable. Less than 90% is not statistically significant by any measure.

    This is basic, basic scientific procedure, and was not invented by elitists to confuse people like you, Mr. Goddard. It’s analogous to the belying techniques used in climbing: yes, everybody does it; no, it’s not immediately obvious to an outsider why we do it that way; no, it is a very bad idea to ignore the procedure because you think the “elitists” have it in for just-folks.

  7. I too am tired of the silliness of interpretation of “trends”. It is just ridiculous to choose one relatively short period of data and ignore what has gone before.

    Having been raised on a farm (sheep station in NSW, Australia), I well remember older folks remarking with seeming wisdom “It’s never been as bad/good/dry/wet (fill in the dots) as this before!”.

    Even the perspectives of a lifetime are not necessarily representative of the whole history of the planet. Nevertheless we seem to be programmed to act as if they were. But that is not a scientific approach is it?

  8. Szilárd discovered nuclear fission while sitting at a red light.

    There was a time when observation was considered an important part of science.

    Er, how did the red light help?

  9. “Science is for everyone, not just the elite who collect government grant money. I’m tired of my children’s science education being controlled by people with a political agenda.”

    Thank you, Anthony, truer words were never spoken! At the University of Illinois School of Public Health, I’m surrounded by the “elite.” What’s been happening with climate change has also been happening with bird flu (remember that?), bioterrorism, maternal/child health and other such silos.

    The educational infrastructure is damn near indestructible, thank you for your service towards intellectual honesty.

  10. Of course there is nothing magic about the 90% level of confidence, but having set that to be his own level of confidence, Tamino is well within his right to state that he doesn’t believe that snow extent hasn’t increased since 1989. His analysis is really straightforward.

    The reason this data doesn’t convince Tamino’s (that is, why it doesn’t exceed the 90% confidence level) is that the snow extent is so variable. Looking at the second chart you have posted one can see that variability. Would I bet it’s going to be higher next year? Not after seeing this graph and reading Tamino’s analysis.

    Statistics is all about trying to stop arguments such as “…it is abundantly clear …”, which always tend to generate more heat than light, and I for one will not fault Tamino from going there.

  11. Steve,
    You will be pleased to know that the propaganda disguised as science routinely fed to school children doesn’t always get through to them.
    On the BBC’s Countryfile tonight a Herefordshire school was was featured showing off its newly erected wind turbine. John Craven was desperately trying to get one of the pupils to say that the turbine was a good thing because it would prevent global warming and save the planet, but all he got was a rather befuddled girl suggesting that it might help the ozone layer!

  12. Ah, Tamino. When he posts, “it is a tale. Told by an idiot, full of sound and fury, Signifying nothing.”

    If that graph were of increasing temps, increasing sea levels, cases of drought, etc, he’d vehemently defend that very same graph.

  13. Robert (12:09:01) :

    You usually have a point to make.
    Nothing jumped out at me, so I’ll attempt to translate:

    Folks can’t possibly understand science unless they become scientists.

    If that was it, it spells real trouble. Science, along with a lot of other subjects, is rapidly disappearing from the classroom.

  14. Steve, they seem to think you are trying to make a mountain out of a mole hill but, in fact they were the ones that were doing exactly that with their hockey stick and bad science. Even today on Science Daily there is an article proclaiming increased global temperatures “could” cause atmospheric blocking and make for stronger storms,etc,etc. It never seems to end.

  15. @ Robert (12:09:01)

    I’ll take what you say about needing a 95% confidence limit on the actual snow extent from 1989 to 2009. It looks to this simple maths grad’s eye that it is obviously upward, but what the heck. It’s Sunday night here in the UK and after a nice meal with nice wine I can’t be bothered to do anything that requires more than a Mk 1 eyeball.

    So, let’s agree that there’s no confidence in that observed result.

    Now, did the models predict that NH snow extent would decline over this period? It’s 20 years so you can’t pull the “it’s just weather” option.

    I assume to get the output of those models published the predictions must have shown a 95% or better confidence limit. Otherwise it would, surely, be meaningless to publish. Just going off what you said here.

    No? If not, then what was the point of them being published (we can all “make stuff up”)?

    If they did have such confidence limits then the models must be (in sound scientific terminology) “pure crap”.

    I look forward to hearing your iPhone’s response.

    Dave

  16. mkurbo,
    Public sentiment is a fickle thing. The CAGW crowd experienced the perfect storm over the last few months with Climategate, Copenhagen, and the winter storms.

    A freakishly hot summer and the public opinion could swing back the other way.

    One thing that I hope is here to stay, however, is the realization that the conspiracy theory in the CAGW camp, that there is Big Oil lurking around every corner and behind every skeptic was, at the very least oversold if not outright paranoia… and that much of the criticism from both the professional and citizen scientist commnities is honest.

    Up until, at the urging of the Montbiots, RC, etc – significant portions of the public and media really thought we were no better than the tobacco lobbyists of 20 years ago. I’m hopeful that, at least at the level it existed at prior, is gone for good.

  17. Leif Svalgaard (12:02:45) :

    Suppose you are in a geography class and are asked to measure the height of one of the hills
    The question is meaningless unless you first have specified the height above what?.

    I’ll go with the obvious for $1,000, Leif. Where the geography class lives.
    In the case of current climate, we have mulitple generations all living at the same time. Measuring climate changes would only become believable if all present were taken into account.

  18. mkurbo (11:57:22) :
    I’d be interested in what other thinks about this ?

    I agree with your observation, but say that the mood is stunned, shocked and upset/hurt as well as frustrated.

  19. Leif Svalgaard (12:02:45) :
    Suppose you are in a geography class and are asked to measure the height of one of the hills

    The question is meaningless unless you first have specified the height above what?.

    In civil engineering and geotechnical circles the measurement is usually expressed as height AMSL (above mean sea level). A good engineer will qualify the measurement, for example, as 1,023m AMSL. So if the person answering the question doesn’t qualify their answer then it is the answer that is meaningless.

  20. Steve Goddard pointed out the failing ‘global warming’ forecast of snowline in the Northern Hemisphere in wintertime moving northward. There is even more snow forecast for Dallas this week.

  21. Leif Svalgaard (12:02:45) :

    Suppose you are in a geography class and are asked to measure the height of one of the hills
    The question is meaningless unless you first have specified the height above what?.

    ————–

    But isn’t that how the AGW questions are framed?

  22. There is inaccurate teaching to children not just in the area of climate. Einstein’s gravity is more accurate than Newton’s. But Newton’s version is still what is taught to children.

    Though I know Steve Goddard’s point is about politics being in the classroom; I detest it also! Children should be challenged to think and explore. There should not be getting indoctrinated and lead to not think. If there is freedom anywhere in public it must be in the classroom!

  23. Leif Svalgaard (12:02:45) : Leif, you postulate: “The question is meaningless unless you first have specified the height above what?”. The answer is: above (relative to) a chosen reference point; in this case, relative to the other hills. All reference datums are relative, whatever the scale, throughout the universe. This includes dubious datums, such as the hyperthetical global average temperature, against which “anomolies” are plotted. Regards, Bob.

  24. It seems to me that the graphs in this article are deliberately manipulated to make the slope more than obvious. The Y-axis doesn’t start at zero, but at 90% of the mean value of the points. It’s easy to see that the ‘slope’ might not be statistically significant. I wonder why Steven Goddard hasn’t done this calculation. Saying things are “abundantly clear” without doing the maths is an abuse of statistics.
    It’s not beyond the wit of man to realise that rising temperatures could possibly cause more snowfall, with more water able to be carried in warmer air, but that would require the author to at least understand the physics of vapour pressure.

    https://wattsupwiththat.com/2009/06/09/co2-condensation-in-antarctica-at-113f/

  25. The heading says ‘SKILLFULL’. English (UK) spelling is ‘skilful’, English (US) spelling is ‘skillful’ (according to WORD spellchecker).

  26. Us folks in San Saba County have a saying for statisticians who use numbers to prove a lie: ” the average person in the San Saba County has one scrotum and one mammary gland.” Its statistically true but obviously false.

    As to the Kansans, all they want is to provide fact-based science to let their kids know that there is another side to the narrative that organic life began from inorganic non-life and then advanced through fortuitous events to create humans. Deniers of atheism, like AGW denialists, simply want the facts on the table without Big Brother imposing its own brand of the truth. In the case of Creation vs purely Naturalistic “science”, the stakes are eternal and therefore far exceed the trillions of dollars in the wasted war against carbon.

  27. “I’m tired of my children’s science education being controlled by people with a political agenda.”
    Move to Kansas…

    I can’t stop laughing.

  28. a minor quibble but paraphrasing Asimov, the sound of scientific discovery is not ‘Eureka’, but more like “Gee, that’s funny…..”

  29. Anthony

    I have followed your work and website for a while. Impressive! You point out the errors and the deviousness of the more unscrupulous players in the AGW camp, and back your statements with meticulous research. It is also very much to your credit that you also point out errors in the thinking of some of the less rigorous skeptics – that is, you apply the same standards to climate-skeptical arguments that you insist should apply to AGW promoters. That’s good, because I have a skeptical argument in need of assessment.

    You have highlighted the inaccuracy of the predictions of the climate models that are central to the arguments of the warmists, and you are spot-on in your analyses. However, I think there is another fundamental issue about these models. It is this:

    The IPCC and most of the advocates of AGW cite the hockey stick and their model predictions as “proof” of the AGW hypothesis. I have a big problem with that. Not because of the inaccuracy of the predictions (and they are certainly inaccurate), but because they would prove nothing about the real drivers of temperature changes even if they were accurate. Ptolemy’s geocentric model of the universe, with its epicycles, deferents and equants, made astronomical predictions that were far more accurate than the climate models’ predictions of the earth’s temperature. Yet we do not live in a geocentric universe. Refer:

    http://www.herkinderkin.com/2010/01/climate-models-and-scientific-consensus-why-they-prove-nothing/

    So far, no AGW suporter has attempted to refute this argument. they simply bang on about their models being accurate in spite of Anthony Watts, Richard Spencer et al. So I have to ask a scientific skeptic. Am I on the right track, or am I in error?

  30. Leif Svalgaard (12:02:45) :

    Suppose you are in a geography class and are asked to measure the height of one of the hills
    The question is meaningless unless you first have specified the height above what?.
    ———————————————————————–

    I beg to differ. The question stands, it is the answer that is meaningless without a reference frame.

    42?

    Hence the need for full and open disclosure of all data and assumptions used during the calculation process.

  31. Surely confidence intervals are designed to show that a particular pattern, or trend is not the result of just noise, and is not necessarily to do with whether or not the future can be predicted.

    It is possible for the “trend” to be rejected at the 95% confidence interval, or even 90% or 80%, but yet still continue into the future. Similarly, a trend could show a very high >95% confidence level, but attempts to predict the future may be totally confounded when the “trend” reverses.

    In other words, confidence levels and trends don’t really help except in one way – in order to compare predictions against observations. In the case of the graph of snow cover, it may be the case that snow cover has increased in the period shown, but what does that tell us about theories of climate change? Not a lot. No more than a similar period of decreasing snow cover or decreasing arctic ice.

    So, if the publisher shows increasing snow cover and uses that to falsify global warming, then someone like Tamino is correct to object on the grounds of confidence levels. But if the publisher is merely saying, look, snow cover has increased, then that is a fact of observation and as long as it is within the error bars of measurement (I assume that it is), then Tamino must accept that as a fact. But then he may ask, What’s your point?

    And I find myself asking the same question. What is the point of this article. It’s like saying “the sun is hot” or “the sea is wet.”

  32. Here in Calgary the airport is at 3,557 ft. However, the airport is on a slightly elevated plain several miles from downtown. For the most part, Calgary is at 3500 feet.

    I lived on a hill that was about 60 feet above its surroundings. Thus, the hill I lived on was 3560 feet high.

    Obviously that makes the hill sound a lot bigger than it actually is, in reality it’s about 60 feet high since the surroundings are all that really count from the perspective of driving there, walking there, providing power there, providing water and sewer service, etc.

    And if you want to get down to it, the 3557′ number is meaningless too, since that is above sea level. But sea level where? Under what conditions? At low or high tide? If all the ice were to melt and sea levels rise, would they change the official measure of my elevation?

  33. I’m wondering about the correcting stickers that school science textbooks will need in a few years:

    “This chapter is now considered to be totally incorrect…”

  34. A little snarky Dr. Svalgaard. Kansans have more at stake in the science of
    climate and its integrity then most. You might want to get out more.

  35. Ref – NickB. (12:54:19) :
    mkurbo,
    “Public sentiment is a fickle thing. The CAGW crowd experienced the perfect storm over the last few months with Climategate, Copenhagen, and the winter storms.
    “A freakishly hot summer and the public opinion could swing back the other way.”
    _____________________________
    So true! Not kidding! The public believes whatever it feels; if it’s “Cold” it’s COLD; if it’s “Hot” it’s HOT. And they hate liers.

    If the AGW faithful and their “scientist” high priests have a better and more accurate weather report for the next and succeeding 72 hours, etc., etc., they gain converts. If the “Scientists” and the “Show-Me” crowd have better weather reports, they’ll gain followers.

    The moral or Climategate and Copenhagen: “Always tell the truth, as often and loud as you can, and Dick & Jane Q. Public will believe in you.”

  36. “Science is for everyone, not just the elite who collect government grant money. I’m tired of my children’s science education being controlled by people with a political agenda.”

    So Homeschool them. (Don’t expect them all to go on and get PhD’s – only one of ours did!)

  37. I hate to agree with Tamino, because like Gavin he is spiteful and hostile to free speech and fair comment, but for once he has half a point. While he should not object to the graph, as it is far more legitimate than (say) anything Mann has produced with his tree ring proxies, he is right that it is not concrete evidence of an underlying upward trend in snow levels. However it is an interesting start, and it will not take many more years like this one before his beloved 95% confidence level is reached, and fewer still to discredit the GCMs’ projections.
    I do hope Tamino remembers that 95% is a purely arbitrary number, and depending on the purpose of the statistic, for instance spend $1 trillion and close down swathes of industry on the one hand, or make a general observation for conversational purposes on the other, higher or lower figures might be appropriate.

  38. Leif Svalgaard (12:34:51): “I’m tired of my children’s science education being controlled by people with a political agenda.” … Move to Kansas.

    Dear Leif, No offense intended, and this is not personal so don’t take it that way, but…

    Modern “science” is a big welfare game. Too much science is taxpayer supported, with no benefit to taxpayers or society. It’s much worse than taxpayer-supported “arts”. Armies of pseudo-scientists and even real scientists are living high on the hog on handouts from the rest of us, justified by false dire reports and false claims that the “science” being performed has any pragmatic value whatsoever.

    You don’t pay my salary; I pay yours. And I am getting zip for my money, or worse than zip. That offends me, and hurts me, and harms my family and community.

    I want every “scientist” on the public dole to provide the taxpayer with a valid reason for funding him or her. And “the furtherance of human knowledge” doesn’t cut it with me. I want practical results, not phony claims, not Chicken Little bull crap. And if they cannot justify their expense, then off with their funding.

    Get a real job, one the free market values and is willing to pay you for. Do “science” in your garage in your spare time. I’m tired of footing the bill and getting BS in return. Will society collapse if taxpayers stop funding “science”? I doubt it sincerely.

  39. I’m not a statistician, so let’s ask one.

    “Anyway, that’s what Mr Jones has said. Reader Francisco González has asked what that “statistically significant” means. It is an excellent question.

    Answer: not much. ”

    http://wmbriggs.com/blog/?p=1958

    “But forget all that, too. Let’s ignore statistics and turn to plain English.

    Suppose, fifteen years ago the temperature (of whatever kind of series you like: global mean, Topeka airport maximums, etc.) was 10o C. And now it is 11o C. Has warming occurred?

    Yes! There is no other answer. It has increased. But now suppose that last year, it was 9o C (this year it is still 11o C). Has warming occurred?

    Yes! And No! Yes, if by “has warming occurred?” we really mean “Is the temperature now higher than it was 15 years ago?” No, if by “has warming occurred?” we really mean “Has the temperature increased each year since 15 years ago?”

    Also Yes, if by “has warming occurred?” we really mean “Has the temperature increased so that is higher now than it was fifteen years ago, but I also allow that it might have bounced around during that fifteen years?”

    Each of these qualifiers corresponds to a different model of the data. Each of them has, that is, a different probabilistic quantification. And so do myriads of other model/statements which we don’t have time to name, each equally plausible for data of this type.

    Which is the correct model? I don’t know, and neither do you. The only way we can tell is when one of these models begins to make skillful predictions of data that was not used in any way to create the model. And this, no climate model (statistical or physical or some combination) has done. “

  40. Before we can even attempt to try and get back on the right track (namely the truth) with climate research, we have to get rid of many of the leading “scientists” that corrupt, twist, hide and/or distort the data and findings. If this doesn’t happen then there is no hope getting back on the right track. That should be pretty obvious if one thinks about it. Now, the only way to get rid of those people is to charge with fraud, and if found guilty put behind bars. If this doesn’t happen then the previous goal will never happen. This is clear from the avalanche of revelations showing that AGW in its present form is a hoax and a fraud. This too should be pretty obvious if one really thinks about it.

  41. Gino (13:30:44) and others:
    I beg to differ. The question stands, it is the answer that is meaningless without a reference frame.

    If the question is something that is arguing against AGW, then a reasonable reference frame would be the time at which AGW proponents claim their effect ‘takes off’, no?
    A common device [‘outlier suppression’] to check if a trend is ‘robust’ is to omit the n lowest and n highest points and see if the trend persists, where n can then vary from 0 and up to a reasonable number, e.g. 10% of the total number of points. This gives you an idea about how much is ‘weather’ vs. how much is ‘climate’.

  42. With all due respect, this post appears to have little to do with observation as a part of science. Someone skilled in the scientific art of observation would simply objectively observe and possibly record data points. All the line-drawing stuff and subsequent analysis would be left to others.

  43. So, if the publisher shows increasing snow cover and uses that to falsify global warming, then someone like Tamino is correct to object on the grounds of confidence levels.

    Well, yes and no.

    Yes, if that is the only evidence provided. No, if more evidence is available.

    If the bulk of the evidence points towards one conclusion, then we can act on that. The fact that each individual piece might only be at 80% becomes less important. (This assumes the evidence is “independent” in a statistical sense.)

    So if we wish to decide whether AGW climate models have been accurate in their predictions, then the amount of snow cover to 80% accuracy is acceptable evidence. Not proof, merely evidence. But then no-one is really claiming that it is proof of the failure of the AGW theory.

    The warmistas will have the Devil’s own job of proving AGW using evidence only above 95%. They don’t even try: do you reckon their calculated glacier melt trends are 95% accurate? How about sea level trends?

    So why should the sceptics be held to much greater evidence? We’re not the ones proposing the outlandish scenario.

  44. Suppose you are in a geography class and are asked to measure the height of one of the hills.

    This sounds like an ideal reason to take the students on a surveying expedition! Take an assortment of modern and classical surveyor’s tools and some appropriate camping gear, and engage in some real hands-on field work. But I guess I’m too old-fashioned, since a few minutes with Google Earth would probably give a good-enough-for-climate-science value. ;->

  45. Speaking of observations it’s quite clear to me but apparently to no one else that the jet streams drifted poleward from 1975 to 2000 but have been drifting back equatorward ever since.

    Now if that simple observation is correct then there are implications but the whole climate establishment seems to be ignoring them.

    However the warmists were happy to announce that the poleward movement was consistent with CO2 forcing and all our fault.

  46. “You usually have a point to make.”

    Yes, I do. The point is that science being for everybody does not mean that it does not require knowledge and skill to do it effectively. Everything worth doing has a learning curve, and it take a basic part of science like seperating trends from noise and dismiss it as “elitist” because you don’t immediately understand how it works is, to borrow a phrase from Rush, retarded.

    @Dave: I lost you when you started speculating about what climate models predicted for snow extent, while admitting you didn’t know. If you care, look it up. Don’t ask me to respond to a controversy that exists only in your imagination.

  47. bbc’s latest ‘science in action’ prog continues on its merry AGW way:

    Public perception of science
    Can we trust science and scientists? It’s a question that is increasingly being asked by the media, and the public, after some high profile apparent mistakes. Did UK scientists manipulate data on global warming? Is the International Panel on Climate Change credible after it admitted that it had made a mistake in asserting that Himalayan glaciers could disappear by 2035? In the past few years there have also been false claims about stem cells, and erroneous warnings about vaccines. Michael Specter is the author of “Denialism”, where he asks why we have begun to fear scientific advances instead of embracing them. He was speaking at TED – Technology, Entertainment, and Design – a conference in California billed as some of the biggest thinkers coming together to spread ideas. This year the theme was “what the world needs now”. Jon Stewart went along to find out more…
    If you’d like to attend TED, the next one is in Oxford, England, in July – there a more details on the Science in Action website. There is a fellowship programme, which focuses on attracting people who have world changing ideas, living or working in the Asia, Africa, the Caribbean, Latin America and the Middle East.
    Ocean acidification
    The oceans are becoming more acidic and at a faster rate than previously measured. This could lead to a massive extinction in the deep seas, according to new research. The ocean is what is known as a carbon sink – it has taken up between a quarter and a third of all atmospheric CO2 since the start of the industrial revolution. A study published in the journal Nature Geoscience shows that the increase in carbon dioxide in the atmosphere is leading to a similar increase in the oceans today. But it is believed that the process is making the seas much more acidic which is damaging the delicate shells of organisms that are critical to the marine food chain. In fact the rate of acidification is now the highest in 55 million years. Danniella Schmidt from the University of Bristol, one of the scientists behind the work, joins us on the programme.
    http://www.bbc.co.uk/programmes/p00673xy

    btw specter didn’t mention AGW in spite of the opening remarks of presenter, John Stewart. he talked of vaccines and GM food and said “trust the scientists”.

    Nature Geoscience: Past constraints on the vulnerability of marine calcifiers to massive carbon dioxide release
    Andy Ridgwell & Daniela N. Schmidt
    http://www.nature.com/ngeo/journal/vaop/ncurrent/abs/ngeo755.html

    Stephen Cauchi in Australia’s Age newspaper, reported the following, which sounds like a Greens’ response to Geoffrey Lean’s ‘rally the green troops’ piece in the UK Tele last week:

    21 Feb: Quadrant: Doomed Planet
    Assault on reason
    The Age reports on a closed meeting by wealthy Green groups to plan their attack on climate sceptics:
    Australia green groups have called a strategy meeting to devise ways to hit back at the climate sceptics movement, amid fears they are losing the PR war.
    The groups, including Greenpeace, the Wilderness Society, World Wide Fund for Nature, Australian Conservation Foundation and Friends of the Earth, have acknowledged that the public mood has shifted following the collapse of the Copenhagen climate talks and blows to the credibility of the IPCC.
    James Norman, of the Australian Conservation Foundation, said the strategy of ignoring climate change sceptics had not worked as it had been taken as confirmation of their claims. ”The stakes are too high to remain silent or disorganised in the face of this systemic disinformation campaign,” Mr Norman said.
    He said the global campaign was being funded by anti-climate-change think tanks such as the American Atlas Economic Research Foundation and the British International Policy Network, which had both received grants from oil company ExxonMobil.
    ”I wouldn’t be surprised if they (ExxonMobil) have connections here in Australia as well,” he said.
    Think tank the Climate Institute, lobby group Get Up, and the Liquor Hospitality and Miscellaneous Union will also attend the Sydney meeting, which is not open to the public or the media.
    Greenpeace spokesman James Lorenz said the meeting was ”a good opportunity for environmental organisations to put their heads together and have a think about what’s going on”…
    http://www.quadrant.org.au/blogs/doomed-planet/2010/02/assault-on-reason

  48. As most skillfully explained by Lewis Carol:

    `When I use a word,‘ Humpty Dumpty said in rather a scornful tone, `it means just what I choose it to mean — neither more nor less.’

    `The question is,’ said Alice, `whether you can make words mean so many different things.’

    `The question is,’ said Humpty Dumpty, `which is to be master – – that’s all.’

    Alice in Wonderland Ch 6 p 364

    The question is, will we chose McGraw Hill’s or Webster’s definition of Science or Humpty Dumpty’s? Who shall be the master?

  49. @steven mosher (13:23:57) :

    A trend that doesn’t reach 95% confidence (or sometimes 90%) is not statistically significant by definition. Your question is akin to asking if I demand that all triangles have interior angles totaling 180 degrees. We’re talking definitions here.

  50. David (14:15:34) :
    Re Vincent (13:32:01) What is the point?
    The point was that the climate models all predicted a decrease in snow cover over this same period

    The models [as shown by Steve’s post] show a decline since the 1960s, so ‘this same period’ need to be since 1960s, no?

  51. Stuff like this is why ‘climate’ scientists don’t design machines. Machines have to actually work. That ain’t easy in the real world.

  52. Leif –
    I choose as my base the Roman Warm Period. If you want, you can choose the middle of the last ice age. I get a slight downward slope. You get one devil of an upward slope. Steve’s points across the last several days have been fairly good. They are not their to extrapolate any trends – but I don’t believe he has claimed to do such. Unlike our AGW friends….

  53. The difference is, Steve Goddard, that time series have peculiar properties even when the input data is random, and so special care has to be taken to distinguish that which may be a signal from random noise.

    So its no use complaining about Tamino on this issue. If the time series has significant autocorrelation (and lots of climate data does, especially tree rings and hydrological series like the Nile-ometer) then a random series can display spurious trends over short periods of time which mean precisely nothing.

    That’s the problem with your series: its truncated for no reason, it has significant autocorrelation, the R2 is low. The forecast based on it has to be taken with a massive amount of salt.

  54. Steve,

    “…it is ridiculous to claim that snow extent has not risen since 1989, based on the logic that the linear trend from 1967-2010 is neutral.”

    Nobody has claimed that the historical numbers are wrong and that the snow extent would not have increased from a local minimum in the data. You have put up the weakest of straw men here.

    But why do you continue to use a plot of northern hemisphere winter snow extents to back your claim that the data refutes the models. The models are predicting the January North America extent only, which the data shows has no significant trend:

    http://climate.rutgers.edu/snowcover/chart_anom.php?ui_set=1&ui_region=nam&ui_month=1

    Nor do the models over this period. There’s no contradiction.

  55. Tamino has a good sense of humor. His t-tests are based on untenable assumptions and can be dismissed.

  56. Robert (14:15:01) :

    Of course. A 24th mag galaxy is lost in the background noise of 25th mag skyglow the same way that sea-level rise of the past 100 years is lost in the noise of the tides. Both are fuzzy pictures.
    An observer looking at 2 pictures taken 100 years apart cannot tell that a sea-level rise event has taken place.
    And a current trend is lost in the trees whereupon the forest cannot be seen.
    The best one can do is to roll with the current punches… adapt.
    Only the current punches are shoving the place towards paralysis of adaptation through a very greedy Climate Bill that is a diode allowing only one direction of change.
    i.e. – the big threat is not the climate, it’s the monolith of Agenda.
    That’s not science it’s policy, and the outcome is not uncertain, given the eventuality of the swings of climate, should Agenda prevail in the name of science.

  57. Right!

    Last night I saw the excellent presentation of Dr. Lindzen, and I would like to ask a question to Dr. Lindzen. Therefore I would really apprectaite if you Anthony could forward this question to Dr. Lindzen.

    Dear Dr. Lindzen,

    In your presentation you stated that a couple of months or years with accurate observations of climate dynamics and radiation balance possibly could reveal the most important mechanims in our climate. Now, given that many ocean cycles are multi decadal, how could it be possible to determine the governing mechanisms in the climate with a fraction of the samples required to reproduce the various climate signals?

    http://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem

    My opinion has been that at least 100 years of accurate Argo ocean data would be required to reveal the characteristics of our climate, and I would be most happy if you could explain why this is not required.

    Yours Sincerely

    Invariant

  58. “Robert (12:09:01) :

    A confidence interval is about distinguishing a random distribution from a pattern. By convention, you need to be 95% confident in your trend in order to reject the null hypothesis. 90% is on the bleeding edge of acceptable. Less than 90% is not statistically significant by any measure”

    i am sorry, that is completely false; firstly you have to know the distribution of your data and the distribution of your error(s). it is quite possible for you to have a signal that has a Poisson distribution and that the error follows a normal distribution. Moreover, it is also more than likely that your data and error is non-normal and that the distributions are different; in the case of measuring average temperature, the two distributions probably vary during the seasons.

  59. Confidence – or better “confidence intervals” (as I was taught to call them) relate to a the confidence one has in a given probability distribution. In this case, there is a probability of 1.00 that each measured point of historical snow cover extent is correct (assuming each has been measured correctly – if not, then each point has its own probability distribution to do with correctness of measuring – but let’s heroically assume no measurement errors). Note that there’s no confidence interval (or confidence) involved in historical data. The straight line drawn thorugh the graph is a simple first order regression (root of sum of squares of differences of each measurement from the average).

    Now, when you project that straight line into the future, for each future time period you calculate a max and min value, based on the historical measured variation of observed points from the historical regression line. The max and mins are calculated based on a given “confidence level”, conventionally 90%. This means you’re 90% confident that the future observed value for any time period will fall between the calculated min and max values for that time period. (Btw, this assumes that future variability is the same as historical variation, but let’s assume – operhaps more heroically – that it is.)

    The thing that really annoys me about much of the AGW reporting is the way that the most alarming of the two intervals is always stated: for example, max values for temperatures; min values for Arctic ice extent. That’s not science, it’s flim-flam.

  60. Hey Leif,
    From where I am standing, your question, when measured against the responses it engendered, was way over their heads. How high is that?

  61. As an aside for Leif Svalgaard – I researched a comment you made in an earlier post and found your assessment to be correct. Sorry for misunderstanding – Mk

  62. I’m not sure how clear this will come out, but I have to try. Symon, your statement:
    “It’s not beyond the wit of man to realise that rising temperatures could possibly cause more snowfall, with more water able to be carried in warmer air”
    is correct, but not totally germaine to the graphs in question. The total volume of precipitation does depend on the amount of water vapor in the air, which warming could increase, thus “more snow.” However, the graphs in question are of snow extent, which is more how far south the snow comes, not the amount of snow. I live in northern Arkansas. The precipitation fronts that come through my area in the winter often have rain in the south portion, ice (and downed power lines) in the middle, and snow in the northern portion. Whether we get our precipitation as rain, ice, or snow, depends not on the volume of precipitation, but on how far south the freezing temps get. So, in my part of the state and areas south of me at least, warmer is very unlikely to cause more snow. It could cause more rain, but we have to be colder to get more snow.
    KW

  63. After inferential statistical testing a 95% confidence level means that for every 100 times an experiment is conducted and if for only five times the result differs from the rest this means that those five are likely to be by chance. A 0.05 p-value is the same thing noted as a probability and is the usual threshold value for psychology experiments. If an hypothesis is supported by the experimental evidence at the 0.05 level then we can say it is moderately reliably supported, we can trust the findings. For experiments testing the safety of new medicines much more stringent p-values are required, 0.01 or 0.001, because peoples’ lives are at risk. Such simple stats deal in how often you think your results are by chance-as others have said, how much ‘noise’ versus how much ‘signal’.
    The big problem with the alarmists is that we can’t apply such tests to their data because their theories do not generate testable hypotheses. Much like the theories of Freud, which I call a typical ‘expanding bag’ theory, whatever observations or results you toss into the bag it just expands a little more and is proclaimed to explain everything. Heavy snow is generated by global warming, heat waves are generated by global warming, much as in Freudian theory you may be diagnosed with a desire to sleep with your mother and when you reject that diagnosis you are told you are in denial, that whatever you say simply goes to convincingly prove this incestuous desire.
    Don’t even get me started on the gigantic expanding bag which is religion…

  64. Leif Svalgaard (14:00:19) :

    A common device [‘outlier suppression’] to check if a trend is ‘robust’ is to omit the n lowest and n highest points and see if the trend persists, where n can then vary from 0 and up to a reasonable number, e.g. 10% of the total number of points. This gives you an idea about how much is ‘weather’ vs. how much is ‘climate’.

    When I was working as an applied mathematician in space sciences, the software would always eliminate the outliers so that we could get an valid trajectory analysis. We were working to at least 8 decimal places much more than the accuracy needed for climate studies. (I always wondered how we can get accuracy to at 10th or 100th of a degree when majority of data is no better than accurate to 1/2 of a degree)

    As a data administrator, I always build software tools to analyze the data and identify data that was suspect. I spent probably 20 times as much time on validation and verification then i did on collection, computing and publishing results.

  65. I love this post. It is a classic.

    Sceptics have spent a huge amount of effort trying to show that evidence of global warming is not significant. But now there might be a cooling trend and “85% would be a pretty good number for betting. A good gambler would bet on 55%.”

    So which is it? Does evidence for climate change have to be proven beyond any doubt, or should we take action on a 55% probability?

    “Secondly, the confidence number is used for predicting future trends.”
    Wrong.

  66. Ref – NickB. (12:54:19) :
    mkurbo,
    “Public sentiment is a fickle thing. The CAGW crowd experienced the perfect storm over the last few months with Climategate, Copenhagen, and the winter storms.

    >>>

    While I agree with the public being fickle, they don’t like being lied too and that feeling is starting to sink in with many comments I read lately. Only my opinion… ..but I think it will be hard to gain peoples trust worldwide again on this subject.

  67. “Science is for everyone, not just the elite who collect government grant money. I’m tired of my children’s science education being controlled by people with a political agenda.”

    This is not a scientific statement, merely a political cheap-shot. Skeptics should practise what they preach and stick to the science.

    So if I Follow through on the logic of this article, there has been warming since 1995 as defined by Jones in his recent BBC interview (0.12C per decade), being that “statistical significance” is apparently not significant.

  68. Steven, you must come up with an objective reason for selecting your start point if you are going to make any claims about trends in NH winter snow extent. Simply claiming that you think you see the legs of a cycle in the data isn’t good enough. (And I do not see them. All I see is very noisy data.)

    Why would snow extent be cyclic anyway? Does it correlate with the PDO cycle, or the solar cycle or something else? You cannot simply claim that all weather is cyclic, that doesn’t fly either. You need to firm up you argument as to why snow extent would be cyclic, to defend your start point selection, because without that you cannot defend yourself against the “cherry-picking” criticisms.

    I think you would have been better off if you had used all the data (which displays zero trend) to debunk the model’s claim of a decline, rather than reaching for the claim of a recent increasing trend. That was a bridge too far.

  69. “There was a time when observation was considered an important part of science. Climate science has gone the opposite direction, with key players rejecting observation when reality disagrees with computer models and statistics. Well known examples include making the MWP disappear, and claiming that temperatures continue to rise according to IPCC projections – in spite of all evidence to the contrary.” -SG
    ________________________
    Integrity. An insignificant collection of letters. A word that is, as Humpty Dumpty would say, “what you want it to mean”. It plays in the dealings of the individual with his craft. It plays in the dealings of the craftsman with his peers. It plays in the dealings of the trade with its members and with the world with the trade.

    Many, if not most, “Scientists” –and the lessor order called “scientists”– believe it does NOT apply to them or their craft. That it only applies on a case by case basis, and lately only to a group called “Climate-ologists”.

    To bring skillful observation back to Science today, one needs a long strong 4″x4″ — or a Baseball Bat. When all Scientists have the integrity of all scientists all is lost and we are as we are today, in Wonderland.

  70. Robert (14:15:01)

    Does your iPhone have an opinion on the graphs from climate models shown at https://wattsupwiththat.com/2010/02/19/north-america-snow-models-miss-the-mark/ then?

    If the model predictions are wrong then they are, er, wrong. Let me make it clear – I’m defining “wrong” as being different to what actually happened. Call me old fashioned but I do have a soft spot for data.

    Sorry to go on about the iPhone stuff but, hey, why not.

    Refuting those evil “deniers”? There’s an app for that :)

    I don’t have an iPhone (I do have an iPod Touch, or iPad Nano as I now call it). WordPress do an excellent job of formatting their sites for such devices.

    Dave

  71. 1. My heart is broken by Scientific American, once the best magazine in the world and now whore for AGW. Not just AGW, but no debate or discussion of skeptics….a real tragedy.
    2. In big Pharma, we see every day the result the moneyed interests cheating their brains out on drug evaluations. The most recent examples are so obvious and despicable that I am amazed more of these researchers don’t end up in jail.
    3. Even in mechanical situations, such as Toyota, the cheap fix of the throttle problem has a major software component that Toyota does not mention. Several of the accidents had no problems with the pedals as the problems were likely caused by a malfunction in the drive by wire.
    4. A temp chart from two thousand years ago, shows a natural variation, and that we are leaving an ice age.
    5. A temp chart of 400,000 years show temp changes that show sunlight due to orbit, inclination, and possibly cosmic radiation and solar wind are part of the temperature equation, certainly no AGW.
    6. The computer model for AGW including its scientific basis has been shown to be completely false. This seals it for me. The issue of temp change is unimportant. The AGW facts are shown t be worthless, and their proponents are total cheaters. That is where the science is at.

  72. @Dave

    “Does your iPhone . . .”

    Dave, I don’t know if your job at the Seven-Eleven doesn’t pay you enough, or what, but you seem obsessed with my phone and the fact that I have a climate science app on there. In fact, I look things up on all kinds of sources, all the time, if that helps you. We can’t all just repeat what a talking dog tells us.

    “If the model predictions are wrong then they are, er, wrong.”

    You, er, don’t have the slightest idea what you’re, er, talking about. The models say the Arctic ice cover will decrease, and it’s decreasing. The magnitude of the decrease is greater than expected. So what? You evidently cannot tell the difference between a scientific prediction and a crystal ball.

  73. As pointed out by others, “confidence” is an important statistical term with a very precise definition, and I should add that it has nothing to do with betting or with gut feelings or eyeball readings. Your argument is indefensible, as there is (currently) no statistically significant trend in winter snow cover, regardless of the dates you pick.

    On the other hand, the trend in decreasing NH spring snow cover IS statistically significant (see Dery and Brown, 2007), and this is what climate models project (IPCC WG1, or, if you prefer their reference, here: http://www.acia.uaf.edu/PDFs/ACIA_Science_Chapters_Final/ACIA_Ch06_Final.pdf).

    You conveniently overlooked this fact when you generalized that “the climate models were wrong”. Hopefully, future posts will address this oversight.

  74. “The big problem with the alarmists is that we can’t apply such tests to their data because their theories do not generate testable hypotheses.”

    That’s just not true, Dr Fallone. The most basic testable hypothesis is that the world is getting warming, and you can demonstrate the statistical significance of that trend quite easily.

    You can test the hypothesis that CO2 and other GHGs heat the earth by absorbing and re-radiating long-wave radiation by looking at the earth’s emission spectrum:

    Look at the pattern compared to a black-body distribution, and do a chi-squared test on the pattern of the holes.

    You can also test the effect of GHGs in the lab, and this has been done, and these experiments can also be assessed for statistical significance.

  75. Isn’t Leif Svalgaard’s comment aimed at why use the average of the 1960/1990 temperatures as the ‘norm’ against which the common global temperature sets assess anomalies? How do we know that this norm is the ideal temperature and how do we know that warming above this norm is of concern? If we take a different base as the norm, then a different result is achieved. For example, perhaps the ideal temperature for mankind is the MWP or the RWP and if so we are still negative against those temperature norms such that any present day warming is a good thing and any warming towards such norms should be a cause for celebration, not a cause for concern.

  76. “Robert (15:42:53) :
    […]
    The models say the Arctic ice cover will decrease, and it’s decreasing. The magnitude of the decrease is greater than expected. ”

    Your iPhone must be Al Gore.

  77. Why is the graph of yearly snow extent drawn as continuous line graphs and not a bar charts?
    As an outsider to this field, I am often surprised to see discrete values in the primary data presented as a continuum. A continuum is justified in analysis of the primary data in looking for a trend — and that is where the dispute lies, in the analysis. The analogy of continuous temperature readings (or ocean heat content etc) with mountain sections is valid – but not with snow extent. Moreover, connecting the original discrete values means that a level of randomness that seem to be apparent in the data is obscured. At least that is how I see it from what I learned in elementary school, and, so I see from my daughter’s homework, this is still taught.

  78. A number of the comments here display the very lack of observational ability that this article is about.mmMark Twain said “There are three kinds of lies: lies, damned lies and statistics.”

    Winter snow cover has been increasing for the last twenty years. From 2001-2010, 7 out of 10 years have been above 45,000,000 km2. But from 1989-2000, only 3 out of 11 years were above 45,000,000 km2. The current year is the second highest on record.

    BTW- Tamino calculated 99% confidence for that graph before applying his undocumented “cherry picking test.”

    Here is an exercise for Robert et. al Show me statistical significance in the geologic record between CO2 and temperature. Good luck with that.

  79. Creative imagination asks simple questions of unexampled subtlety, confirmed by observation but not originating in technical expertise. Galileo asked, Will falling cannonballs of different weights hit Earth at the same time? Newton asked, If white light produces a spectrum, cannot a spectrum then produce white light? Olbers asked, Why are night skies dark? Einstein first asked himself, If I were riding a lightbeam and looked back, what would I see? And then again, Does a falling body feel its own weight?

    A child could ask these questions, but somehow only genius ever does. True, hypotheses are verified by observation, but fudging observations is not by any means unknown. The key is replication of results… Aristotle’s notion of “impetus” is nonsense on its face, but it persisted nigh two thousand years. As Lucy said one Columbus Day to Charlie Brown, “Yeh, that Flat Earth stuff was really stupid. What do we think now?”

    Climate models’ linear extrapolations of the atmosphere’s complex dynamic system violate fundamental principles of math and physics. No wonder Warmists’ kites are down the sewer.

  80. I see a lot of willful misunderstanding of Steven’s article here by the scientists. as Dr Anthony Fallone (15:10:13) tells us: “After inferential statistical testing a 95% confidence level means that for every 100 times an experiment is conducted and if for only five times the result differs from the rest this means that those five are likely to be by chance. A 0.05 p-value is the same thing noted as a probability and is the usual threshold value for psychology experiments. If an hypothesis is supported by the experimental evidence at the 0.05 level then we can say it is moderately reliably supported, we can trust the findings.”

    This 95% confidence interval, then applies to 1) laboratory trials in an experimental situation or 2) a hypothesis that builds on a specific theory that is to be tested observationally, as in the case of climate science, the validity of which is borne out by future observations. Where, in this post, is Steven putting forward any predictions of future conditions? He specifically states: “Can we infer that snow cover will continue to increase? Of course not.” He’s not conducting experiments either. The entire point of his article is historical: he has the historical data that shows that Tamino and the AGW scientists have got their predictions wrong. Does history need a confidence interval? If so, that’s news to me.

    I’d like to see you geniuses go back and prove why the fisheries science models from the 1980s were right, with all their statistical confidence intervals built in, and then tell me where all the Northern cod on the Grand Banks went to.

  81. Leif,

    Most school kids would understand the concept of measuring the height of a hill, person, tree, building, television, etc. How about you?

  82. Again, no matter how you slice the charts or rationalize it away, it takes MORE heat, not less to create heavy snowfall. The coldest place on earth is also one of the driest (in terms of annual precipitation) and that’s Anarctica. These are the basic laws of physics…it takes heat to evaporate moisture, we have very high temps right now in the oceans and troposphere, and when you combine that with a negative AO– bingo, you snow, and lots of it in places not normally seen.

    So if the trend toward more snow covrer (DURING WINTER) is accurate, then in can only mean that on average we are seeing more moisture and thus more heat and evaporation from the oceans during these months. How that discredits AGW in any way is beyond me…

  83. Leif Svalgaard (14:00:19) :

    If the question is something that is arguing against AGW, then a reasonable reference frame would be the time at which AGW proponents claim their effect ‘takes off’, no?


    I think using the time at which AGW proponents claim their effect ‘takes off’ is meaningless. Consider taking a 3/4 lambda measurement sampled across a pure sine wave. With an initial starting point at zero crossing just after the peak, you achieve a very significant positive trend. Move the reference backwards just 1/4 wavelength and it completely reverses the sign with a trend exactly opposite of the first measurement period. If, however, you use a measurement period of one full lambda you would get a zero trend no matter where you choose as the start point. Choosing an arbitrary start point which supposes their effect ‘takes off’ is meaningless if the measurement period is shorter than the cycle under measurement. Climate variability is cyclical so I believe there is a transferable analogy here.

  84. I am 6 feet tall. I’m standing at 5,000 foot elevation. Therefore my height is 5006 feet. Think I will put that on my driver’s license application.

  85. Did anyone read what Steve wrote?! And did anyone read the text describing the model predictions of snow cover?! Steve said he chose 1989-2010 because that’s when the trend turned upward. The observations are the observations. The trend from 1989-2010 is clearly upward at 100% confidence. If you add years before 1989 or after 2010, of course the trend will be different, but Steve was clear he was talking about 1989-2010. The models clearly predicted a downward trend in snow cover from 1989-2010 – look at the graphs. Summer snow cover, full year snow cover, blah blah blah is all irrelevant. The model predictions were for JANUARY snow cover, hence the use of WINTER OBSERVED snow cover as the comparison. Steve’s point was clear, that “North American snow models miss the mark – observed trend opposite of the predictions”, and he showed you why.

  86. Note that the X-axis of the graph is time. Therefore the confidence level would be used to predict “future” behaviour. i.e. further to the right along the x-axis. Anyone trying to claim that snow extent hasn’t increased since 1989 is simply not thinking clearly.

  87. R. Gates,

    Do you understand the difference between snow depth and extent? Extent increases when it snows in Florida. Snow in Florida is not due to heat – it is due to cold.

  88. “The trend from 1989-2010 is clearly upward at 100% confidence.”

    I’m sorry. That’s just not how it works.

  89. “Here is an exercise for Robert et. al”

    Who are my et al? Have I acquired a scientific posse without realizing it?

    “Show me statistical significance in the geologic record between CO2 and temperature.”

    I am not the whiz with computers that some people here are, but I’d be happy to take a look.

  90. This is reminiscent of the controversy that surrounded the claim that snowpack in the Cascades shrank by 50 percent in the last half century. The statistic was used by government and advocacy groups to push their climate change agenda in Washington State. The only problem is that the statistic was dead wrong. The unfolding scandal revealed extensive corruption of the science by one researcher, Philip Mote, at the University of Washington. The Seattle Times published a good summary of the story when it first broke a few years back:

    http://seattletimes.nwsource.com/html/localnews/2003618979_warming15m.html

    The same story was revisited by the Air Vent in July of 2009 and Jeff Id carefully dissected the methods used by Mote et al to reach their fraudulent conclusions. The article is well worth reading given the many similarities between Mote’s dubious methods and the methods purportedly being used to discount NH snow extent:

    http://noconsensus.wordpress.com/2009/07/21/snowmen/

    It’s also interesting to note that just a few months ago, the odious NPR environmental reporter, Richard Harris, gave an extremely distorted picture of the Climategate scandal on the NPR program “All Things Considered”. That by itself is not terribly surprising. However, what shocked me most during the segment was how Harris completely misrepresented John Christy’s contention that his research paper on Sierra Nevada snowpack melt had been suppressed for political reasons. Harris casually concludes that Christy’s concerns were simply a matter of paranoia stemming from Christy being personally attacked in the CRU emails.

    Please read the transcript (below) and carefully note who actually reviewed and rejected Christy’s paper on snowpack melt. Then reread the article I posted above on the Cascade snowpack fraud. Finally, revisit Christy’s contention that his paper was intentionally suppressed for political reasons. See if you reach the same conclusion that Harris does and report back.

    TRANSCRIPT:

    That said, many of the complaints about the journal review process come from people who think mainstream science is overstating climate hazards. John Christy is at the University of Alabama at Huntsville. He accepts global warming is happening, but he says there’s a lot of uncertainty about its causes and impacts. And he says he has trouble getting some of his results published.

    Professor JOHN CHRISTY (Director of the Earth, System Science Center, University of Alabama, Huntsville): I’ve done a pretty thorough study of snowfall in the Sierra Nevada mountains of California. And the Southern Sierra show no downward trend in snowfall.

    HARRIS: That’s important because snowfall is forecast to decline due to global warming. And that would seriously affect California’s water supply. Christy says he’s tried three times to get his paper published. So far it’s been rejected, and he suspects it’s because scientists are trying to stifle his message.

    Prof. CHRISTY: Everyone from the secretary of energy who has talked about the snowfall in the Sierra going away will not find any comfort in the fact that the trends in snowfall are essentially zero for the last hundred years.

    HARRIS: So is it being suppressed? Philip Mote at Oregon State University was one of the scientists who reviewed the paper. He said the science in the paper was fine.

    Dr. PHILIP MOTE (Oregon Climate Change Research Institute, Oregon State University): To my knowledge, there’s no suppression going on. It’s simply that it’s not news.

    HARRIS: Mote himself published a paper four years ago showing that snowfall in the Southern Sierra hasn’t diminished. In fact, he says there are about 10 papers on the subject, certainly not identical to Christy’s but still reaching that same broad conclusion.

    Dr. MOTE: It’s not controversial because it’s already well known.

    HARRIS: Still, it’s easy to see why Christy suspects deeper motivations. The stolen emails contain sharp personal attacks against him. He says the politics inside climate science are making life harder for him, not just in publishing papers but in getting money to do research. Mote says dissent is important in science. He doesn’t agree with everything Christy says, but he says he should have a voice.

    —-

    Full transcript can be found here:

    http://www.npr.org/templates/transcript/transcript.php?storyId=120846593

  91. B.D.

    You are the voice of a true scientist! Someone who reads, thinks and observes carefully, and trusts his own eyes.

  92. R Gates
    “So if the trend toward more snow covrer (DURING WINTER) is accurate”

    The trend is moving south. It’s called extent.

    That means it’s colder further south, where it was not colder before.
    Moisture without cold, is called rain.

    When the extent of snow cover increases, that means it’s colder.

  93. Re: Robert (14:20:54) :

    I don’t understand why you think that the 95% confidence limit or the 0.05 significance level is there somehow by definition in statistics. Confidence limits are arbitrarily set. They are subjective not objective. There is nothing in the definition of statistical significance which demands that a certain significance level be used. It just so happens that people have come to use the 0.05 level of significance for many studies. That doesn’t mean that people are right to use 0.05 just that a lot of people do. Something can be statistically significant at the 85% confidence interval. It may not be significant at the 95% CI but it doesn’t change the fact that by the very natural of statistics that it is statistically significant at the 85% confidence interval. Saying something is statistically significant is not a valid statement unless it is paired with the levle at which that significance was tested.

    To claim a 95% confidence interval is correct because of definition is ludicrous because no such definition exists. If you would like to present an argument for why 95% is the holy grain of significance levels other than that because when it comes to plucking significance levels out of thin air most people tend to pick 95% out of tradition then I would like to hear it. Why 95%? Why not 96%? Why not 94%? What is so special about 95% which, regardless of how many people arbitrarily select it, doesn’t make its selection arbitrary?

  94. BerneiL
    Using bar charts makes it difficult to determine trends.
    Scatter charts are better for that putpose (snow cover, temperature or whatever on the Y axis, time on the X axis).

    From a practical point of view, I still prefer line charts, as I am used to intrepreting these.
    But if you are picky, then use scatter plots.

  95. I think this is funny. Does anyone remember why statistical analysis and confidence levels and so on are so important? I shall answer my own question.

    Human being percieve data differently than computers do. The value of computers is that a) they can manipulate data FASTER than human beings can and b) they can manipulate MORE data than human beings can. The problem this creates is that we no longer percieve the data directly, we percieve it through the limitations of the computer analysis that manipulates it for us. Anyone who has worked for example, in a custom metal fabrication facility, knows that drawings come in from engineers who used Autocad that could have been far simpler to fabricate other ways. I know a professor of architecture who sends his students to a particular building here in town and asks them to use Autocad to draw the stairwell. They can’t, which is the purpose of making them try and do it. Computer tools allow analysis that human beings could never accomplish with calculators or pencils and paper. But it also means we need mechanisms to validate that what we see in the computer analysis output is what we thing it is. Hence statistical analysis and confidence levels to understand if the graph we are looking at, which may be several generations of analysis from the original data, says what our human perceptions believe it says. There’s too much data for us to look at several million measurements, a graph the computer built, and go… yeah, that looks about right.

    The point of that ramble, is that human perception and analysis is still superior to that of a computer program, provided that the data being observed and considered is small enough for us to “fit it in our heads”. In Steve Goddard’s example of the hills, this is exactly the case. Take ten thousand altitude measurements across those hills, and a computer program will in fact tell you that the average is flat, that there is no trend, that the variability is within error, and it will be correct. That’s how computers see data and that’s why it is so easy to take big chunks of data and get answers that are technicaly accurate and totaly meaningless.

    Anyone can look at the graph of the hills and answer the question. Yes they need to say compared to what? the valley? see level? where I’m standing? But it is obvious that saying there are no hills at all, its flat, could be accomplished ONLY by computer analysis, not by a human being looking at it.

    I don’t need a computer to analyze 40 years of snow extent data. I might have some questions as to how it was measured and so on, but having been satisfied that the data collected is valid…. looks to me like snow extent is highly variable, that there was a declining trend from the beginning of the graph that was accompanied by decreased variability, followed by an increase in snow extent at the end of the graph that seems to be accompanied by increasing variability.

    We’ve gotten so used to looking for the errors that poor math or bad data in conmputer models can produce, that we forgot the purpose was to ensure we were looking at what we thought we were looking at, and if the data set is something we can percieve in its entirety without a computer, then there is no need for the statistical analysis. We can actually just look at it.

  96. Robert,

    Nice try. That wasn’t the question I asked. The data you provided demonstrated that CO2 concentration follows temperature in the narrow range of the glacial record over the last few hundred thousand years. During that time CO2 only varied in a very narrow range.

    I pointed you to a graph of the geologic record going back 600 million years plus where CO2 varied by 1000-2000%, which shows very little if any correlation between CO2 and temperature. Please try again.

    Here is another one.

    Over geologic time, there is little if any correlation between CO2 concentration and temperature.

  97. ‘There is 100% confidence that the trend from 1989-2010 is upwards.’

    I’m intrigued by this statement Steve. The main problem I have with this graph is the lack of error bounds. In my experience I have seen many wonderful ground breaking trends which magically disappear once I add the error bound along with my hopes of a tier 1 journal paper.

    Even with empirical data there is still the question of how well it represents reality. This is why I disagree with another statement you made about statistics only being for predicting future trends. Carl Popper says that we use statistics to make up for a lack of knowledge and I’m sorry to say that I like his interpretation better.

    If you make three observations under the same conditions and get different results then there is something you don’t know. Now you want to change the conditions and see if you can measure a change in the result. The problem is that the result changes even when you don’t change the conditions because of what you don’t know. So how can you say anything about changes in results due to changing conditions when results change without conditions? Well we all know the answer to that one and it is statistics. You compare the changes that occur under constant conditions (what you don’t know) with the changes that occur under changed conditions (the sum of what you do and don’t know) and compare them to see if you know enough to make inferences.

    I don’t think this process involves predicting future trends, its simply analysing past events for significant patterns.

  98. Robert re your evidence for AGW.

    The missing gap is how the other, far more important players in the climate interact with the effect of CO2.
    Some of these include water vapour fluctiations, cloud cover, earth – atmosphere -ocean interchanges and cosmic rays (currently being tested in the Cloud experiment at CERN). There are probably others I have omitted.

    The problem I have with AGW is that I have taken NCDC temperature data from 1880, removed the long term linear trend and the 65 year zig zag cyclic component. I am left with short term fluctiations cause by el Ninos, volcanos and higher harmonics of the 65 year cycle.

    These short term fluctiations have no trend (r squared value very close to zero).
    There is no visable evidence of any CO2 effect in these residuals.

    (You will see that I have completely ignored the increasing evidence that the basic temperatrure data may have been biased upwards systematically, which would have artifically raised the long term trend.)

    On top of that is the evidence, growing daily, that the supporting arguements for glacier loss, floating ice loss, snow loss, drought, fire flood, storms, rate of increase in sea levels, polar bears etc are all either exagerated or completely false. That suggests that the scientists at the heart of the AGW scare are pursuing an agenda rather than scientific research.

    I do not consider myself a denier or a skeptic. My position is that I do not see sufficient evidence that AGW is real. I am an accountant by profession with both econimic and statistical training. I realise the devastation that the proposed massive reductions in using fossel fuels woud cause.

    That deveatation would be far, far more severe than the goverments around the world are prepared to admit, or perhaps even realise. Perhaps that is because so few politicans have no experience of the difficulties of actually creating wealth that all of us rely on to survive.

    Making the huge sacrifice of abandoning carbon would be extremely difficult, with misery for all and death for many. To make such monumental change without good cause would be completely wicked.

  99. I’d just like to state for the record that the Wikidpedia picture of Sir Issac Newton looks remarkably like a guy who works in my community theatre.

  100. R. Gates (16:29:54) :

    “How that discredits AGW in any way is beyond me…”
    ==============
    How do you define AGW ? (Show your work).

  101. Robert (12:09:01) :

    A confidence interval is about distinguishing a random distribution from a pattern. By convention, you need to be 95% confident in your trend in order to reject the null hypothesis. 90% is on the bleeding edge of acceptable.

    Excellent. Then we can ignore the IPCC AR4 almost completely, as it almost never reaches 95% (“extremely likely, by their assessment) confidence levels.

    Thanks.

    Can I interest you in a game of chance, by the way, only $10 a point…..

  102. Steve Goddard (16:24:35) :
    Most school kids would understand the concept of measuring the height of a hill, person, tree, building, television, etc. How about you?
    It becomes a bit more tricky if the person, etc is standing on a steep slope. Perhaps there are several persons. One could define the highest person as the one being being able to see farther. A valid analysis would have been simply to plot your curve on the same graph as the models, lining them up on some point in the past, e.g. when the model was run or the last data point used as input to the model. Then there would be no discussion, anybody could just by eye get a feeling for how well the models were doing. Try that.

  103. This is reminiscent of the controversy that surrounded the claim that snowpack in the Cascades shrank by 50 percent in the last half century. The statistic was used by government and advocacy groups to push their climate change agenda in Washington State. The only problem is that the statistic was wrong. The unfolding scandal revealed extensive corruption of the science by one researcher, Philip Mote, at the University of Washington. The Seattle Times published a good summary of the story when it first broke a few years back:

    http://tinyurl.com/259rsh

    The same story was revisited by the Air Vent in July of 2009 and Jeff Id carefully dissected the methods used by Mote et al to reach their fraudulent conclusions. The Air Vent discussion is well worth reading given the many similarities between Mote’s dubious methods and the methods purportedly being used to discount NH snow extent:

    http://tinyurl.com/ydp58zf

    It’s also interesting to note that just a few months ago, the odious NPR environmental reporter, Richard Harris, gave an extremely distorted picture of the Climategate scandal on All Things Considered. That by itself is not terribly surprising. However, what shocked me most during the segment was how Harris completely misrepresented John Christy’s contention that his research paper on Sierra Nevada snowpack melt had been suppressed for political reasons. Harris casually implies that Christy is a conspiracy theorist and that his paranoia stems from being personally attacked in the CRU emails.

    Let’s do an experiment. Since Richard Harris is apparently an incompetent journalist, let’s revisit Christy’s contention that his paper was intentionally suppressed for political reasons. First, please read the article linked to above about the fraudulent claims made by Philip Mote regarding an increasing trend in snowpack melt in the Cascades. Next, please read the NPR transcript below and carefully note who actually reviewed and rejected Christy’s paper on the lack of a downward trend in Sierra Nevada snowfall. Finally, see if you are able to reach the same conclusion as Richard Harris and report back.

    _____

    TRANSCRIPT:

    That said, many of the complaints about the journal review process come from people who think mainstream science is overstating climate hazards. John Christy is at the University of Alabama at Huntsville. He accepts global warming is happening, but he says there’s a lot of uncertainty about its causes and impacts. And he says he has trouble getting some of his results published.

    Professor JOHN CHRISTY (Director of the Earth, System Science Center, University of Alabama, Huntsville): I’ve done a pretty thorough study of snowfall in the Sierra Nevada mountains of California. And the Southern Sierra show no downward trend in snowfall.

    HARRIS: That’s important because snowfall is forecast to decline due to global warming. And that would seriously affect California’s water supply. Christy says he’s tried three times to get his paper published. So far it’s been rejected, and he suspects it’s because scientists are trying to stifle his message.

    Prof. CHRISTY: Everyone from the secretary of energy who has talked about the snowfall in the Sierra going away will not find any comfort in the fact that the trends in snowfall are essentially zero for the last hundred years.

    HARRIS: So is it being suppressed? Philip Mote at Oregon State University was one of the scientists who reviewed the paper. He said the science in the paper was fine.

    Dr. PHILIP MOTE (Oregon Climate Change Research Institute, Oregon State University): To my knowledge, there’s no suppression going on. It’s simply that it’s not news.

    HARRIS: Mote himself published a paper four years ago showing that snowfall in the Southern Sierra hasn’t diminished. In fact, he says there are about 10 papers on the subject, certainly not identical to Christy’s but still reaching that same broad conclusion.

    Dr. MOTE: It’s not controversial because it’s already well known.

    HARRIS: Still, it’s easy to see why Christy suspects deeper motivations. The stolen emails contain sharp personal attacks against him. He says the politics inside climate science are making life harder for him, not just in publishing papers but in getting money to do research. Mote says dissent is important in science. He doesn’t agree with everything Christy says, but he says he should have a voice.

    _____

    Full transcript can be found here:

    http://tinyurl.com/ydk64bu

  104. “But it is believed that the process is making the seas much more acidic which is damaging the delicate shells of organisms that are critical to the marine food chain.”

    Drivel. Unsubstantiated propaganda.

  105. Steve Goddard (17:55:23) :
    Over geologic time, there is little if any correlation between CO2 concentration and temperature.
    Methinks I showed you there was, when you asked me to ‘prove it’… Have you already forgotten?

  106. Robert, thank you so much.

    Since you brought all of this up about r2 I have learned a big lesson. R2 is never an indication of confidence. It is a description of slope when using a linear trend. A flat trend line ALWAYS has an R2 of 0. A near vertical trend line ALWAYS has an R2 near 1. Bingo. It doesn’t matter how many of where the data points are around the trend line when speaking of a time-series. Robert, keep up the good work.

    Everone, try it in Excel. Your view of R2 will never be the same again.

  107. Steve Goddard:

    A number of the comments here display the very lack of observational ability that this article is about.mmMark Twain said “There are three kinds of lies: lies, damned lies and statistics.”

    Actually Mark Twain attributed the quote to British PM Benjamin Disraeli, except that Disraeli (according to his biographers) never made such a statement.

    That’s why you should check your sources.

    Statistics is not a concentrated form of lying, but liars can make fools of us all by bending statistical analyses to a particular conclusion.

    Statistics is a mathematical formalism that asks questions about data and trends which could arise by chance.

    So for example, a time series which exhibits long term persistence will easily produce short term trends which could be the result of a stochastic process.

    You have produced a model showing an apparent linear trend in a time series. Is the series significant? There are a battery of tests created by statisticians which give a likelihood as to whether your claimed trend could have a risen by chance.

    Berating the rest of us for grasping that point because it eludes you is neither skeptical nor scientific.

  108. Leif Svalgaard (12:34:51) :

    “I’m tired of my children’s science education being controlled by people with a political agenda.”
    Move to Kansas…

    and since I happen to find evolutionary theory convincing…what say you now?

  109. wayne (19:14:27) :
    A near vertical trend line ALWAYS has an R2 near 1. Bingo. It doesn’t matter how many of where the data points are around the trend line when speaking of a time-series.
    None of this is correct.

  110. Steve Goddard said: (17:07:57) :

    “R. Gates,

    Do you understand the difference between snow depth and extent? Extent increases when it snows in Florida. Snow in Florida is not due to heat – it is due to cold.”

    Again with insults? Why? You know that cold is only half the equation to produce snow. All the moisture that fell on the south in the form of snow came from subtopical waters– either the Gulf or the Pacific or both. Generally speaking, COLD=DRY in climate terms. This is climatology 101.

    But to answer your question: You know full well why there was enough cold in Florida for it to snow– a very negative AO. And if you’ve done lick of homework, you also know that negative AO’s have their ROOTs not in the arctic, but in the the tropics where warm air is forced up through the troposphere and carried northward to decend on the arctic, forcing the cold arctic air south, and creating high pressure over arctic regions in the process as well. I’ve displayed previously the charts showing the high pressure we had over Greenland this winter with the concurrent higher temps, when it was warmer in Greenland than in much of the southern states.

    Again, deep snow, more snow, gtreater extent, however you want to measure it has it roots in HEATING of the oceans. You are somehow trying to equate a greater snow cover with a colder overall climate…and that’s just plain wrong.

    Anarctica is cold and dry
    The last glacial period was cold & dry
    Cold=dry and Warm=wet

    The fact that we have an El Nino year, plus record temps in the troposphere, plus the several negative AO events, (related to warm tropics) is precisely why we had big snows in odd places this year, and so, if your charts are correct, and we’ve had more snow since 1989, then that only proves there is more heat around in the winter to evaporate and transport all that moisture.

    The simple (and wrong) approach that cold=more snow, defies the laws of both physics and the history of the climate on earth.

  111. Leif Svalgaard (19:33:23) :
    wayne (19:14:27) :
    A near vertical trend line ALWAYS has an R2 near 1. Bingo. It doesn’t matter how many of where the data points are around the trend line when speaking of a time-series.
    None of this is correct.
    I should rephrase this. A trend line that is significant enough has an R2 near 1. Does not have to be vertical. Try in excel the data 2 4 6 8 10 in one column and 1 2 3 4 5 in another and see what R2 is. Time series has nothing to do with anything.

  112. h2o273kk9 (19:32:59) :
    “Move to Kansas…”
    and since I happen to find evolutionary theory convincing…what say you now?

    Not discussing evolution [which is a fact, so no discussion needed] per se, just pointing out that if you want schools controlled by another agenda than politics, Kansas is a good example. Schools should not be controlled by ANY agenda IMHO. Many will disagree and claim that schools should teach the values of the society in which they are located. This is hard to avoid and, as I said, many will think it desirable.

  113. Steve that article was really disjointed, the ideas are mixed and unfocussed.

    “There was a time when observation was considered an important part of science.” Then you referenced the MWP as being observed and your lead up was Newton under an apple tree, Szilard at a red light and Archimedes in a bathtub quite irrelevantly. I don’t know why you tried to use analogies or link it with observing the MWP because there is quite a bit of information over what happened during the MWP and if it was a local or global phenomenon.. that’s called scientific progress (the ability to interpret new information and judge its merit). I don’t know why you bothered yourself starting the article this way because it was very well presented when you became specific with your hills in the Appalachian Plateau analogy.

  114. “Not discussing evolution [which is a fact, so no discussion needed] …”

    Unbelievable! A fact? I’m probably with you in that the preponderance of evidence suggests this is likely correct and I am also an atheist who has no need for supernatural interference. However, the “no discussion needed” is just so AGW like.

    Discussion is always needed if only to sharpen the arguments and keep the “priests of science” humble.

    And your attitude is, in my humble opinion, just another example of

    “science education being controlled by people with a political agenda.”

    Just tonight, I watched a lecture about evolution where it was mentioned that Darwin’s justification for validation was the same model taken by supporters of “Huygen’s wave theory of light”

    And we see how that turned out.

  115. Leif Svalgaard (19:33:23) :

    Why does Excel report is as so?

    And Leif, once again I didn’t specify exactly the case.

    And I know r2 is proper when using two random variables x and y. That is one place where it exactly should apply.

    This is only a special case of high x series of year numbers with random y values placed around the tren line. The slope greatly dictates the r2 value. My x was 1000 to 2010. The y was rand()*100 for each. Plot x-y scatter. Pressing f9 (re-calc) gives you a new set of 1010 y values, but r2 is always very near zero. But if you add to each y value and increasing value as rand()*100+row(), you get an upward sloping line with random points around it and the r2 is always near one no matter how many times you press f9 to refresh to regenerate the data. If you use row()/10 the r2 is always right at 0.5, why?

    The equation of r2 of course has everything to do with this behavior. R = Sxy / sqrt(Sxx * Syy). Still digging as to why in this special case this appears. (But this special case is the form most charts ploting yearly temperatures or snowfall take). It was a new view of r2 to me.

  116. Robert (15:55:28) :
    You can test the hypothesis that CO2 and other GHGs heat the earth by absorbing and re-radiating long-wave radiation by looking at the earth’s emission spectrum

    Sure you can. If you ignore all other factors that occur in the real world as opposed a model’s virtual world. Which is precisely why your models are so useless for real life. Interesting exercise, nothing more.

  117. h2o273kk9 (20:10:40) :
    Unbelievable! A fact? I’m probably with you in that the preponderance of evidence suggests this is likely correct
    There comes a point when the evidence becomes so strong that scientists stop arguing [lay people go on long after that]. At that point, something is elevated to a ‘fact’. The wave theory of light is still valid [we still speak about wave lengths – e.g green light at 520 nm – don’t we?]. The Earth is still round, etc. As we know more later on, the facts get integrated into a larger and richer picture [The Earth is not a sphere, although it is round]. AGW has not reached that point, so is still debated.

  118. davidmhoffer (17:49:17) :
    That’s how computers see data and that’s why it is so easy to take big chunks of data and get answers that are technicaly accurate and totaly meaningless.

    BINGO! A sure nomination for quote of the week! It was one of the first things drilled into us when I was learning to be an ORSA (Operations Research Systems Analyst). It’s also the first lesson forgotten as we become enamored with our fancy toolkits.

  119. R. Gates (19:34:08) :
    Again, deep snow, more snow, gtreater extent, however you want to measure it has it roots in HEATING of the oceans.

    More like ocean heat content being released to the atmosphere. And from there into space.
    But go ahead, keep trying the Jedi mind trick if it makes you happy.

  120. JLKrueger said: “More like ocean heat content being released to the atmosphere. And from there into space”

    Heat content from the oceans “being released” would be called? Yep, find that word…starts with an “e”, and involves a change of state for water. And no, it doesn’t just go from there into space. It enters the troposhere, thus going from the hyrdosphere to the tropsphere via a change of state called evaporation. Once in the hyrdosphere, water vapor joins the other greenhouse gases in trapping heat at the surface.

  121. Correction to last post: “Once in the TROPOSHERE, water vapor joins the other greenhouse gases in trapping heat…”

  122. wayne (20:26:09) :
    But if you add to each y value and increasing value as rand()*100+row(), you get an upward sloping line with random points around it and the r2 is always near one no matter how many times you press f9 to refresh to regenerate the data. If you use row()/10 the r2 is always right at 0.5, why?

    Because r2 depends on the signal-to-noise [S-N] ratio. [is, in fact, a measure of that]. By dividing by 10 you reduce the signal [‘the trend’] so r2 goes down. If you were to reduce the noise [use rand()*10] you would restore the S-N ratio and r2 would go up again. Now r2 in itself is not measure of significance. It also depends on the ‘number of degrees of freedom’ [number of independent data points]. E.g. if you only had 2 data points r2 would always be 1, but there would be no significance. With a million data points, even an r2 of 0.01 might be significant. Another way of expressing what r2 means is that r2 is the fraction of the variability that is ‘explained’ by the trend.

  123. Leif

    “There comes a point when the evidence becomes so strong that scientists stop arguing [lay people go on long after that]. At that point, something is elevated to a ‘fact’. The wave theory of light is still valid [we still speak about wave lengths – e.g green light at 520 nm – don’t we?].”

    The irony here is that this thread starts out with a pic of Newton. Huygen’s theory was heresy in Darwin’s time. Newton’s theory was elevated to “fact” except, of course, when it didn’t fit the narrative…Fresnel, etc.

    The wave theory of light is valid when we are experimenting under certain conditions. The particle theory is valid under other conditions. Finally, geometic optics serves us just find in still other conditions.

    Kind of like “Newtonian mechanics” vs. “Einsteinian relativity”…Mercury’s precession, anyone?

    That’s just two!

    So your “facts” may end up with a giant asterisk beside them some day.

    Where we agree is that AGW “science” isn’t even close to being solidified.

  124. “Robert,

    Nice try. That wasn’t the question I asked.”

    You do realize what you wrote is still visible, right? “statistical significance in the geologic record between CO2 and temperature.” Asked and answered. Game over. Nothing shreds your credibility more thoroughly than being completely unable to admit you’ve made a mistake. There is a statistically significant correlation between CO2 and temperature in the geologic record. Deal with the facts.

  125. @ JLKrueger

    Most science involves isolating a variable in order to test it. Controlled, repeatable experiments rarely reflect the real world in every particular. The question was whether the theory of AGW makes testable, falsifiable predictions. It does. Your hostility to modeling is a different subject entirely.

  126. R. Gates (19:34:08) :
    Again, deep snow, more snow, gtreater extent, however you want to measure it has it roots in HEATING of the oceans.

    Couldn’t it be due to the winds, maybe currents? Total world-wide humidity is always close to flat. The sun’s output is not way up last time I checked. So where is all of this HEATING you keep speaking coming from? Whereever there is HEATING, there is equal COOLING somewhere else; unless you are speaking of long year+ time periods.

  127. h2o273kk9 (21:06:52) :
    So your “facts” may end up with a giant asterisk beside them some day.
    None of your cases overturns the old ‘fact’. Newer theories incorporates the older ones as limiting cases. At the time of Darwin, evolution was not a fact. He didn’t know the mechanism by which it works. We do today, and that is why today it is a fact. Undoubtedly, we’ll learn more and more about the details, but that does not ‘unfact it’. That we have learnt that the Earth is more of a pear-shaped distorted ellipsoid does not ‘unfact’ that it is round. Another example of a ‘fact’ is that the Sun shines from fusion of Hydrogen in its core. As early as 30 years ago, that was not a ‘fact’ and many solar physicists [including myself] were worried about that. Today the resolution of the solar neutrino problem has removed that worry and Hydrogen fusion in the Sun is accepted as a fact [Oliver and other crackpots not withstanding].

  128. h2o273kk9 (21:06:52) :
    The irony here is that this thread starts out with a pic of Newton. Huygen’s theory was heresy in Darwin’s time. Newton’s theory was elevated to “fact” except,…
    Except it was the other way around… Einstein put Newton back in the game, and as it turn out both Huygens and Newton had a side of the truth.

  129. wayne (21:26:52) :
    The sun’s output is not way up last time I checked. So where is all of this HEATING you keep speaking coming from?
    Wayne, there are people [even on this blog] who claim that the Sun’s heat is hidden in the oceans for hundreds of years and now and then surfaces to HEAT us, so perhaps that is where it comes from ;-)

  130. Leif

    “None of your cases overturns the old ‘fact’. Newer theories incorporates the older ones as limiting cases. ”

    Really? Which is it? Particle theory limits Wave theory or Wave theory limits particle theory?

    Robert

    “The question was whether the theory of AGW makes testable, falsifiable predictions. It does. ”

    I want specific predictions. And Hansen’s 3 scenarios fail, BTW.

  131. I continue to be astonished that there are people who have seen the graphs, read the news, and still doubt that winter snow extent has increased in the last 20 years. Look at the graphs. Look outside the window, Just look, and think and see.

    Linear fitting shows that over the past 20 years it has been increasing by an average of 100,000 km2/year. Over the last ten years it has been increasing by over 200,00 km2 per year.

    Are these trends a reliable way to predict the future? I’ll leave that pointless conversation to people who like to spend their time on statistics. I personally don’t think so.

  132. Leif
    “Except it was the other way around… Einstein put Newton back in the game, and as it turn out both Huygens and Newton had a side of the truth.”

    Except that I offered that it was Huygen’s who was supplanting Newton’s “fact” during Darwin’s time…nothing to do with Einstein. No straw arguments please.

  133. Re: Leif Svalgaard (20:30:22)

    Scientific hypotheses can be elevated to theories by fact, and theories can have differing degrees of empirical content but a scientific theory can never ever be a fact. Scientific theories are about trying to discern universal statements from singular statements. The reason we do this is because humans are capable of knowing singular statements but are incapable of knowing universal statements. Singular statements are facts; they are things that you are able to state that you know because they have been observed. We can never state a universal statement is fact because we cannot observe universal statements. You cannot see the law of gravity; you can only see singular events which you assume are governed by the law of gravity. You do not know that if you drop an apple it will fall towards the earth; however that shouldn’t stop you assuming with high confidence that it will. If you drop the apple and it falls to the ground then you can make a factual statement based on that singular event.

  134. Leif Svalgaard (21:28:07) :

    Newer theories incorporates the older ones as limiting cases. At the time of Darwin, evolution was not a fact. He didn’t know the mechanism by which it works. We do today, and that is why today it is a fact.

    _________________________

    Whaaat? Darwin’s name is a household word because he did know the mechanism by which EVOLUTION worked: namely, Natural Selection, which functions through competition for scarce resources between members of the same species. What Darwin did not know was the mechanism that created the variations between individuals that gave some a competitive edge – and when that mechanism was discovered, Darwinism suffered an ‘eclipse’ as people jumped on a Mendelian-Lamarckian variation on evolutionary theory. It was not until population biology added to our understanding of how genes work in populations that Darwinism was fully reinstated, in the late 1950s. Even without that knowledge, evolution became an accepted theory (not a fact) following Darwin, because he provided overwhelming evidence — through hundreds of different observations — that it had occurred. (No stats involved in this one, either.) I agree with the general drift of your comments, though, especially concerning Newton and Huygens.

  135. Simple explanation from William M Briggs, statistician.

    Let’s ignore statistics and turn to plain English.
    Suppose, fifteen years ago the temperature (of whatever kind of series you like: global mean, Topeka airport maximums, etc.) was 10 C. And now it is 11 C. Has warming occurred? Yes! There is no other answer. It has increased.

    http://wmbriggs.com/blog/?p=1958

  136. h2o273kk9 (21:43:01) :
    Really? Which is it? Particle theory limits Wave theory or Wave theory limits particle theory?
    They are both correct. We can talk of the wave length and frequency of light and at the same time talk about the momentum of photons. Even in optics we still talk about light ‘rays’. In the limit of very long waves, we don’t use the particle picture. We talk about ‘radio waves’ not ‘radio photons’. In the limit of very short waves we tend to talk about X-ray and gamma rays as photons [particles] rather than waves. So there are two limits and two limiting cases.

  137. Robert,

    You aren’t answering the question because you can’t. Below are two interpretations of CO2 vs. temperature through the geologic record of the last 600 million years

    Neither shows much, if any, correlation between CO2 and temperature.

    As far as the ice cores go, all that they show is that CO2 is less soluble in warmer seawater (take physical geology 101 at your community college.) That is a small effect of 50% change in CO2 concentration which follows large changes in air temperature.

    The much larger effects of 1000-2000% changes in CO2 are seen in the geologic record.

    Don’t try to BS your way out of it.

  138. vigilant fish

    “I agree with the general drift of your comments, though, especially concerning Newton and Huygens.”

    As I’ve pointed out…which is it?

    “Particle theory limits Wave theory or Wave theory limits particle theory?”

    In the case of “Newtonian mechanics” vs. “Einsteinian relativity”…Mercury’s precession, anyone?

    I have to wonder whether holographic gravitation might play a similar role 100 years from now.

  139. vigilantfish (21:51:38) :
    Whaaat? Darwin’s name is a household word because he did know the mechanism by which EVOLUTION worked
    He did not know how natural selection could work its magic, namely by the digital nature of heredity that ensures fidelity in preserving that which has been selected. Today we know the gory details [DNA, genetic code, etc] and that is what makes evolution a fact. We may have a slightly different view on what a ‘fact’ is. My view is that it is a fact when it is no longer under debate by people who know the field. In this view, ‘facts’ are not absolute, but then nothing is [except in mathematics – but there perhaps ‘fact’ is not the best word to use – mathematicians don’t use it themselves].

  140. Leif
    “They are both correct. We can talk of the wave length and frequency of light and at the same time talk about the momentum of photons.”

    ” So there are two limits and two limiting cases.”

    And that’s my point. One did not limit the other. They became different narratives depending on the circumstances.

    In other words, it’s not a “fact” to discuss wave theory in some circumstances and particle theory in others (two slit experiment for example).

    It’s contextual…at least thus far.

  141. Steve Goddard (21:54:18) :
    Simple explanation from William M Briggs, statistician.
    Has warming occurred? Yes! There is no other answer. It has increased.

    Today was warmer than yesterday. Has warming occurred? Yes! there is no other answer. It has increased.
    But is it a climate change? No.

  142. The Met Office uses climate models to make their (almost always incorrect) seasonal forecasts. They have been consistently wrong since 2007, when they predicted a sizzling summer that turned out to be the most miserable wet summer on record.

  143. Leif

    “Simple explanation from William M Briggs, statistician.
    Has warming occurred? Yes! There is no other answer. It has increased.
    Today was warmer than yesterday. Has warming occurred? Yes! there is no other answer. It has increased.
    But is it a climate change? No.”

    Aaargh. Climate is always changing. You know full well that the question is “is it Anthropological”. I don’t believe the case is proven…and we probably agree on that…but “No.” Sheesh. A little more humility, please!

  144. h2o273kk9 (22:13:09) :
    In other words, it’s not a “fact” to discuss wave theory in some circumstances and particle theory in others (two slit experiment for example).
    You have lost me. They are both facts.

  145. Leif,

    If winter snow extent has increased, does that means it has decreased – as the models predicted? Are the models correct?

  146. Leif
    Particle theory does not explain the two slit experiment, for example. They are accepted facts, yes, but self-limiting with their own domain.

    In other words, it still leaves the door open for a better explanation.

  147. Leif,
    I apologize for not being more clear. Let me put it this way…does Huygen’s theory mean that light is in “fact” a wave or just that it acts like you would expect a wave to act.

    That’s the difference.

  148. Leif Svalgaard (21:40:37) :

    You are exactly right. Wish I had never used the word “storage” many posts ago, it might have given other fertile minds the false impression that you could store it away above physics laws and local temperatures. Big wording mistake!

    Wish WUWT has a tab on “Common Scientific Truths” that are unquestionable; so those constant realities didn’t have to be said over and over and over again. That re-stating is where one wrong word and here comes everyone to tell you how wrong you are or how little you know, missing the whole point of the comment. I’m not good in wording so I get a good dose of that. I’d prefer to say I know and adhere to the Common Scientific Truths and here’s the real message of my comment.

    While commenting to you, don’t you get the sense that some here are trying to sequence items in climate science instead of viewing a continuum of processes? Like, it’s not going to evaporate until … then it will evaporate, and when it evaporates … and then …. Seems they don’t grasp that bulk matter always radiates, bulk matter always absorbs radiation, water always evaporates, and water always condenses, always simultaneously. It is the differential of parameters that matters at the interface and even when the difference is exactly zero, both sides are still happening only in equal proportion.

    A clarification of that fact would help many frequenting here. Something like a mini-course you could just point them to; without Wikipedia to scrabble their minds.

  149. h2o273kk9 (22:28:33) :
    “Today was warmer than yesterday. Has warming occurred? Yes! there is no other answer. It has increased.
    But is it a climate change? No.”
    Aaargh. Climate is always changing.

    The weather is always changing. I would not call it a climate change if it over 1 day, or one year, or 15 years. Up around 30 years it begins to be climate change. That’s how far my humility goes.

  150. R. Gates,

    I guess I’ll have to watch “The Day After Tomorrow” again to understand how global warming is going to cause an ice age.

  151. Probably best to stop digging, Steve. I enjoy Watts, but your first article was full of holes. This one would have been embarrassing if it had come out of a school statistics class.

    (By the way, Szilard didn’t discover fission at a red light. It was the concept of the chain reaction that occurred to him while at a red light. Wasn’t it Hahn and Strassman that discovered fission? In a lab….)

  152. Arctic Ice area is back to within one std dev of the mean. No wonder NSIDC is talking about an “Arctic death spiral.”

  153. Comments from the director of the Rutgers Snow Lab
    http://www.google.com/hostednews/ap/article/ALeqM5g1jo1gT0843vxrD4oRUd1Ufm4F5AD9DRBO880

    This is after a month that saw the most snow cover for any December in North America in the 43 years that records have been kept. And then came January 2010, which ranked No. 8 among all months for North American snow cover, with more than 7.03 million square miles of white.
    The all-time record is February 1978, with 7.31 million square miles. There is a chance this February could break that. There is also a chance that this could go down as the week with the most snow cover on record, Robinson said.

  154. “Leif Svalgaard (19:01:26) :
    Steve Goddard (17:55:23) :
    Over geologic time, there is little if any correlation between CO2 concentration and temperature.
    Methinks I showed you there was, when you asked me to ‘prove it’… Have you already forgotten?”

    He asked you to prove it too? Hilarious!

    “ginckgo (19:44:11) :
    not even wrong.”
    Post of the year. Says what my heart feels. gincko, you’re a poet.

    “I want every “scientist” on the public dole to provide the taxpayer with a valid reason for funding him or her. And “the furtherance of human knowledge” doesn’t cut it with me. I want practical results, not phony claims, not Chicken Little bull crap. And if they cannot justify their expense, then off with their funding.”

    Tell me what I want to hear or I cut off your funding! Yes, I’m sure that approach will get you better science.

    “Peter of Sydney (13:53:21) :
    “Before we can even attempt to try and get back on the right track (namely the truth) with climate research, we have to get rid of many of the leading “scientists” that corrupt, twist, hide and/or distort the data and findings.””

    But, Peter, shouldn’t we have at least one person who is a “skeptic” of AGW? Wouldn’t your rule decimate their ranks?

  155. Wayne said: “Whereever there is HEATING, there is equal COOLING somewhere else; unless you are speaking of long year+ time periods.”

    What get’s cool when the solar energy arrives from the sun? What we’re talking about the the exchange of energy. So yes, energy cannot be created or destroyed, only changed in form, and furthermore, we know that the law of entropy will tend to run this old universe down until there is equal cooling everywhere…hundreds of billions of years from now.

    Be on earth, since the majority of energy comes from the sun, and this is nuclear energy transformed into EM radiation that travels to earth, enters the atmosphere, and then heats up the land, the oceans, and the molecules of the air itself. All the energy (or at least the vast amount) in the oceans comes from the sun, and unless there was some way for this energy to get released from the oceans, they would get way to hot. In this regards, be thankful for hurricanes and big storms. They release such incredible amounts of energy.

    And here is personal experience to illustrate how the HEAT from oceans can lead directily to big snowfalls. Back in the winter of 1982, just a few days before Christmas a big hurricane was brewing out in the pacific. It eventually was downgraded to a tropical storm, but the moisture from that headed in over California and eventually wound up right here in Colorado where I live. That Christmas Blizzard of 1982 was one of the biggest on record, and all started from WARM waters over the pacific, that eventually combined with cold air over Colorado and created a monster storm. In Denver specifically, the SNOWIEST months are usually March and April, when the warmer temps over the oceans can draw up huge amounts of moisture off the Pacific and the Gulf of Mexico and slam it into the cold air over Rocky Mountain. These same storms often head southeast of here over Oklahoma and bring snow, and if the condidtions are right, tornadoes. All this because of WARMTH, not COLD. Cold is lack of energy in the atmosphere, and needs HEAT to bring in moisture before you can get snow…just like the Colorado Christmas Blizzard of 1982, and just like the East Coast storms of 2009-2010.

  156. “We may have a slightly different view on what a ‘fact’ is. My view is that it is a fact when it is no longer under debate by people who know the field.”

    Well, well…a return to the good old days when scientific “LAWS” were all
    the rage. I was afraid that such bold pronouncements of academic
    superiority had gone the way of the Dodo…only now I see that that “Laws”
    have merely been replaced with….”Facts”. I

  157. Leif

    “The weather is always changing. I would not call it a climate change if it over 1 day, or one year, or 15 years. Up around 30 years it begins to be climate change. That’s how far my humility goes.”

    And this 30 year time frame was established where, precisely? I must have missed that meeting.

    So, if I throw out your arbitrary 30 year time frame and pick, oh, say an EON…can I now say that the climate is “always changing”?

    I’m not trying to bust your chops…ok, maybe just a bit…but without the appropriate reference frame…these statements are most certainly challengable.

  158. R. Gates (23:11:40) :

    What get’s cool when the solar energy arrives from the sun?

    An equal amount of LW radiation + albedo of incoming solar radiation, therefore cooling. Look at an energy buget drawing.

    R. Gates, it’s a balance. Think more of the closely balanced temperature over thousands and thousands of years, only a few degrees one way or the other. Input equals output, or this globe would be a block of ice or a cinder.

    Even if there are small variance of solar irradiance the changes to matter here would be very slow and ruled by conductivity, over years.

    It sounds like you are talking on a month scale event. That is where if something here is WARMER, something there is COOLER. Stay in physics.

  159. Leif:

    IMO, evolution over long periods of time is an observable fact: just look at the fossil record. However, the absence of gradualism in that, at least to my mind, challenges neo-Darwinism as providing a complete theory. Maybe one day it will have its “Einstein” come along to significantly refine it.

    Where this relates more to matters in hand is that there are AGW supporters who are just as wedded to orthodoxy as are neo-Darwinists, and any departure from that is a dangerous pursuit for a career scientist. One lot gets labelled as oil-funded deniers, and the other, as crypto-Creationists.

    A fellow can’t hold a dissenting view without being vilified, and that is very bad for science in the long run, because some of its most admired geniuses were at one time dissenters. Science can’t evolve without dissension, and sure, what you pay for that is having to put up with some whose dissension is unwarranted.

    IMO, neither neo-Darwinism nor AGW can fully explain the past or predict the future. The former doesn’t explain the lack of gradualism and didn’t predict the size of the human genome, and the latter doesn’t explain the past (such as the MWP or LIA), and didn’t predict the increasing snow extent over the period Steve has focussed on. And that, for me, is what I’m getting from his post. I’m not, and I don’t think Steve was (he can correct me if I err), attempting to say much more than that. This kind of stuff is interesting for folk like me who are engaged by the climate controversy, and it doesn’t require me to understand statistics in-depth, which I can’t and don’t pretend to do.

    Sometimes, forgive me for saying so, your tone sounds rather haughty and dismissive, and I often don’t quite see what it is you are driving at or why. Folk like me like to be here and to try to learn, and I immensely enjoy the exchange of ideas that go far beyond the obvious, delving into many interesting highways and byways. One learns about much more here than simply climate science, and I feel it’s a shame to disturb convivial debate. If I have misinterpreted your tone, please accept my apologies, but even so, maybe it’s useful for you to be aware of how you are coming across, at least to this one responder.

  160. AlexB (21:48:12) :
    Robert (21:25:27) :
    vigilantfish (16:23:54) :
    AlexB (17:35:46) :
    AlexB (21:48:12) :

    Re: Leif Svalgaard (20:30:22)

    ‘Scientific hypotheses can be elevated to theories by fact, and theories can have differing degrees of empirical content but a scientific theory can never ever be a fact.’
    Steve Goddard (21:54:18) :
    You have the scientific method round the back of your neck, as Nottingham people say it when someone tells something in reverse.
    A theory is generated by becoming aware through study of a gap or an anomaly or an interesting possibility in the research in a particular field. This theory then generates hypotheses (with sufficient ingenuity) that can be tested, supported or falsified. Experiments or observations are carried out and statistical tests used to find out if the results found are likely to be by chance. If the conventional p-value is achieved then it is likely that the hypotheses are reliably supported as not due to chance-if not, the null hypothesis or hypotheses has to be accepted and you find that your theory does not stand the test of rigorous examination by the scientific method. Time to start again with another bright idea.
    The word ‘fact’ should never be part of a scientist’s vocabulary because nothing is an established ‘fact’-repeat, nothing! I point you in the direction of the way this word is used in the TV program ‘The Office’-do any of you wish to be associated with that? All information used in science is temporary and contingent, likely to be overturned at any moment by fresh information that has gone through the testing process. The only people who cling to outdated scientific findings or an overall paradigm are crusty old professors who have an emotional investment in them.

    ‘Simple explanation from William M Briggs, statistician.

    Let’s ignore statistics and turn to plain English.
    Suppose, fifteen years ago the temperature (of whatever kind of series you like: global mean, Topeka airport maximums, etc.) was 10 C. And now it is 11 C. Has warming occurred? Yes! There is no other answer. It has increased.’
    The point is ‘Is the increase statistically significant?’ A simple numerical increase means nothing, it can be by chance and could just as easily reverse itself. As well, the amount of the observed increase has to be enough to be statistically significant. I drummed into my students that you cannot support or falsify an hypothesis with descriptive statistics; that could only be done with inferential tests.
    All the statistical tests referred to in these comments are based on correlation; even regression is ultimately based on correlation and however you dress it up the old saying ‘Correlation is not causation’ holds true. There was a mention of Chi-squared but that is one of the weakest statistical tests. Climate science seems to be difficult to pin down and torture with the stronger inferential tests, on the results of which some reliance could be placed. Correlatory results provide some very thin ice on which I would not like to stand.
    The latter part of my comment, about ‘expanding bags’ and alarmists having it both ways every which way, seems to have been ignored but it is at the core of my complaint about them: no testable hypotheses can be generated from a theory that accepts all information as supporting its claims. Let them have the courage to design a set of hypotheses that may be tested by strong inferential statistical tests and thereby stand or fall. If those hypotheses fall then they must close their case, slink away into the sunset and the rest will be blessed silence.

  161. Steve,

    Your anaolgy is useful, but probably incomplete.

    The task before us is to determine if the section of the range of hills that we have sampled to date (starting from the West) is actually overlayed on some underlying, larger or longer, hill or valley. I think we can all safely assume that this range of hills cannot continue to rise (or fall) into infinity.

  162. Robert (17:38:37) :
    Shot yourself, not in the foot but in the head.
    You obviously missed the “TEMPERATURE LEADING BY 1000 YEARS”.

  163. The key to good science is actually knowing what the most important things to measure ARE.

    Experimental science usually starts with measuring/monitoring things where instruments are already around. You know the sort of thing. Looking at Arctic Ice extent for a year or so – your schoolkids could do that as a project, couldn’t they?

    Next is to try and discern trends, which again is taught using long-standing records. You could look at CET, couldn’t you? You could look at SOI?? You learn some maths, some graph plotting and you hope that some start to sniff out some features.

    Then, and only then, do you come to prediction. You usually assume that the past is a guide to the future, because if not, how do you predict with accuracy? So you start trying to predict next years’ winter snowfall, next winters’ ice maximum etc etc. Some will be right, some will be wrong. You eliminate a bunch of theories in Year 1 and a few stand up.

    Now then: there will be some who, by luck, chance, design or intuition think they might be rumbling nature. They make predictions for 7 – 10 years and they’re on the money. Who IS this genius?? What do they know that I don’t?? And, once again, they are admired because they called it right for a while.

    Then something happens to bust ’em out. Maybe the PDO shifted? Maybe AMO changed? AO, NAO, IPWP?? Maybe the data sets weren’t consistent or accurate? Hadn’t factored that in, had they?? Bummer. Hero to zero in one failed crop season. The dangers of hype and infallibility…

    In each iteration, a subset of key parameters are flushed out which contribute to understanding and, hence, prediction skill. In each iteration, new measurement tools are created, false theories are put forward, tested and fail. In some rounds, salesmen are lauded before the theory they are selling has been substantiated. Religion has entered the fray…..

    And so a crisis of faith comes along sooner or later, where two polar opposites collide. And out of that mess can either come a war and a Dark Age or yet deeper understanding of systems so subtle, so complex yet showing, to the discerning, clear signposts to reach the next plateau of understanding.

    It took 10 minutes to write this, but 25 years of a journey to experience the components of it.

    That’s why you don’t learn to be a scientist reading a blog or a paper. Because they distil 10 years of struggle into two columns of print.

    ‘I am a father’ encapsulates meeting, romancing, courting, marrying and starting a family. Four words. 10 years of honing who you are and what you became.

    It’s the same in science.

    And it’s a crying shame that no science textbooks that I was deputed to read explained science quite like that. And no school education programme nurtured scientific enquiry in that way.

    That’s life.

    Maybe the 21st century will prepare the world to embrace the reality of long-term science in the 22nd, eh?

  164. Philosophers have spent their lives and written millions of pages about “facts” for a reason .
    There is no straightforward definition accepted by everybody about what a “fact” is .
    Actually it covers a very large spectrum going from what everybody can detect by his senses without needing any interpretation frame to completely belief driven convictions only based on interpretation frames .
    If you look at the sun with naked eye you will become blind is a fact that everybody agrees with , can validate (if he dares) and doesn’t need any interpretation .
    .
    Scientific “facts” are sometimes tricky because they critically depend on a theory to be valid . If the theory is valid , its mathematical being allows inferences and predictions . If the thing predicted is observed , it increases our belief that the theory is correct and tends to upgrade its statements to “facts” .
    However one has to be clear that this kind of “facts” are only conditional and the history of science is full of such “facts” that stopped being facts once the theory was proven to be wrong .
    A good example are the black holes .
    Nobody had flown around one , thrown objects in it , measured its temperature and otherwise experimented LOCALLY with them .
    Yet it is a not so difficult to derive their existence and properties from the GR .
    So depending on the faith you have in the validity of the GR equations , you will say that the black holes are a fact or not .
    If you happen to interpret some radiation observations in terms of black holes and it sticks , your faith will increase even if you have still not experimented locally with one .
    Perhaps the GR will be busted one day . And that day the black holes can stop being the “fact” they are today .
    .
    AGW is still far even from that hypothetical “fact” stage because there is not a mathematically formulated theory that allows inferences and predictions . Sure computer simulations make a kind of probabilistic scenarios but that is not a fully formulated predictive theory .
    And no , absorption/emission properties of CO2 or vague correlations are not enough to draw even qualitative conclusions .
    Sofar AGW is just one size fits all working hypothesis and no fact .

  165. It would be interesting to do a little Hurst analysis on any of the data. It would basically show the length of time a model is good for (if it is good for anything) before it goes to pot. Weather like the Nile floods (which were a product of weather) are a mathematically chaotic process.

  166. The Golden Calf of many a fake science, as the so called climate science, is about to fall down…The “turn of the screw” is here!

  167. Atticus,

    Learn how to read a graph before commenting.
    https://wattsupwiththat.files.wordpress.com/2010/02/dec-feb_snow_ext.png?w=510&h=291&h=291
    Winter snow cover has increased over the last 20 years. Anyone with a high school science or math education can see that.

    Comments from the director of the Rutgers Snow Lab
    http://www.google.com/hostednews/ap/article/ALeqM5g1jo1gT0843vxrD4oRUd1Ufm4F5AD9DRBO880

    This is after a month that saw the most snow cover for any December in North America in the 43 years that records have been kept. And then came January 2010, which ranked No. 8 among all months for North American snow cover, with more than 7.03 million square miles of white.
    The all-time record is February 1978, with 7.31 million square miles. There is a chance this February could break that. There is also a chance that this could go down as the week with the most snow cover on record, Robinson said.

    Simple explanation from William M Briggs, statistician.

    Let’s ignore statistics and turn to plain English.
    Suppose, fifteen years ago the temperature (of whatever kind of series you like: global mean, Topeka airport maximums, etc.) was 10 C. And now it is 11 C. Has warming occurred? Yes! There is no other answer. It has increased.

    http://wmbriggs.com/blog/?p=1958

  168. Richard,

    Given that the earth is billions of years old and is neither covered with snow, nor snow free – it is safe to assume that the long term trend of snow cover is flat and has a slope of zero. That tells us nothing about the last 20 years.

  169. Anthony Fallone,

    At no point have I ever made any attempt to predict the future of snow cover. I have not made any hypothesis about a cause and effect relationship between the year and the snow cover. I have simply observed the undeniable observation that winter snow cover has increased over the last 20 years. Statistics has nothing to do with it.

    Had I made a prediction of how further increases in the X-Axis affect the Y-Axis, then your argument might be valid. But that is not the case.

  170. h2o273kk9 (22:37:24) :
    does Huygen’s theory mean that light is in “fact” a wave or just that it acts like you would expect a wave to act.
    If it looks like a duck, quacks like a duck, etc.
    At some point we attach descriptive words to phenomena. Anything that has a ‘wave length’ is called a wave.. ‘is in fact’ is a different question, namely what is reality? The ‘fact’ I was referring to is that light has a well-defined wave length.

    Michael Larkin (01:32:46) :
    IMO, evolution over long periods of time is an observable fact: just look at the fossil record. However, the absence of gradualism in that, at least to my mind, challenges neo-Darwinism as providing a complete theory.
    Things can be facts without being fully described by a ‘complete’ theory.

    Dr Anthony Fallone (02:25:00) :
    ‘Scientific hypotheses can be elevated to theories by fact, and theories can have differing degrees of empirical content but a scientific theory can never ever be a fact.’
    Nobody said that or, at least, implied that. The theory is about facts or tries to describe facts. Kepler’s laws are not facts, but a description of observed positions of the planets [those were the facts], and eventually General Relativity is a theory of and explanation for the fact of gravity. Darwin’s work was mainly a description of the fact of evolution with a theory attached that gave a plausible explanation for that fact.
    That the Earth is round is a fact, which can be explained by Newton’s laws [which also explain why the Earth is not a sphere, but has a flattening brought about by the rotation of the Earth].
    Gravity is a fact with or without Newton. Evolution is a fact with or without Darwin. Both men provided a description and a theory of the underlying facts.

  171. Nice, simple, straight forward insight.

    I’d have thought that any measurement is in the end about whether it leads to something testable and practical. “There are no hills” is obviously an impractical finding.

    The focus on the last 200 years or so of surface temps, which ignores the recent 10 or 15 years as “too short”, and ignores anything beyond 300 as “too long ago to matter nor understand” — that focus on the last 200 years should provide us with a hypothesis that we can test and use for practical problem solving. All it seems to have done is produce models forecasting scenarios 100 years out that we can’t test. If we can’t test then what’s the point?

    If we can’t test, it is not science. So sure, “we have to do something” — I’d just as well rely on my own intuition. That’s what most people seem to do. You can’t publish intuition, but you can act on it. My intuition says there’s too many good things that might happen in the future which we don’t want to jeopardise by imposing silly resource cuts.

  172. h2o273kk9 (23:31:32) :
    And this 30 year time frame was established where, precisely? I must have missed that meeting.
    You were not even born then. Long before there was a climate debate, meteorologists [formalized by The World Meteorological Organization (WMO)] established and generally agreed upon the dividing line between weather and climate being at 30 years. This is, of course, arbitrary in a sense. Perhaps it should be 31 years or 29, but the order of magnitude is about right, or so they found empirically, and have agreed upon as a useful standard or norm in order to be able to compare data from different providers.

  173. Very late coming to this party :-)

    Robert wrote:

    “A confidence interval is about distinguishing a random distribution from a pattern. By convention, you need to be 95% confident in your trend in order to reject the null hypothesis. 90% is on the bleeding edge of acceptable. Less than 90% is not statistically significant by any measure.”

    Robert, you need to attend one of my Intro Physics course lab sessions where students routinely show me how they have fit a straight line to their data and the computer spits out a r^2 value of 95%. They then confidently tell me how that has to be the correct result because of that r^2 value. They, like you, ignore what their eyes are telling them about the real data trend which can be a power function, etc. They quickly learn that the 95% confidence interval is a deceptive statistic and being dependent on it leads one to conclusions that are not valid.

  174. Steve,
    The discussion is interesting and it does show us something about how all things climate can be looked at.

    Obviously, snow extend has risen. The data shows that there’s more snow. But is it noise or a trend? If it were a stock chart that I was looking at, I’d say, “More noise than trend” and move on to a clearer chart.

    The funny thing is, that’s what climate and weather really are, a whole lot of noise and not much trend. It both validates Tamino’s argument and supports the notion of AGW Alarmism as utter foolishness over normal and natural variation. MOST of the climate data I see charted looks like a stock I would not want to trade. There’s no “robust” trend, especially when you adjust for normal climate cycles (ENSO, etc.).

    Mark

  175. Ref – R. Gates (16:29:54) :
    “..on average we are seeing more moisture and thus more heat and evaporation from the oceans during these months. How that discredits AGW in any way is beyond me…”
    ________________________
    Seems reasonable, until you beg the question “How that discredits AGW in any way is beyond me..”. AGW doesn’t forecast another 80K year glacial phase, just the opposite, so its likely not AGW “if” the world’s ice increases.

    _______________________________________
    Observation to all –
    Academics is a fine and worthwhile endeavour. To read through all the bits and pieces of discourse here at WUWT is a fascinating way for me to waste time while waiting for the Grim Reaper, but the question of “Are We or Aren’t We causing changes in the Earth’s climate?” is just that “academic”. Just as fascinating –often more so– as the question about the accuracy of the comments, is the so very human emotional component of the discourse. Plato might not hang around here too long, but everyone of you adds a special something to the discussion in my book. Thank you (and I do mean everyone). You make my day:-)

  176. Every sufficiently long noisy signal will contain runs that look like a signal but are in fact noise. Nobody disagrees that Winter snow cover has, on average, increased since 1989. It has and that is a fact, and as you correctly point out, does not constitute a prediction that the increase will continue. I think the disagreement is whether this increase represents noise or signal. There are objective tests for the hypothesis that a rising or falling signal is embedded in a subset of the sequence. Anyone can loook them up.

    In the context of this discussion, when you say, “Winter snow cover has increased over the last 20 years,” I hear an assertion that the time sequence of N. Hemisphere Winter snow cover contains a rising signal during the last 20 years that is distinct from the superimposed noise. Otherwise, why say it? It would serve no purpose to say that this or that subset of pure noise is rising or falling.

    Either you are making an observation that the last 20 years of noise has randomly produced a rising trend, or you are saying that there is a rising signal embedded in the last 20 years of noisy data.

    Which is it?

    If it is the latter, what recognized objective test have you applied to validate your observation. If it is the former, why even mention it?

    And just to be clear, we are both playing for the same team in the AGW debate. I am just asking you to step up your game so that our team can win sooner and more decisively.

  177. Caveman,

    The only reason to establish statistical significance would be to establish a cause and effect relationship between the X-axis (time) and the Y-axis (snow extent). That would be an important piece of information if we were going to try to predict ice extent moving forwards. I am making no attempt to do that however.

    Regardless, Tamino calculated 99% confidence before he applied his undocumented “cherry picking” test.

    Speaking of cherry picking, a favorite study at Copenhagen was this Greenland melt study, which showed that Greenland melted a lot between 2003 and 2007.
    http://www.spiegel.de/international/world/0,1518,661192,00.html
    I’m really surprised that Tamino did not apply his cherry picking test to that.

  178. Steve Goddard (07:53:35) :
    The only reason to establish statistical significance would be to establish a cause and effect relationship between the X-axis (time) and the Y-axis (snow extent).
    plot the snow cover data on the SAME graph as the model graphs you have been showing, and plot ALL the data for both.

  179. Back in the early 90’s when I was a fresh faced Navy Wx observer,we took old school Airway observations. Then we went to Metar obs, a flawed and not as detailed observation method. Now, except in rare cases, all obs are ASOS generated which won’t report cloud decks below 10K.

    I miss the old obs. The taking of them is a lost art and much valuable info is lost.expeciallly cloud type.

  180. JLKrueger (20:41:48) :
    davidmhoffer (17:49:17) :
    That’s how computers see data and that’s why it is so easy to take big chunks of data and get answers that are technicaly accurate and totaly meaningless. >
    BINGO! A sure nomination for quote of the week! It was one of the first things drilled into us when I was learning to be an ORSA (Operations Research Systems Analyst). It’s also the first lesson forgotten as we become enamored with our fancy toolkits.

    Thanks JLK. I’ve been selling technology for 30 years, much of it to heavy weight researchers. I keep using the Autocad/Stairwell story to show that when we rely on computers, we become limited by the capabilities of the tool we are using.

    Along the way I had some amusing stories. The average researcher is far better equipped to deal with computers than they were 20+ years ago, but here’s a few fun ones:

    Them; This 24 cpu computer you sold us runs exactly as fast as the 1 cpu computer we had before
    Me; OK, remember that conversation we had about converting your code from single threaded to multi-threaded? Did you do that?

    Them; This RISC computer you sold us corrupts the data every time we move it from our Intel computer.
    Me; OK, remember that conversation we had about byte ordering and that Intel and RISC use opposite byte ordering when reading and writing data? Did you account for that?

    But the funniest one (I could NOT make this up)

    Them; We bought a blade server farm with 4 cpu blades from vendor X and it ran 1/4 as fast as it should. We bought a blade server farm with 8 cpu blades from vendor Y, and its even worse, it only runs 1/8 as fast as it should. We’d like to try a blade server farm from your company.
    Me; Uhm… I’ve reviewed the list of apps you are running, they are all single threaded, so they can only run on 1 cpu of each blade. My product won’t act any different. What I suggest is that you run the FREE version of VMWare on your blades, virtualize each blade into one O/S instance per cpu, and then run your application O/S on top of that. No cost to you and you can use the h/w you already have. Or you can spend $2 million with me for a product that will have the same problem as the other two.
    Them; (I did NOT make this up) I smell some sort of agenda here, I just can’t figure out what it is….

  181. I’ve tried arguing this very same argument with AGW believers for some time now, but to no avail. Invariably, when you show that 1998-2009 shows no appreciable warming they will fall back on their “timescale insignificant” trope.

    The point is, however, that length of time scale is less significant if you are dealing with cyclical changes. My example is a sine wave. if you have a sine wave with a wave length of 10cm you will get vastly different predictions of future trends with a sampling of anything less than 10cm. Furthermore, predicting the next 5cm of that wave really depends less on the length (below 10cm) and more on the where the sample is taken.

    In a blind evaluation a 2cm sample at the apex of the curve would show a down trend while the 5cm sample on the up slope would incorrectly diagnose a rising trend for the next 5 cm. In this example the shorter trend is a better descriptor.

  182. Robert, I read your first comment and I skipped to the end here. I didn’t put on my snowboarding gear, though.

    If we accept only 90% confidence interval as *bleeding* edge
    Then review the AR4 based on data of similar noise, the merger of multiple types of data and data sets with variable sampling rates and intervals and data populations,

    to create first order LINEAR trends

    for variable and possibly CYCLIC systems,

    and call these first order linear trends climate models,

    that they can predict reasonably to 50% into the future of the time that most of our data reasonably goes into the past,

    to confidently identify that greenhouse gasses are the largest factor (such that we can discount others to simplify our model,)

    and then have people go and attack OTHER people that suggest that 95% confidence interval would be necessary for refuting data,

    I am 100% confident that I just crapped my pants.

  183. They might argue that the average slope across the plateau is zero, therefore there are no hills
    Or worse, that those hills are the product of curved space or from an entanglement of strings or its differences in level have been caused by the attraction of a tiny nearby “baby black hole”…
    So we must get back to straight reason and leave behind all that nightmarish post modern “science”.

  184. Leif Svalgaard (19:01:26) :

    “Steve Goddard (17:55:23) :
    Over geologic time, there is little if any correlation between CO2 concentration and temperature.
    Methinks I showed you there was, when you asked me to ‘prove it’… Have you already forgotten?”

    Leif, if that proof was not private and you have the time could you please post the reference. It would be most appreciated.

  185. Harry Eagar (11:56:31) :

    Wholly agree that selecting a short segment of a long, squiggly line and declaring that whatever trend that segment shows will extend forever into the future is a sin.

    But I am not too clear about Szilard ‘discovering nuclear fission.’ I thought that was Hahn. Do you mean, Szilard’s insight into the explosive implications of uncontrolled fission?

    Hahn had no idea what he was looking at in 1938 when he “discovered” fission. He thought he was looking at transuranium elements that behaved like barium, etc. It was Lisa Meitner and her nephew, Otto Frisch, who saw that Hahn’s results pointed toward fission, and they promptly put together an experiment to prove their conjecture even though Meitner was a refuge from Germany at the time, had no real laboratory, and barely had any institution at which to work.

  186. Steve Goddard (08:37:46) :
    Here is the image of the entire Rutgers winter data set
    Now plot the graph of top of the model predictions [on the SAME graph]. I have lost count, but methinks this is the fifth or so time I ask.

    David Porter (09:12:28) :
    “Steve Goddard (17:55:23) :
    Over geologic time, there is little if any correlation between CO2 concentration and temperature.
    Methinks I showed you there was, when you asked me to ‘prove it’… Have you already forgotten?”

    Leif, if that proof was not private and you have the time could you please post the reference. It would be most appreciated.

    Over on topic https://wattsupwiththat.com/2010/02/17/northern-hemisphere-snow-extent-second-highest-on-record/
    Steve showed a graph of CO2 and T for the past 4500 million years and claimed that the Figure showed no correlation:
    “Steve Goddard (08:33:37) :
    You claim a correlation in the CO2 vs. temperature graph. Please prove it.”

    My answer then [and now] was:
    Reading off your graph at 500 million year intervals [one has to use equidistant times – otherwise one could read off a million points between 1.1 and 1.2 million years ago, say, and get any correlation one wants] I get:
    Mya CO2 dT
    0 280 1
    500 300 4
    1000 1000 5
    1500 1500 6.5
    2000 1900 7.5
    2500 2000 8
    3000 2100 8.5
    3500 2200 9
    4000 2300 9.5
    4500 2500 10
    linear correlation dT = 1.5 + 0.0034 CO2[ppm] with R^2 = 0.9358. Highly significant. A t-stat of 10.8 for the trend with p-value 5×10^(-6).

  187. R Gates

    “It takes MORE heat, not less, to produce heavy snowfall.”

    Oh, of course! So those great big dunes in the Sahara are made of… yellow snow?

  188. Steve

    “It is abundantly clear that there are “peaks” on the left and right side of the graph, and that there is a “valley” in the middle. It is abundantly clear that there is a “hill” from 1989-2010.”

    It isn’t clear to me. It looks like noisy data points around a fairly steady mean. Flat trend if anything over the time frame shown. But that’s a good thing, IMHO.

  189. Leif,

    That was complete BS. There is almost no data available from the Precambrian.

    Try doing the same analysis at 50 million year intervals starting in the Cambrian Era at 570 million years ago.

  190. Leif Svalgaard (06:08:00) :

    h2o273kk9 (23:31:32) :
    And this 30 year time frame was established where, precisely? I must have missed that meeting.

    You were not even born then. Long before there was a climate debate, meteorologists [formalized by The World Meteorological Organization (WMO)] established and generally agreed upon the dividing line between weather and climate being at 30 years. This is, of course, arbitrary in a sense. Perhaps it should be 31 years or 29, but the order of magnitude is about right, or so they found empirically, and have agreed upon as a useful standard or norm in order to be able to compare data from different providers.

    ————–

    I would like to see a historical reference on this one. This preoccupation wth climate as a function of shortish periods of time sounds awfully recent to me.

    Leif, you stated that Darwin “… did not know how natural selection could work its magic, namely by the digital nature of heredity that ensures fidelity in preserving that which has been selected. Today we know the gory details [DNA, genetic code, etc] and that is what makes evolution a fact.”

    Darwin did know about genetics, Mendelian or otherwise, even though Mendel tried to draw his attention to it. However, he succeeded in making evolution an accepted theory even in the absence of this information. Your explicit description of heredity as having a ‘digital’ nature is philosophically interesting, and shows how often our scientific understanding of nature is shaped by technological metaphors. When Descartes’s understanding of nature prevailed,and God was seen as a clockmaker, organisms were seen as being comprised of mechanical parts. The ‘digital’ analogy of the 4 DNA bases that always combine as base pairs was explicitly drawn by geneticists and scientists and the public began to talk about how people are ‘wired’. It was a powerful metaphor, but has not been borne out by the results of genetic mapping. The one-gene-one-enzyme paradigm has had to be jettisoned and many of the preconceptions about genetic coding were bought into question when ten years ago the human genome was discovered to contain only around 30,000 genes – about 1/3 to 1/10 of the predicted outcome based on the complexity of the human organism. The interactions between the DNA, RNA and other cellular components are far more complex than any computer code.
    Incidentally, while we use technological analogies to describe biological phenomena, scientists also always use economic analogies to analyze and describe the environment. Ecology as a branch of science is profoundly influenced by economic theory.

  191. Steve Goddard (05:41:56) :

    Anthony Fallone,

    At no point have I ever made any attempt to predict the future of snow cover. I have not made any hypothesis about a cause and effect relationship between the year and the snow cover. I have simply observed the undeniable observation that winter snow cover has increased over the last 20 years. Statistics has nothing to do with it.

    Had I made a prediction of how further increases in the X-Axis affect the Y-Axis, then your argument might be valid. But that is not the case.’
    Leif Svalgaard (05:54:52) :
    ‘Dr Anthony Fallone (02:25:00) :
    ‘Scientific hypotheses can be elevated to theories by fact, and theories can have differing degrees of empirical content but a scientific theory can never ever be a fact.’
    Nobody said that or, at least, implied that. The theory is about facts or tries to describe facts. Kepler’s laws are not facts, but a description of observed positions of the planets [those were the facts], and eventually General Relativity is a theory of and explanation for the fact of gravity. Darwin’s work was mainly a description of the fact of evolution with a theory attached that gave a plausible explanation for that fact.
    That the Earth is round is a fact, which can be explained by Newton’s laws [which also explain why the Earth is not a sphere, but has a flattening brought about by the rotation of the Earth].
    Gravity is a fact with or without Newton. Evolution is a fact with or without Darwin. Both men provided a description and a theory of the underlying facts.’

    I am baffled at these two ‘responses’: Steve Goddard is ranting at me for making an argument to him which I did not, while Leif Svalgaard has taken a quote within my comment as my own words and proceeded to shred it, quite properly. The only thing I object to in his analysis is the frequent use of the term ‘fact’, which, as I stated previously, does not have any place in any scientific discourse. There are no such things, just temporary states and positions waiting to be overthrown by fresh experimentation and observation. This is, I know, a difficult view for any human mind to take, being counter to normal human thinking, which demands certainties-the reason why ‘the science is settled’ was received so readily by the general public, at first. We would all like everything to be neat and clean, in bite-sized chunks called ‘facts’-in the same way that the quantum view of the atom will never displace the ‘miniature solar system billiard ball’ view in the general imagination.

  192. Steve Goddard (10:08:46) :
    That was complete BS. There is almost no data available from the Precambrian. Try doing the same analysis at 50 million year intervals starting in the Cambrian Era at 570 million years ago.
    Cherry picking again…
    Your graph showed data in the Precambrian.
    The dips in temperatures are when there were significant glaciations [likely due to orbital changes combined with distribution of land and open/close of sea straits].
    And there is a good deal of data from the Precambrian. Your Figure is a bit deficient, I’l agree to that, here is more on T billions of years ago: http://isotope.colorado.edu/~geol5700/8.pdf The correlation between T and pCO2 can be found on slide 10.

  193. Steven Goddard:

    Your article was a bit confusing to me. The title indicates that you want to emphasize the importance of skillful observations in science. Specifically you focus on observing the extent of snow cover for the last 20 or so years and point out that snow extent has increased and state that this proves the AGW predictions about snow extent were wrong.

    Was it wise for the AGWers to make any predictions about snow extent from 1989 on? Isn’t snow extent for a 20 year period more a weather thing than a climate thing? We can skillfully observe snow extent increasing for the last 20 years but it neither proves nor disproves anything about AGW.

    Analyzing ocean heat content trends and measurement limitations and dynamics seems much more relevant to climate (to me).

    You mentioned something about relaxing the standards from 90% confidence to 55%. That is exactly the wrong direction to go in this climate debate.

    You mentioned that science is for everybody. Not sure what you meant by that. Most everybody can benefit from studying science but few have the ability and preparation to advance science.

    Unlike almost everybody who writes articles for this site, I haven’t seen a summary of your background.

    The example about the hill height was a great example to show how important context is. As Dr. Leif points out, context is everything. Maybe you are thinking of building a house there and want to know where it is relative to the 100 year flood plain. Maybe you want to know how high it is from where you are standing. If the height of the hill is listed on a map, it is probably listed as distance above sea level.

  194. Leif,

    The graph you pointed to didn’t have any real data points. It just showed some theoretically derived curves. The point of that presentation was to say that we don’t really know what early earth temperatures were.

    The Precambrian was typified by a lack of fossils and oceans. Almost all remaining Precambrian rock material is igneous. Data from that time period is not very reliable.

    During the early Tertiary, CO2 levels varied by a factor of three with almost no change in temperature.

  195. vigilantfish (10:15:50) :
    I would like to see a historical reference on this one.
    I’m your historical reference on this :-)
    TThe 30 years what drilled into me when I studied [and worked in Meteorology] 40 years ago]. It is hard to come up with something from so long ago. Here is what the Canadian weather office says:
    http://www.climate.weatheroffice.gc.ca/climate_normals/climate_info_e.html
    “There are many ways to calculate “climate normals”; the most useful ones adhere to accepted standards. The WMO considers thirty years long enough to eliminate year-to-year variations. Thus the WMO climatological standard period for normals calculations are “averages of climatological data computed for consecutive periods of 30 years as follows: 1 January 1901 to 31 December 1930, 1 January 1931 to 31 December 1960, etc.” and should be updated every decade. In addition, the WMO established that normals should be arithmetic means calculated for each month of the year from daily data.”
    Note the examples given: 1901 to 1930, etc.
    It is hard to find the origin of that 30-year interval. Perhaps it goes back to the so-called Bruckner-periods of 34 years.

    Enough about Darwin and what he didn’t know. The 4-base code is universal. And there seems to be some misunderstanding of the difference between the fact and a theory about the fact.

  196. Steve Goddard (10:58:04) :
    The graph you pointed to didn’t have any real data points. It just showed some theoretically derived curves.
    try to look at some of the other graphs. Compare 5 [CO2] and 8 [T].

  197. Steve Koch,

    My objection is with people who use statistical manipulations to hide physical realities. Many scientists jump right into detailed math without thinking about the accuracy of the underlying assumptions.

    I have described my background many times – 30+ years in science and engineering with degrees in both.

  198. Can I ask Leif a few questions?
    This is not to be answered by Science or Statistics, but by Yes or No as in a Court Case and telling the TRUTH.
    Was there more snow extent in each year after 1989 than in 1989?
    Was there more snow extent in each year bar 2007 after 1999 than in 1999?
    Does MORE mean Increase/increasing in the English language?

  199. vigilantfish (10:15:50) :
    I would like to see a historical reference on this one.
    http://www.climate4you.com/NormalClimateNormalPeriod.htm

    “Official climatic normals cover a 30-year period of record, and are supposed to be updated through the end of each decade ending in zero (e.g., 1951-1980, 1961-1990, 1971-2000, etc.). The concept of a normal climate goes back to the first part of the 20th century.”

    WMO Normals: World Meteorological Organization Standard Normals

    http://lwf.ncdc.noaa.gov/oa/climate/normals/usnormalshist.html#wmo :
    “Every 30 years the international meteorological community comes together to produce a document that summarizes the “normal” climate for all of the nations of the world. The effort was originated by the International Meteorological Committee in 1872 as an effort to assure comparability between data collected at various stations. International agreements eventually determined that the appropriate interval for computing a normal would be 30 years (Guttman, 1989).”

  200. Leif,

    It isn’t realistic to do comparisons vs. the Precambrian. There weren’t any significant oceans or life for much of the Precambrian. And the properties of the sun may have been substantially different. You probably knew that.

    Climate sensitivity needs to be calculated during more recent periods when oceans and life forms were more mature, like starting in the Cambrian Era.

  201. @ Mike Monce

    A 95% confidence in rejecting the null hypothesis and an r^2 value are two different things. You need an n value to relate the one to the other.

  202. A C Osborn (11:24:59) :
    Was there more snow extent in each year after 1989 than in 1989?
    Was there more snow extent in each year bar 2007 after 1999 than in 1999?
    Does MORE mean Increase/increasing in the English language?

    A good lawyer for the defense would ask the expert witness these questions in return:
    Was there less snow in 1999 than in 1993?
    Was there less in 2007 than in 1991?
    Was there less in 2009 than in 2001?
    Does LESS mean decrease/decreasing in the English language?

  203. ” jorgekafkazar (18:59:19) :

    “But it is believed that the process is making the seas much more acidic which is damaging the delicate shells of organisms that are critical to the marine food chain.”

    Drivel. Unsubstantiated propaganda.”

    What makes it “drivel” or “propaganda” besides the fact that it is awkward in the context of your political beliefs?

    The theory of ocean acidification is basic chemistry and observations back it up:

    http://en.wikipedia.org/wiki/Ocean_acidification

  204. Steve Goddard (11:34:18) :
    It isn’t realistic to do comparisons vs. the Precambrian.
    Of course, but that is not the point. I was just discussing what your own Figure was purporting to show. Perhaps it was not the best one to bring up?
    Now, how about that overplot [6th time] of Rudgers on Models?

  205. Robert (11:34:56) :
    95% confidence in rejecting the null hypothesis and an r^2 value are two different things. You need an n value to relate the one to the other.
    n was known. r^2 was not, hence asking for r^2 is proper.
    The claim was about how the observations and Models compare, so an appropriate graphs would have been a plot showing both, and a scatter plot showing observations versus models. For that last one, r^2 [and n] would be very illuminating. What I’m trying to convey is that there is a better [and time-honored, standard] way of comparing prediction and observations. Beats me why Steve is so averse to do that.

  206. Leif,

    The average from 1989-2000 was 44,500,000. The average from 2001-2010 is 45,600,000 . Seven of the top ten were after 2000. Eight of the bottom ten were prior to 2001.

    No matter how you look at it, winter snow extent has increased since 1989, and is near a current record now. All your protesting isn’t going to change a thing.

  207. Leif,

    I don’t have the raw Columbia data to do a comparison against – do you?

    But the point of this article is that such analysis is unnecessary at a qualitative level. Winter snow extent has been increasing while the models predicted it should be decreasing.

  208. Steve: Using Excel or some other software to draw a linear fit or other trend line through some data doesn’t mean that the observed slope is meaningful. Some points will be above the line and some will be below. No problem, you say; that’s just noise in the data – and you’d be right. However, if these deviations are really noise, then you need to accept the fact that, BY CHANCE, some of the time, more of the upward deviations will be on the right side of the graph and more of the downward deviations will be on the left side of the graph. This will increase the observed slope of the graph. (You may not see this pattern in this data, but if it were possible to repeat the experiment many times, random noise is certainly going to show this pattern some of the time. Unfortunately, you won’t notice the unusual arrangement of deviations because the linear fit will be steeper and appear to be correct.) Based on the magnitude in the scatter of the data above and below the line and the number of data points, one can calculate a confidence interval for slope of your line. A confidence interval provides a context for interpreting the slope of your line. It is very tiresome seeing blogs jump to conclusions about linear fits without a confidence interval.

    If you are using EXCEL, turn on the data analysis tools and select regression from the package. Looking at your 1989-2010 snow data, I find that the slope for your line is +97,000 mi^2/yr with a 95% confidence interval of 21,000-172,000 mi^2/yr. This is an 0.21% increase per year (or 21% per century if you translate into the usual timeframe for climate change). The 95% confidence interval is 0.05%-0.38% increase per year. So you have some confidence that there has been an increase in snow cover, but a very little idea (with a 7-fold confidence interval) of how fast or slow the increase is.

    EXCEL lets you choose the confidence interval. Most scientists and scientific journals aren’t interested in placing bets on an 85% chance and having the conclusions reached by one out of every six papers be due to chance arrangement of noise in the data. So they traditionally look for >=95% confidence before allowing a conclusion in the abstract and are happier with >99%. [The IPCC’s “more likely than not”, “likely”, and “very likely” are political BS, not proper science.]

    You should also look at the other output from the regression analysis. The plot of residuals shows no significant trends or correlation from one residual to another, showing that a linear fit is reasonable. The normal probability plot shows that much larger deviations from the mean are found in the few years when the snow cover is highest than when it is lowest. [A quick look at Descriptive Statistics shows that both skewness and kurtosis are significant, about than 3-fold the standard error estimated by SQRT(6/N) and SQRT(24/N) respectively]. This suggests that the data may not be normally distributed, and increasing skepticism that the 95% confidence interval derived on the assumption of a normal distribution of noise is wide enough.

    You could repeat this analysis for the 22 years starting in 1967, 1968, 1969, etc.; giving you 23 chances to find a 22-year time frame where the snow coverage is increasing. Using a 95% confidence interval, you will probably find BY CHANCE an average of one 22-year period where the slope is significantly positive OR negative with 95% confidence. So you leave yourself open to a charge of cherry-picking data if you don’t have a particularly good reason for choosing 1989-2010. Let’s say you are interested in recent trends in snow cover – that provides a rational for ending in 2010 and not going all the way back to 1967. You still need a rational for picking 1989 as the starting year. So a scrupulous analysis might look at the trend for the last 15 and 25 years or the last 10, 20 and 30 years, hoping to demonstrate that the strength of your conclusion is independent of your choice of a starting date. Unfortunately, there IS something unusual in the finish date: You are doing this analysis because 2010 is an outlier with unusually heavy snowfall. So a scrupulous analysis would omit 2010 (which isn’t finished in any case). For the 20 years (picking a multiple of five without looking at the data) from 1990-2009, the change in snow cover is +0.10% per year (-0.05% – +0.26%; 95% confidence). (The t-stat is 1.38 which gives a 9% probability that the slope could be zero or less by chance.)

    Is there a recent upward trend in snow coverage? Should you claim to have “proven” that the IPCC’s projections were wrong? With some statistical context, what do you feel like concluding?

    A true “climate skeptic” should be as skeptical of data that agrees with one’s expectations as one is of the “scientific consensus”.

  209. Re: Mike D. (Feb 21 13:46),

    Get a real job, one the free market values and is willing to pay you for. Do “science” in your garage in your spare time. I’m tired of footing the bill and getting BS in return. Will society collapse if taxpayers stop funding “science”? I doubt it sincerely.

    This caught my attention.

    Depends what you mean by collapse. If there is no public funding for science and we go back to the philanthropically financed universities and only elite science, the most probable outcome is that we will find ourselves in a 19th century society. That would stop CO2 footprints for sure.

    The great and accelerated progress in the technological world we live in has come from the funded by the public education and research, because a lot more brilliant people could have access to education. The same is true of the concurrent economic progress, the existence of a middle class and an eight hour day is tightly tied with this progress in technology.

    Ideally, if one makes a science fiction extrapolation of technological advances, all humans will end as people of income lived in the 19th century, but served by robots, and the problems will be the problems of a leisure class : how to occupy oneself.

    Though there is a lot in what you say about cliques and free loading if possible, one should not throw the baby out with the bathwater. The house should be put in order but that does not mean to stop public funding of universities and science.

  210. Steve Goddard (11:34:18) :
    Climate sensitivity needs to be calculated during more recent periods when oceans and life forms were more mature, like starting in the Cambrian Era.
    A time-honored method is to try to investigate things in isolation [see e.g. http://www.leif.org/research/suipr699.pdf on how to separate and discover the contributions of the many different factors that together cause geomagnetic activity], if possible. It seems reasonable that by stripping away complicating factors that the effect of CO2 itself may show up.

  211. “No matter how you look at it, winter snow extent has increased since 1989, and is near a current record now. All your protesting isn’t going to change a thing.”

    At least a half-a-dozen people, not just Leif, have explained to you that there is no statistically significant trend. Has it occurred to you that perhaps there is a point here that you are missing?

  212. Leif:

    Thanks for the reference, which vindicates my observation that the idea of climate being defined by 30-year intervals must be of recent origin (remember, I’m a historian – 1960 is recent). As your first source states:

    ” The concept of a normal climate goes back to the first part of the 20th century. At that time, lasting to about 1960, it was generally believed that for all practical purposes climate could be considered constant, no matter how obvious year-to-year fluctuations might have been. On this basis meteorologist then decided to operate with an average or normal climate, defined by a 30-year period, called the normal period. Later, people became aware of the fact that climate is not constant, but undergo variations in itself.”

    Although 1960 is mentioned as the last year in which people still held onto some notion of a constant climate, your source is still somewhat ambiguous as to exactly when the 30-year intervals were decided upon to measure changes in climate once the new paradigm was accepted.

  213. Tamino’s back with some more charts, hoping to inundate with volume.

    He attacked Lucy Skywalker for not showing the anomaly(correctly in my view), but then he starts off by showing total snow cover.

    Then look at the next post, where he labels a sphere 2 dimensional and a circle one dimensonal.

    “The n-sphere is the n-dimensional surface of an n+1-dimensional ball. The 1-sphere is just a circle; the 2-sphere is the ordinary sphere we’re all familiar with; “

  214. vigilantfish (12:26:36) :
    Thanks for the reference, which vindicates my observation that the idea of climate being defined by 30-year intervals must be of recent origin (remember, I’m a historian – 1960 is recent).

    The concept goes back to 1872. Is that also recent? My point is that the 30-year interval is unrelated to the current debate.

  215. Tamino should demonstrate that the climate model predictions of declining winter (January) snow cover are correct.

  216. Frank,

    Why do you call 2010 an outlier? 2008 had the third highest monthly extent on record and possibly higher than any in 2010. Should we throw out 2008 as well?

  217. vigilantfish (12:26:36) :
    the idea of climate being defined by 30-year intervals
    If you read carefully you’ll see that the 30-year mean should be updated every 10 years. So, every 10 years we can have a new climate ‘assessment’, if you like.

  218. @Robert (15:42:53) :

    “Dave, I don’t know if your job at the Seven-Eleven doesn’t pay you enough, or what, but you seem obsessed with my phone and the fact that I have a climate science app on there. In fact, I look things up on all kinds of sources, all the time, if that helps you. We can’t all just repeat what a talking dog tells us.”

    Thanks Robert. I sort of knew you’d go there and your comment about my job is about as accurate as I would expect you to be.

    For reasons that you will never know, you have just made my life significantly easier and I thank you for that.

    Anyway, that’s enough from me. Have to get back to work. Them there shelves ain’t going to stack themselves, you know – well, that’s what my dog is telling me…

    Dave

  219. Leif,

    You can’t strip away oceans and vegetation from CO2 analysis, because they are responsible for the vast majority of CO2 emissions and absorption. Not to mention that the ocean holds almost all of the latent heat in the climate system.

  220. Also, deserts get hot during the day and cold at night for a reason – the lack of vegetation. It wouldn’t make any sense to do a CO2 sensitivity analysis at a time before vegetation covered the planet.

  221. Suppose you were in a geography class and you were asked the height of the hills?

    That is like being asked how snowy the climate is.

    Suppose you were in a geography class and asked whether the hills get higher towards the right?

    That would be like asking whether the climate is changing.

    And if you are trying to answer the second question, measuring the slope up one hill would be dishonest.

  222. One has to be very careful when applying elementary statistics. The simplicity and convenience of elementary statistical tests is often attained at the price of some very stringent requirements for validity, and the “p-value” will be in error, often grossly so, when those requirements are violated.

    For example, when determining the statistical significance of the slope of a line, you are not allowed to select a portion of the data for analysis based upon your visual inspection of the data. You can use all of the data, or you can randomly pick a starting point without looking at the data, but you cannot choose a starting point for the analysis based upon when you think the trend starts. If you violate this rule, the p-value obtained will be invalid, and likely smaller than the true value.

    Now this doesn’t mean that you can’t statistically analyze data in which the slope actually changes at some point–it just means that you can’t apply the basic linear regression analysis that you find in all of the elementary texts–you have to apply a more sophisticated analysis that accounts properly for the additional degrees of freedom that this more complex statistical model introduces.

  223. Steve Goddard (15:11:50) :
    You can’t strip away oceans and vegetation from CO2 analysis, because they are responsible for the vast majority of CO2 emissions and absorption.
    I thought the C)2 molecules were the culprits…

    Not to mention that the ocean holds almost all of the latent heat in the climate system.
    I thought all that came from the Sun. Now, I’ll grant that the Sun was dimmer back then, so the influence of CO2 must have been even more important. Perhaps the absence of plants helped warm the Earth…

  224. Leif Svalgaard (13:17:56) :

    vigilantfish (12:26:36) :
    the idea of climate being defined by 30-year intervals
    If you read carefully you’ll see that the 30-year mean should be updated every 10 years. So, every 10 years we can have a new climate ‘assessment’, if you like.

    Leif:

    Thanks for the information. I’ve learned stuff from you today. I have to say that I’ve enjoyed your contributions at WUWT.

  225. Leif,

    What is the point of the dry sarcasm? It isn’t particularly clever.

    Show me a “statistically significant” correlation between CO2 and temperature during the Phanerozic.

  226. Michael Hauber,

    The height of a hill is a fairly simple concept which most people can understand. I continue to be surprised by those who can’t.

  227. Steve Goddard (17:49:20) :
    What is the point of the dry sarcasm? It isn’t particularly clever.
    I don’t do sarcasm, and especially not if not clever. I really mean that having a clean cut radiation-driven determination of the emission/absorption issue with complicating circumstances would teach us something. I even gave you an example of how such separation was used to unravel how geomagnetic activity depends on the solar wind: http://www.leif.org/research/suipr699.pdf
    I have a feeling that you didn’t even look…
    Now for the 8th time, how about overplotting the snow cover data on the model graph? That would at a glance show how the two compare.

  228. Leif,

    Look closer at the graph. The relationship between CO2 and temperature in the Cretaceous was coincidental. It was totally different in the Tertiary.

    If people want to claim a dependent relationship between CO2 and temperature demonstrating the greenhouse effect, then they need to include all the relevant data.

  229. “Show me a “statistically significant” correlation between CO2 and temperature during the Phanerozic.”

    If you know (because I demonstrated it) that CO2 and temperature are tightly correlated over the past 800,000 years, why on earth would you want to make an issue of what the correlation was 500 million years ago, when accurate measurements are going to be much harder to come by? If the relationship has held for 800,000 years, why would you think it was any different in the distant past, or if for some strange reason it were, why would we care?

    If there was a relationship between CO2 and temperature for 800,000 years, then whatever that tells us continues to apply today. Speaking of using your common sense.

  230. Steve Goddard: http://i224.photobucket.com/albums/dd137/gorebot/Geological_Timescale_op_927×695.jpg

    Why is it that Scotese assembles the worst graphs on T/CO2 record on the web, and yet his are the most cited?

    The Cainozoic record is utterly wrong (where’s the PETM? The Oligocene Ice age? The Miocene Climatic Optimum?); the Jurassic was cool?; the Late Carboniferous/Early Permian Ice Age is shifted to the Mid-Carboniferous; the very brief end Ordovician Ice Age covers most of the period; and where’s the Snowball Earth in the Neoprterozoic? I won’t even start on the apparently random squiggle that represent CO2.

  231. ginckgo (18:52:46) :
    Why is it that Scotese assembles the worst graphs on T/CO2 record on the web
    The question is why Steve cherry picked that worst graph? That doesn’t even prove his point.

  232. Ron Broberg,

    People who are attempting to show a dependent relationship between CO2 and temperature have to demonstrate statistical significance in order to forecast future temperature trends.

    I am not making any attempt to claim a causal relationship between the date and snow extent, and am not making any attempt to forecast future trends. I am just showing that snow extent has increased during the last 20 years.

    Do you see the difference? There is absolutely no need for statistical analysis to prove that winter snow extent is greater now than it was 20 years ago. All it requires is a few measurements. It is remarkable how confused people seem to be about this.

  233. Re: Robert (Feb 22 18:51),

    If there was a relationship between CO2 and temperature for 800,000 years, then whatever that tells us continues to apply today. Speaking of using your common sense.

    I agree. The ice core record http://upload.wikimedia.org/wikipedia/commons/thumb/c/c2/Vostok-ice-core-petit.png/400px-Vostok-ice-core-petit.png shows clear correlation.

    It is of course important not to lose sight of the fact that correlation is not causation, and that the same record shows an 800 year lag at least of CO2 to temperature.

    In fact, from elementary physics of solids liquids and gases one could hand wave that the concentration of any trace gas would be correlated with the temperature.

    Why are we in this discussion on this board?

  234. Robert,

    The ice core records show CO2 in a narrow range between 200 and 300ppm, following ocean temperature changes, due to changes in CO2 solubility in seawater. They tell us little or nothing about climate sensitivity. What they show is that seawater outgasses CO2 at higher temperatures and absorbs it at lower temperatures.

    The geologic record for the last 600 million years shows much larger swings in CO2 – up to 4,000ppm. Don’t you think they should provide much more useful information than ice cores in calculating sensitivity?

  235. ginckgo, Leif,

    Why don’t you two get together, find your favorite CO2/temperature graph, and calculate the climate sensitivity and statistical significance of the relationship between CO2 and temperature?

  236. Again, quoting William Briggs :

    Let’s ignore statistics and turn to plain English.
    Suppose, fifteen years ago the temperature (of whatever kind of series you like: global mean, Topeka airport maximums, etc.) was 10 C. And now it is 11 C. Has warming occurred? Yes! There is no other answer. It has increased.

    http://wmbriggs.com/blog/?p=1958

  237. Re: ginckgo (Feb 22 18:52),

    Can you give a better link?
    If the link you provided was supposed to be it, it does not work, it gives:
    photobucket.com/findstuff/?httpstatus=404 (took the http off so it would not become a useless link)

  238. Leif,

    You didn’t read ginckgo’s comment very carefully before firing your volley. ginckgo said that Scotese’s graph was the “most cited.” Do you think that using the “most cited” graph is “cherry picking?”

    Or more likely you just didn’t read very carefully.

    Here is another widely used one which shows even less correlation.

  239. A couple of things about the geological record of T and CO2:
    GHGs have varying influence on temperature depending on other factors that can swamp their impact (eg changes in continental arrangements and ocean currents).
    CO2 does not have to be the primary driver for every climate change in the past for it to fulfill that role today.
    The CO2 values for the deep past are much more difficult to calculate than for the past few 100kyr, and yet they are happily taken to be accurate here (selective skepticism?).
    The 800 year lag is also problematic, because I gather that the error margin on sample dates ranges from several 100 years to over 1000 years.
    If CO2 is more effective a GHG at low concentrations (which appears to be correct), then the 1000+ppm of the past are not overly relevant today.

  240. anna v (20:46:40) :
    Re: Robert (Feb 22 18:51)

    Ever considered the ~800 year lag could be related to the ~1600 year thermocline current turn-over time? Half of a cycle?

  241. Re: Leif Svalgaard (Feb 22 09:32),

    I am puzzled. You must be using this graph?

    If I were presenting it in a lecture, I would point out that there is a correlation 500 million years ago, where the fine structure is not measurable. In the last 500 years, where there is more detail ( if correct) correlations are moot. There are times that the CO2 is much in fore and the temperatures are constant, defeating the point that CO2 is a driver of temperature.

    Why are we having this discussion? I insist one can fit 100 angels on the point of a pin.

  242. Steve Goddard: People who are attempting to show a dependent relationship between CO2 and temperature have to demonstrate statistical significance in order to forecast future temperature trends.

    Gibberish. Forecasts of future temperature trends are not made from ‘statistical significant current trends.’ Climate models run simulations based on a range of calculated and parametrized physical properties of the climate. Those forecasts do not depend on whether or not the last 15 years show a statistically significant trend.

    I am not making any attempt to claim a causal relationship between the date and snow extent, and am not making any attempt to forecast future trends. I am just showing that snow extent has increased during the last 20 years.

    And here I am just showing that global temperature anomalies have increased during the last 20 years.

    You cannot draw a ‘trend’ in a time series and then claim that you are not drawing a relationship between the date and the snow extant. [b]The linear trend is a mathematical expression of the relationship between the data and the snow extant.[/b] Read that again. You are doing one thing (literally drawing a relationship between time and snow extant) and then denying that you are doing it. Is it just confusion? I hope so.

    Do you see the difference? There is absolutely no need for statistical analysis to prove that winter snow extent is greater now than it was 20 years ago.

    Here is your problem, Steve, if you are actually confused and not just being defensive.

    It is a physical fact that NH snow extant in Jan 2010 was greater than in Jan 1989.

    It is also a physical fact that global temp anomaly was greater in Jan 2010 than in Jan 1989.

    It is also a fact that a fitted linear trend is positive for both data sets.

    It is also a fact that a fitted linear trend is a mathematical expression between the date and the variable being charted.

    Your reply that statistically significance should be applied to temperature anomaly trends and not to snow trends because statistical significance is required to ‘prove’ CO2 warming is nonsense. Statistical significance (or lack thereof) is a property of time series, a way of separating real trends from noisy data. You have identified a statistically insignificant trend (that straight line you keep drawing) in a noisy data set and, when that fact is pointed out to you, you have resorted to defenses that are silly.

    Admit the mistake (I’m a newbie at stats, I make lots of them) and move on.

  243. Re: wayne (Feb 22 22:04),

    Ever considered the ~800 year lag could be related to the ~1600 year thermocline current turn-over time? Half of a cycle?

    Who knows? For sure the turn over from the lower ocean waters to the surface is important but one would need data on the current at those times to have a quantitative estimate.

  244. Szilárd discovered nuclear fission while sitting at a red light.

    From page 28 of Richard Rhodes’ The Making of the Atomic Bomb:

    “This sort of set me pondering as I was walking in the streets of London, and I remember that I stopped for a red light at the intersection of Southampton Row… I was pondering whether Lord Rutherford might not prove to be wrong.”

    “It occurred to me that neutrons, in contrast to alpha particles, do not ionize [i.e., interact electrically with] the substance through which they pass.

    “Consequently, neutrons need not stop until they hit a nucleus with which they may react.”

    “As the light changed to green and I crossed the street,” Szilard recalls, “it…suddenly occurred to me that if we could find an element which is split by neutrons and which would emit two neutrons when it absorbs one neutron, such an element, if assembled in sufficiently large mass, could sustain a nuclear chain reaction.

    “I didn’t see at the moment just how one would go about finding such an element, or what experiments would be needed, but the idea never left me. In certain circumstances it might be possible to set up a nuclear chain reaction, liberate energy on an industrial scale, and construct atomic bombs.”

    Leo Szilard stepped up onto the sidewalk. Behind him the light changed to red.

    Leo Szilard discovered the concept of a nuclear chain reaction, not fission itself. He later went on to take out a British patent for the nuclear chain reaction. This patent was later transferred, in secret, to the British government when the military applications seemed imminent. The War Office turned down the patent offer noting “there appears to be no reason to keep the specification secret so far as the War Department is concerned.” Later the Admiralty accepted the patent.

    I strongly recommend the above book for anyone’s library. It is a wonderful journey through history and science.

  245. Ron Broberg,

    You continue to be confused – and you demonstrate the point of this article. There is absolutely no question that snow extent has increased over the last 20 years.
    https://wattsupwiththat.files.wordpress.com/2010/02/north_american_winter_dec-feb_snow_extent_1989-2010.png?w=510&h=312

    However, people who claim that there is a physical relationship between increasing CO2 and increasing temperature have to demonstrate this over all time periods, not just the last twenty years.

    My claim is that Northern Hemisphere and North American snowfall have increased for the past twenty years, and they unequivocally have. If think that I am incorrect, and that the current record maximum is an artifact of bad analysis, then good luck with that.

  246. ginckgo,

    You have made the key point “CO2 is more effective a GHG at low concentrations”

    Exactly correct. The first 30 ppm create the vast majority of CO2 warming. That is why further increases in CO2 have less and less impact on temperature. Which is the opposite of a tipping point.

    Your argument about different continent location, etc. is spurious. Regardless of the shape, location, etc. of the continents – temperature should still follow CO2 according to promoters of catastrophic climate change. The absolute value may change but not the relative relationship.

  247. Steve Goddard (21:06:47) :
    Here is another widely used one which shows even less correlation.
    If the two Figures show different amounts of correlation, then it seems likely to me that the Figures are faulty to begin with. Depending on what one wants to show, one can then [cherry] pick the one that fits the best, as you apparently did.

    Now, where is [9th time] the overplot of the snow cover on the models predictions?

  248. Ron,

    Let me turn this around. Tamino claims (based on his statistical analysis) that winter snow extent has not increased over the last twenty years. This is his graph.

    Is he correct, or has winter snow extent increased? Do you think that winter snow extent has increased over the last twenty years?

  249. Leif,

    Find the raw data for the prediction graphs if you want to see a comparison. The images in the article are much too low resolution to make an overlay.

    There was an ice age in the Ordovician with CO2 levels about 10-20X current. All of the graphs show that.

  250. Steve: Do you think that winter snow extent has increased over the last twenty years?

    (Assuming negligible measurement errors …)
    As I stated above, it is a fact that snow extant in Jan 2010 is greater than in Jan 1988.

    What I am curious about is that straight line you drew, that trend I want to know if it is statistically significant. If it is, then there is likely an underlying climatic mechanism which is driving increasing snow extant. If the trend is not statistically significant, then the fact that snow extant is greater in 2010 than in 1988 is just random noise – significant of nothing.

    Now you don’t need a statistically significant increasing snow trend to analyze the skill in the models you quoted in an earlier post. Those models predicted a declining trend. In essence, you could reverse the analysis that is being done for temperature anamolies and the IPCC. See the following links:

    Chad @ Wood For Trees
    AR4 Model Hypothesis Tests

    Lucia @ The Blackboard
    Multi-Model Mean Projection Rejects: GISSTemp, start dates ‘50, ‘60, ‘70, ‘80.

    HadCrut Compared to IPCC Simulations Ending Dec. 2009.

    Tamino @ Open Mind
    Models

    Nick @ Moyhu
    Testing the performance of GCM models

  251. Steve Goddard (06:17:29) :
    Find the raw data for the prediction graphs if you want to see a comparison. The images in the article are much too low resolution to make an overlay.
    If the eye can tell the difference an overlay is possible. I’ll make one for you, then.

    There was an ice age in the Ordovician with CO2 levels about 10-20X current. All of the graphs show that.
    Milankovic and Plate Tectonics beat CO2 most of the time. That’s why one must exclude those sharp dips from the comparison.

  252. Steve Goddard (06:12:18) :
    Let me turn this around. Tamino claims (based on his statistical analysis) that winter snow extent has not increased over the last twenty years. This is his graph…
    I have a problem with his graph [and a bit with yours too]. It concerns 2010. He sets it to 49 million, you have it at somewhere above 48 million. When I add up the Rudgers data I get 47.740 for the 2010 winter, through week 7.

  253. Steve Goddard (07:14:46) :
    Here is an overlay of predicted vs. actual winter Northern hemisphere snow cover.
    No, that is North American snow cover, not hemispheric. And why start in 1989. Show all of the data. And on the full scale plot that you showed long ago of the models.

  254. Leif,

    The average latitude of land during the Ordovician ice age wasn’t hugely different than it is now. The main difference is that land was mainly centered around the South Pole, whereas now it is mainly centered around North Pole (except for Antarctica)

  255. Steve Goddard (07:21:50) :
    The average latitude of land during the Ordovician ice age wasn’t hugely different than it is now.
    And that is why we have an ice age now and had one back then too.

  256. Leif,

    The climate model predictions were for North America (remember?) That is why I overlaid North America snow on top of them. The last twenty years were when the decline was supposed to happen. That is why I used the last 20 years of data.

  257. Leif,

    C)2 levels were 10-20X higher during the Ordovician. Ice age does not equal runaway global warming.

  258. Ron,

    My point is that Tamino spent a lot of time doing statistical analysis, and came up with the wrong answer about the behaviour of winter snow over the last 20 years.

    I have absolutely no idea what is causing all the snow the last few years. But it is happening. I also have no idea if it will continue. I’m just making observations.

  259. Leif Svalgaard (11:35:10) :
    Thankyou, I can now see that you are incapable of telling the TRUTH where this subject is concerned.
    The trick of finding 3 out of 20 years to prove a counterpoint is typical.

  260. Steve Goddard (07:31:47) :
    The climate model predictions were for North America (remember?)
    Yet, in your post on this you compared the models to the hemispheric data (remember). You possibly did that because the ‘increase’ was more systematic for the whole hemisphere.

    The last twenty years were when the decline was supposed to happen. That is why I used the last 20 years of data.
    Some of the models showed a decline long before that.

    To do a valid analysis you simply plot all the data for the same area on the same graph without cutting and selecting.

  261. Steve Goddard (07:42:27) :
    Some might disagree with your assertion that we are currently having an ice age.
    That’s because they don’t know their stuff. We are in an ice age which has lasted several million years and will probably continue so for perhaps the same amount of time. During Ice Ages several glaciations occur controlled by orbital changes [that always occur]. There are interglacials between the glaciations. We live near the end of one of those interglacials. For an ice age to occur something else [land distribution, ocean straights, volcanism, whatever] must set the stage for Milankovic to work.

  262. Leif,

    North America is part of the Northern Hemisphere, but in the article I used the entire Northern Hemisphere because it made it more clear that this is not a local trend. In fact, the North American trend is even strongly upwards than the rest of the hemisphere.

  263. A C Osborn (07:45:04) :
    The trick of finding 3 out of 20 years to prove a counterpoint is typical.
    First, your questions would not be allowed in court since all data has already been presented. The standard objection is ‘asked and answered’. This objection is usually sustained, because otherwise the questions would amount to leading the witness [and rhetorically influencing the jury].
    Second, the upward trend is based on just three years. Remove 2003, 2008, and [the incomplete] 2010 and you have nothing.

  264. Leif,

    Seven out of the last ten years have been above 45 million km2, and eight out of the previous eleven were below 45 million km2. That is an upwards trend, ?que no? Speaking of statistics, that is a several sigma event.

  265. Steve Goddard (08:36:40) :
    North America is part of the Northern Hemisphere, but in the article I used the entire Northern Hemisphere because it made it more clear that this is not a local trend.
    The models were only about N.A. and do not claim they show a global trend. For valid analysis you compare apples and apples.

    In fact, the North American trend is even strongly upwards than the rest of the hemisphere.
    The N.A. data is even more reliant on a single [incomplete] data point, 2010, so its significance is less.

  266. Steve Goddard (08:47:02) :
    Seven out of the last ten years have been above 45 million km2, and eight out of the previous eleven were below 45 million km2. That is an upwards trend, ?que no? Speaking of statistics, that is a several sigma event.
    Now we are back to Hemispheric data, not N.A. [remember?] that you started with. And your above/below ‘analysis’ is not valid unless you quantify how much above/below. Suppose there were only 1 km^2 above/below. The so difference has to be quantified and compared to the usual noise in the data. There are standard methods of doing that [Tamino is good at it :-) ]. Lean about them and try them.

  267. Leif,

    So you agree with Tamino that winter snow extent has not increased over the last twenty years?

  268. Leif Svalgaard (08:57:57) :
    You see, you just can’t admit it can you?
    You just keep avoiding the questions.
    I am not talking Trends I am talking about reality and you just don’t get it, WHY?
    Please answer those 3 questions as I put them.
    I will answer yours about the 3 odd Years and yes they had less snow.
    Your turn, yes or no?

  269. Steve Goddard (09:13:36) :
    So you agree with Tamino that winter snow extent has not increased over the last twenty years?
    Statistically it hasn’t, because the change is compatible with the natural variation and noise. A single year’s data cannot be said to be ‘statistically significant’. For a series of data points one can ask if the trend in the data is statically significant. That is not the same as whether it is ‘real’. The data are what they are. The question is if one can derive a climatologically significant trend from the series. And Tamino is right, one cannot from the data presented. But that is not your real issue, which was [remember?] whether the models were correct in their predictions and it certainly looks to me that they were not, because the divergence between the two data sets is probably too large. And the downward trend in the models does seem to be statistically significant because the variation in the prediction is rather small [although from one model to the next, but within one model], so even if the observations are consistent with no trend [flat], the models are not. Now, the decrease in summer snow cover is worrisome for you [or should be], being a more sensitive indicator of climate. Glaciations come about when the snow at higher latitudes begin not to melt in the summer.

  270. Leif,

    The last few years have seen exceptional snowfall in the Northern Hemisphere. People shoveling snow don’t care about scientists playing games with statistics

    Changes in summer snow/ice cover which occurred prior to 1989 (as I have written many articles about) is largely due to soot, Hansen/Nazarenko, Zander etc.

    Note Tamino’s graph showing that August snow cover is about the same as twenty years ago.

  271. Leif Svalgaard (09:46:41) :
    See you still can’t do it.
    Perhaps if that was your income and I subtracted those amounts from it (instead of added those kinds of amounts), perhaps you might find it “SIGNIFICANT” then, as the people living in the NH are finding it now with Snow blighting their lives.
    There is just no point in reading what you have to say as you are evasive in your answers.

  272. Steve Goddard (10:01:23) :
    I don’t know why you are still bothering, Leif is not going to agree unless we have at least another 10 years of heavy snow, but I admire you trying.

  273. A C Osborn,

    Scientists love math and statistics. They are very important when appropriate, but sometimes people just need to look out the window. ;^)

  274. Steve Goddard (10:01:23) :
    The last few years have seen exceptional snowfall in the Northern Hemisphere. People shoveling snow don’t care about scientists playing games with statistics

    I took one of the models you were talking about. It is here:

    According to the model there should be a significant increase in snow cover during 1985-2020, as observed. [my arrow in the box]. This is to show that you can’t draw conclusions on a few years of data. The variability is just too large. That is what statistics can tell you.

  275. If you are only “talking about reality” then it is obvious from your graph that some years it is higher than in some previous years and some years it is lower. So if you say, for example, “it was lower in 2009 than it was in 2008,” in the same way you might say, “it was cloudy yesterday but clear today,” without pretending that it has any meaning beyond idle observations about the weather, that is fine.

    On the other had, if you want to say something general about what is happening overall (e.g. “snow cover has been increasing/decreasing”), or suggest that the measurements have any meaning, then you are doing statistics, and you need to do it correctly. Leaving out considerations of trend or statistical significance or the impact of choosing to analyze only one particular subset of the time record does not get you away from doing statistics–it just means that you are doing bad statistics.

  276. sometimes people just need to look out the window.

    If I could see the whole Northern Hemisphere from my window – and if my window had a “rewind” button – you might have a point here. Otherwise, this is a non sequitur/i>.

    From my window in Boston, I’ve hardly seen any snow at all this winter. Washington is up to something like five feet.

  277. Steve Goddard (10:01:23) :
    don’t care about scientists playing games with statistics

    I took one of the models you were talking about. It is here:

    and overlaid the snow cover data [the heavy orange graph]. You can see that the model does a reasonable job, or rather that the variations [error bars] are so great that even if it looks good you can’t really make any conclusions. That is what statistics also would tell you.

  278. Leif, I have a question. Why do you call the variation of the curves an “error bar”?

    I do not know how the plot was made. I would expect that somebody integrated the area covered by snow. The error on this measurement will be something like maybe 1 kilometer? 10 kilometers? I would not expect it to be larger as satellites are accurate.

    It is a variation, maybe we cannot explain it, but it is not an error in the measurement. The peaks and troughs are probably chaotic , but probably also, in the same way that one can have large waves and small waves on average depending on the wind, one can have high snowfalls and low snowfalls depending on the ENSO or whatever alphabet combination. I would not call them an error.

    Now the model, which by construction of GCMs purports to predict a curve, does not have errors either. And the errors it does not have are much more important, because they do no error propagation from their parameters. So they throw a curve on the table with wiggles. One is legalized to look at the total wiggle pattern, and the trend in the data and the model but not to talk of the variations as errors.

    I agree that there are not enough peaks and troughs to say whether it will be probably rising in the future, in the same way that watching a few high waves does not define that the wind is rising. If they get to be 100 and growing , that would give the relevant statistical error.

  279. Dang, all those news stories the last few years about record snow in Texas, Florida, Rome, Iraq, Saudi Arabia, Buenos Aires, etc. must have been lying. The Rutgers data about near record snow extent in 2008 and 2010 must also be wrong.

    We have statisticians who say otherwise. Can I make a suggestion? If you want to pick a fight about trends in snow cover, don’t do it during a period of record high snow extent.

  280. Is winter snowfall going up fro the last 20 years, or is it going down?

    Talk, blah, blah, statistics blah, blah, blah, cherry picking, blah blah blah …….

    What advice would Kartman give?

  281. anna v (13:01:33) :
    Why do you call the variation of the curves an “error bar”?
    Poor choice of words that then takes on a life of its own. What I meant [and mistakenly assumed was obvious] was that with a regression line, you can draw two other lines [slightly curved], one on either side that shows the confidence ‘bands’ for a given level of confidence. You can draw several such sets of curves for different levels, 99%, 95%, 90%, 68%, etc. The width of each band is what I meant by using the shorthand ‘error bar’.

  282. Statistics are not helpful in a situation where the physical processes involved are unknown.

    Instead one can legitimately employ informed judgement.

    It is clear that global tropospheric temperatures cycle up and down but are subject to a good deal of erratic or chaotic behaviour in the process.

    So whatever statistics say it is becoming quite clear that a peak of global temperature was recently passed and we could now be on an accelerating downslope. The current extent of northern hemisphere snow cover is one of a number of real world phenomena pointing in the same direction regardless of statistics.

    Steve is quite right to point to the past 20 years of slow but still small increase in snow cover over time at certain parts of the year.

    Arguing that a single parameter is not statistically significant on it’s own is fair enough but we actually have several such indicators all pointing the same way and taken as a group I suggest there is statistical significance or if there is not then the statistical methods need adjusting rather than our powers of judgement.

    “Lies, damned lies and statistics.”

    That is a judgement call and statistics don’t help.

  283. Leif Svalgaard (13:17:56) :

    vigilantfish (12:26:36) :
    the idea of climate being defined by 30-year intervals
    If you read carefully you’ll see that the 30-year mean should be updated every 10 years. So, every 10 years we can have a new climate ‘assessment’, if you like.

    I have been following the conversation between you two, and I have one thing I’d like to add. It is darned difficult to identify a trend (secular increase) in data known to contain cycles. We have this same problem in manufacturing. The idea of having a 30 year dividing line between climate and weather is fine, but if a very significant cycle is longer than 30 years, maybe we should make the dividing line at this cycle length. So, for example, the PDO cycle length looks to be 60 years to me, so why wouldn’t we try to enclose it so we don’t chase our tails running after climate change that is actually cyclic? Make weather 60.

    The problem may be that we never find a longest cycle to encompass, but that would be very telling in its own right wouldn’t it?

  284. Steve Goddard, I’m glad you brought up the Ordovician Ice Age: It did indeed occur sandwiched between Greenhouse climates. But this glacial episode only lasted a few million years at the most, and was possibly as short as 500,000 years. But it was also very severe. Continental rearrangements can’t be the answer, as they happen slowly, and thus wouldn’t have changed dramatically for 10 million years either side.

    It appears that CO2 may be the answer after all:
    http://www.eurekalert.org/pub_releases/2009-10/osu-vpp102609.php
    Turns out that if you look at detailed isotope records, there was a large amount of volcanism along the proto-Atlantic margin, causing the high CO2 levels you mention, and a corresponding Greenhouse Climate. But these emissions were somewhat held in check by the massive erosion of the uplifting Appalachian mountains which sequestered a lot of CO2. Then, for some reason, the volcanism abruptly stopped, but the weathering continued, causing a massive drawdown of CO2 from the atmosphere. The Hirnantian Ice Age was the result.

    So one lesson to take away from this is that you should be more skeptical, rather than accept graphs that suit your idea of how things work.

  285. Stephen Wilde (14:22:05) :
    Statistics are not helpful in a situation where the physical processes involved are unknown.
    On the contrary, that when we need statistics the most. If we know the physics we don’t need statistics.

  286. Kevin Kilty (15:41:46) :
    So, for example, the PDO cycle length looks to be 60 years to me, so why wouldn’t we try to enclose it so we don’t chase our tails running after climate change that is actually cyclic?
    When the 30-yr period became established there was a lot of talk about the Bruckner period, which was 35 years, so that may have played a role in the choice. As you say, one could keep discovering longer and longer periods, 88 yrs, 200 yrs, 1500 yrs, 2300 yrs ,…, and where do you stop? 30 years has turned out to work quite well, so is still with us after 140 years.

  287. I would agree with Leif. A trend it might be but is it significant? Depends on your error bars and the period used to determine average. I see nothing unusual about snow in Alabama, though my Aunt in Louisiana is claiming that the world has come to an end and it is the day after tomorrow. This year’s snow line, last year’s snow line, and the year before that is anecdotal till we see frozen pond scum in summer in the bayou (and yes I know that is an exaggeration…used only to make a point and amuse, not to be scientifically correct).

  288. Steve Goddard (10:01:23) :
    don’t care about scientists playing games with statistics
    I took one of the models you were talking about. It is here:

    and overlaid the snow cover data [the heavy orange graph].
    As you can see the model does a good job at predicting the run of snow cover. This is obvious without statistics by your argument, so your assertion that the models do poorly seems to be unfounded.

  289. Leif,

    You must be correct.

    The near record high winter snow cover is right in line with model predictions of rapidly declining winter snow cover.

    Not

  290. ginckgo (18:46:43) :
    So we’re just ignoring my response to the “Ordovician Ice Age isn’t caused by CO2″ post
    No, we are not. It is recognized that unusual circumstances triggered the ice age. The Milankovic cycles are always there, so something unusual must have made the glaciations possible, and made them disappear. For the argument here, it is immaterial really what the exact cause was.

  291. ginckgo,

    I used to be a geologist and understand that a very active imagination is necessary. That explanation for the Ordovician Ice Age sounded pretty imaginative!

  292. Leif,

    Looks like the trend line and predictions are at right angles in your graph. Difficult to imagine any worse correspondence.

  293. Steve Goddard (19:15:15) :
    Looks like the trend line and predictions are at right angles in your graph. Difficult to imagine any worse correspondence.
    Look at the data instead of the phony trend lines. The arrow shows what I would call a Steve-trend, from the lowest to the highest point [“do you deny that snow cover now is the highest in 20 years?”]. The heavy wiggly ‘trend’ lines come from the models and are 9-year running averages, so on your graph would include data for 1985-2014. Perhaps your trend would look like that in four years time. Anyway, look at the individual years of the model prediction. Do you deny that the arrow shows points from the lowest to the highest value so showing a definite steve-trend predicted by the model?

  294. Steve Goddard writes, “Tamino calculated 99% confidence for the trend, before he did his undocumented “cherry picking” analysis.”

    You are talking about where Tamino explains, “this is the number you get if you use the wrong analysis.” Once again, if you use a simple linear regression and a test for significance of the slope, you are not allowed to select a subset of the data to analyze based upon visual inspection of the data. If you do this, your p value will be wrong, period. (On the other hand, it would have been perfectly legitimate to randomly select a starting point for the analysis without having looked at the data. If you don’t understand why picking the starting point that looks right to you invalidates the analysis, while picking a starting point completely at random does not, you are missing a very fundamental, very important point about statistics).

    What Tamino then did that was really lovely was to use a standard statistical method, “monte carlo” analysis, to determine just how much selecting the starting point throws off the standard calculation of p-value. It’s not really accurate to call it “undocumented,” considering that he provided a very cogent explanation of what he did and why.

  295. Steve Goddard (19:15:15) :
    Difficult to imagine any worse correspondence.
    since you seem to have difficulties with your eyesight, here is blown-up version:
    Yes, indeed, as the direct comparison shows:

  296. Leif Svalgaard (19:59:12) :
    Steve Goddard (19:15:15) :
    Difficult to imagine any worse correspondence.
    That the model predictions match is of course pure nonsense and coincidence and cherry picking [as others do not], but illustrates that the variability is just too large and the time span just too short and the power of cherry picking just too strong to make any ‘statistically’ or physically significant conclusions either way. Looking out the window just ain’t science.

  297. Looking out the window is absolutely one of the most important methods of observing nature. Most people are aware of changes in their environment, and their cumulative knowledge is infinitely more valuable than climate model predictions.

  298. Steve Goddard, you’ve got to be joking. I present a detailed scientific study based on lots of data showing that your conclusion based on eye-balling highly generalised graphs is wrong, and you call it “imagination”?

    And Leif, it’s not immaterial to the debate what the causes were – that is what the whole debate is about. The Ordovician constantly gets thrown up as a shining example against GHG driven climate change, and yet here’s a demonstrated possibility that it’s cause was dominated by large swings in CO2. The postulated causes are indeed unusual, as are the massive amounts of fossil carbon entering the system at the moment.

  299. Steve Goddard (20:36:27) :
    Three (2003, 2008 and 2010) out of four of the highest winter snow extents in the forty-four year record have been in the last decade. Your claim that winter snow is declining is unsupportable and absurd.

    don’t you at least look at my graphs?

    Look at the arrow. It that pointing downwards? Look how closely its upwards striving show that the model predicts the matching observations. I cannot magnify the graph anymore to compensate for your poor eyesight, so make an effort, please.

  300. Steve Goddard (20:36:27) :
    Three (2003, 2008 and 2010) out of four of the highest
    Here are the top ten [only two (not three) out of the four have been in the last decade]:
    1978 48.981
    2010 47.74
    2008 47.459
    1985 47.046
    2003 47.017
    1979 46.941
    1986 46.609
    1967 46.479
    1972 46.461
    1969 46.453

    Here are the bottom ten:
    1968 44.652
    1990 44.652
    1992 44.333
    1976 44.282
    1995 44.137
    2007 43.873
    1980 43.872
    1999 43.844
    1989 43.436
    1975 42.979
    1981 40.696
    Note 2007.

    The model I showed you has lots of high values in the current decade and in the next [for that matter]. Nice prediction, I would say.

  301. Lets try and be rational about this controversy:

    It is true that the data on snow cover have very small errors , so one can establish a hierarchy of “peaks”. That is a fact, as with the mountains, it cannot be disputed.

    The meaning attributed can though.

    It is also true that if one wants to establish a trend with statistical significance (i.e. peaks are growing in the y direction) one needs the normal statistics of number of peaks to be able to give a probability number. As long as “looking out of the window” means “something may be happening”, that is fine by me, the same as with watching for sunspots : I say “aha, they are coming thicker”, maybe we are out of the trough, but statistically we need more to make sure.

    Now as for the models, the plots are not worth the paper they are printed on ( or the bits that display them). Talk about cherry picking.
    If the true errors on those curves were displayed, they would inundate the graph making it meaningless. It is the reason error propagation is not done.

    I explain: each spaghetti line on a GCM output is picked by the intuition of the modeler, i.e. that the CO2 feedback loop is heating up the earth fast and temperatures are rising. If temperatures are rising the snow line will be retreating and so we have the plots. The wiggles can have no meaning when their true errors are imposed, which must be thousands of kilometers in this case ( by analogy to the large spread in errors of future temperature, I have seen estimates of -1C to +2.5C on a 0.2C anomaly).

    So yes, we cannot talk of a significant trend now, but we can speculate and have fun. Speculation is not science, but it is the seed that might bring fruit in the future.

  302. gingcko,
    That paper was a very determined attempt to force fit the geologic record into CAGW theory.

    Leif,
    This decade and particularly this year has seen a lot of snow at lower latitudes. Deal with it.

    anna,
    Thank you for thinking like a scientist!

  303. Leif,

    There is only “good” agreement between the model and observations during the hindcast portion of the model run (up to Jan 1 2001). After that, it is clear, especially from your blown-up graph, that the model “forecasts” a downward trend in January snow cover and the observations show a distinct uptrend. You don’t need a statistical test to see that. Steve is still right – the model missed the mark.

  304. Leif, you produced a list of top ten which included 2003, 2008 and 2010 – and then declared that there were “only two years in the last decade.” It is really pointless discussing mathematics with you.

  305. B.D. (05:57:15) :
    There is only “good” agreement between the model and observations during the hindcast portion of the model run (up to Jan 1 2001). After that, it is clear, especially from your blown-up graph, that the model “forecasts” a downward trend in January snow cover and the observations show a distinct uptrend.

    I don’t know why this is so hard. Here is the relevant part of the blown-up version:

    Both the model prediction and the Steve-trend are up. What I’m saying is that for both the model and for the observations, the variation is too large and the time-span too short to draw any conclusions at all [and statistical analysis shows that clear enough for the observations]. Especially the silly claim that since 2010 was higher than 1989 there is a significant ‘trend’. The model has a similar ‘trend’ as shown by the arrow. In both cases, this means nothing.

  306. B. D.

    One would think that it is apparent that you shouldn’t be on a long term downwards trend and simultaneously be near a record maximum. Yet we have people here arguing that point. They argue that this is not a “statistically significant” upwards trend, and at the same time argue that the trend is downwards.

    It is truly Alice in Wonderland

  307. Steve Goddard (06:13:12) :
    Leif, you produced a list of top ten which included 2003, 2008 and 2010 – and then declared that there were “only two years in the last decade.” It is really pointless discussing mathematics with you.

    I pointed out that your statement:
    Three (2003, 2008 and 2010) out of four of the highest winter snow extents in the forty-four year record have been in the last decade.”
    was incorrect, and that it’s only two out of four of … in the last decade”

    Steve Goddard (07:15:25) :
    Your graph is incorrect.
    My graph cannot be incorrect since it is a clearly identified graph [and carefully cherry picked] from one of the models. Your graph is an equally carefully cherry picked graph from one of the other models. At least mine is identified, while yours is not. This goes to show how powerful cherry picking can be. Pick what supports you thesis, then pretend it is science, or perhaps just out-of-the-window-looking.

  308. Leif,

    umm… The last decade is the last ten years. And you produced a graph recently showing the last decade as being 2001-2010.

    If you need things explained to you in more detail, three of four top ten winters have occurred during the last ten years. It is becoming harder and harder to take you seriously on this topic. The trend is up, the models are down. What is it that you don’t understand?

    And you are confusing model error bars with their trend.

  309. Steve Goddard (07:48:54) :
    umm… The last decade is the last ten years.

    You said:
    “Three (2003, 2008 and 2010) out of four of the highest winter snow extents…”

    This means that if we take the four highest extents [in the last decade]:
    1978 48.981
    2010 47.74
    2008 47.459
    1985 47.046
    there should have been 2003, 2007, and 2010 among them according to you. I’m simply pointing out that such is not the case. And that is “what I don’t understand”.

  310. Leif,

    You are doing the wrong calculation. I calculated average winter extent for each Dec-Feb period. You are using peak, which is less interesting and tells nothing about the rest of the winter.

    1978 48401983
    2010 48079834.3333333
    2008 46909030.3333333
    2003 46829439

    1979 46730553.3333333
    1985 46726720.3333333
    1986 46578984
    1972 46517476.6666667
    1971 46315551
    1969 46297133.6666667

  311. Leif,

    This discussion has always been about average winter extent. Why did you try to slip a different metric under the radar?

  312. Steve Goddard (09:21:34) :
    Actually, I don’t know what you are doing. Your numbers just look wrong.
    I computed the average of snow extent for the winter weeks using the data table from Rutgers. Winter weeks are 49 wrap around through 8. Used weeks because months are not yet available for 2010.

    Look at the Rutgers winter graph.
    Top four are 1978, 2010, 2008, 2003

    Only goes to 2009…

  313. Steve Goddard (09:29:35) :
    Look at the Rutgers winter graph.
    I count 8 above 46 million km2 during the first half of the data and only 2 above during the last half, so sure looks like climatologically winter snow cover is decreasing…

  314. Leif,

    You know perfectly well that 2010 will be #1 or #2, and the trend will be obvious once that is posted on the Rutgers graph

    Correct calcs are

    1978 48401983
    2010 ~48000000
    2008 46909030.3333333
    2003 46829439
    1979 46730553.3333333
    1985 46726720.3333333
    1986 46578984
    1972 46517476.6666667
    1971 46315551
    1969 46297133.6666667

  315. Steve Goddard (08:54:08) :
    I calculated average winter extent for each Dec-Feb period. You are using peak, which is less interesting and tells nothing about the rest of the winter.

    No, I calculate the AVERAGE from week 49 through 8. If you use months, then you cannot have a value for 2010, as the last Rutgers monthly data is for January.

    Steve Goddard (10:05:12) :
    The article is about the trend over the last 20 years, remember?
    The last 20 years is the period 1991-2010. Does not start in 1989, nor in 1990.

  316. Leif,

    Winter is defined as Dec-Feb by Rutgers and everyone else in the meteorological world. 2010 will be #1 or #2. The top four are

    1978
    2010
    2008
    2003

    The point is moot, the trend is up, and you are not saying anything useful. You are doing everything you can to obfuscate the facts and I have to wonder what your intentions are.

  317. Steve Goddard (10:47:02) :
    The point is moot, the trend is up, and you are not saying anything useful. You are doing everything you can to obfuscate the facts and I have to wonder what your intentions are.

    Changing from weeks to months does change things a bit. Giving you the benefit of the doubt and setting 2010 February to 50, with months the top ten are [calculated correctly in millions of km2]:
    48.315 2010
    47.978 1978
    46.911 2008
    46.795 2003
    46.707 1979
    46.677 1985
    46.541 1986
    46.470 1972
    46.328 1971
    46.242 1969
    Now, your original claim was for North America. And for N.A. the numbers are:
    18.397 2010
    18.245 1979
    17.835 2008
    17.834 1978
    17.828 1985
    17.780 1993
    17.764 2001
    17.674 1984
    17.632 2004
    17.631 1982
    Only two in the top four.
    This illustrates how sensitive such a ranking is to the precise selection.

    My intention is to educate you a bit on how to do these things correctly so that WUWT does not become the a laughing stock.

    The trend is not climatologically significant any more than the fact that yesterday was a bit warmer than today.

    I hope we can then close the thread.

  318. Mr. Pot, meet Mr. Kettle:

    Here is the top ten from UAH:

    I guess the ‘statistically insignificant’ point is moot re: warming. The trend is up and ‘skeptics’ are not saying anything useful. They are doing everything they can to obfuscate the facts and we have to wonder what their intentions are.

    UAH
    1. 1998 52
    2. 2005 34
    3. 2002 32
    4. 2007 28
    5. 2003 28
    6. 2006 26
    7. 2009 26
    8. 2001 20
    9. 2004 20
    10. 1991 12

  319. Ron,

    UAH has three decades of data. The most recent decade is the warmest. There is no question that the trend for the last 30 years has been up. Is there a mystery there?

    Leif,

    When Rutgers updates their winter extent data in a few weeks, you know perfectly well that the top four will be 1978, 2010, 2003, 2008. Cut the BS.

    WUWT is the #1 skeptic site on the Internet. Your pointless, argumentative posts have no effect one way or another.

  320. Steve Goddard (12:06:03) :
    There is no question that the trend for the last 30 years has been up. Is there a mystery there?
    Absolutely not. It is well known that AGW causes more snow, no?
    Or perhaps that Rutgers plot you showed says otherwise:
    http://climate.rutgers.edu/snowcover/chart_seasonal.php?ui_set=nhland&ui_season=1
    I count 8 above 46 million km2 during the first half of the data and only 2 above during the last half…

    top four will be 1978, 2010, 2003, 2008. Cut the BS.
    Your original post was for N.A. And there the top four will be
    18.397 2010
    18.245 1979
    17.835 2008
    17.834 1978

    WUWT is the #1 skeptic site on the Internet.
    So we need to keep it that way by being somewhat correct in our assertions.

  321. Guys, Guys! Take it outside will you? Please? Surely you know each other’s email addresses? This is very cringe-inducing to look at!!

  322. Steve Goddard, “That paper was a very determined attempt to force fit the geologic record into CAGW theory.” is your way of saying “they tested a hypothesis I personally don’t agree with, and even though the data supports it, I refuse to even contemplate it’s possibility”. How very ironic considering the topic of this post. And you’re surprised that people use the term “deniers”? Because you certainly are not behaving like a skeptic should.

  323. Leif,

    It is well known that the climate models and the climatologists predicted less snow cover due to AGW. Isn’t that was this article is about?

    The original article which got Tamino hysterical was about the Northern Hemisphere. Are you suggesting that Rutgers is going to change their current third place 2003 winter based on your analysis?

    They currently have 2008 as #2 and 2003 as #3. After this week, both of those will shift down a notch. Your claim that 1978 comes after 2008 is simply incorrect. Nothing that happens this week is going to swap 1978 with 2003.
    http://climate.rutgers.edu/snowcover/chart_seasonal.php?ui_set=nhland&ui_season=1

  324. ginckgo,

    That paper presents an imaginative and not credible theory from my point of view. I do have a geology degree and worked many years as a geologist/geochemist. Nobody would drill for oil based on anything that speculative.

  325. Steve Goddard (16:05:50) :
    Your claim that 1978 comes after 2008 is simply incorrect. Nothing that happens this week is going to swap 1978 with 2003.
    Yes, that was indeed a typo. Thanks for pointing that out.

    It is well known that the climate models and the climatologists predicted less snow cover due to AGW. Isn’t that was this article is about?
    http://climate.rutgers.edu/snowcover/chart_seasonal.php?ui_set=nhland&ui_season=1

    Yes, and that is what the graph shows so clearly. AGW didn’t start in 1989, did it?
    I count 8 above 46 million km2 during the first half of the data and only 3 above during the last half [if we include 2010], so the prediction is pretty well borne out by the data. This does not mean that models are any good, just that they have not been falsified so far.

  326. Leif,

    The models don’t show any decline prior to about 1990. So isn’t it reasonable to start the measurements there? Snow extent is supposed to have been declining since about 1990 and it has been increasing instead. BTW- the late 1960s and the 1970s were exceptionally snow periods, and we have returned to that level.

    If you take a step back and a deep breath you will see that my claims are … correct.

  327. Leif Svalgaard (16:26:57) :
    Steve Goddard (16:05:50) :
    Your claim that 1978 comes after 2008 is simply incorrect. Nothing that happens this week is going to swap 1978 with 2003.
    “Yes, that was indeed a typo. Thanks for pointing that out.”
    I take that back. I thought you were talking about NH and not NA.

    For NA, the numbers are:
    2008:02 17.76 29 515.04
    2008:01 17.89 31 554.59 17.83494505
    2007:12 17.85 31 553.35

    1978:02 18.94 28 530.32
    1978:01 18.23 31 565.13 17.83433333
    1977:12 16.44 31 509.64

    4th column is 2nd [snow cover]* 3rd [days in month]. 5th is sum(4th)/sum(3rd)
    To calculate correctly, you must take into account that the months have different number of days. One could argue that perhaps 28 days for February should be used for 2008 as well. In that case the average becomes 17.83577778 [higher, in fact].
    So with correct calculation 1978 comes below 2008.

  328. Steve Goddard (16:44:08) :
    The models don’t show any decline prior to about 1990.
    Some do. http://www.leif.org/research/Snow-Cover-1850-2100-Model.png
    So isn’t it reasonable to start the measurements there?
    No, for many reasons [one being that model showed decline since the 1930s].

    the late 1960s and the 1970s were exceptionally snow periods, and we have returned to that level.
    No, I count 8 above 46 million km2 during the first half of the data and only 3 above during the last half [if we include 2010],
    http://climate.rutgers.edu/snowcover/chart_seasonal.php?ui_set=nhland&ui_season=1

    The point is that a few years of anything does not make any significant difference to the long-term trend.

  329. Leif,

    The earth is neither covered with snow, nor is it bare, so the long-term trend has to be approximately flat. If you pick a long enough time period to measure snow extent you will definitely see no trend. Do you think there is anything surprising about that?

    The point of the article is that climate models predicted decline for the last twenty years. Get it? That is the interesting period of time.

  330. Steve Goddard (17:41:56) :
    The point of the article is that climate models predicted decline for the last twenty years.
    No, http://www.leif.org/research/Snow-Cover-1850-2100-Model.png

    Steve Goddard (17:44:03) :
    When Rutgers updates their chart, it will show 1978, 2010, 2008, 2003 as the top 4.
    If it does that, they have calculated the values incorrectly. Did you understand my calculation:

    For NA, the numbers are:
    2008:02 17.76 29 515.04
    2008:01 17.89 31 554.59 17.83494505
    2007:12 17.85 31 553.35

    1978:02 18.94 28 530.32
    1978:01 18.23 31 565.13 17.83433333
    1977:12 16.44 31 509.64

    and why not?

  331. Re: Steve Goddard (06:57:42) :

    Agreed – so much for looming catastrophe.

    Re: Leif Svalgaard (16:26:57) :

    Any model output prior to 2001 is not a prediction. From 2001 on, the model does not predict well at all. The model does not work.

  332. Steve Goddard (17:44:03) :
    When Rutgers updates their chart, it will show 1978, 2010, 2008, 2003 as the top 4.
    Grrr, you switched again between NH and NA. We do not disagree for NH.

  333. Steve Goddard (17:41:56) :
    If you pick a long enough time period to measure snow extent you will definitely see no trend.
    On the contrary, that is where the real trend will show up. Over centuries and millenia. If you pick short enough time periods you will definitely see lots of trends, but they will be spurious.

  334. B.D. (18:18:34) :
    Any model output prior to 2001 is not a prediction. From 2001 on, the model does not predict well at all. The model does not work.
    Then the decline since 1930s is not a prediction, but an observed fact, no?
    Of course, the models don’t work. That is not the point. The point is that the ‘trend’ in snow cover it is not statistically significant [ http://www.leif.org/research/Snow-Cover-1966-2010-NA-Winter.png ], and even then matches that of a nonsense model [ http://www.leif.org/research/Snow-Cover-1850-2100-Overlay2.png ].

  335. Re: Leif Svalgaard (18:51:16) :

    Of course, the models don’t work. That is not the point.

    That was exactly the point of Steve’s post. Just because it devolved into all of the irrelevant nonsense about statistics does not change that.

  336. B.D. (19:06:58) :
    “Of course, the models don’t work. That is not the point.”
    That was exactly the point of Steve’s post. Just because it devolved into all of the irrelevant nonsense about statistics does not change that.

    The models don’t work because they disagree among themselves. Steve’s post is not relevant for this, because he postulates a trend that is not significant. I found one of the models in fair agreement with that [spurious] trend. Steve has not invalidated anything, because his analysis is not correct. And no amount of ‘looking out the window’ or quibbling about whether 1978 or 2008 is the higher down in the fifth decimal digit will change that. I would say that he should stop shoveling.

  337. Leif,

    How can there be a long term trend for snow cover? If there was, snow would eventually either completely disappear or completely cover the hemisphere, depending on the polarity. NH winter extent has increased by nearly five million km2 over the last twenty years. At that rate it wouldn’t take more than a few hundred years for snow to become ubiquitous. Clearly the trend will have to reverse at some point in the not too distant future and start declining again like it did in the 1980s.

    The only trend that makes sense over the long term is something cyclical centered around a mean.

  338. Steve Goddard (19:57:01) :
    NH winter extent has increased by nearly five million km2 over the last twenty years. At that rate it wouldn’t take more than a few hundred years for snow to become ubiquitous.

    I think that just shows that your ‘trend’ is spurious to begin with. To call 22 years a trend interval is not right. The trend shows up over 100 years. The models show such a trend out to year 2100 [that they likely are wrong (or right for the wrong reason – as the case may be) is another point]. If the ‘trend’ from 2008 to 2009 held up all snow would be gone in 22 years. Clearly that is nonsense, so we must increase the time span to more than 1 year. Statistics can tell us how many more years [depends on the variance and the autocorrelation at various lags]. That is what statistics is for.

    There has surely been a trend downwards the last 15,000 years until about 7,000 years ago, and there will be one upwards the next 90,000 years or so. So on a 100,000 years scale the trend is indeed cyclic.

  339. In particle physics we have great experience in trying to squeeze a meaning out of few data points.

    We do things differently.
    1) We do not join measured points with lines to create a cardiogram like plot. It is confusing the issues.

    2) we plot histograms if the y axis is a direct number measure that can give the statistical error by simple calculation. If it is a convolution and the error comes another way, we give the error bar on each point. If there is a systematic error in addition we plot that indicating it on the bars.

    3) Model outputs drawn on the same plots, could be either computer fits to the data, or direct predictions. In the first case the parameters fitted have the errors with their significance, and in the second the models have an error band.

    If I plotted this data plot, my histograms would have tiny errors on top. If I could fit a model its parameters would be highly constrained. Since there is no question of a GCM model fitting the data computationally, the model output should be given in a similar histogram to the data with an error band. The error bands for the GCM models are enormous making moot the choice of particular wiggles and any real one to one comparison with data. It is meaningless. The spaghetti model plots taken together show a downtrend, but it is a choice trend not a statistically given trend, as if one drew it by hand as long as there is no error band on each output curve.

    This leaves us with the snow plot. If this were cross section versus energy, for example, there are too few high points to constrain a trend that would definitively say ” crossections are increasing at the x sigma level”.

    More data are needed, more winters in this case.

    BTW the sun seems to be really getting out of the slump :).

  340. anna v (21:34:29) :
    We do not join measured points with lines to create a cardiogram like plot.
    My plot of this looks like this:

    My error bands were picked to produce two outliers [marked by red crosses] on either side.
    As you say, not enough data.

  341. 2100 45906814.4414759
    2101 49024311.5922443
    2102 48728902.7733808
    2103 44604745.8706235
    2104 43931558.6416875
    2105 47023540.9121646
    2106 44483031.473973
    2107 46779228.4531988
    2108 43938557.5740536
    2109 50910102.9993307
    2110 45141125.1358875
    2111 45787005.3961709
    2112 48959117.3375803
    2113 51987871.6211294
    2114 45167912.9101635
    2115 46557349.1996
    2116 48252292.2840534
    2117 54106557.6805669
    2118 50421001.6557413
    2119 49232275.8428081
    2120 52676808.0614631

  342. Leif,

    What is shows is that snow cover has increased by about 5 million km2 over the last 20 years, which was not predicted by all nine GCMs which predicted declining snow cover. You can protest all you want, but the change over the last 20 years has been in the wrong direction.

  343. Steve Goddard (22:18:15) :
    Decimal point in the wrong position. 0.279
    Steve Goddard (22:19:58) :
    2100 45906814.4414759
    […]
    2120 52676808.0614631

    With a t-stat at 2.71 this would ordinarily be significant at the 98.6% level, provided that the end points are selected at random. At an example of what happens with non-random selection, I now decide to exclude the first three points. That pumps R^2 up to 0.46 and increases the statistical significance considerably: by throwing away the three points, the remaining data is now significant at the 99.8% level. So with less data I get a much better result.

    Similarly, if I throw away data before 1995 on your NH graph, I pump R^2 up to 0.334. This may be a subtle point, but the criterion for selecting the data should not be derived from looking at the data. The models in the paper start their predictions around 2000, so that would have been an un-biased starting point, but now we begin to run out of data points, so even if R^2 is high, the significance goes down. After all, with only two points left R^2 is 1, but the significance is zero.

  344. All this arguing about statistics and statistical significance is in one sense irrelevant: the level of snow coverage is, as a matter of fact, either higher, lower or exactly the same as it was at some time in the past. Looking at Steve’s graph from 1989 to the present, it is clear that currently there is more snow (unless we distrust the measurements).

    The issue of statistical significance only arises if we have some model in mind (for example, that snow is increasing at the rate shown by the straight line shown in the same graph). If we test that model (as Tamino apparently did) we find that it fails tests of statistical significance.

    Steve, Leif and others, you would do well to read Matt Briggs’s article at http://wmbriggs.com/blog/?p=1958#comments. So would Tamino.

  345. Steve Goddard (22:25:40) :
    What is shows is that snow cover has increased by about 5 million km2 over the last 20 years, which was not predicted by all nine GCMs which predicted declining snow cover. You can protest all you want, but the change over the last 20 years has been in the wrong direction.

    It irks me a bit that you don’t respond to my comments, while I try hard to respond in detail to every one of yours. You say: “increased … the last 20 years, which was not predicted by all nine GCMs” and ignore completely my demonstration that model INM-CM3.0 for example does predict something very similar to the observations:

    Check out the blue curve. Is it not much higher at the right-hand side of the box? You confuse weather with climate. That a few years are higher or lower does not mean that the climate has changed. The models try to predict climate, not weather. Many of them don’t do a good job, but even if they were correct, the deviations caused by weather are normal and expected.
    I have a feeling that you don’t even do me the courtesy to cast a minimal glance at what I labor to produce, e.g. this one: http://www.leif.org/research/Snow-Cover-1966-2010-NH-Winter.png
    As proof that you have seen it, quote me the number #NNNN shown in the lower right-hand corner.

    As Alex Heyworth (01:13:46) points out:
    “The issue of statistical significance only arises if we have some model in mind (for example, that snow is increasing at the rate shown by the straight line shown in the same graph). If we test that model (as Tamino apparently did) we find that it fails tests of statistical significance.”

    the model he is talking about is not the models of the paper, but your assumption of a trend.

  346. Alex Hayworth,

    Tamino calculated 99% significance on that trend line, but then applied an undocumented “cherry picking” correction which he claimed reduced it to “less than 90%.” I was (not) very surprised to find that he found a way to disagree with my assertion that the climate models were failing.

  347. Daily fun trivia:

    The kerfing or edging around coins (such as the US dime, quarter dollar, …) is an invention of Sir Isaac when he was appointed Master of the British Mint; this is done to preserve coins in circulation from wear

  348. Leif,

    All of the models predicted declining extent during the 21st century.

    So far, they are all wrong. It is astonishing that you would deny this.

  349. Steve Goddard (06:20:28) :
    All of the models predicted declining extent during the 21st century.
    Beginning about 1950, so that should be your starting point. Since we have no data 1950-1965, you have to start at 1966. It is astonishing that you won’t acknowledge that.

    So far, they are all wrong. It is astonishing that you would deny this.
    I do not deny that. I simply point out that your analysis is seriously flawed. Proper analysis shows that over 1966-2010 the trend is flat. R^2 = 0.0001. Since the models show a decrease and observations are flat, the models in aggregate are wrong [as I have said so many times], but it is equally wrong to claim a significant increase in snow cover. Perhaps you can recognize this, so we can close this silliness. What is the #NNNN number on my graph?

  350. Steve Goddard (05:35:23) :
    Tamino calculated 99% significance on that trend line, but then applied an undocumented “cherry picking” correction
    Not ‘undocumented’ since the Monte Carlo simulation is a standard procedure to examine the sensitivity to assumptions.

  351. Leif,

    Suppose that your stock broker recommended that you short a stock based on his prediction that the stock would go down. 20 years later the stock has gone up by 15% and you have lost a lot of money.

    How would you respond if he told you that your lost money is not “statistically significant?” You would probably punch him in the nose.

  352. There is no better language in the world than that of logic. It is music to my ears (which behind THAT art is pure mathematical logic). I would rather listen to logic over a good joke (which I usually don’t get anyway) any time of the day or night.

  353. Leif,

    Over the last 20,000 years snow cover has declined substantially. Over the last four billion years snow cover has increased substantially. That is fascinating but my article covered the last 20 years, not the last 44 years, not the last 20,000 years, not the last four billion years.

  354. Pamela,

    Statistics is anything but pure mathematics. It is highly subjective.

    “There are three kinds of lies: lies, damned lies, and statistics.”

  355. Steve Goddard (07:05:00) :
    How would you respond if he told you that your lost money is not “statistically significant?” You would probably punch him in the nose.
    Not my style. Luckily, I’m perfectly capable of figuring out on my own what the signifiance is.

  356. Steve Goddard: “Tamino calculated 99% significance on that trend line, but then applied an undocumented “cherry picking” correction which he claimed reduced it to “less than 90%.” I was (not) very surprised to find that he found a way to disagree with my assertion that the climate models were failing.”

    Tamino applied a statistical test that is known to be only valid when the starting point has not been pre-selected based on “eyeballing” the data (you can use a random starting point or use all the data, but you don’t get to choose where to begin based upon what “looks right”) and he got a value of 99%–for doing it wrong. Then he asked: Just how much does doing it wrong cause the value to be in error? And he answered the question by applying a basic procedure documented in virtually any statistic text–monte carlo analysis. The answer to the question turned out to be: doing it wrong makes the value very much in error–and if you do it right, the “statistical significance” vanishes.

    Note that a general conclusion like “the climate models are failing” is a statistical assertion, because the climate models make only statistical predictions about trends, and no prediction at all about whether the snow cover on one particular year is going to be greater or less than the snow cover on another particular year. So to make any conclusion about whether the predicted trend agrees with the actual trend, you have to give up looking out your window and actually do statistics–and if you are going to do statistics, then you need to do them right.

  357. trrll,

    So what you are saying is that just because I looked at the Rutgers data and noticed that snowfall has been increasing since 1989, snowfall isn’t really increasing. Had I not looked, it would be increasing.

    I didn’t realize that I had so much power or influence that I could actually change the past by looking at it. Sort of an inverse Heisenberg principle.

  358. Steve Goddard (15:06:43) :
    So what you are saying is that just because I looked at the Rutgers data and noticed that snowfall has been increasing since 1989
    Had you looked three years ago it wouldn’t have. And possibly next year or the year after that it won’t have. So, confusing weather with climate isn’t a very fruitful thing to do.

  359. Leif,

    You sound like a broken record. The top four NH snow years are 1978, 2010, 2008, 2003. I have yet to see a weather forecast which predicts trends for a period of three years, eight years or twenty years. Snow extent has been increasing for a long time and you just refuse to see it.

    Weather models are good for three days, not decades.

  360. Steve Goddard (04:52:49) :
    Snow extent has been increasing for a long time
    You are laboring hard to show that Global Warming causes [or is associated with] more extended snow cover. Unfortunately, that is just wishful thinking on your part, as ‘one swallow does not make a summer’. The data simply does not support your contention, but then we knew that all along, didn’t we?

  361. Leif,

    I have made it more than abundantly clear that I am not trying to associate any physical cause with the observation that NH winter snow extent has been increasing for the last 20 years. The data definitively supports my conclusion, but you knew that all along, didn’t you?

  362. aping doesn’t become you. I think you have demonstrated that no conclusions can be drawn from the data as yet. I agree that that does not prevent people from drawing wrong or unsubstantiated ones.

  363. Steve Goddard (04:52:49) :
    broken record: The top four NH snow years are 1978, 2010, 2008, 2003.
    If next two years snow cover is 40 million km2, the top four years would stay the same, and you would still say that snow cover has increased the last 24 years, right?

  364. Leif,

    If the trend changes in the future (and it will) then we will see that reflected in the graphs. That is the whole point of this article.

    Having said that, there has only been one year in the last decade below 44 million, so your speculation about 40 million seems extremely unlikely.

  365. Leif Svalgaard (11:58:57) :
    Steve Goddard (04:52:49) :
    “broken record: The top four NH snow years are 1978, 2010, 2008, 2003.”

    And the point was that the top four years say nothing about what next year will bring. So few extremes are not indicative of any significant trend.

  366. “Tamino objects to the graph below because it has “less than 90% confidence” using his self-concocted “cherry picking” analysis.”

    Well, Tamino uses a time series of more than 40 years and you use a time series of 20 years, when everybody knows that statistical analysis of climate data should be done over periods of at least 30 years. So cherry-picking by cherry-picking, his makes more sense.

  367. Miguel,

    Does it make sense to do a linear estimation across two legs of a cyclical function? Of course not.

    “Everybody knows” doesn’t carry much weight. Time to start thinking instead.

Comments are closed.