Guest post by Steve Goddard
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.
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.
“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.
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.
“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.
Robert (14:15:01)
Does your iPhone have an opinion on the graphs from climate models shown at http://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
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.
@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.
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.
“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:
http://www.columbia.edu/~mhs119/Storms/Storms_Fig.05.gif
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.
I can’t find Tamino’s article to which you refer. Could you supply a link? TIA.
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.
“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.
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.
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.
http://wattsupwiththat.files.wordpress.com/2010/02/dec-feb_snow_ext.png
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.
http://i224.photobucket.com/albums/dd137/gorebot/Geological_Timescale_op_927x695.jpg
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.
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.
Leif,
Most school kids would understand the concept of measuring the height of a hill, person, tree, building, television, etc. How about you?
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…
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.
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.
For those that are interested, Ziliak & McCloskey have written a thought provoking little article on “The Cult of Statistical Significance”.
http://www.statlit.org/pdf/2009ZiliakMcCloskeyASA.pdf
The bottom line: the concept of statistical significance is much confused (and abused).
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.
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.
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.
The man doth protest too much, methinks.