After years of headscratching, Michael Mann thinks he has finally devised a new set of statistical procedures that will revive his discredited "hockeystick" graph. An email about it from John A [email@example.com] below:
The BBC in their traditional position of custodian of climate science orthodoxy, have announced that Michael Mann has produced yet another Hockey Stick:
A new study by climate scientists behind the controversial 1998 "hockey stick" graph suggests their earlier analysis was broadly correct. Michael Mann's team analysed data for the last 2,000 years, and concluded that Northern Hemisphere temperatures now are "anomalously warm".
Yep, all the omens are good.
In their latest study, Dr Mann's group collated more than 1,200 proxy records - the majority from the Northern Hemisphere - and used different statistical methods to analyse their cumulative message. We used two different methods that are quite complementary in the assumptions they make about data, so that provides a test of the sensitivity of data to the methods used," he told BBC News. "We also made use of a far wider network of proxy data than previously available. "Ten years ago, the availability of data became quite sparse by the time you got back to 1,000 AD, and what we had then was weighted towards tree-ring data; but now you can go back 1,300 years without using tree-ring data at all and still get a verifiable conclusion."
Of course Dr Mann made this claim of robustness to the removal of dendroclimatic records last time, which turned out to be a flat out lie: The Hockey Stick shape disappeared when the bristlecone pines of Western Colorado were removed as Mann himself knows because he tested for their removal and then failed to report that salient and inconvenient truth (ie the CENSORED directories).
The same basic pattern emerged when tree-ring data - whose reliability has been questioned - was excluded from the analysis. "I think that having this extra data and using more methods to analyse it makes the conclusions more robust," commented Gabi Hegerl from the University of Edinburgh, UK, who was not involved in the research.
Yep. Of course Gabi Hegerl was involved in making her own proxy reconstruction of past climate using Michael Mann's same flawed method and incorporating Mann's PC1 as a proxy within her own limited set of proxies (where of course, it dominated the result). See here for this. Not exactly an unbiased observer, is she?
Do not smooth times series, you hockey puck!
Below a climate statistician helps break Mann's revived 'Hockey Stick'. The author below, Dr. William M. Briggs specializes in the statistics of forecast evaluation, serves on the American Meteorological Society's Probability and Statistics Committee and is an Associate Editor of Monthly Weather Review
The advice which forms the title of this post would be how Don Rickles, if he were a statistician, would explain how not to conduct times series analysis. Judging by the methods I regularly see applied to data of this sort, Don's rebuke is sorely needed. The advice is particularly relevant now because there is a new hockey stick controversy brewing. Mann and others have published a new study melding together lots of data and they claim to have again shown that the here and now is hotter than the then and there. Go to climateaudit.org and read all about it. I can't do a better job than Steve, so I won't try. What I can do is to show you what not to do. I'm going to shout it, too, because I want to be sure you hear.
Mann includes at this site a large number of temperature proxy data series. Here is one of them called wy026.ppd (I just grabbed one out of the bunch). Here is the picture of this data:
The various black lines are the actual data! The red-line is a 10-year running mean smoother! I will call the black data the real data, and I will call the smoothed data the fictional data. Mann used a "low pass filter" different than the running mean to produce his fictional data, but a smoother is a smoother and what I'm about to say changes not one whit depending on what smoother you use.
Now I'm going to tell you the great truth of time series analysis. Ready? Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses! If the data is measured with error, you might attempt to model it (which means smooth it) in an attempt to estimate the measurement error, but even in these rare cases you have to have an outside (the learned word is "exogenous") estimate of that error, that is, one not based on your current data.
If, in a moment of insanity, you do smooth time series data and you do use it as input to other analyses, you dramatically increase the probability of fooling yourself! This is because smoothing induces spurious signals-signals that look real to other analytical methods. No matter what you will be too certain of your final results! Mann et al. first dramatically smoothed their series, then analyzed them separately. Regardless of whether their thesis is true-whether there really is a dramatic increase in temperature lately-it is guaranteed that they are now too certain of their conclusion.
There. Sorry for shouting, but I just had to get this off my chest. Now for some specifics, in no particular order.
A probability model should be used for only one thing: to quantify the uncertainty of data not yet seen. I go on and on and on about this because this simple fact, for reasons God only knows, is difficult to remember.
The corollary to this truth is the data in a time series analysis is the data. This tautology is there to make you think. The data is the data! The data is not some model of it. The real, actual data is the real, actual data. There is no secret, hidden "underlying process" that you can tease out with some statistical method, and which will show you the "genuine data". We already know the data and there it is. We do not smooth it to tell us what it "really is" because we already know what it "really is."
Thus, there are only two reasons (excepting measurement error) to ever model time series data: To associate the time series with external factors. This is the standard paradigm for 99% of all statistical analysis. Take several variables and try to quantify their correlation, etc. To predict future data. We do no need to predict the data we already have. Let me repeat that for ease of memorization: Notice that we do no need to predict the data we already have. We can only predict what we do not know, which is future data. Thus, we do not need to predict the tree ring proxy data because we already know it.
The tree ring data is not temperature (say that out loud). This is why it is called a proxy. It is a perfect proxy? Was that last question a rhetorical one? Was that one, too? Because it is a proxy, the uncertainty of its ability to predict temperature must be taken into account in the final results. Did Mann do this? And just what is a rhetorical question?
There are hundreds of time series analysis methods, most with the purpose of trying to understand the uncertainty of the process so that future data can be predicted, and the uncertainty of those predictions can be quantified (this is a huge area of study in, for example, financial markets, for good reason). This is a legitimate use of smoothing and modeling.
We certainly should model the relationship of the proxy and temperature, taking into account the changing nature of proxy through time, the differing physical processes that will cause the proxy to change regardless of temperature or how temperature exacerbates or quashes them, and on and on. But we should not stop, as everybody has stopped, with saying something about the parameters of the probability models used to quantify these relationships. Doing so makes use, once again, far too certain of the final results. We do not care how the proxy predicts the mean temperature, we do care how the proxy predicts temperature.
We do not need a statistical test to say whether a particular time series has increased since some time point. Why? If you do not know, go back and read these points from the beginning. It's because all we have to do is look at the data: if it has increased, we are allowed to say "It increased." If it did not increase or it decreased, then we are not allowed to say "It increased." It really is as simple as that.
You will now say to me "OK Mr Smarty Pants. What if we had several different time series from different locations? How can we tell if there is a general increase across all of them? We certainly need statistics and p-values and Monte Carol routines to tell us that they increased or that the `null hypothesis' of no increase is true." First, nobody has called me "Mr Smarty Pants" for a long time, so you'd better watch your language. Second, weren't you paying attention? If you want to say that 52 out 413 times series increased since some time point, then just go and look at the time series and count! If 52 out of 413 times series increased then you can say "52 out of 413 time series increased." If more or less than 52 out of 413 times series increased, then you cannot say that "52 out of 413 time series increased." Well, you can say it, but you would be lying. There is absolutely no need whatsoever to chatter about null hypotheses etc.
If the points-it really is just one point-I am making seem tedious to you, then I will have succeeded. The only fair way to talk about past, known data in statistics is just by looking at it. It is true that looking at massive data sets is difficult and still somewhat of an art. But looking is looking and it's utterly evenhanded. If you want to say how your data was related with other data, then again, all you have to do is look.
The only reason to create a statistical model is to predict data you have not seen. In the case of the proxy/temperature data, we have the proxies but we do not have temperature, so we can certainly use a probability model to quantify our uncertainty in the unseen temperatures. But we can only create these models when we have simultaneous measures of the proxies and temperature. After these models are created, we then go back to where we do not have temperature and we can predict it (remembering to predict not its mean but the actual values; you also have to take into account how the temperature/proxy relationship might have been different in the past, and how the other conditions extant would have modified this relationship, and on and on).
What you can not, or should not, do is to first model/smooth the proxy data to produce fictional data and then try to model the fictional data and temperature. This trick will always-simply always-make you too certain of yourself and will lead you astray. Notice how the read fictional data looks a hell of a lot more structured than the real data and you'll get the idea.
Next step is to start playing with the proxy data itself and see what is to see. As soon as I am granted my wish to have each day filled with 48 hours, I'll be able to do it.
WARMING, COOLING, IT'S ALL CAUSED BY CO2 SAYS CONSERVATION GROUP
Nothing like a bald assertion devoid of any scientific reasoning!
Forget global warming - the latest problem is global cooling. Conservation group WWF has blamed climate change for the coldest August in Sydney for more than 60 years.
The freezing temperatures are proof of the urgent need to cut carbon pollution, according to WWF development and sustainability program manager Paul Toni. "We can expect more extremes in climate," Mr Toni said. He said climate records had tumbled over the past year.
Australia had its driest May on record, Perth had its wettest April on record, and Tasmania recorded its hottest ever temperature, according to Mr Toni. He said climate extremes were affecting southern Australia in particular.
"This is consistent with climate modelling showing the southern states will feel the effects of climate change most severely," he said. Mr Toni said if action was not taken, more volatile weather would be on the radar.
Comment from Allan MacRae:
Some of us have been joking among ourselves that once natural cyclical cooling started, the global warming alarmists would blame it all on CO2. Well here we are - it has happened. "Conservation group WWF has blamed climate change for the coldest August in Sydney for more than 60 years." Could it be that the alarmists have known for years that warming has stopped, and is this why they changed their mantra from "stop global warming" to "stop climate change"? Scientifically, their AGW theory cannot support CO2 as the primary driver of both warming and cooling - this hypothesis is nonsense.
The only logical scientific conclusion, given the data, is that warming and cooling are overwhelmingly natural cycles, and the impact of human-made CO2 on these cycles is negligible.
The Russian bear submerging Greenie concerns in Germany
Chancellor Angela Merkel (Christian Democratic Union) has again reiterated the need for Germany to build coal-fired power plants. Merkel said in Hamm, North Rhine - Westphalia on Friday [29 August] that Germany would only be able to continue meeting its own demand for electricity if it built new and efficient power plants. Those preventing the construction of new power plants would accept "serious risks" for the economy, the labour market and the future of Germany. Rejecting new and modern power plants was also "counterproductive" in terms of environmental and climate policies.
On the occasion of the energy group RWE laying the cornerstone for a new hard-coal power plant, Merkel also came out in support of maintaining the energy mix consisting of coal, nuclear power and renewable energies. "We need both fossil fuels and the extension of renewable energies," she said. In view of the continuing debate over the phasing-out of nuclear power, she appealed to all parties to take the "initiative to let common sense prevail". "The operating life cycles of nuclear power plants must be determined in such a way that pricing is done on a reasonable basis."
The chancellor stressed that Germany must not become dependent on power supplies from abroad also in the future. "Generating electricity is Germany's strong point as an industrial nation," Merkel added. Also in view of the successful export deals involving German-made technology for modern coal-fired power plants, it was imperative to use the technology at home as well.
The Russian bear submerging Greenie concerns in Britain
Jeremy Leggett has an article up on Comment is Free urging people to "Beware the bear trap". Essentially, his case is that we need to pile on the renewable capacity in order to prevent Russia being able to use its fossil fuel resources as a weapon against us.
The first thing to note is that, while Western Europe is in an unenviable position relying on Russia for its gas, the Russian position isn't quite as strong as it looks. Each time oil and gas resources are used as a weapon they lose their impact. By making it clear that supplies aren't reliable you encourage your customer to put more effort into seeking alternatives or other sources of supply.
There is no doubt that recent Kremlin bolshiness has strengthened the case for Western Europe to revive its nuclear industry, for example, which could well mean threats to the gas supply are less potent next time around. We can only hope that there is someone in the European political elite with the basic strategic vision needed. Business, at least, will probably put more effort into exploiting alternative sources of hydrocarbons like Canadian tar sands.
The major problem with Leggett's article is that he sees renewables as part of the solution, rather than part of the problem. In reality, one of the reasons why Britain is in such trouble is that over the last ten years we've had a Government with a fondness for airy, unrealistic fantasies that renewables can provide a substantial portion of the electricity we need. Our energy policy has been based, for a decade, on the ludicrous idea that a combination of gas and renewable energy can provide the stable, affordable and secure capacity we need.
While renewables can provide power, albeit often at great cost, their unreliability means they can't provide significant capacity when you need it (peak load capacity). The situation is stated pretty clearly in this REF report (PDF, pg. 94). As such, their contribution to energy security is negligible. If Russia were to cut off the gas all the wind power in the world would do pretty much nothing stop the lights going out on a cold evening. Other renewables have, at present, a limited ability to provide remotely affordable power. Unless unreliable or exceptionally expensive electricity is felt to be acceptable renewables can't deliver energy security.
So long as politicians listen to people like Jeremy Leggett, and his renewable energy fairy tales, serious solutions like Enhanced Oil Recovery in the North Sea and building coal and nuclear capacity won't get the attention they deserve. By the time we wake up, it might be too late.
AMBULANCE-CHASING GREEN/LEFTISTS LOVE A HURRICANE
Spare a thought for anyone on the Environment beat at the Guardian newspaper. It must be like working for Pravda during the Breznhev era. There, as the economy became ever more dysfunctional, reporters were obliged to pump out ever more absurd stories saluting record productivity and efficiency records. The triumph over capitalism was imminent!
A different time and a different place: but at the Graun [Guardian], the ideology is "Climate Change" - and the number of narratives permissible is similarly narrow, and rigidly defined from the top. For as regular readers of the paper will know, the climate can only change in one direction: for the worse. Apocalypse is imminent!
It's in this context you should spare a thought for David Adam, the newspaper's environment correspondent. He certainly has our sympathies. With hurricane Gustav set to devastate New Orleans, Adam was tasked with the job of showing how it's all down to Global Warming.
Tasteless ambulance-chasing like this is now commonplace. Both Believers and Skeptics are both guilty of making too much of the latest weather, and extrapolating from it a trend that suits them. Weather is not climate. But extreme weather tends to excite one side rather more than the other: because it follows the simple moral fable in which man's wickedness causes unnatural events. This pagan superstition was evident three years ago, the last time New Orleans took a battering. Barely a week had elapsed after Katrina struck, before Al Gore addressed the nation to blame it all on sinful mankind for causing Global Warming. Gore quoted Chamberlain - "this is only the beginning of the reckoning" - and for good measure, castigated American's "moral health".
So from the outset, it must have dawned on our heroic Graun correspondent that he had a task worthy of Hercules. Adam couldn't quote anything that contradicted the theological foundations of the orthodoxy that the occasion demanded - since that, presumably, would result in a rapid descent into Farringdon Road's piranha tank. But the problem is, there just isn't much evidence to support the idea that a warmer climate means worse weather, and the closer you look, the harder this is to prove. And so his soul-searching struggle is laid bare.
Adams begins confidently - "Meteorologists are predicting a more active hurricane season than usual this year..." But realises it's a lost cause almost immediately. "... but there is no way to know whether global warming has caused an individual event such as a hurricane, or whether it has made such storms worse," he writes.
That's not a promising start - and certainly not what the editors and eco-activists want to read. So like a hastily-constructed sea defence, the doubt is rapidly sandbagged: "On the other hand, some scientists argue that severe storms such as Gustav are more likely in a warming world, because warmer seas make more powerful storms," he continues. Phew.
Actually, it's more accurate to say that while non-scientists, such as Gore, are only too keen to draw links between warming and extreme weather (remember, man is responsible for all things unnatural), recent years have seen fading support for the notion.
Tom Knutson of NOAA's fluid dynamics lab published a paper this year arguing that a warmer climate means fewer hurricanes: 18 per cent fewer by the end of the century, he proposed. In another 2008 study, NOAA's Chris Landsea saw "nothing in the US hurricane damage record that indicates global warming has caused a significant increase in destruction along our coasts". And even the media's favourite hurricane doom-monger, Kerry Emanuel at MIT, an advocate of the link between a warmer climate and nastier storms for 20 years, is surprised by what his models now predict: a warmer planet means fewer hurricanes in 200 years.
(A caveat: like the much vaunted Global Climate Models (GCMs) Knutson and Emanuel's own models involve "parameterization". What this means is that left alone, computer models produce completely ridiculous results: "too many hurricanes", is how Knutson puts it. So the models are frigged massaged to produce something that's plausibly scary, but not so ridiculous that people notice. Such is the way "science" is conducted in the 21st Century...)
But back at El Graun, the unspeakable remains unpublishable. So instead of outlining the recent work, we get such platitudes such as "if anything, the science has become fuzzier in the years after Katrina", and "it is also unclear how reliably historical records of hurricane strength can be compared", and "no firm conclusion can be made at this point".
Well, wasn't that worthwhile? At least the party line remains intact. There's a slight problem, however. When the information needed to look beyond the "fuzziness" is at one's fingertips, and can be found in only a few seconds with Google, why would anyone want to bother with a report? There's one casualty of global warming that no one seems to have discussed yet - newspapers.
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