This is from Vincent Gray. He is referring to CB not CU.
Our observation analysis finds that increases in cumulonimbus (Cb) cloud intensity and frequency brings about a decrease in upper tropospheric water-vapor, not an upper tropospheric moistening as the model simulations show.
Steven Mosher redefined the Global Average Temperature to be a prediction.
No. He, like many, infers warming from a particular statistic’s evolution over time, but he did not redefine that statistic.* Also for the record: The concept of an average is to arrive at a single-value measure of the centering tendency of a distribution of values, e.g., an annual average global temperature. It can be and is calculated in different ways and so it is usually important to specify how the calculation is done when the discussion is quantitative..
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* SM did not even define it in his comment nor was it necessary.
“the planet is getting warmer.
man does contribute to it.
the question is “how much””
Yep!
Three serious points follow.
1. Climate research is not representative of all current science. Much good science continues. Please do not tar all science with the black carbon brush of the climate gloomerati. The temporary limelight radiating from this newby bastard child is mainly from poor scientists whose excellent superiors are pushed into the shadows. In a self.serving way, poor scientists can almost be identified by their personal preferences for science by press release.
2. There were gross structural distortions created during the drift over the last 50 years of scientific funding from the profits of scientific achievement, to funding from taxes. Scientists perform better when they know that their future incomes as scientists are linked fairly clearly to their success in generating visible income. Yes there are projects for which funding from taxation is most appropriate, such as some types of warfare work or large projects like lunar landings. (This has ever been the case, only it used to be managed better). The danger of funding from taxes is that individuals can get to call the shots – and most individuals have a corruption price.
3. Many countries and organisations are reprehensible for allowing inacceptable scientific corruption to pass unpunished. There needs to be more strength behind procedures that reward honest science and punish dishonest. Why is so little heard of disincentives for dishonesty in climate science in particular? Where is the equivalent process to reward a person who kills another after a false claim to be authorised to perform brain surgery?
To illustrate, a light story from the 1970s. The tiny airfield at Katherine, Northern Territory, had a rudimentary shed with a glass box for public notices. These grew over time, something like this.
Become a light aircraft pilot. Ten hours to solo, 50 hours to full licence, Contact …..
Become a light aircraft twin pilot. 20 hours and 200 dollars to solo, 1:50 hourd to commercial licence. Ace Flying School, contact …
Go fully commercial Train for Connair DC3 captain, you will need 1000 hours and 1000 dollars instruction fees.
Qantas seeking First Officers. Minimum 2000 hours multi captain pistons.
Prime Ministers’ Flight now has two Boeing 707 jets. Become a Captain, tour the world. Youl will needlevel A security clearance, 10,000 hour cap bash and impeccable flying record.
Become a Helimuster chopper pilot. Need two cold cartons of Fosters for the instructor and a half hour ride on a merry-go-round.
The lesson? Every day, Flimate science looks more like the cattle roundup.
Lots of interesting points raised – the ‘idealised’ scientific method relies on falsifiability and therefore confirmation or falsification of previous work, but the modern practice of science particularly in academia is based on novelty of research and on many procedures that are borderline impossible to reproduce and replicate.
Obviously, a lot of what is published will be proven wrong in the future, and as such having something published in a ‘peer reviewed journal’ is no guarantee of the rightness of the findings – the best you can hope is that the study has been carried out correctly. Obviously there are areas that fall under the broad canopy of science for which this is a huge issue – nutrition and food science being one where papers with opposing conclusions seem to be published on an almost weekly basis (e.g. low carb / high protein or high carb / low protein, cholesterol etc)
One thing I was told at the outset of my PhD was ‘there is no such thing as bad data, just wrong interpretation’. Now clearly this is a little tongue in cheek, as you can have data that is misleading for any number of reasons (poor sampling, incorrect sample labelling or handling, inappropriate analytical methods, equipment failure being the first few I can think of), but the interpretation is then whether to include or exclude, and whether something mathematical / statistical can be done to improve the reliability of the data (e.g. applying a correction for instrumental drift). The key point however is that the raw data is there both for further investigation and for checking by others.
The Feynman anecdote up thread shows that these are not new problems, and as an aside I will say that in both my MSc and PhD research I failed to reproduce results consistent with previously published work. Now, whether this was through my incompetence in the lab or inadequate documentation of the methods in the literature is unknowable.
However, I think that there are reasons for thinking the issues around reproducibility and erroneous (although not fraudulent or fabricated) research are getting worse, for a variety of reasons:
1 – The ever-increasing prevalence of ‘publish or perish’ attitudes
2 – ‘Impact factors’, and the tendency of the most high profile journals (Nature, Science etc) to focus on headline-grabbing research, which of course is frequently those papers that find the most unexpected (and therefore most likely to be incorrect) results. Wasn’t it Carl Wunsch who said something along the line of ‘just because something is published in Nature doesn’t mean it is necessarily wrong’.
3 – Over-reliance on (and an infatuation with) technology. Think Monty Python’s ‘machine that goes ping’. It’s very easy to think of complex equipment or computerised statistical processes as ‘black boxes’. Input something at one end, push a couple of buttons and get something out the other that you treat as data. I suspect in many cases researchers know less than they are letting on about the processes within these black boxes, and so can often make inappropriate choices that impact the output. And then they have to try to explain it in an article (with some benefit from the supplementary information) to a degree that someone else could replicate the process. Of course, climate modelling has this issue writ large.
4 – Sometimes an unwillingness to share data, code or statistical processes in a manner that allows reasonable replication. Anyone who has followed ClimateAudit for a while knows the difficulties encountered in reverse-engineering the stats procedures used in some palaeo-climate papers and the unwillingness of researchers to share information. It’s back to Phil Jones’s comment of why should I give you my data, you’ll only try to find something wrong with it…
In most cases though, I think it is important to remember to never attribute to conspiracy (or malpractice) what can equally well be explained by cock-up.
Ben/Richard
You don’t see the link between someone writing about others not having the courage to stand up and support what they believe, and then not being willing to write down their own name???
Sources of bias and bad results:
1) No one will investigate un-PC theories (see prior post on the Iris)
2) Gate-keeping by the consensus
3) Insisting that a cloud of points is “consistent with” something
4) Never admitting any data is bad (Tiljander sediment, for example)
5) Never admitting a statistical method is being used wrong
6) Ignoring and not citing contradictory data/theories (IPCC is big on this, for example ignoring Svensmark, Earth’s electric field, cloud feedbacks…)
7) never admitting a theory/model is wrong
Rob: Sorry not to be clearer. I have often made the point Steve has here and have the scars to prove it.
“there is also the motivation to produce a result that will support perceived ‘good’ societal objectives”
This seems to be the key point and is true. This has always been occurring, but the issue is an increasing concern now because of the increase in the speed of worldwide communications.
I said speak Oracle. For yourself. Passing gas don’t count
Can bad scientific practices be fixed? Part of the problem is that no-one is incentivised to be right. Instead, scientists are incentivised to be productive and innovative.
A number of years ago the software company I worked for was concerned that the sales guys weren’t selling enough maintenance updates, so the next year they increased the commission on maintenance.
Sales guys are smart, they work on the deals that pay the best, the next year maintenance sales were way up, but license sales were down.
At the annual sales meeting the President got up and basically said, be careful what you wish for, you might get it (very paraphrased).
But is it any wonder we now have this problem in science?
But it also makes me feel better about my climate work, I’ll probably never get it published, I don’t have the CV to get past the gatekeeper, but because I get paid for doing something else, I’m not beholding to anyone, except my customers.
Ben read harder.
Global Average Surface Temperature
Mike thermometers are necessary. But even they produce predictions. They don’t actually measure temperature. You knew that.. Right?
I said speak Oracle. For yourself. Passing gas don’t count
hmmm… :o)
Now, a four-year study of the area in the vicinity of the shrine is causing archaeologists and other authorities to revisit the notion that intoxicating fumes loosened the lips of the Pythia.
The study, reported in the August issue of Geology, reveals that two faults intersect directly below the Delphic temple. The study also found evidence of hallucinogenic gases rising from a nearby spring and preserved within the temple rock.
“Plutarch made the right observation,” said Jelle De Boer, a geologist at Wesleyan University in Middletown, Connecticut, and co-author of the study. “Indeed, there were gases that came through the fractures.”
http://news.nationalgeographic.com/news/2001/08/0814_delphioracle.html
I think retraction watch is doing a very real public service. I look at their RSS feed everyday. Not only is it important work, but it can also be entertainment. It was a lot of fun to post over at retraction watch when the Lewendowsky paper was retracted.
1) No one will investigate un-PC theories (see prior post on the Iris)
I remember sitting in a meeting of many skeptics and Hans Von Storch.
he offerred up GCM time to test any skeptical idea. NOBODY had any ideas to test. Its hard to investigate UNICORNS. Folks who are skeptical are free to postulate any crap idea they want. but they dont. There only idea is ABC; anything but c02.
2) Gate-keeping by the consensus
It is harder than it should be to publish crappy skeptical papers. Thats true. Ask Monkton… opps
3) Insisting that a cloud of points is “consistent with” something
That’s largely true. Clouds of points also are INCONSISTENT with some theories. Every day I use clouds of points to rule out things.
4) Never admitting any data is bad (Tiljander sediment, for example)
Yup.. this afflicts many people. Some skeptics for example still use old charts that have been deprecated. old data too.
5) Never admitting a statistical method is being used wrong
Yup. I especialy like the guys who do simple averages of temperature data, even when every reader of Climate Audit could show them why they are wrong, but wont.
6) Ignoring and not citing contradictory data/theories (IPCC is big on this, for example ignoring Svensmark, Earth’s electric field, cloud feedbacks…)
Read your last few papers. use that as a standard. How much contradictory data did you show? take the paper you did with Scafetta as a gold standard..
7) never admitting a theory/model is wrong
they are all wrong. the question always is “do you have an improvement?”
it didnt stop jeff Id, steve McIntyre, Anthony watts, Me, Zeke.
Stop blaming the Referee and play your best shot.
Mr. Mosher,
Fair enough – you have read one model but are not a model expert.
Can you then answer these questions:
1) Do the models share a similar computational backbone? I.e. while they are not the same, they share the same engine.
2) You said that feedbacks are not explicitly set in the models, but are an emergent phenomena. I assume what you mean is that there isn’t a “cloud_feedback” variable, but that the effect of clouds on the model comes from the setting of various values. So, is the emergent value of cloud feedback in the one model you examined?
3) You say there is no such thing as a valid hypothesis, yet you call the iris theory invalid. Can you explain what the difference is?
4) If in fact the models are correct because they’re physics based – why then is there such a radical divergence from reality such that Hans von Storch stated:
“So far, no one has been able to provide a compelling answer to why climate change seems to be taking a break. We’re facing a puzzle. Recent CO2 emissions have actually risen even more steeply than we feared. As a result, according to most climate models, we should have seen temperatures rise by around 0.25 degrees Celsius (0.45 degrees Fahrenheit) over the past 10 years. That hasn’t happened. In fact, the increase over the last 15 years was just 0.06 degrees Celsius (0.11 degrees Fahrenheit) — a value very close to zero. This is a serious scientific problem that the Intergovernmental Panel on Climate Change (IPCC) will have to confront when it presents its next Assessment Report late next year.”
