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Comment on The 2.8% effect by R. Gates, Skeptical Warmist

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Ulric Lyons said:

” Anyone making a case for even more Arctic warming is simultaneously making the case for accelerated global cooling.”
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This makes no sense at all. Regional warming of the Arctic is ultimately due to both the net climate forcing as well as localized positive feedbacks occurring because of the biosphere and cryosphere changes going on in the Arctic. If anything, those positive feebacks are likely to accelerate the overall global warming, reaching well beyond their regional signficance and futher enhancing the accumulation of energy in Earth’s climate system.


Comment on Uncertainty in Arctic temperatures by Matthew R Marler

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Mike Flynn: <i> I was asking the author </i> So you were. Now he has no need to answer you, if he felt one in the first place.

Comment on The 2.8% effect by R. Gates, Skeptical Warmist

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Jim asks:

“…what happens if the current possible recovery of the extent and volume of Arctic sea ice continues?”

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Depends on how long it continues for. Much like the so-called pause in tropospheric temperatures, if Arctic sea ice were to undergo a mult-year recovery, something more that 5 or 10 years, then it definitely is worth looking at. I personally look at Arctic Sea ice and Ocean Heat Content as two of the stronger physical reasons for my “warmist” position. Should either (or both) of these reverse direction for a decade or more, my warmist postion would change direction as well. Given that both the cryosphere and the oceans have far greater thermal inertia than the rather fickle troposphere, a long-term reversal of these two would be more likely caused by some actual external forcing, as opposed to natural or internal variability.

Comment on The 2.8% effect by Schrodinger's Cat

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Kim, I think that is a very honest answer. Thank You.

Comment on The 2.8% effect by Kip Hansen

Comment on The 2.8% effect by climatereason

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RGates

In your (excellent) post at Nevens you said;

“Certainly declines in Arctic sea ice may be changing the patterns of NH weather, the research on these effects is ongoing. In terms of the winter of 2013, we can trace the change in England’s weather this winter almost to the day of the onset of the large SSW event over the pole. If you look at the chart above for the SSW that peaked around the 6th of January, and then take a look at the Central England Temperature record for the winter, you’ll see this…”

We had several conversations here about it when you said the declining sea ice had an effect on the weather in the UK as could be witnessed in the hard winter of 2013. I don’t want to misrepresent you, so perhaps you can clarify if you believe that declining sea ice impacts on our winter weather as evidenced by 2013?

tonyb

Comment on The 2.8% effect by jeremyp99

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Kip,

Thank you. Bookmarked.

Comment on The 2.8% effect by WebHubTelescope (@whut)

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DocMartyn,
Ahhh, so you don’t like what you see, so you reflexively knee-jerk.

Obviously the models are not in synch with the measurements. No model analysis exists that assumes a gaping empty space in the middle of Africa or toward the poles. Yet the data has these gaps. One works to correct this …

… or if you are like lil kim, you write awful haiku and then declare victory.


Comment on 20 tips for interpreting scientific claims by GaryM

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No offense, but anyone who needs to be told any of those 20 tips shouldn’t be making policy anyway.

Comment on 20 tips for interpreting scientific claims by Brian H

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Edit: “whether the risk is [taken] voluntarily or not”

#11 – Causes precede (and cannot follow) effects.

Comment on 20 tips for interpreting scientific claims by jim2

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(Jim2)And we need to keep in mind pathological science.

From the link:
Irving Langmuir1 has identified several recurring patterns in cases of pathological science:

The maximum effect that is observed is produced by a causative agent of barely detectable intensity, and the magnitude of the effect is substantially independent of the intensity of the cause.

The effect is of a magnitude that remains close to the limit of detectability, or many measurements are necessary because of the very low statistical significance of the results.

Theories outside the field’s paradigm are suggested.

Criticisms are met by ad hoc excuses thought up on the spur of the moment.

The ratio of supporters to critics rises and then falls gradually to oblivion.

To these we may add the following:

The remarkable result is specific for a “special” system.

Some special technique or equipment is involved.

The result requires a stunning departure from the paradigms that fully determine results in all other comparable systems, including those studied by the authors. — N.J.T.

http://www.columbia.edu/cu/21stC/issue-3.4/turro.sb1.html

And that was included in:

http://www.columbia.edu/cu/21stC/issue-3.4/turro.html

Comment on 20 tips for interpreting scientific claims by Steven Mosher

Comment on 20 tips for interpreting scientific claims by Brian H

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Doc;
Sounds cruel: “throwing away two outliners”. Because they lie out beyond the margins? :p

Comment on 20 tips for interpreting scientific claims by jim2

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More from that article:
Advice for the working revolutionist
Clearly, scientific progress would be impossible if researchers always played it safe within a dominant paradigm, discarding disturbing results or shying away from daring hypotheses. Some of today’s most robust discoveries and most promising research subjects–manned space flight, wave-particle duality, C60 (buckminsterfullerene or “buckyball”) molecules, high-temperature superconductivity, ad infinitum–once struck mainstream scientific opinion as completely implausible. Working researchers have practical steps they can take to lower the chances that today’s “eureka!” will be tomorrow’s Ig Nobel:

Always generate and test several plausible hypotheses to explain a result.

Use imaginative experimental design to increase objectivities and decrease the chances that the initial observation contains artifacts.

Let the best available paradigm be your guide, until you’re certain that your results require revision of the paradigm.

Be conservative about the concepts of statistical significance and margin of error, especially when analyzing phenomena on the threshold between signal and noise.

Reproduce, reproduce, reproduce.

Discuss surprising findings openly with peers (through both formal and informal channels, inside and outside one’s own specialty), and make constructive use of the critiques that arise.

When discussing research with non-scientists–especially those holding microphones, cameras, notebooks, or checkbooks–avoid the temptations to overinterpret results, oversimplify your explanations, or promise the moon in practical applications.

If further studies falsify your hypothesis, acknowledge it with grace and learn from the experience. Blind leads are nothing to be ashamed of; they are inseparable from the progress of science. Any number of pathological investigations give way eventually to one like quantum mechanics–which necessitated a few adjustments to the law of conservation of mass but ultimately withstood criticism, explained results that Newtonian theory couldn’t explain, and revolutionized physics. The same communal corrective processes that falsified one theory verified the other; that’s how science operates and why it almost always works.

Do the unthinkable: Try your very best to find faults in your experiment or to falsify your interpretation. If this is done fairly, objectively, and passionately, even if you turn out to be wrong, you will be true to your science, and you will be admired by the community for your intellectual courage and dedication to the scientific ethos.

Comment on Interpretation of UK temperatures since 1956 by Greg Goodman

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What “snide” remarks? I told you I found your presentation was a barrier to seeing what you were presenting. Make what you will of that information, it’s not “snide”.

It may have been rather blunt because you were badgering me with comments like “one last time” and repeated insisting that I look at you article.

What do you mean “blag what I have done” ? Are you suggesting I’ve stolen your brilliance ideas? That would be pretty hard since I can’t understand it (or rather don’t have the time to battle through trying to).

From your description here it seems to be based on AMO/PDO which I haven’t mentioned so what are you on about?

I did “blag” Clive and Euans idea of looking at station data because I’m responding to their paper.


Comment on 20 tips for interpreting scientific claims by Jim Cripwell

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Measurements and estimates are completely different things.

Comment on Fallacies of risk by johanna

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Toby, I also found this article to be a somewhat pompous exercise in “teaching your grandmother how to suck eggs.”

Modern social “science” practitioners just love creating lists and tables comprising either things we already know and/or things they just made up, in the hope that one day their particular list or table will be cited as ground-breaking stuff. A few of these have previously been published here, and they all have the same characteristics.

That said, the author does make some interesting points in discussing his typology, and some good comments have been generated here as a result.

But as you point out, making policy is not about ticking off boxes to make sure that you haven’t fallen for some “fallacy” or another. Politicians, potentates and military leaders have been making decisions for thousands of years without the benefit of modern sociology – and the successful ones made very good decisions nonetheless. I don’t think that Julius Caesar or Elizabeth 1 would have done anything differently because someone generated a list or a table of “fallacies.”

Comment on 20 tips for interpreting scientific claims by Greg Scott

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Beware of press releases as they often misrepresent science with sensationalist spin.

Comment on 20 tips for interpreting scientific claims by DocMartyn

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Jim(2), pretty much everything you have listed there could be applied to the field of xenohormone research.
We have to get rid of all sorts of xenoestrogens/androgens to observe effects; so most plastic pippet tips are out. The effects you see are subtle and typically you need to remove all the natural hormones in the system to see a reproducible effect.
Thing is, hormone mimitics are real, we just don’t know how much harm they might be doing.

Comment on Interpretation of UK temperatures since 1956 by Greg Goodman

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Thanks for seeing the benefits, blue.

I hasten to add this is not my invention. It’s been a standard choice in real-time audio processing for a long time, primarily because it can be implemented very efficiently with the fast execution time that is obligatory for real-time throughput.

It’s frequency characteristics are very similar to gaussian with the added benefit of a zero stop that can be targeted at an particular frequency like mains hum in audio, or annual in climate.

Pekka seems to have dug himself into a hole to defend the crappy running mean “boxcar” average. Perhaps he has published work that depends on it or he’s been teaching it for years, I don’t know. His reaction seems a bit odd, but he’s smart so I’m sure he has his reasons.

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