I tried posting this question last week, not much result:
A comparative table of climate feedback loops, positive & negative, side-by-side, from largest effect to smallest. Is there one out there? Note: not a box diagram, not a flow chart. A table.
Admittedly, I haven’t pored over the IPCC science reports, so maybe it’s buried in there somewhere.
Comment on Open thread by Rhyzotika
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Comment on Open thread by angech
WEB you state
The Southern Oscillation embedded with the ENSO behavior is what is called a dipole [1], or in other vernacular, a standing wave. Whenever the atmospheric pressure at Tahiti is high, the pressure at Darwin is low, and vice-versa.
This is not true.
There is no physical reason for the atmospheric pressures at Tahiti and Darwin to be aligned in a standing wave. You can have the same atmospheric pressure at both places on the same day if not at the same time many times a month.
You can of course drop a standing wave on it by thousands of different mechanisms and become excited at fin ding a fit temporarily.
The pressures like ENSO take a random walk around set limits and patterns evolve which are random WEB.
“That is why the standing wave is not perfect and far from being a classic sine wave.””
You state “the Anti-correlation between Tahiti and Darwin. The correlation coefficient is calculated to be 0.55 or 55/100.Note that this correlation coefficient is “only” 0.55 when comparing the two time-series, yet the two sets of data are clearly aligned. What this tells us is that other factors, such as noise in the measurements, can easily drop correlated waveforms well below unity.
A correlation of 0.55 as you choose to calculate it is simply not flipping enough coins. Go back a few hundred years and you will find series where the correlation is 0.45, ie the opposite of what you statel. Take it all into account and you will arrive at 0.5 ie toss a coin.
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Comment on Open thread by Rob Ellison
‘Furthermore, these characteristics were found in each season around the same “centers of action” with only minor variation in their position or in the shape of the areas under their influence. Since a major portion of the Southern Hemisphere was affected by the phenomenon (and there was already a North Pacific Oscillation and a North Atlantic Oscillation), Walker decided to call it the “Southern Oscillation” and described it as “the tendency of pressure at stations in the Pacific … to increase, while pressure in the region of the Indian Ocean decreases.”
‘ http://www.esrl.noaa.gov/psd/enso/misc/hxsoi.html
Oh – for God’s sake learn something and don’t just pull nonsense out of your arse.
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Comment on Open thread by angech
Paul_K made comments on BEST methodology which needs further exploration(Comment #131322) at the Blackboard July 23rd, 2014
model assumption does not recognise any time-dependence in the geographical component other than the time-dependent variation in the global average temperature. Under this model assumption, the expected value of temperature at each location on the planet moves in lockstep with the global average temperature. In simplistic terms, the model structure recognises that the poles are colder than the tropics, but it does not recognise in any structural sense that the amplitude of temperature variation in the high northern latitudes is observably greater than the amplitude in the high southern latitudes which is greater than the amplitude in the tropics. The model has no structural recognition of the “geographical component” of temperature variation which is associated with amplification of temperature change as one moves towards the higher latitudes. (I will call this ‘polar amplification’ for short, even though it is not quite the context the term is normally used in.) Yet this is undoubtedly a realworld phenomenon, and one which manifests itself in the data input (weather stations).
Hence when a variogram or correlation-distance analysis is done on the real world datapoints under this model assumption, the only element remaining to be mapped is apparently the weather plus error terms. In the real data, it is the weather plus the temperature variation associated with polar amplification (plus error terms). There is then a very high correlation retention associated with polar amplification, and this results in a very large distance parameter being applied (with radius over 3100 kms) to the weather term, now an obvious misnomer, which results in an unjustified smoothing of all smaller-scale features.
There is another way to look at this, which is to say that since the weather term W is the only fluctuation which is left in the mathematical model, then any variation (like polar amplification) which is not explained by the stable geographical component already accounted for MUST be included as a fluctuation in the weather term. Therefore, you need the large correlation distances to correctly account for polar amplification. This is actually highlighting a structural problem in the model – which results in trying to get the same data to do two incompatible things.
I have come to the conclusion that the problem is better described as a structural deficiency rather than a choice of data problem. At the moment, the BEST methodology is trying to get one spatial characterisation to do two incompatible things – map relatively small-scale weather features and map large-scale temperature variation associated with amplitude dependence on latitude. They need to be separated.
What BEST does at the moment does not seem like a bad approach to generating an average global temperature series, and this should be relatively insensitive to choice of maximum correlation length. Its main problem is in the mapping (and the associated error calculation).
Is this worth putting up to Zeke for response and/or Cowtan and Way who may have used this method which seems to have flaws.
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Comment on Open thread by David Appell
You misunderstood the link to the article about Lovejoy’s work — it doesn’t say the world is cooling.
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Comment on Open thread by stevepostrel
The failure of people like Orestes and Hertzberg (in links above) to aggressively embrace nuclear power in their every public pronouncement is a sign that they are more interested in bemoaning “obstructionism” than overcoming or coopting it. Quite apart from the fact that maintaining anything like our current standard of living while drastically cutting CO2 emissions would require massive nuclear deployment, grasping the nuclear nettle in more than a pro forma way would go a great distance to establishing sincerity with skeptical members of the public. When the Urgent Mitigation cause just conveniently happens to coincide entirely with the cultural and policy predilections of dreamy greens, anti-market zealots, anti-consumptionists, and control-freak technocrats, it’s hard to overcome the “of course they’d say that” reaction of those not sharing these predilections. But strong advocacy for nukes, which go 100% against those instincts, would be a powerful signal of sincerity about the cause.
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Comment on Open thread by David Appell
If CO2 is so good for plants, why are there no plants on Venus, whose atmosphere is 96.5% CO2?
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Comment on Open thread by maksimovich
It is the absence of Venusians (as well as an absence of a magnetosphere) that allows co2 to be high.
http://www.nature.com/nature/journal/v340/n6233/abs/340457a0.html
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Comment on Open thread by ClimateGuy
“If CO2 is so good for plants, why are there no plants on Venus, whose atmosphere is 96.5% CO2?”
For the same reason that the doctors shouldn’t have used pure O2 in the incubator after you were born, David Appell!
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Comment on Open thread by David Springer
Time series in, time series out. [yawn]
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Comment on The 97% feud by Joshua
==> “He has completely ignored all the substantive, surreal issues I pointed out with the study, most centrally:”
Objectively speaking. As a non-ideological scientist. Of course.
Joe – do you really think that your own ideological or cultural or partisan (in the sense of your professional outlook) identifications are not influential in your definition of what is or isn’t “surreal?”
To me, you seem,actually, very ideological here.
I assume that you’re familiar with Kahan’s work on motivated reasoning.
By what means do you exclude yourself from those whose reasoning is motivated?
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Comment on The 97% feud by Wagathon
Is Michael Mann is the Lance Armstrong of climate science? See the link on PubPeer–e.g., The incentives to fabricate data are strong: it is so much easier to publish quickly and to obtain high-profile results if you cheat.
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Comment on The 97% feud by ianl8888
You missed the intermediate step of first taking all the money through tax
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Comment on The 97% feud by Joshua
bentabou -
==> “:Even if you had Judith pegged to a T as a Really Bad Person, ”
I don’t think she’s a hypocrite. I don’t think she’s a bad person.
I think that she offers flawed arguments. I think that she, sometimes, applies her standards inconsistently.
We all make bad arguments and, at times, apply standards inconsistently.
Her application of the concept of motivated reasoning is very uneven. She has dismissed it out of hand as applied to “skeptics” – in fact denigrates the theory when it is applied to “skeptics” – but then turns around and regularly applies it to “realists.”
In point of fact, a key aspect of the theory is that it is a product of human reasoning – not a phenomenon that is contingent on one’s orientation in the climate wars.
If Judith is going to refer to the theory, then she shouldn’t distort it to advance partisan rhetoric – when such a selective application is actually inconsistent with the theory itself.
None of that makes her a “bad person.”
Don’t be so defensive.
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Comment on The 97% feud by Rud Istvan
Joshua, this is just my personal opinion.
You are obtuse enough, immune to factual rebuttal enough, and just insinuative of poster malmotives enough ( especially of Dr. Curry herself), that had I been proprieter of this blog you would have been permanently banned long ago. On grounds of offensive language and irrational conduct unbecoming of any scientific discourse.
Please go away. If you stay, post with less personally offensive and more fact reasoned replies. You pollute this dialog otherwise, and always have IIRC. And you contribute nothing of scientific substance except in your own minds feeble assertions. Which you have more than once now proven via your own posts is small, shriveled, and biased.
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Comment on The 97% feud by willard (@nevaudit)
Crickets.
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Comment on The 97% feud by Don B
In the Oregon Senate election, pitting a Dr. against the incumbent, a bumper sticker reads, Keep your Doctor, Change your Senator.
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Comment on The 97% feud by ianl8888
>Tension building along the fault line
One may hope, but the MSM will not report it, ever
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Comment on The 97% feud by willard (@nevaudit)
> The study turns out to be a scam, based on random politicial activists reading and rating climate science abstracts, the focus of their activism, where they passionately desired a particular outcome, and by virtue of their subjective ratings were in a position to deliver that outcome.
Let’s try the Duarte argument [1]:
These random political activists could be Club of Rome or Greenpeace minions and it wouldn’t change anything to what they did, and Duarte’s not addressing anything by probing for motivations.
In other words, C13 stands on its own merit, just like Duarte’s rant does, whether or not Duarte holds pro-Israel positions:
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Like a secularist libertarian would say, Jesus, what a joke. Is this just the usual God rigmarole of ringtones or what.
[1] http://judithcurry.com/2014/07/27/the-97-feud/#comment-612289
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Comment on Understanding adjustments to temperature data by Stephen Rasey
@Mi Cro 7/26 at 7:05 am |
The is no loss of resolution, and doing it the way I am I have a smaller error.
Is your error smaller? There is no free lunch here. There is missing data. There is bad data. These contribute error and uncertainty into the result.
Where do you have a summary of the process? Is there an error analysis in it?
What is it that you are performing your linear fit over? What is the distribution of lengths in time over the least Square fit? What is the distribution of points in each fit? What is the uncertainty in the slope of each piece? Do these slope uncertainties build as you move from today into history? (They should).
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