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Atmospheric CO2 has gone up 20% since 1960

CoolSceptic said:
Nevertheless, Prof K's work has not yet been falsified in the peer-reviewed literature. So here's your opportunity. If you can find fault with it, publish. Nobody else has managed it yet. I've even started the abstract for you:
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However, other researchers have come to different conclusions, e.g.,

http://adsabs.harvard.edu/abs/2009PhyA..388.2492H

We find that rates comparable with the observed global warming are very rarely generated by the model of natural variability (the probability is less than 2.3×10‑4). Thus, natural agencies are not a plausible explanation for the observed global warming unless all the paleoclimate reconstructions through which the model is parameterized are underestimating natural variance by a factor of at least four.
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Great news! Everybody is off the hook as far as global warming is concerned . It wasn't caused by fossil fuel burning. And nope it wasn't an act of God. Nope It was those nefarious mad military scientists and something called HAARP. Apparently a military installation in Alaska is beaming a huge amount of extra low frequency waves up into the atmosphere causing the ionosphere to bulge. The stratosphere then bulges up in response. That in turn alters the Jet Stream and the rest is well, history! Or so a show I watched on the History Channel said last night. All part of a plot to destroy nations in war by altering their weather. Moving on to earthquakes purposely brought about by same mad scientists through the emanations of elf ----
 
TellyKNeasuss said:
However, other researchers have come to different conclusions, e.g.,

http://adsabs.harvard.edu/abs/2009PhyA..388.2492H
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At last - some interesting scientific content, and no surprise to find it is TellyKNeasuss who provides it. Sorry about spelling your name wrong in the previous post...

The paper you link looks very interesting, and not one I have yet read. Unfortunately the only copy I can find from home is behind a paywall so I cannot read it :(

From the abstract, it seems as if they are using paleoclimatic indicators to set up a model. It will be interesting to see how they deal with the issues raised by Koutsoyiannis and Montanari 2006 (link to preprint), particularly to the issues of inconsistencies between paleoclimate reconstructions discussed at the top of page 16 of that document. (Note I can see that this paper is referenced by Halley 2009, so the author must be aware of the issue)

Unfortunately, without seeing the new paper, I cannot see how they deal with these problems. If you know of a preprint or free copy let me know, otherwise it will have to wait until the next time I am at a library that holds or can access this paper.
 
Wangler said:
Coolsceptic, did you look at the links that lomiller posted:

I would be intested in your opinion of these criticisms.
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Sorry, I missed these earlier.

On "How Red Are Your Proxies", the best technical response is already given by Professor Koutsoyiannis on the thread. However, this debate is not easy to follow unless you are familiar with the territory. I can try and explain the problems with the Ritson method, although I'm not sure how to pitch it technically - it depends on how familiar you are with the topic.

Autocorrelation in time series is when a point in a series has some dependency on the previous point (or points). The presence of autocorrelation in a time series has huge implications for analysis performed on that data set. To deal with it, we impose a model which defines the autocorrelation. The simplest of these is Markovian dependency, or autoregressive model, commonly referred to as AR(1). Prof Ritson's estimator works well (as do other classical estimators) against this type of model.

The problem with Prof Ritson's method is that it collapses if the underlying time series has a different type of autocorrelation. The Hurst phenomenon is one example, but even this is unnecessary - the Ritson estimator fails spectacularly against other types of Markovian models, such as an ARMA model. The ARMA model is like the simple autoregressive model, but adds a moving average component. This model is useful and can be used to accurately represent simple AR(1) models with uncorrelated noise added - such as noise added during the measurement process. Estimating underlying values of ARMA processes is more challenging, classical estimators do an okay job but the Ritson estimator produces estimates which are very wide of the mark.

This means the Ritson estimator is only valid to use if you are very confident indeed that there is no risk of the data set being anything other than a simple AR type. Of course, there is no evidence presented for this, and the assumption is quite obviously dangerous to make. Koutsoyiannis outlines the technical reason for the problem with the estimator in the thread (comments 35 on). A few days after this, others found the problems through Monte Carlo analysis and the thread had comments shut off rather promptly.

As for the second article, Prof Koutsoyiannis chose not to respond on realclimate, but instead at ClimateAudit. I would encourage you to read his response here. The issues addressed here are of a less technical nature, and probably do not require any additional commentary from me :)
 
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CoolSceptic said:
Sorry, I missed these earlier.

On "How Red Are Your Proxies", the best technical response is already given by Professor Koutsoyiannis on the thread. However, this debate is not easy to follow unless you are familiar with the territory. I can try and explain the problems with the Ritson method, although I'm not sure how to pitch it technically - it depends on how familiar you are with the topic.

Autocorrelation in time series is when a point in a series has some dependency on the previous point (or points). The presence of autocorrelation in a time series has huge implications for analysis performed on that data set. To deal with it, we impose a model which defines the autocorrelation. The simplest of these is Markovian dependency, or autoregressive model, commonly referred to as AR(1). Prof Ritson's estimator works well (as do other classical estimators) against this type of model.

The problem with Prof Ritson's method is that it collapses if the underlying time series has a different type of autocorrelation. The Hurst phenomenon is one example, but even this is unnecessary - the Ritson estimator fails spectacularly against other types of Markovian models, such as an ARMA model. The ARMA model is like the simple autoregressive model, but adds a moving average component. This model is useful and can be used to accurately represent simple AR(1) models with uncorrelated noise added - such as noise added during the measurement process. Estimating underlying values of ARMA processes is more challenging, classical estimators do an okay job but the Ritson estimator produces estimates which are very wide of the mark.

This means the Ritson estimator is only valid to use if you are very confident indeed that there is no risk of the data set being anything other than a simple AR type. Of course, there is no evidence presented for this, and the assumption is quite obviously dangerous to make. Koutsoyiannis outlines the technical reason for the problem with the estimator in the thread (comments 35 on). A few days after this, others found the problems through Monte Carlo analysis and the thread had comments shut off rather promptly.

As for the second article, Prof Koutsoyiannis chose not to respond on realclimate, but instead at ClimateAudit. I would encourage you to read his response here. The issues addressed here are of a less technical nature, and probably do not require any additional commentary from me :)
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Thanks for the response...I'm still lost, but working on it.
 
As for the second article, Prof Koutsoyiannis chose not to respond on realclimate, but instead at ClimateAudit.
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fancy that....:rolleyes:

next.......:garfield:
Wangler

http://www.realclimate.org/index.ph...-testing-and-long-term-memory/comment-page-3/

snip - don't burnt oo many midnight oils....

With that in mind, I now turn to the latest paper that is getting the inactivists excited by Demetris Koutsoyiannis and colleagues. There are very clearly two parts to this paper – the first is a poor summary of the practice of climate modelling – touching all the recent contrarian talking points (global cooling, Douglass et al, Karl Popper etc.) but is not worth dealing with in detail (the reviewers of the paper include Willie Soon, Pat Frank and Larry Gould (of Monckton/APS fame) – so no guessing needed for where they get their misconceptions). This is however just a distraction (though I’d recommend to the authors to leave out this kind of nonsense in future if they want to be taken seriously in the wider field).
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There isn't a snowball's chance in hell that Prof. Koutsoyiannis is correct.

Since you are so sure you are correct, you'll have to give me odds; My $500 vs. your $2500 that in five years time his ideas have not taken over the climate debate.
 
BenBurch said:
There isn't a snowball's chance in hell that Prof. Koutsoyiannis is correct.

Since you are so sure you are correct, you'll have to give me odds; My $500 vs. your $2500 that in five years time his ideas have not taken over the climate debate.
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Heck, those are good odds. If I understood more about it, I'd take you up on that.

Of course, before we finalized the bet, we'd have to make some formal scientific definitions to frame the specifics, other than snowballs and such.

Do you have any in mind?
 
The heart of it.....

It’s important to realise that there is nothing magic about processes with long term persistence. This is simply a property that complex systems – like the climate – will exhibit in certain circumstances. However, like all statistical models that do not reflect the real underlying physics of a situation, assuming a form of LTP – a constant Hurst parameter for instance, is simply an assumption that may or may not be useful. Much more interesting is whether there is a match between the kinds of statistical properties seen in the real world and what is seen in the models (see below).

So what did Koutsoyiannis et al do? They took a small number of long station records and compared them to co-located grid points in single realisations of a few models and correlate their annual and longer term means. Returning to the question we asked at the top, what hypothesis is being tested here? They are using single realisations of model runs, and so they are not testing the forced component of the response (which can only be determined using ensembles or very long simulations). By correlating at the annual and other short term periods they are effectively comparing the weather in the real world with that in a model.Even without looking at their results, it is obvious that this is not going to match (since weather is uncorrelated in one realisation to another, let alone in the real world). Furthermore, by using only one to four grid boxes for their comparisons, even the longer term (30 year) forced trends are not going to come out of the noise
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as I said - not worth the effort to dissect since it's been done by the climatologists already and dismissed.......

If you have questions about modelling the RC guys will answer on the site and if relevant by email. They are approachable within reason.

Let's not conflate weather with climate....

Hydrologist are primarily concerned with risk in quite narrow geographic regions rather than the scope of climate - they will be a busy lot soon enough from the consequences of AGW
 
Wangler said:
Heck, those are good odds. If I understood more about it, I'd take you up on that.
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BenBurch's odds are not good. Remember Prof K's hypothesis is a competing hypothesis to AGW; no experiment yet exists to separate the two views; new data samples take 20-30 years to arrive; the AGW view is deeply entrenched in the scientific community.

Deeply entrenched scientific positions do not overturn easily. Would you have bet on Alfred Wegener's views on continental drift when he first came up with them in 1915, as opposed to the "land bridge" explanation? It took nearly 40 years for mainstream science to adopt Wegener's views over the preferred theory of the day. Likewise, if you were in Russia in the late 1930s would you have voted for Lysenkoism or genetics to dominate Russian science in five years time?

You are betting on whether there will be a breakthrough in scientific research that clearly resolves two positions within five years that is so clear cut that even deeply entrenched views are overturned. History tells us these things take longer than that, unfortunately, even in supposedly objective communities.
 
BenBurch said:
There isn't a snowball's chance in hell that Prof. Koutsoyiannis is correct.

Since you are so sure you are correct, you'll have to give me odds; My $500 vs. your $2500 that in five years time his ideas have not taken over the climate debate.
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Why are you still in the Flat Earth Society of Warmers?

We know today, that snowballs have a good chance in the Arctic or Antarctic of the planet Earth, which only in the hysterical rantings of Warmers is considered Hell.
 
Why are you still in the Flat Earth Society of Warmers?
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:dl: the irony the irony with apologies to Coppola...

I think in sentencing terms the deniers are now within the "faint hope" clause boundaries.....:garfield:

denying observation AND physics....:boggled:
 
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