Please don’t take my post as a criticism of your approach. You’re absolutely right that statistical analysis may be all we’ve got. My opinion is that climate researchers should aknowledge that fact more openly, and not pretend to do physics when they don’t. Finding trends and correlations only tells you what’s been happening in the past, but is no guarantee about the future. It may invalidate some model predictions, but then again, never at 100%.

But one thing I’ve learned with my own miserable attempt at playing with CO2 data is that it’s almost imposible to extract a meaningful conclusion from them. I thought I had one, and then I realized I was wrong. But my consolation is that everybody else seems to be wrong, in that case about the CO2 cycle response time. We wee a lot of claims about a 50 year lifetime, but in no way can you get that number from the data. Any lifetime seems to fit the data just as well, and if anything, shorter ones seemed to give a lightly better fit, but only marginally so.

So even good data may not be sufficient to allow discriminating between good and bad models.

The real sin is to proclaim certainty when there isn’t any. So trend analysis is at least useful in that regard.

Have you done a similar analysis for past – longer – periods, to see if that apparently systemic difference persists?

1) I’ve done it with randomly generated data. It is not a feature of the method.
2) I’ve done it for many past 8 year periods while testing a question John V asked me about uncertainty intervals. CO sometimes gives larger and sometimes smaller trends when tested over the historical record.

I suspect the reason for the very similar relation for different start dates here is that the periods of time are overlapping. So, for the recent period, CO happens to be lower in these five instances (two of which are separated from each other by only 1 year.)

Interesting graph. I’m curious as to why the CO number for any given year is always less than the corresponding OLS number. Looks like a systemic difference to me.

Have you done a similar analysis for past – longer – periods, to see if that apparently systemic difference persists?

Any thoughts?

I prefer to think I take a 16th century view!

Bacon’s philosophy of using an inductive approach to nature – to abandon assumption and to attempt to simply observe with an open mind – was in strict contrast with the earlier, Aristotelian approach of deduction, by which analysis of “known facts” produced further understanding. In practice, of course, many scientists (and philosophers) believed that a healthy mix of both was needed—the willingness to question assumptions, yet also interpret observations assumed to have some degree of validity.

In the end, physics rules, not statistics (and of course I say this because I’m a physicist!).

I agree with you– and I’m a mechanical engineer. But, also, empiricism trumps modeling, because I admire Francis Bacon more than Aristotle.

A agree with the idea of testing fingerprints with respect to attribution. The difficulties are:
a) Identifying fingerprints all modelers agree to be fingerprints of AGW and not just warming and
b) Getting a hold of data climatologists agree is sufficiently accurate to test the finger print.

I was about to start looking at stratosphere data when the JohnV suggested I compare the uncertainty intervals to history data. So I’ve been puzzling over that. (Particularly as at first I concluded that my uncertainty intervals might be too small. But then, it turned out the “evidence” of they might be too small all came from the wild oscillations in trends when the jet-inlet to bucket and back transitions were occuring! So, they may be just fine!)

But, in anycase, when I turn to the stratosphere, I’ll also need to read to figure out how accurate people think those are. (OTOH, I could just do the blog-worthy posts, show what the current data made available to the general public show. Some climatologists may not like it.. but… my response to that is: So? Then be more careful with your data sets in the first place! )

Beautiful work as always. But one should always remember that the trend analysis says nothing about the role of increased CO2 on the observed trend. The trend is whatever it is, and its underlying cause is something else. It is still a possibility that increased CO2 contributes next to nothing to global warming, because of unknown negative feedbacks. Or it could be that it contributes much more, but that it is offset by “natural” countertrends.

In this sense, “fingerprint” studies are more useful. IOW, what other symptoms would there be that could point to the role of CO2 (and other GHG’s) in climate change. But that too is not easy. For example, increased water vapour would be a symptom of any sort of warming (or forcing), according to the water vapor feedback theory. So it’s impossible to distinguish between GHG forcing and other forcings, like solar, for example. It may turn out that in a complex system like climate, indirect forcings (of feedbacks, if you want) are as important, if not more, than direct forcings. The simplistic view that equates direct forcing to warming would then be grossly in error.

As a matter of fact, a simple observation of the historical climate records (including paleo climate records) points to a highly nonlinear behavior, that cannot be described by a simple linear model. Just look at glaciations, that result from a tiny change in insolation.

So, even though the trend analysis tells us something about what is likely not to happen, it tells nothing about the underlying causes of climate change.

In the end, physics rules, not statistics (and of course I say this because I’m a physicist!).

The graphs Anthony shows start in 1950 rather than the late 1800s we see for surface temperature. The behavior between 1950-1960 oscillates wildly. Is that weather? Or is it an artifact of measurment problems with a new system to measure something?

After that, it might be interesting if someone compares globally and vertically integrated water vapor to model hind casts. (Maybe someone has or will!)

I guess the bee in my bonnet isn’t so much which metrics are compared to the simulation predictions of hindcasts. I’d just like to see some selected by the IPCC as standard, have the IPCC itself report how each individual modes on which they base their prediction does relative to that metric. Then, it would be if they included the results of the evaluations in a digested form. That is, things like Taylor diagrams etc. rather than a collection of maps with colors super-imposed.

Obviously, the IPCC would have an incentive to select metrics based on things that have been measured with greater confidence rather than lesser confidence. They would also likely pick those models do better on rather than worse on– but…that’s ok. It’s better than what I consider to be the vague qualitative stuff in the AR4.

And a mystery; why weren’t the models compared with humidity data? Scandalous.
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http://wattsupwiththat.wordpre.....re-part-2/

The McLean and Quirk paper is here;

http://mclean.ch/climate/Aust_temps_alt_view.pdf

Have you considered the possibility that all the missing heat is simply being sucked into the earth’s mantle as a prelude to an AGW-caused planetary explosion?

Of course. I even considered buying a Tom Chalko bioresonant t-shirt to support his efforts to come up with an anti-gravity machine. This invention would permit us to do all sort of things withoug use of energy, and so reduce the need for carbon generating power plants.

However, I’m in a quandaery: What if the carbon emissions associated with shipping the t-shirt here result in immediate warming, causing the earth to explode before Tom manages to invent and deploy the anti-gravity machines?

Have you considered the possibility that all the missing heat is simply being sucked into the earth’s mantle as a prelude to an AGW-caused planetary explosion? Isn’t this illusory cooling thing a predictor of the big boom?

Sincerely,

Frightened in Melbourne

So the trends have been falling since 1990.

Well… be careful drawing conclusions. Obviously, the 2001-now data is a subset of the 1990-now data. The trend appears to drop because the 2001-now data has a lower trend than 1990-2001.

But yes, the trend has slowed down– but at least part of that slow down is consistent with weather variability. The other part may be the result of volcanos. From 1990-2001, part of the ramp up was due to GHGs, part to clearing of the volcanic aerosols, and then, possibly part due to ghgs. Since about 2001, we the volcanic aerosols have been clear, so we lost that bit.

So, quite likely, right now the natural is off setting the ghg’s.