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Lumifer comments on Open thread, Sep. 28 - Oct. 4, 2015 - Less Wrong Discussion
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The long and hard answer is about a book's length in size and might well be more.
As to data, there are several "official" series, IIRC from NASA, from NOAA, and from the Hadley Centre. See e.g. this. Data is freely available, so you can plot your own.
However I don't know why there is controversy over the existence of hiatus if even the IPCC 2013 report accepts it as existing and spends a few pages (Ch. 9) discussing it.
Science moves on... are you suggesting that just because it was in the IPCC report the matter is fully settled and over with? Even though I agree with the conclusions of the IPCC report, I'm sure there are many things in the report which will have to be revised in the future.
Just like gjm, I think you're confusing existence and interpretation.
Outside of political posturing, I don't know why someone would claim that hiatus as a feature of the historical data set does not exist. It does and it's pretty clear. That's existence. What does the hiatus mean is a different and a much more complicated question. You can claim it's just an artifact of random variation. You can claim it reflects multi-year cycles in global climate patterns. You can claim it shows that our models are deficient and we don't understand climate variation. You can claim many things -- but a claim that the hiatus just does not exist doesn't seem reasonable to me.
Not sure why you're using this unusual terminology, but I'm arguing about what you call existence. It seems that you're arguing that the 'hiatus' exists with either absolute certainty (in which case you'd have to provide a logical proof) or at least with very high likelihood. However, I see no reason we should assign a very high likelihood to its existence.
The 'existence' of a 'trend' or 'hiatus' in general time series data is part of the map, not the territory. If the climate temperature data were just a smooth line (like this - graph not relevant to the discussion) then I'd agree with you, but it's not. It looks like this.
What's unusual about my terminology?
I am not sure about that. In your "smooth line" example, is the trend part of the map or the territory? More generally, what can I say about a time series that you would consider to be territory and not map?
Oh, and if you want to be technical about it, the time series you're looking at is not part of the territory to start with. It's a complex model-dependent aggregate.
If the temperature graph looked like the first graph, then inference of a trend (which is, again, part of the map) with high probability might be made. But it does not look like that.
That f(t) = x.
For the sake of discussion of the existence of a hiatus I'm assuming the temperature graph is a given. But you're right in that the big picture is that the temperature graph itself is not part of the territory.
In this case I am not sure what do you mean by "exists".
Can you give a definition, preferfably a hard one, that is, an algorithm into which I can feed the time series and it will tell me whether a particular feature (e.g. a hiatus) exists or not?
You're getting close to understanding the problem. What you're really asking about is an inference method, and the optimal inference method is Bayesian inference, which requires specification of what you would expect to see in the temperature record if the current warming rate were zero and also the specification of a prior probability. For the latter, an uninformative prior assigning equal weight to warming and cooling would probably be most suitable here. The former is a bit tricky, and that is precisely the problem with saying "the existence of the hiatus is obvious."
I am sorry, I do nothing of that sort. You asked a question about whether something exists and it turned out that you have a different meaning (or, maybe, context) for that word than I envisioned. So I am asking you what do you mean by "exists" -- not about the optimal methods of inference.
Given your comment, I think what you are asking is not whether the hiatus exists (as I use the word), but rather whether the warming has stopped -- or maybe whether our confidence in the current climate models is not as high as it used to be.
Again, yes you are, because you're asking about inferring some property (the hiatus e.g. relative slowdown in increase of global surface temperatures) from the data, not directly about the data (which is only a function mapping points in time to instantaneous temperature recordings and by itself says nothing about trends). One way of calculating a trend is simply smoothing/windowing and taking the derivative, and then saying 'a hiatus is happening if the derivative is this close to zero'. That is a kind of inference, although not the kind that I would personally use for data like this.
What you are talking about is also probabilistic inference in the strictest sense, because the confidence in your estimate of existence of the hiatus depends directly on how much data you have. In this case, only a few years' worth --- if you had 100 years' worth of data to go on, a much stronger estimate could be made. Conversely, if you had only 1-2 years of data, then no such hiatus would be 'apparent' even if it was occurring.