“Suitability of climate models” is a… complex subject. I don’t think there is a short and easy answer other than that most models overstate the certainty of their conclusions.
Whether the hiatus (“pause”) exists is a much easier question. Just take a look at temperature plots for the last 50 years or so and check what your eyes tell you :-)
Just take a look [...] and check what your eyes tell you :-)
This is the same procedure that leads a lot of people to lose a lot of money trying to pick stocks, and a lot of other people to believe in the efficacy of prayer. Human eyes attached to human brains are very good at seeing patterns that aren’t really there.
I am not a climate scientist and haven’t looked at the data in detail. But, for what it’s worth, when I eyeball the plots what I see is a highly noisy time series whose last 10 years or so do indeed look cooler than trend but not so much so that I’d want to rule out random variation as a sufficient explanation. And at least some people who have looked at the data in detail have arrived at the same conclusion; see e.g. passive_fist’s second and third links.
I don’t know whether those people are right, but what they say seems to me obviously credible enough that saying “just eyeball the data, it’s obvious” is really bad advice.
[EDITED to add:] Maybe what’s actually going here is different interpretations of “the hiatus exists”. It seems fairly uncontroversial that (e.g.) the slope of the least-squares straight line fit to mean surface temperature from 1998 to 2013 is somewhat smaller than that of a corresponding fit from 1983 to 1998. (Though IIUC this is in fact what the NOAA guys are calling into question.) On the other hand, it’s obvious from, er, eyeballing the data that whatever medium-to-long-term trends there may be are overlaid with a lot of short-term variation, and the question is: does that fact about slopes of 15-year fits actually give us reason to think that whatever processes are responsible for the longer-term increase in measured temperatures have stopped or slowed or otherwise changed? The answer to that could well be negative, and my own impression on eyeballing the data is that it probably is negative, and that’s what I mean by saying it’s doubtful whether there has actually been a hiatus.
Maybe what’s actually going here is different interpretations of “the hiatus exists”
“Hiatus exists” is a simple and plain phrase. I think you just confused yourself by trying to add a lot of meaning to this phrase (in particular, whether “random variation” might or might not be a sufficient explanation).
“just eyeball the data, it’s obvious” is really bad advice
You are confusing observation and interpretation. Having said that, I’m a big fan of the Intraocular Trauma significance test :-)
… which can mean at least two reasonable things: (1) a particular set of measurements stopped increasing so fast; (2) the underlying process stopped or slowed. It seems clear that #2 is the more interesting of these.
I’m a big fan of the Intraocular Trauma significance test
(Interocular: between the eyes.) It makes for a good soundbite, but I don’t think it’s usually the best criterion. There’s a reason why fancier and more objective significance tests have been developed!
It seems clear that #2 is the more interesting of these
It also seems clear that we don’t have a good handle on the underlying process so claims about what it does or does not should not be expressed in plain and simple phrases.
I don’t think it’s usually the best criterion
I didn’t say it was—I said I liked it. Fancier significance tests are fancier, but also easier to trick oneself with.
claims about what it does or does not should not be expressed in plain and simple phrases
It appears that from this you draw the conclusion that any given plain and simple phrase can and should and will be clearly understood to refer to something easier to make such claims about with confidence. I draw a different conclusion: we shouldn’t make claims with plain and simple phrases that are liable to be understood in terms of things we don’t have a good handle on.
easier to trick oneself with
I am not at all convinced. It is very, very easy to trick oneself into seeing patterns that aren’t there, and they will quite often appear to hit you between the eyes. Have a look at some random noise:
These are twelve randomly generated datasets with statistics crudely resembling those of the global warming data from 1960 to 2014. None of them has any sort of hiatus in the underlying process; they’re all ramp + white noise. I’d say at least half have “hiatuses” inflicting at least as much interocular trauma as the actual global mean surface temperature graph’s “hiatus” does.
If you have MATLAB you can generate similar graphs yourself:
n=55; f=7.5; x=1:n; for i=1:12; y=(1:n)+f*randn(1,n); subplot(3,4,i); plot(x, y, 'r-'); bestj=0; bestm=1; bestk=0; for j=1:(n-14); x1=j:(j+14); y1=y(x1); c=lscov([0*x1'+1 x1'], y1'); if c(2)<bestm; bestj=j; bestm=c(2); bestk=c(1); end; end; if bestm<=0.5; x1=bestj+(0:14); hold on; plot(x1,bestk+bestm*x1,'b-','LineWidth',3); hold off; end; end;
(This only plots the 15-year trend lines when the gradient over those 15 years is ⇐ half the underlying gradient. You will notice that in my plots, every subplot has a trend line plotted. Yours probably will too.)
For a better simulation of the interocular trauma from actual climate data, I did the same as above but after finding the best “hiatus” in the 55-year data I extended the data on the left (same ramp, same-distribution white noise) to give us 55 years with that “hiatus” at the end. Here are the results:
I reckon that numbers 4,5,7,8,9,10,12 are about as impressive as the “hiatus” in the actual data. That’s just over half.
None of your plots satisfy my Interocular Trauma test (by the way, you’re right that it’s interocular, though the intraocular might be a Continental variation, coup d’oeil and all that :-D). Even the bright blue LOOK AT ME! lines don’t help.
And if we’re throwing pictures around and talking about “objective” statistical metrics, I give you the Anscombe’s quartet.
None of your plots satisfy my Interocular Trauma test
Several of them are as convincing to me as the “hiatus” in the actual temperature data.
Anscombe’s quartet
I’m familiar with Anscombe’s quartet, but what’s its relevance here? I mean, I take it you’re saying something more sensible than “Knowing a few statistics computed from a dataset may tell you far less than everything there is to know about it; therefore we should judge whether or not global warming has slowed or stopped by eyeballing the graph rather than applying any statistical tests”, but what?
Nope. I’m merely convinced that the existence of the hiatus in the measured temperatures isn’t very strong evidence of anything beyond itself. Very similar effects can be produced by noise; therefore seeing such an effect isn’t good evidence of anything more than noise. Of course it might have some more interesting cause, but if want to see better evidence to be convinced that it does.
Eh?
The trouble with merely pointing at things and saying “Behold!” rather than making an actual argument is that teen your readers need to guess what argument you’re hinting at. In this case the best guess I could come up with seemed unlikely, which is why I wrote “I take it you’re saying something more sensible than …, but what?”. Perhaps you might explain what you did have in mind?
I’m merely convinced that the existence of the hiatus in the measured temperatures isn’t very strong evidence of anything beyond itself.
So, in this thread, who are you arguing against? Did someone say “this hiatus certainly means X”?
The trouble with merely pointing at things and saying “Behold!” rather than making an actual argument
If you were to bother looking at the start of this subthread, you would have seen that the original issue was
the disagreement was just over the existence of a recent hiatus in land-ocean surface temperature warming
Questions about existence are adequately answered by merely pointing at things and saying “Behold!”
I have a feeling you are searching for an opponent who would claim something along the lines of “The hiatus is a incontrovertible proof that global warming isn’t happening” and are disappointed that such an opponent is unwilling to present himself.
who are you arguing against? [...] just over the existence of a recent hiatus in land-ocean surface temperature warming
OK, so let’s try to be really clear about this. I suggest that there are three possible claims here. GRAPH: “if you look at the temperature graph, its gradient is lower circa 2005 than circa 1990”. SIGNIFICANT: “GRAPH, and furthermore the difference in gradients is too large to be adequately explained by noise”. MECHANISM: “SIGNIFICANT, and furthermore the best explanation is that something has changed in whatever underlying warming phenomenon may have been going on”.
What were the original questions at issue? Well, in passive_fist’s comment three papers (one “pro-hiatus”, two “anti-hiatus” are cited. The first argues for SIGNIFICANT and suggests two possible explanations, one of which is MECHANISM. The second argues both against GRAPH (it claims that the data need adjusting) and against SIGNIFICANT (it points out that the reduction gets smaller if you include the latest data, including the very warm 2014, and if you don’t start at the cherry-picked El Niño year of 1998). The third argues against SIGNIFICANT on the basis that if you do the statistics right there isn’t actually evidence for a reduction in warming, and explains that the question is important because of possible implications for MECHANISM.
So it doesn’t look to me as if the question was only ever about GRAPH.
Now, perhaps you were only ever talking about GRAPH. But if so, your comments were (I’m sorry to have to say) entirely irrelevant to the points actually at issue.
I have a feeling [...]
Nope, nothing of the sort. Sorry to be less made-of-straw than you might like.
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.
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.
The ‘existence’ of a ‘trend’ or ‘hiatus’ in general time series data is part of the map, not the territory.
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.
More generally, what can I say about a time series that you would consider to be territory and not map?
That f(t) = x.
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.
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.”
What you’re really asking about is an inference method
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.
which isn’t so. You are asking about inferring some property, and I’m asking about the meaning of the words you are using.
However, getting to the meat of the issue, I’d like to make two points.
Point one is distinguishing between sample statistics and estimates of the parameters of the underlying process. In our case we have an underlying process (warming, let’s say we define it as the net energy balance of the planet integrated over a suitable interval) which we cannot observe directly, and some data (land and ocean temperatures) which we can.
The data that we have is, in statistical terminology, a sample and we commonly try to figure out properties of the underlying process by looking at the sample that we have. The thing is, sample statistics are not random. If I have some data (e.g. a time series of temperatures) and I calculate its mean, that mean is not a random variable. The probability of it is 1 -- we observed it, it happened. There is no inference involved in calculating sample means, just straight math. Now, if you want estimates of a mean of the underlying process, that’s a different issue. It’s going to be an uncertain estimate and we will have to specify some sort of a model to even produce such and estimate and talk about how likely it is.
In this case, when I’m talking about the hiatus as a feature of the data, it’s not a probabilistic, there is nothing to infer. But if you want to know whether there is a hiatus in the underlying process of global warming, it’s a different question and much more complicated, too.
Point two is more general and a bit more interesting. It’s common to think in terms of data and models: you have some data and you fit some models to it. You can describe your data without using any models—for example, calculate the sample mean. However as your description of data grows more complex, at some point you cross a (fuzzy) line and start to talk about the same data in terms of models, implied or explicit. Where that fuzzy line is located is subject to debate. For example, you put that line almost at the end of the spectrum when you say that the only thing we can say about a time series without involving models or inferences is that x=f(t) and that’s all. I find that not very useful and my line is further away. I’m not claiming any kind of precision here, but a full-blown ARIMA representation of a time series I would call a model, and something like an AR(1) coefficient would be right on the boundary: is it just a straightforward math calculation, or are you fitting an autoregressive model to the time series?
If there’s something wrong with the article, it seems like you should be able to say what it is rather than making insinuations about one of its authors.
(Lewandowsky is strongly disliked by those whose position on global warming differs from the mainstream scientific consensus, no doubt. So far as I can tell he doesn’t have a reputation for dishonesty or incompetence among groups without a strong motivation to put him down.)
“Suitability of climate models” is a… complex subject. I don’t think there is a short and easy answer other than that most models overstate the certainty of their conclusions.
Whether the hiatus (“pause”) exists is a much easier question. Just take a look at temperature plots for the last 50 years or so and check what your eyes tell you :-)
This is the same procedure that leads a lot of people to lose a lot of money trying to pick stocks, and a lot of other people to believe in the efficacy of prayer. Human eyes attached to human brains are very good at seeing patterns that aren’t really there.
I am not a climate scientist and haven’t looked at the data in detail. But, for what it’s worth, when I eyeball the plots what I see is a highly noisy time series whose last 10 years or so do indeed look cooler than trend but not so much so that I’d want to rule out random variation as a sufficient explanation. And at least some people who have looked at the data in detail have arrived at the same conclusion; see e.g. passive_fist’s second and third links.
I don’t know whether those people are right, but what they say seems to me obviously credible enough that saying “just eyeball the data, it’s obvious” is really bad advice.
[EDITED to add:] Maybe what’s actually going here is different interpretations of “the hiatus exists”. It seems fairly uncontroversial that (e.g.) the slope of the least-squares straight line fit to mean surface temperature from 1998 to 2013 is somewhat smaller than that of a corresponding fit from 1983 to 1998. (Though IIUC this is in fact what the NOAA guys are calling into question.) On the other hand, it’s obvious from, er, eyeballing the data that whatever medium-to-long-term trends there may be are overlaid with a lot of short-term variation, and the question is: does that fact about slopes of 15-year fits actually give us reason to think that whatever processes are responsible for the longer-term increase in measured temperatures have stopped or slowed or otherwise changed? The answer to that could well be negative, and my own impression on eyeballing the data is that it probably is negative, and that’s what I mean by saying it’s doubtful whether there has actually been a hiatus.
“Hiatus exists” is a simple and plain phrase. I think you just confused yourself by trying to add a lot of meaning to this phrase (in particular, whether “random variation” might or might not be a sufficient explanation).
You are confusing observation and interpretation. Having said that, I’m a big fan of the Intraocular Trauma significance test :-)
… which can mean at least two reasonable things: (1) a particular set of measurements stopped increasing so fast; (2) the underlying process stopped or slowed. It seems clear that #2 is the more interesting of these.
(Interocular: between the eyes.) It makes for a good soundbite, but I don’t think it’s usually the best criterion. There’s a reason why fancier and more objective significance tests have been developed!
It also seems clear that we don’t have a good handle on the underlying process so claims about what it does or does not should not be expressed in plain and simple phrases.
I didn’t say it was—I said I liked it. Fancier significance tests are fancier, but also easier to trick oneself with.
It appears that from this you draw the conclusion that any given plain and simple phrase can and should and will be clearly understood to refer to something easier to make such claims about with confidence. I draw a different conclusion: we shouldn’t make claims with plain and simple phrases that are liable to be understood in terms of things we don’t have a good handle on.
I am not at all convinced. It is very, very easy to trick oneself into seeing patterns that aren’t there, and they will quite often appear to hit you between the eyes. Have a look at some random noise:
These are twelve randomly generated datasets with statistics crudely resembling those of the global warming data from 1960 to 2014. None of them has any sort of hiatus in the underlying process; they’re all ramp + white noise. I’d say at least half have “hiatuses” inflicting at least as much interocular trauma as the actual global mean surface temperature graph’s “hiatus” does.
If you have MATLAB you can generate similar graphs yourself:
(This only plots the 15-year trend lines when the gradient over those 15 years is ⇐ half the underlying gradient. You will notice that in my plots, every subplot has a trend line plotted. Yours probably will too.)
For a better simulation of the interocular trauma from actual climate data, I did the same as above but after finding the best “hiatus” in the 55-year data I extended the data on the left (same ramp, same-distribution white noise) to give us 55 years with that “hiatus” at the end. Here are the results:
I reckon that numbers 4,5,7,8,9,10,12 are about as impressive as the “hiatus” in the actual data. That’s just over half.
None of your plots satisfy my Interocular Trauma test (by the way, you’re right that it’s interocular, though the intraocular might be a Continental variation, coup d’oeil and all that :-D). Even the bright blue LOOK AT ME! lines don’t help.
And if we’re throwing pictures around and talking about “objective” statistical metrics, I give you the Anscombe’s quartet.
Several of them are as convincing to me as the “hiatus” in the actual temperature data.
I’m familiar with Anscombe’s quartet, but what’s its relevance here? I mean, I take it you’re saying something more sensible than “Knowing a few statistics computed from a dataset may tell you far less than everything there is to know about it; therefore we should judge whether or not global warming has slowed or stopped by eyeballing the graph rather than applying any statistical tests”, but what?
So are you convinced that the “hiatus” is just an artifact of noise in the data?
Eh? Where is this lovely piece coming from?
Nope. I’m merely convinced that the existence of the hiatus in the measured temperatures isn’t very strong evidence of anything beyond itself. Very similar effects can be produced by noise; therefore seeing such an effect isn’t good evidence of anything more than noise. Of course it might have some more interesting cause, but if want to see better evidence to be convinced that it does.
The trouble with merely pointing at things and saying “Behold!” rather than making an actual argument is that teen your readers need to guess what argument you’re hinting at. In this case the best guess I could come up with seemed unlikely, which is why I wrote “I take it you’re saying something more sensible than …, but what?”. Perhaps you might explain what you did have in mind?
So, in this thread, who are you arguing against? Did someone say “this hiatus certainly means X”?
If you were to bother looking at the start of this subthread, you would have seen that the original issue was
Questions about existence are adequately answered by merely pointing at things and saying “Behold!”
I have a feeling you are searching for an opponent who would claim something along the lines of “The hiatus is a incontrovertible proof that global warming isn’t happening” and are disappointed that such an opponent is unwilling to present himself.
OK, so let’s try to be really clear about this. I suggest that there are three possible claims here. GRAPH: “if you look at the temperature graph, its gradient is lower circa 2005 than circa 1990”. SIGNIFICANT: “GRAPH, and furthermore the difference in gradients is too large to be adequately explained by noise”. MECHANISM: “SIGNIFICANT, and furthermore the best explanation is that something has changed in whatever underlying warming phenomenon may have been going on”.
What were the original questions at issue? Well, in passive_fist’s comment three papers (one “pro-hiatus”, two “anti-hiatus” are cited. The first argues for SIGNIFICANT and suggests two possible explanations, one of which is MECHANISM. The second argues both against GRAPH (it claims that the data need adjusting) and against SIGNIFICANT (it points out that the reduction gets smaller if you include the latest data, including the very warm 2014, and if you don’t start at the cherry-picked El Niño year of 1998). The third argues against SIGNIFICANT on the basis that if you do the statistics right there isn’t actually evidence for a reduction in warming, and explains that the question is important because of possible implications for MECHANISM.
So it doesn’t look to me as if the question was only ever about GRAPH.
Now, perhaps you were only ever talking about GRAPH. But if so, your comments were (I’m sorry to have to say) entirely irrelevant to the points actually at issue.
Nope, nothing of the sort. Sorry to be less made-of-straw than you might like.
Irrelevant to the debate you were having inside your mind, probably. Unfortunately, I was not part of it.
Do you, seriously, think you are being reasonable in this discussion?
I am the very embodiment of reasonableness, am I not? :-P
OK, tapping out now. (By the way, none of the downvotes you’ve received in this thread come from me.)
I’m not afraid of a long and hard answer, if you have one.
Looking at the official data released by NASA, there is no warming hiatus.
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.
To start with, there is some confusion—you say
which isn’t so. You are asking about inferring some property, and I’m asking about the meaning of the words you are using.
However, getting to the meat of the issue, I’d like to make two points.
Point one is distinguishing between sample statistics and estimates of the parameters of the underlying process. In our case we have an underlying process (warming, let’s say we define it as the net energy balance of the planet integrated over a suitable interval) which we cannot observe directly, and some data (land and ocean temperatures) which we can.
The data that we have is, in statistical terminology, a sample and we commonly try to figure out properties of the underlying process by looking at the sample that we have. The thing is, sample statistics are not random. If I have some data (e.g. a time series of temperatures) and I calculate its mean, that mean is not a random variable. The probability of it is 1 -- we observed it, it happened. There is no inference involved in calculating sample means, just straight math. Now, if you want estimates of a mean of the underlying process, that’s a different issue. It’s going to be an uncertain estimate and we will have to specify some sort of a model to even produce such and estimate and talk about how likely it is.
In this case, when I’m talking about the hiatus as a feature of the data, it’s not a probabilistic, there is nothing to infer. But if you want to know whether there is a hiatus in the underlying process of global warming, it’s a different question and much more complicated, too.
Point two is more general and a bit more interesting. It’s common to think in terms of data and models: you have some data and you fit some models to it. You can describe your data without using any models—for example, calculate the sample mean. However as your description of data grows more complex, at some point you cross a (fuzzy) line and start to talk about the same data in terms of models, implied or explicit. Where that fuzzy line is located is subject to debate. For example, you put that line almost at the end of the spectrum when you say that the only thing we can say about a time series without involving models or inferences is that x=f(t) and that’s all. I find that not very useful and my line is further away. I’m not claiming any kind of precision here, but a full-blown ARIMA representation of a time series I would call a model, and something like an AR(1) coefficient would be right on the boundary: is it just a straightforward math calculation, or are you fitting an autoregressive model to the time series?
http://www.nature.com/articles/srep16784
Lewandowsky has an… interesting reputation.
If there’s something wrong with the article, it seems like you should be able to say what it is rather than making insinuations about one of its authors.
(Lewandowsky is strongly disliked by those whose position on global warming differs from the mainstream scientific consensus, no doubt. So far as I can tell he doesn’t have a reputation for dishonesty or incompetence among groups without a strong motivation to put him down.)
I haven’t read the article, just glanced at the front page, saw the name of the lead author and thought “Hmm… that name looks familiar”.