After what feels like a very long hiatus, it seems that people are writing interesting stuff about climate sensitivity again. Just last week on Twitter I saw Andrew Dessler tweeting about his most recent manuscript which is up on ACP(D) for comment. My eyebrow was slightly raised at the range of values he found when analysing outputs of the MPI ensemble, 2.1 to 3.9K, until I realised that these were the outliers from their 100-member ensemble and eyeballing the histogram suggests the standard error on individual estimates (which I didn't see quoted) is around 0.5C or lower. Worth considering, but not a show-stopper in the context of other uncertainties we have to deal with. It would, I think, be interesting to consider whether more precise estimates can be calculated with a more comprehensive use of the data, such as by fitting a simple model to the time series rather than just using the difference between two snapshots. Which, coincidentally (or not) is something I might have more to talk about in the not too distant future.
Then just today, a new paper using interannual variability as an emergent constraint. By chance I bumped into one of the authors last week in Leeds so had a good idea what was coming but have not had time to consider in much detail. (The nature paper is paywalled but sci-hub.hk has a copy already.) Here's a screenshot of the main analysis for those who can't be bothered downloading it. The x-axis is a measure of interannual variability over the observational period, and the letters are CMIP models.
Using interannual variability to diagnose the equilibrium response has a somewhat chequered history, eg here and here for my previous posts though the links to the underlying papers are dead now so I've put the new ones here:
The central problem with the Schwartz approach is the strong (and wrong) assumption that the climate system has a single dominant time scale. It is easy to show (I may return to this in a future post) that the short time scale response simply cannot in principle directly constrain the equilibrium response of a two-time scale system. So this may be why the idea has not been followed up all that much (though in fact Andrew Dessler has done some work on this, such as this paper for example).
The latest paper gets round this by essentially using climate models to provide the link between interannual variability and equilibrium response. It remains possible that the models all get this wrong in a similar manner and thus the real climate system lies outside of their prediction, but this “unknown unknown” issue intrinsically applies to just about everything we ever do and isn't a specific criticism of this paper. My instinct is their result is probably over-optimistic and future work will find more uncertainties than they have presented, but that could just be a reflexive bias on my part. For example, it is not clear from what is written that they have accounted for observational uncertainty in their constraint, which (if they have not done) will probably bias the estimate low as uncorrelated errors will reduce their estimate of the real system's autocorrelation relative to the models where obs are perfect. There is also a hint of p-hacking in the analysis but they have done some quite careful investigation and justification of their choices. It will certainly provide an interesting avenue for more research.
12 comments:
If I understand your last sentence correctly, their current estimate is biased somewhat high and correcting for autocorrelation will lower their current estimate somewhat. Is that, more or less, correct?
I meant your next to last sentence. Sorry about that.
OK, I've taken a very quick look at the paper. so that their 'optimism' is with respect to their for a somewhat lower upper bound for ECS (e. g. less of a fat upper bound tail).
Sorry for any confusion on my part.
Precisely opposite to your first comment, I was speculating that the noise in the obs had pushed their autocorrelation down a bit which would lower their estimate of S. A quick calculation (which I've now done) suggests that this effect is negligible however.
Tried to read the paper and understand what they did, but not sure I do. Essentially they've defined a "variability metric" and tried to calculate equivalent values for that in models and real world global average surface temperature data.
Their Figure 2a plot seems to show that there was little disagreement or ECS correlation in their variability metric in the early 20th Century, but that quickly changes post-1950. Which indicates this is largely about differences in forced response, both to natural (mostly volcanic) and anthropogenic factors.
The difference between observational datasets is interesting. The NOAA analysis is an outlier on the high side, but then GISS indicates the smallest variability despite being essentially the same data with an infilled Arctic. The r^2 value for annual average anomalies between the two datasets is >0.99. It suggests their detrending procedure is quite sensitive and is a big factor in the results.
Agree and this is why I wondered about p-hacking, on the other hand their results don't look to be hugely sensitive to the details so long as they include the recent warming/forced period.
And on checking it seems unlikely that the uncorrelated obs errors matter, according to HadCRUT4 they are only about 0.02C per year which I think is too small to make a difference.
Actually, it is possible that they used NOAA and GISS data with different ERSST versions. The upgrades to v5 were happening around the same time that they submitted the paper. Even so, I don't think there's that much difference between v4 and v5.
Wonder what you'd get applying it to the recent SST reconstruction by Cowtan et al.? My suspicion, just looking at trajectories in comparison to the HadSST3 version and CMIP5 mean is that it would support significantly higher sensitivities.
"Precisely opposite to your first comment ..."
Yeah, I thought so. It took three tries, and still, the 3rd one still doesn't quite parse out correctly. Thanks.
The question I have is why this latest paper should be given more credibility than the seeming scores of emergent constraint papers lately. Some of the other ones if I recall found higher ECS in the 4C range. There are literally thousands of potential emergent properties of models. It seems to me like a fruitless exercise albeit one that is easy to perform and write a paper about. Of much more concern are some recent negative results showing for example that ECS of a GCM is quite sensitive to details of convection modeling and that there was no obvious observational constraints to set these parameters. Lots of work on aggregation too suggests that its important and absent in current models. Also low clouds in models seem to show incorrect time histories resulting in increased forcing at the surface. All of this is totally unsurprising but should be motivating an urgent search for answers.
Dessler et al looks to me like an interesting exercise perhaps about the deficiencies of the model studied but any real world implications are totally dependent on the skill of the model used.
So I'm not sure this recent uptick of papers has done much to help us in constraining ECS. Maybe that's your point too.
The ECS estimate in our paper was not intended to be a rigorous estimate; rather, it's just an example of what you can do with our framework. In fact, it’s an emergent constraint estimate, which, if you read my twitter feed, you’d know I’m not crazy about. To understand why it’s in there, you have to know something about the history of the paper. We had previously submitted a version that does not have the ECS estimate in it to another journal. The reviewers seemed confused about the utility of our revised energy balance framework and questioned what the point was. In response, we decided we had to better show what the potential uses were, so we put that short discussion of ECS in the paper. However, you’ll notice that we don’t cite those numbers in the abstract or the conclusions — that’s a signal that the values are for illustration and not to be considered an important result in our paper. We have another paper that is basically ready to submit that has a more rigorous ECS range (likely 2.4-4.5 K) based on our revised energy balance framework.
Getting out my handy Denier Rolodex, thumbing thorough it, ha ha, found it ...
https://en.wikipedia.org/wiki/List_of_fallacies
https://en.wikipedia.org/wiki/Divine_fallacy
Argument from incredulity, I see.
Arguments from incredulity are called non sequiturs.
So be it.
I believe Cox et al. used the Cowtan correction for 2m temperature warming faster than SST and filling of unobserved regions. Didn't make any difference, though. From some back of the enveloping, I also doubt the uncorrelated errors in observations will give you any appreciable bias.
No, it would seem to me the real assumption is whether or not models can be used for the calibration of the coefficients in the relationship between lag-1 autocorrelation and longterm ECS. Here I could see that there might be some unaccounted for biases, though I doubt they are really big.
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