Neven published this post in the ASIB :
http://neven1.typepad.com/blog/2018/08/aerosols-and-arctic-sea-ice-loss.htmlwhich is essentially a summary of an article in the Guardian, it which he was quoted :
https://www.theguardian.com/environment/climate-consensus-97-per-cent/2018/aug/03/pollution-is-slowing-the-melting-of-arctic-sea-ice-for-nowThe interesting claim from that article is this one :
So how much of an effect do aerosols have? It turns out 23% of the warming caused by greenhouse gases was offset by the cooling from aerosols.
I always like to check the science on such claims, and after Michael Sweet in the ASIB comment section found a free copy of the paper (Mueller et al 2018), I decided to review it :
https://dspace.library.uvic.ca/bitstream/handle/1828/7669/Mueller_Bennit_MSc_2016.pdf?sequence=1I admit that I know very little about aerosols, and have not been following the literature about it.
So it may very well be that the better informed people on this fine forum find all of the following rather boring. But for me, it was pretty exciting and educative.
What I really wanted to know was how they determined that aerosols had a significant impact on Arctic Sea Ice decline.
Overall, I find the paper extremely thorough, and well argued.
I especially like their careful and formal handling of uncertainty in the data, and I learned a lot just reading the methods they use.
At the core, their method is pretty straightforward : They use CMIP5 GCM simulations of ALL, GHG only and NAT only forcings and use (multi-variable) linear regression to tease out these signals from the observed SIE over the 1953-2012 period. Something like this :
SIEobs. = βoant*SIEoant + βnat*SIEnat + βghg*SIEghg
where SIEnat is the CMIP5 simulation of SIE (Sea Ice Exent) with Natural forcings only, SIEghg is the CMIP5 simulation of SIE with well mixed GHGs only, and SIEoant is the CMIP5 simulation of SIE with "everything else" (which is mostly aerosols).
In CMIP5, there is no "OANT" simulation, so they use OANT = ALL - GHG - NAT. Which makes sense. Just remember that OANT is basically "everything else" that is not GHG or Natural driven. That's mostly aerosols, but not exclusively.
SIEobs is the observed Arctic Sea Ice extent in September.
For SIEobs, they use three different SIE data sets : HadISST2 (which is a bit dated), Walsh and Chapman (WC) which is a great dataset, which we extensively discussed in the comment section here :
http://neven1.typepad.com/blog/2016/01/september-arctic-sea-ice-extent-1935-2014.htmland a new dataset by Piron and Pasalodos (PP) which I did not know about before and will certainly take a look at, especially since they date back to 1933.
PP and WC are apparently very similar for the 1953-2012 period that Mueller et al used.
The β's are scaling factors.
Here it gets interesting.
If a β factor is close to unity (1), that suggests that the simulation is very consistent with the actual observed SIE. If a β factor is much different from 1, there may be something fishy going on. For example, if a β factor is close to 0, the signal is not detected at all. That would mean the simulated signal is not detectable in real live observations. If the β factor is much bigger than 1, there may be more causes for the signal in the observed data set than the simulations suggest.
Now just keep that in mind for a moment, because I will get back to that.
In my opinion, the real impressive part (the awe factor) in this paper is the way in which they deal with uncertainties. They have truly set up a Detection and Attribution mechanism, where the calculate formally how the uncertainties in the estimations propagate through the system. And there are many uncertainties to deal with : uncertainties in the GHG / aerosol / NAT forcings, uncertainties in the modeling of their effect on Arctic SIE, the uncertainties in the SIE record etc etc.
There are several formal statistical methods they use (like regularized optimal fingerprinting (ROF), and the residual consistency test (RCT)) that I can learn from, and could apply to my own method of predicting SIE in September based on earlier (June) data :
https://forum.arctic-sea-ice.net/index.php/topic,103.msg162418.html#msg162418When they apply these methods, the signals for GHG increases, Natural forcing and OANT (mostly aerosols) clearly are present in all 3 SIE data sets. They all come out of the noise, with a 90% certainty. That's impressive.
So overall, I really like this paper.
The only question I have is regarding the β factor they obtain for OANT (everything else but GHG and NAT forcings). I attached the results, from Figure 3.3 in the paper.
This suggests that the OANT signal has a β factor of about 1.7 or 1.8. That means that the OANT (aerosols mostly) signal shows up 1.7 to 1.8 stronger in the actual SIE record than the simulations suggest. So either aerosols have a much stronger effect on SIE than simulations suggest, or there is another signal present in reality (maybe something like land snow-cover or so) which is similar to the aerosol, which is there in reality, but is not properly taken into account by the GCM CMIP5 simulations.
Also, I don't see the 23% number from the Guardian anywhere in the paper.
All I see is a 30% number (from the conclusions) :
OANT has offset about 30% of the decline that would have been
expected in the absence of OANT forcing due to the combined climate response from
GHG and NAT forcing.
I suspect that the difference (23% versus 30%) is caused by the fact that aerosols do not fully cover the OANT (everything except for GHG and NATural) forcings.
So, overall a great paper, with the notion that maybe they overestimated the influence of aerosols on Arctic Sea Ice extent by a factor of 1.7 - 1.8.