Six Sigma Deviation for Antarctic Sea Ice(twitter.com) |
Six Sigma Deviation for Antarctic Sea Ice(twitter.com) |
(I’m just commenting to compel someone to correct me and expand on this subthread really :) )
> so it is typically safe to use when you know little about the real distribution.
That was what the quants doing risk assessment at the big banks thought pre-2008, which is the other context I associate with the n-sigma notation for probability
The last few years have been further and further from the [1981 - 2010] "normal flux cycle".
This may help visualise the data better:
https://nsidc.org/arcticseaicenews/charctic-interactive-sea-...
( click the antartic button to switch poles; the default is to grey the thrirty year normal data band and to only show last year and this year, other choices can be made by selection on the data table )
Don't Look Up was right. We're dumb idiots of a species.
Unfortunately the people who can actually do anything about this lack the parts of their brain that could ever hope to understand.
They say once in 7.5 million years, so why don’t they show the previous 7.5 million years?
If that's not possible, and we only have 1989-2023 data, then the 7.5 million year comment is particularly ridiculous, as is making any generalization with data for only 0.0001% of the time span in question.
Antarctic sea ice levels dive in 'five-sigma event' (abc.net.au)
https://www.abc.net.au/news/2023-07-24/antarctic-sea-ice-lev...
34 points by adrian_mrd 1 day ago https://news.ycombinator.com/item?id=36839757
has it a bit over five sigma and easily less than six.
Still hella significant.
But the interpretations given to 34 years of highly-correlated time-series data are highly questionable and largely unwarranted.
Put briefly, we have very little long-term global remote-sensing data largely because remote sensing didn't exist until the 1960s, and largely came of age long after then. As with this data series which begins in 1989.
We *ABSOLUTELY DO* have many other long-term data series showing tremendous changes and associated climatic conditions: ice-core data going back 800,000 years, sea-level measurements going back a billion+ years (at which point plate tectonics are a major confounding factor), plant growth distributions and patterns dating back millions of years, and global temperature inferences also dating back on the order of a billion years or more.
And yes, assuming a normal distribution, a six-sigma event is extraordinarily rare. But to make accurate inferences of such extreme-outlier events based on 34 measurements is statistical malpractice.
Call this "unprecedented in the data record". Call it "extremely concerning". Find other data series with which this pattern can be correlated and from which stronger inferences might be drawn.
(Note that ice-field extant data before the age of satellite observation are very thin, though outlier events such as bergs being sited in temperate waters might well occur, and that shipping logs do tend to record numerous events of interest and date back about 500 years over a fairly wide area. Indigenous records from, say, Tierra del Fuego might also note sitings over a longer period.)
Sources: three years of stats courses at uni, work in stats and data reporting professionally and at an amateur level, though not an actual statistician.
( https://www.nature.com/articles/s41598-022-05449-8 ) Persistent extreme ultraviolet irradiance in Antarctica despite the ozone recovery onset ?
That is an Earth change I could sink my teeth into because our measurements of ozone are first rate, the crisis continues and Antarctica is a known target of the phenomenon.
To show this, reader, try to do a small experiment yourself - something your interested in - but make it really, really small. Can be testing products you use often to see which is the best for you.
It is REALLY hard! And it's extremely easy for people to pick it apart. "Oh, you know more than the guys who wrote the standards?" "I've been using that for years, you're just using it wrong" "Here's a picture of me doing xyz it works fine"
They spent two seconds, you almost certainly spent days and days on your really small test.
This is why you won't see people back up these claims. They didn't spend time or effort getting there, and they won't spend that time or effort backing them up.
So taking it apart, given some restrictions, any sum of randomly distributed data is gaussian distributed.
If you take an average of some value. E.g. sea ice extent at fixed date x, e.g. January 1st every year, you have a sum distribution.
So you're not talking about the random distribution of any date, but of the random distribution of the average value. Only this has the Gaussian distribution.
Now there are some restrictions IID - independently identically distributed. This is the part the quants got wrong. Identical distribution is usually not the issue, we can, for a certain timespan, assume that the random distribution stays roughly the same.
But independent was the issue. If one event is correlated with the next, the central limit theorem may hold for a bit, but if the correlation is, too extreme will break down, like in the quant models of yore.
Their estimates for the housing market risk were ok as long as the credit defaults were not highly correlated, but as soon as the crisis started some vicious cycles formed between the foreclosures and tumbling house prices causing more foreclosures.
The models broke down.
Back to the ice sheats, if we assume the melting of the ice sheats won't increase (or decrease) the melting of the ice sheats we're good. I don't know about causative mechanisms here, but it could be that the models do break down in these times of extreme change.
That doesn't mean that the extreme change is nothing to worry about, since only by being extreme might it break the models.