I'm finding it a little hard to work today with this in my head.
Antarctic ice extent is now 6.4 standard deviations below the mean. That is, I'm reliably told, a one in 13 billion year event.
We're about to see a lot of shit hit a lot of fans. And we are far from ready.
Business as usual is over. Politics as usual is over. We need to be putting our effort into building systems that can help us survive what greed and power and wilful blindness have wrought.
@TomQuinn yeah, it's a probability factor. The number is correct, and mind boggling.
IF all else were equal, the chances of this happening would be once in the age of the universe.
The point is, all else is not equal. As someone said on Bluesky, it's measuring the same ice in the same Antarctica, but the whole world is different.
@timhollo @TomQuinn Decimal place error?
6σ ~= once every 1.38 million years (given the time axis is daily events)
https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule#Table_of_numerical_values
@TomQuinn @timhollo using standard deviations to talk about this is wrong because the "probability distribution" we're measuring from is constantly changing, shifting in one direction
we'd have to have a model that corrects for the average change in average sea ice we already know about and looks at the extent with that effect already factored out in order for stddev "1 in x years" statements to make sense
@TomQuinn Based on a normal distribution and standard deviations, ~13 billion years is indeed what the maths say, if the trend is taken to be annual. (Divide by 12 for months, or by 365.25 for days.)
The problem as I've pointed out here https://toot.cat/@dredmorbius/110773687361166325, is that the data series is far too short to be drawing conclusions at this span. We're seeing 34 years of highly-correlated time-series data.
It absolutely is alarming enough as it stands. But @timhollo's post greatly oversells it.
@[email protected] Not discounting the severity of this data at all ... Keep in mind that using standard deviations to measure for expected occurrence rate relies on both a normal distribution and some defined sampling basis. Both of these assumptions are ... challenged here. Should we be considering this as an *annual* event or a *monthly* one? And we *do* know that Antarctica, all of it, not merely the seas surrounding it, has been ice-free in the past. And that Earth has likely been entirely entombed in ice ("Snowball Earth"), probably multiple times. So the data record we *have* is at best partial. I'd be comfortable saying that this is far outside all previous measured data. I'd caution on drawing inferences *based on statistics alone*, and would strongly urge that previous geological / climactic *evidence* (and the associated atmospheric and other conditions influencing climate) be considered. Stats get really hairy on the thin margins, especially with comparatively small samples. We're looking at *34 years* of data here, not hundreds, thousands, millions, or billions. In statistics, n ~=30 is just where "large sample" methods start becoming applicable, but typically *not* for drawing inferences at the 1:1,000,000,000 scale.
@timhollo Not discounting the severity of this data at all ...
Keep in mind that using standard deviations to measure for expected occurrence rate relies on both a normal distribution and some defined sampling basis.
Both of these assumptions are ... challenged here. Should we be considering this as an annual event or a monthly one?
And we do know that Antarctica, all of it, not merely the seas surrounding it, has been ice-free in the past. And that Earth has likely been entirely entombed in ice ("Snowball Earth"), probably multiple times. So the data record we have is at best partial.
I'd be comfortable saying that this is far outside all previous measured data. I'd caution on drawing inferences based on statistics alone, and would strongly urge that previous geological / climactic evidence (and the associated atmospheric and other conditions influencing climate) be considered.
Stats get really hairy on the thin margins, especially with comparatively small samples. We're looking at 34 years of data here, not hundreds, thousands, millions, or billions. In statistics, n ~=30 is just where "large sample" methods start becoming applicable, but typically not for drawing inferences at the 1:1,000,000,000 scale.
@stooovie Absolutely, I'm not contesting that AT ALL.
The point I'm drawing is of using a very limited amount of highly-correlated time-series data to extrapolate over 10 billion+ years. That's simply ... indefensible.
We have other data series which are sufficiently, and quite genuinely, alarming: atmospheric CO2 levels from ice core data, correlated geological evidence of atmospheric temperatures, ancient sea levels (parallel to your comment, a huge number of pre-modern human / hominid settlements are now under water due to previous sea-level rise), patterns of plant growth and distribution (particularly from lake sediments), etc., etc. etc.
All of which are really solid data.
One problem with many present highly-relevant measures especially those based on remote-sensing data is that they extend back at most to the early 1960s and satellite observations, many far more recently than that (e.g., this series, to 1989). The current awareness these measurements give is extremely valuable, and the fact that we can now make measurements over the entire planet (and hence avoid objections based on potential local human influences as with, say, urban heat-island effects).
But as a long-term data line ... not so much.
What would be useful is to correlate these data with other long-term measures, as has been done with some of the examples I've given above.
@timhollo I'm sure this will be the subject of this week's therapy session.
I don't know how we've come to this. I was at a science conference (#SciFoo) last week where a climate scientist was painting a slightly optimistic picture in terms of "We're no longer completely fucked, but we're not unfucked." - It felt reassuring to hear that but the cognitive dissonance was strong in the face of graphs like these.
I was a Greenpeace activist in the 90s. We knew. It achieved nothing. 😢
The massive funding behind climate denialism continues.
Malign influence campaigns funded by dark money from #KochNetwork alone is over $145 million USD as estimated by Greenpeace.
https://www.theguardian.com/commentisfree/2021/oct/27/climate-crisis-villains-americas-dirty-dozen
@_MillieRooney yeah, I know.
FWIW, my answer to you is keep doing what you’re doing, coz it’s so damn important
@timhollo
"We might end up in a new state. That would be quite concerning to the sustainability of human conditions on Earth, I suspect.
I think a lot of people have the time line too long out, saying this won't affect them. I'm pretty convinced that this is something my generation will experience."
- Dr Petra Heil, Australian Antarctic Program
https://www.abc.net.au/news/2023-07-24/antarctic-sea-ice-levels-nosedive-five-sigma-event/102635204
Antarctic sea ice has usually been able to recover in winter. But this time it's different, with levels taking a sharp downward turn at a time of year when sea ice usually forms reliably — and experts are worried.
@timhollo The standard deviation number, as well as the age-of-the-universe one, just say that the effect is not random. We know it's not random, we've known for a hundred years.
Over-dramatizing this only undermines the importance of the data, which is a shame, because it is important.
Well said (wrote)
Although, l do often feel like we are up against a sociopolitical & economic "brickwall" that simply echoes it's ignorance, climate apathy & moral grandstanding back at us.
However, as you inferred, due to their (generally rich folk that control society, often by using punitive regulations) ecological (environmental) neglect, the eco-degrading methods of business as usual is approaching that ecological "cliff" edge.
Time to mitigate for a softer landing
@timhollo From a rather morbid perspective I really enjoy watching Evolution at work!
Survival of the fittest doesn't mean those that go to the gym or those with the biggest amount of money.
Survival of the fittest means the species as a whole.
I'd really love to see the result of this huge experiment that we started! Into what the species homo sapiens will evolve.
But I assume that I won't be around to see that.
@timhollo Highly recommend this piece by @micefearboggis and linked to in the piece essay by @ZLabe for good context on #SeaIce and #statistics
Which is not to say it's not unusual or worrying but yeah ... we still don't know enough about #Antarctica and it's place in the #Climate system.
@Ruth_Mottram @micefearboggis @ZLabe yeah, thanks for that useful blog, and I totally get the point. I’m not personally a scientist, but have been working in the field of environmental and climate advocacy and activism for 25 years, and have a very solid understanding of the uncertainties, what we know we know and what we know we don’t know.
I guess my point here is that, if we were relying on this one data point to make statements that things are dire, that would be dumb and misleading.
But as one new data point in a gathering storm of data that is showing we’re rapidly tipping into completely new territory, it is a valid thing to look at with serious concern, I would have thought.
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