Six Sigma Deviation for Antarctic Sea Ice

The specific heat of water is 4.190 J/(g°C). It means that it takes 4.190 Joules to heat 1 g of water by 1°C.

The same energy that melts 0°C ice to 0°C water will raise 0°C water to 79.7°C which is more than three quarters of the way to boiling.

We're facing higher and higher amounts of trapped energy as a result of increasing thermal insulation from CO2 ( and more and more from methane and water vapor ).

While we have large amounts of ice as a buffer that energy has a sink that won't drastically* raise near surface tempretures.

As the ice caps dwindle more and more energy goes towards greater and greater rates of tempreture increase.

The earth is a large system with lots of inertia. When some aspect of it changes relatively quickly, it's natural to be uneasy. Particularly when the downstream consequences of that change are already becoming visible.

To wit CO2 levels over the last 1my.

https://www.epa.gov/climate-indicators/climate-change-indica...

The notion of increased CO2 leading to higher atmospheric heat capture dates back to at least 1856:

https://static1.squarespace.com/static/5a2614102278e77e59a04...

So the combination of well-established science connecting CO2 to atmospheric heat, a relatively massive increase in CO2, and the emergeng changes predicted by a atmospheric heat increase - that is a valid reason for concern.

> We want the poles to be livable.

If it was possible to make this happen in isolation, maybe it would be a desirable goal. Given that this is not possible, it has to be viewed in context of the costs it also imposes. More severe weather is part of it, but also loss of both coastline due to sealevel rise and inland areas that become unlivably hot or newly unsuitable for agriculture.

> These changes are absolutely unpredictable and the models these researchers use are so heavily interlinked and layered one small mistake in the foundational data can skew the entire output. Almost as if thats how you lie with statistics...

Here you make the other sort of error - the error where you throw up complexity as a smokescreen to distract from the potential need to take action. If you're waiting for a complete set of totally accurate predictive models before you're willing to take action, you'll be waiting for a long time.

> Even so, I want Greenland to be habitable within the next 100 years. Even at the expense of coast line.

Have you consulted the people that live on the coastline? It doesn't take a whole lot of sea level rise before you're displacing hundreds of millions of people. (Not to mention the other associated costs of higher atmospheric energy.)

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.

[1] https://en.m.wikipedia.org/wiki/Central_limit_theorem