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Comment by _alternator_ | Hacker News Reader

With a headline like that the graph should be at the beginning. Usually you'd put it up front and then talk about the graph, not put it at the bottom and talk about how people aren't talking about it.

Anyway, I scrolled down to the graph and skipped the text. We are currently 4 std deviations above the mean with respect to El Niño temperature.

But there's also a historical line -4 std deviations from the mean. Was that an eventful year too? I can't tell and the graph is at such low resolution that the source URL isn't visible. If the graph and data is so important, shouldn't we care more about presentation? This is either super sloppy or deliberate obfuscation.

Look, I'm all for good reporting on climate. This just doesn't feel like it.

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I didn't find the source URL unreadable. It's here: https://climatereanalyzer.org/clim/sst_daily/?dm_id=nino3.4 The lowest ones were 1988 and 1989.

Fair, I had to click into the full size (I pinch zoomed on my phone). But now the fact that 1988/1989 are outliers raise a few more questions about the variance calculation. In particular, the title suggests that those weren't included in the variance calculation.

A 3.5-sigma event is a 1-in-1000 chance. Yet we have 2 of them in our data set that goes back only 40ish years? Something is suspect. Maybe I'm not accounting for the likelihood of a random walk excursion probability, but if a 3.5 sigma event isn't a 1-in-1000 event I have difficulty interpreting.

Why are you assuming gaussianity?

The tails could be heavy, it's true. This could make my usual 1-in-1000 heuristic overly conservative. Let's go with that, heavy tails. The data shows we are at something between a 1-in40 and 1-in-20 deviation. Normally (pun intended) that's ~2-sigma deviation. So... I guess it doesn't really seem like an epoch defining El Niño.

I think a 3.5-sigma event being a 1/1000 probability is only under the assumption of random data - a constant climate would deliver more-or-less random data around some mean.

The appearance of two 3.5-sigma events suggests that the data is no longer being randomly generated around that mean, but is instead drifting away from that mean.

More concretely, if all the data were within 2-sigma, then you could invoke the null-hypothesis and refute climate change. Beyond 2-sigma (and even worse two 3.5-sigma events) refuting climate change becomes (much) less reasonable.

> The appearance of two 3.5-sigma events suggests that the data is no longer being randomly generated around that mean, but is instead drifting away from that mean.

Not if the two 3.5-sigma events are in opposite directions, as in this case.

> ... opposite directions ...

Yes, I'd forgotten that detail. Still, though, chaotic bifurcation could be present with Il Niño and La Niña driving high & low extremes. Same mean, maybe, but with a bimodal distribution?

The event in this case is a daily observation, so in the last 45 years there have been 16k events. Under Gaussian assumption, 3.5 sigma outliers in positive direction are 1 in ~4,000 events, in either direction 1 in ~2,000, so approx. 8 days would be outliers in the recorded period. Judging by the looks, it's pretty close. And the data is absolutely not Gaussian: at best it's the result of a Gaussian process, as the daily recordings are not independent, but dependent on the previous day.

The graph represents 44 years of statistics, and shouldn't be dismissed out of hand. But you can not determine how these stats relate to longer term time frames such as a 100 years or more. Obviously the article wants you to believe the absolute worst about how these modern indicators compare back even further than the data shows, but how much of that is confirmation bias, and how much is actual science?

confirmation bias, how?

they gradually swapped in biased thermometers, or the thermometers were always biased to start showing temperature rise around 2026? what motivates thermometers to commit confirmation bias?

By choosing what data to show. There is a lot of data to choose from in climate science. How do you think we end up with such strong arguments on the issue? Cherry picking is as big a problem in climate science as anything else.

a lot of those thermometers have been operating for years or decades, in order to cherry pick each year again, you'd have to censor traces that were still used the year before, so at least such cherry picking should leave measurable traces

can you identify cherry-picked "retired thermometers" from this dataset and earlier ones by the same source(s)?

I believe they were responding to your "confirmation bias, how?" question, rather than the narrative around thermometers. So from that lens, the confirmation bias is choosing to show temperature (objectively measured with assumed-reliable instruments / thermometers), but arguably choosing to hide other kinds/categories of data.

Fwiw, I am not backing them up, since I do not know what other data they might be referring to (and I am not very knowledgeable in topics within climate science).

You're surprised in the context of global warming that the physical quantity of temperature was selected? The temperature quantity itself was cherry-picked?

Does anyone besides you expect them to demonstrate global warming but please without mentioning temperature - that's obviously cherry-picked of course... /s

1 - the area was cherry picked. 2 - only this year's data is highlighted in red - the data from last year wasn't highlighted. It's hard to know if this is a trend or it's just a weird year. 3 - the time horizon is cherry picked. Why mean on a given day rather than the mean for a week?

Imagine someone else cherry picks the line that is really low and highlights it. Or they pick a different area which shows cooling. Now you have some idiot claiming global cooling.

The thing about cherry picking is that it works so well. When someone pointed out the cherry picked data, you couldn't even come to the conclusion it was cherry picked. And you are on hacker news. Imagine the really really silly people.

It seems like you are ignoring the hundreds of other signals that tell a similar story to this graph?

Not ignoring anything, just commenting on what the graph says as a singular piece of evidence. And it's "the hundreds of other signals that tell a similar story" that can lead to confirmation bias (ie. following a narrative arc) and potentially interpreting this piece of evidence more strongly than it actually deserves.

Ah yes, the classic case of additional independent evidence making the claim less likely! How could I forget…

See, you're part of the problem. This perfectly demonstrates the point of confirmation bias. You've already decided what the evidence shows, so you don't need to analyze it independently and objectively. "We already know what it means! Stop thinking!"

I don't feel you are engaging me in good faith. You are misinterpreting what I'm saying. Good luck to you.

I had to assume there must be ads displayed before the "payoff"- luckily, wouldn't know

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