[timr]

> Your summary of the article is wrong. The authors model temperature using time series over solar irradiance, volcanic activity, and southern oscillation. They calibrate that model using time series over global surface temperatures. This allows them to isolate and remove each of the three listed confounding factors.

No, it isn’t. You’re just rephrasing what I said with more words: they attempted to adjust for three of the biggest factors that affect temperature, then did a piecewise regression to estimate a 10-year window.

You can’t do it in a statistically valid way. Full stop. The authors admit this, but want you to ignore it.

[cbility]

> You can't do it in a statistically valid way.

They use an established methodology (https://doi.org/10.1088/1748-95 9326/6/4/044022 - the methodology retains the average warming rate over the period since 1970 while smoothing fluctuations) to remove predictable temperature variations so they can isolate the effect they are trying to measure.

Just because they don't know exactly what past global temperatures would have been in the absence of El Niño doesn't mean it's statistically invalid to try and account for it.

Besides, temperature data to 2024 already shows accelerated warming with a confidence level that "exceeds 90% in two of the five data sets".

Add another year or two and it's likely we won't even need to smooth the curve to show accelerated warming at 95% confidence.

[timr]

They used a published methodology. That doesn't mean the methodology is uncontroversial, and it certainly doesn't mean that they used it in a way that makes sense in the current context. One can commit an almost infinite number of horrible abuses via bog-standard linear regression.

Even setting aside the dubious nature of the adjustments, doing a regression on a 10-year window of a system that we know has multi-decade cycles -- or longer -- is just blatantly trying to dress up bad point extrapolations as science. Then, when they don't get the results they want to see from that abuse, they start subtracting the annoying little details in the data that are getting in their way.

> Just because they don't know exactly what past global temperatures would have been in the absence of El Niño doesn't mean it's statistically invalid to try and account for it.

You can't go back in time, invent counterfactual histories by subtracting primary signals, and declare the net result to be "significant". This isn't even statistics -- it's just massaging data via statistical tools.

> Besides, temperature data to 2024 already shows accelerated warming with a confidence level that "exceeds 90% in two of the five data sets".

https://xkcd.com/882/

> Add another year or two and it's likely we won't even need to smooth the curve to show accelerated warming at 95% confidence.

I guess we'll find out.

[cbility]

If you were trying to determine if the quantity of daylight increased over a week in spring, would you account for the differences caused by day and night? What about cloud cover? Or is that just massaging the data?

p.s. the cited methodology has >300 citations in peer reviewed publications, ref Web of Science

[timr]

> If you were trying to determine if the quantity of daylight increased over a week in spring, would you account for the differences caused by day and night? What about cloud cover? Or is that just massaging the data?

Just to draw a better analogy to the low quality of the current work, let's say you wanted to compare average daylight last week, globally, to all of recorded history. Then you made a model that had terms for (say) astronomical daylight, longitude, latitude and, I dunno...altitude of the measurement. Then you made a regression, subtracted three terms, and claimed that the residual was still "significantly darker". Then you run around waving your arms and shouting that if we only extrapolate forward N weeks from last week, soon we'll be living in a fully dark world!

You'd be rightfully laughed out of any room you were in.

[cbility]

I think you are missing my point, and the point of the article: they are demonstrating that global temperature change that is not driven by volcanism, solar variation or El Niño is (in all likelihood, given the data) accelerating. They can do this because the effects of volcanism, solar variation and El Niño on global temperature can all be predicted from external measurements.

[bjourne]

Actually, I used fewer words. I don't think you understand what the authors are doing. They are modeling temperature T per year as a sum of four terms: T = E + S + V + R---(E)l Nino, (S)solar irradiance, (V)olcanic activity, and (R)emaining factors. Then they subtract E, S, and V. Then they show that R fits a super-linear curve. Why there would be no "statistically valid way" to do this is beyond me, the authors, and the article's peer reviewers. If this is "bad methodology", lodge your complaints on https://pubpeer.com/.

[timr]

1) Their model is inherently dumb. The system is much more complicated and inseparable.

2) They openly admit that “subtracting E, S and V”, as you say, cannot actually be done.

3) They’re arbitrarily removing sources of variation so that they can claim “significance” in a narrow window. The entire exercise is designed to achieve a predetermined outcome, and statistical significance cannot be calculated in those circumstances.