Comment by timr
7 hours ago
Setting aside the names of the authors, this is a very bad paper. They take temperature data sets, "adjust" [1] them by attempting to remove the biggest recent factors (volcanism, solar and el nino cycles) affecting temperatures, then do a piece-wise regression analysis to look at trends in 10-year chunks. This is just bad methodology, akin to what a junior graduate student with a failing thesis might do to find signal in a dataset that isn't being cooperative to their hypothesis.
Climate data is inherently noisy, and there are multiple interconnected cyclic signals, ranging from the "adjusted" factors to cycles that span decades, which we don't understand at all. "Adjusting" for a few of these, then doing a regression over the subset of the data is classic cherry-picking in search of a pre-determined conclusion. The overall dubious nature of the conclusion is called out in the final paragraph of the text:
> Although the world may not continue warming at such a fast pace, it could likewise continue accelerating to even faster rates.
They're literally just extrapolating from an unknown point value that they synthesized from data massage, and telling you that's a coin toss as to whether the extrapolation will be valid.
I am not a climate scientist so you can ignore me if you like, but I am "a scientist" who believes the earth is warming, and that we are the primary cause. Nonetheless, if I saw this kind of thing in a paper in my own field, it would be immediately tossed in the trash.
[1] You can't actually adjust for these things, which the authors admit in the text. They just dance around it so that lay-readers won't understand:
> Our method of removing El Niño, volcanism, and solar variations is approximate but not perfect, so it is possible that e.g. the effect of El Niño on the 2023 and 2024 temperature is not completely eliminated.
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. The resulting time series fits a super-linear curve -> accelerating global warming.
> 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.
This has always been the big issue I have with the conclusions draw in climate publications. I encourage anyone with strong opinion on climate change to do a deep dive on the temperature analysis.
The best example I can think of is the "global warming hiatus" that was discussed in depth in the top climate journals in the mid-2010s. Nature Climate Change even devoted an entire month to it.[1]
5 years later publications were saying "there was no hiatus at all".[2]
And as you said, when you dive into the paper, you realize that temperature measures are not objective at all. And I would ask - If everyone was in agreement that temperature increases paused, then 5 years later everyone agrees they didn't, how much confidence do we really have in the measures themselves.*
As someone who conudcted scientific research, this has a ton of inherent problems. It doesn't matter what I'm measuring, if the data collection is not objective, and there is no consensus (or at least trong evidence for adjustments), then the data itself is very unreliable.
If I tried to publish a chemical paper in a top journal and manually went in and adjusted data (even with a scientific rationale) the paper would be immediately rejected.
[1] https://www.nature.com/collections/sthnxgntvp [2] https://www.sciencenews.org/article/global-warming-pause-cli...
> And as you said, when you dive into the paper, you realize that temperature measures are not objective at all.
I don't know if I'd go that far. The measurements are as objective as they can be given the limits of technology and time, but what we do with the datasets afterward is usually filled with subjective decisions. In the worst cases, you get motivated actors doing statistically invalid analysis to reach a preferred conclusion.
This happens in every field of science, but it's often worse in fields that touch politics.
I think research ranges from this paper to ones more rigorous, but the problem of "adjustments" is consistent.
And the issue is not so much the research is being done, but rather how it's reported on. Scientists know the limits of rigor in climate science, but the public doesn't. So catastrophic predictions are viewed by the public as a sure thing, versus one particular prediction with wide error bares.
> This happens in every field of science, but it's often worse in fields that touch politics.
Indeed. Nobody plays fast and lose with papers on the structure of some random enzyme for political purposes.