Who's Shouting in the Mountains: a Person or the Wind?

Original: "On the attribution of weather events to climate change using a fit to extreme value distributions"
arXiv:2308.07560 · 2023-08-15 · CC BY 4.0 · 1 min
Nature can fake a human fingerprint in extreme weather.

In the mountains, an echo repeats every sound, making it hard to tell who shouted: a person or the wind. The same goes for weather disasters. Scientists want to know if humans are responsible for a heat wave or drought. They look for a link between local weather and the warming of the whole planet. But nature has its own fluctuations that can look like a human fingerprint.

The authors tested the standard method on three cases: the Siberian heat wave, the Australian fires, and the Madagascar drought. They took a computer climate model with no human influence and applied the same method to it. It turned out that even without humans, the model shows a strong link between local heat and global temperature. For Australia, for instance, the link was even stronger than in real observations.

Inside the climate there are natural fluctuations, such as the warm El Niño current. It changes weather around the world and can create a false human fingerprint.

This does not mean that humans do not affect the climate. They do, but for each individual event we need to be more careful. Previously, researchers thought that a detected link pointed straight to humans. Now it is clear: nature can fake this signal. So scientists propose additional checks. That way we won't mistake a person's voice for the echo of the wind.

Verkhoyansk, where 38 °C was recorded in 2020, is considered one of the cold poles of the Northern Hemisphere: in winter, temperatures there drop below −60 °C.

In Kim Stanley Robinson's novel 'The Ministry for the Future', heat in India becomes a catalyst for global action, but the attribution of a specific event remains a matter of debate, as in this paper.

\mu = \mu_0 + \alpha T
The location parameter shifts proportionally to the change in global temperature; the coefficient α indicates the sensitivity of extremes to warming.
GEV(x; \mu, \sigma, \xi) = \frac{1}{\sigma} t(x)^{\xi+1} e^{-t(x)}
Describes the statistics of block maxima, such as annual temperature maxima, with location, scale, and shape parameters.
GPD(x; u, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \frac{x-u}{\sigma}\right)^{-(1/\xi + 1)}
Describes values above a given threshold, used for distribution tails, such as extreme precipitation.
Concepts
precipitation anomaly interannual variability climate temperature anomaly monsoon Westerly wind burst probabilistic forecast global warming NOAA CPC GPCP natural variability IRI ERA5 forecast seasonal forcing annual cycle power law distribution random forest climate model velocity distribution