Collapse

3240 readers

1 users here now

We have moved to https://lemm.ee/c/collapse -- please adjust your subscriptions

This is the place for discussing the potential collapse of modern civilization and the environment.

Collapse, in this context, refers to the significant loss of an established level or complexity towards a much simpler state. It can occur differently within many areas, orderly or chaotically, and be willing or unwilling. It does not necessarily imply human extinction or a singular, global event. Although, the longer the duration, the more it resembles a ‘decline’ instead of collapse.

RULES

1 - Remember the human

2 - Link posts should come from a reputable source

3 - All opinions are allowed but discussion must be in good faith.

4 - No low effort posts.

Related lemmys:

founded 4 years ago

MODERATORS

[email protected]

Frequency of heat days may be systematically underestimated in many studies (phys.org)

submitted 8 months ago by [email protected] to c/[email protected]

1 comments fedilink hide all child comments

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 1 points 8 months ago

https://www.nature.com/articles/s41467-024-46349-x

Pitfalls in diagnosing temperature extremes

Lukas Brunner & Aiko Voigt

Nature Communications volume 15, Article number: 2087 (2024)

Abstract

Worsening temperature extremes are among the most severe impacts of human-induced climate change. These extremes are often defined as rare events that exceed a specific percentile threshold within the distribution of daily maximum temperature. The percentile-based approach is chosen to follow regional and seasonal temperature variations so that extremes can occur globally and in all seasons, and frequently uses a running seasonal window to increase the sample size for the threshold calculation. Here, we show that running seasonal windows as used in many studies in recent years introduce a time-, region-, and dataset-depended bias that can lead to a striking underestimation of the expected extreme frequency. We reveal that this bias arises from artificially mixing the mean seasonal cycle into the extreme threshold and propose a simple solution that essentially eliminates it. We then use the corrected extreme frequency as reference to show that the bias also leads to an overestimation of future heatwave changes by as much as 30% in some regions. Based on these results we stress that running seasonal windows should not be used without correction for estimating extremes and their impacts.