这篇文章发布于2026年6月7日，这是一个未来的时间点，表明这是一篇预测性内容。这个时间点对于理解文章中的预测和趋势分析很重要，但需要读者意识到这是前瞻性内容而非已发生的事件。

Met Office-Analyse: https://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/forecasts/co2-forecast-for-2025

Der CO₂-Gehalt der Atmosphäre wird 2024 weiter steigen, so dass die vom IPCC erarbeiteten Pfade, um das 1,5°-Ziel einzuhalten, nicht mehr eingehalten werden können. Das ergibt sich aus einer Studie des britischen Met Office, die sich auf die Daten des Mauna Loa-Observatoriums in Hawai stützt. (Die obere Grenze der Unsicherheitsbereiche dieser Pfade ist erreicht, selbst wenn der El-Niño-Einfluss abgezogen wird. Ein Einhalten der Pfade würde ein sofortiges Absinken des CO₂-Gehalts erfordern.) https://www.liberation.fr/environnement/climat-les-concentrations-de-co2-cette-annee-menacent-la-limite-de-15c-daugmentation-globale-des-temperatures-20240119_6JIALPQDBNADFGNHS4MVDXR5QA/?redirected=1

Animation: https://youtu.be/RYPDvTWDi0E?si=wWEUnypFxQO8M9D7

Bericht: https://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/forecasts/co2-forecast-for-2024

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My thoughts on Climatic Associations of British Species Distributions Show Good Transferability in Time but Low Predictive Accuracy for Range Change by Rapacciuolo et al. (2012).

It's interesting that the ensembles perform best overall but more poorly for predicting changes in occupancy. It seems possible that ensembling multiple methods is basically resulting in a more static prediction, i.e., something closer to a naive baseline.

This highlights the importance of comparing forecasts to baseline predictions to determine the skill of the forecast vs. the basic stability of the pattern.

This is a really important result and my favorite figure in this ms. For cells that changed occupancy status (e.g., a cell that has occupied at t_1 and was unoccupied at t_2) most models had about a 50% chance of getting the change right (i.e., a coin flip).

This is arguably the single most important point in this paper. It is equivalent to comparing forecasts to simple baseline forecasts as is typically done in weather forecasting. In weather forecasting it is typical to talk about the "skill" of the forecast, which is how much better it does than a simple baseline. In this case the the baseline is a species range that doesn't move at all. This would be equivalent to a "naive" forecast in traditional time-series analysis since we only have a single previous point in time and the baseline is simply the prediction based on this value not changing.