Data/hora: 06/06/2024 03:36:12

Provedor de dados: 	5	
País: 	France	
Título: 	A novel probabilistic forecast system predicting anomalously warm 2018-2022 reinforcing the long-term global warming trend	
Autores: 	Sevellec, Florian; Drijfhout, Sybren S.	
Data: 	2018-08	
Ano: 	2018	
Resumo: 	In a changing climate, there is an ever-increasing societal demand for accurate and reliable interannual predictions. Accurate and reliable interannual predictions of global temperatures are key for determining the regional climate change impacts that scale with global temperature, such as precipitation extremes, severe droughts, or intense hurricane activity, for instance. However, the chaotic nature of the climate system limits prediction accuracy on such timescales. Here we develop a novel method to predict global-mean surface air temperature and sea surface temperature, based on transfer operators, which allows, by-design, probabilistic forecasts. The prediction accuracy is equivalent to operational forecasts and its reliability is high. The post-1998 global warming hiatus is well predicted. For 2018–2022, the probabilistic forecast indicates a warmer than normal period, with respect to the forced trend. This will temporarily reinforce the long-term global warming trend. The coming warm period is associated with an increased likelihood of intense to extreme temperatures. The important numerical efficiency of the method (a few hundredths of a second on a laptop) opens the possibility for real-time probabilistic predictions carried out on personal mobile devices.	
Tipo: 	Text	
Idioma: 	Inglês	
Identificador: 	https://archimer.ifremer.fr/doc/00454/56545/58254.pdf

https://archimer.ifremer.fr/doc/00454/56545/58255.pdf

DOI:10.1038/s41467-018-05442-8

https://archimer.ifremer.fr/doc/00454/56545/	
Editor: 	Nature Publishing Group	
Formato: 	application/pdf	
Fonte: 	Nature Communications (2041-1723) (Nature Publishing Group), 2018-08 , Vol. 9 , N. 1 , P. 12p.	
Fonte: 	The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the...

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