{"title":"用深度学习探索气候的未来","authors":"Alaa Al Khourdajie","doi":"10.1038/s41558-025-02350-w","DOIUrl":null,"url":null,"abstract":"Glancing forward to view alternative futures for limiting global warming requires understanding complex societal–environmental systems that drive future emissions. Now a study explores the potential, and limits, of deep learning to generate core characteristics of these futures.","PeriodicalId":18974,"journal":{"name":"Nature Climate Change","volume":"27 1","pages":""},"PeriodicalIF":29.6000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring climate futures with deep learning\",\"authors\":\"Alaa Al Khourdajie\",\"doi\":\"10.1038/s41558-025-02350-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Glancing forward to view alternative futures for limiting global warming requires understanding complex societal–environmental systems that drive future emissions. Now a study explores the potential, and limits, of deep learning to generate core characteristics of these futures.\",\"PeriodicalId\":18974,\"journal\":{\"name\":\"Nature Climate Change\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":29.6000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Climate Change\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1038/s41558-025-02350-w\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Climate Change","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1038/s41558-025-02350-w","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Glancing forward to view alternative futures for limiting global warming requires understanding complex societal–environmental systems that drive future emissions. Now a study explores the potential, and limits, of deep learning to generate core characteristics of these futures.
期刊介绍:
Nature Climate Change is dedicated to addressing the scientific challenge of understanding Earth's changing climate and its societal implications. As a monthly journal, it publishes significant and cutting-edge research on the nature, causes, and impacts of global climate change, as well as its implications for the economy, policy, and the world at large.
The journal publishes original research spanning the natural and social sciences, synthesizing interdisciplinary research to provide a comprehensive understanding of climate change. It upholds the high standards set by all Nature-branded journals, ensuring top-tier original research through a fair and rigorous review process, broad readership access, high standards of copy editing and production, rapid publication, and independence from academic societies and other vested interests.
Nature Climate Change serves as a platform for discussion among experts, publishing opinion, analysis, and review articles. It also features Research Highlights to highlight important developments in the field and original reporting from renowned science journalists in the form of feature articles.
Topics covered in the journal include adaptation, atmospheric science, ecology, economics, energy, impacts and vulnerability, mitigation, oceanography, policy, sociology, and sustainability, among others.