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Using machine learning to understand climate change

利用机器学习探索气候变化

关键词:
来源:
EurekAlert
全文链接1:
http://agri.ckcest.cn/topic/downloadFile/e9b7dd1a-0283-4215-b4c7-62b2450d4b06
全文链接2:
https://www.eurekalert.org/pub_releases/2019-10/uor-uml100819.php
类型:
前沿资讯
语种:
英语
原文发布日期:
2019-10-08
摘要:
Methane is a potent greenhouse gas that is being added to the atmosphere through both natural processes and human activities, such as energy production and agriculture. To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs--both natural and human--as well as the outputs. They also need to know how long methane resides in the atmosphere. To help develop this understanding, Tom Weber, an assistant professor of earth and environmental sciences at the University of Rochester; undergraduate researcher Nicola Wiseman '18, now a graduate student at the University of California, Irvine; and their colleague Annette Kock at the GEOMAR Helmholtz Centre for Ocean Research in Germany, used data science to determine how much methane is emitted from the ocean into the atmosphere each year. Their results, published in the journal Nature Communications, fill a longstanding gap in methane cycle research and will help climate scientists better assess the extent of human perturbations. The study is part of Weber's effort to use data science to better understand how various greenhouse gases, including nitrogen and carbon dioxide, affect global climate systems.
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