Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab4e55 |
Machine learning and artificial intelligence to aid climate change research and preparedness | |
Huntingford, Chris1; Jeffers, Elizabeth S.2; Bonsall, Michael B.2; Christensen, Hannah M.3; Lees, Thomas4; Yang, Hui1,5 | |
2019-12-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2019 |
卷号 | 14期号:12 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Peoples R China |
英文摘要 | Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature 566 195?204, Schneider et al 2017 Geophys. Res. Lett. 44 12396?417). Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations. |
英文关键词 | climate change global warming extreme weather drought artificial intelligence machine learning climate simulations |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000499949100001 |
WOS关键词 | CALCIUM-OXALATE CRYSTALS ; INVERSE PROBLEMS ; SHIP TRACKS ; PRECIPITATION ; SYSTEM ; LAND ; RESOLUTION ; SENSITIVITY ; AEROSOL ; COVER |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/224719 |
专题 | 环境与发展全球科技态势 |
作者单位 | 1.Ctr Ecol & Hydrol, Wallingford OX10 8BB, Oxon, England; 2.Univ Oxford, Dept Zool, South Parks Rd, Oxford OX1 3PS, England; 3.Univ Oxford, Atmospher Ocean & Planetary Phys, Dept Phys, Clarendon Lab, Oxford OX1 3PU, England; 4.Univ Oxford, Sch Geog & Environm, South Parks Rd, Oxford OX1 3QY, England; 5.Peking Univ, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100871, Peoples R China |
推荐引用方式 GB/T 7714 | Huntingford, Chris,Jeffers, Elizabeth S.,Bonsall, Michael B.,et al. Machine learning and artificial intelligence to aid climate change research and preparedness[J]. ENVIRONMENTAL RESEARCH LETTERS,2019,14(12). |
APA | Huntingford, Chris,Jeffers, Elizabeth S.,Bonsall, Michael B.,Christensen, Hannah M.,Lees, Thomas,&Yang, Hui.(2019).Machine learning and artificial intelligence to aid climate change research and preparedness.ENVIRONMENTAL RESEARCH LETTERS,14(12). |
MLA | Huntingford, Chris,et al."Machine learning and artificial intelligence to aid climate change research and preparedness".ENVIRONMENTAL RESEARCH LETTERS 14.12(2019). |
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