Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1038/s41467-020-15195-y |
Causal networks for climate model evaluation and constrained projections | |
Nowack, Peer1,2,3,4; Runge, Jakob1,5; Eyring, Veronika6,7; Haigh, Joanna D.1,2 | |
2020-03-16 | |
发表期刊 | NATURE COMMUNICATIONS
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ISSN | 2041-1723 |
出版年 | 2020 |
卷号 | 11期号:1 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Germany |
英文摘要 | Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections. |
领域 | 地球科学 ; 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000521328700001 |
WOS关键词 | ATMOSPHERIC TELECONNECTIONS ; ENSO TELECONNECTIONS ; CMIP5 ; PERFORMANCE ; ENSEMBLE ; UNCERTAINTY ; CIRCULATION ; FEEDBACKS ; METRICS ; IMPACT |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
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/249672 |
专题 | 资源环境科学 |
作者单位 | 1.Imperial Coll London, Grantham Inst, London SW7 2AZ, England; 2.Imperial Coll London, Fac Nat Sci, Dept Phys, London SW7 2AZ, England; 3.Imperial Coll London, Data Sci Inst, London SW7 2AZ, England; 4.Univ East Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England; 5.German Aerosp Ctr, Inst Data Sci, D-07745 Jena, Germany; 6.Deutsch Zentrum Luft & Raumfahrt DLR, Inst Phys Atmosphare, Muenchener Str 20, D-82234 Wessling, Germany; 7.Univ Bremen, Inst Environm Phys, D-28359 Bremen, Germany |
推荐引用方式 GB/T 7714 | Nowack, Peer,Runge, Jakob,Eyring, Veronika,et al. Causal networks for climate model evaluation and constrained projections[J]. NATURE COMMUNICATIONS,2020,11(1). |
APA | Nowack, Peer,Runge, Jakob,Eyring, Veronika,&Haigh, Joanna D..(2020).Causal networks for climate model evaluation and constrained projections.NATURE COMMUNICATIONS,11(1). |
MLA | Nowack, Peer,et al."Causal networks for climate model evaluation and constrained projections".NATURE COMMUNICATIONS 11.1(2020). |
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