Applying big data beyond small problems in climate research

被引:46
|
作者
Knusel, Benedikt [1 ,2 ]
Zumwald, Marius [1 ,2 ]
Baumberger, Christoph [1 ]
Hadorn, Gertrude Hirsch [1 ]
Fischer, Erich M. [2 ]
Bresch, David N. [1 ,3 ]
Knutti, Reto [2 ]
机构
[1] Swiss Fed Inst Technol, Inst Environm Decis, Zurich, Switzerland
[2] Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, Zurich, Switzerland
[3] MeteoSwiss, Fed Off Meteorol & Climatol, Zurich, Switzerland
基金
瑞士国家科学基金会;
关键词
ARTIFICIAL NEURAL-NETWORK; THEORY-GUIDED-DATA; DROUGHT INDEX; PREDICTION; ALGORITHM; FUTURE; QUEENSLAND; WEATHER; PRECIPITATION; PATTERNS;
D O I
10.1038/s41558-019-0404-1
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to 'small problems', which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research.
引用
收藏
页码:196 / 202
页数:7
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