气候变化领域集成服务门户(CCPortal): Artificial intelligence reconstructs missing climate information

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DOI	10.1038/s41561-020-0582-5
	Artificial intelligence reconstructs missing climate information
	Kadow C.; Hall D.M.; Ulbrich U.
发表日期	2020
ISSN	17520894
起始页码	408
结束页码	413
卷号	13 期号:6
英文摘要	Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records. © 2020, The Author(s), under exclusive licence to Springer Nature Limited.
英文关键词	artificial intelligence; climate conditions; climate modeling; CMIP; global climate; nineteenth century; paleoclimate; paleotemperature; reconstruction
语种	英语
来源期刊	Nature Geoscience
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206797
作者单位	German Climate Computing Center (DKRZ), Hamburg, Germany; Institute of Meteorology, Freie Universität Berlin, Berlin, Germany; NVIDIA, Santa Clara, CA, United States
推荐引用方式 GB/T 7714	Kadow C.,Hall D.M.,Ulbrich U.. Artificial intelligence reconstructs missing climate information[J],2020,13(6).
APA	Kadow C.,Hall D.M.,&Ulbrich U..(2020).Artificial intelligence reconstructs missing climate information.Nature Geoscience,13(6).
MLA	Kadow C.,et al."Artificial intelligence reconstructs missing climate information".Nature Geoscience 13.6(2020).