Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1038/s41561-020-0582-5 |
Artificial intelligence reconstructs missing climate information | |
Kadow, Christopher1,2; Hall, David Matthew3; Ulbrich, Uwe2 | |
2020-06-01 | |
发表期刊 | NATURE GEOSCIENCE |
ISSN | 1752-0894 |
EISSN | 1752-0908 |
出版年 | 2020 |
卷号 | 13期号:6页码:408-+ |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; USA |
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 degrees 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 Nino 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. An artificial intelligence-based method may infill gaps in historical temperature data more effectively than conventional techniques. Application of this method reveals a stronger global warming trend between 1850 and 2018 than estimated previously. |
领域 | 地球科学 ; 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000539293600005 |
WOS关键词 | COUPLED MODEL ; SYSTEM MODEL ; VARIABILITY ; CMIP5 ; EARTH ; SIMULATION ; EPISODE |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
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/273358 |
专题 | 地球科学 气候变化 |
作者单位 | 1.German Climate Comp Ctr DKRZ, Hamburg, Germany; 2.Free Univ Berlin, Inst Meteorol, Berlin, Germany; 3.NVIDIA, Santa Clara, CA USA |
推荐引用方式 GB/T 7714 | Kadow, Christopher,Hall, David Matthew,Ulbrich, Uwe. Artificial intelligence reconstructs missing climate information[J]. NATURE GEOSCIENCE,2020,13(6):408-+. |
APA | Kadow, Christopher,Hall, David Matthew,&Ulbrich, Uwe.(2020).Artificial intelligence reconstructs missing climate information.NATURE GEOSCIENCE,13(6),408-+. |
MLA | Kadow, Christopher,et al."Artificial intelligence reconstructs missing climate information".NATURE GEOSCIENCE 13.6(2020):408-+. |
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