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DOI | 10.1088/1748-9326/aae2be |
Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations | |
Nowack, Peer1,2,3; Braesicke, Peter4; Haigh, Joanna1,2; Abraham, Nathan Luke5,6; Pyle, John5,6; Voulgarakis, Apostolos2 | |
2018-10-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS
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ISSN | 1748-9326 |
出版年 | 2018 |
卷号 | 13期号:10 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Germany |
英文摘要 | A number of studies have demonstrated the importance of ozone in climate change simulations, for example concerning global warming projections and atmospheric dynamics. However, fully interactive atmospheric chemistry schemes needed for calculating changes in ozone are computationally expensive. Climate modelers therefore often use climatological ozone fields, which are typically neither consistent with the actual climate state simulated by each model nor with the specific climate change scenario. This limitation applies in particular to standard modeling experiments such as preindustrial control or abrupt 4xCO(2) climate sensitivity simulations. Here we suggest a novel method using a simple linear machine learning regression algorithm to predict ozone distributions for preindustrial and abrupt 4xCO(2) simulations. Using the atmospheric temperature field as the only input, the regression reliably predicts three-dimensional ozone distributions at monthly to daily time intervals. In particular, the representation of stratospheric ozone variability is much improved compared with a fixed climatology, which is important for interactions with dynamical phenomena such as the polar vortices and the Quasi-Biennial Oscillation. Our method requires training data covering only a fraction of the usual length of simulations and thus promises to be an important stepping stone towards a range of new computationally efficient methods to consider ozone changes in long climate simulations. We highlight key development steps to further improve and extend the scope of machine learning-based ozone parameterizations. |
英文关键词 | climate change climate sensitivity ozone parameterization machine learning big data climate modeling |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000447053100003 |
WOS关键词 | STRATOSPHERIC OZONE ; POLAR VORTEX ; ATMOSPHERIC CHEMISTRY ; QUADRUPLED CO2 ; CIRCULATION ; MODEL ; RADIATION ; FEEDBACK ; IMPACT ; CMIP5 |
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/26063 |
专题 | 气候变化 |
作者单位 | 1.Imperial Coll London, Grantham Inst Climate Change, London, England; 2.Imperial Coll London, Blackett Lab, Dept Phys, London, England; 3.Imperial Coll London, Data Sci Inst, London, England; 4.Karlsruhe Inst Technol, IMK ASF, Karlsruhe, Germany; 5.Natl Ctr Atmospher Sci, Leeds, W Yorkshire, England; 6.Univ Cambridge, Dept Chem, Cambridge, England |
推荐引用方式 GB/T 7714 | Nowack, Peer,Braesicke, Peter,Haigh, Joanna,et al. Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations[J]. ENVIRONMENTAL RESEARCH LETTERS,2018,13(10). |
APA | Nowack, Peer,Braesicke, Peter,Haigh, Joanna,Abraham, Nathan Luke,Pyle, John,&Voulgarakis, Apostolos.(2018).Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations.ENVIRONMENTAL RESEARCH LETTERS,13(10). |
MLA | Nowack, Peer,et al."Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations".ENVIRONMENTAL RESEARCH LETTERS 13.10(2018). |
条目包含的文件 | 条目无相关文件。 |
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