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| DOI | 10.1016/j.accre.2022.06.001 |
| Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network | |
| Xiao H.-X.; Liu X.; Yu R.; Yao B.; Zhang F.; Wang Y.-Q. | |
| 发表日期 | 2022 |
| ISSN | 1674-9278 |
| 起始页码 | 459 |
| 结束页码 | 472 |
| 卷号 | 13期号:4 |
| 英文摘要 | Roles of internal climate variabilities regulating global and ocean temperature changes is a hot but complex issue of scientific concern, influencing the comprehensive policy-making in response to global and regional warming. In this study, the time series of monthly global and ocean mean surface temperature (GST and OST, respectively) since 1866 is successfully reconstructed via natural and anthropogenic forcing factors and internal climate variability by using a Multi-Layer Perceptron (MLP) neural network technique. The MLP demonstrates prominent monthly GST and OST reconstruction skills on both interannual and annual time scales. Most of the warming in GST and OST since 1866 is found to be attributable to anthropogenic forcing, while the multidecadal and interannual GST and OST variations are considerably dominated by Atlantic Multidecadal Oscillation (AMO). Internal climate variabilities like Interdecadal Pacific Oscillation (IPO) can amplify the GST and OST changes and explain the global warming slowdown since 1998. Southern Oscillation Index (SOI) performs a similar role as IPO but to a lesser extent. Changes in OST caused by solar forcing are more considerable than those in GST. Moreover, the ‘biased warmth’ during the Second World War is successfully reconstructed in MLP. AMO and IPO can explain most annual and even sub-annual temperature variations during this period, offering an explanation for the existence of this abnormal warm period other than that it was entirely caused by instrumental errors. The generally high accuracy of reconstructions on interannual and annual time scales can enhance the ability to monitor the prompt feedback of specific external radiative forcings and internal variabilities to changes in climate. © 2022 The Authors |
| 英文关键词 | Annual and interannual timescales; Attribution analysis; Global and ocean surface temperature; Internal climate variability; Multi-layer perceptron neural network |
| 语种 | 英语 |
| 来源期刊 | Advances in Climate Change Research
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/262048 |
| 作者单位 | Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing, 210041, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China; Shanghai Qi Zhi Institute, Shanghai, 200232, China; Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China; Institute of Artificial Intelligence for Meteorology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China |
| 推荐引用方式 GB/T 7714 | Xiao H.-X.； Liu X.； Yu R.； Yao B.； Zhang F.； Wang Y.-Q.. Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network[J],2022,13(4). |
| APA | Xiao H.-X.； Liu X.； Yu R.； Yao B.； Zhang F.； Wang Y.-Q..(2022).Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network.Advances in Climate Change Research,13(4). |
| MLA | Xiao H.-X.； Liu X.； Yu R.； Yao B.； Zhang F.； Wang Y.-Q.."Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network".Advances in Climate Change Research 13.4(2022). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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