Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural networkOA
Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network
Hai-Xia XIAO;Xi LIU;Rong YU;Bin YAO;Feng ZHANG;Ya-Qiang WANG
Key Laboratory of Transportation Meteorology of China Meteorological Administration,Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041,ChinaKey Laboratory of Transportation Meteorology of China Meteorological Administration,Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041,ChinaState Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,ChinaKey Laboratory of Transportation Meteorology of China Meteorological Administration,Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210041,ChinaShanghai Qi Zhi Institute,Shanghai 200232,ChinaDepartment of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China
Global and ocean surface temperatureAttribution analysisMulti-layer perceptron neural networkInternal climate variabilityAnnual and interannual timescales
Global and ocean surface temperatureAttribution analysisMulti-layer perceptron neural networkInternal climate variabilityAnnual and interannual timescales
《气候变化研究进展(英文版)》 2022 (4)
459-472,14
This study was supported by the Special Funds for Basic Research Fund of the Chinese Academy of Meteorological Sciences(2020Z011,2021Y010 and 2021Y005).We are grateful to three anonymous reviewers and editors for their constructive comments.We thank Dr.Xu-Dong Wang from Fudan University for his helpful suggestions and Sklearn(https://scikit-learn.org/stable/)for providing the machine learning algorithms.
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