Evaluation and prediction of the effects of planetary orbital variations to earth's temperature changes
文献类型： 外文期刊
作者： Cao, Mengmeng 1 ; Mao, Kebiao 1 ; Bateni, Sayed M. 2 ; Chen, Jing M. 4 ; Heggy, Essam 6 ; Kug, Jong-Seong 8 ; Shen, Xinyi 9 ;
作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing, Peoples R China
2.Univ Hawaii Manoa, Dept Civil & Environm Engn, Honolulu, HI USA
3.Univ Hawaii Manoa, Water Resources Res Ctr, Honolulu, HI USA
4.Fujian Normal Univ, Sch Geog Sci, Fuzhou, Peoples R China
5.Univ Toronto, Dept Geog & Planning, Toronto, ON, Canada
6.Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA USA
7.CALTECH, Jet Prop Lab, Pasadena, CA USA
8.Pohang Univ Sci & Technol, Div Environm Sci & Engn, Pohang, South Korea
9.Univ Wisconsin Milwaukee, Sch Freshwater Sci, Milwaukee, WI USA
关键词： Planetary motion; Earth's temperature; Greenhouse gases
期刊名称：INTERNATIONAL JOURNAL OF DIGITAL EARTH （ 影响因子：4.9； 五年影响因子：4.7 ）
ISSN： 1753-8947
年卷期： 2025 年 18 卷 1 期
页码：
收录情况： SCI
摘要： The influence of planetary orbital changes on Earth's temperature has been poorly quantified and subject to speculation. Here, we delineated the effects of greenhouse gases and planetary orbital changes on Earth's temperature and forecasted the latter. Our results indicate that Earth's revolution around the Sun and its rotation explain similar to 75.36% and 15.91% of Earth's temperature variations in one year, while the Moon's revolution around the Earth and other planet motions account for 8.26% and 0.26%, respectively. Orbital forcings contributed similar to 11.5% global warming since 1837 and will continue to warm the Earth by similar to 0.13 degrees C from 2020 to 2027. However, orbital forcings may contribute to similar to 0.25 degrees C cooling of Earth from 2027 to 2050, but this effect remains insufficient to offset the warming caused by CO2
- 相关文献
作者其他论文 更多>>
-
Joint optimization of AI large and small models for surface temperature and emissivity retrieval using knowledge distillation
作者：Dai, Wang;Guo, Zhonghua;Xiao, Liurui;Dai, Wang;Mao, Kebiao;Qin, Zhihao;Shi, Jiancheng;Bateni, Sayed M.
关键词：Artificial intelligence; Large models; Knowledge distillation; Automated machine learning; Remote sensing parameter retrieval
-
A Time-Constrained and Spatially Explicit AI Model for Soil Moisture Inversion Using CYGNSS Data
作者：Yang, Changzhi;Yang, Changzhi;Mao, Kebiao;Shi, Jiancheng;Guo, Zhonghua;Bateni, Sayed M.;Bateni, Sayed M.
关键词：Data models; Satellites; Vegetation mapping; Soil moisture; Reflectivity; Monitoring; Mathematical models; Accuracy; Land surface; Microwave theory and techniques; Cyclone-GNSS (CYGNSS); global navigation satellite system-reflectometry (GNSS-R); soil moisture (SM); soil moisture active passive (SMAP); time-constrained and spatially explicit artificial intelligence (TCSE-AI) model
-
An AI-Based Nested Large-Small Model for Passive Microwave Soil Moisture and Land Surface Temperature Retrieval Method
作者：Liang, Mengjie;Meng, Fei;Mao, Kebiao;Mao, Kebiao;Shi, Jiancheng;Bateni, Sayed M.;Bateni, Sayed M.
关键词：passive microwave remote sensing; soil moisture; land surface temperature; multi-hierarchy classification; joint iterative retrieval
-
A normal form for synchronous land surface temperature and emissivity retrieval using deep learning coupled physical and statistical methods
作者：Wang, Han;Mao, Kebiao;Mao, Kebiao;Shi, Jiancheng;Bateni, Sayed M.;Bateni, Sayed M.;Altantuya, Dorjsuren;Sainbuyan, Bayarsaikhan;Bao, Yuhai
关键词：Land surface temperature (LST); Land surface emissivity (LSE); Retrieval; Deep learning (DL); Physical and statistical methods
-
Review of GNSS-R Technology for Soil Moisture Inversion
作者：Yang, Changzhi;Mao, Kebiao;Guo, Zhonghua;Yang, Changzhi;Mao, Kebiao;Yuan, Zijin;Shi, Jiancheng;Bateni, Sayed M.;Bateni, Sayed M.
关键词：remote sensing; GNSS-R; soil moisture; machine learning; transfer learning
-
A General Paradigm for Retrieving Soil Moisture and Surface Temperature from Passive Microwave Remote Sensing Data Based on Artificial Intelligence
作者：Mao, Kebiao;Wang, Han;Mao, Kebiao;Mao, Kebiao;Wang, Han;Shi, Jiancheng;Heggy, Essam;Heggy, Essam;Wu, Shengli;Bateni, Sayed M. M.;Bateni, Sayed M. M.;Du, Guoming
关键词：deep learning; geophysical logical reasoning; interleaved iterative optimization; soil moisture; land surface temperature; collaborative retrieval
-
Granulation-based LSTM-RF combination model for hourly sea surface temperature prediction
作者：Cao, Mengmeng;Mao, Kebiao;Mao, Kebiao;Du, Yongming;Mao, Kebiao;Bateni, Sayed M.;Bateni, Sayed M.;Jun, Changhyun;Shi, Jiancheng;Du, Guoming;Mao, Kebiao
关键词：SST prediction; adaptive granulation method; LSTM; RF; error reciprocal method
