Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6249-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6249-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
23 Aug 2024
Model experiment description paper |
| 23 Aug 2024
| 23 Aug 2024
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Yangke Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Qing Bao
CORRESPONDING AUTHOR
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Bian He
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Xiaofei Wu
School of Atmospheric Sciences/Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China
Jing Yang
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yimin Liu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Guoxiong Wu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Siyuan Zhou
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yao Tang
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Ankang Qu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Yalan Fan
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Anling Liu
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Dandan Chen
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Zhaoming Luo
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Xing Hu
National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
Tongwen Wu
Center for Earth System Modeling and Prediction, China Meteorological Administration, Beijing 100081, China
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Cited
12 citations as recorded by crossref.
- Advancing Asian Monsoon Climate Prediction under Global Change: Progress, Challenges, and Outlook B. Wang et al.
- 2024年初中国东部严重冻雨事件的机理与次季节预测研究 可. 张 & 邦. 徐
- Mechanisms and subseasonal prediction of severe freezing precipitation events in East China in early 2024 K. Zhang & P. Hsu
- The stochastic skeleton model for the Madden–Julian Oscillation with time-dependent observation-based forcing N. Ehstand et al.
- Subseasonal Prediction Skill in the CAMS-CSM Subseasonal-to-Seasonal Forecast System Y. Yan et al.
- Dynamical prediction of sub-seasonal tropical cyclones: IAP-CAS model advances D. Lamichhane et al.
- Global performance benchmarking of artificial intelligence models in atmospheric river forecasting L. Zhang et al.
- Enhancing Ready-to-Implementation subseasonal crop growth predictions in central Southwestern Asia: A machine learning-climate dynamical hybrid strategy T. Zhu et al.
- Shifts in MJO behavior enhance predictability of subseasonal precipitation whiplashes T. Cheng et al.
- Bridging the “Last-mile Gap” in Climate Services Delivery: A Dynamical-AI Hybrid Framework for Next-Month Wildfire Danger Prediction and Emergency Action Y. Pan et al.
- Assimilating summer sea ice thickness enhances predictions of Arctic sea ice and surrounding atmosphere within two months A. Liu et al.
- Impact of Madden–Julian Oscillation on the forecast of marine heatwaves in the southeastern tropical Indian ocean Z. Cui et al.
12 citations as recorded by crossref.
- Advancing Asian Monsoon Climate Prediction under Global Change: Progress, Challenges, and Outlook B. Wang et al.
- 2024年初中国东部严重冻雨事件的机理与次季节预测研究 可. 张 & 邦. 徐
- Mechanisms and subseasonal prediction of severe freezing precipitation events in East China in early 2024 K. Zhang & P. Hsu
- The stochastic skeleton model for the Madden–Julian Oscillation with time-dependent observation-based forcing N. Ehstand et al.
- Subseasonal Prediction Skill in the CAMS-CSM Subseasonal-to-Seasonal Forecast System Y. Yan et al.
- Dynamical prediction of sub-seasonal tropical cyclones: IAP-CAS model advances D. Lamichhane et al.
- Global performance benchmarking of artificial intelligence models in atmospheric river forecasting L. Zhang et al.
- Enhancing Ready-to-Implementation subseasonal crop growth predictions in central Southwestern Asia: A machine learning-climate dynamical hybrid strategy T. Zhu et al.
- Shifts in MJO behavior enhance predictability of subseasonal precipitation whiplashes T. Cheng et al.
- Bridging the “Last-mile Gap” in Climate Services Delivery: A Dynamical-AI Hybrid Framework for Next-Month Wildfire Danger Prediction and Emergency Action Y. Pan et al.
- Assimilating summer sea ice thickness enhances predictions of Arctic sea ice and surrounding atmosphere within two months A. Liu et al.
- Impact of Madden–Julian Oscillation on the forecast of marine heatwaves in the southeastern tropical Indian ocean Z. Cui et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences...
