Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4805-2022
© Author(s) 2022. 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-15-4805-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
23 Jun 2022
Model evaluation paper |
| 23 Jun 2022
| 23 Jun 2022
An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation
Jingzhe Sun
Beijing Institute of Applied Meteorology, Beijing, China
Yingjing Jiang
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Shaoqing Zhang
CORRESPONDING AUTHOR
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Weimin Zhang
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai,
China
Lv Lu
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Guangliang Liu
Shandong Provincial Key Laboratory of Computer Networks, Qilu
University of Technology (Shandong Academy of Sciences), Jinan, China
Yuhu Chen
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
Xiang Xing
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai,
China
Xiaopei Lin
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
Lixin Wu
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, China
Qingdao Pilot National Laboratory for Marine Science and Technology
(QNLM), Qingdao, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, China
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EGUsphere, https://doi.org/10.5194/egusphere-2025-6479, https://doi.org/10.5194/egusphere-2025-6479, 2026
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Deep integration of Artificial intelligence (AI) algorithms and traditional scientific models is crucial for progress, but Fortran-based scientific codes and Python-based AI are difficult to combine. We develop a Python–Fortran hybrid procedure that enables mutual invocation of AI and scientific modules. Applied to climate and weather models, it supports strongly coupled data assimilation and high-precision prediction, promoting future advances in both AI and scientific modeling.
Ruijian Gou, Yaocheng Deng, Yingzhe Cui, Qi Shu, Hong Wang, Shengpeng Wang, Lixin Wu, and Gerrit Lohmann
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This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Under anthropogenic warming, Arctic warming is nearly four times faster than global average. One key reason is the ice-albedo feedback – sea ice melting reveals the darker ocean water, which absorbs more solar radiation. Using a climate model of more refined grids, we find that the ocean absorbs more solar radiation. We attribute it to the better represented short-term extreme ocean warming events. They cause more short-term sea ice melting, allowing the ocean to absorb more solar radiation.
Xiaojing Lv, Zhao Liu, Yuxuan Li, Shaoqing Zhang, Haohuan Fu, Xiaohui Duan, Shiming Xu, Yang Gao, Yujing Fan, Lifeng Yan, Haopeng Huang, Haitian Lu, Lingfeng Wan, Haoran Lin, Qixin Chang, Chenlin Li, Quanjie He, Yangyang Yu, Qinghui Lin, Sheng Jia, Tengda Zhao, Weiguo Liu, and Guangwen Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-5297, https://doi.org/10.5194/egusphere-2025-5297, 2025
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This study introduces a highly-efficient optimization approach that integrates automated and fine-grained optimizations for kilometer-level Earth System Models on heterogeneous many-core supercomputers. Our optimization achieves full parallel coverage for code segments exceeding 1 % of runtime. The optimized 5-km/3-km coupled model reaches 222 Simulated Days Per Day. This work signifies a pivotal advancement in ESMs, providing a robust platform for HR climate simulations.
Wenbin Kou, Yang Gao, Dan Tong, Xiaojie Guo, Xiadong An, Wenyu Liu, Mengshi Cui, Xiuwen Guo, Shaoqing Zhang, Huiwang Gao, and Lixin Wu
Atmos. Chem. Phys., 25, 3029–3048, https://doi.org/10.5194/acp-25-3029-2025, https://doi.org/10.5194/acp-25-3029-2025, 2025
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Unlike traditional numerical studies, we apply a high-resolution Earth system model, improving simulations of surface ozone and large-scale circulations such as atmospheric blocking. Besides local heat waves, we quantify the impact of atmospheric blocking on downstream ozone concentrations, which is closely associated with the blocking position. We identify three major pathways of Rossby wave propagation, stressing the critical role of large-scale circulation in regional air quality.
Lijing Cheng, Yuying Pan, Zhetao Tan, Huayi Zheng, Yujing Zhu, Wangxu Wei, Juan Du, Huifeng Yuan, Guancheng Li, Hanlin Ye, Viktor Gouretski, Yuanlong Li, Kevin E. Trenberth, John Abraham, Yuchun Jin, Franco Reseghetti, Xiaopei Lin, Bin Zhang, Gengxin Chen, Michael E. Mann, and Jiang Zhu
Earth Syst. Sci. Data, 16, 3517–3546, https://doi.org/10.5194/essd-16-3517-2024, https://doi.org/10.5194/essd-16-3517-2024, 2024
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Observational gridded products are essential for understanding the ocean, the atmosphere, and climate change; they support policy decisions and socioeconomic developments. This study provides an update of an ocean subsurface temperature and ocean heat content gridded product, named the IAPv4 data product, which is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1955) for monthly and 1° × 1° temporal and spatial resolutions.
Feifan Yan, Hang Su, Yafang Cheng, Rujin Huang, Hong Liao, Ting Yang, Yuanyuan Zhu, Shaoqing Zhang, Lifang Sheng, Wenbin Kou, Xinran Zeng, Shengnan Xiang, Xiaohong Yao, Huiwang Gao, and Yang Gao
Atmos. Chem. Phys., 24, 2365–2376, https://doi.org/10.5194/acp-24-2365-2024, https://doi.org/10.5194/acp-24-2365-2024, 2024
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PM2.5 pollution is a major air quality issue deteriorating human health, and previous studies mostly focus on regions like the North China Plain and Yangtze River Delta. However, the characteristics of PM2.5 concentrations between these two regions are studied less often. Focusing on the transport corridor region, we identify an interesting seesaw transport phenomenon with stagnant weather conditions, conducive to PM2.5 accumulation over this region, resulting in large health effects.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yang Gao, Xiaopei Lin, Zhao Liu, and Xiaojing Lv
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-10, https://doi.org/10.5194/gmd-2024-10, 2024
Preprint withdrawn
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The hardware-related perturbations caused by the heterogeneous many-core architectures can blend with software or human errors, which can affect the accuracy of the model consistency verification. We develop a deep learning-based consistency test tool for ESMs on the heterogeneous systems (ESM-DCT) and evaluate it in CESM on new Sunway system. The ESM-DCT can detect the existence of software or human errors when taking hardware-related perturbations into account.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
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Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 23, 10713–10730, https://doi.org/10.5194/acp-23-10713-2023, https://doi.org/10.5194/acp-23-10713-2023, 2023
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New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
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To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Huayang Cai, Hao Yang, Pascal Matte, Haidong Pan, Zhan Hu, Tongtiegang Zhao, and Guangliang Liu
Ocean Sci., 18, 1691–1702, https://doi.org/10.5194/os-18-1691-2022, https://doi.org/10.5194/os-18-1691-2022, 2022
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Quantifying spatial–temporal water level dynamics is essential for water resources management in estuaries. In this study, we propose a simple yet powerful regression model to examine the influence of the world’s largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the Yangtze River estuary. The presented method is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled estuaries worldwide.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Lu Yang, Hongli Fu, Xiaofan Luo, Shaoqing Zhang, and Xuefeng Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-92, https://doi.org/10.5194/tc-2022-92, 2022
Revised manuscript not accepted
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During the melting season in Arctic, sea ice thickness is difficult to detect directly by the satellite remote sensing. A bivariate regression model is put forward in this study to construct sea ice thickness. Comparisons with observations show that the new sea ice thickness data has some advantages over other data sets. The experiment shows that the model is expected to provide an available data for improving the forecast accuracy of sea ice variables in the Arctic sea ice melting season.
Zhao Liu, Shaoqing Zhang, Yang Shen, Yuping Guan, and Xiong Deng
Nonlin. Processes Geophys., 28, 481–500, https://doi.org/10.5194/npg-28-481-2021, https://doi.org/10.5194/npg-28-481-2021, 2021
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A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
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Short summary
An online ensemble coupled data assimilation system with the Community Earth System Model is designed and evaluated. This system uses the memory-based information transfer approach which avoids frequent I/O operations. The observations of surface pressure, sea surface temperature, and in situ temperature and salinity profiles can be effectively assimilated into the coupled model. That will facilitate a long-term high-resolution climate reanalysis once the algorithm efficiency is much improved.
An online ensemble coupled data assimilation system with the Community Earth System Model is...
