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IOCAS-IR

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How Well Do CMIP6 and CMIP5 Models Simulate the Climatological Seasonal Variations in Ocean Salinity? 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2022, 页码: 23
作者:  Liu, Yuanxin;  Cheng, Lijing;  Pan, Yuying;  Tan, Zhetao;  Abraham, John;  Zhang, Bin;  Zhu, Jiang;  Song, Junqiang
Adobe PDF(9919Kb)  |  收藏  |  浏览/下载：163/0  |  提交时间：2022/07/18
salinity  climatology  seasonal cycle  CMIP5  CMIP6  upper ocean  
A Hybrid Coupled Ocean-Atmosphere Model and Its Simulation of ENSO and Atmospheric Responses 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2019, 卷号: 36, 期号: 6, 页码: 643-657
作者:  Hu, Junya;  Zhang, Rong-Hua;  Gao, Chuan
Adobe PDF(7642Kb)  |  收藏  |  浏览/下载：307/0  |  提交时间：2019/08/28
IOCAS ICM  hybrid coupled model  ENSO simulation  atmospheric response  
Interannual Salinity Variability in the Tropical Pacific in CMIP5 Simulations 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2019, 卷号: 36, 期号: 4, 页码: 378-396
作者:  Zhi, Hai;  Zhang, Rong-Hua;  Lin, Pengfei;  Yu, Peng
Adobe PDF(5481Kb)  |  收藏  |  浏览/下载：335/0  |  提交时间：2019/08/28
mixed-layer salinity  salt budget  interannual variability  tropical Pacific  model simulation  
ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 卷号: 35, 期号: 7, 页码: 853-867
作者:  Tao, Ling-Jiang;  Gao, Chuan;  Zhang, Rong-Hua
Adobe PDF(1354Kb)  |  收藏  |  浏览/下载：349/0  |  提交时间：2019/08/21
El Nino prediction  initial condition errors  target observations  
Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2018, 卷号: 35, 期号: 4, 页码: 410-422
作者:  Gao, Chuan;  Zhang, Rong-Hua;  Wu, Xinrong;  Sun, Jichang
Adobe PDF(1370Kb)  |  收藏  |  浏览/下载：273/0  |  提交时间：2019/08/21
intermediate coupled model  ENSO modeling  4D-Var data assimilation system  optimization of model parameter and initial condition  
Initial error-induced optimal perturbations in ENSO predictions, as derived from an intermediate coupled model 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2017, 卷号: 34, 期号: 6, 页码: 791-803
作者:  Tao, Ling-Jiang;  Zhang, Rong-Hua;  Gao, Chuan
Adobe PDF(1934Kb)  |  收藏  |  浏览/下载：264/0  |  提交时间：2017/09/29
El Nino Predictability  Initial Errors  Intermediate Coupled Model  Spring Predictability Barrier  
Optimal precursors triggering the Kuroshio Extension state transition obtained by the Conditional Nonlinear Optimal Perturbation approach 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2017, 卷号: 34, 期号: 6, 页码: 685-699
作者:  Zhang, Xing;  Mu, Mu;  Wang, Qiang;  Pierini, Stefano
Adobe PDF(9596Kb)  |  收藏  |  浏览/下载：256/0  |  提交时间：2017/09/29
Kuroshio Extension  States Transition  Cnop Approach  Optimal Precursor  Ocean Modeling  
Role of the oceanic channel in the relationships between the basin/dipole mode of SST anomalies in the tropical Indian Ocean and ENSO transition 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2016, 卷号: 33, 期号: 12, 页码: 1386-1400
作者:  Zhao, Xia;  Yuan, Dongliang;  Yang, Guang;  Zhou, Hui;  Wang, Jing
Adobe PDF(7013Kb)  |  收藏  |  浏览/下载：280/0  |  提交时间：2017/03/21
Indian Ocean Sstas  Dipole Mode  Basin Mode  Enso Transition  Oceanic Channel  
Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2016, 卷号: 33, 期号: 7, 页码: 875-888
作者:  Gao, Chuan;  Wu, Xinrong;  Zhang, Rong-Hua
Adobe PDF(1168Kb)  |  收藏  |  浏览/下载：319/0  |  提交时间：2016/09/21
Four-dimensional Variational Data Assimilation  Intermediate Coupled Model  Twin Experiment  Enso Prediction  
Assessment of Interannual Sea Surface Salinity Variability and Its Effects on the Barrier Layer in the Equatorial Pacific Using BNU-ESM 期刊论文
ADVANCES IN ATMOSPHERIC SCIENCES, 2016, 卷号: 33, 期号: 3, 页码: 339-351
作者:  Zhi, Hai;  Zhang, Rong-Hua;  Zheng, Fei;  Lin, Pengfei;  Wang, Lanning;  Yu, Peng
Adobe PDF(6781Kb)  |  收藏  |  浏览/下载：327/0  |  提交时间：2016/05/03
Feedback  Interannual Variability  Sea Surface Salinity  Barrier Layer Thickness