2021-2023

1. Lu, L., S. Zhang, Y. Jiang, X. Yu, M. Li, Y. Chen, P. Chang, G. Danabasoglu, Z. Liu, C. Zhu, X. Lin, and L. Wu, 2023: An improved coupled data assimilation system with a CGCM using multi-time-scale high-efficiency EnOI-like filtering. Journal of Climate, 36(17): 6045-6067.

2. Zhang S., S. Xu, H. Fu, L. Wu, Z. Liu, Y. Gao, C. Zhao, W. Wan, L. Wan, H. Lu, C. Li, Y. Liu, X. Lv, J. Xie, Y. Yu, J. Gu, X. Wang, Y. Zhang, C. Ning, Y. Fei, X. Guo, Z. Wang, X. Wang, Z. Wang, B. Qu, M. Li, H. Zhao, Y. Jiang, G. Yang, L. Lu, H. Wang, H. An, X. Zhang, Y. Zhang, W. Ma, F. Yu, J. Xu, X. Lin, X. Shen, 2023: Toward Earth system modeling with resolved clouds and ocean submesoscales on heterogeneous many-core HPCs. National Science Review, 10(6): nwad069.

3. Zhu, C., Z. Liu, S. Zhang, L. Wu, 2023:  Likely accelerated weakening of Atlantic overturning circulation emerges in optimal salinity fingerprint. Nature Communications, 14: 1245.

4. Lin, Z., S. Zhang, Z. Zhang, X. Yu, Y. Gao, 2023: The Rossby normal mode as a physical linkage in a machine learning forecast model for the SST and SSH of South China Sea deep basin. Journal of Geophysical Research: Oceans, 128, e2023JC019851.

5.Yong J., S. Zhang, Z. Liu, Y. Gao, L. Wu, J. Li, L. Lu, Y. Jiang, X. Yu, M. Li, H. Zhao, X. Lin, 2023: The linear behavior of the joint initial-boundary-value predictability of the climate system. Climate Dynamics, 60(3): 913-925.

6.Diao X., A. Stössel, P. Chang, G. Danabasoglu, S. G. Yeager, A. Gopal, H. Wang, S. Zhang, 2022: On the Intermittent Occurrence of Open-Ocean Polynyas in a Multi-Century High-Resolution Preindustrial Earth System Model Simulation. Journal of Geophysical Research: Oceans, 127(4): e2021JC017672.

7.Guo X., Y. Gao, S. Zhang, L. Wu, P. Chang, W. Cai, J. Zscheischler, L. R. Leung, J. Small, G. Danabasoglu, L. Thompson, H. Gao, 2022: Threat by marine heatwaves to adaptive large marine ecosystems in an eddy-resolving model. Nature Climate Change, 12(2): 179-186.

8.Jiang Y., W. Cheng, F. Gao, S. Zhang, S. Wang, C. Liu, J. Liu, 2022: A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites. Remote Sensing, 14(10): 2314.

9.Liu, X., P. Chang, D. Fu, R. Saravanan, H. Wang, N. Rosenbloom, S. Zhang, L. Wu, 2022: Improved Simulations of Atmospheric River Climatology and Variability in High‐Resolution CESM. Journal Of Advances In Modeling Earth Systems, 14(9): e2022MS003081.

10.Ma M., Y. Gao, A. Ding, H. Su, H. Liao, S. Wang, X. Wang, B. Zhao, S. Zhang, P. Fu, A. B. Guenther, M. Wang, S. Li, B. Chu, X. Yao, H. Gao, 2022: Development and Assessment of a High-Resolution Biogenic Emission Inventory from Urban Green Spaces in China. Environmental Science & Technology, 56(1): 175-184.

11.Mao K., F. Gao, S. Zhang, C. Liu, 2022: An Initial Field Intelligent Correcting Algorithm for Numerical Forecasting Based on Artificial Neural Networks under the Conditions of Limited Observations: Part I—Focusing on Ocean Temperature. Journal of Marine Science and Engineering, 10(3): 311.

12.Sun J., Y. Jiang, S. Zhang, W. Zhang, L. Lu, G. Liu, Y. Chen, X. Xing, X. Lin, L. Wu, 2022: An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation. Geoscientific Model Development, 15(12): 4805-4830.

13.Yang G., M. Li, S. Zhang, Y. Jin, C. Zhu, Z. Wang, X. Yu, H. Wang, Y. Chen, 2022: An assessment of the simulation of East-Asia precipitation in the high-resolution community earth system model. Climate Dynamics, Published online.

14.Yu Y., S. Zhang, H. Fu, L. Wu, D. Chen, Y. Gao, Z. Wei, D. Jia, X. Lin, 2022: Characterizing uncertainties of Earth system modeling with heterogeneous many-core architecture computing. Geoscientific Model Development, 15(17): 6695-6708.

15.Zhu C., J. Zhang, Z. Liu, B. L. Otto-Bliesner, C. He, E. C. Brady, R. Tomas, Q. Wen, Q. Li, C. Zhu, S. Zhang, L. Wu, 2022: Antarctic Warming during Heinrich Stadial 1 in a Transient Isotope-Enabled Deglacial Simulation. Journal of Climate, 35(22): 3753-3765.

16.Ren, S., S. Zhang, L. Lu, Y. Jiang and Y. Ma, 2021: Impact of tropical Atlantic Warming on the Pacific Walker circulation with numerical experiments of CGCM. Advances in Climate Change Research, 12(6): 757-771.

17.Zhao, L., S. Zhang, Y. Shen, Y. Guan and X. Deng, 2021: A study of capturing Atlantic meridional overturning circulation (AMOC) regime transition through observation-constrained model parameters. Nonlinear Processes in Geophysics, 28(4): 481-500.

18.Zhu, C., Z. Liu, S. Zhang and L. Wu, 2021: Global Oceanic Overturning Circulation Forced by the Competition between Greenhouse Gases and Continental Ice Sheets during the Last Deglaciation. Journal of Climate, 34(18): 7555–7570.

19.Liu, X., J. Yao, T. Wu, S. Zhang, F. Xu, L. Zhang, W. Jie, W. Zhou, Q. Li, X. Liang, M. Chu, J. Yan, S. Nie and Y. Cheng, 2021: Development of coupled data assimilation with the BCC climate system model: Highlighting the role of sea-ice assimilation for global analysis. Journal of Advances in Modeling Earth Systems, 13(4), e2020MS002368.

20.Liu, C., S. Zhang, Y. Gao, Y. Wang, L. Sheng, H. Gao and J. Fung, 2021: Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model. Science of The Total Environment, 773(6): 145580.

21.Ma, Y., J. Li, S. Zhang and H. Zhao, 2021: A multi‑model study of atmosphere predictability in coupled ocean–atmosphere systems. Climate Dynamics, 56: 3489–3509.

22.Wang, X., S. Zhang, X. Lin, B. Qiu and L. Yu, 2021: Characteristics of 3-Dimensional Structure and Heat Budget of Mesoscale Eddies in the South Atlantic Ocean. Journal of Geophysical Research-Oceans, 126: e2020JC016922.

23.Zheng, J., S. Zhang, D. Wang and J. Jiang, 2021: Optimization for the Assessment of Spudcan Peak Resistance in Clay–Sand–Clay Deposits. Journal of Marine Science and Engineering, 9(7): 689.

24.Zhao, H., S. Zhang, J. Li and Y. Ma, 2021: A Study of Predictability of Coupled Ocean–Atmosphere System Using Attractor Radius and Global Attractor Radius. Climate Dynamics, 56: 1317–1334.

2016-2020

1.Chang, P., S. Zhang, G. Danabasoglu, S. G. Yeager, H. Fu, H. Wang, F. S. Castruccio, Y. Chen, J. Edwards, D. Fu, Y. Jia, L. C. Laurindo, X. Liu, N. Rosenbloom, R. J. Small, G. Xu, Y. Zeng, Q. Zhang, J. Bacmeister, D. A. Bailey, X. Duan, A. K. DuVivier, D. Li, Y. Li, R. Neale, A. Stössel, L. Wang, Y. Zhuang, A. Baker, S. Bates, J. Dennis, X. Diao, B. Gan, A. Gopal, D. Jia, Z. Jing, X. Ma, R. Saravanan, W. G. Strand, J. Tao, H. Yang, X. Wang, Z. Wei and L. Wu, 2020: An Unprecedented Set of High-Resolution Earth System Simulations for Understanding Multiscale Interactions in Climate Variability and Change. Journal of Advances in Modeling Earth Systems, 12(12): e2020MS002298.

2.Zhao, H., S. Zhang, J. Li and Y. Ma, 2020:A Study of Predictability of Coupled Ocean–Atmosphere System Using Attractor Radius and Global Attractor Radius. Climate Dynamics, in press.

3.Gao, Y., H. Shan, S. Zhang, L. Sheng, J. Li, J. Zhang, M. Ma, H. Meng, K. Luo, H. Gao and X. Yao, 2020: Characteristics and sources of PM2.5 with focus on two severe pollution events in a coastal city of Qingdao, China. Chemosphere,247: 125861-125861.

4.Gao, Y., L. Zhang, G. Zhang, F. Yan, S. Zhang, L. Sheng, J. Li, M. Wang, S. Wu, J. S. Fu, X. Yao and H. Gao, 2020: The climate impact on atmospheric stagnation and capability of stagnation indices in elucidating the haze events over North China Plain and Northeast China. Chemosphere,258: 127335-127335.

5.Jiang, Y., S. Zhang, J. Tian, Z. Zhang, J. Gan and C. R. Wu, 2020: An Examination of Circulation Characteristics in the Luzon Strait and the South China Sea Using High‐Resolution Regional Atmosphere‐Ocean Coupled Models. Journal of geophysical research. Oceans, 125(6): e2020JC016253.

6.Lee, J. H., Y. S. Chang and S. Zhang, 2020: Assessment of the JMA Serial Observation Lines in the Northwestern Pacific in OSSE Studies with the GFDL Ensemble Coupled Data Assimilation System. Journal of geophysical research. Oceans, 125(3): e2019JC015686.

7.Li, J. and S. Zhang, 2020: Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20. Geoscientific model development, 13(3): 1035-1054.

8.Li, M., S. Zhang, L. Wu, X. Lin, P. Chang, G. Danabasoglu, Z. Wei, X. Yu, H. Hu, X. Ma, W. Ma, D. Jia, X. Liu, H. Zhao, K. Mao, Y. Ma, Y. Jiang, X. Wang, G. Liu and Y. Chen, 2020: A high-resolution Asia-Pacific regional coupled prediction system with dynamically downscaling coupled data assimilation. Science bulletin,65(21): 1849-1858.

9.Li, M., S. Zhang, L. Wu, X. Lin, P. Chang, G. Danabasoglu, Z. Wei, X. Yu, H. Hu, X. Ma, W. Ma, H. Zhao, D. Jia, X. Liu, K. Mao, Y. Ma, Y. Jiang, X. Wang, G. Liu and Y. Chen, 2020: An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization. Advances in atmospheric sciences, 37(9): 939-950.

10.Lu, L., S. Zhang, S. G. Yeager, G. Danabasoglu, P. Chang, L. Wu, X. Lin, A. Rosati and F. Lu, 2020: Impact of Coherent Ocean Stratification on AMOC Reconstruction by Coupled Data Assimilation with a Biased Model. Journal of climate, 33(17): 7319-7334.

11.Roberts, M. J., L. C. Jackson, C. D. Roberts, V. Meccia, D. Docquier, T. Koenigk, P. Ortega, E. Moreno‐Chamarro, A. Bellucci, A. Coward, S. Drijfhout, E. Exarchou, O. Gutjahr, H. Hewitt, D. Iovino, K. Lohmann, D. Putrasahan, R. Schiemann, J. Seddon, L. Terray, X. Xu, Q. Zhang, P. Chang, S. G. Yeager, F. S. Castruccio, S. Zhang and L. Wu, 2020: Sensitivity of the Atlantic Meridional Overturning Circulation to Model Resolution in CMIP6 HighResMIP Simulations and Implications for Future Changes. Journal of advances in modeling earth systems, 12(8): e2019MS002014.

12.Sun, J., Z. Liu, F. Lu, W. Zhang and S. Zhang, 2020: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part III: Assimilation of Real World Reanalysis. Monthly weather review, 148(6): 2351-2364.

13.Zhang, L., Y. Gao, S. Wu, S. Zhang, K. R. Smith, X. Yao and H. Gao, 2020: Global impact of atmospheric arsenic on health risk: 2005 to 2015. Proceedings of the National Academy of Sciences – PNAS, 117(25): 13975-13982.

14.Zhang, S., H. Fu, L. Wu, Y. Li, H. Wang, Y. Zeng, X. Duan, W. Wan, L. Wang, Y. Zhuang, H. Meng, K. Xu, P. Xu, L. Gan, Z. Liu, S. Wu, Y. Chen, H. Yu, S. Shi, L. Wang, S. Xu, W. Xue, W. Liu, Q. Guo, J. Zhang, G. Zhu, Y. Tu, J. Edwards, A. Baker, J. Yong, M. Yuan, Y. Yu, Q. Zhang, Z. Liu, M. Li, D. Jia, G. Yang, Z. Wei, J. Pan, P. Chang, G. Danabasoglu, S. Yeager, N. Rosenbloom and Y. Guo, 2020: Optimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platform. Geosci. Model Dev., 13(10): 4809-4829.

15.Zhang, S., Z. Liu, X. Zhang, X. Wu, G. Han, Y. Zhao, X. Yu, C. Liu, Y. Liu, S. Wu, F. Lu, M. Li and X. Deng, 2020: Coupled data assimilation and parameter estimation in coupled ocean-atmosphere models: a review. Climate dynamics, 54(11-12): 5127-5144.

16.Chikamoto, Y., A. Timmermann, M. J. Widlansky, S. Zhang and M. A. Balmaseda, 2019: A Drift-Free Decadal Climate Prediction System for the Community Earth System Model. Journal of climate, 32(18): 5967-5995.

17.Hu, H., F. Huang, S. Zhang, C. Ruan, S. Gao and P. Li, 2019: Case Study of Fog Predictability for an Event with Cold-Front Synoptic Pattern. Journal of Ocean University of China, 18(2): 271-281.

18.Ma, M., Y. Gao, Y. Wang, S. Zhang, L. R. Leung, C. Liu, S. Wang, B. Zhao, X. Chang, H. Su, T. Zhang, L. Sheng, X. Yao, H. Gao and R. W. A. Pacific Northwest National Lab, 2019: Substantial ozone enhancement over the North China Plain from increased biogenic emissions due to heat waves and land cover in summer 2017. Atmospheric chemistry and physics, 19(19): 12195-12207.

19.Zhao, Y., X. Deng, S. Zhang, Z. Liu and C. Liu, 2019: Sensitivity determined simultaneous estimation of multiple parameters in coupled models: part I—based on single model component sensitivities. Climate dynamics, 53(9): 5349-5373.

20.Chang, Y.-S., S. Zhang, A. Rosati, G. A. Vecchi and X. Yang, 2018: An OSSE Study for Deep Argo Array using the GFDL Ensemble Coupled Data Assimilation System. Ocean science journal, 53(2): 179-189.

21.Li, S., S. Zhang, Z. Liu, L. Lu, J. Zhu, X. Zhang, X. Wu, M. Zhao, G. A. Vecchi, R. H. Zhang and X. Lin, 2018: Estimating Convection Parameters in the GFDL CM2.1 Model Using Ensemble Data Assimilation. Journal of advances in modeling earth systems, 10(4): 989-1010.

22.Park, J.-Y., C. A. Stock, X. Yang, J. P. Dunne, A. Rosati, J. John and S. Zhang, 2018: Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability. Journal of advances in modeling earth systems, 10(3): 891-906.

23.Yu, H., J. Li, K. Wu, Z. Wang, H. Yu, S. Zhang, Y. Hou and R. M. Kelly, 2018: A global high-resolution ocean wave model improved by assimilating the satellite altimeter significant wave height. ITC journal, 70: 43-50.

24.Yu, X., S. Zhang, J. Li, L. Lu, Z. Liu, M. Li, H. Yu, G. Han, X. Lin, L. Wu and P. Chang, 2018: A Multi‐Timescale EnOI‐like High‐Efficiency Approximate Filter for Coupled Model Data Assimilation. Journal of advances in modeling earth systems, 11(1): 45-63.

25.Zhang, S., Y. Xie, F. Counillon, X. Ma, P. Yu and Z. Jing, 2018: Regional Coupled Model and Data Assimilation. Advances in meteorology, 2018: 1-2.

26.Zhang, S., L. Yang, X. Ma, H. Wang, X. Zhang, X. Yu and L. Lu, 2018: The 'Two oceans and one sea' extended range numerical prediction system with an ultra-high resolution atmosphere-ocean-land regional coupled model. Atmospheric and oceanic science letters = Daqi-he-haiyang-kexue-kuaibao, 11(4): 364-371.

27.Fu, H., J. Yang, W. Li, X. Wu, G. Han, Y. Xie, S. Zhang, X. Zhang, Y. Cao and X. Zhang, 2017: A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation. Advances in meteorology, 2017: 1-13.

28.Hu, H., Q. Zhang, J. Sun, C. Ruan, F. Huang and S. Zhang, 2017: Impact of high-frequency observations on fog forecasting: a case study of OSSE. Tellus. Series A, Dynamic meteorology and oceanography, 69(1): 1396182.

29.Liu, C., S. Zhang, S. Li and Z. Liu, 2017: Impact of the Time Scale of Model Sensitivity Response on Coupled Model Parameter Estimation. Advances in atmospheric sciences, 34(11): 1346-1357.

30.Lu, F., Z. Liu, Y. Liu, S. Zhang and R. Jacob, 2017: Understanding the control of extratropical atmospheric variability on ENSO using a coupled data assimilation approach. Climate dynamics, 48(9-10): 3139-3160.

31.Lu, F., Z. Liu, S. Zhang and R. Jacob, 2017: Assessing extratropical impact on the tropical bias in coupled climate model with regional coupled data assimilation. Geophysical research letters, 44(7): 3384-3392.

32.Yu, X., S. Zhang, X. Lin and M. Li, 2017: Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study. Nonlinear Processes in Geophysics, 24: 125-139.

33.Zhao, Y., X. Deng, S. Zhang, Z. Liu, C. Liu, G. Vecchi, G. Han and X. Wu, 2017: Impact of an observational time window on coupled data assimilation: simulation with a simple climate model. Nonlinear processes in geophysics, 24(4): 681-694.

34.Chang, Y.-S. and S. Zhang, 2016: XBT Effects on the Global Ocean State Estimates Using a Coupled Data Assimilation System. TAO : Terrestrial, atmospheric, and oceanic sciences, 27(6): 1019-1031.

35.Cheng, J., Z. Liu, S. Zhang, W. Lina and D. Peng, 2016: Reply to Parker: Robust response of AMOC interdecadal variability to future intense warming. Proceedings of the National Academy of Sciences of the United States of America, 113: E2762–E2763.

36.Cheng, J., Z. Liu, S. Zhang, W. Liu, L. Dong, P. Liu and H. Li, 2016: Reduced interdecadal variability of Atlantic Meridional Overturning Circulation under global warming. Proceedings of the National Academy of Sciences - PNAS, 113(12): 3175-3178.

37.Fu, H., X. Wu, W. Li, Y. Xie, G. Han and S. Zhang, 2016: Reconstruction of Typhoon Structure Using 3-Dimensional Doppler Radar Radial Velocity Data with the Multigrid Analysis: A Case Study in an Idealized Simulation Context. Advances in meteorology, 2016: 1-10.

38.Li, S., S. Zhang, Z. Liu, X. Yang, A. Rosati, J.-C. Golaz and M. Zhao, 2016: The Role of Large-Scale Feedbacks in Cumulus Convection Parameter Estimation. Journal of climate, 29(11): 4099-4119.

39.Liu, H., F. Lu, Z. Liu, Y. Liu and S. Zhang, 2016: Assimilating atmosphere reanalysis in coupled data assimilation. Journal of meteorological research, 30(4): 572-583.

40.Wu, X., G. Han, S. Zhang and Z. Liu, 2016: A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model. Climate dynamics, 46(3): 711-727.

41.Xinrong, W., S. Zhang and Z. L., 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies. Advances in atmospheric sciences, 33(2): 193-207.

42.Zhang, X., S. Zhang, Z. Liu, X. Wu and G. Han, 2016: Correction of biased climate simulated by biased physics through parameter estimation in an intermediate coupled model. Climate dynamics, 47(5): 1899-1912.

2011-2015

1.Goddard, P. B., J. Yin, S. M. Griffies and S. Zhang, 2015: An extreme event of sea-level rise along the Northeast coast of North America in 2009–2010. Nature communications, 6(1): 6346-6346.

2.Han, G., X. Wu, S. Zhang, Z. Liu, I. M. Navon and W. Li, 2015: A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System. Advances in meteorology, 2015: 1-16.

3.Huang, B., J. Zhu, L. Marx, X. Wu, A. Kumar, Z.-Z. Hu, M. A. Balmaseda, S. Zhang, J. Lu, E. K. Schneider and J. L. Kinter Iii, 2015: Climate drift of AMOC, North Atlantic salinity and arctic sea ice in CFSv2 decadal predictions. Climate dynamics, 44(1): 559-583.

4.Jia, L., X. Yang, G. A. Vecchi, R. G. Gudgel, T. L. Delworth, A. Rosati, W. F. Stern, A. T. Wittenberg, L. Krishnamurthy, S. Zhang, R. Msadek, S. Kapnick, S. Underwood, F. Zeng, W. G. Anderson, V. Balaji and K. Dixon, 2015: Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model. Journal of climate, 28(5): 2044-2062.

5.Karspeck, A. R., D. Stammer, A. Köhl, G. Danabasoglu, M. Balmaseda, D. M. Smith, Y. Fujii, S. Zhang, B. Giese, H. Tsujino and A. Rosati, 2015: Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Climate dynamics, 49(3): 957-982.

6.Lu, F., Z. Liu, S. Zhang and Y. Liu, 2015: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study. Monthly weather review, 143(9): 3823-3837.

7.Lu, F., Z. Liu, S. Zhang, Y. Liu, R. Jacob and A. I. L. Argonne National Lab, 2015: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM Experiments. Monthly weather review, 143(11): 4645-4659.

8.Yang, X., G. A. Vecchi, R. G. Gudgel, T. L. Delworth, S. Zhang, A. Rosati, L. Jia, W. F. Stern, A. T. Wittenberg, S. Kapnick, R. Msadek, S. D. Underwood, F. Zeng, W. Anderson and V. Balaji, 2015: Seasonal Predictability of Extratropical Storm Tracks in GFDL’s High-Resolution Climate Prediction Model. Journal of climate, 28(9): 3592-3611.

9.Zhang, S., G. Han, Y. Xie and J. J. Ruiz, 2015: Data Assimilation in Numerical Weather and Climate Models. Advances in meteorology, 2015: 1-2.

10.Zhang, S., M. Zhao, S. J. Lin, X. Yang, W. Anderson, W. Zhang, A. Rosati, S. Underwood and F. Zeng, 2015: Impact of having realistic tropical cyclone frequency on ocean heat content and transport forecasts in a high‐resolution coupled model. Geophysical research letters, 42(14): 5966-5973.

11.Zhang, X., S. Zhang, Z. Liu, X. Wu and G. Han, 2015: Parameter Optimization in an Intermediate Coupled Climate Model with Biased Physics. Journal of climate, 28(3): 1227-1247.

12.Chang, Y.-S., G. A. Vecchi, A. Rosati, S. Zhang and X. Yang, 2014: Comparison of global objective analyzed T-S fields of the upper ocean for 2008–2011. Journal of marine systems, 137: 13-20.

13.Han, G., X. Zhang, S. Zhang, X. Wu and Z. Liu, 2014: Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model. Nonlinear processes in geophysics, 21(2): 357-366.

14.Liu, Y., Z. Liu, S. Zhang, R. Jacob, F. Lu, X. Rong, S. Wu and A. I. L. Argonne National Lab, 2014: Ensemble-Based Parameter Estimation in a Coupled General Circulation Model. Journal of climate, 27(18): 7151-7162.

15.Liu, Y., Z. Liu, S. Zhang, X. Rong, R. Jacob, S. Wu, F. Lu and A. I. L. Argonne National Lab, 2014: Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method. Journal of climate, 27(11): 4002-4014.

16.Msadek, R., T. L. Delworth, A. Rosati, W. Anderson, G. Vecchi, Y. S. Chang, K. Dixon, R. G. Gudgel, W. Stern, A. Wittenberg, X. Yang, F. Zeng, R. Zhang and S. Zhang, 2014: Predicting a Decadal Shift in North Atlantic Climate Variability Using the GFDL Forecast System. Journal of climate, 27(17): 6472-6496.

17.Vecchi, G. A., T. Delworth, R. Gudgel, S. Kapnick, A. Rosati, A. T. Wittenberg, F. Zeng, W. Anderson, V. Balaji, K. Dixon, L. Jia, H. S. Kim, L. Krishnamurthy, R. Msadek, W. F. Stern, S. D. Underwood, G. Villarini, X. Yang and S. Zhang, 2014: On the Seasonal Forecasting of Regional Tropical Cyclone Activity. Journal of climate, 27(21): 7994-8016.

18.Vecchi, G. A., R. Msadek, W. Anderson, Y. S. Chang, T. Delworth, K. Dixon, R. Gudgel, A. Rosati, B. Stern, G. Villarini, A. Wittenberg, X. Yang, F. Zeng, R. Zhang and S. Zhang, 2014: Reply to Comments on Multiyear Predictions of North Atlantic Hurricane Frequency: Promise and Limitations. Journal of Climate, 27(1): 490-492.

19.Wu, X., W. Li, G. Han, S. Zhang and X. Wang, 2014: A Compensatory Approach of the Fixed Localization in EnKF. Monthly weather review, 142(10): 3713-3733.

20.Zhang, S., Y. S. Chang, X. Yang and A. Rosati, 2014: Balanced and Coherent Climate Estimation by Combining Data with a Biased Coupled Model. Journal of climate, 27(3): 1302-1314.

21.Zhang, S., M. Zhao, S. J. Lin, X. Yang and W. Anderson, 2014: Retrieval of tropical cyclone statistics with a high‐resolution coupled model and data. Geophysical research letters, 41(2): 652-660.

22.Han, G., X. Wu, S. Zhang, Z. Liu and W. Li, 2013: Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model. Journal of climate, 26(24): 10218-10231.

23.Liu, Z., S. Wu, S. Zhang, Y. Liu and X. Rong, 2013: Ensemble Data Assimilation in a Simple Coupled Climate Model:The Role of Ocean-Atmosphere Interaction. Advances in atmospheric sciences, 30(5): 1235-1248.

24.Vecchi, G. A., R. Msadek, W. Anderson, Y.-S. Chang, T. Delworth, K. Dixon, R. Gudgel, A. Rosati, B. Stern, G. Villarini, A. Wittenberg, X. Yang, F. Zeng, R. Zhang and S. Zhang, 2013: Multiyear Predictions of North Atlantic Hurricane Frequency: Promise and Limitations. Journal of climate, 26(15): 5337-5357.

25.Wu, X., S. Zhang, Z. Liu, A. Rosati and T. L. Delworth, 2013: A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model. Climate dynamics, 40(7): 1789-1798.

26.Yang, X., A. Rosati, S. Zhang, T. L. Delworth, R. G. Gudgel, R. Zhang, G. Vecchi, W. Anderson, Y.-S. Chang, T. DelSole, K. Dixon, R. Msadek, W. F. Stern, A. Wittenberg and F. Zeng, 2013: A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System. Journal of climate, 26(2): 650-661.

27.Zhang, S., M. Winton, A. Rosati, T. Delworth and B. Huang, 2013: Impact of Enthalpy-Based Ensemble Filtering Sea Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model. Journal of climate, 26(7): 2368-2378.

28.Chang, Y.-S., S. Zhang, A. Rosati, T. L. Delworth and W. F. Stern, 2012: An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation. Climate dynamics, 40(3-4): 775-803.

29.Wu, X., S. Zhang, Z. Liu, A. Rosati, T. L. Delworth and Y. Liu, 2012: Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model. Monthly weather review, 140(12): 3956-3971.

30.Zhang, S., Z. Liu, A. Rosati and T. Delworth, 2012: A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model. Tellus. Series A, Dynamic meteorology and oceanography, 64(1): 10963-10920.

31.Chang, Y.-S., A. Rosati and S. Zhang, 2011: A construction of pseudo salinity profiles for the global ocean: Method and evaluation. Journal of Geophysical Research, 116(C2): C02002.

32.Chang, Y.-S., S. Zhang and A. Rosati, 2011: Improvement of salinity representation in an ensemble coupled data assimilation system using pseudo salinity profiles: improvement of salinity representation. Geophysical research letters, 38(13): L13609.

33.Mahajan, S., R. Zhang, T. L. Delworth, S. Zhang, A. J. Rosati and Y.-S. Chang, 2011: Predicting Atlantic meridional overturning circulation (AMOC) variations using subsurface and surface fingerprints. Deep-sea research. Part II, Topical studies in oceanography, 58(17-18): 1895-1903.

34.Zhang, S., 2011: Impact of observation-optimized model parameters on decadal predictions: Simulation with a simple pycnocline prediction model: Impact of observation-optimized parameters on decadal predictions. Geophysical research letters, 38(2): L02702.

35.Zhang, S., 2011: A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model. Journal of climate, 24(23): 6210-6226.

2006-2010

1.Zhang, S. and A. Rosati, 2010: An Inflated Ensemble Filter for Ocean Data Assimilation with a Biased Coupled GCM. Monthly weather review, 138(10): 3905-3931.

2.Zhang, S., A. Rosati and T. Delworth, 2010: The Adequacy of Observing Systems in Monitoring the Atlantic Meridional Overturning Circulation and North Atlantic Climate. Journal of climate, 23(19): 5311-5324.

3.Chang, Y.-S., A. J. Rosati, S. Zhang and M. J. Harrison, 2009: Objective analysis of monthly temperature and salinity for the world ocean in the 21st century: Comparison with World Ocean Atlas and application to assimilation validation. Journal of Geophysical Research - Oceans, 114(C2): C02014.

4.Zhang, S., A. Rosati and M. J. Harrison, 2009: Detection of multidecadal oceanic variability by ocean data assimilation in the context of a “perfect” coupled model. Journal of Geophysical Research, 114(C12): C12018.

5.Zhang, S., M. J. Harrison, A. Rosati and A. Wittenberg, 2007: System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies. Monthly weather review, 135(10): 3541-3564.

2001-2005

1.Anderson, J. L., B. Wyman, S. Zhang and T. Hoar, 2005: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. Journal of the atmospheric sciences, 62(8): 2925-2938.

2.Qiao, F., S. Zhang and X. Yin, 2005: Study of Initial Vorticity Forcing for Block Onset by a 4-Dimensional Variational Approach. Advances in atmospheric sciences, 22(2): 246-259.

3.Zhang, S., M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson and V. Balaji, 2005: Initialization of an ENSO Forecast System Using a Parallelized Ensemble Filter. Monthly weather review, 133(11): 3176-3201.

4.Qiao, F., S. Zhang and Y. Yuan, 2004: Unification and applications of modern oceanic/atmospheric data assimilation algorithms. Journal of Hydrodynamics. Ser. B, 5: 501-517.

5.Zhang, S., J. L. Anderson, A. Rosati, M. Harrison, S. P. Khare and A. Wittenberg, 2004: Multiple time level adjustment for data assimilation. Tellus. Series A, Dynamic meteorology and oceanography, 56(1): 2-15.

6.Zhang, S. and F. Qiao, 2004: Impact of diabatic processes in AGCM on 4-dimensional variational data assimilation. Acta meteorologica Sinica, 18(3): 259-282.

7.Zhang, S. and J. L. Anderson, 2003: Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus. Series A, Dynamic meteorology and oceanography, 55(2): 126-147.

8.Zhang, S., Zou and J. E. Ahlquist, 2001: Examination of numerical results from tangent linear and adjoint of discontinuous nonlinear models. Monthly weather review, 129(11): 2791-2804.

9.Zou, X., K. Sriskandarajah, W. Yu and S. Q. Zhang, 2001: Eliminating finite-amplitude non-physical oscillations in the time evolution of adjoint model solutions introduced by the leapfrog time-integration scheme. Tellus. Series A, Dynamic meteorology and oceanography, 53(5): 578-584.

1996-2000

1.Zhang, S., Zou, J. Ahlquist, I. M. Navon and J. G. Sela, 2000: Use of differentiable and nondifferentiable optimization algorithms for variational data assimilation with discontinuous cost functions. Monthly weather review, 128(12): 4031-4044.

2.刘还珠, 张绍晴, 1996: 湿位涡与锋面强降水天气的三维结构. 应用气象学报，7(3): 275-284.

3.张绍晴, 刘还珠, 1996: 各种物理强迫激发的平均经圈环流特征及其对数值天气预报模式系统误差的贡献. 大气科学，(01): 112-122.

1991-1995

1.张绍晴, 刘还珠, 吴国雄, 杨云峰, 1995: Diagnosis of NWP Systematic Forecast Errors in Zonal Mean Circulation. Acta Meteorologica Sinica, 9(3): 288-301.

2.张绍晴, 杨晓梅, 1995: 中期数值预报(T63L16)系统的统计性能评估. 气象，10: 14-19.

3.刘还珠, 张绍晴, 1994: NWP模式热量系统误差的动力诊断分析. 应用气象学报，5(004): 428-435.

4.吴学宏, 刘景涛, 温市耕, 张绍晴, 1994: 1992．07．25大暴雨过程的IPV分析. 内蒙古气象，(04): 1-6.

5.张绍晴, 陈久康, 雷兆崇, 1993: 等熵面位涡图上阻塞过程的演变特征. 南京气象学院学报，2: 221-225.

6.张绍晴, 刘还珠, 1993: 球面三维空间中任意截剖方向的垂直剖面图设计原理. 气象，19(11): 36-40.

7.刘还珠, 张绍晴, 1992: 第四讲 中期数值预报的统计检验分析. 气象，(09): 50-54.

8.张绍晴, 陈久康, 雷兆崇, 1992: 用物质线的轨迹积分方法研究阻塞. 南京气象学院学报，015(003): 315-322.

9.张绍晴, 陈久康, 雷兆崇, 1991: IPV—一种有效实施Lagrangian方法的研究工具. 内蒙古气象，000(004): 1-6.

  山东省青岛市崂山区松岭路238号中国海洋大学物理海洋教育部重点实验室 邮编：266100

  Email: szhang@ouc.edu.cn