主要从事热带海气相互作用、短期气候预报和高影响海气事件可预报性相关研究。

1. 国家自然科学基金青年项目，印度洋偶极子最优误差增长及可预报性的研究，2021-2023，主持

2. 自然资源部第二海洋研究所基本科研业务费专项资金项目，印度洋偶极子集合预报系统和可预报性研究，2020-2022，主持

3. 国家重点研发计划，高影响海-气环境事件预报模式的高分辨率海洋资料同化系统研发，2017-2022，参与

4. 国家自然科学基金重点项目，近135年印度洋偶极子集合预报试验及可预报性研究，2016-2020，参与

1. Song, X., D. Chen, Y. Tang, and T. Liu, 2018a: An intermediate coupled model for the tropical ocean-atmosphere system. Sci. China Earth Sci., 61, 1859–1874, https://doi.org/10.1007/s11430-018-9274-6.

2. Song, X., Y. Tang, and D. Chen, 2018b: Decadal Variation in IOD Predictability During 1881–2016. Geophys. Res. Lett., 45, https://doi.org/10.1029/2018GL080221.

3. Song, X., X. Li, S. Zhang, Y. Li, X. Chen, Y. Tang, and D. Chen, 2022a: A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China. Acta Oceanol. Sin., 41, 51–64, https://doi.org/10.1007/s13131-021-1857-4.

4. Song, X., Y. Tang, X. Li, and T. Liu, 2022b: Decadal Variation of Predictability of the Indian Ocean Dipole during 1880–2017 Using an Ensemble Prediction System. Journal of Climate, 35, 5759–5771, https://doi.org/10.1175/JCLI-D-21-0848.1.

5. Song, X., Y. Tang, T. Liu, and X. Li, 2022c: Predictability of Indian Ocean Dipole Over 138 Years Using a CESM Ensemble‐Prediction System. JGR Oceans, 127, https://doi.org/10.1029/2021JC018210.

6. Zhang, H., X. Liu, R. Wu,F. Liu, L. Yu, X. Shang, Y. Qi, Y. Wnag, X. Song, X. Xie, C. Yang, D. Tian, W. Zhang, 2019: Ocean Response to Successive Typhoons Sarika and Haima (2016) Based on Data Acquired via Multiple Satellites and Moored Array. Remote Sensing, 11, 2360, https://doi.org/10.3390/rs11202360.

7. Liu, T., Y. Tang, D. Yang, X. Song, Z. Hou, Z. Shen, Y. Gao, Y. Wu, X. Li, B. Zhang, 2019: The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years. Clim Dyn, 53, 6947–6960, https://doi.org/10.1007/s00382-019-04967-y.

8. Gao, Y., T. Liu, X. Song, Z. Shen, Y. Tang, and D. Chen, 2020: An extension of LDEO5 model for ENSO ensemble predictions. Clim Dyn, 55, 2979–2991, https://doi.org/10.1007/s00382-020-05428-7.

9. Zhang, H., X. Liu, R. Wu, D. Chen, D. Zhang, X. Shang, Y. Wang, X. Song, W. Jin, L. Yu, Y. Qi, D. Tian, W. Zhang, 2020: Sea surface current response patterns to tropical cyclones. Journal of Marine Systems, 208, 103345, https://doi.org/10.1016/j.jmarsys.2020.103345.

10. Gao, Y., Y. Tang, X. Song, and Z. Shen, 2021: Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction. Remote Sensing, 13, 3923, https://doi.org/10.3390/rs13193923.

11. Li, X., Y. Tang, X. Song, and T. Liu, 2022: Decadal variation of the rainfall predictability over the maritime continent in the wet season. Journal of Climate, 1–21, https://doi.org/10.1175/JCLI-D-21-0862.1.

12. Liu, T., X. Song, and Y. Tang, 2022a: The predictability study of the two flavors of ENSO in the CESM model from 1881 to 2017. Clim Dyn, 59, 3343–3358, https://doi.org/10.1007/s00382-022-06269-2.

13. Liu, T., X. Song, Y. Tang, Z. Shen, and X. Tan, 2022b: ENSO Predictability over the Past 137 Years Based on a CESM Ensemble Prediction System. Journal of Climate, 35, 763–777, https://doi.org/10.1175/JCLI-D-21-0450.1.

14. Yao, W., X. Yan, Y. Tang, D. Yang, X. Tan, X. Song, and T. Liu, 2022: Multidecadal Variation in the Seasonal Predictability of Winter PNA and Its Sources. Geophysical Research Letters, 49, https://doi.org/10.1029/2022GL099393.