教育背景：

2010-2014      浙江海洋大学                               海洋科学专业                 学士学位

2014-2017      中国科学院海洋研究所               环境工程专业                 硕士学位

2017-2020      中国科学院大气物理研究所       地球流体力学专业         博士学位

研究方向：

数值模式，ENSO可预报性，误差发展动力学,人工智能中的蝴蝶效应问题

主持和参与项目：

1. 非线性强迫奇异向量-集合预报方法及其在厄尔尼诺和台风可预报性研究中的应用，国家自然科学基金重点项目，41930971，2020.01-2024.12，参与

2. 泛南海海洋-大气-陆地相互作用及重大灾害预测预警研究，广东省基础与应用基础研究重大项目，参与

3. 国家重点研发计划，“全球变化背景下西太平洋环流与ENSO变异及气候预测研究”，参与，项目骨干

4. ENSO持续预报障碍的时空特征及其产生机制研究，第68批博士后面上项目，主持

5. 基于最优化角度研究初始不确定性和模式不确定性对两类ENSO预报障碍的影响，大气科学和地球流体力学数值模拟国家重点实验室开放课题，2021.1-2022.12，主持

6. 南京信息工程大学人才引进项目，2023.7-2026.6，主持

7. 国家青年科学基金项目，2025-2027， 主持

研究主要成果：

基于条件非线性最优扰动（CNOP）方法考察了ENSO预报模式IOCAS ICM对初始场和模式参数的敏感性，揭示了随季节变化最优观测网对ENSO预测改进的有效性，从模式动力角度明确了热带中太平洋海气耦合过程和东太平洋温跃层过程对El Nino发展的重要作用，为改进模式提高ENSO预测提供了理论指导。此外，从宏观角度考察模式不确定性，提出了新一类资料同化方法-非线性强迫奇异向量（NFSV）同化方法。并基于此，发展了具有ENSO多样性预测能力的ENSO预报系统NFSV-ICM。所发展的NFSV-ICM能够有效提高两类El Nino的空间预测能力，提前两个季度以上预测或者识别所发生的ENSO事件类型。

发表文章：

[24] Wei et al., 2024: The March 2023 MJO and its impacts on the subsequent coastal El Niño, Journal of climate, DOI: 10.1175/JCLI-D-24-0198.1

[23] Li T.Y., L. J. Tao and Zhang R. H.*, 2024: Impacts of the Thermocline Feedback Uncertainty on El Nino Simulations in the Tropical Pacific, Journal of Geophysical Research: Oceans, DOI: 10.1029/2024JC021384

[22] Zhang R. H.*, L. Zhou, C. Gao and L. J. Tao, 2024: Real-time predictions of the 2023–2024 climate conditions in the tropical Pacific using a purely data-driven Transformer model, Science China Earth Sciences, DOI: 10.1007/s11430-024-1396-x

[21] Zhang R. H.*, L. Zhou, C. Gao and L. J. Tao, 2024: A transformer-based coupled ocean-atmosphere model for ENSO studies, Science Bulletin, 69, DOI: 10.1016/j.scib.2024.04.048

[20] Tao, L. J.*, 2024: Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions. Journal of Marine Science and Engineering, 12, 601. DOI: 10.3390/jmse12040601

[19]Li T. Y., L. J. Tao*, and M. Zhang, 2024: Projection of Non-Industrial Electricity Consumption in China’s Pearl River Delta under Global Warming Scenarios. Sustainability, 16. DOI: 10.3390/su16052012

[18]Chao P. J., M. Mu, X. H. Fang*, and L. J. Tao, 2023: Improving the Forecasting of El Nino Amplitude Based on an Ensemble Forecast Strategy for Westerly Wind Bursts. Journal of Climate, 36. DOI: 10.1175/JCLI-D-23-0233.1

[17]Tao L. J.*, M. Mu, L. Wang, X. H. Fang, W. S. Duan, and R. H. Zhang, 2023: Impacts of initial zonal current errors on the predictions of two types of El Niño events. JGR-oceans, 128. https://doi.org/10.1029/2023JC019833

[16]Zheng Y. C., W. S. Duan*, L. J. Tao, and J. J. Ma, 2023: Using an ensemble nonlinear forcing singular vector data assimilation approach to address the ENSO forecast uncertainties caused by the “spring predictability barrier” and El Niño diversity. Climate Dynamics, doi: 10.1007/s00382-023-06834-3

[15]刘等, 2023: 一个ENSO多模式集合预报系统介绍. 中国科学 地球科学, 53, doi:10.1360/N072022-0312.

[14]Liu T.*, Y. Q. Gao, X. S. Song, C. Gao, L. J. Tao, Y. M. Tang, W. S. Duan, R. H. Zhang, and D. Chen, 2022: A Multi-model prediction system for ENSO. SCIENCE CHINA Earth Sciences, 66. https://doi.org/10.1007/s11430-022-1094-0. 

[13]Jiang L., W. S., Duan*, H. Wang, H. L. Liu, and L. J. Tao, 2022: Evaluation of the sensitivity on mesoscale eddy associated with the sea surface height anomaly forecasting. Frontiers in Marine Sciences, DOI:10.3389/fmars.2023.1097209

[12]Zheng, F.*, B. Wu, L. Wang, J.-B. Peng, Y. Yao, H.-F. Zong, Q. Bao, J.-H. Ma, S. Hu, H.-L. Ren, T.-W. Cao, R.-P. Lin, X.-H. Fang, L. J. Tao, T.-J. Zhou, and J. Zhu, 2023: Can Eurasia experience a cold winter under a third-year La Niña in 2022/23? Adv. Atmos. Sci., https://doi.org/10.1007/s00376-022-2331-8.

[11]Tao L J, Duan W S*, Jiang L. Model errors of an intermediate model and their effects on realistic predictions of El Niño diversity. International Journal of Climatology, 2022, 42, 7443-7464. DOI: 10.1002/joc.7656

[10]Tao L J, Duan W S*. A novel precursory signal of the Central Pacific El Niño event: Eastern Pacific cooling mode. Climate Dynamics, 2022, 59:2599–2617. DOI: 10.1007/s00382-022-06229-w

[9]Xu Z Z, Chen J, Mu M*, Tao L J, Dai G K, Wang J Z, Ma Y N, 2021: A stochastic and non-linear representation of model uncertainty in a convective-scale ensemble prediction system. Q J R Meteorol Soc. 148: 2507–2531. DOI: 10.1002/qj.4322.

[8]Tao L J, Duan W S*, Vannitsem S. Improving the forecasts of El Niño diversity: A nonlinear forcing singular vector approach. Climate Dynamics, 2020,55: 739-754. DOI: 10.1007/s00382-020-05292-5

[7]Tao L J, Duan W S*. Using a Nonlinear Forcing Singular Vector Approach to Reduce Model Error Effects in ENSO Forecasting. Weather and Forecasting, 2019, 34(5): 1321-42.

[6]Li J X, Steppeler J, Fang F X, Pain C C, Zhu J, Peng X, Dong L, Li Y Y, Tao L J, Leng W, Wang Y, Zheng J. Potential Numerical Techniques and Challenges for Atmospheric Modeling. Bulletin of the American Meteorological Society, 2019, 100(9): 239-242. DOI: 10.1175/BAMS-D-19-0031.

[5]Tao L J, Gao C, Zhang R H*. Model parameter-related optimal perturbations and their contributions to El Niño prediction errors. Climate Dynamics, 2019, 52(3-4): 1425-41.

[4]Zhang, R H*, L J Tao, and C Gao, 2018: An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model. Climate Dynamics, doi: 10.1007/s00382-017-3919-z

[3]Tao L J, Gao C, Zhang R H*. ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas: A Target Observation Perspective. Adv Atmos Sci, 2018, 35(7): 853-67.

[2]Tao L J, R H Zhang*, and C. Gao, 2017: Initial error-induced optimal perturbations in ENSO predictions, as derived from an intermediate coupled model. Adv Atmos Sci, 34, 791-803.

[1]高川，王宏娜，陶灵江，张荣华*，2017：IOCAS ICM及其ENSO实时预测试验和改进，《海洋与湖沼》，6: 1289-1301.

专著：

1. 张荣华、高川、王宏娜、 陶灵江， 2021： 中间型海洋-大气耦合模式及其 ENSO 模拟和预测，科学出版社