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Dr. Xiaosong Yang

UCAR/CPAESS, Project Scientist

Seasonal to Decadal Predictability and Variability Division, NOAA/GFDL

Princeton University Forrestal Campus

201 Forrestal Road, Princeton, NJ 08540

Phone: 609-452-5311, Fax: 609-987-5063, Email: Xiaosong.Yang@noaa.gov

Education

  • Ph.D. in Marine and Atmospheric Science, Stony Brook University,  Stony Brook, 2006
  • M.S. in Atmospheric Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, 2000
  • B.S. in Meteorology, Ocean University of Qingdao, China, 1995

Research Interests

  • Coupled model initialization for climate prediction
  • Seasonal to decadal scale climate predictability and variability
  • Climate extremes: Attribution and prediction
  • Eddy-mean flow interactions and storm track dynamics

Selected papers on predictability studies

  1. Yang, X., L. Jia, S. Kapnick, T. L. Delworth, G. A. Vecchi, R. G. Gudgel, Seth Underwood, Fanrong Zeng, 2018: On the seasonal prediction of the western United States  El Niño precipitation during the 2015/16 winter, Climate Dynamics, DOI:10.1007/s00382-018-4109-3
  2.  Kapnick SB, Yang X, Vecchi GA, Delworth TL, Gudgel R, Malyshev S, Milly PCD, Shevliakova E, Underwood S, Margulis S, 2018: Potential for Western United States Seasonal Snowpack Prediction. Proceedings of the National Academy of Sciences.  doi:10.1073/pnas1716760115
  3. Jia, L., X. Yang, G. A. Vecchi, R. Gudgel, T. Delworth, S. Fueglistaler, P. Lin, A. Scaife, S. Underwood, S.-J. Lin, 2017: Seasonal Prediction Skill of Northern Extratropical Surface Temperature Driven by the Stratosphere, J. Climate, DOI:10.1175/JCLI-D-16-0475.1
  4.  Jia, Liwei, G. A. Vecchi, X. Yang, R. G. Gudgel, T. L. Delworth, W. F. Stern, K. Paffendorf, S. D. Underwood, and F. Zeng, 2016: The Roles of Radiative Forcing, Sea Surface Temperatures, and Atmospheric and Land Initial Conditions in U.S. Summer Warming Episodes. Journal of Climate, 29(11), DOI:10.1175/JCLI-D-15-0471.1
  5. Yang, X. et al., 2015: Seasonal predictability of extratropical storm tracks in GFDL’s high-resolution climate prediction model, J. Climate, 28, 3592-3611,doi: http://dx.doi.org/10.1175/JCLI-D-14-00517.1
  6. Jia, L., X. Yang,  et al., 2015: Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-resolution GFDL Climate Model, J. Climate, 28, 2044-2062, doi: http://dx.doi.org/10.1175/JCLI-D-14-00112.1.
  7. Yang, X. et al., 2013: A predictable AMO-like pattern in GFDL’s fully-coupled ensemble initialization and decadal forecasting system,  Journal of Climate., 26(2), DOI:10.1175/JCLI-D-12-00231.1 .

Selected papers on data assimilation

  1. DelSole, T., and X. Yang, 2010: State and Parameter Estimation in Stochastic Dynamical Models, Physica D, 239,1781-1788.
  2. Yang, X., and T. DelSole, 2009: Using ensemble Kalman Filter to estimate multiplicative parameters, Tellus A, 61, 601-609.
  3. Yang, X., and T. DelSole, 2009: The diffuse ensemble  filter,  Nonlinear Processes in Geophysics, 16, 475-486

Publication Statistics

GFDL’s Bibliography Page

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