The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model
Tóm tắt
Từ khóa
Tài liệu tham khảo
Birgin, E. G., J. M. Martínez, and M. Raydan, 2001: Algorithm 813: SPG-Software for convex-constrained optimization. ACM Transactions on Mathematical Software (TOMS), 27, 340–349, https://doi.org/10.1145/502800.502803 .
Capotondi, A., and P. D. Sardeshmukh, 2015: Optimal precursors of different types of ENSO events. Geophys. Res. Lett., 42, 9952–9960, https://doi.org/10.1002/2015GL066171 .
Chen, L., W. S. Duan, and H. Xu, 2015: A SVD-based ensemble projection algorithm for calculating the conditional nonlinear optimal perturbation. Science China Earth Sciences, 58, 385–394, https://doi.org/10.1007/s11430-014-4991-4 .
Duan, W. S., M. Mu, and B. Wang, 2004: Conditional nonlinear optimal perturbations as the optimal precursors for El Nino-Southern Oscillation events. J. Geophys. Res., 109(D23), D23105, https://doi.org/10.1029/2004JD004756 .
Duan, W. S., Y. S. Yu, H. Xu, and P. Zhao, 2013: Behaviors of nonlinearities modulating the El Niño events induced by optimal precursory disturbances. Climate Dyn., 40, 1399–1413, https://doi.org/10.1007/s00382-012-1557-z .
Fan, Y., M. R. Allen, D. L. T. Anderson, and M. A. Balmaseda, 2000: How predictability depends on the nature of uncertainty in initial conditions in a coupled model of ENSO. J. Climate, 15, 3298–3313, https://doi.org/10.1175/1520-0442(2000)013<3298:HPDOTN>2.0.CO;2 .
Gao, C., and R. H. Zhang, 2017: The roles of atmospheric wind and entrained water temperature (Te) in the second-year cooling of the 2010–12 La Niña event. Climate Dyn., 48, 597–617, https://doi.org/10.1007/s00382-016-3097-4 .
Gao, C., X. R. Wu, and R. H. Zhang, 2016: Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction. Adv. Atmos. Sci., 11, 875–888, https://doi.org/10.1007/s00376-016-5249-1 .
Gao, C., R. H. Zhang, X. R. Wu, and J. C. Sun, 2018: Idealized experiments for optimizing model parameters using a 4D-variational method in an intermediate coupled model of ENSO. Adv. Atmos. Sci., 55, 410–422, https://doi.org/10.1007/s00376-017-7109-z .
Hu, J. Y., and W. S. Duan, 2016: Relationship between optimal precursory disturbances and optimally growing initial errors associated with ENSO events: Implications to target observations for ENSO prediction. J. Geophys. Res., 121(5), 2901–2917, https://doi.org/10.1002/2015JC011386 .
Keenlyside, N. S., 2001: Improved modelling of zonal currents and SST in the tropical Pacific. PhD dissertation, Dept. of Mathematics and Statistics, Monash University Clayton, Victoria, Australia.
Keenlyside, N. S., and R. Kleeman, 2002: Annual cycle of equatorial zonal currents in the Pacific. J. Geophys. Res., 107, 3093, https://doi.org/10.1029/2000JC000711 .
Lee, H. C., A. Kumar, and W. Q. Wang, 2018: Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model. Climate Dyn., 50, 1747–1767, https://doi.org/10.1007/s00382-017-3719-5 .
Mu, B., S. C. Wen, S. J. Yuan, and H. Y. Li, 2015: PPSO: PCA based particle swarm optimization for solving conditional nonlinear optimal perturbation. Computers & Geosciences, 83, 65–71, https://doi.org/10.1016/j.cageo.2015.06.016 .
Mu, B., J. H. Ren, and S. J. Yuan, 2017: An efficient approach based on the gradient definition for solving conditional nonlinear optimal perturbation. Mathematical Problems in Engineering, 2017, 3208431, https://doi.org/10.1155/2017/3208431 .
Mu, B., J. H. Ren, S. J. Yuan, and F. F. Zhou, 2019: Identifying typhoon targeted observations sensitive areas using the gradient definition based method. Asia-Pacific Journal of Atmospheric Sciences, 55, 195–207, https://doi.org/10.1007/s13143-018-0068-1 .
Mu, M., and W. S. Duan, 2003: A new approach to studying ENSO predictability: Conditional nonlinear optimal perturbation. Chinese Science Bulletin, 48, 1045–1047, https://doi.org/10.1007/BF03184224 .
Mu, M., Y. S. Yu, H. Xu, and T. T. Gong, 2014: Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions. Theor. Appl. Climatol., 225, 461–469, https://doi.org/10.1007/s00704-013-0909-x .
Ren, J. H., S. J. Yuan, and B. Mu, 2016: Parallel modified artificial bee colony algorithm for solving conditional nonlinear optimal perturbation. Proc. 2016 IEEE 18th Int. Conf. on High Performance Computing and Communications; IEEE 14th Int. Conf. on Smart City; IEEE 2nd Int. Conf. on Data Science and Systems, Sydney, NSW, Australia, IEEE, https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0055 .
Rosati, A., K. Miyakoda, and R. Gudgel, 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev., 225, 754–772, https://doi.org/10.1175/1520-0493(1997)125<0754:TIOOIC>2.0.CO;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., 14, 791–803, https://doi.org/10.1007/s00376-017-6266-4 .
Tao, L. J., C. Gao, and R. H. Zhang, 2018: ENSO predictions in an intermediate coupled model influenced by removing initial condition errors in sensitive areas: A target observation perspective. Adv. Atmos. Sci., 15, 853–867, https://doi.org/10.1007/s00376-017-7138-7 .
Wang, B., and X. W. Tan, 2010: Conditional nonlinear optimal perturbations: Adjoint-free calculation method and preliminary test. Mon. Wea. Rev., 138, 1043–1049, https://doi.org/10.1175/2009MWR3022.1 .
Zhang, R. H., and C. Gao, 2016a: Role of subsurface entrainment temperature (Te) in the onset of El Niño events, as represented in an intermediate coupled model. Climate Dyn., 46, 1417–1435, https://doi.org/10.1007/s00382-015-2655-5 .
Zhang, R. H., and C. Gao, 2016b: The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event. Science Bulletin, 61, 1061–1070, https://doi.org/10.1007/s11434-016-1064-4 .
Zhang, R. H., S. E. Zebiak, R. Kleeman, and N. Keenlyside, 2003: A new intermediate coupled model for El Niño simulation and prediction. Geophys. Res. Lett., 30, https://doi.org/10.1029/2003GL018010 .