[101] Liu, T., Tang, Y., and co-authors, 2018: The relationship among\ probabilistic, deterministic and potential skills in predicting the ENSO for t\ he past 161 years, J. Climate, Submitted
[100] Wu, Y., and Tang, Y., 2019: Seasonal predictability of t\ he tropical Indian Ocean SST in the North American Multiple model Ensemble, Cli\ mate Dynamics, Submitted
[99] Yao, Z., Tang, Y., Lian, T., Xu, DF., Li,XJ., Hu,CD.,and \ Zheng, JY. Zhang B. L, 2019: Two types of El simulated in the CESM: Roles of th\ e atmospheric physics and model resolution, Ocean Modeling, Submitted.
[98] Tan, X. X., Tang, Y., Lian, T., Yao, Z. X., Li, X. J. 201\ 9. A study of the effects of westerly wind bursts on ENSO based on CESM, Climat\ e Dynamics, submitted
[97] Zhang, C., Lian, T., Tang, Y., 2019: Uncertainty of the Linear Trend in the Zonal SST Gradient across the Equatorial Pacific since 1881, Atmosphere-Ocean, DOI: 10.1080/07055900.2018.1558044.
[96] Lian, T., Chen, D, Ying, J., Huang, P., and Tang, Y. 2018: Tropical Pacific trends under global warming: El Ni ̃no-like or La Ni ̃na-like? Nat Sci Rev, 2018, Vol. 5, No.6, 810-812.
[95] Zhang, H., R. Wu, D. Chen, X. Liu, H. Hailun, Tang, Y., D. Ke, Z. Shen, J. Li, J. Xie, D. Tian, Modulation of Upper Ocean Thermal Structure by Typhoon Kalmaegi (2014), JGR-Ocean, DOI: 10.1029/2018JC014119.
[94] Lian, T., Chen, D., Tang, Y., Ying, J., 2018: Contrasting trends in sea surface temperature gradient across the equatorial Pacific in observations and CMIP5 models, J. Climate, submitted
[93] Song, S., Tang, Y., D. Chen, 2018: Decadal variatiob of IOD predictability in past 136 years, Geophy. Res. Letts., DOI: 10.1029/2018GL080221
[92] Lian, T., Chen, D., Tang, Y., Liu, X., Zhou, L., 2018: Linkage between westerly wind bursts and tropical cyclones, Geophy. Res. Letts, DOI 10.1029/2018GL079745.
[91] Li,X., Tang, Y., Lei Zhou, Yao, Z., Shen, Z., and Li, JD, 2018: Optimal Error Analysis of the MJO Prediction Associated with the uncer tainties in the Sea Surface Temperature, Climate Dynamics, submitted
[90] Shen,Z., Tang, Y., Li, X; Gao,Y, Li, JD, 2018: On the localization in ensemble coupled data assimilation using a multi-scale Lorenz model, Nonlinear Process in Geoscience, DOI: 10.5194/npg-2018-50
[89] Song, S, D. Chen, Tang, Y., and T. Liu, 2018: An intermediate coupled model for the tropical oceans, Science China(Earth Sciences), DOI: 10.1007/s11430-018-9274-6.
[88] Tang, Y., R-H Zhang, T. Liu, W. Duan, D. Yan, F. Zheng, H. Ren, T. Lian, C. Gao, D. Chen, M. Mu, 2018: Progress in ENSO prediction and predictabilit study, National Science Review, nwy105, https://doi.org/10.1093/nsr/nwy105.
[87] Da Liu, W. Duan and Tang, Y., 2018: Summer Predictability Barrier. of Indian Ocean Dipole Events and Corresponding Error Growth Dynamics, JGR-ocean, DOI: 10.1029/2017JC013739
[86] Yang, D., X. Yang, D. Ye, X. Sun, J. Fang, C. Chu, T. Feng, Y. Jiang, J. Liang, X. Ren, Y. Zhang and Tang, Y., 2018: On the relationship between probabilistic and deterministic skills in dynamical seasonal climate prediction, JGR-Atmosphere, DOI: 10.1029/2017JD028002 (Featured as a Research Spotlight by this Journal).
[85] Lian, T., Shen, Z., Ying, J., Tang, Y., 2018: Investigating the linear trend in global SST in multiple datasets using an improved trend estimator , JGR-Oceans, DOI10.1002/2017JC013410l.
[84] Junde Li, Chujin Liang, Tang, Y., Xiaohui Liu, Weifang Jin, Shen, Z and Li, XJ, 2017: Impacts of temperature and salinity anomalies of IOD on the intermittent Equatorial Undercurrent anomalies, Climate Dynamics, DOI: 10.1007/s00382-017-3961-x.
[83] Shen, Z. and Tang, Y., and X. Li, 2017: A new formulation of vector weights in localized particle filter, Quarterly Journal of the Royal Meteorological Society, OCT 2017DOI: 10.1002/qj.3180
[82] Lei Zhou, Raghu Murtugudde, Dake Chen, and Tang, Y., 2017: Seasonal and Interannual Variabilities of the Central Indian Ocean Mode, J. Climate, DOI: 10.1175/JCLI-D-16-0616.1. May 2017.
[81] Mu,M, Duan, W and Tang, Y., , 2017: Predictability of Atmospheric and Oceanic motions--Restropect and Prospects, Science China (Earth Sciences), DOI: 10.1007/s11430-016-9101-x
[80]Qi Q. Q., Duan W., Zheng F., Tang, Y., 2017: Using an ICM ensemble prediction system outputs to explore the spring predictability barrier for 2015/2016 El Nino event, Science China (Earth Sciences), 60,doi: 10.1007/s11430-017-9087-2
[79]Lian, T. Chen., D. Tang, Y., 2017: Genesis of the 2014-2016 El Nino events, Science China Earth Sciences,doi:10.1007/s11430-016-8315-5.
[78]Lian, T., Tang, Y., Zhou, L., Islum, S., Zhang, C., 2017: Westerly Wind Burst Events Simulated in the Community Atmospheric Model (CAM4), Climate Dynamics, pril 20177, DOI: 10.1007/s00382-017-3689-7
[77]Wang,Q., Tang, Y.,Henk A. Dijkstra, Mu Mu, 2017: An optimization strategy for identifying parameter sensitivity in atmosphere and ocean model, Monthly Weather Review, vol 145, August, 3293-3305
[76]Wang, Q., Tang, Y., S. Pierini, amd M. Mu, 2017: Effects of singular vector-type initial errors on the short-range prediction of Kuroshio Extension transition processes, J. Climate, DOI: 10.1175/JCLI-D-16-0305.1, April 2017, pp 5961-5983
[75]Tao Lian, Tang, Y., 2016: Frequency-Specified EOF Analysis and Its Application to Pacific Decadal Oscillation, Science China (Earth Sciences), Vol. 60: 341. doi:10.1007/s11430-016-0141-x
[74]Zhou, L. R. Murtugudde, D. Chen,and Y. Tang , 2016: A Central Indian Ocean Mode and Heavy Precipitation during Indian Summer Monsoon, J. Climate, JCLI-D-16-0347.1
[73]Yao, Z. X., Tang, Y, Chen, D, Zhou, L.,Islam, S, Li, X. J., 2016: Assessment of the simulation of Indian OceanDipole in CESM -- Impact of Atmospheric Physics and Model Resolution, J. Adv. Model. Earth Syst., 11/2016; DOI:10.1002/2016MS000700
[72]Li, J.,, Liang, C, Tang, Y., Dong, C., Chen, D., Liu, X., and Jin,W., 2016: A new dipole index of the salinity anomalies of the tropical Indian Ocean, Scientific Report, 6, 24260; doi: 10.1038/srep24260 (2016).
[71] Tang, Y., Shen, Z., Gao, Y. 2016: An introduction to ensemble based data assimilation in the earth sciences (book chapter), Eds: "Systems and Control of Nonlinear Equations", ISBN 978-953-51-4714-5
[70]Shen, Z. and Tang, Y.,2016: The Theoretical framework of the ensemble-based data assimilation method and its prospect in oceanic data assimilation, Acta Oceanological Sinica, 38(3),doi:10.3969/j.issn 0253-4193
[69]Islam, S.,Tang, Y., 2016: Simulation of different types of ENSO impacts on South Asian monsoon in CCSM4, Climate Dynamics,DOI: 10.1007/s00382-016-3117-4
[68]Li, X. J., Tang, Y, Zhou, L, Chen, D, Yao, Z. X., Islam, S, 2016: Assessment of the Madden-Julian Oscillation Simulation in CESM, Climate Dynamics, January, 2016, DOI 10.1007/s00382-016-2991-0
[67]Yang, D. J., Yang, X. Q., Zhang, Y. C., Ren, X. J., Tang, Y. 2016: Probabilistic versus Deterministic Skill in Predicting the Western North Pacific- East Asian Summer Monsoon Variability with Multi-Model Ensembles, JGR - Atmosphere, January 2016, 10.1002/2015JD023781
[66]Liu, H.,Tang, Y., Chen, D., 2016: Predictability of Indian Dipole Oscillation in Multiple Coupled Models, Climate Dynamics, DOI 10.1007/s00382-016-3187-3
[65]Younas, W.,Tang, Y., 2016: MJO predictability in AGCM and CGCM Ensemble predictions, J. Climate, submitted
[64]Shen, Z. and Tang, Y.,2015: A comparison of EAKF and SIR-PF: towards a generalized Bayesian-based data assimilation method, Acta Oceanological Sinica, DOI: 10.1007/s13131-015-0757-x
[63]Chen, D., Lian, T., Cane, M., Tang. Y, Murtugudde, R., Song, X., Wu, Q., and Zhou, L., 2015: A New Perspective on El Nin Classification and Genesiss, Nature Geoscience, 04/2015; DOI: 10.1038/ngeo2399.
[62]Yang, C, Min, J. and Tang, Y., 2015: Evaluation of two newly developed Kalman Gain Algorithms for Radar dataassimilation in the WRF model, Tellus, Vol 67 (2015) , 1-13.
[61]Islam, S.,Tang, Y., 2015: Optimal error growth of South Asian Monsoon Forecast Associated with the uncertainties in the Sea Surface Temperature, Climate Dynamics, DOI 10.1007/s00382-015-2686-y.
[60]Shen, Z. and Tang, Y.,2015: A modified ensemble Kalman particle filter for non-Gaussian systems with nonlinear measurement functions,J. Adv. Model. Earth Syst., 07, doi:10.1002/2014MS000373.
[59] Tang, Y.,Chen, D., and Yan, X. , 2014: Information-based seasonal climate potential Predictability in AGCM and CGCM applied to Northern America Surface Temperature, Climate Dynamics,DOI 10.1007/s00382-014-2335-x.
[58]Manoj, K.,Tang, Y.,D. Chen, Y. Cheng, 2014: Reduced rank Sigma point Kalman filter and its application in ENSO model, J. Atmos. Oceanic Technol., Vol. 31, No. 10., 2350-2366.
[57] Lian, T.. Chen, D., Tang, Y.,and Wu, Q., 2014, Effects of Westerly Wind Burst on El Nino: A New Perspective, Geophys. Res. Lett.(1 May 2014), 2014GL059989, doi:10.1002/2014gl059989
[56]Tang, Y., D. Chen, Yan, X , 2014: Potential Predictability of Northern America Surface Temperature, Part I:
Information-based vs signal-to-noise based metrics, J. Climate, Vol 27, 1578-1599.
[55]Tang, Y.,Z. Deng, Monaj, K and D. Chen, 2014: A practical scheme of the sigma-point Kalman Filter for high dimensional systems, Journal of Advances in Modeling Earth Systems (JAMES), 24 JAN 2014 | DOI: 10.1002/2013MS000255.
[54] Lian, T.. Chen, D., Tang, Y.,and Jin, B, 2014: A theoretical Investigation of the Tropical Indo-Pacific Tripole Mode, Chinese Sciences D (Earth Sciences) (English version), http://link.springer.com/article/10.1007%2Fs11430-013-4762-7
[53]Tang, Y., J. Ambadan, D. Chen, 2014: A modification of Kalman Gain for Nonlinear measurement function in Ensemble-Kalman filter, Advance in Atmospheric Sciences, doi: 10.1007/s00376-013-3117
[52]Younas, W.,Tang, Y., 2013: PNA predictability at different time scales, J. Climate, 26 (22), 9090-9114
[51]Islam, S.,Tang, Y.,and P. Jackson, 2013: Asian monsoon simulations by Community Climate Models CAM4 and CCSM4, Climate Dynamics, DOI 10.1007/s00382-013-1762-6.
[50]W. J. Merryfield, W-S Lee, G. J. Boer, V. Kharin, J. F. Scinocca, G. M. Flato, R. S. Ajayamohan, J. C. Fyfe, Y. Tang and S. Polavarapu, 2013: The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization, Mon. Wea. Rev., 141, 2910-2945.
[49]Z. Wu, H. Lin, Y. Li, Y.Tang, 2013: Seasonal Prediction of Killing-Frost Frequency in South-Central Canada during the Cool/Overwintering-Crop\
Growing Season, J Applied Meteorology and Climatology, 52, 102-113.
[48] Tang, Y., D. Chen, D. Yang and T. Lian, 2012: Methods of Estimating Uncertainty of Climate Prediction and Climate Change Projection,
Climate Chnages/ (book chapter) (ISBN 980-953-307-489-9), page 397 - 420.
[47]Tang, Y., S. An, and W. Duan 2012: Climate Variability and Predictability at Various Time Scales, Advances in Meteorology, Volume 2012, Article ID 857831, doi:10.1155/2012/857831.
[46] Deng, Z., Tang, Y., D. Chen, and Wang, G. 2012: A hybrid ensemble generation method for EnKF for Argo data assimilation, Atmos-Ocean, 50, 129-145.
[45]Yan, X , Tang, Y., 2012: An analysis of multi-model ensemble for seasonal
climate predictions, Quarterly Journal of the Royal Meteorological Society, 15 OCT 2012 | DOI: 10.1002/qj.2019
[44]Yang, D. , Tang, Y., Y. Zhang, and Yang, X., 2012: Information-Based Potential Predictability of the Asian Summer Monsoon in a Coupled Model, J. Geophys. Res., doi:10.1029/2011JD016775.
[43] Ambadan, J. and Tang, Y., 2011: Sigma-Point Particle Filter for Parameter Estimation
in a Multiplicative Noise Environment, Journal of Advances in Modeling Earth Systeme, VOL. 3, M12005, 16 PP., 2011
doi:10.1029/2011MS000065.
[42] Cheng, Y, Tang, Y, D. Chen, 2011: Relationship between Predictability and Forecast Skill of ENSO at Various Time Scales, J. Geophys. Res. (ocean), VOL. 116, C12006, 15 PP., 2011, doi:10.1029/2011JC007249
[41]Deng, Z. , Tang, Y., Freeland, H, 2011: Evaluation of several model error
schemes in EnKF assimilation applied to Argo profile for the Pacific Ocean, J.Geophys.Res. (Ocean), 116, C09027, doi:10.1029/2011JC006942.
[40]Tang, Y. and Z. Deng, 2011: ENSO bred vectors and
predictability in a hybrid coupled model from 1881-2000, J. Climat, Vol. 24, No.1, 298-314.
[39]Deng, Z., Tang, Y., and G. Wang, 2010 Assimilation of Argo Temperature and Salinity Profiles using a bias-aware localized EnKF system for the Pacific Ocean, Ocean Modeling. Vol. 25, p187-205.
[38]Cheng, Y., Tang, Y., P. Jackson, D. Chen, and Z. Deng , 2010:
Ensemble Construction and Verification of the Probabilistic ENSO
Prediction in the LDEO5 Model, J. Climate, Vol. 23, p5476-5479.
[37]Cheng, Y., Tang, Y., P. Jackson, D. Chen, X. Zhou and Z. Deng ,
2010: Further Analysis of Singular Vector and ENSO predictability from 1876-2003---PartII: Singular value and predictability, Climate Dynamics, DOI 10.1007/s00382-009-0728-z.
[36] Zhou, X. , and Tang, Y., 2010: Singular vectors
and ENSO predictability . Advance in Geosciences (Atmospheric Science), Vol. 16, 110-120.( reprint from the press)
[35]Tang, Y. and Deng, Z., 2010: Tropical Pacific upper ocean heat content variations and ENSO
predictability during the period from 1881-2000 . Advance in Geosciences (Ocean Science), Vol 18, 87-108.( reprint from the press)
[34]Tang, Y. and Z. Deng, 2010: Low-dimensional nonlinearity of ENSO
and its impact on predictability, Physica D, doi:10.1016/j.physd.2009.11.006.
[33]Tang, Y and J. Amabadan, 2009: Reply to comment on "Sigma-point Kalman Filters for the assimilation of strongly nonlinear systems". J. Atmos. Sci., Vol 66(11), 3501-3503 (Comment here).
[32]Zhou, X., Y. Tang and Z. Deng, 2009: Assimilation of historical SST data for long-term ENSO retrospective forecasts , Ocean Modeling, Vol 30, 143-154.
[31]Cheng, Y., Tang, Y. , X. Zhou, P. Jackson, D. Chen, 2009: Further Analysis of Singular Vector and
ENSO predictability from 1876-2003---Part I: Singular Vector and the Control Factors , Climate Dynamics, 10.1007/s00382-009-0595-7.
[30]Deng, Z. and Tang, Y. , 2009: Reconstructing the past wind stresses over
the tropical Pacific Ocean from 1875 to 1947. J. Applied Meteorology and
Climatology, Vol. 48 (6), 1181-1198.
[29]
Ambadan, T. J., and Y. Tang, 2009: Sigma-point Kalman Filters for the assimilation of strongly nonlinear systems . J. Atmos. Sci., 66(2), 261-285.
[28]Zhou, X., Y. Tang, Y. Cheng and Z. Deng, 2009: Improve El Nino prediction by singular vector analysis in a coupled model. J Atmos. Ocean. Tech, 26(3), 626-634.
[27]
Yu, B., Y. Tang, X. B. Zhang and A. Niitsoo, 2008: An analysis on observed and simulated PNA associated atmospheric diabatic deating . Climate Dynamics, DOI 10.1007/s00382-008-0432-4.
[26] Tang, Y. and B. Yu, 2008: An analysis of nonlinear relationship between the MJO and ENSO. J. Japan Met. Soc., 86(6), 867-881.
[25] Deng, Z. and Y. Tang, 2008: The retrospective prediction of ENSO from 1881-2000 by a hybrid coupled model - (II) Interdecadal and decadal variations in predictability . Climate Dynamics, DOI 10.1007/s00382-008-0398-2.
[24] Deng, Z., Y. Tang and X. Zhou, 2008: The retrospective prediction of ENSO from 1881-2000
by a hybrid coupled model - (I): SST Assimilation with Ensemble Kalman Filter . Climate Dynamics, DOI 10.1007/s00382-008-0399-1.
[23] Tang, Y ., Z. Deng, X. Zhou, Y. Cheng and D. Chen, 2008: Interdecadal Variation of ENSO Predictability in Multiple Models . J. Climate, Vol. 21, 4811-4833.
[22] Tang, Y. and B. Yu, 2008: MJO and its relationship to ENSO . J Geophys Res, 113, D14106, doi:10.1029/2007JD009230.
[21] Tang, Y. , R. Kleeman and A. Moore, 2008: Comparison of Information-based Measures of Forecast Uncertainty in Ensemble ENSO Prediction. J. Climate, Vol. 21, No.2, 230-247.
[20] Tang, Y. , H. Lin and A. Moore, 2008: Measuring the potential predictability of ensemble climate predictions. J Geophys Res, VOL. 113, D04108, doi:10.1029/2007JD008804.
[19] Zhou, X., Y. Tang and Z. Deng, 2007: The impact of nonlinear atmosphere on the fastest error growth of ENSO prediction. Climate Dynamics, DOI: 10.1007/s00382-007-0302-5.
[18] Tang, Y ., H. Lin, J. Derome, and M. K. Tippett 2007: A predictability measure applied to seasonal predictions of the Arctic Oscillation , J. Climate, 20, 4733-4750.
[17] Tang, Y ., Kleeman, R. and Miller, S., 2006: ENSO predictability of a fully coupled GCM model using singular vector analysis. J. Climate, 19, 3361-3377.
[16] Moore, A, J. Zavala-Garay, Y. Tang, R. Kleeman, J. Vialard, A. Weaver, K. Sahami, D. L. T. Anderson and M. Fisher
2006: Optimal Forcing Patterns for Coupled Models of ENSO J. Climate, 19, 4683-4699.
[15] Hacker, J., J. Hansen, J. Berner, Y. Chen, G.Eshel, G. Hakim, S.Lazarus,
S. Majumdar, R. Morss, A. Poje, Y. Tang, C. Webb, 2005: Future Scientific directions: Predictability. Bull. Amer. Meteorol. Soc., Vol 86, 1733-1737
[14] Tang, Y , Kleeman, R., and Moore, A., 2005; On the reliability of ENSO dynamical predictions. J. Atmos. Sci., Vol. 62, No. 6, 1770-1791.
[13] Tippett, M. K., R. Kleeman and Y. Tang, 2004: Measuring the potential utility of seasonal climate predictions. Geophys. Res. Lett., 31, L22201, doi:10.1029/2004GL021575.
[12] Tang, Y , Kleeman, R., and Moore, A., 2004; A simple method for estimating variations in the predictability of ENSO.
Geophy. Res. Letters, Vol. 31, No. 17, L17205 10.1029/2004GL020673.
[11] Tang, Y , Kleeman, R., and Moore, A., 2004; SST
assimilation experiments in a tropical Pacific Ocean model, J Phys. Oceangr.
Vol 34, No. 3, 623-642.
[10] Tang, Y , Kleeman, R., Moore, A., Weaver, A., and Vialard, J.,2004: An off-line numerically efficient initialization scheme in an oceanic general circulation model for ENSO prediction. J. Geophy. Res (ocean), 109, C05014, doi:10.1029/2003JC002159.
[9] Tang, Y , Kleeman, R., Moore, A, Weaver, A. and Vialard, J., 2003:
The use of ocean reanalysis products to initialize ENSO predictions, Geophy. Res.
Letters, Vol. 30, No. 13, 1694, 10.1029/2003GL017664.(selected as AGU journal highlight)
[8] Kleeman, R., Tang, Y , and and Moore, A. 2003: The calculation
of climatically relevant singular vectors in the presence of weather noise, J. Atmos. Sci.
, Vol. 60, 2856-2867.
[7] Tang, Y , and Hsieh, W. W, 2003: Nonlinear modes of
decadal-scale and interannual variability of the subsurface thermal structure in the
Pacific ocean , JGR (ocean), Vol. 108 No. C3, 10.1029/2001JC001236.
[6] Tang, Y , and Hsieh, W. W, 2003: ENSO simulation and
predictions using a hybrid coupled model with data assimilation J. of Japan
Met. Soc., vol. 81, No.1, 1-19.
[5] Tang, Y. , and Kleeman, R. 2002:
A new strategy for SST assimilation for ENSO prediction, Geophysical
Research Letters, 10.1029/2002GL014860,12 September 2002.
[4] Tang, Y., 2002: Hybrid coupled models of the tropical
Pacific -- Interannual variability. Climate Dynamics, 19, 331-342.
[3] Tang, Y., and Hsieh, W. W, 2002: Hybrid coupled models of the tropical
Pacific -- ENSO prediction . Climate Dynamics, 19, 343-353.
[2] Tang, Y. and Hsieh, W. W, Tang, B and Haines, K, 2001: A neural network
atmospheric model for hybrid coupled modeling, Climate Dynamics, 17, 445-455.
[1] Tang, Y. and Hsieh, W. W, 2001:
Coupling neural networks to incomplete dynamical systems via
variational data assimilation , Mon. Wea. Rev., 129, 818-834.
[0] Tang, Y., 2001: ENSO simulation and predictions using hybrid coupled models with
data assimilation , 193pp, Ph.D Thesis, UBC, Canada.