几种统计模型对热带印度洋海温异常的预报

方玥炜; 唐佑民; 李俊德; 刘婷

doi:10.3969/j.issn.1001-909X.2018.01.001

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海洋学研究 ›› 2018, Vol. 36 ›› Issue (1) : 1-15. DOI: 10.3969/j.issn.1001-909X.2018.01.001

研究论文

几种统计模型对热带印度洋海温异常的预报

方玥炜^1,2, 唐佑民^*1,2,3, 李俊德^1,2, 刘婷^1,2

作者信息 +

Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly

FANG Yue-wei^1,2, TANG You-min^*1,2,3, LI Jun-de^1,2, LIU Ting^1,2

Author information +

文章历史 +

摘要

本文利用神经网络模型、多元线性回归模型和马尔科夫模型分别建立了统计预报模型,对热带印度洋海表温度异常(SSTA)和印度洋偶极子(IOD)指数进行了63 a的长时间回报实验,并详细比较了线性和非线性统计预报模型的差异。结果表明:统计模型对IOD指数的预报技巧和现有动力模式预报技巧相差不大,对偶极子指数(DMI)有效预报时效为3个月,东极子指数(EIO)为5～6个月,西极子指数(WIO)达到8～9个月。IOD事件强烈的季节锁相特性使得对秋季的DMI指数可以提前4个月做出有效预报。加入同期的ENSO指数来预报IOD指数,能有效地提高IOD预报技巧,特别是对IOD峰值的预报。复杂的神经网络模型和简单的多元线性回归模型在对SSTA和IOD指数的预报具有同等的效果。

Abstract

The tropical Indian Ocean Sea Surface Temperature Anomaly (SSTA) and the Indian Ocean Dipole (IOD) indices are predicted, using the multiple linear regression model, the Markov model and the neural network model respectively. 63 years' hindcast experiments are set up to compare the differences between linear and nonlinear statistical models in detail. And the results reveal that the statistical models are little different from the complicated dynamic model. Their skillful prediction (correlation coefficients above 0.5) could reach 3 months for DMI, about 5-6 months for EIO index and 8-9 months for WIO. Since the IOD event has a strong seasonal phase lock, the DMI can be predicted previously for 4 months in fall. When the synchronistic ENSO index is added as a predictor, the prediction skill, especially the IOD peak, will be improved. The complicated neural network and the simple regression model are proved to be with a similar prediction skill.

导出引用

方玥炜, 唐佑民, 李俊德, 刘婷. 几种统计模型对热带印度洋海温异常的预报[J]. 海洋学研究. 2018, 36(1): 1-15 https://doi.org/10.3969/j.issn.1001-909X.2018.01.001

FANG Yue-wei, TANG You-min, LI Jun-de, LIU Ting. Several statistical models to predict tropical Indian Ocean Sea Surface Temperature Anomaly[J]. Journal of Marine Sciences. 2018, 36(1): 1-15 https://doi.org/10.3969/j.issn.1001-909X.2018.01.001

中图分类号： P732.6

参考文献

[1] LIU Qin-yu, XIE Shang-ping, ZHENG Xiao-tong. Tropical ocean-atmosphere interaction[M]. Beijing: Higher Education Press,2013:78-79.
刘秦玉,谢尚平,郑小童.热带海洋-大气相互作用[M].北京:高等教育出版社,2013:78-79.
[2] CHAMBERS D P, TAPLEY B D, STEWART R H. Anomalous warming in the Indian Ocean coincident with El Niño[J]. Journal of Geophysical Research Oceans, 1999, 104(C2):3 035-3 047.
[3] KLEIN S A, SODEN B J, LAU N C.Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge[J].Journal of Climate,1999, 12(12): 917-932.
[4] TOURRE Y M, WHITE W B.ENSO signals in global upper-ocean temperature[J]. Journal of Physical Oceanography,1995, 25(6): 1 317-1 332.
[5] SAJI N H, GOSWAMI B N, VINAYACHANDRAN P N, et al. A dipole mode in the tropical Indian Ocean[J].Nature,1999, 401(6 751): 360-363.
[6] WEBSTER P J, MOORE A M, LOSCHNIGG J P,et al. Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997-98[J].Nature,1999, 401(6 751): 356-360.
[7] ASHOK K, GUAN Z, YAMAGATA T. Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO[J].Geophysical Research Letters,2001, 28(23): 4 499-4 502.
[8] BEHERA S K, LUO J J, MASSON S, et al. Paramount impact of the Indian Ocean Dipole on the East African short rains: A CGCM Study[J].Journal of Climate,2005, 18(18): 4 514-4 530.
[9] CAI W, COWAN T, RAUPACH M, et al. Positive Indian Ocean Dipole events precondition southeast Australia bushfires[J].Geophysical Research Letters,2009, 36(19): 387-390.
[10] SAJI N H, YAMAGATA T.Possible impacts of Indian Ocean Dipole Mode events on global climate[J].Climate Research,2003, 25(2): 151-169.
[11] LI C, MU M.The influence of the Indian Ocean Dipole on atmospheric circulation and climate[J].Advances in Atmospheric Sciences,2001, 18(5): 830-843.
[12] XIE S P.Indian ocean capacitor effect on Indo-Western Pacific climate during the summer following El Niño[J]. Journal of Climate,2009, 22(3): 730-747.
[13] YUAN Y, YANG H, ZHOU W, et al. Influences of the Indian Ocean dipole on the Asian summer monsoon in the following year[J]. International Journal of Climatology,2008, 28(14): 1 849-1 859.
[14] GUAN Z, YAMAGATA T.The unusual summer of 1994 in East Asia: IOD teleconnections[J]. Geophysical Research Letters,2003, 30(10): 235-250.
[15] ZHANG L, SIELMANN F, FRAEDRICH K,et al. Variability of winter extreme precipitation in Southeast China: Contributions of SST anomalies[J]. Climate Dynamics,2015, 45(9-10): 2 557-2 570.
[16] LI Jun-de, LIANG Chu-jin, TANG You-ming, et al. A new dipole index of the salinity anomalies of the tropical Indian Ocean[J].Scientific Reports,2016, 6: 24 260.
[17] SHI L, HENDON H H, ALVES O, et al. How predictable is the Indian Ocean Dipole[J]？ Monthly Weather Review,2012, 140(12): 3 867-3 884.
[18] SONG Q, VECCHI G A, ROSATI A J.Predictability of the Indian Ocean sea surface temperature anomalies in the GFDL coupled model[J]. Geophysical Research Letters,2008, 35(2): 196-199.
[19] TOZUKA T, LUO J J, MASSON S, et al. Decadal modulations of the Indian Ocean Dipole in the SINTEX-F1 Coupled GCM[J]. Journal of Climate,2007, 20(13): 2 881-2 894.
[20] WAJSOWICZ R C.Seasonal-to-Interannual forecasting of tropical Indian Ocean Sea surface temperature anomalies: Potential predictability and barriers[J]. Journal of Climate,2007, 20(13): 3 320-3 343.
[21] ZHAO M, WANG G, HENDON H H, et al. Impact of including surface currents on simulation of Indian Ocean variability with the POAMA coupled model[J]. Climate Dynamics,2011, 36(7-8): 1 291-1 302.
[22] LIU H, TANG Y, CHEN D, et al. Predictability of the Indian Ocean Dipole in the coupled models[J]. Climate Dynamics,2017, 48(5-6):1-20.
[23] LIU Hua-feng,ZHANG Xiang-ming,TANG You-min, et al.The progress of the India Ocean Dipole and its predictability[J]. Advances in Marine Science,2014, 32(3):405-414.
刘华锋, 章向明, 唐佑民, 等.印度洋偶极子及其可预报性研究进展[J].海洋科学进展,2014, 32(3):405-414.
[24] LANDMAN W A, MASON S J.Forecasts of near-global sea surface temperatures using canonical correlation analysis[J]. Journal of Climate,1920, 14(18): 3 819-3 833.
[25] KUG J S, KANG I S, LEE J Y, et al. A statistical approach to Indian Ocean sea surface temperature prediction using a dynamical ENSO prediction[J]. Geophysical Research Letters,2004, 31(9): 399-420.
[26] DOMMENGET D, JANSEN M.Predictions of Indian Ocean SST indices with a simple statistical model: a null hypothesis[J]. Journal of Climate,2009, 22(18): 4 930-4 938.
[27] WANG L, ZHENG W F, ZHU J.Preliminary studies on predicting the tropical Indian Ocean Sea surface temperature through combined statistical methods and dynamic ENSO prediction[J]. Atmospheric and Oceanic Science Letters,2013, 6(1): 52-59.
[28] XUE Y, LEETMAA A, JI M.ENSO prediction with Markov models: The impact of sea level[J]. Journal of Climate,2010, 13(13): 849-871.
[29] WU Q, CHEN D.Ensemble forecast of Indo-Pacific SST based on IPCC twentieth-century climate simulations[J]. Geophysical Research Letters,2010, 37(16): 127-137.
[30] WANG Xiao-chuan, SHI Feng, YU Lei, et al. 43 cases analysis of neural network in MATLAB[M]. Beijing: Beihang University Press, 2013:1-3.
王小川,史峰,郁磊,等. MATLAB神经网络43个案列分析[M]. 北京:北京航空航天大学出版社,2013:1-3.
[31] TANGANG F T, HSIEH W W,TANG B.Forecasting the equatorial Pacific sea surface temperatures by neural network models[J]. Climate Dynamics,1997, 13(2): 135-147.
[32] TANG Y, HSIEH W W, TANG B, et al. A neural network atmospheric model for hybrid coupled modelling[J]. Climate Dynamics,2001, 17(17): 445-455.
[33] WU A,HSIEH W W, TANG B.Neural network forecasts of the tropical Pacific sea surface temperatures[J]. Neural Networks,2006, 19(2): 145-154.
[34] ZHU J, HUANG B, KUMAR A,et al. Seasonality in prediction skill and predictable pattern of tropical Indian Ocean SST[J]. Journal of Climate,2015, 28(20): 801-809.
[35] LI T, WANG B, CHANG C P. A theory for Indian Ocean dipole-zonal mode[J]. Journal of Atmospheric Science, 2003,60(17):2 119-2 135.
[36] FENG R, DUAN W, MU M. Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions[J]. Climate Dynamics, 2017,48(3-4):1 173-1 185.

基金

国家自然科学基金面上项目资助(41276029);“全球变化与海气相互作用”专项项目资助(CASI-IPOVAI-06);国家自然科学基金重点项目资助(41530961);国家海洋局第二海洋研究所业务专项项目资助(JG1617,JG1619);卫星海洋环境动力学国家实验室自主课题资助(SOEDZZ1516);国家重点研发计划资助(2016YFC1401703,2016YFC1401701)