Abstract
Based on the high-resolution Regional Ocean Modeling System (ROMS) and the conditional nonlinear optimal perturbation (CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander (LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error (denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely, CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction. The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors.
摘要
借助于区域海洋模式系统和条件非线性最优扰动方法, 本文选取两个算例探索了最优初始误差对日本南部黑潮大弯曲路径预报的影响, 并且揭示了最优初始误差的增长机制. 对于每个大弯曲事件, 我们均得到了两类初始误差也就是CNOP, 分别记为CNOP1何CNOP2, 发现它们的大值区主要位于大弯曲上游即九州岛东南部的2500 m以上区域. 此外, 通过分析两类初始误差的空间分布和非线性发展, 我们发现CNOP1有助于通过向西南方向拉伸的方式加强大弯曲路径; 而CNOP2则与CNOP1有着几乎相反的分布, 它则倾向于通过向东北收缩的方式来减弱大弯曲的幅度. 接着, 采用涡能量分析的方法研究了最优初始误差的增长机制, 结果表明在大弯曲路径的形成过程中, 背景场中的能量通过正压和斜压不稳定性转换到了误差场. 因此推测正压过程和斜压过程在CNOP型最优初始误差的增长过程中均起着重要作用.
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References
Akitomo, K., 2008: Effects of stratification and mesoscale eddies on Kuroshio path variation south of Japan. Deep Sea Research Part I: Oceanographic Research Papers, 55(8), 997–1008, https://doi.org/10.1016/j.dsr.2008.03.013.
Birgin, E. G., J. M. Martínez, and M. Raydan, 2000: Nonmonotone spectral projected gradient methods on convex sets. SIAM Journal on Optimization, 10(4), 1196–1211, https://doi.org/10.1137/S1052623497330963.
Chang, Y. L., and L. Y. Oey, 2014: Analysis of STCC eddies using the Okubo–Weiss parameter on model and satellite data. Ocean Dynamics, 64(2), 259–271, https://doi.org/10.1007/s10236-013-0680-7.
Diaz, H., C. Folland, T. Manabe, D. Parker, R. Reynolds, and S. Woodruff, 2002: Workshop on advances in the use of historical marine climate data. WMO Bulletin, 51(4), 377–380.
Fujii, Y., H. Tsujino, N. Usui, H. Nakano, and M. Kamachi, 2008: Application of singular vector analysis to the Kuroshio large meander. J. Geophys. Res., 113(C7), C07026, https://doi.org/10.1029/2007JC004476.
Hayasaki, M., R. Kawamura, M. Mori, and M. Watanabe, 2013: Response of extratropical cyclone activity to the Kuroshio large meander in northern winter. Geophys. Res. Lett., 40(11), 2851–2855, https://doi.org/10.1002/grl.50546.
Kamachi, M., T. Kuragano, S. Sugimoto, K. Yoshita, T. Sakurai, T. Nakano, N. Usui, and F. Uboldi, 2004: Short-range prediction experiments with operational data assimilation system for the Kuroshio south of Japan. Journal of Oceanography, 60(2), 269–282, https://doi.org/10.1023/B:JOCE.0000038333.97882.51.
Kawabe, M., 1986: Transition processes between the three typical paths of the Kuroshio. J. Oceanogr. Soc. Japan, 42(3), 174–191, https://doi.org/10.1007/BF02109352.
Kawabe, M., 1987: Spectral properties of sea level and time scales of Kuroshio path variations. J. Oceanogr. Soc. Japan, 43(2), 111–123, https://doi.org/10.1007/BF02111887.
Kawabe, M., 1995: Variations of current path, velocity, and volume transport of the Kuroshio in relation with the large meander. J. Phys. Oceanogr., 25(12), 3103–3117, https://doi.org/10.1175/1520-0485(1995)025<3103:VOCPVA>2.0.CO;2.
Komori, N., T. Awaji, Y. Ishikawa, and T. Kuragano, 2003: Shortrange forecast experiments of the Kuroshio path variabilities south of Japan using TOPEX/Poseidon altimetric data. J. Geophys. Res., 108(C1), 10–1–10–16, https://doi.org/10.1029/2001JC001282.
Li, Y. N., S. Q. Peng, and D. L. Liu, 2014: Adaptive observation in the South China Sea using CNOP approach based on a 3-D ocean circulation model and its adjoint model. J. Geophys. Res., 119(12), 8973–8986, https://doi.org/10.1002/2014JC010220.
Miyazawa, Y., S. Yamane, X. Y. Guo, and T. Yamagata, 2005: Ensemble forecast of the Kuroshio meandering. J. Geophys. Res., 110(C10), C10026, https://doi.org/10.1029/2004JC002426.
Miyazawa, Y., and Coauthors, 2009: Water mass variability in the western North Pacific detected in a 15-year eddy resolving ocean reanalysis. J. Oceanogr., 65(6), 737–756, https://doi.org/10.1007/s10872-009-0063-3.
Mu, M., W. S. Duan, and B. Wang, 2003: Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 10(6), 493–501, https://doi.org/10.5194/npg-10-493-2003.
Nakamura, H., A. Nishina, and S. Minobe, 2012: Response of storm tracks to bimodal Kuroshio path states south of Japan. J. Climate, 25(21), 7772–7779, https://doi.org/10.1175/JCLID-12-00326.1.
Oey, L. Y., 2008: Loop current and deep eddies. J. Phys. Oceanogr., 38(7), 1426–1449, https://doi.org/10.1175/2007JPO3818.1.
Taft, B., 1972: Characteristics of the flow of the Kuroshio south of Japan. Kuroshio-Its Physical Aspects, H. M. Stommel and K. Yoshida, Eds., University of Tokyo Press, 165–216.
Tsujino, H., N. Usui, and H. Nakano, 2006: Dynamics of Kuroshio path variations in a high-resolution general circulation model. J. Geophys. Res., 111(C11), C11001, https://doi.org/10.1029/2005JC003118.
Usui, N., H. Tsujino, Y. Fujii, and M. Kamachi, 2006: Shortrange prediction experiments of the Kuroshio path variabilities south of Japan. Ocean Dynamics, 56(5–6), 607–623, https://doi.org/10.1007/s10236-006-0084-z.
Usui, N., H. Tsujino, H. Nakano, and Y. Fujii, 2008: Formation process of the Kuroshio large meander in 2004. J. Geophys. Res., 113(C8), C08047, https://doi.org/10.1029/2007JC004675.
Wang, Q., M. Mu, and H. A. Dijkstra, 2012: Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander. Adv. Atmos. Sci., 29(1), 118–134, https://doi.org/10.1007/s00376-011-0199-0.
Wang, Q., M. Mu, and H. A. Dijkstra, 2013: Effects of nonlinear physical processes on optimal error growth in predictability experiments of the Kuroshio Large Meander. J. Geophys. Res., 118(12), 6425–6436, https://doi.org/10.1002/2013JC009276.
Xu, H. M., H. Tokinaga, and S. P. Xie, 2010: Atmospheric effects of the Kuroshio large meander during 2004–05. J. Climate, 23(17), 4704–4715, https://doi.org/10.1175/2010JCLI3267.1.
Yoshida, T., Y. Shimohira, H. Rinno, K. Yokouchi, and H. Akiyama, 2006: Criteria for the determination of a large meander of the Kuroshio based on its path information. Oceanography in Japan, 15, 499–507.
Zhang, K., Q. Wang, M. Mu, and P. Liang, 2016: Effects of optimal initial errors on predicting the seasonal reduction of the upstream Kuroshio transport. Deep Sea Research Part I: Oceanographic Research Papers, 116, 220–235, https://doi.org/10.1016/j.dsr.2016.08.008.
Acknowledgements
This study was supported by the National Natural Scientific Foundation of China (Grant Nos. 41230420 and 41576015), the Qingdao National Laboratory for Marine Science and Technology (Grant No. QNLM2016ORP0107), the NSFC Innovative Group (Grant No. 41421005), the NSFC–Shandong Joint Fund for Marine Science Research Centers (Grant No. U1606402), and the National Programme on Global Change and Air–Sea Interaction (Grant No. GASI-IPOVAI-06).
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Liu, X., Wang, Q. & Mu, M. Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model. Adv. Atmos. Sci. 35, 1362–1371 (2018). https://doi.org/10.1007/s00376-018-8003-z
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DOI: https://doi.org/10.1007/s00376-018-8003-z