Advanced Search
Article Contents

Interdecadal Enhancement in the Relationship between the Western North Pacific Summer Monsoon and Sea Surface Temperature in the Tropical Central-Western Pacific after the Early 1990s


doi: 10.1007/s00376-023-2200-0

  • This study reveals the strengthened interdecadal relationship between the western North Pacific summer monsoon (WNPSM) and tropical central-western Pacific sea surface temperature anomaly (SSTA) in summer after the early 1990s. In the first period (1979–91, P1), the WNPSM-related precipitation anomaly and horizontal wind anomaly present themselves as an analogous Pacific-Japan (PJ)-like pattern, generally considered to be related to the Niño-3 index in the preceding winter. During the subsequent period (1994–2019, P2), the WNPSM-related precipitation anomaly presents a zonal dipole pattern, correlated significantly with the concurrent SSTA in the Niño-4 and tropical western Pacific regions. The negative (positive) SSTA in the tropical western Pacific and positive (negative) SSTA in the Niño-4 region, could work together to influence the WNPSM, noting that the two types of anomalous SSTA configurations enhance (weaken) the WNPSM by the positive (negative) phase PJ-like wave and Gill response, respectively, with an anomalous cyclone (anticyclone) located in the WNPSM, which shows obvious symmetry about the anomalous circulation. Specifically, the SSTA in Niño-4 impacts the WNPSM by an atmospheric Gill response, with a stronger (weaker) WNPSM along with a positive (negative) SSTA in the Niño-4 region. Furthermore, the SSTA in the tropical western Pacific exerts an influence on the WNPSM by a PJ-like wave, with a stronger (weaker) WNPSM along with a negative (positive) SSTA in the tropical western Pacific. In general, SSTAs in the tropical western Pacific and Niño-4 areas could work together to exert influence on the WNPSM, with the effect most likely to occur in the El Niño (La Niña) developing year in P2. However, the SSTAs in the tropical western Pacific worked alone to exert an influence on the WNPSM mainly in 2013, 2014, 2016, and 2017, and the SSTAs in the Niño-4 region worked alone to exert an influence on the WNPSM mainly in Central Pacific (CP) La Niña developing years. The sensitivity experiments also can reproduce the PJ-like wave/Gill response associated with SSTA in the tropical western Pacific/Niño-4 regions. Therefore, the respective and synergistic impacts from the Niño-4 region and the tropical western Pacific on the WNPSM have been revealed, which helps us to acquire a better understanding of the interdecadal variations of the WNPSM and its associated climate influences.
    摘要: 西北太平洋夏季风(WNPSM)和热带太平洋SST之间的相关性在1900s初经历了显著的年代际变化,其中在1990s以前,WNPSM与Niño3显著相关;1990s初期之后,WNPSM与热带西太平洋和Niño4海温异常显著相关。与WNPSM有关的降雨和环流也显示出类似的特征,在1990s之前出现了显著的太平洋-日本遥相关型(PJ-like型),当去除Niño3的影响时,PJ-like型消失;这意味着,1990s以前,与Niño3有关的海温异常是对WNPSM产生影响的一个重要因素。1990s之后,与WNPSM相关的降水呈纬向偶极分布,与Niño4区和热带西太平洋海温均有显著联系;其中,Niño4中的海温异常通过大气Gill响应对WNPSM产生影响,Niño4的正(负)海温异常,伴随着强(弱)WNPSM;与热带西太平洋相关的海温异常通过PJ-like波对WNPSM产生影响,热带西太平洋负(正)海温异常伴随着强(弱)WNPSM。在1990s初期之后的El Niño (La Niña)发展年,热带西太平洋的负(正)海温异常和Niño4地区的正(负)海温异常可能会影响WNPSM,通过正(负)位相PJ波列和Gill响应,增强(削弱)WNPSM。1990s之后,热带西太平洋的正海温异常通过负位相的PJ-like波列单独作用于WNPSM;以及在CP La Niña发展年,Niño4区的负海温异常通过西北太平洋的异常反气旋,削弱WNPSM。
  • 加载中
  • Figure 1.  The locations to add SSTA in sensitive experiments.

    Figure 2.  (a) The 15-year moving correlation between the WNPSMI and simultaneous JJA SST (9.5°S–9.5°N); light (dark) areas denote the 90% (95%) confidence level. (b) The 15-year moving correlation between the WNPSMI and concurrent Niño-4, with the dashed lines indicating statistical significance at the 90% confidence level. (c) The WNPSMI and JJA Niño-4 index, as well as their correlation between 1979–91 (P1) and 1994–2019 (P2); (d) and (e) same as for (b) and (c), but for the TWPI; (f) and (g) are the same as for (b) and (c), but for the TCWI.

    Figure 3.  (a) The correlation and (b) partial correlation of the WNPSMI and synchronous JJA SST after detaching the impact of Niño-3 in the preceding winter during P1. Panels (c) and (d) the same as (a) and (b), but for P2. Panels (e) and (f) the same as (d), but with the synchronous impact of Niño-4 and the TWPI removed, respectively. Light-to-dark shading indicates statistical significance at the 90%, 95%, and 99% confidence levels.

    Figure 4.  Same as in Fig. 3 but for the WNPSMI and grid GPCP precipitation.

    Figure 5.  Regression map of the (a) 850-hPa horizontal wind (units: m s–1), (b) 200-hPa horizontal wind (units: m s–1), (c) 500-hPa geopotential height (units: gpm), and 2-m temperature (units: °C) on the WNPSMI during P1. (d)–(f) Same as for (a)–(c), but for P2. Red vectors, dots, and red contours indicate statistical significance at the 95% confidence level. The violet shading represents the Tibetan Plateau.

    Figure 6.  (a) Normalized Niño-4 index during P2 and its regression on the (b) 850-hPa horizontal wind (units: m s–1), (c) 200-hPa horizontal wind (units: m s–1), (d) 500-hPa geopotential height (units: gpm), and 2-m temperature (units: °C). (e) The JJA SST during P2, after removing the signals of the TWPI, the SST anomaly is added in the rectangle. (f) The sensitivity experiment minus the control experiment (detailed description in section 2). Red vectors, red contours, and dots represent statistical significance at the 95% confidence level in panels (b)–(d). Light-to-dark shadings represent statistical significance at the 90%, 95%, and 99% confidence levels. Violet shading highlights the Tibetan Plateau.

    Figure 7.  Same as in Fig. 6, but for the TWPI.

    Figure 8.  The composite analyses of the (a) SST, (b) 850-hPa horizontal wind (units: m s–1), (c) 200-hPa horizontal wind (units: m s-1), (d) 500-hPa geopotential height (units: gpm) and 2-m temperature (units: °C), (e) u-component of wind and omega (multiplied by –30) based on 1994, 1997, 2002, 2004, 2015 and 2019 years. Red vectors, red contours, and dots represent statistical significance at the 95% confidence level. Light-to-dark shadings represent statistical significance at the 90%, 95%, and 99% confidence levels. Violet shading denotes the Tibetan Plateau.

    Figure 9.  The difference between the sensitivity experiment and the control experiment, when the negative SSTA is added in the tropical western Pacific and the positive SSTA is added in Niño-4 (marked by a rectangle in Fig. 8a). For a detailed description, see section 2. Horizontal wind differences at (a) 850 hPa and (b) 200 hPa (units: m s–1) are shown. Red vectors indicate key anomalies.

    Figure 10.  Same as in Fig. 8, but for 1996, 1998, and 2010.

    Figure 11.  Same as in Fig. 8, but based on 2013, 2014, 2016, and 2017.

    Figure 12.  Same as in Fig. 8, but based on 1999, 2008, and 2011.

    Figure 13.  (a) Same as in Fig. 6, but for TCWI.

    Table 1.  The six combinations of years with different anomalies of the TWPI and Niño-4, positive (negative) anomalies are defined as beyond (below) the (minus) 0.5 standard deviation.

    TypesYears
    Negative & Positive1994, 1997, 2002, 2004, 2015, 2019
    Positive & Negative1996, 1998, 2010
    Negative & NormalNo
    Positive & Normal2013, 2014, 2016, 2017
    Normal & Negative1999, 2008, 2011
    Normal & Positive2009, 2018
    DownLoad: CSV
  • Adler, R. F., and Coauthors, 2003: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4(6), 1147−1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
    Briegel, L. M., and W. M. Frank, 1997: Large-scale influences on tropical cyclogenesis in the western north Pacific. Mon. Wea. Rev., 125, 1397−1413, https://doi.org/10.1175/1520-0493(1997)125<1397:LSIOTC>2.0.CO;2.
    Cao, X., T. Li, M. Peng, W. Chen, and G. H. Chen, 2014: Effects of monsoon trough interannual variation on tropical cyclogenesis over the western north Pacific. Geophys. Res. Lett., 41, 4332−4339, https://doi.org/10.1002/2014GL060307.
    Choi, K.-S., Y. M. Cha, H.-D. Kim, and S.-D. Kang, 2016: Possible influence of western north Pacific monsoon on TC activity in mid-latitudes of East Asia. Climate Dyn., 46, 1−13, https://doi.org/10.1007/s00382-015-2562-9.
    Chou, C., J.-Y. Tu, and J.-Y. Yu, 2003: Interannual variability of the western north Pacific summer monsoon: Differences between ENSO and non-ENSO years. J. Climate, 16(13), 2275−2287, https://doi.org/10.1175/2761.1.
    Feng, X. F., and L. G. Wu, 2022: Roles of interdecadal variability of the western north Pacific monsoon trough in shifting tropical cyclone formation. Climate Dyn., 58, 87−95, https://doi.org/10.1007/s00382-021-05891-w.
    Fu, C. B., and D. Z. Ye, 1988: The tropical very low-frequency oscillation on interannual scale. Adv. Atmos. Sci., 5, 369−388, https://doi.org/10.1007/BF02656760.
    Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669−700, https://doi.org/10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.
    Guan, B., and J. C. L. Chan, 2006: Nonstationarity of the intraseasonal oscillations associated with the western north Pacific summer monsoon. J. Climate, 19, 622−629, https://doi.org/10.1175/JCLI3661.1.
    He, B., Y. Zhang, T. Li, and W.-T. Hu, 2017: Interannual variability in the onset of the South China Sea summer monsoon from 1997 to 2014. Atmos. Ocean. Sci. Lett., 10, 73−81, https://doi.org/10.1080/16742834.2017.1237853.
    Hu, K. M., and S.-M. Long, 2020: Optimal heat source for the interannual variability of the western north Pacific summer monsoon. Atmos. Ocean. Sci. Lett., 13(1), 41−47, https://doi.org/10.1080/16742834.2019.1680087.
    Huang, Y. Y., B. Wang, X. F. Li, and H. J. Wang, 2018: Changes in the influence of the western Pacific subtropical high on Asian summer monsoon rainfall in the late 1990s. Climate Dyn., 51(1−2), 443−455, https://doi.org/10.1007/s00382-017-3933-1.
    Janowiak, J. E., and P. P. Xie, 2003: A global-scale examination of monsoon-related precipitation. J. Climate, 16(24), 4121−4133, https://doi.org/10.1175/1520-0442(2003)016<4121:AGEOMP>2.0.CO;2.
    Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan Series II, 93, 5−48, https://doi.org/10.2151/jmsj.2015-001.
    Kosaka, Y., and H. Nakamura, 2010: Mechanisms of meridional teleconnection observed between a summer monsoon system and a subtropical anticyclone. Part I: The Pacific–Japan pattern. J. Climate, 23, 5085−5108, https://doi.org/10.1175/2010JCLI3413.1.
    Kwon, M., J.-G. Jhun, B. Wang, S.-I. An, and J.-S. Kug, 2005: Decadal change in relationship between east Asian and WNP summer monsoons. Geophys. Res. Lett., 32(16), L16709, https://doi.org/10.1029/2005GL023026.
    Lee, E.-J., K.-J. Ha, and J.-G. Jhun, 2014: Interdecadal changes in interannual variability of the global monsoon precipitation and interrelationships among its subcomponents. Climate Dyn., 42(9−10), 2585−2601, https://doi.org/10.1007/s00382-013-1762-4.
    Lengaigne, M., J.-P. Boulanger, C. Menkes, P. Delecluse, and J. Slingo, 2004: Westerly wind events in the tropical Pacific and their influence on the coupled ocean-atmosphere system: A review. Earth's Climate: The Ocean-Atmosphere Interaction, C. Wang et al., Eds., American Geophysical Union, 49−69, https://doi.org/10.1029/147GM03.
    Li, T., and B. Wang, 2005: A review on the western north Pacific monsoon: Synoptic-to-interannual variabilities. Terrestrial Atmospheric and Oceanic Sciences, 16, 285−314, https://doi.org/10.3319/TAO.2005.16.2.285(A.
    McPhaden, M. J., F. Bahr, Y. Du Penhoat, E. Firing, S. P. Hayes, P. P. Niiler, P. L. Richardson, and J. M. Toole, 1992: The response of the western equatorial Pacific Ocean to westerly wind bursts during November 1989 to January 1990. J. Geophys. Res. Oceans, 97(C9), 14 289−14 303, https://doi.org/10.1029/92JC01197.
    Molinari, J., and D. Vollaro, 2013: What percentage of western north Pacific tropical cyclones form within the monsoon trough. Mon. Wea. Rev., 141, 499−505, https://doi.org/10.1175/MWR-D-12-00165.1.
    Neale, R. B., and Coauthors, 2012: Description of the NCAR community atmosphere model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 274 pp.
    Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos., 108(D14), 4407, https://doi.org/10.1029/2002JD002670.
    Tan, X. X., Y. M. Tang, T. Lian, S. W. Zhang, T. Liu, and D. K. Chen, 2020: Effects of semistochastic westerly wind bursts on ENSO predictability. Geophys. Res. Lett., 47(14), e2019GL086828, https://doi.org/10.1029/2019GL086828.
    Vega, I., P. Ribera, and D. Gallego, 2020: Characteristics of the onset, withdrawal, and breaks of the western north pacific summer monsoon in the 1949–2014 period. J. Climate, 33(17), 7371−7389, https://doi.org/10.1175/JCLI-D-19-0734.1.
    Wang, B., and X. H. Xu, 1997: Northern Hemisphere summer monsoon singularities and climatological intraseasonal oscillation. J. Climate, 10(5), 1071−1085, https://doi.org/10.1175/1520-0442(1997)010<1071:NHSMSA>2.0.CO;2.
    Wang, B., and Z. Fan, 1999: Choice of south Asian summer monsoon indices. Bull. Amer. Meteor. Soc., 80, 629−638, https://doi.org/10.1175/1520-0477(1999)080<0629:COSASM>2.0.CO;2.
    Wang, B., and LinHo, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15(4), 386−398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.
    Wang, B., R. G. Wu, and X. H. Fu, 2000: Pacific–east Asian teleconnection: How does ENSO affect East Asian climate. J. Climate, 13, 1517−1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
    Wang, B., R. G. Wu, and K.-M. Lau, 2001: Interannual variability of the Asian summer monsoon: Contrasts between the Indian and the western north Pacific-east Asian monsoons. J. Climate, 14, 4073−4090, https://doi.org/10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2.
    Wang, B., R. G. Wu, and T. Li, 2003: Atmosphere-warm ocean interaction and its impacts on Asian–Australian monsoon variation. J. Climate, 16(8), 1195−1211, https://doi.org/10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2.
    Wang, L., and J.-Y. Yu, 2018: A recent shift in the monsoon centers associated with the tropospheric biennial oscillation. J. Climate, 31(1), 325−340, https://doi.org/10.1175/JCLI-D-17-0349.1.
    Wang, L., J.-Y. Yu, and H. Paek, 2017: Enhanced biennial variability in the Pacific due to Atlantic capacitor effect. Nature Communications, 8, 14887, https://doi.org/10.1038/ncomms14887.
    Wu, C.-R., 2013: Interannual modulation of the Pacific Decadal Oscillation (PDO) on the low-latitude western north Pacific. Progress in Oceanography, 110, 49−58, https://doi.org/10.1016/j.pocean.2012.12.001.
    Wu, L., Z. P. Wen, R. H. Huang, and R. G. Wu, 2012: Possible linkage between the monsoon trough variability and the tropical cyclone activity over the western north Pacific. Mon. Wea. Rev., 140, 140−150, https://doi.org/10.1175/MWR-D-11-00078.1.
    Wu, M. M., and L. Wang, 2019: Enhanced correlation between ENSO and western north Pacific monsoon during boreal summer around the 1990s. Atmos. Ocean. Sci. Lett., 12(5), 376−384, https://doi.org/10.1080/16742834.2019.1641397.
    Wu, R. G., 2002: Processes for the northeastward advance of the summer monsoon over the western north Pacific. Journal of the Meteorological Society of Japan Series II, 80(1), 67−83, https://doi.org/10.2151/jmsj.80.67.
    Wu, R. G., and B. Wang, 2000: Interannual variability of summer monsoon onset over the western north Pacific and the underlying processes. J. Climate, 13(14), 2483−2501, https://doi.org/10.1175/1520-0442(2000)013<2483:IVOSMO>2.0.CO;2.
    Xie, S.-P., K. M. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-western Pacific climate during the summer following El Niño. J. Climate, 22, 730−747, https://doi.org/10.1175/2008JCLI2544.1.
    Xie, Y. Y., Q. Huang, J. X. Chang, S. Y. Liu, and Y. M. Wang, 2016: Period analysis of hydrologic series through moving-window correlation analysis method. J. Hydrol., 538, 278−292, https://doi.org/10.1016/j.jhydrol.2016.04.024.
    Xu, K., and R. Y. Lu, 2015: Break of the western north Pacific summer monsoon in early August. J. Climate, 28, 3420−3434, https://doi.org/10.1175/JCLI-D-14-00588.1.
    Xu, K., and R. Y. Lu, 2016: Change in tropical cyclone activity during the break of the western north Pacific summer monsoon in early August. J. Climate, 29, 2457−2469, https://doi.org/10.1175/JCLI-D-15-0587.1.
    Xu, K., and R. Y. Lu, 2018: Decadal change of the western north Pacific summer monsoon break around 2002/03. J. Climate, 31, 177−193, https://doi.org/10.1175/JCLI-D-16-0739.1.
    Xu, P. Q., L. Wang, and W. Chen., 2019: The British–Baikal corridor: A teleconnection pattern along the summertime polar front jet over Eurasia. J. Climate, 32(3), 877−896, https://doi.org/10.1175/JCLI-D-18-0343.1.
    Yim, S.-Y., J.-G. Jhun, and S.-W. Yeh, 2008: Decadal change in the relationship between east Asian-western north Pacific summer monsoons and ENSO in the mid-1990s. Geophys. Res. Lett., 35, L20711, https://doi.org/10.1029/2008GL035751.
    Yu, J.-Y., P.-K. Kao, H. Paek, H.-H. Hsu, C.-W. Hung, M.-M. Lu, and S.-I. An, 2015: Linking emergence of the central Pacific El Niño to the Atlantic multidecadal oscillation. J. Climate, 28, 651−662, https://doi.org/10.1175/JCLI-D-14-00347.1.
    Zeng, X. B., and E. Lu, 2004: Globally unified monsoon onset and retreat indexes. J. Climate, 17(11), 2241−2248, https://doi.org/10.1175/1520-0442(2004)017<2241:GUMOAR>2.0.CO;2.
    Zhang, R. H., A. Sumi, and M. Kimoto, 1996: Impact of El Niño on the East Asian monsoon: A diagnostic study of the ‘86/87 and ‘91/92 events. Journal of the Meteorological Society of Japan Series II, 74, 49−62, https://doi.org/10.2151/jmsj1965.74.1_49.
    Zhao, H. K., S. H. Chen, and P. J. Klotzbach, 2019: Recent strengthening of the relationship between the western north Pacific monsoon and western north Pacific tropical cyclone activity during the boreal summer. J. Climate, 32, 8283−8299, https://doi.org/10.1175/JCLI-D-19-0016.1.
    Zhao, J. W., R. F. Zhan, Y. Q. Wang, and H. M. Xu, 2018: Contribution of the interdecadal Pacific oscillation to the recent abrupt decrease in tropical cyclone genesis frequency over the western north Pacific since 1998. J. Climate, 31(20), 8211−8224, https://doi.org/10.1175/JCLI-D-18-0202.1.
    Zhou, W., and J. C. L. Chan, 2005: Intraseasonal oscillations and the South China Sea summer monsoon onset. International Journal of Climatology, 25, 1585−1609, https://doi.org/10.1002/joc.1209.
    Zhou, W., and J. C. L. Chan, 2007: ENSO and the South China Sea summer monsoon onset. International Journal of Climatology, 27, 157−167, https://doi.org/10.1002/joc.1380.
    Zong, H. J., and L. G. Wu, 2015: Re-examination of tropical cyclone formation in monsoon troughs over the western north Pacific. Adv. Atmos. Sci., 32, 924−934, https://doi.org/10.1007/s00376-014-4115-2.
  • [1] LIU Xiangwen, WU Tongwen, YANG Song, LI Qiaoping, CHENG Yanjie, LIANG Xiaoyun, FANG Yongjie, JIE Weihua, NIE Suping, 2014: Relationships between Interannual and Intraseasonal Variations of the Asian-Western Pacific Summer Monsoon Hindcasted by BCC_CSM1.1(m), ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1051-1064.  doi: 10.1007/s00376-014-3192-6
    [2] Yujie WU, Wansuo DUAN, 2018: Impact of SST Anomaly Events over the Kuroshio-Oyashio Extension on the "Summer Prediction Barrier", ADVANCES IN ATMOSPHERIC SCIENCES, 35, 397-409.  doi: 10.1007/s00376-017-6322-0
    [3] Minmin WU, Xugang PENG, Baiyang CHEN, Lei WANG, Jinwen WENG, Weijian LUO, 2023: Recent Enhancement in Co-Variability of the Western North Pacific Summer Monsoon and the Equatorial Zonal Wind, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1597-1616.  doi: 10.1007/s00376-023-2215-6
    [4] Buwen DONG, LU Riyu, 2013: Interdecadal Enhancement of the Walker Circulation over the Tropical Pacific in the Late 1990s, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 247-262.  doi: 10.1007/s00376-012-2069-9
    [5] Liwei ZOU, Tianjun ZHOU, Jianping TANG, Hailong LIU, 2020: Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 800-816.  doi: 10.1007/s00376-020-9268-6
    [6] LI Shuanglin, CHEN Xiaoting, 2014: Quantifying the Response Strength of the Southern Stratospheric Polar Vortex to Indian Ocean Warming in Austral Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 492-503.  doi: 10.1007/s00376-013-2322-x
    [7] Juan AO, Jianqi SUN, 2016: The Impact of Boreal Autumn SST Anomalies over the South Pacific on Boreal Winter Precipitation over East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 644-655.  doi: 10.1007/s00376-015-5067-x
    [8] HAN Jinping, WANG Huijun, 2007: Interdecadal Variability of the East Asian Summer Monsoon in an AGCM, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 808-818.  doi: 10.1007/s00376-007-0808-0
    [9] Ya GAO, Huijun WANG, Dong CHEN, 2017: Interdecadal Variations of the South Asian Summer Monsoon Circulation Variability and the Associated Sea Surface Temperatures on Interannual Scales, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 816-832.  doi: 10.1007/ s00376-017-6246-8
    [10] Ben TIAN, Hong-Li REN, 2022: Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 427-442.  doi: 10.1007/s00376-021-1189-5
    [11] HU Ruijin, LIU Qinyu, MENG Xiangfeng, J. Stuart GODFREY, 2005: On the Mechanism of the Seasonal Variability of SST in the Tropical Indian Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 451-462.  doi: 10.1007/BF02918758
    [12] Kaiming HU, Yingxue LIU, Gang HUANG, Zhuoqi HE, Shang-Min LONG, 2020: Contributions to the Interannual Summer Rainfall Variability in the Mountainous Area of Central China and Their Decadal Changes, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 259-268.  doi: 10.1007/s00376-019-9099-5
    [13] FU Jianjian, LI Shuanglin, LUO Dehai, 2009: Impact of Global SST on Decadal Shift of East Asian Summer Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 192-201.  doi: 10.1007/s00376-009-0192-z
    [14] Pan SONG, Jiang ZHU, Zhong ZHONG, Linlin QI, Xiaodan WANG, 2016: Impact of Atmospheric and Oceanic Conditions on the Frequency and Genesis Location of Tropical Cyclones over the Western North Pacific in 2004 and 2010, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 599-613.  doi: 10.1007/s00376-015-5046-2
    [15] CAO Xi, CHEN Shangfeng, CHEN Guanghua, CHEN Wen, WU Renguang, 2015: On the Weakened Relationship between Spring Arctic Oscillation and Following Summer Tropical Cyclone Frequency over the Western North Pacific: A Comparison between 1968-1986 and 1989-2007, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1319-1328.  doi: 10.1007/s00376-015-4256-y
    [16] FU Jianjian, LI Shuanglin, 2013: The Influence of Regional SSTs on the Interdecadal Shift of the East Asian Summer Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 330-340.  doi: 10.1007/s00376-012-2062-3
    [17] Shuangmei MA, Congwen ZHU, Juan LIU, 2020: Combined Impacts of Warm Central Equatorial Pacific Sea Surface Temperatures and Anthropogenic Warming on the 2019 Severe Drought in East China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1149-1163.  doi: 10.1007/s00376-020-0077-8
    [18] LIN Pengfei, LIU Hailong, YU Yongqiang, ZHANG Xuehong, 2011: Response of Sea Surface Temperature to Chlorophyll-a Concentration in the Tropical Pacific: Annual Mean, Seasonal Cycle, and Interannual Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 492-510.  doi: 10.1007/s00376-010-0015-2
    [19] LIN Pengfei, LIU Hailong, ZHANG Xuehong, 2007: Sensitivity of the Upper Ocean Temperature and Circulation in the Equatorial Pacific to Solar Radiation Penetration Due to Phytoplankton, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 765-780.  doi: 10.1007/s00376-007-0765-7
    [20] BIAN Lingen, LIN Xiang, 2012: Interdecadal Change in the Antarctic Circumpolar Wave during 1951--2010, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 464-470.  doi: 10.1007/s00376-011-1143-z

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 18 July 2022
Manuscript revised: 27 February 2023
Manuscript accepted: 13 March 2023
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Interdecadal Enhancement in the Relationship between the Western North Pacific Summer Monsoon and Sea Surface Temperature in the Tropical Central-Western Pacific after the Early 1990s

    Corresponding author: Lian-Tong ZHOU, zlt@mail.iap.ac.cn
  • 1. College of Geography and Tourism, Hengyang Normal University, Hengyang 421008, China
  • 2. Center of Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract: This study reveals the strengthened interdecadal relationship between the western North Pacific summer monsoon (WNPSM) and tropical central-western Pacific sea surface temperature anomaly (SSTA) in summer after the early 1990s. In the first period (1979–91, P1), the WNPSM-related precipitation anomaly and horizontal wind anomaly present themselves as an analogous Pacific-Japan (PJ)-like pattern, generally considered to be related to the Niño-3 index in the preceding winter. During the subsequent period (1994–2019, P2), the WNPSM-related precipitation anomaly presents a zonal dipole pattern, correlated significantly with the concurrent SSTA in the Niño-4 and tropical western Pacific regions. The negative (positive) SSTA in the tropical western Pacific and positive (negative) SSTA in the Niño-4 region, could work together to influence the WNPSM, noting that the two types of anomalous SSTA configurations enhance (weaken) the WNPSM by the positive (negative) phase PJ-like wave and Gill response, respectively, with an anomalous cyclone (anticyclone) located in the WNPSM, which shows obvious symmetry about the anomalous circulation. Specifically, the SSTA in Niño-4 impacts the WNPSM by an atmospheric Gill response, with a stronger (weaker) WNPSM along with a positive (negative) SSTA in the Niño-4 region. Furthermore, the SSTA in the tropical western Pacific exerts an influence on the WNPSM by a PJ-like wave, with a stronger (weaker) WNPSM along with a negative (positive) SSTA in the tropical western Pacific. In general, SSTAs in the tropical western Pacific and Niño-4 areas could work together to exert influence on the WNPSM, with the effect most likely to occur in the El Niño (La Niña) developing year in P2. However, the SSTAs in the tropical western Pacific worked alone to exert an influence on the WNPSM mainly in 2013, 2014, 2016, and 2017, and the SSTAs in the Niño-4 region worked alone to exert an influence on the WNPSM mainly in Central Pacific (CP) La Niña developing years. The sensitivity experiments also can reproduce the PJ-like wave/Gill response associated with SSTA in the tropical western Pacific/Niño-4 regions. Therefore, the respective and synergistic impacts from the Niño-4 region and the tropical western Pacific on the WNPSM have been revealed, which helps us to acquire a better understanding of the interdecadal variations of the WNPSM and its associated climate influences.

摘要: 西北太平洋夏季风(WNPSM)和热带太平洋SST之间的相关性在1900s初经历了显著的年代际变化,其中在1990s以前,WNPSM与Niño3显著相关;1990s初期之后,WNPSM与热带西太平洋和Niño4海温异常显著相关。与WNPSM有关的降雨和环流也显示出类似的特征,在1990s之前出现了显著的太平洋-日本遥相关型(PJ-like型),当去除Niño3的影响时,PJ-like型消失;这意味着,1990s以前,与Niño3有关的海温异常是对WNPSM产生影响的一个重要因素。1990s之后,与WNPSM相关的降水呈纬向偶极分布,与Niño4区和热带西太平洋海温均有显著联系;其中,Niño4中的海温异常通过大气Gill响应对WNPSM产生影响,Niño4的正(负)海温异常,伴随着强(弱)WNPSM;与热带西太平洋相关的海温异常通过PJ-like波对WNPSM产生影响,热带西太平洋负(正)海温异常伴随着强(弱)WNPSM。在1990s初期之后的El Niño (La Niña)发展年,热带西太平洋的负(正)海温异常和Niño4地区的正(负)海温异常可能会影响WNPSM,通过正(负)位相PJ波列和Gill响应,增强(削弱)WNPSM。1990s之后,热带西太平洋的正海温异常通过负位相的PJ-like波列单独作用于WNPSM;以及在CP La Niña发展年,Niño4区的负海温异常通过西北太平洋的异常反气旋,削弱WNPSM。

    • The Asian summer monsoon is composed of the Indian summer monsoon (ISM), East Asian summer monsoon (EASM), and the western North Pacific summer monsoon (WNPSM) and is considered to be one of the most important climate systems on Earth (Wang et al., 2000, 2001; Kwon et al., 2005; Lee et al., 2014). Compared to the ISM and EASM, the WNPSM, mainly located at 5°–15°N, 100°–130°E and 20°–30°N, 110°–140°E, has received less attention (Wang et al., 2001). However, a large proportion of the rainfall in the Indochina Peninsula, Philippines, and South China is associated with the WNPSM (Vega et al., 2020), which is prone to large variations, which can affect agriculture and the lives of millions of people (Wu and Wang, 2000; Wu, 2002; Chou et al., 2003). Therefore, understanding the variability of the WNPSM is of great socioeconomic significance.

      The WNPSM features complex spatiotemporal variations, mainly in terms of its burst, withdrawal, and breaks, along with its northward progression. The onset date displays a high interannual variability (Vega et al., 2020), and during this period, the rainfall is mainly located in the South China Sea (Wu and Wang, 2000; Wang and LinHo, 2002). In contrast, the WNPSM withdrawal exhibits a lower interannual variation (Vega et al., 2020), spanning from September to November (Wang and LinHo, 2002; Janowiak and Xie, 2003; Zeng and Lu, 2004). In addition, during the period of northeastward movement, the WNPSM features a distinct monsoon break phenomenon. When the WNPSM passes through the Philippines and the Mariana Islands (Wu and Wang, 2000; Wang and LinHo, 2002; Zhou and Chan, 2007), it presents a prominent interannual variation in either intensity or duration (Wang and Xu, 1997; Xu and Lu, 2015).

      The diverse variations of the WNPSM have different climate impacts, mainly regarding the associated circulation, precipitation, and tropical cyclone (TC) events. A strong (weak) WNPSM connects with the enhanced (reduced) precipitation over the subtropical WNP (Chou et al., 2003) and the negative (positive) rainfall anomalies along the Mei-yu/Baiu front (Wang et al., 2001). A suppressed WNPSM can even remotely influence summer rainfall reductions over the Great Plains of the United States (Wang et al., 2001). During break periods of the WNPSM, striking convection suppression and remarkable decreases in precipitation over regions to the Northeast of the WNP (10°–20°N, 140°–160°E) occur (Xu and Lu, 2016). The WNPSM is also related to extreme precipitation through its influence on tropical cyclones (TCs). The WNPSM break and WNPSM trough can influence the formation location, genesis frequency, and strength of TCs (Gray, 1968; Wu et al., 2012; Molinari and Vollaro, 2013; Zong and Wu, 2015; Choi et al., 2016) by means of supplying the associated dynamic and thermodynamic conditions (Gray, 1968; Briegel and Frank, 1997; Wu et al., 2012; Cao et al., 2014; Zong and Wu, 2015; Zhao et al., 2019), further leading to some extreme rainfall events in associated regions.

      The influences from the WNPSM on associated weather and climate are documented, and in addition, the impacts from other factors on the WNPSM are also investigated, such as intraseasonal oscillations (ISO), atmospheric heat sources, tropical SSTs, etc. The ISO have a significant impact on the interannual variability of onset, breaks in the WNPSM, and atmosphere–ocean interaction is associated with the western Pacific subtropical high (WPSH) (Li and Wang, 2005; Zhou and Chan, 2005; Xu and Lu, 2015; He et al., 2017; Huang et al., 2018; Wang and Yu, 2018; Wu and Wang, 2019), as well as its seasonal cycle (Wu and Wang, 2000; Guan and Chan, 2006). Atmospheric heat sources over the WNP (Wang et al., 2001) and the tropical Indian Ocean (Xie et al., 2009) are important in forming the circulation pattern associated with the WNPSM. Besides, Hu and Long (2020) further considered that the combined action of atmospheric heating (cooling) over the subtropical WNP and cooling (heating) in the tropical Indian Ocean and the midlatitudes from China to southern Japan plays an important role in enhancing (weakening) the WNPSM. In addition, a significant connection between the WNPSM and El Niño–Southern Oscillation (ENSO) has also been investigated (Wang et al., 2001). A weak (strong) WNPSM is more commonplace during the La Niña (El Niño) developing year, and a strong (weak) WNPSM tends to appear in the La Niña (El Niño) decaying year (Wang et al., 2001; Chou et al., 2003). From the 1980s on, the WNPSM has had a strong tendency to begin later (earlier) and finish earlier (later) in an eastern Pacific (EP) El Niño (La Niña) background, and the withdrawal of the WNPSM is delayed (advanced) during a CP La Niña (El Niño) (Vega et al., 2020). The interdecadal modes of variability for the WNPSM trough are mainly related to the Pacific decadal oscillation (PDO) and the interdecadal Pacific oscillation (IPO) in different periods of the past decades (Feng and Wu, 2022). Besides, the decadal change in the WNPSM break after 2002/2003 was attributed to the differences in the evolution of SST in the warm pool region of the western Pacific after 2003 (Xu and Lu, 2018). Yim et al. (2008) considered that the WNPSM in the period 1979–93 is mainly linked to the warming of SST in the Niño 3 region (5°S–5°N, 150°–90°W), but over the period 1994–2006, the characteristics of the WNPSM is more closely associated with the SST warming in the Niño-4 region (5°S–5°N, 160°E–150°W).

      In general, previous studies have mainly focused on the synoptic and interannual variability of the WNPSM and their association with different key sea regions in different situations. However, the premise that different key SST areas may work together to exert an influence on the WNPSM has garnered little attention. The present study explores this issue. We have further confirmed the result shown in Yim et al. (2008) before carrying out the research in this study. Aside from the enhanced influence of the Niño-4 region after 1994, we also find that the influence of the tropical western Pacific on the WNPSM is also enhanced. Namely, the interdecadal strengthening of the relationship between the WNPSM and tropical central-western Pacific after the early 1990s has been found in the study. Moreover, we also explore when Niño-4 and tropical western Pacific work together to impact the WNPSM and when they work alone to influence the WNPSM.

      In this paper, the characteristics and associated physical processes of the interdecadal change in the relationship between the SSTA in the tropical central-western Pacific and WNPSM are investigated. The data and methods used in the study are described in section 2. Section 3 confirms the enhanced interdecadal relationship between the WNPSM and SSTA in the tropical central-western Pacific after the early 1990s. Section 4 outlines how the tropical central and western SSTA influences the WNPSM after the interdecadal shift of the early 1990s. Section 5 discusses when the tropical western Pacific and Niño-4 area work coherently to influence the WNPSM and when they work alone to influence the WNPSM. Finally, conclusions and a discussion are presented in section 6.

    2.   Data and Methods
    • This paper applies the JRA-55 monthly reanalysis data with a 1.25° × 1.25° horizontal resolution (Kobayashi et al., 2015), focusing on the period from 1979 to 2019. We adopted the monthly precipitation data from the Global Precipitation Project (GPCP), with a 2.5° × 2.5° horizontal resolution (Adler et al., 2003). Monthly average SST data (HadISST version 1.1) were obtained from the Hadley Centre, with 1° × 1° horizontal resolution (Rayner et al., 2003). A 15-year sliding correlation is applied in this study, which can extract the active time ranges of two factors acting mutually, resisting signal noise (Xie et al., 2016). Here, the width of the moving window (15 years) was determined considering the sample size.

      The WNPSM index (hereafter WNPSMI) is defined as the normalized difference of the 850-hPa zonal wind between the southern region (5°–15°N, 100°–130°E) average and northern region (20°–30°N, 110°–140°E) average, following Wang and Fan (1999) and Wang et al. (2001). Namely, the bigger the WNPSMI value is, the stronger the WNPSM is. The Niño-3 index is defined as the average SSTAs in the eastern equatorial Pacific (5°S–5°N, 90°–150°W) (e.g. Wang et al., 2001; Yim et al., 2008). Here, the acronym JJA (June, July, and August) refers to the summer. In the figures, “A” and “C” indicate the presence of an anomalous anticyclone and a cyclone, respectively, which are marked in Figs. 511. We also define the tropical western Pacific index (TWPI) using SSTAs averaged over 5°S–5°N, 120°–140°E, and the Niño-4 index using SSTAs averaged over 5°S–5°N, 160°E–150°W. Finally, the tropical central-western Pacific thermal contrast index (TCWI) is defined as the normalized sequential difference between Niño-4 and the TWPI.

      To confirm the relative influence of the Niño-4 and tropical western Pacific on the WNPSM, we perform numerical experiments using the Community Atmospheric Model of version 5.0, which is widely used for a variety of atmosphere- and climate-related studies and offers an ability to be configured with a variety of physical parameterization suites of varying complexity. The model is developed by the National Center for Atmospheric Research with an approximate 1.9° × 2.5° (latitude-longitude) spatial resolution and 31 vertical levels (Neale et al., 2012). We perform one control experiment and three sensitivity experiments. In all experiments, the model is integrated for 20 years. The control experiment serves as a reference for the sensitivity experiment, with climatological monthly SST for the period 1981–2010 specified in the global oceans. In the sensitivity experiments, the SST forcing is composed of the climatological monthly SST with the monthly idealized SST anomalies added in Niño-4 (5°S–5°N, 160°E–150°W), marked in the right box of Fig. 1, in the tropical western Pacific (5°S–5°N, 120°–140°E), marked in the left box of Fig. 1, and in both Niño-4 (5°S–5°N, 160°E–150°W) and the tropical western Pacific (5°S–5°N, 120°–140°E) in the left and right boxes of Fig. 1, all of which are determined based statistical analysis. The specified monthly SST forcing repeats yearly, and the largest SST anomalies are 0.6°C and –0.6°C. In the first sensitivity experiment, positive SST anomalies are added in the central Pacific to the climatological monthly SST, and the results are displayed in Fig. 6f. In the second sensitivity experiment, negative SST anomalies are added in the western Pacific to the climatological monthly SST, and the results are shown in Fig. 7f. In the third sensitivity experiment, positive and negative SST anomalies are added in the above two regions, respectively, to the climatological monthly SST, as shown in Fig. 9. The differences in horizontal wind anomalies between the sensitivity experiments and control experiments from the last 16 years represent the atmospheric circulation response to the JJA SST anomalies.

      Figure 1.  The locations to add SSTA in sensitive experiments.

    3.   Interdecadal change in the relationship between the WNPSM and the tropical central-west Pacific SST
    • As displayed in Fig. 2a, the spatial domains of significant correlation between the WNPSM and the tropical SST are mainly concentrated in the tropical Indian Ocean before the 1990s, but after the 1990s, the significant areas mainly appear in the tropical central-west Pacific. This means that a strong (weak) WNPSM is accompanied by a cold (warm) SSTA in the tropical Indian Ocean before the early 1990s. Whereas, when a positive (negative) SSTA appears in the Niño-4 area (5°S–5°N, 160°E–150°W) and a negative (positive) SSTA appears in the tropical west Pacific (5°S–5°N, 120°–140°E), a strong (weak) WNPSM easily generates after the early 1990s. The key SSTA areas associated with strength of the WNPSM differ greatly before and after 1990, therefore, we divide the research period into two subsets 1979–91 (Period 1, referred to as P1) and 1994–2019 (Period 2, P2).

      Figure 2.  (a) The 15-year moving correlation between the WNPSMI and simultaneous JJA SST (9.5°S–9.5°N); light (dark) areas denote the 90% (95%) confidence level. (b) The 15-year moving correlation between the WNPSMI and concurrent Niño-4, with the dashed lines indicating statistical significance at the 90% confidence level. (c) The WNPSMI and JJA Niño-4 index, as well as their correlation between 1979–91 (P1) and 1994–2019 (P2); (d) and (e) same as for (b) and (c), but for the TWPI; (f) and (g) are the same as for (b) and (c), but for the TCWI.

      As shown in Figs. 2b and 2c, there is an obvious interdecadal change in the relationship between Niño-4 and the WNPSMI, with an insignificant correlation during P1, and a significant correlation, up to 0.41 (p < 0.05), during P2. Similarly, as shown in Figs. 2d and 2e, there is an obvious interdecadal change in the relationship between the TWPI and WNPSMI, which also have an insignificant correlation during P1, and a significant correlation, up to –0.56 (p < 0.01), during P2. As shown in Figs. 2f and 2g, we define the TCWI and calculate its correlation with the WNPSMI, to obtain a correlation coefficient as high as 0.54 (p < 0.01) during P2.

      The correlation between the TWPI and WNPSMI (–0.56) during P2 is more significant than that between Niño-4 and the WNPSMI (0.41) and that between the TCWI and WNPSMI (0.54), which implies that the SSTA in Niño-4 and the TWPI do not always work in coherence and that the tropical western Pacific SST may play a more important role in influencing the WNPSM during P2.

      As displayed in Fig. 3a, the significant correlation area is mainly concentrated in the tropical Indian Ocean in JJA. However, when the impact of Niño-3 in the preceding winter is detached, the correlation decreases dramatically, as presented in Fig. 3b. Hence, the WNPSM is mainly related to SSTA of Niño-3 in the preceding winter during P1, consistent with the result obtained by Yim et al. (2008).

      Figure 3.  (a) The correlation and (b) partial correlation of the WNPSMI and synchronous JJA SST after detaching the impact of Niño-3 in the preceding winter during P1. Panels (c) and (d) the same as (a) and (b), but for P2. Panels (e) and (f) the same as (d), but with the synchronous impact of Niño-4 and the TWPI removed, respectively. Light-to-dark shading indicates statistical significance at the 90%, 95%, and 99% confidence levels.

      As shown in Fig. 3c, the significant region of correlation is mainly located in the tropical western Pacific and central Pacific. However, when the influence from Niño-3 in the preceding winter is removed (Fig. 3d), the significant correlation in the tropical western and central Pacific still remains, which suggests that SSTA associated with the WNPSMI during P2 is not governed by Niño-3 in the preceding winter, which differs greatly from that in P1. When the concurrent influence of Niño-4 (Fig. 3e) is removed, the SSTA in the tropical western Pacific still remains, and only the SSTA in the central Pacific disappears. However, when the influence of the TWPI is removed, the significance over the tropical central-west Pacific decreases dramatically (Fig. 3f), which suggests that the TWPI may play a more important and independent role in exerting influences on the WNPSMI during P2 than Niño-4.

      As displayed in Fig. 4a, the most visible precipitation anomaly is a Pacific-Japan (PJ)-like pattern in JJA during P1. Nevertheless, when the impact from Niño 3 in the preceding winter is removed, the areas significant above the 90% confidence level almost disappear (Fig. 4b), which further indicates that the relationship between the WNPSMI and precipitation over the western Pacific and East Asia is mainly dominated by Niño-3 during P1.

      Figure 4.  Same as in Fig. 3 but for the WNPSMI and grid GPCP precipitation.

      As displayed in Fig. 4c, the significant precipitation anomaly demonstrates a dipole pattern during P2, with a negative correlation to the west of 140°E and a positive correlation to the east of 140°E, which means that a strong WNPSM is accompanied by a positive rainfall anomaly in the tropical western Pacific and a negative rainfall anomaly in the maritime continent. When the influence from Niño-3 in the preceding winter and concurrent Niño-4 is removed, a statistically significant dipole pattern remains (Figs. 4d, e), which bears a similarity to the pattern shown in SSTA in Fig. 3c, except within a smaller scope and a westward shift. In contrast, the dipole pattern obviously weakens with the removal of the influence from the TWPI (Fig. 4f), which means that the precipitation linked with the WNPSM during P2 is mainly connected with both Niño-4 and the TWPI, both of which are minimally affected by the Niño-3 of the preceding winter. Moreover, the tropical western Pacific may play a more important role, similar to the results obtained in Fig. 3.

      The circulations related to the WNPSMI also have different features during P1 and P2. During P1, when the WNPSM is strong, the anomaly of horizontal wind related to the WNPSM exhibits a positive-phase PJ-like pattern, with anomalous cyclones existing at 0°–30°N and mid-high latitudes (45°–60°N) with an anomalous 850-hPa anticyclone between them (30°–45°N) (Fig. 5a), which parallels the pattern displayed in Fig. 4a. A similar tripolar pattern also exists in the 200-hPa horizontal wind field, albeit with an anomalous cyclone near the equator tilting northward, with a stronger middle pole and a weaker cyclone in the northern-most pole (Fig. 5b). During P1, the PJ-like pattern features an equivalent barotropic structure in the lower and upper troposphere. Furthermore, the PJ-like pattern could also be distinguished in the 500-hPa geopotential height field, with the statistically significant area centered at the pole near the equator, and the statistically significant area (p <0.05) in 2-m air temperature only concentrated in the middle pole (Fig. 5c).

      Figure 5.  Regression map of the (a) 850-hPa horizontal wind (units: m s–1), (b) 200-hPa horizontal wind (units: m s–1), (c) 500-hPa geopotential height (units: gpm), and 2-m temperature (units: °C) on the WNPSMI during P1. (d)–(f) Same as for (a)–(c), but for P2. Red vectors, dots, and red contours indicate statistical significance at the 95% confidence level. The violet shading represents the Tibetan Plateau.

      During P1, the correlation is 0.88 between the WNPSMI and PJ index, the latter of which is defined as the first principal component of the EOF (Empirical orthogonal function decomposition) analysis of 850-hPa vorticity field over (0°–60°N, 100°–160°E) based on Kosaka and Nakamura (2010). When the impact of Niño-3 in the preceding winter is detached (figure omitted), the PJ pattern vanishes, similar to Fig. 4b. Therefore, during P1, the anomalous circulation related to the WNPSMI, mainly presents a tripole pattern, which is governed by Niño-3 in the preceding winter.

      In contrast, the circulation anomalies associated with the WNPSMI differ, to some extent, during P2. The anomalous wind field features a positive PJ-like pattern in 850-hPa, with an anomalous cyclone to the south of the equator (Fig. 5d). The positive PJ-like pattern also appears at 200-hPa, with two centers in the northern-most pole, and a narrower middle pole also with an anomalous anticyclone on either side of the equator (Fig. 5e). The circulation anomalies on both sides of equator presents a Gill response-like pattern (Figs. 5d, e). However, during P2, the tripole pattern can also be discerned in terms of the 500-hPa geopotential height anomaly, while the northern-most polarity in the 2-m temperature anomaly is more significant (Fig. 5f), which is different from that in P1. In addition, as shown in Fig. 5f, the anomalous circulation in 500-hPa geopotential height between 40°–80°N seems to be a British-Baikal Corridor-like (BBC-like) wave, with polarities in 60°–80°E, 90°–120°E, 130°–150°E (Xu et al., 2019).

      Based on the above analyses, one may conclude that the correlation between the WNPSM and tropical Pacific SST underwent a conspicuous interdecadal shift around the early 1990s, as the WNPSM became significantly linked to the tropical Indian Ocean, governed by the influence of Niño-3 in the preceding winter during P1, and significantly connected with the tropical western Pacific and Niño-4 SSTA, both of which are minimally influenced by Niño- 3 in the preceding winter during P2. It remains possible that the SSTA in the tropical western Pacific played a more important role during P2, compared with Niño-4, identical to the results obtained in the rainfall and circulation patterns associated with the WNPSM.

    4.   The respective influence of SST in the Niño-4 region and the tropical West Pacific on the WNPSM during P2
    • Many studies have revealed that the WNPSM is closely linked to El Niño–Southern Oscillation (ENSO) (Fu and Ye, 1988; Zhang et al., 1996; Wu and Wang, 2000). Therefore, the relationship between the WNPSM and Niño 3 in the previous winter has not been further studied in this paper. This paper mainly focuses on investigating the relationship between the WNPSM and SSTA in the tropical central-western Pacific on the WNPSM during P2.

      The analyses in section 3 show that Niño-4 and the tropical western Pacific impacted the WNPSM during P2. When the influence of Niño-4 is removed, the anomalies associated with the tropical western Pacific still exist. In addition, the correlation coefficient between Niño-4 and the TWPI is only –0.42 during P2. Both observations imply that it is possible that the SSTA over Niño-4 and the tropical western Pacific do not always work in coherence on the WNPSM. Although previous studies have mainly focused on the impact of Niño-4 on the WNPSM after 1994 (i.e., Yim et al., 2008), it now becomes necessary to explore the respective influence of Niño-4 and the tropical Western Pacific on the WNPSM during P2.

    • As shown in Fig. 6b, when a positive SSTA is located in the Niño-4 area, and the signals associated with the TWPI are removed, an anomalous cyclone is excited in the western North Pacific (WNP), accompanied by a strong WNPSM. But the tripole pattern circulation anomaly associated with the WNPSM does not reappear coherently (Figs. 6b, c compared to Figs. 5d, e, respectively). In terms of the 500-hPa geopotential height and 2-m temperature anomalies in Fig. 6d, the PJ-like wave also does not appear, but the BBC-like wave still exists along 60°–80°N, similar to that in Fig. 5f.

      Figure 6.  (a) Normalized Niño-4 index during P2 and its regression on the (b) 850-hPa horizontal wind (units: m s–1), (c) 200-hPa horizontal wind (units: m s–1), (d) 500-hPa geopotential height (units: gpm), and 2-m temperature (units: °C). (e) The JJA SST during P2, after removing the signals of the TWPI, the SST anomaly is added in the rectangle. (f) The sensitivity experiment minus the control experiment (detailed description in section 2). Red vectors, red contours, and dots represent statistical significance at the 95% confidence level in panels (b)–(d). Light-to-dark shadings represent statistical significance at the 90%, 95%, and 99% confidence levels. Violet shading highlights the Tibetan Plateau.

      As shown in Figs. 6b and 6c, anomalous cyclones generate along the south and north sides to the equator at the 850-hPa level, which transforms into an anomalous 200-hPa anticyclonic circulation (Fig. 6c). The configurations of the anomalous circulation in lower and higher layers show that a positive SSTA induces a Gill-type response in the atmosphere, and the 850-hPa anomalous cyclone at the north side to the equator can enhance the WNPSM, which is consistent with circulation anomalies in 20°S–20°N displayed in Figs. 5d and 5e. When the positive SSTA is added in the Niño-4 area, denoted by the rectangle in Fig. 6e, the sensitivity experiment also can reproduce the Gill response (Fig. 6f), the same as the statistical results in Fig. 6b. So, the anomalous cyclone in WNP associated with Gill response may be an important factor of Niño-4 exerting influences on the WNPSM.

    • As shown in Fig. 7b, when the negative SSTA appears in the tropical western Pacific, and the signals related to Niño-4 are removed, an anomalous positive-phase PJ-like pattern can be reproduced, very similar to that in Fig. 5d, and the polarity over the WNP helps to enhance the WNPSM. The anomalous circulation displayed in Fig. 7c also can reproduce that shown in Fig. 5e. In terms of the 500-hPa geopotential height anomaly and 2-m temperature anomaly, the circulation anomaly in Fig. 5f can also be well reproduced, only with a weaker temperature anomaly and stronger geopotential height anomaly (Fig. 7d). The negative SSTA in the tropical western Pacific excites an influence on the WNPSM by the positive PJ-like circulation anomaly, which can be verified in Fig. 7f, because the differences between the sensitivity and control experiment can also reproduce the statistical results, also displaying a PJ-like pattern. Although the Gill response-like pattern also exists along 20°S–20°N, displayed in Fig. 7c, perhaps this is a signal associated with Niño-4 related to the TWPI, displayed in Fig. 7e.

      Figure 7.  Same as in Fig. 6, but for the TWPI.

      Therefore, we initially believe that the SSTA in Niño-4 (tropical western Pacific) generates its influence on the WNPSM by a Gill response (PJ-like wave).

    5.   The different influences of Niño-4 and the TWPI working in coherence and out of coherence
    • In section 4, the impacts from both Niño-4 and tropical western Pacific SSTA on the WNPSM have been analyzed. Through the comparison among Figs. 2c, 2e, 2g, and Figs. 3c, 3e, 3f, it is evident that the SSTA related to the WNPSM, located in Niño-4 and tropical western Pacific, do not always work in accordance, namely not always with a significant negative (positive) SSTA in the tropical western Pacific and positive (negative) SSTA in the Niño-4 area. It then becomes necessary to further analyze when both have worked in coherence and when the tropical western Pacific SSTA and Niño-4 area worked independently to exert an influence on the WNPSM, respectively.

      As shown in Table 1, we define the year with a value (below) beyond (minus) 0.5 standard deviation as a (negative) positive anomaly year for the TWPI and Niño-4 series, respectively. Because the SSTAs in the Niño-4 and tropical western Pacific regions always present a dipole pattern in the climatological mean, there is no need to study "Negative & Negative”, “Positive & Positive”, and “Normal & Normal” types of SSTA associated with the TWPI and Niño-4. Rather, the necessary combinations are displayed in Table 1. During the years 1994, 1997, 2002, 2004, 2015, and 2019, the negative SSTAs in the tropical Western Pacific and positive SSTAs in Niño-4 area could work in accordance to influence the WNPSM; during the years 1996, 1998, and 2010, the positive SSTA in the tropical Western Pacific and negative SSTA in Niño-4 area, also could work in accordance to influence the WNPSM. During 2013, 2014, 2016, and 2017, the positive SSTA anomaly in the tropical western Pacific could have worked alone to influence the WNPSM; during the years 1999, 2008, and 2011, the negative SSTA anomaly in Niño-4 area could have worked alone to exert impacts on the WNPSM. Due to an absence of (or too few) samples, the types of “Negative & Normal” and “Normal & Positive” cases are not discussed.

      TypesYears
      Negative & Positive1994, 1997, 2002, 2004, 2015, 2019
      Positive & Negative1996, 1998, 2010
      Negative & NormalNo
      Positive & Normal2013, 2014, 2016, 2017
      Normal & Negative1999, 2008, 2011
      Normal & Positive2009, 2018

      Table 1.  The six combinations of years with different anomalies of the TWPI and Niño-4, positive (negative) anomalies are defined as beyond (below) the (minus) 0.5 standard deviation.

      Based on the years 1994, 1997, 2002, 2004, 2015, and 2019, we perform composite analyses of the SST and horizontal wind fields to explore the combined effect of the negative SSTA in the tropical Western Pacific and positive SSTA in the Niño-4 area on the WNPSM (Fig. 8). As shown in Fig. 8a, a positive SSTA in the Niño-4 area is reproduced, but the SSTAs are negative in the tropical western Pacific during 1994, 1997, 2002, 2004, 2015, and 2019 years.

      The wind anomalies in Figs. 8b and 8c bear a great resemblance with those displayed in Figs. 5d and 5e, respectively, which are also more similar to Figs. 7b and 7c, compared to those displayed in Figs. 6b and 6c. Relative to the 500-hPa geopotential height anomalies and 2-m temperature anomalies, the circulation anomaly in Fig. 8d is less similar to that in Fig. 5f, with a less significant temperature anomaly and a more significant geopotential height anomaly in the northern-most pole of PJ-like pattern. From the circulation anomalies in Figs. 8b and 8c, both the positive phase PJ-like pattern and Gill response can be discerned. The anomalous years 1994, 1997, 2002, 2004, and 2015 are all El Niño developing years, except for 2019, based on the criteria that the SSTA must be less (greater) –0.5°C (0.5°C) for six consecutive months, including June, July, and August. So, during the El Niño developing year in P2, the SSTA is positive in Niño-4 and negative in tropical Western Pacific, both of which can work in coherence to influence the WNPSM by an atmospheric Gill response connected with the SSTA in Niño-4 and an anomalous PJ-like circulation driven by the western tropical Pacific. As shown in Fig. 8e, an anomalous anti-Walker circulation, located between the Niño-4 area and the tropical western Pacific, helps to maintain the negative SSTA in the tropical western Pacific and a positive SSTA in the Niño-4 area. As displayed in Fig. 9, the statistical analyses can also be reproduced by the sensitivity experiment when the SSTAs are added in the tropical Western Pacific and Niño-4 area (marked in Fig. 8a). The PJ-like pattern and the Gill response can also be approximately reproduced at 850 hPa (Fig. 9a); however, only the Gill response can be reproduced at 200 hPa (Fig. 9b).

      Figure 8.  The composite analyses of the (a) SST, (b) 850-hPa horizontal wind (units: m s–1), (c) 200-hPa horizontal wind (units: m s-1), (d) 500-hPa geopotential height (units: gpm) and 2-m temperature (units: °C), (e) u-component of wind and omega (multiplied by –30) based on 1994, 1997, 2002, 2004, 2015 and 2019 years. Red vectors, red contours, and dots represent statistical significance at the 95% confidence level. Light-to-dark shadings represent statistical significance at the 90%, 95%, and 99% confidence levels. Violet shading denotes the Tibetan Plateau.

      Figure 9.  The difference between the sensitivity experiment and the control experiment, when the negative SSTA is added in the tropical western Pacific and the positive SSTA is added in Niño-4 (marked by a rectangle in Fig. 8a). For a detailed description, see section 2. Horizontal wind differences at (a) 850 hPa and (b) 200 hPa (units: m s–1) are shown. Red vectors indicate key anomalies.

      Based on 1996, 1998, and 2010, we conducted the composite analyses of SST and horizontal wind fields to explore the combined effect of the positive SSTA in the tropical western Pacific and negative SSTA in Niño-4 area on the WNPSM (Fig. 10). As shown in Fig. 10a, the reproduced SSTA is negative in the Niño-4 area, but positive in the tropical western Pacific during 1996, 1998, and 2010.

      Figure 10.  Same as in Fig. 8, but for 1996, 1998, and 2010.

      The wind anomalies in Figs. 10b and 10c present the negative phase of a PJ-like pattern, with the opposite phase to those displayed in Figs. 8b and 8c. Most notably, the negative PJ-like circulation anomalies at 200 hPa do not satisfy a 95% confidence level and also lack the polarity near the equator (Fig. 10c). In terms of the 500-hPa geopotential height anomaly and 2-m temperature anomaly, the PJ-like pattern is much too weak (Fig. 10d). As shown in Fig. 10e, an anomalous Walker circulation can be seen between the tropical western Pacific and tropical central Pacific, that helps to maintain the positive SSTA in the tropical western Pacific and negative SSTA in Niño-4 area. The anomalous years are La Niña developing years, except for 1996, thus in La Niña developing years in P2, the SSTA is negative in Niño-4 and positive in the tropical western Pacific, both of which can work in coherence to influence the WNPSM, showing obvious symmetry, compared to Fig. 8. Because of the prominent symmetry, there is no need to carry out the opposite sensitivity experiment to Fig. 9.

      As shown in Table 1, 2013, 2014, 2016, and 2017 are positive anomalous years of the tropical western Pacific SSTA and Niño-4 normal year. Based on these years, the composite analyses help to isolate the independent factors responsible for its influence on the tropical western Pacific on the WNPSM. As shown in Fig. 11a, anomalous SSTAs existed in the tropical western Pacific during these years, and no significant anomalies over the Niño-4 area were reproduced. Figure 11b shows that there is also a negative phase PJ-like wave at 850 hPa when there is a positive SSTA in the tropical western Pacific. At 200 hPa, Fig. 11c also bears some similarities to Figs. 5e and 7c, only out of phase. The geopotential height and temperature anomalies in Fig. 11d can also reproduce the out-of-phase circulation pattern anomalies, compared to Fig. 5f. As shown in Fig. 11e, anomalous ascending motion appears in the tropical western Pacific. The analyses about the “Positive & Negative” type, namely a positive SSTA in the tropical western Pacific and a normal SSTA in Niño-4, show that tropical SSTA can influence the WNPSM by PJ-like wave. When the SSTA in the tropical western Pacific is positive (negative), a negative (positive) phase PJ-like wave appears and weakens (strengthens) the WNPSM (Figs. 7af, 10ae).

      Figure 11.  Same as in Fig. 8, but based on 2013, 2014, 2016, and 2017.

      As shown in Table 1, 1999, 2008, and 2011 are normal years for a tropical western Pacific SSTA and negative SSTA in Niño-4. Composite analyses based on these years help isolate the independent, influential factors from Niño-4 on the WNPSM. As shown in Fig. 12a, negatively anomalous SSTAs in the Niño-4 region with no significant anomalies over the tropical western Pacific are reproduced during these years. Figure 12b shows an abnormal anticyclone to the north and south of the equator at 850 hPa; however, at 200 hPa, these features develop into cyclones (Fig. 12c), which further confirms that the Niño-4 influences the WNPSM by a Gill response, consistent with the results obtained in Figs. 6b and 6c. The geopotential height and temperature anomalies in Fig. 12d also present weaker circulation anomalies compared to Fig. 6d. At the same time, the anomalous Walker circulation is very weak, with weak descending motion over the Niño-4 area (Fig. 12e). The years of 1999, 2008, 2011 are featured CP La Niña developing events, in which the negative SSTA in Niño-4 area works alone on the WNPSM. When the SSTA in Niño-4 is negative (positive), the abnormal 850-hPa anticyclone (cyclone) in WNP can weaken (enhance) the WNPSM (Figs. 6af, 12ae). Besides, a BBC-like wave appears within 40°–80°N, along with the occurrence of Gill response (Fig. 12b, c), and whether the BBC plays a role in influencing the WNPSM warrants further investigation in future studies.

      Figure 12.  Same as in Fig. 8, but based on 1999, 2008, and 2011.

      On the whole, during the El Niño (La Niña) developing year in P2, the negative (positive) SSTA in the tropical western Pacific and positive (negative) SSTA in the Niño-4 area, could work together to influence the WNPSM, by enhancing (weakening) the WNPSM, by a positive (negative) phase PJ-like wave, and/or through a Gill response. In some years, the positive SSTA in the tropical western Pacific can work alone to influence the WNPSM in P2, weakening the WNPSM by the negative phase of a PJ-like wave. During CP La Niña developing years, the negative SSTA anomaly in the Niño-4 area can work alone to impact the WNPSM in P2 by weakening the WNPSM by a Gill response associated with an anomalous anticyclone in the WNP. The above analyses further confirm that SSTA in the tropical western Pacific (Niño-4) can exert an influence on the WNPSM through a PJ-like wave (Gill response), consistent with the results displayed in section 4.

    6.   Summary and Discussion
    • In light of the above analyses, one may conclude that the correlation between the WNPSM and SSTA in the tropical Pacific underwent a dramatic interdecadal shift around the early 1990s, characterized by an enhanced linkage to tropical western Pacific and Niño-4 SSTA during the summertime from P1 to P2. The rainfall and circulation connected with the WNPSM also display similar features, attaining a significant PJ-like pattern during P1; yet when the influence of Niño 3 from the preceding winter is removed, the PJ-like pattern disappears. These results imply that the Niño 3-related SSTA is the most important factor influencing the WNPSM during P1. During P2, the associated precipitation shows a zonal dipole pattern, connected with both the Niño-4 area and the tropical western Pacific. The SSTA in the Niño-4 area impacts the WNPSM by an atmospheric Gill response, with a stronger (weaker) WNPSM and a positive (negative) SSTA in Niño-4. In addition, an SSTA associated with the tropical western Pacific influences the WNPSM via a PJ-like wave, with a stronger (weaker) WNPSM associated with negative (positive) SSTAs in the tropical western Pacific.

      In the combination of “Negative & Positive” ( “Positive & Negative”) of the TWPI and Niño-4, namely during the El Niño (La Niña) developing year in P2, the negative (positive) SSTA in the tropical western Pacific and positive (negative) SSTA in the Niño-4 area, could work in accordance to influence the WNPSM, enhancing (weakening) the WNPSM, by the positive (negative) phase PJ-like wave and Gill response, with an anomalous cyclone (anticyclone) located in the WNPSM, which shows obvious symmetry. During the “Positive & Normal” combination, the positive SSTA in the tropical western Pacific can work alone to influence the WNPSM in P2, weakening the WNPSM by a negative phase PJ-like wave. During the combination of “Normal & Positive”, namely during CP La Niña developing years, the negative SSTA anomaly in the Niño-4 area can work alone to impact the WNPSM in P2, weakening the WNPSM by a Gill response with an anomalous anticyclone in the WNP. In addition, westerly wind bursts (WWBs) play an important role in ENSO prediction by affecting atmospheric surface zonal currents and triggering eastward downwelling Kelvin waves (McPhaden et al., 1992; Lengaigne et al., 2004). In the last several years, the use of parameterized WWBs has obviously improved the representation of WWBs in coupled models and has led to more accurate simulations of extreme El Niño and central Pacific El Niño events (Tan et al., 2020). With the improvement of Niño-4 prediction skill, the results obtained in this paper would have important implications for the WNPSM prediction. According to the research results, it may be necessary to adjust the strategy of the WNPSM prediction according to the early SST precursor signals in different decades (before and after the 1990s).

      The interdecadal enhancement of the relationship between the WNPSM and SST in the tropical central-west Pacific from P1 to P2 may be linked to the ENSO decadal shift, with more CP ENSO events in P2, which are more closely related to the enhanced influences from the Atlantic multidecadal oscillation (AMO) (Yu et al., 2015; Wang et al., 2017), the variations of the anomalous cyclone/anticyclone in the western North Pacific, and the atmosphere–ocean interaction related to the WPSH (Wang et al., 2003; Huang et al., 2018), all of which may have a direct or indirect connection with the WNPSM. However, Wu and Wang (2019) argue that the concurrent tropical Atlantic (Indian) ocean SST anomalies could constructively reinforce (destructively mitigate) the WNPSM anomalies induced by the summertime Niño-3.4 SST, thus boosting (muting) the correlation between the summertime Niño-3.4 SST and the WNPSM index after (before) the early 1990, which is possibly associated with more frequent occurrences of CP El Niño events and the interdecadal changes in ENSO-associated SST anomalies. Additionally, the PDO and IPO can modulate the relationship between ENSO and the monsoon. For example, the ENSO has a minor (strong) effect on monsoonal winds during its warm (cold) PDO phase (Wu, 2013). The IPO negative phase corresponds to La Niña-like SST anomalies, which strengthens the Walker circulation in the tropical Pacific (Zhao et al., 2018), potentially influencing the WNPSM. These potential teleconnections need to be further investigated in future research.

      If the TCWI is used instead of Niño-4 and the TWPI to study the influence of tropical central-western Pacific on WNPSM, we could obtain the pattern shown in Fig. 13. The circulation anomaly in Fig. 13b resembles that in Fig. 7b, but the anomalous pattern in Fig. 13c is more similar to that in Fig. 6c. As shown in Fig. 13d, the anomalous circulations in 500-hPa geopotential height and 2-m temperature fields are more similar to Fig. 6d, rather than Fig. 7d. Simply using the TCWI to study the influence of tropical central-western Pacific on the WNPSM cannot distinguish the relative contributions by the Niño-4 and tropical western Pacific to the WNPSM. So, in the paper, we used Niño-4 and the TWPI, but not the TCWI, to study the interdecadal enhancement of the relationship between the WNPSM and SSTAs in the tropical central-western Pacific. Finally, if the reanalysis data used in the paper is replaced by NCEP or EAR-40 data, the results are also robust.

      Figure 13.  (a) Same as in Fig. 6, but for TCWI.

      Acknowledgements. We cordially thank all the dataset providers. This work is supported by the Fund Project of the Hengyang Normal University (2022QD11), and the National Natural Science Foundation of China (Grant No. 42105063).

      Data availability statement. The JRA-55 monthly reanalysis data is freely available at http://jra.kishou.go.jp/JRA-55/. The GPCP data is available at https://psl.noaa.gov/data/gridded/data.gpcp.html. The HadISST data is available at https://www.metoffice.gov.uk/hadobs/hadisst/. The Community Atmospheric Model of version 5.0 can be downloaded at https://www.cesm.ucar.edu/models/cesm1.1/. The analysis scripts are available upon request from the corresponding author.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return