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Causes of a Typical Southern Flood and Northern Drought Event in 2015 over Eastern China


doi: 10.1007/s00376-023-2342-0

  • The spatial distribution of summer precipitation anomalies over eastern China often shows a dipole pattern, with anti-phased precipitation anomalies between southern China and northern China, known as the “southern flooding and northern drought” (SF-ND) pattern. In 2015, China experienced heavy rainfall in the south and the worst drought since 1979 in the north, which caused huge social and economic losses. Using reanalysis data, the atmospheric circulation anomalies and possible mechanisms related to the summer precipitation anomalies in 2015 were examined. The results showed that both El Niño and certain atmospheric teleconnections, including the Pacific Japan/East Asia Pacific (PJ/EAP), Eurasia pattern (EU), British–Baikal Corridor pattern (BBC), and Silk Road mode (SR), contributed to the dipole pattern of precipitation anomalies. The combination of these factors caused a southwards shift of the western Pacific subtropical high (WPSH) and a weakening of the East Asian summer monsoon. Consequently, it was difficult for the monsoon front and associated rain band to migrate northwards, which meant that less precipitation occurred in northern China while more precipitation occurred in southern China. This resulted in the SF-ND event. Moreover, further analysis revealed that global sea surface temperature anomalies (SSTAs) or sea-ice anomalies were key to stimulating these atmospheric teleconnections.
    摘要: 中国东部夏季降水异常的空间分布往往呈现偶极子模态,即南方和北方的降水异常呈现反位相,被称为“南涝北旱”型。2015年夏季,中国南方出现强降雨,北方却遭遇了1979年以来最严重的干旱,造成了巨大的社会和经济损失。利用再分析数据,本文研究了与2015年夏季降水异常相关的大尺度环流异常及其可能的机制。结果表明,厄尔尼诺现象和一些大气遥相关模态均有助于此次偶极子降水异常的形成。这些遥相关模态包括了太平洋-日本/东亚-太平洋型遥相关(PJ/EAP)、欧亚型遥相关(EU)、英国-贝加尔湖走廊型遥相关(BBC)和丝绸之路型遥相关(SRP)。上述因素的协同作用导致了西太平洋副热带高压的南移和东亚夏季风的减弱,使得季风锋面及雨带难以向北推进至华北地区,导致华北降水偏少,而南方降水偏多。此外,进一步分析发现,全球海表温度与海冰异常是激发上述大气遥相关模态的关键因素。
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  • Figure 1.  (a) Precipitation anomaly (units: mm) during July–August 2015 relative to the climatology of 1981–2010. (b) Precipitation index 1 (PI1) and (c) precipitation index 2 (PI2), normalized time series of area-weighted mean precipitation in the subregions (31.5°–48°N, 101.5°–130°E) and (17°–28.5°N, 101.5°–130°E), respectively, during July–August 1979–2016. (d) Precipitation index (PI) during July–August 1979–2016 calculated by PI1 and PI2 as in Eq. (3). A positive value of PI indicates the occurrence of the SF-ND pattern in China. Note that these indices are not detrended.

    Figure 2.  The dipole precipitation anomaly (units: mm) in July–August during (a) positive-phase years and (b) negative-phase years. Areas with slashes are significant at the 99% confidence level based on Student’s t-test. (c) Regression maps of July–August 850-hPa wind anomalies with regard to PI during 1979–2016. Light (dark) shading indicates that the values are significant at the 90% (95%) confidence level based on Student’s t-test. (d) The 850-hPa wind anomaly (vectors; units: m s−1) for July–August 2015. The colour shading indicates the anomalous magnitude of wind. The letters A and C indicate the anomalous anticyclone and cyclone, respectively.

    Figure 3.  (a) Regression maps of summer integrated water vapor flux divergence from 300 hPa to 1000 hPa with regard to simultaneous PI during 1979–2015. The dotted area passed the 90% significance test based on Student’s t-test. (b) Anomalous summer integrated water vapor flux divergence (units: g kg−1 m s−1) in 2015 (relative to the climatology of 1981–2010). (c) EASM index and (d) Northern Hemisphere Subtropical High Ridge Position Index during 1979–2015.

    Figure 4.  Anomalous (a) geopotential height (units: gpm) at 500 hPa and (c) meridional winds (units: m s−1) at 250 hPa in July–August 2015. The climate average was calculated from 1981 to 2010. Regression maps of summer (b) 500-hPa geopotential anomalies and (d) 250-hPa meridional winds with regard to concurrent PI during 1979–2016. Light (dark) shading in (b) indicates that the values are significant at the 90% (95%) confidence level, and the dotted area in (d) passes the 95% significance test based on Student’s t-test.

    Figure 5.  (a) Vertical–horizontal cross section averaged along 5°S–5°N for vertical velocity anomalies (units: m s−1) during July–August 2015 (relative to the climatology of 1981–2010). (b) Composite Walker Circulation anomalies over the equator (5°S–5°N) (units: m s−1) in association with the summer EP El Niño. The vertical velocities are magnified 100 times. (c, d) Anomalies of OLR (units: W m−2) in (c) June and (d) July–August 2015 relative to the climatology of 1981–2010. (e) Composite maps of OLR anomalies in July–August with the summer EP El Niño.

    Figure 6.  (a, b) Heterogeneous correlation map of the first mode of the SVD for the detrended and normalized (a) 850-hPa geopotential height and (b) SST during July–August 1979–2016. (c) The corresponding time series. (d) SSTAs in summer 2015 (units: K), with a climate baseline of 1981–2010.

    Figure 7.  (a) Averaged EU index in July–August, SIC in spring, and snow depth (SD) within (55°–70°N, 30°–60°E) in April and May 1979–2015. (b) Snow cover with regard to SIC values during 1979–2016. The dotted area passes the 95% significance test based on Student’s t-test. (c, d) Heterogeneous correlation map of the first mode of the SVD for the detrended and normalized (c) 500-hPa geopotential height during July–August and (d) ICEC (sea-ice concentration) anomalies during spring 1979–2016. (e) The corresponding time series. (f) ICEC in MAM of 2015, with a climate baseline of 1981–2010.

    Figure 8.  (a) Geopotential height (shading; units: gpm) and wave activity flux (vectors; units: m2 s−2) anomalies at 250 hPa in July–August 2015. (b) Anomalous air temperature at 1000 hPa (units: K) and winds (units: m s−1) in July–August 2015. (c) SSTAs (units: K) in July–August 2015 relative to the 1981–2010 climatology.

    Figure 9.  Schematic of the large-scale circulation and meteorological factors that caused the SF-ND precipitation pattern in China in summer 2015.

    Table 1.  Correlation coefficients between indices. Information on how these indices were calculated can be found in section 2. The 99%, 95%, and 90% confidence levels are ±0.403, ±0.312, ±0.264, respectively, according to Student’s t-test. Bold values passed the 90% confidence levels. All indices were detrended.

    EU patternPJ/EAP patternBBC patternSR pattern
    EU pattern
    PJ/EAP pattern−0.540
    BBC pattern−0.7800.562
    SR pattern−0.0210.264−0.223
    EASM0.398−0.180−0.1120.090
    WPSH0.363−0.348−0.167−0.163
    PI10.576−0.6030.586−0.252
    PI2−0.5090.507−0.423−0.021
    PI−0.6460.662−0.6040.146
    DownLoad: CSV

    Table 2.  Correlation coefficients between Niño3 index and the WPSH. The 99%, 95% and 90% confidence levels are ±0.403, ±0.312 and ±0.264, respectively, according to Student’s t-test. Bold values passed the 90% confidence levels.

    WPSH ridge positionWPSH intensityWPSH area
    Niño3_winter−0.5760.5330.661
    Niño3_spring−0.4700.4430.578
    Niño3_summer−0.293−0.082−0.101
    DownLoad: CSV
  • Bader, J., M. D. S. Mesquita, K. I. Hodges, N. Keenlyside, S. Østerhus, and M. Miles, 2011: A review on Northern Hemisphere sea-ice, storminess and the North Atlantic Oscillation: Observations and projected changes. Atmospheric Research, 101, 809−834, https://doi.org/10.1016/j.atmosres.2011.04.007.
    Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth System Science Data, 5(1), 71−99, https://doi.org/10.5194/essd-5-71-2013.
    Chang, C. P., Y. S. Zhang, and T. Li, 2000: Interannual and interdecadal variations of the East Asian Summer Monsoon and tropical Pacific SSTs. Part I: Roles of the subtropical ridge. J. Climate, 13, 4310−4325, https://doi.org/10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2.
    Chen, G. H., and C. Y. Tam, 2010: Different impacts of two kinds of Pacific Ocean warming on tropical cyclone frequency over the western North Pacific. Geophys. Res. Lett., 37, L01803, https://doi.org/10.1029/2009GL041708.
    Chen, L. J., Y. Yuan, M. Z. Yang, J. Q. Zuo, and W. J. Li, 2013: A review of physical mechanisms of the global SSTA impact on EASM. Journal of Applied Meteorological Science, 24(5), 521−532, https://doi.org/10.3969/j.issn.1001-7313.2013.05.002. (in Chinese with English abstract
    Chowdary, J. S., D. Patekar, G. Srinivas, C. Gnanaseelan, and A. Parekh, 2019: Impact of the Indo-Western Pacific Ocean Capacitor mode on South Asian summer monsoon rainfall. Climate Dyn., 53(3), 2327−2338, https://doi.org/10.1007/s00382-019-04850-w.
    Ding, Q. H., and B. Wang, 2005: Circumglobal teleconnection in the Northern Hemisphere summer. J. Climate, 18(17), 3483−3505, https://doi.org/10.1175/JCLI3473.1.
    Enomoto, T., B. J. Hoskins, and Y. Matsuda, 2003: The formation mechanism of the Bonin high in August. Quart. J. Roy. Meteor. Soc., 129(587), 157−178, https://doi.org/10.1256/qj.01.211.
    Feng, J., and W. Chen, 2021: Roles of the north Indian Ocean SST and tropical North Atlantic SST in the latitudinal extension of the anomalous western North Pacific anticyclone during the El Niño decaying summer. J. Climate, 34, 8503−8517, https://doi.org/10.1175/JCLI-D-20-0802.1.
    Feng, J., W. Chen, C.-Y. Tam, and W. Zhou, 2011: Different impacts of El Niño and El Niño Modoki on China rainfall in the decaying phases. International Journal of Climatology, 31, 2091−2101, https://doi.org/10.1002/joc.2217.
    Gao, T., J.-Y. Yu, and H. Paek, 2017: Impacts of four northern-hemisphere teleconnection patterns on atmospheric circulations over Eurasia and the Pacific. Theor. Appl. Climatol., 129(3-4), 815−831, https://doi.org/10.1007/s00704-016-1801-2.
    Gong, D. Y., Yang, J., Kim, SJ., Gao, Y. Q., Guo, D., Zhou T. J., and Hu M., 2011: Spring Arctic Oscillation-East Asian summer monsoon connection through circulation changes over the western North Pacific. Climate Dyn., 37, 2199−2216, https://doi.org/10.1007/s00382-011-1041-1.
    Guan, Z. Y., and T. Yamagata, 2003: The unusual summer of 1994 in East Asia: IOD teleconnections. Geophys. Res. Lett., 30, 1544, https://doi.org/10.1029/2002GL016831.
    Hall, R., R. Erdélyi, E. Hanna, J. M. Jones, and A. A. Scaife, 2015: Drivers of North Atlantic Polar Front jet stream variability. International Journal of Climatology, 35(8), 1697−1720, https://doi.org/10.1002/joc.4121.
    Hirota, N., and M. Takahashi, 2012: A tripolar pattern as an internal mode of the East Asian summer monsoon. Climate Dyn., 39(9-10), 2219−2238, https://doi.org/10.1007/s00382-012-1416-y.
    Hong, X. W., R. Y. Lu, and S. L. Li, 2018: Asymmetric relationship between the meridional displacement of the Asian westerly jet and the Silk Road Pattern. Adv. Atmos. Sci., 35(4), 389−396, https://doi.org/10.1007/s00376-017-6320-2.
    Huang, R. H., and W. J. Li, 1988: Influence and physical mechanism of heat source anomaly over the tropical western Pacific on the subtropical high over East Asia. Scientia Atmospherica Sinica, 12, 107−116, https://doi.org/10.3878/j.issn.1006-9895.1988.t1.08. (in Chinese with English abstract
    Huang, R. H., and F. F. Sun, 1994: Impacts of the thermal state and the convective activities in the tropical western warm pool on the summer climate anomalies in East Asia. Scientia Atmospherica Sinica, 18(2), 141−151, https://doi.org/10.3878/j.issn.1006-9895.1994.02.02. (in Chinese with English abstract
    Huang, R. H., J. L. Chen, L. Wang, and Z. D. Lin, 2012: Characteristics, processes, and causes of the spatio-temporal variabilities of the East Asian monsoon system. Adv. Atmos. Sci., 29, 910−942, https://doi.org/10.1007/s00376-012-2015-x.
    Huang, W. R., Y. H. Chang, and P. H. Huang, 2019: Relationship between the interannual variations of summer convective afternoon rainfall activity in Taiwan and SSTA (Niño3.4) during 1961−2012: Characteristics and mechanisms. Scientific Reports, 9(1), 9378, https://doi.org/10.1038/s41598-019-45901-w.
    Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. International Journal of Climatology, 25, 865−879, https://doi.org/10.1002/joc.1169.
    Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437−472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
    Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation. Bull. Amer. Meteor. Soc., 82, 247−268, https://doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.
    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(19), 5085−5108, https://doi.org/10.1175/2010JCLI3413.1.
    Kosaka, Y., and S. P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501(7467), 403−407, https://doi.org/10.1038/nature12534.
    Lee, S. H., P. D. Williams, and T. H. A. Frame, 2019: Increased shear in the North Atlantic upper-level jet stream over the past four decades. Nature, 572(7771), 639−642, https://doi.org/10.1038/s41586-019-1465-z.
    Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84−87, https://doi.org/10.1038/nature16467.
    Li, H. M., A. G. Dai, T. J. Zhou, and J. Lu, 2010: Responses of East Asian summer monsoon to historical SST and atmospheric forcing during 1950−2000. Climate Dyn., 34(4), 501−514, https://doi.org/10.1007/s00382-008-0482-7.
    Li, H. X., H. P. Chen, H. J. Wang, J. Q. Sun, and J. H. Ma, 2018: Can Barents sea ice decline in spring enhance summer hot drought events over Northeastern China? J. Climate, 31(12), 4705−4725, https://doi.org/10.1175/JCLI-D-17-0429.1.
    Li, Y. F., and L. R. Leung, 2013: Potential impacts of the arctic on interannual and interdecadal summer precipitation over China. J. Climate, 26(3), 899−917, https://doi.org/10.1175/JCLI-D-12-00075.1.
    Liu, X. D., and M. Yanai, 2002: Influence of Eurasian spring snow cover on Asian summer rainfall. International Journal of Climatology, 22(9), 1075−1089, https://doi.org/10.1002/joc.784.
    Liu, Z. Y., and M. Alexander, 2007: Atmospheric bridge, oceanic tunnel, and global climatic teleconnections. Rev. Geophys., 45, RG2005, https://doi.org/10.1029/2005RG000172.
    Lu, R., and Z. Lin, 2009: Role of subtropical precipitation anomalies in maintaining the summertime meridional teleconnection over the western North Pacific and East Asia. J. Climate, 22, 2058−2072, https://doi.org/10.1175/2008JCLI2444.1.
    Lu, R. Y., J. H. Oh, and B. J. Kim, 2002: A teleconnection pattern in upper-level meridional wind over the North African and Eurasian continent in summer. Tellus A, 54, 44−55, https://doi.org/10.3402/tellusa.v54i1.12122.
    Ma, F., A. Z. Ye, J. You, and Q. Y. Duan, 2018: 2015-16 floods and droughts in China, and its response to the strong El Niño. Science of the Total Environment, 627, 1473−1484, https://doi.org/10.1016/j.scitotenv.2018.01.280.
    Muñoz Sabater, J., 2019: ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.68d2bb30.
    Na, Y., and R. Y. Lu, 2023: The concurrent record-breaking rainfall over Northwest India and North China in September 2021. Adv. Atmos. Sci., 40, 653−662, https://doi.org/10.1007/s00376-022-2187-y.
    Nigam, S., and H. S. Shen, 1993: Structure of oceanic and atmospheric low-frequency variability over the tropical Pacific and Indian oceans. Part I: COADS observations. J. Climate, 6, 657−676, https://doi.org/10.1175/1520-0442(1993)006<0657:SOOAAL>2.0.CO;2.
    Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the northern hemisphere summer circulation. J. Meteor. Soc. Japan, 65, 373−390, https://doi.org/10.2151/jmsj1965.65.3_373.
    Nitta, T., and Z. Z. Hu, 1996: Summer climate variability in China and its association with 500 hPa height and tropical convection. J. Meteor. Soc. Japan, 74(4), 425−445, https://doi.org/10.2151/jmsj1965.74.4_425.
    Pedro-Monzonís, M., Solera, A., Ferrer, J., Estrela, T., and Paredes-Arquiola, J., 2015: A review of water scarcity and drought indexes in water resources planning and management. J. Hydrol, 527, 482−493, https://doi.org/10.1016/j.jhydrol.2015.05.003.
    Paek, H., J.-Y. Yu, and C. C. Qian, 2017: Why were the 2015/2016 and 1997/1998 extreme El Niños different? Geophys. Res. Lett., 44(4), 1848−1856, https://doi.org/10.1002/2016GL071515.
    Plumb, R. A., 1985: On the three-dimensional propagation of stationary waves. J. Atmos. Sci., 42(3), 217−229, https://doi.org/10.1175/1520-0469(1985)042<0217:OTTDPO>2.0.CO;2.
    Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401(6751), 360−363, https://doi.org/10.1038/43854.
    Salvador, C., R. Nieto, C. Linares, J. Díaz, and L. Gimeno, 2020: Effects of droughts on health: Diagnosis, repercussion, and adaptation in vulnerable regions under climate change. Challenges for future research. Science of the Total Environment, 703, 134912, https://doi.org/10.1016/j.scitotenv.2019.134912.
    Schott, F. A., S. P. Xie, and J. P. McCreary Jr., 2009: Indian Ocean circulation and climate variability. Rev. Geophys., 47, RG1002, https://doi.org/10.1029/2007RG000245.
    Shi, N., C. Bueh, L. Ji, and P. Wang, 2009: The impact of mid- and high-latitude Rossby wave activities on the medium-range evolution of the EAP pattern during the pre-rainy period of South China. Acta Meteorologica Sinica, 23(3), 300−314.
    Sullivan, A., J. J. Luo, A. C. Hirst, D. H. Bi, W. J. Cai, and J. H. He, 2016: Robust contribution of decadal anomalies to the frequency of central-Pacific El Niño. Scientific Reports, 6(1), 38540, https://doi.org/10.1038/srep38540.
    Sun, J. Q., H. J. Wang, and W. Yuan, 2008: Decadal variations of the relationship between the summer North Atlantic Oscillation and middle East Asian air temperature. J. Geophys. Res.: Atmos., 113(D15), D15107, https://doi.org/10.1029/2007JD009626.
    Weare, B. C., 1979: A statistical study of the relationship between ocean surface temperatures and Indian monsoon rainfall. J. Atmos. Sci., 36, 2279−2291, https://doi.org/10.1175/1520-0469(1979)036<2279:ASSOTR>2.0.CO;2.
    Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109(4), 784−812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>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(9), 1517−1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
    Wang, H. J., 2001: The weakening of the Asian monsoon circulation after the end of 1970's. Adv. Atmos. Sci., 18(3), 376−386, https://doi.org/10.1007/BF02919316.
    Wang, H. J., 2002: The instability of the East Asian summer monsoon–ENSO relations. Adv. Atmos. Sci., 19(1), 1−11, https://doi.org/10.1007/s00376-002-0029-5.
    Wang, H. J., and S. P. He, 2015: The North China/Northeastern Asia severe summer drought in 2014. J. Climate, 28(17), 6667−6681, https://doi.org/10.1175/JCLI-D-15-0202.1.
    Wang, S. S., X. Yuan, and Y. H. Li, 2017: Does a strong El Niño imply a higher predictability of extreme drought? Scientific Reports, 7(1), 40741, https://doi.org/10.1038/srep40741.
    Wang S., Y. Zhang, and J. Feng. 2015: Drought Events and Its Influence in Summer of 2015 in China. J. Arid Environ., 33(5): 888–893, https://doi.org/10.11755/j.issn.1006-7639(2015)-05-0888. (in Chinese with English abstract)
    Wang, X. F., J. H. He, and Y. Lian, 2013: Effect of the previous anomalous heat content in the western Pacific warm pool on the summer rainfall over Northeast China. Acta Meteorologica Sinica, 71(2), 305−317, https://doi.org/10.11676/qxxb2013.024. (in Chinese with English abstract
    Wei, J., Q. Y. Zhang, and S. Y. Tao, 2004: Physical causes of the 1999 and 2000 summer severe drought in North China. Chinese Journal of Atmospheric Sciences, 28(1), 125−137, https://doi.org/10.3878/j.issn.1006-9895.2004.01.12. (in Chinese with English abstract
    Wen, N., L. Li, and J. J. Luo, 2020: Direct impacts of different types of El Niño in developing summer on East Asian precipitation. Climate Dyn., 55(5−6), 1087−1104, https://doi.org/10.1007/s00382-020-05315-1.
    Weng, H. Y., S. K. Behera, and T. Yamagata, 2009: Anomalous winter climate conditions in the Pacific rim during recent El Niño Modoki and El Niño events. Climate Dyn., 32(5), 663−674, https://doi.org/10.1007/s00382-008-0394-6.
    Weng, H. Y., K. Ashok, S. K. Behera, S. A. Rao, and T. Yamagata, 2007: Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer. Climate Dyn., 29(2−3), 113−129, https://doi.org/10.1007/s00382-007-0234-0.
    Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam, 2006: Warming and earlier spring increase Western U.S. forest wildfire activity. Science, 313(5789), 940−943, https://doi.org/10.1126/science.1128834.
    Xie, S. P., Y. Kosaka, Y. Du, K. M. Hu, J. S. Chowdary, and G. Huang, 2016: Indo-western Pacific ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci., 33(4), 411−432, https://doi.org/10.1007/s00376-015-5192-6.
    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(3), 730−747, https://doi.org/10.1175/2008JCLI2544.1.
    Xu, K., D. W. Yang, H. B. Yang, Z. Li, Y. Qin, and Y. Shen, 2015: Spatio-temporal variation of drought in China during 1961−2012: A climatic perspective. J. Hydrol., 526, 253−264, https://doi.org/10.1016/j.jhydrol.2014.09.047.
    Xu, P. Q., L. Wang, and W. Chen, 2019a: 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.
    Xu, P. Q., L. Wang, W. Chen, J. Feng, and Y. Y. Liu, 2019b: Structural changes in the Pacific–Japan pattern in the late 1990s. J. Climate, 32(2), 607−621, https://doi.org/10.1175/JCLI-D-18-0123.1.
    Yang, F. L., and K. M. Lau, 2004: Trend and variability of China precipitation in spring and summer: Linkage to sea-surface temperatures. International Journal of Climatology, 24, 1625−1644, https://doi.org/10.1002/joc.1094.
    Yang, J. L., Q. Y. Liu, S. P. Xie, Z. Y. Liu, and L. X. Wu, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708, https://doi.org/10.1029/2006GL028571.
    Yang, Q., Z. G. Ma, and B. L. Xu, 2017: Modulation of monthly precipitation patterns over East China by the Pacific Decadal Oscillation. Climatic Change, 144, 405−417, https://doi.org/10.1007/s10584-016-1662-9.
    Yatagai, A., and T. Yasunari, 1994: Trends and decadal-scale fluctuations of surface air temperature and precipitation over China and Mongolia during the recent 40 year period (1951−1990). J. Meteor. Soc. Japan, 72(6), 937−957, https://doi.org/10.2151/jmsj1965.72.6_937.
    Yuan, Y., S. Yang, and Z. Q. Zhang, 2012: Different evolutions of the Philippine Sea anticyclone between the eastern and central Pacific El Niño: Possible effects of Indian Ocean SST. J. Climate, 25, 7867−7883, https://doi.org/10.1175/JCLI-D-12-00004.1.
    Yuan, Y., H. Gao, W. J. Li, Y. J. Liu, L. J. Chen, B. Zhou, and Y. H. Ding, 2017: The 2016 summer floods in China and associated physical mechanisms: A comparison with 1998. J. Meteor. Res., 31(2), 261−277, https://doi.org/10.1007/s13351-017-6192-5.
    Zhai, P. M., and Coauthors, 2016: The strong El Niño in 2015/2016 and its dominant impacts on global and China's climate. Acta Meteorologica Sinica, 74(3), 309−321, https://doi.org/10.11676/qxxb2016.049. (in Chinese with English abstract
    Zhang, J., H. S. Chen, and Q. Zhang, 2019: Extreme drought in the recent two decades in northern China resulting from Eurasian warming. Climate Dyn., 52(5−6), 2885−2902, https://doi.org/10.1007/s00382-018-4312-2.
    Zhang, L. X., P. L. Wu, T. J. Zhou, and C. Xiao, 2018: ENSO transition from La Nina to El Niño drives prolonged spring-summer drought over North China. J. Climate, 31(9), 3509−3523, https://doi.org/10.1175/JCLI-D-17-0440.1.
    Zheng, J. Y., and C. Z. Wang, 2021: Influences of three oceans on record-breaking rainfall over the Yangtze River Valley in June 2020. Science China Earth Sciences, 64(10), 1607−1618, https://doi.org/10.1007/s11430-020-9758-9.
    Zhou, T. J., D. Y. Gong, J. Li, and B. Li, 2009: Detecting and understanding the multi-decadal variability of the East Asian Summer Monsoon: Recent progress and state of affairs. Meteor. Z., 18, 455−467, https://doi.org/10.1127/0941-2948/2009/0396.
    Zhu, Y. L., H. J. Wang, W. Zhou, and J. H. Ma, 2011: Recent changes in the summer precipitation pattern in East China and the background circulation. Climate Dyn., 36(7), 1463−1473, https://doi.org/10.1007/s00382-010-0852-9.
  • [1] WANG Zhongrui, Song FENG, TANG Maocang, 2003: A Relationship between Solar Activity and Frequency of Natural Disasters in China, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 934-939.  doi: 10.1007/BF02915516
    [2] Kexin LI, Fei ZHENG, Jiang ZHU, Qing-Cun ZENG, 2024: El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 1017-1022.  doi: 10.1007/s00376-023-3371-4
    [3] WANG Lin, CHEN Wen, ZHOU Wen, 2014: Assessment of Future Drought in Southwest China Based on CMIP5 Multimodel Projections, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1035-1050.  doi: 10.1007/s00376-014-3223-3
    [4] 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
    [5] Chibuike Chiedozie IBEBUCHI, Cameron C. LEE, 2024: Circulation Pattern Controls of Summer Temperature Anomalies in Southern Africa, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 341-354.  doi: 10.1007/s00376-023-2392-3
    [6] ZENG Gang, Wei-Chyung WANG, SUN Zhaobo, LI Zhongxian, 2011: Atmospheric Circulation Cells Associated with Anomalous East Asian Winter Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 913-926.  doi: 10.1007/s00376-010-0100-6
    [7] Chengyang GUAN, Xin WANG, Haijun YANG, 2023: Understanding the Development of the 2018/19 Central Pacific El Niño, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 177-185.  doi: 10.1007/s00376-022-1410-1
    [8] PENG Jing, DONG Wenjie, YUAN Wenping, ZHANG Yong, 2012: Responses of Grassland and Forest to Temperature and Precipitation Changes in Northeast China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1063-1077.  doi: 10.1007/s00376-012-1172-2
    [9] Zichen LI, Qingxiang LI, Tianyi CHEN, 2024: Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature (CMST) Dataset, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 369-376.  doi: 10.1007/s00376-023-3200-9
    [10] Reshmita NATH, Debashis NATH, Qian LI, Wen CHEN, Xuefeng CUI, 2017: Impact of Drought on Agriculture in the Indo-Gangetic Plain, India, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 335-346.  doi: 10.1007/s00376-016-6102-2
    [11] Yueyue LI, Li DAN, Jing PENG, Junbang WANG, Fuqiang YANG, Dongdong GAO, Xiujing YANG, Qiang YU, 2021: Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1580-1595.  doi: 10.1007/s00376-021-0265-1
    [12] Lixia ZHANG, Xiaojing YU, Tianjun ZHOU, Wenxia ZHANG, Shuai HU, Robin CLARK, 2023: Understanding and Attribution of Extreme Heat and Drought Events in 2022: Current Situation and Future Challenges, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1941-1951.  doi: 10.1007/s00376-023-3171-x
    [13] LI Chun, MA Hao, 2012: Relationship between ENSO and Winter Rainfall over Southeast China and Its Decadal Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1129-1141.  doi: 10.1007/s00376-012-1248-z
    [14] Minwei Qian, N. Loglisci, C. Cassardo, A. Longhetto, C. Giraud, 2001: Energy and Water Balance at Soil-Air Interface in a Sahelian Region, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 897-909.
    [15] LI Chongyin, HE Jinhai, ZHU Jinhong, 2004: A Review of Decadal/Interdecadal Climate Variation Studies in China, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 425-436.  doi: 10.1007/BF02915569
    [16] Shaolei TANG, Jing-Jia LUO, Jiaying HE, Jiye WU, Yu ZHOU, Wushan YING, 2021: Toward Understanding the Extreme Floods over Yangtze River Valley in June−July 2020: Role of Tropical Oceans, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 2023-2039.  doi: 10.1007/s00376-021-1036-8
    [17] Lin WANG, Hong-Li REN, Fang ZHOU, Nick DUNSTONE, Xiangde XU, 2023: Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1998-2012.  doi: 10.1007/s00376-023-2288-2
    [18] Fei ZHENG, Shuai HU, Jiehua MA, Lin WANG, Kexin LI, Bo WU, Qing BAO, Jingbei PENG, Chaofan LI, Haifeng ZONG, Yao YAO, Baoqiang TIAN, Hong CHEN, Xianmei LANG, Fangxing FAN, Xiao DONG, Yanling ZHAN, Tao ZHU, Tianjun ZHOU, Jiang ZHU, 2024: Will the Globe Encounter the Warmest Winter after the Hottest Summer in 2023?, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 581-586.  doi: 10.1007/s00376-023-3330-0
    [19] Chan XIAO, Peili WU, Lixia ZHANG, Robin T. CLARK, 2018: Increasing Flash Floods in a Drying Climate over Southwest China, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1094-1099.  doi: 10.1007/s00376-018-7275-7
    [20] Jinling PIAO, Wen CHEN, Ke WEI, Yong LIU, Hans-F. GRAF, Joong-Bae AHN, Alexander POGORELTSEV, 2017: An Abrupt Rainfall Decrease over the Asian Inland Plateau Region around 1999 and the Possible Underlying Mechanism, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 456-468.  doi: 10.1007/s00376-016-6136-5
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Manuscript History

Manuscript received: 21 April 2022
Manuscript revised: 06 December 2022
Manuscript accepted: 15 May 2023
通讯作者: 陈斌, bchen63@163.com
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Causes of a Typical Southern Flood and Northern Drought Event in 2015 over Eastern China

    Corresponding author: Qing YANG, yangqing@tea.ac.cn
    Corresponding author: Er LU, elu@nuist.edu.cn
  • 1. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Xiongan Institute of Innovation, Chinese Academy of Sciences, Xiongan New Area 071800, China

Abstract: The spatial distribution of summer precipitation anomalies over eastern China often shows a dipole pattern, with anti-phased precipitation anomalies between southern China and northern China, known as the “southern flooding and northern drought” (SF-ND) pattern. In 2015, China experienced heavy rainfall in the south and the worst drought since 1979 in the north, which caused huge social and economic losses. Using reanalysis data, the atmospheric circulation anomalies and possible mechanisms related to the summer precipitation anomalies in 2015 were examined. The results showed that both El Niño and certain atmospheric teleconnections, including the Pacific Japan/East Asia Pacific (PJ/EAP), Eurasia pattern (EU), British–Baikal Corridor pattern (BBC), and Silk Road mode (SR), contributed to the dipole pattern of precipitation anomalies. The combination of these factors caused a southwards shift of the western Pacific subtropical high (WPSH) and a weakening of the East Asian summer monsoon. Consequently, it was difficult for the monsoon front and associated rain band to migrate northwards, which meant that less precipitation occurred in northern China while more precipitation occurred in southern China. This resulted in the SF-ND event. Moreover, further analysis revealed that global sea surface temperature anomalies (SSTAs) or sea-ice anomalies were key to stimulating these atmospheric teleconnections.

摘要: 中国东部夏季降水异常的空间分布往往呈现偶极子模态,即南方和北方的降水异常呈现反位相,被称为“南涝北旱”型。2015年夏季,中国南方出现强降雨,北方却遭遇了1979年以来最严重的干旱,造成了巨大的社会和经济损失。利用再分析数据,本文研究了与2015年夏季降水异常相关的大尺度环流异常及其可能的机制。结果表明,厄尔尼诺现象和一些大气遥相关模态均有助于此次偶极子降水异常的形成。这些遥相关模态包括了太平洋-日本/东亚-太平洋型遥相关(PJ/EAP)、欧亚型遥相关(EU)、英国-贝加尔湖走廊型遥相关(BBC)和丝绸之路型遥相关(SRP)。上述因素的协同作用导致了西太平洋副热带高压的南移和东亚夏季风的减弱,使得季风锋面及雨带难以向北推进至华北地区,导致华北降水偏少,而南方降水偏多。此外,进一步分析发现,全球海表温度与海冰异常是激发上述大气遥相关模态的关键因素。

    • Drought is an extreme climatic event that has multiple negative effects on ecohydrology and socioeconomics, including loss of crops (Lesk et al., 2016), water scarcity (María Pedro-Monzonís et al., 2015), wildfires (Westerling et al., 2006), and indirect health effects (Salvador et al., 2020). Northern China is a traditional settlement area and a key agricultural area in China. The 2015 drought in this area caused an economic loss of more than CNY 9.88 billion according to official 2015 statistics (Wang et al., 2015). The rainfall in northern China mainly occurs during the summer months of July–August. Due to the water vapour transport driven by the summer monsoon circulation, the rainfall during July–August accounts for more than 50% of regional annual rainfall (Zhang et al., 2019). Therefore, raising awareness of the causes of drought in this region during July–August has positive implications for both the government and the public. Drought in the north often occurs concurrently with floods in the south, which is called the “southern flood and northern drought” (SF-ND) pattern in China (Yatagai and Yasunari, 1994; Nitta and Hu, 1996; Zhou et al., 2009; Li et al., 2010). In the summer of 2015, a typical SF-ND event occurred. This study focuses on the causes of this anomaly.

      Many previous studies have analyzed the causes of SF-ND events (Ding and Wang, 2005; Xu et al., 2015; Zhang et al., 2018, 2019). The East Asian summer monsoon (EASM) weakened in the late 1970s, resulting in more precipitation in southern China and less precipitation in northern China (Wang, 2001; Zhou et al., 2009). Most of the systems that influence precipitation in this region do so by affecting the western Pacific subtropical high (WPSH) and the EASM. The Eurasian pattern (EU; Wallace and Gutzler, 1981), a quasi-stationary wave train over Eurasia, is thought to be associated with the summer droughts in northern China during 1999 and 2000 (Wei et al., 2004). The dispersion of Rossby wave energy over the Northern Hemisphere has a significant correlation with abnormal climate in China. On the basis of affecting the summer atmospheric circulation in East Asia, it also affects the precipitation and temperature in summer (Shi et al., 2009; Wang and He, 2015; Wang et al., 2017). The Pacific–Japan pattern (PJ; Nitta, 1987), also referred to as the East Asia–Pacific pattern (EAP; Huang and Li, 1988), is a Rossby wave that travels northwards in the lower troposphere and towards the equator in the upper troposphere (shown in Fig. S1 in the Electronic Supplementary Material, ESM). It has been proven in both observations and models to be one of the main climatic systems that alters the summer rain belt in northern East Asia (Kosaka and Nakamura, 2010; Gong et al., 2011; Huang et al., 2012;). The Silk Road teleconnection (SR; Lu et al., 2002; Enomoto et al., 2003), which propagates along the subtropical westerly jet in the upper troposphere (shown in Fig. S2), is another system that is considered to have an important effect on the summer precipitation in northern China (Wang and He, 2015). Furthermore, the British–Baikal Corridor pattern (BBC; shown in Fig. S3), newly proposed by Xu et al. (2019a), has a potential impact on the climate in China. It propagates along the polar front jet but remains little discussed.

      El Niño, which originates in the Pacific Ocean, has a wide range of effects on China’s climate. Its development and recession often affect precipitation in China by influencing the WPSH and EASM. In previous studies (Weng et al., 2007, 2009; Chen and Tam, 2010; Feng et al., 2011; Yuan et al., 2012; Wen et al., 2020; Feng and Chen, 2021), El Niño was divided into the central Pacific, eastern Pacific (EP), and mixed Pacific types. These different types of El Niño produce different climatic effects. The sea-ice index of the Barents Sea in spring is denoted by the sea-ice concentration (SIC) over (70°–82°N, 0°–55°E) (hereafter simply referred to as the SIC index). In summer, its location and strength determine the intensity and distribution of precipitation in China (Wang et al., 2000; Wang, 2002; Huang et al., 2019). Huang and Sun (1994) emphasized the role that convective activity has on the WPSH, and pointed out that the anomalous anticyclonic circulation near the Philippines could determine the north‒south position of the WPSH. Additionally, 2015/16 was a super El Niño year. During this period, the sea surface temperature (SST) continued to increase from the winter of 2014 until the El Niño phase finally formed in the fall of 2015. Did warming have an impact on atmospheric circulation and precipitation in China in 2015? This is one of the questions to be discussed here.

      On both interannual and interdecadal scales, SSTs modulate the modes of circulations over China (Wang et al., 2000; Yang and Lau, 2004; Zhu et al., 2011; Yang et al., 2017). In the summer of 2014, a tripole precipitation anomaly occurred in East Asia, which caused a severe drought event in northeastern China. It was related to the northeast propagation of Rossby waves triggered by the dipole SST anomaly of the Indian Ocean (Saji et al., 1999; Guan and Yamagata, 2003). Anomalous ocean heat can also lead to weakening of the WPSH and blockage of the Sea of Okhotsk high, which may ultimately lead to drought events in northern China (Wang et al., 2013). Zheng and Wang (2021) found that interactions between SST anomalies (SSTAs) in the Pacific, Indian and Atlantic oceans can result in vorticity anomalies over 200 hPa in northern China and 850 hPa in the South China Sea. The movement of the WPSH driven by this abnormal circulation eventually caused severe flooding events in southern China. The Arctic sea ice and Eurasian snow cover can also affect the climate in northern China. Yuan et al. (2017) highlighted that an anomalous reduction in Eurasian high-latitude snow cover can stimulate a positive EU pattern over China, resulting in anomalously high pressure in the Lake Baikal region and anomalously low pressure in European high latitudes. This pattern ultimately favored the drought event that occurred in 2015. Wang and He (2015) suggested that the Arctic sea-ice anomaly was one of the main reasons for the drought in northeastern China in 2014.

      Although previous studies have considered the drought event in northern China in 2015 from the perspective of established forecast models (Yuan et al., 2017), analysis of the physical mechanism of the large-scale circulation anomalies is limited. Moreover, studies on the climate impact of the BBC mode propagating within the polar front jet at 200 hPa are rare. Therefore, based on reanalysis precipitation and circulation data, we studied the possible formation mechanism of the SF-ND event in July–August 2015 from the perspective of meteorology.

      The rest of the paper is organized as follows. Section 2 describes the data and methods used in this study. The precipitation and circulation anomalies are analyzed in section 3. Section 4 provides a detailed analysis of the possible physical mechanisms responsible for these anomalies. Finally, section 5 summarizes our conclusions.

    2.   Data and methods
    • The monthly mean reanalysis atmospheric data used in this study were obtained from the Kalnay et al. (1996) /Kistler et al. (2001) NCEP–NCAR reanalyses. Surface air temperature, meridional and zonal winds, SST, geopotential height, outgoing longwave radiation, and specific humidity, with a horizontal resolution of 2.5° × 2.5°, were used. To make the precipitation results close to the observed data (Wang and Wang, 2017), the Global Precipitation Climatology Centre monthly rainfall data from 1901 to 2016 were used (Becker et al., 2013), with a 1° × 1° resolution (https://www.esrl.noaa.gov/psd/). Monthly means of global SIC from COBE-SST (Global Sea Surface Temperature Analysis) data (Ishii et al., 2005), with a horizontal resolution of 1° × 1°, were used to analyze the effect of Arctic sea ice. Monthly averaged snow cover data were obtained from ERA5 (the fifth major global reanalysis produced by ECMWF; Muñoz Sabater, 2019), with a horizontal resolution of 0.1° × 0.1°.

      Various indices were used to characterize different teleconnection modes in this study. The EU index, based on the definitions of Wallace and Gutzler (1981), is defined by the following equation (shown in Fig. S4):

      The PJ/EAP is defined as the first empirical orthogonal function (EOF) of the mean July–August 850-hPa relative vorticity field over East Asia (0°–60°N, 100°–160°E) (Kosaka and Nakamura, 2010), and the PJ/EAP index is defined as the first normalized principal component (PC) time series. Following Xu et al. (2019a), the BBC is defined as the first EOF analysis result of the 250-hPa meridional wind over the region (50°–80°N, 20°W–150°E) in July–August, and the BBC index is the first PC time series. The SR is the first leading EOF analysis result of the 200-hPa meridional wind velocity anomalies in July–August over the area (30°–60°N, 30°–130°E) (Lu et al., 2002; Chen et al., 2013), and the SR index is defined as above. The EASM index is defined as the average regional (30°–40°N, 115°–125°E) vorticity at 850 hPa in July–August (Wang, 2002). The Northern Hemisphere Subtropical High Area (Ridge Position) Index is used to describe the area (north–south position) of the WPSH. The Western Ridge Point Index shows the westernmost position of the 5880-m geopotential height (in gpm). These three indices can be found in datasets of the National Climate Center of China (https://cmdp.ncc-cma.net/Monitoring/cn_index_130.php). The Niño-3 and Niño-4 average indexes are used in this paper to represent the strength of El Niño events, which can be found at https://psl.noaa.gov/data/climateindices/list. The following formula was used to calculate the EP El Niño (Sullivan et al., 2016):

      The horizontal distribution of the Plumb wave flux was used to diagnose the generation and propagation of quasi-stationary Rossby waves. Plumb (1985) used the conservation relation of small-amplitude stationary waves propagating in a zonally uniform basic flow and gave the three-dimensional wave activity flux of stationary Rossby waves, which represents the propagation direction of wave energy. The Plumb wave action flux has been widely used since it was proposed, and it can effectively analyze the propagation characteristics of steady Rossby waves.

    3.   Results
    • The July–August precipitation anomalies in 2015 are shown in Fig. 1a. A distinct negative precipitation anomaly existed in northern China, including Hebei, Shaanxi, and most places in Mongolia Province, while a large-scale positive precipitation anomaly occurred in southern China. These anomalies were defined with a 30-yr (1981–2010) baseline as the mean climatology. The anomalous precipitation shows a pattern similar to the SF-ND pattern in China (Yatagai and Yasunari, 1994; Nitta and Hu, 1996; Zhou et al., 2009; Li et al., 2010).

      Figure 1.  (a) Precipitation anomaly (units: mm) during July–August 2015 relative to the climatology of 1981–2010. (b) Precipitation index 1 (PI1) and (c) precipitation index 2 (PI2), normalized time series of area-weighted mean precipitation in the subregions (31.5°–48°N, 101.5°–130°E) and (17°–28.5°N, 101.5°–130°E), respectively, during July–August 1979–2016. (d) Precipitation index (PI) during July–August 1979–2016 calculated by PI1 and PI2 as in Eq. (3). A positive value of PI indicates the occurrence of the SF-ND pattern in China. Note that these indices are not detrended.

      Then, we defined a precipitation index (PI) to describe the intensity of this dipole precipitation mode, as follows:

      where PI1 and PI2 are defined as the regionally weighted normalized July–August precipitation in the subregions indicated in Fig. 1a. A minimum value occurs in 2015 in the PI1 sequence (Fig. 1b), suggesting that the index has a good capacity to represent regional dry‒wet conditions. Figure 1d also displays a similar conclusion: a marked positive value often accompanies an obvious SF-ND event.

      Using the PI values, we selected years greater than 0.5 standard deviations as positive-phase years (1994, 1997, 1999, 2001, 2002, 2006, 2014, 2015, 2016). Likewise, we also had negative-phase years (1981, 1982, 1983, 1985, 1987, 1990, 1993, 1998, 2003, 2010, 2011, 2012), with the positive phase showing the SF-ND precipitation mode, and vice versa. Using these selected years, composite maps of precipitation anomalies were drawn (Figs. 2a and b), both of which exhibit obvious dipole precipitation patterns.

      Figure 2.  The dipole precipitation anomaly (units: mm) in July–August during (a) positive-phase years and (b) negative-phase years. Areas with slashes are significant at the 99% confidence level based on Student’s t-test. (c) Regression maps of July–August 850-hPa wind anomalies with regard to PI during 1979–2016. Light (dark) shading indicates that the values are significant at the 90% (95%) confidence level based on Student’s t-test. (d) The 850-hPa wind anomaly (vectors; units: m s−1) for July–August 2015. The colour shading indicates the anomalous magnitude of wind. The letters A and C indicate the anomalous anticyclone and cyclone, respectively.

      Here, we analyzed anomalies in atmospheric circulations in 2015 and their connection with rainfall anomalies in the period 1979–2016 to understand the causes of this extreme precipitation anomaly, particularly in northern China.

      As shown in Figs. 3c and d, the strength of the EASM was weak, and the position of the WPSH was shifted southward. Each system made it difficult for water vapor to be transported from the ocean to northern China. Therefore, what factors caused the abnormal condition of these two systems?

      Figure 3.  (a) Regression maps of summer integrated water vapor flux divergence from 300 hPa to 1000 hPa with regard to simultaneous PI during 1979–2015. The dotted area passed the 90% significance test based on Student’s t-test. (b) Anomalous summer integrated water vapor flux divergence (units: g kg−1 m s−1) in 2015 (relative to the climatology of 1981–2010). (c) EASM index and (d) Northern Hemisphere Subtropical High Ridge Position Index during 1979–2015.

      The wind anomalies at 850 hPa in July–August 2015 are shown in Fig. 2d. Near the Ural Mountains, a strong blocking system appeared, which was closely related to a negative EU phase. The regressed velocity at 850 hPa against the PI is shown in Fig. 2c. It is likely that a meridional circulation mode (the PJ/EAP) occurred from Japan to the northwestern Pacific, which could have had a close connection with this anomalous precipitation pattern in China. As shown in Figs. 3a and b, anomalous water vapor transport centers appeared in southern China. A similarly blocked circulation also occurred in northern China.

      When the PJ/EAP mode is in the positive phase, there is a cyclonic anomaly over southern China, corresponding to ascending air masses. Therefore, substantial water vapor causes precipitation to concentrate here. Similarly, this circulation pattern can effectively prevent the northward movement of the WPSH. The abnormal anticyclone over northeastern China, corresponding to the descending air masses, made it difficult for water vapor to be transported to northern China. Ultimately, this pattern promoted the occurrence of the abnormal precipitation SF-ND mode.

      Moreover, meridional circulation occurred at high latitudes and presented a significant anticyclonic anomaly on the eastern side of Scandinavia, with a cyclonic anomaly near the Okhotsk Sea. This meridional circulation pattern could also have inhibited the northwards movement of water vapor. The EU pattern is a deep quasi-positive pressure system that can be of influence from 500 hPa down to 700 hPa (Wallace and Gutzler, 1981). Since its impact can reach the height of the lower troposphere, it would have affected the northward direction of the high-pressure ridge and prevented the southern water vapor from being transported to the north.

    • To explore the circulation anomalies corresponding to the precipitation anomalies, the 500-hPa geopotential height anomalies are shown in Fig. 4a. Then, these 500-hPa geopotential heights were regressed with regard to the PI, as shown in Fig. 4b. An obvious negative EU phase appeared, with an obvious anticyclonic circulation pattern over Mongolia. As a result, abnormal northerly winds prevailed in northern China, reducing water vapor transport, which was not conducive to precipitation.

      Figure 4.  Anomalous (a) geopotential height (units: gpm) at 500 hPa and (c) meridional winds (units: m s−1) at 250 hPa in July–August 2015. The climate average was calculated from 1981 to 2010. Regression maps of summer (b) 500-hPa geopotential anomalies and (d) 250-hPa meridional winds with regard to concurrent PI during 1979–2016. Light (dark) shading in (b) indicates that the values are significant at the 90% (95%) confidence level, and the dotted area in (d) passes the 95% significance test based on Student’s t-test.

      At 250 hPa, a significant zonal circulation mode occurred at 60°N, which agrees perfectly with the BBC pattern proposed by Xu et al. (2019a). The BBC pattern propagates along polar front jets. Defined at 250 hPa, four geographic centres are included in this mode; these are located to the west of the British Isles, the Baltic Sea, western Siberia, and Lake Baikal. Xu et al. (2019a) performed a regression between precipitation and the BBC index by using Climate Research Unit data. The result was that the BBC could obviously influence the precipitation at high latitudes, including northern China. Additionally, similar precipitation centers occurred that corresponded to the four meridional circulation centers. The northern regions of eastern China tend to be wet when the BBC is in a positive phase. Generally, the SR or circumglobal teleconnection modes propagating along the subtropical jet stream in the upper atmosphere are believed to be responsible for precipitation anomalies in China (Wang and He, 2015; Hong et al., 2018). A meridional circulation similar to SR can be seen near 30°N in Fig. 4c. Its intensity is slightly less than that of the BBC mode. In Table 1, we calculated the correlation between the different indices. As can be seen, the BBC affects precipitation in northern China and southern China equally, while the SR mainly affects precipitation in northern China. The negative correlation coefficient between SR and PI1 is weak. The correlation between the BBC and PI1 shows a positive relationship, but a negative relationship between the BBC and PI2 was obtained. Therefore, if the BBC is in a negative phase while the SR is in a positive phase, an SF-ND event is very likely to occur. The relationship between the two could form this dipole precipitation pattern.

      EU patternPJ/EAP patternBBC patternSR pattern
      EU pattern
      PJ/EAP pattern−0.540
      BBC pattern−0.7800.562
      SR pattern−0.0210.264−0.223
      EASM0.398−0.180−0.1120.090
      WPSH0.363−0.348−0.167−0.163
      PI10.576−0.6030.586−0.252
      PI2−0.5090.507−0.423−0.021
      PI−0.6460.662−0.6040.146

      Table 1.  Correlation coefficients between indices. Information on how these indices were calculated can be found in section 2. The 99%, 95%, and 90% confidence levels are ±0.403, ±0.312, ±0.264, respectively, according to Student’s t-test. Bold values passed the 90% confidence levels. All indices were detrended.

    4.   Analysis of physical mechanisms
    • In the previous analyses, key circulation factors that possibly affected the anomalous precipitation mode were discussed. Therefore, the next question is what circulation factors caused these anomalies. Further analysis of the physical mechanisms of the circulation anomalies is not only meaningful for the typical precipitation pattern in 2015, but also plays an important role in predicting future droughts and rainfall in this region.

      2015/16 was a typical super El Niño year. The wide range of strong SSTAs had a large impact on atmospheric circulations and precipitation (Zhai et al., 2016; Paek et al., 2017; Ma et al., 2018). Summer drought in northern China is likely related to El Niño. Table 2 shows the correlations between different characteristics of the WPSH and the El Niño index. It can be found that, during the year that El Niño develops, the WPSH often strengthens and increases in area, but the location is southerly. This anomaly leads to water vapor being concentrated in southern China and impedes its transport to northern China.

      WPSH ridge positionWPSH intensityWPSH area
      Niño3_winter−0.5760.5330.661
      Niño3_spring−0.4700.4430.578
      Niño3_summer−0.293−0.082−0.101

      Table 2.  Correlation coefficients between Niño3 index and the WPSH. The 99%, 95% and 90% confidence levels are ±0.403, ±0.312 and ±0.264, respectively, according to Student’s t-test. Bold values passed the 90% confidence levels.

      In addition to affecting the location and intensity of the WPSH, El Niño also has a significant impact on convection in the regions influenced by the sinking branch of the Walker circulation. In particular, enhanced convection over the central and eastern Pacific induces a Rossby wave response that affects the midlatitude circulation and precipitation patterns. The convection over Indonesia and the western Pacific also alters the monsoon systems and the tropical cyclone activity in the region. The changes in the Walker circulation also affect the ocean–atmosphere coupling and the interannual variability of the climate system (Weng et al., 2007; Yuan et al., 2012, 2017; Wen et al., 2020).

      Here, we calculated the July–August mean index values for the different types of El Niño in different years. The years of EP El Niño only are provided here (1965, 1972, 1976, 1997, 2009, 2014 , 2015). The summer of 2015 was a significant El Niño development period (the July–August mean index value is 1.765) that may have had a huge impact on the large-scale circulation.

      Figure 5b illustrates the tropical atmospheric circulation patterns in EP El Niño years. In summer, a large-scale Walker circulation anomaly dominates the eastern Pacific Ocean, with strong upward motion over the central and eastern Pacific and significant downward motion over the western Pacific (Weng et al., 2007). The broad ascending motion from 160°W to 80°W splits into two branches at 120°W: one branch turns eastward in the upper troposphere, associated with the Walker circulation, and the other branch extends westward along the equator. Weak descending motion occurs over the tropical Indian Ocean at 60°E, which eventually joins the main subsidence over Indonesia. Although the 2015 tropical vertical circulation (Fig. 5a) was more westward than the ascending branch of a typical EP El Niño year, and its subsidence branch was stronger, it still shows similar characteristics. In June 2015 (Fig. 5c), the OLR negative anomaly center occupied central and southern parts of southern China, showing significant convective activity in these regions. However, in July and August (Fig. 5d), there was no obvious OLR negative anomaly center and descending motion prevailed in southern China. Therefore, it can be considered that the strong precipitation in the south was mainly affected by the large-scale circulation, rather than regional convection. The position of the descending motion corresponded to the intensity and position of the WPSH, which made it difficult for the precipitation in northern China to be affected because the position of the WPSH was limited to the tropical western Pacific. The OLR anomaly of the composite EP El Niño years also supports the above view (Fig. 5e), as due to the influence of the WPSH, the rain band is located in southern China.

      Figure 5.  (a) Vertical–horizontal cross section averaged along 5°S–5°N for vertical velocity anomalies (units: m s−1) during July–August 2015 (relative to the climatology of 1981–2010). (b) Composite Walker Circulation anomalies over the equator (5°S–5°N) (units: m s−1) in association with the summer EP El Niño. The vertical velocities are magnified 100 times. (c, d) Anomalies of OLR (units: W m−2) in (c) June and (d) July–August 2015 relative to the climatology of 1981–2010. (e) Composite maps of OLR anomalies in July–August with the summer EP El Niño.

    • The PJ/EAP pattern can link SSTAs in the western Pacific with summer precipitation in East Asia. When the PJ/EAP pattern tends to appear in the positive phase, the EASM is weak (Huang and Li, 1988; Chang et al., 2000; Xie et al., 2016). However, consensus has not been reached on the formation and maintenance mechanism of the PJ/EAP. Previous studies can be summarized into three views. The first view is that the PJ/EAP mode is a Rossby wave stimulated by convective activity near the Philippines, and the change in the PJ/EAP mode is driven by SSTAs (Nitta, 1987; Huang and Li, 1988; Xie et al., 2009, 2016; Kosaka and Xie, 2013). The second view is that the PJ/EAP mode is an intrinsic atmospheric mode inherent to the complex fundamental flow in East Asia (Kosaka and Nakamura, 2010). The third view suggests that the midlatitude PJ/EAP can be affected by latent heat released by the mei-yu precipitation. The latent heat acts as a bridge to transfer the western Pacific convective changes to the PJ/EAP (Lu and Lin, 2009). Although there is debate regarding the way that SST affects the PJ/EAP, it is not difficult to find that temperature anomalies, especially in the tropical Indian Ocean, are often an important factor in determining variations in the PJ/EAP by combining the above points. We applied the SVD method to examine the covariability between the northern tropical Indian Ocean and East Asia. The results revealed that a positive SST anomaly in the Indian Ocean is associated with a tripole “anticyclone–cyclone–anticyclone” pattern of anomalies over East Asia, which resembles the PJ/EAP mode (Figs. 6c and d).

      Figure 6.  (a, b) Heterogeneous correlation map of the first mode of the SVD for the detrended and normalized (a) 850-hPa geopotential height and (b) SST during July–August 1979–2016. (c) The corresponding time series. (d) SSTAs in summer 2015 (units: K), with a climate baseline of 1981–2010.

      In previous studies (Hirota and Takahashi, 2012; Xu et al., 2019b), the years in which the PJ/EAP phase was positive were associated with significantly high precipitation in southern China and corresponded to the state of El Niño the following year. In a year when EP El Niño develops, the tropical Pacific Ocean becomes a strong heat source that affects the global climate. It has been widely documented that the SST in the tropical Indian Ocean exhibits a warming tendency in the post-El Niño season (Weare, 1979; Nigam and Shen, 1993; Liu and Alexander, 2007; Schott et al., 2009; Xie et al., 2016). The lagged correlation coefficient between the El Niño signal in the preceding winter and the heat source anomaly in the northern tropical Indian Ocean in summer reaches 0.7 (figure not shown; Xie et al., 2016). Therefore, Indian Ocean warming is often considered as a response to El Niño (Fig. 6a). This view is also consistent with Figs. 5c and d, which show that atmospheric convection is suppressed rather than enhanced over the warming tropical Indian Ocean during the developing and mature phases of El Niño. El Niño influences the Indian Ocean temperature through the capacitor charging and discharging effect (Yang et al., 2007; Xie et al., 2009; Chowdary et al., 2019; Na and Lu, 2023), and the SST anomaly in the northern Indian Ocean acts as a wave source that triggers the PJ/EAP. It can excite Kelvin waves that propagate eastward along the equator, and an anomalous anticyclone forms near the Philippine Sea. Furthermore, a tripole “anticyclone–cyclone–anticyclone” pattern of anomalies over East Asia occurs (Xie et al., 2016).

    • Li and Leung (2013) suggested that the sea-ice changes in spring may have a stronger influence on China’s summer precipitation than the sea-ice changes in summer, as Arctic warming is more pronounced in spring. They also indicated that changes in spring and summer sea ice can affect the atmospheric teleconnection patterns in the mid and high latitudes of Eurasia in summer. Previous studies have shown that the EU pattern over Eurasia in summer modulates the distribution of the East Asian subtropical jet and the westerly belt (Gao et al., 2017), and the changes in these jets influence the northward propagation of the jet stream. Arctic sea ice has undergone a significant decline since the 1980s. Therefore, the sea-ice change in the Arctic in early spring plays an important role in modulating the teleconnection patterns of the mid and high latitudes in Eurasia and the East Asian climate in late summer.

      As mentioned in the Introduction, the sea-ice index of the Barents Sea in spring was denoted by the SIC in the region (70°–82°N, 0°–55°E), which we hereafter refer to simply as the SIC index. The distribution and correlation coefficient of the normalized SIC and EU indexes are displayed in Fig. 7a. The correlation coefficient is 0.46, which suggests that a negative phase of the EU pattern that influences the Chinese region is likely to occur when the sea ice in the Barents Sea is relatively low. The heat flux anomaly of the underlying surface may be affected by substantial changes in Arctic sea ice. The upward turbulent heat flux in regions with less sea ice in the Barents Sea in spring is significantly stronger, and a large amount of heat is transferred from the ocean to the atmosphere. Persistent anomalous heat flux over the underlying surface in spring and summer can provide energy for the development of the summer EU pattern.

      Figure 7.  (a) Averaged EU index in July–August, SIC in spring, and snow depth (SD) within (55°–70°N, 30°–60°E) in April and May 1979–2015. (b) Snow cover with regard to SIC values during 1979–2016. The dotted area passes the 95% significance test based on Student’s t-test. (c, d) Heterogeneous correlation map of the first mode of the SVD for the detrended and normalized (c) 500-hPa geopotential height during July–August and (d) ICEC (sea-ice concentration) anomalies during spring 1979–2016. (e) The corresponding time series. (f) ICEC in MAM of 2015, with a climate baseline of 1981–2010.

      The mid–high latitude circulation may be affected by the lack of Arctic sea ice not only through direct dynamic processes but also through indirect thermodynamic processes involving the snow cover in the Eurasian mid–high latitude region. Previous studies (Liu and Yanni, 2002; Bader et al., 2011; Li et al., 2018) have indicated that sea-ice changes are closely related to snow cover changes in Eurasia, and snow cover can further affect the Eurasian climate by altering the albedo and hydrological conditions. Figure 7b shows the composite snow cover in April and May and SIC index in spring. As can be seen, there is a significant positive correlation between sea ice and snow in Siberia; that is, in years with less sea ice, there is less snow here. The anomalous signal of sea-ice reduction in the Barents Sea in spring can persist until summer, leading to higher air temperatures over the Barents Sea. This favours upward motion and forms an anomalous low pressure in the upper troposphere. This low pressure further stimulates the southward wave train and generates the EU pattern. A low-pressure anomaly emerges over the North Pole because of the reduction in snow cover over the Barents Sea and Eurasia in the previous spring, which stimulates a wave train to propagate from the North Pole to the northeast region, and a high-pressure anomaly occurs in northern China. This anticyclonic circulation anomaly is unfavorable for the accumulation of water vapor in northern China.

      In addition to the contributions of the EU and PJ/EAP discussed above, the BBC and SR teleconnection modes also have an important impact on the precipitation modes in China. Among these, the negative phase of the BBC (shown in Fig. 4d) has a good correlation with the precipitation anomalies in the southern and northern parts of China, while the SR type mainly promotes or weakens the precipitation in northern China (shown in Table 1). The BBC propagates within the polar front jet (Xu et al., 2019a). The SR mode originates near the Caspian Sea and the Mediterranean Sea, and then propagates along the subtropical westerly jet (Lu et al., 2002; Enomoto et al., 2003).

      Here, we used the Plumb wave flux method to analyze the wave activity flux anomaly in 2015. A remarkable heat source emerged on the continent near the Caspian Sea in 2015 (Fig. 8b). This heat source induced an anomalous anticyclonic circulation in the upper troposphere near the Caspian Sea. This region is also situated at the entrance of the Asian westerly jet stream, so the strong warming and the associated low-level convergence and high-level divergence can stimulate Rossby waves (Enomoto et al., 2003; Sun et al., 2008) that propagate along the jet stream (i.e., the SR pattern). Figures 8a and b illustrate the impact of this heat source and the wave activity flux at 60°E. Consequently, a positive geopotential height anomaly occurred over high-latitude Asia and a negative anomaly over midlatitude East Asia.

      Figure 8.  (a) Geopotential height (shading; units: gpm) and wave activity flux (vectors; units: m2 s−2) anomalies at 250 hPa in July–August 2015. (b) Anomalous air temperature at 1000 hPa (units: K) and winds (units: m s−1) in July–August 2015. (c) SSTAs (units: K) in July–August 2015 relative to the 1981–2010 climatology.

      The initial perturbation source over the North Atlantic influences the ducting of the polar jet and the development and propagation of teleconnection patterns (Hall et al., 2015; Lee et al., 2019). A previous study proposed that barotropic instability and the interaction between circulation anomalies in the outlet region of the Atlantic jet and high-frequency synoptic-scale transients may stimulate the BBC (Xu et al., 2019a). The BBC and EU patterns have overlapping positions in northern China during their westward propagation, so they can modulate the precipitation here by enhancing or reducing each other’s intensity. For instance, when the BBC is a cyclonic anomaly and the EU is a west-low east-high anomaly, they will increase the precipitation in northern China; on the contrary, when the BBC is an anticyclonic anomaly and the EU mode is a west-high east-low anomaly, they will decrease the precipitation in northern China. Therefore, this system can affect the precipitation in northern China by strengthening/weakening the intensity of the EU.

    5.   Summary and discussion
    • In this study, reanalysis data were used to analyze the causes of the SF-ND rainfall pattern in China from July to August 2015; and to a certain extent, we also aimed to determine the causes of droughts in northern China associated with SF-ND events. Our research analyzed the circulation anomalies and possible physical mechanisms associated with this precipitation anomaly. Our main conclusions are as follows.

      The appearance of the abnormal precipitation pattern was caused by the combined influence of multiple circulation systems. Figure 9 schematically illustrates the influence of circulation systems on this event. These systems included the PJ/EAP mode in the lower troposphere, the EU teleconnection in the middle troposphere, and the BBC and SR teleconnections in the upper troposphere, which together affected the wind and water vapor distributions. When the PJ/EAP pattern is in the positive phase, an abnormal anticyclone occurs over Japan, and an abnormal cyclone occurs in southern China, which contributes to the rainfall. The blocking in northern China brought by the negative quasi-barotropic EU also exacerbates this process. At higher altitudes, the dispersion of Rossby wave energy brought by the positive SR at 30°N and the negative BBC at 60°N promotes the maintenance of the low-level PJ/EAP pattern and EU pattern, contributing to the southward movement of the WPSH and maintaining the weakening of the EASM. The impact of the super El Niño in 2015/16 cannot be underestimated. During the El Niño development year, due to the anomalous SSTs that persisted from spring to summer, the downdraft continued to accumulate. An abnormal downdraft occurred, making the WPSH ridgeline shift south of 30°N.

      Figure 9.  Schematic of the large-scale circulation and meteorological factors that caused the SF-ND precipitation pattern in China in summer 2015.

      Sea-ice and sea-temperature changes have a major impact on the global atmosphere. SVD analysis revealed that Arctic sea-ice patterns have a non-negligible correlation with the EU pattern. The negative phase of EU is easily triggered by negative and positive anomalies of sea ice in the Barents Sea. Moreover, through the indirect influence of Eurasian snow cover, it also affects the geopotential height anomaly over northern China. The sea ice and the snow cover in West Siberia are relatively small. The positive heat flux anomaly will further strengthen the negative phase of the EU. A positive PJ/EAP phase is related to EP El Niño. The ocean warming in the Indian Ocean region has a lagged effect from the development of the El Niño year. The anomalous warming in the northern Indian Ocean stimulates an anticyclone in this area and finally forms the PJ/EAP. Through Plumb wave flux analysis (Plumb, 1985), the negative BBC may be related to the anomalous warming in the northern North Atlantic Ocean. The warming of the land near the Caspian Sea may also lead to the positive phase of SR.

      These results illustrate the reasons for the emergence of the SF-ND pattern in 2015 in China; however, the interactions among these different factors and the magnitudes of their respective contributions remain unclear. Further in-depth study is needed to examine the possible links.

      Acknowledgements. This work was jointly sponsored by the National Natural Science Foundation of China (Grant Nos. 41991281, 42130613 and 41705073), the UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund, and the Jiangsu Collaborative Innovation Center for Climate Change. The authors declare that they have no competing interests.

      Electronic supplementary material: Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-023-2342-0.

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