Advanced Search
Article Contents

The Asymmetric Connection of SST in the Tasman Sea with Respect to the Opposite Phases of ENSO in Austral Summer


doi: 10.1007/s00376-022-1421-y

  • This study uses linear regression and composite analyses to identify a pronounced asymmetric connection of sea surface temperature (SST) in the Tasman Sea with the two opposite phases of El Niño-Southern Oscillation (ENSO) during austral summer. In El Niño years, the SST anomalies (SSTAs) in the Tasman Sea exhibit a dipolar pattern with weak warmth in the northwest and modest cooling in the southeast, while during La Niña years the SSTAs exhibit a basin-scale warmth with greater amplitude. Investigations into the underlying mechanism suggest that this asymmetry arises from a mechanism related to oceanic heat transport, specifically the anomalous Ekman meridional heat transport induced by the zonal wind stress anomalies, rather than the surface heat fluxes on the air-sea interface. Further analysis reveals that the asymmetry of oceanic heat transport between El Niño and La Niña years is driven by the asymmetric atmospheric circulation over the Tasman Sea stimulated by the asymmetric diabatic heating in the tropical Pacific between the two opposite ENSO phases.
    摘要: 本研究通过线性回归和合成分析,发现南半球夏季塔斯曼海海温与ENSO正、负位相存在非对称联系。El Niño年,塔斯曼海的海温异常表现为偶极型模态,西北部为较弱的暖海温异常,东南部为冷海温异常;La Niña年,塔斯曼海则表现为海盆一致的增暖,且异常值的振幅更强。机制分析表明,ENSO正、负位相期间塔斯曼海海温异常的非对称性主要来自海洋热输送的贡献,特别是由纬向风应力异常引起的经向Ekman热输送,而海-气界面的热通量则起了阻尼作用。进一步分析发现,El Niño年和La Niña年海洋热输送的非对称性受到塔斯曼海上空非对称的大气环流的驱动,而非对称的大气环流由热带太平洋非对称的非绝热加热激发。
  • 加载中
  • Figure 1.  The 200-hPa climatological zonal wind (contour, units: m s–1) contoured every 5 m s–1 above 25 m s–1 and the upper-tropospheric storm track (shaded, units: m s–1) based on the standard deviation of the band-pass filtered (2.5–6-day periods) 300-hPa meridional wind for (a) DJF, (b) MAM, (c) JJA, and (d) SON. Shaded areas denote values greater than 7 m s–1. The box in each panel represents the Tasman Sea region (26°−46°S, 150°−174°E).

    Figure 2.  Spatial distribution of seasonal mean climatological SST (contour, units: °C) and the standard deviation (shaded, units: °C) of detrended SSTAs for (a) DJF, (b) MAM, (c) JJA, and (d) SON. The box in each panel represents the Tasman Sea region as in Fig.1.

    Figure 3.  The DJF-mean global SST anomaly patterns (units: °C) in (a) 1957, (b) 1970, (c) 1972, (d) 1973, (e) 2009, and (f) 1998. The left and right columns show SSTAs in El Niño years and La Niña years, respectively. The year in the top left of each panel is the year of December. The box in each panel represents the Tasman Sea region as in Fig. 1.

    Figure 4.  The SSTAs (units: °C) regressed by the Niño-3.4 index using (a) all samples, (b) samples with positive Niño-3.4 values, and (c) samples with sign-reversed negative Niño-3.4 values. Hatching indicates significance at the 90% confidence level. The black boxes represent the Tasman Sea region as in Fig. 1.

    Figure 5.  Composite DJF mean SSTAs (units: °C) for (a) El Niño years and (b) La Niña years. Panel (c) are the symmetric components estimated by half of the difference of (b) and (a). Panel (d) shows the asymmetric components estimated by half of the sum of (b) and (a). Hatching indicates significance at the 90% confidence level.

    Figure 6.  Composite DJF mean surface heat flux anomalies (positive: downward; units: W m–2) for (a) El Niño events and (b) La Niña events. Panel (c) shows the asymmetric components between (a) and (b). Hatching indicates significance at the 90% confidence level.

    Figure 7.  The region-averaged shortwave (SW), longwave (LW) radiative heat flux, sensible heat (SH) and latent heat (LH) flux anomalies and their sum (units: W m–2) over the Tasman Sea for (a) El Niño events, (b) La Niña events and (c) the asymmetric components between (a) and (b). Positive heat fluxes are directed into the ocean. The number in the top right of each panel indicates the area-averaged SSTAs (units: °C).

    Figure 8.  Composite oceanic current anomalies (vector, units: m s–1) at 5-m depth and the horizontal temperature advection anomalies (shaded, units: 10–8 °C s–1) driven by the currents for (a) El Niño events and (b) La Niña events. Panel (c) shows the asymmetric components between (a) and (b). Only the currents with speeds greater than 0.004 m s–1 are displayed in (a, b) and those greater than 0.002 m s–1 are displayed in (c).

    Figure 9.  As Fig. 8, but for the meridional Ekman heat transport anomalies (shaded, units: W m–2) and wind stress anomalies (vector, units: N m–2). Only the vectors with speeds greater than 0.005 N m–2 are displayed in (a, b) and those greater than 0.0025 N m–2 are displayed in (c).

    Figure 10.  Composite DJF mean SLP anomalies (a–c, units: hPa), 500-hPa (d–f) and 200-hPa (g–i) geopotential height anomalies (units: m) for El Niño events (left column), La Niña events (middle column) and asymmetric components (right column). Dotted areas are significant at the 90% confidence level.

    Figure 11.  Composite SSTAs (T′, units: °C) and mixed-layer heat budget for each term (units: °C month–1) during ONDJ of (a) El Niño, (b) La Niña, and (c) their asymmetric components averaged in the Tasman Sea. The x-axis label “sum” represents the sum of terms on the right side of Eq. (4) except R term.

    Figure 12.  Composite DJF mean SSTAs (a, c, e, g; units: °C) and precipitation anomalies (b, d, f, h; units: mm d–1) for (a, b) El Niño events and (c, d) La Niña events. Panels (e) and (f) are the symmetric components estimated by half of the difference between (c) and (a), and (d) and (b), respectively. Panels (g) and (h) are the asymmetric components estimated by half of the sum of (a) and (c), and (b) and (d), respectively. The areas dotted are significant at the 90% confidence level.

    Figure 13.  (a) The horizontal distribution of the initial diabatic heating anomalies (units: K d−1) at 500 hPa. (b) The vertical heating profile (units: K d−1) for the dot in (a). The vertical coordinate in (b) represents sigma levels in the model.

    Figure 14.  (a) 500-hPa and (b) 200-hPa geopotential height responses (units: m) to the asymmetric diabatic heating simulated by the linearized AGCM in the presence of the 3D DJF-mean flow.

    Figure 15.  Summary diagram of identified influence from tropical middle-eastern Pacific on the Tasman Sea for (a) El Niño events, (b) La Niña events, and (c) the asymmetry between El Niño and La Niña, where “+Precip” indicates positive precipitation anomalies and “–Precip” indicates negative precipitation anomalies; L indicates negative SLP anomalies and H indicates positive SLP anomalies. The solid arrows show the direction of advection driven by oceanic currents and the hollow arrows show the direction of Ekman heat transport.

    Table 1.  Years of El Niño and La Niña during the period of 1950–2018 following the threshold of one standard deviation of DJF mean Niño-3.4 index.

    Years
    El Niño1957, 1965, 1968, 1972, 1982, 1986, 1991, 1994, 1997, 2009, 2015
    La Niña1955, 1970, 1973, 1975, 1988, 1998, 1999, 2007, 2010, 2017
    DownLoad: CSV
  • Alexander, M. A., and J. D. Scott, 2008: The role of Ekman Ocean heat transport in the northern Hemisphere response to ENSO. J. Climate, 21(21), 5688−5707, https://doi.org/10.1175/2008JCLI2382.1.
    Alexander, M. A., I. Bladé, M. Newman, J. R. Lanzante, N.-C. Lau, and J. D. Scott, 2002: The atmospheric bridge: The influence of ENSO teleconnections on air–sea interaction over the global oceans. J. Climate, 15(16), 2205−2231, https://doi.org/10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.
    An, S.-I., F.-F. Jin, and I.-S. Kang, 1999: The role of zonal advection feedback in phase transition and growth of ENSO in the Cane-Zebiak model. J. Meteor. Soc. Japan, 77(6), 1151−1160, https://doi.org/10.2151/jmsj1965.77.6_1151.
    Andrews, J. C., M. W. Lawrence, and C. S. Nilsson, 1980: Observations of the Tasman front. J. Phys. Oceanogr., 10(11), 1854−1869, https://doi.org/10.1175/1520-0485(1980)010<1854:OOTTF>2.0.CO;2.
    Baines, P. G., 1983: A survey of blocking mechanisms, with application to the Australian region. Aust. Meteor. Mag., 31, 27−36.
    Behringer, D., and Y. Xue, 2004: Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. Proc. Eighth Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Seattle, Washington, American Meteorological Society, 2−3.
    Berrisford, P., and Coauthors, 2011: The ERA-Interim Archive Version 2.0. ERA Report Series-No.1, 23 pp.
    Bowen, M., J. Markham, P. Sutton, X. B. Zhang, Q. R. Wu, N. T. Shears, and D. Fernandez, 2017: Interannual variability of sea surface temperature in the Southwest Pacific and the Role of Ocean dynamics. J. Climate, 30(18), 7481−7492, https://doi.org/10.1175/JCLI-D-16-0852.1.
    Browning, S. A., and I. D. Goodwin, 2013: Large-scale influences on the evolution of winter subtropical maritime cyclones affecting Australia’s East coast. Mon. Wea. Rev., 141(7), 2416−2431, https://doi.org/10.1175/MWR-D-12-00312.1.
    Cai, W. J., and P. Van Rensch, 2013: Austral summer teleconnections of Indo-pacific variability: Their nonlinearity and impacts on Australian climate. J. Climate, 26(9), 2796−2810, https://doi.org/10.1175/JCLI-D-12-00458.1.
    Cai, W. J., P. van Rensch, T. Cowan, and A. Sullivan, 2010: Asymmetry in ENSO teleconnection with regional rainfall, its multidecadal variability, and impact. J. Climate, 23(18), 4944−4955, https://doi.org/10.1175/2010JCLI3501.1.
    Cai, W. J., P. van Rensch, T. Cowan, and H. H. Hendon, 2012: An asymmetry in the IOD and ENSO teleconnection pathway and its impact on Australian climate. J. Climate, 25(18), 6318−6329, https://doi.org/10.1175/JCLI-D-11-00501.1.
    Cetina-Heredia, P., M. Roughan, E. van Sebille, and M. A. Coleman, 2014: Long-term trends in the East Australian current separation latitude and eddy driven transport. J. Geophys. Res., 119(7), 4351−4366, https://doi.org/10.1002/2014JC010071.
    Chen, M. C., and T. M. Li, 2021: ENSO evolution asymmetry: EP versus CP El Niño. Climate Dyn., 56, 3569−3579, https://doi.org/10.1007/s00382-021-05654-7.
    Chen, M. Y., P. P. Xie, J. E. Janowiak, and P. A. Arkin, 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. Journal of Hydrometeorology, 3(3), 249−266, https://doi.org/10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.
    Chung, C. T. Y., and S. B. Power, 2017: The non-linear impact of El Niño, La Niña and the Southern Oscillation on seasonal and regional Australian precipitation. Journal of Southern Hemisphere Earth Systems Science, 67(1), 25−45, https://doi.org/10.22499/3.6701.003.
    Chung, C. T. Y., S. B. Power, A. Santoso, and G. M. Wang, 2017: Multiyear variability in the tasman sea and impacts on southern Hemisphere Climate in CMIP5 models. J. Climate, 30(12), 4413−4427, https://doi.org/10.1175/JCLI-D-16-0862.1.
    Ciasto, L. M., and D. W. J. Thompson, 2008: Observations of large-scale ocean–atmosphere interaction in the southern Hemisphere. J. Climate, 21(6), 1244−1259, https://doi.org/10.1175/2007JCLI1809.1.
    Ciasto, L. M., and M. H. England, 2011: Observed ENSO teleconnections to southern Ocean SST anomalies diagnosed from a surface mixed layer heat budget. Geophys. Res. Lett., 38, L09701, https://doi.org/10.1029/2011GL046895.
    Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553−597, https://doi.org/10.1002/qj.828.
    Deser, C., M. A. Alexander, S.-P. Xie, and A. S. Phillips, 2010: Sea surface temperature variability: Patterns and mechanisms. Annual Review of Marine Science, 2(1), 115−143, https://doi.org/10.1146/annurev-marine-120408-151453.
    Du, Y., S.-P. Xie, G. Huang, and K. M. Hu, 2009: Role of air–sea interaction in the long persistence of El Nino–induced North Indian Ocean warming. J. Climate, 22(8), 2023−2038, https://doi.org/10.1175/2008JCLI2590.1.
    Fauchereau, N., S. Trzaska, Y. Richard, P. Roucou, and P. Camberlin, 2003: Sea-surface temperature co-variability in the southern Atlantic and Indian Oceans and its connections with the atmospheric circulation in the southern Hemisphere. International Journal of Climatology, 23, 663−677, https://doi.org/10.1002/joc.905.
    Frankignoul, C., and K. Hasselmann, 1977: Stochastic climate models, Part II Application to sea‐surface temperature anomalies and thermocline variability. Tellus, 29(4), 289−305, https://doi.org/10.3402/tellusa.v29i4.11362.
    Garrabou, J., and Coauthors, 2009: Mass mortality in northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Global Change Biology, 15(5), 1090−1103, https://doi.org/10.1111/j.1365-2486.2008.01823.x.
    Guan, Y. H., B. H. Huang, J. S. Zhu, Z.-Z. Hu, and J. L. Kinter III, 2014: Interannual variability of the South Pacific Ocean in observations and simulated by the NCEP Climate Forecast System, version 2,. Climate Dyn., 43, 1141−1157, https://doi.org/10.1007/s00382-014-2148-y.
    Hagos, S., and Coauthors, 2010: Estimates of tropical diabatic heating profiles: Commonalities and uncertainties. J. Climate, 23(3), 542−558, https://doi.org/10.1175/2009JCLI3025.1.
    Ham, Y.-G., J.-H. Kim, E.-S. Kim, and K.-W. On, 2021: Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data. Science Bulletin, 66, 1358−1366, https://doi.org/10.1016/j.scib.2021.03.009.
    Held, I. M., and M. J. Suarez, 1994: A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. Bull. Amer. Meteor. Soc., 75(10), 1825−1830, https://doi.org/10.1175/1520-0477(1994)075<1825:APFTIO>2.0.CO;2.
    Hill, K. L., S. R. Rintoul, R. Coleman and K. R. Ridgway, 2008: Wind forced low frequency variability of the East Australia Current. Geophys. Res. Lett., 35, L08602, https://doi.org/10.1029/2007GL032912.
    Hobday, A. J., and Coauthors, 2016: A hierarchical approach to defining marine heatwaves. Progress in Oceanography, 141, 227−238, https://doi.org/10.1016/j.pocean.2015.12.014.
    Holbrook, N. J., P. S. L. Chan, and S. A. Venegas, 2005: Oscillatory and propagating modes of temperature variability at the 3–3.5- and 4–4.5-yr time scales in the upper southwest Pacific Ocean. J. Climate, 18(5), 719−736, https://doi.org/10.1175/JCLI-3286.1.
    Holbrook, N. J., I. D. Goodwin, S. McGregor, E. Molina, and S. B. Power, 2011: ENSO to multi-decadal time scale changes in East Australian Current transports and Fort Denison sea level: Oceanic Rossby waves as the connecting mechanism. Deep Sea Research Part II: Topical Studies in Oceanography, 58(5), 547−558, https://doi.org/10.1016/j.dsr2.2010.06.007.
    Hopkins, L. C., and G. J. Holland, 1997: Australian heavy-rain days and associated East Coast Cyclones: 1958–92,. J. Climate, 10(4), 621−635, https://doi.org/10.1175/1520-0442(1997)010<0621:AHRDAA>2.0.CO;2.
    Hoskins, B. J., and D. J. Karoly, 1981: The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci., 38(6), 1179−1196, https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.
    Hu, X. M., S. A. Sejas, M. Cai, P. C. Taylor, Y. Deng, and S. Yang, 2019: Decadal evolution of the surface energy budget during the fast warming and global warming hiatus periods in the ERA-Interim. Climate Dyn., 52, 2005−2016, https://doi.org/10.1007/s00382-018-4232-1.
    Huang, B. Y., Y. Xue, D. X. Zhang, A. Kumar, and M. J. McPhaden, 2010: The NCEP GODAS ocean analysis of the tropical pacific mixed layer heat budget on seasonal to interannual time scales. J. Climate, 23(18), 4901−4925, https://doi.org/10.1175/2010JCLI3373.1.
    Huang, B. Y., and Coauthors, 2017: Extended reconstructed sea surface temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30(20), 8179−8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
    Jin, F.-F., and J. D. Neelin, 1993: Modes of interannual tropical ocean-atmosphere interaction−A unified view. Part I: Numerical results. J. Atmos. Sci., 50(21), 3477−3503, https://doi.org/10.1175/1520-0469(1993)050<3477:MOITOI>2.0.CO;2.
    Jo, H.-S., S.-W. Yeh, and W. J. Cai, 2019: An episodic weakening in the boreal spring SST-precipitation relationship in the western Tropical Pacific since the Late 1990s. J. Climate, 32(13), 3837−3845, https://doi.org/10.1175/JCLI-D-17-0737.1.
    Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77(3), 437−472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
    King, A. D., M. G. Donat, L. V. Alexander, and D. J. Karoly, 2015: The ENSO-Australian rainfall teleconnection in reanalysis and CMIP5,. Climate Dyn., 44, 2623−2635, https://doi.org/10.1007/s00382-014-2159-8.
    Kumar, A., and Z.-Z. Hu, 2012: Uncertainty in the ocean-atmosphere feedbacks associated with ENSO in the reanalysis products. Climate Dyn., 39(3-4), 575−588, https://doi.org/10.1007/s00382-011-1104-3.
    Kwon, Y.-O., and C. Deser, 2007: North Pacific decadal variability in the Community Climate System model version 2,. J. Climate, 20, 2416−2433, https://doi.org/10.1175/JCLI4103.1.
    Kwon, Y.-O., M. A. Alexander, N. A. Bond, C. Frankignoul, H. Nakamura, B. Qiu, and L. A. Thompson, 2010: Role of the Gulf Stream and Kuroshio–Oyashio systems in large-scale atmosphere–ocean interaction: A review. J. Climate, 23(12), 3249−3281, https://doi.org/10.1175/2010JCLI3343.1.
    L’Heureux, M. L., and D. W. J. Thompson, 2006: Observed relationships between the El Niño–Southern Oscillation and the extratropical zonal-mean circulation. J. Climate, 19(2), 276−287, https://doi.org/10.1175/JCLI3617.1.
    Li, R. C. Y., W. Zhou, and T. Li, 2014: Influences of the Pacific–Japan teleconnection pattern on synoptic-scale variability in the western North Pacific. J. Climate, 27(1), 140−154, https://doi.org/10.1175/JCLI-D-13-00183.1.
    Li, S. L., W. A. Robinson, M. P. Hoerling, and K. M. Weickmann, 2007: Dynamics of the extratropical response to a tropical atlantic SST anomaly. J. Climate, 20(3), 560−574, https://doi.org/10.1175/JCLI4014.1.
    Li, T., 2006: Origin of the summertime synoptic-scale wave train in the western North Pacific. J. Atmos. Sci., 63(3), 1093−1102, https://doi.org/10.1175/JAS3676.1.
    Li, Z. Y., N. J. Holbrook, X. B. Zhang, E. C. J. Oliver, and E. A. Cougnon, 2020: Remote forcing of Tasman Sea marine heatwaves. J. Climate, 33(12), 5337−5354, https://doi.org/10.1175/JCLI-D-19-0641.1.
    Liess, S., A. Kumar, P. K. Snyder, J. Kawale, K. Steinhaeuser, F. H. M. Semazzi, A. R. Ganguly, N. F. Samatova, and V. Kumar, 2014: Different modes of variability over the Tasman Sea: Implications for regional climate. J. Climate, 27(22), 8466−8486, https://doi.org/10.1175/JCLI-D-13-00713.1.
    Liu, Z. Y., and M. Alexander, 2007: Atmospheric bridge, oceanic tunnel, and global climatic teleconnections. Rev. Geophys., 45(2), RG2005, https://doi.org/10.1029/2005RG000172.
    McPhaden, M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in earth science. Science, 314(5806), 1740−1745, https://doi.org/10.1126/science.1132588.
    Mills, K. E., and Coauthors, 2013: Fisheries management in a changing climate: Lessons from the 2012 ocean heat wave in the Northwest Atlantic. Oceanography, 26(2), 191−195, https://doi.org/10.5670/oceanog.2013.27.
    Monterey, G. I., and S. Levitus, 1997: Seasonal variability of mixed layer depth for the world ocean, Grigory Monterey and Sydney Levitus. NOAA Atlas NESDIS 14, 96 pp.
    Montoya-Sánchez, R. A., A. Devis-Morales, G. Bernal, and G. Poveda, 2018: Seasonal and interannual variability of the mixed layer heat budget in the Caribbean Sea. J. Mar. Syst., 187, 111−127, https://doi.org/10.1016/j.jmarsys.2018.07.003.
    Nakamura, H., T. Sampe, Y. Tanimoto, and A. Shimpo, 2004: Observed associations among storm tracks, jet streams and midlatitude oceanic fronts. Earth’s Climate: The Ocean–Atmosphere Interaction, C. Wang et al., Eds., American Geophysical Union, 329−346,
    Oliver, E. C. J., J. A. Benthuysen, N. L. Bindoff, A. J. Hobday, N. J. Holbrook, C. N. Mundy, and S. E. Perkins-Kirkpatrick, 2017: The unprecedented 2015/16 Tasman Sea marine heatwave. Nature Communications, 8, 16101, https://doi.org/10.1038/ncomms16101.
    Oliver, E. C. J., and Coauthors, 2018: Longer and more frequent marine heatwaves over the past century. Nature Communications, 9, 1324, https://doi.org/10.1038/s41467-018-03732-9.
    Park, S., C. Deser, and M. A. Alexander, 2005: Estimation of the surface heat flux response to sea surface temperature anomalies over the global oceans. J. Climate, 18, 4582−4599, https://doi.org/10.1175/JCLI3521.1.
    Pattiaratchi, C. B., and P. Siji, 2020: Variability in ocean currents around Australia. State and Trends of Australia’s Ocean Report, A. J. Richardson et al., Eds., Integrated Marine Observing System.
    Perkins-Kirkpatrick, S. E., A. D. King, E. A. Cougnon, N. J. Holbrook, M. R. Grose, E. C. J. Oliver, S. C. Lewis, and F. Pourasghar, 2019: The role of natural variability and anthropogenic climate change in the 2017/18 Tasman Sea Marine Heatwave. Bull. Amer. Meteor. Soc., 100(1), S105−S110, https://doi.org/10.1175/BAMS-D-18-0116.1.
    Philander, S. G. H., T. Yamagata, and R. C. Pacanowski, 1984: Unstable air-sea interactions in the tropics. J. Atmos. Sci., 41(4), 604−613, https://doi.org/10.1175/1520-0469(1984)041<0604:UASIIT>2.0.CO;2.
    Pook, M. J., P. C. McIntosh, and G. A. Meyers, 2006: The synoptic decomposition of cool-season rainfall in the southeastern Australian cropping region. J. Appl. Meteorol. Climatol., 45(8), 1156−1170, https://doi.org/10.1175/JAM2394.1.
    Pook, M. J., J. S. Risbey, P. C. McIntosh, C. C. Ummenhofer, A. G. Marshall, and G. A. Meyers, 2013: The seasonal cycle of blocking and associated physical mechanisms in the Australian region and relationship with rainfall. Mon. Wea. Rev., 141(12), 4534−4553, https://doi.org/10.1175/MWR-D-13-00040.1.
    Power, S., M. Haylock, R. Colman, and X. D. Wang, 2006: The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. J. Climate, 19(19), 4755−4771, https://doi.org/10.1175/JCLI3868.1.
    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., 108(D14), 4407, https://doi.org/10.1029/2002JD002670.
    Ren, H.-L., and F.-F. Jin, 2013: Recharge oscillator mechanisms in two types of ENSO. J. Climate, 26(17), 6506−6523, https://doi.org/10.1175/JCLI-D-12-00601.1.
    Ridgway, K. R., 2007: Long-term trend and decadal variability of the southward penetration of the East Australian Current. Geophys. Res. Lett., 34, L13613, https://doi.org/10.1029/2007GL030393.
    Rintoul, S. R., and M. H. England, 2002: Ekman transport dominates local air–sea fluxes in driving variability of subantarctic mode water. J. Phys. Oceanogr., 32, 1308−1321, https://doi.org/10.1175/1520-0485(2002)032<1308:ETDLAS>2.0.CO;2.
    Risbey, J. S., M. J. Pook, P. C. McIntosh, M. C. Wheeler, and H. H. Hendon, 2009: On the remote drivers of rainfall variability in Australia. Mon. Wea. Rev., 137(10), 3233−3253, https://doi.org/10.1175/2009MWR2861.1.
    Roemmich, D., J. Gilson, R. Davis, P. Sutton, S. Wijffels, and S. Riser, 2007: Decadal spinup of the South Pacific subtropical gyre. J. Phys. Oceanogr., 37(2), 162−173, https://doi.org/10.1175/JPO3004.1.
    Sato, K., J. Inoue, I. Simmonds, and I. Rudeva, 2021: Antarctic Peninsula warm winters influenced by Tasman Sea temperatures. Nature Communications, 12, 1497, https://doi.org/10.1038/s41467-021-21773-5.
    Simpson, R. W., and W. K. Downey, 1975: The effect of a warm mid-latitude sea surface temperature anomaly on a numerical simulation of the general circulation of the southern Hemisphere. Quart. J. Roy. Meteor. Soc., 101, 847−867, https://doi.org/10.1002/qj.49710143010.
    Sloyan, B. M., and T. J. O'Kane, 2015: Drivers of decadal variability in the Tasman Sea. J. Geophys. Res., 120(5), 3193−3210, https://doi.org/10.1002/2014JC010550.
    Sprintall, J., D. Roemmich, B. Stanton, and R. Bailey, 1995: Regional climate variability and ocean heat transport in the Southwest Pacific Ocean. J. Geophys. Res., 100(C8), 15 865−15 871,
    Stammer, D., C. Wunsch, and K. Ueyoshi, 2006: Temporal changes in ocean eddy transports. J. Phys. Oceanogr., 36(3), 543−550, https://doi.org/10.1175/JPO2858.1.
    Tanimoto, Y., H. Nakamura, T. Kagimoto, and S. Yamane, 2003: An active role of extratropical sea surface temperature anomalies in determining anomalous turbulent heat fluxes. J. Geophys. Res., 108, 3304, https://doi.org/10.1029/2002JC001750.
    Verdy, A., J. Marshall, and A. Czaja, 2006: Sea surface temperature variability along the path of the Antarctic Circumpolar Current. J. Phys. Oceanogr., 36, 1317−1331, https://doi.org/10.1175/JPO2913.1.
    Wang, Y. M., S. L. Li, D. H. Luo, and J. J. Fu, 2010: Nonlinearity in the Asian monsoonal climate response to Atlantic multidecadal oscillation. Periodical of Ocean University of China, 40(6), 19−26, https://doi.org/10.3969/j.issn.1672-5174.2010.06.003. (in Chinese with English abstract
    Wei, J., W. G. Wang, Q. X. Shao, Y. S. Rong, W. Q. Xing, and C. Liu, 2020: Influence of mature El Niño-Southern Oscillation phase on seasonal precipitation and streamflow in the Yangtze River Basin, China. International Journal of Climatology, 40(8), 3885−3905, https://doi.org/10.1002/joc.6433.
    Wernberg, T., D. A. Smale, F. Tuya, M. S. Thomsen, T. J. Langlois, T. de Bettignies, S. Bennett, and C. S. Rousseaux, 2013: An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change, 3, 78−82, https://doi.org/10.1038/NCLIMATE1627.
    Wu, B., T. Li, and T. J. Zhou, 2010: Asymmetry of atmospheric circulation anomalies over the western North Pacific between El Niño and La Niña. J. Climate, 23(18), 4807−4822, https://doi.org/10.1175/2010JCLI3222.1.
    Wu, L. X., and Coauthors, 2012: Enhanced warming over the global subtropical western boundary currents. Nature Climate Change, 2, 161−166, https://doi.org/10.1038/nclimate1353.
    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.
    Yuan, X. J., 2004: ENSO-related impacts on Antarctic sea ice: A synthesis of phenomenon and mechanisms. Antarctic Science, 16(4), 415−425, https://doi.org/10.1017/S0954102004002238.
    Zhang, Q., A. Kumar, Y. Xue, W. Q. Wang, and F.-F. Jin, 2007: Analysis of the ENSO cycle in the NCEP coupled forecast model. J. Climate, 20(7), 1265−1284, https://doi.org/10.1175/JCLI4062.1.
    Zhao, S., J. P. Li, Y. J. Li, F.-F. Jin, and J. Y. Zheng, 2019: Interhemispheric influence of Indo-Pacific convection oscillation on Southern Hemisphere rainfall through southward propagation of Rossby waves. Climate Dyn., 52, 3203−3221, https://doi.org/10.1007/s00382-018-4324-y.
  • [1] Xueqian Sun, Shuanglin Li, Stefan Liess, 2022: The asymmetric connection of SST in the Tasman Sea with respect to the opposite phases of ENSO in austral summer, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-0421-y
    [2] Ge Song, RONGCAI REN, 2022: Subsurface and surface Indian Ocean Dipole and their association with ENSO in CMIP6 models, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-2086-2
    [3] Li Guo ping, Lu Jinghua, Jin Bingling, Bu Nima, 2001: The Effects of Anomalous Snow Cover of the Tibetan Plateau on the Surface Heating, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1207-1214.  doi: 10.1007/s00376-001-0034-0
    [4] Xianghui FANG, Fei ZHENG, Kexin LI, Zeng-Zhen HU, Hongli REN, Jie WU, Xingrong CHEN, Weiren LAN, Yuan YUAN, Licheng FENG, Qifa CAI, Jiang ZHU, 2023: Will the Historic Southeasterly Wind over the Equatorial Pacific in March 2022 Trigger a Third-year La Niña Event?, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 6-13.  doi: 10.1007/s00376-022-2147-6
    [5] Kunhui YE, Renguang WU, 2017: Autumn Snow Cover Variability over Northern Eurasia and Roles of Atmospheric Circulation, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 847-858.  doi: 10.1007/s00376-017-6287-z
    [6] Xiaomeng SONG, Renhe ZHANG, Xinyao RONG, 2019: Influence of Intraseasonal Oscillation on the Asymmetric Decays of El Niño and La Niña, ADVANCES IN ATMOSPHERIC SCIENCES, , 779-792.  doi: 10.1007/s00376-019-9029-6
    [7] Yang Haijun, Liu Qinyu, Jia Xujing, 1999: On the Upper Oceanic Heat Budget in the South China Sea: Annual Cycle, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 619-629.  doi: 10.1007/s00376-999-0036-x
    [8] Chunlei LIU, Yazhu YANG, Xiaoqing LIAO, Ning CAO, Jimmy LIU, Niansen OU, Richard P. ALLAN, Liang JIN, Ni CHEN, Rong ZHENG, 2022: Discrepancies in Simulated Ocean Net Surface Heat Fluxes over the North Atlantic, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1941-1955.  doi: 10.1007/s00376-022-1360-7
    [9] MA Yaoming, WANG Jiemin, HUANG Ronghui, WEI Guoan, Massimo MENENTI, SU Zhongbo, HU Zeyong, GAO Feng, WEN Jun, 2003: Remote Sensing Parameterization of Land Surface Heat Fluxes over Arid and Semi-arid Areas, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 530-539.  doi: 10.1007/BF02915496
    [10] MA Yaoming, Massimo MENENTI, Reinder FEDDES, 2010: Parameterization of Heat Fluxes at Heterogeneous Surfaces by Integrating Satellite Measurements with Surface Layer and Atmospheric Boundary Layer Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 328-336.  doi: 10.1007/s00376-009-9024-4
    [11] Fu Congbin, Robert Pyle, Fan Huijun, 1994: A Comparison Study of the Climatological Air-Sea Heat Fluxes Estimated by Different Computational Schemes of Bulk Formula, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 189-200.  doi: 10.1007/BF02666545
    [12] Nan GE, Lei ZHONG, Yaoming MA, Yunfei FU, Mijun ZOU, Meilin CHENG, Xian WANG, Ziyu HUANG, 2021: Estimations of Land Surface Characteristic Parameters and Turbulent Heat Fluxes over the Tibetan Plateau Based on FY-4A/AGRI Data, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-020-0169-5
    [13] Li Wei, Yu Rucong, Liu Hailong, Yu Yongqiang, 2001: Impacts of Diurnal Cycle of SST on the Intraseasonal Variation of Surface Heat Flux over the Western PacificWarm Pool, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 793-806.
    [14] ZHANG Shuwen, QIU Chongjian, ZHANG Weidong, 2004: Estimating Heat Fluxes by Merging Profile Formulae and the Energy Budget with a Variational Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 627-636.  doi: 10.1007/BF02915730
    [15] Qu Shaohou, 1989: Observation Research of the Turbulent Fluxes of Momentum, Sensible Heat and Latent Heat over the West Pacific Tropical Ocean Area, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 254-264.  doi: 10.1007/BF02658021
    [16] Yao YAO, Dehai LUO, 2018: An Asymmetric Spatiotemporal Connection between the Euro-Atlantic Blocking within the NAO Life Cycle and European Climates, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 796-812.  doi: 10.1007/s00376-017-7128-9
    [17] CAO Ning, REN Baohua, ZHENG Jianqiu, 2015: Evaluation of CMIP5 Climate Models in Simulating 1979-2005 Oceanic Latent Heat Flux over the Pacific, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1603-1616.  doi: 10.1007/s00376-015-5016-8
    [18] Sang-Hyun LEE, Jun-Ho LEE, Bo-Young KIM, 2015: Estimation of Turbulent Sensible Heat and Momentum Fluxes over a Heterogeneous Urban Area Using a Large Aperture Scintillometer, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1092-1105.  doi: 10.1007/s00376-015-4236-2
    [19] Long Baosen, 1989: The Latent and Sensible Heat Fluxes over the Western Tropical Pacific and Its Relationship to ENSO, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 467-474.  doi: 10.1007/BF02659080
    [20] S. S. Vaidya, V. N. Lykossov, S. S. Singh, 1993: Effect of Counter-Gradient in the Computation of Turbulent Fluxes of Heat and Moisture in a Regional Model, ADVANCES IN ATMOSPHERIC SCIENCES, 10, 85-94.  doi: 10.1007/BF02656956

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 09 November 2021
Manuscript revised: 19 January 2022
Manuscript accepted: 11 February 2022
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

The Asymmetric Connection of SST in the Tasman Sea with Respect to the Opposite Phases of ENSO in Austral Summer

    Corresponding author: Shuanglin LI, shuanglin.li@mail.iap.ac.cn
  • 1. Climate Change Research Center (CCRC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. Department of Atmospheric Science/Centre for Severe Weather and Climate and Hydro-geological Hazards, China University of Geosciences, Wuhan 430074, China
  • 3. College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Department of Soil, Water, and Climate, University of Minnesota, Twin Cities, St. Paul MN 55108, Minnesota, USA

Abstract: This study uses linear regression and composite analyses to identify a pronounced asymmetric connection of sea surface temperature (SST) in the Tasman Sea with the two opposite phases of El Niño-Southern Oscillation (ENSO) during austral summer. In El Niño years, the SST anomalies (SSTAs) in the Tasman Sea exhibit a dipolar pattern with weak warmth in the northwest and modest cooling in the southeast, while during La Niña years the SSTAs exhibit a basin-scale warmth with greater amplitude. Investigations into the underlying mechanism suggest that this asymmetry arises from a mechanism related to oceanic heat transport, specifically the anomalous Ekman meridional heat transport induced by the zonal wind stress anomalies, rather than the surface heat fluxes on the air-sea interface. Further analysis reveals that the asymmetry of oceanic heat transport between El Niño and La Niña years is driven by the asymmetric atmospheric circulation over the Tasman Sea stimulated by the asymmetric diabatic heating in the tropical Pacific between the two opposite ENSO phases.

摘要: 本研究通过线性回归和合成分析,发现南半球夏季塔斯曼海海温与ENSO正、负位相存在非对称联系。El Niño年,塔斯曼海的海温异常表现为偶极型模态,西北部为较弱的暖海温异常,东南部为冷海温异常;La Niña年,塔斯曼海则表现为海盆一致的增暖,且异常值的振幅更强。机制分析表明,ENSO正、负位相期间塔斯曼海海温异常的非对称性主要来自海洋热输送的贡献,特别是由纬向风应力异常引起的经向Ekman热输送,而海-气界面的热通量则起了阻尼作用。进一步分析发现,El Niño年和La Niña年海洋热输送的非对称性受到塔斯曼海上空非对称的大气环流的驱动,而非对称的大气环流由热带太平洋非对称的非绝热加热激发。

    • The Tasman Sea is a boundary sea off the southeastern coast of Australia, surrounded by Tasmania Island in the west, New Zealand in the east, and the Coral Sea in the north. From an oceanic perspective, the Tasman Sea is affected by a strong western boundary current, the East Australian Current (EAC), which parallels the east coast of Australia and flows southward. Its main mass meanders away from Australia around 32ºS and flows eastward into the Tasman Sea, giving rise to intensified oceanic eddy activity and air-sea exchanges (Sprintall et al., 1995). Meanwhile, the warm water brought poleward by EAC is in strong contrast with the cold water in the south, resulting in an oceanic temperature front called Tasman Front (Andrews et al., 1980; Sloyan and O’Kane, 2015). From an atmospheric perspective, the Tasman Sea is situated in the exit region of the upper atmospheric subtropical westerly jet during austral summer (DJF) (Fig. 1a) and is also closely connected with the climatological storm track of the Southern Hemisphere (SH), although the SH upper westerly jet and the storm track exhibit evident seasonal variations over a year (Fig. 1). In terms of its atmospheric and oceanic features, the Tasman Sea region bears a resemblance to the extension region of the Kuroshio in the Northwest Pacific or to the Gulf Stream in the North Atlantic, where active air-sea interaction can be found and where sea surface temperature (SST), as well as the SST front, may exert a substantial influence on the overlying atmosphere (Nakamura et al., 2004; Kwon et al., 2010). In other words, the SST in the Tasman Sea may provide some potential predictability for the climate system.

      Figure 1.  The 200-hPa climatological zonal wind (contour, units: m s–1) contoured every 5 m s–1 above 25 m s–1 and the upper-tropospheric storm track (shaded, units: m s–1) based on the standard deviation of the band-pass filtered (2.5–6-day periods) 300-hPa meridional wind for (a) DJF, (b) MAM, (c) JJA, and (d) SON. Shaded areas denote values greater than 7 m s–1. The box in each panel represents the Tasman Sea region (26°−46°S, 150°−174°E).

      Previous studies suggest that, on interannual timescales, the Tasman Sea SST is an important factor for local and remote climates. First, it influences the weather and climate of Australia and New Zealand (Hopkins and Holland, 1997; Pook et al., 2006, 2013; Risbey et al., 2009) through impacting the formation and maintenance of the atmospheric blocking high over the Tasman Sea (Simpson and Downey, 1975; Baines, 1983) and the activity of Australian east coastal cyclones (Browning and Goodwin, 2013). Second, it affects the occurrence of extreme marine heatwaves (MHWs), the synoptic-scale anomalous warm water events with durations of five days or longer (Hobday et al., 2016), which may cause devastation of the marine ecosystems and even severe economic tensions (Garrabou et al., 2009; Mills et al., 2013; Wernberg et al., 2013; Oliver et al., 2017). During those years with higher SST, the occurrence of MHWs in the Tasman Sea generally increases (Oliver et al., 2018). Finally, the SST in the Tasman Sea may be a potential precursor for the remote Asian climate by influencing the cross-equatorial atmospheric teleconnection or the lower-tropospheric cross-equatorial flows (Liess et al., 2014; Zhao et al., 2019). A recent study suggests that warming in the Tasman Sea promotes increases in air temperature over the Antarctic Peninsula through a poleward shift of Southern Ocean storm tracks (Sato et al., 2021). Therefore, understanding the variability of SST in the Tasman Sea is of great social and climatic significance.

      The processes responsible for the interannual variability of SST in the Tasman Sea are complicated. Frankignoul and Hasselmann (1977) suggested that a large fraction of the SST variability could be explained as a red-noise oceanic response to shorter time scale atmospheric random forcing such as surface heat flux. Consistent with this, Fauchereau et al. (2003) demonstrated that the SST anomalies (SSTAs) in the Tasman Sea seemed to be a response to anomalous latent heat release related to anomalous near-surface winds. More recently, by using the ECCOv4 ocean reanalysis, Bowen et al. (2017) found that the air-sea heat flux contributes to the SSTAs around New Zealand. In addition to these atmospheric flux forcings, the SST is concurrently affected by the heat transport of the ocean current like the EAC, whose strength and extension respond to changes in the South Pacific wind field (Hill et al., 2008; Holbrook et al., 2011; Wu et al., 2012; Chung et al., 2017; Li et al., 2020). Both Ridgway (2007) and Roemmich et al. (2007) suggested that SST warmth in the Tasman Sea was related to the enhanced southward transport of EAC. In agreement with this, Hill et al. (2008) found the variations in temperature at Tasmania can be explained by the heat transport of EAC, and a recent study by Li et al. (2020) showed that a total of 51% of the historical MHWs in the Tasman Sea was primarily due to the increased poleward transports of EAC. Mechanically, the southward extension of EAC, particularly the portion south of 33°S, often causes an unsteady train of mesoscale eddies, increasing eddy mixing (Stammer et al., 2006).

      Aside from local and adjacent atmospheric and oceanic factors, SSTAs in the Tasman Sea are also influenced by remote forcings like El Niño–Southern Oscillation (ENSO), the most prominent mode of interannual climate variability (Philander et al., 1984; McPhaden et al., 2006; Deser et al., 2010). ENSO excites a Pacific-South America (PSA) teleconnection to influence the southern extratropics (Hoskins and Karoly, 1981). The surface atmospheric feedback processes linked to ENSO may alter the extratropical SST through a so-called atmospheric bridge (Alexander et al., 2002). Verdy et al. (2006) illustrated that ENSO drives a low-level anomalous circulation pattern over the South Pacific, which causes surface heat flux anomalies and subsequently the SSTAs in the regions of the Antarctic Circumpolar Current. Similarly, Ciasto and England (2011) demonstrated that the ENSO-related atmospheric circulation coincides well with the turbulent heat flux and contributes to the SST variability in the Southern Ocean. Guan et al. (2014) suggested that the ENSO-related PSA pattern generates persistent anomalies in sea level pressure and surface winds around New Zealand and the mid-latitudinal South Pacific. These anomalies cause surface air-sea heat flux anomalies through the evaporation-wind feedback or Ekman drift, and the arched SSTA pattern, which consists of the two major centers of the South Pacific Ocean Dipole (SPOD) pattern during austral summer. In addition, the ENSO-associated oceanic flow and oceanic waves may also affect the extratropical SST through an oceanic tunnel (oceanic bridge) (Liu and Alexander, 2007). Holbrook et al. (2005) suggested that the 3–3.5-yr oscillation of oceanic temperature variability in the upper Southwest Pacific was connected to the EAC and its extension, which might be explained as the forced result of westward propagating oceanic Rossby waves. Also, studies demonstrated the ENSO-related SSTAs in the Southern Hemisphere projects strongly onto the ENSO-related Ekman heat transport due to ocean dynamics (Ciasto and Thompson, 2008; Ciasto and England, 2011). Cetina-Heredia et al. (2014) identified a lagged increase/decrease of southward EAC heat transport approximately 6–9 months after the end of an El Niño/La Niña event. The increased/decreased southward heat transport might alter the SST nearby (Ridgway, 2007; Roemmich et al., 2007; Li et al., 2020). These studies suggest a substantial remote influence of ENSO on southern mid-latitudinal SSTAs, including those in the Tasman Sea, where the strongest SST variability occurs in the peak phase of ENSO, December-January-February (DJF) (Fig. 2).

      Figure 2.  Spatial distribution of seasonal mean climatological SST (contour, units: °C) and the standard deviation (shaded, units: °C) of detrended SSTAs for (a) DJF, (b) MAM, (c) JJA, and (d) SON. The box in each panel represents the Tasman Sea region as in Fig.1.

      However, from several randomly selected cases as displayed in Table 1, the connection of SST in the Tasman Sea to ENSO seems asymmetric with respect to the opposite phases of ENSO (Fig. 3). In La Niña years, the SSTAs generally increase over the whole basin (Figs. 3b, d, f). In contrast, in El Niño years there is no general basin cooling, but a dipolar pattern with warmth in the northwest and cooling in the southeast (Figs. 3a, c, e). In our understanding, such an asymmetric relationship has not been sufficiently investigated. Research on this issue will contribute to a better understanding of the interannual variability of SST in the Tasman Sea, also yielding insight into the non-linear relationship between East Australian rainfall and ENSO (Power et al., 2006; Cai et al., 2010; King et al., 2015; Chung and Power, 2017), since the moisture availability in the southwest Pacific has been proposed as a possible mechanism explaining such a relationship (King et al., 2015).

      Years
      El Niño1957, 1965, 1968, 1972, 1982, 1986, 1991, 1994, 1997, 2009, 2015
      La Niña1955, 1970, 1973, 1975, 1988, 1998, 1999, 2007, 2010, 2017

      Table 1.  Years of El Niño and La Niña during the period of 1950–2018 following the threshold of one standard deviation of DJF mean Niño-3.4 index.

      Figure 3.  The DJF-mean global SST anomaly patterns (units: °C) in (a) 1957, (b) 1970, (c) 1972, (d) 1973, (e) 2009, and (f) 1998. The left and right columns show SSTAs in El Niño years and La Niña years, respectively. The year in the top left of each panel is the year of December. The box in each panel represents the Tasman Sea region as in Fig. 1.

      This paper is structured as follows. Section 2 outlines the data and methods adopted in this study. Section 3 examines the connection of SSTAs in the Tasman Sea with those in the tropical central-eastern Pacific and identifies the asymmetric connection with respect to the phase of ENSO. Section 4 analyzes the possible mechanisms for the asymmetry of SST in terms of local air-sea heat fluxes and heat advection by oceanic currents. The roles of the above two factors are further diagnosed by examining a heat budge in the local ocean mixed layer in section 5. Finally, a summary is presented in section 6.

    2.   Data and Methods
    • The National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST monthly data (version5, ERSSTv5) (Huang et al., 2017), with a resolution of 2.5° × 2.5° from 1950 to 2019, is used in this study. Reanalysis of monthly sea level pressure (SLP) and multi-level geopotential height spanning 1950–2019 with a resolution of 2.5° × 2.5° are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP/NCAR) (Kalnay et al., 1996). Monthly zonal and meridional wind stress data from 1950 to 2019 also comes from the NCEP/NCAR, but with a resolution of 1.9° × 1.9°. One previous study (Kumar and Hu, 2012) suggests that the ENSO-related wind stress anomalies from this dataset exhibit a qualitative consistency with several other reanalyses, although the ENSO-related air-sea coupled variabilities inherent to this dataset have the largest biases compared with other reanalyses. Thus, it seems plausible to use wind stress from the NCEP/NCAR dataset.

      The surface heat fluxes, including net surface shortwave and longwave fluxes and sensible heat and latent heat fluxes, come from the ERA-Interim (Dee et al., 2011), which has a resolution of 1° × 1° and spans 1979–2019. One should note that the ERA-Interim cannot precisely reflect the observations due to its lack of observation constraint in the uncoupled data assimilation systems, although the ERA-Interim heat fluxes are widely used to explore the reasons behind the SST anomalies (Montoya-Sánchez et al., 2018; Hu et al., 2019; Jo et al., 2019) and there were substantial improvements regarding the handling of data biases (Berrisford et al., 2011; Dee et al., 2011). The monthly precipitation data, on a 2.5° × 2.5° grid, is obtained from the precipitation reconstruction (PREC) (Chen et al., 2002). In addition, the monthly oceanic temperature, total sea-surface downward heat flux, and the ocean current velocity from the global ocean data assimilation system (GODAS) (Behringer and Xue, 2004) are also employed. The GODAS is developed at the NCEP and spans the period from 1980 to the present with a resolution of 0.333° × 1.0°.

      This study focuses on the austral summer (DJF), when the SST variability in the Tasman Sea (Fig. 2), the ENSO-related anomalies (Wei et al., 2020; Ham et al., 2021), and the ENSO-associated SH teleconnections (Yuan, 2004; Ciasto and England, 2011) are seasonally strongest. Because it crosses a calendar year, for a specific case, the year corresponding to the season is marked as the year of December (the earlier year). As in Cai et al. (2012), all datasets are linearly detrended to ensure that any generated relationship is not a result of long-term trends in the respective time series. The climatological norm is calculated as the mean throughout the period 1981–2010.

    • The main statistical tools used include linear regression and composite analysis. Linear regression analysis is utilized to preliminarily estimate the asymmetry in ENSO teleconnection with the Tasman Sea SSTAs. Composite analysis allows for comparison between the SSTAs in El Niño and La Niña events to further isolate the asymmetric relationship. Halves of the differences (sums) between the composite La Niña and El Niño events are defined as the symmetric (asymmetric) components associated with ENSO (Li et al., 2007; Wang et al., 2010). In other words, the symmetric components are approximated by (La Niña – El Niño)/2, and the asymmetric components are estimated by (La Niña + El Niño)/2.

      ENSO is defined with the Niño-3.4 index (the area-averaged SSTAs over 5°S–5°N, 170°–120°W), and a total of 11 El Niño years and 10 La Niña years during 1950–2018 are selected following the threshold of one standard deviation of DJF mean Niño-3.4 index (Table 1). Given the lengths of different datasets and our purpose of discussing air-sea interactions, only 7 El Niño and 6 La Niña cases since 1980 are used for composite analysis to ensure a relative consistency in the atmospheric and oceanic anomalies during the same periods. The Tasman Sea is bounded by 26°–46°S, 150°–174°E (boxed in Fig. 1), as in Perkins-Kirkpatrick et al. (2019).

    • Following Alexander and Scott (2008), the heat flux due to Ekman drift current is computed using

      where $ {c}_{p} $ is the specific heat capacity of seawater, 4100 J (Kg °C)−1, $ f $ is the Coriolis parameter, $ {\tau }_{x} $ and $ {\tau }_{y} $ are the zonal and meridional wind stress, and $ \partial \mathrm{S}\mathrm{S}\mathrm{T}/\partial x $ and $ \partial \mathrm{S}\mathrm{S}\mathrm{T}/\partial y $ are the zonal and meridional SST gradients. Considering the greater importance of the poleward heat flux linked to EAC, we only analyze the meridional component $ {(c}_{p}{\tau }_{x}\partial \mathrm{S}\mathrm{S}\mathrm{T}/f/\partial y) $ of $ {H}_{\mathrm{e}\mathrm{k}} $.

    • To compare the relative roles of air-sea heat fluxes and oceanic dynamics in driving ENSO-related SSTAs in the Tasman Sea and isolate the relative contributions of different physical processes, we perform the ocean mixed layer heat budget analysis as in previous studies (Zhang et al., 2007; Ren and Jin, 2013). Ignoring the second-order non-linear terms (Ren and Jin, 2013), the mixed layer averaged temperature tendency equation can be expressed as

      where an overbar denotes a climatological mean and a prime represents anomalies relative to the climatological mean; $ T $, $ u $, $ v $, $ w $ are the oceanic temperature, zonal current, meridional current, and vertical current velocity, respectively. Parameters ${Q}_{\mathrm{n}\mathrm{e}\mathrm{t}}^{{'}}$, $ \rho $, $ {c}_{p} $ and $ H $ stand for the net surface heat flux anomaly, the density of seawater ($ \rho $= 1024 kg m–3), the specific heat of seawater$ , $and the climatological monthly depth of mixed layer in the Tasman Sea obtained from the World Ocean Atlas (Monterey and Levitus, 1997), individually. The last term, $ R $ is the residual term, which is not considered in this study.

      The mean vertical advection in Eq. (2) can be further decomposed into two terms in an approximation (Jin and Neelin, 1993; An et al., 1999; Zhang et al., 2007) as follows

      ${T}'_{\mathrm{s}\mathrm{u}\mathrm{b}}$ is the subsurface-layer oceanic temperature anomaly.

      Following Ren and Jin (2013), we regroup Eq. (2) into seven feedback terms as follows

      where

      Here, MC, EK, ZA, MA, TD, and TH represent mean circulation feedback, Ekman pumping feedback, zonal advection feedback, meridional advection feedback, thermodynamic feedback, and thermocline feedback, respectively.

    • To isolate the direct effect of the ENSO-related asymmetric heating, a diagnostic experiment with an anomaly atmospheric general circulation model (AGCM) (Li, 2006) is conducted. This anomaly AGCM is a modified dry version of the Princeton AGCM (Held and Suarez, 1994) with five evenly distributed sigma levels (σ = 0.1, 0.3, 0.5, 0.7, 0.9 from top to bottom level) and a horizontal resolution of T42. It is linearized by the specified three-dimensional seasonal mean basic state so that the model response to a specific heating anomaly can be examined. In our experiment, the 3D DJF-mean state, which comes from the long-term mean of the NCEP–NCAR reanalysis, is prescribed as the model basic state. Rayleigh friction with a strong momentum damping rate of 1 d–1 is applied in the lowest model level (σ = 0.9). Newtonian cooling with an e-folding time scale of 10 days is applied to all model levels in both momentum and heat equations. It takes about 25 days for the AGCM response to reach an equilibrium state under these damping terms, and we choose the last 20 days of a 60-day integration to estimate the atmospheric responses to the tropical forcing. More details about this model can be found in Li (2006) and Li et al. (2014).

    3.   Connection of SST in the Tasman Sea to ENSO
    • As illustrated in the introduction, ENSO affects the interannual variability of the southern extratropical SST. The regression results show that SSTAs in much of the Tasman Sea tend to be cooler when the Niño-3.4 values are positive (Fig. 4a), indicating a negative relationship between them. This is consistent with a significant correlation coefficient of –0.24 (p-value = 0.043) between the area-averaged SSTAs in the Tasman Sea and the Niño-3.4 index. To examine whether the connection is asymmetric or not, we calculate the regression of SSTAs when ENSO is positive (Fig. 4b) and when ENSO is negative (Fig. 4c), respectively. When comparing Fig. 4b with Fig. 4c, a remarkable asymmetry is seen regarding the distribution and the amplitude of SSTAs, in that the El Niño-related SSTAs are manifested as a dipole pattern with modest warming in the northwest and weak cooling in the southeast of the Tasman Sea (Fig. 4b). In contrast, the SSTAs associated with La Niña show significant basin-wide warming (Fig. 4c).

      Figure 4.  The SSTAs (units: °C) regressed by the Niño-3.4 index using (a) all samples, (b) samples with positive Niño-3.4 values, and (c) samples with sign-reversed negative Niño-3.4 values. Hatching indicates significance at the 90% confidence level. The black boxes represent the Tasman Sea region as in Fig. 1.

      To shed light on the asymmetry, a composite analysis is performed for the El Niño and La Niña cases, respectively. The composite SSTAs for the El Niño and La Niña cases (Figs. 5ab) bear a close resemblance to their corresponding SST regressions (Figs. 4bc), with spatial correlation coefficients of 0.86 and 0.64, respectively. There is weak warming in the northwest Tasman Sea along with a modest cooling in the southeast in the El Niño cases (Fig. 5a), but there is significant warming in nearly the entire Tasman Sea in the La Niña cases (Fig. 5b). This indicates greater amplitudes of SSTAs corresponding to La Niña, in addition to the asymmetry between their spatial patterns. The asymmetry of SSTAs in the Tasman Sea between the La Niña and El Niño cases is further seen from their symmetric and asymmetric components (Figs. 5cd). The symmetric component shows remarkable consistency with the composite in the La Niña cases. The asymmetric component exhibits a monopole pattern with the maximum offshore southeast to the Australian coast. One additional calculation is performed based on the HadISST1 dataset (Rayner et al., 2003), and a similar composite pattern has been obtained (not shown), implying that the asymmetry is robust and insensitive to the datasets used. The possible mechanisms responsible for the asymmetrical response to ENSO phase are explored in the next section.

      Figure 5.  Composite DJF mean SSTAs (units: °C) for (a) El Niño years and (b) La Niña years. Panel (c) are the symmetric components estimated by half of the difference of (b) and (a). Panel (d) shows the asymmetric components estimated by half of the sum of (b) and (a). Hatching indicates significance at the 90% confidence level.

    4.   Processes responsible for ENSO’s asymmetric influence on the Tasman Sea
    • Alexander et al. (2002) suggest that the surface heat fluxes are the key components of the atmospheric bridge driving extratropical SSTAs. In austral summer, the SST in the Southwest Pacific is susceptible to air-sea exchanges due to the shallow mixed layer depth (Guan et al., 2014; Bowen et al., 2017). Thus, the surface heat fluxes may be especially important for the formation of SSTAs in the Tasman Sea.

      Figure 6 depicts the composite surface heat flux anomalies of El Niño and La Niña and their asymmetric components. Here, a downward heat flux has a positive sign, and thus a positive (negative) anomaly indicates the forcing role of the above atmosphere (ocean beneath) on the ocean beneath (above atmosphere). In El Niño years, the surface heat flux mainly features insignificant positive anomalies over the Tasman Sea, along with scattered negative anomalies in the center (Fig. 6a). The positive anomalies in the southeast Tasman Sea suppress the cold SST anomalies therein (compare Fig. 6a and Fig. 5a). In La Niña years, the heat flux anomalies manifest themselves as significant negative values, which inhibit the formation of warm SST anomalies (compare Fig. 6b and Fig. 5b). The spatial correlation of the heat flux composite with the corresponding SSTA composite in the Tasman Sea is –0.21 and –0.44 for the El Niño and La Niña years, respectively. Also, the asymmetric component of composite surface heat fluxes is negatively correlated with that of composite SSTAs with a value of –0.48. These indicate that the surface heat fluxes play a damping role in shaping the ENSO-related SSTAs in the Tasman Sea. This result is consistent with previous investigations on the role of surface heat fluxes over other frontal regions like the Kuroshio in the North Pacific and the Gulf Stream in the North Atlantic, where heat fluxes damp the low-frequency SST anomalies (Kwon and Deser, 2007; Kwon et al., 2010) along with a positive correlation of local SST with surface heat flux (where a sign of positive indicates upward) (Tanimoto et al., 2003; Park et al., 2005).

      Figure 6.  Composite DJF mean surface heat flux anomalies (positive: downward; units: W m–2) for (a) El Niño events and (b) La Niña events. Panel (c) shows the asymmetric components between (a) and (b). Hatching indicates significance at the 90% confidence level.

      As for the individual roles of components composing the surface heat flux, area-averaged shortwave (SW) and longwave (LW) radiative flux, and sensible heat (SH) and latent heat (LH) flux, as well as the total heat flux (Sum), are compared in Fig. 7. In the El Niño years, the LW and SH are negligible, and the SW and LH contributed to the positive net heat fluxes that warmed the SST in the Tasman Sea (Fig. 7a). In comparison, in La Niña years, the SW and LW radiative fluxes tend to offset each other. The negative net radiative flux strengthens the negative SH and LH, leading to a negative residual which tends to reduce the SST (Fig. 7b). When considering that the observed composite SSTAs in El Niño and La Niña years are neutral and positive, different from the contribution of net heat flux, then the surface fluxes are not the dominant factor for the SSTAs in the Tasman Sea. This is verified by the diagnosis of the asymmetric component (Fig. 7c), in which the net effect of surface heat flux does not contribute to the asymmetry of the SSTAs.

      Figure 7.  The region-averaged shortwave (SW), longwave (LW) radiative heat flux, sensible heat (SH) and latent heat (LH) flux anomalies and their sum (units: W m–2) over the Tasman Sea for (a) El Niño events, (b) La Niña events and (c) the asymmetric components between (a) and (b). Positive heat fluxes are directed into the ocean. The number in the top right of each panel indicates the area-averaged SSTAs (units: °C).

    • Previous studies revealed a time-lagged relationship between ENSO and the heat transport induced by the EAC but did not focus on the differences in heat transport between the two phases of ENSO (Ridgway, 2007; Cetina-Heredia et al., 2014; Pattiaratchi and Siji, 2020). In this section, the oceanic horizontal heat transport induced by Ekman drift is investigated for the two opposite phases of ENSO. In El Niño years, there is cold oceanic advection related to a weakened EAC in the eastern Tasman Sea (Fig. 8a), which has contributed to the observed negative SSTAs there (compare Fig. 8a and Fig. 5a). In contrast, in La Niña years, almost the entire Tasman Sea is dominated by intensified southward currents, leading to enhanced warm advection and positive SSTAs (compare Fig. 8b with Fig. 5b). An evident difference is seen in the amplitude of advection between two opposite ENSO phases (compare Fig. 8a and Fig. 8b), yielding an asymmetry in the heat advection (Fig. 8c) and then SSTAs (Fig. 5d). Therefore, the ENSO-related horizontal heat advection by oceanic currents may have played a substantial role in the ENSO-related SSTAs in the Tasman Sea as well as their asymmetry. Notably, the intensification of the EAC in the western Tasman Sea in the La Niña years is even more evident than in the El Niño years (Figs. 8ab). This may lead to less predictability in the SSTAs or Marine Heat Waves over the Tasman Sea region for El Niño years compared to La Niña years.

      Figure 8.  Composite oceanic current anomalies (vector, units: m s–1) at 5-m depth and the horizontal temperature advection anomalies (shaded, units: 10–8 °C s–1) driven by the currents for (a) El Niño events and (b) La Niña events. Panel (c) shows the asymmetric components between (a) and (b). Only the currents with speeds greater than 0.004 m s–1 are displayed in (a, b) and those greater than 0.002 m s–1 are displayed in (c).

      Previous studies suggested that the Ekman heat transport is instrumental in creating extratropical SSTAs (Alexander et al., 2002; Rintoul and England, 2002; Ciasto and England, 2011). Figure 9 shows the meridional Ekman heat transport induced by zonal wind stress. The anomalous easterlies in the Tasman Sea drive a poleward Ekman transport during La Niña cases, leading to positive heat transport anomalies (Fig. 9b). The situation is opposite in El Niño events but features weaker westerlies and smaller heat flux anomalies (Fig. 9a). Thus, the spatial pattern of the asymmetric components is dominated by the anomalies in the La Niña events (compare Figs. 9c and 9b). The overall similarity of the SSTAs to the meridional Ekman heat transport anomalies indicates that the anomalous Ekman heat transport dominates the ENSO-related SSTAs in the Tasman Sea (compare Fig. 5 and Fig. 9).

      Figure 9.  As Fig. 8, but for the meridional Ekman heat transport anomalies (shaded, units: W m–2) and wind stress anomalies (vector, units: N m–2). Only the vectors with speeds greater than 0.005 N m–2 are displayed in (a, b) and those greater than 0.0025 N m–2 are displayed in (c).

      Given that the upper atmospheric teleconnections originating from the tropics can propagate into the extratropics and influence the extratropical lower atmospheric circulation through baroclinic adjustment, eddy momentum, and heat flux adjustment, this will further affect the upper oceanic currents through Ekman drift or thermodynamic processes. The ENSO-related atmospheric circulation shows a baroclinic Southern Oscillation (SO) pattern in the tropics and an equivalent barotropic PSA pattern in the southern extratropics (Fig. 10). In El Niño years, the Australia-Tasman Sea sector is dominated by positive SLP anomalies, which causes the anomalous southwesterly current to prevail in the eastern Tasman Sea (compare Fig. 10a and Fig. 8a). In contrast, during La Niña years, the SLP anomalies are characterized by a dipole pattern in the Tasman Sea, which results in the prevalence of anomalous northeasterly currents (compare Fig. 10b and Fig. 8b). Moreover, the anomalous atmospheric circulation is nearly symmetric in the tropics but significantly asymmetric in the Tasman Sea (Figs. 10c, f, i).

      Figure 10.  Composite DJF mean SLP anomalies (a–c, units: hPa), 500-hPa (d–f) and 200-hPa (g–i) geopotential height anomalies (units: m) for El Niño events (left column), La Niña events (middle column) and asymmetric components (right column). Dotted areas are significant at the 90% confidence level.

    5.   Discussion
    • The above analyses illustrate the damping role of the surface heat fluxes and the influential role of the oceanic heat transport in producing the ENSO-related SSTAs as well as their asymmetry with respect to the phase of ENSO. To verify this, as in Chen and Li (2021) and Jo et al. (2019), we perform a heat budget analysis of the oceanic mixed layer temperature for the early phase of ENSO (October-November-December-January, ONDJ) by using GODAS data based on Eq. (4) (see section 2). This approach is effective in understanding and monitoring the SST variability (Huang et al., 2010). The results suggest that the MA term related to the Ekman meridional heat transport and the TD term associated with the surface heat flux are dominant (Fig. 11). In El Niño years, the TD term contributes to the warming tendency and damps the cool SST anomalies, while the MA term offsets the warming tendency and favors cooling anomalies (Fig. 11a). In comparison, in La Niña cases, the MA term contributes to both warm SST anomalies and positive SST tendency with a contribution rate of 0.10°C month–1, but the TD term damps the SSTAs and the SST tendency. The contribution of the MA term is mostly counteracted by the TD term, leading to a much weaker SST tendency of 0.016°C month–1 (Fig. 11b). The asymmetric SSTAs and warming tendency are explained well by the MA term, rather than the TD term (Fig. 11c). Quantitatively, the MA term contributes 89.6% to the tendency of the asymmetric SST. This result verifies the critical role of the meridional heat transport in generating ENSO-related SSTAs. The damping role of the surface heat fluxes, shown in Figs. 11bc is in agreement with the above finding that ENSO’s connection with SST in the Tasman Sea cannot be attributed to the anomalous surface heat flux.

      Figure 11.  Composite SSTAs (T′, units: °C) and mixed-layer heat budget for each term (units: °C month–1) during ONDJ of (a) El Niño, (b) La Niña, and (c) their asymmetric components averaged in the Tasman Sea. The x-axis label “sum” represents the sum of terms on the right side of Eq. (4) except R term.

    • As mentioned above, the asymmetry in anomalous atmospheric circulation between the El Niño and La Niña years may result in asymmetry in SSTAs in the Tasman Sea. Figure 12 shows the composite precipitation anomalies and SSTAs, along with their symmetric and asymmetric components about the phase of ENSO. The positive SSTAs in the tropical eastern Pacific during El Niño are stronger than the negative SSTAs during La Niña, whereas the negative SSTAs in the tropical central Pacific during La Niña are stronger and extend farther westward (compare Fig. 12a and 12c), which leads to dipolar asymmetric SSTAs in the tropical middle-eastern Pacific (Fig. 12g). Corresponding to the SSTAs, the asymmetric precipitation anomalies also show a dipolar pattern with increased precipitation in the tropical eastern Pacific and decreased precipitation in the central Pacific (Fig. 12h). These results are consistent with those of Wu et al. (2010), although the ENSO years they used were selected from 1949–2002. The asymmetry in precipitation anomalies may play an important role in shaping the asymmetry in SSTAs through the direct induction of an asymmetry in atmospheric circulation.

      Figure 12.  Composite DJF mean SSTAs (a, c, e, g; units: °C) and precipitation anomalies (b, d, f, h; units: mm d–1) for (a, b) El Niño events and (c, d) La Niña events. Panels (e) and (f) are the symmetric components estimated by half of the difference between (c) and (a), and (d) and (b), respectively. Panels (g) and (h) are the asymmetric components estimated by half of the sum of (a) and (c), and (b) and (d), respectively. The areas dotted are significant at the 90% confidence level.

      To verify our conjecture and exclude other factors, a diagnostic experiment with an anomaly AGCM is conducted (see section 2.2.4). Previous studies have suggested that the latent heat release by precipitation dominates the total diabatic heating (e.g., Hagos et al., 2010). Thus, we use the asymmetric components of precipitation (Fig. 12h) in the tropical Pacific (150ºE–90ºW, 10ºS–10ºN) to calculate the diabatic heating (Fig. 13). The horizontal pattern of the heating rate at 500 hPa is displayed in Fig. 13a, which shows an identical distribution to that of asymmetric precipitation anomalies (compare Fig. 13a and Fig. 12h). The vertical profile of the diabatic heating prescribed over the dot in Fig. 14a has a maximum at about 500hPa (σ = 0.5) to mimic the tropical rainfall-induced convective heating (Fig. 13b). The AGCM response features a wave-train-like structure with alternative positive and negative centers in the extratropical South Pacific (Fig. 14). It bears a resemblance to the asymmetric atmospheric circulation between the El Niño and La Niña years, although the intensity and position of the centers exhibit certain differences (compare Fig. 14 and Figs. 10c, f, i). The above results illustrate that the diabatic heating derived from the asymmetric precipitation about the phase of ENSO excites a propagating teleconnection wave train in the southern extratropics. Coupled with transient feedback, an anomalous low-level atmospheric circulation appears and influences the Ekman drift as well as the SSTAs.

      Figure 13.  (a) The horizontal distribution of the initial diabatic heating anomalies (units: K d−1) at 500 hPa. (b) The vertical heating profile (units: K d−1) for the dot in (a). The vertical coordinate in (b) represents sigma levels in the model.

      Figure 14.  (a) 500-hPa and (b) 200-hPa geopotential height responses (units: m) to the asymmetric diabatic heating simulated by the linearized AGCM in the presence of the 3D DJF-mean flow.

    6.   Summary
    • This study identifies an asymmetric connection of SST in the Tasman Sea to the opposite phase of ENSO during austral summer. The major findings are summarized below.

      Based on the linear regression, SSTAs in much of the Tasman Sea tend to be negatively correlated with ENSO (Fig. 4a). But this connection is visually asymmetric in terms of both distribution and amplitude of SSTAs between two opposite ENSO phases. Specifically, the El Niño-related SSTAs are manifested as a dipole pattern with weak warming in the northwest and modest cooling in the southeast (Figs. 4b and 5a), while the La Niña-related SSTAs exhibit much stronger warmth on a basin scale (Figs. 4c and 5b).

      The physical mechanisms for ENSO’s asymmetric connection with SST in the Tasman Sea were then investigated. The results suggest a damping role of the local air-sea heat fluxes (Fig. 7) and a contributive role of the oceanic heat transport especially the meridional Ekman heat transport by the zonal wind stress (Figs. 8 and 9) in the connection between ENSO and SST in the Tasman Sea. This conclusion is confirmed by the quantitative diagnosis based on the mixed layer heat budget equation (Fig. 11). Further analysis indicates the asymmetry in oceanic heat transport derives from the asymmetry in the atmospheric circulation over the Tasman Sea (Fig. 10), which may be excited by the asymmetric precipitation anomalies in the tropical Pacific (Fig. 14).

      Finally, the preliminary points about the asymmetric connection of SST in the Tasman Sea to the opposite phase of ENSO are summarized in Fig. 15. In the El Niño years, the anomalous atmospheric circulation features a PSA pattern, and the Australia-Tasman Sea sector is dominated by positive SLP anomalies. The prevailing southwesterly oceanic currents and wind stress bring about cold advection and northward Ekman heat transport, leading to the negative SSTAs therein (Fig. 15a). In the La Niña years, the PSA pattern moves northwestward and the Tasman Sea is characterized by dipole SLP anomalies. The northeasterly oceanic currents and wind stress result in warm advection and southward Ekman heat transport and generate positive SSTAs (Fig. 15b). The asymmetry in diabatic heating between El Niño and La Niña years stimulates the asymmetry in the low-level atmospheric circulation, which further induces the asymmetry in SSTAs through affecting the oceanic advection and Ekman heat transport (Fig. 15c).

      Figure 15.  Summary diagram of identified influence from tropical middle-eastern Pacific on the Tasman Sea for (a) El Niño events, (b) La Niña events, and (c) the asymmetry between El Niño and La Niña, where “+Precip” indicates positive precipitation anomalies and “–Precip” indicates negative precipitation anomalies; L indicates negative SLP anomalies and H indicates positive SLP anomalies. The solid arrows show the direction of advection driven by oceanic currents and the hollow arrows show the direction of Ekman heat transport.

      It should be noted that ENSO varies coherently with the Indian Ocean Basin Mode (IOBM), the dominant mode of SST variability in the Indian Ocean (Du et al., 2009; Xie et al., 2009), and with the southern annular mode (SAM; L’Heureux and Thompson, 2006) in austral summer. Cai and Van Rensch (2013) show that the modulating effect of the SAM is limited; in particular, the SAM does not modify the ENSO teleconnection pattern. However, the IOBM-induced wave train appears to be incorporated onto the ENSO-induced PSA pattern, modifying the structure of the PSA with a rather prominent pressure center situated over the Tasman Sea latitudes during the negative phase of IOBM. Thus, the connection between ENSO and SST in the Tasman Sea may be indirectly modulated by IOBM. The issue deserves a further study.

      Acknowledgements. This work was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070402) and the National Natural Science Foundation of China (Grant Nos. 41790473 and 41731177).

      Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return