Articles | Volume 5, issue 4
https://doi.org/10.5194/wcd-5-1505-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-5-1505-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The role of the Indian Ocean Dipole in modulating the austral spring ENSO teleconnection to the Southern Hemisphere
Luciano Gustavo Andrian
CORRESPONDING AUTHOR
Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
CONICET-Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina
CNRS-IRD-CONICET-UBA, Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL3351 IFAECI), Buenos Aires, Argentina
Marisol Osman
Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
CONICET-Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina
CNRS-IRD-CONICET-UBA, Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL3351 IFAECI), Buenos Aires, Argentina
Carolina Susana Vera
Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
CONICET-Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina
CNRS-IRD-CONICET-UBA, Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL3351 IFAECI), Buenos Aires, Argentina
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Cited articles
Andrian, L. G.: LucianoAndrian/ENSO_IOD: Code_Andrian_etal_2024, Zenodo [code], https://doi.org/10.5281/zenodo.14014116, 2024. a
Cai, W., McPhaden, M., Grimm, A., Rodrigues, R., Taschetto, A., Garreaud, R., Dewitte, B., Poveda, G., Ham, Y.-G., Santoso, A., Ng, B., Anderson, W., Wang, G., Geng, T., Jo, H.-S., Marengo, J., Alves, L., Osman, M., Li, S., and Vera, C.: Climate impacts of the El Niño–Southern Oscillation on South America, Nature Reviews Earth & Environment, 1, 215–231, https://doi.org/10.1038/s43017-020-0040-3, 2020. a, b
Chan, S., Behera, S., and Yamagata, T.: Indian Ocean Dipole influence on South American rainfall, Geophys. Res. Lett., 35, L14S12, https://doi.org/10.1029/2008GL034204, 2008. a, b, c
CRU: CRU TS v. 4.08, CRU [data set], https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/ (last access: 30 October 2024), 2024. a
Fan, L., Liu, Q., Wang, C., and Guo, F.: Indian Ocean Dipole Modes Associated with Different Types of ENSO Development, J. Climate, 30, 2233–2249, https://doi.org/10.1175/JCLI-D-16-0426.1, 2016. a
Gillett, Z., Hendon, H., Arblaster, J., Lin, H., and Fuchs, D.: On the Dynamics of Indian Ocean Teleconnections into the Southern Hemisphere during Austral Winter, J. Atmos. Sci., 79, 2453–2469, https://doi.org/10.1175/JAS-D-21-0206.1, 2022. a, b, c
Harris, I., Osborn, T., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Scientific Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2023. a
Holgate, C., Evans, J. P., Taschetto, A. S., Gupta, A. S., and Santoso, A.: The Impact of Interacting Climate Modes on East Australian Precipitation Moisture Sources, J. Climate, 35, 3147–3159, https://doi.org/10.1175/JCLI-D-21-0750.1, 2022. a
Hong, C.-C., Li, T., LinHo, and Kug, J.-S.: Asymmetry of the Indian Ocean Dipole. Part I: Observational Analysis, J. Climate, 21, 4834–4848, https://doi.org/10.1175/2008JCLI2222.1, 2008. a, b
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons, J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1, 2017. a
IRI: CFSv2 model hindcast and real-time prediction data, IRI [data set], https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/.NCEP-CFSv2/, (last access: 30 October 2024), 2024. a
Karoly, D. J.: Southern Hemisphere Circulation Features Associated with El Niño-Southern Oscillation Events, J. Climate, 2, 1239–1252, https://doi.org/10.1175/1520-0442(1989)002<1239:SHCFAW>2.0.CO;2, 1989. a
Kiladis, G. N.: Observations of Rossby Waves Linked to Convection over the Eastern Tropical Pacific, J. Atmos. Sci., 55, 321–339, https://doi.org/10.1175/1520-0469(1998)055<0321:OORWLT>2.0.CO;2, 1998. a
Kumar, A. and Chen, M.: What is the variability in US west coast winter precipitation during strong El Niño events?, Clim. Dynam., 49, 2789–2802, https://doi.org/10.1007/s00382-016-3485-9, 2017. a, b, c
Kumar, A., Chen, M., Zhang, L., Wang, W., Xue, Y., Wen, C., Marx, L., and Huang, B.: An Analysis of the Nonstationarity in the Bias of Sea Surface Temperature Forecasts for the NCEP Climate Forecast System (CFS) Version 2, Mon. Weather Rev., 140, 3003–3016, https://doi.org/10.1175/MWR-D-11-00335.1, 2012. a
Liguori, G., Mcgregor, S., Singh, M., Arblaster, J., and Di Lorenzo, E.: Revisiting ENSO and IOD contributions to Australian Precipitation, Geophys. Res. Lett., 49, e2021GL094295, https://doi.org/10.1029/2021GL094295, 2022. a, b
McIntosh, P. and Hendon, H.: Understanding Rossby wave trains forced by the Indian Ocean Dipole, Clim. Dynam., 50, 2783–2798, https://doi.org/10.1007/s00382-017-3771-1, 2018. a, b
Mo, K.: Relationships between Low-Frequency Variability in the Southern Hemisphere and Sea Surface Temperature Anomalies, J. Climate, 13, 3599–3610, https://doi.org/10.1175/1520-0442(2000)013<3599:RBLFVI>2.0.CO;2, 2000. a, b
Muller, G. V., Fernández Long, M. E., and Bosch, E.: Relación entre la temperatura de la superficie del mar de diferentes océanos y los rendimientos de maíz en la Pampa Húmeda, Meteorológica, 40, 5–16, 2015. a
Osman, M. and Vera, C.: Climate predictability and prediction skill on seasonal time scales over South America from CHFP models, Clim. Dynam., 49, 2365–2383, https://doi.org/10.1007/s00382-016-3444-5, 2017. a
Osman, M., Shepherd, T., and Vera, C.: The combined influence of the stratospheric polar vortex and ENSO on zonal asymmetries in the southern hemisphere upper tropospheric circulation during austral spring and summer, Clim. Dynam., 59, 2949–2964, https://doi.org/10.1007/s00382-022-06225-0, 2022. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Peña, M., Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP climate forecast system version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a, b
Saji, H. and Yamagata, T.: Structure of SST and Surface Wind Variability during Indian Ocean Dipole Mode Events: COADS Observations, J. Climate, 16, 2735–2751, https://doi.org/10.1175/1520-0442(2003)016<2735:SOSASW>2.0.CO;2, 2003a. a, b, c, d
Saji, H. and Yamagata, T.: Possible impacts of Indian Ocean Dipole Mode events on global climate, Clim. Res., 25, 151–169, https://doi.org/10.3354/cr025151, 2003b. a, b, c, d
Saji, H., Goswami, B. N., Vinayachandran, P., and Yamagata, T.: A dipole mode in the Tropical Indian Ocean, Nature, 401, 360–363, https://doi.org/10.1038/43854, 1999. a, b
Schneider, U., Becker, A., Finger, P., Rustemeier, E., and Ziese, M.: GPCC Full Data Monthly Version 2020 at 0.25°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data, DWD [data set], https://doi.org/10.5676/DWD_GPCC/FD_M_V2020_025, 2020. a, b
Sena, A. C. T. and Magnusdottir, G.: Influence of the Indian Ocean Dipole on the Large-Scale Circulation in South America, J. Climate, 34, 6057–6068, https://doi.org/10.1175/JCLI-D-20-0669.1, 2021. a
Stuecker, M. F., Timmermann, A., Jin, F.-F., Chikamoto, Y., Zhang, W., Wittenberg, A. T., Widiasih, E., and Zhao, S.: Revisiting ENSO/Indian Ocean Dipole phase relationships, Geophys. Res. Lett., 44, 2481–2492, https://doi.org/10.1002/2016GL072308, 2017. a
Sun, S., Lan, J., Fang, Y., Tana, C., and Gao, X.: A Triggering Mechanism for the Indian Ocean Dipoles Independent of ENSO, J. Climate, 28, 5063–5076, https://doi.org/10.1175/JCLI-D-14-00580.1, 2015. a
Takaya, K. and Nakamura, H.: A Formulation of a Phase-Independent Wave-Activity Flux for Stationary and Migratory Quasigeostrophic Eddies on a Zonally Varying Basic Flow, J. Atmos. Sci., 58, 608–627, https://doi.org/10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2, 2001. a
Ummenhofer, C., England, M., McIntosh, P., Meyers, G., Pook, M., Risbey, J., Sen Gupta, A., and Taschetto, A.: What causes Southeast Australia's worst droughts?, Geophys. Res. Lett., 36, L04706, https://doi.org/10.1029/2008GL036801, 2009. a
Vinayachandran, P., Francis, P., and Rao, S.: Indian Ocean dipole: processes and impacts, Current Trends in Science, 46, 569–589, 2010. a
Wang, H., Kumar, A., Murtugudde, R., Narapusetty, B., and Seip, K.: Covariations between the Indian Ocean dipole and ENSO: a modeling study, Clim. Dynam., 53, 5743–5761, https://doi.org/10.1007/s00382-019-04895-x, 2019. a
Yuan, C. and Yamagata, T.: Impacts of IOD, ENSO and ENSO Modoki on the Australian Winter Wheat Yields in Recent Decades, Sci. Rep., 5, 17252, https://doi.org/10.1038/srep17252, 2015. a
Zhao, S., Jin, F., and Stuecker, M.: Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts, Geophys. Res. Lett., 46, 9980–9990, https://doi.org/10.1029/2019GL084196, 2019. a
Short summary
The interplay between the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) is well-researched in the tropical Indian Ocean, but their effects on the Southern Hemisphere's extratropical regions during spring are less studied. We show that the positive phase of the IOD can strengthen the El Niño circulation anomalies, heightening their continental impacts. On the other hand, negative IOD combined with La Niña shows less consistent changes among the different methodologies.
The interplay between the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD)...