Skip to main content

Advertisement

SpringerLink
Log in

Inter-decadal variations in the linkages between ENSO, the IOD and south-eastern Australian springtime rainfall in the past 30 years

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

The 30 year period 1985–2014 experienced a swing of the Inter-decadal Pacific Oscillation (IPO) from the warm phase to the cold phase. Here we investigate variation of the relation between El Niño and the Southern Oscillation (ENSO) and the Indian Ocean Dipole mode (IOD) and resultant changes in the predictability of the IOD and south-eastern Australian (SEA) springtime rainfall associated with this swing in the IPO. Using observational analyses, we show that during the warm phase of the IPO in the 1980s–1990s, the amplitudes of ENSO and the IOD were large, and the correlation between them was high; thus predictability of the IOD was high. Nevertheless, during these decades SEA spring rainfall was only weakly related to ENSO and the IOD, and therefore predictability of SEA rainfall was low. In contrast, during the cold phase of the IPO in the 2000s, the opposite was found: the IOD occurred more independently from ENSO, so the IOD was less predictable. Nonetheless, SEA spring rainfall was more strongly related to ENSO and the IOD, and therefore, SEA rainfall was more predictable in the 2000s than in the 1980s–1990s. The cause of this decadal variation in the relationship of SEA rainfall with ENSO and the IOD between the recent warm and cold states of the IPO appears to be a systematic zonal variation of the rainfall anomalies in the tropical Indo-Pacific associated with the IOD and ENSO and related changes in the Rossby wave train path over Australia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Compared to El Niño, the IPO anomalies are weaker at equatorial latitudes and stronger at higher latitudes (Fig. 1b).

  2. Niño3 index = \(\overline{SSTA}_{{5{^\circ }{\text{S5}}{^\circ }{\text{N,90}}{^\circ } - 150{^\circ }{\text{W}}}}\), where SSTA denotes SST anomalies, and the overbar indicates an areal-average.

  3. DMI = \(\overline{SSTA}_{{10{^\circ }{\text{S}} - 10{^\circ }{\text{N, 50}}{^\circ } - 70{^\circ }{\text{E}}}} - \overline{SSTA}_{{10{^\circ }{\text{S}} - 0,90{^\circ } - 110{^\circ }{\text{E}}}}\), where 10°S–10°N, 50°–70°E is the western pole, and 10°S–0, 90°–110°E is the eastern pole of the IOD.

  4. Var *em /(Var *em  + Varesp) where Var *em is a non-biased estimate of the variance of ensemble mean and Varesp is the variance of ensemble spread around the ensemble mean. Var *em is obtained by Varem – Varesp/N, where Varem is the original ensemble mean variance and N is the number of ensemble members.

  5. Lead time mean a time gap between the initialisation and verification of forecasts. In this study, zero (6 month) lead time means forecasts being initialised on the 1st of September (April) and verified in the following September to November mean period in each year of 1985–2014.

References

  • Abram NJ, Gagan MK, Cole J, Hantoro WS, Mudelsee M (2008) Recent intensification of tropical climate variability in the Indian Ocean. Nat Geosci 1:849–853. doi:10.1038/ngeo357

    Article  Google Scholar 

  • Annamalai H, Xie SP, McCreary JP, Murtugudde R (2005) Impact of Indian Ocean sea surface temperature on developing El Niño. J. Climate 18:302–319. doi:10.1175/JCLI-3268.1

    Article  Google Scholar 

  • Ashok K (2003) Influence of the Indian Ocean Dipole on the Australian winter rainfall. Geophys Res Lett 30:3–6. doi:10.1029/2003GL017926

    Article  Google Scholar 

  • Ashok K, Nakamura H, Yamagata T (2007a) Impacts of ENSO and Indian Ocean Dipole events on the southern hemisphere storm-track activity during austral winter. J. Climate 20:3147–3163

    Article  Google Scholar 

  • Ashok K, Behera SK, Rao SA et al (2007b) El Nino Modoki and its possible teleconnection. J Geophys Res Ocean. doi:10.1029/2006JC003798

    Google Scholar 

  • Barnston AG, Tippett MK, L’Heureux ML et al (2012) Skill of real-time seasonal ENSO model predictions during 2002-11: is our capability increasing? Bull Am Meteorol Soc 93:631–651. doi:10.1175/BAMS-D-11-00111.1

    Article  Google Scholar 

  • Cai W, van Rensch P, Cowan T, Hendon HH (2011) Teleconnection pathways for ENSO and the IOD and the mechanism for impacts on Australian rainfall. J. Climate. 24:3910–3923. doi:10.1175/2011JCLI4129.1

    Article  Google Scholar 

  • Chowdary JS, Xie S-P, Tokinaga H, Okumura YM, Kubota H, Johnson N, Zheng X-T (2012) Interdecadal variations in ENSO teleconnection to the Indo–western Pacific for 1870–2007. J. Climate 25:1722–1744. doi:10.1175/JCLI-D-11-00070.1

    Article  Google Scholar 

  • Chung PH, Li T (2013) Interdecadal relationship between the mean state and El Ni??o types. J Clim 26:361–379. doi:10.1175/JCLI-D-12-00106.1

    Article  Google Scholar 

  • Cole JE, Dunbar RB, McClanahan TR, Muthiga NA (2000) Tropical Pacific forcing of decadal SST variability in the western Indian Ocean over the past two centuries. Science 287:617–619. doi:10.1126/science.287.5453.617

    Article  Google Scholar 

  • Colman, R., L. Deschamps, M. Naughton, L. Rikus, A. Sulaiman, K. Puri, G. Roff, Z. Sun, and G. Embery, 2005: BMRC Atmospheric Model (BAM) version 3.0: Comparison with mean climatology. BMRC Research Report No. 108, Bureau of Meteorology Research Centre, 32 pp. (available from http://www.bom.gov.au/bmrc/pubs/researchreports/researchreports.htm)

  • Cottrill A, Hendon HH et al (2013) Seasonal forecasting in the Pacific using the coupled model POAMA-2. Wea. Forecasting. 28:668–680

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  • Fedorov AV (2000) Is El Niño Changing? Science 288:1997–2002. doi:10.1126/science.288.5473.1997

    Article  Google Scholar 

  • Folland CK, Parker DE, Colman AW, Washington R (1999) Large scale modes of ocean surface temperature since the late nineteenth century. In: Navarra A (ed) Beyond El Niño. Springer, Berlin

    Google Scholar 

  • Folland CK, Renwick JA, Salinger MJ, Mullan AB (2002) Relative influences of the Interdecadal Pacific Oscillation and ENSO on the South Pacific Convergence Zone. Geophy. Res. Lett. 29:1643–1646

    Article  Google Scholar 

  • Franks SW (2004) Multi-decadal climate variability, New South Wales. Australia. Water Science and Technology 49(7):133–140

    Google Scholar 

  • Gallant AJE, Kiem AS, Verdon-Kidd DC et al (2012) Understanding hydroclimate processes in the Murray-Darling Basin for natural resources management. Hydrol Earth Syst Sci 16:2049–2068. doi:10.5194/hess-16-2049-2012

    Article  Google Scholar 

  • Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM (2013) Observations: atmosphere and surface. In: Climate change 2013: the physical science basis. contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change [Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, P.M. M]

  • Henley BJ, Gergis J, Karoly DJ et al (2015) A Tripole Index for the Interdecadal Pacific Oscillation. Clim Dyn 45:3077–3090. doi:10.1007/s00382-015-2525-1

    Article  Google Scholar 

  • Hope P, Lim E-P, Wang G, Hendon HH, Arblaster JM (2015) Contributors to the record high temperatures across Australia in late spring 2014. Bull. Amer. Met. Soc. 96(12):S149–S153

    Article  Google Scholar 

  • Hu Z-Z, Kumar A, Ren H-L et al (2013) Weakened Interannual Variability in the Tropical Pacific Ocean since 2000. J Clim 26:2601–2613. doi:10.1175/JCLI-D-12-00265.1

    Article  Google Scholar 

  • Hudson D, Alves O, Hendon HH, Wang G (2011) The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Climate Dyn. 36:1155–1171

    Article  Google Scholar 

  • Hudson D, Marshall A, Yin Y, Alves O, Hendon H (2013) Improving intraseasonal prediction with a new ensemble generation strategy. Mon. Wea. Rev. 141:4429–4449. doi:10.1175/MWR-D-13-00059.1

    Article  Google Scholar 

  • Hurrell JW, Hack JJ, Shea D, Caron JM, Rosinski J (2008) A new sea surface temperature and sea ice boundary data set for the Community Atmosphere Model. J. Climate 21:5145–5153. doi:10.1175/2008JCLI2292.1.1

    Article  Google Scholar 

  • Ihara C, Kushnir Y, Cane MA (2008) Warming trend of the Indian Ocean SST and Indian Ocean dipole from 1880 to 2004. J. Climate 21:2035–2046. doi:10.1175/2007JCLI1945.1

    Article  Google Scholar 

  • Jia X, Lee J-Y, Lin H, Hendon H, Ha K (2014) Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part II. Predictability and prediction skill. Clim. Dyn. 43:1611–1630. doi:10.1007/s00382-014-2084-x

    Article  Google Scholar 

  • Jin F-F, Ah S-I, Timmermann A, Zhao J (2003) Strong El Nino events and nonlinear dynamical heating. Geophys Res Lett 30(3):1120. doi:10.1029/2002GL016356

    Article  Google Scholar 

  • Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58:233–248

    Article  Google Scholar 

  • Kiem AS, Franks SW (2004) Multi-decadal variability of drought risk, eastern Australia. Hydrol Process 18:2039–2050. doi:10.1002/hyp.1460

    Article  Google Scholar 

  • Kiem AS, Franks SW, Kuczera G (2003) Multi-decadal variability of flood risk. Geophys Res Lett 30(2):1035. doi:10.1029/2002GL015992

    Article  Google Scholar 

  • King AD, Alexander LV, Donat MG (2013) Asymmetry in the response of eastern Australia extreme rainfall to low-frequency Pacific variability. Geophys Res Lett 40:1–7. doi:10.1002/grl.50427

    Article  Google Scholar 

  • Kripalani RH, Kulkarni A (1997) Climatic impact of El Niño/La Niña on the Indian monsoon: a new perspective. Weather 52:39–46. doi:10.1002/j.1477-8696.1997.tb06267.x

    Article  Google Scholar 

  • Kumar A, Chen M, Xue Y, Behringer D (2015) An Analysis of the Temporal Evolution of ENSO Prediction Skill in the Context of the Equatorial Pacific Ocean Observing System. Mon Weather Rev 143:3204–3213. doi:10.1175/MWR-D-15-0035.1

    Article  Google Scholar 

  • Lee J-Y, Ha K-J (2015) Understanding of Interdecadal Changes in Variability and Predictability of the Northern Hemisphere Summer Tropical-Extratropical Teleconnection. J Clim 28:8634–8647. doi:10.1175/JCLI-D-15-0154.1

    Article  Google Scholar 

  • Lee T, McPhaden MJ (2010) Increasing intensity of El Niño in the central- 590 equatorial Pacific. Geophys Res Lett 37:L14603. doi:10.1029/2010GL044007

    Google Scholar 

  • Lim E-P, Hendon HH, Hudson D, Wang G, Alves O (2009) Dynamical forecast of inter-El Niño variations of tropical SST and Australian spring rainfall. Mon. Wea. Rev. 137:3796–3810

    Article  Google Scholar 

  • Lim E, Hendon HH, Arblaster JM et al (2016) Interaction of the recent 50 year SST trend and La Niña 2010: amplification of the Southern Annular Mode and Australian springtime rainfall. Clim Dyn. doi:10.1007/s00382-015-2963-9

    Google Scholar 

  • Meehl GA, Goddard Lisa, Murphy James, Stouffer Ronald J, Boer George, Danabasoglu Gokhan, Dixon Keith, Giorgetta Marco A, Greene Arthur M, Hawkins Ed, Hegerl Gabriele, Karoly David, Keenlyside Noel, Kimoto Masahide, Kirtman Ben, Navarra Antonio, Pulwarty Roger, Smith Doug, Stammer Detlef, Stockdale Timothy (2009) Decadal Prediction. Bull. Amer. Meteor. Soc. 90:1467–1485. doi:10.1175/2009BAMS2778.1

    Article  Google Scholar 

  • Meyers GA, McIntosh PC, Pigot L, Pook MJ (2007) The years of El Niño, La Niña and interactions with the tropical Indian Ocean. J. Climate 20:2872–2880

    Article  Google Scholar 

  • NCL cited 2014: NCAR Command Language, Version 6.2.1. UCAR/NCAR/CISL/VETS. http://dx.doi.org/10.5065/D6WD3XH5

  • Newman M, Compo GP, Alexander MA (2003) ENSO-forced variability of the Pacific Decadal Oscillation. J. Climate 16:3853–3857

    Article  Google Scholar 

  • North GR, Bell TL, Cahalan RF, Moeng FJ (1982) Sampling Errors in the Estimation of Empirical Orthogonal Functions. Mon Weather Rev 110:699–706

    Article  Google Scholar 

  • Parker D, Folland C, Scaife A, Knight J, Colman A, Baines P, Dong B (2007) Decadal to multidecadal variability and the climate change background. J. Geophy. Res. 112:D18115. doi:10.1029/2007JD008411

    Article  Google Scholar 

  • Power S, Colman R (2006) Multi-year predictability in a coupled general circulation model. Clim Dyn 26:247–272

    Article  Google Scholar 

  • Power S, Casey T, Folland C, Colman A, Mehta V (1999) Interdecadal modulation of the impact of ENSO on Australia. Climate Dyn. 15:319–324. doi:10.1007/s003820050284

    Article  Google Scholar 

  • Power S, Haylock M, Colman R, Wang X (2006) The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. J. Climate 19:4755–4771

    Article  Google Scholar 

  • Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J. Climate 15:1609–1625

    Article  Google Scholar 

  • Risbey JS, Pook MJ, McIntosh PC, Wheeler MC, Hendon HH (2009) On the remote drivers of rainfall variability in Australia. Mon. Wea. Rev. 137:3233–3253

    Article  Google Scholar 

  • Rowell DP, Folland CK, Maskell K, Ward MN (1995) Variability of summer rainfall over tropical North Africa (1906–92): observations and modeling. Quart. J. Roy. Meteor. Soc. 121:669–704

    Google Scholar 

  • Saji N, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363

    Google Scholar 

  • Saji NH, Ambrizzi T, Ferraz SET (2005) Indian Ocean dipole mode events and austral surface air temperature anomalies. Dyn Atmos Oceans 39:87–100. doi:10.1016/j.dynatmoce.2004.10.015

    Article  Google Scholar 

  • Salinger MJ, Basher RE, Fitzharris BB et al (1995) Climate trends in the South-west Pacific. Int J Clim 15:285–302. doi:10.1002/joc.3370150305

    Article  Google Scholar 

  • Schiller A, Godfrey JS (2003) Indian Ocean intraseasonal variability in an ocean general circulation model. J. Climate 16:21–39

    Article  Google Scholar 

  • Schiller A, Godfrey JS, McIntosh PC, Meyers G, Smith NR, Alves O, Wang G, Fiedler R (2002) A new version of the Australian Community Ocean Model for seasonal climate prediction. CSIRO Marine Research Report No. 240

  • Shi L, Hendon HH, Alves O et al (2012) How Predictable is the Indian Ocean Dipole? Mon Weather Rev 140:3867–3884. doi:10.1175/MWR-D-12-00001.1

    Article  Google Scholar 

  • Valke S, Terray L, Piacentini A (2000) The OASIS coupled user guide version 2.4. Technical report TR/CMGC/00-10, CERFACS

  • Weller E, Cai W, Du Y, Min S-K (2014) Differentiating flavors of the Indian Ocean Dipole using dominant modes in tropical Indian Ocean rainfall. Geophys Res Lett. doi:10.1002/2014GL062459

    Google Scholar 

  • Yang Y, Xie S-P, Wu L et al (2015) Seasonality and Predictability of the Indian Ocean Dipole Mode: ENSO Forcing and Internal Variability. J Clim 28:8021–8036. doi:10.1175/JCLI-D-15-0078.1

    Article  Google Scholar 

  • Yin Y, Alves O, Oke P (2011) An ensemble ocean data assimilation system for seasonal prediction. Mon. Wea. Rev. 139:786–808

    Article  Google Scholar 

  • Zhang Y, Wallace JM, Battisti DS (1997) ENSO-like inter-decadal variability: 1900-1993. J. Climate 10:1004–1020

    Article  Google Scholar 

  • Zhao M, Hendon HH, Alves O, Liu G, Wang G (2016) Weakened El Niño predictability in the early 21st century. J Clim. http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-15-0876.1 (in print)

Download references

Acknowledgments

This study is supported by the Victorian Climate Initiative, and was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. The authors are grateful to Drs Rob Colman and Guomin Wang in the Bureau of Meteorology and two anonymous reviewers for their valuable constructive comments on this manuscript. We also acknowledge NCAR/UCAR, ECMWF and NOAA for producing and providing Hurrell et al. (2008) SST analysis, ERA-Interim reanalysis and Reynolds OI v2 SST analysis, respectively. The NCAR Command Language (NCL; NCL 2014) was used for data analysis and visualization of the results.

Author information

Authors and Affiliations

  1. Bureau of Meteorology, Melbourne, VIC, 3001, Australia

    Eun-Pa Lim, Harry H. Hendon, Mei Zhao & Yonghong Yin

Authors
  1. Eun-Pa Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harry H. Hendon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yonghong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eun-Pa Lim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lim, EP., Hendon, H.H., Zhao, M. et al. Inter-decadal variations in the linkages between ENSO, the IOD and south-eastern Australian springtime rainfall in the past 30 years. Clim Dyn 49, 97–112 (2017). https://doi.org/10.1007/s00382-016-3328-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-016-3328-8

Keywords

Search

Navigation