Advertisement

Science China Earth Sciences

, Volume 61, Issue 12, pp 1832–1843 | Cite as

The role of initial signals in the tropical Pacific Ocean in predictions of negative Indian Ocean Dipole events

  • Rong Feng
  • Wansuo DuanEmail author
Research Paper
  • 17 Downloads

Abstract

Using reanalysis data, the role of initial signals in the tropical Pacific Ocean in predictions of negative Indian Ocean Dipole (IOD) events were analyzed. It was found that the summer predictability barrier (SPB) phenomenon exists in predictions, which is closely related to initial sea temperature errors in the tropical Pacific Ocean, with type-1 initial errors presenting a significant west-east dipole pattern in the tropical Pacific Ocean, and type-2 initial errors showing the opposite spatial pattern. In contrast, SPB-related initial sea temperature errors in the tropical Indian Ocean are relatively small. The initial errors in the tropical Pacific Ocean induce anomalous winds in the tropical Indian Ocean by modulating the Walker circulation in the tropical oceans. In the first half of the prediction year, the anomalous winds, combined with the climatological winds in the tropical Indian Ocean, induce a basin-wide mode of sea surface temperature (SST) errors in the tropical Indian Ocean. With the reversal of the climatological wind in the second half of the prediction year, a west-east dipole pattern of SST errors appears in the tropical Indian Ocean, which is further strengthened under the Bjerknes feedback, yielding a significant SPB. Moreover, two types of precursors were also identified: a significant west-east dipole pattern in the tropical Pacific Ocean and relatively small temperature anomalies in the tropical Indian Ocean. Under the combined effects of temperature anomalies in the tropical Indian and Pacific oceans, northwest wind anomalies appear in the tropical Indian Ocean, which induce a significant west-east dipole pattern of SST anomalies, and yield a negative IOD event.

Keywords

Negative Indian Ocean Dipole Precursor Initial errors Pacific Ocean 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41506032 & 41530961), and the National Programme on Global Change and Air-Sea Interaction (Grant No. GASI-IPOVAI-06).

References

  1. Alexander M A, Bladé I, Newman M, Lanzante J R, Lau N C, Scott J D. 2002. The atmospheric bridge: The influence of ENSO teleconnections on air-sea interaction over the global oceans. J Clim, 15: 2205–2231CrossRefGoogle Scholar
  2. Ansell T, Reason C J C, Meyers G. 2000. Variability in the tropical southeast Indian Ocean and links with southeast Australian winter rainfall. Geophys Res Lett, 27: 3977–3980CrossRefGoogle Scholar
  3. Ashok K, Guan Z, Yamagata T. 2003. Influence of the Indian Ocean Dipole on the Australian winter rainfall. Geophys Res Lett, 30: 1821CrossRefGoogle Scholar
  4. Behera S K, Luo J J, Masson S, Delecluse P, Gualdi S, Navarra A, Yamagata T. 2005. Paramount impact of the Indian Ocean Dipole on the East African short rains: A CGCM study. J Clim, 18: 4514–4530CrossRefGoogle Scholar
  5. Carton J A, Giese B S. 2008. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Mon Weather Rev, 136: 2999–3017CrossRefGoogle Scholar
  6. Chen D. 2010. Indo-Pacific tripole: An intrinsic mode of tropical climate variability. Adv Geosci, 24: 1–18CrossRefGoogle Scholar
  7. Ding R, Ha K J, Li J P. 2010. Interdecadal shift in the relationship between the East Asian summer monsoon and the tropical Indian Ocean. Clim Dyn, 34: 1059–1071CrossRefGoogle Scholar
  8. Ding R, Li J. 2012. Influences of ENSO teleconnection on the persistence of sea surface temperature in the Tropical Indian Ocean. J Clim, 25: 8177–8195CrossRefGoogle Scholar
  9. Feng R, Duan W S, Mu M. 2014. The “winter predictability barrier” for IOD events and its error growth dynamics: Results from a fully coupled GCM. J Geophys Res-Oceans, 119: 8688–8708CrossRefGoogle Scholar
  10. Feng R, Duan W S. 2017. IOD-related optimal initial errors and optimal precursors for IOD predictions from reanalysis data. Sci China Earth Sci, 60: 156–172CrossRefGoogle Scholar
  11. Feng R, Duan W S. 2018. Investigating the initial errors that cause predictability barriers for Indian Ocean Dipole events using CMIP5 model outputs. Adv Atmos Sci, 35: 1305–1320CrossRefGoogle Scholar
  12. Guan Z Y, Yamagata T. 2003. The unusual summer of 1994 in East Asia: IOD teleconnections. Geophys Res Lett, 30: 1544CrossRefGoogle Scholar
  13. Guo F, Liu Q, Sun S, Yang J. 2015. Three types of Indian Ocean Dipoles. J Clim, 28: 3073–3092CrossRefGoogle Scholar
  14. Kramer W, Dijkstra H A. 2013. Optimal localized observations for advancing beyond the ENSO predictability barrier. Nonlin Processes Geophys, 20: 221–230CrossRefGoogle Scholar
  15. Lee T, Fukumori I, Menemenlis D, Xing Z F, Fu L L. 2002. Effects of the Indonesian throughflow on the Pacific and Indian Oceans. J Phys Oceanogr, 32: 1404–1429CrossRefGoogle Scholar
  16. Li J P, Ding R Q. 2013. Temporal-spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans. Int J Climatol, 33: 1936–1947CrossRefGoogle Scholar
  17. Li T, Wang B, Chang C P, Zhang Y. 2003. A theory for the Indian Ocean Dipole-zonal mode. J Atmos Sci, 60: 2119–2135CrossRefGoogle Scholar
  18. Li Z, Yu W, Li K, Liu B, Wang G. 2015. Modulation of interannual variability of tropical cyclone activity over Southeast Indian Ocean by negative IOD phase. Dyn Atmos Oceans, 72: 62–69CrossRefGoogle Scholar
  19. Lian T, Chen D K, Tang Y M, Jin B G. 2014. A theoretical investigation of the tropical Indo-Pacific tripole mode. Sci China Earth Sci, 57: 174–188CrossRefGoogle Scholar
  20. Lim E P, Hendon H H. 2017. Causes and predictability of the negative Indian Ocean Dipole and its impact on La Niña during 2016. Sci Rep, 7: 12619CrossRefGoogle Scholar
  21. Loschnigg J, Meehl G A, Webster P J, Arblaster J M, Compo G P. 2003. The Asian monsoon, the tropospheric biennial oscillation, and the Indian Ocean zonal mode in the NCAR CSM. J Clim, 16: 1617–1642CrossRefGoogle Scholar
  22. Luo J J, Masson S, Behera S, Shingu S, Yamagata T. 2005. Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J Clim, 18: 4474–4497CrossRefGoogle Scholar
  23. Luo J J, Masson S, Behera S, Yamagata T. 2007. Experimental forecasts of the Indian Ocean Dipole using a coupled OAGCM. J Clim, 20: 2178–2190CrossRefGoogle Scholar
  24. Mann M E, Bradley R S, Hughes M K. 1998. Erratum: Global-scale temperature patterns and climate forcing over the past sixcenturies. Nature, 392: 779–787CrossRefGoogle Scholar
  25. Mu M, Ren H L. 2017. Enlightenments from researches and predictions of 2014–2016 super El Niño event. Sci China Earth Sci, 60: 1569–1571CrossRefGoogle Scholar
  26. Saji N H, Goswami B N, Vinayachandran P N, Yamagata T. 1999. A dipole mode in the tropical Indian Ocean. Nature, 401: 360–363Google Scholar
  27. Saji N H, Yamagata T. 2003. Structure of SST and surface wind variability during Indian Ocean Dipole mode events: COADS observations. J Clim, 16: 2735–2751CrossRefGoogle Scholar
  28. Shi G, Ribbe J, Cai W, Cowan T. 2007. Multidecadal variability in the transmission of ENSO signals to the Indian Ocean. Geophys Res Lett, 34: L09706CrossRefGoogle Scholar
  29. Shi L, Hendon H H, Alves O, Luo J J, Balmaseda M, Anderson D. 2012. How predictable is the Indian Ocean Dipole? Mon Weather Rev, 140: 3867–3884CrossRefGoogle Scholar
  30. Song Q, Vecchi G A, Rosati A J. 2007. Indian Ocean variability in the GFDL coupled climate model. J Clim, 20: 2895–2916CrossRefGoogle Scholar
  31. Song Q, Vecchi G A, Rosati A J. 2008. Predictability of the Indian Ocean sea surface temperature anomalies in the GFDL coupled model. Geophys Res Lett, 35: L02701CrossRefGoogle Scholar
  32. Stuecker M F, Timmermann A, Jin F F, Chikamoto Y, Zhang W, Wittenberg A T, Widiasih E, Zhao S. 2017. Revisiting ENSO/Indian Ocean Dipole phase relationships. Geophys Res Lett, 44: 2481–2492CrossRefGoogle Scholar
  33. Ummenhofer C C, Schwarzkopf F U, Meyers G, Behrens E, Biastoch A, Böning C W. 2013. Pacific Ocean contribution to the asymmetry in eastern Indian Ocean variability. J Clim, 26: 1152–1171CrossRefGoogle Scholar
  34. Vinayachandran P N, Iizuka S, Yamagata T. 2002. Indian Ocean Dipole mode events in an ocean general circulation model. Deep-Sea Res Part II-Top Stud Oceanogr, 49: 1573–1596CrossRefGoogle Scholar
  35. Wajsowicz R C. 2004. Climate variability over the tropical Indian Ocean sector in the NSIPP seasonal forecast system. J Clim, 17: 4783–4804CrossRefGoogle Scholar
  36. Wajsowicz R C. 2005. Potential predictability of tropical Indian Ocean SST anomalies. Geophys Res Lett, 32: L24702CrossRefGoogle Scholar
  37. Wang X, Wang C. 2014. Different impacts of various El Niño events on the Indian Ocean Dipole. Clim Dyn, 42: 991–1005CrossRefGoogle Scholar
  38. Wang X, Tan W, Wang C. 2018. A new index for identifying different types of El Niño modoki events. Clim Dyn, 50: 2753–2765CrossRefGoogle Scholar
  39. Webster P J, Moore A M, Loschnigg J P, Leben R R. 1999. Coupled oceanatmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401: 356–360CrossRefGoogle Scholar
  40. Xie S P, Hu K, Hafner J, Tokinaga H, Du Y, Huang G, Sampe T. 2009. Indian Ocean capacitor effect on Indo-Western Pacific climate during the summer following El Niño. J Clim, 22: 730–747CrossRefGoogle Scholar
  41. Yuan D L, Wang J, Xu T, Xu P, Hui Z, Zhao X, Luan Y, Zheng W, Yu Y. 2011. Forcing of the Indian Ocean Dipole on the interannual variations of the tropical Pacific Ocean: Roles of the Indonesian throughflow. J Clim, 24: 3593–3608CrossRefGoogle Scholar
  42. Yuan D L, Zhou H, Zhao X. 2013. Interannual climate variability over the tropical Pacific Ocean induced by the Indian Ocean Dipole through the Indonesian throughflow. J Clim, 26: 2845–2861CrossRefGoogle Scholar
  43. Zhang J, Duan W S, Zhi X F. 2015. Using CMIP5 model outputs to investigate the initial errors that cause the “spring predictability barrier” for El Niño events. Sci China Earth Sci, 58: 685–696CrossRefGoogle Scholar
  44. Zhang W, Wang Y, Jin F F, Stuecker M F, Turner A G. 2015. Impact of different El Niño types on the El Niño/IOD relationship. Geophys Res Lett, 42: 8570–8576CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LASG, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations