Influence of IOD on the following year s El Niño: robustness across the CMIP models?

Transcription

1 Influence of IOD on the following year s El Niño: robustness across the CMIP models? N.C. Jourdain Climate Change Research Center, University of New South Wales, Sydney M. Lengaigne, J. Vialard, T. Izumo LOCEAN-IPSL,Paris A. Sen Gupta CCRC-UNSW, CoE Climate System Science, Sydney

2 Predictability of NINO34 from the Warm Water Volume (WWV) and the Indian Dipole Mode Index (DMI) 14 months in advance, i.e. beyond the winter-spring barrier (Izumo et al. 2010) Results based on satellite-era SST data over

3 Mechanisms (Izumo et al. 2010) Longitude time section of Indo-Pacific anomalies associated with a negative IOD (the influence of ENSO is linearly removed from these composites)

4 Mechanisms (Izumo et al. 2010) Negative IOD anomalous Easterlies (signature of ENSO removed) Warm Water built-up

5 Mechanisms (Izumo et al. 2010) Eastern Pole quickly recedes Collapse of anomalous Easterlies Development of El Nino

6 Izumo et al. (2010) s results were based on the satellite era. Izumo et al. (2013) Climate Dynamics, in press : SST observations were very sparse in the tropical Indian Ocean before ~1980 (Izumo et al. 2013) => sampling error = 30 to 50% of the interannual variability amplitude in the eastern DMI box Nb data / month / 2x2deg. Izumo et al. (2013)

7 Izumo et al. (2010) s results were based on the satellite era. Izumo et al. (2013) Climate Dynamics, in press : Proxies of the IOD based on EOF analysis (IODhist) can be used => stronger correlations to NINO34 to NINO34 Izumo et al. (2013)

8 Is the lag IOD-ENSO relationship based on a robust mechanism, or is it a statistical artifact? Historical simulations from: - 24 CMIP3 models running over ~ (20c3m) - 35 CMIP5 models running over ~ (historical) Observational SST data: HadISST, HadSST2, ERSST, COBE (all considered over the period ) Reanalysis 20CR-v2 (NOAA/OAR/ESRL PSD) Everything is Hann-filtered (N=25, i.e. half of the signal of T > 13 years is removed)

9 ENSO and IOD multi-model mean seasonal cycle Model distributions (between lower and upper quartiles) The amplitude of the seasonal cycle is overestimated for DMI underestimated for NINO34 ~25% of the CMIP5 simulations present a realistic seasonal cycle for NINO34

10 IOD-ENSO relationships The synchronous ENSOIOD relationship is rea-sonably well reproduced, and improved in CMIP5 IOD tends to lead ENSO by 14 months in the CMIP simulations

11 IOD-ENSO relationships There are some links between synchronous and delayed IOD-ENSO relationships in the models 25% and 75% of PDF integratives

12 Inter-decadal variability of the IOD-ENSO relationships Selection of the 30-year period maximizing the predictability of ENSO from IOD 14 months before Strong inter-decadal variability in the observations and in the models The models are able to reproduce periods comparable with the satellite era.

13 IOD-ENSO relationship for each model Part of the models show a correlation between DMI and the previous year s NINO34. Too strong Indian Ocean capacitor effect? (El Nino anomalies persist through JJA in the Indian Ocean, Xie et al. 2008) No obvious relationship with biases in DMI or NINO34 seasonal cycles (not shown) Lag correlation DMI in SON y-1 vs monthly NINO34 (ranked by 14-month lead correlation)

14 Asymmetry of the IOD-ENSO relationships Positive IODs bring more predictability than negative IODs. This could be related to the sequence asymmetry of NinoNina events

15 The ability of CMIP models to produce an IOD that leads ENSO depends on ENSO s amplitude 25% and 75% of PDF integratives

16 Mechanisms anomalous Easterlies Negative IOD No significant influence of IOD on the W-Equ. Pacific zonal wind in the CMIP models There is a small wind stress signature though the linear method used to remove the influence of ENSO is questionable

17 Conclusion - The amplitude of ENSO s seasonal cycle is underestimated in the CMIP simulations, while the amplitude of IOD s seasonal cycle is overestimated. - The synchronous relationship between ENSO and the IOD (i.e. the tendency for ENSO to induce an IOD just before the ENSO peak season) is reasonably well reproduced, and improved in CMIP5. - IOD events tend to lead ENSO events by ~14 months in the CMIP simulations, but not following the mechanisms described by Izumo et al. (2010)

18 Hypothesis for the IOD-ENSO delayed relationship - The CMIP models capture the delayed IOD-ENSO relationship through the wrong mechanism - Other physical mechanisms account for the delayed IOD-ENSO relationship - The delayed IOD-ENSO relationship is a statistical artifact : could we obtain the delayed correlation from a synchronous IOD-ENSO relationship and some bienniality in DMI? DMI(n)= a.nino34(n)+b.dmi(n- 1)+ε - The delayed IOD-ENSO relationship is a statistical artifact : it arises from the Nino-Nina sequence asymmetry and from the synchronous IOD-ENSO relationship.