The Indo-Australian monsoon and IOD-ENSO interactions in the...

The Indo-Australian monsoon and IOD-ENSO interactions

in the CMIP models

Nicolas C. Jourdain, Alex Sen Gupta, Andrea S. Taschetto, Yue Li CCRC, University of New South Wales / ARC CoE, Sydney, Australia

Matthieu Lengaigne, Jerome Vialard, Takeshi Izumo LOCEAN-IPSL, UPMC/CNRS/MNHN/IRD, Paris, France

Australian  monsoon  

Indian  monsoon  

ENSO  El  Niño  Southern  Oscilla7on  

IOD  Indian  Ocean  Dipole  

TBO Tropospheric Biennial

Oscillation

Weak monsoon during El Niño

Weak monsoon during El Niño

- El Niño and pIOD tend to co-occur (as well as La Niña and nIOD) - Predictability of ENSO from IOD 14 months in advance

??

Historical simulations from: - 24 CMIP3 models running over ~1850-2000 (20c3m) - 35 CMIP5 models running over ~1850-2005 (historical)

Observational SST data: HadISST, HadSST2, ERSST, COBE Observational precip data: GPCP, CMAP, AWAP (Aus.), APHRODITE (India), GPCC Precipitation and SST from reanalyses: NCEP-DOE, NCEP-NCAR, NCEP-CFSR, ERA40, ERAinterim, MERRA, JRA25.

ENSO – monsoon relationship in India

Observations: weak monsoons occur at the early stage of a developing El Niño.

CMIP5CMIP3OBS. / REA.APHRODITE / HadISST

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NINO34 leads

JJAS Indian rainfall vs lagged NINO34 (partial: DMI removed)

year

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Jourdain et al.

Clim Dyn 2013

CMIP models: weak monsoons also occur during terminating El Niños.

Related to the seasonal cycle of ENSO in the CMIP models: late termination, aborted events

ENSO – monsoon relationship in Australia

The synchronous anti-correlation between ENSO and the Australian rainfall is well captured by the CMIP models.

35% of the inter-model variance is explained by the amplitude of NINO34.

CMIP5CMIP3OBS. / REA.AWAP / HadISST

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year

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Jourdain et al.

Clim Dyn 2013

ENSO – monsoon relationship over the Maritime Continent

The synchronous ENSO – rainfall anti-correlation is underestimated in the CMIP simulations.

There is some improvement in CMIP5 compared to CMIP3: 25% of the CMIP5 models are in the range of uncertainty of the observations >> related to better equatorial SSTs in the Eastern Pacific.

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DJFM rainfall vs lagged NINO34

year

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CMIP5CMIP3OBS. / REA.

Jourdain et al.

Clim Dyn 2013

ENSO – monsoon relationship over the Maritime Continent

AustraliaE-C-Indonesia

Papua

No ENSO-rainfall

correlationin DJFM

significantENSO-rainfall

anti-correlationin DJFM

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DJFM Papuan rainfall vs lagged NINO34 (partial: DMI removed)

year

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No ENSO-rainfallcorrelation in DJFM

✔ Ok in the CMIP models

✔ Ok in the CMIP models

✗ Wrong in the CMIP models

Jourdain et al. Clim Dyn 2013

ENSO – monsoon relationship over the Maritime Continent

Papua

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DJFM Papuan rainfall vs lagged NINO34 (partial: DMI removed)

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No ENSO-rainfallcorrelation in DJFM

J FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASOND

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NINO34 leads

DJFM meridional wind stress North of Papua (gray area) vs lagged NINO34

year

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No SST anomaly in the CMIP models => Wrong moisture convergence anomaly

Overall, the effect of ENSO on the surface wind is captured by the CMIP models.

TBO: Indian – Australian monsoon relationship

Meehl et al. (1987, 1997, 2003, 2011) -  A strong Indian monsoon tends to be followed by a strong Australian monsoon. -  A strong Australian monsoon tends to be followed by a weak Indian monsoon.

signi!cant ensemble membernon-signi!cant ensemble membersigni!cant ensemblenon-signi!cant ensemble

Enhanced predictability from the Indian to Australian rainfall transition (compared to the median of a Monte Carlo resampling, significance at 90%)

Li et al. GRL 2012

TBO: Indian – Australian monsoon relationship

Meehl et al. (1987, 1997, 2003, 2011) -  A strong Indian monsoon tends to be followed by a strong Australian monsoon. -  A strong Australian monsoon tends to be followed by a weak Indian monsoon.

signi!cant ensemble membernon-signi!cant ensemble membersigni!cant ensemblenon-signi!cant ensemble

Enhanced predictability from the Indian to Australian rainfall transition (compared to the median of a Monte Carlo resampling, significance at 90%)

Li et al. GRL 2012

Most CMIP models capture this transition

TBO: Indian – Australian monsoon relationship

Meehl et al. (1987, 1997, 2003, 2011) -  A strong Indian monsoon tends to be followed by a strong Australian monsoon. -  A strong Australian monsoon tends to be followed by a weak Indian monsoon.

signi!cant ensemble membernon-signi!cant ensemble membersigni!cant ensemblenon-signi!cant ensemble

Enhanced predictability from the Australian to Indian rainfall transition (compared to the median of a Monte Carlo resampling, significance at 90%)

Li et al. GRL 2012

TBO: Indian – Australian monsoon relationship

Meehl et al. (1987, 1997, 2003, 2011) -  A strong Indian monsoon tends to be followed by a strong Australian monsoon. -  A strong Australian monsoon tends to be followed by a weak Indian monsoon.

signi!cant ensemble membernon-signi!cant ensemble membersigni!cant ensemblenon-signi!cant ensemble

Enhanced predictability from the Australian to Indian rainfall transition (compared to the median of a Monte Carlo resampling, significance at 90%)

Li et al. GRL 2012

Most CMIP models tend to produce transitions

of the wrong sign

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NINO34 leads

JJAS Indian rainfall vs lagged NINO34 (partial: DMI removed)

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Related to the wrong relation-ship with previous El Niños

IOD – ENSO relationship

Observations: DMI enhances the predictability of ENSO 14 months in advance (Takeshi et al. 2010). The TBO has been suggested to be a passive response to this IOD-ENSO relationship (Takeshi et al. 2013).

Could biases in the IOD – ENSO relationship explain biases in the TBO in the CMIP models ?

IOD – ENSO relationship

The synchronous ENSO-IOD relationship is rea- -sonably well reproduced, and improved in CMIP5

IOD tends to lead ENSO by 14 months in the CMIP simulations

This shows that the IOD – ENSO relationship is not due to multi-decadal variability that would be emphasized in the too short observational records

Conclusion The ENSO – monsoon relationship is well captured over Australia in the CMIP models. Over India, the ENSO – monsoon relationship is not well reproduced in the CMIP models, mostly because of the poor seasonality of the simulated ENSO. This seems to affect the out-of-phase TBO transition from Australia to India. The IOD-ENSO delayed relationship is remarkably well reproduced by the CMIP models, and therefore does not account for the poorly simulated TBO.

ENSO – monsoon relationship

Jourdain et al. Clim. Dyn. (published online)

TBO Li et al. GRL 2012

ENSO – IOD connections On-going work…

Thank you

n.jourdain@unsw.edu.au