the indo-australian monsoon and iod-enso interactions in the...
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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
0
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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!0.5!0.4!0.3!0.2!0.100.10.20.3 DJFM Australian rainfall vs lagged NINO34
year
0NINO34 leads
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.
J FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASOND
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NINO34 leads
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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NINO34 leads
DJFM Papuan rainfall vs lagged NINO34 (partial: DMI removed)
year
0
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
J FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASOND!0.6
!0.4
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NINO34 leads
DJFM Papuan rainfall vs lagged NINO34 (partial: DMI removed)
year
0
No ENSO-rainfallcorrelation in DJFM
J FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASOND
!0.8!0.6!0.4!0.200.20.40.60.8
NINO34 leads
DJFM meridional wind stress North of Papua (gray area) vs lagged NINO34
year
0
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
J FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASONDJ FMAMJ J ASOND
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NINO34 leads
JJAS Indian rainfall vs lagged NINO34 (partial: DMI removed)
year
0
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