How well can we predict the Indian Ocean Dipole and its - - PowerPoint PPT Presentation

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How well can we predict the Indian Ocean Dipole and its - - PowerPoint PPT Presentation

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How well can we predict the Indian Ocean Dipole and its teleconnections? www.cawcr.gov.au Sally Langford, Li Shi, Harry Hendon and Oscar Alves. April 2011 The Centre for Australian Weather and Climate Research A partnership between CSIRO and

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

How well can we predict the Indian Ocean Dipole and its teleconnections?

Sally Langford, Li Shi, Harry Hendon and Oscar Alves.

April 2011

www.cawcr.gov.au

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Indian Ocean Dipole

  • Why look at IOD?
  • Most focus has been on ENSO and its impacts, it is not the whole story for

regional climate forecasting - the IOD has an important role.

  • The IOD accounts for some of the El Nino response in South East Australia.
  • How well can we predict the IOD?
  • Conflicting reports in the literature.
  • Assess operational international dynamical seasonal prediction models in a

consistent manner.

  • NINO and IOD indices from POAMA 1.5 and 2.4, NCEP CFS1 and 2, ECMWF

and SINTEX-F.

  • How well can we predict the IOD teleconnections?
  • Hasn’t received much focus, but is important for regional climate forecasting.
  • POAMA and EU ENSEMBLES project - full fields available.
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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Comparison of models

1980-2005 7 9 Wind stress perturbations to generate ensemble of ocean reanalysis, SST perturbations at initial time. ERA-40 direct & singular vectors perturbation HOPE 1.4°×0.3- 1.4°; L29 T159L62 ECMWF Sys3 ENSEMBLES 1982-2006 9 8 Same as above except the ocean model of GODAS is upgraded from MOM3 to MOM4 NCEP Reanalysis-2 direct, but the Atmos. Model is upgraded from T62L28 to T574L64 MOM4 (1.0°×1/4- 1/2°; L40) T126L64 NCEP CFSv2 (CFSV2) Saha et al. 2011 1980-2005 7 9 Wind stress perturbations to generate ensemble of ocean reanalysis, SST perturbations at initial time. ERA-40 anomaly, assimilation for soil moisture HadOM(1. 0°L40) HadAM32. 5x3.75oT38 HadGEM2 (UK Met Office) ENSEMBLES 1982-2006 7 11 OI technique (sea temp., salinity and sea level anomalies; SST relaxed to

  • Obs. with a time scale of 3 days; see

Balmaseda et al. 2006) ERA-40 direct & singular vectors perturbation HOPE 1.4°×0.3- 1.4°; L29 T159L62 ECMWF Sys3 (ECMWF) Molteni et al. 2007 1982-2006 1980-2005 9 30 PEODAS (OASIS coupler with flux correction P24b or no flux correction P24a & P24c) ALI (ERA-40 nudged) MOM2 (2.0°×0.5°; L25) BAM3.1 T47L17 (P24a, b) BAM3.0d (P24c) POAMA Multi- Model Ensemble (PMTMD) 1982-2006 9 8 GODAS (3D variation; Sea Temp.

  • nly; SST relaxed to Obs. with a time

scale of 5 days); SST relaxed to Clim. Synthetic salinity created by T-S relationship NCEP Reanalysis-2 direct MOM3 (1.0°×1/3- 1.0°; L40) T62L64 NCEP CFSv1 (CFSV1) Saha et al. 2006 1982-2006 12 9 No ocean assimilation except SST nudged to observations Forced by nudged SST 2.0°×0.5- 2.0° ; L31 T106L19 SINTEX-F (SINTEX) Luo et al. 2005 1982-2006 1980-2005 9 10 OI technique (sea temp. only; SST relaxed to Obs. with a time scale of 3 days; see Smith et al. 1991); ALI (ERA-40 nudged) Hudson et al. (2010) MOM2 (2.0°×0.5°; L25) BAM3.0d T47L17 POAMA1.5b (P15b) Alves et al. 2003 Climatology Fcast Mnths Ensemble Members Ocean Initial Scheme

  • Atmos. Initial Scheme

Ocean Model

  • Atmos. Model
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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Predicting SST indices

Li Shi, CAWCR

Persistence

Lead time

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Predicting ENSO and IOD teleconnections

Li Shi, CAWCR

Persistence Statistical model

Compare the dynamical models to a statistical model based on the persistence of the IOD and influence of ENSO.

IODp(t+τ) = B1×IODo(t) + B2×NINO3o(t) Start date (months after September 1st)

Standard deviation of IOD

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Predicting IOD and ENSO relationship

Li Shi, CAWCR

Obs

Start date (months after September 1st)

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Predicting ENSO and IOD teleconnections

Li Shi, CAWCR

Include the model’s ability to predict ENSO. Statistical-dynamical model -

IODp(t+τ) = B1×IODo(t) + B2×NINO3p(t+τ)

Statistical model tends towards persistence predictability of IOD at longer lead times. Statistical-dynamical model tends towards model predictability of IOD at longer lead times.

Start date (months after September 1st)

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Li Shi, CAWCR

Skill score - correlation coefficient of IOD in dynamical models, compared to the statistical-dynamical model. Models asymptote to zero skill score at long lead time - this indicates that there is no predictability of the IOD beyond ~4 months, except from the ability to predict the ENSO and its teleconnections.

Predicting ENSO and IOD teleconnections

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

IOD teleconnections to Australian climate

JJA lead time 1 month.

IOD IOD|NINO3 NINO3|IOD NINO3

Observations ECMWF UK Met Office

P15b

PMTMD

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

IOD teleconnections

NCEP Reanalysis ECMWF

Contour interval is 0.3 mbar, 4m and 8m per standard dev of DMI.

Regression of DMI standardised anomaly onto MSLP, 500hPa and 200hPa height anomalies - MSLP 500hPa 200hPa

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

UK Met Office

Contour interval is 0.3 mbar, 4m and 8m per standard dev of DMI.

Regression of DMI standardised anomaly onto MSLP, 500hPa and 200hPa height anomalies -

Comparison of models

POAMA PMTMD ECMWF

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Predicting Australian rainfall

Brier Skill Score, JJA lead time 1 month - above median seasonal rainfall forecast

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Summary

  • Dynamical models show skilful forecasts of IOD are limited to 3-4 months

lead time in SON.

  • There is scope for improvement in models by improving ENSO, IOD

teleconnections.

  • Teleconnections are an outstanding problem in dynamical models.
  • Predicting convection associated with dipole - Rossby wave source.
  • Biases in the mean state through which the Rossby waves are propagating.
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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Sally Langford Email: S.Langford@bom.gov.au Web: www.cawcr.gov.au

Thank you

www.cawcr.gov.au

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

Detrended data – CFS1 and 2 improve.