understanding and predicting teleconnections of the iod · a partnership between csiro and the...

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

Understanding and Predicting Teleconnections of the IOD

Harry Hendon and Maggie Zhao Wenju Cai, Eun-Pa Lim, Sally Langford primary support WAMSI and also WIRADA and MCV


Project 2.1 Overview

• Improve understanding of the large-scale variations of the Indian and Pacific Oceans that drive variability of the marine and terrestrial environment of WA

Predictable Large-scale drivers: ENSO, IOD, global warming Impacts: Interannual variations of Leeuwin Current, regional sea level, SST, winds/rainfall

•Assess the potential to predict at lead times of months to seasons with the POAMA seasonal prediction system

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POAMA Predictive Ocean Atmosphere Model for Australia

•Global coupled climate model ~200 km grids •Initialize global seasonal forecasts from observed state of upper ocean-atmosphere

•Run in real-time by BoM since Oct 2002 to make 9 mnth prediction of upper ocean/atmos

•Prediction research primarily conducted with re-forecasts for 1980-2010

POAMA1 > POAMA 1.5 > POAMA 2 > POAMA 3 (ACCESS model)

2002-2007 2007-2009 2009-11 2012

new ocean I.C.s


El Nino affects WA marine environment through two pathways 1) Oceanic teleconnection through Indonesian Through Flow drives variations of Leeuwin Current (review briefly) 2) Atmospheric teleconnection that alters local winds/rainfall (focus of

todays talk)


82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06

Years

FSLA -Niño3.4 NWHC r(Nin34,FSL)=-0.8

Ming Feng r(FSL,heat content)

El Nino Oceanic Teleconnection


Simultaneous correlation Fremantle sea level with heat content 1987-2002.

Skill at lead 7 mnth for heat content from POAMA 1982-2008


82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 2 03 04 05 06

Years

FSLA Obs FSLA Lead 3 FSLA Lead 6 FSLA Lead 9

Output 2.1.1: POAMA predictions of Fremantle Sea Level (proxy for variations of Leeuwin Current)

Hendon and Wang 2009 (Clim. Dyn)

Realtime forecasts available: poama.bom.gov.au

Obs 500mb ht anomaly El Nino during SON

Atmospheric teleconnection

Easterly anomaly across S-W Australia

U500 wind speed anomaly

Storm track anomalies Z’2500 (2-7 d)

Easterly anomalies associated with reduced “storminess”


Hendon 2003

JJA

SON

DJF

IOD typically develops during El Nino in JJA/SON

SST correlated with Nino34(DJF)


Shinoda, Alexander and Hendon 2004

r=0.75

EOF1 SST SON

PC1 and Nino3

1982 1999

OLR regresses on IOD index

Cai et al 2011

DMI= SSTIOw-SSTIOe

Nino34|DMI

Nino34

1979-2008

Cai et al 2010: IOD SST drives convective anomalies that act as Rossby wave source

SST correlation skill POAMA Hindcasts 1982-2008

3 mnth

6 mnth

9 mnth

1 mnth

Lead time months

Nino34

IOD

Regression of z500 on DMI

OBS

LT0

LT2

JJA SON

(Plots are b*sigmaX)

Mean POAMA Rainfall Bias compared to CMAP

LT3 LT6

Deficient mean rainfall in east IO will impact magnitude of rainfall anomaly during ENSO/IOD, thus leading to weakened teleconnections


Conclusions

IOD is important source of atmospheric teleconnection on its own but especially during ENSO Climate prediction for WA (southern Australia) during ENSO is limited by ability to predict IOD-teleconnection IOD is fundamentally less predictable then ENSO paucity of ocean observations in Indian Ocean model error strong limit on prediction skill Future work better understand model error: pathway to improving model diagnose Rossby wave source explore sensitivity of Rossby wave propagation to model mean state errors

understanding and predicting teleconnections of the iod · a partnership between csiro and the...

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