Understanding and Predicting Teleconnections of the IOD · A partnership between CSIRO and the...
Transcript of 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
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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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The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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)
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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Years
FSLA -Niño3.4 NWHC r(Nin34,FSL)=-0.8
Ming Feng r(FSL,heat content)
El Nino Oceanic Teleconnection
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
Simultaneous correlation Fremantle sea level with heat content 1987-2002.
Skill at lead 7 mnth for heat content from POAMA 1982-2008
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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”
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
Hendon 2003
JJA
SON
DJF
IOD typically develops during El Nino in JJA/SON
SST correlated with Nino34(DJF)
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology
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