Decadal Variability in the Southern
Hemisphere
Xiaojun Yuan1 and Emmi Yonekura2
1Lamont-Doherty
Earth Observatory
Columbia University
2
Department Environment and Earth Sciences
Columbia University
Many thanks to D. Martinson, Y. Kushnir and M. Ting for their inputs
2012 Ocean Sciences Meeting, February 20-24, Salt Lake City, Utah
GloDecH meeting, March 14, 2012
Previous Studies focus on the decadal variability in the
ENSO teleconnections
1. Decadal changes is found in the relationships
between precipitation/ moisture flux/ pressure fields
in the Western Antarctic and ENSO from 1980s to
1990s (Cullather et al., 1996, Bromwich et al.,
2000; Fogt and Bromwich, 2006)
Correlation between SOI and
500mb height anomaly
1980s
Moisture flux convergence in the western
Antarctic and SOI
1990s
Bromwich et al., 2000
Fogt and Bromwich, 2006
Previous Studies focus on the decadal variability in the
ENSO teleconnections
2. Tree ring proxy data reveal a peak at 14 year period from coherency
spectrum of reconstructed Summer Trans-Polar Index and the SOI for the
period of 1866 to 1984 ( Villalba et al, 1997)
Trans-Polar Index
Coherency Spectrum
Power Spectra of Four Climate Modes in the Southern
Hemisphere
Decadal peak
Objective
Investigate decadal variability of the Southern Annular Mode and SST
Data
1.
2.
3.
Weather station pressure and temperature data (READER
dataset)
Available reanalysis datasets
20th century runs from IPCC coupled climate models
4.
Hadley Center’s SST
Method
1.
2.
3.
Spectrum analysis
Spectra peaks are assessed by first order Markov null continuum
method.
Short Time Fourier transform
SAM Indices
Defined as differences of SLP anomalies
between 40S and 65S
Detrended SAM indices calculated from BAS
(black), Visbeck (megenta), ERA40 (red), NCEP
(blue) and NCEP-EOF (blue dash line) after 1950.
Black straight line is the long-term trend removed
from the BAS series.
Reconstructed Visbeck annual SAM series
(blue) and Fogt seasonal SAM series since
1905 (red). All time series were filtered by a
butterworth filter with the filter width of 5
years.
Power Spectra of SAM and SST
SAM
SST
40-50S
Dashed lines indicate 95% confidence level, which is assessed by first order Markov null
continuum method. SAM is an order 6 autoregressive process.
SST
30-40S
SST
50-60S
Cross spectrum between SAM and
SST at 50-60S
mean meridional SST gradient
between 30-60S
(a) Power Spectrum of
Fogt’s seasonal SAM index
(1865-2005) and
(b)spectrogram of short time
analysis as function of
frequency (cycle per season)
and center year of each 50year series. The dash line in
(a) indicates the 95%
confidence level.
Short time Fourier Transform
READER database
Turner et al., 2005
Weather station data
coverage for sea
level pressure (a) and
surface air
temperature (b). The
percentage of
missing data for each
station is listed at
right.
Decadal (8-14 yrs) and interannual (3-7 yrs) Variability in Temperature and
Pressure Records at Weather Stations
Air temperature
Sea Level Pressure
Spectrogram of air temperature at Cape Naturaliste
Orcadas Station
Air temperature
Both decadal and
interannual peaks are
significant at 95%
confidence level
Sea Level Pressure
Both decadal and
interannual peaks are
significant at 90%
confidence level.
Austral summer (DJF) composite of SST anomalies for (a) high SAM
years (1960-65, 1995-2001) and (b) low SAM decades (1970-80).
There are consistent energy peaks at the decadal
frequency band in temperature and pressure fields in
mid-latitudes of the Southern Ocean, which contribute
to the decadal variability in SAM.
Do IPCC coupled climate models simulate this
decadal variability in the Southern Hemisphere?
The SAM index is constructed following Gong and
Wang (1999) -- zonal mean SLP anomaly at 40S
minus zonal mean SLP at 65S, in 20th century runs of
18 IPCC coupled models
Linear Trend in SAM index from IPCC models entire 20th century runs
Linear Trends in SAM index from IPCC 20th century runs for the
period of 1947-1997
Decadal variability in SAM indices, SST of 50-60S and SAM/SST cross spectrum from 18 IPCC
AR4 climate models. The 95% (dark grey) and 90% (light grey) confidence levels are shaded.
Country, Institute
Canada, CCCMA
France, CNRM
Australia, CSIRO
USA,
NOAA/GFDL
USA,
NOAA/GFDL
USA,
NASA/Goddard
USA,
NASA/Goddard
China, LASG
Russia, INM
France, PSL
Japan, CCSR,NIES
Japan, CCSR,NIES
Germany,MaxPlan.
Japan, MRI
USA, NCAR
USA, NCAR
UK, Hadley Centre
UK, Hadley Centre
SST 50-60S
Dec Var.
P eriod (yrs)
SAM/SST
50-60S
CrossSpec. per.
(yrs)
Model Name
cccma
cgcm3.1
cnrm cm3
csiro mk3.0
Time
Series
SAM Dec.
Var. Period
(yrs)
1850-2000
1860-1999
1871-2000
8, 9
11,(9)
12
gfdl cm2.0
1861-2000
8, 10
Y
gfdl cm2.1
1861-2000
12
Y
giss modelE-H
1880-1999
8, 12
giss modelE-R
iap fgoals1.0g
inmcm3.0
ipsl cm4
miroc3.2 hires
miroc3.2
medres
mpi echam5
mri
cgcm2.3.2a
ncar ccsm3.0
ncar pcm1
ukmo hadcm3
ukmo_hadgm1
1880-2003
1850-1999
1871-2000
1860-2000
1900-2000
12
10
8
12
1850-2000
1860-1999
14
12
1851-2000
1870-1999
1890-1999
1860-1999
1860-1999
9
10
9
9
9, (10)
14
Ozone
Forcing
N
Y
Y
8, (11)
Y
12
Y
N
N
N
Y
8
11
14
12
Y
Y
9
N
Y
Y
Y
Y
11
10
When all model runs are considered, 57% of SAM indices yield significant decadal peaks.
Conclusions
1. Decadal variability dominates the monthly time series of
SAM in instrumental data periods. The decadal
variability of SLP and air temperature in mid latitudes
largely contributes to the decadal variability in SAM.
2. The decadal variability in SAM is likely related to the
decadal variability of SST in the subpolar and mid
latitude regions of the Southern Ocean.
3. Most IPCC coupled climate models are able to simulate
the decadal variability of SAM in their 20th century runs
and all models produce the positive long-term trend in
SAM. The results suggest that the decadal variability is a
natural variability captured by the fundamental physics
of these climate models
Thank You!
Yuan, X., and E. Yonekura (2011), Decadal variability in the Southern
Hemisphere, J. Geophys. Res-Atmospheres., 116, D19115,
doi:10.1029/2011JD015673.
READER data span
Power Spectra of zonal mean NCEP/NCAR reanalysis pressure
anomalies as function of latitude bands
Power Spectra of zonal mean NCEP/NCAR reanalysis
temperature anomalies as function of latitude bands