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Boreal Summer Intraseasonal Oscillation (BSISO)
Bin WangDepartment of Meteorology and
IPRC, University of Hawaii
1.MJO and BSISO2.Observed characteristics of BSISO3.Mechanisms for BSISO4.Role of atmosphere-ocean interaction5.Impacts of BSISO6.Modeling, Prediction and Predictability
I. MJO and BSISO
boreal winter (MJO)
boreal summer (BSISO)
Wang and Rui 1990
How ISO behavior is affected by annual cycle?
EOF1 (10%) EOF2 (10%)
EOF1 (12%) EOF2 (8%)
Principal Modes of
ISO (30‐60 day) during DJF
and JJA, 1979‐2010
DJF
JJA
Do we need a bimodal representation of Tropical ISO?
MJO in N.H. Winter MJO in N.H. Summer
From MJO working group website
Propagation
Kikuchi, Wang, Kajikawa, 2011 Climate Dyn
Bimodal
representation of the tropical ISO:
MJO and BSISO
Method
25-90 day filtered OLR
JJA DJFEEOF (0,5,10 day)
BSISO mode
Projection: 25-90 day filtered OLR
BSISO entire time series
MJO entire time series
MJO mode
BSISOEEOF2 EEOF1
MJOEEOF1 EEOF2
Bimodal ISO modes: MJO and BSISO
PCs: MJO, BSISO & WH04
MJO
BSISO
WH04
||OLRBSISO
||
||OL
RM
JO||
MJO vs BSISO
Phase space and category
MJO BSISO
PC1
PC2
phase 1
phase 2 phase 3
phase 6phase 7
phase 8
eastern Indian Ocean
Afr
ica
& w
eter
n IO
Mar
itim
e C
ontin
ent
phase 4
Western Pacific
Bay of Bengalea
ster
n N
P &
equ
ator
ial I
O
Indn
ia
& M
ariti
me
Con
tinen
t
western North Pacific
phase 5
PC2
PC1
Composite Life CycleM
JOB
SISO
Fractional varianceD
JFJJ
AMJO
BSISO
WH04
WH04
month
Ave
rage
num
ber o
f da
ys p
er m
onth
Seasonal variation of the two ISO modes
BSISO
MJO
2. Observed Characteristics of BSISO
OLR
1979~2009
1979~2010
(W/m2)2
1979-20010
Camball-Cook and Wang 2001
Variance distribution Propagation
MJ
Jul
ASO
MJJ
ASO
BSISV: Discovery of northward propagation
•
Yasunari (1979, J. Meteorol. Soc. Japan, 57, 227-242)•
Yasunari (1980, J. Meteorol. Soc. Japan, 58, 225-229)
•
Sikka and Gadgil (1980, MWR, 108, 1840-1853)
Latit
ude
Latit
ude
Time-latitude plots of the location and width of the maximum cloud zone at 90oE (after Sikka and Gadgil 1980)
Time-latitude plots of the location and width of the maximum cloud zone at 90oE (after Sikka and Gadgil 1980)
(a) TMI rain rate
(75- 100E,5S-5N)
20-5
0day
ano
mal
ous r
ain
rate
(red
das
hed,
m
m/d
ay)
Dai
ly ra
in ra
te (b
lue,
mm
/day
)
26 37 26 40
39 43 28
31 32 49 32
36 32 24 30
33 34 26 27 32 29
38 28 28
30 33 37 29
(b) 20-50day 3B42 rain rate (75-100E)
1998
1999
2000
2001
2002
2003
2004
BSISO in EIO and N-propagation
Wang et al. 2005)
Composite life cycle of BSISO rain rate (contour) & SST (shading)
1 2 3
3
4
4
1
1
1 2 3
3
4
4
1
1
Schematic structure/movement of BSISO
Wang, Kikuchi and Webster 2005
Phase1: genesis in western EIO; Phase 2: IntensificationPhase 3: Bifurcation, formation of tilted rain bandPhase 4:Northeastward propagation
30-60 day correlation: WNPSMI Quai-Biweekly correlation WNPSMI
Observed Characteristics of BSISO
1. Action centers of convection shifts to monsoon trough regions (Kemball-Cook and Wang 2001)
2. Initiation in the western Equatorial Indian Ocean (60-70E) (Wang, Webster, Teng
2005)
3. NW-SE tilted rain band (Farantii
et al. 1997, Annamalai and Slingo
2001, Kemball-Cook and Wang 2001, Waliser
et
al. 2003) The equatorial eastward propagating MJO tends to The equatorial eastward propagating MJO tends to bifurcatebifurcate
polewardpoleward
near Sumatra (Maloney and Hartmann near Sumatra (Maloney and Hartmann
1998, 1998, KemballKemball--Cook and Wang 2001, Lawrence and Cook and Wang 2001, Lawrence and Webster 2002). Webster 2002).
4. Northward propagation in the Bay of Bengal (Yasunari 1979, 1980, Sikka
and Gadgel
1980, Krishnamurti
and
Subrahmanyam
1982 among others) and northwestward propagation in the WNP (Nitta 1987)
3. Physical Mechanism of BSISO
How are the active/break cycles of ISM re-initiated or MISO
maintained?
How are the active/break cycles of ISM re-initiated or MISO maintained?
Hypotheses1. Circumglobal propagation of the upper-level divergent waves of MJO (Julian and Madden 1981, Lau and Chan 1986 among many others)2. Midlatitude forcing: Forced by midlatitude
Rossby
wave train
(Hsu et al. 1990) or by injection of PV from SH (Rodwell
1997)3. Forcing from decaying off-equatorial Rosbby waves in IM region re-initiates of equatorial convective anomalies by decaying off-
equatorial Rossby
waves in ISM region (Wang and Xie
1997)4. Feedback between hydrological processes in the atmosphere and radiation processes (Hu
and Randal 1994, Stephens et al. 2004)
5. Self-induction mechanism (Wang, Webster, Teng
2005)
200 hPa
velocity potential has a salient stationary component
phase 1
phase 2
phase 3
phase 4
Upper- level
divergence waves may not be essential for re-
initiation
Circum-global navigation?
:sfc. div. & rainfall:sst & rainfall
Lag
corr
elat
ion
coef
ficie
nt
Lag (days)
Genesis in Central EIO: 5S-5N, 60-70E
Surface convergence leads the genesis by 3-4 days
SS warming leads genesis by 6-7 days.
Low-level convergence and local warming may be important
Re-initiation ScenarioRainfall (contours) and SST (color) Surface winds and divergence
Wang, Webster, Teng
2005
The in situ surface wind convergence and sea surface warming that initiate new rainfall anomalies result from the forcing of the previous active monsoon, suggesting a self-Induction mechanism to sustaine BSISO.
Why does the summer monsoon ISO have a NW-SE slanted structure? How does the tilted rain band form?
Can we simulate this structure?
BSISO Model
The two and half layer model includingObserved Mean flows (U,V,W,T)Realistic qs
or SST
Nonlinear heating (SST dependent trigger function and positive only heating)
Initial value problem
(1)
(2)
(3)
(4)
(5)
vvMy
yuxv
)(
0
pyv
xu
ppMpQ
pCRpSpt c
p
)()(
s
u
s
u
s
u
p
p v
p
p cc
p
pqME
gdpQ
Lgdp
yqv
xqu
gdpqt )(1
uuMx
yutu
)(
Model Equations on equatorial beta-plane
11
)(
HH
ss p
pqpq )( SSTqq ss Wang 1988
Mean Flow Terms
pu
pu
yuv
yuv
xuu
xuuuM ''
''
' ')(
pv
pv
yvv
yvv
xvu
xvuvM ''
''
' ')(
pyv
pyv
pxu
pxu
pM
2
'22
'2
s
u
p
p gdp
yqv
xquqM )(
Structure of three-layer model of ISO
mbPs 1000
mbPe 900
mbP 5002
mbP 1000
mbP 3001
mbP 7003
2
00
111 ,, vu
333 ,, vu
b
bb vu ,PBL
p
p
eq
3q
1q
BSISO Model
The two and half layer model includingObserved Mean flows (U,V,W,T)Realistic qs
or SST
Nonlinear heating (SST dependent trigger function and positive only heating)
Initial value problem
July mean state (ER40)
(a)
(b)
(c)
(e)
(d)
(f)
(g)
EQ20S
20N
EQ20S
20N
EQ20S
20N
EQ20S
20N
EQ20S
20N
EQ20S
20N
EQ20S
20N
0
10
6
2
14
18
22
26
Mean flows and SST distribution trap ISO in eastern hemisphere
Simulated boreal summer convectively coupled Kelvin-Rossby waves
Northward propagation component
In the model, the NW-SE slanted precipitation anomalies in the monsoon regions forms due to emanation of the moist Rossby
waves from the equatorial rainfall anomalies over the maritime continent.
Interaction between moist Rossby
wave
and the vertical shear of the mean monsoon provides a mechanism for the formation of the slanted ISO rain band.
Mean flows removedUniform SST
Only Monsoon vertical Shear included Drbohlav and Wang 2005
What give rise to the northward propagation of ISO over the summer monsoon regions?
Northward and westward propagation mechanism in monsoon regions
Northward Propagation•
The land surface heat fluxes into the boundary layer can destabilize the atmosphere ahead of ascending zone, causing a northward shift of the convection zone (Webster 1983)
•
The continuous northwestward emanation of Rossby waves from the equatorial Kelvin-Rossby
wave packet
when the latter pass through the maritime continent (Wang and Xie
1997)
•
Easterly vertical shear, boundary layer advection and air sea interaction (Drbohlav
and Wang 2004, Jiang
et.
al.2004)Westward Propagation
•
Unstable baroclinic
waves (Lau and Peng1990) •
Equatorial Rossby
waves destabilized by easterly vertical
shear and interactive convective heating (Xie
and Wang 1996)
Webster et al. 1983
Wang and Xie 1997Lawrence and Webster 2001
Review northward propagation of boreal summer ISO
Jiang et al. 2004; Drbohlav and Wang 2005Interaction between vertical shear and convection
Fu et al. 2003Air-sea interaction
The land surface heat fluxes into the boundary layer can destabilize the atmosphere ahead of ascending zone, causing a northward shift of the convection zone
Northward propagation is a component of eastward movement of the slated rain band
ω( 0.04 Pa/s) and relative velocity (×10-6s-1)
phase 4
phase 5
phase 6
phase 7
Observation: Barotropic
vorticity
leads convective anomalies in N-propagation.
ECHAM Model: Vorticity
leads convection anomaliesvertical velocity vorticity geopotential
height
divergence specific humidity temperature
Jiang et al. 2003
y
(North)
x (East)
3u CONV
z
u
1u
PBL
0
CONV
'w
AAn atmospheric internal dynamic mechanism for northward propagatin atmospheric internal dynamic mechanism for northward propagation: monsoon on: monsoon easterly vertical shear provides a easterly vertical shear provides a vorticityvorticity
source, which, upon being twisted by the source, which, upon being twisted by the
northnorth--south varying vertical motion field associated with the south varying vertical motion field associated with the RossbyRossby
waves, waves, generates generates positive positive vorticityvorticity
north of the convection, creating boundary layer north of the convection, creating boundary layer
moisture convergence that favor northward movement of the enhancmoisture convergence that favor northward movement of the enhanced rainfall. ed rainfall.
yUv
t T
How easterly vertical shear pulls the RW Northward
4. Roles of Atmosphere-Ocean Interaction in BSISO
Findings•A-O interaction enhances ISO variability (Flateu
1997, Wang and Xie
1998, Waliser
et al. 1999,…)•AGCM (AMIP run) failed to simulate correct SST-Precipitation relationship: in phase in the AGCM models but 90 degrees out of phase in reality. (Wu et al. 2002)•CGCM and AGCM alone yield fundamentally different ISO solution, coupling leads to realistic SST-precipitation relationship (Fu et al. 2003).•Coupling between atmosphere and ocean add predictability to boreal summer ISO (Fu et al. 2006)
Questions•How does ocean intraseasonal
variability feedback to atmospheric ISO?
•What are precise relationships between the SST and surface heat fluxes?•What are the relative roles of entrainment, upwelling, and advection in controlling SST ISV? To what extent theses processes are dependent of atmospheric forcing? (or does ocean processes add noise to ISO?)
Kemball-Cook and Wang, (2001)
Observed Characteristics of AOI in BSISO
The local SST-rainfall phase relationship differs between the equatorial regions and off-equatorial monsoon regions.
Observed Relationship between SST and rainfall anomalies
Does air-sea coupling impact the northward propagation of monsoon intraseasonal
oscillations? If so, how?
CMAP Rainfall
Coupled
Daily Forced
Mean Forced
Roles of air-sea coupling in northward propagation
(Fu and Wang 2004)
Phase Relationships between Rainfall and SST
Arabian Sea
Bay of Bengal
Fu and Wang, (2004)
How AOI Intensifies Northward Propagating ISO?
Surface Wind &Latent Heat Flux
Cloud & Solar Radiation
Mixed-layer Depth
D
D
D
Propagating Air‐sea interaction mechanism
Fu et al. (2003), Wang et al. (2009)
BSISO over the Philippine Sea
How AOI affects BSISO over the Philippine Sea
Wang and Zhang 2002
5. Impacts of BSISO on Monsoon, extreme and midlatitude
Waliser
2005
BSISO is a dominant mode of Monsoon ISO
Monitoring BSISO Using monsoon circulation indices
Wang, Wu, Lau 2001, JC
Asian summer monsoon ISO:
Time series of Indian Summer Monsoon Index (left)Western
North Pacific Summer Monsoon Index (right).(top) Mean 365-day annual cycle (lower three panels) The thin lines are daily anomaly values, the thick lines are 30-60 day band-passed values for the years 1979, 1992 and 2008.
ISO of Monsoon circulation
ISM EA-WPSM
Origin of Synoptic-Scale Wave Train (SWT) in WNP
Lau and Lau (1990) :An alternative positive and negative vorticity
wave train
withtimescale: 2-8 days,wavelength: 2500 km,propagation: northwestward.
Questions:What is the origin of the synoptic wave train? What determines its zonal wave- length and phase propagation?
(c)
Figure 1 (a) Enhanced and (b) relaxed WNP
monsoon trough during active and break
phases of ISO in Peak TC season from July to
October. Shading denotes 850 hPa
vorticity
and the dots represent locations of the first
RI reported. The green curves represent the
location of the monsoon trough. The dashed
box highlights regions in which the largest
differences between dry and wet phases of
intraseasonal
oscillation (ISO) are observed.
(c) The number of RI in the dashed box (10‐
20 ºN, 110‐150 ºE) as function of ISO in the
composite nine phases of ISO. The
composition was made form 55 cases during
June through October. Phases 1 and 5
represent, respectively, the strongest and
weakest convection phases over the
equatorial western Pacific (EWP, 5oS‐5oN,
140oE‐160oE). Thus, the maximum
occurrence of RI over the WNP lags the peak
wet phase of the EWP ISO by three phases
(about 12 days) and the minimum
occurrence of RI over the WNP lags the peak
wet phase of the WNP ISO by seven phases
(about 28 days).
66
MJO Impacts –
Tropical Cyclones
The MJO often leads to “bursts”
and
“lulls”
in tropical cyclone activity when active
Periods generally last for about two weeks
Due to changes in both lower and upper-level winds and convection
H H H
H
L
L
L
L
MJO and the Record-Breaking East Coast Snowstorms in 2009/10
Bar: Eastern US snowLine: Central Pacific MJO
Fig. 8. The schematic diagrams illustrating the typical patterns of MJO-
teleconnection
at Phase 3 Contour : rotational wind at 300 hPa. Letter A (C) :anticyclonic
(cyclonic) circulation anomaly
The winter mean SST anomaly during El Niño and La Niña is shaded in red (T ≥
0.5°C) and blue (T ≤
-0.5°C), respectively.
The dotted blue (red) arrow denotes cold (warm) advection. The yellow(green) rectangle denotes dry(wet) precipitable
water.
Cloud shape in green represents convection associated with MJO and the cloud shape in orange denotes the subsidence at Phase 3.
Green thick line indicates the succession path of cyclonic and anticyclonic
anomalies departed from the convection.
El Nino
La Nina
Fig. 9. The schematic diagrams illustrating the typical patterns of MJO-
teleconnection
at Phase 7 Contour : rotational wind at 300 hPa. Letter A (C) :anticyclonic
(cyclonic) circulation anomaly
The winter mean SST anomaly during El Niño and La Niña is shaded in red (T ≥
0.5°C) and blue (T ≤
-0.5°C), respectively.
The dotted blue (red) arrow denotes cold (warm) advection. The yellow(green) rectangle denotes dry(wet) precipitable
water.
Cloud shape in green represents convection associated with MJO and the cloud shape in orange denotes the subsidence at Phase 3.
Green thick line indicates the succession path of cyclonic and anticyclonic
anomalies departed from the convection.
El Nino
La Nina
L
L
L
HH
L LL
HHH
H HH
HL
L
Possible positive feedback between the Eurasian wavetrain and ISM on intraseasonal timescale
The strong convection over northern ISM region is initially triggered by the anomalous central Asian high within the wavetrain extending from the northeastern Atlantic to East Asia
The northeastern Atlantic anomalous high is presumably excited by efficient kinetic energy extraction from the basic state (Simmons et al 1983).
Barotropic instability
Vertical shear effect
The increased easterly vertical shear would increase equatorial Rossby wave instability of the atmosphere by providing wave available potential energy, thereby increasing monsoon precipitation (Wang and Xie 1996, Xie and Wang 1996).
The convection in turn excite a Rossby-wave response to reinforce the central Asian high and downstream circulation anomalies of the wavetrain, through Rossby wave dispersion.The baroclinic structure of adjacent circulation associated with the ISM convection indicates the diabatic heating effect of the ISM.
“Monsoon-desert” mechanism
Rossby wave dispersionAnd wave guide effect
the westward retreat of the central Asian high after its establishment and the outbreak of convection over the ISM region can be regarded as a Rossby wave response to the diabatic heating
6. GCM modeling, Prediction and Predictability of BSISO
Progress
BSISV in GCMs has received much less attention compared to the simulation of boreal winter MJO–AGCMs: Fennessy
and Shukla
(1994), Ferranti
et al. (1997), Sperber
et al. (2001), Waliser
et al. (2003)
–CGCMs: Kemball-Cook et al. (2002), Fu et al. (2003), Fu and Wang (2004), Rajendran
and
Kitoh
(2006), Sperber
and Annamalai
(2008)
Modeling•
What is the correct heating partitioning between the convective and stable precipitation?
•
What is the correct heating partitioning between the small-scale high frequency and large-scale, low frequency disturbances?
•
What is typical heating profile for synoptic and ISO time scale?
•
What are the typical vertical profile of specific humidity and moist static energy?
•
What is the precise relationship between anomalous SST, BL moist static energy, convective instability, the boundary layer and lower tropospheric
moisture
convergence, and precipitation associated with ISO?
Fig. 17
Common problems:1.
EIO activity center
2.
Northward pathway in Bay of Bengal
3.
Northwest pathway in the western North Pacific
Common strengths1.
Weakening over the MC
2.
Off equatorial activity centers
SD of 20-90 day filtered rainfall (mm/day) for May-Oct from the CMAP for 1979 to 1998 and for the ten AGCMs (lower). In the case of the models, there were 20 summer seasons of data, i.e. ten members each consisting of two years. From Waliser et al. (2003).
The tilted rainband
•
CLIVAR AAM experiments, 1997/98; 10 member ensembles; weekly SST prescribed
•
Typically, AGCMs poorly represent the BSISV tilted rainband (Waliser et al. 2003, Clim. Dynam., 21, 423-446)
Figures kindly provided by D. Waliser
Figures kindly provided by D. Waliser
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
4 0E 6 0 E 8 0 E1 0 0E1 2 0 E14 0 E1 60 E 1 803 0 S
1 0 S
1 0 N
3 0 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0E 6 0 E 80 E10 0 E1 20 E1 4 0E16 0 E 18 030 S
10 S
10 N
30 N
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
4 0 E 60 E 8 0E1 00 E1 2 0E1 40 E1 6 0E 1 8 030 S
10 S
10 N
30 N
PC4 regression with 20-100 day filtered OLR (Wm-2; best fit to AVHRR Day 10 CsEOF
using pattern correlation)
•
Compared to the GCMs analyzed by Waliser et al. (2003) the newer coupled models are better at representing the BSISV, but…
a) AVHRRa) AVHRR
c) ECHO-Gc) ECHO-G
e) CGCM3.1 (T47) e) CGCM3.1 (T47)
f) CNRM-CM3 f) CNRM-CM3 i) GFDL-CM2.0 i) GFDL-CM2.0
j) GFDL-CM2.1 j) GFDL-CM2.1
k) GISS-AOM k) GISS-AOM
l) IPSL-CM4 l) IPSL-CM4
m) MIROC3_2_hires m) MIROC3_2_hires
n) MIROC3_2_medres n) MIROC3_2_medres
h) ECHAM5/MPI-
OM
h) ECHAM5/MPI-
OM
o) MRI-CGCM2.3.2 o) MRI-CGCM2.3.2
d) CCSM3.0 d) CCSM3.0
b) ECHAM4/OPYCb) ECHAM4/OPYC
g) CSIRO Mk3 g) CSIRO Mk3
1111
99
1212 1414
1212 99
1515
99
Miura et al. (2007).
3B42
MJO simulated by NICAMNICAM 7 km
NICAM 850 vorticity
BSISO Prediction
•What is physical basis for intraseasonal predictability?
•
To what extent the is ISV predictable?•
Improvement of climate models: Cloud resolving models? Multi-scale interaction?
•
Techniques for better initialize coupled models
81
Statistical Predictions –
Jones et al. 2004
• The model uses PCs of EOF analysis of 20-90 day OLR anomalies•
Forecast models use information from 5 most recent pentads to predict future PCs patial
structures are obtained by reconstructing the fields of OLR using the forecasts PCs and the associated EOFs
Forecast Skill
Real-time Multivariate MJO (RMM) index (Wheeler and Hendon04)Observed RMM
Forecasted RMM Eigenvector
Project the combinedeigenvector to predicted variables
(daily OLR, U850,U200)
Forecasted PC1, PC2(RMM index)
Verification method: RMM index
Statistical Predictions –
WH Lagged Linear Regression
Forecast Skill
From Gottschalck
84
RMM1 and RMM2 values for the most recent 40 days and forecasts from the ensemble Global Forecast System (GEFS) for the next 15 days
light gray shading: 90% of members
dark gray shading: 50% of forecasts
Yellow Lines – 20 Individual Members Green Line – Ensemble Mean
Dynamical Predictions –
GEFS
From Gottschalck
85
GEFS MJO Index Forecast Skill
--Ensemble Global Forecast System--Horizontal resolution is T126--21 member ensemble
--Includes all days available(all seasons, non-MJO days so skill is somewhat low)
--In some cases, the GEFS has been able to provide information for MJO initiation and demise
--Only 2.5 years of data available
From Gottschalck
Better Initial Conditions
Boost Intraseasonal
Forecast SkillFu and Wang et al. (GRL, 2009)
The amplitude of convective activities in the NCEP reanalysis (left panel, bottom) was found to be less than observed
(left
panel:
top,
TRMM
3B42;
left
middle:
GPCP)
by
a
factor
of
two
to
three.
Three
forecasting
experiments
were
conducted to explore the impact of strengthening the signal in the NCEP initial conditions on forecasting
aspects
of
the
monsoon
intraseasonal
oscillation
(right
panel). With
the
original
NCEP
reanalysis
as
initial
condition,
the
850‐
hPa
zonal
winds
and
rainfall
are
predictable
with
some
skill
only
about
a
week
in
advance
over
the
global
tropics
(30S–30N) and Southeast Asia (10N–30N,60E–120E). Predictability increases steadily with increasing the amplitudes
to
2
times
and
3
times
the
NCEP
initial
conditions. When
the
signals
in
initial
conditions
are
recovered
to
a
level
similar
to
that
in
the
observations
(the
last
experiment),
monsoon
forecast
skill
reaches
25
days
for
850‐hPa
zonal
winds and 15 days for rainfall over both the global tropics and Southeast Asia.
Impact of Initial Condition on ISO Prediction2
(a) skills of filtered rainfall initialized with the original NCEP_R2; (b) with doubled ISO signals in the NCEP_R2; (c) skills of filtered U850 initialized with the original NCEP_R2; (d) with doubled ISO signals in the NCEP_R2.
Signal CPL Forecast Error
ATM Forecast Error
Air-Sea Coupling Extends the Predictabilityof Monsoon Intraseasonal Oscillation
[ATM: 17 days; CPL: 24 days]Fu et al. (2007)
Monsoon Institute, Honolulu, January 07, 2008
* Waliser
et al. 2003
Signal: Mean amplitude of the ISO variance Forecast Error: Mean variance between ensembles
Control run Forecasts
L
Ljis X
Lij
20, )(
121
M
mij
mije XX
Mij1
20 )(1
Signal & Forecast Error
Potential predictability
Model Studies
NASA/GLA AGCM Waliser
et al. (2003)
ECHAM5 AGCM Liess
et al. (2005)
ECHAM4 AGCM, CGCM Fu et al. (2007)
Dynamical models has potential for ISO prediction
Predictability : Signal to Error Ratio Signal: Mean variance within ISO periodError: Mean variance between ensembles
~40 days
Signal
Error
* Liess
et al 2005
200h Pa Velocity Potential* Perfect model assumption
Potential predictability
0
5
10
15
20
25
30
35
40
45
50
C ER F EC M W IN G V LO D Y M AXP M ETF U KM O S N U 1 S N U 2 N C EP N AS A FS U 2 U H C AM
DEMETER APCC/CliPAS
Potential predictabilityNumber of forecast days until the signal equals the noise
From Kang
1
Better understand physical basis for intraseasonal
prediction.
Estimate
potential
and
practical
predictability
of
ISO
in
a
multi‐model
frame
work.
Developing
optimal
strategies
for
multi‐model
ensemble
(MME)
ISO
prediction
system,
including
effective
initialization
schemes
and
quantification
of
the
MME’s
ISO prediction skills with forecast metrics under operational conditions.
Identify
model
deficiencies
and
suggest
ways
to
improve
models’
convective
and other physical parameterizations.
Revealing
new
physical
mechanisms
associated
with
ISV
that
cannot
be
obtained
from analyses of a single model.
Study
ISO’s
modulation
of
extreme
hydrological
events
and
its
contribution
to
seasonal and interannual
climate variation.
ISVHE Objectives
Experimental Designs
Free coupled runs with AOGCMs or AGCM simulation for at least 20 years
Daily or 6- hourly output
Control Run
ISO hindcast initiated every 10 for at least 45 days with more than 6 ensemble members from 1989 to 2008
Daily or 6-hourly output
ISV Hindcast EXP
Additional EXP for YoTC
period from May 2008 to Sep 2009
6-hourly output
YOTC EXP
1
CliPAS/ISVHE ParticipationsInstitution Participants
ABOM, Australia Harry Hendon, Oscar Alves
CMCC, Italy Antonio Navarra, Annalisa
Cherichi, Andrea Alessandri
CWB, Taiwan Mong-Ming Lu
ECMWF, EU Franco Molteni, Frederic Vitart
GFDL, USA Bill Stern
JMA, Japan Kiyotoshi
Takahashi
MRD/EC, Canada Gilbert Brunet, Hai
Lin
NASA/GMAO, USA S. Schubert
NCEP/CPC Arun
Kumar, Jae-Kyung E. Schemm
PNU, Korea Kyung-Hwan Seo
SNU, Korea In-Sik
Kang
UH/IPRC, USA Bin Wang, Xiouhua
Fu, June-Yi Lee
1
ECMWF
JMACWB
ABOM
ECNCEP
ISVHE MODELS Intraseasonal Variability Hindcast Experiment
The ISVHE is a coordinated multi-institutional ISV hindcast
experiment supported by APCC, NOAA CTB, CLIVAR/AAMP & MJO WG, NOAA CTB, and AMY.
UH IPRC
Supporters
SNUPNU
GFDL NASACMCC
http://iprc.soest.hawaii.edu/users/jylee/clipas.htm
1
BSISO
1.
Evaluation of models: (a) MJJAS variance distribution OLR 20‐90Days, (b) Interannual
variation of ISO variance, (3)
Leading EEOF mode (PCC)
2.
Skill: (a) Predictable mode forecast skill (PC1 and PC2), (b) 200hPa velocity potential, (c) precipitation and U850
3.
Predictability: Signal to noise ratio perfect model approach
4.
Prediction of interanual
variation statistical correction.
1 Evaluation on Control RunsPattern Correlation Coefficient and Normalized Root Mean Square Error for Mean Precipitation and 20-100-day Variance (30S-30N)
1 Evaluation on Control Runs20-100-Day U850, U200 and OLR along the equator (15oS-15oN)
The first two MV-EOF modes of 20-100-day 850-
and 200-hPa zonal
wind and OLR along the equator (15oS-15oN) obtained from obs
and
control simulations. The percentage variance explained by each mode is shown in the lower left of each panel. (Wheeler and Hendon 2004)
Preliminary results from 6 models
1 The MME and Individual Model Skills for MJO
Common Period: 1989-2008Initial Condition: 1st day of each month from Oct to MarchMME1: Simple composite with all modelsMMEB2: Simple composite using the best two modelsMMEB3: Simple composite using the best three models
1 MJO Skill depends on ENSO phase
Taking into account IAV anomaly, the practical TCC skill for the RMM1 and 2 extends about 5 to 10 days depends on model. The improvement is remarkable for the RMM1.
The skill in La Nina years is better than El Nino years in most models.
Any comments and questions?
Thank you !