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The Potential Roles of Background Surface Wind in the SST Variability Associated with
Intraseasonal Oscillations
KAYA KANEMARU
Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan

HIROHIKO MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan(Manuscript received 13 December 2013, in final form 13 June 2014)
ABSTRACTThe current study is aimed at exploring the potential roles of the seasonally altering background surface
wind in the seasonality of the intraseasonal oscillations (ISOs) with a focus on the sea surface temperature(SST) variability. A composite analysis of the ocean mixed layer heat budget in term of ISO phases withvarious satellite data is performed for boreal winter and summer. The scalar wind is found to be a dominantfactor that accounts for the ocean surface heat budget, implying that the background surface wind as well as its
anomaly is important for the SST variability. An easterly anomaly to the east of convection diminishes scalarwind, and thus latent heat flux, when superposed onto a background westerly wind, implying that the presenceof basic westerly wind is important for the development of a warm SST anomaly ahead of the ISO convection.
On the other hand, an easterly anomaly in combination with basic easterly wind magnifies scalar wind andlatent heat flux and cancels out the shortwave heat flux anomaly. The seasonal migration of the backgroundwesterly wind, which is confined to a southern equatorial belt in boreal winter but spread across the northern
Indian Ocean in boreal summer, may offer a mechanism that partly accounts for the seasonal characteristics of
ISO propagation. The northward propagation of the SST variability associated with the boreal summer ISO isfound to also involve a similar mechanism with the meridional wind modulation of scalar wind.

1. Introduction
The intraseasonal oscillation (ISO; 30–90 days) is oneof the dominant modes of atmospheric variability over
the tropics (Madden and Julian 1971, 1972), and its behavior is seasonally changed (Madden 1986; Gutzler andMadden 1989; Wang and Rui 1990; Salby and Hendon
1994; Zhang and Dong 2004). The convective activityof the boreal winter ISO [also known as the Madden–
Julian oscillation (MJO)] initiated in the Indian Ocean
propagates eastward along the equator to the Maritime
Continent, being slightly shifted southward as it eventuallyenters the South Pacific convergence zone (SPCZ) (e.g.,
Hendon and Liebmann 1990; Matthews et al. 1996; Wheeler
and Hendon 2004). The convection associated with theboreal summer ISO (BSISO) moves northeastward from

Corresponding author address: Kaya Kanemaru, Graduate
School of Environmental Studies, Nagoya University, Furo-cho,
Chikusa-ku, Nagoya 464-8601, Japan.
E-mail: kanemaru@satellite.hyarc.nagoya-u.ac.jp
DOI: 10.1175/JCLI-D-13-00774.1
Ó 2014 American Meteorological Society

the Indian Ocean into the southeast Asian continentand northwestern Pacific (e.g., Yasunari 1979, 1980;
Krishnamurti and Subrahmanyam 1982).
Evidence for the air–sea coupling related with the ISOvariability has been observed in previous studies. A
warm sea surface temperature (SST) anomaly propagates preceding the enhanced convection associatedwith the ISO (Nakazawa 1995; Hendon and Glick 1997;
Shinoda et al. 1998; Woolnough et al. 2000), which arises
from an enhancement of insolation and a weakening of
evaporation. This relation between ISO propagationand the SST variability is observed in both the MJO(e.g., Lau and Sui 1997; Hendon and Glick 1997;
Shinoda et al. 1998; Vialard et al. 2008) and BSISO(e.g., Kemball-Cook and Wang 2001; Sengupta et al.
2001; Klingaman et al. 2008), which implies that theSST variability has potential importance in the maintenance of ISO propagation. Some model studies suggestthat the air–sea coupling affects the ISO propagation mechanism (e.g., Flatau et al. 1997; Wang and Xie
1998; Waliser et al. 1999; Kemball-Cook et al. 2002;

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Woolnough et al. 2007). The SST response associatedwith ISOs seasonally and regionally differs over the
Indian Ocean and the western Pacific. Duvel andVialard (2007) pointed out that the amplitude of theSST variability is related to the thickness of the climatological ocean mixed layer and the large SST
variability is observed in the summer hemisphere. Li
et al. (2008) also suggested that a large SST variability is
involved in the phase relationship between shortwave
heat flux and wind-driven latent heat flux depending on
seasonal background winds. As such, surface heat flux
anomalies associated with ISOs are important for explaining the ISO seasonality when superposed on theseasonally varying background wind. Meanwhile, theroles of seasonally altering background wind in the SST
variability associated with ISO have not been extensively studied yet.
Figure 1 shows outgoing longwave radiation (OLR)
and SST anomalies associated with ISO composited foreach ISO phase by Wheeler and Hendon (2004). Details
in the methodology of the composite analysis are described in section 2. A negative OLR anomaly (solid
contours) as an index of the enhanced convective activity travels eastward over the Indian Ocean in theinitial phases and subsequently shifts to southward(northward) in boreal winter (summer). Moreover,positive SST anomalies (color shaded) are found toprecede the enhanced ISO convection in both boreal
winter and summer. This confirms the existing knowledge that a warm SST anomaly emerges ahead of the
ISO convection regardless of season, although the ISO
propagation direction is largely different between theseasons. Inness and Slingo (2003) and Inness et al. (2003)
pointed out that the improvement of basic surface
westerly wind simulated by the air–sea coupling model
results in a better representation of the ISO. A working
hypothesis to examine in this study is that a seasonal
contrast in the background surface wind may be a key
factor that explains the SST variability associated with
the ISO.
A global-scale observational dataset of a variety ofparameters including surface heat fluxes, SST, andrainfall is of key importance for further understanding of
air–sea interactions involved in the ISO (Hendon 2012).
Reanalysis products provide an extremely useful datasetwith continuous coverage in space and time at the expense of their dependence on model parameterizations.
In situ observations offer accurate and frequent measurements while restricted to a limited spatial and temporal coverage. Satellites uniformly observe the entire
globe, although the quality and variety of satellite retrievals may be limited by sensor capability. Recent
advances in satellite remote sensing technology, however,

VOLUME 27

have significantly improved the potential utility of satellite observations by means of an integrated use of satellite
data. In this study, satellite observations are extensively
exploited to understand the SST variability related to the
ISO over the tropical Indian and Pacific Oceans. A seasonally independent ISO index proposed by Wheeler andHendon (2004) is utilized to identify different ISO phases
in boreal winter and summer.
The current study aims to explore the potential rolesof the seasonally altering background surface wind in
the seasonality of the ISOs with focus on the SST variability based on an ocean mixed layer heat budget
analysis conducted with satellite data. The analysis
method is introduced in section 2. The seasonality andregionalism of SST and heat flux changes are shown insection 3. A summary and discussion are presented in
section 4.

2 Data and method
a. Ocean heat budget estimation
This study uses a variety of datasets based on satellite
observations to analyze the ocean mixed layer heatbudget associated with the ISO. The methodology isadopted from Masunaga and L’Ecuyer (2010, 2011) withsome updates in the current study. The tendency equation of ocean mixed layer temperature (MLT) may be
written as
›Ts
Qnet
5
1 ,
›t
rw Cp,w Hml(1)

where Ts, Qnet, rw, Cp,w, Hml, and  are SST, the net heat
flux into the ocean mixed layer, the ocean water density(1030 kg m22), the specific heat of ocean water (4.0 3
103 J kg21 K21), the ocean mixed layer depth, and a residual term that contains currently ignored factors (e.g.,
entrainment cooling and vertical diffusion at the bottomof the ocean mixed layer) and retrieval error, respectively. The net heat flux Qnet is divided into
Qnet 5 QSW 1 QLW 1 QLH 1 QSH
1 Qupw 1 Qadv 1 Qdif ,(2)

where QSW is the shortwave flux absorbed into the ocean
mixed layer, QLW is the net longwave flux at the surface,
QLH is the latent heat flux, QSH is the sensible heat flux,
Qupw is the heat flux by upwelling through the ocean
mixed layer bottom, Qadv is the heat flux transported byhorizontal advection within the ocean mixed layer, andQdif is the horizontal diffusional heat flux. The input
data sources are summarized in Fig. 2.

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FIG. 1. OLR (contours) and SST (shaded) anomalies for each MJO phase by Wheeler and Hendon (2004) in(a) boreal winter and (b) boreal summer. Solid contours indicate 230 and 215 W m22 and dashed contours indicate
15 and 30 W m22. The number of days averaged within each phase is shown on the right of each panel. Maps aresmoothed by a 4.58 3 4.58 moving average.

The downwelling surface shortwave radiation (SSR)
Y
and surface longwave radiation (SLR) flux
flux FSWY
FLW are provided by the Hydrologic Cycle and Earth
Radiation Budget (HERB) algorithm, where radiative fluxes are estimated from cloud and precipitation
properties observed from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)
and TRMM Visible and Infrared Scanner (VIRS)(L’Ecuyer and Stephens 2003; L’Ecuyer and McGarragh2010). The net shortwave heat flux absorbed into the ocean
mixed layer isY,
QSW 5 (1 2 a)PFSW(3)

where a and P are the ocean albedo of 0.07 (Morel andAntoine 1994) and the fraction of incoming shortwave

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FIG. 2. Data sources for each heat flux estimate. Gray shaded boxes indicate climatological data
instead of time series. Details are given in the text.

radiation absorbed by the ocean mixed layer, respectively. In this study, P is estimated as





Hml
Hml
1 V2 exp 2, (4)P 5 1 2 0:57 V1 exp 2
Z1
Z2
where V1, V2, Z1, and Z2 are determined for a given
chlorophyll-a concentration (Table 2 of Morel and Antoine1994). The chlorophyll-a concentration is obtained fromthe level 3 standard mapped image product of the Seaviewing Wide Field-of-view Sensor (SeaWiFS) on board
the OrbView-2 satellite (O’Reilly et al. 2000) and provided by the National Aeronautics and Space Administration (NASA) ocean color website. This study uses
Hml given as the density-based mixed layer depth takenfrom mixed layer depth climatology (MLDC; de Boyer
Montégut et al. 2004).
The net longwave heat flux at the surface is written as
Y
2 sTs4 ,QLW 5 FLW

(5)where s is the Stephan–Boltzmann constant (5.67 31028 W m22 K24) and Ts is SST obtained from the National Oceanic and Atmospheric Administration (NOAA)
daily optimum interpolation SST (OISST) version 2(Reynolds et al. 2007). The OISST dataset is derived fromin situ data and Advanced Very High Resolution Radiometer (AVHRR) and Advanced Microwave ScanningRadiometer for Earth Observing System (AMSR-E)
measurements.
The sensible heat and latent heat fluxes from theocean surface are calculated from the bulk method,
QSH 5 2Cp ra Cu ju10 j(Ts 2 Ta )

and

(6)

QLH 5 2Ly ra CE ju10 j[qy*(Ts ) 2 qy,s ],(7)where Cp, ra, u10, Ta, Ly, qy* (Ts ), and qy,s are the specificheat of dry air at constant pressure (1004 J kg21 K21),

the density of dry air (1.2 kg m23), the wind vector at
10 m above ocean surface, the surface air temperature,
the latent heat of vaporization (2.5 3 106 J kg21 K21),
the saturation water vapor mixing ratio at Ts, and thesurface water vapor mixing ratio, respectively. The bulk
transfer coefficients Cu and CE parameterized asa function of ju10j are adopted from Eqs. (A1a)–(A1c) of
Large et al. (1994). In this study, Ta and qy,s are estimated from a logarithmic vertical profile within thesimple surface layer (SL) model by Kanemaru and
Masunaga (2013),
us 5 uML 1ln(z/zSL )[u( ps , Ts ) 2 uML ]ln(zu /zSL )qy,s 5 qy,ML 1ln(z/zSL )[q*ln(zq /zSL ) y (Ts ) 2 qy,ML ] ,

and

(8)

(9)

where uML, qy,ML, u(ps, Ts), and z are the atmospheric
mixed layer (ML) potential temperature, the ML water
vapor mixing ratio, the potential temperature for Ts andsurface pressure ps, and the observation height, respectively. The Aqua level 2 Advanced Microwave Sounder
Unit (AMSU) and Atmospheric Infrared Sounder (AIRS)
combined retrieval (AIRX2RET; hereafter simply AIRX)
version 6 product (Susskind et al. 2003, 2011) contains ps
and air temperature T and water vapor mixing ratio qy at
the 1000-hPa level. In this study, uML and qy,ML are defined at 1000 hPa. Surface potential temperature us isconverted to
Ta 5 us p Rd /Cp
s

1000

,

(10)

where Rd is the gas constant for dry air (287 J K21 kg21).
The roughness lengths for temperature zu and moisturezq are given by the empirical coefficients of Large et al.
(1994),

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zu 5

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8


103
>
>
>
10
exp
2
k
,
>
<
32:7


>
>
103
>
>
: 10 exp 2
k ,
18:0

if

TABLE 1. Data product resolutions.

Ts $ Ta
and

if

(11)

Ts , Ta



103
k ,
zq 5 10 exp 2
34:6

(12)

where k is the von Kármán constant (0.40). It is assumed
in this study that zSL 5 50 m and z 5 3 m. The AIRX
product also contains Ta and qy,s, which are compared toin situ observations and shown in the appendix. The
Quick Scatterometer (QuikSCAT) SeaWinds version 4
product (Ricciardulli and Wentz 2011) provided by
Remote Sensing Systems contains u10 over the ocean
surface.
The upwelling heat flux is evaluated as
Qupw 5 2rw Cp,w wE H(wE )DTE ,(13)

where H and DTE are the Heaviside function and thetemperature difference between ocean mixed layerwater and upwelling water, respectively. The upwelling
velocity wE is estimated aswE 5

1$  ME .
rw(14)

Product

Spatial resolution

Temporal resolution

SeaWiFSOISST
QuikSCAT
HERB
OSCAR
WOA09
MLDC
AIRX

1/ 128 3 1/ 128

Climatology
1 day
1 day
1 day
5 day
Climatology
Climatology
1 day

0.258 3 0.258
0.258 3 0.258
0.258 3 0.258
18 3 18
18 3 18
28 3 2850-km footprint

The horizontal advection heat flux is evaluated as
Qadv 5 2Cp,w rw Hml u  $Ts ,(17)

where u is the current velocity within the ocean mixed
layer. The Ocean Surface Current Analysis–Real Time
(OSCAR) product (Bonjean and Lagerloef 2002) contains the near-surface current over the 0–30-m layer
mean. This study adopts it as is for the ocean mixed layer
current.
The horizontal diffusion heat flux is written as
Qdif 5 Cp,w rw Hml kh =2 Ts ,(18)

where the turbulent diffusion coefficient kh is 3 3 103 m s21(Bauer et al. 1998).

b. Data preprocessing
The Ekman mass transport ME is evaluated fromrs ME,x 2 fME,y 5 t x 5 ra CD ju10 ju10
rs ME,y 1 fME,x 5 t y 5 ra CD ju10 jy 10 ,

and

(15)
(16)

where rs is the drag coefficient, f is the Coriolis parameter, ME,x and ME,y are the zonal and meridional component of the Ekman mass transport, t x and t y are thezonal and meridional wind stresses, CD is the bulk
transfer coefficient obtained by Eq. (A1a) in Large et al.
(1994), and u10 and y 10 are the zonal and meridional
winds at 10 m above ocean surface. In the current study,
rs is 6.62 3 1026 s21 (Lagerloef et al. 1999). From previous studies, DTE is estimated as the difference between ocean temperature at the ocean mixed layer andeither 10 m (Foltz et al. 2010; Drushka et al. 2012) or
20 m (Chang 1993; Wang and McPhaden 1999) below
the ocean mixed layer over the equatorial Indian andPacific Oceans. In the current study, the estimate of DTE
is adopted from Masunaga and L’Ecuyer (2010, 2011) as
the difference between the depth of 0.5 Hml and Hml 1
15 m using the World Ocean Atlas 2009 (WOA09) temperature profile (Locarnini et al. 2010).

The satellite products have various spatial resolutions
as shown in Table 1. The AIRX product contains swath
data of about 50-km spatial resolution at nadir corresponding to an AMSU-A footprint with an AIRS 3 3 3array with cloud contamination algorithmically cleared.
Other products are provided as gridded data. All thedatasets are projected onto a common 1.58 3 1.58 gridded data. In this study, analyzed data have daily
temporal resolution except for the climatologicalparameters provided by WOA09, SeaWiFS, and MLDC
products. The OSCAR product with a 5-day resolution
is temporally interpolated to daily data. Ascending anddescending orbits in AIRX and QuikSCAT products areaveraged together into daily data. The SSR flux of the
HERB algorithm is diurnally averaged (L’Ecuyer and
McGarragh 2010). The SST tendency of (1) is calculated
by a central difference of daily OISST data.
Data are sampled only where TRMM HERB, AIRX,
and QuikSCAT measurements are available. AIRX and
QuikSCAT estimates are affected by rain and clouds.
The AIRX data are interpolated from the neighboringeight grid pixels averaged together to fill data gaps due
to clouds and rain. This interpolation decreases sampling bias, although the accuracy of satellite estimates is7058

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degraded (Kanemaru and Masunaga 2013). We also use
OLR derived by the AIRX product as a proxy of deep
convection and QuikSCAT surface wind vector data. In
the current study, the 81-day running mean is subtracted
from all the data. If the 81-day running mean is subtracted, both the seasonal cycle and longer-time scalevariability are removed from the data.

c. ISO composite
A composite analysis is performed to reveal the propagation characteristics of the ISO. In the current study,
the real-time multivariate MJO (RMM) index proposedby Wheeler and Hendon (2004) is used. This index isindependent of season and defined from the first two
empirical orthogonal functions of the combined fields of
OLR and zonal winds at 850 and 200 hPa meridionallyaveraged between 158S and 158N. Eight MJO phases
are defined based on a pair of the normalized principal
components (RMM1 and RMM2) and the MJO intensity is given by their amplitude of (RMM12 1 RMM22)1/2.
The current study analyzes only days when the amplitude
normalized by their climatological standard deviation isgreater than 1. Composites in boreal winter (November–March) and boreal summer (May–September) are individually analyzed to study the seasonality of SST changes
associated with the ISO.

3. Resultsa. OLR and SST
Composite analysis results in boreal winter and summer are presented in this section to reveal the seasonality and regionalism of convection and SST changes
associated with ISOs. The composite result shown in
Fig. 1 has been already presented in section 1, but theresult is further examined. In boreal winter (Fig. 1a),a positive OLR anomaly (dashed contours) is locatedover the Maritime Continent and the western Pacific inphases 8 and 1; that is, these phases correspond to the
suppressed convection stage of the MJO. A negative
OLR anomaly or enhanced convection (solid contours)
is initiated over the equatorial Indian Ocean in phase 2
and moves eastward in the subsequent phases. In phases
4 and 5, the negative OLR anomaly continues to propagate eastward but begins shifting slightly southwardfrom the equator to northern Australia. In phases 6 and7, the convection decays as it reaches the western Pacific,
while a suppressed convection begins to develop overthe Indian Ocean. In boreal summer (Fig. 1b), an active
convection anomaly in the beginning of the BSISO is
located over the equatorial Indian Ocean in phase 2 andextended to the northeast side in phase 3. In phases 4

VOLUME 27

and 5, the negative OLR anomaly propagates northeastward extensively from the Bay of Bengal to thewestern Pacific. A suppressed convection over the Indian Ocean appears the southwest of the negative OLR
anomaly and propagates northeastward in phases 6–8.
Over the western Pacific, the negative OLR anomaly
remains stationary or propagates northward rather thaneastward. The propagation characteristics of ISOs shownin Fig. 1 agree with known ISO modes such as the eastward propagating mode in boreal winter, the northeastward propagating mode over the Indian Ocean, and thenorthward propagating mode over the western Pacific inboreal summer (Wang and Rui 1990).
An SST anomaly (color shaded in Fig. 1) is also foundto overall propagate eastward but precede the ISO
convection described above. In boreal winter, a warm
SST anomaly is found to emerge before the MJO convection develops over the Indian Ocean (phases 7, 8, and
1) and the channel north of Australia (phases 2 and 3). In
boreal summer, a positive SST anomaly preceding theenhanced convection associated with the BSISO is located over the equatorial Indian Ocean and the ArabianSea (phases 8 and 1) and the Bay of Bengal (phases 3 and
4). The first four phases show clear evidence that SST
leads convection as the ISO proceeds, while the relationship between OLR and SST is unclear in the next
four phases.
Latitudes with prominent SST variability are seasonally different. In phase 1, for example, positive SST
anomalies are located over the Southern (Northern)
Hemisphere in boreal winter (summer), so that a warm
SST anomaly is shifted to the summer hemisphere. The
SST variability over the summer hemisphere is larger
than the winter hemisphere (Duvel and Vialard 2007; Liet al. 2008). Given that the seasonal cycle has been removed by subtracting the 81-day running mean in thecurrent study, the meridional migration of SST is not
mere a seasonal march but should be a property intrinsic
to ISO propagation.

b. Heat fluxes
The origins of the SST variability are investigated by
the ocean mixed layer heat budget introduced in section
2 and compared with heat fluxes divided into the sevencomponents in (2). Figure 3 shows each heat flux
anomaly in phase 2 of boreal winter. The variations of
QSW and QLH have a dominant contribution over otherfluxes to the heat budget. The spatial distribution of QSWanomaly closely resembles the OLR anomaly pattern,which suggests that QSW is modulated mostly by thesurface insolation strongly reflecting the variability ofhigh cloud amount. The other heat fluxes except for
QSW and QLH have no appreciable contribution. These

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FIG. 3. As in Fig. 1a, but for heat flux breakdown of phase 2.

results are also shown in other ISO phases and theother season (not shown). The ocean mixed layer heatbudget is mainly controlled by the variations of QSWand QLH, which is consistent with the known observational characteristics in boreal winter (e.g., Lau and Sui
1997; Hendon and Glick 1997; Shinoda et al. 1998;
Vialard et al. 2008) and summer (e.g., Kemball-Cookand Wang 2001; Sengupta et al. 2001; Klingaman et al.
2008).
The variation of QSW is mostly explained by the
propagation of cloud systems associated with the ISO.
The variation of QLH is not as easy to interpret because
QLH is a function of scalar wind and moisture deficit
shown in (7). The total derivative of logarithmic transformation of (7) isdQLH dU dCE dDqy
5
1
,
1
U
QLH
CE
Dqy

(19)

where U is ju10j and Dqy is the moisture deficit orqy* (Ts ) 2 qy,s . In the current study, CE is adopted from
Large et al. (1994) as a function of U so the CE term is
integrated into the U term:

dQLH dU dU dCE dDqy
5
1
1
U
CE dU
QLH
Dqy


U dCE dU dDqy
1
5 11.
CE dU U
Dqy

(20)

Equation (20) is applied to the estimation of the anomaly(prime) from the temporal average (overbar) as
2

3


dC
Q
U
E 5 QLH 0
U 1 LH Dq0y .
Q0LH 5 41 1
U
CE dU
Dqy

(21)

The first and second terms on the right-hand side are thecontribution of U and Dqy, respectively. Figure 4 shows
the decomposition of the QLH variability shown in Fig. 3.
The variation of QLH primarily reflects the scalar windvariability, while the contribution of the moisture deficit
and the residual term is relatively small.

c. Latitudinal difference in wind speed variability
Scalar wind is explained by the square root of thequadratic sum of zonal and meridional winds. The same
zonal wind anomaly could enhance or diminish scalar

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FIG. 4. As in Fig. 3, but for each term of QLH shown in (21): (a) the first term for U0 , (b) the second term for Dq0y , and(c) the residual term.

wind when added onto the background wind, depending
whether the anomalous wind direction is the same as or
opposite to the background wind direction. Figure 5
shows the seasonal climatology of zonal wind in boreal
winter and summer. In boreal winter (Fig. 5a), theeasterly trade wind is dominant away from the equator,
while the westerly wind dominates a southern equatorial
band (108S–08) extending from the Indian Ocean to thewestern Pacific and northwest Australia. In boreal
summer (Fig. 5b), the westerly wind spreads across thenorthern Indian Ocean to the western Pacific, and theeasterly wind dominates the rest of the tropical ocean
including the whole Southern Hemisphere and thecentral Pacific.
The influence of background wind direction on zonal
and scalar wind anomalies is illustrated by contrasting
two latitudinal bands of a northern band (NB; 38–98N)
and a southern band (SB; 98–38S). The background winddirection is opposite between NB and SB for each season. Figure 6 shows the ISO phase versus longitude cross

FIG. 5. Climatology of surface zonal wind in (a) boreal winter and (b) boreal summer.

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7061

FIG. 6. ISO phase vs longitude section for the latitudinal bands SB (38–98S) and NB (38–98N) in boreal winter for(a),(c) zonal and (b),(d) scalar wind. The ISO phase is temporally smoothed by 1058 (158 3 7) angle running mean.
Solid contours indicate OLR of 220 and 210 W m22 and dashed contours indicate OLR of 10 and 20 W m22. The
landmass is indicated by gray shading. Crosses indicate the OLR minimum for each longitude.

section of zonal and scalar wind anomalies in boreal
winter for each NB and SB. Zonal wind anomalies havean easterly (westerly) anomaly to the east (west) of the
MJO convection, while scalar wind anomalies exhibit
different behaviors between the NB and SB. In the SB
where the basic wind is westerly in this season, scalarwind anomalies propagate eastward nearly in phase withzonal wind anomalies, and easterly (westerly) anomalies
decrease (increase) scalar wind on the front (back) sideof the propagating convection. In the NB where thebasic wind is easterly, on the contrary, easterly anomalies slightly increase scalar wind ahead of the enhanced
convection although the signal is weaker than in the SB.

The scalar wind patterns are hence reversed between the
NB and SB because of the difference in basic zonal wind
(Fig. 5a).
To more clearly illustrate the regional difference, thecomposite plot is next sorted by the ISO phase angle
relative to the convection peak (Fig. 7). Parameters
shown in Fig. 6 are composited with respect to the OLR
minimum at each longitude and then averaged together
between 758 and 1208E. Easterly (westerly) anomalies inzonal wind are well captured before (after) the convection peak regardless of the areas studied (Fig. 7a). It
is also evident, on the other hand, that scalar wind
anomalies are largely different in phase between the NB

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FIG. 7. Time series in terms of ISO phase angle relative to the
OLR minimum for longitudinal average between 758 and 1208E inboreal winter for (a) zonal wind (solid) and scalar wind (dashed),(b) QSW (solid) and QLH (dashed), and (c) tendency of the estimated ocean MLT (solid) and observed SST (dashed). Black (gray)lines indicate the average in SB (NB).

and SB. The scalar wind anomaly is a key factor that
accounts for the QLH variability shown in Fig. 4. Figure 7bshows the composite time series of QSW and QLH.
Anomalies of QSW in both the NB and SB have negative
anomalies in the enhanced convective stage (08 phaseangle difference) and positive anomalies in the suppressed convection stage (61808). In contrast, QLH
changes its behavior between the areas. In the NB, theQLH variability is nearly 1808 out of phase with QSW andtherefore QSW and QLH cancel out each other, making
the SST tendency extremely small (Fig. 7c). In the SB,on the other hand, the QLH anomaly changes in phasewith the QSW anomaly, so that the QLH and QSWVOLUME 27

FIG. 8. As in Fig. 7, but for boreal summer.

anomalies in tandem lead to a significant variation in themixed layer heat budget that gives rise to an SST warming
prior to the MJO convection. It is suggested that thebackground westerly wind is an important factor thatdrives the SST warming before the active MJO convection.
The background wind direction is an important factor
also in boreal summer, while the SST variability characteristics are quite different from boreal winter. Figure 8
is the same as Fig. 7, but for boreal summer. Zonal wind
anomalies are, similarly to the boreal winter case, in
general easterly (westerly) preceding (following) negative OLR anomalies in both the NB and SB (Fig. 8a).
Scalar wind anomalies have a negative peak in the NB
and a subtle positive peak in the SB before the OLR
minimum occurs, opposite to the boreal winter case. The
contrasting behaviors between the different seasons are
explained by the fact that basic zonal wind between the

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7063

FIG. 9. As in Fig. 5, but for meridional wind.

NB and SB are seasonally reversed as seen in Fig. 5b. In
the NB, although the QLH anomaly lags the QSW anomaly
by about 1/ 4 cycle (Fig. 8b), QSW and QLH work together
rather than against each other to give rise to a distinct SST
variation (Fig. 8c). In the SB, on the other hand, QSW andQLH are out of phase and cancel each other out to someextent, resulting in a relatively weak SST variability (Fig.
8c). The latitudinal difference of the phase relationshipbetween QSW and QLH is consistent with Li et al. (2008)
using reanalysis data. The variability of the estimatedocean MLT tendency agrees with the observed SST
tendency in the NB (Fig. 8c), whereas it fails to agree in
the SB. The disagreement may have arisen from the ignored heat fluxes and/or uncertainty in the heat budget
analysis.

d. Meridional propagation in boreal summer
The BSISO and the SST variability are known to havea northward component of propagation over the Indian
Ocean (Kemball-Cook and Wang 2001; Sengupta et al.
2001; Klingaman et al. 2008). The possibility is explorednext that meridional wind may have a similar mechanism as found for zonal wind that facilitates the northward propagation of SST as well as the eastward
migration. Figure 9 shows the seasonal climatology of
meridional wind in boreal winter and summer. In boreal
winter (Fig. 9a), the basic meridional wind is northerly

north of 108S and southerly south of 108S from the Indian Ocean to the western Pacific. In boreal summer(Fig. 9b), on the other hand, the Eastern Hemisphere isdominated by southerlies except in the southwest of the
Indian subcontinent and in the northern central Pacific.
The ISO phase versus latitude diagrams of zonal, meridional, and scalar wind anomalies are shown in Fig. 10
and present the northward propagating BSISO over the
Bay of Bengal with longitudes averaged between 848 and968E. Wind anomalies and OLR minima move northward from the equator and to the north as the BSISO
proceeds. An easterly (westerly) anomaly precedes(lags) the OLR minimum (Fig. 10a). Moreover, meridional wind anomalies in the Northern Hemisphere arenortherly (southerly), slightly leading (lagging) the
OLR minimum (Fig. 10b). The basic meridional wind inboreal summer is entirely southerly over the Indian
Ocean, so that the northerly (southerly) anomaly decreases (increases) scalar wind (Fig. 10c) in collaboration with anomalous zonal wind being enhanced ordiminished when projected on to the background field.
Figure 11, similar to Figs. 7 and 8, shows the composite
time series in terms of the ISO phase angle relative to
the OLR minimum latitudinally averaged between 38
and 188N. Northerly and easterly anomalies precede theconvective peak in phase with scalar wind. As a result,although the QSW anomaly is about 608 ahead of the7064

JOURNAL OF CLIMATEVOLUME 27

FIG. 10. As in Fig. 6, except for ISO phase vs latitude section for the longitudinal band between 848 and 968E in boreal summer for (a) zonal
wind, (b) meridional wind, and (c) scalar wind.

QLH anomaly, both anomalies enhance together during
the convectively enhanced (suppressed) stage withnegative (positive) anomalies (Fig. 11b). The estimatedtendency of the ocean MLT agrees with the observed
SST tendency (Fig. 11c). From the findings above, it is
not only zonal wind but also meridional wind that has animportant role in the preceding SST variability associated with the BSISO.

4. Summary and discussion
The current study aims to explore the potential rolesof the seasonally altering background surface wind in
the seasonality of the ISOs with a focus on the SST
variability based on an observational data analysis. The
analysis is performed with a variety of satellite data forthe 7 years from November 2002 to September 2009. To
examine the seasonal variation of ISO propagation,
OLR, SST, surface heat fluxes, and ocean surface winds
are composited in terms of ISO phases for each season of
boreal winter (November–March) and summer (May–
September).
Composite analysis results are found to reproduce theeastward propagation of the MJO shifting slightly to thesouth as it proceeds and the northeastward movement of
the BSISO. The seasonal difference in ISO propagationagrees with the propagation characteristics of ISOs as
reported in the literature (Wang and Rui 1990). The
propagation of a warm SST anomaly preceding the enhanced ISO convection from the Indian Ocean to the
Maritime Continent mainly arises from the variability ofshortwave and latent heat fluxes. The shortwave flux
variability is primarily explained by the movement ofcloud systems blocking the incoming solar radiation,
while the scalar wind variation is largely responsible forthe modulation of the latent heat flux. The contributionof moisture deficit for the latent heat flux variability issmall. Zonal wind anomalies are enhanced or diminishedwhen superimposed onto the background field and this has
a notable consequence on scalar wind anomaly. As a result, the geographical distribution of basic wind is important for latent heat anomalies and thus preceding SST
variability involved in the ISO. In boreal winter, a basic
westerly wind dominates a latitudinal band between 108S
and the equator, whereas an easterly anomaly to the eastof the MJO decreases scalar wind and latent heat flux. The
reduced latent heat flux precedes the enhanced convection
stage and tends to warm SST there nearly in phase with theshortwave flux variation. Outside this narrow band of
background westerlies, on the other hand, an easterly
anomaly in combination with a basic easterly wind magnifies scalar wind and the latent heat flux, counteracting
a concurrent positive shortwave flux anomaly.
In boreal summer, a basic westerly wind is locatedover the Northern Hemisphere, and the eastward propagation of the BSISO is found in the Northern Hemisphere in contrast with the boreal winter MJO. The
relationship between northward propagation of the
BSISO and the SST variability is related to not only
the zonal wind but also the meridional wind. A northerly
wind anomaly in the north of the active BSISO convection occurs in the region with background southerly
and thus promotes SST warming, working together withthe effect by anomalous zonal wind as described above.
Some previous studies have addressed the roles of thewarm SST anomaly in the possible mechanisms of ISO
propagation. Flatau et al. (1997) showed that a simulation forced with an empirical relationship between SST
tendency and zonal wind on an aquaplanet produces
more organized and slower propagation. They proposed

15 SEPTEMBER 2014

KANEMARU AND MASUNAGA

FIG. 11. Time series in terms of ISO phase angle relative to the
OLR minimum for latitudinal average between 38 and 188N inboreal summer for (a) zonal (solid black), meridional (dashed), andscalar (solid gray) winds, (b) QSW (solid) and QLH (dashed), and(c) tendency of the estimated ocean MLT (solid) and observed SST(dashed).

that a zonal gradient of SST anomaly creates a corresponding gradient of surface moist static energy andproduces the convection development on the east of theconvective center. Waliser et al. (1999) pointed out thatthe positive SST anomaly tends to reinforce the meridional convergence by comparing the difference in air–sea coupling on the simulation. They pointed out thatthe enhanced meridional convergence is associated withthe frictional wave–convective instability of the second
kind (CISK) mechanism.
An interesting implication in this study is the possibility that ISO propagation may tend to be guided intoa channel of background westerlies. A basic westerly

7065

wind is located from the Indian Ocean to the Maritime
Continent (Lettau 1974; Wang 1988) and shifted to thesummer hemisphere. Lettau (1974) showed that overall
the seasonal distribution of the basic surface westerly
wind is explained by the balance between the meridional
gradient of surface pressure and the Coriolis force.
Lindzen and Hou (1988) pointed out that the surface
westerly wind occurs over the latitudinal band between
the equator and the heating center near the equator
resulting from their model of the Hadley circulation. In
boreal winter, meridional wind from higher latitudes
converges south of the equator over the Indian Ocean.
The northerly cross-equatorial flow is veered to a westerly wind by the Coriolis force in the southern narrow
latitude band. The basic westerly wind extends widely to
the Indian Ocean in boreal summer and northwestern
Australia coast in boreal winter and is sustained bymonsoon troughs over the Indian subcontinent and theAustralian continent (e.g., Webster et al. 1998). The
spatial distribution of the basic westerly wind is closely
related with the seasonal cycle of the Hadley circulationand the monsoon circulation, which suggests that the
ISO propagation paths are affected by a modulation due
to the distribution of the background tropical circulations. Inness and Slingo (2003) and Inness et al. (2003)
pointed out that improvement of the basic surface
westerly wind simulated by the air–sea coupling model
results in a better representation of the ISO.
The temporal variability of the ocean mixed layer
thickness is assumed to be constant in the heat budget
analysis, so that the current study does not take into
account the entrainment cooling. The importance of theentrainment effect on the SST variability associated with
ISOs is suggested by model studies (e.g., Li et al. 2008)
and in situ observations (Drushka et al. 2012). Li et al.
(2008) pointed out that the entrainment effect works inphase with shortwave and latent heat fluxes. The entrainment effect tends to be in phase with surface heat
flux forcing rather than out of phase. The ocean MLT
tendency estimated by the current analysis somewhat
lags behind the observed SST tendency (Figs. 7c, 8c, and11c). These lags may arise from the entrainment effectand other factors. Nevertheless, the SST tendency isoverall captured by the estimated from the MLT budget
analysis, which suggests that the present assumptions
including the constant mixed layer depth (MLD) areverifiable to that extent.
Understanding the geographical distribution of surface winds is important for seasonality and regionalismof the SST variability associated with the ISO. Whether
the SST variability is essentially important for the
propagation mechanism of the ISO is beyond the current study and left for future investigations.

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Acknowledgments. The authors thank the anonymous reviewers for their valuable comments and Tristan
L’Ecuyer for providing the dataset from the HERB
algorithm, which was generated under the supportof National Aeronautics and Space Administration(NASA) Energy and Water Cycle Study (NEWS) Grant
NAG06GC46G. The QSCAT SeaWinds dataset is produced by Remote Sensing Systems (http://www.remss.
com). The AIRS/AMSU level 2 product was acquired
as part of the activities of NASA’s Science Mission
Directorate and is archived and distributed by the
Goddard Earth Sciences (GES) Data and Information
Services Center (DISC). The OSCAR surface current
product was obtained from the Physical Oceanography
Distributed Active Archive Center (PODAAC) at theNASA Jet Propulsion Laboratory, Pasadena, California. SeaWiFS data were acquired from NASA’s
ocean color website (http://oceancolor.gsfc.nasa.gov).
The OISST Climate Data Record (CDR) used in thisstudy was acquired from NOAA’s National Climatic
Data Center (http://www.ncdc.noaa.gov). This CDR
was originally developed by Richard Reynolds andcolleagues for the NOAA’s CDR program. The WOA09
temperature data were obtained from the NOAA/
National Oceanographic Data Center. The mixed layer
depth dataset was downloaded online (http://www.
ifremer.fr/cerweb/deboyer/mld/home.php). The Tropical Atmosphere Ocean (TAO) project office of the
NOAA/ Pacific Marine Environmental Laboratory(PMEL) provided ocean buoy data. The authors thankHatsuki Fujinami for invaluable comments and suggestions and Fumie Akimoto Furuzawa for satellite datacollection and management. This work was partly supported by Japan Society for the Promotion of Science(JSPS) Grant-in-Aid for Scientific Research 26287113.

APPENDIX
Error Analysis of Satellite Data
The heat budget in the current study is estimated fromvarious satellite data. Some key parameters of satellite
estimates are compared with in situ observations. In situ
data are obtained by Tropical Atmosphere Ocean(TAO) arrayed buoys, and turbulent fluxes are computed from the Coupled Ocean–Atmosphere Response
Experiment (COARE) version 3.0b algorithm (Fairall
et al. 2003). These data are provided by the TAO project
office of the NOAA/Pacific Marine Environmental
Laboratory. Parameters are surface air temperature Ta,water vapor mixing ratio qy,s, the downwelling surface
shortwave radiation (SSR) and surface longwave radiation (SLR), the latent heat flux QLH, and the sensible

VOLUME 27

TABLE A1. Errors for each observed parameter. Number of data
used, bias, RMSE, and correlation coefficient r are calculated
against in situ data; ‘‘SL’’ indicates the estimation by the SL modeland ‘‘AIRX’’ indicates the AIRX2RET product.

SL Ta (8C)AIRX Ta (8C)
SL qy,s (g kg21)
AIRX qy,s (g kg21)
SSR (W m22)
SLR (W m22)
QLH (W m22)
QSH (W m22)

No.

Bias

RMSE

r

52422
52422
52422
52422
31209
1571
37137
37137

20.70
21.13
20.08
21.78
36.2
5.68
27.82
22.68

0.93
1.34
0.92
1.86
68.2
14.1
36.7
6.77

0.633
0.534
0.559
0.532
0.555
0.793
0.725
0.511

heat flux QSH. Satellite overpasses and arrayed buoys
located between 308E and 1808 are matched up within
1.58 resolution. The sampling number depends on theexisting observation for each parameter. Table A1
shows sample number, bias, root-mean-square error(RMSE), and correlation coefficient r. In situ Ta and qy,sare compared together with satellite estimates of the
AIRX2RET product and the SL model method described in section 2. The estimations of Ta (qy,s) used by
the SL model have RMSE of 0.98C (0.9 g kg21) and biasof 20.78C (20.1 g kg21). On the other hand, the accuracy of Ta (qy,s) derived from the AIRX2RET producthas RMSE of 1.38C (1.9 g kg21) and bias of 21.18C(21.8 g kg21). The estimation of the SL model is higher
in accuracy and smaller in bias than the AIRX2RET
product. Jackson and Wick (2010) showed that the satellite retrieval RMSE of Ta is 1.68C. Iwasaki and Kubota(2012) showed the typical accuracy of satellite derived
qy,s in the range from 1.2 to 2.0 g kg21. The RMSEs(biases) of heat fluxes are 68.2 (36.2), 14.1 (5.7), 36.7
(27.8), and 6.8 (22.7) W m22 for SSR, SLR, QLH, and
QSH, respectively. L’Ecuyer and McGarragh (2010)
showed that RMSEs of satellite instantaneous retrievalsof SSR and SLR against two ocean buoys are from 87.2
to 106.1 W m22 and from 28.2 to 31.9 W m22. Overestimation of satellite SSR for 36.2 W m22 is also shown
in L’Ecuyer and McGarragh (2010). The large bias ofSSR flux may result in the climatological imbalance ofheat budget, while the bias is removed by the analysis inanomaly fields. Bourras (2006) showed that the accuracyof some satellite-derived QLH is a range from 24.0 to
41.2 W m22. Compared to radiative flux and turbulent
flux, the accuracy of heat fluxes associated with theoceanic internal dynamics is difficult to estimate fromthe in situ data and may result in additional potential
uncertainty of the current analysis.
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