PUBLICATIONSJournal of Geophysical Research: Atmospheres
RESEARCH ARTICLE
10.1002/2016JD026071
Key Points:• SST anomalies associated with front
regulate the vertical structure of MABL
• SST front weakens the accelerationof wind blowing at the opposite
direction of SST gradient• Pressure gradient is the primaryfactor to modify the cross front
wind in MABL

Correspondence to:D. Wang,
dxwang@scsio.ac.cn

Citation:
Shi, R., J. Chen, X. Guo, L. Zeng, J. Li,
Q. Xie, X. Wang, and D. Wang (2017),
Ship observations and numericalsimulation of the marine atmosphericboundary layer over the spring oceanic
front in the northwestern South China
Sea, J. Geophys. Res. Atmos., 122,
doi:10.1002/2016JD026071.
Received 9 OCT 2016
Accepted 16 MAR 2017
Accepted article online 20 MAR 2017

Ship observations and numerical simulation of the marine
atmospheric boundary layer over the spring oceanic
front in the northwestern South China Sea
Rui Shi1 , Ju Chen1, Xinyu Guo2, Lili Zeng1
and Dongxiao Wang1, Jian Li1, Qiang Xie1,3, Xin Wang1,

1
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences,
Guangzhou, China, 2Center for Marine Environmental Studies, Ehime University, Matsuyama, Japan, 3Institute of Deep-Sea
Science and Engineering, Chinese Academy of Sciences, Sanya, China

Abstract The response of the marine atmospheric boundary layer (MABL) structure to an oceanic front
is analyzed using Global Positioning System (GPS) sounding data obtained during a survey in thenorthwestern South China Sea (NSCS) over a period of about 1 week in April 2013. The Weather Researchand Forecasting (WRF) model is used to further examine the thermodynamical mechanisms of the MABL’s
response to the front. The WRF model successfully simulates the change in the MABL structure acrossthe front, which agrees well with the observations. The spatially high-pass-ﬁltered ﬁelds of sea surface
temperature (SST) and 10 m neutral equivalent wind from the WRF model simulation show a tight, positive
coupling between the SST and surface winds near the front. Meanwhile, the SST front works as a damping
zone to reduce the enhancement of wind blowing from the warm to the cold side of the front in thelower boundary layer. Analysis of the momentum budget shows that the most active and signiﬁcant term
affecting horizontal momentum over the frontal zone is the adjustment of the pressure gradient. It isfound that the front in the NSCS is wide enough for slowly moving air parcels to be affected by thechange in underlying SST. The different thermal structure upwind and downwind of the front causes abaroclinic adjustment of the perturbation pressure from the surface to the midlayer of the MABL, which
dominates the change in the wind proﬁle across the front.

1. Introduction
The marine atmospheric boundary layer (MABL) is the lowest part of the troposphere that is directly inﬂuenced by the underlying ocean. It responds to sea surface forcing with a time scale of about an hour or less[Stull, 1988]. The MABL is readily heated locally by warm sea surface temperature (SST) and cooled by cold
SST. Therefore, air temperature, atmospheric turbulent mixing, and atmospheric pressure anomalies in the
MABL are naturally affected by the SST anomalies associated with oceanic fronts. The dynamic and thermodynamic adjustments of the MABL to the underlying SST anomalies result in increased surface wind speeds
and more moisture and clouds over warm SST anomalies, and decreased surface wind speeds and lessmoisture and fewer clouds over cold SST anomalies [Chelton et al., 2004, 2007; Chelton and Xie, 2010; Liuet al., 2007; Small et al., 2008; Boé et al., 2011; Czaja and Blunt, 2011; Xu et al., 2011; Nakamura et al., 2012;
Frenger et al., 2013; Minobe and Takebayashi, 2014].
The dynamical response of the MABL to an SST anomaly usually leads to the increased surface winds overwarm water and decreased surface winds over cold water, termed “positive SST-wind coupling.” There are
two main hypotheses to explain the adjustment of sea surface wind to the sharp SST gradients associatedwith oceanic fronts: the generation of hydrostatic pressure gradients through adjustments of the MABL massﬁelds [Lindzen and Nigam, 1987; Wai and Stage, 1989; Small et al., 2008; Song et al., 2006] and the stabilitydependent modiﬁcation of the vertical mixing of momentum from aloft to the surface [Wallace et al., 1989;
Hayes et al., 1989; Tokinaga et al., 2006; Minobe et al., 2008]. In some cases, both these mechanisms are activein the positive SST-wind coupling over oceanic fronts and eddies [Tanimoto et al., 2011; Takatama et al., 2012;
Shi et al., 2011; Chelton, 2013; Frenger et al., 2013].
©2017. American Geophysical Union.
All Rights Reserved.

SHI ET AL.

Samelson et al. [2006] found an approximately linear relationship between the surface stress and boundary
layer depth under the quasi-equilibrium condition. In quasi-equilibrium the average stress is approximately

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Figure 1. (a) Map of the SCS and the surrounding area with the climatological (2006–2015) SST in April (°C; color shading). (b) CTD and XBT stations of the cruise
survey deployed off Shiyan 3 from 18 to 20 April 2013. The two rectangles in Figure 1a denote the coarse and ﬁne resolution model domains with grid sizes of
9 km and 3 km, respectively. The black line in Figure 1a is the cruise track during the observations. Black contours in Figure 1b represent the bathymetry of thestudy area.

in equilibrium with the steady free-atmospheric pressure forcing. As a result, stronger wind stress is
associated with a thicker MABL over warm SST areas, while weaker wind stress is associated with a thinner
MABL over cold SST areas. Samelson et al. also argued that neither of the two previous hypotheses
is appropriate to interpret the change of wind proﬁle for the quasi-equilibrium condition or the warmto-cold SST transition case. Using a large-eddy simulation model, Skyllingstad et al. [2007] supported
Samelson et al.’s study and further found that the turbulent momentum ﬂux divergence dominates thechange of wind proﬁles. However, the lateral pressure gradient in both studies was set to a small value,
and only two-dimensional results were considered. The application of their theories for realistic andthree-dimensional conditions needs to be examined further.
Spall [2007] reported that the mechanism responsible for the change in surface winds in the vicinity of oceanic fronts varies with the environment of the ambient area. Building up on the ﬁndings of Spall [2007], O’Neillet al. [2010] reported that the dynamical mechanism of the mesoscale air-sea coupling process is more complicated. They concluded that six factors could play prominent roles in affecting this coupling process: thespatial scale of the SST perturbations, the strength of the background wind, the latitude, the presence ofstrong lower tropospheric stratiﬁcation and capping inversions, and proximity to landmasses. Therefore,
the inﬂuence of mesoscale air-sea coupling on regional weather and climate variability at the marginal seas
still needs to be studied further due to its locally dependent characteristics, such as the spatial scale of thefront and the atmospheric environment.
The South China Sea (SCS) is a marginal sea linking the western Paciﬁc Ocean and the eastern Indian Ocean(Figure 1a).The ocean circulation of the SCS is essentially cyclonic in winter and anticyclonic in summer,
responding to the seasonally reversing monsoon [Wyrtki, 1961; Qu, 2000; Liu et al., 2001; Fang et al., 2005;
Gan et al., 2006; Wang et al., 2013]. The interaction between the basin gyre, monsoon, bathymetry, and tideproduces many ocean fronts that can be observed in the northern SCS throughout the year with apparent
seasonal variation [Wang et al., 2001; Hu et al., 2003; Chu and Wang, 2003; Wang et al., 2012; Shi et al.,2014]. Meanwhile, the inﬂuence of complex bathymetry, the local variation of wind stress curl, and theinstability of the ocean circulation all generate ocean eddies embedded in the gyre, as seen in satellite dataand hydrographic observations [Wang et al., 2003; Xiu et al., 2010].

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The inﬂuence of ocean fronts on the
MABL over the SCS has been studied
in the western sea of Luzon by Wanget al. [2012]. They emphasized thesigniﬁcant inﬂuence of topography.
Chow and Liu [2012] investigated
the inﬂuence of SST perturbations
associated with ocean eddies on thesea surface wind over the SCS in winter. The study of SST-wind couplingover the coastal SST fronts yields a
coupling coefﬁcient within the rangeof those observed for midlatitudeand near-equatorial oceans [Shiet al., 2014].The major conclusions ofthese studies are consistent with theresults for open ocean but mainly
Figure 2. OSTIA SST data versus in situ SST from CTD and XBTs during the
focus on the response of atmosphericcruise survey.
variables over the sea surface.
Meanwhile, Toy and Johnson [2014]
showed that the modiﬁcation of the convergence and water vapor transport in the MABL by the SST front
could signiﬁcantly change the spatial distribution of rainfall in the eastern SCS. Apart from these references,
the study of the vertical MABL modiﬁcations by SST fronts is still limited in other areas of the SCS and requires
further study.
In this paper, survey data collected in April 2013 in the northern South China Sea (NSCS) are used to examine
the inﬂuence of oceanic fronts on the surface atmosphere and vertical structure of the MABL. A regional
atmospheric model is used to understand the response of the MABL to SST perturbations represented byhigh spatiotemporal resolution (~6.5 km) SST data. The paper is organized as follows. Section 2 introduces
all the data and the model used in this study. The observations and simulation results are analyzed in sections
3 and 4, respectively. The analysis of the momentum budget is introduced in section 5, and section 6 provides
a summary and discussion.

2. Data and Methods
2.1. Hydrographic Survey Data
The hydrographic investigation was conducted aboard research vessel Shiyan 3 of the South China Sea
Institute of Oceanology (SCSIO), Chinese Academy of Sciences from 15 to 22 April 2013. During this survey,
a total of 63 water temperature proﬁles were measured using a conductivity-temperature-depth proﬁler(CTD, Seabird Electronics 911 Plus) and expendable bathythermographs(XBTs) [Clément et al., 2004](Figures 1b and 2), while 15 Global Positioning System (GPS) radiosondes were released across the front(Figure 3a and 3b). The near surface 5 m average temperatures measured by CTD and XBT were used to validate the reanalysis SST used in the atmospheric model. Changfeng CF-06-A GPS radiosondes were used in
the investigation. This radiosonde has been validated against the Vaisala RS92-SGP radiosonde and wasfound to give very similar readings [Li et al., 2011; Xie et al., 2014].
Using air temperature, pressure, and relative humidity from the GPS soundings, virtual potential air temperature θv can be calculated to describe the structure of the MABL across the SST front [Tanimoto et al., 2009]
using θv = θ(1 + 0.61q), where θ ¼ T ðp0 =pÞR=Cp , q = 0.622e/(p  0.378e), e = rh  ea Here θ, q, T, p, p0, e, and rh
are the potential temperature (K), mixing ratio (g kg1), air temperature (K), pressure (hPa), reference pressure(1000 hPa), water vapor pressure (hPa), and relative humidity, respectively. R is the speciﬁc gas constant fordry air (287.04 J kg1 K1), Cp is the speciﬁc heat constant (1004 J kg1 K1), and ea denotes the saturationwater vapor pressure calculated using the Goff-Gratch equation [Murphy and Koop, 2005].
The height of the MABL top is an essential variable to describe the changes in thermal and dynamical structure due to SST perturbations [Small et al., 2008]. In the present paper, the MABL height is deﬁned as the

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Figure 3. Average SST (°C) during the observation periods from (a) 16 to 17 April and (b) 18 to 20 April 2013 in the northwestern SCS. (c) Average magnitude of SST
gradient for 16 and 17 April. The black triangles show the locations of GPS soundings during the cruise survey.

lowest height where the virtual potential temperature is 1 K greater than its value at the surface (50 m high).
This method gives a full description of the boundary layer structure including the mixed layer andentrainment zone. This criterion has been used in previous studies at frontal areas and correctly described
the change of boundary layer structure across the SST front [Hashizume et al., 2002; Tokinaga et al., 2006].
2.2. SST and Wind Data
The Operational SST and Sea Ice Analysis (OSTIA) data are used in this study. OSTIA data are produced by the
UK Met Ofﬁce with support from the My Ocean project. The OSTIA system makes extensive use of multiple
satellite retrievals and assimilates in situ observations from the Global Telecommunications System to generate a global 1/20° (~6 km) resolution, daily SST map since 2006. It is now used operationally as a boundary
condition for all weather forecast models at the Met Ofﬁce and at the European Centre for Medium-Range
Weather Forecasting [Donlon et al., 2012].
OSTIA SST data have small biases (~0.1–0.2°C) and standard deviations (<0.4°C) in the open ocean and somemarginal seas [Donlon et al., 2008; Xie et al., 2008; Martin et al., 2012]. Its validation in the SCS is limited.
Therefore, the accuracy of OSTIA SST data is examined using in situ CTD and XBT observations in April
2013. The correlation coefﬁcient between OSTIA SST and in situ SST is 0.95, and the root-mean-square error
is 0.37°C (Figure 2). The high accuracy and resolution of OSTIA data could be very helpful for simulating theinﬂuence of mesoscale SST perturbations on the MABL [Song et al., 2009; O’Neill et al., 2010]. Therefore, we
adopt OSTIA SST data as the bottom boundary condition for the atmospheric model.
The sea surface wind observed by Advanced Scatterometer (ASCAT) is used to validate the performance of
WRF model in this study. The data are obtained from the Asia-Paciﬁc Data Research Center. Similar to previous studies, the analysis in this study that utilizes wind data is based on 10 m equivalent neutral winds.
Equivalent neutral wind is the wind that would exist for an idealized condition at a given height if the atmospheric boundary layer was neutrally stratiﬁed.
2.3. Model Conﬁguration
The Weather Research and Forecasting (WRF, version 3.7) model with the Advanced Research WRF dynamic
solver [Skamarock et al., 2005] is used in this study. The WRF model has been used extensively to simulate
atmospheric processes over frontal areas [Song et al., 2009; Perlin et al., 2014; Toy and Johnson, 2014].The
model is set up with two-way nested domains, D1 and D2, with horizontal resolutions of 18 and 6 km, respectively. The inner domain (D2) focuses on the NSCS (Figure 1a). The model domains are initialized with the
National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data. FNL data
are from the NCEP Global Data Assimilation System, which continuously collects global observational data.
The lateral boundary conditions for D1 are updated by FNL data every 6 h to allow large-scale synoptic
weather systems outside our model domain to propagate through the domain.
The simulations are performed using 85 vertical sigma levels including 20 levels below 1500 m. A ﬁne verticalresolution is speciﬁed in the boundary layer to accurately simulate the vertical turbulent momentum

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Figure 4. Daily mean 850 hPa temperature (°C), wind (m s ), and geopotential height (m) from NCEP FNL Operational
Global Analysis data for (a) 16 April 2013 and (b) 20 April 2013. The position of the ship cruise transect is marked by
the solid blue line.

exchange there. The Quasi-Normal Scale Elimination (QNSE) planetary boundary layer scheme [Sukoriansky
et al., 2006; Kleczek et al., 2014], the QNSE surface layer scheme [Sukoriansky, 2008], the explicit
microphysics scheme [Hong et al., 2006], the Kain-Fritsch cumulus scheme [Kain, 2004], the Dudhia shortwave scheme [Dudhia, 1989], and a rapid radiative transfer long wave radiation scheme [Mlawer et al.,1997] are set for both domains. The period of the simulation started at 00:00 UTC on 15 April 2013 andﬁnished at 00:00 UTC on 21 April 2013. OSTIA SST data are used as the bottom boundary condition in the
WRF model and updated every 24 h.

3. Observational Results
3.1. Hydrographic Survey and Weather Conditions
The map of the average SST during the study period indicates the presence of a southwest-northeastward
SST front in the NSCS (Figure 3). Over the frontal area, SST is cold on the continental shelf side and warmin the center of the SCS where the water depth exceeds 2000 m. The scalar variable in Figure 3c (shadedcolor) shows the total magnitude of the SST gradient, computed from the meridional and zonal SST gradients. This gives a measure of the SST change across the front, which is about 0.5–1.0°C per 100 km(Figure 3c), a typical value for subtropical ocean fronts in early spring [Kida et al., 2015]. Another two coastal
fronts near Hainan Island and Vietnam are not considered in this study.
The ship cruised almost perpendicularly across the SST front twice (Figures 3a and 3b), once northwestward across the front from the warm side to the cold side (Figure 3a) and then turning back and cruisingsoutheastward across the front again (Figure 3b). Six GPS soundings (G1 to G6) were launched every 12 hduring the ﬁrst stage from 16 April to 17 April 2013 (Figure 3a), and another nine soundings (G7 to G15)
were launched during the second stage from 18 April to 20 April 2013 (Figure 3b). It needs to note thatthe last six GPS soundings (G10 to G15) were released every 6 h on 20 April, so two adjacent stations
are very close and the SST changes are also small (Figure 3b). The GPS sounding data are very usefulfor describing the adjustment of the dynamical and thermodynamical structure of the MABL across thefront. Moreover, they can also be used to validate the simulation results from the WRF model that are used
for further dynamical investigation.
There was no abrupt change of weather conditions during the observation period. At 850 hPa, the whole SCS
was dominated by a high pressure system, so the general pattern of wind vectors was anticyclonic from the
beginning to the end of the observational period (Figures 4a and 4b). No other synoptic disturbances such as
cyclones or cold surges passed through the study area that could signiﬁcantly change the direction and themagnitude of winds. Therefore, the surface weather conditions varied slowly, and the prevailing wind was

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Figure 5. Time-mean SST (°C), wind (m s ), and sea level pressure (hPa) for 16–20 April 2013 from (a) NCEP FNL and(b) WRF simulation results. The ship cruise transect line is marked by the solid blue line. The blue dashed rectangle in
Figure 5b is deﬁned to examine the horizontal homogeneity of the wind ﬁeld near the front.

southeasterly, approximately corresponding to the cross-frontal direction, from the warm to the cold side of
the SST front throughout (Figure 5). The surface wind was almost perpendicular to the SST contours, so this
scenario can be assumed to be a stable warm-cold front case as presented in other studies [Song et al., 2006;
Skyllingstad et al., 2007; Kilpatrick et al., 2014; Xu and Xu, 2015].
3.2. GPS Sounding Results
The GPS radiosonde observations show that the MABL height gradually varied as the ship cruised over a
distance of about 400 km across the front (Figure 6). The observed MABL height was about 1010 m at G2

1

1

Figure 6. Deviation of virtual potential temperature from 300 K (solid lines), relative humidity × 10 (%; dotted lines), and speciﬁc humidity (g kg ; dashed lines)
proﬁles observed at (a) G2, (b) G4, and (c) G6 when ship cruised from southeast to northwest and (d) G7, (e) G9, and (f) G13 when ship cruised southeastward.
The red dashed lines mark the height of the marine atmospheric boundary layer (h0). The deﬁnition of h0 is described in section 2.1.

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1 1Figure 7. Wind speed (m s ; solid lines) and vertical wind shear (×10 s ; dashed lines) proﬁles observed at (a) G2,(b) G6, (c) G7, and (d) G13. The blue dashed lines mark the height of the MABL.

on 16 April (Figure 6a) and gradually decreased to 370 m at G6 on 18 April (Figure 6c). A 640 m reduction in
the MABL height corresponds to a decrease in the underlying SST of about 1.5°C. When the ship sailed southeastward, the MABL height gradually recovered reaching about 760 m at the southern end of the cruise track(Figures 6d and 6f). A 1.2°C increase in SST leads to a 120 m increase in the MABL height (G7 to G13).The MABL
height at G13 (Figure 6f) was about 200 m lower than at G2 (Figure 6a), although these two radiosondes werereleased at nearly the same location with similar SST (Figure 3). As a result, the observed change of MABL
height across front is smaller in the second stage (from 18 April to 20 April) than in the ﬁrst stage (from 16
April to 18 April).
The variation of the MABL height and vertical structure modulates the vertical transport of water vapor. Overthe cold side of the front, the water vapor mixing ratio gradually decreases, and the relative humidity changes
slightly with height (Figures 6c and 6d). Over the warm side of the front, the strong turbulent mixing facilitates the upward transport of water vapor in the well-developed MABL. Consequently, the mixing ratios
remain almost the same in the MABL, and the relative humidity increases with height below the MABL top(i.e., Figures 6a and 6f). Meanwhile, the strong vertical mixing over warm water also leads to a rapid decreaseof mixing ratios in the near surface layer (lower than 50 m). This may explain why the mixing ratio in the lower
layer of the MABL (50 m to 400 m) is lower over warm SST areas than over cold SST areas (Figures 6a, 6c, 6d,
and 6f).
There are also some discrepancies between our observational results and previous studies in the changes ofwind speed across the front. Four wind proﬁles are examined on the cold and warm SST sides of the front(Figure 7). Over the cold SST, a thick layer with high (>10 m s1) and nearly uniform wind speed is observed
from 100 m to over 1200 m (Figures 7b and 7c). Meanwhile, the surface wind speed is less than 5 m s1.
Therefore, a strong wind shear was observed from the surface to 100 m. Over the warm SST area, the highwind speed layer above 100 m disappeared. The wind speed decreased over the whole MABL, and the windspeed shear became weak near the surface (Figures 7a and 7d).

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Table 1. Comparison Between WRF-Simulated and GPS Soundings Below
2000 m for 15–21 April 2013 for Air Temperature (ta), Mixing Ratios (q),aVirtual Potential Temperature (θv), and Wind Speed (ws), Respectively

The observed variation of the wind
shear across the SST front appears
consistent with the vertical mixing11ta (°C)q (g kg )θv (K)
ws (m s )hypotheses proposed by Wallace
R
0.97
0.95
0.97
0.59
et al. [1989] and Hayes et al.
RMSD
0.99
2.01
0.69
2.56
[1989]. However, the sea surfaceawind speed was higher over cold
R is the correlation coefﬁcient and RMSD is the root-mean-squaredifference.
SST (over 4 m s1; Figure 7b) thanover warm SST (about 2 m s1;
Figure 7c). This indicates that the inﬂuence of SST anomalies on the surface wind might be overridden bya change in the background weather conditions. The spatiotemporal distribution of the GPS soundings is
not dense enough to provide a perspective on the dynamics. Thus, numerical simulations are carried out
to further examine the inﬂuence of large-scale weather on the local air-sea coupling.

4. WRF Model Results
4.1. Validation of Model Results
The results of the simulation are interpolated vertically onto 5 m intervals and compared with GPS observations below 2000 m. The root-mean-square difference (RMSD) is calculated to conﬁrm the accuracy of the
WRF model as follows:
RMSD ¼

rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
XN 2
X imod  X iobs =N
i¼1

(1)

where Xmod denotes the variable from the model results, Xobs is the corresponding variable in the GPS
observations, N is the total number of vertical levels (400), and i is the interpolated vertical level. The verticallyaveraged correlation coefﬁcients and the RMSD between the simulated and observational results for air temperature, mixing ratios, virtual potential temperature computed at each station, and their average results andwind speed are shown in Table 1.
The high correlation coefﬁcients (R > 0.95) indicate that the WRF model performs well in simulating the vertical structure of the temperature and mixing ratios. The corresponding RMSDs of these three variables arealso very small, especially for the virtual potential temperature (Table 1). The correlation coefﬁcient of windspeed is lower than those for the temperature and mixing ratios, although the RMSD of wind speed is a little
large. Meanwhile, the mean sea surface wind ﬁeld of WRF simulation is highly consistent with the NCEP datain the observation period (Figure 5b). Considering that all the simulations were conducted without data
assimilation, the performance of the model was acceptable for simulating the response of the MABL tothe front.
The performance of the WRF model is further validated with the neutral equivalent wind observed by ASCAT.
A 1.5° × 1.5° square Gaussian ﬁlter is constructed to obtain spatially high-pass-ﬁltered ﬁelds of time-averaged
OSTIA SST and 10- neutral equivalent wind during the observation period. Both ASCAT data and the WRF
model simulation show a tight, positive coupling between the SST and surface winds in the vicinity of thefront (Figure 8). The overlaid pattern of SST and surface wind is consistent with other studies [Chelton,
2005; Small et al., 2008; Song et al., 2009; O’Neill et al., 2010]. This implies that the WRF model can successfully
simulate the response of sea surface wind to the change of underlying SST associated with the front.
Moreover, although the GPS soundings do not show an intuitive change of wind proﬁle across the SST front,
the inﬂuence of the SST front on the low-level wind can still be identiﬁed on the perturbation ﬁelds ofscatterometer observations and the WRF simulation.
4.2. Simulated MABL Structure Across the Front
The WRF simulation can correctly present the positive coupling between SST and sea surface wind because it
successfully simulates the variation of the MABL structure across the front. Over warm SST (G2), the simulated
virtual potential temperature proﬁle can almost be superimposed on the observed one (Figure 9a). The WRF
model correctly simulates the well-developed mixed layer and the stable layer above. Over cold SST (G6), thesimulated surface virtual potential temperature is a little warmer than the observations, so the simulated

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Figure 8. Maps of spatially high-pass ﬁltered 5 day (16–20 April 2016) average SST (°C, black contours) and 10 m equivalent neutral wind speed (m s , color shading)from (a) ASCAT data and (b) the WRF simulation. The blue line is the ship cruise transect. The ASCAT data used here are obtained from the Asia-Paciﬁc Data
Research Center.

MABL is a little higher than observed (Figure 9b). This difference may be caused by the bias between themodel SST and the real in situ observations.
The simulated mixing ratios over warm and cold SST are also similar to the observations (Figures 9c and 9d).
The WRF model reproduces the well-developed MABL observed at G2, in which the mixing ratio changes little
with height. The simulated mixing ratio is a little different from that observed at G6. The mixing ratio remains
almost uniform below 400 m because of the excessive mixed-layer depth in the simulation. The simulated
rate of decrease of the mixing ratio in the middle layer (~400–1400 m) is also larger than that observed.
The WRF model approximately simulates the wind speed proﬁles over warm and cold SST areas, although
there are some quantitative differences, especially near the surface (Figures 9e and 9f). Over warm SST, thesimulated wind speed is almost uniform in the MABL and gradually decreases above the MABL (Figure 9e).
Consequently, the wind speed shear is small inside the MABL but becomes large in the air above. Over cold
SST areas, the vertical variation of wind shear reverses (Figure 9f). The simulated wind speed increases withheight, forming a jet at about 600 m, and then gradually decreases with increasing height. Thus, the windspeed shear is large in the lower layer and becomes small in the upper layer.

5. Momentum Budget
The above observations and simulations show that the change of MABL height and water vapor across the
SST front is consistent with previous studies. However, the change of wind speed seems to be different fromthe positive SST-wind coupling process. There is also no linear relation between the MABL height and surface
wind stress (Figure 10a), although the change of surface heat ﬂux is similar to previous studies (Figure 10b).
Thus, we carry out a momentum budget analysis to discuss the dynamical mechanism of the local SSTwind coupling.
In terms of the terrain-following coordinates (η) of the WRF model, the momentum equation for the eastward
wind component u is
 0 0
 0 0
 0 0
∂ uv
∂ uω
∂u
∂u
∂u
∂u
1 ∂p ∂ u u¼ u  v  ω þ f v 



þ Sx
∂t
∂x
∂y
∂η
ρ ∂x
∂x
∂y
∂η
i
ii
iii
iv
v
vi

(2)

Terms in equation (2) are (i) the local tendency of eastward velocity, (ii) the horizontal and vertical advection,(iii) the Coriolis term, (iv) the horizontal pressure gradient, (v) the three-dimensional turbulent stress

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Figure 9. (a, b) Virtual potential temperature (K), (c, d) mixing ratio (g kg ), and (e, f) wind speed (m s ) proﬁles observed by GPS soundings (black solid lines) andsimulated by the WRF model (dashed lines) at sites G2 (Figures 9a, 9c, and 9e) and G6 (Figures 9b, 9d, and 9f).

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Figure 10. Surface cross sections of the simulation results of (a) boundary layer height (×10 m; blue line), wind speed
1
2
1
2
(m s ; solid line), and wind stress (N m ; dashed line); (b) total heat ﬂuxes (×10 W m ; black line), sensible heat
2
1
2
ﬂux (W m ; red line), and latent heat ﬂux (×10 W m ; blue line); and (c) SST (°C; black solid line), surface airtemperature minus SST (°C; orange dashed line), and the deﬁnition of different regions used for the calculation of the
momentum budget with equation (3).

divergence, and (vi) the subgrid-scale parameterization term. Terms (iv) and (v) are the two dominant forcing
terms that determine the adjustment of wind speed across the SST front. Terms (v) and (vi) are both directly
calculated by the QNSE parameterization scheme in the WRF model. During the simulations, each of theright-hand side terms in equation (2) was saved for each time step and averaged hourly. In the following

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Figure 11. Time-averaged momentum budget terms (×10 m s ): (a) pressure gradient, (b) turbulent stress divergence, (c) Coriolis force, and (d) advection (notenonlinear color scale) along the ship section. The coordinate axes are rotated anticlockwise to the direction along the ship section (cross-front) direction andperpendicular to the transect (along-front) direction. Positive tendencies indicate that the ﬂow is accelerating to the northwest, and negative tendencies indicate
decelerating ﬂow. The black dashed line marks the top of the MABL. SST is shown in both bottom panels for ease of comparison.

analysis, the coordinate axes are rotated anticlockwise to lie along the ship transect (cross front) andperpendicular to the transect (along front).
The time-averaged terms for the cross-frontal direction are shown in Figure 11. Overall, the pressure gradientis the main force accelerating the wind along the section (Figure 11a), and the other terms act as a drag on
the wind and balance the pressure gradient term. Below 400 m, the pressure gradient is mainly balanced byturbulent stress divergence (Figure 11b), Coriolis force (Figure 11c), and partially by the advection term
(Figure 11d), which is consistent with the Ekman balance. Above 400 m, the pressure gradient is balancedby the Coriolis force with geostrophic balance (Figures 11a and 11c). Upwind of the front southern shipsection, the pressure gradient is vertically uniform from the surface to 2000 m. Over the SST transitionarea (16.4–17.5°N), the pressure gradient near the surface is smaller than at the midlayer of the MABL
(Figure 11a). Downwind of the front (northern ship section), the pressure gradient is also vertically uniform

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Figure 12. (a) Time series of cross front (blue line) and along-front (red line) wind components between Zone 2 and Zone 3 deﬁned in Figure 10c. The shaded zones in
Figure 12a are plus/minus standard deviation from the mean value at the ship cruising transect. (b) Correlation coefﬁcients versus distances between the ship
cruising transect and other parallel transects in the blue rectangle shown in Figure 5b. (c) Vertical distribution of spatial-temporal averaged cross-front windspeed ratio.

but stronger than upwind. The turbulent stress divergence term mainly acts as a drag on the wind speed, andthis deceleration effect is enhanced downwind of the front (Figure 11b). There is a reversal of the advection
term within the upper MABL (Figure 11d), but the magnitude is much smaller than the pressure gradient andCoriolis force.
Figure 11 shows the change in the terms in equation (2) across the front, but it may not give an intuitive
picture of the role of turbulence versus pressure forcing. Therefore, following Skyllingstad et al. [2007], we
consider the Lagrangian momentum budget for the cross-frontal component of the mean ﬂow as it moves
across the front from one region to another:






0 0
∂ u0 v 0∂ u0 ω0du
1 ∂p ∂ u uþ Sx¼ fv 

∂x
∂y
∂η
dt
ρ ∂x

(3)

where the advection terms in equation (2) are moved to the left-hand side and form the total derivative or
Lagrangian time derivative of the velocity. The overbar on the left-hand side of equation (3) denotes thespatial average of the mean ﬂow in the start and end regions, and the overbar on the right-hand side of equation (3) means the spatial average of momentum terms in the area between two regions. Equation (3) indicates that, as an air parcel moves from one region to another, the total change in the mean ﬂow canmainly be attributed to the local Coriolis force, pressure gradient, and turbulent ﬂux along its pathway. The
subgrid-scale term is much smaller than the other terms and can be ignored in the analysis.
However, the large-eddy simulation of Skyllingstad et al. [2007] is essentially two-dimensional and horizontally homogeneous in a channel domain. It is necessary to examine whether the WRF simulation in ourstudy area is horizontally homogeneous. A rectangular cross-front channel domain is artiﬁcially deﬁned
in the study area, centered on the ship section and with a narrow edge at about 150 km (shown in
Figure 5b). In this domain, the simulated cross and along-front wind components have similar time series(Figure 12b), and the normalized deviations of wind speed from the results of the ship section are also verysmall (Figure 12a). This means the spatial variation of the wind component in this limited region can beneglected, and the wind can be considered horizontally homogeneous.
Meanwhile, the distance the air parcel moves along the front should be smaller than the distance it moves
across the front when the total momentum budget is estimated between two separate regions. This meansthat the cross to along-front wind speed ratio needs to be greater than the length to width ratio of thecalculation domain. For example, when we apply equation (3) to derive the momentum budget from Zone
2 to Zone 3 (deﬁned in Figure 10c), the cross to along-front wind speed ratio needs to be greater than 2.4(180/75). During the simulation period, this condition is satisﬁed below 800 m (Figure 12c). Since most

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Figure 13. Horizontally and temporally averaged terms from the momentum budget in equation (3) moving from (a, b) Zone 1 to Zone 2 and (c, d) Zone 2 to Zone 3.
Figures 13b and 13d include the upstream momentum tendency (solid lines) and the sum of the pressure gradient term (PRS), the turbulent stress divergence(TSD), and Coriolis force (COL) (dashed lines). Terms are deﬁned in equation (2). Positive tendencies denote increases in the total momentum of the air parcel.

changes of the momentum terms across the front occur under the MABL top, the Lagrangian approach to themomentum equation is appropriate in this study.
The cross-front momentum budgets between each region are examined upwind of the front (Zone 1 toZone 2) and in the SST transition area (Zone 2 to Zone 3). The total change in momentum of cross-front ﬂow(Δu/Δt) between the two separate regions is ﬁrst calculated by
Δu=Δt ¼

PPð u2  u1 Þ=N
Δt ðzÞ

(4)

where N is the total number of grid points in the analysis domain, u with subscripts are the cross-front mean
ﬂow in the two regions, Δt ðzÞ ¼ D=Uðz Þ is an estimate of the transition time for the air column to move fromone region to another, D is the distance between the two regions, and UðzÞ denotes the average ﬂow speed
between the two regions. The average vertical ﬂux divergence and horizontal pressure gradient between thetwo regions are shown in Figure 13, which allows us to evaluate the effect of the contribution of each term tothe change in momentum ðΔu=Δt Þ.
Upwind of the front and in the SST transition region, the total momentum increases as the air column moves
across the front. The change in total momentum is not similar in form to any single term in equation (3)(Figures 13a and 13c). However, a good match is obtained by adding together the left-hand terms in equation(3) (Figures 13b and 13d). This proves that the change in total momentum is dominated by the joint effect of
the local pressure gradient, turbulent ﬂux, and Coriolis force. Figure 13 also suggests that the momentum budget is different in the MABL and the free atmosphere. In the MABL (surface to 1200 m), the large positive tendency of the pressure gradient is mainly damped by the negative offset of turbulent ﬂux (Figures 13a and 13c).
Consequently, the magnitude of the total change in momentum is much smaller than the tendency of average pressure forcing. Above the MABL (1200 m), the turbulent ﬂux term vanishes, and the pressure gradientand Coriolis force balance each other, so the total momentum is basically conserved and scarcely changes.
The Coriolis force changes sign over different regions of the front (Figures 13a and 13c). It partially offsets
the pressure gradient over the SST transition region but acts to reinforce it upwind of the SST front.
Although the total change in momentum is not decided by either the pressure gradient or turbulent mixing
alone, the pressure gradient can modify the ﬁne structure of momentum change in the MABL. Upwind of
the front, the average pressure gradient tendency is vertically uniform in the whole MABL, and the change
in total momentum is also vertically uniform (Figure 13a). Over the SST transition region, the average

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Figure 14. (a) Time series of MABL height (m) at G2 and G6 from WRF simulation, (b) differences of MABL height (m) and SST (°C) between G2 and G6, (c) time series–1of SST (°C) at G2 and G6, and (d) rate of variation (m °C ) of the MABL height with the SST changes across the front.

pressure gradient tendency is ~1.2 × 104 m s2 near the surface and increases to ~1.6 × 104 m s2 in the
midlayer of the MABL (700 m) (Figure 13c). Similarly, the magnitude of total momentum change increases
with height, because the acceleration of ﬂow is faster in the midlayer of the MABL than in the surface
layer. Therefore, the change in the total momentum is signiﬁcantly modiﬁed by the pressure gradient fromthe sea surface to the midlayer of the MABL, and this modiﬁcation is more evident when the air column iscloser to the surface.
The turbulent stress divergence term mainly acts as a drag to remove momentum from the ﬂow in the MABL
(Figures 13a and 13c). Upwind of the front, the turbulent ﬂux is almost uniform in the lower MABL and gradually weakens after reaching its maximum value at 300 m (Figure 13a). The turbulent ﬂux vanishes at 700 m,
a little lower than the MABL height. At the SST transition region, the magnitude of the turbulent ﬂux isenhanced, and there is a small change in the slope of the vertical proﬁle below 300 m (Figure 13c). Thechange of the vertical turbulent ﬂux across the front suggests that more momentum is dissipated downwindof the front. This result seems to be contradictory to the depression of the MABL turbulent mixing over cold
water, but it is consistent with results from our simulation and previous studies in which the downward
momentum transport is not appropriate to explain the warm-to-cold ﬂow case.

6. Discussion and Conclusions
The response of the MABL structure to the oceanic front has been analyzed using GPS sounding dataobtained during a survey in the NSCS over a period of about 1 week in April 2013. The observational results
show that the adjustment of the thermal structure in the MABL is direct and very sensitive to the change ofunderlying SST; however, an asymmetric change in the MABL height was observed during the different cruise
periods. The observational results also show that the underlying SST anomalies associated with the front not
only change the MABL height but also modify the atmospheric stability. The MABL layer maintains a weakly
stable or near neutral condition in the cold SST area, but is well mixed in the warm SST area, regardless of thetime of observation. The observed change in the MABL height and the stability over the SST front may cause
further adjustment of the water vapor and wind proﬁles inside the MABL.
Using high-resolution OSTIA SST data, the WRF model successfully simulated the change of MABL structure
across the SST front during the observation time. The simulated proﬁles of virtual potential temperature, mixing ratio, and relative humidity in the MABL agree well with the observations. The WRF simulation explained
why a different MABL height contrast across the front is observed during the northwestward and southeastward cruise periods. On the warm side of the front, the MABL height decreases more than 100 m from 17 to
18 April (Figure 14a), while there is little increase of SST at the same time (Figure 14c). The SST increases by

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Figure 15. Cross sections (height versus cross-front distance) of the downstream anomalies (color shading) of (a) virtual potential temperature and (b) perturbationpressure. The downstream anomalies are calculated as the time-averaged ﬁeld minus the upstream average (from y = 0 to y = 150 km, see Figure 10c). Black contours
in Figures 15a and 15b show the mean virtual potential temperature over the period of simulation and the downstream anomalies, respectively. The whitedashed line is the MABL height.

more than 1°C at the cold side of the front from 17 to 20 April (Figure 14c), corresponding to an increase of
MABL height from 570 to 620 m (Figure 14a). The decrease of MABL height over warm water is the major
reason for the change of MABL height contrast across the front (Figure 14b). It indicates that the changeof MABL height across the front is not simply decided by the SST anomalies, but that the small-scaledisturbances in the upper layer also need to be considered.
Both GPS sounding observations and the WRF simulation show high wind speed over cold SST and low windspeed over warm SST, and this result seems to contradict previous studies. However, the inﬂuence of the SST
front on the low-level wind can still be identiﬁed in the spatially high-pass-ﬁltered SST and 10 m equivalent
neutral wind from ASCAT data and the WRF simulation. Moreover, the momentum budget analysis from the
WRF simulation indicates that the SST front works as a damping zone to reduce the enhancement of thesoutheasterly wind from the warm side to the cold side of the SST front.
The WRF simulation shows that the change of thermal structure initiates the adjustment of momentum in the
MABL. When the air blows from warm-to-cold water, an internal boundary layer forms in the lower boundary
layer because the SST cools down (Figure 15a). This thermally generated internal boundary layer leads to a

1

2 2

Figure 16. Cross sections (height versus cross-front distance) of the (a) vertical wind shear (s , shading) and (b) turbulent kinetic energy (m s , black contours).
Black contours in Figure 16a denote the average wind speed. Shading in Figure 16b denotes the downstream anomalies of turbulent kinetic energy. The whitedashed line is the MABL height.

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2 2Figure 17. Vertical proﬁles of the average (a) turbulent kinetic energy (m sregions shown in Figure 10c.

10.1002/2016JD026071

2), (b) vertical diffusion coefﬁcient, and (c) Brunt-Väisälä frequency (s) in the threebaroclinic adjustment of the perturbation pressure that varies with height (Figure 15b). As the pressure gradient term is the largest term in the momentum equation, the acceleration of warm-to-cold ﬂow gradually
decreases from the top of the internal boundary layer to the sea surface and dominates the change intotal momentum.
On the other hand, the turbulent stress divergence term is magniﬁed by the nonuniform distribution of thevertical wind shear. This is because the different accelerations in the vertical layers separate lower slowmoving air parcels from the higher faster-moving parcels and produce relatively stronger wind shear in
the middle boundary layer than in the surface layer (Figure 16a). The magniﬁcation of turbulent stress divergence does not mean the development of turbulence. At the SST transition region and downstream of thefront, the turbulent kinetic energy is actually weakened at the middle layer, and the height of the turbulencelayer decreases as well (Figures 16b and 17a). This is because the static stability proﬁle is enhanced toward
the surface when the warm-to-cold ﬂow moves across the front (Figure 17c), which pulls down the height
where the static stability has become strong enough to suppress the mechanical generation of turbulence
and conﬁne the upward transport of turbulent kinetic energy.
Note that the perturbation pressure (p0 ) used in this study refers to the deviations from a hydrostaticallybalanced reference state pressure ( p ) in the WRF simulation. It has a different physical meaning to thatusually used in the sea level pressure (SLP) adjustment mechanism. The reference state pressure (p) isin hydrostatic balance and is strictly only a function of height (Figure 18a). The real pressure is obtainedby summing these two parts. Since the hydrostatically balanced part of the pressure is horizontally uniform (Figure 18a), the pressure gradient term in the momentum equation is given by the perturbationpressure part (Figure 18b). During the period of observation, the perturbation pressure is higher at thewarm side than the cold side of the front, and this pattern is dominated by the sea level pressure ﬁeld
(Figure 5). It is difﬁcult for the SST front to reverse this pattern, but the SST front can modify it to acertain extent.
The effect of SST on the sea level pressure is not straightforward to isolate, but it can be indirectly extracted
from the numerical simulations. A sensitivity simulation is carried out using a uniform SST of 29.6°C, thevalue observed at the southern ship transect. The bottom boundary condition was only replaced in the
inner domain (D2) while the other model settings remain the same as in the control run. The results ofthe sensitivity simulation show that the increase of SST leads to a decrease of perturbation pressure at thenorthern end of the transect (Figure 18c). That is, the SST front weakens the decreasing tendency of

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Figure 18. Cross sections (height versus cross-front distance) of the (a) reference pressure (hPa), (b) perturbation pressure (hPa) simulated with the SST front, (c) perturbation pressure (hPa) simulated without the SST front, and (d) perturbation pressure differences between WRF simulations with and without the SST front (real
SST minus uniform warm SST). Dashed lines in Figures 18b and 18c are the MABL heights.

pressure from south to north by adding an artiﬁcial high pressure modiﬁcation over the cold SST side of thefront (Figure 18d) that is consistent with the classical SLP mechanism. Although this modiﬁcation is an orderof magnitude smaller than the mean perturbation pressure, the associated pressure gradient is still strong
enough to work as a primary factor to change the low-level wind across the front.
The momentum budget analysis shows that the pressure gradient term is the most active and signiﬁcantterm affecting horizontal momentum, and the turbulent stress term is the passively responding term. Thisis different from some previous studies that found the turbulent stress divergence to be the primary term
[Samelson et al., 2006; Skyllingstad et al., 2007; Kilpatrick et al., 2014]. The reason why these studies discountthe importance of the pressure gradient could be the disparity in the typical length scale of the pressure gradient term and the Coriolis force, and how it compares with the length scale of the front Lf (m). The lengthscale over which the pressure gradient changes is given approximately by LP = uH2/KT, where u, H, and KT
are characteristic values of average wind speed (m s1), boundary layer height (m), and vertical diffusion
coefﬁcient due to turbulent mixing, respectively [Spall, 2007; Small et al., 2008]. This length scale quantiﬁes
the distance over which the temperature of air parcels adjusts to the change of underlying SST. Similarly,
the inertial length scale over which the Coriolis term changes the velocities is Li = u/f, where f is the Coriolis
parameter (s1).
For this study, u = 5 m s1, h = 900 m, f = 0.4 × 10 4 s1, and KT = 30, which yields Li = 125 km and LP = 135 km,
so the length scale of adjustment of the pressure gradient LP and the inertial length scale Li are narrower than
the width of the front, Lf ≅ 200 km. The weak wind and the spatial scale of the front permit signiﬁcant
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Acknowledgments
Insightful comments and suggestions
from the Editor and anonymous
reviewers are appreciated. The
Operational SST and Sea Ice Analysis(OSTIA) data are now available fromthe Copernicus Marine Environment
Monitoring Service (CMEMS). The
ASCAT data are obtained from theAsia-Paciﬁc Data Research Center. The
processed data used to construct ﬁgures in this work are also available, andanyone who wants to access thesedata may contact the corresponding
author, Dongxiao Wang (Email:dxwang@scsio.ac.cn). This study was
supported by the Innovative researchgroup of National Natural Science
Foundation of China (41521005), theStrategic Priority Research Program of
the Chinese Academy of Sciences(XDA11010403), the CAS/SAFEAInternational Partnership Program for
Creative Research Teams, the National
Natural Science Foundation of China(41676018, 41476014, 41576002,
41106028), JSPS KAKENHI (JPH05821),
the Guangdong Natural Science Funds
for Distinguished Young Scholars(2014A030306049), and the 100 Talent
Program of the Chinese Academy of
Sciences. L. Zeng is also sponsored by
the Pearl River S&T Nova Program of
Guangzhou, the Open Project Programof State Key Laboratory of Tropical
Oceanography (LTOZZ1601), and the
Major State Research Development
Program of China (2016YFC1402603).
The numerical simulation is supportedby the High-Performance Computing
Division of the South China Sea
Institute of Oceanology.

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Figure 19. Schematic summarizing the mechanisms investigated in this study.

temperature changes to the air parcels as they advect across the front. This allows the adjustment of thethermal structure in the MABL to develop a sufﬁcient pressure gradient to alter the momentum in thevicinity of the front, additionally to the effect of the reversal of the Coriolis force (Figure 19). This result isthe same as the scenario when an air parcel moves slowly across a midlatitude front from warm-to-coldwater [Spall, 2007]. It is also consistent with other numerical simulation results under realistic conditions[Song et al., 2006; O’Neill et al., 2010]. In this study, there are no strong precipitation processes during theexperimental period. The inﬂuence of the front on the distribution of water vapor is not discussed in detail
here; this will be the focus of future work.

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