submitted to Journal of	Research:

manuscript submitted to Journal of Geophysical Research: Atmospheres

Oceanic Eddy Modulation of Wintertime Air-Sea Coupling and
Marine Atmospheric Boundary Layer Winds over the East China Sea
Tangxuan Song^1, Haibo Hu^1,#, Haokun Bai^2, Xiu-Qun Yang^1, Xiaoming Zhai^3,
Shengyi Qian^1, Kefeng Mao^4
^1CMA Key Laboratory for Climate Prediction Studies, School of Atmospheric Sciences, Nanjing
University, Nanjing 210023, China.
^2College of Meteorology and Oceanography, National University of Defense Technology,
Changsha 410073, China.
^3Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of
East Anglia, Norwich, Norfolk, England.
^4College of Advanced Interdisciplinary Studies, National University of Defense Technology,
Changsha 410073, China.
#Corresponding author: H.-B. Hu (huhaibo@nju.edu.cn)
Key Points
1.		The Eddy-resolved Coupled (EC) simulation shows clear advantages on the duration of low-level sustained wind events over the Kuroshio.
2.		Relative to uncoupled and low-resolution runs, the EC simulation’s increased eddy pairs boost front intensity and sea surface heat flux.
3.		Oceanic eddy pairs then limit the occurrence of the short-lived wind events but maintain the long ones for two prevailing winds.
Abstract:
This study examined the effects of air-sea coupling and oceanic mesoscale eddies on wintertime low-level wind events within the marine atmospheric boundary layer (MABL) over the Kuroshio front zone in the East China Sea. High-resolution (eddy-resolving, 5 km) and low-resolution (50 km) simulations were conducted, using prescribed sea surface temperature (SST) forcing and air-sea coupled simulations in both resolutions. Among all simulations, the eddy-resolved air-sea coupled (EC) one exhibited the strongest atmosphere-to-SST feedback, closest to reanalysis data. In the EC simulation, complex interactions of numerous oceanic eddies enhanced oceanic front intensity. Compared with uncoupled and low-resolution simulations, it better reproduced wind event statistics from reanalysis. Highly-resolved eddy pairs with positive northwest-to-southeast SST anomalies strengthened SST gradients, altering sea surface heat fluxes and MABL stability, and thereby distinctly modulating wind event durations. Specifically, short-lived events were suppressed while long-lived ones persisted, consistent with reanalysis. These effects are attributed to the influence of numerous oceanic eddy pairs, by enhancing oceanic front intensity, air-sea temperature differences, sea surface heat fluxes, and lower atmospheric stability. Anomalous winds were restrained during initiation but prolonged as events evolved.
Key words: Kuroshio front; East China Sea; air-sea coupling; oceanic eddy pair; MABL; winter sustained wind event
Plain Language Summary

Using sea surface temperature (SST) forcing atmospheric and air-sea coupling models at different horizontal resolutions, this study compares the effects of air-sea coupling and oceanic eddy-pairs on the winter marine atmospheric boundary layer (MABL) over the Kuroshio front zone in the East China Sea. Compared to the uncoupled and low-resolution simulations, the largest SST variability, the strongest oceanic front intensity with the east-west heterogeneity in the front zone are present in the produces sea surface heat flux gradient, low-level atmospheric temperature gradient, and sustained anomalous wind that most closely match the reanalysis data. Furthermore, numerous oceanic eddy-pairs are generated within the Kuroshio front zone in the EC simulations. These eddy-pairs alter the SST gradient, modify sea surface heat fluxes, impact the stability of the lower atmosphere, and ultimately affect the durations of anomalous wind events within the MABL. While previous studies have emphasized the impacts of the unidirectional forcing of the SST anomalies or an isolated oceanic eddy on the MABL, this study focuses more on the free air-sea coupling and the complex combinations of oceanic eddies.

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air-sea coupled (EC)

. However, the EC

1. Introduction
As the most powerful western boundary current in the North Pacific, the Kuroshio Current assumes a pivotal role in regulating air-sea interactions within the East China Sea (ECS). Based on its geographical distribution, the Kuroshio Current can be categorized into two distinct segments: the Kuroshio in the ECS (KECS) and the Kuroshio Extension (KE) that flows from the ECS into the Pacific Ocean. The KECS front predominantly forms along the continental shelf outer margin, where warm, saline waters from the south converge with colder, fresher shelf waters (Zhang et al., 2001; Qiu et al., 2010; Ren et al., 2023). This frontal zone exhibits pronounced sea surface temperature (SST) gradients and vigorous mesoscale eddy activity, playing a crucial role in modulating East Asia's weather patterns and climate system (Taguchi et al., 2009; Bai et al., 2019; Shi et al., 2021).
Current satellite observations in mid-latitude oceanic frontal regions worldwide indicate that local warm SST anomalies correlate with increased wind speeds, demonstrating a direct ocean-to-atmosphere forcing effect (Chelton et al., 2004; Minobe et al., 2008; Bryan et al., 2010; Xu et al., 2011). The KECS front has similarly been found to substantially influence low-level atmospheric wind fields (Xu et al., 
82	persistent low-level wind anomalies, peaking just above the marine atmospheric 83	boundary layer (MABL). Two primary mechanisms explain the formation and 84	maintenance of sustained wind anomalies in the MABL. The first is the sea level 85	pressure (SLP) adjustment mechanism (Lindzen et al., 1987), wherein warm (cold) 86	SST anomalies heat (cool) the overlying atmosphere, reducing (increasing) SLP and 87	generating pressure gradient-driven wind convergence (divergence). The importance 88	of this mechanism can be assessed by analyzing the relationship between wind 89	convergence and the SLP Laplacian (Minobe et al., 2008). The second mechanism 90	involves vertical mixing (Hayes et al., 1989; Wallace et al., 1989), where warm (cold) 91	SST enhances (suppresses) atmospheric turbulence, destabilizing (stabilizing) the 92	MABL and facilitating (inhibiting) the downward momentum transfer of high-altitude 93	winds. Small et al. (2008) found that vertical mixing dominates under high 94	background winds or narrow frontal zones due to stronger thermal advection, whereas 95	SLP adjustment prevails under low winds or broad fronts. Thus, it can be seen that the 96	precise mechanisms by which oceanic fronts force the MABL remain uncertain and 97 require consideration of atmospheric background wind conditions.

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2015; Bai et al., 2019).

et al. (2016) noted that oceanic fronts can

98	In the KECS front zone, numerous mesoscale oceanic eddies with diameters 99	ranging from 50 to 500 km have been identified (Yao et al., 2021; Ren et al., 2023). In 100	the northern hemisphere, these eddies are classified as cold cyclonic eddies 101	(counterclockwise rotation) or warm anticyclonic eddies (clockwise rotation) based 102	on their rotational direction and thermal characteristics. They can also be categorized 103	by formation processes (Ding et al., 2018): one type originates from vorticity 104	anomalies caused by horizontal shear along the Kuroshio flanks, where cyclonic 105	(anticyclonic) eddies preferentially form on the cold (warm) side, typically exhibiting 106	short lifespans; another longer-lived type generated directly by the bend of the 107	Kuroshio Current which produces cyclonic (anticyclonic) eddies on the warm (cold) 108	side. These oceanic eddies generated in the KECS front zone can subsequently 109 influence the Kuroshio path (Yin et al., 2017; Shi et al., 2021).
110	Oceanic eddies significantly influence the MABL through localized forcing (Ma 111 et al., 2015; Chen et al., 2017; Koszalka et al., 2009). These eddies rarely occur in 112 isolation, typically forming complex cyclonic-anticyclonic combinations (Ni et al., 113 2020; Wen et al., 2020). Hu et al. (2021) demonstrated that the combination of two 114 eddies with the same rotational properties in the North Pacific exert stronger 115 atmospheric forcing than individual eddies. Pairs of eddies with opposite properties 116 can modify low-level atmospheric baroclinicity and vertical wave activity by altering 117 meridional SST gradients. Considering the abundance of such non-isolated eddies in 118 the KECS front zone, their collective impact on the MABL warrants further 119 investigation.
120 While some studies have analyzed Kuroshio front-MABL interactions during 121 specific weather or climate events using observational data (Xu et al., 2018; Lü et al., 122 2021), most research has employed regional atmospheric models to simulate these 123 forcing processes. Using the WRF model, Xu et al. (2015) showed that vertical 124 mixing dominates MABL dynamics over the KECS front during spring with 125 cross-frontal winds. Kilpatrick et al. (2016) used idealized WRF simulations to 126 demonstrate that atmospheric responses to mid-latitude fronts intensify with stronger 127 background winds. Gao et al. (2019) incorporated high-resolution satellite SST data 128 into WRF simulations, capturing low-level wind responses to mesoscale SST 129 perturbations in the KE region. Bai et al. (2019) examined wintertime Kuroshio front 130 forcing under different wind conditions, finding SLP adjustment dominant for winds 131 along the front, while both vertical mixing and SLP adjustment mechanisms 132 contribute during winds cross the front. These studies reveal ongoing debate about 133 KECS front impacts, particularly as Bai et al. (2019) noted, discrepancies between 134 prescribed SST atmospheric models and coupled reanalysis data are attributed to 135 missing air-sea feedbacks. The exact mechanisms underlying these differences remain 136 unclear. In addition, there are numerous combinations of oceanic eddies within the 137 KECS front zone. While existing studies focus on isolated eddies, the abundant eddy 138 pairs within the KECS front zone necessitate investigation of their collective 139 influence on front-MABL coupling processes.
140		To resolve these research gaps, this study employs both prescribed SST regional 141 atmospheric modeling and free air-sea coupled simulations at two resolutions 142	(including eddy-resolving scales) for the KECS front zone. Through comparative 143 analysis of multiple simulation ensembles and the coupled reanalysis product, we 144 systematically examine how air-sea coupling processes and mesoscale eddy 145	interactions modulate sustained wintertime low-level wind anomalies.
146		2. Data and Methods
147		The reanalysis data used in this study is the Climate Forecast System Reanalysis 148 (CFSR) (Saha et al., 2010) (https://cfs.ncep.noaa.gov), the first dataset obtained by the 149 National Centers for Environmental Prediction (NCEP) which assimilates global 150 satellite remote sensing data into global reanalysis product and accounts for the 151 coupling of atmosphere, ocean, land, and sea ice. This study selected the wintertime 152 (November, December, January, February) atmospheric and oceanic datasets of the 153 CFSR from 2000 to 2009 with four times per day. The horizontal resolution is 154 0.5°×0.5° with 37 atmospheric and 40 oceanic vertical layers.
155		The regional atmospheric model used in this study is the Weather Research and 156 Forecasting (WRF) model version 3.5.1 (https://www.mmm.ucar.edu/models/wrf) 157	(Skamarock et al., 2008), which is a new generation of mesoscale regional weather 158 forecasting model jointly developed by the NCEP/NCAR (National Center for 159 Atmospheric Research) and other institutions. It adopts fully compressible and 160 non-hydrostatic equilibrium equation constraints and has multiple dynamic kernels 161 along with a three-dimensional variational data assimilation system. Widely 162 recognized for its ability to simulate small-scale weather processes and regional 163	climate, the WRF model is currently one of the most advanced regional atmospheric 164 models (Skamarock et al., 2005). It has been widely applied in medium and 165 small-scale atmospheric research (Carvalho et al., 2014; Xu et al., 2015; Kilpatrick et 166 al., 2016; Bai et al., 2019). The WRF model in this study utilizes the Betts Miller 167 Janjic (BMJ) cumulus convection parameterization scheme (Janjic et al., 1994, 2000), 168 the YSU boundary layer scheme developed by Yonsei University (Hong et al., 2006), 169 as well as the long wave fast radiative transfer scheme (Mlawer et al., 1997) and the 170 short wave Dudhia scheme (Dudhia et al., 1989). In addition, this study used a 171 two-way coupling method based on the Coupled Ocean-Atmosphere-Wave-Sediment 172 Transport model (COAWST) (Warner et al., 2010) to simulate the Kuroshio area in 173 the East China Sea (ECS). As this study focuses on air-sea interaction, only the 174 atmospheric module WRF model and the oceanic module Regional Ocean Model 175 System (ROMS) version 3.0 (https://www.myroms.org) in the COAWST system are 176 used. The ROMS model is a new generation regional ocean modeling system jointly 177 developed by Rutgers University and University of California, Los Angeles (UCLA). 178 It uses a three-dimensional nonlinear free surface oblique primitive equation and a 179 vertically non-uniform layered stretched coordinate system (Shchepetkin et al., 2005). 180 It has been widely used in various fields of oceanic research (Edson et al., 2007; Spall, 181 2007; Gula et al., 2015). The coupling between WRF and ROMS is achieved through 182 the Model Coupling Toolkit (MCT) coupler of the COAWST developed by Argonne 183 National Laboratory (ANL). In the coupling test, after switching on the 184 ATM2OCN_FLUXES compilation option, the WRF atmospheric model transfers the 185 sea surface wind stress (momentum flux), sensible and latent heat fluxes, long- and 186 short-waves radiations, and freshwater flux at each time step to the ROMS oceanic 187 model. Meanwhile, the ROMS model provides the real-time sea surface temperature 188 (SST) to the WRF model.
189 All the simulations are conducted during the winters (November, December, 190 January, February) from year 2000 to 2009. To ensure the accuracy of the initial field 191 and the stability of the simulation operation (Ricchi et al., 2019), November of each 192 year was used as the spin up stage of the simulation. The output data of the last 90 193 days is used for comparison. The simulations cover a latitude and longitude range 194 from approximately 20.2°N to 35.5°N, and 116.5°E to 135.5°E (Fig. S1). In the 195 horizontal direction, both WRF and ROMS models utilize the same grid with 196 resolutions of 5 km or 50 km. In the vertical direction, both WRF and ROMS use 197 terrain following coordinates. The atmosphere is divided into 38 layers from near the 198 ground in the WRF, while ROMS outputs 30 layers of data from the sea surface. Both 199 the initial and boundary fields of WRF and ROMS models are interpolated from the 
CFSR data. The time integration steps of WRF and ROMS models during simulation are both 40 seconds, with a coupling interval of 480 seconds. WRF outputs data every 6 hours, whereas ROMS outputs data every 24 hours.
To investigate the effects of air-sea coupling and oceanic eddies on the winter MABL in the Kuroshio area in the ECS, this study conducted four sets of comparative experiments within the same time and space range. These experiments (Table S1) included a WRF-only simulation with a resolution of 50 km (low-resolution SST forcing simulation, hereinafter referred to as LF), a WRF-ROMS coupled simulation with a resolution of 50 km (low-resolution coupled simulation, hereinafter referred to as LC), a WRF-only simulation with a resolution of 5 km (high-resolution SST forcing simulation, hereinafter referred to as HF), and a WRF-ROMS coupled simulation with a resolution of 5 km (eddy-resolving coupled simulation, hereinafter referred to as EC). Comparisons between forcing and coupled groups help us understand the role air-sea coupling processes play. Comparisons between high and low resolution groups help us understand the role mesoscale oceanic processes (e.g. eddies) play.
To assess potential errors in the EC simulation due to CFSR's coarse-resolution (0.5°) forcing, we performed additional simulations using higher-resolution (1/12°) GLORYS12V1 oceanic initial/boundary conditions. The GLORYS12V1 is based largely on the current real-time global forecasting Copernicus Marine Environment Monitoring Service system (Fernandez & Lellouche, 2021). The simulated climatological fields showed negligible differences between both setups (Fig. S2). Since the air-sea coupling attribute of the CFSR is the critical reference when assessing the simulation results, we maintained the CFSR as the unified forcing conditions of each simulation.
To further validate the reliability of our model evaluations, we have incorporated two independent high-resolution observational datasets. The satellite-derived SST data is the European Space Agency (ESA) SST Climate Change Initiative (CCI) and Copernicus Climate Change Service (C3S) reprocessed sea surface temperature

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analyses (https://doi.org/10.48670/moi-00169). This satellite observation data provides daily SST data in spatial resolution of 0.05°. The surface wind product is the Cross-Calibrated Multi-Platform (CCMP) (Mears et al., 2022) (https://www.remss.com/measurements/ccmp). This observation data provides 6-hourly sea surface (10-meter) winds in spatial resolution of 0.25°.
For the detection of oceanic eddies, this study used an algorithm based on the geometry of the velocity vector field (Nencioli et al., 2010; Wen et al., 2020). The minimum velocity occurs near the center of an eddy, and the tangential velocity increases linearly away from the center of the eddy until reaching its peak, after which it decreases. The distance of the increment is considered as the radius of the eddy. Furthermore, oceanic currents should flow in a consistent direction or pattern. For the detection of eddy pairs, this study focuses on two adjacent eddies with opposite vorticity. Such eddy pairs fall into two categories based on their positional relationships: eddy pairs with a positive northwest-to-southeast SST gradient (EPPG, with a cold cyclonic eddy located north of a warm anticyclonic eddy) and eddy pairs with a negative northwest-to-southeast SST gradient (EPNG, with a warm cyclonic eddy located north of a cold anticyclonic eddy) (Wen et al., 2020; Hu et al., 2021). In this study, the distance between the centers of the two eddies is limited to a specified radius of 300km (Fig. S3). A snapshot of an eddy pair is also shown in Fig. S3.
3.		The impacts of air-sea coupling and eddy-resolving on the MABL
3.1		Air-sea coupling and impacts of model resolution on the simulated MABL
While previous studies have examined oceanic front impacts on the MABL using prescribed SST atmospheric models (Koszalka et al., 2009; Xu et al., 2015; Ma et al., 2015; Chen et al., 2017), such approaches cannot capture air-sea feedbacks, leading to discrepancies with reanalysis data (Bai et al., 2019). Recent work (Hu et al., 2021) further shows oceanic eddy pairs affect the MABL differently than isolated eddies. We therefore compare four simulations (LF, LC, HF, EC) with CFSR data to investigate the coupling and resolving discrepancies within the KECS front zone. The

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average SST and sea surface wind during winter in the Kuroshio region
are shown in the Fig. 1. Considering the strong horizontal velocity shear, which is one of the key characteristics of the Kuroshio current (Qiu et al. 1991; Kawabe et al. 1995), we identified the main axis of the Kuroshio based on the position of the climatological maximum surface current velocity, and further defined the range of the main axis from 2° south to 2° north as the front zone (dashed purple box area in Fig. 1). All subsequent wind statistics (e.g., direction) are calculated as spatial averages over this defined front zone. The climatological SST in all datasets exhibits a relatively consistent distribution, with the fronts of the SST horizontal gradient located at the
edge of the ECS continental shelf. The climatological sea surface wind also aligns

with the background northeast wind in each dataset.

Fig 1. The average SST (contours with colors; °C) and 10-meter wind field (arrow; m/s) during the winter of 2000-2009. The solid purple lines represent the main axis of the Kuroshio determined by the surface current velocity, and the areas within the dashed purple lines represent the Kuroshio front area. (a) The Climate Forecast System Reanalysis (CFSR). (b) The European Space Agency (ESA) SST Climate Change Initiative (CCI) and Copernicus Climate Change Service (C3S) and the Cross-Calibrated Multi-Platform (CCMP). (c) The low-resolution forcing simulation (LF). (d) The low-resolution free air-sea coupled simulation (LC). (e) The 279 simulation (EC). The Kuroshio front areas of observation, LF and HF are determined 280 to be the same as CFSR. The contours with lines represent the average SST in the 281 CFSR.

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simulation (HF). (f) The eddy-resolved free air-sea coupled

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283 Further, the Taylor diagram was used to compare the correlations of the 284 simulated SST gradient, air temperature gradient, sea surface heat flux gradient, and 285 wind speed (using original time series) with those from the CFSR (Fig. 2). In the 286 process of daily variation, each simulation shows certain differences. Since the SST 287 forcing fields in the HF and LF simulations are spatial interpolations of the CFSR data, 288 the Taylor diagrams of the SST (not shown) and SST gradient in the Kuroshio front 289 area are highly consistent with the CFSR data. However, for the EC and LC 290 simulations, the time correlations of the SST gradient between simulations and CFSR 291 data are only about 0.9 and 0.8, which are weaker than that of the two forcing groups. 292 This is attributed to the feedback of the sea surface heat flux on the SST in the free 293 air-sea coupling. The eddy-resolved EC simulation showed a more pronounced 294 variation of the SST gradient compared to the LC simulation. Nevertheless, the four 295 simulated low-layer wind speeds show a high consistency over the KECS front. The 296 sea surface heat flux gradient in the EC simulation along with that in the LF and HF 297 simulations is closer to the CFSR data than that in the LC simulation. In addition, the 298 850 hPa temperature gradient over the front zone also exhibits a higher similarity in 299 the EC simulations, which can be used to characterize the thermodynamic properties 300 of the low-level atmosphere. The above results indicate that the air-sea coupling, as 301 well as the presence of mesoscale oceanic processes, contributes to an increase in the 302 time variability of SST gradients in the front zone. Furthermore, the air-sea coupling 303 and mesoscale oceanic processes also alter the net heat flux at the sea surface and the 304 thermal structure of the low-level atmosphere.

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Fig 2. The Taylor diagram of the average SST gradient, 850 hPa temperature gradient, heat flux gradient and wind velocity of the Kuroshio front zone in the East China Sea during the winter of 2000-2009 in the four groups of simulations, with the CFSR data used as a reference. “1” represents the SST gradient; “2” represents the 850 hPa temperature gradient; “3” represents the sea surface heat flux gradient; “4” represents the 10-meter wind speed. The red, orange, light blue and dark blue dots represent the EC, HF, LC, and LF simulations, respectively.
According to previous studies (Wu et al., 2006; Bai et al., 2019), the lead-lag correlations between the SST (or SST tendency) and sea surface heat flux are important indicators of the air-sea interaction. When the SST leads (SST tendency lags) the upward heat flux of the sea surface, it is considered as ocean forcing on the atmosphere (atmosphere feedback to the ocean). Fig. 3 examines the lead-lag relationship between SST (or SST tendency) and sea surface heat flux (a positive value indicates heat flux from the ocean to the atmosphere) in the four simulations, and compares the results with those of the CFSR, respectively. It can be seen that the forcing effects (positive correlation) of SST on the sea surface heat flux are more obvious in the two coupled simulations (EC and LC) than the two SST forcing simulations (LF and HF) during the leading time of the SST. Meanwhile, the two coupled simulations also show more significant simultaneous feedback to the ocean (negative correlation), especially in the EC experiment. In the LF and HF simulations, the forcing of the sea surface heat flux on the SST tendency is approximately 20%
weaker than that of CFSR. However, the heat flux in the EC simulation causes a rapid
change in SST, leading to the larger variance of the SST in Fig. 2.

Fig 3. Lag-lead correlation of the mean SST (SST tendency) and sea surface heat flux over the Kuroshio front zone in the East China Sea during the winter of 2000-2009 in the four simulations and Climate Forecast System Reanalysis (CFSR). The x-axis represents the number of leading days, where the negative values represent the SST leading, while the positive values represent the heat flux leading. The solid lines are the correlation coefficient between SST and sea surface heat flux, while the dashed lines are the correlation coefficient between the SST tendency and sea surface heat flux. The black lines are the result in CFSR (the black line is the same in the four sub-plots), and the orange lines are the result of the corresponding simulations: (a) The LF simulation; (b) the LC simulation; (c) the HF simulation; (d) the EC simulation. The absolute critical correlation for the 95% confidence level is about 0.205.
Considering the unique characteristics of the KECS front, particularly its intense ^,

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SST gradient, we further examined the relationships between the SST gradient, its daily tendency, and the sea surface heat flux gradient (Fig. 4). Unlike the previous comparison in Fig. 3, the two low-resolution simulations (LC and LF) show poorer consistency with the CFSR, especially the LF simulation. However, due to the better resolve SST details in the high-resolution simulations (EC and HF), especially the addition of numerous oceanic eddies in the EC simulation, significant oceanic forcings on the heat flux exist when the SST leads. During simultaneous air-sea interaction, the EC simulation exhibits the strongest forcing intensity of the heat flux gradient on the SST tendency gradient, showing the closest agreement with the CFSR among all simulations.
Fig 4. Same as Fig 3, but for SST (tendency) gradient and sea surface heat flux gradient.
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359	3.2 Eddy effects on oceanic fields in the air-sea coupled simulations
360	Beyond the comparisons of the low-level atmosphere and the heat flux fields, we
361	further analyzed the differences in the oceanic field among various datasets. Given
362	that the regional atmospheric simulations only use the CFSR-interpolated SST, we 363	focused the comparison on the spatial distributions of the oceanic currents, eddies and
364	SST gradients among the LC simulation, EC simulation, and the CFSR (Fig. 5). The 365	results clearly show that the SST gradient in both air-sea coupled simulations 366	increases significantly compared to the CFSR data. With improved resolution, the 367	oceanic current velocity in the EC simulation was largely strengthened. Notably, the 368	high-value area of the SST gradient in the LC simulation is located to the north of the 369	Kuroshio current front. While the three datasets show similarities in the distribution of 370	the KECS, the EC simulation exhibits a more refined and complex oceanic current 371	field. Li et al. (2015) and Pang et al. (2017) reported that several coastal cold currents 372	in the ECS and the Yellow Sea originate from rivers in the Chinese mainland. These 373	southeastward cold currents are weak in summer but strong in winter, resulting in 374	significant SST fronts in the ECS. On the north side of the Kuroshio, both the LC and 375	EC simulations show an obvious northward current compared to the CFSR data. 376	Given that the initial and boundary conditions are identical, this result likely stems 377	from the free air-sea coupling. The improved resolution also allows the detection of 378	numerous oceanic eddies in the EC simulation (Fig. S4). Additionally, Ma et al. (2016) 379	suggested that the suppression of the interaction between eddies and the atmosphere 380	could change the path of the Kuroshio, which may partly explain the location 381	difference. Although the resolution of the CFSR is comparable to the LC simulation, 382	the CFSR contains slightly more eddies statistically. Regarding oceanic eddy pairs - 383	defined in Section 2 as simultaneously occurring adjacent cyclonic and anticyclonic 384	eddies - these were detected in all three datasets (Fig. 5). Consistent with the 385	distribution of oceanic eddies, the EC simulation exhibits the highest number of
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Fig 5. The average SST gradient (contours; ℃/20km) and sea surface current (arrows; m/s) during the winter of 2000-2009. The solid purple lines represent the main axis of Kuroshio, determined by sea surface current velocity, and the areas within the dashed purple lines represent the Kuroshio front zone. The green triangles represent eddy pairs with a positive SST gradient (EPPG), while the blue inverted triangles represent eddy pairs with a negative SST gradient (EPNG). The eddy pairs in this Figure are derived from the superposition of daily eddies over a ten-year winter average. (a) The CFSR data; (b) The LC simulation; (c) The EC simulation.
To better understand the discrepancies in SST gradients among the three coupled datasets and to investigate the influence of oceanic eddies, we analyzed the distribution of SST gradients with longitude within the KECS front zone (Fig. 6a). The SST gradients in the EC simulation and the CFSR exhibit greater heterogeneity on the east and west sides compared to the LC simulation (particularly with noticeable peaks to the east of 126°E). The presence of oceanic eddy pairs in the front zone maintains the intense SST gradient and enhances east-west heterogeneity in the EC simulation (gray line in Fig. 6a). As shown in Fig. 5c, numerous oceanic eddy pairs with positive and negative SST gradients coexist in the front zone, exerting opposite effects on the total SST gradient. Statistical analysis of the oceanic eddy pairs in the KECS front zone reveals an asymmetry in their distribution, with a predominance of eddy pairs with positive SST gradients (EPPGs) over those with negative SST gradients (EPNGs). This asymmetry is more pronounced on the east side of the front (Fig. S5). We have also calculated the mean SST gradients within annuli of increasing radial distance from the center of each eddy pair, as presented in Fig. 6d. It is shown
that the mean SST gradient decreases as distance from the eddy pair center increases.
The gradients keep strong within distance of about 60 km, consistent with the localized frontal intensification induced by the eddy pairs. These findings explain why
SST gradient values within the front zone are significantly higher in the EC

simulation than in the LC one, particularly on the east side of the study zone.

5$/$

Fig 6. The SST gradient (℃/20km) under the meridional mean of the Kuroshio front zone during the winter of 2000-2009 under (a) all time; (b) northwest winds; (c) southwest winds. The solid black lines represent the CFSR, the dotted blue lines represent the LC simulation, the dotted red lines represent the EC simulation, and the solid gray lines represent the SST gradient after the time average in the surrounding area at each eddy pair in the EC simulation. (d) The histograms of SST gradients (℃/20km) with the distance (km) from the eddy pair centers.
3.3 Air-sea coupling and eddy effects on the low-level winds
Previous studies have demonstrated the forcing effect of the Kuroshio front on the wind field in the MABL (Xu et al., 2015; Bai et al., 2019). While there is no significant difference between the climatological wind speed during winter in the four numerical experiments and the CFSR data (Figs. 1&2), our above analysis reveals significant differences in air-sea interaction in the KECS front zone among the four

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simulations with different model settings. This raises an important question: how do

the air-sea coupling and oceanic eddies affect the wind field? Before answering this question, it is essential to understand the daily variation of the low-level wind pattern in the study zone. Following Bai et al. (2019), we categorized the winds into four predominant directions in the daily sea surface anomalous wind field (defining 0° as from south to north): the northwest (NW, 90°–180°), the northeast (NE, 180°–270°), the southeast (SE, 270°–360°) and the southwest (SW, 0°–90°) wind. Minor variations in the frequency of these four sustained anomalous winds are observed among these datasets (Fig. 7). Anomaly values are calculated by subtracting the corresponding daily climatological mean. The NW and SE winds, which are perpendicular to the Kuroshio main axis, account for a substantial proportion of all four events, each reaching about 30%. Notably, more significant differences exist in the total duration for the NW or SE events among all simulations. Consequently, the NW and SE sustained anomalous wind events were selected as the focus for subsequent work. The EC (LC) simulation with (without) abundant oceanic eddies demonstrates a larger (smaller) SST gradient and a stronger (weaker) east-west heterogeneity than that observed in the CFSR data.

Fig 7. The histogram of the proportion of days of the four 10-meter anomalous wind directions in the five datasets. The y-axis represents the percentage.

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The probability density distributions of the daily durations of the prevailing NW
454 and SE wind events are shown (Fig. 8). For both NW and SE events, the results from 455 the EC simulation at eddy-resolved resolution are closest to the CFSR (and 456 observation winds in the CCMP data) among the other three simulations. We defined 457 wind events of 1 to 2 days as short-lived events, and events lasting more than 3 days 458 as long-lived events. Notably, both the EC simulation and the CFSR results show a 459 reduced proportion of short-lived sustained events and an elevated proportion of the 460 long-lived sustained events compared to the other three simulations, especially for the 461 NW events. For the SE events, relative to the two forcing simulations, the results of 462 the two coupling simulations are closer to the CFSR. The Taylor diagram analysis of 463 climatological winter wind fields demonstrates that the high consistency across 464 simulations does not fully capture the fundamental differences between each 465 simulation and the CFSR. This consistency may arise from the uniform atmospheric 466 horizontal boundary forcing derived from the CFSR in all simulations. The two 467 SST-forcing simulations, as well as the LC simulation, produce more short-lived wind 468 events in the low-level wind field than observed, especially the NW events. In 469 contrast, the EC simulation shows increased numbers of long-lived NW events while 470 maintaining nearly unchanged total event days. This may suggest an enhanced 471 intensity of climatological NW winds, which subsequently reduces the SST in the 472 cold side of the Kuroshio.

474 Fig 8. The proportion of the duration of two events. The x-axis represents the duration 475 (days) and the y-axis represents the percentage. The dark red, orange, light blue, dark 476 blue and black lines represent the CCMP, the EC simulation, the HF simulation, the 477 LC simulation, the LF simulation, and the CFSR. (a) The northwest anomalous wind 478 (NW) events. (b) The southeast anomalous wind (SE) events.
479
480 This section compares results from four simulations (EC, LC, HF, and LF) with 481 CFSR to evaluate free air-sea coupling and oceanic eddy effects on the MABL over 482 the KECS front zone. The daily variation of the wind field in the four numerical 483 simulations does not accurately match the CFSR data. On the other hand, the oceanic 484 currents and SST gradient patterns between the coupled datasets exhibit obvious 485 distinctions. With the presence of additional eddy pairs in the EC simulation, the 486 intense SST gradient and its east-west heterogeneity are better captured. To 487 investigate the sea surface wind discrepancies among simulations, we analyzed 488 probability densities of prevalent NW and SE sustained wind events. The HF and LF 489 simulations, as well as the LC simulation, falsely increase the short-lived wind events 490 while reduce the long-lived ones. This prompts two key questions: (1) What drives the 491 simulation differences in event duration? (2) How do EC simulation's more abundant 492 eddy pairs influence anomalous wind formation and maintenance, given its superior 493 performance? These questions are discussed in the next section.
494 4. The formation and maintenance mechanisms of the long- and short-lived 495 anomalous wind events
496 In the previous section, we found variations in the durations of the anomalous 497 sustained winds in the MABL above the KECS among the four simulations. The 498 results from the EC simulation most closely match the probability densities of 499 sustained anomalous wind observed in the CFSR. On the other hand, it has been well 500 established that the formation and maintenance of the sustained anomalous wind 501 events in the lower atmosphere mainly depend on two mechanisms: the sea level 502 pressure (SLP) adjustment mechanism (Lindzen et al., 1987) and the vertical mixing 503 mechanism (Hayes et al., 1989; Wallace et al., 1989). To further investigate how the eddy pairs force the winds, we conducted a focused comparison between the two coupled simulations during the two prevailing anomalous wind events.
On one hand, the relationship between wind convergence and SLP Laplacian was examined based on the SLP adjustment mechanism (Figs. S6-S9). The SLP Laplacian and sea surface wind convergence of the NW and SE events exhibit linear relationships in the Kuroshio front area, indicating that the SLP adjustment mechanism contributes to both NW and SE events.
On the other hand, to compare the differences of vertical mixing processes between the two simulations, the black dashed line area in Fig. 9 that passes through the center of the Kuroshio front is selected as the vertical section. The results on several vertical sections along the northwest-southeast direction are averaged to reduce the influence brought by the east-west heterogeneity of SST gradients, as discussed in the final section. In agreement with the research by Bai et al. (2019), during the NW (SE) events, the vertical mixing rate anomalies over the Kuroshio front increase (decrease), accompanied by a rise (fall) in the MABL (Fig. S10). It is evident that both the SLP adjustment mechanism and the vertical mixing mechanism play a certain role in the NW and SE events, with no significant difference between the two coupled simulations. We further hypothesize that the greater number of oceanic eddy pairs in the EC simulation, which enhances SST gradients and their asymmetric east-west distribution, may account for the varied durations of the sustained anomalous wind events in different simulations.
As the sea surface turbulent heat flux represents a significant indicator of the forcing relationship between ocean and atmosphere (Wu et al. 2006), we analyzed its variation during the NW and SE sustained wind events. First, the differences in sea surface turbulent heat flux during the NW sustained wind events between the EC and LC were compared (Fig. 9). On the day before the NW sustained wind events (Fig. 9a), the EC simulation exhibits a larger overall sea surface heat flux to the atmosphere (upward) than that of the LC. On the first day of the events (Fig. 9b), significant heat flux from the air to the sea (downward) occurs on the cold side of the Kuroshio front.

533

Meanwhile, southeast winds developed over the front zone in the EC relative to the
LC simulation. The presence of more eddy pairs with positive SST gradients on the east side results in larger SST and heat flux gradients in the EC simulation. By the next day (Fig. 9c), the southeast wind transitions into the northwest background wind. On the third day (Fig. 9d), the sea surface heat flux over the front and the warm side
shifts to an upward distribution in the EC simulation in contrast to the LC. Over time,

the differences between the two simulations gradually diminish.

2
Fig 9. The difference in sea surface heat flux (contours; W/m^2) and wind field (arrows;

m/s) during daily period of NW sustained wind events between the EC and LC

simulation (EC minus LC). “Day:0” represents the first day of the events. The dots have passed the 90% significance test. The green triangles are the oceanic eddy pairs with positive temperature gradients, while the blue inverted triangles are the oceanic eddy pairs with negative temperature gradients. The eddy pairs are the superposition of each event. The solid purple lines are the main axis of the Kuroshio. The areas within the purple dashed lines represent the Kuroshio front zone. The areas delineated by the black dashed lines represent the vertical section. (a) to (f) represent the periods from one day before the events to five days after the events respectively.
The Brunt-Vaisala Frequency (BVF) (Tsuda et al., 1991) quantifies the vertical

553

stability of a fluid and helps elucidate the influences of sea surface heat flux on

sustained anomalous wind events. Accordingly, the BVF values, air temperature and wind fields (EC-LC differences) on the cross-front atmospheric section (within the black dotted line in Fig. 9) are compared in Fig. 10. During the formation stage of the NW sustained wind events (days 0-1), the enhanced SST gradient in the EC simulation generates more pronounced surface heat flux contrasts between the two sides of the Kuroshio relative to the LC simulation. On the cold side in the first day, the substantial downward heat flux induces higher stability, reduced downward momentum transport, and the development of southeast wind. However, starting from the second day (day:1) (Fig. 10c), stronger northwest winds in the EC simulation start to cross the front, coinciding with enhanced upward heat flux throughout the entire front zone. Concurrently, the significant decrease in BVF and lower-atmospheric warming in the EC simulation facilitate downward momentum transfer from upper-level northwest winds, which thereby sustaining the lower northwest wind. The contrasting stability changes between the lower and upper layers in Fig. 10a-c may result from the enhanced surface heating in the EC simulation. The stronger upward heat flux warms and destabilizes the lower MABL, reducing static stability and facilitating downward momentum transfer from the overlying NW winds. As this

571

, it

top of the

Fig 10. The difference in Brunt-Vaisala Frequency (contours in color; s^-2), air temperature (contours in lines, ℃) and wind field (arrows; m/s) along with the comparison of the sea surface heat flux (W/m^2) on the vertical section during the daily period of NW sustained wind events between the EC and LC simulation (EC minus LC). The x-axis represents longitude (°E) and the y-axis represents altitude (m). “Day:0” represents the first day of the events. The dots have passed the 90% significance test. The purple triangles indicate the location of the main axis of the Kuroshio. The black lines are the sea surface heat flux on the vertical section in the EC and the orange lines are that in the LC. The bar charts represent the difference between the two lines. The left y-axis of the bottom half of each Fig shows the value of sea surface heat flux in the two simulations (W/m^2), while the right y-axis shows the difference in sea surface heat flux between the two simulations (W/m^2). (a) to (f) represent the period from one day before the events to five days after the events, respectively.
Analogous variable differences are also calculated for the SE events (Figs. 11&12). During the initial two days (days 0-1) of the SE events (Fig. 11b&c), comparison between the EC and LC simulation reveals upward sea surface heat flux on the cold side but downward flux on the warm side of the Kuroshio, which are

592

opposite to those during the early stage of the NW events. This contrast stems from
differences between SST and surface air temperature (SAT), which is the key
determinant of the direction of sea surface heat flux. As shown in Fig. S11, SST exceeds SAT during NW events, whereas SST falls below SAT during SE events. By day 1 of the SE sustained wind events (Fig. 11c), southeast winds emerge in the EC simulation relative to the LC simulation. On day 3 (Fig. 11d), the heat flux on the warm side also reverses to upward. Subsequently, the disparity in sea surface heat flux

gradually diminishes, and the southeast wind eventually dissipates.

Fig 11. Same as Fig 9, but for SE sustained wind events.

Vertical section analysis for the SE events is presented in Fig. 12. The maximum sea surface heat flux gradient occurs on the first day (day 0 in Fig. 12b). In response to the downward heat flux over the warm side of the Kuroshio, the lower atmosphere, particularly over the cold side is generally stable driven under the influence of the background southeast wind. From the second day (day 1 in Fig. 12c) onward, pronounced southeast develop before weakening over subsequent days. Meanwhile,

609

the SE events, sea surface heat flux shows a gradual increase on both sides
of the Kuroshio. By the third day (Fig. 12d), the heat flux throughout the front zone shifts upward, resulting in reduced stability of lower atmosphere and further

enhancing downward momentum transfer from the background southeast winds.

Fig 12. Same as Fig 10, but for SE sustained wind events.

This section reveals that identical CFSR atmospheric boundary forcing produces negligible differences in sustained wind event development between the two coupled simulations. However, varying SST and turbulent heat flux gradients in the KECS front zone significantly influence MABL wind event durations (primarily NW/SE events). Composite analysis shows upward heat flux weakens on the cold (warm) side before NW (SE) winds cross the front, enhancing atmospheric stability and inhibiting downward momentum transfer. Conversely, when NW (SE) winds traverse the front, the EC simulation exhibits stronger surface heat release on the warm (cold) side compared to LC, creating atmospheric instability that sustains upper-level momentum the EC simulation.

625

reduced

events and increased long-lived events in

5. Conclusions and Summary
Building upon previous studies of sea surface temperature (SST) anomalies and isolated oceanic eddies in the Kuroshio zone of the East China Sea (KECS), this work systematically investigates the influence of air-sea coupling processes and oceanic eddy pairs on marine atmospheric boundary layer (MABL) winds. We employed the Weather Research and Forecasting (WRF) atmospheric model with prescribed SST and WRF- Regional Ocean Model System (ROMS) coupled model to conduct four winter (2000-2009) experiments in the KECS front zone: 1) low-resolution WRF forcing (LF), 2) low-resolution WRF-ROMS coupled (LC), 3) high-resolution WRF forcing (HF), and 4) eddy-resolving WRF-ROMS coupled (EC), all constrained by CFSR reanalysis data.
All simulations reproduce observed sea surface winds and SST patterns, with the EC simulation showing stronger SST variability and more accurate heat flux gradients. These results underscore air-sea coupling's key role in MABL-SST interactions. While both LC and EC simulations employ free air-sea coupling, two simulations show distinct SST gradient patterns in the KECS front zone, with the EC simulation exhibiting stronger gradients and east-west contrasts. Statistical analysis confirms these differences stem from EC's higher eddy pair frequency due to its finer resolution. On the other hand, the northwest (NW) and southeast (SE) winds dominate sustained wind events within the MABL in the KECS front zone. While all simulations reproduce observed wind patterns, their anomalous wind event durations differ markedly. The durations in the EC simulation best match the CFSR, whereas others overestimate short-lived events and underestimate long-lived ones.
Analysis of NW and SE anomalous wind events reveals that enhanced SST gradients and their east-west heterogeneity in the EC simulation drive contrasting heat flux responses across the Kuroshio front (Fig. 13). During initial formation (Fig.

653

heat

on

NW,

for SE)

—caused by eddy pair-induced SST gradients — enhances atmospheric stability, thereby inhibiting downward momentum transfer and suppressing short-lived winds. As winds traverse the front (Fig. 13c&e), increased heat release on the farther (north for NW, south for SE) sides destabilizes the atmosphere, sustaining momentum transfer and prolonging long-lived winds. These effects are amplified on the eastern flank due to pronounced eddy pair and SST gradient heterogeneity.

Fig 13. The conceptual diagram of the mechanism of different anomalous wind events in the MABL influenced by free air-sea coupling and eddy pairs. The blue (orange) circles represent cyclonic (anticyclonic) eddies. The green (blue inverted) triangles represent eddy pairs with a positive (negative) SST gradient. The darker ellipses near eddies or eddy pairs represent the enhancement of SST gradient.
In addition, the Intergovernmental Panel on Climate Change's (IPCC) Sixth Assessment Report (AR6) evaluated future global climate change projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) models (Eyring et al., 2016). Our results indicate that, at least for the winter KECS region, the feedback effects of mesoscale oceanic eddies (and notably eddy pairs) on the MABL are substantial. When accounting for the feedback effects of mesoscale oceanic eddies (eddy pairs) on the MABL, the resulting air-sea coupling simulations could yield significantly different outcomes. Therefore, we recommend that future efforts to net effect of eddy‑scale SST structures—including eddy pairs—on the overlying atmosphere, particularly in western boundary current regions where mesoscale activity is intense.

675

improve climate models consider

parameterizations that can represent the

Acknowledgments
Tangxuan Song and Haibo Hu contribute equally as first authors. This work was supported by the National Key Program for Developing Basic Science (grants 2022YFF0801702 and 2022YFE0106600), the Youth Independent Innovation Science Foundation (Grant ZK23-56), the National Natural Science Foundation of China (grant 42405047), the Jiangsu Province Science Foundation (Grant No. BK20201259). The authors are thankful for the support of the Jiangsu Provincial Innovation Center for Climate Change. The reanalysis datasets used in our study are the NCEP Climate Forecast System Reanalysis 6-hourly data (Saha et al., 2010). The observational datasets used in our study are the ESA SST CCI and C3S reprocessed sea surface temperature analyses (https://doi.org/10.48670/moi-00169) and the Cross-Calibrated Multi-Platform	wind	product	(Mears	et	al.,	2022) (https://www.remss.com/measurements/ccmp).
Declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Data Availability Statement
The reanalysis datasets used in our study are the NCEP Climate Forecast System Reanalysis 6-hourly data (https://cfs.ncep.noaa.gov). The observational datasets used in our study are the ESA SST CCI and C3S reprocessed sea surface temperature analyses (https://doi.org/10.48670/moi-00169) and the Cross-Calibrated Multi-Platform wind product (https://www.remss.com/measurements/ccmp).
Conflicts of Interest Statement
The authors have no conflicts of interest to disclose.
Authors' contributions
Writing‐original draft: Tangxuan Song and Haibo Hu
Writing - Review and Editing: Tangxuan Song, Haibo Hu, Haokun Bai, Xiu-Qun Yang, Xiaoming Zhai, Shengyi Qian and Kefeng Mao
Visualization: Tangxuan Song

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