remote sensing

Article Regional Dependence of Atmospheric Responses to Oceanic Eddies in the North Pacific Ocean

Jinlin Ji 1,2, Jing Ma 3, Changming Dong 4,*, John C. H. Chiang 5 and Dake Chen 2,6

1 College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China; [email protected] 2 Second Institute of Oceanography, MNR, Hangzhou 310012, China; [email protected] 3 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, University of Information Science and Technology, Nanjing 210044, China; [email protected] 4 Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science & Technology, Nanjing 210044, China 5 Department of Geography, University of California Berkeley, CA 94720-4740, USA; [email protected] 6 School of Oceanography, Shanghai Jiao Tong University, Shanghai 200000, China * Correspondence: [email protected]; Tel.: +86-025-5869-5733



Received: 28 February 2020; Accepted: 1 April 2020; Published: 4 April 2020


Abstract: Based on sea surface height anomaly (SSHA) from satellite altimeter and microwave radiometer datasets, this study investigates atmospheric responses to oceanic eddies in four subdomains of the North Pacific Ocean with strongest eddy activity: Kuroshio Extension (KE), Subtropical Front (SF), California Coastal Current (CC) and Aleutian Islands (AI). Analyses show that anticyclonic eddies cause sea surface temperature, surface wind speed and precipitation rate to increase in all four subdomains, and vice versa. Through a further examination of the regional dependence of atmospheric responses to oceanic eddies, it is found that the strongest and the weakest surface wind speed responses (in winter and summer) are observed in the KE and AI region, respectively. For precipitation rate, seasonal variation of the atmospheric responses to oceanic eddies is strongest in winter and weakest in summer in the KE, CC and AI regions, but stronger in summer in the SF area. The reasons for such regional dependence and seasonality are the differences in the strength of SST anomalies, the vertical kinetic energy flux and atmospheric instability in the four subdomains.

Keywords: mesoscale eddies; atmospheric responses; regional dependence; North Pacific Ocean

1. Introduction Mesoscale eddies are ubiquitous in oceans globally [1–6] and play integral roles in redistributing ocean heat, salt, momentum and nutrients [7–20]. These structures span tens to hundreds of kilometers and can occur from tens to hundreds of days. Recently available high-resolution satellite measurements of the global ocean and the rapid development of supercomputing resources has led to more intensive mesoscale air–sea interaction research. Extensive research shows that the majority of sea surface temperature anomaly (SSTA) phenomena are associated with oceanic mesoscale processes such as oceanic mesoscale eddies and fronts [2,9,21–23]. Several studies [24–30], using satellite and numerical data to investigate air–sea interactions over mesoscale oceanic eddies and frontal regions, show a high positive correlation between wind speed and SST. Their results are contrary to basin-scale ocean circulation and variation driven by the overlying atmosphere (wind stress), which portray a negative correlation between wind speed and SST in the extratropics. The effects of transient oceanic eddies on the overlying atmosphere are examined in Kuroshio Extension [24], Kuroshio, Gulf Stream, Agulhas Return Current, and Southern Ocean [28–30] regions, respectively. In these studies, it is found that cyclonic (anticyclonic) eddies can

Remote Sens. 2020, 12, 1161; doi:10.3390/rs12071161 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 1161 2 of 18 promote decreases (increases) in SST, wind speed, cloud liquid and precipitation rate. Recent studies also indicate that oceanic frontal-scale variations in some strong current regions impact the marine atmospheric boundary layer (MABL) [29,31], and even the troposphere [21,24,32]. Two primary mechanisms are responsible for sea surface wind responses to SSTA: (1) the vertical mixing mechanism [32,33], in which the near-surface atmosphere becomes unstable over warmer SSTs, thereby inducing an enhanced downward momentum transport from higher up in the atmospheric column and consequently, intensifying surface winds; (2) the sea level pressure (SLP) adjustment mechanism [34], where SST anomalies alter the MABL’s air temperature, resulting in an SLP increase on the cold side. Over warm SST, these low-pressure anomalies generate cyclonic circulation and induce surface wind convergence. As discussed above, several previous studies have investigated the effects of mesoscale eddies on the overlying atmosphere in specific regions [24,27–32,35–37], such as Kuroshio Extension [24], Gulf Stream, Agulhas Return Current, Southern Ocean, and Kuroshio [27–30] regions. Such separate studies do not answer how the oceanic and atmospheric conditions affect the atmospheric response to the oceanic eddies, which is essential to understand the problem fully. In the present study, we chose four areas to investigate how the oceanic eddies impact the atmospheric physical processes under different oceanic and atmospheric conditions. However, studies on the regional dependence of atmospheric responses to oceanic eddies are still lacking. To bridge this gap, we used high-resolution satellite data and a reanalysis product to establish two air–sea coupled mesoscale eddy data sets in four eddy-rich subdomains of the North Pacific Ocean: Kuroshio Extension [KE, (28◦N–42◦N, 140◦E–180◦E)], Subtropical Frontal region [SF, (15◦N–23◦N, 123◦E–150◦W)], California Coastal Current [CC, (20◦N–40◦N, 105◦W–140◦W)] and Aleutian Islands [AI, (48◦N–56◦N, 165◦E–155◦W)]. We used the two datasets to examine the regional dependence of atmospheric responses to mesoscale eddies with temporal variations by comparisons among the four subdomains. The underlying mechanisms are also discussed. The remainder of the present paper is organized as follows: Section2 introduces the data and method; Section3 investigates eddy characteristics and atmospheric responses to mesoscale eddies. Sections4 and5 are a discussion of the underlying mechanisms and a summary, respectively.

2. Data and Methods

2.1. SSHA Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) produce and distribute altimeter SSHA datasets at a spatial resolution of 1/3 1/3 and a weekly time resolution. ◦ × ◦ Data covering 2002–2010 in the four North Pacific subdomains are used.

2.2. AMSR-E The Advanced Microwave Scanning Radiometer (AMSR) is a passive microwave radiometer with twelve channels and six frequencies installed on the National Aeronautics and Space Administration’s (NASA’s) Aqua satellite. It measures brightness temperatures in vertical and horizontal polarizations at 6.9, 10.7, 18.7, 23.8, 36.5 and 89.0 GHz. Footprint spatial resolutions are 75 43 km, 51 29 km, × × 27 16 km, 32 18 km, 14 8 km and 6 4 km, respectively. Wind speeds can be inversed with × × × × measured brightness temperatures in multi-channels at both low (6.9 and 10.7 GHz) and high (36.5 GHz) frequencies. Moreover, the low frequency 6.9 and 10.7 GHz channels are applied to retrieve SST and the 23.8 GHz channel is chosen as the water vapor absorption frequency. For the precipitation rate and cloud liquid water, inversion techniques are based on brightness temperature at channels 36.5 and 89.0 GHz. The detailed algorithms could be found in Shibata et al. [38]. The present study applies the grid daily data (SST, sea surface wind, water vapor, cloud liquid water and precipitation rate) distributed by Remote Sensing Systems (REMSS) with a spatial resolution of 25 km. We focus on the period 2002–2010 in the four North Pacific subdomains. Remote Sens. 2020, 12, 1161 3 of 18

2.3. J-OFURO3

The latent and sensible heat fluxes are estimated from the bulk formula: Q = ρCpC U (ts S S 10 − ta), QL = ρLECLU (qs qa), with SST, wind speed, air temperature and specific humidity, where Q 10 − S and QL are the latent and sensible heat fluxes; Cp, CS and CL are the specific heat capacity of air, sensible and latent heat transfer coefficients, respectively; LE represents the latent heat of evaporation; U10 is the wind speed at 10 m above the sea surface; ta and ts are the air and sea temperature, respectively; and qs and qa are the 10 m specific humidity at the sea surface and above, respectively. The Japanese Ocean Flux with Use of Remote Sensing Observations (J-OFURO) [39] version 2 dataset is employed to obtain latent and sensible heat fluxes at a spatial resolution of 0.25◦. Both high-resolution fluxes are obtained from satellite data provided by the School of Marine Science and Technology at Tokai University and are available from January 1988 to December 2013. We focus on the period of 2002–2010 in the four North Pacific subdomains.

2.4. CFSR The Climate Forecast System Reanalysis (CFSR) dataset is a global coupled atmosphere–ocean–land surface–sea ice system reanalysis product. SST, surface winds (10 m) and heat fluxes (latent and sensible) data with a spatial resolution of 0.313 0.313 , and wind, vertical velocity and air temperature data ◦ × ◦ at isobaric levels and sea surface pressure on a 0.5 0.5 grid are employed. The 6-hourly product ◦ × ◦ spans from 2002 to 2010.

2.5. Eddy Detection Scheme In this study, we apply the eddy-detection algorithm proposed by Nencioli et al. [40]. This method is based on eddy vector geometry and has successfully used in a variety of earlier eddy-related studies of different oceans [41–43]. The detection scheme is applied to 9 years (2002–2010) of altimetry SSHA-derived geostrophic velocity anomalies to obtain the eddy dataset used in this study. The eddy detection scheme is briefly introduced below; four criteria are defined to determine an eddy center based on the geometry of velocity vectors:

1. Along an east–west (E–W) section, the v0 component of velocity changes sign across the eddy center, and its magnitude increases away from the center.

2. Along a north–south (N–S) section, the u0 component of velocity changes sign across the eddy center, and its magnitude increases away from the center. In addition, the u0 or v0 component should rotate in the same direction. 3. The minimum velocity, which is defined at the eddy center in a region which extends up to a specific grid point. 4. The directions must change with an invariable rotation direction of two neighboring velocity vectors around the eddy center, and be located within the same or two adjacent quadrants.

u0 and v0 are the geostrophic current anomalies as calculated from the AVISO SSHA dataset. Only where a point satisfies all the criteria listed above is an eddy center defined. The outermost closed streamline around the center is then searched for and the eddy boundary is regarded as the point where the velocity magnitude ceases to increase in the radial direction.

2.6. Method of Matching Atmospheric Variables with Oceanic Eddies The fine-scale characteristics of atmospheric and oceanic variables (such as SST, wind speed and precipitation rate) are obtained through an 8◦ zonal moving average subtracted from each variable (the   formula is X(i,k) = X(i,k) X(i,k) , where X represents SST, precipitation rate, wind speed, − 8◦ zonal_average etc.). Eddies with SSTA at the eddy center less than 0.05 C (for cyclonic eddies) and larger than − ◦ 0.05 ◦C (for anticyclonic eddies), and radii larger than 20 km, are selected. As a result, there are about 14,677, 19,636, 16,684 and 12,220 cyclonic eddies, and 14,469, 22,043, 16,747 and 13,195 anticyclonic Remote Sens. 2020. 4 of 18

Remote Sens. 2020. 4 of 18 138 0.05 °C (for anticyclonic eddies), and radii larger than 20 km, are selected. As a result, there are about Remote Sens. 2020 12 139 14677, 19636, 16684, , 1161 and 12220 cyclonic eddies, and 14469, 22043, 16747 and 13195 anticyclonic eddies4 of 18 138 0.05 °C (for anticyclonic eddies), and radii larger than 20 km, are selected. As a result, there are about 140 in the KE, SF, CC and AI regions, respectively. The matching method is adopted from Ma et al. [24]. 139 14677, 19636, 16684 and 12220 cyclonic eddies, and 14469, 22043, 16747 and 13195 anticyclonic eddies 141 Figure 1 is the schematic diagram for the matching method. The 4° × 4° bin is large enough to cover 140 eddiesin the KE, in the SF, KE,CC SF,and CC AI andregions, AI regions, respectively. respectively. The matching The matching method method is adopted is adopted from Ma from et al. Ma [24]. et 142 al.the [ 24oceanic]. Figure eddy1 is to the undertake schematic composite diagram foranalysis. the matching The bin method. 12° × 12° The is selected 4 4 asbin the is background large enough for to 141 Figure 1 is the schematic diagram for the matching method. The 4° × 4° bin◦ × is◦ large enough to cover 143 coverthe eddy the oceanicfield. eddy to undertake composite analysis. The bin 12 12 is selected as the background 142 the oceanic eddy to undertake composite analysis. The bin 12° × 12°◦ × is selected◦ as the background for 143 forthe theeddy eddy field. field.

144 144 145 Figure 1. The schematic diagram for the matching method. Figure 1. The schematic diagram for the matching method. 145 Figure 1. The schematic diagram for the matching method. 146 3. Results 146 3. Results 147 3.1. Statistics of eddy Eddy characteristics Characteristics 148147 3.1. StatisticsEddy kinetickinetic of eddy energyenergy characteristics (EKE)(EKE) is is used used to to characterize characterize eddy eddy activity activity intensities intensities and and is calculated is calculated by theby

1 1 2 222 formula: EKEEKE= 2=+(u (' u+ v v ')), where u0 and v0 are the geostrophic current anomalies. Figure2 shows 149148 the formula:Eddy kinetic energy20 (EKE)0 , where is used u’ and to characterize v’ are the geostrophi eddy activityc current intensities anomalies. and isFigure calculated 2 shows by the 90-day high pass-filtered1 22 surface EKE averaged from 2002 to 2010 in the North Pacific. The highest 150 the 90-day highEKE pass-filtered=+(' u v ') surface EKE averaged from 2002 to 2010 in the North Pacific. The highest 149 EKEthe formula: occurs in the KE2 and SF, regionswhere u’ [42 and,43 ].v’In are addition, the geostrophi comparedc current with adjacentanomalies. areas, Figure EKE 2 shows is also 151 EKE occurs in the KE and SF regions [42-43]. In addition, compared with adjacent areas, EKE is also 150 highthe 90-day in the high AI and pass-filtered CC regions. surface The EKE largest averaged EKE in from the KE 2002 region to 2010 is in primarily the North caused Pacific. by The the highest strong 152 high in the AI and CC regions. The largest EKE in the KE region is primarily caused by the strong 151 horizontalEKE occurs shear in the and KE theand meanderingSF regions [42-43]. of the In Kuroshio addition, path compared [42]. The with higher adjacent EKE areas, in the EKE SF regionis also 153 horizontal shear and the meandering of the Kuroshio path [42]. The higher EKE in the SF region is 152 ishigh predominantly in the AI and modulated CC regions. by The the largest baroclinic EKE instability in the KE ofregion the background is primarily currents caused by [44 the]. The strong CC 154 predominantly modulated by the baroclinic instability of the background currents [44]. The CC 153 regionhorizontal is characterized shear and the by meandering strong eddy of activity the Kuroshio where the path persistent [42]. The equatorward higher EKE wind in the causes SF region coastal is 155 region is characterized by strong eddy activity where the persistent equatorward wind causes coastal 154 upwellingpredominantly fronts. modulated Instability ofby thesethe baroclinic upwelling instability fronts results of inthe eddy background generation. currents The instability [44]. The of CC the 156 upwelling fronts. Instability of these upwelling fronts results in eddy generation. The instability of 155 coastregion shelf is characterized trapped jet causes by strong the eddy eddy formation activity wh inere the the AI region.persistent One equatorward can see that thewind eddy causes generation coastal 157 the coast shelf trapped jet causes the eddy formation in the AI region. One can see that the eddy 156 mechanismsupwelling fronts. are varied Instability in the of four these subdomains, upwelling whichfronts couldresults cause in eddy a di generation.fference in mesoscaleThe instability air–sea of 158 generation mechanisms are varied in the four subdomains, which could cause a difference in 157 interactionthe coast shelf characteristics trapped jet in causes the four the regions. eddy formation in the AI region. One can see that the eddy 159 mesoscale air–sea interaction characteristics in the four regions. 158 generation mechanisms are varied in the four subdomains, which could cause a difference in 159 mesoscale air–sea interaction characteristics in the four regions.

160

161160 Figure 2. 90-day90-day high high pass-filtered pass-filtered eddy kinetic energy (EKE; shaded) averaged from January 2002 to 162 December 2010. The black dashed lines delinea delineatete the four subdomains in this study. 161 Figure 2. 90-day high pass-filtered eddy kinetic energy (EKE; shaded) averaged from January 2002 to 162 December 2010. The black dashed lines delineate the four subdomains in this study.

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163 Satellite altimetry data provides an opportunity to characterize eddies in terms of their basic 164 parameters suchsuch as as polarity, polarity, lifetime, lifetime, size, size, vorticity vorticity and and moving moving speed speed in the in four the subdomains.four subdomains. We utilize We 165 twoutilize eddy two counting eddy counting methods. methods. Eddy number Eddy number is defined is defined using the using trajectory the trajectory method method (Lagrangian (Lagrangian method: 166 countingmethod: counting all the eddies all the over eddies the courseover the of course its lifetime of its as lifetime one eddy as one [1]), eddy and also[1]), throughand also the through snapshot the 167 methodsnapshot (Eulerian method (Eulerian method: method: counting counting each individual each individual eddy on eddy the snapshot on the snapshot in each in moment each moment as one

168 eddyas one [45 eddy]). Figure [45]).3 Figureshows the3 shows histogram the histogram of eddy radii of eddy and radi lifespans.i and lifespans. The eddy The size eddy is the size largest is the in 169 thelargest SF regionin the SF and region the smallest and the insmallest the AI in region the AI (upper region panels (upper in panels Figure in3). Figure Eddy sizes3). Eddy in the sizes KE in and the 170 CCKE and regions CC areregions similar. are similar. The peak The eddy peak radius eddy diradiusffers indiffers each in subdomain, each subdomain, with 60–70 with km60–70 in thekm SFin 171 region,the SF region, about 50 about km in50 the km KE in andthe CCKE regions,and CC andregions, only and 40 km only in the40 km AI region.in the AI Eddy region. size Eddy decreases size 172 fromdecreases the low from to highthe low latitudes. to high Eddy latitudes. sizes atEddy each sizes latitude at each are closelatitude to the are first close baroclinic to the first Rossby baroclinic wave 173 deformationRossby wave radius deformation [46], which radius mainly [46], controls which mainly the eddy controls characteristics. the eddy According characteristics. to Chelton According et al. [46 to], 174 Chelton et al. [46], when the latitude is greater than 5°, the first baroclinic Rossby wave deformation when the latitude is greater than 5◦, the first baroclinic Rossby wave deformation radius decreases 175 poleward.radius decreases Eddy lifespans poleward. are displayedEddy lifespans on the are bottom displayed panels ofon Figure the bottom3. Both panels cyclonic of and Figure anticyclonic 3. Both 176 eddycyclonic numbers and anticyclonic decrease as eddy lifespan numbers increases. decrease Eddies as withlifespan lifespans increases. equal Eddies to and with longer lifespans than 4weeks equal s 177 areto and selected longer for than the present4 week study, are selected which reducesfor the present uncertainties study, in which eddy reduces detection. uncertainties in eddy 178 detection.

179

180 Figure 3. Histograms of eddy sizes (upper panel) and eddy lifespan (bottom panel). Vorticity is one of the main parameters used to describe eddy intensity. Figure4 presents the 181 Vorticity is one of the main parameters used to describe eddy intensity. Figure 4 presents the distributions of the eddy vorticity normalized by the local Coriolis coefficient for the four subdomains 182 distributions of the eddy vorticity normalized by the local Coriolis coefficient for the four subdomains where it can be observed that the pattern follows a normal distribution. The eddy vorticity is defined 183 where it can be observed that the pattern follows a normal distribution. The eddy vorticity is defined at the approximate eddy center. The largest normalized eddy vorticity at the peak occurs in the SF 184 at the approximate eddy center. The largest normalized eddy vorticity at the peak occurs in the SF domain (anticyclone: 0.13, cyclone: 0.14), the smallest in the AI region (anticyclone: 0.03, cyclone: 185 domain (anticyclone: −0.13，cyclone: 0.14), the smallest in the AI region (anticyclone: −0.03, cyclone: 0.03). Normalized vorticity at the peak is about 0.1 (0.1) for anticyclonic (cyclonic) eddies in the KE 186 0.03). Normalized vorticity at the peak is about −0.1 (0.1) for anticyclonic (cyclonic) eddies in the KE region and 0.04 (0.04) in the CC region. The oceanic eddies are stronger in SF and KE due to the 187 region and −0.04 (0.04) in the CC region. The oceanic eddies are stronger in SF and KE due to the strong baroclinic instability in these two areas. In detail, in the SF and KE regions, the strong density 188 strong baroclinic instability in these two areas. In detail, in the SF and KE regions, the strong density front is presented around the year. Due to the baroclinic instability, the high available potential energy 189 front is presented around the year. Due to the baroclinic instability, the high available potential stored in the density front in the SF and KE regions is released to the eddy kinetic energy, causing 190 energy stored in the density front in the SF and KE regions is released to the eddy kinetic energy, strong eddy activities in these two regions. 191 causing strong eddy activities in these two regions.

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192 192 193 Figure 4. 4. HistogramHistogram of normalized of normalized eddy eddy vorticity. vorticity. Upper Upper(bottom) (bottom) panel is the panel anticyclonic is the anticyclonic (cyclonic) 193 Figure 4. Histogram of normalized eddy vorticity. Upper (bottom) panel is the anticyclonic (cyclonic) 194 eddy.(cyclonic) eddy. 194 eddy. 195 Eddy movementmovement is is also also an an essential essential eddy eddy characteristic. characteristic. Westward Westward movement movement is a common is a common feature 195 Eddy movement is also an essential eddy characteristic. Westward movement is a common 196 featurefor the eddiesfor the in eddies all four in domains all four (leftdomains panel (left in Figure panel5 ).in In Figure the SF 5). area, In the westwardSF area, the propagation westward 196 feature for the eddies in all four1 domains (left panel in Figure 5). In the SF area, the westward 197 propagationspeed is between speed 3.8–11.1 is between cm s− 3.8–11.1. Furthermore, cm∙s the. Furthermore, speed decreases the withspeed increasing decreases latitude. with increasing The same 197 propagation speed is between· 3.8–11.1 cm∙s. Furthermore, the speed decreases with increasing1 198 latitude.distribution The is same observed distribution in the CC is observed region but in with the zonalCC region speeds but ranging with zonal from speeds 0.8 to 3.93 ranging cm s− from. In the0.8 198 latitude. The same distribution is observed in the CC region but with zonal speeds ranging· from 0.8 199 toKE 3.93 region, cm∙s the. westward In the KE movementregion, the increases westward with moveme the latitudent increases till 30◦ withN, then the decreases latitude till to almost30 °N, then zero 199 to 3.93 cm∙s. In the KE region, the westward movement increases with the latitude till 30 °N, then 200 decreasesat around 33to ◦almostN. It increases zero at around between 33 33 ° andN. It 36 increases◦N, and thenbetween decreases 33 and with 36 ° theN, latitude.and then Thisdecreases behavior with is 200 decreases to almost zero at around 33 °N. It increases between 33 and 36 °N, and then decreases with 201 thebecause latitude. the main This axisbehavior of the is eastward because KE the current main axis located of the near eastward 35◦N obstructs KE current the westward located near movement 35 °N 201 the latitude. This behavior is because the main axis of the eastward KE current located near 35 °N 202 obstructsof eddies. the In westward the AI domain, movement the eddies of eddies. propagate In the westwardAI domain, with the aeddies small propagate zonal speed westward in a range with of 202 obstructs the westward1 movement of eddies. In the AI domain, the eddies propagate westward with 203 a0 small to 0.93 zonal cm s speed− . These in a resultsrange of are 0 generallyto 0.93 cm∙s congruent. These with results results are generally observed congruent by Chelton with et al.results [46], 203 a small zonal· speed in a range of 0 to 0.93 cm∙s. These results are generally congruent with results 204 whichobserved show by Chelton that eddy et propagational. [46], which speed show is that of theeddy same propagation order as thespeed first is baroclinicof the same Rossby order as wave the 204 observed by Chelton et al. [46], which show that eddy propagation speed is of the same order as the 205 firstpropagation baroclinic speed Rossby decreasing wave withpropagation the latitude. speed The de curvescreasing of thewith northward the latitude. movements The curves of eddies of the in 205 first baroclinic Rossby wave propagation speed decreasing with the latitude. The curves of the 206 northwardthe four subdomains movements share of eddies a similar in "Z-shape"the four subdom (right panelains ofshare Figure a similar5). Eddies "Z-shape" in the southern (right panel part ofof 206 northward movements of eddies in the four subdomains share a similar "Z-shape" (right panel of 207 Figureeach subdomain 5). Eddies tendin the to southern move northward part of each with subdomain larger speeds, tend whileto move eddies northward in the northernwith larger part speeds, move 207 Figure 5). Eddies in the southern part of each subdomain tend to move northward with larger speeds, 208 whilesouthward eddies with in the smaller northern speeds. part move southward with smaller speeds. 208 while eddies in the northern part move southward with smaller speeds.

209 209 210 Figure 5. Variations of zonal and meridional speeds with latitude. Solid (dash) curves represent 210 Figure 5.5. Variations of zonalzonal andand meridionalmeridional speedsspeeds withwith latitude.latitude. Solid (dash) curves represent 211 anticyclonic (cyclonic) eddies, respectively. Units: cm∙s. 211 anticyclonic (cyclonic) eddies,eddies, respectively.respectively. Units:Units: cm∙ss 1.. · − 212 3.2. Atmospheric responses to eddies 212 3.2. Atmospheric responses to eddies 213 3.2.1. Wind Speed 213 3.2.1. Wind Speed

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3.2. Atmospheric Responses to Eddies

3.2.1. Wind Speed

AtmosphericRemoteRemote Sens.Sens. 20202020.. responses to oceanic mesoscale eddies are primarily manifested in sea surface77 ofof 1818 winds, precipitation and heat fluxes. Figures6 and7, respectively, show the winter and summer composites 214 Atmospheric responses to oceanic mesoscale eddies are primarily manifested in sea surface 214of SST andAtmospheric sea surface responses wind speed to oceanic anomalies mesoscale in response eddies toare the primarily oceanic manifested eddies. In in each sea subdomain,surface 215 winds, precipitation and heat fluxes. Figures 6 and 7, respectively, show the winter and summer 215wind winds, speed anomaliesprecipitation are and positive heat fluxes. for anticyclonic Figures 6 and eddies 7, respectively, both in winter show and the summer, winter and and summer negative for 216216 composites of SST and sea surface wind speed anomalies in response to the oceanic eddies. In each cycloniccomposites eddies. of Furthermore, SST and sea surface the maximum wind speed wind anoma speedlies anomaliesin response areto the located oceanic over eddies. the eddyIn each center, 217217 subdomain,subdomain, windwind speedspeed anomaliesanomalies areare positivepositive forfor anticyclonicanticyclonic eddieseddies bothboth inin winterwinter andand summer,summer, corresponding to the maximum SST anomalies. However, the magnitudes of the wind speed anomalies 218218 andand negativenegative forfor cycloniccyclonic eddies.eddies. Furthermore,Furthermore, ththee maximummaximum windwind speedspeed anomaliesanomalies areare locatedlocated overover 219219vary the fromthe eddyeddy region center,center, to region correspondingcorresponding and exhibit toto thethe a distinct maximummaximum seasonal SSTSST anomalies.anomalies. variability. However,However, During thethe the magnitudesmagnitudes wintertime, ofofthe thethe wind 1 220220speed windwind anomaly speedspeed and anomaliesanomalies SSTA isvaryvary the fromfrom strongest regionregion in toto the regionregion KE regionanandd exhibitexhibit (0.5 maa distinctdistincts− , 0.7 seasonalseasonal◦C). However, variability.variability. little DuringDuring difference · 1 ∙s ℃ 221221can bethethe observed wintertime,wintertime, in thethe the windwind other speedspeed three anomalyanomaly subdomains andand SSTASSTA (0.2–0.3 isis thethe strongeststrongest m s− , 0.15–0.3 inin thethe KEKE◦ C).regionregion In summer,(0.5(0.5 mm∙s , wind, 0.70.7 ℃).). speed 222 However, little difference can be observed in the other three· subdomains1 (0.2–0.3 m∙s , 0.15–0.3℃). 222anomaly However, and SSTAlittle difference is still the can largest be observed in the in KE the region other three (0.35 subdomains m s− , 0.7 ◦ (0.2–0.3C) and m the∙s smallest, 0.15–0.3 in℃ the). AI · 223223 InIn summer,summer, wind1wind speedspeed anomalyanomaly andand SSTASSTA isis stillstill thethe largestlargest inin thethe KEKE 1 regionregion (0.35(0.35 mm∙𝑠∙𝑠,, 0.70.7 ℃℃)) region (0.1 m s− ). The wind speed anomaly (SSTA) is about 0.2 m s− (0.25 ◦C) in both SF and CC 224 and the smallest· in the AI region (0.1 m∙s ). The wind speed anomaly ·(SSTA) is about 0.2 m∙s (0.25 224regions. and the Therefore, smallest in regardless the AI region of (0.1 season, m∙s the). The eddy wind impacts speed anomaly on the (SSTA) sea surface is about wind 0.2 m speed∙s (0.25 and sea 225 ℃) in both SF and CC regions. Therefore, regardless of season, the eddy impacts on the sea surface 225surface ℃) temperaturein both SF and are CC the regions. strongest Therefore, in the regardless KE region. of season, the eddy impacts on the sea surface 226226 windwind speedspeed andand seasea surfacesurface temperaturetemperature areare thethe strongeststrongest inin thethe KEKE region.region.

227227

1 228 FigureFigure 6. Composites 6. Composites of windof wind speed speed anomalies anomalies (color; m m∙ss ) )and and sea sea surface surface temperature temperature anomaly anomaly 228 Figure 6. Composites of wind speed anomalies (color; m∙s· −) and sea surface temperature anomaly 229229 (SSTA)(SSTA)(SSTA) (contours; (contours(contours◦C);℃;℃ in)) inin winter. winter.winter. From FromFrom left leftleft to toto right:right: KE,KE, KE, SF,SF, SF, CC,CC, CC, AI.AI. AI. UpperUpper Upper panelspanels panels areare arethethe anticyclonic theanticyclonic anticyclonic 230230 eddies;eddies;eddies; bottom bottombottom panels panelspanels are areare the thethe cyclonic cycloniccyclonic eddies. eddies.eddies. TheThe dotteddotted areasareas areas denotedenote denote datadata data atat 95%95% at 95% confidenceconfidence confidence levellevel level 231231 and theandand plus thethe plusplus sign signsign represents representsrepresents the thethe eddy eddyeddy center. center.center.

232232 233233 FigureFigureFigure 7. 7.7.Same SameSame as asas forfor Figure Figure 6,66,, but but inin in summer.summer. summer.

234234 3.2.2.3.2.2. PrecipitationPrecipitation

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Remote Sens. 2020. 8 of 18 3.2.2. Precipitation 235 As shown by the previous analyses, oceanic e eddiesddies thus contribute to SST and wind speed 236 anomalies. Moreover, mesoscale eddieseddies also influenceinfluence the local cloud coverage and precipitation due 237 to changeschanges inin atmospheric atmospheric stability stability and and anomalous anomalous secondary secondary circulation. circulation. Previous Previous studies studies [6,28 ][6, show 28] 238 showthat this that can this happen can happen in two ways:in two vertical ways: vertical motion causedmotion by caused anomalous by anomalous surface divergence surface divergence (dynamic 239 (dynamiceffect), and effect), changes and in changes the water in vaporthe wate contentr vapor (thermodynamic content (thermodynamic effect). effect). 240 Figure8 8displays displays composites composites of precipitation of precipitatio aboven above oceanic oceanic eddies. eddies. The precipitationrate The precipitationrate anomaly 241 anomalyabove anticyclonic above anticyclonic eddies is eddies positive is (negativepositive (n inegative cyclonic in eddies)cyclonic in eddies) agreement in agreement with previous with 242 previousstudies [ 24studies,28]. However, [24, 28]. theHowever, magnitudes the magnitudes of the precipitation of the precipitation rate anomalies rate dianomaliesffered among differed the 243 amongsubdomains. the subdomains. In winter, the In largestwinter, and the smallestlargest and anomalies smallest occur anomalies in the occur KE and in AIthe regions, KE and respectively.AI regions, 244 Therespectively. CC and The SF regions CC and follow SF regions after follow the KE after region. the KE Summer region. and Summer winter and present winter distinct, present regionaldistinct, 245 regionaldependence dependence of precipitation. of precipitation. The maximum The maximum value is still value located is still in located the KE regionin the KE while region the minimumwhile the 246 valueminimum appears value in appears the CC in region, the CC instead region, of instead the AI of area. the InAI addition,area. In addition, in comparison in comparison with the with winter the 247 winterseason, season, the magnitude the magnitude of the precipitationof the precipitation rate anomaly rate anomaly increases increases in the SFin the region, SF region, in contrast in contrast to the 248 windto the speed wind anomalyspeed anomaly displayed displayed in Figures in6 Figuresand7. This 6 and observation 7. This observation may be related may to be the related di fference to the in 249 differencethe atmospheric in the environmentatmospheric ofenvironment each subdomain. of each More subdomain. detailed More discussions detailed are discussions given in Section are given4. 250 in Section 4.

251 252 Figure 8. Composites of precipitation rate anomalies over oceanic mesoscale cyclonic (contours) and 253 anticyclonic (colors) (colors) eddi eddies.es. From From left left to to right: right: KE, KE, SF, SF, CC, CC, an andd AI AIregion. region. Summer Summer period: period: (a) to (a –(d);d); 254 winter period: ((e)e– hto). (h). The The areas areas with with dots dots indicate indicate the thet-test t test is passed is passed at a at 95% a 95% confidence confidence level level and and the 255 plusthe plus sign sign represents represents the eddythe eddy center. center. Units: Units: mm mmh 1∙h. . · − 256 3.2.3. Heat Heat flux Flux 257 Latent and sensible heat fluxes fluxes are two crucial factors in the transfer of heat between the ocean 258 and atmosphere. When When an an air air mass mass moves moves over over a amesoscale mesoscale eddy, eddy, a aturbulent turbulent heat heat flux flux anomaly anomaly is 259 causedis caused by bydifferences differences in humidity in humidity in addition in addition to SST to and SST wind and speed wind anomalies speed anomalies at the ocean at the surface. ocean 260 Forsurface. anticyclonic For anticyclonic eddies, warm eddies, SST warm anomalies SST anomalies increase increase the temperature the temperature difference diff erencebetween between the ocean the 261 andocean atmosphere, and atmosphere, and increase and increase the sensible the sensible heat heat flux flux transfer transfer from from the the ocean ocean to to the the atmosphere. atmosphere. 262 Simultaneously, thethe anomalousanomalous warming warming also also increases increases the the saturated saturated specific specific humidity, humidity, which which in turn in 263 turnincreases increases the humidity the humidity difference difference between between sea and sea air. and Thus, air. theThus, evaporative the evaporative cooling cooling at the sea at surfacethe sea 264 surfaceis enhanced is enhanced and the and latent the heat latent flux heat increases. flux increa Therefore,ses. Therefore, anticyclonic anticyclonic eddies correspond eddies correspond to upward to 265 upwardturbulent turbulent heat flux heat anomalies; flux anomalies; that is, the that ocean is, the heats ocean the heats atmosphere. the atmosphere. On the other On the hand, other cyclonic hand, 266 cycloniceddies correspond eddies correspond to downward to downward turbulent heatturbulen flux anomalies,t heat flux whereanomalies, the ocean where absorbs the ocean heat fromabsorbs the 267 heatatmosphere from the [24 atmosphere]. [24]. 268 The contours in Figure 9 show that negative latent heat flux anomalies are indeed found above 269 cyclonic eddies in winter and summer (sensible heat flux anomalies are not shown since they have 270 the same pattern). The shading in Figure 9 shows the opposite case of an anticyclonic anomaly. Table

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271 1 shows the maximum sensible and latent heat flux anomalies, enhanced by the oceanic mesoscale 272 eddies in the KE, SF, CC and AI regions. The heat flux altered by oceanic mesoscale eddies is stronger 273 Remotein winter Sens. than2020, 12summer., 1161 The atmospheric response to SST anomalies of oceanic mesoscale eddies9 of 18is 274 the strongest in the KE region and the weakest in the AI region. Here, the influence of eddies on heat 275 flux is slightly stronger in cyclonic than anticyclonic eddies. The contours in Figure9 show that negative latent heat flux anomalies are indeed found above 276 cyclonic eddiesTable in winter1. The maximum and summer heat fluxes (sensible anomalies heat flux over anomalies anticyclonic are (cyclonic) not shown eddies. since they have the same pattern). The shading in Figure9 shows the opposite case of an anticyclonic anomaly. KE SF CC AI Table1 shows Subdomain the maximum sensible and latent heat flux anomalies, enhanced by the oceanic mesoscale eddies in the KE, SF, CC and AI regions.Cyclone The heat fluxCyclone altered by oceanicCyclone mesoscale eddiesCyclone is stronger in winter than summer. The atmospheric/ response to SST/ anomalies of oceanic/ mesoscale eddies/ is the Variables strongest in the KE region andAnticyclone the weakest in theAnticyclone AI region. Here, theAnticyclone influence of eddiesAnticyclone on heat flux is slightly stronger in cyclonic than anticyclonic eddies. Sensible heat flux 12.6 (3.2) 2.9 (1.1) 2.6 (2.0) 2.1(0.5) Winter (Summer)Table 1. The maximum heat/ fluxes anomalies/ over anticyclonic (cyclonic)/ eddies. / Unit: W∙mSubdomain −14.8KE (−4.4) −3.7 SF(−1.7) −3.6CC (−1.9) −3.2AI (−0.6) Variables Cyclone/Anticyclone Cyclone/Anticyclone Cyclone/Anticyclone Cyclone/Anticyclone Sensible heat flux Winter (Summer) 12.6 (3.2)/ 14.8 ( 4.4) 2.9 (1.1)/ 3.7 ( 1.7) 2.6 (2.0)/ 3.6 ( 1.9) 2.1 (0.5)/ 3.2 ( 0.6) − − − − − − − − LatentUnit: heat W m flux2 19.6 (6.9) 9.3 (5.1) 6.9 (4.1) 2.1 (0.5) · − Latent heat flux Winter (Summer) Winter (Summer)2 19.6 (6.9)// 24.5 ( 9.9) 9.3 (5.1)/ /12.1( 7.4) 6.9 (4.1)/ 7.8/ ( 4.5) 2.1 (0.5)/ 2.3/ ( 0.6) Unit: W m− − − − − − − − − Unit: W∙m· −24.5 (−9.9) −12.1(−7.4) −7.8 (−4.5) −2.3 (−0.6)

(a)

277

2 278 Figure 9. Same as for Figure 88,, butbut forfor latentlatent heatheat fluxflux anomalies.anomalies. Units:Units: W∙mm− .. · 4. Possible Mechanisms for Atmospheric Response and Regional Dependence 279 4. Possible mechanisms for atmospheric response and regional dependence To investigate the regional dependence of the atmospheric response to oceanic mesoscale eddies 280 To investigate the regional dependence of the atmospheric response to oceanic mesoscale eddies in the four eddy activity regions, we quantify the change of various properties with the SST anomaly. 281 in the four eddy activity regions, we quantify the change of various properties with the SST anomaly. There is a high correlation between atmospheric parameters (wind speed, precipitation rate) and SSTA 282 There is a high correlation between atmospheric parameters (wind speed, precipitation rate) and (correlation coefficients are not shown here). The regression of SSTA and wind speed (precipitation 283 SSTA (correlation coefficients are not shown here). The regression of SSTA and wind speed rate) anomaly in the four subdomains and seasons are investigated in Figures 10 and 11. In addition, 284 (precipitation rate) anomaly in the four subdomains and seasons are investigated in Figures 10 and Table2 summarizes the linear regression coe fficients of wind speed and precipitation rate anomalies 285 11. In addition, Table 2 summarizes the linear regression coefficients of wind speed and precipitation onto SST anomalies for cyclonic and anticyclonic eddies in each subdomain during winter and summer. 286 rate anomalies onto SST anomalies for cyclonic and anticyclonic eddies in each subdomain during The response of wind speed to oceanic mesoscale eddies is much stronger during winter in the four 287 winter and summer. The response of wind speed to oceanic mesoscale eddies is much stronger during subdomains than during summer. However, regardless of the subdomain, the wind speed anomaly 288 winter in the four subdomains than during summer. However, regardless of the subdomain, the wind above the cyclonic eddies is slightly stronger than that above anticyclonic eddies. Comparing the 289 speed anomaly above the cyclonic eddies is slightly stronger than that above anticyclonic eddies. response of wind speed to SST in all four subdomains, the strongest response occurs in the KE region, 290 Comparing the response of wind speed to SST in all four subdomains, the strongest response occurs followed by CC and SF domains, and the weakest is in the AI region. 291 in the KE region, followed by CC and SF domains, and the weakest is in the AI region. Remote Sens. 2020,. 12, 1161 10 of 18 Remote Sens. 2020. 10 of 18

292 292 293 Figure 10. The regression of SSTA and wind speed anomaly in each subdomain, shading is the 293 Figure 10.10. TheThe regression regression of of SSTA SSTA and and wind wind speed speed anomaly anom inaly each in subdomain, each subdomain, shading shading is the standard is the 294 standard deviation, red (blue) stands for summer (winter). From left to right: KE, SF, CC, AI. Upper 294 standarddeviation, deviation, red (blue) red stands (blue) for st summerands for (winter). summer From(winter). left toFrom right: left KE, to right: SF, CC, KE, AI. SF, Upper CC, AI. panels Upper are 295 panels are the anticyclonic eddies; bottom panels are the cyclonic eddies. 295 panelsthe anticyclonic are the anticyclon eddies; bottomic eddies; panels bottom are thepanels cyclonic are the eddies. cyclonic eddies.

296 296 Figure 11. Same as for Figure 10, but for the regression of SSTA and precipitation rate anomaly. 297 Figure 11. Same as for Figure 10, but for the regression of SSTA and precipitation rate anomaly. 297 Figure 11. Same as for Figure 10, but for the regression of SSTA and precipitation rate anomaly. Table 2. Linear fitting coefficients of atmospheric parameters anomalies with SSTA, showing by how 298 Tablemuch the2. Linear wind speedfitting andcoefficients precipitation of atmospheric rate increase parameters (decrease) anomalies above anticyclonic with SSTA, (cyclonic) showing eddies by how in 298 Table 2. Linear fitting coefficients of atmospheric parameters anomalies with SSTA, showing by how 299 muchresponse the towind an SST speed anomaly and precipitation of 1 ◦C in the rate four increase regions. (decrease) above anticyclonic (cyclonic) eddies 299 much the wind speed and precipitation rate increase (decrease) above anticyclonic (cyclonic) eddies 300 in response to an SST anomaly of 1 ℃ in the four regions. Subdomain 300 in response to an SST anomalyKE Cyclone of/Anticyclone 1 ℃ in the SF four Cyclone regions./Anticyclone CC Cyclone/Anticyclone AI Cyclone/Anticyclone Variables Wind speed Winter (Summer) 0.461 (0.291)KE/0.407 (0.284) 0.246 (0.168)SF/0.234 (0.166) 0.285 (0.241)CC/0.267 (0.201) 0.211 (0.137)AI/0.2 (0.116) Unit:Subdomain m s 1 C 1 − ·◦ − KE SF CC AI Subdomain 2 2 2 2 2 2 2 2 precipitation rate Winter (Summer) 2.27 Cyclone10− (1.52 10 − )/2.22 1.09 Cyclone10− (1.35 10 − )/0.73 1.15 10Cyclone− (0.23 10− )/0.93 1.37 10Cyclone− (1.12 10− )/1.17 1 1 × 2 × 2 × 2 × 2 × 2 × 2 × 2 × 2 Unit: mm h− ◦C− Cyclone10 (1.37 10 ) Cyclone10 (0.94 10 ) Cyclone10 (0.2 10 ) 10Cyclone(0.81 10 ) · × − / × − × − / × − × − / × − × − / × − Variables / / / / Variables Anticyclone Anticyclone Anticyclone Anticyclone Due to the limited observationalAnticyclone data, a CFSRAnticyclone reanalysis datasetAnticyclone is used to provideAnticyclone atmospheric variablesWind both speed at the surface0.461 and (0.291) in the vertical0.246 levels,(0.168) which0.285 allow (0.241) us to further0.211 investigate (0.137) the Wind speed 0.461 (0.291) 0.246 (0.168) 0.285 (0.241) 0.211 (0.137) mechanismWinter (Summer) underlying atmosphere/ responses to oceanic/ eddies. Although/ not shown, composites/ of Winter (Summer) / / / / SST,Unit: wind m speed, s-1∙℃ precipitation-1 0.407 and (0.284) heat flux anomalies0.234 (0.166) from the reanalysis0.267 (0.201) data show0.2 similar (0.116) patterns, but theUnit: magnitude m s-1∙℃-1 is slightly0.407 smaller (0.284) than that0.234 of observed (0.166) data.0.267 Furthermore, (0.201) the SST0.2 anomaly (0.116) has a

good linear relationship with other variables (wind speed, precipitation rate, sensible heat flux and latent heat flux). The maximum2.27×10-2 SST anomaly1.09×10 corresponds-2 to1.15×10 the largest-2 wind speed,1.37×10 precipitation-2 2.27×10-2 1.09×10-2 1.15×10-2 1.37×10-2 rate,precipitation sensible heat rate flux (1.52×10 and latent-2) heat flux(1.35×10 anomalies.-2) Previous(0.23×10 studies-2) show that(1.12×10 the wind-2) speed precipitation rate (1.52×10-2) (1.35×10-2) (0.23×10-2) (1.12×10-2) weakensWinter or (Summer) strengthens above oceanic/ eddies depending/ on the strength/ of atmospheric vertical/ kinetic energyWinter flux (Summer) [24,47]. In this study,/ we propose three/ parameters which/ could influence the/ vertical Unit: mm h-1∙℃-1 2.22×10-2 0.73×10-2 0.93×10-2 (0.2×10- 1.17×10-(0.81×10- energyUnit: transport: mm h-1∙℃ (1)-1 intensity2.22×10 of-2 SST anomalies0.73×10 (or-2 eddy size), (2)0.93×10 stability-2 (0.2×10 of atmospheric- 1.17×10 stratification-(0.81×10- (1.37×10-2) (0.94×10-2) 2) 2) (1.37×10-2) (0.94×10-2) 2) 2) 301 301

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302 Due to the limited observational data, a CFSR reanalysis dataset is used to provide atmospheric 303 variables both at the surface and in the vertical levels, which allow us to further investigate the 304 mechanism underlying atmosphere responses to oceanic eddies. Although not shown, composites of 305 SST, wind speed, precipitation and heat flux anomalies from the reanalysis data show similar patterns, 306 but the magnitude is slightly smaller than that of observed data. Furthermore, the SST anomaly has 307 a good linear relationship with other variables (wind speed, precipitation rate, sensible heat flux and 308 latent heat flux). The maximum SST anomaly corresponds to the largest wind speed, precipitation 309 rate, sensible heat flux and latent heat flux anomalies. Previous studies show that the wind speed 310 weakens or strengthens above oceanic eddies depending on the strength of atmospheric vertical 311 kinetic energy flux [24, 47]. In this study, we propose three parameters which could influence the 312 verticalRemote Sens. energy2020, 12 transport:, 1161 (1) intensity of SST anomalies (or eddy size), (2) stability of atmospheric11 of 18 313 stratification (potential temperature shown in Figure 13), and (3) vertical mixing term (the transient 314 kinetic energy flux shown in Figure 14). (potential temperature shown in Figure 13), and (3) vertical mixing term (the transient kinetic energy 315 The main factor which increases or decreases the wind speed happens through SST anomalies flux shown in Figure 14). 316 due to heat fluxes across the air–sea interface. The eddy size controls the area that can be influenced The main factor which increases or decreases the wind speed happens through SST anomalies 317 by SST anomalies. Sun et al. [48] suggest two main impacts of SST-wind speed coupling: the SST due to heat fluxes across the air–sea interface. The eddy size controls the area that can be influenced 318 gradient associate with mesoscale progresses and wind speed steadiness. For detectable mesoscale by SST anomalies. Sun et al. [48] suggest two main impacts of SST-wind speed coupling: the SST 319 coupling to occur, they determine that a constant wind speed plays a crucial role, especially when gradient associate with mesoscale progresses and wind speed steadiness. For detectable mesoscale 320 the SST gradients associated with mesoscale progresses are relatively weak. Chelton et al. [49] find coupling to occur, they determine that a constant wind speed plays a crucial role, especially when the 321 that variations in the wind directional steadiness mainly modulate mesoscale SST-wind coupling. It SST gradients associated with mesoscale progresses are relatively weak. Chelton et al. [49] find that 322 should be noted that the SST anomaly in this study is defined as a spatial anomaly with respect to variations in the wind directional steadiness mainly modulate mesoscale SST-wind coupling. It should 323 the SST in the surrounding area. The SST anomaly inside an eddy is equivalent to the SST gradient, be noted that the SST anomaly in this study is defined as a spatial anomaly with respect to the SST 324 except that it is divided by the eddy radius. Therefore, one can consider that the relationship between in the surrounding area. The SST anomaly inside an eddy is equivalent to the SST gradient, except 325 the wind speed anomaly and SST anomaly inside an eddy is equivalent to the wind speed anomaly that it is divided by the eddy radius. Therefore, one can consider that the relationship between the 326 and SST gradient, as discussed by Sun et al. [48] and Chelton et al. [49]. As the Kuroshio region carries wind speed anomaly and SST anomaly inside an eddy is equivalent to the wind speed anomaly and 327 the warm water far from the equator, the water temperature contrast in KE to the adjusting area is SST gradient, as discussed by Sun et al. [48] and Chelton et al. [49]. As the Kuroshio region carries 328 large, so the eddies shed off the Kuroshio region are highly energetic, which makes the SST anomaly the warm water far from the equator, the water temperature contrast in KE to the adjusting area is 329 in the KE area is the largest among the four selected areas. As Figures 6 and 7 show, SST anomalies large, so the eddies shed off the Kuroshio region are highly energetic, which makes the SST anomaly in 330 are noticeably larger in the winter regardless of the domain, and the SSTA maximum occurs in the the KE area is the largest among the four selected areas. As Figures6 and7 show, SST anomalies are 331 KE region, followed by the CC and SF area, and the smallest in the AI subdomain. Furthermore, noticeably larger in the winter regardless of the domain, and the SSTA maximum occurs in the KE 332 Figure 12 illustrates the rose scatter distribution and the Probability Distribution Function (PDF) of region, followed by the CC and SF area, and the smallest in the AI subdomain. Furthermore, Figure 12 333 wind speed in the four areas in summer and winter. By comparing the magnitude of wind speed in illustrates the rose scatter distribution and the Probability Distribution Function (PDF) of wind speed 334 winter and summer, it is noted that the wind is stronger in winter than in summer in all subdomains. in the four areas in summer and winter. By comparing the magnitude of wind speed in winter and 335 The PDF of wind speed in the KE and AI regions shows a similar distribution both with a peak value summer, it is noted that the wind is stronger in winter than in summer in all subdomains. The PDF of 336 of 3–4 m∙s . However, the wind speed anomalies in the KE region are stronger than in the AI area1 wind speed in the KE and AI regions shows a similar distribution both with a peak value of 3–4 m s− . 337 (Figures 6 and 7). · However, the wind speed anomalies in the KE region are stronger than in the AI area (Figures6 and7). 338

339

Figure 12. Rose scatter distribution (upper and bottom panels) and the PDF (middle panels) of wind speed in four subdomains in winter (blue) and summer (red).

Wallace et al. [33] proposed that variability in atmospheric stability could influence vertical momentum transport, and hence the sea surface wind. The vertical profile of potential temperature is used to represent the stability of the MABL. Figure 13 displays the composited vertical profiles of potential temperature over the eddy centers in winter and summer. Steeper slopes correspond to weaker stratifications (stronger instability), indicating that kinetic energy can be transported to the sea surface, ultimately leading to larger wind speed anomalies. In the KE region, potential temperature changes little with an altitude below 900 hPa in winter, indicating that the atmosphere is relatively unstable. By Remote Sens. 2020. 12 of 18

340 Figure 12. Rose scatter distribution (upper and bottom panels) and the PDF (middle panels) of wind 341 speed in four subdomains in winter (blue) and summer (red).

342 Wallace et al. [33] proposed that variability in atmospheric stability could influence vertical 343 momentum transport, and hence the sea surface wind. The vertical profile of potential temperature 344 is used to represent the stability of the MABL. Figure 13 displays the composited vertical profiles of 345 potential temperature over the eddy centers in winter and summer. Steeper slopes correspond to Remote Sens. 2020, 12, 1161 12 of 18 346 weaker stratifications (stronger instability), indicating that kinetic energy can be transported to the 347 sea surface, ultimately leading to larger wind speed anomalies. In the KE region, potential 348 contrast,temperature potential changes temperature little with decreases an altitude with below altitude 900 hPa in summer, in winter, suggestive indicating of athat stable the atmosphere.atmosphere 349 Similarly,is relatively the slopeunstable. of potential By contrast, temperature potential profile temperature under 900 hPadecreases in theSF with region altitude is steeper in insummer, winter 350 thansuggestive summer. of a In stable the CC atmosphere. and AI regions, Similarly, the potential the slope temperature of potential increasestemperature quickly profile above under 950 900 hPa hPa in 351 summer,in the SF region rather is than steeper during in winter the winter. than summer. The difference In the inCC atmospheric and AI regions, stability the potential between temperature winter and 352 summerincreases may quickly be the above primary 950 causehPa in of thesummer, seasonal rather dependence than during of atmospheric the winter. responses The difference in theKE in 353 region.atmospheric Among stability the four between subdomains, winter the slopeand ofsummer potential may temperature be the primary profile below cause 900 of hPa the is seasonal steepest 354 independence the KE domain, of atmospheric followed byresponses the SF, AI in and the CCKE regions,region. Among indicative the of four the strongestsubdomains, instability the slope in the of 355 KEpotential region. temperature This observation profile is below potentially 900 hPa linked is steepest to the strongestin the KE surfacedomain, wind followed speed by response the SF, AI in and the 356 KECC region.regions, indicative of the strongest instability in the KE region. This observation is potentially 357 linked to the strongest surface wind speed response in the KE region.

358 359 Figure 13.13. Composites of vertical profilesprofiles of potential temperature along eddy center in thethe zonalzonal 360 direction: ((a)a) KE, ((b)b) SF, ((c)c) CC and ((d)d) AI.AI.   ω 2 + 2 361 The transitionaltransitional kinetickinetic energy energy (TKE) (TKE) flux flux 0 𝛚u0𝑢v+𝑣0is an is importantan important parameter parameter which which is used is 362 toused represent to represent how ocean how eddies ocean affeddiesect the affect above the atmosphere above atmosphere through the through vertical the mixing vertical mechanism, mixing 363 where ω0, u0 and v0 𝛚are 𝑢 synoptic 𝑣 scale transient vertical, zonal and meridional wind velocities at mechanism, where  , and are synoptic scale transient vertical, zonal and meridional wind 2 2 364 isobaricvelocities levels. at isobaric If ω0 u 0 levels.+ v0 isIf positive𝛚𝑢 +𝑣 (negative), is positive the transitional (negative), energy the is transportedtransitional downwardenergy is 365 towardtransported (upward downward and away toward from) (upward the sea and surface, away thus from) increasing the sea surface, (decreasing) thus increasing the wind speed(decreasing) at the  2 2 366 seathe surface.wind speed Figure at the14 shows sea surface. the longitude-height Figure 14 show cross-sections the longitude-height of composite cross-sectionω0 u0 + v of0 compositealong the 367 𝛚 𝑢 +𝑣 along the eddy center. From Figure 214, it 2can be seen that 𝛚 𝑢 +𝑣 is almost eddy center. From Figure 14, it can be seen that ω0 u0 + v0 is almost positive for anticyclonic eddies, 368 positive for anticyclonic eddies, showing downward energy transport; the opposite is true of cyclonic showing downward energy transport; the opposite is true of cyclonic eddies. Further, the maximum 369 eddies. Further, the maximum TKE flux is located in the KE region regardless of the season, followed TKE flux is located in the KE region regardless of the season, followed by the SF and the CC regions, 370 by the SF and the CC regions, and the weakest in the AI area. It is consistent with the wind speed and the weakest in the AI area. It is consistent with the wind speed anomalies shown in Figures6 and7. 371 anomalies shown in Figures 6 and 7. In addition, the maximum TKE flux for anticyclonic eddies is In addition, the maximum TKE flux for anticyclonic eddies is observed above 800 hPa and almost 372 observed above 800 hPa and almost reaches 900 hPa in the SF, CC and AI regions. The TKE flux in reaches 900 hPa in the SF, CC and AI regions. The TKE flux in the AI region is the weakest among the 373 the AI region is the weakest among the four subdomains. Moreover, the maximum TKE flux of the four subdomains. Moreover, the maximum TKE flux of the cyclonic eddies is slightly larger than that of the anticyclonic eddies. Furthermore, the TKE flux is weaker during summer than in winter in all four subdomains. It corresponds to larger wind speed anomaly in winter than in summer (Table2). Remote Sens. 2020. 13 of 18

374 cyclonic eddies is slightly larger than that of the anticyclonic eddies. Furthermore, the TKE flux is Remote Sens. 2020, 12, 1161 13 of 18 375 weaker during summer than in winter in all four subdomains. It corresponds to larger wind speed 376 anomaly in winter than in summer (Table 2).

377   378 Figure 14. Composites of vertical variation of TKE flux (ω𝑢2+𝑣2) along the cyclonic (contours) Figure 14. Composites of vertical variation of TKE flux (ω0 u0 + v0 ) along the cyclonic (contours) 379 andand anticyclonicanticyclonic (colors)(colors) eddyeddy centercenter inin thethe zonalzonal direction.direction. BottomBottom (upper)(upper) panel:panel: winter (summer): 380 ((a)a,e )and KE, (e) (b ,fKE,) SF, (b) (c, gand) CC, (f) ( dSF,,h )(c) AI. and The (g) areas CC, with (d) theand plus (h) signAI. The represent areas passeswith the of plus the t -testsign atrepresent the 95% 381 confidencepasses of the level. t test at the 95% confidence level

The SLP adjustment mechanism was first suggested by Lindzen et al. 1978 [21], which requires 382 The SLP adjustment mechanism was first suggested by Lindzen et al. 1978 [21], which requires that negative pressure anomaly occurs over the anticyclonic eddy, and positive pressure anomaly 383 that negative pressure anomaly occurs over the anticyclonic eddy, and positive pressure anomaly occurs over the cyclonic eddy. Figure 15 shows the composites of SLP anomaly over anticyclonic and 384 occurs over the cyclonic eddy. Figure 15 shows the composites of SLP anomaly over anticyclonic and cyclonic eddies in the four areas. The SLP anomalies are characterized by dipole patterns, different 385 cyclonic eddies in the four areas. The SLP anomalies are characterized by dipole patterns, different from what is expected from the SLP adjustment mechanism. Therefore, we conclude that the SLP 386 from what is expected from the SLP adjustment mechanism. Therefore, we conclude that the SLP adjustment is not a dominant mechanism for the atmospheric responses to the oceanic eddies in these 387 adjustment is not a dominant mechanism for the atmospheric responses to the oceanic eddies in these four regions. 388 four regions. The seasonal difference in precipitation rate anomalies to 1 ◦C of SSTA differs among the four subdomains, with stronger responses in winter over the KE, CC and AI regions, but a larger response in summer over the SF region (Figure8). These results may be related to the seasonal variations in precipitation over these regions. In the SF region, more precipitation occurs in summer due to the influence of the summer monsoon. Wang et al. [50] showed that the SF region is one of the peak rainfall regions during the Asia–Pacific summer monsoon. In the midlatitudes, the majority of winter precipitation is associated with frontal systems and related cyclonic activities. In the KE region, the extratropical storms may be the primary contributor to heavy precipitation in winter [51]. The Aleutian Low (AL) is one of the principal causal factors of both the synoptic and climatic conditions in the AI region. In addition, the AL is a deep baroclinic atmospheric system with a low-pressure center that can almost reach the surface. That is why the wind direction in the AI region displays a circular pattern (Figure 12). The CC region is influenced by a tropical Mediterranean-type climate characterized by wet winters and dry summers [52]. The westerly wind path is located closer to the tropical area in winter than in summer, which means that the path of the extratropical cyclone through California could bring more vapor through the atmospheric river. Winter storm tracks and extratropical cyclones heavily influence rainfall in the CC region. In the summer, northeasterly wind prevails (upper panel in Figure 12), carrying less humidity as east of California is a desert area and induces upwelling along the coast. Cold bottom water is brought up to the surface, thus cooling the air above the current and reducing evaporation.

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Figure 15. Composites of sea level pressure anomalies (colors, hPa) and SSTA (contours, ◦C) over the cyclonic (e–h, m–p) and anticyclonic (a–d, i–l) eddies. Bottom (upper) two panels: winter (summer). (a,e,i,m) KE; (b,f,j,n) SF, (c,g,k,o) CC, (d,h,l,p) AI. The plus sign represents the eddy center.

5. Conclusions and Discussion In this study, we chose four eddy rich regions, which are separated from each other by over 1000 km geographically, so there is no mutual relationship between them and they do not affect each other. Based on the above discussion, one can clearly see that there are common influencing factors and also differences in the atmospheric responses to the oceanic eddies among these four areas. The common parts can be summarized as: The increase in the SSTA can cause increases in the sea surface wind, precipitation rate and heat flux, and vice versa. The underlying mechanism driving the atmosphere responses is the vertical kinetic energy flux among the four areas. The different parts can be summarized as: The atmospheric responses (wind speed, precipitation rate) are the strongest and weakest in the KE and AI (or CC) regions. The strong seasonal variation difference in the atmospheric responses are presented. As any type of observational and reanalysis product, uncertainties exist in the data we use in the present study. Based on the validations by the producer of these products, the level of uncertainty is acceptable for the scientific studies. Therefore, these uncertainties associated with the data used do not change the conclusion reached by the present study. Through the above investigation, the EKE, eddy characteristic, SSTA and atmospheric stability vary across the four subdomains and seasons, and could be responsible for differences in mesoscale air–sea interaction characteristics. In addition, several meteorological quantities (SST, wind speed, precipitation, potential temperature, and the heat and TKE fluxes) are statistically examined and a dynamic analysis is performed. It is observed that anticyclonic eddies lead to an enhancement of 1

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SST, surface wind speed, precipitation and heat flux over the eddy center, while cyclonic eddies show the opposite. By comparing composites for the SST, wind speed and heat flux anomalies in the four subdomains, the strongest response is always observed in the KE region, followed by the CC and the SF regions, and the AI region shows the weakest response. However, it is slightly different for precipitation anomalies over the oceanic eddies. The observations might be related to the different magnitude of seasonal precipitation over the four subdomains. The strongest responses are found in the KE, CC and AI in winter. However, in summer it is worth noting that the weakest response shows in the CC region instead of the AI region like the other variables mentioned above, and the strongest response is still observed in the KE area. In addition, the precipitation in the SF region is contrary to the other three subdomains: the strongest response shows in summer with the influence of the summer monsoon acting as the background of the atmosphere. The same method is applied to the CFSR product. Similar variations for each variable occur in the four subdomains. It also shows that surface wind speed responses to oceanic eddies are stronger in winter than in summer, which can be partially explained by the potential temperature profile and  2 2 the TKE flux ω0 u0 + v0 anomalies. The slope of the potential temperature profile below 900 hPa is steepest in the KE domain, followed by the SF, AI, and CC regions, indicative of the strongest instability in the KE region, which is related to the strongest surface wind speed. The TKE flux is positive above anticyclonic eddies, indicating that energy transport is downward as sea surface wind speed increases; the opposite is true for cyclonic eddies. Through the analysis of SSTA, potential temperature and the TKE flux, it was found that there exists a regional dependence of the atmospheric responses to oceanic eddies.

Author Contributions: All authors have read and agree to the published version of the manuscript. Conceptualization, J.J. and C.D.; methodology, J.M. and J.J.; software, J.J.; validation, J.C.H.C., D.C. and M.J.; formal analysis, J.J.; investigation, J.J.; resources, D.C.; data curation, J.J.; writing—original draft preparation, J.J.; writing—review and editing, J.C.H.C., J.M., C.D., and D.C.; visualization, J.J.; supervision, D.C.; project administration, C.D.; funding acquisition, D.C. and C.D. Funding: This research is funded by the National Key Research and Development Program of China (2017YFA0604100), National Programme on Global Change and Air–Sea Interaction (GASI-IPOVAI-04), the National Key Research and Development Program of China (2016YFA0601803 and 2016YFC1401407),the National Natural Science Foundation of China (41476022, 41490643, 41730535 and 41706008), the Startup Foundation for Introducing Talent of Nanjing University of Information Science & Technology (2014r072), the Program for Innovation Research and Entrepreneurship team in Jiangsu Province (2191061503801), the National Programme on Global Change and Air–Sea Interaction (GASI-IPOVAI-02, GASI-03-IPOVAI-05), the National Science Foundation of China (OCE 06-23011) and the China Ocean Mineral Resources R & D Association (DY135-E2-2-02, DY135-E2-3-01). Acknowledgments: The authors would like to thank Yu Liu for his early stage work. Thanks are also extended to Kenny T.C. Lim Kam Sian for improving the writing. The altimeter products were produced and distributed by Aviso+ (https://www.aviso.altimetry.fr/), as part of the Ssalto ground processing segment. AMSR data are produced by Remote Sensing Systems and were sponsored by the NASA AMSR-E Science Team and the NASA Earth Science MEaSUREs Program. The Japanese ocean flux data set using remote-sensing observations J-OFURO (http://dtsv.scc.u-tokai.ac.jp/j-ofuro/). Conflicts of Interest: The authors declare no conflict of interest.

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