Impacts of Atmospheric Pressure on the Annual Maximum of Monthly Sea-Levels in the Northeast Asian Marginal Seas

Journal of Marine Science and Engineering

Article Impacts of Atmospheric Pressure on the Annual Maximum of Monthly Sea-Levels in the Northeast Asian Marginal Seas

MyeongHee Han 1 , Yang-Ki Cho 1,*, Hyoun-Woo Kang 2 , SungHyun Nam 1 , Do-Seong Byun 3 , Kwang-Young Jeong 3 and Eunil Lee 3 1 School of Earth and Environmental Sciences/Research Institute of Oceanography, Seoul National University, Seoul 08826, Korea; [email protected] (M.H.); [email protected] (S.N.) 2 Ocean Circulation & Climate Research Center, Korea Institute of Ocean Science & Technology, Busan 49111, Korea; [email protected] 3 Ocean Research Division, Korea Hydrographic and Oceanographic Agency, Busan 49111, Korea; [email protected] (D.-S.B.); [email protected] (K.-Y.J.); [email protected] (E.L.) * Correspondence: [email protected]; Tel.: +82-2-880-6749; Fax: +82-2-872-3269

Received: 19 May 2020; Accepted: 7 June 2020; Published: 10 June 2020

Abstract: Monthly mean sea-levels have annual maxima in August in the northeast Asian marginal seas (NEAMS). Based on satellite altimetry data, the rising rate of the August NEAMS sea-level (ANS, 4.2 mm yr 1) is greater than those of the NEAMS (3.6 mm yr 1) and global (3.4 mm yr 1) annual · − · − · − mean sea-levels. Thus, the interannual variations of ANS are classified as relatively high (period H) and low (period L) years and have been analysed because of the high risk of sea-level fluctuation to the coastal regions in August. In period H, there are large atmospheric pressure gradients between the high pressure zone in the Kuroshio Extension (KE) and the low pressure zone in the west of Taiwan (WT). In period L, the atmospheric pressure gradients are small between the above-mentioned zones. Large atmospheric pressure gradients induce strong west-northwestward wind stresses and more Ekman transport from the northwest Pacific Ocean into the NEAMS. The correlation coefficient between August NEAMS sea-level index (ANSI), which is the difference of atmospheric pressure anomalies between the KE and the WT, and the August NEAMS sea-level anomaly (ANSA) is 0.73. Although there is a significant correlation (coefficient: 0.64) between ANSA and the East Asian summer monsoon index (EASMI), ANSI might be more useful in estimating the variability of ANSA.

Keywords: sea-level; interannual variation; August NEAMS sea-level index; atmospheric pressure; wind stress; Ekman transport; satellite altimetry

1. Introduction Sea-level rising due to climate change is seriously threatening the lives of human beings [1]. Sea-level changes can be largely affected by barometric, steric, and mass components [2]. Although there are many ongoing studies on sea-level rise caused by thermal expansion and ice sheet melting [3,4], there are few studies on the impacts of wind stress by atmospheric pressure distributions on sea-level variation in marginal seas. The Yellow Sea (YS), East China Sea (ECS), and East/Japan Sea (EJS) make up the northeast Asian marginal seas (NEAMS), which are surrounded by Korea, China, Russia, Japan, and Taiwan. The NEAMS are connected to the Pacific Ocean through narrow straits and shallow continental shelves (Figure1). The sea-level responds immediately to external forcing by quickly propagating barotropic waves in the NEAMS [5–7]. Temporal variations of volume transport in the Korea Strait (KS), a major strait in the NEAMS, is influenced significantly by along-strait wind stresses, atmospheric pressures,

J. Mar. Sci. Eng. 2020, 8, 425; doi:10.3390/jmse8060425 www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2020, 8, 425 2 of 15 and sea-level differences between the Pacific and the EJS for timescales longer than 100 days [7]. The surface wind stress plays an important role in both the volume transport and sea-level changes in the marginal seas [8–10]. The effect of the zonal wind stress on the meridional migration of a subpolar front in the EJS has been previously reported [11]. The wind stress along the east coast of Sakhalin, an island in the north of Soya Strait (SS), induces interannual variation of volume transport along the SS [12]. The interannual variation of the mean sea-level in the ECS is linked to the surface wind stress in the NorthJ. Mar. Pacific Sci. Eng. [ 92020]., Wind 8, x FOR stressPEER REVIEW and atmospheric pressure were proposed as potential3 of 18 causes of the large seasonal variation of sea-level [13].

Figure 1. Domain of the northeast Asian marginal seas (NEAMS, cyan) and locations of twenty-one tide-gauge stationsFigure 1. Domain (magenta of the squares, northeast available Asian marginal from https: seas (NEAMS,//www.psmsl.org cyan) and loca). Thetions blueof twenty-one dotted line along the 32.25◦ Ntide-gauge between stations China (magenta and Japan squares, is for available meridional from https://www. Ekman transports.psmsl.org). SchematicsThe blue dotted of line the regional circulationalong patterns, the 32.25° with N majorbetween surfaces China and (red Japan or is blue) for meridional and intermediate Ekman transpor (grey)ts. Schematics currents of transporting the warm (red)regional and cold circulation (blue) waterspatterns, inwith the major upper surfaces left box (red (available or blue) and from intermediate http://www.khoa.go.kr (grey) currents ). The transporting warm (red) and cold (blue) waters in the upper left box (available from stronger currentshttp://www.khoa.go.kr). are shown with The thicker stronger arrows, currents while are shown continuous with thicker and arrows, continual while continuous currents areand indicated by solid andcontinual dashed currents arrows, are respectively. indicated by solid KC, and TC, dash anded arrows, TWCare respectively. the Kuroshio KC, TC, Current, and TWC andare the the Taiwan and the TsushimaKuroshio Warm Current, Currents, and the Taiwan respectively. and the Tsushima Warm Currents, respectively.

Moreover,2. Data the and eﬀ Methodsect of typhoons, which induce high sea-levels and frequently occur in the NEAMS in August, needsMonthly to be mean studied. sea-levels The in sea-level August in maxima the NEAMS and were their analysed interannual from 1993 variations to 2018. The in daily August need to be understoodabsolute sodynamic that issues topography such as(ADT) coastal data ﬂooding, provided saltwaterby the Copernicus intrusion, Marine and Environment habitat destruction, Monitoring Service (CMEMS) were gridded with a horizontal resolution of 0.25° from all satellite can be preparedaltimeter beforehand missions. ADT [14 is]. the Sea-level sum of sea-level changes anomalies in the YS, and ECS,the mean and dynamic EJS have topography been reported, (mean mainly with tide-gaugesea-level and driven altimetry by thermodynamic measurements processes [15 in], th bute ocean, the causes[16,17]). andWe used dynamics level four of of August the ADT sea-level changes indata the NEAMS(e.g. dt_global_allsat_phy_l4_20180831_20190101.nc) are yet to be studied. downloaded by ftp from the CMEMS In thisportal study, [18,19]. we try More to information understand regarding the mechanism ADT is available of the on interannual the CMEMS web NEAMS site [20]. sea-level variability Monthly mean sea-level data at twenty-one tide-gauge stations (magenta squares in Figure 1), caused by atmosphericfrom 1993 to 2018, pressure were provided and wind by the stressPermanen int August. Service for The Mean data Sea Level and [21,22]. methods The aresea-level presented in Section2, anddata the are resultsfrom stations and discussionin Korea (nine are stations: described INCHEON, in Sections ANHEUNG,3 and 4MOKPO,, respectively. JEJU, YEOSU, Conclusions are presented in Section5. J. Mar. Sci. Eng. 2020, 8, 425 3 of 15

2. Data and Methods Monthly mean sea-levels in August in the NEAMS were analysed from 1993 to 2018. The daily absolute dynamic topography (ADT) data provided by the Copernicus Marine Environment Monitoring Service (CMEMS) were gridded with a horizontal resolution of 0.25◦ from all satellite altimeter missions. ADT is the sum of sea-level anomalies and the mean dynamic topography (mean sea-level driven by thermodynamic processes in the ocean, [16,17]). We used level four of the ADT data (e.g., dt_global_allsat_phy_l4_20180831_20190101.nc) downloaded by ftp from the CMEMS portal [18,19]. More information regarding ADT is available on the CMEMS web site [20]. Monthly mean sea-level data at twenty-one tide-gauge stations (magenta squares in Figure1), from 1993 to 2018, were provided by the Permanent Service for Mean Sea Level [21,22]. The sea-level data are from stations in Korea (nine stations: INCHEON, ANHEUNG, MOKPO, JEJU, YEOSU, BUSAN, MUKHO, SOKCHO, ULLEUNG), China (three stations: KANMEN, LUSI, and DALIAN), and Japan (nine stations: HAKATA, IZUHARA II, HAMADA II, SAIGO, MIKUNI, OGI, AWA SIMA, OSHORO II, WAKKANAI). The inverted barometer eﬀect was removed from the sea-level observed at the tide-gauge stations with the assumption that 1 hPa atmospheric pressure decreases the water level by 1 cm [23,24]. The tide-gauge data, after removing the inverted barometric eﬀect, were matched to have the same mean sea-level as the satellite altimetry data in August for over 26 years (Figure2b). Monthly mean sea-level atmospheric pressure and surface wind stress data from the European Centre for Medium-Range Weather Forecasts (interim reanalysis) were used with a horizontal resolution of 0.75◦ over 26 years (1993–2018) around the NEAMS. The linear trend over the time period (26 years) was removed to investigate the interannual 2 1 variation. The Ekman transport, per unit width, within the surface Ekman layer (VTEkman, m s− ) was calculated using Equation (1): J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 4 of 18 R 0 R 0 1 ∂τ 1 0 BUSAN, MUKHO,VTEkman SOKCHO,= ULLEUNG),uEkman dz China= (three stations:dz KANMEN,= [ τ ] LUSI,D and DALIAN), DEkman DEkman f ρ ∂z f ρ Ekman (1) and Japan (nine stations: HAKATA,1 IZUHARA II, HAMADA II, SAIGO, τ ( MIKUNI,z=0) OGI, AWA SIMA, = τ (z = 0) τ (z = DEkman) = OSHORO II, WAKKANAI).f ρThe inverted barometer− effect was removed ffromρ the sea-level observed at the tide-gauge stations with the assumption that 1 hPa atmospheric pressure decreases the water 5 Here,leveluEkman by, D1 cmEkman [23,24]., f (= The2Ω tide-gaugesin ϕ), Ω data,( 7.2921 after removing10− ), theϕ, ρinverted, z, and barometricτ denote theeffect, Ekman were velocity 1 ≈ ×1 1 (m s−J. Mar.), Ekman Sci.matched Eng. depth2020 to ,have8, 0 (m the), Coriolissame mean parameter sea-level as (therad satellite s− ), rotationaltimetry data rate in of August the Earth for over (rad 26 syears− ),4 of latitude, 16 (Figure 2b). 3 2 density of sea water ( 1025 kg m− ), depth (m), and wind stress (N m ), respectively. ≈ −

Figure 2. Cont.

Figure 2. (a) Climatology of de-trended monthly mean NEAMS sea-levels from tide-gauge Figure 2. (a) Climatology of de-trended monthly mean NEAMS sea-levels from tide-gauge observations observations averaged from 1993 to 2018. 0 cm represents the trend line of the original tide-gauge averaged from 1993 to 2018. 0 cm represents the trend line of the original tide-gauge data and the data and the inverted barometer effect was not removed. The maximum and minimum are in August inverted barometer eﬀect was not removed. The maximum and minimum are in August and February, and February, respectively. Error bars indicate the positive and negative standard deviations of respectively.monthly Error sea-level bars for indicate 26 years the from positive climatology. and negative (b) Time-series standard of deviations August mean of monthlyNEAMS sea-levels sea-level for 26 yearsfrom from satellite climatology. altimetry ((redb) Time-series open circles) of and August tide-gauge mean observations NEAMS sea-levels (black open from squares) satellite from altimetry 1993 (red open circles) and tide-gauge observations (black open squares) from 1993 to 2018 after removing the inverted barometer eﬀect. The vertical reference datum (0 cm) of the tide-gauge data had been adjusted to have the same mean sea-level in August for 26 years between tide-gauge data and satellite altimetry, and the dashed red and black dotted lines represent their trends.

3. Results

3.1. Long-Term Trend of Sea-Levels in August The mean seasonal variation of twenty-one tide-gauge observations was examined without removing the inverted barometer eﬀect and averaged from 1993 to 2018 in order to determine annual variations of the actual sea-level. Maximum and minimum of de-trended sea-levels are +16.1 cm J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 4 of 18

BUSAN, MUKHO, SOKCHO, ULLEUNG), China (three stations: KANMEN, LUSI, and DALIAN), and Japan (nine stations: HAKATA, IZUHARA II, HAMADA II, SAIGO, MIKUNI, OGI, AWA SIMA, OSHORO II, WAKKANAI). The inverted barometer effect was removed from the sea-level observed at the tide-gauge stations with the assumption that 1 hPa atmospheric pressure decreases the water level by 1 cm [23,24]. The tide-gauge data, after removing the inverted barometric effect, were J. Mar. Sci.matched Eng. 2020 to ,have8, 0 the same mean sea-level as the satellite altimetry data in August for over 26 years4 of 16 (Figure 2b).

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Figure 2. (a) Climatology of de-trended monthly mean NEAMS sea-levels from tide-gauge Figure 2. (a) Climatology of de-trended monthly mean NEAMS sea-levels from tide-gauge observations Figure 2. (a) Climatologyobservations averaged of de-trended from 1993 monthlyto 2018. 0 cm mean repres NEAMSents the trend sea-levels line of the from original tide-gauge tide-gauge observations averaged from 1993 to 2018. 0 cm represents the trend line of the original tide-gauge data and the averaged fromdata 1993 and tothe 2018.inverted 0barometer cm represents effect was not the removed. trend The line maximum of the and original minimum tide-gauge are in August data and the inverted barometer eﬀect was not removed. The maximum and minimum are in August and February, inverted barometerand February, eﬀect wasrespectively. not removed. Error bars Theindicate maximum the positive and and minimumnegative standard are indeviations August of and February, respectively.monthly Error sea-level bars for indicate 26 years the from positive climatology. and negative (b) Time-series standard of deviations August mean of monthlyNEAMS sea-levels sea-level for respectively.26 years Errorfrom from satellite bars climatology. indicate altimetry ((redb the) Time-series open positive circles) of andand August tide-g negativeauge mean observations NEAMS standard sea-levels (black deviations open from squares) satellite of monthlyfrom altimetry 1993 sea-level for 26 years from(red open climatology. circles) and (tide-gaugeb) Time-series observations of August (black open mean squares) NEAMS from sea-levels1993 to 2018 fromafter removing satellite altimetry

(red openthe circles) inverted and barometer tide-gauge effect. observations The vertical reference (black datum open (0 squares) cm) of the from tide-gauge 1993 to data 2018 had after been removing adjusted to have the same mean sea-level in August for 26 years between tide-gauge data and satellite the invertedaltimetry, barometer and the dashed effect. red The and vertical black dotted reference lines represent datum their (0 trends. cm) of the tide-gauge data had been adjusted to have the same mean sea-level in August for 26 years between tide-gauge data and satellite 3. Results altimetry, and the dashed red and black dotted lines represent their trends. 3.1. Long-Term Trend of Sea-Levels in August 3. Results The mean seasonal variation of twenty-one tide-gauge observations was examined without 3.1. Long-Termremoving Trend the invertedof Sea-Levels barometer in August effect and averaged from 1993 to 2018 in order to determine annual variations of the actual sea-level. Maximum and minimum of de-trended sea-levels are +16.1 cm The mean seasonal variation of twenty-one tide-gauge observations was examined without removing the inverted barometer effect and averaged from 1993 to 2018 in order to determine annual variations of the actual sea-level. Maximum and minimum of de-trended sea-levels are +16.1 cm in August and 14.3 cm in February, respectively (Figure2a). Rising rates of sea-levels in August, − from satellite altimetry (4.2 mm yr 1) and tide-gauge observations (4.6 mm yr 1), after removing · − · − the inverted barometer effect from 1993 to 2018, are compared in Figure2b (dashed red and dotted black, respectively). Even though there are several causes of these different rising rates (0.4 mm yr 1), · − including uneven distributions of tide-gauge stations, the variations of the two datasets are similar. Sea-level data from satellite altimetry and from tide-gauge observations, which are independent of each other, have a strong positive correlation with coefficients of 0.95 (August, p-value < 0.01) and 0.97 (annual, p-value < 0.01, not shown), respectively.

3.2. Interannual Variation of Sea-Level Anomalies in August The interannual variations of the August NEAMS sea-level anomalies (ANSA) from satellite altimetry are classiﬁed as years of relatively high (positive anomalies above +2 cm) and low (negative anomalies below 2 cm) sea-levels; period H (1994, 1999, 2001, 2002, 2004, 2012, and 2018) and period L − (1993, 1995, 1996, 1998, and 2005), respectively (Figure3). The selected threshold of 2 cm is the accuracy of TOPEX/Poseidon’s radar altimeter [25]. The wind stress and Ekman transport in Section 3.3 and sea-level atmospheric pressure in Section 3.4 were analysed to determine the positive and negative eﬀects of atmospheric conditions on ANSA based on the periods H and L. We will hereafter use ANSA from satellite altimetry data only. J. Mar. Sci. Eng. 2020, 8, 425 5 of 15 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 6 of 18

Figure 3. Time-series of the de-trended August sea-level anomalies averaged over the NEAMS from Figure 3. Time-series of the de-trended August sea-level anomalies averaged over the NEAMS from 1993 to 2018 are classified as seven high (higher than +2 cm) and five low (lower than −2 cm) years. 1993 to 2018 are classified as seven high (higher than +2 cm) and five low (lower than 2 cm) years. Period H (1994, 1999, 2001, 2002, 2004, 2012, and 2018) and period L (1993, 1995, 1996, 1998, −and 2005) Periodare H represented (1994, 1999, by 2001, red and 2002, blue 2004, dotte 2012,d circles, and respectively. 2018) and period0 cm here L (1993,is the trend 1995, of 1996, August 1998, sea-level, and 2005) are representedthe same as by the red red and dashed blue line dotted in Figure circles, 2b. respectively. 0 cm here is the trend of August sea-level, the same as the red dashed line in Figure2b. 3.3. Ekman Transport and Sea-Level Anomaly 3.3. Ekman Transport and Sea-Level Anomaly The ECS is the main path for the Taiwan and the Tsushima Warm Currents which are the Theprimary ECS seawater is the main advection path for sources the Taiwan in the andNEAMS the Tsushima[26]. The meridional Warm Currents Ekman whichtransport are anomaly the primary seawaterfrom advection the east coast sources of China in the to the NEAMS west coast [26]. of The Japan meridional along 32.25°N Ekman in the transport ECS (blue anomaly dotted line from in the east coastFigure of 1) China was calculated to the west to examine coast of the Japan relationsh alongip 32.25 between◦ N inwind the stress ECS and (blue ANSA. dotted The line correlation in Figure 1) was calculatedbetween the to meridional examine the Ekman relationship transport betweenanomaly along wind 32.25°N stress andin August ANSA. and The ANSA, correlation from 1993 between to 2018, is significant (correlation coefficient 0.69 with p-value < 0.01, Figure 4). Thus, the Ekman the meridional Ekman transport anomaly along 32.25◦ N in August and ANSA, from 1993 to 2018, is significanttransport (correlation anomaly by coetheffi windcient stress 0.69 within thep -valuesouthern< NEAMS0.01, Figure and 4the). Thus,sea-level the anomaly Ekman transportin the NEAMS are closely related in August. anomaly by the wind stress in the southern NEAMS and the sea-level anomaly in the NEAMS are There are strong west-northwestward wind stresses in the south of the NEAMS for the period closelyJ. Mar. related Sci. Eng. in 2020 August., 8, x FOR PEER REVIEW 7 of 18 H around 136°E and 29°N in Figure 5a and weak west-northwestward wind stresses in the south of the NEAMS for the period L around 140°E and 28°N in Figure 5b. The Ekman transport can be calculated by using Equation (1) to examine the relationship between wind stress and the sea-level change. More north-northeastward Ekman transport by strong west-northwestward wind stress brings more seawater from the northwest Pacific Ocean into the NEAMS, and less north-northeastward Ekman transport by weak west-northwestward wind stress brings less seawater into the NEAMS. The directions of wind stresses in the ECS are westward and west-northwestward, which can induce more Ekman transport into the YS and EJS for the period H in Figure 5a. These directions of wind stresses result from the expansion of the high and the low pressures westward and eastward, respectively for the period H . On the contrary, the directions of wind stresses in the ECS are almost northward, which cannot cause Ekman transport into the YS and EJS for the period L in Figure 5b. The northward directions of wind stresses are due to the westward retreat of the low pressure in the south of the NEAMS for the period L .

Figure 4. Time-series of the meridional Ekman transport anomaly, along the 32.25°N (blue dotted line Figure 4. Time-series of the meridional Ekman transport anomaly, along the 32.25◦ N (blue dotted line in Figure 1) from the east coast of China to the west coast of Japan (dotted red), and the de-trended in Figure1) from the east coast of China to the west coast of Japan (dotted red), and the de-trended mean sea-level anomalies averaged over the NEAMS (solid black) from satellite altimetry data in mean sea-level anomalies averaged over the NEAMS (solid black) from satellite altimetry data in August from 1993 to 2018. The correlation coefficient between the sea-level anomaly and the Augustmeridional from 1993 Ekman to 2018. transport The correlationanomaly is 0.69 coe ﬃ(p-valuecient between < 0.01). the sea-level anomaly and the meridional Ekman transport anomaly is 0.69 (p-value < 0.01).

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There are strong west-northwestward wind stresses in the south of the NEAMS for the period H around 136◦ E and 29◦ N in Figure5a and weak west-northwestward wind stresses in the south of the NEAMS for the period L around 140◦ E and 28◦ N in Figure5b. The Ekman transport can be calculated by using Equation (1) to examine the relationship between wind stress and the sea-level change. More north-northeastward Ekman transport by strong west-northwestward wind stress brings more seawater from the northwest Paciﬁc Ocean into the NEAMS, and less north-northeastward Ekman transport by weak west-northwestward wind stress brings less seawater into the NEAMS. The directions of wind stresses in the ECS are westward and west-northwestward, which can induce more Ekman transport into the YS and EJS for the period H in Figure5a. These directions of wind stresses result from the expansion of the high and the low pressures westward and eastward, respectively Figure 4. Time-seriesfor the periodof the meridional H. On Ekman the contrary,transport anomaly, the directions along the 32.25°N of wind (blue dotted stresses line in the ECS are almost northward, in Figure 1) from the east coast of China to the west coast of Japan (dotted red), and the de-trended mean sea-levelwhich anomalies cannot averaged cause over Ekman the NEAMS transport (solid intoblack)the from YS satellite and altimetry EJS for data the periodin L in Figure5b. The northward August fromdirections 1993 to 2018. of windThe correlation stresses coeffi arecient due between to the the westward sea-level anomaly retreat and of the the low pressure in the south of the meridionalNEAMS Ekman transport for the anomal periody is 0.69 L. (p-value < 0.01).J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 8 of 18

Figure 5. Surface wind stress (black vector)Figure 5. in Surface August wind averaged stress (black for vector) periods in August (a) H averaged and (b) for L. periods There are(a) H and (b) L. There are strong (weak) west-northwestward windstrong stresses (weak) west-northwestward around 136◦ E and wind 29 ◦stresseN (140s around◦ E and 136°E 28 and◦ N) 29°N in the (140°E and 28°N) in the south of the NEAMS for the period Hsouth (period of the L). NEAMS Coastlines for the areperiod drawn H (per iniod grey. L). Coastlines are drawn in grey.

The Ekman transports per unit widthThe Ekman were calculatedtransports per based unit width on Equation were calculated (1) and based their on Equation anomalies (1) and their anomalies are plotted in Figure 6a,b. The directions of Ekman transport anomalies in the southern NEAMS are are plotted in Figure6a,b. The directionsnorthward of Ekmanfor the period transport H as shown anomalies in Figure in 6a, the and southernthey are southward NEAMS for are the period L as shown northward for the period H as shownin Figure in Figure 6b. The6a, positive and they sea-level are southwardanomaly is due for to more the period northward L as Ekman shown transport in the south in Figure6b. The positive sea-level anomalyof the NEAMS is due for the to moreperiod northwardH in Figure 6a, Ekman and the transportsea-level anomaly in the from south the ofsatellite observations the NEAMS for the period H in Figurein Figure6a, and 6c theis similar sea-level to that. anomaly The negative from sea-level the satellite anomaly observations is due to less in northward Ekman transport in the south of the NEAMS for the period L in Figure 6b, and the result of the satellite Figure6c is similar to that. The negativeobservation sea-level is similar anomaly to that in is Figure due to 6d. less northward Ekman transport in the south of the NEAMS for the periodThe Lpositive in Figure sea-level6b, andanomaly the resultby Ekman of thetransport satellite in the observation southern NEAMS is can cause the similar to that in Figure6d. sea-level anomaly of the NEAMS to be positive because the high sea-level in the southern NEAMS generates more water transport (onshore transport) from the Pacific Ocean (open ocean) to the YS and EJS (marginal sea). This is due to the high water pressure in the southern NEAMS as shown in Figure 6a,c in the period H. The negative sea-level anomaly by Ekman transport in the southern NEAMS can cause the sea-level anomaly of the NEAMS to be negative because the low sea-level in the southern NEAMS causes less water transport from the Pacific Ocean to the YS and EJS. This is due to low water pressure in the southern NEAMS as shown in Figure 6b,d. There is a strong relationship between wind stress (Ekman transport) and sea-level in the NEAMS as shown in Figure 6; thus, we have determined the area where a maximum correlation is observed between wind stress and sea-level in the NEAMS in Figure 7. The maximum correlation existed in the south of Japan, which was selected as a wind index area (WA). The correlation coefficient between the area averaged wind stress in WA and ANSA is 0.73 (p-value < 0.01, Figure 8). Based on this finding, we can estimate the sea-level anomaly in the NEAMS with wind stress in the WA in August.

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FigureFigure 6. The 6. The Ekman Ekman transport transport anomaly anomaly per per unitunit widthwidth ba basedsed on on Equation Equation (1) (1) during during the the periods periods (a) (a)H andH (b )and L. The(b) L. strength The strength and direction and direction of the of Ekmanthe Ekma transportn transport anomaly anomaly are are shown shown with with color color and and vector. The NEAMSvector. The sea-level NEAMS distribution sea-level distribution derived fromderived satellite from satellite altimetry altimetry observations observations during during the periods the (c) H andperiods (d) L. (c) H and (d) L.

The positive sea-level anomaly by Ekman transport in the southern NEAMS can cause the sea-level anomaly of the NEAMS to be positive because the high sea-level in the southern NEAMS generates more water transport (onshore transport) from the Pacific Ocean (open ocean) to the YS and EJS (marginal sea). This is due to the high water pressure in the southern NEAMS as shown in Figure6a,c in the period H. The negative sea-level anomaly by Ekman transport in the southern NEAMS can cause the sea-level anomaly of the NEAMS to be negative because the low sea-level in the southern NEAMS causes less water transport from the Pacific Ocean to the YS and EJS. This is due to low water pressure in the southern NEAMS as shown in Figure6b,d. There is a strong relationship between wind stress (Ekman transport) and sea-level in the NEAMS as shown in Figure6; thus, we have determined the area where a maximum correlation is observed between wind stress and sea-level in the NEAMS in Figure7. The maximum correlation existed in the south of Japan, which was selected as a wind index area (WA). The correlation coefficient between the area averaged wind stress in WA and ANSA is 0.73 (p-value < 0.01, Figure8). Based on this finding, we can estimate the sea-level anomaly in the NEAMS with wind stress in the WA in August.

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FigureFigure 7. 7.Correlation Correlation mapmap ofof the NEAMS August August sea- sea-levellevel with with the thenorthwestward northwestward (main (mainwind wind directiondirectionFigure in 7. Augustin Correlation August in in the themap NEAMS) NEAMS) of the surfaceNEAMS wind windAugust st stressress sea- from fromlevel 1993 with 1993 to 2018.the to2018. northwestward The contour The contour interval (main interval is wind 0.05 is 0.05 andanddirection conﬁdence confidence in August levels levels lessin less the than thanNEAMS) 95%95% were surface eliminated. eliminated. wind stress A Apositivefrom positive 1993 correlation to correlation 2018. The means contour means that intervalhigh that NEAMS high is 0.05 NEAMS sea-level occurs with strong northwestward wind stress and a negative correlation means that high sea-leveland confidence occurs with levels strong less than northwestward 95% were eliminated. wind stress A positive and acorrelation negative means correlation that high means NEAMS that high NEAMS sea-level occurs with weak northwestward wind stress. Here, the wind index area (WA) NEAMSsea-level sea-level occurs occurswith strong with northwestward weak northwestward wind stress wind and a stress. negative Here, correlation the wind means index that areahigh (WA) showing the maximum positive correlation coefficients is marked with a green dotted box. showingNEAMS the sea-level maximum occurs positive with correlationweak northwestward coeﬃcients wind is markedstress. Here, with the a green wind dottedindex area box. (WA) showing the maximum positive correlation coefficients is marked with a green dotted box.

Figure 8. Time-series of area averaged northwestward wind stress anomaly in the WA (red dashed line,

left y-axis), and August NEAMS sea-level anomaly observed from satellite altimetry (black line, right y-axis) from 1993 to 2018. The correlation coeﬃcient is 0.73 with p-value < 0.01. J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 11 of 18

Figure 8. Time-series of area averaged northwestward wind stress anomaly in the WA (red dashed line, left y-axis), and August NEAMS sea-level anomaly observed from satellite altimetry (black line, right y-axis) from 1993 to 2018. The correlation coefficient is 0.73 with p-value < 0.01.

3.4. Atmospheric Pressure Distribution The wind stress is caused by atmospheric pressure distribution; thus we have examined the correlations between atmospheric pressure in each area and mean sea-level in the NEAMS in August for over 26 years in Figure 9. Positive correlation means that high NEAMS sea-level occurs with high atmospheric pressure and negative correlation means that high NEAMS sea-level occurs with low atmospheric pressure. The areas showing the maximum and the minimum correlation coefficients are Kuroshio Extension (KE) and west of Taiwan (WT) (right and left magenta dotted boxes, J. Mar.respectively Sci. Eng. 2020 in, 8 ,Figure 425 9). We selected these two areas to define the index, August NEAMS sea-level9 of 15 index (ANSI). There are large atmospheric pressure gradients between high pressure in the KE and low 3.4. Atmosphericpressure in the Pressure WT in Distribution the period H in Figure 10a. Large atmospheric pressure gradients result in strong west-northwestward wind stresses. Strong west-northwestward wind stresses cause more The wind stress is caused by atmospheric pressure distribution; thus we have examined the north-northeastward Ekman transport from the northwest Pacific Ocean into the NEAMS as shown correlationsin Figure between 6a,c. There atmospheric are small atmospheric pressure in pressure each area gradients and mean in areas sea-level between in high the NEAMSpressure in in the August for overKE and 26 years low pressure in Figure in9 the. Positive WT in the correlation period L in means Figure that 10b. high Small NEAMS atmospheric sea-level pressure occurs gradients with high atmosphericresult in weak pressure west-northwestward and negative correlation wind stresse meanss. Weak that west-northwestward high NEAMS sea-level wind stresses occurs cause with low atmosphericless north-northeastward pressure. The areas Ekman showing transport the from maximum the northwest and the Pacific minimum Ocean correlation into the NEAMS coeﬃcients as are Kuroshioshown Extension in Fig. 6b,d. (KE) and west of Taiwan (WT) (right and left magenta dotted boxes, respectively in Figure9). We selected these two areas to deﬁne the index, August NEAMS sea-level index (ANSI).

Figure 9. Correlation map of the NEAMS August sea-level with the mean sea-level atmospheric pressureFigure from 9. Correlation 1993 to 2018. map Theof the contour NEAMS interval August is sea-level 0.05 and with conﬁdence the mean levels sea-level less atmospheric than 90% were eliminated.pressure Positivefrom 1993 correlation to 2018. The means contour that interval high is NEAMS 0.05 and sea-level confidence occurs levels withless than high 90% atmospheric were pressureeliminated. and negative Positive correlationcorrelation means means that that high high NEAMS NEAMS sea-level sea-level occurs occurs with with high lowatmospheric atmospheric pressure.pressure The and areas negative showing correlation the maximum means that (Kuroshio high NEAMS Extension sea-level (KE)) occurs and minimumwith low atmospheric (west of Taiwan (WT)) correlation coeﬃcients are marked with magenta dotted boxes to make the index, August

NEAMS sea-level index (ANSI).

There are large atmospheric pressure gradients between high pressure in the KE and low pressure in the WT in the period H in Figure 10a. Large atmospheric pressure gradients result in strong west-northwestward wind stresses. Strong west-northwestward wind stresses cause more north-northeastward Ekman transport from the northwest Paciﬁc Ocean into the NEAMS as shown in Figure6a,c. There are small atmospheric pressure gradients in areas between high pressure in the KE and low pressure in the WT in the period L in Figure 10b. Small atmospheric pressure gradients result in weak west-northwestward wind stresses. Weak west-northwestward wind stresses cause less north-northeastward Ekman transport from the northwest Paciﬁc Ocean into the NEAMS as shown in Figure6b,d. J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 12 of 18

pressure. The areas showing the maximum (Kuroshio Extension (KE)) and minimum (west of Taiwan J. Mar. Sci.(WT)) Eng. 2020 correlation, 8, 425 coefficients are marked with magenta dotted boxes to make the index, August 10 of 15 NEAMS sea-level index (ANSI).

FigureFigure 10. Atmospheric10. Atmospheric pressures pressures in in August August averaged for for the the periods periods (a) (Ha) and H and (b) L (b in) Lthe in northwest the northwest PaciﬁcPacific Ocean Ocean (contour (contour intervals: intervals: 11 hPa).hPa). Atmosphe Atmosphericric pressure pressure contours contours with with the colored the colored August August NEAMSNEAMS sea-level sea-level anomaly anomaly (ANSA) (ANSA) (red: (red: positive,positive, blue: blue: negative) negative) for for the the periods periods (c) H (c and) H ( andd) L. ( Thed) L. The areasareas deﬁning defining the the August August NEAMS NEAMS sea-level sea-level index (ANSI) (ANSI) are are denoted denoted by the by themagenta magenta dotted dotted boxes, boxes, the samethe same as in as Figure in Figure9. Coastlines9. Coastlines are are drawn drawn inin grey.grey.

4. Discussion4. Discussion TheThe sea-level sea-level variation variation by by wind wind hashas beenbeen previously previously investigated investigated in the in theNEAMS NEAMS [8,9,11,27,28]. [8,9,11,27 ,28]. The surfaceThe surface wind wind stress stress is is considered considered asas oneone of th thee most most important important factors factors to change to change sea-level. sea-level. The The monthlymonthly mean mean sea-levels sea-levels in in the the NEAMS NEAMS havehave an annual annual maximum maximum in inAugust, August, between between the years the years 1993 1993 and 2018. Even though there are other causes for sea-level variability, such as surface heat flux, air and 2018. Even though there are other causes for sea-level variability, such as surface heat flux, air and and sea surface temperatures, and geostrophic advection [13], the correlations of ANSA with wind sea surfacestress and temperatures, atmospheric pressure and geostrophic distribution advection were significant. [13], the correlations of ANSA with wind stress and atmosphericIf we define pressure the ANSI distribution as the difference were significant. of atmospheric pressure anomalies between the KE (146.0–149.0If we define °E, the 36.0–38.0 ANSI °N) as theand ditheff erenceWT (116.5–119 of atmospheric.5 °E, 23.0–25.5 pressure °N), the anomalies correlation between coefficient the KE (146.0–149.0between ◦ANSIE, 36.0–38.0 and ANSA◦ N) andis 0.73 the (p-value WT (116.5–119.5 < 0.01) (Figure◦ E, 23.0–25.5 11a). Thus◦ N), ANSA the correlationcan be estimated coeffi cient betweenquantitatively ANSI and with ANSA ANSI. is 0.73 (p-value < 0.01) (Figure 11a). Thus ANSA can be estimated quantitativelyWe compared with ANSI. ANSA and ANSI with other climate indices. The climate indices used in this study were the Aleutian low pressure index (ALPI, [29]), the Arctic Oscillation Index (AOI, [30]), the East Asian summer monsoon index (EASMI, [31]), the North Pacific gyre oscillation (NPGO, [32]), the North Pacific index (NPI, [33]), the Oceanic Niño index (ONI, [34]), the Pacific decadal oscillation (PDO, [32]), and the Siberian high index (SHI, [35]) from 1993 to 2018. The ALPI was only compared

J. Mar. Sci. Eng. 2020, 8, 425 11 of 15 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 14 of 18

Figure 11. (a) Time-series of the ANSI (red dash-dotted line, left y-axis), the East Asian summer Figure 11. (a) Time-series of the ANSI (red dash-dotted line, left y-axis), the East Asian summer monsoon index (EASMI) (blue dotted line, left y-axis), and the ANSA (black solid line, right y-axis) monsoonin August index from (EASMI) 1993 to 2018. (blue The dotted correlation line, leftcoefficients y-axis), between and the ANSA ANSA and (black ANSI, solid and ANSA line, right and y-axis) in AugustEASMI are from 0.73 1993 (p-value to 2018. < 0.01) The and correlation 0.64 (p-value coe < ﬃ0.01),cients respectively. between ANSA(b) Time-series and ANSI, of the and Oceanic ANSA and EASMINiño areIndex 0.73 (ONI) (p-value (red dashed< 0.01) line, and left 0.64 y-axis) (p-value and

We compared ANSA and ANSI with other climate indices. The climate indices used in this study were the Aleutian low pressure index (ALPI, [29]), the Arctic Oscillation Index (AOI, [30]), the East Asian summer monsoon index (EASMI, [31]), the North Pacific gyre oscillation (NPGO, [32]), the North Pacific index (NPI, [33]), the Oceanic Niño index (ONI, [34]), the Pacific decadal oscillation (PDO, [32]), and the Siberian high index (SHI, [35]) from 1993 to 2018. The ALPI was only compared with ANSA and ANSI for the years between 1993 and 2015. There are significant correlations between ANSA and EASMI, and ANSI and EASMI, and their correlation coefficients are 0.64 (p-value < 0.01) and 0.69 (p-value < 0.01) (Figure 11a), respectively. This suggests that the August NEAMS atmospheric pressure distribution and East Asian summer monsoon are well related, and the NEAMS sea-level changes in August are correlated with the East Asian summer monsoon. ANSI is slightly better than EASMI in estimating ANSA. The relationship between ANSI and ANSA (correlation coefficient, 0.73 with p-value < 0.01) can be expressed using Equation (2):

ANSA (cm) = 0.92 ANSI (hPa) (2) × J. Mar. Sci. Eng. 2020, 8, 425 12 of 15

However, correlations of ANSA and ANSI with other climate indices (ALPI, AOI, NPGO, NPI, ONI, PDO, SHI) were not significant with p-value < 0.05. The correlation between ANSA and ONI is 0.35 (p-value < 0.09), which means there is a weak teleconnection between the NEAMS sea-level and central Pacific Ocean sea surface temperature in August. It implies that the NEAMS sea-level in August might be roughly estimated by El Niño and La Niña (Figure 11). There were more typhoons in August which passed the NEAMS for period H than period L. This may be because of atmospheric pressure distribution, and its cause and effect needs to be investigated more in the near future. A correlation map of the August sea-level with the ANSI from 1993 to 2018 around the NEAMS was plotted to determine if the ANSI can reproduce the sea-level figures well (Figure 12). A positive correlation indicates that high sea-level occurs with the positive ANSI. The sea-levels of NEAMS areas are highly correlated with ANSI except for in some areas where there are strong currents such as the Kuroshio, and the Taiwan and the Tsushima Warm Currents in the southern part of the NEAMS, and the East Korea and the Tsushima Warm Currents in the southern EJS. This means that the ANSI can be used in estimating the NEAMS sea-level anomaly, but may not be available in strong current and activeJ. Mar. mesoscale Sci. Eng. 2020 eddy, 8, x FOR regions. PEER REVIEW The northern EJS had a higher correlation than those in 15the of YS18 and around the KS.

Figure 12. Correlation map of the August sea-level with the ANSI from 1993 to 2018. The contour intervalFigure is 0.112. andCorrelation conﬁdence map levels of the less August than sea-level 95% were with eliminated. the ANSI Afrom positive 1993 to correlation 2018. The indicatescontour that highinterval sea-level is 0.1 occurs and withconfidence the positive levels less ANSI. than 95% were eliminated. A positive correlation indicates that high sea-level occurs with the positive ANSI. Strong wind stress enhances seawater volume transport from the Paciﬁc into the NEAMS by Ekman transport, which results in high sea-levels in the NEAMS during the period H (Figure 13a). Weak wind stress decreases seawater volume transport, which results in low sea-level in the NEAMS during the period L (Figure 13a).

Figure 13. Schematic diagrams accounting for the ANSA. (a) Strong west-northwestward wind stress (long black arrow) is a result of large atmospheric pressure gradients (double red and blue circles for H and L, respectively) in the northwest Pacific Ocean. Strong wind stress enhances seawater volume transport from the Pacific into the NEAMS by Ekman transport, which results in high sea-levels in the NEAMS (red) during the period H. (b) Weak wind stress (short black arrow) is a result of small

J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 15 of 18

Figure 12. Correlation map of the August sea-level with the ANSI from 1993 to 2018. The contour J. Mar. Sci. Eng.interval2020, 8is, 4250.1 and confidence levels less than 95% were eliminated. A positive correlation indicates 13 of 15 that high sea-level occurs with the positive ANSI.

FigureFigure 13. Schematic 13. Schematic diagrams diagrams accounting accounting for for the the ANSA. ANSA. ((aa)) Strong west-northwestward west-northwestward wind wind stress stress (long black(long arrow)black arrow) is a result is a result of large of large atmospheric atmospheric pressure pressure gradients gradients (double (double redred andand blue circles circles for for H and L,H respectively) and L, respectively) in the in northwest the northwest Paciﬁc Pacific Ocean. Ocean. Strong Strong wind wind stressstress e enhancesnhances seawater seawater volume volume transporttransport from from the Paciﬁc the Pacific into into the the NEAMS NEAMS by by Ekman Ekman transport, transport, wh whichich results results in in high high sea-levels sea-levels in in the NEAMS (red) during the period H. (b) Weak wind stress (short black arrow) is a result of small the NEAMS (red) during the period H. (b) Weak wind stress (short black arrow) is a result of small atmospheric pressure gradients. Weak wind stress decreases seawater volume transport, which results in low sea-level in the NEAMS (blue) during the period L. Coastlines are drawn in grey.

5. Conclusions Sea-levels in the NEAMS, between 1993 and 2018, reach annual maxima in August and show remarkable interannual variations. The rising rate of the August sea-level from satellite altimetry data is larger than that of the annual mean in the NEAMS, which itself is larger than that of the global mean sea-level. Thus, it is more important to understand the mechanism of August sea-level change. The interannual variation in August of sea-levels in the NEAMS is dominated by the atmospheric pressure gradients between high pressure in the KE and low pressure in the WT in the south of the NEAMS. In the period H, the ANSA becomes positive due to the large atmospheric pressure gradients between the KE and WT in the south of the NEAMS. In the period L, the ANSA becomes negative due to the small atmospheric pressure gradients between the KE and the WT. ANSA is mainly determined by the wind stress in the south of the NEAMS and can be estimated quantitatively by ANSI. Local atmospheric forcing (ANSI; local index) may be more influential on ANSA and give a more accurate estimation of sea-levels than remote indices (EASMI, ONI and other indices; climate index) do. Other steric and non-steric effects on sea-level anomalies in August should be considered to estimate ANSA more accurately. Our results spotlight the significant effect of local atmospheric pressure distribution and wind stress on sea-levels in the vicinity of marginal seas. This can be used to estimate sea-levels with atmospheric pressure differences.

Author Contributions: Conceptualisation, M.H. and Y.-K.C.; methodology, M.H., Y.-K.C., H.-W.K., and S.N.; formal analysis, M.H.; investigation, M.H., Y.-K.C. and H.-W.K.; writing—original draft preparation, M.H.; writing—review and editing, M.H. and Y.-K.C.; visualisation, M.H.; supervision, Y.-K.C.; project administration, D.-S.B., K.-Y.J., and E.L.; All authors have read and agreed to the published version of the manuscript. Funding: This research was a part of the project titled “Analysis and Prediction of Sea Level Change in Response to Climate Change around Korean Peninsula (4)” funded by the Korea Hydrographic and Oceanographic Agency (KHOA), and “Deep Water Circulation and Material Cycling in the East Sea” funded by the Ministry of Oceans and Fisheries (MOF), Republic of Korea. H.-W.K. was supported by KIOST in-house project PE99811. Acknowledgments: Ssalto/Duacs altimeter products which were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (http://www.marine.copernicus.eu), tide-gauge sea-level data provided by the Permanent Service for Mean Sea Level (PSMSL; http://www.psmsl.org/data/obtaining), and atmospheric pressure and wind-stress data from ECMWF interim reanalysis (http://www.ecmwf.int/) were used in this study. Conﬂicts of Interest: The authors declare no conﬂict of interest. J. Mar. Sci. Eng. 2020, 8, 425 14 of 15

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