Remotely-Observed Early Spring Warming in the Southwestern Yellow Sea Due to Weakened Winter Monsoon

remote sensing

Letter Remotely-Observed Early Spring Warming in the Southwestern Yellow Sea Due to Weakened Winter Monsoon

Xiangbai Wu 1, Qing Xu 1,* , Gen Li 1, Yuei-An Liou 2 , Bin Wang 1, Huan Mei 3 and Kai Tong 4 1 College of Oceanography, Hohai University, Nanjing 210098, China; [email protected] (X.W.); [email protected] (G.L.); [email protected] (B.W.) 2 Center for Space and Remote Sensing Research, National Central University, Taoyuan City 32001, Taiwan; [email protected] 3 School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China; [email protected] 4 Troops 91001, People’s Liberation Army, Beijing 100161, China; [email protected] * Correspondence: [email protected]; Tel.: +86-25-8378-7674

Received: 18 September 2019; Accepted: 22 October 2019; Published: 24 October 2019

Abstract: The seasonal warming over the southwestern Yellow Sea (YS) in the spring is of vital importance to the local ecologic environment, especially to the massive green algae blooms of the YS in late spring and early summer. Based on daily optimum interpolation sea surface temperature (SST) data consisting of satellite derived SST from Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements, this study analyzed the spring SST variation over the southwestern YS (SWYS) from 1982 to 2018. The results show that the recent warming trend of spring SST over the SWYS is four-to-six times that of the global average, and as a result, sea water over the Subei Shoal (SBS) shifts about 10–13 days earlier to reach 10 ◦C in early April. This implies that, accordingly, the micro-propagules of green algae over the SBS may have the chance to germinate earlier. SST variability in early April significantly correlates with northerly wind and exhibits a general warming over the SWYS with an intensified warming anchored along the axis of the submarine canyon off the Yangtze estuary. The Moderate Resolution Imaging Spectroradiometer (MODIS) red–green–blue composite images captured the intrusion of the Taiwan Warm Current (TWC) into the SWYS through the submarine canyon during northerly wind relaxation in early April. Ocean remote sensing provides important clues for understanding the regional SST variability in the SWYS. Following this clue, this study finds that the weakening of winter monsoon in the spring leads to northward migration of the TWC and results in enhanced spring warming over the SWYS. The attendant advanced warming in spring, resulting in a favorable temperature condition for early development of green alga, may have contributed to the green tide blooms in the Yellow Sea in the recent decade.

Keywords: sea surface temperature; southwestern Yellow Sea; monsoon relaxation; MODIS; OISST

1. Introduction Sea surface temperature (SST) is one of the most important indicators in quantifying climate change and has strong inﬂuence on regional climate and regional ecosystem studies. The massive green tide blooms of Ulva prolifera (U. prolifera) in the late spring and early summer have been the most striking phenomena recurrent over the Yellow Sea (YS) in the past decade [1–5]. The origin area of the green tide has been identiﬁed as the Subei Shoal (SBS, see Figure1 for location) where the sporadic patches of U. prolifera are spotted as early as middle April [6]. The ecological factors

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44 algae growth have been well studied and understood [6–8]. It is widely accepted that the optimal sea 45 water temperature in the YS facilitates the formation of the green tide [6–9]. Song et al. [9] reported 46 that micro-propagules of green algae in coastal waters of the SBS begin to germinate at 10 °C or a 47 higher water temperature. Xiao et al. [7] found the growth rate and photosynthetic rate of the 48 detached U. prolifera were significantly higher at moderate temperature levels. Hence, sea 49 temperature plays a significant role in the development of the green tides, especially at the early 50 stage. Xuan et al. [10] found that SST and wind in the spring played an important role in the 51 development of the spring phytoplankton bloom. The variation of the spring SST is of vital 52 importance to the ecologic environment over the southwestern Yellow Sea (SWYS, Figure 1). 53 Remote Sens.The 2019variability, 11, 2478 of SST over the Yellow and East China Seas (YECS) has been widely studied2 of 14 in 54 the past decade in terms of different time scales, seasons and datasets [11–17]. The YECS has 55 affexperiencedecting algae a growthrobust, havepersistent been SST well increase studied in and the understood last few decades [6–8]. at It isthe widely rate of accepted two-to-four that times the 56 optimalfaster than sea water the globally temperature averaged in the trend. YS facilitates Various the dynamics formation mechanisms of the green have tide [been6–9]. reported Song et al. to [ 9be] 57 reportedresponsible that for micro-propagules the long-term warming: of green anthropoge algae in coastalnic effects waters [11], of theriver SBS discharge begin to induced germinate barrier at 58 10layer◦Cor [13,18], a higher oceanic water advection temperature. [19,20], Xiao and et air–sea al. [7] foundheat flux the induced growth by rate weakened and photosynthetic winds [14,20,21]. rate 59 ofHowever, the detached whileU. most prolifera of thewere previous significantly studies higher on the atSST moderate variability temperature of YECS have levels. focused Hence, on sea the 60 temperatureannual mean plays SST a, significantsummer or role winter in the season development SST, little of resear the greench has tides, been especially about the at variability the early stage. of the 61 Xuanspring et al.SST. [10 ]Exploring found that the SST andspatial wind pattern in the springof the playedspring an SST important variabilit roley inand the the development mechanisms of 62 theresponsible spring phytoplankton was the goal bloom.of this study. The variation of the spring SST is of vital importance to the ecologic environment over the southwestern Yellow Sea (SWYS, Figure1).

63 Figure 1. Regional map of the southwestern Yellow Sea. Topography data deeper than 100 m are from ETOPO2 (Global elevation and bathymetry on 2 arc-minute grid from the National Geophysical Data 64 Figure 1. Regional map of the southwestern Yellow Sea. Topography data deeper than 100 m are from Center, NOAA, USA) data sets, with 50, 30, 20 and 10 m isobaths from nautical charts. The arrows 65 ETOPO2 (Global elevation and bathymetry on 2 arc-minute grid from the National Geophysical Data in the map indicate the Yellow Sea Warm Current (YSWC), the Yellow Sea Coastal Current (YSCC), 66 Center, NOAA, USA) data sets, with 50, 30, 20 and 10 m isobaths from nautical charts. The arrows in the Korea Coastal Current (KCC), the Tsushima Warm Current (TSWC), Taiwan Warming Current 67 the map indicate the Yellow Sea Warm Current (YSWC), the Yellow Sea Coastal Current (YSCC), the (TWC) and Kuroshio Current (KC). SBS indicates the Subei Shoal, a radial sand ridge system (as shown 68 Korea Coastal Current (KCC), the Tsushima Warm Current (TSWC), Taiwan Warming Current by the 10 and 20 m isobaths) located near the coastline of Yangtze Bank. The blue line box (120–125◦E, 69 (TWC) and Kuroshio Current (KC). SBS indicates the Subei Shoal, a radial sand ridge system (as 30–35◦N) is the study area of the present study, mainly in the southwestern Yellow Sea. The green dash 70 shown by the 10 and 20 m isobaths) located near the coastline of Yangtze Bank. The blue line box line box (122.8–126.6◦E, 28.5–32.3◦N) indicates the area where wind data were averaged in Section 3.3. The labeled red star symbols indicate the stations for the spring sea surface temperature evolution

analysis in Section 3.1.

The variability of SST over the Yellow and East China Seas (YECS) has been widely studied in the past decade in terms of different time scales, seasons and datasets [11–17]. The YECS has experienced a robust, persistent SST increase in the last few decades at the rate of two-to-four times faster than the globally averaged trend. Various dynamics mechanisms have been reported to be responsible for the long-term warming: Anthropogenic effects [11], river discharge induced barrier layer [13,18], oceanic advection [19,20], and air–sea heat flux induced by weakened winds [14,20,21]. However, while most of the previous studies on the SST variability of YECS have focused on the annual Remote Sens. 2019, 11, 2478 3 of 14 mean SST, summer or winter season SST, little research has been about the variability of the spring SST. Exploring the spatial pattern of the spring SST variability and the mechanisms responsible was the goal of this study. In the present study, the variability of the spring SST in April from 1982 to 2018 and related physical mechanism, at the early development stage of the green tide blooms over the SWYS adjacent to the SBS, was examined based on the daily optimum interpolation SST (OISST) data set. The dominant spatial pattern of the spring SST variability over the SWYS was analyzed using empirical orthogonal function (EOF). A hypothesis of the related physical mechanism was put forward using the Moderate Resolution Imaging Spectroradiometer (MODIS) images and daily advanced scatterometer (ASCAT) surface wind data, which were further verified with HYbrid Coordinate Ocean Model (HYCOM) reanalysis data.

2. Materials and Methods The daily SST data from 1982–2018 used in this study were from OISST Analysis version 2(v2) and acquired from the National Ocean and Atmospheric Administration’s (NOAA’s) National Climatic Data Center (NCDC), available at https://www.ncdc.noaa.gov/oisst with a high spatial resolution of 0.25 0.25 . This dataset was generated from several data sources including SST data from the ◦ × ◦ Advanced Very High Resolution Radiometer (AVHRR), sea-ice data, and in situ data from ships and buoys. The relationship between the SST variation and wind stress was diagnosed. Daily ASCAT surface wind fields over the SWYS were used to examine the atmospheric variation during the TWC intrusion in the spring of 2017. ASCAT data were obtained from the Centre de Recherche et d’Exploitation Satellitaire (CERSAT), at the French Research Institute for Exploitation of the Sea (IFREMER), Plouzané (France) [22]: http://apdrc.soest.hawaii.edu/datadoc/ascat.php. The daily wind data used to show the long-term variation of wind were from NCEP_Reanalysis 2 data (a global reanalysis of atmospheric data spanning 1979 to present, created by the National Centers for Environmental Prediction in cooperation with the Department of Energy), provided by the NOAA Earth System Research Laboratory’s Physical Sciences Division (NOAA/OAR/ESRL PSD), Boulder, Colorado, USA via their web site at https://www.esrl.noaa.gov/psd/. Red–green–blue composite images from three visible bands (645 nm: R; 555 nm: G; 469 nm: B) of the MODIS were collected along with MODIS SST images to identify the intrusion of the Taiwan Warm Current to the Jiangsu coast at favorable wind conditions in April. MODIS data were obtained from the Optical Oceanography Laboratory, College of Marine Science, University of South Florida https://optics.marine.usf.edu/cgi-bin/optics_data?roi=YS_ECS¤t=1. To explain and verify the ocean response to wind relaxation and shift, the Hybrid Coordinate Ocean Model (HYCOM) hindcast data were used to analyze the ocean currents and sea water temperature at the sea surface, the 20 m layer, and the bottom layer (Global Ocean Forecasting System 3.1, Experiment 92.8, 41-layer HYCOM and the Navy Coupled Ocean Data Assimilation (NCODA) Global 1/12◦ Analysis, https://www.hycom.org/data/glbv0pt08/expt-92pt8). It had a 0.08◦ spatial resolution between 40◦S and 40◦N and 3-hour temporal resolution. The long-term trend of SST was obtained with a linear regression of the time series. Various dynamics processes have been reported to contribute to this trend [11–17]. To reveal the dominant mechanism, an EOF analysis was used to decompose the spring SST field time series in the SWYS into temporal and spatial components in the present study. An EOF analysis is often used to study the possible spatial modes (i.e., patterns) of the variability of a spatially distributed time series by computing the eigenvectors of the covariance matrix of the data set. The derived eigenvalues provide a measure of the percent variance explained by each mode. Most of the variance can be explained by first few modes. By examining the spatial pattern and principal component (PC), an EOF analysis provides statistical insights into physical processes in an intricate climate system. The spatial patterns and PCs extracted from an EOF analysis may be sensitive to the choice of spatial domain and the time period; therefore, to focus on the variability of SST over the SWYS, the domain of the EOF analysis was limited to (120◦E, 125◦E) and (30◦N, 35◦N), excluding the Kuroshio and Tsushima Warm Current areas. Remote Sens. 2019, 11, x FOR PEER REVIEW 4 of 15

121 was limited to (120°E, 125°E) and (30°N, 35°N), excluding the Kuroshio and Tsushima Warm Current Remote Sens. 2019, 11, 2478 4 of 14 122 areas.

123 3. 3.Results Results and and Discussion Discussion

124 3.1.3.1. Coastal Coastal Water Water Warming Warming over over the the SBS SBS

125 FigureFigure 2a2 ashows shows the the long-term long-term trend trend of of SST SST over over the the SWYS. SWYS. The The SWYS SWYS has has experienced experienced an an SST SST 126 warmingwarming at atan an average average rate rate of of0.2–0.4 0.2–0.4 °C/decade,◦C/decade, which which is istwo-to-three two-to-three times times that that of ofthe the global global mean mean 127 warmingwarming trend, trend, consistent consistent with with previous previous studies studies [14,16,17]. [14,16,17 The]. Therapid rapid warming warming (> 0.4 (> °C/decade)0.4 ◦C/decade) is 128 centeredis centered at the at submarine the submarine canyon canyon off the o ﬀYangtzethe Yangtze River Riverestuary estuary and extends and extends to 33°N to along 33◦N the along 30 m the 129 isobath.30 m isobath. Park et Parkal. [14] et al.pointed [14] pointed out that out spring that springand early and summer early summer warming warming from April from to April June to has June 130 contributedhas contributed to the todominant the dominant warming warming in the inlong-term the long-term trend along trend the along eastern the eastern coast of coast China. of China.The 131 springThe spring warming warming trend trendfrom 1982 from to 1982 2018 to is 2018 presente is presentedd in Figure in Figure 2b, with2b, with a general a general warming warming rate rateof 132 0.4–0.7of 0.4–0.7 °C/decade,◦C/decade, twice twiceas much as muchas that as of that the annual of the annualmean warming mean warming as shown as in shown Figure in 2a. Figure Rapid2a. 133 warmingRapid warming is located is near located the nearYangtze the YangtzeRiver estuary River and estuary the andSBS. the SBS.

134 135 FigureFigure 2. 2. WarmingWarming trend ofof sea sea surface surface temperature temperature (SST) (SST) over over the southwestern the southwestern Yellow Yellow Sea (◦C /Seayear). 136 (°C/year).(a) Annual (a) Annual warming warming trend; (trend;b) warming (b) warming in the in spring the spring (March–May). (March–May). The overlappedThe overlapped bold bold (thin) 137 (thin)white white solid solid line isline the is 50 the m 50 (10 m m) (10 isobath; m) isobat dotedh; doted and dash-dotted and dash-dotted lines arelines the are 30 the and 30 20 and m isobaths, 20 m 138 isobaths,respectively; respectively; black solid black lines solid indicate lines indicate the 95% signiﬁcancethe 95% significance for warming for warming trend regression. trend regression. Four circles 139 Fourin (bcircles) labeled in (b as) labeled A, B, C, as D A, are B, the C, locationsD are the where locations SST where was analyzed SST was and analyzed which and are presentedwhich are in 140 presentedFigure3a–d. in Figure 3a–d.

141 UnderUnder such such a a warming trendtrend during during the the period period of 1982–2018,of 1982–2018, an early an early warming warming could becould expected be 142 expectedat fixed at locations. fixed locations. Four locationsFour locations (shown (shown in Figure in Figure2b) were 2b) were selected selected to show to show SST SST evolution evolution near 143 nearthe the SBS SBS from from February February to Mayto May (Figure (Figures3a–d). 3a–d). An evidentAn evident change change (warming) (warming) of SST of SST around around the the SBS 144 SBSoccurred occurred in 1996in 1996/1997./1997. We We calculated calculated the the diff differenerences ofces the of meanthe mean day day of year of year (DoY) (DoY) at which at which sea watersea 145 waterreached reached certain certain temperature temperature before before and afterand after 1996 /1996/1997.1997. The 8,The 10 8, and 10 15and◦C 15 isotherms °C isotherms shifted shifted earlier 146 earlierby 14.5, by 14.5, 13.2 and13.2 18.4and days,18.4 days, respectively, respectively, at Station at Station A on A the on souththe south edge edge of the of SBSthe SBS at 122 at◦ 122°E,E, 32◦N 147 32°N(Figure (Figure3a). The3a). The earlier earlier shifted shifted days days decreased decrease at thed at locationthe location northwards northwards (Figure (Figures3b–d). 3b–d). 148 TheThe four-station four-station averaged averaged evolution evolution is ispresented presented in in Figure Figure 3e.3e. The The 8 8and and 10 10 °C◦ Ccontours contours show show a a 149 strongstrong trend trend before earlyearly 2000s 2000s and and an insignificantan insignificant trend trend afterwards afterwards with large with interannual large interannual variations. 150 variations.The 15 ◦C The contour 15 °C shows contour relatively shows relatively smaller variability smaller variability with a more with consistent a more trendconsistent from trend 1982 tofrom 2018. 151 1982The to early 2018. warming The early trend warming was significant trend was for significant the five-point-smoothed for the five-point-smoothed 8, 10 and 15 ◦ 8,C isotherms10 and 15 with°C 152 isothermscorrelation with coe fficorrelaticients ofon coefficients0.50, 0.66 and of −0.50,0.84, − respectively.0.66 and −0.84, The datesrespectively. of water The temperature dates of reachingwater − − − 153 temperature8, 10 and 15 reaching◦C in 2000s 8, 10 and and 2010s 15 °C were in 2000s generally and 2010s earlier were than generally that in 1980s. earlier The than overall that in warming 1980s. The over 154 overallthe SBS warming showed over 15.3, the 9.7 SBS and showed 11.6 day 15.3, earlier 9.7 shiftingand 11.6 for day the earlier 8, 10 andshifting 15 ◦C for isotherms, the 8, 10 respectivelyand 15 °C (Figure3e). The 10 ◦C isotherm for the SBS varied around the 100th DoY (the black solid lines in

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155 isotherms, respectively (Figure 3e). The 10 °C isotherm for the SBS varied around the 100th DoY (the

156 blackFigure solid3) in lines the early in Figure April. 3) Thein the date early when April. the The SBS date water when reached the SBS 10 water◦C has reached shifted 10 earlier °C has for shifted about 157 earlier10 days for in about the past 10 decades.days in the The past micro-propagules decades. The micro-propagules of green algae germinated of green atalgae 10 ◦ germinatedC or warmer at [ 2010]. 158 °CTherefore, or warmer the [20]. earlier Therefore, shift of warmingthe earlier implies shift of that warming the micro-propagules implies that the ofmicro-propagules green algae over of the green SBS 159 algaecan germinate over the SBS by 10–13can germinate days earlier, by 10–13 which days may earl castier, importantwhich may ecological cast important impacts ecological on the SBSimpacts and 160 onadjacent the SBS seas. and adjacent seas.

161 162 FigureFigure 3. 3. SSTSST near near Jiangsu Jiangsu shoal shoal as as aa function function of of year year and and year–day: year–day: (a ()–(a–d) at locations indicated indicated in in 163 FigureFigure 22b,b, with 8, 10 and 15 °C◦C contours contours lines lines labelled; labelled; (e ()e )((a)–(a–d)) averaged,averaged, with red, green and and blue blue curves for five-point smoothed 8, 10 and 15 C and colored straight lines for their trends (R = 0.50, 164 curves for five-point smoothed 8, 10 and 15 °C◦ and colored straight lines for their trends (R = −0.50, 0.66, 0.84, respectively, p < 0.01 for all). The black vertical solid lines indicate the 100thth year day. 165 −−0.66, −−0.84, respectively, p < 0.01 for all). The black vertical solid lines indicate the 100 year day. 3.2. Spatial and Temporal Variability of SST over the SWYS 166 3.2. Spatial and Temporal Variability of SST over the SWYS To reveal the spatial and temporal variability of the spring SST over the SWYS, time series of the 167 To reveal the spatial and temporal variability of the spring SST over the SWYS, time series of the 100th DoY SST was further analyzed using EOF (Figure4). The first three modes explained over 73.5%, 168 100th DoY SST was further analyzed using EOF (Figure 4). The first three modes explained over 73.5%, 11.3%, and 3.7% of the total SST variance (Figure4c). The leading mode of the 100th year day SST 169 11.3%, and 3.7% of the total SST variance (Figure 4d). The leading mode of the 100th year day SST showed an overall warming trend over the whole study area, with a large variation along the axis 170 showed an overall warming trend over the whole study area, with a large variation along the axis of of the submarine canyon off the Yangtze estuary to the Jiangsu coast, where the maximum variation 171 the submarine canyon off the Yangtze estuary to the Jiangsu coast, where the maximum variation was located (Figure4a). The PC of the leading EOF mode showed an evident increase trend (r = 0.43, 172 was located (Figure 4a). The PC of the leading EOF mode showed an evident increase trend (r = 0.43, p < 0.01) with a large inter-annual variation (Figure4c). 173 p < 0.01) with a large inter-annual variation (Figure 4c). The Yellow Sea is a shallow, macro-tidal sea, and its variability is dynamically dominated by 174 The Yellow Sea is a shallow, macro-tidal sea, and its variability is dynamically dominated by tidal [23] and seasonal variability [24], which are associated with the Asian monsoon system and 175 tidal [23] and seasonal variability [24], which are associated with the Asian monsoon system and their their interaction with morphology [25]. The spatial pattern of the leading EOF mode of the April SST 176 interaction with morphology [25]. The spatial pattern of the leading EOF mode of the April SST variation suggests that the Taiwan Warm Current flows along the submarine canyon, which might 177 variation suggests that the Taiwan Warm Current flows along the submarine canyon, which might have contributed to the SST variation at the Jiangsu coast. 178 have contributed to the SST variation at the Jiangsu coast. 179

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180 Figure 4. 181 Figure 4. EmpiricalEmpirical orthogonal orthogonal (EOF) (EOF) anal analysisysis of the of theSST SST time time series series of year of year day day 100 100from from 1982 1982 to 2018. to 2018. (a) EOF Mode 1 (73.5%); (b) EOF Mode 2 (11.3%); (c) principal component (PC) time series of first two 182 (a) EOF Mode 1 (73.5%); (b) EOF Mode 2 (11.3%); (c) principal component (PC) time series of first two modes and trend of PC 1. Overlapped bold (thin) solid white line in (a,b) is the 50 m (10 m) isobath; 183 modes and trend of PC 1. Overlapped bold (thin) solid white line in (a) and (b) is the 50 m (10 m) doted white line is 30 m isobath; dash-dotted white line is 20 m isobath. In (a), the thick black line 184 isobath; doted white line is 30 m isobath; dash-dotted white line is 20 m isobath. In (a), the thick black shows the location of the whole section at 31.5 N for net water transport, from 122–125 E; the green 185 line shows the location of the whole section at 31.5°N◦ for net water transport, from 122–125°E;◦ the rectangle shows the western section, 122.3–123.5 E. 186 green rectangle shows the western section, 122.3–123.5°E.◦ 3.3. Influence of Monsoon on the Leading Mode of SST Variation 187 3.3. Influence of Monsoon on the Leading Mode of SST Variation Previous studies have shown that the shelf circulation of the Yellow Sea, the East China Sea and 188 thePrevious canyon studies circulation have o ffshownthe Yangtze that the River shelf mouthcirculation in winter of the areYellow primarily Sea, the forced East China by the Sea northerly and 189 thewinds canyon and circulation modified off by the Yangtze wind stress-curl, River mouth and thein winter canyon are circulation primarily further forced inducesby the northerly the inshore 190 windsbranch and of modified the Taiwan by the Warm wind Current stress-curl, [26]. The and PC the time canyon series circulation of the leading further EOF induces mode the and inshore northerly 191 branchwind of speed the Taiwan show good Warm agreement Current [26]. on the The variation PC time ofseries inter-annual of the leading timescale EOF (Figuremode and5a). northerly A positive 192 windcorrelation speed show was good found agreement between themon the with variation a correlation of inter-annual coefficient timescale of 0.54 (Figure (at the significance5a). A positive level 193 correlation0.01, Figure was5), found suggesting between a strong them northerlywith a correlation wind corresponding coefficient of to 0.54 a lower (at the temperature significance and level weak 194 0.01,northerly Figure 5), or strongsuggesting southerly a strong wind northerly that cause wi thend corresponding warming of the to Jiangsu a lower coast temperature water. Northerly and weak wind 195 northerlyalso leads or strong to sea surfacesoutherly cooling wind throughthat cause sensible the warming and latent of the heat Jiangsu flux. coast However, water. it Northerly cannot explain wind the 196 alsospatial leads patternto sea surface of warming. cooling The through wind drivensensible ocean and circulationlatent heat becomesflux. However, the most it cannot probable explain mechanism the 197 spatialthat accountspattern forof thewarming. strong wind-SSTThe wind relationship. driven ocean circulation becomes the most probable 198 mechanism that accounts for the strong wind-SST relationship.

199

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200 201 Figure 5. Relationship betweenbetween thethe meridional meridional wind wind speed speed (m (m/s)/s) and and the the PC PC of theof the leading leading EOF EOF of SST of 202 SSTin the in southwesternthe southwestern Yellow Yellow Sea inSea Figure in Figure3.( a 3.) TimeThe wind series speed plot; was (b) scatteraveraged plot. over The the wind 122.8–126.5°E, speed was

203 28.6–32.4°Naveraged over (location the 122.8–126.5 shown as◦E, the 28.6–32.4 green dash◦N (location box in Figure shown 1) as from the greenmiddle dash March box to in 10 Figure April1) of from the 204 yearmiddle 1982 March to 2018. to 10 April of the year 1982 to 2018.

205 3.4. Intrusion Intrusion of of Taiwan Warm Current at Northeasterly RelaxationRelaxation inin SpringSpring 206 To verify the hypothesis that the wind influencesinfluences the SST through wind driven circulation near 207 the submarine canyoncanyon ooffff the the Yangtze Yangtze River River estuary, estuary, we we examined examined sea sea surface surface signatures signatures presented presented by 208 bythe the MODIS MODIS images images acquisitioned acquisitioned in latein late March March and and early early April. April. Evidence Evidence of theof the intrusion intrusion events events of 209 Taiwanof Taiwan Warm Warm Current Current to theto the Subei Subei Coast Coast were were documented documented by MODISby MODIS visible visible images images (Figure (Figure6). 6). 210 Figure 66bb isis thethe visiblevisible MODISMODIS imageimage capturedcaptured atat 04:4504:45 GreenwichGreenwich MeanMean TimeTime (GMT)(GMT) onon AprilApril 211 13th, 2017. 2017. It It shows shows a aclear clear water water tongue tongue extending extending northward northward to 32°N to 32 along◦N along the 30 the m 30 isobath. m isobath. The 212 Thequasi-synchronous quasi-synchronous SST image SST image shows shows that warm that warmwater waterreaches reaches the Jiangsu the Jiangsu coast near coast 32°N near (Figure 32◦N 213 (Figure6d). For6d). comparison, For comparison, Figure Figure 6a is6 athe is thenearest nearest image image available available before before the the intrusion intrusion event.event. ItIt waswas 214 captured at 05:05 GMT on 2 April 2017. The The turbid turbidityity plume plume over over the the Yangtze Yangtze bank covers a large 215 area extending southeastward from the Jiangsu coast coast to to the the East East China China Sea Sea shelf shelf [27–29] [27–29] (Figure (Figure 66a).a). 216 The warmwarm waterwater tongue tongue before before the the intrusion intrusion event event is located is located over over the submarinethe submarine canyon canyon with thewith north the 217 northend confined end confined south south of 31◦ Nof o31°Nff the off Yangtze the Yangtze River estuaryRiver estuary (Figure (Figure6c). 6c). 218 Though the the MODIS MODIS data data availability availability over over the the SW SWYSYS was was very very limited limited due due to cloud to cloud cover, cover, the 219 intrusionthe intrusion of clear of clear water water of the of the Taiwan Taiwan Warm Warm Curre Currentnt to to the the Jiangsu Jiangsu coast coast around around early early April April was 220 easily observed by the MODIS many times in recent years (Table 1 1).). SuchSuch anan observationobservation waswas possiblepossible 221 due to the reflectancereflectance background provided byby thethe large-scalelarge-scale turbidityturbidity plume.plume.

Table 1. Date for MODIS-observed north migration of the Taiwan Warm Current in 2010s. 222 Figure 7 shows the time series of ASCAT daily wind speed from late February to early May in 223 2017 over the northern East China Sea andYear the SWYS. Day of Northeasterly Year wind dominated in March and 224 begins to relax in late March and April. It2015 switched 100to southwesterly wind twice in the first half of 225 April, 2017, with a stronger southwesterly2016 wind speed 95 at 10 m/s on 5 April. The MODIS spotted a 226 northward intrusion of the TWC that occurred2017 during 103 the northeasterly wind relaxation, resulting in 227 a warming near the Jiangsu coast (Figure 20186d). The earliest 105 image to capture this phenomenon was on 2019 96 228 11 April with large cloud coverage (not shown), suggesting the intrusion of the TWC should have 229 happened during the northerly wind relaxation from 1 to 5 April 2017 (Figure 7). 230 Figure7 shows the time series of ASCAT daily wind speed from late February to early May in 2017 over the northern East China Sea and the SWYS. Northeasterly wind dominated in March and begins to relax in late March and April. It switched to southwesterly wind twice in the ﬁrst half of April, 2017, with a stronger southwesterly wind speed at 10 m/s on 5 April. The MODIS spotted a

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northward intrusion of the TWC that occurred during the northeasterly wind relaxation, resulting in a warming near the Jiangsu coast (Figure6d). The earliest image to capture this phenomenon was on

11 AprilRemote with Sens. 2019 large, 11, cloud x FOR PEER coverage REVIEW (not shown), suggesting the intrusion of the TWC should8 of 15 have happened during the northerly wind relaxation from 1 to 5 April 2017 (Figure7).

(a) (b)

231 FigureFigure 6. Moderate 6. Moderate Resolution Resolution Imaging Imaging Spectroradiometer Spectroradiometer (MODIS) (MODIS) image image before before [(a) 18:00 [(a) Greenwich18:00 232 MeanGreenwich Time (GMT) Mean on Time April (GMT) 1st, 2017;on April (c) 1st, 05:05 2017; GMT (c) 05:05 on 2 AprilGMT on 2017, 2 April GMT] 2017, and GMT] after and [(b after) 04:45 [(b) GMT 233 04:45 GMT on 13 April 2017; (d) 03:10 GMT on 13 April 2017, GMT] the intrusion of the Taiwan Warm on 13 April 2017; (d) 03:10 GMT on 13 April 2017, GMT] the intrusion of the Taiwan Warm Current 234 Current to the Jiangsu coast via the submarine canyon: (a), (b) visible RGB (red–green–blue) to the Jiangsu coast via the submarine canyon: (a,b) visible RGB (red–green–blue) composite images; 235 composite images; (c), (d) SST (°C). MODIS images were downloaded from: (c,d) SST ( C). MODIS images were downloaded from: https://optics.marine.usf.edu/index.html. 236 https://optics.marine.usf.edu/index.html.◦ The concurrence of northward extending of the TWC and northeasterly wind relaxation indicates a potential causal relationship. Compared to the limited temporal resolution of the MODIS image, HYCOM reanalysis data provided not only higher resolution insight to the wind-SST relationship but also more straightforward ocean dynamic response to wind variation. A numerical reanalysis from HYCOM confirmed that the general northward migration of isotherms and the intensified TWC respond to the northeasterly wind relaxation, leading to the intensified warming over the submarine canyon to the Jiangsu coast (Figure8). On April 1 2017, the wind was still northerly, although it began to relax on 31 March, and the surface flow was uniformly southward at the speed of 0.1–0.15 m/s over the SWYS (Figure8a). The flow was weak at the subsurface layer and bottom layer (Figure8c,e). The sea level tilted to northeast with high sea level located in the Hangzhou bay, the Yangtze River estuary and the Jiangsu coast, probably due to Ekman transport induced by northerly wind. The warm waters of the TWC reach 31◦N from the surface to the bottom layer (Figure8a,c,e). Dominated by a southerly wind at 10 m/s on 5 April 2017, the surface layer showed an overall northward flow of 0.3 m/s in the SWYS (Figure8b). In the 20 m layer, the flow was relatively week compared to the surface layer, 237 and the strong current flow of 0.2 m/s along the 50 m isotherm and the submarine canyon brought

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(a) (b)

(c) (d) Remote Sens. 2019, 11, 2478 9 of 14 231 Figure 6. Moderate Resolution Imaging Spectroradiometer (MODIS) image before [(a) 18:00 232 Greenwich Mean Time (GMT) on April 1st, 2017; (c) 05:05 GMT on 2 April 2017, GMT] and after [(b) 233 04:45 GMT on 13 April 2017; (d) 03:10 GMT on 13 April 2017, GMT] the intrusion of the Taiwan Warm warm water northwestward to the Jiangsu coast (Figure8d). The bottom layer showed a strong ﬂow at 234 Current to the Jiangsu coast via the submarine canyon: (a), (b) visible RGB (red–green–blue) 235the bottomcomposite of the deep images; canyon, (c), suggesting (d) SST an(°C). interaction MODIS ofimages the northward were downloaded mitigating from: current and 236topography https://optics.marine.usf.edu/index.html. (Figure8f).

237 Figure 7. Time series of the advanced scatterometer ASCAT wind speed over the northern East

China Sea and the southern Yellow Sea (30–33◦N; 123–125◦E) from late February to early May in 2017.

Two thick vertical straight lines indicate the date when the MODIS images in Figure4 were acquired.

The tongue of warm water mitigated northward to 31.5◦N following the submarine topography through the water column. HYCOM reanalysis data confirm that, during the northerly wind relaxation, the northward current brought warm water from the south side to push isotherms northward, and the intensified flow at the submarine canyon led to intensified warming along the axis of the canyon to the Jiangsu coast. To verify the physical mechanism that links wind stress and ocean currents, we calculated the net water transport across the sections at 31.5◦N (as shown Figure4a) and analyzed its relation to wind speed change using HYCOM reanalysis data. Net meridional water transport was obtained after a 27-hour moving average was carried out to filter the tidal variations. In 15 strong wind days (events) when the meridional wind speed reached local peaks between the 90–110th DoY from 2015 to 2019 (Figure9), the net meridional water transport was calculated with time lag of 0 day (T + 0), 1 day (T + 1) and 2 days (T + 2). Figure 10 shows the net water transport across the sections at 31.5◦N and its relation with wind speed. The correlation between meridional wind speed and the simultaneous net water transport is weak (Figure 10a). With a one-day lag, the net water transport was significantly correlated with the meridional wind speed at a significant level of above 90% (Figure 10b). A stronger southerly wind brings a stronger northward current, both for the whole section (r = 0.52, p = 0.05) and the western section (r = 0.53, p = 0.04). With a two-day lag, the correlation was not significant between northerly wind and net water transport, which is consistent with the results of Tak et al. that the correlations between the northerly wind and the surface flow weakened overall at the two-day lag from a numerical study [30]. However, southerly wind was found to drive northward net water transport across the sections at 31.5◦N, with a significant correlation (r = 0.65, p = 0.08 for the whole section and r = 0.82, p = 0.01 for the western section, Figure 10c). The residual current response to southerly wind with a lag time of one point five-to-two days has been confirmed by field observations in the western YS [31]. A regression analysis showed that the northward net water transport increases by 0.3 Sv (1 Sv = 1 106 × m3/s) with a southerly wind increase by 5 m/s. The regression intersection shows that northward net transport begins at the speed of 3 m/s (5 m/s) for the whole section (the western section). The response of net meridional water transport to wind change was more significant in the western part of the section, as illustrated by the higher correlation coefficient and higher level of significance. This result Remote Sens. 2019, 11, 2478 10 of 14

indicatesRemote thatSens. 2019 the, 11 southerly, x FOR PEER wind REVIEW has stronger inﬂuence on the ocean current at the western 10 of section15 near the submarine canyon, compared to the whole section.

(a) (b)

(e) (f)

273 FigureFigure 8. Temperature 8. Temperature (◦C), (°C), velocity velocity (m (m/s)/s) andand sea surfac surfacee height height (m) (m) from from HYbrid HYbrid Coordinate Coordinate Ocean Ocean 274 ModelModel (HYCOM) (HYCOM) reanalysis reanalysis data data on on April April 11 (left(left panel)panel) and April April 5, 5, 2017 2017 (right (right panel): panel): (a) (anda,b )(b surface) 275 layer;surface (c,d) 20layer; m layer;(c) and and (d) 20 (e, mf) bottomlayer; and layer. (e) and Data (f) bottom used here layer. were Data averaged used here over were 24 averaged hours toover remove 276 the tidal24 hours variation. to remove Overlapped the tidal variation. bold (thin) Overlapped solid white bold line (thin) is the solid 50 white m (10 line m) is isobath; the 50 m dashed (10 m) white 277 line isisobath; 30 m isobath;dashed white dash-dotted line is 30 whitem isobath; line dash-dotted is 20 m isobath. white line is 20 m isobath.

Remote Sens. 2019, 11, 2478 11 of 14 Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 15

278 279 RemoteFigureFigure Sens. 9.9. 2019 TimeTime, 11 series,series x FOR of ofPEER meridional meridional REVIEW wind wind speed speed (m (m/s)./s). (a) ( 2015a) 2015 and and 2016, 2016, (b) 2017(b) 2017 and and 2018; 2018; colored colored dots 12 of 15 280 denotedots denote the date the anddate wind and wind speed speed selected selected for net for water net water transport transport analysis analysis in Figure in Figure 10. 10.

281 Figure 10 shows the net water transport across the sections at 31.5°N and its relation with wind 282 speed. The correlation between meridional wind speed and the simultaneous net water transport is 283 weak (Figure 10a). With a one-day lag, the net water transport was significantly correlated with the 284 meridional wind speed at a significant level of above 90% (Figure 10b). A stronger southerly wind 285 brings a stronger northward current, both for the whole section (r = 0.52, p = 0.05) and the western 286 section (r = 0.53, p = 0.04). With a two-day lag, the correlation was not significant between northerly 287 wind and net water transport, which is consistent with the results of Tak et al. that the correlations 288 between the northerly wind and the surface flow weakened overall at the two-day lag from a 289 numerical study [30]. However, southerly wind was found to drive northward net water transport 290 across the sections at 31.5°N, with a significant correlation (r = 0.65, p = 0.08 for the whole section and 291 r = 0.82, p = 0.01 for the western section, Figure 10c). The residual current response to southerly wind 292 with a lag time of one point five-to-two days has been confirmed by field observations in the western 293 YS [31]. A regression analysis showed that the northward net water transport increases by 0.3 Sv (1 294 Sv = 1 × 106 m3/s) with a southerly wind increase by 5 m/s. The regression intersection shows that 295 northward net transport begins at the speed of 3 m/s (5 m/s) for the whole section (the western 296 section). The response of net meridional water transport to wind change was more significant in the 297 western part of the section, as illustrated by the higher correlation coefficient and higher level of 298 significance. This result indicates that the southerly wind has stronger influence on the ocean current 299 at the western section near the submarine canyon, compared to the whole section. 300 From middle March to early April, northeasterly (winter) monsoon still occupies the SWYS and 301 northern East China Sea (Figure 11). It relaxes before shifting to southwesterly (summer) monsoon in 302 May or June. We calculated the mean wind speed and counted the days with daily mean wind > 0 303 (southwesterly wind) from middle March to early April. In the past few decades, the winter monsoon 304314 (northeasterly wind) has weakened from > 4 3m/s in 1980s to < 3 m/s in 2010s from middle March to Figure 10. Net meridional water transport (m /s) across the sections at 31.5◦N and its relation with the 305315 earlymeridional AprilFigure over 10. wind Netthe study speedmeridional (marea./s), water withThe reddaystransport crosses in which (m for3/s) the daily across whole mean the section sections wind and speedat blue 31.5 diamonds °Nis southerly and its forrelation thehave western with increased the 306316 fromsection. < meridional5 in 1980s (a)T +windto0 > net 8 speed in meridional 2000s (m/s), and waterwith 2010s, red transport. incrosses agreement (forb) Samethe with whole as (thea), section butchange for and the of Tbluemean+ 1 diamonds day, wind dash speed linesfor the(with 307317 correlationindicatewestern coefficient the section. regression ( aof) T0.89, trend. + 0 net not (c meridional) Sameshown). as ( a Ourwater), but analys fortransport. theis T is+ (consistent2b) day, Same solid as with( linesa), but indicatethe for previous the the T + regression 1 day,studies dash that 308318 havetrend reportedlines between indicate the southerlyweakening the regression wind of andthe trend. netEast meridional(c Asian) Same wi as waternter (a), monsoon transport.but for the Correlation(EAWM) T + 2 day, sinc coe solidﬃe cientsthe lines 1970s, are indicate labelled which the has 309319 beenonly attributedregression for regressions to trend global between above warming the southerly 90% [29,32], signiﬁcant wind the and co level. netupling meri ofdional sea surfacewater transport. warming Correlation with winter coefficients monsoon 310320 and oceanare labelled currents only [21], for and regressions Pacific decadalabove the variatio 90% significantn [17]. With level. the increased south wind frequency, 311 the intrusionFrom middle of the March Taiwan to early Warm April, Current northeasterly is more (winter)frequent. monsoon Northward still flow occupies leads the to SWYS a general and 312 northernwarming Eastover Chinathe SWYS, Sea (Figure and the 11 intensified). It relaxes flow before along shifting the submarine to southwesterly canyon (summer)contributes monsoon to rapid 313 inwarming May or near June. the We Jiangsu calculated coast. the mean wind speed and counted the days with daily mean wind

321 322 Figure 11. (a) The wind speed evolution as a function of year and year–day; (b) time series of spring 323 wind speed (m/s) and count of south wind days. The wind speed (north wind v, m/s) was averaged 324 over the 122.8–126.5°E, 28.6–32.4°N (location shown as the green dash box in Figure 1) from middle

Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 15

Remote Sens. 2019, 11, 2478 12 of 14

>0 (southwesterly wind) from middle March to early April. In the past few decades, the winter monsoon (northeasterly wind) has weakened from >4 m/s in 1980s to < 3 m/s in 2010s from middle March to early April over the study area. The days in which daily mean wind speed is southerly have increased from < 5 in 1980s to >8 in 2000s and 2010s, in agreement with the change of mean wind 314 speed (with correlation coefficient of 0.89, not shown). Our analysis is consistent with the previous 315 studiesFigure that have 10. Net reported meridional the weakeningwater transport of the (m3 East/s) across Asian the winter sections monsoon at 31.5°N (EAWM) and its relation since with the 1970s,the 316 which hasmeridional been attributed wind speed to global (m/s), warmingwith red crosses [29,32], for the the coupling whole ofsection sea surfaceand blue warming diamonds with for winter the 317 monsoonwestern and oceansection. currents (a) T + 0 [net21], meridional and Pacific water decadal transport. variation (b) Same [17]. as With (a), but the for increased the T + 1 southday, dash wind 318 frequency,lines the indicate intrusion the regression of the Taiwan trend. Warm(c) Same Current as (a), isbut more for the frequent. T + 2 day, Northward solid lines flow indicate leads the to a 319 generalregression warming trend over between the SWYS, southerly and the wind intensified and net meri flowdional along water the submarinetransport. Correlation canyon contributes coefficients to 320 rapid warmingare labelled near only the for Jiangsu regressions coast. above the 90% significant level.

321 322 FigureFigure 11. 11.(a) ( Thea) The wind wind speed speed evolution evolution as as a function a function of yearof year and and year–day; year–day; (b )(b time) time series series of springof spring 323 windwind speed speed (m (m/s)/s) and and count count of southof south wind wind days. days. The The wind wind speed speed (north (north wind wind v, mv,/ s)m/s) was was averaged averaged 324 overover the the 122.8–126.5 122.8–126.5°E,◦E, 28.6–32.4 28.6–32.4°N◦N (location (location shown shown as as the the green green dash dash box box in in Figure Figure1) from1) from middle middle March to 10 April of 1982 to 2018, shown as the red box in (a). The black dash box marks the year day 100. 4. Conclusions The water temperature in spring over the SWYS has strong ecological significance. In the present study, we analyzed the variation of spring SST in the time span of 1982–2018. A regression analysis showed that the warming trend of spring SST over the SWYS is four-to-six times faster than that of the global mean SST trend. For the SBS coastal water, the date reaching 10 ◦C each year shifts earlier by 10–13 days. This might lead to important ecologic consequences, since the micro-propagules of green algae in coastal waters of the SBS tend to germinate at 10 ◦C. To understand such a strong warming with important potential ecological consequences, we carried out an EOF analysis to reveal the spatiotemporal characteristics of the early April SST variability. We found a dominant spatial pattern of overall warming over the SWYS with enhanced warming anchored over the submarine topography off the Yangtze River estuary and a significant temporal positive correlation with the northerly wind speed. The winter monsoon in the spring has changed a lot since the 1980s in a warming climate. The weakening of the northerly monsoon and increased southwesterly wind drive the TWC up to the canyon and spread to the SBS coast more frequently. The MODIS red–green–blue composite images captured the intrusion of the TWC into the SWYS through the submarine canyon during northerly wind relaxation in early April. Winds control the Remote Sens. 2019, 11, 2478 13 of 14 spring SST through ocean current advection and adjustment. This mechanism was verified by a HYCOM numerical reanalysis. Our results suggest that the weakening of winter monsoon in middle March to early April in recent decades and the interaction between the TWC and submarine topography are responsible for the early spring warming over the SWYS. Multi-source remote sensing has provided important clues for understanding the regional SST variability in the SWYS. The advanced spring warming provides a favorable temperature conditions for green algae bloom in the Yellow Sea.

Author Contributions: Conceptualization, X.W.; methodology, X.W.; validation, G.L.; Writing—Original Draft preparation, X.W.; Writing—Review and Editing, Y.-A.L., B.W., K.T.; visualization, H.M.; supervision, Q.X. Funding: This research was funded by the Fundamental Research Funds for the Central Universities (Grant No. 2018B52814 and 2016B11614), the Chinese Natural Science Foundation (Grant Nos. 41976163, 41776183, 41706003, 41706023 and 41776004), Dayu Scholar Program of Hohai University (for Q.X.) and the Natural Science Foundation of Jiangsu Province (Grants No. BK20170871). Acknowledgments: We would like to thank Chuanmin Hu of Optical Oceanography Laboratory, College of Marine Science, University of South Florida for providing MODIS images online via https://optics.marine.usf.edu/ cgi-bin/optics_data?roi=YS_ECS¤t=1. NCEP_Reanalysis 2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd. Daily Advanced Scatterometer (ASCAT) surface wind data were obtained from the Centre de Recherche et d’Exploitation Satellitaire (CERSAT), at IFREMER, Plouzané (France) at: http://apdrc.soest.hawaii.edu/datadoc/ascat.php. We thank Yan Li of Xiamen University and Yining Chen of the Second Institute of Oceanography, MNR for insightful discussions that helped improve the framework of this study. We sincerely thank four anonymous reviewers for their constructive comments and suggestions that improved this paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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