Variability in the Sea Surface Temperature Gradient and Its Impacts on Chlorophyll-A Concentration in the Kuroshio Extension

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Article Variability in the Sea Surface Temperature Gradient and Its Impacts on Chlorophyll-a Concentration in the Kuroshio Extension

Yuntao Wang * , Rui Tang, Yi Yu and Fei Ji

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China; [email protected] (R.T.); [email protected] (Y.Y.); [email protected] (F.J.) * Correspondence: [email protected]; Tel.: +86-571-8196-3629

Abstract: Sixteen years of satellite observational data in the Northwestern Pacific Ocean are used to describe the variability in the sea surface temperature (SST) gradient and its impact on chlorophyll-a concentrations (Chl-a). Spatially, a meridional dependence is identified in which the SST gradient increases to the north in association with elevated Chl-a. Temporally, the seasonal variability shows a large SST gradient and high Chl-a in winter and spring, while the SST gradient and Chl-a are much lower in summer. The seasonal variability in Chl-a leads the variability in the SST gradient by one month. A significant correlation between the SST gradient and Chl-a in the anomalous field is obtained only in the western section of the Kuroshio extension (KE) and the highest correlation is identified without any lags. An index for the section is defined as the proportion of the number of times that the SST gradient magnitude is anomalously large in each year, and the index is highly related to the stability of the KE and has a prominent influence on Chl-a in the region. An anomalously large positive (negative) SST gradient magnitude occurs when the KE is unstable (stable) and the Citation: Wang, Y.; Tang, R.; Yu, Y.; Ji, corresponding Chl-a is high (low). F. Variability in the Sea Surface Temperature Gradient and Its Impacts Keywords: sea surface temperature gradient; Kuroshio extension; chlorophyll-a; dynamic stability; on Chlorophyll-a Concentration in remote sensing the Kuroshio Extension. Remote Sens. 2021, 13, 888. https://doi.org/ 10.3390/rs13050888

Academic Editor: Yukiharu Hisaki 1. Introduction In the northwestern Pacific Ocean, there are two major currents, the Kuroshio current Received: 2 January 2021 and the Oyashio current, which flow from the southwest and northeast, respectively [1]. Accepted: 22 February 2021 These currents are crucial for the meridional transport of water and heat in the region and Published: 26 February 2021 play an important role in global circulation [2]. In the subtropical region, water piles up in the western section of the Pacific Ocean, and as the large westward pressure gradient Publisher’s Note: MDPI stays neutral balances with the Coriolis forces, an intense northward current, the Kuroshio current, with regard to jurisdictional claims in is generated following the Sverdrup theory [3]. As one of the major western boundary published maps and institutional affil- currents in the world, the Kuroshio current is characterized by warm temperatures and iations. high salinities [4]. It can extend more than 1000 m in depth and transport substantial quantities of water, i.e., 42 Sv excluding the local recirculation, and energy into the mid- latitude region [5]. Conversely, the Oyashio current has low temperatures and salinities and is high in nutrients, which can impact the mortality rate of small pelagic fishes [6]. The Copyright: © 2021 by the authors. annually averaged transport of the Oyashio current is 31 Sv, with a larger value during Licensee MDPI, Basel, Switzerland. winter [7]. Both currents converge east of Japan, and the confluence flows eastward, where This article is an open access article it is subsequently referred to as the Kuroshio extension (KE) [8]. The region of the KE is distributed under the terms and characterized by a prominent gradient in sea surface height (SSH), with the SSH decreasing conditions of the Creative Commons from the subtropical gyre toward the north [8]. In particular, intense changes in SSH can Attribution (CC BY) license (https:// be found to the east of Japan where the KE transports a large volume of water masses creativecommons.org/licenses/by/ associated with high quantities of nutrients and CO [9,10]. The eastward transport of 4.0/). 2

Remote Sens. 2021, 13, 888. https://doi.org/10.3390/rs13050888 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 888 2 of 19

the KE is indeed found to be highly related to the change in SSH across the KE [5]. The energetic KE is characterized by stationary meanders and sheds mesoscale eddies that separate from the meanders [1]. The zonal extent of the KE is usually defined as the region between the coast of Japan and the 165◦ E meridian, where upstream and downstream of the KE are defined as the regions to the west and east of 153◦ E, respectively [11]. In particular, the upstream region of the KE is characterized by large seasonal and interannual to decadal variability [12]. Because of the convergence of the two major currents, prominent instability is intro- duced, and the eddy kinetic energy (EKE) in the region is one of the largest in the world [12]. In particular, mesoscale processes, e.g., fronts and eddies, are abundant in the KE and play an important role in determining regional dynamics and subsequently influencing regional ecosystems. The oceanic fronts delineate the boundaries between water masses, which are characterized by different temperatures, nutrients, and other parameters [13]. The frontal zone in the ocean can be associated with an extremely large concentration of phytoplankton, e.g., diatoms [13]. The sea surface temperature (SST) is a major feature for delineating water masses, and the SST gradient is widely used to detect fronts [14]. SST gradient-derived fronts have been investigated in major current systems, e.g., eastern boundary currents [15], the Gulf Stream [16], and the Brazil current [17]. Fronts in coastal regions can be highly correlated with the local upwelling, which introduces subsurface cold water to the surface and induces fronts [14,15,17]. Similarly, wind can induce mixing and Ekman pumping in the open ocean, leading to cooling at the surface and driving frontogenesis [18]. In return, the difference in SST on each side of a front can modulate the stability of the atmospheric boundary layer and result in increasing (deceasing) wind over warm (cold) surfaces [16]. Thus, the frontal zone is usually characterized as a highly dynamic region that includes the convergence of water masses [18] associated with high nutrients contents and primary production [13,19]. In particular, when the alongshore wind drives coastal upwelling on the coast of California [15], the upwelled cold and nutrient-rich water converges with warm surface water [14]. Fronts are subsequently generated at the boundary of the upwelling zone, which is associated with increased phytoplankton [20]. Similarly, a connection between the front and phytoplankton is also expected in the KE region, which should be further explored. The biomass of phytoplankton can be gauged by the chlorophyll-a concentrations (Chl-a) [21], which can be measured by remote sensing at a global scale with high spatial resolution, i.e., 1/24◦ or higher [22]. Long-term satellite observations can detangle the major components of Chl-a variability, e.g., the annual cycle and interannual variations [22]. A prominent seasonal cycle has been identified in which high Chl-a levels occur in local summer when wind-induced frontal activities are enhanced [15]. Because surface fronts can be detected via the SST gradient, a simplified approach has been developed by linking the SST gradient with the Chl-a concentration to identify their dependence [23]. In addition to the investigation of seasonal variability, the underlying dynamics between frontal activities and Chl-a have been further confirmed in an anomalous field, which is obtained by removing the monthly climatology [15]. Wang et al. [23] found that as the wind anomaly increases, there are larger SST gradients (fronts) and higher Chl-a levels because wind mixing cools the SST and brings more nutrients into the euphotic zone. Yu et al. [24] revealed a significant dependence between frontal activities and Chl-a and found that high Chl-a is accompanied by high frontal activities both during seasonal cycles and in an anomalous field. Eddies, as another class of mesoscale dynamics, are important for driving vertical mixing and horizontal advection. Upwelling (downwelling) occurs in cyclonic (anticyclonic) eddies that can depress (elevate) the ocean surface, and a closed contour of the sea level anomaly (SLA) can be applied to identify the eddy location [25]. Simultaneously, the mixed layer depth (MLD) will be reduced (increased) in cyclonic (anticyclonic) eddies [26]. Thus, eddies induce a monopole feature at their center, and the amplitude of the monopole decreases with the distance from the center of the eddy [27]. If a prominent horizontal Remote Sens. 2021, 13, 888 3 of 19

gradient exists in water parameters, e.g., SST and Chl-a, the rotation of eddies can advect water and induce a dipole feature [28]. The intensity of the dipole increases with the strength of the background gradient, although the redistribution of parameters induces no change in total SST or Chl-a [29]. Compared with the dipole pattern, the monopole feature is usually dominant in major global oceans, including the Kuroshio current and KE, Gulf Stream and eastern boundary current systems [25,30]. More nutrient-rich subsurface water is transported into the euphotic layer within cyclonic eddies, leading to high surface Chl-a [26,27,31]. In contrast, the limited nutrients in anticyclonic eddies inhibit the growth of phytoplankton and result in low Chl-a [29]. Thus, a negative relationship can be found between the SLA and Chl-a when mesoscale eddies dominate the dynamics [32]. This phenomenon is particularly true for oligotrophic regions, including the region south of the KE, where the growth of phytoplankton is limited by the availability of nutrients [33]. Different mechanisms for generating eddies are found in the KE than in other boundary currents, including pinching off from the main stream [34] and horizontal shear instability [35]. Longer-lived and larger cyclonic (anticyclonic) eddies are found to the south (north) of 35◦ N[36]. Because the water masses inside and outside of the eddy perimeters are usually distinct from each other, the boundary of eddies can be identified as fronts if the difference between them is prominent [30,33]. Many studies have investigated the impact of mesoscale eddies on the Chl-a distribution and ecosystem variability in the KE [32,33,37]; however, the influence of eddy-induced frontal activities on Chl-a has been much less studied. Dynamics are important for driving the regional variability in Chl-a at seasonal scales and in anomalous fields [14,15,29]. Prominent decadal variability in Chl-a has also been identified in the KE [38] and is modulated by large-scale and long-term dynamic processes. The variability of the KE is characterized by two distinct modes: stable and unstable modes [12]. A contour of SSH with a specific value, e.g., 110 cm or 170 cm, can be applied such that a longer (shorter) pathlength of the contour indicates an unstable (a stable) dynamic state [39]. The dynamic processes and associated oceanic parameters show more complex variation when the KE is in the unstable mode and less variation when the KE is in the stable mode [12,39,40]. The dynamic state can be reflected via the SSH anomaly, as the SSH anomaly is positive (negative) during the stable (unstable) mode [39]. The switch between the dynamic states is related to the first Pacific decadal mode, namely, the Pacific decadal oscillation (PDO) [41]. In particular, the positive PDO generates negative SSH anomalies in the eastern Pacific Ocean, which are transported westward via baroclinic Rossby waves [40]. As a Rossby wave propagates westward, it pushes the KE southward, such that the deep KE interacts with the shallow Shatsky Ridge and generates instability, leading to an unstable mode of KE [41]. Conversely, the negative PDO induces positive SSH anomalies that are less favorable for generating instability, and the KE switches to the stable mode. Subsequently, a large (small) instability during the unstable (stable) mode can enhance (depress) eddy activities and subsequently enhance Chl-a variability in the KE [38]. The second Pacific decadal mode, the North Pacific Gyre Oscillation (NPGO) index, can also influence the KE strength and ecosystem variability with a lag of 2 to 4 years [42] because of the time required for the signal to propagate westward [43]. However, the connection between the interannual variability in the stability of the KE and the corresponding SST gradient has not yet been resolved, e.g., the intensity of the SST gradient in response to different modes of KE status and the timing for the response to occur. Additionally, the impact of the interannual variability in the SST gradient on Chl-a should be further explored. Local dynamics in the KE have a prominent impact on marine primary production and subsequently influence the higher trophic levels, e.g., zooplankton [44,45] and fish- ery resources [46]. A clear dependence has been identified between the dynamics and the distribution of Chl-a; however, the detailed relationship at seasonal and interannual scales, and in anomalous fields has not been clearly resolved. In particular, whether this dependence is valid over the entire region or limited to a certain spatial extent, and what Remote Sens. 2021, 13, 888 4 of 20 Remote Sens. 2021, 13, 888 4 of 20

timing of different dynamics impacting Chl-a, are unclear. In this study, long-term satel- timing of different dynamics impacting Chl-a, are unclear. In this study, long-term satellite observations of the SST gradient and Chl-a are used to identify the region with a sig- lite observations of the SST gradient and Chl-a are used to identify the region with a significant relationship between these factors at different scales and with the underlying dy- nificant relationship between these factors at different scales and with the underlying dynamics. The remainder of the paper includes the materials and methods in Section 2, the namics. The remainder of the paper includes the materials and methods in Section 2, the Remote Sens. 2021, 13, 888 results in Section 3, the discussion in Section 4 and the conclusions in Section 5. 4 of 19 results in Section 3, the discussion in Section 4 and the conclusions in Section 5. 2. Materials and Methods 2. Materials and Methods the timingSatellite of differentobservations dynamics of SST impacting and Chl-a Chl-a, are used are unclear.in this study In this to study,represent long-term oceanic Satellite observations of SST and Chl-a are used in this study to represent oceanic satelliteconditions. observations Daily observations of the SST measured gradient and by the Chl-a Moderate are used Resolution to identify Imaging the region Spectrora- with a conditions. Daily observations measured by the Moderate Resolution Imaging Spectrora- significantdiometer (MODIS) relationship onboard between NASA’s these satellite factors atAqua different are obtained scales and with with level-3 the underlyingdatasets at a diometer (MODIS) onboard NASA’s satellite Aqua are obtained with level-3 datasets at a dynamics.spatial resolution The remainder equal to of 1/24°, the paper e.g., includes4.5 km. The the materialsdataset covers and methods a long time in Section period2, thefrom spatial resolution equal to 1/24°, e.g., 4.5 km. The dataset covers a long time period from resultsJuly 2002 in Sectionto the present;3, the discussion please refer in Section to the4 acknowledgements and the conclusions section in Section for 5detailed. infor- Julymation 2002 on to data the present;availability. please The refer cloud to coveragethe acknowledgements is identified and section excluded for detailedbefore distrib- infor- mation2.uting Materials the on dataset.data and availability. Methods Remotely The sensed cloud data coverage have been is identified used widely and excludedfor investigating before distrib- global utingvariabilitySatellite the dataset. in observationsChl-a Remotely [22], frontal of sensed SST activities and data Chl-a have [15,17], are been used dynamics used in this widely studyof mesoscale for to investigating represent eddies oceanic [25,29]global variabilityconditions.and assessing in Daily Chl-a water observations [22], quality frontal [47,48]. measured activities To reduce by[15,17], the the Moderate dynamics impact of Resolution ofcloud mesoscale and Imaging land eddies contamina- Spectro- [25,29] andradiometertion, assessing the data (MODIS) waterfor areas quality onboard within [47,48]. NASA’s 5 km To of satellite reducethe coast the Aqua and impact are the obtained dataof cloud for with areasand level-3land with contamina- clouds datasets are ◦ tion,atdiscarded. a spatialthe data resolutionThe for daily areas Chl-a equal within data to 1/245 arekm ,logarithmicallyof e.g., the 4.5 coast km. and The transformed the dataset data covers for due areas a to long theirwith time log-normalclouds period are discarded.fromdistribution July 2002The [49]. todaily theThe Chl-a present;data havedata please aare high logarithmically refer spatial to the resolution, acknowledgements transformed which is due important section to their forfor log-normal detailedresolving distributioninformationmesoscale dynamics, [49]. on data The availability. data such have as frontsa The high cloud andspatial coverageeddies resolution, [14,23,27], is identified which and is and theirimportant excluded impact for before on resolving marine dis- mesoscaletributingproductivity the dynamics, dataset. [24]. SST Remotelysuch data as have fronts sensed been and data widely eddies have used been [14,23,27], usedto determine widely and fortheir the investigating SSTimpact gradients on globalmarine and productivityvariabilityfrontal activities in [24]. Chl-a toSST [investigate22 data], frontal have oceanic activities been widely processes [15,17 used], dynamicsand to air-seadetermine of interactions mesoscale the SST eddies [23].gradients The [25 ,daily 29and] frontalandSST assessinggradient activities is water calculatedto investigate quality for [47 each oceanic,48]. pixel To reduceprocesses such that the and the impact zonalair-sea of gradient cloud interactions and (G landx) and[23]. contamina- meridional The daily tion, the data for areas within 5 km of the coast and the data for areasx with clouds are SSTgradient gradient (Gy )is are calculated first calculated for each as pixel the ratio such of that the theSST zonal difference gradient among (G ) the and surrounding meridional gradientdiscarded.pixels following (G They) are daily firstthe Sobel Chl-acalculated operator: data areas the logarithmically ratio of the SST transformed difference due among to their the surrounding log-normal pixelsdistribution following [49]. the The Sobel data haveoperator: a high spatial resolution, which is important for resolving × 𝐴(𝑖 − 1, 𝑗) 𝐴(𝑖, 𝑗) 𝐴(𝑖 + 1, 𝑗) ( mesoscale dynamics,𝐺 = −1 such 0 +1as fronts and eddies [14,23,27], and their impact on marine −1 0 +1 × 𝐴(𝑖 − 1, 𝑗) 𝐴(𝑖, 𝑗) 𝐴(𝑖 + 1, 𝑗) (2×𝑑 ) ( productivity𝐺 [24=]. SST data have been widely used to determine the SST gradients 1) and (2×𝑑 ) frontal activities to investigate oceanic processes and air-sea interactions [23]. The daily1) +1 𝐴(𝑖, 𝑗 +1) SST gradient is calculated for each pixel such that the zonal gradient (G ) and meridional +10 ×𝐴(𝑖,𝐴(𝑖,𝑗 +1)𝑗) x gradient (G ) are first calculated as the ratio of the SST difference among the surrounding y −10 ×𝐴(𝑖,𝐴(𝑖,𝑗 −1)𝑗) (2) (2) pixels following the Sobel operator:𝐺 = −1 𝐴(𝑖, 𝑗 −1) 2 × 𝑑 𝐺 = 2 × 𝑑 −1 0 +1 × 𝐴(𝑖 − 1, 𝑗) 𝐴(𝑖, 𝑗) 𝐴(𝑖 + 1, 𝑗) 𝐺 = (1) −1 0 +1 × 𝐴(𝑖 − 1, 𝑗) 𝐴(𝑖, 𝑗) 𝐴(𝑖 + 1, 𝑗) (2×𝑑) 𝐺 = (1) (2×𝑑) +1 𝐴(𝑖, 𝑗 +1) +10 ×𝐴(𝑖,𝐴(𝑖,𝑗 +1)𝑗) （2） −10 ×𝐴(𝑖,𝐴(𝑖,𝑗 −1)𝑗) 𝐺 = （2） −1 𝐴(𝑖, 𝑗 −1) 2 × 𝑑 𝐺 = 2 × 𝑑 where A(i,j) is the SST data with i and j denoting to the pixel number in the meridional where A(i,j) is the SST data with i and j denoting𝑑 𝑑 to the pixel number in the meridional whereand zonalA(i,j) directions, is the SST data respectively, with i and andj denoting and to the are pixel the number corresponding in the meridional distances and be- andtween zonal successive directions, pixels respectively, in the zonal and and 𝑑 meridional and 𝑑 are directions, the corresponding respectively, distances which vary be- zonal directions, respectively, and dx and dy are the corresponding distances between suc- tween successive pixels in the zonal and meridional directions, respectively, which vary cessive pixels in the zonal and meridional directions, respectively,𝐺= which𝐺 vary +𝐺 with latitude. with latitude. The total gradient (G) is then obtainedq using , following with latitude. The total gradient (G) is then obtained= 2 +using2 𝐺=𝐺 +𝐺 , following TheWang total et gradiental. [23]. (G) is then obtained using G Gx Gy , following Wang et al. [23]. WangDailyDaily et al. observations observations[23]. of of the the SSH SSH are are acquired acquired from from Archiving, Archiving, Validation Validation and and Interpreta- Interpre- tiontation ofDaily Satelliteof Satellite observations Oceanographic Oceanographic of the dataSSH (AVISO),dataare acquired (AVISO), which from which are Archiving, provided are provided by Validation the Copernicus by the and Copernicus Interpre- Marine tationServiceMarine of (CMEMS). ServiceSatellite (CMEMS). Oceanographic Please refer Please to the datarefer acknowledgements (AVISO), to the acknowledgements which are section provided for section detailed by thefor information Copernicusdetailed in- Marineonformation data Service availability. on data (CMEMS). Theavailability. SSH Please data The arerefer obtainedSSH to datathe fromacknowledgements are altimetryobtained observations,from altimetrysection andfor observations, detailed the SLA isin- formationdefinedand the as SLA on the datais difference defined availability. betweenas the differenceThe the SSH daily data SSHbetween are and obtained thethe correspondingdaily from SSH altimetryand climatology, the corresponding observations, which andisclimatology, calculated the SLA asiswhich thedefined daily is calculated as averaged the difference as SSH the betweendaily between averaged 1993 the and daily SSH 2010. betweenSSH The and spatial 1993the correspondingand resolution 2010. The is 0.25◦ × 0.25◦, and the dataset has been widely used to identify mesoscale eddies in the climatology,spatial resolution which isis calculated0.25° × 0.25°, as theand daily the averageddataset has SSH been between widely 1993 used and to 2010. identify The global ocean and other dynamic processes [25]. Daily reanalyzed wind data are obtained spatialmesoscale resolution eddies inis the0.25° global × 0.25°, ocean and and the other dataset dynamic has processes been widely [25]. Dailyused reanalyzedto identify from ERA-Interim products, a global atmospheric reanalysis developed by the European mesoscale eddies in the global ocean and other dynamic processes [25]. Daily reanalyzed Centre for Medium-Range Forecasts (ECMWF). The spatial resolution is consistent with SLA observations at 0.25◦, and there is no existing gap in the dataset. Detailed information regarding the reanalysis data can be found in Dee et al. [50]. The time period used in this study is from October 2002 to September 2018, and the time series for all parameters are calculated as monthly averages. Monthly averaging can effectively fill the gaps in the SST and Chl-a data without interfering with the underlying seasonal and interannual variability [23]. Seasonal averages of each parameter Remote Sens. 2021, 13, 888 5 of 19

Remote Sens. 2021, 13, 888 5 of 19

seasonal and interannual variability [23]. Seasonal averages of each parameter are subsequently calculated for winter (January–March), spring (April–June), summer (July–Sep- are subsequently calculated for winter (January–March), spring (April–June), summer tember) and fall (October–December) using the monthly time series. The monthly clima- (July–September) and fall (October–December) using the monthly time series. The monthly tology is obtained as the average for the corresponding month over 16 years calculated climatology is obtained as the average for the corresponding month over 16 years calculated from the monthly averaged observations. from the monthly averaged observations. The vertical distribution of ocean features is important for understanding regional The vertical distribution of ocean features is important for understanding regional dynamics. The vertical profile of temperature is obtained from the World Ocean Atlas dynamics. The vertical proﬁle of temperature is obtained from the World Ocean Atlas (2018) with a spatial resolution equal to 1° [51]. The data describe the monthly climatolog- (2018) with a spatial resolution equal to 1◦ [51]. The data describe the monthly climato- ical temperature distribution by merging multiple sources of observations, including logical temperature distribution by merging multiple sources of observations, including oceanographic casts from Argo and in situ observations [52]. There are 57 vertical layers oceanographic casts from Argo and in situ observations [52]. There are 57 vertical layers for describing the temperature at each depth down to a depth of 5500 m, and the MLD is for describing the temperature at each depth down to a depth of 5500 m, and the MLD is calculated as the depth at which the temperature is 0.8 °C less than that at the surface [53]. calculated as the depth at which the temperature is 0.8 ◦C less than that at the surface [53]. DifferentDifferent deﬁnitionsdefinitions ofof thethe MLDMLD havehave beenbeen utilizedutilized inin previousprevious studies,studies, andand thethe selectedselected methodmethod isis foundfound toto bebe thethe mostmost applicableapplicable forfor thethe studystudy region,region, whichwhich rangesranges betweenbetween 3030°◦ NN andand 3939°◦ NN andand betweenbetween 130130°◦ EE andand 175175°◦ EE (Figure(Figure1 ).1). Please Please note note that that the the area area within within thisthis rangerange thatthat isis locatedlocated toto thethe westwest ofof JapanJapan isis excludedexcluded fromfrom thethe currentcurrent study. study.

FigureFigure 1.1. TopographyTopography (m), (m), currents currents and and major major dynamic dynamic processes processes in inthe the Kuroshio Kuroshio extension extension (KE) (KE) region. region. The major The major bath- ◦ ◦ bathymetricymetric features features with with shallow shallow topography topography are are the the Izu-Ogasawara Izu-Ogasawara Ridge Ridge (139.7° (139.7 E)E),, the the Shatsky Shatsky Rise (159.8(159.8° E) andand thethe EmperorEmperor Seamount Seamount (171.4 (171.4°◦ E). E). The The schematic schematic cyclonic cyclonic (CE) (CE) and and anticyclonic anticyclonic (AE) (AE) circulations circulations that that are are frequently frequently present present to theto the south south of Japanof Japan are are labeled labeled [1]. [1].

3.3. ResultsResults 3.1.3.1. MeanMean FieldField andand SeasonalSeasonal VariabilityVariability ofof MajorMajor FeaturesFeatures inin thethe KuroshioKuroshio ExtensionExtension TheThe waterwater depth in the the northwestern northwestern Pacific Pacific Ocean Ocean is is generally generally deep, deep, the the majority majority of ofthe the region region is deeper is deeper than than 3000 3000 m, m,and and the the coas coastaltal region region that that is less is less than than 300 300 kmkm from from the thecoastline coastline has hasdepths depths less lessthan than 1000 1000 m (Figure m (Figure 1). 1There). There is a issubmarine a submarine ridge, ridge, i.e., i.e.,the theIzu- Izu-OgasawaraOgasawara Ridge, Ridge, that that originates originates from from central central Japan Japan (35.6° (35.6 N,◦ N,139.7° 139.7 E)◦ andE) and extends extends to- towardward the the south, south, where where it itis isassociated associated with with a few a few islands. islands. The The largest largest depth depth is found is found im- immediatelymediately east east of ofthe the ridge, ridge, where where the the depth depth can can reach reach more more than 8000 m. There areare twotwo otherother submarinesubmarine ridges ridges located located to to the the east, east, i.e., i.e., the Shatskythe Shatsky Rise andRise theand Emperor the Emperor Seamount. Sea- Themount. Kuroshio The Kuroshio current andcurrent Oyashio and Oyashio current are curre schematicallynt are schematically shown in shown Figure 1in, togetherFigure 1, withtogether the KE.with The the meander KE. The ofmeander the Kuroshio of the currentKuroshio can current induce can intense induce dynamic intense processes, dynamic suchprocesses, as mesoscale such as eddies,mesoscale along eddies, the main along stream the main of the stream Kuroshio of the current Kuroshio [12,25 current]. Because [12,25]. of theBecause curvature of the of curvature the Japanese of coastthe Japanese and the shallowcoast and bathymetry the shallow around bathymetry the Izu-Ogasawara around the Ridge,Izu-Ogasawara the meander Ridge, and the southward meander and meridional southward flow meridional of the Kuroshio flow of current the Kuroshio can induce cur- relativelyrent can induce stable relatively cyclonic and stable anticyclonic cyclonic and circulation anticyclonic to the circulation south of Japan to the [ 40south], which of Japan are schematically[40], which are denoted schematically by CE anddenoted AE, respectivelyby CE and AE, (Figure respectively1). The large (Figure EKE 1). of The the large KE canEKE generate of the KE a great can numbergenerate ofa eddiesgreat number and fronts of eddies [36], similar and fronts to other [36], western similar boundary to other currentwestern systems boundary [25 current]. It is important systems [25]. to point It is important out that the to point stability out ofthat the the KE stability [41] exhibits of the largeKE [41] interannual exhibits large variability interannual that is variability not captured that in is the not figure. captured in the figure. The overall average SST ranges between 16 ◦C and 24 ◦C generally decrease northward (Figure2a), particularly in the open ocean. In the coastal region, the Kuroshio current shows a prominent warm signal that originates from the southwest and is distributed along

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The overall average SST ranges between 16 °C and 24 °C generally decrease northward (Figure 2a), particularly in the open ocean. In the coastal region, the Kuroshio current shows a prominent warm signal that originates from the southwest and is distributed along the coast up to the east of Japan. Additionally, the cold Oyashio current can be identified to the northeast of Japan, and it is distributed along the coast southward until it converges with the Kuroshio current to the east of Japan. Consistently, the averaged Chl- a in the open ocean shows a prominent meridional dependence, with larger values in the north and smaller values in the south. The region with Chl-a values less than −0.8 log10 (mg/m3) is characterized by low productivity [54]. Chl-a monotonically increases toward the coast and is especially high in the region northeast of Japan, where the value can be greater than −0.4 log10 (mg/m3). The general dependence between SST and Chl-a is that high Chl-a are associated with low temperatures, which is consistent with the findings of previous studies [24]. Clearly, Chl-a are much higher in the Oyashio current than in the Kuroshio current. The mean SST gradient is very weak south of the Kuroshio current and KE (Figure 2b), and the corresponding value is less than 0.02 °C/km. A large SST gradient is found at the northern boundary of the Kuroshio current, where a large difference in SST is gener- Remote Sens. 2021, 13, 888 6 of 19 ated between the cold coastal surface water and the warm current, which is consistent with previous findings [55]. It is interesting to note that the corresponding high SST gradient, which is 0.03 °C/km or larger, is limited to only a narrow band near the coast, ap- the coast upproximately to the east of 100 Japan. km Additionally,offshore. A high the coldSST gradient Oyashio occupies current can a much be identiﬁed larger zonal to extent in the northeastthe of north, Japan, where and it the is distributed region is dominated along the coastby the southward Oyashio current until it convergesand KE. In particular, with the Kuroshiothe high current SST gradient, to the east e.g., of Japan. 0.03 °C/km, Consistently, in the theKE averagedextends more Chl-a than in the 1000 open km from the ocean showscoast a prominent to the open meridional ocean. The dependence, SST gradient with has been larger widely values used in the as an north indicator and for detect- 3 smaller valuesing insea the surface south. Thefronts, region and with the Chl-a applied values thre lessshold than to− 0.8define log 10fronts(mg/m is )approximatelyis characterized0.03 by °C/km low productivity at mid-latitudes [54]. Chl-a[15]. Thus, monotonically the KE region increases possesses toward abundant the coast and intense and is especiallyfrontal high activities. in the The region region northeast with a ofhigh Japan, large where averaged the valueSST gradient can be greateris generally associ- 3 than −0.4 logated10 (mg/mwith a large). The temporal general standard dependence deviation between as well SST and(not Chl-ashown) is because that high of the strong Chl-a are associatedseasonal and with interannual low temperatures, variability which in the is KE. consistent A comparison with the of the ﬁndings SST, Chl-a of and SST previous studiesgradient [24 ].data Clearly, shows Chl-a a clear are pattern much higherrelated in to the theOyashio meander current of the KE, than exhibiting in the a curved Kuroshio current.pattern in the corresponding distributions, and their spatial locations are highly similar.

Figure 2. (a) Averaged sea surface temperature (SST, ◦C) overlaid with contours of the averaged 3 ◦ Chl-a (log10 (mg/m )), (b) the averaged SST gradient ( C/km), and (c) the averaged wind speed magnitude (shading) overlaid with wind vectors in the KE region.

The mean SST gradient is very weak south of the Kuroshio current and KE (Figure2b ), and the corresponding value is less than 0.02 ◦C/km. A large SST gradient is found at the northern boundary of the Kuroshio current, where a large difference in SST is generated between the cold coastal surface water and the warm current, which is consistent with previous ﬁndings [55]. It is interesting to note that the corresponding high SST gradient, which is 0.03 ◦C/km or larger, is limited to only a narrow band near the coast, approximately 100 km offshore. A high SST gradient occupies a much larger zonal extent in the north, where the region is dominated by the Oyashio current and KE. In particular, the high SST gradient, e.g., 0.03 ◦C/km, in the KE extends more than 1000 km from the coast to the open ocean. The SST gradient has been widely used as an indicator for detecting sea surface fronts, and the applied threshold to deﬁne fronts is approximately 0.03 ◦C/km at mid-latitudes [15]. Thus, the KE region possesses abundant and intense frontal activities. The region with a high large averaged SST gradient is generally associated with a large temporal standard deviation as well (not shown) because of the strong seasonal and interannual variability in the KE. A comparison of the SST, Chl-a and SST gradient data shows a clear pattern related to the meander of the KE, exhibiting a curved pattern in the corresponding distributions, and their spatial locations are highly similar. Remote Sens. 2021, 13, 888 7 of 19

Remote Sens. 2021Figure, 13, 888 2. (a) Averaged sea surface temperature (SST, °C) overlaid with contours of the averaged Chl-a (log10 (mg/m7 of3 19)), (b) the averaged SST gradient (°C/km), and (c) the averaged wind speed magnitude (shading) overlaid with wind vectors in the KE region.

The windThe field wind is mostlyfield is characterizedmostly characterized as westerly as westerly for the entirefor the region entire (Figureregion 2(Figurec). 2c). For the offshoreFor the offshore region, theregion, wind the speed wind is speed generally is gene greaterrally ingreater the north in the than north in thethan south, in the south, ◦ ◦ and a weakand convergencea weak convergence can be identifiedcan be identified between between 34 N and 34° 38N andN as 38° the N northward as the northward componentcomponent of wind of decreases wind decreases toward thetoward north. the In north. the region In theclose region to close Japan, to the Japan, wind the wind field is mostlyfield is blowingmostly blowing toward thetoward southeast the southeast perpendicular perpendicular to the coastline, to the coastline, with a large with a large variancevariance in direction. in direction. The intensity The intensity of wind of is clearlywind is influencedclearly influenced by the orography, by the orography, as the as the ◦ ◦ wind speedwind is speed reduced is reduced to the leeward to the leeward side of theside main of the island, maine.g., island, 36 e.g.,N, 142 36° N,E, and142° E, and ◦ ◦ intensifiedintensified among mountain among mountain gaps, e.g., gaps, 33 e.g.,N, 139 33° E.N, 139° E. The variabilityThe variability in the SST in andthe SST Chl-a and is furtherChl-a is studiedfurther usingstudied their using seasonal their seasonal averages averages (Figure3(Figure), and prominent 3), and prominent seasonal seasonal differences differences are identified are identified in both parameters. in both parameters. During During ◦ ◦ winter (Figurewinter3 (Figurea), the SST3a), the is between SST is between 10 C and10 °C 20 andC, 20 which °C, which is the is lowest the lowest throughout throughout the the year,year, and theand Kuroshiothe Kuroshio current current clearly clearly delivers delivers warm warm water water to high to high latitudes latitudes near near the the coast coast of Japan.of Japan. Simultaneously, Simultaneously, the the spatially spatially averaged averag Chl-aed Chl-a over over the entirethe entire region region reaches reaches the the annual maximum. This phenomenon is particularly true for the region to the south annual maximum. This phenomenon is particularly true for the3 region to the south of the of the KE where the Chl-a values are greater than −0.7 log10 (mg/m ) at all locations. In KE where the Chl-a values are greater than −0.7 log10 (mg/m3) at all locations. In spring spring (Figure3b), the transport of warm water by the Kuroshio current is still prominent (Figure 3b), the transport of warm water by the Kuroshio current is still prominent since since the surrounding water is cold (less than 22 ◦C). The Chl-a values in the south are the surrounding water is cold (less than 22 °C). The Chl-a values in the south are generally generally lower in spring than in winter, except in the coastal area that are characterized lower in spring than in winter, except in the coastal area that are characterized by cyclonic by cyclonic and anticyclonic circulation (CE and ACE), as shown in Figure1. Conversely, and anticyclonic circulation (CE and ACE), as shown in Figure 1. Conversely, the Chl-a the Chl-a values impacted by the Oyashio current are clearly large in winter and reach the values impacted by the Oyashio3 current are clearly large in winter and reach the annual annual maximum of −0.2 log10 (mg/m ). As the SST in the entire region peaks in summer maximum of −0.2 log10 (mg/m3). As the SST in the entire region peaks in summer (Figure (Figure3c ), the Kuroshio current can hardly be distinguished because the surroundings 3c), the Kuroshio current can hardly be distinguished because the surroundings are also are also warm (26 ◦C or higher). In fall, all the factors show a transitional pattern in which warm (26 °C or higher). In fall, all the factors show a transitional pattern in which SST SST decreases (Figure3d) and Chl-a increases compared with the values in summer. decreases (Figure 3d) and Chl-a increases compared with the values in summer.

3 ◦ Figure 3. SeasonallyFigure averaged 3. Seasonally Chl-a (log10 averaged (mg/m3)) Chl-a overlaid (log with10 (mg/m SST (°C).)) overlaid The seasons with are SST (a) ( winterC). The (January–March), seasons are (b) spring (April–June),(a) winter (c) summer (January–March), (July–September), (b) spring and ( (April–June),d) fall (October–December). (c) summer (July–September), and (d) fall (October–December).

Remote Sens. 2021, 13, 888 8 of 19 Remote Sens. 2021, 13, 888 8 of 19

The seasonal variability in the SST gradient is prominent (Figure4), although the The seasonal variability in the SST gradient is prominent (Figure 4), although the general spatial pattern, e.g., the meridional difference, is consistent with the annual average. general spatial pattern, e.g., the meridional difference, is consistent with the annual aver- The SST gradient is high for the entire region during winter, especially in the northern age. The SST gradient is high for the entire region during winter, especially in the northern section along the Oyashio current, and the corresponding peak value can be as high as section along the Oyashio current, and the corresponding peak value can be as high as 0.05 ◦C/km (Figure4a). To the south of KE, the value is much lower, e.g., 0.01 ◦C/km 0.05 °C/km (Figure 4a). To the south of KE, the value is much lower, e.g., 0.01 °C/km or or less. During spring, the SST gradient in the majority of the region reaches an annual maximum,less. especiallyDuring spring, around the the SST Oyashio gradient current in the andmajority along of the the meander region reaches of the Kuroshioan annual max- currentimum, (Figure especially4b), which around is consistent the Oyashio with the current large and SST along changes the during meander the of same the periodKuroshio cur- (Figurerent3b). (Figure In summer, 4b), thewhich SST is gradient consistent (Figure with4 c)the generally large SST decreases, changes especiallyduring the in same the period Oyashio(Figure current 3b). where In summer, the value the reaches SST gradient the lowest (Figure value 4c) throughout generally decreases, the year (Figure especially4c). in the This phenomenonOyashio current is largely where attributed the value to reaches the warm the lowest SST over value the throughout entire region the (Figure year (Figure3c). 4c). The SSTThis gradient phenomenon increases is inlargely autumn, attributed acting to as the the warm transitional SST over period the entire between region summer (Figure 3c). and winterThe SST (Figure gradient3d). increases in autumn, acting as the transitional period between summer and winter (Figure 3d).

Figure 4. Seasonal variabilityFigure 4. ofSeasonal the sea variabilitysurface temper of theature sea surface(SST) gradient temperature (°C/km). (SST) The gradient seasons (are◦C/km). (a) winter The seasons(January– March), (b) spring (April–June),are (a) winter (c (January–March),) summer (July–September), (b) spring and (April–June), (d) fall (October–December). (c) summer (July–September), and (d) fall (October–December). Wind speed also shows a clear seasonal variance after removing the overall average Windfrom speed the seasonal also shows average a clear (Figure seasonal 5). During variance boreal after removingwinter, the the wind overall near average the coast and from thein the seasonal northern average section (Figure is intensified5 ). During toward boreal the winter, northeast the and wind southeast, near the respectively coast and (Fig- in theure northern 5a). In section particular, is intensified the wind speed toward reache the northeasts the annual and maximum southeast, over respectively the Oyashio cur- (Figurerent5a ). and In particular, remains highly the wind consistent speed with reaches the theannual annual average maximum over the over Kuroshio the Oyashio current and currentthe and majority remains of highly the KE. consistent In spring, with the wind the annual speed average is weakest over over the the Kuroshio entire region current (Figure and the5b), majority especially of the for KE. the In region spring, along the windthe Kurosh speedio is current. weakest Please over the note entire that regionthe range of (Figureshading5b ), especially and the for unity the region wind alongvector the in KuroshioFigure 5 current.differs from Please that note in that Figure the range2c, and the of shadingchange and in the wind unity is generally wind vector weaker in Figure than 5the differs overall from average. that in Thus, Figure the2c, negative and the value of changewind in wind change is generally can be related weaker to the than reduced the overall magnitude average. and Thus, change the in negativedirection. value The strong- of windest change wind can can be identified related to theduring reduced the summer magnitude (Figure and 5c), change when in the direction. wind direction The is strongestgenerally wind can consistent be identified with the during overall the average summer bu (Figuret acts to5c), intensify when the the wind wind direction field. This phe- is generallynomenon consistent is particularly with the true overall for the average KE, wher but actse the to wind intensify intensifies the wind the field.mean Thiswind field, which can be attributed to the warm Kuroshio current (Figure 3c) that reduces the stability

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phenomenon is particularly true for the KE, where the wind intensifies the mean wind field, which can be attributed to the warm Kuroshio current (Figure3c) that reduces the stabilityof of the the atmospheric atmospheric boundary boundary layer layer and and increase increasess the the wind wind fields fields [16]. [16 The]. The wind wind pattern in pattern infall fall shows shows a cyclonic a cyclonic feature feature over over the the entire entire re regiongion east east of of Japan Japan (Figure (Figure 55d),d), e.g., intensi- intensifiedfied southeastward southeastward windwind toto the south of of Japa Japan,n, northward northward component component to to the the east east (between ◦ ◦ (between150° 150 E Eand and 160° 160 E)E) and and southwestward southwestward wind wind over over the the Oyashio Oyashio current. current. Similar Similar to other to otherfeatures, features, the wind inin fallfall alsoalso actsacts asas aa transitional transitional phase phase due due to to the the corresponding corresponding small small magnitudemagnitude in in the the wind wind changes. changes.

Figure 5. Seasonal Figurechanges 5. inSeasonal wind speed changes (shading) in wind overlaid speed with (shading) the wind overlaid vector with (m/s). the The wind seasonal vector changes (m/s). Theare obtained by removingseasonal the annual changes average are from obtained the seasonal by removing averag thee. The annual seasons average are (a) from winter the (January–March), seasonal average. (b) The spring (April–June), (c) summerseasons (July–September), are (a) winter (January–March), and (d) fall (October–December). (b) spring (April–June), (c) summer (July–September), and (d) fall (October–December). 3.2. Dynamic relationship among factors in the Kuroshio extension 3.2. Dynamic Relationship among Factors in the Kuroshio Extension The dynamics influencing the variability of Chl-a are further investigated in an Theanomalous dynamics influencingfield, which the is obtained variability using of Chl-a the monthly are further time investigated series and in removing an anoma- the over- lous field,all average which is for obtained the corresponding using the monthly month, e.g. time, climatological series and removing monthly the average. overall The time averageseries for the of corresponding the Chl-a anomalies month, and e.g., SST climatological gradient anomalies monthly average. (Figure The6) are time positively series corre- of the Chl-alated anomalies in the meander and SST of gradientthe Kuroshio anomalies current (Figure and upstream6) are positively of the KE correlated [8], where in the cor- the meanderrelation of theis significant Kuroshio currentat the 95% and confidence upstream oflevel. the KEThe [ 8confidence], where the level correlation is determined is by significantcomparing at the 95% the confidence correlation level. with a The threshold confidence critical level value is determined of 0.28, which by comparingis calculated based the correlationon the degrees with a thresholdof freedom critical of the valueindependent of 0.28, observations which is calculated [15]. Different based onleads the and lags degreesare of freedomapplied to of the the time independent series of the observations SST gradient [15 and]. DifferentChl-a data leads to identify and lags potential are lead– appliedlag to the relationships, time series ofand the the SST largest gradient correlation and Chl-a is obtained data to identify when both potential time lead–lagseries are simul- relationships,taneous. and Thus, thelargest when the correlation SST gradient is obtained anomaly when in the both main time stream series of are the simultane- Kuroshio current ous. Thus, when the SST gradient anomaly in the main stream of the Kuroshio current and the center of the KE (Figure 1) is large (small), the corresponding Chl-a anomaly is and the center of the KE (Figure1) is large (small), the corresponding Chl-a anomaly is high (low). The region where the variability in Chl-a is related to the SST gradient is pre- high (low). The region where the variability in Chl-a is related to the SST gradient is cisely delineated in Figure 6, and its boundary in the north–south (east–west) direction is precisely delineated in Figure6, and its boundary in the north–south (east–west) direction shown as dashed cyan (solid blue) lines. Meridionally, the boundary is defined as the is shown as dashed cyan (solid blue) lines. Meridionally, the boundary is defined as the range within one standard deviation of the meridional variation from the center. In its western section, i.e., west of 142° E, the region lies roughly between 100 km and 300 km from the coast, and the coastal area is characterized by a low correlation. In the eastern

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range within one standard deviation of the meridional variation from the center. In its western section, i.e., west of 142◦ E, the region lies roughly between 100 km and 300 km from the coast, and the coastal area is characterized by a low correlation. In the eastern section, the correlation is especially large within the KE and gradually decreases eastward. It should be noted that the location of the center of the region with the largest correlation in the meridional direction is also identiﬁed to the west of 134◦ E and to the east of 149◦ E, but the corresponding correlation is not signiﬁcant.

Table 1. The correlation coefficient between the monthly time series of chlorophyll concentrations (Chl-a) and different parameters in climatological and anomalous fields for the enclosed region shown in Figure6. The anomalous field is obtained using the monthly time series by removing the overall average for the corresponding month. The number in the bracket indicates the number of months for the parameter to obtain the correlation with Chl-a with the largest magnitude, where a positive (negative) value indicates the parameter is leading (lagging) the variability in Chl-a. The dash symbol indicates that the correlation is not significant at the 95% confidence level. Because mixed layer depth (MLD) is obtained as a monthly climatology, the correlation in the anomalous field is not available. Remote Sens. 2021, 13, 888 10 of 19

Parameters Climatology Anomaly Sea surface temperature (SST) −0.92 (0) −0.22 (-) section, theSST correlation gradient is especially large +0.92 within (−1) the KE and gradually decreases +0.51 (0) eastward. It shouldSea level be anomalynoted that (SLA) the location of the−0.81 center (−1) of the region with the− largest0.57 (0) correlation in the meridionalMLD direction is also identified +0.87 to (+2) the west of 134° E and to the - east of 149° E, but the corresponding correlation is not significant.

Figure 6. Correlation between the time series of the SST gradient and Chl-a in the anomalous field. The correlation is Figure 6. Correlation between the time series of the SST gradient and Chl-a in the anomalous field. The correlation is significant at the 95% confidence level if the critical value is greater than and less than 0.28 and −0.28, respectively. The − significantsolid (dashed) at the cyan 95% line confidence represents level the if loca thetion critical of the value center is greater (boundary) than andof the less region than with 0.28 anda large0.28, correlation, respectively. > 0.28, The in solidthe meridional (dashed) cyan direction. line represents The boundary the location is defined of the as centerthe rang (boundary)e within one of thestandard region deviatio with a largen of correlation,the meridional >0.28, variation in the meridionalfrom the center. direction. The Thesolid boundary blue lines is repr definedesent as the the averaged range within boundary one standard of the region deviation with of a the significant meridional correlation variation in from the thezonal center. direction. The solid The region blue lines with represent a significant the averagedcorrelation boundary is used to of obtain the region Figures with 7–9 a significantand Table 1. correlation in the zonal direction. The region with a significant correlation is used to obtain Figures7–9 and Table1. The variability in Chl-a and other factors in the meander of the Kuroshio current and upstreamThe variability of the KE, inwhich Chl-a is andcharacterized other factors by a in significant the meander correlation of the Kuroshioin Figure current6, is in- andvestigated upstream to assess of the the KE, seasonal which is variability characterized (Figure by 7). a significant Prominent correlation seasonal cycles in Figure are iden-6, is investigatedtified in the climatological to assess the seasonalmonthly variabilityaverage of (Figureall the factors;7). Prominent for example, seasonal Chl-a cycles peaks are in identifiedApril and inrapidly the climatological decreases until monthly August average befo ofre allincreasing the factors; again for example,(Figure 7a). Chl-a Previous peaks instudies April have and rapidlyrevealed decreases that the untilvariability August in beforeChl-a increasingis related to again the SST (Figure and7 a).MLD Previous during studiesthe seasonal have revealedcycle [24] that and the that variability locally high in Chl-a Chl-a is values related occurs to the in SST local and winter MLD duringin associa- the seasonaltion with cycle a low [ 24SST] and and that deep locally MLD. high In the Chl-a current values study, occurs the in monthl local wintery time inseries association consist- withently acaptured low SST a and low deep SST MLD.and deep In the MLD current in winter study, (Figure the monthly 7b). A significant time series negative consistently cor- capturedrelation is a lowfound SST between and deep SST MLD and in Chl-a winter (Figure (Figure 7b),7b). and A significant the largest negative correlation correlation occurs iswhen found the between SST variations SST and are Chl-a simultaneous (Figure7b), with and Chl-a; the largest a significant correlation negative occurs correlation when the is SSTalso variationsfound between are simultaneous MLD and Chl-a, with Chl-a;and the a significantlargest correlation negative occurs correlation when is MLD also found leads between MLD and Chl-a, and the largest correlation occurs when MLD leads Chl-a by two Chl-a by two months. Additionally, the SST gradient also shows a distinctive seasonal months. Additionally, the SST gradient also shows a distinctive seasonal cycle, peaking cycle, peaking in April and May and then decreasing rapidly until August (Figure 7c). The in April and May and then decreasing rapidly until August (Figure7c). The variability in variability in Chl-a and the SST gradient are highly consistent with each other, with the SST gradient slightly lagging by one month, indicating that the oceanic dynamics are important for driving the seasonal variability in Chl-a. The dependence between Chl-a and other factors during the climatological seasonal cycle is summarized in Table 1. Table 1. The correlation coefficient between the monthly time series of chlorophyll concentrations (Chl-a) and different parameters in climatological and anomalous fields for the enclosed region shown in Figure 6. The anomalous field is obtained using the monthly time series by removing the overall average for the corresponding month. The number in the bracket indicates the number of months for the parameter to obtain the correlation with Chl-a with the largest magnitude, where a positive (negative) value indicates the parameter is leading (lagging) the variability in Chl-a. The dash symbol indicates that the correlation is not significant at the 95% confidence level. Because mixed layer depth (MLD) is obtained as a monthly climatology, the correlation in the anomalous field is not available.

Parameters Climatology Anomaly Sea surface temperature (SST) −0.92 (0) −0.22 (-) SST gradient +0.92 (−1) +0.51 (0) Sea level anomaly (SLA) −0.81 (−1) −0.57 (0) MLD +0.87 (+2) -

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Chl-a and the SST gradient are highly consistent with each other, with the SST gradient slightly lagging by one month, indicating that the oceanic dynamics are important for Remote Sens. 2021, 13, 888 11 of 19 driving the seasonal variability in Chl-a. The dependence between Chl-a and other factors during the climatological seasonal cycle is summarized in Table1.

3 ◦ Figure 7. Monthly averaged (a)Figure Chl-a (log 7. Monthly10 (mg/m averaged3)), (b) SST (a) (blue, Chl-a °C) (log and10 (mg/m MLD (magenta,)), (b) SST m), (blue, andC) (c) and SST MLD gradient (magenta, m), Remote Sens. 2021, 13, 888 12 of 19 (°C/km). The vertical dashed linesand represent (c) SST gradient the annual (◦C/km). peaks The for vertical Chl-a dashed(black) linesand SST represent gradient the annual(green) peaksand annual for Chl-a (black) troughs for SST (blue) and MLDand (magenta SST gradient). The (green)correlation and between annualtroughs Chl-a and for other SST (blue) factors and and MLD the (magenta).associated lag The to correlation obtain the largest correlation arebetween labeled in Chl-a the andcorresponding other factors panels, and the where associated a positive lag to (negative) obtain the value largest indicates correlation that arethe labeled in parameter is leading (lagging) theandthe variability correspondingChl-a variability. in Chl-a. panels, Thus, where the a corresponding positive (negative) relationship value indicates between that thewind parameter and Chl-a is leading is not included(lagging) thein this variability study. in Chl-a. The impact of major dynamic processes on Chl-a, such as those indicated by the SST gradient and SLA, is further quantified in an anomalous field for the region delineated in Figure 6. The SST gradient magnitude (Figure 8a) and SLA (Figure 8b) are divided into eight bins, and the corresponding Chl-a is averaged for each bin. The relationship is clearly captured from their scatterplots showing that abnormally high Chl-a values are associated with a positive anomaly in SST gradient magnitude and a negative anomaly in SLA. It should be noted that no lag is applied since the largest correlation is obtained when the time series are simultaneous (Table 1). Because the correlation between SST and Chl- a is not significant (Table 1), the corresponding information is not included in the figure. Linear regressions are applied using the bin-averaged values of the SST gradient magnitude and SLA and the bin-averaged Chl-a to gauge the dependence of Chl-a on these factors, and the acquired regression coefficients are significant at the 95% confidence level [15]. This method offers a quantitative approach to estimate the change in Chl-a based on 33 ◦ Figure 8. Scatterplots ofoceanic Chl-a (log(log processes.1010 (mg/m ))The)) versus versus applied ( (aa)) SST SST linear gradient gradient regression (°C/km) ( C/km) anddoes and (b (bnot) )SLA SLA imply (m) (m) in inthat the the anomalousthe anomalous dependence field. field. In is (a), black dots are the averagedlinear, but Chl-a it can for eachbe used bin ofas the an SSTindicator gradient, to estimate with vertical the barsChl-a representing anomalies the that corresponding are induced standard deviation. Similarly, Similarly,by certain in in ( (b bprocesses),), cyan cyan dots dots and are are tothe compare averaged Chl-a Chl-a valuesfor each at bin different of SLA. times.The solid The lines SST are gradient obtained is using linear regression furtherbetween applied the values to representof bins and the bin-av bin-averaged stateeraged of the Chl-a, Chl-a, ocean, and and and both both three of of th them groupsem are are significant significantare separated at at the based 95% confidenceconfidence level. The obtainedobtainedon their regressionregression SST gradient isis labeled labeled magnitude in in the the corresponding corresponding anomalies. panel. Thepanel. groups Dots Dots between between are defined the the magenta magenta as those dashed dashed with lines lineslarge in in (a) represent small magnitudes of the SST gradient. (a) represent small magnitudesnegative of theanomaly SST gradient. values (< −1.5×10−3 °C/km), large positive anomaly values (> 1.5×10−3 °C/km) and those with anomaly values close to zero. There are roughly the same 3.3. Impact of Kuroshio Extension Stability on Modulating Oceanic Features number of SST gradient magnitude anomalies in each group. The groups of SST gradient magnitudesThe areanomaly useful of for SST defining gradient the magnitude dynamic state in ea ofch the month KE, whichcan be willclassified be explained into one of in thethe following three groups paragraph. depending It should on its bevalue, noted e.g that., large there positive, is no significant large negative dependence and the re- betweenmaining the windwith smallfield andmagnitude. Chl-a in Then,either thethe raticlimatologicalo between theor anomalousdifference infields, the numberwhich of mightmonths be because with largethe wind anomaly is determined values (i.e., by thelarge-scale number atmospheric of months with circulation large positive and mod- values ifiedminus by mesoscale the number air-sea of monthsinteractions with [56], large but negative has little values) influential and the on numberregional ofdynamics months excluding those with a small magnitude is obtained for each year (Figure 9). Here, a positive (negative) ratio indicates more months characterized by anomalously large (small) SST gradient magnitude within the year. Prominent interannual variability is identified in which a positive (negative) gradient state occurs between 2005 and 2009 and after 2016

(before 2003 and between 2010 and 2014). Only two years, 2004 and 2015, are characterized by a neutral state with the same number of months with anomalously large and small SST gradient magnitudes, or more than 50% of months are characterized as near-zero magnitude of SST gradient anomalies within a year. Thus, the switch between different states occurs in a rapid manner, and each state can be maintained for a few years. Comparing the time series with the previously identified stability of the KE [39], a clear dependence is identified between the stability of the KE and the SST gradient state. A positive (negative) anomaly in the SST gradient magnitude is generally associated with the unstable (stable) mode of the KE, and the gradient state leads by one or two years. Thus, the state of the SST gradient magnitude can be applied as an index to predict the interannual variability in the stability of the KE in the following years.

Remote Sens. 2021, 13, 888 12 of 19

and Chl-a variability. Thus, the corresponding relationship between wind and Chl-a is not included in this study.

Figure 8. Scatterplots of Chl-a (log10 (mg/m3)) versus (a) SST gradient (°C/km) and (b) SLA (m) in the anomalous field. In (a), black dots are the averaged Chl-a for each bin of the SST gradient, with vertical bars representing the corresponding standard deviation. Similarly, in (b), cyan dots are the averaged Chl-a for each bin of SLA. The solid lines are obtained using linear regression between the values of bins and bin-averaged Chl-a, and both of them are significant at the 95% confidence level. The obtained regression is labeled in the corresponding panel. Dots between the magenta dashed lines in (a) represent small magnitudes of the SST gradient.

3.3. Impact of Kuroshio Extension Stability on Modulating Oceanic Features The anomaly of SST gradient magnitude in each month can be classified into one of the three groups depending on its value, e.g., large positive, large negative and the remaining with small magnitude. Then, the ratio between the difference in the number of months with large anomaly values (i.e., the number of months with large positive values minus the number of months with large negative values) and the number of months excluding those with a small magnitude is obtained for each year (Figure 9). Here, a positive (negative) ratio indicates more months characterized by anomalously large (small) SST gradient magnitude within the year. Prominent interannual variability is identified in which a positive (negative) gradient state occurs between 2005 and 2009 and after 2016 (before 2003 and between 2010 and 2014). Only two years, 2004 and 2015, are characterized by a neutral state with the same number of months with anomalously large and small SST gradient magnitudes, or more than 50% of months are characterized as near-zero magnitude of SST gradient anomalies within a year. Thus, the switch between different states occurs in a rapid manner, and each state can be maintained for a few years. Comparing the time series with the previously identified stability of the KE [39], a clear dependence is identified between the stability of the KE and the SST gradient state. A positive (negative) anomaly in the SST gradient magnitude is generally associated with the unstable Remote Sens. 2021, 13, 888 (stable) mode of the KE, and the gradient state leads by one or two years. Thus, the12 state of 19 of the SST gradient magnitude can be applied as an index to predict the interannual variability in the stability of the KE in the following years.

Figure 9. Gradient state based on the annual percentage of the anomaly in the SST gradient magnitude. Red (blue) dots indicate a high percentage of large positive (negative) anomalies in SST gradient magnitudes. The dashed gray line represents the percentage where the positive and negative SST gradient magnitudes are equal, with hollow dots indicating a neutral state. If more than 50% of the time in a year the month is characterized as a near-zero anomaly in SST gradient magnitude, the corresponding year is also deﬁned as a neutral state.

The impact of major dynamic processes on Chl-a, such as those indicated by the SST gradient and SLA, is further quantified in an anomalous field for the region delineated in Figure6. The SST gradient magnitude (Figure8a) and SLA (Figure8b) are divided into eight bins, and the corresponding Chl-a is averaged for each bin. The relationship is clearly captured from their scatterplots showing that abnormally high Chl-a values are associated with a positive anomaly in SST gradient magnitude and a negative anomaly in SLA. It should be noted that no lag is applied since the largest correlation is obtained when the time series are simultaneous (Table1). Because the correlation between SST and Chl-a is not significant (Table1), the corresponding information is not included in the figure. Linear regressions are applied using the bin-averaged values of the SST gradient magnitude and SLA and the bin-averaged Chl-a to gauge the dependence of Chl-a on these factors, and the acquired regression coefficients are significant at the 95% confidence level [15]. This method offers a quantitative approach to estimate the change in Chl-a based on oceanic processes. The applied linear regression does not imply that the dependence is linear, but it can be used as an indicator to estimate the Chl-a anomalies that are induced by certain processes and to compare Chl-a values at different times. The SST gradient is further applied to represent the state of the ocean, and three groups are separated based on their SST gradient magnitude anomalies. The groups are defined as those with large negative anomaly values (<−1.5×10−3 ◦C/km), large positive anomaly values (>1.5×10−3 ◦C/km) and those with anomaly values close to zero. There are roughly the same number of SST gradient magnitude anomalies in each group. The groups of SST gradient magnitudes are useful for defining the dynamic state of the KE, which will be explained in the following paragraph. It should be noted that there is no significant dependence between the wind field and Chl-a in either the climatological or anomalous fields, which might be because the wind is determined by large-scale atmospheric circulation and modified by mesoscale air-sea interactions [56], but has little influential on regional dynamics and Chl-a variability. Thus, the corresponding relationship between wind and Chl-a is not included in this study.

3.3. Impact of Kuroshio Extension Stability on Modulating Oceanic Features The anomaly of SST gradient magnitude in each month can be classiﬁed into one of the three groups depending on its value, e.g., large positive, large negative and the remaining with small magnitude. Then, the ratio between the difference in the number of months with large anomaly values (i.e., the number of months with large positive values minus the number of months with large negative values) and the number of months excluding those with a small magnitude is obtained for each year (Figure9). Here, a positive (negative) ratio indicates more months characterized by anomalously large (small) SST gradient magnitude within the year. Prominent interannual variability is identiﬁed in which a Remote Sens. 2021, 13, 888 13 of 19

positive (negative) gradient state occurs between 2005 and 2009 and after 2016 (before 2003 and between 2010 and 2014). Only two years, 2004 and 2015, are characterized by a neutral state with the same number of months with anomalously large and small SST gradient magnitudes, or more than 50% of months are characterized as near-zero magnitude of SST gradient anomalies within a year. Thus, the switch between different states occurs in a rapid manner, and each state can be maintained for a few years. Comparing the time series with the previously identified stability of the KE [39], a clear dependence is identified between the stability of the KE and the SST gradient state. A positive (negative) anomaly in the SST gradient magnitude is generally associated with the unstable (stable) mode of the KE, and the gradient state leads by one or two years. Thus, the state of the SST gradient magnitude can be applied as an index to predict the interannual variability in the stability of the KE in the following years. The SST gradient states described above are used to investigate the changes in the KE during different states. The SST gradient, Chl-a, SLA and wind field are individually averaged in each pixel for the period when the anomaly of SST gradient magnitude is large, either with a positive or a negative value. The difference for each factor is obtained by subtracting the average during the period with a large negative gradient state from the average during the period with a large positive gradient state (Figure 10). A clear positive dependence can be observed between the SST gradient magnitude and Chl-a in the anomalous field (Figure8a). Thus, a predominantly positive SST gradient magnitude and Chl-a are found for the identified region (Figure 10a,b). Consistently, the negative dependence between SLA and Chl-a in the anomalous field (Figure8b) is revealed as the negative difference in SLA (Figure 10c). For all the features, the difference is much weaker in the western section, which is the meander of the Kuroshio Current, than in the eastern section, which is upstream of the KE. The wind speed is intensified over the identified region (Figure 10d) with a prominent northward component, although the wind speed actually decreases near shore and in the north. Noticeably, the difference in the SST gradient magnitude is found only within the identified region, but the difference in Chl-a, SLA and wind extends farther along the meander of the KE with a similar spatial pattern and is associated with a decrease in magnitude until fading occurs near 158◦ E. As the SST gradient state becomes positive and large upstream of the KE due to the increasing instability of the KE, the corresponding Chl-a and wind values increase, while the SLA decreases throughout the KE. Remote Sens. 2021, 13, 888 13 of 19

Figure 9. Gradient state based on the annual percentage of the anomaly in the SST gradient magnitude. Red (blue) dots indicate a high percentage of large positive (negative) anomalies in SST gradient magnitudes. The dashed gray line represents the percentage where the positive and negative SST gradient magnitudes are equal, with hollow dots indicating a neutral state. If more than 50% of the time in a year the month is characterized as a near-zero anomaly in SST gradient magnitude, the corresponding year is also defined as a neutral state.

The SST gradient states described above are used to investigate the changes in the KE during different states. The SST gradient, Chl-a, SLA and wind field are individually averaged in each pixel for the period when the anomaly of SST gradient magnitude is large, either with a positive or a negative value. The difference for each factor is obtained by subtracting the average during the period with a large negative gradient state from the average during the period with a large positive gradient state (Figure 10). A clear positive dependence can be observed between the SST gradient magnitude and Chl-a in the anomalous field (Figure 8a). Thus, a predominantly positive SST gradient magnitude and Chl-a are found for the identified

region (Figure 10a,bFigure 10a; Figure 10b). Consistently, the negative dependence between SLA and Chl-a in the anomalous field (Figure 8b) is revealed as the negative difference in SLA (Figure 10c). For all the features, the difference is much weaker in the western section, which is the meander of the Kuroshio Current, than in the eastern section, which is upstream of the KE. The wind speed is intensified over the identified region (Figure 10d) with a prominent northward component, although the wind speed actually decreases near shore and in the north. Noticeably, the difference in the SST gradient magnitude is found only within the identified region, but the difference in Chl-a, SLA and wind extends farther along the meander of the KE with a similar spatial pattern and is associated with a decrease in magnitude until fading occurs near 158° E. As the SST gradient state becomes positive and large upstream of the KE due to the Remote Sens. 2021, 13, 888 14 of 19 increasing instability of the KE, the corresponding Chl-a and wind values increase, while the SLA decreases throughout the KE.

Figure 10. 10. DifferencesDifferences in in the the (a ()a )SST SST gradient gradient magnitude, magnitude, (b ()b Chl-a,) Chl-a, (c) ( SLA,c) SLA, and and (d) (windd) wind speed speed overlaid with the wind vector between times whenwhen thethe SSTSST gradientgradient magnitudemagnitude isis abnormallyabnormally large. The ﬁelds are obtained by subtracting the monthly average when the anomaly in the SST gradient magnitude is largely negative from the time when the large anomaly in the SST gradient magnitude is largely positive. The region used to obtain the SST gradient magnitude is shown as the area enclosed by the dashed cyan lines in (a), which is consistent with the region delineated in Figure6.

4. Discussion The current study focuses on the KE region, in the northwestern Pacific Ocean at mid-latitudes, where the upper ocean is characterized as highly dynamic with prominent variability. The supply of nutrients via dynamic processes can enhance the growth of phytoplankton and subsequently increase Chl-a [57], in contrast to coastal regions where nutrients are persistently high [58]. The KE region is characterized by predominant seasonal variability in major features, e.g., Chl-a, SST, SST gradient and wind field. The seasonal cycle of different factors captures a clear dependence of Chl-a on regional oceanic dynamics. For instance, high Chl-a occurs in winter and spring when the deep MLD introduces nutrients from the subsurface into the euphotic zone, thereby enhancing the growth of phytoplankton [31]. In particular, the peak of Chl-a during spring (Figures3b and7a) is consistent with the spring bloom identified in previous studies, when shoaling of the MLD and increasing light intensity stimulate the growth of phytoplankton [59,60]. The corresponding wind is actually weak over the Kuroshio current and KE, indicating that wind is not a factor driving the increase in Chl-a [24]. A detailed investigation found that high Chl-a in the study area (Figure3) were initially identified in the west at 134◦ E in March and gradually migrated eastward to 150◦ E within one month (not shown), yielding a propagation speed of approximately 0.65 m/s, which is consistent with previous observations [61]. During the same period, the high SST gradient indicates active ocean dynamics, e.g., fronts and eddies, which can aggregate and transport nutrients in the upper ocean and subsequently increase Chl-a [37]. It is important to note that the monthly time series of the MLD leads that of Chl-a by two months and the monthly time series of the SST gradient lags that of Chl-a by one month (Figure7). Previous Remote Sens. 2021, 13, 888 15 of 19

studies have identified that the factors usually vary simultaneously [24], but in the KE, the seasonal cycles of the MLD and SST gradients are slightly different, and the subsequent variability in Chl-a varies between them (Figure7). This is likely due to the biological responses lagging the dynamic processes by several days, i.e., the time required for the growth of phytoplankton [62]. The SLA represents the vertical processes and a negative (positive) value is associated with local upwelling (downwelling) [27–29] that increases (decreases) the nutrient content in the upper ocean [31]. Consistently, Sasai et al. [33] found that cyclonic eddies, which are characterized by low SLAs [25], elevate high-nutrient subsurface water into the euphotic zone and result in high Chl-a even in summer; thus, a significant correlation is persistently found between SLA and Chl-a in anomalous fields (Figure8b). Most parameters are characterized by seasonal variability [63], which can always be correlated with each other with certain lags [64], although there is no valid underlying dynamic relationship. The anomalous field, which is calculated by removing the seasonal or annual cycles [15], is subsequently investigated because it can effectively increase the number of independent observations [65] to obtain a solid relationship. Although the entire KE region is dynamically energetic, only the captured zone is characterized by a significant correlation between the SST gradient and Chl-a in the anomalous field (Figure6). When upwelling or mixing is anomalously large [24], the SST gradient increases, the SLA decreases, and substantial nutrients are transported from the subsurface into the upper layer [66], leading to a simultaneous increase in Chl-a (Figure8). Indeed, the major marine features are dynamically bound to each other, and it is important to remove the seasonal cycle to reveal the underlying relationships. Prominent interannual variability is identified in the SST gradient, and the period with a large SST gradient indicates more instability during this period. An index of the SST gradient is defined in the current study based on the magnitude of the SST gradient anomaly over a year (Figure8). The determined period with a large or small gradient state (Figure9) is consistent with the stability of the KE identified in previous studies [ 32], but the change in gradient state leads by one to two years in the current study. Thus, the SST gradient state can be used as an indicator for forecasting the stability of the KE and predicting Chl-a in the region. A significant correlation is identified between the SST gradient index and annually averaged PDO [41], with the latter leading by two years, and the corresponding correlation coefficient equals 0.74. A significant correlation is also identified between the SST gradient index and annually averaged NPGO, with the NPGO leading by one year and a corresponding correlation coefficient of −0.62. This phenomenon reveals that the large-scale climatic pattern in the eastern Pacific Ocean can impact the northwestern Pacific Ocean, and the lag indicates the time required for the signal to propagate across the ocean basin [43]. There are other oceanic features that can be used to indicate regional dynamic processes, which are not incorporated in the current study. For example, the EKE is widely used to describe regional dynamics [67], where a high (low) EKE is identified during the period when the KE is unstable (stable) [38]. The EKE can be calculated using the geostrophic current, which is usually obtained using the field of SLAs [36]. A negative (positive) SLA may indicate a cyclonic (anticyclonic) eddy in the surrounding region if there is a closed SLA contour [28]. Thus, an eddy-induced EKE can be gauged using SLAs, and a large SLA, regardless of whether it is positive or negative, indicates strong eddy activity [25]. In the current study, we are particularly interested in cyclonic eddies because induced upwelling can transport nutrients and mix the subsurface Chl-a maximum (SCM) into the upper layer [29]. Because the number of cyclonic eddies is approximately consistent with that of anticyclonic eddies in the KE, a small SLA can represent few eddies of both polarities and low magnitude of the EKE in general, and vice versa [36]. Additionally, the wind stress is highly correlated with SST at the mesoscale, and high (low) SST is associated with strong (weak) winds [56], as identified in the current study (Figures3 and5). As wind blows perpendicular to the SST gradient, it generates Remote Sens. 2021, 13, 888 16 of 19

a wind stress curl [68] that can, in turn, supply feedback to the distribution of SST in the KE via Ekman pumping [69], leading to a fully coupled air-sea system [70]. Wind can impact the variability in Chl-a by Ekman pumping of subsurface water [24] and influence vertical mixing [71]. Corresponding information is incorporated in the seasonal cycle of MLD (Figure7b) and changes in the SST gradient anomaly (Figure8a), although a direct significant correlation between wind and Chl-a is not identified. Thus, the underlying dynamics of wind among other factors are related to those of the incorporated factors, and the effects of these other factors are already considered in the applied features. Remote sensing data effectively and accurately capture ocean surface features in dynamic regions [72]; however, subsurface information is not incorporated. For example, the fronts in the KE can extend to a depth of more than 300 m, where large anomalies can be identified in the temperature and other features [73,74]. Additionally, even in areas where Chl-a is low at the surface, a subsurface Chl-a bloom can be identified by a profiling float equipped with biogeochemical sensors [66,75]. Compared with surface Chl-a, the integrated Chl-a within the euphotic zone shows less prominent seasonal variability throughout the year [60]. Three-dimensional observations in the future could help more fully describe the physical and biological status of the ocean.

5. Conclusions Sixteen years of satellite observations of the KE are used to investigate the regional dynamics and their impact on modulating the variability in Chl-a. As a typical mid-latitude area, the seasonal cycle dominates the entire region, which is revealed by the distinctive seasonal pattern in Chl-a. The seasonal variability in Chl-a is simultaneously modulated by the horizontal and vertical structures of water masses, which are delineated by the SST gradient and MLD, respectively. The underlying influence of dynamic processes, e.g., fronts and eddies, on Chl-a is confirmed in the anomalous field in which a significant correlation between Chl-a and other factors is identified only upstream of the KE. The SST gradient for the area is used to indicate the dynamic status of the region and can further be used to predict the interannual variability in stability of KE and Chl-a. The current study offers a universally applicable method to investigate the impact of marine dynamics on Chl-a and other factors, and it can help assess the carbon cycle [76] and evaluate habitat for fishes and other high trophic level marine animals in global oceans.

Author Contributions: Visualization, funding, writing, Y.W.; investigation, data curation, R.T.; conceptualization, Y.Y.; review of the manuscript, F.J. All authors have read and agreed to the published version of the manuscript. Funding: The study was funded by the National Natural Science Foundation of China [nos. 41806026, 41730536 and 41890805], the Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR [no. SOEDZZ2102], the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University and Scientific Research Fund of the Second Institute of Oceanography, MNR [no. SL2020ZD201]. Data Availability Statement: Sea surface temperature (SST) and sea surface chlorophyll-a (Chl-a) datasets are available from National Aeronautics and Space Administration (NASA) [77,78], the sea level data is obtained from the E.U. Copernicus Marine Service (CMEMS) (https://resources.marine.copernicus. eu/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_ 008_046, last accessed on 16 October 2020), the ERA-Interim reanalysis product of wind is re-leased by the European Center for Medium-Range Weather Forecasts (ECMWF) (http://apps.ecmwf.int/ datasets/data/interim-full-daily/levtype=sfc/, last accessed on 16 October 2020) [79], and the dataset of World Ocean Atlas is accessed from the National Oceanic and Atmospheric Administration (NOAA) (http://www.nodc.noaa.gov/, last accessed on 16 October 2020) [51]. Acknowledgments: We are very thankful to the shared code for providing the colormap to generates the figures [80]. Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2021, 13, 888 17 of 19

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