Teleconnection Driven Winter Sea Surface Temperature regime shift and Ecosystem reorganization in the Western marginal Sea of the Northwest Pacific

Teleconnection Driven Winter Sea Surface Temperature regime shift and Ecosystem reorganization in the Western marginal Sea of the Northwest Pacific | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Teleconnection Driven Winter Sea Surface Temperature regime shift and Ecosystem reorganization in the Western marginal Sea of the Northwest Pacific Hae Kun Jung, Chun IL Lee, Hyo Keun Jang, In Seong Han, Huitae Joo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7614994/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The western marginal sea of the northwest Pacific has warmed rapidly in recent decades, yet the mechanisms of its recent reorganization remain unclear. Using 1990–2024 observations, we identify a winter sea-surface-temperature (SST) regime shift around 2015, supported by non-parametric change-point tests and the persistence of positive anomalies thereafter. Before 2015, SST variability reflected a balance between atmospheric forcing and East Korea Warm Current (EKWC) transport; afterward, oceanic processes EKWC intensification and a northward Kuroshio displacement—became dominant, concurrent with a meridional reorganization of the Aleutian Low. Despite stable chlorophyll-a, phytoplankton size structure shifted toward pico-dominance at the expense of nano-classes, consistent with enhanced stratification and altered nutrient pathways. These results show that teleconnection-driven boundary-current variability now governs both physical and biological states in this marginal sea, highlighting its role as a sentinel of basin-scale Pacific decadal variability and its relevance for inter-basin climate linkages and decadal prediction. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The Western marginal sea of the Norhwest Pacific (WNP) is a semi-enclosed marginal sea bordered by Korea, Japan, and Russia, situated at the interface between the northwestern Pacific and the Asian continent. Its hydroclimate is shaped by the combined influence of North Pacific atmospheric and oceanic circulation 1 , 2 . The Tsushima Warm Current (TWC) and East Korea Warm Current (EKWC), both branches of the Kuroshio Current, supply heat and salt and exert major control over Sea surface temperature (SST) variability, with more than 80% of the EKWC inflow in winter originating from Kuroshio waters 2 – 4 . On the atmospheric side, variability in the Siberian High (SH) and Aleutian Low (AL) influences stratification 5 , 6 , mixed layer depth 1 , 7 , and SST 5 – 7 , and these regional forcings are further modulated by large-scale modes such as the Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) 1 , 2 , 8 , 9 . In recent decades, the WNP has undergone rapid environmental change, with notable increases in ocean heat content (OHC), intensified stratification, and sustained SST warming 7 , 10 . Since 2015, the OHC accumulation rate has been nearly 9.3 times higher than the 67-year mean , coinciding with winter SST anomalies that reached up to + 1.5°C above average 7 . These physical changes are closely tied to variability in the AO, SH, AL, PDO, and the East Asian Winter Monsoon (EAWM). For example, during the positive AO phase, intensification of the polar vortex suppresses southward cold-air intrusion, weakening the SH by more than 20% and reducing surface wind speed by 1–2 m s⁻¹ 8,11,12 . This weakening reduces surface heat loss, thereby promoting anomalous winter warming. Over the past decade, the WNP has also experienced more frequent and persistent marine heatwaves, underscoring the vulnerability of this marginal sea to coupled climate forcing 13 . Beyond atmospheric drivers, ocean circulation dynamics exert a decisive role in shaping the WNP environment. PDO-related basin-scale adjustments intensify the AL, strengthen midlatitude westerlies, and shift the Kuroshio axis eastward in the East China Sea 2 , 9 , 14 . This shift reduces EKWC inflow into the WNP 2 , 3 , limiting heat and salt supply and altering stratification and mixed layer depth 15 . Such mechanisms highlight how basin-scale climate variability directly modulates WNP hydrography through both atmospheric and oceanic pathways. Historically, climate regime shifts (CRS) in the North Pacific such as those in 1976–1977, 1988–1989, and 1998–1999 have been accompanied by profound physical and ecological reorganizations 1 , 16 . A more recent CRS in the mid-2010s was characterized by a horseshoe-shaped warming pattern and extreme marine heatwaves of + 3–6°C across the North Pacific 17 , 18 , but its specific impacts on the WNP remain underexplored. These physical reorganizations have direct ecological implications. In the WNP, enhanced stratification has suppressed nutrient supply, contributing to long-term declines in primary production 19 – 21 . In the WNP, including the Ulleung Basin, primary production and phytoplankton community structure are tightly coupled to winter hydrography 22 . Notably, recent satellite and in situ observations have documented an expansion of pico-phytoplankton dominance across Korean seas 23 , consistent with warming-driven shoaling of the mixed layer that favors smaller taxa adapted to oligotrophic conditions 24 , 25 . These findings emphasize that changes in winter stratification and circulation may propagate into lower-trophic-level dynamics and alter ecosystem structure on seasonal to decadal scales. Despite these advances, most prior studies have been limited to correlations between individual climate indices and SST or have focused on specific short intervals 1 , 2 , 13 . Quantitative assessments of the relative contributions of multiple atmospheric and oceanic drivers remain scarce 8 , and few have considered how spatial shifts in key drivers such as the AL center or Kuroshio axis modulate SST variability 2 , 14 , 26 . In particular, comparative analyses of the mechanisms driving the post-2015 warming in the WNP versus earlier warm and cold regimes are lacking. Accordingly, this study investigates winter SST variability in the WNP from 1990 to 2024 using an integrated atmosphere–ocean–ecosystem framework. We assess whether a statistically significant regime shift occurred around 2015 and examine how the relative influence of atmospheric and oceanic drivers changed across this transition. We further explore whether comparable SST increases in different periods were governed by distinct physical mechanisms and evaluate whether these reorganizations propagated into phytoplankton community structure. By linking atmosphere–ocean variability with ecosystem responses, this study provides a comprehensive understanding of the causes and ecological consequences of recent environmental change in the WNP. Results Detection of a Winter SST Regime Shift and Associated Changes in Atmospheric and Oceanic Drivers in the WNP To statistically evaluate whether the WNP experienced a significant winter SST regime shift, we first applied Pettitt’s test and sequential Mann–Whitney U tests to annual mean SST anomalies from 1990 to 2024. Pettitt’s test suggested 2015 as the most probable shift year, although the p-value (p = 0.119) indicated marginal significance. The Mann–Whitney U tests, which compared SST distributions before and after each candidate year, yielded a more robust result: 2018 (p = 0.0019), 2016 (p = 0.0046), and 2015 (p = 0.0070) emerged as the three most statistically significant thresholds. Based on the convergence of independent non-parametric tests—Pettitt’s test favoring 2015 (p = 0.119) and sequential Mann–Whitney U tests highlighting 2015–2018 as candidate thresholds—and considering the broader North Pacific context of a basin-wide regime shift, we adopt 2015 as the working change point for subsequent analyses, supported by both statistical evidence and the immediate, persistent sign shift of anomalies thereafter (Fig. 1 ). Following the 2015 transition, the SST anomaly record showed remarkable persistence. Except for 2018, when anomalies briefly approached climatological values, all years from 2016 to 2024 exhibited positive winter SST anomalies relative to the 1990–2014 mean. The magnitude of anomalies ranged between + 0.3 and + 1.5°C, with 2019 and 2020 standing out as extreme warm winters. This indicates not only a one-time change but also a sustained reorganization of the thermal baseline of the WNP, consistent with the definition of a climatic regime shift(Fig. 1 ). To identify the mechanisms underlying this transition, we conducted multiple linear regression analyses using air temperature (AT), wind speed (WS), latent heat flux (LHF), and EKWC strength as explanatory variables, with SST as the response variable. Separate regressions were performed for the pre-shift (1990–2015) and post-shift (2016–2024) periods. Before 2015, the model explained 75.1% of SST variability (R² = 0.751, p < 0.05). Both AT (β = 0.529, p = 0.023) and EKWC (β = 1.709, p = 0.043) had statistically significant positive effects, suggesting that SST anomalies reflected a combined influence of atmospheric thermal forcing and oceanic heat advection. WS and LHF, despite showing expected signs, did not reach statistical significance (p > 0.5) (Table 1 ). Table 1 Multiple linear regression results for the effects of atmospheric and oceanic variables in SST in the WES before and after 2015. Period Variable β Coefficient Std. Error t-value p-value Sig. Before 2015 (R² = 0.75) AT 0.529 0.189 2.798 0.02 * WS 0.274 0.388 0.706 0.5 LHF -0.005 0.015 -0.334 0.75 EKWC 1.709 0.71 2.408 0.04 * After 2015 (R² = 0.86) AT -0.473 0.711 -0.665 0.54 WS -1.149 0.978 -1.175 0.29 LHF 0.078 0.035 2.257 0.07 + EKWC 4.177 1.997 2.092 0.09 + Significance level: p < 0.05 (*), p < 0.1 (+) and non significant (no symbol) After 2015, however, the explanatory power of the model increased further to 85.8% (R² = 0.858, p < 0.05). Interestingly, EKWC became the dominant predictor with a much larger coefficient (β = 4.177, p = 0.091), while AT not only lost significance but also switched to a negative coefficient (β = − 0.473, p = 0.535). LHF emerged as a marginal predictor (β = 0.078, p = 0.074), highlighting the role of air–sea turbulent flux processes in modulating SST after the shift. Together, these results demonstrate a transition from a balanced atmosphere–ocean control of SST before 2015 to an ocean-dominated regime afterward, with EKWC variability emerging as the primary driver (Table 1 ). Relative Contribution of Atmospheric (ΔT) and Oceanic (EKWC) Factors To quantify the relative roles of atmospheric and oceanic forcing, we restructured the regression model to include ΔT (SST minus AT, representing air–sea thermal contrast) and EKWC strength as predictors. During the pre-shift period (1990–2015), regression coefficients for ΔT and EKWC were 0.29 and 2.02, respectively, with an overall R² of 0.26. This indicates that only about a quarter of SST variability was explained, and both atmospheric and oceanic drivers contributed moderately(Fig. S1 ). In contrast, during the post-shift period (2016–2024), the coefficient for ΔT slightly declined to 0.23, whereas EKWC increased dramatically to 3.58, reflecting a ~ 77% strengthening in its apparent contribution. Despite the similar R² (0.23), the redistribution of regression weights suggests a structural shift toward ocean-dominance. A Z-test comparing EKWC coefficients across the two periods yielded Z = − 0.54 (p = 0.59), confirming that the change was not statistically significant given the short post-2016 record. Nevertheless, the consistent increase in magnitude and direction of EKWC’s coefficient across multiple analyses strongly suggests enhanced oceanic control (Fig. S1 ). Expanding correlation analyses reinforced these findings. The correlation between SST and EKWC remained positive and stable before 2015 but increased further afterward, reaching r > 0.6 in the early 2020s (Fig. S2). In contrast, correlations between SST and AT remained flat, while SST–ΔT correlations declined nearly to zero after 2015. Fisher’s Z-tests confirmed that none of these changes were individually significant at the 0.05 level, but the systematic pattern points to a reorganization of forcing structure. Importantly, standardized regression analyses showed that EKWC’s relative contribution increased from 61.9% in 1990–2015 to 70.0% in 2016–2024, while ΔT’s contribution fell from 38.1% to 29.9%. Although bootstrap validation indicated the differences were not statistically significant (p = 0.802), the directionality and consistency of results across methods emphasize the growing dominance of oceanic processes. Intensification and Atmospheric Linkages of the East Korea Warm Current The EKWC itself underwent substantial changes after the mid-2010s. A two-sample t-test confirmed a significant increase in mean EKWC strength after 2015 (p = 0.0018). This intensification was not only a local phenomenon but also coincided with persistent positive SST anomalies. The correlation between EKWC and SST across the full record was r = 0.564 (p = 0.0006), underscoring the tight coupling between ocean advection and regional thermal state (Fig. 2 ). Spatial correlation maps between EKWC intensity and mean sea level pressure (SLP) further revealed coherent atmospheric linkages. EKWC strength showed positive correlations with SLP over the Korean Peninsula and Kuroshio Extension, while exhibiting negative correlations over polar latitudes (Fig. 2 ). These patterns indicate that interannual variations in EKWC intensity are strongly influenced by mid-latitude pressure anomalies. For instance, during winters when the AL weakened and the SH retreated, pressure anomalies near Korea and the Kuroshio Extension (KE) region reinforced stronger northward transport of Kuroshio waters, enhancing EKWC inflow. This coupling highlights the joint operation of regional and basin-scale climate drivers in setting WNP conditions (Fig. 2 ). Meridional Shifts of the Kuroshio Current as a Key Driver Analysis of long-term Kuroshio Current indices revealed a significant northward migration of the mean axis after 2015 (p = 0.0016), even though current intensity did not undergo a regime shift (p = 0.138) (Fig. 2 ). Correlation analysis indicated that stronger flows tended to coincide with a more northerly axis (r = 0.563, p = 0.0008) (Fig. S3). Importantly, meridional shifts of the Kuroshio (MKC) were positively correlated with WNP SST (r = 0.355, p = 0.046), whereas Kuroshio Current Intensity (KCI) alone showed no meaningful relationship. This emphasizes that the geographical pathway of warm water transport, rather than the magnitude of transport, exerts greater influence on the WNP. SST Variability and Dominant Climate Drivers Across the full observational period, meridional shifts of the Aleutian Low (MIAL) showed the strongest relationship with SST (r = 0.4821) (Fig. S3). Regression models confirmed that combinations involving MIAL consistently explained the most variance. For instance, Arctic oscillation index (AOI)–MIAL achieved R² = 0.3261 (p = 0.00181), while Pacific Decadal Oscillation index (PDOI)–MIAL and North Pacific index(NPI)–MIAL also performed strongly. These results underscore MIAL’s persistent role as the most robust atmospheric predictor of WNP SST variability (Table S1 ). Period-specific analyses highlighted clear contrasts. From 1990–2015, the AOI, PDOI, and MIAL dominated, with the AOI–PDOI–MIAL model achieving R² = 0.4594 (p = 0.00317). This indicates that hemispheric-scale atmospheric circulation and decadal ocean–atmosphere variability jointly controlled SST variability during the earlier regime. After 2015, however, AOI, East Asian Winter Monsoon Index (EAWMI), and MIAL emerged as the key predictors. The AOI–EAWMI–MIAL combination explained R² = 0.4058, though limited by the short sample length. These shifts suggest a weakening of PDO influence and strengthening of seasonal atmospheric modes in the post-2015 regime (Table S1 ). Across both eras, MIAL remained central, demonstrating the fundamental importance of Aleutian Low meridional positioning. Atmospheric Pattern Transitions Across the SST Regime Shift To examine large-scale atmospheric linkages of WNP winter SST, we first computed spatial correlations between SST anomalies and Northern Hemisphere atmospheric fields for the full 1990–2024 period. SST correlated positively with SLP near Korea and the KE, but negatively east of the Aleutians, forming a zonal dipole. Correlations with wind speed revealed a latitudinal contrast, with negative values around 30° N—where weaker winds were linked to warming—and positive values north of 45° N (Fig. 1 ). To further clarify structural changes across the mid-2010s regime shift, we performed composite anomaly analyses for winters with above-normal WNP SST before and after 2015. Prior to 2015 (1990, 1992, 1995, 1998, 1999, 2000, 2002, 2004, 2007, 2008, 2009, hear after Group A), warm events were accompanied by widespread positive AT anomalies across Eurasia, including Siberia, reflecting a predominantly continental-scale warming pattern. WS anomalies displayed moderate weakening near 30–35° N and strengthening at higher latitudes, while SLP anomalies exhibited a clear east–west dipole across the Aleutians, consistent with longitudinal modulation of the AL (Fig. 3 ). After 2015 (2016, 2017, 2019, 2020, 2021, 2022, 2023, 2024, hear after Group B ), the AT anomaly pattern shifted toward an oceanic focus, with weaker anomalies over Siberia and stronger warming over lower-latitude marine regions, notably the East China Sea and the western subtropical Pacific. WS anomalies displayed a strengthened latitudinal dipole, with pronounced weakening in the 30–35° N band adjacent to the Korean Peninsula and enhanced strengthening near the Aleutians. SLP anomalies transitioned to a meridional structure, with reduced Arctic anomalies, lower pressures around the Aleutians, and higher pressures across the central North Pacific and the Korean Peninsula. This evolution from a zonal to a meridional anomaly configuration indicates a fundamental reorganization of atmospheric wave activity and air–sea coupling processes in the post-2015 regime (Fig. 3 ). Correlation Patterns Among Key Ocean–Atmosphere Variables Pearson correlation matrices confirmed robust associations among indices. EKWC (r = 0.56, p < 0.01) and MIAL (r = 0.47, p < 0.01) both strongly correlated with SST. MKC also correlated with SST (r = 0.35, p < 0.05) and was itself correlated with both EKWC and MIAL (r = 0.45 each), suggesting basin-scale coherence (Fig. S3). Expanding cumulative correlations highlighted temporal evolution. From 2000–2015, SST variability was tightly governed by atmospheric indices, especially AOI (r = 0.73, p = 0.002). After 2015, atmospheric influence collapsed: AOI showed no correlation (r = − 0.03), while EKWC strengthened dramatically, exceeding r = 0.65 after 2020. MKC also became significant in the most recent years. These results clearly indicate a regime shift in the mid-2010s from atmosphere-dominated to ocean-dominated control of SST (Fig S2). Long-term Variation of Chlorophyll-a and Phytoplankton Size Composition Annual mean surface chlorophyll-a (chl- a ) concentrations between 2003 and 2024 ranged from 0.57 to 1.00 mg m⁻³, with no significant trend. The mean values before and after 2015 (0.77 ± 0.11 vs. 0.77 ± 0.07 mg m⁻³) were statistically indistinguishable (p > 0.05). This suggests that total phytoplankton biomass has remained relatively stable despite substantial physical changes (Fig. 4 ). However, phytoplankton size class (PSC) revealed striking shifts. Pico-phytoplankton (pico) dominated areas increased steadily (slope = + 1.4% per year, r = 0.66, p < 0.01), while nano-phytoplankton (nano) areas declined symmetrically (slope = − 1.4% per year, r = 0.66, p < 0.01). Micro-phytoplankton (pico) remained nearly constant. Comparing pre- and post-2015, pico-dominated areas rose from 59.4% to 71.9% (+ 12.6%), while nano-dominated areas fell from 38.1% to 24.2% (–13.8%). These shifts in community structure, despite stable total chl- a , imply a profound reorganization of lower-trophic-level dynamics, likely linked to stratification and nutrient redistribution associated with intensified EKWC inflow and suppressed vertical mixing (Fig. 4 ). Regime-dependent phytoplankton–climate relationships During the before 2015, chl- a and micro exhibited significant positive correlations with the PDOI (chl- a : r = 0.65, p = 0.017; micro: r = 0.59, p = 0.033), while nanoand pico showed significant associations with the NPI, but with opposite directions (nano: r = − 0.56, p = 0.045; pico: r = 0.64, p = 0.017), suggesting that basin-scale climate variability was a dominant driver before 2015. In contrast, during the after 2015, these large-scale linkages weakened or disappeared; chl-a showed no significant association with any index, micro was significantly negatively correlated with LHF (r = − 0.70, p = 0.035), and nano was significantly negatively correlated with the EKWC (r = − 0.71, p = 0.032), while pico exhibited no significant correlations, indicating a transition toward regional oceanic and air–sea flux controls (Table S2). This regime-dependent shift in phytoplankton–climate coupling was confirmed by multivariate tests: PERMANOVA detected a significant difference between pre- and post-shift structures (F = 3.05, R² = 0.132, p = 0.007), and a Procrustes test comparing PCA loading structures also indicated a significant reorganization (M² = 0.845, p = 0.011; Table S3). Consistently, PCA biplots showed that phytoplankton variables clustered with PDOI and NPI in the pre-shift period but aligned more closely with SST, EKWC, and LHF in the post-shift period. Standardized multiple regression further quantified these shifts: in the pre-shift period, chl-a and micro were mainly explained by PDOI, while nano and pico were best accounted for by NPI, with moderate explanatory power across models (R² = 0.39–0.61); however, in the post-shift period, PDOI and NPI were no longer retained, and instead chl-a and micro were linked to LHF, AOI, and KCI, whereas nano and pico were explained by EKWC, SST, and MKC, with comparable model fits (R² = 0.57–0.61), confirming that phytoplankton variability became more sensitive to regional processes such as ocean currents and air–sea fluxes after 2015 (Table S4). Discussion The long-term variability of winter SST in the WNP is strongly governed by large-scale atmospheric and oceanic drivers. Among these, the AO and the meridional position of the MIAL consistently emerged as dominant predictors, underscoring their pivotal role in modulating interannual SST anomalies. Regression analyses repeatedly identified the AOI and MIAL as key explanatory variables, highlighting their fundamental contribution to regional SST variability 8 , 13 , 27 . This finding is consistent with earlier studies that link the AO to interannual–decadal SST changes and the occurrence of marine heatwaves (MHWs) in the Northwest Pacific 8 , 13 . During positive AO phases, anomalous anticyclonic wind stress curl develops over the WNP, driving surface convergence and Ekman downwelling, which suppresses the upward supply of cold subsurface waters 13 . This reduced entrainment allows anomalously warm waters to persist, thereby increasing the likelihood of MHWs 8 . In addition, AO-related modulation of the SH exerts a critical influence on East Asian winter monsoon (EAWM) strength 27 , 28 . Positive AO phases strengthen the polar vortex and confine cold air to high latitudes, weakening SH development, whereas negative AO phases weaken the vortex and promote southward cold-air intrusions, strengthening the SH and reinforcing cold advection into the WNP 8 . These AO–polar vortex transitions thus constitute a key dynamical pathway linking the SH, EAWM variability, and WNP SST. The AL plays a complementary role by regulating monsoon strength through its central position. When the AL shifts southward, SLP over the KE region decreases, enhancing the SLP gradient between the SH and KE and driving stronger northwesterly advection of cold, dry air from Siberia into the WNP 28 , 29 . Conversely, northward AL displacement weakens the pressure gradient, reduces cold-air advection, and favors warm SST anomalies 1 . Together, AO state transitions, SH intensity changes, and AL displacements strongly modulate winter monsoon dynamics and SST variability in the WNP. A regime shift in WNP SST was identified around 2015, after which winter SSTs remained predominantly above the climatological mean in nearly all years. This “WNP regime shift” coincided with basin-scale atmospheric–oceanic reorganizations, including strengthening of the East Korea Warm Current (EKWC), northward migration of the Kuroshio axis, and displacement of the AL. The dominant drivers of SST also shifted: before 2015, the AO, MIAL, and PDO exerted primary influence, whereas after 2015, AO, meridional AL position, and the EAWM index emerged as key explanatory factors 30 , 31 . These results are consistent with the broader framework of North Pacific climate regime shifts (CRS), which have historically occurred in 1976–77, 1988–89, and 1998–99 16,32 . Earlier CRS were largely PDO-centered, accompanied by AL intensity shifts and Pacific–North American (PNA) teleconnection patterns 33 – 37 . PDO variability, as the leading EOF of North Pacific SST anomalies, explains ~ 25% of variance and strongly couples with AL intensity via ENSO–PNA linkages 33 , 34 . Intensification of the AL during positive PDO phases enhances central North Pacific wind stress curl, alters thermocline depth, and generates westward-propagating Rossby waves, ultimately modulating Kuroshio Current (KC) strength and KE position 38 – 40 . By contrast, the post-2015 CRS was distinct, reflecting a departure from PDO-dominated variability and a growing dominance of the Victoria Mode (VM) 17 , 34 , 41 , 42 . The VM, as the second EOF of North Pacific SST anomalies, accounts for ~ 12–15% of variance and exhibits a subtropical–midlatitude dipole structure 34 . Its variability is closely linked to the West Pacific (WP) teleconnection, characterized by an anomalous high over the western subtropics and a low over the high-latitude North Pacific 37 . Sustained VM activity drives north–south AL displacements, reorganizes wind stress curl, and produces thermocline changes that propagate to the KE via westward Rossby wave transmission 36 , 38 – 40 . Unlike the PDO, which primarily governs KC intensity through AL strength, the VM directly and sensitively controls KC meridional position 31 , 41 . Our findings indicate that after 2015, even without strong AL intensification, a northward AL shift alone was sufficient to reorganize wind stress curl patterns, deepen the thermocline, and displace the KC and KE northward 39 , 40 . These results suggest a structural transition in WNP variability from PDO-driven to VM-driven forcing. The strengthened role of oceanic processes is further supported by satellite-derived MHW analyses, which revealed that cumulative MHW intensity in the WNP increased by 29.6°C·days per decade from 1982 to 2020, more than twice the global mean rate 43 . These trends reflect enhanced contributions from EKWC and Kuroshio variability rather than surface fluxes alone. Indeed, regression results show that after 2015, the effect of EKWC on winter SST was much stronger than that of air temperature, pointing to an ocean-dominated regime. This interpretation is consistent with long-term heat budget analyses demonstrating that in boundary current regions, horizontal and vertical heat advection dominates SST variability, while air–sea fluxes often damp anomalies rather than generate them 44 – 46 . For the EKWC, enhanced advection redistributes heat along its pathway, producing regional contrasts in SST trends: acceleration in some regions, suppression in others 47 . These findings indicate an increasing role of current-driven variability for SST trends in the WNP. The role of atmospheric modes after 2015 should not be overlooked. AO spatial patterns weakened during warm years, which may be linked to the quasi-biennial oscillation (QBO). Stratospheric conditions associated with QBO phases modulate polar vortex strength and planetary wave propagation, thereby influencing the AO–AL connection 48 . During westerly QBO phases, stronger polar vortices enhance AO–AL teleconnections, while easterly phases weaken them. Thus, the reduced AO influence on SST variability after 2015 may partly reflect stratospheric modulation. Nevertheless, ocean circulation changes, particularly EKWC strengthening, emerged as the dominant factor explaining post-2015 SST anomalies. The ecological implications of this structural shift are significant. In the WNP, where terrestrial inputs are minimal, winter hydrography and stratification strongly precondition nutrient availability and spring bloom dynamics 49 , 50 . Enhanced stratification under warming shoals the mixed layer, suppresses nutrient entrainment, and shifts phytoplankton composition toward smaller size classes adapted to oligotrophic conditions 24 , 25 , 51 . Our analysis revealed that while long-term chl-a biomass exhibited no significant trend during 2003–2024, phytoplankton size composition reorganized: pico-dominant areas expanded while nano dominance declined, especially after 2015. These patterns align with global reports of smaller phytoplankton dominance under warming 52 – 54 and with PSC-based analyses in Korean waters showing expansion of pico-dominant areas in the WNP 22 , 23 , 55 . The mechanistic linkage between SST-driven stratification and nutrient redistribution has been further clarified by recent studies. Jung et al. (2025) demonstrated that post-2015 winter stratification intensification in the Northwest Pacific was primarily driven by rising SST, which steepened vertical density gradients and enhanced nitrate accumulation below the pycnocline 10 . This suggests that surface warming not only raises SST but also reorganizes subsurface nutrient reservoirs, constraining upward flux and altering pre-bloom conditions. Our findings are consistent with these insights suggesting that enhanced EKWC transport coincided with PSC reorganization, indicating that winter hydrographic shifts act as precursors shaping lower-trophic-level dynamics. While multiple lines of evidence support a post-2015 transition, short post-shift samples limit formal detection power. We mitigated this with robust tests and persistence diagnostics, but future work should extend the record, incorporate ocean reanalyses for full heat-budget closure, and explore hierarchical or Bayesian change-point frameworks to jointly estimate timing and driver weights. In summary, the 2015 regime shift in the WNP represents a transition from atmosphere-dominated to ocean-dominated forcing of winter SST variability. Before 2015, SST anomalies were primarily linked to AO and PDO driven atmospheric circulation, whereas after 2015, VM-driven meridional shifts of AL and EKWC intensification became the principal controls. These physical changes enhanced winter stratification, altered nutrient pathways, and reshaped phytoplankton community structure. The WNP thus exemplifies how coupled atmosphere–ocean reorganizations in the North Pacific propagate into boundary current systems and ultimately into ecosystem dynamics. Sustained, long-term monitoring of stratification and phytoplankton size composition will be essential to assess ecosystem resilience under continued climate change. More broadly, the identified regime shift in the WNP reflects basin-scale Pacific decadal reorganization that is consistent with known teleconnections involving the PDO and AL, with potential implications for cross-basin climate variability. These results suggest the potential relevance of marginal sea processes in the northwest Pacific to Pacific–Atlantic inter-basin linkages, underscoring the need to incorporate such regional dynamics into decadal climate prediction frameworks. Despite these findings, several limitations should be acknowledged. First, the post-regime-shift period (2016–2024) is relatively short, which constrains the statistical power to detect structural changes; however, the observed trends remain consistent with our hypotheses and conceptual framework. Second, the ecological responses were inferred from phytoplankton size-class composition derived from satellite observations, which may not fully capture in situ community dynamics. Previous studies, however, have demonstrated significant correspondence between satellite-derived size classes and field measurements, lending confidence to this approach. Third, ecosystem changes in the WNP were assessed mainly in relation to surface SST, whereas ecological restructuring cannot be fully explained by upper-ocean thermal variability alone. Future studies integrating water-column structure, nutrient dynamics, and primary production from field observations will be required to better evaluate the mechanisms of ecosystem change. Conclusion Our results reveal a statistically significant winter SST regime shift in the WNP around 2015, after which anomalies remained persistently positive. Before 2015, SST variability reflected a balance between atmospheric forcing and oceanic advection, whereas after 2015, oceanic processes—particularly EKWC intensification and a northward Kuroshio displacement—became dominant. This transition marks a shift from PDO- to Victoria Mode–driven teleconnections, reorganizing the AL and altering regional air–sea coupling. Despite stable chl- a biomass, phytoplankton communities underwent structural reorganization toward pico-dominance, consistent with enhanced stratification and nutrient redistribution. Collectively, these findings demonstrate that teleconnection-driven ocean circulation now governs both physical and ecological states in this marginal sea, underscoring its sentinel role for Pacific decadal variability and the importance of sustained monitoring for prediction and ecosystem resilience. Data and Methods Sea Surface Temperature (SST) Data SST data were obtained from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) product developed by the UK Met Office. OSTIA provides daily global-scale SST and sea ice fields on a 0.05° latitude–longitude grid, and the dataset is reanalyzed for public use based on field observations from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS). These high-resolution foundation SST fields combine satellite observations and in situ measurements through optimal interpolation techniques. In this study, daily SST values were averaged into monthly means, and then aggregated to generate winter seasonal averages, defined as the mean of January, February, and March (JFM) for each year from 1990 to 2024. Spatially, SST data were extracted from a subregion of the WNP, bounded by 36°–38°N latitude and 127°–131°E longitude (Fig. 6 ). This domain was selected to characterize wintertime surface thermal conditions in the nearshore environment off the Korean Peninsula. Atmospheric Variables in north hemisphere To characterize large-scale atmospheric conditions influencing wintertime SST variability in the WNP, we used three key surface variables from the ERA5 reanalysis dataset: 2 m air temperature (T2M), 10 m wind speed (U10, V10), and SLP. All variables were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 monthly averaged data on a 0.25° × 0.25° horizontal resolution grid. For each variable, we extracted data over the Northern Hemisphere (20°–90°N) and computed seasonal winter means by averaging monthly values from December, January, and February (DJF) for each year during the 1990–2024 period. The resulting 35-year winter climatology was used in spatial correlation analyses and compositing to assess large-scale atmospheric patterns associated with interannual SST anomalies in the WNP. Latent Heat Flux (LHF) Data To estimate wintertime LHF, we used hourly ocean buoy observations from the Korea Meteorological Administration (KMA) Open Data Portal ( https://data.kma.go.kr/ ) for the WNP. The dataset included wind speed, sea-level pressure, relative humidity, air temperature, and SST from January to March for the period 2002–2024(Fig. 6 ). LHF (W m⁻²) was calculated using the bulk aerodynamic formula: $$\:{Q}_{L}\:=\:{\rho\:}_{a}\:{L}_{v}\:{C}_{E}\:U\:({q}_{s}\:-\:{q}_{a})$$ where ρ_a is the air density (kg m⁻³), L_v is the latent heat of vaporization (J kg⁻¹), C_E is the bulk transfer coefficient for moisture (dimensionless), U is the wind speed (m s⁻¹), q_s is the saturation specific humidity at the sea surface, and q_a is the specific humidity of the overlying air. Hourly flux values were averaged to produce seasonal means for subsequent analysis. Climate index To investigate the climatic drivers of SST variability in the WNP, we selected five large-scale climate indices commonly used in studies of North Pacific climate variability: the AOI, PDOI, NPI, EAWMI, and SHI. These indices represent major modes of atmospheric circulation and pressure systems that influence regional oceanographic conditions such as SST, SLP, and winter monsoon strength 29 . The AOI represents the leading mode of sea level pressure variability in the Northern Hemisphere (20°–90°N, 180°E–180°W) and is indicative of the strength of the polar vortex and the westerly jet 56 . The PDOI is a long-term mode of Pacific SST variability, calculated as the leading principal component of monthly SST anomalies in the North Pacific Ocean poleward of 20°N, after removing the global mean SST anomaly 9 . The NPI is defined as the area-weighted mean SLP over the region 30°–65°N and 160°E–140°W and is used as an index for the strength of the AL 33 . The EAWMI is calculated from zonal wind speeds at 300 hPa over East Asia (27.5°–37.5°N, 110°–170°E) and serves as a proxy for the intensity of the East Asian winter monsoon 27 . Finally, the SHI quantifies the strength of the SH based on winter mean SLP averaged over continental Asia (40°–60°N, 70°–120°E), reflecting continental cooling and its associated anticyclonic system 57 . Detection of Kuroshio current intensity and latitudinal shifit To assess both the intensity and meridional displacement of the Kuroshio Current over the period 1993–2024, we utilized delayed-time satellite altimetry products provided by AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic data), which include gridded fields of absolute dynamic topography (ADT) and geostrophic surface velocities (ugos and vgos). The data were retrieved at a spatial resolution of 1/4° and daily temporal resolution, and then averaged over the winter season (January to March) to construct annual mean fields. The domain selected for analysis spans 30°–40°N latitude and 140°–160°E longitude, encompassing the eastward-flowing section of the Kuroshio east of Japan. Current intensity was quantified by computing the geostrophic speed as the magnitude of the horizontal velocity vector: where u and v represent the eastward (ugos) and northward (vgos) components of the surface velocity, respectively. To isolate the Kuroshio jet core, a dynamic topography threshold was applied, retaining only those grid cells where ADT ranged from 0.9 to 1.0 meters, consistent with the typical elevation of the current axis (Fig. 6 ). Within each longitudinal grid column, the location of the maximum geostrophic speed within this ADT band was extracted, and the corresponding latitude was recorded as the local center of the current. Averaging these values zonally provided an estimate of the annual mean MKC. Likewise, the geostrophic speed at these core locations was averaged to produce a yearly KCI. East Korea Warm Current Intensity The intensity of the EKWC was quantified as the wintertime volume transport through the western channel of the Korea Strait. Following the method described by Lee et al. (2022), the volume transport (V) was estimated using sea level differences between Izuhara, Japan and Busan, South Korea, based on daily tide gauge records provided by the Japan Meteorological Agency (JMA) and the Korea Hydrographic and Oceanographic Agency (KHOA) (Fig. 6 ). To represent winter conditions, monthly mean sea level values for December, January, and February were averaged to compute a seasonal mean for each year from 1992 to 2024. The geostrophic volume transport was calculated using the following formula: $$\:V\:=\:1/f/\rho\:*\varDelta\:p/\varDelta\:x$$ 1 where V is the volume transport (hm 3 /s), f is the Coriolis force, ρ is the density of seawater (kg/m 3 ), Δp is the pressure difference between each tidal station (hPa), and Δx is the distance between each tidal station (51.17 km) Detection of the Meridional and Zonal Position of the Aleutian Low To estimate the meridional position of the AL in boreal winter, we used monthly SLP fields from the ERA5 reanalysis dataset provided by ECMWF, covering the period from 1990 to 2024. Winter averages were constructed using data from December of the previous year and January–February of the current year, resulting in seasonal DJF means. From the full SLP field, we selected a North Pacific domain spanning 30°N–60°N and 150°E–210°E, which encompasses the typical range of the AL. Within this region, we extracted all grid points where MSLP was less than or equal to 1005 hPa, a threshold commonly used to represent the AL core 37 , 58 . Among these grid points, we selected the lowest 5% of MSLP values, which best capture the core low-pressure center of the AL while minimizing the influence of surrounding synoptic systems. For each year, the mean latitude and longitude of these lowest-pressure grid cells were calculated to define the annual center position of the AL. The mean latitude was used as the MIAL, and the mean longitude as the zonal position index of AL (ZIAL). This approach is consistent with prior methodologies and provides a robust, objective representation of the interannual shift in the AL's geographic location. Identifying Climatic Drivers of SST Variability Before and After 2015 To investigate whether the dominant climatic drivers of SST variability in the WNP changed across the regime shift year, a period-specific analytical framework was employed. Based on the detection of 2015 as a statistically significant SST change point, the full observation period (1990–2024) was divided into two subperiods: pre-shift (1990–2015; Group A) and post-shift (2016–2024; Group B). A total of seven large-scale climate indices were selected as candidate predictors representing distinct atmospheric systems and modes of variability: AOI, EAWMI, NPI, PDOI, SHI, MIAL, ZIAL. These indices were chosen to capture polar teleconnections, mid-latitude pressure variability, and decadal-scale ocean–atmosphere interactions across the North Pacific. For each subperiod and the full period, Pearson correlation analysis was performed to assess the linear relationships between annual SST anomalies and each climate index. Only statistically significant correlations (p < 0.05) were retained for interpretation. To quantify the joint influence of multiple climate indices on SST variability, multiple linear regression models were constructed with SST as the dependent variable. All possible combinations of two to six predictors were tested. Model performance was evaluated using adjusted coefficients of determination (adjusted R²) and significance levels (p-values). This step enabled identification of optimal predictor sets and assessment of explanatory power across different levels of model complexity. To examine temporal shifts in the role of individual indices, interperiod comparisons of regression coefficients were conducted using Z-tests for independent estimates. In addition, expanding window correlation analysis was employed to visualize the evolving strength of relationships between SST and its potential drivers throughout the study period. Fisher’s Z-transformation was applied to test for statistically significant differences in correlation coefficients before and after 2015 (Fig. 4 ). Satellite data and Phytoplankton Size Classes(PSCs) algorithm The PSC algorithm used in the WNP was based on a deep neural network (DNN) model developed by Kang et al. (2022) 59 and further applied to analyze the long-term patterns of PSCs in the littoral seas of Korea by Jang et al. (2025) 23 . The model architecture consisted of eight fully connected layers. Input variables included SST, Total Suspended Solids (TSS), and total chl- a concentration, while output variables were the fractional contributions of micro (> 20 µm), nano (2–20 µm), and pico (< 2 µm) for each pixel. To obtain the input data for the PSC algorithm, satellite-based ocean color data were acquired from MODIS-Aqua Level-3 products (4 km × 4 km resolution) provided by the NASA Goddard Space Flight Center ( https://oceandata.sci.gsfc.nasa.gov/ ). We used monthly mean SST, chl- a , and remote sensing reflectance at 555 nm (Rrs555). TSS was not directly available from NOAA, so it was estimated using an empirical algorithm specifically designed and validated for Korean waters 60 . Rrs555 was used due to its strong sensitivity to water quality parameters and its widespread application in regional optical water quality assessments 60 . The analysis focused on the region between 36°–38°N and 127°–131°E. For PSCs, the annual variability was derived from monthly data (2003–2024) by determining the most frequent (mode) size class for each pixel and then calculating the proportion of each class within the defined spatial domain. For chl- a , annual means were computed from the corresponding monthly data over the same period. Map generation Figures 1 – 3 were generated using MATLAB R2023b (MathWorks, Natick, MA, USA; https://www.mathworks.com/ ) with built-in coastline data from the Mapping Toolbox. Figure 6 was generated using Ocean Data View (ODV, version 5.7.0; https://odv.awi.de ). Declarations Author contributions statement H.K.J. Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing the original draft. H.J. Data curation, Formal analysis, Investigation, Writing the original draft. H.K.J. Visualization, Writing the original draft. C.I.L. Investigation, Visualization. I.S.H. Data curation, Funding acquisition, Methodology, Project administration. Competing interests The authors declare no competing interests. Funding This research was supported by the National Institute of Fisheries Science, Ministry of Oceans and Fisheries, Korea (R2025014). Data availability statement All datasets used in this study are publicly available from open-access repositories and institutional portals. Sea surface temperature (SST): OSTIA product from the Copernicus Marine Service (https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001/description). Atmospheric variables (U/V wind components, sea-level pressure, air temperature): ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). Latent heat flux (LHF): Hourly ocean buoy observations from the Korea Meteorological Administration (KMA) Open Data Portal (https://data.kma.go.kr/). Climate indices: Arctic Oscillation Index (AOI; https://www.ncei.noaa.gov/access/monitoring/ao/), Pacific Decadal Oscillation Index (PDOI; https://www.ncei.noaa.gov/access/monitoring/pdo/), North Pacific Index (NPI; https://psl.noaa.gov/data/timeseries/month/NP/), and East Asian Winter Monsoon Index (EAWMI) and Siberian High Index (SHI) derived from ERA5 sea-level pressure fields. Absolute dynamic topography and geostrophic currents: AVISO absolute dynamic topography and U/V geostrophic currents from the Copernicus Marine Service (https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057/services). East Korea Warm Current (EKWC): Calculated from sea-level differences between Izuhara (Japan Meteorological Agency; https://www.jma.go.jp/) and the Korea Hydrographic and Oceanographic Agency (KHOA; https://www.khoa.go.kr/oceangrid/koofs/kor/observation/obs_real.do). Chlorophyll-a, SST, and Rrs555 for PSC algorithm: MODIS-Aqua Level-3 products provided by the NASA Goddard Space Flight Center, Ocean Biology Processing Group (OBPG; https://oceandata.sci.gsfc.nasa.gov/). Additional information Supplementary Information The online version contains supplementary material available at [insert DOI link once assigned]. Correspondence and requests for materials Correspondence and requests for materials should be addressed to H.K.J. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7614994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":528354193,"identity":"d85a8646-1282-4f1d-a05f-2c7e94bfe28d","order_by":0,"name":"Hae Kun Jung","email":"","orcid":"","institution":"National Institute of Fisheries Science","correspondingAuthor":false,"prefix":"","firstName":"Hae","middleName":"Kun","lastName":"Jung","suffix":""},{"id":528354194,"identity":"26127736-8f30-4eb7-ba9a-26151f081d15","order_by":1,"name":"Chun IL Lee","email":"","orcid":"","institution":"Gangneung-Wonju National 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Joo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBACAwbGBiBlYwDhHiBOSyNQTxpJWsDWHCZBi7lEcvuDHxXnjfmlDx/8wHDmHmEtljMSGxt7ztw2k+xLS5ZguFFMhMNuJDY28LbdtjE4w2MgwfAhgTgtjX//nQNq4f/8g2gtzbwNB8yAtrABHUaMljMPG2fLHEs2luxhM7NIOEOMluPpDz6+qbEz7OdhfnzjwzEitKACkjWMglEwCkbBKMAOAAQ2PrAXm0sqAAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Fisheries Science","correspondingAuthor":true,"prefix":"","firstName":"Huitae","middleName":"","lastName":"Joo","suffix":""}],"badges":[],"createdAt":"2025-09-15 00:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7614994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7614994/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93600539,"identity":"de711428-faaa-4fb6-bd32-c4450ea7feda","added_by":"auto","created_at":"2025-10-15 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14:43:42","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161800,"visible":true,"origin":"","legend":"","description":"","filename":"f7913957411a47af8ecefb8315d48ae91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/ab5118cfe7fcc462263bf7a8.xml"},{"id":93600536,"identity":"ae7de6d7-cf51-41a0-9d57-cf48ac8739c9","added_by":"auto","created_at":"2025-10-15 14:44:09","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173735,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/3b8e795d76cb31b3744b7b2b.html"},{"id":93600504,"identity":"3015be4c-07f7-4598-ba72-534b023fff8a","added_by":"auto","created_at":"2025-10-15 14:43:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":395826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAtmospheric reorganization associated with the winter SST regime shift in the Western marginal sea of the Northwest Pacific (WNP). \u003c/strong\u003e(a) Winter SST anomalies relative to the 1990–2024 climatology, showing the persistent positive anomalies after 2015. (b) Cumulative anomalies highlighting the abrupt baseline shift around 2015 and the persistence of positive values thereafter. (c–e) Correlation maps between WNP winter SST and large-scale atmospheric fields for 1990–2024: (c) sea level pressure, (d) wind speed, and (e) near-surface air temperature. Red contours mark statistically significant regions (p \u0026lt; 0.05). The blue polygon denotes the North Pacific region used for correlation analysis. Maps were generated using MATLAB R2024b(MathWorks, Natick, MA, USA; https://www.mathworks.com/)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/ae424dd2a7c617c9c998a6bf.png"},{"id":93600534,"identity":"365961a0-f1e2-42fe-9c46-f32a4ffb933c","added_by":"auto","created_at":"2025-10-15 14:44:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240555,"visible":true,"origin":"","legend":"\u003cp\u003eTeleconnection-driven variability of the East Korea Warm Current (EKWC) and the Kuroshio Current.\u003cstrong\u003e \u003c/strong\u003e(a) Time series of EKWC anomalies during 1990–2024, showing a significant intensification after the mid-2010s. (b) Correlation map between EKWC strength and winter SLP over the Northern Hemisphere. Red contours mark statistically significant regions (p \u0026lt; 0.05). The blue polygon outlines the North Pacific domain, highlighting the role of mid-latitude atmospheric teleconnections in modulating EKWC transport. (c) Long-term variability of the meridional position of the Kuroshio Current axis (MKC), indicating a significant northward shift after 2015. (d) Time series of Kuroshio Current intensity (KCI), which exhibits interannual fluctuations but no regime shift. Maps were generated using MATLAB R2024b (MathWorks, Natick, MA, USA; https://www.mathworks.com/).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/4ae06f417b0a236ad0b63123.png"},{"id":93600501,"identity":"51acf241-4195-4825-9f8e-b0693805cab3","added_by":"auto","created_at":"2025-10-15 14:43:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":540064,"visible":true,"origin":"","legend":"\u003cp\u003eAtmospheric anomalies associated with warm winters before and after the SST regime shift. (a–c) Composite anomalies for warm years before 2015 (1990, 1992, 1995, 1998, 1999, 2000, 2002, 2004, 2007, 2008, 2009): (a) SLP, (b) AT, (c) WS. (d–f) Same fields for warm years after 2015 (2016, 2017, 2019, 2020, 2021, 2022, 2023, 2024). Red (blue) shading indicates positive (negative) anomalies relative to the 1990–2024 climatology. The blue polygon outlines the North Pacific domain used for SST averaging. Maps were generated using MATLAB R2024b (MathWorks, Natick, MA, USA; https://www.mathworks.com/).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/b705c22876241d489cfeebc8.png"},{"id":93600540,"identity":"aed9705d-89a2-4fd6-b341-f092f2ad303f","added_by":"auto","created_at":"2025-10-15 14:44:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":289649,"visible":true,"origin":"","legend":"\u003cp\u003eAnomalies in annual mean chl-\u003cem\u003ea\u003c/em\u003e (a) and in the annual mean proportion of areas dominated by micro (b), nano (c), and pico size (d) phytoplankton in the WES (36–38ºN, 127–131ºE) during 2003–2024. Pannels (e-f) show the spatial distribution of dominant phytoplankton areas, with micro- and nano-size groups in blue and pico-size groups in red, for 2003–2015 (e) and 2016–2024 (f). Maps were generated using MATLAB R2023b (MathWorks, Natick, MA, USA; https://www.mathworks.com/).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/f417e69552a437df12deb967.png"},{"id":93600517,"identity":"3128af9a-fe2c-4a69-8281-11739de285b6","added_by":"auto","created_at":"2025-10-15 14:44:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":375566,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual schematic of the winter SST regime shift in the Northwest Pacific (WNP). Schematic summary of atmosphere–ocean interactions before (top, PDO-mode dominant) and after (bottom, Victoria Mode–dominant) the mid-2010s SST regime shift. During the pre-2015 period, positive Arctic Oscillation (AO) phases were clearly linked to Eurasian warming, an eastward-displaced and weakened Aleutian Low (AL), and weaker atmospheric forcing relative to oceanic influences, resulting in modest EKWC anomalies. In contrast, the post-2015 regime was characterized by unclear AO influence, a westward-shifted but strong AL, enhanced meridional SLP anomalies, and strengthened oceanic forcing via the EKWC. These conditions produced sustained SST warming in the WNP. The inset boxplot compares winter SST anomalies before and after, confirming a statistically significant upward shift in the thermal baseline.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/c6374276e3cb403dce11b5af.png"},{"id":93600505,"identity":"d5c49c0a-e887-47ed-9f73-6237402d9d78","added_by":"auto","created_at":"2025-10-15 14:43:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":213228,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and major ocean currents in the Western marginal sea of the Northwest Pacific (WNP) and surrounding regions generated using Ocean Data View (ODV, version 5.7.0; https://odv.awi.de). Panel (a) denotes the WNP, including the analysis domain (black box, 36°–38° N, 127°–131° E) and the main pathway of the East Korea Warm Current (EKWC). Panel (b) denotes the Kuroshio Extension region east of Japan. Locations of buoy stations (red triangles) and tide gauge stations (black circles) used in this study are indicated. The broader North Pacific context, including the Tsushima Warm Current (TWC) and the Kuroshio Current pathway in the East China Sea, is also shown.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/d8554295929b30b507a06579.png"},{"id":93601489,"identity":"8b49f5a5-8320-48f6-945f-5d1bb6a5e4ae","added_by":"auto","created_at":"2025-10-15 14:47:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3480542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/cb6295b2-c911-41f0-b6fd-2d51ab8c73ef.pdf"},{"id":93600414,"identity":"f4b5bcdf-eb9a-41f6-944d-9fb9ce998ff8","added_by":"auto","created_at":"2025-10-15 14:43:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":482411,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7614994/v1/a48f1bcbe22728a89c337554.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Teleconnection Driven Winter Sea Surface Temperature regime shift and Ecosystem reorganization in the Western marginal Sea of the Northwest Pacific","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Western marginal sea of the Norhwest Pacific (WNP) is a semi-enclosed marginal sea bordered by Korea, Japan, and Russia, situated at the interface between the northwestern Pacific and the Asian continent. Its hydroclimate is shaped by the combined influence of North Pacific atmospheric and oceanic circulation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The Tsushima Warm Current (TWC) and East Korea Warm Current (EKWC), both branches of the Kuroshio Current, supply heat and salt and exert major control over Sea surface temperature (SST) variability, with more than 80% of the EKWC inflow in winter originating from Kuroshio waters\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. On the atmospheric side, variability in the Siberian High (SH) and Aleutian Low (AL) influences stratification\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, mixed layer depth\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and SST\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and these regional forcings are further modulated by large-scale modes such as the Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent decades, the WNP has undergone rapid environmental change, with notable increases in ocean heat content (OHC), intensified stratification, and sustained SST warming\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Since 2015, the OHC accumulation rate has been nearly \u003cb\u003e9.3 times higher than the 67-year mean\u003c/b\u003e, coinciding with winter SST anomalies that reached up to \u003cb\u003e+\u0026thinsp;1.5\u0026deg;C above average\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These physical changes are closely tied to variability in the AO, SH, AL, PDO, and the East Asian Winter Monsoon (EAWM). For example, during the positive AO phase, intensification of the polar vortex suppresses southward cold-air intrusion, weakening the SH by more than 20% and reducing surface wind speed by 1\u0026ndash;2 m s⁻\u0026sup1;\u003csup\u003e8,11,12\u003c/sup\u003e. This weakening reduces surface heat loss, thereby promoting anomalous winter warming. Over the past decade, the WNP has also experienced more frequent and persistent marine heatwaves, underscoring the vulnerability of this marginal sea to coupled climate forcing\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBeyond atmospheric drivers, ocean circulation dynamics exert a decisive role in shaping the WNP environment. PDO-related basin-scale adjustments intensify the AL, strengthen midlatitude westerlies, and shift the Kuroshio axis eastward in the East China Sea\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This shift reduces EKWC inflow into the WNP\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, limiting heat and salt supply and altering stratification and mixed layer depth\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Such mechanisms highlight how basin-scale climate variability directly modulates WNP hydrography through both atmospheric and oceanic pathways. Historically, \u003cb\u003eclimate regime shifts (CRS)\u003c/b\u003e in the North Pacific such as those in 1976\u0026ndash;1977, 1988\u0026ndash;1989, and 1998\u0026ndash;1999 have been accompanied by profound physical and ecological reorganizations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. A more recent CRS in the mid-2010s was characterized by a horseshoe-shaped warming pattern and extreme marine heatwaves of \u003cb\u003e+\u0026thinsp;3\u0026ndash;6\u0026deg;C\u003c/b\u003e across the North Pacific\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, but its specific impacts on the WNP remain underexplored.\u003c/p\u003e\u003cp\u003eThese physical reorganizations have direct ecological implications. In the WNP, enhanced stratification has suppressed nutrient supply, contributing to long-term declines in primary production\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In the WNP, including the Ulleung Basin, primary production and phytoplankton community structure are tightly coupled to winter hydrography\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Notably, recent satellite and in situ observations have documented an expansion of pico-phytoplankton dominance across Korean seas\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, consistent with warming-driven shoaling of the mixed layer that favors smaller taxa adapted to oligotrophic conditions \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These findings emphasize that changes in winter stratification and circulation may propagate into lower-trophic-level dynamics and alter ecosystem structure on seasonal to decadal scales.\u003c/p\u003e\u003cp\u003eDespite these advances, most prior studies have been limited to correlations between individual climate indices and SST or have focused on specific short intervals\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Quantitative assessments of the relative contributions of multiple atmospheric and oceanic drivers remain scarce\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and few have considered how spatial shifts in key drivers such as the AL center or Kuroshio axis modulate SST variability\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In particular, comparative analyses of the mechanisms driving the post-2015 warming in the WNP versus earlier warm and cold regimes are lacking.\u003c/p\u003e\u003cp\u003eAccordingly, this study investigates winter SST variability in the WNP from 1990 to 2024 using an integrated atmosphere\u0026ndash;ocean\u0026ndash;ecosystem framework. We assess whether a statistically significant regime shift occurred around 2015 and examine how the relative influence of atmospheric and oceanic drivers changed across this transition. We further explore whether comparable SST increases in different periods were governed by distinct physical mechanisms and evaluate whether these reorganizations propagated into phytoplankton community structure. By linking atmosphere\u0026ndash;ocean variability with ecosystem responses, this study provides a comprehensive understanding of the causes and ecological consequences of recent environmental change in the WNP.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDetection of a Winter SST Regime Shift and Associated Changes in Atmospheric and Oceanic Drivers in the WNP\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo statistically evaluate whether the WNP experienced a significant winter SST regime shift, we first applied Pettitt\u0026rsquo;s test and sequential Mann\u0026ndash;Whitney U tests to annual mean SST anomalies from 1990 to 2024. Pettitt\u0026rsquo;s test suggested 2015 as the most probable shift year, although the p-value (p\u0026thinsp;=\u0026thinsp;0.119) indicated marginal significance. The Mann\u0026ndash;Whitney U tests, which compared SST distributions before and after each candidate year, yielded a more robust result: 2018 (p\u0026thinsp;=\u0026thinsp;0.0019), 2016 (p\u0026thinsp;=\u0026thinsp;0.0046), and 2015 (p\u0026thinsp;=\u0026thinsp;0.0070) emerged as the three most statistically significant thresholds. Based on the convergence of independent non-parametric tests\u0026mdash;Pettitt\u0026rsquo;s test favoring 2015 (p\u0026thinsp;=\u0026thinsp;0.119) and sequential Mann\u0026ndash;Whitney U tests highlighting 2015\u0026ndash;2018 as candidate thresholds\u0026mdash;and considering the broader North Pacific context of a basin-wide regime shift, we adopt 2015 as the working change point for subsequent analyses, supported by both statistical evidence and the immediate, persistent sign shift of anomalies thereafter (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFollowing the 2015 transition, the SST anomaly record showed remarkable persistence. Except for 2018, when anomalies briefly approached climatological values, all years from 2016 to 2024 exhibited positive winter SST anomalies relative to the 1990\u0026ndash;2014 mean. The magnitude of anomalies ranged between +\u0026thinsp;0.3 and +\u0026thinsp;1.5\u0026deg;C, with 2019 and 2020 standing out as extreme warm winters. This indicates not only a one-time change but also a sustained reorganization of the thermal baseline of the WNP, consistent with the definition of a climatic regime shift(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo identify the mechanisms underlying this transition, we conducted multiple linear regression analyses using air temperature (AT), wind speed (WS), latent heat flux (LHF), and EKWC strength as explanatory variables, with SST as the response variable. Separate regressions were performed for the pre-shift (1990\u0026ndash;2015) and post-shift (2016\u0026ndash;2024) periods. Before 2015, the model explained 75.1% of SST variability (R\u0026sup2; = 0.751, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Both AT (β\u0026thinsp;=\u0026thinsp;0.529, p\u0026thinsp;=\u0026thinsp;0.023) and EKWC (β\u0026thinsp;=\u0026thinsp;1.709, p\u0026thinsp;=\u0026thinsp;0.043) had statistically significant positive effects, suggesting that SST anomalies reflected a combined influence of atmospheric thermal forcing and oceanic heat advection. WS and LHF, despite showing expected signs, did not reach statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.5) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple linear regression results for the effects of atmospheric and oceanic variables in SST in the WES before and after 2015.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBefore 2015 (R\u0026sup2; = 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEKWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter 2015 (R\u0026sup2; = 0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEKWC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificance level: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*), p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (+) and non significant (no symbol)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter 2015, however, the explanatory power of the model increased further to 85.8% (R\u0026sup2; = 0.858, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interestingly, EKWC became the dominant predictor with a much larger coefficient (β\u0026thinsp;=\u0026thinsp;4.177, p\u0026thinsp;=\u0026thinsp;0.091), while AT not only lost significance but also switched to a negative coefficient (β = \u0026minus;\u0026thinsp;0.473, p\u0026thinsp;=\u0026thinsp;0.535). LHF emerged as a marginal predictor (β\u0026thinsp;=\u0026thinsp;0.078, p\u0026thinsp;=\u0026thinsp;0.074), highlighting the role of air\u0026ndash;sea turbulent flux processes in modulating SST after the shift. Together, these results demonstrate a transition from a balanced atmosphere\u0026ndash;ocean control of SST before 2015 to an ocean-dominated regime afterward, with EKWC variability emerging as the primary driver (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eRelative Contribution of Atmospheric (ΔT) and Oceanic (EKWC) Factors\u003c/h2\u003e\u003cp\u003eTo quantify the relative roles of atmospheric and oceanic forcing, we restructured the regression model to include ΔT (SST minus AT, representing air\u0026ndash;sea thermal contrast) and EKWC strength as predictors. During the pre-shift period (1990\u0026ndash;2015), regression coefficients for ΔT and EKWC were 0.29 and 2.02, respectively, with an overall R\u0026sup2; of 0.26. This indicates that only about a quarter of SST variability was explained, and both atmospheric and oceanic drivers contributed moderately(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, during the post-shift period (2016\u0026ndash;2024), the coefficient for ΔT slightly declined to 0.23, whereas EKWC increased dramatically to 3.58, reflecting a\u0026thinsp;~\u0026thinsp;77% strengthening in its apparent contribution. Despite the similar R\u0026sup2; (0.23), the redistribution of regression weights suggests a structural shift toward ocean-dominance. A Z-test comparing EKWC coefficients across the two periods yielded Z = \u0026minus;\u0026thinsp;0.54 (p\u0026thinsp;=\u0026thinsp;0.59), confirming that the change was not statistically significant given the short post-2016 record. Nevertheless, the consistent increase in magnitude and direction of EKWC\u0026rsquo;s coefficient across multiple analyses strongly suggests enhanced oceanic control (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExpanding correlation analyses reinforced these findings. The correlation between SST and EKWC remained positive and stable before 2015 but increased further afterward, reaching r\u0026thinsp;\u0026gt;\u0026thinsp;0.6 in the early 2020s (Fig. S2). In contrast, correlations between SST and AT remained flat, while SST\u0026ndash;ΔT correlations declined nearly to zero after 2015. Fisher\u0026rsquo;s Z-tests confirmed that none of these changes were individually significant at the 0.05 level, but the systematic pattern points to a reorganization of forcing structure. Importantly, standardized regression analyses showed that EKWC\u0026rsquo;s relative contribution increased from 61.9% in 1990\u0026ndash;2015 to 70.0% in 2016\u0026ndash;2024, while ΔT\u0026rsquo;s contribution fell from 38.1% to 29.9%. Although bootstrap validation indicated the differences were not statistically significant (p\u0026thinsp;=\u0026thinsp;0.802), the directionality and consistency of results across methods emphasize the growing dominance of oceanic processes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIntensification and Atmospheric Linkages of the East Korea Warm Current\u003c/h3\u003e\n\u003cp\u003eThe EKWC itself underwent substantial changes after the mid-2010s. A two-sample t-test confirmed a significant increase in mean EKWC strength after 2015 (p\u0026thinsp;=\u0026thinsp;0.0018). This intensification was not only a local phenomenon but also coincided with persistent positive SST anomalies. The correlation between EKWC and SST across the full record was r\u0026thinsp;=\u0026thinsp;0.564 (p\u0026thinsp;=\u0026thinsp;0.0006), underscoring the tight coupling between ocean advection and regional thermal state (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpatial correlation maps between EKWC intensity and mean sea level pressure (SLP) further revealed coherent atmospheric linkages. EKWC strength showed positive correlations with SLP over the Korean Peninsula and Kuroshio Extension, while exhibiting negative correlations over polar latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These patterns indicate that interannual variations in EKWC intensity are strongly influenced by mid-latitude pressure anomalies. For instance, during winters when the AL weakened and the SH retreated, pressure anomalies near Korea and the Kuroshio Extension (KE) region reinforced stronger northward transport of Kuroshio waters, enhancing EKWC inflow. This coupling highlights the joint operation of regional and basin-scale climate drivers in setting WNP conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMeridional Shifts of the Kuroshio Current as a Key Driver\u003c/h3\u003e\n\u003cp\u003eAnalysis of long-term Kuroshio Current indices revealed a significant northward migration of the mean axis after 2015 (p\u0026thinsp;=\u0026thinsp;0.0016), even though current intensity did not undergo a regime shift (p\u0026thinsp;=\u0026thinsp;0.138) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Correlation analysis indicated that stronger flows tended to coincide with a more northerly axis (r\u0026thinsp;=\u0026thinsp;0.563, p\u0026thinsp;=\u0026thinsp;0.0008) (Fig. S3). Importantly, meridional shifts of the Kuroshio (MKC) were positively correlated with WNP SST (r\u0026thinsp;=\u0026thinsp;0.355, p\u0026thinsp;=\u0026thinsp;0.046), whereas Kuroshio Current Intensity (KCI) alone showed no meaningful relationship. This emphasizes that the geographical pathway of warm water transport, rather than the magnitude of transport, exerts greater influence on the WNP.\u003c/p\u003e\n\u003ch3\u003eSST Variability and Dominant Climate Drivers\u003c/h3\u003e\n\u003cp\u003eAcross the full observational period, meridional shifts of the Aleutian Low (MIAL) showed the strongest relationship with SST (r\u0026thinsp;=\u0026thinsp;0.4821) (Fig. S3). Regression models confirmed that combinations involving MIAL consistently explained the most variance. For instance, Arctic oscillation index (AOI)\u0026ndash;MIAL achieved R\u0026sup2; = 0.3261 (p\u0026thinsp;=\u0026thinsp;0.00181), while Pacific Decadal Oscillation index (PDOI)\u0026ndash;MIAL and North Pacific index(NPI)\u0026ndash;MIAL also performed strongly. These results underscore MIAL\u0026rsquo;s persistent role as the most robust atmospheric predictor of WNP SST variability (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePeriod-specific analyses highlighted clear contrasts. From 1990\u0026ndash;2015, the AOI, PDOI, and MIAL dominated, with the AOI\u0026ndash;PDOI\u0026ndash;MIAL model achieving R\u0026sup2; = 0.4594 (p\u0026thinsp;=\u0026thinsp;0.00317). This indicates that hemispheric-scale atmospheric circulation and decadal ocean\u0026ndash;atmosphere variability jointly controlled SST variability during the earlier regime. After 2015, however, AOI, East Asian Winter Monsoon Index (EAWMI), and MIAL emerged as the key predictors. The AOI\u0026ndash;EAWMI\u0026ndash;MIAL combination explained R\u0026sup2; = 0.4058, though limited by the short sample length. These shifts suggest a weakening of PDO influence and strengthening of seasonal atmospheric modes in the post-2015 regime (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Across both eras, MIAL remained central, demonstrating the fundamental importance of Aleutian Low meridional positioning.\u003c/p\u003e\n\u003ch3\u003eAtmospheric Pattern Transitions Across the SST Regime Shift\u003c/h3\u003e\n\u003cp\u003eTo examine large-scale atmospheric linkages of WNP winter SST, we first computed spatial correlations between SST anomalies and Northern Hemisphere atmospheric fields for the full 1990\u0026ndash;2024 period. SST correlated positively with SLP near Korea and the KE, but negatively east of the Aleutians, forming a zonal dipole. Correlations with wind speed revealed a latitudinal contrast, with negative values around 30\u0026deg; N\u0026mdash;where weaker winds were linked to warming\u0026mdash;and positive values north of 45\u0026deg; N (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo further clarify structural changes across the mid-2010s regime shift, we performed composite anomaly analyses for winters with above-normal WNP SST before and after 2015. Prior to 2015 (1990, 1992, 1995, 1998, 1999, 2000, 2002, 2004, 2007, 2008, 2009, hear after Group A), warm events were accompanied by widespread positive AT anomalies across Eurasia, including Siberia, reflecting a predominantly continental-scale warming pattern. WS anomalies displayed moderate weakening near 30\u0026ndash;35\u0026deg; N and strengthening at higher latitudes, while SLP anomalies exhibited a clear east\u0026ndash;west dipole across the Aleutians, consistent with longitudinal modulation of the AL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter 2015 (2016, 2017, 2019, 2020, 2021, 2022, 2023, 2024, hear after Group B ), the AT anomaly pattern shifted toward an oceanic focus, with weaker anomalies over Siberia and stronger warming over lower-latitude marine regions, notably the East China Sea and the western subtropical Pacific. WS anomalies displayed a strengthened latitudinal dipole, with pronounced weakening in the 30\u0026ndash;35\u0026deg; N band adjacent to the Korean Peninsula and enhanced strengthening near the Aleutians. SLP anomalies transitioned to a meridional structure, with reduced Arctic anomalies, lower pressures around the Aleutians, and higher pressures across the central North Pacific and the Korean Peninsula. This evolution from a zonal to a meridional anomaly configuration indicates a fundamental reorganization of atmospheric wave activity and air\u0026ndash;sea coupling processes in the post-2015 regime (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Patterns Among Key Ocean\u0026ndash;Atmosphere Variables\u003c/h2\u003e\u003cp\u003ePearson correlation matrices confirmed robust associations among indices. EKWC (r\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and MIAL (r\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) both strongly correlated with SST. MKC also correlated with SST (r\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and was itself correlated with both EKWC and MIAL (r\u0026thinsp;=\u0026thinsp;0.45 each), suggesting basin-scale coherence (Fig. S3).\u003c/p\u003e\u003cp\u003eExpanding cumulative correlations highlighted temporal evolution. From 2000\u0026ndash;2015, SST variability was tightly governed by atmospheric indices, especially AOI (r\u0026thinsp;=\u0026thinsp;0.73, p\u0026thinsp;=\u0026thinsp;0.002). After 2015, atmospheric influence collapsed: AOI showed no correlation (r = \u0026minus;\u0026thinsp;0.03), while EKWC strengthened dramatically, exceeding r\u0026thinsp;=\u0026thinsp;0.65 after 2020. MKC also became significant in the most recent years. These results clearly indicate a regime shift in the mid-2010s from atmosphere-dominated to ocean-dominated control of SST (Fig S2).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLong-term Variation of Chlorophyll-a and Phytoplankton Size Composition\u003c/h3\u003e\n\u003cp\u003eAnnual mean surface chlorophyll-a (chl-\u003cem\u003ea\u003c/em\u003e) concentrations between 2003 and 2024 ranged from 0.57 to 1.00 mg m⁻\u0026sup3;, with no significant trend. The mean values before and after 2015 (0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 vs. 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 mg m⁻\u0026sup3;) were statistically indistinguishable (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This suggests that total phytoplankton biomass has remained relatively stable despite substantial physical changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHowever, phytoplankton size class (PSC) revealed striking shifts. Pico-phytoplankton (pico) dominated areas increased steadily (slope\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1.4% per year, r\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while nano-phytoplankton (nano) areas declined symmetrically (slope = \u0026minus;\u0026thinsp;1.4% per year, r\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Micro-phytoplankton (pico) remained nearly constant. Comparing pre- and post-2015, pico-dominated areas rose from 59.4% to 71.9% (+\u0026thinsp;12.6%), while nano-dominated areas fell from 38.1% to 24.2% (\u0026ndash;13.8%). These shifts in community structure, despite stable total chl-\u003cem\u003ea\u003c/em\u003e, imply a profound reorganization of lower-trophic-level dynamics, likely linked to stratification and nutrient redistribution associated with intensified EKWC inflow and suppressed vertical mixing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRegime-dependent phytoplankton–climate relationships\u003c/h3\u003e\n\u003cp\u003eDuring the before 2015, chl-\u003cem\u003ea\u003c/em\u003e and micro exhibited significant positive correlations with the PDOI (chl-\u003cem\u003ea\u003c/em\u003e: r\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;=\u0026thinsp;0.017; micro: r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;=\u0026thinsp;0.033), while nanoand pico showed significant associations with the NPI, but with opposite directions (nano: r = \u0026minus;\u0026thinsp;0.56, p\u0026thinsp;=\u0026thinsp;0.045; pico: r\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;=\u0026thinsp;0.017), suggesting that basin-scale climate variability was a dominant driver before 2015. In contrast, during the after 2015, these large-scale linkages weakened or disappeared; chl-a showed no significant association with any index, micro was significantly negatively correlated with LHF (r = \u0026minus;\u0026thinsp;0.70, p\u0026thinsp;=\u0026thinsp;0.035), and nano was significantly negatively correlated with the EKWC (r = \u0026minus;\u0026thinsp;0.71, p\u0026thinsp;=\u0026thinsp;0.032), while pico exhibited no significant correlations, indicating a transition toward regional oceanic and air\u0026ndash;sea flux controls (Table S2). This regime-dependent shift in phytoplankton\u0026ndash;climate coupling was confirmed by multivariate tests: PERMANOVA detected a significant difference between pre- and post-shift structures (F\u0026thinsp;=\u0026thinsp;3.05, R\u0026sup2; = 0.132, p\u0026thinsp;=\u0026thinsp;0.007), and a Procrustes test comparing PCA loading structures also indicated a significant reorganization (M\u0026sup2; = 0.845, p\u0026thinsp;=\u0026thinsp;0.011; Table S3). Consistently, PCA biplots showed that phytoplankton variables clustered with PDOI and NPI in the pre-shift period but aligned more closely with SST, EKWC, and LHF in the post-shift period. Standardized multiple regression further quantified these shifts: in the pre-shift period, chl-a and micro were mainly explained by PDOI, while nano and pico were best accounted for by NPI, with moderate explanatory power across models (R\u0026sup2; = 0.39\u0026ndash;0.61); however, in the post-shift period, PDOI and NPI were no longer retained, and instead chl-a and micro were linked to LHF, AOI, and KCI, whereas nano and pico were explained by EKWC, SST, and MKC, with comparable model fits (R\u0026sup2; = 0.57\u0026ndash;0.61), confirming that phytoplankton variability became more sensitive to regional processes such as ocean currents and air\u0026ndash;sea fluxes after 2015 (Table S4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe long-term variability of winter SST in the WNP is strongly governed by large-scale atmospheric and oceanic drivers. Among these, the AO and the meridional position of the MIAL consistently emerged as dominant predictors, underscoring their pivotal role in modulating interannual SST anomalies. Regression analyses repeatedly identified the AOI and MIAL as key explanatory variables, highlighting their fundamental contribution to regional SST variability\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This finding is consistent with earlier studies that link the AO to interannual\u0026ndash;decadal SST changes and the occurrence of marine heatwaves (MHWs) in the Northwest Pacific\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. During positive AO phases, anomalous anticyclonic wind stress curl develops over the WNP, driving surface convergence and Ekman downwelling, which suppresses the upward supply of cold subsurface waters\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This reduced entrainment allows anomalously warm waters to persist, thereby increasing the likelihood of MHWs\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, AO-related modulation of the SH exerts a critical influence on East Asian winter monsoon (EAWM) strength\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Positive AO phases strengthen the polar vortex and confine cold air to high latitudes, weakening SH development, whereas negative AO phases weaken the vortex and promote southward cold-air intrusions, strengthening the SH and reinforcing cold advection into the WNP\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These AO\u0026ndash;polar vortex transitions thus constitute a key dynamical pathway linking the SH, EAWM variability, and WNP SST.\u003c/p\u003e\u003cp\u003eThe AL plays a complementary role by regulating monsoon strength through its central position. When the AL shifts southward, SLP over the KE region decreases, enhancing the SLP gradient between the SH and KE and driving stronger northwesterly advection of cold, dry air from Siberia into the WNP\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Conversely, northward AL displacement weakens the pressure gradient, reduces cold-air advection, and favors warm SST anomalies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Together, AO state transitions, SH intensity changes, and AL displacements strongly modulate winter monsoon dynamics and SST variability in the WNP.\u003c/p\u003e\u003cp\u003eA regime shift in WNP SST was identified around 2015, after which winter SSTs remained predominantly above the climatological mean in nearly all years. This \u0026ldquo;WNP regime shift\u0026rdquo; coincided with basin-scale atmospheric\u0026ndash;oceanic reorganizations, including strengthening of the East Korea Warm Current (EKWC), northward migration of the Kuroshio axis, and displacement of the AL. The dominant drivers of SST also shifted: before 2015, the AO, MIAL, and PDO exerted primary influence, whereas after 2015, AO, meridional AL position, and the EAWM index emerged as key explanatory factors\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These results are consistent with the broader framework of North Pacific climate regime shifts (CRS), which have historically occurred in 1976\u0026ndash;77, 1988\u0026ndash;89, and 1998\u0026ndash;99\u003csup\u003e16,32\u003c/sup\u003e. Earlier CRS were largely PDO-centered, accompanied by AL intensity shifts and Pacific\u0026ndash;North American (PNA) teleconnection patterns\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. PDO variability, as the leading EOF of North Pacific SST anomalies, explains\u0026thinsp;~\u0026thinsp;25% of variance and strongly couples with AL intensity via ENSO\u0026ndash;PNA linkages\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Intensification of the AL during positive PDO phases enhances central North Pacific wind stress curl, alters thermocline depth, and generates westward-propagating Rossby waves, ultimately modulating Kuroshio Current (KC) strength and KE position\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBy contrast, the post-2015 CRS was distinct, reflecting a departure from PDO-dominated variability and a growing dominance of the Victoria Mode (VM)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The VM, as the second EOF of North Pacific SST anomalies, accounts for ~\u0026thinsp;12\u0026ndash;15% of variance and exhibits a subtropical\u0026ndash;midlatitude dipole structure\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Its variability is closely linked to the West Pacific (WP) teleconnection, characterized by an anomalous high over the western subtropics and a low over the high-latitude North Pacific\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Sustained VM activity drives north\u0026ndash;south AL displacements, reorganizes wind stress curl, and produces thermocline changes that propagate to the KE via westward Rossby wave transmission\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Unlike the PDO, which primarily governs KC intensity through AL strength, the VM directly and sensitively controls KC meridional position\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that after 2015, even without strong AL intensification, a northward AL shift alone was sufficient to reorganize wind stress curl patterns, deepen the thermocline, and displace the KC and KE northward\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. These results suggest a structural transition in WNP variability from PDO-driven to VM-driven forcing.\u003c/p\u003e\u003cp\u003eThe strengthened role of oceanic processes is further supported by satellite-derived MHW analyses, which revealed that cumulative MHW intensity in the WNP increased by 29.6\u0026deg;C\u0026middot;days per decade from 1982 to 2020, more than twice the global mean rate\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These trends reflect enhanced contributions from EKWC and Kuroshio variability rather than surface fluxes alone. Indeed, regression results show that after 2015, the effect of EKWC on winter SST was much stronger than that of air temperature, pointing to an ocean-dominated regime. This interpretation is consistent with long-term heat budget analyses demonstrating that in boundary current regions, horizontal and vertical heat advection dominates SST variability, while air\u0026ndash;sea fluxes often damp anomalies rather than generate them\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. For the EKWC, enhanced advection redistributes heat along its pathway, producing regional contrasts in SST trends: acceleration in some regions, suppression in others\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. These findings indicate an increasing role of current-driven variability for SST trends in the WNP.\u003c/p\u003e\u003cp\u003eThe role of atmospheric modes after 2015 should not be overlooked. AO spatial patterns weakened during warm years, which may be linked to the quasi-biennial oscillation (QBO). Stratospheric conditions associated with QBO phases modulate polar vortex strength and planetary wave propagation, thereby influencing the AO\u0026ndash;AL connection\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. During westerly QBO phases, stronger polar vortices enhance AO\u0026ndash;AL teleconnections, while easterly phases weaken them. Thus, the reduced AO influence on SST variability after 2015 may partly reflect stratospheric modulation. Nevertheless, ocean circulation changes, particularly EKWC strengthening, emerged as the dominant factor explaining post-2015 SST anomalies.\u003c/p\u003e\u003cp\u003eThe ecological implications of this structural shift are significant. In the WNP, where terrestrial inputs are minimal, winter hydrography and stratification strongly precondition nutrient availability and spring bloom dynamics\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Enhanced stratification under warming shoals the mixed layer, suppresses nutrient entrainment, and shifts phytoplankton composition toward smaller size classes adapted to oligotrophic conditions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Our analysis revealed that while long-term chl-a biomass exhibited no significant trend during 2003\u0026ndash;2024, phytoplankton size composition reorganized: pico-dominant areas expanded while nano dominance declined, especially after 2015. These patterns align with global reports of smaller phytoplankton dominance under warming\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and with PSC-based analyses in Korean waters showing expansion of pico-dominant areas in the WNP\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The mechanistic linkage between SST-driven stratification and nutrient redistribution has been further clarified by recent studies. Jung et al. (2025) demonstrated that post-2015 winter stratification intensification in the Northwest Pacific was primarily driven by rising SST, which steepened vertical density gradients and enhanced nitrate accumulation below the pycnocline\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This suggests that surface warming not only raises SST but also reorganizes subsurface nutrient reservoirs, constraining upward flux and altering pre-bloom conditions. Our findings are consistent with these insights suggesting that enhanced EKWC transport coincided with PSC reorganization, indicating that winter hydrographic shifts act as precursors shaping lower-trophic-level dynamics. While multiple lines of evidence support a post-2015 transition, short post-shift samples limit formal detection power. We mitigated this with robust tests and persistence diagnostics, but future work should extend the record, incorporate ocean reanalyses for full heat-budget closure, and explore hierarchical or Bayesian change-point frameworks to jointly estimate timing and driver weights.\u003c/p\u003e\u003cp\u003eIn summary, the 2015 regime shift in the WNP represents a transition from atmosphere-dominated to ocean-dominated forcing of winter SST variability. Before 2015, SST anomalies were primarily linked to AO and PDO driven atmospheric circulation, whereas after 2015, VM-driven meridional shifts of AL and EKWC intensification became the principal controls. These physical changes enhanced winter stratification, altered nutrient pathways, and reshaped phytoplankton community structure. The WNP thus exemplifies how coupled atmosphere\u0026ndash;ocean reorganizations in the North Pacific propagate into boundary current systems and ultimately into ecosystem dynamics. Sustained, long-term monitoring of stratification and phytoplankton size composition will be essential to assess ecosystem resilience under continued climate change. More broadly, the identified regime shift in the WNP reflects basin-scale Pacific decadal reorganization that is consistent with known teleconnections involving the PDO and AL, with potential implications for cross-basin climate variability. These results suggest the potential relevance of marginal sea processes in the northwest Pacific to Pacific\u0026ndash;Atlantic inter-basin linkages, underscoring the need to incorporate such regional dynamics into decadal climate prediction frameworks.\u003c/p\u003e\u003cp\u003eDespite these findings, several limitations should be acknowledged. First, the post-regime-shift period (2016\u0026ndash;2024) is relatively short, which constrains the statistical power to detect structural changes; however, the observed trends remain consistent with our hypotheses and conceptual framework. Second, the ecological responses were inferred from phytoplankton size-class composition derived from satellite observations, which may not fully capture in situ community dynamics. Previous studies, however, have demonstrated significant correspondence between satellite-derived size classes and field measurements, lending confidence to this approach. Third, ecosystem changes in the WNP were assessed mainly in relation to surface SST, whereas ecological restructuring cannot be fully explained by upper-ocean thermal variability alone. Future studies integrating water-column structure, nutrient dynamics, and primary production from field observations will be required to better evaluate the mechanisms of ecosystem change.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results reveal a statistically significant winter SST regime shift in the WNP around 2015, after which anomalies remained persistently positive. Before 2015, SST variability reflected a balance between atmospheric forcing and oceanic advection, whereas after 2015, oceanic processes—particularly EKWC intensification and a northward Kuroshio displacement—became dominant. This transition marks a shift from PDO- to Victoria Mode–driven teleconnections, reorganizing the AL and altering regional air–sea coupling. Despite stable chl-\u003cem\u003ea\u003c/em\u003e biomass, phytoplankton communities underwent structural reorganization toward pico-dominance, consistent with enhanced stratification and nutrient redistribution. Collectively, these findings demonstrate that teleconnection-driven ocean circulation now governs both physical and ecological states in this marginal sea, underscoring its sentinel role for Pacific decadal variability and the importance of sustained monitoring for prediction and ecosystem resilience.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003ch2\u003eSea Surface Temperature (SST) Data\u003c/h2\u003e\u003cp\u003eSST data were obtained from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) product developed by the UK Met Office. OSTIA provides daily global-scale SST and sea ice fields on a 0.05° latitude–longitude grid, and the dataset is reanalyzed for public use based on field observations from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS). These high-resolution foundation SST fields combine satellite observations and in situ measurements through optimal interpolation techniques.\u003c/p\u003e\u003cp\u003eIn this study, daily SST values were averaged into monthly means, and then aggregated to generate winter seasonal averages, defined as the mean of January, February, and March (JFM) for each year from 1990 to 2024. Spatially, SST data were extracted from a subregion of the WNP, bounded by 36°–38°N latitude and 127°–131°E longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This domain was selected to characterize wintertime surface thermal conditions in the nearshore environment off the Korean Peninsula.\u003c/p\u003e\u003ch2\u003eAtmospheric Variables in north hemisphere\u003c/h2\u003e\u003cp\u003eTo characterize large-scale atmospheric conditions influencing wintertime SST variability in the WNP, we used three key surface variables from the ERA5 reanalysis dataset: 2 m air temperature (T2M), 10 m wind speed (U10, V10), and SLP. All variables were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 monthly averaged data on a 0.25° × 0.25° horizontal resolution grid.\u003c/p\u003e\u003cp\u003eFor each variable, we extracted data over the Northern Hemisphere (20°–90°N) and computed seasonal winter means by averaging monthly values from December, January, and February (DJF) for each year during the 1990–2024 period. The resulting 35-year winter climatology was used in spatial correlation analyses and compositing to assess large-scale atmospheric patterns associated with interannual SST anomalies in the WNP.\u003c/p\u003e\u003cp\u003eLatent Heat Flux (LHF) Data\u003c/p\u003e\u003cp\u003eTo estimate wintertime LHF, we used hourly ocean buoy observations from the Korea Meteorological Administration (KMA) Open Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.kma.go.kr/\u003c/span\u003e\u003cspan address=\"https://data.kma.go.kr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the WNP. The dataset included wind speed, sea-level pressure, relative humidity, air temperature, and SST from January to March for the period 2002–2024(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLHF (W m⁻²) was calculated using the bulk aerodynamic formula:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{L}\\:=\\:{\\rho\\:}_{a}\\:{L}_{v}\\:{C}_{E}\\:U\\:({q}_{s}\\:-\\:{q}_{a})$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere ρ_a is the air density (kg m⁻³), L_v is the latent heat of vaporization (J kg⁻¹), C_E is the bulk transfer coefficient for moisture (dimensionless), U is the wind speed (m s⁻¹), q_s is the saturation specific humidity at the sea surface, and q_a is the specific humidity of the overlying air. Hourly flux values were averaged to produce seasonal means for subsequent analysis.\u003c/p\u003e\u003ch2\u003eClimate index\u003c/h2\u003e\u003cp\u003eTo investigate the climatic drivers of SST variability in the WNP, we selected five large-scale climate indices commonly used in studies of North Pacific climate variability: the AOI, PDOI, NPI, EAWMI, and SHI. These indices represent major modes of atmospheric circulation and pressure systems that influence regional oceanographic conditions such as SST, SLP, and winter monsoon strength\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe AOI represents the leading mode of sea level pressure variability in the Northern Hemisphere (20°–90°N, 180°E–180°W) and is indicative of the strength of the polar vortex and the westerly jet\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The PDOI is a long-term mode of Pacific SST variability, calculated as the leading principal component of monthly SST anomalies in the North Pacific Ocean poleward of 20°N, after removing the global mean SST anomaly\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The NPI is defined as the area-weighted mean SLP over the region 30°–65°N and 160°E–140°W and is used as an index for the strength of the AL\u003csup\u003e33\u003c/sup\u003e. The EAWMI is calculated from zonal wind speeds at 300 hPa over East Asia (27.5°–37.5°N, 110°–170°E) and serves as a proxy for the intensity of the East Asian winter monsoon\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Finally, the SHI quantifies the strength of the SH based on winter mean SLP averaged over continental Asia (40°–60°N, 70°–120°E), reflecting continental cooling and its associated anticyclonic system\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eDetection of Kuroshio current intensity and latitudinal shifit\u003c/h2\u003e\u003cp\u003eTo assess both the intensity and meridional displacement of the Kuroshio Current over the period 1993–2024, we utilized delayed-time satellite altimetry products provided by AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic data), which include gridded fields of absolute dynamic topography (ADT) and geostrophic surface velocities (ugos and vgos). The data were retrieved at a spatial resolution of 1/4° and daily temporal resolution, and then averaged over the winter season (January to March) to construct annual mean fields.\u003c/p\u003e\u003cp\u003eThe domain selected for analysis spans 30°–40°N latitude and 140°–160°E longitude, encompassing the eastward-flowing section of the Kuroshio east of Japan. Current intensity was quantified by computing the geostrophic speed as the magnitude of the horizontal velocity vector:\u003c/p\u003e\u003cp\u003ewhere u and v represent the eastward (ugos) and northward (vgos) components of the surface velocity, respectively. To isolate the Kuroshio jet core, a dynamic topography threshold was applied, retaining only those grid cells where ADT ranged from 0.9 to 1.0 meters, consistent with the typical elevation of the current axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin each longitudinal grid column, the location of the maximum geostrophic speed within this ADT band was extracted, and the corresponding latitude was recorded as the local center of the current. Averaging these values zonally provided an estimate of the annual mean MKC. Likewise, the geostrophic speed at these core locations was averaged to produce a yearly KCI.\u003c/p\u003e\u003ch2\u003eEast Korea Warm Current Intensity\u003c/h2\u003e\u003cp\u003eThe intensity of the EKWC was quantified as the wintertime volume transport through the western channel of the Korea Strait. Following the method described by Lee et al. (2022), the volume transport (V) was estimated using sea level differences between Izuhara, Japan and Busan, South Korea, based on daily tide gauge records provided by the Japan Meteorological Agency (JMA) and the Korea Hydrographic and Oceanographic Agency (KHOA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo represent winter conditions, monthly mean sea level values for December, January, and February were averaged to compute a seasonal mean for each year from 1992 to 2024. The geostrophic volume transport was calculated using the following formula:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:V\\:=\\:1/f/\\rho\\:*\\varDelta\\:p/\\varDelta\\:x$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere V is the volume transport (hm\u003csup\u003e3\u003c/sup\u003e/s), f is the Coriolis force, ρ is the density of seawater (kg/m\u003csup\u003e3\u003c/sup\u003e), Δp is the pressure difference between each tidal station (hPa), and Δx is the distance between each tidal station (51.17 km)\u003c/p\u003e\u003ch2\u003eDetection of the Meridional and Zonal Position of the Aleutian Low\u003c/h2\u003e\u003cp\u003eTo estimate the meridional position of the AL in boreal winter, we used monthly SLP fields from the ERA5 reanalysis dataset provided by ECMWF, covering the period from 1990 to 2024. Winter averages were constructed using data from December of the previous year and January–February of the current year, resulting in seasonal DJF means.\u003c/p\u003e\u003cp\u003eFrom the full SLP field, we selected a North Pacific domain spanning 30°N–60°N and 150°E–210°E, which encompasses the typical range of the AL. Within this region, we extracted all grid points where MSLP was less than or equal to 1005 hPa, a threshold commonly used to represent the AL core\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Among these grid points, we selected the lowest 5% of MSLP values, which best capture the core low-pressure center of the AL while minimizing the influence of surrounding synoptic systems.\u003c/p\u003e\u003cp\u003eFor each year, the mean latitude and longitude of these lowest-pressure grid cells were calculated to define the annual center position of the AL. The mean latitude was used as the MIAL, and the mean longitude as the zonal position index of AL (ZIAL). This approach is consistent with prior methodologies and provides a robust, objective representation of the interannual shift in the AL's geographic location.\u003c/p\u003e\u003ch2\u003eIdentifying Climatic Drivers of SST Variability Before and After 2015\u003c/h2\u003e\u003cp\u003eTo investigate whether the dominant climatic drivers of SST variability in the WNP changed across the regime shift year, a period-specific analytical framework was employed. Based on the detection of 2015 as a statistically significant SST change point, the full observation period (1990–2024) was divided into two subperiods: pre-shift (1990–2015; Group A) and post-shift (2016–2024; Group B). A total of seven large-scale climate indices were selected as candidate predictors representing distinct atmospheric systems and modes of variability: AOI, EAWMI, NPI, PDOI, SHI, MIAL, ZIAL. These indices were chosen to capture polar teleconnections, mid-latitude pressure variability, and decadal-scale ocean–atmosphere interactions across the North Pacific. For each subperiod and the full period, Pearson correlation analysis was performed to assess the linear relationships between annual SST anomalies and each climate index. Only statistically significant correlations (p \u0026lt; 0.05) were retained for interpretation. To quantify the joint influence of multiple climate indices on SST variability, multiple linear regression models were constructed with SST as the dependent variable. All possible combinations of two to six predictors were tested. Model performance was evaluated using adjusted coefficients of determination (adjusted R²) and significance levels (p-values). This step enabled identification of optimal predictor sets and assessment of explanatory power across different levels of model complexity. To examine temporal shifts in the role of individual indices, interperiod comparisons of regression coefficients were conducted using Z-tests for independent estimates. In addition, expanding window correlation analysis was employed to visualize the evolving strength of relationships between SST and its potential drivers throughout the study period. Fisher’s Z-transformation was applied to test for statistically significant differences in correlation coefficients before and after 2015 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eSatellite data and Phytoplankton Size Classes(PSCs) algorithm\u003c/h2\u003e\u003cp\u003eThe PSC algorithm used in the WNP was based on a deep neural network (DNN) model developed by Kang et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e and further applied to analyze the long-term patterns of PSCs in the littoral seas of Korea by Jang et al. (2025)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The model architecture consisted of eight fully connected layers. Input variables included SST, Total Suspended Solids (TSS), and total chl-\u003cem\u003ea\u003c/em\u003e concentration, while output variables were the fractional contributions of micro (\u0026gt; 20 µm), nano (2–20 µm), and pico (\u0026lt; 2 µm) for each pixel.\u003c/p\u003e\u003cp\u003eTo obtain the input data for the PSC algorithm, satellite-based ocean color data were acquired from MODIS-Aqua Level-3 products (4 km × 4 km resolution) provided by the NASA Goddard Space Flight Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://oceandata.sci.gsfc.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://oceandata.sci.gsfc.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We used monthly mean SST, chl-\u003cem\u003ea\u003c/em\u003e, and remote sensing reflectance at 555 nm (Rrs555). TSS was not directly available from NOAA, so it was estimated using an empirical algorithm specifically designed and validated for Korean waters\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Rrs555 was used due to its strong sensitivity to water quality parameters and its widespread application in regional optical water quality assessments\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe analysis focused on the region between 36°–38°N and 127°–131°E. For PSCs, the annual variability was derived from monthly data (2003–2024) by determining the most frequent (mode) size class for each pixel and then calculating the proportion of each class within the defined spatial domain. For chl-\u003cem\u003ea\u003c/em\u003e, annual means were computed from the corresponding monthly data over the same period.\u003c/p\u003e\u003ch2\u003eMap generation\u003c/h2\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were generated using MATLAB R2023b (MathWorks, Natick, MA, USA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mathworks.com/\u003c/span\u003e\u003cspan address=\"https://www.mathworks.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with built-in coastline data from the Mapping Toolbox. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e was generated using Ocean Data View (ODV, version 5.7.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://odv.awi.de\u003c/span\u003e\u003cspan address=\"https://odv.awi.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.K.J. Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing the original draft. H.J. Data curation, Formal analysis, Investigation, Writing the original draft. H.K.J. Visualization, Writing the original draft. C.I.L. Investigation, Visualization. I.S.H. Data curation, Funding acquisition, Methodology, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Institute of Fisheries Science, Ministry of Oceans and Fisheries, Korea (R2025014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this study are publicly available from open-access repositories and institutional portals. Sea surface temperature (SST): OSTIA product from the Copernicus Marine Service (https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001/description). \u003c/p\u003e\n\u003cp\u003eAtmospheric variables (U/V wind components, sea-level pressure, air temperature): ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). Latent heat flux (LHF): Hourly ocean buoy observations from the Korea Meteorological Administration (KMA) Open Data Portal (https://data.kma.go.kr/). \u003c/p\u003e\n\u003cp\u003eClimate indices: Arctic Oscillation Index (AOI; https://www.ncei.noaa.gov/access/monitoring/ao/), Pacific Decadal Oscillation Index (PDOI; https://www.ncei.noaa.gov/access/monitoring/pdo/), North Pacific Index (NPI; https://psl.noaa.gov/data/timeseries/month/NP/), and East Asian Winter Monsoon Index (EAWMI) and Siberian High Index (SHI) derived from ERA5 sea-level pressure fields. Absolute dynamic topography and geostrophic currents: \u003c/p\u003e\n\u003cp\u003eAVISO absolute dynamic topography and U/V geostrophic currents from the Copernicus Marine Service (https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057/services). \u003c/p\u003e\n\u003cp\u003eEast Korea Warm Current (EKWC): Calculated from sea-level differences between Izuhara (Japan Meteorological Agency; https://www.jma.go.jp/) and the Korea Hydrographic and Oceanographic Agency (KHOA; https://www.khoa.go.kr/oceangrid/koofs/kor/observation/obs_real.do).\u003c/p\u003e\n\u003cp\u003eChlorophyll-a, SST, and Rrs555 for PSC algorithm: MODIS-Aqua Level-3 products provided by the NASA Goddard Space Flight Center, Ocean Biology Processing Group (OBPG; https://oceandata.sci.gsfc.nasa.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003cbr\u003e The online version contains supplementary material available at [insert DOI link once assigned].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence and requests for materials\u003cbr\u003e Correspondence and requests for materials should be addressed to H.K.J. (email: [email protected]).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReprints and permissions information\u003cbr\u003e Reprints and permissions information is available at \u003c/strong\u003e\u003cstrong\u003ehttps://www.nature.com/reprints/\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003cbr\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003cbr\u003e This study used publicly available data from the Korea Oceanographic Data Center (KODC), accessible at \u003c/strong\u003e\u003cstrong\u003ehttps://www.nifs.go.kr/kodc/eng/index.kodc\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJung, H.-K. et al. 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Climatol. 72, 1\u0026ndash;9 (2002). https://doi.org/10.1007/s007040200008\u003c/li\u003e\n\u003cli\u003eSurry, A. M. \u0026amp; King, J. R. A new method for calculating ALPI: the Aleutian Low Pressure Index. Can. Tech. Rep. Fish. Aquat. Sci. 3135, v + 31 p (Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, BC; 2015). \u003c/li\u003e\n\u003cli\u003eKang, J. J. et al. Estimation of phytoplankton size classes in the littoral sea of Korea using a new algorithm based on deep learning. J. Mar. Sci. Eng. 10, 1450 (2022). https://doi.org/10.3390/jmse10101450\u003c/li\u003e\n\u003cli\u003eMoon, J.-E., Ahn, Y.-H., Ryu, J.-H. \u0026amp; Palanisamy, S. Development of ocean environmental algorithms for Geostationary Ocean Color Imager. Korean J. Remote Sens. 26, 198\u0026ndash;207 (2010).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7614994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7614994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe western marginal sea of the northwest Pacific has warmed rapidly in recent decades, yet the mechanisms of its recent reorganization remain unclear. Using 1990\u0026ndash;2024 observations, we identify a winter sea-surface-temperature (SST) regime shift around 2015, supported by non-parametric change-point tests and the persistence of positive anomalies thereafter. Before 2015, SST variability reflected a balance between atmospheric forcing and East Korea Warm Current (EKWC) transport; afterward, oceanic processes EKWC intensification and a northward Kuroshio displacement\u0026mdash;became dominant, concurrent with a meridional reorganization of the Aleutian Low. Despite stable chlorophyll-a, phytoplankton size structure shifted toward pico-dominance at the expense of nano-classes, consistent with enhanced stratification and altered nutrient pathways. These results show that teleconnection-driven boundary-current variability now governs both physical and biological states in this marginal sea, highlighting its role as a sentinel of basin-scale Pacific decadal variability and its relevance for inter-basin climate linkages and decadal prediction.\u003c/p\u003e","manuscriptTitle":"Teleconnection Driven Winter Sea Surface Temperature regime shift and Ecosystem reorganization in the Western marginal Sea of the Northwest Pacific","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 14:25:33","doi":"10.21203/rs.3.rs-7614994/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T16:07:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T07:17:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157521438075282173104011978052155056981","date":"2026-04-08T06:35:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282383772179217317957973584830624520006","date":"2026-01-05T14:47:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-04T10:21:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75979348844819114180181144191672009982","date":"2025-10-04T08:50:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T14:37:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T09:58:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T10:22:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-18T08:04:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"598d4c17-8fca-4d6f-a695-d659c7625240","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56155974,"name":"Earth and environmental sciences/Climate sciences"},{"id":56155975,"name":"Biological sciences/Ecology"},{"id":56155976,"name":"Earth and environmental sciences/Ecology"},{"id":56155977,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2026-05-22T06:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 14:25:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7614994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7614994","identity":"rs-7614994","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}