We use a coupled sea ice–wave modeling framework that integrates the Lagrangian neXt-generation Sea Ice Model (neXtSIM) with the WAVEWATCH III (WW3) spectral wave model through the OASIS-MCT coupler . NeXtSIM provides evolving sea ice concentration, thickness, and floe-size distribution (FSD), which govern wave attenuation and the directional filtering of wave energy. Unlike Eulerian models, neXtSIM employs a moving triangular mesh that undergoes periodic local remeshing to maintain a nominal resolution equivalent to the 25 km stereographic grid used by WW3. The two components operate on different grids, with fields exchanged every 30 min via interpolation onto the WW3 exchange grid. NeXtSIM is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) atmospheric fields and the GLORYS12V1 ocean reanalysis, and does not impose lateral boundary conditions for sea ice. Instead, sea ice drifts freely across open boundaries, with inflowing ice assumed to have properties consistent with the adjacent interior ice . WW3 simulates wave propagation and attenuation in sea ice using the IS2+IC2 attenuation scheme, which accounts for scattering, inelastic flexure, and under-ice friction . The southern boundary of the regional WW3 domain is located at 54° N, where lateral wave spectra are prescribed using the Ifremer global WW3 Modélisation et Analyse pour le Recherche Côtière (MARC) hindcast. WW3 is forced with the same ERA5 winds as neXtSIM and does not include ocean currents, neglecting wave–current interactions. This configuration reproduces realistic wave–ice interactions in the Barents and Beaufort Seas and yields MIZ extents consistent with ICESat-2 observations .

Simulations cover the pan-Arctic domain for 2018–2022, with three-hourly output from the coupled system. At 25 km grid resolution, the marginal ice is represented as a mesoscale transition zone, without resolving individual floes and small leads. Model configuration follows and Table S1 in the Supplement lists all model variables used in this study. The framework does not include a prognostic ocean mixed layer, so surface waves do not feed back on stratification or turbulence. Ocean stratification and MLD are prescribed from GLORYS12V1 reanalysis. Langmuir-related metrics presented here represent the potential for mechanically forced mixing inferred from surface forcing and do not capture the fully realized ocean response.

The coupled framework provides key advantages over approaches that combine sea ice fields with externally derived wave products or empirical Stokes drift formulations . In neXtSIM–WW3, Stokes drift and wave radiation stress are computed directly from the ice-attenuated wave spectrum, while the prognostic floe-size distribution modulates wave attenuation in a physically consistent manner. In contrast, ERA5 treats sea ice as land above a concentration threshold and cannot represent wave attenuation, directional filtering, or their influence on Stokes drift. These processes are essential for capturing LT forcing in dynamic sea ice conditions, and for capturing how wave–ice interactions modify Lagrangian shear relevant to LT parameterizations.

To assess the fidelity of the neXtSIM–WW3 inputs most relevant for LT diagnostics, we evaluate the surface winds, surface shear, Stokes drift, and the representation of heterogeneous sea ice and MIZ conditions. ERA5 winds, which force both neXtSIM and WW3, show increased uncertainty under strong wind and high-latitude conditions, particularly near sharp ice–open-water transitions. Consistent with this, comparison against Cross-Calibrated Multi-Platform (CCMP) v3.1 data shows that ERA5 winds are systematically weaker than satellite-derived winds, and that this bias propagates directly into the diagnosed friction velocity u*. Over 2018–2022, area-weighted Arctic mean 10 m winds shows a mean bias of -1.46 m s⁻¹ (ERA5–CCMP), an RMSE of 1.47 m s⁻¹, and a correlation of 0.99 (Fig. S1 in the Supplement), indicating high fidelity in synoptic variability despite a low mean state bias. Because CCMP assimilates ERA5 as a background field, this comparison primarily constrains mean state uncertainty.

The fidelity of neXtSIM sea ice concentration, ice-edge location, and deformation has been demonstrated in multiple studies. report pan-Arctic sea ice extent RMSE of 0.76×106 km² and show that neXtSIM reproduces observed patterns of ice drift and deformation from OSI-SAF products, supporting its ability to represent heterogeneous ice fields that modulate wave penetration and Stokes drift pathways. Accurate estimation of Stokes drift us(0) further depends on realistic representation of short-wave attenuation in ice. The IS2+IC2 attenuation scheme implemented in WW3 has been shown to reproduce observed wave decay and spectral evolution in the Beaufort MIZ , and to yield realistic pan-Arctic MIZ extents and wave-affected ice regimes consistent with ICESat-2–derived freeboard variability and floe-scale ice properties . Based on sensitivity analyses and independent observational constraints, prior studies indicate that residual uncertainty in the short-wave spectrum, and hence in inferred Stokes drift, is dominated by uncertainties in wind forcing and sea ice concentration, with magnitude on the order of tens of percent rather than order-unity errors .

To characterize momentum input into the ocean mixed layer under partial ice cover, we compute an effective surface stress that partitions momentum between the ice–ocean and atmosphere–ocean interfaces. Following the framework of , the net ocean surface stress is defined as an area-weighted combination of ice–ocean and atmosphere–ocean stresses, scaled by the local sea ice concentration: 1τocn=Aτio+(1-A)τao where A is the sea ice concentration (0 = open ocean, 1 = fully ice covered), and the direct atmosphere–ocean stress is given by: 2τao=ρaCao|ua|ua with ρa as air density, ua the 10 m wind velocity, and Cao the air–sea drag coefficient over open water. Subsequently, we define an effective water-side friction velocity u*, which represents the shear strength associated with the net surface stress transmitted to the ocean under partial ice cover: 3u*=|τocn|ρo. where ρo is the density of seawater. It provides the fundamental scaling for wind-driven mixing processes. The net surface stress τao represents an upper bound on the momentum flux available to drive mixed-layer shear during periods of active wave growth. This primarily affects the absolute magnitude of the diagnosed friction velocity u*, while its spatial structure and relative variability remain more robust.

In addition to wind shear, surface waves modify upper-ocean momentum through Stokes drift, the net Lagrangian transport of water particles arising from wave orbital motion. In WW3, the surface Stokes drift components (z=0) are computed from the two-dimensional wave energy spectrum F(k,θ) as: 4Usxz=0=g∫∫k2cos⁡(θ)σ2F(k,θ)dθdk, and 5Usyz=0=g∫∫k2sin⁡(θ)σ2F(k,θ)dθdk.

Here, σ is the wave frequency, k is the wave number and θ the propagation direction. These expressions define the eastward and northward components of the surface Stokes drift vector us. The effective friction velocity and surface Stokes drift together characterize the surface shear and wave forcing that govern the mixing potential for LT under varying sea ice conditions.

The Langmuir number (Lat) is a widely used parameter for quantifying the relative contributions of wind stress and wave-induced Stokes drift to upper ocean turbulence . It is defined as: 6Lat=u*us(0), where u* is the friction velocity associated with the effective surface stress applied to the ocean, and us(0) is the surface Stokes drift magnitude. In the open ocean, typical values of Lat range between 0.2 and 0.5 , suggesting strong wave influence and active Langmuir circulation development, although Lat can reach values near or above 1 when wave effects are weak and wind-driven processes dominate . These ranges are consistent with results from LES and field observations showing that stronger LT and deeper mixing are associated with lower Lat . Lat is used in ocean modeling as a diagnostic of upper-ocean mixing regimes and to inform turbulence parameterizations. However, the traditional formulation implicitly assumes that the wind stress and Stokes drift are aligned. In realistic wave fields, especially in the Arctic, where mixed swell, turning winds, and ice-induced attenuation are common, misalignment can strongly reduce the effective Stokes shear that drives Langmuir circulations .

To account for wind–wave misalignment, we adopt the projected Langmuir number of , which incorporates the dynamic orientation of the dominant Langmuir cells. In this framework, Langmuir cells do not necessarily align with the wind, but with the direction of maximum Lagrangian shear, set by a balance between Eulerian shear, Stokes drift, stratification, and Coriolis rotation. The orientation angle αL represents the direction of the dominant Langmuir cells relative to the wind. Incorporating this angle yields the generalized projected Langmuir number: 7Laproj≡|u*|cos⁡(αL)|us(0)|cos⁡(θww-αL)1/2, where θww is the angle between the wind stress and Stokes drift vectors.

The Langmuir cell orientation αL depends on the vertical structure of Lagrangian shear and cannot be directly evaluated from surface forcing alone. We use the bulk approximation αLOW proposed by , which provides an a priori estimate of the cell orientation based on depth-averaged Lagrangian shear. This estimate assumes that the Eulerian shear follows a law-of-the-wall profile, that cross-wind shear is negligible, and that Stokes shear is known from wave fields. The Lagrangian shear is expressed as the sum of Eulerian and Stokes contributions, with the Eulerian shear represented using a law-of-the-wall profile: 8tan⁡αLOW≈∂vs/∂zDL-u*/(κz)+∂us/∂zDL, where angle brackets denote a depth average over the Langmuir-affected layer DL, and κ is the von Kármán constant. This approximation provides a practical estimate of Langmuir cell orientation across a range of wind–wave misalignment conditions in LES, although it remains an idealized representation .

Figure 1 summarizes the seasonal variability of surface forcing through open-water (OW) exceedance of friction velocity (u*), Stokes drift (us(0)), and their joint occurrence (see Eqs. 10–11). The top panels show probability density functions (PDFs) of exceedance fractions across all ice-covered grid cells (SIC≥0.15), and the bottom panels map the spatial distribution of joint exceedance relative to seasonal median sea ice concentration (SIC) contours. The PDFs (Fig. 1a–c) reveal an asymmetry between atmospheric and wave forcing. Exceedance of wind stress (u*) spans a broad range across all seasons, indicating that atmospheric forcing in ice-covered regions frequently reaches magnitudes comparable to OW conditions. In contrast, Stokes drift (us(0)) exceedance is strongly skewed toward low values, with a rapid decay toward higher exceedance fractions, reflecting the attenuation of wave energy within ice-covered regions (; ; ). As a result, joint exceedance closely follows the Stokes drift distribution, highlighting the dominant role of wave forcing in setting LT-relevant mixing conditions.

Spatially (Fig. 1d–g), joint exceedance is concentrated along the MIZ, closely aligned with the 15 %–80 % SIC contours. The highest occurrence is found in the Barents, Greenland, and Chukchi Seas, where fragmented, mobile ice reduces wave attenuation and allows intermittent penetration of wave energy into the ice-covered ocean. Because the OW benchmark is defined separately for each season, exceedance represents a measure of relative forcing intensity rather than absolute magnitude. Across all seasons, joint exceedance remains limited, rarely exceeding 0.2, indicating that simultaneous strong wind and wave forcing beneath sea ice is uncommon. Seasonal differences therefore reflect both variations in forcing and shifts in the reference state. During winter and transitional seasons (DJF, MAM, SON), exceedance is more spatially continuous along the MIZ, consistent with enhanced wave–ice interaction under storm-driven conditions. However, winter shows comparatively low exceedance despite strong winds and wave heights, because the open-water reference state is also elevated, raising the threshold for exceedance. In contrast, fall (SON) shows the highest frequency of elevated joint exceedance values, even though wave heights are more moderate than in winter (Fig. S2). This reflects the combination of a lower open-water reference and more efficient wave penetration into fragmented ice cover, allowing sea ice conditions to more frequently approach OW forcing. Summer (JJA) shows a different regime. Weaker open-water winds and waves reduce the seasonal benchmark, such that moderate forcing more readily exceeds the reference. As a result, exceedance is spatially widespread. However, the magnitude of exceedance remains low, indicating that this broad signal corresponds to weak LT-relevant forcing.

Based on the exceedance metrics, LT-relevant mixing is primarily controlled by the availability and intermittency of wave forcing. Limited wave penetration beneath sea ice suppresses Stokes drift and inhibits LT, favoring predominantly shear-driven mixing, while episodic storm-driven events in the MIZ can temporarily establish open-water-like forcing beneath the ice.

To further explore the controls on LT mixing potential, we examine the spatial and seasonal structure of the turbulent Langmuir number (Lat). All Lat medians are computed over the full spatial domain shown in Fig. 2, without restricting the analysis based on sea ice concentration. The five-year climatological median (Fig. 2a) reveals a persistent band of low Lat encircling the perennial ice pack and closely following the climatological 15 % SIC contour and consistent with patterns shown in Fig. 1. Elevated median Lat values (>1) dominate the central Arctic under compact ice cover, indicating a shear-dominated mixing regime with limited wave influence. In contrast, moderate to low median Lat values (<0.45), which show LT mixing potential, are confined to narrow, seasonally evolving bands along the ice edge. In OW conditions (SIC <0.15), median Lat values are generally below 0.35, consistent with the range identified by as favorable for LT mixing. The spatial and seasonal patterns in Lat closely reflect the exceedance statistics, reinforcing that wave-driven mixing is intermittent and strongly modulated by the sea ice cover. As ice concentration increases, wave attenuation limits Stokes-driven forcing, shifting the balance toward shear-dominated mixing and reducing LT mixing potential.

Seasonal medians (Fig. 2b–e) further depict how transitions in ice state modulate the balance between wave and wind forcing. During winter and spring (Fig. 2b–c), sharp gradients in Lat delineate the transition from wave-influenced conditions near the ice edge to shear-dominated regimes within the consolidated ice. Compact ice cover strongly attenuates wave energy, limiting the penetration of Stokes drift in ice-covered regions, even under periods of strong wind forcing. In contrast, summer and fall (Fig. 2d–e) show a broader and more continuous band of reduced Lat extending into the seasonal sea ice zone. Lower ice concentration and increased ice mobility can reduce wave attenuation and allow wave-driven forcing to extend farther into the seasonal MIZ, even without stronger wave conditions. These patterns reinforce that departures from open-ocean Lat values are primarily associated with modulation of Stokes drift by sea ice combined with changes in wind forcing. The seasonal MIZ emerges as a distinct transition zone, where the balance between shear- and wave-driven turbulence can shift toward Langmuir-favorable conditions, while the interior pack remains persistently shear-dominated year-round.

Figure 3 synthesizes the spatial structure, temporal variability, and persistence of upper-ocean mixing regimes under ice-covered conditions (SIC≥0.15), based on the regime classification defined in Eq. (12) and the transition framework described in Eqs. (13)–(16). Panel (a) shows the dominant mixing regime at each grid cell, defined as the regime occurring for at least 50 % of ice-covered time. The Arctic interior is characterized primarily by shear-driven mixing (Lat≥0.94), while mixed (0.43≤Lat<0.94) and wave-driven (Lat<0.43) regimes are confined to the seasonal MIZ and regions near the ice edge, where wind and wave forcing increasingly compete. Panel (c) maps the normalized frequency of regime transitions, expressed as the number of regime changes per ice-covered day. Enhanced regime instability is strongly localized within the MIZ, particularly along sectors exposed to episodic wave activity and intermittent OW conditions. This spatial pattern is consistent with fetch-limited wave growth in partial ice cover, where intermittent Stokes drift enhances variability in the relative balance between wind and wave forcing . In contrast, pack ice has uniformly low transition rates, indicating stable and persistent forcing conditions. Localized transitions within consolidated ice likely reflect episodic openings (e.g., leads and polynyas) that temporarily allow wave generation and induce short-lived shifts in mixing regime.

Panels (b) and (d), restricted to MIZ grid cells (0.15≤SIC≤0.8), further resolve the temporal characteristics of regime variability. Regime instability (panel b), defined as the fraction of MIZ grid cells undergoing at least one transition within a 30 d window, reveals a pronounced seasonal cycle with peaks during periods of ice advance and retreat. These peaks precede maxima in MIZ area, indicating that enhanced regime variability occurs prior to, rather than as a consequence of, MIZ expansion. This behavior is consistent with wind, wave, and ice forcing becoming comparable during seasonal transitions in the MIZ, such that no single regime dominates. Small variations in forcing can shift the balance between regimes, leading to frequent transitions and elevated instability along the evolving MIZ boundary. Panel (d) relates regime instability to persistence by comparing the mean time between regime transitions with the longest continuous duration of a single regime within MIZ grid cells. Median regime persistence increases with increasing inter-transition time, although the relationship is nonlinear. Persistence rises rapidly at short timescales before approaching a plateau at longer intervals, with the transition occurring at inter-transition timescales of approximately several weeks (∼ 20–50 d), depending on location within the MIZ. The broad interquartile range at short transition intervals reflects substantial variability in intermittently forced regions near the ice edge, where regimes are frequently disrupted. In contrast, longer transition intervals correspond to more sustained and dynamically stable regimes. Peak instability (∼ 0.1 transitions per day) corresponds to approximately 2–3 transitions per month, with mean intervals of ∼ 10–30 d between transitions. Despite this relatively frequent switching, individual regimes persist for ∼ 50–150 d, increasing to ∼ 100–200 d under low transition frequencies. Overall, this indicates that MIZ uper ocean mixing is characterized by intermittent regime shifts superimposed on otherwise persistent states, rather than continuous or rapid switching, with regime evolution occurring on sub-seasonal timescales.

Figure 4 provides complementary insight into how both local and spatially aggregated values of Lat depend on SIC, and helps contextualize the spatial regime structure from in Fig. 3. Panel (a) shows that median Lat generally increases with SIC across all seasons, indicating a systematic shift toward shear-dominated mixing as ice cover increases. Conditions associated with strong LT mixing potential (Lat<0.43) are largely confined to OW or low SIC, whereas shear-dominated regimes (Lat>0.94) dominate at moderate to high SIC. This behavior is consistent with increasing attenuation of wave energy and reduced Stokes drift under higher ice concentrations. During summer, however, the relationship deviates from this monotonic trend. At high SIC (≳0.8), median Lat decreases, indicating a relative increase in wave influence despite high ice concentration. Additional diagnostics (Fig. S3) show that in this regime Lat exhibits limited dependence on sea ice thickness, but decreases rapidly with increasing significant wave height (hs) over the range 0–1 m. This suggests enhanced sensitivity of Lat to wave forcing, where even modest increases in wave height can substantially increase Stokes drift and reduce Lat. This is consistent with melt-season conditions in which the ice cover becomes mechanically weakened and increasingly spatially heterogeneous. Partial fragmentation and floe-scale openings allow intermittent wave transmission or local, fetch-limited wave generation, while spatial variability in wind stress over melting ice may reduce shear-driven turbulence. Although these processes are not explicitly resolved at model resolution, the results indicate that wave forcing can remain dynamically relevant even at high SIC. In this context, SIC provides a first-order constraint on mixing regimes, but does not uniquely determine the relative importance of wave-driven turbulence during the melt season.

Panel (b) highlights the role of local spatial variability by showing the distribution of Lat within 3×3 grid-cell neighborhoods. The distributions of Latmin⁡, Latmean, and Latmax⁡ demonstrate that a wide range of mixing states can coexist locally, even under similar SIC conditions. In particular, Latmin⁡ captures the most wave-influenced conditions within a neighborhood, while Latmax⁡ reflects the most shear-dominated state. The large spread between these values indicates strong heterogeneity in wave and shear forcing, consistent with a dynamic ice cover containing a mixture of wave-active and wave-suppressed regions. Panel (c) further quantifies this heterogeneity as the difference between local maximum and minimum Lat within each neighborhood. Heterogeneity increases from low SIC and peaks within the MIZ (SIC ≈ 0.3–0.5), reflecting the coexistence of wave- and shear-dominated regimes. At higher SIC, heterogeneity decreases, indicating a transition toward more spatially uniform forcing conditions. Overall, these show that while SIC governs the large-scale transition between mixing regimes, the realized Lat within a given SIC range depends strongly on local ice structure. Fragmentation and spatial heterogeneity in sea ice conditions modulate the intermittency of wave forcing, resulting in highly variable mixing conditions.

Mechanically driven dissipation provides a bulk measure of wind and wave energy input to the upper ocean and complements the regime-based analysis of LT. In our framework, εmech quantifies the magnitude of mechanically mediated energy input, while Lat determines how this energy is partitioned between shear- and wave-driven processes. Figure 5a shows that the spatial distribution of median εmech is strongly structured by surface forcing, with enhanced dissipation along the MIZ and in peripheral seas exposed to open-ocean winds and wave activity. In contrast, the central pack ice exhibits lower dissipation, consistent with reduced momentum transfer and attenuated wave forcing beneath consolidated ice. This pattern reflects the large-scale distribution of mechanically driven energy input and highlights regions where wind–wave forcing recurrently couples to the upper ocean. Panel (b) shows dissipation intermittency, defined as log⁡10(P90/median) of εmech, where the 90th percentile and median are computed from the temporal distribution at each grid cell. This metric quantifies the relative importance of extreme events compared to the background state. High intermittency is strongly localized to narrow MIZ regions and ice-edge corridors, indicating that dissipation in these areas is dominated by episodic events. In contrast, regions with elevated median dissipation but low intermittency reflect more persistent, but still moderate, energy input.

The spatial correspondence between highly intermittent dissipation and regime-transition hotspots suggests that frequent mixing-regime changes arise where wind-, wave-, and ice-mediated forcing alternately dominate on short timescales. This link is further supported by the Arctic-wide time series in panel (c), which shows that variability in εmech is driven by intermittent extremes superimposed on a low median background. The large interquartile range relative to the median indicates that rare, high-energy events contribute disproportionately to temporal variability. This indicates that mechanical dissipation in the Arctic is not controlled by the mean state, but by intermittent high-energy events that dominate variability. Panel (d) shows that dissipation is strongly modulated by season, with peaks in late summer and early autumn and a minimum during late winter under consolidated ice. This seasonal cycle reflects the combined influence of increasing wind stress, reduced ice cover, and enhanced wave activity, all of which act to amplify mechanical energy input into the mixed layer. In the MIZ, mean forcing sets the spatial structure of dissipation, while intermittent events control its variability.

The contribution of wave-induced forcing to upper-ocean mixing depends not only on wave strength but also on the relative alignment between wind and waves. To quantify how directional misalignment modulates LT mixing potential, we compare wind-aligned and dynamically projected Langmuir metrics across the Arctic. Misalignment variability is evaluated using the temporal standard deviation of the log-ratio between projected and aligned Langmuir numbers, σt[log⁡(Laproj/Lat)], which captures fluctuations in the effective projection of Stokes forcing onto the Langmuir cell axis. Pan-Arctic spatial patterns (Fig. 6a) show that variability in the projected-to-aligned Langmuir number ratio ranges from approximately 0.05 to 0.30, with the largest values confined to the MIZ, particularly in regions of persistent wave–ice interaction such as the Barents and Greenland Seas. In contrast, the central Arctic exhibits consistently low variability (≲0.1), reflecting weak wave forcing in compact ice cover. Despite this pronounced spatial variability in diagnostic LT metrics, the impact on LT energetics is limited. The 90th percentile change in VKE (Fig. 6b) remains below ∼ 1 % across most of the ice-covered Arctic and exceeds 4 %–6 % only in localized regions near the ice edge. These regions correspond to partial ice cover and active wave propagation, where Stokes drift is sufficiently strong for directional effects to influence the projected shear. These magnitudes indicate that although misalignment can significantly perturb diagnostic LT metrics, its effect on the resulting mixing is comparatively weak.

To assess how frequently wind–wave misalignment occurs and how it relates to LT variability, we examine the distribution of misalignment angles together with their co-occurrence with wave forcing and the resulting LT response (Fig. 7). The angle distribution (Fig. 7a) shows that moderate-to-large misalignment is relatively common, with more than half of cases exceeding ∼ 30°. However, the joint distribution with wave strength (Fig. 7b) indicates that these larger angles are most often associated with relatively weak wave forcing. The energetic response remains small across all angles (Fig. 7c), with changes in VKE close to zero in most cases and only limited variability at larger misalignment. These suggest that variability in projected LT metrics does not translate directly into changes in mixing, and that the influence of misalignment on LT is secondary compared to variations in wave forcing.

The MIZ emerges not simply as a geometric transition zone, but as a dynamically distinct mixing regime in which wave, wind, and ice processes interact to produce intermittent LT forcing. While conditions resembling open-water forcing occur primarily within the MIZ, LT activity is not controlled by MIZ extent alone. Instead, it is governed by the intensity and coherence of wind–wave forcing, which varies seasonally and episodically. As a result, periods of broad MIZ coverage do not necessarily correspond to enhanced LT activity, whereas dynamically active ice-edge regions can support strong, transient LT forcing. The mixing regime-based analysis further shows that LT variability is closely linked to threshold behavior between shear-, mixed-, and wave-dominated states. Regime instability peaks where grid cells frequently cross these physically defined boundaries, indicating that mixing variability arises from fluctuations in forcing that shift the balance between competing processes, rather than from large-scale ice extent alone. This highlights the MIZ as a region of enhanced variability and frequent reorganization of surface forcing, where LT emerges intermittently in response to episodic wave–ice interaction. This behavior extends earlier work showing that ice-edge variability is governed by dynamical thresholds rather than ice extent alone .

SIC provides a first-order constraint on the mean balance between wave- and shear-driven turbulence by regulating the attenuation of Stokes drift. For most seasons, median Lat increases with SIC, reflecting the progressive suppression of wave-driven forcing relative to wind stress as ice cover increases. This leads to predominantly shear-dominated conditions in the central Arctic. However, SIC alone does not fully explain the variability in LT forcing. Significant departures from the mean SIC–Lat relationship arise across seasons, indicating that additional processes modulate the realized balance between wave and shear forcing. In particular, reduced Lat at higher SIC during summer reflects intermittent wave penetration enabled by thinner, more fragmented ice and enhanced floe-scale heterogeneity. Under these conditions, long-period swell can episodically access nominally ice-covered regions, temporarily enhancing Stokes drift without establishing sustained wave-driven mixing. This indicates that SIC constrains the background forcing state but does not capture the processes that govern LT variability and intermittency.

Energetic metrics show that variability in LT mixing is governed primarily by the intermittency of mechanical forcing rather than its mean magnitude. Highly intermittent dissipation is strongly localized to the same MIZ corridors that exhibit frequent regime transitions, indicating that episodic events, such as storms and transient wave penetration, dominate the temporal evolution of mixing. This behavior is consistent with previous studies showing that turbulent mixing in both open-ocean and ice-covered environments is controlled by intermittent, high-energy events rather than by time-mean forcing . Wind–wave misalignment introduces an additional geometric constraint on LT efficiency by reducing the effective projection of Stokes shear onto the Langmuir cell axis. While spatial diagnostics indicate substantial variability in projected LT metrics, the statistical analysis of misalignment angles shows that near-aligned conditions dominate, with most angles below ∼ 30°, and larger misalignment occurring primarily in the MIZ. The energetic response to misalignment is weak. Hence, despite misalignment perturbing LT metrics, its effect on mixing is secondary, with typical changes in VKE remaining small relative to variability driven by wave forcing. LT energetics are controlled primarily by the magnitude and intermittency of wave forcing, while geometric effects act only as a weak modulation. In summary, LT in the Arctic is primarily controlled by intermittent wave forcing within the MIZ, with sea ice setting the background state and misalignment playing a secondary role.

Our results show that LT in the Arctic is strongly constrained by wave–ice interactions. Wave attenuation beneath consolidated ice limits Stokes drift and confines wave-driven mixing primarily to the MIZ. Thus, parameterizations based on open-ocean conditions are likely to overestimate both the extent and persistence of Langmuir-driven mixing in ice-covered regions. LT forcing is also highly intermittent, driven by episodic wind–wave events along the evolving ice edge. Capturing this variability requires parameterizations that respond to changes in Stokes drift, rather than relying solely on bulk ice properties or steady forcing assumptions. In contrast, wind–wave misalignment plays a secondary role. Although it modifies diagnostic LT metrics, its effect on energetics is small relative to variability associated with wave forcing. These results suggest that improving the representation of wave attenuation and Stokes drift under sea ice is more important than explicitly resolving directional effects for modeling Arctic mixed-layer dynamics.

Our approach is based on bulk diagnostics and empirically derived scalings and therefore carries several important limitations. The use of ∼ 25 km resolution fields limits our ability to resolve fine-scale processes such as submesoscale eddies, floe-scale wave attenuation, and narrow leads and polynyas, all of which can locally modulate LT and upper-ocean mixing. While neighborhood-based statistics partially capture local heterogeneity, direct assessment of these processes requires higher-resolution modeling and targeted in situ observations. In addition, our analysis relies on a coupled sea ice–wave framework in which the ocean mixed layer does not respond dynamically to wind–wave forcing, and stratification feedbacks are not explicitly resolved. Mixed-layer depth and buoyancy effects are prescribed, limiting the realism of the diagnosed vertical mixing response. In particular, buoyancy-driven convection and LT are expected to interact nonlinearly in ice-covered and meltwater-influenced environments, yet their relative contributions remain poorly constrained. As a result, the metrics presented here should be interpreted as indicators of LT mixing potential, rather than predictions of realized turbulent states.

Addressing these challenges ultimately requires fully coupled ocean–wave–ice models that resolve stratification, wave propagation beneath ice, and wind–wave misalignment simultaneously. Recent and ongoing studies are beginning to resolve Langmuir turbulence under partially ice-covered and weakly stratified regimes using large-eddy simulations, regional modeling, and coordinated observations e.g.,. These efforts provide a clear pathway toward stratification-aware, ice-modified Langmuir parameterizations.

Complementary observational efforts are also essential. Coordinated field campaigns employing autonomous profilers, SWIFT drifters, and satellite altimetry will be critical for evaluating Langmuir diagnostics and parameterizations in ice-covered waters. In particular, observational and modeling studies should prioritize the MIZ, where wave–ice–wind interactions are most dynamically active. Finally, quantifying the impact of LT on vertical tracer transport, stratification erosion, and ice–ocean heat exchange in climate models will be essential for assessing its broader role in the evolving Arctic system.