Enhancing Ocean Temperature and Salinity Reconstruction with Deep Learning: The Role of Surface Waves

Abstract

In oceanographic research, reconstructing the three-dimensional (3D) distribution of temperature and salinity is essential for understanding global climate dynamics, predicting marine environmental changes, and evaluating their impacts on ecosystems. While previous studies have largely concentrated on the effects of various modeling approaches on reconstructing oceanic variables, limited attention has been paid to the role of surface waves in reconstruction. This study, based on sea surface data, employs a deep learning-based neural network model, U-Net, to reconstruct 3D temperature and salinity across the North Pacific and Equatorial Pacific within the upper 200 m. The input of wave information includes the significant wave height (SWH), Langmuir number (La), and Langmuir enhancement factor (ε); the latter two indicate the strength of Langmuir turbulence, which promotes vertical mixing in the ocean surface layer and thereby affects profiles of temperature and salinity. The results indicate that incorporating wave information, particularly the La and ε, significantly enhances the model’s ability to reconstruct ocean temperature and salinity. This highlights the critical role of surface waves in enhancing the reconstruction of 3D ocean temperature and salinity.

1. Introduction

The three-dimensional (3D) structure of subsurface temperature determines the characteristics of the mixed layer and thermocline, directly influencing the evolution of key dynamic phenomena such as mesoscale eddies and internal waves [1,2]. Salinity, as a critical factor affecting ocean density gradients, plays a pivotal role in driving global circulation systems, including the Atlantic Meridional Overturning Circulation (AMOC) and thermohaline circulation [3,4,5]. However, due to the limitations of in situ observations, obtaining continuous, high-resolution profiles of the subsurface temperature and salinity remains a significant challenge. The lack of subsurface observations leaves many aspects of the internal ocean’s variability poorly understood [6]. Therefore, reconstructing subsurface temperature and salinity is of great importance for advancing our understanding of the 3D dynamics of the ocean [7].

Numerous techniques have been explored for reconstructing subsurface information using surface data, and some of them have been adopted for 3D ocean reconstruction, including statistical empirical analysis methods, dynamical models, and artificial intelligence techniques [8,9,10]. The statistical empirical analysis methods typically rely on historical data to infer oceanic characteristics by establishing statistical relationships between surface and subsurface data. Many empirical analysis methods for reconstruction are based on the Empirical Orthogonal Function (EOF), which extracts primary spatial modes by decomposing observational data [11]. Reduced Optimal Interpolation (ROI), an extension of the EOF method introduced by Kaplan et al. (1997) [12], was initially developed to reconstruct the sea surface temperature (SST) from sparse data and has since been applied to different variables, such as sea level pressure [13], sea level anomalies [14], and 3D velocity fields [15]. Such empirical analysis methods, while highly flexible and independent of complex physical assumptions, can be limited when dealing with highly nonlinear relationships, which are usually present in ocean state variables (temperature, salinity, and currents [16]). Another widely used approach involves developing dynamic models based on numerical simulation methods. These models use idealized dynamical equations to establish the relationship between surface variables and subsurface parameters [17]. However, they are typically tailored to specific regions and demand extensive computational resources, which often precludes their applicability for large-scale, real-time operational use [18].

In contrast, deep-learning-based artificial intelligence methods can capture complex nonlinear relationships between surface and subsurface characteristics through nonlinear mapping [19]. Deep learning models are adept at handling large-scale data and effectively extracting features that link surface and subsurface parameters. In 3D ocean reconstruction tasks where subsurface observations are sparse, deep learning methods can achieve precise reconstructions by leveraging large training datasets to map surface features to subsurface characteristics [20]. Significant progress has recently been made in the reconstruction of ocean state variables using deep learning. For example, Cheng et al. employed a back-propagation neural network (BPNN) to determine the subsurface temperature in the North Pacific [21]. Tian et al. utilized a feed-forward neural network (FFNN) to reconstruct high-resolution subsurface salinity fields [22]. Zhang et al. introduced a multivariate temporal self-attention network to reconstruct subsurface thermal and saline structures in the Pacific [23]. Furthermore, Liu et al. introduced a CNN (Convolutional Neural Network) to retrieve temperature and salinity profiles within mesoscale eddies in the regions of the Kuroshio Extension and tidal ocean currents [24].

Most studies utilize surface wind, surface temperature, surface heat flux, freshwater flux, wind stress curl, and sea surface height anomalies (SSHAs) to train deep learning models for reconstructing subsurface temperature and salinity [25]. To the best of our knowledge, surface waves have rarely been employed in reconstructing ocean state variables using deep learning, leaving their potential benefits largely untapped. Surface waves influence ocean temperature and salinity, particularly in the mixed layer, through wave–current and wave–wind interactions—key mechanisms driving upper-ocean dynamics [26]. This study incorporates wave-related parameters into the reconstruction of ocean temperature and salinity using an improved U-Net model based on convolutional neural networks [27]. This architecture has been widely recognized for its effectiveness in reconstructing and predicting complex physical fields.

An important process through which surface waves influence ocean temperature and salinity is Langmuir turbulence [28]. Langmuir turbulence is a type of surface-layer turbulence caused by the interaction between surface waves and ocean currents. Langmuir turbulence is typically in the form of counter-rotating vortices with horizontal axes parallel to the wind direction [29]. It induces the significant vertical mixing of temperature and salinity in the ocean surface boundary layer [30], and therefore can influence the profiles of these variables. The strength of Langmuir turbulence can be indicated by the turbulent Langmuir number (La) [31]. The definition of the turbulent Langmuir number does not include the angle θ between winds and waves. Previous studies have shown that misaligned winds and waves can reduce the strength of Langmuir turbulence [32,33]. Therefore, the Langmuir number La and wave-wind angle θ can be combined to form a so-called Langmuir enhancement factor (ε), which better indicates the strength of Langmuir turbulence [34,35].

By integrating SWH and these wave-related parameters (La, ε), this study seeks to investigate whether they can improve the accuracy and fidelity of deep-learning-based reconstructions of ocean temperature and salinity.

The structure of this paper is as follows. Section 2 provides a detailed description of the study regions, data sources, and methodologies. Section 3 presents the experimental results, evaluating the impact of incorporating SWH, La, and ε on the reconstruction accuracy across two representative regions. Finally, Section 4 discusses the implications of the findings, concluding with broader insights into the role of wave dynamics in oceanographic modeling.

2. Data and Methods

2.1. Study Regions

We selected two representative regions in the Pacific Ocean (Figure 1), which reflect diverse ocean wave conditions. Region A (5° N to 5° S, 220° E to 240° E), located in the central Equatorial Pacific, covers part of the core region of the El Niño–Southern Oscillation (ENSO) [36]. This region generally experiences relatively calm waves, with an annually averaged wave height of 1 m.

Region B (30° N to 40° N, 170° E to 190° E) is located within the North Pacific subtropical convergence zone and extends into the Kuroshio Extension, a highly energetic region characterized by strong currents and mesoscale eddies [37]. The interaction between wave dynamics and the vigorous currents of the Kuroshio Extension creates complex patterns, leading to significant variations in wave height. The annual average wave height is approximately 3 m, with substantial influence from monsoons.

2.2. Data

This study uses reanalysis data from the Copernicus Marine Environment Monitoring Service (CMEMS)—one of the core services under the European Union’s Copernicus Programme—as its training samples. As a flagship Earth observation initiative of the EU, CMEMS is dedicated to providing systematic, continuous reference information on physical oceanography, marine biogeochemistry, and sea-ice conditions for both the global ocean and European regional seas. Its high-quality Global Ocean Reanalysis and Simulation Ensemble (GLOSEA) has become fundamental infrastructure for oceanographic research. CMEMS’s product portfolio encompasses 340 distinct lines: 218 “Blue Ocean” products (physical ocean-state parameters), 78 “Green Ocean” products (biogeochemical parameters), and 44 “White Ocean” products (sea-ice parameters), thereby offering invaluable data support across ocean science, climate research, and marine resource management [38].

The temperature and salinity reanalysis data employed in this study are drawn from CMEMS’s Global Ocean Ensemble product, which features a spatial resolution of 0.25° and provides daily averages of temperature, salinity, currents, and ice variables on 75 vertical levels since 1993. This globally consistent, high-quality three-dimensional ocean dataset represents a multi-model ensemble that integrates numerical ocean models with satellite observations and in situ measurements. In this study, the temperature and salinity data were obtained from the aforementioned reanalysis product, while the significant wave height (SWH) data were taken from the CMEMS Global Wave Reanalysis product (WAVERYS). As the objective of our study was to evaluate the impact of waves on the reconstruction of oceanic variables, no additional variables were included to avoid interference. Furthermore, the Stokes drift data and wind stress data required for calculating the Langmuir number were also sourced from CMEMS, ensuring consistency and reliability in the dataset.

We used the reanalysis data from 2018/01 to 2021/06 as the training set (approximately 70% of the total), data from 2021/07 to 2021/12 as the cross-validation set (approximately 10%), and the entire year of 2022 as the test set (approximately 20%). In the North Pacific and Equatorial regions, 31 depth layers (reaching 200 m) of data were used. This chronological split ensured an objective evaluation of the model’s predictive performance on future time points and prevented temporal data leakage. Since our primary goal was to investigate the impact of wave information on the marine environment of a specific region, we extracted a 41×81 grid covering the target area for our experiments, without including data from the surrounding waters. The model utilized surface parameter data as input to reconstruct the 3D temperature and salinity fields.

To ensure the consistency of the training data, all samples were preprocessed before being fed into the model. First, the nearest-neighbor interpolation method was employed to map all datasets from different sources (including wave data, Stokes drift, and wind stress) onto the temperature and salinity grid, achieving a unified representation with a spatial resolution of 0.25° and a temporal resolution of 1 day. Second, to eliminate the interference caused by amplitude differences among various variables and to ensure the accuracy of the reconstruction, a uniform normalization procedure was applied to all data prior to model input.

To more intuitively illustrate the model’s input and output fields, we used study area B (the Kuroshio Extension region in the North Pacific, 30–40° N, 170–190° E) as an example, presenting the 2022 annual mean multi-channel input feature maps alongside the corresponding three-dimensional temperature and salinity (Figure 2). The input features are drawn from five key air–sea interface parameters: SST and sea surface salinity (SSS) at 0.5 m depth, SWH, La, and ε. From the 31 available vertical levels, we selected four representative depths (≈20 m, 50 m, 100 m, and 200 m) to illustrate the output temperature and salinity profiles.

In order to explore the roles of surface waves on the accuracy of ocean temperature and salinity reconstruction, we designed four sets of experiments (

Table 1. List of four experiments.

Experiment Name	Inputs	Outputs
Exp1: Baseline (none wave)	SST	3D Temperature
Exp2: swh	SST, SWH
Exp3: La	SST, La
Exp4: ε	SST, ε

). Taking the reconstruction of temperature as an example:

La characterizes the intensity of Langmuir turbulence. It is defined as follows:

$$\mathrm{L}\mathrm{a}=({U_{\mathrm{*}}/U_s)}^{{\displaystyle \frac{1}{2}}}$$

(1)

Here, $U_{\mathrm{*}}$, known as the friction velocity, represents the turbulent velocity induced by wind at the ocean surface. It is calculated as follows:

$$U_{*}=\sqrt{{\displaystyle \frac{{\tau }_0}{\rho }}}$$

(2)

where ${\tau }_0$ is the wind stress and $\rho$ is the seawater density. The denominator $U_s$ in Equation (1) is the surface Stokes drift velocity. A small La indicates strong Langmuir turbulence.

The Langmuir enhancement factor ε serves as a measure of the Langmuir turbulence intensity, incorporating the influence of the wind–wave θ [35]:

ε = 0.2(cosθ+1) La^−2/3

(3)

The Langmuir enhancement factor represents the enhancement of the boundary-layer turbulence and, thus, the enhancement of the vertical mixing. A small Langmuir number with aligned wind and waves (θ = 0) yields a large enhancement factor ε and thereby strong vertical mixing.

2.3. Methods

We employ a U-Net deep-learning model to reconstruct the vertical profiles of temperature and salinity. Originally proposed for biomedical image analysis, U-Net is both computationally efficient and highly capable of pattern recognition within multi-scale feature images [28]. U-Net consists of two parts: an “encoder” path that compresses the spatial resolution of input variables through repeated convolutions while expanding the number of feature maps representing data patterns, and an “expanding decoder” path that uses the feature maps and spatial information to construct an output field with the same spatial resolution as the input (Figure 3).

The batch size is set to 32, with 100 epochs, and the mean squared error is chosen as the loss function during training. To comprehensively evaluate the reconstruction performance of the model, we use Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and the coefficient of determination (R²) as evaluation metrics to measure the prediction accuracy and goodness of fit of the model.

The calculation formulas are as follows:

$$RMSE=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_r-y_p\right)}^2}{n}}}$$

(4)

$$NRMSE={\displaystyle \frac{\sqrt{\frac{1}{n}{\sum }_{i=1}^n{\left(y_r-y_p\right)}^2}}{y_{rmax}-y_{rmin}}}$$

(5)

$$R^2=1-{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_r-y_p\right)}^2/n}{{\sum }_{i=1}^n{\left(y_r-\overline{y_r}\right)}^2/n}}$$

(6)

Here, $y_r$ represents the reanalysis data (the “truth”), $\overline{y_r}$ represents the average of the reanalysis data, $y_p$ denotes the predictions generated by the deep learning model.

3. Results

3.1. Temperature

3.1.1. Region A

From the reconstructed temperature profiles using U-Net (Figure 4), the model demonstrates a high level of consistency with reanalysis data in the upper ocean (0–50 m), effectively capturing temperature variations near the sea surface. In Figure 4, “Reanalysis” refers to the reanalysis data, “none” represents the baseline model without waves (Exp.1), “swh” indicates the incorporation of SWH (Exp.2), “La” denotes the inclusion of the Langmuir number (Exp.3), and “ε” represents the addition of the Langmuir enhancement factor (Exp.4). Incorporating wave-related variables further reduces the NRMSE by 10–20% (Figure 4e). However, as depth increases—especially below the mixed layer—the discrepancy between the model and reanalysis data grows. In the 50–100 m range, the RMSE and NRMSE increase significantly, peaking at the thermocline, indicating that the temperature coupling between the deep ocean and the surface weakens [39], while processes such as internal waves and mesoscale eddies become dominant (Figure 4b,d). Figure 4c shows that while SWH can reduce the RMSE at certain upper-layer depths, it may also increase it at others. The study region, located in the central equatorial Pacific—an important area for ENSO research—generally experiences low wave heights, with wave influences becoming particularly pronounced during extreme events but relatively weak under normal conditions [40]. In contrast, the La and ε better indicate the Langmuir turbulence intensity within the 0–100 m range, enabling a more accurate representation of wave-driven mixing processes. In deeper layers (100–200 m), the wave-inclusive model remains stable, with SWH helping to reduce the RMSE by approximately 0.05 m near the thermocline. Overall, in this key ENSO research region, a baseline model relying solely on SST performs suboptimally across most depths compared to models that incorporate wave-related variables.

In comparing the horizontal distribution of temperatures at the mixed layer and below the mixed layer base (Figure 5a), it can be observed that regardless of the depth layer, the reconstruction results of the U-Net model and the reanalysis data overall exhibit a high degree of consistency in spatial patterns and detailed features. In Figure 5, we compare the reanalysis data with four different model outputs: the baseline model without waves (none), and models incorporating swh, La, and ε, respectively. The mixed layer depth shown in the figure is defined by the temperature difference exceeding 0.2 °C compared to the surface temperature. However, the RMSE distribution (Figure 5b) highlights a distinct weak area: on the north side of the equator (approximately 1–5° N), the baseline model shows systematically higher errors, indicating that relying solely on SST information is insufficient to characterize the more complex wind–wave–current coupling effects in this region. In fact, this belt experiences stronger winds and relatively more intense wave breaking and Langmuir turbulence, which leads to a more active thermal transport process between the mixed layer and below the mixed layer base [41]. To address this deficiency, when La and ε are incorporated as auxiliary inputs into the deep learning model, the high error zones originally in the mixed layer and below the mixed layer base can be significantly reduced.

Physically, the La parameter introduces information on the intensity of Langmuir turbulence, helping the U-Net model to implicitly learn the vertical mixing mechanism formed by the coupling of Stokes drift and background current, a mechanism that deepens the mixed layer depth by enhancing surface ocean mixing [42]. Meanwhile, ε can better represent the misalignment between wind and waves; a significant wind–wave misalignment can reduce the strength of Langmuir turbulence and its induced vertical mixing, affecting the vertical profiles of temperature [33,34].

The vertical profiles shown in Figure 5c,d further analyze the model’s predictive ability in the vertical dimension. Overall, considering that the U-Net reconstruction of the mixed layer and below the mixed layer base is highly consistent with the reanalysis results, the RMSE is only between 0.09 and 0.38 °C, and at 150–200 m, it remains at a low error level of 0.38–0.66 °C. However, in the 100–150 m depth range, an error band of 1.5–2.5 °C appears, with spatial partitioning in longitude: 230° E–240° E (located at the edge of the equatorial eastern Pacific cold tongue) mainly accumulates errors in the mixed layer and below the mixed layer base; 220° E–230° E exhibits larger deviations concentrated in the mid-layer (100–150 m), just above the core area of the equatorial undercurrent (EUC) [43,44]. This difference may be jointly caused by locally wind-driven upwelling and the shear instability of the EUC, leading to the complexity of the thermal structure [45,46].

It is worth noting that, as expected with the significant role of La in the mixed layer, the introduction of La not only significantly enhances the reconstruction accuracy of the mixed layer and below the mixed layer base, but also noticeably narrows the error in the 100–150 m depth range. This may be because the model, through the additional channel of La, “senses” the cross-layer effects of the interactions between waves, turbulence, and the current field, thereby correcting the baseline model’s temperature reconstruction error in the 100–150 m layer [47].

3.1.2. Region B

From the vertical profile in Figure 6a, it can be seen that the temperature in region B uniformly decreases from 20 °C at the surface; below 20 m it enters the thermocline transitional zone, where the temperature gradient increases sharply (>0.03 °C/m) and drops to 14 °C at a depth of 200 m. However, within the thermocline range of 30–50 m, the RMSE and NRMSE of the model reconstruction reach approximately 1.5 °C and 0.08, respectively (Figure 6b,d), which are significantly higher than the error level in the upper 10 m of the surface (only 0.1–0.2 °C). The thermocline often becomes a challenge for reconstruction due to its steep temperature gradient and complex mixing process [48].

Region B is located at the northern edge of the Kuroshio Extension and is an area with frequent mesoscale eddy activity. It is influenced not only by the strong western boundary current of the Kuroshio Extension but also compounded by the path of mid-latitude storms, resulting in significant changes in sea surface waves [49] and exhibiting an even stronger coupling of Langmuir turbulence with the shear of the background current, which creates an extremely unstable dynamic state within the mixed layer and the thermocline [50].

On this basis, the introduction of wave-related variables can provide the model with key information to capture the interactions between waves and currents. In particular, the addition of La and ε enables the model to explicitly reflect the enhanced vertical mixing effect induced by waves. Within the thermocline (30–50 m), this improvement reduces the RMSE by approximately 0.15 °C, corresponding to an improvement of about 10% in reconstruction accuracy (Figure 6c); meanwhile, within the upper 10 m of the surface, the improvement in NRMSE even reaches 20% (Figure 6e). Similar to region A, the reconstruction performance is significantly enhanced after the inclusion of wave variables, with the contribution of ε being particularly prominent.

We further examine the horizontal distribution of temperature within the mixed layer and below the mixed layer base in Region B (Figure 7a,b). From the model’s reconstruction performance, it is evident that the spatial temperature distribution is well reproduced both in the mixed layer and below the mixed layer base. The baseline model shows notable high-error regions near the Kuroshio Extension, similar to Region A. With the inclusion of SWH, La, and ε, the extent of these high-error regions is significantly reduced. Among these, similar to the effects observed in the vertical profiles, the inclusion of ε is particularly effective, further lowering both the intensity and the spatial range of the high-error zones.

The vertical sections of temperature align closely with the reanalysis data (Figure 7c,d). The RMSE is relatively higher in the 30–50 m depth range, corresponding to the transition zone from the mixed layer to below the mixed layer base. This zone is significantly influenced by multi-scale dynamic processes, such as eddies and internal waves, making it more challenging for the model to reconstruct its complexity [51].

Additionally, Figure 7d reveals a slightly lower reconstruction performance on the left side of the region compared to the right. The left side is closer to the Kuroshio Extension’s main axis, where the dynamics are complicated and more challenging for the model to reconstruct its complexity [52]. The inclusion of SWH, La, and ε substantially improves the reconstruction capability, with a lower RMSE.

3.2. Salinity

Unlike the reconstruction of the temperature, the salinity reconstruction does not exhibit the same layer-wide improvement after incorporating SWH, La, and ε. The enhancements are more pronounced in the upper ocean and are accompanied by noticeable fluctuations. This phenomenon may be attributed to the differences in the physical properties and dynamic controlling processes of temperature and salinity [53].

Temperature in the ocean surface layer is strongly influenced by atmospheric forcing, making it more sensitive to wave- and current-driven mixing processes [54]. In contrast, the spatial distribution of salinity is more governed by processes such as precipitation, evaporation, runoff, and groundwater discharge, which exhibit significant non-uniformity in spatiotemporal scales [55]. Furthermore, compared to temperature, salinity typically has smaller vertical gradients, especially in the deep ocean, where salinity variations are generally less than 0.1 PSU [56]. These distribution characteristics mean that the baseline model already performs well in reconstructing deep-sea salinity, leaving limited room for improvement when wave-related variables are introduced.

3.2.1. Region A

The vertical profile of salinity in Region A (Figure 8a) exhibits the typical “sandwich structure” of equatorial oceans [57]. In the homogeneous surface layer (0–30 m), salinity remains at 34.8–34.9 psu, which is mainly attributed to the strong turbulent mixing driven by the equatorial trade winds (such as Langmuir turbulence and wave breaking), effectively distributing the surface freshwater uniformly and thereby suppressing the vertical salinity gradient [58]. The U-Net model based on SSS as the baseline input can well reconstruct the salinity in this mixed layer (RMSE = 0.04 psu, NRMSE = 0.005); with the further introduction of La and ε, the reconstruction accuracy in the surface layer is further improved, with the RMSE reduced in some depth layers, and the improvement in the NRMSE in the improvement layer reaching up to 15–18%. It is worth noting that the introduction of SWH instead weakens the performance, which may be due to the multiple solutions between SWH and surface salinity: high SWH may reflect the characteristic of strong wind-driven mixing, but may also be accompanied by freshwater input from precipitation events, thereby introducing noise in the model learning process [59].

In the halocline (30–100 m), salinity increases with a steep gradient of about 0.02 psu/m to 35.2 psu. Unlike in the mixed layer, the wave-related variables do not improve the salinity reconstruction in this water layer, which may be because the salinity change in the halocline is mainly controlled by the horizontal salt transport of EUC and upwelling, and the wave variables are difficult to capture for processes such as subsurface current fields or internal wave mixing [60]. Moreover, the energy penetration depth of the wave variables may be limited by the mixed layer, significantly weakening the physical association between these variables and the salinity variability in the halocline [61]. Entering the mid-layer (100–200 m), salinity drops back to 34.8 psu, forming a subsurface low-salinity core that creates a reverse gradient with the high-salinity morphology in the upper layer.

Overall, although the introduction of La and ε significantly improves the reconstruction performance of the U-Net model in the surface mixed layer, the salinity distribution in the halocline is mainly dominated by complex processes such as advection, internal wave mixing, and water mass interactions, which the existing input features cannot adequately cover; therefore, no corresponding reconstruction improvement is obtained in the halocline region. However, the performance improvement in the surface mixed layer is still commendable.

Region A’s horizontal salinity distribution in the mixed layer shows a clear latitudinal asymmetry (Figure 9a): salinity on the south side of the equator (–5°~0°) is generally above 35.0 psu, while on the north side (0°~5°N), it is maintained at about 34.5 psu. This difference reflects the different ocean dynamic processes and atmospheric precipitation patterns on both sides of the equator—stronger trade winds in the southern hemisphere help transport the high-salinity South Pacific Tropical Water (SPTW) to the south equatorial region, whereas the north side receives relatively more freshwater input [62,63,64]. Meanwhile, areas in the mixed layer with a higher RMSE (>0.1 psu) are mainly distributed between 4 and 5°N (Figure 9b); this belt is located at the convergence zone of the North Equatorial Countercurrent (NECC) and the South Equatorial Current (SEC), where the baseline model, relying solely on SSS, struggles to capture the dynamic balance, and thus tends to produce systematic bias [65,66]; similarly, the high-error zones below the mixed layer base are also concentrated in the northern hemisphere region.

Although the U-Net model can reconstruct the salinity structure well in most vertical sections, in the region around 100 m, salinity is noticeably higher and shows significant longitudinal variations (Figure 9c,d): near 220° E, salinity reaches a maximum of 35.2 psu, and gradually decreases to below 35 psu toward the east. This phenomenon may originate from the strong local currents or frontal effects on the left side of the region, leading to the concentration of high-salinity water masses. Surprisingly, although the vertical section in Figure 8 shows that the improvement of wave-related variables below the mixed layer base is limited, in the error core area of 220° E–223° E, after the introduction of La and ε, the RMSE was reduced by about 15%, with the improvement effect of La being the most significant. This contradictory phenomenon suggests that although wave-related variables generally have a weak influence on the reconstruction below the mixed layer base, in local areas, wave-related variables, by reflecting the effects of Stokes drift and vertical mixing, can effectively enhance the representation of the subsurface mixing process, thereby achieving the significant strengthening of local high-error regions [67].

3.2.2. Region B

Region B’s salinity reconstruction exhibits both greater stability and accuracy compared to Region A, especially in the mixed layer (0–30 m) where the RMSE is stably at 0.03 psu and the NRMSE remains ≤ 0.01 (Figure 10). Although the salinity between 100 and 200 m is affected by the low-salinity core of North Pacific Intermediate Water (NPIW), the RMSE in the deep layer of this region is also controlled within 0.06–0.08 psu [68,69]. Unlike Region A, which is mainly dominated by advection processes (such as EUC transport and upwelling), Region B, as a frontal zone of the Kuroshio Extension, is influenced by strong western boundary currents and mid-latitude storm mixing, with wave–current interactions and mesoscale eddy activities occurring abnormally frequently; its vertical salinity distribution is mainly regulated by mixing processes such as wind and waves [70].

Different from the partial surface effects observed in Region A, after the introduction of La and the ε, the model’s reconstruction performance in the mixed layer is significantly improved. A continuous decrease in RMSE is observed in each shallow layer, with NRMSE improvements of up to 20%. This may be because La and ε are directly related to mixing efficiency, and mixing is precisely the core controlling factor of the vertical salinity distribution in Region B [71]. In contrast, after the introduction of SWH, the model performance slightly declines, which may be attributed to the multiple solutions and noise interference of SWH when reflecting the influence of sea surface waves on salinity.

From the horizontal distribution of salinity in Region B (Figure 11a), a clear spatial differentiation characterized by “high in the south, low in the north, fresh in the west, and salty in the east” is observed, with the overall salinity ranging between 34 and 35 psu. Specifically, the southern part of the region (30–35°N) is mainly influenced by the North Pacific subtropical mode water transported by the Kuroshio warm current, resulting in a salinity generally above 34.8 psu [72]; meanwhile, in the northern part (35–40° N), due to increased freshwater input from the coastal cold current and the westerly drift, salinity gradually decreases to below 34.5 psu [73]. In addition, the vertical distribution of salinity in the longitudinal direction (Figure 11c) indicates that in the western part of the region (170–180° E), due to its closer proximity to the separation zone of the Kuroshio main axis and active mesoscale eddies and sub-mesoscale fronts, a distinct vertical “low–high–low” variation trend is exhibited [74]. In contrast, the eastern part of the region (180–190° E) is in a relatively stable area of the Kuroshio Extension, where the vertical gradient of salinity is relatively weak and the lowest salinity appears at a depth around 200 m near 190° E in the east.

The spatial distribution of the RMSE for salinity reconstruction overall follows the longitudinal trend of the temperature field (Figure 7d and Figure 9d), showing a trend of higher values in the west and lower in the east. In the western region, due to the influence of strong eddy activity and frontal dynamic processes [75], both the salinity and temperature fields exhibit higher spatiotemporal variability, significantly increasing the difficulty of the model reconstruction, resulting in the RMSE generally remaining at a high level of 0.07–0.08 psu; in contrast, the eastern region has relatively stable ocean conditions, with salinity and temperature changes being more uniform, and the RMSE is effectively controlled within 0.01–0.04 psu. In addition, although the improvement below the mixed layer base (30–50 m) is relatively limited, within the mixed layer (0–30 m) the introduction of wave-related variables (SWH, La, and ε) also significantly enhances the model performance (Figure 11b), with the RMSE in some areas further reducing from 0.02 psu to around 0.01 psu. This significant improvement can be attributed to the introduction of wave-related variables, which more accurately represent the vertical mixing effects within the mixed layer, enabling the model to better capture both the horizontal and vertical characteristics of the salinity field in Region B where wave-current interactions are frequent.

3.3. Seasonality

In terms of correlation coefficients R2 (Figure 12), it is evident that the reconstruction results across all seasons exhibit high consistency. For temperature reconstruction, Region A shows the highest RMSE in spring, possibly due to the more active air–sea coupling processes and greater seasonal variability in this tropical region during spring [76]. In contrast, Region B has the highest RMSE in summer, likely due to more pronounced sea temperature fluctuations and diverse forcing factors in mid-latitudes during summer [77]. Moreover, the inclusion of SWH, La, and ε in Region B consistently reduces RMSE, with the greatest improvement observed in autumn, reaching up to 26.9%.

For salinity reconstruction, the seasonal patterns are generally similar to those of temperature, but the effect of SWH is less favorable, while La and ε show a relatively stable performance. Despite minor RMSE increases in certain seasons of Region A (e.g., spring), the inclusion of La and ε allows the model to maintain high R2 values. Particularly in the seasons with the highest RMSE (spring in Region A and summer in Region B), the inclusion of La or ε significantly improves the model’s R2, further highlighting the critical role of waves and related variables in enhancing the accuracy of temperature and salinity reconstructions.

4. Discussion

This study uses comparative analysis to reveal the differentiated roles of wave parameters in reconstructing the ocean’s three-dimensional temperature and salinity structure. The results show that incorporating only SWH does not consistently improve the reconstruction accuracy; however, the inclusion of La and ε often yields superior outcomes. This enhancement primarily arises from the nonlinear coupling between the wave field and ocean dynamic processes: wave-induced vertical mixing and water mass transport, via mechanisms such as Stokes drift and Langmuir turbulence, exert spatially heterogeneous, multi-scale influences on vertical stratification. The distinct hydrodynamic information contained in different wave parameters leads to varying contributions to the reconstruction [78].

The introduction of La and ε reflects a deeper consideration of wave–current interactions. In particular, ε additionally incorporates the wind–wave angle. Using SWH alone is insufficient to accurately capture Langmuir mixing; in contrast, La and ε precisely characterize Langmuir turbulence induced by wave–current interactions, whose intensity and penetration depth depend sensitively on the coupled state of wind, currents, and waves. By introducing these two parameters, one effectively embeds a composite factor accounting for wave–current–wind interactions, thereby enabling the model to better represent the ocean’s vertical structure [35]. Consequently, embedding wave variables into temperature and upper-ocean salinity reconstructions is an effective approach. Nevertheless, improvements in deep-ocean salinity remain limited, underscoring the differential impact of wave information on various physical parameters.

We also found that, because our dataset only includes core-region observations and lacks hydrologically continuous information from surrounding waters, reconstruction errors at the domain boundaries are significantly higher than in the central region. To mitigate such boundary effects, future work could expand the study area, integrate observations from beyond the boundaries, or adopt more sophisticated boundary condition treatments to enhance the model’s spatial generalization capability. Furthermore, the enhancement in temperature and salinity reconstruction in the Kuroshio Extension region (Region B) was markedly greater than in the ENSO core region of the central equatorial Pacific (Region A). This difference likely stems from the complex wave field generated by the interaction of strong midlatitude currents and wind, which, through Stokes drift and Langmuir turbulence, exerts a more pronounced direct effect on upper-ocean structure. These regional variations provide a valuable reference for deploying wave-dynamic parameters in different marine areas to optimize future deep learning reconstruction algorithms.

Finally, our study advances a “data-driven, physics-informed” hybrid paradigm in the geosciences by seamlessly integrating wave physical processes with deep-learning techniques. This approach not only improves model accuracy but also enhances physical interpretability, demonstrating that—even in the face of deep learning’s “black box” nature—physical oceanography knowledge can still guide models toward more accurate reconstructions of ocean structure.

5. Conclusions

This study investigates the impact of SWH, La, and ε on the accuracy of temperature and salinity reconstruction with deep learning. The experimental results from different regions, such as the North Pacific and Equatorial Pacific, demonstrate that wave information can significantly enhance the temperature and salinity reconstruction accuracy under specific conditions, particularly with the inclusion of La and ε.

For temperature reconstruction, incorporating wave-related variables notably improved the simulation accuracy of deep-layer temperatures across regions, with improvements ranging from 15% to 30% at certain depths. However, for salinity, La and ε are proven to be more effective in the upper ocean. Under certain oceanic conditions, the inclusion of SWH did not significantly enhance the salinity accuracy and could even lead to accuracy declines.

The reconstruction performance for salinity and temperature below the mixed layer base is less ideal compared to other layers. Future research could focus on developing or selecting models that are better suited to the characteristics of these transition layers. Additionally, integrating more environment variables that reflect regional characteristics, particularly for different depth layers and complex current environments, could further enhance the accuracy of ocean dynamics reconstruction.

Furthermore, this study underscores the complexity of applying deep learning methods to multivariate ocean reconstructions. Different physical variables may necessitate distinct combinations of input features to accurately characterize their unique vertical distribution patterns. By integrating various representations of physical variables, physical guidance will be implicitly introduced into the deep learning model, which may be an effective approach for enabling the model to learn, reconstruct, and simulate ocean processes more accurately.