Three-Dimensional Structural Analysis of Sea Temperature During Typhoon Transit

Abstract

This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone dynamics. By analyzing historical sea surface temperature data and the typhoon’s trajectory, the three-dimensional response of sea temperature during typhoon “Saola” was explored. The key findings are as follows: 1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days after the typhoon. In deep-sea areas, the highest warming occurs within five days, while the lowest cooling occurs within two days. 2. The nearshore water layers respond quickly to the typhoon, while the deep-sea water layers primarily respond in the middle depths, with a delayed effect. 3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer.

1. Introduction

Tropical cyclones (TCs) are among the most intense atmospheric events over tropical and subtropical oceans [1]. These warm cyclone systems develop in tropical waters, including the Western Pacific Ocean (typhoons), the Atlantic and Eastern Pacific Oceans (hurricanes), and the Indian and South Pacific Oceans (cyclonic storms) [2]. Typhoons are significant forms of ocean–atmosphere interaction [3,4]. As a typhoon passes, it triggers heavy rainfall, strong winds, and intense vertical mixing in the upper ocean due to wind stress [5,6]. This mixing alters the sea surface temperature (SST) and affects the temperature and salinity structure of the upper ocean, particularly the mixed-layer depth (MLD), mixed-layer temperature (MLT), and mixed-layer salinity (MLS) [7,8,9]. The upper ocean also experiences “heat pump” and “cold suction” effects [10,11,12]. After a typhoon passes, SST gradually recovers while subsurface thermal anomalies persist, resulting in a deeper thermocline (the “heat pump” effect). Conversely, the “cold suction” effect refers to the strong uplift of the thermocline caused by cyclonic wind stress [13,14,15]. Understanding these effects on ocean stratification is central to studying the response of upper ocean circulation to typhoons. Ocean temperature plays a crucial role in energy exchange between the ocean and typhoons, making it an important factor in understanding ocean responses [16,17,18].

The Northwest Pacific experiences the highest frequency and intensity of typhoons globally. Its upper ocean circulation, along with that of the South China Sea, are driven by the subtropical gyre, tropical circulation from trade winds, shallow overturning circulation from buoyancy fluxes, and monsoon-driven circulation. These large-scale systems are further influenced by mesoscale and sub-mesoscale eddies. Typhoons influence these smaller oceanic circulations and can also impact large-scale ocean currents by altering the upper ocean’s heat and salinity structure [19,20,21]. The interaction between the ocean and atmosphere during a typhoon is complex. The South China Sea, located in the Northwest Pacific, is mostly a marginal sea with a complex marine environment and dynamic system. Typhoon passages, characterized by strong winds and pressure gradients, cause intense air–sea heat exchange and mixing, leading to variations in wave patterns and SST.

International studies have noted that upwelling caused by typhoons leads to SST decreases, with the cooling center typically positioned to the left of the typhoon’s path [22,23]. Air–sea fluxes influence deep waters through dynamic and thermal processes [24]. The typhoon center draws cold subsurface water to the surface, mixing it with warm surface water and leading to the outward transport of mixed water, particularly near the typhoon’s edges. Research suggests that moisture flux, SST, upper-level jets, and outflow at the typhoon’s base are major factors influencing typhoon intensity in China’s offshore areas. Previous studies identified three main responses of ocean temperature to typhoons: (1) Typhoons cause SST to drop and deepen the mixed layer, forming a cold wake to the right of the typhoon’s path [25]; (2) the ocean’s structure can suppress cooling, intensifying the typhoon; (3) unique topography and climate conditions can affect upper ocean temperature responses. Current observation methods during typhoons include satellite observation, Unmanned Surface Vessels (USVs), drifting buoys for surface observation, and ARGO buoys and underwater gliders for subsurface observations [26,27]. However, none of these methods fully reflect the typhoon’s real response, highlighting the need for effective assessment methods to analyze marine environmental changes under typhoon influence.

In recent years, several tools such as ocean and marine numerical models and numerical simulation have been used for ocean forecasting and the prediction of ocean flow fields, ocean temperature, sea surface height, and other information. With advances in simulation technology, researchers increasingly rely on mathematical models to predict typhoon paths and assess SST changes. For example, studies on double typhoons found that SST decreased, with cooling patterns matching wave distributions [28]. Analyses also showed that 2–3 days after a typhoon passes, SST drops more significantly on the right side of the typhoon’s path compared to the left [29]. Other studies focused on offshore platforms provide insights for marine engineering design, disaster prevention, and mitigation [30]; SST response is influenced by initial mixed-layer depth and upper ocean heat gradient mechanisms [31]. SST response is also influenced by the initial mixed-layer depth and upper ocean heat gradient mechanisms [32]. The thermocline beneath the mixed layer also responds to typhoons, though mesoscale anomalies in subsurface heat structures are challenging to observe in real time [33]. Winds in tropical circulations induce localized upper ocean mixing [34], and stable circulations driven by heating, cooling, evaporation, and precipitation directly affect winds [35]. Wind stress enhances ocean vertical mixing and SST cooling [36,37,38].

This study used meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) (https://cds.climate.copernicus.eu) (accessed on 31 May 2023) and outputs from the Hybrid Coordinate Ocean Model (HYCOM) (http://apdrc.soest.hawaii.edu) (accessed on 3 June 2023) to construct a simulation model for Typhoon Saola (designated Typhoon No. 9, 2023). We selected this typhoon for simulation due to its significance in the 2023 Pacific typhoon season, as it illustrates the complexity and unpredictability of tropical cyclone dynamics.

Typhoon Saola moved northwest along a complex and variable path, producing strong winds and heavy rain that significantly impacted southern China. Additionally, it caused severe damage in the Philippines and surrounding regions, with strong winds, intense rainfall, and storm surges resulting in casualties, economic losses, and infrastructure damage. Understanding Saola’s characteristics is valuable for advancing knowledge on tropical cyclone formation, development, and track prediction. The wind speed and track of Saola are shown in Figure 1, with key parameters obtained from the China Central Meteorological Observatory Typhoon Network (https://typhoon.weather.com.cn/) (accessed on 31 May 2023).

Furthermore, controlled experiments were conducted to analyze the typhoon’s response to sea surface temperature (SST) variations in the South China Sea and to temperature anomalies at various ocean depths. This analysis was conducted in both horizontal and vertical profiles to gain insights into the influences of oceanic conditions on typhoon behavior.

2. Material and Methods

2.1. Study Area

The study area of Typhoon “Saola” covers 104° E to 127° E and 15° N to 28° N (Figure 1). To test the simulation model and its accuracy, tidal level (T1, T2, T3, T4) and current (S1, S2, S3, S4) data from four observation stations along with two temperature observation data (C1, C2) from two stations were used. The Pearl River coastal area (Region A, Figure 1) and the Kaohsiung offshore area (Region B, Figure 1) were key areas for analyzing SST outliers in different water layers in this study. The data came from the National Marine Science Data Center of China (https://mds.nmdis.org.cn/) (accessed on 3 June 2023).

2.2. The Construction of the Numerical Hydrodynamic Model

The Finite-Volume Community Ocean Model (FVCOM) is widely used to simulate marine dynamic environmental changes [39,40,41,42]. In this study, a hydrodynamic model of the South China Sea was established to provide the necessary conditions for analyzing Typhoon Saola’s impact on regional sea temperature. The governing equations include momentum, continuity, temperature, salinity, and density (Formulas (1)–(7)). The boundary conditions consist of free surface, seabed, and lateral solid boundaries (Formulas (8)–(12)). The variables are described in Table 1.

(1)${\displaystyle \frac{\partial u}{\partial t}}+u{\displaystyle \frac{\partial u}{\partial x}}+v{\displaystyle \frac{\partial u}{\partial y}}+w{\displaystyle \frac{\partial u}{\partial z}}-fv=-{\displaystyle \frac{1}{{\rho }_0}}{\displaystyle \frac{\partial P}{\partial x}}+{\displaystyle \frac{\partial }{\partial z}}(K_m{\displaystyle \frac{\partial u}{\partial z}})+F_u$

(2)${\displaystyle \frac{\partial v}{\partial t}}+u{\displaystyle \frac{\partial v}{\partial x}}+v{\displaystyle \frac{\partial v}{\partial y}}+w{\displaystyle \frac{\partial v}{\partial z}}+fu=-{\displaystyle \frac{1}{{\rho }_0}}{\displaystyle \frac{\partial P}{\partial y}}+{\displaystyle \frac{\partial }{\partial z}}(K_m{\displaystyle \frac{\partial v}{\partial z}})+F_v$

(3)${\displaystyle \frac{\partial P}{\partial z}}=-\rho g$

(4)${\displaystyle \frac{\partial u}{\partial x}}+{\displaystyle \frac{\partial v}{\partial y}}+{\displaystyle \frac{\partial w}{\partial z}}=0$

(5)${\displaystyle \frac{\partial T}{\partial t}}+u{\displaystyle \frac{\partial T}{\partial x}}+v{\displaystyle \frac{\partial T}{\partial y}}+w{\displaystyle \frac{\partial T}{\partial z}}={\displaystyle \frac{\partial }{\partial z}}(K_h{\displaystyle \frac{\partial T}{\partial z}})+F_T$

(6)${\displaystyle \frac{\partial S}{\partial t}}+u{\displaystyle \frac{\partial S}{\partial x}}+v{\displaystyle \frac{\partial S}{\partial y}}+w{\displaystyle \frac{\partial S}{\partial z}}={\displaystyle \frac{\partial }{\partial z}}(K_h{\displaystyle \frac{\partial S}{\partial z}})+F_S$

(7)$\rho =\rho (T,S)$

(8)$\begin{array}{c}{K}_m({\displaystyle \frac{\partial u}{\partial z}},{\displaystyle \frac{\partial v}{\partial z}})={\displaystyle \frac{1}{{\rho }_0}}({\tau }_{sx},{\tau }_{sy}),w={\displaystyle \frac{\partial \zeta }{\partial x}}+u{\displaystyle \frac{\partial \zeta }{\partial x}}+v{\displaystyle \frac{\partial \zeta }{\partial y}}+{\displaystyle \frac{\widehat{E}-\widehat{P}}{\rho }},\\ atz=\zeta (x,y,t)\end{array}$

(9)$\begin{array}{c}{\displaystyle \frac{\partial T}{\partial z}}={\displaystyle \frac{1}{\rho c_{\rho }{K}_h}}[Q_n(x,y,t)-SW(x,y,\zeta ,t)],{\displaystyle \frac{\partial S}{\partial z}}={\displaystyle \frac{S(\widehat{P}-\widehat{E})}{\rho K_h}}\mathit{cos}\gamma ,\\ atz=\zeta (x,y,t)\end{array}$

(10)$\begin{array}{c}{K}_m({\displaystyle \frac{\partial u}{\partial z}},{\displaystyle \frac{\partial v}{\partial z}})={\displaystyle \frac{1}{{\rho }_0}}({\tau }_{bx},{\tau }_{by}),w=-u{\displaystyle \frac{\partial H}{\partial x}}-v{\displaystyle \frac{\partial H}{\partial x}}+{\displaystyle \frac{Q_b}{\Omega }},\\ atz=-H(x,y)\end{array}$

(11)$\begin{array}{c}{\displaystyle \frac{\partial T}{\partial z}}=-{\displaystyle \frac{A_h\mathit{tan}\alpha }{K_h}}{\displaystyle \frac{\partial T}{\partial n}},{\displaystyle \frac{\partial S}{\partial z}}=-{\displaystyle \frac{A_h\mathit{tan}\alpha }{K_h}}{\displaystyle \frac{\partial S}{\partial n}},\\ atz=-H(x,y)\end{array}$

(12)$v_n=0,{\displaystyle \frac{\partial T}{\partial n}}=0,{\displaystyle \frac{\partial S}{\partial n}}=0$

The model employs an unstructured triangular grid (Figure 2), with 37,309 nodes and 71,327 cells, featuring a minimum grid resolution of 2 m in coastal areas. Bathymetric data were obtained from the GEBCO website, and the model’s forcing conditions included wind, sensible and latent heat, evaporation, precipitation, and tidal boundaries.

2.3. The Construction of the Holland and FVCOM Coupled Model

To accurately simulate changes in the seawater environment during typhoon transit, appropriate wind field data were used as the model’s upper boundary. The Holland typhoon model, commonly used for typhoon forecasting, has proven to be highly reliable [43,44]. In this study, we applied the Holland model, combining different maximum wind radius values and B parameters. The pressure and wind gradient equations are provided in Formulas (13) and (14):

(13)$P(r)=P_c+(P_n-P_c)(-{\displaystyle \frac{R_{\max }}{r}})B$

(14)$V_g(r)=\sqrt{(P_n-P_c){\displaystyle \frac{B}{{\rho }_a}}{({\displaystyle \frac{R_{\max }}{r}})}^B\exp {(-{\displaystyle \frac{R_{\max }}{r}})}^B+{({\displaystyle \frac{rf}{2}})}^2}-{\displaystyle \frac{rf}{2}}$

where P_c is the central pressure of the typhoon; R_max is the maximum wind radius; ρ_a is the air density; f is the Coriolis force; r is the distance from the typhoon center; and parameter B determines the kurtosis and intensity of the typhoon.

The B parameter and R_max are the two key parameters that determine the Holland wind field. When other parameters remain unchanged, the wind speed at the radius of the maximum wind speed gradually increases with the increase in parameter B, while the wind speed at the position far away from the typhoon center becomes weaker, and the wind field energy becomes more concentrated. On the contrary, the wind speed distribution is relatively average with distance.

In this study, we selected typhoon “Saola”, the ninth typhoon of 2023, to simulate its process. Typhoon “Saola” was a significant weather system in the 2023 Pacific typhoon season, showcasing the complexities and uncertainties of tropical cyclone dynamics. The study of typhoon “Saola” is essential for understanding the formation, development, and path prediction of tropical cyclones. The wind field parameters include latitude, longitude, maximum wind radius, central moving speed, typhoon central pressure, and radial pressure distribution parameters. The maximum wind radius (R_max) and radial pressure distribution parameters (B) are calculated by the typhoon characteristic parameters (Formulas (15) and (16)).

(15)$R_{\max }=\exp (3.015-6.291\times {10}^{-5}\Delta P^2+0.0337\mathsf{\Phi })$

(16)$B=2.0-(P_c-900)/160$

where ΔP is the difference between the central pressure of the typhoon and the normal external pressure; Φ is the geographical latitude.

The typhoon wind field simulated by the Holland typhoon model is integrated with the wind field data of ECMWF to establish a fusion wind field. By integrating wind fields, FVCOM can more realistically simulate the hydrodynamic environment during typhoon transit.

3. Results and Discussion

3.1. Hydrodynamic Model Validation

The measured results of each observation station were compared with the calculated results of the model (Figure 3). The tidal current values calculated by the model align well with the natural characteristics of tidal current propagation. The simulated hydrological values, including tide level, velocity, and flow direction at each station, show good agreement with the measured values, effectively reflecting the hydrodynamic conditions of the study area. Similarly, the SST values calculated by the model are consistent with natural SST variations, and the simulated temperature values at the observation stations align well with the measured data, effectively capturing the SST status in the study area.

In order to evaluate the accuracy of model results, it is necessary to measure the model through evaluation criteria. The parameters used for evaluation are Skill score (Skill), correlation coefficient (R), and root mean square error (RMSE) [45,46,47]. Skill classification criteria are as follows: (0.65,1) is excellent; (0.2,0.65) is good; and (0,0.2) is poor. The closer R is to 1, the closer RMSE is to 0, indicating that the simulation result is closer to the measured value. The equations are as follows (Formulas (18) and (19)):

(17)$Skill=1-{\displaystyle \frac{{{\displaystyle {\sum }_{i=1}^N\left(x_m-x_o\right)}}^2}{{\displaystyle {\sum }_{i=1}^N{\left(\left|x_m-\overline{x_m}\right|+\left|x_o-\overline{x_o}\right|\right)}^2}}}$

(18)$R={\displaystyle \frac{{\displaystyle {\sum }_{i=1}^N\left(x_m-\overline{x_m}\right)}\left(x_o-\overline{x_o}\right)}{{\left[{{\displaystyle {\sum }_{i=1}^N\left(x_m-\overline{x_m}\right)}}^2{{\displaystyle {\sum }_{i=1}^N\left(x_o-\overline{x_o}\right)}}^2\right]}^{1/2}}}$

(19)$RMSE={\left[{\displaystyle {\sum }_{i=1}^N\frac{{\left(x_m-x_o\right)}^2}{N}}\right]}^{1/2}$

where x_m is the model simulation result; x_o is the measured result of the tide measuring station; N is the sample size; $\overline{x_m}$ is the average value of the simulation results; $\overline{x_o}$ is the average value of the measured results.

The results of the evaluation parameters Skill and R values of the observation stations (T1, T2, T3, T4) models are greater than 0.95, and the RMSE value is less than 0.3 m, indicating that the model tidal level simulation output fits well with the measured results. For current speed observation stations and current direction observation stations (S1, S2, S3, S4), the measured and simulation results are good, with Skill values above 0.7 and R above 0.8. For the sea temperature observation stations (C1, C2), Skill and R values are higher, both above 0.9. This model can reflect the characteristics of water environment change in the study area.

3.2. The Hydrodynamic Characteristics of the Area

The tidal verification confirmed the reliability of the model. Since this study focuses on the impact of summer typhoons on SST, and residual flow plays a key role in material exchange after tidal influences are removed, this section analyzes the residual flow field to better understand the region’s water environment characteristics (Figure 4).

In the eastern part of the study area, where the wind direction is predominantly southeast, the residual flow deflects from the waters east of Taiwan towards the central Taiwan Strait. This is due to the Taiwan Strait’s location on the continental shelf of the East China Sea, characterized by complex seafloor topography with alternating hills and valleys. Flow velocity increases as currents converge on the northern side of the strait, with most of the flow moving along the coast. A portion of the flow weakens as it disperses into the waters off Kaohsiung, the only deep-water area near Taiwan, resulting in reduced current velocity.

In the western part of the study area, some residual flow moves along the coast of Northern Vietnam, originating from the interior of the Beibu Gulf. Another portion is deflected towards the northern South China Sea, driven by southwest winds as it passes Hainan Island. The seafloor topography of the Beibu Gulf slopes gradually from the gulf’s top to its mouth, creating relatively flat terrain. This, combined with the influence of the Qiongzhou Strait, generates vortices on the eastern side of Hainan Island. Around the Xisha Islands, the residual flow intensifies, likely due to the numerous islands enhancing the current in that region.

In the central part of the study area, which is primarily affected by the typhoon, the residual flow from the Taiwan Strait interacts with the flow from the southern side of Hainan Island, creating a low-velocity zone near the Pearl River Estuary where these opposing flows cancel each other out. The seafloor of the Pearl River Delta is composed of loose sediment and multiple islands, leading to a sharp increase in current velocity near the river mouth.

The characteristics of the Euler residual current in the study area during typhoon transit illustrate the trend in regional energy transport. Calculating the Euler residual flow field in this region will facilitate subsequent studies of SST.

3.3. The Plane Analysis of Temperature Response in South China Sea

In order to analyze the SST changes brought by typhoons to water bodies of each layer in the study area, the SST changes were evaluated by calculating $\Delta SST$ using the following formula:

$\Delta SST=SS{T}_p-SS{T}_b$

In this equation, $SS{T}_b$ represents the temperature field three days before the typhoon passes through, and $SS{T}_p$ represents a certain moment during the typhoon.

Figure 5 shows the changes in SST after Typhoon “Saola”. The most significant SST decreasing occurred around five days after landfall, with greater cooling on the right side of the typhoon’s path compared to the left. The maximum SST increase was recorded five days post-typhoon, reaching up to 2.42 °C, while the most significant SST decrease occurred approximately seven days later, dropping by −8.81 °C. By combining the flow fields with wind speed analysis during the typhoon, it was found that seasonal changes in the surface currents of the South China Sea are significant. In summer, the southwest monsoon prevails, causing surface currents to flow northeastward, with stronger currents in the western and central areas. Two days after the typhoon, disturbances and regional flow characteristics caused nearshore water to be transported offshore, resulting in an SST increase. Offshore water from the west of Hainan Island moved toward the coast, leading to an SST decrease. Five days after the typhoon, the typhoon center moved northwestward, transporting colder deep-sea water towards the coast, which reduced the SST, while nearshore water accumulated along the coast, leading to an SST rise. Seven days post-typhoon, areas far from the typhoon center returned to pre-typhoon conditions, while the nearshore SST decreased due to runoff and heavy rainfall brought by the typhoon. The cooling and circulation centers both appeared on the right side of the typhoon’s path, consistent with previous research showing the left–right asymmetry of typhoon impacts on SST [48,49,50,51]. This section outlines the temperature and flow field responses following Typhoon “Saola” and connects these observations to previous research findings.

Our calculations indicate that the maximum temperature drop nearshore reached 5.13 °C, while the maximum increase was 1.91 °C. In contrast, in the offshore area, the maximum temperature drop was 0.98 °C, and the maximum increase was only 0.32 °C. Notably, the timing of both the maximum increase and maximum drop in temperature for both nearshore and offshore areas coincided, occurring between 121 and 168 h after the typhoon’s passage. The temperature change was more pronounced nearshore than in offshore areas, although the overall regional temperature variation was larger than that observed within these two key study zones.

Combining the flow fields with wind speed analysis during the typhoon, it was found that seasonal changes in the surface currents of the South China Sea are significant. In summer, the southwest monsoon prevails, causing surface currents to flow northeastward, with stronger currents in the western and central regions. Two days post-typhoon, disturbances and regional flow characteristics caused nearshore water to be transported offshore, resulting in an SST increase. Meanwhile, offshore water from west of Hainan Island moved toward the coast, leading to an SST decrease. Five days post-typhoon, the typhoon center moved northwestward, transporting colder deep-sea water towards the coast, reducing the SST, while nearshore water accumulated along the coast, causing the SST to rise. Seven days post-typhoon, areas far from the typhoon center returned to pre-typhoon conditions, while the nearshore SST decreased due to runoff and heavy rainfall brought by the typhoon. Both the cooling and circulation centers appeared on the right side of the typhoon’s path, consistent with the findings of Wang Linyan and others [16]. This section outlines the temperature and flow field responses following Typhoon “Saola” and links these observations to previous research findings (Figure 6).

3.4. The Profile Analysis of Temperature Response in the South China Sea

To explore the differences in SST responses under various topographical conditions, we computed SST profiles for deep-sea and nearshore areas along the typhoon’s path, focusing on three days before its passage, as well as two, five, and seven days post-landfall (Figure 7).

The topography along the typhoon’s path shows significant variation. The maximum depth in the nearshore area is only 80 m, while the deep-sea area reaches depths of 3000 m. Before the typhoon, temperatures in the nearshore area ranged from 14 °C to 30 °C, whereas the deep-sea area had lower temperatures, with surface temperatures of 18 °C and deep-water temperatures of 4 °C. In the nearshore area, the maximum SST increase and minimum SST decrease both occur within five days post-typhoon, with the highest increase reaching 4.24 °C and the lowest decrease reaching 2.99 °C. In contrast, the deep-sea area shows a maximum temperature increase and decrease on days five and two, respectively, with the highest increase being 0.33 °C and the lowest decrease being 0.26 °C (Figure 8).

The model calculations illustrate the average water temperature on either side of the typhoon’s path from the time of its passage to seven days afterward. Figure 9 depicts the stratified water depths across the overall study area, as well as in the nearshore and offshore regions. It is evident that the nearshore area, characterized by shallower depths, exhibits thinner water layers in comparison to the deeper offshore region.

The results indicate that the nearshore area responds more dramatically to the typhoon, with changes observed from the surface to the bottom layers; however, the middle and bottom layers exhibit a delayed response. In contrast, the deep-sea area shows a weaker temperature response, mainly affecting the middle layer, while the bottom layer displays no significant changes (Figure 10).

3.5. Analysis of Sea Surface Anomaly Mechanism Under Typhoon Action

To discuss the degree of influence that typhoon “Saola” had on the ΔSST anomalies at different water layers, we analyzed the temperature anomalies and transport processes in the main path of the typhoon within seven days of its passage.

To investigate the mechanisms by which the typhoon affected both surface and deep water, we focused on the nearshore area (Region A) and the deep offshore area (Region B) (Figure 1). Temperature anomalies were computed based on average temperatures from three days before the typhoon’s passage, and analyzed for two days, between two and five days, and between five and seven days post-landfall (Figure 11).

In the two to seven days following the typhoon, surface temperature analysis in the nearshore area showed negative anomalies, with the largest negative anomaly occurring between two and five days post-landfall. The negative anomalies exhibited an initial increase followed by a decrease, with the strongest responses near the Pearl River Estuary and north of Dongsha Island. In the deep-sea area, surface temperatures also showed negative anomalies, peaking between two and five days post-landfall, with a maximum increase of 0.32 °C. Overall, the trend of negative anomalies was less pronounced in the deep-sea area compared to the nearshore area (Figure 11). The cold SST centers followed the path of the typhoon, consistent with previous research [52].

During the two to seven days post-landfall, subsurface temperatures in the nearshore area (depths of 5 to 18 m) showed both positive and negative anomalies. The negative anomalies gradually diminished, while the positive anomalies initially increased and then decreased. The negative anomalies were concentrated on the right side of the typhoon path and the landing area, whereas the positive anomalies were concentrated on the left side and adjacent coastal areas. In the deep-sea area (depths of 180 to 600 m), subsurface temperatures displayed both positive and negative anomalies, with positive anomalies being more pronounced, although generally weak, during this period (Figure 12).

In the nearshore area, the three-layer water depth (15 to 35 m) exhibited both positive and negative temperature anomalies. Positive anomalies were found along the typhoon’s path, while negative anomalies were concentrated in the landing area, with both types initially increasing and then decreasing. In the deep-sea area, the three-layer depth (600 to 1200 m) showed weak positive anomalies (Figure 13).

In the nearshore area, the four-layer depth (30 to 50 m) displayed both positive and negative anomalies, with the positive anomalies being more intense, particularly a positive anomaly that occurred in the Pearl River Estuary two to five days post-typhoon. In the deep-sea area, the four-layer depth showed no significant anomalies between two and five days but displayed weak positive anomalies between five and seven days (Figure 14).

For the five-layer depth in the nearshore area (40 to 65 m), temperature anomalies were predominantly positive and gradually expanding, while negative anomalies were observed in the landing area and along the coast, initially increasing and then decreasing. In the deep-sea area (1200 to 2300 m), no significant anomalies were observed between two and five days, but weak positive anomalies were noted between five and seven days, similar to the fourth-layer characteristics (Figure 15).

During the two to seven days following the typhoon’s passage, the nearshore area (50 to 90 m) experienced extensive positive temperature anomalies, while negative anomalies were relatively weak. In the deep-sea area (1400 to 3400 m), no significant temperature anomalies were observed (Figure 16).

To compare the responses of the nearshore and deep-sea areas in terms of depth and temperature, we carried out stratified and comprehensive analyses of temperature variations during the period from 24 h (two days) to 168 h (seven days) after the typhoon (Figure 17). In Region A, the maximum temperature increase (5.26 °C) occurred in the sixth layer, and the minimum decrease (−5.17 °C) occurred in the fifth layer, both within two to five days after landfall. In the deep-sea area, the maximum increase (0.49°C) occurred in the subsurface layer, and the minimum decrease (−0.98 °C) occurred at the surface layer during the same period. Two days before the typhoon’s passage, the cooler water in the middle layer of the nearshore area was drawn upwards to the surface, resulting in a decrease in surface and subsurface temperatures. Surface waters near Hong Kong moved downwards, increasing bottom water temperatures. Between two and five days after the typhoon, the overall trend was similar, reaching extreme values during this period. From five to seven days after the typhoon, temperature changes decreased, although the overall trend persisted, with a weakened effect on water temperatures. In the deep-sea area, the temperature change during each time period of typhoon passage was minor, with the maximum being only 0.4877 °C and the minimum being only −0.9809 °C. As a whole, Figure 2 indicates that in Region A, from the surface layer to the bottom layer, the cooling phenomenon (blue) 48 h after the typhoon’s passage was from deep to shallow, representing the difference in the change in outliers. In other words, the warm water in the surface layer of the South China Sea is thin, which enables the cold water in the lower layer to be easily mixed into the surface layer [53]. Due to the upwelling of deep cold water caused by Ekman suction induced by the typhoon, the warming of the surface layer was weakened, so the warming of each layer (red) from 48 h to 168 h always manifested as shallow to deep [15,54], i.e., the “cold suction” effect. From 48 h to 168 h, the abnormal increase and decrease trend of heat and cold in each layer of water reflected the fact that the lower layer of water was lifted by “cold suction” and then gradually decreased due to the “heat pump” phenomenon. In Region A, these two phenomena were more prominent, and the reason was that the mixing effect was stronger in the nearshore region [55]; when the typhoon passed, the mixing was more intense. In Region B, only the surface layer (layers one to three) had weak cold suction, and the heat pump action was stable, which might be related to the thickness of the layers. Since sigma stratification was adopted in this paper, the layers were thicker in the deep-sea area (Figure 9) and the mixing of cold and heat exchange was not conspicuous.

The regional topography influences the distribution of water layers in the nearshore and deep-sea study areas, leading to differences in their responses. In the deep-sea area, the overall trend in seawater temperature change remained stable during the typhoon, with deep-water temperatures largely unchanged. In contrast, the nearshore area shows regional differences in temperature changes. The nearshore water body responds from the bottom up, as the typhoon brings significant water vapor and precipitation. Cloud accumulation reduces short-wave radiation, resulting in lower temperatures and higher humidity in the nearshore area. As the typhoon progresses, the difference between air and sea temperatures increases, accelerating air–sea interactions and causing a decrease in sea temperature. This observation aligns with previous studies [52,56]. Toward the later stages of the typhoon, the sea surface temperature (SST) gradually balances with the air temperature, and the negative anomaly flattens out. In the deep-sea area, responses are observed up to a depth of about 500 m. The thicker water layer results in a weaker response compared to the nearshore area. The SST anomaly response depends on the upwelling and mixing of lower water caused by the typhoon. In areas with a deep ocean mixed layer, the SST cold zone is narrow, consistent with model simulations [57]. Additionally, the typhoon brings heavy precipitation, and fresh water flows into the nearshore area, reducing salinity and affecting temperature. In contrast, salinity changes in the deep-sea water body are minimal, leading to a weaker temperature influence. The uneven water flow and heat distribution in the nearshore area, influenced by topography, further exacerbate temperature differences.

4. Conclusions

This paper employs a simple typhoon model to examine the three-dimensional temperature structure during Typhoon “Saola”. The control test results for tide levels and temperatures closely match observed data, effectively characterizing the typhoon’s impact. Using simulations of Typhoon “Saola” No. 09 in 2023, we studied the response of sea surface temperature (SST) at different depths, leading to the following conclusions:

1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days post-typhoon. In deep-sea areas, the maximum warming occurs within five days, while the lowest cooling is observed within two days.

2. Nearshore water layers respond quickly to the typhoon, whereas deep-sea water layers primarily respond at middle depths, with a delayed effect.

3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer.

Based on this study, we conclude that examining three-dimensional temperature variations in the ocean provides valuable insights into subsurface changes. Future research could explore the effects of harmful elements introduced by human activities on the marine environment. Furthermore, typhoons not only stimulate ocean thermodynamics but also significantly impact the marine ecosystem, such as promoting plankton growth and increasing chlorophyll concentrations in the water [58]. However, the current study’s use of sigma stratification has limitations, particularly regarding the accuracy of deep-sea stratification. These limitations should be addressed in future research to improve the simulation’s accuracy. While this study effectively simulates sea temperature changes, it does not sufficiently account for vertical variations in subsurface currents. Future research could benefit from examining vertical current variations and lag time, which would add depth to our understanding of these processes.

Additionally, this study focuses on an overall analysis of temperature variations in coastal and deep-sea regions. Future work could incorporate a block-level analysis to explore localized differences, offering a more detailed understanding of how temperature variations manifest in different areas.

Author Contributions

Software, T.W.; Data curation, Y.F.; Writing—original draft, L.Y.; Project administration, F.S.; Funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data rights belong to the National Ocean Data Center, Dalian Ocean University Branch Center.

Acknowledgments

We thank the Data Support Team from the National Marine Scientific Data Center (Dalian), National Science & Technology Infrastructure of China (http://odc.dlou.edu.cn/), for providing valuable data and information. We also thank the reviewers for carefully reviewing the manuscript and providing valuable comments to help improve this paper.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. Observation stations and the study area of typhoon “Saola”.

Figure 2. Grid and Depth.

Figure 3. Comparison chart of tidal flow and SST verification.

Figure 3. Comparison chart of tidal flow and SST verification.

Figure 4. Euler residual flow field during the passage of typhoon “Saola”.

Figure 5. Temperature changes in the 7-day temperature of typhoon “Saola” ((a) typhoon transit for two days (48 h), (b) typhoon transit for two to five days (49 to 120 h), (c) typhoon transit for five to seven days (121 to 168 h)).

Figure 5. Temperature changes in the 7-day temperature of typhoon “Saola” ((a) typhoon transit for two days (48 h), (b) typhoon transit for two to five days (49 to 120 h), (c) typhoon transit for five to seven days (121 to 168 h)).

Figure 6. Current changes in the 7-day temperature of typhoon “Saola” ((a) typhoon transit for two days (48 h), (b) typhoon transit for two to five days (49 to 120 h), (c) typhoon transit for five to seven days (121 to 168 h)).

Figure 6. Current changes in the 7-day temperature of typhoon “Saola” ((a) typhoon transit for two days (48 h), (b) typhoon transit for two to five days (49 to 120 h), (c) typhoon transit for five to seven days (121 to 168 h)).

Figure 7. Average profile temperature for three days before transit ((a) is the nearshore area and (b) is the deep-sea area).

Figure 7. Average profile temperature for three days before transit ((a) is the nearshore area and (b) is the deep-sea area).

Figure 8. Temperature changes in the profile of the nearshore region (Region A) and the deep-sea region (Region B) within seven days of the typhoon’s transit: (a) 48 h of typhoon’s transit in the nearshore region; (b) 48 h of the typhoon’s transit in the deep-sea region; (c) 49 h~120 h of the typhoon’s transit in the nearshore region; (d) 49 h~120 h of the typhoon’s transit in the deep-sea region; (e) 121 h~168 h of the typhoon’s transit in the nearshore region; (f) 121 h~168 h of deep-sea area typhoon transit.

Figure 8. Temperature changes in the profile of the nearshore region (Region A) and the deep-sea region (Region B) within seven days of the typhoon’s transit: (a) 48 h of typhoon’s transit in the nearshore region; (b) 48 h of the typhoon’s transit in the deep-sea region; (c) 49 h~120 h of the typhoon’s transit in the nearshore region; (d) 49 h~120 h of the typhoon’s transit in the deep-sea region; (e) 121 h~168 h of the typhoon’s transit in the nearshore region; (f) 121 h~168 h of deep-sea area typhoon transit.

Figure 9. The water depth stratification of the regional model (where (a) is a coastal area (Region A) and (b) is a coastal area (Region A)).

Figure 10. Model water layer temperature time variation diagram (where (a) is a shallow water area (Region A) and (b) is a deep-sea area (Region B)).

Figure 11. Simulation of the surface temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after a typhoon passes: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 12. Simulation of the subsurface temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after a typhoon passes: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 13. Simulation of the third-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 14. Simulation of the fourth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 14. Simulation of the fourth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 15. Simulation of the fifth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 15. Simulation of the fifth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 16. Simulation of the sixth-layer temperature changes in the nearshore area (Region A) and deep-sea area (Region B) within seven days after the typhoon passed: (a) represents the typhoon passing over the nearshore area after 48 h; (b) represents the typhoon passing over the deep-sea area after 48 h; (c) represents the typhoon passing over the nearshore area after 49 h to 120 h; (d) represents the typhoon passing over the deep-sea area after 49 h to 120 h; (e) represents the typhoon passing over the nearshore area after 121 h to 168 h; and (f) represents the typhoon passing over the deep-sea area after 121 h to 168 h).

Figure 17. The maximum and minimum of each layer from 48 h to 168 h (two to seven days) after the typhoon’s transit.

Table 1

Variables in the numerical hydrodynamic model.

Variable	Description	Variable	Description
X	east axes in the Cartesian coordinate system	Fu	x component horizontal momentum diffusion term
Y	north axes in the Cartesian coordinate system	Fv	y component horizontal momentum diffusion term
Z	vertical axes in the Cartesian coordinate system	FT	thermal momentum diffusion term
P	sum of air pressure at the sea surface and hydrostatic pressure	FS	salt momentum diffusion term
Km	vertical eddy viscosity coefficient	Kh	vertical eddy diffusion coefficient
SW	shortwave flux incident at thesea surface	Ah	horizontal eddy diffusion coefficient
Qb	groundwater volume flux atthe bottom	vn	velocity component normal to the boundary
u	x velocity component	g	acceleration of gravity
v	y velocity component	f	Coriolis force parameter
w	z velocity component	S	salinity of seawater
T	temperature of seawater	cp	specific heat of seawater
Qn	surface net heat flux	ρ 0	reference density of seawater
τsx	x component of surface wind	τsy	y component of surface wind
τbx	x component of bottom stresses	τby	y component of bottom stresses
α	the slope of the bottom bathymetry	ζ	surface elevation

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