Upwelling Proxy Improvement and Validation Using Satellite Remote Sensing along Southwest of the East Sea: Case Study in 2019

Abstract

Coastal upwelling is a significantly imperative process for understanding the interactions between physical and ecological processes and has been investigated incessantly. In this study, we explored the upwelling index, specifically upwelling age (UA). UA enabled us to observe the initiating, sustaining, and decaying upwelling processes. Although the sensitivity of many other geophysical parameters to estimate UA has been investigated, the wind direction has not been evaluated. Thus, we assessed the appropriate wind direction for the UA and obtained efficient upwelling signals from the four coastal stations. Furthermore, we applied the UA and compared it with the satellite sea level anomaly, sea surface temperature, and chlorophyll-a changes to validate how UA depicts their spatial extents. Thus, UA can predict the timing of coastal upwelling events using predicted geophysical parameters.

1. Introduction

One of the crucial dynamic coastal processes is upwelling. It results mainly from sea surface winds, forming rich nutrients on the sea surface from upwelled cold subsurface seawater. This phenomenon has been researched for a long time based on dynamic processes(Lentz and Chapman, 2004) and ecological changes (Shin et al., 2017). Owing to the efforts of many oceanographers, we can understand it better in terms of initiation, sustaining, and fading stages based on field measurements (Lentz and Chapman, 2004), theory (Rossi et al., 2013), numerical models (Jacox et al., 2018), and satellite observations (Chen et al., 2012). In addition to analytical investigation, practical applications have been extended to monitor coastal upwelling processes based on the upwelling index (UI).

The first aspect of the UI is based exclusively on wind stress and the Coriolis parameter developed by Bakun (1975). This UI is still used globally, but there are some arguments regarding its application in certain domains. Coastal regions have very complex structures in terms of bottom bathymetry, coastal geological orientations, and diverse sea state conditions, including all geophysical components. The concept of upwelling age (UA) was initially proposed by Garvine (2004) and later investigated by Jiang et al. (2012). UA is a non dimensional number for deducing two different time scales: the wind stress and advection time scale on the sea surface and the seawater, respectively. The ratio of the two timescales can suggest upwelling initiation and its intensities on the sea surface. Knowing the strength of upwelling is vital for understanding the occurrence of ecological influences, as demonstrated by Shin et al. (2017).

We evaluated the UA off the coast of Korea, as depicted in Fig. 1. The southwest coast of the East Sea is affected by a branch of the Kuroshio Current called the East Korea Warm Current (EKWC), which is transported through the Korea Strait. The EKWC transport was reported as roughly 1.5 Sv, accounting for approximately 60% of the entire volume through the Korea Strait (Takikawa et al., 2005). Therefore, this study focuses on (1) investigating UA performance along four locations and (2) UA correlation with the spatial extent of sea level anomaly (SLA), sea surface temperature (SST), and Chlorophyll-a concentration (CHL) measured by satellites. It is worth noting that current influences on coastal upwelling, primarily in the southern coastal regions of Pohang. The complementary forces by current and winds based on the Burger Number were investigated (Kim et al., 2022).

Fig. 1. Bathymetry maps shallower than 200 m depth where the coastal upwelling occurs. NKCC, EKWC, and OB portray the North Korea Cold Current, East Korea Warm Current, and Offshore Branch of the Tsushima Warm Current, respectively.

The remainder of this paper is organized as follows. Section 2 introduces the data sources and analysis methods. The specific results for the examination and performance are further analyzed in Section 3. The overall findings and discussion are summarized in Section 4.

2. Data and Methods

1) Data sources

To estimate upwelling processes off the southwest coast of the East Sea, alongshore wind stress, bottom topography, and vertical water structure were acquired to formulate the UA from January 1993 to October 2019. Wind stress was estimated using the ERA-Interim 10-m wind data downloaded from the EuropeanCenter for Medium-Range Weather Forecasts with a spatial resolution of 0.75° (https://www.ecmwf.int). The shelf slope was estimated utilizing ETOPO1 data from the global relief model of the Earth’s surface (https://www. ngdc.noaa.gov/mgg/global/global.html). The mixed layer depth and thermocline depth were evaluated from daily Hybrid Coordinate Ocean Model data with a spatial resolution of 1/12° (https://www.hycom.org).

To distinguish upwelling-induced cold water on the sea surface, multi-sensor ultra-high-resolution sea surface temperature (MURSST) data were employed. The MURSST was available with a daily 4 km resolution (https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4- GLOB-v4.1#). In addition, the visible infrared imaging radiometer suite (VIIRS) ocean color observations (https://oceancolor.gsfc.nasa.gov) and temperature time series from tide gauges maintained by the Korea Hydrographic and Oceanographic Agency (https:// www.khoa.go.kr/) were used. SLA data were also used to scrutinize upwelling signals. SLA data were obtained from Ssalto/Duacs and distributed by AVISO (https:// www.aviso.oceanobs.com/es/data/products/sea-surface- height-products/global/index.html). The gridded SLA was provided using a Mercator 1/4° grid. The resolutions in kilometers in latitude and longitude are thus identical and vary with the cosine of the latitude.

2) Methodology

Estimation of UI: According to Bakun (1975), UI can be defined as,

\(U I = - \frac { \tau _ { y } } { \rho _ { f } }\)       (1)

Where, τ_y is the alongshore wind stress, ρ is water density, and f is the Coriolis parameter.

Estimation of UA: Jiang et al. (2012) employed the Regional Ocean Model System, a numerical model to validate the UA. Thus, we calculated the UA derived by Jiang et al. (2012) for regions along the southwest coast of the East Sea, using satellite and reanalysis datasets. UA (Γ) is defined as:

\(\Gamma = \frac { t _ { \text { wind } } } { t _ { a d } }\)      (2)

Where t_wind is a time scale used to measure the upwelling-favorable wind duration, and tad stands for advection time. According to Jiang et al. (2012), the timescale of these processes can be scaled as:

\(t _ { a d } = \frac { \rho f d ( H _ { 0 } - H _ { 1 } ) } { \alpha \tau }\)       (3)

The first and second terms are the climbing and upwelling time scales, respectively. The first term is a function of the wind stress (τ), bottom slope (α), water density (ρ), Coriolis parameter (f), bottom layer thickness (d), depth of the thermocline (H0), and switch-over depth between the climbing and upwelling processes (transition depth, H1). Because UA is expressed as a non-dimensional index, it can be employed to determine the critical conditions for initiating upwelling. ≫ Accordingly, when Γ 1 results in the commencement of accumulation of upwelled water on the sea surface, large offshore transport generate a surface front. When Γ≤ 1, upwelling is nonexistent due to no overturning (Chen et al., 2013; Jiang et al., 2012). To estimate the alongshore wind stress (τ), the wind data for each grid were separated into alongshore and cross-shore components. The shelf slope, α, was computed as the ratio of the depth of 200 m to the regions between the coastline (0 m depth) and 200 m depth along the cross shore direction.

3. Results and Discussion

1) Sensitivity of wind direction to derive coastal upwelling events

Kim et al. (2022) investigated the sensitivity of geophysical parameters. However, wind direction has not yet been studied. Fig. 2 displays the UA with two different wind directions at Hupo station: 45°and 89°, respectively. The geographic location is denoted in the upper-left corner by a red circle. The time series of the sea surface temperature in 2019 is illustrated. The SST directly indicated three upwelling events on May 18, June 27, and July 19 according to UA larger than 1. Fig. 2(a) depicts the cold upwelled water in the third (about May 26) and the end week of May through early June (May 26 to June 6). However, this low SST during the periods was not identified by the UA (lower panel of Fig. 2(a)). Thus, the direction of the wind to the coastline was examined. Fig. 2(b) depicts SST and UA after the wind direction was oriented at 89°. Unlike UA in Fig. 2(b), the corrected UA specifically indicates upwelling events.

Fig. 2. Sensitivity of wind direction to the coastal line. The sea surface temperature (SST) in the Hupo station is compared with Upwelling Age (UA) and Upwelling Index (UI) including wind direction at 45° (a) and 89° (b). All other parameters are identical, except wind direction in (a) and (b). The UA and UI have units for non-dimensional and m3sec–1per 100 m of coastline, respectively. The UA is more extensive or smaller than 1 and is indicated with red and blue bars, respectively. Similarly, the red and blue bars of UI represent upwelling favorable or non-favorable conditions, respectively.

Fig. 3. SST and UA at four stations off the coast of the East Sea. The corrected UA with wind direction is illustrated in Fig. 2. The UA and UI have units for non-dimension and m3sec–1per 100 m of coastline, respectively. All dashed red boxes signify the significant upwelling events according to UA, UI, and relevant low SST.

Compared to UI, UA with 89° wind direction performs well in indicating starting date, strength, and duration (Fig. 2(b)). It is worth noting that the lowest temperature around June 2 resulted from strong upwelling beginning from May 19 and continued the subsurface water to the sea surface; thus, UA and corresponding SST do not show coincident accordingly. While UA for the 2nd upwelling on June 27 is slightly higher than 1, UI on the same day is not different from other days, and thus it is hard to consider whether this upwelling event actually happened or not. The third upwelling event can be considered as clearly strong event based on UA and UI.

Post UA calculation with a wind direction of 89°, the UA at the four stations was compared (Fig. 3). Fig. 3(a)–3(d) represents the SST and UA from the northern to southern stations. The red dashed box indicates the upwelling duration based on UA. It can be observed that the UA can show low SST signals for most periods. It is worth noting that there was a time lag in initiating upwelling, as indicated in Fig. 3. Cross-correlation between UA and water temperature anomaly, where seasonality is removed by subtracting daily climatology, demonstrates a lag of order of 1–10 days, depending on the stations.

2) Sea surface height, temperature, and CHL changes due to upwelling

Previously, we demonstrated point comparisons between the SST and UA. Because upwelling events can be recognized in satellite measurements, we examined them using SLA. Based on the quasi geostrophic and barotropic theories, the relationship between sea level and wind stress can be expressed as (Fu and Davidson, 1995):

\(\frac { \partial } { \partial t } \nabla ^ { 2 } \eta + \beta \frac { \partial \eta } { \partial x } - \frac { f } { D } ( \frac { \partial \eta } { \partial x } \frac { \partial D } { \partial y } - \frac { \partial \eta } { \partial y } \frac { \partial D } { \partial x } ) = \frac { f } { p g } [ \nabla ^ { \times } \times ( \frac { \tau } { D } ) ] _ { z }\)       (4)

Where ηis the sea level height, τis the sea wind stress, Dis the water depth, ρis the water density, f=2Ωsin (latitude), Ω is the Earth’s rotation rate, β=df/dy, and g is the local gravitational constant. Eq. (4) correlates sea surface height to the wind stress applied to the ocean surface. To maintain balance in Eq. (4), the sea surface height adjusts itself in response to changes in the wind stress forcing. By employing the scale analysis of Eq. (4), we obtain:

\(\frac { h } { L ^ { 2 } T } \sim \frac { f } { p g } \frac { \Gamma } { D L }\)       (5)

Where Lis the spatial scale, Tis the temporal scale, h is the scale of the sea-level height, and Γ is the characteristic wind stress. From Eq. (5), we obtain:

\(h \sim \frac { f T L \Gamma } { p g D }\)       (6)

Eq. (6) depicts the scale relationship between the sea surface height anomaly and the wind stress forcing. f= 7.25 × 10^–4s^–1, ρ= 1027 kg/m³, g= 9.8 m/s², D= 50 m, T= 2 weeks, L= 50 km, and Γ= 5 × 10^–2. h can be calculated as 10.1 cm, which is sufficient to discriminate from normal sea surface height changes, such as wind-induced sea level changes in the open ocean (Yan et al., 2004).

SLA was performed on July 23, 2019, to evaluate upwelling signals in sea level changes (Fig. 4). Higher SLA can be attributed to warm eddies, especially over Ulleung Island. One can see the upwelling-like signals indicated by white lines. Although the SLA signal can indicate approximate upwelling signals, it is challenging to address the origin of upwelled water and define the spatial ranges of upwelled water owing to relatively coarse resolutions compared to ocean color remote sensing observations, as illustrated in Fig. 5(d). SST signals with UA along the coastal regions were coherent enough for upwelling intensity and spatial regions of upwelling validation (Fig. 5). It is noteworthy that the SLA also showed negative cross-correlation with UA one for 1-10 days, similar to water temperature for all stations except for Ulsan, where SLA can be considerably influenced by background EKWC. This lag between the UA and environmental measurements can be due to an unresolved time-dependent frictional adjustment mechanism. Specifically, the time scale required for the velocity fields of the coastal region to reach steady-state conditions can be governed by the frictional adjustment time scale h/γ; where his the water depth and γ≈5×10^–4) m/s (Chapman, 2002; Lentz and Chapman, 2004). Choosing h≈200 m, corresponding to the study area (Fig. 1), yielded a frictional adjustment time of about five days, the order of which clearly matched the order of the lag.

Fig. 4. Sea Level Anomaly (SLA) on July 23, 2019. White lines indicate the regions of upwelled water, which is compared with SST in Fig. 5(d).

Fig. 5. Distribution of sea surface temperature and current (vector) with upwelling age at each station (a, d). Distribution of 8-day chlorophyll-a concentration (b, e). Distribution of wind velocity with wind speed (c, f). The white lines in Fig. 5(d) represent the regions of upwelled water, the same as in Fig. 4.

In addition to sea level height variation for upwelling signals, the distributions of SST, CHL, and wind speed were compared to verify the spatiotemporal consistency of upwelling on May 29 and July 23 (Fig. 5). The color of the circle on the coastline indicated with the sea surface temperature signifies that UA has a value of 1 or higher as the color becomes reddish (Fig. 5(a) and Fig. 5(d)). This depicts the two cases of spatial distributions of upwelling occurrence, which are a usual pattern and an extended pattern due to the relatively stronger and more persistent wind. In general, during coastal upwelling, the southwesterly wind blows for a few days and the surface cold water proceeds to the north along the coast via the EKWC (Fig. 5(a) and Fig. 5(d)). These cold waters, including nutrients upwelled from the subsurface, in turn, induce high primary production inferred by chlorophyll-a distributions (Fig. 5(b)). However, when the upwelling-favorable wind became stronger than usual and was more persistent for a couple of days, the cold surface waters extended beyond the boundary of the EKWC (Fig. 5(c) and Fig. 5(f)). Phytoplankton also thrives in upwelled waters, advecting through the extended surface water mass. Apart from the case presented above, most cases are also consistent with a reduction in sea surface temperature and escalation of CHL when UA is larger than 1 (not represented here).

It was noted in Fig. 2 that the low SST at low UA on May 29 (Fig. 5(a)) is due to strong upwelling that began on May 19 (Fig. 3), and the upwelling continued the subsurface water to the sea surface. Thus, UA and corresponding SST do not show coincident accordingly. At the same time, CHL is high in low SST (Fig. 5(b)) due to advected water by prior upwelling and sea surface current. UA can show when unwilling starts, and satellite observations can reveal the resultant features.

In addition to the current work, two additional future research can be proceeded. (1) Although current UA can be estimated based on satellite and additional numerical model output, subsurface temperature derived from multiple satellite observations (Jeong et al., 2019) can replace numerical data sources (e.g., thermocline depth). (2) There was generally known that blooms by upwelling are especially important for understanding marine ecosystem changes. Thus, upwelling processes should be monitored for their cause and effect on bloom evolution based on underline processes. However, ocean color observations are limited due to mainly cloud presence. Thus, if well-quality controlled reconstructed CHL is available, the continuous evolution of bloom in response to coastal upwelling can be analyzed.

4. Conclusions

In this study, we evaluated the UI to measure the intensity of the wind stress along the southwest coast of the East Sea. UA represents the degree of upwelling evolution and quantitative magnitudes. In particular, the wind direction was critical for simulating the processes of coastal upwelling. After examining the wind direction for the UA, we established that the wind direction should be near 90° to the coastline, which is a favorable wind condition. By comparing four coastal stations, UA could potentially be employed to predict the initiation of an upwelling event using predicted wind and ocean state information. Furthermore, we validated the spatial extent of the upwelled water using satellite sea surface height, temperature, and CHL measurements. This information also depicted the amount of upwelling water that advected offshore and phytoplankton bloom growth.