Spatial Distribution Characteristics of Seasonal Thermocline in Korea
1, 2
Dong-Young Yoon1 and Hyun-Woo Choi2*
Oceanographic Data & Information Center, Korea Institute of Ocean Science & Technology (KIOST)
787 Haean-ro(st) Sangnok-gu, Ansan-si, Gyunggi-do, Korea,
[email protected], [email protected]
*Corresponding author: [email protected]
ABSTRACT
In low-latitude waters such as the equatorial region, 20 °C isothermal line is defined as the permanent
thermocline. However, mid-latitude waters have variable vertical profile of water temperature with respect
to the season and region. Therefore, in this study we developed an algorithm for quantifying seasonal
thermocline in order to extract parameters for vertical temperature profiles of Korean waters in summer.
In addition, quantified parameters were used to identify spatial distribution characteristics of thermocline,
and a model for relationship between thermocline parameters was established to help understand the
structure of thermocline.
Keyword: seasonal thermocline, thermocline parameters, spatial distribution, Korean waters
INTRODUCTION
Research of spatial characteristics of water temperature in the sea, which is a three-dimensional space,
has been mostly limited to studies of horizontal distribution. In other words, research on vertical
temperature characteristics, including the thermocline, is lacking. In the vertical temperature profile of the
sea, the layer below the mixed layer in which the water temperature rapidly decreases with depth is
referred to as the thermocline. This layer is important to understand marine physical and biological
phenomenon.
In order to study the thermocline, one must first determine if it has been existed and then extract the
parameters of the thermocline structure. The most difficult step is determining at which depth the
thermocline top and base points. For this purpose, one may visually inspect the vertical temperature
profile. However, this method is impractical when analyzing vertical temperature profile dataset over a
wide region and for an extended period of time.
In research of the equatorial Pacific Ocean, Kessler (1990) has defined the depth of 20 °C isothermal
line as the medium depth of the thermocline and this definition has since been used [1]. The NOAA
(2012) has recently used this definition in discovering the relationship between El Niño/La Niña and
thermocline [2]. In contrast to low-latitude waters, mid-latitude waters have seasonal thermocline and various
vertical temperature profiles. Several studies have attempted to identify vertical temperature gradient (ΔT)
conditions for different seasons and regions in order to extract depths of the thermocline top and base points [3,
4, 5].
Limitations of these studies are that there is no prior step for checking the formation of thermocline and
that an absolute threshold ΔT has been used to extract thermocline. Such threshold will vary with season and
region, which implies that an absolute value cannot be applied generally to mid-latitude waters. To solve this
problem, we have developed an algorithm for detecting thermoclines and extracting relevant parameters in the
various vertical temperature profiles in the southern Sea of Korea during summer [6]. In the study, ΔT
threshold conditions were used to detect thermoclines, and structural characteristics of the hyperbolic tangent
function were used to extract thermocline parameters. Although the developed algorithm is capable of
extracting thermocline parameters, it is unable to achieve the fundamental goal of “detecting the thermocline
and extracting its parameters regardless of the shape of the vertical temperature profile”.
In this study, we aimed to develop an improved algorithm for detecting thermocline and extracting
parameters, only using the structure of the hyperbolic tangent function. In addition, the algorithm was applied
to the Korean waters having various vertical temperature profiles for establishing a model of relationship
between thermocline parameters and understanding spatial distribution characteristics of thermocline.
MATERIALS AND METHODS
(1) Study area and data
The Korean waters located at mid-latitude regions was selected as study area (Figure 1). These region
is directly affected by the Kuroshio Warm Current and the North Korea Cold Current, and has various
forms of seasonal thermocline during summer. The water temperature data used in this study was
monitored by the NFRDI (National Fishers Research and Development Institute) at 205 sites during
summer (August) for 30 years (from 1981 to 2010), as part of NSO (NFRDI Serial Oceanographic
observation) project. The data contains water temperature at 14 standard depths (0, 10, 20, 30, 50, 75, 100,
125, 150, 200, 250, 300, 400, and 500 m). In order to obtain water temperature data with 1 m interval,
monotone piecewise cubic interpolation was applied to the water temperature data of standard depths [7].
Figure 1. Study area with temperature observed stations by NFRDI
(2) Thermocline detection
The thermocline detection algorithm used in this study was based on the idea that the structure of the
hyperbolic tangent function is similar to the thermocline structure, as shown in Figure 2. The concept of
differential hyperbolic tangent function (DHTF) has been applied to the vertical structure of ΔT. The xcomponent of the DHTF is the depth and the y-component is the ΔT, and the depths at which ΔT is 42%
of the maximum ΔT were defined as the top and base point depths of thermocline [6]. Occurrence of
thermocline was determined by the existence of depths that have gradients that are 42% of maximum ΔT.
The following 10 parameters were extracted from the datasets in which thermocline exists: 1) sea surface
temperature (SST), 2, 3) thermocline top point depth & temperature, 4, 5) maximum ΔT point depth &
temperature, 6, 7) thermocline base point depth & temperature, 8) maximum ΔT, 9) thickness of
thermocline, and 10) difference of temperature between thermocline top and base point.
Figure 2. The vertical temperature and ΔT profile using a) hyperbolic tangent function and b)
differential hyperbolic tangent function to understand the structure
RESULT
(1) Thermocline occurrence frequency
Of total 5,329 datasets, 3,231 were found to have thermocline. Thermocline occurrence frequency for
each station is shown in Figure 3. There are 15 stations with frequency below 25%, 33 stations with
frequency between 25% and 50%, 89 stations with frequency between 50% and 75%, and 68 stations with
frequency above 75%. Regions close to the land with shallow depth had relatively lower thermocline
occurrence frequency compared to the outer sea. To investigate the spatial distribution characteristics of
thermocline, average value for ten parameters was calculated at each station with occurrence frequency
above 50%.
Figure 3. Map of thermocline occurrence frequency
(2) Distribution of thermocline parameters
The spatial distribution maps were created for the four parameters representing the thermocline,
which are SST, thermocline top point depth, maximum ΔT, and thickness of thermocline (Figure 4). The
range of SST of the Korean waters during summer was from 22 °C to 30 °C, with southeastern area of the
Jeju Island being the highest and the East Sea being the lowest. This is most likely because the
southeastern area of the Jeju Island is affected by the Kuroshio Warm Current and the East Sea is affected
by the North Korea Cold Current. The top point depth of thermocline had a range of 9 m to 27 m, with it
being the deepest near Tsushima Island. Maximum ΔT was higher in the west of Jeju Island compared to
east, while the thickness of thermocline showed an opposite trend. From the results, it was concluded that
thermoclines in the Korean waters are formed more strongly in the western region compared to the eastern
region of Jeju Island.
Figure 4. Spatial distribution pattern of thermocline parameters a) SST, b) top point depth,
c) maximum ΔT, d) thickness of thermocline
(3) Correlation analysis
Correlation analysis was performed to identify the relationship between the four parameters, which
had various spatial distribution pattern (Table 1). SST was positively correlated with maximum ΔT, and
SST was negatively correlated with thickness of thermocline. This implies that higher the SST, maximum
ΔT becomes larger and thickness of thermocline becomes smaller. Therefore, SST was found to influence
the strength of thermocline. In contrast to SST, top point depth of thermocline was negatively correlated
with maximum ΔT and positively correlated with thickness of thermocline. Also, maximum ΔT and the
thickness were negatively correlated. Therefore, relational model of thermocline parameters can be used to
deduce the relationship of SST with other parameters.
Table 1. Pearson’s correlation coefficients between thermocline parameters
SST (A)
Thermocline top point depth (B)
Maximum ΔT (C)
B
C
D
0.129
0.189*
-0.407**
-0.362*
0.497**
-0.645**
Thickness of thermocline (D)
p-value (**<0.01, *<0.05), total number of data is 157
CONCLUSION
We developed an algorithm for detecting and quantifying thermoclines in various vertical
temperature profiles, and used it to extract parameters for seasonal thermocline in the mid-latitude waters.
By quantifying parameters related to thermocline, spatial distribution characteristics of thermocline in the
Korean waters during summer has been identified for the first time. Also, we established a model of
thermocline structure through correlation analysis of the parameters. It is anticipated that the algorithm
developed in this study can be used for the analysis of relationship between thermocline with climate
change index and marine organisms in the mid-latitude waters.
ACKNOWLEDGMENT
This study was supported by KIOST project (PE98971) “Development of Algorithm for Detecting
and Extracting Thermocline”.
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