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Article

Statistical Characteristics of Remote Sensing Extreme Temperature Anomaly Events in the Taiwan Strait

by
Ze-Feng Jin
1,2,3,4 and
Wen-Zhou Zhang
1,2,3,4,*
1
College of Ocean and Earth Sciences, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China
2
Coastal and Ocean Management Institute (COMI), Xiamen University, Xiamen 361102, China
3
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
4
Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361102, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(16), 3091; https://doi.org/10.3390/rs16163091
Submission received: 16 July 2024 / Revised: 15 August 2024 / Accepted: 18 August 2024 / Published: 22 August 2024
(This article belongs to the Section Ocean Remote Sensing)
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Abstract

With global warming, the global ocean is experiencing more and stronger marine heatwaves (MHWs) and less and weaker marine cold spells (MCSs). On the regional scale, the complex circulation structure means that the changes in sea surface temperature (SST) and extreme temperature anomaly events in the Taiwan Strait (TWS) exhibit unique regional characteristics. In summer (autumn), the SST in most regions of the TWS has a significant increasing trend with a regionally averaged rate of 0.22 °C (0.19 °C) per decade during the period 1982–2021. In winter and spring, the SST in the western strait shows a significant decreasing trend with a maximum decreasing rate of −0.48 °C per decade, while it shows an increasing trend in the eastern strait. The annual mean results show that the TWS is experiencing more MHWs and MCSs with time. The frequency of the MHWs in the eastern strait is increasing faster than that in the western strait. In the western region controlled by the Zhe-Min Coastal Current, the MCSs have an increasing trend while in the other areas they have a decreasing trend. The MHWs occur in most areas of the TWS in summer and autumn, but the MCSs are mainly concentrated in the west of the TWS in spring and winter. The cooling effect of summer upwelling tends to inhibit the occurrence of MHWs and enhance MCSs. The rising background SST is a dominant driver for the increasing trend of summer MHWs. By contrast, both the SST decreasing trend and internal variability contribute to the winter MCSs increasing trend in the strait.

1. Introduction

Anthropogenic global warming with the effects of sun activity [1] and cloud cover [2] leads to frequent extreme climate events such as heatwaves and extreme precipitation [3]. In many areas of the world’s oceans, sea surface temperature (SST) has experienced significant rise due to global warming [4,5,6]. As a key thermal factor in the ocean, SST profoundly affects the dynamic and ecological environments of the global ocean, especially the marine extreme temperature anomaly events related to it. Marine hot extremes are called “marine heatwaves (MHWs)”. Hobday et al. [7] defined an MHW as a discrete prolonged anomalously warm water event in a specific region, which can last for weeks or even months and affect an area of thousands of kilometers wide [8]. In recent years, MHWs have swept the globe, from open oceans to marginal and coastal seas [8,9,10,11,12,13,14,15]. MHWs have devastating effects on marine ecosystems, including reduced growth of seagrass [16], widespread coral bleaching [10], mass mortality of marine organisms [17], changes in community structure and species geographic distribution [8,17,18], and impacting marine ranching and fisheries around the world with significant economic and political implications [19]. Marine cold extremes are correspondingly called “marine cold spells (MCSs)” [20], which can also cause strong ecological responses, such as fish deaths [21,22], coral bleaching [23], and species range shrinkage [24].
Oliver et al. [25] found that the global average frequency and duration of MHWs increased by 34% and 17% during 1925–2016, respectively, resulting in a 54% increase in global annual MHW days. Over the past decades, MHWs have become longer lasting and more frequent, intense and extensive, with the average spatial extent of MHWs 21 times bigger than that in the preindustrial period, and this trend will accelerate further in the future [26]. By contrast, the global average frequency, duration, and intensity of MCSs show a decreasing trend [27,28], which is mainly caused by the mean SST warming [27,29]. As a result, MCSs receive less global attention. However, on the regional scale, some local processes, such as upwellings, may induce severe MCSs [12,30] and moderate the occurrence of MHWs [31]. Yao et al. [28] believed that the weakening of the Atlantic meridional overturning circulation would increase the occurrence of MCSs in the subpolar North Atlantic. In addition to the long-term warming trend of SST, MHWs and MCSs are also regulated by SST internal natural variability. A series of low frequency climate modes, such as the El Niño Southern Oscillation (ENSO) [25,32], Pacific Decadal Oscillation (PDO) [18], Atlantic Multidecadal Oscillation (AMO) [25] and Indian Ocean Dipole (IOD) [33], induce MHWs and MCSs by affecting air–sea heat flux, wind field, and ocean circulation. Thus, global MHWs are extremely uneven in space. Areas with large SST variability, such as the western boundary current extension, are hotspots of MHWs [25].
Under the influence of the western Pacific warm pool and Kuroshio, the marginal seas around China have also experienced varying degrees of warming [34], accompanied by more frequent MHWs [35] and fewer MCSs [36]. The Taiwan Strait (TWS) to the southeast of the Chinese mainland is an important waterway connecting the South China Sea (SCS) and the East China Sea (ECS, Figure 1), and plays a crucial role in the material and heat transport and geographical pattern of species distribution in the regional seas. The SST in the TWS is affected by complex currents and dynamic systems, such as the intrusion of Kuroshio [37], the SCS Warm Current (SCSWC), the cold Zhe-Min Coastal Current [38,39], and upwelling systems [40]. It shows obvious spatial differences. Affected by the East Asian monsoon, there are significant seasonal variations in the circulation structure [37] and the SST distribution in the TWS. The western part of the strait is alternately controlled by different coastal currents with different temperature and flowing direction due to monsoon transition. Meanwhile, the East Asian monsoon can also affect the horizontal heat transport of the Kuroshio through wind-driven Ekman advection [5]. In addition, the average water depth in the TWS is only 60 m, and shallow coastal sea areas are more susceptible to temperature fluctuations and are more sensitive to short-term local forcing than deep open oceans [12]. Due to the complex spatiotemporal variations in the SST in the TWS, the characteristics of extreme temperature anomaly events (MHWs and MCSs) have not been well revealed in the strait, and their seasonal variations and long-term trends under global warming are still unclear. Therefore, this study aims to analyze the characteristics and their spatial distributions, temporal variations and long-term trends of MHWs and MCSs in the TWS, based on high-resolution satellite remote sensing SST data, and investigate the main processes influencing the MHWs and MCSs.
The remainder of this paper is organized as follows: The data and methods used in this study are introduced in Section 2. Section 3 presents the seasonal and spatial differences and long-term trends of extreme temperature anomaly events in the TWS. The main factors driving the long-term trends of MHWs and MCSs are analyzed and discussed in Section 4. Finally, the conclusions are summarized in Section 5.

2. Data and Methods

2.1. Data

The OSTIA [41] global SST reprocessed product from the Copernicus Marine Environmental Monitoring Service (CMEMS) was used to analyze the long-term trend of SST in the TWS from 1982 to 2021, as well as the characteristics of MHWs and MCSs. The dataset is produced through the combination of satellite data and in situ observations at a 0.05° latitude ×0.05° longitude horizontal resolution. These daily SST data reveal well the spatiotemporal characteristics of MHWs in the Japan/East Sea [42].

2.2. Methods

2.2.1. MHW and MCS Identification

We adopted the definition of MHWs proposed by Hobday et al. [7] and Oliver et al. [25] to detect MHWs in the TWS. An MHW is identified when the SSTs at one location are above the corresponding seasonally varying 90th percentile threshold for at least five consecutive days. The 90th percentile threshold on each calendar day is first calculated using daily SSTs within an 11-day window centered on the date across all years within the climatology period of 1982–2011, and then all threshold values are smoothed by applying a 31-day moving average. Two successive MHW events separated by a 2-day or shorter break are considered as a single event. The definition and identification methods of MCSs are similar to those for MHWs, except that the SSTs below the corresponding 10th percentile threshold for at least 5 consecutive days are considered for the MCSs. Likewise, consecutive MCS events with an interval of 2 days or less are taken as a single event [20].

2.2.2. Metrics of MHWs and MCSs

Following Hobday et al. [7], six metrics were used to characterize the properties of identified MHWs, including the total days (the accumulated days of all MHWs during a certain period), frequency (the number of MHWs during the period), duration (the time period between the start and end dates of an individual MHW), mean intensity (mean temperature anomaly during the MHW), maximum intensity (highest temperature anomaly value during the MHW), and the cumulative intensity (the sum of daily temperature anomalies during the MHW). MCSs have the same metrics. All of the above metrics for MHWs/MCSs are described in detail in Table 1.

2.2.3. Mann–Kendall Test

Linear trend analysis was conducted to reveal long-term trends of both the SST and the properties of MHWs and MCSs in the TWS. Then, the Mann–Kendall (MK) non-parametric test [43,44] was used to determine whether the linear trends were significant. This method has been widely used to detect trends in the characteristics of extreme temperature anomaly events [32,36]. For time series x n ( n is the length of the data set), the MK test is given as follows:
S = ∑ i = 1 n − 1 ∑ k = i + 1 n s g n x k − x i ,
s g n x k − x i = 1 , x k − x i > 0 0 , x k − x i = 0 − 1 , x k − x i < 0 ,
V a r S = n ( n − 1 ) ( 2 n + 5 ) 18 ,
Z = S − 1 V a r ( S ) , S > 0 0 , S = 0 S + 1 V a r ( S ) , S < 0 .
When Z > Z 1 − α / 2 , x n is considered to have a significant trend. … is the absolute value operator. Z 1 − α / 2 is obtained from the standard normal distribution table and α is the significance level of the test.

2.2.4. Evaluating Effects of the SST Long-Term Trend and Variability on MHWs/MCSs

Both the SST long-term trend (shift in the probability density function of SST) and the internal variability (changes in SST variance) can lead to long-term trends of the properties of extreme temperature anomaly events [29]. To determine the main drivers of the observed trends in MHWs/MCSs via the method proposed by Lee et al. [15], we obtained a new SST time series after eliminating the long-term trend from the original SST data. MHWs and MCSs were identified again using the detrended SST time series and the corresponding metrics were recalculated. According to Marin et al. [45] and Li et al. [36], we can isolate the effect of the long-term SST trend on the MHWs/MCSs metrics by the following formula:
                    M H W S S T t r e n d = M H W S S T − M H W ( S S T d e t r e n d ) ,
where M H W S S T and M H W ( S S T d e t r e n d ) are the MHW metrics obtained from the original SST time series and the detrended SST time series, respectively. The same approach can be used for MCSs. Then, the trend attributional ratio (TAR) [45] was used to evaluate the relative contribution of the SST long-term trend or the variability to the total variability of the MHWs/MCSs metrics:
T A R = r a t e t r e n d − r a t e d e t r e n d m a x r a t e t r e n d ,   r a t e d e t r e n d ,
where r a t e t r e n d and r a t e d e t r e n d are trends in the metrics of MHWs/MCSs attributed to the SST long-term trend and the SST internal variability, respectively. The TAR ranges from −1 to 1. If the TAR is quite close to 1 (−1), the SST long-term trend (internal variability) is the dominant driver of the observed trends in MHWs/MCSs. The long-term trend and internal variability of SST have a comparable effect on the observed trends when the TAR is close or equal to 0.

3. Results

3.1. Characteristics and Long-Term Trend of SST in the TWS

It can be seen from the seasonal mean SST in the TWS (Figure 2a–d) that the TWS is controlled by different water masses with different temperature properties in each season. The spatial distribution of the SST shows that there is a temperature gradient front between the western and eastern parts of the TWS. Generally, the temperature in the west of the TWS is lower than that in the east. This is mainly because the western strait is often affected by the southward-flowing cold current (ZCC) and cold subsurface water uplifted by upwelling while the eastern strait is principally occupied by the northward-flowing warm currents (SCSWC and KBC). Since the currents in the TWS are partly driven and adjusted by the East Asian monsoon, it plays an important role in circulation patterns in the TWS. In summer, under the influence of the summer southwest monsoon (southwesterly wind), the TWS is dominated by the northeastward and northward currents: the YCC near the west coast, the SCSWC in the middle and west, and the KBC in the east (Figure 1b). Due to wind-driven Ekman advection, the tidal effect and the bottom topographic effect [40], upwelling often occurs in the four regions near PT, DS, PH, and TB, resulting in lower SST than that in the other regions of the TWS (Figure 2b). In winter, driven by the strong winter northeast monsoon (northeasterly wind), the ZCC is very strong and can reach DS and TB along the west coast of the TWS while the SCSWC and KBC become weak and hardly flow through the TWS uninterruptedly. Because of the very low ZCC temperature, a strong cross-strait temperature gradient occurs along its east flank in the middle strait (Figure 2d). During the monsoon transition (autumn) from summer to winter, the ZCC gradually develops and extends into the TWS with the northeast monsoon appearing and intensifying, as indicated by the SST distribution in the TWS (Figure 2c). By contrast, the southwestward-flowing ZCC in the west of the TWS begins to retreat due to weakening of the northeast monsoon during the monsoon transition (spring) from winter to summer and the northeastward and northward warm currents become strong and extend northward, as can be seen from Figure 2a.
The seasonal 90th (10th) percentile SST shows the seasonally averaged baseline for MHWs (MCSs). Figure 2e–h demonstrate that, on average, the southwestward cold ZCC and upwelling (northeastward and northward warm currents) are weaker (stronger) than their counterparts, as shown in the seasonal mean SST maps (Figure 2a–d), when MHWs happen. By contrast, the situation is just reversed (Figure 2i–l) when MCSs appear. These suggest that the ocean circulations and upwelling may be crucial for the occurrence of MHWs and MCSs in the TWS.
During 1982–2021, the SST in most regions of the TWS in summer and autumn shows a significant increasing trend above the 95% level ( p < 0.05 , Figure 3b,c) with a regionally averaged increasing rate of 0.22 °C per decade in summer and 0.19 °C per decade in autumn. It is consistent with the results reported by Qi and Cai [34]. It should be noted that in the summer upwelling regions near DS and TB (Figure 2b), the SST does not show a significant warming trend ( p > 0.05 , Figure 3b), indicating that upwelling may have suppressed the long-term rising trend of local SST in these regions.
Interestingly, the SSTs in the east and west of the TWS show opposite trends in both spring and winter (Figure 3a,d). The SST in the west shows a significant decreasing trend, and the maximum decreasing rate reaches −0.48 °C per decade. Qi and Cai [34] also found that the SST in the coastal regions south of the Yangtze river estuary shows a significant decreasing trend in winter and spring, which may be related to the enhancement of the ZCC after 1998 [46]. By contrast, the east side of the TWS shows a warming trend in winter and spring, and the maximum warming rate appears in the central and northeastern regions of the TWS in spring, exceeding 0.4 °C per decade.

3.2. Annual Mean Characteristics of Extreme Temperature Anomaly Events in the TWS

3.2.1. MHWs in the TWS

Figure 4 shows the annual mean results and linear trends for the total days, frequency, duration, mean intensity, cumulative intensity, and the maximum intensity of MHWs in the TWS from 1982 to 2021. The regionally averaged (116°E–121°E, 22°N–26°N) total days of MHWs are 34 days per year (Figure 4a), with most regions showing an increasing trend above the 95% significance level (Figure 4g). The linear trend is mostly higher in the east of the strait than that in the west. The frequency is low in the west of the strait and high in the east with a regionally averaged frequency of 3.1 times per year (Figure 4b). Similar to the total days, the frequency in the east of the strait has a higher increasing rate (1–2 times per decade) than that (near zero) in the west (Figure 4h). Actually, there is no significant increase in the MHW frequency during the period of 1982–2021 in the western nearshore region. The duration is high in the regions near the west coast of the TWS and around the TB, with a maximum duration of 12 days (Figure 4c). The linear trend of duration is higher in the northwest to Taiwan Island and in the south of TWS (Figure 4i), with a maximum increasing rate of 5.3 days per decade.
The mean and maximum intensity of MHWs and their long-term trends show similar spatial distribution characteristics (Figure 4d,f,j,l). The annual mean MHWs intensity ranges from 0.6 °C to 2.7 °C and increases by 0.1 °C–0.4 °C per decade averaged in space. The high values of the mean intensity and maximum intensity are concentrated in the ZY. The long-term trends of both the mean intensity and maximum intensity are obviously higher in the east of the TWS than in the west. Their high values appear in the sea area northwest of Taiwan Island and in the south of the strait. As for the duration, there is no distinct difference in the cumulative intensity between the east and west parts of the strait (Figure 4e), and the high values of its linear trend are also concentrated in the sea area northwest of Taiwan Island and in the south of the strait (Figure 4k).

3.2.2. MCSs in the TWS

Figure 5 shows the annual mean properties and long-term trends of MCSs from 1982 to 2021. The annual mean total days of MCSs range from 17 to 31 days per year (Figure 5a) and show a linear trend of −20 to 10 days per decade (Figure 5g). The linear trends in most regions are significant above the 95% significance level. The annual mean frequency ranges from 1.6 to 3.2 times per year (Figure 5b), and changes linearly by −1.9–0.9 times per decade (Figure 5h). The annual mean duration is 4.5–12.9 days (Figure 5c), and the corresponding linear trend ranges from −4.1 to 3 days per decade (Figure 5i). As shown in Figure 5a–c, the western TWS experiences longer-lasting and more frequent MCSs, resulting in more MCS total days, compared with the eastern TWS. The annual total days, mean frequency and mean duration of MCSs in the west of the strait show an increasing trend, while they show a decreasing trend in the east.
The mean, cumulative, and maximum intensity of MCSs, as well as their respective long-term trends, all exhibit similar spatial patterns (Figure 5d–f,j–l). The regions of high intensity are located in the central and eastern TWS, with a maximum intensity of −2.9 °C. The mean, cumulative, and maximum intensities of MCSs in the west of the strait and around the ZY show a significant increasing trend during 1982–2021 with rates of −0.1 °C per decade, −2.2 °C days per decade, and −0.2 °C per decade (negative value indicating a strengthening trend of MCSs), respectively, above the 95% significance level (Figure 5j–l). In contrast, the MCS intensity in other regions has an opposite trend (weakening).

3.3. Seasonal Variability of Extreme Temperature Anomaly Events in the TWS

Figure 6 shows the seasonal mean characteristics of MHWs in the TWS for each season from 1982–2021. Among all seasons, the total days, frequency, duration and cumulative intensity of MHWs in winter are the lowest, with regional averages of 7.26 days, 0.67 times, 4.87 days, and 8.25 °C days, respectively (Table 2), and the spatial distribution of maximum intensity is similar to the mean intensity. Most of the low values are concentrated in the west of the TWS where the ZCC is dominant in winter (Figure 6d,h,l,p,t). The mean intensity and cumulative intensity of MHWs in winter are basically higher in the middle and eastern parts of the strait than in the western part, particularly around the ZY (Figure 6p,t). In spring, when the ZCC weakens and retreats northward, the low values of the MHW metrics are confined to the northeast corner of the TWS (Figure 6a,e,i,m,q). It is noted that the mean intensity of the MHWs is clearly higher in spring with a highest regionally averaged value of 0.84 °C (Figure 6m, Table 2), compared with other seasons. This is especially obvious in the northeast of the strait.
The regionally averaged total days of MHWs in summer are 9.92 days, which is the highest value in the year (Figure 6b, Table 2). And the highest MHW frequency also occurs in summer, with a regional average of 0.91 times (Figure 6f, Table 2). Low values of the MHW total days and frequency occur in the upwelling regions around the PH, DS, and TB. The regionally averaged total days and frequency of MHWs in the DS upwelling area are 7.89 days and 0.76 times, respectively. The cumulative intensity of each MHW event, on average, is relatively low in summer, mainly because of the short duration (Figure 6j,r). In autumn, the total days and frequency of MHWs fall between summer and winter (Figure 6c,g). It is clear that the MHW in autumn has the longest duration but the weakest intensity among four seasons (Figure 6k,o). Its regionally averaged duration is up to 6.42 days (Table 2).
In autumn, all metrics of MHWs in the TWS generally show a significant increasing trend (Figure 7c,g,k,o,s). The regionally averaged increasing rates of MHWs total days, duration, mean intensity, and cumulative intensity in autumn are the highest among four seasons. They are 3.3 days per decade, 2.1 days per decade, 0.1 °C per decade, and 3.2 °C days per decade, respectively. In summer, the MHWs metrics have a significant increasing trend in most regions of the TWS. The maximum regionally averaged increasing rate of MHWs frequency occurs in summer with a value of 0.2 times per decade. However, the metrics of summer MHWs in the DS and TB upwelling regions (Figure 2b) do not show a significant increasing trend and even have a decreasing trend (Figure 7b), which corresponds to no significant increasing trend in summer SST in these two regions (Figure 3b).
In winter, the most obvious feature in the linear trends of the MHW metrics is that the trends are basically negative (decreasing) in the west of the TWS while they are positive (increasing) in the east (Figure 7d,h,l,p,t). These patterns are consistent with the SST linear trend pattern in winter (Figure 3d). In spring, the total days, duration and cumulative intensity of MHWs tend to increase with positive trends in the TWS (Figure 7a,i,q). For the frequency and mean intensity of MHWs, they are positive in most regions of the TWS but negative in the western coastal waters of the strait (Figure 7e,m).
Different from MHWs mostly happening in summer, MCSs in the TWS mainly occur in winter (the fourth column in Figure 8 and Table 3). In terms of spatial distribution, the total days, frequency and duration of winter MCSs in the west of the TWS are higher than their counterparts in the east, with the highest values of 15.7 days, 1.5 times, and 10 days, respectively. Similar to MHWs in winter (Figure 6p,t), the mean and cumulative intensity of MCSs is stronger around the ZY in the middle and east of the strait, compared with other regions in the strait (Figure 8p,t). Here, the strongest mean intensity of MCSs reaches −2.3 °C. The absolute values of the MCSs metrics are quite a lot smaller in spring than in winter, but they have similar spatial patterns to their counterparts in winter (the first column in Figure 8). In summer and autumn, MCSs are very few and almost negligible according to their metrics in the TWS, except for the upwelling area around DS in summer. In the DS upwelling area, the average total days, frequency, duration, and mean intensity of MCSs in summer are 8 days (Figure 8b), 0.9 times (Figure 8f), 5.4 days (Figure 8j), and −1.3 °C (Figure 8n), respectively.
As can be seen from the long-term trends of the MCSs metrics during 1982–2021 (Figure 9), the MCSs in spring and winter have significantly increasing trends in total days, frequency, and duration, and strengthening trends in both mean intensity and cumulative intensity in the western regions of the TWS (the first and fourth columns in Figure 9). In winter, these trends are more obvious, and additionally, the MCSs in the middle and eastern part of the TWS have similar trends. Their regionally averaged rates of change in frequency, duration, mean intensity, and cumulative intensity are 0.2 times per decade (Figure 9h), 1.5 days per decade (Figure 9l), −0.24 °C per decade (Figure 9p), and −3.7 °C days per decade (Figure 9t), respectively. In summer and autumn, the MCSs have decreasing trends in total days, frequency and duration, and weakening trends in both mean intensity and cumulative intensity from 1982 to 2021 in the TWS except for the summer upwelling regions around DS and PH (the second and third columns in Figure 9). In the DS upwelling region, the MCSs have slightly increasing or strengthening trends in summer, but they are not significant.
It is interesting that the regional high values of both MHW and MCS intensity appear around the ZY (Figure 6p,t and Figure 8p,t). Oliver et al. [25] found that hotspots of high MHW intensity occur in areas with large SST variability, such as the western boundary current extension. The ZY is located at the quasi-static SST front between the cold ZCC coastal water and the warm Kuroshio branch water in winter [38]. The SST at this position is obviously affected by the competition between these two waters, so there is a large SST variability, resulting in intense MHWs and MCSs.

4. Discussion

As identified from the results presented in Section 3.3, there are obvious seasonal differences between MHWs and MCSs in the TWS. MHWs are concentrated in summer and autumn while MCSs mainly occur in winter and spring. And they have different spatial distribution patterns in the strait. In order to further examine the main spatial patterns of MHWs/MCSs and their linear trends in the TWS, an empirical orthogonal function (EOF) analysis was conducted for the MHWs in summer and the MCSs in winter. Since total intensity can simultaneously reflect the frequency, duration, and intensity of MHWs/MCSs [32,42], we applied EOF analysis to the seasonal total intensity of summer MHWs and winter MCSs. Here, the seasonal total intensity is the sum of the MHWs/MCSs intensity during their total days in a season. For the total intensity of summer MHWs (winter MCSs), the first and second EOF modes account for 45.62% (34.98%) and 17.57% (12.09%) of the total variance, respectively.
The spatial pattern of the first EOF mode (EOF1) of the summer MHWs total intensity (Figure 10a) is similar to the spatial distribution of the long-term trends in the summer MHWs metrics (Figure 7). Most of the regions in the TWS show high positive values and only upwelling areas around DS and TB have low values or even negative values. Varela et al. [31] reported that upwelling can modulate the occurrence of MHWs. Yao and Wang [32] found that the cooling effect of upwelling in summer can effectively inhibit the occurrence of MHWs in the South China Sea. Figure 10b demonstrates that the principal component of EOF1 (PC1) has a significant increasing trend above the 99% significance level, indicating that the TWS, except in upwelling areas, tends to experience more and/or stronger MHWs.
The spatial pattern of EOF1 of the winter MCSs total intensity shows negative values in the TWS (Figure 10c). Very low values are concentrated in the western and central areas of the TWS where MCSs frequently happened (Figure 8). Its PC1 shows a significant increasing trend with the significance level of 95% (Figure 10d), suggesting that over the past four decades, more and/or stronger MCSs occurred in the western and central areas of the TWS.
The linear regression analyses for both metrics of seasonal MHWs/MCSs and the PC1 of seasonal MHWs/MCSs first EOF mode demonstrate that there are long-term increasing trends in summer MHWs and winter MCSs during the period of 1982–2021. Interestingly, the summer SST in the TWS, except in upwelling areas, also has an increasing trend during the period and the winter SST in the western TWS has a decreasing trend, which corresponds to the increasing trends of the summer MHWs and winter MCSs. Previous studies have shown that the long-term SST warming trend is the dominant driver of both the increasing trend of global MHWs and the decreasing trend of global MCSs [27,29]. Since there are regional differences in SST trends [15,42], the local influence of SST trends on MHWs and MCSs in the TWS is not clear.
In order to evaluate the influence of the SST long-term trend on extreme temperature anomaly events, we removed the linear trend from the original SST to obtain detrended SST. Based on the same climatology baseline, the detrended SST time series were used to identify the MHWs and MCSs again, which are only related to the SST internal variability, rather than the SST long-term trend. Taking summer MHWs and winter MCSs as indicators, the contributions of the long-term trend and the internal variability of SST to the total intensity variability of extreme temperature anomaly events during 1982–2021 were obtained and compared (Figure 11). The time series of the summer MHWs seasonal total intensity calculated from original SSTs shows a significant increasing trend above the 99% significance level while that from detrended SSTs has no significant trend (Figure 11a). Their difference (the former minus the latter) is attributed to the SST long-term trend and it shows a similar trend to that from the original SST. The TAR value is 0.93, which indicates that the SST long-term trend is the main reason for the observed long-term trend of summer MHWs total intensity [45]. Figure 11b shows that the time series from the original SST, that from the detrended SST, and their difference all have decreasing trends (their absolute values show increasing trends). The TAR value of −0.15 indicates that the SST long-term trend is as important as the SST internal variability for the long-term trend of the winter MCSs total intensity in the TWS.
Following Oliver [29] and Lee et al. [15], the probability distribution of the regionally averaged SST was plotted for the first decade (1982–1991) and the last decade (2012–2021), based on the original and detrended SST time series, respectively (Figure 12). The results in summer show that in the original SST distribution (Figure 12a), the mean summer SST increases from 27.19 °C (1982–1991) to 27.81 °C (2012–2021), but there is little change in the SST variance (from 0.74 °C to 0.62 °C). It is noted that the likelihood of more and stronger MHWs (red shaded area in Figure 12a) increases. However, this phenomenon does not exist in the detrended SST distribution (Figure 12b). These results confirm that the SST long-term warming, rather than SST internal variability, is the dominant driver for the observed increasing trends of summer MHWs properties in the TWS. In winter, the mean SST decreases from 16.71 °C in the first decade to 16.38 °C in the last decade and the SST variance increases from 1.61 °C to 3.30 °C (Figure 12c). As a result, the likelihood of more and stronger MCSs increased (blue shaded area in Figure 12c). In the detrended SST, the SST variance increases from 2.11 °C to 2.68 °C. Although the SST long-term trend has been removed, the likelihood of more and stronger winter MCSs in the detrended SST during the last decade is still greater than that during the first decade (blue shaded area in Figure 12d), which should be attributed to the increase in the SST variance. Compared with the results from the original SST (Figure 12c), the difference in the likelihood of MCSs between the two decades is smaller. These observations suggest that both the SST long-term trend and the SST internal variability contribute to the increasing trend of the winter MCSs metrics in the TWS.

5. Summary

In this study, we used the high-resolution global satellite SST product to reveal the annual mean characteristics, spatial differences, seasonal variations, and long-term trends of extreme temperature anomaly events (MHWs and MCSs) in the TWS from 1982 to 2021 and discussed potential influencing processes. The main conclusions are summarized as follows:
  • The SST and its long-term trend in the Taiwan Strait show different spatial patterns in different seasons.
  • The regionally averaged annual total days and frequency of MHWs in the TWS are 34 days and 3.1 times, respectively, and show a significant increasing trend during 1982–2021. The increasing rate in the east of the Taiwan Strait is higher than that in the west. The maximum MHW duration can reach 12 days, and the maximum increasing rate can reach 5.3 days per decade. The annual mean intensity of MHWs ranges from 0.6 °C to 2.7 °C and increases by 0.1 °C~0.4 °C per decade. The MCSs mainly occur in the west of the strait. The annual total days of MCSs range from 17 to 31 days and show a linear trend of −20 to 10 days per decade. The metrics of MCSs show an increasing trend in the western strait but a decreasing trend in the eastern.
  • In summer and autumn, MHWs occur in most areas of the TWS, and their metrics tend to increase with time during the period of 1982–2021. The metrics of MHWs in spring and winter show a decreasing trend in the west of the strait and an increasing trend in the east. The spring and winter MCSs are concentrated in the western TWS affected by the ZCC and show an increasing trend. In summer and autumn, the metrics of MCSs show an obvious decreasing trend. The cooling effect of summer upwelling around DS and TB tends to inhibit the occurrence of MHWs but enhances MCSs.
  • The rising background SST is the dominant driver for the increasing trend of summer MHWs in the TWS while both the SST decreasing trend and internal variability contribute to the increasing trend of winter MCSs in the strait.

Author Contributions

Conceptualization, W.-Z.Z. and Z.-F.J.; methodology, Z.-F.J.; software, Z.-F.J.; validation, Z.-F.J. and W.-Z.Z.; formal analysis, Z.-F.J.; investigation, Z.-F.J. and W.-Z.Z.; resources, Z.-F.J.; data curation, Z.-F.J.; writing—original draft preparation, Z.-F.J.; writing—review and editing, W.-Z.Z.; visualization, Z.-F.J.; supervision, W.-Z.Z.; project administration, W.-Z.Z.; funding acquisition, W.-Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was jointly supported by the State Key R&D project (2022YFF0801404) and the National Natural Science Foundation of China (41776015).

Data Availability Statement

The daily SST data can be downloaded from https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_REP_OBSERVATIONS_010_011/download?dataset=METOFFICE-GLO-SST-L4-REP-OBS-SST_202003 (accessed on 1 February 2024).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Scafetta, N. Empirical assessment of the role of the Sun in climate change using balanced multi-proxy solar records. Geosci. Front. 2023, 14, 101650. [Google Scholar] [CrossRef]
  2. Svensmark, H.; Svensmark, J.; Enghoff, M.B.; Shaviv, N.J. Atmospheric ionization and cloud radiative forcing. Sci. Rep. 2021, 11, 19668. [Google Scholar] [CrossRef]
  3. Coumou, D.; Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2012, 2, 491–496. [Google Scholar] [CrossRef]
  4. Kennedy, J.J. A review of uncertainty in in situ measurements and data sets of sea surface temperature. Rev. Geophys. 2014, 52, 1–32. [Google Scholar] [CrossRef]
  5. Cai, R.; Tan, H.; Kontoyiannis, H. Robust Surface Warming in Offshore China Seas and Its Relationship to the East Asian Monsoon Wind Field and Ocean Forcing on Interdecadal Time Scales. J. Clim. 2017, 30, 8987–9005. [Google Scholar] [CrossRef]
  6. Tan, H.; Cai, R.; Huo, Y.; Guo, H. Projections of changes in marine environment in coastal China seas over the 21st century based on CMIP5 models. J. Oceanol. Limnol. 2020, 38, 1676–1691. [Google Scholar] [CrossRef]
  7. Hobday, A.J.; Alexander, L.V.; Perkins, S.E.; Smale, D.A.; Straub, S.C.; Oliver, E.C.J.; Benthuysen, J.A.; Burrows, M.T.; Donat, M.G.; Feng, M.; et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 2016, 141, 227–238. [Google Scholar] [CrossRef]
  8. Wernberg, T.; Smale, D.A.; Tuya, F.; Thomsen, M.S.; Langlois, T.J.; de Bettignies, T.; Bennett, S.; Rousseaux, C.S. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 2012, 3, 78–82. [Google Scholar] [CrossRef]
  9. Di Lorenzo, E.; Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 2016, 6, 1042–1047. [Google Scholar] [CrossRef]
  10. Hughes, T.P.; Kerry, J.T.; Álvarez-Noriega, M.; Álvarez-Romero, J.G.; Anderson, K.D.; Baird, A.H.; Babcock, R.C.; Beger, M.; Bellwood, D.R.; Berkelmans, R.; et al. Global warming and recurrent mass bleaching of corals. Nature 2017, 543, 373–377. [Google Scholar] [CrossRef] [PubMed]
  11. Oliver, E.C.J.; Benthuysen, J.A.; Bindoff, N.L.; Hobday, A.J.; Holbrook, N.J.; Mundy, C.N.; Perkins-Kirkpatrick, S.E. The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun. 2017, 8, 16101. [Google Scholar] [CrossRef] [PubMed]
  12. Schlegel, R.W.; Oliver, E.C.J.; Wernberg, T.; Smit, A.J. Nearshore and offshore co-occurrence of marine heatwaves and cold-spells. Prog. Oceanogr. 2017, 151, 189–205. [Google Scholar] [CrossRef]
  13. Hobday, A.; Oliver, E.; Sen Gupta, A.; Benthuysen, J.; Burrows, M.; Donat, M.; Holbrook, N.; Moore, P.; Thomsen, M.; Wernberg, T.; et al. Categorizing and Naming Marine Heatwaves. Oceanography 2018, 31, 162–173. [Google Scholar] [CrossRef]
  14. Rodrigues, R.R.; Taschetto, A.S.; Sen Gupta, A.; Foltz, G.R. Common cause for severe droughts in South America and marine heatwaves in the South Atlantic. Nat. Geosci. 2019, 12, 620–626. [Google Scholar] [CrossRef]
  15. Lee, S.; Park, M.S.; Kwon, M.; Park, Y.G.; Kim, Y.H.; Choi, N. Rapidly Changing East Asian Marine Heatwaves Under a Warming Climate. J. Geophys. Res. Ocean. 2023, 128, e2023JC019761. [Google Scholar] [CrossRef]
  16. Diaz-Almela, E.; MarbÀ, N.; Duarte, C.M. Consequences of Mediterranean warming events in seagrass (Posidonia oceanica) flowering records. Glob. Change Biol. 2006, 13, 224–235. [Google Scholar] [CrossRef]
  17. Cavole, L.; Demko, A.; Diner, R.; Giddings, A.; Koester, I.; Pagniello, C.; Paulsen, M.-L.; Ramirez-Valdez, A.; Schwenck, S.; Yen, N.; et al. Biological Impacts of the 2013–2015 Warm-Water Anomaly in the Northeast Pacific: Winners, Losers, and the Future. Oceanography 2016, 29, 273–285. [Google Scholar] [CrossRef]
  18. Bond, N.A.; Cronin, M.F.; Freeland, H.; Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 2015, 42, 3414–3420. [Google Scholar] [CrossRef]
  19. Mills, K.; Pershing, A.; Brown, C.; Chen, Y.; Chiang, F.-S.; Holland, D.; Lehuta, S.; Nye, J.; Sun, J.; Thomas, A.; et al. Fisheries Management in a Changing Climate: Lessons From the 2012 Ocean Heat Wave in the Northwest Atlantic. Oceanography 2013, 26, 191–195. [Google Scholar] [CrossRef]
  20. Schlegel, R.W.; Darmaraki, S.; Benthuysen, J.A.; Filbee-Dexter, K.; Oliver, E.C.J. Marine cold-spells. Prog. Oceanogr. 2021, 198, 102684. [Google Scholar] [CrossRef]
  21. Aretxabaleta, A.; Nelson, J.R.; Blanton, J.O.; Seim, H.E.; Werner, F.E.; Bane, J.M.; Weisberg, R. Cold event in the South Atlantic Bight during summer of 2003: Anomalous hydrographic and atmospheric conditions. J. Geophys. Res. Ocean. 2006, 111, C06007. [Google Scholar] [CrossRef]
  22. Aretxabaleta, A.; Blanton, B.O.; Seim, H.E.; Werner, F.E.; Nelson, J.R.; Chassignet, E.P. Cold event in the South Atlantic Bight during summer of 2003: Model simulations and implications. J. Geophys. Res. Ocean. 2007, 112, C05022. [Google Scholar] [CrossRef]
  23. Zapata, F.A.; Jaramillo-González, J.; Navas-Camacho, R. Extensive bleaching of the coral porites lobata at Malpelo Island, Colombia, during a cold water episode in 2009. Boletín Investig. Mar. Y Costeras-INVEMAR 2011, 40, 185–193. [Google Scholar]
  24. Firth, L.B.; Mieszkowska, N.; Grant, L.M.; Bush, L.E.; Davies, A.J.; Frost, M.T.; Moschella, P.S.; Burrows, M.T.; Cunningham, P.N.; Dye, S.R.; et al. Historical comparisons reveal multiple drivers of decadal change of an ecosystem engineer at the range edge. Ecol. Evol. 2015, 5, 3210–3222. [Google Scholar] [CrossRef]
  25. Oliver, E.C.J.; Donat, M.G.; Burrows, M.T.; Moore, P.J.; Smale, D.A.; Alexander, L.V.; Benthuysen, J.A.; Feng, M.; Sen Gupta, A.; Hobday, A.J.; et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 2018, 9, 1324. [Google Scholar] [CrossRef]
  26. Frölicher, T.L.; Fischer, E.M.; Gruber, N. Marine heatwaves under global warming. Nature 2018, 560, 360–364. [Google Scholar] [CrossRef]
  27. Wang, Y.; Kajtar, J.B.; Alexander, L.V.; Pilo, G.S.; Holbrook, N.J. Understanding the Changing Nature of Marine Cold-Spells. Geophys. Res. Lett. 2022, 49, e2021GL097002. [Google Scholar] [CrossRef]
  28. Yao, Y.; Wang, C.; Fu, Y. Global Marine Heatwaves and Cold-Spells in Present Climate to Future Projections. Earth’s Future 2022, 10, e2022EF002787. [Google Scholar] [CrossRef]
  29. Oliver, E.C.J. Mean warming not variability drives marine heatwave trends. Clim. Dyn. 2019, 53, 1653–1659. [Google Scholar] [CrossRef]
  30. Sun, D.; Liu, Z.; Chiu, L.; Yang, R.; Singh, R.P.; Kafatos, M. Anomalous cold water detected along Mid-Atlantic Coast. Eos Trans. Am. Geophys. Union 2004, 85, 152. [Google Scholar] [CrossRef]
  31. Varela, R.; Rodríguez-Díaz, L.; de Castro, M.; Gómez-Gesteira, M. Influence of Eastern Upwelling systems on marine heatwaves occurrence. Glob. Planet. Change 2021, 196, 103379. [Google Scholar] [CrossRef]
  32. Yao, Y.; Wang, C. Variations in Summer Marine Heatwaves in the South China Sea. J. Geophys. Res. Ocean. 2021, 126, e2021JC017792. [Google Scholar] [CrossRef]
  33. Tan, H.-J.; Cai, R.-S.; Wu, R.-G. Summer marine heatwaves in the South China Sea: Trend, variability and possible causes. Adv. Clim. Change Res. 2022, 13, 323–332. [Google Scholar] [CrossRef]
  34. Qi, Q.; Cai, R. Analysis on climate characteristics of sea surface temperature extremes in coastal China seas. Haiyang Xuebao 2019, 41, 6. [Google Scholar] [CrossRef]
  35. Li, Y.; Ren, G.; Wang, Q.; You, Q. More extreme marine heatwaves in the China Seas during the global warming hiatus. Environ. Res. Lett. 2019, 14, 104010. [Google Scholar] [CrossRef]
  36. Li, Y.; Ren, G.; Wang, Q.; Mu, L. Changes in marine hot and cold extremes in the China Seas during 1982–2020. Weather Clim. Extrem. 2023, 39, 100553. [Google Scholar] [CrossRef]
  37. Hu, J.Y.; Kawamura, H.; Li, C.Y.; Hong, H.S.; Jiang, Y.W. Review on Current and Seawater Volume Transport through the Taiwan Strait. J. Oceanogr. 2010, 66, 591–610. [Google Scholar] [CrossRef]
  38. Chang, Y.; Lee, K.-T.; Lee, M.-A.; Lan, K.-W. Satellite Observation on the Exceptional Intrusion of Cold Water in the Taiwan Strait. Terr. Atmos. Ocean. Sci. 2009, 20, 661–669. [Google Scholar] [CrossRef]
  39. Liao, E.; Oey, L.Y.; Yan, X.-H.; Li, L.; Jiang, Y. The Deflection of the China Coastal Current over the Taiwan Bank in Winter. J. Phys. Oceanogr. 2018, 48, 1433–1450. [Google Scholar] [CrossRef]
  40. Hu, J.Y.; Kawamura, H.; Hong, H.S.; Pan, W.R. A review of research on the upwelling in the Taiwan Strait. Bull. Mar. Sci. 2003, 73, 605–628. [Google Scholar]
  41. Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; et al. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. [Google Scholar] [CrossRef]
  42. Wang, D.; Xu, T.; Fang, G.; Jiang, S.; Wang, G.; Wei, Z.; Wang, Y. Characteristics of Marine Heatwaves in the Japan/East Sea. Remote Sens. 2022, 14, 936. [Google Scholar] [CrossRef]
  43. Mann, H.B. Nonparametric test against trend. Econometrica 1945, 13, 245–259. [Google Scholar] [CrossRef]
  44. Kendall, M.G. Rank Correlation Measures; Charles Griffin: London, UK, 1975. [Google Scholar]
  45. Marin, M.; Feng, M.; Phillips, H.E.; Bindoff, N.L. A Global, Multiproduct Analysis of Coastal Marine Heatwaves: Distribution, Characteristics, and Long-Term Trends. J. Geophys. Res. Ocean. 2021, 126, e2020JC016708. [Google Scholar] [CrossRef]
  46. Zhang, C.; Huang, Y.; Ding, W. Enhancement of Zhe-Min coastal water in the Taiwan Strait in winter. J. Oceanogr. 2020, 76, 197–209. [Google Scholar] [CrossRef]
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Figure 1. (a) The location of the TWS and its adjacent seas. The current patterns in the TWS in (b) summer and (c) winter. The shading shows the bathymetry in meters (m). In each panel, PT, ZY, PH, TB, DS, YCC, SCSWC, KBC, and ZCC denote Pingtan Island, Zhangyun Ridge, the Penghu Islands, Taiwan Bank, Dongshan Island, the Yuedong Coastal Current, the South China Sea Warm Current, the Kuroshio Branch Current, and the Zhe-Min Coastal Current, respectively.
Figure 1. (a) The location of the TWS and its adjacent seas. The current patterns in the TWS in (b) summer and (c) winter. The shading shows the bathymetry in meters (m). In each panel, PT, ZY, PH, TB, DS, YCC, SCSWC, KBC, and ZCC denote Pingtan Island, Zhangyun Ridge, the Penghu Islands, Taiwan Bank, Dongshan Island, the Yuedong Coastal Current, the South China Sea Warm Current, the Kuroshio Branch Current, and the Zhe-Min Coastal Current, respectively.
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Figure 2. Seasonal mean SST (°C, (a–d)), seasonal 90th percentile SST (e–h), seasonal 10th percentile SST (i–l) in spring (March–May, first column), summer (June–August, second column), autumn (September–November, third column), and winter (December–February, fourth column) obtained from the period 1982–2011.
Figure 2. Seasonal mean SST (°C, (a–d)), seasonal 90th percentile SST (e–h), seasonal 10th percentile SST (i–l) in spring (March–May, first column), summer (June–August, second column), autumn (September–November, third column), and winter (December–February, fourth column) obtained from the period 1982–2011.
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Figure 3. SST long-term trends ( ℃   d e c a d e − 1 ) in spring (a), summer (b), autumn (c), and winter (d) during 1982–2021. Hatching indicates the trend is significant above the 95% significance level ( p < 0.05 ).
Figure 3. SST long-term trends ( ℃   d e c a d e − 1 ) in spring (a), summer (b), autumn (c), and winter (d) during 1982–2021. Hatching indicates the trend is significant above the 95% significance level ( p < 0.05 ).
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Figure 4. The annual mean metrics of MHWs during 1982–2021: (a) total days, (b) frequency, (c) duration, (d) mean intensity, (e) cumulative intensity, (f) maximum intensity; the linear trends of the metrics of MHWs during 1982–2021 recorded per decade: (g) total days, (h) frequency, (i) duration, (j) mean intensity, (k) cumulative intensity, (l) maximum intensity. Hatching indicates the trend is significant above the 95% significance level ( p < 0.05 ).
Figure 4. The annual mean metrics of MHWs during 1982–2021: (a) total days, (b) frequency, (c) duration, (d) mean intensity, (e) cumulative intensity, (f) maximum intensity; the linear trends of the metrics of MHWs during 1982–2021 recorded per decade: (g) total days, (h) frequency, (i) duration, (j) mean intensity, (k) cumulative intensity, (l) maximum intensity. Hatching indicates the trend is significant above the 95% significance level ( p < 0.05 ).
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Figure 5. The annual mean metrics of MCSs during 1982–2021: (a) total days, (b) frequency, (c) duration, (d) mean intensity, (e) cumulative intensity, (f) maximum intensity; the linear trends of the metrics of MCSs during 1982–2021 recorded per decade: (g) total days, (h) frequency, (i) duration, (j) mean intensity, (k) cumulative intensity, (l) maximum intensity. Hatching indicates the trend is significant above the 95% significance level ( p < 0.05 ).
Figure 5. The annual mean metrics of MCSs during 1982–2021: (a) total days, (b) frequency, (c) duration, (d) mean intensity, (e) cumulative intensity, (f) maximum intensity; the linear trends of the metrics of MCSs during 1982–2021 recorded per decade: (g) total days, (h) frequency, (i) duration, (j) mean intensity, (k) cumulative intensity, (l) maximum intensity. Hatching indicates the trend is significant above the 95% significance level ( p < 0.05 ).
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Figure 6. The spatial distribution of seasonal mean MHWs metrics in TWS during 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
Figure 6. The spatial distribution of seasonal mean MHWs metrics in TWS during 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
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Figure 7. The linear trends of the corresponding MHWs metrics in Figure 6 from 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
Figure 7. The linear trends of the corresponding MHWs metrics in Figure 6 from 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
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Figure 8. The spatial distribution of the seasonal mean MCSs metrics in TWS during 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
Figure 8. The spatial distribution of the seasonal mean MCSs metrics in TWS during 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
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Figure 9. The linear trends of the corresponding MCSs metrics in Figure 8 from 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
Figure 9. The linear trends of the corresponding MCSs metrics in Figure 8 from 1982–2021: (a–d) total days, (e–h) frequency, (i–l) duration, (m–p) mean intensity, (q–t) cumulative intensity. The first to the fourth columns are the results for spring, summer, autumn, and winter, respectively.
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Figure 10. Empirical orthogonal function (EOF) analysis of total intensity for (a,b) summer MHWs and (c,d) winter MCSs in TWS during 1982–2021: (a,c) spatial patterns of the first EOF modes for MHWs and MCSs, and (b,d) their corresponding principal components.
Figure 10. Empirical orthogonal function (EOF) analysis of total intensity for (a,b) summer MHWs and (c,d) winter MCSs in TWS during 1982–2021: (a,c) spatial patterns of the first EOF modes for MHWs and MCSs, and (b,d) their corresponding principal components.
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Figure 11. Temporal variations of (a) MHWs and (b) MCSs seasonal total intensity (°C) during 1982–2021. The red curve in (a) and blue curve in (b) are calculated from original SST time series (original results) and black curves in (a,b) are from detrended SST time series (detrended results). The green lines are the differences between the original results and detrended results (the former minus the latter). Dashed lines represent the linear trends.
Figure 11. Temporal variations of (a) MHWs and (b) MCSs seasonal total intensity (°C) during 1982–2021. The red curve in (a) and blue curve in (b) are calculated from original SST time series (original results) and black curves in (a,b) are from detrended SST time series (detrended results). The green lines are the differences between the original results and detrended results (the former minus the latter). Dashed lines represent the linear trends.
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Figure 12. Probability distribution function for the regionally averaged SST in (a,b) summer and (c,d) winter. In each panel, the SST density distributions for the first decade (1982–1991) and last decade (2012–2021) are shown as grey and black curves, respectively. The first column is obtained from the original SST data and the second column is from the detrended SST data. Red shades in (a,b) and blue shades in (c,d) represent the SST range for MHWs and MCSs, respectively.
Figure 12. Probability distribution function for the regionally averaged SST in (a,b) summer and (c,d) winter. In each panel, the SST density distributions for the first decade (1982–1991) and last decade (2012–2021) are shown as grey and black curves, respectively. The first column is obtained from the original SST data and the second column is from the detrended SST data. Red shades in (a,b) and blue shades in (c,d) represent the SST range for MHWs and MCSs, respectively.
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Table 1. Definitions of MHWs/MCSs metrics.
Table 1. Definitions of MHWs/MCSs metrics.
IndexFormulasUnit
Total days ∑ i = 1 N D i Days
Duration D = t e − t s + 1 Days
FrequencyNTimes
Mean intensity ∑ i = t s t e ( T i − T m i ) M °C
Maximum intensity max ⁡ T i − T m i for MHWs
min ⁡ T i − T m i for MCSs
            t s ≤ i ≤ t e 		°C
Cumulative intensity ∑ i = t s t e ( T i − T m ( i ) ) •1 day°C days
Note: t s , t e : dates on which a MHW/MCS begins and ends, respectively. T i and T m i are the daily SST and climatological mean SST, respectively, for day i . M is the number of days in the duration.
Table 2. Regionally averaged MHWs metrics in four seasons.
Table 2. Regionally averaged MHWs metrics in four seasons.
SeasonTotal Days
(Days)		Frequency
(Times)		Duration
(Days)		Mean Intensity
(°C)		Cumulative Intensity
(°C Days)
Spring8.910.815.730.849.60
Summer9.920.915.740.778.48
Autumn9.540.776.420.709.43
Winter7.260.674.870.748.25
Table 3. Regionally averaged MCSs metrics in four seasons.
Table 3. Regionally averaged MCSs metrics in four seasons.
SeasonTotal Days
(Days)		Frequency
(Times)		Duration
(Days)		Mean Intensity
(°C)		Cumulative Intensity
(°C Days)
Spring3.440.352.33−0.41−4.20
Summer2.600.291.87−0.30−2.64
Autumn2.340.231.69−0.23−2.35
Winter4.630.423.16−0.52−6.03
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Jin, Z.-F.; Zhang, W.-Z. Statistical Characteristics of Remote Sensing Extreme Temperature Anomaly Events in the Taiwan Strait. Remote Sens. 2024, 16, 3091. https://doi.org/10.3390/rs16163091

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Jin Z-F, Zhang W-Z. Statistical Characteristics of Remote Sensing Extreme Temperature Anomaly Events in the Taiwan Strait. Remote Sensing. 2024; 16(16):3091. https://doi.org/10.3390/rs16163091

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Jin, Ze-Feng, and Wen-Zhou Zhang. 2024. "Statistical Characteristics of Remote Sensing Extreme Temperature Anomaly Events in the Taiwan Strait" Remote Sensing 16, no. 16: 3091. https://doi.org/10.3390/rs16163091

APA Style

Jin, Z.-F., & Zhang, W.-Z. (2024). Statistical Characteristics of Remote Sensing Extreme Temperature Anomaly Events in the Taiwan Strait. Remote Sensing, 16(16), 3091. https://doi.org/10.3390/rs16163091

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