Estimating Typhoon-Induced Sea Surface Cooling Based Upon Satellite Observations

Home , Typhoon Ketsana, Typhoon Krovanh (2003), Typhoon Lingling (2019), Typhoon Lionrock, Typhoon Rammasun, Typhoon Rammasun (2008)

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Article Estimating Typhoon-Induced Sea Surface Cooling Based upon Satellite Observations

Dan Song 1,2 , Lulu Xiang 1, Linghui Guo 1 and Bo Li 3,*

1 Institute of Physical Oceanography and Remote Sensing, Ocean College, Zhejiang University, Zhoushan 316021, China; [email protected] (D.S.); [email protected] (L.X.); [email protected] (L.G.) 2 Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Zhoushan 316021, China 3 Department of Oceanography, College of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China * Correspondence: [email protected]

Received: 10 September 2020; Accepted: 27 October 2020; Published: 1 November 2020

Abstract: Typhoons frequently occur in the summer in the northwestern Pacific Ocean, and the responses of the upper ocean to typhoons have drawn extensive attention for decades. In the present work, a modified grid-based maximum response (GMR) method was proposed to estimate the sea surface cooling (SSC) caused by typhoons. The current algorithm (CA) is different from the original GMR method mainly in two aspects: (1) it uses a 5 day average rather than a 2 day average of the sea surface temperature (SST) before the typhoon as the reference temperature; (2) it modifies the fixed radius of 400 km to the level-7 Beaufort scale wind-force (~17.1 m/s) radius to determine the area where the SSC should be calculated. Then the MW-IR OISST data derived from satellite observations were used to compare the SSC estimated by different algorithms in four typhoon cases, Megi, LionRock, Trami and KongRey. The results show that, in all cases, maximum response methods have approached similar results, while the others seemed to have underestimated the SSC in degrees. In the slow-moving LionRock case, grid-based methods were found to have better performance, while in the successive typhoon cases, Trami and KongRey, CA showed an improved result in representing the pre-existing sea surface status before the typhoon KongRey by using the pentad mean SST as the reference temperature. In addition, the use of level-7 wind-force coverage made the results much livelier. In a word, the algorithm proposed here is valid in general. It has advantages in estimating the SSC caused by both slow-moving typhoons and successive typhoons, and should be further applied to related research.

Keywords: sea surface cooling; grid-based method; maximum response; slow-moving typhoons; successive typhoons

1. Introduction Tropical cyclones are some of the most severe natural disasters. They have great destructive power and usually bring strong winds, heavy rain, huge waves, and storm surges to coastal areas. The tropical cyclones generated in the northwestern Paciﬁc Ocean (NWPO) often have a long period of air–sea interaction before making landfall, which can easily strengthen them to form so-called typhoons. In other words, the NWPO environment is conducive to the generation and development of strong tropical cyclones [1]. Some recent studies have shown that the ratio of strong typhoons to weak ones happening in the NWPO is slowly increasing due to global warming [2]. Taking China as an example, it has an average of nine tropical cyclones per year in its coastal regions [2,3], causing severe economic losses and casualties. Having a comprehensive understanding of the air–sea interactions

Water 2020, 12, 3060; doi:10.3390/w12113060 www.mdpi.com/journal/water Water 2020, 12, 3060 2 of 12 during typhoons may improve our typhoon monitoring and early warning capabilities to help reduce associated damage [4]. The response of the upper ocean to a typhoon’s passage has been a hot issue since the 1980s [5,6]. Two remarkable aspects have been suggested as responses to a typhoon’s wind force [7,8]: (1) the strengthening and changes in direction of sea surface flow, and (2) the reduction in the sea surface temperature (SST) due to strong vertical mixing. An SST drop is the most obvious feature of the upper ocean’s response to a typhoon, and it can easily be measured by means of remote satellite sensing. Researchers found that the SST reduction caused by typhoon transit could vary from 1 to 9 ◦C[9–13]. Dare et al. [14] used SST data from 1981 to 2008 to study SST changes in response to global typhoons and found that the largest decreases in SST mostly occurred within one day of typhoon transit in the affected area. They further found that the SST would recover to 44% of its original climatic state within 5 days and 88% within 30 days. It was found that the intensity and speed of a typhoon affect the range and recovery time of the SST response. The greater the typhoon intensity and the slower the movement speed is, the stronger the vertical pumping and mixing effect is and, therefore, the greater the temperature drop is [14–17]. When a typhoon is moving at a slow speed, the strength and speed of the typhoon affect the SST cooling, while the temperature drop caused by the rapid movement of a typhoon is mainly controlled by its strength [5]. Song et al. [18] also found that a relatively lower sea level (or cold core eddy) favors causing intense sea surface cooling (SSC) through the study of five strong and super typhoons in 2016. There is a noticeable asymmetry in the cooling path caused by a typhoon. Due to the resonance effect between the local wind and the near-inertial flow on the right side of the typhoon track in the northern hemisphere, the cooling rate on the right side is larger than that on the left side [3,5,19–23]. Xu [24] and others used statistical analysis to study SST changes due to the influence of typhoons in the NWPO from 2000 to 2005. Statistics show that in the sea area and time-period under study, the general situation of typhoon-induced cooling is that the SST on the day of the typhoon’s transit decreases the most. On the second and third days of transit, the SST will continue to decrease and reach a minimum, after which the temperature will slowly rise. Liu et al. [25] analyzed the response of the upper ocean to the passage of tropical cyclones using Argo buoy observation data from 2001 to 2004 and found that the deepening of the mixed layer and the decrease in temperature were most obvious within 5 days of the typhoon passing. Park et al. and Wang et al. [26,27] studied the Argo observation data in the North Pacific Ocean from 1996 to 2012 and found that the recovery time of the SST was about 5 to 20 days. The shallow surface layer (about 10 m) can quickly recover to its state before the typhoon’s transit, but the response of the near-surface layer lasts longer. Recently, Zhang et al. [28] studied the relationship between cold wake size and the storm size, which highlighted the importance of cold wake size in evaluating the power dissipation and ocean heat uptake. Precise estimation of the SSC is required for statistical analysis to understand the responses of the upper ocean to the typhoon’s wind-force and translation speed, as well as the pre-existing sea surface status. It may further be applied to estimate the heat-flux through the air–sea surface to improve coupled numerical models. Generally, the SSC can be recognized as the difference between a minimum temperature (MT) caused by a typhoon and a reference temperature (RT) before the typhoon’s arrival. Thus, the divergences remain in how to define the MT and RT. Here listed four main algorithms used by the former researchers:

A1 The SST of the ﬁrst day after the typhoon’s passage (MT) minus that of the day before the typhoon’s arrival (RT) [5,29–31]; A2 The minimum SST during the typhoon’s passage (MT) minus the average SST over three days before the typhoon’s arrival (RT) [32]; A3 The SST averaged over 24 h to four days after the typhoon passage (MT) minus the SST averaged during 10 to 3 days before the typhoon’s arrival (RT) [33]; Water 2020, 12, 3060 3 of 12

A4 The so-called grid-based maximum response (GMR) method, which treats the average SST 2 days before the initial forcing time as RT and the minimum SST from the arrival to 5 days after the terminal forcing time as MT [34]. Hereafter, this will also be referred to as the LI method;

WaterMany 2020 researchers, 12, x FOR PEER also REVIEW analyzed and compared the daily SST changes during typhoons3 of instead 12 of calculating the SSC [35–37]. Despite the importance of the sea surface response to typhoons, few papers have discussedMany researchers the algorithm also analyzed to estimate and the compared SSC, whose the daily aspects SST havechanges not during been illustratedtyphoons instead and studied in detailof calculating till now. the Therefore, SSC [35–37 a]. modiﬁedDespite the GMR importance method of the is proposed sea surface by response the present to typhoons, work. few It will be describedpapers have along discussed with the the data algorithm used in to this estimate study the in SSC, Section whose2. aspects In Section have3 ,not comparison been illustrated with and the four abovementionedstudied in detail algorithms till now. Therefore, will be shown. a modified Section GMR4 contains method ais detailedproposed discussion, by the present and work. a conclusion It will be described along with the data used in this study in Section 2. In Section 3, comparison with will be drawn in Section5. the four abovementioned algorithms will be shown. Section 4 contains a detailed discussion, and a 2. Dataconclusion and Methods will be drawn in Section 5.

2.1.2. Data Data and Methods 2.1. Data 2.1.1. Typhoon Track Data and Basic Information 2.1.1.The Typhoon typhoon Track information Data and used Basic in Information this study, including the time, central location, maximum wind speed (MWS),The typhoon central information pressure, used transnational in this study, including speed, the and time, seventh-level central location, wind maximum circle wind every 6 h, wasspeed obtained (MWS), from central the pressure, Central transnational Meteorological speed, Observatory and seventh-level (http: wind//typhoon.nmc.cn circle every 6 h,/web.html was ). Theobta typhoonsined from named theLionRock Central Meteorological and Megi in 2016 Observatory and Trami (http://typhoon.nmc.cn/web.html). and KongRey in 2018 are discussed The in this study.typhoons Information named LionRock on the four and typhoons Megi in 2016 is displayed and Trami inand Table KongRey1, while in 2018 the tracksare discussed and intensities in this of thesestudy. four Information typhoons are on shownthe four in typhoons Figure1 is. displayed in Table 1, while the tracks and intensities of these four typhoons are shown in Figure 1. Table 1. Information about the four typhoons. Table 1. Information about the four typhoons.

NameName CodeCode No. No. Duration Duration (h) (h) Max Max Wind Wind Speed Speed (m/s) (m /s)Min Pressure Min Pressure (hPa) (hPa) LionRockLionRock 16101610 276 276 50 50935 935 MegiMegi 16171617 135 135 52 52935 935 TramiTrami 18241824 222 222 60 60920 920 KongReyKongRey 18251825 180 180 60 60920 920

FigureFigure 1. 1.Path Path map map of Typhoon Typhoon LionRock LionRock (1610), (1610), Megi Megi (1617), (1617), Trami (1824), Trami and (1824), KongRey and KongRey(1825). The (1825). Thecolored colored dotted dotted lines lines show show the trajectories the trajectories of the typhoons. of the typhoons. The different The colors diﬀ representerent colors the typhoon represent the typhoonintensities intensities set by the set China by the Meteorological China Meteorological Administration. Administration. The time interval The between time the interval dots is between24 h. the dots is 24 h.

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2.1.2. Satellite Observations The SST data used in this study are microwave and infrared OI SST (MW-IR OISST) data from the remote sensing system (http://data.remss.com/sst/daily/mw/v05.0/netcdf/) with a spatial resolution of 9 km and a time resolution of 1 day.

2.2. Methods Here, we would like to introduce another GMR method differing from the LI method in some physical considerations. Firstly, for the reference temperature, the SST averaged over 5 days before the typhoon’s arrival is suggested to smooth the possible influence of weather processes such as thunderstorms. This modification is expected to better represent the pre-existing sea surface status. Another reason is that pentad data are commonly used to index the monsoons (e.g., Xie et al., 1998 [38], Wang et al., 2004 [39]), and the influential time scale of a typhoon often exceed 5 days but is apparently shorter than that of the monsoons. Secondly, the minimum temperatures are taken from the arrival day of the typhoon to 5 days after its passage, since most of the significant cooling was found to happen during that period [25]. This thought is consistent with the LI method. Thirdly, only areas covered under the level-7 Beaufort scale (BS) wind-force will be counted, because it will be recognized as a storm only if the wind-force exceeds level-7 BS (~17.1 m/s). For the first step, the average value of the four-direction level-7 BS wind-force diameter is calculated to identify the area to apply the current algorithm. The second step is to determine the time at which a point in the area is affected by the typhoon. For any point affected by the typhoon, we define the moment when the typhoon comes and the point is only affected by the typhoon (i.e., the moment when the typhoon level-7 BS wind-force begins to cover this point) as t_s, and the moment when the typhoon passes and the point is no longer affected by the typhoon (i.e., the moment when the typhoon’s level-7 BS wind-force leaves the point) as t_e. The third step is to calculate the RT (SST_0) of a point before it is affected by the typhoon and the MT (SST_t) of the point during the influence of the typhoon. Referring to the experience of previous scholars, we assume that SST_0 is the average value of the SST 5 days before t_s. That is,

SST_0 = (SSTt_s 1 + SSTt_s 2 + SSTt_s 3 + SSTt_s 4 + SSTt_s 5)/5 (1) − − − − − Similarly, we deﬁne SST_0 as the minimum SST during t_s and 5 days after t_e. That is,

SST_t = min(SSTt_s, SSTt_s+1, ..., SSTt_e, ..., SSTt_e+5) (2)

Then the magnitude of the SST drop caused by the typhoon is

SSC = SST_t SST_0 (3) − For comparison, the former algorithms will be referred to as A1, A2, A3, and A4 (described in the Introduction). The algorithm proposed in this paper will be called CA. Only areas covered by the level-7 BS wind-force will be calculated except for the calculation area of A4 is 400 km.

3. Results

3.1. Megi Megi (1617) was generated at 8 o’clock on 23 September on the surface of the NWPO 2140 km southeast of Taiwan Province with an MWS of 18 m/s and a center pressure of 998 hPa. In the early morning of 27 September, when its center was located at 22.4◦ N and 123.8◦ E, it strengthened into a super typhoon. The MWS near the center was 52 m/s, the lowest center pressure was 935 hPa, and the radius of the seventh-level wind circle was 350 to 450 km. On the same day, it made landfall in Hualian Water 2020, 12, 3060 5 of 12

County, Taiwan Province, and weakened after that. Then it crossed the Taiwan Strait and made landfall again in Hui’an County, Fujian Province. As shown in Figure2, the respective ranges of SSC values caused by Megi when calculated using A1 through A4 are 2.16 to 0.5 ◦C, 3.8167 to 1.0 ◦C, 1.9029 to 1.0 ◦C, and 3.835 to 0 ◦C, and that Water 2020, 12, x FOR PEER− REVIEW − − − 5 of 12 calculated using CA is 3.818 to 0 C. Since the accuracy of the satellite-based SST data is around − ◦ 0.5 C, differential values between 1 and 1 C are within the error range which could be ignored. °C, ◦differential values between −1 and− 1 °C are◦ within the error range which could be ignored. What Whatthe five the algorithms’ five algorithms’ results results have havein common in common is that is the that regions the regions where where the cooling the cooling is most is most distinct distinct are areall to all the to theright right of the of the typhoon typhoon’s’s path. path. The The cooling cooling ranges ranges of of CA CA,, A2 A2 and and A4 A4 were were the the closest closest and the largest,largest, andand thethe cooling cooling centers centers of of them them were were the the most most similar, similar which, which were were diff erentdifferent from from the otherthe other two. Fortwo A4,. For even A4, even though though the calculated the calculated area isarea larger is larger than thethan other the other four methods, four methods, it does it notdoes show not show more obviousmore obvious cooling coolingpoints. Compared points. Compa to CA,red A2 toand CA, A4, A1 A2 and and A3 A4, underestimate A1 and A3 the underestimate temperature drop the bytemperature almost 2 ◦ C.drop In these by almost two graphs, 2 °C. In there these was two more graphs, than onethere maximum was more cooling than one center, maximum and it appeared cooling incenter, different and it typhoon appeared stages in different along thetyphoon typhoon’s stages path. along This the typhoon phenomenon’s path. is This at odds phenomenon with existing is at researchodds with and ex ouristing perceptions. research and Both our A1 perceptions. and A3 assume Both that A1 the and SST A3 drops assume only that after the the SST typhoon drops passes, only butafter actually the typhoon it begins passes to drop, but during actually the it typhoon begins to and drop the during temperature the typhoon of the a ffandected the sea temperature area earlier of is likelythe affected to have sea started area to earlier rise by is the likely time to the have typhoon started disappears. to rise by Therefore, the time the the minimum typhoon temperaturedisappears. isTherefore, omitted, the resulting minimum in the temperature lower result is ofomitted, these two resulting algorithms. in the lower result of these two algorithms.

Figure 2. TheThe sea sea surface surface cooling cooling (SSC) (SSC) results results of of Megi Megi using using A1, A1, A2, A2, A3, A3, A4, A4, and and CA. CA. The The colored colored bar barrepresents represents the thecooling cooling degree degreess in inCelsius. Celsius. The The colors colors on on the the trajectory trajectory are are consistent consistent with with those in Figure1 1,, showing showing the the intensities intensities of of Megi. Megi. 3.2. LionRock 3.2. LionRock LionRock (1610) began with a low-pressure area that formed on the west of Wake Island on LionRock (1610) began with a low-pressure area that formed on the west of Wake Island on 15 15 August. On 25 August, LionRock sluggishly moved northwestward. Then a sudden turning August. On 25 August, LionRock sluggishly moved northwestward. Then a sudden turning occurred, and it moved to the north of Japan on the second day. On the afternoon of 28 August, occurred, and it moved to the north of Japan on the second day. On the afternoon of 28 August, LionRock developed into a super typhoon with an MWS of 52 m/s and a center pressure of 935 hpa. LionRock developed into a super typhoon with an MWS of 52 m/s and a center pressure of 935 hpa. At 5 p.m. on 30 August, it landed near Otsuto City, Iwate Prefecture, Japan. In the early morning of At 5 p.m. on 30 August, it landed near Otsuto City, Iwate Prefecture, Japan. In the early morning of 31 August, it made landfall again near the city of Vladivostok in Russia’s Primorsky Krai. The formation 31 August, it made landfall again near the city of Vladivostok in Russia’s Primorsky Krai. The location of LionRock was at 33.1 N, making it the most northern storm in the 21st century. The path of formation location of LionRock ◦was at 33.1° N, making it the most northern storm in the 21st century. LionRock was too complicated to predict because it not only moved in a spiral shape over the southern The path of LionRock was too complicated to predict because it not only moved in a spiral shape over edge of Japan but also crossed the eastern part of Honshu Island and entered the Sea of Japan. the southern edge of Japan but also crossed the eastern part of Honshu Island and entered the Sea of The cooling ranges of LionRock obtained by using 5 algorithms are 6.16 to 3 C (A1), 5.95 to Japan. − ◦ − 0 C (A2), 4.3033 to 4.0 C (A3), 5.655 to 0 C (A4), and 5.566 to 0 C (CA) respectively, as shown in ◦ The cooling− ranges ◦of LionRock− obtained◦ by using 5− algorithms◦ are −6.16 to 3 °C (A1), −5.95 to 0 Figure3. Although A1 has the greatest cooling among the ﬁve algorithms, the maximum cooling center °C (A2), −4.3033 to 4.0 °C (A3), −5.655 to 0 °C (A4), and −5.566 to 0 °C (CA) respectively, as shown in appeared in the Sea of Japan when the typhoon weakened and was about to make landfall, which is Figure 3. Although A1 has the greatest cooling among the five algorithms, the maximum cooling inconsistent with theory and practice. In addition, abnormally positive values only appeared in A1 and center appeared in the Sea of Japan when the typhoon weakened and was about to make landfall, which is inconsistent with theory and practice. In addition, abnormally positive values only appeared in A1 and A3. CA, A2 and A4 still give the most similar results, from which we can see more evidently that during the passage of LionRock, there was a cool tail along its path with a rightward bias. Moreover, the cooling was greatest at the turning point of the track or when the typhoon wind speed was relatively high, and the cooling areas around the turning point were concentrated in circular areas.

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A3. CA, A2 and A4 still give the most similar results, from which we can see more evidently that during Waterthe passage 2020, 12, ofx FOR LionRock, PEER REVIEW there was a cool tail along its path with a rightward bias. Moreover, the cooling6 of 12 was greatest at the turning point of the track or when the typhoon wind speed was relatively high, Water 2020, 12, x FOR PEER REVIEW 6 of 12 and the cooling areas around the turning point were concentrated in circular areas.

Figure 3. SSC result of LionRock using A1, A2, A3, A4, and CA. The colored bar represents the cooling Figure 3. degrees inSSC Celsius result. The of LionRock colors on using the trajectory A1, A2, A3, are A4, consistent and CA. Thewith colored those in bar Figure represents 1, showing the cooling the Figuredegrees 3.in SSC Celsius. result of The LionRock colors onusing the A1, trajectory A2, A3, are A4, consistent and CA. The with colored those bar in Figurerepresents1, showing the cooling the intensities of LionRock. degreeintensitiess in ofCelsius LionRock.. The colors on the trajectory are consistent with those in Figure 1, showing the intensities of LionRock. In order to better observe SST variation near the turning points, the line diagram of daily In order to better observe SST variation near the turning points, the line diagram of daily temperature changes at four randomly selected points in this area is shown in Figure 4. From 21 to temperatureIn order changes to better at observe four randomly SST variation selected near points the in turning this area points, is shown the inline Figure diagram4. From of 21daily to 23 August, S2 was affected by LionRock. The SST also suddenly dropped during this period, and the temperature23 August, S2 changes was aﬀ ectedat four by randomly LionRock. selected The SST points also suddenly in this area dropped is shown during in Figure this period, 4. From and 21 the to temperature began to rise slowly after a drop of up to 4.5 °C . S1 was covered by LionRock from 24 to 23temperature August, S2 began was affected to rise slowly by LionRock. after a drop The SST of up also to 4.5suddenlyC. S1 wasdropped covered during by LionRock this period from, and 24 the to 26 August with an SSC of 5.5 °C. During the period of 24 ◦to 28 August, LionRock caused a SST drop temperature26 August with began an SSC to rise of 5.5slowlyC.During after a drop the period of up ofto 244.5 to °C 28. S1 August, was covered LionRock by causedLionRock a SST from drop 24 to of of about 5 °C in S3. LionRock ◦entered the area of the S4 on 27 August and left on 29 August, resulting 26about August 5 C with in S3. an LionRock SSC of 5.5 entered °C. During the area the of period the S4 of on 24 27 to August 28 August and, leftLionRock on 29 August, caused resultinga SST drop in in a SSC◦ of about 3 °C. The calculation results of the four points under the five algorithms are shown ofa SSC about of 5 about °C in 3 S3.C. LionRock The calculation entered results the area of of the the four S4 pointson 27 August under the and ﬁve left algorithms on 29 August, are shownresulting in in Table 2. The data◦ in the table show that the calculation result of CA, A2 and A4 are very close and Tablein a SSC2. The of about data 3 in °C. the The table calculation show that results the calculation of the four result points of under CA, A2 the and five A4 algorithms are very are close shown and reasonable, while the results for A1 and A3 are about 2 °C lower than those in Figure 4. inreasonable, Table 2. The while data the in results the table for show A1 and that A3 the are calculation about 2 ◦C result lower of than CA, those A2 and in FigureA4 are4 .very close and reasonable, while the results for A1 and A3 are about 2 °C lower than those in Figure 4.

Figure 4. DaiDailyly sea sea surface surface temperature temperature (SST) (SST) variation variation during during the the applying applying period period of of the the current algorithm,algorithm, at four selected points shown in Figure 33.. Figure 4. Daily sea surface temperature (SST) variation during the applying period of the current algorithm, at four selected points shown in Figure 3. Table 2. SSC in S1–S4 calculated by different methods.

Dots TableA1 ( °2.C) SSC in A2S1– S4(°C) calculated A3 by (° C)different A4 methods (°C) . CA (°C) DotsS1 A1−3.11 (°C) A2−4.58 (°C) A3−2.02 (°C) A4−4.69 (°C) CA−4.59 (°C) S2 −2.40 −4.22 −2.27 −4.43 −4.25 S1 −3.11 −4.58 −2.02 −4.69 −4.59 S3 −2.76 −5.02 −2.50 −4.81 −4.80 S2 −2.40 −4.22 −2.27 −4.43 −4.25 S4 −1.25 −3.07 −1.21 −2.70 −2.78 S3 −2.76 −5.02 −2.50 −4.81 −4.80 S4 −1.25 −3.07 −1.21 −2.70 −2.78

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Table 2. SSC in S1–S4 calculated by diﬀerent methods.

Dots A1 (◦C) A2 (◦C) A3 (◦C) A4 (◦C) CA (◦C) S1 3.11 4.58 2.02 4.69 4.59 − − − − − S2 2.40 4.22 2.27 4.43 4.25 − − − − − Water 2020, 12, x FORS3 PEER REVIEW2.76 5.02 2.50 4.81 4.80 7 of 12 − − − − − S4 1.25 3.07 1.21 2.70 2.78 − − − − − 3.3. Successive Typhoons: Trami and KongRey 3.3. Successive Typhoons: Trami and KongRey Trami (1824) formed in NWPO on 16 September. It moved steadily northwestward and developedTrami (1824) into a formed super in typhoon NWPO on on 16 25 September. September. It Thenmoved it steadily slowed northwestward down and turned and developed to move northeastward.into a super typhoon Trami made on 25 landfall September. on the Then coast it slowedof Wakayama down Prefecture, and turned Honshu to move Island, northeastward. Japan on 30Trami September made landfall with an on MWS the coast of 42 of m/s Wakayama and a minimum Prefecture, center Honshu pressure Island, of 952 Japan hPa. on 30 September with an MWSOn 29 of September, 42 m/s and one a minimum day before center Trami pressure made landfall of 952 hPa., KongRey developed into a tropical storm on theOn NWPO 29 September, northeast one of day Pohnpei. before TramiAfter madethat, KongRey landfall, KongRey moved steadily developed northwest into a tropicalward, stormand the on intensitythe NWPO gradually northeast increased. of Pohnpei. KongRey After that, followed KongRey Trami’s moved steadily track to northwestward, the northwest and and the gradually intensity intensifiedgraduallyincreased. until it became KongRey a super followed typhoon Trami’s at 5 trackp.m. toon the 1 October. northwest It andmade gradually landfall intensifiedon the coast until of Southit became Gyeongsang a super typhoon Province at in 5 p.m.South on Korea 1 October. around It made 8:40 on landfall 6 October, on the and coast the of MWS South at Gyeongsang the center wasProvince 30 m/s. in SouthAfter that, Korea its around intensity 8:40 continued on 6 October, to weaken. and the KongRey MWS at the crossed center the was same 30 m ocean/s. After area that, to Tramiits intensity so that continued part of the to sea weaken. area was KongRey continuously crossed affected the same by oceantwo typhoons. area to Trami so that part of the sea areaAs shown was continuously in Figure 5 (upper), affected the by twoSSC typhoons.ranges of Trami calculated by the five algorithms are: −5.2 to 0.5As °C shown (A1), −6.8367 in Figure to5 0(upper), °C (A2) the, −5.2119 SSC ranges to 0.6262 of Trami °C (A3) calculated, −6.94 to by 0 the °C five(A4) algorithms and −6.932 are: to 0 °C5.2 − (CA),to 0.5 respectivelyC (A1), 6.8367. The topositive 0 C (A2), value 5.2119of SSC toless 0.6262 than 1C °C (A3), is considered6.94 to 0 toC be (A4) within and the6.932 acceptable to 0 C ◦ − ◦ − ◦ − ◦ − ◦ error(CA), range. respectively. As with The the positive previous value two oftyphoons, SSC less the than result 1 ◦Cs isof considered A2, A4 and to CA be withinwere the the closest, acceptable and theerror maximum range. As cooling with the areas previous were twoalmost typhoons, the same the (i.e., results it was of A2, on A4the and right CA side were of thethe closest, typhoon and path the whenmaximum the intens coolingity areas was were at a almost maximum). the same A1 (i.e.,and itA was3 significantly on the right underestimated side of the typhoon the coolingpath when in degreesthe intensity and showed was at a smaller maximum). cooling A1 areas. and A3 significantly underestimated the cooling in degrees and showed smaller cooling areas.

Figure 5. Cont.

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FigureFigure 5. 5. TheThe SSC SSC results results of of Trami Trami (upper (upper) )and and KongRey KongRey (bottom (bottom) )using using A1, A1, A2, A2, A3, A3, A4, A4, and and CA. CA. TheThe colored colored bar bar represents represents the the cooling cooling degree degreess in in Celsius. Celsius. The The colors colors on on the the trajectory trajectory are are consistent consistent Figure 5. The SSC results of Trami (upper) and KongRey (bottom) using A1, A2, A3, A4, and CA. withwith those those in in Figure Figure 11,, showing thethe intensitiesintensities ofof thethe typhoons.typhoons. The colored bar represents the cooling degrees in Celsius. The colors on the trajectory are consistent withThe those SSC rangesin Figure for 1, KongRey,showing the as intensities obtained of bythe fivetyphoons. algorithms respectively, are 4.38 to 4.0 C The SSC ranges for KongRey, as obtained by five algorithms respectively, are −4.38− to 4.0 °C◦ (A1), 4.5333 to 2.0 ◦C (A2), 5.6157 to 1.0 ◦C (A3), 4.245 to 0.5 ◦C (A4), and 4.468 to 1.0 ◦C (CA). (A1), The−4.5333− SSC to ranges 2.0 °C for (A2) KongRey,, −5.6157− asto 1.0obtained °C (A3) by, −4.−five245 algorithms to −−0.5 °C respectively,(A4), and −4.468− are to−4.38 1.0 to°C 4.0(CA) °C. As Figure5 (bottom) shows, the distribution of SSC caused by KongRey has no obvious characteristics, As(A1) Figure, −4.5333 5 to(bottom) 2.0 °C ( A2) shows,, −5.6157 the to distribution 1.0 °C (A3) , of−4. 245 SSC to caused −0.5 °C by(A4) KongRey, and −4.468 has to no1.0 °C obvious (CA). and no obvious cooling center can be found in the five pictures. What is worth noting is that the sea characteristics,As Figure 5 (bottom)and no obvious shows, cooling the distribution center can be of found SSC in caused the five by pictures. KongRey What has is worth no obvious noting surface warming occurred when KongRey developed into a super typhoon. This might be because the ischaracteristics, that the sea surface and no warming obvious occurredcooling center when can KongRey be found developed in the five into pictures. a super What typhoon is worth. This notingmight pre-existing SSTs were still in a cold status influenced by the previous typhoon, Trami, which may beis thatbecause the sea the surface pre-exist warminging SSTs occurred were still when in a cold KongRey status developedinfluenced intoby the a super previous typhoon typhoon,. This Trami, might have reduced the heat-flux during the passage of typhoon KongRey. whichbe because may thehave pre reduced-existing the SSTs heat were-flux still durin in ag coldthe passage status influenced of typhoon by KongRey. the previous typhoon, Trami, Considering that Typhoon KongRey once again affected part of the sea area where Trami passed, whichConsidering may have reducedthat Typhoon the heat KongRey-flux durin onceg againthe passage affected of part typhoon of the KongRey. sea area where Trami passed, four points were selected from the sea area under the continuous influence of Trami and KongRey. four pointsConsidering were selected that Typhoon from theKongRey sea area once under again the affected continuous part of influence the sea area of Trami where and Trami KongRey passed,. The variations of daily SST at these points are shown in Figure6. Trami and KongRey passed through Thefour variations points were of dailyselected SST from at these the points sea area are under shown the in continuousFigure 6. Trami influence and KongRey of Trami passed and KongRey through. the area from 25 to 29 September and from 3 to 5 October, respectively, so the temperature at the thThee area variations from 25 of todaily 29 SeptemberSST at these and points from are 3 shownto 5 October, in Figure respectively, 6. Trami and so KongReythe temperature passed throughat the 4 4 points cooled down twice. The first cooling occurred from 24 to 29 September. In this process, the SST pointsthe area cooled from down25 to 29 twice. September The first and cooling from 3occurred to 5 October, from respectively,24 to 29 September so the .temperature In this process, at the the 4 of S5, S6, S7 and S8 decreased by about 3, 5, 3.5, and 2 ◦C, respectively, which should be the result SSTpoints of S5,cooled S6, S7 down and S8twice. decreased The first by aboutcooling 3, 5occurred, 3.5, and from 2 °C, 24respectively, to 29 September which. shouldIn this beprocess, the result the caused by Trami. The second SSC occurring between 1 and 4 October should be the result of KongRey’s causedSST of S5, by S6, Trami S7 and. The S8 decreased second SSC by about occurr 3ing, 5, 3.5,between and 2 1°C, and respectively, 4 October which should should be the be the result result of influence. In this process, the SST at S7 declined the most significantly by about 4 ◦C, followed by S8, KongReycaused by’s influenceTrami. The. In secondthis process, SSC occurr the SSTing at between S7 declined 1 and the 4 mostOctober significantly should be by the about result 4 °C, of by about 3 ◦C, S5 by slightly more than 1 ◦C and S6 by less than 1 ◦C. followedKongRey by’s influenceS8, by about. In 3this °C, process, S5 by slightly the SST more at S7than declined 1 °C and the S6 most by less significantly than 1 °C. by about 4 °C, followed by S8, by about 3 °C, S5 by slightly more than 1 °C and S6 by less than 1 °C.

FigureFigure 6. 6. DailyDaily SST SST variation variation during during the the applying applying period period of of the the current current algorithm, algorithm, at at four four selected selected pointspoints shown shown i inn Figure Figure 5.. Figure 6. Daily SST variation during the applying period of the current algorithm, at four selected points shown in Figure 5.

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4. DiscussionWater 2020, 12, x FOR PEER REVIEW 9 of 12 The SSC ranges calculated by all the algorithms are listed in Table3. It is clearly that A2, A4, 4. Discussion and CA approached similar results. This is because all of them are essentially maximum response methods.The Slight SSC ranges divergences calculated come by from all the the algorithms choices of are the listed MT in and Table the 3. RT. It is clearly that A2, A4, and CA approached similar results. This is because all of them are essentially maximum response methods. SlightTable divergences 3. The SSC rangescome fro of them the four choices typhoons of the calculated MT and by the di ﬀRT.erent algorithms.

NameTable 3. The A1 SSC (◦ C)ranges of the A2 four (◦C) typhoons A3 calculated (◦C) by A4different (◦C) algorithms CA (◦. C) Megi 2.16–1 3.82–0.5 1.90–1 3.84~0 3.82–0 Name − A1 (°C) − A2 (°C) A3− (°C) A4− (°C) CA− (°C) LionRock 6.16–3 5.95–0 4.30–4 5.66–0 5.57–0 − − − − − TramiMegi 5.2–0.5−2.16–1 −6.84–03.82–0.5 −5.21–0.631.90–1 −3.84~06.94–0 −3.826.93–0–0 − − − − − KongReyLionRock 4.38–4−6.16–3 −4.53–25.95–0 −4.305.62–1–4 −5.664.25––0 0.5 −5.574.47–1–0 Trami − −5.2–0.5 −−6.84–0 −5.21− –0.63 −6.94–−0 −6.93− –0 KongRey −4.38–4 −4.53–2 −5.62–1 −4.25–−0.5 −4.47–1 Although A1 and A3 seem to have generally underestimated the SSC degrees, one minimum was capturedAlthough by A1 A1 and in theA3 LionRockseem to have case generally and another underestimated was captured the SSC by A3degrees, in the one KongRey minimum case. Sincewas the captured MT defined by A1 in in A1 the will LionRock also be case considered and another in other was algorithmscaptured by except A3 in the for KongRey A3, it is reasonable case. Since to assumethe MT that defined A1 will in take A1 somewill also higher be considered RT values intoin other calculation. algorithms The except idea of for calculating A3, it is reasonable the day-to-day to diffassumeerences that brings A1 will abnormally take some positive higher results.RT values The into same calculation. situation The happens idea of incalculat A3, whiching the calculates day-to- theday mean-to-mean differences di bringsfferences. abnormal In thely KongRey positive results. case, the The minimum same situation in A3 might happens come in from A3, awhich higher RTcalculates because it the takes mean the-to SST-mean averaged differences. over In 10 the to KongRey 3 days before case, the minimum arrival of in the A3 typhoon. might come It covered from a perioda higher even RT beforebecause the it priortakes typhoon,the SST averaged Trami. over 10 to 3 days before the arrival of the typhoon. It coveredComparing a period this even to A4 before and the CA, prior ignoring typhoon, the possibilityTrami. that the maximum response may happen after theCompar typhoon’sing this passage to A4 and might CA, beignoring the main the possibility problemof that A2. the Tomaximum prove this, response the days may happen from the typhoon’safter the leaving typhoon date’s passage to when might the MT be thewas main captured problem have of been A2. countered To prove this, at each the grid days and from shown the in Figuretyphoon7.’ Thiss leaving shows date that to inwhen a fairly the MT large was area, captured strongest have cooling been countered occurred at within each grid 1 to and 5 days shown after thein typhoon Figure 7. left. This Situations shows that were in a morefairly obviouslarge area, in strongest the downstream cooling occurred area of thewith successivein 1 to 5 days typhoons, after Tramithe andtyphoon KongRey. left. Situations were more obvious in the downstream area of the successive typhoons, Trami and KongRey.

Figure 7. The days from the time typhoon leaving to the time when the minimum temperature (MT) Figure 7. The days from the time typhoon leaving to the time when the minimum temperature (MT) occurred. The diﬀerent colors represent diﬀerent days. occurred. The different colors represent different days.

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The algorithm proposed here (CA) has a lot in common with the LI method (A4). Those differing from the others are both grid-based, which may improve the results in slow-moving typhoon cases. If we treat the typhoon’s lifetime as the forcing time, applying it to every grid in the whole domain (non-grid-based), wrong MTs might be chosen in the upstream area while wrong RTs might be counted in the downstream area. This situation happened in the case of LionRock, which moved very slowly around the southwestern tip of its path and turned almost totally to move northeastward [18]. In Figure3, both A1 and A3 showed abnormal surface warming results in the downstream area, while both A1 and A2 calculated significant cooling in the Sea of Japan that is not shown in CA and A4. A cooling center located around (36◦ N, 144◦ E) on the path was detected by CA and A4, but it was not shown in the result of A2. The major divergence between CA and A4 is the definition of RT before the typhoon’s arrival and the slight differences laid in the KongRey case (Figure5), while CA calculated a surface warming around S6, but this was not shown in the result of A4. From Figure6, it is clear that the SST around S6 was in a continuous recovery from the significant influence of the prior typhoon, Trami, during the typhoon KongRey’s forcing time. Therefore, a surface warming result seems to be reasonable. Then the pentad mean RT is argued to be more representative of the pre-existing sea surface status than the 2 day average which was applied in A4. Another slight difference is that CA calculates the SSC in the area covered under the level-7 BS wind-force, while A4 estimates the SSC in the area less than 400 km away from the path. This brings hardly any difference in the results, except that the changing shapes of the affected area in the CA results seem to be consistent with the evolution of the typhoons, which makes the results much livelier.

5. Conclusions A new algorithm (CA) to estimate the sea surface cooling caused by typhoons has been proposed based upon the grid-based maximum response method suggested by Li et al. [33]. It differs from the LI method by defining the reference temperature as the pentad mean SST before the typhoon, and counting the area covered under the level-7 BS wind-force instead of that within a radius of 400 km. The MW-IR OISST dataset was used to calculate the SSC caused by four typhoon cases, Megi, LionRock, Trami, and KongRey. The results of different algorithms were compared to valid the improvement of CA. In all the four cases, maximum response methods (CA, A2, and A4) produced similar results, while A1 and A3 seem to underestimate the SSC in degrees. In the LionRock case, grid-based methods (CA and A4) showed improvement to solve the slow-moving problems. Meanwhile, in the successive typhoon cases, Trami and KongRey, the results of CA seem to have reasonably detected some surface warming, showing an improvement in representing the pre-existing sea surface status before the typhoon’s arrival by applying the pentad mean SST as reference. The slight change in using level-7 BS wind-force coverage brings nothing but livelier results. Although the performance of CA will still be questionable when the interval between successive typhoons is less than 5 days, there were only nine such cases during the past 6 years (2014–2019). Therefore, the algorithm has been validated in general and should be recommended for use in future research.

Author Contributions: Conceptualization, D.S.; Methodology, D.S. and L.X.; Software, L.X. and L.G.; Validation, D.S., L.X. and L.G.; Formal Analysis, B.L.; Investigation, B.L.; Resources, L.X. and B.L.; Data Curation, L.G. and B.L.; Writing—Original Draft, L.X.; Writing—Review and Editing, D.S. and B.L.; Visualization, L.X. and L.G.; Supervision, D.S.; Project Administration, D.S.; Funding Acquisition, D.S. and B.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Zhejiang Provincial Natural Science Foundation of China under grant number LY17D060003, the Bureau of Science and Technology of Zhoushan under grant number 2018C81032, the National Natural Science Foundation of China under grant number 41706022 and 41830533, and the Key Laboratory of Ocean Circulation and Waves of the Chinese Academy of Sciences under grant number KLOCW1704. Water 2020, 12, 3060 11 of 12

Acknowledgments: We would like to thank Jinsong Yang, Shuangyan He, Jiawang Chen and some other colleagues for their remarkable advice on the present work. We also deeply thank all the reviewers for their valuable comments and discussions. Conﬂicts of Interest: The authors declare no conﬂict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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