remote sensing

Article Upper Ocean Response to Kalmaegi and Sarika in the South Sea from Multiple-Satellite Observations and Numerical Simulations

Xinxin Yue 1, Biao Zhang 1,* ID , Guoqiang Liu 1,2, Xiaofeng Li 3 ID , Han Zhang 4 and Yijun He 1 ID 1 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected] (X.Y.); [email protected] (G.L.); [email protected] (Y.H.) 2 Bedford Institute of Oceanography, Fisheries and Oceans, Dartmouth, NS B2Y 4A2, Canada 3 GST at National Oceanic and Atmospheric Administration (NOAA)/NESDIS, College Park, MD 20740-3818, USA; [email protected] 4 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou 310012, China; [email protected] * Correspondence: [email protected]

Received: 12 December 2017; Accepted: 22 February 2018; Published: 24 February 2018

Abstract: We investigated ocean surface and subsurface physical responses to Kalmaegi and Sarika in the Sea, utilizing synergistic multiple-satellite observations, in situ measurements, and numerical simulations. We found significant typhoon-induced sea surface cooling using satellite sea surface temperature (SST) observations and numerical model simulations. This cooling was mainly caused by vertical mixing and upwelling. The maximum amplitudes were 6 ◦C and 4.2 ◦C for Typhoons Kalmaegi and Sarika, respectively. For Typhoon Sarika, Argo temperature profile measurements showed that temperature response beneath the surface showed a three-layer vertical structure (decreasing-increasing-decreasing). Satellite salinity observations showed that the maximum increase of sea surface salinity (SSS) was 2.2 psu on the right side of Typhoon Sarika’s track, and the maximum decrease of SSS was 1.4 psu on the left. This SSS seesaw response phenomenon is related to the asymmetrical rainfall on both sides of the typhoon track. Acoustic Doppler Current Profilers measurements and numerical simulations both showed that subsurface current velocities rapidly increased as the typhoon passed, with peak increases of up to 1.19 m/s and 1.49 m/s. Typhoon-generated SST cooling and current velocity increases both exhibited a rightward bias associated with a coupling between typhoon wind-stress and mixed layer velocity.

Keywords: upper ocean response; typhoon; satellite observations; numerical simulations

1. Introduction Global warming has given rise to increased intensity of tropical in the northwest Pacific Ocean [1–3]. Typhoons are destructive tropical cyclones that periodically cause devastating weather-related natural disasters, such as flooding from intense rainfall and storm surges, which pose a growing threat to human populations and infrastructure in coastal regions [4,5]. As typhoons move over the ocean, ocean and atmosphere interactions change the storm intensity and drive energy, momentum, heat and mass exchanges. Satellite remote sensing observations, numerical simulations of the ocean and atmosphere, and a variety of in situ measurements provide a great ability to study the characteristics and mechanisms of how the upper ocean responds to typhoons. Ocean-typhoon interaction has been a cutting-edge research topic over the past few decades, and scientists have made great efforts to investigate oceanic response and feedback to typhoons. The upper ocean’s responses to a moving tropical (TC) can be divided into two stages: a forced stage and then a relaxation stage [6]. During the forced stage, strong typhoon winds induce

Remote Sens. 2018, 10, 348; doi:10.3390/rs10020348 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 348 2 of 22 strong vertical mixing and upwelling, which accounts for decreased sea surface temperature (SST), increased sea surface salinity (SSS), and increased mixed layer depth. Early studies have shown that entrainment and vertical mixing are dominant mechanisms decreasing SST beneath a moving TC [7,8]. One consequence of the TC-induced large vertical shear is that the mixed layer thickness increases and entrainment becomes enhanced. Observed TC tracks and coupled ocean-atmosphere hurricane models were used to estimate the net ocean heating induced by global activity, thus suggesting that TCs may play an important role in driving the thermohaline circulation [9]. It has been reported that approximately 15% of the peak ocean heat transport may be associated with the vertical mixing induced by TCs [10]. On global scale, TCs are responsible for 1.87 PW (11.05 W·m−2) of heat transfer annually from the global ocean to the atmosphere during the passage of a storm [11]. SST response to typhoons is of particular interest because SST plays a key role in energy exchange between typhoons and the ocean. The (SCS) is a large, semi-enclosed marginal sea, and is subject to frequent typhoons. Thus, proper characterization of typhoon-induced SST cooling may help improve understanding of typhoon mechanisms and improve typhoon intensity predictions. Previous research found that SST dropped 9 ◦C after the passage of Typhoon Kai-Tak and 11 ◦C following [12,13]. Satellite measurements and three-dimensional numerical simulations have shown that typhoon-induced decreases in SST depend strongly on the typhoon’s maximum surface wind speed, translation speed, and size [14,15]. SST cooling amplitude also strongly depends on depth of the mixed layer and thermocline stratification prior to the storm [16,17]. During strong typhoons (greater than or equal to category 4), the subsurface warming expected from vertical mixing is comparable in magnitude to the near-surface cooling; however, for weak typhoons (less than or equal to category 3), only surface cooling occurs, thus suggesting that air-sea heat exchange and vertical advection possibly play an important role in the case of weak typhoons [18]. Rapid intensification occurs when a TC pass over warm eddies owing to the increasing ocean heat content, while storm-induced mixing and SST cooling are suppressed by the warm ocean features. Composited advanced very-high-resolution radiometer observations and buoy measurements showed SST decreases of only about 0.5 ◦C–1 ◦C when Hurricane Opal passed over a warm core ring in the Gulf of Mexico [19]. In addition, when , a category 5 storm, encountered a warm ocean eddy in the western North Pacific, there was an analogous SST cooling of approximately 1 ◦C[20]. Typhoon Maemi’s self-induced SST cooling was suppressed by a thicker upper ocean mixed layer in the warm eddy. This prevented cold water from the deep ocean from being entrained into the upper ocean mixed layer. Moreover, the near-surface barrier layer also suppressed the typhoon-induced upper ocean cooling, leading to temperature changes that were smaller than expected [21]. When typhoon-induced forcing is strong enough to break through the barrier layer base into the thermocline, the barrier layer reduces the cooling magnitude [22]. Upper ocean current response to typhoons in the northwestern Pacific Ocean has been characterized through analysis of ocean current observations from Surface Velocity Program (SVP) drifters and Joint Typhoon Warning Center (JTWC) best track data. Near-surface current speeds greater than 2 m/s were observed during major typhoons, with strongest mean currents to the right of the storm track [23]. SVP drifter observations showed a maximum mixed layer current velocity of 2.6 m/s for Typhoon Maon [24]. Furthermore, the investigation also demonstrated that current velocity in the mixed layer depended on both the typhoon’s translation speed and its intensity scale. For a slow-moving and super strong typhoon, the maximum mixed layer current velocity is roughly twice that of a fast-moving and weak typhoon. Typhoon-induced near-inertial motions are dominant during the relaxation stage, and barotropic subinertial waves and baroclinic near-inertial oscillations were both observed at this time, indicating a subinertial wave period of 2.8–4.1 days [25]. Near-inertial oscillations decayed more rapidly in shallow water than in the deep ocean, which emphasized the importance of frictional effects for characterizing responses to typhoon forcing in the shallow ocean [26]. Upper ocean current responses to typhoons have been investigated using numerical simulations and in situ observations. Strong near-surface current with maximum speed of 1.5 m/s was Remote Sens. 2018, 10, 348 3 of 22 observed on the right side of the track of Typhoon Kalmaegi by Acoustic Doppler Current Profilers (ADCP) [27]. This rightward bias in the surface current response can be attributed to coupling between typhoon wind-stress and mixed layer current velocity [7]. Numerical simulations suggested that typhoon-induced cyclonic currents flowed in a roughly circular pattern around a mesoscale cold eddy, with a maximum velocity of 2.5 m/s [28]. Moreover, previous research revealed that strong near-inertial rotating currents with maxima of approximately 1–1.4 m/s were induced in the mixed layer by the passage of Hurricane Gilbert [29]. Typhoon-generated near-inertial currents associated with surface cooling and deepening of the mixed layer also showed this rightward bias, with peak magnitudes of up to 0.6 m/s [7,30]. Salinity plays a significant role in upper ocean typhoon response. Observations from Argo profiling floats showed that a deepening of the mixed layer and cooling showed a significant rightward bias, whereas salinity decreased to the left of the typhoon track and increased to the right [31]. Moreover, Argo salinity profile measurements of typhoons in the western North Pacific Ocean have shown decreasing salinity 70–80 m beneath the surface to the left and right of typhoon tracks [32]. Instruments on an underwater glider also observed changes in upper ocean salinity before, during, and after the passage of Hurricane Gonzalo, showing that wind-forced mixing caused a 0.6 psu salinity increase at depths above 20 m and a 0.4 psu salinity decrease between 20 m and 130 m deep [21]. Both three-dimensional numerical simulations and in situ observations have revealed that salinity anomalies generally show a three-layer vertical structure, with the surface layer becoming cooler and saltier, the subsurface layer becoming warmer and fresher, and the lower layer becoming cooler and saltier again [22]. Although Argo floats and underwater gliders can measure upper ocean salinity, they provide scarce salinity observations on both sides of typhoon track. Our main goal is to improve the understanding of upper ocean responses to typhoon force winds. To accomplish this, we assessed ocean conditions (SST, near-sea surface currents, and SSS) before (pre-storm), during, and after (post-storm) the passage of Typhoons Kalmaegi and Sarika in the SCS. Our assessment used synergistic multi-platform satellite remote sensing observations, numerical simulations, and in situ measurements. Satellite observations revealed sea surface typhoon responses, such as significant SST decreases and increases in near-surface current velocity. Typhoon-induced SSS variabilities were first quantified using spaceborne L-band radiometer observations. Both in situ (Argo, Conductivity Temperature Depth (CTD), and ADCP) measurements and numerical simulations showed complex subsurface responses. We further compared CTD-measured temperatures and ADCP-measured current velocities with temperatures and current velocities simulated by a three-dimensional oceanic numeric model. We used these comparisons to evaluate model performance in simulating upper ocean response during a typhoon. We also discuss some potential mechanisms of upper ocean response to typhoon. The remainder of this paper is organized as follows. In Section2, the dataset used in this study and methodology are described. In Section3, the results and discussions are presented. The conclusions are given in Section4.

2. Dataset and Methodology

2.1. Multiple-Satellite Remote Sensing Data

2.1.1. Sea Surface Temperature (SST) In this study, we used microwave optimally interpolated (OI) SST data to study typhoon-induced cooling of the sea surface. These data were provided by Remote Sensing Systems (data available at www.remss.com). The SST data were produced using observations from the Tropical Rainfall Measuring Mission Microwave Imager (TMI), the Advanced Microwave Scanning Radiometers for the Earth Observing System (AMSR-E and AMSR2) satellites, and the WindSat polarimetric radiometer aboard the Department of Defense Coriolis satellite. The ability of microwave radiometers to measure SST through clouds provided a valuable global SST map. The data were enhanced with interpolation to Remote Sens. 2018, 10, 348 4 of 22

fill areas missing due to orbit transition and other environmental factors, achieving a 0.25◦ resolution (~25 km). All OI SST values use a diurnal model to create the foundation SST for representing a noon temperature.

2.1.2. Sea Surface Salinity (SSS) The Soil Moisture Active Passive (SMAP) satellite was launched in January 2015. Although SMAP was designed to measure soil moisture, its L-band radiometer can also be used to measure SSS. The SMAP has a high spatial resolution of 40 km and a wide swath of 1000 km that allows for global coverage in three days, with an eight-day repeat cycle. The Aquarius mission provided global maps of SSS with 150 km resolution on a monthly timescale. The finer spatial resolution of the SMAP makes it noisier than Aquarius, but the noise is reduced by the temporal averaging of the data over days or weeks. Therefore, SMAP provides only eight-day running mean and monthly SSS product. Remote Sensing Systems processed SMAP SSS data as Level 2C, Level 3 8-day running, and Level 3 Monthly data [33], which are available at www.remss.com. The Level 3 SSS product is gridded at 0.25◦ × 0.25◦. In this study, we used SMAP Level 3 8-day running SSS data to analyze salinity changes before and during typhoon passage.

2.1.3. Rainfall Rate We analyzed typhoon-induced rainfall using rainfall rates from WindSat and Global Precipitation Measurement (GPM) satellite observations. WindSat brightness temperatures acquired at 18.7, 23.8, and 37.0 GHz were used to retrieve rainfall rates. The daily files consisted of WindSat daily rainfall rate measurements mapped to a 0.25◦ × 0.25◦ grid, complete with data gaps between ascending and descending orbits, via the Remote Sensing Systems website (www.remss.com). For GPM, we used Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall rate products (https://pmm.nasa.gov/data-access/downloads/gpm). IMERG is a Level-3 algorithm intended to intercalibrate, merge, and interpolate all available passive microwave (PMW) estimates from the GPM constellation (e.g., GMI, TMI, SSMIS, AMSR2, and MHS), using PMW-calibrated infrared estimates and rain gauge observations. IMERG rainfall rate data have spatial and temporal resolutions of 0.1 degree and 30 min, respectively.

2.1.4. Sea Surface Height Anomalies (SSHA) and Geostrophic Velocity Anomalies (GVA) The altimetry products of the SL-TAC multi-mission altimeter data processing system are distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS). The system processed SSHA and GVA using observations from all the following altimeter missions: Topex/Poseidon (T/P), Environmental Satellite (ENVISAT), Geosat Follow-On (GFO), European Remote Sensing satellites (ERS-1 and ERS-2), Jason-1, Jason-2, Jason-3, Sentinel-3A, HY-2A, Saral/AltiKa, and Cryosat-2. The gridded SSHA and geostrophic velocity anomaly products have spatial resolution of 0.25◦ × 0.25◦ and temporal resolution of one day. For Typhoon Kalmaegi, we focus on the 6 (pre-storm) and 16 (post-storm) SSHA and GVA in September 2014, while for Sarika, we focus on the 8 (pre-storm) and 18 (post-storm) anomalies in October 2016.

2.2. In Situ Observation Data

2.2.1. Current Velocity Profiles To investigate upper ocean typhoon responses, we deployed a cross-shaped observational array consisting of five moored buoys and four subsurface moorings organized into five stations in the SCS. As an example, Figure1 shows the design of Station 4, which consisted of a buoy and a mooring. Observed variables included standard meteorological parameters, ocean surface waves, currents at depths from 0 to 850 m, and temperature and salinity measurements from 0 to 400 m and near the ocean bottom. We focused on the upper ocean response to typhoons from 0 to 100 m, thus mooring-measured RemoteRemote Sens.Sens.2018 2016,,10 8,, x 348 5 of5 of 22 4

bottom. We focused on the upper ocean response to typhoons from 0 to 100 m, thus mooring-measured datasets were not used. Stations 1 and 4 were located on the right of the track of Typhoon Kalmaegi, datasets were not used. Stations 1 and 4 were located on the right of the track of Typhoon Kalmaegi, whereas Station 2 was on the left. All these stations were located within the maximum wind radius of whereas Station 2 was on the left. All these stations were located within the maximum wind radius Kalmaegi. Station 2 is much closer to the track than stations 1 and 4. Currents were measured using of Kalmaegi. Station 2 is much closer to the track than stations 1 and 4. Currents were measured using ADCP at each station: a 150 kHz (Stations 1 and 2) or 300 kHz (Station 4) downward-looking ADCP ADCP at each station: a 150 kHz (Stations 1 and 2) or 300 kHz (Station 4) downward-looking ADCP binned at 8-m (Stations 1 and 2) or 4-m (Station 4) intervals with the first bin at either 14 m (Stations 1 binned at 8-m (Stations 1 and 2) or 4-m (Station 4) intervals with the first bin at either 14 m (Stations and 2) or 8 m (Station 4) and the last bin at either 246 m (Stations 1 and 2) or 124 m (Station 4) below the 1 and 2) or 8 m (Station 4) and the last bin at either 246 m (Stations 1 and 2) or 124 m (Station 4) below ± surface.the surface. The The 75 kHz 75 kHz ADCP ADCP velocity velocity accuracy accuracy is 1% is of1% water of water velocity velocity relative relative to theto the ADCP ADCP 0.01±0.01 m/s, m/s, ± ± andand thethe 150150 andand 300300 kHzkHz ADCPADCP accuraciesaccuracies are are 1% 1% ± 0.005 m/s and and 0.5% 0.5% ± 0.0050.005 m/s, m/s, respectively. respectively.

FigureFigure 1.1. BuoyBuoy andand mooringmooring designdesign atat StationStation 4.4.

2.2.2. Temperature and Salinity Profiles 2.2.2. Temperature and Salinity Profiles Argo is an array of 3887 active profiling floats, distributed almost uniformly across the global ocean,Argo that is provide an array temperature of 3887 active and profiling salinity profilesfloats, distributed from the surfacealmost touniformly 2000 m deep.across Thethe global array ◦ hasocean, approximately that provide one temperature float for every and salinity 3 of latitude profiles and from longitude the surface and to provides 2000 m adeep. new The profile array every has 10approximately days. Overall, one Argo float provides for every accurate 3° of latitude and comprehensive and longitude and measurements provides a ofnew changes profile toevery ocean 10 temperaturedays. Overall, and Argo salinity, provides enabling accurate systematic and assessmentscomprehensive of themeasurements physical state of of changes the upper to ocean.ocean Intemperature this study, weand used salinity, Argo enabling temperature systematic and salinity assessm profilesents of along the physical with CTD-measured state of the upper temperature ocean. andIn this salinity study, profiles we used to exploreArgo temperature the oceanic and impacts salinity of Typhoons profiles along Kalmaegi with andCTD-measured Sarika. The temperaturetemperature and salinity profiles to explore the oceanic impacts of Typhoons Kalmaegi and Sarika. The

Remote Sens. 2018, 10, 348 6 of 22 and salinity profiles of Typhoon Kalmaegi were measured every 2 min by CTD recorders from 0 m to 400 m placed at 20 m intervals. The CTD accuracy is ±0.002 ◦C for temperature and ±0.0003 S/m for electrical conductivity. There were no temperature and salinity data from Station 1 because its string of CTD recorders was lost. In addition, we extracted the Argo float profiles from the real-time quality-controlled Argo database of the China Argo Real-time Data Center (http://www.argo.org.cn). Vertical profiles from Argo floats 5904562 and 5904746 allowed us to investigate the ocean’s responses to typhoons. The temporal window for the pre-storm Argo profile is 4 days before the storm and the post-storm Argo profile is also 4 days after storm passage. The differences in the locations of the pre-storm, storm and post-storm Argo profiles are within 0.11◦–0.18◦, and the horizontal distance can minimize the influences of background signals. The distances of Argo floats 5904562 and 5904746 are 158.5 km and 155.6 km relative to the center of the storm track, respectively.

2.3. Ocean Model Setup We used the three-dimensional-ocean model, Regional Ocean Modeling System (ROMS), to numerically investigate upper ocean responses to Typhoons Kalmaegi and Sarika in the SCS. The ROMS model is a free-surface, hydrostatic, primitive equation ocean model extensively used in estuarine, coastal, and basin-scale research applications (http://www.myroms.org)[34–36]. The air–sea interface boundary Momentum and buoyancy fluxes are parameterized using the Coupled Ocean-Atmosphere Response Experiment (COARE) 3.0 bulk-flux formation [37,38], and we used the K-profile parameterization (KPP) mixing scheme detailed in [39] for vertical turbulent mixing. The KPP model has shown a reasonable level of accuracy in simulating typhoon-induced mixing [40]. The domain used in the ROMS model covers part of the SCS (14◦N–23◦N, 111E◦–119◦E). To obtain the fine oceanic structure in the region, a high resolution horizontal grid is applied ~1/32◦ (≈3 km). The domain has 72 terrain-following vertical levels with 0.5–2.7 m vertical resolution in the upper 100 m and then 11.5–426.1 m resolution in the interior ocean. The grid dimensions (latitude, longitude, and depth) are 271 × 304 × 72. The internal mode time step is 20 s, while the external mode time step is around 0.3 s, hourly outputs are stored for case studies. The bathymetry for the domain is taken from ETOPO2 gridded dataset, and the slop parameter is 0.2. We obtained atmospheric forcing conditions from Climate Forecast System Version 2 (CFSv2; http://cfs.ncep.noaa.gov), a global high-resolution atmospheric hind-cast model. This version has been upgraded in respect to nearly all aspects of the system’s data assimilation and forecast model components [41]. CFSv2 provides air temperature, rainfall, and humidity, which determine the atmosphere-ocean buoyancy (heat and freshwater) fluxes, and far-field pressure and wind vectors. The initial and lateral open boundary conditions for temperature, salinity, and current were obtained from the Hybrid Coordinate Ocean Model (HYCOM), with a 1/12◦ spatial resolution (http://tds.hycom.org)[42]. The choice of model used for initial and boundary conditions was largely based upon data availability in this study region.

3. Results and Discussion We analyzed ocean surface and subsurface physical responses to Typhoons Kalmaegi and Sarika using multiple-satellite observations, in situ measurements, and numerical simulations. Figure2 illustrates the lifespans of Typhoons Kalmaegi and Sarika. Typhoon Kalmaegi was generated at 12:00 UTC on 10 September 2014 in the western North Pacific Ocean. It then crossed the with a 33.4 m/s speed and a 974 mb minimum sea-level pressure at 00:00 UTC on 15 September 2014. It moved northwestward and gradually decayed after making landfall. Typhoon Sarika formed as a tropical depression at 06:00 UTC on 13 October 2016 near 13◦N, 130◦E, traveling northwestward and reaching typhoon strength at 18:00 UTC on 14 October 2016. As Typhoon Sarika made landfall in the Philippines, it reached a maximum sustained wind speed of 59.1 m/s. The storm subsequently weakened after passing over the Philippines and moved toward the SCS, gradually decaying after making landfall over Island. Remote Sens. 2018, 10, 348 7 of 22 Remote Sens. 2016, 8, x 7 of 4

FigureFigure 2.2. BestBest track of Typhoons KalmaegiKalmaegi (2014)(2014) andand Sarika (2016) from the Joint Typhoon Warning Center.Center. BuoyBuoy arrayarray positionspositions areare markedmarked byby redred dotsdots (B1,(B1, B2,B2, andand B4).B4). TheThe redred andand blueblue starsstars denotedenote thethe positionspositions ofof ArgoArgo floatsfloats 59045625904562 andand 5904746.5904746. TheThe redred andand blueblue boxesboxes markmark thethe focusedfocused regionsregions ofof thethe TyphoonsTyphoons Kalmaegi Kalmaegi (2014) (2014) and and Sarika Sarika (2016). (2016) The. The color color bar showsbar shows maximum maximum sustained sustained wind speedwind (m/s),speed (m/s), which which indicates indicates typhoon typhoon intensity. intensity.

3.1.3.1. Temperature ResponseResponse FigureFigure3 3 shows shows SST SST evolution evolution during during thethe passagepassage ofof Typhoon Typhoon Kalmaegi Kalmaegi from from 13 13 September September to to 1717 SeptemberSeptember 2014. 2014. Before Before Kalmaegi’s Kalmaegi’s arrival, arrival, the SCS the was SCS characterized was characterized by warm SSTs by predominately warm SSTs greaterpredominately than 30 greater◦C (Figure than3 a).30 °C Typhoon (Figure Kalmaegi3a). Typhoon entered Kalmaegi the SCS entered at 18:00 the UTCSCS at on 18:00 14 September UTC on 14 (FigureSeptember3b), (Figure and decreased 3b), and SST decreased as shown SST in as Figure shown3c. in As Figure it moved 3c. As to it the moved northwest, to the Typhoonnorthwest, KalmaegiTyphoon Kalmaegi induced significant induced significant SST cooling SST on cooling 16 September, on 16 September, with a minimum with a minimum SST of 24.5SST ◦ofC 24.5 found °C atfound the centerat the(20.4 center◦N, (20.4°N, 114.3◦E) 114.3°E) of the cold of poolthe cold (Figure pool3d). (Figure After Typhoon3d). After Kalmaegi’s Typhoon departure,Kalmaegi’s SSTdeparture, gradually SST recovered gradually (Figure recovered3e). Figure(Figure3f shows3e). Figure the SST 3f changesshows the between SST changes 16 September between and 16 14September September, and with 14 September, SST cooling with occurring SST cooling from occu 15◦Nrring to 21from◦N and15°N 113 to ◦21°NE to and 117◦ 113°EE. SST to dropped 117°E. SST as muchdropped as 6as◦ Cmuch compared as 6 °C to compared conditions to prior conditions to the typhoonprior to the (30.5 typhoon◦C). Figure (30.54 °C).shows Figure a similar 4 shows SST a coolingsimilar phenomenonSST cooling phenomenon for Typhoon for Sarika. Typhoon Figure Sarika4c shows. Figure large 4c patches shows oflarge cool patches waterranging of cool fromwater 25.2ranging◦Cto from 27.3 25.2◦C, °C and to a27.3 2.7 °C,◦C and drop a compared2.7 °C drop with compared the initial with stage the initial on 16 stage October on 16 2016. October Over 2016. the followingOver the following days, the days, cooled the cooled patch increasedpatch increased in size in andsize and grew grew along along the the typhoon typhoon track track (Figure (Figure4 4d).d). FigureFigure4 4ff shows shows a a maximum maximum SST SST decrease decrease of of 4.2 4.2 ◦°C.C. Both satellite observations and three-dimensional numericalnumerical simulationssimulations revealed revealed that that typhoon-produced typhoon-produced SST cooling SST cooling strongly strongly depends depends on the typhoon’s on the characteristics,typhoon’s characteristics, such as intensity such asas measured intensity byas itsmeasured maximum by surface its maximum wind speed, surface translation wind speed, andtranslation size [14 speed,,15]. The and minimum size [14,15]. SST The related minimum to Typhoon SST related Kalmaegi to Typhoon appeared Kalmaegi on 16 September appeared on 2014 16 andSeptember the minimum 2014 and for the Typhoon minimum Sarika for occurredTyphoon on Sari 18ka October occurred 2016. on The18 October maximum 2016. wind The speeds maximum and translationwind speeds speeds and translation of these storms speeds were of these 36 m/s storms and were 38.5 m/s 36 m/s and and 5.8 38.5 m/s m/s and and 6.9 5.8 m/s, m/s respectively. and 6.9 m/s, respectively.We estimate the vertically integrated ocean heat content (OHC) with Equation (1), We estimate the vertically integrated ocean heat content (OHC) with Equation (1),

OHC = ρ0Cp y ∆T Tdhdxdy (1) OHC = ρC ∆Tdhdxdy (1) where ρ0 and Cp are the density and heat capacity of sea water, respectively, ∆T is the magnitude of the temperature anomaly between pre- and post-storm, h is the depth of temperature anomaly where ρ and C are the density and heat capacity of sea water, respectively, ∆T is the magnitude of extends,the temperature and x and anomalyy are between the meridional pre- and and post-storm the zonal, h extends,is the depth respectively, of temperature of the anomaly storm-induced extends, ρ 3 temperatureand x and y anomaly. are the meridional For all calculations, and the zonal 0extends,and Cp rearespectively, constant ofand the storm-induced equal to 1025 kg/mtemperatureand −1 ◦ −1 4200 J/kg · C , respectively. The depth of vertical mixing (h) is assumed3 as a constant and−1 − it1 anomaly. For all calculations, ρ and C are constant and equal to 1025 kg/m and 4200 J/kg ·°C , equalsrespectively. to 200 The m. The depth OHC of variationsvertical mixing between (h) is the assumed pre- and as post-storm a constantfor and Typhoons it equals Kalmaegito 200 m. andThe SarikaOHC variations are shown between in Figure the5b,d. pre- After and Typhoon post-storm Kalmaegi for Typhoons passage, Kalmaegi the maximum and Sarika ocean heatare shown loss was in Figure 5b,d. After Typhoon Kalmaegi passage, the maximum ocean heat loss was 1.6 × 108 J/m2 (Figure 5b), which is corresponding to the significant SST cooling of 6 °C (Figure 3f). For Typhoon Sarika,

Remote Sens. 2018, 10, 348 8 of 22

8 2 ◦ 1.6Remote× 10 Sens.J/m 2016, (Figure8, x 5b), which is corresponding to the significant SST cooling of 6 C (Figure83 off). 4 For Typhoon Sarika, however, the ocean heat loss was only 0.5 × 108 J/m2 (Figure5d) and the SST drophowever, was 4.2the ◦oceanC (Figure heat 4lossf). Thewas only amplitude 0.5 × 10 of8 J/m SST2 (Figure cooling 5d) and and OHC the SST decrease drop was owing 4.2 °C to (Figure Typhoon 4f). SarikaThe amplitude is less than of that SST of cooling Typhoon and Kalmaegi OHC decrease because Typhoonowing to SarikaTyphoon passed Sarika through is less a coldthan cyclone that of eddyTyphoon (Figure Kalmaegi5c). Therefore, because the Typhoon pre-storm Sarika oceanic passe conditionsd through also a affectcold thecyclone upper eddy ocean (Figure response 5c). toTherefore, typhoons. the pre-storm oceanic conditions also affect the upper ocean response to typhoons.

FigureFigure 3. 3.Microwave Microwave optimally optimally interpolated interpolated SST SST data data showing showing SST SST as Typhoonas Typhoon Kalmaegi Kalmaegi passed: passed: (a) on(a) 13on September 13 September 2014; 2014; (b) ( onb) on 14 14 September September 2014; 2014; (c )(c on) on 15 15 September September 2014; 2014; ( d(d)) onon 1616 SeptemberSeptember 20142014;; andand (e(e)) onon 17 17 September September 2014. 2014. ((ff)) SSTSST differencedifference betweenbetween 1616 andand 14 14 September September 2014.2014. TheThe colorcolor barbar ◦ denotesdenotes thethe temperature,temperature, in units of °C.C. The The colored colored circles circles mark mark the the location location of of the the typhoon typhoon center center at atdifferent different times, times, and and the the colors colors represent represent the themaximum maximum sustained sustained wind wind speed speed (m/s): (m/s): yellow yellow for 31–35 for 31–35m/s and m/s magenta and magenta for 36–40 for 36–40m/s. The m/s. black The star black marks star marksthe position the position of Argo of float Argo 5904562. float 5904562.

Remote Sens. 2018, 10, 348 9 of 22 Remote Sens. 2016, 8, x 9 of 4

FigureFigure 4.4.Microwave Microwaveoptimally optimallyinterpolated interpolatedSST SSTdata datashowing showing SSTSSTas asTyphoon Typhoon Sarika Sarika passed: passed:( a(a)) on on 1515 OctoberOctober 2016;2016; ((bb)) onon 1616 October 2016; ( (cc)) on on 17 17 October October 2016; 2016; (d (d) )on on 18 18 October October 2016; 2016; and and (e) ( eon) on 19 19October October 2016. 2016 (.(f) fSST) SST difference difference between between 18 18 and and 16 16 October. October. The The color color bar bar denotes thethe temperature,temperature, inin units units of of◦ °C.C. TheThe coloredcolored circlescircles markmark thethelocation locationof ofthe thetyphoon typhoon centercenter atatdifferent differenttimes, times,and andthe the colorscolors representrepresent the the maximum maximum sustained sustained wind wind speed speed (m/s): (m/s): black black for for <20 <20 m/s; m/s; greengreenfor for 20–30 20–30 m/s; m/s; yellowyellow for for 30–35 30–35 m/s; m/s; magentamagenta forfor 35–40 35–40 m/s; m/s; andand redred forfor >40 >40 m/s.m/s. The black starstar marksmarks thethe positionposition ofof ArgoArgo float float 5904746. 5904746.

Figure6 shows SST variations that were induced by Typhoons Kalmaegi and Sarika on the left and right sides of the track. The maximum SST cooling for Typhoons Kalmaegi and Sarika occurred on the right side of the track, with corresponding locations of 39.7 km and 66.2 km from the storm center, respectively. These two locations were within the maximum wind radius, which was 113.9 km for Typhoon Kalmaegi and 138.9 km for Typhoon Sarika. We estimate the power spectral density of wind speed and mixed layer current velocity at the maximum cooling position on the right side of the typhoon. As shown in Figure7, the peaks of power density agree well between the typhoon wind forcing and mixed layer current at the near inertial frequency. We found that a rightward SST bias occurred in the satellite observations, which is consistent with previous studies. It is mainly caused by the fact that the storm wind-stress vector turns clockwise over time on the right side of the track, and was roughly resonant with mixed layer current [7,43].

Remote Sens. 2018, 10, 348 10 of 22 Remote Sens. 2016, 8, x 10 of 4

FigureFigure 5. TheThe altimeter-derived altimeter-derived SSHA SSHA and and GVA GVA between between post-storm post-storm and pre-storm and pre-storm conditions conditions for: (a) for:Typhoon (a) Typhoon Kalmaegi; Kalmaegi; and (c) Typhoon and (c) Typhoon Sarika. The Sarika. color bar The denote color bars SSHA denotes variation SSHA in variationm. The ROMS- in m. Thesimulated ROMS-simulated OHC variations OHC between variations post- between and pre-storm post- and conditions pre-storm for: conditions (b) Typhoon for: Kalmaegi; (b) Typhoon and Kalmaegi;(d) Typhoon and Sarika. (d) Typhoon The color Sarika. bar denotes The color the barOHC denotes variation the in OHC J/m2 variation. The black in stars J/m 2mark. The the black position stars markof the theArgo position floats. ofThe the colored Argo floats.circles Themark colored the loca circlestion of mark the typhoon the location center of theat different typhoon times, center and at differentthe colors times, represent andthe thecolors maximum represent sustained the maximum wind speed sustained (m/s): black wind for speed <20 (m/s):m/s; green black for for 20–30 <20 m/s;m/s; Remotegreenyellow Sens. for2016 for 20–30, 30–358, x m/s; m/s; yellow magenta for for 30–35 35–40 m/s; m/s; magenta and red for for 35–40 >40 m/s. m/s; and red for >40 m/s. 11 of 4

Figure 6 shows SST variations that were induced by Typhoons Kalmaegi and Sarika on the left and right sides of the track. The maximum SST cooling for Typhoons Kalmaegi and Sarika occurred on the right side of the track, with corresponding locations of 39.7 km and 66.2 km from the storm center, respectively. These two locations were within the maximum wind radius, which was 113.9 km for Typhoon Kalmaegi and 138.9 km for Typhoon Sarika. We estimate the power spectral density of wind speed and mixed layer current velocity at the maximum cooling position on the right side of the typhoon. As shown in Figure 7, the peaks of power density agree well between the typhoon wind forcing and mixed layer current at the near inertial frequency. We found that a rightward SST bias occurred in the satellite observations, which is consistent with previous studies. It is mainly caused by the fact that the storm wind-stress vector turns clockwise over time on the right side of the track, and was roughly resonant with mixed layer current [7,43].

FigureFigure 6.6. SST changeschanges inducedinduced byby Typhoons:Typhoons: ((aa)) Kalmaegi;Kalmaegi; andand ((bb)) Sarika.Sarika. TheThe intersectionintersection ofof thethe dotteddotted lines marks marks the the location location of of the the typhoon typhoon cent center,er, and and the the centers centers of the of theTyphoons Typhoons Kalmaegi Kalmaegi and andSarika Sarika are at are 18.9°N, at 18.9 114°E◦N, 114 and◦E 18.1°N, and 18.1 111.5°E,◦N, 111.5 and◦E, at and 12:00 at 12:00UTC UTCon 15 onSeptember 15 September 2014 and 2014 at and 18:00 at 18:00UTC UTCon 17 on October 17 October 2016, 2016, respectively. respectively. The The negative negative direction direction of of the the horizontal horizontal axis axis denotes thethe typhoon’styphoon’s translationtranslation direction.direction. The top and bottom halveshalves of the verticalvertical axisaxis representrepresent thethe leftleft andand rightright sidessides ofof thethe typhoontyphoon track,track, respectively.respectively. TheThe colorcolor barbar denotesdenotes SSTSST variation,variation, inin unitsunits ofof◦ °C.C.

Figure 7. PSD of the wind speed (red lines) and current speed (blue lines) for Typhoon: (a) Kalmaegi; and (b) Sarika. The magenta lines denote the near-inertial frequency.

We only used satellite observations to study SST response to typhoons. To investigate subsurface temperature responses, we analyzed vertical temperature changes measured by Argo floats before and during Typhoons Kalmaegi and Sarika, the results of which are shown in Figure 8. Figure 8b,d

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Figure 6. SST changes induced by Typhoons: (a) Kalmaegi; and (b) Sarika. The intersection of the dotted lines marks the location of the typhoon center, and the centers of the Typhoons Kalmaegi and Sarika are at 18.9°N, 114°E and 18.1°N, 111.5°E, and at 12:00 UTC on 15 September 2014 and at 18:00 UTC on 17 October 2016, respectively. The negative direction of the horizontal axis denotes the typhoon’s translation direction. The top and bottom halves of the vertical axis represent the left and Remote Sens. 2018, 10, 348 11 of 22 right sides of the typhoon track, respectively. The color bar denotes SST variation, in units of °C.

FigureFigure 7. 7.PSD PSDof ofthe thewind wind speedspeed (red(red lines)lines) andand currentcurrent speedspeed (blue(blue lines)lines) forfor Typhoon:Typhoon: ((aa)) Kalmaegi;Kalmaegi; andand ( b(b)) Sarika. Sarika. The The magenta magenta lines lines denote denote the the near-inertial near-inertial frequency. frequency.

We only used satellite observations to study SST response to typhoons. To investigate subsurface We only used satellite observations to study SST response to typhoons. To investigate subsurface temperature responses, we analyzed vertical temperature changes measured by Argo floats before temperature responses, we analyzed vertical temperature changes measured by Argo floats before and and during Typhoons Kalmaegi and Sarika, the results of which are shown in Figure 8. Figure 8b,d during Typhoons Kalmaegi and Sarika, the results of which are shown in Figure8. Figure8b,d show ◦ ◦ a decrease of 0.1 C–1.3 C in the temperature above 95 m, and the depth of mixed layer was 36 m on 15 September 2014. The Argo-measured OHC decreases from 1.09 × 108 J/m2 to 0.89 × 108 J/m2. This suggests that the heat exchange at the air–sea interface and advection contribute to the upper ocean temperature cooling in the initial stage of typhoon forcing on 15 September 2014. Figure8d shows the depth of mixed layer deepening from 36 m to 46 m on 19 September 2014. Four days after the passage of Typhoon Kalmaegi, there was a decrease of 0.4 ◦C–0.6 ◦C in the temperature above 36 m, but an increase of 0.1 ◦C–1.1 ◦C in the temperature between 36 m and 80 m. The mixing induced by Typhoon Kalmaegi plays an important role on the surface cooling and subsurface warming. Figure8f reveals that Typhoon Sarika triggered a different subsurface temperature response than Typhoon Kalmaegi. The subsurface temperature response demonstrates a three-layer vertical structure, with a 0.8 ◦C–1.8 ◦C temperature decrease above 45 m, a 0.3 ◦C–1.5 ◦C increase between 45 m and 70 m, and a 0.6 ◦C–1.6 ◦C decrease between 70 m and 95 m. The OHC decreases from 1.41 × 108 J/m2 to 0.86 × 108 J/m2, and Figure8h shows the mixed layer deepening by 20 m during the passage of Typhoon Sarika. Four days after Typhoon Sarika, SST recovered to 29.3 ◦C (SST was 29.6 ◦C before the typhoon), whereas the subsurface temperatures remained warm state. This was owing to the net heat input to the subsurface that was retained by mixing while the surface temperature gradually recovered. Many studies have shown that the “heat pump” effect is critical to global circulation and heat transport [9,10,44,45]. Remote Sens. 2016, 8, x 12 of 4 show a decrease of 0.1 °C–1.3 °C in the temperature above 95 m, and the depth of mixed layer was 36 m on 15 September 2014. The Argo-measured OHC decreases from 1.09 × 108 J/m2 to 0.89 × 108 J/m2. This suggests that the heat exchange at the air–sea interface and advection contribute to the upper ocean temperature cooling in the initial stage of typhoon forcing on 15 September 2014. Figure 8d shows the depth of mixed layer deepening from 36 m to 46 m on 19 September 2014. Four days after the passage of Typhoon Kalmaegi, there was a decrease of 0.4 °C–0.6 °C in the temperature above 36 m, but an increase of 0.1 °C–1.1 °C in the temperature between 36 m and 80 m. The mixing induced by Typhoon Kalmaegi plays an important role on the surface cooling and subsurface warming. Figure 8f reveals that Typhoon Sarika triggered a different subsurface temperature response than Typhoon Kalmaegi. The subsurface temperature response demonstrates a three-layer vertical structure, with a 0.8 °C–1.8 °C temperature decrease above 45 m, a 0.3 °C–1.5 °C increase between 45 m and 70 m, and a 0.6 °C–1.6 °C decrease between 70 m and 95 m. The OHC decreases from 1.41 × 108 J/m2 to 0.86 × 108 J/m2, and Figure 8h shows the mixed layer deepening by 20 m during the passage of Typhoon Sarika. Four days after Typhoon Sarika, SST recovered to 29.3 °C (SST was 29.6 °C before the typhoon), whereas the subsurface temperatures remained warm state. This was owing to the net heat input to the subsurface that was retained by mixing while the surface temperature gradually recovered. Many studies have shownRemote that Sens. the2018 “heat, 10, 348 pump” effect is critical to global circulation and heat transport [9,10,44,45].12 of 22

Figure 8. VerticalFigure 8. temperatureVertical temperature and salinity and salinity measured measured by Ar bygo Argo floats floats at profiling at profiling interval interval of of four four days: (a) the tracksdays: of Argo (a) the floats tracks (black of Argo line); floats and (black (e line);) the andlocations (e) the of locations the profiles of the profilesused in used (b,c in,f,g (b), c(red,f,g) dots). (red dots). The blue line denoted the typhoon tracks, and the yellow and magenta dots denote the The blue line denoted the typhoon tracks, and the yellow and magenta dots denote the center of the center of the typhoons. (b) Temperature; and (c) salinity differences on 11 September, 15 September, typhoons. and(b) 19Temperature; September 2014; and and (f()c) temperature; salinity differences and (g) salinity on differences September on 13 11, October, September 17 October, 15, and Septemberand 19 212014; October and2016. (f) temperature; The blue solid linesand denote(g) salinity the changes differences during theon andOctober before 13, storm, October the blue 17, and October 21dashed 2016. The lines blue denote solid the changeslines denote between the the chan post-ges and during pre-storm, the andand the before red solid storm, lines the denote blue the dashed lines denotezero the line. changes The mixed between layer depth the post- measured and by pre-storm, Argo float (andd) 5904562 the red during solid Typhoon lines denote Kalmaegi the (2014) zero line. (maximum sustained wind of 33.4 m/s) and (h) 5904746 during Typhoon Sarika (2016) (maximum The mixed layer depth measured by Argo float (d) 5904562 during Typhoon Kalmaegi (2014) sustained wind of 38.6 m/s). (maximum sustained wind of 33.4 m/s) and (h) 5904746 during Typhoon Sarika (2016) (maximum sustained windFigure of9 shows38.6 m/s). CTD-measured and ROMS-simulated temperature evolutions for Typhoon Kalmaegi between 0 and 200 m depths at stations B2 and B4. Again, both in situ measurements and model simulations showed significant SST cooling and deepening of the mixed layer when the typhoon passed. ROMS-simulated SST changes before and during the passage of Typhoon Kalmaegi are shown in Figure 10. Compared to satellite observations (Figure3c), the model-simulated SST cooling has a smaller amplitude than observations, a discrepancy that is largely linked to atmospheric forcing. Moreover, the stratification defined by the buoyancy frequency (N)[19] is as follows, Remote Sens. 2016, 8, x 13 of 4

Figure 9 shows CTD-measured and ROMS-simulated temperature evolutions for Typhoon Kalmaegi between 0 and 200 m depths at stations B2 and B4. Again, both in situ measurements and model simulations showed significant SST cooling and deepening of the mixed layer when the typhoon passed. ROMS-simulated SST changes before and during the passage of Typhoon Kalmaegi are shown in Figure 10. Compared to satellite observations (Figure 3c), the model-simulated SST cooling has a smaller amplitude than observations, a discrepancy that is largely linked to atmospheric forcing.Remote Moreover, Sens. 2018, 10 the, 348 stratification defined by the buoyancy frequency () [19] is as follows,13 of 22

̅ 2 =−g ∂ρ (2) N = − (2) ρ0 ∂z ≈9.8 m/s wherewhere g ≈ 9.8 m/s, 2 , isρ theis thepotential potential density density of ofseawater seawater temperature temperature and and salinity salinity at at depth depth z. The ROMS-simulatedThe ROMS-simulated maximum maximum buoyancy buoyancy frequency frequency (MBP) (MBP) occurred occurred atat 54.2 54.2 m m (N (=4.9=4.9×10× 10−2 s−1), s ), whereaswhereas the theCTD-measured CTD-measured MBP MBP occurred occurred atat 39.239.2 m m ( N(==2.7×102.7 × 10−2 s− s1).). It isIt obviousis obvious that that the the ROMS-simulatedROMS-simulated buoyancy buoyancy frequency frequency is is stronger stronger than thethe CTD CTD measurements, measurements, and and the the stronger stronger stratificationstratification yields yields smaller smaller SST SST cooling cooling amplitude. amplitude.

FigureFigure 9. (a 9.,b()a ,CTD-measuredb) CTD-measured temperatures from from 0–200 0–200 m deep m deep at two at different two different stations; andstations; (c,d) ROMS- and (c,d) ROMS-simulatedsimulated temperatures temperatures from from 0–200 0–200 m deep m deep at two at two different different stations. stations. The The color color bar denotesbar denotes the the temperature,temperature, in units in units of of°C.◦C.

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FigureFigure 10.10. ROMS-simulatedROMS-simulated sea sea surface surface temperature temperature differences differences between between 16 16September September and and 14 14September September 2014.2014. The The color color bar bar denotes denotes the the temperatur temperaturee difference, difference, in in units units of of °C.◦C. The color circlescircles showshow thethe location location of of the the typhoon typhoon center center at different at different times andtimes the and colors the denote colors the denote maximum the maximum sustained windsustained speed wind (m/s): speed yellow (m/s): for yellow 31–35 m/s;for 31–35 magenta m/s; formagenta 36–40 for m/s. 36–40 m/s.

3.2. Salinity Response 3.2. Salinity Response Figure 11 shows the SMAP Level 3 Running 8-day SSS maps before (Figure 11a), during (Figure 11b,c) Figure 11 shows the SMAP Level 3 Running 8-day SSS maps before (Figure 11a), during (Figure 11b,c) and after (Figure 11d,e) Typhoon Sarika. Figure 11f illustrates typhoon-induced SSS changes. The and after (Figure 11d,e) Typhoon Sarika. Figure 11f illustrates typhoon-induced SSS changes. maximum SSS increase was 2.2 psu to the right side of Typhoons Sarika’s track, whereas the The maximum SSS increase was 2.2 psu to the right side of Typhoons Sarika’s track, whereas the maximum salinity decrease was 1.4 psu to the left side. SSS was increased compared to pre-typhoon maximum salinity decrease was 1.4 psu to the left side. SSS was increased compared to pre-typhoon conditions on the right side because the wind stress vector rotates clockwise on the right side of the conditions on the right side because the wind stress vector rotates clockwise on the right side of the typhoon track and remains parallel with the inertially rotating mixed layer current. This results in typhoon track and remains parallel with the inertially rotating mixed layer current. This results in strong vertical mixing, and thus, brings saltier water from the deep ocean to the surface. Figure 12 strong vertical mixing, and thus, brings saltier water from the deep ocean to the surface. Figure 12 shows that high rainfall rates occurred on the left side of Typhoon Sarika’s track. Many studies have shows that high rainfall rates occurred on the left side of Typhoon Sarika’s track. Many studies have shown that the remarkable asymmetric TC rainfall distribution is caused by the vertical wind shear, shown that the remarkable asymmetric TC rainfall distribution is caused by the vertical wind shear, tropical cyclones motion, and water vapor flux or topography, in which the vertical wind shear is the tropical cyclones motion, and water vapor flux or topography, in which the vertical wind shear is the primary factor [46–53]. Moreover, the rainfall maximum in the inner-core region of a TC is always primary factor [46–53]. Moreover, the rainfall maximum in the inner-core region of a TC is always located on the downshear left side for TCs in the Northern Hemisphere regardless of the storm located on the downshear left side for TCs in the Northern Hemisphere regardless of the storm motion motion and shear orientation [52,53]. The decreasing SSS on the left side was induced by intense and shear orientation [52,53]. The decreasing SSS on the left side was induced by intense rainfall rainfall injecting fresh water into the ocean. Moreover, the typhoon-induced SSS drop on the left side injecting fresh water into the ocean. Moreover, the typhoon-induced SSS drop on the left side of of the typhoon’s track was also confirmed by Argo observations. The GPM-measured rainfall rate the typhoon’s track was also confirmed by Argo observations. The GPM-measured rainfall rate was was 6.8 mm/h at the Argo position, which led to a salinity decrease of 0.02–0.41 psu above 70 m on 6.8 mm/h at the Argo position, which led to a salinity decrease of 0.02–0.41 psu above 70 m on the left the left side as shown in Figure 8g. side as shown in Figure8g.

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Figure 11.11. SMAP Level 3 8-day Running sea surface salinitysalinity (SSS) data showing the SSS as Typhoon Sarika passed:passed: (a)) on 11 October 2016; ((b)) on 14 October 2016; (c)) on 17 October 2016; (d) on 20 October 2016; (e)) and on 23 October 2016. ( f) SSS difference between 17 October andand 1111 October 2016.2016. The color bar denotesdenotes thethe salinity,salinity, in in units units of of PSU. PSU. The The colored colored circles circles denote denote the the location location of of the the typhoon typhoon center center at differentat different times times and and the the colors colors denote denote the the maximum maximum sustained sustained wind wind speed speed (m/s): (m/s): black black for for <20 <20 m/s;m/s; green for 20–30 m/s; yellow yellow for for 30–35 30–35 m/s; m/s; magenta magenta fo forr 35–40 35–40 m/s; m/s; and and red red for for>40 >40 m/s. m/s. The Theblack black star starmarks marks the position the position of Argo of Argo float float 5904746. 5904746.

Salinity profiles profiles from from CTD CTD observations observations and and ROMS ROMS simulations, simulations, are areillustrated illustrated in Figure in Figure 13, both 13, bothrevealed revealed that subsurface that subsurface salinity salinity increased increased at stations atstations B2 and B4 B2 during and B4 Typhoon during TyphoonKalmaegi. Kalmaegi. The CTD Themeasurements CTD measurements clearly showed clearly maximu showedm salinity maximum increases salinity in the increases subsurface in the layer subsurface at stations layer B2 and at stationsB4 of 0.32 B2 and B40.17 of psu, 0.32 andrespectively 0.17 psu, (Figure respectively 13a,b). (Figure ROMS 13 simulationsa,b). ROMS simulationsalso exhibited also subsurface exhibited subsurfacesalinity increases salinity at increases the two atstations the two, with stations, respective with respectivevalues of 0.39 values and of 0.26 0.39 psu, and as 0.26 shown psu, asin shownFigure in14c,d. Figure The 14 CSFv2-derivedc,d. The CSFv2-derived rainfall rates rainfall at stations rates atB2 stations and B4 B2were and 4.6 B4 mm/h were 4.6and mm/h 2.6 mm/h, and 2.6while mm/h, the whileWindSat-measured the WindSat-measured rainfall rates rainfall were rates 6.5 mm/h were an 6.5d mm/h 4.3 mm/h. and Model-simulated 4.3 mm/h. Model-simulated salinity maxima salinity are maximaslightly higher are slightly than those higher measured than those by measured CTD, which by is CTD, possibly which associated is possibly with associated the lower withCSFv2-derived the lower CSFv2-derivedrainfall rate for rainfallthe model rate input. for the Moreover, model input. Argo Moreover, salinity Argoprofile salinity measurements profile measurements also confirm alsothat confirmsubsurface that salinity subsurface increased salinity by up increased to 0.16 bypsu up be totween 0.16 40 psu m betweenand 100 m, 40 mas andshown 100 in m, Figure as shown 8c. The in Figurerainfall8 c.rate The was rainfall 12 mm/h rate in wasthe Argo 12 mm/h position in the(Figure Argo 14), position resulting (Figure in a salinity 14), resulting decrease in of a 0.14–0.24 salinity decreasepsu above of 40 0.14–0.24 m. psu above 40 m.

Remote Sens. 2016, 8, x 16 of 4

Remote Sens. 2018, 10, 348 16 of 22

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Figure 12. GPM-measured rain rates at 00:00 UTC on 17 October 2016. The color bar denotes rainfall Figure 12. GPM-measured rain rates at 00:00 UTC on 17 October 2016. The color bar denotes rainfall Figure 12.rate GPM-measured in mm/h. White indicates rain rates no rain. at 00:00 The black UTC star on denotes 17 October the position 2016. of ArgoThe floatcolor 5904746. bar denotes The rainfall raterate in mm/h.in mm/h.colored White White circles indicatesindicates show the locationno no rain. rain. of Thethe The typhoon black black stcenterar star denotes at denotesdifferent the times theposition and position the ofcolors Argo ofrepresent Argofloat the 5904746. float 5904746. The The colored circlesmaximum show sustained the locationwind speed of (m/s): the typhoonblack for <20 center m/s; green at different for 20–30 timesm/s; yellow and for the 30–35 colors m/s; represent the colored circlesmagenta show for 35–40 the m/s; location and red offor >40the m/s. typhoon center at different times and the colors represent the maximummaximum sustained sustained wind wind speed speed (m/s): (m/s): black black for for <20 <20 m/s;m/s; green for 20–30 m/s; yellow yellow for for 30–35 30–35 m/s; m/s; magentamagenta for for 35–40 35–40 m/s; m/s; andand red for for >40 >40 m/s. m/s.

Figure 13. (a,b) CTD-measured salinity at 0–200 m deep at two different stations; and (c,d) ROMS- simulated salinity at 0–200 m deep at two different stations. The color bar denotes the salinity, in units of PSU. Remote Sens. 2016, 8, x 17 of 4

Figure 13. (a,b) CTD-measured salinity at 0–200 m deep at two different stations; and (c,d) ROMS- Remotesimulated Sens. 2018, salinity10, 348 at 0–200 m deep at two different stations. The color bar denotes the salinity, in units17 of 22 of PSU.

Figure 14. WindSat-measured rainfallrainfall ratesrates atat 10:1210:12 UTCUTC onon 15 September 2014.2014. The color bar denotes rainfallrainfall raterate inin mm/h.mm/h. The The black black star star denotes the position of Argo floatfloat 5904562.5904562. The colored circlescircles show thethe location location of of the the typhoon typhoon center center at different at different times andtimes the and colors the denote colors the denote maximum the maximum sustained windsustained speed wind (m/s): speed yellow (m/s): for yellow 31–35 m/s;for 31–35 magenta m/s; magenta for 36–40 for m/s. 36–40 m/s.

3.3. Current Response Strong typhoon-forcedtyphoon-forced winds can enhanceenhance near-surfacenear-surface ocean currentcurrent velocity.velocity. OceanOcean currentcurrent response toto TyphoonTyphoon Kalmaegi Kalmaegi was was clearly clearly observed observed by by ADCP ADCP at at stations stations B1, B1, B2, B2, and and B4, B4, as as shown shown in Figurein Figure 15 a–c.15a–c. B1 B1 and and B4 B4 stations stations were were on on the the right right side side of Typhoonof Typhoon Kalmaegi’s Kalmaegi’s track track and and B2 wasB2 was on theon the left. left. Before Before Typhoon Typhoon Kalmaegi’s Kalmaegi’s arrival, arrival, ADCP-measured ADCP-measured current current velocity velocity maxima maxima were were 0.5 0.5 m/s m/s at B1,B1, 0.30.3 m/sm/s at B2, and 0.4 m/sm/s at at B4, B4, occurring depths of 30, 14, and 22 m,m, respectively.respectively. DuringDuring TyphoonTyphoon Kalmaegi’sKalmaegi’s passage,passage, thethe currentcurrent velocityvelocity maximamaxima measuredmeasured atat thethe same depths increased to 1.18,1.18, 0.86,0.86, andand 1.191.19 m/s,m/s, respectively. The ADCP-measured subsurface current velocities at stationsstations B1 and B4 are strongerstronger thanthan thosethose measuredmeasured atat B2B2 becausebecause the windwind stress isis betterbetter coupled with the inertialinertial currents on on the the right right side side than than that that those those to tothe the left left side. side. After After Typhoon Typhoon Kalmaegi Kalmaegi passed, passed, the themaximum maximum current current velocities velocities were were 1.22, 1.22, 1.01, 1.01, and and 1.23 1.23 m/s m/s at stations at stations B1, B1,B2, B2,and and B4, B4,at depths at depths of 54, of 54,46, and 46, and 94 m, 94 respectively. m, respectively. It is indicated It is indicated that th thate mixed the mixed layer depth layer depthwas deepened, was deepened, which was which mainly was mainlycaused causedby near-inertial by near-inertial current currentshear at shear the base at the of basethe mixed of the layer. mixed layer. We also used a ROMS model to simulate surface and subsurface current responses to typhoons. Figure 15d–f shows model-simulated current velocities at stations B1, B2, and B4. This demonstrates that model simulations can reproduce ADCP-observed current response characteristics well. Maximum current velocities from ROMS model simulation were 1.49 m/s at station B1, 1.23 m/s at station B2, and 1.41 m/s at station B4, respectively. Generally, the large wind stresses lead to strong ocean current. This drag coefficient in the COARE3.0 algorithm is larger than that used in [54] for high wind speeds. Therefore, it might cause that the model-simulated current velocities are larger than those from ADCP measurements. Moreover, Figure 16 shows near-surface current velocity differences, from the ROMS simulation, before and during Typhoon Kalmaegi passes. This demonstrates that current velocity changes also presented a rightward bias; current velocities on the right side of typhoon track are higher than on the left.

Remote Sens. 2018, 10, 348 18 of 22 Remote Sens. 2016, 8, x 18 of 4

Figure 15. Time series of current velocity profiles observed by ADCP at: (a) station B1; (b) station B2; Figure 15. Time series of current velocity profiles observed by ADCP at: (a) station B1; (b) station B2; and (c) station B4 during the passage of Typhoon Kalmaegi. Time series of current velocity profiles and (c) station B4 during the passage of Typhoon Kalmaegi. Time series of current velocity profiles simulated by the ROMS at: (d) station B1; (e) station B2; and (f) station B4 during the passage of simulated by the ROMS at: (d) station B1; (e) station B2; and (f) station B4 during the passage of Typhoon Kalmaegi. The color bar denotes the current velocity, in units of m/s. Typhoon Kalmaegi. The color bar denotes the current velocity, in units of m/s.

We also used a ROMS model to simulate surface and subsurface current responses to typhoons. Figure 15d–f shows model-simulated current velocities at stations B1, B2, and B4. This demonstrates that model simulations can reproduce ADCP-observed current response characteristics well. Maximum current velocities from ROMS model simulation were 1.49 m/s at station B1, 1.23 m/s at station B2, and 1.41 m/s at station B4, respectively. Generally, the large wind stresses lead to strong ocean current. This drag coefficient in the COARE3.0 algorithm is larger than that used in [54] for high wind speeds. Therefore, it might cause that the model-simulated current velocities are larger

Remote Sens. 2016, 8, x 19 of 4 than those from ADCP measurements. Moreover, Figure 16 shows near-surface current velocity differences, from the ROMS simulation, before and during Typhoon Kalmaegi passes. This Remotedemonstrates Sens. 2018 ,that10, 348 current velocity changes also presented a rightward bias; current velocities on19 of the 22 right side of typhoon track are higher than on the left.

Figure 16. ROMS-simulatedROMS-simulated near-surface near-surface current current velo velocitycity difference difference between between 15 15September September and and 14 14September September 2014. 2014. The The color color bar bar denotes denotes the the current current velocity velocity difference, difference, in units of m/s.m/s. The colored circles denote the location of the typhoon center at different times and the colors denote the maximum sustained wind speedspeed (m/s):(m/s): yellow yellow for for 31–35 31–35 m/s; m/s; magenta magenta for for 36–40 36–40 m/s. m/s.

4. Conclusions 4. Conclusions We quantified physical responses of the ocean surface to Typhoons Kalmaegi and Sarika, based We quantified physical responses of the ocean surface to Typhoons Kalmaegi and Sarika, based on multi-satellite observations, in situ measurements, and a numerical ocean model. Using satellite on multi-satellite observations, in situ measurements, and a numerical ocean model. Using satellite observations and ocean simulations, we confirmed that strong typhoon winds pumped cold and salty observations and ocean simulations, we confirmed that strong typhoon winds pumped cold and salty deep waters from the mixed layer to the sea surface, which resulted SST cooling and SSS increases. deep waters from the mixed layer to the sea surface, which resulted SST cooling and SSS increases. Typhoon force winds induced strong vertical mixing and upwelling, leading to enhanced near-surface Typhoon force winds induced strong vertical mixing and upwelling, leading to enhanced near-surface ocean currents. SST and current responses both exhibited obvious rightward biases because of the ocean currents. SST and current responses both exhibited obvious rightward biases because of the rotation resonance of the wind vector with wind-driven inertial currents. rotation resonance of the wind vector with wind-driven inertial currents. The maximum amplitudes of SST decrease were 6 °C and 4.2 °C for Typhoons Kalmaegi and The maximum amplitudes of SST decrease were 6 ◦C and 4.2 ◦C for Typhoons Kalmaegi and Sarika, respectively. Regarding Typhoon Sarika, measurements from Argo temperature profiles showed Sarika, respectively. Regarding Typhoon Sarika, measurements from Argo temperature profiles showed that the subsurface temperature response had a three-layer vertical structure, with a 0.8 °C–1.8 °C that the subsurface temperature response had a three-layer vertical structure, with a 0.8 ◦C–1.8 ◦C temperature decrease above 45 m, a 0.3 °C–1.5 °C increase between 45 m and 70 m, and a 0.6 °C–1.6 °C temperature decrease above 45 m, a 0.3 ◦C–1.5 ◦C increase between 45 m and 70 m, and a 0.6 ◦C–1.6 ◦C decrease between 70 m and 95 m. After Typhoon Sarika passed, SST recovered to their pre-typhoon decrease between 70 m and 95 m. After Typhoon Sarika passed, SST recovered to their pre-typhoon condition, whereas subsurface temperatures remained in a warm state. This is due to the net heat condition, whereas subsurface temperatures remained in a warm state. This is due to the net heat input to the subsurface that is retained by mixing, while the surface temperature gradually recovers. input to the subsurface that is retained by mixing, while the surface temperature gradually recovers. Many studies have shown that the “heat pump” effect have important effects on the global circulation Many studies have shown that the “heat pump” effect have important effects on the global circulation and the heat transport [9,10,44,45]. and the heat transport [9,10,44,45]. SMAP Level 3 8-day Running SSS maps show that the maximum SSS increase was 2.2 psu on SMAP Level 3 8-day Running SSS maps show that the maximum SSS increase was 2.2 psu on the right side of Typhoon Sarika’s track, whereas the maximum salinity decrease was 1.4 psu on the the right side of Typhoon Sarika’s track, whereas the maximum salinity decrease was 1.4 psu on the left. This SSS seesaw response phenomenon is related to asymmetrical rainfall on both sides of the left. This SSS seesaw response phenomenon is related to asymmetrical rainfall on both sides of the typhoon track. SSS on the right side was higher than pre-typhoon conditions because typhoon-induced typhoon track. SSS on the right side was higher than pre-typhoon conditions because typhoon-induced vertical mixing brings saltier deep water to the surface. Decreasing SSS on the left side is induced by vertical mixing brings saltier deep water to the surface. Decreasing SSS on the left side is induced by intense rainfall injecting fresh water into the ocean. Moreover, Argo salinity profile measurements intense rainfall injecting fresh water into the ocean. Moreover, Argo salinity profile measurements also also confirmed the left side SSS decrease. confirmed the left side SSS decrease. Both numerical simulations and ADCP observations revealed upper ocean current response to typhoons. When Typhoon Kalmaegi passed, the highest current velocities measured by ADCP at depths of 30, 14, and 22 m, were 1.18, 0.86, and 1.19 m/s, respectively. After Typhoon Kalmaegi Remote Sens. 2018, 10, 348 20 of 22 passed, the maximum current velocities were 1.22, 1.01, and 1.23 m/s at stations B1, B2, and B4, with corresponding depths of 54, 46, and 94 m. It is indicated that the mixed layer depth was deepened, which was mainly caused by near-inertial current shear at the base of the mixed layer. Factors such as accurate atmospheric forcing fields, suitable drag coefficient under high wind speed, vertical parameterization schemes, and the reason to use coupled ocean–atmosphere models, depending on the condition will directly affect the simulation results. Furthermore, we will use the wind field of high-resolution microwave radiometers to provide atmospheric forcing and study ocean response before, during, and after the passage of a typhoon. We will further analyze the physical mechanism of the changes in detail using the simulation results, including the roles of entrainment, KPP mixing, and upwelling.

Acknowledgments: This work as supported in part by the National Key Research and Development Program of China under Grant 2016YFC1401001, in part by the National Science Foundation of China for Outstanding Young Scientist under Grant 41622604, in part by the Excellent Youth Science Foundation of Jiangsu Province under Grant BK20160090. Cross-shaped observations were organized under the support of National Basic Research Program of China (2013CB430302) and the National Program on Global Change and Air–ASea Interaction (GASI-IPOVAI-04). Microwave OS SST data are produced by Remote Sensing Systems and sponsored by National Oceanographic Partnership Program and the NASA Earth Science Physical Oceanography Program. We would like to thank the four anonymous reviewers for their helpful comments on improving the manuscript. The views, opinions, and findings contained in the paper are those of the authors and should not be construed as an official NOAA or U.S. Government position, policy, or decision. Author Contributions: Biao Zhang and Guoqiang Liu conceived the original idea of the study, suggested for the topic, and contributed to the interpretation of the results; Xinxin Yue carried out satellite data analysis, numerical simulations and prepared all of the figures; Han Zhang processed and analyzed in situ data; Xiaofeng Li and Yijun He assisted in manuscript preparation and revision. Xinxin Yue and Biao Zhang wrote the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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