Observed Sea Surface Temperature Research Articles

Spatial variability in the community structure of microphytoplankton along the southeastern Arabian sea during an unusually warm winter monsoon season

The microphytoplankton community structure and composition were investigated along the South Eastern Arabian Sea (SEAS) during the late winter monsoon season (January 2020). A total of 186 species of phytoplankton were identified, with cyanobacteria Trichodesmium erythraeum (72%) being the most dominant, followed by diatoms (11%) and dinoflagellates (2.7%). Major blooms of T. erythraeum were observed towards the southern region of SEAS characterised by high SST (28.75 ± 0.7 °C), high surface salinity (34.6 ± 1.2 psu) and low wind speed (∼3.6 m s−1). The T. erythraeum bloom areas generally observed high nitrate and low silicate and phosphate concentrations with high chlorophyll a concentration (8.96 ± 2.92 mg m−3). In non-bloom regions, centric diatoms like Chaetoceros spp., Bacteriastrum spp. and Leptocylindrus sp. dominated. The abundance of diatoms observed a decreasing trend towards the southern extend of SEAS. The remote sensing data derived for the area observed sea surface temperatures are about 1.0 °C higher than the climatological mean of January. The higher temperatures (29 °C–32 °C) are clearly visible in the Indian coastal region from Kanyakumari to the Gujarat coast, suggesting unusual warming in the eastern coast of the Arabian Sea in January 2020. This warming which was more prominent towards the southern extend of SEAS, might have augmented the proliferation of T. erythraeum along the surface waters of SEAS.

Southwestern Atlantic Ocean Fronts Detected from Satellite-Derived SST and Chlorophyll

The Southern Ocean front (SOF) is an important factor that affects the heat exchange and material transport of the Southern Ocean. In the past two decades, with the advancements in satellite remote-sensing technology, the study of the spatio-temporal variability of the Southern Ocean front has become a new hot topic. Nevertheless, the southwestern Atlantic, as an important part of the Southern Ocean, is poorly studied in this regard. Based on the 16-year (2004–2019) high-resolution satellite observations of sea surface temperature (SST) and 13-year (2007–2019) observations of chlorophyll (CHL), this study detected and analyzed the position and seasonal variation of the SOF in the southwestern Atlantic using a gradient-based frontal detection method. According to the experimental results, the thermal front (derived from the SST data) disappeared in winter due to the spatially uniform surface cooling, whereas the ocean color front (derived from the CHL data) existed without remarkable spatio-temporal changes. Furthermore, the exact position and seasonal variation of the SOF in the southwestern Atlantic are determined by comparing the paths of the two fronts. Since the formation of the Kuroshio front in the East China Sea (ECS) is similar to the SOF in the southwestern Atlantic, the seasonal distributions of the two fronts were compared. Apart from that, the Kuroshio thermal fronts were mostly distributed in winter and less in summer, while the Southern Ocean thermal fronts showed the opposite. These results indicated that the ocean current properties significantly influence the spatio-temporal variability of the front.

State Estimates and Forecasts of the Northern Philippine Sea Circulation including Ocean Acoustic Travel Times

AbstractThe 2010–11 North Pacific Acoustic Laboratory (NPAL) Philippine Sea experiment measured travel times between six acoustic transceiver moorings in a 660-km diameter ocean acoustic tomography array in the northern Philippine Sea (NPS). The travel-time series compare favorably with travel times computed for a yearlong series of state estimates produced for this region using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system constrained by satellite sea surface height and sea surface temperature observations and by Argo temperature and salinity profiles. Fluctuations in the computed travel times largely match the fluctuations in the measurements caused by the intense mesoscale eddy field in the NPS, providing a powerful test of the observations and state estimates. The computed travel times tend to be shorter than the measured travel times, however, reflecting a warm bias in the state estimates. After processing the travel times to remove tidal signals and extract the low-frequency variability, the differences between the measured and computed travel times were used in addition to SSH, SST, and Argo temperature and salinity observations to further constrain the model and generate improved state estimates. The assimilation of the travel times reduced the misfit between the measured and computed travel times, while not increasing the misfits with the other assimilated observations. The state estimates that used the travel times are more consistent with temperature measurements from an independent oceanographic mooring than the state estimates that did not incorporate the travel times.

Seasonal predictability of baroclinic wave activity

Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.

Modulation of observed sea surface temperature variation by the quasi‐biweekly oscillation in the tropical western Pacific during boreal summer

AbstractUsing 17‐year in situ buoy observations, this study investigates how sea surface temperature (SST) over the tropical western Pacific Ocean is modulated by the quasi‐biweekly oscillation (QBWO) during boreal summer. The 10‐buoy mean phase‐by‐phase composites of SST indicate that SST evolution lags behind the convection cycle by approximately one quarter, with the maximum SST occurring in Phase −90° (29.68°C) and the minimum SST occurring in Phase 90° (29.37°C). The diurnal cycle of SST (dSST) is in phase with the convection cycle, with the minimum dSST occurring during the convectively active phase (0.34°C) and the maximum dSST occurring during the convectively suppressed phase (0.61°C). The fluctuation in the dSST reaches 0.27°C, which is similar to that in the SST (0.31°C). This kind of oscillation can be well explained by the evolution of the sea surface forcing parameters, especially the shortwave radiation and wind speeds, observed from the buoys. In addition, the composite range of the dSST suggests an interesting asymmetry between Phase −90° and 90°. The interaction of the background summer mean state and the 10–30‐day intraseasonal anomalies causes the asymmetry in wind speed between Phase −90° and 90°, which in turn results in the asymmetry in the dSST between these two phases. Since QBWOs are more likely to occur during El Niño developing summers than during El Niño decaying summers, the 10–30‐day intraseasonal SST and dSST variances during El Niño developing summers are stronger than those during El Niño decaying summers.

Evaluation and projection of the AMO and PDO variabilities in the CMIP5 models under different warming scenarios part1: Evaluation

The capabilities of the CMIP5 models to simulate the two most important interdecadal modes, the Atlantic Multi-decadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), are evaluated by comparing the observed sea surface temperature data from 1880 to 2009 and the CMIP5 simulations under the historical scenario. The multi-model ensemble (MME) historical simulations show deficiency in reproducing characteristics of the PDO mode in the tropics but well simulated the spatial patterns of the AMO and PDO in the high latitudes. The amplitude of the two modes is obviously weaker in the historical simulations than the observation. In terms of periodic simulations, the MME can simulate the 60–70 year period of the AMO and the double periods, namely 20 and 60–70 years, of the PDO. The MME show more realistic simulations of observed spatial and temporal features of AMO and PDO than most models. The CESM1-CAM5, FGOALS-g2, GISS-E2-R-CC, GISS-E2-H-CC and MIROC-ESM-CHEM are the top-performing models. By comparing the individual models, the variances of the AMO and PDO modes decrease when the simulated global mean sea surface temperatures are higher.

An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000\u20131849

This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.

Impact of the Agulhas Current on southern Africa precipitation: a modelling study

AbstractThe Agulhas Current (AC) creates a sharp temperature gradient with the surrounding ocean, leading to a large turbulent flux of moisture from ocean to atmosphere. We use two simulations of the Weather Research and Forecasting (WRF) model to show the seasonal impact of the warm core of the AC on southern Africa precipitation. In one simulation the sea surface temperature (SST) of the AC is similar to satellite observations, while the second uses satellite SST observations spatially smoothed to reduce the temperature of the core of the AC by ~1.5°C. We show that decreasing the SST of the AC reduces the precipitation of the wettest seasons (austral summer and autumn) inland. Over the ocean, reducing the SST reduces precipitation, low-level wind convergence, SST and SLP Laplacian above the AC in all seasons, consistent with the pressure adjustment mechanism. Moreover, winter precipitation above the Current may be also related to increased latent flux. In summer and autumn, the AC SST reduction is also associated with decreased precipitation further inland (more than 1.5 mm/day), caused by an atmospheric circulation that decreases the horizontal moisture flux from the AC to South Africa. The reduction is also associated with higher geopotential height extending from the surface east and over the AC to the mid-troposphere over southeastern Africa. The westward tilted geopotential height is consistent with the linear response to shallow diabatic heating in midlatitudes. An identical mechanism occurs in spring but is weaker. Winter rainfall response is confined above the AC.

Recovery of Tropical Cyclone Induced SST Cooling Observed by Satellite in the Northwestern Pacific Ocean

Based on the satellite observed sea surface temperature (SST), the recovery of SST cooling induced by the tropical cyclones (TCs) over the northwestern Pacific Ocean is investigated. The results show that the passage of a TC induces a mean maximum cooling in the SST of roughly −1.25 °C. It was also found that most of this cooling (~87%) is typically erased within 30 days of TC passage. This recovery time depends upon the degree of cooling, with stronger (weaker) SST cooling corresponding to longer (shorter) recovery time. Further analyses show that the mixed layer depth (MLD) and the upper layer thermocline temperature gradient (UTTG) also play an important role in the SST response to TCs. The maximum cooling increases ~0.1 °C for every 7 m decrease in the MLD or every 0.04 °C/m increase in the UTTG. The combined effects of MLD and TC intensity and translation speed on the SST response are also discussed.

High-Resolution Ocean Currents from Sea Surface Temperature Observations: The Catalan Sea (Western Mediterranean)

Current observations of ocean currents are mainly based on altimetric measurements of Sea Surface Heights (SSH), however the characteristics of the present-day constellation of altimeters are only capable to retrieve surface currents at scales larger than 50–70 km. By contrast, infrared and visible radiometers reach spatial resolutions thirty times higher than altimeters under cloud-free conditions. During the last years, it has been shown how the Surface Quasi-Geostrophic (SQG) approximation is able to reconstruct surface currents from measured Sea Surface Temperature (SST), but it has not been yet used to retrieve velocities at scales shorter than those provided by altimeters. In this study, the velocity field of ocean structures with characteristic lengths between 10 and 20 km has been derived from infrared SST using the SQG approach and compared to the velocities derived from the trajectories of Lagrangian drifters. Results show that the SQG approach is able to reconstruct the direction of the velocity field with observed RMS errors between 8 and 15 degrees and linear correlations between 0.85 and 0.99. The reconstruction of the modulus of the velocity is more problematic due to two limitations of the SQG approach: the need to calibrate the level of energy and the ageostrophic contributions. If drifter trajectories are used to calibrate velocities and the analysis is restricted to small Rossby numbers, the RMS error in the range of 10 to 16 cm/s and linear correlations can be as high as 0.97.

Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning

AbstractRecent satellite sea surface height (SSH) and sea surface temperature (SST) observations have shown that abnormal eddies, that is, warm cyclonic eddies and cold anticyclonic eddies occur sporadically in some regions, which triggers an essential question on the spatiotemporal distribution of abnormal eddies in the global ocean. In this study, a deep learning framework was developed to systematically mine information from the synergy of satellite‐sensed global SSH and SST data over the 1996–2015, 20‐year period. Abnormal eddies account for a surprising one‐third of total eddies and are active along the Equatorial Current and high unstable currents. Normal (abnormal) eddies are stronger in winter (summer) in the North Hemisphere and vice versa in the Southern Hemisphere. The annual mean amplitudes of normal eddies are larger than that of abnormal eddies. Crucially, the daily number of normal (abnormal) eddies increased (decreased) 9.68 (11.80) every year.

Sub-Surface Ocean Structure from Satellite Surface Observations in the North Indian Ocean

In this study interior plus Surface Quasi-Geostrophic (isQG) methodology is used to reconstruct subsurface density anomaly using sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). The study is carried out in the Arabian Sea and Bay of Bengal of the Northern Indian ocean region. isQG is first tested for identical twin experiments, where the surface data and the stability profile (N2) were taken from a numerical ocean model. The root mean square error (RMSE) between isQG and model density anomalies lie within the error bars of model density anomaly in most of the levels. The impact of expected errors in synthetic observations of SST and SSS on isQG retrieved density anomalies was studied and it is found that error in SSS results in greater RMSE in isQG density anomaly profile in the Arabian sea than in the Bay of Bengal. isQG method is then applied to the satellite observations of SST, SSS and SSH and retrieved density profiles are then compared with model outputs and in-situ observations from RAMA buoy for the year 2019. The comparison indicates seasonal dependency and effect of N2 on the performance of isQG method.

Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation

The Atlantic Meridional Overturning Circulation (AMOC), a major ocean current system transporting warm surface waters toward the northern Atlantic, has been suggested to exhibit two distinct modes of operation. A collapse from the currently attained strong to the weak mode would have severe impacts on the global climate system and further multi-stable Earth system components. Observations and recently suggested fingerprints of AMOC variability indicate a gradual weakening during the last decades, but estimates of the critical transition point remain uncertain. Here, a robust and general early-warning indicator for forthcoming critical transitions is introduced. Significant early-warning signals are found in eight independent AMOC indices, based on observational sea-surface temperature and salinity data from across the Atlantic Ocean basin. These results reveal spatially consistent empirical evidence that, in the course of the last century, the AMOC may have evolved from relatively stable conditions to a point close to a critical transition. The Atlantic Meridional Overturning Circulation (AMOC) is currently strong, but transition to its weak mode could see significant changes in the climate system. This work presents an observation-based early-warning system for such transitions and shows that the AMOC may be approaching a transition.

THE PERFORMANCE SEA SURFACE TEMPERATURE ANOMALY OVER PACIFIC OCEAN AND NIÑO 3.4 AREA BY USING INTERMEDIATE COUPLED MODEL (ICM)

In this research, climate variability is discussed as the main important problem in the world. Because the extreme event has impacted the naturally changed and the results of the naturally changed are many severe damages varying accordingly in a wide range of territory over the world. These damages include storms, sea-level rise, floods, and droughts. The El Nino-Southern Oscillation (ENSO) is one of the primaries of the climate variability under current concern. Therefore, the aim of this research is to study the mechanism of Sea Surface Temperature Anomaly (SSTA) during 2011–2020 and simulate SSTA over the Pacific Ocean and Nino 3.4 area in advance. The results of ICM are compared with the Extended Reconstructed Sea Surface Temperature (ERSST) observation dataset. Through time series analysis, the result from the ICM model can capture the highest value in 2016, similar to the ERSST observation. However, in the time series pattern, the results from Case I (simulation six months in advance) exhibited a good trend than Case II (simulation 12 months in advance). In statistical analysis, the values from statistical analysis, the statistical values (R, RMSE, ME, and MAE) of six months in advance revealed a good accuracy value from 12 months in advance. In conclusion, the ICM model showed high-performance results indicating simulation of SSTA over the Pacific Ocean and Nino 3.4 area, especially in the case of six months in advance.

The sudden stratospheric warming in January 2021

Using the ERA5 reanalysis, sea surface temperature and sea ice observations, and the real-time multivariate Madden–Julian index, this study explores a sudden stratospheric warming (SSW) in January 2021, its favorable conditions, and the near surface impact. Wavenumbers 1 and 2 alternately contributed to the total eddy heat flux from mid-December 2020 to late January 2021, and the wavenumber 2 during the onset period nearly split the stratospheric polar vortex. In mid-December 2020 and during the 2021 New Year period (1–5 January 2021), a blocking developed over the Urals, which enhanced the local ridge and the climatological wavenumber 2. Composite results confirm that the Arctic sea ice loss in autumn and La Niña favor the deepening of the high latitude North Pacific low and the increase of the Urals height ridge, which together enhance the planetary waves and hence disturb the stratospheric polar vortex. However, the Madden–Julian oscillation (MJO) in the tropics was dormant in mid-to-late December 2020 and early January 2021, and the well-established statistical relationship between the MJO convection over the western Pacific and the SSW is not applicable to this special case. The cold air outbreak in China during the 2021 New Year period before the January 2021 SSW onset is not explained by the SSW signal which developed in the stratosphere. In contrast, the downward-propagating signal reached the near surface in mid-February 2021, which may contribute to the cold air outbreak in US and may help to explain the extreme coldness of Texas in middle February.

Inversion of Initial Field Based on a Temperature Transport Adjoint

The setting of initial values is one of the key problems in ocean numerical prediction, with the accuracy of sea water temperature (SWT) simulation and prediction greatly affected by the initial field quality. In this paper, we describe the development of an adjoint assimilation model of temperature transport used to invert the initial temperature field by assimilating the observed data of sea surface temperature (SST) and vertical temperature. Two ideal experiments were conducted to verify the feasibility and validity of this method. By assimilating the “observed data”, the mean absolute error (MAE) between the simulated temperature data and the “observed data” decreased from 1.74 °C and 1.87 °C to 0.13 °C and 0.14 °C, respectively. The spatial distribution of SST difference and the comparison of vertical data also indicate that the regional error of vertical data assimilation is smaller. In the practical experiment, the monthly average temperature field provided by World Ocean Atlas 2018 was selected as background filed and optimized by assimilating the SST data and Argo vertical temperature observation data, to invert the temperature field at 0 a.m. on 1 December 2014 in the South China Sea. Through data assimilation, MAE was reduced from 1.29 °C to 0.65 °C. In terms of vertical observations data comparison and SST spatial distribution, the temperature field obtained by inversion is in good agreement with SST and Argo observations.

Ocean satellite data assimilation using the implicit equal-weights variational particle smoother

The implicit equal-weights variational particle smoother (IEWVPS) is a combination of the particle filter (PF) and weak-constraint 4-dimensional variational (4D-Var) method, that inherits the merits of both. The IEWVPS avoids the filter degeneracy of particle filters through an implicit equal-weights scheme and reduces the root mean square deviations (RMSDs) by introducing the 4D-Var method. This method has been tested using the Lorenz 96 model in a previous study, and we now implement it in the Regional Ocean Model System (ROMS), which is a realistic and complex ocean model. Two key problems, the representation of the analysis error covariance and the choice of the parameter α, were solved during this implementation. With an eddy-permitting model, satellite-based sea surface height (SSH) and sea surface temperature (SST) observations were assimilated with a set of 40 particles IEWVPS scheme. Compared with the ensemble 4D-Var method, the IEWVPS can reduce the bias introduced by perturbed atmospheric forcing, effectively improving temperature simulations in the upper 50 m while maintaining the RMSD of SSH at the same level. Therefore, the cooling effect caused by typhoons in the upper ocean is better characterized under the IEWVPS scheme than with previously used method. The ratio of RMSD to the ensemble spread indicates that the ensemble quality of the IEWVPS is much better than that of the ensemble 4D-Var. In addition, the computational cost of the IEWVPS is only slightly larger than that of the ensemble 4D-Var. One additional tangent linear model integration, one additional nonlinear model integration, and perturbation fields inputs/outputs are still needed.

Correcting Observational Biases in Sea Surface Temperature Observations Removes Anomalous Warmth during World War II

AbstractMost historical sea surface temperature (SST) estimates indicate warmer World War II SSTs than expected from forcing and internal climate variability. If real, this World War II warm anomaly (WW2WA) has important implications for decadal variability, but the WW2WA may also arise from incomplete corrections of biases associated with bucket and engine room intake (ERI) measurements. To better assess the origins of the WW2WA, we develop five different historical SST estimates (reconstructions R1–R5). Using uncorrected SST measurements from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) version 3.0 (R1) gives a WW2WA of 0.41°C. In contrast, using only buckets (R2) or ERI observations (R3) gives WW2WAs of 0.18° and 0.08°C, respectively, implying that uncorrected biases are the primary source of the WW2WA. We then use an extended linear-mixed-effect method to quantify systematic differences between subsets of SSTs and develop groupwise SST adjustments based on differences between pairs of nearby SST measurements. Using all measurements after applying groupwise adjustments (R4) gives a WW2WA of 0.13°C [95% confidence interval (c.i.): 0.01°–0.26°C] and indicates that U.S. and U.K. naval observations are the primary cause of the WW2WA. Finally, nighttime bucket SSTs are found to be warmer than their daytime counterparts during WW2, prompting a daytime-only reconstruction using groupwise adjustments (R5) that has a WW2WA of 0.09°C (95% c.i.: −0.01° to 0.18°C). R5 is consistent with the range of internal variability found in either the CMIP5 (95% c.i.: −0.10° to 0.10°C) or CMIP6 ensembles (95% c.i.: −0.11° to 0.10°C). These results support the hypothesis that the WW2WA is an artifact of observational biases, although further data and metadata analyses will be important for confirmation.

The Effect of Atmosphere-Ocean Coupling on the Structure and Intensity of Tropical Cyclone Bejisa in the Southwest Indian Ocean

A set of numerical simulations is relied upon to evaluate the impact of air-sea interactions on the behaviour of tropical cyclone (TC) Bejisa (2014), using various configurations of the coupled ocean-atmosphere numerical system Meso-NH-NEMO. Uncoupled (SST constant) as well as 1D (use of a 1D ocean mixed layer) and 3D (full 3D ocean) coupled experiments are conducted to evaluate the impact of the oceanic response and dynamic processes, with emphasis on the simulated structure and intensity of TC Bejisa. Although the three experiments are shown to properly capture the track of the tropical cyclone, the intensity and the spatial distribution of the sea surface cooling show strong differences from one coupled experiment to another. In the 1D experiment, sea surface cooling (∼1 °C) is reduced by a factor 2 with respect to observations and appears restricted to the depth of the ocean mixed layer. Cooling is maximized along the right-hand side of the TC track, in apparent disagreement with satellite-derived sea surface temperature observations. In the 3D experiment, surface cooling of up to 2.5 °C is simulated along the left hand side of the TC track, which shows more consistency with observations both in terms of intensity and spatial structure. In-depth cooling is also shown to extend to a much deeper depth, with a secondary maximum of nearly 1.5 °C simulated near 250 m. With respect to the uncoupled experiment, heat fluxes are reduced from about 20% in both 1D and 3D coupling configurations. The tropical cyclone intensity in terms of occurrence of 10-m TC wind is globally reduced in both cases by about 10%. 3D-coupling tends to asymmetrize winds aloft with little impact on intensity but rather a modification of the secondary circulation, resulting in a slight change in structure.

Investigating the Response of Temperature and Salinity in the Agulhas Current Region to ENSO Events

The Agulhas Current is a critical component of global ocean circulation and has been observed to respond to El Niño Southern Oscillation (ENSO) events via its temperature and salinity signatures. In this research, we use sea surface salinity (SSS) from the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) satellite, sea surface temperature (SST) observations from the Canadian Meteorological Centre (CMC), sea surface height (SSH) anomalies from altimetry, and the Oceanic Niño Index to study the SMAP satellite time period of April 2015 through March 2020 (to observe full years of study). We see warming and high salinities after El Niño, cooling and fresher surface waters after La Niña, and a stronger temperature response than that of salinity. About one year after the 2015 El Niño, there is a warming of the entire region except at the Antarctic Circumpolar Current. About two years after the event, there is an increase in salinity along the eastern coast of Africa and in the Agulhas Current region. About two years after the 2016 and 2018 La Niñas, there is a cooling south of Madagascar and in the Agulhas Current. There are no major changes in salinity seen in the Agulhas Current, but there is a highly saline mass of water west of the Indonesian Throughflow about two years after the La Niña events. Wavelet coherence analysis finds that SSS and ENSO are most strongly correlated a year after the 2015 El Niño and two years after the 2016 La Niña.