Sensed Sea Surface Temperature Research Articles

Estimation of sea surface salinity from MODIS-Aqua data in the northern South China Sea

Sea Surface Salinity (SSS) is a key variable in physical oceanography and biogeochemical research. Microwave remote sensing technology has been used to monitor SSS in open ocean areas. However, the limited spatial resolution and suboptimal precision exhibited by microwave remote sensing derived SSS data have led to the omission of crucial oceanic information, consequently constraining its utilization. In this study, an artificial neural network (ANN) model for the regional SSS inversion was constructed utilizing in situ SSS measurements in the northern South China Sea and remote sensing sea surface temperature and reflectance at wavelengths of 412, 443, 488, 555 and 667 nm derived from MODIS-Aqua satellite Level 2 data. The model showed a good performance of root mean square error (RMSE) of 0.32 and 0.34 for the model training and testing datasets, with mean bias (MB) of 0 and 0.01, and correlation coefficients (R2) reaching 0.93. The model validation indicated that the ANN model reproduced the spatial distribution of SSS highly consistent with the measured SSS (RMSE = 2.75, MB = −1.67, R2 = 0.95), demonstrating its strong applicability in the NSCS. In contrast to other local SSS inversion models, our ANN model clearly showed the fine-scale SSS feature under the intrusion of Pearl River plume near to Dongsha Island with the help of anticyclonic-cyclonic eddies. This prediction closely aligned with the observations from the Soil Moisture Active Passive SSS dataset. The ANN model was applied to MODIS-Aqua data to generate the monthly SSS from 2003 to 2022. A decreasing SSS trend in SSS of 0.02 psu y−1 during 2005–2013 and 2017–2020 and a slightly increasing SSS trend of 0.01 psu y−1 from 2013 to 2017 were observed. Further analysis shows the low-frequency variability of SSS is significantly correlated with the Pacific Decadal Oscillation.

Real-time estimation of underwater sound speed profiles with a data fusion convolutional neural network model

Sound speed profiles (SSPs) characterize the spatial distribution of sound velocity, significantly impacting the trajectory of acoustic signals. Consequently, they play a crucial role in determining the efficiency and precision of communication, localization, and other practical applications. Recently, SSP inversion methods significantly improve real-time performance compared to the direct measurement method, but it is highly dependent on sonar observation data so that the application scope is limited. To obtain real-time sound speed distribution in larger scale areas, we proposes a data-fusion-driven multi-input multi-output convolutional regression neural network (DF-MIMO-RCNN) , which combines satellite-based real-time remote sensing sea surface temperature (SST), historical SSP feature vector, historical mean temperature below the sea surface with corresponding spatial coordinate information. The model gets rid of the dependence on sonar observation data and can be applied to a wider spatial region. Then two sets of experiments were conducted based on the Argo database and marine experimental data respectively. Experimental results based on Argo data show that the proposed method is not only suitable for deep-sea environments at the same depth, but even for shallow water of different depths. At the same time, compared with other algorithms, the root mean square error (RMSE) and RMSE distributions are smaller and more robust. Finally, we conducted deep-sea experiments in the South China Sea. The results show that even in non-strictly gridded sea areas, our proposed model still has lower RMSE and higher robustness, which demonstrates the superiority of our proposed algorithm.

Interannual Variations in the Summer Coastal Upwelling in the Northeastern South China Sea

This study scrutinizes interannual (2003–2023) variations in coastal upwelling along the Guangdong Province during summers (June–August) in the northeastern South China Sea (NESCS) by comprehensively applying the moderate-resolution imaging spectroradiometer (MODIS) remote sensing sea surface temperature (SST) and chlorophyll concentration (CHL) data and the model reanalysis product. The results show that SST and upwelling intensity in the sea area have significant (p < 0.05) rising trends in the last 21 years. The CHL shows an upward but insignificant trend, which is affected simultaneously by the rise in SST and the enhancement of upwelling. Further analysis reveals that the interannual variations in upwelling are robustly related to the wind fields’ variations in the coastal region. A clockwise/counter-clockwise anomaly in the wind field centered on the NESCS facilitates alongshore/onshore winds near the Guangdong coast, which can strengthen/weaken coastal upwelling. Based on the correlation between wind field variations and large-scale climate factors, long-term variations in the upwelling intensity can be primarily predicted by the Oceanic Niño Index.

Estimating Three‐Dimensional Structures of Eddy in the South Indian Ocean From the Satellite Observations Based on the isQG Method

AbstractBy incorporating the high‐resolution satellite remote sensing sea surface temperature (SST) with low‐resolution sea level anomaly and sea surface salinity (SSS), this study explores the reconstructability of the three‐dimensional (3D) eddy structures via the “interior + surface quasigeostrophic” (isQG) method in the South Indian Ocean. We apply the incorporation of high‐resolution SST to improve the spatial resolution of the reconstruction. We also propose a correction scheme for density reconstruction within the mixed layer to offset the absence of mixed layer dynamics in the SQG framework. Comparison against the in situ observations demonstrates a satisfactory reconstructability for subsurface velocity and density anomalies. Statistically, the zonal velocity reconstructability outperforms its meridional counterpart and the corresponding velocity phase. The reconstructed shallow‐layer velocity exhibits a superior skill in eddy‐active regions, when compared with drogued drifter observations. Reconstructed subsurface velocities reproduce the spatial structures of eddy‐induced velocity anomaly along the GO‐SHIP observation transect, although present smaller magnitudes. Results demonstrate the potential applicability of the isQG method for reconstructing mesoscale eddies, particularly in the ocean at mid‐to‐high latitudes, where subsurface dynamics are strongly influenced by barotropic and the first baroclinic modes. With the upcoming high‐advanced satellite observations, the isQG framework is expected to achieve better subsurface estimations.

Cross-shelf transport off the northern Taiwan, East China Sea

Cross-shelf transport is an important material exchange process between the shelf seas and open seas. The distribution characteristics of phytoplankton, surface sediment particle size, minerals, etc., all indicate the possible existence of a cross-shelf transport conduit from the inner shelf to the outer shelf off northern Taiwan. This study confirmed cross-shelf transport off northern Taiwan based on remote sensing sea surface temperature (SST) data and heat flux data, which can be divided into two patterns: cold eddy and cold water tongue. Furthermore, combined with AVISO sea surface level data, CCMP wind field data, the dynamic formation factors of different morphological characteristics of cross-shelf transport are studied.Topographic changes mainly induce cross-shelf transport off northern Taiwan and occur at the curve of isobath to deep water. When there is a strong thermal front along the coast, cross-shelf transport is more likely to occur. Against the steady, strong northerly wind, the Zhe-Min coastal current's southward flow and Kuroshio's invasion strengthened the temperature front and facilitated cross-shelf transport. Typhoon strengthened cross-shelf transport over northern Taiwan after passing through the southern part of the study area. This research has important implications to find the channel and transporting pattern for material exchange between the East China Sea and the West Pacific, which will contribute to the study of ecological environmental protection and seabed muddy area formation.

Remote Sensing Analysis of Typhoon-Induced Storm Surges and Sea Surface Cooling in Chinese Coastal Waters

Inthis study, remote sensing measurements were utilized to examine the characteristics of storm surges and sea surface cooling in Chinese coastal waters caused by typhoons. Altimetric data from satellite altimeters were used to determine the magnitude, cross-shelf decaying scale, and propagating speed of storm surges from typhoons. The results were in agreement with estimates obtained from a theoretical model and tide gauge data, showing that the two storm surges propagated as continental shelf waves along the southeastern coast of China. The sea surface cooling, driven by Typhoons 1319Usagi and 1323Fitow, was analyzed using the remote sensing sea surface temperature product, named the global 1 km sea surface temperature (G1SST) dataset, revealing a considerable decrease in the temperature, with the largest decrease reaching 4.5 °C after the passage of 1319Usagi, in line with buoy estimates of 4.6 °C. It was found that 1323Fitow and 1324Danas jointly impacted the southeastern coast of China, resulting in a significant temperature drop of 4.0 °C. Our study shows that incorporating remotely sensed measurements into the study of oceanic responses to typhoons has significant benefits and complements the traditional tide gauge network and buoy data.

Comparing eight remotely sensed sea surface temperature products and Bayesian maximum entropy-based data fusion products

Sea surface temperature is an important oceanographic physical attribute. In this work, 5 kinds of infrared SST data sets (including FY3C-VIRR, MetopB-AVHRR3, MODIS-Aqua, MODIS-Terra, SNPP-VIIRS) and 3 kinds of microwave SST data sets (including AMSR-2, GMI, WindSat) were considered for fusion purposes. The area with geographical coordinates ranging from 100° W to 180° W and from 10° N to 10° S was chosen as the study area. Using the Bayesian maximum entropy (BME) method, four SST fusion products were generated from January 1 to January 31, 2020, with temporal resolution of 1 day, spatial resolution of 0.25 decimal degrees, and coverage rate of 100%. The Holdout cross-validation method was employed to evaluate the performance of BME for SST interpolation purposes by using two data sets, i.e., WinSat SST and MODIS-Terra SST data; and the validation results showed that the mean absolute error (MAE) values of the WindSat SST data and the MODIS-Terra SST data were 0.117 mg/m3 and 0.191 mg/m3, respectively, and the root mean square error (RMSE) values were 0.158 mg/m3 and 0.321 mg/m3, respectively. In addition, four SST interpolation products were compared with the Optimum Interpolation Sea Surface Temperature (OISST) and the Group for High-Resolution Sea Surface Temperature (GHRSST) products by combining Argo data with Buoy data treated as in-situ measured data sets. Three (MW-WindSat SST product, ALL-Terra SST product and ALL-WindSat SST product) of the four interpolation products performed better than the OISST and GHRSST data sets. Among them, the ALL-WindSat SST product considering WindSat SST data and other 7 SST data as hard and soft data exhibited the best performance in terms of all three indicators: the coefficient of determination value (0.744), the MAE value (0.82 mg/m3) and the RMSE value (1.082 mg/m3). In sum, the present work made full use of the inclusiveness and integration abilities of the BME method for various types of data, and demonstrated its high accuracy in a number of real world data, thus demonstrating its considerable potential in the context of marine data interpolation and fusion.

A Hybrid ENSO Prediction System Based on the FIO−CPS and XGBoost Algorithm

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO−CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO−CPS−HY. The latest 15 years (2007–2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Niño3.4 index from FIO−CPS v2.0 to FIO−CPS−HY for 7− to 13−month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 °C to 0.65 °C), respectively. The real−time predictions from FIO−CPS−HY indicated that the sea surface state of the Niño3.4 area would likely be in neutral conditions in 2023. Although FIO−CPS−HY still has some biases in real−time prediction, this study provides possible ideas and methods to enhance short−term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications.

Hidden heatwaves and severe coral bleaching linked to mesoscale eddies and thermocline dynamics

The severity of marine heatwaves (MHWs) that are increasingly impacting ocean ecosystems, including vulnerable coral reefs, has primarily been assessed using remotely sensed sea-surface temperatures (SSTs), without information relevant to heating across ecosystem depths. Here, using a rare combination of SST, high-resolution in-situ temperatures, and sea level anomalies observed over 15 years near Moorea, French Polynesia, we document subsurface MHWs that have been paradoxical in comparison to SST metrics and associated with unexpected coral bleaching across depths. Variations in the depth range and severity of MHWs was driven by mesoscale (10s to 100s of km) eddies that altered sea levels and thermocline depths and decreased (2007, 2017 and 2019) or increased (2012, 2015, 2016) internal-wave cooling. Pronounced eddy-induced reductions in internal waves during early 2019 contributed to a prolonged subsurface MHW and unexpectedly severe coral bleaching, with subsequent mortality offsetting almost a decade of coral recovery. Variability in mesoscale eddy fields, and thus thermocline depths, is expected to increase with climate change, which, along with strengthening and deepening stratification, could increase the occurrence of subsurface MHWs over ecosystems historically insulated from surface ocean heating by the cooling effects of internal waves.

Marine Heatwaves in the South China Sea: Tempo-Spatial Pattern and Its Association with Large-Scale Circulation

A marine heatwave (MHW) can significantly harm marine ecosystems and fisheries. Based on a remotely sensed sea surface temperature (SST) product, this study investigated MHWs behaviors in the South China Sea (SCS) throughout the warm season (May to September) from 1982 to 2020. The distributions of the three MHW indices used in this study showed significant latitudinal variations: more frequent, longer, and more intense MHWs appear in the northern SCS, and less frequent, shorter, and weaker MHWs appear in the southern SCS. Using the empirical orthogonal function (EOF) method, we found that the first leading modes of the three MHW indices account for more than half of the total variance. The first leading modes reveal uniform anomalies throughout the SCS, with the maximum in the deep central portion and its surroundings. Their corresponding time series showed significant interdecadal variations, with a turning point around 2009. Since 2010, the SCS has seen an increase in the frequency, length, and severity of MHWs. The incidence of MHWs has been linked to the presence of stable near-surface anticyclonic anomalies, which reduced cloud cover and increased solar radiation. This abnormal pattern was usually accompanied by the intensification and westward shift of the western North Pacific subtropical high (WNPSH). The findings imply that MHWs in the SCS may be predictable on interannual and decadal scales.

On the Reconstruction of Missing Sea Surface Temperature Data from Himawari-8 in Adjacent Waters of Taiwan Using DINEOF Conducted with 25-h Data

Satellite remote sensing sea surface temperature (SST) data are lost due to cloud cover. Missing data often cause inconvenience in subsequent applications and thus need to be reconstructed. In this study, the Data Interpolating Empirical Orthogonal Function (DINEOF) method was used to reconstruct the hourly SST data missing from the Himawari-8 satellite in the waters near Taiwan. The SST characteristics in the waters around Taiwan are quite complex, with high SST at Kuroshio in the east of Taiwan and great variation in the SST west of Taiwan due to the influence of tides. Therefore, the analysis with DINEOF was conducted using 25-h data to match the tidal cycle. The influence of SST characteristics on the accuracy of SST reconstruction is also discussed. The results show that in the western sea area where the variation of SST is large, the average root-mean-square error of SST between the original SST and the reconstructed SST is the lowest and the average coefficient of determination is the highest. The accuracy of the reconstructed SST is positively correlated with the SST variation. Furthermore, the statistical results also show that the DINEOF method can effectively reconstruct the SST regardless of the missing data rate.

Spatial and temporal variability and connectivity of the marine environment of the South Sandwich Islands, Southern Ocean

The South Sandwich Islands form the eastern boundary to the highly biologically productive Scotia Sea in the southwest Atlantic sector of the Southern Ocean and are part of a large Marine Protected Area. The South Sandwich Islands have a complex marine environment that is influenced by both the Antarctic Circumpolar Current and the Weddell Gyre, and seasonal sea ice. Here we investigate the local and regional dynamics and variability of the ocean and sea ice to inform management of the region. Remotely sensed sea surface temperature (SST), sea ice concentration and chlorophyll a data from 2009 to 2021 are used to define the mean seasonal cycle in the environment and the associated temporal and spatial variability. While sea surface temperature and sea ice have a clearly defined seasonality, local chlorophyll blooms are irregular in timing, location and magnitude. Interannual variability in summer SST is strongly positively correlated along the island arc. The islands experience very different winter sea ice conditions from year to year, with marked variability in sea ice distribution and duration. Surface chlorophyll blooms develop in most years close to the island arc, but there is little spatial consistency and there are years where blooms are not observed. The timing and pattern of sea ice retreat appears to be a key driver in the formation of chlorophyll blooms, with their propagation affected by local circulation, but additional local processes are also important. Trajectories of near-surface satellite tracked surface buoys and Argo floats, together with an analysis of sea surface height output from a global reanalysis product, demonstrate the connectivity of the South Sandwich Islands to the wider regional marine system. Enhanced current flows around and between the South Sandwich Islands are likely to affect the transport and exchange of material along the island arc. The South Sandwich Islands are connected with the Scotia and Weddell seas, with contribution from the different regions varying according to latitude along the island arc. There are also connections with islands downstream including Bouvet, Crozet and Kerguelen Islands and seamounts, with possible return flow via the Weddell Gyre. Our analyses indicate that accounting for the complexity and variability in the South Sandwich Islands marine environment will be crucial in the development of conservation and fisheries management procedures.

4D-Var data assimilation using satellite sea surface temperature to improve the tidally-driven interior ocean dynamics estimates in the Indo-Australian Basin

Estimating ocean processes in regions with sparse observations, and where dynamics are dominated by strong barotropic and baroclinic tides, poses a significant modelling challenge. In this study, a Four-Dimensional Variational (4D-Var) data assimilation method, incorporating remotely sensed sea surface temperature (SST) data observations only, was used to improve estimates of the internal tide dynamics in the ocean between Australia and Indonesia. In this region, local internal tide generation, propagation and interference of the internal tides is highly variable and dependent on the 3D density field and mesoscale dynamics. The results of this study show that assimilation of remotely sensed SST data not only reduced the surface temperature root mean squared error (RMSE) from 0.8 °C to 0.3 °C, and removed a 0.4 °C cold bias (compared to a non-assimilative model), but also improved estimates of through-water-column variables — with the latter evaluated against ARGO profiling floats and two independent mooring sites. Overall, the depth-averaged temperature RMSE was reduced by as much as 38%, and ocean currents by ∼20% at the two mooring sites. The improvements were most significant in the top 100 m, where the internal tide signature was significantly corrected despite no depth-varying variables being assimilated. The study also showed that assimilating 2 km resolution SST observations resulted in more realistic SST wavenumber power spectral density estimates than those without assimilating data; in particular, the scheme reduced the energy at mesoscale length scales and improved the spectral slope towards the smaller submesoscale dynamics. It is concluded that 4D-Var assimilation using only SST improves estimates of upper 100 m ocean dynamics in tropical regions where there is sensitivity to the air–sea fluxes.

Coping with El Niño: phenotypic flexibility of reproductive traits in red squat lobster determines recruitment success

Abstract Many organisms display changes in behaviour and life-history traits when facing variabilities in environmental conditions. A subset of these changes comprises reversible within-individual variations, known as phenotypic flexibility. Using red squat lobster (Pleuroncodes monodon) individuals harvested at the Humboldt Current Ecosystem (HCE), we evaluated how changes in habitat temperatures associated with warm El Niño (EN) conditions and cold La Niña conditions induce phenotypic flexibility in reproductive traits and how this flexibility affects recruitment success. The biological data were obtained from swept area surveys conducted between 2015 and 2020. Remotely sensed sea surface temperature (SST) data were used to compute anomalies (SSTA). Our results showed that females facing warmer environmental conditions carried more eggs with smaller sizes, and under cold conditions, females carried fewer eggs with larger sizes. The recruitments lagged by 1 year correlated positively with the egg density and negatively with the egg size. Our evidence shows that for phenotypic flexibility to be expressed in recruitment success, the warm conditions experienced by females should match good food availability for the planktonic stages. We discussed how climate change predictions for HCE will amplify responses of the reproductive traits of red squat lobster with strong impacts on recruitment likely.

Relationships between environmental variables and spatial and temporal distribution of jack mackerel (Trachurus japonicus) in the Beibu Gulf, South China Sea.

The jack mackerel (Trachurus japonicus) is both a dominant pelagic fish species and an important fishing target in the Beibu Gulf, South China Sea. However, the resource status of this species fluctuates dramatically, and it has recently been added to a “red list” of threatened species of the International Union for Conservation of Nature (IUCN). Despite its economic importance and decreasing population status, limited research on its spatiotemporal distribution has been undertaken over the last decades. In order to evaluate the most crucial factors that influence the spatiotemporal variability of T. japonicus and to determine GAM performance and predictability, we analyze catch per unit effort (CPUE) of T. japonicus from Beibu Gulf over four seasons (months) from 2013 to 2014. A generalized additive model (GAMs) is populated with water depth and remotely sensed sea surface temperature (SST), sea surface salinity (SSS), sea surface chlorophyll-a concentration (Chl-a) and sea level anomaly (SLA). The CPUE of T. japonicus varies seasonally, with higher CPUE in summer and autumn than in spring and winter, and the highest CPUE in summer. GAM results explain 57% of the deviation explained in CPUE, with the most important variables being SLA, Month, Depth, SSS, and SST , each explaining 21.2%, 18.7%, 10.7%, 5.1%, and 1.3% of the variation in CPUE, respectively. This species occurs mainly between 50 and 75 m depth, SSS values 32.3–33.5 PSU and SST 25–30.5 °C. High CPUE sites occur near SLA ≤ 0 m, on the edge of cold eddies, and there is a certain catch near the sea surface with SLA ≥ 0 m. The spatial and temporal distribution of T. japonicus is affected by the season and the marine hydrological environment. This study might contribute to a better understanding of the distributional patterns of T. japonicus as well as provide a basis for sustainable management in the Beibu Gulf.

Characterization of fronts in the Western Mediterranean with a special focus on the North Balearic Front

We focus on the characterization of thermohaline fronts in the Western Mediterranean, with a particular focus on the North Balearic Front (NBF), separating the Atlantic Waters that spread into the Algerian Basin from the saltier and colder waters of the Liguro-Provençal area. We use a simple gradient-based method of front detection applied to a 20-year (from June 1993 to June 2013) reanalysis of the Mediterranean. Statistics of daily frontal indices are used to identify areas of recurrent fronts, i.e., frontal zones. Comparisons with data from glider transects and remotely sensed sea surface temperature and altimetry data have been used to validate our approach from daily to seasonal and interannual time scales. Our method yielded two co-existing frontal zones in the area of the NBF. One with an almost permanent haline surface frontal zone extending southeastward from the Balearic Islands to Sardinia, representing the northern limit of the fresher Atlantic Water that spreads via instabilities of the Algerian Current and associated Algerian Eddies. Between years, its position shifts by about 1° of latitude, possibly due to processes associated with both the deep water formation in the Provençal Basin and the spreading of southern Algerian Eddies. The second frontal zone is seasonal and thermally driven extending off the northeast of Menorca to the northwest of Corsica. The Pyrenees Front, a sharp thermal front off Cap de Creus which marks the boundary between the warm surface waters of the Balearic Sea and the cooler waters in the Gulf of Lion in late summer, appears to facilitate the formation of the aforementioned seasonal thermal front through the northeastward advection of its waters toward the West Corsican Current. The divergent eastward extensions of the two frontal zones, and their differences in nature, structure, and spatio-temporal variability, call for a revisit of the NBF appellation and a clarification of its dynamics.

Effects of Buoyancy Flux on Upper‐Ocean Turbulent Mixing Generated by Non‐Breaking Surface Waves Observed in the South China Sea

AbstractThe surface waves are the most energetic motions, which have great contribution to the turbulent mixing in the upper ocean. In this study, a novel turbulent mixing scheme is proposed in terms of the non‐breaking wave velocity shear module with the buoyancy flux. In the scheme, the mixing coefficients can be calculated empirically from the significant wave height, the wave number, the wave frequency, the buoyancy frequency, and the turbulence Prandtl number. The buoyancy fluxes, as well as the non‐breaking wave velocity shear production, are important for the upper‐ocean turbulent mixing, and make the calculated turbulence dissipation rate closer to in situ observations, which are provided by the “Responses of Marine Hazards to Climate Change in the Western Pacific (ROSE)” Project. The effects of the buoyancy flux on the turbulent mixing are examined based on three numerical experiments using the MASNUM ocean model. In the experiments, the topography, the lateral boundaries, initialization conditions, and the surface forcing fluxes are taken from the GEBCO, HYCOM/NCODA, and ERA5 data, respectively. Comparing to the AVHRR remote sensing sea surface temperature (SST) products and in situ observations, the simulated results with the non‐breaking wave‐generated turbulent mixing gain significant improvement in the SST, upper‐ocean temperature structure, and the mixed layer compared with the classic Mellor‐Yamada scheme. The results show that the buoyancy flux is able to suppress the enhanced non‐breaking wave‐generated turbulent mixing, so that the improved model simulates the observations better than that without the buoyancy effects.

Spatiotemporal distribution of Decapterus maruadsi in spring and autumn in response to environmental variation in the northern South China Sea

Understanding relationships between the spatiotemporal variability of fishery resources and marine environmental variables forms the basis of effective development and utilization of the northern South China Sea (NSCS) fishery resources. Decapterus maruadsi is one important pelagic economic fish which inhabits the coastal warm waters of the NSCS. We examine the spatiotemporal distribution and central of gravity (CoG) of D. maruadsi in the NSCS in spring and autumn from 2015 to 2017 with generalized additive models (GAMs), using fishing trawl data, and remote sensed sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), chlorophyll a concentration (Chl-a), and the sea level anomaly (SLA) data. During spring D. maruadsi occurs mainly in a small area from 110.5–111 °E and 18.25–19.25 °N, at about 72 m, SST 24.4–26.8 °C, SSS 32.9–34.0 PSU, and SLA -0.04 to 0.04 m. During autumn these ranges shift to 112.25–114 °E and 20.25–21.25 °N, 62 m, 28.5–28.8 °C, and 33.4–34.1 PSU, respectively (D. maruadsi was insensitive to SLA during autumn). Accordingly, we deem the most important variables affecting the distribution of this species to be water depth and SSS in spring and SSS in autumn. CPUE of D. maruadsi was high in autumn and low in spring, with the CoG shifting from spring to autumn northwards by 0.4 °N and eastwards by 0.7 °E. This shift is likely influenced by this species’ life habits. Higher D. maruadsi CPUE in 2016 could partly be associated with La Nina and Typhoon climate anomalies. Our results provide a basis for scientific assessment and effective conservation of NSCS fishery resources.

Projected shifts in loggerhead sea turtle thermal habitat in the Northwest Atlantic Ocean due to climate change

It is well established that sea turtles are vulnerable to atmospheric and oceanographic shifts associated with climate change. However, few studies have formally projected how their seasonal marine habitat may shift in response to warming ocean temperatures. Here we used a high-resolution global climate model and a large satellite tagging dataset to project changes in the future distribution of suitable thermal habitat for loggerheads along the northeastern continental shelf of the United States. Between 2009 and 2018, we deployed 196 satellite tags on loggerheads within the Middle Atlantic Bight (MAB) of the Northwest Atlantic continental shelf region, a seasonal foraging area. Tag location data combined with depth and remotely sensed sea surface temperature (SST) were used to characterize the species’ current thermal range in the MAB. The best-fitting model indicated that the habitat envelope for tagged loggerheads consisted of SST ranging from 11.0° to 29.7 °C and depths between 0 and 105.0 m. The calculated core bathythermal range consisted of SSTs between 15.0° and 28.0 °C and depths between 8.0 and 92.0 m, with the highest probability of presence occurred in regions with SST between 17.7° and 25.3 °C and at depths between 26.1 and 74.2 m. This model was then forced by a high-resolution global climate model under a doubling of atmospheric CO2 to project loggerhead probability of presence over the next 80 years. Our results suggest that loggerhead thermal habitat and seasonal duration will likely increase in northern regions of the NW Atlantic shelf. This change in spatiotemporal range for sea turtles in a region of high anthropogenic use may prompt adjustments to the localized protected species conservation measures.

Statistical Validation of MODIS-Based Sea Surface Temperature in Shallow Semi-Enclosed Marginal Sea: A Comparison between Direct Matchup and Triple Collocation

Validating remotely sensed sea surface temperature (SST) is a fundamental step in establishing reliable biological/physical models that can be used in different marine applications. Mapping SST using accurate models would assess in understanding critical mechanisms of marine and coastal zones, such as water circulations and biotic activities. This study set out to validate MODIS SSTs with a spatial resolution of 1-km in the Arabian Gulf (24–30° N, 48–57° E) and to assess how well direct comparison of dual matchups and triple collocation analyses perform. For the matchup process, three data sets, MODIS-Aqua, MODIS-Terra, and iQuam, were co-located and extracted for 1-pixel box centered at each actual in situ measurement location with a time difference window restricted to a maximum of ±3 h of the satellite overpass. Over the period July 2002 to May 2020, the MODIS SSTs (N = 3786 triplets) exhibited a slight cool night-time bias compared to iQuam SSTs, with a mean ± SD of −0.36 ± 0.77 °C for Aqua and −0.27 ± 0.83 °C for Terra. Daytime MODIS SST observations (N = 5186 triplets) had a lower negative bias for both Aqua (Bias = −0.052 °C, SD = 0.93 °C) and Terra (Bias = −0.24 °C, SD = 0.90 °C). Using extended triple collocation analysis, the statistical validation of system- and model-based products against in situ-based product indicated the highest ETC-based determination coefficients (ρt,X2 ≥ 0.98) with the lowest error variances (σε2 ≤ 0.32), whereas direct comparison underestimated the determination coefficients and overestimated the error estimates for all MODIS algorithms. The ETC-based error variances for MODIS Aqua/Terra NLSSTs were 0.25/0.19 and 0.26/0.32 in daytime and night-time, respectively. In addition, MODIS-Aqua was relatively more sensitive to the SST signal than MODIS-Terra at night and vice versa as seen in the unbiased signal-to-noise ratios for all observation types.