Inconsistent Coral Bleaching Risk Indicators Between Temperature Data Sources

In recent decades, satellites have played a pivotal role in observing ocean dynamics, providing diverse datasets with varying spatial resolutions. Notably, within these datasets, sea surface height (SSH) data typically exhibit low resolution, while sea surface temperature (SST) data have significantly higher resolution. This study introduces a Transfer Learning-enhanced Generative Adversarial Network (TLGAN) for reconstructing high-resolution SSH fields through the fusion of heterogeneous SST data. In contrast to alternative deep learning approaches that involve directly stacking SSH and SST data as input channels in neural networks, our methodology utilizes bifurcated blocks comprising Residual Dense Module and Residual Feature Distillation Module to extract features from SSH and SST data, respectively. A pixelshuffle module-based upscaling block is then concatenated to map these features into a common latent space. Employing a hybrid strategy involving adversarial training and transfer learning, we overcome the limitation that SST and SSH data should share the same time dimension and achieve significant resolution enhancement in SSH reconstruction. Experimental results demonstrate that, when compared to interpolation method, TLGAN effectively reduces reconstruction errors and fusing SST data could significantly enhance in generating more realistic and physically plausible results.

The Spanish surface fishery operates mainly during the summer season in the waters of the Bay of Biscay. Sea surface temperature (SST) data recovered from satellite images are being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve the understanding of the variations in catch distribution and rate needed to properly manage fisheries. The images used for retrieval of SST often present gaps due to the existence of clouds or satellite malfunction periods. The data gaps can totally or partially affect the area of interest. Within this study, an application of a technique for the reconstruction of missing data called DINEOF (data interpolating empirical orthogonal functions) is analysed, with the aim of testing its applicability in operational SST retrieval during summer months. In this case study, the Bay of Biscay is used as the target area. Three months of SST Moderate Resolution Imaging Spectroradiometer (MODIS) images, ranging from 1 May 2006 to 31 July 2006, were used. The main objective of this work is to test the overall performance of this technique, under potential operational use for the support of the fleet during the summer fishing season. The study is designed to analyse the sensitivity of the results of this technique to several details of the methodology used in the reconstruction of SST, such as the number of empirical orthogonal functions (EOFs) retained, the handling of the seasonal cycle or the length (number of images) of the SST database used. The results are tested against independent SST data from International Comprehensive Ocean–Atmosphere Data Set (ICOADS) ship reports and standing buoys and estimations of the error of the reconstructed SST fields are given. Conclusions show that over this area three months of data are enough for efficient SST reconstruction, which yields four EOFs as the optimal number needed for this case study. An extended EOF experiment with SST and SST with a lag of one day was carried out to analyse whether the autocorrelation of the SST data allows better performance in the SST reconstruction, although the experiment did not improve the results. The validation studies show that the reconstructed SSTs can be trusted, even when the amount of missing data is very high. The mean absolute deviation maps show that the error is greatest near to the coast and mainly in the upwelling areas close to the French and north-western Spanish coasts.

BackgroundThe 2023–24 El Niño event caused extreme marine heat stress and widespread coral bleaching. Coral reefs at the Reserva Biológica Isla del Caño and the northern coast of the Osa Peninsula, Costa Rica, underpin critical ecosystem services, including biodiversity conservation and marine tourism, and have previously withstood similar events with minimal coral loss. Evaluating the ecological impacts of the 2023–24 El Niño is essential to assess coral reef resilience and guide future management.MethodsCoral Reef Watch sea surface temperature (SST) data (1985–2025; CoralTemp V3.1) were used to calculate long-term SST trends and degree heating weeks (DHW). Reef surveys were conducted at nine sites between 2019 and 2025, with primary benthic composition and coral health data collected in 2024–25. Benthic cover was assessed using point-intercept, line-intercept, and quadrat methods, while coral diversity, abundance, and health were measured via belt transects. Beta regression was used to assess the effect of temperature on coral cover, and multivariate analyses, including principal component analysis (PCA) and similarity percentage analysis (SIMPER), evaluated benthic community changes and bleaching patterns. An Ecological Recovery Feasibility Index (ERFI) was developed using PCA loadings and benthic indicators to rank sites by recovery potential.ResultsSST increased significantly over the past 40 years (∼0.23 °C/decade), with the 2023–24 El Niño recording peak SST (31.2 °C). Bleaching threshold exceedance days increased, while cool days declined. Twelve coral taxa were recorded; Pocillopora spp. and Porites lobata were present at all sites. Coral diversity varied, with Cueva and Ancla highest, and San Josecito lowest. Estimated baseline bleaching prevalence was ∼23%, highest in Pocillopora spp. (33.9%). SIMPER and PCA revealed a shift from coral to algal dominance: turf algae increased by 70.62%, dead coral declined 80.71%, and coral cover fell 40.44%. Major coral declines were statistically significant at Ancla, Esquina, and Tina. Bayesian regression confirmed coral decline at Chorro, Cueva, Tina, and Ancla, alongside turf algae increases. Coral cover was higher at warmer sites, though non-temperature site-specific factors were also influential. Chorro and Esquina had the highest recovery potential; Ancla, San Josecito, and Barco Profundo the lowest.ConclusionThere is an urgent need to develop and implement a coral reef restoration strategy for Isla del Caño that addresses site-specific conditions, integrates tourism management, and promotes long-term resilience. Under continued climate change, localized, targeted restoration will be essential to maintain the ecological function of these historically resilient but increasingly vulnerable reefs in Costa Rica’s Eastern Tropical Pacific.

The sea surface temperature (SST) mapping could be performed with a wide spatial and temporal extent in a reasonable time limit. The space-borne sensor of AVHRR was widely used for the purpose. However, the current SST retrieval techniques for infrared channels were limited only for the cloud-free area, because the electromagnetic waves in the infrared wavelengths could not penetrate the cloud. Therefore, the SST availability was low for the single image. To overcome this problem, we studied to produce the composite of three day's SST map. The diurnal changes of SST data are quite stable through a short period of time if no abrupt natural disaster occurrence. Therefore, the SST data of three consecutive days with nearly coincident daily time were merged in order to create a three day's composite SST data. The composite image could increase the SST availability. In this study, we acquired the level 1b AVHRR (Advanced Very High Resolution Radiometer) images from Malaysia Center of Remote Sensing (MACRES). The images were first preprocessed and the cloud and land areas were masked. We made some modifications on the technique of obtaining the threshold value for cloud masking. The SST was estimated by using the day split MCSST algorithm. The cloud free water pixels availability were computed and compared. The mean of SST for three day's composite data were calculated and a SST map was generated. The cloud free water pixels availability were computed and compared. The SST data availability was increased by merging the SST data.

Sea surface temperature (SST) and salinity (SSS) are essential variables at the ocean and atmosphere interface when considering risk factors for disease in farmed and wild fish stocks. Ecological research has witnessed a recent trend in use of digital and satellite technologies, including remote-sensing tools. We explored spatial coverage of remotely-sensed SST and SSS data and compared them with in situ measurements of water temperatures and salinity, which led to suggested adjustments to the remotely-sensed data for its use in aquaculture research. The in situ data were from farms and wild surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. Concurrent SST and SSS values were extracted from remotely-sensed products and compared with 20,513 and 20,038 in situ records for water temperature and salinity, respectively, from 232 different sites. Among nine SST products evaluated, the UKMO OSTIA SST (UK Meteorological Office) had the highest retrieval, and highest concordance correlation coefficient (0.86), highest index of agreement (0.93), fewest missing values, and smallest mean and SD values for bias, when compared to in situ measurements. A mixed linear regression model with UKMO OSTIA SST as the predictor for in situ measurements estimated an adjustment coefficient of 0.89 °C for UKMO OSTIA SST. None of the three SSS products evaluated provided appropriate corresponding values for in situ sites, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO OSTIA SST is currently best suited for aquaculture studies in coastal BC. The near real-time availability of these data with the estimated adjustment would allow their use in forecast models, surveillance of pathogens, and the creation of risk maps.

Multi-sensor data from different satellites are used to identify an upwelling area in the sea off northeast Taiwan. Sea surface temperature (SST) data derived from infrared and microwave, as well as sea surface height anomaly (SSHA) data derived from satellite altimeters are used for this study. An integration filtering algorithm based on SST data is developed for detecting the cold patch induced by the upwelling. The center of the cold patch is identified by the maximum negative deviation relative to the spatial mean of a SST image within the study area and its climatological mean of each pixel. The boundary of the cold patch is found by the largest SST gradient. The along track SSHA data derived from satellite altimeters are then used to verify the detected cold patch. Applying the detecting algorithm, spatial and temporal characteristics and variations of the cold patch are revealed. The cold patch has an average area of 1.92 × 104 km2. Its occurrence frequencies are high from June to October and reach a peak in July. The mean SST of the cold patch is 23.8 °C. In addition to the annual and the intraseasonal fluctuation with main peak centered at 60 days, the cold patch also has a variation period of about 4.7 years in the interannual timescale. This implies that the Kuroshio variations and long-term and large scale processes playing roles in modifying the cold patch occurrence frequency.

This study tried to observe the correlation and coherency between sea surface temperature (SST) and wind velocity in the Malacca Strait at the year of 2015. The SST and wind velocity data with 6 hours interval step have been used in this study. S-Transform, the Cross Wavelet Transform, and the Wavelet Coherency were applied to observe the influence of the variation of sea surface temperature to the wind velocity in Malacca Strait. These methods could produce the phase lag and the time of occurrence between them. S-Transform was used to show the spectrum energy of the sea surface temperature variation. The strongest correlation between them has the period of 32 days during July to August and October to November at each point with significance level of 95 %. The coherency of them has the range of 4 to 64 days at each point. The last result is the spectrum energy of SST variation that has the period of 5 to 50 days at each point. It was similar to the result of the correlation and coherence period between the wind velocity and the SST data

<p>The Western Tropical Atlantic is a crucial region when it comes to understanding the CO<sub>2</sub> dynamics in the tropics, as it is subject to large inputs of freshwater from the Amazon River and the ITCZ rainfall, as well as the input of CO<sub>2</sub>-rich waters from upwelling of subsurface water. This study aims to reconstruct the surface marine carbonate system from 1998 to 2018 using sea surface temperature (SST) and sea surface salinity (SSS) data from the PIRATA buoy at 8°N 38°W and describe its variability in time. Two empirical models were used to calculate total alkalinity (TA) and dissolved inorganic carbon (DIC) from SSS. From these two parameters and SST data, it was possible to calculate pH and CO<sub>2</sub> fugacity (<em>f</em>CO<sub>2</sub>) values. Only DIC, pH and <em>f</em>CO<sub>2</sub> showed a statistically significant trend in time, where DIC showed an increase of 0.717 µmol kg<sup>-1</sup> year<sup>-1</sup>, pH decreased 0.001394 pH units year<sup>-1</sup>, and <em>f</em>CO<sub>2</sub> had an increase of 1.539 µatm year<sup>-1</sup>. Two different seasons were observed when data were analyzed: a dry season from January to June, when SSTs were lower (around 27°C) and SSS was stable around 36, matching the period when the ITCZ is over the South American continent, Amazon river plume is restricted to western shelf areas and Equatorial upwelling is more active, and a rainy season from July to December, when SSTs were higher (around 28.5°C) and SSS had higher variability (from 31 to 36), matching the period when the ITCZ is at its northern range, the Amazon plume is spread eastwards through the North Brazil Current’s retroflection and the Equatorial upwelling is less intense. Along with that, TA, DIC and pH varied positively with SSS, with higher values (TA around 2350 µmol kg<sup>-1</sup>, DIC around 2025 µmol kg<sup>-1</sup> and pH around 8.060 pH units) during dry season and lower values (TA around 2300 µmol kg<sup>-1</sup>, DIC around 1990 µmol kg<sup>-1</sup> and pH around 8.050 pH units) during rainy season. On the other hand, <em>f</em>CO<sub>2</sub> varied positively with SST, with lower values (around 385 µatm) during dry, upwelling season and higher values (around 390 µatm) during rainy season, showing that both SSS and SST variability play an important role in the CO<sub>2</sub> solubility in the region.</p>

Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) of ten years (2000-2009) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color data of 13 years (1998-2010), the Kuroshio oceanic front exists between two entirely different water masses in the East China Sea, namely, the Kuroshio and continental shelf waters. The Kuroshio thermal front (derived from the SST data) disappears in summer due to the spatially uniform surface heating, whereas the Kuroshio ocean color front in summer is distinct and exhibits the same spatial pattern along the shelf break of the deep Okinawa Trough as in winter. The Kuroshio thermal front has one bifurcation at about (126° E, 28° N) and extends northward toward the Cheju Island lasting from December to March, but this kind of branch is absent in the ocean color data.

<p indent="0mm">Sea surface temperature (SST) is a crucial physical parameter in meteorology and oceanography. This study demonstrates that the influence of earth incidence angle (EIA) on the SST retrieved from the microwave radiation imager (MWRI) onboard FengYun-3 (FY-3) meteorological satellites should not be ignored. Compared with algorithms that do not consider the influence of EIA in the regression, those that integrate the EIA into the regression can enhance the accuracy of SST retrievals. Subsequently, based on the recalibrated Level 1B data from the FY-3/MWRI, a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression. The reprocessed SST data, including FY-3B/MWRI SST during 2010–2019, FY-3C/MWRI SST during 2013–2019, and FY-3D/MWRI SST during 2018–2020, were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis (OSTIA). The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset. Compared with the Copernicus Climate Change Service V2.0 SST, the absolute deviation of the reprocessed SST, with a quality flag of 50, was less than 1.5°C. The root mean square errors of the FY-3/MWRI orbital, daily, and monthly SSTs, with a quality flag of 50, were approximately 0.82°C, 0.69°C, and 0.37°C, respectively. The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current. Overall, this reprocessed SST product is recommended for El Niño and La Niña events monitoring.

Spatiotemporal time series prediction plays a crucial role in a wide range of applications. However, in most of the studies, spatial information was ignored and predictions were carried out either on a few points or on average values. In this study, 37 different configurations of 4 traditional ML models and 3 Neural Network (NN) based models were utilized to provide a comprehensive comparison and evaluate the spatiotemporal data prediction capabilities of the ML models. Additionally, to reveal the importance of spatial data for the time series prediction process, the best configuration of each ML model was evaluated with and without using spatial information. The utilized models were: (i) Linear Regression (LR), (ii) K-Nearest Neighbors (KNN), (iii) Decision-Trees (DT), (iv) Support Vector Machine (SVM), (v) Multi-Layer Perceptron (MLP), (vi) Long Short-Term Memory (LSTM), and (vii) Gated Recurrent Unit (GRU). The study was performed on the Sea Surface Temperature (SST) data collected by satellite radiometers via infrared measurements. The models were evaluated according to their one-month ahead spatiotemporal SST prediction performance over the southern coasts of Turkey, and the effects of spatial information on model performance were presented. Results reveal that the spatial information increased the prediction performance by approximately 25%, in terms of RMSE. Additionally, acquired results show that the LSTM model outperforms all other ML models and gives the smallest prediction errors in all metrics.

Sea surface temperature (SST) plays an important role in the typhoon forecast. By comparison, satellite retrieval products can provide more accurate SST data than reanalysis data in typhoon simulations. However, the effect of SST data from Chinese Fengyun-3 (FY-3) satellites on typhoon simulation hasn’t been evaluated yet. In this paper, the impact of FY-3D SST retrieval data with ascending and descending orbits on the forecast of typhoon In-Fa, 2021, is investigated with the Weather Research Forecast (WRF) model. Compared to the control experiments with SST data from Global Data Assimilation System (GDAS) data, the replacement experiments with FY-3D SST data significantly improve the forecast of typhoon central sea level pressure, track, and precipitation. Especially, the SST from the descending orbit satellite data provides the optimal track and intensity forecast, which are verified against the best track data from the Japan Meteorological Agency.

Traditional reconstructions of sea‐surface temperatures (SSTs) produced by the Pliocene Research Interpretations and Synoptic Mapping (PRISM) Group indicate that mid‐Pliocene surface ocean temperatures were unchanged or slightly cooler than modern at the tropics and low latitudes and significantly warmer at higher latitudes, particularly in the North Atlantic. This change in the latitudinal pattern of SSTs has been attributed to enhanced meridional ocean heat transport generated by more vigorous surface ocean gyres and/or thermohaline circulation (THC). This study derives new SST estimates using the alkenone paleothermometer in the Pacific Ocean. These estimates, combined with published alkenone SST data from two Atlantic sites, are located in tropical and subtropical regions where the distribution of PRISM sites is sparse. A fully coupled ocean‐atmosphere general circulation model (OAGCM) running for the mid‐Pliocene was also used to predict SST. Significant differences are noted between absolute PRISM SSTs interpolated to our core locations and alkenone‐derived SSTs. These differences may be related to errors in the PRISM and alkenone paleothermometry estimates or to regional differences between localities where data was collected. However, the available alkenone and model‐based SST estimates are consistent in the sign of temperature change they predict and provide the first indication of warmer SSTs during the mid‐Pliocene in the tropics and subtropics. This contrasts with PRISM's estimates of unchanged or slightly cooler SSTs for the same geographical regions. The pattern of SSTs, produced by alkenone estimates and modeling, is not characteristic of that produced by enhanced meridional ocean heat transport or THC. Instead, the pattern is similar to that which might be expected as a result of higher concentrations of atmospheric CO2, which would act to warm the oceans at the tropics and other latitudes. Furthermore, model diagnostics indicate that reduced sea ice and terrestrial ice sheet extents and a strong ice‐albedo feedback played a major role in forcing mid‐Pliocene warmth. Further work aimed at validating these conclusions should concentrate on expanding the mid‐Pliocene SST data set, particularly at low latitudes, as well as undertaking model/model comparisons to assess model dependency of the results.

Pandeglang district is one of the fishery production centers in Banten province which is the largest producer of high economic fish in Banten province. One of the dominant species caught is mackerel (Rastrelliger spp). Production of mackerel is fluctuated due to the condition of fishing areas that are always changing related to the fertility of the waters. Lack of information to fishermen associated with fishing areas leads to unstable catches. This study aims to provide an overview of the fertility of waters around Pandeglang waters using sea surface temperature and chlorophyll-a parameters based on satellite data during the western and transitional season (January to May) 2017. The method used in this study is survey method by collecting primary data in the form of catch and capture data, sea surface temperature and chlorophyll-a data in some fishing areas, and secondary data of sea surface temperature and chlorophyll-a during the west season and transitional seasons taken from AQUA MODIS satellite images. The results of this study indicated that the highest yield of mackerel fish obtained in May is about 98 kg/trip with sea surface temperature conditions ranging from 30.49 °C - 31.10 °C and chlorophyll-a concentrations between 0.21 - 0.58 mg/L. This condition can be used as a reference in determining potential fishing areas that are beneficial to fishermen, especially in Pandeglang regency.

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future.