Sea Surface Temperature Products Research Articles

A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data

The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research.

Variations of surface marine heatwaves in the Northwest Pacific during 1993–2019

Parameters of surface marine heatwaves (MHWs) in the Northwest Pacific during 1993–2019 are derived from two sea surface temperature (SST) products: the Optimum Interpolation SST based on satellite remote sensing (OISST V2.1) and the Global Ocean Physics Reanalysis based on data-assimilative global ocean model (GLORYS12V1). Similarities and differences between the MHW parameters derived from the two datasets are identified. The spatial distributions of the mean annual MHW total days, frequency, duration, mean intensity and cumulative intensity, and interannual variations of these parameters are generally similar, while the MHW total days and duration from GLORYS12V1 are usually higher than that from OISST V2.1. Based on seasonal-mean values from GLORYS12V1, longer MHW total days (>7) have the largest spatial coverage in both the shelf and deep waters in summer, while the smallest coverage in spring. In selected representative regions, interannual variations of the MHW total days are positively correlated with the SST anomalies. In summer, the MHW total days have positive correlations with the Western Pacific Subtropical High intensity, and negative correlations with the East Asia Monsoon intensity, over nearly the whole South China Sea (SCS) and the low-latitude Pacific. In winter, positive correlations with both the Subtropical High and Monsoon intensities present over the western part of SCS. Strong El Niño is followed by longer MHW total days over the western half of SCS in winter, and over the whole SCS and low-latitude Pacific in summer of the next year. These correlation relationships are valuable for developing forecasts of MHWs in the region.

Pliocene Weakening of Gradients in Temperature but Not in Productivity in the Eastern Equatorial Pacific

AbstractThe modern eastern equatorial Pacific Ocean (EEP) exhibits strong upwelling, producing pronounced gradients in sea surface temperature (SST), nutrient concentration, and biological productivity between 80° and 140°W. During the globally warmer late Pliocene (3.0–3.6 Ma), the EEP may have experienced permanent El Niño‐like conditions, supported by a reduced SST gradient across the equatorial Pacific. However, the weakened east‐west SST gradient has been controversial, with disparate results depending on the proxy used to monitor Western Warm Pool SSTs. We present new Pliocene alkenone‐based SST and paleoproductivity records from four Ocean Drilling Program (ODP) cores spanning an east‐west transect across the EEP, which present an internally consistent picture of SST and productivity gradients in the modern cold tongue, resolved at orbital‐scale variability. Strong agreement between core top reconstructions and satellite estimates indicates that alkenone paleotemperature and paleoproductivity proxies are appropriate for reconstructing Pliocene EEP conditions. The average SST gradient between 90° and 120°W was reduced from the modern 1.8°C gradient to 0.9°C in the late Pliocene. Despite the weakened SST gradient, the surface productivity gradient was stronger during the late Pliocene compared to modern, based on calibrated X‐ray fluorescence biogenic opal and alkenone average accumulation rates. Contrary to modern El Niño SST and productivity patterns, reduced Pliocene surface productivity did not accompany the weakened SST gradient. Instead, strong Pliocene biogenic opal and alkenone concentration accumulation gradients in the eastern EEP suggest that subsurface tilting of the nutricline and thermocline persisted to supply vigorous upwelling of warm but nutrient‐rich subsurface waters in a warmer climate.

Global Sea Surface Temperature Analysis Based on Domestic Ocean Satellite Data. Part II: Comparative Analysis of Multiple Sets of Global Sea Surface Temperature Products in 2022

With more and more global gap-free fusion products of the sea surface temperature, understanding the consistency and discrepancy of the different SST fusion products will not only help data providers to improve their algorithms, but also help them to select the one that may better suit their applications. In this article, we have compared and analysed 10 sets of global gap-free fusion products of sea surface temperature in 2022, with different fusion techniques and related configurations. It is found that each SST analysis product has the same spatial distribution, with the minimum NMEFC mean value (20.11) and the maximum MGDSST mean value (20.31). Compared with ARGO in-situ data, the RMSE ranges are from 0.3233 (OSTIA) to 0.5180 (MGDSST). The RMSE between NMEFC fusion products and Argo in-situ data is 0.3861, ranked fifth out of 10 sets of fusion products. Compared with GMPE analysis among 9 sets of fusion products, the RMSE ranges are from 0.1579 (CMC) to 0.3199 (K10), and the NMEFC fusion product has a RMSE of 0.3040, which is at the intermediate level, ranked sixth out of 9 fusion products.

An Operational Global Near-Real-Time High-Resolution Seamless Sea Surface Temperature Products From Satellite-Based Thermal Infrared Measurements

An Operational Global Near-Real-Time High-Resolution Seamless Sea Surface Temperature Products From Satellite-Based Thermal Infrared Measurements

Quality Assessment of FY-4A/AGRI Official Sea Surface Temperature Product

Quality Assessment of FY-4A/AGRI Official Sea Surface Temperature Product

Deriving fine-scale patterns of sea surface temperature in coral reef habitats using the Landsat 8 thermal infrared sensor

The available sea surface temperature (SST) products are too coarse to assess the fine-scale (<1 km) SST variations related to coral bleaching. In this study, we proposed an optimal SST inversion model using Landsat 8 thermal infrared sensor (TIRS) images to derive fine-scale SST patterns in the coral reef habitats of the Xisha Islands, South China Sea. Our study included two parts: 1) six SST inversion models were developed using the radiative transfer method and the split window (SW) algorithm in the hot season and cool season, from which the optimal SST inversion model was determined; and 2) the optimal model was applied to 47 Landsat 8 TIRS images to derive the SST spatial and temporal pattern among the geomorphic zones of six reefs in hot and cool season conditions. Compared with the measured sea temperature data and the verified MODIS SST products, the SST6 model using the SW algorithm was optimal, with an RMSE of approximately 0.64°C in the hot season. The average SST results usually had a pattern of reef flat > lagoon > reef slope/offshore sea. The reef flat was usually approximately 0.05°C–0.2°C hotter than the lagoon in the hot season. The SST in the lagoon also increased from south to north and the shallow lagoon was usually warmer than the deep lagoon in the hot season. Our results suggested that scleractinian corals in the reef flat and the lagoon were more susceptible to bleaching-level thermal stress than other geomorphic zones. During the cool season, the SST fluctuated markedly among coral reefs and geomorphic zones.

SEA SURFACE TEMPERATURE ANOMALIES FORMATION IN THE GULF OF MEXICO UNDER THE INFLUENCE OF HURRICANE SULLY ON SEPTEMBER11–17, 2020

Problem Statement and Purpose. Tropical cyclones are one of the most destructive natural phenomena on Earth, causing great social and economic losses. Given the destructive impact of tropical cyclones, the question of their study and the impact of climate change on their behavior is considerable scientific interest. Tropical cyclones "draw" energy from the surface of the ocean. Tropical cyclones arise and develop over the water surface, the temperature of which is not lower than 26 °C. In the case when a well-developed tropical cyclone hits the colder surface of the ocean, it begins to fill up quickly. Thus, a change in the SST on the path of the tropical cyclone may affect the further development and intensity of the storm, and as a result, its destructive power. The purpose of the work is to determine the deviations of the ocean surface temperature in the Gulf of Mexico under the influence of the passage of Hurricane Sally on September 11–17, 2020. Data & Methods. The analysis of sea surface temperature anomalies was performed on the basis of data from the NOAA OI SST V2 High Resolution Dataset Optimum Daily Interpolation of Sea Surface Temperature product, which is long-term climate data (period 1971–2000) and allows determination of daily of sea surface temperature anomalies with a step of 1/4°. Results. Against the background of positive Sea Surface Temperature anomalies associated with the tendency to increase Sea Surface Temperature in the Gulf of Mexico, which has been observed in recent decades, in the places where the trajectory of Hurricane Sally passed during the next day, the appearance of negative Sea Surface Temperature anomalies was noted, which had a focal character and were comparable in area to horizontal dimensions of the hurricane. Over time, cells of negative Sea Surface Temperature anomalies increased in area and temperature deviation. The highest Sea Surface Temperature anomalies values were observed on September 15–16, 2020 and were –1 °C. Negative anomalies persisted after the passage of Sally for a month, after which a positive Sea Surface Temperature anomalies formed on the surface of the Gulf of Mexico, occupying the entire water area of the Gulf. This duration of preservation of anomalies may be related to the circulation of the waters.

Multi-proxy reconstruction of climate changes in the Lower St. Lawrence Estuary, Canada, during the Middle and Late-Holocene

The micropaleontological and palynological content, and geochemical and isotopic composition of a marine sediment core collected off Pointe-des-Monts in eastern Québec, Canada, reveal regional palaeoclimatic and paleoceanographic conditions in the Lower St. Lawrence Estuary over the last ~8200 years. The pollen and spore content allows comparison with the terrestrial palynostratigraphy, whereas dinoflagellate cysts and benthic foraminifera are used to reconstruct sea-surface conditions and bottom water properties, respectively. The dinocyst-based reconstructions indicate shifts between estuarine and oceanic conditions with important changes in sea-surface temperature, salinity, and primary productivity. Both the dinocyst assemblages and the quantitative sea-surface estimates highlight a distinct transition at ca. 4200 cal years BP. It is notably marked by a change towards higher salinity, which suggests reduced freshwater discharge, hence lower precipitation in the watershed, during the Late-Holocene. The isotopic composition (δ18O and δ13C) and assemblages of the benthic foraminifera indicate centennial to millennial frequency variability of bottom water properties, over a general trend towards decreasing temperatures and increasing ventilation from the beginning of the Middle Holocene until the last century. Since then, reverse trends with abrupt warming and decreasing dissolved oxygen content in bottom water have been observed.

Inconsistent Coral Bleaching Risk Indicators Between Temperature Data Sources

AbstractCoral reefs are facing severe threats and are at risk of accelerated decline due to climate change‐induced changes in their environment. Ongoing efforts to understand the mechanisms of coral response to warming rely on multiple sources of temperature data. Yet, it remains uncertain whether the Sea Surface Temperature (SST) data used for coral reef studies are consistent among different data products, despite potential implications for conservation. A better understanding of the consistency among the different SST data applied to coral reefs may facilitate the fusion of data into a standard product. This will improve monitoring and understanding of the impact of global warming on coral reefs. Four types of SST data across North‐Western and South‐Western Australia are compared to assess their differences and ability to observe high thermal stress during historical coral bleaching events. The four SST data sources included those derived from Global Circulation Models, NOAA CoralTemp SST product, ESA CCI SST product, and coral core derived SST. Coral bleaching risk indicators, Degree Heating Week (DHW), and Degree Heating Month (DHM) were calculated using these sources and compared for consistency. DHW and DHM were inconsistent among data sets and did not accurately reflect high thermal stress metrics during moderate and severe bleaching events. Some reefs did not experience bleaching in spite of high DHWs and DHMs, suggesting a mismatch in data scales, or perhaps other oceanographic factors and coral adaptation. By exploring the differences and similarities among these four data sources, this study highlights the need to compare existing indicators of thermal stress from different data sets.

Examining the Consistency of Sea Surface Temperature and Sea Ice Concentration in Arctic Satellite Products

Available observations and a theoretical simulation are used to explore the consistency and relationship between sea surface temperature (SST) and sea ice concentration (SIC) within open-ocean-sea ice mixed satellite pixels as a function of grid resolution. The maximum limiting SST value for a specified SIC and spatial resolution is first examined within collocated satellite-derived products contained within existing Level 4 SST analyses distributed using the data specification from the Group for High Resolution Sea Surface Temperature. The shape of the interdependence is further validated with manually quality-controlled buoy SST and SIC collocations. A parametric equation for the limiting SST value is derived from simulations of a mixed ocean/ice pixel with specified ice fraction and a linear SST gradient extending away from the ice edge. The exponential curve matching the observed interdependence suggests a maximum 5 km pixel-averaged SST at SIC values approaching zero between 6 and 8 °C. This maximum value is significantly greater than the previously assumed limiting values of ~3 °C and the corresponding SST gradient is larger than those typically observed with satellite SST products, but agrees well with recent Saildrone SST observations near ice. The curve provides a conservative limit with which inconsistent SST/SIC pairings can be identified, not only near the ice edge but at intermediate ice concentrations. Application of the filter improves the agreement between the SST/SIC relationship in satellite products and available Saildrone observations as well as the internal consistency of the different satellite products.

Validation of Himawari-8 Sea Surface Temperature Retrievals Using Infrared SST Autonomous Radiometer Measurements

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero.

The temporal and spatial distribution of sea fog in offshore of China based on FY-4A satellite data

Satellite remote sensing is an important instrument of sea fog monitoring. In order to study the temporal and spatial distribution characteristics of sea fog and the influence of sea surface temperature (SST) on the sea fog, based on sea fog monitoring and SST products from FY-4A satellite, the monthly variation of sea fog and monthly average SST in five typical sea areas (including Bohai Sea, Yellow Sea, East China Sea, Taiwan Strait and South China Sea) are statistically analyzed. The results show that the sea fog in offshore has strong regional and seasonal characteristics. From winter to autumn, the sea fog first appears in the Beibu Gulf of the South China Sea, the sea fog extends from South China Sea to East China Sea, and then to Yellow Sea, finally to Bohai Sea. Sea fog is related to sea surface temperature. By comparing the sea fog with the SST distribution, the optimal SST is from 7 °C to 25 °C when the sea fog happens.

Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products.

An inversion method of subsurface thermohaline field based on deep learning and remote sensing data

ABSTRACT Seawater temperature and salinity are basic marine environmental parameters, which can be used to calculate other marine environmental parameters. However, most of the on-site observation data have the problems of uneven spatial distribution and time discontinuity, and it is difficult for remote sensing observation methods to obtain subsurface information. In this study, we proposed a deep learning model with combining remote sensing temperature and salinity as well as in-situ measured data by Argo profiles, and the nonlinear relationship was revealed. An effective and direct inversion method was realized for underwater three-dimensional thermohaline structure based on remote sensing temperature and salinity at accurate points on global scale. The SST data were obtained from the FY3C-VIRR daily sea surface temperature product, the SSS data were acquired from the SMAP Level 3 8-day running averages sea surface salinity product, and the Argo scatter data were got from the ‘Global Ocean Argo Scatter Data Set’. Based on the temporal and spatial location information of the data, this paper matched the remote sensing data from 2016 to 2019 with the Argo data, 64751 valid pairing points were obtained. The deep learning model was constructed as a multilayer perceptron model with 5 hidden layers. The RMSE of temperature had a maximum value of 2.106°C in 130 m depth and a minimum value of 0.367°C in 1000 m with an average of 1.174°C for validation dataset. And the RMSE of salinity had a maximum value of 0.356 psu in 0 m and a minimum value of 0.045 psu in 1000 m with an average of 0.202 psu. Compared with other methods based on fixed mesh products, this study realized a direct inversion method at any location in the global ocean and improved the inversion accuracy, which provides a reliable data support for refined marine monitoring.

Understanding Differences in Sea Surface Temperature Intercomparisons

Abstract Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products’ original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance Statement Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for.

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.

Improving the combined use of reflectance and thermal channels for ocean and coastal cloud detection for the Sea and Land Surface Temperature Radiometer (SLSTR)

Reflectance imagery is used to aid daytime cloud detection in thermal remote sensing. This paper presents new approaches to utilization of reflectance for sea surface temperature (SST) remote sensing for the Sea and Land Surface Temperature Radiometer (SLSTR). SLSTR is an along-track scanning sensor with a complex instrumental viewing geometry, and no co-registration of the fields-of-view of reflectance and thermal pixels. Reflectance channels have twice the spatial resolution of thermal channels, and observations are placed on compatible “image grids” for the convenience of users of Level-1 data. We highlight limitations of simple methods, based on these image grids, of using reflectance imagery to inform cloud detection at the thermal resolution. We present improvements from averaging the N-nearest reflectance observations directly to the infrared instrument geometry, where N = 10 is chosen in this study when using the A and B stripes together. We show that the standard deviation of the N-nearest reflectance observations is another calculable quantity of use to improve the discrimination of clouds in the infrared image, beneficially reducing the weight placed on coarser-scale thermal spatial variability. The developments are illustrated by case studies in coastal zones over optically bright waters and around strong ocean fronts, and the benefit for SST products is quantified by the impacts on coverage and validation statistics. In a case study over optically bright waters, the clear-sky fraction increases from 46.2% to 93.1%. Coastal zone validation shows a 23.8-31.6% reduction in the false alarm rate and a corresponding 27.1-33.3% increase in the cloud detection true skill score. Globally the robust standard deviation for clear-sky matches between SLSTR-A and drifting buoys reduces from 0.3 to 0.29 with a 6% reduction in data due to improved screening of scattered cloud.

Impacts of Ocean Deoxygenation on Marine Benthos in the Gulf of Mexico

Ocean deoxygenation is a growing concern globally. Oxygen is less soluble in warm water, and warming temperatures also result in the slowdown of ocean circulation which limits oxygen delivery to deeper waters. Anthropogenic eutrophication has also contributed to the development of hypoxic conditions in many coastal areas. Here we investigate biodiversity structure along an environmental gradient in the Gulf of Mexico to see how patterns of spatiotemporal turnover can inform future biotic response of benthos to ocean deoxygenation. Live and dead assemblages of bivalve mollusks were collected at 15 stations offshore Louisiana, Alabama, and Florida. Abundance and body size data were collected, and specimens were classified functionally using information about feeding, attachment, life position, and body size. Environmental conditions were characterized using multi-decadal mean sea surface temperature (SST), dissolved oxygen (DO), and net primary productivity (NPP), and grain size data from our field samples. Stations in the north-central Gulf affected by Mississippi River discharge are characterized by higher NPP, lower DO, and higher percentages of silt and clay than stations in the northeastern Gulf. Both taxonomic and functional diversity significantly covary with this environmental gradient, with the lowest diversities observed at stations in the core of Louisiana’s “dead zone.” Analyses of spatiotemporal turnover patterns reveal shifts in the dominant feeding mode, with hypoxic environments containing a greater abundance of deposit and mixed feeders, compared with more oxygenated environments that host an abundance of suspension feeders and are characterized by a greater variety of feeding ecologies. Live-dead analyses reveal a shift in taxonomic and functional diversity in coastal Louisiana, that appears to coincide with the onset of anthropogenic eutrophication in these coastal settings.