Sea Surface Temperature Measurements Research Articles

Acoustic telemetry captures the full annual migration of alewife between Chesapeake Bay and the Gulf of Maine

Anadromous fish are declining at a global scale, and a more holistic approach to management is needed that addresses threats across their freshwater, estuarine, and ocean habitats. In this study, we used acoustic telemetry to track adult alewife Alosa pseudoharengus in Chesapeake Bay, USA, to evaluate, for the first time, habitat use throughout the entire annual migration cycle. Fifty adult alewife were tagged in the Choptank River, Maryland, in spring 2022, and detection data were obtained via collaborative acoustic telemetry networks along the Eastern Seaboard of the USA and Canada. Water temperature data were also collected using data loggers in the Choptank River and from satellite measurements of sea surface temperature in Chesapeake Bay and the ocean. In total, 48 tags (96%) were detected at least once, 14 tagged fish (28%) were detected in the ocean migrating north to the Gulf of Maine and Bay of Fundy, and 5 tagged fish (10%) returned to the Choptank River in spring 2023. With few exceptions, tagged fish were detected at temperatures of 7-16°C across all habitats (river to ocean and back). Alewife made extensive use of tidal and non-tidal portions of the Choptank River during spring, migrated to summer habitats that are experiencing rapid warming, and passed through areas of high incidental catch on both the outgoing and return migration. This study highlights how acoustic telemetry can refine our understanding of river-specific migrations of anadromous fish and the management implications of their movements in regions with collaborative acoustic telemetry networks.

Impact of river-discharged freshwater on surface ocean environments revealed by synergistic use of satellite measurements in the East China Sea

River discharge forms a plume of low-salinity water that spreads offshore, delivering terrestrial substances into the ocean and thus plays a critical role in controlling marine environments as well as the carbon cycle. This study investigated how freshwater discharged from the Changjiang impacts the physical and biological responses and oceanic uptake of carbon dioxide (CO2) in the northern East China Sea (ECS) by combining the recently available sea surface salinity (SSS) product from the Soil Moisture Active Passive (SMAP) mission with other satellite measurements of sea surface temperature (SST) and chlorophyll-a (chl-a) concentration. The partial pressure of CO2 (pCO2) and its interannual variability were estimated using empirical regression with satellite-derived environmental variables. The bias-corrected SMAP SSS revealed that river discharge largely contributed to the distinct interannual SSS variations, with a seasonal cycle reaching a maximum in winter and a minimum in summer. Compared to the SST and chl-a anomalies, we observed an increase in SST and primary production in the region where sea surface freshening was robust. Freshwater from rivers contributes to sea surface warming by trapping heat from the atmosphere at the surface layer. Nutrient-enriched freshwater within the buoyant plume enhances phytoplankton production, which in turn enriches the ocean surface with chl-a. Simultaneously, the pCO2 was relatively low in the region where the SST and primary production were high, highlighting that the heat and riverine nutrients trapped within the buoyant plume contributed to the reduction in pCO2 by promoting the biological uptake of CO2. The estimates conducted here illustrate the synergistic utility of multiple satellite measurements for the evaluation of CO2 uptake capacity, complemented by in situ measurements of river-dominated marginal seas.

Time series analysis of sea surface temperature change in the coastal seas of Türkiye

Sea surface temperature (SST) is a crucial geophysical parameter in assessing heat exchange between the air and sea surface. Changes in SST and its accurate prediction play a pivotal role in explaining the global heat balance, determining atmospheric circulations, and constructing global climate models. This work aims to reveal a model for one-month-ahead forecasting of SST time series data along the Türkiye coasts, encompassing the Mediterranean, Aegean, Marmara, and Black Seas, and their long-term future forecast. A long short-term memory (LSTM) neural network and seasonal autoregressive integrated moving average (SARIMA) models are used for this purpose. The ECMWF ERA5 (0.5ox0.5°) monthly SST dataset spanning the years 1970–2023 is used for model development. The results obtained from the LSTM and SARIMA models show that there will be an increasing trend in SSTs along these seacoasts until 2050. The SST measurements of 23.4 °C, 20.2 °C, 17.0 °C, and 16.6 °C recorded along the Mediterranean, Aegean, Marmara, and Black Seas in 2023 are expected to rise to 25.1 °C, 21.9 °C, 18.1 °C, and 18.8 °C, respectively, by 2050. These figures indicate an increase of 7.3%, 8.4%, 6.5%, and 13.3% in the SST values across these coastal seas over the next quarter century.

System Design of Ocean Temperature Measurement System Using Brillouin Lidar Based on Dual Iodine Cells

Ocean temperature profile information plays a key role in understanding the marine environment. The passive remote-sensing technique can provide sea surface temperature measurements over large areas. However, it is sensitive to the atmospheric environment and cannot provide seawater temperature profile information. The lidar technique is the only way to carry out seawater temperature profile measurements over large areas. However, it is insufficient for measuring speed, the receiving field, stability, spectral integrity, simple system structures, and so on. Therefore, we propose a Brillouin lidar method combining two iodine cells at different temperatures to realize temperature measurements, where one iodine cell is used as a filter to absorb the elastic scattering and the other as an edge detection discriminator to obtain the seawater temperature measurement. The system has a fast measurement speed, a large receiving field, a simple system structure, and high stability. The system feasibility was verified via principle simulation and real iodine absorption curve measurements. For an ocean temperature of [5 °C, 15 °C], a laser wavelength of 532.10495 nm was more appropriate, corresponding to the iodine pool temperature combinations of 50 °C and 78 °C. For an ocean temperature of [15 °C, 32 °C], a laser wavelength of 532.10518 nm was more appropriate, corresponding to the iodine cell temperature combinations of 60 °C and 78 °C. When the laser intensity reached a measurement precision of 1‰, the temperature could be predicted with an accuracy of up to 0.2 K. This work shows promise as a potential solution for seawater temperature profile measurement.

California Current temperatures adjacent to Southern California: 1949–2020

The southward flowing California Current, which lies adjacent to the California Pacific coastline as part of the North Pacific Ocean gyre circulation, is an important element in characterizing California's climate variability. This study improves and extends the historic record of California Current temperatures along the Southern California coastline for the last 71 years. The data come from long-term sea surface temperature measurements determined by the California Cooperative Oceanic Fisheries Investigations (CalCOFI) partners. The sea-surface temperature measurements come from a grid of 31 oceanic sampling sites within the core of the California Current off the Southern California coast. The sites have been sampled intermittently over the course of each year with ∼100 measurements/year on average. The Southern California Current temperatures in our grid have risen ∼2.5 °C since 1949.The California current temperatures are strongly correlated with the larger-scale North Pacific Ocean Pacific Decadal Oscillation (PDO) index and the North Pacific Gyre Oscillation (NGPO) index. All three show distinctive correlation with decadal (∼14-yr) California rainfall cyclicity.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

Characterization and Validation of ECOSTRESS Sea Surface Temperature Measurements at 70 m Spatial Scale

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities.

ANALYSIS OF CHLOROPHYLL-α AND SEA SURFACE TEMPERATURE DISTRIBUTION USING SATELITE IMAGE TO ESTIMATE OF FISHING ZONES FOR YELLOWFIN TUNA (Thunnus albacares) IN WEST SUMATERA PROVINCE WATERS

The potential of tuna in the waters of West Sumatra reaches 300,000 tons per year, and only 25% of its existing potential has been exploited. The existence of these fish can be determined by measuring the concentration of chlorophyll-α as an indicator of food sources in the waters and measuring sea surface temperature as an indicator of the fish environment, which can be done using remote sensing technology. This research was conducted from August 05 to September 29, 2022, using Aqua MODIS satellite imagery data. The results obtained during the study showed that the concentration of chlorophyll-α had an average of 2.01 mg/m3. The highest concentration occurred in the period 13-20 August 2022 with an average value of 3.17 mg/m3, and the lowest occurred in the period 06-13 September 2022 with an average value of 0.85 mg/m3. Sea surface temperature has a relatively similar value, which ranges from 29.5–30.8oC with an average of 30.5oC. Determination of the alleged fishing area for yellowfin tuna was carried out using the overlay technique by shading the area from chlorophyll-a criteria >0.1 mg/m3 with the sea surface temperature criteria of 29-30oC. The accuracy level of the yellowfin tuna fishing area estimation has an average accuracy of 28.7%. The highest accuracy value occurs from August 29 to September 05 2022, with a percentage of 69.8%, and the lowest occurs from August 13 to 20, 2022, with a rate of 3.9%.

Diurnal warming rectification in the tropical Pacific linked to sea surface temperature front

Sharp and rapid changes in the sea surface temperature (SST) associated with fronts and the diurnal cycle can drive changes in the atmospheric boundary-layer stability and circulation. Here we show how a one-dimensional surface ocean model forced with either high-resolution or daily averaged surface fluxes can be used to distinguish diurnal versus frontal SST anomalies observed from an uncrewed surface vehicle. The model, forced with daily satellite fluxes, shows that the diurnal warming is largest within the equatorial Pacific cold tongue of SST. The strong persistent SST front north of the cold tongue is evident in both the oceanic and atmospheric boundary-layer stability scales and, as a consequence, in the magnitude of the diurnal ocean warming. Using SST, barometric pressure and surface wind measurements from moorings at 0°, 95° W and 2° N, 95° W, we show that the front in the SST diurnal warming results in a weakened SST front in the afternoon and a corresponding reduced meridional gradient in the barometric pressure that appears to contribute to a diurnal pulsing of the surface meridional winds. To the extent that these modulate the surface branch of the Hadley cell, these diurnal variations may have remote impacts.

Diel variation of seawater volatile organic compounds, DMSP-related compounds, and microbial plankton inside and outside a tropical coral reef ecosystem

Biogenic volatile organic compounds (VOCs) play key roles in coral reef ecosystems, where, together with dimethylated sulfur compounds, they are indicators of ecosystem health and are used as defense strategies and infochemicals. Assessment and prediction of the exchange rates of VOCs between the oceans and atmosphere, with implications for atmospheric reactivity and climate, are hampered by poor knowledge of the regulating processes and their temporal variability, including diel cycles. Here, we measured the variation over 36h of the concentrations of DMSPCs (dimethylsulfoniopropionate (DMSP)-related compounds, namely DMSP, dimethylsulfoxide, acrylate, dimethylsulfide, and methanethiol as dimethyl disulfide) and VOCs (COS, CS2, isoprene, the iodomethanes CH3I and CH2ClI, and the bromomethanes CHBr3 and CH2Br2), in surface waters inside the shallow, northern coral-reef lagoon of Mo’orea (French Polynesia) and 4 km offshore, in the tropical open ocean. Comparisons with concurrent measurements of sea surface temperature, solar radiation, biogeochemical variables (nutrients, organic matter), and the abundances and taxonomic affiliations of microbial plankton were conducted with the aim to explain interconnections between DMSPCs, VOCs, and their environment across diel cycles. In open ocean waters, deeper surface mixing and low nutrient levels resulted in low phytoplankton biomass and bacterial activity. Consequently, the diel patterns of VOCs were more dependent on photochemical reactions, with daytime increases for several compounds including dissolved dimethylsulfoxide, COS, CS2, CH3I, and CH2ClI. A eukaryotic phytoplankton assemblage dominated by dinoflagellates and haptophytes provided higher cell-associated DMSP concentrations, yet the occurrence of DMSP degradation products (dimethylsulfide, dimethyl disulfide) was limited by photochemical loss. Conversely, in the shallow back reef lagoon the proximity of seafloor sediments, corals and abundant seaweeds resulted in higher nutrient levels, more freshly-produced organic matter, higher bacterial activity, and larger algal populations of Mamiellales, diatoms and Cryptomonadales. Consequently, DMSP and dimethylsulfoxide concentrations were lower but those of most VOCs were higher. A combination of photobiological and photochemical processes yielded sunny-daytime increases and nighttime decreases of dimethylsulfide, dimethyl disulfide, COS, isoprene, iodomethanes and bromomethanes. Our results illustrate the important role of solar radiation in DMSPC and VOC cycling, and are relevant for the design of sampling strategies that seek representative and comparable measurements of these compounds.

Evaluation of in situ observations on Marine Weather Observer during Typhoon Sinlaku

Abstract. The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper.

High-precision retrieval of offshore sea surface temperature: A machine learning framework based on MODIS and in-situ measurements

High-precision sea surface temperature (SST) data is essential for monitoring and management of offshore environment. SST retrievals from thermal infrared remote sensing have the advantages in time frequency and space coverage over measurements from ships and buoys. However, traditional SST retrieval models are not fully applicable for offshore waters where seawater conditions change a lot, due to simple functional forms and empirical fitting of model parameters. Machine learning (ML) is introduced because it doesn’t need to define specific model rules or unknown parameters, which is more intelligent to deal with complex retrieval problems in a data-driven way. But the applicability of ML has not been investigated in the North Yellow Sea of China, and the selection of driving features for model construction has not been fully discussed. Here, we designed a high-precision SST retrieval framework based on random forest (RF) with optimal feature combination using MODIS data and in-situ SST measurements during 2013–2020 in the northern part of the North Yellow Sea. The performances of different feature combinations were assessed based on model testing accuracies and the RF output feature importance. Compared with previous ML-based SST retrieval studies, we added two time-related features (i.e., day of the year and month) and one feature as initial temperature (i.e., SST retrieval from split-window algorithm), which reduced the testing error by around 15 % and 11 %, respectively. The retrieval results were validated with in-situ measurements, showing that the RF model achieved better accuracies (R2 = 0.987, SD = 0.842 °C, MAE = 0.675 °C, RMSE = 0.841 °C) than the improved split-window algorithm (named as ISW) and deep neural network. Compared with ISW, the spatial distribution of RF retrieval map exhibited similar variation across four seasons, mainly differed in the coastal areas or at the formation of temperature fronts. Note that the constructed RF model was still reliable when applied to the independent samples in 2021. This study contributes to improve the accuracy of offshore SST retrieval under different seawater conditions and provides references for SST monitoring.

Characterizing oceanographic conditions near Coiba Island and Pacific Panama using 20 years of satellite-based wind stress, SST and chlorophyll-a measurements

Coiba Island and the associated Special Zone of Marine Protection represent an important, yet poorly studied marine reserve along the Pacific coast of Panama. While efforts have recently began to establish monitoring programs in the region, a range of historical, marine-related environmental measurements already exist, derived from satellite-based observations. The goal of this paper was to use long-term datasets for key variables to provide qualitative insights (i.e. descriptive oceanography) of climatological conditions and interannual variability in the Pacific Panama region. These are underpinned with numerical assessments, providing an important baseline for ongoing and future studies, particularly in the Coiba Island/Gulf of Chiriqui region. In particular, we examined 20 years (January 2003-December 2022) of wind stress, sea surface temperature (SST), and chlorophyll-a (Chl-a), spanning the neritic and pelagic regions of the Pacific Panama coast. During the dry season (northern winter), the well-known, seasonal, regional Panama wind jet appeared across the Gulf of Panama, leading to surface mixing and SST cooling that eventually extended across most of the Panama Bight. West of the Azuero Peninsula, SST increased and surface warming extended further offshore from January through April. The SST in the Gulf of Chiriqui during this period was about 1 °C warmer on average than east of Coiba Island. By July and August, offshore SST gradients became largely longitudinal, cooling occured across the season, and the SST on either side of Coiba Island was nearly the same. The influence of the Panama jet in the Gulf of Panama was clear in the Chl-a data as well, with upwelling-driven values peaking in February/March (up to 11 mg m-3, with a monthly climatological value of around 2 mg m-3 during this period). During the rest of the year, the Chl-a concentration in this region averaged around 0.5-1.0 mg m-3. In the Gulf of Chiriqui and the region east of Coiba Island, the climatological monthly averages were roughly 0.3-0.5 mg m-3 and 0.4-0.6 mg m-3, respectively. Somewhat surprisingly, very high Chl-a values were present in the satellite data for the Gulf of Chiriqui during May 2007 and June 2008, peaking at 16 mg m-3 and 32 mg m-3 at a location just west of Coiba Island, respectively. It remains unclear as to the cause of these apparent blooms. Even when the high Chl-a values were excluded in the calculation of climatological averages in the Gulf of Chiriqui, however, there is a suggestion of modest seasonality in Chl-a values, with slightly elevated values (~ 0.4 mg m-3) peaking around May and October. During the extreme El Niño event of 2015-2016, the monthly-averaged SST along the Panama Pacific coast was warmer than average, with elevated levels of up to + 2 °C and lasting 12 months in the Gulf of Chiriqui. In the Gulf of Panama, the monthly-averaged SST anomalies were up to + 1.7 °C, although the temperatures returned to near-seasonal averages after roughly 5 months.

Optimizing Back-Propagation Neural Network to Retrieve Sea Surface Temperature Based on Improved Sparrow Search Algorithm

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network’s initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters.

A Warming Southern Gulf of Mexico: Reconstruction of Anthropogenic Environmental Changes From a Siderastrea siderea Coral on the Northern Coast of Cuba

AbstractThe Gulf of Mexico is a vital region for the Atlantic Meridional Overturning Circulation (AMOC), that fuels the exchange of heat between the tropics and the polar regions. A weakening of the AMOC would have dire consequences for the planet. First observations and ocean models show that this process has already started. Very limited knowledge of the components that are part of the AMOC such as the Loop Current (LC) make it difficult to understand its dynamics as well as changes in strength or temperature since the onset of the Industrial Revolution. Currently, there are no continuous in situ sea surface temperature or salinity measurements for the southeastern Gulf of Mexico or reconstruction attempts for this region, showing the necessity for high‐resolution climate archives. A Siderastrea siderea coral core was retrieved from the northwestern Cuban coast and used as a sub‐seasonally resolved sea surface temperature and hydroclimate archive. The approach is based on skeletal δ18O, and trace and minor element contents show an increase in temperature over 160 years since 1845 of 2.6–3.3°C. A possible stagnation of the warming trend set in after the 1980s, indicating a potential weakening of the Loop Current. Impacts in sea surface salinity such as El Niño events in the Pacific region can still be detected in the Gulf of Mexico as decreases in salinity in 1998 from the reconstructed δ18OSW coral record. In situ measurements remain crucial to understand the dynamics in the LC and its influence on the AMOC.

Diversity of Ceratium Schrank (Dinophyceae) species in the surface waters of Dhamra, Odisha, Eastern India

Aim: To study the diversity of Ceratium Schrank (Dinophyceae) species in the surface waters of Dhamra, Odisha, Eastern India. Methodology: The present study was carried out at six GPS coordinated stations in the coastal waters of Dhamra, Odisha during pre-monsoon, monsoon and post-monsoon periods (March 2021, October 2021 and February 2022). Standard methods were followed for measuring sea surface temperature, transparency, dissolved oxygen, pH, alkalinity, salinity, silicate, nitrite, nitrate and phosphate. Samples were collected by filtration as well as towing method. Light microscopy and scanning electron microscopy were carried out for the identification of Ceratium species. Results: A total of twenty-eight species of Ceratium were observed. Out of the reported twenty-eight species, C. massiliense var. armatum and C. breve var. breve have not been reported earlier in the coastal waters of Dhamra, Odisha. Diversity of Ceratium species was influenced by various parameters. Analyses of water quality parameters, showed that the transparency, salinity, alkalinity, nitrite and phosphate were maximum during pre-monsoon whereas temperature and silicate concentration were higher in monsoon. Similarly, during post-monsoon, pH and nitrate concentration were maximum. Availability of nutrients might have probably influenced the distribution of Ceratium species in the coastal waters and one of the sources was from the riverine influx. Interpretation: Abundance of Ceratium species is probably regulated by alkalinity, high transparency, salinity and phosphate concentration in the surrounding water. Dhamra coastal water is conducive for the growth of Ceratium sp. which primarily uses phosphate for developing the horns. Key words: Abundance plot, Bray-Curtis, Ceratium, Dinophyceae, Species diversity

Impact of Giant Iceberg A68A on the Physical Conditions of the Surface South Atlantic, Derived Using Remote Sensing

AbstractGiant icebergs release cold, fresh meltwater as they drift, perturbing the physical conditions of the surface ocean. This study uses satellite‐derived sea surface salinity and temperature measurements to explore the physical impact of supergiant iceberg A68A between September 2020 and June 2021. During A68A's drift through the Scotia Sea in austral spring, gradual but persistent edge‐wasting contributed to a freshening of several psu extending hundreds of kilometers ahead of the iceberg, whilst the cooling signal was more pronounced in the iceberg's wake. The magnitude of the physical perturbation intensified during A68A's breakup near South Georgia. Several large meltwater lenses surrounding the descendant icebergs displayed temperature anomalies of up to −4.5°C, whilst the salinity measurements indicated a surface (skin‐depth) anomaly regularly exceeding order −10 psu. The perturbations stretched at times >1,000 km and persisted for >2 months following A68A's melt in April 2021.

Data Reconstruction of Sea Surface Temperature in Indonesia’s Fish Management Area 713 (IFMA-713) Using Machine Learning

The IFMA-713 in Indonesia is water that has dynamic of temperature changes due to interactions with the Pacific Ocean and the surrounding. Sea surface temperature data can be obtained by measuring with satellite imagery. However, satellite imagery measurements of sea surface temperature can be incomplete due to cloud cover. In this study, a machine learning method was used to reconstruct sea surface temperature data using a backpropagation neural network algorithm. The data used in this research is data captured with MODIS Satellite. Then, the reconstruction of sea surface temperature data is carried with four scenarios with missing data percentages: empty data, zero values, average values at the point of data collection, and Indonesia’s average sea surface temperature. Accurate results were obtained in reconstructing sea surface temperature where the scenarios had a positive correlation. The most accurate scenarios for reconstructing sea surface temperature data with missing data were those in which the empty data was filled with average values at the point of data collection or Indonesia’s average sea surface temperature.

A data-assimilative modeling investigation of Gulf Stream variability

An advanced data-assimilative ocean circulation model is used to investigate Gulf Stream (GS) variability during 2017–2018. The modeling system applies a strong-constraint, 4D variational data assimilation algorithm. It assimilates satellite-based sea surface height and sea surface temperature measurements and in situ temperature and salinity profiles. Model skill assessment metrics along with comparisons of GS position and GS's three-dimensional mean kinetic energy with historical observations are applied to validate the data-assimilative model. The resulting time- and space-continuous ocean state estimates are used to diagnose eddy kinetic energy conversion and cross-stream eddy heat and salt fluxes over the two-year study period. The processes leading to kinetic energy conversion are primarily due to GS meanders. Significant inverse energy cascading (EKE→MKE and EKE→EPE) can occur during GS-eddy interactions, particularly during onshore intrusions or offshore meanderings of the GS. Throughout the two-year study period, the cross-stream eddy heat and salt fluxes off Cape Hatteras were predominantly positive (onshore). Both GS offshore meandering (occurring 44% of the time and associated with shelf/slope water export) and GS intrusion (occurring 56% of the time) contribute to onshore heat and salt transport. Improved understanding of these processes and dynamics requires strong integration of an advanced observational infrastructure that combines remote sensing; fixed, mobile, and shore-based observing components; and high-resolution data assimilative models.

Compact and modular autonomous surface vehicle for water research: The Naval Operating Research Drone Assessing Climate Change (NORDACC)

Research, monitoring, and management of marine and aquatic ecosystems often require surface water samples to measure biogeochemical and optical parameters. Traditional sampling with a boat and several personnel onboard can be labor-intensive and safety requirements limit sampling activities in high-risk environments. This paper describes the Naval Operating Research Drone Assessing Climate Change (NORDACC). NORDACC is an open source, light-weight, and portable autonomous surface vehicle that can acquire surface water samples while also measuring sea surface temperature and salinity for the duration of its deployment. NORDACC is ideal for operations in remote areas where resources and personnel are limited. Two sample bottles, each one liter in volume, can be filled, either at pre-programmed sampling stations or manually, using the remote control. A trimaran design provides buoyancy and stability, with hulls constructed of vacuum-formed acrylonitrile butadiene styrene (ABS) plastic. NORDACC can navigate autonomously between waypoints and features first person view capabilities for enhanced situational awareness. NORDACC’s performance was validated in Aarhus Bay, Denmark, collecting multiple surface water samples in winds in excess of 8 ms−1 and steep, choppy waves.