Copernicus Marine Environment Monitoring Service Research Articles

Numerical Model Test of Spilled Oil Transport Near the Korean Coasts Using Various Input Parametric Models

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

Retrieval of sea ice drift in the Fram Strait based on data from Chinese satellite HaiYang (HY-1D)

Abstract. Melting of sea ice in the Arctic has accelerated due to global warming. The Fram Strait (FS) serves as a crucial pathway for sea ice export from the Arctic to the North Atlantic Ocean. Monitoring sea ice drift (SID) in the FS provides insight into how Arctic sea ice responds to the climate change. The SID has been retrieved from Sentinel-1 synthetic aperture radar (SAR), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and further exploration is needed for the retrieval of SID using optical imagery. In this paper, we retrieve SID in the FS using the Chinese HaiYang1-D (HY-1D) satellite equipped with the Coastal Zone Imager (CZI). A multi-template matching technique is employed to calculate the cross-correlation, and subpixel estimation is used to locate displacement vectors from the cross-correlation matrix. The dataset covering March to May 2021 was divided into hourly and daily intervals for analysis, and validation was performed using Copernicus Marine Environment Monitoring Service (CMEMS) SAR-based product and International Arctic Buoy Programme (IABP) buoy. A comparison with the CMEMS SID product revealed a high correlation with the daily interval dataset; however, due to the spatial and temporal variability in the sea ice motion, differences are observed with the hourly interval dataset. Additionally, validation with the IABP buoys yielded a velocity bias of −0.005 m s−1 and RMSE of 0.031 m s−1 for the daily interval dataset, along with a flow direction bias of 0.002 rad and RMSE of 0.009 rad, respectively. For the hourly interval dataset, the velocity bias was negligible (0 m s−1), with a RMSE of 0.036 m s−1, while the flow direction bias was 0.003 rad, with a RMSE of 0.010 rad. In addition, during the validation with buoys, we found that the accuracy of retrieving the SID flow direction is distinctly interrelated with the sea ice displacement.

Neural network approach for detecting spatial changes in catch probability of Engraulis ringens during El Niño‐Southern Oscillation events in northern Chile

AbstractEngraulis ringens (anchovy) is a small pelagic fish of the Family Engraulidae that inhabits the neretic‐coastal zone from northern Peru to south‐central Chile. It is the main resource species of industrial fishing of northern Chile, representing 80% of the annual landings of the purse seine fleet. The history of this fishery (1985–2023) shows a strong decrease in annual industrial landings, especially during extreme El Niño–Southern Oscillation (ENSO) events. The greatest decrease in landings in two decades occurred in 2020, coinciding with a cold La Niña event, which had not been observed in previous La Niña events. We evaluated whether the current decrease in annual landings of E. ringens is associated with oceanographic changes in northern Chile during El Niño or La Niña events. We applied a neuronal network model to identify the spatial and temporal distribution of E. ringens using the catch probability of each boat of the industrial purse seine fleet. The selected oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height and currents) for the 2003–2020 period were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS program) and used as predictor variables of the monthly landings of E. ringens. The neural network model explained 97% of the monthly variability of catch probability of E. ringens by the industrial purse seine fleet. The spatial distribution of catch probability of E. ringens was analyzed independently for El Niño (2015), La Niña (2007, 2013, and 2020), and Neutral (2004) years. We found that catch probability extended further west during La Niña events (except for 2020), occupying a greater area, but were limited to a 10 nautical mile coastal strip during the El Niño event. The spatial distribution of catch probability in the Neutral condition was near the coast, although not as restricted as during the El Niño event. The higher catch probabilities in the La Niña event of 2020 were near the coast, in contrast to the previous La Niña events of 2007 and 2013, due to the restriction of the optimal habitat of E. ringens in response to changes in oceanographic conditions. The application of the results of this study will allow understanding and probably anticipating the consequences that extreme ENSO events could have on the catch probability of the industrial anchovy purse seine fleet in northern Chile.

Spatial-temporal characteristics of temperature in Indonesian Sea based on a high-resolution reanalysis data

Influenced by both the Pacific and Indian Oceans, the temperature field in the Indonesian Sea are complicated. In order to reveal the spatial-temporal variability, the surface, thermocline and intermediate layers of the temperature field are studied based on the global ocean reanalysis data of Copernicus Marine Environment Monitoring Service (CMEMS) from 1993 to 2019 and the Empirical Orthogonal Function (EOF) analysis method. The results show that there is a warming trend of 1.36×10−2 °C /year in the surface layer. The first two modes both show reverse changes close to the Pacific and Indian Oceans. The correlation coefficients between the first and second modal time coefficients and the Niño 3.4 index are 0.62 and 0.48, respectively. And the correlation coefficient between the second modal time coefficient and the ITF inflow is -0.45. In the thermocline, there is a warmer trend, which is 4.78×10−2 °C /year. The correlation index of the first and second modal time coefficient with the Niño 3.4 and DMI indices are 0.87 and 0.43, respectively. The correlation index between the first and second modal time coefficient and the ITF inflow are -0.60 and 0.38, respectively. In the intermediate layer, the warming trend is 2.18×10−2 °C /year. From 1993 to 1999, from 2000 to 2016 and from 2017 to 2019, the Sulu Sea and northern Halmahera Sea experienced three periods of warming, cooling and warming, respectively. The study is helpful for further understanding the variation of the temperature field in the Indonesian Sea.

Thermal efficiency dataset around Cuban seas (TEDACS)

Currently, the generation of electrical energy in Cuba is supported by oil and natural gas. These sources, as it is known, are directly linked to large emissions of pollutants that are released into the environment. Therefore, it is necessary to search for new energy options that are directed towards sustainable development, allowing the preservation of natural ecosystems. Owing to the location and geographical characteristics of Cuba, it is necessary to assess the energy possibilities of the seas that surround it and to search for the most feasible areas to obtain energy from the sea temperature. This renewable energy source, in addition to being used to generate electricity, can also be used in derived technologies, such as desalination, refrigeration, and aquaculture. Hence, a dataset is presented with the calculation of the thermal efficiency for the exploitation of thermal energy from the sea, which is based on the thermal gradient between the sea potential temperatures between the shore and the level of depth being analyzed. Outputs of 27 years of daily data from the Copernicus Marine Environmental Monitoring Service (CMEMS) GLOBAL_MULTIYEAR_PHY_001_030 product with a spatial resolution of 1/12° were used. The calculation was made using a Python script of the daily thermal efficiency at depths of 763, 902, and 1062 m, as these are the levels that are traditionally studied for the exploitation of sea thermal energy. In this way, 27 files of each level were generated for a total of 81 files in text format separated by commas. Each file is presented with the date, level, coordinates, and thermal efficiency. The dataset is available from the Science Data Bank repository (https://doi.org/10.57760/sciencedb.10037).

CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system

Abstract. Observation-based data reconstructions of global surface ocean carbonate system variables play an essential role in monitoring the recent status of ocean carbon uptake and ocean acidification, as well as their impacts on marine organisms and ecosystems. So far, ongoing efforts are directed towards exploring new approaches to describe the complete marine carbonate system and to better recover its fine-scale features. In this respect, our research activities within the Copernicus Marine Environment Monitoring Service (CMEMS) aim to develop a sustainable production chain of observation-derived global ocean carbonate system datasets at high space–time resolutions. As the start of the long-term objective, this study introduces a new global 0.25∘ monthly reconstruction, namely CMEMS-LSCE (Laboratoire des Sciences du Climat et de l'Environnement) for the period 1985–2021. The CMEMS-LSCE reconstruction derives datasets of six carbonate system variables, including surface ocean partial pressure of CO2 (pCO2), total alkalinity (AT), total dissolved inorganic carbon (CT), surface ocean pH, and saturation states with respect to aragonite (Ωar) and calcite (Ωca). Reconstructing pCO2 relies on an ensemble of neural network models mapping gridded observation-based data provided by the Surface Ocean CO2 ATlas (SOCAT). Surface ocean AT is estimated with a multiple-linear-regression approach, and the remaining carbonate variables are resolved by CO2 system speciation given the reconstructed pCO2 and AT; 1σ uncertainty associated with these estimates is also provided. Here, σ stands for either the ensemble standard deviation of pCO2 estimates or the total uncertainty for each of the five other variables propagated through the processing chain with input data uncertainty. We demonstrate that the 0.25∘ resolution pCO2 product outperforms a coarser spatial resolution (1∘) thanks to higher data coverage nearshore and a better description of horizontal and temporal variations in pCO2 across diverse ocean basins, particularly in the coastal–open-ocean continuum. Product qualification with observation-based data confirms reliable reconstructions with root-mean-square deviation from observations of less than 8 %, 4 %, and 1 % relative to the global mean of pCO2, AT (CT), and pH. The global average 1σ uncertainty is below 5 % and 8 % for pCO2 and Ωar (Ωca), 2 % for AT and CT, and 0.4 % for pH relative to their global mean values. Both model–observation misfit and model uncertainty indicate that coastal data reproduction still needs further improvement, wherein high temporal and horizontal gradients of carbonate variables and representative uncertainty from data sampling would be taken into account as a priority. This study also presents a potential use case of the CMEMS-LSCE carbonate data product in tracking the recent state of ocean acidification. The data associated with this study are available at https://doi.org/10.14768/a2f0891b-763a-49e9-af1b-78ed78b16982 (Chau et al., 2023).

Exploring the interplay of chlorophyll-a, sea surface temperature, and sea surface salinity in aceh waters during january and july 2022

This study investigates the relationship among chlorophyll-a (Chl-a), sea surface temperature (SST), and sea surface salinity (SSS) in Aceh Waters, Indonesia using data from January and July 2022. Chl-a, SST, and SSS data were retrieved from the Copernicus Marine Environment Monitoring Service (CMEMS) database. Pearson correlation analysis was employed to assess the connections among Chl-a, SST, and SSS within the Aceh Waters region. The findings reveal that all three parameters - Chl-a, SST, and SSS - conform to the seasonal monsoon patterns observed in January and July 2022. The correlation analysis conducted for January revealed the following relationships: a negative correlation between Chl-a and SST (-0.649), an inverse correlation between Chl-a and SSS (-0.215), and a positive correlation between SST and SSS (0.493). Conversely, correlations for July reveal a negative correlation between Chl-a and SST (-0.503), a positive correlation between Chl-a and SSS (0.039), and a negative correlation between SST and SSS (-0.478). Overall, this study elucidates the complex relationship among Chl-a, SST, and SSS in Aceh Waters, which is influenced by seasonal monsoon variations. Understanding this relationship is essential for assessing marine environmental dynamics and their potential impacts on ecosystems and human activities in the region.

The effect of offshore wind farms on the variation of the phytoplankton population

Ocean and coastal areas have the opportunity to harness renewable energy with a rapidly growing rate of investment. Among the categories of marine energy, offshore wind energy produces an enormous amount of electricity for over 15 countries. The devices used for harvesting renewable energy can directly or indirectly affect habitat change, climate change, material-energy cycling, and the development of new communities. Studying their impacts can help fill the knowledge gap related to topics such as global carbon management and the reduction of ecological risks. This study aims to investigate changes in phytoplankton population after offshore wind farm (OWF) construction events at intervals of 2, 4, 7, 11, 18, 30, 50, and 80 days. The wind farms selected for this study are located in the North Sea, which is the most active area for OWF activity. The necessary information for this study was gathered from the Copernicus Marine Environment Monitoring Service (CMEMS) and the 4 C Offshore database. Python package in Jupyter Notebook and SPSS (version 25) were used to identify significant changes with a 95% confidence interval, along with their effect size. The results of the study are reported in four sets based on the eutrophication status of the studied OWFs. The events that have a diminishing effect on the phytoplankton population include foundation installation, array cable installation, and fully commissioning. Turbine installation was the only event where primary producers experienced growth afterward. The occurrence and duration of phytoplankton population changes during different eutrophication zones vary. The findings of this study can be beneficial for governments and various organizations when making decisions about the fishing industry, coastal management, ecosystem-based management, and ecological studies.

Intercomparing the quality of recent reanalyses for offshore wind farm planning in Germany's exclusive economic zone of the North Sea

Abstract. In order to facilitate offshore wind farm tenders, Deutscher Wetterdienst (DWD, Germany's national meteorological service) provides reanalysis data and quality assessments to Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency). The regional reanalysis COSMO-REA6 is used besides the global reanalysis ERA5. New reanalyses and derived products getting available are (i) the regional reanalysis CERRA (C3S), (ii) COSMO-R6G2, a successor of COSMO-REA6 which is currently produced by DWD and (iii) HoKliSim-De, a convection-permitting climate simulation for Germany with COSMO-CLM as a regional downscaling of ERA5. In the present study, the quality of the different data sets for offshore wind energy application is compared using in-situ measurements of the wind speed and wind direction from the top anemometer and vane of the FINO1 research platform and satellite-based data of the near-surface wind speed from the Copernicus Marine Environment Monitoring Service (CMEMS) and the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). Evaluation at FINO1 focuses on the time period prior to the installation of nearby wind farms to avoid wake effects. COSMO-REA6, CERRA and HoKliSim-De show only small biases and resemble the observed distribution of the wind speed at FINO1 whereas ERA5 shows slightly lower values of the wind speed at 100 m. All model-based products tend to slightly underestimate the occurrence of south-westerly wind directions and overestimate wind directions from West to Northwest. Smallest directional biases are analysed for COSMO-REA6. Analysis of the windstorm CHRISTIAN suggests that ensemble information is required for the representation of individual extreme events. Evaluation of the near-surface wind speed using satellite-based data is performed for an area around the German Exclusive Economic Zone (EEZ) of the North Sea. The median bias of ERA5 and COSMO-REA6 is close to zero. CERRA shows a systematic overestimation of the near-surface wind speed compared to the satellite-based reference datasets. By contrast, a slight underestimation is analysed for HoKliSim-De. The bias distribution analysed for a first simulation stream of COSMO-R6G2 is similar to COSMO-REA6 which provides initial indication for the applicability of the new product.

Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model

Abstract. Local analytical optimal nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-resolution (3–5 km) pan-Arctic coupled ocean and sea ice modeling and prediction system based on the HYbrid Coordinate Ocean Model (HYCOM version 2.2.98) and the Los Alamos multi-category sea ice model (CICE version 5.1.2), with the LAON for data assimilation. In this study, our focus is on the LAON assimilation of AMSR2 SIC, which is designed to update the model SIC in every time step such that the analysis will eventually reach the optimal estimate. The SIC innovation (observation minus model) is designed to be proportionally distributed to the multiple sea ice categories. A hindcast experiment is performed with and without the LAON assimilation for the period 1 January 2021 to 30 April 2022, in which the extra computational cost for the LAON assimilation is about 5 % of the free run without assimilation. The results show that the LAON assimilation greatly improves the simulated sea ice concentration, extent, area, thickness, and volume, as well as the sea surface temperature (SST). It also produces significantly more accurate sea ice edge and marginal zone (MIZ) than the observed AMSR2 SIC that is assimilated when evaluated against the Norwegian Ice Service (NIS) ice chart. The results are also compared with the Copernicus Marine Environment Monitoring Service (CMEMS) operational SIC analyses from NEMO, TOPAZ4, and neXtSIM, which use ensemble Kalman filters and direct insertion for data assimilation. It is shown that the LAON assimilation produces significantly lower integrated ice edge error (IIEE) and integrated MIZ error (IME) than the CMEMS SIC analyses when evaluated against the NIS ice chart. LAON also produces a continuous and smooth evolution of sub-daily SIC, which avoids abrupt jumps often seen in other assimilated products. This efficient and accurate method is promising for data assimilation in global and high-resolution models.

The response of tuna to ocean acidification in Indonesian waters (Case study: Gulf of Bone)

There is growing concern about ocean acidification (decrease in pH of the ocean as a result of increased atmospheric carbon dioxide absorption by ocean) as one threat of climate change that may have significant impacts on marine organisms, such as fish. Recent studies suggest that adult fish are not directly impaired by OA, however, for the earliest fish stages, a number of direct effects have been observed. Hence, we observed the response of OA on monthly larvae density of yellowfin tuna in the Indonesian water, especially in the Gulf of Bone. The pH on the total scale (pH) and surface aqueous partial pressure of CO2 (pCO2) data were derived from Copernicus Marine Environment Monitoring Service (CMEMS) model product; meanwhile, fish data from 2014-2016 were derived from daily Infrastructure Development for Space Oceanography (INDESO) tuna population model outputs. This study indicates that the variability of pCO2 tends to increase while the pH tends to decline. During the northwest monsoon periods, pH in the Gulf of Bone tends to be lower. The larvae and juvenile of yellowfin tuna in the Gulf of Bone waters have various spatial correlations with pH and pCO2. Both have the potential to decrease with the declined pH and elevated pCO2.

Spatiotemporal Variation of Anticyclonic Eddies in the South China Sea during 1993–2019

Based on the absolute dynamic topography data from the Copernicus Marine Environment Monitoring Service, this paper applies the Topographic Position Index to develop a new approach for mapping the anticyclonic eddies in the South China Sea (SCS). The results show that anticyclonic eddies are active in the deep basin of SCS, and the five selected parameters (number or frequency, lifetime, kinetic energy, amplitude, and area or radius) of anticyclonic eddies have a similar temporal variation and a similar spatial distribution pattern. (1) As for monthly variations, anticyclonic eddies are active in late spring and most active in summer. (2) The El Niño–Southern Oscillation had a stronger impact on the inter-annual variations of anticyclonic eddies in the SCS before 2013, resulting in a significant transition of inter-annual variations of these five parameters in around 2004. After 2013, most of these five parameters had a minimum in 2015 and a maximum in 2017. (3) Analyses show that the eddy activities in the SCS are significantly influenced by the monsoon wind and the western boundary current like Kuroshio. Therefore, the areas southwest of Taiwan Island and east of Vietnam are the two areas where the anticyclonic eddies are most active, with much larger eddy kinetic energy and much higher eddy amplitude.

Analysis of chlorophyll-a, sea surface temperature, and sea surface salinity in the Bay of Bengal

The waters of the Bay of Bengal (BoB) are influenced by the northeast monsoon (November-February) and the southwest monsoon (June-September). This study aims to determine the relationship between sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chl-a) during January 2021 in the BoB. The method used in this study is the method of correlation analysis, hypothesis testing, and analysis of variance using monthly average data for January 2021. The data comes from the Copernicus Marine Environment Monitoring Service (CMEMS) portal. Our analysis showed that high Chl-a was inversely related to low SST and SSS conditions and vice versa. This finding confirms the results of other previous studies. Medium and negative correlations occurred for the Chl-a - SST and Chl-a - SSS pairs. Meanwhile, the SST- SSS pair shows a strong and positive correlation. The hypothesis test shows that the conclusions drawn from these correlation relationships are acceptable. Furthermore, from the analysis of variance obtained, it was found that Chl-a had a significant effect on variations in SST and SSS, while SST had a significant effect on variations in SSS.

STUDY OF POTENTIAL INSTALLATION OF OCEAN THERMAL ENERGY CONVERSION (OTEC) IN THE NORTH WATERS OF LEMBATA, NTT

Ocean thermal energy conversion is an attempt to convert potential energy in the variances heat content of seawater into other energy by utilizing the temperature change between the sea surface and deep sea at least 20°C. The Lembata waters is near to the equator, sea surface temperature tends to be warm and stable. This research was conducted to estimate the potential energy generated from a closed cycle OTEC system in North Lembata Waters. This study used temperature data from Global Ocean Physics Reanalysis from Copernicus Marine Environment Monitoring Service (CMEMS) for 9 years (2012-2020) in 6 stations. Validation was performed using the primary CTD Lembata OTEC Team of the Marine Geological Institute (MGI). Temperature data validation results on the MSE (Mean Square Error), RMSE (Root Mean Square Error), and MAPE (Mean Absolute Percentage Error) methods are considered to represent field temperature conditions. The variability value shows the station point in the North Lembata Waters has a temperature with slight differences. The vertical temperature change (ΔT) shows between 20.98°C to 23.44°C. Potential electric power resulting from the OTEC system using the technical estimation formula. The average net power generated from those temperature gradients ranges from 5.65 MW-7.56 MW, respectively. The Lembata waters have temperature conditions suitable for OTEC installations. Station C-4 has a power potential of 6.84 MW with a depth of 763 m and the distance of 1.86 km from the coastline. Station C-4 in the Omesuri sub-district is the best point for OTEC installation in North Lembata Waters.

Reconstructing three-dimensional salinity field of the South China Sea from satellite observations

High-resolution salinity information is of great significance for understanding the marine environment. We here propose a deep learning model denoted the “Attention U-net network” to reconstruct the daily salinity fields on a 1/4° grid in the interior of the South China Sea (SCS) from satellite observations of surface variables including sea surface salinity, sea surface temperature, sea level anomaly, and sea surface wind field. The vertical salinity profiles from the GLORYS2V4 reanalysis product provided by Copernicus Marine Environment Monitoring Service were used for training and evaluating the network. Results suggest that the Attention U-net model performs quite well in reconstructing the three-dimensional (3D) salinity field in the upper 1000 m of the SCS, with an average root mean square error (RMSE) of 0.051 psu and an overall correlation coefficient of 0.998. The topography mask of the SCS in the loss function can significantly improve the performance of the model. Compared with the results derived from the model using Huber loss function, there is a significant reduction of RMSE in all vertical layers. Using sea surface salinity as model inputs also helps to yield more accurate subsurface salinity, with an average RMSE near the sea surface being reduced by 16.4%. The good performance of the Attention U-net model is also validated by in situ mooring measurements, and case studies show that the reconstructed high-resolution 3D salinity field can effectively capture the evolution of underwater signals of mesoscale eddies in the SCS. The resolution and accuracy of sea surface variables observed by satellites will continue to improve in the future, and with these improvements, more precise 3D salinity field reconstructions will be possible, which will bring new insights about the multi-scale dynamics research in the SCS.

Long-term thermohaline trends in the Sulawesi Sea based on a high-resolution reanalysis data

Based on the global ocean reanalysis data of COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS) from 1993 to 2019, the long-term thermohaline trends in the Sulawesi Sea are analyzed. The results show that the temperature of the upper middle layer of the Sulawesi Sea has a long-term increasing trend, and the fastest warming is located at 130m, which is 0.05°C/yr, implying a total of 1.35°C in the 27 years. For salinity, there is a whole negative trend, except for the surface layer. Further study shows that the temperature and salinity interannual variations show positive and negative correlation with Niño index, respectively.

The Respondence of Wave on Sea Surface Temperature in the Context of Global Change

Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over global seas from 1993–2020. The European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Marine Environment Monitoring Service (CMEMS), current and sea level were used as the forcing fields in the WW3 model. The validation of modelling simulations against the measurements from the National Data Buoy Center (NDBC) buoys and Haiyang-2B (HY-2B) altimeter yielded a root mean square error (RMSE) of 0.49 m and 0.63 m, with a correlation (COR) of 0.89 and 0.90, respectively. The terms calculated by WW3-simulated waves, i.e., breaking waves, nonbreaking waves, radiation stress, and Stokes drift, were included in the water temperature simulation by a numerical circulation model named the Stony Brook Parallel Ocean Model (sbPOM). The water temperature was simulated in 2005–2015 using the high-quality Simple Ocean Data Assimilation (SODA) data. The validation of sbPOM-simulated results against the measurements obtained from the Array for Real-time Geostrophic Oceanography (Argo) buoys yielded a RMSE of 1.12 °C and a COR of 0.99. By the seasonal variation, the interrelation of the currents, sea level anomaly, and significant wave heights (SWHs) were strong in the Indian Ocean. In the strong current areas, the distribution of the sea level was consistent with the SWHs. The monthly variation of SWHs, currents, sea surface elevation, and sea level anomalies revealed that the upward trends of SWHs and sea level anomalies were consistent from 1993–2015 over the global ocean. In the Indian Ocean, the SWHs were obviously influenced by the SST and sea surface wind stress. The rise of wind stress intensity and sea level enlarges the growth of waves, and the wave-induced terms strengthen the heat exchange at the air–sea layer. It was assumed that the SST oscillation had a negative response to the SWHs in the global ocean from 2005–2015. This feedback indicates that the growth of waves could slow down the amplitude of water warming.

An Evaluation of a 1/18o Resolution Regional Ocean Circulation Model of CROCO in the Southern Sunda Shelf

The Coastal and Regional Ocean Community Model (CROCO) was applied to simulate the three dimensional ocean circulation on the southern Sunda Shelf with a horizontal resolution of 1/18o and 40 vertical layers. This configuration is called as CROCO18-SSS. The initial and open boundary conditions were derived from a global reanalysis product at a resolution of 1/12o from the Copernicus Marine Environment Monitoring Service (CMEMS). The regional model was driven by atmospheric, tides, and river discharges data from a wide variety of datasets, each with a different resolution. To test the model performance and capabilities of the CROCO18-SSS model outputs including sea level anomaly, current velocity, temperature, salinity, and volume transport, we have compared in the simulation results with other reanalysis product and satellite-derived datasets. The CROCO18-SSS model outputs reproduced the regional ocean circulation well from observations and reanalysis products. For example, the reversal of the monsoonal current in the southern sunda shelf, as well as strong persistent southward Makassar throughflow and their interaction in the southern Makassar Strait, can be seen clearly. The vertical structure of the zonal current component was comparable to that of the reanalysis product reference. The seasonal shift in temperature and salinity, with the water becoming colder and saltier during the northwest monsoon and warmer and fresher during the southeast monsoon, was also accurately reproduced by the  CROCO18-SSS model.  Additionally, the mean volume transport calculated by the CROCO18-SSS model for the Makassar and Karimata Straits was comparable to that reported in previous studies.

Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

Evaluating the influence of environmental variables on the length-weight relationship and prediction modelling in flathead grey mullet, Mugil cephalus Linnaeus, 1758.

Fish stocks that are grown under diverse environmental conditions have different biometric relationships and growth patterns. The biometric length-weight relationship (LWR) is an essential fishery assessment tool, as fish growth is continuous and depends on genetic and environmental factors. The present study attempts to understand the LWR of the flathead grey mullet, Mugil cephalus Linnaeus, 1758, from different locations. The study area encompassed its distribution in the wild across freshwater location (one), coastal habitats (eight locations), and estuaries (six locations) in India to determine the relationship between various environmental parameters. Specimens (n = 476) of M. cephalus were collected from commercial catches and the length and weight of individual specimens were recorded. Monthly data from the study locations were extracted for nine environmental variables from the datasets downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the Copernicus Marine Environment Monitoring Service (CMEMS) over 16 years (2002 to 2017) on the Geographical Information System platform. The parameters of the LWR, intercept 'a' and slope or regression coefficient 'b', varied from 0.005321 to 0.22182 and 2.235 to 3.173, respectively. The condition factor ranged from 0.92 to 1.41. The partial least squares (PLS) score scatter plot matrix indicated differences in the environmental variables between the locations. PLS analysis of the regression coefficient and environment parameters revealed that certain environment variables viz., sea surface temperature, salinity, dissolved oxygen, nitrate, and phosphate, played a positive role. However, chlorophyll, pH, silicate, and iron played a negative role in influencing weight growth across various locations. The results revealed that the M. cephalus specimens from three locations, Mandapam, Karwar, and Ratnagiri, possessed significantly higher fitness to their environment than those from the other six locations. The PLS model can be used to predict weight growth under the various environmental conditions of different ecosystems. The three identified locations are useful sites for the mariculture of this species considering their growth performance, the environmental variables, and their interactions. The results of this study will improve the management and conservation of exploited stocks in regions affected by climate change. Our results will also aid in making environment clearance decisions for coastal development projects and will improve the efficiency of mariculture systems.