Copernicus Marine Environment Monitoring Service Research Articles

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 byin situmooring 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.

Rapid Response of the Norwegian Atlantic Slope Current to Wind Forcing

Abstract We explore drivers of variability in the Norwegian Atlantic Slope Current, which carries relatively warm Atlantic Water toward the Barents Sea and Arctic Ocean, using Copernicus Marine Environment Monitoring Service (CMEMS) satellite altimetry data and TOPAZ4 ocean reanalysis data. Previous studies have pointed to a variety of causes, on a variety of time scales. We use data with daily resolution to investigate day-to-day changes in ocean transport across three sections crossing the shelf-slope of Norway (Svinøy, Gimsøy, and the Barents Sea Opening). The highest (lowest) extremes in transport at all sections develop over two days as a cyclonic (anticyclonic) atmospheric pressure system approaches from the southwest, piling up (extracting) water at the coast of Norway. The actual peak is reached when the pressure system passes the site of measurement, and the transport then relaxes for the next two days as the system continues northward along the coast. Other sources of short-term variability, such as propagating continental shelf waves and baroclinic instability, are unlikely to yield covariability over large separations. Monthly variability in the current can also be explained by passing weather systems since their numbers and intensity vary greatly from month to month. Many studies of longer-term variability, especially in the Barents Sea Opening, have pointed to the North Atlantic Oscillation (NAO) as the main cause of variability. Our results show that passing weather systems offer a better explanation of month-to-month variability.

Caspian Sea Level Change Observed by Satellite Altimetry

We analyze satellite altimeter observed Caspian Sea level (CSL) changes over the period January 1993 to December 2021 using the lake level series from the Hydroweb project and global sea level anomalies (SLA) grids provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The two altimeter-based CSL series agree well at interannual and longer time scales, but show significantly large discrepancies at seasonal and shorter time scales. The large discrepancies are found to be introduced by the approximately inverted barometer (IB) correction applied to the CMEMS SLA over the Caspian Sea. The IB correction over the Caspian Sea or any enclosed lakes needs to be treated separately from the ocean by using the correct reference mean pressure. The actual IB effects over the Caspian Sea are significantly smaller than those applied in the CMEMS SLA grids. After applying an improved IB correction using the global mean sea level pressure fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis atmospheric model, the two CSL series agree remarkably well. Altimeter observed CSL series show a significant decreasing trend on top of strong seasonal variations. The estimated linear trends for the Hydroweb and CMEMS CSL series are −5.37 ± 0.11 and −5.40 ± 0.11 cm/yr, respectively. Annual amplitudes are 17.03 ± 1.33 vs. 15.79 ± 1.30 cm, with nearly the same phases. The CSL change shows notable acceleration in the decreasing trend since around 2005, and the estimated trends have increased to −8.86 ± 0.10 and −8.81 ± 0.10 cm/yr, respectively for the two-altimeter CSL series.

Merged Multi-Sensor Ocean Colour Chlorophyll Product Evaluation for the British Columbia Coast

Phytoplankton phenology studies require a dataset that is continuous in time and space since missing data have been shown to affect the accuracy of seasonality metrics. The interpolated GlobColour product provided by the Copernicus Marine Environment Monitoring Service (CMEMS) meets these requirements by being ‘gap filled’, thus yielding the highest spatial coverage. Despite being validated on a global scale, a regional comparison to in situ Chl-a concentrations should be conducted to enable product application in optically complex waters. This study aims to evaluate the performance of the GlobColour interpolated product in British Columbia coastal waters via a statistical match-up analysis and a qualitative analysis to determine whether the data reflect the region’s large-scale seasonal trends and latitudinal dynamics. Additionally, the statistical performance of the GlobColour interpolated product was compared to the original GlobColour and Ocean Colour Climate Change Initiative (OC-CCI) merged chlorophyll-a products based on in situ observations. The GlobColour interpolated product performed relatively well and was comparable to the best-performing product for each water type (RMSE = 0.28, r2 = 0.77, MdAD = 1.5, BIAS = 0.90). The statistics for all the products degraded in Case 2 waters, thus highlighting the dilemma of applying algorithms designed for Case 1 waters in Case 2 waters. Our results indicate how the quality of products can vary in different environmental conditions.

Multiple Sea Ice Type Retrieval Using the HaiYang-2B Scatterometer in the Arctic

Sea ice type classification is of great significance for the exploration of waterways, fisheries, and offshore operations in the Arctic. However, to date, there is no multiple remote sensing method to detect sea ice type in the Arctic. This study develops a multiple sea ice type algorithm using the HaiYang-2B Scatterometer (HY-2B SCA). First, the parameters most applicable to classify sea ice type are selected through feature extraction, and a stacking model is established for the first time, which integrates decision tree and image segmentation algorithms. Finally, multiple sea ice types are classified in the Arctic, comprising Nilas, Young Ice, First Year Ice, Old Ice, and Fast Ice. Comparing the results with the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Sea Ice Type dataset (SIT) indicates that the sea ice type classified by HY-2B SCA (Stacking-HY2B) is similar to OSI-SAF SIT with regard to the changing trends in extent of sea ice. We use the Copernicus Marine Environment Monitoring Service (CMEMS) high-resolution sea ice type data and EM-Bird ice thickness data to validate the result, and accuracies of 87% and 88% are obtained, respectively. This indicates that the algorithm in this work is comparable with the performance of OSI-SAF dataset, while providing information of multiple sea ice types.

Spatio-temporal assessment of the impacts of the trends in physical and biogeochemical parameters on the primary production of the Gulf of Guinea

This study applied ocean models data from Copernicus Marine Environment Monitoring Service (CMEMS) in assessing the impacts of the trends in key ocean parameters on the primary production of the Gulf of Guinea (GoG). Trend analyses, from 1993 to 2020, were done using linear regression and Mann-Kendall significance test methods to ascertain inter-annual and inter-seasonal variations and check the significance of the trends, respectively. Results affirm that temperature, salinity, nutrients, and oxygen play significant roles in the primary production of the GoG. Also, parameters such as temperature, salinity, chlorophyll-A, net primary production, phosphate, and dissolved oxygen have been experiencing increases between the study duration while silicate and nitrate have been declining in the GoG. However, there are regions and years with contrary values to the average trends. The varying level of significance of the trend showed that the impacts of the climate on the primary production of the GoG vary basin-wide.

Identification of eddy-induced chlorophyll in the Northeastern Natuna Sea

We detected a robust cyclonic eddy in the northeastern Natuna Island in the Northwest Monsoon during an extremely negative El Niño-Southern Oscillation (ENSO) period (i.e., extreme La Niña event) using the Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis results. In the intense La Niña event from 1999 to 2001, we also found an increase in the abundance of chlorophyll coincided with the eddy strength pattern using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. Furthermore, this chlorophyll intensity may be related to the upwelling of water mass to the surface at the eddy’s center. The intensification of chlorophyll compared to the average value at the corresponding year is 83,4% (1999), 37.8% (2000), 37.3% (2008), and 72.5% (2011). In this paper, we revisit the identification of solid eddies in extreme La Niña events from 1999 to 2012 and further discuss the mechanism of the relationship between chlorophyll intensity and eddy strength, especially during the months of the extreme La Niña event.

A systematic method of estimation of alongshore windstress and Ekman transport associated with coastal upwelling

A standard method for estimating alongshore windstress (AWS) and related cross-shore Ekman transport (ET) is proposed. In the absence of standard methodologies for estimating coastal angles required for AWS estimation, an estimation is typically derived on a case-to-case basis, often approximating entire coastlines with a single line segment. A novel standard parametric method for estimating this coastal angle is developed in this manuscript consisting:•Computation of Coastal Angle from Global Self-consistent Hierarchical High-resolution Shoreline (GSHHS) coastline data through line simplification•Estimation of Windstress components at coastal points from Copernicus Marine Environment Monitoring Service (CMEMS) wind data•Estimation of AWS and associated Ekman TransportThe method developed was demonstrated for the eastern and western coasts of India. While, a single line approximation was adequate for representing the west coast of India (a nearly linear coastline), an ET variation greater than 20% was introduced by oversimplification of the coastline for the east coast, which has significant curvature. This demonstrated the relevance of our systematic approach for coastline simplification, emphasizing the significance of accurate determination of coastal angles for computing ET simultaneously.

Satellite Oceanographic Data Processing and Analysis: Correlation Between Nino 3.4 Sea Surface Temperature & Sea Surface High and Wind

El Nino Southern Oscillation (ENSO) is an irregular climate oscillation induced by sea surface temperature anomalies (SSTA) in the Equatorial Pacific Ocean. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina. The objectives of this study are to: reproduce Oceanic Nino Index (ONI) based on MW OISST; produce Nino Region 3.4 wind and Sea Surface High (SSH); analyze the correlation between SST and Wind & SSH; discuss Typhoon Soudelor based on SST, Wind, and SSH; and analyze the correlation of El Nino and Precipitation in specific area. MWOI-SST was used to produce monthly mean SST over Nino 3.4 region from January 1998 – May 2020. Monthly wind data was obtained from QuickScat. Daily Sea Surface High (SSH) was obtained from Copernicus Marine Environment Monitoring Service (CMEMS). Daily precipitation from TRMM 3B42 over Bandung City (Indonesia) was used to assess the correlation between El Nino and precipitation in specific area. The results show that in Nino 3.4 region, 2015 is the hottest year during 1998-2020 period with average SST of 28.5oC, and 1999 is the coldest year with average SST of 25.7oC. The result shows that the MWOI-SST Ocean Nino Index has very strong correlation with ERSST.v5 with coefficient of correlation is 0.92 and RMSE is 0.36oC. the wind speed of Nino 3.4 region is range from 5.23 m/s to 7.97 m/s. Unlike Sea Surface Temperature (SST), annual average wind speed is more stable with monthly variation. The wind speed is observed high in the beginning and the end of years. Sea Surface Height (SSH) over Nino 3.4 region varied from 65.8 cm to 106.8 cm. 2015 is the highest SSH with annual average of 96 cm, whereas 1999 is the lowest SSH with annual average of 71.4 cm. It is observed that Sea Surface Temperature (SST) has negative correlation with wind speed with coefficient of correlation of 0.28. Conversely, Sea Surface Temperature (SST) over Nino 3.4 region has positive correlation with Sea Surface High (SSH) with coefficient of correlation of 0.30. which mean the higher temperature, the higher Sea Surface Height. During the passage of Typhoon Soudelor, there is evident cool trail along its track with rightward bias. We can assume that the decreasing precipitation in Bandung City might be affected by strong El Nino occurrence in 2015.

A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models

A 15-year reanalysis (2007–2021) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf is described. The analysis uses the Regional Ocean Modeling System (ROMS) and four-dimensional variational (4D-Var) data assimilation (DA) of observations from in situ platforms, coastal radars, and satellites. The reanalysis downscales open boundary information from the Copernicus Marine Environmental Monitoring Service (CMEMS) global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions.A complementary analysis of the mean seasonal cycle of regional circulation, also computed using ROMS 4D-Var but with climatological mean observations and forcing, is used to reduce biases in the CMEMS boundary data and to provide a dynamically and kinematically constrained Mean Dynamic Topography to use in conjunction with the assimilation of satellite altimeter sea level anomaly observations.The configuration of ROMS 4D-Var used is described, presenting details of the comprehensive suite of observations assembled, data pre-processing and quality control procedures, and background and observation error hypotheses. Control variables of the DA are the initial conditions, surface forcing, and boundary conditions of a sequence of non-overlapping 3-day analysis cycles.Comparisons to a non-assimilative version of the same ROMS model configuration show the added skill brought by assimilation of local observations. The improvement that downscaling with assimilation achieves over ocean state estimates from CMEMS and the U.S. Naval Research Laboratory Global Ocean Forecast System (GOFS) is demonstrated by the reduction in residuals of the DA, and by comparison to independent (unassimilated) observations. Wherever data volumes allow, skill assessments are made with the respect to anomalies from the mean seasonal cycle to emphasize performance at the ocean mesoscale.To highlight the utility of the analysis to inform studies related to coastal sea level variability and marine ecosystems, comparisons are made to unassimilated coastal sea level gauges and novel observations from sensors on fishing gear. The assimilation of coastal satellite altimetry data produces coastal sea level results that are coherent with observations across all time scales from interannual to tidal, while bias and correlation metrics show that bottom temperatures in regions of commercial fishing activity in the Mid-Atlantic Bight and the Gulf of Maine are modeled well.

Detection and Identification of Mesoscale Eddies in the South China Sea Based on an Artificial Neural Network Model—YOLOF and Remotely Sensed Data

Mesoscale eddies are typical mesoscale ocean phenomena that exist widely in all oceans and marginal seas around the world, playing important roles in ocean circulation and material transport. They also have important impacts on the safe navigation of ships and underwater acoustic communications. Traditional mesoscale eddy identification methods are subjective and usually depend on parameters that must be pre-defined or adjusted by experts, meaning that their accuracy cannot be guaranteed. With the rise of deep learning, the “you only look once” (YOLO) series target recognition models have been shown to present certain advantages in eddy detection and recognition. Based on sea level anomaly (SLA) data provided over the past 30 years by the Copernicus Marine Environment Monitoring Service (CMEMS), as well as deep transfer learning, we propose a method for oceanic mesoscale eddy detection and identification based on the “you only look once level feature” (YOLOF) model. Using the proposed model, the mesoscale eddies in the South China Sea from 1993 to 2021 were detected and identified. Compared with traditional recognition methods, the proposed model had a better recognition effect (with an accuracy of 91%) and avoided the bias associated with subjectively set thresholds; to a certain extent, the model also improved the detection of and the identification speed for mesoscale eddies. The method proposed in this paper not only promotes the development of deep learning in the field of oceanic mesoscale eddy detection and identification, but also provides an effective technical method for the study of mesoscale eddy detection using sea surface height data.