Effects of Mesoscale Eddies On Intraseasonal Variability of Intermediate Water East of Taiwan

The variability of intermediate water (IW) east of Taiwan was investigated utilizing 17 months of long-term, continuous and synchronous measurements of temperature, salinity and current from mooring sites deployed at 122ºE/23ºN from January 2016 to May 2017. For the first time, we prove that the intraseasonal variability in the IW within significant periods of ~80 days was caused by mesoscale eddies propagating westward from the Subtropic Counter Current (STCC) area. The correlation coefficients between sea level anomalies (SLAs) and the Kuroshio, and between SLAs and the minimum salinity in the intermediate layer, were 0.63 and 0.52, respectively. The anticyclonic (cyclonic) eddies from the STCC, increased (decreased) the speed of the Kuroshio as well as increase (decrease) the temperature and salinity in the 400–600 m in east of Taiwan. Combines Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) products data, showed that temperature and salinity increased (decreased) in the intermediate layer due to the downward (upward) vertical movement of the water mass by anticyclonic (cyclonic) eddies. Anticyclonic eddies strengthened the Kuroshio and benefitted SCSIW flowing through the Luzon Strait to enhance salinity, while cyclonic eddies weakened the Kuroshio and favored relatively low-salt NPIW, in the area east of Taiwan.

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns.

Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cantconsequences in coastal operations, such as naval engineering and navigation. Conventional sealevel predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elementsand require long-term historical sea level data. In this paper, two deep learning approachesare applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), andthe second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (InexpensiveDevice for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained themodel for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1stMay to 1st August. We compared forecasting results with RNN and LSTM with the results of theconventional method, namely tidal harmonic analysis. The LSTM’s results showed better performancethan the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 andan RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodatenon-tidal harmonic data such as sea level anomalies.

Assemblage Structure of the Ichthyoplankton and Its Relationship With Environmental Factors in Spring and Autumn off the Pearl River Estuary

Ichthyoplankton assemblages and their relationship with environmental variables are investigated in waters off the Pearl River Estuary in spring and autumn of 2019. Of 80 ichthyoplankton taxa identified using DNA barcode and morphological methods, 61 are identified to species. The most abundance families (Carangidae, Trichiuridae, Mullidae, and Scombridae) account for 61.34% of the horizontal total catch in spring, while Menidae and Carangidae are the most abundant families identified in autumn, accounting for 89.72% of the horizontal total catch. Cluster analysis identifies three species assemblages in spring, and four in autumn based on horizontal trawls. Relationships between assemblage structure and environmental variables (in situ and remote sensed) are determined by canonical correspondence analysis. Ichthyoplankton assemblage structure appears to be strongly influenced by sea level anomalies, salinity, water depth, temperature at 10 m depth, and distance from shore. We demonstrate the efficacy of using DNA barcode to identify ichthyoplankton, and suggest how these data can be used to protect fish spawning grounds in waters off the Pearl River Estuary.

North SEAL: a new dataset of sea level changes in the North Sea from satellite altimetry

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal, and economic issues. This article reports about a new sea level dataset for the North Sea (named North SEAL) of monthly sea level anomalies (SLAs), absolute sea level trends, and amplitudes of the mean annual sea level cycle over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data preprocessed with coastal dedicated approaches and gridded with an innovative least-squares procedure including an advanced outlier detection to a 6–8 km wide triangular mesh. The comparison of SLAs and tide gauge time series shows good consistency, with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8 %–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best consistency with a median difference of 0.04±1.15 mm yr−1 is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections, and studies on coastal adaptation measures. The North SEAL data are available at https://doi.org/10.17882/79673 (Müller et al., 2021).

Positive Regional Sea Level Anomalies in Southern Brazil Due to Changes in Austral Atmospheric Circulation

Pressure gradients and winds play an important role in Southern Hemisphere (SH) sea levels, which are currently associated with the positive trend of the Southern Annular Mode (SAM). This study investigated regional sea level anomalies (SLAs) in the southern coast Brazil using altimeter data (1993–2019), post-processed by the X-TRACK (CTOH/LEGOS). We observed a negative SLA from 1993 to 2009 and a positive SLA from 2010 to 2019, with upward trends throughout the evaluation period. We analyzed wind stress curl, pressure, and wind fields at sea level (FNMOC and ERA 5, respectively) in addition to sea surface temperature and height anomalies (SSTA/SSHA-OISST) in the South Atlantic Ocean (SAO) for 1993–2009 and 2010–2019. In relation to the first period, the second shows the enhancement in Hadley and Walker cells and trade winds, in addition to greater SSTA and SSHA in SAO. The SAO subtropical gyre and zonal winds at 45°S contribute to the intensification of the western boundary current. A greater pressure gradient between the SAO surface and the southeast of South America is noteworthy. Regionally, the positive SAM brings an increase in sea level to the study area, caused by greater wind stress and variability in heat flows.

Absolute Baltic Sea Level Trends in the Satellite Altimetry Era: A Revisit

The absolute sea level trend from May 1995 to May 2019 in the Baltic Sea is analyzed by means of a regional monthly gridded dataset based on a dedicated processing of satellite altimetry data. In addition, we evaluate the role of the North Atlantic Oscillation and the wind patterns in shaping differences in sea level trend and variability at a sub-basin scale. To compile the altimetry dataset, we use information collected in coastal areas and from leads within sea-ice. The dataset is validated by comparison with tide gauges and the available global gridded altimetry products. The agreement between trends computed from satellite altimetry and tide gauges improves by 9%. The rise in sea level is statistically significant in the entire region of study and higher in winter than in summer. A gradient of over 3 mm/yr in sea level rise is observed, with the north and east of the basin rising more than the south-west. Part of this gradient (about 1 mm/yr) is directly explained by a regression analysis of the wind contribution on the sea level time series. A sub-basin analysis comparing the northernmost part (Bay of Bothnia) with the south-west reveals that the differences in winter sea level anomalies are related to different phases of the North-Atlantic Oscillation (0.71 correlation coefficient). Sea level anomalies are higher in the Bay of Bothnia when winter wind forcing pushes waters through Ekman transport from the south-west toward east and north. The study also demonstrates the maturity of enhanced satellite altimetry products to support local sea level studies in areas characterized by complex coastlines or sea-ice coverage. The processing chain used in this study can be exported to other regions, in particular to test the applicability in regions affected by larger ocean tides.

Cross-Calibrations of the HY-2B Altimeter Using Jason-3 Satellite During the Period of April 2019–September 2020

In 2018, the Haiyang-2B (HY-2B) satellite altimeter was sent to orbit as a follow-up mission of the HY-2A satellite altimeter. The performance of the HY-2B system over the global oceans is considered to be critical. However, its performance is not fully known at the present time. In the present study, the first global quality assessment of the HY-2B Geophysical Data Record (GDR) was presented using comparison and crossover analysis processes of the main parameters and sea level anomalies (SLAs) with Jason-3 GDR data. This study’s assessment results demonstrated that the editing proportion of unqualified data for the HY-2B was 2.67%, which was at a similar level as the Jason-3 (2.86%). In addition, this study’s assessment results of the HY-2B key parameters (mainly the backscatter coefficients, significant wave heights, sea state bias, wet troposphere delays, and ionosphere delays) showed good agreement with the Jason-3, and there were no abnormal trends observed. The mean and standard deviations (STDs) were determined to be (0.21 ± 6.70) cm and (−3.4 ± 6.25) cm for the SLA differences at the self-crossover points of the HY-2B and dual-crossover points between the HY-2B and Jason-3 satellites, respectively. In addition, the SLA crossover analysis results indicated that the accuracy of the sea surface heights for the HY-2B was close to that of the Jason-3 satellite. The spatial distributions of the SLA differences showed no significant errors in the geographic characteristics. The SLA measurements were assessed using a wavenumber spectra method. The obtained results suggested that the power spectrum of the SLAs of the HY-2B satellite followed the regular patterns of the traditional Jason-3 altimeter. Furthermore, based on the spectrum analysis results, it was revealed that the noise level of the HY-2B was lower than that of the Jason-3, indicating a good overall performance of the HY-2B.