The sea ice conditions in the Kara Sea have important impacts on Arctic shipping, oil and gas production, and marine environmental changes. In this study, sea ice coverage (C_R) less than 30% is considered as open water, its onset and end dates are defined as T_open and T_close, respectively. The sea ice melt onset (T_melt) is defined as the date when ice-sea freshwater flux initially changes from ice into the ocean. Satellite-based sea ice concentration (SIC) from 1989 to 2019 shows a negative correlation between T_open and T_close (r = –0.77, p < 0.01) in the Kara Sea. This phenomenon is also obtained through analyzing the hindcast simulation from 1994 to 2015 by a coupled ocean and sea-ice model (NAPA1/4). The model results reveal that thermodynamics dominate the sea ice variations, and ice basal melt is greater than the ice surface melt. Heat budget estimation suggests that the heat flux is significant correlated with T_open (r = –0.95, p < 0.01) during the melt period (the duration of multi-year averaged T_melt to T_open) influenced by the sea ice conditions. Additionally, this heat flux is also suggested to dominate the interannual variation of the heat input during the whole heat absorption process (r = 0.81, p < 0.01). The more heat input during this process leads to later T_close (r = 0.77, p < 0.01). This is the physical basis of the negative correlation between T_open and T_close. Therefore, the duration of open water can be predicted by T_open and thence support earlier planning of marine activities.

Sea ice velocity impacts the distribution of sea ice, and the flux of exported sea ice through the Fram Strait increases with increasing ice velocity. Therefore, improving the accuracy of estimates of the sea ice velocity is important. We introduce a pyramid algorithm into the Horn-Schunck optical flow (HS-OF) method (to develop the PHS-OF method). Before calculating the sea ice velocity, we generate multilayer pyramid images from an original brightness temperature image. Then, the sea ice velocity of the pyramid layer is calculated, and the ice velocity in the original image is calculated by layer iteration. Winter Arctic sea ice velocities from 2014 to 2016 are obtained and used to discuss the accuracy of the HS-OF method and PHS-OF (specifically the 2-layer PHS-OF (2LPHS-OF) and 4-layer PHS-OF (4LPHS-OF)) methods. The results prove that the PHS-OF method indeed improves the accuracy of sea ice velocity estimates, and the 2LPHS-OF scheme is more appropriate for estimating ice velocity. The error is smaller for the 2LPHS-OF velocity estimates than values from the Ocean and Sea Ice Satellite Application Facility and the Copernicus Marine Environment Monitoring Service, and estimates of changes in velocity by the 2LPHS-OF method are consistent with those from the National Snow and Ice Data Center. Sea ice undergoes two main motion patterns, i.e., transpolar drift and the Beaufort Gyre. In addition, cyclonic and anticyclonic ice drift occurred during winter 2016. Variations in sea ice velocity are related to the open water area, sea ice retreat time and length of the open water season.

We report on a hexactinellid sponge new to science, Walteria demeterae sp. nov., which was collected from the northwestern Pacific seamounts at depths of 1 271–1 703 m. Its tubular and basiphytous body, extensive lateral processes, numerous oval lateral oscula which are irregularly situated in the body wall, the presence of microscleres with oxyoidal, discoidal and onychoidal outer ends, and the absence of anchorate discohexasters, indicate it belongs to the genus Walteria of family Euplectellidae, which is also supported by molecular phylogenetic evidence from 18S, 28S, 16S rRNA and cytochrome c oxidase subunit I (COI) gene sequences. The unique morphotype, which is structured by a thin and rigid framework of body wall and lateral processes consisting of diactins, characterizes it as a new species. Local aggregations of individuals of this new species coupled with their associated macrofauna in the Suda Seamount are reported, highlighting its functional significance in providing biogenic microhabitats in the deep sea.

The present climate simulation and future projection of the mixed layer depth (MLD) and subduction process in the subtropical Southeast Pacific are investigated based on the geophysical fluid dynamics laboratory earth system model (GFDL-ESM2M). The MLD deepens from May and reaches its maximum (>160 m) near (24°S, 104°W) in September in the historical simulation. The MLD spatial pattern in September is non-uniform in the present climate, which shows three characteristics: (1) the deep MLD extends from the Southeast Pacific to the West Pacific and leads to a “deep tongue” until 135°W; (2) the northern boundary of the MLD maximum is smoothly near 18°S, and MLD shallows sharply to the northeast; (3) there is a relatively shallow MLD zone inserted into the MLD maximum eastern boundary near (26°S, 80°W) as a weak “shallow tongue”. The MLD non-uniform spatial pattern generates three strong MLD fronts respectively in the three key regions, promoting the subduction rate. After global warming, the variability of MLD spatial patterns is remarkably diverse, rather than deepening consistently. In all the key regions, the MLD deepens in the south but shoals in the north, strengthing the MLD front. As a result, the subduction rate enhances in these areas. This MLD antisymmetric variability is mainly influenced by various factors, especially the potential-density horizontal advection non-uniform changes. Notice that the freshwater flux change helps to deepen the MLD uniformly in the whole basin, so it hardly works on the regional MLD variability. The study highlights that there are regional differences in the mechanisms of the MLD change, and the MLD front change caused by MLD non-uniform variability is the crucial factor in the subduction response to global warming.

Alentiana has only one member, A. aurantiaca (Verrill, 1885), commensal with sea anemone (Bolocera tuediae). Here, Alentiana palinpoda sp. nov., a second member in the genus Alentiana (subfamily Lepidastheniinae), a new commensal polynoid is described, based on a specimen collected from a deep-water seamount in the Northwest Pacific Ocean. The new species is characterized by large elytra that completely cover the dorsum and are present in every third segment in the posterior regions. Neuropodia are truncated, with two types of neurochaetae; supraacicular chaetae toothed on one side with a slightly inflated end, and subacicular chaetae that are heavy and smooth; the prechaetal lobe is larger than the postchaetal lobe, with the ventral part rolling upward and backward.

The influences of the three types of reanalysis wind fields on the simulation of three typhoon waves occurred in 2015 in offshore China were numerically investigated. The typhoon wave model was based on the simulating waves nearshore model (SWAN), in which the wind fields for driving waves were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-interim), the National Centers for Environmental Prediction climate forecast system version 2 (CFSv2) and cross-calibrated multi-platform (CCMP) datasets. Firstly, the typhoon waves generated during the occurrence of typhoons Chan-hom (1509), Linfa (1510) and Nangka (1511) in 2015 were simulated by using the wave model driven by ERA-interim, CFSv2 and CCMP datasets. The numerical results were validated using buoy data and satellite observation data, and the simulation results under the three types of wind fields were in good agreement with the observed data. The numerical results showed that the CCMP wind data was the best in simulating waves overall, and the wind speeds pertaining to ERA-Interim and CCMP were notably smaller than those observed near the typhoon centre. To correct the accuracy of the wind fields, the Holland theoretical wind model was used to revise and optimize the wind speed pertaining to the CCMP near the typhoon centre. The results indicated that the CCMP wind-driven SWAN model could appropriately simulate the typhoon waves generated by three typhoons in offshore China, and the use of the CCMP/Holland blended wind field could effectively improve the accuracy of typhoon wave simulations.

Authigenic carbonate samples were collected from the northern Okinawa Trough. Based on their carbon and oxygen isotopes, these samples were found to be methane-related carbonates precipitated by the anaerobic oxidation of methane (AOM). Petrological analysis revealed numerous framboidal pyrites that had been partly oxidized. In order to trace the variation and diagenetic information of these framboidal pyrites, their diameters and geochemical components were studied using an electron probe. The results showed that their diameters varied from 4 µm to 17 µm (n = 60; geometric mean of 9.9 µm) and were of a normal distribution. The diameters of single pyrite that formed the framboidal pyrites varied from 1 µm to 2 µm. The framboidal pyrites with diameters of 6–14 µm accounted for ~80% of the total. The geometric mean of 9.9 µm indicates that they are probably diagenetic pyrites that were precipitated in a lower dysoxic environment (weakly oxygenated bottom waters). The S/Fe ratio of the framboidal minerals ranged from 0 to 1.67, and the pyrite content of single framboid varied between 0% and 86.4%. Therefore, numerous pyrites were oxygenated to iron oxides or oxyhydroxides, and were retained as pseudomorphism pyrites. The size of framboidal pyrites precipitated in cold seeps can be used to trace the redox environment; however, acquisition of additional data via investigation of different cold seeps is necessary to obtain more persuasive results.

Arctic absolute sea level variations were analyzed based on multi-mission satellite altimetry data and tide gauge observations for the period of 1993–2018. The range of linear absolute sea level trends were found −2.00 mm/a to 6.88 mm/a excluding the central Arctic, positive trend rates were predominantly located in shallow water and coastal areas, and negative rates were located in high-latitude areas and Baffin Bay. Satellite-derived results show that the average secular absolute sea level trend was (2.53±0.42) mm/a in the Arctic region. Large differences were presented between satellite-derived and tide gauge results, which are mainly due to low satellite data coverage, uncertainties in tidal height processing and vertical land movement (VLM). The VLM rates at 11 global navigation satellite system stations around the Arctic Ocean were analyzed, among which 6 stations were tide gauge co-located, the results indicate that the absolute sea level trends after VLM corrected were of the same magnitude as satellite altimetry results. Accurately calculating VLM is the primary uncertainty in interpreting tide gauge measurements such that differences between tide gauge and satellite altimetry data are attributable generally to VLM.

Spilled oil floats and travels across the water’s surface under the influence of wind, currents, and wave action. Wave-induced Stokes drift is an important physical process that can affect surface water particles but that is currently absent from oil spill analyses. In this study, two methods are applied to determine the velocity of Stokes drift, the first calculates velocity from the wind-related formula based upon a one-dimensional frequency spectrum, while the second determines velocity directly from the wave model that was based on a two-dimensional spectrum. The experimental results of numerous models indicated that: (1) oil simulations that include the influence of Stokes drift are more accurate than that those do not; (2) for medium and long-term simulations longer than two days or more, Stokes drift is a significant factor that should not be ignored, and its magnitude can reach about 2% of the wind speed; (3) the velocity of Stokes drift is related to the wind but is not linear. Therefore, Stokes drift cannot simply be replaced or substituted by simply increasing the wind drift factor, which can cause errors in oil spill projections; (4) the Stokes drift velocity obtained from the two-dimensional wave spectrum makes the oil spill simulation more accurate.

Mesoscale eddies, which are mainly caused by baroclinic effects in the ocean, are common oceanic phenomena in the Northwest Pacific Ocean and play very important roles in ocean circulation, ocean dynamics and material energy transport. The temperature structure of mesoscale eddies will lead to variations in oceanic baroclinity, which can be reflected in the sea level anomaly (SLA). Deep learning can automatically extract different features of data at multiple levels without human intervention, and find the hidden relations of data. Therefore, combining satellite SLA data with deep learning is a good way to invert the temperature structure inside eddies. This paper proposes a deep learning algorithm, eddy convolution neural network (ECN), which can train the relationship between mesoscale eddy temperature anomalies and sea level anomalies (SLAs), relying on the powerful feature extraction and learning abilities of convolutional neural networks. After obtaining the temperature structure model through ECN, according to climatic temperature data, the temperature structure of mesoscale eddies in the Northwest Pacific is retrieved with a spatial resolution of 0.25° at depths of 0–1 000 m. The overall accuracy of the ECN temperature structure is verified using Argo profiles at the locations of cyclonic and anticyclonic eddies during 2015–2016. Taking 10% error as the acceptable threshold of accuracy, 89.64% and 87.25% of the cyclonic and anticyclonic eddy temperature structures obtained by ECN met the threshold, respectively.