Antarctic coastal polynyas are biological hotspots in the Southern Ocean that support the abundance of high-trophic-level predators and are important for carbon cycling in the high-latitude oceans. In this study, we examined the interannual variation of summertime phytoplankton biomass in the Marguerite Bay polynya (MBP) in the western Antarctic Peninsula area, and linked such variability to the Southern Annular Mode (SAM) that dominated the southern hemisphere extratropical climate variability. Combining satellite data, atmosphere reanalysis products and numerical simulations, we found that the interannual variation of summer chlorophyll-a (Chl-a) concentration in the MBP is significantly and negatively correlated with the spring SAM index, and weakly correlated with the summer SAM index. The negative relation between summer Chl-a and spring SAM is due to weaker spring vertical mixing under a more positive SAM condition, which would inhibit the supply of iron from deep layers into the surface euphotic layer. The negative relation between spring mixing and spring SAM results from greater precipitation rate over the MBP region in positive SAM phase, which leads to lower salinity in the ocean surface layer. The coupled physical-biological mechanisms between SAM and phytoplankton biomass revealed in this study is important for us to predict the future variations of phytoplankton biomasses in Antarctic polynyas under climate change.

The recognition on the trend of wind energy stability is still extremely rare, although it is closely related to acquisition efficiency, grid connection, equipment lifetime, and costs of wind energy utilization. Using the 40-year (1979–2018) ERA-Interim data from the European Center for Medium-Range Weather Forecasts, this study presented the spatial-temporal distribution and climatic trend of the stability of global offshore wind energy as well as the abrupt phenomenon of wind energy stability in key regions over the past 40 years with the climatic analysis method and Mann-Kendall (M-K) test. The results show the following 5 points. (1) According to the coefficient of variation (C _v) of the wind power density, there are six permanent stable zones of global offshore wind energy: the southeast and northeast trade wind zones in the Indian, Pacific and Atlantic oceans, the Southern Hemisphere westerly, and a semi-permanent stable zone (North Indian Ocean). (2) There are six low-value zones for both seasonal variability index (S _v) and monthly variability index (M _v) globally, with a similar spatial distribution as that of the six permanent stable zones. M _v and S _v in the Arabian Sea are the highest in the world. (3) After C _v, M _v and S _v are comprehensively considered, the six permanent stable zones have an obvious advantage in the stability of wind energy over other sea areas, with C _v below 0.8, M _v within 1.0, and S _v within 0.7 all the year round. (4) The global stability of offshore wind energy shows a positive climatic trend for the past four decades. C _v, M _v and S _v have not changed significantly or decreased in most of the global ocean during 1979 to 2018. That is, wind energy is flat or more stable, while the monthly and seasonal variabilities tend to shrink/smooth, which is beneficial for wind energy utilization. (5) C _v in the low-latitude Pacific and M _v and S _v in both the North Indian Ocean and the low-latitude Pacific have an obvious abrupt phenomenon at the end of the 20th century.

During the 10th Chinese Arctic scientific expedition carried out in the summer of 2019, the surface current in the high-latitude areas of the Arctic Ocean was observed using a self-developed surface drifting buoy, which was initially deployed in the Chukchi Sea. The buoy traversed the Chukchi Sea, Chukchi Abyssal Plain, Mendeleev Ridge, Makarov Basin, and Canada Basin over a period of 632 d. After returning to the Mendeleev Ridge, it continued to drift toward the pole. Overall, the track of the buoy reflected the characteristics of the transpolar drift and Chukchi Slope Current, as well as the inertial flow, cross-ridge surface flow, and even the surface disorganized flow for some time intervals. The results showed that: (1) the transpolar drift mainly occurs in the Chukchi Abyssal Plain, Mendeleev Ridge, and western Canada Basin to the east of the ridge where sea ice concentration is high, and the average northward flow velocity in the region between 79.41°N and 86.32°N was 5.1 cm/s; (2) the average surface velocity of the Chukchi Slope Current was 13.5 cm/s, and while this current moves westward along the continental slope, it also extends northwestward across the continental slope and flows to the deep sea; and (3) when sea ice concentration was less than 50%, the inertial flow was more significant (the maximum observed inertial flow was 26 cm/s, and the radius of the inertia circle was 3.6 km).

An obvious trend shift in the annual mean and winter mixed layer depth (MLD) in the Antarctic Circumpolar Current (ACC) region was detected during the 1960–2021 period. Shallowing trends stopped in mid-1980s, followed by a period of weak trends. The MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage, the areas north to Victoria Land and Wilkes Land, and the central parts of the South Indian sector. The newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian sector. The saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.

Understanding the topographic patterns of the seafloor is a very important part of understanding our planet. Although the science involved in bathymetric surveying has advanced much over the decades, less than 20% of the seafloor has been precisely modeled to date, and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data. In this study, we introduce a pretrained visual geometry group network (VGGNet) method based on deep learning. To apply this method, we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter, which has a larger spatial coverage, based on the former, which is considered the true value and is more accurate. After obtaining the corrected high-precision gravity model, it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation. We choose four data pairs collected from different environments, i.e., the Southern Ocean, Pacific Ocean, Atlantic Ocean and Caribbean Sea, to evaluate the topographic correction results of the model. The experiments show that the coefficient of determination (R ²) reaches 0.834 among the results of the four experimental groups, signifying a high correlation. The standard deviation and normalized root mean square error are also evaluated, and the accuracy of their performance improved by up to 24.2% compared with similar research done in recent years. The evaluation of the R ² values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research. Finally, the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21% within 1% of the total water depths, which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.

Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion. Characterizing by rotational speed, the eddy intensity is one of the most fundamental properties of an eddy. However, the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective. In this study, we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set. The results suggest that the eddy intensity has a distinct seasonal variation, reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere. The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15° and 30° in the western Pacific Ocean, the northwestern Atlantic Ocean, and the eastern Indian Ocean because baroclinic instability in these areas changes sharply. Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.

The Indonesian Throughflow (ITF), which connects the tropical Pacific and Indian oceans, plays important roles in the inter-ocean water exchange and regional or even global climate variability. The Makassar Strait is the main inflow passage of the ITF, carrying about 77% of the total ITF volume transport. In this study, we analyze the simulated ITF in the Makassar Strait in the Simple Ocean Data Assimilation version 3 (SODA3) datasets. A total of nine ensemble members of the SODA3 datasets, of which are driven by different surface forcings and bulk formulas, and with or without data assimilation, are used in this study. The annual mean water transports (i.e., volume, heat and freshwater) are related to the combination of surface forcing and bulk formula, as well as whether data assimilation is employed. The phases of the seasonal and interannual variability in water transports cross the Makassar Strait, are basically consistent with each other among the SODA3 ensemble members. The interannual variability in Makassar Strait volume and heat transports are significantly correlated with El Niño-Southern Oscillation (ENSO) at time lags of −6 to 7 months. There is no statistically significant correlation between the freshwater transport and the ENSO. The Makassar Strait water transports are not significantly correlated with the Indian Ocean Dipole (IOD), which may attribute to model deficiency in simulating the propagation of semi-annual Kelvin waves from the Indian Ocean to the Makassar Strait.

The classification of the springtime water mass has an important influence on the hydrography, regional climate change and fishery in the Taiwan Strait. Based on 58 stations of CTD profiling data collected in the western and southwestern Taiwan Strait during the spring cruise of 2019, we analyze the spatial distributions of temperature (T) and salinity (S) in the investigation area. Then by using the fuzzy cluster method combined with the T-S similarity number, we classify the investigation area into 5 water masses: the Minzhe Coastal Water (MZCW), the Taiwan Strait Mixed Water (TSMW), the South China Sea Surface Water (SCSSW), the South China Sea Subsurface Water (SCSUW) and the Kuroshio Branch Water (KBW). The MZCW appears in the near surface layer along the western coast of Taiwan Strait, showing low-salinity (<32.0) tongues near the Minjiang River Estuary and the Xiamen Bay mouth. The TSMW covers most upper layer of the investigation area. The SCSSW is mainly distributed in the upper layer of the southwestern Taiwan Strait, beneath which is the SCSUW. The KBW is a high temperature (core value of 26.36℃) and high salinity (core value of 34.62) water mass located southeast of the Taiwan Bank and partially in the central Taiwan Strait.

The southeastern Indian Ocean is characterized by the warm barrier layer (BL) underlying the cool mixed layer water in austral winter. This phenomenon lasts almost half a year and thus provides a unique positive effect on the upper mixed layer heat content through the entrainment processes at the base of the mixed layer, which has not been well evaluated due to the lack of proper method and dataset. Among various traditional threshold methods, here it is shown that the 5 m fixed depth difference can produce a reliable and accurate estimate of the entrainment heat flux (EHF) in this BL region. The comparison between the daily and monthly EHF warming indicates that the account for high-frequency EHF variability almost doubles the warming effect in the BL period, which can compensate for or even surpass the surface heat loss. This increased warming is a result of stronger relative rate of the mixed layer deepening and larger temperature differences between the mixed layer and its immediate below in the daily-resolving data. The interannual EHF shows a moderately increasing trend and similar variabilities to the Southern Annular Mode (SAM), likely because the mixed layer deepening under the positive SAM trend is accompanied by enhanced turbulent entrainment and thus increases the BL warming.