Acta Oceanologica Sinica最新文献

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 (R²) 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 R² 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.

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).

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys. A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas. This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors. The fishery ranching index (FRI1, FRI2) was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan, using Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellite data with a special resolution of 2 m. In the second step, the environmental factors such as sea surface temperature (SST), chlorophyll a (Chl-a) concentration, current and tide, suspended sediment concentration (SSC) in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail. The results show the following three points. (1) For the extraction of the mussel aquaculture area, FRI1 and FRI2 are complementary, and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area. As for the large yellow croaker aquaculture area extraction, FRI2 is suitable. (2) Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters. The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined. (3) This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future. the fishery ranching index (FRI1, FRI2) in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

Marine oil spill emulsions are difficult to recover, and the damage to the environment is not easy to eliminate. The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments. However, the spectrum of oil emulsions changes due to different water content. Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions. Nonetheless, hyperspectral data can also cause information redundancy, reducing classification accuracy and efficiency, and even overfitting in machine learning models. To address these problems, an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established, and feature bands that can distinguish between crude oil, seawater, water-in-oil emulsion (WO), and oil-in-water emulsion (OW) are filtered based on a standard deviation threshold-mutual information method. Using oil spill airborne hyperspectral data, we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions, analyzed the transferability of the model, and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions. The results show the following. (1) The standard deviation-mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO, OW, oil slick, and seawater. The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data and from 126 to 100 on the S185 data. (2) With feature selection, the overall accuracy and Kappa of the identification results for the training area are 91.80% and 0.86, respectively, improved by 2.62% and 0.04, and the overall accuracy and Kappa of the identification results for the migration area are 86.53% and 0.80, respectively, improved by 3.45% and 0.05. (3) The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations, with an overall accuracy of more than 80%, Kappa coefficient of more than 0.7, and F₁ score of 0.75 or more for each category. (4) As the spectral resolution decreasing, the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW. Based on the above experimental results, we demonstrate that the oil emulsion identification model with spatial-spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data, and can be applied to images under different spatial and temporal conditions. Furthermore, we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process. These

Negative Indian Ocean Dipole (nIOD) can exert great impacts on global climate and can also strongly influence the climate in China. Early nIOD is a major type of nIOD, which can induce more pronounced climate anomalies in summer than La Niña-related nIOD. However, the characteristics and triggering mechanisms of early nIOD are unclear. Our results based on reanalysis datasets indicate that the early nIOD and La Niña-related nIOD are the two major types of nIOD, and the former accounts for over one third of all the nIOD events in the past six decades. These two types of nIODs are similar in their intensities, but are different in their spatial patterns and seasonal cycles. The early nIOD, which develops in spring and peaks in summer, is one season earlier than the La Niña-related nIOD. The spatial pattern of the wind anomaly associated with early nIOD exhibits a winter monsoon-like pattern, with strong westerly anomalies in the equatorial Indian Ocean and eastly anomalies in the northern Indian Ocean. Opposite to the triggering mechanism of early positve IOD, the early nIOD is induced by delayed Indian summer monsoon onset. The results of this study are helpful for improving the prediction skill of IOD and its climate impacts.

The effects of surf zone eddy generated by alongshore currents on the deformation and transport of dye are still poorly understood, and related tracer release experiments are lacking. Therefore, a tracer release laboratory experiment was conducted under monochromatic, unidirectional incident waves with a large incident angle (30°) on a plane beach with a 1:100 slope in a large wave basin. A charge-coupled device suspended above the basin recorded the dye patch image. The evolution of eddy dye patch was observed and the transport and diffusion were analyzed based on the collected images. Subsequently, a linear instability numerical model was adopted to calculate the perturbation velocity field at the initial stage. The observation and image processing results show that surf zone eddy patches occurred and were separated from the original dye patches. Our numerical analysis results demonstrate that the structure of the perturbation velocity field is consistent with the experimental observations, and that the ejection of eddy patches shoreward or offshore may be ascribed to the double vortex.

The importance of the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO) in influencing zonally asymmetric changes in Antarctic surface air temperature (SAT) has been established. However, previous studies have primarily concentrated on examining the combined impact of the contrasting phases of the AMO and IPO, which have been dominant since the advent of satellite observations in 1979. This study utilizes long-term reanalysis data to investigate the impact of four combinations of +AMO+IPO, −AMO−IPO, +AMO−IPO, and −AMO+IPO on Antarctic SAT over the past 115 years. The +AMO phase is characterized by a spatial mean temperature amplitude of up to 0.5°C over the North Atlantic Ocean, accompanied by positive sea surface temperature (SST) anomalies in the tropical eastern Pacific and negative SST anomalies in the extratropical-mid-latitude western Pacific, which are indicative of the +IPO phase. The Antarctic SAT exhibits contrasting spatial patterns during the +AMO+IPO and +AMO−IPO periods. However, during the −AMO+IPO period, apart from the Antarctic Peninsula and the vicinity of the Weddell Sea, the entire Antarctic region experiences a warming trend. The most pronounced signal in the SAT anomalies is observed during the austral autumn, whereas the combination of −AMO and −IPO exhibits the smallest magnitude across all the combinations. The wavetrain excited by the SST anomalies associated with the AMO and IPO induces upper-level and surface atmospheric circulation anomalies, which alter the SAT anomalies. Furthermore, downward longwave radiation anomalies related to anomalous cloud cover play a crucial role. In the future, if the phases of AMO and IPO were to reverse (AMO transitioning to a negative phase and IPO transitioning to a positive phase), Antarctica could potentially face more pronounced warming and accelerated melting compared to the current observations.

Antarctic sea ice is an important part of the Earth’s atmospheric system, and satellite remote sensing is an important technology for observing Antarctic sea ice. Whether Chinese Haiyang-2B (HY-2B) satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series, as other radar altimetry satellites can, needs further investigation. This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data. We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature (IST) product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images. Second, a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights. We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation. Finally, the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate (ASPeCt) ship-based observed sea ice thickness. The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable, and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m. The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products; this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.