The distribution process of trace elements between coral aragonite skeletons and seawater is a key link to control the incorporating of trace elements in seawater into coral skeletons. The distribution coefficient (K_D) characterizing this distribution process is often used to reconstruct the contents of trace elements in seawater in time series. Lacking of in situ research on corals and seawater, the limited knowledge related to the distribution behavior of trace elements between coral aragonite skeletons and seawater seriously hindered our acquisition of accurate chemical quantitative data on seawater. In this study we selected two typical reef-building coral species (Goniastrea retiformis and Platygyra yaeyamaensis) and five trace elements (Cu, Zn, Pb, Cd and Cr) in the surrounding in-situ surface seawater of Weizhou Island in the northern South China Sea, and estimated the distribution coefficients of these trace elements between the coral aragonite skeleton and seawater. Results revealed that the K_D value of G. retiformis for Cu, Zn, Pb, Cd, Cr was 3.65, 0.57, 1.06, 0.88, 0.66 respectively; the K_D value of P. yaeyamaensis was 3.49, 0.97, 1.42, 1.01, 0.60 respectively. We found a significant difference among different coral species in the K_D values of Cu, Zn, Pb and Cd, but no significant difference for Cr. The results indicate that the K_D values of trace elements are affected by the Rayleigh fractionation, which is related to the numerical values of K_D. The study provided relatively accurate K_D values for coral aragonite skeleton and seawater, represented important basic data for future research on coral reefs, and expanded our knowledge of chemical signature in biogenic lattices associated with marine organism.

Sea ice numerical model is an effective way to study the dynamical and thermal state parameters of sea ice and the connections between them. The current assessment of the results of numerical ice thickness simulation is much less than the sea ice extent/area and concentration, and the study of the influence of ice velocity and sea ice deformation on ice thickness distribution is still lacking. We simulated the Arctic sea ice variability from 1980 to 2018 using the Los Alamos sea ice model (CICE), and validated and comparison the CICE simulation results using remote sensing and assimilated ice thickness data. We further analyzed the effects of simulated ice velocity and sea ice deformation on ice thickness, and calculated the contributions of ice velocity divergence and shear bias to ice thickness bias. The results show that the interannual variability of the mean ice thickness and ice speed in the Arctic north of 70°N is reasonable, but the multi-year trends of the simulated mean ice thickness and ice speed are smaller than the variability of the assimilated data; the differences in the spatial distribution of the simulated and observed ice thickness are closely related to the deviations of the ice speed and deformation rate, mainly in the positive deviation in the Beaufort Sea and the negative deviation in the Arctic central zone to Fram Strait. The contribution of divergence and shear deviation to ice thickness deviation in the pan-Arctic region fluctuates between 13% and 16% before March, and jumps from 16% to 27% in March-April. The divergence bias dominates the positive bias of ice thickness in the Beaufort Sea region in November and December, while the shear bias dominates the negative ice thickness bias in the winter in the ocean north of the Canadian archipelago and in the region of the Arctic transpolar drift.

Sea ice motion is an important factor affecting Arctic sea ice advection and spatial redistribution of mass balance. Based on the data collected by 66 ice-based drifting buoys deployed in the Arctic Ocean from September 2018 to August 2019, combined with atmospheric reanalysis data and passive microwave sea ice concentration product, we calculated the sea ice drifting speed, the ice-wind speed ratio and the inertial intensity of sea ice motion, so as to characterize the temporal and spatial changes of Arctic sea ice kinematic parameters over a full ice season, and analyse the correlation between ice-wind speed ratio and sea ice concentration. Seasonally, the sea ice drift speed in the Beaufort-Chukchi Sea, the central eastern Arctic Ocean and the central western Arctic Ocean showed a decreasing trend in autumn and winter and an increasing trend in spring and summer. Spatially, the Greenland Sea ((0.32 ± 0.06) m/s) had the highest sea ice drift speed, followed by the Fram Strait ((0.17 ± 0.07) m/s), and those in the Beaufort-Chukchi Sea ((0.14 ± 0.05) m/s), the central eastern Arctic Ocean ((0.09 ± 0.02) m/s) and the central western Arctic Ocean ((0.07 ± 0.03) m/s) were relatively low. On the monthly scale, the ice-wind speed ratio was mainly dominated by sea ice drift speed. The ice-wind speed ratio in the Fram Strait and the Greenland Sea was relatively larger, due to the strong ocean surface current. While the ice-wind speed ratio in the central western Arctic Ocean, the central eastern Arctic Ocean, and the Beaufort-Chukchi Sea was relative low and tended to be distributed between 0−0.02 with the increase in sea ice concentration. The monthly averaged IMI for all buoys was 0.158 ± 0.144, and the response of ice to wind and the inertial signal of ice motion gradually weakened during the autumn-winter transition and started to strengthen again in spring and summer. Therefore, both of the above parameters can be used as important indicators to indicate the consolidation of ice field.

Based on the statistical data of direct economic losses of typhoon storm surge in China from 1989 to 2021, the economic losses of storm surge disasters in China during 32 years showed a significant downward trend, showing a thick-tailed distribution as a whole, and a normal distribution after logarithmic processing. The periodic changes of the direct economic losses of typhoon storm surge in China were analyzed by Morlet wavelet analysis method. According to the t-test, there were two quasi-high-frequency oscillations in the whole region, 1−2 years and 7−8 years oscillation , but the annual cycle gradually shortened to 3−5 years with the change of time. It indicated that the economic loss sequence of storm surge had high-frequency oscillation and multi-period nested low-frequency oscillation. On this basis, Daubechies wavelet decomposition was used to separate high frequency signal and low frequency signal. According to the results of root mean square error (RMSE) and signal-to-noise ratio, Daubechies wavelet base was set the vanishing moment is 7 and the number of decomposition layers is 2 for the direct economic loss time series of typhoon storm surges in China from 1989 to 2021, which had the optimal decomposition and reconstruction effect. Based on the results of stationarity test and white noise test of wavelet coefficients of each decomposition layer, the combined wavelet decomposed−ARMA model was established. The simulation accuracy and prediction accuracy were both better than that of single Autoregressive Integrated Moving Average model and Fourier series expansion model, which verified the reliability and superiority of wavelet decomposition method for rapid assessment of economic loss of typhoon storm surge in China.

Under the background of global climate change, the extreme storm surge events caused by tropical cyclones in the Changjiang River Estuary and adjacent coastal area present non-stationary feature. In this study, a storm surge model for the Changjiang River Estuary was constructed using the ADCIRC model to reproduce the storm surges during 241 tropical cyclones affecting the Changjiang River Estuary from 1979 to 2019. By combining the non-stationary generalized extreme value distribution with the state space approach, a statistical model for capturing the non-stationarity of extreme storm surges was built to investigate the spatiotemporal variability of the extreme storm surges in the Changjiang River Estuary and its adjacent coastal area. The statistical model can well reproduce the non-stationary feature of extreme storm surges, which was mainly represented by the time-dependent location parameter. The time-dependent location parameters at the tidal gauge stations were stationary before 2008 and presented increasing trends afterwards, which was mainly caused by the increase of the annual second- and third-largest storm surges. The reoccurrence period of storm surge event with 100-year return period under the stationary assumption was reduced to around 40–80 years, indicating an increased flood risk in the Changjiang River Estuary. Combined with the changes in the intensity and path of the tropical cyclones that caused the annual second- and third-largest storm surges, it was concluded that the increasing trends of extreme storm surges were mainly caused by the increase in the intensity of the tropical cyclone that tracking northward to the offshore of the Changjiang River Estuary and veering eastwards.

Polar ships always collide with various types of sea ice in varying degrees during their voyages in ice-covered regions, so sufficient fatigue strength reserves are essential. This paper proposes a cumulative damage analysis method for ice-induced fatigue on polar ship structures based on field measured ice loads. Firstly, according to the statistical analysis of the field data of ice thickness and sailing speed of RV Xue Long during the China’s 8th Arctic Scientific Expedition, ice-induced fatigue conditions are constructed within the ice thickness range of 0.5−2.5 m and the sailing speed range of 2−12 kn. The joint probability distribution of the two parameters is taken as the occurrence probability of fatigue conditions. Then, the time histories of ice loads under typical conditions are identified based on the support vector machine method. The key positions and corresponding hot spot stresses are determined by dynamic analysis. The rainflow counting algorithm is adopted to count the number of stress cycles. Finally, the fatigue damage during the voyage is further calculated by S-N curve and Miner linear cumulative damage theory, which verifies the ice navigation safety of RV Xue Long. This paper has certain reference significance for the ice-resistant design and safety evaluation of polar ship structures.

Phytoplankton blooms in polar regions with seasonal sea ice cover show a unimodal seasonality. However, the bloom processes are controlled by multiple physical and biogeochemical factors, including sea ice, light availability, mixed layer depth, and nutrients; those may result in great uncertainties in simulating phytoplankton bloom by the Earth System Models (ESMs). In this study, the results of 11 Coupled Model Intercomparison Phase-6 (CMIP6) ESMs were analyzed and evaluated with various types of observational products in order to determine whether those ESMs can correctly model the phytoplankton blooms in three Arctic shelf seas, Barents Sea, Chukchi Sea, and Bering Sea. By calculating multiple indices that represent light and nutrient limitations, the error sources of simulated surface chlorophyll a concentrations were comprehensively analyzed. Our results show that the 11 ESMs can be divided into three groups based on ice-adjusted photoperiod, rate of change of mixed layer depth, and surface nitrate concentration. Some groups are characterized by the smallest bias between modeled indices and observation-based reference, and those ESMs perform best in simulating phytoplankton bloom characteristics. The other groups of ESMs differ significantly from the reference values in terms of surface nitrate and/or rate of change of mixed layer depth, resulting in delayed occurrences of annual chlorophyll a peak concentration and greater differences in corresponding peak values. In general, in addition to the two primary constraints of light and nutrients, the ESMs should also well represent the upper mixed layer controlled by temperature and salinity distributions, so as to accurately simulate the seasonal variation of surface chlorophyll a concentration. The above analyses indicate ESMs can be used in assessing polar planktonic ecosystems, and there is room for improving ecosystem-related parametrization in future ESM development.

Most of the small rivers in the Shandong Peninsula have clear sediment sources and relatively small mineral deposition differentiation, and their study can provide regional similarities for the analysis of the sources of large rivers into the sea, as well as the characteristics of the material sources in the Shandong Peninsula for the deposition of the Yellow Sea shelf and Bohai Sea shelf. In this paper, the distribution patterns of detrital minerals in surface sediment samples taken from riverbeds, estuaries and beaches were analysed at multiple grain levels, taking the Xin’an River, a small short-source river in the northeastern part of the Shandong Peninsula, as an example. The study shows that the Xin’an River basin and beach sediments are mostly medium and fine sands, with moderate heavy mineral content in the whole sample, ranging from 1.47% to 8.19%. Twenty-seven heavy minerals and eight light minerals occur, with the main heavy minerals hornblende (41.7%), chlorite (16.7%), actinolite (10.6%) and limonite (6.7%) and the main light minerals quartz (49.0%), plagioclase (26.5%) and potassium feldspar (20.7%), which are much more abundant than the other detrital minerals in the riverbed, estuary and beach, and the mineral assemblage remains constant, with less significant variation in relative content between them, while there is a significant non-linear correlation between non-dominant mineral species and clastic grain size. Mineral species and content in the Xin’an River are controlled by the regional lithological sources, i.e. acidic and moderately acidic intrusive rocks and regional metamorphic rocks, and the relatively stable output of amphibole group minerals across the different features of the Xin’an River is responsible for their homogeneity. The short source rivers of the Shandong Peninsula are derived from similar geological features and rock types, although the types and content of detrital minerals vary. The main controlling factors for mineral composition variation are regional sources and particle size sorting, followed by sedimentary environmental differences. In contrast, the high content of mica group and carbonate minerals in a specific grain size and the low content of amphibole group minerals are the aspects that distinguish the Huanghe River from the river source materials in the northern and southern coastal waters of the Shandong Peninsula.

In order to quickly obtain a large-scale, quasi-real-time internal structure of the ocean, sea surface remote sensing data are widely used to construct the vertical structure of the temperature profiles, but satellite remote sensing can only obtain relatively accurate ocean surface or near-surface data. In order to improve the accuracy of temperature profile inversion, this paper takes the depth-fixed temperature as the constraint, and the nonlinear mapping between the temperature profiles and the sea surface remote sensing data such as sea surface temperature (SST) and sea level anomaly (SLA) is generated through the radial basis function (RBF) neural network, and discuss the theoretical basis for constrained depth selection. The inversion results of the temperature profiles in the South China Sea show that the first empirical orthogonal function (EOF) coefficient can characterize the vertical displacement of the thermocline. And there is a strong correlation between the temperature at the depth corresponding to the extreme point of the first EOF and the first EOF coefficient. Therefore, when the temperature at this depth is added as a constraint, the inversion accuracy of the thermocline is about 0.35℃ higher than that of only using sea surface remote sensing data, and the mean root mean square error of temperature profile inversion is about 0.33℃.

Seagrass coverage ratio is an important indicator reflecting the ecological status of seagrass beds. In this paper, through the design of aerial photography scheme and flight condition test, the high-resolution seagrass image map of the Dongjiao Coconut Forest sea area was obtained by using UAV aerial photography. Combined with the image classification tools of ArcGIS software and 3D tools, a new method for calculating the coverage ratio of seagrass were obtained and the coverage of seagrass was calculated. The station location of simulating survey method of the traditional seagrass coverage ratio was compared and discussed. Seagrasses in the coastal sea bed of the Dongjiao Coconut Forest are distributed on the coral reefs within 300 m from the shore with patches and intervals. Using the new method, the concentrated distribution area of seagrass at sea bed of the Dongjiao Coconut Forest is about 23 221 m², and average concentration distribution ratio is 17.79%. The distribution area of seagrass in this study area is about 16 423 m², and the coverage ratio of seagrass is 12.58%. The coverage ratio of seagrass is higher, and the ecological condition of seagrass bed is good. Sargassum is densely distributed in the southeast area of the study area, with a distribution area of 755.6 m² and a coverage of 0.5%, and grows as a single cylinder floating. By simulating the investigation station location of the sample frame method and sample line method of traditional seagrass coverage survey, seagrass coverage ratio changes with different stations, sample frame, and sample line positions changing randomly, which is the reason for the representativeness and comparability of the traditional survey results. The research results of this project have the promotion and application value in the investigation of seagrass ecological monitoring area.