The Arctic near-surface air temperatures are increasing more than twice as fast as the global average–a feature known as Arctic amplification (AA). A modified AA index is constructed in this paper to emphasize the contrast of warming rate between polar and mid-latitude regions, as well as the spatial and temporal characteristics of AA and their influence on atmospheric circulation over the Northern Hemisphere. Results show that AA has a pronounced annual cycle. The positive or negative phase activities are the strongest in autumn and winter, the weakest in summer. After experiencing a remarkable decadal shift from negative to positive phase in the early global warming hiatus period, the AA has entered into a state of being enlarged continuously, and the decadal regime shift of AA in about 2002 is affected mainly by decadal shift in autumn. In terms of spatial distribution, AA has maximum warming near the surface in almost all seasons except in summer. Poleward of 20°N, AA in autumn has a significant influence on the atmospheric circulation in the following winter. The reason may be that the autumn AA increases the amplitude of planetary waves, slows the wave speeds and weakens upper-level zonal winds through the thermal wind relation, thus influencing surface air temperature in the following winter. The AA correlates to negative phase of the Arctic oscillation (AO) and leads AO by 0–3 months within the period 1979–2002. However, weaker relationship between them is indistinctive after the decadal shift of AA.

The third Chinese National Arctic Research Expedition (CHINARE) was conducted in the summer of 2008. During the survey, the surface seawater partial pressure of CO₂ (pCO₂) was measured, and sea water samples were collected for CO₂ measurement in the Canada Basin. The distribution of pCO₂ in the Canada Basin was determined, the influencing factors were addressed, and the air-sea CO₂ flux in the Canada Basin was evaluated. The Canada Basin was divided into three regions: the ice-free zone (south of 77°N), the partially ice-covered zone (77°–80°N), and the heavily ice-covered zone (north of 80°N). In the ice-free zone, pCO₂ was high (320 to 368 μatm, 1 μatm=0.101 325 Pa), primarily due to rapid equilibration with atmospheric CO₂ over a short time. In the partially ice-covered zone, the surface pCO₂ was relatively low (250 to 270 μatm) due to ice-edge blooms and ice-melt water dilution. In the heavily ice-covered zone, the seawater pCO₂ varied between 270 and 300 μatm due to biological CO₂ removal, the transportation of low pCO₂ water northward, and heavy ice cover. The surface seawater pCO₂ during the survey was undersaturated with respect to the atmosphere in the Canada Basin, and it was a net sink for atmospheric CO₂. The summertime net CO₂ uptake of the ice-free zone, the partially ice-covered zone and the heavily ice-covered zone was (4.14±1.08), (1.79±0.19), and (0.57±0.03) Tg/a (calculated by carbon, 1 Tg=10¹² g), respectively. Overall, the net CO₂ sink of the Canada Basin in the summer of 2008 was (6.5±1.3) Tg/a, which accounted for 4%–10% of the Arctic Ocean CO₂ sink.

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming. This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions. The results indicate that sea ice extent reduction during 1979–2013 is most significant in summer, following by that in autumn, winter and spring. In years with rich sea ice, sea ice extent anomaly with seasonal cycle removed changes with a period of 4–6 years. The year of 2003–2006 is the ice-rich period with diverse regional difference in this century. In years with poor sea ice, sea ice margin retreats further north in the Arctic. Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic. Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes, which shows an coincident change in the Arctic and sub regions. Since 2002, Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic, followed by C1 and C3. Sea ice changes in different regions show three relationships. The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high, suggesting good consistency of ice variation. In the Atlantic sector, sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea. Sea ice changes in the central Arctic are affected by surrounding seas.

This study was to investigate bacterial and archaeal community structure of pan-Arctic Ocean sediments by pyrosequencing. In total, investigation of three marine sediments revealed 15 002 bacterial and 4 362 archaeal operational taxonomic units (OTUs) at the 97% similarity level. Analysis of community structure indicated that these three samples had high bacterial and archaeal diversity. The most relatively abundant bacterial group in Samples CC1 and R05 was Proteobacteria, while Firmicutes was dominant in Sample BL03. Thaumarchaeota was the most relatively abundant archaeal phylum in Samples CC1 and R05, and the relative abundance of Thaumarchaeota was almost as high as that of Euryarchaeota in Sample BL03. These two phyla accounted for nearly 100% of the archaeal OTUs. δ-Proteobacteria and γ-Proteobacteria were the two most relatively abundant classes at Proteobacterial class level, and their relative abundance was more than 60% in Samples CC1 and R05. There were also differences in the top 10 relatively abundant bacterial and archaeal OTUs among the three samples at the 97% similarity, and only 12 core bacterial OTUs were detected. Overall, this study indicated that there were distinct microbial communities and many unique OTUs in these three samples.

Time-series InSAR analysis (e.g., permanent scatterers (PSInSAR)) has been proven as an effective technology in monitoring ground deformation over urban areas. However, it is a big challenge to apply this technology in coastal regions due to the lack of man-made targets. An distributed scatterers interferometric synthetic aperture radar (DSInSAR) is developed to solve the problem of insufficient samples and low reliability in monitoring coastal lowland subsidence, by applying a spatially adaptive filter and an eigendecomposition algorithm to estimating the optimal phase of statistically homogeneous distributed scatterers (DSs). Twenty-four scenes of COSMO-SkyMed images acquired between 2013 and 2015 are used to retrieve the land subsidence over the Shangyu District on south coast of the Hangzhou Bay, Zhejiang Province, China. The spatial pattern of the land subsidence obtained by the PS-InSAR and the DSInSAR coincides with each other, but the density of the DSs is three point five times higher than the permanent scatterers (PSs). Validated by precise levelling data over the same period, the DSInSAR method achieves an accuracy of ±5.0 mm/a which is superior to the PS-InSAR with ±5.5 mm/a. The land subsidence in the Shangyu District is mainly distributed in the urban areas, industrial towns and land reclamation zones, with a maximum subsidence rate –30.2 mm/a. The analysis of geological data, field investigation and historical reclamation data indicates that human activities and natural compaction of reclamation material are major causes of the detected land subsidence. The results demonstrate that the DSInSAR method has a great potential in monitoring the coastal lowland subsidence and can be used to further investigate subsidence-related environmental issues in coastal regions.

In order to predict the bottom backscattering strength more accurately, the stratified structure of the seafloor is considered. The seafloor is viewed as an elastic half-space basement covered by a fluid sediment layer with finite thickness. On the basis of calculating acoustic field in the water, the sediment layer, and the basement, four kinds of scattering mechanisms are taken into account, including roughness scattering from the water-sediment interface, volume scattering from the sediment layer, roughness scattering from the sediment-basement interface, and volume scattering from the basement. Then a backscattering model for a stratified seafloor applying to low frequency (0.1–10 kHz) is established. The simulation results show that the roughness scattering from the sediment-basement interface and the volume scattering from the basement are more prominent at relative low frequency (below 1.0 kHz). While with the increase of the frequency, the contribution of them to total bottom scattering gradually becomes weak. And the results ultimately approach to the predictions of the high-frequency (10–100 kHz) bottom scattering model. When the sound speed and attenuation of the shear wave in the basement gradually decrease, the prediction of the model tends to that of the full fluid model, which validates the backscattering model for the stratified seafloor in another aspect.

On the basis of the salinity distribution of isopycnal (σ₀=27.2 kg/m³) surface and in salinity minimum, the Antarctic Intermediate Water (AAIW) around South Australia can be classified into five types corresponding to five regions by using in situ CTD observations. Type 1 is the Tasman AAIW, which has consistent hydrographic properties in the South Coral Sea and the North Tasman Sea. Type 2 is the Southern Ocean (SO) AAIW, parallel to and extending from the Subantarctic Front with the freshest and coldest AAIW in the study area. Type 3 is a transition between Type 1 and Type 2. The AAIW transforms from fresh to saline with the latitude declining (equatorward). Type 4, the South Australia AAIW, has relatively uniform AAIW properties due to the semi-enclosed South Australia Basin. Type 5, the Southeast Indian AAIW, progressively becomes more saline through mixing with the subtropical Indian intermediate water from south to north. In addition to the above hydrographic analysis of AAIW, the newest trajectories of Argo (Array for real-time Geostrophic Oceanography) floats were used to constructed the intermediate (1 000 m water depth) current field, which show the major interocean circulation of AAIW in the study area. Finally, a refined schematic of intermediate circulation shows that several currents get together to complete the connection between the Pacific Ocean and the Indian Ocean. They include the South Equatorial Current and the East Australia Current in the Southwest Pacific Ocean, the Tasman Leakage and the Flinders Current in the South Australia Basin, and the extension of Flinders Current in the southeast Indian Ocean.

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including QuickBird and WorldView-2 remote sensing images with their original spatial resolution (2.4/2.0 m) and four kinds of reducing spatial resolution (4, 8, 16 and 32 m), and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and 16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors (MRE) of QuickBird and WorldView-2 bathymetry are 21.2% and 13.1%, compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors (MAE) are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

The purpose is to study the accuracy of ocean wave parameters retrieved from C-band VV-polarization Sentinel-1 Synthetic Aperture Radar (SAR) images, including both significant wave height (SWH) and mean wave period (MWP), which are both calculated from a SAR-derived wave spectrum. The wind direction from in situ buoys is used and then the wind speed is retrieved by using a new C-band geophysical model function (GMF) model, denoted as C-SARMOD. Continuously, an algorithm parameterized first-guess spectra method (PFSM) is employed to retrieve the SWH and the MWP by using the SAR-derived wind speed. Forty–five VV-polarization Sentinel-1 SAR images are collected, which cover the in situ buoys around US coastal waters. A total of 52 subscenes are selected from those images. The retrieval results are compared with the measurements from in situ buoys. The comparison performs good for a wind retrieval, showing a 1.6 m/s standard deviation (STD) of the wind speed, while a 0.54 m STD of the SWH and a 2.14 s STD of the MWP are exhibited with an acceptable error. Additional 50 images taken in China’s seas were also implemented by using the algorithm PFSM, showing a 0.67 m STD of the SWH and a 2.21 s STD of the MWP compared with European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis grids wave data. The results indicate that the algorithm PFSM works for the wave retrieval from VV-polarization Sentinel-1 SAR image through SAR-derived wind speed by using the new GMF C-SARMOD.