The reduction in Arctic sea ice in summer has been reported to have a significant impact on the global climate. In this study, Arctic sea ice/snow at the end of the melting season in 2018 was investigated during CHINARE-2018, in terms of its temperature, salinity, density and textural structure, the snow density, water content and albedo, as well as morphology and albedo of the refreezing melt pond. The interior melting of sea ice caused a strong stratification of temperature, salinity and density. The temperature of sea ice ranged from –0.8°C to 0°C, and exhibited linear cooling with depth. The average salinity and density of sea ice were approximately 1.3 psu and 825 kg/m³, respectively, and increased slightly with depth. The first-year sea ice was dominated by columnar grained ice. Snow cover over all the investigated floes was in the melt phase, and the average water content and density were 0.74% and 241 kg/m³, respectively. The thickness of the thin ice lid ranged from 2.2 cm to 7.0 cm, and the depth of the pond ranged from 1.8 cm to 26.8 cm. The integrated albedo of the refreezing melt pond was in the range of 0.28–0.57. Because of the thin ice lid, the albedo of the melt pond improved to twice as high as that of the mature melt pond. These results provide a reference for the current state of Arctic sea ice and the mechanism of its reduction.

The extremely low temperature, high humidity and limited power supply pose considerable challenges when using spectrometers within the Arctic sea ice. The feasibility of using a miniature low-power near-infrared spectrometer module to measure solar radiation in Arctic sea ice environments was investigated in this study. Temperature and integration time dependences of the spectrometer module were examined over the entire target operating range of –50°C to 30°C, well below the specified operating range of this spectrometer. Using these observations, a dark output prediction model was developed to represent dark output as a function of temperature and integration time. Temperature-induced biases in the saturation output and linear operating range of the spectrometer were also determined. Temperature and integration time dependences of the signal output were evaluated. Two signal output correction models were developed and compared, to convert the signal output at any temperature within the operating temperature range and integration time to that measured at the reference temperature and integration time. The overall performance of the spectrometer was evaluated by integrating it into a refined fiber optic spectrometry system and measuring solar irradiance distribution in the ice cover with thickness of 1.85 m in the Arctic during the 9th Chinese National Arctic Research Expedition. The general shape of the measured solar irradiance above the snow surface agreed well with that measured by other commercial oceanographic spectroradiometers. The measured optical properties of the sea ice were generally comparable to those of similar ice measured using other instruments. This approach provides a general framework for assessing the feasibility of using spectrometers for applications in cold environments.

Based on an ice concentration threshold of 90%, it has been identified that two polynya events occurred in the region north of Greenland during the 2017/2018 ice season. The winter event lasted from February 20 to March 3, 2018 and the summer event persisted from August 2 to September 5, 2018. The minimum ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) observations was 72% and 65% during the winter and summer events, respectively. The occurrence of both events can be related to strengthened southerly winds associated with an increased east-west zonal surface level air pressure gradient across the north Greenland due to perturbation of mid-troposphere polar vortex. The relatively warm air temperature during the 2017/2018 freezing season in comparison with previous years, together with the occurrence of the winter polynya, formed favourable pre-conditions for ice field fracturing in summer, which promoted the formation of the summer polynya. Diminished southerly winds and increased cover of new ice over the open water were the dominant factors for the disappearance of the winter polynya, whereas increased ice inflow from the north was the primary factor behind the closure of the summer polynya. Sentinel-1 Synthetic Aperture Radar (SAR) images were found better suited than AMSR2 observations for quantification of a new ice product during the polynya event because the SAR images have high potential for mapping of different sea ice regimes with finely spatial resolution. The unprecedented polynya events north of Greenland in 2017/2018 are important from the perspective of Arctic sea ice loss because they occurred in a region that could potentially be the last “Arctic sea ice refuge” in future summers.

The spatial structure of the Arctic sea ice concentration (SIC) variability and the connection to atmospheric as well as radiative forcing during winter and summer for the 1979–2017 period are investigated. The interannual variability with different spatial characteristics of SIC in summer and winter is extracted using the empirical orthogonal function (EOF) analysis. The present study confirms that the atmospheric circulation has a strong influence on the SIC through both dynamic and thermodynamic processes, as the heat flux anomalies in summer are radiatively forced while those in winter contain both radiative and “circulation-induced” components. Thus, atmospheric fluctuations have an explicit and extensive influence to the SIC through complex mechanisms during both seasons. Moreover, analysis of a variety of atmospheric variables indicates that the primary mechanism about specific regional SIC patterns in Arctic marginal seas are different with special characteristics.

Clay minerals deposited at the southern Mendeleev Ridge in the Arctic Ocean have a unique provenance, which can be used to reconstruct changes in the local sedimentary environment. We show that sediments in core ARC7-E23 record high-frequency changes in clay minerals since the penultimate interglacial. The clay minerals, grain size, and ice-rafted debris indicate the extent of the East Siberia Ice Sheet (ESIS). During the glacial periods of Marine Isotope Stage 2 (MIS2) and MIS4, the southern Mendeleev Ridge was likely covered by an ESIS-extended ice shelf, blocking almost all sediment input from the Canadian Arctic and Laptev Sea, but allowing transport of fine-grained sediments from the East Siberian and Chukchi Sea shelves. After ESIS retreat, the Beaufort Gyre and Transpolar Drift became the primary transport mechanism for the distally sourced sediments. Climate conditions in MIS3 enhanced both the oceanic circulation and sediment transport.

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships, while limited by the capability of climate models. A bias correction methodology in this study was proposed and performed on raw products from two climate models, the First Institute Oceanography Earth System Model (FIOESM) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), to improve 60 days predictions for Arctic sea ice. Both models were initialized on July 1, August 1, and September 1 in 2018. A 60-day forecast was conducted as a part of the official sea ice service, especially for the ninth Chinese National Arctic Research Expedition (CHINARE) and the China Ocean Shipping (Group) Company (COSCO) Northeast Passage voyages during the summer of 2018. The results indicated that raw products from FIOESM underestimated sea ice concentration (SIC) overall, with a mean bias of SIC up to 30%. Bias correction resulted in a 27% improvement in the Root Mean Square Error (RMSE) of SIC and a 10% improvement in the Integrated Ice Edge Error (IIEE) of sea ice edge (SIE). For the CFS, the SIE overestimation in the marginal ice zone was the dominant features of raw products. Bias correction provided a 7% reduction in the RMSE of SIC and a 17% reduction in the IIEE of SIE. In terms of sea ice extent, FIOESM projected a reasonable minimum time and amount in mid-September; however, CFS failed to project both. Additional comparison with subseasonal to seasonal (S2S) models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

We analyzed the biogenic silica (BSi) content and produced a diatom-based summer sea-surface temperature (SST) reconstruction for sediment core GC4 from the Holsteinsborg Dyb, West Greenland. Our aim was to reconstruct marine productivity and climatic fluctuations during the last millennium. Increased BSi content and diatom abundance suggest relatively high marine productively during the interval of AD 1000–1400, corresponding in time to the Medieval Warm Period (MWP). The summer SST reconstruction indicates relatively warm conditions during AD 900–1100, followed by cooling after AD 1100. An extended cooling period during AD 1400–1900 is characterized by prolonged low in reconstructed SST and high sea-ice concentration. The BSi values fluctuated during this period, suggesting varying marine productivity during the Little Ice Age (LIA). There is no significant correlation between the BSi content and SST during the last millennium, suggesting that the summer SST has little influence on marine productively in the Holsteinsborg Dyb. A good correspondence between the BSi content and the element Ti counts in core GC4 suggests that silicate-rich meltwater from the Greenland ice sheet was likely responsible for changes in marine productively in the Holsteinsborg Dyb.

During the 3rd Chinese National Arctic Research Expedition cruise in the summer of 2008, nutrients (${\rm{NO}}_3^ - $, ${\rm{NO}}_2^ - $, ${\rm{SiO}}_3^ {2-} $, and ${\rm{PO}}_4^ {3-} $) and dissolved oxygen were measured in the western Arctic Ocean, to derive the vertical distribution of nutrient tracers and its relationship to water structure and biogeochemical processes. The nutrient data show that surface waters had the lowest ${\rm{NO}}_3^ - $/${\rm{PO}}_4^ {3-} $ (mean of 0.5) and ${\rm{SiO}}_3^ {2-} $/${\rm{PO}}_3^ {-} $ (mean of 2.8) values in the water column, suggesting an excess of phosphate. Winter Bering Shelf water (wBSW) had high Si* (16.7 μmol/L; Si*=[Si(OH)₄]–[${\rm{NO}}_3^ - $]) with negative N* (−11.7 μmol/L; N*=[${\rm{PO}}_4^ {3-} $]−16[${\rm{PO}}_4^ {3-} $]+3.5 μmol/L) in the water column, indicating nitrate deficiency. The warm Atlantic layer had positive N* (0.8 μmol/L) and negative Si* (−5.4 μmol/L) compared with Pacific source water. The vertical distribution of nutrients indicates that wBSW can be characterized by N* minimum and Si* maximum. In contrast, minima of Si* and ${\rm{SiO}}_3^ {2-} $/${\rm{PO}}_4^ {3-} $ below 200 m indicate the distribution of Atlantic warm water.

The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information. However, traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone. A successive corrections analysis using variational optimization method, called spatial multi-scale recursive filter (SMRF), has been designed in this paper to extract multi-scale information resolved by sea ice observations. It is a combination of successive correction methods (SCM) and minimization algorithms, in which various observational scales, from longer to shorter wavelengths, can be extracted successively. As a variational objective analysis scheme, it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time, and also, the specification of parameters is more convenient. Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals. Further, it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration (SIC) experiment with the real observations from Special Sensor Microwave/Imager SIC (SSMI).