2020 Vol. 39, No. 9

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2020-9 Contents
2020, (9): 1-2.
Exploring the Arctic Ocean under Arctic amplification
Ruibo Lei, Zexun Wei
2020, 39(9): 1-4. doi: 10.1007/s13131-020-1642-9
Characterization of the unprecedented polynya events north of Greenland in 2017/2018 using remote sensing and reanalysis data
Ruibo Lei, Dawei Gui, Zhuoli Yuan, Xiaoping Pang, Ding Tao, Mengxi Zhai
2020, 39(9): 5-17. doi: 10.1007/s13131-020-1643-8
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.
Comparison of summer Arctic sea ice surface temperatures from in situ and MODIS measurements
Na Li, Bingrui Li, Ruibo Lei, Qun Li
2020, 39(9): 18-24. doi: 10.1007/s13131-020-1644-7
Ship-borne infrared radiometric measurements conducted during the Chinese National Arctic Research Expedition (CHINARE) in 2008, 2010, 2012, 2014, 2016 and 2017 were used for in situ validation studies of the Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice surface temperature (IST) product. Observations of sea ice were made using a KT19.85 radiometer mounted on the Chinese icebreaker Xuelong between July and September over six years. The MODIS-derived ISTs from the satellites, Terra and Aqua, both show close correspondence with ISTs derived from radiometer spot measurements averaged over areas of 4 km×4 km, spanning the temperature range of 262–280 K with a ±1.7 K (Aqua) and ±1.6 K (Terra) variation. The consistency of the results over each year indicates that MODIS provides a suitable platform for remotely deriving surface temperature data when the sky is clear. Investigation into factors that cause the MODIS IST bias (defined as the difference between MODIS and KT19.85 ISTs) shows that large positive bias is caused by increased coverage of leads and melt ponds, while large negative bias mostly arises from undetected clouds. Thin vapor fog forming over Arctic sea ice may explain the cold bias when cloud cover is below 20%.
Physical and optical characteristics of sea ice in the Pacific Arctic Sector during the summer of 2018
Xiaowei Cao, Peng Lu, Ruibo Lei, Qingkai Wang, Zhijun Li
2020, 39(9): 25-37. doi: 10.1007/s13131-020-1645-6
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.
Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018
Yuanren Xiu, Zhijun Li, Ruibo Lei, Qingkai Wang, Peng Lu, Matti Leppäranta
2020, 39(9): 38-49. doi: 10.1007/s13131-020-1646-5
In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from −3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.
The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018
Jiechen Zhao, Qi Shu, Chunhua Li, Xingren Wu, Zhenya Song, Fangli Qiao
2020, 39(9): 50-59. doi: 10.1007/s13131-020-1578-0
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.
Role of atmospheric factors in forcing Arctic sea ice variability
Yu Liang, Haibo Bi, Yunhe Wang, Zehua Zhang, Haijun Huang
2020, 39(9): 60-72. doi: 10.1007/s13131-020-1629-6
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.
Electrolytic enrichment method for tritium determination in the Arctic Ocean using liquid scintillation counter
Feng Lin, Tao Yu, Wen Yu, Jialin Ni, Li Lin
2020, 39(9): 73-77. doi: 10.1007/s13131-020-1647-4
A method of measuring the tritium in seawater based on electrolytic enrichment and ultra-low background liquid scintillation counting techniques was established. The different factors influencing the detection limit were studied, including the counting time, the electrolytic volume of the seawater samples, the selection of background water, scintillation solution and their ratio. After optimizing the parameters and electrolyzing 350 mL volume of samples, the detection limit of the method was as low as 0.10 Bq/L. In order to test the optimization of system for this method, of the 84 seawater samples collected from the Arctic Ocean we measured, 92% were above the detection limit (the activity of this samples ranged from 0.10 Bq/L to 1.44 Bq/L with an average of (0.30±0.24) Bq/L). In future research, if we need to accurately measure the tritium activity in samples, the volume of the electrolytic samples will be increased to further reduce the minimum detectable activity.
Biogenic silica concentration as a marine primary productivity proxy in the Holsteinsborg Dyb, West Greenland, during the last millennium
Longbin Sha, Dongling Li, Yanguang Liu, Bin Wu, Yanni Wu, Karen Luise Knudsen, Zhongqiao Li, Hao Xu
2020, 39(9): 78-85. doi: 10.1007/s13131-020-1648-3
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.
Ice sheet controls on fine-grained deposition at the southern Mendeleev Ridge since the penultimate interglacial
Liming Ye, Xiaoguo Yu, Weiyan Zhang, Rong Wang
2020, 39(9): 86-95. doi: 10.1007/s13131-020-1649-2
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.
Sources of particulate organic matter in the Chukchi and Siberian shelves: clues from carbon and nitrogen isotopes
Renming Jia, Xinyue Mu, Min Chen, Jing Zhu, Bo Wang, Xiaopeng Li, A S Astakhov, Minfang Zheng, Yusheng Qiu
2020, 39(9): 96-108. doi: 10.1007/s13131-020-1650-9
The stable isotopic composition (δ13C and δ15N) and carbon/nitrogen ratio (C/N) of particulate organic matter (POM) in the Chukchi and East Siberian shelves from July to September, 2016 were measured to evaluate the spatial variability and origin of POM. The δ13CPOC values were in the range of −29.5‰ to −17.5‰ with an average of −25.9‰±2.0‰, and the δ15NPN values ranged from 3.9‰ to 13.1‰ with an average of 8.0‰±1.6‰. The C/N ratios in the East Siberian shelf were generally higher than those in the Chukchi shelf, while the δ13C and δ15N values were just the opposite. Abnormally low C/N ratios (<4), low δ13CPOC (almost −28‰) and high δ15NPN (>10‰) values were observed in the Wrangel Island polynya, which was attributed to the early bloom of small phytoplankton. The contributions of terrestrial POM, bloom-produced POM and non-bloom marine POM were estimated using a three end-member mixing model. The spatial distribution of terrestrial POM showed a high fraction in the East Siberian shelf and decreased eastward, indicating the influence of Russian rivers. The distribution of non-bloom marine POM showed a high fraction in the Chukchi shelf with the highest fraction occurring in the Bering Strait and decreased westward, suggesting the stimulation of biological production by the Pacific inflow in the Chukchi shelf. The fractions of bloom-produced POM were highest in the winter polynya and gradually decreased toward the periphery. A negative relationship between the bloom-produced POM and the sea ice meltwater inventory was observed, indicating that the net sea ice loss promotes early bloom in the polynya. Given the high fraction of bloom-produced POM, the early bloom of phytoplankton in the polynyas may play an important role on marine production and POM export in the Arctic shelves.
Vertical distribution of nutrient tracers in the western Arctic Ocean and its relationship to water structure and biogeochemical processes
Yanpei Zhuang, Hongliang Li, Haiyan Jin, Shengquan Gao, Jianfang Chen, Yangjie Li, Youcheng Bai, Zhongqiang Ji
2020, 39(9): 109-114. doi: 10.1007/s13131-020-1651-8
During the 3rd Chinese National Arctic Research Expedition cruise in the summer of 2008, nutrients (\begin{document}${\rm{NO}}_3^ - $\end{document}, \begin{document}${\rm{NO}}_2^ - $\end{document}, \begin{document}${\rm{SiO}}_3^ {2-}\! $\end{document}, and \begin{document}${\rm{PO}}_4^ {3-} \!$\end{document}) and dissolved oxygen were measured in the western Arctic Ocean, to derive the vertical distribution of nutrient tracers and its relationship to water structure and biogeochemical processes. The nutrient data show that surface waters had the lowest \begin{document}${\rm{NO}}_3^ - \!$\end{document}/\begin{document}${\rm{PO}}_4^ {3-} $\end{document} (mean of 0.5) and \begin{document}${\rm{SiO}}_3^ {2-} \!$\end{document}/\begin{document}${\rm{PO}}_3^ {-} $\end{document} (mean of 2.8) values in the water column, suggesting an excess of phosphate. Winter Bering Shelf water (wBSW) had high Si* (16.7 μmol/L; Si*=[Si(OH)4]–[\begin{document}${\rm{NO}}_3^ - $\end{document}]) with negative N* (−11.7 μmol/L; N*=[\begin{document}${\rm{PO}}_4^ {3-} $\end{document}]−16[\begin{document}${\rm{PO}}_4^ {3-} $\end{document}]+3.5 μmol/L) in the water column, indicating nitrate deficiency. The warm Atlantic layer had positive N* (0.8 μmol/L) and negative Si* (−5.4 μmol/L) compared with Pacific source water. The vertical distribution of nutrients indicates that wBSW can be characterized by N* minimum and Si* maximum. In contrast, minima of Si* and \begin{document}${\rm{SiO}}_3^ {2-}\! $\end{document}/\begin{document}${\rm{PO}}_4^ {3-} $\end{document} below 200 m indicate the distribution of Atlantic warm water.
Feasibility study of miniature near-infrared spectrometer for the measurement of solar irradiance within Arctic snow-cover sea ice
Liwen Nan, Xiaoping Wang, Hangzhou Wang, Hongliang Zhou, Ying Chen
2020, 39(9): 115-124. doi: 10.1007/s13131-020-1632-y
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.
Under-ice ambient noise in the Arctic Ocean: observations at the long-term ice station
Xiao Han, Jingwei Yin, Yanming Yang, Hongtao Wen, Longxiang Guo
2020, 39(9): 125-132. doi: 10.1007/s13131-020-1652-7
Under-ice ambient noise in the Arctic Ocean is studied using the data recorded by autonomous hydrophones at the long-term ice station during the 9th Chinese National Arctic Research Expedition. Time-frequency analysis of two 7-s-long ice-induced noise samples shows that both ice collision and ice breaking noise have many outliers in the time-domain (impulsive characteristic) and abundant frequency components in the frequency-domain. Ice collision noise lasts for several seconds while the duration of ice breaking noise is much shorter (i.e., less than tens of milliseconds). Gaussian distribution and symmetric alpha stable (sαs) distribution are used in this paper to fit the impulsive under-ice noise. The sαs distribution can achieve better performance as it can track the heavy tails of impulsive noise while Gaussian distribution fails. This paper also analyzes the meteorological variables during the under-ice noise observation experiment and deduces that the impulsive ambient noise was caused by the combined force of high wind speed and increasing atmosphere temperature on the ice canopy. The Pearson correlation coefficients between long-term power spectral density variations of under-ice ambient noise and meteorological variables are also studied in this paper.
An improved least mean square/fourth direct adaptive equalizer for under-water acoustic communications in the Arctic
Yanan Tian, Xiao Han, Jingwei Yin, Hongxia Chen, Qingyu Liu
2020, 39(9): 133-139. doi: 10.1007/s13131-020-1653-6
An improved least mean square/fourth direct adaptive equalizer (LMS/F-DAE) is proposed in this paper for underwater acoustic communication in the Arctic. It is able to process complex-valued baseband signals and has better equalization performance than LMS. Considering the sparsity feature of equalizer tap coefficients, an adaptive norm (AN) is incorporated into the cost function which is utilized as a sparse regularization. The norm constraint changes adaptively according to the amplitude of each coefficient. For small-scale coefficients, the sparse constraint exists to accelerate the convergence speed. For large-scale coefficients, it disappears to ensure smaller equalization error. The performance of the proposed AN-LMS/F-DAE is verified by the experimental data from the 9th Chinese National Arctic Research Expedition. The results show that compared with the standard LMS/F-DAE, AN-LMS/F-DAE can promote the sparse level of the equalizer and achieve better performance.
A variational successive corrections approach for the sea ice concentration analysis
Xuefeng Zhang, Lu Yang, Hongli Fu, Dong Li, Zheqi Shen, Lianxin Zhang, Xuhui Hu
2020, 39(9): 140-154. doi: 10.1007/s13131-020-1654-5
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).