The role of Arctic clouds in the recent rapid Arctic warming has attracted much attention. However, Arctic cloud water paths (CWPs) from reanalysis datasets have not been well evaluated. This study evaluated the CWPs as well as LWPs (cloud liquid water paths) and IWPs (cloud ice water paths) from five reanalysis datasets (MERRA-2, MERRA, ERA-Interim, JRA-55, and ERA5) against the COSP (Cloud Feedback Model Intercomparison Project Observations Simulator Package) output for MODIS from the MERRA-2 CSP (COSP satellite simulator) collection (defined as M2Modis in short). Averaged over 1980–2015 and over the Arctic region (north of 60°N), the mean CWPs of these five datasets range from 49.5 g/m² (MERRA) to 82.7 g/m² (ERA-Interim), much smaller than that from M2Modis (140.0 g/m²). However, the spatial distributions of CWPs, show similar patterns among these reanalyses, with relatively small values over Greenland and large values over the North Atlantic. Consistent with M2Modis, these reanalyses show larger LWPs than IWPs, except for ERA-Interim. However, MERRA-2 and MERRA underestimate the ratio of IWPs to CWPs over the entire Arctic, while ERA-Interim and JRA-55 overestimate this ratio. ERA5 shows the best performance in terms of the ratio of IWPs to CWPs. All datasets exhibit larger CWPs and LWPs in summer than in winter. For M2Modis, IWPs hold seasonal variation similar with LWPs over the land but opposite over the ocean. Following the Arctic warming, the trends in LWPs and IWPs during 1980~2015 show that LWPs increase and IWPs decrease across all datasets, although not statistically significant. Correlation analysis suggests that all datasets have similar interannual variability. The study further found that the inclusion of re-evaporation processes increases the humidity in the atmosphere over the land and that a more realistic liquid/ice phase can be obtained by independently treating the liquid and ice water contents.

在北极深海沉积物生态系统中,微生物的群落结构由有机质输入、能量的可用性及其他环境因素决定.然而,全球气候变暖及其导致的冰盖提前融化正在影响微生物的多样性.为描述北极深海沉积物中的微生物群落结构及其与环境因素的相关性,我们利用罗氏454对北极深海沉积物样品的16S rDNA扩增子进行了测序,对细菌和古菌群落的丰富度、成分、结构及其系统发育分类地位进行了描述.硫还原和化能有机营养类是细菌群落中的主要类群;而古细菌群落主要是由微生物的关系最为密切的氨氧化奇古菌门(96.66%)和产甲烷古生菌界(3.21%).这项研究描述了北极极点附近深海沉积物(> 3500米)中的微生物多样性,将为以后研究类似环境中微生物代谢过程和途径等功能分析奠定基础.

在过去半个世纪，随着全球气候的变暖北极海冰快速减退。本文主要分析了北极及其各边缘海海冰覆盖范围的变化特征。结果表明北极海冰在1979-2013年持续减退，夏季海冰减退尤为显著，其次为秋季、冬季、春季。在北极海冰厚重时期，消除季节变化后的海冰覆盖范围存在一个4-6年的增减变化特征。2003-06年为本世纪的重冰年，但各年的区域差异较大。在轻冰年，各扇区海冰外缘线北退。弗拉姆海峡的海冰外缘线的变化跟整个北冰洋基本相反。融冻期海冰覆盖率指数是海冰冰情的重要体现，反映了北极和各边缘海的海冰变化情况。自2002年以来，太平洋扇区北部的C2区海冰的变化对北极中央区贡献最大，其次是C1区，C3区。近些年来北极各海区之间冰情变化体现了3种相关性：楚科奇海和东西伯利亚海海冰覆盖率指数的相关系数较大，海冰变化较为一致；由于来自巴伦支海的暖流进入喀拉海，大西洋扇区的喀拉海和巴伦之海之间海冰变化较为一致。北极中央区海冰的变化受到周边海域的海冰的影响。

好氧不产氧光合(AAP)细菌因其具有利用溶解性有机物及光能的能力,在海洋碳循环及能量流动中发挥着重要的作用。该类细菌广泛分布在海洋环境中,其在不同生境中的多样性已被调查。但到目前为止,人们对于高纬度地区好氧不产氧光合细菌的认识还较为缺乏。有鉴于此,本研究基于编码光反应复合物上一个色素结合蛋白亚基的pufM基因,对北极王湾及南极乔治王岛近岸水体中夏季好氧不产氧光合细菌的多样性进行了检测。针对2个王湾站位和2个南极麦克斯维尔湾站位构建了4个pufM基因克隆文库,获得674个阳性克隆子。北极克隆子全部由α-变形细菌组成,而南极克隆子则包括α-变形细菌及β-变形细菌。来源于类似红细菌科的pufM基因在所有样品中皆占据优势。此外,与一株滴状亚硫酸盐杆菌中质粒编码的pufM基因存在亲缘关系的序列,在南、北极样品中均占优势。结果表明海洋环境中的pufM基因存在跨极甚至是环球分布。与此同时,南、北极序列之间的差异,也表明了极地地方种的存在。这些结果显示,作为好氧不产氧光合细菌的红细菌科在两极的近岸水体中具有重要的地位。

The Arctic Oscillation (AO) has important effects on the sea ice change in terms of the dynamic and thermodynamic processes. However, while the dynamic processes of AO have been widely explored, the thermodynamic processes of AO need to be further discussed. In this paper, we use the fifth state-of-the-art reanalysis at European Centre for Medium-Range Weather Forecasts (ERA5) from 1979 to 2020 to investigate the relationship between AO and the surface springtime longwave (LW) cloud radiative forcing (CRF), summertime shortwave (SW) CRF in the Arctic region (65°−90°N). In addition, the contribution of CRF induced by AO to the sea ice change is also discussed. Results indicate that the positive (negative) anomalies of springtime LW CRF and summertime SW CRF are generally detected over the Arctic Ocean during the enhanced positive (negative) AO phase in spring and summer, respectively. Meanwhile, while the LW (SW) CRF generally has a positive correlation with AO index (AOI) in spring (summer) over the entire Arctic Ocean, this correlation is statistically significant over 70°−85°N and 120°W−90°E (i.e., region of interest (ROI)) in both seasons. Moreover, the response of CRF to the atmospheric conditions varies in spring and summer. We also find that the positive springtime (summertime) AOI tends to decrease (increase) the sea ice in September, and this phenomenon is especially prominent over the ROI. The sensitivity study among sea ice extent, CRF and AOI further reveals that decreases (increases) in September sea ice over the ROI are partly attributed to the springtime LW (summertime SW) CRF during the positive AOI. The present study provides a new pattern of AO affecting sea ice change via cloud radiative effects, which might benefit the sea ice forecast improvement.

With the accelerated warming of the world, the safety and use of Arctic passages is receiving more attention. Predicting visibility in the Arctic has been a hot topic in recent years because of navigation risks and opening of ice-free northern passages. Numerical weather prediction and statistical prediction are two methods for predicting visibility. As microphysical parameterization schemes for visibility are so sophisticated, visibility prediction using numerical weather prediction models includes large uncertainties. With the development of artificial intelligence, statistical prediction methods have received increasing attention. In this study, we constructed a statistical model with a physical basis, to predict visibility in the Arctic based on a dynamic Bayesian network, and tested visibility prediction over a 1°×1° grid area averaged daily. The results show that the mean relative error of the predicted visibility from the dynamic Bayesian network is approximately 14.6% compared with the inferred visibility from the artificial neural network. However, dynamic Bayesian network can predict visibility for only 3 days. Moreover, with an increase in predicted area and period, the uncertainty of the predicted visibility becomes larger. At the same time, the accuracy of the predicted visibility is positively correlated with the time period of the input evidence data. It is concluded that using a dynamic Bayesian network to predict visibility can be useful over Arctic regions for projected climatic changes.

Based on the climatological reanalysis data of the European Center for Medium-Range Weather Forecasts and the Arctic sea ice data of the National Snow and Ice Data Center, the relationship between the Arctic sea ice area (SIA) and the interannual variation of atmospheric meridional heat transport (AMHT) was analyzed. The results show that the atmospheric meridional heat transported by transient eddy (TAMHT) dominates the June AMHT in mid-high latitudes of the Northern Hemisphere, while the western Baffin Bay (B) and the eastern Greenland (G) are two gates for TAMHT entering the Arctic. TAMHT in the western Baffin Bay (B-TAMHT) and eastern Greenland (G-TAMHT) has a concurrent variation of reverse phase, which is closely related to the summer Arctic SIA. Possible mechanism is that the three Arctic atmospheric circulation patterns (AD, AO and NAO) in June can cause the concurrent variation of TAMHT in the B and G regions. This concurrent variation helps to maintain AD anomaly in summer through wave action and changes the polar air temperature, thus affecting the summer Arctic SIA. Calling the heat entering the Arctic as warm transport and the heat leaving Arctic as cold transport, then the results are classified into three situations based on B-TAMHT and G-TAMHT: warm B corresponding to cold G (WC), cold B corresponding to warm G (CW), cold B corresponding to cold G (CC), while warm B corresponding to warm G is virtually non-existent. During the WC situation, the SIA in the Pacific Arctic sediments and Kara Sea decreases; during the CW situation, the SIA in the Laptev Sea and Kara Sea decreases; during the CC situation, the SIA in the Kara Sea, Laptev Sea and southern Beaufort Sea increases.

The surface heat budget of the Arctic Ocean (SHEBA) project has shown that the study of the surface heat budget characteristics is crucial to understanding the interface process and environmental change in the polar region. An arctic single-column model (ARCSCM) of Colorado University is used to simulate the arctic surface radiation and energy budget during the summertime. The simulation results are analyzed and compared with the SHEBA measurements. Sensitivity analyses are performed to test microphysical and radiative parameterizations in this model. The results show that the ARCSCM model is able to simulate the surface radiation and energy budget in the arctic during the summertime, and the different parameterizations have a significant influence on the results. The combination of cloud microphysics and RRTM parameterizations can fairly derive the surface solar shortwave radiation and downwelling longwave radiation flux. But this cloud microphysics parameterization scheme deviates notably from the simulation of surface sensible and latent heat flux. Further improvement for the parameterization scheme applied to the Arctic Regions is necessary.

Fifty-seven stations (48 grid stations and nine stratified stations) were sampled across the study region (67.000°-88.394°N, 152.500°-178.643°W) during the 4th Chinese National Arctic Research Expedition (CHINARE 4) from July to August 2010 by the icebreaker R/V Xuelong. A total of 24 species of Hydromedusae were identified from 130 zooplankton samples, of which seven species belonged to Automedusa, eight species to Anthomedusae, four species to Leptomedudae, and three species to Siphonophora. Catablema multicirratum Kishinouye, 1910, Bougainvillia bitentaculata Uchida, 1925, and Euphysa japonica (Maas, 1909) were recorded for the first time in the Arctic sea. In the present paper, 18 species of Hydromedusae were described and illustrated, of which three species were described for the first time in the Arctic sea, and 15 species were described for the first time in China.

With the onset of winter, polar marine microalgae would have faced total darkness for a period of up to 6 months. A natural autumn community of Arctic sea ice microalgae was collected for dark survival experiments from the Greenland Sea during the ARKTIS-XI/2 Expedition of RV Polarstern in October 1995. After a dark period of 161 days, species dominance in the algal assemblage have changed from initially pennate diatoms to small phytoflagellates (< 20 μm). Over the entire dark period, the mean algal growth rate was -0.01 d^-1. Nearly all diatom species had negative growth rates, while phytoflagellate abundance increased. Resting spore formation during the dark period was observed in less than 4.5% of all cells and only for dinoflagellates and the diatom Chaetoceros spp. We assume that facultative heterotrophy and energy storage are the main processes enabling survival during the dark Arctic winter. After an increase in light intensity, microalgal cells reacted with fast growth within days. Phytoflagellates had the highest growth rate, followed by Nitzschia frigida. Further investigations and experiments should focus on the mechanisms of dark survival (mixotrophy and energy storage) of polar marine microalgae.