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An assessment of Arctic cloud water paths in atmospheric reanalyses
Mingyi Gu, Zhaomin Wang, Jianfen Wei, Xiaoyong Yu
2021, 40(3): 46-57. doi: 10.1007/s13131-021-1706-5  Published:2021-04-30
Keywords: Arctic, clouds, cloud water paths (CWPs), reanalysis evaluation
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.
Bacterial and archaeal community structures in the Arctic deep-sea sediment
LI Yan, LIU Qun, LI Chaolun, DONG Yi, ZHANG Wenyan, ZHANG Wuchang, XIAO Tian
2015, 34(2): 93-113. doi: 10.1007/s13131-015-0624-9
Keywords: Arctic, deep-sea sediment, microbial community structure, pyrosequencing
Microbial community structures in the Arctic deep-sea sedimentary ecosystem are determined by organic matter input, energy availability, and other environmental factors. However, global warming and earlier ice-cover melting are affecting the microbial diversity. To characterize the Arctic deep-sea sediment microbial diversity and its rela-tionship with environmental factors, we applied Roche 454 sequencing of 16S rDNA amplicons from Arctic deep-sea sediment sample. Both bacterial and archaeal communities' richness, compositions and structures as well as tax-onomic and phylogenetic affiliations of identified clades were characterized. Phylotypes relating to sulfur reduction and chemoorganotrophic lifestyle are major groups in the bacterial groups; while the archaeal community is domi-nated by phylotypes most closely related to the ammonia-oxidizing Thaumarchaeota (96.66%) and methanogenic Euryarchaeota (3.21%). This study describes the microbial diversity in the Arctic deep marine sediment (>3 500 m) near the North Pole and would lay foundation for future functional analysis on microbial metabolic processes and pathways predictions in similar environments.
Variation of sea ice extent in different regions of the Arctic Ocean
CHEN Ping, ZHAO Jinping
2017, 36(8): 9-19. doi: 10.1007/s13131-016-0886-x
Keywords: Arctic, sea ice extent, period of 4-6 years, sea ice margin, sea ice coverage indices
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.
Diversity of the aerobic anoxygenic phototrophy gene pufM in Arctic and Antarctic coastal seawaters
ZENG Yinxin, DONG Peiyan, QIAO Zongyun, ZHENG Tianling
2016, 35(6): 68-77. doi: 10.1007/s13131-016-0877-y
Keywords: diversity, aerobic anoxygenic phototrophic bacteria, pufM, Arctic, Antarctic
Aerobic anoxygenic phototrophic (AAP) bacteria serve important functions in marine carbon and energy cycling because of their capability to utilize dissolved organic substrates and harvest light energy. AAP bacteria are widely distributed in marine environments, and their diversity has been examined in marine habitats. However, information about AAP bacteria at high latitudes remains insufficient to date. Therefore, this study determined the summer AAP bacterial diversity in Arctic Kongsfjorden and in the Antarctic coastal seawater of King George Island on the basis of pufM, a gene that encodes a pigment-binding protein subunit of the reaction center complex. Four pufM clone libraries were constructed, and 674 positive clones were obtained from four investigated stations (two in Kongsfjorden and two in the Antarctic Maxwell Bay). Arctic clones were clustered within the Alphaproteobacteria, whereas Antarctic clones were classified into the Alphaproteobacteria and Betaproteobacteria classes. Rhodobacteraceae-like pufM genes dominated in all samples. In addition, sequences closely related to pufM encoded on a plasmid in Sulfitobacter guttiformis were predominant in both Arctic and Antarctic samples. This result indicates the transpolar or even global distribution of pufM genes in marine environments. Meanwhile, differences between the Arctic and Antarctic sequences may prove polar endemism. These results indicate the important role of Rhodobacteraceae as AAP bacteria in bipolar coastal waters.
Impact of Arctic Oscillation on cloud radiative forcing and September sea ice retreat
Yanxing Li, Liang Chang, Guoping Gao
2022, 41(10): 131-139. doi: 10.1007/s13131-022-2010-8  Published:2022-10-27
Keywords: Arctic, Arctic Oscillation, cloud radiative forcing, sea ice retreat
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.
Prediction of visibility in the Arctic based on dynamic Bayesian network analysis
Shijun Zhao, Yulong Shan, Ismail Gultepe
2022, 41(4): 57-67. doi: 10.1007/s13131-021-1826-z  Published:2022-04-01
Keywords: Arctic, visibility prediction, artificial neural network, dynamic Bayesian network
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.
The impact of concurrent variation of atmospheric meridional heat transport in western Baffen Bay and eastern Greenland on summer Arctic sea ice
Le Wang, Lujun Zhang, Wenfa Yang
2020, 39(8): 14-23. doi: 10.1007/s13131-020-1614-0  Published:2020-08-25
Keywords: Arctic, atmospheric meridional heat transport, transient eddy, sea ice, Arctic dipole
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.
Simulation of arctic surface radiation and energy budget during the summertime using the single-column model
LI Xiang, WANG Hui, ZHANG Zhanhai, WU Huiding
2008(1): 1-12.
Keywords: ARCSCM, surface radiation and energy budget, arctic, simulation
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.
Hydromedusae from the Arctic in 2010 during the 4th Chinese National Arctic Research Expedition (CHINARE 4)
WANG Chunguang, HUANG Jiaqi, XIANG Peng, WANG Yanguo, XU Zhenzu, GUO Donghui, LIN Mao
2014, 33(6): 95-102. doi: 10.1007/s13131-014-0494-6
Keywords: Arctic, Hydromedusae, taxonomy
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.
Competition within the marine microalgae over the polar dark period in the Greenland Sea of high Arctic
Zhang Qing, Rolf Gradinger, Zhou Qingsong
2003(2): 233-242.
Keywords: Competition, marine microalgae, dark, the Greenland Sea, Arctic
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.
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