A 41-year Antarctic sea ice concentration (SIC) dataset derived from satellite passive microwave radiometers during the period of 1979–2019 has been used to analyze sea ice changes in recent decades. The trends of SIC and sea ice extent (SIE) are calculated during the periods of 1979–2019, 1979–2013, and 2014–2019. The trends show regionally dependent features. The SIC shows an increasing trend in most of the regions except the Bellingshausen Sea and Amundsen Sea (BA) during 1979–2019 and 1979–2013. The SIE trend shows a decreasing or decelerating trend in the period of 1979–2019 ((6 835±2 210) km²/a) compared with the 1979–2013 period ((18 600±2 203) km²/a). In recent years (2014–2019), the SIC and SIE have exhibited decreasing trends (–(34 567±3 521) km²/month), especially in the Weddell Sea (WS) and Ross Sea (RS) during summer and autumn. The trends are related to regionally dependent causes. The analyses show that the SIC and SIE decreased in response to the warming trend of 2 m air temperature (T_a-2m) and have exhibited a good relationship with T_a-2m in summer and autumn in recent years. The sea ice decrease in the Antarctic is mainly caused by increases in absorbed energy and southward energy transportation in recent years, such as the increase in gained solar radiation and moist static energy from the south, which demonstrate notable regional characteristics. In the WS region, the local positive feedback from the additional absorbed solar radiation, resulting in warmer air and reduced sea ice, is the main reason for the sea ice decrease in recent years. The increase in southward energy transport has also favored a decrease in sea ice. In the RS region, the increase in southward-transported moist static energy has contributed to the decrease in sea ice, and the increases in cloud cover and longwave radiation have prevented sea ice growth.

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.

The importance of the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO) in influencing zonally asymmetric changes in Antarctic surface air temperature (SAT) has been established. However, previous studies have primarily concentrated on examining the combined impact of the contrasting phases of the AMO and IPO, which have been dominant since the advent of satellite observations in 1979. This study utilizes long-term reanalysis data to investigate the impact of four combinations of +AMO+IPO, –AMO–IPO, +AMO–IPO, and –AMO+IPO on Antarctic SAT over the past 115 years. The +AMO phase is characterized by a spatial mean temperature amplitude of up to 0.5℃ over the North Atlantic Ocean, accompanied by positive sea surface temperature (SST) anomalies in the tropical eastern Pacific and negative SST anomalies in the extratropical-mid-latitude western Pacific, which are indicative of the +IPO phase. The Antarctic SAT exhibits contrasting spatial patterns during the +AMO+IPO and +AMO–IPO periods. However, during the –AMO+IPO period, apart from the Antarctic Peninsula and the vicinity of the Weddell Sea, the entire Antarctic region experiences a warming trend. The most pronounced signal in the SAT anomalies is observed during the austral autumn, whereas the combination of –AMO and –IPO exhibits the smallest magnitude across all the combinations. The wavetrain excited by the SST anomalies associated with the AMO and IPO induces upper-level and surface atmospheric circulation anomalies, which alter the SAT anomalies. Furthermore, downward longwave radiation anomalies related to anomalous cloud cover play a crucial role. In the future, if the phases of AMO and IPO were to reverse (AMO transitioning to a negative phase and IPO transitioning to a positive phase), Antarctica could potentially face more pronounced warming and accelerated melting compared to the current observations.

The key functional genes involved in temperature adaption of the Antarctic psychrotrophic bacterium Psychrobacter sp. G. were identified by transcriptomic sequencing. We analyzed the global transcriptional profile of Psychrobacter sp. G under cold stress (0℃) and heat stress (30℃), with the optimal growth temperature 20℃ as the control. There were large alterations of the transcriptome profile, including significant upregulation of 11 and 12 transcripts as well as significant downregulation of 47 and 42 transcripts in the cold and heat stress groups, respectively, compared to the control. The expression of various genes encoding enzymes and transcriptional regulators, including PfpI and TetR family transcriptional regulators under heat stress, as well as the expression of DEAD/DEAH box helicase and the IclR family of transcriptional regulators under cold stress, were upregulated significantly. The expression of several genes, most affiliated with TonB-dependent receptor and siderophore receptor, was downregulated significantly under both heat and cold stress. Many of the genes associated with the metabolism of fatty acid and ABC transporters were regulated differentially under different temperature stress. The results of this survey of transcriptome and temperature stress-relevant genes contribute to our understanding of the stress-resistant mechanism in Antarctic bacteria.

An Antarctic sea ice identification algorithm on the HY-2A scatterometer (HSCAT) employs backscattering coefficient (σ₀) and active polarization ratio (APR) for a preliminary sea ice identification. Then standard deviation (STD) filtering and space filtering are carried out. Finally, it is used to identify sea ice. A process uses a σ₀, STD threshold and an APR as sea ice indicators. The sea ice identification results are verified using the sea ice distribution data of the ASMR2 released by the National Snow and Ice Data Center as a reference. The results show very good consistence of sea ice development trends, seasonal changes, area distribution, and sea ice edge distribution of the sea ice identification results obtained by this algorithm relative to the ASMR2 sea ice results. The accuracy of a sea ice coverage is 90.8% versus the ASMR2 sea ice results. This indicates that this algorithm is reliable.

Sea-ice concentration is a key item in global climate change research. Recent progress in remotely sensed sea-ice concentration product has been stimulated by the use of a new sensor, advanced microwave scanning radiometer for EOS (AMSR-E), which offers a spatial resolution of 6 km×4 km at 89GHz. A new inversion algorithm named LASI (linear ASI) using AMSR-E 89GHz data was proposed and applied in the antarctic sea areas. And then comparisons between the LASI ice concentration products and those retrieved by the other two standard algorithms, ASI (arctic radiation and turbulence interaction study sea-ice algorithm) and bootstrap, were made. Both the spatial and temporal variability patterns of ice concentration differences, LASI minus ASI and LASI minus bootstrap, were investigated. Comparative data suggest a high result consistency, especially between LASI and ASI. On the other hand, in order to estimate the LASI ice concentration errors introduced by the tie-points uncertainties, a sensitivity analysis was carried out. Additionally an LASI algorithmerror estimation based on the field measurements was also completed. The errors suggest that themoderate to high ice concentration areas (>70%) are less affected (never exceeding 10%) than those in the low ice concentration. LASI and ASI consume 75 and 112 s respectively when processing the same AMSR-E time series thourghout the year 2010. To conclude, by using the LASI algorithm, not only the seaice concentration can be retrieved with at least an equal quality as that of the two extensively demonstrated operational algorithms, ASI and bootstrap, but also in a more efficient way than ASI.

Dimethylsulfoniopropionate (DMSP) is mainly produced by marine phytoplankton as an osmolyte, antioxidant, predator deterrent, or cryoprotectant. DMSP is also an important carbon and sulfur source for marine bacteria. Bacteria may metabolize DMSP via the demethylation pathway involving the DMSP demethylase gene (dmdA) or the cleavage pathway involving several different DMSP lyase genes. Most DMSP released into seawater is degraded by bacteria via demethylation. To test a hypothesis that the high gene frequency of dmdA among major marine taxa results in part from horizontal gene transfer (HGT) events, a total of thirty-one bacterial strains were isolated from Arctic Kongsfjorden seawater in this study. Analysis of 16S rRNA gene sequences showed that, except for strains BSw22118, BSw22131 and BSw22132 belonging to the genera Colwellia, Pseudomonas and Glaciecola, respectively, all bacteria fell into the genus Pseudoalteromonas. DmdA genes were detected in five distantly related bacterial strains, including four Arctic strains (Pseudoalteromonas sp. BSw22112, Colwellia sp. BSw22118, Pseudomonas sp. BSw22131 and Glaciecola sp. BSw22132) and one Antarctic strain (Roseicitreum antarcticum ZS2–28). Their dmdA genes showed significant similarities (97.7%-98.3%) to that of Ruegeria pomeroyi DSS–3, which was originally isolated from temperate coastal seawater. In addition, the sequence of the gene transfer agent (GTA) capsid protein gene (g5) detected in Antarctic strain ZS2–28 exhibited a genetically closely related to that of Ruegeria pomeroyi DSS–3. Among the five tested strains, only Pseudomonas sp. BSw22131 could grow using DMSP as the sole carbon source. The results of this study support the hypothesis of HGT for dmdA among taxonomically heterogeneous bacterioplankton, and suggest a wide distribution of functional gene (i.e., dmdA) in global marine environments.

A set of 27 marine planktonic bacteria isolated from the polar regions was characterized by 16S rDNA sequencing and physiological and biochemical testing. More than half of these bacteria were positive for caseinase, gelatinase and β-glucosidase, and could utilize glucose, maltose or malic acid as carbon source for cell growth. Twelve isolates expressed nitrate reduction activities. Except for one antarctic isolate BSw10175 belonging to Actinobacteria phylum, these isolates were classified as γ-Proteobacteria, suggesting that γ-Proteobacteria dominated in cultivable marine bacterioplankton at both poles. Genus Pseudoalteromonas was the predominant group in the Chukchi Sea and the Bering Sea, and genus Shewanella dominated in cultivable bacterioplankton in the Prydz Bay. With sequence similarities above 97%, genus Psychrobacter was found at both poles. These 27 isolates were psychrotolerant, and significant 16S rDNA sequence similarities were found not only between arctic and antarctic marine bacteria (>99%), but also between polar marine bacteria and bacteria from other aquatic environments (≥ 98.8%), including temperate ocean, deep sea, pond and lake, suggesting that in the polar oceans less temperature-sensitive bacteria may be cosmopolitan and have a bipolar, even global, distribution at the species level.

In this paper, on the basis of the Antarctic sea ice data from 1972 to 1989 issued by the America Joint Ice Center, the distribution features of the Antarctic sea ice is analyzed, the net sea ice area indexes are calculated. and the long-range variation periods of the sea ice area index are analyzed with the maximum entropy spectrum, finally the distribution pattern of the Antarctic sea ice and its variation features are obtained.
According to its spatial distribution feature, the Antarctic Sea ice is divided into three large regions. Region Ⅰ (0°-120°E) is a zonal area which includes the Prydz Bay area, and sea ice area extending from the Weddell Sea, Region Ⅰ (120°E-120°W) mainly includes the Ross Sea area, and Region Ⅱ(120°W-0°) mainly the Weddell Sea area. Of all the regions, the ice area in Region,is the largest, and that in Region Ⅰ is the smallest.

The characteristic low-frequency oscillation of the sea surface temperature anomaly (SSTA) of ENSO related regions, Niño 1+2, Niño 3, Niño 4 and Niño West, and the Southern Oscillation index (SOI) is analyzed with the method of maximum entropy spectrum.Antarctic sea ice is divided into 4 regions, i.e.East Antarctic is Region Ⅰ (0°-120°E), the region dominated by Ross Sea ice is Region Ⅱ (120°E-120°W), the region dominated by Ross Sea ice is Region Ⅲ (120°W-0°), and the whole Antarctic sea ice area is Region Ⅳ.Also, the month-to-month correlation series of the sea ice with ENSO from contemporary to 5-years lag is calculated.The optimum correlation period is selected from the series.The characteristics and the rules obtained are as follows.1.There are a common 4-years main period of the SSTA of Niños 1+2, 3 and 4, a rather strong 4-years secondary period and a quasi-8-years main period of that of Niño West.