Search Results (189)

Sea ice parameters are crucial for polar ship design. During China’s 39th Antarctic Scientific Expedition, ice condition from the entire navigation process of the research vessel Xuelong 2 was recorded using shipborne cameras. To obtain sea ice parameters, two deep learning models, Ice-Deeplab and U-Net, were employed to automatically obtain sea ice concentration (SIC) and sea ice thickness (SIT), providing high-frequency data at 5-min intervals. During the observation period, ice navigation accounted for 32 days, constituting less than 20% of the total 163 voyage days. Notably, 63% of the navigation was in ice fields with less than 10% concentration, while only 18.9% occurred in packed ice (concentration > 90%) or level ice regions. SIT ranges from 100 cm to 234 cm and follows a normal distribution. The results demonstrate that, to achieve enhanced navigation efficiency and fulfill expedition objectives, the research vessel substantially reduced duration in high-concentration ice areas. Additionally, the results of SIC extracted from shipborne camera images were compared with the data from the Copernicus Marine Environment Monitoring Service (CMEMS) satellite remote sensing. In summary, the sea ice parameter data obtained from shipborne camera images offer high spatial and temporal resolution, making them more suitable for engineering applications in establishing sea ice environmental parameters. Full article

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the effects of oceanic mesoscale activity during the periods of maximum and minimum Antarctic sea ice extent (September 2019 and February 2020), numerical experiments were conducted using a coupled regional model and an online two-dimensional spatial filter to remove high-frequency sea surface temperature (SST) oscillations. The largest SST anomalies were observed in the Brazil–Malvinas Confluence and along oceanic fronts in September, with maximum SST anomalies reaching 4.23 °C and −3.71 °C. In February, the anomalies were 2.18 °C and −3.06 °C. The influence of oceanic mesoscale activity was evident in surface atmospheric variables, with larger anomalies also observed in September. This influence led to changes in the vertical structure of the atmosphere, affecting the development of the marine atmospheric boundary layer (MABL) and influencing the free atmosphere above the MABL. Modulations in precipitation patterns were observed, not only in oceanic regions, but also in adjacent continental areas. This research provides a novel perspective on ocean–atmosphere thermodynamic coupling, highlighting the mesoscale role and importance of its representation in the study region. Full article

Antarctic navigation encounters substantial challenges due to the dynamic and perilous characteristics of sea ice, which pose threats to vessel safety and operational efficiency. Existing risk assessment methodologies frequently lack real-time adaptability, while strategies for icebreaker convoys remain insufficiently quantified. To address these deficiencies, this study introduces an integrated framework that combines satellite-based sea ice monitoring, operational risk prediction, and icebreaker escort optimization. First, polar research routes and hydrographic conditions are systematically analyzed to enhance navigation planning. Second, a risk assessment system is developed by leveraging satellite-derived sea ice density and thickness data, facilitating a near-real-time hazard assessment (subject to satellite data latency) evaluation with 96.3% accuracy in ice type classification and a 15% improvement in risk prediction precision compared to conventional methods. Finally, kinematic safety criteria for icebreaker-escorted convoys are established, specifying speed-dependent distance thresholds to minimize collision risks, achieving optimal speeds of 1.4–2.3 knots for PC3-class vessels and 10–20% speed improvements for escorted vessels in cleared channels. The findings offer actionable insights into polar route optimization, risk mitigation, and safe ice navigation protocols, thereby directly supporting operational decision making in Antarctic waters. Full article

Blowing snow is a common phenomenon over the Antarctic ice sheet and sea ice regions, playing a crucial role in the Antarctic climate system. Previous research developed an optimized machine learning (ML) model to diagnose blowing snow occurrence using meteorological fields from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). This paper extends that work by optimizing an ML model to estimate blowing snow height and optical depth for operational data production. Observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) serve as ground truth for training. The optimization process involves selecting relevant input features and identifying the most effective ML regressor. As a result, 21 MERRA-2 fields were identified as key input features, and Extreme Gradient Boosting emerged as the most effective regressor. Feature importance analysis highlights wind components and surface pressure as the most significant predictors for blowing snow height and optical depth. Individual models were developed for each month. Using 10 years of CALIPSO data (2007–2016) for training, these optimized models can be applied across the full MERRA-2 dataset, spanning from 1980 to the present. This enables the generation of hourly blowing snow height and optical depth data on the MERRA-2 grid for the entire MERRA-2 time span. Full article

The environmental drivers of fin whale (Balaenoptera physalus) acoustic presence in Eastern Antarctic waters were investigated based on passive acoustic recordings from four sites, 2013–2019. Fin whale 20 Hz pulses were detected from late austral summer to early winter. Daily values of sea-ice concentration (SIC) were compared with the number of days with fin whale 20 Hz acoustic presence using a generalized additive model approach. At the Southern Kerguelen Plateau, Casey, and Dumont d’Urville sites, SIC correlated with fin whale calling activity, but less so at the Prydz site. Changes in SIC between sites resulted in variation in acoustic presence: Earlier sea-ice formation at Dumont d’Urville resulted in less acoustic presence in comparison to the Southern Kerguelen Plateau, where sea ice formed later in the season. Interannual variability in SIC impacted yearly acoustic presence, with a later onset of high SIC resulting in greater acoustic presence and later departure (migration timing) of the animals. Identifying the environmental drivers of fin whale presence is key to informing how this migratory species may be affected by environmental variability resulting from climate change. Full article

This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch. Full article

Based on a laboratorial analysis of nitrous oxide (N₂O) concentrations collected in gas bottles (glass flask) at the Zhongshan Station on the East Antarctic coast from 2008 to 2021, the variation characteristics and trends in the background concentration of N₂O at the station were analyzed and compared with the N₂O data from other Antarctic stations. The results showed that the annual average concentration of atmospheric N₂O along the East Antarctic coast increased from 320.40 ppb in 2008 to 333.31 ppb in 2021, with an overall increasing trend of 0.99 ppb per year. Pronounced seasonal variability was observed, with elevated concentrations occurring during austral spring–summer and reduced levels in autumn–winter, consistent with the seasonal patterns documented at other Antarctic sites. The overall variation trend of the N₂O concentration at Zhongshan Station is basically consistent with the observation results at other stations in Antarctica, suggesting that the station’s background N₂O measurements are representative of continental-scale atmospheric composition dynamics. Combined with the analysis of air mass tracks, this seasonal variation in N₂O is mainly related to the mass movement of air mass and, to a certain extent, is influenced by the seasonal melting of sea ice and the exchange between the troposphere and stratosphere. The results supplement important basic data on N₂O concentrations along the East Antarctic coast and have potential reference significance for further understanding the causes of atmospheric N₂O variations in the Antarctic region. Full article

Polynyas play a crucial role in polar ecosystems, influencing biodiversity, climate regulation, and oceanic processes. This study employs Synthetic Aperture Radar (SAR) data to determine the optimal sea ice concentration threshold for polynya identification, which is established at 75%. We present a dataset of daily polynya distribution in the Arctic and Antarctic from 2013 to 2022, analyzing their spatial patterns, interannual variability, and seasonal dynamics. Our results indicate that coastal polynyas, primarily located near landmasses, dominate both polar regions. The total polynya area in the Antarctic remained relatively stable, averaging approximately 1.86 × 10⁸ km² per year, with an interannual fluctuation of −3.1 × 10⁵ km² per year. In the Arctic, the average polynya area is around 1.59 × 10⁸ km² per year, with an interannual fluctuation of −7.1 × 10⁵ km² per year. Both regions exhibit distinct seasonal cycles: Arctic polynyas peak in May and reach their minimum in September, whereas Antarctic polynyas expand in November and contract to their smallest extent in February. The polynya formation and development result from a complex interplay of multiple factors, with no single variable fully explaining variations in polynyas’ extent. Additionally, the polynya area in the NOW, and Weddell Sea polynyas, exhibit consistent trends with chlorophyll-a concentration, highlighting their role as critical habitats for primary productivity in polar regions. These findings provide key insights into polynya dynamics and their broader implications for climate and ecological processes in polar regions. Full article

Sea-ice thickness (SIT) from satellites is an essential climate variable for characterizing the ice-covered ocean and evaluating numerical models. Although satellite altimetry is a promising option to obtain sustainable circum-Antarctic SIT estimates, its application in the Antarctic remains challenging due to the scarcity of systematic in situ observations for validation, and the most recent intercomparison exercise covered the period 2004 to 2008. In this study, we compared three empirical methods (ERM, BERM, and OLM) and one lidar-only method (ZIF) to determine SIT from lidar freeboard observations, one method combining lidar and radar freeboard observations (FDM), and one that uses both lidar freeboard observations and an independent snow depth dataset from passive microwaves (SICC). We first compared the methods in 2019, which is the only data-rich year during the overlapping period from 2019 to 2023. While the methods agreed on the broad spatial patterns of SIT, they clustered in two groups that have significant magnitude differences, with SICC and FDM estimating thicker ice and the lidar-based methods producing the thinnest estimates. Based on the limited set of available data, we did not find any single best performing method, and we recommend using the methods in a complementary way and to establish a network of concerted and continued field measurements for method assessments. Full article

The Antarctic sea ice concentration (SIC) plays a crucial role in global climate dynamics by influencing atmospheric and oceanic circulation. This study examines SIC variability and its relationship with major climate modes, including the El Niño-Southern Oscillation (ENSO), Pacific-South American (PSA) pattern, Southern Annular Mode (SAM), and Antarctic Dipole (ADP). Using NSIDC satellite-derived sea ice data and ERA5 reanalysis from 1980 to 2022, we analyzed SIC anomalies in the Weddell, Ross, and Bellingshausen and Amundsen (B&A) Seas, assessing their response to climatic forcings across different timescales. Our findings reveal strong linkages between SIC variability and large-scale atmospheric circulation. ENSO-related teleconnections drive a dipolar SIC response, with warming in the Pacific sector and cooling in the Atlantic during El Niño, and the opposite pattern during La Niña. PSA and ADP further modulate this response by altering Rossby wave propagation and heat fluxes, leading to significant SIC fluctuations. The ADP emerges as a dominant driver of interannual SIC anomalies, showing an out-of-phase relationship between the Atlantic and Pacific sectors of the Southern Ocean. Regional SIC trends exhibit contrasting patterns: the Ross Sea shows a significant positive SIC trend, while the B&A and Weddell Seas experience persistent negative anomalies due to enhanced meridional heat transport and stronger westerly winds. SAM strongly influences SIC, particularly in the Atlantic sector, with delayed responses of up to six months, likely due to ice-albedo feedbacks and ocean memory effects. These results enhance our understanding of Antarctic sea ice variability and its sensitivity to large-scale climate oscillations. Given the observed trends and ongoing climate change, further research is needed to assess how these processes will evolve under future warming scenarios. This study highlights the importance of continuous satellite observations and high-resolution climate modeling for improving projections of Antarctic sea ice behavior and its implications for the global climate system. Full article

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications. Full article

This study examines the trends and interannual variability of extreme precipitation in Antarctica, using six decades (1963–2023) of daily precipitation data from Russia’s Novolazarevskaya Station in East Antarctica. The results reveal declining trends in both the annual number of extreme precipitation days and the total amount of extreme precipitation, as well as a decreasing ratio of extreme to total annual precipitation. These trends are linked to changes in northward water vapor flux and enhanced downward atmospheric motion. The synoptic pattern driving extreme precipitation events is characterized by a dipole of negative and positive height anomalies to the west and east of the station, respectively, which directs southward water vapor flux into the region. Interannual variability in extreme precipitation days shows a significant correlation with the Niño 3.4 index during the austral winter semester (May–October). This relationship, weak before 1992, strengthened significantly afterward due to shifting wave patterns induced by tropical Pacific sea surface temperature anomalies. These findings shed light on how large-scale atmospheric circulation and tropical-extratropical teleconnections shape Antarctic precipitation patterns, with potential implications for ice sheet stability and regional climate variability. Full article

This study investigates the climatic background of winter PM2.5 (particulate matter with a diameter of 2.5 micrometers or smaller) concentrations in Hubei Province (DJF-HBPMC) and evaluates its predictability. The key findings are as follows: (1) Elevated DJF-HBPMC levels are associated with an upper-tropospheric northerly anomaly, a deepened southern branch trough (SBT) that facilitates southwesterly flow into central and eastern China, and a weakened East Asian winter monsoon (EAWM), which reduces the frequency and intensity of cold air intrusions. Near-surface easterlies and an anomalous anticyclonic circulation over Hubei contribute to reduced precipitation, thereby decreasing the dispersion of pollutants and leading to higher PM2.5 concentrations. (2) Significant correlations are observed between DJF-HBPMC and sea surface temperature (SST) anomalies in specific oceanic regions, as well as sea-ice concentration (SIC) anomalies near the Antarctic. For the atmospheric pattern anomalies over Hubei Province, the North Atlantic SST mode (NA) promotes the southward intrusion of northerlies, while the Northwest Pacific (NWP) and South Pacific (SPC) SST modes enhance wet deposition through increased precipitation, showing a negative correlation with DJF-HBPMC. Conversely, the South Atlantic–Southwest Indian Ocean SST mode (SAIO) and the Ross Sea sea-ice mode (ROSIC) contribute to more stable local atmospheric conditions, which reduce pollutant dispersion and increase PM2.5 accumulation, thus exhibiting a positive correlation with DJF-HBPMC. (3) A multiple linear regression (MLR) model, using selected seasonal SST and SIC indices, effectively predicts DJF-HBPMC, showing high correlation coefficients (CORR) and anomaly sign consistency rates (AS) compared to real-time values. (4) In daily HBPMC forecasting, both the Reversed Unrestricted Mixed-Frequency Data Sampling (RU-MIDAS) and Reversed Restricted-MIDAS (RR-MIDAS) models exhibit superior skill using only monthly precipitation, and the RR-MIDAS offers the best balance in prediction accuracy and trend consistency when incorporating monthly precipitation along with monthly SST and SIC indices. Full article

The Ross Sea I glaciation, marked by the northward advance of the Ross Ice Sheet (RIS) in the Ross Sea, east Antarctica, corresponds with the last major expansion of the West Antarctic Ice Sheet during the last glacial period. During its advance, the RIS was grounded along the southern Victoria Land coast, completely blocking the mouths of several of the McMurdo Dry Valleys (MDVs). Several authors have proposed that very large paleolakes, proglacial to the RIS, existed in many of the MDVs. Studies of these large paleolakes have been key in the interpretation of the regional landscape, climate, hydrology, and glacier and ice sheet movements. By far the most studied of these large paleolakes is Glacial Lake Washburn (GLW) in Taylor Valley. Here, we present a comprehensive review of literature related to GLW, focusing on the waters supplying the paleolake, signatures of the paleolake itself, and signatures of past glacial movements that controlled the spatial extent of GLW. We find that while a valley-wide proglacial lake likely did exist in Taylor Valley during the early stages of the Ross Sea I glaciation, during later stages two isolated lakes occupied the eastern and western sections of the valley, confined by an expansion of local alpine glaciers. Lake levels above ~140 m asl were confined to western Taylor Valley, and major lake level changes were likely driven by RIS movements, with climate variables playing a more minor role. These results may have major implications for our understanding of the MDVs and the RIS during the Ross Sea I glaciation. Full article

The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes. Full article