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Drifting icebergs represent a significant hazard for polar navigation and are able to impact the ocean environment around them. Freshwater flux and the associated cooling from melting icebergs can locally decrease salinity and temperature and thus affect ocean circulation, biological activity, sea ice, and –on larger spatial scales– the whole climate system. However, despite their potential impact, the large-scale operational monitoring of drifting icebergs in sea ice-covered regions is as of today typically restricted to giant icebergs, larger than 18.5 km in length. This is due to difficulties in accurately identifying and following the motion of much smaller features in the polar ocean from space. So far, tracking of smaller icebergs from satellite imagery thus has been limited to open-ocean regions not covered by sea ice. In this study, a novel automated iceberg tracking method, based on a machine learning-approach for automatic iceberg detection, is presented. To demonstrate the applicability of the method, a case study was performed for the Weddell Sea region, Antarctica, using 1213 Advanced Synthetic Aperture Radar (ASAR) satellite images acquired between 2002 and 2011. Overall, a subset of 414 icebergs (3134 re-detections in total) with surface areas between 3.4 km2 and 3612 km2 were investigated with respect to their prevalent drift patterns, size variability, and average disintegration. The majority of the tracked icebergs drifted between 1.3 km and 2679.2 km westward around the Antarctic continent, following the Antarctic Coastal Current (ACoC) and the Weddell Gyre, at an average drift speed of 3.6 ± 7.4 km day−1. The method also allowed us to estimate an average daily disintegration (i.e. iceberg area decrease) rate of ~0.13% (~37% year−1) for all icebergs. Using the sum of all detected individual surface area reductions, we estimate a total iceberg mass decrease of ~683 Gt year−1, which can be freshwater input and/or new 'child' icebergs calved from larger icebergs. The extension to an automated long-term tracking method for icebergs is challenging as the iceberg shape can vary significantly due to abrupt disintegration or calving of bergy bits. However, our machine learning approach extended by automatic shape-based tracking capabilities proved to be a reliable alternative for automatic detection and tracking of icebergs, even under the ambiguous SAR background signatures often found in the Southern Ocean. In particular, the method works in the challenging near-coastal environment where the presence of sea ice and coastal ocean dynamics such as surface waves usually pose major obstacles for other approaches. [ABSTRACT FROM AUTHOR]

Two decades into the 21st century there is growing evidence for global impacts of Antarctic and Southern Ocean climate change. Reliable estimates of how the Antarctic climate system would behave under a range of scenarios of future external climate forcing are thus a high priority. Output from new model simulations coordinated as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides an opportunity for a comprehensive analysis of the latest generation of state‐of‐the‐art climate models following a wider range of experiment types and scenarios than previous CMIP phases. Here the main broad‐scale 21st century Antarctic projections provided by the CMIP6 models are shown across four forcing scenarios: SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5. End‐of‐century Antarctic surface‐air temperature change across these scenarios (relative to 1995–2014) is 1.3, 2.5, 3.7 and 4.8°C. The corresponding proportional precipitation rate changes are 8, 16, 24 and 31%. In addition to these end‐of‐century changes, an assessment of scenario dependence of pathways of absolute and global‐relative 21st century projections is conducted. Potential differences in regional response are of particular relevance to coastal Antarctica, where, for example, ecosystems and ice shelves are highly sensitive to the timing of crossing of key thresholds in both atmospheric and oceanic conditions. Overall, it is found that the projected changes over coastal Antarctica do not scale linearly with global forcing. We identify two factors that appear to contribute: (a) a stronger global‐relative Southern Ocean warming in stabilisation (SSP2‐4.5) and aggressive mitigation (SSP1‐2.6) scenarios as the Southern Ocean continues to warm and (b) projected recovery of Southern Hemisphere stratospheric ozone and its effect on the mid‐latitude westerlies. The major implication is that over coastal Antarctica, the surface warming by 2100 is stronger relative to the global mean surface warming for the low forcing compared to high forcing future scenarios. [ABSTRACT FROM AUTHOR]

Mass loss around the Antarctic Ice Sheet is driven by basal melting and iceberg calving, which constitute the two dominant paths of freshwater flux into the Southern Ocean. Although of similar magnitude, icebergs play an important and still not fully understood role in the balance of heat and freshwater around Antarctica. This lack of understanding is partly due to operational difficulties in large‐scale monitoring in polar regions, despite observational and remote sensing efforts. In this study, a novel machine learning approach, augmented by visual inspection, was applied to three Synthetic Aperture Radar (SAR) mosaics of the whole Antarctic continent and its adjacent coastal zone. Although originally intended for a mapping of the Antarctic continent, the SAR mosaics allow us to document the evolution and distribution of the size (and mass) of icebergs in the pan‐Antarctic near‐coastal zone for the years 1997, 2000, and 2008. Our novel algorithm identified 7,649 icebergs in 1997, 13,712 icebergs in 2000, and 7,246 icebergs in 2008 with surface areas between 0.1 and 4,567.82 km2 and total masses of 4,641.53, 6,862.81, and 5,263.69 Gt, respectively. Large regional variability was observed, although a zonal pattern distribution is present. This has implications for future climate modeling studies that try to estimate the freshwater flux from melting icebergs, which demands a realistic representation of the interannually varying near‐coastal iceberg pattern to initialize the simulations. Plain Language Summary: When icebergs melt in the Southern Ocean, they cool the surrounding ocean. They also distribute freshwater, which potentially impacts the circulation, biological activity, sea‐ice cover, and the formation of the densest waters of the world's oceans. However, all these influences are not fully understood because we are lacking reliable methods to detect icebergs from space via satellites. This study has the main objective to determine how iceberg mass is distributed in the coastal zone around the Antarctic continent and how this distribution changes between individual years. We show that a novel machine learning approach can be applied to this problem, which is capable to identify icebergs in satellite images of the near‐coastal ocean region almost automatically. The method also works in severe conditions, e.g. when the icebergs are affected by ocean waves or when they are surrounded by sea ice. Our results complement the ongoing discussion about the distribution of Antarctic icebergs in open‐ocean regions that are not affected by sea ice. The resulting data can also be used in computer models that simulate the input of iceberg freshwater into the ocean. Key Points: A novel automatic machine learning approach is applied to SAR imagery, with minimal visual inspectionNear‐coastal iceberg size distributions are presented for 1997, 2000, and 2008Mass distribution for smaller icebergs appears to be relatively stable, while less frequent large icebergs strongly bias the distribution [ABSTRACT FROM AUTHOR]