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In this article, we analyze the impacts of climate change on Antarctic marine ecosystems. Observations demonstrate large-scale changes in the physical variables and circulation of the Southern Ocean driven by warming, stratospheric ozone depletion, and a positive Southern Annular Mode. Alterations in the physical environment are driving change through all levels of Antarctic marine food webs, which differ regionally. The distributions of key species, such as Antarctic krill, are also changing. Differential responses among predators reflect differences in species ecology. The impacts of climate change on Antarctic biodiversity will likely vary for different communities and depend on species range. Coastal communities and those of sub-Antarctic islands, especially range-restricted endemic communities, will likely suffer the greatest negative consequences of climate change. Simultaneously, ecosystem services in the Southern Ocean will likely increase. Such decoupling of ecosystem services and endemic species will require consideration in the management of human activities such as fishing in Antarctic marine ecosystems. Expected final online publication date for the Annual Review of Marine Science Volume 12 is January 3, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical models at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss., Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.

In order to understand and mitigate for the potential impacts of anthropogenic disturbance on marine predators, it is fundamental to gain insight into the drivers behind the temporal variation in their current distribution. With this in mind, we used platform terminal transmitter devices to track Adelie Pygoscelis adeliae and Chinstrap Penguins Pygoscelis antarctica breeding on the South Orkney Islands during the pre-moult phase of their annual cycles. We show that Adelie Penguins have an affinity to forage near and moult on sea-ice, and that Chinstraps remain over the shallower shelf waters during pre-moult, and return to the colony to moult. However, habitat models developed to predict the preferred foraging habitats of penguins during pre-moult had low predictive power. This indicates that predictive models may be insufficient to understand the distribution and foraging behaviour of penguins during certain stages of the life-cycle, and that collecting empirical data from individual colonies or archipelagos is vital if we are to understand the potential implications of future climate warming, or indeed the overlap with potentially competing fisheries.

Interspecific competition can drive niche partitioning along multidimensional axes, including allochrony. Competitor matching will arise where the phenology of sympatric species with similar ecological requirements responds to climate change at different rates such that allochrony is reduced.Our study quantifies the degree of niche segregation in foraging areas and depths that arises from allochrony in sympatric Adélie and chinstrap penguins and explores its resilience to climate change.Three‐dimensional tracking data were sampled during all stages of the breeding season and were used to parameterise a behaviour‐based model that quantified spatial overlap of foraging areas under different scenarios of allochrony.The foraging ranges of the two species were similar within breeding stages, but differences in their foraging ranges between stages, combined with the observed allochrony of 28 days, resulted in them leapfrogging each other through the breeding season such that they were exploiting different foraging locations on the same calendar dates. Allochrony reduced spatial overlap in the peripheral utilisation distribution of the two species by 54.0% over the entire breeding season, compared to a scenario where the two species bred synchronously.Analysis of long‐term phenology data revealed that both species advanced their laying dates in relation to October air temperatures at the same rate, preserving allochrony and niche partitioning. However, if allochrony is reduced by just a single day, the spatial overlap of the core utilisation distribution increased by an average of 2.1% over the entire breeding season.Niche partitioning between the two species by allochrony appears to be resilient to climate change and so competitor matching cannot be implicated in the observed population declines of the two penguin species across the Western Antarctic Peninsula., Reductions in allochrony due to climate change may increase competition among sibling species. The authors investigated this in sympatric Adélie and chinstrap penguins and found that allochrony induced leapfrog foraging, substantially reducing spatial overlap compared to synchronous breeding. However, phenology of both species shifted in parallel, preserving allochrony and niche partitioning.

Warming of the Antarctic Peninsula in the latter half of the twentieth century was greater than any other terrestrial environment in the Southern Hemisphere, and clear cryospheric and biological consequences have been observed. Under a global 1.5◦C scenario, warming in the Antarctic Peninsula is likely to increase the number of days above 0◦C, with up to 130 of such days each year in the northern Peninsula. Ocean turbulence will increase, making the circumpolar deep water (CDW) both warmer and shallower, delivering heat to the sea surface and to coastal margins. Thinning and recession of marine margins of glaciers and ice caps is expected to accelerate to terrestrial limits, increasing iceberg production, after which glacier retreat may slow on land. Ice shelves will experience continued increase in meltwater production and consequent structural change, but not imminent regional collapses. Marine biota can respond in multiple ways to climatic changes, with effects complicated by past resource extraction activities. Southward distribution shifts have been observed in multiple taxa during the last century and these are likely to continue. Exposed (ice free) terrestrial areas will expand, providing new habitats for native and non-native organisms, but with a potential loss of genetic diversity. While native terrestrial biota are likely to benefit from modest warming, the greatest threat to native biodiversity is from non-native terrestrial species.

Antarctic krill (Euphausia superba) and salps are major macroplankton contributors to Southern Ocean food webs and krill are also fished commercially. Managing this fishery sustainably, against a backdrop of rapid regional climate change, requires information on distribution and time trends. Many data on the abundance of both taxa have been obtained from net sampling surveys since 1926, but much of this is stored in national archives, sometimes only in notebooks. In order to make these important data accessible we have collated available abundance data (numerical density, no. m−2) of postlarval E. superba and salps (combined aggregate and solitary stages and species) into a central database, KRILLBASE, together with environmental information, standardisation and metadata. The aim is to provide a temporal-spatial data resource to support a variety of research such as biogeochemistry, autecology, higher predator foraging and food web modelling in addition to fisheries management and conservation. Previous versions of KRILLBASE have led to a series of papers since 2004 which illustrate some of the potential uses of this database. With increasing numbers of requests for these data we here provide an updated version of KRILLBASE that contains data from 15,194 net hauls, including 12,758 with krill abundance data and 9,726 with salp abundance data. These data were collected by 10 nations and span 56 seasons in two epochs (1926–1939 and 1976–2016). Here, we illustrate the seasonal, inter-annual, regional and depth coverage of sampling, and provide both circumpolar- and regional-scale distribution maps. Krill abundance data have been standardised to accommodate variation in sampling methods, and we have presented these as well as the raw data. Information is provided on how to screen, interpret and use KRILLBASE to reduce artefacts in interpretation, with contact points for the main data providers.

The cemetery at Whalers Bay, Deception Island, was the largest in the Antarctic prior to being partially buried and partially washed away during a volcanic eruption in 1969. In 2002 a wooden cross at Argentina's Decepción Station was identified as being that of the Norwegian whaler Peder Knapstad. It was removed, secured in a base, and re-erected at Whalers Bay.