Sea Ice Concentration Research Articles

Decline in the West Greenland population of a zooplanktivorous seabird, the little auk Alle alle

The warming of the Arctic had lead to a diverse range of impacts on local biota, including northward shifts of some species range. Here, we report past and present distribution and abundance of an Arctic zooplanktivorous seabird, the little auk Alle alle in West Greenland south of 74° N, and examine the changes in sea surface temperature (SST) and sea ice concentration (SIC) in the birds foraging areas in 1850–2007. We estimated the little auk population in the studied region to be 5,200 pairs in the 1930s, 6,000–6,500 pairs in the 1940-1970s and 70–80 pairs by the 2000s. We found that periods with increased SST and reduced SIC, especially in the last few decades, coincided with little auk population declines. Besides, years with little auk presence in breeding sites were characterized by either low SST and low to moderate SIC or higher SST but moderate to high SIC. Observed contraction of the breeding range and a decrease in abundance of the little auk may be attributed to more complex climate-driven changes in the marine ecosystem at finer spatial and temporal scales and/or cannot be easily detected given the coarseness of data used. It is possible that the population in this region has never been very numerous being subjected to local impacts such as disease, bycatch, predation, etc. The climate warming that is currently being observed, along with corresponding shifts in zooplankton communities, may lead to extirpation of the studied little auk populations.

Arctic Wind, Sea Ice, and the Corresponding Characteristic Relationship

In efforts to fulfill the objectives of taking part in pragmatic cooperation in the Arctic, constructing the “Silk Road on Ice”, and ensuring ships’ safety and risk assessment in the Arctic, the two biggest hazards, which concern ships’ navigation in the Arctic, are wind and sea ice. Sea ice can result in a ship being besieged or crashing into an iceberg, endangering both human and property safety. Meanwhile, light winds can assist ships in breaking free of a sea-ice siege, whereas strong winds can hinder ships’ navigation. In this work, we first calculated the spatial and temporal characteristics of a number of indicators, including Arctic wind speed, sea-ice density, the frequency of different wind directions, the frequency of a sea-ice density of less than 20%, the frequency of strong winds of force six or above, etc. Using the ERA5 wind field and the SSMI/S sea-ice data, and applying statistical techniques, we then conducted a joint analysis to determine the correlation coefficients between the frequencies of various wind directions, the frequency of strong winds and its impact on the density of sea ice, the frequency of a sea-ice concentration (SIC) of less than 20%, and the correlation coefficient between winds and sea-ice density. In doing so, we determined importance of factoring the wind’s contribution into sea-ice analysis.

Influence of the Atlantic and Pacific Multidecadal Variability on Arctic Sea Ice in Pacemaker Simulations during 1920–2013

Abstract The Atlantic multidecadal variability (AMV) and Pacific multidecadal variability (PMV) can influence Arctic sea ice and modulate its trend, but to what extent the AMV and PMV can affect Arctic sea ice and which processes are dominant are not well understood. Here, we analyze the Community Earth System Model, version 1, idealized and time-varying pacemaker ensemble simulations to investigate these issues. These experiments show that the sea ice concentration varies mainly over the marginal Arctic Ocean, while the sea ice thickness variations occur over the entire Arctic Ocean. The internal components of AMV and PMV can enhance or weaken the decadal sea ice loss rates over the marginal Arctic Ocean by more than 50%. The AMV- or PMV-induced anomalous atmospheric energy transport and downward longwave radiation related to low clouds (thermodynamical processes) and sea ice motion (dynamical processes) contribute to the Arctic surface air temperature and sea ice concentration and thickness changes. Anomalous oceanic heat flux is mainly a response to rather than a cause of sea ice variations. The dynamic processes contribute to the winter Arctic sea ice variations as much as the thermodynamic processes, but they contribute less (more) to the summer Arctic sea ice variability than the thermodynamic processes over the marginal Arctic Ocean (parts of the central Arctic Ocean). Sea ice loss enhances air–sea heat fluxes, which cause oceanic heat convergence and warm near-surface air and the lower troposphere, which in turn melt more sea ice.

The role of atmospheric conditions in the Antarctic sea ice extent summer minima

Abstract. Understanding the variability of Antarctic sea ice is still a challenge. After decades of modest growth, an unprecedented minimum in the sea ice extent (SIE) was registered in summer 2017, and, following years of anomalously low SIE, a new record was established in early 2022. These two memorable minima have received great attention as single cases, but a comprehensive analysis of summer SIE minima is currently lacking. Indeed, other similar events are present in the observational record, although they are minor compared to the most recent ones, and a full analysis of all summer SIE minima is essential to separate potential common drivers from event-specific dynamics in order to ultimately improve our understanding of the Antarctic sea ice and climate variability. In this work, we examine sea ice and atmospheric conditions during and before all summer SIE minima over the satellite period up to 2022. We use observations and reanalysis data and compare our main findings with results from an ocean–sea ice model (NEMO–LIM) driven by prescribed atmospheric fields from ERA5. Examining SIE and sea ice concentration (SIC) anomalies, we find that the main contributors to the summer minima are the Ross and Weddell sectors. However, the two regions play different roles, and the variability of the Ross Sea explains most of the minima, with typical negative SIE anomalies about twice as large as the ones in the Weddell Sea. Furthermore, the distribution of SIC anomalies is also different: in the Weddell Sea, they exhibit a dipolar structure, with increased SIC next to the continent and decreased SIC at the sea ice margin, while the Ross Sea displays a more homogenous decrease. We also examine the role of wintertime sea ice conditions before the summer SIE minima and find mixed results depending on the period: the winter conditions are relevant in the most recent events, after 2017, but they are marginal for previous years. Next, we consider the influence of the atmosphere on the SIE minima, which is shown to play a major role: after analyzing the anomalous atmospheric circulation during the preceding spring, we find that different large-scale anomalies can lead to similar regional prevailing winds that drive the summer minima. Specifically, the SIE minima are generally associated with dominant northwesterly anomalous winds in the Weddell Sea, while a southwesterly anomalous flow prevails in the Ross Sea. Finally, we investigate the relative contribution of dynamic (e.g., ice transport) and thermodynamic (e.g., local melting) processes to the summer minima. Our results indicate that the exceptional sea ice loss in both the Ross and Weddell sectors is dominated at the large scale by thermodynamic processes, while dynamics are also present but with a minor role.

Retrieving compressive sea ice strength in the Beaufort Sea using the inverse visco-plastic model

We apply a simplified 2d Visco-Plastic (VP) sea ice model with a spatially variable representation of the sea ice rheological parameters for retrieving maximum compressive sea ice strength from satellite and in situ observations. A set of Observing System Simulation Experiments (OSSEs) demonstrates feasibility of optimizing rheological parameter of the VP sea ice model through the variational data assimilation approach during the periods of strong sea ice convergence if accurate sea ice observations are available. Following this strategy, the developed variational data assimilation VP model was applied to the sea ice velocity (https://nsidc.org/data/nsidc-0116/versions/4), sea ice concentration (https://nsidc.org/data/) and CryoSat-2 sea ice thickness observations collected in the vicinity of three moorings in the Beaufort Sea during periods of intensive sea ice convergence. Ice velocities from moorings and atmospheric wind speed (NCEP-NCAR) were used as well. Our results show that conventional maximum compressive sea ice strength (Hibler, 1979) may depend on sea ice thickness or other parameters partly controlled by the sea ice thickness, which is driven by the seasonal cycle.

Multi-proxy record of Late Holocene climate events and westerly winds in Maxwell Bay, South Shetland Islands, Antarctica

Glacial expression of climate change during the Holocene was neither uniform geographically nor synchronous in Antarctica, highlighting the influence of regional drivers and local feedbacks on environmental change. Here, we present new data from sediment cores collected in Maxwell Bay, a broad open bay located in the South Shetland Islands, Antarctica. We reconstruct past intervals of glacial marine conditions using diatom assemblage and stable isotope proxy data from the sediment cores. These data suggest that there were episodes of stronger westerly winds in the South Shetland Islands, evidenced by the increased presence of the offshore diatom Fragilariopsis kerguelensis and increased abundance of planktonic foraminifera that are associated with low sea-ice concentration and presence of relatively warm water masses. With an expanded sedimentary archive from the proximal bay, we reconstruct the last 2500 years of environmental conditions in Maxwell Bay and interrogate the record within the context of over a dozen published records in the region. Four major climate intervals are identified: (1) relatively stable, cold climate with high productivity from ∼2500 to ∼1850 calibrated years Before Present (cal yr BP), followed by (2) environmental instability until ∼900 cal yr BP, (3) a prominent Medieval Climatic Anomaly from ∼875 to ∼475 cal yr BP, and (4) cooling during the Little Ice Age between ∼475 and ∼ 60 cal yr BP. More recently, the Antarctic Peninsula experienced rapid regional warming during the second half of the Twentieth Century, followed by a regional reversal since the late 1990s; it is imperative to understand the glacial response to past climate events like this to inform models of future change.

A global analysis of ice phenology for 3702 lakes and 1028 reservoirs across the Northern Hemisphere using Sentinel-2 imagery

As existing global lake ice studies have predominantly focused on medium to large lakes, and reservoir ice studies have been limited to regional scales, very few studies of ice phenology have combined both lakes and reservoirs of different sizes. This study aims to characterize the freeze-up and break-up dates of 3702 lakes and 1028 reservoirs from 1 to 31,000 km2 across the Northern Hemisphere, and to analyze spatial patterns and relationships between ice phenological dates and driving factors. The freeze-up and break-up dates of these water bodies were retrieved from Sentinel-2 imagery using an ice detection algorithm through the Google Earth Engine platform from 2019 to 2023. The algorithm was verified by comparing phenology dates with an independent database based on observations from passive microwave sensors, with a mean absolute error of 18 days for both freeze-up and break-up dates. This newly established ice phenology database along with various geographic, morphometric, and climatic characteristics of the water bodies, was used to develop a random forest model for predicting ice phenology dates. While the predictive model performance is at a fair level (mean absolute error of 12 days for both freeze-up and break-up), challenges were encountered in certain high-elevation areas where cloudy conditions as well as black ice resulted in delayed freeze-up dates. Among the variables included in the random forest model, latitude and accumulation of freezing degree days were identified as the main drivers of ice phenology dates. Despite the challenges of applying a single, straightforward method on a global scale, this study has allowed the creation of a vast and comprehensive database of lake and reservoir freeze-up and break-up dates that can be used by the community to further analyze ice patterns.

The atmospheric connection between the Arctic and Eurasia is underestimated in simulations with prescribed sea ice

Numerous analyses have demonstrated the impact of Arctic sea ice loss on Eurasia during wintertime. However, dynamical models inconsistently support the observed Arctic-Eurasia connection. The critical physical processes causing discrepancies remain unclear. Here, through numerical simulation, we found that the Arctic-Eurasian connection is underestimated when the model is forced with prescribed sea ice concentrations. The suppressed turbulent heat flux over the Arctic sea ice surface, due to the oversimplified sea ice states, is likely an important physical process leading to model spread. By incorporating the reanalyzed turbulent heat flux into the model, we enhance the heat transfer and reproduce the Arctic-Eurasian connection. The weakened Siberian Storm Track and reduced baroclinicity favors the strengthened Siberian High through eddy feedback forcing. These findings highlight the vital role of turbulent heat flux related to sea ice loss, emphasizing the urgent need for enhanced model fidelity in representing the sea ice processes.

Arctic September Sea Ice Concentration Biases in CMIP6 Models and Their Relationships with Other Model Variables

Abstract The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models.

Large-Scale Climate Modes Drive Low-Frequency Regional Arctic Sea Ice Variability

Abstract Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple time scales that introduces large uncertainties in projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) at lead times of 4–20 years, with up to 60% of observed low-frequency variability explained at a 5-yr lead time. The dominant driver of low-frequency SIC variability is the interdecadal Pacific oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years but with large uncertainty between GCMs and internal variability realization. The Niño-3.4 index and Atlantic multidecadal oscillation have better agreement between GCMs of being positively and negatively related, respectively, with low-frequency SIC anomalies for at least 10-yr lead times. The large variations between GCMs and between members within large ensembles indicate the diverse simulation of teleconnections between the tropics and Arctic sea ice and the dependence on the initial climate state. Further, the influence of the Niño-3.4 index was found to be sensitive to the background climate. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade. Significance Statement The purpose of this study is to better understand the drivers of low-frequency variability of Arctic sea ice. Teasing out the complicated relationships within the climate system takes a large number of examples. Here, we use 42 of the latest generation of global climate models to construct a simple linear model based on dominant named climate features to predict regional low-frequency sea ice anomalies at a lead time of 2–20 years. In 2022, these modes of variability happen to be in the phases most conducive to low Arctic sea ice concentration anomalies. Given the context of the longer-term trend of sea ice loss due to global warming, our results suggest accelerated Arctic sea ice loss in the next decade.

Future sea ice weakening amplifies wind-driven trends in surface stress and Arctic Ocean spin-up

Arctic sea ice mediates atmosphere-ocean momentum transfer, which drives upper ocean circulation. How Arctic Ocean surface stress and velocity respond to sea ice decline and changing winds under global warming is unclear. Here we show that state-of-the-art climate models consistently predict an increase in future (2015–2100) ocean surface stress in response to increased surface wind speed, declining sea ice area, and a weaker ice pack. While wind speeds increase most during fall (+2.2% per decade), surface stress rises most in winter (+5.1% per decade) being amplified by reduced internal ice stress. This is because, as sea ice concentration decreases in a warming climate, less energy is dissipated by the weaker ice pack, resulting in more momentum transfer to the ocean. The increased momentum transfer accelerates Arctic Ocean surface velocity (+31–47% by 2100), leading to elevated ocean kinetic energy and enhanced vertical mixing. The enhanced surface stress also increases the Beaufort Gyre Ekman convergence and freshwater content, impacting Arctic marine ecosystems and the downstream ocean circulation. The impacts of projected changes are profound, but different and simplified model formulations of atmosphere-ice-ocean momentum transfer introduce considerable uncertainty, highlighting the need for improved coupling in climate models.

Projection of a winter ice-free Barents-Kara Sea by CMIP6 models with the CCHZ-DISO method

The winter sea ice over the Barents-Kara Sea (BKS) is reducing at an alarming rate, which impacts the Arctic shipping routes and the local ecosystems as well as the climate system across other regions. Consequently, it is imperative to project the winter ice-free state in this region to adequately prepare for future climate change and its impacts. However, most models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) show higher climatological values, weaker decreasing trends, and lower interannual variabilities in winter BKS sea ice concentration compared to observation. Additionally, it can be also seen that there exist great uncertainties in the projections of different models on whether the BKS region will be ice-free in future winters and the ice-free period, which spans almost the entire 21st century. Therefore, the study adopts two approaches developed from distance between indices of simulation and observation (DISO) method to project the winter ice-free period of the BKS under different emission scenarios. The results indicate that under SSP1–2.6, the winter BKS will not be ice-free by 2100. For SSP2–4.5, SSP3–7.0, and SSP5–8.5, the winter BKS is projected to be ice-free during 2076–2086, 2063–2068, and 2049–2061, respectively. By employing the DISO method, the projection uncertainty is reduced and the results highlight the urgency of reducing greenhouse gas emissions to delay the winter ice-free period over the BKS. These projections are intended to provide a reference for policymakers.

Analysis of Arctic Sea Ice Concentration Anomalies Using Spatiotemporal Clustering

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies.

The out‐of‐phase pattern of summer precipitation over northern China and the possible mechanisms

AbstractThis study explored the interannual characteristics of the out‐of‐phase pattern of summer precipitation over northern China during the past years, as well as the possible underlying mechanisms. The out‐of‐phase pattern is characterized by the positive precipitation anomaly in Northwest China (NWC) and the negative anomaly in North China (NC). Our analyses indicate that the variation of Asian westerly jet (AWJ) is found evidently associated with this out‐of‐phase pattern of summer precipitation. The meridional displacement of AWJ induces opposite trends in water vapour transport and circulation anomalies between NWC and NC, leading to the out‐of‐phase pattern. Further analyses suggest that the anomalies of Arctic sea ice concentration (SIC), North Atlantic and Northwest Pacific sea surface temperature (SST) can impact the variation of AWJ by triggering the Eurasian (EU), polar‐Eurasia (POL) and Pacific‐Japan (PJ) teleconnection patterns, respectively, which in turn modulate the formation of this pattern. The precipitation in NWC is primarily affected by the North Atlantic and Northwest Pacific SST anomalies, whereas the precipitation in NC is notably influenced by both SST anomalies and the Arctic SIC anomaly. However, the role of SST anomalies on precipitation in NWC and NC exhibits an evident contrast, leading to the out‐of‐phase pattern of summer precipitation.

The AutoICE Challenge

Abstract. Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants’ submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results.

Influence of sea ice on ship routes and speed along the Arctic Northeast Passage

The accelerated melting of sea ice and the growing influx of ships in the Arctic pose significant challenges to navigational safety and the resilient development of the Arctic routes. This study aims to explore the influence of sea ice on ship routes and speed by analyzing the spatiotemporal correlation of Sea Ice Concentration (SIC), Sea Ice Thickness (SIT), Sea Ice Volume (SIV), and Automatic Identification System (AIS) data along the Northeast Passage (NEP) in 2022. Our findings indicate that during the navigation window period (July–October), 115 ships traversed the NEP. It was observed that ships preferred navigating at speeds ranging from 6kn to 14kn when the SIC was below 10% and the SIT was less than 0.1m. Compared with cargo ships and tankers, the trajectory and speed distributions of other ships show greater differences under the influence of sea ice. Furthermore, our results suggest that the SIV, compared to the SIC and SIT, could better reflect the influence of sea ice on ship navigation. These insights are crucial for developing management strategies in Arctic routes.

Role of atmospheric and oceanic processes on interannual summertime (2016–2017) decrease of sea ice in the Antarctic regions of the Southern Ocean

In this article, the interannual variability of sea ice in the Antarctic sea ice regions between 2013–2018 is studied using a global ocean sea ice coupled model and satellite observation. The numerical model reasonably well simulates satellite observed interannual variability of sea ice concentration (SIC) and sea surface temperature (SST) in the Antarctic regions of Southern Ocean during all four austral seasons; summer (December–February), autumn (March–May), winter (June–August), and spring (September–November).A comparison of satellite and model shows that, during last two decades between 2001–2020, summertime of 2016–2017 had the lowest (highest) SIC (SST) across the Antarctic sea ice regions. Mixed layer heat budget analysis has been performed to comprehend how thermodynamic processes affect changes in SIC and SST in the Antarctic sea ice regions. The strong positive net atmospheric heat flux and the negative ocean vertical entrainment during summertime of 2016–2017 resulted in increased SST compared to other years, which lead to decreased SIC during above years. Also, loss of sea ice during summertime of 2016–2017 in the Antarctic sea ice regions are linked with significant decrease of wind stress magnitude and increase of wind stress curl.

Analysis of the Antarctic Marginal Ice Zone Based on Unsupervised Classification of Standalone Sea Ice Model Data

AbstractThe Antarctic marginal ice zone, the regularly wave‐affected outer band of the sea ice covered Southern Ocean, typically contains an unconsolidated ice cover comprised of smaller, thinner floes than the inner ice pack. Thus, it is a highly dynamic region and susceptible to rapid expansion and contraction, making it a focal area for understanding and predicting the response of Antarctic sea ice to a changing climate. This novel study uses unsupervised statistical clustering of sea ice data simulated by a global sea ice model (standalone CICE6 combined with a wave propagation module and prescribed ocean) to address the outstanding challenge of separating the marginal ice zone from the inner ice pack in sea ice data sets. The method identifies a marginal ice zone with the desired characteristics and floe size is shown to be the key variable in the classification. Simulated marginal ice zone widths are similar to those derived from satellite observations of wave penetration distances, but contrast with those using the standard 15%–80% areal sea ice concentration proxy, particularly during austral winter. The simulated marginal ice zone is found to undergo a seasonal transition due to new ice formation in winter, increased drift in spring, and increased rates of wave‐induced breakup and melting in summer. The understanding gained from the study motivates incorporation of wave and floe‐scale processes in sea ice models, and the methods are available for application to outputs from high‐resolution and coupled sea ice–ocean–wave models for more detailed studies of the marginal ice zone (in both hemispheres).

Seasonal resource tracking and use of sea-ice foraging habitats by albatrosses and large petrels

The Antarctic seasonal sea-ice zone (SIZ) is one of the most extensive and dynamic habitats on Earth. In summer, increased insolation and ice melt cause primary production to peak, sustaining large populations of locally-breeding seabirds. Due to their hypermobility, large Procellariiformes, including albatrosses, breeding in the subantarctic also have the potential to access the SIZ and track macroscale resource waves over the Sothern Ocean but the extent to which they do this is poorly known. Here, we analysed the foraging movements of breeding albatrosses and large petrels (seven species, 1298 individuals) recorded using GPS loggers and satellite-transmitters to quantify their use of sea-ice habitats and test whether they tracked seasonal drivers of primary production. Foraging latitudes of white-chinned petrels Procellaria aequinoctialis and black-browed Thalassarche melanophris, grey-headed T. chrysostoma and wandering albatrosses Diomedea exulans varied sinusoidally over the breeding season, presumably in response to lagged effects of solar irradiance on primary production. Foraging latitudes of northern and southern giant petrels (Macronectes halli and M. giganteus), and light-mantled albatrosses Phoebetria palpebrata, exhibited no strong seasonal trend, but the latter two species spent ≥ 20 % of their time in the SIZ during incubation and post-brood, prior to or at the time of the spring ice breakup. Southern giant petrels travelled 100 s of km into the pack ice, encountering sea-ice concentrations up to 100 %, whereas light-mantled albatrosses remained almost exclusively in open water near the Marginal Ice Zone (MIZ). The remaining species spent up to 15 % of their time in the SIZ, typically from 5-7 weeks after breakup, and avoided the MIZ. This supports hypotheses that sea ice presents albatrosses but not giant petrels with physical barriers to flight or foraging, and that open-water-affiliated species use the SIZ only after primary production stimulated by ice melt transfers to intermediate trophic levels. Given that all seven species used the SIZ, it is likely that the phenology and demography of these and many other subantarctic-breeding seabirds are mechanistically linked to sea-ice dynamics. Declines in Antarctic sea ice predicted under climate change could therefore modulate and exacerbate the already unsustainable anthropogenic impacts being experienced by these populations.

Mid to Late-Holocene environmental dynamics recorded in Lake Pup Lagoon, East Antarctica: Insights from environmental magnetism and biogeochemical proxies

Paleoenvironmental archives in East Antarctica have revealed significant changes during the Holocene, marked by ice sheet retreat leading to the isolation of submarine basins. These basins offer valuable insights into past climate, glaciology, and oceanography shifts that impact sedimentary processes. In this study, environmental magnetism and biogeochemical proxies to investigate the Mid-to-Late-Holocene transitions in Pup Lagoon, a coastal isolation basin is presented. Our findings reveal distinct stratigraphic zones reflecting shifts from marine to lacustrine environments. The results reveal predominant mechanical weathering in the Stornes region, producing coarse-grained “soft” ferrimagnetic minerals. Notably, a period of warm oceanographic conditions between 6000 and 4722 cal. yr BP was characterized by mixed magnetic grain sizes and ultrafine superparamagnetic grains, indicating relatively oxic open waters in the basin. Subsequent shifts to reducing conditions coincide with persistent marine sea ice cover from 4722 to 2634 cal. yr BP, favoring the retention of coarse-grained ferrimagnets. Finer magnetic grain sizes between 2634 and 2185 cal. yr BP was attributed to the increased freshwater inputs associated with the Mid-Holocene Hypsithermal. Further, diagenetic changes under persistent sea ice cover between 2185 and 1970 cal. yr BP led to the selective dissolution of fine-grained ferrimagnets. Transitioning to freshwater isolated basin conditions between 1970 and 588 cal. yr BP, fine ferrimagnet precipitation indicate oxic to suboxic conditions alongside drier conditions. Biogenic productivity increased post-isolation, which was reflected in increased (Total Organic Carbon) TOC and (Total Nitrogen) TN percentages. Additionally, the presence of greigite in the isolated phase sediment indicates reducing conditions owing to organic matter decomposition. Notably, χfd% exhibits an inverse trend to sea ice concentration, potentially indicating anoxic-dysoxic conditions due to the presence of sea ice. These observations align with broader regional sea ice concentration changes, emphasizing the interconnected behavior of local and regional factors shaping Antarctic coastal environments.