Explore the vast range of titles available.

The 2023 Antarctic sea ice extent (SIE) maximum on 7 September was the lowest annual maximum in the satellite era (16.98 × 106 km2), with the largest contributions to the anomaly coming from the Ross (37.7%, −0.57 × 106 km2) and Weddell (32.9%, −0.49 × 106 km2) Seas. The SIE was low due to anomalously warm (>0.3°C) upper‐ocean temperatures combined with anomalously strong northerly winds impeding the ice advance during the fall and winter. Northerly winds of >12 ms−1 in the Weddell Sea occurred because of negative pressure anomalies over the Antarctic Peninsula, while those in the Ross Sea were associated with extreme blocking episodes off the Ross Ice Shelf. The Ross Sea experienced an unprecedented SIE decrease of −1.08 × 103 km2 d−1 from 1 June till the annual maximum. The passage of quasi‐stationary and explosive polar cyclones contributed to periods of southward ice‐edge shift in both sectors. Plain Language Summary Sea ice provides a vital habitat for life in the Southern Ocean, and plays an important role in the ocean circulation, the dynamics of the Earth's climate, the biogeochemical cycle, and the regional ecosystem. Climatologically, Antarctic sea ice expands northwards from the continent each autumn and winter. However, in 2023 an unprecedented slow ice expansion occurred in the Southern Ocean ahead of the annual maximum on 7 September of 16.98 × 106 km2, which was 1.46 × 106 km2 below the long‐term average. In fact, the area covered by ice remained at a record low level every day from 21 April 2023 until 11 November 2023. Our findings suggest that an impact of upper‐ocean warming and changes in winds, combined with heat and moisture fluxes, extreme winds and high ocean waves associated with polar cyclones (storms), contributed to these record low ice conditions. In particular, cyclones caused episodes of exceptional slow ice expansion or even retreat, leading to negative ice anomalies. For instance, the ice‐edge in the Weddell Sea was moved southwards quickly in a few days (up to 256 km southward) with an ice area loss of ∼2.3 × 105 km2, equivalent to the size of United Kingdom. Key Points The 2023 Antarctic sea ice extent maximum on 7 September (16.98 × 106 km2) was the lowest annual maximum in the satellite era Anomalous upper‐ocean warming and strong northerly winds contributed to impeding the ice expansion in the Ross and Weddell Seas Quasi‐stationary and explosive polar cyclones contributed to periods of southward ice‐edge shift in both sectors

The Canadian Beaufort Sea has been categorized as an oligotrophic system with the potential for enhanced production due to a nutrient‐rich intermediate layer of Pacific‐origin waters. Using under‐ice hydrographic data collected near the ice‐edge of a shallow Arctic bay, we documented an ice‐edge upwelling event that brought nutrient‐rich waters to the surface during June 2008. The event resulted in a 3‐week long phytoplankton bloom that produced an estimated 31 g C m−2 of new production. This value was approximately twice that of previous estimates for annual production in the region, demonstrating the importance of ice‐edge upwelling to the local marine ecosystem. Under‐ice primary production estimates of up to 0.31 g C m−2 d−1 showed that this production was not negligible, contributing up to 22% of the daily averaged production of the ice‐edge bloom. It is suggested that under‐ice blooms are a widespread yet under‐documented phenomenon in polar regions, which could increase in importance with the Arctic's thinning ice cover and subsequent increase in transmitted irradiance to the under‐ice environment.

Sentinel species, like Adélie penguins, have been used to assess the impact of environmental changes, and their link with sea ice has eceived considerable attention. Here, we tested if foraging Adélie penguins from 2 colonies in East Antarctica target the distant sea-ice edge or take advantage of closer open waters that are readily available near their colony. We examined the foraging behaviour of penguins during the incubation trips of females in 2016 and males in 2017, using GPS tracking and diet data in view of daily sea-ice data and bathymetry. In 2016−2017, sea-ice cover was extensive during females’ trips but flaw leads and polynyas were close to both study sites. Sea ice receded rapidly during males’ trips in 2017−2018. Despite close open water near both colonies in both years, females and males preferentially targeted the continental slope and the sea-ice edge to forage. In addition, there was no difference in the diet of penguins from both colonies: all penguins fed mostly on Antarctic krill and males also foraged on Antarctic silverfish. Our results highlight the importance of the sea-ice edge for penguins, an area where food abundance is predictable. It is likely that resource availability was not sufficient in closer open water areas at such an early stage in the breeding season. The behaviours displayed by the penguins from both colonies were similar, suggesting a common behaviour across colonies in Terre Adélie, although additional sites would be necessary to confirm this hypothesis.

Sea ice is a major threat to marine operations around Svalbard, and accurate short-term (1–5 days) forecasts of sea ice edge (SIE) and marginal ice zone (MIZ) are crucial for safe marine operations. In this paper, we investigate the effects of assimilating the AMSR2 sea ice concentration (SIC), the Norwegian sea ice chart, and the OSTIA sea surface temperature (SST) on the short-term forecasts of SIE and MIZ around Svalbard. The used model, Barents-LAON, is based on the coupled ROMS-CICE model with the Local Analytical Optimal Nudging (LAON) for data assimilation. The assimilation effects are evaluated through seven model experiments, from Free run to the full assimilation of OSTIA SST, AMSR2 SIC, and ice chart. The results show that the Free run of Barents-LAON contains a large cold bias, which significantly overestimates the sea ice extent and underestimates the SST. Assimilation of SST mildly improves the analyses of SIE and MIZ, and additional assimilations of AMSR2 SIC and ice chart considerably improve the analyses and forecasts. We show that 1–3 days of forecasts of SIE and MIZ with assimilations of both SIC and SST outperform the CMEMS operational forecasts TOPAZ5 and neXtSIM, the US Navy GOFS3.1 system, and the Norwegian Meteorological Institute’s Barents-EPS. The assimilation of both ice chart and OSTIA SST is shown to have the largest improvement for MIZ analysis and forecasts. All the Barents-LAON short-term SIE forecasts with assimilations of SIC and SST outperform the sea ice chart persistence forecasts after the first day. However, all the MIZ forecasts, regardless of using the operational models or the current model experiments, are shown to have lower skills than the sea ice chart persistence. This suggests two possible defects: 1) the present AMSR2 SIC is not sufficiently accurate for separating MIZ from dense pack ice, and 2) some important physical processes may be lacking for the transformation between dense pack ice and MIZ in the present coupled ocean and sea ice models.

In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea-ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill.An important improvement in SEAS5 is the reduction of the equatorial Pacific cold tongue bias, which is accompanied by a more realistic El Niño amplitude and an improvement in El Niño prediction skill over the central-west Pacific. Improvements in 2 m temperature skill are also clear over the tropical Pacific. Sea-surface temperature (SST) biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF 2 m temperature prediction skill in this region. The prognostic sea-ice model improves seasonal predictions of sea-ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in 2 m temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in JJA. In summary, development and added complexity since System 4 has ensured that SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in the El Niño Southern Oscillation (ENSO) prediction.

Idealized models and a simple vertically averaged vorticity equation illustrate the effects of an upwelling favorable wind and a spatially variable landfast ice cover on the circulation beneath landfast ice. For the case of no along‐shore variations in ice, upwelling favorable winds seaward of the ice edge result in vortex squashing beneath the landfast ice leading to (1) large decreases in coastal and ice edge sea levels, (2) cross‐shore sea level slopes and weak (<∼.05 m s−1) under‐ice currents flowing upwind, (3) strong downwind ice edge jets, and (4) offshore transport in the under‐ice and bottom boundary layers of the landfast ice zone. The upwind under‐ice current accelerates quickly within 2–4 days and then slows as cross‐shore transport gradually decreases the cross‐shore sea level slope. Near the ice edge, bottom boundary layer convergence produces ice edge upwelling. Cross‐ice edge exchanges occur in the surface and above the bottom boundary layer and reduce the under‐ice shelf volume by 15% in 10 days. Under‐ice along‐shore pressure gradients established by along‐ and cross‐shore variations in ice width and/or under‐ice friction alter this basic circulation pattern. For a landfast ice zone of finite width and length, upwelling‐favorable winds blowing seaward of and transverse to the ice boundaries induce downwind flow beneath the ice and generate vorticity waves that propagate along‐shore in the Kelvin wave direction. Our results imply that landfast ice dynamics, not included explicitly herein, can effectively convert the long‐wavelength forcing of the wind into shorter‐scale ocean motions beneath the landfast ice. Key Points Upwelling winds seaward of an ice edge decrease the under ice sea level Ice ocean friction magnitude determines the magnitude of sea level decrease Alongshore differences in ice width or ice friction generate along‐shore flow

A coupled ocean-ice-wave model is used to study ice-edge jet and eddy genesis during surface gravity wave dissipation in a frazil-pancake ice zone. With observational data from the Beaufort Sea, possible wave dissipation processes are evaluated using sensitivity experiments. As wave energy dissipated, energy was transferred into ice floe through radiation stress. Later, energy was in turn transferred into current through ocean-ice interfacial stress. Since most of the wave energy is dissipated at the ice edge, ice-edge jets, which contained strong horizontal shear, appeared both in the ice zone and the ocean. Meanwhile, the wave propagation direction determines the velocity partition in the along-ice-edge and cross-ice-edge directions, which in turn determines the strength of the along-ice-edge jet and cross-ice-edge velocity. The momentum applied in the along-ice-edge (cross-ice-edge) direction increased (decreased) with larger incident angle, which is favorable condition for producing stronger mesoscale eddies, vice versa. The dissipation rate increases (decreases) with larger (smaller) wavenumber, which enhances (reduces) the jet strength and the strength of the mesoscale eddy. The strong along-ice-edge jet may extend to a deep layer (> 200 m). If the water depth is too shallow (e.g., 80 m), the jet may be largely dampened by bottom drag, and no visible mesoscale eddies are found. The results suggest that the bathymetry and incident wavenumber (magnitude and propagation direction) are important for wave-driven current and mesoscale eddy genesis.

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.

In this study the impact of extreme cyclones on Arctic sea ice in summer is investigated. Examined in particular are relative thermodynamic and dynamic contributions to sea ice volume budgets in the vicinity of Arctic summer cyclones in 2012 and 2016. Results from this investigation illustrate that sea ice loss in the vicinity of the cyclone trajectories during each year was associated with different dominant processes: thermodynamic processes (melting) in the Pacific sector of the Arctic in 2012, and both thermodynamic and dynamic processes in the Pacific sector of the Arctic in 2016. Comparison of both years further suggests that the Arctic minimum sea ice extent is influenced by not only the strength of the cyclone, but also by the timing and location relative to the sea ice edge. Located near the sea ice edge in early August in 2012, and over the central Arctic later in August in 2016, extreme cyclones contributed to comparable sea ice area (SIA) loss, yet enhanced sea ice volume loss in 2012 relative to 2016. Central to a characterization of extreme cyclone impacts on Arctic sea ice from the perspective of thermodynamic and dynamic processes, we present an index describing relative thermodynamic and dynamic contributions to sea ice volume changes. This index helps to quantify and improve our understanding of initial sea ice state and dynamical responses to cyclones in a rapidly warming Arctic, with implications for seasonal ice forecasting, marine navigation, coastal community infrastructure, and designation of protected and ecologically sensitive marine zones.

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice edge error, Brier score, and spatial probability score. We find that the CMIP6 multimodel mean captures the mean annual cycle and 1979–2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic subregions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.