Extreme Sea Ice Research Articles

Model Biases in Simulating Extreme Sea Ice Loss Associated With the Record January 2022 Arctic Cyclone

AbstractIn January 2022, the strongest Arctic cyclone on record resulted in a record weekly loss in sea ice cover in the Barents‐Kara‐Laptev seas. While ECMWF operational forecasts skillfully predicted the cyclone, the loss in sea ice was poorly predicted. We explore the ocean's response to the cyclone using observations from an Argo float that was profiling in the region, and investigate model biases in simulating the observed sea ice loss in a fully coupled GCM. The observations showed changes over the whole ocean column in the Barents Sea after the passage of the storm, cooling and mixing with enough implied heat release to melt roughly 1 m of sea ice. We replicate the observed cyclone in the GCM by nudging the model's winds to observations above the boundary layer. In these simulations, the associated loss of sea ice is only about 10%–15% of the observed loss, and the ocean exhibits very small changes in response to the cyclone. With the use of a simple 1‐D ice‐ocean model, we find that the overly strong ocean stratification in the GCM may be a significant source of model bias in its simulated response to the cyclone. However, even initialized with observed stratification profiles, the 1‐D model also underestimated mixing and sea ice melt relative to the observations.

An assessment of the CMIP6 performance in simulating Arctic sea ice volume flux via Fram Strait

Numerical models serve as an essential tool to investigate the causes and effects of Arctic sea ice changes. Evaluating the simulation capabilities of the most recent CMIP6 models in sea ice volume flux provides references for model applications and improvements. Meanwhile, reliable long-term simulation results of the ice volume flux contribute to a deeper understanding of the sea ice response to global climate change. In this study, the sea ice volume flux through six Arctic gateways over the past four decades (1979–2014) were estimated in combination of satellite observations of sea ice concentration (SIC) and sea ice motion (SIM) as well as the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) reanalysis sea ice thickness (SIT) data. The simulation capability of 17 CMIP6 historical models for the volume flux through Fram Strait were quantitatively assessed. Sea ice volume flux simulated from the ensemble mean of 17 CMIP6 models demonstrates better performance than that from the individual model, yet IPSL-CM6A-LR and EC-Earth3-Veg-LR outperform the ensemble mean in the annual volume flux, with Taylor scores of 0.86 and 0.50, respectively. CMIP6 models display relatively robust capability in simulating the seasonal variations of volume flux. Among them, CESM2-WACCM performs the best, with a correlation coefficient of 0.96 and a Taylor score of 0.88. Conversely, NESM3 demonstrates the largest deviation from the observation/reanalysis data, with the lowest Taylor score of 0.16. The variability of sea ice volume flux is primarily influenced by SIM and SIT, followed by SIC. The extreme large sea ice export through Fram Strait is linked to the occurrence of anomalously low air temperatures, which in turn promote increased SIC and SIT in the corresponding region. Moreover, the intensified activity of Arctic cyclones and Arctic dipole anomaly could boost the southward sea ice velocity through Fram Strait, which further enhance the sea ice outflow.

Atmospheric Response to Antarctic Sea-Ice Reductions Drives Ice Sheet Surface Mass Balance Increases

Abstract The mass balance of the Antarctic ice sheet is intricately linked to the state of the surrounding atmosphere and ocean. As a direct result, improving projections of future sea level change requires understanding change in the Antarctic atmosphere and Southern Ocean, and the processes that couple these systems. Here, we examine the influence of sea ice cover on the overlying atmosphere and subsequently the surface mass balance (SMB) of the adjacent Antarctic ice sheet. We investigate these processes both over the observational era using the ERA5 atmospheric reanalysis and in ensemble simulations of the Community Earth System Model 2.1 (CESM2) where only sea ice coverage is altered. Comparing extreme high and low sea ice over the satellite era in ERA5 reveals atmospheric and ice sheet SMB anomalies that largely mirror anomalies simulated by CESM2 in response to sea ice loss. Results highlight significant near-surface atmospheric warming in response to sea ice reductions that are particularly pronounced in nonsummer seasons and driven by significant ocean-to-atmosphere turbulent heat fluxes. In areas of sea ice loss, significant ocean surface evaporation increases occur. On the eastern flank of climatological low pressure systems, moisture is readily advected toward the ice sheet, driving positive anomalies in the ice sheet SMB. These results indicate that underestimation of Antarctic sea ice, which is common in many current-generation coupled climate models, may lead to overestimation of the ice sheet SMB and therefore underestimation of Antarctica’s contributions to global sea level. Significance Statement The Antarctic ice sheet is the largest potential source of global sea level rise. Its sea level contributions depend in part on how much snow accumulates across its surface. Through observation-incorporating reanalysis data and climate model sensitivity studies, we find that Southern Ocean sea ice coverage exerts an important influence on the near-surface climate and mass balance of the Antarctic ice sheet. Our results show that reductions in Antarctic sea ice promote enhanced ocean surface evaporation and subsequent increases in snowfall across the Antarctic ice sheet. Because current climate models tend to simulate too little Antarctic sea ice, we conclude they may therefore overestimate Antarctic ice sheet snowfall, leading to underprediction of future sea level rise.

Impacts of Observed Extreme Antarctic Sea Ice Conditions on the Southern Hemisphere Atmosphere

The Antarctic sea ice has undergone dramatic changes in recent years, with the highest recorded sea ice extent in 2014 and the lowest in 2017. We investigated the impacts of the observed changes in these two extremes of Antarctic sea ice conditions on the atmospheric circulation in the Southern Hemisphere. We conducted three numerical simulations with different seasonal cycles of Antarctic sea ice forcings using the Community Atmosphere Model Version 5: the maximum sea ice extent in 2014 (ICE_14), the minimum sea ice extent in 2017 (ICE_17), and the average sea ice extent between 1981 and 2010 (ICE_clm, reference simulation). Our results suggest that the atmospheric response in the Southern Hemisphere showed strong seasonal variations and the atmospheric circulation in winter was more sensitive to the decreased Antarctic sea ice in 2017 than the increased sea ice in 2014. In ICE_14, the westerlies over the polar region were enhanced in summer, but there was no significant change in the zonal-averaged wind in winter. In contrast, in ICE_17, there was a clear equatorward shift in the subtropical jet in winter, but no significant change in summer. The temperature responses were limited to the Antarctic coast, where there were changes in the sea ice in ICE_14 and ICE_17. The warming on the coast of the Amundsen Sea in summer led to a slight increase in precipitation in both simulations.

The relative role of the subsurface Southern Ocean in driving negative Antarctic Sea ice extent anomalies in 2016–2021

The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent.

Turbulent Heat Flux, Downward Longwave Radiation, and Large-Scale Atmospheric Circulation Associated with Wintertime Barents–Kara Sea Extreme Sea Ice Loss Events

AbstractWe investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Niño–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

A Comparison of Factors That Led to the Extreme Sea Ice Minima in the Twenty-First Century in the Arctic Ocean

Abstract The extreme Arctic sea ice minima in the twenty-first century have been attributed to multiple factors, such as anomalous atmospheric circulation, excess solar radiation absorbed by open ocean, and thinning sea ice in a warming world. Most likely it is the combination of these factors that drives the extreme sea ice minima, but how the factors rank in setting the conditions for these events has not been quantified. To address this question, the sea ice budget of an Arctic regional sea ice–ocean model forced by atmospheric reanalysis data is analyzed to assess the development of the observed sea ice minima. Results show that the ice area difference in the years 2012, 2019, and 2007 is driven to over 60% by the difference in summertime sea ice area loss due to air–ocean heat flux over open water. Other contributions are small. For the years 2012 and 2020 the situation is different and more complex. The air–ice heat flux causes more sea ice area loss in summer 2020 than in 2012 due to warmer air temperatures, but this difference in sea ice area loss is compensated by reduced advective sea ice loss out of the Arctic Ocean mainly caused by the relaxation of the Arctic dipole. The difference in open water area in early August leads to different air–ocean heat fluxes, which distinguishes the sea ice minima in 2012 and 2020. Further, sensitivity experiments indicate that both the atmospheric circulation associated with the Arctic dipole and extreme storms are essential conditions for a new low record of sea ice extent.

Understanding the Forecast Skill of Rapid Arctic Sea Ice Loss on Subseasonal Time Scales

Abstract The predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) time scales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability; in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on nonextreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and nonextreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events and also that, while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization. Significance Statement We studied weather model forecasts of changes in Arctic sea ice extent on day-to-day time scales in different regions and seasons. We were especially interested in extreme sea ice loss days, or days in which sea ice melts very quickly or is reduced due to diverging forces such as winds, ocean currents, and waves. We find that forecast models generally capture the observed timing of extreme sea ice loss days. We also find that forecasts of sea ice extent are worse on extreme sea ice loss days compared to typical days, and that forecast errors remain elevated following extreme sea ice loss events.

Characteristics of Arctic Summer Inversion and Its Correlation with Extreme Sea Ice Anomalies

Low tropospheric temperature inversion is very common in the Arctic region. Based on the hyperspectral Atmospheric Infrared Sounder (AIRS) profiles from 2002 to 2020, this study provides a comprehensive analysis of the characteristics and anomalies for low tropospheric inversions in the entire Arctic, especially during the summer period. Three types of inversion are classified here, representing the inversions under the clear-sky condition (“clear” inversion), under the cloudy condition with clouds under the inversion layer top (“cloud-I” inversion), and without clouds under the inversion layer top (“cloud-II” inversion). Obvious seasonality is revealed in these three types of inversion, which is stronger in winter than in summer, as per previous studies. We further found that a “summer” peak of inversions occurs in the Arctic, notably in July. Averaged over the study region (60−90° N, 180° W−180° E), the frequencies of “cloud-I” and “cloud-II” inversions peak in July with values of about 22.1% and 34.6%, respectively. Moreover, the three inversion types all display a small “July” peak of inversion strength, ranging from 2.14 to 3.19 K. The result reveals that when the frequency and strength of summer inversions are both with high positive anomalies, there would be a drop in sea ice concentration in September. This implied that the high positive anomalies, both in inversion frequency and strength in summer, might be a predicted signal for the extreme low sea ice event in September. It is also noted that during the extreme low sea ice events in 2007 and 2020, the summer inversion has a strong positive anomaly. However, the summer inversion in 2012, when the sea ice extent also broke the low record, was not extreme as in 2007 and 2020. Further study needs to be supported by follow-up models and observations to evaluate the impact of the inversions on the sea ice.

Seasonal Arctic sea ice forecasting with probabilistic deep learning

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.

High‐Frequency Sea Ice Variability in Observations and Models

AbstractWe characterize high‐frequency variability of sea ice extent (HFVSIE) in observations and climate models. We find that HFVSIE in models is biased low with respect to observations, especially at synoptic timescales (<20 days) in the Arctic year‐round and at monthly timescales (30–60 days) in Antarctica in winter. Models show large spread in HFVSIE, especially in Antarctica. This spread is partly explained by sea ice mean‐state while model biases in sea level pressure (SLP) and wind variability do not appear to play a major role in HFVSIE spread. Extreme sea ice extent (SIE) changes are associated with SLP anomaly dipoles aligned with the sea ice edge and winds directed on‐ice (off‐ice) during SIE loss (gain) events. In observations, these events are also associated with distinct ocean wave states during the cold season, when waves are greater (smaller) and travel toward (away from) the sea ice edge during SIE loss (gain) events.

Role of Intense Arctic Storm in Accelerating Summer Sea Ice Melt: An In Situ Observational Study

AbstractIntense storms have been more frequently observed in the Arctic during recent years, in coincidence with extreme sea ice loss events. However, it is still not fully understood how storms drive such events due to deficient observations and modeling discrepancies. Here we address this problem by analyzing in situ observations acquired during an Arctic expedition, which uniquely captured an intense storm in summer 2016. The result shows a pronounced acceleration of sea ice loss during the storm process. Diagnostic analysis indicates a net energy loss at the ice surface, not supporting the accelerated melting. Although the open water surface gained net heat energy, it was insufficient to increase the mixed‐layer temperature to the observed values. Dynamic analysis suggests that storm‐driven increase in ocean mixing and upward Ekman pumping of the Pacific‐origin warm water tremendously increased oceanic heat flux. The thermal advection by the Ekman pumping led to a warmed mixed layer by 0.05°C–0.12°C and, in consequence, an increased basal sea ice melt rate by 0.1–1.7 cm day−1.

Key barriers to the commercial use of the Northern Sea Route: View from China with a fuzzy DEMATEL approach

The unprecedented reduction in the Arctic sea ice may lead to shipping shortcuts through the Arctic region. Among these, the Northern Sea Route (NSR) linking Europe and Asia has received wide attention because of its potential to save time and energy compared with the conventional southerly transit. However, limited by various factors, the NSR has not witnessed sustained commercial operation, that is, no shipping companies have started regular services there. By reviewing existing literature, we classify 10 factors that have influenced NSR development into four categories: natural conditions, commercial costs, service support, and political considerations. We evaluate the interdependent relationships among these factors through a fuzzy decision-making trial and evaluation laboratory model (DEMATEL), an effective approach in identifying the cause–effect chain in an uncertain and complex decision-making environment. The most critical elements restricting Arctic NSR development include extreme climate, political considerations, and sea ice condition based on expert inputs. This study discusses relations among these factors as well as their policy implications, which could contribute to the decision making of shipping companies and policy focus of the government.

Extraordinary Carbon Fluxes on the Shallow Pacific Arctic Shelf During a Remarkably Warm and Low Sea Ice Period

The shallow Pacific Arctic shelf has historically acted as an effective carbon sink, characterized by tight benthic pelagic coupling. However, the strength of the biological carbon pump in the Arctic has been predicted to weaken with climate change due to increased duration of the open-water period for primary production, enhanced nutrient limitation, and increased pelagic heterotrophy. In order and gain insights into how the biological carbon pump is functioning under the recent conditions of extreme warming and sea ice loss on the Pacific Arctic shelf, we measured sinking particulate organic carbon (POC) fluxes from drifting and moored sediment traps, as well as rates of primary production and particle-associated microbial respiration in June 2018. We measured high sinking POC fluxes similar to and/or higher than previous measurements from this region (up to 2.3 g C m-2 d-1), reaching flux levels among the highest ever documented in the global oceans. Furthermore, high export ratios averaging 82% and low rates of particle-associated microbial respiration also indicated negligible recycling of sinking POC in the water column during spring of 2018. These results highlight the extraordinary strength of biological carbon pump on the Pacific Arctic shelf during an unusually warm and low-sea ice year. While additional measurements and time are needed to confirm the ultimate trajectory of these fluxes in response to ongoing climate change, these results call into question the prevailing hypothesis that the strength of the biological carbon pump in the Pacific Arctic will weaken under these conditions.

Variability of Arctic Sea Ice Based on Quantile Regression and the Teleconnection with Large-Scale Climate Patterns

AbstractUnder global warming, Arctic sea ice has declined significantly in recent decades, with years of extremely low sea ice occurring more frequently. Recent studies suggest that teleconnections with large-scale climate patterns could induce the observed extreme sea ice loss. In this study, a probabilistic analysis of Arctic sea ice was conducted using quantile regression analysis with covariates, including time and climate indices. From temporal trends at quantile levels from 0.01 to 0.99, Arctic sea ice shows statistically significant decreases over all quantile levels, although of different magnitudes at different quantiles. At the representative extreme quantile levels of the 5th and 95th percentiles, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Pacific–North American pattern (PNA) have more significant influence on Arctic sea ice than El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO). Positive AO as well as positive NAO contribute to low winter sea ice, and a positive PNA contributes to low summer Arctic sea ice. If, in addition to these conditions, there is concurrently positive AMO and PDO, the sea ice decrease is amplified. Teleconnections between Arctic sea ice and the climate patterns were demonstrated through a composite analysis of the climate variables. The anomalously strong anticyclonic circulation during the years of positive AO, NAO, and PNA promotes more sea ice export through Fram Strait, resulting in excessive sea ice loss. The probabilistic analyses of the teleconnections between the Arctic sea ice and climate patterns confirm the crucial role that the climate patterns and their combinations play in overall sea ice reduction, but particularly for the low and high quantiles of sea ice concentration.

Application of Remote Sensing to Identify and Monitor Seasonal and Interannual Changes of Water Turbidity in Yellow River Estuary, China

AbstractWater turbidity is an important indicator for water security and environmental security in the Yellow River estuary. However, due to the complex terrain and harsh climatic environment, it is difficult to monitor the water turbidity over the complex surface of the estuary. In this study we applied a self‐organizing map clustering method, an artificial neural network clustering method, to extract turbidity patterns from the long‐term remote sensing data sets in the Yellow River estuary. Based on the Moderate Resolution Imaging Spectroradiometer data from 2000 to 2015, six turbidity patterns were identified by using the self‐organizing map clustering method: high turbidity pattern, moderate turbidity pattern, low turbidity pattern, very low turbidity pattern, extreme high turbidity pattern and sea ice pattern, and the first four patterns appear every year. All patterns have significant seasonal characteristics, and monthly turbidity is dominated by one of these turbidity patterns. The water turbidity in the estuary has decreased in the past 16 years, and the interannual variation of the turbidity pattern is the result of the combination of the sediment transported into the sea by the Yellow River and the wind and waves on the sea surface.

Moisture transport and Antarctic sea ice: austral spring 2016 event

Abstract. In austral spring 2016 the Antarctic region experienced anomalous sea ice retreat in all sectors, with sea ice extent in October and November 2016 being the lowest in the Southern Hemisphere over the observational period (1979–present). The extreme sea ice retreat was accompanied by widespread warming along the coastal areas as well as in the interior of the Antarctic continent. This exceptional event occurred along with a strong negative phase of the Southern Annular Mode (SAM) and the moistest and warmest spring on record, over large areas covering the Indian Ocean, the Ross Sea and the Weddell Sea. In October 2016, the positive anomalies of the totally integrated water vapor (IWV) and 2 m air temperature (T2m) over the Indian Ocean, western Pacific, Bellingshausen Sea and southern part of Ross Sea were unprecedented in the last 39 years. In October and November 2016, when the largest magnitude of negative daily sea ice concentration anomalies was observed, repeated episodes of poleward advection of warm and moist air took place. These results suggest the importance of moist and warm air intrusions into the Antarctic region as one of the main contributors to this exceptional sea ice retreat event.

Identifying metabolic pathways for production of extracellular polymeric substances by the diatom Fragilariopsis cylindrus inhabiting sea ice

Diatoms are significant primary producers in sea ice, an ephemeral habitat with steep vertical gradients of temperature and salinity characterizing the ice matrix environment. To cope with the variable and challenging conditions, sea ice diatoms produce polysaccharide-rich extracellular polymeric substances (EPS) that play important roles in adhesion, cell protection, ligand binding and as organic carbon sources. Significant differences in EPS concentrations and chemical composition corresponding to temperature and salinity gradients were present in sea ice from the Weddell Sea and Eastern Antarctic regions of the Southern Ocean. To reconstruct the first metabolic pathway for EPS production in diatoms, we exposed Fragilariopsis cylindrus, a key bi-polar diatom species, to simulated sea ice formation. Transcriptome profiling under varying conditions of EPS production identified a significant number of genes and divergent alleles. Their complex differential expression patterns under simulated sea ice formation was aligned with physiological and biochemical properties of the cells, and with field measurements of sea ice EPS characteristics. Thus, the molecular complexity of the EPS pathway suggests metabolic plasticity in F. cylindrus is required to cope with the challenging conditions of the highly variable and extreme sea ice habitat.

Extreme Sea Ice Loss over the Arctic: An Analysis Based on Anomalous Moisture Transport

The Arctic system has experienced in recent times an extreme reduction in the extent of its sea ice. The years 2007 and 2012 in particular showed maxima in the loss of sea ice. It has been suggested that such a rapid decrease has important implications for climate not only over the system itself but also globally. Understanding the causes of this sea ice loss is key to analysing how future changes related to climate change can affect the Arctic system. For this purpose, we applied the Lagrangian FLEXible PARTicle dispersion (FLEXPART) model to study the anomalous transport of moisture for 2006/2007 and 2011/2012 in order to assess the implications for the sea ice. We used the model results to analyse the variation in the sources of moisture for the system (backward analysis), as well as how the moisture supply from these sources differs (forward analysis) during these years. The results indicate an anomalous transport of moisture for both years. However, the pattern differs between events, and the anomalous moisture supply varies both in intensity and spatial distribution for all sources.

SIPEX 2012: Extreme sea-ice and atmospheric conditions off East Antarctica

In 2012, Antarctic sea-ice coverage was marked by weak annual-mean climate anomalies that consisted of opposing anomalies early and late in the year (some setting new records) which were interspersed by near-average conditions for most of the austral autumn and winter. Here, we investigate the ocean-ice-atmosphere system off East Antarctica, prior to and during the Sea Ice Physics and Ecosystems eXperiment [SIPEX] 2012, by exploring relationships between atmospheric and oceanic forcing together with the sea-ice and snow characteristics. During August and September 2012, just prior to SIPEX 2012, atmospheric circulation over the Southern Ocean was near-average, setting up the ocean-ice-atmosphere system for near-average conditions. However, below-average surface pressure and temperature as well as strengthened circumpolar winds prevailed during June and July 2012. This led to a new record (19.48×106km2) in maximum Antarctic sea-ice extent recorded in late September. In contrast to the weak circum-Antarctic conditions, the East Antarctic sector (including the SIPEX 2012 region) experienced positive sea-ice extent and concentration anomalies during most of 2012, coincident with negative atmospheric pressure and sea-surface temperature anomalies. Heavily deformed sea ice appeared to be associated with intensified wind stress due to increased cyclonicity as well as an increased influx of sea ice from the east. This increased westward ice flux is likely linked to the break-up of nearly 80% of the Mertz Glacier Tongue in 2010, which strongly modified the coastal configuration and hence the width of the westward coastal current. Combined with favourable atmospheric conditions the associated changed coastal configuration allowed more sea ice to remain within the coastal current at the expense of a reduced northward flow in the region around 141°–145°E. In addition a westward propagating positive anomaly of sea-ice extent from the western Ross Sea during austral winter 2012 has been identified to have fed into the westward current of the SIPEX 2012 region. A pair of large grounded icebergs appears to have modified the local stress state as well as the structure of the ice pack upstream and also towards the Dalton Glacier Tongue. Together with the increased influx of sea ice into the regions, this contributed to the difficulties in navigating the SIPEX 2012 region.