Sea Ice Mass Research Articles

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

AbstractThe state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models.

Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns.

Snow thermal conductivity and conductive flux in the Central Arctic: Estimates from observations and implications for models

During the Arctic winter, the conductive heat flux through the sea ice and snow balances the radiative and turbulent heat fluxes at the surface. Snow on sea ice is a thermal insulator that reduces the magnitude of the conductive flux. The thermal conductivity of snow, that is, how readily energy is conducted, is known to vary significantly in time and space from observations, but most forecast and climate models use a constant value. This work begins with a demonstration of the importance of snow thermal conductivity in a regional coupled forecast model. Varying snow thermal conductivity impacts the magnitudes of all surface fluxes, not just conduction, and their responses to atmospheric forcing. Given the importance of snow thermal conductivity in models, we use observations from sea ice mass balance buoys installed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition to derive the profiles of thermal conductivity, density, and conductive flux. From 13 sites, median snow thermal conductivity ranges from 0.33 W m−1 K−1 to 0.47 W m−1 K−1 with a median from all data of 0.39 W m−1 K−1 from October to February. In terms of surface energy budget closure, estimated conductive fluxes are generally smaller than the net atmospheric flux by as much as 20 W m−2, but the average residual during winter is −6 W m−2, which is within the uncertainties. The spatial variability of conductive heat flux is highest during clear and cold time periods. Higher surface temperature, which often occurs during cloudy conditions, and thicker snowpacks reduce temporal and spatial variability. These relationships are compared between observations and the coupled forecast model, emphasizing both the importance and challenge of describing thermodynamic parameters of snow cover for modeling the Arctic as a coupled system.

Temperature and salinity in the Bohai Sea landfast ice: Observations and modelling

Temperature and salinity are essential thermodynamic properties of sea ice. A 40-day long field campaign was carried out in winter 2020/2021 in the northern Bohai Sea in the landfast sea ice to investigate the seasonal sea ice temperature and salinity evolution. Ice thickness was measured routinely. A thermistor string was used to measure the ice temperature. Ice samples were collected for salinity by coring (Sco) and crumbing (Scr) methods. The mean vertical values of Sco and Scr were 4.8–5.8 psu and 8.3–13.8 psu, respectively. In cold ice, Sco and Scr were very close to each other. When ice temperature increased to −5 °C, Sco and Scr tended to differ from each other, and the difference was at largest at ice bottom. The brine volume fraction (BVF) ranged between 0.03 and 0.21 for Sco and 0.03 and 0.32 for Scr. The growth of ice reached a maximum of 3.7 cm/day in very cold conditions, while the average growth rate was 0.8 cm/day comparable to other seasonally ice-covered seas. A numerical sea ice thermodynamic model was applied to simulate the ice thickness with particular attention to the effect of Sco and Scr on the ice mass balance. The modeled overall ice growth rate was 0.7 cm/day and agreed well with the observations but during cold periods the simulation was biased down (1.1 cm/day); a possible reason is underestimation of the thermal conductivity in the model. The modeled ice thickness differed by 3 cm when using Sco and Scr as the input salinity profiles indicating that salinity has a minor impact on sea ice mass balance during mid-winter.

Sea ice heat and mass balance measurements from four autonomous buoys during the MOSAiC drift campaign

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day−1, with summer melt rates (June to July) as large as 5 cm day−1. Air temperatures changed as much as 2°C hour−1 but were dampened to <0.3°C hour−1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m−2, with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m−1 °C−1, higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget.

Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula: Cryospheric, biotic and societal implications

The seasonal movement of the zero-degree isotherm across the Southern Ocean and Antarctic Peninsula drives major changes in the physical and biological processes around maritime Antarctica. These include spatial and temporal shifts in precipitation phase, snow accumulation and melt, thawing and freezing of the active layer of the permafrost, glacier mass balance variations, sea ice mass balance and changes in physiological processes of biodiversity. Here, we characterize the historical seasonal southward movement of the monthly near-surface zero-degree isotherm latitude (ZIL), and quantify the velocity of migration in the context of climate change using climate reanalyses and projections. From 1957 to 2020, the ZIL exhibited a significant southward shift of 16.8 km decade−1 around Antarctica and of 23.8 km decade−1 in the Antarctic Peninsula, substantially faster than the global mean velocity of temperature change of 4.2 km decade−1, with only a small fraction being attributed to the Southern Annular Mode (SAM). CMIP6 models reproduce the trends observed from 1957 to 2014 and predict a further southward migration around Antarctica of 24 ± 12 km decade−1 and 50 ± 19 km decade−1 under the SSP2-4.5 and SSP5-8.5 scenarios, respectively.The southward migration of the ZIL is expected to have major impacts on the cryosphere, especially on the precipitation phase, snow accumulation and in peripheral glaciers of the Antarctic Peninsula, with more uncertain changes on permafrost, ice sheets and shelves, and sea ice. Longer periods of temperatures above 0 °C threshold will extend active biological periods in terrestrial ecosystems and will reduce the extent of oceanic ice cover, changing phenologies as well as areas of productivity in marine ecosystems, especially those located on the sea ice edge.

Comparative Analysis and Assessment of Artic Sea Ice: Predictions from CMIP6 Models Amid Global Climate Change

Amid global climate change urgency, accurate model predictions are paramount for informed interventions. However, the disparity among prediction models highlights a pressing research gap requiring a deeper and more comprehensive comparison of the differences and consistency of the different models in climate change prediction. This research undertook a comprehensive analysis of 18 CMIP-6 models, aiming to provide a comprehensive comparison and cohesive prediction of ice mass and ice area from 1960 to 2050. The models consistently forecast a substantial decline in Arctic ice coverage and volume. Without marked reductions in carbon emissions, Arctic glaciers are projected to vanish entirely by the close of the century. Furthermore, the correlation analysis results indicate that there is a significant interdependence between sea ice area and sea ice mass was observed, with Pearson R2 approximately 0.908. Notably, models varied regarding the precise year of the Arctic's complete ice loss, spotlighting the inherent uncertainties of current predictions and the pressing need for mitigation measures. The study illuminates the pressing urgency for a globally coordinated response to carbon emissions and serves as a clarion call for enhanced research into the intricacies of climate change and glacier melting processes to refine future predictions.

Rainy Days in the Arctic

Abstract The Arctic is warming faster than anywhere on Earth, and with these warming temperatures, there is likely to be more precipitation falling as rain. This precipitation phase change will have profound impacts on the hydrologic cycle, energy balance, and snow and sea ice mass budgets. Here, we examine the number of rainfall days in the Arctic from three reanalyses, ERA-Interim, ERA5, and MERRA-2, over 1980–2016. We show that the number of rainfall days has increased over this period, predominantly in the autumn and in the North Atlantic and peripheral seas, and the length of the rain season has increased in all reanalyses. This is positively correlated to the number of days with above freezing air temperatures and a lengthening of the warm season. ERA-Interim produces significantly more rainfall days than other reanalyses and CloudSat observations, as well as significantly more rainfall when temperatures are below freezing. Investigation into the cloud microphysics schemes revealed that the scheme employed by ERA-Interim allowed for mixed-phase clouds to form rain at temperatures below freezing following a temperature-dependent phase partitioning function between 250 and 273 K. This simple diagnostic treatment erroneously overestimates rain at temperatures below 273 K and produces unrealistic rainfall compared to ERA5 and MERRA-2. This work highlights the importance of having accurate physics and improving microphysical schemes in models for simulating precipitation in the Arctic and the caution that is warranted for interpreting reanalysis trends.

Morphological changes and future projections of the Getz Ice shelf, western Antarctica—A statistical and geospatial approach

The study assessed the extent of the Getz Ice Shelf over a 19-year period using satellite data and divided it into three sectors. The average of rates (AOR), end point rate (EPR), and linear regression (LR) methods were employed to estimate the rate of change, with the LR method showing the strongest correlation. The analysis revealed a shrinkage rate of 42 m/year during February from 2003 to 2019. Over the last 17 years, recession was observed in 60% of transects, while progradation occurred in 40%. The recent decline in Getz ice shelf extent is closely linked to the record low sea ice extent in the Amundsen Sea sector. Predictions for 2024 and 2029 indicated progradation in Sector I and recession in Sectors II and III, as determined by LR analysis. The root mean square error values of transects in Sectors II and III showed good correlation with past positions and satellite-based observations of ice shelves. To validate the predicted ice shelf extent cross-verified with the satellite and predicted data from 2020 to 2022. These findings highlight the complex interactions between climatic forcings, such as wind speed, sea surface temperature (SST), and the Southern Annular Mode (SAM). Positive SAM and increased zonal winds can contribute to warm water upwelling near the Antarctic coast, impacting ice shelf extent. The relationship between ice mass loss and SST provides valuable insights into the dynamic processes of melting and progradation in the Getz Ice Shelf during February. Ocean warming plays a significant role in increased basal melting and changes in sea ice mass. The study emphasizes the significant impact of ocean-atmospheric factors on Antarctic ice shelf dynamics and highlights the necessity for ongoing satellite observations and enhanced comprehension of these processes to accurately predict future changes in Antarctic ice shelves.

Annual evolution of the ice–ocean interaction beneath landfast ice in Prydz Bay, East Antarctica

Abstract. High-frequency observations of the ice–ocean interaction and high-precision estimation of the ice–ocean heat exchange are critical to understanding the thermodynamics of the landfast ice mass balance in Antarctica. To investigate the oceanic contribution to the evolution of the landfast ice, an integrated ocean observation system, including an acoustic Doppler velocimeter (ADV), conductivity–temperature–depth (CTD) sensors, and a sea ice mass balance array (SIMBA), was deployed on the landfast ice near the Chinese Zhongshan Station in Prydz Bay, East Antarctica, from April to November 2021. The CTD sensors recorded the ocean temperature and salinity. The ocean temperature experienced a rapid increase in late April, from −1.62 to the maximum of −1.30 ∘C, and then it gradually decreased to −1.75 ∘C in May and remained at this temperature until November. The seawater salinity and density exhibited similar increasing trends during April and May, with mean rates of 0.04 psu d−1 and 0.03 kg m−3 d−1, respectively, which was related to the strong salt rejection caused by freezing of the landfast ice. The ocean current observed by the ADV had mean horizontal and vertical velocities of 9.5 ± 3.9 and 0.2 ± 0.8 cm s−1, respectively. The domain current direction was ESE (120∘)–WSW (240∘), and the domain velocity (79 %) was 5–15 cm s−1. The oceanic heat flux (Fw) estimated using the residual method reached a peak of 41.3 ± 9.8 W m−2 in April, and then it gradually decreased to a stable level of 7.8 ± 2.9 W m−2 from June to October. The Fw values calculated using three different bulk parameterizations exhibited similar trends with different magnitudes due to the uncertainties of the empirical friction velocity. The spectral analysis results suggest that all of the observed ocean variables exhibited a typical half-day period, indicating the strong diurnal influence of the local tidal oscillations. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variations in the ocean variables, revealing the important relationship between the large-scale and local phenomena. The high-frequency and cross-seasonal observations of oceanic variables obtained in this study allow us to deeply investigate their diurnal and seasonal variations and to evaluate their influences on the landfast ice evolution.

Snowmelt contribution to Arctic first-year ice ridge mass balance and rapid consolidation during summer melt

Sea ice ridges are one of the most under-sampled and poorly understood components of the Arctic sea ice system. Yet, ridges play a crucial role in the sea ice mass balance and have been identified as ecological hotspots for ice-associated flora and fauna in the Arctic. To better understand the mass balance of sea ice ridges, we drilled and sampled two different first-year ice (FYI) ridges in June–July 2020 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Ice cores were cut into 5 cm sections, melted, then analyzed for salinity and oxygen (δ18O) isotope composition. Combined with isotope data of snow samples, we used a mixing model to quantify the contribution of snow to the consolidated sea ice ridge mass. Our results demonstrate that snow meltwater is important for summer consolidation and overall ice mass balance of FYI ridges during the melt season, representing 6%–11% of total ridged ice mass or an ice thickness equivalent of 0.37–0.53 m. These findings demonstrate that snowmelt contributes to consolidation of FYI ridges and is a mechanism resulting in a relative increase of sea ice volume in summer. This mechanism can also affect the mechanical strength and survivability of ridges, but also contribute to reduction of the habitable space and light levels within FYI ridges. We proposed a combination of two pathways for the transport of snow meltwater and incorporation into ridge keels: percolation downward through the ridge and/or lateral transport from the under-ice meltwater layer. Whether only one pathway or a combination of both pathways is most likely remains unclear based on our observations, warranting further research on ridge morphology.

On the ability of OMIP models to simulate the ocean mixed layer depth and its seasonal cycle in the Arctic Ocean

We evaluate the skills of ocean–sea ice general circulation models involved in the Ocean Modeling Intercomparison Project in simulating the ocean mixed layer depth and its seasonal cycle in the Arctic region. During summer months, all models consistently underestimate the mixed layer depth compared to observational data from the Monthly Isopycnal Mixed layer Ocean Climatology and the Ice Tethered Profilers. In fall and winter, the models exhibit great variability compared to observational data, and inter-model comparison reveals differences up to several tens of meters. We analyze the origin of the fall and winter model biases in ice-covered regions, where the seasonal cycle of the surface salinity and mixed layer depth is strongly influenced by brine rejection resulting from ocean–sea ice interactions.Focusing first on the central Arctic Ocean, defined here as the region north of 80° N, we show that all models simulate more or less the same vertical sea ice mass balance and thus similar salt fluxes into the ocean during sea ice freezing. Furthermore, the model ensemble features a strong relationship between the stratification profile in September and the mixed layer depth at the end of winter. The models whose stratification compares the best to observational data also display the most realistic values of the mixed layer depth at the end of winter. We argue that the discrepancies between models are therefore not so much linked to the surface salt balance but rather to the accuracy with which those models reproduce the vertical salinity profile. In short, a weakly stratified ocean tends to create a deep mixed layer, while strong stratification leads to a shallow mixed layer. To substantiate this conclusion, we apply a simple conceptual model, which simulates the month-to-month evolution of the mixed layer depth using as input the vertical salinity gradients and the surface salt fluxes from general circulation models. Quite surprisingly, this simplified dynamics captures very well the behavior of the general circulation models, emphasizing the role of the different vertical stratification in the control of the mixed layer depth. Furthermore, this interplay may also significantly account for the large mixed layer biases observed in other ice-covered regions of the pan-Arctic seas, even though sea–ice ocean interaction is not the only driver of mixed layer variability in fall and winter there.

On the turbulent heat fluxes: A comparison among satellite-based estimates, atmospheric reanalyses, and in-situ observations during the winter climate over Arctic sea ice

The surface energy budget is crucial for Arctic sea ice mass balance calculation and climate systems, among which turbulent heat fluxes significantly affect the air–sea exchanges of heat and moisture in the atmospheric boundary layer. Satellite observations (e.g. CERES and APP-X) and atmospheric reanalyses (e.g., ERA5) are often used to represent components of the energy budget at regional and pan-Arctic scales. However, the uncertainties of the satellite-based turbulent heat fluxes are largely unknown, and cross-comparisons with reanalysis data and in-situ observations are limited. In this study, satellite-based turbulent heat fluxes were assessed against in-situ observations from the N-ICE2015 drifting ice station (north of Svalbard, January–June 2015) and ERA5 reanalysis. The turbulent heat fluxes were calculated by two approaches using the satellite-based ice surface temperature and radiative fluxes, surface atmospheric parameters from ERA5, and snow/sea ice thickness from the pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). We found that the bulk-aerodynamic formula based results could better capture the variations of turbulent heat fluxes, while the maximum entropy production based estimates are comparable with ERA5 in terms of root-mean-square error (RMSE). CERES-based estimates outperform the APP-X-based ones but ERA5 performs the best in all seasons (RMSE of 18 and 7 W m−2 for sensible and latent heat flux, respectively). The air–ice temperature/humidity differences and the surface radiation budget were found the primary driving factors in the bulk-formula method and maximum entropy production (MEP) method, respectively. Furthermore, errors in the surface and near-surface temperature and humidity explain almost 50% of the uncertainties in the estimates based on the bulk-formula, whereas errors in the net radiative fluxes explain more than 50% of the uncertainties in the MEP-based results.

Temporal evolution of under-ice meltwater layers and false bottoms and their impact on summer Arctic sea ice mass balance

Low-salinity meltwater from Arctic sea ice and its snow cover accumulates and creates under-ice meltwater layers below sea ice. These meltwater layers can result in the formation of new ice layers, or false bottoms, at the interface of this low-salinity meltwater and colder seawater. As part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), we used a combination of sea ice coring, temperature profiles from thermistor strings and underwater multibeam sonar surveys with a remotely operated vehicle (ROV) to study the areal coverage and temporal evolution of under-ice meltwater layers and false bottoms during the summer melt season from mid-June until late July. ROV surveys indicated that the areal coverage of false bottoms for a part of the MOSAiC Central Observatory (350 by 200 m2) was 21%. Presence of false bottoms reduced bottom ice melt by 7–8% due to the local decrease in the ocean heat flux, which can be described by a thermodynamic model. Under-ice meltwater layer thickness was larger below first-year ice and thinner below thicker second-year ice. We also found that thick ice and ridge keels confined the areas in which under-ice meltwater accumulated, preventing its mixing with underlying seawater. While a thermodynamic model could reproduce false bottom growth and melt, it could not describe the observed bottom melt rates of the ice above false bottoms. We also show that the evolution of under-ice meltwater-layer salinity below first-year ice is linked to brine flushing from the above sea ice and accumulating in the meltwater layer above the false bottom. The results of this study aid in estimating the contribution of under-ice meltwater layers and false bottoms to the mass balance and salt budget for Arctic summer sea ice.

Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC

Abstract. Information about sea ice surface topography and related deformation is crucial for studies of sea ice mass balance, sea ice modeling, and ship navigation through the ice pack. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), part of the National Aeronautics and Space Administration (NASA) Earth Observing System, has been on orbit for over 4 years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of sea ice surface topography are required. During the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS), achieving an overlap of more than 100 km. Despite the comparably short data set, the high-resolution centimeter-scale measurements of the ALS can be used to evaluate the performance of ICESat-2 products. Our goal is to investigate how the sea ice surface roughness and topography are represented in different ICESat-2 products as well as how sensitive ICESat-2 products are to leads and small cracks in the ice cover. Here, we compare the ALS measurements with ICESat-2's primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). By applying a ridge-detection algorithm, we find that 16 % (4 %) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42 % (30 %) are achieved when using the UMD product. While only one lead is indicated in ATL07 for the underflight, the ALS reveals many small, narrow, and only partly open cracks that appear to be overlooked by ATL07.

Seasonal and interannual variability of the landfast ice mass balance between 2009 and 2018 in Prydz Bay, East Antarctica

Abstract. Landfast ice (LFI) plays a crucial role for both the climate and the ecosystem of the Antarctic coastal regions. We investigate the snow and LFI mass balance in Prydz Bay using observations from 11 sea ice mass balance buoys (IMBs). The buoys were deployed offshore from the Chinese Zhongshan Station (ZS) and Australian Davis Station (DS), with the measurements covering the ice seasons of 2009–2010, 2013–2016, and 2018. The observed annual maximum ice thickness and snow depth were 1.59 ± 0.17 and 0.11–0.76 m off ZS and 1.64 ± 0.08 and 0.11–0.38 m off DS, respectively. Early in the ice growth season (May–September), the LFI basal growth rate near DS (0.6 ± 0.2 cm d−1) was larger than that around ZS (0.5 ± 0.2 cm d−1). This is attributed to cooler air temperature (AT) and lower oceanic heat flux at that time in the DS region. Air temperature anomalies were more important in regulating the LFI growth rate at that time because of thinner sea ice having a weaker thermal inertia relative to thick ice in later seasons. Interannual and local spatial variabilities for the seasonality of LFI mass balance identified at ZS are larger than at DS due to local differences in topography and katabatic wind regime. Snow ice contributed up to 27 % of the LFI total ice thickness at the offshore site close to ground icebergs off ZS because of the substantial snow accumulation. Offshore from ZS, the supercooled water was observed at the sites close to the Dålk Glacier from July to October, which reduced the oceanic heat flux and promoted the LFI growth. During late austral spring and summer, the increased oceanic heat flux led to a reduction of LFI growth at all investigated sites. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades. Based on the results derived from this study, we argue that an increased understanding of snow (on LFI) processes, local atmospheric and oceanic conditions, as well as coastal morphology and bathymetry, are required to improve the Antarctic LFI modeling.

Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework

Abstract. Sea ice is a key component of the Earth's climate system as it modulates the energy exchanges and associated feedback processes at the air–sea interface in polar regions. These exchanges have been suggested to strongly depend on openings in the sea ice cover, which are associated with fine-scale sea ice deformations, but the importance of these processes remains poorly understood as most numerical models struggle to represent these deformations without using very costly horizontal resolutions (≃ 5 km). In this study, we present results from a 12 km resolution ocean–sea ice coupled model, the first that uses a brittle rheology to represent the mechanical behaviour of sea ice. This rheology has been shown to reproduce observed characteristics and complexity of fine-scale sea ice deformations at relatively coarse resolutions. We evaluate and discuss the Arctic sea ice mass balance of this coupled model for the period 2000–2018. We first assess sea ice quantities relevant for climate (volume, extent, and drift) and find that they are consistent with satellite observations. We evaluate components of the mass balance for which observations are available, i.e. sea ice volume export through Fram Strait and winter mass balance in the Arctic marginal seas for the period 2003–2018. Model values show a good match with observations, remaining within the estimated uncertainty, and the interannual variability of the dynamic contribution to the winter mass balance is generally well captured. We discuss the relative contributions of dynamics and thermodynamics to the sea ice mass balance in the Arctic Basin for 2000–2018. Using the ability of the model to represent divergence motions at different scales, we investigate the role of leads and polynyas in ice production. We suggest a way to estimate the contribution of leads and polynyas to ice growth in winter, and we estimate this contribution to add up to 25 %–35 % of the total ice growth in pack ice from January to March. This contribution shows a significant increase over 2000–2018. This coupled framework opens up new opportunities to understand and quantify the interplay between small-scale sea ice dynamics and ocean properties.

Database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.

Antarctic Sea Ice Holds the Fate of Antarctic Ice-Shelf Basal Melting in a Warming Climate

Abstract Changes in the Antarctic ice sheet play a critical role in the Southern Ocean and global climates. Although many studies have pointed out that enhanced ocean heat delivery onto the Antarctic continental shelf regions can cause significant changes in Antarctic ice-shelf basal melting, the associated physical mechanisms require further research. Here, we perform numerical experiments using an ocean–sea ice model with an ice-shelf component to simulate future projections in Antarctic ice-shelf basal melting in a warming climate, focusing on the driving mechanism and the physical linkages with the seasonal Antarctic sea ice fields and coastal water masses. The model projects a distinct superlinear response of ice-shelf basal melting to future atmospheric warming, demonstrating that future projections of the Antarctic and Southern Ocean climate bifurcate with the level of global warming. Detailed examinations of sea ice and water masses show that in an extreme warming scenario, a combination of enhanced intrusions of warm deep water and warm summertime surface water can cause the nonlinear response of Antarctic ice-shelf basal melting. A large reduction in Antarctic coastal sea ice and the associated ocean freshening by decreasing coastal sea ice production in winter provide favorable conditions for summertime warm surface water formation and warm deep water intrusions onto some continental shelves. The model results demonstrate that disappearing summertime sea ice along the Antarctic coastal margins in a warming climate heralds the nonlinear increase in Antarctic ice-shelf basal melting, presumably contributing to the negative mass balance of the Antarctic ice sheet and the sea level rise.

The evolution of the Fram Strait sea ice volume export decomposed by age: estimating with parameter-optimized sea ice-ocean model outputs

Sea ice export through the Fram Strait is crucial in the dynamic evolution of Arctic sea ice and can further modulate Arctic sea ice mass balance as well as the ocean thermohaline circulation. In this study, based on outputs from a parameter-optimized and fully physical ocean–sea ice coupled model and sea ice age observation, we estimate sea ice volume (SIV) flux and its age evolution via the Fram Strait. The estimate of mean annual SIV flux is about 1605 315 km3 yr−1 without a significant trend for 1979–2021. Combining with sea ice age data, the variation of the sea ice age and its corresponding SIV flux are obtained for 1984–2020. The SIV flux of 1st-year ice significantly increases as expected, but it still contributes very little to the total flux in the 2010s with a proportion of 3.5%. SIV fluxes of different ages in multi-year ice present diverse variations. The proportions of 2nd-year ice and 3rd-year ice in the annual SIV flux show an extreme increase from 6.8% and 25.0% in the 1980s to 49.0% and 38.8% in the 2010s, respectively, while the proportions of 4th-year ice and 5th-year and older (5+ year) ice significantly decrease from 22.8% and 45.0% in the 1980s to 7.1% and 1.6% in the 2010s, respectively. Meanwhile, the prevailing age of annual volume export via Fram Strait shifts from 4th-year and 5+ year ice to 2nd-year and 3rd-year ice around 2007/2008. It is worth noticing that the variation in Fram Strait ice export modulates the variation in Arctic SIV prior to 2008, but the reverse is true after 2008, indicating a decreasing influence of Fram Strait SIV export on Artic SIV variability with decreasing sea ice age. The results are beneficial to promote the understanding of the evolution of Fram Strait SIV export under the warming Arctic.