Trophic ecology of epibenthic communities exposed to different sea-ice concentrations across the Canadian Arctic Ocean

Icebergs drifting within sea ice represent a serious hazard to Arctic maritime activities, yet predicting their drift remains highly uncertain. We show that sea ice governs the drift of five icebergs in the Greenland Sea (IB-I to III) and Barents Sea (IB-IV, V) between 2012 and 2025. Those icebergs were either locked in the sea ice drift or were dragged by a combination of sea ice, water and wind. We investigate the simulation error by ocean and sea ice input data and the model formulation of the used Lagrangian iceberg drift model. The error is quantified as the difference between simulated and observed drift distance and direction, and it varies across cases and environmental conditions, with low errors for IB-I and IB-IV and high errors for IB-II and IB-III. A new sea ice drag formulation substantially reduces these large errors, particularly for trajectories in regions with high sea ice concentration. This formulation enables the small Arctic icebergs to follow the sea ice motion more realistically, either through a lowered lock-in concentration threshold of 0.7 or by applying a sea ice concentration dependent drag coefficient. Contributing to the overall simulation error, differences in the ocean and sea ice input led to substantial simulation spread of 0 . 2 to 1 . 5 × the observed drift distance and 13 to 8 5 ∘ drift direction. Parts of this error originate from the input sea ice concentration fields, which, though consistent with operational sea ice charts, show a slight positive bias from local observations. • Sea ice drags or locks in iceberg and is thus essential in iceberg drift simulations. • The drag by sea ice accounts for large parts of the iceberg drift distance. • Uncertain ocean and sea ice input cause large spread in the iceberg simulations. • The simulation error is decreased by a modified sea-ice-drag-equation.

The marginal sea ice zone has been identified as a source of different climate active gases to the atmosphere due to its unique biogeochemistry. However, it remains highly undersampled and the impact of changes in sea ice concentration on the distributions of these gases is poorly understood. To address this, we present measurements of dissolved methanol, acetone, acetaldehyde, dimethyl sulfide and isoprene in the sea ice zone of the Canadian Arctic from the surface down to 60 m. The measurements were made using a Segmented Flow Coil Equilibrator coupled to a Proton Transfer Reaction Mass Spectrometer. These gases varied in concentrations with depth, with the highest concentrations generally observed near the surface. Underway (3–4 m) measurements showed broadly higher concentrations in partial sea ice cover compared to ice-free waters. The large number of depth profiles at different sea ice coverages enables proposition of the likely dominant production processes of these compounds in this area. Methanol concentrations appear to be controlled by specific biological consumption processes. Acetone and acetaldehyde concentrations are influenced by the penetration depth of light and the mixed layer depth, implying dominant photochemical sources in this area. Dimethyl sulfide and isoprene both display higher surface concentrations in partial sea ice coverage compared to ice-free waters due to ice edge blooms. Dimethyl sulfide concentrations sometimes display a subsurface maximum in ice -free conditions, while isoprene displays more reliably a subsurface maximum. Surface gas concentrations were used to estimate their air – sea fluxes. Despite obvious in situ production, we estimate that the sea ice zone is absorbing methanol and acetone from the atmosphere. In contrast, DMS and isoprene are consistently emitted from the ocean, with marked episodes of high emissions during ice-free conditions, suggesting that these gases are produced in ice-covered areas and emitted once the ice has melted. Our measurements show that the seawater concentrations and air-sea fluxes of these gases are clearly impacted by sea ice concentration. These novel measurements and insights will allow us to better constrain the cycling of these gases in the polar regions and their effect on the oxidative capacity and aerosol budget in the Arctic atmosphere.

The present study investigated the impacts of autumn Arctic sea ice concentration (SIC) changes on the East Asian winter monsoon (EAWM) and associated climate and circulation on the interannual time scale. It is found that the Arctic SIC anomalies have little impact on the southern mode of EAWM, but the northern mode is significantly associated with both western and eastern Arctic SIC anomalies. When there is less (more) SIC in eastern (western) Arctic, the EAWM tends to be stronger. The concurrent surface air temperature anomalies are induced both locally due to the direct effect of ice cover and in remote regions through anomalous wind advection. Analysis showed that eastern Arctic SIC anomalies have a larger effect on local atmospheric stability of the lower troposphere than western Arctic SIC anomalies. Winter temperature over the midlatitudes of East Asia is lower when there is more (less) SIC in the western (eastern) Arctic. The atmospheric response to the Arctic SIC anomalies is dominantly barotropic in autumn, and changes to baroclinic over the midlatitudes of Asia, but remains barotropic in other regions in winter. The mid- to high-latitude circulation systems, including the Siberian high, the East Asian trough, and the East Asian westerly jet stream, play important roles in connecting autumn Arctic SIC anomalies and the northern mode of the EAWM variability. No obvious concurrent sea surface temperature anomalies accompany Arctic SIC variations on the interannual time scale, indicating that the Arctic SIC anomalies have independent impacts on the East Asian winter climate.

Changes in the UV Lambertian equivalent reflectivity in the Southern Ocean: Influence of sea ice and cloudiness

Sea ice kilometer‐scale characteristics are not explicitly represented in weather and climate model systems. However, due to the large differences of the surface temperature between sea ice and ocean, sea ice‐free areas (leads, polynyas, etc.) can have a strong impact on the surface heat fluxes and, in turn, on atmospheric dynamics. In the present study we analyze the atmospheric response to changes in the sea ice characteristics in the marginal ice zone of the Fram Strait and Barents Sea. A kilometer‐scale convection permitting atmospheric modeling system is used. The model sensitivity experiment is idealized, since there is no complete information on sea ice on these scales available. Instead, we are merging information from the passive microwave satellite product of sea ice concentration with a sea ice lead product based on thermal infrared sensors. An important constraint is that the sea ice‐free areas are introduced locally, but on scales of 100 km the sea ice coverage is kept constant for both the experiments. The sensitivity of air‐sea fluxes, atmospheric temperature, and winds is analyzed between the model experiments. In general, the standard deviation of the changes in surface air temperature and winds is, on large scales, about 2 K and 2 m/s, respectively. Those values are in the same order as the mean 12‐hr forecast error of advanced weather forecast models in this region.

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

Climate warming has enabled the Arctic region to achieve seasonal navigation, and sea ice concentration is an important factor affecting the navigation of the Arctic waterways. This article uses the Arctic sea ice concentration data of the three highest temperatures in 2016, 2019, and 2020, combined with the Arctic summer sea level pressure, wind field, temperature, temperature anomaly, ice age, and sea ice movement data to analyze the spatial and temporal variation of sea ice and connectivity in the Northeast Passage (NEP) of the Arctic in Summer in three hot years, and summarizes the causes of sea ice anomalies. The results show that: (1) the summer Arctic sea ice extent in 2016, 2019 and 2020 were all lower than the multi-year average sea ice extent, and the summer sea ice extent in 2020 had the largest change trend; (2) in October of these three years, the sea ice was all negative anomalies, extending the opening time of the NEP; (3) when the sea ice concentration was 30% as the threshold, the navigation period of the NEP in 2016 was from mid-August to late October, 2019 was from the beginning of August to mid-October, 2020 was from the end of July to the end of October, and 2020 was the longest year since the opening of the NEP; (4) when the sea ice concentration was 10% as the threshold, the navigation period of the NEP in 2016 was from the end of August to the end of October, 2019 was from early August to mid-October, and 2020 was from the beginning of August to the end of October; (5) the key navigable areas of the NEP in the past three years were the central waters of the East Siberian Sea, the New Siberian Islands and the Vilkitsky Strait; (6) the navigation period of the NEP in 2016, 2019 and 2020 was longer. The main reasons were that the temperature of the NEP in the past 3 years was relatively high, the wind was weak, the sea ice movement had little effect, and the sea ice age in the key navigable areas was first year ice, which was easy to melt, which greatly promoted the opening of the NEP.

Sea ice is an integral part of the marine ecosystem in the Arctic and important habitat for ringed seals and polar bears. To study changes in sea ice characteristics indicative of ringed seal habitat (and linked, through predator/prey relationships, to polar bear habitat), we examined historical changes in sea ice concentration and type within M'Clintock Channel and the Gulf of Boothia, two regions of the Canadian Arctic Archipelago, during 1980-2000. Results suggest large interannual variability in winter ice conditions over the 21-year study period. In M'Clintock Channel, first-year ice types dominated consistently, while in the Gulf of Boothia, thick ice types dominated in some years. For breakup and consolidation, the regional spatial patterns differed significantly, occurring in opposite directions (N-S vs. S-N) in the two regions. The dates showed considerable interannual variability in both regions, suggesting no clear pattern of either earlier breakup or later consolidation. Analysis of satellite data confirmed the results obtained from digital ice charts. Ringed seal habitat suitability indices (HSI) indicate that both regions contained primary, secondary, and tertiary HSI classes. No trends were evident in the secondary or tertiary classes, but changes in the primary class were evident in M'Clintock Channel over the five-year period 1997-2001. Dynamic and thermodynamic sea ice processes are important to ringed seal habitat (and ultimately, polar bear habitat) at regional and hemispheric scales in the current context of climate variability and change.

Arctic sea ice variation in the Northwest Passage in 1979–2017 and its response to surface thermodynamics factors

Abstract. Local analytical optimal nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-resolution (3–5 km) pan-Arctic coupled ocean and sea ice modeling and prediction system based on the HYbrid Coordinate Ocean Model (HYCOM version 2.2.98) and the Los Alamos multi-category sea ice model (CICE version 5.1.2), with the LAON for data assimilation. In this study, our focus is on the LAON assimilation of AMSR2 SIC, which is designed to update the model SIC in every time step such that the analysis will eventually reach the optimal estimate. The SIC innovation (observation minus model) is designed to be proportionally distributed to the multiple sea ice categories. A hindcast experiment is performed with and without the LAON assimilation for the period 1 January 2021 to 30 April 2022, in which the extra computational cost for the LAON assimilation is about 5 % of the free run without assimilation. The results show that the LAON assimilation greatly improves the simulated sea ice concentration, extent, area, thickness, and volume, as well as the sea surface temperature (SST). It also produces significantly more accurate sea ice edge and marginal zone (MIZ) than the observed AMSR2 SIC that is assimilated when evaluated against the Norwegian Ice Service (NIS) ice chart. The results are also compared with the Copernicus Marine Environment Monitoring Service (CMEMS) operational SIC analyses from NEMO, TOPAZ4, and neXtSIM, which use ensemble Kalman filters and direct insertion for data assimilation. It is shown that the LAON assimilation produces significantly lower integrated ice edge error (IIEE) and integrated MIZ error (IME) than the CMEMS SIC analyses when evaluated against the NIS ice chart. LAON also produces a continuous and smooth evolution of sub-daily SIC, which avoids abrupt jumps often seen in other assimilated products. This efficient and accurate method is promising for data assimilation in global and high-resolution models.

The accurate representation of clouds and their phase is crucial to enable a correct representation of the Earth’s radiation balance. This has been demonstrated by large radiative errors in global models over the Southern Ocean mainly caused by an incorrect representation of supercooled liquid lowlevel clouds. In contrast to lowlevel clouds, midlevel clouds are rarely investigated. To fill this gap, this study investigates active satellite observations of midlevel clouds over the Southern Ocean and the Arctic Ocean from 2007 and 2008 with a comprehensive comparison to observations of lowlevel clouds. Midlevel and lowlevel clouds are distinguished by cloud base height. The DARDAR dataset provides a detailed phase categorization based on CloudSat and CALIPSO measurements. We have analyzed the cloud phase partitioning as a function of cloud top temperature, vertical cloud thickness, and the horizontal cloud extent. A local minimum in the mean liquid fraction within a cloud column can be observed for a cloud top temperature of -15 °C. An exception to this observation occurs for lowlevel clouds over the Arctic Ocean, which feature a plateau instead of a minimum. This hints at processes producing ice at these temperatures, which could be habit dependent vapor growth, secondary ice production, or a combination of both processes, as already discussed in other studies. Furthermore, daily sea ice concentrations from a passive microwave instrument are collocated to investigate their correlation with the cloud phase. At equal cloud top temperature, lowlevel clouds over the Southern Ocean and the Arctic Ocean have a higher liquid fraction, if they occur over sea ice. Midlevel clouds over the Southern Ocean show the same behaviour, while midlevel clouds over the Arctic Ocean show no significant phase dependence on the sea ice concentration. In addition, collocated CAMS reanalysis data are used to investigate the influence of different concentrations of various aerosol types such as sea salt, dust, black carbon, or organic matter on the cloud phase. Preliminary results show a stronger influence of the mixing ratio of sea salt on the phase of lowlevel clouds, but also the phase of midlevel clouds over the Southern Ocean seems to be influenced. Future work will further investigate the influence of different parameters, such as sea ice concentration, aerosol concentration, and cloud top temperature on the cloud phase by applying a machine learning model and exploring the relative importance of these parameters for the cloud phase, as well as their interactions and synergies.

Abstract. Sea ice coverage is a key indicator of changes in polar and global climate. Observational estimates of the area and extent of sea ice are primarily derived from passive microwave surface emissions, which are used to develop gridded products of sea ice concentration (SIC). Passive microwave (PM) satellite sensors remain the sole global product for understanding SIC variability. Here, in Part I of a two-part study, we use a dataset of more than 70 000 high-resolution airborne optical classified images from Operation IceBridge, and we first identify biases in commonly used passive microwave products in areas with thin sea ice fractures. We find that passive-microwave-derived SIC products overestimate true SIC with biases on average 4.4 % in winter and 3.2 % in summer. Despite the low mean bias in the summer, uncertainty increases in the summer due to complex surface conditions, leading to a wider spread in SIC biases than in winter. We show that ICESat-2, a laser altimeter operational since 2018, has the capacity to sample these thin fractures, with good agreement between ICESat-2 surface-type classifications and near-coincident WorldView and Sentinel-2 data in winter. Using the ICESat-2 surface-type classifications, we introduce a new derived parameter, the linear ice fraction (LIF), and discuss its potential for representing a two-dimensional sea ice concentration field. This paper highlights the biases present in PM-derived SIC and makes a case for considering the integration of ICESat-2 and its high-precision measurements of the sea ice surface to enhance future SIC estimations. In Part II, we identify and evaluate biases associated with the LIF through emulation of ICESat-2 overflights of the data explored here and develop a gridded LIF product, which we compare to gridded PM-SIC data.

Unlike in many polar regions, the spatial extent and duration of the sea ice season have increased in the Ross Sea sector of the Southern Ocean during the satellite era. Simultaneously, populations of Adélie penguins, a sea ice obligate, have been stable or increasing in the region. Relationships between Adélie penguin population growth and sea ice concentration (SIC) are complex, with sea ice driving different, sometimes contrasting, demographic patterns. Adélie penguins undergo a complete molt annually, replacing all their feathers while fasting shortly after the breeding season. Unlike most penguin species, a majority of Adélies are thought to molt on sea ice, away from the breeding colonies, which makes this period particularly difficult to study. Here, we evaluate the hypothesis that persistent areas of high SIC provide an important molting habitat for Adélie penguins. We analyzed data from geolocating dive recorders deployed year-round on 195 adult penguins at two colonies in the Ross Sea from 2017 to 2019. We identified molt by detecting extended gaps in postbreeding diving activity and used associated locations to define two key molting areas. Remotely sensed data indicated that SIC during molt was anomalously low during the study and has declined in the primary molt area since 1980. Further, annual return rates of penguins to breeding colonies were positively correlated with SIC in the molt areas over 20 y. Together these results suggest that sea ice conditions during Adélie penguin molt may represent a previously underappreciated annual bottleneck for adult survival.

The ASI algorithm uses the same sea ice and seawater tie-points when inverting polar sea ice concentration (SIC), but this approach does not fully consider the differences between different polar sea regions and the impact of different sea ice characteristics on SIC results. To make up for this deficiency, the SIC inversion algorithm based on different types of Arctic sea ice is proposed. The proposed algorithm selects pure ice and pure water sample points in different sea regions to derive SIC inversion formulas, and subsequently obtains SIC retrieval results for the entire Arctic. Compare the results of this study with those of traditional ASI algorithm, and perform local validation based on the sea ice distribution obtained from Landsat-8 data. The results show that compared with the traditional ASI algorithm, the proposed algorithm has improved the accuracy of SIC inversion in different sea ice regions by 2%-6%, with an average improvement of 3.3%. Overall, our research has improved the ASI algorithm, which is of great significance for obtaining higher precision polar SIC.

<p>Ocean reanalyses (ORAs) are used extensively in polar research, hence their realism should be assessed regularly. Here the ORAs performance in the Antarctic region is analyzed with specific emphasis on sea ice concentration and thickness. We used four global ocean-sea ice products: C-GLORSv7, FOAM-GLOSEA5v13, GLORYS2v4, and ORAS5, and their ensemble mean GREP (provided by CMEMS) within the 1993 to 2018 period. All ORAs use the NEMO ocean model in a global eddy-permitting configuration (1/4° horizontal resolution and 75 vertical levels) and are forced by the ECMWF ERA-Interim atmospheric reanalysis.</p><p>Here we examine the ability of ORAs to reproduce sea ice properties in the Southern Ocean taking into account regional characteristics and sea ice types. Seasonal and interannual variability of sea ice concentration (SIC) and sea ice thickness (SIT) is examined in the hemispheric domain and in five sub-regions for three different sea ice classes: pack ice (SIC ≥ 80%), marginal ice zone (MIZ) (15% ≤ SIC < 80%), and sparse ice (0 < SIC <15%).  Modeled sea ice properties are compared to a set of satellite products: NSIDC CDR, Ifremer/CERSAT, and EUMETSAT OSI-SAF for SIC and Envisat and CryoSat-2 for SIT, together with PIOMAS and GIOMAS reanalyses. We revealed shortcomings of reanalysis systems to be improved in the future representation of Antarctic sea ice. Additionally, we focused on the assessment of the GREP ensemble mean product. We found that for certain metrics GREP minimizes the single errors and outperforms individual members. The evidence from this study implies that GREP can be a feasible product for a number of applications.</p>