Mesoscale atmospheric processes over an Arctic fjord as observed during a research aircraft campaign in winter

Arctic ecosystems are especially vulnerable to the impacts of climate change because of the limit to possible northward shifts for species dependent on land or continental shelf and because the rate of warming of the region has been 2-4 x the global average in recent decades. The decline in sea ice in the Arctic has both direct and indirect impacts on the species that live in association with ice, breeding on it, traveling over it, feeding on other ice-dependent species or avoiding competition with subarctic species that cannot exploit resources in ice-covered areas. Herein, we present a metamodel of a top-level predator, the polar bear (Ursus maritimus), and two of its key prey species, ringed seals (Pusa hispida) and bearded seals (Erignathus barbatus), which are important in maintaining current polar bear densities and in turn are strongly influenced by bear predation. We used a metamodel that links Population Viability Analyses of the three species in order to examine how the impacts of declining spring land-fast sea ice on the fjords of Svalbard (Norway) and Frans Josef Land (Russia) can cascade through this predator-prey system. As the ice conditions that allow ringed seals to raise pups in snow-covered lairs on the frozen fjords diminish, or even disappear, ringed seal populations using the land-fast sea ice will collapse due to lack of successful recruitment. Consequently, the polar bear population, which relies heavily on hunting ringed seals in the land-fast sea ice to be able to raise their own offspring is also likely to decline. Our models suggest time-lags of decades, with the polar bear population not entering into decline until the lack of recruitment of ringed seals results in the depletion of breeding age ringed seals – starting in the third decade from the start point of the model and dropping below the initial population size only some decades later. Although lags between climate change and impacts on the ice-associated fauna are expected, the sea ice conditions have already changed dramatically in the northern Barents Sea region, including the Svalbard Archipelago, and the collapse of this Arctic species assemblage might already be underway.

Arctic sea ice is rapidly declining in presence, thickness and extent. The consequences that this has for the overall biodiversity in Arctic marine ecosystems are poorly addressed. Especially the so-called sympagic meiofauna, the many tiny organisms living in sea ice, is rarely identified to species level. Here we present the first record of the hydrozoan Sympagohydra tuuli living in sea ice in the Svalbard fjords (European Arctic). Previously, this tiny ice-cnidarian has only been reported from sea ice of Barrow (Alaska), the Canadian Arctic and the central Arctic Ocean. In April 2015, two small hydrozoans were recorded in the landfast sea ice in Van Mijenfjorden (West Spitsbergen). Both of them were preserved in ethanol and one specimen was successfully identified with Sanger sequencing. DNA barcoding confirmed it to be the Protohydridae S. tuuli. Little is known about S. tuuli lifecycle, but its occurrence within the sea ice of seasonal ice-covered fjords in Western Svalbard with no sea-ice connection to the Arctic Ocean strengthens the theory about a sympago-benthic life strategy. We propose that S. tuuli has a pan-Arctic distribution and only spends parts of its life cycle in sea ice.

<p>The Danish Meteorological Institute (DMI) is the authority in charge of the sea ice service that supports mariners off the Greenlandic coast. DMI has offered ice charts and in-person consultancy for many years. Over time, the portfolio of sea ice products has extended to include forecast and automated retrievals and translations of remotely sensed data to sea ice products. Recently, DMI has launched an improved, high-resolution sea ice operational forecast system (SIOFS). The newly-launched DMI-SIOFS is updated with an improved model configuration with a nominal horizontal resolution of 5 km that predicts the ocean and sea ice states. The system is based on the coupling of the 3D ocean model Hybrid Coordinate Ocean Model (HYCOM; e.g., Chassignet et al. (2007)) with the Community Ice CodE model (CICE; e.g., Hunke and Dukowicz (1997); Hunke (2001)). Apart from the horizontal resolution, the sea ice component is improved with parameterization of features such as landfast sea ice and melt-ponds, as well as enhanced sea-ice salinity and thermodynamics schemes. The freshwater discharge from Greenland has also been upgraded by using inputs from the Geological Survey of Denmark and Greenland (GEUS; https://eng.geus.dk/) that improves the coastal ocean currents and, consequently, the sea ice transport nearshore Greenland. As a final product, a 144 hourly forecast of sea ice conditions is produced twice a day and released on Polarportal (http://polarportal.dk/en/home/).</p><p>With a focus on the waters surrounding Greenland, this study introduces the DMI-SIOFS and provides the first evaluation for it in terms of predicted sea ice edge location – an essential diagnostic for mariners. To do so, we make use of the Integrated Ice Edge Error (IIEE) metric introduced by Goessling et al. (2016). In addition, we provide insights on the model response to different forcing.</p><p> </p>

The effect of freshwater sources on wintertime sea-ice CO2processes was studied from the glacier front to the outer Tempelfjorden, Svalbard, in sea ice, glacier ice, brine and snow. March–April 2012 was mild, and the fjord was mainly covered with drift ice, in contrast to the observed thicker fast ice in the colder April 2013. This resulted in different physical and chemical properties of the sea ice and under-ice water. Data from stable oxygen isotopic ratios and salinity showed that the sea ice at the glacier front in April 2012 contained on average 54% of frozen-in glacial meltwater. This was five times higher than in April 2013, where the ice was frozen seawater. In April 2012, the largest excess of sea-ice total alkalinity (AT), carbonate ion ([CO32−]) and bicarbonate ion concentrations ([HCO3−]) relative to salinity was mainly related to dissolved dolomite and calcite incorporated during freezing of mineral-enriched glacial water. In April 2013, the excess of these variables was mainly due to ikaite dissolution as a result of sea-ice processes. Dolomite dissolution increased sea-iceATtwice as much as ikaite and calcite dissolution, implying different buffering capacity and potential for ocean CO2uptake in a changing climate.

The sea ice edge presents a region of many feedback processes between the atmosphere, ocean, and sea ice (Maslowski et al.). Here the authors focus on the impact of on-ice atmospheric and oceanic flows at the sea ice edge. Mesoscale jet formation due to the Coriolis effect is well understood over sharp changes in surface roughness such as coastlines (Hunt et al.). This sharp change in surface roughness is experienced by the atmosphere and ocean encountering a compacted sea ice edge. This paper presents a study of a dynamic sea ice edge responding to prescribed atmospheric and oceanic jet formation. An idealized analytical model of sea ice drift is developed and compared to a sea ice climate model [the Los Alamos Sea Ice Model (CICE)] run on an idealized domain. The response of the CICE model to jet formation is tested at various resolutions.It is found that the formation of atmospheric jets at the sea ice edge increases the wind speed parallel to the sea ice edge and results in the formation of a sea ice drift jet in agreement with an observed sea ice drift jet (Johannessen et al.). The increase in ice drift speed is dependent upon the angle between the ice edge and wind and results in up to a 40% increase in ice transport along the sea ice edge. The possibility of oceanic jet formation and the resultant effect upon the sea ice edge is less conclusive. Observations and climate model data of the polar oceans have been analyzed to show areas of likely atmospheric jet formation, with the Fram Strait being of particular interest.

Spatio-temporal dynamics in microalgal communities in Arctic land-fast sea ice

<p>The Antarctic Climate is characterized by strong interactions between the Southern Ocean, its sea ice cover, and the overlying atmosphere taking place over a wide range of spatio-temporal scales. This coupling constrains our ability to isolate the role of specific components of the climate system on the dynamics of the Antarctic Climate, especially with stand-alone approaches neglecting the feedbacks at play. Based on coupled model simulations, we explore how the ocean can drive the interactions with the cryosphere and atmosphere at two distinct spatio-temporal scales. First, the role of ocean mesoscale eddies is investigated. We describe the imprint of mesoscale eddies on the sea ice and atmosphere in a high-resolution simulation of the Adélie Land sector (East Antarctica) performed with a regional coupled ocean--sea ice--atmosphere model (NEMO-MAR). Specific attention is given to the role of the sea ice in the modulation of the air-sea interactions at mesoscale and to the influence of eddy-driven fluxes on the ocean and sea ice. We show that mesoscale eddies affect near-surface winds and air temperature both in ice-free and ice-covered conditions due to their imprint on the sea ice cover. In addition, eddies promote northward sea ice transport and decrease momentum transfer by surface stress to the ocean. In a second section, we move to larger spatial and temporal scales and delve into the influence of the ocean on the seasonal to interannual variability of the sea ice, atmosphere, and ice shelves basal melt at the scale of the Southern Ocean. This work is based on early results from a new coupled ocean–sea ice--atmosphere--ice sheet configuration with explicit under-ice shelf cavities called PARASO. We focus on subsurface heat content variability and its influence on the interactions between the ocean, the sea ice, the atmosphere, and the Antarctic Ice Sheet.</p>

Landfast sea ice (LFSI) refers to sea ice attached to the shoreline with little or no horizonal motion in contrast to drifting sea ice. The LFSI plays an important role in the Arctic marine environmental and biological systems. Therefore, it is crucial to accurately monitor the spatiotemporal changes in the LFSI distribution. Here we present an automatic LFSI retrieval method for the Laptev Sea, eastern Arctic Ocean, based on a conditional generative adversarial network Pix2Pix using the true-color images of Moderate Resolution Imaging Spectroradiometer (MODIS). The spatial resolution of the derived product is 1.25 km, with a temporal interval of 7 days. Compared to the manually identified data from the true-color images of MODIS, the average precision of the LFSI area derived from LFSI mapping model reaches 91.4%, with the recall reaching 98.7% and F1-score reaching 94.5%. The LFSI coverage is consistent with the traditional large-scale LFSI products, but provides more details. Intraseasonal and interannual variations in LFSI area of the Laptev Sea in spring (March–May) during the period of 2002–2021 are investigated using the new product. The spring LFSI area in this region decreases at a rate of 0.67 × 103 km2 per year during this period (R2 = 0.117, p < 0.01). According to the spatial and temporal changes, we conclude that the LFSI is becoming more stable while the area is shrinking. The method is fully-automatic and computationally efficient, which can be further applied to the entire Arctic Ocean for LFSI identification and monitoring.

This study aims to explore the joint usage of multisource scatterometer measurements in polar sea water and ice discrimination. All radar backscatter measurements from current operating satellite scatterometers are considered, including the C-band ASCAT scatterometer on board the MetOp series satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT). By performing seven experiments that use the same support vector machine (SVM) classifier method but with different input data, we find that the SVM model with all available HSCAT, CSCAT, and ASCAT scatterometer data as inputs gives the best performance. In addition to the SVM outputs, we employ the image erosion/dilation techniques and area growth method to reduce misclassifications of sea water and ice. The sea ice extent obtained in this study shows a good agreement with the National Snow and Ice Data Center (NSIDC) sea ice concentration data from the years 2019 to 2021. More specifically, the sea ice areas are closer to the sea ice areas calculated using 15% as the threshold for NSIDC sea ice concentration data in both Arctic and Antarctic. The sea ice edges acquired by the multisource scatterometer show a close correlation with sea ice edges from the Sentinel-1 Synthetic Aperture Radar (SAR) images. In addition, we found that the coverage of multisource scatterometer data in a half-day is usually above 97%, and more importantly, the sea ice areas obtained on the basis of half-day and daily multisource scatterometer data are very close to each other. The presented work can serve as guidance on the usage of all available scatterometer measurements in sea ice monitoring.

Landfast sea ice (LFI) in Terra Nova Bay, East Antarctica where the Jangbogo Antarctic Research Station is located, has significant influences on marine ecosystem and the sailing of an icebreaker. Therefore, it is essential to analyze the spatio-temporal variation of the LFI in Terra Nova Bay. In this study, we chose interferometric pairs with the temporal baseline from 1 to 9 days out of a total of 62 COSMO–SkyMed synthetic aperture radar (SAR) images over Terra Nova Bay obtained from December 2010 to January 2012, and then constructed the coherence image of each pair. The LFI showed coherence values higher than 0.3 even in the interferometric SAR (InSAR) pairs of up to 9-days of temporal baseline. This was because the LFI was fixed at coastline and thus showed low temporal phase decorrelation. Based on the characteristics of the coherence on LFI, We defined the areas of LFI that show spatially homogeneous coherence values higher than 0.3. Pack ice (PI) and open water showed coherence values lower than 0.3 due to large temporal phase decorreation caused by current and wind. Distinguishing PI from open water in the coherence images was difficult due to their similarly low coherence values. PI was identified in SAR amplitude images by investigating cracks on the ice. The extents of the LFI and PI were estimated from the coherence and SAR amplitude images and their temporal variations were analyzed. The extent of the LFI increased from March to July (maximum extent of 170.7 km2) and decreased from October. The extent of the PI increased from February to May and decreased from May to July when the LFI increases dramatically. The extent of the LFI and air temperature showed an inverse correlation with a time lag of about 2 months, i.e., the extent of the LFI decreases after 2 months of the increase in the air temperature. Meanwhile the correlation between wind speed and the extent of the LFI was very low. This represents that the extent of LFI in Terra Nova Bay are influenced more by the air temperature than wind speed.

Although studies of biogeochemical processes in polar sea ice have been increasing, similar research on relatively warm low-salinity sea ice remains sparse. In this study, we investigated biogeochemical properties of the landfast sea ice cover in the brackish Bothnian Bay (Northern Baltic Sea) and the possible role of this sea ice in mediating the exchange of greenhouse gases, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) across the water column–sea ice–atmosphere interface. Observations of total alkalinity and dissolved inorganic carbon in both landfast sea ice and the water column suggest that the carbonate system is mainly driven by salinity. While high CH4 and N2O concentrations were observed in both the water column (up to 14.3 and 17.5 nmol L–1, respectively) and the sea ice (up to 143.6 and 22.4 nmol L–1, respectively), these gases appear to be enriched in sea ice compared to the water column. This enrichment may be attributable to the sea ice formation process, which concentrates impurities within brine. As sea ice temperature and brine volume decrease, gas solubility decreases as well, promoting the formation of bubbles. Gas bubbles originating from underlying sediments may also be incorporated within the ice cover and contribute to the enrichment in sea ice. The fate of these greenhouse gases within the ice merits further research, as storage in this low-salinity seasonal sea ice is temporary.

The threshold of sea ice concentration (SIC) is the basis for accurately calculating sea ice extent based on passive microwave (PM) remote sensing data. However, the PM SIC threshold at the sea ice edge used in previous studies and released sea ice products has not always been consistent. To explore the representable value of the PM SIC threshold corresponding on average to the position of the Arctic sea ice edge during summer in recent years, we extracted sea ice edge boundaries from the Moderate-resolution Imaging Spectroradiometer (MODIS) sea ice product (MOD29 with a spatial resolution of 1 km), MODIS images (250 m), and sea ice ship-based observation points (1 km) during the fifth (CHINARE-2012) and sixth (CHINARE-2014) Chinese National Arctic Research Expeditions, and made an overlay and comparison analysis with PM SIC derived from Special Sensor Microwave Imager Sounder (SSMIS, with a spatial resolution of 25 km) in the summer of 2012 and 2014. Results showed that the average SSMIS SIC threshold at the Arctic sea ice edge based on ice-water boundary lines extracted from MOD29 was 33%, which was higher than that of the commonly used 15% discriminant threshold. The average SIC threshold at sea ice edge based on ice-water boundary lines extracted by visual interpretation from four scenes of the MODIS image was 35% when compared to the average value of 36% from the MOD29 extracted ice edge pixels for the same days. The average SIC of 31% at the sea ice edge points extracted from ship-based observations also confirmed that choosing around 30% as the SIC threshold during summer is recommended for sea ice extent calculations based on SSMIS PM data. These results can provide a reference for further studying the variation of sea ice under the rapidly changing Arctic.

<p>The Totten Glacier in East Antarctica is of major climate interest because of the large fluctuation of its grounding line and of its potential vulnerability to climate change. The ocean above the continental shelf in front of the Totten ice shelf exhibits large extents of landfast sea ice with low interannual variability. Landfast sea ice is mostly not or sole crudely represented in current climate models. These models are potentially omitting or misrepresenting important effects related to this type of sea ice, such as its influence on coastal polynya locations. Yet, the impact of the landfast sea<br>ice on the ocean – ice shelf interactions is poorly understood. Using a series of high-resolution, regional NEMO-LIM-based experiments including an<br>explicit treatment of ocean – ice shelf interactions over the years 2001-2010, we simulate a realistic landfast sea ice extent in the area of Totten Glacier<br>through a combination of a sea ice tensile strength parameterisation and a grounded iceberg representation. We show that the presence of landfast sea<br>ice impacts seriously both the location of coastal polynyas and the ocean mixed layer depth along the coast, in addition to favouring the intrusion of<br>mixed Circumpolar Deep Water into the ice shelf cavities. Depending on the local bathymetry and the landfast sea ice distribution, landfast sea ice affects ice shelf cavities in different ways, either by increasing the ice melt (+28% for the Moscow University ice shelf) or by reducing its seasonal cycle<br>(+10% in March-May for the Totten ice shelf). This highlights the importance of including an accurate landfast sea ice representation in regional and<br>eventually global climate models</p>

Modelling landfast sea ice and its influence on ocean–ice interactions in the area of the Totten Glacier, East Antarctica

The spatial variability of near-surface variables and turbulent surface fluxes was investigated in three Arctic fjords in Svalbard applying the Weather Research and Forecasting (WRF) mesoscale model. Ten real cases from winter and spring 2008, representing the most common large-scale flow directions, were simulated at 9, 3 and 1 km resolutions for 36 h each. Validation against tower observations and radiosoundings showed fairly good agreement, although a systematic warm and moist bias and slightly overestimated wind speeds were found close to the surface. The spatial variability within a fjord was large and it often reached levels comparable to the temporal variability. The spatial variability of the surface fluxes of sensible and latent heat was mostly controlled by the air and sea surface temperatures instead of wind speed. The same cases were also simulated without any topography over Svalbard. The topography increased the spatial variability but the influence on the mean values was not systematic, except that a clear warming effect was seen in all the fjords studied. The role of surface type increased with increasing air—sea temperature difference and was dominating over topographic effects for the air temperature, specific humidity and turbulent heat fluxes.