Anja Rösel

Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice

The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16u2009±u20096u2009gu2009C m−2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.

Observations of flooding and snow-ice formation in a thinner Arctic sea-ice regime during the N-ICE2015 campaign: Influence of basal ice melt and storms

Seven ice mass balance instruments deployed near 83°N on different first-year and second-year ice floes, representing variable snow and ice conditions, documented the evolution of snow and ice conditions in the Arctic Ocean north of Svalbard in January–March 2015. Frequent profiles of temperature and thermal diffusivity proxy were recorded to distinguish changes in snow depth and ice thickness with 2 cm vertical resolution. Four instruments documented flooding and snow-ice formation. Flooding was clearly detectable in the simultaneous changes in thermal diffusivity proxy, increased temperature, and heat propagation through the underlying ice. Slush then progressively transformed into snow-ice. Flooding resulted from two different processes: (i) after storm-induced breakup of snow-loaded floes and (ii) after loss of buoyancy due to basal ice melt. In the case of breakup, when the ice was cold and not permeable, rapid flooding, probably due to lateral intrusion of seawater, led to slush and snow-ice layers at the ocean freezing temperature (−1.88°C). After the storm, the instruments documented basal sea-ice melt over warm Atlantic waters and ocean-to-ice heat flux peaked at up to 400 W m−2. The warm ice was then permeable and flooding was more gradual probably involving vertical intrusion of brines and led to colder slush and snow-ice (−3°C). The N-ICE2015 campaign provided the first documentation of significant flooding and snow-ice formation in the Arctic ice pack as the slush partially refroze. Snow-ice formation may become a more frequently observed process in a thinner ice Arctic.

Snow contribution to first-year and second-year Arctic sea ice mass balance north of Svalbard

The salinity and water oxygen isotope composition (δ18O) of twenty-nine first-year (FYI) and second-year (SYI) Arctic sea ice cores (total length 32.0 m) from the drifting ice pack north of Svalbard were examined to quantify the contribution of snow to sea ice mass. Five cores (total length 6.4 m) were analyzed for their structural composition showing variable contribution of 10-30% by granular ice. In these cores snow had been entrained in 6 to 28% of the total ice thickness. We found evidence of snow contribution in about three quarter of the sea ice cores, when surface granular layers had very low δ18O values. Snow contributed 7.5-9.7% to sea ice mass balance on average (including also cores with no snow) using δ18O mass balance calculations. In SYI cores snow fraction by mass (12.7-16.3%) was much higher than in FYI cores (3.3-4.4%), while the bulk salinity of FYI (4.9) was distinctively higher than for SYI (2.7). We surmise that oxygen isotopes and salinity profiles can give information on the age of the ice and allows to distinguish between FYI and SYI (or older) ice in the area north of Svalbard. This article is protected by copyright. All rights reserved.

The seeding of ice algal blooms in Arctic pack ice : the multiyear ice seed repository hypothesis

During the Norwegian young sea ICE expedition (N-ICE2015) from January to June 2015 the pack ice in the Arctic Ocean north of Svalbard was studied during four drifts between 83° and 80° N. This pack ice consisted of a mix of second-year, first-year and young ice. The physical properties and ice algal community composition was investigated in the three different ice types during the winter-spring-summer transition. Our results indicate that algae remaining in sea ice that survived the summer melt season are subsequently trapped in the upper layers of the ice column during winter and may function as an algal seed repository. Once the connectivity in the entire ice column is established, as a result of temperature-driven increase in ice porosity during spring, algae in the upper parts of the ice are able to migrate towards the bottom and initiate the ice-algal spring bloom. Furthermore, this algal repository might seed the bloom in younger ice formed in adjacent leads. This mechanism was studied in detail for the often dominating ice diatom Nitzschia frigida.The proposed seeding mechanism may be compromised due to the disappearance of older ice in the anticipated regime shift towards a seasonally ice-free Arctic Ocean.

Spring snow conditions on Arctic sea ice north of Svalbard, during the Norwegian Young Sea ICE (N-ICE2015) expedition

Snow is crucial over sea ice due to its conflicting role in reflecting the incoming solar energy and reducing the heat transfer so that its temporal and spatial variability are important to estimate. During the Norwegian Young Sea ICE (N-ICE2015) campaign, snow physical properties and variability were examined, and results from April until mid-June 2015 are presented here. Overall, the snow thickness was about 20xa0cm higher than the climatology for second-year ice, with an average of 55xa0±xa027xa0cm and 32xa0±xa020xa0cm on first-year ice. The average density was 350–400xa0kgxa0m−3 in spring, with higher values in June due to melting. Due to flooding in March, larger variability in snow water equivalent was observed. However, the snow structure was quite homogeneous in spring due to warmer weather and lower amount of storms passing over the field camp. The snow was mostly consisted of wind slab, faceted, and depth hoar type crystals with occasional fresh snow. These observations highlight the more dynamic character of evolution of snow properties over sea ice compared to previous observations, due to more variable sea ice and weather conditions in this area. The snowpack was isothermal as early as 10 June with the first onset of melt clearly identified in early June. Based on our observations, we estimate than snow could be accurately represented by a three to four layers modeling approach, in order to better consider the high variability of snow thickness and density together with the rapid metamorphose of the snow in springtime.

Investigation into Different Polarimetric Features for Sea Ice Classification Using X-Band Synthetic Aperture Radar

Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH-VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.

Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic Ocean

During the Norwegian young sea ICE (N-ICE2015) campaign in early 2015, a deep snow pack was observed, almost double the climatology for the region north of Svalbard. There were significant amounts of snow-ice in second-year ice (SYI), while much less in first-year ice (FYI). Here we use a 1-D snow/ice thermodynamic model, forced with reanalyses, to show that snow-ice contributes to thickness growth of SYI in absence of any bottom growth, due to the thick snow. Growth of FYI is tightly controlled by the timing of growth onset relative to precipitation events. A later growth-onset can be favorable for FYI growth due to less snow accumulation, which limits snow-ice formation. We surmise these findings are related to a phenomenon in the Atlantic sector of the Arctic, where frequent storm events bring heavy precipitation during autumn and winter, in a region with a thinning ice cover.

Sea ice thermohaline dynamics and biogeochemistry in the Arctic Ocean: Empirical and model results

Large changes in the sea ice regime of the Arctic Ocean have occurred over the last n decades justifying the development of models to forecast sea ice physics and biogeochemistry. n The main goal of this study is to evaluate the performance of the Los Alamos Sea Ice n Model (CICE) to simulate physical and biogeochemical properties at time scales of n a few weeks and to use the model to analyze ice algal bloom dynamics in different n types of ice. Ocean and atmospheric forcing data and observations of the evolution n of the sea ice properties collected from 18 April to 4 June 2015, during the Norwegian n young sea ICE expedition, were used to test the CICE model. Our results show the following: n (i) model performance is reasonable for sea ice thickness and bulk salinity; good n for vertically resolved temperature, vertically averaged Chl a concentrations, and standing stocks; and poor for vertically resolved Chl a concentrations. (ii) Improving current knowledge about nutrient exchanges, ice algal n recruitment, and motion is critical to improve sea ice biogeochemical modeling. (iii) n Ice algae may bloom despite some degree of basal melting. (iv) Ice algal motility n driven by gradients in limiting factors is a plausible mechanism to explain their n vertical distribution. (v) Different ice algal bloom and net primary production (NPP) n patterns were identified in the ice types studied, suggesting that ice algal maximal n growth rates will increase, while sea ice vertically integrated NPP and biomass will n decrease as a result of the predictable increase in the area covered by refrozen leads n in the Arctic Ocean.

Comparison of Freeboard Retrieval and Ice Thickness Calculation From ALS, ASIRAS, and CryoSat‐2 in the Norwegian Arctic to Field Measurements Made During the N‐ICE2015 Expedition

We present freeboard measurements from airborne laser scanner (ALS), the Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS), and CryoSat-2 SIRAL radar altimeter; ice thickness measurements from both helicopter-borne and ground-based electromagnetic-sounding; and point measurements of ice properties. This case study was carried out in April 2015 during the N-ICE2015 expedition in the area of the Arctic Ocean north of Svalbard. The region is represented by deep snow up to 1.12 m and a widespread presence of negative freeboards. The main scattering surfaces from both CryoSat-2 and ASIRAS are shown to be closer to the snow freeboard obtained by ALS than to the ice freeboard measured in situ. This case study documents the complexity of freeboard retrievals from radar altimetry. We show that even under cold (below −15°C) conditions the radar freeboard can be close to the snow freeboard on a regional scale of tens of kilometers. We derived a modal sea-ice thickness for the study region from CryoSat-2 of 3.9 m compared to measured total thickness 1.7 m, resulting in an overestimation of sea-ice thickness on the order of a factor 2. Our results also highlight the importance of year-to-year regional scale information about the depth and density of the snowpack, as this influences the sea-ice freeboard, the radar penetration, and is a key component of the hydrostatic balance equations used to convert radar freeboard to sea-ice thickness.

Algal hot spots in a changing Arctic Ocean: Sea-ice ridges and the snow-ice interface

During the N-ICE2015 drift expedition north-west of Svalbard, we observed the establishment and development of algal communities in first-year ice (FYI) ridges and at the snow-ice interface. Despite some indications of being hot spots for biological activity, ridges are under-studied largely because they are complex structures that are difficult to sample. Snow infiltration communities can grow at the snow-ice interface when flooded. They have been commonly observed in the Antarctic, but rarely in the Arctic, where flooding is less common mainly due to a lower snow-to-ice thickness ratio. Combining biomass measurements and algal community analysis with under-ice irradiance and current measurements as well as light modeling, we comprehensively describe these two algal habitats in an Arctic pack ice environment. High biomass accumulation in ridges was facilitated by complex surfaces for algal deposition and attachment, increased light availability, and protection against strong under-ice currents. Notably, specific locations within the ridges were found to host distinct ice algal communities. The pennate diatoms Nitzschia frigida and Navicula species dominated the underside and inclined walls of submerged ice blocks, while the centric diatom Shionodiscus bioculatus dominated the top surfaces of the submerged ice blocks. Higher light levels than those in and below the sea ice, low mesozooplankton grazing, and physical concentration likely contributed to the high algal biomass at the snow-ice interface. These snow infiltration communities were dominated by Phaeocystis pouchetii and chain-forming pelagic diatoms (Fragilariopsis oceanica and Chaetoceros gelidus). Ridges are likely to form more frequently in a thinner and more dynamic ice pack, while the predicted increase in Arctic precipitation in some regions in combination with the thinning Arctic icescape might lead to larger areas of sea ice with negative freeboard and subsequent flooding during the melt season. Therefore, these two habitats are likely to become increasingly important in the new Arctic with implications for carbon export and transfer in the ice-associated ecosystem.