Salinity-controlled distribution of prokaryotic communities in the Arctic sea-ice melt ponds

The thawing of snow and sea ice produces distinctive melt ponds on the surface of the Arctic sea ice, which covers a significant portion of the surface sea ice during summer. Melt-pond salinity impacts heat transfer to the ice below and the melting rate. It is widely known that melt ponds play a significant role in heat fluxes, ice-albedo feedback, and sea-ice energy balance. However, not much attention has been given to the fact that melt ponds also serve as a unique microbial ecosystem where microbial production begins as soon as they are formed. Here, we investigated the role of melt pond salinity in controlling the diversity and distribution of prokaryotic communities using culture-dependent and -independent approaches. The 16 S rRNA gene amplicon based next generation sequencing analysis retrieved a total of 14 bacterial phyla, consisting of 146 genera, in addition to two archaeal phyla. Further, the culture-dependent approaches of the study allowed for the isolation and identification of twenty-four bacterial genera in pure culture. Flavobacterium, Candidatus_Aquiluna, SAR11 clade, Polaribacter, Glaciecola, and Nonlabens were the dominant genera observed in the amplicon analysis. Whereas Actimicrobium, Rhodoglobus, Flavobacterium, and Pseudomonas were dominated in the culturable fraction. Our results also demonstrated that salinity, chlorophyll a, and dissolved organic carbon were the significant environmental variables controlling the prokaryotic community distribution in melt ponds. A significant community shift was observed in melt ponds when the salinity changed with the progression of melting and deepening of ponds. Different communities were found to be dominant in melt ponds with different salinity ranges. It was also observed that melt pond prokaryotic communities significantly differed from the surface ocean microbial community. Our observations suggest that complex prokaryotic communities develop in melt ponds immediately after its formation using dissolved organic carbon generated through primary production in the oligotrophic water.

Climate change influence on the levels and trends of persistent organic pollutants (POPs) and chemicals of emerging Arctic concern (CEACs) in the Arctic physical environment - a review

Climate change brings about significant changes in the physical environment in the Arctic. Increasing temperatures, sea ice retreat, slumping permafrost, changing sea ice regimes, glacial loss and changes in precipitation patterns can all affect how contaminants distribute within the Arctic environment and subsequently impact the Arctic ecosystems. In this review, we summarized observed evidence of the influence of climate change on contaminant circulation and transport among various Arctic environment media, including air, ice, snow, permafrost, fresh water and the marine environment. We have also drawn on parallel examples observed in Antarctica and the Tibetan Plateau, to broaden the discussion on how climate change may influence contaminant fate in similar cold-climate ecosystems. Significant knowledge gaps on indirect effects of climate change on contaminants in the Arctic environment, including those of extreme weather events, increase in forests fires, and enhanced human activities leading to new local contaminant emissions, have been identified. Enhanced mobilization of contaminants to marine and freshwater ecosystems has been observed as a result of climate change, but better linkages need to be made between these observed effects with subsequent exposure and accumulation of contaminants in biota. Emerging issues include those of Arctic contamination by microplastics and higher molecular weight halogenated natural products (hHNPs) and the implications of such contamination in a changing Arctic environment is explored.

Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice

In order to satisfy the demand of key sea ice parameters, including melt pond depth Hp and underlying ice thickness Hi, in studies of Arctic sea ice change in summer, four algorithms of retrieving Hp and Hi were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of Hp. For the retrieval of Hi, Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of Hi for Lu18 agreed better with field measurements for thin ice (Hi < 1 m) than that for thick ice, but those results of Hp were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated Hp, while showing a good agreement with Hp < 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve Hp and Hi. Malimka18 also showed the ability to retrieve Hi, except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of Hp and Hi under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer.

High Concentrations of Perfluoroalkyl Acids in Arctic Seawater Driven by Early Thawing Sea Ice

Poly- and perfluoroalkyl substances are synthetic chemicals that are widely present in the global environment including the Arctic. However, little is known about how these chemicals (particularly perfluoroalkyl acids, PFAA) enter the Arctic marine system and cycle between seawater and sea ice compartments. To evaluate this, we analyzed sea ice, snow, melt ponds, and near-surface seawater at two ice-covered stations located north of the Barents Sea (81 °N) with the aim of investigating PFAA dynamics in the late-season ice pack. Sea ice showed high concentrations of PFAA particularly at the surface with snow-ice (the uppermost sea ice layer strongly influenced by snow) comprising 26-62% of the total PFAA burden. Low salinities (<2.5 ppt) and low δ18OH20 values (<1‰ in snow and upper ice layers) in sea ice revealed the strong influence of meteoric water on sea ice, thus indicating a significant atmospheric source of PFAA with subsequent transfer down the sea ice column in meltwater. Importantly, the under-ice seawater (0.5 m depth) displayed some of the highest concentrations notably for the long-chain PFAA (e.g., PFOA 928 ± 617 pg L-1), which were ≈3-fold higher than those of deeper water (5 m depth) and ≈2-fold higher than those recently measured in surface waters of the North Sea infuenced by industrial inputs of PFAAs. The evidence provided here suggests that meltwater arising early in the melt season from snow and other surface ice floe components drives the higher PFAA concentrations observed in under-ice seawater, which could in turn influence the timing and extent of PFAA exposure for organisms at the base of the marine food web.

Retrieval of Melt Pond Fraction over Arctic Sea Ice during 2000–2019 Using an Ensemble-Based Deep Neural Network

The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.

Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

The tipping times in an Arctic sea ice system under influence of extreme events

In light of the rapid recent retreat of Arctic sea ice, the extreme weather events triggering the variability in Arctic ice cover has drawn increasing attention. A non-Gaussian α-stable Lévy process is thought to be an appropriate model to describe such extreme events. The maximal likely trajectory, based on the nonlocal Fokker-Planck equation, is applied to a nonautonomous Arctic sea ice system under α-stable Lévy noise. Two types of tipping times, the early-warning tipping time and the disaster-happening tipping time, are used to predict the critical time for the maximal likely transition from a perennially ice-covered state to a seasonally ice-free one and from a seasonally ice-free state to a perennially ice-free one, respectively. We find that the increased intensity of extreme events results in shorter warning time for sea ice melting and that an enhanced greenhouse effect will intensify this influence, making the arrival of warning time significantly earlier. Meanwhile, for the enhanced greenhouse effect, we discover that increased intensity and frequency of extreme events will advance the disaster-happening tipping time, in which an ice-free state is maintained throughout the year in the Arctic Ocean. Finally, we identify values of the Lévy index α and the noise intensity ϵ in the αϵ-space that can trigger a transition between the Arctic sea ice state. These results provide an effective theoretical framework for studying Arctic sea ice variations under the influence of extreme events.

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD.

Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes

Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R2 value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf. The methods presented here pave the way to upscaling throughout the Landsat 8 and Sentinel-2 observational record across Antarctica to produce a first-ever continental dataset of supraglacial lake volumes. Such a dataset will improve our understanding of the influence of surface hydrology on ice shelf stability, and thus, future projections of Antarctica’s contribution to sea level rise.

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Regions of open water and melting sea ice drive new particle formation in North East Greenland

Atmospheric new particle formation (NPF) and growth significantly influences the indirect aerosol-cloud effect within the polar climate system. In this work, the aerosol population is categorised via cluster analysis of aerosol number size distributions (9–915 nm, 65 bins) taken at Villum Research Station, Station Nord (VRS) in North Greenland during a 7 year record (2010–2016). Data are clustered at daily averaged resolution; in total, we classified six categories, five of which clearly describe the ultrafine aerosol population, one of which is linked to nucleation events (up to 39% during summer). Air mass trajectory analyses tie these frequent nucleation events to biogenic precursors released by open water and melting sea ice regions. NPF events in the studied regions seem not to be related to bird colonies from coastal zones. Our results show a negative correlation (r = −0.89) between NPF events and sea ice extent, suggesting the impact of ultrafine Arctic aerosols is likely to increase in the future, given the likely increased sea ice melting. Understanding the composition and the sources of Arctic aerosols requires further integrated studies with joint multi-component ocean-atmosphere observation and modelling.

The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean

In July 2011, the observation of a massive phytoplankton bloom underneath a sea ice-covered region of the Chukchi Sea shifted the scientific consensus that regions of the Arctic Ocean covered by sea ice were inhospitable to photosynthetic life. Although the impact of widespread phytoplankton blooms under sea ice on Arctic Ocean ecology and carbon fixation is potentially marked, the prevalence of these events in the modern Arctic and in the recent past is, to date, unknown. We investigate the timing, frequency, and evolution of these events over the past 30 years. Although sea ice strongly attenuates solar radiation, it has thinned significantly over the past 30 years. The thinner summertime Arctic sea ice is increasingly covered in melt ponds, which permit more light penetration than bare or snow-covered ice. Our model results indicate that the recent thinning of Arctic sea ice is the main cause of a marked increase in the prevalence of light conditions conducive to sub-ice blooms. We find that as little as 20 years ago, the conditions required for sub-ice blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the ice-covered Arctic Ocean in July permits sub-ice blooms. Recent climate change may have markedly altered the ecology of the Arctic Ocean.

Current use pesticide and legacy organochlorine pesticide dynamics at the ocean-sea ice-atmosphere interface in resolute passage, Canadian Arctic, during winter-summer transition

Here, we present the first detailed analysis of processes by which various current use pesticides (CUPs) and legacy organochlorine pesticides (OCPs) are concentrated in melt ponds that form on Arctic sea ice in the summer, when surface snow is melting and ice eventually breaks up. Four current use pesticides (dacthal, chlorpyrifos, trifluralin, and pentachloronitrobenzene) and one legacy organochlorine pesticide (α-hexachlorocyclohexane) were detected in ponds in Resolute Passage, Canadian Arctic, in 2012. Melt-pond concentrations changed over time as a function of gas exchange, precipitation, and dilution with melting sea ice. Observed increases in melt-pond concentrations for all detected pesticides were associated with precipitation events. Dacthal reached the highest concentration of all current use pesticides in ponds (95±71pgL^-1), a value exceeding measured concentrations in the under-ice (0m) and 5m seawater by >10 and >16 times, respectively. Drainage of dacthal-enriched pond water to the ocean during ice break-up provides an important ice-mediated annual delivery route, adding ~30% of inventory in the summer Mixed Layer (ML; 10m) in the Resolute Passage, and a concentrating mechanism with potential implications for exposures to organisms such as ice algae, and phytoplankton.

Influence of ice thickness and surface properties on light transmission through Arctic sea ice

The observed changes in physical properties of sea ice such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic sea ice. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of sea-ice-melt and under-ice primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of sea ice. We measured spectral under-ice radiance and irradiance using the new Nereid Under-Ice (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H-ROV) designed for both remotely piloted and autonomous surveys underneath land-fast and moving sea ice. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under-ice optical measurements with three dimensional under-ice topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying ice-thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under-ice light field on small scales (<1000 m²), while sea ice-thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of sea ice thickness and surface albedo.

The delivery of organic contaminants to the Arctic food web: why sea ice matters

For decades sea ice has been perceived as a physical barrier for the loading of contaminants to the Arctic Ocean. We show that sea ice, in fact, facilitates the delivery of organic contaminants to the Arctic marine food web through processes that: 1) are independent of contaminant physical-chemical properties (e.g. 2-3-fold increase in exposure to brine-associated biota), and 2) depend on physical-chemical properties and, therefore, differentiate between contaminants (e.g. atmospheric loading of contaminants to melt ponds over the summer, and their subsequent leakage to the ocean). We estimate the concentrations of legacy organochlorine pesticides (OCPs) and current-use pesticides (CUPs) in melt pond water in the Beaufort Sea, Canadian High Arctic, in 2008, at near-gas exchange equilibrium based on Henry's law constants (HLCs), air concentrations and exchange dynamics. CUPs currently present the highest risk of increased exposures through melt pond loading and drainage due to the high ratio of melt pond water to seawater concentration (Melt pond Enrichment Factor, MEF), which ranges from 2 for dacthal to 10 for endosulfan I. Melt pond contaminant enrichment can be perceived as a hypothetical 'pump' delivering contaminants from the atmosphere to the ocean under ice-covered conditions, with 2-10% of CUPs annually entering the Beaufort Sea via this input route compared to the standing stock in the Polar Mixed Layer of the ocean. The abovementioned processes are strongly favored in first-year ice compared to multi-year ice and, therefore, the dynamic balance between contaminant inventories and contaminant deposition to the surface ocean is being widely affected by the large-scale icescape transition taking place in the Arctic.

Mechanisms and implications of α-HCH enrichment in melt pond water on Arctic sea ice

During the summer of 2009, we sampled 14 partially refrozen melt ponds and the top 1 m of old ice in the pond vicinity for α-hexachlorocyclohexane (α-HCH) concentrations and enantiomer fractions (EFs) in the Beaufort Sea. α-HCH concentrations were 3 - 9 times higher in melt ponds than in the old ice. We identify two routes of α-HCH enrichment in the ice over the summer. First, atmospheric gas deposition results in an increase of α-HCH concentration from 0.07 ± 0.02 ng/L (old ice) to 0.34 ± 0.08 ng/L, or ~20% less than the atmosphere-water equilibrium partitioning concentration (0.43 ng/L). Second, late-season ice permeability and/or complete ice thawing at the bottom of ponds permit α-HCH rich seawater (~0.88 ng/L) to replenish pond water, bringing concentrations up to 0.75 ± 0.06 ng/L. α-HCH pond enrichment may lead to substantial concentration patchiness in old ice floes, and changed exposures to biota as the surface meltwater eventually reaches the ocean through various drainage mechanisms. Melt pond concentrations of α-HCH were relatively high prior to the late 1980-s, with a Melt pond Enrichment Factor >1 (MEF; a ratio of concentration in surface meltwater to surface seawater), providing for the potential of increased biological exposures.