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.

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.

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.

Arctic Sea Ice Surface Roughness Estimated from Multi-Angular Reflectance Satellite Imagery

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values were nearly identical for areas with a roughness < 20 cm, but for rougher regions, the MISR-estimated roughness had a narrower range of values than the ATM data. The algorithm was able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-estimated roughness data have a variance about half that of ATM roughness data.

Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic

The vertical diffuse attenuation coefficient for downward plane irradiance ( K d ) is an apparent optical property commonly used in primary production models to propagate incident solar radiation in the water column. In open water, estimating K d is relatively straightforward when a vertical profile of measurements of downward irradiance, E d , is available. In the Arctic, the ice pack is characterized by a complex mosaic composed of sea ice with snow, ridges, melt ponds, and leads. Due to the resulting spatially heterogeneous light field in the top meters of the water column, it is difficult to measure at single-point locations meaningful K d values that allow predicting average irradiance at any depth. The main objective of this work is to propose a new method to estimate average irradiance over large spatially heterogeneous area as it would be seen by drifting phytoplankton. Using both in situ data and 3D Monte Carlo numerical simulations of radiative transfer, we show that (1) the large-area average vertical profile of downward irradiance, E d ¯ ( z ) , under heterogeneous sea ice cover can be represented by a single-term exponential function and (2) the vertical attenuation coefficient for upward radiance ( K L u ), which is up to two times less influenced by a heterogeneous incident light field than K d in the vicinity of a melt pond, can be used as a proxy to estimate E d ¯ ( z ) in the water column.

Thermo-mechanical loads of confined sea ice on structures

Records of the ice pressures on the joggle skirt of the coal quay in Spitsbergen were performed over winter seasons in 2013 and 2015. The ice thickness below the quay was above 2 m. Ice temperature over the ice thickness and water pressure at the sea bed were measured synchronously. It was discovered that sea water migrates through the ice confined inside the joggle skirt under the influence of tidal changes of the water pressure below the ice. It leads to the formation of floods on the ice surface during high tide. The ice surface becomes dry during low tide. Ice temperature and ice pressure on the joggle skirt are changed according to the semidiurnal cycle. Spectral analysis and correlation analysis are performed to analyse the loads caused by thermal expansion of the ice. A thermo-mechanical model of ice based on elastic-plastic rheology with thermal effect is used in numerical simulations of the observed phenomena. Numerical simulations are performed with finite element software.This article is part of the theme issue 'Modelling of sea-ice phenomena'.

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.

Evidence for ice-ocean albedo feedback in the Arctic Ocean shifting to a seasonal ice zone

Ice-albedo feedback due to the albedo contrast between water and ice is a major factor in seasonal sea ice retreat, and has received increasing attention with the Arctic Ocean shifting to a seasonal ice cover. However, quantitative evaluation of such feedbacks is still insufficient. Here we provide quantitative evidence that heat input through the open water fraction is the primary driver of seasonal and interannual variations in Arctic sea ice retreat. Analyses of satellite data (1979–2014) and a simplified ice-upper ocean coupled model reveal that divergent ice motion in the early melt season triggers large-scale feedback which subsequently amplifies summer sea ice anomalies. The magnitude of divergence controlling the feedback has doubled since 2000 due to a more mobile ice cover, which can partly explain the recent drastic ice reduction in the Arctic Ocean.

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.

Evidence for marine origin and microbial-viral habitability of sub-zero hypersaline aqueous inclusions within permafrost near Barrow, Alaska

Cryopegs are sub-surface hypersaline brines at sub-zero temperatures within permafrost; their global extent and distribution are unknown. The permafrost barrier to surface and groundwater advection maintains these brines as semi-isolated systems over geological time. A cryopeg 7 m below ground near Barrow, Alaska, was sampled for geochemical and microbiological analysis. Sub-surface brines (in situtemperature of -6 °C, salinity of 115 ppt), and an associated sediment-infused ice wedge (melt salinity of 0.04 ppt) were sampled using sterile technique. Major ionic concentrations in the brine corresponded more closely to other (Siberian) cryopegs than to Standard seawater or the ice wedge. Ionic ratios and stable isotope analysis of water conformed to a marine or brackish origin with subsequent Rayleigh fractionation. The brine contained ∼1000× more bacteria than surrounding ice, relatively high viral numbers suggestive of infection and reproduction, and an unusually high ratio of particulate to dissolved extracellular polysaccharide substances. A viral metagenome indicated a high frequency of temperate viruses and limited viral diversity compared to surface environments, with closest similarity to low water activity environments. Interpretations of the results underscore the isolation of these underexplored microbial ecosystems from past and present oceans.

Bounds on the complex permittivity of polycrystalline materials by analytic continuation

An analytic continuation method for obtaining rigorous bounds on the effective complex permittivity ε * of polycrystalline composite materials is developed. It is assumed that the composite consists of many identical anisotropic crystals, each with a unique orientation. The key step in obtaining the bounds involves deriving an integral representation for ε *, which separates parameter information from geometrical information. Forward bounds are then found using knowledge of the single crystal permittivity tensor and mean crystal orientation. Inverse bounds are also developed, which recover information about the mean crystal orientation from ε *. We apply the polycrystalline bounds to sea ice, a critical component of the climate system. Different ice types, which result from different growth conditions, have different crystal orientation and size statistics. These characteristics significantly influence the fluid transport properties of sea ice, which control many geophysical and biogeochemical processes important to the climate and polar ecosystems. Using a two-scale homogenization scheme, where the single crystal tensor is numerically computed, forward bounds for sea ice are obtained and are in excellent agreement with columnar sea ice data. Furthermore, the inverse bounds are also applied to sea ice, helping to lay the groundwork for determining ice type using remote sensing techniques.

Onset of convective instabilities in under-ice melt ponds

The onset of double-diffusive natural convection in under-ice melt ponds is investigated through a linear stability analysis. The three-layer configuration is composed by a fluid layer (melt pond) overlying a saturated porous medium (ice matrix), which in turn overlies another fluid layer (under-ice melt pond). Water density inversion is taken into account by adopting a density profile with a quadratic temperature dependence and a linear concentration dependence. We show that the key parameter affecting stability is the depth of the ice matrix, while the depths of the upper and lower fluid layers play a marginal role. A Hopf bifurcation is observed in the whole range of parameters studied, and the size of the convection cells depends on ice permeability. The influence of the external temperature gradient is investigated by means of the definition of an extra thermal parameter accounting for the relative position of the density maximum. It is shown that convection is favored by larger temperature gradients, which occur during Arctic summer.