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.

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.

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.

Production rate estimation of mycosporine-like amino acids in two Arctic melt ponds by stable isotope probing with NAH(13) CO3

The net carbon uptake rate and net production rate of mycosporine-like amino acids (MAAs) were measured in phytoplankton from 2 different melt ponds (MPs; closed and open type pond) in the western Arctic Ocean using a (13) C stable isotope tracer technique. The Research Vessel Araon visited ice-covered western-central basins situated at 82°N and 173°E in the summer of 2012, when Arctic sea ice declined to a record minimum. The average net carbon uptake rate of the phytoplankton in polycarbonate (PC) bottles in the closed MP was 3.24 mg C · m(-3) · h(-1) (SD = ±1.12 mg C · m(-3) · h(-1) ), while that in the open MP was 1.3 mg C · m(-3) · h(-1) (SD = ±0.05 mg C · m(-3) · h(-1) ). The net production rate of total MAAs in incubated PC bottles was highest (1.44 (SD = ±0.24) ng C · L(-1) · h(-1) ) in the open MP and lowest (0.05 (SD = ±0.003) ng C · L(-1) · h(-1) ) in the closed MP. The net production rate of shinorine and palythine in incubated PC bottles at the open MP presented significantly high values 0.76 (SD = ±0.12) ng C · L(-1) · h(-1) and 0.53 (SD = ±0.06) ng C · L(-1) · h(-1) . Our results showed that high net production rate of MAAs in the open MP was enhanced by a combination of osmotic and UVR stress and that in situ net production rates of individual MAA can be determined using (13) C tracer in MPs in Arctic sea ice.