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  1. Deming, J. ; Nicolaus, M. (Ed.)

    As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day−1, with summer melt rates (June to July) as large as 5 cm day−1. Air temperatures changed as much as 2°C hour−1 but were dampened to <0.3°C hour−1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m−2, with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m−1 °C−1, higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget.

     
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    Free, publicly-accessible full text available November 29, 2024
  2. Abstract Atmospheric gaseous elemental mercury (GEM) concentrations in the Arctic exhibit a clear summertime maximum, while the origin of this peak is still a matter of debate in the community. Based on summertime observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a modeling approach, we further investigate the sources of atmospheric Hg in the central Arctic. Simulations with a generalized additive model (GAM) show that long-range transport of anthropogenic and terrestrial Hg from lower latitudes is a minor contribution (~2%), and more than 50% of the explained GEM variability is caused by oceanic evasion. A potential source contribution function (PSCF) analysis further shows that oceanic evasion is not significant throughout the ice-covered central Arctic Ocean but mainly occurs in the Marginal Ice Zone (MIZ) due to the specific environmental conditions in that region. Our results suggest that this regional process could be the leading contributor to the observed summertime GEM maximum. In the context of rapid Arctic warming and the observed increase in width of the MIZ, oceanic Hg evasion may become more significant and strengthen the role of the central Arctic Ocean as a summertime source of atmospheric Hg. 
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    Free, publicly-accessible full text available December 1, 2024
  3. Low-salinity meltwater from Arctic sea ice and its snow cover accumulates and creates under-ice meltwater layers below sea ice. These meltwater layers can result in the formation of new ice layers, or false bottoms, at the interface of this low-salinity meltwater and colder seawater. As part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), we used a combination of sea ice coring, temperature profiles from thermistor strings and underwater multibeam sonar surveys with a remotely operated vehicle (ROV) to study the areal coverage and temporal evolution of under-ice meltwater layers and false bottoms during the summer melt season from mid-June until late July. ROV surveys indicated that the areal coverage of false bottoms for a part of the MOSAiC Central Observatory (350 by 200 m2) was 21%. Presence of false bottoms reduced bottom ice melt by 7–8% due to the local decrease in the ocean heat flux, which can be described by a thermodynamic model. Under-ice meltwater layer thickness was larger below first-year ice and thinner below thicker second-year ice. We also found that thick ice and ridge keels confined the areas in which under-ice meltwater accumulated, preventing its mixing with underlying seawater. While a thermodynamic model could reproduce false bottom growth and melt, it could not describe the observed bottom melt rates of the ice above false bottoms. We also show that the evolution of under-ice meltwater-layer salinity below first-year ice is linked to brine flushing from the above sea ice and accumulating in the meltwater layer above the false bottom. The results of this study aid in estimating the contribution of under-ice meltwater layers and false bottoms to the mass balance and salt budget for Arctic summer sea ice.

     
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  4. Abstract. The annual sea ice freeze–thaw cycle plays a crucial role in theArctic atmosphere—ice–ocean system, regulating the seasonal energy balanceof sea ice and the underlying upper-ocean. Previous studies of the sea icefreeze–thaw cycle were often based on limited accessible in situ or easilyavailable remotely sensed observations of the surface. To better understandthe responses of the sea ice to climate change and its coupling to the upperocean, we combine measurements of the ice surface and bottom usingmultisource data to investigate the temporal and spatial variations in thefreeze–thaw cycle of Arctic sea ice. Observations by 69 sea ice mass balancebuoys (IMBs) collected from 2001 to 2018 revealed that the average ice basalmelt onset in the Beaufort Gyre occurred on 23 May (±6 d),approximately 17 d earlier than the surface melt onset. The average icebasal melt onset in the central Arctic Ocean occurred on 17 June (±9 d), which was comparable with the surface melt onset. This difference wasmainly attributed to the distinct seasonal variations of oceanic heatavailable to sea ice melt between the two regions. The overall average onsetof basal ice growth of the pan Arctic Ocean occurred on 14 November (±21 d), lagging approximately 3 months behind the surface freezeonset. This temporal delay was caused by a combination of cooling the seaice, the ocean mixed layer, and the ocean subsurface layer, as well as thethermal buffering of snow atop the ice. In the Beaufort Gyre region, both(Lagrangian) IMB observations (2001–2018) and (Eulerian) moored upward-looking sonar (ULS) observations (2003–2018) revealed a trend towardsearlier basal melt onset, mainly linked to the earlier warming of thesurface ocean. A trend towards earlier onset of basal ice growth was alsoidentified from the IMB observations (multiyear ice), which we attributed tothe overall reduction of ice thickness. In contrast, a trend towards delayedonset of basal ice growth was identified from the ULS observations, whichwas explained by the fact that the ice cover melted almost entirely by theend of summer in recent years. 
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  5. The western Arctic Ocean is rapidly acidifying due to sea ice loss. 
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  6. During the Arctic melt season, relatively fresh meltwater layers can accumulate under sea ice as a result of snow and ice melt, far from terrestrial freshwater inputs. Such under-ice meltwater layers, sometimes referred to as under-ice melt ponds, have been suggested to play a role in the summer sea ice mass balance both by isolating the sea ice from saltier water below, and by driving formation of ‘false bottoms’ below the sea ice. Such layers form at the interface of the fresher under-ice layer and the colder, saltier seawater below. During the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition in the Central Arctic, we observed the presence of under-ice meltwater layers and false bottoms throughout July 2020 at primarily first-year ice locations. Here, we examine the distribution, prevalence, and drivers of under-ice ponds and the resulting false bottoms during this period. The average thickness of observed false bottoms and freshwater equivalent of under-ice meltwater layers was 0.08 m, with false bottom ice comprised of 74–87% FYI melt and 13–26% snow melt. Additionally, we explore these results using a 1D model to understand the role of dynamic influences on decoupling the ice from the seawater below. The model comparison suggests that the ice-ocean friction velocity was likely exceptionally low, with implications for air-ice-ocean momentum transfer. Overall, the prevalence of false bottoms was similar to or higher than noted during other observational campaigns, indicating that these features may in fact be common in the Arctic during the melt season. These results have implications for the broader ice-ocean system, as under-ice meltwater layers and false bottoms provide a source of ice growth during the melt season, potentially reduce fluxes between the ice and the ocean, isolate sea ice primary producers from pelagic nutrient sources, and may alter light transmission to the ocean below. 
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  7. Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond. 
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  8. Sea ice thickness is a key parameter in the polar climate and ecosystem. Thermodynamic and dynamic processes alter the sea ice thickness. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provided a unique opportunity to study seasonal sea ice thickness changes of the same sea ice. We analyzed 11 large-scale (∼50 km) airborne electromagnetic sea thickness and surface roughness surveys from October 2019 to September 2020. Data from ice mass balance and position buoys provided additional information. We found that thermodynamic growth and decay dominated the seasonal cycle with a total mean sea ice thickness increase of 1.4 m (October 2019 to June 2020) and decay of 1.2 m (June 2020 to September 2020). Ice dynamics and deformation-related processes, such as thin ice formation in leads and subsequent ridging, broadened the ice thickness distribution and contributed 30% to the increase in mean thickness. These processes caused a 1-month delay between maximum thermodynamic sea ice thickness and maximum mean ice thickness. The airborne EM measurements bridged the scales from local floe-scale measurements to Arctic-wide satellite observations and model grid cells. The spatial differences in mean sea ice thickness between the Central Observatory (<10 km) of MOSAiC and the Distributed Network (<50 km) were negligible in fall and only 0.2 m in late winter, but the relative abundance of thin and thick ice varied. One unexpected outcome was the large dynamic thickening in a regime where divergence prevailed on average in the western Nansen Basin in spring. We suggest that the large dynamic thickening was due to the mobile, unconsolidated sea ice pack and periodic, sub-daily motion. We demonstrate that this Lagrangian sea ice thickness data set is well suited for validating the existing redistribution theory in sea ice models. Our comprehensive description of seasonal changes of the sea ice thickness distribution is valuable for interpreting MOSAiC time series across disciplines and can be used as a reference to advance sea ice thickness modeling. 
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  9. Abstract The accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant ( p < 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (<11.6%), and spatial-temporal resolutions are higher in the NSIDC product, which makes it more suitable for estimating sea-ice deformation. 
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