Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition.
Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.
Abstract. The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM.We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.
more »
« less
- Award ID(s):
- 1724551
- PAR ID:
- 10355627
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 15
- Issue:
- 16
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 6429 to 6449
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.more » « less
-
Abstract The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.more » « less
-
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.more » « less
-
more » « less
Abstract Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km2drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using
in-situ measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-15-6429-2022
