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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 Observator...
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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.
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Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for...
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Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month?1 for FYI, 0.24 m month?1 for previously ponded SYI) exceeding that of thick ice (0.2 m month?1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development.
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The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL)...
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The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL) effectively backscatters solar radiation and keeps the surface albedo of melting ice relatively high compared to ice with the SSL manually removed. Measurements of albedo provide information on how incoming shortwave radiation is partitioned by the SSL and have been pivotal to improving climate model parameterizations. However, the relationship between the physical and optical properties of the SSL is still poorly constrained. Until now, radiative transfer models have been the only way to infer the microstructure of the SSL. During the MOSAiC expedition of 2019–2020, we took samples and, for the first time, directly measured the microstructure of the SSL on bare sea ice using X-ray micro-computed tomography. We show that the SSL has a highly anisotropic, coarse, and porous structure, with a small optical diameter and density at the surface, increasing with depth. As the melting surface ablates, the SSL regenerates, maintaining some aspects of its microstructure throughout the melt season. We used the microstructure measurements with a radiative transfer model to improve our understanding of the relationship between physical properties and optical properties at 850 nm wavelength. When the microstructure is used as model input, we see a 10–15% overestimation of the reflectance at 850 nm. This comparison suggests that either a) spatial variability at the meter scale is important for the two in situ optical measurements and therefore a larger sample size is needed to represent the microstructure or b) future work should investigate either i) using a ray-tracing approach instead of explicitly solving the radiative transfer equation or ii) using a more appropriate radiative transfer model.
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Arctic cyclones are a fundamental component of Arctic climate, influencing atmospheric heat and moisture transport into the region and surface energy, moisture, and momentum fluxes. Arctic cyclones can also drive changes in sea ic...
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Arctic cyclones are a fundamental component of Arctic climate, influencing atmospheric heat and moisture transport into the region and surface energy, moisture, and momentum fluxes. Arctic cyclones can also drive changes in sea ice and energize ocean waves. Here we investigate a record low sea level pressure (SLP) Arctic cyclone which formed in East Greenland and tracked NE over the Barents and Kara seas between 21 and 27 January 2022. At its peak intensity on 24 January, the cyclone reached an estimated depth of 932.2 mb at 79.5°N 20°E. North of 70°N, this is the lowest SLP in the ERA-5 reanalysis over 1979 to present. The cyclone resulted in a record (over the period 1979-2022) weekly loss of regional sea ice area and surface wind speeds,and generated ocean waves exceeding 8 m that impinged on sea ice in the Barents sea, observed via satellite altimetry as large waves-in-sea ice up to 2 m in amplitude more than 100 km into the ice pack. Surface heat fluxes were strongly impacted by the cyclone, with record atmosphere-to-surface turbulent fluxes. However,the direct atmospheric thermodynamic impact on sea ice loss was modest, and the record sea ice changes were likely mainly driven by dynamical and/or ocean processes. While the storm was well predicted up to 8 days in advance, subsequent changes in sea ice cover were not, likely due to biases in the forecasts' sea ice initial conditions and missing physics in the forecast model such as wave-sea ice interaction.
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In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this “new” Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian track...
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In recent decades, Arctic sea ice has shifted toward a younger, thinner, seasonal ice regime. Studying and understanding this “new” Arctic will be the focus of a year-long ship campaign beginning in autumn 2019. Lagrangian tracking of sea ice floes in the Community Earth System Model Large Ensemble (CESM-LE) during representative “perennial” and “seasonal” time periods allows for understanding of the conditions that a floe could experience throughout the calendar year. These model tracks, put into context a single year of observations, provide guidance on how observations can optimally shape model development, and how climate models could be used in future campaign planning. The modeled floe tracks show a range of possible trajectories, though a Transpolar Drift trajectory is most likely. There is also a small but emerging possibility of high-risk tracks, including possible melt of the floe before the end of a calendar year. We find that a Lagrangian approach is essential in order to correctly compare the seasonal cycle of sea ice conditions between point-based observations and a model. Because of high variability in the melt season sea ice conditions, we recommend in situ sampling over a large range of ice conditions for a more complete understanding of how ice type and surface conditions affect the observed processes. We find that sea ice predictability emerges rapidly during the autumn freeze-up and anticipate that process-based observations during this period may help elucidate the processes leading to this change in predictability.
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Abstract Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Therefore, their areal coverage, which can undergo rapid change, is crucial to mo...
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Abstract Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Therefore, their areal coverage, which can undergo rapid change, is crucial to monitor. We present a revised method to extract melt pond fraction (MPF) from Sentinel‐2 satellite imagery, which is evaluated by MPF products from higher‐resolution satellite and helicopter‐borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed ice part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs are 17% before and 23% after the major drainage. Arctic‐wide analysis of MPF for years 2017–2021 shows a consistent seasonal cycle in all regions and years.
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Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challeng...
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Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow-ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow-ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.
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Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challeng...
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Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow-ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow-ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.
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Melinda Webster is a Research Physical Scientist at NASA's Goddard Space Flight Center. Her work uses a combination of remote sensing, in situ, and model data to investigate the polar sea ice covers. Prior to joining NASA in 2017,...
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Melinda Webster is a Research Physical Scientist at NASA's Goddard Space Flight Center. Her work uses a combination of remote sensing, in situ, and model data to investigate the polar sea ice covers. Prior to joining NASA in 2017, she received her B.S., M.S., and Ph.D. degrees in Oceanography from the University of Washington. She's actively engaged with the broader community, serving as a co-lead for the Interagency Arctic Research Policy Commission's Sea Ice Collaboration Team and contributing to several reports including the Arctic Report Card and the Arctic Monitoring and Assessment Programme's Snow, Water, Ice and Permafrost in the Arctic Assessment.
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