Spring sea-ice evolution in Rijpfjorden (80° N), Svalbard, from in situ measurements and ice mass-balance buoy (IMB) data

AbstractRijpfjorden (808 N, 22° E) is a high-Arctic fjord on Nordaustlandet in the Svalbard archipelago. To monitor the thermodynamic change of sea ice in spring, an ice mass-balance buoy (IMB) was deployed for 2.5 months (10 April–26 June 2011), with accompanying in situ measurements, sea-ice sampling on three occasions and ice-core analysis. Uncertainties and sources of error in in situ measurements and IMB data are discussed. The in situ measurements, ice-core analysis and IMB data together depict the development of snow and ice in spring. Snow and ice thickness exhibited large spatial and temporal variability. After relatively stable conditions with only little change in ice thickness and accumulation of snow, a layer of superimposed ice ∼0.06 m thick formed at the snow-ice interface due to refreezing of snow meltwater in late spring. Ice thickness (except for growth of superimposed ice) did not change significantly based on in situ observations. In contrast, the under-ice sonar data from the IMB show reflections from a layer deeper than the underside of the ice during the melting phase. This can be explained as a reflection of the sonar pulses from an interface between a freshwater layer under the ice and more saline water below, or as a false-bottom formation.

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Winter Arctic sea ice bottom evolution detected by thermistor string-based ice mass balance buoys (SIMBA)

10.5194/egusphere-egu2020-22171 ◽

2020 ◽

Author(s):

Bin Cheng ◽

Timo Vihma ◽

Zeling Liao ◽

Ruibo Lei ◽

Mario Hoppmann ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Mass Balance ◽

Arctic Sea Ice ◽

Heating Element ◽

The Arctic ◽

Ice Thickness ◽

The Arctic Ocean ◽

Ice Mass Balance ◽

Snow And Ice

A thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) has been developed in recent years and used for monitoring snow and ice mass balance in the Arctic Ocean. SIMBA measures vertical environment temperature (ET) profiles through the air-snow-sea ice-ocean column using a thermistor string (5 m long, sensor spacing 2cm). Each thermistor sensor equipped with a small identical heating element. A small voltage was applied to the heating element so that the heat energy liberated in the vicinity of each sensor is the same. The heating time intervals lasted 60 s and 120 s, respectively. The heating temperatures (HT) after these two intervals were recorded. The ET was measured 4 times a day and once per day for the HT.A total 15 SIMBA buoys have been deployed in the Arctic Ocean during the Chinese National Arctic Research Expedition (CHINARE) 2018 and the Nansen and Amundsen Basins Observational System (NABOS) 2018 field expeditions in late autumn. We applied a recently developed SIMBA algorithm to retrieve snow and ice thickness using SIMBA ET and HT temperature data. We focus particularly on sea ice bottom evolution during Arctic winter.In mid-September 2018, 5 SIMBA buoys were deployed in the East Siberian Sea (NABOS2018) where snow was in practical zero cm and ice thickness ranged between 1.8 m &#8211; 2.6 m. By the end of May, those SIMBA buoys were drifted in the central Arctic where snow and ice thicknesses were around 0.05m - 0.2m and 2.6m &#8211; 3.2m, respectively. For those 10 SIMBA buoys deployed by the CHINARE2018 in the Chukchi Sea and Canadian Basin, the initial snow and ice thickness were ranged between 0.05m &#8211; 0.1cm and 1.5m &#8211; 2.5m, respectively. &#160;By the end of May, those SIMBA buoys were drifted toward the north of Greenland where snow and ice thicknesses were around 0.2m - 0.3m and 2.0m &#8211; 3.5m, respectively. The ice bottom evolution derived by SIMBA algorithm agrees well with SIMBA HT identified ice-ocean interfaces. We also perform a preliminary investigation of sea ice bottom evolution measured by several SIMBA buoys deployed during the MOSAiC leg1 field campaign in winter 2019/2020.&#160;&#160;

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Snow depth uncertainty and its implications on satellite derived Antarctic sea ice thickness

10.5194/tc-2018-92 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daniel Price ◽

Iman Soltanzadeh ◽

Wolfgang Rack

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Snow Accumulation ◽

In Situ Measurements ◽

Ice Thickness ◽

Growth Season ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Fast Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.

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Fate of sea ice in the 'New Arctic': A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

10.5194/tc-2021-297 ◽

2021 ◽

Author(s):

Sean Horvath ◽

Linette Boisvert ◽

Chelsea Parker ◽

Melinda Webster ◽

Patrick Taylor ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Mass Balance ◽

Air Temperature ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Ice Mass Balance ◽

Temperature And Pressure

Abstract. Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and extent and transitioned to a seasonal ice cover. This shift to thinner, seasonal ice in the 'New Arctic' is accompanied by a reshuffling of energy flows at the surface. Understanding the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. Building on previous work, this dataset includes remotely sensed radiative and turbulent fluxes from which the surface energy budget can be calculated. Additionally, flags indicate when sea ice parcels travel within cyclones, recording distance and direction from the cyclone center. The database drift track was evaluated by comparison with sea ice mass balance buoys. Results show ice parcels generally remain within 100km of the corresponding buoy, with a mean distance of 82.6 km and median distance of 54 km. The sea ice mass balance buoys also provide recordings of sea ice thickness, snow depth, and air temperature and pressure which were compared to this database. Ice thickness and snow depth typically are less accurate than air temperature and pressure due to the high spatial variability of the former two quantities when compared to a point measurement. The correlations between the ice parcel and buoy data are high, which highlights the accuracy of this Lagrangian database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. Applications such as these may shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.

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Inter-annual variation of lake ice composition in European Arctic: observations based on high-resolution thermistor strings

10.5194/essd-2021-135 ◽

2021 ◽

Author(s):

Bin Cheng ◽

Yubing Cheng ◽

Timo Vihma ◽

Anna Kontu ◽

Fei Zheng ◽

...

Keyword(s):

Mass Balance ◽

Air Temperature ◽

Snow Depth ◽

Ice Thickness ◽

Lake Ice ◽

Data Set ◽

Finnish Lapland ◽

Increasing Trend ◽

Ice Mass Balance ◽

Snow And Ice

Abstract. Climate change and global warming strongly impact the cryosphere. The rise of air temperature and change of precipitation patterns lead to dramatic responses of snow and ice heat and mass balance. Sustainable field observations on lake air-snow-ice-water temperature regime have been carried out in Lake Orajärvi in the vicinity of the Finnish Space Centre, a Flagship Supersite in Sodankylä in Finnish Lapland since 2009. A thermistor string-based snow and ice mass balance buoy called “Snow and ice mass balance apparatus (SIMBA)” was deployed in the lake at the beginning of each ice season. In this paper, we describe snow and ice temperature regimes, snow depth, ice thickness, and ice compositions retrieved from SIMBA observations as well as meteorological variables based on high-quality observations at the Finnish Space Centre. Ice thickness in Lake Orajärvi showed an increasing trend. During the decade of data collection: 1) The November-May mean air temperature had an increasing trend of 0.16º C/year, and the interannual variations were highly correlated (r = 0.93) with the total seasonal accumulated precipitation; 2) The maximum granular ice thickness ranged from 15 to 80 % of the maximum total ice thickness; 3) The snow depth on lake ice was not correlated (r = 0.21) with the total precipitation. The data set can be applied to investigate the lake ice surface heat balance and the role of snow on lake ice mass balance, and to improve the parameterization of snow to ice transformation in snow/ice models. The data are archived at https://zenodo.org/record/4559368#.YIKOOpAzZPZ (Cheng et al., 2021)

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Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

The Cryosphere ◽

10.5194/tc-13-1409-2019 ◽

2019 ◽

Vol 13 (4) ◽

pp. 1409-1422

Author(s):

Daniel Price ◽

Iman Soltanzadeh ◽

Wolfgang Rack ◽

Ethan Dale

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Snow Accumulation ◽

In Situ Measurements ◽

Ice Thickness ◽

Growth Season ◽

Mcmurdo Sound ◽

Sea Ice Thickness ◽

Antarctic Sea Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

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In situ measurements of the direct-current conductivity of Antarctic Sea ice: implications for airborne electromagnetic Sounding of Sea-ice thickness

Annals of Glaciology ◽

10.3189/172756406781811772 ◽

2006 ◽

Vol 44 ◽

pp. 217-223 ◽

Cited By ~ 15

Author(s):

J.E. Reid ◽

A. Pfaffling ◽

A.P. Worby ◽

J.R. Bishop

Keyword(s):

Sea Ice ◽

Direct Current ◽

Sea Water ◽

Vertical Direction ◽

In Situ Measurements ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Direct Current Conductivity

AbstractAirborne, Ship-borne and Surface low-frequency electromagnetic (EM) methods have become widely applied to measure Sea-ice thickness. EM responses measured over Sea ice depend mainly on the Sea-water conductivity and on the height of the Sensor above the Sea-ice–sea-water interface, but may be Sensitive to the Sea-ice conductivity at high excitation frequencies. We have conducted in Situ measurements of direct-current conductivity of Sea ice using Standard geophysical geoelectrical methods. Sea-ice thickness estimated from the geoelectrical Sounding data was found to be consistently underestimated due to the pronounced vertical-to-horizontal conductivity anisotropy present in level Sea ice. At five Sites, it was possible to determine the approximate horizontal and vertical conductivities from the Sounding data. The average horizontal conductivity was found to be 0.017 Sm–1, and that in the vertical direction to be 9–12 times higher. EM measurements over level Sea ice are Sensitive only to the horizontal conductivity. Numerical modelling has Shown that the assumption of zero Sea-ice conductivity in interpretation of airborne EM data results in a negligible error in interpreted thickness for typical level Antarctic Sea ice.

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Spatially distributed simulations of the effect of snow on mass balance and flooding of Antarctic sea ice

Journal of Glaciology ◽

10.1017/jog.2021.54 ◽

2021 ◽

pp. 1-19

Author(s):

Nander Wever ◽

Katherine Leonard ◽

Ted Maksym ◽

Seth White ◽

Martin Proksch ◽

...

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Single Point ◽

Weddell Sea ◽

Ice Formation ◽

Ice Thickness ◽

One Dimensional ◽

Antarctic Sea Ice ◽

Distributed Simulations ◽

Snow And Ice

Abstract Southern Ocean sea ice can exhibit widespread flooding and subsequent snow-ice formation, due to relatively thick snow covers compared to the total ice thickness. Considerable subkilometer scale variability in snow and ice thickness causes poorly constrained uncertainties in determining the amount of flooding that occurs. Using datasets of snow depth and ice thickness acquired in the Weddell Sea during austral winter 2013 (AWECS campaign) from three floes, we demonstrate large spatial variability of a factor 10 and 5 for snow and combined snow and ice thickness, respectively. The temporal evolution after the floe visit was recorded by automatic weather station and ice mass balance buoys. Using a physics-based, multi-layer snow/sea ice model in a one-dimensional and distributed mode to simulate the thermodynamic processes, we show that the distributed simulations, modeling flooding across the entire heterogeneous floe, produced vastly different amounts of flooding than one-dimensional single point simulations. Three times the flooding is produced in the one-dimensional simulation for the buoy location than distributed (floe-averaged) simulations. The latter is in close agreement with buoy observations. The results suggest that using point observations or one-dimensional simulations to extrapolate processes on the floe-scale can overestimate the amount of flooding and snow-ice formation.

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Inter-annual variation in lake ice composition in the European Arctic: observations based on high-resolution thermistor strings

Earth System Science Data ◽

10.5194/essd-13-3967-2021 ◽

2021 ◽

Vol 13 (8) ◽

pp. 3967-3978

Author(s):

Bin Cheng ◽

Yubing Cheng ◽

Timo Vihma ◽

Anna Kontu ◽

Fei Zheng ◽

...

Keyword(s):

Mass Balance ◽

Air Temperature ◽

Snow Depth ◽

Ice Thickness ◽

Lake Ice ◽

Data Set ◽

Finnish Lapland ◽

Increasing Trend ◽

Ice Mass Balance ◽

Snow And Ice

Abstract. Climate change and global warming strongly impact the cryosphere. The rise of air temperature and change of precipitation patterns lead to dramatic responses of snow and ice heat and mass balance. Sustainable field observations on lake air–snow–ice–water temperature regime have been carried out in Lake Orajärvi in the vicinity of the Finnish Space Centre, a Flagship Supersite in Sodankylä in Finnish Lapland since 2009. A thermistor-string-based snow and ice mass balance buoy called “Snow and ice mass balance apparatus (SIMBA)” was deployed in the lake at the beginning of each ice season. In this paper, we describe snow and ice temperature regimes, snow depth, ice thickness, and ice compositions retrieved from SIMBA observations as well as meteorological variables based on high-quality observations at the Finnish Space Centre. Ice thickness in Lake Orajärvi showed an increasing trend. During the decade of data collection (1) the November–May mean air temperature had an increasing trend of 0.16 ∘C per year, and the interannual variations were highly correlated (r = 0.93) with the total seasonal accumulated precipitation; (2) the maximum granular ice thickness ranged from 15 % to 80 % of the maximum total ice thickness; and (3) the snow depth on lake ice was not correlated (r = 0.21) with the total precipitation. The data set can be applied to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and to improve the parameterization of snow to ice transformation in snow and ice models. The data are archived at https://doi.org/10.5281/zenodo.4559368 (Cheng et al., 2021).

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Implications of surface flooding on airborne estimates of snow depth on sea ice

The Cryosphere ◽

10.5194/tc-15-2819-2021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 2819-2833

Author(s):

Anja Rösel ◽

Sinead Louise Farrell ◽

Vishnu Nandan ◽

Jaqueline Richter-Menge ◽

Gunnar Spreen ◽

...

Keyword(s):

Sea Ice ◽

Validation Studies ◽

Snow Depth ◽

The Arctic ◽

Airborne Radar ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Thickness Measurements ◽

Snow And Ice

Abstract. Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

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From points to Poles: extrapolating point measurements of sea-ice mass balance

Annals of Glaciology ◽

10.3189/172756406781811204 ◽

2006 ◽

Vol 44 ◽

pp. 188-192 ◽

Cited By ~ 17

Author(s):

Don Perovich ◽

Jacqueline A. Richter-Menge

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Large Scale ◽

Heat Budget ◽

Ice Cover ◽

The Arctic ◽

Bottom Surface ◽

Ice Thickness ◽

Ice Mass Balance ◽

Remote Sensing Techniques

AbstractThe amount of ice growth and ablation are key measures of the thermodynamic state of the ice cover. While ice extent and even ice thickness can be determined using remote-sensing techniques, this is not the case for the mass balance. Mass-balance measurements require an ability to attribute the change, establishing whether a change in the thickness of the ice cover occurs at the top or bottom surface and whether it is a result of growth or ablation. We have developed and implemented a tool that can be used to measure thermodynamic changes in sea-ice mass balance at individual locations: the ice mass-balance buoy (IMB). The primary limitation of the IMB is that it provides a point measurement of the ice mass balance, defined by a particular combination of snow and ice conditions. Determining if, and how, such point measurements can be extrapolated is critical to understanding the large-scale mass balance of the sea-ice cover. We explore the potential for extrapolation using mass-balance observations from the Surface Heat Budget of the Arctic (SHEBA) field experiment. During SHEBA, mass-balance measurements were made at over 100 sites covering a 100 km2 area. Results indicate that individual point measurements can provide reasonable estimates for undeformed and unponded multi-year ice, which represented more than two-thirds of the ice cover at SHEBA and is the dominant ice type in the perennial pack. A key is carefully selecting a representative location for the instrument package. The contribution of these point measurements can be amplified by integrating them with other tools designed to measure ice thickness and assimilating these combined data into sea-ice models.

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