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

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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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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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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Autonomous observations of sea ice mass balance during MOSAiC

10.5194/egusphere-egu21-5748 ◽

2021 ◽

Author(s):

Don Perovich ◽

Ian Raphael ◽

Ryleigh Moore ◽

David Clemens-Sewall

Keyword(s):

Mass Balance ◽

Snow Depth ◽

Heat Fluxes ◽

Growth Surface ◽

Ice Thickness ◽

Distributed Network ◽

Growth Season ◽

Air Temperatures ◽

Snow Drift ◽

Ice Mass Balance

Four seasonal ice mass balance buoys were deployed as part of the MOSAiC distributed network. These instruments measured vertical profiles of snow and ice temperature, as well as snow depth and ice thickness every six hours. Ice growth, surface melt, and bottom melt, as well as temporally averaged estimates of ocean heat fluxes, were calculated from these measurements. The buoys were installed in October 2019, with durations ranging from February 2020 to July 2020. Three of the buoys were destroyed in ridging events in February, March, and June 2020. The fourth buoy lasted until floe breakup in July 2020. The sites were separated by tens of kilometers, but had very similar air temperatures. While air temperatures were similar, snow &#8211; ice interface temperatures at different buoys varied by as much as 15 C due to differences in snow depth and ice thickness. Initial ice thicknesses ranged from 0.30 to 1.36 meters. During the growth season snow depths typically were around 0.1 to 0.2 meters, except for one case where the buoy was in a snow drift and the snow depth exceeded 0.5 meter. Peak growth rates of about 0.8 cm per day occurred in January. In mid-January there was a rapid increase in ice thickness associated with an aggregation of platelet ice. This aggregation only lasted for two weeks. In mid-April, air temperatures increased to nearly 0 C, almost ending the growth season.

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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 and ice thickness derived from SIMBA ice mass balance buoy data using an automated algorithm

International Journal of Digital Earth ◽

10.1080/17538947.2018.1545877 ◽

2018 ◽

Vol 12 (8) ◽

pp. 962-979 ◽

Cited By ~ 4

Author(s):

Zeliang Liao ◽

Bin Cheng ◽

JieChen Zhao ◽

Timo Vihma ◽

Keith Jackson ◽

...

Keyword(s):

Mass Balance ◽

Snow Depth ◽

Ice Thickness ◽

Automated Algorithm ◽

Ice Mass Balance ◽

Buoy Data

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

Annals of Glaciology ◽

10.3189/2013aog62a135 ◽

2013 ◽

Vol 54 (62) ◽

pp. 253-260 ◽

Cited By ~ 12

Author(s):

Caixin Wang ◽

Liqiong Shi ◽

Sebastian Gerland ◽

Mats A. Granskog ◽

Angelika H.H. Renner ◽

...

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Ice Core ◽

In Situ Measurements ◽

Ice Thickness ◽

Core Analysis ◽

Svalbard Archipelago ◽

Ice Mass Balance ◽

Snow And Ice

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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Modeling interdecadal variations of lake-ice thickness and sensitivity to climatic change in northernmost Alaska

Annals of Glaciology ◽

10.3189/172756400781819905 ◽

2000 ◽

Vol 31 ◽

pp. 339-347 ◽

Cited By ~ 26

Author(s):

T. Zhang ◽

M. O. Jeffries

Keyword(s):

Air Temperature ◽

Snow Depth ◽

Field Measurements ◽

Ice Thickness ◽

Transfer Model ◽

Lake Ice ◽

Ice Growth ◽

The North ◽

Thickness Variability ◽

Methane Efflux

AbstractA physically based finite-element heat-transfer model with phase change is used to simulate ice growth and thickness variability on shallow, thaw lakes on the North Slope of Alaska during the period 1947–97. The basic inputs to the model are air temperature and snow depth as recorded at the U.S. National Weather Service station, Barrow, Alaska. The simulated long-term mean maximum ice thickness was 1.91 ±0.21 m with a range from 1.33 m (1962) to 2.47 m (1976). Variations in the seasonal snow cover played a much greater role than air temperatures in controlling ice-thickness variability during the 50 year simulation period. The sensitivity of lake-ice growth to extremes of snow depth, air temperature and snow bulk thermal conductivity is investigated. This study shows that lake-ice thickness has varied significantly from year to year in northern Alaska. Continued variability combined with potential climate change could affect the area of ice that freezes completely to the bottom of lakes each winter, resulting in changes in water storage and availability, permafrost thermal regime and talik dynamics beneath lakes, and methane efflux and energy fluxes to the atmosphere. It is concluded that quantification and a full understanding of these potential effects will require systematic and continuous field measurements that will provide better forcing and validation fields for improved models.

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Inter-annual variations and large-scale atmospheric forcing on ice thickness and composition during the last decade in an Arctic lake

10.5194/egusphere-egu21-14840 ◽

2021 ◽

Author(s):

Bin Cheng ◽

Yubing Cheng ◽

Timo Vihma ◽

Fei Zheng

Keyword(s):

Mass Balance ◽

Large Scale ◽

Warming Trend ◽

Ice Thickness ◽

Lake Ice ◽

Arctic Lake ◽

Annual Variations ◽

The Pacific ◽

Average Maximum ◽

Ice Mass Balance

A thermistor-string-based Snow and Ice Mass Balance Apparatus (SIMBA) was deployed in an Arctic lake Oraj&#228;rvi in northern Finland (67.36&#176;N, 26.83&#176;E) during winter seasons 2011/2012 - 2019/2020. The snow depth and ice thickness (total and separately for congelation ice and granular ice) were retrieved from SIMBA temperature measurements. The average maximum ice thickness was 72 cm with a standard deviation of 10 cm. The interannual variability of lake ice composition was large. In the past 3 ice seasons, the granular ice dominated the total ice thickness. For example, granular ice accounted 80% of the total ice thickness in May 2020. A high-resolution thermodynamic snow/ice model was applied to simulate ice mass balance, with special attention to the lake ice composition. Local weather station data and ECMWF reanalysis products were used as model forcing.&#160;The increase of granular ice formation is a result of more snow precipitation during the ice season, increased variability of seasonal air temperature, and a warming trend. The observed snow thickness on land showed a high correlation with snow-ice thickness on top of lake ice. The relationships between the ratio of snow-ice to total ice thickness and the large-scale atmospheric circulation indexes were investigated. Precipitation and, consequently, snow ice thickness on Lake Oraj&#228;rvi correlated with the phase of the Pacific Decadal Oscillation, which is in line with previous results for precipitation and ice conditions in northern Finland, but an eventual causal teleconnection still requires further studies.&#160;

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Model simulation of the effects of climate variability and change on lake ice in central Alaska, USA

Annals of Glaciology ◽

10.3189/172756405781813663 ◽

2005 ◽

Vol 40 ◽

pp. 113-118 ◽

Cited By ~ 18

Author(s):

Kim Morris ◽

Martin Jeffries ◽

Claude Duguay

Keyword(s):

Climate Variability ◽

Air Temperature ◽

Snow Depth ◽

Model Simulation ◽

Ice Cover ◽

Ice Formation ◽

Future Climate Change ◽

Ice Thickness ◽

Climate Variability And Change ◽

Lake Ice

AbstractThe Canadian Lake Ice Model (CLIMo) is used to simulate the effects of climate variability and change on lake ice phenology (freeze-up (FU), break-up (BU), ice-cover duration) and total thickness and composition (snow ice, congelation ice) in central Alaska, USA. The model results suggest that, due to the Pacific Decadal Oscillation shift that occurred in 1976, the ice-cover duration is shorter (4 days) due to earlier BU in spring, and thinner (0.05 m) due to a reduction in the formation of snow ice. Sensitivity tests indicate that air-temperature changes cause the duration change, and snow-depth changes cause the total ice-thickness and composition change. The role of annual and monthly air-temperature and snow-depth changes is examined further in a series of experiments designed to elucidate the possible effects of future climate change. Air temperature is varied in 1˚C increments between –4 and +4˚C, and snow depth is varied in 25% increments between –100% and +100% of the long-term averages for 1952–75 and 1977–2000. The FU and BU dates (ice duration) are most affected by annual air-temperature change. Total ice thickness and composition are most affected by annual and monthly snow-depth change. A key finding is that snow-depth increases cause total ice thickness to increase as a consequence of increased snow-ice formation on top of the ice cover at the expense of congelation-ice formation at the bottom of the ice cover.

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Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements

Sensors ◽

10.3390/s20143909 ◽

2020 ◽

Vol 20 (14) ◽

pp. 3909

Author(s):

Patrick Pomerleau ◽

Alain Royer ◽

Alexandre Langlois ◽

Patrick Cliche ◽

Bruno Courtemanche ◽

...

Keyword(s):

Snow Depth ◽

Continuous Wave ◽

Snow Water Equivalent ◽

Low Cost ◽

Ice Thickness ◽

Lake Ice ◽

Wide Range ◽

Thickness Measurements ◽

24 Ghz

Monitoring the evolution of snow on the ground and lake ice—two of the most important components of the changing northern environment—is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

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