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

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

<p>A thermistor-string-based Snow and Ice Mass Balance Apparatus (SIMBA) was deployed in an Arctic lake Orajärvi in northern Finland (67.36°N, 26.83°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.</p><p> </p><p>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 Orajärvi 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.</p><p> </p>

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

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) 

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

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).

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

2006 ◽  
Vol 44 ◽  
pp. 188-192 ◽  
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.

Autonomous observations of sea ice mass balance during MOSAiC

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

<p>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 – 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.</p>

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

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

<p>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.</p><p>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.</p><p>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 – 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 – 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 – 0.1cm and 1.5m – 2.5m, respectively.  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 – 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.  </p>

scholarly journals 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

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.

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

2018 ◽  
Vol 12 (8) ◽  
pp. 962-979 ◽  
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

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

2013 ◽  
Vol 54 (62) ◽  
pp. 253-260 ◽  
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.

scholarly journals Comparison of seasonal sea-ice thickness change in the Transpolar Drift observed by local ice mass-balance observations and floe-scale EM surveys

2011 ◽  
Vol 52 (57) ◽  
pp. 97-102 ◽  
Author(s):  
Christian Haas ◽  
Herve Le Goff ◽  
Samuel Audrain ◽  
Don Perovich ◽  
Jari Haapala
Keyword(s):  
Mass Balance ◽  
Electromagnetic Induction ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Thickness Change ◽  
Ice Mass Balance ◽  
Transpolar Drift ◽  
Thickness Measurements ◽  
Good Agreement

AbstractLocal and transect ice-thickness measurements were performed between May and November 2007 on an ice floe in the Transpolar Drift of the Arctic Ocean using an ice mass-balance buoy and electromagnetic induction (EM) sounding. Repeated EM surveys along an originally 2160m long profile including level and deformed ice showed that between June and September modal and mean thicknesses decreased by 0.6 and 0.86m respectively. the modal thickness decrease is in good agreement with the thinning of 0.6m observed by the ice mass-balance buoy at one location on unponded ice during the same period, although the local observations do not capture the different melt rates on level and rough ice. the paper discusses methodological and operational challenges in sustaining both measurements over periods of several months, and concludes that more work needs to be done to better understand their representativeness.

scholarly journals The influence of albedo parameterization for improved lake ice simulation

2020 ◽  
Author(s):  
Alexis L. Robinson ◽  
Sarah S. Ariano ◽  
Laura C. Brown
Keyword(s):  
Field Measurements ◽  
Time Lapse ◽  
Ice Cover ◽  
High Arctic ◽  
Arctic Lakes ◽  
Ice Thickness ◽  
Lake Ice ◽  
Temperate Region ◽  
Climate Conditions ◽  
Arctic Lake

Abstract. Lake ice models can be used to study the latitudinal differences of current and projected changes in ice covered lakes under a changing climate. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in composition between northern and temperate ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of the High Arctic ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show both an underestimation in ice thickness (~ 4 to 18 cm) and ice-off timing (~ 25 to 30 days). Field measurements were used to adjust the albedo parameterization used in CLIMo, which resulted in improvements to both simulated ice thickness, within 0.1 cm to 10 cm of manual measurements, and ice-off timing, within 1 to 7 days of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations. These results further our knowledge regarding of the response of temperate and High Arctic lake ice regimes to climate conditions.

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