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

2000 ◽  
Vol 31 ◽  
pp. 339-347 ◽  
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.

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

2005 ◽  
Vol 40 ◽  
pp. 113-118 ◽  
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.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

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

Sensors ◽  
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.

scholarly journals Laboratory study of initial sea-ice growth: properties of grease ice and nilas

2012 ◽  
Vol 6 (4) ◽  
pp. 729-741 ◽  
Author(s):  
A. K. Naumann ◽  
D. Notz ◽  
L. Håvik ◽  
A. Sirevaag
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Solid Fraction ◽  
Interstitial Water ◽  
Mass Conservation ◽  
Ice Formation ◽  
Ice Thickness ◽  
Ice Growth ◽  
Bulk Salinity ◽  
A Current

Abstract. We investigate initial sea-ice growth in an ice-tank study by freezing an NaCl solution of about 29 g kg−1 in three different setups: grease ice grew in experiments with waves and in experiments with a current and wind, while nilas formed in a quiescent experimental setup. In this paper we focus on the differences in bulk salinity, solid fraction and thickness between these two ice types. The bulk salinity of the grease-ice layer in our experiments remained almost constant until the ice began to consolidate. In contrast, the initial bulk-salinity evolution of the nilas is well described by a linear decrease of about 2.1 g kg−1 h−1 independent of air temperature. This rapid decrease can be qualitatively understood by considering a Rayleigh number that became maximum while the nilas was still less than 1 cm thick. Comparing three different methods to measure solid fraction in grease ice based on (a) salt conservation, (b) mass conservation and (c) energy conservation, we find that the method based on salt conservation does not give reliable results if the salinity of the interstitial water is approximated as being equal to the salinity of the underlying water. Instead the increase in salinity of the interstitial water during grease-ice formation must be taken into account. In our experiments, the solid fraction of grease ice was relatively constant with values of 0.25, whereas it increased to values as high as 0.50 as soon as the grease ice consolidated at its surface. In contrast, the solid fraction of the nilas increased continuously in the first hours of ice formation and reached an average value of 0.55 after 4.5 h. The spatially averaged ice thickness was twice as large in the first 24 h of ice formation in the setup with a current and wind compared to the other two setups, since the wind kept parts of the water surface ice free and therefore allowed for a higher heat loss from the water. The development of the ice thickness can be reproduced well with simple, one dimensional models that only require air temperature or ice surface temperature as input.

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.

scholarly journals The role of snow and ice thickness on river ice process in Songhua River basin, Northeast China

2019 ◽  
Author(s):  
Qian Yang ◽  
Kaishan Song ◽  
Xiaohua Hao ◽  
Zhidan Wen ◽  
Yue Tan ◽  
...  
Keyword(s):  
River Basin ◽  
Air Temperature ◽  
Snow Depth ◽  
Northeast China ◽  
River Ice ◽  
Ice Thickness ◽  
Songhua River ◽  
Songhua River Basin ◽  
Ice Phenology

Abstract. Songhua River basin is a sensitive area to global warming in Northeast China that could be indicated by changes in lake and river ice development. The regional role and trends of ice characteristics of this area have been scarcely investigated, which are critical for aquatic ecosystem, climate variability, and human activities. Based on the ice record of hydrological stations, we examined the spatial variations of the ice phenology and ice thickness in Songhua River basin in Northeast China from 2010 to 2015 and explored the role of ice thickness, snow during ice-on and ice-off process. All five river ice phenology including freeze-up start, freeze-up end, break-up start, break-up end and complete frozen duration showed latitudinal distribution and a changing direction from southeast to northwest, and five typically geographic zones were identified based on rotated empirical orthogonal function. Maximum ice thickness had a higher correlation with five parameters than that of average snow depth and air temperature on bank. A linear regression function was established between ice thickness and snow depth on ice and indicated ice thickness was closely associated with snow depth on ice. The air temperature had higher correlation with ice phenology and influenced the lake ice phenology significantly, and snow cover did not show significant correlation with the ice phenology. However, snow cover correlated with ice thickness significantly and positively during the periods when the freshwater is completely frozen.

scholarly journals Lake ice growth and decay in central Alaska, USA: observations and computer simulations compared

2005 ◽  
Vol 40 ◽  
pp. 195-199 ◽  
Author(s):  
Martin O. Jeffries ◽  
Kim Morris ◽  
Claude R. Duguay
Keyword(s):  
Meteorological Data ◽  
Measured Data ◽  
Ice Thickness ◽  
Lake Ice ◽  
One Dimensional ◽  
Ice Growth ◽  
Unsteady Heat Conduction ◽  
Input Variables ◽  
Thickness Measurements ◽  
Ice Model

AbstractThe Canadian Lake Ice Model (CLIMo), a one-dimensional, thermodynamic model with unsteady heat conduction and penetrating solar radiation, is used to simulate ice growth and decay on shallow ponds in and near Fairbanks, central Alaska, USA. Simulations are compared with observations of ice thickness and composition (snow ice, congelation ice), freeze-up, break-up and duration. Simulations run using US National Weather Service meteorological data as input variables do not agree well with ice-thickness measurements. The simulations improve significantly when the model is run with more representative, locally measured data for air temperature and depth of snow on the ice. The causes of some discrepancies between simulations and observations are discussed and some suggestions for model improvements are made.

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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