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

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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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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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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The ice phenomena dynamics of small anthropogenic water bodies in the Silesian Upland, Poland

Environmental & Socio-economic Studies ◽

10.1515/environ-2017-0022 ◽

2017 ◽

Vol 5 (4) ◽

pp. 74-81 ◽

Cited By ~ 1

Author(s):

Maksymilian Solarski

Keyword(s):

Air Temperature ◽

Water Body ◽

Correlation Coefficients ◽

Ice Cover ◽

Water Bodies ◽

Ice Formation ◽

Ice Thickness ◽

Small Water ◽

Cover Thickness ◽

Ice Phenomena

AbstractThe aim of this study was to determine the dynamics of the process of a course of ice creation phenomena in two small water bodies located in the Silesian Upland. The studies and observations of ice formation on the water bodies were conducted during the period 10th November 2011 to 23rd March 2012. The following parameters were determined each day: degree of ice coverage on each water body, thickness and ice structure and thickness of snow cover on each water body. From the studies it results that a course of the ice formation of both water bodies was almost identical. The same maximum ice thickness was recorded in both cases. It was 36 cm in that season, with slight differences in average thickness. The course of particular phases of ice formation in different water regions was also very similar. The number of days with the ice phenomena and the number of days from the beginning to the end of the ice phenomena were identical in both cases, being 96 and 131 days, respectively. The slight differences over several days were recorded in the case of: number of days with shore ice (lb), number of days with partial ice cover (lcz), number of days with an incomplete ice cover (lnp), number of breaks in the ice cover (B). Additionally, with daily measurements of ice cover thickness the relationships between the course of the average daily air temperature from the meteorological station of Faculty of Earth Sciences of University of Silesia and the daily changes in the ice thickness in the water regions in question were determined by using Spearman’s correlation coefficient. In both cases the relationships were strong and they were r= −0,84(p<0,001) for the Amendy water body and r= −0,87 (p<0,001) for the Żabie Doły S water body. The maximum and average ice thickness, duration of the ice phenomena and ice cover and the obtained correlation coefficients between the air temperature and the changes in the ice thickness show that the water bodies in question are characterized by a quasi-natural ice regime.

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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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Trends and abrupt changes in 104 years of ice cover and water temperature in a dimictic lake in response to air temperature, wind speed, and water clarity drivers

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1681-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1681-1702 ◽

Cited By ~ 39

Author(s):

Madeline R. Magee ◽

Chin H. Wu ◽

Dale M. Robertson ◽

Richard C. Lathrop ◽

David P. Hamilton

Keyword(s):

Wind Speed ◽

Water Temperature ◽

Air Temperature ◽

Water Clarity ◽

Ice Cover ◽

Ice Thickness ◽

Lake Mendota ◽

The Third ◽

Abrupt Changes ◽

Hypolimnetic Heating

Abstract. The one-dimensional hydrodynamic ice model, DYRESM-WQ-I, was modified to simulate ice cover and thermal structure of dimictic Lake Mendota, Wisconsin, USA, over a continuous 104-year period (1911–2014). The model results were then used to examine the drivers of changes in ice cover and water temperature, focusing on the responses to shifts in air temperature, wind speed, and water clarity at multiyear timescales. Observations of the drivers include a change in the trend of warming air temperatures from 0.081 °C per decade before 1981 to 0.334 °C per decade thereafter, as well as a shift in mean wind speed from 4.44 m s−1 before 1994 to 3.74 m s−1 thereafter. Observations show that Lake Mendota has experienced significant changes in ice cover: later ice-on date(9.0 days later per century), earlier ice-off date (12.3 days per century), decreasing ice cover duration (21.3 days per century), while model simulations indicate a change in maximum ice thickness (12.7 cm decrease per century). Model simulations also show changes in the lake thermal regime of earlier stratification onset (12.3 days per century), later fall turnover (14.6 days per century), longer stratification duration (26.8 days per century), and decreasing summer hypolimnetic temperatures (−1.4 °C per century). Correlation analysis of lake variables and driving variables revealed ice cover variables, stratification onset, epilimnetic temperature, and hypolimnetic temperature were most closely correlated with air temperature, whereas freeze-over water temperature, hypolimnetic heating, and fall turnover date were more closely correlated with wind speed. Each lake variable (i.e., ice-on and ice-off dates, ice cover duration, maximum ice thickness, freeze-over water temperature, stratification onset, fall turnover date, stratification duration, epilimnion temperature, hypolimnion temperature, and hypolimnetic heating) was averaged for the three periods (1911–1980, 1981–1993, and 1994–2014) delineated by abrupt changes in air temperature and wind speed. Average summer hypolimnetic temperature and fall turnover date exhibit significant differences between the third period and the first two periods. Changes in ice cover (ice-on and ice-off dates, ice cover duration, and maximum ice thickness) exhibit an abrupt change after 1994, which was related in part to the warm El Niño winter of 1997–1998. Under-ice water temperature, freeze-over water temperature, hypolimnetic temperature, fall turnover date, and stratification duration demonstrate a significant difference in the third period (1994–2014), when air temperature was warmest and wind speeds decreased rather abruptly. The trends in ice cover and water temperature demonstrate responses to both long-term and abrupt changes in meteorological conditions that can be complemented with numerical modeling to better understand how these variables will respond in a future climate.

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

The Cryosphere ◽

10.5194/tc-6-729-2012 ◽

2012 ◽

Vol 6 (4) ◽

pp. 729-741 ◽

Cited By ~ 5

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.

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On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

The Cryosphere ◽

10.5194/tc-12-993-2018 ◽

2018 ◽

Vol 12 (3) ◽

pp. 993-1012 ◽

Cited By ~ 8

Author(s):

Lu Zhou ◽

Shiming Xu ◽

Jiping Liu ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Microwave Remote Sensing ◽

Ice Cover ◽

Ice Thickness ◽

Laser Altimetry ◽

Sea Ice Thickness ◽

Passive Remote Sensing ◽

L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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On the Retrieval of Sea Ice Thickness and Snow Depth using Concurrent Laser Altimetry and L-Band Remote Sensing Data

10.5194/tc-2017-133 ◽

2017 ◽

Author(s):

Lu Zhou ◽

Shiming Xu ◽

Jiping Liu ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Ice Cover ◽

Retrieval Algorithm ◽

Ice Thickness ◽

Laser Altimetry ◽

Sea Ice Thickness ◽

Passive Remote Sensing ◽

L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, are key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved, as compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry, as well the uncertainty associated with the radiation model are potential sources of error. The proposed retrieval algorithm (or methodology) can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat and ICESat-2.

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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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