Canadian Lake Ice Model Research Articles

Forward modelling of synthetic aperture radar backscatter from lake ice over Canadian Subarctic Lakes

Lake ice provides important social and economic services to local communities, in addition to being a sensitive indicator of climate change. The reduction of ground observations of freshwater ice has led to an increased reliance on the use of satellite remote sensing data. There is currently interest in the retrieval of lake ice properties (e.g., ice thickness, bubble radius, roughness) using synthetic aperture radar (SAR). Roughness at the ice-water interface is particularly important as it has been identified as the dominant mechanism for increasing SAR backscatter throughout the ice season and must be considered in numerical radiative transfer models. Therefore, this study determines optimal ice-water interface roughness height for two subarctic lakes in northern Canada and models backscatter throughout two ice seasons using the snow microwave radiative transfer (SMRT) model. The two lakes for this study are Noell Lake and Malcolm Ramsay Lake. Field observations of ice thickness, snow depth, snow density, and the Canadian Lake Ice Model (CLIMo) are used to parameterize SMRT. Modelled L, C, and X-band backscatter at different incidence angles is assessed using SAR imagery from multiple satellite missions. Root mean square errors ranged from 0.38 to 1.45 dB for Noell Lake and 0.70 to 2.33 dB for Malcolm Ramsay Lake. Discrepancies between modelled and observed backscatter were found to be connected to the representation of roughness at different interfaces within the ice column and changes that occurred during freeze-melt events. These results provide insight into how changes in ice properties impact backscatter throughout the ice season. SMRT is valuable for modelling backscatter from lake ice during the cold season and could be used to develop retrieval algorithms for estimating ice-water interface roughness. This would allow for the development of other inversion models for retrieval of surface ice conditions and ice thickness.

The Influence of Snow and Ice Albedo towards Improved Lake Ice Simulations

Lake ice models are a vital tool for studying the response of ice-covered lakes to changing climates throughout the world. 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 ice composition between northern and temperate region lake 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 High Arctic lake 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 good ice-on timing, but an under-representation of ice thickness, and earlier complete ice-off timing (~3 to 5 weeks). Field measurements were used to adjust the albedo values used in CLIMo, which resulted in improvements to both simulated ice thickness (~3 cm MAE compared to manual measurements), and ice-off timing, within 0 to 7 days (2 days MAE) of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations, which further our knowledge of the response of temperate and High Arctic lake ice regimes to climate conditions.

Improvement of Lake Ice Thickness Retrieval From MODIS Satellite Data Using a Thermodynamic Model

Observations of ice thickness are limited in high latitude regions, at a time when they are increasingly being requested by operational ice centers. This study aims to improve the retrieval of lake ice thickness using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA’s Aqua (P.M.) and Terra (A.M.) satellites. The accuracy of ice thickness retrievals based on MODIS lake ice surface temperature (LIST) is investigated using a commonly used heat balance equation and the retrieved ice thicknesses are compared to in situ measurements from the Canadian Ice Service. The accuracy of ice thickness estimates is improved when using snow depth from the 1-D thermodynamic lake ice model Canadian Lake Ice Model (CLIMo) rather than an empirical relationship between snow depth and ice thickness utilized in the recent investigations. Taking into account all data over the study period (2002–2014), the mean bias error and the root-mean-square error are reduced from −0.42 to 0.07 m and 0.58 to 0.17 m, respectively, with the novel approach proposed herein. However, this approach is limited to ice thickness estimations of less than ca. 1.7 m.

Freshwater lake ice thickness derived using surface-based X- and Ku-band FMCW scatterometers

Time series of ground-based X- and Ku-band frequency-modulated continuous-wave (FMCW) radar data are used to derive ice thickness for bubbled freshwater lake ice with heterogeneous snow cover under the assumption of interactions in range occurring at the ice–snow and ice–water interface. Once adjusted for the refractive index of ice and slant range, the distance between peak returns agrees with in-situ ice thickness observations. Ice thicknesses are derived from the distance of peak returns in range acquired in the off-nadir incidence angle range 21°–60°. Derived ice thicknesses are compared to in-situ measurements, an upward-looking acoustic sonar (Shallow Water Ice-Profiler (SWIP)), and a one-dimensional thermodynamic lake ice model (Canadian Lake Ice Model (CLIMo)). Median ice thicknesses derived with University of Waterloo X- and Ku-band scatterometers (UW-Scat) agreed well with in-situ measurements (R2=0.953 and 0.964), SWIP (R2=0.842 and 0.893), and Canadian Lake Ice Model (CLIMo) simulations using 25% of terrestrial snowpack scenario, respectively. UW-Scat derived ice thicknesses produced root mean square error (RMSE) values of 0.053 and 0.088m for X- and Ku-band, respectively, relative to in-situ ice thickness measurements. This study is the first FMCW X- and Ku-band off-nadir approach to observe interactions at the snow–ice and ice–water interface to derive ice thickness.

Ice Freeze-up and Break-up Detection of Shallow Lakes in Northern Alaska with Spaceborne SAR

Shallow lakes, with depths less than ca. 3.5–4 m, are a ubiquitous feature of the Arctic Alaskan Coastal Plain, covering up to 40% of the land surface. With such an extended areal coverage, lakes and their ice regimes represent an important component of the cryosphere. The duration of the ice season has major implications for the regional and local climate, as well as for the physical and biogeochemical processes of the lakes. With day and night observations in all weather conditions, synthetic aperture radar (SAR) sensors provide year-round acquisitions. Monitoring the evolution of radar backscatter (σ°) is useful for detecting the timing of the beginning and end of the ice season. Analysis of the temporal evolution of C-band σ° from Advanced Synthetic Aperture Radar (ASAR) Wide Swath and RADARSAT-2 ScanSAR, with a combined frequency of acquisitions from two to five days, was employed to evaluate the potential of SAR to detect the timing of key lake-ice events. SAR observations from 2005 to 2011 were compared to outputs of the Canadian Lake Ice Model (CLIMo). Model simulations fall within similar ranges with those of the SAR observations, with a mean difference between SAR observations and model simulations of only one day for water-clear-of-ice (WCI) from 2006 to 2010. For freeze onset (FO), larger mean differences were observed. SAR analysis shows that the mean FO date for these shallow coastal lakes is 30 September and the mean WCI date is 5 July. Results reveal that greater variability existed in the mean FO date (up to 26 days) than in that of melt onset (MO) (up to 12 days) and in that of WCI (6 days). Additionally, this study also identifies limitations and provides recommendations for future work using C-band SAR for monitoring the lake- ice phenology of shallow Arctic lakes.

Modelled and satellite‐derived surface albedo of lake ice – part II: evaluation of MODIS albedo products

AbstractAlbedo products (MOD10A1, MYD10A1, and MCD43A3) from the Moderate Resolution Imaging Spectrometer (MODIS) have the potential to be integrated directly into lake ice models such as the Canadian lake ice model (CLIMo) to improve the simulation of freshwater break‐up (ice‐off). The high albedo of snow and lake ice has been shown to affect the timing of break‐up. Therefore, the surface energy balance parameterization of CLIMo requires accurate estimates of albedo when modelling phenology. MOD10A1, MYD10A1, and MCD43A3 were evaluated against in situ snow and ice albedo observations taken over a partially snow‐covered freshwater lake (Malcolm Ramsay Lake) near Churchill, Manitoba, during the ice growth period (15 February 2012 to 25 April 2012). The MODIS albedo products were then compared with the CLIMo's albedo parameterization during the ice break‐up period.The MODIS albedo products MOD10A1, MYD10A1, and MCD43A3 retrieved snow and ice albedo with root mean square error values of 0.07, 0.08, and 0.06, respectively, compared with spatially averaged in situ albedo measurements during ice growth. MODIS albedo products compared with CLIMo's melting ice parameterization during the melt season indicate that CLIMo's albedo estimates have a mean difference of at least 0.14 compared with the MODIS retrievals during melt. The quality of the albedo retrievals over lake ice from MODIS and the need for more accurate albedo simulations during the melt season suggest that the assimilation of MODIS albedo products into CLIMo could be beneficial for the determination of break‐up (ice‐off) dates. Copyright © 2014 John Wiley & Sons, Ltd.

Modelled and satellite‐derived surface albedo of lake ice – Part I: evaluation of the albedo parameterization scheme of the Canadian Lake Ice Model

AbstractAccurate simulations of freshwater lake ice are integral for the study of climatic variability in northern environments. Surface albedo, a component often parameterized in lake ice models, has been shown to affect the timing of ice break‐up during the melt season. In situ snow and ice albedo measurements were taken over a partially snow‐covered freshwater lake near Churchill, Manitoba for the evaluation of the albedo parameterization of the Canadian Lake Ice Model (CLIMo) and the Moderate Resolution Imaging Spectrometer (MODIS) albedo products (MOD10A1/MYD10A1 and MCD43A3). The albedo simulations using CLIMo were performed with and without snow integrated into the model and evaluated against in situ albedo measurements recorded over clear ice, snow ice and snow‐covered ice. The simulated snow albedo from CLIMo for the entire ice growth season evaluated against snow albedo observations had a root mean square error of 0.07, a mean absolute error of 0.06, and a mean bias error (MBE) of −0.01. With snow removed from CLIMo, the albedo parameterization overestimated albedo values measured in the field over snow‐free clear ice and snow ice with MBE values of 0.13 and 0.10, respectively. These findings suggest that CLIMo's bare ice albedo parameterization needs to be revised to account for albedo differences between ice types.The evaluation of the MODIS albedo products with in situ snow and ice albedo observations and the comparison of these satellite products with CLIMo's albedo parameterization during the melt period, when in situ radiation measurement stations needed to be removed from the lake‐ice surface, are addressed in paper Part II (this issue). Copyright © 2014 John Wiley & Sons, Ltd.

Response of ice cover on shallow lakes of the North Slope of Alaska to contemporary climate conditions (1950–2011): radar remote-sensing and numerical modeling data analysis

Abstract. Air temperature and winter precipitation changes over the last five decades have impacted the timing, duration, and thickness of the ice cover on Arctic lakes as shown by recent studies. In the case of shallow tundra lakes, many of which are less than 3 m deep, warmer climate conditions could result in thinner ice covers and consequently, in a smaller fraction of lakes freezing to their bed in winter. However, these changes have not yet been comprehensively documented. The analysis of a 20 yr time series of European remote sensing satellite ERS-1/2 synthetic aperture radar (SAR) data and a numerical lake ice model were employed to determine the response of ice cover (thickness, freezing to the bed, and phenology) on shallow lakes of the North Slope of Alaska (NSA) to climate conditions over the last six decades. Given the large area covered by these lakes, changes in the regional climate and weather are related to regime shifts in the ice cover of the lakes. Analysis of available SAR data from 1991 to 2011, from a sub-region of the NSA near Barrow, shows a reduction in the fraction of lakes that freeze to the bed in late winter. This finding is in good agreement with the decrease in ice thickness simulated with the Canadian Lake Ice Model (CLIMo), a lower fraction of lakes frozen to the bed corresponding to a thinner ice cover. Observed changes of the ice cover show a trend toward increasing floating ice fractions from 1991 to 2011, with the greatest change occurring in April, when the grounded ice fraction declined by 22% (α = 0.01). Model results indicate a trend toward thinner ice covers by 18–22 cm (no-snow and 53% snow depth scenarios, α = 0.01) during the 1991–2011 period and by 21–38 cm (α = 0.001) from 1950 to 2011. The longer trend analysis (1950–2011) also shows a decrease in the ice cover duration by ~24 days consequent to later freeze-up dates by 5.9 days (α = 0.1) and earlier break-up dates by 17.7–18.6 days (α = 0.001).

Simulation of surface temperature and ice cover of large northern lakes with 1-D models: a comparison with MODIS satellite data and in situ measurements

ABSTRACTLake surface temperature (LST) and ice phenology were simulated for various points differing in depth on Great Slave Lake and Great Bear Lake, two large lakes located in the Mackenzie River Basin in Canada's Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002–2010 period, forced with data from three weather stations (Yellowknife, Hay River and Deline). LST model results were compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. Simulated ice thickness and freeze-up/break-up dates were also compared to in situ observations. Both models showed a good agreement with daily average MODIS LSTs on an annual basis (0.935 ≤ relative index of agreement ≤ 0.984 and 0.94 ≤ mean bias error ≤ 4.83). The absence of consideration of snow on lake ice in FLake was found to have a large impact on estimated ice thicknesses (25 cm thicker on average by the end of winter compared to in situ measurements; 9 cm thicker for CLIMo) and break-up dates (6 d earlier in comparison with in situ measurements; 3 d later for CLIMo). The overall agreement between the two models and MODIS LST products during both the open water and ice seasons was good. Remotely sensed data are a promising data source for assimilation into numerical weather prediction models, as they provide the spatial coverage that is not captured by in situ data.

The fate of lake ice in the North American Arctic

Abstract. Lakes comprise a large portion of the surface cover in northern North America, forming an important part of the cryosphere. The timing of lake ice phenological events (e.g. break-up/freeze-up) is a useful indicator of climate variability and change, which is of particular relevance in environmentally sensitive areas such as the North American Arctic. Further alterations to the present day ice regime could result in major ecosystem changes, such as species shifts and the disappearance of perennial ice cover. The Canadian Lake Ice Model (CLIMo) was used to simulate lake ice phenology across the North American Arctic from 1961–2100 using two climate scenarios produced by the Canadian Regional Climate Model (CRCM). Results from the 1961–1990 time period were validated using 15 locations across the Canadian Arctic, with both in situ ice cover observations from the Canadian Ice Database as well as additional ice cover simulations using nearby weather station data. Projected changes to the ice cover using the 30-year mean data between 1961–1990 and 2041–2070 suggest a shift in break-up and freeze-up dates for most areas ranging from 10–25 days earlier (break-up) and 0–15 days later (freeze-up). The resulting ice cover durations show mainly a 10–25 day reduction for the shallower lakes (3 and 10 m) and 10–30 day reduction for the deeper lakes (30 m). More extreme reductions of up to 60 days (excluding the loss of perennial ice cover) were shown in the coastal regions compared to the interior continental areas. The mean maximum ice thickness was shown to decrease by 10–60 cm with no snow cover and 5–50 cm with snow cover on the ice. Snow ice was also shown to increase through most of the study area with the exception of the Alaskan coastal areas.

A comparison of simulated and measured lake ice thickness using a Shallow Water Ice Profiler

AbstractIn northern regions where observational data is sparse, lake ice models are ideal tools as they can provide valuable information on ice cover regimes. The Canadian Lake Ice Model was used to simulate ice cover for a lake near Churchill, Manitoba, Canada throughout the 2008/2009 and 2009/2010 ice covered seasons. To validate and improve the model results, in situ measurements of the ice cover through both seasons were obtained using an upward‐looking sonar device Shallow Water Ice Profiler (SWIP) installed on the bottom of the lake. The SWIP identified the ice‐on/off dates as well as collected ice thickness measurements. In addition, a digital camera was installed on shore to capture images of the ice cover through the seasons and field measurements were obtained of snow depth on the ice, and both the thickness of snow ice (if present) and total ice cover. Altering the amounts of snow cover on the ice surface to represent potential snow redistribution affected simulated freeze‐up dates by a maximum of 22 days and break‐up dates by a maximum of 12 days, highlighting the importance of accurately representing the snowpack for lake ice modelling. The late season ice thickness tended to be under estimated by the simulations with break‐up occurring too early, however, the evolution of the ice cover was simulated to fall between the range of the full snow and no snow scenario, with the thickness being dependant on the amount of snow cover on the ice surface. Copyright © 2011 John Wiley & Sons, Ltd.

Northwest Territories and Nunavut Snow Characteristics from a Subarctic Traverse: Implications for Passive Microwave Remote Sensing

Abstract During April 2007, a coordinated series of snow measurements was made across the Northwest Territories and Nunavut, Canada, during a snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. The purpose of the measurements was to document the general nature of the snowpack across this region for the evaluation of satellite- and model-derived estimates of snow water equivalent (SWE). Although detailed, local snow measurements have been made as part of ongoing studies at tundra field sites (e.g., Daring Lake and Trail Valley Creek in the Northwest Territories; Toolik Lake and the Kuparak River basin in Alaska), systematic measurements at the regional scale have not been previously collected across this region of northern Canada. The snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (<40 cm deep), usually with fewer than six layers. Where snow was deposited on lake and river ice, it was shallower, denser, and more metamorphosed than where it was deposited on tundra. Although highly variable locally, no longitudinal gradients in snow distribution, magnitude, or structure were detected. This regional homogeneity allowed us to identify that the observed spatial variability in passive microwave brightness temperatures was related to subgrid fractional lake cover. Correlation analysis between lake fraction and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature showed frequency dependent, seasonally evolving relationships consistent with lake ice drivers. Simulations of lake ice thickness and snow depth on lake ice produced from the Canadian Lake Ice Model (CLIMo) indicated that at low frequencies (6.9, 10.7 GHz), correlations with lake fraction were consistent through the winter season, whereas at higher frequencies (18.7, 36.5 GHz), the strength and direction of the correlations evolved consistently with the penetration depth as the influence of the subice water was replaced by emissions from the ice and snowpack. A regional rain-on-snow event created a surface ice lens that was detectable using the AMSR-E 36.5-GHz polarization gradient due to a strong response at the horizontal polarization. The appropriate polarization for remote sensing of the tundra snowpack depends on the application: horizontal measurements are suitable for ice lens detection; vertically polarized measurements are appropriate for deriving SWE estimates.

Modeling ice growth on Canadian lakes using artificial neural networks

This paper presents artificial neural network (ANN) models designed to predict ice in Canadian lakes and reservoirs during the early winter ice thickness growth period. The models fit ice thickness measurements at one or more monitored lakes and predict ice thickness during the growth period either at the same locations for dates without measurements (local ANN models) or at any site in the region (regional ANN model), provided that the required meteorological input variables are available. The input variables were selected after preliminary assessments and were adapted from time series of daily mean air temperature, rainfall, cloud cover, solar radiation, and average snow depth. The results of the ANN models compared well with those of the deterministic physics‐driven Canadian Lake Ice Model (CLIMO) in terms of root‐mean‐square error and in terms of relative root‐mean‐square errors. The ANN models predictions were also marginally more precise than a revised version of Stefan's law (RSL), presented herein. They reproduced some intrawinter and interannual growth rate fluctuations that were not accounted for by RSL. The performance of the models results in good part from a careful choice of input variables, inspired from the work on deterministic models such as CLIMO. ANN models of ice thickness show good potential for the use in contexts where ad hoc adjustments are desirable because of the limited availability of measurements and where poor data nature, availability, and quality precludes using deterministic physics‐driven models.

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

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.

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.

Simulation of ice phenology on Great Slave Lake, Northwest Territories, Canada

AbstractA one‐dimensional thermodynamic lake ice model (Canadian Lake Ice Model or CLIMo) is used to simulate ice phenology on Great Slave Lake (GSL) in the Mackenzie River basin, Northwest Territories, Canada. Model simulations are validated against freeze‐up and break‐up dates, as well as ice thickness and on‐ice snow depth measurements made in situ at three sites on GSL (Back Bay near Yellowknife, 1960–91; Hay River, 1965–91; Charlton Bay near Fort Reliance, 1977–90). Freeze‐up and break‐up dates from the lake ice model are also compared with those derived from SSM/I 85 GHz passive microwave imagery over the entire lake surface (1988–99).Results show a very good agreement between observed and simulated ice thickness and freeze‐up/break‐up dates over the 30–40 years of observations, particularly for the Back Bay and Hay River sites. CLIMo simulates the ice thickness and annual freeze‐up/break‐dates with a mean error of 7 cm and 4 days respectively. However, some limitations have been identified regarding the rather simplistic approach used to characterize the temporal evolution of snow cover on ice. Future model improvements will therefore focus on this particular aspect, through linkage or coupling to a snow model. Copyright © 2002 John Wiley & Sons, Ltd.