Lakes comprise a large portion of the surface cover in northern North America, forming an important part of the cryosphere. 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. Lake ice has been shown to both respond to, and play a role in the local/regional climate. The timing of lake ice phenological events (e.g. break-up/freeze-up) is a useful indicator of climate variability and change. Trends in ice phenology have typically been associated with variations in air temperatures while trends found in ice thickness tend to be associated more with changes in snow cover. The inclusion of lakes and lake ice in climate modelling is an area of increased attention in recent studies and the ability to accurately represent ice cover on lakes will be an important step in the improvement of global circulation models, regional climate models and numerical weather forecasting. This thesis aimed to further our understanding of lake ice and climate interactions, with an emphasis on ice cover modelling. The Canadian Lake Ice Model (CLIMo) was used throughout for lake ice simulations. To validate and improve the model results, in situ measurements of the ice cover for two seasons in Churchill, MB 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 dependent on the amount of snow cover on the ice surface. CLIMo was then 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. While the most suitable way to undertake wide scale lake ice modeling is to force the models with climate model output or reanalysis data, a variety of different lake morphometric conditions could exist within a given grid cell leading to different durations of ice cover within the grid cell. Both the daily IMS product (4 km) and the MODIS snow product (500 m) were assessed for their utility at determining lake ice phenology at the sub-grid cell level throughout the province of Quebec. Both products were useful for detecting ice-off, however, the MODIS product was advantageous for detecting ice-on, mainly due to the finer resolution and resulting spatial detail of the lake ice. The sub-grid cell variability was typically less than 2%, although it ranged as high as 10% for some grid cells. An indication of whether or not the simulated ice-on/off dates were within the sub-grid cell variability was determined and on average across the entire province, were found to be within the variability 62% of the time for ice-off and 80% of the time for ice-on. Forcing the model with the future climate scenarios from CRCM predicts ice cover durations throughout the region will decrease by up to 50 days from the current 1981-2010 means to the 2041-2070 means, and decrease from 15 to nearly 100 days shorter between the contemporary and 2071-2100 means. Overall, this work examined the climate-lake-ice interactions under both contemporary and future climate conditions, as well as provided new insight into sub-grid cell variability of lake ice.

Lakes comprise a large portion of the surface cover in northern North America, forming an important part of the cryosphere. 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. Lake ice has been shown to both respond to, and play a role in the local/regional climate. The timing of lake ice phenological events (e.g. break-up/freeze-up) is a useful indicator of climate variability and change. Trends in ice phenology have typically been associated with variations in air temperatures while trends found in ice thickness tend to be associated more with changes in snow cover. The inclusion of lakes and lake ice in climate modelling is an area of increased attention in recent studies and the ability to accurately represent ice cover on lakes will be an important step in the improvement of global circulation models, regional climate models and numerical weather forecasting. This thesis aimed to further our understanding of lake ice and climate interactions, with an emphasis on ice cover modelling. The Canadian Lake Ice Model (CLIMo) was used throughout for lake ice simulations. To validate and improve the model results, in situ measurements of the ice cover for two seasons in Churchill, MB 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 dependent on the amount of snow cover on the ice surface. CLIMo was then 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. While the most suitable way to undertake wide scale lake ice modeling is to force the models with climate model output or reanalysis data, a variety of different lake morphometric conditions could exist within a given grid cell leading to different durations of ice cover within the grid cell. Both the daily IMS product (4 km) and the MODIS snow product (500 m) were assessed for their utility at determining lake ice phenology at the sub-grid cell level throughout the province of Quebec. Both products were useful for detecting ice-off, however, the MODIS product was advantageous for detecting ice-on, mainly due to the finer resolution and resulting spatial detail of the lake ice. The sub-grid cell variability was typically less than 2%, although it ranged as high as 10% for some grid cells. An indication of whether or not the simulated ice-on/off dates were within the sub-grid cell variability was determined and on average across the entire province, were found to be within the variability 62% of the time for ice-off and 80% of the time for ice-on. Forcing the model with the future climate scenarios from CRCM predicts ice cover durations throughout the region will decrease by up to 50 days from the current 1981-2010 means to the 2041-2070 means, and decrease from 15 to nearly 100 days shorter between the contemporary and 2071-2100 means. Overall, this work examined the climate-lake-ice interactions under both contemporary and future climate conditions, as well as provided new insight into sub-grid cell variability of lake ice.

Snow and lake ice have very high albedos compared to other surfaces found in nature. Surface albedo is an important component of the surface energy budget especially when albedos are high since albedo governs how much shortwave radiation is absorbed or reflected at a surface. In particular, snow and lake ice albedos have been shown to affect the timing of lake ice break-up. Lakes are found throughout the Northern Hemisphere and lake ice has been shown to be sensitive to climatic variability. Therefore, the modelling of lake ice phenology, using lake ice models such as the Canadian Lake Ice Model (CLIMo), is important to the study of climatic variability in the Arctic and sub-Arctic regions and accurate snow and lake ice albedo measurements are required to ensure the accuracy of the simulations. However, snow and lake ice albedo can vary from day-to-day depending on factors such as air temperature, presence of impurities, age, and composition. Some factors are more difficult than others to model (e.g. presence of impurities). It would be more straight forward to just gather field measurements, but such measurements would be costly and lakes can be in remote locations and difficult to access. Instead, CLIMo contains an albedo parameterization scheme that models the evolution of snow and lake ice albedo in its simulations. However, parts of the albedo parameterization are based on sea-ice observations (which inherently have higher albedos due to brine inclusions) and the albedo parameterization does not take ice type (e.g. clear ice or snow ice) into account. Satellite remote sensing via the Moderate Resolution Imaging Spectroradiometer (MODIS) provides methods for retrieving albedo that may help enhance CLIMo’s albedo parameterization. CLIMo’s albedo parameterization as well the MODIS daily albedo products (MOD10A1 and MYD10A1) and 16-day product (MCD43A3) were evaluated against in situ albedo observations made over Malcolm Ramsay Lake near Churchill, Manitoba, during the winter of 2012. It was found that the snow albedo parameterization of CLIMo performs well when compared to average in situ observations, but the bare ice parameterization overestimated bare ice albedo observations. The MODIS albedo products compared well when evaluated against the in situ albedo observations and were able to capture changes in albedo throughout the study period. The MODIS albedo products were also compared against CLIMo’s melting ice parameterization, because the equipment had to be removed from the lake to prevent it from falling into the water during the melt season. Cloud cover interfered with the MODIS observations, but the comparison suggests that MODIS albedo products retrieved higher albedo values than the melting ice parameterization of CLIMo. The MODIS albedo products were then integrated directly into CLIMo in substitution of the albedo parameterization to see if they could enhance break-up date (ice off) simulations. MODIS albedo retrievals (MOD10A1, MYD10A1, and MCD43A3) were collected over Back Bay, Great Slave Lake (GSL) near Yellowknife, Northwest Territories, from 2000-2011. CLIMo was then run with and without the MODIS albedos integrated and compared against MODIS observed break-up dates. Simulations were also run under three difference snow cover scenarios (0%, 68%, and 100% snow cover). It was found that CLIMo without MODIS albedos performed better with the 0% snow cover scenario than with the MODIS albedos integrated in. Both simulations (with and without MODIS albedos) performed well with the snow cover scenarios. The MODIS albedo products slightly improved CLIMo break-up simulations when integrated up to a month in advance of actual lake ice break-up for Back Bay. With the MODIS albedo products integrated into CLIMo, break-up dates were simulated within 3-4 days of MODIS observed break-up. CLIMo without the MODIS albedos still performed very well simulating break-up within 4-5 days of MODIS observed break-up. It is uncertain whether this was a significant improvement or not with such a small study period and with the investigation being conducted at a single site (Back Bay). However, it has been found that CLIMo performs well with the original albedo parameterization and that MODIS albedos could potentially complement lake-wide break-up simulations in future studies.

No description available.

Current North American operational satellite passive microwave snow water equivalent (SWE) retrieval algorithms consistently underestimate SWE levels for tundra environments when compared to four years of regional snow surveys conducted in the Northwest Territories and northern Manitoba, Canada. Almost all contemporary SWE algorithms are based on the brightness temperature difference between the 37GHz and 19GHz frequencies found onboard both past and present spaceborne sensors. This underestimation is likely a result of the distribution and deposition of the tundra snow, coupled with the influence of tundra lakes on brightness temperatures at the 19GHz frequency. To better our understanding concerning the underestimation of passive microwave SWE retrievals on the tundra, Environment Canada collected in situ measurements of SWE, snow depth, and density at 87 sites within a 25km by 25km study domain located near Churchill, Manitoba in March 2006. Coincident multi-scale passive microwave airborne (70m & 500m resolution) and spaceborne (regridded to 12.5km & 25km resolution depending on frequency) data were measured at 6.9GHz, 19GHz, 37GHz and 89 GHz frequencies during the same time period. The snow survey data highlighted small-scale localized patterns of snow distribution and deposition on the tundra that likely influences current SWE underestimation. Snow from the open tundra plains is re-distributed by wind into small-scale vegetated features and micro-topographic depressions such as narrow creekbeds, lake edge willows, small stands of coniferous trees and polygonal wedge depressions. The very large amounts of snow deposited in these spatially-constrained features has little influence on the microwave emission measured by large-scale passive microwave spaceborne sensors and is therefore unaccounted for in current methods of satellite SWE estimation. The analysis of the passive microwave airborne data revealed that brightness temperatures at the 19GHz were much lower over some tundra lakes, effectively lowering SWE at the satellite scale by reducing the 37-19GHz brightness temperature difference used to estimate SWE. The unique emission properties of lakes in the wide open expanse of the tundra plains, coupled with an insensitivity to the large amounts of SWE deposited in small-scale features provides an explanation for current passive microwave underestimation of SWE in the tundra environment.

Current North American operational satellite passive microwave snow water equivalent (SWE) retrieval algorithms consistently underestimate SWE levels for tundra environments when compared to four years of regional snow surveys conducted in the Northwest Territories and northern Manitoba, Canada. Almost all contemporary SWE algorithms are based on the brightness temperature difference between the 37GHz and 19GHz frequencies found onboard both past and present spaceborne sensors. This underestimation is likely a result of the distribution and deposition of the tundra snow, coupled with the influence of tundra lakes on brightness temperatures at the 19GHz frequency. To better our understanding concerning the underestimation of passive microwave SWE retrievals on the tundra, Environment Canada collected in situ measurements of SWE, snow depth, and density at 87 sites within a 25km by 25km study domain located near Churchill, Manitoba in March 2006. Coincident multi-scale passive microwave airborne (70m & 500m resolution) and spaceborne (regridded to 12.5km & 25km resolution depending on frequency) data were measured at 6.9GHz, 19GHz, 37GHz and 89 GHz frequencies during the same time period. The snow survey data highlighted small-scale localized patterns of snow distribution and deposition on the tundra that likely influences current SWE underestimation. Snow from the open tundra plains is re-distributed by wind into small-scale vegetated features and micro-topographic depressions such as narrow creekbeds, lake edge willows, small stands of coniferous trees and polygonal wedge depressions. The very large amounts of snow deposited in these spatially-constrained features has little influence on the microwave emission measured by large-scale passive microwave spaceborne sensors and is therefore unaccounted for in current methods of satellite SWE estimation. The analysis of the passive microwave airborne data revealed that brightness temperatures at the 19GHz were much lower over some tundra lakes, effectively lowering SWE at the satellite scale by reducing the 37-19GHz brightness temperature difference used to estimate SWE. The unique emission properties of lakes in the wide open expanse of the tundra plains, coupled with an insensitivity to the large amounts of SWE deposited in small-scale features provides an explanation for current passive microwave underestimation of SWE in the tundra environment.

No description available.

No description available.

No description available.

No description available.