Snow Albedo Feedback (SAF) in response to climate change is a process that can amplify the climate warming response to increases in anthropogenic atmospheric CO2 concentrations from the 20th to the 21st Century. Warmer surface air temperature may induce snowmelt and expose darker underlying surfaces which absorb more incoming solar radiation and further increase the ambient temperature. Springtime SAF in the fully Coupled Model Intercomparison Project Phase 3 (CMIP3) models is associated with summertime circulation. However, no clear physical mechanism explaining this link has been found. Furthermore, there is a large intermodel spread in the projection of SAF among the CMIP3 models which is primarily controlled through the parameterization of snow albedo in each model. Limited work was conducted on assessing the response of SAF to that of an isolated controlling parameter such as snow albedo. Here, the uncoupled Geophysical Fluid Dynamics Laboratory Atmospheric Model 2.1 (AM2.1) was used to diagnose SAF in the CMIP3 models by conducting a set of sensitivity experiments with perturbed snow albedo. This was performed to remove indirect external climate factors that may influence SAF and to use the simplified uncoupled model to understand the behaviours exhibited by the complex coupled models. Snow cover extent (SNC) and snow metamorphosis as a function of temperature (TEM) that influences SAF, as well as the knock-on effects of SAF on soil moisture, snow mass, snow melt and circulation were analyzed using both the CMIP3 and AM2.1 models. In addition, it was hypothesized that summertime Land Sea Contrast response to climate change (dLSC) is a physical mechanism that induces summertime circulation patterns in relation to springtime SAF. It is found that the AM2.1 can similarly reproduce SNC and TEM as well as the spread in SAF exhibited in the CMIP3 models. However, no robust link can be determined between SAF and its knock-on effects. Furthermore, the correlation between SAF and dLSC is not significant and thus dLSC is not a physical mechanism that influences the summertime circulation patterns in response to climate change. It is the expectation that these research results can provide an in-depth understanding of the role of SAF among fully coupled GCMs through tests performed by the uncoupled simulation.

Snow Albedo Feedback (SAF) in response to climate change is a process that can amplify the climate warming response to increases in anthropogenic atmospheric CO2 concentrations from the 20th to the 21st Century. Warmer surface air temperature may induce snowmelt and expose darker underlying surfaces which absorb more incoming solar radiation and further increase the ambient temperature. Springtime SAF in the fully Coupled Model Intercomparison Project Phase 3 (CMIP3) models is associated with summertime circulation. However, no clear physical mechanism explaining this link has been found. Furthermore, there is a large intermodel spread in the projection of SAF among the CMIP3 models which is primarily controlled through the parameterization of snow albedo in each model. Limited work was conducted on assessing the response of SAF to that of an isolated controlling parameter such as snow albedo. Here, the uncoupled Geophysical Fluid Dynamics Laboratory Atmospheric Model 2.1 (AM2.1) was used to diagnose SAF in the CMIP3 models by conducting a set of sensitivity experiments with perturbed snow albedo. This was performed to remove indirect external climate factors that may influence SAF and to use the simplified uncoupled model to understand the behaviours exhibited by the complex coupled models. Snow cover extent (SNC) and snow metamorphosis as a function of temperature (TEM) that influences SAF, as well as the knock-on effects of SAF on soil moisture, snow mass, snow melt and circulation were analyzed using both the CMIP3 and AM2.1 models. In addition, it was hypothesized that summertime Land Sea Contrast response to climate change (dLSC) is a physical mechanism that induces summertime circulation patterns in relation to springtime SAF. It is found that the AM2.1 can similarly reproduce SNC and TEM as well as the spread in SAF exhibited in the CMIP3 models. However, no robust link can be determined between SAF and its knock-on effects. Furthermore, the correlation between SAF and dLSC is not significant and thus dLSC is not a physical mechanism that influences the summertime circulation patterns in response to climate change. It is the expectation that these research results can provide an in-depth understanding of the role of SAF among fully coupled GCMs through tests performed by the uncoupled simulation.

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.

A computer program was written to calculate snow albedos for the months of January, March, and May in western Canada. Snow depth as well as water equivalent depth data was obtained from snow cover records and climatic maps. It was found that for the months of January and March, the snow depths were all greater than 10 cm and so the snow albedo was not a function of the surface type rather only the snow cover. For May, however, snow depths of less than 10 cm were obtained and the albedo became a function of both the water equivalent as well as surface type. The method of data collection is criticized primarily because of the instances of measurements and methods of measurement. Also, the equation in which the snow albedo is calculated is criticized because it only takes into consideration snow depth and not other important factors such as snow age density and crystal structure. However, age, density, and crystal structure are difficult measures to obtain data for on a large scale typical of GCMs. Good comparisons are made with the snow albedo values of forested sites obtained in this study with those in the literature. / Thesis / Bachelor of Science (BSc)

The snow cover plays an important role not only for the whole climate system but also for tourism and economy in the Lapland winter (e.g. dog sledding, snow mobile, etc). Snow constitutes a shelter for animals and plants during the winter due to thermal isolation, but, on the range of this investigation, it can make grazing difficult for reindeers, if the conditions are not favorable. Different approaches to the study have been made; the first and most important part of the investigation was a campaign in Järämä, in Swedish Lapland. During 3 days (between the 3rd and 5th of March 2009), a series of snow pits were done, recording snow grain size, snow layers depth, snow hardness/compactness, density and temperature. The hardness in the snow was evaluated through ram penetration tests. It was additionally studied the correspondence between the snow layers found in situ and the Sámi terminology. Another approach of the study consisted of satellite observations during the winter season 2008/2009 with day light in the region. The type of imagery used was MODIS (The Moderate Resolution Imaging Spectroradiometer) daily snow albedo and 8-day surface reflectance products. Measurements of temperature, precipitation, snow depth were used to cover the polar night time when satellite images were missing. According to these weather observations some snow metamorphisms were also studied, and their influence on the snowpack conditions. Through the comparison between all these forms of data it was found that in the winter season 2008/2009 the conditions for reindeers grazing were not good due to the formation of ice encapsulating the lichens and grass. Additionally several hard snow layers have been found in the snowpack which increase the difficulty to dig in the snow and may cause problems to the reindeers’ digestion. Snow hardness measurements with a ram penetrometer, manual tests and visual grain size observation proved these discovers. Several periods of positive temperature may cause melting/refreezing cycles contributing to the formation of hard snow layers. These conclusions are supported by the snow albedo and surface reflectance satellite imagery. In these images is visible a period with snow albedo decreasing a lot in the beginning of autumn, after the first lasting snowfall. The weather conditions in early fall, when the first durable snow occurs, are of extreme importance for the reindeers’ grazing, and in the case of the studied winter season, the conditions were not favorable.

Atmospheric particulate matter (PM) is linked to various adverse environmental and health impacts. PM in the atmosphere reduces visibility, alters precipitation patterns by acting as cloud condensation nuclei (CCN), and changes the Earth’s radiative balance by absorbing or scattering solar radiation in the atmosphere. The long-range transport of pollutants leads to increase in PM concentrations even in remote locations such as polar regions and mountain ranges. One significant effect of PM on the earth’s climate occurs while light absorbing PM, such as Black Carbon (BC), deposits over snow. In the Arctic, BC deposition on highly reflective surfaces (e.g. glaciers and sea ices) has very intense effects, causing snow to melt more quickly. Thus, characterizing PM sources, identifying long-range transport pathways, and quantifying the climate impacts of PM are crucial in order to inform emission abatement policies for reducing both health and environmental impacts of PM. Chemical transport models provide mathematical tools for better understanding atmospheric system including chemical and particle transport, pollution diffusion, and deposition. The technological and computational advances in the past decades allow higher resolution air quality and weather forecast simulations with more accurate representations of physical and chemical mechanisms of the atmosphere. Due to the significant role of air pollutants on public health and environment, several countries and cities perform air quality forecasts for warning the population about the future air pollution events and taking local preventive measures such as traffic regulations to minimize the impacts of the forecasted episode. However, the costs associated with the complex air quality forecast models especially for simulations with higher resolution simulations make “forecasting” a challenge. This dissertation also focuses on applications, performance analysis, and optimization of meteorology and air quality modeling forecasting models. This dissertation presents several modeling studies with various scales to better understand transport of aerosols from different geographical sources and economic sectors (i.e. transportation, residential, industry, biomass burning, and power) and quantify their climate impacts. The simulations are evaluated using various observations including ground site measurements, field campaigns, and satellite data. The sector-based modeling studies elucidated the importance of various economical sector and geographical regions on global air quality and the climatic impacts associated with BC. This dissertation provides the policy makers with some implications to inform emission mitigation policies in order to target source sectors and regions with highest impacts. Furthermore, advances were made to better understand the impacts of light absorbing particles on climate and surface albedo. Finally, for improving the modeling speed, the performances of the models are analyzed, and optimizations were proposed for improving the computational efficiencies of the models. Theses optimizations show a significant improvement in the performance of Weather Research and Forecasting (WRF) and WRF-Chem models. The modified codes were validated and incorporated back into the WRF source code to benefit all WRF users. Although weather and air quality models are shown to be an excellent means for forecasting applications both for local and hemispheric scale, further studies are needed to optimize the models and improve the performance of the simulations.

Land ice is losing mass to the worlds oceans at an accelerated rate. The worlds glaciers contain much less ice than the ice sheets but contribute equally to eustatic sea level rise and are expected to continue to do so over the coming centuries if global temperatures continue to rise. It is therefore important to characterize the mass balance of these glaciers and its relationship to climate trends and variability. In the Canadian High Arctic, analysis of long-term surface mass balance records shows a shift to more negative mass balances after 1987 and is coincident with a change in the mean location of the July circumpolar vortex, a mid-troposphere cyclonic feature known to have a strong influence on Arctic summer climate. Since 1987 the occurrence of July vortices centered in the Eastern Hemisphere have increased significantly. This change is associated with an increased frequency of tropospheric ridging over the Canadian High Arctic, higher surface air temperatures, and more negative glacier mass balance. However, regional scale mass balance modeling is needed to determine whether or not the long-term mass balance measurements in this region accurately reflect the mass balance of the entire Canadian High Arctic. The Canadian High Arctic is characterized by high relief and complex terrain that result in steep horizontal gradients in surface mass balance, which can only be resolved if models are run at high spatial resolutions. For such runs, models often require input fields such as air temperature that are derived by downscaling of output from climate models or reanalyses. Downscaling is often performed using a specified relationship between temperature and elevation (a lapse rate). Although a constant lapse rate is often assumed, this is not well justified by observations. To improve upon this assumption, near-surface temperature lapse rates during the summer ablation season were derived from surface measurements on 4 Arctic glaciers. Near-surface lapse rates vary systematically with free-air temperatures and are less steep than the free-air lapse rates that have often been used in mass balance modeling. Available observations were used to derive a new variable temperature downscaling method based on temperature dependent daily lapse rates. This method was implemented in a temperature index mass balance model, and results were compared with those derived from a constant linear lapse rate. Compared with other approaches, model estimates of surface mass balance fit observations much better when variable, temperature dependent lapse rates are used. To better account for glacier-climate feedbacks within mass balance models, more physically explicit representations of snow and ice processes must be used. Since absorption of shortwave radiation is often the single largest source of energy for melt, one of the most important parameters to model correctly is surface albedo. To move beyond the limitations of empirical snow and ice albedo parameterizations often used in surface mass balance models, a computationally simple, theoretically-based parameterization for snow and ice albedo was developed. Unlike previous parameterizations, it provides a single set of equations for the estimation of both snow and ice albedo. The parameterization also produces accurate results for a much wider range of snow, ice, and atmospheric conditions.

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