Spaceborne microwave sensors are powerful tools for monitoring the impacts of global climate change on the Greenland ice sheet. This dissertation focuses on refining methods for applying microwave data in Greenland studies by using new simple theoretical and empirical models to investigate (1) azimuth anisotropies in the data, (2) the microwave signature of the snow surface, (3) detection of snow melt, and (4) classification of snow melt. The results are applicable for identifying geophysical properties of the snow surface and monitoring changes on the ice sheet in relation to melt duration/extent, accumulation, and wind patterns. Azimuth dependence of the normalized radar cross-section (sigma-0) over the Greenland ice sheet is modeled with a simple surface scattering model. The model assumes that azimuth anisotropy in 1-100 meter scale surface roughness is the primary mechanism driving the azimuth modulation. This model is inverted to estimate snow surface properties using sigma-0 measurements from the C-band European Remote Sensing Advanced Microwave Instrument (ERS) in scatterometer mode. The largest roughness estimates occur in the lower portions of the dry snow zone. Estimates of the preferential direction in surface roughness are highly correlated with katabatic wind fields over Greenland. A new observation model is introduced that uses a limited number of parameters to characterize the snow surface based on the dependence of radar backscatter on incidence angle, azimuth angle, spatial gradient, and temporal rate of change. The individual model parameters are discussed in depth with examples using data from the NASA Scatterometer (NSCAT) and from the ERS. The model may be applied for increased accuracy in scatterometer, SAR, and wide-angle SAR studies. Examples illustrating the use of the model are included with one application focusing on analysis of inter-annual change and another focusing on increased sensitivity in studies of intra-annual change. Six different melt detection method/sensor combinations are compared using data for the summer of 2000. The sensors include the Special Spectral Microwave Imager (SSM/I), SeaWinds on QuikSCAT (QSCAT), and ERS. A new method of melt detection is introduced that is based on a simple physical model relating the moisture content and depth of a layer of wet surface snow to a single channel melt detection threshold. The model can be applied to both active and passive sensors. Model-based melt estimates from different sensors are highly correlated and do not exhibit the unnatural phenomenon observed with previous methods. Trends in SSM/I channel ratios are used to differentiate subsurface and surface melt. For ablation estimation, this separation is important due to expected differences in the ablation rate for the two melt types. Evidences of the daily melt refreeze cycle are observed in the diurnal variation of the different brightness temperature channel ratios. The polarization ratio increases during periods of surface melt while the frequency ratio remains relatively constant. The frequency ratio increases during periods of expected subsurface melt. Similar trends are observed in brightness temperature measurements from in situ data collected by other investigators.

Western Antarctica has been experiencing significant warming for at least the past fifty years. While higher Net Surface Mass Balance (SMB) over West Antarctica during this period of warming is expected, SMB reconstructions from ice cores reveal a more complex pattern during the period of warming. The mechanisms giving rise to SMB variability over the West Antarctic Ice Sheet (WAIS) are not well understood due to lack of instrumental data. The Southern Annular Mode (SAM) and El Niño Southern Oscillation (ENSO) are believed to contribute to WAIS SMB variability but the assumption has not been rigorously tested. SMB during years where SAM and ENSO are in extreme phases is compared to average SMB from the period 1979-2010. Additionally, atmospheric circulation anomalies are used to assess what circulation patterns accompany extreme modes of climate during the same period. The results suggest that significantly lower SMB occurs when SAM is in an extremely positive phase or ENSO is in an extremely negative phase. Additionally, atmospheric circulation anomalies show that certain circulation patterns accompany extreme modes of climate, which contribute to SMB variability over the WAIS. Ultimately, the location of low and high pressure cells is the best predictor for extreme accumulation events over the WAIS. These results are verified by assessing observed net SMB trends from a network of firn cores located from the central WAIS. Seven new firn cores are added to improve the spatial network of regional net SMB measurements. Reconstructed net SMB is calculated from new firn core records, and compared to the existing cores. The new suite of preliminary firn core records show the same significant decreasing trend that is observed in existing cores. This represents a negative region-wide SMB trend that is likely in part due to trends in SAM and ENSO.

Glaciers adjust their sizes as a response to changing climatic conditions which make them a good indicator of climate change. Remote-sensing based glacier monitoring provides a robust way to inventory the health of glaciers and are estimated as a measure of changes in their area, length, volume and mass balance over a period. This research uses remote sensing methods to map glacier extents from satellite images and explores the efficacy of three machine learning algorithms for accurate glacier classification. The results indicated that the Columbia icefield lost 42 km2 of its area cover between 1985 and 2018. It was observed that smaller glaciers lost more of their area at a faster pace than larger ones. Change analysis showed the Columbia glacier experienced the highest area loss (-5.62 km2) and retreat (-3.37 km) while the Athabasca glacier recorded the highest mass ice lose (-2.54 m w.e.) over the study period.

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Essential oils are natural products used to flavor food and beverages. With the increase in nutrition conscious consumers, manufacturers of food additives and food products are faced with the challenge of making healthy alternatives. In particular, food products going to market with label claims stating reductions in sugar and salt, organic certified, organic compliant, and all natural; moreover the ingredients used in flavors must meet these label claims as well. More often than not, the challenge in using ingredients that follow these requirements is the pricing, the sourcing and the variability among those sources. Variability is common in the ingredients coming from nature such as fruits and plants because the area of cultivation can vary by the soil at the sight of planting and/or the climate in the region. Pricing is also problematic in naturally grown ingredients because it is a matter of supply and demand. Stock could be depleted from natural disasters, disease carrying pest(s), pests that consume the crop, and/or other causes for scarce supply of crop(s). Essential oils are natural byproducts of fruit, peels, and leaves from plants that contribute to flavor formulae for a large variety of food products. Because the essential oils are a crop based commodity, every variety has inherent differences based on the growing conditions and their ripening stages [1]. Nevertheless, each type of oil has marker chemicals that make up the majority of its composition; these marker chemicals have the tendency to degrade over time based on their interaction with light, oxygen exposure, and temperature. For companies that manufacture flavorings, understanding the variability among sources of essential oils as well as the possible degradants of essential oils is valuable information to obtain because it is possible the variants and degradants will negatively impact the flavor profile. Flavor is without question the most important attribute of the food we consume and by default stability of said flavor(s) need to be understood [30]. The content in this dissertation involves the stability analysis of a common essential oil, Oil Mandarin Italian Select, from Citrus Reticulata Blanco. It has known off notes that form from unknown causes. Most common is the plastic note that has formed in carbonated products like soda. Studying this particular essential oil in various conditions is intended to shed light on what those degradants are and under which conditions they form to give mandarin oil an off-note when applied to high acid and carbonated beverage applications. Once the note is reproduced, a correlation between analytical data and sensory interpretation of the oil will be developed. Mandarin essential oil being in the Citrus genus is traditionally analyzed via gas chromatography (GC) because of the high quantities of volatile constituents that give an oil high aroma activity. The volatile fraction of mandarin oil to be studied includes stability of methyl-N-methylanthranilate (MNMA), a major component giving mandarin its distinct grapey character, as well as gamma terpinene, thymol, sinensal, alpha pinene, beta pinene, myrcene, para cymene, alpha terpineol, and beta caryophyllene. Each of these ten compounds contributes to the unique flavor profile of mandarin oils when compared to orange and tangerine essential oils [1]. It was the common knowledge that para cymene can be perceived as rancid in aroma and the many interconversions the terpenes make that cause para cymene formation in Citrus oils, which made monitoring the changes of this chemical in the three stability environments crucial. Attention is being paid to para-cymene, as a specific marker of degradation in Citrus. The data obtained from the applied stability studies were challenging to understand as the marker chemicals are volatile and sensitive to chemical change. In this work the chemical changes and trends were analyzed under various storage conditions. Significant statistical analyses were employed to help define criteria of usability. The analyses were required because of natural variants and apparent inconsistencies of the data. Dixon Q Test and the Z Test were applied to determine outliers. Additionally, the Bland Altman method was applied to compare storage conditions and to determine if this statistical approach could be used to define significant changes in the marker chemical stability. The Bland Altman plots suggest that each component met the statistical limits of agreement, meaning the samplings were not significantly changing, statistically speaking. A final approach to assess the analytical data of the mandarin oil for significant change was the mass balance of each marker chemical from week 0 to week 24. Instrumental fluctuations have an acceptable range of +/- 20% in the industry; hence, a significant change criterion for a chemical in the mass balance must be one that exceeded +/- 20%. Unlike classical statistic methods, the mass balance was indicative that significant change had occurred to the compounds in the three studies. Upon sensory analysis of the oil samples, display of plastic note, oxidation, and overall loss of characteristic mandarin notes, the mass balance was found to correlate best to the significant change detected by sensory evaluation of the oil samplings. Due to the inadequate number of validated methods on Citrus essential oil research and the absence of large groupings of terpenes validated in a unified methodology, reconciliation of mass balance is an underutilized method of assessment in the literature. As a final assessment of the GC method validated, a product containing the selected mandarin oil was analyzed to evaluate the ability of the method to separate the oil components within a significantly more complicated matrix than the initial samples. The method was successful though not all marker chemicals were detected due to their low formulation concentration being below the level of detection of the method. This should not be seen as a failure of the method. For the major components of the essential oil studied, the method was quantitatively successful, meeting industry requirements. / Chemistry

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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The objective of this research is to develop a system capable of simulating snow depth and snow water equivalent in the Sherbrooke to Mount-Megantic area of Quebec's Eastern Townships using meteorological and digital terrain data as input.The working hypothesis is that meteorological data may drive a point energy and mass balance snow cover model.The model used was developed by the Hydrologic Research Lab (National Weather Service) which was calibrated for local conditions using field data collected during two winters at several sites on Mount-Megantic. Snow water equivalent and depth are used for calibration and validation of the model. Automated snow sensors were also used to obtain temperature calibration data.The snow surveys and correction of the air temperature for elevation improves the estimates of snow depth and water equivalent.The results suggest that data from the Sherbrooke meteorological stations can be used to estimate the snow cover over the area of Eastern Townships. Air temperature extrapolation across the field area is a challenge. However the simulated snow cover conforms generally well with data observed at several stations throughout the region.