Melt ponds greatly increase the transmission of solar radiation through sea ice relative to snow covered or bare ice. This rise in transmittance has the potential to enhance water column heating and primary production. I examine how spatially variable sea ice surfaces control the under-ice salinity, temperature and photosynthetically active radiation (PAR) and provide estimates of solar heating and primary production during melt. Conductivity, temperature and PAR profiles were measured in the Canadian Arctic under snow covered ice, leads, bare ice and melt ponds. The under-ice light field to a depth of 10 to 13 m was highly variable, controlled by increased transmission under melt ponds and shading by bare ice. Below, the light field became relatively homogeneous showing the depth the surface heterogeneity had an effect on transmitted PAR. Furthermore, one water column profile is not representative of the PAR, salinity or temperature under a spatially heterogeneous surface.

During the fourth International Polar Year, an interdisciplinary study was conducted to examine the couplings between sea ice, ocean, atmosphere, and ecosystem in the southeastern Beaufort Sea. This thesis examines components of the system that control the air–sea exchange of carbon dioxide. Using eddy covariance measurements, we found enhanced CO2 exchange associated with new ice formation in winter flaw leads. This exchange was typically directed towards the surface, although we also measured one instance of outgassing. Sea surface dissolved CO2 measurements (pCO2sw) in Amundsen Gulf showed significant undersaturation with respect to the atmosphere at freeze–up, followed by a slow increase over the winter until spring phytoplankton blooms caused strong undersaturation at break–up. Over the summer, pCO2sw increased until becoming slightly supersaturated due to surface warming. Along the southern margins of Amundsen Gulf and on the Mackenzie Shelf we found pCO2sw supersaturations in the fall due to wind–driven coastal upwelling. In the spring, this upwelling occurred along the landfast ice edges of Amundsen Gulf. By combining observations of enhanced winter gas exchange with observations of pCO2sw in Amundsen Gulf, we derived an annual budget of air–sea CO2 exchange for the region. This exercise showed that uptake through the winter season was as important as the open water season, making the overall annual uptake of CO2 about double what had previously been calculated. Prior to this work, the prevailing paradigm of air–sea CO2 cycling in Arctic polynya regions posited that strong CO2 absorption occurs in the open water seasons, and that a potential outgassing during the winter is inhibited by the sea ice cover. As a new paradigm, we propose that the spatial and temporal variability of many processes – including phytoplankton blooms, sea surface temperature and salinity changes, upwelling, river input, continental shelf processes, and the potential for high rates of winter gas exchange – need to be considered in order to understand the carbon source/sink status of a given Arctic polynya region. A paradigm that considers such varied processes is useful in understanding how climate change in the Arctic can impact air–sea CO2 exchange.

Implementing oxygen isotopes (H218O, H216O) in coupled climate models provides both an important test of the individual model's hydrological cycle, and a powerful tool to mechanistically explore past climate changes while producing results directly comparable to isotope proxy records. The addition of oxygen isotopes in the University of Victoria Earth System Climate Model (UVic ESCM) is described. Equilibrium simulations are performed for preindustrial and Last Glacial Maximum (LGM) conditions. The oxygen isotope content in the model's preindustrial climate is compared against observations for precipitation and seawater. The distribution of oxygen isotopes during the LGM is compared against available paleo-reconstructions. Records of temporal variability in the oxygen isotopic composition of biogenic carbonates from ocean sediment cores inform our understanding of past continental ice volume and ocean temperatures. Interpretation of biogenic carbonate d18O variability typically neglects changes due to factors other than ice volume and temperature, equivalent to assuming constant local seawater isotopic composition. This investigation focuses on whether sea ice, which fractionates seawater during its formation, could shift the isotopic value of seawater during distinct climates. Glacial and interglacial states are simulated with the isotope-enabled UVic ESCM, and a global analysis is performed. Results indicate that interglacial-glacial sea ice variability produces as much as a 0.13 permil shift in local seawater, which corresponds to a potential error in local paleotemperature reconstruction of approximately 0.5 C. Isotopic shifts due to sea ice variability are concentrated in the Northern Hemisphere, specifically in the Labrador Sea and northeastern North Atlantic. / Graduate

The ocean is the largest sink of anthropogenic carbon from the atmosphere and therefore the magnitude of ocean carbon uptake largely determines the airborne fraction of emissions and the ultimate severity of surface climate change. However, climate-feedbacks on ocean carbon uptake over the historical period and in the future are uncertain. In particular, much uncertainty in the ocean carbon response hinges on the influence of wind-driven changes in the Southern Ocean, which is the most significant region of anthropogenic carbon uptake. Here I show that the Southern Hemisphere westerly winds simulated by the Coupled Model Intercomparison Project Phase 3 (CMIP3) and CMIP5 climate models have significant biases in their pre-industrial and satellite era-climatologies, relative to observationally based estimates. I also show that the models project the westerlies to intensify and shift poleward under anthropogenic forcing over the 20th and 21st centuries, but that they significantly underestimate the trends over the satellite era. I then use a novel experimental design, wherein I isolate the influence of the model’s pre-industrial wind bias on simulations of ocean carbon uptake and climate. I do this by using the UVic Earth System Climate Model (ESCM) with an ensemble of members, each forced by the winds from an individual CMIP model. I show here that the climate model pre-industrial wind bias can significantly increase ocean carbon uptake in transient climate change simulations, reducing the airborne fraction and projected climate change. By contrast, the simulated wind-changes over the 20th and 21st centuries reduce ocean carbon uptake, largely through an increase in outgassing from the Southern Ocean. However, I show that this transient- wind effect is i) smaller than the pre-industrial bias effect and ii) does not occur when using a variable formulation for the Gent-McWilliams coefficient of eddy diffusivity in the coarse resolution model, under simulated or observed wind-changes. I then go on to demonstrate that the simulated transient wind-changes significantly reduce the Antarctic sea-ice area simulated by the UVic ESCM. I also test the influence of fresh water input to the Southern Ocean from dynamic Antarctic Ice Sheet mass loss, which is a forcing absent from the CMIP5 models. The magnitude of the fresh water effect is small and has little influence on the sea-ice area trends simulated by the CMIP5 models over the historical era. These results have significant implications for previous model-based studies of the ocean carbon cycle, as well as for the quantification of the wind-induced uncertainty in future climate projections by current Earth System Models. / Graduate / 0725 / 0425 / 0415

Spatial intensity variation introduced by illumination changes is a challenging problem for image segmentation and classification. Many techniques have been proposed which focus on removing this illumination variation by estimating or modelling it. There is limited research on developing an illumination invariant classification technique which does not use any preprocessing. A major focus of this research is on automatically classifying synthetic aperture radar (SAR) images. These are large satellite images which pose many challenges for image classification including the incidence angle effect which is a strong illumination variation across the image. Mapping of full scene satellite images of sea-ice is important for navigational purposes for ships and also for climate research. The images obtained from the RADARSAT-2 satellite are dual band, high quality images. Currently, sea ice chart are produced manually by ice analysts at the Canadian Ice Service. However, this process can be automated to reduce processing time and obtain more detailed pixel-level ice maps. An automated classification algorithm to achieve sea ice and open water separation will greatly help the ice analyst by providing sufficient guidance in the initial stages of creating an ice map. It would also help the analyst to improve the accuracy while finding ice concentrations and remove subjective bias. The existing Iterative Region Growing by Semantics (IRGS) algorithm is not effective for full scene segmentation because of the incidence angle effect. This research proposes a "glocal" (global as well as local) approach to solve this problem. The image is divided in a rectangular grid and each rectangle is segmented using IRGS. This is viewed as an over-segmentation of the original image. Finally, IRGS is used globally to glue together the over-segmented regions. This method yields acceptable results with the denoised images. The proposed technique can also be used for general image classification purposes. Extensive testing was done to investigate the best set of parameters for the proposed approach. Images were simulated with the SAR illumination variation and multiplicative speckle noise. The technique was effective for general classification and attained accurate results for full scene SAR segmentation.

Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

Thesis (Ph.D.)--McMaster University (Canada), 1991. / Source: Dissertation Abstracts International, Volume: 53-01, Section: B, page: 0496. Supervisor: Simon Haykin.

Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2004. / Includes bibliographical references (p. 111-113).

Thesis (M.S.)--University of Wisconsin--Madison, 1965. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 118-122).

Thesis (Ph. D.), Memorial University of Newfoundland, 1999. / Restricted until June 2000. Bibliography: p. 306-330.