Where once the Arctic basin held predominantly old, thick perennial sea ice, it is now increasingly occupied by young, thin seasonal ice. The sea ice surface topography, which affects and is affected by many of the physical processes operating at the interface between ocean, sea ice and atmosphere, is closely related to the age and type of sea ice cover. In this thesis, new methods are presented for measuring and understanding sea ice topography using Light Detection and Ranging (LiDAR) technology. A new technique is presented for parameterizing the micro-scale roughness of sea ice using terrestrial LiDAR. Field, laboratory and numerical experiments have been carried out to test the precision and accuracy of the technique, and calibrations have been developed for correcting field observations of surface roughness for known biases. Results obtained using this technique have been applied in several microwave remote sensing and electromagnetic-wave scattering model studies of snow-covered and melting sea ice. Terrestrial and satellite LiDAR observations are acquired and combined in a further study to examine how sea ice surface topography regulates the melting of ice during the Arctic summer. Observations from a field program in the Canadian Arctic show that minor variations in the roughness of pre-melt sea ice topography can affect significant variations in the melt pond coverage at the ice surface in summer. Numerical simulations are used to develop a quantitative understanding of these findings and, when applied to satellite observations, explain most of the spatial variation in Arctic summer ice melting rates. Results suggest that a recent reduction in sea ice roughness, caused by progressive changes in the type of sea ice resident in the Arctic Ocean, has accelerated the summer melting and decline of the Arctic sea ice cover. / May 2016

The Arctic sea-ice cover plays an important role for the global climate system. Sea ice and the overlying snow cover reflect up to eight times more of the solar radiation than the underlying ocean. Hence, they are important for the global energy budget, and changes in the sea-ice cover can have a large impact on the Arctic climate and beyond. In the past 36 years the ice cover reduced significantly. The largest decline is observed in September, with a rate of more than 12% per decade. The negative trend is accompanied by large inter-annual sea-ice variability: in September the sea-ice extent varies by up to 27% between years. The processes controlling the large variability are not well understood. In this thesis the atmospheric contribution to the inter-annual sea-ice variability is explored. The focus is specifically on the thermodynamical effects: processes that are associated with a temperature change of the ice cover and sea-ice melt. Atmospheric reanalysis data are used to identify key processes, while experiments with a state-of-the-art climate model are conducted to understand their relevance throughout different seasons. It is found that in years with a very low September sea-ice extent more heat and moisture is transported in spring into the area that shows the largest ice variability. The increased transport is often associated with similar atmospheric circulation patterns. Increased heat and moisture over the Arctic result in positive anomalies of water vapor and clouds. These alter the amount of downward radiation at the surface: positive cloud anomalies allow for more longwave radiation and less shortwave radiation. In spring, when the solar inclination is small, positive cloud anomalies result in an increased surface warming and an earlier seasonal melt onset. This reduces the ice cover early in the season and allows for an increased absorption of solar radiation by the surface during summer, which further accelerates the ice melt. The modeling experiments indicate that cloud anomalies of similar magnitude during other seasons than spring would likely not result in below-average September sea ice. Based on these results a simple statistical sea-ice prediction model is designed, that only takes into account the downward longwave radiation anomalies or variables associated with it. Predictive skills are similar to those of more complex models, emphasizing the importance of the spring atmosphere for the annual sea-ice evolution. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p>

Accumulation of dynamic and thermodynamic forces in the Arctic are decreasing the extent of thin annual sea ice which polar bear rely on for survival. It is imperative that we identify the preferred habitat of polar bears to fully understand their future requirements. In this thesis, surveys of polar bear tracks and the surrounding sea ice variables, at various scales, were recorded. Sea ice roughness was measured through surveys of the sample area in situ, with an electromagnetic induction (EM) system (IcePIC) mounted to a helicopter, and analyzed through advanced synthetic aperture radar (ASAR) images of the study area. In situ Polar bear tracks provided a limited association with the EM sea ice roughness and a negative association to ASAR sea ice roughness. Results indicate a significant association between EM mean values and ASAR brightness means. In addition, EM statistics and ASAR texture statistics were correlated through a best fit regression model. These associations display a remote method to identify preferred polar bear habitat and provide a potential linkage between the regional (EM) and remotely sensed (ASAR) assessment of sea ice roughness. / October 2008

Open water in sea ice, such as leads and polynyas, plays a crucial role in determining the formation of deep- and bottom-water, as well as their long-term global properties and circulation. Ocean general circulation models (GCMs) designed for studies of the long-term thermohaline circulation have typically coarse resolution, making it inevitable to parameterize subgrid-scale features such as leads and convective plumes. In this study, a hierarchy of higher-resolution sea-ice models is developed to reduce uncertainties due to coarse resolution, while keeping the ocean component at coarse resolution to maintain the efficiency of the GCM to study the long-term deep-ocean properties and circulation. The higher-resolved sea-ice component is restricted to the Southern Ocean. Compared with the coarse sea-ice model, the intermediate, higher-resolution version yields more detailed coastal polynyas, a realistically sharp ice edge, and an overall enhanced lead fraction. The latter gives enhanced rates of Antarctic Bottom Water formation through enhanced near-boundary convection. Sensitivity experiments revealed coastal katabatic winds accounted for in the higher resolution version, are the main reason for producing such an effect. For a more realistic coastline, satellite passive-microwave data for fine-grid land/ice-shelf – seaice/ ocean boundary were used. With a further enhancement of the resolution of the Southern Ocean’s sea-ice component, a grid spacing of 22 km is reached. This is about the size of the pixel resolution of satellite-passive microwave data from which ice concentration is retrieved. This product is used in this study to validate the sea-ice component of the global ocean GCM. The overall performance of the high-resolution sea-ice component is encouraging, particularly the representation of the crucial coastal polynyas. Enhancing the resolution of the convection parameterization reduces spurious coarse-grid polynyas. Constraining the upper-ocean temperature and modifying the plume velocity removes unrealistic small-scale convection within the ice pack. The observed highfrequency variability along the ice edge is to some extent captured by exposing the ice pack to upper-ocean currents that mimic tidal variability. While these measures improve several characteristics of the Southern Ocean sea-ice pack, they deteriorate the global deepocean properties and circulation, calling for further refinements and tuning to arrive at presently observed conditions.

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Accumulation of dynamic and thermodynamic forces in the Arctic are decreasing the extent of thin annual sea ice which polar bear rely on for survival. It is imperative that we identify the preferred habitat of polar bears to fully understand their future requirements. In this thesis, surveys of polar bear tracks and the surrounding sea ice variables, at various scales, were recorded. Sea ice roughness was measured through surveys of the sample area in situ, with an electromagnetic induction (EM) system (IcePIC) mounted to a helicopter, and analyzed through advanced synthetic aperture radar (ASAR) images of the study area. In situ Polar bear tracks provided a limited association with the EM sea ice roughness and a negative association to ASAR sea ice roughness. Results indicate a significant association between EM mean values and ASAR brightness means. In addition, EM statistics and ASAR texture statistics were correlated through a best fit regression model. These associations display a remote method to identify preferred polar bear habitat and provide a potential linkage between the regional (EM) and remotely sensed (ASAR) assessment of sea ice roughness.

This dissertation attempts to improve the understanding of spatial distribution and evolution of snow-covered sea ice as related to polar bear habitat. This will be accomplished at both the local (i.e. 1m spatial resolution) and regional scales (i.e. 100 km spatial resolution) for various types of first-year sea ice (FYI) through four primary objectives. The first primary objective (i.e. Chapter 3) examines the observed and modeled temporal evolution of snow over smooth FYI, as well as the estimation of on-ice meteorological conditions. Results suggest that increases in observed snowdrifts and changes to the orientation of the drifts are related to snowfall and drifting events. Modeling these changes over time using a spatially distributed snow model is not able to accurately estimate the snow distribution. As well, only the on-ice temperature and humidity can be estimated from land-based station data, limiting the modeling of snow over sea ice. The second primary objective (i.e. Chapter 4) extends this research to rough FYI types, more relevant to polar bear habitat. More specifically this objective studies the spatial pattern of snow distribution over rough ice and ridges and the relationship between ice roughness and meteorological conditions. Results suggest that ice roughness influences the magnitude of snow depth, while the wind direction during periods of snow deposition and/or blowing snow events will impact the spatial pattern. The third primary objective (i.e. Chapter 5) focuses on developing a more feasible method of deriving surface roughness. This objective attempts to use satellite imagery and texture analysis to derive surface roughness for snow-covered sea ice. Results suggest that a Gamma speckle reduction filter, coupled with a grey-level co-occurrence matrix texture measure (Entropy or Angular Second Moment) is able to account for more than 88% of the variability in the surface roughness. The final primary objective (i.e. Chapter 6) examines the temporal evolution and factors controlling the changes in sea ice characteristics over regional scale for a period from 1978 to 2002. Observed anomalies in sea ice characteristics within some of the polar bear subpopulations may be explained by thermodynamic and/or dynamic factors. Results suggest that published reduction in polar bear population and condition within the subpopulations co-occur with these observed changes in sea ice characteristics.

Seasonal ice cover off Canada's east coast was examined in relation to synoptic scale atmospheric events. Ice concentration information derived from Nimbus-7 scanning multichannel microwave radiometer (SMMR) measurements of surface brightness temperatures, supplemented by AES composite ice charts, provided timely coverage of the study area during the 1971/72, 1980/81 and 1984/85 ice seasons, 1971/72 and 1984/85 seasons with high ice concentrations and 1980/81 a season with low concentrations. / Atmospheric variability was monitored using the 850 hPa height at three upper air stations along the western edge of the study region. Additional information was drawn from storm track records, providing an indication of surface variability. Properties of specific storm events were obtained from the storm track data, permitting evaluation of the ice response to passing synoptic disturbances. / Results of this investigation indicate that, (1) passive microwave-derived ice information can be used to monitor high frequency variability in the marginal ice, (2) despite short time series, spectral relationships between ice concentration variability and 850 hPa pressure height indicate a strong association between the two at synoptic frequencies zone, and (3) variability in ice cover extent and concentration can be related to the passage of individual synoptic events.

Accumulation of dynamic and thermodynamic forces in the Arctic are decreasing the extent of thin annual sea ice which polar bear rely on for survival. It is imperative that we identify the preferred habitat of polar bears to fully understand their future requirements. In this thesis, surveys of polar bear tracks and the surrounding sea ice variables, at various scales, were recorded. Sea ice roughness was measured through surveys of the sample area in situ, with an electromagnetic induction (EM) system (IcePIC) mounted to a helicopter, and analyzed through advanced synthetic aperture radar (ASAR) images of the study area. In situ Polar bear tracks provided a limited association with the EM sea ice roughness and a negative association to ASAR sea ice roughness. Results indicate a significant association between EM mean values and ASAR brightness means. In addition, EM statistics and ASAR texture statistics were correlated through a best fit regression model. These associations display a remote method to identify preferred polar bear habitat and provide a potential linkage between the regional (EM) and remotely sensed (ASAR) assessment of sea ice roughness.