Geological records of past sea ice, such as those contained in Arctic marine sediments, offer an opportunity to strengthen our understanding of long-term sea ice variability, provided unambiguous paleo-sea ice proxies can be developed. One such recently proposed proxy is IP₂₅, a highly branched isoprenoid alkene biosynthesized exclusively by sea-ice dwelling diatoms (Haslea spp.), which is well preserved in marine sediments and could be used to reconstruct past changes in spring sea-ice extent. However, little is known about regional-scale controls on IP₂₅ production in sea ice, limiting its wider applicability as a paleo-sea-ice proxy. To address this issue we examined the distributions of IP₂₅ and the marine productivity biomarkers dinosterol and brassicasterol in a suite of surface sediment samples distributed across the Arctic. We find a statistically significant, logarithmic relationship between IP₂₅ and spring sea ice cover in samples from arctic and subarctic sites in the Pacific (n = 96, r² = 0.67, P < 0.0001) and the Atlantic n = 25, r² = 0.50, P < 0.0001), though the absolute concentrations of IP₂₅ are generally higher in the Atlantic (1.6 - 166.4 [mu]g/g OC) than in the Pacific (0 - 38.5 [mu]g/g OC) for equivalent sea-ice cover, and there are regional and basin-specific differences in the slope of the IP₂₅ - sea ice relationship. After normalization of IP₂₅ concentrations to that of a productivity biomarker (e.g., dinosterol; the PDIP₂₅ index) the proxy-sea ice relationship in greatly improved for all regions (r² = 0.86 and r² = 0.75 for Atlantic and Pacific, respectively) and most of the basin specific differences in the rate of change of IP₂₅ with sea ice are removed. This suggests that productivity plays an important secondary role in controlling IP₂₅ concentrations. However, the use of the PDIP₂₅ index does not change the absolute differences in concentrations seen in the Atlantic and the Pacific, and previously published data from Fram Strait remain anomalous when compared to the rest of our data. This suggests that there are additional, yet unidentified controls on the IP₂₅ proxy - sea ice relationship, which may hinder the development of an Arctic-wide calibration but that the PDIP₂₅ index is a viable tool for local and regional sea ice reconstructions. / text

Previous research indicates that future reductions in Arctic sea ice cover (SIC) could alter storm tracks and precipitation patterns in western North America and negatively impact water resources in the American southwest. Other research suggests that multiple periods of increased precipitation and/or cooler temperatures in the American southwest during the Little Ice Age (LIA) were due to strong El Niño events; historical records also describe expanded Arctic SIC at this time. We use 16th-19th century Arctic SIC records from the ACSYS Historical Ice Chart Archive as a basis for expanding Arctic SIC from 1870 HadISST data to theoretical LIA extents. Then, in a suite of sensitivity studies, we investigate the relative influences of and interactions between El Niño-Southern Oscillation (ENSO) related sea surface temperature (SST) variation and varying Arctic SIC in controlling storm tracks, precipitation patterns, and overall climate conditions in the American southwest. We find that tropical Pacific SSTs greatly influence climate system response to variability in Arctic SIC, with ENSO-Neutral SSTs permitting the greatest response. Additionally, the degree of expansion and symmetry of Arctic SIC also influence precipitation regime response. These findings suggest that the climate response to future Arctic SIC retreat may not only be highly dependent on the spatial patterns and extent of SIC reductions, but also upon ENSO variability, such that El Nino events may reduce the potential climate impact of ice reductions as compared to Neutral or La Nina events. / Master of Science

Arctic sea ice has undergone a dramatic transformation in recent decades, including a substantial reduction in sea ice extent in summer months. Such changes, combined with relatively recent advancements in seasonal (1-12 months) to decadal forecasting, have prompted a rapidly-growing body of research on forecasting Arctic sea ice on seasonal timescales. These forecasts are anticipated to benefit a vast array of end-users whose activities are dependent on Arctic sea ice conditions. The research goal of this thesis is to address fundamental challenges pertaining to seasonal forecasts of Arcitc sea ice, with a particular focus placed on improving operational sea ice forecasts in the Canadian Seasonal to Interannual Prediction System (CanSIPS). Seasonal forecasts are strongly dependent on the accuracy of observations used as initial condition inputs. A key challenge initializing Arctic sea ice is the sparse availability of Arctic sea ice thickness (SIT) observations. I present on the development of three statistical models that can be used for estimating Arctic SIT in real time for sea ice forecast initialization. The three statistical models are shown to vary in their ability to capture the recent thinning of sea ice, as well as their ability to capture interannual variations in SIT anomalies; however, each of the models is shown to dramatically improve the representation of SIT compared to the climatological SIT estimates used to initialize CanSIPS. I conduct a thorough assessment of sea ice hindcast skill using the Canadian Climate Model, version 3 (one of two models used in CanSIPS), in which the dependence of hindcast skill on SIT initialization is investigated. From this assessment, it can be concluded that all three statistical models are able to estimate SIT sufficiently to improve hindcast skill relative to the climatological initialization. However, the accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with two statistical models that adequately represent both processes. The final goal of this thesis is to improve the quantification of uncertainty in seasonal forecasts of regional Arctic sea ice coverage. Information regarding forecast uncertainty is crucial for end-users who want to quantify the risk associated with trusting a particular forecast. I develop statistical post-processing methodology for improving probabilistic forecasts of Arctic SIC. The first of these improvements is intended to reduce sampling uncertainty by fitting ensemble SIC forecasts to a parametric probability distribution, namely the zero- and one- inflated beta (BEINF) distribution. It is shown that overall, probabilistic forecast skill is improved using the parametric distribution relative to a simpler count-based approach; however, model biases can degrade this skill improvement. The second of these improvements is the introduction of a novel calibration method, called trend-adjusted quantile mapping (TAQM), that explicitly accounts for SIC trends and is specifically designed for the BEINF distribution. It is shown that applying TAQM greatly reduces model errors, and results in probabilistic forecast skill that generally surpasses that of a climatological reference forecast, and to some degree that of a trend-adjusted climatological reference forecast, particularly at shorter lead times. / Graduate

The seasonal cycle of snow and sea ice is a fundamental feature of the Arctic climate system. In the Northern Hemisphere, about 55 million km2 of sea ice and snow undergo complete melt and freeze processes every year. Because snow and sea ice are much brighter (higher albedo) than the underlying surface, their presence reduces absorption of incoming solar energy at high latitudes. Therefore, changes of the sea-ice and snow cover have a large impact on the Arctic climate and possibly at lower latitudes. One of the most important determining factors of the seasonal snow and sea-ice cover is the timing of the seasonal melt-freeze transitions. Hence, in order to better understand Arctic climate variability, it is key to continuously monitor these transitions. This thesis presents an algorithm for obtaining melt-freeze transitions using scatterometers over both the land and sea-ice domains. These satellite-borne instruments emit radiation at microwave wavelengths and measure the returned signal. Several scatterometers are employed: QuikSCAT (1999–2009), ASCAT (2009–present), and OSCAT (2009–present). QuikSCAT and OSCAT operate at Ku-band (λ=2.2 cm) and ASCAT at C-band (λ=5.7 cm), resulting in slightly different surface interactions. This thesis discusses these dissimilarities over the Arctic sea-ice domain, and juxtaposes the time series of seasonal melt-freeze transitions from the three scatterometers and compares them with other, independent datasets. The interactions of snow and sea ice with other components of the Arctic climate system are complex. Models are commonly employed to disentangle these interactions. But this hinges upon robust and well-formulated models, reached by perpetual testing against observations. This thesis also presents an evaluation of how well eleven state-of-the-art global climate models reproduce the Arctic sea-ice cover and the summer length—given by the melt-freeze transitions—using surface observations of air temperature. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: In press. Paper 4: Submitted.</p>

No description available.

<div> <div> <div> <p>Rapid melting of sea ice and an increased warming have been observed over the Arctic since 1990s and is expected to continue in future climate projections. Possible linkage between the Arctic sea ice and the Northern Hemisphere mid-latitude circulation has been studied previously but is not yet fully understood. This dissertation investigates the influence of the Arctic on the mid-latitudes and the underlying dynamical mechanisms. Specifically, we hypothesize that the stratosphere and its coupling with the troposphere play an important role in amplifying and extending the mid-latitude circulation response to arctic warming. </p><p><br></p> <p>First, we assess the robustness of the stratospheric pathway in linking the sea ice variability, specifically over the Barents-Kara Sea (BKS), in late autumn and early winter to the mid-latitude circulation in the subsequent winter using an ensemble of global climate model simulations. We analyze two groups of models from the Coupled Model Intercomparison Project phase 5 (CMIP5) archive, one with a well-resolved stratosphere (high-top models) and the other with a poorly-resolved stratosphere (low-top models) to distinguish the role of the stratospheric pathway. It has been found that, collectively, high-top models are able to capture the persistent mid-latitude circulation response in the subsequent winter. The response in low-top models is, however, weaker and not as long-lasting most likely due to lack of stratospheric variability. Diagnosis of eddy heat flux reveals that stronger vertical wave propagation leads to a stronger response in stratospheric polar vortex in high- top models. The results robustly demonstrate that multi-model ensemble of CMIP5 high-top models are able to capture the prolonged impact of sea ice variability on the mid-latitude circulation and outperforms the low-top models in this regard.</p><p><br></p></div></div></div><div><div><div> <p>We further explore the dynamical linkage between the BKS sea ice loss and the Siberian cold anomalies using a comprehensive Atmospheric General Circulation Model (AGCM), with a well-resolved stratosphere, with prescribed sea ice loss over BKS region. Decomposition of dynamic and thermodynamic components suggests a dynamically induced warm Arctic cold Siberia pattern in the winter following sea ice loss over the BKS in late autumn. Specifically, the results show that the meridional component of the horizontal temperature advection, from the Arctic into the Siberia, dominates in driving a cold temperature anomaly. Additionally, we conduct targeted experiments in order to quantitatively measure the role of the stratospheric pathway. We find that the stratosphere plays a critical role in the tropospheric circulation anomaly characterized by an intensified ridge-trough pattern that is attributable for the enhanced meridional temperature advection from the Arctic into the Siberia. </p><p><br></p> <p>Next, we extend our study to investigate the sensitivity to geographical location of Arctic sea ice loss and associated warming in modulating the atmospheric circulation. In particular, we assess the linear additivity of the regional Arctic sea ice loss and Arctic Amplification (AA), using a simplified dry dynamical core model. We find that the responses to regional AA over three key regions of the Arctic, i.e. Barents- Kara Sea, East Siberia-Chukchi sea and Baffin Bay-Labrador Sea, separately, show similar equatorward shift of the tropospheric jet but differences in the stratospheric polar vortex. In addition, responses to regional Arctic Amplification are not linearly additive and the residual resembles a positive Northern Annular Mode-like structure. Additional targeted experiments further diagnose the role of the stratosphere in the non-linearity. It is found that the stratosphere-troposphere coupling plays an important role in driving the non-linear circulation response to regional AA. </p><p><br></p> <p>The findings of our research leads to a systematic understanding of the role of the stratospheric pathway in modulating the mid-latitude circulation response to Arctic sea ice loss and accompanied surface warming. Our study suggests that the representation of the stratosphere in climate models plays an important role in correctly simulating the mid-latitude circulation response and could be accountable for the some of the discrepancies among recent studies. Additionally, the result indicates that studying the regional sea ice loss might not provide the full picture of pan-Arctic sea ice melting and caution the use of regional sea ice to explain the recent trend.</p></div></div></div>

The sea ice extent change and variability of the Canadian Arctic Archipelago (CAA) are quite different compared to the Arctic as a whole due to its unique geographic settings. In this thesis, the sea ice retreat processes, the connection with other Arctic regions, and the linkages to the surface radiation flux in the CAA are examined. The sea ice retreat processes in the CAA follow a four-phase process: a slow ice melt phase that usually lasts until early June (phase 1); a quick melt phase with large daily sea ice extent change which lasts close to half-a-month (phase 2); a slow melt phase that looks like slow sea ice melt or even a small ice increase that lasts another half-a-month (phase 3); and a steady ice decrease phase (phase 4). With the help of Moderate-Resolution Imaging Spectroradiometer (MODIS) data, it is identified that the quick melt in phase 2 is actually melt ponding, with melt ponds being falsely identified as open water by passive microwave. A simplified data assimilation method is then developed to improve the passive microwave sea ice concentration estimation by fusion with MODIS ice surface temperature data. The ice concentration from the analysis is found to improve the original passive microwave sea ice concentration estimation, with the largest improvements during sea ice melt. The sea ice retreat patterns in the CAA region are correlated with the sea ice retreat patterns in other regions of the Arctic. A decision tree classifier is designed to segment the sea ice retreat patterns in the CAA into several classes and classification maps are generated. These maps are effective in identifying the geographic locations that have large changes in the sea ice retreat patterns through the years. The daily progressions of the surface radiation components are described in detail. Due to the lack of multiple reflection, the percentage of shortwave radiation at the top of atmosphere that reaches the surface is influenced by the form of melt ponds over ice surface. The roles that each surface radiation component plays in forcing sea ice retreat are different in different years.

This dissertation presents new methods for detecting dynamic and thermodynamic characteristics of Arctic sea ice using radar remote sensing. A new technique for sea ice motion detection from sequential satellite synthetic aperture radar (SAR) images was developed and thoroughly validated. The accuracy of the system is 0.43 km obtained from a comparison between SAR-derived ice motion vectors and in-situ sea ice beacon trajectories. For the first time, we evaluated ice motion tracking results derived from co-polarization (HH) and cross-polarization (HV) channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice motion tracking. Sea ice motion is substantially controlled by surface winds. Two new models for ocean surface wind speed retrieval from C-band SAR data have been developed and validated based on a large body of statistics on buoy observations collocated and coincided with RADARSAT-1 and -2 ScanSAR images. The proposed models without wind direction input demonstrated a better accuracy than conventionally used algorithms. As a combination of the developed methods we designed a wind speed-ice motion product which can be a useful tool for studying sea ice dynamics processes in the marginal ice zone. To effectively asses the thermodynamic properties of sea ice advanced tools for modeling electromagnetic (EM) wave scattering from rough natural surfaces are required. In this dissertation we present a new analytical formulation for EM wave scattering from rough boundaries interfacing inhomogeneous media based on the first-order approximation of the small perturbation method. Available solutions in the literature represent special cases of our general solution. The developed scattering theory was applied to experimental data collected at three stations (with different snow thicknesses) in the Beaufort Sea from the research icebreaker Amundsen during the Circumpolar Flaw Lead system study. Good agreement between the model and experimental data were observed for all three case studies. Both model and experimental radar backscatter coefficients were considerably higher for thin snow cover (4 cm) compared to the thick snow cover case (16 cm). Our findings suggest that, winter snow thickness retrieval may be possible from radar observations under particular scattering conditions.

This thesis examines the role of remote sensing technology in providing information to northern residents of Kugluktuk and Cambridge Bay, Kitikmeot region of Nunavut, Western Canadian Arctic, for the purpose of improving sea ice trafficability and safety. The main objectives of this thesis include 1) the identification of northern community sea ice information needs that can be addressed using remote sensing, and 2) the creation of remote sensing-based products showing sea ice surface roughness information useful to community sea ice trafficability and safety. Thesis outcomes include the refinement and dissemination of information and products with these communities. Research methods involved interviews with northern community members that were analysed using thematic analysis, as well as quantitative assessments of sea ice roughness using satellite datasets. Maps of sea ice surface roughness were created using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging Spectroradiometer, and were evaluated against fine-scale airborne LiDAR data. / Graduate / 2020-07-31

No description available.