Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies

Geostatistical and statistical classification of sea-ice properties and provinces from SAR data

Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line

Snow observations from Arctic Ocean Soviet drifting stations: legacy and new directions

The Arctic Ocean is one of the most rapidly changing regions on the planet. Its warming climate has driven reductions in the region's sea ice cover which are likely unprecedented in recent history, with many of the environmental impacts being mediated by the overlying snow cover. As well as impacting energetic and material fluxes, the snow cover also obscures the underlying ice from direct satellite observation. While the radar waves emitted from satellite-mounted altimeters have some ability to penetrate snow cover, an understanding of snow geophysical properties remains critical to remote sensing of sea ice thickness. The paucity of Arctic Ocean snow observations was recently identified as a key knowledge gap and uncertainty by the Intergovernmental Panel on Climate Change's Special Report on Oceans and Cryosphere in a Changing Climate. This thesis aims to address that knowledge gap. Between 1937 and 1991 the Soviet Union operated a series of 31 crewed stations which drifted around the Arctic Ocean. During their operation, scientists took detailed observations of the atmospheric conditions, the physical oceanography, and the snow cover on the sea ice. This thesis contains four projects that feature these observations. The first two consider a well known snow depth and density climatology that was compiled from observations at the stations between 1954 & 1991. Specifically, Chapter two considers the role of seasonally evolving snow density in sea ice thickness retrievals, and Chapter three considers the impact of the climatological treatment itself on satellite estimates of sea ice thickness variability and trends. Chapter four presents a statistical model for the sub-kilometre distribution of snow depth on Arctic sea ice through analysis of snow depth transect data. Chapter five then compares the characteristics of snow melt onset at the stations with satellite observations and results from a recently developed model

Evaluation of Operation IceBridge quick-look snow depth estimates on sea ice

We evaluate Operation IceBridge (OIB) ‘quick-look’ (QL) snow depth on sea ice retrievals using in situ measurements taken over immobile first-year ice (FYI) and multi-year ice (MYI) during March of 2014. Good agreement was found over undeformed FYI (-4.5 cm mean bias) with reduced agreement over deformed FYI (-6.6 cm mean bias). Over MYI, the mean bias was -5.7 cm but 54% of retrievals were discarded by the OIB retrieval process as compared to only 10% over FYI. Footprint scale analysis revealed a root mean square error (RMSE) of 6.2 cm over undeformed FYI with RMSE of 10.5 cm and 17.5 cm in the more complex deformed FYI and MYI environments. Correlation analysis was used to demonstrate contrasting retrieval uncertainty associated with spatial aggregation and ice surface roughness

Estimation of Sea Ice Thickness Distributions through the Combination of Snow Depth and Satellite Laser Altimetry Data

Combinations of sea ice freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea ice thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High resolution airborne laser altimeter retrievals of snow-ice freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high resolution satellite altimeter measurements with low resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin ice allowing the development of a method for estimating sea ice thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and ice thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year ice from AMSR-E, and snow depth over multiyear ice from climatological data. Due to the non-linear dependence of heat flux on ice thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for ice thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and ice growth rate using snow and ice thickness values at the 70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25 km mean ice thickness values

Mapping Polar Bear Maternal Denning Habitat in the National Petroleum Reserve–Alaska with an IfSAR Digital Terrain Model

The National Petroleum Reserve–Alaska (NPR-A) in northeastern Alaska provides winter maternal denning habitat for polar bears (Ursus maritimus) and also has high potential for recoverable hydrocarbons. Denning polar bears exposed to human activities may abandon their dens before their young are able to survive the severity of Arctic winter weather. To ensure that wintertime petroleum activities do not threaten polar bears, managers need to know the distribution of landscape features in which maternal dens are likely to occur. Here, we present a map of potential denning habitat within the NPR-A. We used a fine-grain digital elevation model derived from Interferometric Synthetic Aperture Radar (IfSAR) to generate a map of putative denning habitat. We then tested the map’s ability to identify polar bear denning habitat on the landscape. Our final map correctly identified 82% of denning habitat estimated to be within the NPR-A. Mapped denning habitat comprised 19.7 km2 (0.1% of the study area) and was widely dispersed. Though mapping denning habitat with IfSAR data was as effective as mapping with the photogrammetric methods used for other regions of the Alaskan Arctic coastal plain, the use of GIS to analyze IfSAR data allowed greater objectivity and flexibility with less manual labor. Analytical advantages and performance equivalent to that of manual cartographic methods suggest that the use of IfSAR data to identify polar bear maternal denning habitat is a better management tool in the NPR-A and wherever such data may be available.La réserve pétrolière nationale–Alaska (NPR-A), située dans le nord-est de l’Alaska (NPR-A), constitue un habitat hivernal de tanières de mise bas pour l’ours polaire (Ursus maritimus) et présente de grandes possibilités du point de vue des hydrocarbures récupérables. Les ours polaires des tanières qui sont exposés aux activités de l’être humain peuvent abandonner leur tanière avant que leurs petits ne soient prêts à survivre les rigueurs de l’hiver de l’Arctique. Afin de faire en sorte que les activités d’exploitation pétrolière hivernales ne posent pas de menaces aux ours polaires, les gestionnaires doivent connaître la répartition des caractéristiques du paysage où les tanières de mise bas sont susceptibles de se trouver. Ici, nous présentons une carte sur laquelle sont indiqués des habitats de tanières possibles au sein de la NPR-A. Nous avons utilisé un système de modélisation numérique des hauteurs à haute définition dérivé du radar interférométrique à synthèse d’ouverture (IfSAR) pour produire une carte putative de l’habitat de tanières. Ensuite, nous avons mis la carte à l’épreuve pour déterminer son aptitude à repérer l’habitat de tanières de mise bas au sein du paysage. Notre carte finale a repéré avec exactitude 82 % de l’habitat de tanières qui se trouverait à l’intérieur de la NPR-A. L’habitat de tanières cartographié s’étendait sur 19,7 km2 (0,1 % de l’aire étudiée) et était largement dispersé. Même si la cartographie de l’habitat de tanières au moyen des données de l’IfSAR était aussi efficace que la cartographie des méthodes photogrammétriques employées dans d’autres régions de la plaine côtière arctique de l’Alaska, l’utilisation du SIG pour analyser les données de l’IfSAR a donné lieu à une plus grande objectivité et flexibilité, avec moins de main-d’oeuvre. Les avantages analytiques et l’exécution équivalant à celles des méthodes de carto-graphie manuelles suggèrent que le recours aux données de l’IfSAR pour repérer l’habitat de tanières de mise bas d’ours polaires constitue un outil de gestion supérieur au sein de la NPR-A et de n’importe quel autre endroit où ces données sont disponibles

Impact of spatial aliasing on sea-ice thickness measurements

We explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and \u3e1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er = bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice

Ice and snow thickness variability and change in the high Arctic Ocean observed by in-situ measurements

In April 2017 we collected unique, extensive in-situ data of sea ice and snow thickness. At ten sampling sites, located under a CryoSat-2 overpass, between Ellesmere Island and 87.1°N mean and modal total ice thicknesses ranged between 2 to 3.4 m and 1.8 to 2.9 m respectively. Coincident snow thicknesses ranged between 0.3 to 0.47 m (mean), and 0.1 to 0.5 m (mode). The profile spanned the complete multiyear ice zone in the Lincoln Sea, into the first-year ice zone further north. Complementary snow thickness measurements near the North Pole showed a mean thickness of 0.31 m. Compared with scarce measurements from other years, multiyear ice was up to 0.75 m thinner than in 2004, but not significantly different from 2011 and 2014. We found excellent agreement with a commonly used snow climatology and with published long-term ice thinning rates. There was reasonable agreement with CryoSat-2 thickness retrieval

Impact of spatial aliasing on sea-ice thickness measurements

We explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and \u3e1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er = bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats