Recent Precipitation Decrease Across the Western Greenland Ice Sheet Percolation Zone

The mass balance of the Greenland Ice Sheet (GrIS) in a warming climate is of critical interest in the context of future sea level rise. Increased melting in the GrIS percolation zone due to atmospheric warming over the past several decades has led to increased mass loss at lower elevations. Previous studies have hypothesized that this warming is accompanied by a precipitation increase, as would be expected from the Clausius–Clapeyron relationship, compensating for some of the melt-induced mass loss throughout the western GrIS. This study tests that hypothesis by calculating snow accumulation rates and trends across the western GrIS percolation zone, providing new accumulation rate estimates in regions with sparse in situ data or data that do not span the recent accelerating surface melt. We present accumulation records from sixteen 22–32m long firn cores and 4436 km of ground-penetrating radar, covering the past 20–60 years of accumulation, collected across the western GrIS percolation zone as part of the Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) project. Trends from both radar and firn cores, as well as commonly used regional climate models, show decreasing accumulation rates of 2:4±1:5%a-1 over the 1996–2016 period, which we attribute to shifting storm tracks related to stronger atmospheric summer blocking over Greenland. Changes in atmospheric circulation over the past 20 years, specifically anomalously strong summertime blocking, have reduced GrIS surface mass balance through both an increase in surface melting and a decrease in accumulation rates

Multi-Channel Ground-Penetrating Radar for the Continuous Quantification of Snow and Firn Density, Depth, and Accumulation

A priority of ice sheet surface mass balance (SMB) prediction is ascertaining the surface density and annual snow accumulation. These forcing data are inputs for firn density models and can be used to inform remotely sensed ice sheet surface processes and to assess Regional Climate Model (RCM) skill. The Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) retrieved 16 shallow firn cores and dug 42 snow pits along the Western percolation zone of the Greenland Ice Sheet (GrIS) during May and June of 2016 and 2017. I deployed and maintained a multi-channel 500 MHz ground-penetrating radar in a multi-offset configuration throughout the two traverse campaigns. The multi-channel radar technique accurately and independently estimates density, depth, and annual snow accumulation -- between the firn core and snow pit sites -- by horizon velocity analysis of common midpoint radar reflections from the snow and shallow firn. I analyzed a 45 km section of the traverse in a high accumulation zone, known as the GreenTrACS Core 15 Western Spur. Deviations in surface density up to +- 15 kg/m3 from the transect mean correlate with surface elevation and surface slope angle. Spatial variation in mean annual accumulation of ~0.175 m w.e. ɑ-1 occurs across a trough in the surface topography ~5 km wide. The reported variability of density and accumulation demonstrates that RCMs must be down-scaled to resolutions within 5 km to assess subtle yet significant contributions to the GrIS SMB

Ice Core Records of West Greenland Melt and Climate Forcing

Remote sensing observations and climate models indicate that the Greenland Ice Sheet (GrIS) has been losing mass since the late 1990s, mostly due to enhanced surface melting from rising summer temperatures. However, in situ observational records of GrIS melt rates over recent decades are rare. Here we develop a record of frozen meltwater in the west GrIS percolation zone preserved in seven firn cores. Quantifying ice layer distribution as a melt feature percentage (MFP), we find significant increases in MFP in the southernmost five cores over the past 50 years to unprecedented modern levels (since 1550 CE). Annual to decadal changes in summer temperatures and MFP are closely tied to changes in Greenland summer blocking activity and North Atlantic sea surface temperatures since 1870. However, summer warming of ~1.2°C since 1870–1900, in addition to warming attributable to recent sea surface temperature and blocking variability, is a critical driver of high modern MFP levels

Advancements in Measuring and Modeling the Mechanical and Hydrological Properties of Snow and Firn: Multi-sensor Analysis, Integration, and Algorithm Development

Estimating snow mechanical properties – such as elastic modulus, stiffness, and strength – is important for understanding how effectively a vehicle can travel over snow-covered terrain. Vehicle instrumentation data and observations of the snowpack are valuable for improving the estimates of winter vehicle performance. Combining in-situ and remotely-sensed snow observations, driver input, and vehicle performance sensors requires several techniques of data integration. I explored correlations between measurements spanning from millimeter to meter scales, beginning with the SnowMicroPenetrometer (SMP) and instruments applied to snow that were designed for measuring the load bearing capacity and the compressive and shear strengths of roads and soils. The spatial distribution of snow’s mechanical properties is still largely unknown. From this initial work, I determined that snow density remains a useful proxy for snowpack strength. To measure snow density, I applied multi-sensor electromagnetic methods. Using spatially distributed snowpack, terrain, and vegetation information developed in the subsequent chapters, I developed an over-snow vehicle performance model. To measure the vehicle performance, I joined driver and vehicle data in the coined Normalized Difference Mobility Index (NDMI). Then, I applied regression methods to distribute NDMI from spatial snow, terrain, and vegetation properties. Mobility prediction is useful for the strategic advancement of warfighting in cold regions. The security of water resources is climatologically inequitable and water stress causes international conflict. Water resources derived from snow are essential for modern societies in climates where snow is the predominant source of precipitation, such as the western United States. Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. In this work, I combined high-spatial resolution light detection and ranging (LiDAR) measured snow depths with ground-penetrating radar (GPR) measurements of two-way travel-time (TWT) to solve for snow density. Then using LiDAR derived terrain and vegetation features as predictors in a multiple linear regression, the density observations are distributed across the SnowEx 2020 study area at Grand Mesa, Colorado. The modeled density resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation. The integration of radar and LiDAR sensors shows promise as a technique for estimating SWE across entire river basins and evaluating observational- or physics-based snow-density models. Accurate estimation of SWE is a means of water security. In our changing climate, snow and ice mass are being permanently lost from the cryosphere. Mass balance is an indicator of the (in)stability of glaciers and ice sheets. Surface mass balance (SMB) may be estimated by multiplying the thickness of any annual snowpack layer by its density. Though, unlike applications in seasonal snowpack, the ages of annual firn layers are unknown. To estimate SMB, I modeled the firn depth, density, and age using empirical and numerical approaches. The annual SMB history shows cyclical patterns representing the combination of atmospheric, oceanic, and anthropogenic climate forcing, which may serve as evaluation or assimilation data in climate model retrievals of SMB. The advancements made using the SMP, multi-channel GPR arrays, and airborne LiDAR and radar within this dissertation have made it possible to spatially estimate the snow depth, density, and water equivalent in seasonal snow, glaciers, and ice sheets. Open access, process automation, repeatability, and accuracy were key design parameters of the analyses and algorithms developed within this work. The many different campaigns, objectives, and outcomes composing this research documented the successes and limitations of multi-sensor estimation techniques for a broad range of cryosphere applications

A Comparison of Global Climate Reanalysis and Climate of South Greenland and the North Atlantic

Global climate reanalysis models are regularly used in many scientific fields concerning climate and atmospheric observation. This thesis utilizes reanalysis models in two chapters in order to gain insight into North Atlantic climate teleconnections and their relation to precipitation across South Greenland. This first chapter of this thesis compares the four most recent reanalysis models – ECMWF Reanalysis Interim (ERA-I), NCEP Climate Forecast System Reanalysis (CFSR), JMA 55-year Reanalysis (JRA-55), and NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) – and develops from these models a monthly-mean ensemble average of common meteorological variables for the period 1979-2013. Results from this analysis shows that the reanalyses are in good agreement above the friction layer in the atmosphere, whereas significant model differences are found near the surface. The second chapter of this thesis utilizes the previous results to investigate the relative importance of the North Atlantic Oscillation (NAO) (high-frequency atmospheric) and the Atlantic Multidecadal Oscillation (AMO) (low-frequency sea-surface temperature) climate teleconnections as well as the Icelandic Low, Azores High, and blocking patterns in modulating precipitation across South Greenland. Key findings from this second chapter include: 1) years of extreme high and low precipitation in West Greenland are linked with the Icelandic Low, blocking patterns, and the westerly winds; and, 2) the long-term precipitation signal shows an increase of annual total precipitation and variability over southwest Greenland after the year 1995, suggesting an influence from the increase in both temperature and meridional flux of moisture and heat accompanied by a decrease in the zonal component of the westerlies. This work could be expanded upon in the future by identifying changes in synoptic fields during years of extreme high and low precipitation. Output from the four-member global climate reanalysis ensemble produced as part of this thesis will be made available online for community use

The SUMup Dataset: Compiled Measurements of Surface Mass Balance Components over Ice Sheets and Sea Ice with Analysis over Greenland

Increasing atmospheric temperatures over ice cover affect surface processes, including melt, snowfall, and snow density. Here, we present the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset, a standardized dataset of Arctic and Antarctic observations of surface mass balance components. The July 2018 SUMup dataset consists of three subdatasets, snow/firn density (https://doi.org/10.18739/A2JH3D23R), at least near-annually resolved snow accumulation on land ice (https://doi.org/10.18739/A2DR2P790), and snow depth on sea ice (https://doi.org/10.18739/A2WS8HK6X), to monitor change and improve estimates of surface mass balance. The measurements in this dataset were compiled from field notes, papers, technical reports, and digital files. SUMup is a compiled, community-based dataset that can be and has been used to evaluate modeling efforts and remote sensing retrievals. Active submission of new or past measurements is encouraged. Analysis of the dataset shows that Greenland Ice Sheet density measurements in the top 1m do not show a strong relationship with annual temperature. At Summit Station, Greenland, accumulation and surface density measurements vary seasonally with lower values during summer months. The SUMup dataset is a dynamic, living dataset that will be updated and expanded for community use as new measurements are taken and new processes are discovered and quantified

Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland

Increasing atmospheric temperatures over ice cover affect surface processes, including melt, snowfall, and snow density. Here, we present the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset, a standardized dataset of Arctic and Antarctic observations of surface mass balance components. The July 2018 SUMup dataset consists of three subdatasets, snow/firn density (https://doi.org/10.18739/A2JH3D23R), at least near-annually resolved snow accumulation on land ice (https://doi.org/10.18739/A2DR2P790), and snow depth on sea ice (https://doi.org/10.18739/A2WS8HK6X), to monitor change and improve estimates of surface mass balance. The measurements in this dataset were compiled from field notes, papers, technical reports, and digital files. SUMup is a compiled, community-based dataset that can be and has been used to evaluate modeling efforts and remote sensing retrievals. Active submission of new or past measurements is encouraged. Analysis of the dataset shows that Greenland Ice Sheet density measurements in the top 1&thinsp;m do not show a strong relationship with annual temperature. At Summit Station, Greenland, accumulation and surface density measurements vary seasonally with lower values during summer months. The SUMup dataset is a dynamic, living dataset that will be updated and expanded for community use as new measurements are taken and new processes are discovered and quantified.</p

Utilizing Ground-Penetrating Radar to Estimate the Spatial Distribution of Snow Depth over Lake Ice in Canada’s Sub-Arctic

With the expected rise in air temperature, it becomes important to understand how snow will respond in different climate scenarios. The presence of snow over lake ice largely influences the ice thickness, and as Canada’s Arctic and sub-arctic regions are experiencing warming at twice the global rate, concerns rise as changes in the snowpack will significantly impact northern communities that rely on lake ice as a means of transportation, source for drinking water, and feeding their families. The distribution of snow depth is highly sensitive to changes in climate over time, as such a slight increase in air temperature or change in precipitation can substantially alter snowpack dynamics, which in-turn, directly impacts the rate of lake ice growth. The heterogeneity of snow depth over lake ice is driven by wind redistribution and snowpack metamorphism which creates an inconsistent ice thickness across the lake. Currently, daily snow depth measurements are represented as one value, collected at a weather station on land, near lake shorelines, but previous studies show that this data is not representative of the distribution of snow across different landscapes, more specifically lake ice. Due to the exposed nature of lakes, it is shown that snow depth will be redistributed greatly over lake ice, as there is a lack of vegetation compared to land surfaces with differences in topography. To identify the snow spatial distribution, extensive snow depth measurements must be collected across the entire lake. However, the collection of accurate snow depth measurements over lake ice is challenging and requires a great deal of time spent in the field. Studies have explored the use of remote sensing techniques to map snow distribution over land, however our understanding of such over lake ice is minimal. Accurate measurements of the spatial distribution of snow depth over lake ice is limited due to logistical difficulties in manual measurement techniques (i.e., ruler, snow depth probe). This study presents the use of ground-penetrating radar (GPR) and in-situ observations (snow depth and density) to develop a systematic method to estimate the spatial distribution of snow depth over lake ice. Focused on four lakes located in the North Slave Region, Northwest Territories (Landing Lake, Finger Lake, Vee Lake, Long Lake) the snow depth is derived using GPR two-way travel time. Through utilizing a combination of ground-based techniques, this study proposed a methodology to ease the collection process required to get accurate snow depth measurements on a larger spatial scale than current methods allow. The findings of this thesis will benefit the snow and ice community as we can increase our availability of accurate snow depth data over lake ice through an efficient method of collecting larger snow depth datasets. Specifically, with the availability of snow depth data over lake ice, the accuracy of thermodynamic lake ice model can be improved significantly