Melt on Antarctic ice shelves: observing surface melt duration from microwave remote sensing and modeling the dynamical impacts of subshelf melting

Dissertation (Ph.D.) University of Alaska Fairbanks, 2021Melt on the surface and underside of Antarctic ice shelves are important to the mass balance and stability of the ice sheet, and therefore pose significance to global sea levels. Satellite-based passive microwave observations provide daily or near-daily coarse resolution surface observations from 1978 on, and we use this record to identify days in which melt water is present on the ice sheet and ice shelf surfaces, called melt days. There are significant differences in the results of melt detection methods however, and we evaluate four different passive microwave melt detection algorithms. There is a lack of sufficient ground truth observations, so we use Google Earth Engine to build time series of Sentinel-1 Synthetic Aperture Radar images from which we can also detect melt to serve as a comparison dataset. A melt detection method using a Kmeans clustering algorithm developed here is shown to be the most effective on ice shelves, so we further apply this method to quantify melt days across all Antarctica ice shelves for every year from 1979/80 to 2019/20. The highest sums of melt days occur on the Antarctic Peninsula at 89 melt days per year, and we find few linear trends in the annual melt days on ice shelves around the continent. The primary mode of spatial variability in the melt day dataset is closely related to the Southern Annular Mode, a climate index for the southward migration of Southern Westerly Winds, which has been increasing in recent decades. Positive Southern Annular Mode index values are associated with decreased melt days in some regions of Antarctica. We also present a novel application of passive microwave analysis to detect changes in firn structure due to unusually large melt events in some regions and we show how this method detects ice lens formation and grain growth on specific ice shelves. To study the impacts of subshelf melt we focus on the Filchner-Ronne region of Antarctica, which contains the second largest ice shelf on the continent. We performed an ensemble of ice sheet model runs for a set of ocean warming scenarios. Each ensemble used a realistic range of physical parameters to control ice dynamics and sliding, generated by a Bayesian analysis of a surrogate model and observed velocities. Increased ocean temperatures were associated with increased mass loss, and by the year 2100 this region contributed 14 mm to sea level per degree of ocean warming at depth between +0°C and +4°C of ocean potential temperature. Beyond +4°C, the rate mass loss increased substantially. This mass loss corresponded to grounding line retreat across the region.NSF Award #1543432, NASA grant #80NSSC17K056

Snowmelt Detection on Alpine Glaciers using Synthetic Aperture Radar Time Series

Hindu Kush Himalayan (HKH) glaciers serve as some of the most sensitive indicators of changes in global climate. These glaciers shape the hydrologic dynamics of river systems supplying freshwater to over 2 billion people throughout Asia and regulate the geochemistry of sensitive aquatic alpine ecosystems. As snowmelt onsets sooner, lasts longer, and snowfields retreat due to increases in global temperature, the hydrologic dynamics of catchments draining HKH threaten to change the availability of surface freshwater resources for nearly one fifth of the global population, disturb sensitive aquatic habitat, and precipitate hazards associated with glacier wasting. Informed planning and decision-making around adaption to a changing climate requires operational monitoring of glacier melt dynamics to improve study of predicted disturbances to HKH hydrologic systems. This research presents a method for spatially resolved alpine glacier melt detection using synthetic aperture radar (SAR) time series. Building on research into melt detection from passive microwave scatterometers over large ice sheets, this study detects melt characteristics from Sentinel-1 SAR backscatter intensity time series over glacier surfaces using a classification threshold based on a decrease in backscatter intensity relative to average values across the frozen season. Statistical analysis of the radiometric response to dielectric loss on glaciated area within the study region (70,789 km2) shows that cross-polarized melt classification accounts for 24% more of glacier surface area than co-polarized observations. Illustrative comparison of melt classification results to optical imagery captured near the end of seasonal melt reveals that dual polarized melt measurements are concentrated within areas of apparent glacier accumulation yet cross-polarized melt detection occurs more homogeneously across glacier surfaces relative to co-polarized observations. The results of this study suggest that physical characteristics of the glacier surface may be radiometrically distinct across positive and negative zones of glacier mass balance. Improvements to radiometric terrain correction of SAR data in complex high mountain terrain would improve the accuracy of temporal thresholding algorithms for melt detection

Spatio-temporal variability in Southern Hemisphere glacier snowline altitudes from 2000-2020

The glacierised Southern Hemisphere is vulnerable to continued shrinkage under climate change, but representation of these mountainous regions in climate research is limited by hemispheric and altitudinal scarcity of meteorological observations. End-of-summer snowline altitude (SLAEOS) indicates glacier response to climatic forcing, though has been estimated with low spatio-temporal coverage for the Southern Hemisphere. This study presents the first Southern Hemisphere-wide quantification of SLAEOS, with analysis of regional and intra-regional trends. An automated approach was implemented in Google Earth Engine, in which glacier snow cover was classified in Landsat scenes using Otsu image segmentation and SLAEOS was estimated as the lowest altitude from which snow cover ratio was continuously > 0.5. Results encompassed 6485 glaciers of the Southern Alps, Andes, and Antarctic Peninsula, with trends calculated from 2000-2020. Snowlines underwent widespread retreat in this period; mean rates of SLAEOS rise were between 2.19 and 6.28 m yr-1 for regions, between 1.63 and 7.55 m yr-1 for east/west sub-regions, and were mostly accelerated for the recent decade (2010-2020). Mean SLAEOS lowering (-30 to -1 m yr-1) indicated stability in the southernmost Andes, contrasting to rapid SLAEOS rise (10 to 30 m yr-1) in the southern Central Chilean Andes, and eastern slopes generally experienced increased rates of SLAEOS rise compared to western slopes. SLAEOS variability was reflected in periods of summer warming and reductions in summer snowfall, though correlation with these variables was not consistently identified. East-west and north-south disparities in absolute SLAEOS and rates of SLAEOS change were linked to spatial variability in terrain elevation and prevailing moisture transport, with the latter evidencing the variability and impact of large-scale climatic modes. Given implications of observed trends for glacier mass loss, continued research may involve developing an annually-updated global dataset, investigating additional drivers of SLAEOS variability, and estimating glacier response times

Multispectral classification and reflectance of glaciers: in situ data collection, satellite data algorithm development, and application in Iceland & Svalbard

Glaciers and ice caps (GIC) are central parts of the hydrological cycle, are key to understanding regional and global climate change, and are important contributors to global sea level rise, regional water resources and local biodiversity. Multispectral (visible and near-infrared) remote sensing has been used for studying GIC and their changing characteristics for several decades. Glacier surfaces can be classified into a range of facies, or zones, which can be used as proxies for annual mass balance and also play a significant role in understanding glacier energy balance. However, multispectral sensors were not designed explicitly for snow and ice observation, so it is not self-evident that they should be optimal for remote sensing of glaciers. There are no universal techniques for glacier surface classification which have been optimized with in situ reflectance spectra. Therefore, the roles that the various spectral, spatial, and radiometric properties of each sensor play in the success and output of resulting classifications remain largely unknown. Therefore, this study approaches the problem from an inverse perspective. Starting with in situ reflectance spectra from the full range of surfaces measured on two glaciers at the end of the melt season in order to capture the largest range of facies (Midtre Lovénbreen, Svalbard & Langjökull, Iceland), optimal wavelengths for glacier facies identification are investigated with principal component analysis. Two linear combinations are produced which capture the vast majority of variance in the data; the first highlights broadband albedo while the second emphasizes the difference in reflectance between blue and near-infrared wavelengths for glacier surface classification. The results confirm previous work which limited distinction to snow, slush, and ice facies. Based on these in situ data, a simple, and more importantly completely transferrable, classification scheme for glacier surfaces is presented for a range of satellite multispectral sensors. Again starting with in situ data, application of relative response functions, scaling factors, and calibration coefficients shows that almost all simulated multispectral sensors (at certain gain settings) are qualified to classify glacier accumulation and ablation areas but confuse classification of partly ash-covered glacier surfaces. In order to consider the spatial as well as the spectral properties of multispectral sensors, airborne data are spatially degraded to emulate satellite imagery; while medium-resolution sensors (~20-60 m) successfully reproduce high-resolution (2 m) observations, low-resolution sensors (i.e. 250 m+) are unable to do so. These results give confidence in results from current sensors such as ASTER and Landsat ETM+ as well as ESA’s upcoming Sentinel-2 and NASA’s recently launched LDCM. In addition, images from the Landsat data archive are used to classify glacier facies and calculate the albedo of glaciers on the Brøgger Peninsula, Svalbard. The time series is used to observe seasonal and interannual trends and investigate the role of melt-albedo feedback in thinning of Svalbard glaciers. The dissertation concludes with recommendations for glacier surface classification over a range of current and future multispectral sensors. Application of the classification schemes suggested should help to improve the understanding of recent and continuing change to GIC around the world.My doctoral studies were supported by a graduate studentship from Trinity College, Cambridge as well as by the National Science Foundation Graduate Research Fellowship Programme under Grant No. DGE-1038596. Further research support came from UK Natural Environment Research Council’s Field Spectroscopy Facility, ARCFAC (the European Centre for Arctic Environmental Research), Trinity College Cambridge, Sigma Xi, the Norwegian Marshall Fund, the Explorers Club, the National Geographic Society Young Explorers Program, the Scott Polar Research Institute, the Cambridge University Geography Department, the Cambridge University Department of Anglo-Saxon, Norse, and Celtic Studies, and the Cambridge University Worts Fund

Climate and surface mass balance of coastal West Antarctica resolved by regional climate modelling

West Antarctic climate and surface mass balance (SMB) records are sparse. To fill this gap, regional atmospheric climate modelling is useful, providing that such models are employed at sufficiently high horizontal resolution and coupled with a snow model. Here we present the results of a high-resolution (5.5 km) regional atmospheric climate model (RACMO2) simulation of coastal West Antarctica for the period 1979–2015. We evaluate the results with available in situ weather observations, remote-sensing estimates of surface melt, and SMB estimates derived from radar and firn cores. Moreover, results are compared with those from a lower-resolution version, to assess the added value of the resolution. The high-resolution model resolves small-scale climate variability invoked by topography, such as the relatively warm conditions over ice-shelf grounding zones, and local wind speed accelerations. Surface melt and SMB are well reproduced by RACMO2. This dataset will prove useful for picking ice core locations, converting elevation changes to mass changes, for driving ocean, ice-sheet and coupled models, and for attributing changes in the West Antarctic Ice Sheet and shelves to changes in atmospheric forcing

UAVs for Science in Antarctica

Remote sensing is a very powerful tool that has been used to identify, map and monitor Antarctic features and processes for nearly one century. Satellite remote sensing plays the main role for about the last five decades, as it is the only way to provide multitemporal views at continental scale. But the emergence of small consumer-grade unoccupied aerial vehicles (UAVs) over the past two decades has paved the way for data in unprecedented detail. This has been also verified by an increasing noticeable interest in Antarctica by the incorporation of UAVs in the field activities in diversified research topics. This paper presents a comprehensive review about the use of UAVs in scientific activities in Antarctica. It is based on the analysis of 190 scientific publications published in peer-reviewed journals and proceedings of conferences which are organised into six main application topics: Terrestrial, Ice and Snow, Fauna, Technology, Atmosphere and Others. The analysis encompasses a detailed overview of the activities, identifying advantages and difficulties, also evaluating future possibilities and challenges for expanding the use of UAV in the field activities. The relevance of using UAVs to support numerous and diverse scientific activities in Antarctica becomes very clear after analysing this set of scientific publications, as it is revolutionising the remote acquisition of new data with much higher detail, from inaccessible or difficult to access regions, in faster and cheaper ways. Many of the advances can be seen in the terrestrial areas (detailed 3D mapping; vegetation mapping, discrimination and health assessment; periglacial forms characterisation), ice and snow (more detailed topography, depth and features of ice-sheets, glaciers and sea-ice), fauna (counting penguins, seals and flying birds and detailed morphometrics) and in atmosphere studies (more detailed meteorological measurements and air-surface couplings). This review has also shown that despite the low environmental impact of UAV-based surveys, the increasing number of applications and use, may lead to impacts in the most sensitive Antarctic ecosystems. Hence, we call for an internationally coordinated effort to for planning and sharing UAV data in Antarctica, which would reduce environmental impacts, while extending research outcomes.info:eu-repo/semantics/publishedVersio

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing