Glacier-climate interactions: a synoptic approach

The reliance on freshwater released by mountain glaciers and ice caps demands that the effects of climate change on these thermally-sensitive systems are evaluated thoroughly. Coupling climate variability to processes of mass and energy exchange at the glacier scale is challenged, however, by a lack of climate data at an appropriately fine spatial resolution. The thesis addresses this challenge through attempting to reconcile this scale mismatch: glacier boundary-layer observations of meteorology and ablation at Vestari Hagafellsjökull, Iceland, and Storglaciären, Sweden, are related to synoptic-scale meteorological variability recorded in gridded, reanalysis data. Specific attention is directed toward synoptic controls on: i) near-surface air temperature lapse rates; ii) stationarity of temperature-index melt model parameters; and iii) glacier-surface ablation. A synoptic weather-typing procedure, which groups days of similar reanalysis meteorology into weather categories , forms the basis of the analytical approach adopted to achieve these aims. Lapse rates at Vestari Hagafellsjökull were found to be shallowest during weather categories characterised by warm, cloud-free weather that encouraged katabatic drainage; steep lapse rates were encountered in weather categories associated with strong synoptic winds. Quantitatively, 26% to 38% of the daily lapse-rate variability could be explained by weather-category and regression-based models utilizing the reanalysis data: a level of skill sufficient to effect appreciable improvements in the accuracy of air temperatures extrapolated vertically over Vestari Hagafellsjökull. Weather categories also highlighted the dynamic nature of the temperature-ablation relationship. Notably, the sensitivity of ablation to changes in air temperature was observed to be non-stationary between weather categories, highlighting vulnerabilities of temperature-index models. An innovative solution to this limitation is suggested: the relationship between temperature and ablation can be varied as a function of weather-category membership. This flexibility leads to an overall improvement in the simulation of daily ablation compared to traditional temperature-index formulations (up to a 14% improvement in the amount of variance explained), without the need for additional meteorological data recorded in-situ. It is concluded that weather categories are highly appropriate for evaluating synoptic controls on glacier meteorology and surface energetics; significant improvements in the parameterization of boundary-layer meteorology and ablation rates are realised through their application

Spatial and temporal patterns of snowmelt refreezing in a Himalayan catchment

Recent progress has been made in quantifying snowmelt in the Himalaya. Although the conditions are favorable for refreezing, little is known about the spatial variability of meltwater refreezing, hindering a complete understanding of seasonal snowmelt dynamics. This study aims to improve our understanding about how refreezing varies in space and time. We simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment, Nepalese Himalaya, covering a 5-year period. Meteorological forcing data were derived from a unique elaborate network of meteorological stations and high-resolution meteorological simulations. The results show that the annual catchment average refreezing amounts to 122 mm w.e. (21% of the melt), and varies strongly in space depending on elevation and aspect. In addition, there is a seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. Substantial intra-annual variability resulted from fluctuations in snowfall. Daily refreezing simulations decreased by 84% (annual catchment average of 19 mm w.e.) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture melt-refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to changes in air temperature as a 2°C increase leads to a refreezing decrease of 35%

Anisotropy parameterization development and evaluation for glacier surface albedo retrieval from satellite observations.

Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its variability. However, corrections of anisotropic reflectance of glacier surface have been established for specific shortwave bands only, such as Landsat 5 Thematic Mapper (L5/TM) band 2 and band 4, which is a major limitation of current retrievals of glacier broadband albedo. In this study, we calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets of Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model, referred to from here on as the ‘updated model’, to retrieve albedo from L5/TM, Landsat 8 Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world. Our results show that the updated model: (1) can accurately estimate anisotropic factors of reflectance for snow and ice surfaces; (2) generally performs better than prior approaches for L8/OLI albedo retrieval but is not appropriate for L5/TM; (3) generally retrieves MODIS albedo better than the MODIS standard albedo product (MCD43A3) in both absolute values and glacier albedo temporal evolution, i.e., exhibiting both fewer gaps and better agreement with field observations. As the updated model enables anisotropy correction of a maximum of 10 multispectral bands and is implemented in Google Earth Engine (GEE), it is promising for observing and analyzing glacier albedo at large spatial scales

Energy and Water Cycles in the Third Pole

As the most prominent and complicated terrain on the globe, the Tibetan Plateau (TP) is often called the “Roof of the World”, “Third Pole” or “Asian Water Tower”. The energy and water cycles in the Third Pole have great impacts on the atmospheric circulation, Asian monsoon system and global climate change. On the other hand, the TP and the surrounding higher elevation area are also experiencing evident and rapid environmental changes under the background of global warming. As the headwater area of major rivers in Asia, the TP’s environmental changes—such as glacial retreat, snow melting, lake expanding and permafrost degradation—pose potential long-term threats to water resources of the local and surrounding regions. To promote quantitative understanding of energy and water cycles of the TP, several field campaigns, including GAME/Tibet, CAMP/Tibet and TORP, have been carried out. A large amount of data have been collected to gain a better understanding of the atmospheric boundary layer structure, turbulent heat fluxes and their coupling with atmospheric circulation and hydrological processes. The focus of this reprint is to present recent advances in quantifying land–atmosphere interactions, the water cycle and its components, energy balance components, climate change and hydrological feedbacks by in situ measurements, remote sensing or numerical modelling approaches in the “Third Pole” region

Data Fusion and Synergy of Active and Passive Remote Sensing; An application for Freeze Thaw Detections

There has been a recent evolvement in the field of remote sensing after increase of number satellites and sensors data which could be fused to produce new data and products. These efforts are mainly focused on using of simultaneous observations from different platforms with different spatial and temporal resolutions. The research dissertation aims to enhance the synergy use of active and passive microwave observations and examine the results in detection land freeze and thaw (FT) predictions. Freeze thaw cycles particularly in high-latitude regions have a crucial role in many applications such as agriculture, biogeochemical transitions, hydrology and ecosystem studies. The dielectric change between frozen ice and melted water can dramatically affect the brightness temperature (TB) signal when water transits from the liquid to the solid phase which makes satellite-based microwave remote sensing unique for characterizing the surface freeze thaw status. Passive microwave (PMW) sensors with coarse resolution (about 25 km) but more frequent observations (at least twice a day and more frequent in polar regions) have been successfully utilized to define surface state in terms of freeze and thaw in the past. Alternatively, active microwave (AMW) sensors provide much higher spatial resolution (about 100 m or less) though with less temporal resolution (each 12 days). Therefore, an integration of microwave data coming from different sensors may provide a more complete estimation of land freeze thaw state. In this regard, the overarching goal of this research is to explore estimating high spatiotemporal freeze and thaw states using the combination of passive and active microwave observations. To obtain a high temporal resolution TB, this study primarily builds an improved diurnal variation of land surface temperature from integration of infrared sensors. In the next step, a half an hourly diurnal cycle of TB based on fusion of different passive sensors is estimated. It should be mentioned that each instrument has its own footprint, resolution, viewing angle, as well as frequency and consequently their data need to be harmonized in order to be combined. Later, data from an AMW sensor with fine spatial resolution are merged and compared to the corresponding passive data in order to find a relation between TB and backscatter data. Subsequently, PMW TB map can be downscaled to a higher spatial resolution or AMW backscatter timeseries can be generalized to high temporal resolution. Eventually, the final high spatiotemporal resolution TB product is used to examine the freeze thaw state for case studies areas in Northern latitudes

Simulating past and future mass balance of Place Glacier using a physically-based, distributed glacier mass balance model

The objective of this study is to develop a physically-based distributed glacier mass balance (GMB) model for Place Glacier, British Columbia, Canada, and apply the model to develop the historic and the future mass balance. The model is forced with climate data from Regional Atmospheric Modeling System (RAMS) mesoscale atmospheric model output from 1979-2008 for developing historic mass balance on Place Glacier. The model is also run in the future (2009-2040) to develop a projection of mass balance. The model simulated the historic glacier-wide summer and winter balance on Place Glacier satisfactorily. For all years, root mean squared error (RMSE) in simulated summer and winter balance are 0.43 m water equivalent (w.e.) and 0.27 m w.e., respectively. Over the period of 29 years, the model simulated a cumulative net mass balance of -33.72 m w.e. The model outperformed both empirical temperature index (TI) and enhanced TI models in simulating summer balance on Place Glacier when forced with the same RAMS variables. A linear regression model based on Singular Value Decomposition (SVD) technique is used for downscaling future climate projections from a suite of Global Climate Models (GCMs). The cross-validation of downscaled daily air temperature showed a strong correlation with the validation dataset (r~ =0.85, p <0.05). However, the RMSE in downscaled daily air temperature is large (=2.4~C). With spatially average correlation of 0.38 and RMSE of 7.5 mm day\u207b~ , the model for daily precipitation performed less satisfactorily in downscaling large-scale precipitation. For all variables, the error statistics improved with the monthly model. Future GCM projections form CanESM2, MIROC-ESM, MPI-ESM-LR, and HadGEM2-ES, are considered for downscaling. CanESM2 predicted a large negative glacier-wide net mass balance of -2.50 m w.e. for Place Glacier in the future. For the remaining GCMs, the average of net mass balance is \u20130.96 m w.e. The average of the cumulative mass loss predicted from GCMs other than CanESM2 is -31 m w.e. From 2009-2040, CanESM2, MIROC, MPI and HadGEM2 predicted an area loss of 52%, 28% and 22%, respectively. Overall, all downscaled GCMs, except CanESM2, performed better in predicting future mass balance for Place Glacier.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b205525

From processes to predictions in hydrological modelling of glacierized basins

Glacierized mountain headwaters act as water towers, providing critical water resources to downstream environments when other sources are unavailable. These headwaters are currently witnessing a shift in their coupled hydrological and glaciological systems. This shift is reducing glacier volume, extent and elevation range, in addition to changing the snow dynamics across both glacierized and non-glacierized areas. These interconnected changes occur simultaneously, driven by complex physical feedbacks, and they impact streamflow generation processes. To properly characterize this transition period and predict future hydrological behaviour in these glacierized basins, physically based glacio-hydrological models representing the full range of both glacier and basin hydrological processes are needed. However, obtaining the data to apply such modelling approaches is complicated by the scarce data availability in mountain regions. New approaches to collect the required data and parametrize these complex processes need to be developed in parallel with increased process representations in glacio-hydrological models. This thesis aims to assess the impact of future climate and glacier change on glacierized basin hydrological processes and streamflow generation. Its specific objectives are to (1) develop and apply innovative approaches to characterize hydro-glaciological processes in glacierized basins, (2) diagnose hydrological and glaciological processes resulting in streamflow generation and variability and (3) assess the coupled impacts of climate and landscape change on the hydrological processes and streamflow generation in a glacierized basin. Field-based investigations of streamflow measurement uncertainty, sub-debris melt and surface energy balance were conducted and used to guide new and revised algorithms for the Cold Region Hydrological Modelling (CRHM) platform. Using CRHM with the newly added process representations for katabatic wind turbulent transfer, hourly energy balance and sub-debris melt, a physically based glacio-hydrological model was developed and tested in the Peyto Glacier Research Basin, a 53% glacierized headwater basin (as of 2013) located in the Canadian Rockies. This glacio-hydrological model was used to investigate the recent past and current (1990-2020) hydrology of the basin using in-situ weather observations. Over the 32 years, strong inter-annual variability in the meteorological forcings caused highly variable streamflow in this cold alpine basin. Snowmelt always provided the largest fraction of annual streamflow (44 to 89%), with lower snowmelt contributions occurring in high streamflow years. Ice melt provided between 10 to 45% of total streamflow, with a higher contribution associated with high flow years. Both rainfall-runoff and firn melt contributed less than 13% of annual streamflow. Years with high streamflow were on average 1.43˚C warmer than low streamflow years, and high streamflow years had lower winter snow accumulation, earlier snowmelt and higher summer rain than years with low streamflow. The glacier hydrology of current (2000-2015) and future periods (2085-2100) was compared, using bias-corrected, dynamically downscaled, convection-permitting high-resolution atmospheric model outputs. The simulations show that the end-of-century increase in precipitation, mainly expressed as an increase in rainfall at the expense of snowfall, will nearly compensate for the decreased ice melt associated with almost complete deglaciation, resulting in a decrease of 7% in annual streamflow. However, the timing of streamflow will advance substantially, with the timing of peak flow shifting from July to June, and August streamflow dropping by 68%. To examine the sensitivity of future hydrology to possible future post-glacial landscapes, the end-of-century simulations were run under a range of boundary conditions and were most sensitive to initial ice volume and surface water storage. This research provides better modelling techniques to represent the complex systems of headwater glacierized basins, as well as robust estimates of future glacier contributions to streamflow in reference basins of the Canadian Rockies and should be useful for water availability studies and water management mitigation strategies

Quantifying supraglacial debris thickness at local to regional scales

Supraglacial debris thickness is a key control on the surface energy balance of debris-covered glaciers, which are common in temperate mountain ranges around the world. As such, it is an important input variable to the sorts of models that are used to understand and predict glacier change, which are essential for determining future water supply in glacierised regions and glacier contributions to sea-level rise. However, to quantify supraglacial debris thickness is difficult: making direct measurements is laborious and existing remote sensing approaches have not been thoroughly validated, so there is a general paucity of supraglacial debris thickness data. This thesis investigates methods of quantifying supraglacial debris thickness at local to regional scales. First, it makes in-situ field measurements of debris thickness at the local scale on glaciers in the Himalaya and the European Alps by manual excavation and by ground-penetrating radar (GPR). Second, it uses some of these field measurements to test and develop thermal remote sensing approaches to quantifying supraglacial debris thickness at the glacier scale. Third, it uses a dynamic energy-balance model in an inverse approach to quantify debris thickness on the glaciers of three watersheds in High Mountain Asia from thermal satellite imagery and high-resolution meteorological reanalysis data. At the local scale, GPR is found to be useful for measuring supraglacial debris thickness accurately and precisely, at least in the range 0.16-4.9 m. Debris thickness is highly variable over horizontal distances of < 10 m on individual glaciers due to gravitational reworking, which necessarily implies higher sub-debris ice melt rates than if debris thickness was spatially invariable. At the glacier scale, thermal remote sensing approaches can reproduce field measurements, and remote sensing estimates of supraglacial debris thickness can be used successfully to model sub-debris melting. If well-distributed field measurements are available, supraglacial debris thickness should be extrapolated using remote sensing-derived pseudo daily mean surface temperatures. Otherwise, it should be determined iteratively by minimising the mismatch between remotely sensed surface temperatures, preferably from night-time thermal images, and surface temperatures determined using a dynamic energy-balance model. At the regional scale, thermal satellite imagery and high-resolution meteorological reanalysis data can be used to provide reasonable estimates of supraglacial debris thickness. However, modelled uncertainties are not always able to explain ground-truth measurements, and there is a tendency towards underestimation due to problems associated with supraglacial ponds and ice cliffs and the spatial resolution of input data. The findings of this thesis will lead to improvements in the quantification of supraglacial debris thickness at a range of scales and, therefore, in the understanding and prediction of glacier change in temperate mountain ranges.Funded by NERC DTP grant number NE/L002507/1. CASE sponsorship provided by Reynolds International Ltd

Multivariate data assimilation in snow modelling at Alpine sites

The knowledge of snowpack dynamics is of critical importance to several real-time applications such as agricultural production, water resource management, flood prevention, hydropower generation, especially in mountain basins. Snowpack state can be estimated by models or from observations, even though both these sources of information are affected by several errors