The floating extent of the Antarctic Ice Sheet -- the ice shelves -- play a critical role in stabilizing the ice sheet through a process known as buttressing. This effect slows the flow of grounded ice into the ocean and thereby helps regulating the ice sheet's sea level rise contribution. However, ice shelves are highly sensitive to (climate-driven) changes, which can cause thinning and structural weakening. This, in turn, can diminish their stabilizing influence and accelerate ice loss from the ice sheet. Given Antarctica’s vast potential to contribute to sea level rise, understanding the processes affecting ice shelf stability is essential for predicting future changes and reducing associated uncertainties.

Ocean-driven melting at the base of an ice shelf significantly influences its stability by driving ice thinning, grounding line retreat, and through basal channel formation. These channels, formed by meltwater plumes carving pathways along the ice base, are shaped by ice draft geometry, ocean dynamics and temperature. Basal channels concentrate melting and can weaken ice shelves by acting as structural weak points and promoting fractures that may lead to calving and retreat. On the other hand, basal channels can also stabilize ice shelves by localizing melt, potentially reducing overall thinning. Their evolution -- including changes in size, location, and intensity of melting -- is influenced by changes in ice flow and the availability and temperature of circumpolar deep water, which is expected to increase under climate change. Understanding basal channels and their role in ice shelf (in)stability is thus essential for accurately assessing the future behavior of the Antarctic Ice Sheet and its contributions to sea level rise.

In this thesis a method for detecting basal melting at high spatial resolution, called BURGEE (Basal melt rates Using REMA and Google Earth Engine), was developed and described in Chapter 2. BURGEE combines stereo-imagery from the Reference Elevation Model of Antarctica (REMA) with CryoSat-2 elevation data to obtain high-resolution ice shelf elevation changes, which through a mass conservation approach can be translated into basal melt rates. BURGEE's 50 m posting allows for capturing detailed melt patterns previously unresolved in coarser remote sensing products. Applied to the Dotson Ice Shelf, BURGEE revealed spatial variability within a major melt channel, influenced by a pinning point that affects ocean plume pathways. This method was developed to be scalable allowing for applications to other ice shelves to better understand ice shelf melt dynamics and stability across several ice shelves.

Using BURGEE in Chapter 3, high-resolution basal melt maps revealed that melt rates within ice shelf channels have been underestimated by 42-50% in products relying on altimetry-only. This underestimation has a significant impact on ice shelf stability assumptions, for which channel breakthrough times can be used as a proxy. As breakthrough times are highly controlled by the melt rate within the channels, these altimetry-only studies also significantly underestimate the time it would take for a channel to break through. While so far channels have not been observed to actually break through, they have been observed to cause significant fracturing once they reach a thin and vulnerable state. Channel-induced fracturing has further been observed to lead to ice shelf calving and retreat. The faster-than-previously-assumed channel breakthrough times -- and thus weakening -- exacerbates the vulnerability of ice shelves to channelized melting and consequent fracturing and retreat. Incorporating basal melting at high resolution into ice-sheet and ocean models is thus crucial for improving projections of ice shelf stability and global sea level rise.

In Chapter 4, BURGEE has further revealed sudden changes within the basal channel system on George VI Ice Shelf, marked by a 23 m surface lowering over just nine years. This rapid development coincided with increased ocean temperatures and salinity during the 2015 El Niño event, highlighting the influence of large-scale climate patterns on basal melting. The high resolution further revealed subtle shifts in ice flow indicative of fracturing, suggesting a combined weakening effect from basal melting and structural integrity causing changes and possible re-routing of the channel system. Such findings underscore the importance of monitoring dynamic ice shelf channels at a high resolution to better understand and predict their role in ice shelf weakening.

Together, these findings represent a significant advancement in our understanding of basal melting and its impact on ice shelf stability. This thesis has provided the tools and insights needed to detect, quantify, and analyze the spatial variability of basal melting at high spatial resolution. By uncovering the underestimation of channelized melting, identifying key drivers of channel evolution, and linking these processes to ice shelf weakening and retreat, this work has filled critical knowledge gaps. It emphasizes the importance of high-resolution observations and models in capturing the complex interactions between ocean dynamics, basal melting (especially within channels), and ice shelf integrity.

Antarctica, the coldest, windiest, and most remote continent on our planet, plays a crucial role in the global climate system. Its ice mass loss is a major driver of rising sea levels, with projections indicating a potential rise of several meters in the coming centuries. However, there remains considerable uncertainty about the future trajectory of Antarctic mass loss. A major area of uncertainty is the fate of ice shelves—floating extensions of land ice that surround much of Antarctica and act as barriers, slowing the flow of glaciers into the ocean. Ice shelves are affected by warm water from below, which thins them and increases their vulnerability to cracking, as well as by warm air from above, which melts the surface and forms ponds of meltwater.

This research focuses on surface melt, a phenomenon where meltwater forms and either refreezes or accumulates on the ice shelf surface. When the water accumulates, it can seep into cracks, causing them to deepen and widen, which can weaken the ice shelves. In today’s era of abundant satellite imagery and advanced deep learning techniques, we can efficiently process large volumes of data, enabling more comprehensive research on surface melt dynamics. The aim of this dissertation is to enhance the mapping and understanding of surface meltwater on Antarctic ice shelves using remote sensing and deep learning methods.

The introductory chapter provides an overview of the Antarctic Ice Sheet, emphasizing the continent's immense scale and importance. Written in an accessible style, it presents key concepts about Antarctica and explores how ice shelves and surface melt influence the continent. The chapter also describes the use of satellite data to map surface melt and discusses advancements in computational resources and deep learning, which have significantly improved our ability to analyze the expanding catalog of satellite data. It concludes with an overview of the research questions addressed in the thesis.

In the second chapter, various remote sensing datasets are compared to illustrate how and why satellite observations of surface melt differ. Using state-of-the-art melt detection algorithms, we analyze surface melt patterns and observe large differences, especially in icy areas, regions with subsurface melt, and during winter. These differences arise from factors such as satellite overpass times, spatial resolution, signal penetration, cloud cover, and detection methods. Despite these challenges, the variations create opportunities to combine data from multiple satellites, enhancing the overall accuracy of surface melt detection across Antarctica.

The third chapter builds on the previous findings and addresses the challenge of balancing spatial and temporal resolution in satellite observations. Surface melt in Antarctica is highly dynamic and varies regionally, making high-resolution mapping essential. To tackle this, we develop UMelt, a surface melt dataset for all Antarctic ice shelves with high spatial (500 m) and temporal (12 h) resolution, covering the period from 2016 to 2021. Our deep learning model integrates data from multiple satellites, allowing for detailed detection of surface melt while maintaining high temporal resolution. UMelt offers the potential for new insights into how ice shelves respond to changing atmospheric conditions.

In the fourth chapter, we shift from mapping the presence of surface melt to estimating its volume. Since surface melt is mainly driven by local processes, high-resolution regional climate models (RCMs) are necessary. However, current RCMs have a coarse resolution (25--30 km) that is insufficient for capturing small-scale melt processes. To address this, we introduce SUPREME, a deep learning method that downscales surface melt to 5.5 km resolution using a physically-informed super-resolution model. This model combines remote sensing data on albedo and elevation with a 27 km resolution Regional Atmospheric Climate Model (RACMO), accounting for the diverse drivers of surface melt across Antarctica. SUPREME demonstrates the potential of super-resolution techniques with physical constraints for high-resolution surface melt mapping, providing valuable insights into localized melting patterns.

The fifth chapter examines the hydrology of surface meltwater lakes on Antarctica, investigating whether they refreeze or drain into fractures at the end of the melt season, potentially destabilizing ice shelves. Monitoring these lakes with optical satellite imagery is often limited by cloud cover, complicating the tracking of their changes over time. To overcome this, we develop a spatiotemporal deep learning model using radar imagery from Sentinel-1, which allows us to classify the evolution of meltwater lakes regardless of cloud conditions. Our findings reveal no clear connections between lake evolution and ice shelf parameters, highlighting the need for further research and model refinement. The study is an initial step in using deep learning and Sentinel-1 data to monitor the evolution of supraglacial lakes on Antarctic ice shelves.

The sixth and final chapter reflects on the research and outlines future directions. It begins by summarizing the state of Antarctic surface melt research at the start of my PhD. The chapter then highlights the key contributions of this thesis and concludes with three proposed research ideas aimed at advancing our understanding of surface melt processes in Antarctica.

The melting of the Antarctic ice sheet is anticipated to play a significant role in sea level rise over the upcoming decades. Long-term mass and volume changes of the Antarctic ice sheet are predominantly caused by changes in the movement of the ice layer, referred to as ice dynamics. Mass and volume changes of the Antarctic ice sheet are monitored by gravimetry and altimetry satellites. Their data are corrected for glacial isostatic adjustment and changes in the surface climate, namely cumulated surface mass balance anomalies and firn thickness changes, to obtain ice-dynamical mass and volume time series. These time series are modelled using dynamic state-space models in this thesis.

State-space models decompose the data into several components. These components consist of parameters that are constant with time and states that vary with time. This thesis considers two modelling approaches to estimate the parameters and states, referred to as the the Frequentist and Bayesian approaches. The Frequentist approach entails estimating model parameters using maximum likelihood estimation and subsequently determining the states at each epoch using the Kalman Filter and Smoother. The Frequentist parameter estimates are deterministic. As a result, potential stochasticity of parameters is not accounted for when determining the states. This may lead to overconfident small uncertainties in Frequentist models. The Bayesian approach remedies this by considering model parameters to be stochastic. Following the Bayesian approach, the marginal posterior distributions of the parameters are estimated using Markov Chain Monte Carlo methods. Samples from these distributions are used to estimate the conditional distributions of the states at each epoch using the simulation smoother. Because parameter samples are used to estimate the states, stochasticity of the parameters is accounted for when sampling the states following the Bayesian approach.

Three distinct Antarctic ice drainage basins are investigated in this thesis. For their ice-dynamical ice mass data, significant differences between the uncertainties of Frequentist and Bayesian models are found. The Bayesian uncertainties are consistently larger than the Frequentist uncertainties. The largest differences between Frequentist and Bayesian uncertainties are found for the slope components of state-space models. Depending on the complexity of the data that are being modelled, the Bayesian uncertainty of a slope component can be up to 4 times larger than its Frequentist uncertainty.

Frequentist and Bayesian methods to combine the trends of gravimetry-based and altimetry-based state-space models of ice-dynamical ice mass in Antarctic ice basins are also investigated in this thesis. Epoch-wise weighted averaging, with weights based on the uncertainties in the gravimetry-based and altimetry-based models, is done to combine the trends. It is found that the gravimetry data is of significantly higher quality than the altimetry data, having much smaller uncertainties. As a result, the weighted average of the trends aligns closely to the gravimetry-based trend. Finally, several choices that have to be made when working with Bayesian Markov Chain Monte Carlo methods for state-space modelling and their impact on the results are discussed.

Establishing an accurate global unified vertical reference frame (VRF) is a long-standing objective of geodesy. However, that objective has still not been achieved. One particular application where the lack of such a VRF is evident, is the improvement of hydrodynamic models by assimilating total water levels acquired by tide gauges. Indeed, to facilitate a straightforward assimilation requires that both the observed and modeled water levels refer to the same vertical datum. The required accuracy is high; it is expected to be in the order of 1centimeter. The best alternative VRF for the area of interest, the northwest European continental shelf, is the European Vertical Reference Frame 2019 (EVRF2019). The EVRF2019, however, still lacks complete coverage and the required accuracy. The key reason is that it is solely based on geopotential differences from spirit leveling/gravimetry, which are not available between benchmarks separated by large water bodies. This thesis exploits model-based hydrodynamic leveling to provide these differences. The specific objective is to assess the potential of including these data in realizing of European Vertical Reference System (EVRS).

Climate change is driving rapid transformations in the Arctic, with one of the most prominent changes being the significant decline in sea ice cover. As Arctic sea ice recedes at an alarming rate, its impact extends across various domains, necessitating a closer examination of these effects. This thesis focuses on one such domain: the relationship between Arctic sea ice and global tides. Current global hydrodynamic tidal models do not account for the energy dissipation due to sea ice nor the impact of variations in sea ice cover on tides. The seasonal fluctuations in sea ice are known to modulate tides through ice-water frictional dissipation. Given the ongoing decline in sea ice, it is imperative to develop accurate models that incorporate this dissipation. Global tidal models are crucial for several applications, including navigation and coastal flood management, highlighting the need for an efficient parameterization to account for sea ice-induced tidal dissipation.

The first research objective of this thesis was to develop a physically consistent parameterization for modeling sea ice-induced dissipation in barotropic global tide models. Chapter 2 explores the physics of air-ice-ocean interactions and the challenges of acquiring accurate sea ice drift velocities on tidal time scales. A parametric approach is introduced, which incorporates dissipation from three distinct sea ice regimes: landfast ice, drifting sea ice with internal stresses, and free-drift sea ice. The findings indicate that landfast sea ice can significantly influence the seasonal modulation of the M2 tide, particularly in regions like Hudson Bay, where it can reach up to 0.25 meters, underlining the need for further research on the long-term effects of Arctic sea ice decline.

Chapter 3 delves into the dissipation of tides caused by free drift sea ice. While this dissipation is negligible in deep, open ocean areas without wind influence, its impact in shallow water regions, such as the Spitzbergen Shelf, remains uncertain. An analysis using a beacon dataset and a physics-based ice model in the Barents Sea suggests that under low wind conditions, the dissipation from free drift sea ice is minimal, contributing only 2-3% of the bottom frictional stress.

Chapter 4 presents a new parametric approach for modeling tidal energy dissipation due to sea ice, dividing the sea ice cover into regions dominated by either Vertical Shear (VS) or Horizontal Shear (HS) energy dissipation. This approach is based on a non-dimensional Friction Number (F) that depends on sea ice thickness and concentration. The new parameterization demonstrates a stronger correlation (0.6) with altimetry data compared to current state-of-the-art methods (0.4). Additionally, it shows lower differences when compared to tide gauge observations, making it more suitable for studying the impact of sea ice decline on tides.

A significant challenge in this research was the lack of accurate, time-specific observations of tidal currents or water levels in the Arctic. To address this, the second research objective focused on developing a method for deriving tidal current constituents from GNSS buoy data, resulting in the novel ‘Model-derived fitting method’ detailed in Chapter 5. This method is evaluated through case studies, demonstrating its accuracy and robustness, especially in regions with dominant barotropic or baroclinic tidal currents.

This thesis successfully addresses key questions regarding the impact of sea ice on tides, introducing innovative parameterizations and exploring new data sources for tidal current estimation, thus advancing the understanding of Arctic tidal dynamics and ice-water interactions.

Extreme (still) sea levels and the possibly associated coastal floods, are generally linked to (high) tides and storm surges. The risk of coastal floods will likely intensify in the future. This is because, on the one hand, the population of coastal zones is expected to continue to grow, and, on the other hand, climate change may lead to an increase in the frequency and magnitude of extreme sea levels. Although observations suggest that on the global scale, sea level rise is the primary driver behind the increase in extreme sea levels, locally the increase in extreme sea levels may be amplified or even dominated by changes in stormsurges and tidal dynamics...

Satellite radar altimetry is often considered to be the most succesful spaceborne remote sensing technique ever. Satellite radar altimeters were designed for static geodetic and ocean dynamics applications. The goal of the geodetic mission phases, which have a dense ground-track spacing, is primarily to acquire information about the marine gravity field. This enables the estimation of mean dynamic topography (geographical sea surface height patterns due to ocean currents) and deep-ocean bathymetry. The primary goal of the oceanographic mission phases is to gain information about time-varying currents and ocean dynamics. TOPEX/Poseidon is the first altimetry mission to reveal sea surface height variations related to ocean dynamics as the El Niño Southern Oscillation (ENSO). During the mission it became clear that secular changes in sea level could also be monitored. Already in 1995, Nerem (1995) computed a Global Mean Sea Level (GMSL) time series from the TOPEX/Poseidon data. Currently, the GMSL record spans 26 years, in which TOPEX/Poseidon time series is extended with the Jason-1&2&3 observations. The estimated secular trend of GMSL over the altimetry era is approximately 3 mm yr−1. The succes of the TOPEX/Poseidon mission spawned the Argo project with the deployment of the first floats in the year 2000. One argued that Argo would support the future Jason missions in separating changes into the two components (density and mass) of sea level. The Argo project aims to estimate temperature and salinity over a depth of 2000 meter using floats, which enable the estimation of density or steric sea level changes. By subtracting the steric signal from the absolute sea level measured by Jason (steric-corrected altimetry), the second component of sea level changes, mass, is estimated. The launch of the Gravity Recovery And Climate Experiment (GRACE) satellites in 2002 made it possible to independently validate oceanic mass variations. If the sum of the mass and steric components equals total sea level within the uncertainties, the sea level is said to be closed. Besides these two oceanic components, ocean bottom deformation or Vertical Land Motion (VLM) also affects the sea level observed by altimeters. Over the open ocean VLM signals are generally small after a correction for Glacial Isostatic Adjustment (GIA), but near large mass variations they might become significant. Additionally, tide-gauge records are affected by VLM changes, because they are connected to land. Therefore they measure sea level relative to the sea floor, while the satellite altimeters observe the absolute variations. To bring tide gauges in the same reference frame as the altimeters, corrections for VLM have to be applied, which is usually done with nearby Global Navigation Satellite System (GNSS) data...

The Greenland Ice Sheet has a total volume of 2900000 km3. In recent decades, the ice-sheet has been losing mass rapidly and has nearly doubled its contribution to sea-level rise. One main contributing factor has been the recent widespread acceleration of the tidewater glaciers that terminate in deep and narrow glacial fjords. However, our understanding of the subsurface water properties causing this acceleration has been extremely limited owing to the lack of observations around these locations. We hypothesize that while the ice-sheet is not coupled to the ocean in the current General Circulation Models (GCMs), they can still be used to improve our understanding of the subsurface warming around these regions. In this study, we first evaluate two GCMs (HADGEM2-ES and GISS-E2-R) around three tidewater glacier locations, by interpolating the high resolution CTD data to the GCM grid. Our results substantiate that GISS-E2-R performs better than the HADGEM2-ES model along the Western Margin. However, along the Eastern Margin, we find that the HADGEM2-ES model is more consistent with the observations. The modelled spatio-temporal variability of water masses was investigated in a Pre-Industrial and Historical simulation, later used to quantify the evolution of subsurface warming under a future warming (RCP 8.5) scenario. Our results show that the Kangerlussuaq glacier (shelf) is more sensitive to greenhouse gas (GHG) forcings than Helheim and Jakobshavn. With respect to the Pre-Industrial climate, the mean warming of the Kangerlussuaq shelf under the RCP 8.5 scenario is 3.76°C, which is considerably greater than Helheim (2.38°C) and Jakobshavn (1.92°C). This is predominantly driven by the subsurface Return Waters of the Arctic Atlantic which are seen to warm the most under a future warming (RCP 8.5) scenario. For each of the three simulation, we decompose the Ocean Heat Content (OHC) time-series into intrinsic oscillatory modes so as to discern and quantify the high frequency variability from the multi-year/decadal variability and trends in the OHC. Where the contribution from the high-frequency modes to the total energy contained in the OHC signal is considerable in a Pre-Industrial scenario; the Historical and RCP 8.5 derived OHC are seen to contain a very dominant low-frequency response to GHG forcings, which contains (almost) all of the energy contained in the signal. For the RCP 8.5 scenario, we infer that at Kangerlussuaq, such a response is considerably greater than other locations. Furthermore, a consistent long-term increasing trend is seen in the upper ocean heat content for Kangerlussuaq, throughout the 21st century (1.01 × 10^8 J/m^2/month), unlike our inferences from other locations. This trend is found to be an order of magnitude higher than Helheim (2.36 × 10^7 J/m^2/month) and Jakobshavn (1.27 × 10^7 J/m^2/month). Our results also indicate that sea-ice free months at the Nares Strait (A.D. 2055 onwards) allows for a relatively greater mixing of the Lincoln Sea and Baffin Bay waters, which is consistent with the decline and stagnation of the rising OHC at Jakobshavn.

The Greenland ice sheet (GrIS) is currently losing mass, as a result of complex mechanisms of ice-climate interaction that need to be understood for reliable projections of future sea level rise. The thesis focuses on the estimation of mass anomalies in Greenland using data from the GRACE satellite gravity mission. Monthly GRACE gravity field solutions are post-processed using a new variant of the "mascon approach''. Greenland is covered with multiple distinctive "mascons'', assuming the mass anomalies within each one are laterally-homogeneous.

Gravity disturbances at mean satellite altitude are synthesized from the GRACE spherical harmonic coefficients. They are used as pseudo-observations to estimate the mascon mass anomalies using weighted least-squares techniques. No regularization is applied. The full noise covariance matrix of gravity disturbances is propagated from the full noise covariance matrix of spherical harmonic coefficients using the law of covariance propagation. Those matrices represent a complete stochastic description of random noise in the data, provided that it is Gaussian. The inverse noise covariance matrix is used as a weight matrix in the weighted least-squares estimate of the mascon mass anomalies. The limited spectral content of the gravity disturbances is accounted for by applying a low-pass filter to the design matrix providing a spectrally consistent functional model.

Using numerical experiments with simulated signal and data, we demonstrate the importance of the data weighting and of the spectral consistency between the mascon model and the pseudo-observations. The developed methodology is applied to process real GRACE data using CSR RL05 monthly gravity field solutions with full noise covariance matrices. We distinguish five GrIS drainage systems. The obtained mass anomaly estimates per mascon are integrated over individual drainage systems, as well as over entire Greenland. We find that using a weighted least-squares estimator reduces random noise in the estimates by factors ranging from 1.5 to 3.0, depending on the drainage system. Furthermore, we compare the de-trended mascon mass anomaly time-series with similar time-series from the Regional Atmospheric Climate Model (RACMO 2.3), which describes the Surface Mass Balance (SMB). We show that the weighted least-squares estimate reduces the discrepancies between the time-series by 24\%--47\%.

Then, we combine GRACE mass anomaly estimates, SMB model outputs, and ice discharge data to systematically analyze the mass budget of Greenland at various temporal and spatial scales. Among others, we reveal a substantial seasonal meltwater storage, which peaks in July, reaching in total $100 \pm 20$ Gt. Meltwater storage is particularly intense in the northern, northwestern and southeastern drainage systems. An analysis of outlet glacier velocities shows that the contribution of ice discharge to the seasonal mass variations is minor, at a level of only a few Gt. In addition, we propose a simple way to use GRACE data for validating SMB model outputs in winter, based on the fact that ice discharge cannot be negative.

Finally, we use numerical simulations and real data to identify the optimal GRACE data processing strategy (primarily the size of the mascons) for three temporal scales of interest: monthly mass anomalies, mean mass anomalies per calendar month, and long-term linear trends. We show that the two major contributors to the error budgets are random errors and parameterization (model) errors; the latter are caused by a spatial variability of actual mass anomalies within individual mascons. We find that the errors in long-term linear trend estimates are mainly caused by the parameterization errors, and that accurate estimates require small size mascons in combination with the ordinary least-squares estimator. The error budget of mean mass anomalies per calendar month is dominated by the parameterization error when the size of mascons is large and by random errors otherwise. Hence, accurate estimates require mascons of intermediate size in combination with a weighted least-squares estimator. Finally, we find that random errors are the dominant error source in monthly mass anomalies. We advise to use in this case large mascons and a weighted least-squares estimator.

Our new variant of the mascon approach and the results of this thesis can be used in support of future research on GrIS hydrology, glacier dynamics, and surface mass balance, as well as their mutual interactions.

The main goal of this thesis involves the development of a refined methodology to
separate the mass change signals associated with glacial isostatic adjustment (GIA)
from those of surface ice/firn by exploiting the strengths of independent data sets,
such as those from gravimetry, altimetry, climate data, and others. To achieve this,
various research efforts were conducted addressing specific aspects of the methodology and subsequent data processing. This led to a number of new contributions to the topic,