Ward van Pelt

Glacier retreat transforms landscapes in polar and mountainous regions. Yet, the topography of the emerging terrain remains poorly known. Here, we present a physically consistent, global map of the ice-covered topography beneath all glaciers on Earth distinct from the ice sheets, derived from the three-dimensional higher-order Instructed Glacier Model, and constrained by extensive observational datasets. The map allows us to identify > 50,000 possible future lakes in the presently ice-covered landscape, with a maximum total volume of 3,138 km ³ —enough to store 7 mm sea-level equivalent (SLE). Additionally, we estimate the total global glacier volume at 149.41 ± 29.28 × 10 ³ km ³ (308 ± 60 mm SLE). Large overdeepenings near glacier fronts in High Mountain Asia suggest an increased risk for glacier lake outburst floods under glacier retreat. The subglacial topography and ice thickness data offer new opportunities for diverse cryospheric and Earth system studies, including refined projections of glacier changes and landscape evolution of deglaciated terrain.

Bromine exists in multiple chemical forms in the atmosphere, with bromide (Br ⁻ ) being the predominant species in snow. Here, we detect and track the formation of bromate (BrO ₃ ⁻ ) in Arctic snow and propose a mechanism for its production. Our observations reveal relevant BrO ₃ ⁻ concentrations, reaching up to 5% of total bromine, during springtime. The evidence for the persistence of BrO ₃ ⁻ in the snowpack and its production as a function of solar radiation suggests a snow-driven photochemical process, with negligible contributions from direct aerosol deposition, and emphasizes the role of the snowpack as a reactive matrix for photooxidation, providing insights into the bromine cycle in the Arctic. Our findings are supported by quantum chemical calculations, which explore both radical and ionic reaction mechanisms and the matrix effect, thereby supporting the role of snowpack in promoting BrO ₃ ⁻ formation. The presence of BrO ₃ ⁻ in snowpack represents a reservoir of nonreactive bromine, with potential implications for understanding halogen chemistry in polar environments.

Glaciers distinct from the ice sheets in Greenland and Antarctica are experiencing accelerating retreat. Consequently, landscapes so-far hidden beneath the ice will emerge, yet their characteristics are poorly constrained by existing observations and models. At the same time, the subglacial topography itself controls retreat patterns and magnitudes. Hence, an improved representation of the glacier bed is key for improved future projections of glacier evolution. Here, we present a physically plausible map of subglacial topography for all >200,000 glaciers in the world, called Topography of a Deglaciated Earth (TOPO-DE). The map is constrained by an inverse modeling approach that relies on the higher-order Instructed Glacier Model (IGM), a wealth of surface observations, and automatic Bayesian calibration against thickness observations. We find a global glacier volume of 149±29 × 103 km3 (316±61 mm sea level equivalent) and a mean glacier thickness of 212 m. While the global total is consistent with previous work, regionally variable discrepancies highlight the differences between this study and previous reconstructions based on shallow ice-flow physics. The landscapes beneath the ice are characterized by a large potential to host future lakes, quantified as a combined potential lake volume of >3,000 km3, or 2% of the global glacier volume, and a potential areal lake coverage of the presently ice-covered lands of 6%. We show where previous studies produced unphysical bed features and compare that to solutions of our model. Our freely-available bed product offers new insights into landscapes emerging after glacier retreat and can serve as an input for future projections of glacier change and its consequences.

In this contribution, we will demonstrate the first prototype of the Svalbard cryosphere digital twin (SvalbardDT v1; https://svalbarddt.org/), which is a Digital Twin Component of ESAs Digital Twin Earth (DTE). An environmental digital twin observes a physical entity (i.e. the cryosphere), fuses multi-modal observations together, then generates what if scenarios to improve human decision-making. In this way, there are two-way flows of information (i.e. data) between the physical and digital assets. In Svalbard, where rates of warming are six times faster than the global average, digital twins have the potential to be used by researchers to analyse trends in the cryosphere, by local communities to improve navigation, and by policy-makers to improve decision-making. Furthermore, Svalbard is considered a ‘super site’ of in situ observations in a pan-Arctic context owing to the permanent infrastructure and long history of international scientific collaboration on the archipelago, hence it is a prime location to develop Arctic digital twin technology. Here, we will provide a demonstration of the capabilities of SvalbardDT version 1 as a new tool for monitoring Svalbard’s cryosphere in the 21st century (2010-present), the underlying architecture, and the next steps towards full operationalisation.SvalbardDT represents a digital twin of Svalbard’s cryosphere covering glaciers, snow and sea ice observed through a combination of Earth Observation data sets and reanalysis data products. These data products are multi-modal i.e. they are collected at different resolutions, scales and time/spatial periods. SvalbardDT fine-tunes a deep learning foundational model to ingest the relevant data products and harmonise them into a 4D data cube describing the data set variable, x-dimension, y-dimension, and its changes over time. This produces weekly to monthly data cubes describing ~21 parameters. SvalbardDT focuses on the application of two case studies which are accessed through an online dashboard: (1) exploring the current state of cryosphere conditions in Svalbard to simulate terrestrial and marine navigation routes across the archipelago; and (2) forecast extreme Rain on Snow and Ice (ROSI) events using an AI foundation model. The associated toolboxes help to improve our understanding of the interconnecting processes shaping glaciers and ice caps across Svalbard.

Behavioural plasticity is likely to influence how individuals continue to access resources under rapid climate change. Plasticity will be particularly important at highly dynamic, prey‐rich foraging areas such as upwelling fronts of marine‐terminating glaciers in the high Arctic, where profitability varies significantly across space and time. Understanding individual variation in plasticity and its adaptive potential is crucial to understand a populations flexibility to future climate scenarios. By analysing GPS data from 186 black‐legged kittiwakes Rissa tridactyla breeding in the high Arctic over six years, we quantified individual variation in behavioural plasticity in use of glacial fronts and its relationship with the number of chicks produced. Variation in the relationship between glacial use and levels of discharged meltwater was primarily explained by differences in food availability between years. Whereas there was no significant relationship between discharge rates and glacier use in years of low zooplankton biomass, the probability of glacial front use and time spent at glaciers decreased in years when food was more abundant, despite high discharge and likely good conditions at the front. Interestingly, neither glacial use nor plasticity in foraging during the breeding season correlated with the number of surviving chicks, suggesting that all individuals still obtained enough food for reproduction. Understanding the complex nature of individual variation in plasticity and when it is likely to be adaptive will be the first step in highlighting when plasticity can be used to predict how species will respond to rapidly changing environments.

Knowledge of the thickness, volume, and subglacial topography of glaciers is crucial for a range of glaciological, hydrological, and societal issues, including studies on climate-warming-induced glacier retreat and associated sea level rise. This is not in the least true for Svalbard, one of the fastest-warming places in the world. Here, we present new maps of the ice thickness and subglacial topography for every glacier on Svalbard. Using remotely sensed observations of surface height, ice velocity, rate of surface elevation change, and glacier boundaries in combination with a modelled mass balance product, we apply an inverse method that leverages state-of-the-art ice flow models to obtain the shape of the glacier bed. Specifically, we model large glaciers with the Parallel Ice Sheet Model (PISM) at 500 m resolution, while we resolve smaller mountain glaciers at 100 m resolution using the physics-informed deep-learning-based Instructed Glacier Model (IGM). Actively surging glaciers are modelled using a perfect-plasticity model. We find a total glacier volume (excluding the island Kvitøya) of 6800 ± 238 km³, corresponding to 16.3 ± 0.6 mm sea level equivalent. Validation against thickness observations shows high statistical agreement, and the combination of the three methods is found to reduce uncertainties. We discuss the remaining sources of errors, differences from previous ice thickness maps of the region, and future applications of our results.

An uncertain factor in assessing future sea level rise is the melt water runoff buffering capacity of snow and firn on glaciers and ice caps. Field studies have resulted in observations of perennial firn aquifers (PFAs), which are bodies of water present deep in the firn layer and sheltered from cold surface conditions. PFAs can store surface melt, thereby acting as a buffer for sea level rise, and influence the thermodynamics of the firn layer. Furthermore, ice dynamics might be affected by the presence of liquid water through hydrofracturing and water transport to the bed. In this study, we present results of applying an existing groundwater model MODLFOW 6 to an observed perennial firn aquifer on the Lomonosovfonna ice cap in central Svalbard. The observations span a three-year period, where a ground penetrating radar was used to measure the water table depth of the aquifer. We calibrate our model against observations to infer hydraulic conductivity 6.4 * 10^-4 m s^-1, and then use the model to project the aquifer evolution over the period 1957–2019. We find that the aquifer was present in 1957, and that it steadily grew over the modelled period with relative increases of about 11 % in total water content and 15 % in water table depth. Water table depth is found to be more sensitive to transient meltwater input than firn density changes at this location on the long term. On an annual basis, the aquifer exhibits sharp water table increases during the melt season, followed by slow seepage through the cold season.

An uncertain factor in assessing future sea level rise is the melt water runoff buffering capacity of snow and firn on glaciers and ice caps. Field studies have resulted in observations of perennial firn aquifers (PFAs), which are bodies of water present deep in the firn layer and sheltered from cold surface conditions. PFAs can store surface melt, thereby acting as a buffer for sea level rise, and influence the thermodynamics of the firn layer. Furthermore, ice dynamics might be affected by the presence of liquid water through hydrofracturing and water transport to the bed. In this study, we present results of applying an existing groundwater model MODLFOW 6 to an observed perennial firn aquifer on the Lomonosovfonna ice cap in central Svalbard. The observations span a three-year period, where a ground penetrating radar was used to measure the water table depth of the aquifer. We calibrate our model against observations to infer hydraulic conductivity 6.4 * 10^-4 m s^-1, and then use the model to project the aquifer evolution over the period 1957–2019. We find that the aquifer was present in 1957, and that it steadily grew over the modelled period with relative increases of about 11 % in total water content and 15 % in water table depth. Water table depth is found to be more sensitive to transient meltwater input than firn density changes at this location on the long term. On an annual basis, the aquifer exhibits sharp water table increases during the melt season, followed by slow seepage through the cold season.

Knowledge of the thickness, volume and sub-glacial topography of glaciers is crucial for a range of glaciological, hydrological and societal issues, including, e.g., studies on climate-warming induced glacier retreat and associated sea-level rise. This is not the least true for Svalbard, one of the fastest-warming places in the world. Here, we present a new map of ice thickness and sub-glacial topography for every glacier on Svalbard. Using remotely-sensed observations of surface height, ice velocity, rate of surface elevation change, and glacier boundaries in combination with a modeled mass balance product, we apply an inverse method that leverages state-of-the-art ice flow models to obtain the shape of the glacier bed. Specifically, we model large glaciers with the Parallel Ice Sheet Model (PISM) at 500 m resolution, while we resolve smaller mountain glaciers at 100 m resolution using the physics-informed deep learning-based Instructed Glacier Model (IGM). Actively surging glaciers are modeled using a perfect-plasticity model. We find a total glacier volume (excluding Kvitøya) of 6,800 km±238 km³, and a mean ice thickness of 205±7 m. Validation against thickness observations shows high statistical agreement, and combining the three methods is found to reduce uncertainties. We discuss remaining sources of errors, differences to previous ice thickness maps of the region, and future applications of our results.