Patagonia and Tierra del Fuego (Austral Andes) are the most glacierised regions in the Southern Hemisphere, where glaciers have experienced significant mass changes in recent decades. Understanding glacier–climate–water interactions is crucial for addressing future climate challenges. Open-access data play a key role in advancing geoscience research, improving models and assessing the impacts of hazards and sea-level rise impacts. Here, we present QFuego-Patagonia, a free glacier-related GIS dataset and web portal covering Patagonia and Tierra del Fuego, which provides essential geospatial information across four scientific topics: Glaciology, Atmosphere, Terrain Models, and Glacial Geology and Geomorphology. This initiative aims to foster interdisciplinary research and collaboration, synthesise current knowledge and establish an advanced glacier data repository that will be continuously updated as new data and insights become available.

Ice sheet mass loss is typically provided for grounded ice, because changes in floating ice are more difficult to measure and contribute minimally to sea level rise. However, gross freshwater mass flow rate across ice sheet boundaries, including floating ice, is a better metric of ice sheet health. Here, we present total mass flows across ice sheet boundaries for both the Greenlandic and Antarctic ice sheets and their peripheral glaciers from 2010 through 2019. In addition to total mass flow, we provide constituent terms and gross rather than net values, including components that combine to provide surface mass balance. Ice mass loss in Greenland is 330 $\pm$50 Gt yr-1 which is $\sim$30% larger than the 255 $\pm$40 Gt yr-1 grounded ice mass loss estimates that neglect floating ice changes. Ice mass loss in Antarctica is 450 $\pm$270 Gt yr-1 which is $\sim$2.4x the 190 $\pm$115 Gt yr-1 grounded ice mass loss estimates. Freshwater mass flow rate from Greenland is $\sim$1065 $\pm$120 Gt yr-1 or $\sim$3x mass loss ($\sim$4x grounded mass loss), and from Antarctica is $\sim$3110 $\pm$1515 Gt yr-1, or $\sim$7x mass loss ($\sim$16x grounded mass loss).

Ice shelves regulate ice sheet dynamics, with their stability influenced by horizontal flow and vertical flexure. MacAyeal and others (2021) developed the theoretical foundation for a coupled flow-flexure model (the “M21 model”), combining the Shallow Shelf Approximation with thin-beam flexure, providing a computationally efficient tool for studying phenomena like ice shelf rumpling and lake drainage. However, the M21 model relies on proprietary software, is unstable under compressive flow conditions, and does not incorporate fracture processes critical for capturing ice-shelf damage evolution. We present an open-source version of the M21 model addressing these limitations. Using the free Python libraries Firedrake and icepack, we introduce a plastic failure mechanism, effectively limiting bending stresses and thereby stabilizing the model. This enhancement expands the viscous M21 model into a viscoplastic flow-flexure-fracture (3F) framework. We validate the 3F model through test cases replicating key ice shelf phenomena, including marginal rumpling and periodic surface meltwater drainage. By offering this tool as open-source software, we aim to enable broader adoption, with the ultimate aim of representing surface meltwater induced flow-flexure-fracture processes in large-scale ice sheet models.

The specific surface area (SSA) of snow is a critical structural quantity that controls snow properties and constantly evolves over time. An accurate understanding and modeling of the dynamical evolution of the SSA is thus of primary importance for snowpack models. For this, we simulate the evolution of randomly generated bi-continuous microstructures that resemble real snow microstructures and investigate the control of the ambient conditions (temperature and temperature gradien) and of the physical processes at play on the rate of change of the SSA. Based on this, we propose a new physically informed governing law for the coarsening of microstructures. This law could also benefit other materials than snow, for instance metal alloys. It involves three new material properties, corresponding to the sensitivity to isothermal and temperature-gradient conditions and a length scale controlling the transition from kinetics-limited to diffusion-limited regimes. This law accurately predicts the simulation outputs under various conditions. We also propose a potential parameterization of the three newly introduced material properties and evaluate it against snow coarsening data. This work opens the path to a closed system of physics-based equations governing snow microstructural evolution, with direct applicability in snowpack models.

Diffuse optical spectroscopy (DOS) techniques characterize scattering media by examining their optical response to laser illumination. Time-domain DOS methods involve illuminating the medium with a laser pulse and using a fast photodetector to measure the time-dependent intensity of light that exits the medium after multiple scattering events. While DOS research traditionally focused on characterizing biological tissues, we demonstrate that time-domain diffuse optical measurements can also be used to characterize snow. We introduce a model that predicts the time-dependent reflectance of a dry snowpack as a function of its density, grain size, and black carbon content. We develop an algorithm that retrieves these properties from measurements at two wavelengths. To validate our approach, we assembled a two-wavelength lidar system to measure the time-dependent reflectance of snow samples with varying properties. Rather than measuring direct surface returns, our system captures photons that enter and exit the snow at different points, separated by a small distance (4–10 cm). We observe clear, linear correlations between our retrievals of density and black carbon concentration, and ground truth. For black carbon concentration the correlation is nearly one-to-one. We also find that our method is capable of distinguishing between small and large grain sizes.