The uncertainty of glacier change projections is largely influenced by glacier models. In this study, we focus on temperature-index mass-balance (MB) models and their calibration. Using the Open Global Glacier Model (OGGM), we examine the influence of different surface-type dependent degree-day factors, temporal climate resolutions (daily, monthly) and downscaling options (temperature lapse rates, temperature and precipitation corrections) for 88 glaciers with in-situ observations. Our findings indicate that higher spatial and temporal resolution observations enhance MB gradient representation due to an improved calibration. The addition of surface-type distinction in the model also improves MB gradients, but the lack of independent observations limits our ability to demonstrate the added value of increased model complexity. Some model choices have systematic effects, for example weaker temperature lapse rates result in smaller projected glaciers. However, we often find counter balancing effects, such as the sensitivity to different degree-day factors for snow, firn and ice, which depends on how the glacier accumulation area ratio changes in the future. Similarly, using daily versus monthly climate data can affect glaciers differently depending on the shifting balance between melt and solid precipitation thresholds. Our study highlights the importance of considering minor model design differences to predict future glacier volumes and runoff accurately.

Knowledge of frontal ablation from marine-terminating glaciers (i.e., mass lost at the calving face) is critical for constraining glacier mass balance, improving projections of mass change, and identifying the processes that govern frontal mass loss. Here, we discuss the challenges involved in computing frontal ablation and the unique issues pertaining to both glaciers and ice sheets. Frontal ablation estimates require numerous datasets, including glacier terminus area change, thickness, surface velocity, density, and climatic mass balance. Observations and models of these variables have improved over the past decade, but significant gaps and regional discrepancies remain, and better quantification of temporal variability in frontal ablation is needed. Despite major advances in satellite-derived large-scale datasets, large uncertainties remain with respect to ice thickness, depth-averaged velocities, and the bulk density of glacier ice close to calving termini or grounding lines. We suggest ways in which we can move toward globally complete frontal ablation estimates, highlighting areas where we need improved datasets and increased collaboration.

Rock debris covers ~30% of glacier ablation areas in the Central Himalaya and modifies the impact of atmospheric conditions on mass balance. The thermal properties of supraglacial debris are diurnally variable but remain poorly constrained for monsoon-influenced glaciers over the timescale of the ablation season. We measured vertical debris profile temperatures at 12 sites on four glaciers in the Everest region with debris thickness ranging from 0.08 to 2.8 m. Typically, the length of the ice ablation season beneath supraglacial debris was 160 days (15 May to 22 October)—a month longer than the monsoon season. Debris temperature gradients were approximately linear (r2 > 0.83), measured as −40°C m–1 where debris was up to 0.1 m thick, −20°C m–1 for debris 0.1–0.5 m thick, and −4°C m–1 for debris greater than 0.5 m thick. Our results demonstrate that the influence of supraglacial debris on the temperature of the underlying ice surface, and therefore melt, is stable at a seasonal timescale and can be estimated from near-surface temperature. These results have the potential to greatly improve the representation of ablation in calculations of debris-covered glacier mass balance and projections of their response to climate change.

The response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.