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arXiv2026

The Glacier Model Intercomparison Project (GlacierMIP): Towards standardisation of global glacier projections

The Glacier Model Intercomparison Project (GlacierMIP) provides an international framework for the coordinated evaluation and comparison of global glacier evolution models and their projections of glacier change. Its fourth phase, GlacierMIP4, updates previous global glacier projections through a standardised experimental design. For the first time, the protocol includes simulations extending to 2300 and an overshoot scenario for a subset of climate projections and requires monthly output of individual mass-balance components such as melt, accumulation and refreezing, in addition to annual results for glacier mass and area. While permitting model-specific approaches to calibration and other aspects of model implementation, the protocol prescribes common atmospheric forcing data, inventory boundary conditions, and reporting conventions to enable consistent analyses of inter-model differences. Here, we describe the GlacierMIP4 experimental protocol, including the simulation domain, initialisation, atmospheric forcing, and standardised output requirements. More broadly, we emphasize the need for standardised terminology, units, sign convention and file formats in global glacier modelling to improve the traceability, interoperability, and comparability of results, and recommend this protocol as a blueprint for future global glacier modelling studies.

physics.geo-ph↗

arXiv2026

Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation

The cryosphere regulates global cycles of water, energy, and carbon, affecting societies and ecosystems. By assimilating observations into models, we can infer the past trajectory of state variables, generating a cryospheric reanalysis. Existing cryospheric reanalyses typically treat each water year independently, with no pooling of information across years. With sparse and noisy observations, such 'climate-assumed' reanalyses perform poorly, reverting to an assumed yet uncalibrated background climatology. Conversely, Bayesian calibration with static parameters completely pools information across water years, inferring climatological parameters without capturing inter-annual variability. Here, we propose a new hierarchical Bayesian approach using partial pooling, which we coin 'climate-informed' cryospheric reanalysis. It jointly infers state trajectories, annual parameters, and local climatological hyperparameters using a nested hybrid particle smoothing workflow. We test the approach through three experiments by assimilating in situ snow water equivalent, satellite-based fractional snow-covered area, and glacier-wide mass balance data into a temperature index model at eight study areas, from the Swiss Alps to the High Arctic. Hierarchical reanalysis with partial pooling outperforms annually independent cryospheric reanalysis (no pooling) and static parameter calibration (complete pooling) for all experiments during calibration and for all but one during validation. Overall, it yields a 51% improvement in continuous ranked probability score relative to the prior, markedly exceeding that of annual reanalysis (38%) and static calibration (19%). Compared to a particle Markov chain Monte Carlo benchmark, our workflow reduces computational cost by several orders of magnitude through recycling and expectation maximization, with comparable or improved skill.

physics.geo-ph↗