Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation
Abstract
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.
Related
- DMSP special sensor H (SSH) data rescue and assimilation in reanalysis
- Introducing a new normalized cryospheric index (NCI) to categorize sub-watersheds on arid environments
- Self-supervised temporal reconstruction of reanalysis fields at subhourly scales
- Satellites and Reanalysis Reveal the Dominance of Dynamics over Precipitation in Marine Cold-Air Outbreak Cloud Break-Up
- All-weather lake-ice phenology from Sentinel-1 and reanalysis thermodynamics: a validated, transferable method applied to 131 Adirondack lakes
BibTeX
@misc{aalstad2026climateinformed,
title = {{Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation}},
author = {Kristoffer Aalstad and Esteban Alonso-González and Joel Fiddes and Brian Groenke and Gregoire Guillet and Regine Hock and Bartłomiej Luks and Norbert Pirk and Sebastian Westermann and Yeliz A. Yılmaz},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.36077},
eprint = {2609.36077},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.36077},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation
AU - Kristoffer Aalstad
AU - Esteban Alonso-González
AU - Joel Fiddes
AU - Brian Groenke
AU - Gregoire Guillet
AU - Regine Hock
AU - Bartłomiej Luks
AU - Norbert Pirk
AU - Sebastian Westermann
AU - Yeliz A. Yılmaz
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.36077
UR - https://arxiv.org/abs/2609.36077
AB - 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.
ER -