Ruitang Yang

• Aalstad, Kristoffer; Alonso-González, Esteban; Pirk, Norbert; Westermann, Sebastian; Willmes, Clarissa & Yang, Ruitang (2026). Evolving beyond collapse: an adaptive particle batch smoother for cryospheric data assimilation. Geoscientific Model Development. ISSN 1991-959X. 19(18), p. 8565–8596. doi: 10.5194/gmd-19-8565-2026. Full text in Research Archive Show summary

  We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. The proposed approach seeks to combine some of the advantages of two widely used classes of schemes: particle methods and iterative ensemble Kalman methods. Specifically, it extends the Particle Batch Smoother (PBS) that is commonly used in cryospheric data assimilation, with the Adaptive Multiple Importance Sampling algorithm. This adaptive formulation transforms the PBS into an iterative scheme with improved resilience against ensemble collapse and the ability to implement early-stopping strategies. As such, computational cost is automatically adapted to the complexity of the problem at hand, even down to the grid-cell and water year level in distributed multiyear simulations. In homage to the schemes that it builds on, we coin this new algorithm the Adaptive Particle Batch Smoother (AdaPBS) and we test it across a range of scenarios. First, we conducted an intercomparison of some of the most commonly used cryospheric data assimilation algorithms using Markov Chain Monte Carlo (MCMC) simulation as a costly gold-standard benchmark in a simplified temperature index model assimilating snow depth observations. We further evaluated AdaPBS by assimilating snow depth observations from the ESMSnowMIP project at 6 different sites spanning 3 continents, using an ensemble of simulations generated with the more complex Flexible Snow Model (FSM2). Our results demonstrate that AdaPBS is a robust and reliable tool, outperforming or at least matching the performance of other commonly used algorithms and successfully handling complex cases with dense observational datasets. All experiments were carried out using the open-source Multiple Snow Data Assimilation System (MuSA) toolbox, which now includes AdaPBS and MCMC among the growing list of available cryospheric data assimilation methods. Beyond our cryospheric focus, the scheme has the potential to be applied directly to the closely related fields of land surface and hydrological data assimilation as well as more general geoscientific Bayesian inference problems.

• Petersen, Eric Ivan; Hock, Regine; Loso, Michael G.; Guo, Wanqin; Markovsky, Cameron & Yang, Ruitang [Show all 9 contributors for this article] (2025). Multi‐Year Glaciological and Meteorological Observations on Debris‐Covered Kennicott Glacier, Alaska, 2016–2023. Geoscience Data Journal. 12(4). doi: 10.1002/gdj3.70032. Full text in Research Archive Show summary

  ABSTRACT Despite increasing availability of satellite‐derived products, in situ glacier observations are pivotal to accurately monitor glacier change and to calibrate and validate glacier models. However, comprehensive multi‐variable field observations are especially rare on large glaciers and on debris‐covered glaciers. Here we present extensive field observations from Kennicott Glacier, a heavily debris‐covered glacier in central Alaska covering more than 400 km 2 . The multi‐year data set includes point glacier mass balances, meteorological data from several weather stations on and off the glacier, debris thickness and temperature, ice cliff back wasting derived from time‐lapse photography of horizontal stakes drilled into several cliffs, and bathymetry, water temperature, and water level of proglacial and supraglacial lakes. Cumulated summer melt of more than 8 m was observed at the lowest clean‐ice sites. Melt rates over clean ice correlate well with elevation, while the rates over debris‐covered ice lack any strong elevation dependence. Melt rates drop exponentially with increasing debris thickness and tend to be much lower than for clean ice at similar elevations. Melt rates determined for ice cliffs in areas of otherwise continuous debris cover were up to 10× those for debris‐covered ice, and even exceeded standard clean ice melt rates. Debris‐cover thickness measurements at 150 sites vary from < 1 to 69 cm with an average of 17 ± 11 cm (±standard deviation). Debris thickens down‐glacier, but with high spatial variability–thickness was observed to vary by tens of cm within a ~15 m radius. Depth‐averaged thermal heat conductivity derived from supraglacial debris temperature profiles at 12 sites ranges from 0.53 to 1.86 W m −1 K −1 . Interconnected proglacial lakes covered 1.61 km 2 in 2018 with observed water depths of more than 60 m in the two largest lakes. The dataset can be downloaded at https://doi.org/10.5281/zenodo.14625691 (Petersen, Hock, Loso, Guo, et al., 2024) and will be useful for glaciological and glacier meteorological studies.

• Yang, Ruitang; Hock, Regine; Rounce, David & Kang, Shichang (2025). Regional-Scale Response of Glacier Speed to Seasonal Runoff Variations on the Kenai Peninsula, Alaska. Geophysical Research Letters. ISSN 0094-8276. 52(8). doi: 10.1029/2025GL115248. Full text in Research Archive Show summary

  Seasonal variations in glacier motion are influenced by water supply and associated changes in subglacial drainage systems. Few studies have investigated how year-to-year variations in glacier runoff modify these seasonal speed patterns. We analyze more than 200 correlations between glacier surface speed and runoff for 77 glaciers (∼3,070 km2) on the Kenai Peninsula, Alaska, from 2015 to 2019. Correlations for the same-month or same-season were typically weak or insignificant. However, lower-than-average ice speeds in a given month or season often correspond with higher-than-average runoff in preceding months or seasons. Delays up to 9 months (e.g., correlations between April speed and preceding July runoff), suggest the impact of interannual runoff variations on ice speed lasts longer than previously documented. Unlike previous studies in the region and elsewhere, winter speed and summer runoff are weakly correlated or uncorrelated, while winter speed negatively correlates with October/November runoff.

• Hartl, Lea; Schmitt, Carl G.; Stuefer, Martin; Jenckes, Jordan; Page, Benjamin P. & Crawford, Christopher J. [Show all 9 contributors for this article] (2025). Leveraging airborne imaging spectroscopy and multispectral satellite imagery to map glacial sediment plumes in Kachemak Bay, Alaska. Journal of Hydrology Regional Studies. 57. doi: 10.1016/j.ejrh.2024.102121. Full text in Research Archive Show summary

  Study Region Kachemak Bay is a fjord-type estuary in the northern Gulf of Alaska. Water quality and habitat characteristics are strongly influenced by freshwater and sediment input from multiple glacierized catchments. Study Focus We present a new method combining imaging spectroscopy from an airborne survey with Landsat and Sentinel-2 imagery to map water surface turbidity originating from glacial runoff based on spectral abundance. We compare the spectral characteristics of turbid glacial water to clear water and generate a high resolution reference map of glacial turbidity in Kachemak Bay. This informs the subsequent analysis of a homogenized, Rayleigh corrected time series of Landsat and Sentinel-2 images and seasonal patterns of turbidity. New Hydrological Insights for the Region Our results provide the most comprehensive data set on water surface turbidity in Kachemak Bay to date and improve understanding of spatial and seasonal variability of glacial turbidity in a data sparse region. July and August have the largest plumes with median sizes around 150 km2, or around a quarter of Kachemak Bay. Plume sizes typically decrease with decreasing glacier runoff in September and October. We show that imaging spectroscopy aids assessments of turbid water in glacial marine catchments across scales. Leveraging high resolution spectral information allows for water color analyses that are customized to local conditions and catchment characteristics as well as scalable to wider regions.

• Piermattei, Livia; Zemp, Michael; Sommer, Christian; Brun, Fanny; Braun, Matthias H. & Andreassen, Liss M. [Show all 35 contributors for this article] (2024). Observing glacier elevation changes from spaceborne optical and radar sensors - an inter-comparison experiment using ASTER and TanDEM-X data. The Cryosphere. ISSN 1994-0416. 18(7), p. 3195–3230. doi: 10.5194/tc-18-3195-2024. Full text in Research Archive Show summary

  Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies.

• Yang, Ruitang; Hock, Regine; Kang, Shichang; Guo, Wanqin; Shangguan, Donghui & Jiang, Zongli [Show all 7 contributors for this article] (2022). Glacier Surface Speed Variations on the Kenai Peninsula, Alaska, 2014–2019. Journal of Geophysical Research (JGR): Earth Surface. ISSN 2169-9003. 127(3). doi: 10.1029/2022JF006599. Full text in Research Archive Show summary

  To characterize the spatiotemporal variations of glacier surface speed on the Kenai Peninsula, Alaska (∼3,900 km2), we derived 92 surface speed fields between October 2014 and December 2019 using intensity offset tracking on Sentinel-1 data. On average, speeds are 50% greater in spring (March-May) than the annual mean (69 m a−1) while winter speeds are close to the annual mean. While marine-terminating glaciers have their maximum speed near the terminus, both land- and lake-terminating glaciers flow fastest around the median glacier elevation. On average, the lake-terminating and tidewater glaciers flow 1.7 and 2.3 times faster than the land-terminating glaciers, respectively. Monthly variations over the 5-year period are strikingly synchronous regardless of terminus type suggesting that regional-scale meteorological drivers govern the temporal variability. Mean annual speeds fluctuate roughly ±10% of the period mean without an apparent trend. At lake-terminating Bear Glacier, a short-term tripling in ice speed in fall 2019 over the area below an ice-dammed lake coincides with an observed glacier lake outburst flood (GLOF). An earlier GLOF caused a persistent breach of the beach barrier between the proglacial lake and ocean which likely led to overall speed-up of the lower glacier part throughout 2019. A significant speedup was also observed at the lower part of the lake-terminating Ellsworth Glacier and attributed to rapid glacier retreat and lake expansion, probably further amplified by the terminus area becoming buoyant and a large tabular iceberg breaking off. Our results highlight the impact of GLOFs and proglacial characteristics in spatial and temporal glacier speed variations.

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• Yılmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire; Rounce, David; Tober, Brandon & Yang, Ruitang [Show all 8 contributors for this article] (2026). Probabilistic calibration of glacier projections using data assimilation. doi: 10.5194/egusphere-egu26-14845. Full text in Research Archive Show summary

  Global glacier mass loss is accelerating, yet considerable spread in projected glacier changes remains due to model structure, forcing, and parameter uncertainty. Most global glacier projections rely on relatively simple models where calibration approaches are essential to constrain uncertainty by using scarce observations. Probabilistic calibration strategies that help with quantifying uncertainities have recently incorporated into a limited number of global glacier models. As a next step, we suggest employing Bayesian data assimilation methods with untapped potential to integrate multi source observations (in-situ, satellite, reanalysis) when calibrating global glacier model projections.Here, we present a probabilistic calibration framework for the Python Glacier Evolution Model (PyGEM) based on ensemble-based data assimilation. Two data assimilation techniques (PBS and AdaPBS) are used to calibrate mass balance parameters to constrain future projections of glacio-hydrological variables (surface mass balance and runoff) between 2015 and 2100 under four SSP climate scenarios across the Scandinavian region. In this work, we compare our calibration results with commonly used deterministic and probabilistic glacier model calibration algorithms which are already in use in PyGEM. Our probabilistic calibration framework based on data assimilation has the potential to quantify and disentangle uncertainties from climate forcing, model structure, and parameters. Better constrained and uncertainty-aware glacier models increases confidence in projections of future glacier change and their relevant impacts. This work paves the way for producing policy relevant global glacier projections and scenarios with their uncertainty estimates within the ERC-AdG GLACMASS project.

• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Mazzolini, Marco; Guillet, Gregoire; Yang, Ruitang & Hock, Regine (2025). Data assimilation for snow and glacier modeling in climate applications. Full text in Research Archive
• Yang, Ruitang (2025). Frontal ablation modeling in the global glacier model. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire; Rounce, David; Tober, Brandon & Yang, Ruitang [Show all 7 contributors for this article] (2025). Calibrating future glacier projections using data assimilation. doi: https:/imc2025.info/imc25/abstract/calibrating-future-glacier-projections-using-data-assimilation/. Full text in Research Archive Show summary

  Global glacier mass is changing rapidly, and projections of global glacier mass balance under changing climatic conditions are crucial for informed decision-making. Existing glacier projections use relatively simple glacier models constrained by sparse observations. In these projections, model calibration plays a key role in constraining uncertainty. The use of Bayesian data assimilation methods for calibration by integrating multiple emerging observational data sets (in-situ mass balance measurements, climate reanalyses, and satellite remote sensing) to constrain model parameters and their dynamics remains relatively unexplored. Such a probabilistic calibration strategy could enable us to quantify and disentangle uncertainties related to the glacier model, the selected climate model forcing, and internal climate variability. The Python Glacier Evolution Model (PyGEM) is one of a handful of global glacier models that allow us to simulate the evolution of the mass balance of all glaciers in the world. In this work, we adopt ensemble-based data assimilation methods to calibrate PyGEM model parameters and thus constrain future projections of glacier mass balance across Scandinavia. We compare our results with traditional glacier model calibration algorithms and the Bayesian gold standard Markov Chain Monte Carlo (MCMC) method in PyGEM for glacio-hydrological indicators (surface mass balance and runoff projections) between 2015 and 2100 with four SSP scenarios. This work serves as the kernel for a scalable glacier data assimilation framework to produce policy relevant global glacier projections and scenarios within the recently funded ERC-AdG GLACMASS project. The probabilistic calibration framework developed in this study can in principle be adapted for a wide range of cryospheric applications.

• Aalstad, Kristoffer; Guillet, Gregoire; Yang, Ruitang; Yilmaz, Yeliz & Hock, Regine (2025). Glacier reanalysis using hierarchical data assimilation. Full text in Research Archive
• Yang, Ruitang; Hock, Regine; Rounce, David & Kang, Shichang (2024). Regional scale response of glacier speed to seasonal runoff variations on the Kenai Peninsula, Alaska. Full text in Research Archive
• Yang, Ruitang; Ultee, Lizz; Hock, Regine; Debolskiy, Matvey V. & Rounce, David (2024). Modeling frontal ablation in a global glacier evolution model. Full text in Research Archive
• Yang, Ruitang (2024). Modeling frontal ablation in a global glacier evolution model. Full text in Research Archive
• Yang, Ruitang (2023). Towards more physical modeling of frontal ablation of marine- and lake-terminating glaciers in a global glacier evolution model. Full text in Research Archive
• Yang, Ruitang (2022). Influence of glacier runoff on seasonal ice-speed variations on the Kenai Peninsula, Alaska. Full text in Research Archive

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