Kristoffer Aalstad

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

Headwater streams and lakes exchange greenhouse gases with the atmosphere, but how their gas saturation varies between years with contrasting snow conditions remains poorly understood in cold regions. Groundwater discharge may contribute to this variability by transporting dissolved greenhouse gases to surface waters. At an alpine sub-Arctic site in Norway, we sampled streams, lakes, groundwater and snow in summer 2020 and 2021. We measured carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) in surface waters and groundwater to calculate saturation relative to atmospheric equilibrium. A Bayesian mixing model estimated groundwater contributions from water chemistry, and separate regression models assessed their relationships with gas saturation. Our results show that 2020 was an extremely snow-rich year, with the longest snow-cover duration in the 21-year satellite record, whereas snow-cover duration in 2021 was close to the long-term average. CO₂ saturation was higher in 2020 than in 2021, both overall (medians 306 % and 221 %) and at most sites sampled in both years. Higher aquatic CO₂ saturation coincided with reduced terrestrial carbon uptake reported for 2020 in the companion study by Pirk et al. (2023). Median CH₄ saturation exceeded atmospheric equilibrium in both years (320 % and 198 %). CH₄ saturation differed among surface-water sources in 2021. N₂O medians were near equilibrium (109 % and 110 %), with no differences detected among surface-water sources. CO₂ saturation tended to increase with estimated groundwater contribution after accounting for year and surface-water source. However, after accounting for uncertainty in the groundwater estimates, no statistically significant relationship was detected for any of the three gases. Estimated groundwater contributions tended to be higher in 2021, when CO₂ saturation was lower. The clearest between-year contrast was observed for CO₂, whereas CH₄ and N₂O showed no consistent directional change. Multi-year observations linking snow conditions, hydrological pathways and direct gas fluxes are needed to distinguish transport from biological processing and atmospheric exchange.

Headwater streams and lakes exchange greenhouse gases with the atmosphere, but how their gas saturation varies between years with contrasting snow conditions remains poorly understood in cold regions. Groundwater discharge may contribute to this variability by transporting dissolved greenhouse gases to surface waters. At an alpine sub-Arctic site in Norway, we sampled streams, lakes, groundwater and snow in summer 2020 and 2021. We measured carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) in surface waters and groundwater to calculate saturation relative to atmospheric equilibrium. A Bayesian mixing model estimated groundwater contributions from water chemistry, and separate regression models assessed their relationships with gas saturation. Our results show that 2020 was an extremely snow-rich year, with the longest snow-cover duration in the 21-year satellite record, whereas snow-cover duration in 2021 was close to the long-term average. CO₂ saturation was higher in 2020 than in 2021, both overall (medians 306 % and 221 %) and at most sites sampled in both years. Higher aquatic CO₂ saturation coincided with reduced terrestrial carbon uptake reported for 2020 in the companion study by Pirk et al. (2023). Median CH₄ saturation exceeded atmospheric equilibrium in both years (320 % and 198 %). CH₄ saturation differed among surface-water sources in 2021. N₂O medians were near equilibrium (109 % and 110 %), with no differences detected among surface-water sources. CO₂ saturation tended to increase with estimated groundwater contribution after accounting for year and surface-water source. However, after accounting for uncertainty in the groundwater estimates, no statistically significant relationship was detected for any of the three gases. Estimated groundwater contributions tended to be higher in 2021, when CO₂ saturation was lower. The clearest between-year contrast was observed for CO₂, whereas CH₄ and N₂O showed no consistent directional change. Multi-year observations linking snow conditions, hydrological pathways and direct gas fluxes are needed to distinguish transport from biological processing and atmospheric exchange.

Modeling frontal ablation of marine-terminating glaciers is critical for accurately modeling mass change but remains challenging. We present a hybrid, physically consistent and numerically efficient modelling framework that integrates the Simple Estimator of Retreat Magnitude and ice flux (SERMeQ) into two coupled global glacier models (OGGM and PyGEM). The adaptive particle‑batch smoother, a Bayesian data assimilation method, is used for joint calibration of all parameters. The coupled framework simulates monthly length changes along flowlines and mass‑balance components of individual glaciers. Applied to 71 marine-terminating glaciers across Svalbard during the period of 2000–2019, the model simulates distinct seasonal variations and annual length changes of glacier calving fronts, while reproducing decadal climatic mass balance estimates well. However, frontal ablation tends to be overestimated possibly due to uncertainties in initial geometry and modelled ice velocities. Overall, the good alignment of the regional distributions highlights that the modeling framework, including the efficient joint Bayesian calibration, provides a promising tool for large-scale frontal ablation modeling in glacier evolution models.

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.

Measurements of surface-atmosphere carbon dioxide (CO ₂ ) and methane (CH ₄ ) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO ₂ flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO ₂ fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH ₄ fluxes. For aquatic ecosystems, we split CH ₄ fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO ₂ and CH ₄ concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH ₄ fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO ₂ dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH ₄ flux site-months (3098) were more frequent than CO ₂ flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO ₂ flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH ₄ data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our understanding of climate feedbacks. The data can be accessed at ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2448 (Virkkala et al., 2026).

We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. 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. 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.

We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. 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. 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.

We present a new adaptive particle-based data assimilation scheme for cryospheric applications that leverages promising developments in importance sampling. 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. 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.