Clarissa Willmes

• 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.

• Pismeniuk, Anfisa; Dörsch, Peter; Ippach, Mats Rouven; Willmes, Clarissa; Sheffield, Sunniva Bratli & Pirk, Norbert [Show all 7 contributors for this article] (2026). Wintertime production and storage of methane in thermokarst ponds of subarctic Norway. Biogeosciences. ISSN 1726-4170. 23(4), p. 1497–1514. doi: 10.5194/bg-23-1497-2026. Full text in Research Archive Show summary

  The ongoing climate change in permafrost areas can trigger abrupt thaw processes, leading to the formation of thermokarst lakes and ponds. These water bodies, especially in organic-rich areas, are recognized as strong methane (CH4) emitters during the ice-free periods and have the potential to accumulate large CH4 amounts in and below the ice, which is released during the ice melt. We estimated winter CH4 bottom flux in nine shallow ponds within two permafrost peatlands in Northern Norway, Iškoras and Áidejávri, during the 2023–2024 ice cover season. The cumulative winter CH4 bottom flux ranged from 0.6 to 24 g CH4-C m−2 which at the Iškoras site contributed up to 40 % to the annual CH4 budget. The heterogeneity of the CH4 wintertime accumulation is related to pond depth, differences in vegetation, and the thermokarst pond formation age. The latter was investigated using a space-for-time substitution approach along chronosequences of thermokarst formation spanning more than 70 years. The winter CH4 bottom flux increased from 2 mg CH4-C m−2 d−1 in two-year-old pond to 106 mg CH4-C m−2 d−1 in a pond formed between 30 and 60 years ago. Pond that formed more than 70 years ago and is currently experiencing sedge regrowth exhibited large winter CH4 bottom flux of 59 mg CH4-C m−2 d−1, while older ponds dominated by Sphagnum mosses showed 4 to 10 times smaller CH4 bottom fluxes.

• Aga, Juditha; Willmes, Clarissa; Sinitsyn, Anatoly; Arlov, Thor Bjørn; Boike, Julia & Westermann, Sebastian (2025). Impact of snow and building management on ground surface temperatures in permafrost environments - A case study from the historical mining town Ny-Ålesund, Svalbard. Cold Regions Science and Technology. ISSN 0165-232X. 237, p. 1–12. doi: 10.1016/j.coldregions.2025.104516. Full text in Research Archive Show summary

  Permafrost is warming due to changing climatic conditions, a trend that might threaten infrastructure and livelihood across the Arctic. Historical structures are especially vulnerable, as they were not designed to withstand these rapid environmental changes. In the high-Arctic settlement Ny-Ålesund, Svalbard, a large number of historical buildings exist, making the village a suitable case study for investigating the interplay between historical buildings and permafrost. This study analysed two years of ground surface temperature (GST) measurements and snow observations to assess the impact of snow and building management on permafrost conditions in Ny-Ålesund. The mean annual ground surface temperature (MAGST) was found between −1.9°C and 1.9°C in 2022/23, and between −3.1°C and 1.1°C in 2023/24. The results reveal that GST varied substantially within the village area and exceeded the freezing point in several locations. We identified influencing factors contributing to these differences in GST: (i) Snow redistribution by wind and snow ploughing caused large variations in GST with the highest MAGST beneath artificial snow deposits. (ii) Building type, particularly crawl space ventilation, also affected GST. Ventilated crawl spaces tended to lower the MAGST beneath the buildings, while enclosed crawl spaces increased MAGST to positive values. (iii) Building wall orientation further influenced GST, with southern exposures exhibiting higher values compared to northern exposures. Our findings highlight the importance of snow and building management on GST and permafrost stability. Understanding these small scale effects is crucial for the preservation of historical infrastructure on permafrost under changing climatic conditions.

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• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Mazzolini, Marco & Willmes, Clarissa (2025). Adventures in snow data assimilation. Full text in Research Archive Show summary

  In this talk I will provide a guided tour of some of our adventures in snow data assimilation. The role that snow reanalysis can play in filling key observational gaps from both established and emerging Earth observations will be a recurring theme. This will be highlighted through examples from some ongoing work on trying to estimate global seasonal snow water equivalent using optical satellite data. Through our adventures, I hope to showcase the still largely untapped potential that data assimilation holds for adding value to Earth observations and constraining snow model uncertainty. 

• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Blandini, Giulia; Guidicelli, Matteo & Willmes, Clarissa (2025). Adventures in terrestrial cryospheric data assimilation. Full text in Research Archive Show summary

  In this talk I will provide a guided tour of our adventures applying data assimilation to various terrestrial cryospheric problems. While the focus will primarily be on seasonal snow, I will also present some related developments for glacier and permafrost modeling. The role that targeted terrestrial cryospheric reanalysis can play in filling key observational gaps from both established and emerging Earth observations will be a recurring theme. This will be highlighted through examples from some ongoing work on trying to estimate global seasonal snow water equivalent using optical satellite data. Throughout the talk, I will provide several examples of combining machine learning and data assimilation to both speed up and enhance inference. Moreover, I will introduce some recent forays into hybrid and adaptive particle schemes as well as some experimental work on ensemble-based hierarchical inference. Through our adventures, I hope to showcase the still largely untapped potential that data assimilation holds for adding value to observations and constraining model uncertainty across a broader range of problems.

• Aga, Juditha & Willmes, Clarissa (2024). Ground surface temperature measurements and snow observations in Ny-Ålesund, Svalbard (2022-2024). doi: 10.5281/zenodo.14380247. Full text in Research Archive
• Aalstad, Kristoffer; Willmes, Clarissa; Mazzolini, Marco & Kreynen, Dylan (2024). Snow data assimilation at UiO. Full text in Research Archive
• Aga, Juditha; Westermann, Sebastian; Landgren, Oskar Andreas; Willmes, Clarissa & Sinitsyn, Anatoly (2023). Cultural heritage in a changing climate. In al., - et (Eds.), Svalbard Science Conference 2023. Book of Abstracts. SSC . ISSN 9780000000019. Full text in Research Archive
• Aga, Juditha; Westermann, Sebastian; Landgren, Oskar Andreas; Willmes, Clarissa & Sinitsyn, Anatoly (2023). Cultural heritage in a changing climate: The crucial role of snow management and the influence of warming permafrost on historical buildings in Ny-Ålesund. In al., - et (Eds.), Svalbard Science Conference 2023. Book of Abstracts. SSC . ISSN 9780000000019. p. 235–235. Full text in Research Archive
• Sinitsyn, Anatoly; Aga, Juditha; Arlov, Thor Bjørn; Bekele, Yared Worku; Seljelv, Ingalill Johansen & Landgren, Oskar Andreas [Show all 13 contributors for this article] (2023). Polar Climate and Cultural Heritage: outline of the PCCH-Arctic project and first results. In al., - et (Eds.), Svalbard Science Conference 2023. Book of Abstracts. SSC . ISSN 9780000000019. p. 234–234. Full text in Research Archive

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