Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-8565-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-8565-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Sep 2026
Development and technical paper |
| 16 Sep 2026
| 16 Sep 2026
Evolving beyond collapse: an adaptive particle batch smoother for cryospheric data assimilation
Kristoffer Aalstad
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Esteban Alonso-González
CORRESPONDING AUTHOR
Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE-CSIC), Jaca, Spain
Norbert Pirk
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Sebastian Westermann
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Clarissa Willmes
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Ruitang Yang
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Related authors
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Tiffany Windholz, Kyle A. Arndt, Gerard Rocher-Ros, Mathias Göckede, David Olefeldt, Edward A. G. Schuur, David Bastviken, Richard O'Keefe, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Mika Aurela, Sivakiruthika Balathandayuthabani, Katrin Attermeyer, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andreas Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Beth Holmes, Macall Hock, David Holl, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Cheristy P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natascha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Miska Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martikainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Daniel F. Nadeau, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykänen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleksi Räsänen, Helena Rautakoski, Riikka Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Tarmo Virtanen, Elena Veretennikova, Timo Vesala, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data, 18, 6357–6409, https://doi.org/10.5194/essd-18-6357-2026, https://doi.org/10.5194/essd-18-6357-2026, 2026
Short summary
Short summary
This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
Ruitang Yang, Lizz Ultee, Kristoffer Aalstad, Matvey V. Debolskiy, Regine Hock, Patrick Schmitt, David Rounce, and Tian Li
EGUsphere, https://doi.org/10.5194/egusphere-2026-1081, https://doi.org/10.5194/egusphere-2026-1081, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We present a new framework to model frontal ablation of marine-terminating glaciers by linking two global glacier models with a simple physics-based calving scheme and Bayesian calibration. Applied to 71 Svalbard glaciers (2000–2019) and projected to 2100, it reproduces seasonal front changes but overestimates ablation due to uncertain geometry and velocity. This efficient approach can improve regional to global simulations.
Esteban Alonso-González, Adrian Harpold, Jessica D. Lundquist, Cara Piske, Laura Sourp, Kristoffer Aalstad, and Simon Gascoin
The Cryosphere, 20, 209–225, https://doi.org/10.5194/tc-20-209-2026, https://doi.org/10.5194/tc-20-209-2026, 2026
Short summary
Short summary
Simulating the snowpack is challenging, as there are several sources of uncertainty due to e.g. the meteorological forcing. Using data assimilation techniques, it is possible to improve the simulations by fusing models and snow observations. However in forests, observations are difficult to obtain, because they cannot be retrieved through the canopy. Here, we explore the possibility of propagating the information obtained in forest clearings to areas covered by the canopy.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
The Cryosphere, 19, 4759–4783, https://doi.org/10.5194/tc-19-4759-2025, https://doi.org/10.5194/tc-19-4759-2025, 2025
Short summary
Short summary
Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.
Clarissa Willmes, Kristoffer Aalstad, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3142, https://doi.org/10.5194/egusphere-2025-3142, 2025
Short summary
Short summary
In permafrost areas, the spatial variability of the snow cover significantly affects the ground thermal regime. This study presents an algorithm that integrates satellite data into a permafrost model to enhance the spatial detail of ground temperature simulations. At a site on Svalbard, the algorithm improves simulations of ground temperatures especially for wind-blown ridges with little snow, as well as snow-drift sites.
Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler
The Cryosphere, 19, 3831–3848, https://doi.org/10.5194/tc-19-3831-2025, https://doi.org/10.5194/tc-19-3831-2025, 2025
Short summary
Short summary
In this work, we showcase the use the satellite laser altimeter ICESat-2, which is able to retrieve snow depth in areas where snow amounts are still poorly estimated despite the importance of these water resources. We can update snow models with these observations through algorithms that spatially propagate the information beyond the satellite profiles. The positive results show the potential of the approach to improve snow simulations, in terms of average snow depth and spatial distribution.
Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk
Biogeosciences, 22, 4163–4186, https://doi.org/10.5194/bg-22-4163-2025, https://doi.org/10.5194/bg-22-4163-2025, 2025
Short summary
Short summary
Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected spectral anomalies in satellite data associated with the herds. Our method can be used for diverse point sources, thereby improving emission inventories.
Juditha Aga, Livia Piermattei, Luc Girod, Kristoffer Aalstad, Trond Eiken, Andreas Kääb, and Sebastian Westermann
Earth Surf. Dynam., 12, 1049–1070, https://doi.org/10.5194/esurf-12-1049-2024, https://doi.org/10.5194/esurf-12-1049-2024, 2024
Short summary
Short summary
Coastal rock cliffs on Svalbard are considered to be fairly stable; however, long-term trends in coastal-retreat rates remain unknown. This study examines changes in the coastline position along Brøggerhalvøya, Svalbard, using aerial images from 1970, 1990, 2010, and 2021. Our analysis shows that coastal-retreat rates accelerate during the period 2010–2021, which coincides with increasing storminess and retreating sea ice.
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
Short summary
Short summary
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
Short summary
Short summary
The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Cas Renette, Kristoffer Aalstad, Juditha Aga, Robin Benjamin Zweigel, Bernd Etzelmüller, Karianne Staalesen Lilleøren, Ketil Isaksen, and Sebastian Westermann
Earth Surf. Dynam., 11, 33–50, https://doi.org/10.5194/esurf-11-33-2023, https://doi.org/10.5194/esurf-11-33-2023, 2023
Short summary
Short summary
One of the reasons for lower ground temperatures in coarse, blocky terrain is a low or varying soil moisture content, which most permafrost modelling studies did not take into account. We used the CryoGrid community model to successfully simulate this effect and found markedly lower temperatures in well-drained, blocky deposits compared to other set-ups. The inclusion of this drainage effect is another step towards a better model representation of blocky mountain terrain in permafrost regions.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
Short summary
Short summary
Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, https://doi.org/10.5194/amt-15-7293-2022, 2022
Short summary
Short summary
In this study, we show how sparse and noisy drone measurements can be combined with an ensemble of turbulence-resolving wind simulations to estimate uncertainty-aware surface energy exchange. We demonstrate the feasibility of this drone data assimilation framework in a series of synthetic and real-world experiments. This new framework can, in future, be applied to estimate energy and gas exchange in heterogeneous landscapes more representatively than conventional methods.
Joel Fiddes, Kristoffer Aalstad, and Michael Lehning
Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, https://doi.org/10.5194/gmd-15-1753-2022, 2022
Short summary
Short summary
This study describes and evaluates a new downscaling scheme that addresses the need for hillslope-scale atmospheric forcing time series for modelling the local impact of regional climate change on the land surface in mountain areas. The method has a global scope and is able to generate all model forcing variables required for hydrological and land surface modelling. This is important, as impact models require high-resolution forcings such as those generated here to produce meaningful results.
Jiawen Zhu, Linyang Guo, Matvey V. Debolskiy, Kjetil S. Aas, Lei Cai, Fang Li, Sebastian Westermann, and Hanna Lee
EGUsphere, https://doi.org/10.5194/egusphere-2026-4332, https://doi.org/10.5194/egusphere-2026-4332, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Frozen ground across the Arctic stores large amounts of carbon and supports fragile ecosystems. We improved a computer model to include excess ice and compared it with two other model setups. All showed that this excess ice can slow warming at first, but their estimates of excess ice loss, soil change, and plant growth differed greatly. Better comparisons between models are needed to improve predictions of Arctic change and its effects on the global climate.
Tazio Strozzi, Barbara Widhalm, Nina Jones, Julia Boike, Sofia Antonova, Birgit Heim, Sebastian Westermann, Andreas Kääb, and Annett Bartsch
EGUsphere, https://doi.org/10.5194/egusphere-2026-5477, https://doi.org/10.5194/egusphere-2026-5477, 2026
This preprint is open for discussion and under review for Earth Observation (EO).
Short summary
Short summary
Monitoring long-term surface subsidence induced by ground ice thaw across the pan-Arctic is crucial to detect permafrost changes and assess impacts caused by recent atmospheric warming. Using Sentinel-1 satellite radar data, we successfully measured subsidence rates in Siberia’s Lena Delta and found that they vary remarkably among the three main geomorphological units. To enable continuous, large-scale monitoring, satellite data records must maintain coherence from one summer season to the next.
Juditha Aga, Antoni G. Lewkowicz, and Sebastian Westermann
The Cryosphere, 20, 5225–5245, https://doi.org/10.5194/tc-20-5225-2026, https://doi.org/10.5194/tc-20-5225-2026, 2026
Short summary
Short summary
In this study, we explore the influence of a warming climate on landslides in ice-rich permafrost slopes. Using climate, terrain and soil data, we built a model that simulates permafrost thaw and its impact on slope stability over time. We tested the model on Banks Island, Canada, and it reproduced years with many new landslide observations and identified the most susceptible slopes. The model can also be applied to other permafrost regions, and to both past and future climate conditions.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Tiffany Windholz, Kyle A. Arndt, Gerard Rocher-Ros, Mathias Göckede, David Olefeldt, Edward A. G. Schuur, David Bastviken, Richard O'Keefe, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Mika Aurela, Sivakiruthika Balathandayuthabani, Katrin Attermeyer, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andreas Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Beth Holmes, Macall Hock, David Holl, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Cheristy P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natascha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Miska Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martikainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Daniel F. Nadeau, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykänen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleksi Räsänen, Helena Rautakoski, Riikka Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Tarmo Virtanen, Elena Veretennikova, Timo Vesala, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data, 18, 6357–6409, https://doi.org/10.5194/essd-18-6357-2026, https://doi.org/10.5194/essd-18-6357-2026, 2026
Short summary
Short summary
This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
Javier Bandrés, Jorge Pey, Esteban Alonso-González, Pablo Domínguez-Aguilar, Jesús Revuelto, Eñaut Izagirre, Francisco Rojas-Heredia, Eric Allan Sproles, and Juan Ignacio López-Moreno
EGUsphere, https://doi.org/10.5194/egusphere-2026-4306, https://doi.org/10.5194/egusphere-2026-4306, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Snow in the Pyrenees is increasingly affected by African dust and black carbon, which darken the snow surface and accelerate melting. We developed a simple field experiment to measure how realistic amounts of these particles change snow properties and melt. Mineral dust consistently produced stronger effects than black carbon, showing that even common dust events can substantially shorten snow cover and improve estimates of snowmelt in mountain regions.
Astrid Vatne, Norbert Pirk, Kolbjørn Engeland, Ane V. Vollsnes, and Lena M. Tallaksen
Hydrol. Earth Syst. Sci., 30, 5245–5279, https://doi.org/10.5194/hess-30-5245-2026, https://doi.org/10.5194/hess-30-5245-2026, 2026
Short summary
Short summary
Measurements of evaporation are important to understand how evaporation modifies the water balance of northern ecosystems. However, evaporation data in these regions are scarce. We explored a new dataset of evaporation measurements from four wetland sites in Norway and found that up to 30 % of the annual precipitation evaporate back to the atmosphere. Our results indicate that earlier snow melt-out and drier air can increase annual evaporation in the region.
Tamara Mathys, Sergey S. Marchenko, Sebastian Westermann, Luke Gregor, Léo C. P. Martin, Murataly Duishonakunov, Christian Hauck, Mohammad Farzamian, Teddi Herring, Christin Hilbich, Ruslan Kenzhebaev, Mukhammad Esenaman uulu, Timur Musaev, Zhoodarbeshim Bektursunov, Ryskul Usubaliev, Isabella Brining, Tomas Saks, and Martin Hoelzle
EGUsphere, https://doi.org/10.5194/egusphere-2026-2756, https://doi.org/10.5194/egusphere-2026-2756, 2026
This preprint is open for discussion and under review for Earth Surface Dynamics (ESurf).
Short summary
Short summary
CryoGrid simulations constrained by geophysical and borehole data were used to assess permafrost thermal conditions and thaw depth in the Inner Tien Shan (Kyrgyzstan) at 30 m resolution. Surface-type-specific stratigraphies derived from geophysical surveys improved representation of subsurface heterogeneity. Results indicate widespread but spatially variable permafrost, with evidence of approximately 1 °C warming since the 1980s and increasing thaw depths.
Joana Baptista, Sebastian Westermann, and Gonçalo Vieira
EGUsphere, https://doi.org/10.5194/egusphere-2026-1914, https://doi.org/10.5194/egusphere-2026-1914, 2026
Short summary
Short summary
Permafrost underlies most ice-free areas of the Antarctic Peninsula being crucial for ecosystems. Yet its response to climate warming is uncertain. Using the CryoGrid Community Model at five PERMANTAR observatories, we simulated past and future permafrost evolution. Results revealed a warming of 0.22 °C dec⁻¹ since 1950–2020 and a progressive degradation under all CMIP6 scenarios, with positive MAGTs at 20 m depth before 2100, driven by elevation, snow cover, and ocean–atmosphere interactions.
Ruitang Yang, Lizz Ultee, Kristoffer Aalstad, Matvey V. Debolskiy, Regine Hock, Patrick Schmitt, David Rounce, and Tian Li
EGUsphere, https://doi.org/10.5194/egusphere-2026-1081, https://doi.org/10.5194/egusphere-2026-1081, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We present a new framework to model frontal ablation of marine-terminating glaciers by linking two global glacier models with a simple physics-based calving scheme and Bayesian calibration. Applied to 71 Svalbard glaciers (2000–2019) and projected to 2100, it reproduces seasonal front changes but overestimates ablation due to uncertain geometry and velocity. This efficient approach can improve regional to global simulations.
Esther Karin Bender, Matvey Debolskiy, Kjetil Aas, Sebastian Westermann, Fang Li, Jiawen Zhu, and Hanna Lee
EGUsphere, https://doi.org/10.5194/egusphere-2026-1039, https://doi.org/10.5194/egusphere-2026-1039, 2026
Short summary
Short summary
This paper introduces a new method to represent ice-rich permafrost soils in the Community Land Model used for climate prediction. The approach includes key processes previously missing, enabling simulation of rapid thaw events from ground-ice melt. It improves representation of soil temperature, hydrology, and landscape dynamics, leading to more accurate future greenhouse-gas estimates.
Anfisa Pismeniuk, Peter Dörsch, Mats Ippach, Clarissa Willmes, Sunniva Sheffield, Norbert Pirk, and Sebastian Westermann
Biogeosciences, 23, 1497–1514, https://doi.org/10.5194/bg-23-1497-2026, https://doi.org/10.5194/bg-23-1497-2026, 2026
Short summary
Short summary
Thermokarst ponds in high latitudes are important methane (CH4) sources in summer. Meanwhile, these ponds are ice-covered for around 60 % of the year and can accumulate CH4 in the ice and within the underlying water column, which potentially results in high emissions during the ice-off. Here, we present data on wintertime CH4 storage of ponds located within two peat plateaus in Northern Norway. Our results show that the wintertime CH4 storage can contribute up to 40 % to the annual CH4 budget.
Lotte Wendt, Line Rouyet, Hanne H. Christiansen, Tom Rune Lauknes, and Sebastian Westermann
The Cryosphere, 20, 1179–1197, https://doi.org/10.5194/tc-20-1179-2026, https://doi.org/10.5194/tc-20-1179-2026, 2026
Short summary
Short summary
In permafrost environments, the ground surface moves due to the formation and melt of ice in the ground. This study compares ground surface displacements measured from satellite images against field data of ground ice contents. We find good agreement between the detected seasonal subsidence and observed ground ice melt. Our results show the potential of satellite remote sensing for mapping ground ice variability, but also indicate that ice in excess of the pore space must be considered.
Robin B. Zweigel, Dashtseren Avirmed, Khurelbaatar Temuujin, Clare Webster, Hanna Lee, and Sebastian Westermann
The Cryosphere, 20, 1001–1023, https://doi.org/10.5194/tc-20-1001-2026, https://doi.org/10.5194/tc-20-1001-2026, 2026
Short summary
Short summary
Two years of data along a forest disturbance gradient in Mongolia show a larger annual ground surface temperature range in dead and logged forests than intact forest, while the range is dampened in stands of young regrowth. Compared to intact forest, mean annual ground surface temperatures are 0.5 °C colder in dead and logged forest and dense stands of young regrowth. This is linked to differences in vegetation and surface cover due to the disturbance and patterns in livestock activity.
Esteban Alonso-González, Adrian Harpold, Jessica D. Lundquist, Cara Piske, Laura Sourp, Kristoffer Aalstad, and Simon Gascoin
The Cryosphere, 20, 209–225, https://doi.org/10.5194/tc-20-209-2026, https://doi.org/10.5194/tc-20-209-2026, 2026
Short summary
Short summary
Simulating the snowpack is challenging, as there are several sources of uncertainty due to e.g. the meteorological forcing. Using data assimilation techniques, it is possible to improve the simulations by fusing models and snow observations. However in forests, observations are difficult to obtain, because they cannot be retrieved through the canopy. Here, we explore the possibility of propagating the information obtained in forest clearings to areas covered by the canopy.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
The Cryosphere, 19, 4759–4783, https://doi.org/10.5194/tc-19-4759-2025, https://doi.org/10.5194/tc-19-4759-2025, 2025
Short summary
Short summary
Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.
Clarissa Willmes, Kristoffer Aalstad, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3142, https://doi.org/10.5194/egusphere-2025-3142, 2025
Short summary
Short summary
In permafrost areas, the spatial variability of the snow cover significantly affects the ground thermal regime. This study presents an algorithm that integrates satellite data into a permafrost model to enhance the spatial detail of ground temperature simulations. At a site on Svalbard, the algorithm improves simulations of ground temperatures especially for wind-blown ridges with little snow, as well as snow-drift sites.
Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler
The Cryosphere, 19, 3831–3848, https://doi.org/10.5194/tc-19-3831-2025, https://doi.org/10.5194/tc-19-3831-2025, 2025
Short summary
Short summary
In this work, we showcase the use the satellite laser altimeter ICESat-2, which is able to retrieve snow depth in areas where snow amounts are still poorly estimated despite the importance of these water resources. We can update snow models with these observations through algorithms that spatially propagate the information beyond the satellite profiles. The positive results show the potential of the approach to improve snow simulations, in terms of average snow depth and spatial distribution.
Joana Pedro Baptista, Gonçalo Brito Guapo Teles Vieira, António Manuel de Carvalho Soares Correia, Hyoungseok Lee, and Sebastian Westermann
The Cryosphere, 19, 3459–3476, https://doi.org/10.5194/tc-19-3459-2025, https://doi.org/10.5194/tc-19-3459-2025, 2025
Short summary
Short summary
Permafrost underlies ice-free areas of Antarctica, but its response to long-term warming is unclear due to a limited number of monitoring sites. To address this, we used the CryoGrid model, forced with climate data, to estimate permafrost temperatures and active layer thickness at King Sejong Station since 1950. The results show ground temperatures rising 0.25 °C per decade and the active layer thickening by 2 m. Warming has accelerated since 2015, highlighting the need for continued monitoring.
Alouette van Hove, Kristoffer Aalstad, Vibeke Lind, Claudia Arndt, Vincent Odongo, Rodolfo Ceriani, Francesco Fava, John Hulth, and Norbert Pirk
Biogeosciences, 22, 4163–4186, https://doi.org/10.5194/bg-22-4163-2025, https://doi.org/10.5194/bg-22-4163-2025, 2025
Short summary
Short summary
Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected spectral anomalies in satellite data associated with the herds. Our method can be used for diverse point sources, thereby improving emission inventories.
Jacqueline K. Knutson, François Clayer, Peter Dörsch, Sebastian Westermann, and Heleen A. de Wit
Biogeosciences, 22, 3899–3914, https://doi.org/10.5194/bg-22-3899-2025, https://doi.org/10.5194/bg-22-3899-2025, 2025
Short summary
Short summary
Thawing permafrost at Iškoras in northern Norway is transforming peat plateaus into thermokarst ponds and wetlands. These small ponds show striking oversaturation of dissolved greenhouse gases, such as carbon dioxide (CO2) and methane (CH4), partly owing to organic matter processing. Streams nearby emit CO2, driven by turbulence. As permafrost disappears, carbon dynamics will change, potentially increasing emissions of CH4. This study highlights the need to integrate these changes into climate models.
Lou-Anne Chevrollier, Adrien Wehrlé, Joseph M. Cook, Norbert Pirk, Liane G. Benning, Alexandre M. Anesio, and Martyn Tranter
The Cryosphere, 19, 1527–1538, https://doi.org/10.5194/tc-19-1527-2025, https://doi.org/10.5194/tc-19-1527-2025, 2025
Short summary
Short summary
Light-absorbing particles (LAPs) are often present as a mixture on snow surfaces and are important to disentangle because their darkening effects vary but also because the processes governing their presence and accumulation on snow surfaces are different. This study presents a novel method to retrieve the concentration and albedo-reducing effect of different LAPs present at the snow surface from surface spectral albedo. The method is then successfully applied to ground observations on seasonal snow.
Thibault Xavier, Laurent Orgogozo, Anatoly S. Prokushkin, Esteban Alonso-González, Simon Gascoin, and Oleg S. Pokrovsky
The Cryosphere, 18, 5865–5885, https://doi.org/10.5194/tc-18-5865-2024, https://doi.org/10.5194/tc-18-5865-2024, 2024
Short summary
Short summary
Permafrost (permanently frozen soil at depth) is thawing as a result of climate change. However, estimating its future degradation is particularly challenging due to the complex multi-physical processes involved. In this work, we designed and ran numerical simulations for months on a supercomputer to quantify the impact of climate change in a forested valley of central Siberia. There, climate change could increase the thickness of the seasonally thawed soil layer in summer by up to 65 % by 2100.
Robin Benjamin Zweigel, Avirmed Dashtseren, Khurelbaatar Temuujin, Anarmaa Sharkhuu, Clare Webster, Hanna Lee, and Sebastian Westermann
Biogeosciences, 21, 5059–5077, https://doi.org/10.5194/bg-21-5059-2024, https://doi.org/10.5194/bg-21-5059-2024, 2024
Short summary
Short summary
Intense grazing at grassland sites removes vegetation, reduces the snow cover, and inhibits litter layers from forming. Grazed sites generally have a larger annual ground surface temperature amplitude than ungrazed sites, but the net effect depends on effects in the transitional seasons. Our results also suggest that seasonal use of pastures can reduce ground temperatures, which can be a strategy to protect currently degrading grassland permafrost.
Sigrid Trier Kjær, Sebastian Westermann, Nora Nedkvitne, and Peter Dörsch
Biogeosciences, 21, 4723–4737, https://doi.org/10.5194/bg-21-4723-2024, https://doi.org/10.5194/bg-21-4723-2024, 2024
Short summary
Short summary
Permafrost peatlands are thawing due to climate change, releasing large quantities of carbon that degrades upon thawing and is released as CO2, CH4 or dissolved organic carbon (DOC). We incubated thawed Norwegian permafrost peat plateaus and thermokarst pond sediment found next to permafrost for up to 350 d to measure carbon loss. CO2 production was initially the highest, whereas CH4 production increased over time. The largest carbon loss was measured at the top of the peat plateau core as DOC.
Juditha Aga, Livia Piermattei, Luc Girod, Kristoffer Aalstad, Trond Eiken, Andreas Kääb, and Sebastian Westermann
Earth Surf. Dynam., 12, 1049–1070, https://doi.org/10.5194/esurf-12-1049-2024, https://doi.org/10.5194/esurf-12-1049-2024, 2024
Short summary
Short summary
Coastal rock cliffs on Svalbard are considered to be fairly stable; however, long-term trends in coastal-retreat rates remain unknown. This study examines changes in the coastline position along Brøggerhalvøya, Svalbard, using aerial images from 1970, 1990, 2010, and 2021. Our analysis shows that coastal-retreat rates accelerate during the period 2010–2021, which coincides with increasing storminess and retreating sea ice.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
Short summary
Short summary
Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Josep Bonsoms, Juan I. López-Moreno, Esteban Alonso-González, César Deschamps-Berger, and Marc Oliva
Nat. Hazards Earth Syst. Sci., 24, 245–264, https://doi.org/10.5194/nhess-24-245-2024, https://doi.org/10.5194/nhess-24-245-2024, 2024
Short summary
Short summary
Climate warming is changing mountain snowpack patterns, leading in some cases to rain-on-snow (ROS) events. Here we analyzed near-present ROS and its sensitivity to climate warming across the Pyrenees. ROS increases during the coldest months of the year but decreases in the warmest months and areas under severe warming due to snow cover depletion. Faster snow ablation is anticipated in the coldest and northern slopes of the range. Relevant implications in mountain ecosystem are anticipated.
Moritz Langer, Jan Nitzbon, Brian Groenke, Lisa-Marie Assmann, Thomas Schneider von Deimling, Simone Maria Stuenzi, and Sebastian Westermann
The Cryosphere, 18, 363–385, https://doi.org/10.5194/tc-18-363-2024, https://doi.org/10.5194/tc-18-363-2024, 2024
Short summary
Short summary
Using a model that can simulate the evolution of Arctic permafrost over centuries to millennia, we find that post-industrialization permafrost warming has three "hotspots" in NE Canada, N Alaska, and W Siberia. The extent of near-surface permafrost has decreased substantially since 1850, with the largest area losses occurring in the last 50 years. The simulations also show that volcanic eruptions have in some cases counteracted the loss of near-surface permafrost for a few decades.
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.
Bernd Etzelmüller, Ketil Isaksen, Justyna Czekirda, Sebastian Westermann, Christin Hilbich, and Christian Hauck
The Cryosphere, 17, 5477–5497, https://doi.org/10.5194/tc-17-5477-2023, https://doi.org/10.5194/tc-17-5477-2023, 2023
Short summary
Short summary
Permafrost (permanently frozen ground) is widespread in the mountains of Norway and Iceland. Several boreholes were drilled after 1999 for long-term permafrost monitoring. We document a strong warming of permafrost, including the development of unfrozen bodies in the permafrost. Warming and degradation of mountain permafrost may lead to more natural hazards.
Anatoly O. Sinitsyn, Sara Bazin, Rasmus Benestad, Bernd Etzelmüller, Ketil Isaksen, Hanne Kvitsand, Julia Lutz, Andrea L. Popp, Lena Rubensdotter, and Sebastian Westermann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2950, https://doi.org/10.5194/egusphere-2023-2950, 2023
Preprint archived
Short summary
Short summary
This study looked at under the ground on Svalbard, an archipelago close to the North Pole. We found something very surprising – there is water under the all year around frozen soil. This was not known before. This water could be used for drinking if we manage it carefully. This is important because getting clean drinking water is very difficult in Svalbard, and other Arctic places. Also, because the climate is getting warmer, there might be even more water underground in the future.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
Short summary
Short summary
Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Juditha Aga, Julia Boike, Moritz Langer, Thomas Ingeman-Nielsen, and Sebastian Westermann
The Cryosphere, 17, 4179–4206, https://doi.org/10.5194/tc-17-4179-2023, https://doi.org/10.5194/tc-17-4179-2023, 2023
Short summary
Short summary
This study presents a new model scheme for simulating ice segregation and thaw consolidation in permafrost environments, depending on ground properties and climatic forcing. It is embedded in the CryoGrid community model, a land surface model for the terrestrial cryosphere. We describe the model physics and functionalities, followed by a model validation and a sensitivity study of controlling factors.
Matan Ben-Asher, Florence Magnin, Sebastian Westermann, Josué Bock, Emmanuel Malet, Johan Berthet, Ludovic Ravanel, and Philip Deline
Earth Surf. Dynam., 11, 899–915, https://doi.org/10.5194/esurf-11-899-2023, https://doi.org/10.5194/esurf-11-899-2023, 2023
Short summary
Short summary
Quantitative knowledge of water availability on high mountain rock slopes is very limited. We use a numerical model and field measurements to estimate the water balance at a steep rock wall site. We show that snowmelt is the main source of water at elevations >3600 m and that snowpack hydrology and sublimation are key factors. The new information presented here can be used to improve the understanding of thermal, hydrogeological, and mechanical processes on steep mountain rock slopes.
Brian Groenke, Moritz Langer, Jan Nitzbon, Sebastian Westermann, Guillermo Gallego, and Julia Boike
The Cryosphere, 17, 3505–3533, https://doi.org/10.5194/tc-17-3505-2023, https://doi.org/10.5194/tc-17-3505-2023, 2023
Short summary
Short summary
It is now well known from long-term temperature measurements that Arctic permafrost, i.e., ground that remains continuously frozen for at least 2 years, is warming in response to climate change. Temperature, however, only tells half of the story. In this study, we use computer modeling to better understand how the thawing and freezing of water in the ground affects the way permafrost responds to climate change and what temperature trends can and cannot tell us about how permafrost is changing.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
Short summary
Short summary
Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
Ixeia Vidaller, Eñaut Izagirre, Luis Mariano del Rio, Esteban Alonso-González, Francisco Rojas-Heredia, Enrique Serrano, Ana Moreno, Juan Ignacio López-Moreno, and Jesús Revuelto
The Cryosphere, 17, 3177–3192, https://doi.org/10.5194/tc-17-3177-2023, https://doi.org/10.5194/tc-17-3177-2023, 2023
Short summary
Short summary
The Aneto glacier, the largest glacier in the Pyrenees, has shown continuous surface and ice thickness losses in the last decades. In this study, we examine changes in its surface and ice thickness for 1981–2022 and the remaining ice thickness in 2020. During these 41 years, the glacier has shrunk by 64.7 %, and the ice thickness has decreased by 30.5 m on average. The mean ice thickness in 2022 was 11.9 m, compared to 32.9 m in 1981. The results highlight the critical situation of the glacier.
Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Erin Emily Thomas, and Sebastian Westermann
The Cryosphere, 17, 2941–2963, https://doi.org/10.5194/tc-17-2941-2023, https://doi.org/10.5194/tc-17-2941-2023, 2023
Short summary
Short summary
Here, we present high-resolution simulations of glacier mass balance (the gain and loss of ice over a year) and runoff on Svalbard from 1991–2022, one of the fastest warming regions in the Arctic. The simulations are created using the CryoGrid community model. We find a small overall loss of mass over the simulation period of −0.08 m yr−1 but with no statistically significant trend. The average runoff was found to be 41 Gt yr−1, with a significant increasing trend of 6.3 Gt per decade.
Justyna Czekirda, Bernd Etzelmüller, Sebastian Westermann, Ketil Isaksen, and Florence Magnin
The Cryosphere, 17, 2725–2754, https://doi.org/10.5194/tc-17-2725-2023, https://doi.org/10.5194/tc-17-2725-2023, 2023
Short summary
Short summary
We assess spatio-temporal permafrost variations in selected rock walls in Norway over the last 120 years. Ground temperature is modelled using the two-dimensional ground heat flux model CryoGrid 2D along nine profiles. Permafrost probably occurs at most sites. All simulations show increasing ground temperature from the 1980s. Our simulations show that rock wall permafrost with a temperature of −1 °C at 20 m depth could thaw at this depth within 50 years.
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
Short summary
Short summary
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
Short summary
Short summary
The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Josep Bonsoms, Juan Ignacio López-Moreno, and Esteban Alonso-González
The Cryosphere, 17, 1307–1326, https://doi.org/10.5194/tc-17-1307-2023, https://doi.org/10.5194/tc-17-1307-2023, 2023
Short summary
Short summary
This work analyzes the snow response to temperature and precipitation in the Pyrenees. During warm and wet seasons, seasonal snow depth is expected to be reduced by −37 %, −34 %, and −27 % per degree Celsius at low-, mid-, and high-elevation areas, respectively. The largest snow reductions are anticipated at low elevations of the eastern Pyrenees. Results anticipate important impacts on the nearby ecological and socioeconomic systems.
Cas Renette, Kristoffer Aalstad, Juditha Aga, Robin Benjamin Zweigel, Bernd Etzelmüller, Karianne Staalesen Lilleøren, Ketil Isaksen, and Sebastian Westermann
Earth Surf. Dynam., 11, 33–50, https://doi.org/10.5194/esurf-11-33-2023, https://doi.org/10.5194/esurf-11-33-2023, 2023
Short summary
Short summary
One of the reasons for lower ground temperatures in coarse, blocky terrain is a low or varying soil moisture content, which most permafrost modelling studies did not take into account. We used the CryoGrid community model to successfully simulate this effect and found markedly lower temperatures in well-drained, blocky deposits compared to other set-ups. The inclusion of this drainage effect is another step towards a better model representation of blocky mountain terrain in permafrost regions.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
Short summary
Short summary
Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, https://doi.org/10.5194/amt-15-7293-2022, 2022
Short summary
Short summary
In this study, we show how sparse and noisy drone measurements can be combined with an ensemble of turbulence-resolving wind simulations to estimate uncertainty-aware surface energy exchange. We demonstrate the feasibility of this drone data assimilation framework in a series of synthetic and real-world experiments. This new framework can, in future, be applied to estimate energy and gas exchange in heterogeneous landscapes more representatively than conventional methods.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary
Short summary
Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Juri Palmtag, Jaroslav Obu, Peter Kuhry, Andreas Richter, Matthias B. Siewert, Niels Weiss, Sebastian Westermann, and Gustaf Hugelius
Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, https://doi.org/10.5194/essd-14-4095-2022, 2022
Short summary
Short summary
The northern permafrost region covers 22 % of the Northern Hemisphere and holds almost twice as much carbon as the atmosphere. This paper presents data from 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region. We use this dataset together with ESA's global land cover dataset to estimate soil organic carbon and total nitrogen storage up to 300 cm soil depth, with estimated values of 813 Pg for carbon and 55 Pg for nitrogen.
Anders Lindroth, Norbert Pirk, Ingibjörg S. Jónsdóttir, Christian Stiegler, Leif Klemedtsson, and Mats B. Nilsson
Biogeosciences, 19, 3921–3934, https://doi.org/10.5194/bg-19-3921-2022, https://doi.org/10.5194/bg-19-3921-2022, 2022
Short summary
Short summary
We measured the fluxes of carbon dioxide and methane between a moist moss tundra and the atmosphere on Svalbard in order to better understand how such ecosystems are affecting the climate and vice versa. We found that the system was a small sink of carbon dioxide and a small source of methane. These fluxes are small in comparison with other tundra ecosystems in the high Arctic. Analysis of temperature sensitivity showed that respiration was more sensitive than photosynthesis above about 6 ℃.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
Short summary
Short summary
The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Joel Fiddes, Kristoffer Aalstad, and Michael Lehning
Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, https://doi.org/10.5194/gmd-15-1753-2022, 2022
Short summary
Short summary
This study describes and evaluates a new downscaling scheme that addresses the need for hillslope-scale atmospheric forcing time series for modelling the local impact of regional climate change on the land surface in mountain areas. The method has a global scope and is able to generate all model forcing variables required for hydrological and land surface modelling. This is important, as impact models require high-resolution forcings such as those generated here to produce meaningful results.
Sarah E. Chadburn, Eleanor J. Burke, Angela V. Gallego-Sala, Noah D. Smith, M. Syndonia Bret-Harte, Dan J. Charman, Julia Drewer, Colin W. Edgar, Eugenie S. Euskirchen, Krzysztof Fortuniak, Yao Gao, Mahdi Nakhavali, Włodzimierz Pawlak, Edward A. G. Schuur, and Sebastian Westermann
Geosci. Model Dev., 15, 1633–1657, https://doi.org/10.5194/gmd-15-1633-2022, https://doi.org/10.5194/gmd-15-1633-2022, 2022
Short summary
Short summary
We present a new method to include peatlands in an Earth system model (ESM). Peatlands store huge amounts of carbon that accumulates very slowly but that can be rapidly destabilised, emitting greenhouse gases. Our model captures the dynamic nature of peat by simulating the change in surface height and physical properties of the soil as carbon is added or decomposed. Thus, we model, for the first time in an ESM, peat dynamics and its threshold behaviours that can lead to destabilisation.
Bernd Etzelmüller, Justyna Czekirda, Florence Magnin, Pierre-Allain Duvillard, Ludovic Ravanel, Emanuelle Malet, Andreas Aspaas, Lene Kristensen, Ingrid Skrede, Gudrun D. Majala, Benjamin Jacobs, Johannes Leinauer, Christian Hauck, Christin Hilbich, Martina Böhme, Reginald Hermanns, Harald Ø. Eriksen, Tom Rune Lauknes, Michael Krautblatter, and Sebastian Westermann
Earth Surf. Dynam., 10, 97–129, https://doi.org/10.5194/esurf-10-97-2022, https://doi.org/10.5194/esurf-10-97-2022, 2022
Short summary
Short summary
This paper is a multi-authored study documenting the possible existence of permafrost in permanently monitored rockslides in Norway for the first time by combining a multitude of field data, including geophysical surveys in rock walls. The paper discusses the possible role of thermal regime and rockslide movement, and it evaluates the possible impact of atmospheric warming on rockslide dynamics in Norwegian mountains.
Cited articles
Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites, The Cryosphere, 12, 247–270, https://doi.org/10.5194/tc-12-247-2018, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m
Alonso-González, E.: Inputs (forcing and observations) ready for use by “MuSA: The Multiscale Snow Data Assimilation System (v1.0)”, Zenodo [code], https://doi.org/10.5281/zenodo.7248635, 2022. a
Alonso-González, E. and Aalstad, K.: MuSA: v2.3 AdaPBS submission, Zenodo [code], https://doi.org/10.5281/zenodo.17292981, 2025. a, b, c
Alonso González, E. and Aalstad, K.: Code and data to reproduce the results and figures in: Evolving beyond collapse: An adaptive particle batch smoother for cryospheric data assimilation, Zenodo [code and data set], https://doi.org/10.5281/zenodo.21244337, 2026. a
Alonso-González, E., López-Moreno, J. I., Gascoin, S., García-Valdecasas Ojeda, M., Sanmiguel-Vallelado, A., Navarro-Serrano, F., Revuelto, J., Ceballos, A., Esteban-Parra, M. J., and Essery, R.: Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014, Earth Syst. Sci. Data, 10, 303–315, https://doi.org/10.5194/essd-10-303-2018, 2018. a
Alonso-González, E., Gutmann, E., Aalstad, K., Fayad, A., Bouchet, M., and Gascoin, S.: Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area, Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, 2021. a, b, c, d, e
Alonso-González, E., Aalstad, K., Baba, M. W., Revuelto, J., López-Moreno, J. I., Fiddes, J., Essery, R., and Gascoin, S.: The Multiple Snow Data Assimilation System (MuSA v1.0), Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab
Alonso-González, E., Aalstad, K., Pirk, N., Mazzolini, M., Treichler, D., Leclercq, P., Westermann, S., López-Moreno, J. I., and Gascoin, S.: Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation, Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, 2023. a, b, c, d, e, f
Bannister, R. N.: A review of operational methods of variational and ensemble‐variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation Techniques in Oceanography, Int. Stat. Rev., 71, 223–241, https://doi.org/10.1111/j.1751-5823.2003.tb00194.x, 2003. a, b
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, https://doi.org/10.1002/hyp.3360060305, 1992. a, b, c
Campelo, F. and Aranha, C.: Lessons from the Evolutionary Computation Bestiary, Artificial Life, 29, 421–432, https://doi.org/10.1162/artl_a_00402, 2023. a
Charrois, L., Cosme, E., Dumont, M., Lafaysse, M., Morin, S., Libois, Q., and Picard, G.: On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model, The Cryosphere, 10, 1021–1038, https://doi.org/10.5194/tc-10-1021-2016, 2016. a, b
Chopin, N.: A sequential particle filter method for static models, Biometrika, 89, 539–552, https://doi.org/10.1093/biomet/89.3.539, 2002. a, b, c
Clark, M. P., Slater, A. G., Barrett, A. P., Hay, L. E., McCabe, G. J., Rajagopalan, B., and Leavesley, G. H.: Assimilation of snow covered area information into hydrologic and land-surface models, Adv. Water Resour., 29, 1209–1221, https://doi.org/10.1016/j.advwatres.2005.10.001, 2006. a
Cleary, E., Garbuno-Inigo, A., Lan, S., Schneider, T., and Stuart, A.: Calibrate, emulate, sample, J. Comput. Phys., 424, 109716, https://doi.org/10.1016/j.jcp.2020.109716, 2021. a
Cluzet, B., Lafaysse, M., Cosme, E., Albergel, C., Meunier, L.-F., and Dumont, M.: CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework, Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, 2021. a, b, c
Cluzet, B., Magnusson, J., Quéno, L., Mazzotti, G., Mott, R., and Jonas, T.: Exploring how Sentinel-1 wet-snow maps can inform fully distributed physically based snowpack models, The Cryosphere, 18, 5753–5767, https://doi.org/10.5194/tc-18-5753-2024, 2024. a
Cortés, G. and Margulis, S.: Impacts of El Niño and La Niña on interannual snow accumulation in the Andes: Results from a high-resolution 31 year reanalysis: El Niño Effects on Andes Snow, Geophys. Res. Lett., 44, 6859–6867, https://doi.org/10.1002/2017GL073826, 2017. a, b
De Lannoy, G. J. M., Reichle, R. H., Arsenault, K. R., Houser, P. R., Kumar, S., Verhoest, N. E. C., and Pauwels, V. R. N.: Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado, Water Resour. Res., 48, W01522, https://doi.org/10.1029/2011WR010588, 2012. a
De Lannoy, G. J. M., Bechtold, M., Busschaert, L., Heyvaert, Z., Modanesi, S., Dunmire, D., Lievens, H., Getirana, A., and Massari, C.: Contributions of Irrigation Modeling, Soil Moisture and Snow Data Assimilation to High-Resolution Water Budget Estimates Over the Po Basin: Progress Towards Digital Replicas, J. Adv. Model. Earth Sy., 16, e2024MS004433, https://doi.org/10.1029/2024MS004433, 2024. a
Del Moral, P.: Feynman-Kac Formulae, Springer, https://doi.org/10.1007/978-1-4684-9393-1, 2004. a, b
Doucet, A., Godsill, S., and Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering, Stat. Comput., 10, 197–208, https://doi.org/10.1023/A:1008935410038, 2000. a
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial distribution of snow water equivalent in the world’s mountains, Wires Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016. a, b
Duan, J.-C., Li, S., and Xu, Y.: Sequential Monte Carlo optimization and statistical inference, WIREs Comput. Stat., 15, e1598, https://doi.org/10.1002/wics.1598, 2023. a
Durand, M., Molotch, N. P., and Margulis, S. A.: A Bayesian approach to snow water equivalent reconstruction, J. Geophys. Res.-Atmos., 113, D20117, https://doi.org/10.1029/2008JD009894, 2008. a
Elias Chereque, A., Kushner, P. J., Mudryk, L., Derksen, C., and Mortimer, C.: A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products, The Cryosphere, 18, 4955–4969, https://doi.org/10.5194/tc-18-4955-2024, 2024. a
Elvira, V., Martino, L., Bugallo, M. F., and Djuric, P. M.: Elucidating the Auxiliary Particle Filter via Multiple Importance Sampling [Lecture Notes], IEEE Signal Process. Mag., 36, 145–152, https://doi.org/10.1109/MSP.2019.2938026, 2019. a
Elvira, V., Martino, L., and Robert, C.: Rethinking the Effective Sample Size, Int. Stat. Rev., 90, 525–550, https://doi.org/10.1111/insr.12500, 2022. a
Emerick, A. A. and Reynolds, A. C.: Combining the Ensemble Kalman Filter with Markov Chain Monte Carlo for Improved History Matching and Uncertainty Characterization, SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, February 2011, SPE-141336-MS, https://doi.org/10.2118/141336-MS, 2011. a
Essery, R.: A factorial snowpack model (FSM 1.0), Geosci. Model Dev., 8, 3867–3876, https://doi.org/10.5194/gmd-8-3867-2015, 2015. a
Essery, R., Mazzotti, G., Barr, S., Jonas, T., Quaife, T., and Rutter, N.: A Flexible Snow Model (FSM 2.1.1) including a forest canopy, Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, 2025. a, b, c, d
Euskirchen, E. S., Goodstein, E. S., and Huntington, H. P.: An estimated cost of lost climate regulation services caused by thawing of the Arctic cryosphere, Ecol. Appl., 23, 1869–1880, https://doi.org/10.1890/11-0858.1, 2013. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143, https://doi.org/10.1029/94JC00572, 1994. a, b, c
Farchi, A. and Bocquet, M.: Review article: Comparison of local particle filters and new implementations, Nonlin. Processes Geophys., 25, 765–807, https://doi.org/10.5194/npg-25-765-2018, 2018. a, b, c, d
Fiddes, J., Aalstad, K., and Westermann, S.: Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering, Hydrol. Earth Syst. Sci., 23, 4717–4736, https://doi.org/10.5194/hess-23-4717-2019, 2019. a, b, c
Garbuno-Inigo, A., Hoffmann, F., Li, W., and Stuart, A.: Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler, SIAM J. Appl. Dynam. Syst., 19, 412–441, https://doi.org/10.1137/19M1251655, 2020. a, b, c
Gascoin, S.: A call for an accurate presentation of glaciers as water resources, WIREs Water, 11, e1705, https://doi.org/10.1002/wat2.1705, 2024. a
Gascoin, S., Luojus, K., Nagler, T., Lievens, H., Masiokas, M., Jonas, T., Zheng, Z., and De Rosnay, P.: Remote sensing of mountain snow from space: status and recommendations, Front. Earth Sci., 12, 138323, https://doi.org/10.3389/feart.2024.1381323, 2024. a
Gilks, W. R. and Berzuini, C.: Following a moving target-Monte Carlo inference for dynamic Bayesian models, J. Roy. Stat. Soc. Ser. B, 63, 127–146, https://doi.org/10.1111/1467-9868.00280, 2001. a, b, c
Girotto, M., Margulis, S., and Durand, M.: Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques, Hydrol. Process., 28, 3875–3895, https://doi.org/10.1002/hyp.9887, 2014. a
Girotto, M., Musselman, K. N., and Essery, R. L. H.: Data Assimilation Improves Estimates of Climate-Sensitive Seasonal Snow, Curr. Clim. Change Rep., 6, 81–94, https://doi.org/10.1007/s40641-020-00159-7, 2020. a, b
Girotto, M., Formetta, G., Azimi, S., Bachand, C., Cowherd, M., De Lannoy, G., Lievens, H., Modanesi, S., Raleigh, M. S., Rigon, R., and Massari, C.: Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals, Sci. Total Environ., 906, 167312, https://doi.org/10.1016/j.scitotenv.2023.167312, 2024. a, b, c
Gneiting, T. and Raftery, A. E.: Strictly Proper Scoring Rules, Prediction, and Estimation, J. Am. Stat. Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a
Gottlieb, A. R. and Mankin, J. S.: Evidence of human influence on Northern Hemisphere snow loss, Nature, 625, 293–300, https://doi.org/10.1038/s41586-023-06794-y, 2024. a
Groenke, B., Langer, M., Nitzbon, J., Westermann, S., Gallego, G., and Boike, J.: Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer, The Cryosphere, 17, 3505–3533, https://doi.org/10.5194/tc-17-3505-2023, 2023. a, b
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in Snowpack Simulations – Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point-Scale Energy Balance Snow Model Performance, Water Resour. Res., 55, 2779–2800, https://doi.org/10.1029/2018WR023403, 2019. a
Hammersley, J. and Morton, K.: Poor Man's Monte Carlo, J. Roy. Stat. Soc. Ser. B, 16, 23–38, https://doi.org/10.1111/j.2517-6161.1954.tb00145.x, 1954. a
Hastings, W.: Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika, 57, https://doi.org/10.2307/2334940, 1970. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c, d, e
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003. a
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y., Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin, S., Orlove, B., and Steltzer, H.: High Mountain Areas, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, C. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge, 131–202, https://doi.org/10.1017/9781009157964.004, 2019. a
Holland, J.: Adaptation in Natural and Artificial Systems, MIT, 2nd Edn., ISBN 9780262581110, 1992. a
Immerzeel, W., Lutz, A., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B., Elmore, A., Emmer, A., Feng, M., Fernánde, A., Haritashya, U., Kargel, J., Koppes, M., Kraaijenbrink, P., Kulkarni, A., Mayewski, P., Nepal, S., Pacheco, P., Painter, T., Pellicciotti, F. Rajaram, H., Rupper, S., Sinisalo, A., Srestha, A., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J.: Importance and vulnerability of the world’s water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. a
Jazwinski, A.: Stochastic Processes and Filtering Theory, Academic Press, Vol. 64, 376 pp., https://doi.org/10.1016/S0076-5392(09)X6022-4, 1970. a
Keetz, L. T., Aalstad, K., Fisher, R. A., Poppe Terán, C., Naz, B., Pirk, N., Yilmaz, Y. A., and Skarpaas, O.: Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning, J. Adv. Model. Earth Sy., 17, e2024MS004542, https://doi.org/10.1029/2024MS004542, 2025. a, b, c, d
Kitagawa, G.: Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models, J. Comput. Graph. Stat., 5, 1–25, https://doi.org/10.1080/10618600.1996.10474692, 1996. a, b, c
Koblents, E. and Míguez, J.: A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models, Stat. Comput., 25, 407–425, https://doi.org/10.1007/s11222-013-9440-2, 2015. a, b
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a
Landmann, J. M., Künsch, H. R., Huss, M., Ogier, C., Kalisch, M., and Farinotti, D.: Assimilating near-real-time mass balance stake readings into a model ensemble using a particle filter, The Cryosphere, 15, 5017–5040, https://doi.org/10.5194/tc-15-5017-2021, 2021. a, b
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Wisntral, A., and Margulis, S.: Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review, Front. Earth Sci., 8, 325, https://doi.org/10.3389/feart.2020.00325, 2020. a, b, c
Law, K. and Stuart, A.: Evaluating Data Assimilation Algorithms, Mon. Weather Rev., 140, 3757–3782, https://doi.org/10.1175/MWR-D-11-00257.1, 2012. a, b, c
Leisenring, M. and Moradkhani, H.: Snow water equivalent prediction using Bayesian data assimilation methods, Stoch. Env. Res. Risk Assess., 25, 253–270, https://doi.org/10.1007/s00477-010-0445-5, 2011. a, b, c
Li, T., Bolic, M., and Djuric, P.: Resampling Methods for Particle Filtering: Classification, implementation, and strategies, IEEE Signal Process. Mag., 32, 70–86, https://doi.org/10.1109/MSP.2014.2330626, 2015. a
Liston, G. E. and Elder, K.: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet, J. Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006b. a
Liu, Y., Fang, Y., and Margulis, S. A.: Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset, The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, 2021. a, b, c
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, 2018. a
Magnusson, J., Winstral, A., Stordal, A. S., Essery, R., and Jonas, T.: Improving physically based snow simulations by assimilating snow depths using the particle filter, Water Resour. Res., 53, 1125–1143, https://doi.org/10.1002/2016WR019092, 2017. a, b, c
Margulis, S. A., Cortés, G., Girotto, M., and Durand, M.: A landsat-era Sierra Nevada snow reanalysis (1985-2015, J. Hydrometeorol., 17, 1203–1221, https://doi.org/10.1175/JHM-D-15-0177.1, 2016. a, b, c, d
Marin, J.-M., Pudlo, P., and Sedki, M.: Consistency of adaptive importance sampling and recycling schemes, Bernoulli, 25, 1977–1998, https://doi.org/10.3150/18-BEJ1042, 2019. a
Maussion, F., Butenko, A., Champollion, N., Dusch, M., Eis, J., Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T., and Marzeion, B.: The Open Global Glacier Model (OGGM) v1.1, Geosci. Model Dev., 12, 909–931, https://doi.org/10.5194/gmd-12-909-2019, 2019. a, b
Ménard, C. and Essery, R.: ESM-SnowMIP meteorological and evaluation datasets at ten reference sites (in situ and bias corrected reanalysis data), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.897575, 2019. a
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N.: Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019. a, b, c
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C., Ottersen, G., Pritchard, H., and Schuur, E. A. G.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, C. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge, 203–320, https://doi.org/10.1017/9781009157964.005, 2019. a
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., and Teller, E.: Equation of State Calculations by Fast Computing Machines, J. Chem. Phys., 26, 1087–1092, https://doi.org/10.1063/1.1699114, 1953. a
Morzfeld, M., Hodyss, D., and Snyder, C.: What the collapse of the ensemble Kalman filter tells us about particle filters, Tellus A, 69, 1–14, https://doi.org/10.1080/16000870.2017.1283809, 2017. a, b, c, d
Navari, M., Margulis, S. A., Bateni, S. M., Tedesco, M., Alexander, P., and Fettweis, X.: Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures, The Cryosphere, 10, 103–120, https://doi.org/10.5194/tc-10-103-2016, 2016. a, b, c
Neal, R.: Annealed importance sampling, Stat. Comput., 11, 125–139, https://doi.org/10.1023/A:1008923215028, 2001. a
Neal, R.: MCMC Using Hamiltonian Dynamics, in: Handbook of Markov Chain Monte Carlo, edited by: Brooks, S., Gelman, A., Jones, G., and Meng, X.-L., chap. 5, CRC Press, 113–162, https://doi.org/10.1201/b10905, 2011. a, b
Oberrauch, M., Cluzet, B., Magnusson, J., and Jonas, T.: Improving Fully Distributed Snowpack Simulations by Mapping Perturbations of Meteorological Forcings Inferred From Particle Filter Assimilation of Snow Monitoring Data, Water Resour. Res., 60, e2023WR036994, https://doi.org/10.1029/2023WR036994, 2024. a, b, c, d, e
Owen, A. and Zhou, Y.: Safe and Effective Importance Sampling, J. Am. Stat. Assoc., 95, 135–143, https://doi.org/10.1080/01621459.2000.10473909, 2000. a, b
Piazzi, G., Thirel, G., Campo, L., and Gabellani, S.: A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment, The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, 2018. a, b, c
Pirk, N., Aalstad, K., Westermann, S., Vatne, A., van Hove, A., Tallaksen, L. M., Cassiani, M., and Katul, G.: Inferring surface energy fluxes using drone data assimilation in large eddy simulations, Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, 2022. a, b, c, d, e, f, g, h, i
Pirk, N., Aalstad, K., Mannerfelt, E. S., Clayer, F., de Wit, H., Christiansen, C. T., Althuizen, I., Lee, H., and Westermann, S.: Disaggregating the Carbon Exchange of Degrading Permafrost Peatlands Using Bayesian Deep Learning, Geophys. Res. Lett., 51, e2024GL109283, https://doi.org/10.1029/2024GL109283, 2024. a, b, c, d, e, f
Reich, S. and Cotter, C.: Probabilistic Forecasting and Bayesian Data Assimilation, Cambridge, https://doi.org/10.1017/CBO9781107706804, 2015. a, b, c, d
Revuelto, J., Azorin-Molina, C., Alonso-González, E., Sanmiguel-Vallelado, A., Navarro-Serrano, F., Rico, I., and López-Moreno, J. I.: Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017, Earth Syst. Sci. Data, 9, 993–1005, https://doi.org/10.5194/essd-9-993-2017, 2017. a
Revuelto, J., Alonso-Gonzalez, E., Vidaller-Gayan, I., Lacroix, E., Izagirre, E., Rodríguez-López, G., and López-Moreno, J.: Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain, Cold Reg. Sci. Technol., 190, 103344, https://doi.org/10.1016/j.coldregions.2021.103344, 2021. a, b
Riihelä, A., Bright, R. M., and Anttila, K.: Recent strengthening of snow and ice albedo feedback driven by Antarctic sea-ice loss, Nat. Geosci., 14, 832–836, https://doi.org/10.1038/s41561-021-00841-x, 2021. a
Robert, C.: The Bayesian Choice, Springer, 2nd Edn., https://doi.org/10.1007/0-387-71599-1, 2007. a, b, c
Robert, C. and Casella, G.: Monte Carlo Statistical Methods, Springer, https://doi.org/10.1007/978-1-4757-4145-2, 2004. a, b, c, d
Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023. a, b, c, d, e, f, g
Smith, A. F. M. and Gelfand, A. E.: Bayesian Statistics without Tears: A Sampling–Resampling Perspective, Am. Stat., 46, 84–88, https://doi.org/10.1080/00031305.1992.10475856, 1992. a
Smith, T., Sharma, A., Marshall, L., Mehrotra, R., and Sisson, S.: Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments, Water Resour. Res., 46, https://doi.org/10.1029/2010WR009514, 2010. a, b
Smyth, E. J., Raleigh, M. S., and Small, E. E.: Particle Filter Data Assimilation of Monthly Snow Depth Observations Improves Estimation of Snow Density and SWE, Water Resour. Res., 55, 1296–1311, https://doi.org/10.1029/2018WR023400, 2019. a, b
Sörensen, K.: Metaheuristics – the metaphor exposed, Int. Trans. Oper. Res., 22, 3–18, https://doi.org/10.1111/itor.12001, 2015. a
Stordal, A. S. and Elsheikh, A. H.: Iterative ensemble smoothers in the annealed importance sampling framework, Adv. Water Resour., 86, 231–239, https://doi.org/10.1016/j.advwatres.2015.09.030, 2015. a, b, c
Sun, H., Fang, Y., Margulis, S. A., Mortimer, C., Mudryk, L., and Derksen, C.: Evaluation of the Snow Climate Change Initiative (Snow CCI) snow-covered area product within a mountain snow water equivalent reanalysis, The Cryosphere, 19, 2017–2036, https://doi.org/10.5194/tc-19-2017-2025, 2025. a, b, c, d
Tang, B., Frye, H., Gelfand, A., and Silander, J. A.: Zero-Inflated Beta Distribution Regression Modeling, J. Agr., Biol. Environ. Stat., 28, 117–137, 2023. a
van Hove, A., Aalstad, K., Lind, V., Arndt, C., Odongo, V., Ceriani, R., Fava, F., Hulth, J., and Pirk, N.: Inferring methane emissions from African livestock by fusing drone, tower, and satellite data, Biogeosciences, 22, 4163–4186, https://doi.org/10.5194/bg-22-4163-2025, 2025. a, b, c
van Hove, A., Aalstad, K., and Pirk, N.: Actively inferring methane sources with drones, Environ. Data Sci., 5, e2, https://doi.org/10.1017/eds.2026.10029, 2026. a, b
van Leeuwen, P. J.: Particle Filtering in Geophysical Systems, Mon. Weather Rev., 137, 4089–4114, https://doi.org/10.1175/2009MWR2835.1, 2009. a, b, c
van Leeuwen, P. J. and Evensen, G.: Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation, Mon. Weather Rev., 124, 2898–2913, https://doi.org/10.1175/1520-0493(1996)124<2898:DAAIMI>2.0.CO;2, 1996. a, b, c
Vihola, M.: Robust adaptive Metropolis algorithm with coerced acceptance rate, Stat. Comput., 22, 997–1008, https://doi.org/10.1007/s11222-011-9269-5, 2012. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli, A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem, A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C., Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin, E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A., Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich, J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner, J., Kulick, J., Schönberger, J. L., de Miranda Cardoso, J. V., Reimer, J., Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M., Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O., Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T., Pingel, T. J., Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y. O., and Vázquez-Baeza, Y.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Meth., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, https://doi.org/10.1029/2002WR001642, 2003. a, b
Vrugt, J. A., ter Braak, C. J. F., Clark, M. P., Hyman, J. M., and Robinson, B. A.: Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resour. Res., 44, W00B09, https://doi.org/10.1029/2007WR006720, 2008. a, b
Westermann, S., Ingeman-Nielsen, T., Scheer, J., Aalstad, K., Aga, J., Chaudhary, N., Etzelmüller, B., Filhol, S., Kääb, A., Renette, C., Schmidt, L. S., Schuler, T. V., Zweigel, R. B., Martin, L., Morard, S., Ben-Asher, M., Angelopoulos, M., Boike, J., Groenke, B., Miesner, F., Nitzbon, J., Overduin, P., Stuenzi, S. M., and Langer, M.: The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere, Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, 2023. a, b, c, d
Wikle, C. K. and Berliner, L. M.: A Bayesian tutorial for data assimilation, Phys. D, 230, 1–16, https://doi.org/10.1016/j.physd.2006.09.017, 2007. a
Zschenderlein, L., Luojus, K., Takala, M., Venäläinen, P., and Pulliainen, J.: Evaluation of passive microwave dry snow detection algorithms and application to SWE retrieval during seasonal snow accumulation, Remote Sens. Environ., 288, 113476, https://doi.org/10.1016/j.rse.2023.113476, 2023. a
Short summary
AdaPBS (Adaptive Particle Batch Smoother) is a new algorithm to combine observations with cryospheric numerical models. AdaPBS is an iterative algorithm that automatically adjusts computing effort to the task, allowing the implementation of early stopping strategies. We tested AdaPBS at multiple sites with different models, matching or outperforming standard methods, when compared against more complex (computationally expensive) algorithms.
AdaPBS (Adaptive Particle Batch Smoother) is a new algorithm to combine observations with...
