Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-889-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-889-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards long-term records of rain-on-snow events across the Arctic from satellite data
b.geos GmbH, Industriestrasse 1, 2100 Korneuburg, Austria
Helena Bergstedt
b.geos GmbH, Industriestrasse 1, 2100 Korneuburg, Austria
Georg Pointner
b.geos GmbH, Industriestrasse 1, 2100 Korneuburg, Austria
Xaver Muri
b.geos GmbH, Industriestrasse 1, 2100 Korneuburg, Austria
Kimmo Rautiainen
Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Leena Leppänen
Earth Observation Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Arctic Centre, University of Lapland, Pohjoisranta 4, 96101 Rovaniemi, Finland
Kyle Joly
Gates of the Arctic National Park and Preserve, Arctic Inventory and Monitoring Program, National Park Service, 99709 Fairbanks, Alaska, USA
Aleksandr Sokolov
Arctic Research Station, Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences, Zelenaya Gorka Str. 21, Labytnangi, Yamal-Nenets Autonomous District, Russia
Pavel Orekhov
Arctic Research Station, Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences, Zelenaya Gorka Str. 21, Labytnangi, Yamal-Nenets Autonomous District, Russia
Dorothee Ehrich
Department of Arctic and Marine Biology, UiT, The Arctic University of Norway, 9037 Tromsø, Norway
Eeva Mariatta Soininen
Department of Arctic and Marine Biology, UiT, The Arctic University of Norway, 9037 Tromsø, Norway
Related authors
Kseniia Ivanova, Anna-Maria Virkkala, Victor Brovkin, Tobias Stacke, Barbara Widhalm, Annett Bartsch, Carolina Voigt, Oliver Sonnentag, and Mathias Göckede
EGUsphere, https://doi.org/10.5194/egusphere-2025-3968, https://doi.org/10.5194/egusphere-2025-3968, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We measured over 13,000 methane fluxes at a site in the Canadian Arctic and linked them with drone and free satellite images. We tested four machine-learning methods and two map scales. Metre-scale maps captured small wet and dry features that strongly affect methane release, while coarser maps blurred them. Different models shifted the monthly methane estimate. This helps choose the right data and tools to map methane, design monitoring networks, and check climate models.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
Short summary
Short summary
We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Valeria Briones, Hélène Genet, Elchin E. Jafarov, Brendan M. Rogers, Jennifer D. Watts, Anna-Maria Virkkala, Annett Bartsch, Benjamin C. Maglio, Joshua Rady, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-226, https://doi.org/10.5194/essd-2025-226, 2025
Manuscript not accepted for further review
Short summary
Short summary
Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar.
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmentiynen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1358, https://doi.org/10.5194/egusphere-2025-1358, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic. Significant correlations north of 60°N was more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could be specifically determined for Eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate parameters and causative effects across the Arctic.
Julia Wagner, Juliane Wolter, Justine Ramage, Victoria Martin, Andreas Richter, Niek Jesse Speetjens, Jorien E. Vonk, Rachele Lodi, Annett Bartsch, Michael Fritz, Hugues Lantuit, and Gustaf Hugelius
EGUsphere, https://doi.org/10.5194/egusphere-2025-1052, https://doi.org/10.5194/egusphere-2025-1052, 2025
Short summary
Short summary
Permafrost soils store vast amounts of organic carbon, key to understanding climate change. This study uses machine learning and combines existing data with new field data to create detailed regional maps of soil carbon and nitrogen stocks for the Yukon coastal plain. The results show how soil properties vary across the landscape highlighting the importance of data selection for accurate predictions. These findings improve carbon storage estimates and may aid regional carbon budget assessments.
Barbara Widhalm, Annett Bartsch, Tazio Strozzi, Nina Jones, Artem Khomutov, Elena Babkina, Marina Leibman, Rustam Khairullin, Mathias Göckede, Helena Bergstedt, Clemens von Baeckmann, and Xaver Muri
The Cryosphere, 19, 1103–1133, https://doi.org/10.5194/tc-19-1103-2025, https://doi.org/10.5194/tc-19-1103-2025, 2025
Short summary
Short summary
Mapping soil moisture in Arctic permafrost regions is crucial for various activities, but it is challenging with typical satellite methods due to the landscape's diversity. Seasonal freezing and thawing cause the ground to periodically rise and subside. Our research demonstrates that this seasonal ground settlement, measured with Sentinel-1 satellite data, is larger in areas with wetter soils. This method helps to monitor permafrost degradation.
Annett Bartsch, Xaver Muri, Markus Hetzenecker, Kimmo Rautiainen, Helena Bergstedt, Jan Wuite, Thomas Nagler, and Dmitry Nicolsky
The Cryosphere, 19, 459–483, https://doi.org/10.5194/tc-19-459-2025, https://doi.org/10.5194/tc-19-459-2025, 2025
Short summary
Short summary
We developed a robust freeze–thaw detection approach, applying a constant threshold to Copernicus Sentinel-1 data that is suitable for tundra regions. All global, coarser-resolution products, tested with the resulting benchmarking dataset, are of value for freeze–thaw retrieval, although differences were found depending on the seasons, particularly during the spring and autumn transition.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
The Cryosphere, 18, 4703–4722, https://doi.org/10.5194/tc-18-4703-2024, https://doi.org/10.5194/tc-18-4703-2024, 2024
Short summary
Short summary
Lakes are common features in Arctic permafrost areas. Land cover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibman
Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, https://doi.org/10.5194/hess-28-2421-2024, 2024
Short summary
Short summary
Wetness gradients and landcover diversity for the entire Arctic tundra have been assessed using a novel satellite-data-based map. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are represented at 10 m. About 40 % of the area north of the treeline falls into three units of dry types, with limited shrub growth. Wetter regions have higher landcover diversity than drier regions.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
Short summary
Short summary
The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Georg Pointner, Annett Bartsch, Yury A. Dvornikov, and Alexei V. Kouraev
The Cryosphere, 15, 1907–1929, https://doi.org/10.5194/tc-15-1907-2021, https://doi.org/10.5194/tc-15-1907-2021, 2021
Short summary
Short summary
This study presents strong new indications that regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of ice of Lake Neyto in northwestern Siberia are related to strong emissions of natural gas. Spatio-temporal dynamics and potential scattering and formation mechanisms are assessed. It is suggested that exploiting the spatial and temporal properties of Sentinel-1 SAR data may be beneficial for the identification of similar phenomena in other Arctic lakes.
Kseniia Ivanova, Anna-Maria Virkkala, Victor Brovkin, Tobias Stacke, Barbara Widhalm, Annett Bartsch, Carolina Voigt, Oliver Sonnentag, and Mathias Göckede
EGUsphere, https://doi.org/10.5194/egusphere-2025-3968, https://doi.org/10.5194/egusphere-2025-3968, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We measured over 13,000 methane fluxes at a site in the Canadian Arctic and linked them with drone and free satellite images. We tested four machine-learning methods and two map scales. Metre-scale maps captured small wet and dry features that strongly affect methane release, while coarser maps blurred them. Different models shifted the monthly methane estimate. This helps choose the right data and tools to map methane, design monitoring networks, and check climate models.
Sara Hyvärinen, Maria Katariina Tenkanen, Aki Tsuruta, Anttoni Erkkilä, Kimmo Rautiainen, Hermanni Aaltonen, Motoki Sasakawa, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-2794, https://doi.org/10.5194/egusphere-2025-2794, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed spring methane emissions from northern high-latitude wetlands using satellite thaw data and inverse modeling (2011–2021). Comparing region-based and grid-based approaches, we found that emissions varied with the length of the melting season, which depended on air temperature. We found spring melting season emissions ranged from 0.45 Tg to 1.83 Tg depending on the approach, with no clear trend over the period. Our methods allow for seasonal methane monitoring across different scales.
Constanze Reinken, Victor Brovkin, Philipp de Vrese, Ingmar Nitze, Helena Bergstedt, and Guido Grosse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1817, https://doi.org/10.5194/egusphere-2025-1817, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Thermokarst lakes are dynamic features of ice-rich permafrost landscapes, altering energy, water and carbon cycles, but have so far mostly been modeled on site-level scale. A deterministic modelling approach would be challenging on larger scales due to the lack of extensive high-resolution data of sub-surface conditions. We therefore develop a conceptual stochastic model of thermokarst lake dynamics that treats the involved processes as probabilistic.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
Short summary
Short summary
We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Valeria Briones, Hélène Genet, Elchin E. Jafarov, Brendan M. Rogers, Jennifer D. Watts, Anna-Maria Virkkala, Annett Bartsch, Benjamin C. Maglio, Joshua Rady, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-226, https://doi.org/10.5194/essd-2025-226, 2025
Manuscript not accepted for further review
Short summary
Short summary
Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar.
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmentiynen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1358, https://doi.org/10.5194/egusphere-2025-1358, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic. Significant correlations north of 60°N was more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could be specifically determined for Eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate parameters and causative effects across the Arctic.
Kimmo Rautiainen, Manu Holmberg, Juval Cohen, Arnaud Mialon, Mike Schwank, Juha Lemmetyinen, Antonio de la Fuente, and Yann Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-68, https://doi.org/10.5194/essd-2025-68, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The SMOS Soil Freeze Thaw State product uses satellite data to monitor seasonal soil freezing and thawing globally, with a focus on high latitude regions. This is important for understanding greenhouse gas emissions, as frozen soil is associated with methane release. The product provides accurate data on key events such as the first day of soil freezing in autumn, helping scientists to study climate change, ecosystem dynamics and its impact on our planet.
Julia Wagner, Juliane Wolter, Justine Ramage, Victoria Martin, Andreas Richter, Niek Jesse Speetjens, Jorien E. Vonk, Rachele Lodi, Annett Bartsch, Michael Fritz, Hugues Lantuit, and Gustaf Hugelius
EGUsphere, https://doi.org/10.5194/egusphere-2025-1052, https://doi.org/10.5194/egusphere-2025-1052, 2025
Short summary
Short summary
Permafrost soils store vast amounts of organic carbon, key to understanding climate change. This study uses machine learning and combines existing data with new field data to create detailed regional maps of soil carbon and nitrogen stocks for the Yukon coastal plain. The results show how soil properties vary across the landscape highlighting the importance of data selection for accurate predictions. These findings improve carbon storage estimates and may aid regional carbon budget assessments.
Barbara Widhalm, Annett Bartsch, Tazio Strozzi, Nina Jones, Artem Khomutov, Elena Babkina, Marina Leibman, Rustam Khairullin, Mathias Göckede, Helena Bergstedt, Clemens von Baeckmann, and Xaver Muri
The Cryosphere, 19, 1103–1133, https://doi.org/10.5194/tc-19-1103-2025, https://doi.org/10.5194/tc-19-1103-2025, 2025
Short summary
Short summary
Mapping soil moisture in Arctic permafrost regions is crucial for various activities, but it is challenging with typical satellite methods due to the landscape's diversity. Seasonal freezing and thawing cause the ground to periodically rise and subside. Our research demonstrates that this seasonal ground settlement, measured with Sentinel-1 satellite data, is larger in areas with wetter soils. This method helps to monitor permafrost degradation.
Ella Kivimäki, Tuula Aalto, Michael Buchwitz, Kari Luojus, Jouni Pulliainen, Kimmo Rautiainen, Oliver Schneising, Anu-Maija Sundström, Johanna Tamminen, Aki Tsuruta, and Hannakaisa Lindqvist
EGUsphere, https://doi.org/10.5194/egusphere-2025-249, https://doi.org/10.5194/egusphere-2025-249, 2025
Short summary
Short summary
We investigate how environmental variables influencing natural methane fluxes explain the large-scale seasonal variability of satellite-observed methane at Northern high latitudes. Our findings show that soil moisture, snow cover, and soil temperature have the strongest influence, with snowmelt playing a surprisingly significant role, likely through soil isolation and wetting. This study highlights the value of multi-satellite observations for understanding large-scale wetland emissions.
Annett Bartsch, Xaver Muri, Markus Hetzenecker, Kimmo Rautiainen, Helena Bergstedt, Jan Wuite, Thomas Nagler, and Dmitry Nicolsky
The Cryosphere, 19, 459–483, https://doi.org/10.5194/tc-19-459-2025, https://doi.org/10.5194/tc-19-459-2025, 2025
Short summary
Short summary
We developed a robust freeze–thaw detection approach, applying a constant threshold to Copernicus Sentinel-1 data that is suitable for tundra regions. All global, coarser-resolution products, tested with the resulting benchmarking dataset, are of value for freeze–thaw retrieval, although differences were found depending on the seasons, particularly during the spring and autumn transition.
Juliette Ortet, Arnaud Mialon, Alain Royer, Mike Schwank, Manu Holmberg, Kimmo Rautiainen, Simone Bircher-Adrot, Andreas Colliander, Yann Kerr, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3963, https://doi.org/10.5194/egusphere-2024-3963, 2025
Short summary
Short summary
We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperatures time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is excessively promising for monitoring ground temperature below the snowpack and studying the spatiotemporal variability thanks to 15 years of observations over the whole Arctic area.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
The Cryosphere, 18, 4703–4722, https://doi.org/10.5194/tc-18-4703-2024, https://doi.org/10.5194/tc-18-4703-2024, 2024
Short summary
Short summary
Lakes are common features in Arctic permafrost areas. Land cover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibman
Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, https://doi.org/10.5194/hess-28-2421-2024, 2024
Short summary
Short summary
Wetness gradients and landcover diversity for the entire Arctic tundra have been assessed using a novel satellite-data-based map. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are represented at 10 m. About 40 % of the area north of the treeline falls into three units of dry types, with limited shrub growth. Wetter regions have higher landcover diversity than drier regions.
Noriaki Ohara, Benjamin M. Jones, Andrew D. Parsekian, Kenneth M. Hinkel, Katsu Yamatani, Mikhail Kanevskiy, Rodrigo C. Rangel, Amy L. Breen, and Helena Bergstedt
The Cryosphere, 16, 1247–1264, https://doi.org/10.5194/tc-16-1247-2022, https://doi.org/10.5194/tc-16-1247-2022, 2022
Short summary
Short summary
New variational principle suggests that a semi-ellipsoid talik shape (3D Stefan equation) is optimum for incoming energy. However, the lake bathymetry tends to be less ellipsoidal due to the ice-rich layers near the surface. Wind wave erosion is likely responsible for the elongation of lakes, while thaw subsidence slows the wave effect and stabilizes the thermokarst lakes. The derived 3D Stefan equation was compared to the field-observed talik thickness data using geophysical methods.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
Short summary
Short summary
The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Georg Pointner, Annett Bartsch, Yury A. Dvornikov, and Alexei V. Kouraev
The Cryosphere, 15, 1907–1929, https://doi.org/10.5194/tc-15-1907-2021, https://doi.org/10.5194/tc-15-1907-2021, 2021
Short summary
Short summary
This study presents strong new indications that regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of ice of Lake Neyto in northwestern Siberia are related to strong emissions of natural gas. Spatio-temporal dynamics and potential scattering and formation mechanisms are assessed. It is suggested that exploiting the spatial and temporal properties of Sentinel-1 SAR data may be beneficial for the identification of similar phenomena in other Arctic lakes.
Cited articles
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a
Bartsch, A.: Spring snowmelt and midwinter thaw and refreeze north of 60∘ N based
on Seawinds QuikScat 2000–2009, supplement to: Bartsch, Annett (2010): Ten
Years of SeaWinds on QuikSCAT for Snow Applications, Remote Sens., 2,
1142–1156, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.834198, 2010a. a
Bartsch, A.: Monitoring of Terrestrial Hydrology at High Latitudes with
Scatterometer Data, in: Geoscience and Remote Sensing, New Achievements,
edited by: Imperatore, P. and Riccio, D., Intechweb, Vokuvar, 247–262, ISBN 9789537619978, 2010c. a
Bartsch, A., Kidd, R. A., Wagner, W., and Bartalis, Z.: Temporal and Spatial
Variability of the Beginning and End of Daily Spring Freeze/Thaw Cycles
Derived from Scatterometer Data, Remote Sens. Environ., 106,
360–374, https://doi.org/10.1016/j.rse.2006.09.004, 2007. a, b
Bartsch, A., Wagner, W., and Kidd, R.: Remote Sensing of Spring Snowmelt in
Siberia, in: Environmental Change in Siberia, Earth Observation, Field
Studies and Modelling, edited by: Balzter, H., Springer, 135–155,
https://doi.org/10.1007/978-90-481-8641-9_9, 2010b. a
Bartsch, A., Höfler, A., Kroisleitner, C., and Trofaier, A. M.: Land Cover
Mapping in Northern High Latitude Permafrost Regions with Satellite Data:
Achievements and Remaining Challenges, Remote Sens., 8, 979,
https://doi.org/10.3390/rs8120979, 2016. a
Bartsch, A., Widhalm, B., Leibman, M., Ermokhina, K., Kumpula, T., Skarin, A.,
Wilcox, E. J., Jones, B. M., Frost, G. V., Hofler, A., and Pointner, G.:
Feasibility of tundra vegetation height retrieval from Sentinel-1 and
Sentinel-2 data, Remote Sens. Environ., 237, 111515,
https://doi.org/10.1016/j.rse.2019.111515, 2020. a
Bartsch, A., Pointner, G., Bergstedt, H., Widhalm, B., Wendleder, A., and Roth,
A.: Utility of Polarizations Available from Sentinel-1 for Tundra Mapping,
in: 2021 IEEE International Geoscience and Remote Sensing Symposium
IGARSS, IEEE, Brussels, Belgium, 2021, 1452–1455, https://doi.org/10.1109/igarss47720.2021.9553993, 2021. a
Bartsch, A., Bergstedt, H., Pointner, G., Muri, X., and Rautiainen, K.:
Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and
SMOS, 2011/2012–2021/2022, Zenodo [data set], https://doi.org/10.5281/zenodo.7575927, 2023. a
Bergstedt, H., Zwieback, S., Bartsch, A., and Leibman, M.: Dependence of C-Band
Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic
Permafrost Regions, Remote Sens., 10, 142, https://doi.org/10.3390/rs10010142, 2018. a, b
Bergstedt, H., Bartsch, A., Neureiter, A., Hofler, A., Widhalm, B., Pepin, N.,
and Hjort, J.: Deriving a Frozen Area Fraction From Metop ASCAT Backscatter
Based on Sentinel-1, IEEE Transactions on Geoscience and Remote Sensing,
58, 6008–6019, https://doi.org/10.1109/tgrs.2020.2967364, 2020. a, b, c
Bjerke, J. W., Treharne, R., Vikhamar-Schuler, D., Karlsen, S. R., Ravolainen,
V., Bokhorst, S., Phoenix, G. K., Bochenek, Z., and Tømmervik, H.:
Understanding the drivers of extensive plant damage in boreal and Arctic
ecosystems: Insights from field surveys in the aftermath of damage, Sci. Total Environ., 599–600, 1965–1976,
https://doi.org/10.1016/j.scitotenv.2017.05.050, 2017. a
Derksen, C., Xu, X., Dunbar, R. S., Colliander, A., Kim, Y., Kimball, J. S.,
Black, T. A., Euskirchen, E., Langlois, A., Loranty, M. M., Marsh, P.,
Rautiainen, K., Roy, A., Royer, A., and Stephens, J.: Retrieving landscape
freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and
radiometer measurements, Remote Sens. Environ., 194, 48–62,
https://doi.org/10.1016/j.rse.2017.03.007, 2017. a, b
Dolant, C., Langlois, A., Montpetit, B., Brucker, L., Roy, A., and Royer, A.:
Development of a rain-on-snow detection algorithm using passive microwave
radiometry, Hydrol. Process., 30, 3184–3196, https://doi.org/10.1002/hyp.10828,
2016. a, b, c
Ehrich, D., Cerezo, M., Rodnikova, A. Y., Sokolova, N. A., Fuglei, E., Shtro,
V. G., and Sokolov, A. A.: Vole abundance and reindeer carcasses determine
breeding activity of Arctic foxes in low Arctic Yamal, Russia, BMC Ecology,
17, 32, https://doi.org/10.1186/s12898-017-0142-z, 2017. a, b
Figa-Saldaña, J., Wilson, J., Attema, E., Gelsthorpe, R., Drinkwater, M.,
and Stoffelen, A.: The advanced scatterometer (ASCAT) on the meteorological
operational (MetOp) platform: A follow on for European wind scatterometers,
Can. J. Remote Sens., 28, 404–412, https://doi.org/10.5589/m02-035,
2002. a
Forbes, B. C., Kumpula, T., Meschtyb, N., Laptander, R., Macias-Fauria, M.,
Zetterberg, P., Verdonen, M., Skarin, A., Kim, K.-Y., Boisvert, L. N.,
Stroeve, J. C., and Bartsch, A.: Sea ice, rain-on-snow and tundra reindeer
nomadism in Arctic Russia, Biol. Lett., 12, 20160466, https://doi.org/10.1098/rsbl.2016.0466, 2016. a, b, c, d, e, f, g, h, i, j, k, l
Freund, K. and Bartsch, A.: Midwinter thaw events over Greenland derived from
Seawinds QuikScat 2000–2008, Pangaea [data set], https://doi.org/10.1594/PANGAEA.911298, 2020. a
Grenfell, T. C. and Putkonen, J.: A Method for the Detection of the Severe
Rain-on-Snow Event on Banks Island, October 2003, Using Passive
Microwave Remote Sensing, Water Resour. Res., 44, W03425, https://doi.org/10.1029/2007WR005929, 2008. a, b
Hallikainen, M., Ulaby, F., and Abdelrazik, M.: Dielectric properties of snow
in the 3 to 37 GHz range, IEEE T. Antenn. Propag.,
34, 1329–1340, https://doi.org/10.1109/tap.1986.1143757, 1986. a
Joly, K., Couriot, O., Cameron, M. D., and Gurarie, E.: Behavioral,
Physiological, Demographic and Ecological Impacts of Hematophagous and
Endoparasitic Insects on an Arctic Ungulate, Toxins, 12, 334,
https://doi.org/10.3390/toxins12050334, 2020. a
Lamarche, C., Santoro, M., Bontemps, S., d'Andrimont, R., Radoux, J.,
Giustarini, L., Broc kmann, C., Wevers, J., Defourny, P., and Arino, O.:
Compilation and Validation of SAR and Optical Data Products for a Complete
and Global Map of Inland/Ocean Water Tailored to the Climate Modeling
Community, Remote Sens., 9, 36, https://doi.org/10.3390/rs9010036, 2017. a
Langlois, A., Johnson, C.-A., Montpetit, B., Royer, A., Blukacz-Richards, E.,
Neave, E., Dolant, C., Roy, A., Arhonditsis, G., Kim, D.-K., Kaluskar, S.,
and Brucker, L.: Detection of rain-on-snow (ROS) events and ice layer
formation using passive microwave radiometry: A context for Peary caribou
habitat in the Canadian Arctic, Remote Sens. Environ., 189, 84–95,
https://doi.org/10.1016/j.rse.2016.11.006, 2017. a, b, c, d, e
Lemmetyinen, J., Schwank, M., Rautiainen, K., Kontu, A., Parkkinen, T.,
Mätzler, C., Wiesmann, A., Wegmüller, U., Derksen, C., Toose, P.,
Roy, A., and Pulliainen, J.: Snow density and ground permittivity retrieved
from L-band radiometry: Application to experimental data, Remote Sens.
Environ., 180, 377–391, https://doi.org/10.1016/j.rse.2016.02.002, 2016. a, b
Leppänen, L., Kontu, A., Hannula, H.-R., Sjöblom, H., and Pulliainen,
J.: Sodankylä manual snow survey program, Geoscientific Instrumentation,
Methods and Data Systems, 5, 163–179, https://doi.org/10.5194/gi-5-163-2016, 2016. a
Lindsley, R. D. and Long, D. G.: Enhanced-Resolution Reconstruction of ASCAT
Backscatter Measurements, IEEE T. Geosci. Remote, 54, 2589–2601, https://doi.org/10.1109/tgrs.2015.2503762, 2016. a
Long, D. G.: Comparison of SeaWinds Backscatter Imaging Algorithms, IEEE
J. Sel. Top. Appl.,
10, 2214–2231, https://doi.org/10.1109/jstars.2016.2626966, 2017. a
Mousavi, M., Colliander, A., Miller, J. Z., Entekhabi, D., Johnson, J. T.,
Shuman, C. A., Kimball, J. S., and Courville, Z. R.: Evaluation of Surface
Melt on the Greenland Ice Sheet Using SMAP L-Band Microwave Radiometry,
IEEE J. Sel. Top. Appl., 14, 11439–11449, https://doi.org/10.1109/jstars.2021.3124229, 2021. a, b, c
Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S., and Wagner, W.: An
Improved Soil Moisture Retrieval Algorithm for ERS and METOP
Scatterometer Observations, IEEE Trans. Geosci. Remote Sens., 47, 1999–2013, https://doi.org/10.1109/tgrs.2008.2011617, 2009. a, b
Naeimi, V., Paulik, C., Bartsch, A., Wagner, W., Kidd, R., Park, S. E., Elger,
K., and Boike, J.: ASCAT Surface State Flag (SSF):
Extracting Information on Surface Freeze/Thaw Conditions From
Backscatter Data Using an Empirical Threshold-Analysis
Algorithm, IEEE Trans. Geosci. Remote Sens., 50,
2566–2582, https://doi.org/10.1109/TGRS.2011.2177667, 2012. a, b, c
Nagler, T. and Rott, H.: Retrieval of Wet Snow by Means of Multitemporal SAR
Data, IEEE Trans. Geosci. Remote Sens., 38, 754–765, https://doi.org/10.1109/36.842004, 2000. a, b
Nagler, T., Rott, H., Ripper, E., Bippus, G., and Hetzenecker, M.: Advancements
for Snowmelt Monitoring by Means of Sentinel-1 SAR, Remote Sens., 8, 348,
https://doi.org/10.3390/rs8040348, 2016. a
Pellarin, T., Mialon, A., Biron, R., Coulaud, C., Gibon, F., Kerr, Y.,
Lafaysse, M., Mercier, B., Morin, S., Redor, I., Schwank, M., and
Völksch, I.: Three years of L-band brightness temperature measurements in
a mountainous area: Topography, vegetation and snowmelt issues, Remote Sens. Environ., 180, 85–98, https://doi.org/10.1016/j.rse.2016.02.047, 2016. a
Potin, P.: Sentinel-1 User Handbook, ESA, issue 1, https://sedas.satapps.org/wp-content/uploads/2015/07/Sentinel-1_User_Handbook.pdf (last access: 6 February 2023), 2013. a
Potin, P., Rosich, B., Roeder, J., and Bargellini, P.: Sentinel-1 Mission
operations concept, in: 2014 IEEE Geosci. Remote Se., 1465–1468, https://doi.org/10.1109/IGARSS.2014.6946713, 2014. a
Putkonen, J. and Roe, G.: Rain-on-Snow Events Impact Soil Temperatures and
Affect Ungulate Survival, Geophys. Res. Lett., 30, 1188, https://doi.org/10.1029/2002GL016326, 2003. a, b, c
Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A.,
Ikonen, J., Derksen, C., Davydov, S., Davydova, A., Boike, J., Langer, M.,
Drusch, M., and Pulliainen, J.: SMOS prototype algorithm for detecting
autumn soil freezing, Remote Sens. Environ., 180, 346–360,
https://doi.org/10.1016/j.rse.2016.01.012, 2016. a
Raynolds, M. K., Walker, D. A., Balser, A., Bay, C., Campbell, M., Cherosov,
M. M., Daniëls, F. J., Eidesen, P. B., Ermokhina, K. A., Frost, G. V.,
Jedrzejek, B., Jorgenson, M. T., Kennedy, B. E., Kholod, S. S., Lavrinenko,
I. A., Lavrinenko, O. V., Magnússon, B., Matveyeva, N. V.,
Metúsalemsson, S., Nilsen, L., Olthof, I., Pospelov, I. N., Pospelova,
E. B., Pouliot, D., Razzhivin, V., Schaepman-Strub, G., Šibík,
J., Telyatnikov, M. Y., and Troeva, E.: A raster version of the Circumpolar
Arctic Vegetation Map (CAVM), Remote Sens. Environ., 232, 111297,
https://doi.org/10.1016/j.rse.2019.111297, 2019. a, b, c, d
Schubert, A., Miranda, N., Geudtner, D., and Small, D.: Sentinel-1A/B Combined
Product Geolocation Accuracy, Remote Sens., 9, 607,
https://doi.org/10.3390/rs9060607, 2017. a
Schwank, M., Mätzler, C., Wiesmann, A., Wegmäller, U., Pulliainen, J.,
Lemmetyinen, J., Rautiainen, K., Derksen, C., Toose, P., and Drusch, M.: Snow
Density and Ground Permittivity Retrieved from L-Band Radiometry: A
Synthetic Analysis, IEEE J. Sel. Top. Appl., 8, 3833–3845,
https://doi.org/10.1109/jstars.2015.2422998, 2015. a, b
Serreze, M. C., Gustafson, J., Barrett, A. P., Druckenmiller, M. L., Fox, S.,
Voveris, J., Stroeve, J., Sheffield, B., Forbes, B. C., Rasmus, S.,
Laptander, R., Brook, M., Brubaker, M., Temte, J., McCrystall, M. R., and
Bartsch, A.: Arctic rain on snow events: bridging observations to understand
environmental and livelihood impacts, Environ. Res. Lett., 16,
105009, https://doi.org/10.1088/1748-9326/ac269b, 2021. a, b, c, d, e
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res., 113, C02S03,
https://doi.org/10.1029/2005jc003384, 2008. a, b, c, d
Tao, S., Ao, Z., Wigneron, J.-P., Saatchi, S., Ciais, P., Chave, J., Le Toan, T., Frison, P.-L., Hu, X., Chen, C., Fan, L., Wang, M., Zhu, J., Zhao, X., Li, X., Liu, X., Su, Y., Hu, T., Guo, Q., Wang, Z., Tang, Z., Liu, Y., and Fang, J.: C-band Scatterometer (CScat): the first global long-term satellite radar backscatter data set with a C-band signal dynamic, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-264, in review, 2022. a
Treharne, R., Bjerke, J. W., Tømmervik, H., and Phoenix, G. K.: Extreme
event impacts on CO2 fluxes across a range of high latitude,
shrub-dominated ecosystems, Environ. Res. Lett., 15, 104084,
https://doi.org/10.1088/1748-9326/abb0b1, 2020. a
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022. a
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing,
Active and Passive, Vol. III, Artech House, Inc, ISBN 0890061920, 1986. a
Vickers, H., Malnes, E., and Eckerstorfer, M.: A Synthetic Aperture Radar Based
Method for Long Term Monitoring of Seasonal Snowmelt and Wintertime
Rain-On-Snow Events in Svalbard, Front. Earth Sci., 10, 868945,
https://doi.org/10.3389/feart.2022.868945, 2022.
a
Westermann, S., Boike, J., Langer, M., Schuler, T. V., and Etzelmüller, B.: Modeling the impact of wintertime rain events on the thermal regime of permafrost, The Cryosphere, 5, 945–959, https://doi.org/10.5194/tc-5-945-2011, 2011. a
Wilson, R. R., Bartsch, A., Joly, K., Reynolds, J. H., Orlando, A., and Loya,
W. M.: Frequency, timing, extent, and size of winter thaw-refreeze events in
Alaska 2001–2008 detected by remotely sensed microwave
backscatter data, Polar Biol., 36, 419–426,
https://doi.org/10.1007/s00300-012-1272-6, 2012. a, b, c, d
Woodhouse, I. H.: Introduction to Microwave Remote Sensing, Taylor & Francis, New York, p. 400, https://doi.org/10.1201/9781315272573, 2006. a
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In...