Articles | Volume 15, issue 24
https://doi.org/10.5194/amt-15-7293-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-7293-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2022
Research article |
| 20 Dec 2022
| 20 Dec 2022
Inferring surface energy fluxes using drone data assimilation in large eddy simulations
Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
Kristoffer Aalstad
Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
Sebastian Westermann
Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
Astrid Vatne
Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
Alouette van Hove
Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
Lena Merete Tallaksen
Department of Geosciences, University of Oslo, Sem Sælands vei 1, 0371 Oslo, Norway
Massimo Cassiani
NILU – Norwegian Institute for Air Research, Instituttveien 18, 2007 Kjeller, Norway
Gabriel Katul
Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall, Durham, NC, 27708, USA
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Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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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
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Mohammad Allouche, Vladislav I. Sevostianov, Einara Zahn, Mark A. Zondlo, Nelson Luís Dias, Gabriel G. Katul, Jose D. Fuentes, and Elie Bou-Zeid
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Riccardo Biella, Ansastasiya Shyrokaya, Monica Ionita, Raffaele Vignola, Samuel Sutanto, Andrijana Todorovic, Claudia Teutschbein, Daniela Cid, Maria Carmen Llasat, Pedro Alencar, Alessia Matanó, Elena Ridolfi, Benedetta Moccia, Ilias Pechlivanidis, Anne van Loon, Doris Wendt, Elin Stenfors, Fabio Russo, Jean-Philippe Vidal, Lucy Barker, Mariana Madruga de Brito, Marleen Lam, Monika Bláhová, Patricia Trambauer, Raed Hamed, Scott J. McGrane, Serena Ceola, Sigrid Jørgensen Bakke, Svitlana Krakovska, Viorica Nagavciuc, Faranak Tootoonchi, Giuliano Di Baldassarre, Sandra Hauswirth, Shreedhar Maskey, Svitlana Zubkovych, Marthe Wens, and Lena Merete Tallaksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2069, https://doi.org/10.5194/egusphere-2024-2069, 2024
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Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-1404, https://doi.org/10.5194/egusphere-2024-1404, 2024
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Moritz Langer, Jan Nitzbon, Brian Groenke, Lisa-Marie Assmann, Thomas Schneider von Deimling, Simone Maria Stuenzi, and Sebastian Westermann
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The Cryosphere, 17, 5477–5497, https://doi.org/10.5194/tc-17-5477-2023, https://doi.org/10.5194/tc-17-5477-2023, 2023
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Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
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In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
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
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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.
Matti Räsänen, Mika Aurela, Ville Vakkari, Johan P. Beukes, Juha-Pekka Tuovinen, Pieter G. Van Zyl, Miroslav Josipovic, Stefan J. Siebert, Tuomas Laurila, Markku Kulmala, Lauri Laakso, Janne Rinne, Ram Oren, and Gabriel Katul
Hydrol. Earth Syst. Sci., 26, 5773–5791, https://doi.org/10.5194/hess-26-5773-2022, https://doi.org/10.5194/hess-26-5773-2022, 2022
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The productivity of semiarid grazed grasslands is linked to the variation in rainfall and transpiration. By combining carbon dioxide and water flux measurements, we show that the annual transpiration is nearly constant during wet years while grasses react quickly to dry spells and drought, which reduce transpiration. The planning of annual grazing strategies could consider the early-season rainfall frequency that was linked to the portion of annual transpiration.
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
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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.
Stefano Manzoni, Simone Fatichi, Xue Feng, Gabriel G. Katul, Danielle Way, and Giulia Vico
Biogeosciences, 19, 4387–4414, https://doi.org/10.5194/bg-19-4387-2022, https://doi.org/10.5194/bg-19-4387-2022, 2022
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Increasing atmospheric carbon dioxide (CO2) causes leaves to close their stomata (through which water evaporates) but also promotes leaf growth. Even if individual leaves save water, how much will be consumed by a whole plant with possibly more leaves? Using different mathematical models, we show that plant stands that are not very dense and can grow more leaves will benefit from higher CO2 by photosynthesizing more while adjusting their stomata to consume similar amounts of water.
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
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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
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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 ℃.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
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Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
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
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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
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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
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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
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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.
Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, https://doi.org/10.5194/hess-25-4455-2021, 2021
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Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
Léo C. P. Martin, Jan Nitzbon, Johanna Scheer, Kjetil S. Aas, Trond Eiken, Moritz Langer, Simon Filhol, Bernd Etzelmüller, and Sebastian Westermann
The Cryosphere, 15, 3423–3442, https://doi.org/10.5194/tc-15-3423-2021, https://doi.org/10.5194/tc-15-3423-2021, 2021
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It is important to understand how permafrost landscapes respond to climate changes because their thaw can contribute to global warming. We investigate how a common permafrost morphology degrades using both field observations of the surface elevation and numerical modeling. We show that numerical models accounting for topographic changes related to permafrost degradation can reproduce the observed changes in nature and help us understand how parameters such as snow influence this phenomenon.
Juditha Undine Schmidt, Bernd Etzelmüller, Thomas Vikhamar Schuler, Florence Magnin, Julia Boike, Moritz Langer, and Sebastian Westermann
The Cryosphere, 15, 2491–2509, https://doi.org/10.5194/tc-15-2491-2021, https://doi.org/10.5194/tc-15-2491-2021, 2021
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This study presents rock surface temperatures (RSTs) of steep high-Arctic rock walls on Svalbard from 2016 to 2020. The field data show that coastal cliffs are characterized by warmer RSTs than inland locations during winter seasons. By running model simulations, we analyze factors leading to that effect, calculate the surface energy balance and simulate different future scenarios. Both field data and model results can contribute to a further understanding of RST in high-Arctic rock walls.
Thomas Schneider von Deimling, Hanna Lee, Thomas Ingeman-Nielsen, Sebastian Westermann, Vladimir Romanovsky, Scott Lamoureux, Donald A. Walker, Sarah Chadburn, Erin Trochim, Lei Cai, Jan Nitzbon, Stephan Jacobi, and Moritz Langer
The Cryosphere, 15, 2451–2471, https://doi.org/10.5194/tc-15-2451-2021, https://doi.org/10.5194/tc-15-2451-2021, 2021
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Climate warming puts infrastructure built on permafrost at risk of failure. There is a growing need for appropriate model-based risk assessments. Here we present a modelling study and show an exemplary case of how a gravel road in a cold permafrost environment in Alaska might suffer from degrading permafrost under a scenario of intense climate warming. We use this case study to discuss the broader-scale applicability of our model for simulating future Arctic infrastructure failure.
Pavel Alekseychik, Gabriel Katul, Ilkka Korpela, and Samuli Launiainen
Atmos. Meas. Tech., 14, 3501–3521, https://doi.org/10.5194/amt-14-3501-2021, https://doi.org/10.5194/amt-14-3501-2021, 2021
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Drones with thermal cameras are powerful new tools with the potential to provide new insights into atmospheric turbulence and heat fluxes. In a pioneering experiment, a Matrice 210 drone with a Zenmuse XT2 thermal camera was used to record 10–20 min thermal videos at 500 m a.g.l. over the Siikaneva peatland in southern Finland. A method to visualize the turbulent structures and derive their parameters from thermal videos is developed. The study provides a novel approach for turbulence analysis.
Jan Nitzbon, Moritz Langer, Léo C. P. Martin, Sebastian Westermann, Thomas Schneider von Deimling, and Julia Boike
The Cryosphere, 15, 1399–1422, https://doi.org/10.5194/tc-15-1399-2021, https://doi.org/10.5194/tc-15-1399-2021, 2021
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We used a numerical model to investigate how small-scale landscape heterogeneities affect permafrost thaw under climate-warming scenarios. Our results show that representing small-scale heterogeneities in the model can decide whether a landscape is water-logged or well-drained in the future. This in turn affects how fast permafrost thaws under warming. Our research emphasizes the importance of considering small-scale processes in model assessments of permafrost thaw under climate change.
Simone Maria Stuenzi, Julia Boike, William Cable, Ulrike Herzschuh, Stefan Kruse, Luidmila A. Pestryakova, Thomas Schneider von Deimling, Sebastian Westermann, Evgenii S. Zakharov, and Moritz Langer
Biogeosciences, 18, 343–365, https://doi.org/10.5194/bg-18-343-2021, https://doi.org/10.5194/bg-18-343-2021, 2021
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Boreal forests in eastern Siberia are an essential component of global climate patterns. We use a physically based model and field measurements to study the interactions between forests, permanently frozen ground and the atmosphere. We find that forests exert a strong control on the thermal state of permafrost through changing snow cover dynamics and altering the surface energy balance, through absorbing most of the incoming solar radiation and suppressing below-canopy turbulent fluxes.
Peter Horvath, Hui Tang, Rune Halvorsen, Frode Stordal, Lena Merete Tallaksen, Terje Koren Berntsen, and Anders Bryn
Biogeosciences, 18, 95–112, https://doi.org/10.5194/bg-18-95-2021, https://doi.org/10.5194/bg-18-95-2021, 2021
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We evaluated the performance of three methods for representing vegetation cover. Remote sensing provided the best match to a reference dataset, closely followed by distribution modelling (DM), whereas the dynamic global vegetation model (DGVM) in CLM4.5BGCDV deviated strongly from the reference. Sensitivity tests show that use of threshold values for predictors identified by DM may improve DGVM performance. The results highlight the potential of using DM in the development of DGVMs.
Lei Cai, Hanna Lee, Kjetil Schanke Aas, and Sebastian Westermann
The Cryosphere, 14, 4611–4626, https://doi.org/10.5194/tc-14-4611-2020, https://doi.org/10.5194/tc-14-4611-2020, 2020
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A sub-grid representation of excess ground ice in the Community Land Model (CLM) is developed as novel progress in modeling permafrost thaw and its impacts under the warming climate. The modeled permafrost degradation with sub-grid excess ice follows the pathway that continuous permafrost transforms into discontinuous permafrost before it disappears, including surface subsidence and talik formation, which are highly permafrost-relevant landscape changes excluded from most land models.
Sigrid J. Bakke, Monica Ionita, and Lena M. Tallaksen
Hydrol. Earth Syst. Sci., 24, 5621–5653, https://doi.org/10.5194/hess-24-5621-2020, https://doi.org/10.5194/hess-24-5621-2020, 2020
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This study provides an in-depth analysis of the 2018 northern European drought. Large parts of the region experienced 60-year record-breaking temperatures, linked to high-pressure systems and warm surrounding seas. Meteorological drought developed from May and, depending on local conditions, led to extreme low flows and groundwater drought in the following months. The 2018 event was unique in that it affected most of Fennoscandia as compared to previous droughts.
Kerstin Stahl, Jean-Philippe Vidal, Jamie Hannaford, Erik Tijdeman, Gregor Laaha, Tobias Gauster, and Lena M. Tallaksen
Proc. IAHS, 383, 291–295, https://doi.org/10.5194/piahs-383-291-2020, https://doi.org/10.5194/piahs-383-291-2020, 2020
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Numerous indices exist for the description of hydrological drought, some are based on absolute thresholds of overall streamflows or water levels and some are based on relative anomalies with respect to the season. This article discusses paradigms and experiences with such index uses in drought monitoring and drought analysis to raise awareness of the different interpretations of drought severity.
Cited articles
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite retrieved
fractional snow-covered area at a high-Arctic site using terrestrial
photography, Remote Sens. Environ., 239, 111618,
https://doi.org/10.1016/j.rse.2019.111618, 2020. 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
Alonso-González, E., Aalstad, K., Baba, M. W., Revuelto, J., López-Moreno, J. I., Fiddes, J., Essery, R., and Gascoin, S.: MuSA: The Multiscale Snow Data Assimilation System (v1.0), Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-137, in review, 2022. a, b
Ardeshiri, H., Cassiani, M., Park, S. Y., Stohl, A., Pisso, I., and Dinger,
A. S.: On the Convergence and Capability of the Large-Eddy
Simulation of Concentration Fluctuations in Passive Plumes for a
Neutral Boundary Layer at Infinite Reynolds Number, Bound.-Lay.
Meteorol., 176, 291–327, https://doi.org/10.1007/s10546-020-00537-6, 2020. a
Arenas, A. and Chorin, A. J.: On the Existence and Scaling of Structure
Functions in Turbulence According to the Data, P. Natl.
Acad. Sci. USA, 103, 4352–4355, https://doi.org/10.1073/pnas.0600482103, 2006. a
Asadzadeh, S., de Oliveira, W. J., and de Souza Filho, C. R.: UAV-based
Remote Sensing for the Petroleum Industry and Environmental Monitoring:
State-of-the-art and Perspectives, J. Petrol. Sci.
Eng., 208, 109633, https://doi.org/10.1016/j.petrol.2021.109633, 2022. a
Banner, K. M., Irvine, K. M., and Rodhouse, T. J.: The Use of Bayesian
Priors in Ecology: The Good, the Bad and the Not Great, Method.
Ecol. Evolut., 11, 882–889, https://doi.org/10.1111/2041-210X.13407, 2020. a
Bannister, R. N.: A Review of Operational Methods of Variational and
Ensemble-variational Data Assimilation, Q. J. Roy.
Meteorol. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. a
Barbieri, L., Kral, S., Bailey, S., Frazier, A., Jacob, J., Reuder, J., Brus,
D., Chilson, P., Crick, C., Detweiler, C., Doddi, A., Elston, J., Foroutan,
H., González-Rocha, J., Greene, B., Guzman, M., Houston, A., Islam, A.,
Kemppinen, O., Lawrence, D., Pillar-Little, E., Ross, S., Sama, M.,
Schmale, D., Schuyler, T., Shankar, A., Smith, S., Waugh, S., Dixon, C.,
Borenstein, S., and de Boer, G.: Intercomparison of Small Unmanned
Aircraft System (sUAS) Measurements for Atmospheric Science
during the LAPSE-RATE Campaign, Sensors, 19, 2179,
https://doi.org/10.3390/s19092179, 2019. a
Båserud, L., Reuder, J., Jonassen, M. O., Bonin, T. A., Chilson, P. B.,
Jiménez, M. A., and Durand, P.: Potential and Limitations in
Estimating Sensible-Heat-Flux Profiles from Consecutive Temperature
Profiles Using Remotely-Piloted Aircraft Systems, Bound.-Lay.
Meteorol., 174, 145–177, https://doi.org/10.1007/s10546-019-00478-9, 2020. a
Bassi, E.: From Here to 2023: Civil Drones Operations and the
Setting of New Legal Rules for the European Single Sky, J. Intellig. Robot. Syst., 100, 493–503,
https://doi.org/10.1007/s10846-020-01185-1, 2020. 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
Bonin, T., Chilson, P., Zielke, B., and Fedorovich, E.: Observations of the
Early Evening Boundary-Layer Transition Using a Small Unmanned Aerial
System, Bound.-Lay. Meteorol., 146, 119–132,
https://doi.org/10.1007/s10546-012-9760-3, 2013. a
Bou-Zeid, E., Anderson, W., Katul, G. G., and Mahrt, L.: The Persistent
Challenge of Surface Heterogeneity in Boundary-Layer Meteorology:
A Review, Bound.-Lay. Meteorol., 177, 227–245,
https://doi.org/10.1007/s10546-020-00551-8, 2020. a
Box, G. E. P.: Science and Statistics, J. Am. Stat.
Assoc., 71, 791–799, https://doi.org/10.1080/01621459.1976.10480949, 1976. a
Box, G. E. P. and Youle, P. V.: The Exploration and Exploitation of
Response Surfaces: An Example of the Link between the Fitted
Surface and the Basic Mechanism of the System, Biometrics, 11, 287,
https://doi.org/10.2307/3001769, 1955. a
Bretthorst, G.: Bayesian Spectrum Analysis and Parameter Estimation,
Springer, https://doi.org/10.1007/978-1-4684-9399-3, 1988. a
Burgers, G., Jan van Leeuwen, P., and Evensen, G.: Analysis Scheme in the
Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719–1724,
https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998. a
Caparrini, F., Castelli, F., and Entekhabi, D.: Estimation of Surface
Turbulent Fluxes through Assimilation of Radiometric Surface
Temperature Sequences, J. Hydrometeorol., 5, 145–159,
https://doi.org/10.1175/1525-7541(2004)005<0145:EOSTFT>2.0.CO;2, 2004. a
Chopin, N. and Papaspiliopoulos, O.: An Introduction to Sequential Monte
Carlo, Springer, https://doi.org/10.1007/978-3-030-47845-2, 2020. a, b, c, d
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn,
M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D.,
Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G.,
Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai,
A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough,
C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H.,
Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law,
B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K.,
Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E.,
Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell,
E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C.,
Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A.,
Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of
Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux
Sites, Agr. Forest Meteorol., 301–302, 108350,
https://doi.org/10.1016/j.agrformet.2021.108350, 2021. a
Codling, E. A., Plank, M. J., and Benhamou, S.: Random Walk Models in Biology,
J. Roy. Soc. Interf., 5, 813–834,
https://doi.org/10.1098/rsif.2008.0014, 2008. a
Cosme, E., Verron, J., Brasseur, P., Blum, J., and Auroux, D.: Smoothing
Problems in a Bayesian Framework and Their Linear Gaussian Solutions,
Mon. Weather Rev., 140, 683–695, https://doi.org/10.1175/MWR-D-10-05025.1, 2012. a
Couvreux, F., Bazile, E., Rodier, Q., Maronga, B., Matheou, G., Chinita, M. J.,
Edwards, J., van Stratum, B. J. H., van Heerwaarden, C. C., Huang, J.,
Moene, A. F., Cheng, A., Fuka, V., Basu, S., Bou-Zeid, E., Canut, G., and
Vignon, E.: Intercomparison of Large-Eddy Simulations of the Antarctic
Boundary Layer for Very Stable Stratification, Bound.-Lay.
Meteorol., 176, 369–400, https://doi.org/10.1007/s10546-020-00539-4, 2020. a
Dagon, K., Sanderson, B. M., Fisher, R. A., and Lawrence, D. M.: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, 2020. a
Daube, C., Conley, S., Faloona, I. C., Arndt, C., Yacovitch, T. I., Roscioli, J. R., and Herndon, S. C.: Using the tracer flux ratio method with flight measurements to estimate dairy farm CH4 emissions in central California, Atmos. Meas. Tech., 12, 2085–2095, https://doi.org/10.5194/amt-12-2085-2019, 2019. a
De Roo, F., Zhang, S., Huq, S., and Mauder, M.: A Semi-Empirical Model of the
Energy Balance Closure in the Surface Layer, PLOS ONE, 13, e0209022,
https://doi.org/10.1371/journal.pone.0209022, 2018. a
Deardorff, J. W.: Stratocumulus-Capped Mixed Layers Derived from a
Three-Dimensional Model, Bound.-Lay. Meteorol., 18, 495–527,
https://doi.org/10.1007/BF00119502, 1980. a
Defforge, C. L., Carissimo, B., Bocquet, M., Bresson, R., and Armand, P.:
Improving Numerical Dispersion Modelling in Built Environments with
Data Assimilation Using the Iterative Ensemble Kalman Smoother,
Bound.-Lay. Meteorol., 179, 209–240, https://doi.org/10.1007/s10546-020-00588-9,
2021. a
Desjardins, R. L., MacPherson, J. I., Schuepp, P. H., and Karanja, F.: An
Evaluation of Aircraft Flux Measurements of CO2, Water Vapor and Sensible
Heat, Bound.-Lay. Meteorol., 47, 55–69, https://doi.org/10.1007/BF00122322, 1989. a
Dunbar, O., Duncan, A., Stuart, A., and Wolfram, M.-T.: Ensemble Inference
Methods for Models With Noisy and Expensive Likelihoods, SIAM J.
Appl. Dynam. Syst., 21, 1539–1572, https://doi.org/10.1137/21M1410853,
2022a. a
Dunbar, O., Howland, M., Schneider, T., and Stuart, A.: Ensemble-based
experimental design for targeting data acquisition to inform climate models,
J. Adv. Model. Earth Syst., 14, e2022MS002997,
https://doi.org/10.1029/2022MS002997, 2022b. a
Elston, J., Argrow, B., Stachura, M., Weibel, D., Lawrence, D., and Pope, D.:
Overview of Small Fixed-Wing Unmanned Aircraft for Meteorological
Sampling, J. Atmos. Ocean. Techno., 32, 97–115,
https://doi.org/10.1175/JTECH-D-13-00236.1, 2015. a
Emerick, A. A. and Reynolds, A. C.: Ensemble Smoother with Multiple Data
Assimilation, Comput. Geosci., 55, 3–15,
https://doi.org/10.1016/j.cageo.2012.03.011, 2013. a, b, c, d
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
Evensen, G.: Analysis of Iterative Ensemble Smoothers for Solving Inverse
Problems, Comput. Geosci., 22, 885–908,
https://doi.org/10.1007/s10596-018-9731-y, 2018. a, b, c
Evensen, G.: Accounting for Model Errors in Iterative Ensemble Smoothers,
Comput. Geosci., 23, 761–775, https://doi.org/10.1007/s10596-019-9819-z,
2019. a, b
Evensen, G., Vossepoel, F. C., and van Leeuwen, P. J.: Data Assimilation
Fundamentals: A Unified Formulation of the State and Parameter
Estimation Problem, Springer Textbooks in Earth Sciences,
Geography and Environment, Springer International Publishing,
Cham, https://doi.org/10.1007/978-3-030-96709-3, 2022. a, b, c, d, e, f
Ferreira-Filho, E. B. and Pimenta, L. C.: Abstraction Based Approach for
Segregation in Heterogeneous Robotic Swarms, Robot. Auto. Syst.,
122, 103295, https://doi.org/10.1016/j.robot.2019.103295, 2019. a
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
Finkelstein, P. L. and Sims, P. F.: Sampling Error in Eddy Correlation Flux
Measurements, J. Geophys. Res.-Atmos., 106, 3503–3509,
https://doi.org/10.1029/2000JD900731, 2001. a, b
Foken, T.: 50 Years of the Monin – Obukhov Similarity
Theory, Bound.-Lay. Meteorol., 119, 431–447,
https://doi.org/10.1007/s10546-006-9048-6, 2006. a
Foken, T. and Wichura, B.: Tools for Quality Assessment of Surface-Based Flux
Measurements, Agr. Forest Meteorol., 78, 83–105,
https://doi.org/10.1016/0168-1923(95)02248-1, 1996. a
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., and Pezzulo,
G.: Active Inference and Epistemic Value, Cog. Neurosci., 6,
187–214, https://doi.org/10.1080/17588928.2015.1020053, 2015. a
Gandin, L. S.: Complex Quality Control of Meteorological Observations,
Mon. Weather Rev., 116, 1137–1156,
https://doi.org/10.1175/1520-0493(1988)116<1137:CQCOMO>2.0.CO;2, 1988. a
Garbuno-Inigo, A., Hoffmann, F., Li, W., and Stuart, A. M.: 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
Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., and Rubin, D.:
Bayesian Data Analysis, Chapman and Hall/CRC, 3 Edn., https://doi.org/10.1201/b16018,
2013. a, b, c
Helbig, M., Waddington, J. M., Alekseychik, P., Amiro, B. D., Aurela, M., Barr,
A. G., Black, T. A., Blanken, P. D., Carey, S. K., Chen, J., Chi, J., Desai,
A. R., Dunn, A., Euskirchen, E. S., Flanagan, L. B., Forbrich, I., Friborg,
T., Grelle, A., Harder, S., Heliasz, M., Humphreys, E. R., Ikawa, H.,
Isabelle, P.-E., Iwata, H., Jassal, R., Korkiakoski, M., Kurbatova, J.,
Kutzbach, L., Lindroth, A., Löfvenius, M. O., Lohila, A., Mammarella, I.,
Marsh, P., Maximov, T., Melton, J. R., Moore, P. A., Nadeau, D. F., Nicholls,
E. M., Nilsson, M. B., Ohta, T., Peichl, M., Petrone, R. M., Petrov, R.,
Prokushkin, A., Quinton, W. L., Reed, D. E., Roulet, N. T., Runkle, B. R. K.,
Sonnentag, O., Strachan, I. B., Taillardat, P., Tuittila, E.-S., Tuovinen,
J.-P., Turner, J., Ueyama, M., Varlagin, A., Wilmking, M., Wofsy, S. C., and
Zyrianov, V.: Increasing Contribution of Peatlands to Boreal
Evapotranspiration in a Warming Climate, Nat. Clim. Change, 10, 555–560,
https://doi.org/10.1038/s41558-020-0763-7, 2020. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
Hoffmann, H., Nieto, H., Jensen, R., Guzinski, R., Zarco-Tejada, P., and Friborg, T.: Estimating evaporation with thermal UAV data and two-source energy balance models, Hydrol. Earth Syst. Sci., 20, 697–713, https://doi.org/10.5194/hess-20-697-2016, 2016. a
Högström, U.: Non-dimensional wind and temperature profiles in the
atmospheric surface layer: A re-evaluation, Bound.-Lay. Meteorol., 42,
55–78, https://doi.org/10.1007/BF00119875, 1988. a
Hutchinson, M., Oh, H., and Chen, W.-H.: A Review of Source Term Estimation
Methods for Atmospheric Dispersion Events Using Static or Mobile Sensors,
Info. Fusion, 36, 130–148, https://doi.org/10.1016/j.inffus.2016.11.010, 2017. a
Iglesias, M. and Yang, Y.: Adaptive regularisation for ensemble Kalman
inversion, Inverse Prob., 37, 025008, https://doi.org/10.1088/1361-6420/abd29b,
2021. a, b, c
Iglesias, M. A., Law, J. H., and Stuart, A. M.: Ensemble Kalman methods for
inverse problems, Inverse Prob., 29, 045001,
https://doi.org/10.1088/0266-5611/29/4/045001, 2013. a, b
Jaynes, E.: Probability Theory: The Logic of Science, Cambridge University
Press, 727, https://doi.org/10.1017/CBO9780511790423, 2003. a, b, c
Jazwinski, A.: Stochastic Processes and Filtering Theory, Academic Press,
376, ISBN 9780486462745, 1970. a
Katul, G. and Hsieh, C.: Flux-Variance Similarity Relationships for Heat and
Water Vapour in the Unstable Atmospheric Surface Layer, Bound.-Lay.
Meteorol., 90, 327–338, https://doi.org/10.1023/A:1001747925158, 1999. a
Katzfuss, M., Stroud, J. R., and Wikle, C. K.: Understanding the Ensemble
Kalman Filter, The Am. Stat., 70, 350–357,
https://doi.org/10.1080/00031305.2016.1141709, 2016. a
Katzfuss, M., Stroud, R. S., and Wikle, C. K.: Ensemble Kalman Methods for
High-Dimensional Hierarchical Dynamic Space-Time Models, J.
Am. Stat. Assoc., 115, 866–885,
https://doi.org/10.1080/01621459.2019.1592753, 2020. a
Kim, M.-S. and Kwon, B. H.: Estimation of Sensible Heat Flux and
Atmospheric Boundary Layer Height Using an Unmanned Aerial Vehicle,
Atmosphere, 10, 363, https://doi.org/10.3390/atmos10070363, 2019. a
Lee, T., Buban, M., Dumas, E., and Baker, C.: On the Use of Rotary-Wing
Aircraft to Sample Near-Surface Thermodynamic Fields: Results from
Recent Field Campaigns, Sensors, 19, 10, https://doi.org/10.3390/s19010010, 2018. a
Lin, D., Khan, B., Katurji, M., Bird, L., Faria, R., and Revell, L. E.: WRF4PALM v1.0: a mesoscale dynamical driver for the microscale PALM model system 6.0, Geosci. Model Dev., 14, 2503–2524, https://doi.org/10.5194/gmd-14-2503-2021, 2021. a
Lunderman, S., Morzfeld, M., Glassmeier, F., and Feingold, G.: Estimating
Parameters of the Nonlinear Cloud and Rain Equation from a Large-Eddy
Simulation, Phys. D: Nonlin. Pheno., 410, 132500,
https://doi.org/10.1016/j.physd.2020.132500, 2020. a
Mahrt, L.: Flux Sampling Errors for Aircraft and Towers, J.
Atmos. Ocean. Technol., 15, 416–429,
https://doi.org/10.1175/1520-0426(1998)015<0416:FSEFAA>2.0.CO;2, 1998. a
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.: A Particle
Batch Smoother Approach to Snow Water Equivalent Estimation, J.
Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1, 2015. a, b
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a
Masutani, M., Schlatter, T. W., Errico, R. M., Stoffelen, A., Andersson, E.,
Lahoz, W., Woollen, J. S., Emmitt, G. D., Riishøjgaard, L.-P., and Lord,
S. J.: Observing System Simulation Experiments, in: Data
Assimilation, edited by: Lahoz, W., Khattatov, B., and Menard, R.,
647–679 pp., Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-74703-1_24, 2010. a
Moncrieff, J., Massheder, J., de Bruin, H., Elbers, J., Friborg, T.,
Heusinkveld, B., Kabat, P., Scott, S., Soegaard, H., and Verhoef, A.: A
System to Measure Surface Fluxes of Momentum, Sensible Heat, Water Vapour and
Carbon Dioxide, J. Hydrol., 188–189, 589–611,
https://doi.org/10.1016/S0022-1694(96)03194-0, 1997. a
Moncrieff, J., Clement, R., Finnigan, J., and Meyers, T.: Averaging,
Detrending, and Filtering of Eddy Covariance Time Series, in:
Handbook of Micrometeorology, edited by: Lee, X., Massman, W., and Law,
B., vol. 29, 7–31 pp., Kluwer Academic Publishers, Dordrecht,
https://doi.org/10.1007/1-4020-2265-4_2, 2005. a
Monin, A. and Obukhov, A.: Basic Laws of Turbulent Mixing in the Surface Layer
of the Atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 24, 163–187, 1954. a
Neal, R. M.: Sampling from Multimodal Distributions Using Tempered Transitions,
Stat. Comput., 6, 353–366, https://doi.org/10.1007/BF00143556, 1996. a
Neumann, P. P. and Bartholmai, M.: Real-Time Wind Estimation on a Micro
Unmanned Aerial Vehicle Using Its Inertial Measurement Unit, Sens.
Actua. A: Phys., 235, 300–310, https://doi.org/10.1016/j.sna.2015.09.036, 2015. a, b, c
norberp: Resources for “Inferring surface energy fluxes using drone data assimilation in large eddy simulations” by Pirk et al., Zenodo [data set], https://doi.org/10.5281/zenodo.6769683, 2022. a
Palomaki, R. T., Rose, N. T., van den Bossche, M., Sherman, T. J., and
De Wekker, S. F. J.: Wind Estimation in the Lower Atmosphere Using
Multirotor Aircraft, J. Atmos. Ocea. Technol., 34,
1183–1191, https://doi.org/10.1175/JTECH-D-16-0177.1, 2017. a, b, c
Papadakis, N., Mémin, E., Cuzol, A., and Gengembre, N.: Data Assimilation
with the Weighted Ensemble Kalman Filter, Tellus A: Dynam. Meteorol.
Oceanogr., 62, 673–697, https://doi.org/10.1111/j.1600-0870.2010.00461.x, 2010. a, b
Perez-Cruz, F.: Kullback-Leibler Divergence Estimation of Continuous
Distributions, in: 2008 IEEE International Symposium on Information
Theory, 1666–1670 pp., IEEE, Toronto, ON, Canada,
https://doi.org/10.1109/ISIT.2008.4595271, 2008. a
Pirk, N., Sievers, J., Mertes, J., Parmentier, F.-J. W., Mastepanov, M., and Christensen, T. R.: Spatial variability of CO2 uptake in polygonal tundra: assessing low-frequency disturbances in eddy covariance flux estimates, Biogeosciences, 14, 3157–3169, https://doi.org/10.5194/bg-14-3157-2017, 2017. a
Raasch, S. and Schröter, M.: PALM - A Large-Eddy Simulation Model
Performing on Massively Parallel Computers, Meteorol. Z., 10,
363–372, https://doi.org/10.1127/0941-2948/2001/0010-0363, 2001. a
Ramtvedt, E. N. and Pirk, N.: A Methodology for Providing
Surface-Cover-Corrected Net Radiation at Heterogeneous Eddy-Covariance
Sites, Bound.-Lay. Meteorol., 184, 173–193,
https://doi.org/10.1007/s10546-022-00704-x, 2022. a
Ristic, B., Gilliam, C., Moran, W., and Palmer, J. L.: Decentralised
Multi-Platform Search for a Hazardous Source in a Turbulent Flow, Info.
Fus., 58, 13–23, https://doi.org/10.1016/j.inffus.2019.12.011, 2020. a
Schillings, C. and Stuart, A. M.: Analysis of the Ensemble Kalman Filter for
Inverse Problems, SIAM J. Num. Anal., 55, 1264–1290,
https://doi.org/10.1137/16M105959X, 2017. a, b
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to
High-Dimensional Particle Filtering, Mon. Weather Rev., 136,
4629–4640, https://doi.org/10.1175/2008MWR2529.1, 2008. a, b, c
Steinfeld, G., Letzel, M. O., Raasch, S., Kanda, M., and Inagaki, A.: Spatial
Representativeness of Single Tower Measurements and the Imbalance Problem
with Eddy-Covariance Fluxes: Results of a Large-Eddy Simulation Study,
Bound.-Lay. Meteorol., 123, 77–98, https://doi.org/10.1007/s10546-006-9133-x,
2007. a, b
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
Stoy, P. C., Mauder, M., Foken, T., Marcolla, B., Boegh, E., Ibrom, A., Arain,
M. A., Arneth, A., Aurela, M., Bernhofer, C., Cescatti, A., Dellwik, E.,
Duce, P., Gianelle, D., van Gorsel, E., Kiely, G., Knohl, A., Margolis, H.,
McCaughey, H., Merbold, L., Montagnani, L., Papale, D., Reichstein, M.,
Saunders, M., Serrano-Ortiz, P., Sottocornola, M., Spano, D., Vaccari, F.,
and Varlagin, A.: A Data-Driven Analysis of Energy Balance Closure across
FLUXNET Research Sites: The Role of Landscape Scale Heterogeneity,
Agr. Forest Meteorol., 171–172, 137–152,
https://doi.org/10.1016/j.agrformet.2012.11.004, 2013. a, b
Stuart, A. M.: Inverse Problems: A Bayesian Perspective, Acta Num., 19,
451–559, https://doi.org/10.1017/S0962492910000061, 2010. a, b, c
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Atmospheric
Sciences Library, Kluwer Academic Publishers, Dordrecht, Boston, 666 pp., https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Sühring, M., Metzger, S., Xu, K., Durden, D., and Desai, A.: Trade-Offs
in Flux Disaggregation: A Large-Eddy Simulation Study, Bound.-Lay.
Meteorol., 170, 69–93, https://doi.org/10.1007/s10546-018-0387-x, 2019. a, b
Tajfar, E., Bateni, S. M., Margulis, S. A., Gentine, P., and Auligne, T.:
Estimation of Turbulent Heat Fluxes via Assimilation of Air
Temperature and Specific Humidity into an Atmospheric Boundary Layer
Model, J. Hydrometeorol., 21, 205–225,
https://doi.org/10.1175/JHM-D-19-0104.1, 2020. a
Tans, P. P., Conway, T. J., and Nakazawa, T.: Latitudinal Distribution of the
Sources and Sinks of Atmospheric Carbon Dioxide Derived from Surface
Observations and an Atmospheric Transport Model, J. Geophys.
Res., 94, 5151, https://doi.org/10.1029/JD094iD04p05151, 1989. a
Thompson, R. L., Nisbet, E. G., Pisso, I., Stohl, A., Blake, D., Dlugokencky, E. J., Helmig, D., and White, J. W. C.: Variability in atmospheric methane from fossil fuel and microbial sources over the last three decades. Geophys. Res. Lett., 45, 11499–11508, https://doi.org/10.1029/2018GL078127, 2018. a
van de Boer, A., Moene, A. F., Graf, A., Schüttemeyer, D., and Simmer,
C.: Detection of Entrainment Influences on Surface-Layer Measurements
and Extension of Monin – Obukhov Similarity Theory,
Bound.-Lay. Meteorol., 152, 19–44, https://doi.org/10.1007/s10546-014-9920-8,
2014. a
van der Valk, L. D., Teuling, A. J., Girod, L., Pirk, N., Stoffer, R., and van Heerwaarden, C. C.: Understanding wind-driven melt of patchy snow cover, The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, 2022. a
van Leeuwen, P. J.: Representation Errors and Retrievals in Linear and
Nonlinear Data Assimilation, Q. J. Roy. Meteorol.
Soc., 141, 1612–1623, https://doi.org/10.1002/qj.2464, 2015. a, b
van Leeuwen, P. J.: A Consistent Interpretation of the Stochastic Version of
the Ensemble Kalman Filter, Q. J. Roy. Meteorol.
Soc., 146, 2815–2825, https://doi.org/10.1002/qj.3819, 2020. a
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
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich,
S.: Particle Filters for High-dimensional Geoscience Applications: A
Review, Q. J. Roy. Meteorol. Soc., 145,
2335–2365, https://doi.org/10.1002/qj.3551, 2019.
a, b
Wikle, C. K. and Berliner, L. M.: A Bayesian Tutorial for Data
Assimilation, Phys. D: Non. Pheno., 230, 1–16,
https://doi.org/10.1016/j.physd.2006.09.017, 2007. a
Wildmann, N., Mauz, M., and Bange, J.: Two fast temperature sensors for probing of the atmospheric boundary layer using small remotely piloted aircraft (RPA), Atmos. Meas. Tech., 6, 2101–2113, https://doi.org/10.5194/amt-6-2101-2013, 2013. a
Wyngaard, J. C.: Turbulence in the Atmosphere, Cambridge University Press,
Cambridge, UK, New York, 393 pp., https://doi.org/10.1017/CBO9780511840524, 2010. a
Xu, T., Bateni, S. M., Neale, C. M. U., Auligne, T., and Liu, S.: Estimation of
Turbulent Heat Fluxes by Assimilation of Land Surface Temperature
Observations From GOES Satellites Into an Ensemble Kalman Smoother
Framework, J. Geophys. Res.-Atmos., 123, 2409–2423,
https://doi.org/10.1002/2017JD027732, 2018. a
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.
In this study, we show how sparse and noisy drone measurements can be combined with an ensemble...
