Articles | Volume 20, issue 11
https://doi.org/10.5194/bg-20-2031-2023
© 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/bg-20-2031-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jun 2023
Research article |
| 06 Jun 2023
| 06 Jun 2023
Snow–vegetation–atmosphere interactions in alpine tundra
Department of Geosciences, University of Oslo, Oslo, Norway
Kristoffer Aalstad
Department of Geosciences, University of Oslo, Oslo, Norway
Yeliz A. Yilmaz
Department of Geosciences, University of Oslo, Oslo, Norway
Astrid Vatne
Department of Geosciences, University of Oslo, Oslo, Norway
Andrea L. Popp
Department of Geosciences, University of Oslo, Oslo, Norway
Hydrological Research Unit, Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
Peter Horvath
Natural History Museum, University of Oslo, Oslo, Norway
Anders Bryn
Natural History Museum, University of Oslo, Oslo, Norway
Ane Victoria Vollsnes
Department of Biosciences, University of Oslo, Oslo, Norway
Sebastian Westermann
Department of Geosciences, University of Oslo, Oslo, Norway
Terje Koren Berntsen
Department of Geosciences, University of Oslo, Oslo, Norway
Frode Stordal
Department of Geosciences, University of Oslo, Oslo, Norway
Lena Merete Tallaksen
Department of Geosciences, University of Oslo, Oslo, Norway
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Elin Ristorp Aas, Inge Althuizen, Hui Tang, Sonya Geange, Eva Lieungh, Vigdis Vandvik, and Terje Koren Berntsen
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Marco Mazzolini, Kristoffer Aalstad, Esteban Alonso-González, Sebastian Westermann, and Désirée Treichler
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Preprint archived
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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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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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Here, we present high-resolution simulations of glacier mass balance (the gain and loss of ice over a year) and runoff on Svalbard from 1991–2022, one of the fastest warming regions in the Arctic. The simulations are created using the CryoGrid community model. We find a small overall loss of mass over the simulation period of −0.08 m yr−1 but with no statistically significant trend. The average runoff was found to be 41 Gt yr−1, with a significant increasing trend of 6.3 Gt per decade.
Justyna Czekirda, Bernd Etzelmüller, Sebastian Westermann, Ketil Isaksen, and Florence Magnin
The Cryosphere, 17, 2725–2754, https://doi.org/10.5194/tc-17-2725-2023, https://doi.org/10.5194/tc-17-2725-2023, 2023
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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.
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.
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, https://doi.org/10.5194/amt-15-7293-2022, 2022
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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.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
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In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
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.
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.
Caitlyn A. Hall, Sheila M. Saia, Andrea L. Popp, Nilay Dogulu, Stanislaus J. Schymanski, Niels Drost, Tim van Emmerik, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, https://doi.org/10.5194/hess-26-647-2022, 2022
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Impactful open, accessible, reusable, and reproducible hydrologic research practices are being embraced by individuals and the community, but taking the plunge can seem overwhelming. We present the Open Hydrology Principles and Practical Guide to help hydrologists move toward open science, research, and education. We discuss the benefits and how hydrologists can overcome common challenges. We encourage all hydrologists to join the open science community (https://open-hydrology.github.io).
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.
Sara Marie Blichner, Moa Kristina Sporre, and Terje Koren Berntsen
Atmos. Chem. Phys., 21, 17243–17265, https://doi.org/10.5194/acp-21-17243-2021, https://doi.org/10.5194/acp-21-17243-2021, 2021
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In this study we quantify how a new way of modeling the formation of new particles in the atmosphere affects the estimated cooling from aerosol–cloud interactions since pre-industrial times. Our improved scheme merges two common approaches to aerosol modeling: a sectional scheme for treating early growth and the pre-existing modal scheme in NorESM. We find that the cooling from aerosol–cloud interactions since pre-industrial times is reduced by 10 % when the new scheme is used.
Stefanie Falk, Ane V. Vollsnes, Aud B. Eriksen, Lisa Emberson, Connie O'Neill, Frode Stordal, and Terje Koren Berntsen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-260, https://doi.org/10.5194/bg-2021-260, 2021
Revised manuscript not accepted
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Subarctic vegetation is threatened by climate change and ozone. We assess essential climate variables in 2018/19. 2018 was warmer and brighter than usual in Spring with forest fires and elevated ozone in summer. Visible damage was observed on plant species in 2018. We find that generic parameterizations used in modeling ozone dose do not suffice. We propose a method to acclimate these parameterizations and find an ozone-induced biomass loss of 2.5 to 17.4 % (up to 6 % larger than default).
Stefanie Falk, Ane V. Vollsnes, Aud B. Eriksen, Frode Stordal, and Terje Koren Berntsen
Atmos. Chem. Phys., 21, 15647–15661, https://doi.org/10.5194/acp-21-15647-2021, https://doi.org/10.5194/acp-21-15647-2021, 2021
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We evaluate regional and global models for ozone modeling and damage risk mapping of vegetation over subarctic Europe. Our analysis suggests that low-resolution global models do not reproduce the observed ozone seasonal cycle at ground level, underestimating ozone by 30–50 %. High-resolution regional models capture the seasonal cycle well, still underestimating ozone by up to 20 %. Our proposed gap-filling method for site observations shows a 76 % accuracy compared to the regional model (80 %).
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.
Sara M. Blichner, Moa K. Sporre, Risto Makkonen, and Terje K. Berntsen
Geosci. Model Dev., 14, 3335–3359, https://doi.org/10.5194/gmd-14-3335-2021, https://doi.org/10.5194/gmd-14-3335-2021, 2021
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Aerosol–cloud interactions are the largest contributor to climate forcing uncertainty. In this study we combine two common approaches to aerosol representation in global models: a sectional scheme, which is closer to first principals, for the smallest particles forming in the atmosphere and a log-modal scheme, which is faster, for the larger particles. With this approach, we improve the aerosol representation compared to observations, while only increasing the computational cost by 15 %.
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.
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.
Marianne T. Lund, Borgar Aamaas, Camilla W. Stjern, Zbigniew Klimont, Terje K. Berntsen, and Bjørn H. Samset
Earth Syst. Dynam., 11, 977–993, https://doi.org/10.5194/esd-11-977-2020, https://doi.org/10.5194/esd-11-977-2020, 2020
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Achieving the Paris Agreement temperature goals requires both near-zero levels of long-lived greenhouse gases and deep cuts in emissions of short-lived climate forcers (SLCFs). Here we quantify the near- and long-term global temperature impacts of emissions of individual SLCFs and CO2 from 7 economic sectors in 13 regions in order to provide the detailed knowledge needed to design efficient mitigation strategies at the sectoral and regional levels.
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
Aas, K. S., Gisnås, K., Westermann, S., and Berntsen, T. K.: A Tiling
Approach to Represent Subgrid Snow Variability in Coupled Land
Surface–Atmosphere Models, J. Hydrometeorol., 18,
49–63, https://doi.org/10.1175/JHM-D-16-0026.1, 2017. a
Adler, B., Gohm, A., Kalthoff, N., Babić, N., Corsmeier, U., Lehner, M.,
Rotach, M. W., Haid, M., Markmann, P., Gast, E., Tsaknakis, G., and
Georgoussis, G.: CROSSINN: A Field Experiment to Study the
Three-Dimensional Flow Structure in the Inn Valley, Austria, Bull.
Am. Meteorol. Soc., 102, E38–E60,
https://doi.org/10.1175/BAMS-D-19-0283.1, 2021. a
Arias, P., Bellouin, N., Coppola, E., et al.: Climate Change
2021: The Physical Science Basis, Contribution of Working Group14
I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, Technical Summary, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 33–144, https://doi.org/10.1017/9781009157896.002, 2021. a
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A.,
Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W.,
Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala,
T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the
Temporal and Spatial Variability of Ecosystem–Scale
Carbon Dioxide, Water Vapor, and Energy Flux Densities, Bull. Am. Meteorol. Soc., 82, 2415–2434,
https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001. a
Baldocchi, D. D.: How Eddy Covariance Flux Measurements Have Contributed to Our
Understanding of Global Change Biology, Glob. Change Biol.,
26, 242–260, https://doi.org/10.1111/gcb.14807, 2020. a
Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and
Persistence of Low-Frequency Atmospheric Circulation Patterns,
Mon. Weather Rev., 115, 1083–1126,
https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2, 1987. a
Beigaitė, R., Tang, H., Bryn, A., Skarpaas, O., Stordal, F., Bjerke, J. W.,
and Žliobaitė, I.: Identifying Climate Thresholds for Dominant
Natural Vegetation Types at the Global Scale Using Machine Learning:
Average Climate versus Extremes, Glob. Change Biol., 28, 3557–3579,
https://doi.org/10.1111/gcb.16110, 2022. a
Bintanja, R. and Andry, O.: Towards a Rain-Dominated Arctic, Nat. Clim.
Change, 7, 263–267, https://doi.org/10.1038/nclimate3240, 2017. a
Blume-Werry, G., Milbau, A., Teuber, L. M., Johansson, M., and Dorrepaal, E.:
Dwelling in the Deep – Strongly Increased Root Growth and Rooting
Depth Enhance Plant Interactions with Thawing Permafrost Soil, New
Phytol., 223, 1328–1339, https://doi.org/10.1111/nph.15903, 2019. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Bryn, A. and Horvath, P.: Kartlegging Av NiN Naturtyper i Må lestokk
1:5000 Rundt Flux-Tå rnet Og På Hansbunuten, Finse
(Vestland), Naturhistorisk museum, Universitetet i Oslo, ISBN: 978-82-7970-122-4, https://doi.org/10.13140/RG.2.2.35946.75205, 2020. a
Carrillo-Rojas, G., Silva, B., Rollenbeck, R., Célleri, R., and Bendix,
J.: The Breathing of the Andean Highlands: Net Ecosystem Exchange and
Evapotranspiration over the Páramo of Southern Ecuador, Agr.
Forest Meteorol., 265, 30–47, https://doi.org/10.1016/j.agrformet.2018.11.006,
2019. a
Chipman, H. A., George, E. I., and McCulloch, R. E.: BART: Bayesian
Additive Regression Trees, Ann. Appl. Stat., 4, 266–298,
https://doi.org/10.1214/09-AOAS285, 2010. a
Christensen, T. R., Lund, M., Skov, K., Abermann, J., López-Blanco, E.,
Scheller, J., Scheel, M., Jackowicz-Korczynski, M., Langley, K., Murphy,
M. J., and Mastepanov, M.: Multiple Ecosystem Effects of Extreme
Weather Events in the Arctic, Ecosystems, 24, 122–136,
https://doi.org/10.1007/s10021-020-00507-6, 2021. a
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J.,
Allard, V., Aubinet, M., Buchmann, N., Bernhofer, C., Carrara, A.,
Chevallier, F., De Noblet, N., Friend, A. D., Friedlingstein, P.,
Grünwald, T., Heinesch, B., Keronen, P., Knohl, A., Krinner, G., Loustau,
D., Manca, G., Matteucci, G., Miglietta, F., Ourcival, J. M., Papale, D.,
Pilegaard, K., Rambal, S., Seufert, G., Soussana, J. F., Sanz, M. J.,
Schulze, E. D., Vesala, T., and Valentini, R.: Europe-Wide Reduction in
Primary Productivity Caused by the Heat and Drought in 2003, Nature, 437,
529–533, https://doi.org/10.1038/nature03972, 2005. a
Dahl, E.: Rondane, I kommisjon hos Aschehoug, 1956. a
Derksen, C. and Brown, R.: Spring Snow Cover Extent Reductions in the 2008–2012
Period Exceeding Climate Model Projections: SPRING SNOW COVER EXTENT
REDUCTIONS, Geophys. Res. Lett., 39, L19504,
https://doi.org/10.1029/2012GL053387, 2012. a
Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R.,
and Mearns, L. O.: Climate Extremes: Observations, Modeling, and
Impacts, Science, 289, 2068–2074, https://doi.org/10.1126/science.289.5487.2068,
2000. a
Erlandsen, H. B., Beldring, S., Eisner, S., Hisdal, H., Huang, S., and
Tallaksen, L. M.: Constraining the HBV Model for Robust Water Balance
Assessments in a Cold Climate, Hydrol. Res., 52, 356–372,
https://doi.org/10.2166/nh.2021.132, 2021. 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, b
Frei, E. R. and Henry, G. H.: Long-Term Effects of Snowmelt Timing and Climate
Warming on Phenology, Growth, and Reproductive Effort of Arctic Tundra
Plant Species, Arct. Sci., 8, 700–721, https://doi.org/10.1139/as-2021-0028, 2022. a
Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., and Thomas, C. K.:
Revealing the Morning Transition in the Mountain Boundary Layer Using
Fiber-Optic Distributed Temperature Sensing, Geophys. Res.
Lett., 48, e2020GL092238, https://doi.org/10.1029/2020GL092238, 2021. a
Gisnås, K., Westermann, S., Schuler, T. V., Litherland, T., Isaksen, K., Boike, J., and Etzelmüller, B.: A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover, The Cryosphere, 8, 2063–2074, https://doi.org/10.5194/tc-8-2063-2014, 2014. a
Gleason, H. A.: The Individualistic Concept of the Plant Association, B. Torrey Bot. Club, 53, 7–26, 1926. a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale Geospatial Analysis for
Everyone, Remote Sens. Environ., 202, 18–27,
https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
Gregorutti, B., Michel, B., and Saint-Pierre, P.: Correlation and Variable
Importance in Random Forests, Stat. Comp., 27, 659–678,
https://doi.org/10.1007/s11222-016-9646-1, 2017. a, b
Griebel, A., Bennett, L. T., Metzen, D., Cleverly, J., Burba, G., and Arndt,
S. K.: Effects of Inhomogeneities within the Flux Footprint on the
Interpretation of Seasonal, Annual, and Interannual Ecosystem Carbon
Exchange, Agr. Forest Meteorol., 221, 50–60, 2016. a
Groendahl, L., Friborg, T., and Soegaard, H.: Temperature and Snow-Melt
Controls on Interannual Variability in Carbon Exchange in the High
Arctic, Theor. Appl. Climatol., 88, 111–125, 2007. a
Gu, L., Massman, W. J., Leuning, R., Pallardy, S. G., Meyers, T., Hanson,
P. J., Riggs, J. S., Hosman, K. P., and Yang, B.: The Fundamental Equation of
Eddy Covariance and Its Application in Flux Measurements, Agr.
Forest Meteorol., 152, 135–148, https://doi.org/10.1016/j.agrformet.2011.09.014,
2012. a, b
Hall, D. K., Riggs G., A., Solomonson, V., and SIPS, N. M.: MODIS/Aqua
Snow Cover Daily L3 Global 500m SIN Grid, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/MODIS/MYD10A1.006, 2015a. a
Hall, D. K., Riggs G., A., Solomonson, V., and SIPS, N. M.: MODIS/Terra
Snow Cover Daily L3 Global 500m SIN Grid, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set],
https://doi.org/10.5067/MODIS/MOD10A1.006, 2015b. a
Hall, D. K., Riggs, G. A., DiGirolamo, N. E., and Román, M. O.: Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record, Hydrol. Earth Syst. Sci., 23, 5227–5241, https://doi.org/10.5194/hess-23-5227-2019, 2019. a
Halvorsen, R., Skarpaas, O., Bryn, A., Bratli, H., Erikstad, L., Simensen, T.,
and Lieungh, E.: Towards a Systematics of Ecodiversity: The EcoSyst
Framework, Global Ecol. Biogeogr., 29, 1887–1906,
https://doi.org/10.1111/geb.13164, 2020. a
Hampe, A. and Petit, R. J.: Conserving Biodiversity under Climate Change: The
Rear Edge Matters: Rear Edges and Climate Change, Ecol. Lett., 8,
461–467, https://doi.org/10.1111/j.1461-0248.2005.00739.x, 2005. a
Hanssen-Bauer, I., Førland, E. J., Haddeland, I., Hisdal, H.,
Lawrence, D., Mayer, S., Nesje, A., Nilsen, J., Sandven, S., Sandø, A. B.,
Sorteberg, A., and Ådlandsvik, B.: Climate in Norway 2100 – a Knowledge Base for Climate
Adaptation, NCCS report, 1, Norwegian Centre for Climate Services, ISSN: 2387-3027, 2017. a
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
Heliasz, M., Johansson, T., Lindroth, A., Mölder, M., Mastepanov, M.,
Friborg, T., Callaghan, T. V., and Christensen, T. R.: Quantification of
C Uptake in Subarctic Birch Forest after Setback by an Extreme Insect
Outbreak: CARBON UPTAKE SETBACK BY INSECT OUTBREAK, Geophys. Res.
Lett., 38, L01704, https://doi.org/10.1029/2010GL044733, 2011. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
Global Reanalysis, Q. J. Roy. Meteorol. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Horvath, P.: geco-nhm/NiN_Finse: Finse_publication (finse), Zenodo [data set], https://doi.org/10.5281/zenodo.8005237, 2023. a
Jasechko, S., Birks, S. J., Gleeson, T., Wada, Y., Fawcett, P. J., Sharp,
Z. D., McDonnell, J. J., and Welker, J. M.: The Pronounced Seasonality of
Global Groundwater Recharge, Water Resour. Res., 50, 8845–8867,
https://doi.org/10.1002/2014WR015809, 2014. a
Jia, G. J., Epstein, H. E., and Walker, D. A.: Greening of Arctic Alaska,
1981–2001, Geophys. Res. Lett., 30, 2003GL018268,
https://doi.org/10.1029/2003GL018268, 2003. a, b
Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J.,
Williamson, G. J., and Bowman, D. M. J. S.: Climate-Induced Variations in
Global Wildfire Danger from 1979 to 2013, Nat. Commun., 6, 7537,
https://doi.org/10.1038/ncomms8537, 2015. a
Karlsson, P. S.: Patterns of Carbon Allocation above Ground in a Deciduous
(Vaccinium Uliginosum) and an Evergreen (Vaccinium Vitis-Idaea) Dwarf
Shrub, Physiol. Plant., 63, 1–7,
https://doi.org/10.1111/j.1399-3054.1985.tb02809.x, 1985. a
Kausrud, K. L., Mysterud, A., Steen, H., Vik, J. O., Østbye, E., Cazelles,
B., Framstad, E., Eikeset, A. M., Mysterud, I., Solhøy, T., and Stenseth,
N. C.: Linking Climate Change to Lemming Cycles, Nature, 456, 93–97,
https://doi.org/10.1038/nature07442, 2008. a
Kim, Y., Johnson, M. S., Knox, S. H., Black, T. A., Dalmagro, H. J., Kang, M.,
Kim, J., and Baldocchi, D.: Gap-filling Approaches for Eddy Covariance
Methane Fluxes: A Comparison of Three Machine Learning Algorithms and a
Traditional Method with Principal Component Analysis, Glob. Change Biol.,
26, 1499–1518, https://doi.org/10.1111/gcb.14845, 2020. a
Kljun, N., Calanca, P., Rotach, M. W., and Schmid, H. P.: A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP), Geosci. Model Dev., 8, 3695–3713, https://doi.org/10.5194/gmd-8-3695-2015, 2015. a
Kolari, T. H. M., Kumpula, T., Verdonen, M., Forbes, B. C., and Tahvanainen,
T.: Reindeer Grazing Controls Willows but Has Only Minor Effects on Plant
Communities in Fennoscandian Oroarctic Mires, Arct. Antarct.
Alp. Res., 51, 506–520, https://doi.org/10.1080/15230430.2019.1679940, 2019. a
Körner, C. and Renhardt, U.: Dry Matter Partitioning and Root
Length/Leaf Area Ratios in Herbaceous Perennial Plants with Diverse
Altitudinal Distribution, Oecologia, 74, 411–418, https://doi.org/10.1007/BF00378938,
1987. a
Lackner, G., Domine, F., Nadeau, D. F., Parent, A.-C., Anctil, F., Lafaysse,
M., and Dumont, M.: On the Energy Budget of a Low-Arctic Snowpack, The
Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, 2022. a
Lehner, M. and Rotach, M. W.: Current Challenges in Understanding and
Predicting Transport and Exchange in the Atmosphere over Mountainous Terrain,
Atmosphere, 9, 276, https://doi.org/10.3390/atmos9070276, 2018. a
Liljedahl, A. K., Hinzman, L. D., Harazono, Y., Zona, D., Tweedie, C. E.,
Hollister, R. D., Engstrom, R., and Oechel, W. C.: Nonlinear Controls on
Evapotranspiration in Arctic Coastal Wetlands, Biogeosciences, 8, 3375–3389,
https://doi.org/10.5194/bg-8-3375-2011, 2011. a
Lloret, F., Escudero, A., Iriondo, J. M., Martínez-Vilalta, J., and
Valladares, F.: Extreme Climatic Events and Vegetation: The Role of
Stabilizing Processes, Glob. Change Biol., 18, 797–805,
https://doi.org/10.1111/j.1365-2486.2011.02624.x, 2012. a
Mahrt, L., Thomas, C. K., Grachev, A. A., and Persson, P. O. G.: Near-Surface
Vertical Flux Divergence in the Stable Boundary Layer, Bound.-Lay.
Meteorol., 169, 373–393, https://doi.org/10.1007/s10546-018-0379-x, 2018. 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., Kluwer Academic Publishers, Dordrecht, Vol. 29, 7–31,
https://doi.org/10.1007/1-4020-2265-4_2, 2005. a
Moriana-Armendariz, M., Nilsen, L., and Cooper, E. J.: Natural Variation in
Snow Depth and Snow Melt Timing in the High Arctic Have Implications for
Soil and Plant Nutrient Status and Vegetation Composition, Arct. Sci., 8,
767–785, https://doi.org/10.1139/as-2020-0025, 2022. a
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover Dynamics:
Review on Wind-Driven Coupling Processes, Front. Earth Sci., 6,
197, https://doi.org/10.3389/feart.2018.00197, 2018. a
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M.,
Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical
Northern Hemisphere Snow Cover Trends and Projected Changes in the
CMIP6 Multi-Model Ensemble, The Cryosphere, 14, 2495–2514,
https://doi.org/10.5194/tc-14-2495-2020, 2020. a
Myers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein,
H. E., Assmann, J. J., John, C., Andreu-Hayles, L., Angers-Blondin, S.,
Beck, P. S. A., Berner, L. T., Bhatt, U. S., Bjorkman, A. D., Blok, D., Bryn,
A., Christiansen, C. T., Cornelissen, J. H. C., Cunliffe, A. M., Elmendorf,
S. C., Forbes, B. C., Goetz, S. J., Hollister, R. D., de Jong, R., Loranty,
M. M., Macias-Fauria, M., Maseyk, K., Normand, S., Olofsson, J., Parker,
T. C., Parmentier, F.-J. W., Post, E., Schaepman-Strub, G., Stordal, F.,
Sullivan, P. F., Thomas, H. J. D., Tømmervik, H., Treharne, R., Tweedie,
C. E., Walker, D. A., Wilmking, M., and Wipf, S.: Complexity Revealed in the
Greening of the Arctic, Nat. Clim. Change, 10, 106–117,
https://doi.org/10.1038/s41558-019-0688-1, 2020. a
Nicholls, E. M. and Carey, S. K.: Evapotranspiration and Energy Partitioning
across a Forest-Shrub Vegetation Gradient in a Subarctic, Alpine Catchment,
J. Hydrol., 602, 126790, https://doi.org/10.1016/j.jhydrol.2021.126790,
2021. a
Niittynen, P. and Luoto, M.: The Importance of Snow in Species Distribution
Models of Arctic Vegetation, Ecography, 41, 1024–1037,
https://doi.org/10.1111/ecog.03348, 2018. a
Niittynen, P., Heikkinen, R. K., and Luoto, M.: Snow Cover Is a Neglected
Driver of Arctic Biodiversity Loss, Nat. Clim. Change, 8, 997–1001,
https://doi.org/10.1038/s41558-018-0311-x, 2018. a, b
Niittynen, P., Heikkinen, R. K., Aalto, J., Guisan, A., Kemppinen, J., and
Luoto, M.: Fine-Scale Tundra Vegetation Patterns Are Strongly Related to
Winter Thermal Conditions, Nat. Clim. Change, 10, 1143–1148,
https://doi.org/10.1038/s41558-020-00916-4, 2020. a
Niu, S., Xing, X., Zhang, Z., Xia, J., Zhou, X., Song, B., Li, L., and Wan, S.:
Water-Use Efficiency in Response to Climate Change: From Leaf to Ecosystem in
a Temperate Steppe: water-use efficiency in responses to climate change,
Glob. Change Biol., 17, 1073–1082,
https://doi.org/10.1111/j.1365-2486.2010.02280.x, 2011. a
Odland, A. and Munkejord, H. K.: Plants as Indicators of Snow Layer Duration in
Southern Norwegian Mountains, Ecol. Indic., 8, 57–68,
https://doi.org/10.1016/j.ecolind.2006.12.005, 2008. a, b
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.:
Scikit-Learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. 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
Pirk, N., Aalstad, K., Westermann, S., Vatne, A., van Hove, A., Tallaksen, L. M., Cassiani, M., and Katul, G.: Inferring surface energy fluxes using drone data assimilation in large eddy simulations, Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, 2022. a
Pirk, N., Aalstad, K., Yilmaz, Y. A., Vatne, A., Popp, A. L., Horvath, P.,
Bryn, A., Vollsnes, A. V., Westermann, S., Berntsen, T. K., Stordal, F.,
and Tallaksen, L. M.: Resources for “Snow-vegetation-atmosphere interactions in alpine tundra”, Zenodo [data set], https://doi.org/10.5281/zenodo.7566641, 2023. 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
Rasmussen, C. E. and Williams, C. K. I.: Gaussian Processes for Machine
Learning, The MIT Press, https://doi.org/10.7551/mitpress/3206.001.0001, 2005. a
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier,
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T.,
Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila,
A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M.,
Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G.,
Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the Separation of
Net Ecosystem Exchange into Assimilation and Ecosystem Respiration: Review
and Improved Algorithm, Glob. Change Biol., 11, 1424–1439,
https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005. a
Rixen, C., Høye, T. T., Macek, P., Aerts, R., Alatalo, J. M., Anderson,
J. T., Arnold, P. A., Barrio, I. C., Bjerke, J. W., Björkman, M. P.,
Blok, D., Blume-Werry, G., Boike, J., Bokhorst, S., Carbognani, M.,
Christiansen, C. T., Convey, P., Cooper, E. J., Cornelissen, J. H. C.,
Coulson, S. J., Dorrepaal, E., Elberling, B., Elmendorf, S. C., Elphinstone,
C., Forte, T. G., Frei, E. R., Geange, S. R., Gehrmann, F., Gibson, C.,
Grogan, P., Halbritter, A. H., Harte, J., Henry, G. H., Inouye, D. W., Irwin,
R. E., Jespersen, G., Jónsdóttir, I. S., Jung, J. Y., Klinges, D. H.,
Kudo, G., Lämsä, J., Lee, H., Lembrechts, J. J., Lett, S., Lynn,
J. S., Mann, H. M., Mastepanov, M., Morse, J., Myers-Smith, I. H.,
Olofsson, J., Paavola, R., Petraglia, A., Phoenix, G. K., Semenchuk, P.,
Siewert, M. B., Slatyer, R., Spasojevic, M. J., Suding, K., Sullivan, P.,
Thompson, K. L., Väisänen, M., Vandvik, V., Venn, S., Walz, J., Way,
R., Welker, J. M., Wipf, S., and Zong, S.: Winters Are Changing: Snow Effects
on Arctic and Alpine Tundra Ecosystems, Arctic Science, 8, 572–608,
https://doi.org/10.1139/as-2020-0058, 2022. a
Rizzi, J., Nilsen, I. B., Stagge, J. H., Gisnås, K., and Tallaksen, L. M.:
Five Decades of Warming: Impacts on Snow Cover in Norway, Hydrol.
Res., 49, 670–688, https://doi.org/10.2166/nh.2017.051, 2018. a
Roos, R. E., Asplund, J., Birkemoe, T., Halbritter, A. H., Olsen, S. L.,
Vassvik, L., van Zuijlen, K., and Klanderud, K.: Three Decades of
Environmental Change Studies at Alpine Finse, Norway: Climate Trends
and Responses across Ecological Scales, Arctic Science, 9, 430–450,
https://doi.org/10.1139/AS-2020-0051, 2022. a
Ross, L. C., Austrheim, G., Asheim, L.-J., Bjarnason, G., Feilberg, J., Fosaa,
A. M., Hester, A. J., Holand, Ø., Jónsdóttir, I. S., Mortensen,
L. E., Mysterud, A., Olsen, E., Skonhoft, A., Speed, J. D. M., Steinheim, G.,
Thompson, D. B. A., and Thórhallsdóttir, A. G.: Sheep Grazing in the
North Atlantic Region: A Long-Term Perspective on Environmental
Sustainability, Ambio, 45, 551–566, https://doi.org/10.1007/s13280-016-0771-z, 2016. a
Rotach, M. W., Calanca, P., Graziani, G., Gurtz, J., Steyn, D. G., Vogt, R.,
Andretta, M., Christen, A., Cieslik, S., Connolly, R., Wekker, S. F. J. D.,
Galmarini, S., Kadygrov, E. N., Kadygrov, V., Miller, E., Neininger, B.,
Rucker, M., Gorsel, E. V., Weber, H., Weiss, A., and Zappa, M.: Turbulence
Structure and Exchange Processes in an Alpine Valley: The Riviera
Project, Bull. Am. Meteorol. Soc., 85, 1367–1386,
https://doi.org/10.1175/BAMS-85-9-1367, 2004. a
Salomonson, V. and Appel, I.: Development of the Aqua MODIS NDSI Fractional
Snow Cover Algorithm and Validation Results, IEEE Trans. Geosci.
Remote Sens., 44, 1747–1756, https://doi.org/10.1109/TGRS.2006.876029, 2006. a
Scharnagl, K., Johnson, D., and Ebert-May, D.: Shrub Expansion and Alpine
Plant Community Change: 40-Year Record from Niwot Ridge, Colorado,
Plant Ecol. Div., 12, 407–416,
https://doi.org/10.1080/17550874.2019.1641757, 2019. a
Schlesinger, W. H.: Biogeochemistry: An Analysis of Global Change, Elsevier,
SanDiego, 4th Edn., ISBN: 978-0-12-385874-0, https://doi.org/10.1016/C2010-0-66291-2, 2020. a
Sievers, J., Papakyriakou, T., Larsen, S. E., Jammet, M. M., Rysgaard, S.,
Sejr, M. K., and Sørensen, L. L.: Estimating Surface Fluxes Using Eddy
Covariance and Numerical Ogive Optimization, Atmos. Chem.
Phys., 15, 2081–2103, https://doi.org/10.5194/acp-15-2081-2015, 2015. a
Skaugen, T., Stranden, H. B., and Saloranta, T.: Trends in Snow Water
Equivalent in Norway (1931–2009), Hydrol. Res., 43,
489–499, https://doi.org/10.2166/nh.2012.109, 2012. a
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
Sturm, M. and Wagner, A. M.: Using Repeated Patterns in Snow Distribution
Modeling: An Arctic Example: repeated snow patterns, Water Resour.
Res., 46, W12549, https://doi.org/10.1029/2010WR009434, 2010. a
Tonjer, L.-R., Thoen, E., Morgado, L., Botnen, S., Mundra, S., Nybakken, L.,
Bryn, A., and Kauserud, H.: Fungal Community Dynamics across a
Forest – Alpine Ecotone, Mol. Ecol., 30, 4926–4938,
https://doi.org/10.1111/mec.16095, 2021. a
Trouet, V. and Van Oldenborgh, G. J.: KNMI Climate Explorer: A Web-Based
Research Tool for High-Resolution Paleoclimatology, Tree-Ring Res.,
69, 3–13, https://doi.org/10.3959/1536-1098-69.1.3, 2013. 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
Vestergren, T.: Om Den Olikformiga Snöbetäckningens Inflytande På
Vegetationen i Sarekfjällen, Bot. Not., 55, 241–268, 1902. a
Vickers, D. and Mahrt, L.: Quality Control and Flux Sampling Problems
for Tower and Aircraft Data, J. Atmos. Ocean.
Technol., 14, 512–526,
https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997. a, b
Walker, D. A., Billings, W. D., and De Molenaar, J. G.: Snow-Vegetation Interactions in Tundra Environments, chap. 6, in: Snow ecology: an
interdisciplinary examination of snow-covered ecosystems, edited by: Jones, H. G., Pomeroy, J. W., Walker, D. A., and Hoham, R. W., Cambridge University Press, 266, 324, ISBN: 9780521584838, 2001. a
Wang, L., Li, M., Wang, J., Li, X., and Wang, L.: An Analytical Reductionist
Framework to Separate the Effects of Climate Change and Human Activities on
Variation in Water Use Efficiency, Sci. Total Environ., 727,
138306, https://doi.org/10.1016/j.scitotenv.2020.138306, 2020. a
Whiteman, C. D. and Doran, J. C.: The Relationship between Overlying
Synoptic-Scale Flows and Winds within a Valley, J. Appl.
Meteorol., 32, 1669–1682,
https://doi.org/10.1175/1520-0450(1993)032<1669:TRBOSS>2.0.CO;2, 1993. a
Whittaker, R. H.: Vegetation of the Great Smoky Mountains, Ecol.
Monogr., 26, 1–80, https://doi.org/10.2307/1943577, 1956. a
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier,
P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom,
A., Law, B., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W.,
Tenhunen, J., Valentini, R., and Verma, S.: Energy Balance Closure at
FLUXNET Sites, Agr. Forest Meteorol., 113, 223–243,
https://doi.org/10.1016/S0168-1923(02)00109-0, 2002. a
Wipf, S., Stoeckli, V., and Bebi, P.: Winter Climate Change in Alpine Tundra:
Plant Responses to Changes in Snow Depth and Snowmelt Timing, Climatic
Change, 94, 105–121, https://doi.org/10.1007/s10584-009-9546-x, 2009. a, b
Wolpert, D. and Macready, W.: No Free Lunch Theorems for Optimization, IEEE
Trans. Evolut. Comp., 1, 67–82,
https://doi.org/10.1109/4235.585893, 1997.
a
Zona, D., Lafleur, P. M., Hufkens, K., Bailey, B., Gioli, B., Burba, G.,
Goodrich, J. P., Liljedahl, A. K., Euskirchen, E. S., Watts, J. D., Farina,
M., Kimball, J. S., Heimann, M., Göckede, M., Pallandt, M., Christensen,
T. R., Mastepanov, M., López-Blanco, E., Jackowicz-Korczynski, M.,
Dolman, A. J., Marchesini, L. B., Commane, R., Wofsy, S. C., Miller, C. E.,
Lipson, D. A., Hashemi, J., Arndt, K. A., Kutzbach, L., Holl, D., Boike, J.,
Wille, C., Sachs, T., Kalhori, A., Song, X., Xu, X., Humphreys, E. R., Koven,
C. D., Sonnentag, O., Meyer, G., Gosselin, G. H., Marsh, P., and Oechel,
W. C.: Earlier Snowmelt May Lead to Late Season Declines in Plant
Productivity and Carbon Sequestration in Arctic Tundra Ecosystems,
Sci. Rep., 12, 3986, https://doi.org/10.1038/s41598-022-07561-1, 2022. a
Short summary
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years....
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