Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6605-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6605-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
29 Oct 2021
Model evaluation paper |
| 29 Oct 2021
Comparing an exponential respiration model to alternative models for soil respiration components in a Canadian wildfire chronosequence (FireResp v1.0)
John Zobitz
Department of Mathematics, Statistics, and Computer Science, Augsburg University, Minneapolis, Minnesota, USA
Heidi Aaltonen
Department of Environmental and Biological Sciences,
University of Eastern Finland, Kuopio, Finland
Xuan Zhou
Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
Frank Berninger
Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
Jukka Pumpanen
Department of Environmental and Biological Sciences,
University of Eastern Finland, Kuopio, Finland
Department of Forest Sciences, University of Helsinki,
Helsinki, Finland
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Taija Saarela, Xudan Zhu, Helena Jäntti, Mizue Ohashi, Jun'ichiro Ide, Henri Siljanen, Aake Pesonen, Heidi Aaltonen, Anne Ojala, Hiroshi Nishimura, Timo Kekäläinen, Janne Jänis, Frank Berninger, and Jukka Pumpanen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-225, https://doi.org/10.5194/bg-2022-225, 2022
Manuscript not accepted for further review
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This study investigated the molecular composition and carbon dioxide production of water samples collected from two subarctic rivers that represent contrasting types of catchment characteristics. The results highlight the role of clearwater environments in microbial degradation and greenhouse gas dynamics of subarctic catchments.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
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We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Oleg Sizov, Ekaterina Ezhova, Petr Tsymbarovich, Andrey Soromotin, Nikolay Prihod'ko, Tuukka Petäjä, Sergej Zilitinkevich, Markku Kulmala, Jaana Bäck, and Kajar Köster
Biogeosciences, 18, 207–228, https://doi.org/10.5194/bg-18-207-2021, https://doi.org/10.5194/bg-18-207-2021, 2021
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In changing climate, tundra is expected to turn into shrubs and trees, diminishing reindeer pasture and increasing risks of tick-borne diseases. However, this transition may require a disturbance. Fires in Siberia are increasingly widespread. We studied wildfire dynamics and tundra–forest transition over 60 years in northwest Siberia near the Arctic Circle. Based on satellite data analysis, we found that transition occurs in 40 %–85 % of burned tundra compared to 5 %–15 % in non-disturbed areas.
Xuefei Li, Outi Wahlroos, Sami Haapanala, Jukka Pumpanen, Harri Vasander, Anne Ojala, Timo Vesala, and Ivan Mammarella
Biogeosciences, 17, 3409–3425, https://doi.org/10.5194/bg-17-3409-2020, https://doi.org/10.5194/bg-17-3409-2020, 2020
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We measured CO2 and CH4 fluxes and quantified the global warming potential of different surface areas in a recently created urban wetland in Southern Finland. The ecosystem has a small net climate warming effect which was mainly contributed by the open-water areas. Our results suggest that limiting open-water areas and setting a design preference for areas of emergent vegetation in the establishment of urban wetlands can be a beneficial practice when considering solely the climate impact.
Christopher P. O. Reyer, Ramiro Silveyra Gonzalez, Klara Dolos, Florian Hartig, Ylva Hauf, Matthias Noack, Petra Lasch-Born, Thomas Rötzer, Hans Pretzsch, Henning Meesenburg, Stefan Fleck, Markus Wagner, Andreas Bolte, Tanja G. M. Sanders, Pasi Kolari, Annikki Mäkelä, Timo Vesala, Ivan Mammarella, Jukka Pumpanen, Alessio Collalti, Carlo Trotta, Giorgio Matteucci, Ettore D'Andrea, Lenka Foltýnová, Jan Krejza, Andreas Ibrom, Kim Pilegaard, Denis Loustau, Jean-Marc Bonnefond, Paul Berbigier, Delphine Picart, Sébastien Lafont, Michael Dietze, David Cameron, Massimo Vieno, Hanqin Tian, Alicia Palacios-Orueta, Victor Cicuendez, Laura Recuero, Klaus Wiese, Matthias Büchner, Stefan Lange, Jan Volkholz, Hyungjun Kim, Joanna A. Horemans, Friedrich Bohn, Jörg Steinkamp, Alexander Chikalanov, Graham P. Weedon, Justin Sheffield, Flurin Babst, Iliusi Vega del Valle, Felicitas Suckow, Simon Martel, Mats Mahnken, Martin Gutsch, and Katja Frieler
Earth Syst. Sci. Data, 12, 1295–1320, https://doi.org/10.5194/essd-12-1295-2020, https://doi.org/10.5194/essd-12-1295-2020, 2020
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Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale to support systematic model intercomparisons and model development in Europe.
Michael Boy, Erik S. Thomson, Juan-C. Acosta Navarro, Olafur Arnalds, Ekaterina Batchvarova, Jaana Bäck, Frank Berninger, Merete Bilde, Zoé Brasseur, Pavla Dagsson-Waldhauserova, Dimitri Castarède, Maryam Dalirian, Gerrit de Leeuw, Monika Dragosics, Ella-Maria Duplissy, Jonathan Duplissy, Annica M. L. Ekman, Keyan Fang, Jean-Charles Gallet, Marianne Glasius, Sven-Erik Gryning, Henrik Grythe, Hans-Christen Hansson, Margareta Hansson, Elisabeth Isaksson, Trond Iversen, Ingibjorg Jonsdottir, Ville Kasurinen, Alf Kirkevåg, Atte Korhola, Radovan Krejci, Jon Egill Kristjansson, Hanna K. Lappalainen, Antti Lauri, Matti Leppäranta, Heikki Lihavainen, Risto Makkonen, Andreas Massling, Outi Meinander, E. Douglas Nilsson, Haraldur Olafsson, Jan B. C. Pettersson, Nønne L. Prisle, Ilona Riipinen, Pontus Roldin, Meri Ruppel, Matthew Salter, Maria Sand, Øyvind Seland, Heikki Seppä, Henrik Skov, Joana Soares, Andreas Stohl, Johan Ström, Jonas Svensson, Erik Swietlicki, Ksenia Tabakova, Throstur Thorsteinsson, Aki Virkkula, Gesa A. Weyhenmeyer, Yusheng Wu, Paul Zieger, and Markku Kulmala
Atmos. Chem. Phys., 19, 2015–2061, https://doi.org/10.5194/acp-19-2015-2019, https://doi.org/10.5194/acp-19-2015-2019, 2019
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The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date and aimed to strengthen research and innovation regarding climate change issues in the Nordic region. The paper presents an overview of the main scientific topics investigated and provides a state-of-the-art comprehensive summary of what has been achieved in CRAICC.
Mari Mäki, Hermanni Aaltonen, Jussi Heinonsalo, Heidi Hellén, Jukka Pumpanen, and Jaana Bäck
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-22, https://doi.org/10.5194/bg-2018-22, 2018
Preprint withdrawn
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Vegetation emissions of volatile organic compounds (VOCs) are intensively studied world-wide, but remains largely unknown how effectively belowground VOCs are produced and released into the atmosphere. We demonstrate that boreal forest soil is a diverse source and storage of VOCs, because more than 50 VOCs were detected in the soil air. Our results give evidence that VOC production processes and storages partly differ from those VOCs that are simultaneously emitted from the soil surface.
Fabio Gennaretti, Guillermo Gea-Izquierdo, Etienne Boucher, Frank Berninger, Dominique Arseneault, and Joel Guiot
Biogeosciences, 14, 4851–4866, https://doi.org/10.5194/bg-14-4851-2017, https://doi.org/10.5194/bg-14-4851-2017, 2017
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A model–data fusion approach is used to study how boreal forests assimilate and allocate carbon depending on weather/climate conditions. First, we adapted the MAIDEN ecophysiological forest model to consider important processes for boreal tree species. We tested the modifications on black spruce gross primary production and ring width data. We show that MAIDEN is a powerful tool for understanding how environmental factors interact with tree ecophysiology to influence boreal forest carbon fluxes.
Eero Nikinmaa, Tuomo Kalliokoski, Kari Minkkinen, Jaana Bäck, Michael Boy, Yao Gao, Nina Janasik-Honkela, Janne I. Hukkinen, Maarit Kallio, Markku Kulmala, Nea Kuusinen, Annikki Mäkelä, Brent D. Matthies, Mikko Peltoniemi, Risto Sievänen, Ditte Taipale, Lauri Valsta, Anni Vanhatalo, Martin Welp, Luxi Zhou, Putian Zhou, and Frank Berninger
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-141, https://doi.org/10.5194/bg-2017-141, 2017
Manuscript not accepted for further review
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We estimated the impact of boreal forest management on climate, considering the effects of carbon, albedo, aerosols, and effects of industrial wood use. We made analyses both in current and warmer climate of 2050. The aerosol effect was comparable to that of carbon sequestration. Deciduous trees may have a large potential for mitigation due to their high albedo and aerosol effects. If the forests will be used more intensively and mainly for pulp and energy, the warming influence is clear.
B. Tupek, K. Minkkinen, J. Pumpanen, T. Vesala, and E. Nikinmaa
Biogeosciences, 12, 281–297, https://doi.org/10.5194/bg-12-281-2015, https://doi.org/10.5194/bg-12-281-2015, 2015
A. Virkkula, J. Levula, T. Pohja, P. P. Aalto, P. Keronen, S. Schobesberger, C. B. Clements, L. Pirjola, A.-J. Kieloaho, L. Kulmala, H. Aaltonen, J. Patokoski, J. Pumpanen, J. Rinne, T. Ruuskanen, M. Pihlatie, H. E. Manninen, V. Aaltonen, H. Junninen, T. Petäjä, J. Backman, M. Dal Maso, T. Nieminen, T. Olsson, T. Grönholm, J. Aalto, T. H. Virtanen, M. Kajos, V.-M. Kerminen, D. M. Schultz, J. Kukkonen, M. Sofiev, G. De Leeuw, J. Bäck, P. Hari, and M. Kulmala
Atmos. Chem. Phys., 14, 4473–4502, https://doi.org/10.5194/acp-14-4473-2014, https://doi.org/10.5194/acp-14-4473-2014, 2014
J. F. J. Korhonen, M. Pihlatie, J. Pumpanen, H. Aaltonen, P. Hari, J. Levula, A.-J. Kieloaho, E. Nikinmaa, T. Vesala, and H. Ilvesniemi
Biogeosciences, 10, 1083–1095, https://doi.org/10.5194/bg-10-1083-2013, https://doi.org/10.5194/bg-10-1083-2013, 2013
Related subject area
Biogeosciences
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
Ocean biogeochemistry in the coupled ocean-sea ice-biogeochemistry model FESOM2.1-REcoM3
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
Modeling of non-structural carbohydrate dynamics by the spatially explicitly individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC ver1.0)
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation
Representation of the phosphorus cycle in the Joint UK Land Environment Simulator (vn5.5_JULES-CNP)
CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)
The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0
A validation standard for area of habitat maps for terrestrial birds and mammals
Soil Cycles of Elements simulator for Predicting TERrestrial regulation of greenhouse gases: SCEPTER v0.9
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
A map of global peatland extent created using machine learning (Peat-ML)
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model
Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET
Predicting global terrestrial biomes with the LeNet convolutional neural network
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020
A dynamic local-scale vegetation model for lycopsids (LYCOm v1.0)
Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model
Global evaluation of the Ecosystem Demography model (ED v3.0)
A new snow module improves predictions of the isotope-enabled MAIDENiso forest growth model
Calibrating the soil organic carbon model Yasso20 with multiple datasets
The PFLOTRAN Reaction Sandbox
A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil) for northern and temperate peatlands
Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
Locating trees to mitigate outdoor radiant load of humans in urban areas using a metaheuristic hill-climbing algorithm – introducing TreePlanter v1.0
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Özgür Gürses, Laurent Oziel, Onur Karakus, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, and Judith Hauck
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-2, https://doi.org/10.5194/gmd-2023-2, 2023
Revised manuscript accepted for GMD
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This paper is assessing the biogeochemical model REcoM3 coupled to the ocean-sea ice model FESOM2.1. The model can be used e.g. to simulate the carbon uptake or release of the ocean on time scales of several hundred years. A detailed analysis of nutrients, ocean productivity and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well in simulating the observed mean biogeochemical state and variability, and is comparable to other ocean-biogeochemical models.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
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Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
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Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
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We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
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Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
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A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
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Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
EGUsphere, https://doi.org/10.5194/egusphere-2022-835, https://doi.org/10.5194/egusphere-2022-835, 2022
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Non-structural carbohydrates (NSCs) are important carbon storage in plants against the climate change. We added a new NSC module into an individual-based ecosystem model: SEIB-DGVM. The amounts of simulated NSCs and the general seasonality agree well with observed NSCs on a point scale and global scale. The model has a high potential to simulate the biotic effects resulting from insufficient NSC that are otherwise difficult to measure in terrestrial ecosystems globally.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
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The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
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Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
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Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
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Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, https://doi.org/10.5194/gmd-15-5713-2022, 2022
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This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
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A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Mahdi André Nakhavali, Lina M. Mercado, Iain P. Hartley, Stephen Sitch, Fernanda V. Cunha, Raffaello di Ponzio, Laynara F. Lugli, Carlos A. Quesada, Kelly M. Andersen, Sarah E. Chadburn, Andy J. Wiltshire, Douglas B. Clark, Gyovanni Ribeiro, Lara Siebert, Anna C. M. Moraes, Jéssica Schmeisk Rosa, Rafael Assis, and José L. Camargo
Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
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In tropical ecosystems, the availability of rock-derived elements such as P can be very low. Thus, without a representation of P cycling, tropical forest responses to rising atmospheric CO2 conditions in areas such as Amazonia remain highly uncertain. We introduced P dynamics and its interactions with the N and P cycles into the JULES model. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low-fertility soils.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
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Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
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We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev., 15, 5093–5105, https://doi.org/10.5194/gmd-15-5093-2022, https://doi.org/10.5194/gmd-15-5093-2022, 2022
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This paper describes the validation of area of habitat (AOH) maps produced for terrestrial birds and mammals. The main objective was to assess the accuracy of the maps based on independent data. We used open access data from repositories, such as ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. The majority of AOH maps were found to have a high accuracy.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 15, 4959–4990, https://doi.org/10.5194/gmd-15-4959-2022, https://doi.org/10.5194/gmd-15-4959-2022, 2022
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Increasing carbon dioxide in the atmosphere is an urgent issue in the coming century. Enhanced rock weathering in soils can be one of the most efficient C capture strategies. On the basis as a weathering simulator, the newly developed SCEPTER model implements bio-mixing by fauna/humans and enables organic matter and crushed rocks/minerals at the soil surface with an option to track their particle size distributions. Those features can be useful for evaluating the carbon capture efficiency.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
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We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot
Geosci. Model Dev., 15, 4709–4738, https://doi.org/10.5194/gmd-15-4709-2022, https://doi.org/10.5194/gmd-15-4709-2022, 2022
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Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022, https://doi.org/10.5194/gmd-15-4313-2022, 2022
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Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
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We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Nicolas Azaña Schnedler-Meyer, Tobias Kuhlmann Andersen, Fenjuan Rose Schmidt Hu, Karsten Bolding, Anders Nielsen, and Dennis Trolle
Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, https://doi.org/10.5194/gmd-15-3861-2022, 2022
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We present the Water Ecosystems Tool (WET) – a new modular aquatic ecosystem model configurable to a wide array of physical setups, ecosystems and research questions based on the popular FABM–PCLake model. We aim for the model to become a community staple, thus helping to consolidate the state of the art under a few flexible models, with the aim of improving comparability across studies and preventing the
re-inventions of the wheelthat are common to our scientific modeling community.
Hamze Dokoohaki, Bailey D. Morrison, Ann Raiho, Shawn P. Serbin, Katie Zarada, Luke Dramko, and Michael Dietze
Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022, https://doi.org/10.5194/gmd-15-3233-2022, 2022
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We present a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon
reanalysisproduct that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022, https://doi.org/10.5194/gmd-15-3121-2022, 2022
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Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
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By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Elodie Salmon, Fabrice Jégou, Bertrand Guenet, Line Jourdain, Chunjing Qiu, Vladislav Bastrikov, Christophe Guimbaud, Dan Zhu, Philippe Ciais, Philippe Peylin, Sébastien Gogo, Fatima Laggoun-Défarge, Mika Aurela, M. Syndonia Bret-Harte, Jiquan Chen, Bogdan H. Chojnicki, Housen Chu, Colin W. Edgar, Eugenie S. Euskirchen, Lawrence B. Flanagan, Krzysztof Fortuniak, David Holl, Janina Klatt, Olaf Kolle, Natalia Kowalska, Lars Kutzbach, Annalea Lohila, Lutz Merbold, Włodzimierz Pawlak, Torsten Sachs, and Klaudia Ziemblińska
Geosci. Model Dev., 15, 2813–2838, https://doi.org/10.5194/gmd-15-2813-2022, https://doi.org/10.5194/gmd-15-2813-2022, 2022
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A methane model that features methane production and transport by plants, the ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model, which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4, was calibrated and evaluated on 14 peatland sites. Results show that the model is sensitive to temperature and substrate availability over the top 75 cm of soil depth.
Suman Halder, Susanne K. M. Arens, Kai Jensen, Tais W. Dahl, and Philipp Porada
Geosci. Model Dev., 15, 2325–2343, https://doi.org/10.5194/gmd-15-2325-2022, https://doi.org/10.5194/gmd-15-2325-2022, 2022
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A dynamic vegetation model, designed to estimate potential impacts of early vascular vegetation, namely, lycopsids, on the biogeochemical cycle at a local scale. Lycopsid Model (LYCOm) estimates the productivity and physiological properties of lycopsids across a broad climatic range along with natural selection, which is then utilized to adjudge their weathering potential. It lays the foundation for estimation of their impacts during their long evolutionary history starting from the Ordovician.
Dóra Hidy, Zoltán Barcza, Roland Hollós, Laura Dobor, Tamás Ács, Dóra Zacháry, Tibor Filep, László Pásztor, Dóra Incze, Márton Dencső, Eszter Tóth, Katarína Merganičová, Peter Thornton, Steven Running, and Nándor Fodor
Geosci. Model Dev., 15, 2157–2181, https://doi.org/10.5194/gmd-15-2157-2022, https://doi.org/10.5194/gmd-15-2157-2022, 2022
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Biogeochemical models used by the scientific community can support society in the quantification of the expected environmental impacts caused by global climate change. The Biome-BGCMuSo v6.2 biogeochemical model has been created by implementing a lot of developments related to soil hydrology as well as the soil carbon and nitrogen cycle and by integrating crop model components. Detailed descriptions of developments with case studies are presented in this paper.
Lei Ma, George Hurtt, Lesley Ott, Ritvik Sahajpal, Justin Fisk, Rachel Lamb, Hao Tang, Steve Flanagan, Louise Chini, Abhishek Chatterjee, and Joseph Sullivan
Geosci. Model Dev., 15, 1971–1994, https://doi.org/10.5194/gmd-15-1971-2022, https://doi.org/10.5194/gmd-15-1971-2022, 2022
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We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking datasets demonstrated the model’s capability to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has the potential to be linked with remote sensing observations to address key scientific questions.
Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, Aliénor Lavergne, Robert Field, and Laia Andreu-Hayles
Geosci. Model Dev., 15, 1931–1952, https://doi.org/10.5194/gmd-15-1931-2022, https://doi.org/10.5194/gmd-15-1931-2022, 2022
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We modify the numerical model of forest growth MAIDENiso by explicitly simulating snow. This allows us to use the model in boreal environments, where snow is dominant. We tested the performance of the model before and after adding snow, using it at two Canadian sites to simulate tree-ring isotopes and comparing with local observations. We found that modelling snow improves significantly the simulation of the hydrological cycle, the plausibility of the model and the simulated isotopes.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
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We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
Glenn E. Hammond
Geosci. Model Dev., 15, 1659–1676, https://doi.org/10.5194/gmd-15-1659-2022, https://doi.org/10.5194/gmd-15-1659-2022, 2022
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This paper describes a simplified interface for implementing and testing new chemical reactions within the reactive transport simulator PFLOTRAN. The paper describes the interface, providing example code for the interface. The paper includes several chemical reactions implemented through the interface.
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.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
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The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Nils Wallenberg, Fredrik Lindberg, and David Rayner
Geosci. Model Dev., 15, 1107–1128, https://doi.org/10.5194/gmd-15-1107-2022, https://doi.org/10.5194/gmd-15-1107-2022, 2022
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Exposure to solar radiation on clear and warm days can lead to heat stress and thermal discomfort. This can be alleviated by planting trees providing shade in particularly warm areas. Here, we use a model to locate trees and optimize their blocking of solar radiation. Our results show that locations can differ depending, e.g., tree size (juvenile or mature) and number of trees that are positioned simultaneously. The model is available as a tool for accessibility by researchers and others.
Cited articles
Aaltonen, H., Köster, K., Köster, E., Berninger, F., Zhou, X., Karhu,
K., Biasi, C., Bruckman, V., Palviainen, M., and Pumpanen, J.: Forest Fires
in Canadian Permafrost Region: The Combined Effects of Fire and
Permafrost Dynamics on Soil Organic Matter Quality, Biogeochemistry, 143,
257–274, https://doi.org/10.1007/s10533-019-00560-x, 2019a. a, b, c, d, e, f
Aaltonen, H., Palviainen, M., Zhou, X., Köster, E., Berninger, F.,
Pumpanen, J., and Köster, K.: Temperature Sensitivity of Soil Organic
Matter Decomposition after Forest Fire in Canadian Permafrost Region,
J. Environ. Manag., 241, 637–644,
https://doi.org/10.1016/j.jenvman.2019.02.130, 2019b. a, b, c, d, e, f, g, h
Abbott, B. W., Jones, J. B., Schuur, E. A. G., Chapin III, F. S., Bowden,
W. B., Bret-Harte, M. S., Epstein, H. E., Flannigan, M. D., Harms, T. K.,
Hollingsworth, T. N., Mack, M. C., McGuire, A. D., Natali, S. M., Rocha,
A. V., Tank, S. E., Turetsky, M. R., Vonk, J. E., Wickland, K. P., Aiken,
G. R., Alexander, H. D., Amon, R. M. W., Benscoter, B. W., Bergeron, Y.,
Bishop, K., Blarquez, O., Ben Bond-Lamberty, Breen, A. L., Buffam, I., Cai,
Y., Carcaillet, C., Carey, S. K., Chen, J. M., Chen, H. Y. H., Christensen,
T. R., Cooper, L. W., Cornelissen, J. H. C., de Groot, W. J., DeLuca,
T. H., Dorrepaal, E., Fetcher, N., Finlay, J. C., Forbes, B. C., French, N.
H. F., Gauthier, S., Girardin, M. P., Goetz, S. J., Goldammer, J. G., Gough,
L., Grogan, P., Guo, L., Higuera, P. E., Hinzman, L., Hu, F. S., Hugelius,
G., Jafarov, E. E., Jandt, R., Johnstone, J. F., Jan Karlsson, Kasischke,
E. S., Kattner, G., Kelly, R., Keuper, F., Kling, G. W., Kortelainen, P.,
Kouki, J., Kuhry, P., Laudon, H., Laurion, I., Macdonald, R. W., Mann, P. J.,
Martikainen, P. J., McClelland, J. W., Molau, U., Oberbauer, S. F., Olefeldt,
D., Paré, D., Parisien, M.-A., Payette, S., Peng, C., Pokrovsky, O. S.,
Rastetter, E. B., Raymond, P. A., Raynolds, M. K., Rein, G., Reynolds, J. F.,
Robards, M., Rogers, B. M., Schädel, C., Schaefer, K., Schmidt, I. K.,
Shvidenko, A., Sky, J., Spencer, R. G. M., Starr, G., Striegl, R. G.,
Teisserenc, R., Tranvik, L. J., Virtanen, T., Welker, J. M., and Zimov, S.:
Biomass Offsets Little or None of Permafrost Carbon Release from Soils,
Streams, and Wildfire: An Expert Assessment, Environmental Research Letters,
11, 034 014, https://doi.org/10.1088/1748-9326/11/3/034014, 2016. a
Aber, J. D., Ollinger, S. V., and Driscoll, C. T.: Modeling Nitrogen Saturation
in Forest Ecosystems in Response to Land Use and Atmospheric Deposition,
Ecol. Model., 101, 61–78, https://doi.org/10.1016/S0304-3800(97)01953-4, 1997. a
Akaike, H.: A New Look at the Statistical Model Identification, IEEE
T. Automat. Contr., 19, 716–723,
https://doi.org/10.1109/TAC.1974.1100705, 1974. a
Allison, S. D.: Modeling Adaptation of Carbon Use Efficiency in Microbial
Communities, Front. Microbiol., 5, 571, https://doi.org/10.3389/fmicb.2014.00571,
2014. a
Allison, S. D., Wallenstein, M. D., and Bradford, M. A.: Soil-Carbon Response
to Warming Dependent on Microbial Physiology, Nat. Geosci., 3, 336–340,
https://doi.org/10.1038/ngeo846, 2010. a
Allison, S. D., Romero-Olivares, A. L., Lu, Y., Taylor, J. W., and Treseder,
K. K.: Temperature Sensitivities of Extracellular Enzyme Vmax and
Km across Thermal Environments, Glob. Change Biol., 24,
2884–2897, https://doi.org/10.1111/gcb.14045, 2018. a
Anderson, J. P. E. and Domsch, K. H.: Quantification of Bacterial and Fungal
Contributions to Soil Respiration, Arch. Mikrobiol., 93, 113–127,
https://doi.org/10.1007/BF00424942, 1973. a
Beck, T., Joergensen, R. G., Kandeler, E., Makeschin, F., Nuss, E., Oberholzer,
H. R., and Scheu, S.: An Inter-Laboratory Comparison of Ten Different Ways of
Measuring Soil Microbial Biomass C, Soil Biol. Biochem., 29,
1023–1032, https://doi.org/10.1016/S0038-0717(97)00030-8, 1997. a, b
Bond-Lamberty, B. and Thomson, A.: A Global Database of Soil Respiration
Data, Biogeosciences, 7, 1915–1926, https://doi.org/10.5194/bg-7-1915-2010, 2010. a, b
Bosatta, E. and Ågren, G. I.: Theoretical Analysis of Decomposition of
Heterogeneous Substrates, Soil Biol. Biochem., 17, 601–610, 1985. a
Bosatta, E. and Ågren, G. I.: Dynamics of Carbon and Nitrogen in the
Organic Matter of the Soil: A Generic Theory, Am. Nat., 138,
227–245, 1991. a
Bosatta, E. and Ågren, G. I.: Quality and Irreversibility: Constraints on
Ecosystem Development, Proc. R. Soc. Lond. B, 269, 203–210,
https://doi.org/10.1098/rspb.2001.1865, 2002. a, b
Burnham, K. P. and Anderson, D. R., (Eds.): Model Selection and Multimodel
Inference, Springer New York, New York, NY, 2002. a
Chakrawal, A., Herrmann, A. M., Koestel, J., Jarsjö, J., Nunan, N.,
Kätterer, T., and Manzoni, S.: Dynamic Upscaling of Decomposition
Kinetics for Carbon Cycling Models, Geosci. Model Dev., 13,
1399–1429, https://doi.org/10.5194/gmd-13-1399-2020, 2020. a
Chen, H. and Tian, H.-Q.: Does a General Temperature-Dependent Q10
Model of Soil Respiration Exist at Biome and Global Scale?,
J. Integr. Plant Biol., 47, 1288–1302,
https://doi.org/10.1111/j.1744-7909.2005.00211.x, 2005. a
Christiansen, B.: Ensemble Averaging and the Curse of
Dimensionality, J. Clim., 31, 1587–1596,
https://doi.org/10.1175/JCLI-D-17-0197.1, 2018. a
Davidson, E. A., Belk, E., and Boone, R. D.: Soil Water Content and Temperature
as Independent or Confounded Factors Controlling Soil Respiration in a
Temperate Mixed Hardwood Forest, Glob. Change Biol., 4, 217–227,
https://doi.org/10.1046/j.1365-2486.1998.00128.x, 1998. a
Davidson, E. A., Janssens, I. A., and Luo, Y.: On the Variability of
Respiration in Terrestrial Ecosystems: Moving beyond Q10, Glob. Change
Biol., 12, 154–164, https://doi.org/10.1111/j.1365-2486.2005.01065.x, 2006. a, b, c
Elzhov, T. V., Mullen, K. M., Spiess, A.-N., and Bolker, B.: Minpack.Lm: R
Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found
in MINPACK, Plus Support for Bounds, R package version 1.2-1, available at: https://CRAN.R-project.org/package=minpack.lm (last access: 1 April 2021), 2016. a
Famiglietti, C. A., Smallman, T. L., Levine, P. A., Flack-Prain, S., Quetin,
G. R., Meyer, V., Parazoo, N. C., Stettz, S. G., Yang, Y., Bonal, D., Bloom,
A. A., Williams, M., and Konings, A. G.: Optimal Model Complexity for
Terrestrial Carbon Cycle Prediction, Biogeosciences, 18, 2727–2754,
https://doi.org/10.5194/bg-18-2727-2021, 2021. a
Fan, Z., Jastrow, J. D., Liang, C., Matamala, R., and Miller, R. M.: Priming
Effects in Boreal Black Spruce Forest Soils: Quantitative
Evaluation and Sensitivity Analysis, PLOS ONE, 8, e77880,
https://doi.org/10.1371/journal.pone.0077880, 2013. a
German, D. P., Marcelo, K. R. B., Stone, M. M., and Allison, S. D.: The
Michaelis–Menten Kinetics of Soil Extracellular Enzymes in
Response to Temperature: A Cross-Latitudinal Study, Glob. Change Biol.,
18, 1468–1479, https://doi.org/10.1111/j.1365-2486.2011.02615.x, 2012. a, b
Graf, A., Weihermüller, L., Huisman, J. A., Herbst, M., Bauer, J., and
Vereecken, H.: Measurement Depth Effects on the Apparent Temperature
Sensitivity of Soil Respiration in Field Studies, Biogeosciences, 5,
1175–1188, https://doi.org/10.5194/bg-5-1175-2008, 2008. a
Hamdi, S., Moyano, F., Sall, S., Bernoux, M., and Chevallier, T.: Synthesis
Analysis of the Temperature Sensitivity of Soil Respiration from Laboratory
Studies in Relation to Incubation Methods and Soil Conditions, Soil Biol. Biochem., 58, 115–126, https://doi.org/10.1016/j.soilbio.2012.11.012, 2013. a
Hanson, P., Edwards, N., Garten, C., and Andrews, J.: Separating Root and Soil
Microbial Contributions to Soil Respiration: A Review of Methods and
Observations, Biogeochemistry, 48, 115–146, https://doi.org/10.1023/A:1006244819642,
2000. a
Härkönen, S., Lehtonen, A., Eerikäinen, K., Peltoniemi, M., and
Mäkelä, A.: Estimating Forest Carbon Fluxes for Large Regions Based
on Process-Based Modelling, NFI Data and Landsat Satellite Images,
Forest Ecol. Manag., 262, 2364–2377,
https://doi.org/10.1016/j.foreco.2011.08.035, 2011. a
Harmon, M. E., Bond-Lamberty, B., Tang, J., and Vargas, R.: Heterotrophic
Respiration in Disturbed Forests: A Review with Examples from North
America, J. Geophys. Res., 116, G00K04, https://doi.org/10.1029/2010JG001495,
2011. a
Holden, S. R., Rogers, B. M., Treseder, K. K., and Randerson, J. T.: Fire
Severity Influences the Response of Soil Microbes to a Boreal Forest Fire,
Environ. Res. Lett., 11, 035004,
https://doi.org/10.1088/1748-9326/11/3/035004, 2016. a
Hu, T., Sun, L., Hu, H., Weise, D. R., and Guo, F.: Soil Respiration of the
Dahurian Larch (Larix Gmelinii) Forest and the
Response to Fire Disturbance in Da Xing'an Mountains,
China, Sci. Rep., 7, 2967, https://doi.org/10.1038/s41598-017-03325-4,
2017. a
Hugelius, G., Tarnocai, C., Broll, G., Canadell, J. G., Kuhry, P., and Swanson,
D. K.: The Northern Circumpolar Soil Carbon Database: Spatially
Distributed Datasets of Soil Coverage and Soil Carbon Storage in the Northern
Permafrost Regions, Earth Syst. Sci. Data, 5, 3–13,
https://doi.org/10.5194/essd-5-3-2013, 2013. a
Ito, E., Ikemoto, Y., and Yoshioka, T.: Thermodynamic Implications of High
Q10 of thermoTRP Channels in Living Cells, Biophysics,
11, 33–38, https://doi.org/10.2142/biophysics.11.33, 2015. a
Jian, S., Li, J., Wang, G., Kluber, L. A., Schadt, C. W., Liang, J., and Mayes,
M. A.: Multi-Year Incubation Experiments Boost Confidence in Model
Projections of Long-Term Soil Carbon Dynamics, Nat. Commun., 11,
5864, https://doi.org/10.1038/s41467-020-19428-y, 2020. a
Kalyn, A. L. and Van Rees, K. C. J.: Contribution of Fine Roots to Ecosystem
Biomass and Net Primary Production in Black Spruce, Aspen, and Jack Pine
Forests in Saskatchewan, Agr. Forest Meteorol., 140,
236–243, https://doi.org/10.1016/j.agrformet.2005.08.019, 2006. a
Karhu, K., Hilasvuori, E., Fritze, H., Biasi, C., Nykänen, H., Liski, J.,
Vanhala, P., Heinonsalo, J., and Pumpanen, J.: Priming Effect Increases with
Depth in a Boreal Forest Soil, Soil Biol. Biochem., 99, 104–107,
https://doi.org/10.1016/j.soilbio.2016.05.001, 2016. a
Keener, J., Sneyd, J., Antman, S., Marsden, J., and Sirovich, L., eds.:
Mathematical Physiology, Vol. 8/1, Interdisciplinary Applied
Mathematics, Springer New York, New York, NY,
https://doi.org/10.1007/978-0-387-75847-3, 2009. a
Knicker, H.: How Does Fire Affect the Nature and Stability of Soil Organic
Nitrogen and Carbon? A Review, Biogeochemistry, 85, 91–118,
https://doi.org/10.1007/s10533-007-9104-4, 2007. a
Köster, E., Köster, K., Berninger, F., Aaltonen, H., Zhou, X., and
Pumpanen, J.: Carbon Dioxide, Methane and Nitrous Oxide Fluxes from a Fire
Chronosequence in Subarctic Boreal Forests of Canada, Sci.
Total Environ., 601/602, 895–905, https://doi.org/10.1016/j.scitotenv.2017.05.246,
2017. a, b, c, d, e, f, g, h, i, j
Kraemer, G., Camps-Valls, G., Reichstein, M., and Mahecha, M. D.: Summarizing
the State of the Terrestrial Biosphere in Few Dimensions, Biogeosciences, 17,
2397–2424, https://doi.org/10.5194/bg-17-2397-2020, 2020. a
Kulmala, L., Aaltonen, H., Berninger, F., Kieloaho, A.-J., Levula, J.,
Bäck, J., Hari, P., Kolari, P., Korhonen, J. F. J., Kulmala, M.,
Nikinmaa, E., Pihlatie, M., Vesala, T., and Pumpanen, J.: Changes in
Biogeochemistry and Carbon Fluxes in a Boreal Forest after the Clear-Cutting
and Partial Burning of Slash, Agr. Forest Meteorol., 188,
33–44, https://doi.org/10.1016/j.agrformet.2013.12.003, 2014. a
Lloyd, J. and Taylor, J. A.: On the Temperature Dependence of Soil
Respiration, Funct. Ecol., 8, 315–323, https://doi.org/10.2307/2389824, 1994. a
Luo, Y., Weng, E., Wu, X., Gao, C., Zhou, X., and Zhang, L.: Parameter
Identifiability, Constraint, and Equifinality in Data Assimilation with
Ecosystem Models, Ecol. Appl., 19, 571–574,
https://doi.org/10.1890/08-0561.1, 2009. a
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F.,
Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.,
McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra,
C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., van Groenigen,
K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J., Xu, X., Xu,
X., and Zhou, T.: Toward More Realistic Projections of Soil Carbon Dynamics
by Earth System Models, Global Biogeochem. Cy., 30, 40–56,
https://doi.org/10.1002/2015GB005239, 2016. a
Marschmann, G. L., Pagel, H., Kügler, P., and Streck, T.: Equifinality,
Sloppiness, and Emergent Structures of Mechanistic Soil Biogeochemical
Models, Environ. Model. Softw., 122, 104518,
https://doi.org/10.1016/j.envsoft.2019.104518, 2019. a
Masrur, A., Petrov, A. N., and DeGroote, J.: Circumpolar Spatio-Temporal
Patterns and Contributing Climatic Factors of Wildfire Activity in the
Arctic Tundra from 2001–2015, Environ. Res. Lett.,
13, 014019, https://doi.org/10.1088/1748-9326/aa9a76, 2018. a
McGuire, A. D., Anderson, L. G., Christensen, T. R., Dallimore, S., Guo, L.,
Hayes, D. J., Heimann, M., Lorenson, T. D., Macdonald, R. W., and Roulet, N.:
Sensitivity of the Carbon Cycle in the Arctic to Climate Change,
Ecol. Monogr., 79, 523–555, https://doi.org/10.1890/08-2025.1, 2009. a, b
Meigs, G. W., Donato, D. C., Campbell, J. L., Martin, J. G., and Law, B. E.:
Forest Fire Impacts on Carbon Uptake, Storage, and Emission:
The Role of Burn Severity in the Eastern Cascades, Oregon, Ecosystems,
12, 1246–1267, https://doi.org/10.1007/s10021-009-9285-x, 2009. a
Michaelis, L. and Menten, M.: Die Kinetik Der Invertin Wirkung, Biochem.
Z., 49, 334–336, 1913. a
Morgan, R. B., Herrmann, V., Kunert, N., Bond-Lamberty, B., Muller-Landau,
H. C., and Anderson-Teixeira, K. J.: Global Patterns of Forest Autotrophic
Carbon Fluxes, Glob. Change Biol., 27, 2840–2855,
https://doi.org/10.1111/gcb.15574, 2021. a
Moyano, F. E., Manzoni, S., and Chenu, C.: Responses of Soil Heterotrophic
Respiration to Moisture Availability: An Exploration of Processes and
Models, Soil Biol. Biochem., 59, 72–85,
https://doi.org/10.1016/j.soilbio.2013.01.002, 2013. a
Muñoz-Rojas, M., Lewandrowski, W., Erickson, T. E., Dixon, K. W., and
Merritt, D. J.: Soil Respiration Dynamics in Fire Affected Semi-Arid
Ecosystems: Effects of Vegetation Type and Environmental Factors, Sci. Total Environ., 572, 1385–1394,
https://doi.org/10.1016/j.scitotenv.2016.02.086, 2016. a
Nash, J. C.: Nonlinear Parameter Optimization Using R Tools, Chichester,
West Sussex, 1st Edn., Wiley, Chichester, West Sussex, 2014. a
Nash, J. C. and Murdoch, D.: Nlsr: Functions for Nonlinear Least Squares
Solutions, R package version 2021.8.19, available at: https://CRAN.R-project.org/package=nlsr (last access: 9 May 2021), 2019. a
Neumann, M., Godbold, D. L., Hirano, Y., and Finér, L.: Improving Models of
Fine Root Carbon Stocks and Fluxes in European Forests, J.
Ecol., 108, 496–514, https://doi.org/10.1111/1365-2745.13328, 2020. a, b
Niu, B., Zhang, X., Piao, S., Janssens, I. A., Fu, G., He, Y., Zhang, Y., Shi,
P., Dai, E., Yu, C., Zhang, J., Yu, G., Xu, M., Wu, J., Zhu, L., Desai,
A. R., Chen, J., Bohrer, G., Gough, C. M., Mammarella, I., Varlagin, A.,
Fares, S., Zhao, X., Li, Y., Wang, H., and Ouyang, Z.: Warming Homogenizes
Apparent Temperature Sensitivity of Ecosystem Respiration, Sci. Adv.,
7, eabc7358, https://doi.org/10.1126/sciadv.abc7358, 2021. a
O'Donnell, J. A., Harden, J. W., McGuire, A. D., and Romanovsky, V. E.:
Exploring the Sensitivity of Soil Carbon Dynamics to Climate Change, Fire
Disturbance and Permafrost Thaw in a Black Spruce Ecosystem, Biogeosciences,
8, 1367–1382, https://doi.org/10.5194/bg-8-1367-2011, 2011. a
Pavelka, M., Acosta, M., Marek, M. V., Kutsch, W., and Janous, D.: Dependence
of the Q10 Values on the Depth of the Soil Temperature Measuring Point,
Plant Soil, 292, 171–179, https://doi.org/10.1007/s11104-007-9213-9, 2007. a
Phillips, C. L., Nickerson, N., Risk, D., and Bond, B. J.: Interpreting Diel
Hysteresis between Soil Respiration and Temperature, Glob. Change Biol.,
17, 515–527, https://doi.org/10.1111/j.1365-2486.2010.02250.x, 2011. a
Phillips, C. L., Bond-Lamberty, B., Desai, A. R., Lavoie, M., Risk, D., Tang,
J., Todd-Brown, K., and Vargas, R.: The Value of Soil Respiration
Measurements for Interpreting and Modeling Terrestrial Carbon Cycling, Plant
Soil, 413, 1–25, https://doi.org/10.1007/s11104-016-3084-x, 2017. a
Pumpanen, J., Ilvesniemi, H., and Hari, P.: A Process-Based Model for
Predicting Soil Carbon Dioxide Efflux and Concentration, Soil Sci.
Soc. Am. J., 67, 402–413, https://doi.org/10.2136/sssaj2003.4020, 2003. a
Pumpanen, J., Ilvesniemi, H., Kulmala, L., Siivola, E., Laakso, H., Kolari, P.,
Helenelund, C., Laakso, M., Uusimaa, M., and Hari, P.: Respiration in
Boreal Forest Soil as Determined from Carbon Dioxide Concentration
Profile, Soil Sci. Soc. Am. J., 72, 1187–1196,
https://doi.org/10.2136/sssaj2007.0199, 2008. a
Rayment, M. B. and Jarvis, P. G.: Temporal and Spatial Variation of Soil
CO2 Efflux in a Canadian Boreal Forest, Soil Biol.
Biochem., 32, 35–45, https://doi.org/10.1016/S0038-0717(99)00110-8, 2000. a
Reichstein, M. and Beer, C.: Soil Respiration across Scales: The Importance
of a Model – Data Integration Framework for Data Interpretation,
J. Plant Nutr. Soil Sci., 171, 344–354,
https://doi.org/10.1002/jpln.200700075, 2008. a, b
Ribeiro-Kumara, C., Köster, E., Aaltonen, H., and Köster, K.:
Forest-fires-GHG: A dataset derived from a literature review on soil greenhouse gas emissions after forest fires in upland boreal forests, Mendeley dataset, V1, https://doi.org/10.17632/v7gxtvv9z3.1, 2020a. a
Schuur, E. A. G., Bockheim, J., Canadell, J. G., Euskirchen, E., Field, C. B.,
Goryachkin, S. V., Hagemann, S., Kuhry, P., Lafleur, P. M., Lee, H.,
Mazhitova, G., Nelson, F. E., Rinke, A., Romanovsky, V. E., Shiklomanov, N.,
Tarnocai, C., Venevsky, S., Vogel, J. G., and Zimov, S. A.: Vulnerability of
Permafrost Carbon to Climate Change: Implications for the
Glob. Carbon Cy., BioScience, 58, 701–714, https://doi.org/10.1641/B580807, 2008. a, b
Shao, P., Zeng, X., Moore, D. J. P., and Zeng, X.: Soil Microbial Respiration
from Observations and Earth System Models, Environ. Res.
Lett., 8, 034034, https://doi.org/10.1088/1748-9326/8/3/034034, 2013. a
Shiklomanov, A. N., Bond-Lamberty, B., Atkins, J. W., and Gough, C. M.:
Structure and Parameter Uncertainty in Centennial Projections of Forest
Community Structure and Carbon Cycling, Glob. Change Biol., 26,
6080–6096, https://doi.org/10.1111/gcb.15164, 2020. a
Sihi, D., Gerber, S., Inglett, P. W., and Inglett, K. S.: Comparing Models of
Microbial – Substrate Interactions and Their Response to Warming,
Biogeosciences, 13, 1733–1752, https://doi.org/10.5194/bg-13-1733-2016, 2016. a, b, c
Song, J., Liu, Z., Zhang, Y., Yan, T., Shen, Z., and Piao, S.: Effects of
Wildfire on Soil Respiration and Its Heterotrophic and Autotrophic Components
in a Montane Coniferous Forest, J. Plant Ecol., 12, 336–345,
https://doi.org/10.1093/jpe/rty031, 2019. a
Steele, S. J., Gower, S. T., Vogel, J. G., and Norman, J. M.: Root Mass, Net
Primary Production and Turnover in Aspen, Jack Pine and Black Spruce Forests
in Saskatchewan and Manitoba, Canada, Tree Physiol., 17,
577–587, https://doi.org/10.1093/treephys/17.8-9.577, 1997. a
Subke, J.-A. and Bahn, M.: On the “Temperature Sensitivity” of Soil
Respiration: Can We Use the Immeasurable to Predict the Unknown?, Soil
Biol. Biochem., 42, 1653–1656,
https://doi.org/10.1016/j.soilbio.2010.05.026, 2010. a
Subke, J.-A., Inglima, I., and Cotrufo, M. F.: Trends and Methodological
Impacts in Soil CO2 Efflux Partitioning: A Metaanalytical Review,
Glob. Change Biol., 12, 921–943, https://doi.org/10.1111/j.1365-2486.2006.01117.x,
2006. a
Sulman, B. N., Moore, J. A. M., Abramoff, R., Averill, C., Kivlin, S.,
Georgiou, K., Sridhar, B., Hartman, M. D., Wang, G., Wieder, W. R., Bradford,
M. A., Luo, Y., Mayes, M. A., Morrison, E., Riley, W. J., Salazar, A.,
Schimel, J. P., Tang, J., and Classen, A. T.: Multiple Models and Experiments
Underscore Large Uncertainty in Soil Carbon Dynamics, Biogeochemistry, 141, 109–123,
https://doi.org/10.1007/s10533-018-0509-z, 2018. a
Tang, J. and Zhuang, Q.: Equifinality in Parameterization of Process-Based
Biogeochemistry Models: A Significant Uncertainty Source to the
Estimation of Regional Carbon Dynamics, J. Geophys. Res., 113,
G04010, https://doi.org/10.1029/2008JG000757, 2008. a, b
Taylor, K. E.: Summarizing Multiple Aspects of Model Performance in a Single
Diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000JD900719, 2001. a
Todd-Brown, K. E. O., Hopkins, F. M., Kivlin, S. N., Talbot, J. M., and
Allison, S. D.: A Framework for Representing Microbial Decomposition in
Coupled Climate Models, Biogeochemistry, 109, 19–33,
https://doi.org/10.1007/s10533-011-9635-6, 2012. a, b
van't Hoff, J. H. and Lehfeldt, R. A.: Lectures in Theoretical and Physical
Chemistry: Part I : Chemical Dynamics, Edward Arnold, London, 1898. a
Vargas, R., Baldocchi, D. D., Allen, M. F., Bahn, M., Black, T. A., Collins,
S. L., Yuste, J. C., Hirano, T., Jassal, R. S., Pumpanen, J., and Tang, J.:
Looking Deeper into the Soil: Biophysical Controls and Seasonal Lags of Soil
CO2 Production and Efflux, Ecol. Appl., 20, 1569–1582,
https://doi.org/10.1890/09-0693.1, 2010. a, b
Vereecken, H., Schnepf, A., Hopmans, J. W., Javaux, M., Or, D., Roose, T.,
Vanderborght, J., Young, M. H., Amelung, W., Aitkenhead, M., Allison, S. D.,
Assouline, S., Baveye, P., Berli, M., Brüggemann, N., Finke, P., Flury,
M., Gaiser, T., Govers, G., Ghezzehei, T., Hallett, P., Franssen, H. J. H.,
Heppell, J., Horn, R., Huisman, J. A., Jacques, D., Jonard, F., Kollet, S.,
Lafolie, F., Lamorski, K., Leitner, D., McBratney, A., Minasny, B., Montzka,
C., Nowak, W., Pachepsky, Y., Padarian, J., Romano, N., Roth, K., Rothfuss,
Y., Rowe, E. C., Schwen, A., Šimůnek, J., Tiktak, A., Dam, J. V.,
van der Zee, S. E. A. T. M., Vogel, H. J., Vrugt, J. A., Wöhling, T., and
Young, I. M.: Modeling Soil Processes: Review, Key Challenges,
and New Perspectives, Vadose Zone J., 15, 1–57,
https://doi.org/10.2136/vzj2015.09.0131, 2016. a
Vogel, J., Valentine, D., and Ruess, R.: Soil and Root Respiration in Mature
Alaskan Black Spruce Forests That Vary in Soil Organic Matter
Decomposition Rates, Canadian Journal of Forest Research-revue Canadienne De
Recherche Forestiere, Can. J. Forest Res., 35, 161–174, https://doi.org/10.1139/x04-159,
2005. a
Vogel, J. G., Bronson, D., Gower, S. T., and Schuur, E. A.: The Response of
Root and Microbial Respiration to the Experimental Warming of a Boreal Black
Spruce Forest, Can. J. Forest Res., 44, 986–993,
https://doi.org/10.1139/cjfr-2014-0056, 2014. a
Walsh, J. E., Ballinger, T. J., Euskirchen, E. S., Hanna, E., Mård, J.,
Overland, J. E., Tangen, H., and Vihma, T.: Extreme Weather and Climate
Events in Northern Areas: A Review, Earth-Sci. Rev., 209, 103324,
https://doi.org/10.1016/j.earscirev.2020.103324, 2020. a
Wang, W., Wang, H., Zu, Y., Li, X., and Koike, T.: Characteristics of the
Temperature Coefficient, Q10, for the Respiration of Non-Photosynthetic
Organs and Soils of Forest Ecosystems, Front. Forestr. China, 1,
125–135, https://doi.org/10.1007/s11461-006-0018-4, 2006. a
Wang, Y.-P., Zhang, H., Ciais, P., Goll, D., Huang, Y., Wood, J. D., Ollinger,
S. V., Tang, X., and Prescher, A.-K.: Microbial Activity and Root
Carbon Inputs Are More Important than Soil Carbon Diffusion in
Simulating Soil Carbon Profiles, J. Geophys. Res.-Biogeo., 126, e2020JG006205, https://doi.org/10.1029/2020JG006205, 2021. a
Wei, W., Weile, C., and Shaopeng, W.: Forest Soil Respiration and Its
Heterotrophic and Autotrophic Components: Global Patterns and Responses
to Temperature and Precipitation, Soil Biol. Biochem., 42,
1236–1244, https://doi.org/10.1016/j.soilbio.2010.04.013, 2010. a
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global Soil Carbon Projections
Are Improved by Modelling Microbial Processes, Nat. Clim. Change, 3,
909–912, https://doi.org/10.1038/nclimate1951, 2013. a, b, c
Wieder, W. R., Allison, S. D., Davidson, E. A., Georgiou, K., Hararuk, O., He,
Y., Hopkins, F., Luo, Y., Smith, M. J., Sulman, B., Todd-Brown, K., Wang,
Y.-P., Xia, J., and Xu, X.: Explicitly Representing Soil Microbial Processes
in Earth System Models, Global Biogeochem. Cy., 29, 1782–1800,
https://doi.org/10.1002/2015GB005188, 2015. a
Zhang, Q., Katul, G. G., Oren, R., Daly, E., Manzoni, S., and Yang, D.: The
Hysteresis Response of Soil CO2 Concentration and Soil Respiration to
Soil Temperature, J. Geophys. Res.-Biogeo., 120,
1605–1618, https://doi.org/10.1002/2015JG003047, 2015. a
Zhao, B., Zhuang, Q., Shurpali, N., Köster, K., Berninger, F., and
Pumpanen, J.: North American Boreal Forests Are a Large Carbon Source Due
to Wildfires from 1986 to 2016, Sci. Rep., 11, 7723,
https://doi.org/10.1038/s41598-021-87343-3, 2021. a
Zhou, X., Sun, H., Pumpanen, J., Sietiö, O.-M., Heinonsalo, J., Köster,
K., and Berninger, F.: The Impact of Wildfire on Microbial
Zhu, D., Ciais, P., Krinner, G., Maignan, F., Jornet Puig, A., and Hugelius,
G.: Controls of Soil Organic Matter on Soil Thermal Dynamics in the Northern
High Latitudes, Nat. Commun., 10, 3172, https://doi.org/10.1038/s41467-019-11103-1,
2019. a, b
Zhuang, Q., A. D. McGuire, K. P. O’Neill, J. W. Harden, V. E. Romanovsky, and J. Yarie, Modeling soil thermal and carbon dynamics of a fire chronosequence in interior Alaska, J. Geophys. Res., 107, 8147, https://doi.org/10.1029/2001JD001244, 2002. a
Zobitz, J., Desai, A., Moore, D., and Chadwick, M.: A Primer for Data
Assimilation with Ecological Models Using Markov Chain Monte Carlo
(MCMC), Oecologia, 167, 599–611, https://doi.org/10.1007/s00442-011-2107-9, 2011.
a
Zobitz, J., Aaltonen, H., Zhou, X., Berninger, F., Pumpanen, J., and Köster,
K.: FireResp (v1.0.2), Zenodo [code], https://doi.org/10.5281/zenodo.5542011, 2021. a
Zobitz, J. M., Moore, D. J. P., Sacks, W. J., Monson, R. K., Bowling, D. R.,
and Schimel, D. S.: Integration of Process-Based Soil Respiration Models with
Whole-Ecosystem CO2 Measurements, Ecosystems, 11, 250–269,
https://doi.org/10.1007/s10021-007-9120-1, 2008. a, b, c, d
Short summary
Forest fires heavily affect carbon stocks and fluxes of carbon in high-latitude forests. Long-term trends in soil respiration following forest fires are associated with recovery of aboveground biomass. We evaluated models for soil autotrophic and heterotrophic respiration with data from a chronosequence of stand-replacing forest fires in northern Canada. The best model that reproduced expected patterns in soil respiration components takes into account soil microbe carbon as a model variable.
Forest fires heavily affect carbon stocks and fluxes of carbon in high-latitude forests....
