Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2011-2023
© 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/gmd-16-2011-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Apr 2023
Model description paper |
| 13 Apr 2023
| 13 Apr 2023
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
Winslow D. Hansen
CORRESPONDING AUTHOR
Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA
Adrianna Foster
National Center for Atmospheric Research, Boulder, CO 80035, USA
Benjamin Gaglioti
Water and Environmental Research Center, Institute of Northern
Engineering, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Rupert Seidl
School of Life Sciences, Technical University of Munich, 85354
Freising, Germany
Berchtesgaden National Park, 83471 Berchtesgaden, Germany
Werner Rammer
School of Life Sciences, Technical University of Munich, 85354
Freising, Germany
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Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Anna Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-526, https://doi.org/10.5194/essd-2024-526, 2024
Revised manuscript accepted for ESSD
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Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a data set of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Cornelius Senf and Rupert Seidl
Biogeosciences, 18, 5223–5230, https://doi.org/10.5194/bg-18-5223-2021, https://doi.org/10.5194/bg-18-5223-2021, 2021
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Europe was affected by an extreme drought in 2018. We show that this drought has increased forest disturbances across Europe, especially central and eastern Europe. Disturbance levels observed 2018–2020 were the highest on record for 30 years. Increased forest disturbances were correlated with low moisture and high atmospheric water demand. The unprecedented impacts of the 2018 drought on forest disturbances demonstrate an urgent need to adapt Europe’s forests to a hotter and drier future.
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
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Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
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Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
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Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
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Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
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Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
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We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
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When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us see what changes on our lands.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Cited articles
Abbott, B. W. and Jones, J. B.: Permafrost collapse alters soil carbon
stocks, respiration, CH4, and N2O in upland tundra, Glob.Change Biol.,
21, 4570–4587, https://doi.org/10.1111/gcb.13069, 2015.
Albrich, K., Rammer, W., Turner, M. G., Ratajczak, Z., Braziunas, K. H.,
Hansen, W. D., and Seidl, R.: Simulating forest resilience: A review, Global
Ecol. Biogeogr., 29, 2082–2096, https://doi.org/10.1111/geb.13197,
2020.
Alexander, H. D. and Mack, M. C.: A canopy shift in interior Alaskan boreal
forests: Consequences for above- and belowground carbon and nitrogen pools
during post-fire succession, Ecosystems, 19, 98–114,
https://doi.org/10.1007/s10021-015-9920-7, 2016.
Anderegg, W. R. L., Wu, C., Acil, N., Carvalhais, N., Pugh, T. A. M.,
Sadler, J. P., and Seidl, R.: A climate risk analysis of Earth's forests in
the 21st century, Science, 377, 1099–1103,
https://doi.org/10.1126/science.abp9723, 2022.
Anderson, P. M., Edwards, M. E., and Brubaker, L. B.: Results and
paleoclimate implications of 35 years of paleoecological research in Alaska,
in: Developments in Quaternary Sciences, vol. 1, Elsevier, 427–440,
https://doi.org/10.1016/S1571-0866(03)01019-4, 2003.
Baltzer, J. L., Veness, T., Chasmer, L. E., Sniderhan, A. E., and Quinton,
W. L.: Forests on thawing permafrost: fragmentation, edge effects, and net
forest loss, Glob. Change Biol., 20, 824–834,
https://doi.org/10.1111/gcb.12349, 2014.
Baltzer, J. L., Day, N. J., Walker, X. J., Greene, D., Mack, M. C.,
Alexander, H. D., Arseneault, D., Barnes, J., Bergeron, Y., Boucher, Y.,
Bourgeau-Chavez, L., Brown, C. D., Carrière, S., Howard, B. K.,
Gauthier, S., Parisien, M.-A., Reid, K. A., Rogers, B. M., Roland, C.,
Sirois, L., Stehn, S., Thompson, D. K., Turetsky, M. R., Veraverbeke, S.,
Whitman, E., Yang, J., and Johnstone, J. F.: Increasing fire and the decline
of fire adapted black spruce in the boreal forest, P. Natl. Acad. Sci. USA, 118, e2024872118,
https://doi.org/10.1073/pnas.2024872118, 2021.
Beer, C., Lucht, W., Gerten, D., Thonicke, K., and Schmullius, C.: Effects
of soil freezing and thawing on vegetation carbon density in Siberia: A
modeling analysis with the Lund-Potsdam-Jena Dynamic Global Vegetation Model
(LPJ-DGVM), Global Biogeochem. Cy., 21, GB1012,
https://doi.org/10.1029/2006GB002760, 2007.
Bennett, K. E., Cherry, J. E., Balk, B., and Lindsey, S.: Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska, Hydrol. Earth Syst. Sci., 23, 2439–2459, https://doi.org/10.5194/hess-23-2439-2019, 2019.
Bonan, G.: Surface Energy Fluxes, in: Climate Change and Terrestrial
Ecosystem Modeling, University of Cambridge Press, Cambridge, UK, 101–114, https://doi.org/10.1017/9781107339217,
2019.
Bonan, G. B.: A biophysical surface energy budget analysis of soil
temperature in the boreal forests of interior Alaska, Water Resour.
Res., 27, 767–781, https://doi.org/10.1029/91WR00143, 1991.
Bonan, G. B. and Korzuhin, M. D.: Simulation of moss and tree dynamics in
the boreal forests of interior Alaska, Vegetatio, 84, 31–44,
https://doi.org/10.1007/BF00054663, 1989.
Bormann, K. J., Westra, S., Evans, J. P., and McCabe, M. F.: Spatial and
temporal variability in seasonal snow density, J. Hydrol., 484,
63–73, https://doi.org/10.1016/j.jhydrol.2013.01.032, 2013.
Brown, C. D. and Johnstone, J. F.: Once burned, twice shy: Repeat fires
reduce seed availability and alter substrate constraints on Picea mariana
regeneration, Forest Ecol. Manage., 266, 34–41,
https://doi.org/10.1016/j.foreco.2011.11.006, 2012.
Brown, D. R. N., Jorgenson, M. T., Kielland, K., Verbyla, D. L., Prakash,
A., and Koch, J. C.: Landscape effects of wildfire on permafrost
distribution in interior Alaska derived from remote sensing, Remote Sensing,
8, 654, https://doi.org/10.3390/rs8080654, 2016.
Buma, B., Hayes, K., Weiss, S., and Lucash, M.: Short-interval fires
increasing in the Alaskan boreal forest as fire self-regulation decays
across forest types, Sci. Rep., 12, 4901,
https://doi.org/10.1038/s41598-022-08912-8, 2022.
Burns, R. M. and Honkala, B. H.: Silvics Manual Volume 1-Conifers and Volume
2-Hardwoods, 2nd Edn., U.S. Department of Agriculture, Forest Service,
Washington DC, US Library of Congress catalog number: 86-60058,
1990.
Chapin, F. S., Randerson, J. T., McGuire, A. D., Foley, J. A., and Field, C.
B.: Changing feedbacks in the climate–biosphere system, Front.
Ecol. Environ., 6, 313–320, https://doi.org/10.1890/080005,
2008.
Chylek, P., Folland, C., Klett, J. D., Wang, M., Hengartner, N., Lesins, G.,
and Dubey, M. K.: Annual Mean Arctic Amplification 1970–2020: Observed and
Simulated by CMIP6 Climate Models, Geophys. Res. Lett., 49,
e2022GL099371, https://doi.org/10.1029/2022GL099371, 2022.
Cook, B. I., Bonan, G. B., Levis, S., and Epstein, H. E.: The
thermoinsulation effect of snow cover within a climate model, Clim.
Dynam., 31, 107–124, https://doi.org/10.1007/s00382-007-0341-y, 2008.
Dearborn, K. D. and Baltzer, J. L.: Unexpected greening in a boreal
permafrost peatland undergoing forest loss is partially attributable to tree
species turnover, Glob. Change Biol., 27, 2867–2882,
https://doi.org/10.1111/gcb.15608, 2021.
Farouki, O. T.: The thermal properties of soils in cold regions, Cold
Reg. Sci. Technol., 5, 67–75,
https://doi.org/10.1016/0165-232X(81)90041-0, 1981.
Fisher, J. P., Estop-Aragonés, C., Thierry, A., Charman, D. J., Wolfe,
S. A., Hartley, I. P., Murton, J. B., Williams, M., and Phoenix, G. K.: The
influence of vegetation and soil characteristics on active-layer thickness
of permafrost soils in boreal forest, Glob. Change Biol., 22, 3127–3140,
https://doi.org/10.1111/gcb.13248, 2016.
Fisher, R. A. and Koven, C. D.: Perspectives on the future of land surface
models and the challenges of representing complex terrestrial systems,
J. Adv. Model. Earth Sy., 12, e2018MS001453,
https://doi.org/10.1029/2018ms001453, 2020.
Foley, J. A., Kutzbach, J. E., Coe, M. T., and Levis, S.: Feedbacks between
climate and boreal forests during the Holocene epoch, Nature, 371, 52–54,
https://doi.org/10.1038/371052a0, 1994.
Foster, A. C., Armstrong, A. H., Shuman, J. K., Shugart, H. H., Rogers, B.
M., Mack, M. C., Goetz, S. J., and Ranson, K. J.: Importance of tree- and
species-level interactions with wildfire, climate, and soils in interior
Alaska: Implications for forest change under a warming climate, Ecol.
Model., 409, 108765, https://doi.org/10.1016/j.ecolmodel.2019.108765,
2019.
Foster, A. C., Shuman, J. K., Rogers, B. M., Walker, X. J., Mack, M. C.,
Bourgeau-Chavez, L. L., Veraverbeke, S., and Goetz, S. J.: Bottom-up drivers
of future fire regimes in western boreal North America, Environ. Res. Lett.,
17, 025006, https://doi.org/10.1088/1748-9326/ac4c1e, 2022.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M.,
Worley, P. H., Yang, Z.-L., and Zhang, M.: The Community Climate System
Model Version 4, J. Climate, 24, 4973–4991,
https://doi.org/10.1175/2011JCLI4083.1, 2011.
Gibson, C. M., Chasmer, L. E., Thompson, D. K., Quinton, W. L., Flannigan,
M. D., and Olefeldt, D.: Wildfire as a major driver of recent permafrost
thaw in boreal peatlands, Nat. Commun., 9, 3041,
https://doi.org/10.1038/s41467-018-05457-1, 2018.
Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S.
F., Thulke, H.-H., Weiner, J., Wiegand, T., and DeAngelis, D. L.:
Pattern-oriented modeling of agent-based complex systems: Lessons from
ecology, Science, 310, 987–991, https://doi.org/10.1126/science.1116681,
2005.
Gustafson, E. J., Miranda, B. R., Shvidenko, A. Z., and Sturtevant, B. R.:
Simulating growth and competition on wet and waterlogged soils in a forest
landscape model, Front. Ecol. Evol., 8, 598775,
https://doi.org/10.3389/fevo.2020.598775, 2020.
Hansen, W. D., Braziunas, K. H., Rammer, W., Seidl, R., and Turner, M. G.:
It takes a few to tango: Changing climate and fire regimes can cause
regeneration failure of two subalpine conifers, Ecology, 99, 966–977,
https://doi.org/10.1002/ecy.2181, 2018.
Hansen, W. D., Abendroth, D., Rammer, W., Seidl, R., and Turner, M.: Can
wildland fire management alter 21st-century subalpine fire and forests in
Grand Teton National Park, Wyoming, USA?, Ecol. Appl., 30,
e02030, https://doi.org/10.1002/eap.2030, 2020.
Hansen, W. D., Fitzsimmons, R., Olnes, J., and Williams, A. P.: An alternate
vegetation type proves resilient and persists for decades following forest
conversion in the North American boreal biome, J. Ecol., 109,
85–98, https://doi.org/10.1111/1365-2745.13446, 2021.
Hansen, W. D., Schwartz, N. B., Williams, A. P., Albrich, K., Kueppers, L. M.,
Rammig, A., Reyer, C. P. O., Staver, A. C., and Seidl, R.: Global forests are
influenced by legacies of past inter-annual temperature variability,
Environ. Res. Ecol., 1, 011001, https://doi.org/10.1088/2752-664X/ac6e4a, 2022a.
Hansen, W. D., Foster, A., Gaglioti, B., Seidl, R., and Rammer, W.: Data and code associated with: The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0, Cary Institute [code and data set], https://doi.org/10.25390/caryinstitute.21339090.v3, 2022b.
Hengl, T., Jesus, J. M. de, Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda,
M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Héon, J., Arseneault, D., and Parisien, M.-A.: Resistance of the boreal
forest to high burn rates, P. Natl. Acad. Sci. USA,
111, 13888–13893, https://doi.org/10.1073/pnas.1409316111, 2014.
Hermosilla, T., Bastyr, A., Coops, N. C., White, J. C., and Wulder, M. A.:
Mapping the presence and distribution of tree species in Canada's forested
ecosystems, Remote Sens. Environ., 282, 113276,
https://doi.org/10.1016/j.rse.2022.113276, 2022.
Hijmans, R. J.: Spatial Data analysis, Rpackage version 1.7-22 [software], https://rspatial.org/ (last access: 1 March 2022), 2021.
Hinzman, L. D., Kane, D. L., Gieck, R. E., and Everett, K. R.: Hydrologic
and thermal properties of the active layer in the Alaskan Arctic, Cold
Reg. Sci. Technol., 19, 95–110,
https://doi.org/10.1016/0165-232X(91)90001-W, 1991.
Hughes, T. P., Linares, C., Dakos, V., van de Leemput, I. A., and van Nes, E.
H.: Living dangerously on borrowed time during slow, unrecognized regime
shifts, Trends Ecol. Evol., 28, 149–55,
https://doi.org/10.1016/j.tree.2012.08.022, 2013.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021.
Jafarov, E. E., Nicolsky, D. J., Romanovsky, V. E., Walsh, J. E., Panda, S.
K., and Serreze, M. C.: The effect of snow: How to better model ground
surface temperatures, Cold Reg. Sci. Technol., 102, 63–77,
https://doi.org/10.1016/j.coldregions.2014.02.007, 2014.
Jean, M., Melvin, A. M., Mack, M. C., and Johnstone, J. F.: Broadleaf litter
controls feather moss growth in black spruce and birch forests of interior
Alaska, Ecosystems, 23, 18–33, https://doi.org/10.1007/s10021-019-00384-8,
2020.
Johnstone, J. and Chapin, F.: Effects of soil burn severity on post-fire
tree recruitment in boreal forest, Ecosystems, 9, 14–31,
https://doi.org/10.1007/s10021-004-0042-x, 2006.
Johnstone, J., Boby, L., Tissier, E., Mack, M., Verbyla, D., and Walker, X.:
Postfire seed rain of black spruce, a semiserotinous conifer, in forests of
interior Alaska, Can. J. Forest Res., 39, 1575–1588,
https://doi.org/10.1139/X09-068, 2009.
Johnstone, J. F. and Kasischke, E. S.: Stand-level effects of soil burn
severity on postfire regeneration in a recently burned black spruce forest,
Can. J. Forest Res., 35, 2151–2163, 2005.
Johnstone, J. F., Hollingsworth, T. N., Chapin, F. S., and Mack, M. C.:
Changes in fire regime break the legacy lock on successional trajectories in
Alaskan boreal forest, Glob. Change Biol., 16, 1281–1295,
https://doi.org/10.1111/j.1365-2486.2009.02051.x, 2010a.
Johnstone, J. F., Chapin, F. S., Hollingsworth, T. N., Mack, M. C.,
Romanovsky, V., and Turetsky, M.: Fire, climate change, and forest
resilience in interior Alaska, Can. J. Forest Res., 40,
1302–1312, https://doi.org/10.1139/X10-061, 2010b.
Johnstone, J. F., Allen, C. D., Franklin, J. F., Frelich, L. E., Harvey, B.
J., Higuera, P. E., Mack, M. C., Meentemeyer, R. K., Metz, M. R., Perry, G.
L., Schoennagel, T., and Turner, M. G.: Changing disturbance regimes,
ecological memory, and forest resilience, Front. Ecol.
Environ., 14, 369–378, https://doi.org/10.1002/fee.1311, 2016.
Johnstone, J. F., Celis, G., Chapin, F. S., Hollingsworth, T. N., Jean, M.,
and Mack, M. C.: Factors shaping alternate successional trajectories in
burned black spruce forests of Alaska, Ecosphere, 11, e03129,
https://doi.org/10.1002/ecs2.3129, 2020.
Jorgenson, M. T., Romanovsky, V., Harden, J., Shur, Y., O'Donnell, J.,
Schuur, E. A. G., Kanevskiy, M., and Marchenko, S.: Resilience and
vulnerability of permafrost to climate change, Can. J. Forest
Res., 40, 1219–1236, https://doi.org/10.1139/X10-060, 2010.
Kannenberg, S. A., Schwalm, C. R., and Anderegg, W. R. L.: Ghosts of the
past: how drought legacy effects shape forest functioning and carbon
cycling, Ecol. Lett., 23, 891–901, https://doi.org/10.1111/ele.13485,
2020.
Karra, S., Painter, S. L., and Lichtner, P. C.: Three-phase numerical model for subsurface hydrology in permafrost-affected regions (PFLOTRAN-ICE v1.0), The Cryosphere, 8, 1935–1950, https://doi.org/10.5194/tc-8-1935-2014, 2014.
Kasischke, E. S. and Johnstone, J. F.: Variation in postfire organic layer
thickness in a black spruce forest complex in interior Alaska and its
effects on soil temperature and moisture, Can. J. Forest
Res., 35, 2164–2177, https://doi.org/10.1139/x05-159, 2005.
Kruse, S., Stuenzi, S. M., Boike, J., Langer, M., Gloy, J., and Herzschuh, U.: Novel coupled permafrost–forest model (LAVESI–CryoGrid v1.0) revealing the interplay between permafrost, vegetation, and climate across eastern Siberia, Geosci. Model Dev., 15, 2395–2422, https://doi.org/10.5194/gmd-15-2395-2022, 2022.
Lorenz, K. and Lal, R.: Carbon Dynamics and Pools in Major Forest Biomes of
the World, in: Carbon Sequestration in Forest Ecosystems, edited by: Lorenz,
K. and Lal, R., Springer Netherlands, Dordrecht, 159–205,
https://doi.org/10.1007/978-90-481-3266-9_4, 2010.
Mack, M. C., Walker, X. J., Johnstone, J. F., Alexander, H. D., Melvin, A.
M., Jean, M., and Miller, S. N.: Carbon loss from boreal forest wildfires
offset by increased dominance of deciduous trees, Science, 372, 280–283,
https://doi.org/10.1126/science.abf3903, 2021.
Malone, T., Liang, J., and Packee, E. C.: Cooperative Alaska Forest
Inventory, United States Department of Agriculture, Forest Service, Pacific
Northwester Research Station, https://doi.org/10.2737/PNW-GTR-785, 2009.
Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F., and Rogers,
B. M.: Expansion of high-latitude deciduous forests driven by interactions
between climate warming and fire, Nature Plants, 5, 952–958,
https://doi.org/10.1038/s41477-019-0495-8, 2019.
Melvin, A. M., Mack, M. C., Johnstone, J. F., Mcguire, A. D., Genet, H., and
Schuur, E. A. G.: Differences in ecosystem carbon distribution and nutrient
cycling linked to forest tree species composition in a mid-successional
boreal forest, Ecosystems, 18, 1472–1488,
https://doi.org/10.1007/s10021-015-9912-7, 2015.
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H.,
Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov,
A., Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda,
S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin,
J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling
for 2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316,
https://doi.org/10.1016/j.earscirev.2019.04.023, 2019.
O'Donnell, J. A., Romanovsky, V. E., Harden, J. W., and McGuire, A. D.: The
effect of moisture content on the thermal conductivity of moss and organic
soil horizons from black spruce ecosystems in interior Alaska, Soil Sci.,
174, 646–651, https://doi.org/10.1097/SS.0b013e3181c4a7f8, 2009.
Ogle, K., Barber, J. J., Barron-Gafford, G. A., Bentley, L. P., Young, J.
M., Huxman, T. E., Loik, M. E., and Tissue, D. T.: Quantifying ecological
memory in plant and ecosystem processes, Ecol. Lett., 18, 221–235,
https://doi.org/10.1111/ele.12399, 2015.
Pastick, N. J., Jorgenson, M. T., Wylie, B. K., Nield, S. J., Johnson, K.
D., and Finley, A. O.: Distribution of near-surface permafrost in Alaska:
Estimates of present and future conditions, Remote Sens. Environ.,
168, 301–315, https://doi.org/10.1016/j.rse.2015.07.019, 2015.
Perreault, J., Fortier, R., and Molson, J. W.: Numerical modelling of
permafrost dynamics under climate change and evolving ground surface
conditions: application to an instrumented permafrost mound at Umiujaq,
Nunavik (Québec), Canada, Écoscience, 28, 377–397,
https://doi.org/10.1080/11956860.2021.1949819, 2021.
Phillips, C. A., Rogers, B. M., Elder, M., Cooperdock, S., Moubarak, M., Randerson, J. T., and Frumhoff, P. C.: Escalating carbon emissions from North American boreal forest wildfires and the climate mitigation potential of fire management, Sci. Adv., 8, eabl7161, https://doi.org/10.1126/sciadv.abl7161, 2022.
Potter, S., Solvik, K., Erb, A., Goetz, S. J., Johnstone, J. F., Mack, M.
C., Randerson, J. T., Román, M. O., Schaaf, C. L., Turetsky, M. R.,
Veraverbeke, S., Walker, X. J., Wang, Z., Massey, R., and Rogers, B. M.:
Climate change decreases the cooling effect from postfire albedo in boreal
North America, Glob. Change Biol., 26, 1592–1607,
https://doi.org/10.1111/gcb.14888, 2020.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, [software], https://www.R-project.org/ (last access: 1 March 2022), 2021.
Riseborough, D. W.: Exploring the Parameters of a Simple Model of the
Permafrost – Climate Relationship, Carleton University, Ottawa, Canada, 282
pp., https://doi.org/10.22215/etd/2004-05938, 2004.
Rogers, B. M., Randerson, J. T., and Bonan, G. B.: High-latitude cooling associated with landscape changes from North American boreal forest fires, Biogeosciences, 10, 699–718, https://doi.org/10.5194/bg-10-699-2013, 2013.
Ruess, R. W., Winton, L. M., and Adams, G. C.: Widespread mortality of
trembling aspen (Populus tremuloides) throughout interior Alaskan boreal
forests resulting from a novel canker disease, PLOS ONE, 16, e0250078,
https://doi.org/10.1371/journal.pone.0250078, 2021.
Running, S. W. and Coughlan, J. C.: A general model of forest ecosystem
processes for regional applications I. Hydrologic balance, canopy gas
exchange and primary production processes, Ecol. Model., 42,
125–154, https://doi.org/10.1016/0304-3800(88)90112-3, 1988.
Schuur, E. A. G. and Mack, M. C.: Ecological response to permafrost thaw and
consequences for local and global ecosystem services, Annu. Rev.
Ecol. Evol. S., 49, 279–301,
https://doi.org/10.1146/annurev-ecolsys-121415-032349, 2018.
Schurr, E. A. G., McGuire, A. D., Romanovsky, V. E., Schädel, C., and
Mack, M.: Chapter 11: Arctic and boreal carbon, in: Second State of the
Carbon Cycle Report (SOCCR2): A Sustained Assessment Report, U.S. Global
Change Research Program, Washington, DC, 428–468, https://doi.org/10.7930/SOCCR2.2018., 2018.
Seidl, R.: To model or not to model, that is no longer the question for
ecologists, Ecosystems, 20, 222–228,
https://doi.org/10.1007/s10021-016-0068-x, 2017.
Seidl, R. and Turner, M. G.: Post-disturbance reorganization of forest
ecosystems in a changing world, P. Natl. Acad. Sci. USA, 119, e2202190119, https://doi.org/10.1073/pnas.2202190119, 2022.
Seidl, R., Rammer, W., Scheller, R. M., and Spies, T. A.: An
individual-based process model to simulate landscape-scale forest ecosystem
dynamics, Ecol. Model., 231, 87–100,
https://doi.org/10.1016/j.ecolmodel.2012.02.015, 2012a.
Seidl, R., Spies, T. A., Rammer, W., Steel, E. A., Pabst, R. J., and Olsen,
K.: Multi-scale drivers of spatial variation in old-growth forest carbon
density disentangled with lidar and an individual-based landscape model,
Ecosystems, 15, 1321–1335, https://doi.org/10.1007/s10021-012-9587-2,
2012b.
Seidl, R., Rammer, W., and Spies, T. A.: Disturbance legacies increase the
resilience of forest ecosystem structure, composition, and functioning,
Ecol. Appl., 24, 2063–2077, 2014a.
Seidl, R., Rammer, W., and Blennow, K.: Simulating wind disturbance impacts
on forest landscapes: Tree-level heterogeneity matters, Environ.
Modell. Softw., 51, 1–11,
https://doi.org/10.1016/j.envsoft.2013.09.018, 2014b.
Seidl, R., Honkaniemi, J., Aakala, T., Aleinikov, A., Angelstam, P.,
Bouchard, M., Boulanger, Y., Burton, P. J., De Grandpré, L., Gauthier,
S., Hansen, W. D., Jepsen, J. U., Jõgiste, K., Kneeshaw, D. D.,
Kuuluvainen, T., Lisitsyna, O., Makoto, K., Mori, A. S., Pureswaran, D. S.,
Shorohova, E., Shubnitsina, E., Taylor, A. R., Vladimirova, N., Vodde, F.,
and Senf, C.: Globally consistent climate sensitivity of natural
disturbances across boreal and temperate forest ecosystems, Ecography, 43,
967–978, https://doi.org/10.1111/ecog.04995, 2020.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model,
Glob. Change Biol., 9, 161–185,
https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
Smith, S. L., O'Neill, H. B., Isaksen, K., Noetzli, J., and Romanovsky, V.
E.: The changing thermal state of permafrost, Nat. Rev. Earth Environ., 3,
10–23, https://doi.org/10.1038/s43017-021-00240-1, 2022.
Stuenzi, S. M., Boike, J., Gädeke, A., Herzschuh, U., Kruse, S.,
Pestryakova, L. A., Westermann, S., and Langer, M.: Sensitivity of
ecosystem-protected permafrost under changing boreal forest structures,
Environ. Res. Lett., 16, 084045, https://doi.org/10.1088/1748-9326/ac153d,
2021.
Thornton, P. E., Shrestha, R., Thornton, M., Kao, S.-C., Wei, Y., and
Wilson, B. E.: Gridded daily weather data for North America with
comprehensive uncertainty quantification, Sci. Data, 8, 190,
https://doi.org/10.1038/s41597-021-00973-0, 2021.
Trugman, A. T., Fenton, N. J., Bergeron, Y., Xu, X., Welp, L. R., and
Medvigy, D.: Climate, soil organic layer, and nitrogen jointly drive forest
development after fire in the North American boreal zone, J.
Adv. Model. Earth Sy., 8, 1180–1209,
https://doi.org/10.1002/2015MS000576, 2016.
Turetsky, M. R., Mack, M. C., Hollingsworth, T. N., and Harden, J. W.: The
role of mosses in ecosystem succession and function in Alaska's boreal
forest, Can. J. Forest Res., 40, 1237–1264,
https://doi.org/10.1139/X10-072, 2010.
Turetsky, M. R., Baltzer, J. L., Johnstone, J. F., Mack, M. C., Mccann, K.,
and Schuur, E. A. G.: Losing legacies, ecological release, and transient
responses: Key challenges for the future of northern ecosystem science,
Ecosystems, 20, 23–30, https://doi.org/10.1007/s10021-016-0055-2, 2016.
Turner, M. G., Braziunas, K. H., Hansen, W. D., Hoecker, T. J., Rammer, W.,
Ratajczak, Z., Westerling, A. L., and Seidl, R.: The magnitude, direction,
and tempo of forest change in Greater Yellowstone in a warmer world with
more fire, Ecol. Monogr., 92, e01485,
https://doi.org/10.1002/ecm.1485, 2022.
Van Cleve, K. and Viereck, L. A.: Forest Succession in Relation to Nutrient
Cycling in the Boreal Forest of Alaska, in: Forest Succession: Concepts and
Application, edited by: West, D. C., Shugart, H. H., and Botkin, D. B.,
Springer, New York, NY, 185–211,
https://doi.org/10.1007/978-1-4612-5950-3_13, 1981.
Veraverbeke, S., Rogers, B. M., Goulden, M. L., Jandt, R. R., Miller, C. E., Wiggins, E. B., and Randerson, J. T.: Lightning as a major driver of recent large fire years in North American boreal forests, Nat. Clim. Change 7, 529–534, 2017.
Walker, X. and Johnstone, J. F.: Widespread negative correlations between
black spruce growth and temperature across topographic moisture gradients in
the boreal forest, Environ. Res. Lett., 9, 064016–064016,
https://doi.org/10.1088/1748-9326/9/6/064016, 2014.
Walker, X. J., Rogers, B. M., Baltzer, J. L., Cumming, S. G., Day, N. J.,
Goetz, S. J., Johnstone, J. F., Schuur, E. A. G., Turetsky, M. R., and Mack,
M. C.: Cross-scale controls on carbon emissions from boreal forest
megafires, Glob. Change Biol., 24, 4251–4265,
https://doi.org/10.1111/gcb.14287, 2018.
Walker, X. J., Baltzer, J. L., Cumming, S. G., Day, N. J., Ebert, C., Goetz,
S., Johnstone, J. F., Potter, S., Rogers, B. M., Schuur, E. A. G., Turetsky,
M. R., and Mack, M. C.: Increasing wildfires threaten historic carbon sink
of boreal forest soils, Nature, 572, 520–523,
https://doi.org/10.1038/s41586-019-1474-y, 2019.
Walker, X. J., Rogers, B. M., Veraverbeke, S., Johnstone, J. F., Baltzer, J.
L., Barrett, K., Bourgeau-Chavez, L., Day, N. J., de Groot, W. J., Dieleman,
C. M., Goetz, S., Hoy, E., Jenkins, L. K., Kane, E. S., Parisien, M.-A.,
Potter, S., Schuur, E. A. G., Turetsky, M., Whitman, E., and Mack, M. C.:
Fuel availability not fire weather controls boreal wildfire severity and
carbon emissions, Nat. Clim. Change, 10, 1130–1136,
https://doi.org/10.1038/s41558-020-00920-8, 2020.
Walsh, J. E., Bhatt, U. S., Littell, J. S., Leonawicz, M., Lindgren, M., Kurkowski, T. A., Bieniek, P. A., Thoman, R., Gray, S., and Rupp, T. S.: Downscaling of climate model output for Alaskan stakeholders, Environ. Modell. Softw., 110, 38–51, 2018.
Wang, J. A., Sulla-Menashe, D., Woodcock, C. E., Sonnentag, O., Keeling, R.
F., and Friedl, M. A.: Extensive land cover change across Arctic–Boreal
Northwestern North America from disturbance and climate forcing, Glob.
Change Biol., 26, 807–822, https://doi.org/10.1111/gcb.14804, 2020.
Wang, J. A., Baccini, A., Farina, M., Randerson, J. T., and Friedl, M. A.:
Disturbance suppresses the aboveground carbon sink in North American boreal
forests, Nat. Clim. Change, 11, 435–441,
https://doi.org/10.1038/s41558-021-01027-4, 2021.
Westermann, S., Langer, M., Boike, J., Heikenfeld, M., Peter, M., Etzelmüller, B., and Krinner, G.: Simulating the thermal regime and thaw processes of ice-rich permafrost ground with the land-surface model CryoGrid 3, Geosci. Model Dev., 9, 523–546, https://doi.org/10.5194/gmd-9-523-2016, 2016.
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D.,
François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M.,
Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J.,
Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke,
C., Woo, K., and Yutani, H.: Welcome to the {tidyverse}, J. Open Source Softw., 4, 1686,
https://doi.org/10.21105/joss.01686, 2019.
Yi, Y., Kimball, J., and Miller, C. E.: ABoVE: High Resolution Cloud-Free
Snow Cover Extent and Snow Depth, Alaska, 2001–2017, ORNL DAAC [data set],
https://doi.org/10.3334/ORNLDAAC/1757, 2020.
Yokohata, T., Saito, K., Takata, K., Nitta, T., Satoh, Y., Hajima, T.,
Sueyoshi, T., and Iwahana, G.: Model improvement and future projection of
permafrost processes in a global land surface model, Prog. Earth Planet Sci.,
7, 69, https://doi.org/10.1186/s40645-020-00380-w, 2020.
Young-Robertson, J. M., Ogle, K., and Welker, J. M.: Thawing seasonal ground
ice: An important water source for boreal forest plants in Interior Alaska,
Ecohydrology, 10, e1796–e1796, https://doi.org/10.1002/eco.1796, 2017.
Short summary
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.
Permafrost and the thick soil-surface organic layers that insulate permafrost are important...
