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
Related authors
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.
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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Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
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
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES-HYDRO V1.0)
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
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
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
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Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
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Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
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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
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The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0
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Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
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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
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A dynamic local-scale vegetation model for lycopsids (LYCOm v1.0)
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Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
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.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, and Yanxu Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-89, https://doi.org/10.5194/gmd-2023-89, 2023
Revised manuscript accepted for GMD
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical‐ecosystem ocean model (High-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
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.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-66, https://doi.org/10.5194/gmd-2023-66, 2023
Revised manuscript accepted for GMD
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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.
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.
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.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla T. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-47, https://doi.org/10.5194/gmd-2023-47, 2023
Revised manuscript accepted for GMD
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (eg, basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits from C sequestration. ERW could drive changes in the soil emissions of non-CO2 GHGs (N2O), and trace gases (NO & NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
EGUsphere, https://doi.org/10.5194/egusphere-2023-278, https://doi.org/10.5194/egusphere-2023-278, 2023
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This paper introduces a plant hydrodynamic model for the DOE-sponsored dynamic vegetation model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We have identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
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.
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.
Jianghui Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-281, https://doi.org/10.5194/gmd-2022-281, 2023
Revised manuscript accepted for GMD
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Trace elements and isotopes (TEIs) are important tools to study the changes of the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
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.
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.
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...
