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
A. Park Williams, Winslow D. Hansen, Caroline S. Juang, John T. Abatzoglou, Volker C. Radeloff, Bowen Wang, Jazlynn Hall, Jatan Buch, and Gavin D. Madakumbura
EGUsphere, https://doi.org/10.5194/egusphere-2025-2934, https://doi.org/10.5194/egusphere-2025-2934, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The new WULFFSS is a monthly gridded forest-fire model to simulate forest fires across the western United States in response to vegetation, topographic, anthropogenic, and climate factors. This effort is motivated by the ten-fold increase in western U.S. annual forest area burned over the past 40 years. The WULFFSS is highly skillful, accounting for over 80 % of the observed variability in annual forest-fire area and capturing observed spatial, intra-annual variations, and trends.
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
Short summary
Short summary
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.
A. Park Williams, Winslow D. Hansen, Caroline S. Juang, John T. Abatzoglou, Volker C. Radeloff, Bowen Wang, Jazlynn Hall, Jatan Buch, and Gavin D. Madakumbura
EGUsphere, https://doi.org/10.5194/egusphere-2025-2934, https://doi.org/10.5194/egusphere-2025-2934, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The new WULFFSS is a monthly gridded forest-fire model to simulate forest fires across the western United States in response to vegetation, topographic, anthropogenic, and climate factors. This effort is motivated by the ten-fold increase in western U.S. annual forest area burned over the past 40 years. The WULFFSS is highly skillful, accounting for over 80 % of the observed variability in annual forest-fire area and capturing observed spatial, intra-annual variations, and trends.
Anna C. 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, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
Short summary
Short summary
Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset 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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Related subject area
Biogeosciences
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
pyVPRM: a next-generation vegetation photosynthesis and respiration model for the post-MODIS era
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Implementing a process-based representation of soil water movement in a second-generation dynamic vegetation model: application to dryland ecosystems (LPJ-GUESS-RE v1.0)
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
CROMES v1.0: A flexible CROp Model Emulator Suite for climate impact assessment
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0)
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Data-Informed Inversion Model (DIIM): a framework to retrieve marine optical constituents in the BOUSSOLE site using a three-stream irradiance model
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Benjamin F. Meyer, João P. Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025, https://doi.org/10.5194/gmd-18-4643-2025, 2025
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening the European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show its ability to capture species-specific evapotranspiration responses to drought and to reproduce flux observations of both gross primary production and evapotranspiration.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
Geosci. Model Dev., 18, 4713–4742, https://doi.org/10.5194/gmd-18-4713-2025, https://doi.org/10.5194/gmd-18-4713-2025, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS, and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025, https://doi.org/10.5194/gmd-18-4317-2025, 2025
Short summary
Short summary
We developed fast machine learning models to predict forest regrowth and carbon dynamics under climate change. These models mimic the outputs of a complex vegetation model but run 95 % faster, enabling global analyses and supporting climate solutions in large modeling frameworks such as LandSyMM.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025, https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Short summary
Physical–biogeochemical ocean global models are required to analyze difficult oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
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.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions 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.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization 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, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Wim Verbruggen, David Wårlind, Stéphanie Horion, Félicien Meunier, Hans Verbeeck, and Guy Schurgers
EGUsphere, https://doi.org/10.5194/egusphere-2025-1259, https://doi.org/10.5194/egusphere-2025-1259, 2025
Short summary
Short summary
We improved the representation of soil water movement in a state-of-the-art dynamic vegetation model. This is especially important for dry ecosystems, as they are often driven by changes in soil water availability. We showed that this update resulted in a generally better match with observations, and that the updated model is more sensitive to soil texture. This updated model will help scientists to better understand the future of dry ecosystems under climate change.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts 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 capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Christian Folberth, Artem Baklanov, Nikolay Khabarov, Thomas Oberleitner, Juraj Balkovič, and Rastislav Skalský
EGUsphere, https://doi.org/10.5194/egusphere-2025-862, https://doi.org/10.5194/egusphere-2025-862, 2025
Short summary
Short summary
Global gridded crop models (GGCMs) are important tools in agricultural climate impact assessments but computationally costly. An emergent approach to derive crop productivity estimates similar to those from GGCMs are emulators that mimic the original model, but typically with considerable bias. Here we present a modelling package that trains emulators with very high accuracy and high computational gain, providing a basis for more comprehensive scenario assessments.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
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.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
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.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
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
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Felix Nößler, Thibault Moulin, Oksana Buzhdygan, Britta Tietjen, and Felix May
EGUsphere, https://doi.org/10.5194/egusphere-2024-3798, https://doi.org/10.5194/egusphere-2024-3798, 2024
Short summary
Short summary
To predict the response of grassland plant communities to management and climate change, we developed the computer model GrasslandTraitSim.jl. Unlike other models, it uses measurable plant traits such as height, leaf thinness, and root structure as inputs, rather than hard-to-measure species data. This allows realistic simulation of many species. The model tracks daily changes in above- and below-ground biomass, plant height, and soil water, linking plant community composition to biomass supply.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Carlos Enmanuel Soto López, Fabio Anselmi, Mirna Gharbi Dit Kacem, and Paolo Lazzari
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-174, https://doi.org/10.5194/gmd-2024-174, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Our goal was to use an analytical expression to estimate the density of optical constituents, allowing us to have an interpretable formulation consistent with the laws of physics. We focused on a probabilistic approach, optimizing the model and retrieving quantities with their respective uncertainty. Considering future application to Big Data, we also explored a Neural Network based method, retrieving computationally efficient estimates, maintaining consistency with the analytical expression.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
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
Short summary
Short summary
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.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
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
Short summary
Short summary
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.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Joshua Coupe, Nicole S. Lovenduski, Luise S. Gleason, Michael N. Levy, Kristen Krumhardt, Keith Lindsay, Charles Bardeen, Clay Tabor, Cheryl Harrison, Kenneth G. MacLeod, Siddhartha Mitra, and Julio Sepúlveda
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-94, https://doi.org/10.5194/gmd-2024-94, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We develop a new feature in the atmosphere and ocean components of the Community Earth System Model version 2. We have implemented ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation. The new feature will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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...
