Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2325-2022
© Author(s) 2022. 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-15-2325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A dynamic local-scale vegetation model for lycopsids (LYCOm v1.0)
Institute of Plant Science and Microbiology, Ecological Modeling, Universität Hamburg,
Ohnhorststr. 18, 22609 Hamburg, Germany
Susanne K. M. Arens
GLOBE Institute, University of Copenhagen, Øster Voldgade 5–7, 1350 Copenhagen,
Denmark
Kai Jensen
Institute of Plant Science and Microbiology, Applied Plant Ecology, Universität Hamburg,
Ohnhorststr. 18, 22609 Hamburg, Germany
Tais W. Dahl
GLOBE Institute, University of Copenhagen, Øster Voldgade 5–7, 1350 Copenhagen,
Denmark
Philipp Porada
Institute of Plant Science and Microbiology, Ecological Modeling, Universität Hamburg,
Ohnhorststr. 18, 22609 Hamburg, Germany
Related authors
No articles found.
Youssef Saadaoui, Christian Beer, Peter Mueller, Friederike Neiske, Joscha N. Becker, Annette Eschenbach, and Philipp Porada
EGUsphere, https://doi.org/10.5194/egusphere-2024-1756, https://doi.org/10.5194/egusphere-2024-1756, 2024
Short summary
Short summary
Estuarine marshes are vital for capturing carbon and benefiting the climate. Our research explored how plant-microbe interactions affect carbon cycling, focusing on traits like root oxygen loss. Using a model, we found that accounting for these trait variations significantly changes carbon balance estimates. This suggests that including plant diversity in ecosystem models improves predictions about carbon dynamics in estuarine marshes, highlighting their importance in climate regulation.
Yunyao Ma, Bettina Weber, Alexandra Kratz, José Raggio, Claudia Colesie, Maik Veste, Maaike Y. Bader, and Philipp Porada
Biogeosciences, 20, 2553–2572, https://doi.org/10.5194/bg-20-2553-2023, https://doi.org/10.5194/bg-20-2553-2023, 2023
Short summary
Short summary
We found that the modelled annual carbon balance of biocrusts is strongly affected by both the environment (mostly air temperature and CO2 concentration) and physiology, such as temperature response of respiration. However, the relative impacts of these drivers vary across regions with different climates. Uncertainty in driving factors may lead to unrealistic carbon balance estimates, particularly in temperate climates, and may be explained by seasonal variation of physiology due to acclimation.
Hao Tang, Stefanie Nolte, Kai Jensen, Roy Rich, Julian Mittmann-Goetsch, and Peter Mueller
Biogeosciences, 20, 1925–1935, https://doi.org/10.5194/bg-20-1925-2023, https://doi.org/10.5194/bg-20-1925-2023, 2023
Short summary
Short summary
In order to gain the first mechanistic insight into warming effects and litter breakdown dynamics across whole-soil profiles, we used a unique field warming experiment and standardized plant litter to investigate the degree to which rising soil temperatures can accelerate belowground litter breakdown in coastal wetland ecosystems. We found warming strongly increases the initial rate of labile litter decomposition but has less consistent effects on the stabilization of this material.
Hao Tang, Susanne Liebner, Svenja Reents, Stefanie Nolte, Kai Jensen, Fabian Horn, and Peter Mueller
Biogeosciences, 18, 6133–6146, https://doi.org/10.5194/bg-18-6133-2021, https://doi.org/10.5194/bg-18-6133-2021, 2021
Short summary
Short summary
We examined if sea-level rise and plant genotype interact to affect soil microbial functioning in a mesocosm experiment using two genotypes of a dominant salt-marsh grass characterized by differences in flooding sensitivity. Larger variability in microbial community structure, enzyme activity, and litter breakdown in soils with the more sensitive genotype supports our hypothesis that effects of climate change on soil microbial functioning can be controlled by plant intraspecific adaptations.
Svenja Reents, Peter Mueller, Hao Tang, Kai Jensen, and Stefanie Nolte
Biogeosciences, 18, 403–411, https://doi.org/10.5194/bg-18-403-2021, https://doi.org/10.5194/bg-18-403-2021, 2021
Short summary
Short summary
By conducting a flooding experiment with two genotypes of the salt-marsh grass Elymus athericus, we show considerable differences in biomass response to flooding within the same species. As biomass production plays a major role in sedimentation processes and thereby salt-marsh accretion, we emphasise the importance of taking intraspecific differences into account when evaluating ecosystem resilience to accelerated sea level rise.
Félix Pellerin, Philipp Porada, and Inga Hense
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2020-55, https://doi.org/10.5194/esd-2020-55, 2020
Revised manuscript not accepted
Short summary
Short summary
While several biological processes are similar among terrestrial and marine ecosystems, their representation in Earth System Models may differ. By comparing the terrestrial and marine modules of 17 Earth System Models, we found multiple evidences of unjustified differences in processes representation. These inconsistencies may lead to wrong predictions about the role of biosphere in the climate system and skew our perception of the relative influence of each ecosystem on climate.
Philipp Porada, Alexandra Tamm, Jose Raggio, Yafang Cheng, Axel Kleidon, Ulrich Pöschl, and Bettina Weber
Biogeosciences, 16, 2003–2031, https://doi.org/10.5194/bg-16-2003-2019, https://doi.org/10.5194/bg-16-2003-2019, 2019
Short summary
Short summary
The trace gases NO and HONO are crucial for atmospheric chemistry. It has been suggested that biological soil crusts in drylands contribute substantially to global NO and HONO emissions, based on empirical upscaling of laboratory and field observations. Here we apply an alternative, process-based modeling approach to predict these emissions. We find that biological soil crusts emit globally significant amounts of NO and HONO, which also vary depending on the type of biological soil crust.
Stefano Manzoni, Petr Čapek, Philipp Porada, Martin Thurner, Mattias Winterdahl, Christian Beer, Volker Brüchert, Jan Frouz, Anke M. Herrmann, Björn D. Lindahl, Steve W. Lyon, Hana Šantrůčková, Giulia Vico, and Danielle Way
Biogeosciences, 15, 5929–5949, https://doi.org/10.5194/bg-15-5929-2018, https://doi.org/10.5194/bg-15-5929-2018, 2018
Short summary
Short summary
Carbon fixed by plants and phytoplankton through photosynthesis is ultimately stored in soils and sediments or released to the atmosphere during decomposition of dead biomass. Carbon-use efficiency is a useful metric to quantify the fate of carbon – higher efficiency means higher storage and lower release to the atmosphere. Here we summarize many definitions of carbon-use efficiency and study how this metric changes from organisms to ecosystems and from terrestrial to aquatic environments.
Peter Mueller, Lisa M. Schile-Beers, Thomas J. Mozdzer, Gail L. Chmura, Thomas Dinter, Yakov Kuzyakov, Alma V. de Groot, Peter Esselink, Christian Smit, Andrea D'Alpaos, Carles Ibáñez, Magdalena Lazarus, Urs Neumeier, Beverly J. Johnson, Andrew H. Baldwin, Stephanie A. Yarwood, Diana I. Montemayor, Zaichao Yang, Jihua Wu, Kai Jensen, and Stefanie Nolte
Biogeosciences, 15, 3189–3202, https://doi.org/10.5194/bg-15-3189-2018, https://doi.org/10.5194/bg-15-3189-2018, 2018
Christian Beer, Philipp Porada, Altug Ekici, and Matthias Brakebusch
The Cryosphere, 12, 741–757, https://doi.org/10.5194/tc-12-741-2018, https://doi.org/10.5194/tc-12-741-2018, 2018
Short summary
Short summary
Idealized model experiments demonstrate that, in addition to a gradual climate change, changing daily to weekly variability of meteorological variables and extreme events will also have an impact on mean annual ground temperature in high-latitude permafrost areas. In fact, results of the land surface model experiments show that the projected increase of variability of meteorological variables leads to cooler permafrost soil in contrast to an otherwise soil warming in response to climate change.
Sarah E. Chadburn, Gerhard Krinner, Philipp Porada, Annett Bartsch, Christian Beer, Luca Belelli Marchesini, Julia Boike, Altug Ekici, Bo Elberling, Thomas Friborg, Gustaf Hugelius, Margareta Johansson, Peter Kuhry, Lars Kutzbach, Moritz Langer, Magnus Lund, Frans-Jan W. Parmentier, Shushi Peng, Ko Van Huissteden, Tao Wang, Sebastian Westermann, Dan Zhu, and Eleanor J. Burke
Biogeosciences, 14, 5143–5169, https://doi.org/10.5194/bg-14-5143-2017, https://doi.org/10.5194/bg-14-5143-2017, 2017
Short summary
Short summary
Earth system models (ESMs) are our main tools for understanding future climate. The Arctic is important for the future carbon cycle, particularly due to the large carbon stocks in permafrost. We evaluated the performance of the land component of three major ESMs at Arctic tundra sites, focusing on the fluxes and stocks of carbon.
We show that the next steps for model improvement are to better represent vegetation dynamics, to include mosses and to improve below-ground carbon cycle processes.
Philipp Porada, Ulrich Pöschl, Axel Kleidon, Christian Beer, and Bettina Weber
Biogeosciences, 14, 1593–1602, https://doi.org/10.5194/bg-14-1593-2017, https://doi.org/10.5194/bg-14-1593-2017, 2017
Short summary
Short summary
Lichens and bryophytes have been shown to release nitrous oxide, which is a strong greenhouse gas and atmospheric ozone-depleting agent. Here we apply a process-based computer model of lichens and bryophytes at the global scale, to estimate growth and respiration of the organisms. By relating respiration to nitrous oxide release, we simulate global nitrous oxide emissions of 0.27 (0.19–0.35) Tg yr−1. Moreover, we quantify different sources of uncertainty in nitrous oxide emission rates.
Philipp Porada, Altug Ekici, and Christian Beer
The Cryosphere, 10, 2291–2315, https://doi.org/10.5194/tc-10-2291-2016, https://doi.org/10.5194/tc-10-2291-2016, 2016
Short summary
Short summary
Bryophyte and lichen cover on the forest floor at high latitudes insulates the ground and thus decreases soil temperature. This can protect permafrost soil, stabilising it against global warming. To quantify the insulating effect, we integrate a novel, process-based model of bryophyte and lichen growth into the global land surface model JSBACH. We find an average cooling effect of the bryophyte and lichen cover of 2.7 K, which implies a significant impact on soil temperature at high latitudes.
Christian Beer, Philipp Porada, Altug Ekici, and Matthias Brakebusch
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-210, https://doi.org/10.5194/tc-2016-210, 2016
Preprint withdrawn
Short summary
Short summary
Models suggest thawing permafrost in future due to climate change. In addition to warming, day-to-day variability of air temperature and precipitation is projected to increase. In an idealized theoretical model experiment we show that such changing short-term variability will reduce soil warming as a consequence of air warming by up to 1 K due to effects on snow and moss insulating layers. This shows the need of a mechanistic representation of such layers in Earth system models.
C. Buendía, S. Arens, T. Hickler, S. I. Higgins, P. Porada, and A. Kleidon
Biogeosciences, 11, 3661–3683, https://doi.org/10.5194/bg-11-3661-2014, https://doi.org/10.5194/bg-11-3661-2014, 2014
A. Kleidon, M. Renner, and P. Porada
Hydrol. Earth Syst. Sci., 18, 2201–2218, https://doi.org/10.5194/hess-18-2201-2014, https://doi.org/10.5194/hess-18-2201-2014, 2014
P. Porada, B. Weber, W. Elbert, U. Pöschl, and A. Kleidon
Biogeosciences, 10, 6989–7033, https://doi.org/10.5194/bg-10-6989-2013, https://doi.org/10.5194/bg-10-6989-2013, 2013
Related subject area
Biogeosciences
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
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
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)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
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
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
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
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
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
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
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)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
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
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
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.
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.
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.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
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.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 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.
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.
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.
Benjamin Franklin Meyer, João Paulo 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening 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 it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
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.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
Short summary
Short summary
Physical–biogeochemical ocean global models is difficult to analyze 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 model is an important tool for studying physical–biogeochemical processes.
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.
Elchin E. Jafarov, Helene 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. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
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.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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.
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.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Cited articles
Aghamiri, R. R. and Schwartzman, D. W.: Weathering rates of bedrock by lichens:
a mini watershed study, Chem. Geol., 188, 249–259, 2002. a
Algeo, T. J. and Scheckler, S. E.: Land plant evolution and weathering rate
changes in the Devonian, J. Earth Sci., 21, 75–78, 2010. a
Andrews, J. A. and Schlesinger, W. H.: Soil CO2 dynamics, acidification, and
chemical weathering in a temperate forest with experimental CO2 enrichment,
Global Biogeochem. Cy., 15, 149–162, 2001. a
Atkin, O. K., Bruhn, D., Hurry, V. M., and Tjoelker, M. G.: Evans Review No. 2:
The hot and the cold: unravelling the variable response of plant respiration
to temperature, Funct. Plant Biol., 32, 87–105, 2005. a
Bateman, R. M., Crane, P. R., DiMichele, W. A., Kenrick, P. R., Rowe, N. P.,
Speck, T., and Stein, W. E.: Early evolution of land plants: phylogeny,
physiology, and ecology of the primary terrestrial radiation, Annu. Rev.
Ecol. Syst., 29, 263–292, 1998. a
Bergman, N. M., Lenton, T. M., and Watson, A. J.: COPSE: a new model of
biogeochemical cycling over Phanerozoic time, Am. J. Sci.,
304, 397–437, 2004. a
Berner, E., Berner, R., and Moulton, K.: Plants and mineral weathering: present and past, Treatise on Geochemistry, 5, 169–188, https://doi.org/10.1016/B0-08-043751-6/05175-6, 2003. a
Berner, R. A.: Atmospheric carbon dioxide levels over Phanerozoic time,
Science, 249, 1382–1386, 1990. a
Berner, R. A.: Weathering, plants, and the long-term carbon cycle, Geochim.
Cosmochim. Ac., 56, 3225–3231, 1992. a
Bluth, G. J. and Kump, L. R.: Lithologic and climatologic controls of river
chemistry, Geochim. Cosmochim. Ac., 58, 2341–2359, 1994. a
Boyce, C. K. and Lee, J.-E.: Plant evolution and climate over geological
timescales, Annu. Rev. Earth Pl. Sc., 45, 61–87, 2017. a
Carriquí, M., Roig-Oliver, M., Brodribb, T. J., Coopman, R., Gill, W.,
Mark, K., Niinemets, Ü., Perera-Castro, A. V., Ribas-Carbó, M., Sack,
L., and Tosens, T.: Anatomical constraints to nonstomatal diffusion conductance and
photosynthesis in lycophytes and bryophytes, New Phytol., 222,
1256–1270, 2019. a
Chang, Y. and Graham, S. W.: Inferring the higher-order phylogeny of mosses
(Bryophyta) and relatives using a large, multigene plastid data set, Am.
J. Bot., 98, 839–849, 2011. a
Chater, C., Kamisugi, Y., Movahedi, M., Fleming, A., Cuming, A. C., Gray,
J. E., and Beerling, D. J.: Regulatory mechanism controlling stomatal
behavior conserved across 400 million years of land plant evolution, Curr.
Biol., 21, 1025–1029, 2011. a
Cochran, M. F. and Berner, R. A.: Enhancement of silicate weathering rates by
vascular land plants: quantifying the effect, Chem. Geol., 107,
213–215, 1993. a
Colbourn, G., Ridgwell, A., and Lenton, T.: The time scale of the silicate
weathering negative feedback on atmospheric CO2, Global Biogeochem.
Cy., 29, 583–596, 2015. a
Dahl, T. W. and Arens, S. K.: The impacts of land plant evolution on Earth's
climate and oxygenation state – An interdisciplinary review, Chem. Geol.,
547, 119665, https://doi.org/10.1016/j.chemgeo.2020.119665, 2020. a
Drever, J. I.: The effect of land plants on weathering rates of silicate
minerals, Geochim. Cosmochim. Ac., 58, 2325–2332, 1994. a
Elick, J. M., Driese, S. G., and Mora, C. I.: Very large plant and root traces from the Early to Middle Devonian: Implications for early
terrestrial ecosystems and atmospheric p(CO2), Geology, 26, 143–146, 1998. a
Fan, J., McConkey, B., Wang, H., and Janzen, H.: Root distribution by depth for
temperate agricultural crops, Field Crop. Res., 189, 68–74, 2016. a
Farquhar, G. D. and Von Caemmerer, S.: Modelling of photosynthetic response to environmental
conditions, in: Physiological plant ecology, Vol. 12 B: Water relations and photosynthetic
productivity, edited by: Lange, O. L., Nobel, P. S., Osmond, C. B., Ziegler, H., Springer-Verlag, Berlin
Heidelberg New York, 549–587, https://doi.org/10.1007/978-3-642-68150-9_17, 1982. a, b
Foster, G. L., Royer, D. L., and Lunt, D. J.: Future climate forcing
potentially without precedent in the last 420 million years, Nat.
Commun., 8, 14845, https://doi.org/10.1038/ncomms14845, 2017. a
Gensel, P. G., Kotyk, M. E., and Basinger, J. F.: Morphology of Above-and Below-Ground
Structures in Early Devonian (Pragian–Emsian) Plants, Plants invade the land: evolutionary
and environmental perspectives, edited by: Gensel, P. G. and Edwards, D., Columbia University Press, 83–102, https://doi.org/10.7312/gens11160-006, 2001. a
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019. a, b, c, d
Gibling, M., Davies, N., Falcon-Lang, H., Bashforth, A. R., DiMichele, W. A.,
Rygel, M., and Ielpi, A.: Palaeozoic co-evolution of rivers and vegetation: a
synthesis of current knowledge, P. Geologist. Assoc.,
125, 524–533, 2014. a
Green, J. K., Konings, A. G., Alemohammad, S. H., Berry, J., Entekhabi, D.,
Kolassa, J., Lee, J.-E., and Gentine, P.: Regionally strong feedbacks between
the atmosphere and terrestrial biosphere, Nat. Geosci., 10, 410–414,
2017. a
Halder, S. and Porada, P.: A dynamic local scale vegetation model for lycopsids (LYCOm), Zenodo [code], https://doi.org/10.5281/zenodo.4960947, 2021. a
Hetherington, A. J. and Dolan, L.: The evolution of lycopsid rooting
structures: conservatism and disparity, New Phytol., 215, 538–544, 2017. a
Hetherington, A. J., Bridson, S. L., Jones, A. L., Hass, H., Kerp, H.,
and Dolan, L.: An evidence-based 3D reconstruction of Asteroxylon
mackiei the most complex plant preserved from the Rhynie
chert, eLife, 10, e69447, https://doi.org/10.7554/eLife.69447, 2021. a
Hikosaka, K. and Hirose, T.: Leaf angle as a strategy for light competition:
optimal and evolutionarily stable light-extinction coefficient within a leaf
canopy, Ecoscience, 4, 501–507, 1997. a
Hilley, G. E. and Porder, S.: A framework for predicting global silicate
weathering and CO2 drawdown rates over geologic time-scales, P. Natl. Acad. Sci. USA, 105, 16855–16859, 2008. a
Ibarra, D. E., Rugenstein, J. K. C., Bachan, A., Baresch, A., Lau, K. V.,
Thomas, D. L., Lee, J.-E., Boyce, C. K., and Chamberlain, C. P.: Modeling the
consequences of land plant evolution on silicate weathering, Am. J. Sci., 319, 1–43, https://doi.org/10.2475/01.2019.01, 2019. a, b, c
Jabro, J. D., Sainju, U. M., Stevens, W. B., and Evans, R. G.: Estimation of
CO2 diffusion coefficient at 0–10 cm depth in undisturbed and tilled soils,
Arch. Agron. Soil Sci., 58, 1–9, https://doi.org/10.1080/03650340.2010.506482, 2012. a
June, T., Evans, J. R., and Farquhar, G. D.: A simple new equation for the
reversible temperature dependence of photosynthetic electron transport: a
study on soybean leaf, Funct. Plant Biol., 31, 275–283, 2004. a
Kelly, E. F., Chadwick, O. A., and Hilinski, T. E.: The effect of plants on
mineral weathering, Biogeochemistry, 42, 21–53, 1998. a
Kenrick, P. and Crane, P. R.: The origin and early evolution of plants on land,
Nature, 389, 33–39, 1997. a
Kenrick, P. and Strullu-Derrien, C.: The origin and early evolution of roots,
Plant Physiol., 166, 570–580, 2014. a
Kenrick, P., Wellman, C. H., Schneider, H., and Edgecombe, G. D.: A timeline for terrestrialization:
consequences for the carbon cycle in the Palaeozoic, Philos. T. R.
Soc. B., 367, 519–536, https://doi.org/10.1098/rstb.2011.0271, 2012. a
Kotyk, M. E., Basinger, J. F., Gensel, P. G., and de Freitas, T. A.:
Morphologically complex plant macrofossils from the Late Silurian of Arctic
Canada, Am. J. Bot., 89, 1004–1013, 2002. a
Lawson, T., von Caemmerer, S., and Baroli, I.: Photosynthesis and
stomatal behaviour, in: Progress in botany, edited by: Lüttge, U. E., Beyschlag, W., Büdel, B., Francis, D.,
Springer, Berlin, 265–304, https://doi.org/10.1007/978-3-642-13145-5_11, 2010. a
Lee, J.-E. and Boyce, K.: Impact of the hydraulic capacity of plants on water
and carbon fluxes in tropical South America, J. Geophys. Res.-Atmos., 115, D23123, https://doi.org/10.1029/2010JD014568, 2010. a
Le Hir, G., Donnadieu, Y., Goddéris, Y., Meyer-Berthaud, B., Ramstein, G.,
and Blakey, R. C.: The climate change caused by the land plant invasion in
the Devonian, Earth Planet. Sc. Lett., 310, 203–212, 2011. a
Maher, K.: The role of fluid residence time and topographic scales in
determining chemical fluxes from landscapes, Earth Planet. Sc.
Lett., 312, 48–58, 2011. a
Maher, K. and Chamberlain, C.: Hydrologic regulation of chemical weathering and
the geologic carbon cycle, Science, 343, 1502–1504, 2014. a
Matsunaga, K. K. and Tomescu, A. M.: Root evolution at the base of the
lycophyte clade: insights from an Early Devonian lycophyte, Ann. Bot.-London,
117, 585–598, https://doi.org/10.1093/aob/mcw006, 2016. a, b, c, d
McAdam, S. A. and Brodribb, T. J.: Ancestral stomatal control results in a
canalization of fern and lycophyte adaptation to drought, New Phytol.,
198, 429–441, 2013. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A comparison of three methods
for selecting values of input variables in the analysis of output from a
computer code, Technometrics, 42, 55–61, 2000. a
Medlyn, B., Loustau, D., and Delzon, S.: Temperature response of parameters of
a biochemically based model of photosynthesis. I. Seasonal changes in mature
maritime pine (Pinus pinaster Ait.), Plant Cell Environ., 25,
1155–1165, https://doi.org/10.1046/j.1365-3040.2002.00890.x, 2002. a, b
Montanez, I. P.: Modern soil system constraints on reconstructing deep-time
atmospheric CO2, Geochim. Cosmochim. Ac., 101, 57–75, 2013. a
Monteith, J.: Evaporation and surface temperature, Q. J.
Roy. Meteor. Soc., 107, 1–27, https://doi.org/10.1002/qj.49710745102, 1981. a
Morris, J. L., Puttick, M. N., Clark, J. W., Edwards, D., Kenrick, P., Pressel, S., Wellman, C. H., Yang, Z., Schneider, H., and Donoghue, P. C. J.: The timescale of
early land plant evolution, P. Natl. Acad. Sci. USA, 115, E2274–E2283, https://doi.org/10.1073/pnas.1719588115, 2018. a
Moulton, K. L., West, J., and Berner, R. A.: Solute flux and mineral mass
balance approaches to the quantification of plant effects on silicate
weathering, Am. J. Sci., 300, 539–570, 2000. a
Otto-Bliesner, B. L., Brady, E. C., Zhao, A., Brierley, C. M., Axford, Y., Capron, E., Govin, A., Hoffman, J. S., Isaacs, E., Kageyama, M., Scussolini, P., Tzedakis, P. C., Williams, C. J. R., Wolff, E., Abe-Ouchi, A., Braconnot, P., Ramos Buarque, S., Cao, J., de Vernal, A., Guarino, M. V., Guo, C., LeGrande, A. N., Lohmann, G., Meissner, K. J., Menviel, L., Morozova, P. A., Nisancioglu, K. H., O'ishi, R., Salas y Mélia, D., Shi, X., Sicard, M., Sime, L., Stepanek, C., Tomas, R., Volodin, E., Yeung, N. K. H., Zhang, Q., Zhang, Z., and Zheng, W.: Large-scale features of Last Interglacial climate: results from evaluating the lig127k simulations for the Coupled Model Intercomparison Project (CMIP6)–Paleoclimate Modeling Intercomparison Project (PMIP4), Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, 2021. a
Pavlick, R., Drewry, D. T., Bohn, K., Reu, B., and Kleidon, A.: The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs, Biogeosciences, 10, 4137–4177, https://doi.org/10.5194/bg-10-4137-2013, 2013. a
Petersen, K. B. and Burd, M.: The adaptive value of heterospory: Evidence from
Selaginella, Evolution, 72, 1080–1091, 2018. a
Porada, P., Weber, B., Elbert, W., Pöschl, U., and Kleidon, A.: Estimating global carbon uptake by lichens and bryophytes with a process-based model, Biogeosciences, 10, 6989–7033, https://doi.org/10.5194/bg-10-6989-2013, 2013. a, b, c, d
Porada, P., Lenton, T., Pohl, A., Weber, B., Mander, L., Donnadieu, Y., Beer,
C., Pöschl, U., and Kleidon, A.: High potential for weathering and
climate effects of non-vascular vegetation in the Late Ordovician, Nat.
Commun., 7, 1–13, https://doi.org/10.1038/ncomms12113, 2016. a, b
Proctor M. C.: The diversification of bryophytes and vascular plants in evolving terrestrial
environments, in: Photosynthesis in bryophytes and early land plants, edited by: Hanson, D. T. and Rice, S. K.,
Springer, Dordrecht, 59–77, 2014. a
Qiu, Y.-L., Li, L., Wang, B., Chen, Z., Knoop, V., Groth-Malonek, M.,
Dombrovska, O., Lee, J., Kent, L., Rest, J., Estabrook, G. F., Hendry, T. A., Taylor, D. W., Testa,
C. M., Ambros, M., Crandall-Stotler, B., Duff, R. J., Stech, M., Frey, W., Quandt, D., and Davis C. C.: The deepest divergences
in land plants inferred from phylogenomic evidence, P.
Natl. Acad. Sci. USA, 103, 15511–15516, https://doi.org/10.1073/pnas.0603335103, 2006. a
Quirk, J., Leake, J. R., Johnson, D. A., Taylor, L. L., Saccone, L., and
Beerling, D. J.: Constraining the role of early land plants in Palaeozoic
weathering and global cooling, P. Roy. Soc. B-Biol.
Sci., 282, 20151115, https://doi.org/10.1098/rspb.2015.1115, 2015. a
Randerson, J. T., Hoffman, F. M., Thornton, P. E., Mahowald, N. M., Lindsay,
K., Lee, Y. H., Nevison, C. D., Doney, S. C., Bonan, G., Stöckli, R.,
Covey, C., Running, S. W., and Fung, I. Y.: Systematic assessment of terrestrial biogeochemistry in coupled
climate–carbon models, Global Change Biol., 15, 2462–2484, https://doi.org/10.1111/j.1365-2486.2009.01912.x, 2009. a
Raven, J. A.: Selection pressures on stomatal evolution, New Phytol., 153,
371–386, 2002. a
Ruszala, E. M., Beerling, D. J., Franks, P. J., Chater, C., Casson, S. A.,
Gray, J. E., and Hetherington, A. M.: Land plants acquired active stomatal
control early in their evolutionary history, Curr. Biol., 21, 1030–1035,
2011. a
Savir, Y., Noor, E., Milo, R., and Tlusty, T.: Cross-species analysis traces
adaptation of Rubisco toward optimality in a low-dimensional landscape,
P. Natl. Acad. Sci. USA, 107, 3475–3480, https://doi.org/10.1073/pnas.0911663107, 2010. a, b
Sellers, P., Randall, D., Collatz, G., Berry, J., Field, C., Dazlich, D.,
Zhang, C., Collelo, G., and Bounoua, L.: A revised land surface
parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation,
J. Climate, 9, 676–705, 1996. a
Shukla, J. and Mintz, Y.: Influence of land-surface evapotranspiration on the
earth's climate, Science, 215, 1498–1501, 1982. a
Song, H., Wignall, P. B., Song, H., Dai, X., and Chu, D.: Seawater temperature
and dissolved oxygen over the past 500 million years, J. Earth
Sci., 30, 236–243, 2019. a
Spencer, V., Nemec Venza, Z., and Harrison, C. J.: What can lycophytes teach us
about plant evolution and development? Modern perspectives on an ancient
lineage, Evol. Dev., 23, 174–196, 2021. a
Stein, W. E., Berry, C. M., Hernick, L. V., and Mannolini, F.: Surprisingly
complex community discovered in the mid-Devonian fossil forest at Gilboa,
Nature, 483, 78–81, 2012. a
Taylor, E. L., Taylor, T. N., and Krings, M.: Paleobotany: the biology
and evolution of fossil plants, Academic Press, Burlington, NY, USA, MA, edn. 2, ISBN 978-0-12-373972-8, 2009. a
Thomas, B. A. and Watson, J.: A rediscovered 114-foot Lepidodendron from
Bolton, Lancashire, Geol. J., 11, 15–20, 1976. a
Tosens, T., Nishida, K., Gago, J., Coopman, R. E., Cabrera, H. M.,
Carriquí, M., Laanisto, L., Morales, L., Nadal, M., Rojas, R., Talts, E., Tomas, M., Hanba, Y.,
Niinemets, U., and Flexas, J.:
The photosynthetic capacity in 35 ferns and fern allies: mesophyll CO2
diffusion as a key trait, New Phytol., 209, 1576–1590, 2016. a, b, c, d, e
Valdespino, I. A.: Two New Species and a New Record of Selaginella
(Selaginellaceae) from Bolivia, Novon, 24, 96–105, https://doi.org/10.3417/2011022,
2015a. a
Valdespino, I. A.: Two new species and a new record of Selaginella
(Selaginellaceae) from Bolivia, Novon,
24, 96–105, 2015b. a
Vanderwel, M. C., Slot, M., Lichstein, J. W., Reich, P. B., Kattge, J., Atkin,
O. K., Bloomfield, K. J., Tjoelker, M. G., and Kitajima, K.: Global
convergence in leaf respiration from estimates of thermal acclimation across
time and space, New Phytol., 207, 1026–1037, 2015. a
Vannoppen, W., De Baets, S., Keeble, J., Dong, Y., and Poesen, J.: How do root
and soil characteristics affect the erosion-reducing potential of plant
species?, Ecol. Eng., 109, 186–195, 2017. a
Weedon, G., Gomes, S., Viterbo, P., Shuttleworth, W., Blyth, E.,
Österle, H., Adam, J., Bellouin, N., Boucher, O., and Best, M.:
Creation of the WATCH Forcing Data and Its Use to Assess
Global and Regional Reference Crop Evaporation over Land
during the Twentieth Century, J. Hydrometeorol., 12, 823–848, https://doi.org/10.1175/2011JHM1369.1, 2011. a
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI Meteorological Forcing Data, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/486N-8109, 2018. a
Wellman, C. H. and Gray, J.: The microfossil record of early land plants,
Philos. T. Roy. Soc. B, 355, 717–732, 2000. a
Wellman, C. H., Osterloff, P. L., and Mohiuddin, U.: Fragments of the earliest
land plants, Nature, 425, 282–285, 2003. a
Wellman, C. H., Steemans, P., and Vecoli, M.: Palaeophytogeography of
Ordovician–Silurian land plants, Geological Society, London, Memoirs, 38,
461–476, 2013. a
Wickett, N. J., Mirarab, S., Nguyen, N., Warnow, T., Carpenter, E., Matasci,
N., Ayyampalayam, S., Barker, M. S., Burleigh, J. G., Gitzendanner, M. A.,
Ruhfel, B. R., Wafula, E., Der, J. P., Graham, S. W., Mathews, S., Melkonian,
M., Soltis, D. E., Soltis, P. S., Miles, N. W., Rothfels, C. J., Pokorny L., Shaw, A. J., DeGironimo, L.,
Stevenson, D. W., Surek, B., Villarreal, J. C., Roure, B., Philippe, H., dePamphilis, C. W., Chen, T.,
Deyholos, M. K., Baucom, R. S., Kutchan, T. M., Augustin, M. M., Wang, J., Zhang, Y., Tian, Z., Yan,
Z., Wu, X., Sun, X., Wong, G. K., and Leebens-Mack, J.: Phylotranscriptomic analysis of the origin and early diversification
of land plants, P. Natl. Acad. Sci. USA, 111,
E4859–E4868, https://doi.org/10.1073/pnas.1323926111, 2014. a
Yin-Long, Q.: Phylogeny and evolution of charophytic algae and land plants,
J. Syst. Evol., 46, 287–306, 2008. a
Zhang, L., Hu, Z., Fan, J., Zhou, D., and Tang, F.: A meta-analysis of the
canopy light extinction coefficient in terrestrial ecosystems, Front.
Earth Sci.-PRC, 8, 599–609, https://doi.org/10.1007/s11707-014-0446-7, 2014. a
Zier, J., Belanger, B., Trahan, G., and Watkins, J. E.: Ecophysiology of four
co-occurring lycophyte species: an investigation of functional convergence,
AoB Plants, 7, plv137, https://doi.org/10.1093/aobpla/plv137, 2015. a
Short summary
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.
A dynamic vegetation model, designed to estimate potential impacts of early vascular vegetation,...