Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-893-2019
© Author(s) 2019. 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-12-893-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Mar 2019
Model description paper |
| 08 Mar 2019
| 08 Mar 2019
LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model
Dynamic Macroecology/Land Change Science, Swiss Federal Institute for
Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Michael Mischurow
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Erik Lindström
Centre for Mathematical Sciences, Department for Mathematics Lund
University, Lund, Sweden
Dörte Lehsten
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Heike Lischke
Dynamic Macroecology/Land Change Science, Swiss Federal Institute for
Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Related authors
Karolina Janecka, Kerstin Treydte, Silvia Piccinelli, Loïc Francon, Marçal Argelich Ninot, Johannes Edvardsson, Christophe Corona, Veiko Lehsten, and Markus Stoffel
EGUsphere, https://doi.org/10.5194/egusphere-2025-79, https://doi.org/10.5194/egusphere-2025-79, 2025
Short summary
Short summary
Peatlands hold valuable insights about past climate, but the link between tree growth and water conditions remains unclear. We analyzed tree-ring stable isotopes from Scots pines in Swedish peatlands to study their response to water levels and climate. Unlike tree-ring widths, stable isotopes showed strong, consistent signals of water table levels and summer climate. This improves our ability to reconstruct past climate changes from peatland trees.
Deborah Zani, Veiko Lehsten, and Heike Lischke
Geosci. Model Dev., 15, 4913–4940, https://doi.org/10.5194/gmd-15-4913-2022, https://doi.org/10.5194/gmd-15-4913-2022, 2022
Short summary
Short summary
The prediction of species migration under rapid climate change remains uncertain. In this paper, we evaluate the importance of the mechanisms underlying plant migration and increase the performance in the dynamic global vegetation model LPJ-GM 1.0. The improved model will allow us to understand past vegetation dynamics and predict the future redistribution of species in a context of global change.
Joel Dawson White, Lena Ström, Veiko Lehsten, Janne Rinne, and Dag Ahrén
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-353, https://doi.org/10.5194/bg-2021-353, 2022
Revised manuscript not accepted
Short summary
Short summary
Microbes that produce CH4 play an important role to climate. Microbes which emit CH4 from wetlands is poorly understood. We observed that microbial community was of importance in explaining CH4 emission. We found, that microbes that produce CH4 hold the ability to produce and consume CH4 in multiple ways. This is important in terms of future climate scenarios, where wetlands are expected to shift. Therefore, we expect the community to be highly adaptive to future climate scenarios.
Florian Sallaba, Stefan Olin, Kerstin Engström, Abdulhakim M. Abdi, Niklas Boke-Olén, Veiko Lehsten, Jonas Ardö, and Jonathan W. Seaquist
Earth Syst. Dynam., 8, 1191–1221, https://doi.org/10.5194/esd-8-1191-2017, https://doi.org/10.5194/esd-8-1191-2017, 2017
Short summary
Short summary
The UN sustainable development goals for eradicating hunger are at high risk for failure in the Sahel. We show that the demand for food and feed biomass will begin to outstrip its supply in the 2040s if current trends continue. Though supply continues to increase it is outpaced by a greater increase in demand due to a combination of population growth and a shift to diets rich in animal proteins. This underscores the importance of policy interventions that would act to mitigate such developments.
M. Baudena, S. C. Dekker, P. M. van Bodegom, B. Cuesta, S. I. Higgins, V. Lehsten, C. H. Reick, M. Rietkerk, S. Scheiter, Z. Yin, M. A. Zavala, and V. Brovkin
Biogeosciences, 12, 1833–1848, https://doi.org/10.5194/bg-12-1833-2015, https://doi.org/10.5194/bg-12-1833-2015, 2015
Karolina Janecka, Kerstin Treydte, Silvia Piccinelli, Loïc Francon, Marçal Argelich Ninot, Johannes Edvardsson, Christophe Corona, Veiko Lehsten, and Markus Stoffel
EGUsphere, https://doi.org/10.5194/egusphere-2025-79, https://doi.org/10.5194/egusphere-2025-79, 2025
Short summary
Short summary
Peatlands hold valuable insights about past climate, but the link between tree growth and water conditions remains unclear. We analyzed tree-ring stable isotopes from Scots pines in Swedish peatlands to study their response to water levels and climate. Unlike tree-ring widths, stable isotopes showed strong, consistent signals of water table levels and summer climate. This improves our ability to reconstruct past climate changes from peatland trees.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
Short summary
Short summary
Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Deborah Zani, Veiko Lehsten, and Heike Lischke
Geosci. Model Dev., 15, 4913–4940, https://doi.org/10.5194/gmd-15-4913-2022, https://doi.org/10.5194/gmd-15-4913-2022, 2022
Short summary
Short summary
The prediction of species migration under rapid climate change remains uncertain. In this paper, we evaluate the importance of the mechanisms underlying plant migration and increase the performance in the dynamic global vegetation model LPJ-GM 1.0. The improved model will allow us to understand past vegetation dynamics and predict the future redistribution of species in a context of global change.
Joel Dawson White, Lena Ström, Veiko Lehsten, Janne Rinne, and Dag Ahrén
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-353, https://doi.org/10.5194/bg-2021-353, 2022
Revised manuscript not accepted
Short summary
Short summary
Microbes that produce CH4 play an important role to climate. Microbes which emit CH4 from wetlands is poorly understood. We observed that microbial community was of importance in explaining CH4 emission. We found, that microbes that produce CH4 hold the ability to produce and consume CH4 in multiple ways. This is important in terms of future climate scenarios, where wetlands are expected to shift. Therefore, we expect the community to be highly adaptive to future climate scenarios.
Guillaume Monteil, Grégoire Broquet, Marko Scholze, Matthew Lang, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Naomi E. Smith, Rona L. Thompson, Ingrid T. Luijkx, Emily White, Antoon Meesters, Philippe Ciais, Anita L. Ganesan, Alistair Manning, Michael Mischurow, Wouter Peters, Philippe Peylin, Jerôme Tarniewicz, Matt Rigby, Christian Rödenbeck, Alex Vermeulen, and Evie M. Walton
Atmos. Chem. Phys., 20, 12063–12091, https://doi.org/10.5194/acp-20-12063-2020, https://doi.org/10.5194/acp-20-12063-2020, 2020
Short summary
Short summary
The paper presents the first results from the EUROCOM project, a regional atmospheric inversion intercomparison exercise involving six European research groups. It aims to produce an estimate of the net carbon flux between the European terrestrial ecosystems and the atmosphere for the period 2006–2015, based on constraints provided by observed CO2 concentrations and using inverse modelling techniques. The use of six different models enables us to investigate the robustness of the results.
Matthias J. R. Speich, Massimiliano Zappa, Marc Scherstjanoi, and Heike Lischke
Geosci. Model Dev., 13, 537–564, https://doi.org/10.5194/gmd-13-537-2020, https://doi.org/10.5194/gmd-13-537-2020, 2020
Short summary
Short summary
Climate change is expected to substantially affect natural processes, and simulation models are a valuable tool to anticipate these changes. In this study, we combine two existing models that each describe one aspect of the environment: forest dynamics and the terrestrial water cycle. The coupled model better described observed patterns in vegetation structure. We also found that including the effect of water availability on tree height and rooting depth improved the model.
Matthias J. R. Speich, Heike Lischke, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 22, 4097–4124, https://doi.org/10.5194/hess-22-4097-2018, https://doi.org/10.5194/hess-22-4097-2018, 2018
Short summary
Short summary
To simulate the water balance of, e.g., a forest plot, it is important to estimate the maximum volume of water available to plants. This depends on soil properties and the average depth of roots. Rooting depth has proven challenging to estimate. Here, we applied a model assuming that plants dimension their roots to optimize their carbon budget. We compared its results with values obtained by calibrating a dynamic water balance model. In most cases, there is good agreement between both methods.
Florian Sallaba, Stefan Olin, Kerstin Engström, Abdulhakim M. Abdi, Niklas Boke-Olén, Veiko Lehsten, Jonas Ardö, and Jonathan W. Seaquist
Earth Syst. Dynam., 8, 1191–1221, https://doi.org/10.5194/esd-8-1191-2017, https://doi.org/10.5194/esd-8-1191-2017, 2017
Short summary
Short summary
The UN sustainable development goals for eradicating hunger are at high risk for failure in the Sahel. We show that the demand for food and feed biomass will begin to outstrip its supply in the 2040s if current trends continue. Though supply continues to increase it is outpaced by a greater increase in demand due to a combination of population growth and a shift to diets rich in animal proteins. This underscores the importance of policy interventions that would act to mitigate such developments.
M. Baudena, S. C. Dekker, P. M. van Bodegom, B. Cuesta, S. I. Higgins, V. Lehsten, C. H. Reick, M. Rietkerk, S. Scheiter, Z. Yin, M. A. Zavala, and V. Brovkin
Biogeosciences, 12, 1833–1848, https://doi.org/10.5194/bg-12-1833-2015, https://doi.org/10.5194/bg-12-1833-2015, 2015
Related subject area
Biogeosciences
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
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
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
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 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
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)
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
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
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
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
H2MV (v1.0): Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation
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
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
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
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
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)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
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.
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.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
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 have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
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.
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.
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.
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.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
Short summary
Short summary
Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
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.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
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.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
Short summary
Short summary
We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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.
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.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Cited articles
Bradshaw, R. H. W. and Lindbladh, M.: Regional spread and stand-scale
establishment of Fagus sylvatica and Picea abies in Scandinavia, Ecology, 86,
1679–1686, https://doi.org/10.1890/03-0785, 2005.
Bronstein, I. N., Semendjajew, K. A., Musiol, C., and Mühlig, H.: Taschenbuch
der Mathematik, Verlag Harri Deutsch, Frankfurt am Main, 1995.
Bugmann, H. K. M., Brang, P., Elkin, C., Henne, P., Jakoby, O.,
Lévesque, M., Lischke, H., Psomas, A., Rigling, A., Wermelinger, B., and
Zimmermann, N. E.: Climate change impacts on tree species, forest properties,
and ecosystem services, in: CH2014-Impacts (2014): Toward Quantitative
Scenarios of Climate Change Impacts in Switzerland, edited by: Foen, O.,
Meteoswiss, 2014.
Clarke, L., Glendinning, I., and Hempel, R.: The MPI Message Passing
Interface Standard, in: Programming Environments for Massively Parallel
Distributed Systems, edited by: Decker, R. R. and Birkhäuser, K. M.,
Basel, 1994.
Cooley, J. W. and Tukey, J. W.: An Algorithm for the Machine Calculation of
Complex Fourier Series Mathematics of Computation An Algorithm for the
Machine Calculation of Complex Fourier Series, Source, Math. Comput., 19,
297–301, https://doi.org/10.2307/2003354, 1965.
Dullinger, S., Willner, W., Plutzar, C., Englisch, T., Schratt-Ehrendorfer,
L., Moser, D., Ertl, S., Essl, F., and Niklfeld, H.: Post-glacial migration
lag restricts range filling of plants in the European Alps, Global Ecol.
Biogeogr., 21, 829–840, https://doi.org/10.1111/j.1466-8238.2011.00732.x, 2012.
Engler, R. and Guisan, A.: MigClim: Predicting plant distribution and
dispersal in a changing climate, Divers. Distrib., 15, 590–601,
https://doi.org/10.1111/j.1472-4642.2009.00566.x, 2009.
Epstein, H. E., Yu, Q. Y. Q., Kaplan, J. O., and Lischke, H.: Simulating
Future Changes in Arctic and Subarctic Vegetation, Comput. Sci. Eng., 9,
12–23, https://doi.org/10.1109/MCSE.2007.84, 2007.
Feurdean, A., Bhagwat, S. A., Willis, K. J., Birks, H. J. B., Lischke,
H., and Hickler, T.: Tree Migration-Rates: Narrowing the Gap between Inferred
Post-Glacial Rates and Projected Rates, PLoS One, 8, e71797,
https://doi.org/10.1371/journal.pone.0071797, 2013.
Foley, J. A., Levis, S., Prentice, I. C., Pollard, D. and Thompson, S. L.:
Coupling dynamic models of climate and vegetation, Glob. Change Biol., 4,
561–579, https://doi.org/10.1046/j.1365-2486.1998.00168.x, 1998.
Gonzalez, R. C. and Woods, R. E.: Digital Image Processing, 2nd Edn.,
Prentice Hall, 2002.
He, H. S., Gustafson, E. J., and Lischke, H.: Modeling forest landscapes in a
changing climate: theory and application, Landscape Ecol., 32, 1299–1305,
https://doi.org/10.1007/s10980-017-0529-4, 2017.
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L.,
Giesecke, T., Fronzek, S., Carter, T. R., Cramer, W., Kühn, I., and
Sykes, M. T.: Projecting the future distribution of European potential
natural vegetation zones with a generalized, tree species-based dynamic
vegetation model, Global Ecol. Biogeogr., 21, 50–63,
https://doi.org/10.1111/j.1466-8238.2010.00613.x, 2012.
Kruse, S., Gerdes, A., Kath, N. J., and Herzschuh, U.: Implementing spatially
explicit wind-driven seed and pollen dispersal in the individual-based larch
simulation model: LAVESI-WIND 1.0, Geosci. Model Dev., 11, 4451–4467,
https://doi.org/10.5194/gmd-11-4451-2018, 2018.
Lehsten, D., Dullinger, S., Huber, K., Schurgers, G., Cheddadi, R., Laborde,
H., Lehsten, V., Francois, L., Dury, M., and Sykes, M. T.: Modelling the
Holocene migrational dynamics of Fagus sylvatica L. and Picea abies (L.) H.
Karst, Global Ecol. Biogeogr., 23, 658–668, https://doi.org/10.1111/geb.12145, 2014.
Lehsten, V., Sykes, M. T., Scott, A. V., Tzanopoulos, J., Kallimanis, A.,
Mazaris, A., Verburg, P. H., Schulp, C. J. E., Potts, S. G., and Vogiatzakis,
I.: Disentangling the effects of land-use change, climate and CO2 on
projected future European habitat types, Global Ecol. Biogeogr., 24,
653–663, https://doi.org/10.1111/geb.12291, 2015.
Lehsten, V., Arneth, A., Spessa, A., Thonicke, K., and Moustakas, A.: The
effect of fire on tree-grass coexistence in savannas: A simulation study,
Int. J. Wildland Fire, 25, 137–146, https://doi.org/10.1071/WF14205, 2016.
Lehsten, L., Mischurow, M., Lindström, E., Lehsten, D., and Lischke, H.: Tree migration in virtual
landscape, https://doi.org/10.18161/migration_lehsten_2018, 2019a.
Lehsten, L., Mischurow, M., Lindström, E., Lehsten, D., and Lischke, H.: Matlab code for supplementary material
for Lehsten et al. GMD 2019, https://doi.org/10.18161/seed_disp_code_2018, 2019b.
Lindeskog, M., Arneth, A., Bondeau, A., Waha, K., Seaquist, J., Olin, S., and
Smith, B.: Implications of accounting for land use in simulations of
ecosystem carbon cycling in Africa, Earth Syst. Dynam., 4, 385–407,
https://doi.org/10.5194/esd-4-385-2013, 2013.
Lischke, H., Löffler, T. J. and Fischlin, A.: Aggregation of individual
trees and patches in forest succession models: Capturing variability with
height structured, random, spatial distributions, Theor. Popul. Biol., 54,
213–226, https://doi.org/10.1006/tpbi.1998.1378, 1998.
Lischke, H., Zimmermann, N. E., Bolliger, J., Rickebusch, S., and
Löffler, T. J.: TreeMig: A forest-landscape model for simulating
spatio-temporal patterns from stand to landscape scale, Ecol. Model., 199,
409–420, https://doi.org/10.1016/j.ecolmodel.2005.11.046, 2006.
Meier, E. S., Lischke, H., Schmatz, D. R., and Zimmermann, N. E.: Climate,
competition and connectivity affect future migration and ranges of European
trees, Global Ecol. Biogeogr., 21, 164–178,
https://doi.org/10.1111/j.1466-8238.2011.00669.x, 2012.
Mladenoff, D. J.: LANDIS and forest landscape models, Ecol. Model., 180,
7–19, https://doi.org/10.1016/j.ecolmodel.2004.03.016, 2004.
Nabel, J. E. M. S.: Upscaling with the dynamic two-layer classification
concept (D2C): TreeMig-2L, an efficient implementation of the
forest-landscape model TreeMig, Geosci. Model Dev., 8, 3563–3577,
https://doi.org/10.5194/gmd-8-3563-2015, 2015.
Nabel, J. E. M. S. and Lischke, H.: Upscaling of spatially explicit and
linked time- and space-discrete models simulating vegetation dynamics under
climate change, in: 27th International Conference on Environmental
Informatics for Environmental Protection, Sustainable Development and Risk
Management, EnviroInfo 2013, edited by: Page, B., Göbel, F. A. G. J., and
Wohlgemuth, V., Hamburg, 842–850, 2013.
Nathan, R., Horvitz, N., He, Y., Kuparinen, A., Schurr, F. M., and Katul, G.
G.: Spread of North American wind-dispersed trees in future environments,
Ecol. Lett., 14, 211–219, https://doi.org/10.1111/j.1461-0248.2010.01573.x, 2011.
Neilson, R. P., Pitelka, L. F., Solomon, A. M., Nathan, R., Midgley, G. F., Fragoso, J. M. V., Lischke, H., and Thompson, K.: Forecasting regional to global plant migration in response
to climate change, Bioscience, 55, 749–759, 2005.
Nobis, M. P. and Normand, S.: KISSMig – a simple model for R to account for
limited migration in analyses of species distributions, Ecography, 37,
1282–1287, https://doi.org/10.1111/ecog.00930, 2014.
Powell, J.: Spatiotemporal models in ecology; an introduction to integro-
difference equations, Utah State University, available at:
http://www.math.usu.edu/powell/wauclass/labs.pdf (last access: 4 March 2019),
2001.
Pueyo, Y., Kefi, S., Alados, C. L., and Rietkerk, M.: Dispersal strategies
and spatial organization of vegetation in arid ecosystems, Oikos, 117,
1522–1532,
https://doi.org/10.1111/j.0030-1299.2008.16735.x, 2008.
Quillet, A., Peng, C., and Garneau, M.: Toward dynamic global vegetation
models for simulating vegetation–climate interactions and feedbacks: recent
developments, limitations, and future challenges, Environ. Rev., 18,
333–353, https://doi.org/10.1139/A10-016, 2010.
Sato, H. and Ise, T.: Effect of plant dynamic processes on African
vegetation responses to climate change: Analysis using the spatially explicit
individual-based dynamic global vegetation model (SEIB-DGVM), J. Geophys.
Res., 117, G03017, https://doi.org/10.1029/2012JG002056, 2012.
Sato, H., Itoh, A., and Kohyama, T.: SEIB–DGVM: A new Dynamic Global
Vegetation Model using a spatially explicit individual-based approach, Ecol.
Model., 200, 279–307, https://doi.org/10.1016/j.ecolmodel.2006.09.006, 2007.
Scherstjanoi, M., Kaplan, J. O., and Lischke, H.: Application of a
computationally efficient method to approximate gap model results with a
probabilistic approach, Geosci. Model Dev., 7, 1543–1571,
https://doi.org/10.5194/gmd-7-1543-2014, 2014.
Schumacher, S., Bugmann, H., and Mladenoff, D. J.: Improving the formulation
of tree growth and succession in a spatially explicit landscape model, Ecol.
Model., 180, 175–194, https://doi.org/10.1016/j.ecolmodel.2003.12.055, 2004.
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, 2012.
Shaw, M. W., Harwood, T. D., Wilkinson, M. J., and Elliott, L.: Assembling
spatially explicit landscape models of pollen and spore dispersal by wind for
risk assessment, P. Roy. Soc. B Biol. Sci., 273, 1705–1713, https://doi.org/10.1098/rspb.2006.3491,
2006.
Shifley, S. R., He, H. S., Lischke, H., Wang, W. J., Jin, W., Gustafson, E.
J., Thompson, J. R., Thompson, F. R., Dijak, W. D., and Yang, J.: The past
and future of modeling forest dynamics: from growth and yield curves to
forest landscape models, Landscape Ecol., 32, 1307–1325,
https://doi.org/10.1007/s10980-017-0540-9, 2017.
Shiryaev, A. N.: Probability, 3rd Edn., Springer Verlag, New York, 2016.
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, 2003.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol. Biogeogr.,
10, 621–637, 2001.
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg,
J., and Zaehle, S.: Implications of incorporating N cycling and N limitations
on primary production in an individual-based dynamic vegetation model,
Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014,
2014.
Snell, R. S.: Simulating long-distance seed dispersal in a dynamic
vegetation model, Global Ecol. Biogeogr., 23, 89–98, https://doi.org/10.1111/geb.12106,
2014.
Snell, R. S., Huth, A., Nabel, J. E. M. S., Bocedi, G., Travis, J. M. J.,
Gravel, D., Bugmann, H., Gutiérrez, A. G., Hickler, T., Higgins, S. I.,
Reineking, B., Scherstjanoi, M., Zurbriggen, N., and Lischke, H.: Using
dynamic vegetation models to simulate plant range shifts, Ecography,
37, 1184–1197, https://doi.org/10.1111/ecog.00580, 2014.
Strang, G.: Wavelets, Am. Sci., 82, 250–255, 1994.
Yue, C., Ciais, P., Luyssaert, S., Li, W., McGrath, M. J., Chang, J., and Peng, S.: Representing anthropogenic gross
land use change, wood harvest, and forest age dynamics in a
global vegetation model ORCHIDEE-MICT v8.4.2, Geosci. Model Dev., 11, 409–428,
https://doi.org/10.5194/gmd-11-409-2018, 2018.
Short summary
To assess the effect of climate on vegetation, dynamic vegetation models simulate their response e.g. to climate change. Most currently used dynamic vegetation models ignore the fact that for colonization of a new area not only do the climatic conditions have to be suitable, but seeds also need to arrive at the site to allow the species to migrate there. In this paper we are developing a novel method which allows us to simulate migration within dynamic vegetation models even at large scale.
To assess the effect of climate on vegetation, dynamic vegetation models simulate their response...
