Articles | Volume 17, issue 15
https://doi.org/10.5194/gmd-17-5961-2024
© Author(s) 2024. 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-17-5961-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
09 Aug 2024
Model evaluation paper |
| 09 Aug 2024
| 09 Aug 2024
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Imperial College London, Department of Physics, South Kensington Campus, London SW7 2AZ, United Kingdom
Frank Hagedorn
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Margaux Moreno Duborgel
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
ETH Zurich, Department of Earth Sciences, Sonneggstrasse 5, 8092 Zurich, Switzerland
Luisa I. Minich
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
ETH Zurich, Department of Earth Sciences, Sonneggstrasse 5, 8092 Zurich, Switzerland
Heather D. Graven
Imperial College London, Department of Physics, South Kensington Campus, London SW7 2AZ, United Kingdom
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Claudia Guidi, Sia Gosheva-Oney, Markus Didion, Roman Flury, Lorenz Walthert, Stephan Zimmermann, Brian J. Oney, Pascal A. Niklaus, Esther Thürig, Toni Viskari, Jari Liski, and Frank Hagedorn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3788, https://doi.org/10.5194/egusphere-2024-3788, 2024
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Predicting soil organic carbon (SOC) stocks in forests is crucial for assessing C balance, yet drivers of SOC stocks remain uncertain at large scales. Across a broad environmental gradient in Switzerland, we compared measured SOC stocks with those modelled by Yasso20, commonly used for GHG budgets. Our results show that soil mineral properties and climate are main controls of SOC stocks, indicating that better accounting of these processes will advance accuracy of SOC stock predictions.
Marco M. Lehmann, Josie Geris, Ilja van Meerveld, Daniele Penna, Youri Rothfuss, Matteo Verdone, Pertti Ala-Aho, Matyas Arvai, Alise Babre, Philippe Balandier, Fabian Bernhard, Lukrecija Butorac, Simon Damien Carrière, Natalie C. Ceperley, Zuosinan Chen, Alicia Correa, Haoyu Diao, David Dubbert, Maren Dubbert, Fabio Ercoli, Marius G. Floriancic, Teresa E. Gimeno, Damien Gounelle, Frank Hagedorn, Christophe Hissler, Frédéric Huneau, Alberto Iraheta, Tamara Jakovljević, Nerantzis Kazakis, Zoltan Kern, Karl Knaebel, Johannes Kobler, Jiří Kocum, Charlotte Koeber, Gerbrand Koren, Angelika Kübert, Dawid Kupka, Samuel Le Gall, Aleksi Lehtonen, Thomas Leydier, Philippe Malagoli, Francesca Sofia Manca di Villahermosa, Chiara Marchina, Núria Martínez-Carreras, Nicolas Martin-StPaul, Hannu Marttila, Aline Meyer Oliveira, Gaël Monvoisin, Natalie Orlowski, Kadi Palmik-Das, Aurel Persoiu, Andrei Popa, Egor Prikaziuk, Cécile Quantin, Katja T. Rinne-Garmston, Clara Rohde, Martin Sanda, Matthias Saurer, Daniel Schulz, Michael Paul Stockinger, Christine Stumpp, Jean-Stéphane Venisse, Lukas Vlcek, Stylianos Voudouris, Björn Weeser, Mark E. Wilkinson, Giulia Zuecco, and Katrin Meusburger
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-409, https://doi.org/10.5194/essd-2024-409, 2024
Preprint under review for ESSD
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This study describes a unique large-scale isotope dataset to study water dynamics in European forests. Researchers collected data from 40 beech and spruce forest sites in spring and summer 2023, using a standardized method to ensure consistency. The results show that water sources for trees change between seasons and vary by tree species. This large dataset offers valuable information for understanding plant water use, improving ecohydrological models, and mapping water cycles across Europe.
Frank Hagedorn, Joesphine Imboden, Pavel Moiseev, Decai Gao, Emmanuel Frossard, Daniel Christen, Konstantin Gavazov, and Jasmin Fetzer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2622, https://doi.org/10.5194/egusphere-2024-2622, 2024
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At treeline, plant species change abruptly from low stature plants in tundra to trees in forests. Our study documents that from tundra towards forest, the litter layer gets strongly enriched in nutrients. We show that these litter quality changes alter nutrient processing by soil microbes and increase the nutrient release during decomposition in forest than in tundra. The associated improvement of nutrient availability in the forest potentially stimulates tree growth and treeline shifts.
Giulia Zazzeri, Lukas Wacker, Negar Haghipour, Philip Gautchi, Thomas Laemmel, Sönke Szidat, and Heather Graven
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-123, https://doi.org/10.5194/amt-2024-123, 2024
Revised manuscript accepted for AMT
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Radiocarbon (14C) is an optimal tracer of methane (CH4) emissions, as 14C measurements enable distinguishing fossil from biogenic methane. However, these measurements are particularly challenging, mainly due to technical difficulties in the sampling procedure. With this work we made the sample extraction much simpler and time efficient, providing a new technology that can be used by any research group, with the goal of expanding 14C measurements for an improved understanding of methane sources.
Hannah Chawner, Eric Saboya, Karina E. Adcock, Tim Arnold, Yuri Artioli, Caroline Dylag, Grant L. Forster, Anita Ganesan, Heather Graven, Gennadi Lessin, Peter Levy, Ingrid T. Luijkx, Alistair Manning, Penelope A. Pickers, Chris Rennick, Christian Rödenbeck, and Matthew Rigby
Atmos. Chem. Phys., 24, 4231–4252, https://doi.org/10.5194/acp-24-4231-2024, https://doi.org/10.5194/acp-24-4231-2024, 2024
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The quantity of atmospheric potential oxygen (APO), derived from coincident measurements of carbon dioxide (CO2) and oxygen (O2), has been proposed as a tracer for fossil fuel CO2 emissions. In this model sensitivity study, we examine the use of APO for this purpose in the UK and compare our model to observations. We find that our model simulations are most sensitive to uncertainties relating to ocean fluxes and boundary conditions.
Tatjana C. Speckert, Jeannine Suremann, Konstantin Gavazov, Maria J. Santos, Frank Hagedorn, and Guido L. B. Wiesenberg
SOIL, 9, 609–621, https://doi.org/10.5194/soil-9-609-2023, https://doi.org/10.5194/soil-9-609-2023, 2023
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Soil organic carbon (SOC) is key player in the global carbon cycle. Afforestation on pastures potentially alters organic matter input and SOC sequestration. We investigated the effects of a Picea abies L. afforestation sequence (0 to 130 years) on a former subalpine pasture on SOC stocks and dynamics. We found no difference in the SOC stock after 130 years of afforestation and thus no additional SOC sequestration. SOC composition was altered due to a modified SOC input following afforestation.
Eric Saboya, Giulia Zazzeri, Heather Graven, Alistair J. Manning, and Sylvia Englund Michel
Atmos. Chem. Phys., 22, 3595–3613, https://doi.org/10.5194/acp-22-3595-2022, https://doi.org/10.5194/acp-22-3595-2022, 2022
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Continuous measurements of atmospheric methane concentrations and its carbon-13 isotope have been made in central London since early 2018. These measurements were used to evaluate methane emissions reported in global and UK-specific emission inventories for the London area. Compared to atmospheric methane measurements from March 2018 to October 2020, both inventories are under-reporting natural gas leakage for the London area.
Jasmin Fetzer, Emmanuel Frossard, Klaus Kaiser, and Frank Hagedorn
Biogeosciences, 19, 1527–1546, https://doi.org/10.5194/bg-19-1527-2022, https://doi.org/10.5194/bg-19-1527-2022, 2022
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As leaching is a major pathway of nitrogen and phosphorus loss in forest soils, we investigated several potential drivers in two contrasting beech forests. The composition of leachates, obtained by zero-tension lysimeters, varied by season, and climatic extremes influenced the magnitude of leaching. Effects of nitrogen and phosphorus fertilization varied with soil nutrient status and sorption properties, and leaching from the low-nutrient soil was more sensitive to environmental factors.
Severin-Luca Bellè, Asmeret Asefaw Berhe, Frank Hagedorn, Cristina Santin, Marcus Schiedung, Ilja van Meerveld, and Samuel Abiven
Biogeosciences, 18, 1105–1126, https://doi.org/10.5194/bg-18-1105-2021, https://doi.org/10.5194/bg-18-1105-2021, 2021
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Controls of pyrogenic carbon (PyC) redistribution under rainfall are largely unknown. However, PyC mobility can be substantial after initial rain in post-fire landscapes. We conducted a controlled simulation experiment on plots where PyC was applied on the soil surface. We identified redistribution of PyC by runoff and splash and vertical movement in the soil depending on soil texture and PyC characteristics (material and size). PyC also induced changes in exports of native soil organic carbon.
Hannah Gies, Frank Hagedorn, Maarten Lupker, Daniel Montluçon, Negar Haghipour, Tessa Sophia van der Voort, and Timothy Ian Eglinton
Biogeosciences, 18, 189–205, https://doi.org/10.5194/bg-18-189-2021, https://doi.org/10.5194/bg-18-189-2021, 2021
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Understanding controls on the persistence of organic matter in soils is essential to constrain its role in the carbon cycle. Emerging concepts suggest that the soil carbon pool is predominantly comprised of stabilized microbial residues. To test this hypothesis we isolated microbial membrane lipids from two Swiss soil profiles and measured their radiocarbon age. We find that the ages of these compounds are in the range of millenia and thus provide evidence for stabilized microbial mass in soils.
Tessa Sophia van der Voort, Utsav Mannu, Frank Hagedorn, Cameron McIntyre, Lorenz Walthert, Patrick Schleppi, Negar Haghipour, and Timothy Ian Eglinton
Biogeosciences, 16, 3233–3246, https://doi.org/10.5194/bg-16-3233-2019, https://doi.org/10.5194/bg-16-3233-2019, 2019
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The carbon stored in soils is the largest reservoir of organic carbon on land. In the context of greenhouse gas emissions and a changing climate, it is very important to understand how stable the carbon in the soil is and why. The deeper parts of the soil have often been overlooked even though they store a lot of carbon. In this paper, we discovered that although deep soil carbon is expected to be old and stable, there can be a significant young component that cycles much faster.
Kieran Brophy, Heather Graven, Alistair J. Manning, Emily White, Tim Arnold, Marc L. Fischer, Seongeun Jeong, Xinguang Cui, and Matthew Rigby
Atmos. Chem. Phys., 19, 2991–3006, https://doi.org/10.5194/acp-19-2991-2019, https://doi.org/10.5194/acp-19-2991-2019, 2019
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We investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of California's fossil fuel CO2 emissions. Our results indicate that uncertainties in posterior total state fossil fuel CO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider.
Emily F. Solly, Valentino Weber, Stephan Zimmermann, Lorenz Walthert, Frank Hagedorn, and Michael W. I. Schmidt
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-33, https://doi.org/10.5194/bg-2019-33, 2019
Revised manuscript not accepted
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Soils are the largest reservoir of carbon on land. In the context of global change, it is important to assess which environmental variables are needed to describe changes in the content of soil organic carbon. We assessed how climatic, vegetation and edaphic variables explain the variance of soil organic carbon content in Swiss forests. Our results provide a first indication that considering the effective cation exchange capacity of soils in future biogeochemical simulations could be beneficial.
Jobin Joseph, Christoph Külls, Matthias Arend, Marcus Schaub, Frank Hagedorn, Arthur Gessler, and Markus Weiler
SOIL, 5, 49–62, https://doi.org/10.5194/soil-5-49-2019, https://doi.org/10.5194/soil-5-49-2019, 2019
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By coupling an OA-ICOS with hydrophobic but gas-permeable membranes placed at different depths in acidic and calcareous soils, we investigated the contribution of abiotic and biotic components to total soil CO2 release. In calcareous Gleysol, CO2 originating from carbonate dissolution contributed to total soil CO2 concentration at detectable degrees, probably due to CO2 evasion from groundwater. Inward diffusion of atmospheric CO2 was found to be pronounced in the topsoil layers at both sites.
Heather Graven, Colin E. Allison, David M. Etheridge, Samuel Hammer, Ralph F. Keeling, Ingeborg Levin, Harro A. J. Meijer, Mauro Rubino, Pieter P. Tans, Cathy M. Trudinger, Bruce H. Vaughn, and James W. C. White
Geosci. Model Dev., 10, 4405–4417, https://doi.org/10.5194/gmd-10-4405-2017, https://doi.org/10.5194/gmd-10-4405-2017, 2017
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Modelling of carbon isotopes 13C and 14C in land and ocean components of Earth system models provides opportunities for new insights and improved understanding of global carbon cycling, and for model evaluation. We compiled existing historical datasets to define the annual mean carbon isotopic composition of atmospheric CO2 for 1850–2015 that can be used in CMIP6 and other modelling activities.
James C. Orr, Raymond G. Najjar, Olivier Aumont, Laurent Bopp, John L. Bullister, Gokhan Danabasoglu, Scott C. Doney, John P. Dunne, Jean-Claude Dutay, Heather Graven, Stephen M. Griffies, Jasmin G. John, Fortunat Joos, Ingeborg Levin, Keith Lindsay, Richard J. Matear, Galen A. McKinley, Anne Mouchet, Andreas Oschlies, Anastasia Romanou, Reiner Schlitzer, Alessandro Tagliabue, Toste Tanhua, and Andrew Yool
Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, https://doi.org/10.5194/gmd-10-2169-2017, 2017
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The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
Chris D. Jones, Vivek Arora, Pierre Friedlingstein, Laurent Bopp, Victor Brovkin, John Dunne, Heather Graven, Forrest Hoffman, Tatiana Ilyina, Jasmin G. John, Martin Jung, Michio Kawamiya, Charlie Koven, Julia Pongratz, Thomas Raddatz, James T. Randerson, and Sönke Zaehle
Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, https://doi.org/10.5194/gmd-9-2853-2016, 2016
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How the carbon cycle interacts with climate will affect future climate change and how society plans emissions reductions to achieve climate targets. The Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) is an endorsed activity of CMIP6 and aims to quantify these interactions and feedbacks in state-of-the-art climate models. This paper lays out the experimental protocol for modelling groups to follow to contribute to C4MIP. It is a contribution to the CMIP6 GMD Special Issue.
Tessa Sophia van der Voort, Frank Hagedorn, Cameron McIntyre, Claudia Zell, Lorenz Walthert, Patrick Schleppi, Xiaojuan Feng, and Timothy Ian Eglinton
Biogeosciences, 13, 3427–3439, https://doi.org/10.5194/bg-13-3427-2016, https://doi.org/10.5194/bg-13-3427-2016, 2016
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This study explores heterogeneity in 14C content of soil organic matter (SOM) at different spatial scales and across climatic and geologic gradients, which is essential for a better understanding of SOM stability. Results reveal that despite dissimilar environmental conditions, 14C contents in topsoils is relatively uniform and 14C trends with depth are similar. Plot-scale variability is significant. Statistical analysis found a significant correlation of 14C contents (0–5 cm) and temperature.
D. D. Lucas, C. Yver Kwok, P. Cameron-Smith, H. Graven, D. Bergmann, T. P. Guilderson, R. Weiss, and R. Keeling
Geosci. Instrum. Method. Data Syst., 4, 121–137, https://doi.org/10.5194/gi-4-121-2015, https://doi.org/10.5194/gi-4-121-2015, 2015
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Multiobjective optimization is used to design Pareto optimal greenhouse gas (GHG) observing networks. A prototype GHG network is designed to optimize scientific performance and measurement costs. The Pareto frontier is convex, showing the trade-offs between performance and cost and the diminishing returns in trading one for the other. Other objectives and constraints that are important in the design of practical GHG monitoring networks can be incorporated into our method.
J. Schwaab, M. Bavay, E. Davin, F. Hagedorn, F. Hüsler, M. Lehning, M. Schneebeli, E. Thürig, and P. Bebi
Biogeosciences, 12, 467–487, https://doi.org/10.5194/bg-12-467-2015, https://doi.org/10.5194/bg-12-467-2015, 2015
Related subject area
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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
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
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
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Lambda-PFLOTRAN 1.0: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling
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
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
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Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
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
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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
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
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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 and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-34, https://doi.org/10.5194/gmd-2024-34, 2024
Revised manuscript accepted for GMD
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The newly developed Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate respiration and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow via a Jupyter Notebook interface, that digests raw organic matter chemistry data via FTICR-MS, develops the representative reaction network, and completes a biogeochemical simulation with the open source, parallel reactive flow and transport code PFLOTRAN.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Cited articles
Abramoff, R. and Xu, X.: rabramoff/Millennial: First release of Millennial V2, Zenodo [code], https://doi.org/10.5281/ZENODO.6353519, 2022. a
Abramoff, R. Z., Xu, X., Hartman, M., O'Brien, S., Feng, W., Davidson, E., Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.: The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century, Biogeochemistry, 137, 51–71, https://doi.org/10.1007/s10533-017-0409-7, 2018. a
Abramoff, R. Z., Guenet, B., Zhang, H., Georgiou, K., Xu, X., Viscarra Rossel, R. A., Yuan, W., and Ciais, P.: Improved global-scale predictions of soil carbon stocks with Millennial Version 2, Soil Biol. Biochem., 164, 108466, https://doi.org/10.1016/j.soilbio.2021.108466, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Ahrens, B., Braakhekke, M. C., Guggenberger, G., Schrumpf, M., and Reichstein, M.: Contribution of sorption, DOC transport and microbial interactions to the 14C age of a soil organic carbon profile: Insights from a calibrated process model, Soil Biol. Biochem., 88, 390–402, https://doi.org/10.1016/j.soilbio.2015.06.008, 2015. a
Ahrens, B., Guggenberger, G., Rethemeyer, J., John, S., Marschner, B., Heinze, S., Angst, G., Mueller, C. W., Kögel-Knabner, I., Leuschner, C., Hertel, D., Bachmann, J., Reichstein, M., and Schrumpf, M.: Combination of energy limitation and sorption capacity explains 14C depth gradients, Soil Biol Biochem., 148, 107912, https://doi.org/10.1016/j.soilbio.2020.107912, 2020. a, b
Armstrong, D. G., Cook, H., and Thomas, B.: The lignin and cellulose contents of certain grassland species at different stages of growth, J. Agr. Sci., 40, 93–99, https://doi.org/10.1017/S002185960004555X, 1950. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a
Baisden, W. T., Amundson, R., Cook, A. C., and Brenner, D. L.: Turnover and storage of C and N in five density fractions from California annual grassland surface soils, Global Biogeochem. Cy., 16, 64-1–64-16, https://doi.org/10.1029/2001GB001822, 2002. a, b, c, d
Berhe, A. A., Harden, J. W., Torn, M. S., Kleber, M., Burton, S. D., and Harte, J.: Persistence of soil organic matter in eroding versus depositional landform positions, J. Geophys. Res.-Biogeo., 117, G02019, https://doi.org/10.1029/2011JG001790, 2012. a
Braakhekke, M. C., Beer, C., Hoosbeek, M. R., Reichstein, M., Kruijt, B., Schrumpf, M., and Kabat, P.: SOMPROF: A vertically explicit soil organic matter model, Ecol. Modell., 222, 1712–1730, https://doi.org/10.1016/j.ecolmodel.2011.02.015, 2011. a, b
Braakhekke, M. C., Wutzler, T., Beer, C., Kattge, J., Schrumpf, M., Ahrens, B., Schöning, I., Hoosbeek, M. R., Kruijt, B., Kabat, P., and Reichstein, M.: Modeling the vertical soil organic matter profile using Bayesian parameter estimation, Biogeosciences, 10, 399–420, https://doi.org/10.5194/bg-10-399-2013, 2013. a
Braakhekke, M. C., Beer, C., Schrumpf, M., Ekici, A., Ahrens, B., Hoosbeek, M. R., Kruijt, B., Kabat, P., and Reichstein, M.: The use of radiocarbon to constrain current and future soil organic matter turnover and transport in a temperate forest, J. Geophys. Res.-Biogeo., 119, 372–391, https://doi.org/10.1002/2013JG002420, 2014. a, b, c
Brunmayr, A. S.: asb219/evaluate-SOC-models, Zenodo [code], https://doi.org/10.5281/zenodo.10575139, 2024. a, b
Cerli, C., Celi, L., Kalbitz, K., Guggenberger, G., and Kaiser, K.: Separation of light and heavy organic matter fractions in soil – Testing for proper density cut-off and dispersion level, Geoderma, 170, 403–416, https://doi.org/10.1016/j.geoderma.2011.10.009, 2012. a
Chen, J., Zhu, Q., Riley, W. J., He, Y., Randerson, J. T., and Trumbore, S.: Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model, J. Geophys. Res.-Biogeo., 124, 1098–1114, https://doi.org/10.1029/2018JG004795, 2019. a
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011. a
Coleman, K. and Jenkinson, D. S.: RothC-26.3 - A Model for the turnover of carbon in soil, in: Evaluation of Soil Organic Matter Models, edited by: Powlson, D. S., Smith, P., and Smith, J. U., Springer Berlin Heidelberg, Berlin, Heidelberg, 237–246, ISBN 978-3-642-64692-8 978-3-642-61094-3, https://doi.org/10.1007/978-3-642-61094-3_17, 1996. a, b
Crowther, T. W., Van Den Hoogen, J., Wan, J., Mayes, M. A., Keiser, A. D., Mo, L., Averill, C., and Maynard, D. S.: The global soil community and its influence on biogeochemistry, Science, 365, eaav0550, https://doi.org/10.1126/science.aav0550, 2019. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Doetterl, S., Stevens, A., Six, J., Merckx, R., Van Oost, K., Casanova Pinto, M., Casanova-Katny, A., Muñoz, C., Boudin, M., Zagal Venegas, E., and Boeckx, P.: Soil carbon storage controlled by interactions between geochemistry and climate, Nat. Geosci., 8, 780–783, https://doi.org/10.1038/ngeo2516, 2015. a
Dungait, J. A. J., Hopkins, D. W., Gregory, A. S., and Whitmore, A. P.: Soil organic matter turnover is governed by accessibility not recalcitrance, Glob. Change Biol., 18, 1781–1796, https://doi.org/10.1111/j.1365-2486.2012.02665.x, 2012. a
Eckmeier, E., Van der Borg, K., Tegtmeier, U., Schmidt, M. W. I., and Gerlach, R.: Dating Charred Soil Organic Matter: Comparison of Radiocarbon Ages from Macrocharcoals and Chemically Separated Charcoal Carbon, Radiocarbon, 51, 437–443, https://doi.org/10.1017/S0033822200055831, 2009. a
Fan, X., Gao, D., Zhao, C., Wang, C., Qu, Y., Zhang, J., and Bai, E.: Improved model simulation of soil carbon cycling by representing the microbially derived organic carbon pool, ISME J., 15, 2248–2263, https://doi.org/10.1038/s41396-021-00914-0, 2021. a
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A., Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526, https://doi.org/10.1175/JCLI-D-12-00579.1, 2014. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora, V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L., Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, 2022. a
Gan, T.: CSDMS SoilGrids Data Component, Zenodo [code], https://doi.org/10.5281/ZENODO.10368883, 2023. a
Gaudinski, J. B., Trumbore, S. E., Davidson, E. A., and Zheng, S.: Soil carbon cycling in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates and partitioning of fluxes, Biogeochemistry, 51, 33–69, https://doi.org/10.1023/A:1006301010014, 2000. a, b
Georgiou, K., Koven, C. D., Wieder, W. R., Hartman, M. D., Riley, W. J., Pett-Ridge, J., Bouskill, N. J., Abramoff, R. Z., Slessarev, E. W., Ahlström, A., Parton, W. J., Pellegrini, A. F. A., Pierson, D., Sulman, B. N., Zhu, Q., and Jackson, R. B.: Emergent temperature sensitivity of soil organic carbon driven by mineral associations, Nat. Geosci., 17, 205–212, https://doi.org/10.1038/s41561-024-01384-7, 2024. a
German, D. P., Marcelo, K. R. B., Stone, M. M., and Allison, S. D.: The Michaelis-Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study, Glob.Change Biol., 18, 1468–1479, https://doi.org/10.1111/j.1365-2486.2011.02615.x, 2012. a
Golchin, A., Oades, J., Skjemstad, J., and Clarke, P.: Study of free and occluded particulate organic matter in soils by solid state 13C CP/MAS NMR spectroscopy and scanning electron microscopy, Soil Res., 32, 285, https://doi.org/10.1071/SR9940285, 1994. a
González-Domínguez, B., Niklaus, P. A., Studer, M. S., Hagedorn, F., Wacker, L., Haghipour, N., Zimmermann, S., Walthert, L., McIntyre, C., and Abiven, S.: Temperature and moisture are minor drivers of regional-scale soil organic carbon dynamics, Sci. Rep., 9, 6422, https://doi.org/10.1038/s41598-019-42629-5, 2019. a, b
Grant, K., Hilton, R., and Galy, V.: Global patterns of radiocarbon depletion in subsoil linked to rock-derived organic carbon, Geochem. Perspect. Lett., 25, 36–40, https://doi.org/10.7185/geochemlet.2312, 2023. a
Graven, H., Allison, C. E., Etheridge, D. M., Hammer, S., Keeling, R. F., Levin, I., Meijer, H. A. J., Rubino, M., Tans, P. P., Trudinger, C. M., Vaughn, B. H., and White, J. W. C.: Compiled records of carbon isotopes in atmospheric CO2 for historical simulations in CMIP6, Geosci. Model Dev., 10, 4405–4417, https://doi.org/10.5194/gmd-10-4405-2017, 2017. a, b, c, d
Graven, H., Keeling, R. F., and Rogelj, J.: Changes to Carbon Isotopes in Atmospheric CO2 Over the Industrial Era and Into the Future, Global Biogeochem. Cy., 34, e2019GB006170, https://doi.org/10.1029/2019GB006170, 2020. a
Griepentrog, M., Bodé, S., Boeckx, P., Hagedorn, F., Heim, A., and Schmidt, M. W. I.: Nitrogen deposition promotes the production of new fungal residues but retards the decomposition of old residues in forest soil fractions, Glob. Change Biol., 20, 327–340, https://doi.org/10.1111/gcb.12374, 2014. a
Griepentrog, M., Eglinton, T. I., Hagedorn, F., Schmidt, M. W. I., and Wiesenberg, G. L. B.: Interactive effects of elevated CO2 and nitrogen deposition on fatty acid molecular and isotope composition of above- and belowground tree biomass and forest soil fractions, Glob. Change Biol., 21, 473–486, https://doi.org/10.1111/gcb.12666, 2015. a
Hajdas, I., Schlumpf, N., Minikus-Stary, N., Hagedorn, F., Eckmeier, E., Schoch, W., Burga, C., Bonani, G., Schmidt, M. W., and Cherubini, P.: Radiocarbon ages of soil charcoals from the southern Alps, Ticino, Switzerland, Nucl. Instrum. Meth. B, 259, 398–402, https://doi.org/10.1016/j.nimb.2007.02.075, 2007. a
Harden, J. W., Fries, T. L., and Pavich, M. J.: Cycling of Beryllium and Carbon through hillslope soils in Iowa, Biogeochemistry, 60, 317–336, https://doi.org/10.1023/A:1020308729553, 2002. a
He, Y., Trumbore, S. E., Torn, M. S., Harden, J. W., Vaughn, L. J. S., Allison, S. D., and Randerson, J. T.: Radiocarbon constraints imply reduced carbon uptake by soils during the 21st century, Science, 353, 1419–1424, https://doi.org/10.1126/science.aad4273, 2016. a, b, c, d
Heckman, K.: Pedogenesis & Carbon Dynamics Across A Lithosequence Under Ponderosa Pine, Ph.D. thesis, Zenodo, https://doi.org/10.5281/zenodo.1486081, 2010. a
Heckman, K., Lawrence, C. R., and Harden, J. W.: A sequential selective dissolution method to quantify storage and stability of organic carbon associated with Al and Fe hydroxide phases, Geoderma, 312, 24–35, https://doi.org/10.1016/j.geoderma.2017.09.043, 2018. a
Heckman, K., Hicks Pries, C. E., Lawrence, C. R., Rasmussen, C., Crow, S. E., Hoyt, A. M., Von Fromm, S. F., Shi, Z., Stoner, S., McGrath, C., Beem-Miller, J., Berhe, A. A., Blankinship, J. C., Keiluweit, M., Marín-Spiotta, E., Monroe, J. G., Plante, A. F., Schimel, J., Sierra, C. A., Thompson, A., and Wagai, R.: Beyond bulk: Density fractions explain heterogeneity in global soil carbon abundance and persistence, Glob. Change Biol., 28, 1178–1196, https://doi.org/10.1111/gcb.16023, 2022. a, b
Herrera-Ramírez, D., Muhr, J., Hartmann, H., Römermann, C., Trumbore, S., and Sierra, C. A.: Probability distributions of nonstructural carbon ages and transit times provide insights into carbon allocation dynamics of mature trees, New Phytol., 226, 1299–1311, https://doi.org/10.1111/nph.16461, 2020. a
Hicks Pries, C. E., Castanha, C., Porras, R. C., and Torn, M. S.: The whole-soil carbon flux in response to warming, Science, 355, 1420–1423, https://doi.org/10.1126/science.aal1319, 2017. a
Hicks Pries, C. E., Sulman, B. N., West, C., O'Neill, C., Poppleton, E., Porras, R. C., Castanha, C., Zhu, B., Wiedemeier, D. B., and Torn, M. S.: Root litter decomposition slows with soil depth, Soil Biol. Biochem., 125, 103–114, https://doi.org/10.1016/j.soilbio.2018.07.002, 2018. a
IBS Center for Climate Physics, National Center for Atmospheric Research, Danabasoglu, G., Deser, C., Rodgers, K., and Timmermann, A.: CESM2 Large Ensemble Community Project, https://doi.org/10.26024/KGMP-C556, 2021. a
International Soil Radiocarbon Database: ISRaD database files (v 2.5.5.2023-09-20), GitHub [code], https://github.com/International-Soil-Radiocarbon-Database/ISRaD/blob/629b14634d0ddaadf523c662f2cc45b4dad29ae5/ISRaD_data_files/database/ISRaD_database_files.zip (last access: 30 July 2024), 2023. a
ISIMIP: ISIMIP Repository, https://data.isimip.org, last access: 30 July 2024. a
Jagadamma, S., Lal, R., Ussiri, D. A. N., Trumbore, S. E., and Mestelan, S.: Evaluation of structural chemistry and isotopic signatures of refractory soil organic carbon fraction isolated by wet oxidation methods, Biogeochemistry, 98, 29–44, https://doi.org/10.1007/s10533-009-9374-0, 2010. a
Kleber, M., Nico, P. S., Plante, A., Filley, T., Kramer, M., Swanston, C., and Sollins, P.: Old and stable soil organic matter is not necessarily chemically recalcitrant: implications for modeling concepts and temperature sensitivity, Glob. Change Biol., 17, 1097–1107, https://doi.org/10.1111/j.1365-2486.2010.02278.x, 2011. a, b
Kleber, M., Eusterhues, K., Keiluweit, M., Mikutta, C., Mikutta, R., and Nico, P. S.: Mineral–Organic Associations: Formation, Properties, and Relevance in Soil Environments, in: Advances in Agronomy, Elsevier, vol. 130, 1–140, ISBN 978-0-12-802137-8, https://doi.org/10.1016/bs.agron.2014.10.005, 2015. a
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins, W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4, Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, 2013. a
Kyker-Snowman, E.: EmilyKykerSnowman/MIMICS-CN-for-publication v1.0, Zenodo [code], https://doi.org/10.5281/ZENODO.3534561, 2019. a
Kyker-Snowman, E., Wieder, W. R., Frey, S. D., and Grandy, A. S.: Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0), Geosci. Model Dev., 13, 4413–4434, https://doi.org/10.5194/gmd-13-4413-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m
Lavallee, J. M., Soong, J. L., and Cotrufo, M. F.: Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century, Glob. Change Biol., 26, 261–273, https://doi.org/10.1111/gcb.14859, 2020. a, b, c, d
Lawrence, C. R., Beem-Miller, J., Hoyt, A. M., Monroe, G., Sierra, C. A., Stoner, S., Heckman, K., Blankinship, J. C., Crow, S. E., McNicol, G., Trumbore, S., Levine, P. A., Vindušková, O., Todd-Brown, K., Rasmussen, C., Hicks Pries, C. E., Schädel, C., McFarlane, K., Doetterl, S., Hatté, C., He, Y., Treat, C., Harden, J. W., Torn, M. S., Estop-Aragonés, C., Asefaw Berhe, A., Keiluweit, M., Della Rosa Kuhnen, Á., Marin-Spiotta, E., Plante, A. F., Thompson, A., Shi, Z., Schimel, J. P., Vaughn, L. J. S., von Fromm, S. F., and Wagai, R.: An open-source database for the synthesis of soil radiocarbon data: International Soil Radiocarbon Database (ISRaD) version 1.0, Earth Syst. Sci. Data, 12, 61–76, https://doi.org/10.5194/essd-12-61-2020, 2020. a, b, c, d
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Lehmann, J. and Kleber, M.: The contentious nature of soil organic matter, Nature, 528, 60–68, https://doi.org/10.1038/nature16069, 2015. a, b
Leifeld, J.: Biased 14C-derived organic carbon turnover estimates following black carbon input to soil: an exploration with RothC, Biogeochemistry, 88, 205–211, https://doi.org/10.1007/s10533-008-9209-4, 2008. a, b
Leifeld, J., Zimmermann, M., and Fuhrer, J.: Simulating decomposition of labile soil organic carbon: Effects of pH, Soil Biol. Biochem., 40, 2948–2951, https://doi.org/10.1016/j.soilbio.2008.08.019, 2008. a
Leifeld, J., Zimmermann, M., Fuhrer, J., and Conen, F.: Storage and turnover of carbon in grassland soils along an elevation gradient in the Swiss Alps, Glob. Change Biol., 15, 668–679, https://doi.org/10.1111/j.1365-2486.2008.01782.x, 2009. a, b, c
Liu, K., Xu, Y., Feng, W., Zhang, X., Yao, S., and Zhang, B.: Modeling the dynamics of protected and primed organic carbon in soil and aggregates under constant soil moisture following litter incorporation, Soil Biol. Biochem., 151, 108039, https://doi.org/10.1016/j.soilbio.2020.108039, 2020. a
Lybrand, R. A., Heckman, K., and Rasmussen, C.: Soil organic carbon partitioning and 14C variation in desert and conifer ecosystems of southern Arizona, Biogeochemistry, 134, 261–277, https://doi.org/10.1007/s10533-017-0360-7, 2017. a
Malamoud, K., McBratney, A. B., Minasny, B., and Field, D. J.: Modelling how carbon affects soil structure, Geoderma, 149, 19–26, https://doi.org/10.1016/j.geoderma.2008.10.018, 2009. a
Marín-Spiotta, E., Swanston, C. W., Torn, M. S., Silver, W. L., and Burton, S. D.: Chemical and mineral control of soil carbon turnover in abandoned tropical pastures, Geoderma, 143, 49–62, https://doi.org/10.1016/j.geoderma.2007.10.001, 2008. a
McFarlane, K. J., Torn, M. S., Hanson, P. J., Porras, R. C., Swanston, C. W., Callaham, M. A., and Guilderson, T. P.: Comparison of soil organic matter dynamics at five temperate deciduous forests with physical fractionation and radiocarbon measurements, Biogeochemistry, 112, 457–476, https://doi.org/10.1007/s10533-012-9740-1, 2013. a
Meyer, S., Leifeld, J., Bahn, M., and Fuhrer, J.: Free and protected soil organic carbon dynamics respond differently to abandonment of mountain grassland, Biogeosciences, 9, 853–865, https://doi.org/10.5194/bg-9-853-2012, 2012. a, b
Mikutta, R., Turner, S., Schippers, A., Gentsch, N., Meyer-Stüve, S., Condron, L. M., Peltzer, D. A., Richardson, S. J., Eger, A., Hempel, G., Kaiser, K., Klotzbücher, T., and Guggenberger, G.: Microbial and abiotic controls on mineral-associated organic matter in soil profiles along an ecosystem gradient, Sci. Rep., 9, 10294, https://doi.org/10.1038/s41598-019-46501-4, 2019. a
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S.: Analysis of Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands, Soil Sci. Soc. Am. J., 51, 1173–1179, https://doi.org/10.2136/sssaj1987.03615995005100050015x, 1987. a, b
Poeplau, C., Don, A., Six, J., Kaiser, M., Benbi, D., Chenu, C., Cotrufo, M. F., Derrien, D., Gioacchini, P., Grand, S., Gregorich, E., Griepentrog, M., Gunina, A., Haddix, M., Kuzyakov, Y., Kühnel, A., Macdonald, L. M., Soong, J., Trigalet, S., Vermeire, M.-L., Rovira, P., van Wesemael, B., Wiesmeier, M., Yeasmin, S., Yevdokimov, I., and Nieder, R.: Isolating organic carbon fractions with varying turnover rates in temperate agricultural soils – A comprehensive method comparison, Soil Biol. Biochem., 125, 10–26, https://doi.org/10.1016/j.soilbio.2018.06.025, 2018. a, b, c
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b
Rahman, M. M., Tsukamoto, J., Rahman, M. M., Yoneyama, A., and Mostafa, K. M.: Lignin and its effects on litter decomposition in forest ecosystems, Chem. Ecol., 29, 540–553, https://doi.org/10.1080/02757540.2013.790380, 2013. a
Rasmussen, C., Heckman, K., Wieder, W. R., Keiluweit, M., Lawrence, C. R., Berhe, A. A., Blankinship, J. C., Crow, S. E., Druhan, J. L., Hicks Pries, C. E., Marin-Spiotta, E., Plante, A. F., Schädel, C., Schimel, J. P., Sierra, C. A., Thompson, A., and Wagai, R.: Beyond clay: towards an improved set of variables for predicting soil organic matter content, Biogeochemistry, 137, 297–306, https://doi.org/10.1007/s10533-018-0424-3, 2018a. a
Rasmussen, C., Throckmorton, H., Liles, G., Heckman, K., Meding, S., and Horwath, W.: Controls on Soil Organic Carbon Partitioning and Stabilization in the California Sierra Nevada, Soil Syst., 2, 41, https://doi.org/10.3390/soilsystems2030041, 2018b. a
Reisser, M., Purves, R. S., Schmidt, M. W. I., and Abiven, S.: Pyrogenic Carbon in Soils: A Literature-Based Inventory and a Global Estimation of Its Content in Soil Organic Carbon and Stocks, Front. Earth Sci., 4, 80, https://doi.org/10.3389/feart.2016.00080, 2016. a
Roberts, M. L. and Southon, J. R.: A Preliminary Determination of the Absolute 14C/12C Ratio of OX-I, Radiocarbon, 49, 441–445, https://doi.org/10.1017/S0033822200042363, 2007. a
Robertson, A. D., Paustian, K., Ogle, S., Wallenstein, M. D., Lugato, E., and Cotrufo, M. F.: Unifying soil organic matter formation and persistence frameworks: the MEMS model, Biogeosciences, 16, 1225–1248, https://doi.org/10.5194/bg-16-1225-2019, 2019. a
Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., Bódai, T., Chung, E.-S., Huang, L., Kim, W. M., Lamarque, J.-F., Lombardozzi, D. L., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, 2021. a, b
Rosenzweig, C., Arnell, N. W., Ebi, K. L., Lotze-Campen, H., Raes, F., Rapley, C., Smith, M. S., Cramer, W., Frieler, K., Reyer, C. P. O., Schewe, J., Van Vuuren, D., and Warszawski, L.: Assessing inter-sectoral climate change risks: the role of ISIMIP, Environ. Res. Lett., 12, 010301, https://doi.org/10.1088/1748-9326/12/1/010301, 2017. a
Rowley, M. C., Grand, S., and Verrecchia, E. P.: Calcium-mediated stabilisation of soil organic carbon, Biogeochemistry, 137, 27–49, https://doi.org/10.1007/s10533-017-0410-1, 2018. a
Salazar, A., Sulman, B. N., and Dukes, J. S.: Microbial dormancy promotes microbial biomass and respiration across pulses of drying-wetting stress, Soil Biol. Biochem., 116, 237–244, https://doi.org/10.1016/j.soilbio.2017.10.017, 2018. a
Schimel, J.: Modeling ecosystem-scale carbon dynamics in soil: The microbial dimension, Soil Biol. Biochem., 178, 108948, https://doi.org/10.1016/j.soilbio.2023.108948, 2023. a
Schmidt, M. W. I., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D. A. C., Nannipieri, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.: Persistence of soil organic matter as an ecosystem property, Nature, 478, 49–56, https://doi.org/10.1038/nature10386, 2011. a
Schrumpf, M. and Kaiser, K.: Large differences in estimates of soil organic carbon turnover in density fractions by using single and repeated radiocarbon inventories, Geoderma, 239-240, 168–178, https://doi.org/10.1016/j.geoderma.2014.09.025, 2015. a
Schrumpf, M., Kaiser, K., Guggenberger, G., Persson, T., Kögel-Knabner, I., and Schulze, E.-D.: Storage and stability of organic carbon in soils as related to depth, occlusion within aggregates, and attachment to minerals, Biogeosciences, 10, 1675–1691, https://doi.org/10.5194/bg-10-1675-2013, 2013. a, b, c, d, e
Schrumpf, M., Kaiser, K., Mayer, A., Hempel, G., and Trumbore, S.: Age distribution, extractability, and stability of mineral-bound organic carbon in central European soils, Biogeosciences, 18, 1241–1257, https://doi.org/10.5194/bg-18-1241-2021, 2021. a, b
Segoli, M., De Gryze, S., Dou, F., Lee, J., Post, W., Denef, K., and Six, J.: AggModel: A soil organic matter model with measurable pools for use in incubation studies, Ecol. Modell., 263, 1–9, https://doi.org/10.1016/j.ecolmodel.2013.04.010, 2013. a
Shi, Z., Allison, S. D., He, Y., Levine, P. A., Hoyt, A. M., Beem-Miller, J., Zhu, Q., Wieder, W. R., Trumbore, S., and Randerson, J. T.: The age distribution of global soil carbon inferred from radiocarbon measurements, Nat. Geosci., 13, 555–559, https://doi.org/10.1038/s41561-020-0596-z, 2020. a
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., and Janssens, I.: Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture, J. Adv. Model. Earth Sy., 7, 335–356, https://doi.org/10.1002/2014MS000358, 2015. a
Sierra, C. A., Müller, M., Metzler, H., Manzoni, S., and Trumbore, S. E.: The muddle of ages, turnover, transit, and residence times in the carbon cycle, Glob. Change Biol., 23, 1763–1773, https://doi.org/10.1111/gcb.13556, 2017. a
Solly, E. F., Brunner, I., Helmisaari, H.-S., Herzog, C., Leppälammi-Kujansuu, J., Schöning, I., Schrumpf, M., Schweingruber, F. H., Trumbore, S. E., and Hagedorn, F.: Unravelling the age of fine roots of temperate and boreal forests, Nat. Commun., 9, 3006, https://doi.org/10.1038/s41467-018-05460-6, 2018. a
Soucémarianadin, L., Reisser, M., Cécillon, L., Barré, P., Nicolas, M., and Abiven, S.: Pyrogenic carbon content and dynamics in top and subsoil of French forests, Soil Biol. Biochem., 133, 12–15, https://doi.org/10.1016/j.soilbio.2019.02.013, 2019. a
Stamati, F. E., Nikolaidis, N. P., Banwart, S., and Blum, W. E.: A coupled carbon, aggregation, and structure turnover (CAST) model for topsoils, Geoderma, 211-212, 51–64, https://doi.org/10.1016/j.geoderma.2013.06.014, 2013. a
Stoner, S. W., Schrumpf, M., Hoyt, A., Sierra, C. A., Doetterl, S., Galy, V., and Trumbore, S.: How well does ramped thermal oxidation quantify the age distribution of soil carbon? Assessing thermal stability of physically and chemically fractionated soil organic matter, Biogeosciences, 20, 3151–3163, https://doi.org/10.5194/bg-20-3151-2023, 2023. a
Sulman, B.: bsulman/CORPSE-fire-response: v1.0, Zenodo [code], https://doi.org/10.5281/ZENODO.10624992, 2024a. a, b, c
Sulman, B.: bsulman/CORPSE-N: v1.0, Zenodo [code], https://doi.org/10.5281/ZENODO.10624990, 2024b. a
Sulman, B. N., Brzostek, E. R., Medici, C., Shevliakova, E., Menge, D. N. L., and Phillips, R. P.: Feedbacks between plant N demand and rhizosphere priming depend on type of mycorrhizal association, Ecol. Lett., 20, 1043–1053, https://doi.org/10.1111/ele.12802, 2017. a
Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J. G., Jackson, R. B., Arneth, A., Chang, J., Chen, G., Ciais, P., Gerber, S., Ito, A., Huang, Y., Joos, F., Lienert, S., Messina, P., Olin, S., Pan, S., Peng, C., Saikawa, E., Thompson, R. L., Vuichard, N., Winiwarter, W., Zaehle, S., Zhang, B., Zhang, K., and Zhu, Q.: The Global N2O Model Intercomparison Project, B. Am. Meteorol. Soc., 99, 1231–1251, https://doi.org/10.1175/BAMS-D-17-0212.1, 2018. a
Tipping, E. and Rowe, E. C.: Modelling the physical states, element stoichiometries and residence times of topsoil organic matter, European J. Soil Sci., 70, 321–337, https://doi.org/10.1111/ejss.12785, 2019. a, b
Trumbore, S. E., Sierra, C. A., and Hicks Pries, C. E.: Radiocarbon Nomenclature, Theory, Models, and Interpretation: Measuring Age, Determining Cycling Rates, and Tracing Source Pools, in: Radiocarbon and Climate Change, edited by: Schuur, E. A., Druffel, E., and Trumbore, S. E., Springer International Publishing, Cham, 45–82, ISBN 978-3-319-25641-2, https://doi.org/10.1007/978-3-319-25643-6_3, 2016. a, b, c
Van der Voort, T. S., Zell, C. I., Hagedorn, F., Feng, X., McIntyre, C. P., Haghipour, N., Graf Pannatier, E., and Eglinton, T. I.: Diverse Soil Carbon Dynamics Expressed at the Molecular Level: Molecular Level Soil Carbon Dynamics, Geophys. Res. Lett., 44, 11840–11850, https://doi.org/10.1002/2017GL076188, 2017. a, b
van der Voort, T. S., Mannu, U., Hagedorn, F., McIntyre, C., Walthert, L., Schleppi, P., Haghipour, N., and Eglinton, T. I.: Dynamics of deep soil carbon – insights from 14C time series across a climatic gradient, Biogeosciences, 16, 3233–3246, https://doi.org/10.5194/bg-16-3233-2019, 2019. a
Vogel, C., Mueller, C. W., Höschen, C., Buegger, F., Heister, K., Schulz, S., Schloter, M., and Kögel-Knabner, I.: Submicron structures provide preferential spots for carbon and nitrogen sequestration in soils, Nat. Commun., 5, 2947, https://doi.org/10.1038/ncomms3947, 2014. a
von Lützow, M., Kögel-Knabner, I., Ekschmitt, K., Flessa, H., Guggenberger, G., Matzner, E., and Marschner, B.: SOM fractionation methods: Relevance to functional pools and to stabilization mechanisms, Soil Biol. Biochem., 39, 2183–2207, https://doi.org/10.1016/j.soilbio.2007.03.007, 2007. a, b, c
Wagai, R., Mayer, L. M., and Kitayama, K.: Nature of the “occluded” low-density fraction in soil organic matter studies: A critical review, Soil Sci. Plant Nutr., 55, 13–25, https://doi.org/10.1111/j.1747-0765.2008.00356.x, 2009. a, b
Wang, G.: Microbial-ENzyme Decomposition (MEND) model (commit 92323c7 in https://github.com/wanggangsheng/MEND), Zenodo [code], https://doi.org/10.5281/zenodo.10576664, 2024. a, b
Wang, G. and Brunmayr, A. S.: Microbial-ENzyme Decomposition (MEND) model – GitHub fork asb219/MEND, Zenodo [code], https://doi.org/10.5281/zenodo.11065513, 2024. a, b
Wang, G., Post, W. M., and Mayes, M. A.: Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses, Ecol. Appl., 23, 255–272, https://doi.org/10.1890/12-0681.1, 2013. a
Wang, G., Jagadamma, S., Mayes, M. A., Schadt, C. W., Megan Steinweg, J., Gu, L., and Post, W. M.: Microbial dormancy improves development and experimental validation of ecosystem model, ISME J., 9, 226–237, https://doi.org/10.1038/ismej.2014.120, 2015. a
Wang, Y.-P.: Vertically resolved soil carbon model (model codes and site data), CSIRO [data set], https://doi.org/10.25919/843A-W584, 2020. a, b, c
Wang, Y.-P., Zhang, H., Ciais, P., Goll, D., Huang, Y., Wood, J. D., Ollinger, S. V., Tang, X., and Prescher, A.-K.: Microbial Activity and Root Carbon Inputs Are More Important than Soil Carbon Diffusion in Simulating Soil Carbon Profiles, J. Geophys. Res.-Biogeo., 126, e2020JG006205, https://doi.org/10.1029/2020JG006205, 2021. a, b, c, d, e
Wickham, H., Hester, J., Chang, W., and Bryan, J.: devtools: Tools to Make Developing R Packages Easier, r package version 2.4.5, https://CRAN.R-project.org/package=devtools (last access: 30 July 2024), 2022. a
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.: Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences, 11, 3899–3917, https://doi.org/10.5194/bg-11-3899-2014, 2014. a
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., Taylor, P. G., and Bonan, G. B.: Representing life in the Earth system with soil microbial functional traits in the MIMICS model, Geosci. Model Dev., 8, 1789–1808, https://doi.org/10.5194/gmd-8-1789-2015, 2015. a
Woolf, D.: domwoolf/somic1: SOMic v 1.00, Zenodo [code], https://doi.org/10.5281/ZENODO.10578048, 2024. a, b, c
Woolf, D. and Brunmayr, A. S.: SOMic – GitHub fork asb219/somic1, Zenodo [code], https://doi.org/10.5281/ZENODO.11068749, 2023. a, b
Xiao, L., Wang, G., Chang, J., Chen, Y., Guo, X., Mao, X., Wang, M., Zhang, S., Shi, Z., Luo, Y., Cheng, L., Yu, K., Mo, F., and Luo, Z.: Global depth distribution of belowground net primary productivity and its drivers, Global Ecol. Biogeogr., 32, 1435–1451, https://doi.org/10.1111/geb.13705, 2023. a
Yu, L., Ahrens, B., Wutzler, T., Schrumpf, M., and Zaehle, S.: Jena Soil Model (JSM v1.0; revision 1934): a microbial soil organic carbon model integrated with nitrogen and phosphorus processes, Geosci. Model Dev., 13, 783–803, https://doi.org/10.5194/gmd-13-783-2020, 2020. a
Zhang, H., Goll, D. S., Wang, Y.-P., Ciais, P., Wieder, W. R., Abramoff, R., Huang, Y., Guenet, B., Prescher, A.-K., Viscarra Rossel, R. A., Barré, P., Chenu, C., Zhou, G., and Tang, X.: Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon, Glob. Change Biol., 26, 2668–2685, https://doi.org/10.1111/gcb.14994, 2020. a, b
Zhang, Y., Lavallee, J. M., Robertson, A. D., Even, R., Ogle, S. M., Paustian, K., and Cotrufo, M. F.: Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model, Biogeosciences, 18, 3147–3171, https://doi.org/10.5194/bg-18-3147-2021, 2021. a, b, c
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.
A new generation of soil models promises to more accurately predict the carbon cycle in soils...
