Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1349-2024
© 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-17-1349-2024
© 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
| 
15 Feb 2024
Model evaluation paper |
| 15 Feb 2024
| 15 Feb 2024
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Konstantin Aiteew
CORRESPONDING AUTHOR
Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, Braunschweig, Germany
Jarno Rouhiainen
Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, Braunschweig, Germany
Claas Nendel
Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, Müncheberg, Germany
Institute of Biochemistry and Biology, University of Potsdam, Am Mühlenberg 3, Potsdam, Germany
René Dechow
Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, Braunschweig, Germany
Related authors
No articles found.
Christina Franziska Radtke, Xiaoqiang Yang, Christin Müller, Jarno Rouhiainen, Ralf Merz, Stefanie R. Lutz, Paolo Benettin, Hong Wei, and Kay Knöller
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-109, https://doi.org/10.5194/hess-2024-109, 2024
Preprint under review for HESS
Short summary
Short summary
Most studies assume no difference between transit times of water and nitrate, because nitrate is transported by water. With an 8-year high-frequency dataset of isotopic signatures of both, water and nitrate, and a transit time model, we show the temporal varying difference of nitrate and water transit times. This finding is highly relevant for applied future research related to nutrient dynamics in landscapes under anthropogenic forcing and for managing impacts of nitrate on aquatic ecosystems.
Roland Baatz, Gohar Ghazaryan, Michael Hagenlocher, Claas Nendel, Andrea Toreti, and Ehsan Eyshi Rezaei
EGUsphere, https://doi.org/10.5194/egusphere-2024-1069, https://doi.org/10.5194/egusphere-2024-1069, 2024
Short summary
Short summary
Our analysis of over 130,000 peer-reviewed articles on drought research reveals critical shifts towards interdisciplinary approaches. Research priorities are identified in methodological advancements of drought forecasting and in plant genetics. The systemic nature of drought impacts is demonstrated. Challenges identified are the integration of plant physiological response in forecasting, fostering machine learning and early warning systems, and more systemic drought resilience frameworks.
Balázs Grosz, Reinhard Well, Rene Dechow, Jan Reent Köster, Mohammad Ibrahim Khalil, Simone Merl, Andreas Rode, Bianca Ziehmer, Amanda Matson, and Hongxing He
Biogeosciences, 18, 5681–5697, https://doi.org/10.5194/bg-18-5681-2021, https://doi.org/10.5194/bg-18-5681-2021, 2021
Short summary
Short summary
To assure quality predictions biogeochemical models must be current. We use data measured using novel incubation methods to test the denitrification sub-modules of three models. We aim to identify limitations in the denitrification modeling to inform next steps for development. Several areas are identified, most urgently improved denitrification control parameters and further testing with high-temporal-resolution datasets. Addressing these would significantly improve denitrification modeling.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Related subject area
Climate and Earth system modeling
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
Short summary
Short summary
We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
Short summary
Short summary
This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
Short summary
Short summary
Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Cited articles
Abrahamsen, P. and Hansen, S.: Daisy: an open soil-crop-atmosphere system model, Environ. Model. Softw., 15, 313–330, https://doi.org/10.1016/S1364-8152(00)00003-7, 2000.
Agumas, B., Blagodatsky, S., Balume, I., Musyoki, M. K., Marhan, S., and Rasche, F.: Microbial carbon use efficiency during plant residue decomposition: Integrating multi-enzyme stoichiometry and C balance approach, Appl. Soil Ecol., 159, 103820, https://doi.org/10.1016/j.apsoil.2020.103820, 2021.
Aiteew, K., Rouhiainen, J., Nendel, C., and Dechow, R.: Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1) (3.3.1), Zenodo [code], https://doi.org/10.5281/zenodo.8380341, 2023a.
Aiteew, K., Rouhiainen, J., Nendel, C., and Dechow, R.: Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1) – MONICA model source code and data, Zenodo [data set], https://doi.org/10.5281/zenodo.8380332, 2023b.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, Fao, Rome, 300, D05109, M-56, ISBN 92-5-104219-5, 1998.
Amelung, W., Bossio, D., de Vries, W., Kögel-Knabner, I., Lehmann, J., Amundson, R., Bol, R., Collins, C., Lal, R., Leifeld, J., Minasny, B., Pan, G., Paustian, K., Rumpel, C., Sanderman, J., van Groenigen, J. W., Mooney, S., van Wesemael, B., Wander, M., and Chabbi, A.: Towards a global-scale soil climate mitigation strategy, Nat. Commun., 11, 5427, https://doi.org/10.1038/s41467-020-18887-7, 2020.
Amundson, R. and Biardeau, L.: Opinion: Soil carbon sequestration is an elusive climate mitigation tool, P. Natl. Acad. Sci. USA, 115, 11652–11656, 2018.
Aon, M. A., Cabello, M. N., Sarena, D., Colaneri, A., Franco, M., Burgos, J., and Cortassa, S.: I. Spatio-temporal patterns of soil microbial and enzymatic activities in an agricultural soil, Appl. Soil Ecol., 18, 239–254, 2001.
Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P. J., Rötter, R. P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P. K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Kersebaum, K. C., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., Wallach, D., White, J. W., Williams, J. R., and Wolf, J.: Uncertainty in simulating wheat yields under climate change, Nat. Clim. Change, 3, 827–832, https://doi.org/10.1038/nclimate1916, 2013.
Bassu, S., Brisson, N., Durand, J.-L., Boote, K., Lizaso, J., Jones, J. W., Rosenzweig, C., Ruane, A. C., Adam, M., Baron, C., Basso, B., Biernath, C., Boogaard, H., Conijn, S., Corbeels, M., Deryng, D., De Sanctis, G., Gayler, S., Grassini, P., Hatfield, J., Hoek, S., Izaurralde, C., Jongschaap, R., Kemanian, A. R., Kersebaum, K. C., Kim, S.-H., Kumar, N. S., Makowski, D., Müller, C., Nendel, C., Priesack, E., Pravia, M. V., Sau, F., Shcherbak, I., Tao, F., Teixeira, E., Timlin, D., and Waha, K.: How do various maize crop models vary in their responses to climate change factors?, Global Change Biol., 20, 2301–2320, https://doi.org/10.1111/gcb.12520, 2014.
Berg, G., Köberl, M., Rybakova, D., Müller, H., Grosch, R., and Smalla, K.: Plant microbial diversity is suggested as the key to future biocontrol and health trends, FEMS Microbiol. Ecol., 93, fix050, https://doi.org/10.1093/femsec/fix050, 2017.
Bolinder, M. A., Janzen, H. H., Gregorich, E. G., Angers, D. A., and VandenBygaart, A. J.: An approach for estimating net primary productivity and annual carbon inputs to soil for common agricultural crops in Canada, Agricult. Ecosyst. Environ., 118, 29–42, https://doi.org/10.1016/j.agee.2006.05.013, 2007.
Bonett, D. G. and Wright, T. A.: Sample size requirements for estimating pearson, kendall and spearman correlations, Psychometrika, 65, 23–28, https://doi.org/10.1007/BF02294183, 2000.
Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R., and Wood, S. A.: Understanding the dominant controls on litter decomposition, J. Ecol., 104, 229–238, https://doi.org/10.1111/1365-2745.12507, 2016.
Bruun, S. and Jensen, L. S.: Initialisation of the soil organic matter pools of the Daisy model, Ecol. Model., 153, 291–295, 2002.
Bruun, S., Christensen, B. T., Hansen, E. M., Magid, J., and Jensen, L. S.: Calibration and validation of the soil organic matter dynamics of the Daisy model with data from the Askov long-term experiments, Soil Biol. Biochem., 35, 67–76, https://doi.org/10.1016/S0038-0717(02)00237-7, 2003.
Buckeridge, K. M., Mason, K. E., McNamara, N. P., Ostle, N., Puissant, J., Goodall, T., Griffiths, R. I., Stott, A. W., and Whitaker, J.: Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization, Commun. Earth Environ., 1, 36, https://doi.org/10.1038/s43247-020-00031-4, 2020.
Cajamarca, S. M. N., Martins, D., da Silva, J., Fontenelle, M. R., Guedes, Í. M. R., de Figueiredo, C. C., and Pacheco Lima, C. E.: Heterogeneity in the Chemical Composition of Biofertilizers, Potential Agronomic Use, and Heavy Metal Contents of Different Agro-Industrial Wastes, Sustainability, 11, 1995, 2019.
Campolongo, F., Cariboni, J., and Saltelli, A.: An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509–1518, https://doi.org/10.1016/j.envsoft.2006.10.004, 2007.
Čapek, P., Starke, R., Hofmockel, K. S., Bond-Lamberty, B., and Hess, N.: Apparent temperature sensitivity of soil respiration can result from temperature driven changes in microbial biomass, Soil Biol. Biochem., 135, 286–293, https://doi.org/10.1016/j.soilbio.2019.05.016, 2019.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., Mu, M., Saatchi, S., Santoro, M., Thurner, M., Weber, U., Ahrens, B., Beer, C., Cescatti, A., Randerson, J. T., and Reichstein, M.: Global covariation of carbon turnover times with climate in terrestrial ecosystems, Nature, 514, 213–217, https://doi.org/10.1038/nature13731, 2014.
Chandel, A. K., Jiang, L., and Luo, Y.: Microbial Models for Simulating Soil Carbon Dynamics: A Review, J. Geophys. Res.-Biogeo., 128, e2023JG007436, https://doi.org/10.1029/2023JG007436, 2023.
Churchman, G. J., Singh, M., Schapel, A., Sarkar, B., and Bolan, N.: Clay minerals as the key to the sequestration of carbon in soils, Clays Clay Miner., 68, 135–143, https://doi.org/10.1007/s42860-020-00071-z, 2020.
Coleman, K. and Jenkinson, D. S.: RothC-26.3 - A Model for the turnover of carbon in soil, Evaluation of Soil Organic Matter Models, Berlin, Heidelberg, 237–246, 1996.
Conant, R. T., Drijber, R. A., Haddix, M. L., Parton, W. J., Paul, E. A., Plante, A. F., Six, J., and Steinweg, J. M.: Sensitivity of organic matter decomposition to warming varies with its quality, Global Change Biol., 14, 868–877, 2008.
Confalonieri, R., Bellocchi, G., Bregaglio, S., Donatelli, M., and Acutis, M.: Comparison of sensitivity analysis techniques: A case study with the rice model WARM, Ecol. Model., 221, 1897–1906, https://doi.org/10.1016/j.ecolmodel.2010.04.021, 2010.
Creamer, C. A., Foster, A. L., Lawrence, C., McFarland, J., Schulz, M., and Waldrop, M. P.: Mineralogy dictates the initial mechanism of microbial necromass association, Geochim. Cosmochim. Ac., 260, 161–176, https://doi.org/10.1016/j.gca.2019.06.028, 2019.
Curiel Yuste, J., Baldocchi, D. D., Gershenson, A., Goldstein, A., Misson, L., and Wong, S.: Microbial soil respiration and its dependency on carbon inputs, soil temperature and moisture, Global Change Biol., 13, 2018–2035, https://doi.org/10.1111/j.1365-2486.2007.01415.x, 2007.
Dechow, R., Franko, U., Kätterer, T., and Kolbe, H.: Evaluation of the RothC model as a prognostic tool for the prediction of SOC trends in response to management practices on arable land, Geoderma, 337, 463–478, https://doi.org/10.1016/j.geoderma.2018.10.001, 2019.
Doran, J., Mielke, L., and Power, J.: Microbial activity as regulated by soil water-filled pore space, Transactions 14th International Congress of Soil Science, Kyoto, Japan, Volume III, 94–99, August 1990.
DWD: Climate predictions and climate projections, Deutscher Wetterdienst, 36, 2021.
DWD: Index of /climate_environment/CDC/, Climate Data Center [data set], https://opendata.dwd.de/climate_environment/CDC/, last access: 17 October 2022.
Falloon, P., Smith, P., Coleman, K., and Marshall, S.: Estimating the size of inert organic matter pool from total soil organic carbon content for use the Rothamsted Carbon Model, Soil Biol. Biochem., 30, 1207–1211, https://doi.org/10.1016/S0038-0717(97)00256-3, 1998.
Fang, C. and Moncrieff, J. B.: The dependence of soil CO2 efflux on temperature, Soil Biol. Biochem., 33, 155–165, https://doi.org/10.1016/S0038-0717(00)00125-5, 2001.
Farina, R., Sándor, R., Abdalla, M., Álvaro-Fuentes, J., Bechini, L., Bolinder, M. A., Brilli, L., Chenu, C., Clivot, H., De Antoni Migliorati, M., Di Bene, C., Dorich, C. D., Ehrhardt, F., Ferchaud, F., Fitton, N., Francaviglia, R., Franko, U., Giltrap, D. L., Grant, B. B., Guenet, B., Harrison, M. T., Kirschbaum, M. U. F., Kuka, K., Kulmala, L., Liski, J., McGrath, M. J., Meier, E., Menichetti, L., Moyano, F., Nendel, C., Recous, S., Reibold, N., Shepherd, A., Smith, W. N., Smith, P., Soussana, J.-F., Stella, T., Taghizadeh-Toosi, A., Tsutskikh, E., and Bellocchi, G.: Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils, Global Change Biol., 27, 904–928, https://doi.org/10.1111/gcb.15441, 2021.
Fierer, N., Colman, B. P., Schimel, J. P., and Jackson, R. B.: Predicting the temperature dependence of microbial respiration in soil: A continental-scale analysis, Global Biogeochem. Cycles, 20, 1–10, https://doi.org/10.1029/2005GB002644, 2006.
Fissore, C., Jurgensen, M. F., Pickens, J., Miller, C., Page-Dumroese, D., and Giardina, C. P.: Role of soil texture, clay mineralogy, location, and temperature in coarse wood decomposition – a mesocosm experiment, Ecosphere, 7, e01605, https://doi.org/10.1002/ecs2.1605, 2016.
Flessa, H., Dörsch, P., and Beese, F.: Seasonal variation of N2O and CH4 fluxes in differently managed arable soils in southern Germany, J. Geophys. Res.-Atmos., 100, 23115–23124, https://doi.org/10.1029/95JD02270, 1995.
Flessa, H., Fuß, R., Andres, M., Augustin, J., Christen, O., Dittert, K., Hegewald, H., Heilmann, H., Huth, V., Kage, H., Kern, J., Kesenheimer, K., Knieß, A., Köbke, S., Lewandowski, I., Mallast, J., Moffat, A. M., Mühling, K.-H., Öhlschläger, G., and Stichnothe, H.: Minderung von Treibhausgasemissionen im Rapsanbau unter besonderer Berücksichtigung der Stickstoffdüngung, Johann Heinrich von Thünen-Institut, Braunschweig, 2017.
Fomina, M. and Skorochod, I.: Microbial Interaction with Clay Minerals and Its Environmental and Biotechnological Implications, Minerals, 10, 861, 2020.
Fox, J., Weisberg, S., Price, P., Adler, D., Bates, D., Baud-Bovy, G., Bolker, B., Ellison, S., Firth, D., Friendly, M., Gorjanc, G., Graves, S., Heiberger, R., Krivitsky, P., Fox, J., Weisberg, S., Price, P., Adler, D., Bates, D., Baud-Bovy, G., Bolker, B., Ellison, S., Firth, D., Friendly, M., Gorjanc, G., Graves, S., Heiberger, R., Krivitsky, P., Laboissiere, R., Maechler, M., Monette, G., Murdoch, D., Nilsson, H., Ogle, D., Ripley, B., Venables, W., Walker, S., Winsemius, D., and Zeileis, A.: car: Companion to Applied Regression, Sage [code], https://r-forge.r-project.org/projects/car/ (last access: 5 April 2022), 2019.
Franko, U., Oelschlägel, B., and Schenk, S.: Simulation of temperature-, water- and nitrogen dynamics using the model CANDY, Ecol. Model., 81, 213–222, https://doi.org/10.1016/0304-3800(94)00172-E, 1995.
Franko, U., Kolbe, H., Thiel, E., and Ließ, E.: Multi-site validation of a soil organic matter model for arable fields based on generally available input data, Geoderma, 166, 119–134, https://doi.org/10.1016/j.geoderma.2011.07.019, 2011.
Franzluebbers, A. J.: Microbial activity in response to water-filled pore space of variably eroded southern Piedmont soils, Appl. Soil Ecol., 11, 91–101, https://doi.org/10.1016/S0929-1393(98)00128-0, 1999.
Freibauer, A., Rounsevell, M. D. A., Smith, P., and Verhagen, J.: Carbon sequestration in the agricultural soils of Europe, Geoderma, 122, 1–23, https://doi.org/10.1016/j.geoderma.2004.01.021, 2004.
Frolking, S. E., Mosier, A. R., Ojima, D. S., Li, C., Parton, W. J., Potter, C. S., Priesack, E., Stenger, R., Haberbosch, C., Dörsch, P., Flessa, H., and Smith, K. A.: Comparison of N2O emissions from soils at three temperate agricultural sites: simulations of year-round measurements by four models, Nutr. Cycl. Agroecosys., 52, 77–105, https://doi.org/10.1023/A:1009780109748, 1998.
Fujisaki, K., Chevallier, T., Chapuis-Lardy, L., Albrecht, A., Razafimbelo, T., Masse, D., Ndour, Y. B., and Chotte, J.-L.: Soil carbon stock changes in tropical croplands are mainly driven by carbon inputs: A synthesis, Agricul. Ecosyst. Environ., 259, 147–158, https://doi.org/10.1016/j.agee.2017.12.008, 2018.
Fuss, S., Lamb, W. F., Callaghan, M. W., Hilaire, J., Creutzig, F., Amann, T., Beringer, T., de Oliveira Garcia, W., Hartmann, J., Khanna, T., Luderer, G., Nemet, G. F., Rogelj, J., Smith, P., Vicente, J. L. V., Wilcox, J., del Mar Zamora Dominguez, M., and Minx, J. C.: Negative emissions – Part 2: Costs, potentials and side effects, Environ. Res. Lett., 13, 063002, https://doi.org/10.1088/1748-9326/aabf9f, 2018.
Gabriel, C.-E. and Kellman, L.: Investigating the role of moisture as an environmental constraint in the decomposition of shallow and deep mineral soil organic matter of a temperate coniferous soil, Soil Biol. Biochem., 68, 373–384, https://doi.org/10.1016/j.soilbio.2013.10.009, 2014.
Gelman, A. and Rubin, D. B.: Inference from iterative simulation using multiple sequences, Stat. Sci., 7, 457–472, 1992.
Geyer, K., Schnecker, J., Grandy, A. S., Richter, A., and Frey, S.: Assessing microbial residues in soil as a potential carbon sink and moderator of carbon use efficiency, Biogeochemistry, 151, 237–249, https://doi.org/10.1007/s10533-020-00720-4, 2020.
Gillabel, J., Cebrian-Lopez, B., Six, J., and Merckx, R.: Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition, Global Change Biol., 16, 2789–2798, https://doi.org/10.1111/j.1365-2486.2009.02132.x, 2010.
Gilmour, J. T., Norman, R. J., Mauromoustakos, A., and Gale, P. M.: Kinetics of Crop Residue Decomposition: Variability among Crops and Years, Soil Sci. Soc. Am. J., 62, 750–755, https://doi.org/10.2136/sssaj1998.03615995006200030030x, 1998.
Gleixner, G.: Soil organic matter dynamics: a biological perspective derived from the use of compound-specific isotopes studies, Ecol. Res., 28, 683–695, 2013.
Gross, A. and Glaser, B.: Meta-analysis on how manure application changes soil organic carbon storage, Sci. Rep.-UK, 11, 5516, https://doi.org/10.1038/s41598-021-82739-7, 2021.
Gurung, R. B., Ogle, S. M., Breidt, F. J., Williams, S. A., and Parton, W. J.: Bayesian calibration of the DayCent ecosystem model to simulate soil organic carbon dynamics and reduce model uncertainty, Geoderma, 376, 114529, https://doi.org/10.1016/j.geoderma.2020.114529, 2020.
Haddix, M., Steinweg, M., Conant, R., Plante, A., Paul, E., and Six, J.: Effect of Temperature on the Dynamics of Different Soil Organic Matter Fractions, The 18th World Congress of Soil Science, Philadelphia, Pennsylvania, USA, 15 July 2006.
Hansen, S., Jensen, H., Nielsen, N., and Svendsen, H.: Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY, Fert. Res., 27, 245–259, 1991.
Hastings, W. K.: Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97–109, https://doi.org/10.1093/biomet/57.1.97, 1970.
Höper, H. and Meesenburg, H.: 30 Jahre Bodendauerbeobachtung in Niedersachsen, GeoBerichte, 39, 275 pp., https://doi.org/10.48476/geober_39_2021, 2021.
Iooss, B., Da Veiga, S., Janon, A., and Pujol, G.: Sensitivity: Global Sensitivity Analysis of Model Outputs, R package version 1.26.0, 2020.
Jacobs, A., Poeplau, C., Weiser, C., Fahrion-Nitschke, A., and Don, A.: Exports and inputs of organic carbon on agricultural soils in Germany, Nutr. Cycl. Agroecosys., 118, 249–271, https://doi.org/10.1007/s10705-020-10087-5, 2020.
Jargowsky, P. A. and Yang, R.: Descriptive and Inferential Statistics, in: Encyclopedia of Social Measurement, edited by: Kempf-Leonard, K., Elsevier, New York, 659–668, https://doi.org/10.1016/B0-12-369398-5/00145-6, 2005.
Jeuffroy, M.-H. and Ney, B.: Crop physiology and productivity, Field Crops Research, 53, 3–16, https://doi.org/10.1016/S0378-4290(97)00019-1, 1997.
Joergensen, R. G., Brookes, P. C., and Jenkinson, D. S.: Survival of the soil microbial biomass at elevated temperatures, Soil Biol. Biochem., 22, 1129–1136, https://doi.org/10.1016/0038-0717(90)90039-3, 1990.
Jordon, M. W. and Smith, P.: Modelling soil carbon stocks following reduced tillage intensity: A framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe, Soil Till. Res., 222, 105428, https://doi.org/10.1016/j.still.2022.105428, 2022.
Kallenbach, C. M., Frey, S. D., and Grandy, A. S.: Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls, Nat. Commun., 7, 13630, https://doi.org/10.1038/ncomms13630, 2016.
Kendall, M. G.: A New Measure of Rank Correlation, Biometrika, 30, 81–93, https://doi.org/10.2307/2332226, 1938.
Kendall, M. G.: Rank correlation methods, Oxford University Press, London 1948.
Kersebaum, K. C.: Application of a simple management model to simulate water and nitrogen dynamics, Ecol. Model., 81, 145–156, https://doi.org/10.1016/0304-3800(94)00167-G, 1995.
Kersebaum, K. C.: Modelling nitrogen dynamics in soil–crop systems with HERMES, Nutr. Cycl. Agroecosys., 77, 39–52, 2006.
Kindler, R., Miltner, A., Richnow, H.-H., and Kästner, M.: Fate of gram-negative bacterial biomass in soil – mineralization and contribution to SOM, Soil Biol. Biochem., 38, 2860–2870, https://doi.org/10.1016/j.soilbio.2006.04.047, 2006.
Koishi, A., Bragazza, L., Maltas, A., Guillaume, T., and Sinaj, S.: Long-Term Effects of Organic Amendments on Soil Organic Matter Quantity and Quality in Conventional Cropping Systems in Switzerland, Agronomy, 10, 1977, https://doi.org/10.3390/agronomy10121977, 2020.
Kollas, C., Kersebaum, K. C., Nendel, C., Manevski, K., Müller, C., Palosuo, T., Armas-Herrera, C. M., Beaudoin, N., Bindi, M., Charfeddine, M., Conradt, T., Constantin, J., Eitzinger, J., Ewert, F., Ferrise, R., Gaiser, T., Cortazar-Atauri, I. G. d., Giglio, L., Hlavinka, P., Hoffmann, H., Hoffmann, M. P., Launay, M., Manderscheid, R., Mary, B., Mirschel, W., Moriondo, M., Olesen, J. E., Öztürk, I., Pacholski, A., Ripoche-Wachter, D., Roggero, P. P., Roncossek, S., Rötter, R. P., Ruget, F., Sharif, B., Trnka, M., Ventrella, D., Waha, K., Wegehenkel, M., Weigel, H.-J., and Wu, L.: Crop rotation modelling – A European model intercomparison, Eur. J. Agron., 70, 98–111, https://doi.org/10.1016/j.eja.2015.06.007, 2015.
Kostková, M., Hlavinka, P., Pohanková, E., Kersebaum, K. C., Nendel, C., Gobin, A., Olesen, J. E., Ferrise, R., Dibari, C., Takáč, J., Topaj, A., Medvedev, S., Hoffmann, M. P., Stella, T., Balek, J., Ruiz-Ramos, M., Rodríguez, A., Hoogenboom, G., Shelia, V., Ventrella, D., Giglio, L., Sharif, B., Oztürk, I., Rötter, R. P., Balkovič, J., Skalský, R., Moriondo, M., Thaler, S., Žalud, Z., and Trnka, M.: Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe, J. Agric. Sci., 159, 69–89, https://doi.org/10.1017/S0021859621000216, 2021.
Kothari, K., Battisti, R., Boote, K. J., Archontoulis, S. V., Confalone, A., Constantin, J., Cuadra, S. V., Debaeke, P., Faye, B., Grant, B., Hoogenboom, G., Jing, Q., van der Laan, M., Macena da Silva, F. A., Marin, F. R., Nehbandani, A., Nendel, C., Purcell, L. C., Qian, B., Ruane, A. C., Schoving, C., Silva, E. H. F. M., Smith, W., Soltani, A., Srivastava, A., Vieira, N. A., Slone, S., and Salmerón, M.: Are soybean models ready for climate change food impact assessments?, Eur. J. Agron., 135, 126482, https://doi.org/10.1016/j.eja.2022.126482, 2022.
Lashermes, G., Nicolardot, B., Parnaudeau, V., Thuriès, L., Chaussod, R., Guillotin, M. L., Linères, M., Mary, B., Metzger, L., Morvan, T., Tricaud, A., Villette, C., and Houot, S.: Indicator of potential residual carbon in soils after exogenous organic matter application, Eur. J. Soil Sci., 60, 297–310, https://doi.org/10.1111/j.1365-2389.2008.01110.x, 2009.
LBEG (Landesamt für Bergbau, Energie und Geologie): Das Boden-Dauerbeobachtungsprogramm von Niedersachsen, https://www.lbeg.niedersachsen.de/boden_grundwasser/bodenmonitoring/bodendauerbeobachtung/das-boden-dauerbeobachtungsprogramm-von-niedersachsen-572.html, LBEG [data set], last access: 24 November 2023.
Lehuger, S., Gabrielle, B., Oijen, M. v., Makowski, D., Germon, J. C., Morvan, T., and Hénault, C.: Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model, Agric. Ecosyst. Environ., 133, 208–222, https://doi.org/10.1016/j.agee.2009.04.022, 2009.
Leidel, S., Augustin, J., Köppen, D., and Merbach, W.: Einfluss Unterschiedlicher Organisch-Mineralischer N-Düngung auf die Lachgas- und Methanemission eines ackerbaulich genutzten standortes Norddeutschlands, Arch. Agron. Soil Sci., 45, 453–469, https://doi.org/10.1080/03650340009366141, 2000.
Levene, H.: Robust Test for Equality of Variances, in: Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, edited by: Olkin, I., Stanford University Press, Palo Alto, 278–292, 1960.
Li, C., Frolking, S., Crocker, G. J., Grace, P. R., Klír, J., Körchens, M., and Poulton, P. R.: Simulating trends in soil organic carbon in long-term experiments using the DNDC model, Geoderma, 81, 45–60, https://doi.org/10.1016/S0016-7061(97)00080-3, 1997.
Liang, C. and Balser, T. C.: Microbial production of recalcitrant organic matter in global soils: implications for productivity and climate policy, Nat. Rev. Microbiol., 9, 75, https://doi.org/10.1038/nrmicro2386-c1, 2011.
Liddle, K., McGonigle, T., and Koiter, A.: Microbe Biomass in Relation to Organic Carbon and Clay in Soil, Soil Syst., 4, 41, https://doi.org/10.3390/soilsystems4030041, 2020.
Liebig, M., Jones, A., Doran, J., and Mielke, L.: Potential soil respiration and relationship to soil properties in ridge tillage, Soil Sci. Soc. Am. J., 59, 1430–1435, 1995.
Linn, D. M. and Doran, J. W.: Effect of water-filled pore space on carbon dioxide and nitrous oxide production in tilled and nontilled soils, Soil Sci. Soc. Am. J., 48, 1267–1272, 1984.
Liu, Y., He, N., Wen, X., Xu, L., Sun, X., Yu, G., Liang, L., and Schipper, L. A.: The optimum temperature of soil microbial respiration: Patterns and controls, Soil Biol. Biochem., 121, 35–42, https://doi.org/10.1016/j.soilbio.2018.02.019, 2018.
Louis, B. P., Maron, P.-A., Viaud, V., Leterme, P., and Menasseri-Aubry, S.: Soil C and N models that integrate microbial diversity, Environ. Chem. Lett., 14, 331–344, https://doi.org/10.1007/s10311-016-0571-5, 2016.
Lovenduski, N. S. and Bonan, G. B.: Reducing uncertainty in projections of terrestrial carbon uptake, Environ. Res. Lett., 12, 044020, https://doi.org/10.1088/1748-9326/aa66b8, 2017.
Lowder, S. K., Skoet, J., and Raney, T.: The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide, World Development, 87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041, 2016.
Lugato, E., Bampa, F., Panagos, P., Montanarella, L., and Jones, A.: Potential carbon sequestration of European arable soils estimated by modelling a comprehensive set of management practices, Global Change Biol., 20, 3557–3567, https://doi.org/10.1111/gcb.12551, 2014.
Luo, Z., Wang, E., and Sun, O. J.: Can no-tillage stimulate carbon sequestration in agricultural soils? A meta-analysis of paired experiments, Agric. Ecosyst. Environ., 139, 224–231, https://doi.org/10.1016/j.agee.2010.08.006, 2010.
Luo, Z., Feng, W., Luo, Y., Baldock, J., and Wang, E.: Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions, Global Change Biol., 23, 4430–4439, https://doi.org/10.1111/gcb.13767, 2017.
Ma, S., He, F., Tian, D., Zou, D., Yan, Z., Yang, Y., Zhou, T., Huang, K., Shen, H., and Fang, J.: Variations and determinants of carbon content in plants: a global synthesis, Biogeosciences, 15, 693–702, https://doi.org/10.5194/bg-15-693-2018, 2018.
Mallast, J., Stichnothe, H., Flessa, H., Fuß, R., Lucas-Moffat, A., Petersen-Schlapkohl, U., Augustin, J., Hagemann, U., Kesenheimer, K., Ruser, R., Suárez, T., Prochnow, A., Dittert, K., Köbke, S., Huth, V., Glatzel, S., Räbiger, T., Knieß, A., Kage, H., and Christen, O.: Multi-variable experimental data set of agronomic data and gaseous soil emissions from maize, oilseed rape and other energy crops at eight sites in Germany, Open Data Journal for Agricultural Research, 7, 11–19, https://doi.org/10.18174/odjar.v7i0.16124, 2021.
Manzoni, S., Taylor, P., Richter, A., Porporato, A., and Ågren, G. I.: Environmental and stoichiometric controls on microbial carbon-use efficiency in soils, New Phytologist, 196, 79–91, https://doi.org/10.1111/j.1469-8137.2012.04225.x, 2012.
McCartney, D. H., Block, H. C., Dubeski, P. L., and Ohama, A. J.: Review: The composition and availability of straw and chaff from small grain cereals for beef cattle in western Canada, Can. J. Anim. Sci., 86, 443–455, https://doi.org/10.4141/A05-092, 2006.
McGill, W. B., Cannon, K. R., Robertson, J. A., and Cook, F. D.: Dynamics of soil microbial biomass and water-soluble organic C in Breton L after 50 years of cropping to two rotations, Can. J. Soil Sci., 66, 1–19, 1986.
Menichetti, L., Reyes Ortigoza, A. L., García, N., Giagnoni, L., Nannipieri, P., and Renella, G.: Thermal sensitivity of enzyme activity in tropical soils assessed by the Q10 and equilibrium model, Biol. Fert. Soils, 51, 299–310, https://doi.org/10.1007/s00374-014-0976-x, 2015.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equation of state calculations by fast computing machines, J. Chem. Phys., 21, 1087–1092, 1953.
Meyer, N., Welp, G., and Amelung, W.: The Temperature Sensitivity (Q10) of Soil Respiration: Controlling Factors and Spatial Prediction at Regional Scale Based on Environmental Soil Classes, Global Biogeochem. Cycles, 32, 306–323, https://doi.org/10.1002/2017GB005644, 2018.
Miltner, A., Bombach, P., Schmidt-Brücken, B., and Kästner, M.: SOM genesis: microbial biomass as a significant source, Biogeochemistry, 111, 41–55, https://doi.org/10.1007/s10533-011-9658-z, 2012.
Minasny, B., Malone, B. P., McBratney, A. B., Angers, D. A., Arrouays, D., Chambers, A., Chaplot, V., Chen, Z.-S., Cheng, K., Das, B. S., Field, D. J., Gimona, A., Hedley, C. B., Hong, S. Y., Mandal, B., Marchant, B. P., Martin, M., McConkey, B. G., Mulder, V. L., O'Rourke, S., Richer-de-Forges, A. C., Odeh, I., Padarian, J., Paustian, K., Pan, G., Poggio, L., Savin, I., Stolbovoy, V., Stockmann, U., Sulaeman, Y., Tsui, C.-C., Vågen, T.-G., van Wesemael, B., and Winowiecki, L.: Soil carbon 4 per mille, Geoderma, 292, 59–86, https://doi.org/10.1016/j.geoderma.2017.01.002, 2017.
Minunno, F., Van Oijen, M., and Pereira, J.: Selecting Parameters for Bayesian Calibration of a Process-Based Model: A Methodology Based on Canonical Correlation Analysis, Journal on Uncertainty Quantification, 1, 370–385, https://doi.org/10.1137/120891344, 2013.
Möller, K. and Schultheiß, U.: Chemical characterization of commercial organic fertilizers, Arch. Agron. Soil Sci., 61, 989–1012, https://doi.org/10.1080/03650340.2014.978763, 2015.
Mondal, S., Chakraborty, D., Bandyopadhyay, K., Aggarwal, P., and Rana, D. S.: A global analysis of the impact of zero-tillage on soil physical condition, organic carbon content, and plant root response, Land Degrad. Dev., 31, 557–567, https://doi.org/10.1002/ldr.3470, 2020.
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational Experiments, Technometrics, 33, 161–174, https://doi.org/10.1080/00401706.1991.10484804, 1991.
Mueller, T., Jensen, L. S., Hansen, S., and Nielsen, N. E.: Simulating soil carbon and nitrogen dynamics with the soil-plant-atmosphere system model DAISY, Evaluation of Soil Organic Matter Models, Berlin, Heidelberg, 275–281, 1996.
Murphy, B.: Soil Carbon Sequestration as an Elusive Climate Mitigation Tool, in: No-till Farming Systems for Sustainable Agriculture: Challenges and Opportunities, edited by: Dang, Y. P., Dalal, R. C., and Menzies, N. W., Springer International Publishing, Cham, 337–353, https://doi.org/10.1007/978-3-030-46409-7_20, 2020.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nendel, C.: MONICA: A Simulation Model for Nitrogen and Carbon Dynamics in Agro-Ecosystems, in: Novel Measurement and Assessment Tools for Monitoring and Management of Land and Water Resources in Agricultural Landscapes of Central Asia, edited by: Mueller, L., Saparov, A., and Lischeid, G., Springer International Publishing, Cham, 389–405, https://doi.org/10.1007/978-3-319-01017-5_23, 2014.
Nendel, C., Berg, M., Kersebaum, K. C., Mirschel, W., Specka, X., Wegehenkel, M., Wenkel, K., and Wieland, R.: The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics, Ecol. Model., 222, 1614–1625, 2011.
Nendel, C., Kersebaum, K. C., Mirschel, W., and Wenkel, K. O.: Testing farm management options as climate change adaptation strategies using the MONICA model, Eur. J. Agron., 52, 47–56, https://doi.org/10.1016/j.eja.2012.09.005, 2014.
Nguye, T.-T. and Marschner, P.: Respiration in mixes of sandy and clay soils: influence of clay type and addition rate, J. Soil Sci. Plant Nutr,, 14, 881–887, 2014.
Oakley, J. E. and O'Hagan, A.: Uncertainty in prior elicitations: a nonparametric approach, Biometrika, 94, 427–441, https://doi.org/10.1093/biomet/asm031, 2007.
Parton, W. J.: Ecosystem model comparisons: science or fantasy world?, Evaluation of Soil Organic Matter Models, Berlin, Heidelberg, 133–142, 1996.
Paustian, K., Parton, W. J., and Persson, J.: Modeling soil organic matter in organic-amended and nitrogen-fertilized long-term plots, Soil Sci. Soc. Am. J., 56, 476–488, 1992.
Peltre, C., Christensen, B., Dragon, S., Icard, C., Kätterer, T., and Houot, S.: RothC simulation of carbon accumulation in soil after repeated application of widely different organic amendments, Soil Biol. Biochem., 52, 49–60, https://doi.org/10.1016/j.soilbio.2012.03.023, 2012.
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Model. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016.
Pietikäinen, J., Pettersson, M., and Bååth, E.: Comparison of temperature effects on soil respiration and bacterial and fungal growth rates, FEMS Microbiol Ecol, 52, 49–58, https://doi.org/10.1016/j.femsec.2004.10.002, 2005.
Plummer, M., Best, N., Cowles, K., and Vines, K.: CODA: convergence diagnosis and output analysis for MCMC, R news, 6, 7–11, 2006.
Poeplau, C. and Don, A.: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis, Agriculture, Ecosyst. Environ., 200, 33–41, https://doi.org/10.1016/j.agee.2014.10.024, 2015.
Poeplau, C., Jacobs, A., Don, A., Vos, C., Schneider, F., Wittnebel, M., Tiemeyer, B., Heidkamp, A., Prietz, R., and Flessa, H.: Stocks of organic carbon in German agricultural soils – Key results of the first comprehensive inventory, J. Plant Nutr. Soil Sci., 183, 665–681, https://doi.org/10.1002/jpln.202000113, 2020.
Poeplau, C., Don, A., and Schneider, F.: Roots are key to increasing the mean residence time of organic carbon entering temperate agricultural soils, Global Change Biol., 27, 4921–4934, https://doi.org/10.1111/gcb.15787, 2021.
Pujol, G.: Simplex-based screening designs for estimating metamodels, Reliability Engineering & System Safety, 94, 1156–1160, https://doi.org/10.1016/j.ress.2008.08.002, 2009.
Qiao, Y., Wang, J., Liang, G., Du, Z., Zhou, J., Zhu, C., Huang, K., Zhou, X., Luo, Y., Yan, L., and Xia, J.: Global variation of soil microbial carbon-use efficiency in relation to growth temperature and substrate supply, Sci. Rep.-UK, 9, 5621, https://doi.org/10.1038/s41598-019-42145-6, 2019.
Riaz, M. and Marschner, P.: Sandy Soil Amended with Clay Soil: Effect of Clay Soil Properties on Soil Respiration, Microbial Biomass, and Water Extractable Organic C, J. Soil Sci. Plant Nutr., 20, 2465–2470, https://doi.org/10.1007/s42729-020-00312-z, 2020.
Richardson, J., Chatterjee, A., and Darrel Jenerette, G.: Optimum temperatures for soil respiration along a semi-arid elevation gradient in southern California, Soil Biol. Biochem., 46, 89–95, https://doi.org/10.1016/j.soilbio.2011.11.008, 2012.
Riggers, C., Poeplau, C., Don, A., Bamminger, C., Höper, H., and Dechow, R.: Multi-model ensemble improved the prediction of trends in soil organic carbon stocks in German croplands, Geoderma, 345, 17–30, https://doi.org/10.1016/j.geoderma.2019.03.014, 2019.
Riggers, C., Poeplau, C., Don, A., Frühauf, C., and Dechow, R.: How much carbon input is required to preserve or increase projected soil organic carbon stocks in German croplands under climate change?, Plant Soil, 460, 417–433, https://doi.org/10.1007/s11104-020-04806-8, 2021.
Roß, C.-L., Baumecker, M., Ellmer, F., and Kautz, T.: Organic Manure Increases Carbon Sequestration Far beyond the “4 per 1000 Initiative”; Goal on a Sandy Soil in the Thyrow Long-Term Field Experiment DIV.2, Agriculture, 12, 170, 2022.
Rötter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., Ferrise, R., Hlavinka, P., Moriondo, M., and Nendel, C.: Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models, Field Crops Res., 133, 23–36, 2012.
Saifuddin, M., Bhatnagar, J. M., Segrè, D., and Finzi, A. C.: Microbial carbon use efficiency predicted from genome-scale metabolic models, Nat. Commun., 10, 3568, https://doi.org/10.1038/s41467-019-11488-z, 2019.
Salo, T. J., Palosuo, T., Kersebaum, K. C., Nendel, C., Angulo, C., Ewert, F., Bindi, M., Calanca, P., Klein, T., and Moriondo, M.: Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization, J. Agric. Sci., 154, 1218–1240, 2016.
Sanderman, J., Hengl, T., and Fiske, G. J.: Correction for Sanderman et al., Soil carbon debt of 12,000 years of human land use, P. Natl. Acad. Sci. USA, 115, E1700–E1700, https://doi.org/10.1073/pnas.1800925115, 2018.
Sarrazin, F., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis of environmental models: Convergence and validation, Environ. Model. Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016.
Schapel, A., Marschner, P., and Churchman, J.: Clay amount and distribution influence organic carbon content in sand with subsoil clay addition, Soil Till. Res., 184, 253–260, https://doi.org/10.1016/j.still.2018.08.001, 2018.
Schimel, J., Balser, T. C., and Wallenstein, M.: Microbial stress-response physiology and its implications for ecosystem function, Ecology, 88, 1386–1394, https://doi.org/10.1890/06-0219, 2007.
Schmädeke, F.: Lachgas- und Methaneinflüsse eines Gley-Auenbodens unter dem Einfluss einer Rapsfruchtfolge und in Abhängigkeit von der N-Düngung, Dissertation, Fakultät für Forstwissenschaften und Waldökologie, Georg-August-Universität Göttingen, 149 pp., https://doi.org/10.53846/goediss-2312, 1998.
Schnug, E., Oswald, P., and Haneklaus, S.: Organic manure management and efficiency: Role of organic fertilizers and their management practices, in: Fertilizers and Environment: Proceedings of the International Symposium “Fertilizers and Environment”, Salamanca, Spain, 26–29 September 1994, edited by: Rodriguez-Barrueco, C., Springer Netherlands, Dordrecht, 259–265, https://doi.org/10.1007/978-94-009-1586-2_44, 1996.
Schoups, G. and Vrugt, J. A.: A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors, Water Resour. Res., 46, 1–17, https://doi.org/10.1029/2009WR008933, 2010.
Schroeder, J., Jannoura, R., Beuschel, R., Pfeiffer, B., Dyckmans, J., Murugan, R., Chavannavar, S., Wachendorf, C., and Joergensen, R. G.: Carbon use efficiency and microbial functional diversity in a temperate Luvisol and a tropical Nitisol after millet litter and N addition, Biol. Fert. Soils, 56, 1139–1150, https://doi.org/10.1007/s00374-020-01487-4, 2020.
Scott, N. A., Cole, C. V., Elliott, E. T., and Huffman, S. A.: Soil Textural Control on Decomposition and Soil Organic Matter Dynamics, Soil Sci. Soc. Am. J., 60, 1102–1109, https://doi.org/10.2136/sssaj1996.03615995006000040020x, 1996.
Seitz, D., Fischer, L. M., Dechow, R., Wiesmeier, M., and Don, A.: The potential of cover crops to increase soil organic carbon storage in German croplands, Plant Soil, 488, 157–173, https://doi.org/10.1007/s11104-022-05438-w, 2022.
Semenov, V., Pautova, N., Lebedeva, T., Khromychkina, D., Semenova, N., and lopes de Gerenyu, V.: Plant Residues Decomposition and Formation of Active Organic Matter in the Soil of the Incubation Experiments, Eurasian Soil Science, 52, 1183–1194, https://doi.org/10.1134/S1064229319100119, 2019.
Shapiro, S. S. and Wilk, M. B.: An Analysis of Variance Test for Normality (Complete Samples), Biometrika, 52, 591–611, https://doi.org/10.2307/2333709, 1965.
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.
Shoemaker, W. R., Jones, S. E., Muscarella, M. E., Behringer, M. G., Lehmkuhl, B. K., and Lennon, J. T.: Microbial population dynamics and evolutionary outcomes under extreme energy limitation, P. Natl. Acad. Sci. USA, 118, e2101691118, https://doi.org/10.1073/pnas.2101691118, 2021.
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.
Six, J. and Paustian, K.: Aggregate-associated soil organic matter as an ecosystem property and a measurement tool, Soil Biol. Biochem., 68, A4–A9, https://doi.org/10.1016/j.soilbio.2013.06.014, 2014.
Smith, P., Smith, J., Powlson, D., McGill, W., Arah, J., Chertov, O., Coleman, K., Franko, U., Frolking, S., and Jenkinson, D.: A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments, Geoderma, 81, 153–225, 1997.
Smith, P., Andrén, O., Karlsson, T., Perälä, P., Regina, K., Rounsevell, M., and Van Wesemael, B.: Carbon sequestration potential in European croplands has been overestimated, Global Change Biol., 11, 2153–2163, https://doi.org/10.1111/j.1365-2486.2005.01052.x, 2005.
Sokol, N. W. and Bradford, M. A.: Microbial formation of stable soil carbon is more efficient from belowground than aboveground input, Nat. Geosci., 12, 46–53, https://doi.org/10.1038/s41561-018-0258-6, 2019.
Sommer, R. and Bossio, D.: Dynamics and climate change mitigation potential of soil organic carbon sequestration, J. Environ. Manage., 144C, 83–87, https://doi.org/10.1016/j.jenvman.2014.05.017, 2014.
Specka, X., Nendel, C., Hagemann, U., Pohl, M., Hoffmann, M., Barkusky, D., Augustin, J., Sommer, M., and van Oost, K.: Reproducing CO2 exchange rates of a crop rotation at contrasting terrain positions using two different modelling approaches, Soil Till. Res., 156, 219–229, https://doi.org/10.1016/j.still.2015.05.007, 2016.
Spohn, M., Pötsch, E. M., Eichorst, S. A., Woebken, D., Wanek, W., and Richter, A.: Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland, Soil Biol. Biochem., 97, 168–175, https://doi.org/10.1016/j.soilbio.2016.03.008, 2016.
Steinmann, T., Welp, G., Wolf, A., Holbeck, B., Große-Rüschkamp, T., and Amelung, W.: Repeated monitoring of organic carbon stocks after eight years reveals carbon losses from intensively managed agricultural soils in Western Germany, J. Plant Nutr. Soil Sci., 179, 355–366, https://doi.org/10.1002/jpln.201500503, 2016.
Sulman, B. N., Moore, J. A. M., Abramoff, R., Averill, C., Kivlin, S., Georgiou, K., Sridhar, B., Hartman, M. D., Wang, G., Wieder, W. R., Bradford, M. A., Luo, Y., Mayes, M. A., Morrison, E., Riley, W. J., Salazar, A., Schimel, J. P., Tang, J., and Classen, A. T.: Multiple models and experiments underscore large uncertainty in soil carbon dynamics, Biogeochemistry, 141, 109–123, 2018.
Svensson, M., Jansson, P.-E., Gustafsson, D., Kleja, D. B., Langvall, O., and Lindroth, A.: Bayesian calibration of a model describing carbon, water and heat fluxes for a Swedish boreal forest stand, Ecol. Model., 213, 331–344, https://doi.org/10.1016/j.ecolmodel.2008.01.001, 2008.
Taghizadeh-Toosi, A., Cong, W.-F., Eriksen, J., Mayer, J., Olesen, J. E., Keel, S. G., Glendining, M., Kätterer, T., and Christensen, B. T.: Visiting dark sides of model simulation of carbon stocks in European temperate agricultural soils: allometric function and model initialization, Plant Soil, 450, 255–272, https://doi.org/10.1007/s11104-020-04500-9, 2020.
Tardy, V., Spor, A., Mathieu, O., Lévèque, J., Terrat, S., Plassart, P., Regnier, T., Bardgett, R. D., van der Putten, W. H., Roggero, P. P., Seddaiu, G., Bagella, S., Lemanceau, P., Ranjard, L., and Maron, P.-A.: Shifts in microbial diversity through land use intensity as drivers of carbon mineralization in soil, Soil Biol. Biochem., 90, 204–213, https://doi.org/10.1016/j.soilbio.2015.08.010, 2015.
Tesar, M. B. (Ed.): Physiological Basis of Crop Growth and Development, American Society of Agronomy, Inc., and the Crop Science Society of America, Inc., Madison, Wisconsin, USA, https://doi.org/10.2135/1984.physiologicalbasis, 1984.
Throckmorton, H. M., Bird, J. A., Dane, L., Firestone, M. K., and Horwath, W. R.: The source of microbial C has little impact on soil organic matter stabilisation in forest ecosystems, Ecol. Lett., 15, 1257–1265, 2012.
Thuriès, L., Pansu, M., Feller, C., Herrmann, P., and Rémy, J. C.: Kinetics of added organic matter decomposition in a Mediterranean sandy soil, Soil Biol. Biochem., 33, 997–1010, https://doi.org/10.1016/S0038-0717(01)00003-7, 2001.
Trumbore, S.: Age of soil organic matter and soil respiration: Radiocarbon constraints on belowground C dynamics, Ecol. Appl., 10, 399–411, https://doi.org/10.1890/1051-0761(2000)010[0399:AOSOMA]2.0.CO;2, 2000.
van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., and Yau, C.: Bayesian statistics and modelling, Nature Reviews Methods Primers, 1, 1, https://doi.org/10.1038/s43586-020-00001-2, 2021.
van Genuchten, M. T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Van Oijen, M., Rougier, J., and Smith, R.: Bayesian calibration of process-based forest models: bridging the gap between models and data, Tree Physiol., 25, 915–927, https://doi.org/10.1093/treephys/25.7.915, 2005.
Vanhala, P., Karhu, K., Tuomi, M., Sonninen, E., Jungner, H., Fritze, H., and Liski, J.: Old soil carbon is more temperature sensitive than the young in an agricultural field, Soil Biol. Biochem., 39, 2967–2970, 2007.
Vanuytrecht, E., Raes, D., and Willems, P.: Global sensitivity analysis of yield output from the water productivity model, Environ. Model. Softw., 51, 323–332, https://doi.org/10.1016/j.envsoft.2013.10.017, 2014.
Vilkienė, M., Ambrazaitienė, D., Karčauskienė, D., and Dabkevičius, Z.: Assessment of soil organic matter mineralization under various management practices, Acta Agr. Scand. B-S. P., 66, 641–646, https://doi.org/10.1080/09064710.2016.1162845, 2016.
Vos, C., Jaconi, A., Jacobs, A., and Don, A.: Hot regions of labile and stable soil organic carbon in Germany – Spatial variability and driving factors, SOIL, 4, 153–167, https://doi.org/10.5194/soil-4-153-2018, 2018.
Vos, C., Don, A., Hobley, E. U., Prietz, R., Heidkamp, A., and Freibauer, A.: Factors controlling the variation in organic carbon stocks in agricultural soils of Germany, Eur. J. Soil Sci., 70, 550–564, https://doi.org/10.1111/ejss.12787, 2019.
Vrugt, J. A.: Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation, Environ. Model. Softw., 75, 273–316, https://doi.org/10.1016/j.envsoft.2015.08.013, 2016.
Wang, J., Xiong, Z., and Kuzyakov, Y.: Biochar stability in soil: meta-analysis of decomposition and priming effects, GCB Bioenergy, 8, 512—523, https://doi.org/10.1111/gcbb.12266, 2016.
Wei, H., Guenet, B., Vicca, S., Nunan, N., Asard, H., AbdElgawad, H., Shen, W., and Janssens, I. A.: High clay content accelerates the decomposition of fresh organic matter in artificial soils, Soil Biol. Biochem., 77, 100–108, https://doi.org/10.1016/j.soilbio.2014.06.006, 2014.
Wiesler, F., Hund-Rinke, K., Gäth, S. G., Eckhard, Greef, J. M., Hölzle, L. E., Holz, F. H., Kurt-Jürgen, Rudolf, P., Severin, K., Frede, H.-G., Blum, B., Schenkel, H., Horst, W., Dittert, K., Ebertseder, T., Osterburg, B., Philipp, W., and Pietsch, M.: Anwendung von organischen Düngern und organischen Reststoffen in der Landwirtschaft, Bundesministerium für Landwirtschaft und Ernährung, Berichte über Landwirtschaft, https://doi.org/10.12767/buel.v94i1.124, 2016.
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Climate Res., 30, 79–82, 2005.
Winkhart, F., Mösl, T., Schmid, H., and Hülsbergen, K.-J.: Effects of Organic Maize Cropping Systems on Nitrogen Balances and Nitrous Oxide Emissions, Agriculture, 12, 907, https://doi.org/10.3390/agriculture12070907, 2022.
Wolf, U., Fuß, R., Höppner, F., and Flessa, H.: Contribution of N2O and NH3 to total greenhouse gas emission from fertilization: results from a sandy soil fertilized with nitrate and biogas digestate with and without nitrification inhibitor, Nutr. Cycl. Agroecosys., 100, 121–134, 2014.
Zambrano-Bigiarini, M.: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series (0.5-4), Zenodo [code], https://doi.org/10.5281/zenodo.839854, 2020.
Zhang, Y., Liu, Q., Zhang, W., Wang, X., Mao, R., Tigabu, M., and Ma, X.: Linkage of aggregate formation, aggregate-associated C distribution, and microorganisms in two different-textured ultisols: A short-term incubation experiment, Geoderma, 394, 114979, https://doi.org/10.1016/j.geoderma.2021.114979, 2021.
Zhang, Z.-S., Dong, X.-J., Xu, B.-X., Chen, Y.-L., Zhao, Y., Gao, Y.-H., Hu, Y.-G., and Huang, L.: Soil respiration sensitivities to water and temperature in a revegetated desert, J. Geophys. Res.-Biogeo., 120, 773-787, https://doi.org/10.1002/2014JG002805, 2015.
Zhao, F., Wu, Y., Hui, J., Sivakumar, B., Meng, X., and Liu, S.: Projected soil organic carbon loss in response to climate warming and soil water content in a loess watershed, Carbon Balance and Management, 16, 24, https://doi.org/10.1186/s13021-021-00187-2, 2021.
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
This study evaluated the biogeochemical model MONICA and its performance in simulating soil...
