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.
Victoria Nasser, René Dechow, Mirjam Helfrich, Ana Meijide, Pauline Sophie Rummel, Heinz-Josef Koch, Reiner Ruser, Lisa Essich, and Klaus Dittert
SOIL, 11, 489–506, https://doi.org/10.5194/soil-11-489-2025, https://doi.org/10.5194/soil-11-489-2025, 2025
Short summary
Short summary
This study evaluated the impact of contrasting cover crops on topsoil mineral nitrogen (SMN), N2O emissions, and carbon (C) sequestration. Non-legume cover crops reduced SMN levels, showed potential for mitigating indirect N2O emissions, and increased C sequestration but did not reduce cumulative N2O emissions compared to fallow. The results highlight the need for tailored cover crop strategies to balance SMN capture, N2O emissions, and C sequestration effectively.
Roland Baatz, Gohar Ghazaryan, Michael Hagenlocher, Claas Nendel, Andrea Toreti, and Ehsan Eyshi Rezaei
Hydrol. Earth Syst. Sci., 29, 1379–1393, https://doi.org/10.5194/hess-29-1379-2025, https://doi.org/10.5194/hess-29-1379-2025, 2025
Short summary
Short summary
Our global review of 130 000 peer-reviewed articles reveals that drought forecasting and plant genetics dominate drought research priorities. Using topic modelling, we traced evolving themes from 1901 to 2022, highlighting a shift from ecology to cutting-edge technologies. By applying unsupervised machine learning, we offer insights into how integrated approaches matter, guiding future priorities to strengthen drought resilience and to safeguard water resources.
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
Revised manuscript not accepted
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.
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
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
FACA v1 – Fully Automated Co-Alignment of UAV Point Clouds
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary
Short summary
This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Short summary
Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary
Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
Short summary
Short summary
To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Nick Schüßler, Jewgenij Torizin, Claudia Gunkel, Karsten Schütze, Lars Tiepolt, Dirk Kuhn, Michael Fuchs, and Steffen Prüfer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-209, https://doi.org/10.5194/gmd-2024-209, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
FACA – Fully Automated Co-Alignment – is a tool designed to generate co-aligned point clouds. We aim to accelerate the application of the co-alignment method and achieve fast results with evolving temporal data and minimal site-specific preparation. FACA offers multiple ways to interact with the workflow, enabling new users to quickly generate initial results through the custom interface, as well as integration into larger automated workflows via the command line. Test datasets are provided.
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...
