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
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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
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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.
Victoria Nasser, René Dechow, Mirjam Helfrich, Ana Meijide, Pauline Sophie Rummel, Heinz-Josef Koch, Reiner Ruser, Lisa Essich, and Klaus Dittert
EGUsphere, https://doi.org/10.5194/egusphere-2024-2849, https://doi.org/10.5194/egusphere-2024-2849, 2024
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This study evaluated the impact of diverse 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 significantly 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.
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 under review for HESS
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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.
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
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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
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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
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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.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
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. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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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.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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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 key 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 irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
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...
