Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3603-2021
© Author(s) 2021. 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-14-3603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Jun 2021
Model description paper |
| 15 Jun 2021
| 15 Jun 2021
Model of Early Diagenesis in the Upper Sediment with Adaptable complexity – MEDUSA (v. 2): a time-dependent biogeochemical sediment module for Earth system models, process analysis and teaching
Dépt. d'Astrophysique, Géophysique et Océanographie, Université de Liège, 4000 Liège, Belgium
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Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Nathaelle Bouttes, Lester Kwiatkowski, Elodie Bougeot, Manon Berger, Victor Brovkin, and Guy Munhoven
EGUsphere, https://doi.org/10.5194/egusphere-2024-3738, https://doi.org/10.5194/egusphere-2024-3738, 2024
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Coral reefs are under threat due to warming and ocean acidification. It is difficult to project future coral reef production due to uncertainties in climate models, socio-economic scenarios and coral adaptation to warming. Here we have included a coral reef module within a climate model for the first time to evaluate the range of possible futures. We show that coral reef production decreases in most future scenarios, but in some cases coral reef carbonate production can persist.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Guy Munhoven
Geosci. Model Dev., 14, 4225–4240, https://doi.org/10.5194/gmd-14-4225-2021, https://doi.org/10.5194/gmd-14-4225-2021, 2021
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SolveSAPHE (Munhoven, 2013) was the first package to calculate pH reliably from any physically sensible pair of total alkalinity (AlkT) and dissolved inorganic carbon (CT) data and to do so in an autonomous and efficient way. Here, we extend it to use CO2, HCO3 or CO3 instead of CT. For each one of these pairs, the new SolveSAPHE calculates all of the possible pH values (0, 1, or 2), again without any prior knowledge of the solutions.
Takasumi Kurahashi-Nakamura, André Paul, Guy Munhoven, Ute Merkel, and Michael Schulz
Geosci. Model Dev., 13, 825–840, https://doi.org/10.5194/gmd-13-825-2020, https://doi.org/10.5194/gmd-13-825-2020, 2020
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Chemical processes in ocean-floor sediments have a large influence on the marine carbon cycle, hence the global climate, at long timescales. We developed a new coupling scheme for a chemical sediment model and a comprehensive climate model. The new coupled model outperformed the original uncoupled climate model in reproducing the global distribution of sediment properties. The sediment model will also act as a
bridgebetween the ocean model and paleoceanographic data.
Thomas Kleinen, Victor Brovkin, and Guy Munhoven
Clim. Past, 12, 2145–2160, https://doi.org/10.5194/cp-12-2145-2016, https://doi.org/10.5194/cp-12-2145-2016, 2016
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We investigate trends in atmospheric CO2 during three recent interglacials – the Holocene, the Eemian and MIS 11 – using an earth system model of intermediate complexity. Our model experiments show a considerable improvement in the modelled CO2 trends for all three interglacials if peat accumulation and shallow water CaCO3 sedimentation are included, forcing the model only with orbital and sea level changes. The Holocene CO2 trend requires anthropogenic emissions of CO2 only after 3 ka BP.
G. Munhoven
Geosci. Model Dev., 6, 1367–1388, https://doi.org/10.5194/gmd-6-1367-2013, https://doi.org/10.5194/gmd-6-1367-2013, 2013
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Nathaelle Bouttes, Lester Kwiatkowski, Elodie Bougeot, Manon Berger, Victor Brovkin, and Guy Munhoven
EGUsphere, https://doi.org/10.5194/egusphere-2024-3738, https://doi.org/10.5194/egusphere-2024-3738, 2024
Short summary
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Coral reefs are under threat due to warming and ocean acidification. It is difficult to project future coral reef production due to uncertainties in climate models, socio-economic scenarios and coral adaptation to warming. Here we have included a coral reef module within a climate model for the first time to evaluate the range of possible futures. We show that coral reef production decreases in most future scenarios, but in some cases coral reef carbonate production can persist.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
Short summary
Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Guy Munhoven
Geosci. Model Dev., 14, 4225–4240, https://doi.org/10.5194/gmd-14-4225-2021, https://doi.org/10.5194/gmd-14-4225-2021, 2021
Short summary
Short summary
SolveSAPHE (Munhoven, 2013) was the first package to calculate pH reliably from any physically sensible pair of total alkalinity (AlkT) and dissolved inorganic carbon (CT) data and to do so in an autonomous and efficient way. Here, we extend it to use CO2, HCO3 or CO3 instead of CT. For each one of these pairs, the new SolveSAPHE calculates all of the possible pH values (0, 1, or 2), again without any prior knowledge of the solutions.
Takasumi Kurahashi-Nakamura, André Paul, Guy Munhoven, Ute Merkel, and Michael Schulz
Geosci. Model Dev., 13, 825–840, https://doi.org/10.5194/gmd-13-825-2020, https://doi.org/10.5194/gmd-13-825-2020, 2020
Short summary
Short summary
Chemical processes in ocean-floor sediments have a large influence on the marine carbon cycle, hence the global climate, at long timescales. We developed a new coupling scheme for a chemical sediment model and a comprehensive climate model. The new coupled model outperformed the original uncoupled climate model in reproducing the global distribution of sediment properties. The sediment model will also act as a
bridgebetween the ocean model and paleoceanographic data.
Thomas Kleinen, Victor Brovkin, and Guy Munhoven
Clim. Past, 12, 2145–2160, https://doi.org/10.5194/cp-12-2145-2016, https://doi.org/10.5194/cp-12-2145-2016, 2016
Short summary
Short summary
We investigate trends in atmospheric CO2 during three recent interglacials – the Holocene, the Eemian and MIS 11 – using an earth system model of intermediate complexity. Our model experiments show a considerable improvement in the modelled CO2 trends for all three interglacials if peat accumulation and shallow water CaCO3 sedimentation are included, forcing the model only with orbital and sea level changes. The Holocene CO2 trend requires anthropogenic emissions of CO2 only after 3 ka BP.
G. Munhoven
Geosci. Model Dev., 6, 1367–1388, https://doi.org/10.5194/gmd-6-1367-2013, https://doi.org/10.5194/gmd-6-1367-2013, 2013
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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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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
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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.
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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.
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.
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.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. 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.
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.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, 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.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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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 climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous 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 sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1941, https://doi.org/10.5194/egusphere-2024-1941, 2024
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We studied the coupled carbon-nitrogen cycle effect in Earth System Models by developing a carbon-nitrogen coupling in a reduced complexity model, MAGICC. Our model successfully emulated the global carbon-nitrogen cycle dynamics seen in CMIP6 complex models. Results indicate consistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100. Our findings suggest that nitrogen deficiency could reduce future land carbon sequestration.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
EGUsphere, https://doi.org/10.5194/egusphere-2024-2279, https://doi.org/10.5194/egusphere-2024-2279, 2024
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We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over time scales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Cited articles
Archer, D.: A data-driven model of the global calcite lysocline, Global
Biogeochem. Cy., 10, 511–526, https://doi.org/10.1029/96GB01521, 1996a. a, b, c
Archer, D. and Maier-Reimer, E.: Effect of deep-sea sedimentary calcite preservation on atmospheric CO2 concentration, Nature, 367, 260–263, https://doi.org/10.1038/367260a0, 1994. a, b
Archer, D., Kheshgi, H., and Maier-Reimer, E.: Dynamics of Fossil Fuel CO2 Neutralization by Marine CaCO3, Global Biogeochem. Cy., 12, 259–276, https://doi.org/10.1029/98GB00744, 1998. a
Archer, D., Winguth, A., Lea, D., and Mahowald, N.: What Caused the Glacial-Interglacial Atmospheric pCO2 Cycles?, Rev. Geophys., 38, 159–189, https://doi.org/10.1029/1999RG000066, 2000. a, b
Archer, D. E.: Modeling the Calcite Lysocline, J. Geophys. Res., 96, 17037–17050, https://doi.org/10.1029/91JC01812, 1991. a, b, c, d
Archer, D. E.: An atlas of the distribution of calcium carbonate in sediments of the deep sea, Global Biogeochem. Cy., 10, 159–174, https://doi.org/10.1029/95GB03016, 1996b. a
Archer, D. E., Morford, J. L., and Emerson, S. R.: A model of suboxic sedimentary diagenesis suitable for automatic tuning and gridded domains, Global Biogeochem. Cy., 16, 1017, https://doi.org/10.1029/2000GB001288, 2002. a, b, c, d
Arndt, S., Regnier, P., Goddéris, Y., and Donnadieu, Y.: GEOCLIM reloaded (v 1.0): a new coupled earth system model for past climate change, Geosci. Model Dev., 4, 451–481, https://doi.org/10.5194/gmd-4-451-2011, 2011. a, b, c, d
Berner, R. A.: Inclusion of adsorption in the modelling of early diagenesis,
Earth Planet. Sc. Lett., 29, 333–340, https://doi.org/10.1016/0012-821X(76)90137-0,
1976. a
Boudreau, B. P.: On the equivalence of non-local and radial-diffusion models for porewater irrigation, J. Mar. Res., 42, 731–735, https://doi.org/10.1357/002224084788505924, 1984. a
Boudreau, B. P.: The Mathematics of Tracer Mixing in Sediments: I. Spatially-Dependent, Diffusive Mixing, Am. J. Sci., 286, 161–198, https://doi.org/10.2475/ajs.286.3.161, 1986. a
Boudreau, B. P.: Asymptotic forms and solutions of the model for silica-opal diagenesis in bioturbated sediments, J. Geophys. Res., 95, 7367–7379, https://doi.org/10.1029/JC095iC05p07367, 1990. a
Boudreau, B. P.: Is burial velocity a master parameter for bioturbation?, Geochim. Cosmochim. Ac., 58, 1243–1249, https://doi.org/10.1016/0016-7037(94)90378-6, 1994. a
Boudreau, B. P.: A method-of-lines code for carbon and nutrient diagenesis in aquatic sediments, Comput. Geosci., 22, 479–496, https://doi.org/10.1016/0098-3004(95)00115-8, 1996. a, b
Boudreau, B. P.: Mean mixed depth of sediments: The wherefore and the why, Limnol. Oceanogr., 43, 524–526, https://doi.org/10.4319/lo.1998.43.3.0524, 1998. a
Brovkin, V., Ganopolski, A., Archer, D., and Munhoven, G.: Glacial CO2 cycle as a succession of key physical and biogeochemical processes, Clim. Past, 8, 251–264, https://doi.org/10.5194/cp-8-251-2012, 2012. a, b
Bruckner, T., Bashmakov, I. A., Mulugetta, Y., Chum, H., De la Vega Navarro, A., Edmonds, J., Faaij, A., Fungtammasan, B., Garg, A., Hertwich, E., Honnery, D., Infield, D., Kainuma, M., Khennas, S., Kim, S., Nimir, H. B., Riahi, K., Strachan, N., Wiser, R., and Zhang, X.: Energy Systems, chap. 7, in: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Minx, J. C., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., and Zwickel, T., Cambridge University Press, Cambridge, UK and New York, USA, 2014. a
Capet, A., Meysman, F. J. R., Akoumianaki, I., Soetaert, K., and Grégoire, M.: Integrating sediment biogeochemistry into 3D oceanic models: A study of benthic-pelagic coupling in the Black Sea, Ocean Model., 101, 83–100, https://doi.org/10.1016/j.ocemod.2016.03.006, 2016. a
Crucifix, M.: Traditional and novel approaches to palaeoclimate modelling, Quaternary Sci. Rev., 57, 1–16, https://doi.org/10.1016/j.quascirev.2012.09.010, 2012. a
Deleersnijder, E., Campin, J.-M., and Delhez, E. J. M.: The concept of age in marine modelling – I. Theory and preliminary model results, J. Marine Syst., 28, 2503–2517, https://doi.org/10.1016/S0924-7963(01)00026-4, 2001. a, b
Delhez, E. J. M., Campin, J.-M., Hirst, A. C., and Deleersnijder, E.: Toward a general theory of the age in ocean modelling, Ocean Model., 1, 17–27,
https://doi.org/10.1016/S1463-5003(99)00003-7, 1999. a, b
Dixit, S., Van Cappellen, P., and van Bennekom, A. J.: Processes controlling solubility of biogenic silica and pore water build-up of silicic acid in marine sediments, Mar. Chem., 73, 333–352, https://doi.org/10.1016/S0304-4203(00)00118-3, 2001. a
Dunne, J. P., Sarmiento, and Gnanadesikan, A.: A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor, Global Biogeochem. Cy., 21, GB4006, https://doi.org/10.1029/2006GB002907, 2007. a, b
Eby, M., Zickfeld, K., Montenegro, A., Archer, D., Meissner, K. J., and Weaver, A. J.: Lifetime of Anthropogenic Climate Change: Millennial Time Scales of Potential CO2 and Surface Temperature Perturbations, J. Climate, 22, 2501–2511, https://doi.org/10.1175/2008JCLI2554.1, 2009. a, b
Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A., Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F.: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity, Clim. Past, 9, 1111–1140, https://doi.org/10.5194/cp-9-1111-2013, 2013. a
Emerson, S.: Organic carbon preservation in marine sediments, in: The Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present, edited by Sundquist, E. T. and Broecker, W. S., AGU, Washington, DC, Geophys. Monogr. Ser., 32, 78–87, https://doi.org/10.1029/GM032p0078, 1985. a
Ermakov, I., Crucifix, M., and Munhoven, G.: Emulation of the MBM-MEDUSA model: exploring the sea level and the basin-to-shelf transfer influence on the system dynamics, EGU General Assembly, Vienna, Austria, 7–12 April 2013, EGU2013-9011-2, available at: https://meetingorganizer.copernicus.org/EGU2013/EGU2013-9011-2.pdf (last access: 27 May 2021), 2013. a
Fiadeiro, M. E. and Veronis, G.: On weighted-mean schemes for the finite difference approximation to the advection-diffusion equation, Tellus, 29, 512–522, https://doi.org/10.3402/tellusa.v29i6.11385, 1977. a
Gehlen, M., Bopp, L., Emprin, N., Aumont, O., Heinze, C., and Ragueneau, O.: Reconciling surface ocean productivity, export fluxes and sediment composition in a global biogeochemical ocean model, Biogeosciences, 3, 521–537, https://doi.org/10.5194/bg-3-521-2006, 2006. a, b
Heinze, C. and Maier-Reimer, E.: The Hamburg Oceanic Carbon Cycle Circulation Model Version “HAMOCC2s” for long time integrations, Technical Report 20, Deutsches Klimarechenzentrum, Hamburg (DE), https://doi.org/10.2312/WDCC/DKRZ_Report_No20, 1999. a
Heinze, C., Maier-Reimer, E., and Winn, K.: Glacial p
Heinze, C., Maier-Reimer, E., Winguth, A. M. E., and Archer, D.: A global ocean sediment model for long-term climate studies, Global Biogeochem. Cy., 13, 221–250, https://doi.org/10.1029/98GB02812, 1999. a, b, c, d
Heinze, C., Kriest, I., and Maier-Reimer, E.: Age offsets among different biogenic and lithogenic components of sediment cores revealed by numerical modeling, Paleoceanography, 24, PA4214, https://doi.org/10.1029/2008PA001662, 2009. a
Hoffert, M. I., Callegari, A. J., and Hsieh, C.-T.: A Box-Diffusion Carbon Cycle Model with Upwelling, Polar Bottom Water Formation and a Marine Biosphere, in: Carbon Cycle Modelling, edited by: Bolin, B., John Wiley & Sons, Chichester, NY, SCOPE, 16, 285–305, 1981. a
Hülse, D., Arndt, S., Wilson, J. D., Munhoven, G., and Ridgwell, A.: Understanding the causes and consequences of past marine carbon cycling variability through models, Earth-Sci. Rev., 171, 349–382, https://doi.org/10.1016/j.earscirev.2017.06.004, 2017. a
Hülse, D., Arndt, S., Daines, S., Regnier, P., and Ridgwell, A.: OMEN-SED 1.0: a novel, numerically efficient organic matter sediment diagenesis module for coupling to Earth system models, Geosci. Model Dev., 11, 2649–2689, https://doi.org/10.5194/gmd-11-2649-2018, 2018. a, b, c
Hundsdorfer, W. and Verwer, J.: Numerical Solution of Time-Dependent Advection-Diffusion-Reaction Equations, Springer Series in Computational Mathematics, Springer, Berlin, https://doi.org/10.1007/978-3-662-09017-6, 2003. a, b
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth system model in different CMIP5 experimental realizations, J. Adv. Model. Earth Sy., 5, 287–315, https://doi.org/10.1029/2012MS000178, 2013a. a, b, c
Ilyina, T., Wolf-Gladrow, D., Munhoven, G., and Heinze, C.: Assessing the potential of calcium-based artificial ocean alkalinization to mitigate rising atmospheric CO2 and ocean acidification, Geophys. Res. Lett., 40, 5909–5914, https://doi.org/10.1002/2013GL057981, 2013b. a
Jones, I. W., Munhoven, G., Tranter, M., Huybrechts, P., and Sharp, M. J.: Modelled glacial and non-glacial HCO
Jourabchi, P., Van Cappellen, P., and Regnier, P.: Quantitative interpretation of pH distributions in aquatic sediments: A reaction-transport modeling approach, Am. J. Sci., 305, 919–956,
https://doi.org/10.2475/ajs.305.9.919, 2005. a, b, c, d
Jourabchi, P., Meile, C., Pasion, L. R., and Van Cappellen, P.: Quantitative interpretation of pore water O2 and pH distributions in deep-sea sediments, Geochim. Cosmochim. Ac., 72, 1350–1364, https://doi.org/10.1016/j.gca.2007.12.012, 2008. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad
Keir, R. S.: Dissolution of calcite in the deep-sea: theoretical prediction for the case of uniform size particles settling into a well-mixed sediment, Am. J. Sci., 282, 193–236, https://doi.org/10.2475/ajs.282.3.193, 1982. a, b
Keir, R. S.: On the Late Pleistocene Ocean Geochemistry and Circulation, Paleoceanography, 3, 413–445, https://doi.org/10.1029/PA003i004p00413, 1988. a, b
Keir, R. S. and Berger, W. H.: Atmospheric CO2 content in the last 120,000
years: The phosphate-extraction model, J. Geophys. Res., 88, 6027–6038, https://doi.org/10.1029/JC088iC10p06027, 1983. a, b
Kurahashi-Nakamura, T., Paul, A., Munhoven, G., Merkel, U., and Schulz, M.: Coupling of a sediment diagenesis model (MEDUSA) and an Earth system model (CESM1.2): a contribution toward enhanced marine biogeochemical modelling and long-term climate simulations, Geosci. Model Dev., 13, 825–840, https://doi.org/10.5194/gmd-13-825-2020, 2020. a, b, c, d, e
Luff, R. and Moll, A.: Seasonal dynamics of the North Sea sediments using a three-dimensional coupled sediment-water model system, Cont. Shelf Res., 24, 1099–1127, https://doi.org/10.1016/j.csr.2004.03.010, 2004. a
Luff, R., Wallmann, K., Grandel, S., and Schlüter, M.: Numerical modeling of benthic processes in the deep Arabian Sea, Deep-Sea Res. Pt. II, 47, 3039–3072, https://doi.org/10.1016/S0967-0645(00)00058-8, 2000. a, b
Maier-Reimer, E.: Towards a global ocean carbon model, in: Interactions between Climate and Biosphere, edited by: Lieth, H., Fantechi, R., and Schnitzler, H., Swets & Zeitlinger, Lisse, NL, Progress in Biometeorology, 3, 295–310, 1984. a
Maier-Reimer, E., Kriest, I., Segschneider, J., and Wetzel, P.: The HAMburg Ocean Carbon Cycle Model HAMOCC 5.1 – Technical Description Release 1.1, Berichte zur Erdsystemforschung [Reports on Earth System Science] 14, Max-Planck-Institut für Meteorologie, Hamburg, DE, https://doi.org/10.17617/2.994983, 2005. a, b, c
Markwardt, C. B.: Non-linear Least Squares Fitting in IDL with MPFIT, in: Proceedings of Astronomical Data Analysis Software and Systems XVIII, Quebec, Canada, edited by: Bohlender, D., Dowler, P., and Durand, D., Astronomical Society of the Pacific, San Francisco, CA, ASP Conference Series, 411, 251–254, 2009. a
Martin, W. R. and Sayles, F. L.: CaCO3 dissolution in sediments of the Ceara Rise, western equatorial Atlantic, Geochim. Cosmochim. Ac., 60, 243–263, https://doi.org/10.1016/0016-7037(95)00383-5, 1996. a
Moore, J. K., Doney, S. C., and Lindsay, K.: Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model, Global Biogeochem. Cy., 18, GB4028, https://doi.org/10.1029/2004GB002220, 2004. a, b, c, d
Moore, J. K., Doney, S. C., and Lindsay, K.: The Role of Ecosystem Dynamics on the Global Ocean Carbon Cycle: A JGOFS Model-Data Synthesis, U.S. JGOFS [data set], iPub, available at: http://usjgofs.whoi.edu/mzweb/smpdatadocs/moore_becmodel.html (last access: 10 January 2020), 2005. a
Moore, J. K., Lindsay, K., Doney, S. C., Long, M. C., and Misumi, K.: Marine Ecosystem Dynamics and Biogeochemical Cycling in the Community Earth System Model [CESM1(BGC)]: Comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 Scenarios, J. Climate, 26, 9291–9312, https://doi.org/10.1175/JCLI-D-12-00566.1, 2013. a
Moreira Martinez, S., Roche, D. M., Munhoven, G., and Waelbroeck, C.: Coupling MEDUSA sediment model to iLOVECLIM (v1.1β) Earth system model, in: 12th International Conference on Paleoceanography (ICP12), Utrecht, The Netherlands, 29 August 2016–2 September 2016, P–368, available at: http://hdl.handle.net/2268/219749 (last access: 28 May 2021), 2016. a
Munhoven, G.: Model of Early Diagenesis in the Upper Sediment with Adaptable complexity – MEDUSA (v. 2): a time-dependent biogeochemical sediment module for Earth System Models, process analysis and teaching (Version 2.0), Zenodo [software], https://doi.org/10.5281/zenodo.4677682, 2020a. a
Munhoven, G.: The Fortran 95 Library µXML, (Version 1.0), Zenodo [software], https://doi.org/10.5281/zenodo.4677788, 2020b. a
Munhoven, G.: THDYCT (THermoDYnamic ConsTants) – a legacy FORTRAN 77 library for carbonate system speciation calculations, Zenodo [software], https://doi.org/10.5281/zenodo.4677790, 2020c. a
Munhoven, G. and François, L. M.: Glacial-interglacial variability of atmospheric CO2 due to changing continental silicate rock weathering: A model study, J. Geophys. Res., 101, 21423–21437, https://doi.org/10.1029/96JD01842, 1996. a, b
Palastanga, V., Slomp, C. P., and Heinze, C.: Long-term controls on ocean phosphorus and oxygen in a global biogeochemical model, Global Biogeochem. Cy., 25, GB3024, https://doi.org/10.1029/2010GB003827, 2011. a
Ridgwell, A.: Interpreting transient carbonate compensation depth changes by marine sediment core modeling, Paleoceanography, 22, PA4102, https://doi.org/10.1029/2006PA001372, 2007. a, b, c, d
Schink, D. R. and Guinasso Jr., N. L.: Modelling the Influence of Bioturbation and Other Processes on Calcium Carbonate Dissolution at the Sea Floor, in: The Fate of Fossil Fuel CO2 in the Oceans, edited by: Andersen, N. R. and Malahoff, A., Plenum Press, New York, NY, 375–399, 1977. a
Seiter, K., Hensen, C., Schröter, J., and Zabel, M.: Organic carbon content in surface sediments–defining regional provinces, Deep-Sea Res. Pt. I, 51, 2001–2026, https://doi.org/10.1016/j.dsr.2004.06.014, 2004. a, b, c
Sigman, D. M., McCorkle, D. C., and Martin, W. R.: The calcite lysocline as a constraint on glacial/interglacial low-latitude production changes, Global Biogeochem. Cy., 12, 409–427, https://doi.org/10.1029/98GB01184, 1998. a, b
Soetaert, K., Herman, P. M. J., and Middelburg, J. J.: A model of early diagenetic processes from the shelf to abyssal depths, Geochim. Cosmochim. Ac., 60, 1019–1040, https://doi.org/10.1016/0016-7037(96)00013-0, 1996. a, b
Soetaert, K., Middelburg, J. J., Herman, P. M. J., and Buis, K.: On the coupling of benthic and pelagic biogeochemical models, Earth-Sci. Rev., 51, 173–201, https://doi.org/10.1016/S0012-8252(00)00004-0, 2000.
a
Sulpis, O., Boudreau, B. P., Mucci, A., Jenkins, C., Trossman, D. S., Arbic, B. K., and Key, R. M.: Current CaCO3 dissolution at the seafloor caused by anthropogenic CO2, P. Natl. Acad. Sci. USA, 115, 11700–11705, https://doi.org/10.1073/pnas.1804250115, 2018. a
Sundquist, E. T.: Geologic Analogs: Their Value and Limitations in Carbon
Dioxide Research, in: The Changing Carbon Cycle: A Global Analysis, edited by: Trabalka, J. R. and Reichle, D. E., Springer-Verlag, New York (NY), chap. 19, 371–402, https://doi.org/10.1007/978-1-4757-1915-4_19, 1986. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Tschumi, T., Joos, F., Gehlen, M., and Heinze, C.: Deep ocean ventilation, carbon isotopes, marine sedimentation and the deglacial CO2 rise, Clim. Past, 7, 771–800, https://doi.org/10.5194/cp-7-771-2011, 2011. a, b
Van Cappellen, P. and Wang, Y.: Cycling of Iron and Manganese in Surface Sediments: A General Theory for the Coupled Transport and Reaction of Carbon, Oxygen, Nitrogen, Sulfur, Iron, and Manganese, Am. J. Sci., 296, 197–243, https://doi.org/10.2475/ajs.296.3.197, 1996. a
Völker, C., Ye, Y., Butzin, M., Köhler, P., and Munhoven, G.: Role of sediment in the marine C cycle–insights from a coupled ocean-sediment model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18562, https://doi.org/10.5194/egusphere-egu2020-18562, 2020. a
W3C: Extensible Markup Language (XML) 1.0 (Fifth Edition), W3C recommendation
26 November 2008, W3C, available at:
http://www.w3.org/TR/2008/REC-xml-20081126 (last access 2 February 2017), 2008. a
Walker, J. C. G. and Opdyke, B. C.: Influence of variable rates of neritic carbonate deposition on atmospheric carbon dioxide and pelagic sediments, Paleoceanography, 10, 415–427, https://doi.org/10.1029/94PA02963, 1995. a
Wenzhöfer, F., Adler, M., Kohls, O., Hensen, C., Strotmann, B., Boehme, S., and Schulz, H.: Calcite dissolution driven by benthic mineralization in the deep-sea: In situ measurements of Ca2+, pH, pCO2 and O2, Geochim. Cosmochim. Ac., 65, 2677–2690, https://doi.org/10.1016/S0016-7037(01)00620-2, 2001. a, b
Yool, A., Popova, E. E., and Anderson, T. R.: Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain, Geosci. Model Dev., 4, 381–417, https://doi.org/10.5194/gmd-4-381-2011, 2001. a, b
Zickfeld, K., MacDougall, A. H., and Matthews, H. D.: On the proportionality between global temperature change and cumulative CO2 emissions during periods of net negative CO2 emissions, Environ. Res. Lett., 11, 055006, https://doi.org/10.1088/1748-9326/11/5/055006, 2016. a
Short summary
Sea-floor sediments play an important role in biogeochemical cycling of elements (e.g. carbon, silicon, nutrients) in the ocean. Realistic sediment modules are, however, not yet commonly used in global ocean biogeochemical models. Here we present MEDUSA, a model of the processes taking place in the surface sea-floor sediments which control the interaction between the sediments and the ocean. MEDUSA can be configured to meet the exact needs of any given ocean biogeochemical model.
Sea-floor sediments play an important role in biogeochemical cycling of elements (e.g. carbon,...
