Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-1869-2019
© Author(s) 2019. 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-12-1869-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 May 2019
Development and technical paper |
| 10 May 2019
| 10 May 2019
CO2 drawdown due to particle ballasting by glacial aeolian dust: an estimate based on the ocean carbon cycle model MPIOM/HAMOCC version 1.6.2p3
Malte Heinemann
CORRESPONDING AUTHOR
Institute of Geosciences, Kiel University, Ludewig-Meyn-Strasse 10,
24118 Kiel, Germany
Joachim Segschneider
Institute of Geosciences, Kiel University, Ludewig-Meyn-Strasse 10,
24118 Kiel, Germany
Birgit Schneider
Institute of Geosciences, Kiel University, Ludewig-Meyn-Strasse 10,
24118 Kiel, Germany
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The Antarctic Ice Sheet has expanded and retracted often in the past, but, so far, studies have not identified which environmental driver is most important: air temperature, snowfall, ocean conditions or global sea level. In a modeling study of 400 000 years of Antarctic Ice Sheet variability we isolated different drivers and found that no single driver dominates. Air temperature and sea level are most important and combine in a synergistic way, with important implications for future change.
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The northern Indian Ocean hosts an extensive oxygen minimum zone (OMZ), which intensified due to human-induced global changes. This includes the occurrence of anoxic events on the Indian shelf and affects benthic ecosystems and the pelagic ecosystem structure in the Arabian Sea. Consequences for biogeochemical cycles are unknown, which, in addition to the poor representation of mesoscale features, reduces the reliability of predictions of the future OMZ development in the northern Indian Ocean.
Dipayan Choudhury, Axel Timmermann, Fabian Schloesser, Malte Heinemann, and David Pollard
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Michelle Tigchelaar, Axel Timmermann, Tobias Friedrich, Malte Heinemann, and David Pollard
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Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
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M. Sarnthein, B. Schneider, and P. M. Grootes
Clim. Past, 9, 2595–2614, https://doi.org/10.5194/cp-9-2595-2013, https://doi.org/10.5194/cp-9-2595-2013, 2013
A. Regenberg, B. Schneider, and R. Gangstø
Biogeosciences Discuss., https://doi.org/10.5194/bgd-10-11343-2013, https://doi.org/10.5194/bgd-10-11343-2013, 2013
Revised manuscript not accepted
V. Cocco, F. Joos, M. Steinacher, T. L. Frölicher, L. Bopp, J. Dunne, M. Gehlen, C. Heinze, J. Orr, A. Oschlies, B. Schneider, J. Segschneider, and J. Tjiputra
Biogeosciences, 10, 1849–1868, https://doi.org/10.5194/bg-10-1849-2013, https://doi.org/10.5194/bg-10-1849-2013, 2013
P. Bakker, E. J. Stone, S. Charbit, M. Gröger, U. Krebs-Kanzow, S. P. Ritz, V. Varma, V. Khon, D. J. Lunt, U. Mikolajewicz, M. Prange, H. Renssen, B. Schneider, and M. Schulz
Clim. Past, 9, 605–619, https://doi.org/10.5194/cp-9-605-2013, https://doi.org/10.5194/cp-9-605-2013, 2013
F. Joos, R. Roth, J. S. Fuglestvedt, G. P. Peters, I. G. Enting, W. von Bloh, V. Brovkin, E. J. Burke, M. Eby, N. R. Edwards, T. Friedrich, T. L. Frölicher, P. R. Halloran, P. B. Holden, C. Jones, T. Kleinen, F. T. Mackenzie, K. Matsumoto, M. Meinshausen, G.-K. Plattner, A. Reisinger, J. Segschneider, G. Shaffer, M. Steinacher, K. Strassmann, K. Tanaka, A. Timmermann, and A. J. Weaver
Atmos. Chem. Phys., 13, 2793–2825, https://doi.org/10.5194/acp-13-2793-2013, https://doi.org/10.5194/acp-13-2793-2013, 2013
J. Segschneider, A. Beitsch, C. Timmreck, V. Brovkin, T. Ilyina, J. Jungclaus, S. J. Lorenz, K. D. Six, and D. Zanchettin
Biogeosciences, 10, 669–687, https://doi.org/10.5194/bg-10-669-2013, https://doi.org/10.5194/bg-10-669-2013, 2013
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Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
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Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
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Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
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Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
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We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Cited articles
Abe-Ouchi, A., Segawa, T., and Saito, F.: Climatic Conditions for modelling
the Northern Hemisphere ice sheets throughout the ice age cycle, Clim. Past,
3, 423–438, https://doi.org/10.5194/cp-3-423-2007, 2007. a
Albani, S., Mahowald, N. M., Murphy, L. N., Raiswell, R., Moore, J. K.,
Anderson, R. F., McGee, D., Bradtmiller, L. I., Delmonte, B., Hesse, P. P.,
and Mayewski, P. A.: Paleodust variability since the Last Glacial Maximum
and implications for iron inputs to the ocean, Geophys. Res. Lett.,
43, 3944–3954, 2016. a, b, c, d, e
Anderson, R. F., Barker, S., Fleisher, M., Gersonde, R., Goldstein, S. L.,
Kuhn, G., Mortyn, P. G., Pahnke, K., and Sachs, J. P.: Biological response
to millennial variability of dust and nutrient supply in the Subantarctic
South Atlantic Ocean, Philos. T. R. Soc. A, 372, 20130054,
2014. a
Bach, L. T., Boxhammer, T., Larsen, A., Hildebrandt, N., Schulz, K. G., and
Riebesell, U.: Influence of plankton community structure on the sinking
velocity of marine aggregates, Global Biogeochem. Cy., 30, 1145–1165,
2016. a
Berelson, W. M.: Particle settling rates increase with depth in the ocean,
Deep-Sea Res. Pt. II, 49, 237–251,
2001. a
Berman-Frank, I., Cullen, J. T., Shaked, Y., Sherrell, R. M., and Falkowski,
P. G.: Iron availability, cellular iron quotas, and nitrogen fixation in
Trichodesmium, Limnol. Oceanogr., 46, 1249–1260, 2001. a
Bopp, L., Kohfeld, K. E., Le Quéré, C., and Aumont, O.: Dust impact on
marine biota and atmospheric CO2 during glacial periods,
Paleoceanography, 18, 1046, https://doi.org/10.1029/2002PA000810, 2003. a, b, c
Boyd, P. W. and Trull, T. W.: Understanding the export of biogenic particles
in oceanic waters: Is there consensus?, Prog. Oceanogr., 72,
276–312, 2007. a
Boyd, P. W., Jickells, T., Law, C. S., Blain, S., Boyle, E. A., Buesseler,
K. O., Coale, K. H., Cullen, J. J., de Baar, H. J. W., Follows, M., Harvey,
M., Lancelot, C., Levasseur, M., Owens, N. P. J., Pollard, R., Rivkin, R. B.,
Sarmiento, J., Schoemann, V., Smetacek, V., Takeda, S., Tsuda, A., Turner,
S., and Watson, A. J.: Mesoscale Iron Enrichment Experiments 1993–2005:
Synthesis and Future Directions, Science, 315, 612–617, 2007. a, b
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
Bullard, J. E.: Contemporary glacigenic inputs to the dust cycle, Earth
Surf. Proc. Land., 38, 71–89, 2013. a
Chikamoto, M. O., Abe-Ouchi, A., Oka, A., Ohgaito, R., and Timmermann, A.:
Quantifying the ocean's role in glacial CO2 reductions, Clim. Past,
8, 545–563, https://doi.org/10.5194/cp-8-545-2012, 2012. a
Dunne, J. P., Sarmiento, J. L., 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, 1–16, 2007. a
Falkowski, P. G., Barber, R. T., and Smetacek, V.: Biogeochemical Controls and
Feedbacks on Ocean Primary Production, Science, 281, 200–206, 1998. a
Fischer, G. and Karakaş, G.: Sinking rates and ballast composition of
particles in the Atlantic Ocean: implications for the organic carbon fluxes
to the deep ocean, Biogeosciences, 6, 85–102,
https://doi.org/10.5194/bg-6-85-2009, 2009. a
François, R., Honjo, S., Krishfield, R., and Manganini, S.: Factors
controlling the flux of organic carbon to the bathypelagic zone of the
ocean, Global Biogeochem. Cy., 16, 34-1–34-20, 2002. a
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J.,
Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K.,
Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L.,
Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan,
F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R.,
Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J.,
Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.:
Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations
for the Coupled Model Intercomparison Project phase 5, J. Adv.
Model. Earth Sy., 5, 572–597, 2013. a
Hain, M. P., Sigman, D. M., and Haug, G. H.: Carbon dioxide effects of
Antarctic stratification, North Atlantic Intermediate Water formation, and
subantarctic nutrient drawdown during the last ice age: Diagnosis and
synthesis in a geochemical box model, Global Biogeochem. Cy., 24, GB4023,
https://doi.org/10.1029/2010GB003790, 2010. a
Heinemann, M., Timmermann, A., Elison Timm, O., Saito, F., and Abe-Ouchi, A.:
Deglacial ice sheet meltdown: orbital pacemaking and CO2 effects,
Clim. Past, 10, 1567–1579, https://doi.org/10.5194/cp-10-1567-2014, 2014. a
Heinze, C., Reimer, E. M., Winguth, A. M. E., and Archer, D.: A global oceanic
sediment model for long-term climate studies, Global Biogeochemical Cycles,
13, 221–250, 1999. a
Henson, S. A., Sanders, R., and Madsen, E.: Global patterns in efficiency of
particulate organic carbon export and transfer to the deep ocean, Global
Biogeochem. Cy., 26, GB1028, https://doi.org/10.1029/2011GB004099, 2012. a, b, c
Hofmann, M. and Schellnhuber, H.-J.: Oceanic acidification affects marine
carbon pump and triggers extended marine oxygen holes, P. Natl. Acad. Sci. USA, 106,
3017–3022, 2009. a
Honjo, S., Manganini, S. J., and Cole, J. J.: Sedimentation of biogenic matter
in the deep ocean, Deep-Sea Res., 29, 609–625, 1982. a
Howard, M. T., Winguth, A. M. E., Klaas, C., and Reimer, E. M.: Sensitivity of
ocean carbon tracer distributions to particulate organic flux
parameterizations, Global Biogeochem. Cy., 20, 1–11, 2006. a
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, 2013. a, b, c, d, e, f
Ittekkot, V.: The abiotically driven biological pump in the ocean and
short-term fluctuations in atmospheric CO2 contents, Global
Planet. Change, 8, 17–25, 1993. a
Jaccard, S. L. and Galbraith, E. D.: Large climate-driven changes of oceanic
oxygen concentrations during the last deglaciation, Nat. Geosci., 5,
151–156, 2012. a
Jungclaus, J. H., Lorenz, S. J., Timmreck, C., Reick, C. H., Brovkin, V.,
Six, K., Segschneider, J., Giorgetta, M. A., Crowley, T. J., Pongratz, J.,
Krivova, N. A., Vieira, L. E., Solanki, S. K., Klocke, D., Botzet, M., Esch,
M., Gayler, V., Haak, H., Raddatz, T. J., Roeckner, E., Schnur, R., Widmann,
H., Claussen, M., Stevens, B., and Marotzke, J.: Climate and carbon-cycle
variability over the last millennium, Clim. Past, 6, 723–737,
https://doi.org/10.5194/cp-6-723-2010, 2010. a
Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D.,
Mikolajewicz, U., Notz, D., and von Storch, J. S.: Characteristics of the
ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean
component of the MPI-Earth system model, J. Adv. Model.
Earth Sy., 5, 422–446, 2013. a
Klaas, C. and Archer, D. E.: Association of sinking organic matter with
various types of mineral ballast in the deep sea: Implications for the rain
ratio, Global Biogeochem. Cy., 16, 1116, https://doi.org/10.1029/2001GB001765, 2002. a, b
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., and Fischer,
H.: A 156 kyr smoothed history of the atmospheric greenhouse gases
CO2, CH4, and N2O and their radiative forcing, Earth
Syst. Sci. Data, 9, 363–387, https://doi.org/10.5194/essd-9-363-2017, 2017. a
Komar, P. D., Morse, A. P., Small, L. F., and Fowler, S. W.: An analysis of
sinking rates of natural copepod and euphausiid fecal pellets1, Limnol. Oceanogr., 26, 172–180, 1981. a
Kriest, I. and Evans, G. T.: A vertically resolved model for phytoplankton
aggregation, J. Earth Syst. Sci., 109, 453–469, 2000. a
Kustka, A. B., Wilhelmy, S. A. S., Carpenter, E. J., Capone, D., Burns, J., and
Sunda, W. G.: Iron requirements for dinitrogen- and ammonium-supported
growth in cultures of Trichodesmium (IMS 101): Comparison with nitrogen
fixation rates and iron: carbon ratios of field populations, Limnol.
Oceanogr., 48, 1869–1884, 2003. a
Lam, P. J. and Marchal, O.: Insights into Particle Cycling from Thorium and
Particle Data, Annual Review of Marine Science, 7, 159–184, 2015. a
Lüthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.-M.,
Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K., and
Stocker, T. F.: High-resolution carbon dioxide concentration record
650,000–800,000 years before present, Nature, 453, 379–382, 2008. a
Mahowald, N. M., Baker, A. R., Bergametti, G., Brooks, N., Duce, R. A.,
Jickells, T. D., Kubilay, N., Prospero, J. M., and Tegen, I.: Atmospheric
global dust cycle and iron inputs to the ocean, Global Biogeochem. Cy., 19,
GB4025, https://doi.org/10.1029/2004GB002402, 2005. a, b, c
Mahowald, N. M., Muhs, D. R., Levis, S., Rasch, P. J., Yoshioka, M., Zender,
C. S., and Luo, C.: Change in atmospheric mineral aerosols in response to
climate: Last glacial period, preindustrial, modern, and doubled carbon
dioxide climates, J. Geophys. Res.-Sol. Ea., 111, D10202,
https://doi.org/10.1029/2005JD006653, 2006. a, b, c, d, e, f, g, h, i, j
Maier-Reimer, E., Kriest, I., Segschneider, J., and Wetzel, P.: The HAMburg
Ocean Carbon Cycle Model HAMOCC5.1 – Technical Description Release 1.1,
Berichte zur Erdsystemforschung, Max-Planck-Institut für Meteorologie,
Hamburg, 14 pp., 2005. a
Marcott, S. A., Bauska, T. K., Buizert, C., Steig, E. J., Rosen, J. L., Cuffey,
K. M., Fudge, T. J., Severinghaus, J. P., Ahn, J., Kalk, M. L., McConnell,
J. R., Sowers, T., Taylor, K. C., White, J. W. C., and Brook, E. J.:
Centennial-scale changes in the global carbon cycle during the last
deglaciation, Nature, 514, 616–619, 2014. a
Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M., and Röske, F.: The
Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear
coordinates, Ocean Model., 5, 91–127, 2003. a
Martin, J. H., Knauer, G. A., Karl, D. M., and Broenkow, W. W.: VERTEX: carbon
cycling in the northeast Pacific, Deep-Sea Res. Pt. A, 34, 267–285, 1987. a
Martinez-Garcia, A., Sigman, D. M., Ren, H., Anderson, R. F., Straub, M.,
Hodell, D. A., Jaccard, S. L., Eglinton, T. I., and Haug, G. H.: Iron
Fertilization of the Subantarctic Ocean During the Last Ice Age, Science,
343, 1347–1350, 2014. a
Moore, J. K. and Doney, S. C.: Iron availability limits the ocean nitrogen
inventory stabilizing feedbacks between marine denitrification and nitrogen
fixation, Global Biogeochem. Cy., 21, GB2001, https://doi.org/10.1029/2006GB002762,
2007. a, b
Raddatz, T. J., Reick, C. H., Knorr, W., Kattge, J., Roeckner, E., Schnur, R.,
Schnitzler, K. G., Wetzel, P., and Jungclaus, J.: Will the tropical land
biosphere dominate the climate–carbon cycle feedback during the
twenty-first century?, Clim. Dynam., 29, 565–574, 2007. a
Ridgwell, A. J.: An end to the “rain ratio” reign?, Geochem. Geophy.
Geosy., 4, 1–5, 2003. a
Röske, F.: Global oceanic heat and fresh water forcing datasets based on
ERA-40 and ERA-15, Max-Planck-Institut für Meteorologie, Hamburg,
Berichte zur Erdsystemforschung, 13 pp., 2005. a
Röske, F.: A global heat and freshwater forcing dataset for ocean models,
Ocean Model., 11, 235–297, 2006. a
Sigman, D. M. and Boyle, E. A.: Glacial/interglacial variations in atmospheric
carbon dioxide, Nature, 407, 859–869, 2000. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I.,
Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and
Roeckner, E.: Atmosphere component of the MPI-M Earth System Model: ECHAM6,
J. Adv. Model. Earth Sy., 5, 146–172, 2013. a
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishiik,
M., Midorikawak, T., Nojiril, Y., Körtzinger, A., Tobias Steinhoffm, M.
H. n., Hoppema, M., Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen,
T., Olsen, A., Bellerby, R., Wong, C. S., Delille, B., Bates, N. R., and
de Baar, H. J. W.: Climatological mean and decadal change in surface ocean
pCO2, and net sea–air CO2 flux over the global oceans,
Deep-Sea Res. Pt. II, 56, 554–577, 2009. a, b
van der Jagt, H., Friese, C., Stuut, J. B. W., Fischer, G., and Iversen, M. H.:
The ballasting effect of Saharan dust deposition on aggregate dynamics and
carbon export: Aggregation, settling, and scavenging potential of marine
snow, Limnol. Oceanogr., 63, 1386–1394, 2018. a
Wagener, T., Guieu, C., and Leblond, N.: Effects of dust deposition on iron
cycle in the surface Mediterranean Sea: results from a mesocosm seeding
experiment, Biogeosciences, 7, 3769–3781,
https://doi.org/10.5194/bg-7-3769-2010, 2010. a
Weber, T., Cram, J. A., and Leung, S. W.: Deep ocean nutrients imply large
latitudinal variation in particle transfer efficiency, P. Natl. Acad. Sci. USA, 113,
8606–8611, 2016. a
Werner, M., Tegen, I., Harrison, S. P., Kohfeld, K. E., Prentice, I. C.,
Balkanski, Y., Rodhe, H., and Roelandt, C.: Seasonal and interannual
variability of the mineral dust cycle under present and glacial climate
conditions, J. Geophys. Res., 107, 4744, https://doi.org/10.1029/2002JD002365, 2002. a, b, c, d
Wetzel, P., Maier-Reimer, E., Botzet, M., Jungclaus, J., Keenlyside, N., and
Latif, M.: Effects of Ocean Biology on the Penetrative Radiation in a
Coupled Climate Model, J. Climate, 19, 3973–3987, 2006. a
Ye, Y. and Völker, C.: On the Role of Dust-Deposited Lithogenic Particles
for Iron Cycling in the Tropical and Subtropical Atlantic, Global
Biogeochem. Cy., 31, 1543–1558, 2017. a
Ye, Y., Wagener, T., Völker, C., Guieu, C., and Wolf-Gladrow, D. A.: Dust
deposition: iron source or sink? A case study, Biogeosciences, 8, 2107–2124,
https://doi.org/10.5194/bg-8-2107-2011, 2011. a
Short summary
Ocean CO2 uptake played a crucial role for the global cooling during ice ages. Dust formation, e.g. by ice scraping over bedrock, potentially contributed to this CO2 uptake because (1) the iron in the dust is a fertilizer and (2) the heavy dust particles can accelerate sinking organic matter (ballasting hypothesis). This study tests the glacial dust ballasting hypothesis for the first time, using an ocean model. It turns out, however, that the ballasting effect probably played a minor role.
Ocean CO2 uptake played a crucial role for the global cooling during ice ages. Dust formation,...
