Articles | Volume 12, issue 4
https://doi.org/10.5194/gmd-12-1541-2019
© 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-12-1541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Apr 2019
Model description paper |
| 18 Apr 2019
| 18 Apr 2019
The [simple carbon project] model v1.0
Cameron M. O'Neill
CORRESPONDING AUTHOR
Research School of Earth Sciences, Australian National University, Canberra, Australia
Andrew McC. Hogg
Research School of Earth Sciences, Australian National University, Canberra, Australia
Michael J. Ellwood
Research School of Earth Sciences, Australian National University, Canberra, Australia
Stephen M. Eggins
Research School of Earth Sciences, Australian National University, Canberra, Australia
Bradley N. Opdyke
Research School of Earth Sciences, Australian National University, Canberra, Australia
Related authors
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Bradley N. Opdyke, and Stephen M. Eggins
Clim. Past, 17, 171–201, https://doi.org/10.5194/cp-17-171-2021, https://doi.org/10.5194/cp-17-171-2021, 2021
Short summary
Short summary
We undertake a model–data study of the last glacial–interglacial cycle of atmospheric CO2, spanning 0–130 ka. We apply a carbon cycle box model, constrained with glacial–interglacial observations, and solve for optimal model parameter values against atmospheric and ocean proxy data. The results indicate that the last glacial drawdown in atmospheric CO2 was delivered mainly by slowing ocean circulation, lower sea surface temperatures and also increased Southern Ocean biological productivity.
Wilma G. C. Huneke, Andy McC. Hogg, Martin Dix, Daohua Bi, Arnold Sullivan, Shayne McGregor, Chiara Holgate, Siobhan P. O'Farrell, and Micael J. T. Oliveira
EGUsphere, https://doi.org/10.5194/egusphere-2025-1006, https://doi.org/10.5194/egusphere-2025-1006, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component is introduced. The new version of the coupled climate model was designed to better capture smaller-scale ocean motions. While this configuration improves the representation of many aspects of the climate system, some biases from the existing lower-resolution version persist.
Claire K. Yung, Madelaine G. Rosevear, Adele K. Morrison, Andrew McC Hogg, and Yoshihiro Nakayama
EGUsphere, https://doi.org/10.5194/egusphere-2024-3513, https://doi.org/10.5194/egusphere-2024-3513, 2024
Short summary
Short summary
Ocean models are used to understand how the ocean interacts with the Antarctic Ice Sheet, but they are too coarse in resolution to capture the small-scale ocean processes driving melting and require a parameterisation to predict melt. Previous parameterisations ignore key processes occurring in some regions of Antarctica. We develop a parameterisation with the feedback of stratification on melting and test it in idealised and regional ocean models, finding changes to melt rate and circulation.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
Short summary
Short summary
Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
Short summary
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Hakase Hayashida, Meibing Jin, Nadja S. Steiner, Neil C. Swart, Eiji Watanabe, Russell Fiedler, Andrew McC. Hogg, Andrew E. Kiss, Richard J. Matear, and Peter G. Strutton
Geosci. Model Dev., 14, 6847–6861, https://doi.org/10.5194/gmd-14-6847-2021, https://doi.org/10.5194/gmd-14-6847-2021, 2021
Short summary
Short summary
Ice algae are tiny plants like phytoplankton but they grow within sea ice. In polar regions, both phytoplankton and ice algae are the foundation of marine ecosystems and play an important role in taking up carbon dioxide in the atmosphere. However, state-of-the-art climate models typically do not include ice algae, and therefore their role in the climate system remains unclear. This project aims to address this knowledge gap by coordinating a set of experiments using sea-ice–ocean models.
Cameron M. O'Neill, Andrew McC. Hogg, Michael J. Ellwood, Bradley N. Opdyke, and Stephen M. Eggins
Clim. Past, 17, 171–201, https://doi.org/10.5194/cp-17-171-2021, https://doi.org/10.5194/cp-17-171-2021, 2021
Short summary
Short summary
We undertake a model–data study of the last glacial–interglacial cycle of atmospheric CO2, spanning 0–130 ka. We apply a carbon cycle box model, constrained with glacial–interglacial observations, and solve for optimal model parameter values against atmospheric and ocean proxy data. The results indicate that the last glacial drawdown in atmospheric CO2 was delivered mainly by slowing ocean circulation, lower sea surface temperatures and also increased Southern Ocean biological productivity.
Andrew E. Kiss, Andrew McC. Hogg, Nicholas Hannah, Fabio Boeira Dias, Gary B. Brassington, Matthew A. Chamberlain, Christopher Chapman, Peter Dobrohotoff, Catia M. Domingues, Earl R. Duran, Matthew H. England, Russell Fiedler, Stephen M. Griffies, Aidan Heerdegen, Petra Heil, Ryan M. Holmes, Andreas Klocker, Simon J. Marsland, Adele K. Morrison, James Munroe, Maxim Nikurashin, Peter R. Oke, Gabriela S. Pilo, Océane Richet, Abhishek Savita, Paul Spence, Kial D. Stewart, Marshall L. Ward, Fanghua Wu, and Xihan Zhang
Geosci. Model Dev., 13, 401–442, https://doi.org/10.5194/gmd-13-401-2020, https://doi.org/10.5194/gmd-13-401-2020, 2020
Short summary
Short summary
We describe new computer model configurations which simulate the global ocean and sea ice at three resolutions. The coarsest resolution is suitable for multi-century climate projection experiments, whereas the finest resolution is designed for more detailed studies over time spans of decades. The paper provides technical details of the model configurations and an assessment of their performance relative to observations.
Robert McKay, Neville Exon, Dietmar Müller, Karsten Gohl, Michael Gurnis, Amelia Shevenell, Stuart Henrys, Fumio Inagaki, Dhananjai Pandey, Jessica Whiteside, Tina van de Flierdt, Tim Naish, Verena Heuer, Yuki Morono, Millard Coffin, Marguerite Godard, Laura Wallace, Shuichi Kodaira, Peter Bijl, Julien Collot, Gerald Dickens, Brandon Dugan, Ann G. Dunlea, Ron Hackney, Minoru Ikehara, Martin Jutzeler, Lisa McNeill, Sushant Naik, Taryn Noble, Bradley Opdyke, Ingo Pecher, Lowell Stott, Gabriele Uenzelmann-Neben, Yatheesh Vadakkeykath, and Ulrich G. Wortmann
Sci. Dril., 24, 61–70, https://doi.org/10.5194/sd-24-61-2018, https://doi.org/10.5194/sd-24-61-2018, 2018
O. Friedrich, R. D. Norris, P. A. Wilson, and B. N. Opdyke
Sci. Dril., 19, 39–42, https://doi.org/10.5194/sd-19-39-2015, https://doi.org/10.5194/sd-19-39-2015, 2015
Short summary
Short summary
This workshop brought together specialists from various fields to develop a drilling proposal to fill the “Oligo-Miocene Gap” that exists in our understanding of the functions of Earth’s systems. We propose to establish the first continuous high-deposition record of the Oligo-Miocene through International Ocean Discovery Program (IODP) drilling in the North Atlantic. We give a short overview of the major topics discussed during the workshop and the scientific goals of the resulting pre-proposal.
Related subject area
Biogeosciences
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
H2MV (v1.0): Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Including the Phosphorus cycle into the LPJ-GUESS Dynamic Global Vegetation Model (v4.1, r10994) – Global patterns and temporal trends of N and P primary production limitation
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Process-based Modeling of Solar-induced Chlorophyll Fluorescence with VISIT-SIF version 1.0
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
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.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1914, https://doi.org/10.5194/egusphere-2024-1914, 2024
Short summary
Short summary
Under climate change, the conditions for wildfires to form are becoming more frequent in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a crucial platform for future developments.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
Short summary
Short summary
When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those 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 see what changes on our lands.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Cited articles
Amiotte Suchet, P., Probst, J., and Ludwig, W.: Worldwide distribution of
continental rock lithology: Implications for the atmospheric/soil CO2 uptake
by continental weathering and alkalinity river transport to the oceans,
Global Biogeochem. Cy., 17, 7–1 – 7–14, 2003. a
Anderson, L. A. and Sarmiento, J. L.: Redfield ratios of remineralization
determined by nutrient data analysis, Global Biogeochem. Cy., 8, 65–80,
1994. a
Anderson, R., Ali, S., Bradtmiller, L., Nielsen, S., Fleisher, M., Anderson,
B., and Burckle, L.: Wind-driven upwelling in the Southern Ocean and the
deglacial rise in atmospheric CO2, Science, 323, 1443–1448, 2009. a
Anderson, R. F., Fleishera, M. Q., Laoc, Y., and Winckler, G.: Modern
CaCO3
preservation in equatorial Pacific sediments in the context of
late-Pleistocene glacial cycles, Mar. Chem., 111, 30–46, 2007. a
Annan, J. D. and Hargreaves, J. C.: A new global reconstruction of
temperature changes at the Last Glacial Maximum, Clim. Past, 9, 367–376,
https://doi.org/10.5194/cp-9-367-2013, 2013. a, b, c
Archer, D. and Maier-Reimer, E.: Effect of deep-sea sedimentary calcite
preservation on atmospheric CO2 concentration, Nature, 367, 260–263, 1994. a
Barker, S., Knorr, G., Vautravers, M., Diz, P., and
Skinner, L.: Extreme
deepening of the Atlantic overturning circulation during deglaciation,
Nat. Geosci., 3, 567–571, 2010. a
Battaglia, G., Steinacher, M., and Joos, F.: A probabilistic assessment of
calcium carbonate export and dissolution in the modern ocean, Biogeosciences,
13, 2823–2848, https://doi.org/10.5194/bg-13-2823-2016, 2016. a, b
Bouttes, N., Paillard, D., Roche, D. M., Brovkin, V., and Bopp, L.: Last
Glacial Maximum CO2 and δ13C successfully reconciled,
Geophys. Res. Lett., 38,
LO2705, https://doi.org/10.1029/2010GL044499,
2011. a
Broecker, W. S.: The great ocean conveyor, Oceanography, 4, 79–89, 1991. a
Bryan, S., Marchitto, T., and Lehman, S.: The release of 14C-depleted carbon
from the deep ocean during the last deglaciation: Evidence from the Arabian
Sea, Earth Planet. Sc. Lett., 298, 244–254, 2010. a
Ciais, P., Tagliabue, A., Cuntz, M., Bopp, L., Scholze, M., Hoffmann, G.,
Lourantou, A., Harrison, S. P., Prentice, I. C., Kelley, D. I., Koven, C.,
and Piao, S. L.: Large inert carbon pool in the terrestrial biosphere during
the Last Glacial Maximum, Nat. Geosci., 5, 74–79, 2012. a, b, c, d, e, f, g
Clark, P., Dyke, A., Shakun, J., Carlson, A., Clark, J., Wohlfarth, B.,
Mitrovica, J., Hostetler, S., and McCabe, A.: The Last Glacial Maximum,
Science, 325, 710–714, 2009. a
Compton, J., Mallinson, D., Glenn, C., and Zanin, Y.: Variations in the global
phosphorus cycle, Society of Sedimentary Geology, 66, 21–33, 2000. a
Craig, H.: The natural distribution of radiocarbon and the exchange time of
carbon dioxide between atmosphere and sea, Tellus, 9, 1–17, 1957. a
Curry, W., Duplessy, J., Labeyrie, L., and Shackleton, N.: Changes in the
distribution of δ13C of deep water PCO2 between the last glaciation and the
Holocene, Paleoceanography, 3, 317–341, 1988. a
Curry, W. B. and Oppo, D. W.: Glacial water mass geometry and the distribution
of δ13C of CO2 in the western Atlantic Ocean, Paleoceanography, 20, PA1017,
https://doi.org/10.1029/2004PA001021, 2005. a, b, c, d
Davies-Walczak, M., Mix, A., Stoner, J., Southon, J., Cheseby, M., and Xuan,
C.: Late Glacial to Holocene radiocarbon constraints on North Pacific
Intermediate Water ventilation and deglacial atmospheric CO2 sources,
Earth Planet. Sc. Lett., 397, 57–66, 2014. a
Dickson, R. R. and Brown, J.: The production of North Atlantic Deep Water:
Sources, rates, and pathways, J. Geophys. Res., 99,
12319–12341,
1994. a
Emerson, S. and Hedges, J. I.: Sediment diagenesis and benthic flux, in:
Treatise on Geochemistry, edited by: Holland, H. D. and Turekian., K. K.,
vol. 6, chap. 9, Elsevier, Amsterdam, 2003. a
Fontugne, M. and Duplessy, J.: Carbon Isotope Ratio of Marine Phytoplankton
related to surface water masses, Earth Planet. Sc. Lett., 41,
365–371, 1978. a
Gebbie, G., Peterson, C., Lisiecki, L., and Spero, H.: Global-mean marine
δ13C
and its uncertainty in a glacial state estimate, Quaternary Sci. Rev.,
125, 144–159, 2015. a
Gloege, L., McKinley, G. A., Mouw, C. B., and Ciochetto, A. B.: Global eva-
luation of particulate organic carbon flux parameterizations and implications
for atmospheric pCO2, Global Biogeochem. Cy., 31,
1192–1215, 2017. a
Gordon, A.: The role of thermohaline circulation in global climate change,
in:
Lamont-Doherty Geological Observatory Report 1990/91, Tech. rep.,
Lamont-Doherty Geological Observatory of Columbia University, Palisades, New
York, 1991. 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, b, c, d, e
Harman, I., Trudinger, C., and Raupach, M.: SCCM – the Simple Carbon-Climate
Model: Technical Documentation, CAWCR Technical Report 047, CSIRO Centre for
Australian Weather and Climate Research, CSIRO Marine and Atmospheric
Research, FC Pye Laboratory, G.P.O. Box 3023, Canberra, ACT, 2601, Australia,
2011. a, b
Harrison, W., Head, E., Horne, E., Irwin, B., Li, W., Longhurst, A.,
Paranjape,
M., and Platt, T.: The western North Atlantic bloom experiment, Deep-Sea Res. Pt. II, 40, 279–305, 1993. a
Henson, S. A., Sanders, R., Madsen, E., Morris, P. J., Moigne, F. L., and
Quartly, G. D.: A reduced estimate of the strength of the ocean's biological
carbon pump, Geophys. Res. Lett., 38, L04606, https://doi.org/10.1029/2011GL046735, 2011. a
Hesse, T., Butzin, M., Bickert, T., and Lohmann, G.: A model data comparison of
d13C in the glacial Atlantic Ocean, Paleoceanography, 26, PA3220,
https://doi.org/10.1029/2010PA002085, 2011. a, b
Hogg, A. M. C.: Glacial cycles and carbon dioxide: A conceptual model, Geophys.
Res. Lett., 35, L01701, https://doi.org/10.1029/2007GL032071, 2008. a
Hoogakker, B. A. A., Smith, R. S., Singarayer, J. S., Marchant, R., Prentice,
I. C., Allen, J. R. M., Anderson, R. S., Bhagwat, S. A., Behling, H.,
Borisova, O., Bush, M., Correa-Metrio, A., de Vernal, A., Finch, J. M.,
Fréchette, B., Lozano-Garcia, S., Gosling, W. D., Granoszewski, W., Grimm,
E. C., Grüger, E., Hanselman, J., Harrison, S. P., Hill, T. R., Huntley,
B., Jiménez-Moreno, G., Kershaw, P., Ledru, M.-P., Magri, D., McKenzie, M.,
Müller, U., Nakagawa, T., Novenko, E., Penny, D., Sadori, L., Scott, L.,
Stevenson, J., Valdes, P. J., Vandergoes, M., Velichko, A., Whitlock, C., and
Tzedakis, C.: Terrestrial biosphere changes over the last 120 kyr, Clim.
Past, 12, 51–73, https://doi.org/10.5194/cp-12-51-2016, 2016. a, b, c
Hughes, P. and Gibbard, P.: A stratigraphical basis for the Last Glacial
Maximum (LGM), Quatern. Int., 383, 174–185, 2015. a
Hughes, P., Gibbard, P., and Ehlers, J.: Timing of glaciation during the last
glacial cycle: evaluating the concept of a global “Last Glacial Maximum”
(LGM), Earth-Sci. Rev., 125, 171–198, 2013. a
IPCC: Annex II: Climate System Scenario Tables, Climate Change 2013: The
Physical Science Basis, Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change,
1395–1445, Cambridge University Press, Cambridge, United Kingdom and New
York, USA, 2013a. a, b, c, d
Jansen, H., Zeebe, R., and Wolf-Gladrow, D.: Modeling the dissolution of
settling CaCO3 in the ocean, Global Biogeochem. Cy., 16, 11-1–11-15, 2002. a
Jones, C., Robertson, E., Arorab, V., Friedlingstein, P., Shevliakova, E.,
Bopp, L., Brovkin, V., Hajima, T., Kato, E., Kawamiya, M., S., Liddicoat,
Lindsay, K., Reick, C., Roelandt, C., Segschneider, J., and Tjiputra, J.:
Twenty-First-Century Compatible CO2 Emissions and Airborne Fraction Simulated
by CMIP5 Earth System Models under Four Representative Concentration
Pathways, J. Climate, 26, 4398–4413, 2013. a, b, c, d, e
Kara, A., Rochford, P., and Hurlburt, H.: Mixed layer depth variability over
the global ocean, J. Geophys. Res., 108, 3079,
https://doi.org/10.1029/2000JC000736, 2003. a, b
Keeling, C. and Bolin, B.: The simultaneous use of chemical tracers in oceanic
studies 11. A three-reservoir model of the North and South Pacific Oceans,
Tellus, 20, 17–53, 1968. 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
Knox, F. and McElroy, M.: Changes in Atmospheric CO2: Influence of
the Marine
Biota at High Latitude, J. Geophys. Res., 89, 4269–4637,
1984. a
Kuhlbrodt, T. A., Griesel, M., Montoya, A., Levermann, M., Hofmann, M., and
Rahmstorf, S.: On the driving processes of the Atlantic meridional
overturning circulation, Rev. Geophys., 45, RG2001,
https://doi.org/10.1029/2004RG000166, 2007. a
Liu, C., Kohl, A., Liu, Z., Wang, F., and Stammer, D.: Deep-reaching
thermocline mixing in the equatorial pacific cold tongue, Nat. Commun., 7, 11576, https://doi.org/10.1038/ncomms11576, 2016. a, b
Lueker, T. J., Dickson, A. G., and Keeling, C. D.: Ocean pCO2
calculated from
dissolved inorganic carbon, alkalinity, and equations for K-1 and K-2:
validation based on laboratory measurements of CO2 in gas and seawater at
equilibrium, Mar. Chem., 70, 105–119, 2000. a
Lumpkin, R. and Speer, K.: Global ocean meridional overturning, J. Phys. Oceanogr., 37, 550–562, 2007. a
Lund, D. C., Adkins, J. F., and Ferrari, R.: Abyssal Atlantic circulation
during the Last Glacial Maximum: Constraining the ratio between transport and
vertical mixing, Paleoceanography, 26, PA1213, https://doi.org/10.1029/2010PA001938, 2011. a
Lynch-Stieglitz, J., Stocker, T., Broecker, W., and Fairbanks, R.: The
influence of air-sea exchange on the isotopic composition of oceanic carbon:
Observations and modeling, Global Biogeochem. Cy., 9, 653–665, 1995. a
Marchitto, T., Lehman, S., Ortiz, J., Flückiger, J., and van Geen, A.:
Marine Radiocarbon Evidence for the Mechanism of Deglacial Atmospheric
CO2
Rise, Science, 316, 1456–1459, 2007. a
Marcott, S., Bauska, T., Buizert, C., Steig, E., Rosen, J., Cuffey, K., Fudge,
T. J., Severinghaus, J. P., Ahn, J., Kalk, M. L., McConnell, J. R., Sowers,
T., Taylor, K., White, J. W. C., and Brook, E.: Centennial-scale changes in
the global carbon cycle during the last deglaciation, Nature, 514, 616–621, 2014. a
Meehl, G., Stocker, T., Collins, W., Friedlingstein, P., Gaye, A., Gregory, J.,
Kitoh, A., Knutti, R., Murphy, J., Noda, A., Raper, S., Watterson, I.,
Weaver, A., and Zhao, Z.: Global climate projections, Climate Change 2007: The Physical
Science Basis, 747–845, Cambridge University Press, 2007. a, b
Mekik, F. A., Anderson, R. F., Loubere, P., François, R., and Richaud, M.:
The mystery of the missing deglacial carbonate preservation maximum,
Quaternary Sci. Rev., 39, 60–72, 2012. a
Moore, J. K., Fu, W., Primeau, F., Britten, G., Lindsay, K., Long, M., Doney,
S., Mahowald, N., Hoffman, F., and Randerson, J.: Sustained climate warming
drives declining marine biological productivity, Science, 359, 1139–1143,
https://doi.org/10.1126/science.aao6379,
2018. a, b
Morrison, A. and Hogg, A.: On the Relationship between Southern Ocean
Overturning and ACC Transport, J. Phys. Oceanogr., 43,
140–148, 2013. a
Morse, J. W. and Berner, R. A.: Dissolution kinetics of calcium carbonate in
sea water. II: A kinetic origin for the lysocline, Am. J. Sci., 272,
840–851, https://doi.org/10.2475/ajs.272.9.840, 1972. a, b, c, d
Mucci, A.: The solubility of calcite and aragonite in seawater at various
salinities, temperatures, and one atmosphere total pressure, Am. J. Sci., 283,
780–799, 1983. a
Oliver, K. I. C., Hoogakker, B. A. A., Crowhurst, S., Henderson, G. M.,
Rickaby, R. E. M., Edwards, N. R., and Elderfield, H.: A synthesis of marine
sediment core δ13C data over the last 150 000 years, Clim.
Past, 6, 645–673, https://doi.org/10.5194/cp-6-645-2010, 2010. a, b
Olsen, A., Key, R. M., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X.,
Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S.,
Steinfeldt, R., Jeansson, E., Ishii, M., Pérez, F. F., and Suzuki, T.: The
Global Ocean Data Analysis Project version 2 (GLODAPv2) – an internally
consistent data product for the world ocean, Earth Syst. Sci. Data, 8,
297–323, https://doi.org/10.5194/essd-8-297-2016, 2016. a, b, c
O'Neill, C. M., Hogg, A. McC., Ellwood, M. J.,
Opdyke, B. N., and Eggins, S. M.: [simple carbon project] model (Version V1.0), Zenodo, https://doi.org/10.5281/zenodo.1310161, 2018. a, b, c, d
Opdyke, B. and Walker, J.: Return of the coral reef hypothesis: Basin to shelf
partitioning of CaCO3 and its effect on atmospheric CO2, Geology, 20,
733–736, 1992. a
Redfield, A. C., Ketchum, B. H., and Richards, F. A.: The influence of
organisms on the composition of seawater, The Sea, 2, 26–77, 1963. a
Reimer, P., Baillie, M., Bard, E., Bayliss, A., Beck, J., Blackwell, P.,
Ramsey, C. B., Buck, C., Burr, G., Edwards, R., Friedrich, M., Grootes, P.,
Guilderson, T., Hajdas, I., Heaton, T., Hogg, A., Hughen, K., Kaiser, K.,
Kromer, B., McCormac, F., Manning, S., Reimer, R., Richards, D., Southon, J.,
Talamo, S., Turney, C., van der Plicht, J., and Weyhenmeyer, C.: IntCal09 and
Marine09 radiocarbon age calibration curves, 0–50,000 years cal BP.,
Radiocarbon, 51, 1111–1150, 2009. a
Ridgewell, A.: An end to the “rain ratio” reign?, Geochem. Geophys. Geosyst.,
4, 1051, https://doi.org/10.1029/2003GC000512, 2003. a
Ridgewell, A., Watson, A., Maslin, M., and Kaplan, J.: Implications of coral
reef buildup for the controls on atmospheric CO2 since the Last Glacial
Maximum, Paleoceanography, 18, 1083, https://doi.org/10.1029/2003PA000893, 2003. a
Ronge, T., Tiedemann, R., Lamy, F., Kohler, P., Alloway, B., Pol-Holz, R. D.,
Pahnke, K., Southon, J., and Wacker, L.: Radiocarbon constraints on the
extent and evolution of the South Pacific glacial carbon pool, Nat. Commun., 7, 11487, https://doi.org/10.1038/ncomms11487, 2016. a, b
Sarmiento, J. L., Dunne, J., Gnanadesikan, A., Key, R. M., Matsumoto, K., and
Slater, R.: A new estimate of the CaCO3 to organic carbon export ratio,
Global Biogeochem. Cy., 16, 1107, https://doi.org/10.1029/2002GB001919, 2002. a, b
Schmittner, A., Gruber, N., Mix, A. C., Key, R. M., Tagliabue, A., and
Westberry, T. K.: Biology and air–sea gas exchange controls on the
distribution of carbon isotope ratios (δ13C) in the ocean,
Biogeosciences, 10, 5793–5816, https://doi.org/10.5194/bg-10-5793-2013,
2013. a
Schmitz, W. J.: On the World Ocean Circulation: Volume I. Some global
features/North Atlantic circulation, Woods Hole Oceanographic Institution
Technical
Report, WHOI-96-03, 144, 1996. a
Siegenthaler, U. and Wenk, T.: Rapid atmospheric CO2 variations and
ocean
circulation, Nature, 308, 624–626, 1984. a
Sikes, E., Samson, C., Guilderson, T., and Howard, W.: Old radiocarbon ages in
the southwest Pacific Ocean during the last glacial period and deglaciation,
Nature, 405, 555–559, 2000. a
Sikes, E., Cook, M., and Guilderson, T.: Reduced deep ocean ventilation in the
Southern Pacific Ocean during the last glaciation persisted into the
deglaciation, Earth Planet. Sc. Lett., 438, 130–138, 2016. a
Skinner, L. and Shackleton, N. J.: Rapid transient changes in northeast
Atlantic deep water ventilation age across Termination I, Paleoceanography,
19, PA2005, https://doi.org/10.1029/2003PA000983, 2004. a, b
Skinner, L., Primeau, F., Freeman, E., de la Fuente, M., Goodwin, P. A.,
Gottschalk, J., Huang, E., McCave, I. N., Noble, T. L., and Scrivner, A. E.:
Radiocarbon constraints on the glacial ocean circulation and its impact on
atmospheric CO2, Nat. Commun., 8, 16010, https://doi.org/10.1038/ncomms16010, 2017. a, b, c, d
Stommel, H.: Thermohaline convection with two stable regimes of flow, Tellus,
13, 224–230, 1961. a
Stuiver, M. and Polach, H.: Reporting of 14C data, Radiocarbon, 19,
355–363,
1977. a
Sverdrup, H., Johnson, N., and Fleming, R.: The Oceans, Prentice-Hall,
Englewood Cliffs, NJ, 1941. a
Tagliabue, A., Bopp, L., Roche, D. M., Bouttes, N., Dutay, J.-C., Alkama, R.,
Kageyama, M., Michel, E., and Paillard, D.: Quantifying the roles of ocean
circulation and biogeochemistry in governing ocean carbon-13 and atmospheric
carbon dioxide at the last glacial maximum, Clim. Past, 5, 695–706,
https://doi.org/10.5194/cp-5-695-2009, 2009. a
Talley, L.: Freshwater transport estimates and the global overturning
circulation: Shallow, deep and throughflow components, Prog. Oceanogr., 78, 257–303, 2008. a
Toggweiler, J. and Sarmiento, J.: Glacial to interglacial changes in
atmospheric carbon dioxide: The critical role of ocean surface water in high
latitudes, The Carbon Cycle and Atmospheric CO2: Natural Variations
Archean to Present, Geophysical Monograph Series, American Geophysical Union,
32, 163–184, 1985. a, b, c, d, e, f, g
Toggweiler, J. R., Russell, J. L., and Carson, S. R.: Midlatitude westerlies,
atmospheric CO2, and climate change during ice ages, Paleoceanography,
21, PA2005, https://doi.org/10.1029/2005PA001154, 2006. a
Trent-Staid, M. and Prell, W. L.: Sea surface temperature at the Last Glacial
Maximum: A reconstruction using the modern analog technique, Paleoceanography,
17, 1065, https://doi.org/10.1029/2000PA000506, 2002. a, b, c
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
Turnbull, J. C., Mikaloff Fletcher, S. E., Ansell, I., Brailsford, G. W.,
Moss, R. C., Norris, M. W., and Steinkamp, K.: Sixty years of radiocarbon
dioxide measurements at Wellington, New Zealand: 1954–2014, Atmos. Chem.
Phys., 17, 14771–14784, https://doi.org/10.5194/acp-17-14771-2017,
2017.
a, b
Volk, T. and Hoffert, M. I.: Ocean carbon pumps: Analysis of relative strengths
and efficiencies in ocean-driven atmospheric CO2 changes, in The Carbon Cycle
and Atmospheric CO2: Natural Variations Archean to Present, Geophys. Monogr.
Ser., 32, 99–110, 1985. a
Wang, L., Huang, J., Luo, Y., and Zhao, Z.: Narrowing the spread in CMIP5 model
projections of air-sea CO2 fluxes, Nature Scientific Reports, 6, 37548,
https://doi.org/10.1038/srep37548, 2016. a
Watson, A., Bakker, D. C. E., Ridgwell, A. J., Boyd, P. W., and Law, C.: Effect
of iron supply on Southern Ocean CO2 uptake and implications for glacial
atmospheric CO2, Nature, 407, 730–733, 2000. a
Watson, A., Vallis, G. K., and Nikurashin, M.: Southern Ocean Buoyancy Forcing
of Ocean Ventilation and Glacial Atmospheric CO2, Nat. Geosci., 8,
861–864, 2015. a
Yokoyama, Y., Lambeck, K., Deckker, P. D., Johnston, P., and Field, K.: Timing
of the Last Glacial Maximum from observed sea-level minima, Nature, 406,
713–716, 2000. a
Yu, J., Anderson, R., Jin, Z., Menviel, L., Zhang, F., Ryerson, F., and
Rohling,
E.: Deep South Atlantic carbonate chemistry and increased interocean deep
water exchange during last deglaciation, Quaternary Sci. Rev., 90,
80–89, 2014a. a
Zeebe, R. E.: LOSCAR: Long-term Ocean-atmosphere-Sediment CArbon cycle
Reservoir Model v2.0.4, Geosci. Model Dev., 5, 149–166,
https://doi.org/10.5194/gmd-5-149-2012, 2012. a, b
Short summary
The [simple carbon project] model v1.0 (SCP-M) was constructed for simulations of the paleo and modern carbon cycle. In this paper we show its application to the carbon cycle transition from the Last Glacial Maximum to the Holocene period. Our model–data experiment uses SCP-M's fast run time to cover a large range of possible inputs. The results highlight the role of varying the strength of ocean circulation to account for large fluctuations in atmospheric CO2 across the two periods.
The [simple carbon project] model v1.0 (SCP-M) was constructed for simulations of the paleo and...
