Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-813-2023
© Author(s) 2023. 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-16-813-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Feb 2023
Development and technical paper |
| 02 Feb 2023
| 02 Feb 2023
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
School of Earth Sciences, University of Bristol, Bristol, BS8 1RJ, UK
Fanny M. Monteiro
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Jamie D. Wilson
School of Earth Sciences, University of Bristol, Bristol, BS8 1RJ, UK
Daniela N. Schmidt
School of Earth Sciences, University of Bristol, Bristol, BS8 1RJ, UK
Related authors
Kirsty M. Edgar, Maria Grigoratou, Fanny M. Monteiro, Ruby Barrett, Rui Ying, and Daniela N. Schmidt
Biogeosciences, 22, 3463–3483, https://doi.org/10.5194/bg-22-3463-2025, https://doi.org/10.5194/bg-22-3463-2025, 2025
Short summary
Short summary
Planktic foraminifera are microscopic marine organisms whose calcium carbonate shells provide valuable insights into past ocean conditions. A promising means of understanding foraminiferal ecology and their environmental interactions is to constrain their key functional traits relating to feeding, symbioses, motility, calcification, and reproduction. Here we review what we know of their functional traits, key gaps in our understanding, and suggestions on how to fill them.
Marco Puglia, Thomas Bibby, Jamie Wilson, and Ben Ward
EGUsphere, https://doi.org/10.5194/egusphere-2025-3050, https://doi.org/10.5194/egusphere-2025-3050, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Mixotrophs use both photosynthesis and predation as source of nutrition. Simulations show they can increase ocean carbon storage, but long-term effects are not yet understood. Using a low-resolution ocean-ecology model that ran for 10,000 years, we compared simulations with and without mixotrophs. Mixotrophs increased global carbon storage by trapping more organic carbon in the ocean interior, although interactions with the ocean circulation offset these effects in the North Atlantic.
Kirsty M. Edgar, Maria Grigoratou, Fanny M. Monteiro, Ruby Barrett, Rui Ying, and Daniela N. Schmidt
Biogeosciences, 22, 3463–3483, https://doi.org/10.5194/bg-22-3463-2025, https://doi.org/10.5194/bg-22-3463-2025, 2025
Short summary
Short summary
Planktic foraminifera are microscopic marine organisms whose calcium carbonate shells provide valuable insights into past ocean conditions. A promising means of understanding foraminiferal ecology and their environmental interactions is to constrain their key functional traits relating to feeding, symbioses, motility, calcification, and reproduction. Here we review what we know of their functional traits, key gaps in our understanding, and suggestions on how to fill them.
Mara Y. McPartland, Tomas Lovato, Charles D. Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
EGUsphere, https://doi.org/10.5194/egusphere-2025-3246, https://doi.org/10.5194/egusphere-2025-3246, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
David A. Stappard, Jamie D. Wilson, Andrew Yool, and Toby Tyrrell
EGUsphere, https://doi.org/10.5194/egusphere-2025-436, https://doi.org/10.5194/egusphere-2025-436, 2025
Short summary
Short summary
This research explores nutrient limitations in oceanic primary production. While traditional experiments identify the immediate limiting nutrient at specific locations, this study aims to identify the ultimate limiting nutrient (ULN), which governs long-term productivity. A mathematical model incorporating nitrogen, phosphorus, and iron nutrient cycles is used. The model's results are compared with ocean observational data to assess its effectiveness in investigating the ULN.
Ruby Barrett, Joost de Vries, and Daniela N. Schmidt
Biogeosciences, 22, 791–807, https://doi.org/10.5194/bg-22-791-2025, https://doi.org/10.5194/bg-22-791-2025, 2025
Short summary
Short summary
Planktic foraminifers are a plankton whose fossilised shell weight is used to reconstruct past environmental conditions such as seawater CO2. However, there is debate about whether other environmental drivers impact shell weight. Here we use a global data compilation and statistics to analyse what controls their weight. We find that the response varies between species and ocean basin, making it important to use regional calibrations and consider which species should be used to reconstruct CO2.
Joost de Vries, Fanny Monteiro, Gerald Langer, Colin Brownlee, and Glen Wheeler
Biogeosciences, 21, 1707–1727, https://doi.org/10.5194/bg-21-1707-2024, https://doi.org/10.5194/bg-21-1707-2024, 2024
Short summary
Short summary
Calcifying phytoplankton (coccolithophores) utilize a life cycle in which they can grow and divide into two different phases. These two phases (HET and HOL) vary in terms of their physiology and distributions, with many unknowns about what the key differences are. Using a combination of lab experiments and model simulations, we find that nutrient storage is a critical difference between the two phases and that this difference allows them to inhabit different nitrogen input regimes.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Rachel A. Kruft Welton, George Hoppit, Daniela N. Schmidt, James D. Witts, and Benjamin C. Moon
Biogeosciences, 21, 223–239, https://doi.org/10.5194/bg-21-223-2024, https://doi.org/10.5194/bg-21-223-2024, 2024
Short summary
Short summary
We conducted a meta-analysis of known experimental literature examining how marine bivalve growth rates respond to climate change. Growth is usually negatively impacted by climate change. Bivalve eggs/larva are generally more vulnerable than either juveniles or adults. Available data on the bivalve response to climate stressors are biased towards early growth stages (commercially important in the Global North), and many families have only single experiments examining climate change impacts.
Markus Adloff, Andy Ridgwell, Fanny M. Monteiro, Ian J. Parkinson, Alexander J. Dickson, Philip A. E. Pogge von Strandmann, Matthew S. Fantle, and Sarah E. Greene
Geosci. Model Dev., 14, 4187–4223, https://doi.org/10.5194/gmd-14-4187-2021, https://doi.org/10.5194/gmd-14-4187-2021, 2021
Short summary
Short summary
We present the first representation of the trace metals Sr, Os, Li and Ca in a 3D Earth system model (cGENIE). The simulation of marine metal sources (weathering, hydrothermal input) and sinks (deposition) reproduces the observed concentrations and isotopic homogeneity of these metals in the modern ocean. With these new tracers, cGENIE can be used to test hypotheses linking these metal cycles and the cycling of other elements like O and C and simulate their dynamic response to external forcing.
Joost de Vries, Fanny Monteiro, Glen Wheeler, Alex Poulton, Jelena Godrijan, Federica Cerino, Elisa Malinverno, Gerald Langer, and Colin Brownlee
Biogeosciences, 18, 1161–1184, https://doi.org/10.5194/bg-18-1161-2021, https://doi.org/10.5194/bg-18-1161-2021, 2021
Short summary
Short summary
Coccolithophores are important calcifying phytoplankton with an overlooked life cycle. We compile a global dataset of marine coccolithophore abundance to investigate the environmental characteristics of each life cycle phase. We find that both phases contribute to coccolithophore abundance and that their different environmental preference increases coccolithophore habitat. Accounting for the life cycle of coccolithophores is thus crucial for understanding their ecology and biogeochemical impact.
Katherine A. Crichton, Jamie D. Wilson, Andy Ridgwell, and Paul N. Pearson
Geosci. Model Dev., 14, 125–149, https://doi.org/10.5194/gmd-14-125-2021, https://doi.org/10.5194/gmd-14-125-2021, 2021
Short summary
Short summary
Temperature is a controller of metabolic processes and therefore also a controller of the ocean's biological carbon pump (BCP). We calibrate a temperature-dependent version of the BCP in the cGENIE Earth system model. Since the pre-industrial period, warming has intensified near-surface nutrient recycling, supporting production and largely offsetting stratification-induced surface nutrient limitation. But at the same time less carbon that sinks out of the surface then reaches the deep ocean.
Sophie Kendall, Felix Gradstein, Christopher Jones, Oliver T. Lord, and Daniela N. Schmidt
J. Micropalaeontol., 39, 27–39, https://doi.org/10.5194/jm-39-27-2020, https://doi.org/10.5194/jm-39-27-2020, 2020
Short summary
Short summary
Changes in morphology during development can have profound impacts on an organism but are hard to quantify as we lack preservation in the fossil record. As they grow by adding chambers, planktic foraminifera are an ideal group to study changes in growth in development. We analyse four different species of Jurassic foraminifers using a micro-CT scanner. The low morphological variability suggests that strong constraints, described in the modern ocean, were already acting on Jurassic specimens.
Anna Mikis, Katharine R. Hendry, Jennifer Pike, Daniela N. Schmidt, Kirsty M. Edgar, Victoria Peck, Frank J. C. Peeters, Melanie J. Leng, Michael P. Meredith, Chloe L. C. Jones, Sharon Stammerjohn, and Hugh Ducklow
Biogeosciences, 16, 3267–3282, https://doi.org/10.5194/bg-16-3267-2019, https://doi.org/10.5194/bg-16-3267-2019, 2019
Short summary
Short summary
Antarctic marine calcifying organisms are threatened by regional climate change and ocean acidification. Future projections of regional carbonate production are challenging due to the lack of historical data combined with complex climate variability. We present a 6-year record of flux, morphology and geochemistry of an Antarctic planktonic foraminifera, which shows that their growth is most sensitive to sea ice dynamics and is linked with the El Niño–Southern Oscillation.
Jamie D. Wilson, Stephen Barker, Neil R. Edwards, Philip B. Holden, and Andy Ridgwell
Biogeosciences, 16, 2923–2936, https://doi.org/10.5194/bg-16-2923-2019, https://doi.org/10.5194/bg-16-2923-2019, 2019
Short summary
Short summary
The remains of plankton rain down from the surface ocean to the deep ocean, acting to store CO2 in the deep ocean. We used a model of biology and ocean circulation to explore the importance of this process in different regions of the ocean. The amount of CO2 stored in the deep ocean is most sensitive to changes in the Southern Ocean. As plankton in the Southern Ocean are likely those most impacted by future climate change, the amount of CO2 they store in the deep ocean could also be affected.
Maria Grigoratou, Fanny M. Monteiro, Daniela N. Schmidt, Jamie D. Wilson, Ben A. Ward, and Andy Ridgwell
Biogeosciences, 16, 1469–1492, https://doi.org/10.5194/bg-16-1469-2019, https://doi.org/10.5194/bg-16-1469-2019, 2019
Short summary
Short summary
The paper presents a novel study based on the traits of shell size, calcification and feeding behaviour of two planktonic foraminifera life stages using modelling simulations. With the model, we tested the cost and benefit of calcification and explored how the interactions of planktonic foraminifera among other plankton groups influence their biomass under different environmental conditions. Our results provide new insights into environmental controls in planktonic foraminifera ecology.
Marcus P. S. Badger, Thomas B. Chalk, Gavin L. Foster, Paul R. Bown, Samantha J. Gibbs, Philip F. Sexton, Daniela N. Schmidt, Heiko Pälike, Andreas Mackensen, and Richard D. Pancost
Clim. Past, 15, 539–554, https://doi.org/10.5194/cp-15-539-2019, https://doi.org/10.5194/cp-15-539-2019, 2019
Short summary
Short summary
Understanding how atmospheric CO2 has affected the climate of the past is an important way of furthering our understanding of how CO2 may affect our climate in the future. There are several ways of determining CO2 in the past; in this paper, we ground-truth one method (based on preserved organic matter from alga) against the record of CO2 preserved as bubbles in ice cores over a glacial–interglacial cycle. We find that there is a discrepancy between the two.
Ben A. Ward, Jamie D. Wilson, Ros M. Death, Fanny M. Monteiro, Andrew Yool, and Andy Ridgwell
Geosci. Model Dev., 11, 4241–4267, https://doi.org/10.5194/gmd-11-4241-2018, https://doi.org/10.5194/gmd-11-4241-2018, 2018
Short summary
Short summary
A novel configuration of an Earth system model includes a diverse plankton community. The model – EcoGEnIE – is sufficiently complex to reproduce a realistic, size-structured plankton community, while at the same time retaining the efficiency to run to a global steady state (~ 10k years). The increased capabilities of EcoGEnIE will allow future exploration of ecological communities on much longer timescales than have so far been examined in global ocean models and particularly for past climate.
M. Wall, F. Ragazzola, L. C. Foster, A. Form, and D. N. Schmidt
Biogeosciences, 12, 6869–6880, https://doi.org/10.5194/bg-12-6869-2015, https://doi.org/10.5194/bg-12-6869-2015, 2015
Short summary
Short summary
We investigated the ability of cold-water corals to deal with changes in ocean pH. We uniquely combined morphological assessment with boron isotope analysis to determine if changes in growth are related to changes in control of calcification pH. We found that the cold-water coral Lophelia pertusa can maintain the skeletal morphology, growth patterns as well as internal calcification pH. This has important implications for their future occurrence and explains their cosmopolitan distribution.
L. A. Melbourne, J. Griffin, D. N. Schmidt, and E. J. Rayfield
Biogeosciences, 12, 5871–5883, https://doi.org/10.5194/bg-12-5871-2015, https://doi.org/10.5194/bg-12-5871-2015, 2015
Short summary
Short summary
Using Finite element modelling (FEM) we show that a simplified geometric FE model can predict the structural strength of the coralline algal skeleton. We compared a series of 3D geometric FE-models with increasing complexity to a biologically accurate model derived from computed tomography (CT) scan data. Using geometric models provides the basis for a better understanding of the potential effect of climate change on the structural integrity of these organisms.
J. D. Wilson, A. Ridgwell, and S. Barker
Biogeosciences, 12, 5547–5562, https://doi.org/10.5194/bg-12-5547-2015, https://doi.org/10.5194/bg-12-5547-2015, 2015
Short summary
Short summary
We explore whether ocean model transport rates, in the form of a transport matrix, can be used to estimate remineralisation rates from dissolved nutrient concentrations and infer vertical fluxes of particulate organic carbon. Estimated remineralisation rates are significantly sensitive to uncertainty in the observations and the modelled circulation. The remineralisation of dissolved organic matter is an additional source of uncertainty when inferring vertical fluxes from remineralisation rates.
C. V. Davis, M. P. S. Badger, P. R. Bown, and D. N. Schmidt
Biogeosciences, 10, 6131–6139, https://doi.org/10.5194/bg-10-6131-2013, https://doi.org/10.5194/bg-10-6131-2013, 2013
A. G. M. Caromel, D. N. Schmidt, and J. C. Phillips
Biogeosciences Discuss., https://doi.org/10.5194/bgd-10-6763-2013, https://doi.org/10.5194/bgd-10-6763-2013, 2013
Revised manuscript not accepted
Daniela N. Schmidt, Jeremy R. Young, Shirley Van Heck, and Jackie Lees
J. Micropalaeontol., 28, 91–93, https://doi.org/10.1144/jm.28.1.91, https://doi.org/10.1144/jm.28.1.91, 2009
Related subject area
Biogeosciences
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
pyVPRM: a next-generation vegetation photosynthesis and respiration model for the post-MODIS era
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Implementing a process-based representation of soil water movement in a second-generation dynamic vegetation model: application to dryland ecosystems (LPJ-GUESS-RE v1.0)
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
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
CROMES v1.0: A flexible CROp Model Emulator Suite for climate impact assessment
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
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
Simple Eulerian-Lagrangian approach to solve equations for sinking particulate organic matter in the ocean
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0)
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
Data-Informed Inversion Model (DIIM): a framework to retrieve marine optical constituents in the BOUSSOLE site using a three-stream irradiance model
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
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
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
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)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
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
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
Benjamin F. Meyer, João P. Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
Geosci. Model Dev., 18, 4643–4666, https://doi.org/10.5194/gmd-18-4643-2025, https://doi.org/10.5194/gmd-18-4643-2025, 2025
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening the European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show its ability to capture species-specific evapotranspiration responses to drought and to reproduce flux observations of both gross primary production and evapotranspiration.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
Geosci. Model Dev., 18, 4713–4742, https://doi.org/10.5194/gmd-18-4713-2025, https://doi.org/10.5194/gmd-18-4713-2025, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS, and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
Geosci. Model Dev., 18, 4317–4333, https://doi.org/10.5194/gmd-18-4317-2025, https://doi.org/10.5194/gmd-18-4317-2025, 2025
Short summary
Short summary
We developed fast machine learning models to predict forest regrowth and carbon dynamics under climate change. These models mimic the outputs of a complex vegetation model but run 95 % faster, enabling global analyses and supporting climate solutions in large modeling frameworks such as LandSyMM.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
Short summary
Short summary
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.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025, https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Short summary
Physical–biogeochemical ocean global models are required to analyze difficult oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène 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., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
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.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
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 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.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization 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, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Wim Verbruggen, David Wårlind, Stéphanie Horion, Félicien Meunier, Hans Verbeeck, and Guy Schurgers
EGUsphere, https://doi.org/10.5194/egusphere-2025-1259, https://doi.org/10.5194/egusphere-2025-1259, 2025
Short summary
Short summary
We improved the representation of soil water movement in a state-of-the-art dynamic vegetation model. This is especially important for dry ecosystems, as they are often driven by changes in soil water availability. We showed that this update resulted in a generally better match with observations, and that the updated model is more sensitive to soil texture. This updated model will help scientists to better understand the future of dry ecosystems under climate change.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
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
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
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.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently 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 basis for future improvements.
Christian Folberth, Artem Baklanov, Nikolay Khabarov, Thomas Oberleitner, Juraj Balkovič, and Rastislav Skalský
EGUsphere, https://doi.org/10.5194/egusphere-2025-862, https://doi.org/10.5194/egusphere-2025-862, 2025
Short summary
Short summary
Global gridded crop models (GGCMs) are important tools in agricultural climate impact assessments but computationally costly. An emergent approach to derive crop productivity estimates similar to those from GGCMs are emulators that mimic the original model, but typically with considerable bias. Here we present a modelling package that trains emulators with very high accuracy and high computational gain, providing a basis for more comprehensive scenario assessments.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
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.
Vladimir Maderich, Igor Brovchenko, Kateryna Kovalets, Seongbong Seo, and Kyeong Ok Kim
EGUsphere, https://doi.org/10.5194/egusphere-2025-491, https://doi.org/10.5194/egusphere-2025-491, 2025
Short summary
Short summary
We have developed a new simple Eulerian-Lagrangian approach to solve equations for sinking particulate organic matter in the ocean. We rely on the known parameterizations, but our approach to solving the problem differs, allowing the algorithm to be incorporated into biogeochemical global ocean models with relative ease. New analytical and numerical solutions confirmed that feedback between degradation rate and sinking velocity significantly changes particulate matter fluxes.
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.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
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.
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.
Felix Nößler, Thibault Moulin, Oksana Buzhdygan, Britta Tietjen, and Felix May
EGUsphere, https://doi.org/10.5194/egusphere-2024-3798, https://doi.org/10.5194/egusphere-2024-3798, 2024
Short summary
Short summary
To predict the response of grassland plant communities to management and climate change, we developed the computer model GrasslandTraitSim.jl. Unlike other models, it uses measurable plant traits such as height, leaf thinness, and root structure as inputs, rather than hard-to-measure species data. This allows realistic simulation of many species. The model tracks daily changes in above- and below-ground biomass, plant height, and soil water, linking plant community composition to biomass supply.
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.
Carlos Enmanuel Soto López, Fabio Anselmi, Mirna Gharbi Dit Kacem, and Paolo Lazzari
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-174, https://doi.org/10.5194/gmd-2024-174, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Our goal was to use an analytical expression to estimate the density of optical constituents, allowing us to have an interpretable formulation consistent with the laws of physics. We focused on a probabilistic approach, optimizing the model and retrieving quantities with their respective uncertainty. Considering future application to Big Data, we also explored a Neural Network based method, retrieving computationally efficient estimates, maintaining consistency with the analytical expression.
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.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
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.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
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.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Joshua Coupe, Nicole S. Lovenduski, Luise S. Gleason, Michael N. Levy, Kristen Krumhardt, Keith Lindsay, Charles Bardeen, Clay Tabor, Cheryl Harrison, Kenneth G. MacLeod, Siddhartha Mitra, and Julio Sepúlveda
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-94, https://doi.org/10.5194/gmd-2024-94, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We develop a new feature in the atmosphere and ocean components of the Community Earth System Model version 2. We have implemented ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation. The new feature will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.
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.
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.
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.
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.
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.
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.
Cited articles
Andersen, K. H., Berge, T., Gonçalves, R. J., Hartvig, M., Heuschele,
J., Hylander, S., Jacobsen, N. S., Lindemann, C., Martens, E. A., Neuheimer,
A. B., Olsson, K., Palacz, A., Prowe, A. E. F., Sainmont, J., Traving, S.
J., Visser, A. W., Wadhwa, N., and Kiørboe, T.: Characteristic Sizes of
Life in the Oceans, from Bacteria to Whales, Annu. Rev. Mar.
Sci., 8, 217–241, https://doi.org/10/f3pdzr, 2016.
Anderson, O. R. and Bé, A. W. H.: The ultrastructure of a planktonic
foraminifer, Globigerinoides sacculifer (Brady), and its symbiotic
dinoflagellates, J. Foramin. Res., 6, 1–21,
https://doi.org/10.2113/gsjfr.6.1.1, 1976.
Anderson, O. R., Spindler, M., Bé, A. W. H., and Hemleben, Ch.: Trophic
activity of planktonic foraminifera, J. Mar. Biol. Ass., 59, 791–799,
https://doi.org/10.1017/S002531540004577X, 1979.
Anderson, R. P.: When and how should biotic interactions be considered in
models of species niches and distributions?, J. Biogeogr., 44,
8–17, https://doi.org/10.1111/jbi.12825, 2017.
Anderson, T. R.: Plankton functional type modelling: running before we can
walk?, J. Plankton Res., 27, 1073–1081,
https://doi.org/10.1093/plankt/fbi076, 2005.
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015.
Aze, T., Ezard, T. H. G., Purvis, A., Coxall, H. K., Stewart, D. R. M.,
Wade, B. S., and Pearson, P. N.: A phylogeny of Cenozoic macroperforate
planktonic foraminifera from fossil data, Biol. Rev., 86, 900–927,
https://doi.org/10/dvjwx6, 2011.
Barker, S. and Elderfield, H.: Foraminiferal Calcification Response to
Glacial-Interglacial Changes in Atmospheric CO2, Science, 297, 833–836,
https://doi.org/10.1126/science.1072815, 2002.
Be, A. W. H. and Hamlin, W. H.: Ecology of Recent Planktonic Foraminifera:
Part 3: Distribution in the North Atlantic during the Summer of 1962,
Micropaleontology, 13, 87–106, https://doi.org/10.2307/1484808, 1967.
Bé, A. W. H., Spero, H. J., and Anderson, O. R.: Effects of symbiont
elimination and reinfection on the life processes of the planktonic
foraminifer Globigerinoides sacculifer, Mar. Biol., 70, 73–86,
https://doi.org/10.1007/BF00397298, 1982.
Bec, B., Collos, Y., Vaquer, A., Mouillot, D., and Souchu, P.: Growth
rate peaks at intermediate cell size in marine photosynthetic
picoeukaryotes, Limnol. Oceanogr., 53, 863–867,
https://doi.org/10.4319/lo.2008.53.2.0863, 2008.
Bopp, L., Aumont, O., Kwiatkowski, L., Clerc, C., Dupont, L., Ethé, C., Gorgues, T., Séférian, R., and Tagliabue, A.: Diazotrophy as a key driver of the response of marine net primary productivity to climate change, Biogeosciences, 19, 4267–4285, https://doi.org/10.5194/bg-19-4267-2022, 2022.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M., and West, G. B.:
Toward a Metabolic Theory of Ecology, Ecology, 85, 1771–1789,
https://doi.org/10.1890/03-9000,
2004.
Buckley, L. B., Urban, M. C., Angilletta, M. J., Crozier, L. G., Rissler, L.
J., and Sears, M. W.: Can mechanism inform species' distribution models?,
Ecol. Lett., 13, 1041–1054,
https://doi.org/10.1111/j.1461-0248.2010.01479.x, 2010.
Buitenhuis, E. T., Vogt, M., Moriarty, R., Bednaršek, N., Doney, S. C., Leblanc, K., Le Quéré, C., Luo, Y.-W., O'Brien, C., O'Brien, T., Peloquin, J., Schiebel, R., and Swan, C.: MAREDAT: towards a world atlas of MARine Ecosystem DATa, Earth Syst. Sci. Data, 5, 227–239, https://doi.org/10.5194/essd-5-227-2013, 2013.
Buitenhuis, E. T., Quéré, C. L., Bednaršek, N., and Schiebel,
R.: Large Contribution of Pteropods to Shallow CaCO3 Export, Global
Biogeochem. Cy., 33, 458–468, https://doi.org/10/gjpnzt, 2019.
Cao, L., Eby, M., Ridgwell, A., Caldeira, K., Archer, D., Ishida, A., Joos, F., Matsumoto, K., Mikolajewicz, U., Mouchet, A., Orr, J. C., Plattner, G.-K., Schlitzer, R., Tokos, K., Totterdell, I., Tschumi, T., Yamanaka, Y., and Yool, A.: The role of ocean transport in the uptake of anthropogenic CO2, Biogeosciences, 6, 375–390, https://doi.org/10.5194/bg-6-375-2009, 2009.
Caromel, A. G. M., Schmidt, D. N., Phillips, J. C., and Rayfield, E. J.:
Hydrodynamic constraints on the evolution and ecology of planktic
foraminifera, Mar. Micropaleontol., 106, 69–78,
https://doi.org/10.1016/j.marmicro.2014.01.002, 2014.
Castellani, M., Våge, S., Strand, E., Thingstad, T. F., and Giske, J.:
The Scaled Subspaces Method: A new trait-based approach to model communities
of populations with largely inhomogeneous density, Ecol. Model.,
251, 173–186, https://doi.org/10.1016/j.ecolmodel.2012.12.006, 2013.
Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre,
M.-F., Weber, S., Alcamo, J., Alexeev, V., Berger, A., Calov, R.,
Ganopolski, A., Goosse, H., Lohmann, G., Lunkeit, F., Mokhov, I., Petoukhov,
V., Stone, P., and Wang, Z.: Earth system models of intermediate complexity:
closing the gap in the spectrum of climate system models, Clim. Dynam.,
18, 579–586, https://doi.org/10.1007/s00382-001-0200-1, 2002.
Daniels, C. J., Poulton, A. J., Balch, W. M., Marañón, E., Adey, T., Bowler, B. C., Cermeño, P., Charalampopoulou, A., Crawford, D. W., Drapeau, D., Feng, Y., Fernández, A., Fernández, E., Fragoso, G. M., González, N., Graziano, L. M., Heslop, R., Holligan, P. M., Hopkins, J., Huete-Ortega, M., Hutchins, D. A., Lam, P. J., Lipsen, M. S., López-Sandoval, D. C., Loucaides, S., Marchetti, A., Mayers, K. M. J., Rees, A. P., Sobrino, C., Tynan, E., and Tyrrell, T.: A global compilation of coccolithophore calcification rates, Earth Syst. Sci. Data, 10, 1859–1876, https://doi.org/10.5194/essd-10-1859-2018, 2018.
Droop, M. R.: Vitamin B12 and Marine Ecology. IV. The Kinetics of Uptake,
Growth and Inhibition in Monochrysis Lutheri, J. Mar. Biol. Ass., 48,
689–733, https://doi.org/10.1017/S0025315400019238, 1968.
Edgar, K. M., Bohaty, S. M., Gibbs, S. J., Sexton, P. F., Norris, R. D., and
Wilson, P. A.: Symbiont “bleaching” in planktic foraminifera during the
Middle Eocene Climatic Optimum, Geology, 41, 15–18,
https://doi.org/10/f4jwbp, 2013.
Edwards, K. F., Thomas, M. K., Klausmeier, C. A., and Litchman, E.:
Allometric scaling and taxonomic variation in nutrient utilization traits
and maximum growth rate of phytoplankton, Limnol. Oceanogr., 57, 554–566,
https://doi.org/10.4319/lo.2012.57.2.0554, 2012.
Edwards, N. R. and Marsh, R.: Uncertainties due to transport-parameter
sensitivity in an efficient 3-D ocean-climate model, Clim. Dynam., 24, 415–433,
https://doi.org/10/fcvq9k, 2005.
Ezard, T. H. G., Aze, T., Pearson, P. N., and Purvis, A.: Interplay Between
Changing Climate and Species' Ecology Drives Macroevolutionary Dynamics,
Science, 332, 349–351, https://doi.org/10/bd77gm, 2011.
Fiksen, Ø., Follows, M. J., and Aksnes, D. L.: Trait-based models of
nutrient uptake in microbes extend the Michaelis-Menten framework, Limnol.
Oceanogr., 58, 193–202, https://doi.org/10.4319/lo.2013.58.1.0193, 2013.
Fisher, R. A., Corbet, A. S., and Williams, C. B.: The Relation Between the
Number of Species and the Number of Individuals in a Random Sample of an
Animal Population, J. Anim. Ecol., 12, 42–58,
https://doi.org/10.2307/1411, 1943.
Flynn, K. J.: The importance of the form of the quota curve and control of
non-limiting nutrient transport in phytoplankton models, J. Plankton Res., 30, 423–438, https://doi.org/10.1093/plankt/fbn007, 2008.
Follows, M. J. and Dutkiewicz, S.: Modeling Diverse Communities of Marine
Microbes, Annu. Rev. Mar. Sci., 3, 427–451, https://doi.org/10/b3w27x,
2011.
Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W.: Emergent
Biogeography of Microbial Communities in a Model Ocean, Science, 315,
1843–1846, https://doi.org/10/bf6j95, 2007.
Fraile, I., Schulz, M., Mulitza, S., and Kucera, M.: Predicting the global distribution of planktonic foraminifera using a dynamic ecosystem model, Biogeosciences, 5, 891–911, https://doi.org/10.5194/bg-5-891-2008, 2008.
Fraile, I., Schulz, M., Mulitza, S., Merkel, U., Prange, M., and Paul, A.:
Modeling the seasonal distribution of planktonic foraminifera during the
Last Glacial Maximum, Paleoceanography, 24, PA2216, https://doi.org/10.1029/2008PA001686, 2009.
Frick, H., Chow, F., Kuhn, M., Mahoney, M., Silge, J., and Wickham, H.: rsample: General Resampling Infrastructure, https://rsample.tidymodels.org, last access: July 2022.
Gaskell, D. E., Ohman, M. D., and Hull, P. M.: Zooglider-Based Measurements
of Planktonic Foraminifera in the California Current System, J. Foramin. Res., 49, 390–404,
https://doi.org/10.2113/gsjfr.49.4.390, 2019.
Geider, R. J., Maclntyre, H. L., and Kana, T. M.: A dynamic regulatory model
of phytoplanktonic acclimation to light, nutrients, and temperature, Limnol.
Oceanogr., 43, 679–694, https://doi.org/10.4319/lo.1998.43.4.0679, 1998.
Gregoire, L. J., Valdes, P. J., Payne, A. J., and Kahana, R.: Optimal tuning
of a GCM using modern and glacial constraints, Clim. Dynam., 37, 705–719,
https://doi.org/10.1007/s00382-010-0934-8, 2011.
Grigoratou, M., Monteiro, F. M., Schmidt, D. N., Wilson, J. D., Ward, B. A., and Ridgwell, A.: A trait-based modelling approach to planktonic foraminifera ecology, Biogeosciences, 16, 1469–1492, https://doi.org/10.5194/bg-16-1469-2019, 2019.
Grigoratou, M., Monteiro, F. M., Wilson, J. D., Ridgwell, A., and Schmidt,
D. N.: Exploring the impact of climate change on the global distribution of
non-spinose planktonic foraminifera using a trait-based ecosystem model,
Glob. Change Biol., 28, 1063–1076, https://doi.org/10.1111/gcb.15964, 2021a.
Grigoratou, M., Monteiro, F. M., Ridgwell, A., and Schmidt, D. N.:
Investigating the benefits and costs of spines and diet on planktonic
foraminifera distribution with a trait-based ecosystem model, Mar. Micropaleontol., 166, 102004, https://doi.org/10/gkbn65, 2021b.
Hemer, M. A. and Trenham, C. E.: Evaluation of a CMIP5 derived dynamical
global wind wave climate model ensemble, Ocean Model., 103, 190–203,
https://doi.org/10.1016/j.ocemod.2015.10.009, 2016.
Hemleben, C., Spindler, M., and Anderson, O. R.: Modern Planktonic Foraminifera,
Springer Verlag, New York, 112–127, 134–136,
1989.
Henehan, M. J., Ridgwell, A., Thomas, E., Zhang, S., Alegret, L., Schmidt,
D. N., Rae, J. W. B., Witts, J. D., Landman, N. H., Greene, S. E., Huber, B.
T., Super, J. R., Planavsky, N. J., and Hull, P. M.: Rapid ocean
acidification and protracted Earth system recovery followed the
end-Cretaceous Chicxulub impact, P. Natl. Acad. Sci. USA, 116, 22500–22504,
https://doi.org/10/ggbnrm, 2019.
Holling, C. S.: The Functional Response of Predators to Prey Density and its
Role in Mimicry and Population Regulation, Mem. Entomol. Soc. Can., 97,
5–60, https://doi.org/10/fhjtms, 1965.
Hönisch, B., Ridgwell, A., Schmidt, D. N., Thomas, E., Gibbs, S. J.,
Sluijs, A., Zeebe, R., Kump, L., Martindale, R. C., Greene, S. E.,
Kiessling, W., Ries, J., Zachos, J. C., Royer, D. L., Barker, S., Marchitto,
T. M., Moyer, R., Pelejero, C., Ziveri, P., Foster, G. L., and Williams, B.:
The Geological Record of Ocean Acidification, Science, 335, 1058–1063,
https://doi.org/10/gdj3zf, 2012.
Jonkers, L. and Kučera, M.: Global analysis of seasonality in the shell flux of extant planktonic Foraminifera, Biogeosciences, 12, 2207–2226, https://doi.org/10.5194/bg-12-2207-2015, 2015.
Keller, D. P., Oschlies, A., and Eby, M.: A new marine ecosystem model for the University of Victoria Earth System Climate Model, Geosci. Model Dev., 5, 1195–1220, https://doi.org/10.5194/gmd-5-1195-2012, 2012.
Kiørboe, T., Visser, A., and Andersen, K. H.: A trait-based approach to
ocean ecology, ICES J. Mar. Sci., 75, 1849–1863,
https://doi.org/10.1093/icesjms/fsy090, 2018.
Kretschmer, K., Kucera, M., and Schulz, M.: Modeling the distribution and
seasonality of Neogloboquadrina pachyderma in the North Atlantic Ocean
during Heinrich Stadial 1, Paleoceanography, 31, 986–1010,
https://doi.org/10.1002/2015PA002819, 2016.
Kretschmer, K., Jonkers, L., Kucera, M., and Schulz, M.: Modeling seasonal and vertical habitats of planktonic foraminifera on a global scale, Biogeosciences, 15, 4405–4429, https://doi.org/10.5194/bg-15-4405-2018, 2018.
Kucera, M. and Schonfeld, J.: The origin of modern oceanic foraminiferal
faunas and Neogene climate change, in: Deep-Time Perspectives on Climate
Change: Marrying the Signal from Computer Models and Biological Proxies,
edited by: Williams, M., Haywood, A. M., Gregory, F. J., and Schmidt, D. N.,
The Geological Society of London on behalf of The Micropalaeontological
Society, 409–425, https://doi.org/10.1144/TMS002.18, 2007.
Legendre, P. and Legendre, L.: Numerical Ecology, 2nd edn., Elsevier, 316–317, ISBN 0-444089249-4, 1998.
LeKieffre, C., Spero, H. J., Russell, A. D., Fehrenbacher, J. S., Geslin,
E., and Meibom, A.: Assimilation, translocation, and utilization of carbon
between photosynthetic symbiotic dinoflagellates and their planktic
foraminifera host, Mar. Biol., 165, 104, https://doi.org/10.1007/s00227-018-3362-7, 2018.
Lombard, F., Labeyrie, L., Michel, E., Bopp, L., Cortijo, E., Retailleau, S., Howa, H., and Jorissen, F.: Modelling planktic foraminifer growth and distribution using an ecophysiological multi-species approach, Biogeosciences, 8, 853–873, https://doi.org/10.5194/bg-8-853-2011, 2011.
Marsh, R., Müller, S. A., Yool, A., and Edwards, N. R.: Incorporation of the C-GOLDSTEIN efficient climate model into the GENIE framework: “eb_go_gs” configurations of GENIE, Geosci. Model Dev., 4, 957–992, https://doi.org/10.5194/gmd-4-957-2011, 2011.
Mitra, A., Flynn, K. J., Tillmann, U., Raven, J. A., Caron, D., Stoecker, D.
K., Not, F., Hansen, P. J., Hallegraeff, G., Sanders, R., Wilken, S.,
McManus, G., Johnson, M., Pitta, P., Våge, S., Berge, T., Calbet, A.,
Thingstad, F., Jeong, H. J., Burkholder, J., Glibert, P. M., Granéli,
E., and Lundgren, V.: Defining Planktonic Protist Functional Groups on
Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse
Mixotrophic Strategies, Protist, 167, 106–120, https://doi.org/10/f3p5h2,
2016.
Monteiro, F. M., Follows, M. J., and Dutkiewicz, S.: Distribution of diverse
nitrogen fixers in the global ocean, Global Biogeochem. Cy., 24, GB3017,
https://doi.org/10/ctkc4h, 2010.
Moore, J. K., Doney, S. C., Kleypas, J. A., Glover, D. M., and Fung, I. Y.:
An intermediate complexity marine ecosystem model for the global domain,
Deep-Sea Res. Pt. II, 49, 403–462,
https://doi.org/10/bp99zn, 2001.
Ohman, M. D.: A sea of tentacles: optically discernible traits resolved from
planktonic organisms in situ, ICES J. Mar. Sci., 76,
1959–1972, https://doi.org/10.1093/icesjms/fsz184, 2019.
Ortiz, J. D., Mix, A. C., and Collier, R. W.: Environmental control of
living symbiotic and asymbiotic foraminifera of the California Current,
Paleoceanography, 10, 987–1009, https://doi.org/10/ft8jc7, 1995.
Pianosi, F. and Wagener, T.: A simple and efficient method for global
sensitivity analysis based on cumulative distribution functions,
Environ. Modell. Softw., 67, 1–11, https://doi.org/10/f677qs,
2015.
Quéré, C. L., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T.,
Aumont, O., Bopp, L., Claustre, H., Cunha, L. C. D., Geider, R., Giraud, X.,
Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin, R.
B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gladrow, D.:
Ecosystem dynamics based on plankton functional types for global ocean
biogeochemistry models, Glob. Change Biol., 11, 2016–2040,
https://doi.org/10/cm9nzc, 2005.
Rae, J. W. B., Gray, W. R., Wills, R. C. J., Eisenman, I., Fitzhugh, B.,
Fotheringham, M., Littley, E. F. M., Rafter, P. A., Rees-Owen, R., Ridgwell,
A., Taylor, B., and Burke, A.: Overturning circulation, nutrient limitation,
and warming in the Glacial North Pacific, Science Advances, 6, eabd1654,
https://doi.org/10/ghrj7m, 2020.
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: July 2022), 2021.
Renaud, S. and Schmidt, D. N.: Habitat tracking as a response of the
planktic foraminifer Globorotalia truncatulinoides to environmental
fluctuations during the last 140 kyr, Mar. Micropaleontol., 49, 97–122,
https://doi.org/10/bgp3cz, 2003.
Ridgwell, A. and Hargreaves, J. C.: Regulation of atmospheric CO2 by deep-sea sediments in an Earth system model, Global Biogeochem. Cy., 21, GB2008,
https://doi.org/10.1029/2006GB002764, 2007.
Ridgwell, A. and Schmidt, D. N.: Past constraints on the vulnerability of
marine calcifiers to massive carbon dioxide release, Nat. Geosci., 3,
196–200, https://doi.org/10.1038/ngeo755, 2010.
Ridgwell, A., Hargreaves, J. C., Edwards, N. R., Annan, J. D., Lenton, T. M., Marsh, R., Yool, A., and Watson, A.: Marine geochemical data assimilation in an efficient Earth System Model of global biogeochemical cycling, Biogeosciences, 4, 87–104, https://doi.org/10.5194/bg-4-87-2007, 2007.
Ridgwell, A., Ying R., Reinhard, C., van de Velde, S., Adloff, M., Monteiro, F., Hülse, D., Wilson, J., Ward, B., Vervoort, P., Kirtland, S., Turner, S., and Li, M.: ruiying-ocean/cgenie.muffin: ForamEcoGENIE (v0.9.26), Zenodo [code and data set], https://doi.org/10.5281/zenodo.6808760, 2022.
Roy, T., Lombard, F., Bopp, L., and Gehlen, M.: Projected impacts of climate change and ocean acidification on the global biogeography of planktonic Foraminifera, Biogeosciences, 12, 2873–2889, https://doi.org/10.5194/bg-12-2873-2015, 2015.
Salter, I., Schiebel, R., Ziveri, P., Movellan, A., Lampitt, R., and Wolff,
G. A.: Carbonate counter pump stimulated by natural iron fertilization in
the Polar Frontal Zone, Nat. Geosci., 7, 885–889,
https://doi.org/10.1038/ngeo2285, 2014.
Sarrazin, F., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis of
environmental models: Convergence and validation, Environ. Modell. Softw., 79, 135–152, https://doi.org/10/f8n8kp, 2016.
Schiebel, R.: Planktic foraminiferal sedimentation and the marine calcite
budget, Global Biogeochem. Cy., 16, 1065,
https://doi.org/10/bdxfhs, 2002.
Schiebel, R. and Hemleben, C.: Planktic Foraminifers in the Modern Ocean,
Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-662-50297-6, 2017.
Schiebel, R. and Movellan, A.: First-order estimate of the planktic foraminifer biomass in the modern ocean, Earth Syst. Sci. Data, 4, 75–89, https://doi.org/10.5194/essd-4-75-2012, 2012.
Schmidt, D. N., Thierstein, H. R., Bollmann, J., and Schiebel, R.: Abiotic
Forcing of Plankton Evolution in the Cenozoic, Science, 303, 207–210,
https://doi.org/10/b37mvn, 2004a.
Schmidt, D. N., Renaud, S., Bollmann, J., Schiebel, R., and Thierstein, H.
R.: Size distribution of Holocene planktic foraminifer assemblages:
biogeography, ecology and adaptation, Mar. Micropaleontol., 50,
319–338, https://doi.org/10/b6nqrj, 2004b.
van Sebille, E., Scussolini, P., Durgadoo, J. V., Peeters, F. J. C.,
Biastoch, A., Weijer, W., Turney, C., Paris, C. B., and Zahn, R.: Ocean
currents generate large footprints in marine palaeoclimate proxies, Nat.
Commun., 6, 6521, https://doi.org/10/f67xqv, 2015.
Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L.,
Tagliabue, A., Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J.,
Gehlen, M., Ilyina, T., John, J. G., Li, H., Long, M. C., Luo, J. Y.,
Nakano, H., Romanou, A., Schwinger, J., Stock, C., Santana-Falcón, Y.,
Takano, Y., Tjiputra, J., Tsujino, H., Watanabe, M., Wu, T., Wu, F., and
Yamamoto, A.: Tracking Improvement in Simulated Marine Biogeochemistry
Between CMIP5 and CMIP6, Curr. Clim. Change Rep., 6, 95–119,
https://doi.org/10.1007/s40641-020-00160-0, 2020.
Siccha, M. and Kucera, M.: ForCenS, a curated database of planktonic
foraminifera census counts in marine surface sediment samples, Sci. Data, 4,
170109, https://doi.org/10.1038/sdata.2017.109, 2017.
Spero, H. J. and Parker, S. L.: Photosynthesis in the symbiotic planktonic
foraminifer Orbulina universa, and its potential contribution to oceanic
primary productivity, J. Foramin. Res., 15, 273–281,
https://doi.org/10/c2rt2q, 1985.
Suggett, D. J., Warner, M. E., and Leggat, W.: Symbiotic Dinoflagellate
Functional Diversity Mediates Coral Survival under Ecological Crisis, Trends
Ecol. Evol., 32, 735–745,
https://doi.org/10.1016/j.tree.2017.07.013, 2017.
Sunagawa, S., Acinas, S. G., Bork, P., Bowler, C., Eveillard, D., Gorsky,
G., Guidi, L., Iudicone, D., Karsenti, E., Lombard, F., Ogata, H., Pesant,
S., Sullivan, M. B., Wincker, P., and de Vargas, C.: Tara Oceans: towards
global ocean ecosystems biology, Nat. Rev. Microbiol., 18, 428–445,
https://doi.org/10.1038/s41579-020-0364-5, 2020.
Takagi, H., Kimoto, K., Fujiki, T., Saito, H., Schmidt, C., Kucera, M., and Moriya, K.: Characterizing photosymbiosis in modern planktonic foraminifera, Biogeosciences, 16, 3377–3396, https://doi.org/10.5194/bg-16-3377-2019, 2019.
Takahashi, K. and Be, A. W. H.: Planktonic foraminifera: factors controlling
sinking speeds, Deep-Sea Res., 31,
1477–1500, https://doi.org/10.1016/0198-0149(84)90083-9, 1984.
Tierney, J. E., Poulsen, C. J., Montañez, I. P., Bhattacharya, T., Feng,
R., Ford, H. L., Hönisch, B., Inglis, G. N., Petersen, S. V., Sagoo, N.,
Tabor, C. R., Thirumalai, K., Zhu, J., Burls, N. J., Foster, G. L.,
Goddéris, Y., Huber, B. T., Ivany, L. C., Kirtland Turner, S., Lunt, D.
J., McElwain, J. C., Mills, B. J. W., Otto-Bliesner, B. L., Ridgwell, A.,
and Zhang, Y. G.: Past climates inform our future, Science, 370, eaay3701,
https://doi.org/10/gh6c3g, 2020.
Todd, C. L., Schmidt, D. N., Robinson, M. M., and Schepper, S. D.: Planktic
Foraminiferal Test Size and Weight Response to the Late Pliocene
Environment, Paleoceanography and Paleoclimatology, 35, e2019PA003738,
https://doi.org/10/ghrd4r, 2020.
Tréguer, P., Bowler, C., Moriceau, B., Dutkiewicz, S., Gehlen, M.,
Aumont, O., Bittner, L., Dugdale, R., Finkel, Z., Iudicone, D., Jahn, O.,
Guidi, L., Lasbleiz, M., Leblanc, K., Levy, M., and Pondaven, P.: Influence
of diatom diversity on the ocean biological carbon pump, Nat. Geosci.,
11, 27–37, https://doi.org/10/gcxznd, 2018.
Uhle, M. E., Macko, S. A., Spero, H. J., Lea, D. W., Ruddiman, W. F., and
Engel, M. H.: The fate of nitrogen in the Orbulina universa
foraminifera-symbiont system determined by nitrogen isotope analyses of
shell-bound organic matter, Limnol. Oceanogr., 44, 1968–1977,
https://doi.org/10/ffgtfw, 1999.
Våge, S., Castellani, M., Giske, J., and Thingstad, T. F.: Successful
strategies in size structured mixotrophic food webs, Aquat. Ecol., 47,
329–347, https://doi.org/10.1007/s10452-013-9447-y, 2013.
van de Velde, S. J., Hülse, D., Reinhard, C. T., and Ridgwell, A.: Iron and sulfur cycling in the cGENIE.muffin Earth system model (v0.9.21), Geosci. Model Dev., 14, 2713–2745, https://doi.org/10.5194/gmd-14-2713-2021, 2021.
Ward, B. A. and Follows, M. J.: Marine mixotrophy increases trophic transfer
efficiency, mean organism size, and vertical carbon flux, P. Natl. Acad. Sci.
USA, 113, 2958–2963, https://doi.org/10/ggnmm5, 2016.
Ward, B. A., Wilson, J. D., Death, R. M., Monteiro, F. M., Yool, A., and Ridgwell, A.: EcoGEnIE 1.0: plankton ecology in the cGEnIE Earth system model, Geosci. Model Dev., 11, 4241–4267, https://doi.org/10.5194/gmd-11-4241-2018, 2018.
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011.
Waterson, A. M., Edgar, K. M., Schmidt, D. N., and Valdes, P. J.:
Quantifying the stability of planktic foraminiferal physical niches between
the Holocene and Last Glacial Maximum: Niche Stability of Planktic
Foraminifera, Paleoceanography, 32, 74–89, https://doi.org/10/f9vbtb, 2017.
Watterson, I. G.: Non-Dimensional Measures of Climate Model Performance,
Int. J. Climatol., 16, 379–391,
https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<379::AID-JOC18>3.0.CO;2-U, 1996.
Watterson, I. G., Bathols, J., and Heady, C.: What Influences the Skill of
Climate Models over the Continents?, B. Am. Meteorol.
Soc., 95, 689–700, https://doi.org/10.1175/BAMS-D-12-00136.1, 2014.
West, G. B., Brown, J. H., and Enquist, B. J.: A General Model for the
Origin of Allometric Scaling Laws in Biology, Science, 276, 122–126,
https://doi.org/10.1126/science.276.5309.122, 1997.
Wilson, J. D., Andrews, O., Katavouta, A., de Melo Viríssimo, F.,
Death, R. M., Adloff, M., Baker, C. A., Blackledge, B., Goldsworth, F. W.,
Kennedy-Asser, A. T., Liu, Q., Sieradzan, K. R., Vosper, E., and Ying, R.:
The biological carbon pump in CMIP6 models: 21st century trends and
uncertainties, P. Natl. Acad. Sci. USA, 119, e2204369119,
https://doi.org/10.1073/pnas.2204369119, 2022.
Zakharova, L., Meyer, K. M., and Seifan, M.: Trait-based modelling in
ecology: A review of two decades of research, Ecol. Model., 407,
108703, https://doi.org/10.1016/j.ecolmodel.2019.05.008, 2019.
Žarić, S., Schulz, M., and Mulitza, S.: Global prediction of planktic foraminiferal fluxes from hydrographic and productivity data, Biogeosciences, 3, 187–207, https://doi.org/10.5194/bg-3-187-2006, 2006.
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure...
