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.
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
No articles found.
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
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)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
A global behavioural model of human fire use and management: WHAM! v1.0
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): calibration-free calculations of water and energy fluxes
biospheremetrics v1.0.1: An R package to calculate two complementary terrestrial biosphere integrity indicators: human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Optimal enzyme allocation leads to the constrained enzyme hypothesis: The Soil Enzyme Steady Allocation Model (SESAM v3.1)
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
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.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Preprint under review for GMD
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.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James Millington
EGUsphere, https://doi.org/10.5194/egusphere-2023-2162, https://doi.org/10.5194/egusphere-2023-2162, 2023
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.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
EGUsphere, https://doi.org/10.5194/egusphere-2023-1626, https://doi.org/10.5194/egusphere-2023-1626, 2023
Short summary
Short summary
Numerous estimations of water and energy balances heavily depend on empirical equations that necessitate site-specific calibration. This equifinality poses the risk of obtaining 'right answers for wrong reasons.' In this paper, we introduce novel formulations based on first-principles to calculate calibration-free quantities, such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
EGUsphere, https://doi.org/10.5194/egusphere-2023-2503, https://doi.org/10.5194/egusphere-2023-2503, 2023
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 >25 % of the potential pre-industrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of land, water, and fertilizer use, as well as climate change.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Elin Ristorp Aas, Heleen A. de Wit, and Terje Koren Berntsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2069, https://doi.org/10.5194/egusphere-2023-2069, 2023
Short summary
Short summary
By including microbial processes in soil models, we learn about how the soil system interacts with its environment, and responds to climate change. We present a soil process model, MIMICS+, able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also found that when adding nitrogen, the relationship between soil microbes changed notably. Coupling the model to a vegetation model, will allow further investigation of these mechanisms.
Yannek Käber, Florian Hartig, and Harald Bugmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2114, https://doi.org/10.5194/egusphere-2023-2114, 2023
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 with forest inventory data, and climatic effects are challenging to capture.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
EGUsphere, https://doi.org/10.5194/egusphere-2023-2003, https://doi.org/10.5194/egusphere-2023-2003, 2023
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.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
EGUsphere, https://doi.org/10.5194/egusphere-2023-1492, https://doi.org/10.5194/egusphere-2023-1492, 2023
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 behavior at low microbial biomass led us formulate the constrained enzyme hypothesis. We now can better describe how microbial mediated linkages of elemental fluxes adapt across decades.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-1287, https://doi.org/10.5194/egusphere-2023-1287, 2023
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predict their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that important questions related to plant-atmosphere interactions can be addressed, such as the effects of rising CO2 and ozone pollution on carbon uptake of the biosphere. The model has been well validated with both ground and satellite observations.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
Short summary
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
Short summary
Short summary
A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
Short summary
Short summary
The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
Short summary
Short summary
Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
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...