Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6025-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6025-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Oct 2021
Model description paper |
| 08 Oct 2021
| 08 Oct 2021
FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth
Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg, Germany
Helmholtz-Zentrum Hereon, Institute of Coastal Systems – Analysis and Modeling, Geesthacht, Germany
Prima Anugerahanti
Earth SURFACE System Research Center, Research Institute for Global Change, JAMSTEC, Yokosuka, Japan
Sherwood Lan Smith
Earth SURFACE System Research Center, Research Institute for Global Change, JAMSTEC, Yokosuka, Japan
Related authors
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.
Onur Kerimoglu, Yoana G. Voynova, Fatemeh Chegini, Holger Brix, Ulrich Callies, Richard Hofmeister, Knut Klingbeil, Corinna Schrum, and Justus E. E. van Beusekom
Biogeosciences, 17, 5097–5127, https://doi.org/10.5194/bg-17-5097-2020, https://doi.org/10.5194/bg-17-5097-2020, 2020
Short summary
Short summary
In this study, using extensive field observations and a numerical model, we analyzed the physical and biogeochemical structure of a coastal system following an extreme flood event. Our results suggest that a number of anomalous observations were driven by a co-occurrence of peculiar meteorological conditions and increased riverine discharges. Our results call for attention to the combined effects of hydrological and meteorological extremes that are anticipated to increase in frequency.
Onur Kerimoglu, Richard Hofmeister, Joeran Maerz, Rolf Riethmüller, and Kai W. Wirtz
Biogeosciences, 14, 4499–4531, https://doi.org/10.5194/bg-14-4499-2017, https://doi.org/10.5194/bg-14-4499-2017, 2017
Short summary
Short summary
In this study, we present a coupled physical–biogeochemical model, in which the acclimation of autotrophs to their resource environment is resolved based on optimality arguments. The model is implemented in the southern North Sea, a shallow coastal system. Based on comparisons with multiple data sources, we show that the model can reproduce the variability in the system for the period 2000–2010 and analyze the mechanisms driving the large-scale, persistent coastal gradients in the system.
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.
Yoshikazu Sasai, Sherwood Lan Smith, Eko Siswanto, Hideharu Sasaki, and Masami Nonaka
Biogeosciences, 19, 4865–4882, https://doi.org/10.5194/bg-19-4865-2022, https://doi.org/10.5194/bg-19-4865-2022, 2022
Short summary
Short summary
We have investigated the adaptive response of phytoplankton growth to changing light, nutrients, and temperature over the North Pacific using two physical-biological models. We compare modeled chlorophyll and primary production from an inflexible control model (InFlexPFT), which assumes fixed carbon (C):nitrogen (N):chlorophyll (Chl) ratios, to a recently developed flexible phytoplankton functional type model (FlexPFT), which incorporates photoacclimation and variable C:N:Chl ratios.
Guillaume Le Gland, Sergio M. Vallina, S. Lan Smith, and Pedro Cermeño
Geosci. Model Dev., 14, 1949–1985, https://doi.org/10.5194/gmd-14-1949-2021, https://doi.org/10.5194/gmd-14-1949-2021, 2021
Short summary
Short summary
We present an ecological model called SPEAD wherein various phytoplankton compete for nutrients. Phytoplankton in SPEAD are characterized by two continuously distributed traits: optimal temperature and nutrient half-saturation. Trait diversity is sustained by allowing the traits to mutate at each generation. We show that SPEAD agrees well with a more classical discrete model for only a fraction of the cost. We also identify realistic values for the mutation rates to be used in future models.
Onur Kerimoglu, Yoana G. Voynova, Fatemeh Chegini, Holger Brix, Ulrich Callies, Richard Hofmeister, Knut Klingbeil, Corinna Schrum, and Justus E. E. van Beusekom
Biogeosciences, 17, 5097–5127, https://doi.org/10.5194/bg-17-5097-2020, https://doi.org/10.5194/bg-17-5097-2020, 2020
Short summary
Short summary
In this study, using extensive field observations and a numerical model, we analyzed the physical and biogeochemical structure of a coastal system following an extreme flood event. Our results suggest that a number of anomalous observations were driven by a co-occurrence of peculiar meteorological conditions and increased riverine discharges. Our results call for attention to the combined effects of hydrological and meteorological extremes that are anticipated to increase in frequency.
Prima Anugerahanti, Shovonlal Roy, and Keith Haines
Biogeosciences, 15, 6685–6711, https://doi.org/10.5194/bg-15-6685-2018, https://doi.org/10.5194/bg-15-6685-2018, 2018
Short summary
Short summary
Minor changes in the biogeochemical model equations lead to major dynamical changes. We assessed this structural sensitivity for the MEDUSA biogeochemical model on chlorophyll and nitrogen concentrations at five oceanographic stations over 10 years, using 1-D ensembles generated by combining different process equations. The ensemble performed better than the default model in most of the stations, suggesting that our approach is useful for generating a probabilistic biogeochemical ensemble model.
Bingzhang Chen and Sherwood Lan Smith
Geosci. Model Dev., 11, 467–495, https://doi.org/10.5194/gmd-11-467-2018, https://doi.org/10.5194/gmd-11-467-2018, 2018
Short summary
Short summary
Marine phytoplankton accounts for half of global primary production. Phytoplankton size is an important trait affecting its fitness and ecosystem functioning. We have developed a plankton model with continuous size distribution for phytoplankton and applied it in the North Pacific. This model is able to capture the general patterns of phytoplankton size distribution in the real ocean and can be used for understanding the mechanisms controlling phytoplankton size structure and diversity.
Onur Kerimoglu, Richard Hofmeister, Joeran Maerz, Rolf Riethmüller, and Kai W. Wirtz
Biogeosciences, 14, 4499–4531, https://doi.org/10.5194/bg-14-4499-2017, https://doi.org/10.5194/bg-14-4499-2017, 2017
Short summary
Short summary
In this study, we present a coupled physical–biogeochemical model, in which the acclimation of autotrophs to their resource environment is resolved based on optimality arguments. The model is implemented in the southern North Sea, a shallow coastal system. Based on comparisons with multiple data sources, we show that the model can reproduce the variability in the system for the period 2000–2010 and analyze the mechanisms driving the large-scale, persistent coastal gradients in the system.
Esteban Acevedo-Trejos, Gunnar Brandt, S. Lan Smith, and Agostino Merico
Geosci. Model Dev., 9, 4071–4085, https://doi.org/10.5194/gmd-9-4071-2016, https://doi.org/10.5194/gmd-9-4071-2016, 2016
Short summary
Short summary
Marine phytoplankton plays a prominent role in regulating Earth’s climate. Numerical models are important tools that help us investigate the interactions between these microbes and their environment. We proposed PhytoSFDM as an open-source model to quantify size structure and functional diversity of marine phytoplankton communities. This tool allows us, in a manageable and computationally efficient way, to study patterns in planktonic ecosystems and their feedbacks with a changing environment.
Related subject area
Biogeosciences
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
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal 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)
MEDFATE 2.8.1: A trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
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)
Modeling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation
Representation of the phosphorus cycle in the Joint UK Land Environment Simulator (vn5.5_JULES-CNP)
CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)
The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0
A validation standard for area of habitat maps for terrestrial birds and mammals
Soil Cycles of Elements simulator for Predicting TERrestrial regulation of greenhouse gases: SCEPTER v0.9
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
A map of global peatland extent created using machine learning (Peat-ML)
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model
Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET
Predicting global terrestrial biomes with the LeNet convolutional neural network
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020
A dynamic local-scale vegetation model for lycopsids (LYCOm v1.0)
Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model
Global evaluation of the Ecosystem Demography model (ED v3.0)
A new snow module improves predictions of the isotope-enabled MAIDENiso forest growth model
Calibrating the soil organic carbon model Yasso20 with multiple datasets
The PFLOTRAN Reaction Sandbox
A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil) for northern and temperate peatlands
Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)
Locating trees to mitigate outdoor radiant load of humans in urban areas using a metaheuristic hill-climbing algorithm – introducing TreePlanter v1.0
Sensitivity of asymmetric oxygen minimum zones to mixing intensity and stoichiometry in the tropical Pacific using a basin-scale model (OGCM-DMEC V1.4)
The importance of turbulent ocean–sea ice nutrient exchanges for simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2
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).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
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.
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.
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, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-243, https://doi.org/10.5194/gmd-2022-243, 2022
Revised manuscript accepted for GMD
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, a novel model of forest dynamics for its application over a region in the Western Mediterranean Basin.
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.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-176, https://doi.org/10.5194/gmd-2022-176, 2022
Revised manuscript accepted for GMD
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 utilisation by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, https://doi.org/10.5194/gmd-15-5713-2022, 2022
Short summary
Short summary
This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
Short summary
Short summary
A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
Short summary
Short summary
We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Mahdi André Nakhavali, Lina M. Mercado, Iain P. Hartley, Stephen Sitch, Fernanda V. Cunha, Raffaello di Ponzio, Laynara F. Lugli, Carlos A. Quesada, Kelly M. Andersen, Sarah E. Chadburn, Andy J. Wiltshire, Douglas B. Clark, Gyovanni Ribeiro, Lara Siebert, Anna C. M. Moraes, Jéssica Schmeisk Rosa, Rafael Assis, and José L. Camargo
Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
Short summary
Short summary
In tropical ecosystems, the availability of rock-derived elements such as P can be very low. Thus, without a representation of P cycling, tropical forest responses to rising atmospheric CO2 conditions in areas such as Amazonia remain highly uncertain. We introduced P dynamics and its interactions with the N and P cycles into the JULES model. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low-fertility soils.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
Short summary
Short summary
Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
Short summary
Short summary
We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev., 15, 5093–5105, https://doi.org/10.5194/gmd-15-5093-2022, https://doi.org/10.5194/gmd-15-5093-2022, 2022
Short summary
Short summary
This paper describes the validation of area of habitat (AOH) maps produced for terrestrial birds and mammals. The main objective was to assess the accuracy of the maps based on independent data. We used open access data from repositories, such as ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. The majority of AOH maps were found to have a high accuracy.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 15, 4959–4990, https://doi.org/10.5194/gmd-15-4959-2022, https://doi.org/10.5194/gmd-15-4959-2022, 2022
Short summary
Short summary
Increasing carbon dioxide in the atmosphere is an urgent issue in the coming century. Enhanced rock weathering in soils can be one of the most efficient C capture strategies. On the basis as a weathering simulator, the newly developed SCEPTER model implements bio-mixing by fauna/humans and enables organic matter and crushed rocks/minerals at the soil surface with an option to track their particle size distributions. Those features can be useful for evaluating the carbon capture efficiency.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
Short summary
Short summary
We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot
Geosci. Model Dev., 15, 4709–4738, https://doi.org/10.5194/gmd-15-4709-2022, https://doi.org/10.5194/gmd-15-4709-2022, 2022
Short summary
Short summary
Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022, https://doi.org/10.5194/gmd-15-4313-2022, 2022
Short summary
Short summary
Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Short summary
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Nicolas Azaña Schnedler-Meyer, Tobias Kuhlmann Andersen, Fenjuan Rose Schmidt Hu, Karsten Bolding, Anders Nielsen, and Dennis Trolle
Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, https://doi.org/10.5194/gmd-15-3861-2022, 2022
Short summary
Short summary
We present the Water Ecosystems Tool (WET) – a new modular aquatic ecosystem model configurable to a wide array of physical setups, ecosystems and research questions based on the popular FABM–PCLake model. We aim for the model to become a community staple, thus helping to consolidate the state of the art under a few flexible models, with the aim of improving comparability across studies and preventing the
re-inventions of the wheelthat are common to our scientific modeling community.
Hamze Dokoohaki, Bailey D. Morrison, Ann Raiho, Shawn P. Serbin, Katie Zarada, Luke Dramko, and Michael Dietze
Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022, https://doi.org/10.5194/gmd-15-3233-2022, 2022
Short summary
Short summary
We present a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon
reanalysisproduct that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022, https://doi.org/10.5194/gmd-15-3121-2022, 2022
Short summary
Short summary
Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
Short summary
Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Elodie Salmon, Fabrice Jégou, Bertrand Guenet, Line Jourdain, Chunjing Qiu, Vladislav Bastrikov, Christophe Guimbaud, Dan Zhu, Philippe Ciais, Philippe Peylin, Sébastien Gogo, Fatima Laggoun-Défarge, Mika Aurela, M. Syndonia Bret-Harte, Jiquan Chen, Bogdan H. Chojnicki, Housen Chu, Colin W. Edgar, Eugenie S. Euskirchen, Lawrence B. Flanagan, Krzysztof Fortuniak, David Holl, Janina Klatt, Olaf Kolle, Natalia Kowalska, Lars Kutzbach, Annalea Lohila, Lutz Merbold, Włodzimierz Pawlak, Torsten Sachs, and Klaudia Ziemblińska
Geosci. Model Dev., 15, 2813–2838, https://doi.org/10.5194/gmd-15-2813-2022, https://doi.org/10.5194/gmd-15-2813-2022, 2022
Short summary
Short summary
A methane model that features methane production and transport by plants, the ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model, which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4, was calibrated and evaluated on 14 peatland sites. Results show that the model is sensitive to temperature and substrate availability over the top 75 cm of soil depth.
Suman Halder, Susanne K. M. Arens, Kai Jensen, Tais W. Dahl, and Philipp Porada
Geosci. Model Dev., 15, 2325–2343, https://doi.org/10.5194/gmd-15-2325-2022, https://doi.org/10.5194/gmd-15-2325-2022, 2022
Short summary
Short summary
A dynamic vegetation model, designed to estimate potential impacts of early vascular vegetation, namely, lycopsids, on the biogeochemical cycle at a local scale. Lycopsid Model (LYCOm) estimates the productivity and physiological properties of lycopsids across a broad climatic range along with natural selection, which is then utilized to adjudge their weathering potential. It lays the foundation for estimation of their impacts during their long evolutionary history starting from the Ordovician.
Dóra Hidy, Zoltán Barcza, Roland Hollós, Laura Dobor, Tamás Ács, Dóra Zacháry, Tibor Filep, László Pásztor, Dóra Incze, Márton Dencső, Eszter Tóth, Katarína Merganičová, Peter Thornton, Steven Running, and Nándor Fodor
Geosci. Model Dev., 15, 2157–2181, https://doi.org/10.5194/gmd-15-2157-2022, https://doi.org/10.5194/gmd-15-2157-2022, 2022
Short summary
Short summary
Biogeochemical models used by the scientific community can support society in the quantification of the expected environmental impacts caused by global climate change. The Biome-BGCMuSo v6.2 biogeochemical model has been created by implementing a lot of developments related to soil hydrology as well as the soil carbon and nitrogen cycle and by integrating crop model components. Detailed descriptions of developments with case studies are presented in this paper.
Lei Ma, George Hurtt, Lesley Ott, Ritvik Sahajpal, Justin Fisk, Rachel Lamb, Hao Tang, Steve Flanagan, Louise Chini, Abhishek Chatterjee, and Joseph Sullivan
Geosci. Model Dev., 15, 1971–1994, https://doi.org/10.5194/gmd-15-1971-2022, https://doi.org/10.5194/gmd-15-1971-2022, 2022
Short summary
Short summary
We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking datasets demonstrated the model’s capability to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has the potential to be linked with remote sensing observations to address key scientific questions.
Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, Aliénor Lavergne, Robert Field, and Laia Andreu-Hayles
Geosci. Model Dev., 15, 1931–1952, https://doi.org/10.5194/gmd-15-1931-2022, https://doi.org/10.5194/gmd-15-1931-2022, 2022
Short summary
Short summary
We modify the numerical model of forest growth MAIDENiso by explicitly simulating snow. This allows us to use the model in boreal environments, where snow is dominant. We tested the performance of the model before and after adding snow, using it at two Canadian sites to simulate tree-ring isotopes and comparing with local observations. We found that modelling snow improves significantly the simulation of the hydrological cycle, the plausibility of the model and the simulated isotopes.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
Short summary
Short summary
We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
Glenn E. Hammond
Geosci. Model Dev., 15, 1659–1676, https://doi.org/10.5194/gmd-15-1659-2022, https://doi.org/10.5194/gmd-15-1659-2022, 2022
Short summary
Short summary
This paper describes a simplified interface for implementing and testing new chemical reactions within the reactive transport simulator PFLOTRAN. The paper describes the interface, providing example code for the interface. The paper includes several chemical reactions implemented through the interface.
Sarah E. Chadburn, Eleanor J. Burke, Angela V. Gallego-Sala, Noah D. Smith, M. Syndonia Bret-Harte, Dan J. Charman, Julia Drewer, Colin W. Edgar, Eugenie S. Euskirchen, Krzysztof Fortuniak, Yao Gao, Mahdi Nakhavali, Włodzimierz Pawlak, Edward A. G. Schuur, and Sebastian Westermann
Geosci. Model Dev., 15, 1633–1657, https://doi.org/10.5194/gmd-15-1633-2022, https://doi.org/10.5194/gmd-15-1633-2022, 2022
Short summary
Short summary
We present a new method to include peatlands in an Earth system model (ESM). Peatlands store huge amounts of carbon that accumulates very slowly but that can be rapidly destabilised, emitting greenhouse gases. Our model captures the dynamic nature of peat by simulating the change in surface height and physical properties of the soil as carbon is added or decomposed. Thus, we model, for the first time in an ESM, peat dynamics and its threshold behaviours that can lead to destabilisation.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
Short summary
Short summary
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Nils Wallenberg, Fredrik Lindberg, and David Rayner
Geosci. Model Dev., 15, 1107–1128, https://doi.org/10.5194/gmd-15-1107-2022, https://doi.org/10.5194/gmd-15-1107-2022, 2022
Short summary
Short summary
Exposure to solar radiation on clear and warm days can lead to heat stress and thermal discomfort. This can be alleviated by planting trees providing shade in particularly warm areas. Here, we use a model to locate trees and optimize their blocking of solar radiation. Our results show that locations can differ depending, e.g., tree size (juvenile or mature) and number of trees that are positioned simultaneously. The model is available as a tool for accessibility by researchers and others.
Kai Wang, Xiujun Wang, Raghu Murtugudde, Dongxiao Zhang, and Rong-Hua Zhang
Geosci. Model Dev., 15, 1017–1035, https://doi.org/10.5194/gmd-15-1017-2022, https://doi.org/10.5194/gmd-15-1017-2022, 2022
Short summary
Short summary
We use observational data of dissolved oxygen (DO) and organic nitrogen to calibrate a basin-scale model (OGCM-DEMC V1.4) and then evaluate model capacity for simulating mid-depth DO in the tropical Pacific. Sensitivity studies show that enhanced vertical mixing combined with reduced biological consumption performs well in reproducing asymmetric oxygen minimum zones (OMZs). We find that DO is more sensitive to biological processes in the upper OMZs but to physical processes in the lower OMZs.
Pedro Duarte, Philipp Assmy, Karley Campbell, and Arild Sundfjord
Geosci. Model Dev., 15, 841–857, https://doi.org/10.5194/gmd-15-841-2022, https://doi.org/10.5194/gmd-15-841-2022, 2022
Short summary
Short summary
Sea ice modeling is an important part of Earth system models (ESMs). The results of ESMs are used by the Intergovernmental Panel on Climate Change in their reports. In this study we present an improvement to calculate the exchange of nutrients between the ocean and the sea ice. This nutrient exchange is an essential process to keep the ice-associated ecosystem functioning. We found out that previous calculation methods may underestimate the primary production of the ice-associated ecosystem.
Cited articles
Aksnes, D. L. and Egge, J.: A theoretical model for nutrient uptake in
phytoplankton, Mar. Ecol. Prog. Ser., 70, 65–72,
1991. a
Anderson, T. R. and Pondaven, P.: Non-redfield carbon and nitrogen cycling in
the Sargasso Sea: Pelagic imbalances and export flux, Deep-Sea Res. Pt.
I, 50, 573–591,
https://doi.org/10.1016/S0967-0637(03)00034-7, 2003. a
Anugerahanti, P., Kerimoglu, O., and Smith, S. L.: Enhancing Ocean
Biogeochemical Models With Phytoplankton Variable Composition, Front.
Mar. Sci., 8, 675428, https://doi.org/10.3389/fmars.2021.675428, 2021. a, b
Armstrong, R. A.: An optimization-based model of iron–light–ammonium
colimitation of nitrate uptake and phytoplankton growth, Limnol. Oceanogr.,
44, 1436–1446, https://doi.org/10.4319/lo.1999.44.6.1436, 1999. a
Ayata, S. D., Lévy, M., Aumont, O., Sciandra, A., Sainte-Marie, J.,
Tagliabue, A., and Bernard, O.: Phytoplankton growth formulation in marine
ecosystem models: Should we take into account photo-acclimation and variable
stoichiometry in oligotrophic areas?, J. Marine Syst., 125,
29–40, https://doi.org/10.1016/j.jmarsys.2012.12.010, 2013. a, b
Behrenfeld, M. J., Westberry, T. K., Boss, E. S., O'Malley, R. T., Siegel, D. A., Wiggert, J. D., Franz, B. A., McClain, C. R., Feldman, G. C., Doney, S. C., Moore, J. K., Dall'Olmo, G., Milligan, A. J., Lima, I., and Mahowald, N.: Satellite-detected fluorescence reveals global physiology of ocean phytoplankton, Biogeosciences, 6, 779–794, https://doi.org/10.5194/bg-6-779-2009, 2009. a
Bergeron, M. and Tremblay, J.-E.: Shifts in biological productivity inferred
from nutrient drawdown in the southern Beaufort Sea (2003–2011) and
northern BaffinBay (1997–2011), Canadian Arctic, Geophys. Res. Lett., 41,
3979–3987, https://doi.org/10.1002/2014GL059649.Received, 2014. a
Bonachela, J. A., Allison, S. D., Martiny, A. C., and Levin, S. A.: A model for variable phytoplankton stoichiometry based on cell protein regulation, Biogeosciences, 10, 4341–4356, https://doi.org/10.5194/bg-10-4341-2013, 2013. a
Branco, P., Egas, M., Elser, J. J., and Huisman, J.: Eco-Evolutionary Dynamics
of Ecological Stoichiometry in Plankton Communities, Am.
Nat., 192, E000–E000, https://doi.org/10.1086/697472, 2018. a
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical
models, Environ. Modell. Softw., 61, 249–265,
https://doi.org/10.1016/j.envsoft.2014.04.002, 2014. a, b, c, d
Bruggeman, J. and Kooijman, S. A. L. M.: A biodiversity-inspired approach to
aquatic ecosystem modeling, Limonol. Oceanogr., 52, 1533–1544,
https://doi.org/10.4319/lo.2007.52.4.1533, 2007. a
Burchard, H., Bolding, K., Kühn, W., Meister, A., Neumann, T., and
Umlauf, L.: Description of a flexible and extendable physical-biogeochemical
model system for the water column, J. Marine Syst., 61, 180–211,
https://doi.org/10.1016/j.jmarsys.2005.04.011, 2006. a, b, c
Burmaster, D. E.: The Continuous Culture of Phytoplankton: Mathematical
Equivalence Among Three Steady-State Models, Am. Nat., 113,
123–134, https://doi.org/10.1086/283368, 1979. a, b, c, d
Burson, A., Stomp, M., Akil, L., Brussaard, C. P. D., and Huisman, J.:
Unbalanced reduction of nutrient loads has created an offshore gradient from
phosphorus to nitrogen limitation in the North Sea, Limnol.
Oceanogr., 61, 869–888, https://doi.org/10.1002/lno.10257, 2016. a
Button, D. K.: On the theory of control of microbial growth kinetics by
limiting nutrient concentrations, Deep-Sea Res., 25, 1163–1177,
https://doi.org/10.1016/0146-6291(78)90011-5, 1978. a, b, c
Chakraborty, S., Cadier, M., Visser, A. W., Bruggeman, J., and Andersen, K. H.:
Latitudinal Variation in Plankton Traits and Ecosystem Function, Global
Biogeochem. Cy., 32, e2020GB006564, https://doi.org/10.1029/2020GB006564, 2020. a
Chen, B. and Smith, S. L.: Optimality-based approach for computationally
efficient modeling of phytoplankton growth, chlorophyll-to-carbon, and
nitrogen-to-carbon ratios, Ecol. Model., 385, 197–212,
https://doi.org/10.1016/j.ecolmodel.2018.08.001, 2018. a
Christian, J. R.: Biogeochemical cycling in the oligotrophic ocean: Redfield
and non-Redfield models, Limnol. Oceanogr., 50, 646–657,
https://doi.org/10.4319/lo.2005.50.2.0646, 2005. a
Cloern, J., Grenz, C., and Vidergar-Lucas, L.: An empirical model of the
phytoplankton chlorophyll : carbon ratio – the conversion factor between
productivity and growth rate, Limnol. Oceanogr., 40, 1313–1321,
https://doi.org/10.4319/lo.1995.40.7.1313, 1995. a
Doney, S. C., Glover, D. M., and Najjar, R. G.: A new coupled, one-dimensional
biological-physical model for the upper ocean: Applications to the JGOFS
Bermuda Atlantic Time-series study (BATS) site, Deep-Sea Res. Pt. II, 43, 591–624,
https://doi.org/10.1016/0967-0645(95)00104-2, 1996. a
Dugdale, R.: Nutrient Limitation in the Sea: Dynamics, Identification and
Significance, Limnol. Oceanogr., 12, 685–695,
https://doi.org/10.4319/lo.1967.12.4.0685, 1967. a
Dutkiewicz, S., Cermeno, P., Jahn, O., Follows, M. J., Hickman, A. E., Taniguchi, D. A. A., and Ward, B. A.: Dimensions of marine phytoplankton diversity, Biogeosciences, 17, 609–634, https://doi.org/10.5194/bg-17-609-2020, 2020. a, b, c
Edelaar, P. and Bolnick, D. I.: Appreciating the Multiple Processes Increasing
Individual or Population Fitness, Trends Ecol. Evol., 34,
435–446, https://doi.org/10.1016/j.tree.2019.02.001, 2019. a
Flynn, K. J., St John, M., Raven, J. A., Skibinski, D. O., Allen, J. I.,
Mitra, A., and Hofmann, E. E.: Acclimation, adaptation, traits and
trade-offs in plankton functional type models: Reconciling terminology for
biology and modelling, J. Plankton Res., 37, 683–691,
https://doi.org/10.1093/plankt/fbv036, 2015. a, b
Follows, M. J. and Dutkiewicz, S.: Modeling Diverse Communities of Marine
Microbes, Annu. Rev. Mar. Sci., 3, 427–451,
https://doi.org/10.1146/annurev-marine-120709-142848, 2011. a
Geider, R. and La Roche, J.: Redfield revisited: variability of C:N:P in
marine microalgae and its biochemical basis, Eur. J. Phycol.,
37, 1–17, https://doi.org/10.1017/S0967026201003456, 2002. a, b, c
Geider, R., Maclntyre, H., and Kana, T.: 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. a, b, c
Gerloff, G. C. and Skoog, F.: Cell Contents of Nitrogen and Phosphorous as a
Measure of Their Availability for Growth of Microcystis Aeruginosa,
Ecology, 35, 348–353,
1954. a
Grover, J. P.: Is storage an adaptation to spatial variation in resource
availability?, Am. Nat., 173, E44–E61, https://doi.org/10.1086/595751, 2009. a, b
Guidi, L., Jackson, G. A., Stemmann, L., Miquel, J. C., Picheral, M., and
Gorsky, G.: Relationship between particle size distribution and flux in the
mesopelagic zone, Deep-Sea Res. Pt. I,
55, 1364–1374, https://doi.org/10.1016/j.dsr.2008.05.014, 2008. a
Halsey, K. H. and Jones, B. M.: Phytoplankton strategies for photosynthetic
energy allocation, Annu. Rev. Mar. Sci., 7, 265–297,
https://doi.org/10.1146/annurev-marine-010814-015813, 2015. a
Johnson, K. and Goody, R.: The Original Michaelis Constant: Translation of the
1913 Michaelis–Menten Paper, Biochemistry, 50, 8264–8269,
https://doi.org/10.1021/bi201284u, 2011. a
Johnson, K. S., Plant, J. N., Dunne, J. P., Talley, L. D., and Sarmiento,
J. L.: Annual nitrate drawdown observed by SOCCOM profiling floats and the
relationship to annual net community production, J. Geophys.
Res.-Oceans, 122, 6668–6683, https://doi.org/10.1002/2017JC012839, 2017. a
Kerimoglu, O.: OnurKerimoglu/fabm-nflexpd: Version 1.0 release candidate 0 (v1.0-rc0), Zenodo [code], https://doi.org/10.5281/zenodo.4761937, 2021. a
Kerimoglu, O., Straile, D., and Peeters, F.: Role of phytoplankton cell size
on the competition for nutrients and light in incompletely mixed systems.,
J. Theor. Biol., 300, 330–343,
https://doi.org/10.1016/j.jtbi.2012.01.044, 2012. a, b
Kerimoglu, O., Hofmeister, R., Maerz, J., Riethmüller, R., and Wirtz, K. W.: The acclimative biogeochemical model of the southern North Sea, Biogeosciences, 14, 4499–4531, https://doi.org/10.5194/bg-14-4499-2017, 2017. a, b, c
Kerimoglu, O., Große, F., Kreus, M., Lenhart, H.-J., and van Beusekom,
J. E.: A model-based projection of historical state of a coastal ecosystem:
relevance of phytoplankton stoichiometry, Sci. Total Environ.,
639, 1311–1323, https://doi.org/10.1016/j.scitotenv.2018.05.215, 2018. a, b
Kerimoglu, O. Anugerahanti, P., and Smith, S. L.: FABM-NflexPD 1.0: cyclic-equilibria simulated by three model variants in an idealized high-latitude 1D water column, SEANOE [data set], https://doi.org/10.17882/83699, 2021. a
Klausmeier, C., Litchman, E., Daufresne, T., and Levin, S.: Optimal
nitrogen-to-phosphorus stoichiometry of phytoplankton, Nature, 429,
171–174, https://doi.org/10.1029/2001GL014649, 2004. a, b
Kruskopf, M. and Flynn, K. J.: Chlorophyll content and fluorescence responses
cannot be used to gauge reliably phytoplankton biomass, nutrient status or
growth rate, New Phytol., 169, 525–536,
https://doi.org/10.1111/j.1469-8137.2005.01601.x, 2006. a
Kwiatkowski, L., Aumont, O., Bopp, L., and Ciais, P.: The Impact of Variable
Phytoplankton Stoichiometry on Projections of Primary Production, Food
Quality, and Carbon Uptake in the Global Ocean, Global Biogeochem. Cy., 32, 516–528, https://doi.org/10.1002/2017GB005799, 2018. a, b
Laufkötter, C., Vogt, M., Gruber, N., Aita-Noguchi, M., Aumont, O., Bopp, L., Buitenhuis, E., Doney, S. C., Dunne, J., Hashioka, T., Hauck, J., Hirata, T., John, J., Le Quéré, C., Lima, I. D., Nakano, H., Seferian, R., Totterdell, I., Vichi, M., and Völker, C.: Drivers and uncertainties of future global marine primary production in marine ecosystem models, Biogeosciences, 12, 6955–6984, https://doi.org/10.5194/bg-12-6955-2015, 2015. a, b
Laws, E. A. and Chalup, M. S.: A microalgal growth model, Limnol.
Oceanogr., 35, 597–608, https://doi.org/10.4319/lo.1990.35.3.0597, 1990. a
Litchman, E., Klausmeier, C. A., and Yoshiyama, K.: Contrasting size evolution
in marine and freshwater diatoms, P. Natl. Acad.
Sci. USA, 106, 2665–2670, 2009. a
Maar, M., Rindorf, A., Møller, E. F., Christensen, A., Madsen, K. S., and
van Deurs, M.: Zooplankton mortality in 3D ecosystem modelling considering
variable spatial–temporal fish consumptions in the North Sea, Prog.
Oceanogr., 124, 78–91, https://doi.org/10.1016/j.pocean.2014.03.002, 2014. a
Martiny, A. A. C., Pham, C. C. T. A., Primeau, F. F. W., Vrugt, J. A., Moore,
J. K., Levin, S. A., and Lomas, M. W.: Strong latitudinal patterns in the
elemental ratios of marine plankton and organic matter, Nat. Geosci.,
6, 279–283, https://doi.org/10.1038/ngeo1757, 2013. a
Mitra, A., Flynn, K. J., and Fasham, M. J. R.: Accounting for grazing dynamics
in nitrogen-phytoplankton-zooplankton models, Limnol. Oceanogr., 52,
649–661, https://doi.org/10.4319/lo.2007.52.2.0649, 2007. a
Mongin, M., Nelson, D. M., Pondaven, P., Brzezinski, M. A., and Tréguer,
P.: Simulation of upper-ocean biogeochemistry with a flexible-composition
phytoplankton model: C, N and Si cycling in the western Sargasso Sea,
Deep-Sea Res. Pt. I, 50, 1445–1480,
https://doi.org/10.1016/j.dsr.2003.08.003, 2003. a, b
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/10.1016/S0967-0645(01)00108-4, 2002. a
Moore, J. K., Doney, S. C., and Lindsay, K.: Upper ocean ecosystem dynamics
and iron cycling in a global three-dimensional model, Global Biogeochem.
Cy., 18, GB4028, https://doi.org/10.1029/2004GB002220, 2004. a
Moreno, A. R. and Martiny, A. C.: Ecological Stoichiometry of Ocean Plankton,
Annu. Rev. Mar. Sci., 10, 43–69,
https://doi.org/10.1146/annurev-marine-121916-063126, 2018. a, b
Oschlies, A. and Schartau, A.: Basin-scale performance of a locally optimized
marine ecosystem model, Research, J. Marine, 63, 335–358,
https://doi.org/10.1016/b978-0-408-70700-8.50018-5, 2005. a, b
Pahlow, M.: Linking chlorophyll – nutrient dynamics to the Redfield N : C
ratio with a model of optimal phytoplankton growth, Mar. Ecol. Prog.
Ser., 287, 33–43, https://doi.org/10.3354/meps287033, 2005. a, b, c, d
Pahlow, M. and Oschlies, A.: Chain model of phytoplankton P, N and light
colimitation, Mar. Ecol. Prog. Ser., 376, 69–83,
https://doi.org/10.3354/meps07748, 2009.
a
Pahlow, M., Chien, C.-T., Arteaga, L. A., and Oschlies, A.: Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviour, Geosci. Model Dev., 13, 4663–4690, https://doi.org/10.5194/gmd-13-4663-2020, 2020. a, b
Paulson, C. and Simpson, J.: Irradience measurements in the upper ocean,
J. Phys. Oceanogr., 7, 952–956, 1977. a
Platt, T. and Jassby, A. D.: the Relationship Between Photosynthesis and Light for Natural Assemblages of Coastal Marine Phytoplankton, J. Phycol., 12, 421–430, https://doi.org/10.1111/j.1529-8817.1976.tb02866.x, 1976. a
Redfield, A.: On the proportions of organic derivatives in sea water and their
relation to the composition of plankton, in: James Johnstone Memorial
Volume, edited by: Daniel, R., University Press of Liverpool, 177–192,
1934. a
Redfield, A.: The biological control of chemical factors in the environment,
Am. Sci., 46, 205–221, 1958. a
Sailley, S. F., Polimene, L., Mitra, A., Atkinson, A., and Allen, J. I.:
Impact of zooplankton food selectivity on plankton dynamics and nutrient
cycling, J. Plankton Res., 37, 519–529,
https://doi.org/10.1093/plankt/fbv020, 2015. a
Schourup-Kristensen, V., Sidorenko, D., Wolf-Gladrow, D. A., and Völker, C.: A skill assessment of the biogeochemical model REcoM2 coupled to the Finite Element Sea Ice–Ocean Model (FESOM 1.3), Geosci. Model Dev., 7, 2769–2802, https://doi.org/10.5194/gmd-7-2769-2014, 2014. a
Sharoni, S. and Halevy, I.: Nutrient ratios in marine particulate organic
matter, Sci. Adv., 6, 1–10, https://doi.org/10.1126/sciadv.aaw9371, 2020. a
Shuter, B.: A model of physiological adaptation in unicellular algae, J. Theor. Biol., 78, 519–552, 1979. a
Smith, S. L., Yamanaka, Y., Pahlow, M., and Oschlies, A.: Optimal uptake
kinetics: physiological acclimation explains the pattern of nitrate uptake by
phytoplankton in the ocean, Mar. Ecol. Prog. Ser., 384, 1–12,
https://doi.org/10.3354/meps08022, 2009. a, b
Smith, S. L., Pahlow, M., Merico, A., and Wirtz, K. W.: Optimality-based
modeling of planktonic organisms, Limnol. Oceanogr., 56,
2080–2094, https://doi.org/10.4319/lo.2011.56.6.2080, 2011. a, b, c
Smith, S. L., Pahlow, M., Merico, A., Acevedo-Trejos, E., Sasai, Y., Yoshikawa,
C., Sasaoka, K., Fujiki, T., Matsumoto, K., and Honda, M. C.: Flexible
phytoplankton functional type (FlexPFT) model: size-scaling of traits and
optimal growth, J. Plankton Res., 38, 977–992,
https://doi.org/10.1093/plankt/fbv038, 2016. a, b, c, d, e, f, g, h, i, j
Ward, B. A.: Assessing an efficient “Instant Acclimation” approximation of
dynamic phytoplankton stoichiometry, J. Plankton Res., 39, 803–814,
https://doi.org/10.1093/plankt/fbx040, 2017. a, b, c
Wirtz, K. W. and Kerimoglu, O.: Autotrophic Stoichiometry Emerging from
Optimality and Variable Co-limitation, Front. Ecol. Evol.,
4, 131, https://doi.org/10.3389/fevo.2016.00131, 2016. a, b, c
Short summary
In large-scale models, variations in cellular composition of phytoplankton are often insufficiently represented. Detailed modeling approaches exist, but they require additional state variables that increase the computational costs. In this study, we test an instantaneous acclimation model in a spatially explicit setup and show that its behavior is mostly similar to that of a variant with an additional state variable but different from that of a fixed composition variant.
In large-scale models, variations in cellular composition of phytoplankton are often...
