Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-1765-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-1765-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 May 2019
Development and technical paper |
| 06 May 2019
| 06 May 2019
Towards end-to-end (E2E) modelling in a consistent NPZD-F modelling framework (ECOSMO E2E_v1.0): application to the North Sea and Baltic Sea
Ute Daewel
CORRESPONDING AUTHOR
Helmholtz Centre Geesthacht, Institute of Coastal Research,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Corinna Schrum
Helmholtz Centre Geesthacht, Institute of Coastal Research,
Max-Planck-Str. 1, 21502 Geesthacht, Germany
Geophysical Institute, University of Bergen, Allegaten 41, 5007
Bergen, Norway
Jed I. Macdonald
Faculty of Life and Environmental Sciences, University of Iceland, 101
Reykjavík, Iceland
Oceanic Fisheries Programme, Pacific Community (SPC), Noumea BP D5
98848, New Caledonia
Related authors
Lucas Porz, Wenyan Zhang, Nils Christiansen, Jan Kossack, Ute Daewel, and Corinna Schrum
Biogeosciences, 21, 2547–2570, https://doi.org/10.5194/bg-21-2547-2024, https://doi.org/10.5194/bg-21-2547-2024, 2024
Short summary
Short summary
Seafloor sediments store a large amount of carbon, helping to naturally regulate Earth's climate. If disturbed, some sediment particles can turn into CO2, but this effect is not well understood. Using computer simulations, we found that bottom-contacting fishing gears release about 1 million tons of CO2 per year in the North Sea, one of the most heavily fished regions globally. We show how protecting certain areas could reduce these emissions while also benefitting seafloor-living animals.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
Short summary
Short summary
High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
Short summary
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
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.
Laurie M. Charrieau, Karl Ljung, Frederik Schenk, Ute Daewel, Emma Kritzberg, and Helena L. Filipsson
Biogeosciences, 16, 3835–3852, https://doi.org/10.5194/bg-16-3835-2019, https://doi.org/10.5194/bg-16-3835-2019, 2019
Short summary
Short summary
We reconstructed environmental changes in the Öresund during the last 200 years, using foraminifera (microfossils), sediment, and climate data. Five zones were identified, reflecting oxygen, salinity, food content, and pollution levels for each period. The largest changes occurred ~ 1950, towards stronger currents. The foraminifera responded quickly (< 10 years) to the changes. Moreover, they did not rebound when the system returned to the previous pattern, but displayed a new equilibrium state.
Johannes Pein, Annika Eisele, Richard Hofmeister, Tina Sanders, Ute Daewel, Emil V. Stanev, Justus van Beusekom, Joanna Staneva, and Corinna Schrum
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-265, https://doi.org/10.5194/bg-2019-265, 2019
Revised manuscript not accepted
Short summary
Short summary
The Elbe estuary is subject to vigorous tidal forcing from the sea side and considerable biological inputs from the land side. Our 3D numerical coupled physical-biogeochemical integrates these forcing signals and provides highly realistic hindcasts of the associated dynamics. Model simulations show that the freshwater part of Elbe estuary is inhabited by plankton. According to simulations these organism play a key role in converting organic inputs into nitrate, the major inorganic nutrient.
Changjin Zhao, Ute Daewel, and Corinna Schrum
Earth Syst. Dynam., 10, 287–317, https://doi.org/10.5194/esd-10-287-2019, https://doi.org/10.5194/esd-10-287-2019, 2019
Short summary
Short summary
Our study highlights the importance of tides in controlling the spatial and temporal distributions North Sea primary production based on numerical experiments. We identified two different response chains acting in different regions of the North Sea. (i) In the southern shallow areas, strong tidal mixing dilutes phytoplankton concentrations and increases turbidity, thus decreasing NPP. (ii) In the frontal regions, tidal mixing infuses nutrients into the surface mixed layer, thus increasing NPP.
Ute Daewel and Corinna Schrum
Earth Syst. Dynam., 8, 801–815, https://doi.org/10.5194/esd-8-801-2017, https://doi.org/10.5194/esd-8-801-2017, 2017
Short summary
Short summary
Processes behind observed long-term variations in marine ecosystems are difficult to be deduced from in situ observations only. By statistically analysing a 61-year model simulation for the North Sea and Baltic Sea and additional model scenarios, we identified major modes of variability in the environmental variables and associated those with changes in primary production. We found that the dominant impact on changes in ecosystem productivity was introduced by modulations of the wind fields.
Lucas Porz, Wenyan Zhang, Nils Christiansen, Jan Kossack, Ute Daewel, and Corinna Schrum
Biogeosciences, 21, 2547–2570, https://doi.org/10.5194/bg-21-2547-2024, https://doi.org/10.5194/bg-21-2547-2024, 2024
Short summary
Short summary
Seafloor sediments store a large amount of carbon, helping to naturally regulate Earth's climate. If disturbed, some sediment particles can turn into CO2, but this effect is not well understood. Using computer simulations, we found that bottom-contacting fishing gears release about 1 million tons of CO2 per year in the North Sea, one of the most heavily fished regions globally. We show how protecting certain areas could reduce these emissions while also benefitting seafloor-living animals.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
Short summary
Short summary
High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
Johannes Bieser, David J. Amptmeijer, Ute Daewel, Joachim Kuss, Anne L. Soerensen, and Corinna Schrum
Geosci. Model Dev., 16, 2649–2688, https://doi.org/10.5194/gmd-16-2649-2023, https://doi.org/10.5194/gmd-16-2649-2023, 2023
Short summary
Short summary
MERCY is a 3D model to study mercury (Hg) cycling in the ocean. Hg is a highly harmful pollutant regulated by the UN Minamata Convention on Mercury due to widespread human emissions. These emissions eventually reach the oceans, where Hg transforms into the even more toxic and bioaccumulative pollutant methylmercury. MERCY predicts the fate of Hg in the ocean and its buildup in the food chain. It is the first model to consider Hg accumulation in fish, a major source of Hg exposure for humans.
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.
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.
Laurie M. Charrieau, Karl Ljung, Frederik Schenk, Ute Daewel, Emma Kritzberg, and Helena L. Filipsson
Biogeosciences, 16, 3835–3852, https://doi.org/10.5194/bg-16-3835-2019, https://doi.org/10.5194/bg-16-3835-2019, 2019
Short summary
Short summary
We reconstructed environmental changes in the Öresund during the last 200 years, using foraminifera (microfossils), sediment, and climate data. Five zones were identified, reflecting oxygen, salinity, food content, and pollution levels for each period. The largest changes occurred ~ 1950, towards stronger currents. The foraminifera responded quickly (< 10 years) to the changes. Moreover, they did not rebound when the system returned to the previous pattern, but displayed a new equilibrium state.
Johannes Pein, Annika Eisele, Richard Hofmeister, Tina Sanders, Ute Daewel, Emil V. Stanev, Justus van Beusekom, Joanna Staneva, and Corinna Schrum
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-265, https://doi.org/10.5194/bg-2019-265, 2019
Revised manuscript not accepted
Short summary
Short summary
The Elbe estuary is subject to vigorous tidal forcing from the sea side and considerable biological inputs from the land side. Our 3D numerical coupled physical-biogeochemical integrates these forcing signals and provides highly realistic hindcasts of the associated dynamics. Model simulations show that the freshwater part of Elbe estuary is inhabited by plankton. According to simulations these organism play a key role in converting organic inputs into nitrate, the major inorganic nutrient.
Changjin Zhao, Ute Daewel, and Corinna Schrum
Earth Syst. Dynam., 10, 287–317, https://doi.org/10.5194/esd-10-287-2019, https://doi.org/10.5194/esd-10-287-2019, 2019
Short summary
Short summary
Our study highlights the importance of tides in controlling the spatial and temporal distributions North Sea primary production based on numerical experiments. We identified two different response chains acting in different regions of the North Sea. (i) In the southern shallow areas, strong tidal mixing dilutes phytoplankton concentrations and increases turbidity, thus decreasing NPP. (ii) In the frontal regions, tidal mixing infuses nutrients into the surface mixed layer, thus increasing NPP.
Ute Daewel and Corinna Schrum
Earth Syst. Dynam., 8, 801–815, https://doi.org/10.5194/esd-8-801-2017, https://doi.org/10.5194/esd-8-801-2017, 2017
Short summary
Short summary
Processes behind observed long-term variations in marine ecosystems are difficult to be deduced from in situ observations only. By statistically analysing a 61-year model simulation for the North Sea and Baltic Sea and additional model scenarios, we identified major modes of variability in the environmental variables and associated those with changes in primary production. We found that the dominant impact on changes in ecosystem productivity was introduced by modulations of the wind fields.
Related subject area
Biogeosciences
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
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
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
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
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)
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
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
In-silico calculation of soil pH by SCEPTER v1.0
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
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
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
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)
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
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
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.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
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
Revised manuscript accepted 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.
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.
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.
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.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-137, https://doi.org/10.5194/gmd-2023-137, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater pH and bulk soil pH is thus crucial to accurate evaluation of alkalinity required to counteract soil acidification and resulting capture of anthropogenic carbon dioxide through the Enhanced Rock Weathering technique. This has been enabled by the updated reactive-transport SCEPTER code and newly developed framework to simulate soil pH.
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.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
Short summary
Short summary
Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
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.
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).
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.
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.
Cited articles
Anders, K. and Möller, H.: Seasonal fluctuations in macrobenthic fauna of
the Fucus belt in Kiel Fjord (western Baltic Sea), Helgoländer Meeresun.,
36, 277–283, 1983.
Andersson, A., Hajdu, S., Haecky, P., Kuparinen, J., and Wikner, J.:
Succession and growth limitation of phytoplankton in the Gulf of Bothnia
(Baltic Sea), Mar. Biol., 126, 791–801, https://doi.org/10.1007/BF00351346, 1996.
Bagge, O., Thurow, F., Steffensen, E., and Bay, J.: The Baltic cod, Dana, 10,
1–28, 1994.
Barthel, K., Daewel, U., Pushpadas, D., Schrum, C., Årthun, M., and
Wehde, H.: Resolving frontal structures: On the computational costs and
pay-off using a less diffusive but computational more expensive advection
scheme, Ocean Dynam., 62, 1457–1470, https://doi.org/10.1007/s10236-012-0578-9, 2012.
Blackford, J. C., Allen, J. I., and Gilbert, F. J.: Ecosystem dynamics at six
contrasting sites?: a generic modelling study, J. Mar. Syst., 52, 191–215,
https://doi.org/10.1016/j.jmarsys.2004.02.004, 2004.
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y.,
Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin,
G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley,
S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine
biogeochemistry and the ecosystem dynamics of the lower trophic levels,
Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016,
2016.
Callaway, R., Alsvag, J., de Boois, I., Cotter, J., Ford, A., Hinz, H.,
Jennings, S., Kröncke, I., Lancaster, J., Piet, G., Prince, P., and
Ehrich, S.: Diversity and community structure of epibenthic invertebrates and
fish in the North Sea, ICES J. Mar. Sci., 59, 1199–1214,
https://doi.org/10.1006/jmsc.2002.1288, 2002.
Casini, M., Hjelm, J., Molinero, J.-M., Lövgren, J., Cardinale, M.,
Bartolino, V., Belgrano, A., and Kornilovs, G.: Trophic cascades promote
threshold-like shifts, P. Natl. Acad. Sci. USA, 106, 197–202, 2009.
Charles, A. T.: Bio-Socio-Economic Fishery Models: Labour Dyanmics and
Multi-Objective Management, Can. J. Fish. Aquat. Sci., 46, 1313–1322, 1989.
Christensen, V. and Walters, C. J.: Ecopath with Ecosim: methods,
capabilities and limitations, Ecol. Modell., 172, 109–139,
https://doi.org/10.1016/j.ecolmodel.2003.09.003, 2004.
Clarke, A. and Johnston, N. M.: Scaling of metabolic rate with body mass and
temperature in teleost fish, J. Anim. Ecol., 68, 893–905,
https://doi.org/10.1046/j.1365-2656.1999.00337.x, 1999.
Colebrook, J. M., Environment, N., Place, P., and Hoe, T.: Continuous
plankton records: relationships between species of phytoplankton and
zooplankton in the seasonal cycle, Mar. Biol., 323, 313–323, 1984.
Coull, K., Jermyn, A., Newton, A., Henderson, G., and Hall, W.: Length/weight
Relationships for 88 Species of Fish Encountered in the North East Atlantic,
Scottish Fish. Res. Reports, 43, 82 pp., 1989.
Cury, P. and Shannon, L.: Regime shifts in upwelling ecosystems: observed
changes and possible mechanisms in the northern and southern Benguela, Prog.
Oceanogr., 60, 223–243, https://doi.org/10.1016/j.pocean.2004.02.007, 2004.
Cury, P., Shannon, L., and Shin, Y.-J.: The functioning of marine ecosystems,
in Responsible Fisheries in the Marine Ecosystem, edited by: Sinclair, M. and
Valdimarsson, G., FAO, Rome and CABI Publishing, Wallingford, 103–123, 2003.
Daan, N., Bromley, P. J., Hislop, J. R. G., and Nielson, N. A.: Ecology of
North Sea Fish, Neth. J. Sea Res., 26, 343–386, 1990.
Daewel, U. and Schrum, C.: Simulating long-term dynamics of the coupled North
Sea and Baltic Sea ecosystem with ECOSMO II: Model description and
validation, J. Mar. Syst., 119–120, 30–49,
https://doi.org/10.1016/j.jmarsys.2013.03.008, 2013.
Daewel, U., Peck, M. A., Kühn, W., St. John, M. A., Alekseeva, I., and
Schrum, C.: Coupling ecosystem and individual-based models to simulate the
influence of environmental variability on potential growth and survival of
larval sprat (Sprattus sprattus L.) in the North Sea, Fish. Oceanogr., 17,
333–351, https://doi.org/10.1111/j.1365-2419.2008.00482.x, 2008.
Daewel, U., Hjøllo, S. S., Huret, M., Ji, R., Maar, M., Niiranen, S.,
Travers-trolet, M., Peck, M. A., Wolfshaar, K. E., and van de Wolfshaar, K.
E.: Predation control of zooplankton dynamics: a review of observations and
models, ICES J. Mar. Sci., 71, 254–271, https://doi.org/10.1093/icesjms/fst125, 2014.
Daewel, U., Schrum, C., and Gupta, A. K.: The predictive potential of early
life stage individual-based models (IBMs): an example for Atlantic cod Gadus
morhua in the North Sea, Mar. Ecol. Prog. Ser., 534, 199–219,
https://doi.org/10.3354/meps11367, 2015.
Ekeroth, N., Blomqvist, S., and Hall, P. O. J.: Nutrient fluxes from reduced
Baltic Sea sediment: effects of oxygenation and macrobenthos, Mar. Ecol.
Prog. Ser., 544, 77–92, https://doi.org/10.3354/meps11592, 2016.
Elliott, M. and Hemingway, K.: Fishes in Estuaries, Fishes in Estuaries,
i–xx, https://doi.org/10.2134/jeq2003.3750, 2002.
Fennel, W.: Towards bridging biogeochemical and fish-production models, J.
Mar. Syst., 71, 171–194, https://doi.org/10.1016/j.jmarsys.2007.06.008, 2008.
Fennel, W.: Parameterizations of truncated food web models from the
perspective of an end-to-end model approach, J. Mar. Syst., 76, 171–185,
https://doi.org/10.1016/j.jmarsys.2008.05.005, 2009.
Fennel, W.: A nutrient to fish model for the example of the Baltic Sea, J.
Mar. Syst., 81, 184–195, https://doi.org/10.1016/j.jmarsys.2009.12.007, 2010.
Froese, R. and Pauly, D.: FishBase 2000: concepts, design and data sources,
ICLARM, Los Baños, Laguna, Philippines, 2000.
Froese, R., Thorson, J. T., and Reyes Jr., R. B.: A Bayesian approach for
estimating length-weight relationships in fishes, J. Appl. Ichthyol., 30,
78–85, https://doi.org/10.1111/jai.12299, 2014.
Fulton, E., Smith, A., and Punt, A.: Which ecological indicators can robustly
detect effects of fishing?, ICES J. Mar. Sci., 62, 540–551,
https://doi.org/10.1016/j.icesjms.2004.12.012, 2005.
Fulton, E. A.: Approaches to end-to-end ecosystem models, J. Mar. Syst., 81,
171–183, https://doi.org/10.1016/j.jmarsys.2009.12.012, 2010.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M., and Charnov, E.
L.: Effects of size and temperature on metabolic rate., Science, 293,
2248–2251, https://doi.org/10.1126/science.1061967, 2001.
Gogina, M. and Zettler, M. L.: Diversity and distribution of benthic
macrofauna in the Baltic Sea, Data inventory and its use for species
distribution modelling and prediction, J. Sea Res., 64, 313–321,
https://doi.org/10.1016/j.seares.2010.04.005, 2010.
Gogina, M., Glockzin, M., and Zettler, M. L.: Distribution of benthic
macrofaunal communities in the western Baltic Sea with regard to near-bottom
environmental parameters. 1. Causal analysis, J. Mar. Syst., 80, 57–70,
https://doi.org/10.1016/j.jmarsys.2009.10.001, 2010.
Greenstreet, S.: Seasonal variation in the consumption of food by fish in the
North Sea and implications for food web dynamics, ICES J. Mar. Sci., 54,
243–266, https://doi.org/10.1006/jmsc.1996.0183, 1997.
Greenstreet, S. P. R., Bryant, A. D., Broekhuizen, N., Hall, S. J., and
Heath, M. R.: Seasonal variation in the consumption of food by fish in the
North Sea and implications for food web dynamics, ICES J. Mar. Sci., 54,
243–266, https://doi.org/10.1006/jmsc.1996.0183, 1997.
Guerra, A. and Rocha, F.: The life history of Loligo vulgaris and Loligo
forbesi (Cephalopoda: Loliginidae) in Galician waters (NW Spain), Fish. Res.,
21, 43–69, https://doi.org/10.1016/0165-7836(94)90095-7, 1994.
Heath, M. R.: Ecosystem limits to food web fluxes and fisheries yields in the
North Sea simulated with an end-to-end food web model, Prog. Oceanogr., 102,
42–66, 2012.
Heip, C., Basford, D., Craeymeersch, J. A., Dewarumez, J. M., Dorjes, J., de
Wilde, P., Duineveld, G., Eleftheriou, A., Herman, P. M. J., Niermann, U.,
Kingston, P., Kunitzer, A., Rachor, E., Rumohr, H., Soetaert, K., and
Soltwedel, T.: Trends in biomass, density and diversity of North Sea
macrofauna, ICES J. Mar. Sci., 49, 13–22, https://doi.org/10.1093/icesjms/49.1.13, 1992.
Hinrichsen, H.-H., Lehmann, A., Petereit, C., Nissling, A., Ustups, D.,
Bergström, U., and Hüssy, K.: Spawning areas of eastern Baltic cod
revisited: Using hydrodynamic modelling to reveal spawning habitat
suitability, egg survival probability, and connectivity patterns, Prog.
Oceanogr., 143, 13–25, https://doi.org/10.1016/j.pocean.2016.02.004, 2016.
Huang, L., Wu, Y., Wan, R., and Zhang, J.: Carbon, nitrogen and phosphorus
stoichiometry in Japanese anchovy (Engraulis japonicus) from the Huanghai
Sea, China, Acta Oceanol. Sin., 31, 154–161, https://doi.org/10.1007/s13131-012-0229-5,
2012.
Hunter, E., Metcalfe, J. D., and Reynolds, J. D.: Migration route and
spawning area fidelity by North Sea plaice, Proc. Biol. Sci., 270,
2097–2103, https://doi.org/10.1098/rspb.2003.2473, 2003.
ICES: Manual for the International Bottom Trawl Surveys, Series of ICES
Survey Protocols, SISP 1-IBTS VIII, 68 pp., 2012.
ICES: Baltic Sea Ecoregion – Fisheries overview – November, 1–23,
https://doi.org/10.17895/ices.pub.4648, 2018a.
ICES: Greater North Sea Ecoregion – Fisheries overview – November, 1–31,
https://doi.org/10.17895/ices.pub.4647, 2018b.
Jennings, S., Alvsvåg, J., Cotter, A. J. R., Ehrich, S., Greenstreet, S.
P. R., Jarre-Teichmann, A., Mergardt, N., Rijnsdorp, A. D., and Smedstad, O.:
Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort,
diversity and community structure, III. International trawling effort in the
North Sea: An analysis of spatial and temporal trends, Fish. Res., 40,
125–134, https://doi.org/10.1016/S0165-7836(98)00208-2, 1999.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.
C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471,
https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Klaoudatos, D. S., Conides, A. J., Anastasopoulou, A., and Dulčić,
J.: Age, growth, mortality and sex ratio of the inshore population of the
edible crab, Cancer pagurus (Linnaeus 1758) in South Wales (UK), J. Appl.
Ichthyol., 29, 579–586, https://doi.org/10.1111/jai.12122, 2013.
Kröncke, I. and Bergfeld, C.: North sea benthos: a review, Senck. Marit.,
33, 205–268, https://doi.org/10.1007/BF03043049, 2003.
Maar, M., Friis, E., Larsen, J., Skovgaard, K., Wan, Z., She, J., Jonasson,
L., and Neumann, T.: Ecosystem modelling across a salinity gradient from the
North Sea to the Baltic Sea, Ecol. Modell., 222, 1696–1711,
https://doi.org/10.1016/j.ecolmodel.2011.03.006, 2011.
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.
Mackinson, S. and Daskalov, G.: An ecosystem model of the North Sea to
support an ecosystem approach to fisheries management: description and
parameterisation, Sci. Ser. Tech. Rep., 142, 196 pp., 2007.
McCully, S., Scott, F., and Ellis, J.: Lengths at maturity and conversion
factors for skate (Rajidae) around the British Isles, with an analysis of
data in the literature, ICES J. Mar. Sci., 69, 1812–1822,
https://doi.org/10.1093/icesjms/fst048, 2012.
Megrey, B. A., Rose, K. A., Klumb, R. A., Hay, D. E., Werner, F. E.,
Eslinger, D. L., and Smith, S. L.: A bioenergetics-based population dynamics
model of Pacific herring (Clupea harengus pallasi) coupled to a lower trophic
level nutrient – phytoplankton – zooplankton model?: Description,
calibration, and sensitivity analysis, Ecol. Modell., 202, 144–164,
https://doi.org/10.1016/j.ecolmodel.2006.08.020, 2007.
Michaelis, L. and Menten, M. L.: Die Kinetik der Invertinwirkung, Biochem.
Zeitschrift Beiträge zur chem. Physiol. u. Pathol., 49, 333–369,
availalbe at:
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/17273
(last access: 22 August 2012), 1913.
Möllmann, C., Kornilovs, G., and Sidrevics, L.: Long-term dynamics of
main mesozooplankton species in the central Baltic Sea, J. Plankton Res., 22,
2015–2038, 2000.
Monod, J.: Recherches sur la croissance des cultures bacteiriennes, Hermann
& cie, Paris, available at:
http://www.worldcat.org/title/recherches-sur-la-croissance-des-cultures-bacteriennes/oclc/6126763
(last access: 22 August 2012), 1942.
Müller-Navarra, S. H. and Lange, W.: Modelling tides in the Baltic Sea –
A short note on the harmonic analysis of a one-year water level time series,
in: Proceedings of the 6th HIROMB Scientific Workshop, St. Petersburg,
Russia, 8–10 September 2003, 16–20, 2004.
Neumann, T. and Schernewski, G.: Eutrophication in the Baltic Sea and shifts
in nitrogen fixation analyzed with a 3D ecosystem model, J. Mar. Syst., 74,
592–602, https://doi.org/10.1016/j.jmarsys.2008.05.003, 2008.
Nøttestad, L., Giske, J., Holst, J. C., and Huse, G.: A length-based
hypothesis for feeding migrations in pelagic fish, Can. J. Fish. Aquat. Sci.,
56, 26–34, https://doi.org/10.1139/f99-222, 2011.
Oguz, T., Salihoglu, B., and Fach, B.: A coupled plankton–anchovy population
dynamics model assessing nonlinear controls of anchovy and gelatinous biomass
in the Black Sea, Mar. Ecol. Prog. Ser., 369, 229–256, 2008.
Omstedt, A. and Hansson, D.: The Baltic Sea ocean climate system memory and
response to changes in the water and heat balance components, Cont. Shelf
Res., 26, 236–251, https://doi.org/10.1016/j.csr.2005.11.003, 2006.
Peck, M. A., Arvanitidis, C., Butenschön, M., Canu, D. M.,
Chatzinikolaou, E., Cucco, A., Domenici, P., Fernandes, J. A., Gasche, L.,
Huebert, K. B., Hufnagl, M., Jones, M. C., Kempf, A., Keyl, F., Maar, M.,
Mahévas, S., Marchal, P., Nicolas, D., Pinnegar, J. K., Rivot, E.,
Rochette, S., Sell, A. F., Sinerchia, M., Solidoro, C., Somerfield, P. J.,
Teal, L. R., Travers-Trolet, M., and van de Wolfshaar, K. E.: Projecting
changes in the distribution and productivity of living marine resources: A
critical review of the suite of modelling approaches used in the large
European project VECTORS, Estuar. Coast. Shelf Sci., 201, 40–55,
https://doi.org/10.1016/j.ecss.2016.05.019, 2015.
Persson, L.-E.: Temporal and spatial variation in coastal macrobenthic
community structure, Hanö bay (southern Baltic), J. Exp. Mar. Biol.
Ecol., 68, 277–293, 1983.
Pierce, G. J., Boyle, P. R., Hastie, L. C., and Key, L.: The life history of
Loligo forbesi (Cephalopoda: Loliginidae) in Scottish waters, Fish. Res., 21,
17–41, https://doi.org/10.1016/0165-7836(94)90094-9, 1994.
Pinto, C., Travers-Trolet, M., Macdonald, J., Rivot, E., and Vermard, Y.:
Combining multiple data sets to unravel the spatio-temporal dynamics of a
data-limited fish stock, Can. J. Fish. Aquat. Sci.,
https://doi.org/10.1139/cjfas-2018-0149, 2018.
Politikos, D. V., Curchitser, E. N., Rose, K. A., Checkley, D. M., and
Fiechter, J.: Climate variability and sardine recruitment in the California
Current: A mechanistic analysis of an ecosystem model, Fish. Oceanogr., 27,
602–622, https://doi.org/10.1111/fog.12381, 2018.
Radtke, H., Neumann, T., and Fennel, W.: A Eulerian nutrient to fish model of
the Baltic Sea – A feasibility-study, J. Mar. Syst., 125, 61–76,
https://doi.org/10.1016/j.jmarsys.2012.07.010, 2013.
Rees, H. L., Pendle, M. a, Waldock, R., Limpenny, D. S., and Boyd, S. E.: A
comparison of benthic biodiversity in the North Sea, English Channel, and
Celtic Seas, ICES J. Mar. Sci., 56, 228–246, https://doi.org/10.1006/jmsc.1998.0438,
1999.
Rees, H. L., Eggleton, J. D., Rachor, E. and Vanden Berghe, E. (Eds.):
Structure and dynamics of the North Sea benthos, ICES Cooperative Research
Report, ISBN: 87-7482-058-3, 258 pp., 2007.
Reiss, H. and Krönke, I.: Seasonal variability of epibenthic communities
in different areas of the southern North Sea, ICES J. Mar. Sci., 61,
882–905, https://doi.org/10.1016/j.icesjms.2004.06.020, 2004.
Reiss, H. and Krönke, I.: Seasonal variability of infaunal community
structures in three areas of the North Sea under different environmental
conditions, Estuar. Coast. Shelf Sci., 65, 253–274,
https://doi.org/10.1016/j.ecss.2005.06.008, 2005.
Rodhe, J., Tett, P., and Wulff, F.: The Baltic and North Seas: A Regional
Review of some important Physical-Chemical-Biological Interaction Processes,
in: The Sea, Vol. 14 B, edited by: Robinson, A. R. and Brink, K., Harvard
University Press, 1033–1075, 2006.
Rose, K. A., Fiechter, J., Curchitser, E. N., Hedstrom, K., Bernal, M.,
Creekmore, S., Haynie, A., Ito, S., Lluch-Cota, S., Megrey, B. A., Edwards,
C. A., Checkley, D., Koslow, T., McClatchie, S., Werner, F., MacCall, A., and
Agostini, V.: Demonstration of a Fully-Coupled End-to-End Model for Small
Pelagic Fish Using Sardine and Anchovy in the California Current, Prog.
Oceanogr., 138, 348–380, https://doi.org/10.1016/j.pocean.2015.01.012, 2015.
Schlüter, M., Hinkel, J., Bots, P. W. G., and Arlinghaus, R.: Application
of the SES framework for model-based analysis of the dynamics of
social-ecological systems, Ecol. Soc., 19, https://doi.org/10.5751/ES-05782-190136, 2014.
Schrum, C. and Backhaus, J. O.: Sensitivity of atmosphere-ocean heat exchange
and heat content in the North Sea and the Baltic Sea, Tellus A, 51, 526–549,
https://doi.org/10.1034/j.1600-0870.1992.00006.x, 1999.
Schrum, C., Alekseeva, I., and St. John, M.: Development of a coupled
physical–biological ecosystem model ECOSMO, J. Mar. Syst., 61, 79–99,
https://doi.org/10.1016/j.jmarsys.2006.01.005, 2006.
Seinä, A. and Palosuo, E.: The classification of the maximum annual
extent of ice cover in the Baltic Sea 1720–1995, Meri – Report Series of
the Finnish Institute of Marine Research, 20, 79–910, 1996.
Shin, Y.-J. and Cury, P.: Using an individual-based model of fish assemblages
to study the response of size spectra to changes in fishing, Can. J. Fish.
Aquat. Sci., 61, 414–431, https://doi.org/10.1139/f03-154, 2004.
Shin, Y.-J., Travers, M., and Maury, O.: Coupling low and high trophic levels
models: Towards a pathways-orientated approach for end-to-end models, Prog.
Oceanogr., 84, 105–112, 2010.
Shin, Y. and Cury, P.: Exploring fish community dynamics through
size-dependent trophic interactions using a spatialized individual-based
model, Aquat. Living Resour., 14, 65–80, https://doi.org/10.1016/S0990-7440(01)01106-8,
2001.
Skogen, M. D., Søiland, H., and Svendsen, E.: Effects of changing nutrient
loads to the North Sea, J. Mar. Syst., 46, 23–38,
https://doi.org/10.1016/j.jmarsys.2003.11.013, 2004.
Sparholt, H.: An estimate of the total biomass of fish in the North Sea, ICES
J. Mar. Sci., 46, 200–210, https://doi.org/10.1093/icesjms/46.2.200, 1990.
Sterner, R. W. and George, N. B.: Carbon , Nitrogen , and Phosphorus
Stoichiometry of Cyprinid Fishes, Ecology, 81, 127–140, 2000.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Templeman, W.: Length-weight Relationships & Morphometric Characteristics
and Thorniness of Thorny Skate (Raja radiata) from the Northwest Atlantic, J.
Northwest Atl. Fish. Sci., 7, 89–98, 1987.
Thurow, F.: Estimation of the total fish biomass in the Baltic Sea during the
20th century, ICES J. Mar. Sci., 54, 444–461, https://doi.org/10.1006/jmsc.1996.0195,
1997.
Timmermann, K., Norkko, J., Janas, U., Norkko, A., Gustafsson, B. G., and
Bonsdorff, E.: Modelling macrofaunal biomass in relation to hypoxia and
nutrient loading, J. Mar. Syst., 105–108, 60–69,
https://doi.org/10.1016/j.jmarsys.2012.06.001, 2012.
Tomczak, M. T., Niiranen, S., Hjerne, O., and Blenckner, T.: Ecosystem flow
dynamics in the Baltic Proper – Using a multi-trophic dataset as a basis for
food–web modelling, Ecol. Modell., 230, 123–147,
https://doi.org/10.1016/j.ecolmodel.2011.12.014, 2012.
Travers, M., Shin, Y.-J., Jennings, S., and Cury, P.: Towards end-to-end
models for investigating the effects of climate and fishing in marine
ecosystems, Prog. Oceanogr., 75, 751–770, https://doi.org/10.1016/j.pocean.2007.08.001,
2007.
Utne, K. R., Hjøllo, S. S., Huse, G., and Skogen, M.: Estimating the
consumption of Calanus finmarchicus by planktivorous fish in the Norwegian
Sea using a fully coupled 3D model system, Mar. Biol. Res., 8, 527–547,
https://doi.org/10.1080/17451000.2011.642804, 2012.
Vikebø, F., Jørgensen, C., Kristiansen, T., and Fiksen, Ø.: Drift,
growth, and survival of larval Northeast Arctic cod with simple rules of
behaviour, Mar. Ecol. Prog. Ser., 347, 207–219, https://doi.org/10.3354/meps06979, 2007.
Von Storch, H. and Zwiers, F. W.: Statistical Analysis in Climate Research,
Cambridge University Press, Cambridge, UK, 1999.
Watson, J. R., Stock, C. A., and Sarmiento, J. L.: Exploring the role of
movement in determining the global distribution of marine biomass using a
coupled hydrodynamic – Size-based ecosystem model, Prog. Oceanogr., 138,
521–532, https://doi.org/10.1016/j.pocean.2014.09.001, 2014.
Short summary
Here we propose a novel modelling approach that includes an extended food web in a functional-group-type marine ecosystem model (ECOSMO E2E) by formulating new groups for macrobenthos and fish. This enables the estimation of the dynamics of the higher-trophic-level production potential and constitutes a more consistent closure term for the lower-trophic-level ecosystem. Thus, the model allows for the study of the control mechanisms for marine ecosystems at a high spatial and temporal resolution.
Here we propose a novel modelling approach that includes an extended food web in a...
