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
Kubilay Timur Demir, Moritz Mathis, Jan Kossack, Feifei Liu, Ute Daewel, Christoph Stegert, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 2569–2599, https://doi.org/10.5194/bg-22-2569-2025, https://doi.org/10.5194/bg-22-2569-2025, 2025
Short summary
Short summary
This study examines how variations in the ratios of carbon, nitrogen, and phosphorus in organic matter affect carbon cycling in the northwest European shelf seas. Traditional models with fixed ratios tend to underestimate biological carbon uptake. By integrating variable ratios into a regional model, we find that carbon dioxide uptake increases by 9 %–31 %. These results highlight the need to include variable ratios for accurate assessments of regional and global carbon cycles.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
David Johannes Amptmeijer, Elena Mikhavee, Ute Daewel, Johannes Bieser, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2025-1486, https://doi.org/10.5194/egusphere-2025-1486, 2025
Short summary
Short summary
In this study, we analyze mercury bioaccumulation, including both methylated and inorganic Hg. While methylmercury is the primary toxin of concern, modeling inorganic Hg bioaccumulation reveals its role in marine mercury cycling. We find that bioaccumulation strongly influences mercury dynamics, increasing methylmercury levels. This effect is more pronounced in well-mixed coastal waters than in permanently stratified deep waters.
Feifei Liu, Ute Daewel, Jan Kossack, Kubilay Timur Demir, Helmuth Thomas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2025-81, https://doi.org/10.5194/egusphere-2025-81, 2025
Short summary
Short summary
Ocean Alkalinity Enhancement boosts oceanic CO₂ absorption, offering a climate solution. Using a regional model, we examined OAE in the North Sea, revealing that shallow coastal areas achieve higher CO₂ uptake than offshore, where alkalinity is more susceptible to deep-ocean loss. Long-term carbon storage is limited, and pH shifts vary by location. Our findings guide OAE deployment to optimize carbon removal while minimizing ecological effects, supporting global climate mitigation efforts.
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.
Kubilay Timur Demir, Moritz Mathis, Jan Kossack, Feifei Liu, Ute Daewel, Christoph Stegert, Helmuth Thomas, and Corinna Schrum
Biogeosciences, 22, 2569–2599, https://doi.org/10.5194/bg-22-2569-2025, https://doi.org/10.5194/bg-22-2569-2025, 2025
Short summary
Short summary
This study examines how variations in the ratios of carbon, nitrogen, and phosphorus in organic matter affect carbon cycling in the northwest European shelf seas. Traditional models with fixed ratios tend to underestimate biological carbon uptake. By integrating variable ratios into a regional model, we find that carbon dioxide uptake increases by 9 %–31 %. These results highlight the need to include variable ratios for accurate assessments of regional and global carbon cycles.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
David Johannes Amptmeijer, Elena Mikhavee, Ute Daewel, Johannes Bieser, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2025-1486, https://doi.org/10.5194/egusphere-2025-1486, 2025
Short summary
Short summary
In this study, we analyze mercury bioaccumulation, including both methylated and inorganic Hg. While methylmercury is the primary toxin of concern, modeling inorganic Hg bioaccumulation reveals its role in marine mercury cycling. We find that bioaccumulation strongly influences mercury dynamics, increasing methylmercury levels. This effect is more pronounced in well-mixed coastal waters than in permanently stratified deep waters.
Feifei Liu, Ute Daewel, Jan Kossack, Kubilay Timur Demir, Helmuth Thomas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2025-81, https://doi.org/10.5194/egusphere-2025-81, 2025
Short summary
Short summary
Ocean Alkalinity Enhancement boosts oceanic CO₂ absorption, offering a climate solution. Using a regional model, we examined OAE in the North Sea, revealing that shallow coastal areas achieve higher CO₂ uptake than offshore, where alkalinity is more susceptible to deep-ocean loss. Long-term carbon storage is limited, and pH shifts vary by location. Our findings guide OAE deployment to optimize carbon removal while minimizing ecological effects, supporting global climate mitigation efforts.
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
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
Short summary
Short summary
Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
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...
