Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3901-2022
© Author(s) 2022. 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-15-3901-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 May 2022
Model description paper |
| 13 May 2022
| 13 May 2022
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Veli Çağlar Yumruktepe
CORRESPONDING AUTHOR
Nansen Environmental and Remote Sensing Center, Jahnebakken 3, 5007, Bergen, Norway
Annette Samuelsen
Nansen Environmental and Remote Sensing Center, Jahnebakken 3, 5007, Bergen, Norway
Ute Daewel
Helmholtz-Zentrum Hereon, Institute for Coastal Systems – Analysis
and Modelling, Max-Planck-Str. 1, Geesthacht, Germany
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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.
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For assessing the consequences of human-induced climate change for the marine realm, it is necessary to not only look at gradual changes but also at abrupt changes of environmental conditions. We summarise abrupt changes in ocean warming, acidification, and oxygen concentration as the key environmental factors for ecosystems. Taking these abrupt changes into account requires greenhouse gas emissions to be reduced to a larger extent than previously thought to limit respective damage.
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Parameterisation 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, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
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Saleem Shalin, Annette Samuelsen, Anton Korosov, Nandini Menon, Björn C. Backeberg, and Lasse H. Pettersson
Biogeosciences, 15, 1395–1414, https://doi.org/10.5194/bg-15-1395-2018, https://doi.org/10.5194/bg-15-1395-2018, 2018
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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
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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.
A. Samuelsen, C. Hansen, and H. Wehde
Geosci. Model Dev., 8, 2187–2202, https://doi.org/10.5194/gmd-8-2187-2015, https://doi.org/10.5194/gmd-8-2187-2015, 2015
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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
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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
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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.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-57, https://doi.org/10.5194/gmd-2024-57, 2024
Revised manuscript accepted for GMD
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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.
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
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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
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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
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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.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-26, https://doi.org/10.5194/gmd-2024-26, 2024
Revised manuscript accepted for GMD
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Numerical models that capture key features of the global dynamics of fish communities play a crucial role in addressing the impacts of climate change and industrial fishing on ecosystems and societies. Here, we detail an update of the BiOeconomic marine Trophic Size-spectrum model that corrects the model representation of the dynamic of fisheries in the High Seas. This update also allows a better representation of biodiversity to improve future global and regional fisheries studies.
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
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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
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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.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošeľa, Doroteja Bitunjac, Masa Zorana Ostrogovic Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-45, https://doi.org/10.5194/gmd-2024-45, 2024
Revised manuscript accepted for GMD
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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 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.
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
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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
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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
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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.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-962, https://doi.org/10.5194/egusphere-2024-962, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use, whilst taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers were lost due to NH3 emissions. Hot and dry conditions and regions with high pH soils can expect higher NH3 emissions.
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
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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
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, and Christoph Müller
EGUsphere, https://doi.org/10.5194/egusphere-2023-2946, https://doi.org/10.5194/egusphere-2023-2946, 2024
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We present a new approach to model 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, the nitrogen (N) deficit and carbon (C) costs. The new approach improved global sums and spatial patterns of BNF compared to the scientific literature and the models’ ability to project future C and N cycle dynamics.
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
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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
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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
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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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
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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
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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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.
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
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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.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne-Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
EGUsphere, https://doi.org/10.5194/egusphere-2023-1216, https://doi.org/10.5194/egusphere-2023-1216, 2023
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This research looks at how climate change influences forests, particularly how altered wind and insect activities could make forests emit, instead of absorb, carbon. We've updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, like insect outbreaks, can dramatically affect carbon storage, offering crucial insights for tackling climate change.
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
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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
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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
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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
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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.
Cited articles
Assmy, P., Fernández-Méndez, M., Duarte, P., Meyer, A., Randelhoff, A., Mundy, C. J., Olsen, L. M., Kauko, H. M., Bailey, A., Chierici, M., Cohen, L., Doulgeris, A. P., Ehn, J. K., Fransson, A., Gerland, S., Hop, H., Hudson, S. R., Hughes, N., Itkin, P., Johnsen, G., King, J. A., Koch, B. P., Koenig, Z., Kwasniewski, S., Laney, S. R., Nicolaus, M., Pavlov, A. K., Polashenski, C. M., Provost, C., Rösel, A., Sandbu, M., Spreen, G., Smedsrud, L. H., Sundfjord, A., Taskjelle, T., Tatarek, A., Wiktor, J., Wagner, P. M., Wold, A., Steen, H., and Granskog, M. A: Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice, Scientific Reports, Scient. Rep., 7, 1–9, https://doi.org/10.1038/srep40850, 2017.
Bagniewski, W., Fennel, K., Perry, M. J., and D'Asaro, E. A.: Optimizing models of the North Atlantic spring bloom using physical, chemical and
bio-optical observations from a Lagrangian float, Biogeosciences, 8,
1291–1307, https://doi.org/10.5194/bg-8-1291-2011, 2011.
Baumann, K. H., Andruleit, H. A., and Samtleben, C.: Coccolithophores in the
Nordic Seas: comparison of living communities with surface sediment
assemblages, Deep-Sea Res. Pt. II, 47,
1743–1772, 2000.
Bleck, R.: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates, Ocean Modelling, Ocean Model., 4, 55–88, https://doi.org/10.1016/S1463-5003(01)00012-9, 2002.
Boyer, T. P., Garcia, H. E., Locarnini, R. A., Zweng, M. M., Mishonov, A.
V., Reagan, J. R., Weathers, K. A., Baranova, O. K., Seidov, D., and Smolyar, I. V.: World Ocean Atlas 2018 [tempearature, nitrate, silicate, phosphate],
NOAA National Centers for Environmental Information, NOAA [data set] https://accession.nodc.noaa.gov/NCEI-WOA18 (last access: 25 March 2022), 2018.
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical models, Environ. Model. Softw., 61, 249–265, https://doi.org/10.1016/j.envsoft.2014.04.002, 2014.
Burchard, H., Bolding, K., Kühn, K., Meister, A., Neumann, T., and Umlauf, L.: Description of a flexible and extendable physical-biogeochemical model system for the water column, J. Mar. Syst., 61, 180–211, 2006.
Ciavatta, S., Torres, R., Saux-Picard., S., and Allen, J. I.: Can ocean
color assimilation improve biogeochemical hindcasts in shelf seas?, J. Geophys. Res., 116, C12043, https://doi.org/10.1029/2011JC007219, 2011.
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.
Dalpadado, P., Arrigo, K. R., van Dijken, G. L., Skjoldal, H. R., Bagøien, E., Dolgov, A. V., Prokopchuk, I. P., and Sperfeld, E.: Climate effects on temporal and spatial dynamics of phytoplankton and zooplankton in the Barents Sea, Prog. Oceanogr., 185, 102320, https://doi.org/10.1016/j.pocean.2020.102320, 2020.
Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Alonso-Balmaseda, M., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A., van de Berg, L., Bidlot, J.-R., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P. W., Köhler, M., Matricardi, M., McNally, A., Monge-Sanz, B. M., Morcrette, J.-J., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dierssen, H. M.: Perspectives on empirical approaches for ocean color remote
sensing of chlorophyll in a changing climate, P. Natl. Acad. Sci. USA, 107, 17073–17078, https://doi.org/10.1073/pnas.0913800107, 2010.
Dong, K., Kvile, K. Ø., Stenseth, N. C., and Stige, L. C.: Associations
among temperature, sea ice and phytoplankton bloom dynamics in the Barents
Sea, Mar. Ecol.-Prog. Ser., 635, 25–36, https://doi.org/10.3354/meps13218, 2020.
Evans, G. and Parslow, J.: A model of annual plankton cycles, Biol. Oceanogr., 3, 327–347, 1985.
Fröb, F., Olsen, A., Pérez, F. F., Garciá-Ibáñez, M. I.,
Jeansson, E., Omar, A., and Lauvset, S. K.: Inorganic carbon and water masses
in the Irminger Sea since 1991, Biogeosciences, 15, 51–72,
https://doi.org/10.5194/bg-15-51-2018, 2018.
García, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Baranova, O. K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: World Ocean Atlas 2013 Volume 4: Dissolved Inorganic Nutrients (phosphate, nitrate, silicate), edited by: Levitus, S. and Mishonov, A., NOAA Atlas NESDIS 76, 25 pp., NOAA [data set], https://doi.org/10.7289/v5f769gt, 2013.
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: Dynamic model of phytoplankton growth and acclimation: Responses of the balanced growth rate
and the chlorophyll a:carbon ratio to light, nutrient-limitation and temperature, Mar. Ecol.-Prog. Ser., 148, 187–200 ,https://doi.org/10.3354/meps148187, 1997.
Gradinger, R.: Sea-ice algae: Major contributors to primary production and
algal biomass in the Chukchi and Beaufort Seas during May/June 2002, Deep-Sea Res. Pt. II, 56, 1201–1212, https://doi.org/10.1016/j.dsr2.2008.10.016, 2009.
Hop, H., Falk-Petersen, S., Svendsen, H., Kwasniewsky, S., Pavlov, V.,
Pavlova, O., and Søreide, J.: Physical and biological characteristics of
the pelagic system across Fram Strait to Kongsfjorden, Prog. Oceanogr., 71, 182–231, https://doi.org/10.1016/j.pocean.2006.09.007, 2006.
Institute of Marine Research: Næringssalt-, oksygen- og klorofyll- data i norske havområder fra 1980–2017, Institute of Marine Research [data set], http://www.imr.no/forskning/forskningsdata/infrastruktur/viewdataset.html?dataset_id=104, (last access: 25 March 2022), 2018.
Lee, Y. J., Matrai, P. A., Friedrichs, M. A. M., Saba, V. S., Antoine, D., Ardyna, M., Asanuma, I., Babin, M., Bélanger, S., Benoît-Gagné, M., Devred, E., Fernández-Méndez, M., Gentili, B., Hirawake, T., Kang, S.-H., Kameda, T., Katlein, C., Lee, S. H., Lee, Z., Mélin, F., Scardi, M., Smyth, T. J., Tang, S., Turpie, K. R., Waters, K. J., and Westberry, T. K.:: An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models, J. Geophys. Res.-Oceans, 120, 6508–6541, https://doi.org/10.1002/2015JC011018, 2015.
Lisæter, K. A., Counillon, F., Xie, J., Hansen, C., Samuelsen, A., Yumruktepe, V. Ç., Daewel, U., Schrum, C., Evensen, G., and Bertino, L.: HYCOM-ECOSMOII(CHL) a marine biogeochemical model for the North Atlantic and the Arctic model code and configuration, Zenodo [code], https://doi.org/10.5281/zenodo.6387608, 2021.
Longhurst, A. R.: Ecological biogeography of the sea, Academic Press, San
Diego, USA, https://doi.org/10.1016/B978-0-12-455521-1.X5000-1, 1998.
Matrai, P. A., Olson, E., Suttles, S., Hill, V., Codispoti, L. A., Light, B., and Steele, M.: Synthesis of primary production in the Arctic Ocean: I. Surface waters, 1954–2007, Prog. Oceanogr., 110, 93–106, https://doi.org/10.1016/j.pocean.2012.11.004, 2013.
Mayorga. E., Seitzinger, S. P., Harrison, J. A., Dumont, E., Beusend, A. H. W., Bouwmand, A. F., Fekete, B. M., Kroeze, C., and Drechtd, G. V.: Global Nutrient Export from WaterSheds 2 (NEWS 2): Model development and implementation, Environ. Model. Softw., 25, 837–853, https://doi.org/10.1016/j.envsoft.2010.01.007, 2010.
Melle, W. and Skjoldal, H. R.: Reproduction and development of Calanus finmarchicus, C-glacialis and C-hyperboreus in the Barents Sea, Mar. Ecol.-Prog. Ser., 169, 211–228, 1998.
Melle, W., Ellertsen, B., and Skjoldal, H. R.: Zooplankton: the link to higher trophic levels, The Norwegian Sea Ecosystem, Tapir Academic Press, 137–202, ISBN 13 978-8251918411, 2004.
Nilsen, J. E. Ø. O. and Falck, E.: Variations of mixed layer properties
in the Norwegian Sea for the period 1948–1999, Prog. Oceanogr., 70, 58–90, https://doi.org/10.1016/j.pocean.2006.03.014, 2006.
Oki, T. and Sud, Y. C.: Design of Total Runoff Integrating Pathways (TRIP) – A Global River Channel Network, Earth Interact., 2, 1–37, https://doi.org/10.1175/1087-3562(1998)002<0001:DOTRIP>2.3.CO;2, 1998.
Orvik, K., Skagseth, Ø. and Mork, M.: Atlantic infow to the Nordic Seas:
current structure and volume fluxes from moored current meters, VM-ADCP and
SeaSoar-CTD observations, 1995–1999, Deep-Sea Res. Pt. I, 48, 937–957, 2001.
Polyakov, I. V., Alkire, M. B., Bluhm, B. A., Brown, K. A., Carmack, E. C.,
Chierici, M., Danielson, S. L., Ellingsen, I., Ershova, E. A., Gårdfeldt, K., Ingvaldsen, R. B., Pnyushkov, A. V., Slagstad, D., and Wassmann, P.: Borealization of the Arctic Ocean in Response to Anomalous Advection From Sub-Arctic Seas, Front. Mar. Sci., 7, 491, https://doi.org/10.3389/fmars.2020.00491, 2020.
Rey, F.: Primary production estimates in the Norwegian CoastalCurrent between 62∘ N and 72∘ N, in: vol. 2, The Norwegian Coastal Current. Proceedings of the Norwegian Coastal Current symposium, 9–12 September 1980, Geilo, edited by: Sætre, R. and Mork, M. University of Bergen, Norway, 640–648, ISBN OCLC:1223644316, 1981.
Rey, F.: Phytoplankton: the grass of the sea, in: The Norwegian Sea Ecosystem, edited by: Skjoldal, H. R., Tapir Academic Press, Trondheim, ISBN 9788251918411, 2004.
Samuelsen, A., Huse, G., and Hansen, C.: Shelf recruitment of Calanus
finmarchicus off the west coast of Norway: role of physical processes and
timing of diapause termination, Mar. Ecol.-Prog. Ser., 386, 163–180, https://doi.org/10.3354/meps08060, 2009.
Samuelsen, A., Hansen, C., and Wehde, H.: Tuning and assessment of the
HYCOM-NORWECOM V2.1 biogeochemical modeling system for the North Atlantic and Arctic oceans, Geosci. Model Dev., 8, 2187–2202, https://doi.org/10.5194/gmd-8-2187-2015, 2015.
Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin, F., Moore, T. S., Müller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., and Platt, T.: An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019.
Schrum, C., Alekseeva, I., and John, M. S.: Development of a coupled
physical-biological ecosystem model ECOSMO. Part I: Model description and
validation for the North Sea, J. Mar. Syst., 61, 79–99, https://doi.org/10.1016/j.jmarsys.2006.01.005, 2006.
Schubert-Frisius, M. and Feser, F.: Global High Resolution Climate
Reconstruction with ECHAM6 using the spectral nudging technique, run by
Helmholtz-Zentrum Geesthacht, World Data Center for Climate (WDCC) at
DKRZ, https://doi.org/10.1594/WDCC/CLISAP_MPI-ESM-XR_T255L95, 2015.
Seitzinger, S. P., Mayorga, E., Bouwman, A. F., Kroeze, C., Beusen, A. H., Billen, G., Van Drecht, G., Dumont, E., Fekete, B. M., Garnier, J., and Harrison, J. A.: Global river nutrient export: A scenario analysis of past and future trends, Global Biogeochem. Cy., 24, GB0A08, https://doi.org/10.1029/2009GB003587, 2010.
Skogen, M. D. and Moll, A.: Importance of ocean circulation in ecological
modeling: An example from the North Sea, J. Mar. Syst., 57, 289–300,
https://doi.org/10.1016/j.jmarsys.2005.06.002, 2005.
von Schuckmann, K., Le Traon, P.-Y., Alvarez-Fanjul, E., Axell, L., Balmaseda, M., Breivik, L.-A., Brewin, R. J. W., Bricaud, C., Drevillon, M., Drillet, Y., Dubois, C., Embury, O., Etienne, H., Sotillo, M. G., Garric, G., Gasparin, F., Gutknecht, E., Guinehut, S., Hernandez, F., Juza, M., Karlson, B., Korres, G., Legeais, J.-F., Levier, B., Lien, V. S., Morrow, R., Notarstefano, G., Parent, L., Pascual, Á., Pérez-Gómez, B., Perruche, C., Pinardi, N., Pisano, A., Poulain, P.-M., Pujol, I. M., Raj, R. P., Raudsepp, U., Roquet, H., Samuelsen, A., Sathyendranath, S., She, J., Simoncelli, S., Solidoro, C., Tinker, J., Tintoré, J., Viktorsson, L., Ablain, M., Almroth-Rosell, E., Bonaduce, A., Clementi, E., Cossarini, G., Dagneaux, Q., Desportes, C., Dye, S., Fratianni, C., Good, S., Greiner, E., Gourrion, J., Hamon, M., Holt, J., Hyder, P., Kennedy, J., Manzano-Muñoz, F., Melet, A., Meyssignac, B., Mulet, S., Nardelli, B. B., O'Dea, E., Olason, E., Paulmier, A., Pérez-González, I., Reid, R., Racault, M.-F., Raitsos, D. E., Ramos, A., Sykes, P., Szekely, T., and Verbrugge, N.: The Copernicus Marine Environment Monitoring Service Ocean State Report, J. Operat. Oceanogr., 9, s235–s320, https://doi.org/10.1080/1755876X.2016.1273446, 2016.
Yashayaev, I., Bersch, M., and van Aken, H. M.: Spreading of the Labrador Sea
Water to the Irminger and Iceland basins, Geophys. Res. Lett., 34, 1–8,
https://doi.org/10.1029/2006GL028999, 2007.
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.
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional...
