Articles | Volume 9, issue 4
Geosci. Model Dev., 9, 1293–1339, 2016
https://doi.org/10.5194/gmd-9-1293-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 9, 1293–1339, 2016
https://doi.org/10.5194/gmd-9-1293-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper 05 Apr 2016
Model description paper | 05 Apr 2016
ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels
Momme Butenschön et al.
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Huw W. Lewis, Juan Manuel Castillo Sanchez, Jennifer Graham, Andrew Saulter, Jorge Bornemann, Alex Arnold, Joachim Fallmann, Chris Harris, David Pearson, Steven Ramsdale, Alberto Martínez-de la Torre, Lucy Bricheno, Eleanor Blyth, Victoria A. Bell, Helen Davies, Toby R. Marthews, Clare O'Neill, Heather Rumbold, Enda O'Dea, Ashley Brereton, Karen Guihou, Adrian Hines, Momme Butenschon, Simon J. Dadson, Tamzin Palmer, Jason Holt, Nick Reynard, Martin Best, John Edwards, and John Siddorn
Geosci. Model Dev., 11, 1–42, https://doi.org/10.5194/gmd-11-1-2018, https://doi.org/10.5194/gmd-11-1-2018, 2018
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In the real world the atmosphere, oceans and land surface are closely interconnected, and yet prediction systems tend to treat them in isolation. Those feedbacks are often illustrated in natural hazards, such as when strong winds lead to large waves and coastal damage, or when prolonged rainfall leads to saturated ground and high flowing rivers. For the first time, we have attempted to represent some of the feedbacks between sky, sea and land within a high-resolution forecast system for the UK.
Claudie Beaulieu, Harriet Cole, Stephanie Henson, Andrew Yool, Thomas R. Anderson, Lee de Mora, Erik T. Buitenhuis, Momme Butenschön, Ian J. Totterdell, and J. Icarus Allen
Biogeosciences, 13, 4533–4553, https://doi.org/10.5194/bg-13-4533-2016, https://doi.org/10.5194/bg-13-4533-2016, 2016
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Regime shifts have been suggested in the late 1970s and late 1980s in the Gulf of Alaska with important consequences for fisheries. Here we investigate the ability of a suite of ocean biogeochemical models of varying complexity to simulate these regime shifts. Our results demonstrate that ocean models can successfully simulate regime shifts in the Gulf of Alaska region, thereby improving our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.
L. de Mora, M. Butenschön, and J. I. Allen
Geosci. Model Dev., 9, 59–76, https://doi.org/10.5194/gmd-9-59-2016, https://doi.org/10.5194/gmd-9-59-2016, 2016
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To use models to inform policy or to forecast the impact of climate change, the model must first be shown to be a valid representation of the ecosystem. Here we show an novel method to validate a marine model using its ability to represent ecosystem function. These relationships are the community structure, the carbon to chlorophyll ratio and the stoichiometric balance of the ecosystem. These methods are powerful, valid over large spatial scales and independent of the circulation model.
L. Kwiatkowski, A. Yool, J. I. Allen, T. R. Anderson, R. Barciela, E. T. Buitenhuis, M. Butenschön, C. Enright, P. R. Halloran, C. Le Quéré, L. de Mora, M.-F. Racault, B. Sinha, I. J. Totterdell, and P. M. Cox
Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, https://doi.org/10.5194/bg-11-7291-2014, 2014
Y. Artioli, J. C. Blackford, G. Nondal, R. G. J. Bellerby, S. L. Wakelin, J. T. Holt, M. Butenschön, and J. I. Allen
Biogeosciences, 11, 601–612, https://doi.org/10.5194/bg-11-601-2014, https://doi.org/10.5194/bg-11-601-2014, 2014
J. Holt, C. Schrum, H. Cannaby, U. Daewel, I. Allen, Y. Artioli, L. Bopp, M. Butenschon, B. A. Fach, J. Harle, D. Pushpadas, B. Salihoglu, and S. Wakelin
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-1909-2014, https://doi.org/10.5194/bgd-11-1909-2014, 2014
Revised manuscript not accepted
L. de Mora, M. Butenschön, and J. I. Allen
Geosci. Model Dev., 6, 533–548, https://doi.org/10.5194/gmd-6-533-2013, https://doi.org/10.5194/gmd-6-533-2013, 2013
Andrew Yool, Julien Palmiéri, Colin G. Jones, Lee de Mora, Till Kuhlbrodt, Ekatarina E. Popova, A. J. George Nurser, Joel Hirschi, Adam T. Blaker, Andrew C. Coward, Edward W. Blockley, and Alistair A. Sellar
Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, https://doi.org/10.5194/gmd-14-3437-2021, 2021
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The ocean plays a key role in modulating the Earth’s climate. Understanding this role is critical when using models to project future climate change. Consequently, it is necessary to evaluate their realism against the ocean's observed state. Here we validate UKESM1, a new Earth system model, focusing on the realism of its ocean physics and circulation, as well as its biological cycles and productivity. While we identify biases, generally the model performs well over a wide range of properties.
Lee de Mora, Alistair A. Sellar, Andrew Yool, Julien Palmieri, Robin S. Smith, Till Kuhlbrodt, Robert J. Parker, Jeremy Walton, Jeremy C. Blackford, and Colin G. Jones
Geosci. Commun., 3, 263–278, https://doi.org/10.5194/gc-3-263-2020, https://doi.org/10.5194/gc-3-263-2020, 2020
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We use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces for piano. Each of the six pieces help to explain either a scientific principle or a practical aspect of Earth system modelling. We describe the methods that were used to create these pieces, discuss the limitations of this pilot study and list several approaches to extend and expand upon this work.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Mattia Righi, Bouwe Andela, Veronika Eyring, Axel Lauer, Valeriu Predoi, Manuel Schlund, Javier Vegas-Regidor, Lisa Bock, Björn Brötz, Lee de Mora, Faruk Diblen, Laura Dreyer, Niels Drost, Paul Earnshaw, Birgit Hassler, Nikolay Koldunov, Bill Little, Saskia Loosveldt Tomas, and Klaus Zimmermann
Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, https://doi.org/10.5194/gmd-13-1179-2020, 2020
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This paper describes the second major release of ESMValTool, a community diagnostic and performance metrics tool for the evaluation of Earth system models. This new version features a brand new design, with an improved interface and a revised preprocessor. It takes advantage of state-of-the-art computational libraries and methods to deploy efficient and user-friendly data processing, improving the performance over its predecessor by more than a factor of 30.
Thodoris Karpouzoglou, Brigitte Vlaswinkel, and Johan van der Molen
Ocean Sci., 16, 195–208, https://doi.org/10.5194/os-16-195-2020, https://doi.org/10.5194/os-16-195-2020, 2020
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Sustainable operation of floating solar platforms requires knowledge of effects on the marine ecosystem. We modelled effects on water flow and algae growth in a coastal sea. Algae growth was reduced depending on the local currents and on the density of coverage with platforms. The model represented platforms distributed evenly over areas of hundreds of square kilometres. For smaller-scale cases, effects may be smaller, and for more detailed understanding, three-dimensional models are needed.
Huw W. Lewis, Juan Manuel Castillo Sanchez, Alex Arnold, Joachim Fallmann, Andrew Saulter, Jennifer Graham, Mike Bush, John Siddorn, Tamzin Palmer, Adrian Lock, John Edwards, Lucy Bricheno, Alberto Martínez-de la Torre, and James Clark
Geosci. Model Dev., 12, 2357–2400, https://doi.org/10.5194/gmd-12-2357-2019, https://doi.org/10.5194/gmd-12-2357-2019, 2019
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In the real world the atmosphere, oceans and land surface are closely interconnected, and yet the prediction systems used for weather and ocean forecasting tend to treat them in isolation. This paper describes the third version of a regional modelling system which aims to represent the feedback processes between sky, sea and land. The main innovation introduced in this version enables waves to affect the underlying ocean. Coupled results from four different month-long simulations are analysed.
Lee de Mora, Andrew Yool, Julien Palmieri, Alistair Sellar, Till Kuhlbrodt, Ekaterina Popova, Colin Jones, and J. Icarus Allen
Geosci. Model Dev., 11, 4215–4240, https://doi.org/10.5194/gmd-11-4215-2018, https://doi.org/10.5194/gmd-11-4215-2018, 2018
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Climate change is expected to have a significant impact on the Earth's weather, ice caps, land surface, and ocean. Computer models of the Earth system are the only tools available to make predictions about how the climate may change in the future. However, in order to trust the model predictions, we must first demonstrate that the models have a realistic description of the past. The BGC-val toolkit was built to rapidly and simply evaluate the behaviour of models of the Earth's oceans.
Johan van der Molen, Piet Ruardij, Karen Mooney, Philip Kerrison, Nessa E. O'Connor, Emma Gorman, Klaas Timmermans, Serena Wright, Maeve Kelly, Adam D. Hughes, and Elisa Capuzzo
Biogeosciences, 15, 1123–1147, https://doi.org/10.5194/bg-15-1123-2018, https://doi.org/10.5194/bg-15-1123-2018, 2018
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Macroalgae farming may provide biofuel. Modelled macroalgae production is given for four sites in UK and Dutch waters. Macroalgae growth depended on nutrient concentrations and light levels. Macroalgae carbohydrate content, important for biofuel use, was lower for high nutrient concentrations. The hypothetical large-scale farm off the UK north Norfolk coast gave high, stable yields of macroalgae from year to year with substantial carbohydrate content.
Huw W. Lewis, Juan Manuel Castillo Sanchez, Jennifer Graham, Andrew Saulter, Jorge Bornemann, Alex Arnold, Joachim Fallmann, Chris Harris, David Pearson, Steven Ramsdale, Alberto Martínez-de la Torre, Lucy Bricheno, Eleanor Blyth, Victoria A. Bell, Helen Davies, Toby R. Marthews, Clare O'Neill, Heather Rumbold, Enda O'Dea, Ashley Brereton, Karen Guihou, Adrian Hines, Momme Butenschon, Simon J. Dadson, Tamzin Palmer, Jason Holt, Nick Reynard, Martin Best, John Edwards, and John Siddorn
Geosci. Model Dev., 11, 1–42, https://doi.org/10.5194/gmd-11-1-2018, https://doi.org/10.5194/gmd-11-1-2018, 2018
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In the real world the atmosphere, oceans and land surface are closely interconnected, and yet prediction systems tend to treat them in isolation. Those feedbacks are often illustrated in natural hazards, such as when strong winds lead to large waves and coastal damage, or when prolonged rainfall leads to saturated ground and high flowing rivers. For the first time, we have attempted to represent some of the feedbacks between sky, sea and land within a high-resolution forecast system for the UK.
David A. Ford, Johan van der Molen, Kieran Hyder, John Bacon, Rosa Barciela, Veronique Creach, Robert McEwan, Piet Ruardij, and Rodney Forster
Biogeosciences, 14, 1419–1444, https://doi.org/10.5194/bg-14-1419-2017, https://doi.org/10.5194/bg-14-1419-2017, 2017
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This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea. These observations were used to validate two physical–biogeochemical ocean model simulations, each of which used different variants of the widely used European Regional Seas Ecosystem Model (ERSEM). The results suggest the ability of the models to reproduce the observed phytoplankton community structure was dependent on the details of the biogeochemical model parameterizations used.
Evgeniy V. Yakushev, Elizaveta A. Protsenko, Jorn Bruggeman, Philip Wallhead, Svetlana V. Pakhomova, Shamil Kh. Yakubov, Richard G. J. Bellerby, and Raoul-Marie Couture
Geosci. Model Dev., 10, 453–482, https://doi.org/10.5194/gmd-10-453-2017, https://doi.org/10.5194/gmd-10-453-2017, 2017
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This paper presents a new benthic–pelagic biogeochemical model (BROM) that combines a relatively simple pelagic ecosystem model with a detailed biogeochemical model of the coupled cycles of N, P, Si, C, O, S, Mn, Fe in the water column, benthic boundary layer, and sediments, with a focus on oxygen and redox state. BROM should be of interest for the study of a range of environmental applications in addition to hypoxia, such as benthic nutrient recycling, redox biogeochemistry, and eutrophication.
Jason Holt, Patrick Hyder, Mike Ashworth, James Harle, Helene T. Hewitt, Hedong Liu, Adrian L. New, Stephen Pickles, Andrew Porter, Ekaterina Popova, J. Icarus Allen, John Siddorn, and Richard Wood
Geosci. Model Dev., 10, 499–523, https://doi.org/10.5194/gmd-10-499-2017, https://doi.org/10.5194/gmd-10-499-2017, 2017
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Accurately representing coastal and shelf seas in global ocean models is one of the grand challenges of Earth system science. Here, we explore what the options are for improving this by exploring what the important physical processes are that need to be represented. We use a simple scale analysis to investigate how large the resulting models would need to be. We then compare this with how computer power is increasing to provide estimates of when this might be feasible in the future.
Claudie Beaulieu, Harriet Cole, Stephanie Henson, Andrew Yool, Thomas R. Anderson, Lee de Mora, Erik T. Buitenhuis, Momme Butenschön, Ian J. Totterdell, and J. Icarus Allen
Biogeosciences, 13, 4533–4553, https://doi.org/10.5194/bg-13-4533-2016, https://doi.org/10.5194/bg-13-4533-2016, 2016
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Regime shifts have been suggested in the late 1970s and late 1980s in the Gulf of Alaska with important consequences for fisheries. Here we investigate the ability of a suite of ocean biogeochemical models of varying complexity to simulate these regime shifts. Our results demonstrate that ocean models can successfully simulate regime shifts in the Gulf of Alaska region, thereby improving our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.
Jun She, Icarus Allen, Erik Buch, Alessandro Crise, Johnny A. Johannessen, Pierre-Yves Le Traon, Urmas Lips, Glenn Nolan, Nadia Pinardi, Jan H. Reißmann, John Siddorn, Emil Stanev, and Henning Wehde
Ocean Sci., 12, 953–976, https://doi.org/10.5194/os-12-953-2016, https://doi.org/10.5194/os-12-953-2016, 2016
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This white paper addresses key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5–10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European ocean observations, modelling and forecasting technology, coastal operational oceanography, and operational ecology.
Corinne Le Quéré, Erik T. Buitenhuis, Róisín Moriarty, Séverine Alvain, Olivier Aumont, Laurent Bopp, Sophie Chollet, Clare Enright, Daniel J. Franklin, Richard J. Geider, Sandy P. Harrison, Andrew G. Hirst, Stuart Larsen, Louis Legendre, Trevor Platt, I. Colin Prentice, Richard B. Rivkin, Sévrine Sailley, Shubha Sathyendranath, Nick Stephens, Meike Vogt, and Sergio M. Vallina
Biogeosciences, 13, 4111–4133, https://doi.org/10.5194/bg-13-4111-2016, https://doi.org/10.5194/bg-13-4111-2016, 2016
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We present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types, and use the model to assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean. Our results suggest that observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton growth.
Fenjuan Hu, Karsten Bolding, Jorn Bruggeman, Erik Jeppesen, Morgens R. Flindt, Luuk van Gerven, Jan H. Janse, Annette B. G. Janssen, Jan J. Kuiper, Wolf M. Mooij, and Dennis Trolle
Geosci. Model Dev., 9, 2271–2278, https://doi.org/10.5194/gmd-9-2271-2016, https://doi.org/10.5194/gmd-9-2271-2016, 2016
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We present a redesign and further development of a complex and well-known aquatic ecosystem model (PCLake) into the Framework for Aquatic Biogeochemical Models (FABM). So PCLake can run in different hydrodynamic environments, ranging from 0-D to 3-D. We introduce the methods and technical details about how the model was re-designed into a modular structure and the new features of PCLake enabled by FABM. We further present a benchmark test case to verify the new model implementation.
Mingxi Yang, Thomas G. Bell, Frances E. Hopkins, Vassilis Kitidis, Pierre W. Cazenave, Philip D. Nightingale, Margaret J. Yelland, Robin W. Pascal, John Prytherch, Ian M. Brooks, and Timothy J. Smyth
Atmos. Chem. Phys., 16, 5745–5761, https://doi.org/10.5194/acp-16-5745-2016, https://doi.org/10.5194/acp-16-5745-2016, 2016
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Coastal seas are sources of methane in the atmosphere and can fluctuate from emitting to absorbing carbon dioxide. Direct air–sea transport measurements of these two greenhouse gases in near shore regions remain scarce. From a recently established coastal atmospheric station on the south-west coast of the UK, we observed that the oceanic absorption of carbon dioxide peaked during the phytoplankton bloom, while methane emission varied with the tidal cycle, likely due to an estuary influence.
Johan van der Molen, Piet Ruardij, and Naomi Greenwood
Biogeosciences, 13, 2593–2609, https://doi.org/10.5194/bg-13-2593-2016, https://doi.org/10.5194/bg-13-2593-2016, 2016
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The potential large-scale (> 100 km) effects of marine renewable tidal energy generation in the Pentland Firth were studied using a 3-D hydrodynamics–biogeochemistry model. A realistic 800 MW scenario suggested minor effects on tides and biogeochemistry. A massive-expansion 8 GW scenario suggested effects over hundreds of kilometres away with changes of up to 10 % in tidal and ecosystem variables, the latter through clearer waters and increased primary production with associated increases in fauna.
Jonathan Beecham, Jorn Bruggeman, John Aldridge, and Steven Mackinson
Geosci. Model Dev., 9, 947–964, https://doi.org/10.5194/gmd-9-947-2016, https://doi.org/10.5194/gmd-9-947-2016, 2016
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This paper is a description of how very different higher and lower trophic level models (Ecopath with Ecosim) and ERSEM, respectively, can be coupled together using a metadata coupling system together with a number of examples of short- and long-range projections for end to end modelling.
S. M. van Leeuwen, W. F. Le Quesne, and E. R. Parker
Biogeosciences, 13, 441–454, https://doi.org/10.5194/bg-13-441-2016, https://doi.org/10.5194/bg-13-441-2016, 2016
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– Effects of ocean acidification in shelf waters on fish yield can be of the same order of magnitude as climatic impacts.
– Local differences in response within one shelf sea due to different governing hydrodynamic regimes and variation in importance of benthic food web.
– Future fish yields in the North Sea: seasonally stratified areas show increased yield, transitional waters show decreased yield and permanently mixed waters show increased yield moderated by acidification impacts.
L. de Mora, M. Butenschön, and J. I. Allen
Geosci. Model Dev., 9, 59–76, https://doi.org/10.5194/gmd-9-59-2016, https://doi.org/10.5194/gmd-9-59-2016, 2016
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To use models to inform policy or to forecast the impact of climate change, the model must first be shown to be a valid representation of the ecosystem. Here we show an novel method to validate a marine model using its ability to represent ecosystem function. These relationships are the community structure, the carbon to chlorophyll ratio and the stoichiometric balance of the ecosystem. These methods are powerful, valid over large spatial scales and independent of the circulation model.
J. van der Molen, J. van Beek, S. Augustine, L. Vansteenbrugge, L. van Walraven, V. Langenberg, H. W. van der Veer, K. Hostens, S. Pitois, and J. Robbens
Ocean Sci., 11, 405–424, https://doi.org/10.5194/os-11-405-2015, https://doi.org/10.5194/os-11-405-2015, 2015
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The reproduction, survival, and transport of the comb jelly Mnemiopsis leidyi was studied with three models in the Scheldt estuaries and the southern North Sea. The results suggest that (a) the estuaries can retain an overwintering population and seed offshore populations; (b) M. leidyi can survive in the North Sea, and be transported between coastal inlets; and (c) M. leidyi cannot reproduce well in the North Sea, but this might change with global warming. The models need further improvement.
L. Kwiatkowski, A. Yool, J. I. Allen, T. R. Anderson, R. Barciela, E. T. Buitenhuis, M. Butenschön, C. Enright, P. R. Halloran, C. Le Quéré, L. de Mora, M.-F. Racault, B. Sinha, I. J. Totterdell, and P. M. Cox
Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, https://doi.org/10.5194/bg-11-7291-2014, 2014
D. Talmy, J. Blackford, N. J. Hardman-Mountford, L. Polimene, M. J. Follows, and R. J. Geider
Biogeosciences, 11, 4881–4895, https://doi.org/10.5194/bg-11-4881-2014, https://doi.org/10.5194/bg-11-4881-2014, 2014
M. C. H. Tiessen, L. Fernard, T. Gerkema, J. van der Molen, P. Ruardij, and H. W. van der Veer
Ocean Sci., 10, 357–376, https://doi.org/10.5194/os-10-357-2014, https://doi.org/10.5194/os-10-357-2014, 2014
Y. Artioli, J. C. Blackford, G. Nondal, R. G. J. Bellerby, S. L. Wakelin, J. T. Holt, M. Butenschön, and J. I. Allen
Biogeosciences, 11, 601–612, https://doi.org/10.5194/bg-11-601-2014, https://doi.org/10.5194/bg-11-601-2014, 2014
J. Holt, C. Schrum, H. Cannaby, U. Daewel, I. Allen, Y. Artioli, L. Bopp, M. Butenschon, B. A. Fach, J. Harle, D. Pushpadas, B. Salihoglu, and S. Wakelin
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-1909-2014, https://doi.org/10.5194/bgd-11-1909-2014, 2014
Revised manuscript not accepted
L. de Mora, M. Butenschön, and J. I. Allen
Geosci. Model Dev., 6, 533–548, https://doi.org/10.5194/gmd-6-533-2013, https://doi.org/10.5194/gmd-6-533-2013, 2013
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Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
Marine biogeochemical cycling and oceanic CO2 uptake simulated by the NUIST Earth System Model version 3 (NESM v3)
CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance
HR3DHG version 1: modeling the spatiotemporal dynamics of mercury in the Augusta Bay (southern Italy)
BPOP-v1 model: exploring the impact of changes in the biological pump on the shelf sea and ocean nutrient and redox state
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production
HETEROFOR 1.0: a spatially explicit model for exploring the response of structurally complex forests to uncertain future conditions – Part 2: Phenology and water cycle
Dynamic upscaling of decomposition kinetics for carbon cycling models
CE-DYNAM (v1): a spatially explicit process-based carbon erosion scheme for use in Earth system models
A coupled pelagic–benthic–sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model
FORests and HYdrology under Climate Change in Switzerland v1.0: a spatially distributed model combining hydrology and forest dynamics
ORCHIDEE MICT-LEAK (r5459), a global model for the production, transport, and transformation of dissolved organic carbon from Arctic permafrost regions – Part 2: Model evaluation over the Lena River basin
GLOBAL-FATE (version 1.0.0): A geographical information system (GIS)-based model for assessing contaminants fate in the global river network
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?
Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data
Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): multi-scale evaluation of gross primary production
A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996)
Modelling biomass burning emissions and the effect of spatial resolution: a case study for Africa based on the Global Fire Emissions Database (GFED)
A lattice-automaton bioturbation simulator with coupled physics, chemistry, and biology in marine sediments (eLABS v0.2)
Leah Birch, Christopher R. Schwalm, Sue Natali, Danica Lombardozzi, Gretchen Keppel-Aleks, Jennifer Watts, Xin Lin, Donatella Zona, Walter Oechel, Torsten Sachs, Thomas Andrew Black, and Brendan M. Rogers
Geosci. Model Dev., 14, 3361–3382, https://doi.org/10.5194/gmd-14-3361-2021, https://doi.org/10.5194/gmd-14-3361-2021, 2021
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The high-latitude landscape or Arctic–boreal zone has been warming rapidly, impacting the carbon balance both regionally and globally. Given the possible global effects of climate change, it is important to have accurate climate model simulations. We assess the simulation of the Arctic–boreal carbon cycle in the Community Land Model (CLM 5.0). We find biases in both the timing and magnitude photosynthesis. We then use observational data to improve the simulation of the carbon cycle.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
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Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
Leonardo Calle and Benjamin Poulter
Geosci. Model Dev., 14, 2575–2601, https://doi.org/10.5194/gmd-14-2575-2021, https://doi.org/10.5194/gmd-14-2575-2021, 2021
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We developed a model to simulate and track the age of ecosystems on Earth. We found that the effect of ecosystem age on net primary production and ecosystem respiration is as important as climate in large areas of every vegetated continent. The LPJ-wsl v2.0 age-class model simulates dynamic age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.
Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang
Geosci. Model Dev., 14, 2371–2417, https://doi.org/10.5194/gmd-14-2371-2021, https://doi.org/10.5194/gmd-14-2371-2021, 2021
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This study evaluates how well the CLASSIC land surface model reproduces the energy, water, and carbon cycle when compared against a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models and that failing to do so may potentially misguide future model development.
Yuan Zhang, Olivier Boucher, Philippe Ciais, Laurent Li, and Nicolas Bellouin
Geosci. Model Dev., 14, 2029–2039, https://doi.org/10.5194/gmd-14-2029-2021, https://doi.org/10.5194/gmd-14-2029-2021, 2021
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We investigated different methods to reconstruct spatiotemporal distribution of the fraction of diffuse radiation (Fdf) to qualify the aerosol impacts on GPP using the ORCHIDEE_DF land surface model. We find that climatological-averaging methods which dampen the variability of Fdf can cause significant bias in the modeled diffuse radiation impacts on GPP. Better methods to reconstruct Fdf are recommended.
Guillaume Le Gland, Sergio M. Vallina, S. Lan Smith, and Pedro Cermeño
Geosci. Model Dev., 14, 1949–1985, https://doi.org/10.5194/gmd-14-1949-2021, https://doi.org/10.5194/gmd-14-1949-2021, 2021
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We present an ecological model called SPEAD wherein various phytoplankton compete for nutrients. Phytoplankton in SPEAD are characterized by two continuously distributed traits: optimal temperature and nutrient half-saturation. Trait diversity is sustained by allowing the traits to mutate at each generation. We show that SPEAD agrees well with a more classical discrete model for only a fraction of the cost. We also identify realistic values for the mutation rates to be used in future models.
Magnus Dahler Norling, Leah Amber Jackson-Blake, José-Luis Guerrero Calidonio, and James Edward Sample
Geosci. Model Dev., 14, 1885–1897, https://doi.org/10.5194/gmd-14-1885-2021, https://doi.org/10.5194/gmd-14-1885-2021, 2021
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In order to allow researchers to quickly prototype and build models of natural systems, we have created the Mobius framework. Such models can, for instance, be used to ask questions about what the impacts of land-use changes are to water quality in a river or lake, or the response of biogeochemical systems to climate change. The Mobius framework makes it quick to build models that run fast, which enables the user to explore many different scenarios and model formulations.
Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656, https://doi.org/10.5194/gmd-14-1639-2021, https://doi.org/10.5194/gmd-14-1639-2021, 2021
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We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
Yunfei Wang, Yijian Zeng, Lianyu Yu, Peiqi Yang, Christiaan Van der Tol, Qiang Yu, Xiaoliang Lü, Huanjie Cai, and Zhongbo Su
Geosci. Model Dev., 14, 1379–1407, https://doi.org/10.5194/gmd-14-1379-2021, https://doi.org/10.5194/gmd-14-1379-2021, 2021
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This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved by considering the root water uptake, especially when vegetation was experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer in simulating ecosystem functioning.
Guy Munhoven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-447, https://doi.org/10.5194/gmd-2020-447, 2021
Revised manuscript accepted for GMD
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SolveSAPHE (Munhoven, 2013) was the first package to calculate pH reliably from any physically sensible pair of total alkalinity (AlkT) and dissolved inorganic carbon (CT) data and to do so in an autonomous and efficient way. Here, we extend it to use CO2, HCO3 or CO3 instead of CT. For each one of these pairs, the new SolveSAPHE calculates all of the possible pH values (0, 1, or 2), again without any prior knowledge of the solutions.
Lauric Cécillon, François Baudin, Claire Chenu, Bent T. Christensen, Uwe Franko, Sabine Houot, Eva Kanari, Thomas Kätterer, Ines Merbach, Folkert van Oort, Christopher Poeplau, Juan Carlos Quezada, Florence Savignac, Laure N. Soucémarianadin, and Pierre Barré
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-16, https://doi.org/10.5194/gmd-2021-16, 2021
Revised manuscript accepted for GMD
Jaber Rahimi, Expedit Evariste Ago, Augustine Ayantunde, Sina Berger, Jan Bogaert, Klaus Butterbach-Bahl, Bernard Cappelaere, Jérôme Demarty, Abdoul Aziz Diouf, Ulrike Falk, Edwin Haas, Pierre Hiernaux, David Kraus, Olivier Roupsard, Clemens Scheer, Amit Kumar Srivastava, Torbern Tagesson, and Rüdiger Grote
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-417, https://doi.org/10.5194/gmd-2020-417, 2021
Revised manuscript accepted for GMD
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West African Sahelian and Sudanian ecosystems are important regions for global carbon exchange and provide valuable food and fodder resources. Therefore, we simulate net ecosystem exchange and above-ground biomass of typical ecosystems in this region with an improved process-based bio-geochemical model, LandscapeDNDC. Carbon stocks as well as exchange rates correlated in particular with the abundance of trees. Grass and crop yields were increasing with humid climatic conditions.
Hongxing He, Per-Erik Jansson, and Annemieke I. Gärdenäs
Geosci. Model Dev., 14, 735–761, https://doi.org/10.5194/gmd-14-735-2021, https://doi.org/10.5194/gmd-14-735-2021, 2021
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This study presents the integration of the phosphorus (P) cycle into CoupModel (v6.0, Coup-CNP). The extended Coup-CNP, which explicitly considers the symbiosis between soil microbes and plant roots, enables simulations of coupled C, N, and P dynamics for terrestrial ecosystems. Simulations from the new Coup-CNP model provide strong evidence that P fluxes need to be further considered in studies of how ecosystems and C turnover react to climate change.
Theresa Boas, Heye Bogena, Thomas Grünwald, Bernard Heinesch, Dongryeol Ryu, Marius Schmidt, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 14, 573–601, https://doi.org/10.5194/gmd-14-573-2021, https://doi.org/10.5194/gmd-14-573-2021, 2021
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In this study we were able to significantly improve CLM5 model performance for European cropland sites by adding a winter wheat representation, specific plant parameterizations for important cash crops, and a cover-cropping and crop rotation subroutine to its crop module. Our modifications should be applied in future studies of CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.
Kazuyuki Saito, Hirokazu Machiya, Go Iwahana, Tokuta Yokohata, and Hiroshi Ohno
Geosci. Model Dev., 14, 521–542, https://doi.org/10.5194/gmd-14-521-2021, https://doi.org/10.5194/gmd-14-521-2021, 2021
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Soil organic carbon (SOC) and ground ice (ICE) are essential but under-documented information to assess the circum-Arctic permafrost degradation impacts. A simple numerical model of essential SOC and ICE dynamics, developed and integrated north of 50° N for 125,000 years since the last interglacial, reconstructed the history and 1° distribution of SOC and ICE consistent with current knowledge, together with successful demonstration of climatic and topographical controls on SOC evolution.
Felix Leung, Karina Williams, Stephen Sitch, Amos P. K. Tai, Andy Wiltshire, Jemma Gornall, Elizabeth A. Ainsworth, Timothy Arkebauer, and David Scoby
Geosci. Model Dev., 13, 6201–6213, https://doi.org/10.5194/gmd-13-6201-2020, https://doi.org/10.5194/gmd-13-6201-2020, 2020
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Ground-level ozone (O3) is detrimental to plant productivity and crop yield. Currently, the Joint UK Land Environment Simulator (JULES) includes a representation of crops (JULES-crop). The parameters for O3 damage in soybean in JULES-crop were calibrated against photosynthesis measurements from the Soybean Free Air Concentration Enrichment (SoyFACE). The result shows good performance for yield, and it helps contribute to understanding of the impacts of climate and air pollution on food security.
Huilin Huang, Yongkang Xue, Fang Li, and Ye Liu
Geosci. Model Dev., 13, 6029–6050, https://doi.org/10.5194/gmd-13-6029-2020, https://doi.org/10.5194/gmd-13-6029-2020, 2020
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We developed a fire-coupled dynamic vegetation model that captures the spatial distribution, temporal variability, and especially the seasonal variability of fire regimes. The fire model is applied to assess the long-term fire impact on ecosystems and surface energy. We find that fire is an important determinant of the structure and function of the tropical savanna. By changing the vegetation composition and ecosystem characteristics, fire significantly alters surface energy balance.
Virginie Moreaux, Simon Martel, Alexandre Bosc, Delphine Picart, David Achat, Christophe Moisy, Raphael Aussenac, Christophe Chipeaux, Jean-Marc Bonnefond, Soisick Figuères, Pierre Trichet, Rémi Vezy, Vincent Badeau, Bernard Longdoz, André Granier, Olivier Roupsard, Manuel Nicolas, Kim Pilegaard, Giorgio Matteucci, Claudy Jolivet, Andrew T. Black, Olivier Picard, and Denis Loustau
Geosci. Model Dev., 13, 5973–6009, https://doi.org/10.5194/gmd-13-5973-2020, https://doi.org/10.5194/gmd-13-5973-2020, 2020
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The model GO+ describes the functioning of managed forests based upon biophysical and biogeochemical processes. It accounts for the impacts of forest operations on energy, water and carbon exchanges within the soil–vegetation–atmosphere continuum. It includes versatile descriptions of management operations. Its sensitivity and uncertainty are detailed and predictions are compared with observations about mass and energy exchanges, hydrological data, and tree growth variables from different sites.
Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski
Geosci. Model Dev., 13, 5959–5971, https://doi.org/10.5194/gmd-13-5959-2020, https://doi.org/10.5194/gmd-13-5959-2020, 2020
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The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.
Christopher T. Reinhard, Stephanie L. Olson, Sandra Kirtland Turner, Cecily Pälike, Yoshiki Kanzaki, and Andy Ridgwell
Geosci. Model Dev., 13, 5687–5706, https://doi.org/10.5194/gmd-13-5687-2020, https://doi.org/10.5194/gmd-13-5687-2020, 2020
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We provide documentation and testing of new developments for the oceanic and atmospheric methane cycles in the cGENIE Earth system model. The model is designed to explore Earth's methane cycle across a wide range of timescales and scenarios, in particular assessing the mean climate state and climate perturbations in Earth's deep past. We further document the impact of atmospheric oxygen levels and ocean chemistry on fluxes of methane to the atmosphere from the ocean biosphere.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
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We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Petra Lasch-Born, Felicitas Suckow, Christopher P. O. Reyer, Martin Gutsch, Chris Kollas, Franz-Werner Badeck, Harald K. M. Bugmann, Rüdiger Grote, Cornelia Fürstenau, Marcus Lindner, and Jörg Schaber
Geosci. Model Dev., 13, 5311–5343, https://doi.org/10.5194/gmd-13-5311-2020, https://doi.org/10.5194/gmd-13-5311-2020, 2020
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The process-based model 4C has been developed to study climate impacts on forests and is now freely available as an open-source tool. This paper provides a comprehensive description of the 4C version (v2.2) for scientific users of the model and presents an evaluation of 4C. The evaluation focused on forest growth, carbon water, and heat fluxes. We conclude that 4C is widely applicable, reliable, and ready to be released to the scientific community to use and further develop the model.
Zhengang Wang, Jianxiu Qiu, and Kristof Van Oost
Geosci. Model Dev., 13, 4977–4992, https://doi.org/10.5194/gmd-13-4977-2020, https://doi.org/10.5194/gmd-13-4977-2020, 2020
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This study developed a spatially distributed carbon cycling model applicable in an eroding landscape. It includes all three carbon isotopes so that it is able to represent the carbon isotopic compositions. The model is able to represent the observations that eroding area is enriched in 13C and depleted of 14C compared to depositional area. Our simulations show that the spatial variability of carbon isotopic properties in an eroding landscape is mainly caused by the soil redistribution.
Brian N. Bailey, María A. Ponce de León, and E. Scott Krayenhoff
Geosci. Model Dev., 13, 4789–4808, https://doi.org/10.5194/gmd-13-4789-2020, https://doi.org/10.5194/gmd-13-4789-2020, 2020
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Numerous models of plant radiation interception based on a range of assumptions are available in the literature, but the importance of each assumption is not well understood. In this work, we evaluate several assumptions common in simple models of radiation interception in canopies with widely spaced plants by comparing against a detailed 3-D model. This yielded a simple model based on readily measurable parameters that could accurately predict interception for a wide range of architectures.
Julius Vira, Peter Hess, Jeff Melkonian, and William R. Wieder
Geosci. Model Dev., 13, 4459–4490, https://doi.org/10.5194/gmd-13-4459-2020, https://doi.org/10.5194/gmd-13-4459-2020, 2020
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Mostly emitted by the agricultural sector, ammonia has an important role in atmospheric chemistry. We developed a model to simulate how ammonia emissions respond to changes in temperature and soil moisture, and we evaluated agricultural ammonia emissions globally. The simulated emissions agree with earlier estimates over many regions, but the results highlight the variability of ammonia emissions and suggest that emissions in warm climates may be higher than previously thought.
Emily Kyker-Snowman, William R. Wieder, Serita D. Frey, and A. Stuart Grandy
Geosci. Model Dev., 13, 4413–4434, https://doi.org/10.5194/gmd-13-4413-2020, https://doi.org/10.5194/gmd-13-4413-2020, 2020
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Microbes drive carbon (C) and nitrogen (N) transformations in soil, and soil models have started to include explicit microbial physiology and functioning to try to reduce uncertainty in soil–climate feedbacks. Here, we add N cycling to a microbially explicit soil C model and reproduce C and N dynamics in soil during litter decomposition across a range of sites. We discuss model-generated hypotheses about soil C and N cycling and highlight the need for landscape-scale model evaluation data.
Jinxuan Chen, Christoph Gerbig, Julia Marshall, and Kai Uwe Totsche
Geosci. Model Dev., 13, 4091–4106, https://doi.org/10.5194/gmd-13-4091-2020, https://doi.org/10.5194/gmd-13-4091-2020, 2020
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One of the essential challenge for atmospheric CO2 forecasting is predicting CO2 flux variation on synoptic timescale. For CAMS CO2 forecast, a process-based vegetation model is used.
In this research we evaluate another type of model (i.e., the light-use-efficiency model VPRM), which is a data-driven approach and thus ideal for realistic estimation, on its ability of flux prediction. Errors from different sources are assessed, and overall the model is capable of CO2 flux prediction.
Yuma Sakai, Hideki Kobayashi, and Tomomichi Kato
Geosci. Model Dev., 13, 4041–4066, https://doi.org/10.5194/gmd-13-4041-2020, https://doi.org/10.5194/gmd-13-4041-2020, 2020
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Chlorophyll fluorescence is one of the energy release pathways of excess incident light in the photosynthetic process. The canopy-scale Sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf-level photosynthesis to global GPP, can be observed from satellites. We develop the three-dimensional Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind SIF emission from complex forest canopies.
Femke Lutz, Stephen Del Grosso, Stephen Ogle, Stephen Williams, Sara Minoli, Susanne Rolinski, Jens Heinke, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 13, 3905–3923, https://doi.org/10.5194/gmd-13-3905-2020, https://doi.org/10.5194/gmd-13-3905-2020, 2020
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Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Tingting Li, Yanyu Lu, Lingfei Yu, Wenjuan Sun, Qing Zhang, Wen Zhang, Guocheng Wang, Zhangcai Qin, Lijun Yu, Hailing Li, and Ran Zhang
Geosci. Model Dev., 13, 3769–3788, https://doi.org/10.5194/gmd-13-3769-2020, https://doi.org/10.5194/gmd-13-3769-2020, 2020
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Reliable models are required to estimate global wetland CH4 emissions, which are the largest and most uncertain source of atmospheric CH4. This paper evaluated CH4MODwetland and TEM models against CH4 measurements from different continents and wetland types. Based on best-model performance, we estimated 117–125 Tg yr−1 of global CH4 emissions from wetlands for the period 2000–2010. Efforts should be made to reduce estimate uncertainties for different wetland types and regions.
Jennifer E. Dentith, Ruza F. Ivanovic, Lauren J. Gregoire, Julia C. Tindall, and Laura F. Robinson
Geosci. Model Dev., 13, 3529–3552, https://doi.org/10.5194/gmd-13-3529-2020, https://doi.org/10.5194/gmd-13-3529-2020, 2020
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We have added a new tracer (13C) into the ocean of the FAMOUS climate model to study large-scale circulation and the marine carbon cycle. The model captures the large-scale spatial pattern of observations but the simulated values are consistently higher than observed. In the first instance, our new tracer is therefore useful for recalibrating the physical and biogeochemical components of the model.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
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Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Yifei Dai, Long Cao, and Bin Wang
Geosci. Model Dev., 13, 3119–3144, https://doi.org/10.5194/gmd-13-3119-2020, https://doi.org/10.5194/gmd-13-3119-2020, 2020
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NESM v3 is one of the CMIP6 registered Earth system models. We evaluate its ocean carbon cycle component and present its present-day and future oceanic CO2 uptake based on the CMIP6 historical and SSP5–8.5 scenarios. We hope that this paper can serve as a documentation of the marine biogeochemical cycle in NESM v3. Also, the model defects found and their underlying causes analyzed in this paper could help users and further model development.
Joe R. Melton, Vivek K. Arora, Eduard Wisernig-Cojoc, Christian Seiler, Matthew Fortier, Ed Chan, and Lina Teckentrup
Geosci. Model Dev., 13, 2825–2850, https://doi.org/10.5194/gmd-13-2825-2020, https://doi.org/10.5194/gmd-13-2825-2020, 2020
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We transitioned the CLASS-CTEM land surface model to an open-source community model format by modernizing the code base to make the model easier to use and understand, providing a complete software environment to run the model within, developing a benchmarking suite for model evaluation, and creating an infrastructure to support community involvement. The new model, the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC), is now available for the community to use and develop.
Giovanni Denaro, Daniela Salvagio Manta, Alessandro Borri, Maria Bonsignore, Davide Valenti, Enza Quinci, Andrea Cucco, Bernardo Spagnolo, Mario Sprovieri, and Andrea De Gaetano
Geosci. Model Dev., 13, 2073–2093, https://doi.org/10.5194/gmd-13-2073-2020, https://doi.org/10.5194/gmd-13-2073-2020, 2020
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The HR3DHG (high-resolution 3D mercury model) investigates the spatiotemporal behavior, in seawater and marine sediments, of three mercury species (elemental, inorganic, and organic mercury) in a highly polluted marine environment (Augusta Bay, southern Italy). The model shows fair agreement with the experimental data collected during six different oceanographic cruises and can possibly be used for a detailed exploration of the effects of climate change on mercury distribution.
Elisa Lovecchio and Timothy M. Lenton
Geosci. Model Dev., 13, 1865–1883, https://doi.org/10.5194/gmd-13-1865-2020, https://doi.org/10.5194/gmd-13-1865-2020, 2020
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We present here the newly developed BPOP box model. BPOP is aimed at studying the impact of large-scale changes in the biological pump, i.e. the cycle of production, export and remineralization of the marine organic matter, on the nutrient and oxygen concentrations in the shelf and open ocean. This model has been developed to investigate the global consequences of the evolution of larger and heavier phytoplankton cells but can be applied to a variety of past and future case studies.
Benjamin D. Stocker, Han Wang, Nicholas G. Smith, Sandy P. Harrison, Trevor F. Keenan, David Sandoval, Tyler Davis, and I. Colin Prentice
Geosci. Model Dev., 13, 1545–1581, https://doi.org/10.5194/gmd-13-1545-2020, https://doi.org/10.5194/gmd-13-1545-2020, 2020
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Estimating terrestrial photosynthesis relies on satellite data of vegetation cover and models simulating the efficiency by which light absorbed by vegetation is used for CO2 assimilation. This paper presents the P-model, a light use efficiency model derived from a carbon–water optimality principle, and evaluates its predictions of ecosystem-level photosynthesis against globally distributed observations. The model is implemented and openly accessible as an R package (rpmodel).
Louis de Wergifosse, Frédéric André, Nicolas Beudez, François de Coligny, Hugues Goosse, François Jonard, Quentin Ponette, Hugues Titeux, Caroline Vincke, and Mathieu Jonard
Geosci. Model Dev., 13, 1459–1498, https://doi.org/10.5194/gmd-13-1459-2020, https://doi.org/10.5194/gmd-13-1459-2020, 2020
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Given their key role in the simulation of climate impacts on tree growth, phenological and water balance processes must be integrated in models simulating forest dynamics under a changing environment. Here, we describe these processes integrated in HETEROFOR, a model accounting simultaneously for the functional, structural and spatial complexity to explore the forest response to forestry practices. The model evaluation using phenological and soil water content observations is quite promising.
Arjun Chakrawal, Anke M. Herrmann, John Koestel, Jerker Jarsjö, Naoise Nunan, Thomas Kätterer, and Stefano Manzoni
Geosci. Model Dev., 13, 1399–1429, https://doi.org/10.5194/gmd-13-1399-2020, https://doi.org/10.5194/gmd-13-1399-2020, 2020
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Soils are heterogeneous, which results in a nonuniform spatial distribution of substrates and the microorganisms feeding on them. Our results show that the variability in the spatial distribution of substrates and microorganisms at the pore scale is crucial because it affects how fast substrates are used by microorganisms and thus the decomposition rate observed at the soil core scale. This work provides a methodology to include microscale heterogeneity in soil carbon cycling models.
Victoria Naipal, Ronny Lauerwald, Philippe Ciais, Bertrand Guenet, and Yilong Wang
Geosci. Model Dev., 13, 1201–1222, https://doi.org/10.5194/gmd-13-1201-2020, https://doi.org/10.5194/gmd-13-1201-2020, 2020
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In this study we present the Carbon Erosion DYNAMics model (CE-DYNAM) that links sediment dynamics resulting from water erosion with the soil carbon cycle along a cascade of hillslopes, floodplains, and rivers. The model can simulate the removal of soil and carbon from eroding areas and their destination at regional scale. We calibrated and validated the model for the Rhine catchment, and we show that soil erosion is a potential large net carbon sink over the period 1850–2005.
Kelly Kearney, Albert Hermann, Wei Cheng, Ivonne Ortiz, and Kerim Aydin
Geosci. Model Dev., 13, 597–650, https://doi.org/10.5194/gmd-13-597-2020, https://doi.org/10.5194/gmd-13-597-2020, 2020
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We describe an ecosystem model for the Bering Sea. Biological components in the Bering Sea can be found in the water column, on and within the bottom sediments, and within the porous lower layer of seasonal sea ice. This model simulates the exchange of material between nutrients and plankton within all three environments. Here, we thoroughly document the model and assess its skill in capturing key biophysical features across the Bering Sea.
Matthias J. R. Speich, Massimiliano Zappa, Marc Scherstjanoi, and Heike Lischke
Geosci. Model Dev., 13, 537–564, https://doi.org/10.5194/gmd-13-537-2020, https://doi.org/10.5194/gmd-13-537-2020, 2020
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Climate change is expected to substantially affect natural processes, and simulation models are a valuable tool to anticipate these changes. In this study, we combine two existing models that each describe one aspect of the environment: forest dynamics and the terrestrial water cycle. The coupled model better described observed patterns in vegetation structure. We also found that including the effect of water availability on tree height and rooting depth improved the model.
Simon P. K. Bowring, Ronny Lauerwald, Bertrand Guenet, Dan Zhu, Matthieu Guimberteau, Pierre Regnier, Ardalan Tootchi, Agnès Ducharne, and Philippe Ciais
Geosci. Model Dev., 13, 507–520, https://doi.org/10.5194/gmd-13-507-2020, https://doi.org/10.5194/gmd-13-507-2020, 2020
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In this second part of the study, we performed simulations of the carbon and water budget of the Lena catchment with the land surface model ORCHIDEE MICT-LEAK, enabled to simulate dissolved organic carbon (DOC) production in soils and its transport and fate in high-latitude inland waters. We compare simulations using this model to existing data sources to show that it is capable of reproducing dissolved carbon fluxes of potentially great importance for the future of the global permafrost.
Carme Font, Francesco Bregoli, Vicenç Acuña, Sergi Sabater, and Rafael Marcé
Geosci. Model Dev., 12, 5213–5228, https://doi.org/10.5194/gmd-12-5213-2019, https://doi.org/10.5194/gmd-12-5213-2019, 2019
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GLOBAL-FATE is an open-source, multiplatform, and flexible model that simulates the fate of pharmaceutical-like compounds in the global river network. The model considers the consumption of pharmaceuticals by humans, differentiates between pharmaceutical load treated in wastewater treatment plants from that directly delivered to streams and rivers, and integrates lakes and reservoirs in calculations. GLOBAL-FATE is a powerful tool for pollutant impact studies at the global scale.
Luke Gregor, Alice D. Lebehot, Schalk Kok, and Pedro M. Scheel Monteiro
Geosci. Model Dev., 12, 5113–5136, https://doi.org/10.5194/gmd-12-5113-2019, https://doi.org/10.5194/gmd-12-5113-2019, 2019
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The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO2). Historically sparse ship-based measurements of surface ocean CO2 make direct estimates of CO2 exchange changes unreliable. We introduce a machine-learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine-learning algorithms' capability.
Markus Drüke, Matthias Forkel, Werner von Bloh, Boris Sakschewski, Manoel Cardoso, Mercedes Bustamante, Jürgen Kurths, and Kirsten Thonicke
Geosci. Model Dev., 12, 5029–5054, https://doi.org/10.5194/gmd-12-5029-2019, https://doi.org/10.5194/gmd-12-5029-2019, 2019
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This work shows the successful application of a systematic model–data integration setup, as well as the implementation of a new fire danger formulation, in order to optimize a process-based fire-enabled dynamic global vegetation model. We have demonstrated a major improvement in the fire representation within LPJmL4-SPITFIRE in terms of the spatial pattern and the interannual variability of burned area in South America as well as in the modelling of biomass and the distribution of plant types.
Nicolas Vuichard, Palmira Messina, Sebastiaan Luyssaert, Bertrand Guenet, Sönke Zaehle, Josefine Ghattas, Vladislav Bastrikov, and Philippe Peylin
Geosci. Model Dev., 12, 4751–4779, https://doi.org/10.5194/gmd-12-4751-2019, https://doi.org/10.5194/gmd-12-4751-2019, 2019
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In this research, we present a new version of the global terrestrial ecosystem model ORCHIDEE in which carbon and nitrogen cycles are coupled. We evaluate its skills at simulating primary production at 78 sites and at a global scale. Based on a set of additional simulations in which carbon and nitrogen cycles are coupled and uncoupled, we show that the functional responses of the model with carbon–nitrogen interactions better agree with our current understanding of photosynthesis.
Tea Thum, Silvia Caldararu, Jan Engel, Melanie Kern, Marleen Pallandt, Reiner Schnur, Lin Yu, and Sönke Zaehle
Geosci. Model Dev., 12, 4781–4802, https://doi.org/10.5194/gmd-12-4781-2019, https://doi.org/10.5194/gmd-12-4781-2019, 2019
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To predict the response of the vegetation to climate change, we need global models that describe the relevant processes taking place in the vegetation. Recently, we have obtained more in-depth understanding of vegetation processes and the role of nutrients in the biogeochemical cycles. We have developed a new global vegetation model that includes carbon, water, nitrogen, and phosphorus cycles. We show that the model is successful in evaluation against a wide range of observations.
Dave van Wees and Guido R. van der Werf
Geosci. Model Dev., 12, 4681–4703, https://doi.org/10.5194/gmd-12-4681-2019, https://doi.org/10.5194/gmd-12-4681-2019, 2019
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For this paper, a novel high spatial-resolution fire emission model based on the Global Fire Emissions Database (GFED) modelling framework was developed and compared to a coarser-resolution version of the same model. Our findings highlight the importance of fine spatial resolution when modelling global-scale fire emissions, especially considering the comparison of model pixels to individual field measurements and the model representation of heterogeneity in the landscape.
Yoshiki Kanzaki, Bernard P. Boudreau, Sandra Kirtland Turner, and Andy Ridgwell
Geosci. Model Dev., 12, 4469–4496, https://doi.org/10.5194/gmd-12-4469-2019, https://doi.org/10.5194/gmd-12-4469-2019, 2019
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This paper provides eLABS, an extension of the lattice-automaton bioturbation simulator LABS. In our new model, the benthic animal behavior interacts and changes dynamically with oxygen and organic matter concentrations and the water flows caused by benthic animals themselves, in a 2-D marine-sediment grid. The model can address the mechanisms behind empirical observations of bioturbation based on the interactions between physical, chemical and biological aspects of marine sediment.
Cited articles
Aksnes, D. L. and Egge, J. K.: A theoretical model for nutrient uptake in phytoplankton, Mar. Ecol.-Prog. Ser., 70, 65–72, https://doi.org/10.3354/meps070065, 1991.
Allen, J. I. and Somerfield, P. J.: A multivariate approach to model skill assessment, J. Marine Syst., 76, 83–94, https://doi.org/10.1016/j.jmarsys.2008.05.009, 2009.
Allen, J. I., Somerfield, P. J., and Siddonr, J.: Primary and bacterial production in the Mediterranean Sea: a modelling study, J. Marine Syst., 33–34, 473–495, https://doi.org/10.1016/S0924-7963(02)00072-6, 2002.
Allen, J. I., Blackford, J. C., Holt, J., Proctor, R., Ashworth, M., and Siddorn, J.: A highly spatially resolved ecosystem model for the North West European Continental Shelf, Sarsia, 86, 423–440, 2001.
Allen, J. I., Somerfield, P. J., and Gilbert, F. J.: Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models, in: Contributions from Advances in Marine Ecosystem Modelling Research, 27–29 June 2005, Plymouth, UK AMEMR, 64, 3–14, available at: http://www.sciencedirect.com/science/article/pii/S0924796306001035 (last access: 14 August 2015), 2007.
Anderson, T. R., Hessen, D. O., Mitra, A., Mayor, D. J., and Yool, A.: Sensitivity of secondary production and export flux to choice of trophic transfer formulation, J. Marine Syst., 125, 41–53, https://doi.org/10.1016/j.jmarsys.2012.09.008, 2013.
Artioli, Y., Blackford, J. C., Butenschön, M., Holt, J. T., Wakelin, S. L., Thomas, H., Borges, A. V., and Allen, J. I.: The carbonate system in the North Sea: sensitivity and model validation, J. Marine Syst., 102–104, 1–13, https://doi.org/10.1016/j.jmarsys.2012.04.006, 2012.
Artioli, Y., Blackford, J. C., Nondal, G., Bellerby, R. G. J., Wakelin, S. L., Holt, J. T., Butenschön, M., and Allen, J. I.: Heterogeneity of impacts of high CO2 on the North Western European Shelf, Biogeosciences, 11, 601–612, https://doi.org/10.5194/bg-11-601-2014, 2014a.
Artioli, Y., Blackford, J. C., Nondal, G., Bellerby, R. G. J., Wakelin, S. L., Holt, J. T., Butenschön, M., and Allen, J. I.: Heterogeneity of impacts of high CO2 on the North Western European Shelf, Biogeosciences, 11, 601–612, https://doi.org/10.5194/bg-11-601-2014, 2014b.
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.: An ecosystem model of the global ocean including Fe, Si, P colimitations, Global Biogeochem. Cy., 17, 1–23, https://doi.org/10.1029/2001GB001745, 2003.
Barange, M., Merino, G., Blanchard, J. L., Scholtens, J., Harle, J., Allison, E. H., Allen, J. I., Holt, J., and Jennings, S.: Impacts of climate change on marine ecosystem production in societies dependent on fisheries, Nat. Clim. Change, 4, 211–216, https://doi.org/10.1038/nclimate2119, 2014.
Baretta, J., Ruardij, P., Billings, W. D., Golley, F., Lange, O. L., Olson, J. S., and Remmert, H. (Eds.): Tidal Flat Estuaries, vol. 71 of Ecological Studies, Springer Berlin Heidelberg, Berlin, Heidelberg, Germany, available at: http://link.springer.com/10.1007/978-3-642-73753-4 (last access: 14 August 2015), 1988.
Baretta, J. W.: Preface, J. Sea Res., 38, 169–171, https://doi.org/10.1016/S1385-1101(97)00054-3, 1997.
Baretta, J. W., Ebenhöh, W., and Ruardij, P.: The European regional seas ecosystem model, a complex marine ecosystem model, Neth. J. Sea Res., 33, 233–246, https://doi.org/10.1016/0077-7579(95)90047-0, 1995.
Baretta-Bekker, H., Bot, P., Prins, T., and Zevenboom, W.: Report on the second application of the OSPAR Comprehensive Procedure to the Dutch marine waters, Tech. rep., OSPAR Comission, London, 2008.
Baretta-Bekker, J. G.: Note of the editor, Neth. J. Sea Res., 33, 230–231, https://doi.org/10.1016/0077-7579(95)90046-2, 1995.
Baretta-Bekker, J. G., Baretta, J. W., and Rasmussen, E. K.: The microbial food web in the European Regional Seas Ecosystem Model, Neth. J. Sea Res., 33, 363–379, 1995.
Baretta-Bekker, J. G., Baretta, J. W., and Ebenhöh, W.: Microbial dynamics in the marine ecosystem model ERSEM II with decoupled carbon assimilation and nutrient uptake, J. Sea Res., 38, 195–211, https://doi.org/10.1016/S1385-1101(97)00052-X, 1997.
Bellerby, R. G. J., Olsen, A., Furevik, T., and Anderson, L. G.: Response of the surface ocean CO2 system in the Nordic Seas and northern North Atlantic to climate change, in: The Nordic Seas: An Integrated Perspective, edited by: Drange, H., Dokken, T., Furevik, T., Gerdes, R., and Berger, W., American Geophysical Union, Washington, D.C., USA, 189–197, https://doi.org/10.1029/158GM13, 2005.
Blackford, J., Artioli, Y., Kelly-Gerreyn, B., Martin, A., Tyrrell, T., and Somavilla, R.: Sub-model acidification-sensitive calcification rate, including user guide, Project Report, D2.2, EC FP7 MEECE – 212085, Plymouth Marine Laboratory, Plymouth, UK, 2010.
Blackford, J. C.: An analysis of benthic biological dynamics in a North Sea ecosystem model, J. Sea Res., 38, 213–230, https://doi.org/10.1016/S1385-1101(97)00044-0, 1997.
Blackford, J. C. and Burkill, P. H.: Planktonic community structure and carbon cycling in the Arabian Sea as a result of monsoonal forcing: the application of a generic model, J. Marine Syst., 36, 239–267, https://doi.org/10.1016/S0924-7963(02)00182-3, 2002.
Blackford, J. C. and Gilbert, F. J.: pH variability and CO2 induced acidification in the North Sea, Symposium on Advances in Marine Ecosystem Modelling Research, Plymouth, England, 27–29 June 2005, J. Marine Syst., 64, 229–241, https://doi.org/10.1016/j.jmarsys.2006.03.016, 2007.
Blackford, J. C., Allen, J. I., and Gilbert, F. J.: Ecosystem dynamics at six contrasting sites: a generic modelling study, J. Marine Syst., 52, 191–215, 2004.
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013.
Borges, A. V. and Frankignoulle, M.: Daily and seasonal variations of the partial pressure of CO2 in surface seawater along Belgian and southern Dutch coastal areas, J. Marine Syst., 19, 251–266, https://doi.org/10.1016/S0924-7963(98)00093-1, 1999.
Broecker, W. S. and Peng, T. H.: Tracers in the sea, Eldigio Press, New York, USA, 1982.
Broekhuizen, N., Heath, M. R., Hay, S. J., and Gurney, W. S. C.: Modelling the dynamics of the North Sea's Mesozooplankton, Neth. J. Sea Res., 33, 381–406, https://doi.org/10.1016/0077-7579(95)90054-3, 1995.
Brzezinski, M. A.: The Si : C : N ratio of marine diatoms: interspecific variability and the effect of some environmental variables, J. Phycol., 36, 87–96, https://doi.org/10.1111/j.0022-3646.1985.00347.x, 1985.
Burchard, H., Deleersnijder, E., and Meister, A.: A high-order conservative Patankar-type discretisation for stiff systems of production–destruction equations, Appl. Numer. Math., 47, 1–30, https://doi.org/10.1016/S0168-9274(03)00101-6, 2003.
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. Marine Syst., 61, 180–211, 2006.
Butenschön, M., Zavatarelli, M., and Vichi, M.: Sensitivity of a marine coupled physical biogeochemical model to time resolution, integration scheme and time splitting method, Ocean Model., 52–53, 36–53, https://doi.org/10.1016/j.ocemod.2012.04.008, 2012.
Chesson, J.: The estimation and analysis of preference and its relatioship to foraging models, Ecology, 64, 1297–1304, https://doi.org/10.2307/1937838, 1983.
Chisholm, S.: Phytoplankton size, in: Primary Productivity and Biogeochemical Cycles in the Sea, edited by: Falkowski, P. and Woodhead, A., Plenum Press, New York, 213–237, 1992.
Chust, G., Allen, J. I., Bopp, L., Schrum, C., Holt, J., Tsiaras, K., Zavatarelli, M., Chifflet, M., Cannaby, H., Dadou, I., Daewel, U., Wakelin, S. L., Machu, E., Pushpadas, D., Butenschon, M., Artioli, Y., Petihakis, G., Smith, C., Garaçon, V., Goubanova, K., Le Vu, B., Fach, B. A., Salihoglu, B., Clementi, E., and Irigoien, X.: Biomass changes and trophic amplification of plankton in a warmer ocean, Global Change Biol., 20, 2124–2139, https://doi.org/10.1111/gcb.12562, 2014.
Daszykowski, M., Kaczmarek, K., Heyden, Y. V., and Walczak, B.: Robust statistics in data analysis – a review: basic concepts, Chemometr. Intell. Lab., 85, 203–219, https://doi.org/10.1016/j.chemolab.2006.06.016, 2007.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., 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.
de Mora, L., Butenschön, M., and Allen, J. I.: How should sparse marine in situ measurements be compared to a continuous model: an example, Geosci. Model Dev., 6, 533–548, https://doi.org/10.5194/gmd-6-533-2013, 2013.
Dickson, A. G.: Thermodynamics of the dissociation of boric acid in synthetic seawater from 273.15 to 318.15 K, Deep-Sea Res., 37, 755–766, https://doi.org/10.1016/0198-0149(90)90004-F, 1990.
Dickson, A. G., Sabine, C. L., and Christian, J. R.: Guide to Best Practices for Ocean CO2 Measurements, PICES Special Publication 3, PICES, Sidney, British Columbia, available at: http://cdiac.ornl.gov/ftp/oceans/Handbook_2007/Guide_all_in_one.pdf (last access: 14 August 2015), 2007.
Doney, S. C., Ruckelshaus, M., Emmett Duffy, J., Barry, J. P., Chan, F., English, C. A., Galindo, H. M., Grebmeier, J. M., Hollowed, A. B., Knowlton, N., Polovina, J., Rabalais, N. N., Sydeman, W. J., and Talley, L. D.: Climate change impacts on marine ecosystems, Annu. Rev. Mar. Sci., 4, 11–37, https://doi.org/10.1146/annurev-marine-041911-111611, 2012.
Droop, M. R.: The nutrient status of alga cells in continous culture, J. Mar. Biol. Assoc. UK, 54, 825–855, https://doi.org/10.1016/0924-7963(94)00031-6, 1974.
Dugdale, R. C., Wilkerson, F. P., and Minas, H. J.: The role of a silicate pump in driving new production, Deep-Sea Res. Pt. I, 42, 697–719, https://doi.org/10.1016/0967-0637(95)00015-X, 1995.
Ebenhöh, W., Kohlmeier, C., and Radford, P. J.: The benthic biological submodel in the European regional seas ecosystem model, Neth. J. Sea Res., 33, 423–452, https://doi.org/10.1016/0077-7579(95)90056-X, 1995.
Edwards, K. P., Barciela, R., and Butenschön, M.: Validation of the NEMO-ERSEM operational ecosystem model for the North West European Continental Shelf, Ocean Sci., 8, 983–1000, https://doi.org/10.5194/os-8-983-2012, 2012.
Falkowski, P. G. and Raven, J. A.: Aquatic photosynthesis, Princeton University Press, Princeton, USA, 2007.
Fasham, M. J. R., Ducklow, H. W., and McKelvie, S. M.: A nitrogen-based model of plankton dynamics in the oceanic mixed layer, J. Mar. Res., 48, 591–639, 1990.
Fennel, W.: A model of the yearly cycle of nutrients and plankton in the Baltic Sea, J. Marine Syst., 6, 313–329, https://doi.org/10.1016/0924-7963(94)00031-6, 1995.
Finkel, Z. V., Beardall, J., Flynn, K. J., Quigg, A., Rees, T. A. V., and Raven, J. A.: Phytoplankton in a changing world: cell size and elemental stoichiometry, J. Plankton Res., 32, 119–137, https://doi.org/10.1093/plankt/fbp098, 2010.
Flynn, K. J.: Ecological modelling in a sea of variable stoichiometry: dysfunctionality and the legacy of Redfield and Monod, Prog. Oceanogr., 84, 52–65, https://doi.org/10.1016/j.pocean.2009.09.006, 2010.
Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W.: Emergent biogeography of microbial communities in a model ocean, Science, 315, 1843–1846, https://doi.org/10.1126/science.1138544, 2007.
Franks, P. J. S.: Planktonic ecosystem models: perplexing parameterizations and a failure to fail, J. Plankton Res., 31, 1299–1306, https://doi.org/10.1093/plankt/fbp069, 2009.
Gehlen, M., Gangstø, R., Schneider, B., Bopp, L., Aumont, O., and Ethe, C.: The fate of pelagic CaCO3 production in a high CO2 ocean: a model study, Biogeosciences, 4, 505–519, https://doi.org/10.5194/bg-4-505-2007, 2007.
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: A dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and chlorophyll a : carbon ratio to light, nutrient limitation and temperature, Mar. Ecol-Prog. Ser., 148, 187–200, https://doi.org/10.3354/meps148187, 1997.
Gentleman, W., Leising, A., Frost, B., Strom, S., and Murray, J.: Functional responses for zooplankton feeding on multiple resources: a review of assumptions and biological dynamics, Deep-Sea Res. Pt. II, 50, 2847–2875, https://doi.org/10.1016/j.dsr2.2003.07.001, 2003.
Gismervik, I.: Implications of zooplankton stoichiometry on distribution of N and P among planktonic size fractions, J. Plankton Res., 19, 343–356, https://doi.org/10.1093/plankt/19.3.343, 1997.
Glibert, P. M., Icarus Allen, J., Artioli, Y., Beusen, A., Bouwman, L., Harle, J., Holmes, R., and Holt, J.: Vulnerability of coastal ecosystems to changes in harmful algal bloom distribution in response to climate change: projections based on model analysis, Global Change Biol., 20, 3845–3858, https://doi.org/10.1111/gcb.12662, 2014.
Greenwood, N., Parker, E. R., Fernand, L., Sivyer, D. B., Weston, K., Painting, S. J., Kröger, S., Forster, R. M., Lees, H. E., Mills, D. K., and Laane, R. W. P. M.: Detection of low bottom water oxygen concentrations in the North Sea; implications for monitoring and assessment of ecosystem health, Biogeosciences, 7, 1357–1373, https://doi.org/10.5194/bg-7-1357-2010, 2010.
Hansell, D. A.: Recalcitrant dissolved organic carbon fractions, Annu. Rev. Mar. Sci., 5, 421–445, https://doi.org/10.1146/annurev-marine-120710-100757, 2013.
Harris, R.: The L4 time-series: the first 20 years, J. Plankton Res., 32, 577–583, https://doi.org/10.1093/plankt/fbq021, 2010.
Heath, M., Roberston, W., Mardaljevic, J., and Gurney, W. S. G.: Modelling the population dynamics of Calanus in the Fair Isle current of northern Scotland, J. Sea Res., 38, 381–412, https://doi.org/10.1016/S1385-1101(97)00047-6, 1997.
Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., Isada, T., Howell, E., Hashioka, T., Noguchi-Aita, M., and Yamanaka, Y.: Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types, Biogeosciences, 8, 311–327, https://doi.org/10.5194/bg-8-311-2011, 2011.
Holt, J., Butenschön, M., Wakelin, S. L., Artioli, Y., and Allen, J. I.: Oceanic controls on the primary production of the northwest European continental shelf: model experiments under recent past conditions and a potential future scenario, Biogeosciences, 9, 97–117, https://doi.org/10.5194/bg-9-97-2012, 2012.
Holt, J. T. and James, I. D.: An s coordinate density evolving model of the northwest European continental shelf: 1. Model description and density structure, J. Geophys. Res.-Oceans, 106, 14015–14034, https://doi.org/10.1029/2000JC000304, 2001.
ICES: ICES Dataset on Ocean Hydrography, The International Council for the Exploration of the SeaCopenhagen, Copenhagen, Denmark, 2009.
Ingri, N., Kakolowicz, W., Sillén, L. G., and Warnqvist, B.: High-speed computers as a supplement to graphical methods V1: Haltafall, a general program for calculating the composition of equilibrium mixtures, Talanta, 14, 1261–1286, https://doi.org/10.1016/0039-9140(67)80203-0, 1967.
Jassby, A. D. and Platt, T.: Mathematical formulation of relationship between photosynthesis and light for phytoplankton, Limnol. Oceanogr., 21, 540–547, https://doi.org/10.4319/lo.1976.21.4.0540, 1976.
Jiao, N., Herndl, G. J., Hansell, D. A., Benner, R., Kattner, G., Wilhelm, S. W., Kirchman, D. L., Weinbauer, M. G., Luo, T., Chen, F., and Azam, F.: Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean, Nat. Rev. Microbiol., 8, 593–599, https://doi.org/10.1038/nrmicro2386, 2010.
Jiao, N., Robinson, C., Azam, F., Thomas, H., Baltar, F., Dang, H., Hardman-Mountford, N. J., Johnson, M., Kirchman, D. L., Koch, B. P., Legendre, L., Li, C., Liu, J., Luo, T., Luo, Y.-W., Mitra, A., Romanou, A., Tang, K., Wang, X., Zhang, C., and Zhang, R.: Mechanisms of microbial carbon sequestration in the ocean – future research directions, Biogeosciences, 11, 5285–5306, https://doi.org/10.5194/bg-11-5285-2014, 2014.
Jolliff, J. K., Kindle, J. C., Shulman, I., Penta, B., Friedrichs, M. A. M., Helber, R., and Arnone, R. A.: Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment, J. Marine Syst., 76, 64–82, https://doi.org/10.1016/j.jmarsys.2008.05.014, 2009.
Keir, R. S.: The dissolution kinetics of biogenic calcium carbonates in seawater, Geochim. Cosmochim. Acta, 44, 241–252, https://doi.org/10.1016/0016-7037(80)90135-0, 1980.
Kleypas, J. A., Buddemeier, R., W., Archer, D., Gattuso, J.-P., Langdon, C., and Opdyke, B., N. : Geochemical consequences of increased atmospheric carbon dioxide on coral reefs, Science, 284, 118–120, https://doi.org/10.1126/science.284.5411.118, 1999.
Kohlmeier, C.: Modellierung des Spiekerooger Rückseitenwatts mit einem gekoppelten Euler–Lagrange-Modell auf der Basis von ERSEM, PhD thesis, Carl von Ossietzky Universität, Oldenburg, 2004.
Kwiatkowski, L., Yool, A., Allen, J. I., Anderson, T. R., Barciela, R., Buitenhuis, E. T., Butenschön, M., Enright, C., Halloran, P. R., Le Quéré, C., de Mora, L., Racault, M.-F., Sinha, B., Totterdell, I. J., and Cox, P. M.: iMarNet: an ocean biogeochemistry model intercomparison project within a common physical ocean modelling framework, Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, 2014.
Lee, K., Tong, L. T., Millero, F. J., Sabine, C. L., Dickson, A. G., Goyet, C., Park, G.-H., Wanninkhof, R., Feely, R. A., and Key, R. M.: Global relationships of total alkalinity with salinity and temperature in surface waters of the world's oceans, Geophys. Res. Lett., 33, L19605, https://doi.org/10.1029/2006GL027207, 2006.
Lee, Z., Du, K., and Arnone, R.: A model for the diffuse attenuation coefficient of downwelling irradiance, J. Geophys. Res., 110, 1–10, https://doi.org/10.1029/2004JC002275, 2005.
Leeuwen, S. M. v., Molen, J. v. d., Ruardij, P., Fernand, L., and Jickells, T.: Modelling the contribution of deep chlorophyll maxima to annual primary production in the North Sea, Biogeochemistry, 113, 137–152, https://doi.org/10.1007/s10533-012-9704-5, 2012.
Lenhart, H.-J., Mills, D. K., Baretta-Bekker, H., Leeuwen, S. M. v., Molen, J. v. d., Baretta, J. W., Blaas, M., Desmit, X., Kühn, W., Lacroix, G., Los, H. J., Ménesguen, A., Neves, R., Proctor, R., Ruardij, P., Skogen, M. D., Vanhoutte-Brunier, A., Villars, M. T., and Wakelin, S. L.: Predicting the consequences of nutrient reduction on the eutrophication status of the North Sea, J. Marine Syst., 81, 148–170, https://doi.org/10.1016/j.jmarsys.2009.12.014, 2010.
Litchman, E., Klausmeier, C. A., Schofield, O. M., and Falkowski, P. G.: The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level, Ecol. Lett., 10, 1170–1181, https://doi.org/10.1111/j.1461-0248.2007.01117.x, 2007.
Litchman, E., Pinto, P. d. T., Klausmeier, C. A., Thomas, M. K., and Yoshiyama, K.: Linking traits to species diversity and community structure in phytoplankton, Hydrobiologia, 653, 15–28, https://doi.org/10.1007/s10750-010-0341-5, 2010.
Madec, G.: NEMO Ocean Engine, Note du Pole de modélisation, Institut Pierre-Simon Laplace (IPSL), No. 28, Paris, France, 2008.
Mehrbach, C., Culberson, C. H., Hawley, J. E., and Pytkowicx, R. M.: Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure, Limnol. Oceanogr., 18, 897–907, https://doi.org/10.4319/lo.1973.18.6.0897, 1973.
Millero, F. J.: Thermodynamics of the carbon dioxide system in the oceans, Geochim. Cosmochim. Acta, 59, 661–677, https://doi.org/10.1016/0016-7037(94)00354-O, 1995.
Millero, F. J., Lee, K., and Roche, M.: Distribution of alkalinity in the surface waters of the major oceans, Mar. Chem., 60, 111–130, https://doi.org/10.1016/S0304-4203(97)00084-4, 1998.
Moore, C. M., Mills, M. M., Arrigo, K. R., Berman-Frank, I., Bopp, L., Boyd, P. W., Galbraith, E. D., Geider, R. J., Guieu, C., Jaccard, S. L., Jickells, T. D., La Roche, J., Lenton, T. M., Mahowald, N. M., Maranon, E., Marinov, I., Moore, J. K., Nakatsuka, T., Oschlies, A., Saito, M. A., Thingstad, T. F., Tsuda, A., and Ulloa, O.: Processes and patterns of oceanic nutrient limitation, Nat. Geosci., 6, 701–710, https://doi.org/10.1038/ngeo1765, 2013.
Nightingale, P. D., Malin, G., Law, C. S., Watson, A. J., Liss, P. S., Liddicoat, M. I., Boutin, J., and Upstill-Goddard, R. C.: In situ evaluation of air-sea gas exchange parameterizations using novel conservative and volatile tracers, Global Biogeochem. Cy., 14, 373–387, https://doi.org/10.1029/1999GB900091, 2000.
Paasche, E.: Roles of nitrogen and phosphorus in coccolith formation in Emiliania huxleyi (Prymnesiophyceae), Eur. J. Phycol., 33, 33–42, 1998.
Patankar, S. V.: Numerical Heat Transfer and Fluid Flow, McGraw-Hill, New York, USA, 1980.
Platt, T., Harrison, W. G., Irwin, B., Horne, E. P., and Gallegos, C. L.: Photosynthesis and photoadaptation of marine phytoplankton in the arctic, Deep-Sea Res., 29, 1159–1170, https://doi.org/10.1016/0198-0149(82)90087-5, 1982.
Polimene, L., Allen, J. I., and Zavatarelli, M.: Model of interactions between dissolved organic carbon and bacteria in marine systems, Aquat. Microb. Ecol., 43, 127–138, https://doi.org/10.3354/ame043127, 2006.
Polimene, L., Pinardi, N., Zavatarelli, M., Allen, J. I., Giani, M., and Vichi, M.: A numerical simulation study of dissolved organic carbon accumulation in the northern Adriatic Sea, J. Geophys. Res.-Oceans, 112, C03S20, https://doi.org/10.1029/2006JC003529, 2007.
Polimene, L., Archer, S. D., Butenschön, M., and Allen, J. I.: A mechanistic explanation of the Sargasso Sea DMS "summer paradox", Biogeochemistry, 110, 243–255, https://doi.org/10.1007/s10533-011-9674-z, 2012.
Polimene, L., Brunet, C., Butenschön, M., Martinez-Vicente, V., Widdicombe, C., Torres, R., and Allen, J. I.: Modelling a light-driven phytoplankton succession, J. Plankton Res., 36, 214–229, https://doi.org/10.1093/plankt/fbt086, 2014.
Pujo-Pay, M. and Conan, P. and Raimbault, P.: Excretion of dissolved organic nitrogen by phytoplankton assessed by wet oxidation and 15N tracer procedures, Mar. Ecol.-Prog. Ser., 153, 99–111, https://doi.org/10.3354/meps153099, 1997.
Radford, P. and Joint, I.: The application of an ecosystem model to the Bristol Channel and Severn Estuary, Water Pollut. Control, 2, 244–250, 1980.
Ridgwell, A., Zondervan, I., Hargreaves, J. C., Bijma, J., and Lenton, T. M.: Assessing the potential long-term increase of oceanic fossil fuel CO2 uptake due to CO2-calcification feedback, Biogeosciences, 4, 481–492, https://doi.org/10.5194/bg-4-481-2007, 2007.
Riegman, R., Stolte, W., Noordeloos, A. A. M., and Slezak, D.: Nutrient uptake and alkaline phosphatase (ec 3 : 1 : 3 : 1) activity of emiliania huxleyi (PRYMNESIOPHYCEAE) during growth under n and p limitation in continuous cultures, J. Phycol., 36, 87–96, https://doi.org/10.1046/j.1529-8817.2000.99023.x, 2000.
Ruardij, P. and Van Raaphorst, W.: Benthic nutrient regeneration in the ERSEM ecosystem model of the North Sea, Neth. J. Sea Res., 33, 453–483, https://doi.org/10.1016/0077-7579(95)90057-8, 1995.
Saux Picart, S., Butenschön, M., and Shutler, J. D.: Wavelet-based spatial comparison technique for analysing and evaluating two-dimensional geophysical model fields, Geosci. Model Dev., 5, 223–230, https://doi.org/10.5194/gmd-5-223-2012, 2012.
Shutler, J. D., Smyth, T. J., Saux-Picart, S., Wakelin, S. L., Hyder, P., Orekhov, P., Grant, M. G., Tilstone, G. H., and Allen, J. I.: Evaluating the ability of a hydrodynamic ecosystem model to capture inter- and intra-annual spatial characteristics of chlorophyll-a in the north east Atlantic, J. Marine Syst., 88, 169–182, https://doi.org/10.1016/j.jmarsys.2011.03.013, 2011.
Smyth, T. J., Fishwick, J. R., Al-Moosawi, L., Cummings, D. G., Harris, C., Kitidis, V., Rees, A., Martinez-Vicente, V., and Woodward, E. M. S.: A broad spatio-temporal view of the Western English Channel observatory, J. Plankton Res., 32, 585–601, https://doi.org/10.1093/plankt/fbp128, 2010.
Steinberg, D. K., Carlson, C. A., Bates, N. R., Johnson, R. J., Michaels, A. F., and Knap, A. H.: Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry, Deep-Sea Res. Pt. II, 48, 1405–1447, https://doi.org/10.1016/S0967-0645(00)00148-X, 2001.
Stock, C. A., Dunne, J. P., and John, J. G.: Global-scale carbon and energy flows through the marine planktonic food web: an analysis with a coupled physical–biological model, Prog. Oceanogr., 120, 1–28, https://doi.org/10.1016/j.pocean.2013.07.001, 2014.
Sukenik, A., Bennett, J., and Falkowski, P.: Light-saturated photosynthesis Limitation by electron transport or carbon fixation?, BBA-Bioenergetics, 891, 205–215, https://doi.org/10.1016/0005-2728(87)90216-7, 1987.
Takahashi, T., Williams, R. T., and Bos, D. L.: Carbonate chemistry, in: Chapter 3, GEOSECS Pacific Expedition, Hydrographic Data 1973–1974, Vol. 3, US Governemnt Priniting Office, Washington, D.C., USA, 1982.
Talmy, D., Blackford, J., Hardman-Mountford, N. J., Polimene, L., Follows, M. J., and Geider, R. J.: Flexible C : N ratio enhances metabolism of large phytoplankton when resource supply is intermittent, Biogeosciences, 11, 4881–4895, https://doi.org/10.5194/bg-11-4881-2014, 2014.
Timmermans, K. R., van der Wagt, B., Veldhuis, M. J. W., Maatman, A., and de Baar, H. J. W.: Physiological responses of three species of marine pico-phytoplankton to ammonium, phosphate, iron and light limitation, J. Sea Res., 53, 109–120, https://doi.org/10.1016/j.seares.2004.05.003, 2005.
van der Molen, J., Smith, H. C. M., Lepper, P., Limpenny, S., and Rees, J.: Predicting the large-scale consequences of offshore wind turbine array development on a North Sea ecosystem, Cont. Shelf Res., 85, 60–72, https://doi.org/10.1016/j.csr.2014.05.018, 2014.
van Duyl, F. C., van Raaphorst, W., and Kop, A. J.: Benthic bacterial production and nutrient sediment-water exchange in sandy North-Sea sediments, Mar. Ecol-Prog. Ser., 100, 85–95, https://doi.org/10.3354/meps100085, 1993.
Varela, R. A., Cruzado, A., and Gabaldón, J. E.: Modelling primary production in the North Sea using the European Regional Seas Ecosystem Model, Neth. J. Sea Res., 33, 337–361, https://doi.org/10.1016/0077-7579(95)90052-7, 1995.
Veldhuis, M. J. W., Timmermans, K. R., Croot, P., and van der Wagt, B.: Picophytoplankton; a comparative study of their biochemical composition and photosynthetic properties, J. Sea Res., 53, 7–24, https://doi.org/10.1016/j.seares.2004.01.006, 2005.
Vichi, M., Ruardij, P., and Baretta, J. W.: Link or sink: a modelling interpretation of the open Baltic biogeochemistry, Biogeosciences, 1, 79–100, https://doi.org/10.5194/bg-1-79-2004, 2004.
Vichi, M., Pinardi, N., and Masina, S.: A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: Theory, J. Marine Syst., 64, 89–109, 2007.
Villarino, E., Chust, G., Licandro, P., Butenschön, M., Ibaibarriaga, L., Larrañaga, A., and Irigoien, X.: Modelling the future biogeography of North Atlantic zooplankton communities in response to climate change, Mar. Ecol.-Prog. Ser., 531, 121–142, https://doi.org/10.3354/meps11299, 2015.
Wakelin, S. L., Holt, J. T., Blackford, J. C., Allen, J. I., Butenschön, M., and Artioli, Y.: Modeling the carbon fluxes of the northwest European continental shelf: validation and budgets, J. Geophys. Res., 117, C05020, https://doi.org/10.1029/2011JC007402, 2012.
Walve, J. and Larsson, U.: Carbon, nitrogen and phosphorus stoichiometry of crustacean zooplankton in the Baltic Sea: implications for nutrient recycling, J. Plankton Res., 21, 2309–2321, https://doi.org/10.1093/plankt/21.12.2309, 1999
Ward, B. B.: Chapter 5 – Nitrification in marine systems, in: Nitrogen in the marine environment, 2nd Edn., edited by: Capone, D. G., Bronk, D. A., Mulholland, M. R., and Carpenter, E. J., Plenum Press, New York, 199–261, 1992.
Weiss, R. F.: The solubility of nitrogen, oxygen and argon in water and seawater, Deep-Sea Res., 17, 721–735, https://doi.org/10.1016/0011-7471(70)90037-9, 1970.
Weiss, R. F.: Carbon dioxide in water and seawater: the solubility of a non-ideal gas, Mar. Chem., 2, 203–215, https://doi.org/10.1016/0304-4203(74)90015-2, 1974.
Weston, K., Fernand, L., Nicholls, J., Marca-Bell, A., Mills, D., Sivyer, D., and Trimmer, M.: Sedimentary and water column processes in the Oyster Grounds: A potentially hypoxic region of the North Sea, Mar. Environ. Res., 65, 235–249, https://doi.org/10.1016/j.marenvres.2007.11.002, 2008.
Wild-Allen, K., Herzfeld, M., Thompson, P. A., Rosebrock, U., Parslow, J., and Volkman, J. K.: Applied coastal biogeochemical modelling to quantify the environmental impact of fish farm nutrients and inform managers, J. Marine Syst., 81, 134–147, https://doi.org/10.1016/j.jmarsys.2009.12.013, 2010.
Yool, A., Popova, E. E., and Anderson, T. R.: MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies, Geosci. Model Dev., 6, 1767–1811, https://doi.org/10.5194/gmd-6-1767-2013, 2013.
Zavatarelli, M. and Pinardi, N.: The Adriatic Sea modelling system: a nested approach, Ann. Geophys., 21, 345–364, https://doi.org/10.5194/angeo-21-345-2003, 2003.
Zeebe, R. W. and Wolf-Gladrow, D.: CO2 in Seawater: Equilibrium, Kinetics, Isotopes, no. 65 in Elsevier Oceanography Series, Elsevier, Amsterdam, the Netherlands, 2001.
Short summary
ERSEM 15.06 is a model for marine biogeochemistry and the lower trophic levels of the marine food web. It comprises a pelagic and benthic sub-model including the microbial food web and the major biogeochemical cycles of carbon, nitrogen, phosphorus, silicate, and iron using dynamic stochiometry. Further features include modules for the carbonate system and calcification. We present full mathematical descriptions of all elements along with examples at various scales up to 3-D applications.
ERSEM 15.06 is a model for marine biogeochemistry and the lower trophic levels of the marine...
