Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8473-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Nov 2022
Model description paper |
| 22 Nov 2022
| 22 Nov 2022
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Thomas Neumann
CORRESPONDING AUTHOR
Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
Hagen Radtke
Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
Bronwyn Cahill
Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
Martin Schmidt
Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
Gregor Rehder
Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
Related authors
Thomas Neumann, Gerald Schernewski, and René Friedland
EGUsphere, https://doi.org/10.5194/egusphere-2024-3734, https://doi.org/10.5194/egusphere-2024-3734, 2025
Short summary
Short summary
We applied a 3D ecosystem model to the Oder Lagoon in the Baltic Sea and found that 30 % of nitrogen and 10 % of phosphorus are retained in the lagoon before entering the Baltic Sea. This is important for coarse-grained models that do not resolve such coastal structures. Moreover, the coastal filter supports the mitigation of eutrophication in the Baltic Sea.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Henry C. Bittig, Erik Jacobs, Thomas Neumann, and Gregor Rehder
Earth Syst. Sci. Data, 16, 753–773, https://doi.org/10.5194/essd-16-753-2024, https://doi.org/10.5194/essd-16-753-2024, 2024
Short summary
Short summary
We present a pCO2 climatology of the Baltic Sea using a new approach to extrapolate from individual observations to the entire Baltic Sea. The extrapolation approach uses (a) a model to inform on how data at one location are connected to data at other locations, together with (b) very accurate pCO2 observations from 2003 to 2021 as the base data. The climatology can be used e.g. to assess uptake and release of CO2 or to identify extreme events.
Sarah Piehl, René Friedland, Thomas Neumann, and Gerald Schernewski
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-152, https://doi.org/10.5194/bg-2023-152, 2023
Revised manuscript not accepted
Short summary
Short summary
We integrated observations essential for policy decisions with high-resolution 3D model results to improve the reliability of oxygen assessments. Based on our findings, we suggest merging only high temporal and/or vertical resolution station data with model data to increase confidence in oxygen assessments. While showing the strengths and limitations of our approach we show that model simulations are an useful tool for policy-relevant oxygen assessments.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
Short summary
Short summary
Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Thomas Neumann, Sampsa Koponen, Jenni Attila, Carsten Brockmann, Kari Kallio, Mikko Kervinen, Constant Mazeran, Dagmar Müller, Petra Philipson, Susanne Thulin, Sakari Väkevä, and Pasi Ylöstalo
Geosci. Model Dev., 14, 5049–5062, https://doi.org/10.5194/gmd-14-5049-2021, https://doi.org/10.5194/gmd-14-5049-2021, 2021
Short summary
Short summary
The Baltic Sea is heavily impacted by surrounding land. Therefore, the concentration of colored dissolved organic matter (CDOM) of terrestrial origin is relatively high and impacts the light penetration depth. Estimating a correct light climate is essential for ecosystem models. In this study, a method is developed to derive riverine CDOM from Earth observation methods. The data are used as boundary conditions for an ecosystem model, and the advantage over former approaches is shown.
Thomas Neumann, Herbert Siegel, Matthias Moros, Monika Gerth, Madline Kniebusch, and Daniel Heydebreck
Ocean Sci., 16, 767–780, https://doi.org/10.5194/os-16-767-2020, https://doi.org/10.5194/os-16-767-2020, 2020
Short summary
Short summary
The bottom water of the northern Baltic Sea usually is well oxygenated. We used a combined approach of numerical model simulations and in situ observations to investigate processes responsible for a regular ventilation of the Bothnian Bay. Surface water masses from the Bothnian Sea and the Bothnian Bay mix at the link between both regions. In winter, when water temperature is low, the resulting density is large enough that the water descends and replaces old bottom water.
Daniel Neumann, Matthias Karl, Hagen Radtke, Volker Matthias, René Friedland, and Thomas Neumann
Ocean Sci., 16, 115–134, https://doi.org/10.5194/os-16-115-2020, https://doi.org/10.5194/os-16-115-2020, 2020
Short summary
Short summary
The study evaluates how much bioavailable nitrogen is contributed to the nitrogen budget of the western Baltic Sea by deposition of shipping-emitted nitrogen oxides. Bioavailable nitrogen compounds are nutrients for phytoplankton (algae). Excessive input of nutrients into water bodies may lead to eutrophication: more algal blooms with subsequently more oxygen limitation at the seafloor. Hence, reducing shipping emissions might reduce the anthropogenic pressure on the marine ecosystem.
Hagen Radtke, Marko Lipka, Dennis Bunke, Claudia Morys, Jana Woelfel, Bronwyn Cahill, Michael E. Böttcher, Stefan Forster, Thomas Leipe, Gregor Rehder, and Thomas Neumann
Geosci. Model Dev., 12, 275–320, https://doi.org/10.5194/gmd-12-275-2019, https://doi.org/10.5194/gmd-12-275-2019, 2019
Short summary
Short summary
This paper describes a coupled benthic–pelagic biogeochemical model, ERGOM-SED. We demonstrate its use in a one-dimensional physical model, which is horizontally integrated and vertically resolved. We describe the application of the model to seven stations in the south-western Baltic Sea. The model was calibrated using pore water profiles from these stations. We compare the model results to these and to measured sediment compositions, benthopelagic fluxes and bioturbation intensities.
Daniel Neumann, Hagen Radtke, Matthias Karl, and Thomas Neumann
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-365, https://doi.org/10.5194/bg-2018-365, 2018
Publication in BG not foreseen
Short summary
Short summary
The contribution of atmospheric nitrogen deposition to the marine dissolved inorganic nitrogen (DIN) pool of the North and Baltic Sea was assessed for the year 2012. Atmospheric deposition accounted for approximately 10 % to 15 % of the DIN but its residence time differed between both water bodies. The nitrogen contributions of atmospheric shipping and agricultural imissions also were assessed. Particularly the latter source had a large impact in coastal regions.
Daniel Neumann, Matthias Karl, Hagen Radtke, and Thomas Neumann
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-364, https://doi.org/10.5194/bg-2018-364, 2018
Manuscript not accepted for further review
Short summary
Short summary
Atmospheric nitrogen deposition contributes 20 % to 40 % to bioavailable nitrogen inputs into the North Sea and Baltic Sea. Excessive bioavailable nitrogen may lead to intensified algal blooms in these water bodies resulting in several negative consequences for the marine ecosystem. We traced atmospheric nitrogen in the marine ecosystem via an ecosystem model and estimated the contribution of atmospheric nitrogen to plankton biomass in different regions of the North and Baltic Sea over five years.
Daniel Neumann, René Friedland, Matthias Karl, Hagen Radtke, Volker Matthias, and Thomas Neumann
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-71, https://doi.org/10.5194/os-2018-71, 2018
Revised manuscript not accepted
Short summary
Short summary
We found that refining the spatial resolution of nitrogen deposition data had low impact on marine nitrogen compounds compared to the impact by nitrogen deposition data sets of different origin (other model). The shipping sector had a contribution of up to 10 % to the marine dissolved inorganic nitrogen.
Pratirupa Bardhan, Claudia Frey, Gregor Rehder, and Hermann W. Bange
EGUsphere, https://doi.org/10.5194/egusphere-2025-2518, https://doi.org/10.5194/egusphere-2025-2518, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Nitrous oxide (N2O), a potent greenhouse gas, is released from coastal seas & estuaries, yet we don't fully understand how it is formed and consumed. In this study we collected water from several sites in the central Baltic Sea. N2O came from ammonia in oxic waters. Deep waters with low to no oxygen noted more active N2O cycling. The seafloor was a source in some areas. Typically N2O is produced by bacteria, but our results indicate possibility of other players like fungi or chemical reactions.
Thomas Neumann, Gerald Schernewski, and René Friedland
EGUsphere, https://doi.org/10.5194/egusphere-2024-3734, https://doi.org/10.5194/egusphere-2024-3734, 2025
Short summary
Short summary
We applied a 3D ecosystem model to the Oder Lagoon in the Baltic Sea and found that 30 % of nitrogen and 10 % of phosphorus are retained in the lagoon before entering the Baltic Sea. This is important for coarse-grained models that do not resolve such coastal structures. Moreover, the coastal filter supports the mitigation of eutrophication in the Baltic Sea.
Daniel Pönisch, Henry C. Bittig, Martin Kolbe, Ingo Schuffenhauer, Stefan Otto, Peter Holtermann, Kusala Premaratne, and Gregor Rehder
EGUsphere, https://doi.org/10.5194/egusphere-2024-3246, https://doi.org/10.5194/egusphere-2024-3246, 2024
Short summary
Short summary
Rewetted peatlands exhibit natural spatial and temporal biogeochemical heterogeneity, influenced by water level and vegetation. This study investigated the variability of the distribution of GHGs in a brackish-rewetted peatland. Two innovative sensor-equipped landers were used to measure a wide range of marine physicochemical variables at high temporal resolution. The measurements revealed strong fluctuations in CO2 and CH4, expressed as multi-day, diurnal and event-based variability.
Silvie Lainela, Erik Jacobs, Stella-Theresa Luik, Gregor Rehder, and Urmas Lips
Biogeosciences, 21, 4495–4519, https://doi.org/10.5194/bg-21-4495-2024, https://doi.org/10.5194/bg-21-4495-2024, 2024
Short summary
Short summary
We evaluate the variability of carbon dioxide and methane in the surface layer of the north-eastern basins of the Baltic Sea in 2018. We show that the shallower coastal areas have considerably higher spatial variability and seasonal amplitude of surface layer pCO2 and cCH4 than measured in the offshore areas of the Baltic Sea. Despite this high variability, caused mostly by coastal physical processes, the average annual air–sea CO2 fluxes differed only marginally between the sub-basins.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Henry C. Bittig, Erik Jacobs, Thomas Neumann, and Gregor Rehder
Earth Syst. Sci. Data, 16, 753–773, https://doi.org/10.5194/essd-16-753-2024, https://doi.org/10.5194/essd-16-753-2024, 2024
Short summary
Short summary
We present a pCO2 climatology of the Baltic Sea using a new approach to extrapolate from individual observations to the entire Baltic Sea. The extrapolation approach uses (a) a model to inform on how data at one location are connected to data at other locations, together with (b) very accurate pCO2 observations from 2003 to 2021 as the base data. The climatology can be used e.g. to assess uptake and release of CO2 or to identify extreme events.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Mohammad Hadi Bordbar, Volker Mohrholz, and Martin Schmidt
Earth Syst. Dynam., 14, 1065–1080, https://doi.org/10.5194/esd-14-1065-2023, https://doi.org/10.5194/esd-14-1065-2023, 2023
Short summary
Short summary
The Benguela Upwelling System hosts highly productive marine ecosystems, supporting the livelihood of the local inhabitants. Regional distribution of nutrients in this system is affected by upwelling, primarily wind-driven and related to the South Atlantic Anticyclone, which is believed to intensify in the future. We found that this system's southern and northern parts respond to the anticyclone changes differently. Due to climate variability, the uncertainty in the upwelling trend is high.
Sarah Piehl, René Friedland, Thomas Neumann, and Gerald Schernewski
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-152, https://doi.org/10.5194/bg-2023-152, 2023
Revised manuscript not accepted
Short summary
Short summary
We integrated observations essential for policy decisions with high-resolution 3D model results to improve the reliability of oxygen assessments. Based on our findings, we suggest merging only high temporal and/or vertical resolution station data with model data to increase confidence in oxygen assessments. While showing the strengths and limitations of our approach we show that model simulations are an useful tool for policy-relevant oxygen assessments.
Bronwyn E. Cahill, Piotr Kowalczuk, Lena Kritten, Ulf Gräwe, John Wilkin, and Jürgen Fischer
Biogeosciences, 20, 2743–2768, https://doi.org/10.5194/bg-20-2743-2023, https://doi.org/10.5194/bg-20-2743-2023, 2023
Short summary
Short summary
We quantify the impact of optically significant water constituents on surface heating rates and thermal energy fluxes in the western Baltic Sea. During productive months in 2018 (April to September) we found that the combined effect of coloured
dissolved organic matter and particulate absorption contributes to sea surface heating of between 0.4 and 0.9 K m−1 d−1 and a mean loss of heat (ca. 5 W m−2) from the sea to the atmosphere. This may be important for regional heat balance budgets.
Daniel L. Pönisch, Anne Breznikar, Cordula N. Gutekunst, Gerald Jurasinski, Maren Voss, and Gregor Rehder
Biogeosciences, 20, 295–323, https://doi.org/10.5194/bg-20-295-2023, https://doi.org/10.5194/bg-20-295-2023, 2023
Short summary
Short summary
Peatland rewetting is known to reduce dissolved nutrients and greenhouse gases; however, short-term nutrient leaching and high CH4 emissions shortly after rewetting are likely to occur. We investigated the rewetting of a coastal peatland with brackish water and its effects on nutrient release and greenhouse gas fluxes. Nutrient concentrations were higher in the peatland than in the adjacent bay, leading to an export. CH4 emissions did not increase, which is in contrast to freshwater rewetting.
Matthias Gröger, Manja Placke, H. E. Markus Meier, Florian Börgel, Sandra-Esther Brunnabend, Cyril Dutheil, Ulf Gräwe, Magnus Hieronymus, Thomas Neumann, Hagen Radtke, Semjon Schimanke, Jian Su, and Germo Väli
Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
Short summary
Short summary
Comparisons of oceanographic climate data from different models often suffer from different model setups, forcing fields, and output of variables. This paper provides a protocol to harmonize these elements to set up multidecadal simulations for the Baltic Sea, a marginal sea in Europe. First results are shown from six different model simulations from four different model platforms. Topical studies for upwelling, marine heat waves, and stratification are also assessed.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Karol Kuliński, Gregor Rehder, Eero Asmala, Alena Bartosova, Jacob Carstensen, Bo Gustafsson, Per O. J. Hall, Christoph Humborg, Tom Jilbert, Klaus Jürgens, H. E. Markus Meier, Bärbel Müller-Karulis, Michael Naumann, Jørgen E. Olesen, Oleg Savchuk, Andreas Schramm, Caroline P. Slomp, Mikhail Sofiev, Anna Sobek, Beata Szymczycha, and Emma Undeman
Earth Syst. Dynam., 13, 633–685, https://doi.org/10.5194/esd-13-633-2022, https://doi.org/10.5194/esd-13-633-2022, 2022
Short summary
Short summary
The paper covers the aspects related to changes in carbon, nitrogen, and phosphorus (C, N, P) external loads; their transformations in the coastal zone; changes in organic matter production (eutrophication) and remineralization (oxygen availability); and the role of sediments in burial and turnover of C, N, and P. Furthermore, this paper also focuses on changes in the marine CO2 system, the structure of the microbial community, and the role of contaminants for biogeochemical processes.
Martti Honkanen, Jens Daniel Müller, Jukka Seppälä, Gregor Rehder, Sami Kielosto, Pasi Ylöstalo, Timo Mäkelä, Juha Hatakka, and Lauri Laakso
Ocean Sci., 17, 1657–1675, https://doi.org/10.5194/os-17-1657-2021, https://doi.org/10.5194/os-17-1657-2021, 2021
Short summary
Short summary
The exchange of carbon dioxide (CO2) between the sea and the atmosphere is regulated by the gradient of CO2 partial pressure (pCO2) between the sea and the air. The daily variation of the seawater pCO2 recorded at the fixed station Utö in the Baltic Sea was found to be mainly biologically driven. Calculation of the annual net exchange of CO2 between the sea and atmosphere based on daily measurements of pCO2 carried out using the same sampling time every day could introduce a bias of up to 12 %.
Jens Daniel Müller, Bernd Schneider, Ulf Gräwe, Peer Fietzek, Marcus Bo Wallin, Anna Rutgersson, Norbert Wasmund, Siegfried Krüger, and Gregor Rehder
Biogeosciences, 18, 4889–4917, https://doi.org/10.5194/bg-18-4889-2021, https://doi.org/10.5194/bg-18-4889-2021, 2021
Short summary
Short summary
Based on profiling pCO2 measurements from a field campaign, we quantify the biomass production of a cyanobacteria bloom in the Baltic Sea, the export of which would foster deep water deoxygenation. We further demonstrate how this biomass production can be accurately reconstructed from long-term surface measurements made on cargo vessels in combination with modelled temperature profiles. This approach enables a better understanding of a severe concern for the Baltic’s good environmental status.
Thomas Neumann, Sampsa Koponen, Jenni Attila, Carsten Brockmann, Kari Kallio, Mikko Kervinen, Constant Mazeran, Dagmar Müller, Petra Philipson, Susanne Thulin, Sakari Väkevä, and Pasi Ylöstalo
Geosci. Model Dev., 14, 5049–5062, https://doi.org/10.5194/gmd-14-5049-2021, https://doi.org/10.5194/gmd-14-5049-2021, 2021
Short summary
Short summary
The Baltic Sea is heavily impacted by surrounding land. Therefore, the concentration of colored dissolved organic matter (CDOM) of terrestrial origin is relatively high and impacts the light penetration depth. Estimating a correct light climate is essential for ecosystem models. In this study, a method is developed to derive riverine CDOM from Earth observation methods. The data are used as boundary conditions for an ecosystem model, and the advantage over former approaches is shown.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
Short summary
Short summary
We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Erik Jacobs, Henry C. Bittig, Ulf Gräwe, Carolyn A. Graves, Michael Glockzin, Jens D. Müller, Bernd Schneider, and Gregor Rehder
Biogeosciences, 18, 2679–2709, https://doi.org/10.5194/bg-18-2679-2021, https://doi.org/10.5194/bg-18-2679-2021, 2021
Short summary
Short summary
We use a unique data set of 8 years of continuous carbon dioxide (CO2) and methane (CH4) surface water measurements from a commercial ferry to study upwelling in the Baltic Sea. Its seasonality and regional and interannual variability are examined. Strong upwelling events drastically increase local surface CO2 and CH4 levels and are mostly detected in late summer after long periods of impaired mixing. We introduce an extrapolation method to estimate regional upwelling-induced trace gas fluxes.
Trystan Sanders, Jörn Thomsen, Jens Daniel Müller, Gregor Rehder, and Frank Melzner
Biogeosciences, 18, 2573–2590, https://doi.org/10.5194/bg-18-2573-2021, https://doi.org/10.5194/bg-18-2573-2021, 2021
Short summary
Short summary
The Baltic Sea is expected to experience a rapid drop in salinity and increases in acidity and warming in the next century. Calcifying mussels dominate Baltic Sea seafloor ecosystems yet are sensitive to changes in seawater chemistry. We combine laboratory experiments and a field study and show that a lack of calcium causes extremely slow growth rates in mussels at low salinities. Subsequently, climate change in the Baltic may have drastic ramifications for Baltic seafloor ecosystems.
Anna Rose Canning, Peer Fietzek, Gregor Rehder, and Arne Körtzinger
Biogeosciences, 18, 1351–1373, https://doi.org/10.5194/bg-18-1351-2021, https://doi.org/10.5194/bg-18-1351-2021, 2021
Short summary
Short summary
The paper describes a novel, fully autonomous, multi-gas flow-through set-up for multiple gases that combines established, high-quality oceanographic sensors in a small and robust system designed for use across all salinities and all types of platforms. We describe the system and its performance in all relevant detail, including the corrections which improve the accuracy of these sensors, and illustrate how simultaneous multi-gas set-ups can provide an extremely high spatiotemporal resolution.
Meike Becker, Are Olsen, Peter Landschützer, Abdirhaman Omar, Gregor Rehder, Christian Rödenbeck, and Ingunn Skjelvan
Biogeosciences, 18, 1127–1147, https://doi.org/10.5194/bg-18-1127-2021, https://doi.org/10.5194/bg-18-1127-2021, 2021
Short summary
Short summary
We developed a simple method to refine existing open-ocean maps towards different coastal seas. Using a multi-linear regression, we produced monthly maps of surface ocean fCO2 in the northern European coastal seas (the North Sea, the Baltic Sea, the Norwegian Coast and the Barents Sea) covering a time period from 1998 to 2016. Based on this fCO2 map, we calculate trends in surface ocean fCO2, pH and the air–sea gas exchange.
Robert Daniel Osinski, Kristina Enders, Ulf Gräwe, Knut Klingbeil, and Hagen Radtke
Ocean Sci., 16, 1491–1507, https://doi.org/10.5194/os-16-1491-2020, https://doi.org/10.5194/os-16-1491-2020, 2020
Short summary
Short summary
This study investigates the impact of the uncertainty in atmospheric data of a storm event on the transport of microplastics and sediments. The model chain includes the WRF atmospheric model, the WAVEWATCH III® wave model, and the GETM regional ocean model as well as a sediment transport model based on the FABM framework. An ensemble approach based on stochastic perturbations of the WRF model is used. We found a strong impact of atmospheric uncertainty on the amount of transported material.
Samuel T. Wilson, Alia N. Al-Haj, Annie Bourbonnais, Claudia Frey, Robinson W. Fulweiler, John D. Kessler, Hannah K. Marchant, Jana Milucka, Nicholas E. Ray, Parvadha Suntharalingam, Brett F. Thornton, Robert C. Upstill-Goddard, Thomas S. Weber, Damian L. Arévalo-Martínez, Hermann W. Bange, Heather M. Benway, Daniele Bianchi, Alberto V. Borges, Bonnie X. Chang, Patrick M. Crill, Daniela A. del Valle, Laura Farías, Samantha B. Joye, Annette Kock, Jabrane Labidi, Cara C. Manning, John W. Pohlman, Gregor Rehder, Katy J. Sparrow, Philippe D. Tortell, Tina Treude, David L. Valentine, Bess B. Ward, Simon Yang, and Leonid N. Yurganov
Biogeosciences, 17, 5809–5828, https://doi.org/10.5194/bg-17-5809-2020, https://doi.org/10.5194/bg-17-5809-2020, 2020
Short summary
Short summary
The oceans are a net source of the major greenhouse gases; however there has been little coordination of oceanic methane and nitrous oxide measurements. The scientific community has recently embarked on a series of capacity-building exercises to improve the interoperability of dissolved methane and nitrous oxide measurements. This paper derives from a workshop which discussed the challenges and opportunities for oceanic methane and nitrous oxide research in the near future.
Hagen Radtke, Sandra-Esther Brunnabend, Ulf Gräwe, and H. E. Markus Meier
Clim. Past, 16, 1617–1642, https://doi.org/10.5194/cp-16-1617-2020, https://doi.org/10.5194/cp-16-1617-2020, 2020
Short summary
Short summary
During the last century, salinity in the Baltic Sea showed a multidecadal oscillation with a period of 30 years. Using a numerical circulation model and wavelet coherence analysis, we demonstrate that this variation has at least two possible causes. One driver is river runoff which shows a 30-year variation. The second one is a variation in the frequency of strong inflows of saline water across Darss Sill which also contains a pronounced 30-year period.
Thomas Neumann, Herbert Siegel, Matthias Moros, Monika Gerth, Madline Kniebusch, and Daniel Heydebreck
Ocean Sci., 16, 767–780, https://doi.org/10.5194/os-16-767-2020, https://doi.org/10.5194/os-16-767-2020, 2020
Short summary
Short summary
The bottom water of the northern Baltic Sea usually is well oxygenated. We used a combined approach of numerical model simulations and in situ observations to investigate processes responsible for a regular ventilation of the Bothnian Bay. Surface water masses from the Bothnian Sea and the Bothnian Bay mix at the link between both regions. In winter, when water temperature is low, the resulting density is large enough that the water descends and replaces old bottom water.
Robert Daniel Osinski and Hagen Radtke
Ocean Sci., 16, 355–371, https://doi.org/10.5194/os-16-355-2020, https://doi.org/10.5194/os-16-355-2020, 2020
Short summary
Short summary
The idea of this study is to quantify the uncertainty in hindcasts of severe storm events by applying a state-of-the-art ensemble generation technique. Other ensemble generation techniques are tested. The atmospheric WRF model is driven by the ERA5 reanalysis. A setup of the Wavewatch III® wave model for the Baltic Sea is used with the wind fields produced with the WRF ensemble. The effect of different spatio-temporal resolutions of the wind fields on the significant wave height is investigated.
Daniel Neumann, Matthias Karl, Hagen Radtke, Volker Matthias, René Friedland, and Thomas Neumann
Ocean Sci., 16, 115–134, https://doi.org/10.5194/os-16-115-2020, https://doi.org/10.5194/os-16-115-2020, 2020
Short summary
Short summary
The study evaluates how much bioavailable nitrogen is contributed to the nitrogen budget of the western Baltic Sea by deposition of shipping-emitted nitrogen oxides. Bioavailable nitrogen compounds are nutrients for phytoplankton (algae). Excessive input of nutrients into water bodies may lead to eutrophication: more algal blooms with subsequently more oxygen limitation at the seafloor. Hence, reducing shipping emissions might reduce the anthropogenic pressure on the marine ecosystem.
Thomas Holding, Ian G. Ashton, Jamie D. Shutler, Peter E. Land, Philip D. Nightingale, Andrew P. Rees, Ian Brown, Jean-Francois Piolle, Annette Kock, Hermann W. Bange, David K. Woolf, Lonneke Goddijn-Murphy, Ryan Pereira, Frederic Paul, Fanny Girard-Ardhuin, Bertrand Chapron, Gregor Rehder, Fabrice Ardhuin, and Craig J. Donlon
Ocean Sci., 15, 1707–1728, https://doi.org/10.5194/os-15-1707-2019, https://doi.org/10.5194/os-15-1707-2019, 2019
Short summary
Short summary
FluxEngine is an open-source software toolbox designed to allow for the easy and accurate calculation of air–sea gas fluxes. This article describes new functionality and capabilities, which include the ability to calculate fluxes for nitrous oxide and methane, optimisation for running FluxEngine on a stand-alone desktop computer, and extensive new features to support the in situ measurement community. Four research case studies are used to demonstrate these new features.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
Short summary
Short summary
The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Hagen Radtke, Marko Lipka, Dennis Bunke, Claudia Morys, Jana Woelfel, Bronwyn Cahill, Michael E. Böttcher, Stefan Forster, Thomas Leipe, Gregor Rehder, and Thomas Neumann
Geosci. Model Dev., 12, 275–320, https://doi.org/10.5194/gmd-12-275-2019, https://doi.org/10.5194/gmd-12-275-2019, 2019
Short summary
Short summary
This paper describes a coupled benthic–pelagic biogeochemical model, ERGOM-SED. We demonstrate its use in a one-dimensional physical model, which is horizontally integrated and vertically resolved. We describe the application of the model to seven stations in the south-western Baltic Sea. The model was calibrated using pore water profiles from these stations. We compare the model results to these and to measured sediment compositions, benthopelagic fluxes and bioturbation intensities.
Beate Stawiarski, Stefan Otto, Volker Thiel, Ulf Gräwe, Natalie Loick-Wilde, Anna K. Wittenborn, Stefan Schloemer, Janine Wäge, Gregor Rehder, Matthias Labrenz, Norbert Wasmund, and Oliver Schmale
Biogeosciences, 16, 1–16, https://doi.org/10.5194/bg-16-1-2019, https://doi.org/10.5194/bg-16-1-2019, 2019
Short summary
Short summary
The understanding of surface water methane production in the world oceans is still poor. By combining field studies and incubation experiments, our investigations suggest that zooplankton contributes to subthermocline methane enrichments in the central Baltic Sea by methane production within the digestive tract of copepods and/or by methane production through release of methane precursor substances into the surrounding water, followed by microbial degradation to methane.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Judith Hauck, Julia Pongratz, Penelope A. Pickers, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Almut Arneth, Vivek K. Arora, Leticia Barbero, Ana Bastos, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Scott C. Doney, Thanos Gkritzalis, Daniel S. Goll, Ian Harris, Vanessa Haverd, Forrest M. Hoffman, Mario Hoppema, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Truls Johannessen, Chris D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Peter Landschützer, Nathalie Lefèvre, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Craig Neill, Are Olsen, Tsueno Ono, Prabir Patra, Anna Peregon, Wouter Peters, Philippe Peylin, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Matthias Rocher, Christian Rödenbeck, Ute Schuster, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Tobias Steinhoff, Adrienne Sutton, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Nicolas Viovy, Anthony P. Walker, Andrew J. Wiltshire, Rebecca Wright, Sönke Zaehle, and Bo Zheng
Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, https://doi.org/10.5194/essd-10-2141-2018, 2018
Short summary
Short summary
The Global Carbon Budget 2018 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Xi Wen, Viktoria Unger, Gerald Jurasinski, Franziska Koebsch, Fabian Horn, Gregor Rehder, Torsten Sachs, Dominik Zak, Gunnar Lischeid, Klaus-Holger Knorr, Michael E. Böttcher, Matthias Winkel, Paul L. E. Bodelier, and Susanne Liebner
Biogeosciences, 15, 6519–6536, https://doi.org/10.5194/bg-15-6519-2018, https://doi.org/10.5194/bg-15-6519-2018, 2018
Short summary
Short summary
Rewetting drained peatlands may lead to prolonged emission of the greenhouse gas methane, but the underlying factors are not well described. In this study, we found two rewetted fens with known high methane fluxes had a high ratio of microbial methane producers to methane consumers and a low abundance of methane consumers compared to pristine wetlands. We therefore suggest abundances of methane-cycling microbes as potential indicators for prolonged high methane emissions in rewetted peatlands.
Daniel Neumann, Hagen Radtke, Matthias Karl, and Thomas Neumann
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-365, https://doi.org/10.5194/bg-2018-365, 2018
Publication in BG not foreseen
Short summary
Short summary
The contribution of atmospheric nitrogen deposition to the marine dissolved inorganic nitrogen (DIN) pool of the North and Baltic Sea was assessed for the year 2012. Atmospheric deposition accounted for approximately 10 % to 15 % of the DIN but its residence time differed between both water bodies. The nitrogen contributions of atmospheric shipping and agricultural imissions also were assessed. Particularly the latter source had a large impact in coastal regions.
Daniel Neumann, Matthias Karl, Hagen Radtke, and Thomas Neumann
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-364, https://doi.org/10.5194/bg-2018-364, 2018
Manuscript not accepted for further review
Short summary
Short summary
Atmospheric nitrogen deposition contributes 20 % to 40 % to bioavailable nitrogen inputs into the North Sea and Baltic Sea. Excessive bioavailable nitrogen may lead to intensified algal blooms in these water bodies resulting in several negative consequences for the marine ecosystem. We traced atmospheric nitrogen in the marine ecosystem via an ecosystem model and estimated the contribution of atmospheric nitrogen to plankton biomass in different regions of the North and Baltic Sea over five years.
Samuel T. Wilson, Hermann W. Bange, Damian L. Arévalo-Martínez, Jonathan Barnes, Alberto V. Borges, Ian Brown, John L. Bullister, Macarena Burgos, David W. Capelle, Michael Casso, Mercedes de la Paz, Laura Farías, Lindsay Fenwick, Sara Ferrón, Gerardo Garcia, Michael Glockzin, David M. Karl, Annette Kock, Sarah Laperriere, Cliff S. Law, Cara C. Manning, Andrew Marriner, Jukka-Pekka Myllykangas, John W. Pohlman, Andrew P. Rees, Alyson E. Santoro, Philippe D. Tortell, Robert C. Upstill-Goddard, David P. Wisegarver, Gui-Ling Zhang, and Gregor Rehder
Biogeosciences, 15, 5891–5907, https://doi.org/10.5194/bg-15-5891-2018, https://doi.org/10.5194/bg-15-5891-2018, 2018
Short summary
Short summary
To determine the variability between independent measurements of dissolved methane and nitrous oxide, seawater samples were analyzed by multiple laboratories. The results revealed the influences of the different parts of the analytical process, from the initial sample collection to the calculation of the final concentrations. Recommendations are made to improve dissolved methane and nitrous oxide measurements to help preclude future analytical discrepancies between laboratories.
Daniel Neumann, René Friedland, Matthias Karl, Hagen Radtke, Volker Matthias, and Thomas Neumann
Ocean Sci. Discuss., https://doi.org/10.5194/os-2018-71, https://doi.org/10.5194/os-2018-71, 2018
Revised manuscript not accepted
Short summary
Short summary
We found that refining the spatial resolution of nitrogen deposition data had low impact on marine nitrogen compounds compared to the impact by nitrogen deposition data sets of different origin (other model). The shipping sector had a contribution of up to 10 % to the marine dissolved inorganic nitrogen.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Julia Pongratz, Andrew C. Manning, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Robert B. Jackson, Thomas A. Boden, Pieter P. Tans, Oliver D. Andrews, Vivek K. Arora, Dorothee C. E. Bakker, Leticia Barbero, Meike Becker, Richard A. Betts, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Philippe Ciais, Catherine E. Cosca, Jessica Cross, Kim Currie, Thomas Gasser, Ian Harris, Judith Hauck, Vanessa Haverd, Richard A. Houghton, Christopher W. Hunt, George Hurtt, Tatiana Ilyina, Atul K. Jain, Etsushi Kato, Markus Kautz, Ralph F. Keeling, Kees Klein Goldewijk, Arne Körtzinger, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Ivan Lima, Danica Lombardozzi, Nicolas Metzl, Frank Millero, Pedro M. S. Monteiro, David R. Munro, Julia E. M. S. Nabel, Shin-ichiro Nakaoka, Yukihiro Nojiri, X. Antonio Padin, Anna Peregon, Benjamin Pfeil, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Janet Reimer, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Benjamin D. Stocker, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Ingrid T. van der Laan-Luijkx, Guido R. van der Werf, Steven van Heuven, Nicolas Viovy, Nicolas Vuichard, Anthony P. Walker, Andrew J. Watson, Andrew J. Wiltshire, Sönke Zaehle, and Dan Zhu
Earth Syst. Sci. Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018, https://doi.org/10.5194/essd-10-405-2018, 2018
Short summary
Short summary
The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Jukka-Pekka Myllykangas, Tom Jilbert, Gunnar Jakobs, Gregor Rehder, Jan Werner, and Susanna Hietanen
Earth Syst. Dynam., 8, 817–826, https://doi.org/10.5194/esd-8-817-2017, https://doi.org/10.5194/esd-8-817-2017, 2017
Short summary
Short summary
The deep waters of the Baltic Sea host an expanding
dead zone, where low-oxygen conditions favour the natural production of two strong greenhouse gases, methane and nitrous oxide. Oxygen is introduced into the deeps only during rare
salt pulses. We studied the effects of a recent salt pulse on Baltic greenhouse gas production. We found that where oxygen was introduced, methane was largely removed, while nitrous oxide production increased, indicating strong effects on greenhouse gas dynamics.
C. Le Quéré, R. Moriarty, R. M. Andrew, J. G. Canadell, S. Sitch, J. I. Korsbakken, P. Friedlingstein, G. P. Peters, R. J. Andres, T. A. Boden, R. A. Houghton, J. I. House, R. F. Keeling, P. Tans, A. Arneth, D. C. E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L. P. Chini, P. Ciais, M. Fader, R. A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A. K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S. K. Lauvset, N. Lefèvre, A. Lenton, I. D. Lima, N. Metzl, F. Millero, D. R. Munro, A. Murata, J. E. M. S. Nabel, S. Nakaoka, Y. Nojiri, K. O'Brien, A. Olsen, T. Ono, F. F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, I. T. van der Laan-Luijkx, G. R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, https://doi.org/10.5194/essd-7-349-2015, 2015
Short summary
Short summary
Accurate assessment of anthropogenic carbon dioxide emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to understand the global carbon cycle, support the development of climate policies, and project future climate change. We describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on a range of data and models and their interpretation by a broad scientific community.
J. Friedrich, F. Janssen, D. Aleynik, H. W. Bange, N. Boltacheva, M. N. Çagatay, A. W. Dale, G. Etiope, Z. Erdem, M. Geraga, A. Gilli, M. T. Gomoiu, P. O. J. Hall, D. Hansson, Y. He, M. Holtappels, M. K. Kirf, M. Kononets, S. Konovalov, A. Lichtschlag, D. M. Livingstone, G. Marinaro, S. Mazlumyan, S. Naeher, R. P. North, G. Papatheodorou, O. Pfannkuche, R. Prien, G. Rehder, C. J. Schubert, T. Soltwedel, S. Sommer, H. Stahl, E. V. Stanev, A. Teaca, A. Tengberg, C. Waldmann, B. Wehrli, and F. Wenzhöfer
Biogeosciences, 11, 1215–1259, https://doi.org/10.5194/bg-11-1215-2014, https://doi.org/10.5194/bg-11-1215-2014, 2014
G. Jakobs, G. Rehder, G. Jost, K. Kießlich, M. Labrenz, and O. Schmale
Biogeosciences, 10, 7863–7875, https://doi.org/10.5194/bg-10-7863-2013, https://doi.org/10.5194/bg-10-7863-2013, 2013
W. Gülzow, G. Rehder, J. Schneider v. Deimling, T. Seifert, and Z. Tóth
Biogeosciences, 10, 81–99, https://doi.org/10.5194/bg-10-81-2013, https://doi.org/10.5194/bg-10-81-2013, 2013
Related subject area
Biogeosciences
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Short summary
Short summary
This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
Short summary
Short summary
Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Cited articles
Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S., Nojiri, Y., Schuster, U., Steinhoff, T., Sweeney, C., Takahashi, T., Tilbrook, B., Wada, C., Wanninkhof, R., Alin, S. R., Balestrini, C. F., Barbero, L., Bates, N. R., Bianchi, A. A., Bonou, F., Boutin, J., Bozec, Y., Burger, E. F., Cai, W.-J., Castle, R. D., Chen, L., Chierici, M., Currie, K., Evans, W., Featherstone, C., Feely, R. A., Fransson, A., Goyet, C., Greenwood, N., Gregor, L., Hankin, S., Hardman-Mountford, N. J., Harlay, J., Hauck, J., Hoppema, M., Humphreys, M. P., Hunt, C. W., Huss, B., Ibánhez, J. S. P., Johannessen, T., Keeling, R., Kitidis, V., Körtzinger, A., Kozyr, A., Krasakopoulou, E., Kuwata, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lo Monaco, C., Manke, A., Mathis, J. T., Merlivat, L., Millero, F. J., Monteiro, P. M. S., Munro, D. R., Murata, A., Newberger, T., Omar, A. M., Ono, T., Paterson, K., Pearce, D., Pierrot, D., Robbins, L. L., Saito, S., Salisbury, J., Schlitzer, R., Schneider, B., Schweitzer, R., Sieger, R., Skjelvan, I., Sullivan, K. F., Sutherland, S. C., Sutton, A. J., Tadokoro, K., Telszewski, M., Tuma, M., van Heuven, S. M. A. C., Vandemark, D., Ward, B., Watson, A. J., and Xu, S.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383–413, https://doi.org/10.5194/essd-8-383-2016, 2016. a
Bakker, D. C. E., Alin, S. R., Becker, M., Bittig, H. C., Castaño-Primo, R., Feely, R. A., Gkritzalis, T., Kadono, K., Kozyr, A., Lauvset, S. K., Metzl, N., Munro, D. R., Nakaoka, S., Nojiri, Y., O'Brien, K. M., Olsen, A., Pfeil, B., Pierrot, D., Steinhoff, T., Sullivan, K. F., Sutton, A. J., Sweeney, C., Tilbrook, B., Wada, C., Wanninkhof, R., Willstrand W., Anna, A., John, A., L. B., Bates, N., Beatty, C. M., Burger, E. F., Cai, W.-J., Cosca, C. E., Corredor, J. E., Cronin, M., Cross, J. N., De Carlo, E. H., DeGrandpre, M. D., Emerson, S., Enright, M. P., Enyo, K., Evans, W., Frangoulis, C., Fransson, A., García-Ibáñez, M. I., Gehrung, M., Giannoudi, L., Glockzin, M., Hales, B., Howden, S. D., Hunt, C. W., Ibánhez, J. S. P., Jones, S. D., Kamb, L., Körtzinger, A., Landa, C. S., Landschützer, P., Lefèvre, N., Lo Monaco, C., Macovei, V. A., Maenner Jones, S., Meinig, C., Millero, F. J., Monacci, N. M., Mordy, C., Morell, J. M., Murata, A., Musielewicz, S., Neill, C., Newberger, T., Nomura, D., Ohman, M., Ono, T., Passmore, A., Petersen, W., Petihakis, G., Perivoliotis, L., Plueddemann, A. J., Rehder, G., Reynaud, T., Rodriguez, C., Ross, A. C., Rutgersson, A., Sabine, C. L., Salisbury, J. E., Schlitzer, R., Send, U., Skjelvan, I., Stamataki, N., Sutherland, S. C., Sweeney, C., Tadokoro, K., Tanhua, T., Telszewski, M., Trull, T., Vandemark, D., van Ooijen, E., Voynova, Y. G., Wang, H., Weller, R. A., Whitehead, C., and Wilson, D.:
Surface Ocean CO2 Atlas Database Version 2022 (SOCATv2022) (NCEI Accession 0253659), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/1h9f-nb73, 2022. a
Carlson, C. A. and Hansell, D. A.: Chapter 3 – DOM Sources, Sinks, Reactivity, and Budgets, in: Biogeochemistry of Marine Dissolved Organic Matter, 2nd edn., edited by: Carlson, D. A. and Hansell, C. A., Academic Press, Boston, 65–126, https://doi.org/10.1016/B978-0-12-405940-5.00003-0, 2015. a, b
Carlson, C. A., Ducklow, H. W., Hansell, D. A., and Smith Jr., W. O.: Organic carbon partitioning during spring phytoplankton blooms in the Ross Sea polynya and the Sargasso Sea, Limnol. Oceanogr., 43, 375–386, https://doi.org/10.4319/lo.1998.43.3.0375, 1998. a
Chien, C.-T., Pahlow, M., Schartau, M., and Oschlies, A.: Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration, Geosci. Model Dev., 13, 4691–4712, https://doi.org/10.5194/gmd-13-4691-2020, 2020. a
Droop, M. R.: Some Thoughts on Nutrient Limitation in Algae, J. Phycol., 9, 264–272, https://doi.org/10.1111/j.1529-8817.1973.tb04092.x, 1973. a
Eggert, A. and Schneider, B.: A nitrogen source in spring in the surface mixed-layer of the Baltic Sea: Evidence from total nitrogen and total phosphorus data, J. Marine Syst., 148, 39–47, https://doi.org/10.1016/j.jmarsys.2015.01.005, 2015. a
Engel, A.: Direct relationship between CO2 uptake and transparent exopolymer particles production in natural phytoplankton, J. Plankton Res., 24, 49–53, https://doi.org/10.1093/plankt/24.1.49, 2002. a, b
Engel, A., Thoms, S., Riebesell, U., Rochelle-Newall, E., and Zondervan, I.: Polysaccharide aggregation as a potential sink of marine dissolved organic carbon, Nature, 428, 929–932, https://doi.org/10.1038/nature02453, 2004. a
Eppley, R. W.: Temperature and phytoplankton growth, Fish. Bull., 70, 1063–1085, 1972. a
Fransner, F., Gustafsson, E., Tedesco, L., Vichi, M., Hordoir, R., Roquet, F., Spilling, K., Kuznetsov, I., Eilola, K., Mörth, C.-M., Humborg, C., and Nycander, J.: Non-Redfieldian Dynamics Explain Seasonal pCO2 Drawdown in the Gulf of Bothnia, J. Geophys. Res.-Oceans, 123, 166–188, https://doi.org/10.1002/2017JC013019, 2018. a, b, c, d, e, f, g
Geyer, B. and Rockel, B.: coastDat-2 COSMO-CLM Atmospheric Reconstruction [data set], https://doi.org/10.1594/WDCC/coastDat-2_COSMO-CLM, 2013. a, b
Griffies, S. M.: Fundamentals of Ocean Climate Models, Princeton University Press, Princeton, NJ, ISBN 9780691118925, 2004. a
Gustafsson, E., Deutsch, B., Gustafsson, B., Humborg, C., and Mörth, C.-M.: Carbon cycling in the Baltic Sea? The fate of allochthonous organic carbon and its impact on air-sea CO2 exchange, J. Marine Syst., 129, 289–302, https://doi.org/10.1016/j.jmarsys.2013.07.005, 2014a. a
Gustafsson, E., Wällstedt, T., Humborg, C., Mörth, C.-M., and Gustafsson, B. G.: External total alkalinity loads versus internal generation: The influence of nonriverine alkalinity sources in the Baltic Sea, Global Biogeochem. Cy., 28, 1358–1370, https://doi.org/10.1002/2014GB004888, 2014b. a, b, c, d, e, f
Gustafsson, E., Savchuk, O. P., Gustafsson, B. G., and Müller-Karulis, B.: Key processes in the coupled carbon, nitrogen, and phosphorus cycling of the Baltic Sea, Biogeochemistry, 134, 301–317, https://doi.org/10.1007/s10533-017-0361-6, 2017. a, b
Gustafsson, E., Hagens, M., Sun, X., Reed, D. C., Humborg, C., Slomp, C. P., and Gustafsson, B. G.: Sedimentary alkalinity generation and long-term alkalinity development in the Baltic Sea, Biogeosciences, 16, 437–456, https://doi.org/10.5194/bg-16-437-2019, 2019. a, b
Hansell, D. A., Carlson, C. A., Repeta, D. J., and Schlitzer, R.: Dissolved Organic Matter in the Ocean: A Controversy Stimulates New Insights, Oceanography, 22, 202–211, https://doi.org/10.5670/oceanog.2009.109, 2009. a
HELCOM: Sources and pathways of nutrients to the Baltic Sea, Balt. Sea Environ. Proc. No. 153, HELCOM, ISSN 0357-2994, https://helcom.fi/wp-content/uploads/2019/08/BSEP153.pdf
(last access: 15 November 2022),
2018. a
Ho, T.-Y., Quigg, A., Finkel, Z. V., Milligan, A. J., Wyman, K., Falkowski, P. G., and Morel, F. M. M.: The Elemental Composition of Some Marine Phytoplankton, J. Phycol., 39, 1145–1159, https://doi.org/10.1111/j.0022-3646.2003.03-090.x, 2003. a
Hoikkala, L., Kortelainen, P., Soinne, H., and Kuosa, H.: Dissolved organic matter in the Baltic Sea, J. Marine Syst., 142, 47–61, https://doi.org/10.1016/j.jmarsys.2014.10.005, 2015. a
ICES: Database Oceanography [data set], https://www.ices.dk/data/data-portals/Pages/default.aspx, last access: 14 January 2022. a
Kreus, M., Schartau, M., Engel, A., Nausch, M., and Voss, M.: Variations in the elemental ratio of organic matter in the central Baltic Sea: Part I – Linking primary production to remineralization, Cont. Shelf Res., 100, 25–45, https://doi.org/10.1016/j.csr.2014.06.015, 2015. a, b, c
Kriest, I., Oschlies, A., and Khatiwala, S.: Sensitivity analysis of simple global marine biogeochemical models, Global Biogeochem. Cy., 26, GB2029, https://doi.org/10.1029/2011GB004072, 2012. a
Kuznetsov, I., Neumann, T., and Burchard, H.: Model study on the ecosystem impact of a variable
Larsson, U., Hajdu, S., Walve, J., and Elmgren, R.: Baltic Sea nitrogen fixation estimated from the summer increase in upper mixed layer total nitrogen, Limnol. Oceanogr., 46, 811–820, https://doi.org/10.4319/lo.2001.46.4.0811, 2001. a, b, c
Leibniz Institute for Baltic Sea Research: Oceanographic Database Search with Interactive Navigation (ODIN) [data set],
https://odin2.io-warnemuende.de/,
last access: 15 November 2022. a
Leipe, T., Tauber, F., Vallius, H., Virtasalo, J., Uścinowicz, S., Kowalski, N., Hille, S., Lindgren, S., and Myllyvirta, T.: Particulate organic carbon (POC) in surface sediments of the Baltic Sea, Geo-Mar. Lett., 31, 175–188, https://doi.org/10.1007/s00367-010-0223-x, 2010. a
Lips, I. and Lips, U.: The Importance of Mesodinium rubrum at Post-Spring Bloom Nutrient and Phytoplankton Dynamics in the Vertically Stratified Baltic Sea, Frontiers in Marine Science, 4, 407, https://doi.org/10.3389/fmars.2017.00407, 2017. a
Macias, D., Huertas, I. E., Garcia-Gorriz, E., and Stips, A.: Non-Redfieldian dynamics driven by phytoplankton phosphate frugality explain nutrient and chlorophyll patterns in model simulations for the Mediterranean Sea, Prog. Oceanogr., 173, 37–50, https://doi.org/10.1016/j.pocean.2019.02.005, 2019. a
Martin, J. H., Knauer, G. A., Karl, D. M., and Broenkow, W. W.: VERTEX: carbon cycling in the northeast Pacific, Deep-Sea Res., 34, 267–285, https://doi.org/10.1016/0198-0149(87)90086-0, 1987. a
Martiny, A. C., Talarmin, A., Mouginot, C., Lee, J. A., Huang, J. S., Gellene, A. G., and Caron, D. A.: Biogeochemical interactions control a temporal succession in the elemental composition of marine communities, Limnol. Oceanogr., 61, 531–542, https://doi.org/10.1002/lno.10233, 2016. a
Monod, J.: The growth of bacterial cultures, Ann. Rev. Microbiol., 3, 371–394, 1949. a
Müller, J. D., Schneider, B., and Rehder, G.: Long-term alkalinity trends in the Baltic Sea and their implications for CO2-induced acidification, Limnol. Oceanogr., 61, 1984–2002, https://doi.org/10.1002/lno.10349, 2016. a
Nausch, M., Nausch, G., Lass, H. U., Mohrholz, V., Nagel, K., Siegel, H., and Wasmund, N.: Phosphorus input by upwelling in the eastern Gotland Basin (Baltic Sea) in summer and its effects on filamentous cyanobacteria, Estuar. Coast. Shelf S., 83, 434–442, https://doi.org/10.1016/j.ecss.2009.04.031, 2009. a
Neumann, T.: ERGOM 1.2 model hindcast 1948–2019 [data set], https://thredds-iow.io-warnemuende.de/thredds/catalogs/projects/integral/catalog_pocNP_V04R25_3nm_agg_time.html (last access: 10 March 2022), 2021. a
Neumann, T.: Model code and boundary data for “Non-Redfield carbon model for the Baltic Sea (ERGOM version 1.2) – Implementation and Budget estimates” paper [code], https://doi.org/10.5281/zenodo.7252134, last access: 26 October 2022. a, b
Neumann, T., Fennel, W., and Kremp, C.: Experimental simulations with an ecosystem model of the Baltic Sea: A nutrient load reduction experiment, Global Biogeochem. Cy., 16, 7-1–7-19, https://doi.org/10.1029/2001GB001450, 2002. a
Neumann, T., Koponen, S., Attila, J., Brockmann, C., Kallio, K., Kervinen, M., Mazeran, C., Müller, D., Philipson, P., Thulin, S., Väkevä, S., and Ylöstalo, P.: Optical model for the Baltic Sea with an explicit CDOM state variable: a case study with Model ERGOM (version 1.2), Geosci. Model Dev., 14, 5049–5062, https://doi.org/10.5194/gmd-14-5049-2021, 2021. a, b, c, d
Omstedt, A., Gustafsson, E., and Wesslander, K.: Modelling the uptake and release of carbon dioxide in the Baltic Sea surface water, Cont. Shelf Res., 29, 870–885, https://doi.org/10.1016/j.csr.2009.01.006, 2009. a, b
Omstedt, A., Humborg, C., Pempkowiak, J., Perttilä, M., Rutgersson, A., Schneider, B., and Smith, B.: Biogeochemical Control of the Coupled CO2–O2 System of the Baltic Sea: A Review of the Results of Baltic-C, Ambio, 43, 49–59, https://doi.org/10.1007/s13280-013-0485-4, 2014. a
Pahlow, M., Chien, C.-T., Arteaga, L. A., and Oschlies, A.: Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviour, Geosci. Model Dev., 13, 4663–4690, https://doi.org/10.5194/gmd-13-4663-2020, 2020. a
Pfeil, B., Olsen, A., Bakker, D. C. E., Hankin, S., Koyuk, H., Kozyr, A., Malczyk, J., Manke, A., Metzl, N., Sabine, C. L., Akl, J., Alin, S. R., Bates, N., Bellerby, R. G. J., Borges, A., Boutin, J., Brown, P. J., Cai, W.-J., Chavez, F. P., Chen, A., Cosca, C., Fassbender, A. J., Feely, R. A., González-Dávila, M., Goyet, C., Hales, B., Hardman-Mountford, N., Heinze, C., Hood, M., Hoppema, M., Hunt, C. W., Hydes, D., Ishii, M., Johannessen, T., Jones, S. D., Key, R. M., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lourantou, A., Merlivat, L., Midorikawa, T., Mintrop, L., Miyazaki, C., Murata, A., Nakadate, A., Nakano, Y., Nakaoka, S., Nojiri, Y., Omar, A. M., Padin, X. A., Park, G.-H., Paterson, K., Perez, F. F., Pierrot, D., Poisson, A., Ríos, A. F., Santana-Casiano, J. M., Salisbury, J., Sarma, V. V. S. S., Schlitzer, R., Schneider, B., Schuster, U., Sieger, R., Skjelvan, I., Steinhoff, T., Suzuki, T., Takahashi, T., Tedesco, K., Telszewski, M., Thomas, H., Tilbrook, B., Tjiputra, J., Vandemark, D., Veness, T., Wanninkhof, R., Watson, A. J., Weiss, R., Wong, C. S., and Yoshikawa-Inoue, H.: A uniform, quality controlled Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 5, 125–143, https://doi.org/10.5194/essd-5-125-2013, 2013. a
Rehder, G., Müller, J., Bittig, H., Kahru, M., Kaitala, S., Schneider, B., Siiriä, S.-M., Tuomi, L., and Wasmund, N.: Extreme productivity patterns during the spring bloom 2018 in the central Baltic Sea suggest vertical nutrient shuttling: Unforeseen surprises for the fight against eutrophication in a warming world?, in: All abstracts for the ICOS Science Conference 2020, ICOS Integrated Carbon Observation System, https://www.icos-cp.eu/sc2020/abstracts#143 (last access: 15 November 2022), 2020. a
Schneider, B. and Müller, J. D.: Surface Water Biogeochemistry as Derived from pCO2 Observations, Springer International Publishing, Cham, 49–92, https://doi.org/10.1007/978-3-319-61699-5_5, 2018. a, b, c, d
Schneider, B., Kaitala, S., Raateoja, M., and Sadkowiak, B.: A nitrogen fixation estimate for the Baltic Sea based on continuous pCO2 measurements on a cargo ship and total nitrogen data, Cont. Shelf Res., 29, 1535–1540, https://doi.org/10.1016/j.csr.2009.04.001, 2009.
a
Sharoni, S. and Halevy, I.: Nutrient ratios in marine particulate organic matter are predicted by the population structure of well-adapted phytoplankton, Science Advances, 6, eaaw9371, https://doi.org/10.1126/sciadv.aaw9371, 2020. a
Steele, J. H.: Environmental control of photosynthesis in the sea,
Limnol. Oceanogr.,
7,
137–150,
https://doi.org/10.4319/lo.1962.7.2.0137,
1962. a
Szymczycha, B., Maciejewska, A., Winogradow, A., and Pempkowiak, J.: Could submarine groundwater discharge be a significant carbon source to the southern Baltic Sea?, Oceanologia, 56, 327–347, https://doi.org/10.5697/oc.56-2.327, 2014. a
The Swedish Agency for Marine and Water Management and the Swedish Meteorological and Hydrological Institute: Swedish Ocean Archive
(SHARK) [data set], https://sharkweb.smhi.se/hamta-data/, last access: 28 February 2022. a
Wan, Z., Jonasson, L., and Bi, H.:
Wetz, M. S. and Wheeler, P. A.: Release of dissolved organic matter by coastal diatoms, Limnol. Oceanogr., 52, 798–807, https://doi.org/10.4319/lo.2007.52.2.0798, 2007. a
Winton, M.: A Reformulated Three-Layer Sea Ice Model, J. Atmos. Ocean. Tech., 17, 525–531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2, 2000. a
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and...
