Articles | Volume 9, issue 4
https://doi.org/10.5194/gmd-9-1545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-1545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
22 Apr 2016
Model description paper |
| 22 Apr 2016
The ecological module of BOATS-1.0: a bioenergetically constrained model of marine upper trophic levels suitable for studies of fisheries and ocean biogeochemistry
David Anthony Carozza
CORRESPONDING AUTHOR
Department of Earth and Planetary Sciences, McGill University, Montreal, Canada
Daniele Bianchi
Department of Earth and Planetary Sciences, McGill University, Montreal, Canada
School of Oceanography, University of Washington, Seattle, Washington, USA
now at: Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USA
Eric Douglas Galbraith
Department of Earth and Planetary Sciences, McGill University, Montreal, Canada
now at: Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain
now at: Institut de Ciència i Tecnologia Ambientals (ICTA) and Department of Mathematics, Universitat Autonoma de Barcelona, 08193 Barcelona, Spain
Related authors
Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond
Nat. Hazards Earth Syst. Sci., 24, 2577–2595, https://doi.org/10.5194/nhess-24-2577-2024, https://doi.org/10.5194/nhess-24-2577-2024, 2024
Short summary
Short summary
Modelling floods at the street level for large countries like Canada and the United States is difficult and very costly. However, many applications do not necessarily require that level of detail. As a result, we present a flood modelling framework built with artificial intelligence for socioeconomic studies like trend and scenarios analyses. We find for example that an increase of 10 % in average precipitation yields an increase in displaced population of 18 % in Canada and 14 % in the US.
Derek P. Tittensor, Tyler D. Eddy, Heike K. Lotze, Eric D. Galbraith, William Cheung, Manuel Barange, Julia L. Blanchard, Laurent Bopp, Andrea Bryndum-Buchholz, Matthias Büchner, Catherine Bulman, David A. Carozza, Villy Christensen, Marta Coll, John P. Dunne, Jose A. Fernandes, Elizabeth A. Fulton, Alistair J. Hobday, Veronika Huber, Simon Jennings, Miranda Jones, Patrick Lehodey, Jason S. Link, Steve Mackinson, Olivier Maury, Susa Niiranen, Ricardo Oliveros-Ramos, Tilla Roy, Jacob Schewe, Yunne-Jai Shin, Tiago Silva, Charles A. Stock, Jeroen Steenbeek, Philip J. Underwood, Jan Volkholz, James R. Watson, and Nicola D. Walker
Geosci. Model Dev., 11, 1421–1442, https://doi.org/10.5194/gmd-11-1421-2018, https://doi.org/10.5194/gmd-11-1421-2018, 2018
Short summary
Short summary
Model intercomparison studies in the climate and Earth sciences communities have been crucial for strengthening future projections. Given the speed and magnitude of anthropogenic change in the marine environment, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. We describe the Fisheries and Marine Ecosystem Model Intercomparison Project, which brings together the marine ecosystem modelling community to inform long-term projections of marine ecosystems.
Eric D. Galbraith, Abdullah Al Faisal, Tanya Matitia, William Fajzel, Ian Hatton, Helmut Haberl, Fridolin Krausmann, and Dominik Wiedenhofer
Earth Syst. Dynam., 16, 979–999, https://doi.org/10.5194/esd-16-979-2025, https://doi.org/10.5194/esd-16-979-2025, 2025
Short summary
Short summary
The technosphere – including buildings, infrastructure, and all other non-living human creations – is a major part of our planet, but it is not often considered as an integrated part of Earth system processes. Here we propose a refined definition of the technosphere, intended to help with integration. We also characterize the functional end uses, map the global distribution, and discuss the catalytic properties that underlie the exponential growth of the trillion tonne technosphere.
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.
Manuel Grenier, Mathieu Boudreault, David A. Carozza, Jérémie Boudreault, and Sébastien Raymond
Nat. Hazards Earth Syst. Sci., 24, 2577–2595, https://doi.org/10.5194/nhess-24-2577-2024, https://doi.org/10.5194/nhess-24-2577-2024, 2024
Short summary
Short summary
Modelling floods at the street level for large countries like Canada and the United States is difficult and very costly. However, many applications do not necessarily require that level of detail. As a result, we present a flood modelling framework built with artificial intelligence for socioeconomic studies like trend and scenarios analyses. We find for example that an increase of 10 % in average precipitation yields an increase in displaced population of 18 % in Canada and 14 % in the US.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
De'Marcus Robinson, Anh L. D. Pham, David J. Yousavich, Felix Janssen, Frank Wenzhöfer, Eleanor C. Arrington, Kelsey M. Gosselin, Marco Sandoval-Belmar, Matthew Mar, David L. Valentine, Daniele Bianchi, and Tina Treude
Biogeosciences, 21, 773–788, https://doi.org/10.5194/bg-21-773-2024, https://doi.org/10.5194/bg-21-773-2024, 2024
Short summary
Short summary
The present study suggests that high release of ferrous iron from the seafloor of the oxygen-deficient Santa Barabara Basin (California) supports surface primary productivity, creating positive feedback on seafloor iron release by enhancing low-oxygen conditions in the basin.
Daniele Bianchi, Daniel McCoy, and Simon Yang
Geosci. Model Dev., 16, 3581–3609, https://doi.org/10.5194/gmd-16-3581-2023, https://doi.org/10.5194/gmd-16-3581-2023, 2023
Short summary
Short summary
We present NitrOMZ, a new model of the oceanic nitrogen cycle that simulates chemical transformations within oxygen minimum zones (OMZs). We describe the model formulation and its implementation in a one-dimensional representation of the water column before evaluating its ability to reproduce observations in the eastern tropical South Pacific. We conclude by describing the model sensitivity to parameter choices and environmental factors and its application to nitrogen cycling in the ocean.
Priscilla Le Mézo, Jérôme Guiet, Kim Scherrer, Daniele Bianchi, and Eric Galbraith
Biogeosciences, 19, 2537–2555, https://doi.org/10.5194/bg-19-2537-2022, https://doi.org/10.5194/bg-19-2537-2022, 2022
Short summary
Short summary
This study quantifies the role of commercially targeted fish biomass in the cycling of three important nutrients (N, P, and Fe), relative to nutrients otherwise available in water and to nutrients required by primary producers, and the impact of fishing. We use a model of commercially targeted fish biomass constrained by fish catch and stock assessment data to assess the contributions of fish at the global scale, at the time of the global peak catch and prior to industrial fishing.
Eric D. Galbraith
Earth Syst. Dynam., 12, 671–687, https://doi.org/10.5194/esd-12-671-2021, https://doi.org/10.5194/esd-12-671-2021, 2021
Short summary
Short summary
Scientific tradition has left a gap between the study of humans and the rest of the Earth system. Here, a holistic approach to the global human system is proposed, intended to provide seamless integration with natural sciences. At the core, this focuses on what humans are doing with their time, what the bio-physical outcomes of those activities are, and what the lived experience is. The quantitative approach can facilitate data analysis across scales and integrated human–Earth system modeling.
Jordyn E. Moscoso, Andrew L. Stewart, Daniele Bianchi, and James C. McWilliams
Geosci. Model Dev., 14, 763–794, https://doi.org/10.5194/gmd-14-763-2021, https://doi.org/10.5194/gmd-14-763-2021, 2021
Short summary
Short summary
This project was created to understand the across-shore distribution of plankton in the California Current System. To complete this study, we used a quasi-2-D dynamical model coupled to an ecosystem model. This paper is a preliminary study to test and validate the model against data collected by the California Cooperative Oceanic Fisheries Investigations (CalCOFI). We show the solution of our model solution compares well to the data and discuss our model as a tool for further model development.
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.
Olivier Cartapanis, Eric D. Galbraith, Daniele Bianchi, and Samuel L. Jaccard
Clim. Past, 14, 1819–1850, https://doi.org/10.5194/cp-14-1819-2018, https://doi.org/10.5194/cp-14-1819-2018, 2018
Short summary
Short summary
A data-based reconstruction of carbon-bearing deep-sea sediment shows significant changes in the global burial rate over the last glacial cycle. We calculate the impact of these deep-sea changes, as well as hypothetical changes in continental shelf burial and volcanic outgassing. Our results imply that these geological fluxes had a significant impact on ocean chemistry and the global carbon isotopic ratio, and that the natural carbon cycle was not in steady state during the Holocene.
Derek P. Tittensor, Tyler D. Eddy, Heike K. Lotze, Eric D. Galbraith, William Cheung, Manuel Barange, Julia L. Blanchard, Laurent Bopp, Andrea Bryndum-Buchholz, Matthias Büchner, Catherine Bulman, David A. Carozza, Villy Christensen, Marta Coll, John P. Dunne, Jose A. Fernandes, Elizabeth A. Fulton, Alistair J. Hobday, Veronika Huber, Simon Jennings, Miranda Jones, Patrick Lehodey, Jason S. Link, Steve Mackinson, Olivier Maury, Susa Niiranen, Ricardo Oliveros-Ramos, Tilla Roy, Jacob Schewe, Yunne-Jai Shin, Tiago Silva, Charles A. Stock, Jeroen Steenbeek, Philip J. Underwood, Jan Volkholz, James R. Watson, and Nicola D. Walker
Geosci. Model Dev., 11, 1421–1442, https://doi.org/10.5194/gmd-11-1421-2018, https://doi.org/10.5194/gmd-11-1421-2018, 2018
Short summary
Short summary
Model intercomparison studies in the climate and Earth sciences communities have been crucial for strengthening future projections. Given the speed and magnitude of anthropogenic change in the marine environment, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. We describe the Fisheries and Marine Ecosystem Model Intercomparison Project, which brings together the marine ecosystem modelling community to inform long-term projections of marine ecosystems.
Katja Frieler, Stefan Lange, Franziska Piontek, Christopher P. O. Reyer, Jacob Schewe, Lila Warszawski, Fang Zhao, Louise Chini, Sebastien Denvil, Kerry Emanuel, Tobias Geiger, Kate Halladay, George Hurtt, Matthias Mengel, Daisuke Murakami, Sebastian Ostberg, Alexander Popp, Riccardo Riva, Miodrag Stevanovic, Tatsuo Suzuki, Jan Volkholz, Eleanor Burke, Philippe Ciais, Kristie Ebi, Tyler D. Eddy, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Christian Hof, Veronika Huber, Jonas Jägermeyr, Valentina Krysanova, Rafael Marcé, Hannes Müller Schmied, Ioanna Mouratiadou, Don Pierson, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Matthias F. Biber, Richard A. Betts, Benjamin Leon Bodirsky, Delphine Deryng, Steve Frolking, Chris D. Jones, Heike K. Lotze, Hermann Lotze-Campen, Ritvik Sahajpal, Kirsten Thonicke, Hanqin Tian, and Yoshiki Yamagata
Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, https://doi.org/10.5194/gmd-10-4321-2017, 2017
Short summary
Short summary
This paper describes the simulation scenario design for the next phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is designed to facilitate a contribution to the scientific basis for the IPCC Special Report on the impacts of 1.5 °C global warming. ISIMIP brings together over 80 climate-impact models, covering impacts on hydrology, biomes, forests, heat-related mortality, permafrost, tropical cyclones, fisheries, agiculture, energy, and coastal infrastructure.
Nicolas Brown and Eric D. Galbraith
Clim. Past, 12, 1663–1679, https://doi.org/10.5194/cp-12-1663-2016, https://doi.org/10.5194/cp-12-1663-2016, 2016
Short summary
Short summary
An Earth system model is used to explore variability in the global impacts of AMOC disruptions. The model exhibits spontaneous AMOC oscillations under particular boundary conditions, which we compare with freshwater-forced disruptions. We find that the global impacts are similar whether the AMOC disruptions are spontaneous or forced. Freshwater forcing generally amplifies the global impacts, with tropical precipitation and the stability of polar haloclines showing particular sensitivity.
O. Duteil, W. Koeve, A. Oschlies, D. Bianchi, E. Galbraith, I. Kriest, and R. Matear
Biogeosciences, 10, 7723–7738, https://doi.org/10.5194/bg-10-7723-2013, https://doi.org/10.5194/bg-10-7723-2013, 2013
Related subject area
Biogeosciences
ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model
Development and assessment of the physical–biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3
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
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
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
A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0)
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
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
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)
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)
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev., 18, 4103–4117, https://doi.org/10.5194/gmd-18-4103-2025, https://doi.org/10.5194/gmd-18-4103-2025, 2025
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.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
Geosci. Model Dev., 18, 3941–3964, https://doi.org/10.5194/gmd-18-3941-2025, https://doi.org/10.5194/gmd-18-3941-2025, 2025
Short summary
Short summary
Physical–biogeochemical ocean global models are required to analyze difficult 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 developed model is an important tool for studying physical–biogeochemical processes.
Elchin E. Jafarov, Hélène 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., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
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.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
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.
Felix Nößler, Thibault Moulin, Oksana Buzhdygan, Britta Tietjen, and Felix May
EGUsphere, https://doi.org/10.5194/egusphere-2024-3798, https://doi.org/10.5194/egusphere-2024-3798, 2024
Short summary
Short summary
To predict the response of grassland plant communities to management and climate change, we developed the computer model GrasslandTraitSim.jl. Unlike other models, it uses measurable plant traits such as height, leaf thinness, and root structure as inputs, rather than hard-to-measure species data. This allows realistic simulation of many species. The model tracks daily changes in above- and below-ground biomass, plant height, and soil water, linking plant community composition to biomass supply.
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.
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.
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.
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.
Cited articles
Agawin, N. S., Duarte, C. M., and Agusti, S.: Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production, Limnol. Oceanogr., 45, 591–600, https://doi.org/10.4319/lo.2000.45.3.0591, 2000.
Andersen, K. H. and Beyer, J. E.: Asymptotic size determines species abundance in the marine size spectrum, Am. Nat., 168, 54–61, https://doi.org/10.1086/504849, 2006.
Andersen, K. H. and Beyer, J. E.: Size structure, not metabolic scaling rules, determines fisheries reference points, Fish Fish., 16, 1–22, https://doi.org/10.1111/faf.12042, 2013.
Andersen, K. H. and Pedersen, M.: Damped trophic cascades driven by fishing in model marine ecosystems, P. Roy. Soc. B-Biol. Sci., 277, 795–802, https://doi.org/10.1098/rspb.2009.1512, 2010.
Barange, M., Merino, G., Blanchard, J., Scholtens, J., Harle, J., Allison, E. H., Allen, J. I., Holt, J., and Jennings, S.: Impacts of climate change on marine ecosystem production in societies dependent on fisheries, Nature Climate Change, 4, 211–216, https://doi.org/10.1038/NCLIMATE2119, 2014.
Barnes, C., Maxwell, D., Reuman, D. C., and Jennings, S.: Global patterns in predator–prey size relationships reveal size dependency of trophic transfer efficiency, Ecology, 91, 222–232, https://doi.org/10.1890/08-2061.1, 2010.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20, https://doi.org/10.4319/lo.1997.42.1.0001, 1997.
Beverton, R. J. H.: Patterns of reproductive strategy parameters in some marine teleost fishes, J. Fish Biol., 41, 137–160, https://doi.org/10.1111/j.1095-8649.1992.tb03875.x, 1992.
Beverton, R. J. H. and Holt, S. J.: On the Dynamics of Exploited Fish Populations, Springer Science+Business Media Inc., Dordrecht, 1957.
Bissinger, J. E., Montagnes, D. J., Sharples, J., and Atkinson, D.: Predicting marine phytoplankton maximum growth rates from temperature: Improving on the Eppley curve using quantile regression, Limnol. Oceanogr., 53, 487–493, https://doi.org/10.4319/lo.2008.53.2.0487, 2008.
Blanchard, J. L., Jennings, S., Law, R., Castle, M. D., McCloghrie, P., Rochet, M.-J., and Benoît, E.: How does abundance scale with body size in coupled size-structured food webs?, J. Anim. Ecol., 78, 270–280, https://doi.org/10.1111/j.1365-2656.2008.01466.x, 2009.
Blanchard, J. L., Jennings, S., Holmes, R., Harle, J., Merino, G., Allen, J. I., Holt, J., Dulvy, N. K., and Barange, M.: Potential consequences of climate change for primary production and fish production in large marine ecosystems, Philos. T. R. Soc. B-Biol. Sci., 367, 2979–2989, https://doi.org/10.1098/rstb.2012.0231, 2012.
Blueweiss, L., Fox, H., Kudzma, V., Nakashima, D., Peters, R., and Sams, S.: Relationships between body size and some life history parameters, Oecologia, 37, 257–272, https://doi.org/10.1007/BF00344996, 1978.
Boltzmann, L.: Weitere Studien über das Wärmegleichgewicht unter Gasmolekülen, Sitzungsberichte der Mathematisch-Naturwissenschaftlichen Classe der Kaiserlichen Akademie der Wissenschaften Wien, 66, 275–370, 1872.
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013.
Branch, T. A., Jensen, O., Ricard, D., Ye, Y., and Hilborn, R.: Contrasting global trends in marine fishery status obtained from catches and from stock assessments, Conserv. Biol., 25, 777–786, https://doi.org/10.1111/j.1523-1739.2011.01687.x, 2011.
Brander, K.: Impacts of climate change on fisheries, J. Marine Syst., 79, 389–402, https://doi.org/10.1016/j.jmarsys.2008.12.015, 2010.
Brown, J., Gillooly, J., Allen, A., Savage, V., and West, G.: Toward a metabolic theory of ecology, Ecology, 85, 1771–1789, https://doi.org/10.1890/03-9000, 2004.
Carozza, D. A., Bianchi, D., and Galbraith, E. D.: BOATS: A bioenergetically-constrained coupled fisheries ecology and economics model for global studies of harvest and climate change, in review, 2016.
Carr, M.-E., Friedrichs, M. A. M., Schmeltz, M., Noguchi Aita, M., Antoine, D., Arrigo, K. R., Asanuma, I., Aumont, O., Barber, R., Behrenfeld, M., Bidigare, R., Buitenhuis, E. T., Campbell, J., Ciotti, A., Dierssen, H., Dowell, M., Dunne, J., Esaias, W., Gentili, B., Gregg, W., Groom, S., Hoepffner, N., Ishizaka, J., Kameda, T., Le Quéré, C., Lohrenz, S., Marra, J., Mélin, F., Moore, K., Morel, A., Reddy, T. E., Ryan, J., Scardi, M., Smyth, T., Turpie, K., Tilstone, G., Waters, K., and Yamanaka, Y.: A comparison of global estimates of marine primary production from ocean color, Deep-Sea Res. Pt. II, 53, 741–770, https://doi.org/10.1016/j.dsr2.2006.01.028, 2006.
Charnov, E. L., Gislason, H., and Pope, J. G.: Evolutionary assembly rules for fish life histories, Fish Fish., 14, 213–224, https://doi.org/10.1111/j.1467-2979.2012.00467.x, 2012.
Chassot, E., Bonhommeau, S., and Dulvy, N.: Global marine primary production constrains fisheries catches, Ecol. Lett., 13, 495–505, https://doi.org/10.1111/j.1461-0248.2010.01443.x, 2010.
Chen, Y., Jackson, D. A., and Harvey, H. H.: A comparison of von bertalanffy and polynomial functions in modelling fish growth data, Can. J. Fish. Aquat. Sci., 49, 1228–1235, https://doi.org/10.1139/f92-138, 1992.
Chesson, P.: Mechanisms of maintenance of species diversity, Annu. Rev. Ecol. Syst., 31, 343–366, https://doi.org/10.1146/annurev.ecolsys.31.1.343, 2000.
Cheung, W. W. L., Lam, V. W. Y., Sarmiento, J. L., Kearney, K., Watson, R., Zeller, D., and Pauly, D.: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change, Glob. Change Biol., 16, 24–35, https://doi.org/10.1111/j.1365-2486.2009.01995.x, 2010.
Christensen, V., Coll, M., Buszowski, J., Cheung, W. W., L., Frölicher, T., Steenbeek, J., Stock, C. A., Watson, R. A., and Walters, C. J.: The global ocean is an ecosystem: simulating marine life and fisheries, Global Ecol. Biogeogr., 24, 507–517, https://doi.org/10.1111/geb.12281, 2015.
Clarke, A.: Costs and consequences of evolutionary temperature adaptation, Trends Ecol. Evol., 18, 573–581, https://doi.org/10.1016/j.tree.2003.08.007, 2003.
Clarke, A.: Is there a universal temperature dependence of metabolism?, Funct. Ecol., 18, 252–256, https://doi.org/10.1111/j.0269-8463.2004.00842.x, 2004.
Crossland, C. J., Hatcher, B. G., and Smith, S. V.: Role of coral reefs in global ocean production, Coral Reefs, 10, 55–64, https://doi.org/10.1007/BF00571824, 1991.
Cury, P. and Pauly, D.: Patterns and propensities in reproduction and growth of marine fishes, Ecol. Res., 15, 101–106, https://doi.org/10.1046/j.1440-1703.2000.00321.x, 2000.
Dahlberg, M. D.: A review of survival rates of fish eggs and larvae in relation to impact assessments, Mar. Fish. Rev., 41, 1–12, 1979.
Datta, S., Delius, G. W., and Law, R.: A jump-growth model for predator–prey dynamics: derivation and application to marine ecosystems, B. Math. Biol., 72, 1361–1382, https://doi.org/10.1007/s11538-009-9496-5, 2010.
Denman, K. L. and Pena, M. A.: The response of two coupled one-dimensional mixed layer/planktonic ecosystem models to climate change in the NE subarctic Pacific Ocean, Deep-Sea Res. Pt. II, 49, 5739–5757, https://doi.org/10.1016/S0967-0645(02)00212-6, 2002.
Deriso, R. B.: Harvesting strategies and parameter estimation for an age-structured model, Can. J. Fish. Aquat. Sci., 37, 268–282, https://doi.org/10.1139/f80-034, 1980.
Doney, S. C., Ruckelshaus, M., Emmett Duffy, J., Barry, J. P., Chan, F., English, C. A., Galindo, H. M., Grebmeier, J. M., Hollowed, A. B., Knowlton, N., Polovina, J., Rabalais, N. N., Sydeman, W. J., and Talley, L. D.: Climate change impacts on marine ecosystems, Annual Review of Marine Science, 4, 11–37, https://doi.org/10.1146/annurev-marine-041911-111611, 2012.
Duarte, C. M. and Alcaraz, M.: To produce many small or few large eggs: a size-independent reproductive tactic of fish, Oecologia, 80, 401–404, https://doi.org/10.1007/BF00379043, 1989.
Duarte, C. M. and Chiscano, C. L.: Seagrass biomass and production: a reassessment, Aquat. Bot., 65, 159–174, https://doi.org/10.1016/S0304-3770(99)00038-8, 1999.
Dueri, S., Faugeras, B., and Maury, O.: Modelling the skipjack tuna dynamics in the Indian Ocean with APECOSM-E: Part 1. Model formulation, Ecol. Model., 245, 41–54, https://doi.org/10.1016/j.ecolmodel.2012.02.007, 2012.
Dunne, J., Armstrong, R., Gnanadesikan, A., and Sarmiento, J.: Empirical and mechanistic models for the particle export ratio, Global Biogeochem. Cy., 19, GB4026, https://doi.org/10.1029/2004GB002390, 2005.
Duplisea, D. E., Jennings, S., Warr, K. J., and Dinmore, T. A.: A size-based model of the impacts of bottom trawling on benthic community structure, Can. J. Fish. Aquat. Sci., 59, 1785–1795, https://doi.org/10.1139/F02-148, 2002.
Ernest, S. K. M., Enquist, B. J., Brown, J. H., Charnov, E. L., Gillooly, J. F., Savage, V. M., White, E. P., Smith, F. A., Hadly, E. A., Haskell, J. P., Lyons, S. K., Maurer, B. A., Niklas, K. J., and Tiffney, B.: Thermodynamic and metabolic effects on the scaling of production and population energy use, Ecol. Lett., 6, 990–995, https://doi.org/10.1046/j.1461-0248.2003.00526.x, 2003.
FAO: The State of World Fisheries and Aquaculture: Opportunities and Challenges, Rome, Italy, 2014.
Faugeras, B. and Maury, O.: A multi-region nonlinear age–size structured fish population model, Nonlinear Anal.-Real, 6, 447–460, 2005.
Freedman, J. A. and Noakes, D. L. G.: Why are there no really big bony fishes? A point-of-view on maximum body size in teleosts and elasmobranchs, Rev. Fish Biol. Fisher., 12, 403–416, https://doi.org/10.1023/A:1025365210414, 2002.
Friedland, K. D., Stock, C., Drinkwater, K. F., Link, J. S., Leaf, R. T., Shank, B. V., Rose, J. M., Pilskaln, C. H., and Fogarty, M. J.: Pathways between Primary Production and Fisheries Yields of Large Marine Ecosystems, PLoS ONE, 7, e28945, https://doi.org/10.1371/journal.pone.0028945, 2012.
Froese, R. and Pauly, D.: Fishbase, World Wide Web electronic publication, available at: www.fishbase.org (last access: 31 July 2015), version (11/2014), 2014.
Froese, R., Thorson, J. T., and Reyes Jr., R. B.: A Bayesian approach for estimating length-weight relationships in fishes, J. Appl. Ichthyol., 30, 78–85, https://doi.org/10.1111/jai.12299, 2013.
Gislason, H., Daan, N., Rice, J. C., and Pope, J. G.: Size, growth, temperature and the natural mortality of marine fish, Fish Fish., 11, 149–158, https://doi.org/10.1111/j.1467-2979.2009.00350.x, 2010.
Hartvig, M., Andersen, K. H., and Beyer, J. E.: Food web framework for size-structured populations, J. Theor. Biol., 272, 113–122, https://doi.org/10.1016/j.jtbi.2010.12.006, 2011.
Hessen, D. O. and Kaartvedt, S.: Top–down cascades in lakes and oceans: different perspectives but same story?, J. Plankton Res., 36, 914–924, https://doi.org/10.1093/plankt/fbu040, 2014.
Holling, C. S.: Some characteristics of simple types of predation and parasitism, Can. Entomol., 91, 385–398, https://doi.org/10.4039/Ent91385-7, 1959.
Hutchings, J. A., Minto, C., Ricard, D., Baum, J. K., and Jensen, O. P.: Trends in the abundance of marine fishes, Can. J. Fish. Aquat. Sci., 67, 1205–1210, https://doi.org/10.1139/F10-081, 2010.
Irigoien, X., Klevjer, T. A., Røstad, A., Martinez, U., Boyra, G., Acuña, J. L., Bode, A., Echevarria, F., Gonzalez-Gordillo, J. I., Hernandez-Leon, S., Agusti, S., Aksnes, D. L., Duarte, C. M., and Kaartvedt, S.: Large mesopelagic fishes biomass and trophic efficiency in the open ocean, Nature Communications, 5, 3271, https://doi.org/10.1038/ncomms4271, 2014.
Jackson, J. B. C.: Historical overfishing and the recent collapse of coastal ecosystems, Science, 293, 629–637, https://doi.org/10.1126/science.1059199, 2001.
Jennings, S. and Collingridge, K.: Predicting Consumer Biomass, Size-Structure, Production, Catch Potential, Responses to Fishing and Associated Uncertainties in the World's Marine Ecosystems, PLoS ONE, 10, e0133794, https://doi.org/10.1371/journal.pone.0133794, 2015.
Jennings, S., Mélin, F., Blanchard, J. L., Forster, R. M., Dulvy, N. K., and Wilson, R. W.: Global-scale predictions of community and ecosystem properties from simple ecological theory, P. Roy. Soc. B-Biol. Sci., 275, 1375–1383, https://doi.org/10.1098/rspb.2008.0192, 2008.
Kendall Jr., A. W., Alstrom, E. H., and Moser, H. G.: Early life history stages of fishes and their characters, in: Ontogeny and Systematics of Fishes, edited by: Moser, H. G., The American Society of Ichthyologists and Herpetologists, Lawrence, KS, USA, 11–22, 1984.
Kooijmann, S. A. L. M.: Dynamic Energy Mass Budgets in Biological Systems, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511565403, 2000.
Kot, M.: Elements of Mathematical Ecology, Cambridge University Press, Cambridge, 2001.
Krohn, M., Reidy, S., and Kerr, S.: Bioenergetic analysis of the effects of temperature and prey availability on growth and condition of northern cod (Gadus morhua), Can. J. Fish. Aquat. Sci., 54, 113–121, https://doi.org/10.1139/f96-159, 1997.
Lefort, S., Aumont, O., Bopp, L., Arsouze, T., Gehlen, M., and Maury, O.: Spatial and body-size dependent response of marine pelagic communities to projected global climate change, Glob. Change Biol., 21, 154–164, https://doi.org/10.1111/gcb.12679, 2014.
Lester, N. P., Shuter, B. J., and Abrams, P. A.: Interpreting the von Bertalanffy model of somatic growth in fishes: the cost of reproduction, P. Roy. Soc. B-Biol. Sci., 271, 1625–1631, https://doi.org/10.1098/rspb.2004.2778, 2004.
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., and Levitus, S. (Eds.): World Ocean Atlas 2005, volume 1: Temperature, NOAA Atlas NESDIS 61, US Gov. Printing Office, Washington, DC, USA, 2006.
Maranón, E.: Cell size as a key determinant of phytoplankton metabolism and community structure, Annual Review of Marine Science, 7, 241–264, https://doi.org/10.1146/annurev-marine-010814-015955, 2015.
Marquet, P. A., Quiñones, R. A., Abades, S., Labra, F., Tognelli, M., Arim, M., and Rivadeneira, M.: Scaling and power-laws in ecological systems, J. Exp. Biol., 208, 1749–1769, 2005.
Marra, J., Trees, C. C., and O'Reilly, J. E.: Phytoplankton pigment absorption: a strong predictor of primary productivity in the surface ocean, Deep-Sea Res. Pt. I, 54, 155–163, https://doi.org/10.1016/j.dsr.2006.12.001, 2007.
MATLAB: version 7.14.0.739 (R2012a), The MathWorks Inc., Natick, MA, 2012.
Maury, O.: An overview of APECOSM, a spatialized mass balanced “Apex Predators ECOSystem Model” to study physiologically structured tuna population dynamics in their ecosystem, Prog. Oceanogr., 84, 113–117, https://doi.org/10.1016/j.pocean.2009.09.013, 2010.
Maury, O. and Poggiale, J.-C.: From individuals to populations to communities: a dynamic energy budget model of marine ecosystem size-spectrum including life history diversity, J. Theor. Biol., 324, 52–71, https://doi.org/10.1016/j.jtbi.2013.01.018, 2013.
Maury, O., Faugeras, B., Shin, Y.-J., Poggiale, J.-C., Ben Ari, T., and Marsac, F.: Modeling environmental effects on the size-structured energy flow through marine ecosystems. Part 1: The model, Prog. Oceanogr., 74, 479–499, https://doi.org/10.1016/j.pocean.2007.05.002, 2007.
McCauley, D. J., Pinsky, M. L., Palumbi, S. R., Estes, J. A., Joyce, F. H., and Warner, R. R.: Marine defaunation: animal loss in the global ocean, Science, 347, 1255641, https://doi.org/10.1126/science.1255641, 2015.
McGurk, M. D.: Natural mortality of marine pelagic fish eggs and larvae: role of spatial patchiness, Mar. Ecol.-Prog. Ser., 34, 227–242, 1986.
McKendrick, A. G.: Applications of mathematics to medical problems, P. Edinburgh Math. Soc., 3, 98–130, 1926.
Morato, T., Watson, R., Pitcher, T. J., and Pauly, D.: Fishing down the deep, Fish Fish., 7, 24–34, https://doi.org/10.1111/j.1467-2979.2006.00205.x, 2006.
Mullon, C., Freon, P., and Cury, P.: The dynamics of collapse in world fisheries, Fish Fish., 6, 111–120, https://doi.org/10.1111/j.1467-2979.2005.00181.x, 2005.
Munro, J. L. and Pauly, D.: A simple method for comparing the growth of fishes and invertebrates, Fishbyte, 1, 5–6, 1983.
Myers, R. A.: Stock and recruitment: generalizations about maximum reproductive rate, density dependence, and variability using meta-analytic approaches, ICES J. Mar. Sci., 58, 937–951, https://doi.org/10.1006/jmsc.2001.1109, 2001.
Myers, R. A.: Recruitment: understanding density-dependence in fish populations, Handbook of Fish Biology and Fisheries, 1, 123–148, 2002.
Myers, R. A. and Worm, B.: Rapid worldwide depletion of predatory fish communities, Nature, 423, 280–283, https://doi.org/10.1038/nature01610, 2003.
Norse, E. A., Brooke, S., Cheung, W. W., Clark, M. R., Ekeland, I., Froese, R., Gjerde, K. M., Haedrich, R. L., Heppell, S. S., Morato, T., Morgan, L. E., Pauly, D., Sumaila, R., and Watson, R.: Sustainability of deep-sea fisheries, Mar. Policy, 36, 307–320, https://doi.org/10.1016/j.marpol.2011.06.008, 2012.
Paloheimo, J. E. and Dickie, L. M.: Food and growth of fishes: I. A growth curve derived from experimental data, J. Fish. Res. Board Can., 22, 521–542, https://doi.org/10.1139/f65-048, 1965.
Pauly, D.: On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks, ICES J. Mar. Sci., 39, 175–192, https://doi.org/10.1093/icesjms/39.2.175, 1980.
Pauly, D.: The sea around us project: documenting and communicating global fisheries impacts on marine ecosystems, AMBIO, 36, 290–295, https://doi.org/10.1579/0044-7447(2007)36[290:TSAUPD]2.0.CO;2, 2007.
Pauly, D. and Christensen, V.: Primary production required to sustain global fisheries, Nature, 374, 255–257, https://doi.org/10.1038/374255a0, 1995.
Pauly, D. and Zeller, D.: Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining, Nature Communications, 7, 10244, https://doi.org/10.1038/ncomms10244, 2016.
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L., Levin, S. A.: Marine Taxa Track Local Climate Velocities, Science, 341, 1239–1242, https://doi.org/10.1126/science.1239352, 2013.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical Recipes in C (2nd Edn.): the Art of Scientific Computing, Cambridge University Press, New York, NY, USA, 1992.
Pulkkinen, H., Mäntyniemi, S., and Chen, Y.: Maximum survival of eggs as the key parameter of stock–recruit meta-analysis: accounting for parameter and structural uncertainty, Can. J. Fish. Aquat. Sci., 70, 527–533, https://doi.org/10.1139/cjfas-2012-0268, 2013.
Ricker, W. E.: Stock and recruitment, J. Fish. Res. Board Can., 11, 559–623, https://doi.org/10.1139/f54-039, 1954.
Rochet, M.-J., Collie, J. S., Jennings, S., and Hall, S. J.: Does selective fishing conserve community biodiversity? Predictions from a length-based multispecies model, J. Fish. Res. Board Can., 68, 469–486, https://doi.org/10.1139/F10-159, 2011.
Rosenberg, A., Fogarty, M., Cooper, A., Dickey-Collas, M., Fulton, E., Gutiérrez, N., Hyde, K., Kleisner, K., Kristiansen, T., Longo, C., Minte-Vera, C., Minto, C., Mosqueira, I., Chato Osio, G., Ovando, D., Selig, E., Thorson, J., and Ye, Y.: Developing New Approaches to Global Stock Status Assessment and Fishery Production Potential of the Seas, FAO Fisheries and Aquaculture Circular No. 1086, FAO, Rome, Italy, 2014.
Ryther, J. H.: Photosynthesis and fish production in the sea, Science, 166, 72–76, https://doi.org/10.1126/science.166.3901.72, 1969.
Saba, V. S., Friedrichs, M. A. M., Antoine, D., Armstrong, R. A., Asanuma, I., Behrenfeld, M. J., Ciotti, A. M., Dowell, M., Hoepffner, N., Hyde, K. J. W., Ishizaka, J., Kameda, T., Marra, J., Mélin, F., Morel, A., O'Reilly, J., Scardi, M., Smith Jr., W. O., Smyth, T. J., Tang, S., Uitz, J., Waters, K., and Westberry, T. K.: An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe, Biogeosciences, 8, 489–503, https://doi.org/10.5194/bg-8-489-2011, 2011.
Sarmiento, J. L., Slater, R., Barber, R., Bopp, L., Doney, S. C., Hirst, A. C., Kleypas, J., Matear, R., Mikolajewicz, U., Monfray, P., Soldatov, V., Spall, S. A., Stouffer, R.: Response of ocean ecosystems to climate warming, Global Biogeochem. Cy, 18, GB3003, https://doi.org/10.1029/2003GB002134, 2004.
Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B., and Charnov, E. L.: Effects of body size and temperature on population growth, Am. Nat., 163, 429–441, https://doi.org/10.1086/381872, 2004.
Schaefer, M. B.: Some aspects of the dynamics of populations important to the management of the commercial marine fisheries, Bulletin of the Inter-American Tropical Tuna Commission, 1, 27–56, https://doi.org/10.1007/BF02464432, 1954.
Schnute, J.: A general theory for analysis of catch and effort data, Can. J. Fish. Aquat. Sci., 42, 414–429, https://doi.org/10.1139/f85-057, 1985.
Sheldon, R. W., Prakash, A., and Sutcliffe Jr., W.: The size distribution of particles in the ocean, Limnol. Oceanogr., 17, 327–340, https://doi.org/10.4319/lo.1997.42.1.0001, 1972.
Smyth, T. J.: Integration of radiative transfer into satellite models of ocean primary production, J. Geophys. Res., 110, C10014, https://doi.org/10.1029/2004JC002784, 2005.
Steinacher, M., Joos, F., Frölicher, T. L., Bopp, L., Cadule, P., Cocco, V., Doney, S. C., Gehlen, M., Lindsay, K., Moore, J. K., Schneider, B., and Segschneider, J.: Projected 21st century decrease in marine productivity: a multi-model analysis, Biogeosciences, 7, 979–1005, https://doi.org/10.5194/bg-7-979-2010, 2010.
Sumaila, U. R., Cheung, W. W. L., Lam, V. W. Y., Pauly, D., and Herrick, S.: Climate change impacts on the biophysics and economics of world fisheries, Nature Climate Change, 1, 449–456, https://doi.org/10.1038/nclimate1301, 2011.
Taucher, J. and Oschlies, A.: Can we predict the direction of marine primary production change under global warming?, Geophys. Res. Lett., 38, L02603, https://doi.org/10.1029/2010GL045934, 2011.
Taylor, C. C.: Cod growth and temperature, ICES J. Mar. Sci., 23, 357–365, https://doi.org/10.1093/icesjms/23.3.366, 1958.
Tremblay-Boyer, L., Gascuel, D., Watson, R., Christensen, V., and Pauly, D.: Modelling the effects of fishing on the biomass of the world's oceans from 1950 to 2006, Mar. Ecol.-Prog. Ser., 442, 169–185, https://doi.org/10.3354/meps09375, 2011.
Vandermeer, J.: Metabolic theories in ecology, Trends Ecol. Evol., 21, 136–140, https://doi.org/10.1016/j.tree.2005.11.004, 2006.
von Bertalanffy, L.: A quantitative theory of organic growth (inquiries on growth laws. II), Hum. Biol., 10, 181–213, 1938.
von Bertalanffy, L.: Problems of organic growth, Nature, 163, 156–158, https://doi.org/10.1038/163156a0, 1949.
von Bertalanffy, L.: Quantitative laws in metabolism and growth, Q. Rev. Biol., 32, 217–231, 1957.
von Foerster, H.: Some remarks on changing populations, in: The Kinetics of Cellular Proliferation, edited by: Stohlman Jr., F., Grune and Stratton, New York, 1959.
Walsh, H. J., Richardson, D. E., Marancik, K. E., and Hare, J. A.: Long-Term Changes in the Distributions of Larval and Adult Fish in the Northeast U.S. Shelf Ecosystem, PLoS ONE, 10, e0137382, https://doi.org/10.1371/journal.pone.0137382, 2015.
Watson, R. A. and Morato, T.: Fishing down the deep: accounting for within-species changes in depth of fishing, Fish. Res., 140, 63–65, https://doi.org/10.1016/j.fishres.2012.12.004, 2013.
Watson, R. A., Kitchingman, A., and Gelchu, A.: Mapping global fisheries: sharpening our focus, Fish Fish., 5, 168–177, https://doi.org/10.1111/j.1467-2979.2004.00142.x, 2004.
Watson, R. A., Zeller, D., and Pauly, D.: Primary productivity demands of global fishing fleets, Fish Fish., 15, 231–241, https://doi.org/10.1111/faf.12013, 2013a.
Watson, R. A., Cheung, W. W. L., Anticamara, J. A., Sumaila, R. U., Zeller, D., and Pauly, D.: Global marine yield halved as fishing intensity redoubles, Fish Fish., 14, 493–503, https://doi.org/10.1111/j.1467-2979.2012.00483.x, 2013b.
Watson, J. R., Stock, C. A., and Sarmiento, J. L.: Exploring the role of movement in determining the global distribution of marine biomass using a coupled hydrodynamic – size-based ecosystem model, Prog. Oceanogr., https://doi.org/10.1016/j.pocean.2014.09.001, online first, 2014.
West, G. B., Brown, J. H., and Enquist, B. J.: A general model for ontogenetic growth, Nature, 413, 628–631, https://doi.org/10.1038/35098076, 2001.
White, E. P., Ernest, S. K. M., Kerkhoff, A. J., and Enquist, B. J.: Relationships between body size and abundance in ecology, Trends Ecol. Evol., 22, 323–330, https://doi.org/10.1016/j.tree.2007.03.007, 2007.
Woodworth-Jefcoats, P. A., Polovina, J. J., Dunne, J. P., and Blanchard, J. L.: Ecosystem size structure response to 21st century climate projection: large fish abundance decreases in the central North Pacific and increases in the California Current, Glob. Change Biol., 19, 724–733, https://doi.org/10.1111/gcb.12076, 2012.
Xiao, X., White, E. P., Hooten, M. B., and Durham, S. L.: On the use of log-transformation vs. nonlinear regression for analyzing biological power laws, Ecology, 92, 1887–1894, https://doi.org/10.1890/11-0538.1, 2011.
Short summary
We present the ecological module of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model, which takes an Earth-system approach to modeling upper trophic level biomass at the global scale. BOATS employs fundamental ecological principles and takes a simple approach that relies on fewer parameters compared to similar modelling efforts. As such, it enables the exploration of the linkages between ocean biogeochemistry, climate, upper trophic levels, and fisheries at the global scale.
We present the ecological module of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model,...
