Articles | Volume 8, issue 3
https://doi.org/10.5194/gmd-8-697-2015
© Author(s) 2015. 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-8-697-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
23 Mar 2015
Methods for assessment of models |
| 23 Mar 2015
Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1)
J. C. P. Hemmings
CORRESPONDING AUTHOR
National Oceanography Centre, Southampton, SO14 3ZH, UK
Wessex Environmental Associates, Salisbury, UK
P. G. Challenor
National Oceanography Centre, Southampton, SO14 3ZH, UK
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK
National Oceanography Centre, Southampton, SO14 3ZH, UK
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Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
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We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
Biogeosciences, 20, 1195–1257, https://doi.org/10.5194/bg-20-1195-2023, https://doi.org/10.5194/bg-20-1195-2023, 2023
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Ocean alkalinity is critical to the uptake of atmospheric carbon and acidification in surface waters. We review the representation of alkalinity and the associated calcium carbonate cycle in Earth system models. While many parameterizations remain present in the latest generation of models, there is a general improvement in the simulated alkalinity distribution. This improvement is related to an increase in the export of biotic calcium carbonate, which closer resembles observations.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
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The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
Stephanie Woodward, Alistair A. Sellar, Yongming Tang, Marc Stringer, Andrew Yool, Eddy Robertson, and Andy Wiltshire
Atmos. Chem. Phys., 22, 14503–14528, https://doi.org/10.5194/acp-22-14503-2022, https://doi.org/10.5194/acp-22-14503-2022, 2022
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We describe the dust scheme in the UKESM1 Earth system model and show generally good agreement with observations. Comparing with the closely related HadGEM3-GC3.1 model, we show that dust differences are not only due to inter-model differences but also to the dust size distribution. Under climate change, HadGEM3-GC3.1 dust hardly changes, but UKESM1 dust decreases because that model includes the vegetation response which, in our models, has a bigger impact on dust than climate change itself.
Pradeebane Vaittinada Ayar, Laurent Bopp, Jim R. Christian, Tatiana Ilyina, John P. Krasting, Roland Séférian, Hiroyuki Tsujino, Michio Watanabe, Andrew Yool, and Jerry Tjiputra
Earth Syst. Dynam., 13, 1097–1118, https://doi.org/10.5194/esd-13-1097-2022, https://doi.org/10.5194/esd-13-1097-2022, 2022
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The El Niño–Southern Oscillation is the main driver for the natural variability of global atmospheric CO2. It modulates the CO2 fluxes in the tropical Pacific with anomalous CO2 influx during El Niño and outflux during La Niña. This relationship is projected to reverse by half of Earth system models studied here under the business-as-usual scenario. This study shows models that simulate a positive bias in surface carbonate concentrations simulate a shift in the ENSO–CO2 flux relationship.
Reint Fischer, Delphine Lobelle, Merel Kooi, Albert Koelmans, Victor Onink, Charlotte Laufkötter, Linda Amaral-Zettler, Andrew Yool, and Erik van Sebille
Biogeosciences, 19, 2211–2234, https://doi.org/10.5194/bg-19-2211-2022, https://doi.org/10.5194/bg-19-2211-2022, 2022
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Since current estimates show that only about 1 % of the all plastic that enters the ocean is floating at the surface, we look at subsurface processes that can cause vertical movement of (micro)plastic. We investigate how modelled algal attachment and the ocean's vertical movement can cause particles to sink and oscillate in the open ocean. Particles can sink to depths of > 5000 m in regions with high wind intensity and mainly remain close to the surface with low winds and biological activity.
Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor
Geosci. Model Dev., 15, 1913–1929, https://doi.org/10.5194/gmd-15-1913-2022, https://doi.org/10.5194/gmd-15-1913-2022, 2022
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We have adapted machine learning techniques to build a model of the land surface in Great Britain. The model was trained using data from a very complex land surface model called JULES. Our model is faster at producing simulations and predictions and can investigate many different scenarios, which can be used to improve our understanding of the climate and could also be used to help make local decisions.
Josué Bock, Martine Michou, Pierre Nabat, Manabu Abe, Jane P. Mulcahy, Dirk J. L. Olivié, Jörg Schwinger, Parvadha Suntharalingam, Jerry Tjiputra, Marco van Hulten, Michio Watanabe, Andrew Yool, and Roland Séférian
Biogeosciences, 18, 3823–3860, https://doi.org/10.5194/bg-18-3823-2021, https://doi.org/10.5194/bg-18-3823-2021, 2021
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In this study we analyse surface ocean dimethylsulfide (DMS) concentration and flux to the atmosphere from four CMIP6 Earth system models over the historical and ssp585 simulations.
Our analysis of contemporary (1980–2009) climatologies shows that models better reproduce observations in mid to high latitudes. The models disagree on the sign of the trend of the global DMS flux from 1980 onwards. The models agree on a positive trend of DMS over polar latitudes following sea-ice retreat dynamics.
Andrew Yool, Julien Palmiéri, Colin G. Jones, Lee de Mora, Till Kuhlbrodt, Ekatarina E. Popova, A. J. George Nurser, Joel Hirschi, Adam T. Blaker, Andrew C. Coward, Edward W. Blockley, and Alistair A. Sellar
Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, https://doi.org/10.5194/gmd-14-3437-2021, 2021
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The ocean plays a key role in modulating the Earth’s climate. Understanding this role is critical when using models to project future climate change. Consequently, it is necessary to evaluate their realism against the ocean's observed state. Here we validate UKESM1, a new Earth system model, focusing on the realism of its ocean physics and circulation, as well as its biological cycles and productivity. While we identify biases, generally the model performs well over a wide range of properties.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
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Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Lee de Mora, Alistair A. Sellar, Andrew Yool, Julien Palmieri, Robin S. Smith, Till Kuhlbrodt, Robert J. Parker, Jeremy Walton, Jeremy C. Blackford, and Colin G. Jones
Geosci. Commun., 3, 263–278, https://doi.org/10.5194/gc-3-263-2020, https://doi.org/10.5194/gc-3-263-2020, 2020
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We use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces for piano. Each of the six pieces help to explain either a scientific principle or a practical aspect of Earth system modelling. We describe the methods that were used to create these pieces, discuss the limitations of this pilot study and list several approaches to extend and expand upon this work.
Lester Kwiatkowski, Olivier Torres, Laurent Bopp, Olivier Aumont, Matthew Chamberlain, James R. Christian, John P. Dunne, Marion Gehlen, Tatiana Ilyina, Jasmin G. John, Andrew Lenton, Hongmei Li, Nicole S. Lovenduski, James C. Orr, Julien Palmieri, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Charles A. Stock, Alessandro Tagliabue, Yohei Takano, Jerry Tjiputra, Katsuya Toyama, Hiroyuki Tsujino, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, and Tilo Ziehn
Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, https://doi.org/10.5194/bg-17-3439-2020, 2020
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We assess 21st century projections of marine biogeochemistry in the CMIP6 Earth system models. These models represent the most up-to-date understanding of climate change. The models generally project greater surface ocean warming, acidification, subsurface deoxygenation, and euphotic nitrate reductions but lesser primary production declines than the previous generation of models. This has major implications for the impact of anthropogenic climate change on marine ecosystems.
Doug McNeall, Jonny Williams, Richard Betts, Ben Booth, Peter Challenor, Peter Good, and Andy Wiltshire
Geosci. Model Dev., 13, 2487–2509, https://doi.org/10.5194/gmd-13-2487-2020, https://doi.org/10.5194/gmd-13-2487-2020, 2020
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In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required different land surface parameter settings than for other forests. It was unclear if this discrepancy was due to a bias in the modelled climate or an error in the land surface component of the model. Correcting the climate of the model with a statistical model corrects the simulation of the Amazon forest, suggesting that the land surface component of the model is not the source of the discrepancy.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
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As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Matthew P. Couldrey, Kevin I. C. Oliver, Andrew Yool, Paul R. Halloran, and Eric P. Achterberg
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-16, https://doi.org/10.5194/bg-2019-16, 2019
Revised manuscript not accepted
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Determining how much carbon dioxide (CO2) the oceans absorb is key to predicting human-caused climate change. A computer model of the ocean shows how the North Atlantic will change up to the end of the century. Year-to-year variations are mostly caused by changes in ocean temperature and seawater chemistry, altering CO2 solubility. By 2100, human emissions cause the biggest changes. The near term changes are physically driven, which may be more predictable than biological changes.
Ben A. Ward, Jamie D. Wilson, Ros M. Death, Fanny M. Monteiro, Andrew Yool, and Andy Ridgwell
Geosci. Model Dev., 11, 4241–4267, https://doi.org/10.5194/gmd-11-4241-2018, https://doi.org/10.5194/gmd-11-4241-2018, 2018
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A novel configuration of an Earth system model includes a diverse plankton community. The model – EcoGEnIE – is sufficiently complex to reproduce a realistic, size-structured plankton community, while at the same time retaining the efficiency to run to a global steady state (~ 10k years). The increased capabilities of EcoGEnIE will allow future exploration of ecological communities on much longer timescales than have so far been examined in global ocean models and particularly for past climate.
Lee de Mora, Andrew Yool, Julien Palmieri, Alistair Sellar, Till Kuhlbrodt, Ekaterina Popova, Colin Jones, and J. Icarus Allen
Geosci. Model Dev., 11, 4215–4240, https://doi.org/10.5194/gmd-11-4215-2018, https://doi.org/10.5194/gmd-11-4215-2018, 2018
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Climate change is expected to have a significant impact on the Earth's weather, ice caps, land surface, and ocean. Computer models of the Earth system are the only tools available to make predictions about how the climate may change in the future. However, in order to trust the model predictions, we must first demonstrate that the models have a realistic description of the past. The BGC-val toolkit was built to rapidly and simply evaluate the behaviour of models of the Earth's oceans.
Doug McNeall, Jonny Williams, Ben Booth, Richard Betts, Peter Challenor, Andy Wiltshire, and David Sexton
Earth Syst. Dynam., 7, 917–935, https://doi.org/10.5194/esd-7-917-2016, https://doi.org/10.5194/esd-7-917-2016, 2016
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We compare simulated with observed forests to constrain uncertain input parameters of the land surface component of a climate model.
We find that the model is unlikely to be able to simulate the Amazon and other major forests simultaneously at any one parameter set, suggesting a bias in the model's representation of the Amazon.
We find we cannot constrain parameters individually, but we can rule out large areas of joint parameter space.
Claudie Beaulieu, Harriet Cole, Stephanie Henson, Andrew Yool, Thomas R. Anderson, Lee de Mora, Erik T. Buitenhuis, Momme Butenschön, Ian J. Totterdell, and J. Icarus Allen
Biogeosciences, 13, 4533–4553, https://doi.org/10.5194/bg-13-4533-2016, https://doi.org/10.5194/bg-13-4533-2016, 2016
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Regime shifts have been suggested in the late 1970s and late 1980s in the Gulf of Alaska with important consequences for fisheries. Here we investigate the ability of a suite of ocean biogeochemical models of varying complexity to simulate these regime shifts. Our results demonstrate that ocean models can successfully simulate regime shifts in the Gulf of Alaska region, thereby improving our understanding of how changes in physical conditions are propagated from lower to upper trophic levels.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
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In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
T. R. Anderson, W. C. Gentleman, and A. Yool
Geosci. Model Dev., 8, 2231–2262, https://doi.org/10.5194/gmd-8-2231-2015, https://doi.org/10.5194/gmd-8-2231-2015, 2015
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Ecosystem models provide a powerful tool for simulating ocean biology. Care must be exercised when selecting appropriate equations and parameter values to represent chosen marine ecosystems. Here, we present an efficient plankton model testbed, using simplified physics and coded in the freely available language R. Multiple runs can be undertaken for different ocean sites, permitting thorough evaluation of ecosystem model performance. The testbed also serves as an excellent resource for teaching.
L. Kwiatkowski, A. Yool, J. I. Allen, T. R. Anderson, R. Barciela, E. T. Buitenhuis, M. Butenschön, C. Enright, P. R. Halloran, C. Le Quéré, L. de Mora, M.-F. Racault, B. Sinha, I. J. Totterdell, and P. M. Cox
Biogeosciences, 11, 7291–7304, https://doi.org/10.5194/bg-11-7291-2014, https://doi.org/10.5194/bg-11-7291-2014, 2014
B. A. Kelly-Gerreyn, A. P. Martin, B. J. Bett, T. R. Anderson, J. I. Kaariainen, C. E. Main, C. J. Marcinko, and A. Yool
Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, https://doi.org/10.5194/bg-11-6401-2014, 2014
E. E. Popova, A. Yool, Y. Aksenov, A. C. Coward, and T. R. Anderson
Biogeosciences, 11, 293–308, https://doi.org/10.5194/bg-11-293-2014, https://doi.org/10.5194/bg-11-293-2014, 2014
A. Yool, E. E. Popova, and T. R. Anderson
Geosci. Model Dev., 6, 1767–1811, https://doi.org/10.5194/gmd-6-1767-2013, https://doi.org/10.5194/gmd-6-1767-2013, 2013
D. J. McNeall, P. G. Challenor, J. R. Gattiker, and E. J. Stone
Geosci. Model Dev., 6, 1715–1728, https://doi.org/10.5194/gmd-6-1715-2013, https://doi.org/10.5194/gmd-6-1715-2013, 2013
A. Yool, E. E. Popova, A. C. Coward, D. Bernie, and T. R. Anderson
Biogeosciences, 10, 5831–5854, https://doi.org/10.5194/bg-10-5831-2013, https://doi.org/10.5194/bg-10-5831-2013, 2013
S. Henson, H. Cole, C. Beaulieu, and A. Yool
Biogeosciences, 10, 4357–4369, https://doi.org/10.5194/bg-10-4357-2013, https://doi.org/10.5194/bg-10-4357-2013, 2013
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Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
EGUsphere, https://doi.org/10.5194/egusphere-2023-1697, https://doi.org/10.5194/egusphere-2023-1697, 2023
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool-set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool-set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models, to find the most appropriate one.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
EGUsphere, https://doi.org/10.5194/egusphere-2023-1414, https://doi.org/10.5194/egusphere-2023-1414, 2023
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To manage soil organic matter sustainably, we need to better understand the role that soil microbes play in aggregate protection. Here, we propose a the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
EGUsphere, https://doi.org/10.5194/egusphere-2023-1075, https://doi.org/10.5194/egusphere-2023-1075, 2023
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Jalisha Theanutti Kallingal, Johan Lindström, Paul A Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-302, https://doi.org/10.5194/gmd-2022-302, 2023
Revised manuscript accepted for GMD
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This manuscript describes the development of a Bayesian data assimilation framework around the wetland CH4 module in the LPJ-GUESS DGVM. The novel approach we developed combines the Rao-Blackwellised Adaptive Metropolis algorithm with the Global Adaptive Scaling (G-RB AM) for sampling the model parameters. Further, the manuscript demonstrates the application of the DA framework for optimising model process parameters by assimilating daily CH4 flux measurement data.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Rémy Fieuzal, and Eric Ceschia
EGUsphere, https://doi.org/10.5194/egusphere-2023-48, https://doi.org/10.5194/egusphere-2023-48, 2023
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Quantification of Carbon fluxes of crops is an essential brick for the construction of a Monitoring, Reporting and Verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates through an efficient Bayesian approach, high resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in-situ flux towers, yield maps, and analysed at regional scale.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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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.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
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Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
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Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
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We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
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Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
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A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
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Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
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The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
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Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
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Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
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Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, https://doi.org/10.5194/gmd-15-5713-2022, 2022
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This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
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A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Mahdi André Nakhavali, Lina M. Mercado, Iain P. Hartley, Stephen Sitch, Fernanda V. Cunha, Raffaello di Ponzio, Laynara F. Lugli, Carlos A. Quesada, Kelly M. Andersen, Sarah E. Chadburn, Andy J. Wiltshire, Douglas B. Clark, Gyovanni Ribeiro, Lara Siebert, Anna C. M. Moraes, Jéssica Schmeisk Rosa, Rafael Assis, and José L. Camargo
Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
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In tropical ecosystems, the availability of rock-derived elements such as P can be very low. Thus, without a representation of P cycling, tropical forest responses to rising atmospheric CO2 conditions in areas such as Amazonia remain highly uncertain. We introduced P dynamics and its interactions with the N and P cycles into the JULES model. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low-fertility soils.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
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Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
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We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev., 15, 5093–5105, https://doi.org/10.5194/gmd-15-5093-2022, https://doi.org/10.5194/gmd-15-5093-2022, 2022
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This paper describes the validation of area of habitat (AOH) maps produced for terrestrial birds and mammals. The main objective was to assess the accuracy of the maps based on independent data. We used open access data from repositories, such as ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. The majority of AOH maps were found to have a high accuracy.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 15, 4959–4990, https://doi.org/10.5194/gmd-15-4959-2022, https://doi.org/10.5194/gmd-15-4959-2022, 2022
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Increasing carbon dioxide in the atmosphere is an urgent issue in the coming century. Enhanced rock weathering in soils can be one of the most efficient C capture strategies. On the basis as a weathering simulator, the newly developed SCEPTER model implements bio-mixing by fauna/humans and enables organic matter and crushed rocks/minerals at the soil surface with an option to track their particle size distributions. Those features can be useful for evaluating the carbon capture efficiency.
Cited articles
Arhonditsis, G. B., Papantou, D., Zhang, W., Perhar, G., Massos, E., and Shi, M.:. Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management, J. Marine Syst., 73, 8–30, 2008.
Aumont, O. and Bopp, L.: Globalizing results from ocean in situ iron fertilization studies, Global Biogeochem. Cycles, 20, GB2017, https://doi.org/10.1029/2005GB002591, 2006.
Campbell, J. W.: The lognormal distribution as a model for bio-optical variability in the sea, J. Geophys. Res., 100, 13237–13254, 1995.
Doron, M., Brasseur, P., Brankart, J.-M., Losa, S. N., and Melet, A.: Stochastic estimation of biogeochemical parameters from Globcolour ocean colour satellite data in a North Atlantic 3-D ocean coupled physical-biogeochemical model, J. Marine Syst., 117–118, 81–95, 2013.
Dowd, M.: Estimating parameters for a stochastic dynamic marine ecological system, Environmetrics, 22, 501–515, https://doi.org/10.1002/env.1083, 2011.
Fan, W. and Lv, X.: Data assimilation in a simple marine ecosystem model based on spatial biological parameterizations, Ecol. Model., 220, 1997–2008, https://doi.org/10.1016/j.ecolmodel.2009.04.050, 2009.
Fasham, M. J. R. and Evans, G. T.: The use of optimization techniques to model marine ecosystem dynamics at the JGOFS station at 47° N 20° W, Philos. T. Roy. Soc. B, 348, 203–209, 1995.
Fasham, M. J. R., Boyd, P. W., and Savidge, G.: Modeling the relative contributions of autotrophs and heterotrophs to carbon flow at a Lagrangian JGOFS station in the Northeast Atlantic: the importance of DOC, Limnol. Oceanogr., 44, 80–94, 1999.
Fasham, M. J. R., Flynn, K. J., Pondaven, P., Anderson, T. R., and Boyd, P. W.: Development of a robust marine ecosystem model to predict the role of iron in biogeochemical cycles: a comparison of results for iron-replete and iron-limited areas, and the SOIREE iron-enrichment experiment, Deep-Sea Res. Pt. I, 53, 333–366, 2006.
Fiechter, J.: Assessing marine ecosystem model properties from ensemble calculations, Ecol. Model., 242, 164–179, https://doi.org/10.1016/j.ecolmodel.2012.05.016, 2012.
Fiechter, J., Herbei, R., Leeds, W., Brown, J., Milliff, R., Wikle, C., Moore, A., and Powell, T.: A Bayesian parameter estimation method applied to a marine ecosystem model for the coastal Gulf of Alaska, Ecol. Model., 258, 122–133, 2013.
Friedrichs, M. A. M., Hood, R. R., and Wiggert, J. D.: Ecosystem model complexity versus physical forcing: quantification of their relative impact with assimilated Arabian Sea data, Deep-Sea Res. Pt. II, 53, 576–600, 2006.
Friedrichs, M. A. M., Dusenberry, J. A., Anderson, L. A., Armstrong, R. A., Chai, F., Christian, J. R., Doney, S. C., Dunne, J., Fujii, M., Hood, R., McGillicuddy Jr., D. J., Moore, K., Schartau, M., Spitz, Y., and Wiggert, J. D.: Assessment of skill and portability in regional marine biogeochemical models: role of multiple planktonic groups, J. Geophys. Res., 112, C08001, https://doi.org/10.1029/2006JC003852, 2007.
Garcia-Gorriz, E., Hoepffner, N., and Ouberdous, M.: Assimilation of SeaWiFS data in a coupled physical-biological model of the Adriatic Sea, J. Marine Syst., 40–41, 233–252, 2003.
Gregg, W., Ginoux, W. P., Schopf, P. S., and Casey, N. W.: Phytoplankton and iron: validation of a global three-dimensional ocean biogeochemical model, Deep-Sea Res. Pt. II, 50, 3143–3169, 2003.
Hemmings, J. C. P. and Challenor, P. G.: Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha), Geosci. Model Dev., 5, 471–498, https://doi.org/10.5194/gmd-5-471-2012, 2012.
Hemmings, J. C. P., Srokosz, M. A., Challenor, P., and Fasham, M. J. R.: Split-domain calibration of an ecosystem model using satellite ocean colour data, J. Marine Syst., 50, 141–179, 2004.
Hooten, M. B., Leeds, W. B., Fiechter, J., and Wikle, C. K.: Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models, J. Agric. Biol. Envir. S., 16, 475–494, https://doi.org/10.1007/s13253-011-0073-7, 2011.
Hourdin, F. and Armengaud, A.: The use of finite-volume methods for atmospheric advection of trace species. Part I: Test of various formulations in a general circulation model, Mon. Weather Rev., 127, 822–837, 1999.
Huret, M., Gohin, F., Delmas, D., Lunven, M., and Garçon, V.: Use of SeaWiFS data for light availability and parameter estimation of a phytoplankton production model of the Bay of Biscay, J. Marine Syst., 65, 509–531, 2007.
Hurtt, G. C. and Armstrong, R. A.: A pelagic ecosystem model calibrated with BATS and OWSI data, Deep-Sea Res. Pt. I, 46, 27–61, 1999.
Johnson, M., Moore, L., and Ylvisaker, D.: Minimax and maxmin distance designs, J. Stat. Plan. Infer., 26, 131–148, 1990.
Kane, A., Moulin, C., Thiria, S., Bopp, L., Berrada, M., Tagliabue, A., Crépon, M., Aumont, O., and Badran, F.: Improving the parameters of a global ocean biogeochemical model via variational assimilation of in situ data at five time series stations, J. Geophys. Res., 116, C06011, https://doi.org/10.1029/2009JC006005, 2011.
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. Roy. Stat. Soc. B, 63, 425–464, 2001.
Khatiwala, S.: A computational framework for simulation of biogeochemical tracers in the ocean, Global Biogeochem. Cycles, 21, GB3001, https://doi.org/10.1029/2007GB002923, 2007.
Khatiwala, S.: Fast spin up of Ocean biogeochemical models using matrix-free Newton–Krylov, Ocean Model., 23, 121–129, https://doi.org/10.1016/j.ocemod.2008.05.002, 2008.
Kidston, M., Matear, R., and Baird, M. E.: Parameter optimisation of a marine ecosystem model at two contrasting stations in the Sub-Antarctic Zone, Deep-Sea Res. Pt. II, 58, 2301–2315, https://doi.org/10.1016/j.dsr2.2011.05.018, 2011.
Kriest, I., Khatiwala, S., and Oschlies, A.: Towards an assessment of simple global marine biogeochemical models of different complexity, Prog. Oceanogr., 86, 337–360, https://doi.org/10.1016/j.pocean.2010.05.002, 2010.
Kriest, I., Oschlies, A., and Khatiwala, S.: Sensitivity analysis of simple global marine biogeochemical models, Global Biogeochem. Cycles, 26, GB2029, https://doi.org/10.1029/2011GB004072, 2012.
Lee, L. A., Carslaw, K. S., Pringle, K. J., and Mann, G. W.: Mapping the uncertainty in global CCN using emulation, Atmos. Chem. Phys., 12, 9739–9751, https://doi.org/10.5194/acp-12-9739-2012, 2012.
Leeds, W. B., Wikle, C. K., Fiechter, J., Brown, J., and Milliff, R. F.: Modeling 3-D spatio-temporal biogeochemical processes with a forest of 1-D statistical emulators, Environmetrics, 24, 1–12, https://doi.org/10.1002/env.2187, 2013.
Le Quéré, C., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Cotrim da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin, R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gladrow, D.: Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models, Glob. Change Biol., 11, 2016–2040, https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005.
Lévy, M.: The modulation of biological production by oceanic mesoscale turbulence, Lect. Notes Phys., 744, 219–261, https://doi.org/10.1007/978-3-540-75215-8_9, 2008.
Lévy, M., Estublier, A., and Madec, G.: Choice of an advection scheme for biogeochemical models, Geophys. Res. Lett., 28, 3725–3728, 2001.
Losa, S. N., Kivman, G. A., and Ryabchenko, V. A.: Weak constraint parameter estimation for a simple ocean ecosystem model: what can we learn about the model and data?, J. Marine Syst., 45, 1–20, 2004.
Madec, G.: NEMO Reference Manual, Ocean Dynamic Component: NEMO-OPA, Note du Pole de modélisation, l'Institut Pierre-Simon Laplace, Paris, France, No. 27., ISSN 1288–1619, 2008.
Matear, R. J.: Parameter optimization and analysis of ecosystem models using simulated annealing: a case study at Station P, J. Marine Res., 53, 571–607, 1995.
Mattern, J. P., Fennel, K., and Dowd, M.: Estimating time-dependent parameters for a biological ocean model using an emulator approach, J. Marine Syst., 96–97, 32–47, 2012.
McDonald, C. P, Bennington, V., Urban, N. R., and McKinley, G.: A 1-D test-bed calibration of a 3-D Lake Superior biogeochemical model, Ecol. Model., 225, 115–126, 2012.
McKay, M. D., Conover, W. J., and Beckman, R. J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21, 239–245, 1979.
Moore, J. K., Doney, S. C., and Lindsay, K.: Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model, Global Biogeochem. Cy., 18, GB4028, https://doi.org/10.1029/2004GB002220, 2004.
O'Hagan, T.: Bayesian analysis of computer code outputs: a tutorial, Reliab. Eng. Syst. Safe., 91, 1290–1300, 2006.
Oschlies, A. and Schartau, M.: Basin-scale performance of a locally optimized marine ecosystem model, J. Marine Res., 63, 335–358, 2005.
Palmer, J. R. and Totterdell, I. J.: Production and export in a global ocean ecosystem model, Deep-Sea Res. Pt. I, 48, 1169–1198, 2001.
Prieß, M., Koziel, S., and Slawig, T.: Marine ecosystem model calibration with real data using enhanced surrogate-base optimization, J. Comput. Sci., 4, 423–437, https://doi.org/10.1016/j.jocs.2013.04.001, 2013a.
Prieß, M., Piwonski, J., Koziel, S., Oschlies, A., and Slawig, T.: Accelerated parameter identification in a 3-D marine biogeochemical model using surrogate-based optimization, Ocean Model., 68, 22–36, https://doi.org/10.1016/j.ocemod.2013.04.003, 2013b.
Rayner, P. J., Scholze, M., Knorr, W., Kaminski, T., Giering, R., and Widmann, H.: Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS), Global Biogeochem. Cy., 19, GB2026, https://doi.org/10.1029/2004GB002254, 2005.
Sarmiento, J. L., Slater, R. D., Fasham, M. J. R., Ducklow, H. W, Toggweiler, J. R., and Evans, G. T.: A seasonal three-dimensional ecosystem model of nitrogen cycling in the North Atlantic Euphotic Zone, Global Biogeochem. Cy., 7, 417–450, https://doi.org/10.1029/93GB00375, 1993.
Schartau, M. and Oschlies, A.: Simultaneous data-based optimization of a 1-D-ecosystem model at three locations in the North Atlantic: Part I – Method and parameter estimates, J. Mar. Res., 61, 765–793, 2003.
Séférian, R., Bopp, L., Gehlen, M. Orr, J. C., Ethé, C., Cadule, P., Aumont, O., Salas y Mélia, D., Voldoire, A., and Madec, G.: Skill assessment of three earth system models with common marine biogeochemistry, Clim. Dynam., 40, 2549–2573, https://doi.org/10.1007/s00382-012-1362-8, 2013.
Six, K. D. and Maier-Reimer, E.: Effects of plankton dynamics on seasonal carbon fluxes in an ocean general circulation model, Global Biogeochem. Cy., 10, 559–583, 1996.
Stow, C. A., Jolliff, J., McGillicuddy Jr., D. J., Doney, S. C., Allen, J. I., Friedrichs, M. A. M., Rose, K. A., and Wallhead, P.: Skill assessment for coupled biological/physical models of marine systems, J. Marine Syst., 76, 4–15, 2009.
Tjiputra, J. F., Polzin, D., and Winguth, A. M. E.: Assimilation of seasonal chlorophyll and nutrient data into an adjoint three-dimensional ocean carbon cycle model: sensitivity analysis and ecosystem parameter optimization, Global Biogeochem. Cy., 21, GB1001, https://doi.org/10.1029/2006GB002745, 2007.
Van Leer, B.: Towards the ultimate conservative difference scheme IV: a new approach to numerical convection, J. Comput. Phys., 23, 276–299, 1977.
Ward, B. A., Friedrichs, M. A. M, Anderson, T. R., and Oschlies, A.: Parameter optimisation techniques and the problem of underdetermination in marine biogeochemical models, J. Marine Syst., 81, 34–43, 2010.
Ward, B. A, Schartau, M., Oschlies, A., Martin, A. P., Follows, M. J., and Anderson, T. R.: When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites, Prog. Oceanogr., 116, 49–65, 2013.
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P., Jackson, L., and Yamazaki, K.: History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble, Clim. Dynam., 41, 1703–1729, https://doi.org/10.1007/s00382-013-1896-4, 2013.
Xiao, Y. and Friedrichs, M.: The assimilation of satellite-derived data into a one-dimensional lower trophic level marine ecosystem model, J. Geophys. Res.-Oceans, 119, 2691–2712, https://doi.org/10.1002/2013JC009433, 2014.
Yool, A., Popova, E. E., and Anderson, T. R.: MEDUSA-1.0: a new intermediate complexity plankton ecosystem model for the global domain, Geosci. Model Dev., 4, 381–417, https://doi.org/10.5194/gmd-4-381-2011, 2011.
Zhang, W. and Arhonditsis, G. B.: A Bayesian hierarchical framework for calibrating aquatic biogeochemical models, Ecol. Model., 220, 2142–2161, https://doi.org/10.1016/j.ecolmodel.2009.05.023, 2009.
Short summary
Effective calibration of global models is inhibited by the computational demands of 3-D simulations. As a solution for the NEMO-MEDUSA model, we present an efficient emulator of surface chlorophyll as a function of MEDUSA’s biogeochemical parameters. The emulator comprises an array of site-based 1-D simulators and a quantification of uncertainty in their predictions. It is able to produce robust probabilistic estimates of 3-D model output rapidly for comparison with satellite chlorophyll.
Effective calibration of global models is inhibited by the computational demands of 3-D...
