Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7255-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-7255-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
30 Nov 2021
Model description paper |
| 30 Nov 2021
| 30 Nov 2021
Explicit silicate cycling in the Kiel Marine Biogeochemistry Model version 3 (KMBM3) embedded in the UVic ESCM version 2.9
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
present address: GNS Science, 1 Fairway Drive, Avalon 5010, P.O. Box 30368, Lower Hutt 5040, New Zealand
David P. Keller
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Wolfgang Koeve
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Katrin J. Meissner
Climate Change Research Centre, Level 4 Mathews Bldg., UNSW, Sydney, NSW, Australia
ARC Centre of Excellence for Climate Extremes, Sydney, Australia
Christopher J. Somes
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Wanxuan Yao
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Andreas Oschlies
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
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Miriam Tivig, David P. Keller, and Andreas Oschlies
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Henrike Schmidt, Julia Getzlaff, Ulrike Löptien, and Andreas Oschlies
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Jaard Hauschildt, Soeren Thomsen, Vincent Echevin, Andreas Oschlies, Yonss Saranga José, Gerd Krahmann, Laura A. Bristow, and Gaute Lavik
Biogeosciences, 18, 3605–3629, https://doi.org/10.5194/bg-18-3605-2021, https://doi.org/10.5194/bg-18-3605-2021, 2021
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Mariana Hill Cruz, Iris Kriest, Yonss Saranga José, Rainer Kiko, Helena Hauss, and Andreas Oschlies
Biogeosciences, 18, 2891–2916, https://doi.org/10.5194/bg-18-2891-2021, https://doi.org/10.5194/bg-18-2891-2021, 2021
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In this study we use a regional biogeochemical model of the eastern tropical South Pacific Ocean to implicitly simulate the effect that fluctuations in populations of small pelagic fish, such as anchovy and sardine, may have on the biogeochemistry of the northern Humboldt Current System. To do so, we vary the zooplankton mortality in the model, under the assumption that these fishes eat zooplankton. We also evaluate the model for the first time against mesozooplankton observations.
Nicholas King-Hei Yeung, Laurie Menviel, Katrin J. Meissner, Andréa S. Taschetto, Tilo Ziehn, and Matthew Chamberlain
Clim. Past, 17, 869–885, https://doi.org/10.5194/cp-17-869-2021, https://doi.org/10.5194/cp-17-869-2021, 2021
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The Last Interglacial period (LIG) is characterised by strong orbital forcing compared to the pre-industrial period (PI). This study compares the mean climate state of the LIG to the PI as simulated by the ACCESS-ESM1.5, with a focus on the southern hemispheric monsoons, which are shown to be consistently weakened. This is associated with cooler terrestrial conditions in austral summer due to decreased insolation, and greater pressure and subsidence over land from Hadley cell strengthening.
Shannon A. Bengtson, Laurie C. Menviel, Katrin J. Meissner, Lise Missiaen, Carlye D. Peterson, Lorraine E. Lisiecki, and Fortunat Joos
Clim. Past, 17, 507–528, https://doi.org/10.5194/cp-17-507-2021, https://doi.org/10.5194/cp-17-507-2021, 2021
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The last interglacial was a warm period that may provide insights into future climates. Here, we compile and analyse stable carbon isotope data from the ocean during the last interglacial and compare it to the Holocene. The data show that Atlantic Ocean circulation was similar during the last interglacial and the Holocene. We also establish a difference in the mean oceanic carbon isotopic ratio between these periods, which was most likely caused by burial and weathering carbon fluxes.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
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The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Bette L. Otto-Bliesner, Esther C. Brady, Anni Zhao, Chris M. Brierley, Yarrow Axford, Emilie Capron, Aline Govin, Jeremy S. Hoffman, Elizabeth Isaacs, Masa Kageyama, Paolo Scussolini, Polychronis C. Tzedakis, Charles J. R. Williams, Eric Wolff, Ayako Abe-Ouchi, Pascale Braconnot, Silvana Ramos Buarque, Jian Cao, Anne de Vernal, Maria Vittoria Guarino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina A. Morozova, Kerim H. Nisancioglu, Ryouta O'ishi, David Salas y Mélia, Xiaoxu Shi, Marie Sicard, Louise Sime, Christian Stepanek, Robert Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, https://doi.org/10.5194/cp-17-63-2021, 2021
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The CMIP6–PMIP4 Tier 1 lig127k experiment was designed to address the climate responses to strong orbital forcing. We present a multi-model ensemble of 17 climate models, most of which have also completed the CMIP6 DECK experiments and are thus important for assessing future projections. The lig127ksimulations show strong summer warming over the NH continents. More than half of the models simulate a retreat of the Arctic minimum summer ice edge similar to the average for 2000–2018.
Markus Pahlow, Chia-Te Chien, Lionel A. Arteaga, and Andreas Oschlies
Geosci. Model Dev., 13, 4663–4690, https://doi.org/10.5194/gmd-13-4663-2020, https://doi.org/10.5194/gmd-13-4663-2020, 2020
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The stoichiometry of marine biotic processes is important for the regulation of atmospheric CO2 and hence the global climate. We replace a simplistic, fixed-stoichiometry plankton module in an Earth system model with an optimal-regulation model with variable stoichiometry. Our model compares better to the observed carbon transfer from the surface to depth and surface nutrient distributions. This work could aid our ability to describe and project the role of marine ecosystems in the Earth system.
Chia-Te Chien, Markus Pahlow, Markus Schartau, and Andreas Oschlies
Geosci. Model Dev., 13, 4691–4712, https://doi.org/10.5194/gmd-13-4691-2020, https://doi.org/10.5194/gmd-13-4691-2020, 2020
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We demonstrate sensitivities of tracers to parameters of a new optimality-based plankton–ecosystem model (OPEM) in the UVic-ESCM. We find that changes in phytoplankton subsistence nitrogen quota strongly impact the nitrogen inventory, nitrogen fixation, and elemental stoichiometry of ordinary phytoplankton and diazotrophs. We introduce a new likelihood-based metric for model calibration, and it shows the capability of constraining globally averaged oxygen, nitrate, and DIC concentrations.
Nadine Mengis, David P. Keller, Andrew H. MacDougall, Michael Eby, Nesha Wright, Katrin J. Meissner, Andreas Oschlies, Andreas Schmittner, Alexander J. MacIsaac, H. Damon Matthews, and Kirsten Zickfeld
Geosci. Model Dev., 13, 4183–4204, https://doi.org/10.5194/gmd-13-4183-2020, https://doi.org/10.5194/gmd-13-4183-2020, 2020
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In this paper, we evaluate the newest version of the University of Victoria Earth System Climate Model (UVic ESCM 2.10). Combining recent model developments as a joint effort, this version is to be used in the next phase of model intercomparison and climate change studies. The UVic ESCM 2.10 is capable of reproducing changes in historical temperature and carbon fluxes well. Additionally, the model is able to reproduce the three-dimensional distribution of many ocean tracers.
Sabine Mathesius, Julia Getzlaff, Heiner Dietze, Andreas Oschlies, and Markus Schartau
Earth Syst. Sci. Data, 12, 1775–1787, https://doi.org/10.5194/essd-12-1775-2020, https://doi.org/10.5194/essd-12-1775-2020, 2020
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Controlled manipulation of environmental conditions within large enclosures in the ocean, pelagic mesocosms, has become a standard method to explore responses of marine plankton communities to anthropogenic change. Among the challenges of interpreting mesocosm data is the often uncertain role of vertical mixing. This study introduces a mesocosm mixing model that is able to estimate vertical diffusivities and thus provides a tool for future mesocosm data analyses that account for mixing.
Iris Kriest, Paul Kähler, Wolfgang Koeve, Karin Kvale, Volkmar Sauerland, and Andreas Oschlies
Biogeosciences, 17, 3057–3082, https://doi.org/10.5194/bg-17-3057-2020, https://doi.org/10.5194/bg-17-3057-2020, 2020
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Constants of global biogeochemical ocean models are often tuned
by handto match observations of nutrients or oxygen. We investigate the effect of this tuning by optimising six constants of a global biogeochemical model, simulated in five different offline circulations. Optimal values for three constants adjust to distinct features of the circulation applied and can afterwards be swapped among the circulations, without losing too much of the model's fit to observed quantities.
Fabian Reith, Wolfgang Koeve, David P. Keller, Julia Getzlaff, and Andreas Oschlies
Earth Syst. Dynam., 10, 711–727, https://doi.org/10.5194/esd-10-711-2019, https://doi.org/10.5194/esd-10-711-2019, 2019
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This modeling study is the first one to look at the suitability and collateral effects of direct CO2 injection into the deep ocean as a means to bridge the gap between CO2 emissions and climate impacts of an intermediate CO2 emission scenario and a temperature target on a millennium timescale, such as the 1.5 °C climate target of the Paris Agreement.
Tronje P. Kemena, Angela Landolfi, Andreas Oschlies, Klaus Wallmann, and Andrew W. Dale
Earth Syst. Dynam., 10, 539–553, https://doi.org/10.5194/esd-10-539-2019, https://doi.org/10.5194/esd-10-539-2019, 2019
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Oceanic deoxygenation is driven by climate change in several areas of the global ocean. Measurements indicate that ocean volumes with very low oxygen levels expand, with consequences for marine organisms and fishery. We found climate-change-driven phosphorus (P) input in the ocean is hereby an important driver for deoxygenation on longer timescales with effects in the next millennia.
Daniela Niemeyer, Iris Kriest, and Andreas Oschlies
Biogeosciences, 16, 3095–3111, https://doi.org/10.5194/bg-16-3095-2019, https://doi.org/10.5194/bg-16-3095-2019, 2019
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Recent studies suggest spatial variations of the marine particle flux length scale. Using a global biogeochemical ocean model, we investigate whether changes in particle size and size-dependent sinking can explain this variation. We address uncertainties by varying aggregate properties and circulation. Both aspects have an impact on the representation of nutrients, oxygen and oxygen minimum zones. The formation and sinking of large aggregates in productive areas lead to deeper flux penetration.
Yonss Saranga José, Lothar Stramma, Sunke Schmidtko, and Andreas Oschlies
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-155, https://doi.org/10.5194/bg-2019-155, 2019
Revised manuscript accepted for BG
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In situ observations along the Peruvian and Chilean coasts have exhibited variability in the water column oxygen concentration. This variability, which is attributed to the El Niño Southern Oscillation (ENSO), might have implication on the vertical extension of the Eastern Tropical South Pacific (ETSP) oxygen minimum zone. Here using a coupled physical-biogeochemical model, we provide new insights into how ENSO variability affects the vertical extension of the oxygen-poor waters of the ETSP.
Karin F. Kvale, Katherine E. Turner, Angela Landolfi, and Katrin J. Meissner
Biogeosciences, 16, 1019–1034, https://doi.org/10.5194/bg-16-1019-2019, https://doi.org/10.5194/bg-16-1019-2019, 2019
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Drivers motivating the evolution of calcifying phytoplankton are poorly understood. We explore differences in global ocean chemistry with and without calcifiers during rapid climate changes. We find the presence of phytoplankton calcifiers stabilizes the volume of low oxygen regions and consequently stabilizes the concentration of nitrate, which is an important nutrient required for photosynthesis. By stabilizing nitrate concentrations, calcifiers improve their growth conditions.
Olaf Duteil, Andreas Oschlies, and Claus W. Böning
Biogeosciences, 15, 7111–7126, https://doi.org/10.5194/bg-15-7111-2018, https://doi.org/10.5194/bg-15-7111-2018, 2018
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Oxygen-depleted regions of the Pacific Ocean are currently expanding, which is threatening marine habitats. Based on numerical simulations, we show that the decrease in the intensity of the trade winds and the subsequent slowdown of the oceanic currents lead to a reduction in oxygen supply. Our study suggests that the prevailing positive conditions of the Pacific Decadal Oscillation since 1975, a major source of natural variability, may explain a significant part of the current deoxygenation.
Marine Bretagnon, Aurélien Paulmier, Véronique Garçon, Boris Dewitte, Séréna Illig, Nathalie Leblond, Laurent Coppola, Fernando Campos, Federico Velazco, Christos Panagiotopoulos, Andreas Oschlies, J. Martin Hernandez-Ayon, Helmut Maske, Oscar Vergara, Ivonne Montes, Philippe Martinez, Edgardo Carrasco, Jacques Grelet, Olivier Desprez-De-Gesincourt, Christophe Maes, and Lionel Scouarnec
Biogeosciences, 15, 5093–5111, https://doi.org/10.5194/bg-15-5093-2018, https://doi.org/10.5194/bg-15-5093-2018, 2018
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In oxygen minimum zone, the fate of the organic matter is a key question as the low oxygen condition would preserve the OM and thus enhance the biological carbon pump while the high microbial activity would foster the remineralisation and the greenhouse gases emission. To investigate this paradigm, sediment traps were deployed off Peru. We pointed out the influence of the oxygenation as well as the organic matter quantity and quality on the carbon transfer efficiency in the oxygen minimum zone.
Kaitlin A. Naughten, Katrin J. Meissner, Benjamin K. Galton-Fenzi, Matthew H. England, Ralph Timmermann, Hartmut H. Hellmer, Tore Hattermann, and Jens B. Debernard
Geosci. Model Dev., 11, 1257–1292, https://doi.org/10.5194/gmd-11-1257-2018, https://doi.org/10.5194/gmd-11-1257-2018, 2018
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MetROMS and FESOM are two ocean/sea-ice models which resolve Antarctic ice-shelf cavities and consider thermodynamics at the ice-shelf base. We simulate the period 1992–2016 with both models, and with two options for resolution in FESOM, and compare output from the three simulations. Ice-shelf melt rates, sub-ice-shelf circulation, continental shelf water masses, and sea-ice processes are compared and evaluated against available observations.
Andrew Lenton, Richard J. Matear, David P. Keller, Vivian Scott, and Naomi E. Vaughan
Earth Syst. Dynam., 9, 339–357, https://doi.org/10.5194/esd-9-339-2018, https://doi.org/10.5194/esd-9-339-2018, 2018
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Artificial ocean alkalinization (AOA) is capable of reducing atmospheric carbon dioxide concentrations and surface warming while also addressing ocean acidification. We simulate the Earth system response to a fixed addition of AOA under low and high emissions. We explore the regional and global response to AOA. A key finding is that AOA is much more effective at reducing warming and ocean acidification under low emissions, despite lower carbon uptake.
Volkmar Sauerland, Ulrike Löptien, Claudine Leonhard, Andreas Oschlies, and Anand Srivastav
Geosci. Model Dev., 11, 1181–1198, https://doi.org/10.5194/gmd-11-1181-2018, https://doi.org/10.5194/gmd-11-1181-2018, 2018
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We present a concept to prove that a parametric model is well calibrated, i.e., that changes of its free parameters cannot lead to a much better model–data misfit anymore. The intention is motivated by the fact that calibrating global biogeochemical ocean models is important for assessment and inter-model comparison but computationally expensive.
David P. Keller, Andrew Lenton, Vivian Scott, Naomi E. Vaughan, Nico Bauer, Duoying Ji, Chris D. Jones, Ben Kravitz, Helene Muri, and Kirsten Zickfeld
Geosci. Model Dev., 11, 1133–1160, https://doi.org/10.5194/gmd-11-1133-2018, https://doi.org/10.5194/gmd-11-1133-2018, 2018
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There is little consensus on the impacts and efficacy of proposed carbon dioxide removal (CDR) methods as a potential means of mitigating climate change. To address this need, the Carbon Dioxide Removal Model Intercomparison Project (or CDR-MIP) has been initiated. This project brings together models of the Earth system in a common framework to explore the potential, impacts, and challenges of CDR. Here, we describe the first set of CDR-MIP experiments.
Nadine Mengis, David P. Keller, and Andreas Oschlies
Earth Syst. Dynam., 9, 15–31, https://doi.org/10.5194/esd-9-15-2018, https://doi.org/10.5194/esd-9-15-2018, 2018
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The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to systematically select, transparent, nonredundant indicators for a comprehensive assessment of the Earth system state. We show that due to changing climate forcing, such as anthropogenic climate change, the ad hoc assessment indicators might need to be reevaluated. Within an iterative process, this method would allow us to select scientifically consistent and societally relevant assessment indicators.
Karin F. Kvale and Katrin J. Meissner
Biogeosciences, 14, 4767–4780, https://doi.org/10.5194/bg-14-4767-2017, https://doi.org/10.5194/bg-14-4767-2017, 2017
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Climate models containing ocean biogeochemistry contain a lot of poorly constrained parameters, which makes model tuning difficult. For more than 20 years modellers have generally assumed phytoplankton light attenuation parameter value choice has an insignificant affect on model ocean primary production; thus, it is often overlooked for tuning. We show that an empirical range of light attenuation parameter values can affect primary production, with increasing sensitivity under climate change.
Karin F. Kvale, Samar Khatiwala, Heiner Dietze, Iris Kriest, and Andreas Oschlies
Geosci. Model Dev., 10, 2425–2445, https://doi.org/10.5194/gmd-10-2425-2017, https://doi.org/10.5194/gmd-10-2425-2017, 2017
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Computer models of ocean biology and chemistry are becoming increasingly complex, and thus more expensive, to run. One solution is to approximate the behaviour of the ocean physics and store that information outside of the model. That
offlineinformation can then be used to calculate a steady-state solution from the model's biology and chemistry, without waiting for a traditional
onlineintegration to complete. We show this offline method reproduces online results and is 100 times faster.
James C. Orr, Raymond G. Najjar, Olivier Aumont, Laurent Bopp, John L. Bullister, Gokhan Danabasoglu, Scott C. Doney, John P. Dunne, Jean-Claude Dutay, Heather Graven, Stephen M. Griffies, Jasmin G. John, Fortunat Joos, Ingeborg Levin, Keith Lindsay, Richard J. Matear, Galen A. McKinley, Anne Mouchet, Andreas Oschlies, Anastasia Romanou, Reiner Schlitzer, Alessandro Tagliabue, Toste Tanhua, and Andrew Yool
Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, https://doi.org/10.5194/gmd-10-2169-2017, 2017
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The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
Daniela Niemeyer, Tronje P. Kemena, Katrin J. Meissner, and Andreas Oschlies
Earth Syst. Dynam., 8, 357–367, https://doi.org/10.5194/esd-8-357-2017, https://doi.org/10.5194/esd-8-357-2017, 2017
Markus Schartau, Philip Wallhead, John Hemmings, Ulrike Löptien, Iris Kriest, Shubham Krishna, Ben A. Ward, Thomas Slawig, and Andreas Oschlies
Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, https://doi.org/10.5194/bg-14-1647-2017, 2017
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Plankton models have become an integral part in marine ecosystem and biogeochemical research. These models differ in complexity and in their number of parameters. How values are assigned to parameters is essential. An overview of major methodologies of parameter estimation is provided. Aspects of parameter identification in the literature are diverse. Individual findings could be better synthesized if notation and expertise of the different scientific communities would be reasonably merged.
Yonss Saranga José, Heiner Dietze, and Andreas Oschlies
Biogeosciences, 14, 1349–1364, https://doi.org/10.5194/bg-14-1349-2017, https://doi.org/10.5194/bg-14-1349-2017, 2017
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This study aims to investigate the diverse subsurface nutrient patterns observed within anticyclonic eddies in the upwelling system off Peru. Two simulated anticyclonic eddies with opposing subsurface nitrate concentrations were analysed. The results show that diverse nutrient patterns within anticyclonic eddies are related to the presence of water mass from different origins at different depths, responding to variations in depth of the circulation strength at the edge of the eddy.
Iris Kriest, Volkmar Sauerland, Samar Khatiwala, Anand Srivastav, and Andreas Oschlies
Geosci. Model Dev., 10, 127–154, https://doi.org/10.5194/gmd-10-127-2017, https://doi.org/10.5194/gmd-10-127-2017, 2017
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Global biogeochemical ocean models are subject to a high level of parametric uncertainty. This may be of consequence for their skill with respect to accurately describing features of the present ocean and their sensitivity to possible environmental changes. We present the first results from a framework that combines an offline biogeochemical tracer transport model with an estimation of distribution algorithm, calibrating six biogeochemical model parameters against observed oxygen and nutrients.
Fabian Reith, David P. Keller, and Andreas Oschlies
Earth Syst. Dynam., 7, 797–812, https://doi.org/10.5194/esd-7-797-2016, https://doi.org/10.5194/esd-7-797-2016, 2016
Bei Su, Markus Pahlow, and Andreas Oschlies
Biogeosciences, 13, 4985–5001, https://doi.org/10.5194/bg-13-4985-2016, https://doi.org/10.5194/bg-13-4985-2016, 2016
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Previously identified positive feedbacks within the nitrogen cycle in the eastern tropical South Pacific (ETSP) have challenged our understanding of the observed dynamics and stability of the nitrogen inventory. We present a box model analysis of the biological and biogeochemical relations in the ETSP among nitrogen deposition, benthic denitrification and phosphate regeneration. Our results suggest dominant stabilizing feedbacks tending to keep a balanced nitrogen inventory in the ETSP.
I. Kriest and A. Oschlies
Geosci. Model Dev., 8, 2929–2957, https://doi.org/10.5194/gmd-8-2929-2015, https://doi.org/10.5194/gmd-8-2929-2015, 2015
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We use a global model of oceanic P, N, and O2 cycles to investigate consequences of uncertainties in description of organic matter sinking, remineralization, denitrification, and N2-Fixation. After all biogeochemical and physical processes have been spun-up into a dynamically consistent steady-state, particle sinking and oxidant affinities of aerobic and anaerobic remineralization determine the extent of oxygen minimum zones, global nitrogen fluxes, and the oceanic nitrogen inventory.
W. Koeve, H. Wagner, P. Kähler, and A. Oschlies
Geosci. Model Dev., 8, 2079–2094, https://doi.org/10.5194/gmd-8-2079-2015, https://doi.org/10.5194/gmd-8-2079-2015, 2015
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The natural abundance of 14C in CO2 dissolved in seawater is often used to evaluate circulation and age in the ocean and in ocean models. We study limitations of using natural 14C to determine the time elapsed since water had contact with the atmosphere. We find that, globally, bulk 14C age is dominated by two equally important components, (1) the time component of circulation and (2) the “preformed 14C-age”. Considering preformed 14C-age is critical for an assessment of circulation in models.
L. Nickelsen, D. P. Keller, and A. Oschlies
Geosci. Model Dev., 8, 1357–1381, https://doi.org/10.5194/gmd-8-1357-2015, https://doi.org/10.5194/gmd-8-1357-2015, 2015
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In this paper we find that including the marine cycle of the phytoplankton nutrient iron in a global climate model improves the agreement between observed and simulated nutrient concentrations in the ocean and that a better description of the source of iron from the sediment to the ocean is more important than that of iron-containing dust deposition. Finally, we find that the response of the iron cycle to climate warming affects the phytoplankton growth and nutrient cycles.
B. Su, M. Pahlow, H. Wagner, and A. Oschlies
Biogeosciences, 12, 1113–1130, https://doi.org/10.5194/bg-12-1113-2015, https://doi.org/10.5194/bg-12-1113-2015, 2015
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A box model of the eastern tropical South Pacific oxygen minimum zone suggests that anaerobic water-column remineralization rates have to be slower than aerobic remineralization in order to explain the relatively high values of observed nitrate concentrations. Lateral oxygen supply sufficient to oxidize about one-fifth of the export production is required to prevent an anoxic deep ocean. Under these circumstances, the region can be a net source of fixed nitrogen to the surrounding ocean.
W. Koeve, O. Duteil, A. Oschlies, P. Kähler, and J. Segschneider
Geosci. Model Dev., 7, 2393–2408, https://doi.org/10.5194/gmd-7-2393-2014, https://doi.org/10.5194/gmd-7-2393-2014, 2014
A. E. F. Prowe, M. Pahlow, S. Dutkiewicz, and A. Oschlies
Biogeosciences, 11, 3397–3407, https://doi.org/10.5194/bg-11-3397-2014, https://doi.org/10.5194/bg-11-3397-2014, 2014
K. F. Kvale, K. J. Meissner, D. P. Keller, M. Eby, and A. Schmittner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-1709-2014, https://doi.org/10.5194/gmdd-7-1709-2014, 2014
Revised manuscript not accepted
I. Kriest and A. Oschlies
Biogeosciences, 10, 8401–8422, https://doi.org/10.5194/bg-10-8401-2013, https://doi.org/10.5194/bg-10-8401-2013, 2013
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
C. J. Somes, A. Oschlies, and A. Schmittner
Biogeosciences, 10, 5889–5910, https://doi.org/10.5194/bg-10-5889-2013, https://doi.org/10.5194/bg-10-5889-2013, 2013
V. Cocco, F. Joos, M. Steinacher, T. L. Frölicher, L. Bopp, J. Dunne, M. Gehlen, C. Heinze, J. Orr, A. Oschlies, B. Schneider, J. Segschneider, and J. Tjiputra
Biogeosciences, 10, 1849–1868, https://doi.org/10.5194/bg-10-1849-2013, https://doi.org/10.5194/bg-10-1849-2013, 2013
A. Landolfi, H. Dietze, W. Koeve, and A. Oschlies
Biogeosciences, 10, 1351–1363, https://doi.org/10.5194/bg-10-1351-2013, https://doi.org/10.5194/bg-10-1351-2013, 2013
M. El Jarbi, J. Rückelt, T. Slawig, and A. Oschlies
Biogeosciences, 10, 1169–1182, https://doi.org/10.5194/bg-10-1169-2013, https://doi.org/10.5194/bg-10-1169-2013, 2013
L. M. Zamora, A. Oschlies, H. W. Bange, K. B. Huebert, J. D. Craig, A. Kock, and C. R. Löscher
Biogeosciences, 9, 5007–5022, https://doi.org/10.5194/bg-9-5007-2012, https://doi.org/10.5194/bg-9-5007-2012, 2012
Related subject area
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Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
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Exploring the potential of history matching for land surface model calibration
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Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
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The unicellular NUM v.0.91: A trait-based plankton model evaluated in two contrasting biogeographic provinces
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Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
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Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
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
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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
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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.
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
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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.
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
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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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
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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
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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.
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
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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
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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.
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
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
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
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using 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.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
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Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
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
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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
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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.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
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
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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
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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
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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.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1914, https://doi.org/10.5194/egusphere-2024-1914, 2024
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Under climate change, the conditions for wildfires to form are becoming more frequent 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 crucial platform for future developments.
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
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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
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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
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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
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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.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
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When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those 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 see what changes on our lands.
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
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-53, https://doi.org/10.5194/gmd-2024-53, 2024
Revised manuscript accepted for GMD
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We describe and test the size-based NUM model, that define organisms by a single set of parameters, on planktonic unicellular ecosystems in a eutrophic and an oligotrophic site. Results show both sites can be modelled with similar parameters, and a robust performance over a wide range of parameters. The study show that the NUM model is useful for non-experts and applicable for modelling domains with limited ecosystem data. It holds promise for evolutionary scenarios and deep-time climate models.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
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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.
Cited articles
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.: An ecosystem model of
the global ocean including Fe, Si, P colimitations, Global
Biogeochem. Cy., 17, 1060, https://doi.org/10.1029/2001GB001745, 2003. a, b, c, d
Balch, W. M. and Utgoff, P. E.: Potential Interactions Among Ocean
Acidification, Coccolithophores, and the Optical Properties of Seawater,
Oceanography, 22, 146–159, https://doi.org/10.5670/oceanog.2009.104,
2009. a
Battle, M., Bender, M., Sowers, T., Tans, P., Butler, J., Elkins, J., Ellis,
J., Conway, T., Zhang, N., Lang, P., and Clarke, A.: Atmospheric gas
concentrations over the past century measured in air from firn at the South
Pole, Nature, 383, 231–235, https://doi.org/10.1038/383231a0, 1996. a
Berelson, W. M.: Particle settling rates increase with depth in the ocean,
Deep-Sea Res. Pt. II, 49, 237–251,
https://doi.org/10.1016/S0967-0645(01)00102-3, 2001. a
Bidle, K. D. and Azam, F.: Accelerated dissolution of diatom silica by marine
bacterial assemblages, Nature, 397, 508–512, https://doi.org/10.1038/17351, 1999. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols,
M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N.,
Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL
IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set],
https://doi.org/10.22033/ESGF/CMIP6.5195, 2018. a, b, c, d
Boyce, D. G., Lewis, M. R., and Worm, B.: Global phytoplankton decline over the
past century, Nature, 466, 591–596, https://doi.org/10.1038/nature09268, 2010. a
Buitenhuis, E. T., Vogt, M., Moriarty, R., Bednaršek, N., Doney, S. C., Leblanc, K., Le Quéré, C., Luo, Y.-W., O'Brien, C., O'Brien, T., Peloquin, J., Schiebel, R., and Swan, C.: MAREDAT: towards a world atlas of MARine Ecosystem DATa, Earth Syst. Sci. Data, 5, 227–239, https://doi.org/10.5194/essd-5-227-2013, 2013. a, b, c, d, e
Cael, B. B. and Bisson, K.: Particle Flux Parameterizations: Quantitative and
Mechanistic Similarities and Differences, Front. Mar. Sci., 5,
395, https://doi.org/10.3389/fmars.2018.00395, 2018. a
Carr, M.-E., Friedrichs, M. A. M., Schmeltz, M., Aita, M. N., 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.,
Melin, 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, 2006. a, b
Dale, A. W., Paul, K. M., Clemens, D., Scholz, F., Schroller-Lomnitz, U.,
Wallmann, K., Geilert, S., Hensen, C., Plass, A., Liebetrau, V., Grasse, P.,
and Sommer, S.: Recycling and Burial of Biogenic Silica in an Open Margin
Oxygen Minimum Zone, Global Biogeochem. Cy., 35, e2020GB006583,
https://doi.org/10.1029/2020GB006583, 2021. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set],
https://doi.org/10.22033/ESGF/CMIP6.7627, 2019. a, b, c, d
Eby, M.: Earth System Climate Model, available at: http://terra.seos.uvic.ca/model/, last access: 18 October 2021. a
Eby, M., Zickfeld, K., Montenegro, A., Archer, D., Meissner, K. J., and Weaver,
A. J.: Lifetime of Anthropogenic Climate Change: Millennial Time Scales of
Potential CO2 and Surface Temperature Perturbations, J. Climate, 22,
2501–2511, https://doi.org/10.1175/2008JCLI2554.1, 2009. a, b, c
EC-Earth Consortium: EC-Earth-Consortium EC-Earth-3-CC model output
prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4702, 2021. a, b, c, d
Eppley, R.: Temperature and phytoplankton growth in the sea, Fish.
B. NOAA, 70, 1063—1085,
1972. a
Etheridge, D., Steele, L., Langenfelds, R., Francey, R., Barnola, J., and
Morgan, V.: Natural and anthropogenic changes in atmospheric CO2 over the
last 1000 years from air in Antarctic ice and firn, J. Geophys.
Res.-Atmos., 101, 4115–4128, https://doi.org/10.1029/95JD03410, 1996. a
Etheridge, D., Steele, L., Francey, R., and Langenfelds, R.: Atmospheric
methane between 1000 AD and present: Evidence of anthropogenic emissions and
climatic variability, J. Geophys. Res.-Atmos., 103,
15979–15993, https://doi.org/10.1029/98JD00923, 1998. a
Ferretti, D., Miller, J., White, J., Etheridge, D., Lassey, K., Lowe, D.,
Meure, C., Dreier, M., Trudinger, C., van Ommen, T., and Langenfelds, R.:
Unexpected changes to the global methane budget over the past 2000 years,
Science, 309, 1714–1717, https://doi.org/10.1126/science.1115193, 2005. a
Flückiger, J., Dällenbach, A., Blunier, T., Stauffer, B., Stocker,
T. F., Raynaud, D., and Barnola, J.-M.: Variations in atmospheric N2O
concentration during abrupt climatic changes, Science, 285, 227–230,
https://doi.org/10.1126/science.285.5425.227, 1999. a
Flückiger, J., Blunier, T., Stauffer, B., Chappellaz, J., Spahni, R.,
Kawamura, K., Schwander, J., Stocker, T. F., and Dahl-Jensen, D.: N2O and CH4
variations during the last glacial epoch: Insight into global processes,
Global Biogeochem. Cy., 18, GB1020, https://doi.org/10.1029/2003GB002122, 2004. a
Frölicher, T. L., Sarmiento, J. L., Paynter, D. J., Dunne, J. P., Krasting,
J. P., and Winton, M.: Dominance of the Southern Ocean in Anthropogenic
Carbon and Heat Uptake in CMIP5 Models, J. Climate, 28, 862–886,
https://doi.org/10.1175/JCLI-D-14-00117.1, 2015. a
Fu, W., Randerson, J. T., and Moore, J. K.: Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models, Biogeosciences, 13, 5151–5170, https://doi.org/10.5194/bg-13-5151-2016, 2016. a, b, c
Galbraith, E. D., Gnanadesikan, A., Dunne, J. P., and Hiscock, M. R.: Regional impacts of iron-light colimitation in a global biogeochemical model, Biogeosciences, 7, 1043–1064, https://doi.org/10.5194/bg-7-1043-2010, 2010. a
Garcia, H., Locarnini, R. A., Boyer, T. P., Antonov, J., Baranova, O., Zweng,
M. M., Reagan, J., and Johnson, D.: World Ocean Atlas 2013, Volume 3:
Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, Tech.
rep., NOAA Atlas NESDIS 75, U.S. Government Printing Office, Washington,
D.C., 2014a. a, b, c
Garcia, H., Locarnini, R. A., Boyer, T. P., Antonov, J., Zweng, M. M., Reagan,
J., Baranova, O., and Johnson, D.: World Ocean Atlas 2013, Volume 4:
Dissolved Inorganic Nutrients (phosphate, nitrate, silicate), Tech. rep.,
NOAA Atlas NESDIS 76, U.S. Government Printing Office, Washington, D.C.,
2014b. a, b, c, d, e, f, g
Gnanadesikan, A.: A global model of silicon cycling: Sensitivity to eddy
parameterization and dissolution, Global Biogeochem. Cy., 13, 199–220,
https://doi.org/10.1029/1998GB900013, 1999. a, b, c
Heinze, C., Eyring, V., Friedlingstein, P., Jones, C., Balkanski, Y., Collins, W., Fichefet, T., Gao, S., Hall, A., Ivanova, D., Knorr, W., Knutti, R., Löw, A., Ponater, M., Schultz, M. G., Schulz, M., Siebesma, P., Teixeira, J., Tselioudis, G., and Vancoppenolle, M.: ESD Reviews: Climate feedbacks in the Earth system and prospects for their evaluation, Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, 2019. a
Holzer, M., Primeau, F. W., DeVries, T., and Matear, R.: The Southern Ocean
silicon trap: Data-constrained estimates of regenerated silicic acid,
trapping efficiencies, and global transport paths, J. Geophys.
Res.-Oceans, 119, 313–331, https://doi.org/10.1002/2013JC009356, 2014. a, b
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer,
G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones, C. D.,
Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova,
E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P.,
and Wang, Y. P.: Harmonization of land-use scenarios for the period
1500–2100: 600 years of global gridded annual land-use transitions, wood
harvest, and resulting secondary lands, Climatic Change, 109, 117,
https://doi.org/10.1007/s10584-011-0153-2, 2011. a
Iida, T., Odate, T., and Fukuchi, M.: Long-Term Trends of Nutrients and
Apparent Oxygen Utilization South of the Polar Front in Southern Ocean
Intermediate Water from 1965 to 2008, PLOS ONE, 8, 1–7,
https://doi.org/10.1371/journal.pone.0071766, 2013. a
Jin, X., Gruber, N., Dunne, J. P., Sarmiento, J. L., and Armstrong, R. A.:
Diagnosing the contribution of phytoplankton functional groups to the
production and export of particulate organic carbon, CaCO3, and opal from
global nutrient and alkalinity distributions, Global Biogeochem. Cy.,
20, GB2015, https://doi.org/10.1029/2005GB002532, 2006. a, b, c
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K., Ropelewski, C., Wang, J.,
Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year
reanalysis project, B. Am. Meteorol. Soc., 77,
437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Key, R., Olsen, A., van Heuven, S., Lauvset, A., Velo, Lin, X., Schirnick, C.,
Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R.,
Jeansson, E., Ishii, M., Perez, F. F., and Suzuki, T.: Global Ocean Data Analysis Project,
Version 2 (GLODAPv2), Tech. rep., US Department of Energy, Carbon Dioxide
Information Analysis Center, Oak Ridge National Laboratory, US Department of
Energy, Oak Ridge, Tennessee [data set], https://doi.org/10.3334/CDIAC/OTG.NDP093_GLODAPv2, 2015. a, b, c, d
Khatiwala, S.: A computational framework for simulation of biogeochemical
tracers in the ocean, Global Biogeochem. Cy., 21, GB3001,
https://doi.org/10.1029/2007GB002923, 2007. a
Khatiwala, S., Primeau, F., and Hall, T.: Reconstruction of the history of
anthropogenic CO2 concentrations in the ocean, Nature, 462, 346–349,
https://doi.org/10.1038/nature08526, 2009. a
Khatiwala, S., Visbeck, M., and Cane, M.: Accelerated simulation of passive
tracers in ocean circulation models, Ocean Model., 9, 51–69,
https://doi.org/10.1016/j.ocemod.2004.04.002, 2005. a
Koeve, W., Wagner, H., Kähler, P., and Oschlies, A.: 14C-age tracers in global ocean circulation models, Geosci. Model Dev., 8, 2079–2094, https://doi.org/10.5194/gmd-8-2079-2015, 2015. a
Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S.,
Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis,
C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A.,
Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M.,
Hurlin, W., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J.,
Schwarzkopf, D. M., Seman, C. J., Silvers, L., Wyman, B., Zeng, Y., Adcroft,
A., Dunne, J. P., Dussin, R., Guo, H., He, J., Held, I. M., Horowitz, L. W.,
Lin, P., Milly, P., Shevliakova, E., Stock, C., Winton, M., Xie, Y., and
Zhao, M.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP
historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8597, 2018. a, b, c, d
Kriest, I.: Calibration of a simple and a complex model of global marine biogeochemistry, Biogeosciences, 14, 4965–4984, https://doi.org/10.5194/bg-14-4965-2017, 2017. a, b
Kriest, I. and Oschlies, A.: Numerical effects on organic-matter sedimentation
and remineralization in biogeochemical ocean models, Ocean Model., 39,
275–283, https://doi.org/10.1016/j.ocemod.2011.05.001, 2011. a
Kriest, I., Sauerland, V., Khatiwala, S., Srivastav, A., and Oschlies, A.: Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0), Geosci. Model Dev., 10, 127–154, https://doi.org/10.5194/gmd-10-127-2017, 2017. a, b
Kriest, I., Kähler, P., Koeve, W., Kvale, K., Sauerland, V., and Oschlies, A.: One size fits all? Calibrating an ocean biogeochemistry model for different circulations, Biogeosciences, 17, 3057–3082, https://doi.org/10.5194/bg-17-3057-2020, 2020. a
Kvale, K.: Supplementary Data to “Explicit silicate cycling in the Kiel Marine Biogeochemistry Model, version 3 (KMBM3) embedded in the UVic ESCM version 2.9”, OPeNDAP GEOMAR serve [code and data set], available at: https://hdl.handle.net/20.500.12085/34412098-27f9-4cbb-992d-12d0d342aa45 (last access: 18 October 2021), 2020. a
Kvale, K. F. and Meissner, K. J.: Primary production sensitivity to phytoplankton light attenuation parameter increases with transient forcing, Biogeosciences, 14, 4767–4780, https://doi.org/10.5194/bg-14-4767-2017, 2017. a
Kvale, K. F., Khatiwala, S., Dietze, H., Kriest, I., and Oschlies, A.: Evaluation of the transport matrix method for simulation of ocean biogeochemical tracers, Geosci. Model Dev., 10, 2425–2445, https://doi.org/10.5194/gmd-10-2425-2017, 2017. a, b, c
Kvale, K. F., Turner, K. E., Keller, D. P., and Meissner, K. J.: Asymmetric
dynamical ocean responses in warming icehouse and cooling greenhouse
climates, Environ. Res. Lett., 13, 125011,
https://doi.org/10.1088/1748-9326/aaedc3, 2018. a, b
Kvale, K. F., Turner, K. E., Landolfi, A., and Meissner, K. J.: Phytoplankton calcifiers control nitrate cycling and the pace of transition in warming icehouse and cooling greenhouse climates, Biogeosciences, 16, 1019–1034, https://doi.org/10.5194/bg-16-1019-2019, 2019. a, b, c
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1∘ × 1∘ GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, https://doi.org/10.5194/essd-8-325-2016, 2016. a, b, c, d
Leblanc, K., Arístegui Ruiz, J., Armand, L. K., Assmy, P.,
Beker, B., Bode, A., Breton, E., Cornet, V., Gibson, J.,
Gosselin, M.-P., Kopczynska, E. E., Marshall, H. G., Peloquin, J. M.,
Piontkovski, S., Poulton, A. J., Quéguiner, B., Schiebel, R.,
Shipe, R., Stefels, J., van Leeuwe, M. A., Varela, M., Widdicombe,
C. E., and Yallop, M.: Global distributions of diatoms abundance,
biovolume and biomass – Gridded data product (NetCDF) – Contribution to the
MAREDAT World Ocean Atlas of Plankton Functional Types, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.777384, 2012. a, b, c, d
Lovato, T., Peano, D., and Butenschön, M.: CMCC CMCC-ESM2 model output
prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.13195, 2021. a, b, c, d
Luo, Y.-W., Doney, S. C., Anderson, L. A., Benavides, M., Berman-Frank, I., Bode, A., Bonnet, S., Boström, K. H., Böttjer, D., Capone, D. G., Carpenter, E. J., Chen, Y. L., Church, M. J., Dore, J. E., Falcón, L. I., Fernández, A., Foster, R. A., Furuya, K., Gómez, F., Gundersen, K., Hynes, A. M., Karl, D. M., Kitajima, S., Langlois, R. J., LaRoche, J., Letelier, R. M., Marañón, E., McGillicuddy Jr., D. J., Moisander, P. H., Moore, C. M., Mouriño-Carballido, B., Mulholland, M. R., Needoba, J. A., Orcutt, K. M., Poulton, A. J., Rahav, E., Raimbault, P., Rees, A. P., Riemann, L., Shiozaki, T., Subramaniam, A., Tyrrell, T., Turk-Kubo, K. A., Varela, M., Villareal, T. A., Webb, E. A., White, A. E., Wu, J., and Zehr, J. P.: Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates, Earth Syst. Sci. Data, 4, 47–73, https://doi.org/10.5194/essd-4-47-2012, 2012. a, b, c
Luo, Y.-W., Lima, I. D., Karl, D. M., Deutsch, C. A., and Doney, S. C.: Data-based assessment of environmental controls on global marine nitrogen fixation, Biogeosciences, 11, 691–708, https://doi.org/10.5194/bg-11-691-2014, 2014. a, b
Machida, T., Nakazawa, T., Fujii, Y., Aoki, S., and Watanabe, O.: Increase in
the atmospheric nitrous-oxide concentration during the last 250 years,
Geophys. Res. Lett., 22, 2921–2924, https://doi.org/10.1029/95GL02822, 1995. a
Mahowald, N. M., Muhs, D. R., Levis, S., Rasch, P. J., Yoshioka, M., Zender,
C. S., and Luo, C.: Change in atmospheric mineral aerosols in response to
climate: Last glacial period, preindustrial, modern, and doubled carbon
dioxide climates, J. Geophys. Res.-Atmos., 111,
https://doi.org/10.1029/2005JD006653, 2006. a
Matsumoto, K., Sarmiento, J. L., and Brzezinski, M. A.: Silicic acid leakage
from the Southern Ocean: A possible explanation for glacial atmospheric pCO2,
Global Biogeochem. Cy., 16, 5-1–5-23, https://doi.org/10.1029/2001GB001442,
2002. a
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T.,
Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K.,
Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse
gas concentrations and their extensions from 1765 to 2300, Climatic
Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011. a
Meissner, K., Weaver, A., Matthews, H., and Cox, P.: The role of land surface
dynamics in glacial inception: A study with the UVic Earth System Model,
Clim. Dynam., 21, 515–537, https://doi.org/10.1007/s00382-003-0352-2, 2003. a
Mengis, N., Keller, D. P., MacDougall, A. H., Eby, M., Wright, N., Meissner, K. J., Oschlies, A., Schmittner, A., MacIsaac, A. J., Matthews, H. D., and Zickfeld, K.: Evaluation of the University of Victoria Earth System Climate Model version 2.10 (UVic ESCM 2.10), Geosci. Model Dev., 13, 4183–4204, https://doi.org/10.5194/gmd-13-4183-2020, 2020. a
Merico, A., Tyrrell, T., and Cokacar, T.: Is there any relationship between
phytoplankton seasonal dynamics and the carbonate system?, J. Marine
Syst., 59, 120–142, 2006. a
Meure, M. C., Etheridge, D., Trudinger, C., Steele, P., Langenfelds, R., van
Ommen, T., Smith, A., and Elkins, J.: Law Dome CO2, CH4 and N2O ice core
records extended to 2000 years BP, Geophys. Res. Lett., 33, L14810,
https://doi.org/10.1029/2006GL026152, 2006. a
Moore, J. K., Fu, W., Primeau, F., Britten, G. L., Lindsay, K., Long, M.,
Doney, S. C., Mahowald, N., Hoffman, F., and Randerson, J. T.: Sustained
climate warming drives declining marine biological productivity, Science,
359, 1139–1143, https://doi.org/10.1126/science.aao6379, 2018. a
Muglia, J., Somes, C. J., Nickelsen, L., and Schmittner, A.: Combined Effects
of Atmospheric and Seafloor Iron Fluxes to the Glacial Ocean,
Paleoceanography, 32, 1204–1218, https://doi.org/10.1002/2016PA003077, 2017. a, b, c, d
NASA (NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group): Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Particulate Inorganic Carbon Data, 2018 Reprocessing, NASA OB.DAAC, Greenbelt, MD, USA [data set], https://doi.org/data/10.5067/AQUA/MODIS/L3M/PIC/2018, 2018. a, b, c
Neukermans, G., Oziel, L., and Babin, M.: Increased intrusion of warming
Atlantic water leads to rapid expansion of temperate phytoplankton in the
Arctic, Glob. Change Biol., 24, 2545–2553, https://doi.org/10.1111/gcb.14075, 2018. a
Nickelsen, L., Keller, D. P., and Oschlies, A.: A dynamic marine iron cycle module coupled to the University of Victoria Earth System Model: the Kiel Marine Biogeochemical Model 2 for UVic 2.9, Geosci. Model Dev., 8, 1357–1381, https://doi.org/10.5194/gmd-8-1357-2015, 2015 a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Nishioka, J., Obata, H., Ogawa, H., Ono, K., Yamashita, Y., Lee, K., Takeda,
S., and Yasuda, I.: Subpolar marginal seas fuel the North Pacific through the
intermediate water at the termination of the global ocean circulation,
P. Natl. Acad. Sci. USA, 117, 12665–12673,
https://doi.org/10.1073/pnas.2000658117, 2020. a
Olsson, J., Stipp, S., Makovicky, E., and Gislason, S.: Metal scavenging by
calcium carbonate at the Eyjafjallajökull volcano: A carbon capture and
storage analogue, Chem. Geol., 384, 135–148,
https://doi.org/10.1016/j.chemgeo.2014.06.025, 2014. a
Oschlies, A., Duteil, O., Getzlaff, J., Koeve, W., Landolfi, A., and Schmidtko,
S.: Patterns of deoxygenation: sensitivity to natural and anthropogenic
drivers, Philos. T. R. Soc. A, 375, 20160325,
https://doi.org/10.1098/rsta.2016.0325, 2017. a, b
O'Brien, C. J.: Global distributions of coccolithophores
abundance and biomass – Gridded data product (NetCDF) – Contribution to the
MAREDAT World Ocean Atlas of Plankton Functional Types, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.785092, 2012. a, b, c
Paasche, E.: A review of the coccolithophorid Emiliania huxleyi
(Prymnesiophyceae), with particular reference to growth, coccolith
formation, and calcification-photosynthesis interactions, Phycologia, 40,
503–529, 2001. a
Partanen, A., Keller, D. P., Korhonen, H., and Matthews, H. D.: Impacts of sea
spray geoengineering on ocean biogeochemistry, Geophys. Res. Lett.,
43, 7600–7608, https://doi.org/10.1002/2016GL070111, 2016. a
Pasquier, B. and Holzer, M.: Inverse-model estimates of the ocean's coupled phosphorus, silicon, and iron cycles, Biogeosciences, 14, 4125–4159, https://doi.org/10.5194/bg-14-4125-2017, 2017. a
Renaudie, J.: Quantifying the Cenozoic marine diatom deposition history: links to the C and Si cycles, Biogeosciences, 13, 6003–6014, https://doi.org/10.5194/bg-13-6003-2016, 2016. a
Riahi, K., Gruebler, A., and Nakicenovic, N.: Scenarios of long-term
socio-economic and environmental development under climate stabilization,
Technol. Forecast. Soc., 74, 887–935,
https://doi.org/10.1016/j.techfore.2006.05.026, 2007. a
Ridgwell, A., Watson, A., and Archer, D.: Modeling the response of the oceanic
Si inventory to perturbation, and consequences for atmospheric CO2, Global
Biogeochem. Cy., 16, 19-1–19-25, https://doi.org/10.1029/2002GB001877, 2002. a, b
Rousseaux, C. S. and Gregg, W. W.: Recent decadal trends in global
phytoplankton composition, Global Biogeochem. Cy., 29, 1674–1688,
https://doi.org/10.1002/2015GB005139, 2015. a, b
Schmidtko, S., Stramma, L., and Visbeck, M.: Decline in global oceanic oxygen
content during the past five decades, Nature, 542, 335–339,
https://doi.org/10.1038/nature21399, 2017. a
Schmittner, A., Oschlies, A., Giraud, X., Eby, M., and Simmons, H.: A global
model of the marine ecosystem for long-term simulations: Sensitivity to ocean
mixing, buoyancy forcing, particle sinking, and dissolved organic matter
cycling, Global Biogeochem. Cy., 19, GB3004, https://doi.org/10.1029/2004GB002283, 2005. a, b, c
Schmittner, A., Oschlies, A., Matthews, H. D., and Galbraith, E. D.: Future
changes in climate, ocean circulation, ecosystems, and biogeochemical cycling
simulated for a business-as-usual CO2 emission scenario until year 4000 AD,
Global Biogeochem. Cy., 22, GB1013, https://doi.org/10.1029/2007GB002953, 2008. a, b, c, d, e
Seland, y., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A.,
Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A.,
Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller,
J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A.,
Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang,
Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output
prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8036, 2019. a
Smith, S. V. and Gattuso, J.-P.: Balancing the Oceanic Calcium Carbonate Cycle:
Consequences of Variable Water Column Ψ, Aquat. Geochem., 17, 327–337,
https://doi.org/10.1007/s10498-010-9109-9, 2011. a
Smith, S. V. and Mackenzie, F. T.: The Role of CaCO3 Reactions in the
Contemporary Oceanic CO2 Cycle, Aquat. Geochem., 22, 153–175,
https://doi.org/10.1007/s10498-015-9282-y, 2016. a
Somes, C. J., Oschlies, A., and Schmittner, A.: Isotopic constraints on the pre-industrial oceanic nitrogen budget, Biogeosciences, 10, 5889–5910, https://doi.org/10.5194/bg-10-5889-2013, 2013. a, b
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett,
N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G.,
Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L.,
von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5-CanOE
model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set],
https://doi.org/10.22033/ESGF/CMIP6.10260, 2019. a, b
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000JD900719, 2001. a, b, c
Tréguer, P., Bowler, C., Moriceau, B., Dutkiewicz, S., Gehlen, M.,
Aumont, O., Bittner, L., Dugdale, R., Finkel, Z., Iudicone, D., Jahn, O.,
Guidi, L., Lasbleiz, M., Leblanc, K., Levy, M., and Pondaven, P.: Influence
of diatom diversity on the ocean biological carbon pump, Nat. Geosci.,
11, 27–37, https://doi.org/10.1038/s41561-017-0028-x, 2018. a
Tréguer, P. J., Sutton, J. N., Brzezinski, M., Charette, M. A., Devries, T., Dutkiewicz, S., Ehlert, C., Hawkings, J., Leynaert, A., Liu, S. M., Llopis Monferrer, N., López-Acosta, M., Maldonado, M., Rahman, S., Ran, L., and Rouxel, O.: Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean, Biogeosciences, 18, 1269–1289, https://doi.org/10.5194/bg-18-1269-2021, 2021.
a, b, c, d, e, f, g, h, i, j, k
Van Cappellen, P., Dixit, S., and van Beusekom, J.: Biogenic silica dissolution
in the oceans: Reconciling experimental and field-based dissolution rates,
Global Biogeochem. Cy., 16, 23-1–23-10,
https://doi.org/10.1029/2001GB001431, 2002. a
Weaver, A., Eby, M., Wiebe, E., Bitz, C., Duffy, P., Ewen, T., Fanning, A.,
Holland, M., MacFadyen, A., Matthews, H., Meissner, K., Saenko, O.,
Schmittner, A., Wang, H., and Yoshimori, M.: The UVic Earth System Climate
Model: Model description, climatology, and applications to past, present and
future climates, Atmosphere-Ocean, 39, 361–428, 2001. a, b, c
Weber, T. and Deutsch, C.: Oceanic nitrogen reservoir regulated by plankton
diversity and ocean circulation, Nature, 489, 419,
https://doi.org/10.1038/nature11357
2012. a
Wernand, M. R., van der Woerd, H. J., and Gieskes, W. W. C.: Trends in Ocean
Colour and Chlorophyll Concentration from 1889 to 2000, Worldwide, PLOS ONE,
8, 1–20, https://doi.org/10.1371/journal.pone.0063766, 2013. a
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based
primary productivity modeling with vertically resolved photoacclimation,
Global Biogeochem. Cy., 22, GB2024, https://doi.org/10.1029/2007GB003078, 2008. a
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M.,
Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese,
P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V.,
Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger,
C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke,
J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W.,
Nabel, J., Notz, D., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J.,
Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B.,
Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for
CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6595, 2019. a, b
Yao, W., Kvale, K. F., Achterberg, E., Koeve, W., and Oschlies, A.: Hierarchy
of calibrated global models reveals improved distributions and fluxes of
biogeochemical tracers in models with explicit representation of iron,
Environ. Res. Lett., 14, 114009, https://doi.org/10.1088/1748-9326/ab4c52,
2019. a, b, c
Yasunaka, S., Ono, T., Nojiri, Y., Whitney, F. A., Wada, C., Murata, A.,
Nakaoka, S., and Hosoda, S.: Long-term variability of surface nutrient
concentrations in the North Pacific, Geophys. Res. Lett., 43,
3389–3397, https://doi.org/10.1002/2016GL068097, 2016. a
Zhang, Y., Mahowald, N., Scanza, R. A., Journet, E., Desboeufs, K., Albani, S., Kok, J. F., Zhuang, G., Chen, Y., Cohen, D. D., Paytan, A., Patey, M. D., Achterberg, E. P., Engelbrecht, J. P., and Fomba, K. W.: Modeling the global emission, transport and deposition of trace elements associated with mineral dust, Biogeosciences, 12, 5771–5792, https://doi.org/10.5194/bg-12-5771-2015, 2015. a
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Short summary
We present a new model of biological marine silicate cycling for the University of Victoria Earth System Climate Model (UVic ESCM). This new model adds diatoms, which are a key aspect of the biological carbon pump, to an existing ecosystem model. Our modifications change how the model responds to warming, with net primary production declining more strongly than in previous versions. Diatoms in particular are simulated to decline with climate warming due to their high nutrient requirements.
We present a new model of biological marine silicate cycling for the University of Victoria...
