Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-4691-2020
© Author(s) 2020. 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-13-4691-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
02 Oct 2020
Model evaluation paper |
| 02 Oct 2020
| 02 Oct 2020
Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
Markus Pahlow
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
Markus Schartau
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
Andreas Oschlies
GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
Related authors
Na Li, Christopher J. Somes, Angela Landolfi, Chia-Te Chien, Markus Pahlow, and Andreas Oschlies
EGUsphere, https://doi.org/10.5194/egusphere-2024-123, https://doi.org/10.5194/egusphere-2024-123, 2024
Short summary
Short summary
N is an important nutrient that limits phytoplankton growth in large parts of the ocean. The amount of oceanic N is governed by the balance of N2 fixation and denitrification. Here we incorporate benthic denitrification in an Earth system model with variable stoichiometry. Our model compares better to the observed surface nutrient distributions, marine N2 fixation and primary production. Benthic denitrification plays an important role in marine N and C cycling, and hence the global climate.
Chia-Te Chien, Jonathan V. Durgadoo, Dana Ehlert, Ivy Frenger, David P. Keller, Wolfgang Koeve, Iris Kriest, Angela Landolfi, Lavinia Patara, Sebastian Wahl, and Andreas Oschlies
Geosci. Model Dev., 15, 5987–6024, https://doi.org/10.5194/gmd-15-5987-2022, https://doi.org/10.5194/gmd-15-5987-2022, 2022
Short summary
Short summary
We present the implementation and evaluation of a marine biogeochemical model, Model of Oceanic Pelagic Stoichiometry (MOPS) in the Flexible Ocean and Climate Infrastructure (FOCI) climate model. FOCI-MOPS enables the simulation of marine biological processes, the marine carbon, nitrogen and oxygen cycles, and air–sea gas exchange of CO2 and O2. As shown by our evaluation, FOCI-MOPS shows an overall adequate performance that makes it an appropriate tool for Earth climate system simulations.
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
Short summary
Short summary
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.
Tianfei Xue, Jens Terhaar, A. E. Friederike Prowe, Thomas L. Frölicher, Andreas Oschlies, and Ivy Frenger
Biogeosciences, 21, 2473–2491, https://doi.org/10.5194/bg-21-2473-2024, https://doi.org/10.5194/bg-21-2473-2024, 2024
Short summary
Short summary
Phytoplankton play a crucial role in marine ecosystems. However, climate change's impact on phytoplankton biomass remains uncertain, particularly in the Southern Ocean. In this region, phytoplankton biomass within the water column is likely to remain stable in response to climate change, as supported by models. This stability arises from a shallower mixed layer, favoring phytoplankton growth but also increasing zooplankton grazing due to phytoplankton concentration near the surface.
Miriam Tivig, David Peter Keller, and Andreas Oschlies
EGUsphere, https://doi.org/10.5194/egusphere-2024-258, https://doi.org/10.5194/egusphere-2024-258, 2024
Short summary
Short summary
Marine biological production is highly dependent on the availability of nitrogen and phosphorus. Rivers are the main source of phosphorus to the oceans but poorly represented in global model oceans. We include dissolved nitrogen and phosphorus from river export in a global model ocean and find that the addition of riverine phosphorus affects marine biology on millennial timescales more than riverine nitrogen alone. Globally, riverine phosphorus input increase primary production rates.
Na Li, Christopher J. Somes, Angela Landolfi, Chia-Te Chien, Markus Pahlow, and Andreas Oschlies
EGUsphere, https://doi.org/10.5194/egusphere-2024-123, https://doi.org/10.5194/egusphere-2024-123, 2024
Short summary
Short summary
N is an important nutrient that limits phytoplankton growth in large parts of the ocean. The amount of oceanic N is governed by the balance of N2 fixation and denitrification. Here we incorporate benthic denitrification in an Earth system model with variable stoichiometry. Our model compares better to the observed surface nutrient distributions, marine N2 fixation and primary production. Benthic denitrification plays an important role in marine N and C cycling, and hence the global climate.
Katja Fennel, Matthew C. Long, Christopher Algar, Brendan Carter, David Keller, Arnaud Laurent, Jann Paul Mattern, Ruth Musgrave, Andreas Oschlies, Josiane Ostiguy, Jaime B. Palter, and Daniel B. Whitt
State Planet, 2-oae2023, 9, https://doi.org/10.5194/sp-2-oae2023-9-2023, https://doi.org/10.5194/sp-2-oae2023-9-2023, 2023
Short summary
Short summary
This paper describes biogeochemical models and modelling techniques for applications related to ocean alkalinity enhancement (OAE) research. Many of the most pressing OAE-related research questions cannot be addressed by observation alone but will require a combination of skilful models and observations. We present illustrative examples with references to further information; describe limitations, caveats, and future research needs; and provide practical recommendations.
Andreas Oschlies, Lennart T. Bach, Rosalind E. M. Rickaby, Terre Satterfield, Romany Webb, and Jean-Pierre Gattuso
State Planet, 2-oae2023, 1, https://doi.org/10.5194/sp-2-oae2023-1-2023, https://doi.org/10.5194/sp-2-oae2023-1-2023, 2023
Short summary
Short summary
Reaching promised climate targets will require the deployment of carbon dioxide removal (CDR). Marine CDR options receive more and more interest. Based on idealized theoretical studies, ocean alkalinity enhancement (OAE) appears as a promising marine CDR method. We provide an overview on the current situation of developing OAE as a marine CDR method and describe the history that has led to the creation of the OAE research best practice guide.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Iris Kriest, Julia Getzlaff, Angela Landolfi, Volkmar Sauerland, Markus Schartau, and Andreas Oschlies
Biogeosciences, 20, 2645–2669, https://doi.org/10.5194/bg-20-2645-2023, https://doi.org/10.5194/bg-20-2645-2023, 2023
Short summary
Short summary
Global biogeochemical ocean models are often subjectively assessed and tuned against observations. We applied different strategies to calibrate a global model against observations. Although the calibrated models show similar tracer distributions at the surface, they differ in global biogeochemical fluxes, especially in global particle flux. Simulated global volume of oxygen minimum zones varies strongly with calibration strategy and over time, rendering its temporal extrapolation difficult.
Jiajun Wu, David P. Keller, and Andreas Oschlies
Earth Syst. Dynam., 14, 185–221, https://doi.org/10.5194/esd-14-185-2023, https://doi.org/10.5194/esd-14-185-2023, 2023
Short summary
Short summary
In this study we investigate an ocean-based carbon dioxide removal method: macroalgae open-ocean mariculture and sinking (MOS), which aims to cultivate seaweed in the open-ocean surface and to sink matured biomass quickly to the deep seafloor. Our results suggest that MOS has considerable potential as an ocean-based CDR method. However, MOS has inherent side effects on marine ecosystems and biogeochemistry, which will require careful evaluation beyond this first idealized modeling study.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Chia-Te Chien, Jonathan V. Durgadoo, Dana Ehlert, Ivy Frenger, David P. Keller, Wolfgang Koeve, Iris Kriest, Angela Landolfi, Lavinia Patara, Sebastian Wahl, and Andreas Oschlies
Geosci. Model Dev., 15, 5987–6024, https://doi.org/10.5194/gmd-15-5987-2022, https://doi.org/10.5194/gmd-15-5987-2022, 2022
Short summary
Short summary
We present the implementation and evaluation of a marine biogeochemical model, Model of Oceanic Pelagic Stoichiometry (MOPS) in the Flexible Ocean and Climate Infrastructure (FOCI) climate model. FOCI-MOPS enables the simulation of marine biological processes, the marine carbon, nitrogen and oxygen cycles, and air–sea gas exchange of CO2 and O2. As shown by our evaluation, FOCI-MOPS shows an overall adequate performance that makes it an appropriate tool for Earth climate system simulations.
Tianfei Xue, Ivy Frenger, A. E. Friederike Prowe, Yonss Saranga José, and Andreas Oschlies
Biogeosciences, 19, 455–475, https://doi.org/10.5194/bg-19-455-2022, https://doi.org/10.5194/bg-19-455-2022, 2022
Short summary
Short summary
The Peruvian system supports 10 % of the world's fishing yield. In the Peruvian system, wind and earth’s rotation bring cold, nutrient-rich water to the surface and allow phytoplankton to grow. But observations show that it grows worse at high upwelling. Using a model, we find that high upwelling happens when air mixes the water the most. Then phytoplankton is diluted and grows slowly due to low light and cool upwelled water. This study helps to estimate how it might change in a warming climate.
Karin Kvale, David P. Keller, Wolfgang Koeve, Katrin J. Meissner, Christopher J. Somes, Wanxuan Yao, and Andreas Oschlies
Geosci. Model Dev., 14, 7255–7285, https://doi.org/10.5194/gmd-14-7255-2021, https://doi.org/10.5194/gmd-14-7255-2021, 2021
Short summary
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.
Maria-Theresia Verwega, Christopher J. Somes, Markus Schartau, Robyn Elizabeth Tuerena, Anne Lorrain, Andreas Oschlies, and Thomas Slawig
Earth Syst. Sci. Data, 13, 4861–4880, https://doi.org/10.5194/essd-13-4861-2021, https://doi.org/10.5194/essd-13-4861-2021, 2021
Short summary
Short summary
This work describes a ready-to-use collection of particulate organic carbon stable isotope ratio data sets. It covers the 1960s–2010s and all main oceans, providing meta-information and gridded data. The best coverage exists in Atlantic, Indian and Southern Ocean surface waters during the 1990s. It indicates no major difference between methods and shows decreasing values towards high latitudes, with the lowest in the Southern Ocean, and a long-term decline in all regions but the Southern Ocean.
Miriam Tivig, David P. Keller, and Andreas Oschlies
Biogeosciences, 18, 5327–5350, https://doi.org/10.5194/bg-18-5327-2021, https://doi.org/10.5194/bg-18-5327-2021, 2021
Short summary
Short summary
Nitrogen is one of the most important elements for life in the ocean. A major source is the riverine discharge of dissolved nitrogen. While global models often omit rivers as a nutrient source, we included nitrogen from rivers in our Earth system model and found that additional nitrogen affected marine biology not only locally but also in regions far off the coast. Depending on regional conditions, primary production was enhanced or even decreased due to internal feedbacks in the nitrogen cycle.
Henrike Schmidt, Julia Getzlaff, Ulrike Löptien, and Andreas Oschlies
Ocean Sci., 17, 1303–1320, https://doi.org/10.5194/os-17-1303-2021, https://doi.org/10.5194/os-17-1303-2021, 2021
Short summary
Short summary
Oxygen-poor regions in the open ocean restrict marine habitats. Global climate simulations show large uncertainties regarding the prediction of these areas. We analyse the representation of the simulated oxygen minimum zones in the Arabian Sea using 10 climate models. We give an overview of the main deficiencies that cause the model–data misfit in oxygen concentrations. This detailed process analysis shall foster future model improvements regarding the oxygen minimum zone in the Arabian Sea.
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
Short summary
Short summary
In this paper we quantify the subduction of upwelled nitrate due to physical processes on the order of several kilometers in the coastal upwelling off Peru and its effect on primary production. We also compare the prepresentation of these processes in a high-resolution simulation (~2.5 km) with a more coarsely resolved simulation (~12 km). To do this, we combine high-resolution shipboard observations of physical and biogeochemical parameters with a complex biogeochemical model configuration.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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, 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
Short summary
Short summary
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
Short summary
Short summary
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
Maria Moreno de Castro, Markus Schartau, and Kai Wirtz
Biogeosciences, 14, 1883–1901, https://doi.org/10.5194/bg-14-1883-2017, https://doi.org/10.5194/bg-14-1883-2017, 2017
Short summary
Short summary
Observations from different mesocosms exposed to the same treatment level typically show variability that hinders the detection of potential treatments effects. To unearth relevant sources of variability, we developed and performed a data-based model analysis that simulates uncertainty propagation. With this method we investigate the divergence in the outcomes due to the amplification of differences in experimentally unresolved ecological factors within replicates of the same treatment level.
Shubham Krishna and Markus Schartau
Biogeosciences, 14, 1857–1882, https://doi.org/10.5194/bg-14-1857-2017, https://doi.org/10.5194/bg-14-1857-2017, 2017
Short summary
Short summary
This study combines experimental data with results from numerical modelling. Data of an ocean acidification mesocosm experiment are used to constrain parameter values of a plankton model. Three different intensities of calcification are resolved with ensembles of optimised model results. Observed variability in data can be well explained by these ensemble model solutions. The simulated ocean acidification effect on calcification is small compared to the spread of the ensemble model solutions.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Carolin R. Löscher, Hermann W. Bange, Ruth A. Schmitz, Cameron M. Callbeck, Anja Engel, Helena Hauss, Torsten Kanzow, Rainer Kiko, Gaute Lavik, Alexandra Loginova, Frank Melzner, Judith Meyer, Sven C. Neulinger, Markus Pahlow, Ulf Riebesell, Harald Schunck, Sören Thomsen, and Hannes Wagner
Biogeosciences, 13, 3585–3606, https://doi.org/10.5194/bg-13-3585-2016, https://doi.org/10.5194/bg-13-3585-2016, 2016
Short summary
Short summary
The ocean loses oxygen due to climate change. Addressing this issue in tropical ocean regions (off Peru and Mauritania), we aimed to understand the effects of oxygen depletion on various aspects of marine biogeochemistry, including primary production and export production, the nitrogen cycle, greenhouse gas production, organic matter fluxes and remineralization, and the role of zooplankton and viruses.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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
Climate and Earth system modeling
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
Short summary
Short summary
We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
Short summary
Short summary
This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
Short summary
Short summary
Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Cited articles
Anderson, L. A. and Sarmiento, J. L.: Redfield ratios of remineralization
determined by nutrient data analysis, Global Biogeochem. Cycles, 8,
65–80, https://doi.org/10.1029/93GB03318, 1994. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013. a, b
Cavan, E. L., Trimmer, M., Shelley, F., and Sanders, R.: Remineralization of
particulate organic carbon in an ocean oxygen minimum zone, Nat.
Commun., 8, 14847 EP,
https://doi.org/10.1038/ncomms14847, 2017. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van
Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer,
C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E.,
Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch,
P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2),
J. Adv. Model. Earth Syst., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020. a
Deutsch, C. and Weber, T.: Nutrient Ratios as a Tracer and Driver of Ocean
Biogeochemistry, Annu. Rev. Marine Sci., 4, 113–141,
https://doi.org/10.1146/annurev-marine-120709-142821, 2012. a
Deutsch, C., Sarmiento, J. L., Sigman, D. M., Gruber, N., and Dunne, J. P.:
Spatial coupling of nitrogen inputs and losses in the ocean, Nature, 445,
163–167, https://doi.org/10.1038/nature05392, 2007. a
DeVries, T., Deutsch, C., Primeau, F., Chang, B., and Devol, A.: Global rates
of water-column denitrification derived from nitrogen gas measurements,
Nat. Geosci., 5, 547–550,
https://doi.org/10.1038/ngeo1515, 2012. a
Droop, M. R.: Vitamin B12 and Marine Ecology. IV. The Kinetics of Uptake,
Growth and Inhibition in Monochrysis Lutheri, 48, 689–733,
https://doi.org/10.1017/S0025315400019238, 1968. a
Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P.,
Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke, W.,
Dunne, K. A., Griffies, S. M., Hallberg, R. W., Harrison, M. J., Levy, H.,
Wittenberg, A. T., Phillips, P. J., and Zadeh, N.: GFDL's ESM2 Global Coupled
Climate–Carbon Earth System Models. Part II: Carbon System Formulation and
Baseline Simulation Characteristics, J. Climate, 26, 2247–2267,
https://doi.org/10.1175/JCLI-D-12-00150.1, 2013. a, b
Dutkiewicz, S., Ward, B. A., Monteiro, F., and Follows, M. J.: Interconnection
of nitrogen fixers and iron in the Pacific Ocean: Theory and numerical
simulations, Global Biogeochem. Cycles, 26, GB1012, https://doi.org/10.1029/2011GB004039,
2012. a
Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A., Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F.: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity, Clim. Past, 9, 1111–1140, https://doi.org/10.5194/cp-9-1111-2013, 2013. a
Edwards, A. M.: Adding Detritus to a Nutrient–Phytoplankton–Zooplankton
Model:A Dynamical-Systems Approach, J. Plankton Res., 23,
389–413, https://doi.org/10.1093/plankt/23.4.389, 2001. a
Eppley, R. W. and Peterson, B. J.: Particulate organic matter flux and
planktonic new production in the deep ocean, Nature, 282, 677–680,
https://doi.org/10.1038/282677a0, 1979. a
Everett, J. D., Baird, M. E., Buchanan, P., Bulman, C., Davies, C., Downie, R.,
Griffiths, C., Heneghan, R., Kloser, R. J., Laiolo, L., Lara-Lopez, A.,
Lozano-Montes, H., Matear, R. J., McEnnulty, F., Robson, B., Rochester, W.,
Skerratt, J., Smith, J. A., Strzelecki, J., Suthers, I. M., Swadling, K. M.,
van Ruth, P., and Richardson, A. J.: Modeling What We Sample and Sampling
What We Model: Challenges for Zooplankton Model Assessment, Front.
Marine Sci., 4, 77, https://doi.org/10.3389/fmars.2017.00077, 2017. a
Falkowski, P. G.: Evolution of the nitrogen cycle and its influence on the
biological sequestration of CO2 in the ocean, Nature, 387, 272–275,
https://doi.org/10.1038/387272a0, 1997. a
Fasham, M. J. R., Ducklow, H. W., and McKelvie, S. M.: A nitrogen-based model
of plankton dynamics in the oceanic mixed layer, J. Marine Res.,
48, 591–639,
1990. a
Fay, A. R. and McKinley, G. A.: Global open-ocean biomes: mean and temporal variability, Earth Syst. Sci. Data, 6, 273–284, https://doi.org/10.5194/essd-6-273-2014, 2014. a, b, c
Feely, R. A., Sabine, C. L., Schlitzer, R., Bullister, J. L., Mecking, S., and
Greeley, D.: Oxygen Utilization and Organic Carbon Remineralization in the
Upper Water Column of the Pacific Ocean, J. Oceanogr., 60, 45–52,
https://doi.org/10.1023/B:JOCE.0000038317.01279.aa, 2004. a
Fernández-Castro, B., Pahlow, M., Mouriño-Carballido, B.,
Marañón, E., and Oschlies, A.: Optimality-based Trichodesmium
Diazotrophy in the North Atlantic Subtropical Gyre, J. Plankton Res., 38,
946–963, https://doi.org/10.1093/plankt/fbw047, 2016. a, b
Flato, G. M.: Earth system models: an overview, Wiley Interdisciplinary
Reviews: Climate Change, 2, 783–800, https://doi.org/10.1002/wcc.148, 2011. a
Galbraith, E. D. and Martiny, A. C.: A simple nutrient-dependence mechanism for
predicting the stoichiometry of marine ecosystems, P.
Natl. Acad. Sci. USA, 112, 8199–8204, https://doi.org/10.1073/pnas.1423917112, 2015. a
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Mishonov, A. V.,
Baranova, O. K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: Dissolved
Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, in: World Ocean
Atlas 2013, edited by: Levitus, S., vol. 3, NOAA Atlas NESDIS 75,
available at: http://www.nodc.noaa.gov/OC5/indprod.html (last access: 1 August 2018), 2013a. a, b, c
Garcia, H. E., Locarnini, R. A., Boyer, T. P., Antonov, J. I., Mishonov, A. V.,
Baranova, O. K., Zweng, M. M., Reagan, J. R., and Johnson, D. R.: Dissolved
Inorganic Nutrients (phosphate, nitrate, silicate), in: World Ocean Atlas
2013, edited by: Levitus, S., vol. 4, NOAA Atlas NESDIS 76,
available at: http://www.nodc.noaa.gov/OC5/indprod.html (last access: 1 August 2018), 2013b. a, b, c
Geider, R. and Roche, J. L.: Redfield revisited: variability of
Getzlaff, J. and Dietze, H.: Effects of increased isopycnal diffusivity
mimicking the unresolved equatorial intermediate current system in an earth
system climate model, Geophys. Res. Lett., 40, 2166–2170,
https://doi.org/10.1002/grl.50419, 2013. a, b
Harding, K., Turk-Kubo, K. A., Sipler, R. E., Mills, M. M., Bronk, D. A., and
Zehr, J. P.: Symbiotic unicellular cyanobacteria fix nitrogen in the Arctic
Ocean, P. Natl. Acad. Sci. USA, 115, 13371,
https://doi.org/10.1073/pnas.1813658115, 2018. a
Holling, C. S. and Buckingham, S.: A behavioral model of predator-prey
functional responses, Behav. Sci., 21, 183–195,
https://doi.org/10.1002/bs.3830210305, 1976. a
Houlton, B. Z., Wang, Y.-P., Vitousek, P. M., and Field, C. B.: A unifying
framework for dinitrogen fixation in the terrestrial biosphere, Nature, 454,
327–330, https://doi.org/10.1038/nature07028, 2008. a
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and
Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC: Model
architecture and performance as component of the MPI-Earth system model in
different CMIP5 experimental realizations, J. Adv. Model.
Earth Syst., 5, 287–315, https://doi.org/10.1029/2012MS000178, 2013. a, b
Jickells, T. D., Buitenhuis, E., Altieri, K., Baker, A. R., Capone, D., Duce,
R. A., Dentener, F., Fennel, K., Kanakidou, M., LaRoche, J., Lee, K., Liss,
P., Middelburg, J. J., Moore, J. K., Okin, G., Oschlies, A., Sarin, M.,
Seitzinger, S., Sharples, J., Singh, A., Suntharalingam, P., Uematsu, M., and
Zamora, L. M.: A reevaluation of the magnitude and impacts of anthropogenic
atmospheric nitrogen inputs on the ocean, Global Biogeochem. Cycles, 31,
289–305, https://doi.org/10.1002/2016GB005586, 2017. a
Keller, D. P., Oschlies, A., and Eby, M.: A new marine ecosystem model for the University of Victoria Earth System Climate Model, Geosci. Model Dev., 5, 1195–1220, https://doi.org/10.5194/gmd-5-1195-2012, 2012. a, b, c
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
Kriest, I., Oschlies, A., and Khatiwala, S.: Sensitivity analysis of simple
global marine biogeochemical models, Global Biogeochem. Cycles, 26, GB2029,
https://doi.org/10.1029/2011GB004072, 2012. a, b
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
Kwiatkowski, L., Aumont, O., Bopp, L., and Ciais, P.: The Impact of Variable
Phytoplankton Stoichiometry on Projections of Primary Production, Food
Quality, and Carbon Uptake in the Global Ocean, Global Biogeochem. Cycles,
32, 516–528, https://doi.org/10.1002/2017gb005799, 2018. a, b
Kwiatkowski, L., Aumont, O., and Bopp, L.: Consistent trophic amplification of
marine biomass declines under climate change, Global Change Biol., 25,
218–229, https://doi.org/10.1111/gcb.14468, 2019. a
Landolfi, A., Koeve, W., Dietze, H., Kähler, P., and Oschlies, A.: A new
perspective on environmental controls of marine nitrogen fixation,
Geophys. Res. Lett., 42, 4482–4489, https://doi.org/10.1002/2015GL063756,
2015. a
Landolfi, A., Somes, C. J., Koeve, W., Zamora, L. M., and Oschlies, A.: Oceanic
nitrogen cycling and N2O flux perturbations in the Anthropocene, Global
Biogeochem. Cycles, 31, 1236–1255, https://doi.org/10.1002/2017GB005633, 2017. a, b
Li, Z. and Cassar, N.: Satellite estimates of net community production based on
O2∕Ar observations and comparison to other estimates, Global Biogeochem.
Cycles, 30, 735–752, https://doi.org/10.1002/2015GB005314, 2016. a
Loh, A. N. and Bauer, J. E.: Distribution, partitioning and fluxes of dissolved
and particulate organic C, N and P in the eastern North Pacific and Southern
Oceans, Deep-Sea Res. Pt. I, 47, 2287–2316, https://doi.org/10.1016/S0967-0637(00)00027-3, 2000. a
Longhurst, A. R.: Ecological Geography of the Sea, 2nd ed., Academic,
Burlington, Vt., 2007. a
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
Maier-Reimer, E., Mikolajewicz, U., and Winguth, A.: Interactions between ocean
circulation and the biological pumps in the global warming,
Max-Planck-Institut für Meteorologie, Report 171, Hamburg, Germany, 1995. a
Martiny, A. C., Pham, C. T. A., Primeau, F. W., Vrugt, J. A., Moore, J. K.,
Levin, S. A., and Lomas, M. W.: Strong latitudinal patterns in the elemental
ratios of marine plankton and organic matter, Nat. Geosci., 6, 279–283,
https://doi.org/10.1038/ngeo1757, 2013a. a, b
Martiny, A. C., Vrugt, J. A., Primeau, F. W., and Lomas, M. W.: Regional
variation in the particulate organic carbon to nitrogen ratio in the surface
ocean, Global Biogeochem. Cycles, 27, 723–731, https://doi.org/10.1002/gbc.20061,
2013b. a
McElroy, M. B.: Marine biological controls on atmospheric CO2 and climate,
Nature, 302, 328–329, https://doi.org/10.1038/302328a0, 1983. a
McGillicuddy Jr., D. J.: Do Trichodesmium spp. populations in the North
Atlantic export most of the nitrogen they fix?, Global Biogeochem. Cycles,
28, 103–114, https://doi.org/10.1002/2013GB004652, 2014. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A Comparison of Three Methods
for Selecting Values of Input Variables in the Analysis of Output from a
Computer Code, Technometrics, 21, 239–245, 1979. a
Mulholland, M. R., Bernhardt, P. W., Widner, B. N., Selden, C. R., Chappell,
P. D., Clayton, S., Mannino, A., and Hyde, K.: High Rates of N2 Fixation in
Temperate, Western North Atlantic Coastal Waters Expand the Realm of Marine
Diazotrophy, Global Biogeochem. Cycles, 33, 826–840, https://doi.org/10.1029/2018GB006130,
2019. a
Ocean Biology Processing Group: Sea-viewing Wide Field-of-view Sensor
(SeaWiFS) Ocean Color Data, NASA OB.DAAC, NASA Goddard Space Flight
Center, Greenbelt, MD, USA, https://doi.org/10.5067/ORBVIEW-2/SEAWIFS_OC.2014.0,
accessed 2018/07/04, 2014. a, b
Olsen, A., Key, R. M., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Pérez, F. F., and Suzuki, T.: The Global Ocean Data Analysis Project version 2 (GLODAPv2) – an internally consistent data product for the world ocean, Earth Syst. Sci. Data, 8, 297–323, https://doi.org/10.5194/essd-8-297-2016, 2016. a
Pahlow, M.: Linking chlorophyll-nutrient dynamics to the Redfield N:C ratio
with a model of optimal phytoplankton growth, Mar. Ecol. Prog. Ser., 287,
33–43, https://doi.org/10.3354/meps287033, 2005. a
Pahlow, M.: UVic-updates-opem: Optimality-based Plankton Ecosystem Model (OPEM v1.0) for the UVic-ESCM, OceanRep, https://doi.org/10.3289/SW_1_2020, 2020. a
Pahlow, M. and Oschlies, A.: Optimal allocation backs Droop's cell-quota
model, Mar. Ecol. Prog. Ser., 473, 1–5, 2013. a
Prinn, R. G.: Development and application of earth system models, P. Natl. Acad. Sci. USA, 110, 3673,
https://doi.org/10.1073/pnas.1107470109, 2013. a
Reid, P. C., Fischer, A. C., Lewis-Brown, E., Meredith, M. P., Sparrow, M.,
Andersson, A. J., Antia, A., Bates, N. R., Bathmann, U., Beaugrand, G., Brix,
H., Dye, S., Edwards, M., Furevik, T., Gangstø, R., Hátún, H.,
Hopcroft, R. R., Kendall, M., Kasten, S., Keeling, R., Le Quéré, C.,
Mackenzie, F. T., Malin, G., Mauritzen, C., Ólafsson, J., Paull, C.,
Rignot, E., Shimada, K., Vogt, M., Wallace, C., Wang, Z., and Washington, R.:
Chapter 1 Impacts of the Oceans on Climate Change,
Academic Press, 56, 1–150, https://doi.org/10.1016/S0065-2881(09)56001-4, 2009. a
Rödenbeck, C., Bakker, D. C. E., Gruber, N., Iida, Y., Jacobson, A. R., Jones, S., Landschützer, P., Metzl, N., Nakaoka, S., Olsen, A., Park, G.-H., Peylin, P., Rodgers, K. B., Sasse, T. P., Schuster, U., Shutler, J. D., Valsala, V., Wanninkhof, R., and Zeng, J.: Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM), Biogeosciences, 12, 7251–7278, https://doi.org/10.5194/bg-12-7251-2015, 2015. a
Sarmiento, J. L., Slater, R. D., Fasham, M. J. R., Ducklow, H. W., Toggweiler,
J. R., and Evans, G. T.: A seasonal three-dimensional ecosystem model of
nitrogen cycling in the North Atlantic Euphotic Zone, Global Biogeochem.
Cycles, 7, 417–450, https://doi.org/10.1029/93GB00375, 1993. a
Sauerland, V., Kriest, I., Oschlies, A., and Srivastav, A.: Multiobjective
Calibration of a Global Biogeochemical Ocean Model Against Nutrients, Oxygen,
and Oxygen Minimum Zones, J. Adv. Model. Earth Syst., 11, 1285–1308,
https://doi.org/10.1029/2018ms001510, 2019. a, b
Schartau, M., Wallhead, P., Hemmings, J., Löptien, U., Kriest, I., Krishna, S., Ward, B. A., Slawig, T., and Oschlies, A.: Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling, Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, 2017. a
Schmittner, A., Oschlies, A., Giraud, X., Eby, M., and Simmons, H. L.: 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. Cycles, 19, gB3004,
https://doi.org/10.1029/2004GB002283, 2005. a
Shiozaki, T., Fujiwara, A., Ijichi, M., Harada, N., Nishino, S., Nishi, S.,
Nagata, T., and Hamasaki, K.: Diazotroph community structure and the role of
nitrogen fixation in the nitrogen cycle in the Chukchi Sea (western Arctic
Ocean), Limnol. Oceanogr., 63, 2191–2205, https://doi.org/10.1002/lno.10933,
2018. a
Sigman, D. M. and Boyle, E. A.: Glacial/interglacial variations in atmospheric
carbon dioxide, Nature, 407, 859–869, https://doi.org/10.1038/35038000, 2000. a
Sipler, R. E., Gong, D., Baer, S. E., Sanderson, M. P., Roberts, Q. N.,
Mulholland, M. R., and Bronk, D. A.: Preliminary estimates of the
contribution of Arctic nitrogen fixation to the global nitrogen budget,
Limnol. Oceanogr. Lett., 2, 159–166, https://doi.org/10.1002/lol2.10046,
2017. a
Six, K. D. and Maier-Reimer, E.: Effects of plankton dynamics on seasonal
carbon fluxes in an ocean general circulation model, Global Biogeochem.
Cycles, 10, 559–583, https://doi.org/10.1029/96GB02561, 1996. a
Somes, C. J. and Oschlies, A.: On the influence of “non-Redfield”dissolved
organic nutrient dynamics on the spatial distribution of N2 fixation and the
size of the marine fixed nitrogen inventory, Global Biogeochem. Cycles,
29, 973–993, https://doi.org/10.1002/2014GB005050, 2015. a, b
Sterner, R. W. and Elser, J. J.: Ecological Stoichiometry: The Biology of
Elements from Molecules to the Biosphere, Princeton University Press, Princeton, Oxford, 2002. a
Su, B., Pahlow, M., and Prowe, A. E. F.: The role of microzooplankton trophic
interactions in modelling a suite of mesocosm ecosystems, Ecol.
Model., 368, 169–179,
https://doi.org/10.1016/j.ecolmodel.2017.11.013, 2018. a
Thomas, H.: Remineralization ratios of carbon, nutrients, and oxygen in the
North Atlantic Ocean: A field databased assessment, Global Biogeochem.
Cycles, 16, 24-1–24-12, https://doi.org/10.1029/2001GB001452, 2002.
a
Vallina, S. M., Cermeno, P., Dutkiewicz, S., Loreau, M., and Montoya, J. M.:
Phytoplankton functional diversity increases ecosystem productivity and
stability, Ecol. Model., 361, 184–196,
https://doi.org/10.1016/j.ecolmodel.2017.06.020, 2017. a
Vichi, M., Pinardi, N., and Masina, S.: A generalized model of pelagic
biogeochemistry for the global ocean ecosystem. Part I: Theory, J.
Marine Syst., 64, 89–109,
https://doi.org/10.1016/j.jmarsys.2006.03.006, 2007. a
Wanninkhof, R., Park, G.-H., Takahashi, T., Sweeney, C., Feely, R., Nojiri, Y., Gruber, N., Doney, S. C., McKinley, G. A., Lenton, A., Le Quéré, C., Heinze, C., Schwinger, J., Graven, H., and Khatiwala, S.: Global ocean carbon uptake: magnitude, variability and trends, Biogeosciences, 10, 1983–2000, https://doi.org/10.5194/bg-10-1983-2013, 2013. a
Ward, B. A., Dutkiewicz, S., Moore, C. M., and Follows, M. J.: Iron,
phosphorus, and nitrogen supply ratios define the biogeography of nitrogen
fixation, Limnol. Oceanogr., 58, 2059–2075,
https://doi.org/10.4319/lo.2013.58.6.2059, 2013. a
Ward, B. A., Wilson, J. D., Death, R. M., Monteiro, F. M., Yool, A., and Ridgwell, A.: EcoGEnIE 1.0: plankton ecology in the cGEnIE Earth system model, Geosci. Model Dev., 11, 4241–4267, https://doi.org/10.5194/gmd-11-4241-2018, 2018. a
Weaver, A. J., Eby, M., Wiebe, E. C., Bitz, C. M., Duffy, P. B., Ewen, T. L.,
Fanning, A. F., Holland, M. M., MacFadyen, A., Matthews, H. D., Meissner,
K. J., 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,
https://doi.org/10.1080/07055900.2001.9649686, 2001. a, b
Weber, T. and Deutsch, C.: Oceanic nitrogen reservoir regulated by plankton
diversity and ocean circulation, Nature, 489, 419–422,
https://doi.org/10.1038/nature11357, 2012. a
Weber, T. and Deutsch, C.: Local versus basin-scale limitation of marine
nitrogen fixation, P. Natl. Acad. Sci. USA, 111,
8741–8746, https://doi.org/10.1073/pnas.1317193111, 2014. a
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based
primary productivity modeling with vertically resolved photoacclimation,
Global Biogeochem. Cycles, 22, GB2024, https://doi.org/10.1029/2007GB003078, 2008. a
Short summary
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.
We demonstrate sensitivities of tracers to parameters of a new optimality-based...
