Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-4691-2020
https://doi.org/10.5194/gmd-13-4691-2020
Model evaluation paper
 | 
02 Oct 2020
Model evaluation paper |  | 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

Chia-Te Chien, Markus Pahlow, Markus Schartau, and Andreas Oschlies

Related authors

Global impact of benthic denitrification on marine N2 fixation and primary production simulated by a variable-stoichiometry Earth system model
Na Li, Christopher J. Somes, Angela Landolfi, Chia-Te Chien, Markus Pahlow, and Andreas Oschlies
Biogeosciences, 21, 4361–4380, https://doi.org/10.5194/bg-21-4361-2024,https://doi.org/10.5194/bg-21-4361-2024, 2024
Short summary
FOCI-MOPS v1 – integration of marine biogeochemistry within the Flexible Ocean and Climate Infrastructure version 1 (FOCI 1) Earth system model
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
Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 1: Implementation and model behaviour
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

Related subject area

Climate and Earth system modeling
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025,https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025,https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025,https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Baseline Climate Variables for Earth System Modelling
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025,https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025,https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary

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
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.
Share