Articles | Volume 8, issue 3
https://doi.org/10.5194/gmd-8-697-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-697-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Mechanistic site-based emulation of a global ocean biogeochemical model (MEDUSA 1.0) for parametric analysis and calibration: an application of the Marine Model Optimization Testbed (MarMOT 1.1)
J. C. P. Hemmings
CORRESPONDING AUTHOR
National Oceanography Centre, Southampton, SO14 3ZH, UK
Wessex Environmental Associates, Salisbury, UK
P. G. Challenor
National Oceanography Centre, Southampton, SO14 3ZH, UK
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK
National Oceanography Centre, Southampton, SO14 3ZH, UK
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Cited
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- Machine learning-based modeling of chl-a concentration in Northern marine regions using oceanic and atmospheric data M. Aleshin et al. 10.3389/fmars.2024.1412883
- Perturbed Biology and Physics Signatures in a 1-D Ocean Biogeochemical Model Ensemble P. Anugerahanti et al. 10.3389/fmars.2020.00549
- Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
- Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea D. Kaufman et al. 10.5194/bg-15-73-2018
- A Metamodel-Based Analysis of the Sensitivity and Uncertainty of the Response of Chesapeake Bay Salinity and Circulation to Projected Climate Change A. Ross et al. 10.1007/s12237-020-00761-w
- Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling M. Schartau et al. 10.5194/bg-14-1647-2017
- Climate change impacts on southern Ross Sea phytoplankton composition, productivity, and export D. Kaufman et al. 10.1002/2016JC012514
- Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia J. Skákala et al. 10.3389/fmars.2023.1058837
- Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0) I. Kriest et al. 10.5194/gmd-10-127-2017
- A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations P. Anugerahanti et al. 10.5194/bg-15-6685-2018
- Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design D. Ford 10.5194/bg-18-509-2021
- Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products D. Ford & R. Barciela 10.1016/j.rse.2017.03.040
- Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea C. Shu et al. 10.3390/rs14051297
13 citations as recorded by crossref.
- Machine learning-based modeling of chl-a concentration in Northern marine regions using oceanic and atmospheric data M. Aleshin et al. 10.3389/fmars.2024.1412883
- Perturbed Biology and Physics Signatures in a 1-D Ocean Biogeochemical Model Ensemble P. Anugerahanti et al. 10.3389/fmars.2020.00549
- Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
- Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea D. Kaufman et al. 10.5194/bg-15-73-2018
- A Metamodel-Based Analysis of the Sensitivity and Uncertainty of the Response of Chesapeake Bay Salinity and Circulation to Projected Climate Change A. Ross et al. 10.1007/s12237-020-00761-w
- Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling M. Schartau et al. 10.5194/bg-14-1647-2017
- Climate change impacts on southern Ross Sea phytoplankton composition, productivity, and export D. Kaufman et al. 10.1002/2016JC012514
- Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia J. Skákala et al. 10.3389/fmars.2023.1058837
- Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0) I. Kriest et al. 10.5194/gmd-10-127-2017
- A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations P. Anugerahanti et al. 10.5194/bg-15-6685-2018
- Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design D. Ford 10.5194/bg-18-509-2021
- Global marine biogeochemical reanalyses assimilating two different sets of merged ocean colour products D. Ford & R. Barciela 10.1016/j.rse.2017.03.040
- Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea C. Shu et al. 10.3390/rs14051297
Saved (final revised paper)
Latest update: 21 Nov 2024
Short summary
Effective calibration of global models is inhibited by the computational demands of 3-D simulations. As a solution for the NEMO-MEDUSA model, we present an efficient emulator of surface chlorophyll as a function of MEDUSA’s biogeochemical parameters. The emulator comprises an array of site-based 1-D simulators and a quantification of uncertainty in their predictions. It is able to produce robust probabilistic estimates of 3-D model output rapidly for comparison with satellite chlorophyll.
Effective calibration of global models is inhibited by the computational demands of 3-D...