Articles | Volume 5, issue 2
https://doi.org/10.5194/gmd-5-471-2012
© Author(s) 2012. 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-5-471-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
20 Apr 2012
Development and technical paper |
| 20 Apr 2012
Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha)
J. C. P. Hemmings
National Oceanography Centre, European Way, Southampton SO14 3ZH, UK
P. G. Challenor
National Oceanography Centre, European Way, Southampton SO14 3ZH, UK
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- Climate change impacts on southern Ross Sea phytoplankton composition, productivity, and export D. Kaufman et al. 10.1002/2016JC012514
- Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling M. Schartau et al. 10.5194/bg-14-1647-2017
- A general framework for aquatic biogeochemical models J. Bruggeman & K. Bolding 10.1016/j.envsoft.2014.04.002
- Effects of parameter indeterminacy in pelagic biogeochemical modules of Earth System Models on projections into a warming future: The scale of the problem U. Löptien & H. Dietze 10.1002/2017GB005690
- When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites B. Ward et al. 10.1016/j.pocean.2013.06.002
- Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea D. Kaufman et al. 10.5194/bg-15-73-2018
- 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. Hemmings et al. 10.5194/gmd-8-697-2015
- MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies A. Yool et al. 10.5194/gmd-6-1767-2013
- A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations P. Anugerahanti et al. 10.5194/bg-15-6685-2018
- Inter‐Annual Variability in Phytoplankton and Nutrients in the Gulf of Elat/Aqaba H. Berman et al. 10.1029/2022JC019431
- Slow science: the value of long ocean biogeochemistry records S. Henson 10.1098/rsta.2013.0334
- Inferred support for disturbance‐recovery hypothesis of North Atlantic phytoplankton blooms M. Smith et al. 10.1002/2015JC011080
- Enhanced global optimization methods applied to complex fisheries stock assessment models D. Penas et al. 10.1016/j.asoc.2019.01.012
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- The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model Y. Xiao & M. Friedrichs 10.1002/2013JC009433
- Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0) V. Sauerland et al. 10.5194/gmd-11-1181-2018
- Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition P. Anugerahanti et al. 10.3389/fmars.2021.675428
20 citations as recorded by crossref.
- Editorial: The publication of geoscientific model developments v1.0 10.5194/gmd-6-1233-2013
- Reciprocal bias compensation and ensuing uncertainties in model-based climate projections: pelagic biogeochemistry versus ocean mixing U. Löptien & H. Dietze 10.5194/bg-16-1865-2019
- Variations in the elemental ratio of organic matter in the central Baltic Sea: Part II – Sensitivities of annual mass flux estimates to model parameter variations M. Kreus & M. Schartau 10.1016/j.csr.2015.02.004
- Climate change impacts on southern Ross Sea phytoplankton composition, productivity, and export D. Kaufman et al. 10.1002/2016JC012514
- Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling M. Schartau et al. 10.5194/bg-14-1647-2017
- A general framework for aquatic biogeochemical models J. Bruggeman & K. Bolding 10.1016/j.envsoft.2014.04.002
- Effects of parameter indeterminacy in pelagic biogeochemical modules of Earth System Models on projections into a warming future: The scale of the problem U. Löptien & H. Dietze 10.1002/2017GB005690
- When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites B. Ward et al. 10.1016/j.pocean.2013.06.002
- Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea D. Kaufman et al. 10.5194/bg-15-73-2018
- 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. Hemmings et al. 10.5194/gmd-8-697-2015
- MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies A. Yool et al. 10.5194/gmd-6-1767-2013
- A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations P. Anugerahanti et al. 10.5194/bg-15-6685-2018
- Inter‐Annual Variability in Phytoplankton and Nutrients in the Gulf of Elat/Aqaba H. Berman et al. 10.1029/2022JC019431
- Slow science: the value of long ocean biogeochemistry records S. Henson 10.1098/rsta.2013.0334
- Inferred support for disturbance‐recovery hypothesis of North Atlantic phytoplankton blooms M. Smith et al. 10.1002/2015JC011080
- Enhanced global optimization methods applied to complex fisheries stock assessment models D. Penas et al. 10.1016/j.asoc.2019.01.012
- Constraining parameters in marine pelagic ecosystem models – is it actually feasible with typical observations of standing stocks? U. Löptien & H. Dietze 10.5194/os-11-573-2015
- The assimilation of satellite‐derived data into a one‐dimensional lower trophic level marine ecosystem model Y. Xiao & M. Friedrichs 10.1002/2013JC009433
- Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0) V. Sauerland et al. 10.5194/gmd-11-1181-2018
- Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition P. Anugerahanti et al. 10.3389/fmars.2021.675428
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