Articles | Volume 11, issue 3
Geosci. Model Dev., 11, 1181–1198, 2018
Geosci. Model Dev., 11, 1181–1198, 2018
Methods for assessment of models
29 Mar 2018
Methods for assessment of models | 29 Mar 2018

Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0)

Volkmar Sauerland et al.

Related authors

Calibrating a global three-dimensional biogeochemical ocean model (MOPS-1.0)
Iris Kriest, Volkmar Sauerland, Samar Khatiwala, Anand Srivastav, and Andreas Oschlies
Geosci. Model Dev., 10, 127–154,,, 2017
Short summary

Related subject area

FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108,,, 2023
Short summary
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125,,, 2022
Short summary
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540,,, 2022
Short summary
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471,,, 2022
Short summary
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393,,, 2022
Short summary

Cited articles

Anderson, T.: Plankton functional type modelling: running before we can walk?, J. Plankton Res., 27, 1073–1081,, 2005.
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,, 2015.
Barlow, R. E., Bartholomew, D. J., Bremner, J. M., and Brunk, H. D.: Statistical Inference under Order Restrictions, Theory and Application of Isotonic Regression, Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, London,, 1972.
Boyd, S. and Vandenberghe, L.: Convex optimization, Cambridge University Press, 2004.
Brovkin, V., Petoukhov, V., Claussen, M., Bauer, E., Archer, D., and Jaeger, C.: Geoengineering climate by stratospheric sulfur injections: Earth system vulnerability to technological failure, Climatic Change, 92, 243–259,, 2009.
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.