Articles | Volume 11, issue 3
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, Ulrike Löptien, Claudine Leonhard, Andreas Oschlies, and Anand Srivastav

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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.