A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part I: theory

The set of equations for global ocean biogeochemistry deterministic models have been for-mulated in a comprehensive and unified form in order to use them in numerical simulations of the marine ecosystem for climate change studies (PELAGOS, PELAgic biogeochemistry for Global Ocean Simulations). The fundamental approach stems from the representation of marine trophic interactions and major biogeochemical cycles introduced in the European Regional Seas Ecosystem Model (ERSEM). Our theoretical formulation revisits and generalizes the stoichiometric approach of ERSEM by defining the state variables as Chemical Functional Families (CFF). CFFs are further subdivided into living, non-living and inorganic components. Living CFFs are the basis for the definition of Living Functional Groups, the biomass-based functional prototype of the real organisms. Both CFFs and LFGs are theoretical constructs which allow us to relate measurable properties of marine biogeochemistry to the state variables used in deterministic models. This approach is sufficiently generic that may be used to describe other existing biomass-based ecosystem model

Forecast and analysis assessment through skill scores

International audienceThis paper describes a first comprehensive evaluation of the quality of the ten days ocean forecasts produced by the Mediterranean ocean Forecasting System (MFS). Once a week ten days forecasts are produced. The forecast starts on Tuesday at noon and the prediction is released on Wednesday morning with less then 24 hr delay. In this work we have considered 22 ten days forecasts produced from the 16 August 2005 to the 10 January 2006. All the statistical scores have been done for the Mediterranean basin and for 13 regions in which the Mediterranean sea has been subdivided. The forecast evaluation is given here in terms of root mean square (rms) values. The main skill score is computed as the root mean square of the difference between forecast and analysis (FA) and forecast and persistence (FP), where the persistence is defined as the average of the day of the analysis corresponding to the first day of the forecast. A second skill score (SSP) is defined as the ratio between rms of FA and FP, giving the percentage of accuracy of the forecast with respect to the persistence (Murphy 1993). The rms of FA is always better than FP and the FP rms error is double than the rms of FA. It is found that in the surface layers the error growth is controlled mainly by the atmospheric forcing inaccuracies while at depth the forecast errors could be due to adjustments of the data assimilation scheme to the data insertion procedure. The predictability limit for our ocean forecast seems to be 5?6 days connected to atmospheric forcing inaccuracies and to the data availability for assimilation