Split-domain calibration of an ecosystem model using satellite ocean colour data

The application of satellite ocean colour data to the calibration of plankton ecosystem models for large geographic domains, over which their ideal parameters cannot be assumed to be invariant, is investigated. A method is presented for seeking the number and geographic scope of parameter sets which allows the best fit to validation data to be achieved. These are independent data not used in the parameter estimation process. The goodness-of-fit of the optimally calibrated model to the validation data is an objective measure of merit for the model, together with its external forcing data. Importantly, this is a statistic which can be used for comparative evaluation of different models. The method makes use of observations from multiple locations, referred to as stations, distributed across the geographic domain. It relies on a technique for finding groups of stations which can be aggregated for parameter estimation purposes with minimal increase in the resulting misfit between model and observations.The results of testing this split-domain calibration method for a simple zero dimensional model, using observations from 30 stations in the North Atlantic, are presented. The stations are divided into separate calibration and validation sets. One year of ocean colour data from each station were used in conjunction with a climatological estimate of the station’s annual nitrate maximum. The results demonstrate the practical utility of the method and imply that an optimal fit of the model to the validation data would be given by two parameter sets. The corresponding division of the North Atlantic domain into two provinces allows a misfit-based cost to be achieved which is 25% lower than that for the single parameter set obtained using all of the calibration stations. In general, parameters are poorly constrained, contributing to a high degree of uncertainty in model output for unobserved variables. This suggests that limited progress towards a definitive model calibration can be made without including other types of observations

A marine model optimization test-bed for ecosystem model evaluation: MarMOT version 1.0 description and user guide

In response to scientific challenges in the modelling of plankton ecosystems and their role in biogeochemical cycles, the Marine Model Optimization Test-bed (MarMOT) software has been developed as a tool for comprehensive evaluation of plankton ecosystem models against observational data. It provides a common physical and computational environment in which different ecosystem models can be calibrated and compared. The system is designed specifically to support computationally intensive experiments involving parameter optimization, in which models are evaluated many times with different input data in a 1-D framework. The core of the system is the MarMOT Model Evaluator (MME), which is implemented as a specific application within a system called the Generic Function Analyzer (GFAn). The MME runs one or more simulation cases, producing a cost function value summarizing the model-data misfit over all cases, together with detailed model output, diagnostics and misfit data for each case. It provides various options for modelling photosynthesis, each of which can be applied to any ecosystem model implemented.GFAn provides a generic data management framework that adapts to the requirements of the application, together with an optimizer for cost function minimization and a flexible experiment control interface. The data management framework allows different instances of all model inputs (parameters, forcing data and initial conditions) to be easily combined in different ways to drive ensemble simulations for sensitivity and uncertainty analyses or multi-site calibration experiments.A baseline version of the MarMOT system is described in which two ecosystem models are implemented. In future versions, it is expected that a wide range of ecosystem models will be made available for research purposes through collaborative work with different modelling groups. Investigations of all models should benefit from independent improvements in the functionality of the MME application and the GFAn system, extending the power and range of potential analyses.<br/