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Two models have been implemented: the Biome-BGC v4.1.1 Max Planck Institute model, and the newly developed Biome-BGC MuSo 3.0 model. Performance, success or failure of these models are highly dependent on parameter settings and variation. Due to the high number of parameters (around 40 and 60 for 4.1.1 MPI and MuSo respectively) and the non-linear behaviour of the models, there are limited options to find the ‘best’ parametrisation. Sensitivity Analysis (SA) is one of the ways to enhance deeper understanding for better parametrisation of the model according to the model-data-fusion approach.

Who is it for?

Scientists interested in Biome-BGC and professional users of the model for calibrated simulation.

What is it for?

To check the effect of parameter variability on the results; find the most influential parameters on the resulted outputs; restrict the number of parameters to decrease the degree of freedom of the model simulation; understand model behaviour.

How does it work?

SA requires a prior execution of a Biome-BGC Monte Carlo Experiment (MCE), that performs an independent parameter variation within given parameter ranges. Parameters, range of parameter values, output variables and number of randomized repetition has to be set in Biome-BGC MCE workflow, that runs off-line because of the time consuming nature of MCE jobs (usually it takes several days on, for example the EDGeS@home desktop grid). Then one or several SA can be launched based on the results of Biome-BGC MCE completed before.

Expected results

Table of sensitivity values of selected Biome-BGC output variables depending on investigated parameters and bar charts.

Links to workflow and user documentation

Workflow on myExperiment


Biome-BGC Monte Carlo Experiment workflow

Biome-BGC Monte Carlo Experiment workflow documentation

Biome-BGC Model-Data-Fusion framework

Example of use

Twenty parameters (represented along the X-axis) of two model versions were analysed and compared in a Biome-BGC Monte Carlo Experiment and SA. The sensitivity of gross primary production (GPP) of both models were high for specific leaf area (SLA), leaf N in RUBISCO, C:N of leaves and fixation rate of N parameters, however sensitivity of GPP’s are differ considerably. Biome-BGC MuSo is much less sensitive to SLA, while the opposite can be seen in the case of some other ecophysiological parameters.





19 February 2015

At the final review of the project by the EC, one of the reviewers said: “Incredible work done with a community that is not unified. Remarkable work. It opens for new development in a near future. Hope for success. Good project. Happy that you have been financed three plus years ago.”

Read all about the project and its results in the Project Final Report or read the Executive Summary only.