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Comparing differently sized data sets is one main task in model assessmentand calibration. This is due to field data being generally sparse compared tosimulated model results. We tackled this task by the application of a newdiffusion-based kernel density estimator (diffKDE) that approximatesprobability density functions of a data set nearly independent of the amount ofavailable data. We compared the resulting density estimates of measured andsimulated marine particulate organic carbon-13 isotopes qualitatively andquantitatively by the Wasserstein distance. For reference we also show thecorresponding comparison based on equally sized data set with reducedsimulation and field data. The comparison based on all available data reveals abetter fit of the simulation to the field data and shows misleading modelproperties in the masked analysis. A comparison between the diffKDE and atraditional Gaussian KDE shows a better resolution of data features under thediffKDE. We are able to show a promising advantage in the application of KDEsin calibration of models, especially in the application of the diffKDE.

Diffusion-based kernel density estimation improves the assessment of carbon isotope modelling