Quantifying parameter-induced uncertainty and evaluating the effects of model simplification in a marine biogeochemical model

Abstract. Marine biogeochemical models are widely used to investigate plankton ecosystem dynamics and biogeochemical cycling. However, their increasing complexity makes large ensemble analyses of parameter-induced uncertainty computationally demanding. Model simplification can reduce computational cost, but it may also modify how parameter perturbations propagate through ecological and biogeochemical processes. This study examines whether model simplification affects not only mean-state behaviour, but also the magnitude and time-depth distribution of parameter-induced uncertainty. We used the one-dimensional vertical configuration of the Eco3M-MED-V1b model at the DYFAMED station in the northwestern Mediterranean Sea, and compared the full-complexity (FULL) version with two simplified versions: MOD1, designed to preserve the state variables, and MOD2, designed to preserve a set of key ecosystem indicators. A common 512-member Sobol low-discrepancy ensemble was generated by perturbing 30 parameters shared by the three model versions, allowing direct comparison under identical parameter perturbations. The results show that parameter-induced uncertainty in FULL is concentrated in ecologically important periods and depths, including the upper productive layer, near the nutricline, the deep chlorophyll maximum, and during the spring bloom and summer stratification. Model simplification did not systematically increase or decrease uncertainty; instead, its effects varied among variables, ecosystem indicators, periods and depths. MOD1 broadly preserved the main time–depth distribution of uncertainty in the state variables, whereas MOD2 generally preserved the uncertainty magnitude of most ecosystem indicators. However, similar uncertainty magnitudes in indicators integrated over the whole year or water column may mask local differences occurring during specific periods or within specific depth ranges. Diagnostics used to guide model evaluation or simplification should therefore be defined a priori according to the scientific question and intended application. Uncertainty analysis can also help identify complementary diagnostics.