A Bayesian parameter estimation method applied to a marine ecosystem model for the coastal Gulf of Alaska

The present study describes a state-of-the-art methodology based on an adaptive Metropolis–Hastings algorithm to facilitate efficient Bayesian sampling for realistic lower trophic level (LTL) marine ecosystem models. The main objective is to explore the ability to differentiate between biological parameters that can learn from observations and those that cannot. The Bayesian approach is applied to the northwestern coastal Gulf of Alaska region and uses both synthetic and actual (in situ and remotely sensed) observations. LTL ecosystem dynamics in the Bayesian framework are described by a process model consisting of a 1-dimensional Nutrient–Phytoplankton–Zooplankton–Detritus formulation with iron limitation (NPZDFe) and vertical mixing. The results illustrate the ability to determine parameter posterior distributions for fundamental biological rates, such as maximum phytoplankton growth or zooplankton grazing. By using various observational platforms as data stage inputs, the results also demonstrate the impact of spatial and temporal sampling on parameter posterior distributions, as well as the benefits of having concurrent measurements for two or more state variables of the process model (e.g., chlorophyll and nitrate concentrations). Extending the method to multiple parameters is non-trivial, as posterior distributions become impacted by correlated and/or disproportionate contributions for certain model parameters. Controlled experiments with “near perfect data” were useful to characterize parameter identifiability based on information content in the BHM data stage inputs, as well as to separate uncertainties due to sampling issues vs. uncertain ecosystem process interpretation.