The Role of Model Complexity in Determining Patterns of Chlorophyll Variability in the Coastal Northwest North Atlantic

A key feature of the North Atlantic Ocean’s biological dynamics is the annual phytoplankton spring bloom. In the region comprising the continental shelf and adjacent deep ocean of the northwest North Atlantic, we identified two patterns of bloom development: 1) locations with cold temperatures and deep winter mixed layers, where the spring bloom peaks around April and the annual chlorophyll cycle has a large amplitude, and 2) locations with warmer temperatures and shallow winter mixed layers, where the spring bloom peaks earlier in the year, sometimes indiscernible from the fall bloom. These patterns result from a combination of limiting environmental factors and interactions among planktonic groups with different optimal requirements. Simple models that represent the ecosystem with a single phytoplankton (P) and a single zooplankton (Z) group are challenged to reproduce these ecological interactions. Here we investigate the effect that added complexity has on determining spatio-temporal chlorophyll. We compare two ecosystem models, one that contains one P and one Z group, and one with two P and three Z groups. We consider three types of changes in complexity: 1) added dependencies among variables (e.g., temperature dependent rates), 2) modified structural pathways, and 3) added pathways. Subsets of the most sensitive parameters are optimized in each model to replicate observations in the region. For computational efficiency, the parameter optimization is performed using 1D surrogates of a 3D model. We evaluate how model complexity affects model skill, and whether the optimized parameter sets found for each model modify the interpretation of ecosystem functioning. Spatial differences in the parameter sets that best represent different areas hint at the existence of different ecological communities or at physical-biological interactions that are not represented in the simplest model. Our methodology emphasizes the combined use of observations, 1D models to help identifying patterns, and 3D models able to simulate the environment modre realistically, as a means to acquire predictive understanding of the ocean’s ecology.