A causality-based method for multi-model comparison: application to relationships between atmospheric, oceanic and marine biogeochemical variables

Abstract

We introduce a novel causality-based approach to compare Earth system model outputs. The method is based on the PCMCI+ algorithm, which identifies causal relationships between multiple variables. We aim to investigate the causal relationships between atmospheric (North Atlantic Oscillation – NAO), oceanic (gyre strength, stratification, circulation) and biogeochemical variables (nitrate, iron, silicate, phosphate and net primary production) in the North Atlantic subpolar gyre. It is a critical region for the global climate system with a well-characterised multi-year variability in physical and biogeochemical properties in response to the North Atlantic Oscillation. We test a specific multivariate conceptual scheme, involving causal links between these variables. Applying the PCMCI+ method allows us to differentiate between the influence of vertical mixing and horizontal advection on nutrient concentrations and spring bloom intensity, as well as to highlight model-specific dynamics. The analysis of the causal links suggests a dominant contribution of winter vertical mixing to nutrient concentration compared to transport. The strength of the links is variable among models. Stratification is identified as an important factor controlling spring bloom net primary production (NPP) in some, but not all, models. Horizontal transport also significantly influences nutrient concentration. However, horizontal transport generally exhibits lower contributions than vertical mixing to nutrient variability. The limitations of the method are discussed, and directions for future research are suggested.