The balance between particulate inorganic carbon (PIC) and particulate organic carbon (POC) holds significant importance in carbon storage within the ocean. A recent investigation delved into the spatial distribution of phytoplankton and the physiological mechanisms governing their growth. Employing random forests, a machine learning technique, this study unveiled apparent relationships between POC and 10 environmental fields. In this work, we extend the use of random forests to compare how observed PIC and POC respond to environmental conditions. Our findings indicate that while both exhibit similar responses to certain environmental drivers, PIC is less sensitive to iron and more sensitive to light. Intriguingly, both PIC and POC display reduced sensitivity to CO2, contrary to previous studies, possibly due to the elevated pCO2 in our dataset. This research sheds light on the underlying processes influencing carbon sequestration and ocean productivity.

As phytoplankton form the base of the marine food web, understanding the controls on their abundance is fundamental to understanding marine ecology and how it might be altered by global climate change. While many Earth System Models (ESMs) predict phytoplankton biomass, it is unclear whether they properly capture the mechanistic relationships that control this quantity in the real ocean. In this paper, we used Random Forest (RF) analysis to analyze the output of ESMs and observational datasets. We gathered information from 13 ESMs and two observational datasets. The target variable was phytoplankton carbon and the predictors included environmental parameters known to influence phytoplankton, such as nutrients, light, mixed layer depth, salinity, temperature, and upwelling. We examined three questions: (1) What fractions of variability in ESMs and observations can be linked to the large-scale environmental variables simulated by ESMs? (2) What are the dominant predictors and relationships affecting phytoplankton biomass? (3) How well do ESMs simulate phytoplankton carbon and do they simulate the relationships we see in observations? We show that about 88% to 96% of the variability in observational datasets and greater than 98% in the ESMs was accounted for by variables known to influence phytoplankton biomass from large-scale environmental variables. The dominant predictors in the observational datasets were dissolved iron and shortwave radiation. The dominant predictors in the ESMs were dissolved iron, shortwave radiation, and mixed layer depth. While relationships in most of the ESMs matched the general trends seen in the observations, significant quantitative differences were seen. While the assumption made by ESMs that large-scale environmental conditions control phytoplankton biomass appears to hold in the real world, much work remains to be done to ensure that ESMs properly represent these controls.