Using Random Forests to Compare the Sensitivity of Observed Particulate Inorganic and Particulate Organic Carbon to Environmental Conditions

Abstract

AbstractThe 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. PIC and POC exhibit similar responses to certain environmental drivers, suggesting that these do not explain differences in their distribution. However, PIC is less sensitive to iron and more sensitive to light and mixed layer depth. Intriguingly, both PIC and POC display weak sensitivity to CO2, contrary to previous studies, possibly due to the elevated pCO2 in our data set. This research sheds light on the underlying processes influencing carbon sequestration and ocean productivity.