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Using random forests to compare the sensitivity of observed particulate inorganic and particulate organic carbon to environmental conditions
  • Rui Jin,
  • Anand Gnanadesikan,
  • Christopher D. Holder
Rui Jin
Johns Hopkins University

Corresponding Author:[email protected]

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Anand Gnanadesikan
Johns Hopkins University
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Christopher D. Holder
Johns Hopkins University
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Abstract

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.
04 Mar 2024Submitted to ESS Open Archive
04 Mar 2024Published in ESS Open Archive