An Accurate Absorption-Based Net Primary Production Model for the Global Ocean

As a vital living link in the global carbon cycle, understanding how net primary production (NPP) varies through space, time, and across climatic oscillations (e.g. ENSO) is a key objective in oceanographic research. The continual improvement of ocean observing satellites and data analytics now present greater opportunities for advanced understanding and characterization of the factors regulating NPP. In particular, the emergence of spectral inversion algorithms now permits accurate retrievals of the phytoplankton absorption coefficient (a_Φ) from space. As NPP is the efficiency in which absorbed energy is converted into carbon biomass, a_Φ measurements circumvents chlorophyll-based empirical approaches by permitting direct and accurate measurements of phytoplankton energy absorption. It has long been recognized, and perhaps underappreciated, that NPP and phytoplankton growth rates display muted variability when normalized to a_Φ rather than chlorophyll. Here we present a novel absorption-based NPP model that parameterizes the underlying physiological mechanisms behind this muted variability, and apply this physiological model to the global ocean. Through a comparison against field data from the Hawaii and Bermuda Ocean Time Series, we demonstrate how this approach yields more accurate NPP measurements than other published NPP models. By normalizing NPP to satellite estimates of phytoplankton carbon biomass, this presentation also explores the seasonality of phytoplankton growth rates across several oceanic regions. Finally, we discuss how future advances in remote-sensing (e.g. hyperspectral satellites, LIDAR, autonomous profilers) can be exploited to further improve absorption-based NPP models.