Moving toward finer scales in oceanography: Predictive linear functional model of Chlorophyll a profile from light data
Résumé
The Southern Ocean plays a key role in ocean–atmosphere carbon dioxide fluxes. Estimation of carbon
exchanges between ocean and atmosphere must rely on accurate estimations of primary productivity
which require measurements of phytoplankton concentration within the water column. In this paper,
we are interested in relationships between primary productivity and light in the Antarctic ocean. The
originality of this work is twofold. Starting from physical hypothesis, a statistical model is constructed
for the prediction of Chlorophyll a (Chl a) profiles where light profiles are used as a covariate. Taking into
account of the functional nature of the data, solutions are proposed to estimate continuous vertical profiles
from discrete data sampled by elephant seals equipped with a new generation of oceanographic tags.
Bootstrapped prediction intervals show a good quality of prediction of Chl a profiles, giving access to the
shape of the profiles along depth and to the submesoscale structure of phytoplankton within the euphotic
layer of the Southern Ocean.