Contemporaneous disequilibrium of bio‐optical properties in the Southern Ocean. Issue 6 (23rd March 2017) - Kahru, Mati; Lee, Zhongping; Mitchell, B. Greg
Abstract: Significant changes in satellite‐detected net primary production (NPP, mg C m −2  d −1 ) were observed in the Southern Ocean during 2011–2016: an increase in the Pacific sector and a decrease in the Atlantic sector. While no clear physical forcing was identified, we hypothesize that the changes in NPP were associated with changes in the phytoplankton community and reflected in the concomitant bio‐optical properties. Satellite algorithms for chlorophyll a concentration (Chl a, mg m −3 ) use a combination of estimates of the remote sensing reflectance Rrs ( λ ) that are statistically fitted to a global reference data set. In any particular region or point in space/time the estimate produced by the global "mean" algorithm can deviate from the true value. Reflectance anomaly (RA) is supposed to remove the first‐order variability in Rrs ( λ ) associated with Chl a and reveal bio‐optical properties that are due to the composition of phytoplankton and associated materials. Time series of RA showed variability at multiple scales, including the life span of the sensor, multiyear and annual. Models of plankton functional types using estimated Chl a as input cannot be expected to correctly resolve regional and seasonal anomalies due to biases in the Chl a estimate that they are based on. While a statistical model using RA( λ ) time series can predict the times series of NPP with high accuracy ( R 2  = 0.82) in both Pacific and Atlantic regions, the underlying mechanisms in terms of phytoplankton groups and the associated materials remain elusive. Key Points: Primary productivity increasing in the Pacific sector and decreasing in the Atlantic sector of the Southern Ocean Annual cycle in reflectance anomaly is in phase with chlorophyll concentration in the Atlantic Ocean and out of phase in the Pacific Ocean Monthly time series of primary production can be statistically predicted using partial least squares regression of the reflectance anomalies