Satellite estimates of net community production based on O sub(2)/Ar observations and comparison to other estimates

We present two statistical algorithms for predicting global oceanic net community production (NCP) from satellite observations. To calibrate these two algorithms, we compiled a large data set of in situ O sub(2)/Ar-NCP and remotely sensed observations, including sea surface temperature (SST), net primary production (NPP), phytoplankton size composition, and inherent optical properties. The first algorithm is based on genetic programming (GP) which simultaneously searches for the optimal form and coefficients of NCP equations. We find that several GP solutions are consistent with NPP and SST being strong predictors of NCP. The second algorithm uses support vector regression (SVR) to optimize a numerical relationship between O sub(2)/Ar-NCP measurements and satellite observations. Both statistical algorithms can predict NCP relatively well, with a coefficient of determination (R super(2)) of 0.68 for GP and 0.72 for SVR, which is comparable to other algorithms in the literature. However, our new algorithms predict more spatially uniform annual NCP distribution for the world's oceans and higher annual NCP values in the Southern Ocean and the five oligotrophic gyres. Key Points * Two NCP algorithms are developed using O sub(2)/Ar and satellite data * Our algorithms are comparable to others * Our estimates show more spatially uniform distribution than others