Satellite estimates of net community production based on O₂/Ar observations and comparison to other estimates

Abstract

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 O2/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 O2/Ar‐NCP measurements and satellite observations. Both statistical algorithms can predict NCP relatively well, with a coefficient of determination (R2) 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.

Refined Estimates of Net Community Production in the Subarctic Northeast Pacific Derived From ΔO2/Ar Measurements With N2O‐Based Corrections for Vertical Mixing

“…This suggests that the corrected values provide a better estimate of the broad spatial patterns and environmental controls on NCP across our survey region. The relationships we observed between NCP and chl, SST, and mld are consistent with results from previous studies in other oceanic systems (Eppley, ; Li & Cassar, ; Tortell et al, ).…”

Controls on Open‐Ocean North Atlantic ΔpCO2 at Seasonal and Interannual Time Scales Are Different

“…Although the carbon‐based productivity model (CbPM; Westberry et al, ) displays negative correlation between PP and Δ p CO 2 in the subpolar region at seasonal time scales (consistent with other algorithms tested), in the subtropics and at interannual time scales CbPM‐PP is positively correlated with Δ p CO 2 (differing from other algorithms tested). For NCP at the seasonal time scale, the Li and Cassar () model agrees with the Tilstone et al () results, while the Siegel et al () model shows positive, rather than negative, correlations in two midlatitude provinces. At the interannual time scale, both the Li and Cassar () and Siegel et al () algorithms have several regions where NCP and Δ p CO 2 are not significantly correlated.…”

“…For NCP at the seasonal time scale, the Li and Cassar () model agrees with the Tilstone et al () results, while the Siegel et al () model shows positive, rather than negative, correlations in two midlatitude provinces. At the interannual time scale, both the Li and Cassar () and Siegel et al () algorithms have several regions where NCP and Δ p CO 2 are not significantly correlated. Where they are, the sign of the correlation is not necessarily the same as for Tilstone et al ().…”

Interannual variability in net community production at the Western Antarctic Peninsula region (1997–2014)

“…For example, Chang et al . [] and Li and Cassar [] recently estimated NCP for the Southern Ocean and the global oceans using in situ O 2 /Ar‐NCP estimates and various statistical methods including Artificial Neural Networks (ANNs), Support Vector Regression (SVR), and Genetic Programming (GP). Although these approaches estimate NCP relatively well, uncertainties remain significant, in part due to factors controlling NCP varying from one area to another.…”