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We describe the basis of a theory for interpreting measurements of two key biogeochemical fluxes—primary production by phytoplankton ( p , μg C · L −1  · day −1 ) and biological carbon export from the surface ocean by sinking particles ( f , mg C · m −2  · day −1 )—in terms of their probability distributions. Given that  p  and  f  are mechanistically linked but variable and effectively measured on different scales, we hypothesize that a quantitative relationship emerges between  collections  of the two measurements. Motivated by the many subprocesses driving production and export, we take as a null model that large‐scale distributions of  p  and  f  are lognormal. We then show that compilations of  p  and  f  measurements are consistent with this hypothesis. The compilation of  p  measurements is extensive enough to subregion by biome, basin, depth, or season; these subsets are also well described by lognormals, whose log‐moments sort predictably. Informed by the lognormality of both  p  and  f  we infer a statistical scaling relationship between the two quantities and derive a linear relationship between the log‐moments of their distributions. We find agreement between two independent estimates of the slope and intercept of this line and show that the distribution of  f  measurements is consistent with predictions made from the moments of the  p  distribution. These results illustrate the utility of a distributional approach to biogeochemical fluxes. We close by describing potential uses and challenges for the further development of such an approach.

Can Rates of Ocean Primary Production and Biological Carbon Export Be Related Through Their Probability Distributions?