%ACael, B. [Massachusetts Institute of Technology Cambridge MA USA, Woods Hole Oceanographic Institution Woods Hole MA USA]%ACael, B. [Massachusetts Institute of Technology; Cambridge MA USA; Woods Hole Oceanographic Institution; Woods Hole MA USA]%ABisson, Kelsey [University of California Santa Barbara CA USA]%ABisson, Kelsey [University of California; Santa Barbara CA USA]%AFollett, Christopher [Massachusetts Institute of Technology Cambridge MA USA]%AFollett, Christopher [Massachusetts Institute of Technology; Cambridge MA USA]%BJournal Name: Global Biogeochemical Cycles; Journal Volume: 32; Journal Issue: 6; Related Information: CHORUS Timestamp: 2023-09-10 01:07:57 %D2018%IDOI PREFIX: 10.1029 %JJournal Name: Global Biogeochemical Cycles; Journal Volume: 32; Journal Issue: 6; Related Information: CHORUS Timestamp: 2023-09-10 01:07:57 %K %MOSTI ID: 10060247 %PMedium: X %TCan Rates of Ocean Primary Production and Biological Carbon Export Be Related Through Their Probability Distributions? %X

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 thatpandfare mechanistically linked but variable and effectively measured on different scales, we hypothesize that a quantitative relationship emerges betweencollectionsof the two measurements. Motivated by the many subprocesses driving production and export, we take as a null model that large‐scale distributions ofpandfare lognormal. We then show that compilations ofpandfmeasurements are consistent with this hypothesis. The compilation ofpmeasurements 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 bothpandfwe 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 offmeasurements is consistent with predictions made from the moments of thepdistribution. 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.

%0Journal Article