Constraining the Global Marine Copper Cycle through a Data-based Modeling Approach

Hengdi Liang1, James W Moffett1 and Prof Seth John2, (1)University of Southern California, Los Angeles, CA, United States, (2)University of Southern California, Department of Earth Sciences, Los Angeles, CA, United States
Abstract:
Copper plays an essential role as a micronutrient for organisms in the oceans, but it can also be toxic to phytoplankton at higher concentrations. More than 99% of copper in the ocean is bound by organic ligands. Previous experimental studies have suggested that free cupric ions and weakly complexed copper can be utilized by phytoplankton, while the strongly bound copper is not bioavailable. We present an ocean circulation inverse model (OCIM) study of marine copper speciation and distribution. Our model shows that without accounting for copper speciation, the simulated vertical profiles of copper throughout the water column do not exhibit the unique linear increases in concentration which are observed in the data. However, a two-pool model including separate pools of labile copper and inert copper, where biological uptake and reversible scavenging processes act only on labile copper, is better able to reproduce observed Cu distributions in the global ocean. The two pools are not isolated, as there is photodegradation from the inert pool to the labile pool in the upper water column, suggesting a possibly important role of sunlight in the photodegradation of marine copper species. This two-pool model successfully reconciled the simulated global copper distribution with GEOTRACES datasets.

Additionally, we explore the importance of external sources including riverine and eolian inputs, which are balanced by sedimentary burial. Residence times calculated based on river fluxes from previous literatures can range from hundreds to thousands of years, and we find that model output is highly dependent on the residence time used, suggesting that an accurate estimate of global riverine copper delivery to the oceans is a prerequisite for Cu biogeochemical modeling.