NASA - National Aeronautics and Space Administration

Halogenated very short lived substances (VSLS) affect the ozone budget in the atmosphere. Brominated VSLS are naturally emitted from the ocean, and current oceanic emission inventories vary dramatically. We present a new global oceanic emission inventory of Br‐VSLS (bromoform and dibromomethane), considering the physical forcing in the ocean and the atmosphere, as well as the ocean biogeochemistry control. A data‐oriented machine‐learning emulator was developed to couple the air‐sea exchange with the ocean biogeochemistry. The predicted surface seawater concentrations and the surface atmospheric mixing ratios of Br‐VSLS are evaluated with long‐term, global‐scale observations; and the predicted vertical distributions of Br‐VSLS are compared to the global airborne observations in both boreal summer and winter. The global marine emissions of bromoform and dibromomethane are estimated to be 385 and 54 Gg Br per year, respectively. The new oceanic emission inventory of Br‐VSLS is more skillful than the widely used top‐down approaches for representing the seasonal/spatial variations and the annual means of atmospheric concentrations. The new approach improves the model predictability for the coupled Earth system model and can be used as a basis for investigating the past and future ocean emissions and feedbacks under climate change. This model framework can be used to calculate the bidirectional oceanic fluxes for other compounds of interest. Plain Language Summary Halogen atoms released from the man‐made, long‐lived ozone‐are the major cause of the stratospheric ozone depletion. Recent studies found that natural bromine‐containing very short lived substances are of particular importance for the ozone radiative forcing in the lower stratosphere. These bromine‐containing short‐lived ozone‐depleting substances are naturally produced from phytoplankton in seawater and released into the atmosphere. The past decade has seen increased applications of machine‐learning techniques in climate‐related research. In this work, we use a data‐oriented machine‐learning algorithm to calculate the production of bromine‐containing short‐lived substances in the ocean, representing a fairly accurate and computationally efficient solution for addressing future climate predictions.