Ocean Biogeochemistry Control on the Atmospheric Chemistry

The physical and biogeochemical processes in the ocean play a profound role in the climate/Earth system. Many climate-relevant trace gases are naturally produced in the seawater from biotic and abiotic processes, such as production from phytoplankton, photochemistry involving colored dissolved organic materials, etc. The ocean biogeochemistry control on the atmospheric chemistry remains poorly constrained in chemistry-climate models, mainly due to the lack of the mechanistic and quantitative understanding of the biogeochemical processes controlling the productions and removal of these climate-relevant trace gases in the ocean. Most chemistry-climate models and chemical transport models use prescribed (offline) oceanic emissions for trace gases, which do not respond to changes in local conditions and external forcing, with limited predictability for the future marine emissions under climate change.

Here at ACOM, we have developed an online air-sea interface for soluble species (OASISS) for the NCAR CESM (Wang et al., 2019), which calculates the bi-directional oceanic fluxes of trace gases. This model framework is flexible and user-friendly, and has been used to examine the global oceanic emissions of a number of climate-active trace gases, such as acetaldehyde (Wang et al., 2019), brominated very short-lived ozone-depleting substances (Wang et al., 2019), and acetone (Wang et al., in preparation). Moreover, a novel data-oriented machine-learning algorithm is developed to leverage the in situ seawater observations and link the ocean biogeochemistry to the air-sea exchange processes (Wang et al., 2019). The results are evaluated with a variety of oceanic and atmospheric observations (ground-based, airborne), especially the NCAR TOGA measurements from recent airborne campaigns. Together, the online air-sea exchange model framework and the machine-learning algorithm lead to an improved representation of the ocean biogeochemistry control on the atmosphere.

Data-oriented machine-learning algorithm
Figure 1. Schematic diagram of the data-oriented machine-learning algorithm linked to the online air-sea exchange framework for NCAR CESM.

References

Wang, S., Hornbrook, R. S., Hills, A., Emmons, L. K., Tilmes, S., Lamarque, J.-F., et al. (2019). Atmospheric Acetaldehyde: Importance of Air-Sea Exchange and a Missing Source in the Remote Troposphere. Geophysical Research Letters. https://doi.org/10.1029/2019GL082034

Wang, S., Kinnison, D, Montzka, S, Apel, E., Hornbrook, R., Hills, A., et al. (2019). Ocean biogeochemistry control on the marine emissions of brominated very short-lived ozone-depleting substances: a machine-learning approach. Accepted for Publication in Journal of Geophysical Research.