Application of Satellite‐Based Detections of Arctic Bromine Explosion Events Within GEOS‐Chem

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: P. A. Wales, C. A. Keller, K. E. Knowland, S. Pawson, S. Choi, F. Hendrick, M. VanRoozendael, R. J. Salawitch, R. Sulieman, W. F. Swanson
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2023
• Subjects: background ozone; halogens; Arctic; atmospheric chemistry; modeling; Physical geography; GB3-5030; Oceanography; GC1-1581; Arctic Ocean
• Online Access: https://doi.org/10.1029/2022MS003465; https://doaj.org/article/45fe26a54f41422eb58a0b7d93d4227c

Abstract During polar spring, periods of elevated tropospheric bromine drive near complete removal of surface ozone. These events impact the tropospheric oxidative capacity and are an area of active research with multiple approaches for representing the underlying processes in global models. We present a method for parameterizing emissions of molecular bromine (Br2) over the Arctic using satellite retrievals of bromine monoxide (BrO) from the Ozone Monitoring Instrument (OMI). OMI retrieves column BrO with daily near global coverage, and we use the GEOS‐Chem chemical mechanism, run online within the Goddard Earth Observing System Earth System Model to identify hotspots of BrO likely associated with polar processes. To account for uncertainties in modeling background BrO, hotspots are only identified where the difference between OMI and modeled columns exceeds a statistical threshold. The resulting hotspot columns are a lower‐limit for the portion of OMI BrO attributable to bromine explosion events. While these hotspots are correlated with BrO measured in the lower troposphere over the Arctic Ocean, a case study of missing detections of near‐surface BrO is identified. Daily flux of Br2 is estimated from hotspot columns of BrO using internal model parameters. When the emissions are applied, BrO hotspots are modeled with a 5% low bias. The sensitivity of the resulting ozone simulations to the treatment of background uncertainties in the BrO column is demonstrated. While periods of isolated, large (>50%) decreases in surface ozone are modeled, this technique does not simulate the low ozone observed at coastal stations and consistently underestimates ozone loss during March.