5B.4 Evaluation of the Reactive Nitrogen Budget of the Remote Atmosphere in Global Models Using Airborne Measurements

Tuesday, 9 January 2018: 11:15 AM
Room 9 C (ACC) (Austin, Texas)
Lee Thomas Murray, Univ. of Rochester, Rochester, NY; and S. A. Strode, A. M. Fiore, J. F. Lamarque, M. J. Prather, C. Thompson, J. Peischl, T. B. Ryerson, H. Allen, D. R. Blake, W. Brune, J. Crounse, J. W. Elkins, S. Hall, E. J. Hintsa, L. G. Huey, M. Kim, F. L. Moore, K. Ullmann, P. O. Wennberg, and S. C. Wofsy

Nitrogen oxides (NOx ≡ NO + NO2) in the background atmosphere are critical precursors for the formation of tropospheric ozone and OH, thereby exerting strong influence on surface air quality, reactive greenhouse gases, and ecosystem health. The impact of NOx on atmospheric composition and climate is sensitive to the relative partitioning of reactive nitrogen between NOx and longer-lived reservoir species of the total reactive nitrogen family (NOy) such as HNO3, HNO4, PAN and organic nitrates (RONO2). Unfortunately, global chemistry-climate models (CCMs) and chemistry-transport models (CTMs) have historically disagreed in their reactive nitrogen budgets outside of polluted continental regions, and we have lacked in situ observations with which to evaluate them. Here, we compare and evaluate the NOy budget of six global models (GEOS-Chem CTM, GFDL AM3 CCM, GISS E2.1 CCM, GMI CTM, NCAR CAM CCM, and UCI CTM) using new observations of total reactive nitrogen and its member species from the NASA Atmospheric Tomography (ATom) mission. ATom has now completed two of its four planned deployments sampling the remote Pacific and Atlantic basins of both hemispheres with a comprehensive suite of measurements for constraining reactive photochemistry. All six models have simulated conditions climatologically similar to the deployments. The GMI and GEOS-Chem CTMs have in addition performed hindcast simulations using the MERRA-2 reanalysis, and have been sampled along the flight tracks. We evaluate the performance of the models relative to the observations, and identify factors contributing to their disparate behavior using known differences in model oxidation mechanisms, heterogeneous loss pathways, lightning and surface emissions, and physical loss processes.
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