Quantifying the Causes of Differences in Tropospheric OH within Global Models

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Julie M Nicely1, Ross J Salawitch2, Timothy P Canty1, Steve Arnold3, Martyn Chipperfield4, Louisa K Emmons5, Johannes Flemming6, Vincent Huijnen7, Douglas E Kinnison8, J F Lamarque5, Jingqiu Mao9, Sarah A Monks10, Jose M Rodriguez11 and Simone Tilmes12, (1)University of Maryland College Park, College Park, MD, United States, (2)University of Maryland, College Park, MD, United States, (3)University of Leeds, School of Earth and Environment, Leeds, United Kingdom, (4)University of Leeds, Leeds, United Kingdom, (5)National Center for Atmospheric Research, Boulder, CO, United States, (6)European Center for Medium-Range Weather Forecasts, Reading, United Kingdom, (7)Royal Netherlands Meteorological Institute, De Bilt, Netherlands, (8)NCAR, Boulder, CO, United States, (9)Princeton University, Princeton, NJ, United States, (10)NOAA Boulder, Boulder, CO, United States, (11)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (12)Univ. of Leeds, Leeds, United Kingdom
Hydroxyl radical (OH) is the main daytime oxidant in the troposphere and provides the primary loss mechanism for many pollutants and greenhouse gases, including methane. Global mean tropospheric OH differs by as much as 50% between various global models, for reasons that are not understood. We use neural networks (NN), trained using archived model output from global models, to quantify the factors that cause differences in tropospheric OH between any two models, both globally and on a regional scale. This is done by inputting 3-D OH precursor fields from one model into the chemical mechanism of the other model (represented by the trained NN) to quantify the impact of each precursor field on OH. The NN framework also allows the impact of various chemical mechanisms to be assessed. Here, we apply NNs to output from eight chemical transport models (CTMs) that participated in the POLARCAT Model Intercomparison Project (POLMIP). The primary cause of OH variation is the CTM treatment of J(O1D), O3, CO, and CH4; we will show tables and figures documenting why OH differs between each pair of models. Finally, we will outline chemical fields that must be archived for a similar analysis to be conducted for other model intercomparison projects.