A super-simplified OH chemistry scheme for ICON-ART

The monitoring of greenhouse gas (GHG) emissions is essential to reliably assess key drivers of climate change. Accurate GHG inventories provide the quantitative basis for mitigation and adaption strategies under global warming. The ITMS project (“Integriertes Treibhausgas Monitoringsystem”, in English “integrated GHG monitoring system”)[1], is designed to establish an operational GHG data assimilation service at the German Meteorological Service (DWD) based on the model system ICON-ART[2] to enable Germany to operationally monitor the sources and sinks of three important GHGs: carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O).

In the first phase of the ITMS project DWD together with the Karlsruhe Institute of Technology (KIT) and other partners are focusing on the emission, distribution and depletion of methane. In the troposphere, methane is mainly depleted by the chemical reaction with the OH radical. Tropospheric OH is created mostly by photodissociation of ozone (O₃) and thus its abundance depends mainly on the available solar UV radiation and the ozone concentration. The calculation of this chemical system is computationally expensive. Therefore, a simplified calculation of the OH chemistry has to be included in the ICON-ART forward model.

Here, we present a super-simplified OH chemistry scheme for ICON-ART, a data-driven approach based on Minschwaner et al., 2011[3]. The OH concentration is hereby estimated based on the solar zenith angle (SZA) at the respective grid cell. The required parameters are pre-trained on SZA information and OH concentration. We test two independent training datasets – the CAMS global reanalysis (EAC4)[4] and an in‑house chemistry‑climate simulation using the EMAC (ECHAM/MESSy Atmospheric Chemistry) model[5] – and find that the scheme yields reasonable results for both.

[1] www.itms-germany.de

[2] Schröter, J., Rieger, D., Stassen, C., Vogel, H., Weimer, M., Werchner, S., Förstner, J., Prill, F., Reinert, D., Zängl, G., Giorgetta, M., Ruhnke, R., Vogel, B., and Braesicke, P.: ICON-ART 2.1: a flexible tracer framework and its application for composition studies in numerical weather forecasting and climate simulations, Geosci. Model Dev., 11, 4043–4068, https://doi.org/10.5194/gmd-11-4043-2018, 2018.

[3] Minschwaner, K., Manney, G. L., Wang, S. H., and Harwood, R. S.: Hydroxyl in the stratosphere and mesosphere – Part 1: Diurnal variability, Atmos. Chem. Phys., 11, 955–962, https://doi.org/10.5194/acp-11-955-2011, 2011.

[4] Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.

[5] Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., Kern, B., Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geoscientific Model Development, 3, 717-752, https://doi.org/10.5194/gmd-3-717-2010, 2010.