Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0

Abstract

We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust–radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20¿µm, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000¿Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about -0.90 and -0.63¿W¿m-2 and at the top of the atmosphere between -0.20 and -0.28¿W¿m-2. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations. ; This research has been supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 789630. Martina Klose has received funding through the Helmholtz Association's Initiative and Networking Fund (grant agreement no. VH-NG-1533). Jeronimo Escribano and Matthew L. Dawson have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements no. 754433 (Jeronimo Escribano) and no. 747048 (Matthew Dawson). BSC co-authors acknowledge funding from the following: the European Research Council (FRAGMENT (grant no. 773051)); the AXA Research Fund; the Spanish Ministry of Science, Innovation and Universities (grant no. CGL2017- 88911-R); the EU H2020 project FORCES (grant no. 821205); the CMUG-CCI3-TECHPROP contract, an activity carried out under a program of and funded by the European Space Agency (ESA); and the DustClim project, which is part of ERA4CS, an ERA-NET initiated by JPI Climate and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR), with cofunding by the European Union (grant no. 690462). Ron L. Miller is funded by the NASA Modeling, Analysis and Prediction Program (NNG14HH42I). Jasper F. Kok acknowledges support from the National Science Foundation (NSF) under grant nos. 1552519 and 1856389 and the Army Research Office (grant no. W911NF-20- 2-0150). Yue Huang has received funding from the Columbia University Earth Institute Postdoctoral Research Fellowship and from NASA (grant no. 80NSSC19K1346) awarded under the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. ; Peer Reviewed ; Postprint (published version)