A new process-based and scale-respecting desert dust emission scheme for global climate models – Part I: description and evaluation against inverse modeling emissions

Abstract

Abstract. Desert dust accounts for most of the atmosphere’s aerosol burden by mass and produces numerous important impacts on the Earth system. However, current global climate models (GCMs) and land surface models (LSMs) struggle to accurately represent key dust emission processes, in part because of inadequate representations of soil particle sizes that affect the dust emission threshold, surface roughness elements that absorb wind momentum, and boundary-layer characteristics that control wind fluctuations. Furthermore, because dust emission is driven by small-scale (~1 km or smaller) processes, simulating the global cycle of desert dust in GCMs with coarse horizontal resolutions (~100 km) presents a fundamental challenge. This representation problem is exacerbated by dust emission fluxes scaling nonlinearly with wind speed above a threshold wind speed that is sensitive to land surface characteristics. Here, we address these fundamental problems underlying the simulation of dust emissions in GCMs and LSMs by developing improved descriptions of (1) the effect of soil texture on the dust emission threshold, (2) the effects of non-erodible roughness elements (both rocks and green vegetation) on the surface wind stress, and (3) the effects of boundary-layer turbulence on driving intermittent dust emissions. We then use the resulting revised dust emission parameterization to simulate global dust emissions in a standalone model forced by reanalysis meteorology and land surface fields. We further propose (4) a simple methodology to scale up high-resolution gridded dust emissions to the coarse resolution of GCMs. The resulting dust emission simulation shows substantially improved agreement against regional dust emissions observationally constrained by inverse modeling. We thus find that our revised dust emission parameterization can substantially improve dust emission simulations in GCMs and LSMs.

Modelling the European wind-blown dust emissions and their impact on PM concentrations

“…Tyagi et al, 2018;Zhang et al, 2021). As dust emissions scale non-linearly with wind speed that are above a threshold (Leung et al, 2022) this raises the potential to overestimate dust emissions if winds are overestimated.…”

Reducing Resolution Dependency of Dust Emission Modeling Using Albedo‐Based Wind Friction

“…However, models require parameterizations between fluxes and variables averaged spatially over model unit areas (pixels) and temporally over model time steps that is, $$\overline{f}$$ = $$\overline{f}\left(\overline{\boldsymbol{v}}\right)$$, where overbars denote space‐time averaging. The scaling problem of how to obtain $$\overline{f}\left(\overline{\boldsymbol{v}}\right)$$ from f ( v ), is not solved by simply averaging, resampling or interpolating from one grid spacing to another (Leung et al., 2023; Ridley et al., 2013). Scaling requires the conversion of point support to area support (de Vrese and Hagemann, 2016; Kyriakidis and Yoo, 2005; Raupach and Finnigan, 1995; Van Looy et al., 2017).…”

“…Leung et al. (2023) proposed an alternative approach to derive a simple spatial map and upscale the spatial variability of F from fine to coarse resolution.…”

“…Regional and global F models are driven by coarse resolution wind fields modeled over an area or pixel support. Despite being provided on a regular grid, these wind fields are not point support and are therefore incompatible with grain‐scale physics and parameterizations (Gotway and Young, 2002) used in Q and F models (Leung et al., 2023; Meng et al., 2021; Raupach and Lu, 2004; Ridley et al., 2013). For clarity, we use model resolution to refer to different sized areal data or pixel support and in our work we do not use the terms “grid resolution” and “grid spacing” to avoid giving the impression that these data are point support.…”

Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)

“…For the dust emission parameterization, the threshold friction velocity calculated in both BRIFT and DEAD does not account for the spatiotemporal variability of the soil properties (e.g., soil grain size distribution, aggregate state, and static drag partition due to rocks etc. ; Leung et al, 2022; mainly limited by the sparse information; Kok et al, 2014b) in addition to the soil moisture. The current dust module in CAM6.1 also does not consider the roughness effect due to the presence of non-erodible elements (i.e., rocks and pebbles) on the threshold velocity calculation (Marticorena and Bergametti, 1995).…”