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 nonerodible
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 rescale lower-resolution dust emission
simulations to match the spatial variability of higher-resolution emission
simulations in 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.


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

Dust pollution poses significant risks to human health, air quality, and food safety, necessitating the identification of dust occurrence and the development of dust susceptibility maps (DSMs) to mitigate its effects. This research aims to detect dust occurrence using satellite images and prepare a DSM for Bushehr province, Iran, by enhancing the attentive interpretable tabular learning (TabNet) model through three swarm-based metaheuristic algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and hunger games search (HGS). A spatial database incorporating dust occurrence areas was created using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2022, including 15 influential criteria related to climate, soil, topography, and land cover. Four models were employed for modeling and DSM generation: TabNet, TabNet-PSO, TabNet-GWO, and TabNet-HGS. Evaluation of the modeling results using performance metrics indicated that the TabNet-HGS model outperformed the other models in both training (mean absolute error (MAE) = 0.055, root-mean-square error (RMSE) = 0.1, coefficient of determination (R2) = 0.959), and testing (MAE = 0.063, RMSE = 0.114, R2 = 0.947) data. Following TabNet-HGS, the TabNet-PSO, TabNet-GWO, and TabNet models demonstrated progressively lower accuracy. The validation of the DSM was performed by assessing receiver operating characteristic (ROC) curves, revealing that the TabNet-HGS, TabNet-PSO, TabNet-GWO, and TabNet models exhibited the highest modeling accuracy, with corresponding area under the curve (AUC) values of 0.994, 0.986, 0.98, and 0.832, respectively. These results highlight the enhanced accuracy of dust susceptibility modeling achieved by integrating swarm-based metaheuristic algorithms with the TabNet model. The dust susceptibility map provides valuable insights into the sources, pathways, and impacts of dust particles on the environment and human health in the study area. 

Spatial mapping of land susceptibility to dust emissions using optimization of attentive Interpretable Tabular Learning (TabNet) model.

Abstract. The Community Atmosphere Model (CAM6.1), the atmospheric component of the Community Earth System Model (CESM; version 2.1), simulates the life cycle (emission, transport, and deposition) of mineral dust and its interactions with physio-chemical components to quantify the impacts of dust on climate and the Earth system. The accuracy of such quantifications relies on how well dust-related processes are represented in the model. Here we update the parameterizations for the dust module, including those on the dust emission scheme, the aerosol dry deposition scheme, the size distribution of transported dust, and the treatment of dust particle shape. Multiple simulations were undertaken to evaluate the model performance against diverse observations, and to understand how each update alters the modeled dust cycle and the simulated dust direct radiative effect. The modelâobservation comparisons suggest that substantially improved model representations of the dust cycle are achieved primarily through the new more physically-based dust emission scheme. In comparison, the other modifications induced small changes to the modeled dust cycle and modelâobservation comparisons, except the size distribution of dust in the coarse mode, which can be even more influential than that of replacing the dust emission scheme. We highlight which changes introduced here are important for which regions, shedding light on further dust model developments required for more accurately estimating interactions between dust and climate.

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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. 

/pdf/a-new-process-based-and-scale-respecting-desert-dust-1lbvuyi2.pdf

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

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