Dust emission response to precipitation and temperature anomalies under different climatic conditions

The dust-drought nexus has received more attention in recent years, particularly in light of global warming. This study aims to better understand the interacting role of temperature and precipitation climatology on global dust emission. Simulated global arid regions' dust emissions from 11 CMIP6 Earth System Models (ESMs) (65 years from 1950 to 2014) and MERRA2 reanalysis (41 years from 1980 to 2020) are analyzed. We calculate dust emission sensitivity to precipitation and temperature using multiple linear regression with near-surface (10 m) wind speed, precipitation, and 2 m air temperature as predictors. The results show that simulated dust emission from most models is significantly correlated with precipitation and temperature over large arid dust source regions. All ESMs show both positive and negative dust emission sensitivity to precipitation (β_P) and temperature (β_T). The climatological mean state affects the strength of the relationships. The precipitation sensitivity is larger over the cold regions while the temperature sensitivity is larger in wet regions. The precipitation sensitivity symmetrically increases with latitude due to the variability of temperature with latitude. Future changes to mean temperature and precipitation have competing effects on dust emission and are likely to alter the hemispheric balance of atmospheric dust.

Antecedent Soil Moisture Conditions Influenced Vertical Dust Flux: A Case Study in Iran Using WRF-Chem Model

Soil moisture is one of the most important parameters affecting dust emission flux. This study was conducted to investigate the effects of soil moisture on vertical dust flux in the central plateau region of Iran. In this study, the WRF-Chem (Weather Research and Forecast with Chemistry) model, with the GOCART (Global Ozone Chemistry Aerosol Radiation and Transport) scheme, was used to estimate the dust emission flux during a major storm from 19 to 21 July 2015, and to discriminate between dust sources. The results showed that the Kyrgyz deserts in Turkmenistan, the Arabian deserts in Saudi Arabia, the deserts of Iraq, and the Helmand region in Afghanistan are sources of foreign dust. Additionally, the central desert plain was identified as an internal dust source, where the dust level reached 7000 µg m−2 s−1. The results of WRF-Chem simulation were verified with reanalysis data from MERRA2 and AERONET data from Natanz station, which showed good agreement with the simulation. Based on the GLDAS reanalysis, soil moisture content varied between 2.6% and 34%. Linear and nonlinear regression of vertical dust flux values and soil moisture showed nonlinear behavior following the exponential function, with a correlation coefficient of 0.8 and a strong negative association between soil moisture and vertical dust flux.

Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system

A new windblown dust emission treatment was incorporated in the Community Multiscale Air Quality (CMAQ) modeling system. This new model treatment has been built upon previously developed physics-based parameterization schemes from the literature. A distinct and novel feature of this scheme, however, is the incorporation of a newly developed dynamic relation for the surface roughness length relevant to small-scale dust generation processes. Through this implementation, the effect of nonerodible elements on the local flow acceleration, drag partitioning, and surface coverage protection is modeled in a physically based and consistent manner. Careful attention is paid in integrating the new windblown dust treatment in the CMAQ model to ensure that the required input parameters are correctly configured. To test the performance of the new dust module in CMAQ, the entire year 2011 is simulated for the continental United States, with particular emphasis on the southwestern United States (SWUS) where windblown dust concentrations are relatively large. Overall, the model shows good performance with the daily mean bias of soil concentrations fluctuating in the range of ±1 μg m-3 for the entire year. Springtime soil concentrations are in quite good agreement (normalized mean bias of 8.3%) with observations, while moderate to high underestimation of soil concentration is seen in the summertime. The latter is attributed to the issue of representing the convective dust storms in summertime. Evaluations against observations for seven elevated dust events in the SWUS indicate that the new windblown dust treatment is capable of capturing spatial and temporal characteristics of dust outbreaks.

Seasonal dynamics of threshold friction velocity and dust emission in Central Asia

An improved model representation of mineral dust cycle is critical to reducing the uncertainty of dust-induced environmental and climatic impact. Here we present a mesoscale model study of the seasonal dust activity in the semiarid drylands of Central Asia, focusing on the effects of wind speed, soil moisture, surface roughness heterogeneity, and vegetation phenology on the threshold friction velocity (u_*t ) and dust emission during the dust season of 1 March to 31 October 2001. The dust model WRF-Chem-DuMo allows us to examine the uncertainties in seasonal dust emissions due to the selection of dust emission scheme and soil grain size distribution data. To account for the vegetation effects on the u_*t , we use the Moderate Resolution Imaging Spectroradiometer monthly normalized difference vegetation index to derive the dynamic surface roughness parameters required by the physically based dust schemes of Marticorena and Bergametti (1995, hereinafter MB) and Shao et al. (1996, hereinafter Shao). We find the springtime u_*t is strongly enhanced by the roughness effects of temperate steppe and desert ephemeral plants and, to less extent, the binding effects of increased soil moisture. The u_*t decreases as the aboveground biomass dies back and soil moisture depletes during summer. The u_*t dynamics determines the dust seasonality by causing more summer dust emission, despite a higher frequency of strong winds during spring. Due to the presence of more erodible materials in the saltation diameter range of 60-200 µm, the dry-sieved soil size distribution data lead to eight times more season-total dust emission than the soil texture data, but with minor differences in the temporal distribution. On the other hand, the Shao scheme produces almost the same amount of season-total dust emission as the MB scheme, but with a strong shift toward summer due to the strong sensitivity of the u_*t to vegetation. By simply averaging the MB and Shao model experiments, we obtain a mean estimate (Exp_mean) of season-total dust emission of 255.6 Mt (megaton), of which 26.8%, 50.4%, and 22.8% are produced in spring (March-April-May), summer (June-July-August), and autumn (September-October), respectively. The Exp_mean estimate identifies the Ustyurt Plateau, dried seabed of Aral Sea (called Aralkum), Caspian Sea coast, and loess deserts as the strongest dust source areas in Central Asia. The spatial distribution and seasonality of the Exp_mean estimate are in general agreement with ground station dusty weather observations and satellite aerosol optical depth and absorbing aerosol index products. Compared to Cakmur et al. (2006), the Exp_mean estimate suggests Central Asia contributes 10-17% to the global dust emission in 2001.

KEY POINTS

The WRF-Chem-DuMo model is used to study dust seasonality in Central Asia An accurate representation of u_*t is critical for dust seasonality Multiexperiment mean dust emission estimate agrees with observations.