This work examines how environmental variables previously shown to be correlated with dust emissions (temperature, vapor pressure deficit, relative humidity, precipitation, soil moisture, wind speed, and leaf area index [LAI]) explain historical variability in observed fine dust concentrations in the southwestern United States. We quantify fine dust concentrations in the U.S. Southwest dust season, March through July 1995 through 2015, using fine iron measurements from the Interagency Monitoring of Protected Visual Environments network as a dust proxy. We use Coupled Model Intercomparison Project Phase 5 (CMIP5) output to estimate how environmental variables that drive dust concentrations may change in the future. The majority of CMIP5 models that simulate LAI, a quantity anticorrelated with dust concentrations, show increasing LAI in the Southwest United States in the 21st century. Based on linear estimates of how dust concentrations respond to changes in LAI, increases in LAI could result in reduced dust concentrations in the future. However, when environmental variables are selected objectively using lasso regression, LAI is not selected in favor of other variables. When using a linear combination of objectively selected environmental variables, we estimate that future Southwest dust season concentrations will increase by 0.24 μg m⁻³ (12%) by the end of the 21st century for RCP 8.5. This estimated increase in fine dust concentration is driven by decreases in relative humidity, precipitation, and soil moisture and buffered by decreased wind speeds.

中文翻译：

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估算美国西南部未来细粉尘浓度的传播

这项工作研究了以前显示出与粉尘排放相关的环境变量（温度，蒸气压赤字，相对湿度，降水，土壤湿度，风速和叶面积指数[LAI]）如何解释了观测到的粉尘浓度的历史变化。美国西南部。我们使用“受保护的视觉环境”跨部门监视网络中的细铁测量值作为尘代标，对1995年3月至7月至2015年美国西南尘埃季节的细尘浓度进行了量化。我们使用耦合模型比较项目第5阶段（CMIP5）的输出来估算驱动粉尘浓度的环境变量将来可能如何变化。大多数CMIP5模型都模拟LAI（与灰尘浓度成反比的数量），显示21世纪美国西南部的LAI不断增加。根据尘埃浓度如何响应LAI变化的线性估计，LAI的增加可能会导致将来尘埃浓度降低。但是，当使用套索回归客观地选择环境变量时，不会选择LAI来支持其他变量。当使用客观选择的环境变量的线性组合时，我们估计未来西南沙尘季节的浓度将增加0.24μgm对于RCP 8.5，到21世纪末为⁻³（12％）。估计的细粉尘浓度增加是由相对湿度，降水和土壤湿度的降低所驱动，而由风速的降低所缓冲。