Modeling Future Changes of the Dust Emission Flux over Northern China
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摘要: 本研究利用WRF-Chem(Weather Research and Forecasting model with online coupled Chemistry)模式对未来中国北方沙尘起沙过程变化进行了模拟预测。为了提高预测结果的准确度,研究综合考虑了气溶胶、温室气体和植被覆盖率等因素对天气、气候和起沙过程的影响。预测结果显示,2016~2029年西北部沙尘源地起沙量高于北部沙尘源地,地形和气候的差异是导致两地起沙过程及其季节变化差异的主要原因。两个沙尘源地四季起沙通量呈总体减少而部分季节增加的趋势,西北部沙尘源地起沙通量在春季总体呈上升趋势,在夏、秋和冬季呈下降趋势;北部沙尘源地起沙通量在春、夏和冬季呈下降趋势,在秋季呈微弱上升趋势。两个沙尘源地各季起沙通量的变化趋势由近地面风速主导,植被覆盖率、降水和地面温度等因素对起沙通量的年际波动有着重要影响。
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关键词:
- 动力降尺度 /
- WRF-Chem模式 /
- 起沙通量变化预测 /
- 起沙通量变化驱动因素分析
Abstract: This paper employs the Weather Research and Forecasting model with the online coupled Chemistry (WRF-Chem) to study future changes in dust emission in northern China. To improve the accuracy of prediction results, this study comprehensively considers the influence of factors such as aerosols, greenhouse gases, and vegetation fraction on the weather, climate, and dust emission processes. The prediction shows that from 2016 to 2029, the amount of dust emission in the northwestern dust source region is higher than that in the northern dust source region. In addition, differences in topography and climate lead to differences in dust emission processes and their seasonal variations in the two regions. Seasonal mean dust emission fluxes in the northwestern and northern dust source regions from 2016 to 2029 show general decreasing trends, whereas some seasons show increasing trends. The dust emission flux in the northwestern dust region shows a weak increasing trend in spring and decreasing trends in summer, autumn, and winter. The dust emission flux in the northern dust source region shows decreasing trends in spring, summer, and winter and a weak increasing trend in autumn. Variation trends of dust emission fluxes in the two regions are dominated by the near-surface wind speed, whereas the vegetation fraction, precipitation, and surface temperature have important effects on the interannual fluctuation of dust emission fluxes. -
图 2 2016~2018年(a,c)西北部沙尘源地和(b,d)北部沙尘源地模拟及观测(a,b)月平均和(c,d)季平均近地面风速(WS)变化。Rgcm和Rwrf分别为本研究使用的GCM和WRF-Chem模拟结果与MICAPS数据的相关系数,Pgcm和Pwrf为对应的显著性水平;图c和d横坐标中的MAM和SON分别代表March–April–May和September–October–November,即春季和秋季
Figure 2. Simulated and the observed (a, b) monthly mean and (c, d) seasonal mean near-surface wind speed (WS) variations in (a, c) the northwestern and (b, d) northern dust source regions from 2016 to 2018. Rgcm and Rwrf are the correlation coefficients between the Global Climate Model (GCM) and WRF-Chem simulation results used in this study and the Meteorological Information Comprehensive Analysis and Process System (MICAPS) data, respectively. Pgcm and Pwrf are the corresponding significant levels. The MAM and SON in the horizontal coordinates of figures c and d represent March–April–May and September–October–November, i.e., spring and fall, respectively
图 5 2016~2029年(a–d)西北部沙尘源地和(e–h)北部沙尘源地(a,e)春、(b,f)夏、(c,g)秋和(d,h)冬季平均起沙通量变化。图中Slope、R2和P分别代表线性拟合的斜率、拟合度和显著性水平
Figure 5. Variations of seasonal mean dust emission fluxes in (a–d) the northwestern and (e–h) northern dust source regions in (a, e) spring, (b, f) summer, (c, g) autumn, and (d, h) winter from 2016 to 2029. Slope, R2 and P in the figure represent the slope, goodness and significant level of the linear fitting, respectively
表 1 WRF-Chem物理、化学和气溶胶参数化方案设置
Table 1. Namelist settings of physical, chemical, and aerosol parameterizations used in the WRF-Chem model
物理化学过程 方案编号 方案名称 微物理 28 Thompson aerosol-aware
(Thompson and Eidhammer, 2014)长波辐射 4 RRTMG(Mlawer et al., 1997) 短波辐射 4 RRTMG(Iacono et al., 2000) 地表 2 Monin-Obukhov(Pahlow et al., 2001) 陆面过程 2 Unified Noah(Chen et al., 2010) 边界层 2 MYJ(Janjić, 1994) 积云参数化 5 Grell 3D(Grell and Freitas, 2014) 化学和气溶胶 401 Dust only 表 2 各季节起沙通量与其影响因素的相关系数
Table 2. Correlation coefficients between the dust emission flux and its relevant elements in various seasons
西北部沙尘源地 春季 夏季 秋季 冬季 近地面风速 0.51 0.60 0.71 0.15 降水量 −0.02 −0.54 −0.35 0.09 地表温度 −0.12 −0.06 −0.78 0.11 植被覆盖率 0.09 −0.34 −0.62 −0.53 北部沙尘源地 春季 夏季 秋季 冬季 近地面风速 0.72 0.76 0.78 0.57 降水量 −0.15 0.07 −0.53 −0.11 地表温度 −0.38 −0.07 −0.39 0.10 植被覆盖率 −0.31 −0.50 −0.34 −0.15 -
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