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Figure 1 shows the spatial distributions of trends for seasonal precipitation amounts during the past 50years over Mid-Eastern China, estimated using the least squares technique. Decreasing trends of spring precipitation were mainly centered in the regions around the Yangtze River and Southeast China, with the largest decreasing trends (i.e.from–2%/decade to–8%/decade) over the Loess Plateau region. Increasing trends of spring mean precipitation were mainly found over the Northeastern China, Beijing, Tianjin, North China Plain, Yunnan-Guizhou Plateau and Tibet Plateau. The decreasing trends for summer precipitation are larger (i.e.from–1%/decade to–5%/decade) than for spring over Northeast China, Beijing, Tianjin, Shandong Peninsula and Loess Plateau. Positive change trends of summer mean precipitation were found over Southern China, with the largest increasing trends (from 1%/decade to10%/decade) over the Yangtze River Plain. The trends for winter mean precipitation were generally positive over China, except for Beijing, Tianjin and Inner Mongolia. The decreasing trends of precipitation in autumn were distributed more broadly and homogeneously than that in other seasons. Significant decreasing trends of autumn mean precipitation were found over the Yangtze River Plain, Sichuan Plateau, North China Plain and Yunnan-Guizhou Plateau. As a whole, lower amount of precipitation was generated over the Mid-Eastern China, especially over Northeast China, Central China and South China.
Figure 1. Spatial distribution of the trend (%per decade) of precipitation in (a) spring (b) summer (c) autumn (d) winter precipitation amount from 1959 to 2008.
Figure 2 shows the temporal variation of seasonal mean precipitation averaged for all observational stations in the Mid-Eastern China. Increasing trends of precipitation were found for summer and winter whereas decreasing trends were found for spring and autumn. The trend of autumn mean precipitation was more significant over Yangtze-Delta region (i.e.–5.6%/decade) since 1980 and later on, contributed to the characteristic of autumn mean precipitation variations over Mid-Eastern China.
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Figure 3 shows the temporal variations of autumn atmospheric water content averaged over Mid-Eastern China during 1959–2002. The year of 1975 tends to be the turning point of atmospheric water content, indicating a transition from negative to positive trend. The lowest atmospheric water content during the past50 years was found at the end of 1960s, while two peaks at the end of 1990s (i.e.15%) and at the begging of the 21 century (25%) respectively. Most of the regions over Mid-Eastern China experienced a positive change trend of atmospheric water content, especially over Yangtze River, Southeast, Southwest and Northeast of China with an increase of 0.3–0.6mm/decade generally. The least square method was applied to calculate the change trends of autumn mean precipitation during the past 50 years over Mid-Eastern China. Based on the above analysis, no evidences were found to support that the decreasing autumn precipitation trend over Mid-Eastern China was related to changes of the large-scale atmospheric water content. We believe other factors could play more important roles in influencing the autumn precipitation characteristics over Mid-Eastern China.
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Aerosols could directly and indirectly affect the radiation budgets of the earth system, with substantial effects on regional even global climate[31-36]. Thus, further analysis was done to demonstrate the role of aerosols on precipitation over Mid-Eastern China.Fig. 4 shows the spatial distribution of annual mean AOD derived from MODIS retrievals and visibilities from observational data in autumn. AOD values larger than0.6 were found over the Sichuan Basin, Yellow River, the middle and lower reaches of Yangtze River. The spatial variations of annual mean AOD were also consistent with the findings of previous studies (Luo et al.[37], Wang et al.[38]). Visibility data also showed a consistent spatial pattern with that for AOD. It should be noted that the spatial distribution of visibility was of the opposite sign with that of AOD (i.e.larger AOD with smaller visibility), which gave more confidence to represent the characteristics of aerosol particles using visibility datasets.
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The Singular Value Decomposition (SVD) analysis, a useful tool in demonstrating the spatial correlation of two atmospheric variables, has been widely used in various studies[39]. Autumn mean visibility was treated as the left field whereas the autumn mean precipitation was treated as the right field. The coupling relationship between the anomalous distribution of autumn mean precipitation and the variation of visibility has been investigated using SVD analysis. It should be pointed out that only the simultaneous correlation was considered in this study due to the large spatial variation and short life period of aerosol particles.
The variance contribution, cumulative variance and correlation coefficient for the first five SVD modes of singular vector were shown in Table 1. It was found that the first five pairs of singular vector contributed up to 75%of the total variance. Thus, they can be used to depict the coupling relationship of precipitation and visibility variations over Mid-Eastern China. The first mode of visibility and precipitation indicated a significant decrease of temporalcoefficient (R=0.87, P < 0.01). The heterogeneous correlation coefficient of the right field of the mode for visibility and precipitation was dominated by positive values, with negative correlation found over a few small regions. High positive correlation was found over Southeastern China and Northeastern China (17.5°–36°N, 105°–120°E), which was characterized by the significant coupling of the first mode. Thus, it can be inferred from the consistence of spatial variations between aerosol and precipitation that aerosol could have large impacts on precipitation, with more aerosol loadings and lower precipitation.
Singular
vectorVariance
contributionCumulative
varianceCorrelation
coefficientq=1 36.56% 36.56% 0.83 q=2 21.34% 58.19% 0.90 q=3 7.94% 66.13% 0.92 q=4 6.50% 72.63% 0.87 q=5 4.86% 77.47% 0.90 Table 1. The variance contribution, cumulative variance and correlation coefficient explained by the first five pairs (q=1, 2, 3, 4, 5) of singular vector in this study.
By absorbing and scattering radiation, aerosols could alter regional atmospheric stability and vertical motions, and affect the large-scale circulation and hydrologic cycle with significant regional climate effects[40-43]. In addition, aerosol particles serve as condensation nuclei for the formation of both cloud droplets and atmospheric ice particles, exerting large influence on the formation of precipitation[44-48]. With the rapid development of urbanization, pollutant emission has increased dramatically over Mid-Eastern China for the last few decades. Due to the production of more black carbon aerosols and sulfate aerosols, Mid-Eastern China became the unique experimental region for studying the impacts of aerosol on regional climate and hydrological cycle. Next, we try to focus on the effects of aerosol mechanisms on autumn precipitation from the perspective of atmospheric stability conditions and the cloud microphysical conditions.
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Figure 5 shows the temporal variations of CAPE, Convective Inhibition Energy (CIN) and revised visibility in autumn averaged over Mid-Eastern China during the past 50 years. High CAPE was accompanied by low CIN, suggesting an inverse correlation between CAPE and CIN. CIN increased by28.67 J/kg per decade, while CAPE decreased by12.81 J/Kg per decade. More specifically, air stability increased significantly after the 1980s, accompanied by the decrease of CIN by–2.1%/year and the increase of CAPE by 4.7%/year.
Figure 5. The first SVD mode of the autumn visibility and autumn precipitation for the period 1980-2005. (a) The temporal coefficients of autumn visibility (solid line) and autumn precipitation (dash line); (b) The spatial pattern of heterogeneous correlation coefficient.
A decreasing trend (i.e.–2.7%/year) was found for visibility variation, which was consistent with the trend of CAPE. Thus, it can be concluded that aerosols could suppress vertical motion of air and increase regional atmospheric stability, resulting in the decrease of autumn mean precipitation.
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Figure 6 shows the comparison of autumn mean aerosol optical depth and cloud droplet effective radius under different cloud top temperature and liquid water path for 2002–2008. The solid line represents the relationship between aerosols and cloud effective particle radius when liquid water path exceeds 70 g m-2. A positive correlation between cloud droplet effective radius and liquid water path could be found when the magnitude of aerosol optical depth was lower than 0.2. Cloud droplet effective radius decreases quickly with the increase of aerosol optical depth, which is more significant with higher cloud top temperature. A decrease of cloud droplet effective radius (i.e.5μm) was found with the increase of aerosol optical depth when cloud droplet effective radius exceeds 289 K. Thus, it can be inferred that more aerosols in autumn over Mid-Eastern China lead to smaller cloud droplet effective radius, which in turn decreases the conversion efficiency of cloud droplet into raindrop and suppresses the formation of precipitation in autumn.
Figure 6. Time series of convective available potential energy (dash line), convective inhibition energy (solid line) and revised visibility (red histogram) anomalies in autumn over Mid-Eastern China. The red line represents linear trend of revised visibility anomaly.
From the perspective of precipitation formation mechanism, increased atmospheric stability and modified cloud microphysics could result in the decrease of autumn precipitation over Mid-Eastern China. Increased atmospheric stability induced by more aerosols may decrease the surface net radiation, inhibit the ascending motion of air and decrease the formation of precipitation. On the other hand, aerosols also change the characteristics of cloud microphysics by influencing the process of cloud condensation nucleus and lead to decreased precipitation.