Abstract
The objective of this paper is to study the spatial and temporal variation characteristics of the UNESCO aridity index (AI) at annual and seasonal temporal scales in northwestern China during the 55-year study period (1961–2015). Based on climate data from 178 meteorological stations provided by the China Meteorological Data Service Center, AI trends were investigated by the Mann-Kendall test. The results showed that the annual and seasonal AIs displayed a mixed pattern of positive and negative trends for the period of 1961–2015 across the study area. In most regions, both annual and seasonal AIs exhibited slightly positive trends in northwestern China. Significant (P < .05) positive and negative annual trends were detected at 38 and 6 stations, respectively. The highest magnitude of the positive annual trend (0.0037 per year) appeared at Xining station, and the greatest decreasing annual trend (−0.0041 per year) appeared at Minxian station. In terms of seasonal AI, its trend magnitudes varied from −0.0058 to 0.0035 in spring, from −0.0062 to 0.0087 in summer, from −0.0105 to 0.0048 in autumn and from −0.0002 to 0.0443 in winter each year. Our study demonstrated that the AI spatial distribution may tend to be spatially homogeneous in the long term. Abrupt changes varied in different regions and seasons. Nevertheless, the positive trend shifts mostly began in the 1980s and became significant in the following decades. The correlation analysis indicated that the AI in winter and spring was probably mainly affected by the Indian Ocean Warm Pool Strength Index (IOWPSI), while the Multivariate ENSO Index (MEI), the Niño3.4 Sea Surface Temperature Anomaly Index (Niño3.4 SSTA) and the Southern Oscillation Index (SOI) were likely to be the conjoined influencing factors of the AI in summer and autumn. The spatiotemporal features of the long-term AI provided in this study may enhance our scientific understanding of the impacts of recent climate change on dryness/humidity changes in northwestern China; furthermore, this information might potentially be applied in other areas for comparison purposes.
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