Climatological spatial scales of meteorological droughts in China and associated climate variability

Abstract

This study investigates the climatological spatial scales (CSSs) of meteorological droughts in China and the linkages to climate variability. The Global Precipitation Climatology Centre monthly gridded precipitation with a spatial resolution of 25 km × 25 km for 1961–2010 is used. The standardized precipitation index at different timescales (1-, 3-, 6-, 9-, 12-, 24-, 36-, and 48-month) is applied to characterize meteorological droughts. The CSSs of meteorological droughts are calculated using a method considering spatial correlation and anisotropy. The relationships between the CSSs and main monsoons and climate teleconnections in five selected regions are quantified using dynamic spatial panel models. The five regions are South China (SC), the Yangtze River Valley (YRV), North China (NC), Northeast China (NE), and the Tibetan Plateau (TIB). The following results are obtained: (1) The CSSs of multi-timescale meteorological droughts in China are determined. The means of the CSSs at various timescales in China are 68.9, 73.8, 71.8, 69.7, 68.8, 65.9, 62.9, and 60.8 × 104 km2, respectively. The CSSs in eastern China generally show a significant decrease with increasing timescale. (2) Significant quantification relationships (R2 greater than 0.85) suggest that there is not a simple linear relationship between climate anomalies and the CSSs, but rather a complex spatiotemporal interaction including exogenous and endogenous interaction effects. The CSSs not only strongly depend on their neighbors on the same timescale but also significantly depend on the CSSs of neighbors on adjacent timescales. Three monsoon indices (Indian monsoon, East Asian monsoon, and western North Pacific monsoon) have significant impacts on the multi-timescale CSS variations in the five regions, especially for NE, YRV, SC, and TIB, while the El Niño–Southern Oscillation and Pacific Decadal Oscillation affect mainly SC and YRV. These findings could broaden our understanding of the spatiotemporal relationships of droughts and be useful for drought risk management.