A standardized index for assessing sub-monthly compound dry and hot conditions with application in China

[摘要] Compound dry and hot conditions frequently cause large impacts on ecosystems and societies worldwide. A suite of indices is available for the assessment of droughts and heatwaves, yet there is no index available for incorporating the joint variability of dry and hot conditions at the sub-monthly scale. Here we introduce a daily-scale index, called the standardized compound drought and heat index (SCDHI), to assess compound dry-hot conditions. The SCDHI is based on a daily drought index (the standardized antecedent precipitation evapotranspiration index – SAPEI), the daily-scale standardized temperature index (STI), and a joint probability distribution method. The new index is verified against real-world compound dry and hot events and associated observed vegetation impacts in China. The SCDHI can not only capture compound dry and hot events at both monthly and sub-monthly scales, but is also a good indicator for associated vegetation impacts. Using the SCDHI, we quantify the frequency, severity, duration, and intensity of compound dry-hot events during the historical period and assess the ability of climate models to reproduce these characteristics in China. We find that compound events whose severity is at least light and which last longer than 2 weeks generally persisted for 20–35 d in China. Southern China suffered from compound events most frequently, and the most severe compound events were mainly detected in this region. Climate models generally overestimate the frequency, duration, severity, and intensity of compound events in China, especially for western regions, which can be attributed to a too strong dependence between the SAPEI and STI in those models. The SCDHI provides a new tool to quantify sub-monthly characteristics of compound dry and hot events and to monitor their initiation, development, and decay. This is important information for decision-makers and stakeholders to release early and timely warnings.

[学科分类] 妇产科学