Revisiting two indices measuring high‐frequency daily variability

Abstract

AbstractHigh‐frequency daily variability (HFDV), as a fundamental aspect of climate variability, has drawn increasing attention due to its crucial impacts on public health, regional ecosystems and economic growth rates. The statistical metrics are of great importance to measure the changes in HFDV. In this study, we compared two common statistical metrics of monthly HFDV, including the standard deviation (SD) and the day‐to‐day difference (DTDx), for various variables (temperature, specific humidity and horizontal wind) using the NCEP/NCAR reanalysis dataset. Here, we show that the SD of daily variables includes mainly sub‐semiannual variability (<180 days), while DTDx can separate the synoptic variability (<12 days, DTD1), submonthly variability (<25 days, DTD3) and subseasonal variability (<60 days, DTD5) from other temporal scales. Moreover, a wider dominant timescale of DTDx causes a higher climatological mean and a closer relationship with SD. We also find that the linear trend in SD exhibits remarkable differences from that of the DTDx nearly all over the world, which can be explained by the significant change in the autocorrelation with different lag days under climate change. Our findings highlight that the DTDx is superior to SD in measuring HFDV, especially in a changing climate.