Stationarity in the variability of arid precipitation:A case study of arid Central Asia

• 作者: YAO Jun-Qiang CHEN Jing ZHANG Tong-Wen DILINUER Tuoliewubieke MAO Wei-Yi
• 来源期刊: 气候变化研究进展（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: Changes in Climate system
• 页码范围: 172-186
• 页数: 15页
• 语种: 英文

摘要

Precipitation is particularly scarce in arid Central Asia (CA), and is expected to be severely impacted by future warming, and the assessment of the stationarity of precipitation variability is important for managing surface water resources in this region. In this study, we investigated the statistics of stationarity in the totals and extremes of precipitation in CA based on the longest observational records (1881-2006), tree-ring reconstructed records (1756-2012 and 1760-2015), and the Coupled Model Intercomparison Project 5 (CMIP5) simulations, applying the autocorrelation function and testing criteria established based on the statistical definitions of stationarity. We analyzed the longest daily pre-cipitation record (Tashkent station, 1881-2006) and found that the autocorrelation coefficient of the precipitation totals (PRCPTOT) and annual maximum 1-day precipitation amount (Rx1day) were statistically insignificant for all lags, implying stationary behavior. Regionally, nearly all the Global Historical Climatology Network-Daily Database (GHCN-D) observatory sites (1925-2005) indicated likely stationary behavior. The reconstructed records were also indistinguishable from a random process. For the CMIP5 models, the simulated and projected PRCPTOT closely approximated a purely random process; however, the projected Rx1day maintained non-stationary means in most of the models under the representative concentration pathway (RCPs), implying that extreme events would increase in the future. The mean precipitation changes (△P) can be expressed as an exponential function depending on the length of the successive mean periods (μ) and variance (σ2 ). The△P of the next decade is projected to be within ±14.8％of the previous decades mean annual PRCPTOT over CA. The higher the RCPs, the higher the△P over CA. The results show that the detection and prediction of precipitation change will be challenging in arid CA.