Spatiotemporal Analysis of Extreme Temperature Change on the Tibetan Plateau Based On Quantile Regression

Abstract

AbstractTemperature extremes have been extensively observed on the Tibetan Plateau (TP), of which the dynamics were mostly monitored by traditional trend analysis based on linear regression. This method however, is often impacted by a quasi‐periodic heterogeneity disturbance in variance, leading to results susceptible to extreme outliers. In this study, we conducted the comprehensive detection of extreme temperature changes using quantile regression on the TP from 100 weather stations from 1979 to 2019. The use of quantile regression enabled us to characterize the entire density function of the extreme temperature time‐series (the daily minimum, mean and maximum temperature) at each location. Our results revealed that the daily minimum temperature was generally warming faster than the daily maximum and mean temperatures with the respective trend interval of [−0.45, 1.29], [−0.1, 0.98], [−0.01, 1.01] °C/decade among the conditional quantile levels. Furthermore, the warming rate of extremely cold (0.05 quantile level) and hot days (0.95 quantile level) was higher than that of the mean condition (0.5 quantile level) in each extreme temperature time‐series. We also found that there was a significant elevation‐dependent warming only in extremely cold days. In addition, results from the teleconnection analysis showed that the Arctic Oscillation had a strong influence on the extreme cold and hot extremes over the northern and southwestern TP region, while the El Niño‐Southern Oscillation mainly modulated temperatures over the southeastern TP area. This study can potentially offer improved understanding of the temperature extremes over TP in the face of global warming.