The Paris Agreement put forward a goal that limits the increase in the global mean surface temperature to 1.5 ℃ in order to minimize the risks of extreme climate events (Hoegh-Guldberg et al., 2018). The global mean surface temperature was 0.87°C higher in 2006–2015 than in 1850–1900 (IPCC, 2013), which has already caused multiple observed changes in the climate system. Especially, the magnitude and intensity of climate extremes have increased around the world in recent decades, affecting the natural and human systems (IPCC, 2012). Therefore, the investigations about temperature extremes have received more and more attention worldwide due to their impacts (Byers et al., 2018). It has also been projected that more temperature extremes will increase mortality and morbidity in vulnerable groups (Dong et al., 2018), and will have an important influence on global agriculture (Vogel et al., 2019; Yan et al., 2021), vegetation phenology, and productivity (Crabbe et al., 2016). Hence, understanding the variations in temperature extremes is crucial to ascertain the magnitude and pattern of the risks posed by global warming.
Generally, climate extremes are defined as the occurrence of a weather or climate variable at a value above (or below) a threshold value that is near the upper (or lower) ends of the observed values range of the variable (IPCC, 2012). During past periods, research on climate extremes has made great progress, especially the efforts of the Expert Team on Climate Change Detection and Indices (ETCCDI). They categorized climate extremes using percentiles and/or the frequency of days/nights exceeding certain thresholds, and they structured a set of climate extreme indices based on the daily maximum and minimum temperatures, and the daily precipitation (such as the hottest (coldest) day of the year and precipitation extremes) (Alexander et al., 2006; Sillmann et al., 2013). These climate extreme indices provide statistically robust insights into a region’s local climatic conditions with high signal-to-noise ratio (Saleem et al., 2021; Zhou et al., 2016). Moreover, these climate extreme indices provide a comprehensive overview for temperature and precipitation extremes, and enable regional and global datasets (both station and gridded) to be developed in a comparable way. Hence, climate extreme indices have been widely applied in climate variability and trend studies (Zhang et al., 2011).
It should be noted that significant changes in extreme temperatures have been observed on global and some regional scales within different datasets (Alexander et al., 2006; Dong et al., 2018; Zhou et al., 2016). Since the beginning of the twentieth century, the widespread significant variations in temperature extremes in the global are consistent with the warming trends. These changes of the global are more pronounced for indices related to cold extremes than for indices related to warm extremes (Donat et al., 2013). At regional scales, drastic changes in the trends of warm and cold extremes have been reported across the Arabia, with an increasing number of warm days and nights, higher extreme temperature values, shorter cold spell durations and fewer cold days, and nights since the mid-1950s (Donat et al., 2014). A considerable increase in the frequency of warm nights was observed in the Indo-Pacific region during 1971–2005 (Caesar et al., 2011). For China, decreases in cold extremes and increases in warm extremes have also been found during 1961–2010 (Zhou et al., 2016). Several studies have demonstrated that the increasing trend of the minimum temperature index is greater than that of the maximum temperature index in Northeastern China and the Loess Plateau (Yan et al., 2015; Yu and Li, 2015).
It is important to understand the causes of the long-term trends in the observed temperature extremes and the possible influence of external forcing on the climate system (Dong et al., 2018). The variability of climate is believed to be related to several factors, especially the El Niño-Southern Oscillation (ENSO). The ENSO is a climate signal from the oceans and can trigger pronounced changes in climate across the world (Sun et al., 2016). The ENSO plays a robust role in the climate of East Asia, which has been mainly ascribed to the interactions between the ENSO and the East Asian summer and winter monsoons (Miao et al., 2019; Ying et al., 2015). One study investigated that the relationship between ENSO and mean temperature peaks a few months before the monsoon (del Rio et al., 2013). The above studies mainly focused on the impact of the average intensity of ENSO events, but the different types in ENSO were poorly considered. ENSO events can be divided into two types, Eastern Pacific (EP) ENSO events and Central Pacific (CP) ENSO events, which have different influences on the atmospheric circulation in East Asia (Larkin and Harrison, 2005; Weng et al., 2009). It is noticed that CP ENSO events have been frequently observed in recent years (Wang et al., 2019), which may interfere with the robustness of climate predictions in East Asia. Thus, gaining a better understanding of the impact of different types of ENSO events on climate is necessary and would enable the identification of the key factors affecting climate extremes events. Furthermore, previous investigations have reported that large-scale changes in the wind speed and geopotential height are the likely causes of warm extremes that trigger severe heatwave conditions in the presence of a high-pressure system (Gao et al., 2018; Khan et al., 2019). Different climate variabilities will affect the distribution patterns of temperature extremes on a daily time scale. Therefore, in order to detect and attribute the influences of ENSO on observed changes of temperature extremes, the changes in large-scale modes of climate variability caused by the different ENSO types need to be considered.
Although some studies have investigated the variation of temperature extremes and its influence factors using CMIP5 models (Yin et al., 2019; You et al., 2018). little attention has been paid to the response of temperature extremes on the Tibetan Plateau (TP) to different ENSO types. As the largest and highest plateau on earth, the TP is extremely sensitive to warming compared to surrounding areas (Duan and Xiao, 2015), and an increasing number of climate extremes have occurred on the TP in recent decades, especially temperature extremes. The increased number of temperature extremes has exerted an important effect on this region, such as retreat of glaciers, terrestrial vegetation migration, wetland shrinkage, and encroachment upon farmlands (Yin et al., 2019). Therefore, it is of interest to explore the temporal and spatial patterns of temperature extremes over the TP, where temperature extremes are controlled by the rapid warming and unique large-scale atmospheric circulation around the TP. If we can gain a better understanding of the temporal and spatial variations of temperature extremes and their response characteristics to different ENSO types, then, we will be able to give an insight into the reasons behind the rapid changes in climate extremes, and to develop an essential scientific basis for future projections of climate extremes and climate change policy making.
In this study, we examined the variations in temperature extremes across the TP using the meteorological observational dataset, and extracted different ENSO types based on the national standard of China formulated. Then, we applied the composite analysis method to determine the influence of different ENSO types on the variation patterns of temperature extremes over the TP. The goals of this study were to accomplish the following: (1) analyzing temporal and spatial patterns of temperature extremes on the TP; and (2) investigating the influence mechanism of ENSO events on the patterns of temperature extremes on the TP.