Magnitude and Frequency of Temperature and Precipitation Extremes and the Associated Atmospheric Circulation Patterns in the Yellow River Basin (1960–2017), China

Abstract

Since there are many destructive effects caused by extreme climate events in the Yellow River, it is of great theoretical and practical significance to explore the variations of climatic extremes in this key basin. We used a meteorological dataset from 66 stations within the Yellow River basin (YRB) for the period 1960–2017 to calculate magnitude and frequency of precipitation/temperature extremes. We also analyzed the relationships between the main large-scale atmospheric circulation patterns (ACPs) and precipitation/temperature extremes. The trends in precipitation extremes were nonsignificant, only a few stations were characterized by significantly increasing or decreasing anomalies; this indicates the precipitation intensity may have been strengthened, and the extreme rainfall duration appears to have been reduced during 1960–2017. The trends of magnitudes for “cold” extremes were larger than those for “warm” extremes, changes of trends in frost days were higher than those for summer days, and the trends in increasing warm nights were higher than those of warm days. The influence of the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) on temperature extremes outweighed the influence of the North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) for the other extreme climate indices. The YRB might be at risk of increased extreme high temperature events, and more attention should be paid to this higher risk of extreme climatic events.

Changes in daily extreme temperature and precipitation events in mainland China from 1960 to 2016 under global warming

“…In the present study, the strengths of the trends for the temperature indices were lower than those in the Loess Plateau of northwestern China (Sun et al ., 2016), the Xinjiang Autonomous Region of northwestern China (Wang et al ., 2013a), and the Tibetan Plateau of western China (Wang et al ., 2013b), but greater than those for the Yunnan–Guizhou plateau in southwestern China (Li et al ., 2012) and the Yangtze River basin in central and southern China (Wang et al ., 2014). Specifically, the magnitude of the trend for TNn in mainland China was lower than the average value for the Yangtze River Basin (0.42°C·decade −1 ; Guan et al ., 2015), the Yellow River Basin (0.45°C·decade −1 ; Dong et al ., 2019) and the Yarlung Tsangpo River Basin in China (0.48°C·decade −1 ; Liu et al ., 2019). Similarly, the trend for FD (−2.63 days·decade −1 ) was less than that in the Yellow River Basin (−3.72 days·decade −1 ; Dong et al ., 2019), Tibetan Plateau (−5.69 days·decade −1 ; Sun et al ., 2016), Loess Plateau (−3.22 days·decade −1 ; Wang et al ., 2013b), and Yarlung Tsangpo River Basin (−4.39 days·decade −1 ; Liu et al ., 2019), but larger than those in the Wujiang River Basin (−2.19 days·decade −1 ) and Jialing River Basin (−1.93 days·decade −1 ) (Wang et al ., 2014) in China.…”

“…Specifically, the magnitude of the trend for TNn in mainland China was lower than the average value for the Yangtze River Basin (0.42°C·decade −1 ; Guan et al ., 2015), the Yellow River Basin (0.45°C·decade −1 ; Dong et al ., 2019) and the Yarlung Tsangpo River Basin in China (0.48°C·decade −1 ; Liu et al ., 2019). Similarly, the trend for FD (−2.63 days·decade −1 ) was less than that in the Yellow River Basin (−3.72 days·decade −1 ; Dong et al ., 2019), Tibetan Plateau (−5.69 days·decade −1 ; Sun et al ., 2016), Loess Plateau (−3.22 days·decade −1 ; Wang et al ., 2013b), and Yarlung Tsangpo River Basin (−4.39 days·decade −1 ; Liu et al ., 2019), but larger than those in the Wujiang River Basin (−2.19 days·decade −1 ) and Jialing River Basin (−1.93 days·decade −1 ) (Wang et al ., 2014) in China.…”

“…The decreasing trend for CWD in the present study was consistent with the results for Xinjiang (Wang et al ., 2013a) and the Yunnan–Guizhou plateau (Li et al ., 2012), which are in Northwest and Southwest China, respectively, but contrary to the results in the Loess Plateau (Sun et al ., 2016), Tibetan Plateau (Wang et al ., 2013b), and Songhua River (Song et al ., 2015) in China. In addition, the regionally averaged SDII (0.05 mm·day −1 ·decade −1 ) in the present study was larger than the average value for the Xinjiang region (0.04 mm·day −1 ·decade −1 ; Wang et al ., 2013a), the Tibetan Plateau (0.01 mm·day −1 ·decade −1 ; Wang et al ., 2013b), and the Yunnan–Guizhou plateau (0.03 mm·day −1 ·decade −1 ; Li et al ., 2012), but lower than that in the Yellow River Basin (0.06 mm·day −1 ·decade −1 ; Dong et al ., 2019), and Yarlung Tsangpo River Basin (0.10 mm·day −1 ·decade −1 ; Liu et al ., 2019).…”

Extreme precipitation variability across the Lancang‐Mekong River Basin during 1952–2015 in relation to teleconnections and summer monsoons

“…However, Bayazit and Önöz (2007) concluded that such serial correlations have negligible effects on the detection of significant trends in data sets with large samples ( n ≥ 50), like the time series employed by the present study. Both TFPW and RB methods have already been applied by previous studies for exploring variability and trends in extreme precipitation indices in different parts of the world (e.g., Irannezhad et al ., 2016; Dong et al ., 2019).…”

A new method for assessing the performance of general circulation models based on their ability to simulate the response to observed forcing

“…To account for their confounding effects, natural variability modes are considered in our analysis. Modes like the Atlantic multidecadal oscillation (AMO), the Pacific decadal oscillation (PDO), the Southern Oscillation index (SOI), the northern annular mode (NAM), and the North Atlantic Oscillation (NAO) have a stronger influence in the NH regions (Hu et al 2003;Englehart and Douglas 2004;Brönnimann et al 2007;Riaz et al 2017;Brunetti and Kutiel 2011;de Beurs et al 2018;Dong et al 2019). The dipole mode index or Indian Ocean dipole (IOD), southern annular mode (SAM), North Pacific index (NPI), AMO, and SOI influence SH regions (Mason and Jury 1997;Power et al 1999;Tyson and Preston-Whyte 2000;Hendon et al 2007;Fogt et al 2011;Ashcroft et al 2014;Lakhraj-Govender and Grab 2018).…”