Asymmetric impacts of El Niño-Southern Oscillation on summer temperature over the eastern Qinghai-Tibetan Plateau, as revealed by the blue intensity of Picea purpurea

The Qinghai-Tibetan Plateau (QTP) is a region exceptionally sensitive to climate change. Warming in the QTP has more than doubled the global average temperature over the past 50 years. Numerous studies have aimed to assess recent warming by reconstructing the past climate of the QTP through tree-ring analysis. However, these studies primarily utilize ring width as the parameter. Our study marks the development of the blue intensity (BI) chronologies from Picea purpurea in the eastern QTP. Additionally, the latewood BI chronology was employed to reconstruct the mean maximum temperature (Tmax) of July–August for this region. The reconstruction spanned from 1737 to 2020 CE and accounted for 62% of the observed variance during 1961–2020 CE. The reconstruction revealed distinct periods: the cool phase of the Little Ice Age, relative stability in the 19th century, slight warming in the 20th century, and a substantial temperature increase since the 21st century. Moreover, we highlight the El Niño-Southern Oscillation, Atlantic Multidecadal Oscillation, and tropical volcanic eruptions as critical factors influencing July–August Tmax over the eastern QTP. However, the impact of El Niño-Southern Oscillation on regional temperature exhibited asymmetry: La Niña events caused considerable cooling in the study area, while the warming associated with El Niño events was not substantial, demanding further investigation. In conclusion, these findings imply that tree-ring BI holds substantial potential to enhance our comprehension of past temperature fluctuations in the QTP. Its superior temperature sensitivity, in contrast to commonly used ring width data, is particularly noteworthy. •We developed the first blue-intensity chronology on the Qinghai-Tibetan Plateau.•Late summer temperature on the eastern Qinghai-Tibetan Plateau was reconstructed.•ENSO have an asymmetric influence on regional temperature variation.