Spatiotemporal variations of seasonal temperature at different percentiles in the three provinces of northeast China from 1961 to 2019

Abstract

AbstractThe three provinces of northeast China (NEPs) were one of the major grain producing areas in China and has undergone significant climate warming during past decades. However, nowadays researches on climate warming mostly focused on variations of mean temperature or temperature extremes, which missed much information in the other parts of temperature. Therefore, taken NEPs as study area, variations of maximum temperature (Tmax), minimum temperature (Tmin) and diurnal temperature range (DTR) at every 10% percentile from 1961 to 2019 in NEPs were analysed at seasonal scale. The abrupt changes and decadal shifts of Tmax, Tmin and DTR at different percentiles were further highlighted. Results show that (a) for either regional average or station cases, fluctuations of daily Tmax and daily Tmin both decreased in spring and winter, but increased in autumn. In summer, fluctuations of daily Tmin decreased while those of daily Tmax increased. (b) Spatially, Tmax heated up more in south of NEPs at lower percentiles, and in northwest of NEPs at higher percentiles in spring, summer and winter. Tmin and DTR overall showed consistent spatial differences that changes in north and northwest of NEPs were with greater magnitudes. (c) In spring and winter, the higher Tmax/Tmin had earlier abrupt changes than lower Tmax/Tmin, while in summer and autumn, the abrupt changes occurred earlier for lower Tmax/Tmin. (d) In winter, overwhelming positive shifts were significant before 2000, but turned negative after 2000. In summer, Tmax heated up quickly since 1990s and Tmin since 1980s. Shifts in spring and autumn varied during different periods and generally presented alternative warming at higher and lower percentiles. The research can help better depict the warming patterns and figure out the detailed warming procedures in NEPs, which is a strong supplement for current climate change research.