Recent and future changes in the combination of annual temperature and precipitation throughout China

Abstract

Climate involves different combinations of temperature and precipitation, and each year's combination of factors can be assigned a climatic year type (CYT; e.g. Warm‐Humid). Describing the changes in the CYT provides more information than describing the temperature or precipitation data alone. In this study, we defined nine CYTs using the probability density function of annual temperature and precipitation. Recent and future spatiotemporal changes in CYT were analysed using 507‐station observational data and projected data obtained from the CMIP5 multi‐model ensemble under RCP2.6, RCP4.5, and RCP8.5 scenarios. China was divided into six subregions to analyse the spatiotemporal changes. Obvious differences in spatial patterns among the various CYTs reflect the climate regime throughout China. The warmth‐associated CYTs (Warm‐Humid, Warm‐Dry, and Warm‐Normal) mainly occur in West China (e.g. Southwest China). The cold‐associated CYTs (Cold‐Humid, Cold‐Dry, and Cold‐Normal) dominate at high latitudes and high altitudes (e.g. Northeast China and the Tibetan Plateau). The climate in China changed from cold to warm in the last half‐century, accompanying the transformation of Cold‐Humid, Cold‐Dry, and Cold‐Normal before the early 1990s to Warm‐Humid, Warm‐Dry, and Warm‐Normal from the early 1990s onward. In the 21st century, the projected CYTs are mainly Warm‐Humid, Warm‐Dry, and Warm‐Normal in China. Warm‐Humid dominates in West China, North China, and Northeast China. Warm‐Dry is mainly projected in the Yellow River Valley and South China. High‐frequency Warm‐Normal is projected in the Yellow River Valley. Warm‐Humid is projected to increase whereas Warm‐Dry and Warm‐Normal are projected to decrease from 2015 to 2099. All three CYTs are projected to exhibit larger changes in trends under stronger versus weaker RCPs (RCP8.5 > RCP4.5 > RCP2.6). Compared with temperature or precipitation data alone, CYTs provide more complete information on climate change and more accurately characterize regional differences in climate throughout China.

Analysis on the Changes of Agro-Meteorological Thermal Indices in Northeast China under RCP4.5 Scenario Using the PRECIS2.1

“…As produced by PRECIS, changes of the Tmean and Tmin during the 21st century would be in agreement with variability of the radiative forcing trajectories under RCP scenarios [34,35]. In comparison with the Tmean, the Tmin would warm up more obviously, which is consistent with previous findings [34,36]. Relative to observations, overestimation of the simulated …”

Spatial and temporal variations of summer hot days and heat waves and their relationships with large‐scale atmospheric circulations across Northeast China

“…For consistency, the observations were also interpolated to the same grids (0.5° × 0.5°). The relative model error (RME * ) was defined for every model (Liu et al, ) by where RMSE was the RMSEs of regional averaged T max , FHD, and FHW over NEC between each model and observation, was the average value of RMSEs for the 22 GCMs. The negative RME * values indicated that the model performance was better than the majority (50%) of models and vice‐versa.…”

“…For consistency, the observations were also interpolated to the same grids (0.5 × 0.5 ). The relative model error (RME * ) was defined for every model (Liu et al, 2016) by…”

Changes in start, end, and length of frost‐free season across Northeast China

“…We compared the root‐mean‐square errors (RMSEs) of the regional averaged LSFD, FFFD, and LFFS over NEC, which were calculated by the observations and by the models, respectively. The relative model error ( RMSE *) (Liu et al , ) was defined for every model by …”

“…which were calculated by the observations and by the models, respectively. The relative model error (RMSE*) (Liu et al, 2017) was defined for every model by…”