Changes in extreme temperature over China when global warming stabilized at 1.5 °C and 2.0 °C

Abstract

The 1.5 °C global warming target proposed by the Paris Agreement has raised worldwide attention and inspired numerous studies to assess corresponding climate changes for different regions of the world. But CMIP5 models based on Representative Concentration Pathways (RCP) are ‘transient simulations’ and cannot reflect the response of climate warming stabilized at 1.5 °C. The current work presents an assessment of extreme temperature changes in China with simulations from ‘Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) project specially conceived for global warming levels stabilized at 1.5 °C and 2.0 °C. When global warming stabilizes at 1.5 °C/2.0 °C, the areal-mean temperature for whole China increases by about 0.94 °C/1.59 °C (relative to present period, taken from 2006–2015). Notable increase regions are mainly found in Northwest and Northeast-North China, but warm spell duration increases mostly in Southeast China. The effect of the additional 0.5 °C warming is particularly investigated and compared between the transient and stabilized simulations. Changes of mean and extreme temperature are larger in transient simulations than in stabilized simulations. The uncertainty range is also narrower in stabilized simulations. Under stabilized global warming scenario, extreme hot event with return period of 100 years in the present climate becomes event occurring every 4.79 (1.5 °C warming level) and 1.56 years (2.0 °C warming level), extreme cold event with return period of 10 years becomes event occurring every 67 years under 1.5 °C warming and is unlikely to occur under 2.0 °C warming. For geographic distribution, the occurrence probabilities of extreme (hot and cold) events mainly change in the Tibetan Plateau, and the extreme cold events also change in Northeast and Southeast China.

Does Dynamic Downscaling Modify the Projected Impacts of Stabilized 1.5°C and 2°C Warming on Hot Extremes Over China?

“…CESM, Community Earth System Model; WRF, Weather Research and Forecasting. including CMIP5 Shi et al, 2018), HAPPI (Sun et al, 2019), and CESM Low-Warming Zhao & Zhou, 2019) simulations. This suggests that GCMs are basically able to capture the trend of hot extremes due to 1.5°C and 2°C warming over China.…”

“…All rights reserved. the stabilized and transient warming are quite different (King et al, 2020;Sun et al, 2019;Zhao and Zhou, 2019).…”

“…Unfortunately, China is projected to have a higher warming than the global mean in both the 1.5°C and 2°C warming scenarios (Hu et al., 2017; Li, Zhou, et al., 2018; Sun et al., 2019). This intensified warming tends to further exacerbate hot extremes, especially over northern China and Tibetan Plateau (Li, Zhou, et al., 2018; Shi et al., 2018; Sun et al., 2019). However, the risks could be substantially avoided for China by limiting global warming to 1.5°C (e.g., Chen & Sun, 2018; Li, Zhou, et al., 2018; Lin et al., 2018; Yang et al., 2018).…”

“…China is highly vulnerable to climate change, and has suffered from growing hot extremes in recent decades (Song et al., 2015; Sun et al., 2014; Zhou et al., 2019). Unfortunately, China is projected to have a higher warming than the global mean in both the 1.5°C and 2°C warming scenarios (Hu et al., 2017; Li, Zhou, et al., 2018; Sun et al., 2019). This intensified warming tends to further exacerbate hot extremes, especially over northern China and Tibetan Plateau (Li, Zhou, et al., 2018; Shi et al., 2018; Sun et al., 2019).…”

Expansion of drylands in China with an additional half a degree warming

“…Among variables used to calculate PET, surface downwelling longwave radiation and air temperature, including mean, maximum and minimum, have the highest SNRs (Figure 9). The regional warming in China, especially over drylands (Huang et al ., 2017), has a rate that is much faster than the global mean air temperature (see Figure S2; Sun et al, 2019). In addition, the significant increase of maximum air temperature is suggested to make the largest contribution to the increase of PET, for example, over the Yellow River basin (Jian et al ., 2020).…”