The effect of future reduction in aerosol emissions on climate extremes in China Academic Article uri icon


  • © 2016, Springer-Verlag Berlin Heidelberg. This study investigates the effect of reduced aerosol emissions on projected temperature and precipitation extremes in China during 2031–2050 and 2081–2100 relative to present-day conditions using the daily data output from the Community Earth System Model ensemble simulations under the Representative Concentration Pathway (RCP) 8.5 with an applied aerosol reduction and RCP8.5 with fixed 2005 aerosol emissions (RCP8.5_FixA) scenarios. The reduced aerosol emissions of RCP8.5 magnify the warming effect due to greenhouse gases (GHG) and lead to significant increases in temperature extremes, such as the maximum of daily maximum temperature (TXx), minimum of daily minimum temperature (TNn), and tropical nights (TR), and precipitation extremes, such as the maximum 5-day precipitation amount, number of heavy precipitation days, and annual total precipitation from days ˃95th percentile, in China. The projected TXx, TNn, and TR averaged over China increase by 1.2 ± 0.2 °C (4.4 ± 0.2 °C), 1.3 ± 0.2 °C (4.8 ± 0.2 °C), and 8.2 ± 1.2 (30.9 ± 1.4) days, respectively, during 2031–2050 (2081–2100) under the RCP8.5_FixA scenario, whereas the corresponding values are 1.6 ± 0.1 °C (5.3 ± 0.2 °C), 1.8 ± 0.2 °C (5.6 ± 0.2 °C), and 11.9 ± 0.9 (38.4 ± 1.0) days under the RCP8.5 scenario. Nationally averaged increases in all of those extreme precipitation indices above due to the aerosol reduction account for more than 30 % of the extreme precipitation increases under the RCP8.5 scenario. Moreover, the aerosol reduction leads to decreases in frost days and consecutive dry days averaged over China. There are great regional differences in changes of climate extremes caused by the aerosol reduction. When normalized by global mean surface temperature changes, aerosols have larger effects on temperature and precipitation extremes over China than GHG.

altmetric score

  • 0.25

author list (cited authors)

  • Wang, Z., Lin, L., Yang, M., & Xu, Y.

citation count

  • 29

publication date

  • November 2016