Anthropogenic influence on precipitation quantile trends over China

It has been established that the variations/trends in large-scale precipitation over land since the mid-twentieth century can be attributed to forcing changes due to anthropogenic greenhouse gas emissions (Eyring et al.). The detection and attribution (D&A) of changes in regional precipitation regimes, however, remains challenging due to issues such as larger influences from internal variability, as well as larger uncertainty in observed and simulated data (Doblas-Reyes et al.). This study is aimed at exploring the feasibility of attributing the quantile trends in daily precipitation over China to natural and anthropogenic influences, aided by the latest CMIP6 GCM data. The quantile trends in observed and modeled daily precipitation are derived through quantile regression (Koenker and Bassett). The scenarios considered are the historical, natural, anthropogenic greenhouse gas, and anthropogenic aerosol forcings, and the control simulations are employed to reflect natural variability. The D&A is undertaken through the regularized optimal fingerprinting (Ribes et al.). The results show that the increasing trends in winter precipitation at high and extremely high quantile levels, as well as the increasing trends in spring precipitation at all quantile levels, can be attributed to the effects of historical forcing. The effect of anthropogenic greenhouse gas forcing is evident over the domain, to which the increasing precipitation trends at all quantile levels in all seasons can be attributed; this effect can be separated from that of anthropogenic aerosol forcing for winter precipitation trends at high and extremely high quantile levels, and for spring, summer, and autumn trends at low quantile levels. Findings of this research can help improve our knowledge of anthropogenic processes on the climate system, which can further support climate modeling and projections.

 

Reference

Doblas-Reyes, F. J., et al. “Linking Global to Regional Climate Change.” Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by V. Masson-Delmotte et al., Cambridge University Press, 2021.

Eyring, V., et al. “Human Influence on the Climate System.” Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by V. Masson-Delmotte et al., Cambridge University Press, 2021, pp. 423–552, https://doi.org/10.1017/9781009157896.005.

Koenker, Roger, and Gilbert Bassett. “Regression Quantiles.” Econometrica, vol. 46, no. 1, Jan. 1978, p. 33, https://doi.org/10.2307/1913643.

Ribes, Aurélien, et al. “Application of Regularised Optimal Fingerprinting to Attribution. Part I: Method, Properties and Idealised Analysis.” Climate Dynamics, vol. 41, no. 11–12, Dec. 2013, pp. 2817–36, https://doi.org/10.1007/s00382-013-1735-7.