#detection_and_attribution
#aerosols
#large_ensemble_simulations

In 2022, Xuezhi Tan from Sun Yat-sen University and co-authors conducted a detection & attribution study on the 2020-2021 precipitation deficit and drought in Southeastern China (20-28 N, 110-120 E).

Data:

The observed precipitation were from 86 stations. Reanalysis data from ERA5 were also used to calculate the SPEI drought index.

Large ensemble modeled precipitation were from the HadGEM3-GA6 and CESM LENS simulations. The HadGEM3-GA6 operated at 0.56x0.83 degs resolution and participated in the CLIVAR C20C + Detection and Attribution project. The CESM LENS model had single-forcing exclusion experiments. For the aerosol exclusion case, the model considered no industrial aerosols (1920 level throughout 1920-2080), and no aerosolsl from biomass burning in agriculture and wildfires (1920 level throughout 1920-2029).

Methods:

The detection and attribution was conducted using the probability risk ratio (PR) method, which is one of the standard approaches for event-based detection and attribution.

Main findings:

Aerosol emissions from industrial aerosols and biomass burning aerosols have decreasing impacts on precipitation. The impacts are greater than the increasing impacts of than greenhouse gases, resulting in the net decreasing precipitation.

Aerosol emissions impacts on drought events, as defined by the Standardized Precipitation Index, are also detectable, but not greenhouse gases emissions impacts.

https://www.sciencedirect.com/science/article/pii/S0022169422005716#!