Using temperature and precipitation combined to detect and attribute aerosol effects on large-scale climate

Anthropogenic aerosols (AER) have been found to impact both Earth’s energy and water cycle. Like greenhouse gases (GHG) they are an anthropogenic climate forcing, which will play an important role in shaping Earth’s future climate. To improve future predictions, it is, therefore, fundamental to understand and quantify the individual impacts these two forcings have on the climate system. This can be achieved by using detection and attribution methods facilitating the differentiation of the response of the climate system to different forcings.

Separating the signal of individual anthropogenic effects related to greenhouse gas and aerosol emissions is hindered by large uncertainties in the response to aerosol forcing in different climate models. Thus, in this study we investigate a joint change in temperature and precipitation to reduce the signal-to-noise ratio and better constrain the impact of anthropogenic aerosols since 1979. Building on previous findings on how aerosols affect climate, we focus on shifts in tropical precipitation by tracking wet/dry regions as well as changes in the interhemispheric temperature asymmetry (ITA) and the diurnal temperature range (DTR), due to its unique response to different radiative forcings. Individual fingerprints are derived from large-ensembles of historical single-forcing simulations from three models that are part of phase 6 of the Coupled Model Intercomparison Project (CMIP6).

We find inter-model agreement in the trends for wet regions, ITA, and DTR in single-forcing and historical (all-forcing) runs. Contrasting trends in these time series derived for AER-only and GHG-only simulations suggest that aerosols have offset some of the greenhouse gas induced precipitation and temperature changes in the past.  While a drying in the dry regions can be observed for GHG-only simulations, inter-model agreement is not found for aerosols. Early results show an improved constraint on the detection of a greenhouse gas signal when investigating a joint change in wet and dry regions, which is refined by including the other variables and indicators.