Based on climate simulations and a formal detection and attribution analysis accounting for non-linear temporal behavior, we found that human-produced greenhouse gases and particulate atmospheric pollution have influenced, together, global and annual changes in temperature, precipitation and regional aridity in two distinct ways, both statistically identifiable in observations. The dominant signal is characterized by global warming, an intensification of the wet-dry rainfall patterns and a progressive large-scale continental aridity – all largely driven by a slowly evolving increase in anthropogenic greenhouse gas emissions. The second signal captures a temperature contrast between the Northern and Southern Hemispheres yielding to changes in the position of the tropical rain belt. This secondary signal is mainly controlled by the cooling influence of reflective particulate pollution emitted from Europe and North America up until the Clean Air Act, in the 1980s.

In this presentation, we will explore the opposite end of the extreme event spectrum and focus on the changes in extreme precipitation derived from daily rainfall data. Previous studies have shown that climate models project a general intensification of extreme rainfall events in response to global warming associated with the increase of greenhouse gases in the atmosphere. This intensification seems consistent with an intensification of extreme precipitation observed over many land areas in recent decades. In our study, we will investigate the underlying causes of the recent observed changes in extreme precipitation. We will also perform a number of sensitivity studies to explore the robustness of the fingerprint results to model uncertainties, the choice of precipitation extreme indices, and the quality of the observational datasets.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.