Suchergebnisse

Abstract: Australia is adversely affected by global warming (GW), as its well-known cycles of droughts, floods, and extreme weather events are increasingly amplified by GW. Here, the focus is the impacts of GW on populous southeast Australia. Machine Learning attribution techniques have been applied to identify the main drivers of these impacts. This article presents the detection examples of the most relevant drivers, individually and in combination, responsible for observed trends in precipitation and temperature due to GW.