Case studies of atmospheric rivers over China and Australia: new insight into their rainfall generation

Abstract

While the Australia–Asian (A-A) monsoon is a prominent feature of weather and climate in China and Australia, there are significant differences in their dominant weather patterns and climate drivers. In order to explore different characteristics of atmospheric rivers (ARs) affecting weather and climate in these two countries, this paper compares two typical AR events that occurred in the boreal summer (austral winter) in 2016. The event in China produced record-breaking rainfall in North China, whereas the event in Australia was accompanied by a classic Northwest Cloud Band (NWCB) and produced a rainfall belt across the continent. Using global reanalysis products and ground-based observational data, we analysed the synoptic backgrounds, vertical structures, water vapour sources and relationship between ARs and cloud distributions. In both China and Australia, heavy precipitation was triggered by strong water vapour transport by ARs ahead of midlatitude frontal systems. The main differences between these two AR events and their associated rainfall effectiveness were that (i) the AR intensity in the Asian summer monsoon was stronger than that in the austral winter season over Australia; (ii) the centre of AR maximum moisture transport in China was around 850hPa, whereas in Australia, it was located at around 700hPa; and (iii) the AR-induced rainfall was heavier in China than in Australia. These differences were caused by numerous factors, including a lack of topographic influence, a dry climate background in Australia, and different interactions between warm and moist air conveyed by ARs from the tropics with cold air from the midlatitudes. We paid particular attention to the relationship between the Australian AR and its associated cloud structure and rainfall to understand precipitation efficiency of the NWCB. In addition, we assessed the forecast skills of an Australian numerical weather prediction system (ACCESS-APS2) for the two events with different lead times. The model produced reasonable forecasts of the occurrence and intensity of both AR events several days in advance, and the AR forecast skill was better than its forecasts of rainfall location and intensity. This demonstrates the value of using AR analysis in guiding extreme rainfall forecasts with longer lead time.