Advanced risk-based event attribution for heavy regional rainfall events

Abstract

Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning.

Extreme Precipitation in 150-year Continuous Simulations by 20-km and 60-km Atmospheric General Circulation Models with Dynamical Downscaling over Japan by a 20-km Regional Climate Model

“…It has been shown that some recent heavy rainfall events, as well as extreme temperature events were influenced by global warming (Imada et al 2020). There is an increasing need for better information regarding the temporal evolution of these extreme events from now to the end of this century, and on the timing of the emergence of the signal associated with any changes above the noise of natural variability (Hawkins et al 2020).…”

Anthropogenic climate change has changed frequency of past flood during 2010-2013

“…Due to smaller signals in the lower level of warming in the recent past compared to future projections, many climate simulations with and without anthropogenic climate change since the industrial revolution have been compared to evaluate the effects of past anthropogenic effects on climate change. For example, the effects of human-induced global warming on past heavy precipitation events (Imada et al 2020;Li et al 2021) and mean and extreme streamflow (Gudmundsson et al 2021) have been investigated using large ensemble climate simulations. Despite evidence of a general increase in extreme precipitation events in the past (Hartmann et al 2013;Min et al 2011), the observed changes in flooding vary with catchment sizes or hydroclimatological characteristics (Do et al 2017;Sharma et al 2018).…”

Atmospheric Rivers Bring More Frequent and Intense Extreme Rainfall Events Over East Asia Under Global Warming

“…In July 2018 and July 2020, ARs passing through East Asia brought extreme rainfall and large social damages to the region (Araki et al., 2021; Hirockawa et al., 2020; Takemura et al., 2019; Tsuguti et al., 2019; Zhao et al., 2021). The higher frequency of AR‐induced natural disasters in recent years suggest a possible influence of ongoing global warming on the occurrence of extreme events (e.g., Imada et al., 2020; Kamae, Shiogama, et al., 2017; Kawase et al., 2020, 2019).…”