Contribution of Historical Global Warming to Local‐Scale Heavy Precipitation in Western Japan Estimated by Large Ensemble High‐Resolution Simulations

Kawase, Hiroaki; Imada, Yukiko; Sasaki, Hirokazu; Nakaegawa, Tosiyuki; Murata, Akihiko; Nosaka, Masaya; Takayabu, Izuru

doi:10.1029/2018jd030155

Cited by 42 publications

(36 citation statements)

References 38 publications

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“…Attribution of long-term trends in heavy rain frequency Figure 3 shows the difference in heavy rainfall frequency (the number of days with more than 100 mm per day) during the month of July from 1981 to 2010 between the HIST and NonW simulations. It was noted that the impact of global warming on the July 1981-2010 heavy rainfall frequency was strong on the western side of the mountain range because the moisture increase due to warming have directly enhanced the southwesterly moisture convergence 18 . However, the difference was relatively small in eastern Kyushu and the regions of Japan's Inland Sea.…”

Section: Validation Of Model Reproducibilitymentioning

confidence: 99%

“…However, the difference was relatively small in eastern Kyushu and the regions of Japan's Inland Sea. In the eastern Kyushu region, large uncertainty in simulated TCs prevented accurate detection of significant differences 18 . In the coastal regions of Japan's Inland Sea, heavy rainfall differences were difficult to attribute to climate warming due to various kind of atmospheric variabilities from year to year.…”

Section: Validation Of Model Reproducibilitymentioning

confidence: 99%

“…Three-day accumulated rainfall was used for Case2018, and daily rainfall was used for Case2017 and Case1993. Because the simulated daily rainfall of 20 km-RCM slightly underestimated the frequency of intense rainfall of more than 200 mm per day 18 , we used a 50-year return value of each 3000 sample (100 members from 1981 to 2010) of the HIST simulation as a threshold instead of observed values (see 'Methods'). Contrary to Fig.…”

Section: Validation Of Model Reproducibilitymentioning

confidence: 99%

“…Globally, the moisture increase caused by warmer atmospheric temperature agrees with the Clausius-Clapeyron relation of~7% saturated vapour per Kelvin 1 ; however, local precipitation changes are influenced by moisture availability, atmospheric circulation and vertical stability 16,17 . Furthermore, disastrous extreme precipitation is often strongly linked to mesoscale rainfall systems and specific geography 18 . Precipitation takes different forms of atmospheric circulation in each location.…”

Section: Introductionmentioning

confidence: 99%

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Advanced risk-based event attribution for heavy regional rainfall events

et al. 2020

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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.

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Section: Validation Of Model Reproducibilitymentioning

confidence: 99%

Section: Validation Of Model Reproducibilitymentioning

confidence: 99%

Section: Validation Of Model Reproducibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced risk-based event attribution for heavy regional rainfall events

et al. 2020

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“…The d4PDF, which contains a global climate simulation with about 60 km grid spacing and a regional climate simulation with 20 km grid spacing (hereafter referred to as d4PDF20), is a useful dataset for evaluating the impact of global warming on extreme events, such as heat waves (Imada et al 2019), heavy rainfalls (Kawase et al 2019b;Ohba and Sugimoto 2019), and heavy snowfalls (Kawase et al 2016). However, d4PDF20 has insufficient grid spacing to resolve the complex mountains of Japan.…”

Section: Introductionmentioning

confidence: 99%

Changes in extremely heavy and light snow-cover winters due to global warming over high mountainous areas in central Japan

Kawase

Yamazaki

Sugimoto

et al. 2020

Prog Earth Planet Sci

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To investigate future changes in snow cover and snowfall over mountainous areas in central Japan, we conducted regional climate projections using a high-resolution non-hydrostatic regional climate model (NHRCM) with 5 km and 1 km grid spacings. Boundary conditions are derived from the database for Policy Decision making for Future climate change (d4PDF) 20 km regional climate projections (d4PDF20). The d4PDF20 assumes two future climates when global mean surface air temperatures are approximately 2 K and 4 K warmer than in the preindustrial period. Experiments with 5 km grid spacing are conducted by NHRCM for 372 years in d4PDF20 in each climate. Experiments with 1 km grid spacing are performed focusing on 5 years with heavy, median, and light snow cover of mountainous areas in each climate. In the years with heavy snow cover in 2 K and 4 K warming climates, snowfall is enhanced from late December to February at more than 2000 m above sea level (mASL) in the northern parts of Japan's Northern Alps, resulting in heavy snow cover comparable to that in the present climate. Heavy daily snowfall remarkably increases due to global warming in the years with heavy snow cover. At low elevations below 500 mASL, snowfall decreases in all ranges of snowfall intensity in the 4 K warming climate, while the frequency of heavy daily snowfall increases in the 2 K warming climate. Precipitation is enhanced around the Japan-Sea Polarairmass Convergence Zone and the mountainous area facing the Sea of Japan, resulting in strengthened heavy snowfall at high elevations where the winter mean temperature is approximately − 10°C in the present climate. On the other hand, remarkable reductions in snow cover and snowfall are projected in years with light snow cover. Our results indicate that global warming causes heavy and light midwinter snowfalls at high elevations of Japan's Northern Alps that are more extreme than those in the present climate.

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Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

Baba

2022

Atmospheric Science Letters

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Hindcast experiments were performed for heavy rainfall events over Japan in July 2020 using a regional atmospheric model and a regional coupled model to examine the importance of ocean prediction for predicting heavy rainfall events. Both models were able to predict the first peak of accumulated rainfall over western Japan occurring in the first half of July. However, only the coupled model predicted the second peak that occurred in the second half of July. Sea level pressure (SLP) and low-level moisture inflow originating from an existing atmospheric river (AR) were found to differ in each model. In the regional atmospheric model, the error associated with the inaccurate low-level moisture inflow grew with rising excessive latent heat flux, which enhanced convection and resulted in incorrect SLP patterns. This trend seems to be enhanced by having a prescribed sea surface temperature (SST), which affects the surface heat flux. When ocean conditions are predicted as in the coupled model, such error growth is suppressed by changes in SST that adjust surface heat flux, and it leads to generation of the correct SLP patterns. With correct SLP especially for Pacific high in this case, favorable conditions for inflow from the AR can also be predicted, thus making it possible to predict the heavy rainfall. In conclusion, considering the atmospheric feedback on SST, ocean prediction can improve the predictability of heavy rainfall over Japan, the conditions for which are influenced by the nearby AR. Ocean prediction may therefore extend the range of weather forecasting.

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Contribution of Historical Global Warming to Local‐Scale Heavy Precipitation in Western Japan Estimated by Large Ensemble High‐Resolution Simulations

Cited by 42 publications

References 38 publications

Advanced risk-based event attribution for heavy regional rainfall events

Advanced risk-based event attribution for heavy regional rainfall events

Changes in extremely heavy and light snow-cover winters due to global warming over high mountainous areas in central Japan

Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

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