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Dynamical Downscaling Simulation and Future Projection of Extreme Precipitation Activities in Taiwan During the Mei-Yu Seasons

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Dynamical Downscaling Simulation and Future Projection of Extreme Precipitation Activities in Taiwan During the Mei-Yu Seasons AprilJournal 2019 of the Meteorological Society of Japan, 97W.-R.(2), 481−499, HUANG 2019. et al. doi:10.2151/jmsj.2019-028 481 Dynamical Downscaling Simulation and Future Projection of Extreme Precipitation Activities in Taiwan during the Mei-Yu Seasons Wan-Ru HUANG, Po-Han HUANG, Ya-Hui CHANG Department of Earth Sciences, National Taiwan Normal University, Taiwan Chao-Tzuen CHENG National Sciences and Technology Center for Disaster Reduction, Taiwan Huang-Hsiung HSU, Chia-Ying TU Research Center for Environmental Changes, Academia Sinica, Taiwan and Akio KITOH Japan Meteorological Business Support Center, Tsukuba, Japan (Manuscript received 4 August 2018, in final form 21 December 2018) Abstract By using the Weather Research and Forecasting (denoted as WRF) model driven by two super-high-resolution global models, High Resolution Atmospheric Model (denoted as HiRAM) and Meteorological Research Institute Atmospheric General Circulation Model (denoted as MRI), this study investigates the dynamical downscaling simulation and projection of extreme precipitation activities (including intensity and frequency) in Taiwan during the Mei-Yu seasons (May and June). The analyses focus on two time period simulations: the present-day (1979 – 2003, historical run) and the future (2075 – 2099, RCP8.5 scenario). For the present-day simulation, our results show that the bias of HiRAM and MRI in simulating the extreme precipitation activities over Taiwan can be reduced after dynamical downscaling by using the WRF model. For the future projections, both the dynamical downscal- ing models (i.e., HiRAM-WRF and MRI-WRF) project that extreme precipitation will become more frequent and more intense over western Taiwan but less frequent and less intense over eastern Taiwan. The east-west contrast in the projected changes in extreme precipitation in Taiwan are found to be a local response to the enhancement of southwesterly monsoonal flow over the coastal regions of South China, which leads to an increase in water vapor convergence over the windward side (i.e., western Taiwan) and a decrease in water vapor convergence over the leeward side (i.e., eastern Taiwan). Further examinations of the significance of the projected changes in extreme precipitation that affect the agriculture regions of Taiwan show that the southwestern agriculture regions will be affected by extreme precipitation events more frequently and more intensely than the other subregions. This finding highlights the importance of examining regional differences in the projected changes in extreme pre- cipitation over the complex terrain of East Asia. Corresponding author: Wan-Ru Huang, Department of Earth Sciences, National Taiwan Normal University, No. 88, Sec. 4, Tingchou Rd., Wenshan District, Taipei 11677, Taiwan R.O.C. E-mail: [email protected] J-stage Advance Published Date: 10 January 2019 ©The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0). 482 Journal of the Meteorological Society of Japan Vol. 97, No. 2 Keywords dynamical downscaling; extreme precipitation; future projection; East Asia Citation Huang, W.-R., P.-H. Huang, Y.-H. Chang, C.-T. Cheng, H.-H. Hsu, C.-Y. Tu, and A. Kitoh, 2019: Dynam- ical downscaling simulation and future projection of extreme precipitation activities in Taiwan during the Mei-Yu seasons. J. Meteor. Soc. Japan, 97, 481–499, doi:10.2151/jmsj.2019-028. reports (IPCC 2013), most global models project that 1. Introduction extreme precipitation is very likely to become more Extreme precipitation has always been a crucial intense and more frequent over most of the mid-lati- research subject in many countries (e.g., Endo et al. tude land masses and wet tropical regions. However, 2009; Agel et al. 2015; Herman and Schumacher 2016; it is well-known that most global models cannot Loriaux et al. 2017). In Taiwan, long-term climatic capture the regional precipitation features over areas statistics show that the Mei-Yu season (May and June, with complex terrains well (Huang and Wang 2017). or MJ) is one of the major precipitation periods, and Therefore, more studies tend to assess the future approximately 26.3 % of annual precipitation falls changes in precipitation in Taiwan by using dynamical during this period (estimated from Fig. 1 of Chen downscaling methods (i.e., using the outputs of global et al. 2004). Although the Mei-Yu season precipitation models to drive a higher-resolution regional model) can provide the water supply for people’s livelihoods (e.g., Huang et al. 2016a, b, c). For example, Huang in Taiwan, it is common to see extreme precipitation et al. (2016a, b) used Weather Research and Forecast- events during this period bring disastrous floods ing (WRF) driven by two super-high-resolution global and result in economic losses to agriculture. For models and noted that the dynamical downscaling example, heavy rainfall induced by the Mei-Yu front approach adds valuable information in the present-day system from June 2 to June 4, 2017 caused significant simulation of diurnal precipitation over Taiwan and damage in Taiwan. Approximately 6,000 hectares of nearby regions (including South China and Luzon). cropland were destroyed, and the economic losses Huang et al. (2016a, b) and others (e.g., Huang and from this natural disaster reached approximately 9 Wang 2017) showed that most global models have million (U.S. dollars) according to the Council of problems capturing the right timing of the appearance Agriculture, Executive Yuan in Taiwan. Extensive of the diurnal precipitation maximum in Taiwan. studies examining the observational data have found Such a bias can be reduced using the WRF dynamical that Mei-Yu season extreme precipitation in Taiwan is downscaling simulation (Huang et al. 2016a, b). As mostly attributed to the frontal types of precipitation inferred from these documented studies, the use of a events (including the rainstorms embedded in the WRF dynamical downscaling approach might be a frontal system) (e.g., Chen et al. 1989; Chen and Chen good method to reduce the bias of global models in 2003; Yeh and Chen 2004; Chen et al. 2011; Wu et al. simulating the Mei-Yu season extreme precipitation 2016). Huang and Chen (2015) further noted that activities over Taiwan. This issue has not been exam- the occurrence frequency of frontal precipitation in ined by Huang et al. (2016a, b) or other studies, and it Taiwan declined (a negative trend) during the Mei-Yu is examined herein. seasons of 1982 – 2012, which was due to the changes The main objectives of this study are as follows: (1) in the East-Asian monsoonal circulation over the past to clarify whether the WRF dynamical downscaling several decades. It is likely that future changes in the approach can add valuable information when simulat- East-Asian monsoonal circulation might also play an ing the Mei-Yu season extreme precipitation activities important role in affecting the Mei-Yu season extreme (including intensity and frequency) in Taiwan; (2) to precipitation activities in Taiwan. clarify whether the projected changes in the Mei-Yu Many studies have compared the present-day season extreme precipitation activities in Taiwan are simulation and the future projection of extreme location dependent; and (3) to clarify whether the precipitation over various countries by using global findings of issues (1) and (2) are dependent on the models to assess future climate changes (e.g., Walsh models. The analyses were particularly focused on the et al. 2008; Dulière et al. 2011; Shi and Durran 2015; projected changes in extreme precipitation activities Suzuki et al. 2015; Park et al. 2016). According to the over agricultural regions because it can provide useful Intergovernmental Panel on Climate Change (IPCC) information to the local government when establishing April 2019 W.-R. HUANG et al. 483 long-term disastrous prevention policies. Detailed information on the models, the observational data, and the analysis methods are introduced in Section 2. Com- parisons of the abilities of the models to simulate the extreme precipitation activities in Taiwan during the present-day (1979 – 2003, under the historical run) are presented in Section 3. The projected changes, which are defined as the difference between the future projec- tion at the end of the 21st century (2075 – 2099, under the RCP8.5 scenario) and the present-day simulation, are documented in Section 4. A discussion of the pos- sible causes of the projected changes in the extreme precipitation activities in Taiwan is given in Section 5. A concluding remark is provided in Section 6. 2. Models, observations, and methodology Fig. 1. Geographic location of Taiwan and model 2.1 Models domain (dark pink box) used in WRF simula- Throughout this study, the analyses are based on tions. The land areas are shaded in gray. two time period simulations at the present-day (1979 – 2003, under the historical run) and at the end of the 21st century (2075 – 2099, under the RCP8.5 scenario). (Collins et al. 2004) is used and the RCP8.5 green- Here the output from two super-high-resolution global house gas (GHG) concentration is considered in the models are adopted to drive the regional model in longwave radiation calculation. No cumulus param- higher spatial resolution (i.e. dynamical downscaling eterizations are applied for reasons given in Huang models). The first global model we used is the High et al. (2016c). Spectral nudging (Miguez-Macho et al. Resolution Atmospheric Model (HiRAM), which has 2004) is applied to the atmospheric conditions only a horizontal resolution of approximately 25 km and and not to the boundary layer. The wavenumber used originated from the Geophysical Fluid Dynamics for the spectral nudging in WRF is 4, the cutoff wave- Laboratory (GFDL) Atmospheric Model version 2.1 length is approximately 2000 km/4 = 500 km, and the (Zhao et al.
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