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DOI: 10.2151/sola.17A-002.
J-STAGE Advance published date: Dec. 24, 2020
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SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
1 Enhancement of extremely heavy precipitation induced
2 by Typhoon Hagibis (2019) due to historical warming
3
4 Hiroaki Kawase,1 Munehiko Yamaguchi,1 Yukiko Imada,1 Syugo Hayashi,1
5 Akihiko Murata,1 Tosiyuki Nakaegawa,1 Takafumi Miyasaka,2,1 Izuru Takayabu1
6
7 1 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
8 2Japan Meteorological Business Support Center, Tsukuba, Japan
9
10
11
12
13
14
15
16
17 Corresponding author: Hiroaki Kawase, Meteorological Research Institute, 1-1,
18 Nagamine, Tsukuba 305-0052, JAPAN; E-mail. [email protected] © 2020,
19 Meteorological Society of Japan
20 2 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
21 Abstract
22 Impacts of historical warming on extremely heavy rainfall induced by Typhoon Hagibis
23 (2019) are investigated using a storyline event attribution approach with the Japan
24 Meteorological Agency Nonhydrostatic Model (JMA-NHM). Control experiments based
25 on JMA mesoscale analysis data well reproduce the typhoon’s track, intensity, and heavy
26 precipitation. First, two non-warming experiments are conducted: One excludes both 40-
27 year atmospheric and oceanic temperature trends from 1980 to 2019, and the other
28 excludes the oceanic trend only. A comparison between control and non-warming
29 experiments indicates that historical warming strengthens typhoons and increases the
30 amount of total precipitation by 10.9 % over central Japan. The difference between CTL
31 and non-warming experiments without both atmospheric and oceanic temperature trends
32 is larger than that without just the oceanic trend (7.3 %). Additional sensitivity
33 experiments without Japan’s topography indicate that topography enhances not only total
34 precipitation but also the changes in total precipitation due to historical warming. Through
35 the storyline event attribution approach, it is concluded that historical warming intensifies
36 strength of Typhoon Hagibis (2019) and enhances the extremely heavy precipitation
37 induced by the typhoon.
38
39 (Citation: Kawase, H., and co-authors, 2020: Enhancement of extremely heavy
40 precipitation induced by Typhoon Hagibis (2019) due to historical warming. SOLA, X,
41 XXX-XXX, doi:10.2151/sola.XXXX-XXX.)
42 SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
43 1. Introduction
44 Tropical cyclones (hereafter, referred to as TCs) are typical extreme phenomena
45 and cause heavy precipitation, strong winds, and storm surges. Japan is frequently
46 influenced by TCs formed in the western North Pacific, called typhoons. In October 2019,
47 Typhoon Hagibis (2019) hit eastern Japan, including the Tokyo Metropolitan area, and
48 induced extremely heavy precipitation there. Takemi and Unuma (2020) investigated
49 environmental factors causing heavy rainfall during the passage of Typhoon Hagibis
50 (2019). They pointed out that the combination of a nearly moist-adiabatic lapse rate, moist
51 absolute instability, abundant moisture content, and high relative humidity throughout the
52 troposphere resulted in the heavy rainfall.
53 Many previous studies have investigated the impacts of global warming on TCs
54 since the warmer sea surface temperature (SST) and plenty of moisture develop TCs.
55 Studies on the detection and attribution of TCs and future TC changes are summarized in
56 each oceanic basin (Knutson et al. 2019, 2020; Lee et al. 2020; Cha et al. 2020). The
57 number of typhoons approaching Japan will decrease in the future climate, while the
58 precipitation amount induced by strong typhoon will increase (Watanabe et al. 2019;
59 Hatsuzuka et al. 2019; Miyasaka et al. 2020). A pseudo-global warming (PGW)
60 experiment is one method of evaluating the impacts of global warming in the future
61 climate on past TCs. This approach has been applied to typhoons hitting Japan such as
62 Typhoons Vera (1959), Songda (2004), Talas (2011), Canthu (2016), and Lionrock (2016)
63 (Ito et al. 2016; Takemi et al. 2016; Kanada et al. 2017a, 2017b; Nayak et al. 2019; Takemi
64 2019). These studies indicated that extreme typhoons got stronger under the future global
65 warming scenario. Takemi (2019) pointed out that increased precipitable water led to
66 increased rainfall produced by Typhoon Talas (2011) despite the stabilized atmospheric 4 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
67 condition in the future climate; Takemi (2019) also indicated that global warming could
68 enhance long-lasting heavy rainfall caused by slow-moving typhoons.
69 The PGW approach evaluating the impact of historical climate changes on
70 individual extreme events is a kind of storyline event attribution (hereafter, referred to as
71 storyline EA), which investigates the impact on an event intensity, in distinction from the
72 risk-based EA, which investigates the impact on an event frequency (Shepherd et al.
73 2018). Storyline EAs aimed at TC-induced extremes show significant impacts of global
74 warming on heavy rainfall amounts, wind speeds, and storm surges (Takayabu et al. 2015;
75 Patricola and Wehner 2018; Reed et al. 2020). Takayabu et al. (2015) stated that Typhoon
76 Haiyan (2013), which made landfall in Philippines, could become stronger than in the
77 hypothetical preindustrial condition.
78 Typhoon Hagibis (2019), which slowly approached and passed through Japan,
79 brought heavy precipitation for more than 24 hours in eastern Japan. This study applies
80 the storyline EA to Typhoon Hagibis (2019) and investigates the impacts of historical
81 warming on Typhoon Hagibis (2019) and the typhoon-induced heavy precipitation.
82 Additionally, we discuss the sensitivity of the typhoon-induced precipitation to historical
83 atmospheric and oceanic temperature changes, and the effect of topography.
84 2. Experimental design
85 The basic methodology follows that of Kawase et al. (2020), who applied the
86 storyline EA to the Heavy Rain Event of July 2018 in Japan. Numerical experiments were
87 conducted using the Japan Meteorological Agency Nonhydrostatic Model (JMA-NHM;
88 Saito et al. 2007) with 5 km grid spacing. The specifications of NHM used in this study
89 are summarized in Table S1. The initial and lateral boundary conditions of control SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
90 experiments (hereafter, referred to as CTL) are obtained from the JMA mesoscale analysis
91 data (hereafter, referred to as MA). A model domain and the topography around the
92 Kanto-Koshin region are shown in Fig. 1. We conducted ensemble experiments with four
93 initial conditions—00UTC, 03UTC, 06UTC and 09UTC—on October 9, 2019.
94 The Japanese 55-year Reanalysis (JRA-55) (Kobayashi et al. 2015) shows a rapid
95 atmospheric warming around Japan since 1980 (Fig. S1). Non-warming experiments are
96 based on the MA in which the regional mean atmospheric temperature at each vertical
97 level and oceanic temperature trends from 1980 to 2019 are excluded (hereafter, referred
98 to as NonW40), which are derived from JRA-55 and Global Sea Surface Temperature
99 Analysis Data (COBE-SST) (Ishii et al. 2005), respectively (Table 1). Sensitivity
100 experiments using only SST trends are referred to as NonW40_SST. Five types of
101 temperature trends are prepared: monthly means of August, September, and October; and
102 three-month means of August-September-October (ASO) and September-October-
103 November (SON). The ensemble experiments using five different time windows were
104 conducted to reduce uncertainty in the trend estimation. The total number of ensemble
105 experiments is 20. Vertical profiles of the temperature trends are shown in Fig. 1c. Here,
106 we excluded the trend in only November when the number of typhoons rapidly decreases.
107 The relative humidity in NonW40 is the same as that in CTL as with previous studies
108 (e.g., Takemi 2019). The geopotential height is modified to be suitable to atmospheric
109 temperature changes.
110 Takemi and Unuma (2020) pointed out that topography-induced ascent was
111 considered to be the trigger of convection causing heavy precipitation. We conducted CTL
112 and NonW40 without Japan’s topography (hereafter, referred to as CTL_NOTP and
113 NonW40_NOTP, respectively) to investigate the impact of Japan’s topography on the 6 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
114 precipitation changes due to historical warming.
115 The uncertainty of historical warming is crucial for studies on the attribution of
116 extreme events to global warming. We also evaluate the impact of historical warming
117 since the preindustrial period on Typhoon Hagibis (2019) using the Database for Policy
118 Decision-Making for Future Climate Change (d4PDF; Mizuta et al. 2017), which includes
119 100 ensembles of historical and non-warming experiments. This study used extension
120 data of d4PDF from 2008 to 2017 (Imada et al. 2019). We conduct the experiments using
121 the MA in which the differences in regional mean temperature between the historical and
122 non-warming experiments in August, September, October, ASO, and SON are excluded
123 (hereafter, NonW_d4PDF) (Table 1).
124 To detect the central position of the typhoon at a given time, we first calculate a sea
125 level pressure (SLP) averaged over 5 x 5 grids (i.e., 25 km x 25 km). Then the central
126 grid of the 5 x 5 grids domain with the minimum mean SLP is defined as the central
127 position of the typhoon.
128 Hourly radar/rain gauge–analyzed rainfall provided by the JMA is used to compare
129 with precipitation simulated by CTL.
130 3. Results
131 3.1 Simulation skills
132 Large amounts of precipitation are calculated by CTL around the path of Typhoon
133 Hagibis (2019) and over the mountains in the Kanto-Koshin region (Fig. 2a). The total
134 precipitation from 00UTC on October 10 to 23UTC on October 13, 2019, is well
135 simulated by CTL compared to the observation (Fig. 2b–c). The regional mean total
136 precipitation in CTL is 26.2 mm (approximately 10 %) less than the observation (Table SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
137 2). The typhoon tracks in CTL follow the best-track data of the JMA until the typhoon
138 passes through the Kanto-Koshin region (Fig. 2a). The typhoon’s landfall and the peak of
139 precipitation are, however, delayed by 5–6 hours in CTL. If the peak of the observed
140 precipitation is modified to fit CTL, the time series of hourly precipitation simulated by
141 CTL is similar to that observed (Fig. 3). East of Japan, the simulated typhoon passes
142 through a slightly more southerly path as compared to the best-track data. NonW40
143 simulates typhoon tracks similar to those in CTL (Fig. 2a).
144 The differences between the simulated and observed minimum SLPs, which
145 represent the strength of the typhoon, are 5 and 3 hPa when the typhoon reached around
146 28.5°N and made landfall in Japan, respectively (Table 2). These differences are
147 comparable to the intervals of the SLPs analyzed in the best-track data. Considering the
148 current status of TC intensity prediction accuracy (e.g., DeMaria et al. 2014; Yamaguchi
149 et al. 2018), an error of less than 5 hPa is a high accuracy.
150 3.2 Impact of historical warming on typhoon-induced precipitation, typhoon
151 strength, and moisture
152 The total precipitation is widely greater in CTL than that in NonW40, while
153 precipitation slightly decreases at the left (west) of the typhoon track in Japan (Fig. 2e).
154 The total precipitation is 10.9 % greater in CTL than that in NonW40 in the Kanto-Koshin
155 region (Table 2). The regional mean hourly precipitation in NonW40 is less than that in
156 CTL, especially around the peak of precipitation (Fig. 3). The total precipitation in
157 NonW40_SST is also shown to be less than that in CTL (Fig. 2f and Table 2), indicating
158 that the lower SST can inhibit the heavy precipitation induced by Typhoon Hagibis (2019).
159 Minimum SLPs of typhoons are higher in NonW40 than those in CTL, not only
160 when the typhoon exists over the Pacific Ocean but also when the typhoon makes landfall 8 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
161 in Japan (Fig. 4a and Table 2), which means that Typhoon Hagibis (2019) was
162 strengthened due to historical warming. It is noteworthy that minimum SLPs in
163 NonW40_SST are comparable to those in NonW40 north of 30°N. It is pointed out that a
164 high SST due to global warming is most important for the development of strong
165 typhoons (Takemi et al. 2016; Kanada et al. 2020). NonW40, on the other hand, simulates
166 larger precipitation changes than does NonW40_SST (Table 2). The atmospheric
167 temperature changes also affect the typhoon-induced precipitation in addition to the
168 strengthened Typhoon Hagibis (2019).
169 We calculated the maximum total precipitable water during the whole calculation
170 term (hereafter, referred to as maxTPW) in each grid. High maxTPW over 90 mm appears
171 along the typhoon track in CTL (Fig. 4b). The maxTPW increases by approximately 7 %
172 (light and deep orange) around the Kato-Koshin region and partly increases by more than
173 10 % (purple) along the typhoon track (Fig. 4c) in NonW40. The percentages of increase
174 in maxTPW on the right side of the typhoon relative to the moving direction is larger than
175 that on the left side; this is consistent with precipitation changes (Fig. 2e). The percentage
176 of change in water vapor estimated by the Clausius-Clapeyron relation is approximately
177 6.65 % in NonW40 when the regional mean air temperature rises by 0.95 K in NonW40
178 (Table 2).
179 In NonW40_SST, changes in the horizontal distribution of maxTPW completely
180 differ (Fig. S2), although the minimum SLPs in NonW40_SST are comparable to those
181 in NonW40. The remarkable difference in maxTPW appears only along the typhoon path
182 and maxTPW differs by 3–4 % over the Kanto-Koshin region due to SST warming. The
183 impact of SST changes on maxTPW is limited to around the typhoon’s path.
184 We focus on the duration from 06 UTC to 21 UTC October 12, 2019, when the SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
185 typhoon approaches and makes landfall in Japan and heavy precipitation occurred in the
186 Kanto-Koshin region. Heavy precipitation occurs north of the typhoon, especially over
187 the mountainous areas, and is enhanced near the center of the typhoon, and north and east
188 of the typhoon (Fig. 5b). In contrast, precipitation decreases along the northwestern
189 boundary of heavy precipitation areas.
190 Upward flow at 700 hPa prevails over the Kanto-Koshin region and east of the
191 Kanto-Koshin region (Fig. 5c). The windward and leeward sides of Japan’s mountains
192 produce strong upward and downward flows, respectively. The upward and downward
193 flow anomalies appear on the right and left sides of the typhoon relative to the moving
194 direction in NonW40 (Fig. 5d), where the upward and downward flows prevail in CTL,
195 respectively (Fig. 5c). These changes are consistent with the precipitation changes (Fig.
196 5b). On the leeward side of the mountains, the inhibition of precipitation related to
197 enhanced downward flow would exceed the increase in precipitation related to
198 moistening due to warming.
199 4. Discussion
200 4.1 Topographic effects on precipitation changes due to historical warming
201 In CTL_NOTP, the horizontal distribution of total precipitation is smoother than
202 that of CTL because there is no topography-induced precipitation (Fig. 2g). However,
203 CTL_NOTP simulates large amounts of precipitation north of typhoon’s track, which is
204 related to the local front on the north side of the typhoon when the typhoon transforms
205 into an extratropical cyclone (Araki 2020). Araki et al. (2020) quantitatively evaluated
206 the impact of topography on the heavy precipitation and discussed the mechanism of the
207 enhanced precipitation. Our experiments also indicated that the total precipitation is 10 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
208 enhanced approximately 26 % by the topography in the Kanto-Koshin region (Table 2).
209 The horizontal patterns of precipitation changes by historical warming are similar
210 to those in the experiments with topography (Fig. 2g). On the other hand, we found that
211 the experiments with topography enhanced the precipitation induced by Typhoon Hagibis
212 (2019) over the Kanto-Koshin region more than did the experiments without topography.
213 The difference in the regional mean precipitation over the Kanto-Koshin region is 6.8 %
214 between CTL_NOTP and NonW40_NOTP, which is less than the difference between
215 CTL and NonW40 (10.9 %). The value of 6.8 % is similar to the percentage of change in
216 water vapor estimated by the Clausius-Clapeyron relation in NonW40 (6.65 %), as stated
217 above. The enhancement of precipitation near the center of the typhoon due to historical
218 warming (Knutson et al. 2020) and the enhancement of topography-induced precipitation
219 related to the increased water vapor flux can synergistically lead to the increase in heavy
220 precipitation of Typhoon Hagibis (2019).
221 4.2 Uncertainty in the definition of historical warming
222 We have evaluated the impact of historical warming on Typhoon Hagibis based on
223 the JRA-55. However, the JRA-55 includes not only climatic changes due to increased
224 greenhouse gases but also natural decadal variability, such as the Pacific Decadal
225 Oscillation. Uncertainty in the estimation of historical warming is crucial for studies on
226 the attribution of extreme events to global warming. Lastly, we utilize the d4PDF and
227 evaluate the impact of anthropogenic global warming since the preindustrial period on
228 Typhoon Hagibis (2019).
229 The horizontal distribution of changes in total precipitation in NonW_d4PDF is
230 quite similar to that in NonW40 (Fig. 2e and 2h), although the atmospheric temperature
231 changes are different from those in the JRA-55 (Fig. 1c). Precipitation increases by SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
232 13.6 % over that in the Kanto-Koshin region (Table 2), which is larger than that estimated
233 by the JRA-55. This is because the temperature differences in NonW_d4PDF are larger
234 than those in NonW40 (Fig. 1c). Precipitation slightly decreases on the left (west) side of
235 the typhoon, as with the other experiments.
236 The ensemble mean minimum SLP in NonW_d4PDF is 939.5 hPa and 956.9 hPa
237 around 28.5°N and 35°N, respectively, which are comparable to NonW40, although the
238 differences in SST in NonW_d4PDF are approximately 0.4 K larger than those in
239 NonW40 (Table 2). The discrepancy in the relationship between changes in the strength
240 of the typhoon and changes in the SST would be derived from changes in the atmospheric
241 stability (Takemi and Unuma 2020; Kawase et al. 2020). Changes in the atmospheric
242 temperature of d4PDF show larger warming in the upper troposphere (Fig. 1c), meaning
243 that atmospheric stability increases due to historical warming. This stabilization can
244 weaken the impacts of SST warming and moistening on the strength of typhoons.
245 Similar patterns of changes in typhoon-induced precipitation were simulated by
246 using two types of historical warming, i.e., 40-year trends in the JRA55 and climate
247 changes from the preindustrial period up to the present in d4PDF, despite different vertical
248 profiles of air temperature. These results indicate that historical warming including the
249 effects of increased anthropogenic greenhouse gases, contributes to the enhancement of
250 heavy precipitation induced by Typhoon Hagibis (2019).
251 5. Summary
252 This study investigated impacts of historical warming on extremely heavy
253 precipitation induced by Typhoon Hagibis (2019) using a storyline EA approach. The
254 control experiments well reproduced the typhoon’s track, intensity, and the amounts of 12 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
255 precipitation. Sensitivity experiments without atmospheric and oceanic trends from 1980
256 to 2019 showed that the strength of Typhoon Hagibis (2019) was intensified and the
257 typhoon-induced precipitation was increased by 10.9 % over the Kanto-Koshin region
258 due to historical warming. Sensitivity experiments without just the oceanic temperature
259 trends also showed precipitation changes (7.3 %), which is smaller than 10.9 %. The
260 experiments without Japan’s topography, which showed a 6.8 % increase in precipitation,
261 indicated that the impact of historical warming on precipitation changes could be
262 enhanced by mountains in the Kanto-Koshin region.
263 Previous studies pointed out the slowdown of TC related to the northward shift of
264 the subtropical jet due to global warming (Yamaguchi and Maeda 2020; Yamaguchi et al.
265 2020; Kanada et al. 2020). However, it will be a further study because our experiments
266 only modify the vertical profiles of air temperature and SST. The storyline EA can
267 quantitatively evaluate the impact of global warming on the typhoon. To probabilistically
268 evaluate the impacts of historical warming on typhoons approaching Japan, risk-based
269 EAs using large global ensemble experiments are needed (e.g., Stott et al. 2004; Imada et
270 al. 2014, 2020; Shiogama et al. 2016). Furthermore, higher-resolution experiments
271 without convective parameterization are required to represent small-scale convections
272 (Takayabu et al. 2015). Although some future works remain, the present study
273 undoubtedly shows that historical atmospheric and oceanic warming contributes to the
274 intensification of extremely heavy precipitation induced by Typhoon Hagibis (2019).
275 Acknowledgments
276 This research was supported by JSPS KAKENHI Grant Number 19H05697 and by the
277 Integrated Research Program for Advancing Climate Models (TOUGOU) Grant Numbers SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
278 JPMXD0717935561 and JPMXD0717935457 from the Ministry of Education, Culture,
279 Sports, Science and Technology (MEXT), Japan.
280 Supplements
281 Supplement 1 includes one supplemental table and two supplemental figures.
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392 18 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
393 List of Figure Captions
394 Fig. 1: Model domain, analysis domain including Kanto-Koshin region, and vertical
395 profiles of temperature trend/change. (a) Model domain. The area shaded gray
396 represents the model boundary. The rectangular area shows the region where the
397 temperature trends/changes were calculated. (b) Analysis domain. Color shading
398 illustrates topography. A rectangular area represents the Kanto-Koshin region. (c)
399 Vertical profiles of temperature changes estimated by linear trends from 1980 to
400 2019 in the JRA55 (black lines) and the difference in temperature between historical
401 and non-warming experiments in d4PDF (gray lines). Thin lines with blank circles,
402 filled circles, and blank rectangles represent August, September, and October,
403 respectively. Thick and dotted lines represent three-monthly-means of ASO and
404 SON, respectively.
405 Fig. 2: Total precipitation from 00 UTC on October 10 to 23 UTC on October 13, 2019;
406 typhoon tracks; and differences in total precipitation between CTL and non-warming
407 experiments. (a) Total precipitation in CTL and typhoon tracks in CTL and NonW40.
408 Triangles, black circles, and red circles represent the JMA best-track data, CTL, and
409 NonW40, respectively. Large triangle and black circle represent locations of typhoon
410 in best-track data and one CTL run, respectively, on 12 UTC October 12, 2019. (b)
411 Radar/rain gauge–analyzed rainfall. (c) Ensemble mean total precipitation in CTL.
412 (d) Ensemble mean total precipitation in CTL_NOTP. (e–h) Differences in total
413 precipitation between CTL (CTL_NOTP) and four sensitivity experiments. (e)
414 NonW40, (f) NonW40_SST, (g) NonW40_NOTP, and (h) NonW_d4PDF. Here,
415 CTL and non-warming experiments show the averages of 4 and 20 runs, respectively.
416 Blue shading represents that the amount of precipitation is larger in CTL than in the SOLA, 2021, Vol.17A, XXX-XXX, doi: 10.2151/sola.17A-002
417 sensitivity experiments.
418 Fig. 3: Time series of the regional mean hourly and accumulated precipitation over the
419 Kanto-Koshin region from 09 UTC on October 11, 2019, to 15 UTC on October 13,
420 2019. Thin bars represent observation (radar/rain gauge–analyzed rainfall). The
421 observed hourly precipitation is shifted by five hours to fit the precipitation peak in
422 CTL. Thick grey and blue bars represent hourly precipitation, in CTL and NonW40,
423 respectively. The thick black dashed line represents the accumulated precipitation of
424 the observation. Thin and solid black lines are individual CTL and the averages of
425 all CTL, respectively. Thin and solid blue lines are individual NonW40 and the
426 average of all NonW40, respectively. A thick black line on x-axis show the duration
427 of heavy precipitation which focused on Fig. 5.
428 Fig. 4: Minimum SLPs of Typhoon Hagibis (2019) and maximum total precipitable water
429 vapor (maxTPW). (a) Time series of minimum SLPs. The thick white line shows the
430 best-track data. Black, blue, and green lines show CTL, NonW40, and
431 NonW40_SST, respectively. Here, SLP is the average over the 25 x 25 km area, i.e.,
432 5 x 5 grid points. (b) Ensemble mean maxTPW from 09 UTC on October 9 to 12
433 UTC on October 13, 2019, in CTL. (c) Percentage of the difference of maxTPW
434 between CTL and NonW40. CTL and NonW40 show the averages of 4 and 20 runs,
435 respectively.
436 Fig. 5: Changes in the 15-hour total precipitation and 15-hour mean vertical wind at 700
437 hPa (06 UTC–21 UTC, October 12, 2019). (a) Precipitation simulated by CTL and
438 (b) the difference in precipitation between CTL and NonW40. (c) Vertical wind at
439 700 hPa simulated by CTL and (d) the difference in vertical winds between CTL and
440 NonW40. CTL and NonW40 show the averages of 4 and 20 runs, respectively. Cross 20 Kawase et al., Impact of historical warming on Typhoon Hagibis (2019)
441 symbols represent centers of typhoons simulated by one CTL whose initial date is
442 00 UTC on October 9, 2019.
443 List of Table Captions
444 Table 1: Experimental design
445 Table 2: Given temperature and SST changes, minimum SLPs of the typhoon around
446 28.5°N and 35°N, and regional mean total precipitation over the Kanto-Koshin
447 region Fig. 1: Model domain, analysis domain including Kanto-Koshin region, and vertical profiles of temperature trend/change. (a) Model domain. The area shaded gray represents the model boundary. The rectangular area shows the region where the temperature trends/changes were calculated. (b) Analysis domain. Color shading illustrates topography. A rectangular area represents the Kanto- Koshin region. (c) Vertical profiles of temperature changes estimated by linear trends from 1980 to 2019 in the JRA55 (black lines) and the difference in temperature between historical and non- warming experiments in d4PDF (gray lines). Thin lines with blank circles, filled circles, and blank rectangles represent August, September, and October, respectively. Thick and dotted lines represent three-monthly-means of ASO and SON, respectively. Fig. 2: Total precipitation from 00 UTC on October 10 to 23 UTC on October 13, 2019; typhoon tracks; and differences in total precipitation between CTL and non-warming experiments. (a) Total precipitation in CTL and typhoon tracks in CTL and NonW40. Triangles, black circles, and red circles represent the JMA best-track data, CTL, and NonW40, respectively. Large triangle and black circle represent locations of typhoon in best-track data and one CTL run, respectively, on 12 UTC October 12, 2019. (b) Radar/rain gauge‒analyzed rainfall. (c) Ensemble mean total precipitation in CTL. (d) Ensemble mean total precipitation in CTL_NOTP. Fig. 2: (e‒h) Differences in total precipitation between CTL (CTL_NOTP) and four sensitivity experiments. (e) NonW40, (f) NonW40_SST, (g) NonW40_NOTP, and (h) NonW_d4PDF. Here, CTL and non-warming experiments show the averages of 4 and 20 runs, respectively. Blue shading represents that the amount of precipitation is larger in CTL than in the sensitivity experiments. Fig. 3: Time series of the regional mean hourly and accumulated precipitation over the Kanto-Koshin region from 09 UTC on October 11, 2019, to 15 UTC on October 13, 2019. Thin bars represent observation (radar/rain gauge‒analyzed rainfall). The observed hourly precipitation is shifted by five hours to fit the precipitation peak in CTL. Thick grey and blue bars represent hourly precipitation, in CTL and NonW40, respectively. The thick black dashed line represents the accumulated precipitation of the observation. Thin and solid black lines are individual CTL and the averages of all CTL, respectively. Thin and solid blue lines are individual NonW40 and the average of all NonW40, respectively. A thick black line on x-axis show the duration of heavy precipitation which focused on Fig. 5. Fig. 4: Minimum SLPs of Typhoon Hagibis (2019) and maximum total precipitable water vapor (maxTPW). (a) Time series of minimum SLPs. The thick white line shows the best-track data. Black, blue, and green lines show CTL, NonW40, and NonW40_SST, respectively. Here, SLP is the average over the 25 x 25 km area, i.e., 5 x 5 grid points. (b) Ensemble mean maxTPW from 09 UTC on October 9 to 12 UTC on October 13, 2019, in CTL. (c) Percentage of the difference of maxTPW between CTL and NonW40. CTL and NonW40 show the averages of 4 and 20 runs, respectively. Fig. 5: Changes in the 15-hour total precipitation and 15-hour mean vertical wind at 700 hPa (06 UTC‒21 UTC, October 12, 2019). (a) Precipitation simulated by CTL and (b) the difference in precipitation between CTL and NonW40. (c) Vertical wind at 700 hPa simulated by CTL and (d) the difference in vertical winds between CTL and NonW40. CTL and NonW40 show the averages of 4 and 20 runs, respectively. Cross symbols represent centers of typhoons simulated by one CTL whose initial date is 00 UTC on October 9, 2019. Table 1 Experimental design
Assuming year/period number Name Initial date Topography Atmosphere SST of runs
CTL 2019 4
*a NonW40 Realistic 1980 (40-year detrending , JRA55) 20
topography *a NonW40_SST 00,03,06,09UTC 2019 1980 (40-year detrending , JRA55) 20
NonW_d4PDF 9 Oct. 2019 Preindustrial period (non-warming simulation*a, d4PDF)*b 20
CTL_NOTP 2019 4 0 m NonW40_NOTP 1980 (40-year detrending*a, JRA55) 20
*a Monthly means in August, September, and October, and three-monthly means in ASO and SON. *b Non-warming simulation of d4PDF uses the concentration of greenhouse gases in 1850. Table 2. Given temperature and SST changes, minimum SLPs of the typhoon around 28.5N and 35 N, and regional mean total precipitation over Kanto-Koshin region.
Experiment name 1000 hPa SST Minimum SLP [hPa] Regional mean precipitation over Kanto-Koshin region temperature change (standard deviation) amount Differences between CTL and NonXX or change [mm] between CTL_NOTP and NonW40_NOTP 28.5N 35N (standard deviation)
amount [mm] percentage [%]
Observation - - 935 955 262.1 - -
CTL*a - - 930.2 (0.39) 952.0 (0.81) 235.9 - -
NonW40*b -0.95 -0.88 939.0 (1.57) 955.6(0.93) 212.9 23.1 (4.9) 10.9 (2.5)
NonW40_SST*b - -0.88 936.0 (0.38) 958.5 (0.91) 220.0 15.9 (3.2) 7.3 (1.5)
NonW_d4PDF*b -1.37 -1.30 939.5 (0.70) 956.9 (0.98) 207.8 28.1 (5.4) 13.6 (3.0)
CTL_NOTP*a - - 930.6 (0.22) 948.5 (0.37) 173.8 - -
NonW40_NOTP*b -0.95 -0.88 938.8 (1.63) 952.9 (1.31) 162.9 11.0 (5.7) 6.8(3.9)
*a Four runs mean *b Twenty runs mean Table S1. Specifications of NHM
Numerical model Japan Meteorological Agency-Nonhydrostatic Model (JMA-NHM) (Saito et al. 2007) Horizontal resolution 5km Grid number 400x400x50 Microphysics Bulk-type cloud microphysics (Ikawa et al.1991) Kain-Fritsch convective parameterization scheme Convective precipitation (Kain and Fritsch 1993) Clear-sky radiation scheme (Yabu et al. 2005) Radiation process Cloud radiation scheme (Kitagawa et al. 2000) Improved Mellor-Yamada-Nakanishi-Niino (MYNN) Level 3 Boundary-layer process (Nakanishi and Niino 2004)
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Figure S1: Annual variation of air temperature at 1000 hPa in October around Japan shown in Fig. 1a. A red line represents the linear trend from 1980 to 2019.
Figure S2: Differences in maxTPW (a–b) between CTL and NoW40, and (c–d) between CTL and NonW40_SST.