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Quantifying the Effects of Long-Term Climate Change on Tropical Cyclone Rainfall Using a Cloud-Resolving Model: Examples of Two Landfall Typhoons in Taiwan

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Quantifying the Effects of Long-Term Climate Change on Tropical Cyclone Rainfall Using a Cloud-Resolving Model: Examples of Two Landfall Typhoons in Taiwan 66 JOURNAL OF CLIMATE VOLUME 28 Quantifying the Effects of Long-Term Climate Change on Tropical Cyclone Rainfall Using a Cloud-Resolving Model: Examples of Two Landfall Typhoons in Taiwan CHUNG-CHIEH WANG,BO-XUN LIN,CHENG-TA CHEN, AND SHIH-HOW LO Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan (Manuscript received 9 January 2014, in final form 11 September 2014) ABSTRACT To quantify the effects of long-term climate change on typhoon rainfall near Taiwan, cloud-resolving sim- ulations of Typhoon (TY) Sinlaku and TY Jangmi, both in September 2008, are performed and compared with sensitivity tests where these same typhoons are placed in the climate background of 1950–69, which is slightly cooler and drier compared to the modern climate of 1990–2009 computed using NCEP–NCAR reanalysis data. Using this strategy, largely consistent responses are found in the model although only two cases are studied. In control experiments, both modern-day typhoons yield more rainfall than their counterpart in the sensitivity test using past climate, by about 5%–6% at 200–500 km from the center for Sinlaku and roughly 4%–7% within 300 km of Jangmi, throughout much of the periods simulated. In both cases, the frequency of more-intense 2 rainfall (20 to .50 mm h 1) also increases by about 5%–25% and the increase tends to be larger toward higher rain rates. Results from the water budget analysis, again quite consistent between the two cases, indicate that the increased rainfall from the typhoons in the modern climate is attributable to both a moister environment (by 2.5%–4%) as well as, on average, a more active secondary circulation of the storm. Thus, a changing climate may already have had a discernible impact on TC rainfall near Taiwan. While an overall increase in TC rainfall of roughly 5% may not seem large, it is certainly not insignificant considering that the long-term trend observed in the past 40–50 yr, whatever the causes might be, may continue for many decades in the foreseeable future. 1. Research background and motivation change (e.g., Solomon et al. 2007; Stott et al. 2010; Min et al. 2011; Pall et al. 2011). When facing disastrous, extreme, or record-breaking Obviously, the likely changes in tropical cyclones and weather events like tropical cyclones (TCs) and heavy other types of extreme weather events in the future are rainfall, it is generally very difficult to assess the influences of major concern under the global warming scenario. One of anthropogenic forcing (related to ‘‘global warming’’) or common approach to tackle such questions is to perform long-term climate change (including natural forcing and long-term simulations with global or regional climate internal variability as well) since the dynamical forcing models and compare event statistics in the present and in these rare, high-impact events is typically much larger future climates (e.g., Meehl and Tebaldi 2004; Knutson than the climate forcing and favorable factors across et al. 2007, 2008; Zhao et al. 2009; Murakami et al. 2012; a wide range of scales often come together in synergy to produce them. Thus, while an increase in the severity of Sillmann et al. 2013). One issue of this approach is the extreme weather events is consistent with the expected reliability of such models in reproducing the observed effects of climate change, it is generally difficult to at- characteristics of extreme weather events with rather tribute any single event to the warming or climate coarse resolution (typically more than tens of kilometers), raising the need for further dynamical or dynamical– statistical downscaling (e.g., Stott et al. 2010; Knutson et al. 2013; Emanuel 2013). Also, because of the high de- Denotes Open Access content. gree of natural variability involved among the two groups of different events (one in present and the other in future climate) as well as the uncertainties in the pro- Corresponding author address: Prof. Chung-Chieh Wang, jection of future climate, especially at regional scale, Department of Earth Sciences, National Taiwan Normal University, No. 88, Sec. 4, Ting-Chou Rd., Taipei 11677, Taiwan. a large number of samples is often needed to establish the E-mail: [email protected] statistical significance and potentially the confidence for DOI: 10.1175/JCLI-D-14-00044.1 Ó 2015 American Meteorological Society Unauthenticated | Downloaded 10/07/21 09:22 AM UTC 1JANUARY 2015 W A N G E T A L . 67 a thorough assessment (e.g., Stott et al. 2004; Karl et al. 2. Methodology and experiments 2008; Pall et al. 2011 Alexander and Tebaldi 2011; Fischer et al. 2013). To compute the long-term trend of climate change for Instead of assessing the possible changes in typhoons the global atmosphere, a dataset extending back into the in the future, which involves reliability and much higher past for as long as possible is preferred. Therefore, for this uncertainties as mentioned above, in this study we at- purpose we adopt the National Centers for Environmen- tempt to address the following issue quantitatively: How tal Prediction (NCEP)–National Center for Atmospheric much rain, in terms of percentages, from the most rainy Research (NCAR) monthly-mean global gridded re- typhoon cases near Taiwan in modern days can be at- analysis (2.5832.58; Kalnay et al. 1996), as in Chu et al. tributed to the effects of long-term climate change (2012). These reanalysis data are used to compute the (whether due to anthropogenic impact or natural vari- mean climate states for two 20-yr periods, 1950–69 and ability) that we have already seen? To do this, we select 1990–2009, and subsequently their differences for all specific modern-day typhoons near Taiwan and perform major variables at all pressure levels and the surface. For highly realistic simulations of their life cycle using a sea surface temperature (SST), the 18318 Hadley Centre cloud-resolving model (i.e., control runs, one for each Sea Ice and Sea Surface Temperature (HadISST) data typhoon). Then, we place these same typhoons in a cli- (Rayner et al. 2003) for the same periods are employed. mate background representing conditions from about Using 20-yr averages, signals from variations up to de- 40 yr ago, constructed by subtracting the long-term trend cadal time scale are largely removed, and these differ- from gridded analyses during the case period, and run ences (called the delta values or ‘‘D’’ for short) represent sensitivity tests and make direct comparison with the the long-term climate trend during the past half century control simulations. Here, because the long-term trend or so under a mixture of both natural and anthropogenic is computed using reanalysis data based on observations, forcings. Although some multidecadal variability (e.g., the uncertainties involved with climate projection are Pacific decadal oscillation; Trenberth 1990; Biondi et al. not present (although the ultimate causes of the climate 2001) or climate regime shifts (Lo and Hsu 2008) may still change over the period remain debatable). Also, two be present in the D values, it is widely accepted that an- cloud-resolving, high-resolution runs of the same ty- thropogenic influence on global-mean temperature be- phoon (with identical synoptic evolution) are compared came relatively more detectable in recent decades, with the only difference in their mean climate state, and especially since the 1990s (e.g., Solomon et al. 2007). much of the uncertainties from natural variability among Our results of the mean state in modern climate of events and inadequate model resolution are eliminated. 1990–2009 and the D values from 1950–69 to 1990–2009 Thus, quantitative assessments are allowed for indivi- (latter minus former) over the western North Pacific dual events from a small number of model experiments (WNP) are shown in Figs. 1 and 2, respectively. Com- through sensitivity tests, as has long been practiced to pared to the mean state (Fig. 1), the long-term changes address the roles or isolate the impacts of various factors since 1950–69 near Taiwan are quite small as expected (e.g., Gall 1976; Kuo et al. 1991; Stein and Alpert 1993; and include slight increases in northwesterly wind com- Braun and Tao 2000; Wang et al. 2005, 2012, 2013a). ponents at low levels (1000–700 hPa) and in westerly wind Typically, systematic responses in the model and the components in the middle troposphere (600–400 hPa), 2 underlying physics are examined in these tests without both by about 0.5 m s 1 (Figs. 2a,b) and in general strict statistical inference. To our knowledge, such a agreement with Chu et al. (2012).Thereisalso strategy has not been adopted to access the impacts of a warming and moistening trend of roughly 0.5 K and 2 climate change on high-impact weather systems before, 0.1–0.4 g kg 1 at low levels, as well as an increase in and thus this paper, using a small number of cases at first, SST by about 0.6–1.5 K near Taiwan (Figs. 2c,d). also serves to establish the concept of this methodol- Further aloft, weak warming (#0.3 K) exists at 200– ogy. Herein, we report our results on two landfall ty- 300 hPa and mild cooling of about 0.5 K also appears phoons in Taiwan: Typhoon (TY) Sinlaku (2008) and at 100 hPa near Taiwan (not shown), in rough agreement TY Jangmi (2008). Both TCs exhibited a typical track with Vecchi et al. (2013) and Emanuel et al. (2013).More to approach from the southeast, and they are ranked as precisely, the above changes correspond to a weaken- the 3rd and 10th most rainy typhoon in Taiwan over the ing in easterly to southeasterly mean flow by about 2 past 50 yr (Chang et al.
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