1. Introduction
The western North Pacific (WNP), as a part of the warm pool, is the most energetic area of the tropical cyclone (TC) genesis in the world. Nearly 30% of the total annual TC numbers in the world comes from the WNP. The TC-accompanied severe rainfall and wind generally lead to huge disasters and economic lost in the WNP and East Asia. Observations revealed that the warm pool has exhibited a significant warming in recent decades (Hong et al. 2013; Weller et al. 2016), and this warming would continue under global warming in the future based on the model projections of phase 5 of the Coupled Model Intercomparison Project (CMIP5) (IPCC 2013). Given the fact that the WNP TC activity is highly connected with warm sea surface temperature (SST) (Chan 2000; Chia and Ropelewski 2002), how a warmer SST in the warm pool would modify the TC activity in the future becomes an important issue. Investigation on this topic can not only advance our scientific knowledge but also provide more reliable and adequate information for the policy makers for disaster prevention and adaption in a warming climate.
The possible effects of global warming on TC have been investigated by many researchers (e.g., Emanuel et al. 2008; Knutson et al. 2010; Murakami and Sugi 2010; Murakami et al. 2011, 2012; Camargo 2013; Tory et al. 2013; Knutson et al. 2020; Vecchi et al. 2019; Cha et al. 2020; Roberts et al. 2020). Based on the CMIP5 projections, the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC 2013) summarized that the global TC frequency was estimated to decrease at the end of the twenty-first century, consistent with the previous IPCC report of the AR4. However, the changes in TC frequency in individual ocean basins showed a large diversity among different modeling studies (IPCC 2013). One of the uncertainties may come from the biased simulations of TC in the low-resolution CMIP5 models (approximately 100 km on average), which were unable to represent adequately both TC intensity and structure (Camargo 2013; Murakami et al. 2014). Most of the CMIP5 models underestimated TC frequency and intensity in either the WNP or the globe (Yokoi et al. 2012; Camargo 2013). It was reported that the CMIP5 models with the highest horizontal resolution performed the best in terms of global TC activity (Camargo 2013), indicating that a high horizontal resolution model is necessary for examining the future projection of TC activity, particularly that of the TC intensity.
A recent study based on a high-resolution (60 km) model revealed that the annual-mean TC number in the WNP decreased by approximately 30% in the end of twenty-first century (2075–99); however, the intensity increases substantially in the future warming climate [i.e., the representative concentration pathway 8.5 (RCP8.5) scenario] (Murakami et al. 2012). Although the thermodynamic effects of global SST warming and increased moisture are favorable for TC growth, the regional dynamic effects such as enhanced vertical wind shear may lead to unfavorable conditions for TC activities (e.g., Vecchi and Soden 2007; Knutson et al. 2010). The opposing thermodynamic and dynamic effects in individual basins associated with global warming may cause large uncertainty and difficulty in projecting the future changes in TC activity at a regional scale (Grossmann and Morgan 2011). In addition to the effects of mean state changes, the activity of intraseasonal oscillation (ISO) and its interaction with TC may also affect the TC intensity (Hsu et al. 2011; Weng and Hsu 2017; Hong et al. 2018; Zhou et al. 2018). All these changes in large-scale thermodynamic and dynamic conditions and the scale interaction among mean state, ISO and TC would result in future changes in TC activity, including its genesis and intensity. Their relative effects and detailed physical processes responsible for the TC changes in a warmer climate are not fully understood. To advance our understanding about the key factors and processes causing TC intensity change under global warming, we analyze the large-scale environmental changes and utilize the diagnostic method of synoptic-scale eddy kinetic energy budget (Tsou et al. 2014) using the simulations of two high-resolution (~20 km) models in this paper.
The remainder of the paper is organized as follows: In section 2, we introduce datasets, model, and experiment designs. In section 3, we present the future projection of TC activity in the WNP. In section 4, we discuss the changes in large-scale dynamic and thermodynamic conditions under future global warming. In section 5, we investigate the physical processes responsible for the TC intensity based on the eddy kinetic energy budget equation. A summary of these results is given in section 6.
2. Data and methodology
The high-resolution reanalysis data (0.5° × 0.5°) of 1979–2003 provided by the National Centers for Environmental Prediction (NCEP) Coupled Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010) was used for obtaining the large-scale circulations over the WNP. The 6-hourly best track data obtained from the Joint Typhoon Warning Center (JTWC) was used to examine TC activity. The TC genesis location is defined by the geographical position when the maximum sustained wind speed of a tropical storm reaches 34 kt (1 kt ≈ 0.51 m s−1). The Saffir–Simpson hurricane wind scale (Simpson 1974) was used to categorize the strength of TC. According to this scale, severe TCs are the ones with maximum sustained wind speed of ≥ 96 kt [i.e., ≥category 3 (C3)]. The moving speed of TC is derived from the speed of its center. We calculate it every 6 h first, and then take average for the whole TC lifetime. Because nearly 90% of the TCs in the WNP occur during June–November (JJASON, namely, the typhoon season), our analysis and diagnosis focus on the typhoon season.
We conducted simulations using two high-resolution models. One is the High Resolution Atmospheric Model (HiRAM) from the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory, with a near 20-km horizontal resolution and 32 levels. The HiRAM can realistically simulate the extreme events (Lau and Ploshay 2009; Chen et al. 2019) and the seasonal cycle of TC number in the WNP (Tsou et al. 2016). The setting of the 20-km HiRAM model follows Chen and Lin (2011) in which a high resolution of 25 km was utilized for TC simulations. Similarly, the spatial resolution of HiRAM was modified from C192 (50 km) to C384 with a horizontal resolution of about 0.23° longitude × 0.23° latitude (~20 km over the WNP) in this study. The detail of the HiRAM was documented in Zhao et al. (2009) and in Chen and Lin (2011). Time slice experiments using prescribed SST to drive the HiRAM were carried out in this study (Bengtsson et al. 2009; Kusunoki and Mizuta 2013). In the control experiment, the HiRAM was forced by the observed SST and the historical greenhouse gas concentration (Donner et al. 2011), which is denoted as Present-exp. In the global warming run (referred to as Future-exp), greenhouse gas concentrations following RCP8.5 (Meinshausen et al. 2011; see also http://www.pik-potsdam.de/~mmalte/rcps/index.htm) together with the future SST change obtained from the average of 28 CMIP5 models (Mizuta et al. 2014) were applied to force the HiRAM.
The effect of global warming can be estimated by subtracting the results of Present-exp from those of Future-exp. The simulation periods are 1979–2003 and 2075–99 for Present-exp and Future-exp, respectively. To obtain robust results, the same model experimental designs were applied to another high-resolution model of the MRI-AGCM3.2 (Mizuta et al. 2014; Kitoh and Endo 2016; Kusunoki 2018; Okada et al. 2017). The MRI-AGCM3.2 is the updated version of MRI-AGCM3.1, in which the Yoshimura cumulus parameterization scheme (Yukimoto et al. 2011) is used. The MRI-AGCM3.2 used in this study has a 20-km grid (TL959) and 60 vertical levels. The detection of TC in the HiRAM and MRI-AGCM3.2 simulations follows the method proposed by Tsou et al. (2016) and Murakami et al. (2012), respectively. Basically, the TC detection algorithm first identifies the position of intense vortices (i.e., the maximum relative vorticity at 850 hPa exceeds a threshold value) with a warm core for each 6-hourly period. Then, an objective procedure is applied to find the storm trajectories. Finally, TCs with a maximum wind speed at 10-m height exceeding 17.5 m s−1 are considered as TCs in this study. The precipitation within a 200-km radius to the TC center is defined as TC-induced rainfall.
The basic features of WNP TC simulations by the two models can be found in Murakami et al. (2012) and Tsou et al. (2016). The annual cycle of TC genesis number was realistically simulated by 20-km HiRAM and MRI with a very high correlation coefficient (~0.95) with the observation, while their year-to-year variations show some differences in amplitude when compared with the observation. Even so, the two high-resolution models capture the temporal fluctuation of observed TC genesis number with the correlation coefficients of 0.38 (HiRAM) and 0.4 (MRI), which are statistically significant at the 90% confidence level (not shown). (TC intensity is also reasonably simulated, as displayed in Figs. 1 and 3.)
The TC genesis number (number per year in each 2.5° × 2.5° box) and 850-hPa winds for (a) observation and for simulations of (b) HiRAM, (c) MRI, and (d) average of HiRAM and MRI in the typhoon season (JJASON) during the period of present climate (1979–2003). (e)–(h),(i)–(l) As in (a)–(d), but for the TC track frequency (number per year) and TC wind speed at 850 hPa (m s−1), respectively.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
3. Changes of TC activity in future climate
Figure 1 compares TC genesis count, track frequency, and intensity between observation and simulations during the typhoon season (JJASON) in the period 1979–2003. The TC genesis count, intensity, and track frequency (density) are defined as the averaged genesis number, 850-hPa wind speed, and frequency of TC occurrence over a 2.5° × 2.5° box, respectively. Note that the WNP monsoon trough and associated lower-level southwesterly wind are reasonably captured by both HiRAM and MRI (Figs. 1a–c). The spatial distribution of TC genesis count is realistically simulated by the HiRAM (Figs. 1a,b). It is also well reproduced by the MRI except for a slightly northward shift of the main genesis location and an underestimated TC genesis counts to the east of 150°E, which is probably due to the underestimated WNP monsoon trough (Figs. 1a,c). The pattern of simulated TC track density (Figs. 1f,g) is similar to the observed (Fig. 1e), although the MRI model shows less TCs. In contrast to the TC genesis count and track frequency, the TC intensity is underestimated by the HiRAM and overestimated by the MRI (Figs. 1i–k). The underestimated TC intensity in the HiRAM is linked to the much smaller number of severe TCs (≥C3) than that in the observation. The underestimated severe TCs are probably due to a lower vertical resolution in the HiRAM than in the MRI model (Tsou et al. 2016). The comparison of TC activity between the simulations and observation is summarized in Table 1. Overall, the biases in the two models seem to offset each other. Therefore, the TC genesis number, spatial patterns of TC genesis frequency and TC intensity show higher skills in the average of HiRAM and MRI simulations than the individual model results. Because the ensemble mean approach can reduce the uncertainty from individual models, we will use the average of HiRAM and MRI simulations to investigate the change in TC activity under global warming.
Comparison of TC activity over the western North Pacific between observation and high-resolution (20 km) model outputs by HiRAM and MRI. The observation is based on the JTWC data. The numbers with and without parentheses indicate the values in the present climate (1979–2003) and future climate (2075–99), respectively. The pattern correlation denotes the correlation coefficient between the simulated TC frequency pattern and observation in the period 1979–2003. The intensity is defined as the TC mean wind speed at 850 hPa averaged over the subtropical WNP (100°E–180°, 20°–40°N). The maximum precipitation denotes the averaged precipitation rate within 200 km of the TC center at the time of lifetime maximum intensity (LMI).
Figure 2 depicts the change in TC activity in the future climate obtained from the average of HiRAM and MRI simulations. It reveals that both the TC genesis number and track frequency decrease substantially over the WNP in a warmer climate (Figs. 2a,b). The total TC genesis number per year decreases approximately by 50% (from 23.5 to 11.7) from present climate to future climate (Table 1). This change rate in TC genesis number is close to the results of Yoshida et al. (2017) when a high-resolution model was used under the scenario with high greenhouse gas emissions. With lower resolutions and different parameterizations, the projected results of CMIP5 models range widely, but their ensemble mean also suggests a decrease in TC genesis counts (~−12%) under RCP8.5 (Tory et al. 2013). Notably, the significant decrease in TC genesis number appears along the WNP monsoon trough, where the genesis is active climatologically (Fig. 2a). This suggests that the decrease in TC genesis count in the future climate is associated with the weakening of the WNP monsoon trough (i.e., an anticyclonic anomaly over the WNP). The change of spatial pattern for TC track frequency resembles that of the TC genesis number (Figs. 2a,b). The two main paths of TC track frequency, including the westward movement toward South China and the northward recurving toward Japan and the Korean Peninsula (Fig. 1h), both decrease considerably in the future (Fig. 2b). In contrast to the TC genesis number and track frequency, the TC intensity increases by approximately 15% from present climate to future climate (Table 1). The increase in TC mean wind speed over the subtropical WNP (approximately from 20° to 40°N, Fig. 2c) is especially evident. Conversely, a substantial decrease in the tropic (~10°N, from the Philippine Sea to the date line) can be identified. Figure 2 depicts the TC activity changes in far future (2075–99). The TC activity changes in near future (2040–64) relative to present-day climate (1979–2003) are examined by using the HiRAM simulations. Overall, the spatial distributions of mean state change in near future resemble those in far feature except for smaller magnitude (not shown), that is, the changes in large-scale thermodynamic and dynamic conditions associated with TC activity change are approximately proportional to the strength of the warming.
Differences in TC (a) genesis number (number per year), (b) track frequency (number per year), and (c) mean wind speed (m s−1) in JJASON between future climate (2075–99) and present climate (1979–2003) (i.e., future minus present climate) based on the average of two models’ simulations. Stipples indicate the change exceeding the significance test at the 90% significance level.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
The probability density function (PDF) of TC intensity (the maximum sustained wind) further reveals that the frequency of intense typhoons (categories 3–5 with the maximum sustained wind greater than 50 m s−1) increase evidently in the future warmer climate in both models. Conversely, the frequency of mild-to-moderate TC (with intensity weaker than 50 m s−1) is reduced under global warming (Fig. 3). We found that the increase in TC intensity is accompanied by increased TC-induced rainfall. The TC-induced maximum rainfall increases by approximately 35% in the future (Table 1), which was reported in a few recent studies (Murakami et al. 2012; IPCC 2013; Tsou et al. 2016). It is worth mentioning that the increase in the TC-induced maximum rainfall is much stronger than that in wind speed. Thus, we may need to pay more attention to the severe rainfall than to the strong wind surge for TC-induced hazards in the future warmer climate.
PDF distributions of the maximum sustained wind (m s−1) of TCs from observation (bars) and model simulations (colored curves). Light blue (light orange) and dark blue (orange) curves represent the HiRAM (MRI) simulations in present-day (1979–2003) and future climates (2075–99), respectively.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
4. Future changes in large-scale thermodynamic and dynamic conditions
Consistent with the findings of Murakami et al. (2011), results from the two high-resolution models used in this study suggest that the WNP TC genesis count tends to decrease while the TCs show stronger intensity when the climate becomes warmer (Table 1, Figs. 2 and 3). To reveal the potential association between large-scale environments and TC activity under global warming, we compare the seasonal-mean SST and lower-level circulation between the present climate (1979–2003) and future climate (2075–99) in Fig. 4. The future change of SST under the RCP8.5 scenario in the CMIP5 projections exhibits evident warming over the subtropical North Pacific (approximately 20°–40°N; Fig. 4d). This subtropical warming is linked to the increases in relative humidity and vertical velocity in the midlevel atmosphere over the subtropical WNP (Figs. 5a,b). In contrast, the relative humidity in the tropical western Pacific (south of 20°N) decreases, which may be associated with the anticyclonic anomaly-induced moisture divergence in the future climate (Figs. 4d and 5c). Thus, the TC genesis tends to reduce over the western North Pacific. Notably, the larger increases in SST and relative humidity in the subtropical western Pacific (20°–40°N) concur with the increase in TC intensity in the future climate (Figs. 2c and 4c). This suggests that the enhancement of TC intensity under global warming may be related to the change in large-scale thermodynamic conditions of SST and moisture content (Knutson et al. 2010; Murakami et al. 2011).
Climatology of SST (shading; °C) and 850-hPa wind (vectors; m s−1) during JJASON in present climate (1979–2003) for (a) observation and (b) average of HiRAM and MRI simulations. (c) As in (b), but for future climate (2075–99). (d) Future changes in SST and 850-hPa wind field calculated by (c) minus (b). Stipples indicate the change exceeding the significance test at the 90% significance level.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
As in Fig. 3d, but for the future changes in (a) 600-hPa relative humidity (%), (b) 500-hPa omega (10−2 Pa s−1), (c) 850-hPa vorticity (10−6 s−1), and (d) vertical wind shear between 200 and 850 hPa (U200 minus U850; m s−1). Stipples indicate the change exceeding the significance test at the 90% significance level.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Being the key dynamic factor for the WNP TC activity, the monsoon trough influences the TC genesis by modulating the location and intensity of both seasonal-mean and intraseasonal circulations (e.g., Hsu et al. 2017; Weng and Hsu 2017). In Fig. 2a, we can see an anticyclonic anomaly associated with the weakened monsoon trough over the WNP under global warming. This anomalous anticyclone results in a negative lower-level vorticity anomaly and enhances the vertical easterly wind shear in the WNP (Figs. 5c,d); both are not favorable for the WNP TC activity (Chia and Ropelewski 2002; Chan 2005). Meanwhile, the ISO variability is reduced along with the weakened monsoon trough in a warmer climate (Fig. 6). The inactive ISO activity may also lead to the inactive TC activity as it alters the background circulation for the TC genesis at the intraseasonal time scale (Maloney and Hartmann 2001; Hsu et al. 2011; Tsou et al. 2014).
As in Figs. 3b–d, but for 850-hPa wind field (vectors; m s−1) and ISO variance (shading; m2 s−2), defined as the variance of 10–90-day filtered wind speed.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Based on the large-scale thermodynamic and dynamic environments analyzed (Figs. 4–6), we find that the reduced TC genesis counts over the tropical WNP should be attributable to the weakened monsoon trough, and to the associated changes in vertical wind shear and ISO activity under global warming. Notably, the evident reduction of projected TC genesis appears over the Philippine Sea and tropical WNP (10°–20°N, 125°–155°E; referred to as the key region) (Fig. 2a). Focusing on this region, we found that most of the large-scale environmental conditions, including 600-hPa relative humidity (Fig. 5a), 500-hPa vertical motion (Fig. 5b), and 850-hPa vorticity (Fig. 5c), reveal significant contributions to the TC genesis change. The future change in vertical wind shear over the key region is not uniform and less significant (Fig. 5d) compared to the other large-scale fields (Figs. 5a–c). This suggests the effect of vertical wind shear changes on the TC genesis is minor and offset by the negative effects of atmospheric drying and associated anticyclonic and downward anomalies. In contrast, the increase in TC intensity over the extratropical WNP (Fig. 2c) is likely to be related to the thermodynamic effect (large SST and moisture anomalies) to the north of 20°N (Figs. 4d and 5a). In addition to the thermodynamic factors, the change in ISO variability would play a role in TC intensification (Weng and Hsu 2017; Hong et al. 2018; Zhou et al. 2018). So far, we know little about how the global warming affects the scale interaction among mean flow, ISO, and synoptic disturbances, and thus the WNP TC intensification.
5. Possible causes for the enhancement of TC intensity in future
a. Effects of TC lifetime and intensification rate
TC intensity is highly correlated with its lifetime and intensification rate. As a TC stays over the ocean for a longer period or/and at a larger intensification rate, it will have a higher probability of growing into an intense typhoon. Regarding the effect of life span on TC intensity, studies indicated that the TC intensity over the WNP tends to enhance during the El Niño developing summer due to the prolonged TC lifetime in response to the eastward shift of TC genesis location (Chan 2000; Chia and Ropelewski 2002). As shown in Fig. 4, the future SST change in the tropical Pacific exhibits an El Niño–like pattern. The WNP TC intensity in the future would probably increase due to the longer TC lifetime in response to the El Niño–like warming. To verify this, we compare the TC life span in the present climate and future climate (Fig. 7). We can see that the TC lifetime in the present climate (middle boxplot) is realistically simulated by the two models except that the medium of TC lifetime shows a small underestimation (compared to the observation in the left boxplot). Under global warming (right boxplot), the medium of TC lifetime decreases approximately by half a day (from 168 to 156 h) relative to the present climate condition. The results yield that the El Niño–like mean SST change in the future will not prolong the TC lifetime. To confirm the results of Fig. 7, Welch’s t test that assumes two samples with possibly unequal variances and the degree of freedom was applied. It turned out that the significance test results are consistent for both Student’s and Welch’s t tests. Another interesting issue is related to the regional differences of future TC changes. To answer this, we divided the WNP basin into five subregions as in Wang et al. (2013) and then analyzed separately the changes in TC life spans for TCs generated in different subregions. No substantial regional differences were detected (not shown), suggesting the robustness of our analysis.
Boxplots of (a) TC lifetime (h), (b) moving speed of severe TCs (km h−1), and (c) hours required for a TD to develop into a C3 for the observation (the leftmost boxplot) and average of HiRAM and MRI simulations in present climate (middle boxplot) and future climate (the rightmost boxplot). The values shown in each boxplot (from top to bottom) represent the 95th, 75th, 50th, 25th, and 5th percentiles, respectively. The blue dot indicates the mean value, while the red circles denote outliers. We calculate moving speed of the TC center every 6 h first, and then take the average for the whole TC lifetime. The p value in the title indicates the significance test of mean difference between future climate and present climate of the simulations.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Generally, TC intensity is determined by both TC life span and intensification rate. If the TC has a longer life span, it has a chance to grow stronger (even with a consistent intensification rate). On the contrary, TCs can reach a higher intensity when they have a larger intensification rate (even with the same lifetime). Because the average life span of TCs become shorter in the future, we suggest that the change (i.e., increase) in TC intensification rate is the key factor causing the enhanced TC intensity. To understand what causes the shortening of TC lifetime under global warming, we analyzed the changes in TC genesis location and moving speed. Figure 8 depicts that the mean TC genesis location shifts eastward and northward. The northward shift of TC genesis location, probably caused by the subtropical warming (Fig. 4d), tends to shorten the lifetime of TC. However, the eastward shift of TC genesis location, which could be related to the El Niño–like warming, may prolong the duration of TC. Because the effects of eastward and northward shifts of the mean TS genesis location on TC’s lifetime offset each other, the shortening of TC lifetime in the future is difficult to explain by using the change of TC genesis location alone.
As in Fig. 7, but for the mean TC genesis locations of (a) latitude (°N) and (b) longitude (°E). The blue dot indicates the mean value, while the red circles denote outliers. The p values for mean differences in latitude and longitude are 0.037 and 0.028, respectively.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
The boxplot of TC moving speed (Fig. 7b) suggests that the medium of moving speed for the WNP TC increases approximately 2 km h−1 from the present climate to future climate (19–21 km h−1). To understand the effects of anomalous steering flows and relative TC frequency change on the increases in WNP basin-averaged TC translation (Fig. 7), we plotted the spatial distributions of TC translation speed and steering flows (Figs. 9a–c) as well as the relative frequency of TC occurrence (Figs. 9d–f). The TC translation speed shows increases in the tropics (south of 20°N) and East Asia, in which the easterly and southeasterly anomalies appear due to the enhancement of western North Pacific subtropical high (WNPSH) under global warming (Fig. 9c). In contrast, the TC translation speed tends to decrease slightly over the subtropics under global warming, consistent with the results in Zhang et al. (2020). In addition to the contribution of anomalous steering flow, the increases in averaged TC translation speed could also be attributed to the increased frequency of TC over the subtropics (Fig. 9f) where the TCs generally have a higher translation speed (Yoshida et al. 2017). Using the large ensemble simulations of 60-km MRI model, Yamaguchi et al. (2020) also pointed out the increased translation speed of WNP TCs under global warming is due to more TCs occur in the subtropical regions.
Average simulation of steering flow (vectors; m s−1) and TC moving speed (shading; m s−1) for (a) present climate, (b) future climate, and (c) future climate minus present climate. (d)–(f) As in (a)–(c), but for the relative frequency of TC occurrence (number per year). The steering flow is defined as the vertically integrated vector winds from 1000 to 400 hPa. The relative frequency of occurrence was calculated by normalizing the frequency with the climatology of annual-mean TC genesis number for the present and future climate, respectively. Stippling in (e) and (h) indicates the changes exceeding the significant test at the 90% significant level.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Because the averaged TC lifetime tends to shorten due to the increased translation speed in the future (Figs. 7b and 9), the intensification rate of TC may play a role in the enhancement of TC intensity. As expected, TC intensification rate increases substantially: the mean hours requiring for tropical depression developing to C3 decreases by approximately half a day under global warming (from 96 to 84 h; Fig. 7c). The result of enhanced TC intensification rate is consistent when we analyze the time requiring for a tropical depression growing into a category 5 (not shown).
b. Diagnosis of eddy kinetic energy budget
According to Eq. (2), scale interaction occurs only through lower-level barotropic energy conversion (CK), whereas upper-level baroclinic energy conversion (CE) involves only eddy–eddy interaction. A scale analysis reveals that the right-hand side of Eq. (2) is primarily determined by CK and CE, which maximize in the lower and upper levels, respectively (Maloney and Hartmann 2001; Hsu et al. 2011, 2017; Tsou et al. 2014). For the sake of brevity, we focus on the analysis of CK at 850 hPa and CE at 200 hPa. The spatial patterns of CK and CE at these specific levels are similar to the vertically integrated CK and CE, but with slight change in magnitude (not shown).
SSE includes all the synoptic-scale eddies, which may or may not develop into TC. To confirm the SSE changes discussed here are related to the TC cases, both CK and CE were calculated only when the TC passes through each 2.5° × 2.5° box. Therefore, the CK represents the scale interaction between the TC and mean circulation, and that between the TC and ISO; CE represents the TC-associated kinetic energy converted from the TC-associated available potential energy.
Figure 9 depicts the lower-level CK and upper-level CE for observation and simulations. Here, positive CK (CE) indicates the energy conversion from the background kinetic energy (eddy available potential energy) to SSE kinetic energy. Overall, the spatial patterns of CK and CE in the present climate are realistically simulated (pattern correlation coefficients being 0.6 and 0.8 respectively). However, both CK and CE over the WNP are overestimated in the models. The overestimates of both CK and CE over the WNP could result from biased simulations of background circulations, such as monsoon trough and WNP subtropical high in the models (Fig. 1) and the TC intensity (Table 1). A comparison between present climate and future climate in the model simulations reveals that the CK is enhanced over the northwestern sector of the WNP (115°–140°E, 20°–35°N), whereas it is weakened in the South China Sea, Philippine Sea, and the eastern part of the WNP (Figs. 10b–d). The future change of CE to the north of Taiwan and the vicinity of Japan resembles that of CK except that the increase in CE over East China and Korean Peninsula becomes more evident. Moreover, the enhancement of CE extends over the subtropical WNP from East Asia to the date line. Notably, the enhancements of CK and CE to the north of Taiwan are consistent with the increase in mean TC wind speed (Fig. 2c). This suggests that both CK and CE may contribute to intensifying TC wind speed (as the TC gains more kinetic energy for growth) over the subtropical western WNP in the future. A previous study suggested that the CK was the dominant factor to the rapid intensification of TC in the WNP since the 1990s (Hong et al. 2018). In contrast to the historical observation, the simulations reveal that both the upper-level CE and lower-level CK contribute positively to the intensification of TC in the future warming climate (Figs. 10d,h).
Spatial distributions of the 850-hPa barotropic energy conversion (term CK; 10−5 m2 s−3) of the EKE budget [Eq. (1)] for (a) observation, and the average of two models’ simulations of (b) present climate, (c) future climate, and (d) their difference (future minus present climate). (e)–(h) As in (a)–(d), but for the 200-hPa baroclinic energy conversion (term CE). Stippling in (d) and (h) indicates the change exceeding significance test at the 90% significance level. To ensure that the EKE be generated from TC, the EKE is calculated only when a TC passed through a 2.5° × 2.5° box.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
To understand the effect of global warming on scale interactions between mean flow and SSE (CKS–M) and between ISO and SSE (CKS–ISO), we examine the changes in CKS–M and CKS–ISO in the future climate (Fig. 10). Overall, the spatial patterns of CKS–M and CKS–ISO in the present climate are reasonably simulated but overestimated in amplitude (Figs. 11a,b,e–f), as seen in their combined effect of CK (Figs. 10a,b). The changes in CKS–M and CKS–ISO in the future share similar features. That is an increase in the regions of Southeast China, north of Taiwan, and the vicinity of Japan but a decrease in the regions of the eastern sector of the WNP and monsoon trough (south of 20°N). The decreases in CKS–M and CKS–ISO over the WNP monsoon trough area in the future are related to the weakening of the mean state (WNP monsoon trough) and ISO variability (Fig. 6c), leading to reduced TC genesis over the tropical WNP (south of 20°N) in the future climate (Fig. 2a). Based on the further examination of individual terms of CKS–M and CKS–ISO (not shown), we found that the TCs (SSEs) gain more kinetic energy through their interaction with the anomalous convergence of zonal winds associated with both seasonal-mean flow [i.e., the term
As in Fig. 10, but for the decompositions of CK for terms (a)–(d) CKS–M and (e)–(h) CKS–ISO at 850 hPa. Vectors denote the seasonal-mean (in the left panels) and ISO-associated (in the right panels) wind fields (m s−1), respectively.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Figure 10 shows that CE with a zonally elongated pattern over the warmer SST region over the subtropical North Pacific has a larger contribution at high levels to the intensification of TC wind speed in the future climate. The term CE,
TC-associated vertical velocity (Pa s−1) at 300 hPa for (a) observation, and the average of HiRAM and MRI simulations in (b) present climate, (c) future climate, and (d) their difference (future minus present climate). (e)–(h) As in (a)–(d), but for 300-hPa temperature (K). Stippling in (d) and (h) indicates the change exceeding significance test at the 90% level.
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Note that the intensification of TC wind speed for the abrupt change in the late 1990s and the future climate seems to be controlled by different physical processes. The barotropic energy conversion (CK) dominates in the present climate (Hong et al. 2018). In Figs. 10 and 11, both upper-level available potential energy conversion (CE) and the lower-level CK contribute to the increased intensification rate of TC in the future. The enhanced CE is due to enhancement of TC-associated perturbations of temperature and vertical velocity. In contrast, the increase of CK is primarily attributed to the scale interaction among eddy momentum and background seasonal-mean and ISO circulations. The kinetic energy of SSE (TC) shows significant increase over the region (South China, north of Taiwan, and the vicinity of Japan) with anomalous convergence of zonal wind associated with the enhancement of the WNPSH in the future.
6. Conclusions
The future changes in WNP TC activity, particularly the TC intensity and frequency, under global warming were investigated using the high-resolution (20 km) HiRAM and MRI models. Two time slice experiments were conducted for the present climate (1979–2003) and future climate (2075–99), respectively. In the present climate experiment, the models were forced by the observed SST and historical greenhouse gas concentration. In the future climate experiment, the models were forced by the greenhouse gas concentrations following the RCP8.5, together with the future SST obtained from the ensemble average of 28 CMIP5 models’ projections. The changes in TC activities and associated mechanisms responsible for these changes were discussed by comparing the simulated results in present and future climate experiments. Our main findings are illustrated in the schematic diagram (Fig. 13) and summarized as follows:
The TC characteristics over the WNP, including genesis number, genesis location, track density, and intensity, in the present climate were realistically simulated by the average of HiRAM and MRI. This indicates that the high-resolution models of HiRAM and MRI have adequate skills to simulate the changes in WNP TC activity in the future.
The average of the simulations revealed that the WNP TC genesis number in the future would decrease by approximately 50% (from 23.5 to 11.7 yr−1). Conversely, the TC intensity, defined as the 850-hPa mean wind speed of TC, would increase by nearly 15%. The mean genesis location of TCs would sift slightly eastward (related to the El Niño–like warming) and northward (related to the weakened monsoon trough over the tropics). The averaged moving speed of TCs would increase in response to the enhancement of steering flow associated with the change in WNPSH under global warming.
The WNP monsoon trough would decrease remarkably in the future. The weakening of the monsoon trough would result in a lower-level anticyclone anomaly and ISO variability, which would suppress substantially the TC genesis count over the tropical WNP (south of 20°N). Meanwhile, the TC intensity would increase evidently over the subtropical WNP (20°–40°N) where the SST and moisture contents would increase evidently in the future. The strengthened TC intensity mainly results from a larger intensification rate under global warming as the TC lifetime shows little change.
To understand the physical processes responsible for the increased TC intensification rate under global warming, we diagnosed the kinetic energy budget of SSE associated with all TC cases in the present and future climate simulations. The results revealed that the intensification of TC wind speed in the future may be attributable to the increases in available potential energy to kinetic energy conversion of SSE (CE) at the upper levels and the lower-level barotropic energy conversion (CK) from mean flow and ISO kinetic energy to SSE kinetic energy. The increase in CE is due to enhancement of TC-related perturbations of temperature and vertical velocity over the subtropical WNP, where the SST increases significantly, and the descending motion of the Hadley cell tends to decrease under global warming. In contrast, the increase in CK is primarily attributed to the scale interaction between SSE and anomalous convergence of zonal winds associated with both the seasonal-mean flow and ISO, which are closely related to the enhancement of the WNPSH in future.
Schematic diagram to illustrate possible physical processes leading to the increase of TC intensity in far future under a warming climate. The blue and red curves represent the WNP flows in the present and future climates, respectively. Shading regions mark the future changes in circulation (gray area) and EKE conversion processes (blue, purple, and orange areas).
Citation: Journal of Climate 34, 6; 10.1175/JCLI-D-20-0417.1
Our study shows that the El Niño–like warming shifts the mean TC genesis location eastward (Fig. 8a), which may prolong the duration of TC lifetime. However, the mean moving speed of TCs also significantly increases in a warming climate because of the enhancement of steering flow that is largely related to the WNP subtropical high (Fig. 7). The WNP subtropical high enhances in future, which gives enhanced steering flow. Moreover, the mean TC genesis location shifts northward slightly in future. The translation speed of TCs is higher in the higher latitude. In summary, the effect of increased TC moving speed dominates the shortening of TC life span although it is offset partly by the effect of El Niño–like warming on prolonging TC life span. Thus, the net effect of large-scale environmental changes leads to a shortened TC life span under future global warming. The strengthening of WNP subtropical high substantially weakens the WNP monsoon trough, leading to a remarkable decrease of TC genesis number in the WNP in far future. What causes the strengthening of WNP subtropical high is under investigating and will be present in the future.
The results shown in this study are based on AGCM simulations, which lack active air–sea coupling processes. The TC-induced Ekman pumping or the cold wake in the ocean may feedback to the TC intensity change (Duan et al. 2013; Jullien et al. 2014; Wang et al. 2018; Roberts et al. 2020). However, this effect is missing in the AGCM simulations. A future study based on the simulations and projections of the CMIP6 High Resolution Model Intercomparison Project (or other high-resolution coupled GCMs) is needed to understand the contributions of air–sea coupling processes to the TC intensity change.
Acknowledgments
We thank the anonymous reviewers for their valuable comments and suggestions. We also thank Mr. M.-Y. Lee for making the figures. The observational data of atmosphere and ocean used in this study are from the NOAA Earth System Research Laboratory, NCEP/CFSR and NOAA_ERSST_V4. The tropical cyclone data were obtained from the Joint Typhoon Warning Center (JTWC). This study was supported by the Ministry of Science and Technology, Taiwan (under Grants 106-2111-M-845-001, 106-2111-M-001-005, 106-2621-M-865-001, 106-2111-M-003-001, and 107-2119-M-003-001), and the TOUGOU Program of the MEXT, Japan (JPMXD0717935561).
REFERENCES
Bengtsson, L., K. I. Hodges, and N. Keenlyside, 2009: Will extratropical storms intensify in a warmer climate? J. Climate, 22, 2276–2301, https://doi.org/10.1175/2008JCLI2678.1.
Camargo, S. J., 2013: Global and regional aspects of tropical cyclone activity in the CMIP5 models. J. Climate, 26, 9880–9902, https://doi.org/10.1175/JCLI-D-12-00549.1.
Cha, E. J., T. R. Knutson, T. C. Lee, M. Ying, and T. Nakaegawa, 2020: Third assessment on impacts of climate change on tropical cyclones in the Typhoon Committee Region—Part II: Future projections. Trop. Cyclone Res. Rev., 9, 75–86, https://doi.org/10.1016/j.tcrr.2020.04.005.
Chan, J. C. L., 2000: Tropical cyclone activity over the western North Pacific associated with El Niño and La Niña events. J. Climate, 13, 2960–2972, https://doi.org/10.1175/1520-0442(2000)013<2960:TCAOTW>2.0.CO;2.
Chan, J. C. L., 2005: Interannual and interdecadal variations of tropical cyclone activity over the western North Pacific. Meteor. Atmos. Phys., 89, 143–152, https://doi.org/10.1007/s00703-005-0126-y.
Chen, C.-A., H.-H. Hsu, C.-C. Hong, P.-G. Chiu, C.-Y. Tu, S.-J. Lin, and A. Kitoh, 2019: Seasonal precipitation change in the western North Pacific and East Asia under global warming in two high-resolution AGCMs. Climate Dyn., 53, 5583–5605, https://doi.org/10.1007/s00382-019-04883-1.
Chen, J.-H., and S.-J. Lin, 2011: The remarkable predictability of inter-annual variability of Atlantic hurricanes during the past decade. Geophys. Res. Lett., 38, L11804, https://doi.org/10.1029/2011GL047629.
Chia, H.-H., and C. F. Ropelewski, 2002: The interannual variability in the genesis location of tropical cyclones in the northwest Pacific. J. Climate, 15, 2934–2944, https://doi.org/10.1175/1520-0442(2002)015<2934:TIVITG>2.0.CO;2.
Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Climate, 24, 3484–3519, https://doi.org/10.1175/2011JCLI3955.1.
Duan, Y., R. Wu, R. Yu, and X. Liang, 2013: Numerical simulation of changes in tropical cyclone intensity using a coupled air-sea model. Acta Meteor. Sin., 27, 658–672, https://doi.org/10.1007/s13351-013-0503-2.
Emanuel, K., C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. J. Atmos. Sci., 61, 843–858, https://doi.org/10.1175/1520-0469(2004)061<0843:ECOTCI>2.0.CO;2.
Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347–368, https://doi.org/10.1175/BAMS-89-3-347.
Grossmann, I., and M. G. Morgan, 2011: Tropical cyclones, climate change, and scientific uncertainty: What do we know, what does it mean, and what should be done? Climatic Change, 108, 543–579, https://doi.org/10.1007/s10584-011-0020-1.
Hong, C.-C., Y.-K. Wu, T. Li, and C.-C. Chang, 2013: The climate regime shift in Pacific during 1996/1997. Climate Dyn., 43, 435–446, https://doi.org/10.1007/s00382-013-1867-9.
Hong, C.-C., C.-H. Tsou, M.-Y. Lee, C.-C. Chang, H.-H. Hsu, and K.-C. Chen, 2018: Effect of ISO-SSE interaction on accelerating the TS to severe TS development in the WNP since the late 1990s. Geophys. Res. Lett., 45, 12 008–12 014, https://doi.org/10.1029/2018GL079548.
Hsu, P.-C., T. Li, and C.-H. Tsou, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part I: Energetics diagnosis. J. Climate, 24, 927–941, https://doi.org/10.1175/2010JCLI3833.1.
Hsu, P.-C., T.-H. Lee, C.-H. Tsou, P.-S. Chu, Y. Qian, and M. Bi, 2017: Role of scale interactions in the abrupt change of tropical cyclone in autumn over the western North Pacific. Climate Dyn., 49, 3175–3192, https://doi.org/10.1007/s00382-016-3504-x.
IPCC, 2013: Summary for policymakers. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 3–29.
Jullien, S., P. Marchesiello, C. E. Menkes, J. Lefèvre, N. C. Jourdain, G. Samson, and M. Lengaigne, 2014: Ocean feedback to tropical cyclones: Climatology and processes. Climate Dyn., 43, 2831–2854, https://doi.org/10.1007/s00382-014-2096-6.
Kitoh, A., and H. Endo, 2016: Changes in precipitation extremes projected by a 20-km mesh global atmospheric model. Wea. Climate Extremes, 11, 41–52, https://doi.org/10.1016/j.wace.2015.09.001.
Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157–163, https://doi.org/10.1038/ngeo779.
Knutson, T. R., S. J. Camargo, J. C. Chan, K. Emanuel, C. H. Ho, J. Kossin, and L. Wu, 2020: Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming. Bull. Amer. Meteor. Soc., 101, E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1.
Kusunoki, S., 2018: Is the global atmospheric model MRI-AGCM3.2 better than the CMIP5 atmospheric models in simulating precipitation over East Asia? Climate Dyn., 51, 4489–4510, https://doi.org/10.1007/s00382-016-3335-9.
Kusunoki, S., and R. Mizuta, 2013: Changes in precipitation intensity over East Asia during the 20th and 21st centuries simulated by a global atmospheric model with a 60 km grid size. J. Geophys. Res. Atmos., 118, 11 007–11 016, https://doi.org/10.1002/jgrd.50877.
Lau, N. C., and J. J. Ploshay, 2009: Simulation of synoptic- and subsynoptic-scale phenomena associated with the East Asian summer monsoon using a high-resolution GCM. Mon. Wea. Rev., 137, 137–160, https://doi.org/10.1175/2008MWR2511.1.
Maloney, E. D., and D. L. Hartmann, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: Observations. J. Atmos. Sci., 58, 2545–2558, https://doi.org/10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2.
Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z.
Mizuta, R., O. Arakawa, T. Ose, S. Kusunoki, H. Endo, and A. Kitoh, 2014: Classification of CMIP5 future climate responses by the tropical sea surface temperature changes. SOLA, 10, 167–171, https://doi.org/10.2151/sola.2014-035.
Murakami, H., and M. Sugi, 2010: Effect of model resolution on tropical cyclone climate projections. SOLA, 6, 73–76, https://doi.org/10.2151/sola.2010-019.
Murakami, H., B. Wang, and A. Kitoh, 2011: Future change of western North Pacific typhoons: Projections by a 20-km-mesh global atmospheric model. J. Climate, 24, 1154–1169, https://doi.org/10.1175/2010JCLI3723.1.
Murakami, H., and Coauthors, 2012: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Climate, 25, 3237–3260, https://doi.org/10.1175/JCLI-D-11-00415.1.
Murakami, H., P. C. Hsu, O. Arakawa, and T. Li, 2014: Influence of model biases on projected future changes in tropical cyclone frequency of occurrence. J. Climate, 27, 2159–2181, https://doi.org/10.1175/JCLI-D-13-00436.1.
Okada, Y., T. Takemi, H. Ishikawa, S. Kusunoki, and R. Mizuta, 2017: Future changes in atmospheric conditions for the seasonal evolution of the baiu as revealed from projected AGCM experiments. J. Meteor. Soc. Japan, 95, 239–260, https://doi.org/10.2151/jmsj.2017-013.
Roberts, M. J., and Coauthors, 2020: Projected future changes in tropical cyclones using the CMIP6 HighResMIP multi-model ensemble. Geophys. Res. Lett., 47, e2020GL088662, https://doi.org/10.1029/2020GL088662.
Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1.
Simpson, R. H., 1974: The hurricane disaster potential scale. Weatherwise, 27, 169–186, https://doi.org/10.1080/00431672.1974.9931702.
Tory, K. J., S. S. Chand, J. L. McBride, H. Ye, and R. A. Dare, 2013: Projected changes in late-twenty-first-century tropical cyclone frequency in 13 coupled climate models from phase 5 of the Coupled Model Intercomparison Project. J. Climate, 26, 9946–9959, https://doi.org/10.1175/JCLI-D-13-00010.1.
Tsou, C.-H., H.-H. Hsu, and P.-C. Hsu, 2014: The role of multi-scale interaction in synoptic-scale eddy kinetic energy over the western North Pacific in autumn. J. Climate, 27, 3750–3766, https://doi.org/10.1175/JCLI-D-13-00380.1.
Tsou, C.-H., P.-Y. Huang, C.-Y. Tu, C.-T. Chen, T.-P. Tzeng, and C.-T. Chen, 2016: Present simulation and future typhoon activity projection over western North Pacific and Taiwan/East Coast of China in 20-km HiRAM climate model. Terr. Atmos. Oceanic Sci., 27, 687–703, https://doi.org/10.3319/TAO.2016.06.13.04.
Vecchi, G. A., and B. J. Soden, 2007: Increased tropical Atlantic wind shear in model projections of global warming. Geophys. Res. Lett., 34, L08702, https://doi.org/10.1029/2006GL028905.
Vecchi, G. A., and Coauthors, 2019: Tropical cyclone sensitivities to CO2 doubling: Roles of atmospheric resolution, synoptic variability and background climate changes. Climate Dyn., 53, 5999–6033, https://doi.org/10.1007/s00382-019-04913-y.
Wang, C., C. Li, M. Mu, and W. Duan, 2013: Seasonal modulations of different impacts of two types of ENSO events on tropical cyclone activity in the western North Pacific. Climate Dyn., 40, 2887–2902, https://doi.org/10.1007/s00382-012-1434-9.
Wang, X., X. Wang, and P. C. Chu, 2018: Air-sea interactions during rapid intensification of Typhoon Fengshen (2008). Deep-Sea Res. I, 140, 63–77, https://doi.org/10.1016/j.dsr.2018.08.009.
Weller, E., S.-K. Min, W. Cai, F. W. Zwiers, Y.-H. Kim, and D. Lee, 2016: Human-caused Indo-Pacific warm pool expansion. Sci. Adv., 2, e1501719, https://doi.org/10.1126/sciadv.1501719.
Weng, C.-H., and H.-H. Hsu, 2017: Intraseasonal oscillation enhancing C5 typhoon occurrence over the tropical western North Pacific. Geophys. Res. Lett., 44, 3339–3345, https://doi.org/10.1002/2017GL072743.
Yamaguchi, M., J. C. L. Chan, I.-J. Moon, K. Yoshida, and R. Mizuta, 2020: Global warming changes tropical cyclone translation speed. Nat. Commun., 11, 47, https://doi.org/10.1038/s41467-019-13902-y.
Yokoi, S., C. Takahashi, K. Yasunaga, and R. Shirooka, 2012: Multi-model projection of tropical cyclone genesis frequency over the western North Pacific: CMIP5 results. SOLA, 8, 137–140, https://doi.org/10.2151/sola.2012-034.
Yoshida, K., M. Sugi, R. Mizuta, H. Murakami, and M. Ishii, 2017: Future changes in tropical cyclone activity in high-resolution large-ensemble simulations. Geophys. Res. Lett., 44, 9910–9917, https://doi.org/10.1002/2017GL075058.
Yukimoto, S., and Coauthors, 2011: Meteorological Research Institute-Earth System Model v1 (MRIESM1)—Model description. Meteorological Research Institute Tech. Rep. 64, 88 pp., https://www.mri-jma.go.jp/Publish/Technical/DATA/VOL_64/tec_rep_mri_64.pdf.
Zhang, G., H. Murakami, T. Knutson, R. Mizuta, and K. Yoshida, 2020: Tropical cyclone motion in changing climate. Sci. Adv., 6, eaaz7610, https://doi.org/10.1126/sciadv.aaz7610.
Zhao, M., I. M. Held, S. J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 6653–6678, https://doi.org/10.1175/2009JCLI3049.1.
Zhou, H., P.-C. Hsu, and Y. Qian, 2018: Close linkage between quasi-biweekly oscillation and tropical cyclone intensification over the western North Pacific. Atmos. Sci. Lett., 19, e826, https://doi.org/10.1002/asl.826.
