Amplified Bimodal Distribution of Western North Pacific Tropical Cyclone Lifetime Maximum Intensity

Abstract

The impact of a warming climate on tropical cyclones (TCs) remains unclear. Here, we find that the probability density function for western North Pacific TC lifetime maximum intensity (LMI) has an amplified bimodal distribution in recent years. This change implies a trend toward more extreme TCs and fewer moderate TCs. Changes in the TC LMI distribution are associated with alterations in the occurrence of rapidly intensifying TCs. Changes in TC tracks, due to alterations in the steering flow linked to a weakening Hadley cell, cause more TCs to move northwestward into a more favorable environment for intensification with large ocean heat content. Consequently, more rapidly intensifying TCs reach higher intensities, significantly contributing to the observed amplified bimodal distribution. These findings provide new insights into changes in TC intensity and highlight the increasing threat to coastal areas from more intense TCs in a warming climate.

Introduction

Historically, tropical cyclones (TCs) have caused significant economic losses, often resulting in damages amounting to billions of dollars (Klotzbach et al., 2018; Mendelsohn et al., 2012). Forecasting TC intensity remains a significant challenge due to the poor understanding of complex atmospheric interactions, limited predictability, and short reliable observation records (Elsberry et al., 2013; Emanuel, 2018). Lifetime maximum intensity (LMI) is generally regarded as a relatively reliable index due to its geographic consistency and resistance to data uncertainty (Kossin et al., 2014; Zhao et al., 2022). Studies have identified a bimodal probability density function (PDF) of LMI for global TCs, with peaks at approximately 40 and 100 kt (Managanello et al., 2012) and 50 and 120 kt (Kossin et al., 2013), and have highlighted the significant impact of rapid intensification (RI) of TCs on the PDF pattern (Soloviev et al., 2014). The PDF of LMI in individual basins exhibits this bimodal structure primarily due to RI rather than TC duration (Lee et al., 2016). Moreover, there is an increasing trend in annual LMI standard deviations and interquartile ranges, attributed to the rising mean LMI of rapidly intensifying tropical cyclones (RITCs) and the decreasing mean LMI of non-RITCs (Song et al., 2018).

A significant increase in the proportion of category 4–5 TCs has been observed in most TC basins in recent decades (Elsner et al., 2008; Emanuel, 2005; Kang & Elsner, 2012, 2016; Klotzbach et al., 2022; Klotzbach & Landsea, 2015; Knutson et al., 2010; Kossin et al., 2020; Sobel et al., 2016; Webster et al., 2005). This increase has been linked to global warming (Balaguru et al., 2012, 2016; Kang & Elsner, 2016; Mei et al., 2015). Theoretical mechanisms and model simulations suggest that TC intensity will increase on both basin and global scales. Elsner (2020) noted that the intensity of strong TCs has increased, while Garner (2023) showed that TC intensification rates have already changed due to warming oceans (Garner, 2023). Additionally, Wang, Wu, et al. (2022) used ocean current data to demonstrate an intensifying trend for weaker TCs. However, the response of TC intensity to climate change remains uncertain, due to relatively short reliable observational TC records and inconsistent projected changes in a warming climate (Knutson et al., 2019, 2020).

The influence of ocean-atmosphere interactions on TC intensification has been emphasized in various studies, particularly highlighting the roles of sea surface temperatures (SSTs), vertical wind shear, and mid-tropospheric moisture (Balaguru et al., 2024; Emanuel, 2005; Ramsay & Sobel, 2011; Vecchi & Soden, 2007). Previous studies also showed that TC track changes are a potentially important factor influencing TC intensity on a basin-wide scale (Kossin & Camargo, 2009; Wu et al., 2018; Wu & Wang, 2008). These observed track shifts have been related to anthropogenic global warming (Wu et al., 2023; Zhao et al., 2022).

The studies mentioned above suggest that the LMI distribution has already changed due to climate change. However, these studies have mainly focused on hemisphere-scale and global-scale analyses, with limited physical analyses of the changes in the LMI distribution. Since the LMI distribution varies across different ocean basins (Lee et al., 2016), understanding the distribution in each basin is essential for a comprehensive analysis of the evolving trends. Given that western North Pacific (WNP) TCs, on average, account for approximately one-third of all TCs worldwide (Schreck et al., 2014; Wang, Wu, et al., 2022; Zhao et al., 2010), this study focuses on the LMI distribution of TCs in the WNP over the past four decades. We find that the LMI distribution has transformed from a quasi-trimodal to a bimodal PDF. We investigate the underlying causes and establish a connection with changes in RITCs. The changing zonal and meridional winds altered TC tracks, steering them into more favorable environmental conditions for rapid intensification (RI). This study elucidates the specific mechanisms driving these changes in the context of global warming.

Data and Methods

TC data including latitude, longitude, and maximum sustained wind speed at 6-hr intervals are obtained from the Joint Typhoon Warning Center's (JTWC) best track data set that is archived in the International Best Track Archive for Climate Stewardship (IBTrACS) (Knapp et al., 2010). TC intensity records from JTWC exhibit dynamical consistency with changes in the atmospheric-oceanic environment (Wu & Zhao, 2012). All TCs with 1-min TC maximum sustained wind (MSW) reaching 34 kt are considered. We also validate our results using data sets from the Japan Meteorological Agency (JMA; RSMC Tokyo); the China Meteorological Administration's Shanghai Typhoon Institute (CMA), and the Hong Kong Observatory (HKO) agencies as archived in IBTrACS. Owing to different wind speed averaging periods used in reporting the TC MSW for different agencies, the MSW from all agencies are converted to a 1-min MSW following Knapp and Kruk (2010) to facilitate a more direct comparison with JTWC. The exact formula used for converting the winds from CMA, HKO and JMA to 1-min MSW can be found in Table S1 in Supporting Information S1. The adjusted TC intensity from the CMA, HKO and JMA agencies are used for validation, unless otherwise specified.

Generally, RI has been defined as an intensity change threshold of 30 kt in 24 hr, which is equivalent to approximately the 95th percentile of 24-hr overwater intensity changes (Kaplan et al., 2010; Wang et al., 2015; X. Wang & Liu, 2016). In this study, to avoid ambiguity in determining the onset and duration of the RI process, we follow B. Wang & Zhou (2008) and use the following definition. All three of these criteria must be met.

• An increase of ≥ 5 kt in TC intensity in the first 6 hr,

• An increase of ≥ 10 kt in TC intensity in the first 12 hr,

• An increase of ≥ 30 kt in TC intensity in 24 hr.

We focus on each TC that underwent RI at least once during its lifetime. If a TC experienced multiple RI events separated by times when RI did not occur, then each RI event is counted. The TC intensification rate is defined as the average 24-hr intensity change during the RI period. The RI occurrence latitude (longitude) is defined as the latitude (longitude) where RI began.

Monthly atmospheric environmental parameters are derived from the ERA5 monthly reanalysis on a 0.25° × 0.25° latitude-longitude grid (Hersbach et al., 2020). Monthly sea surface temperature (SST) data on a 2.0° × 2.0° latitude-longitude grid box are obtained from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature version 5 (ERSST v5) data set (Huang, Thorne, Banzon, Boyer, Chepurin, Lawrimore, & et al., 2017). The Pacific Decadal Oscillation (PDO) index from NOAA is defined as the first EOF mode time series of monthly mean SST anomalies north of 20°N in the North Pacific Ocean (Mantua & Hare, 2002). Global mean SSTs (GMSST) are calculated over the oceanic domain between 40°S and 60°N, as has been done in several previous studies (Liguori et al., 2020; Smith & Reynolds, 1998; Zhao et al., 2022). Monthly mean oceanic temperatures are obtained from the Simple Ocean Data Assimilation (SODA) data set version 3, on a 0.25° × 0.25° latitude-longitude grid and with 50 vertical layers (Carton et al., 2018).

Signals on decadal or longer time scales are defined as the low-pass time series that is calculated as a five-point running mean of the raw time series. The statistical significance of correlations and linear trends is tested using a two-tailed Student's t-test. We test statistical significance of correlation coefficients after five-point smoothing using an effective degrees of freedom calculation following previous studies (Li et al., 2013; Pyper & Peterman, 1998; Xie et al., 2019). Effective degrees of freedom are calculated with the following expression: 1 $$\frac{1}{{N}^{eff}}\mathit{\approx }\frac{1}{N}+\frac{2}{N}\sum\limits _{k=1}^{N}\frac{N-k}{N}{r}_{1}(k){r}_{2}(k),$$ where N is the sample size and $${r}_{1}(k)$$ and $${r}_{2}(k)$$ are the autocorrelations of the two time series with a lag k, respectively.

In this research, vertical wind shear (VWS) is calculated as the magnitude of the wind vector difference between the 200 and 850-hPa pressure levels. TC heat potential (TCHP) is used to calculate ocean heat content (Leipper & Volgenau, 1972): 2 $$TCHP={c}_{p}\rho \underset{D26{}}{\overset{0}{\int }}[T(z)-26]dz,$$ where c_p is the specific heat taken as 4178 J kg⁻¹ °C⁻¹; ρ is the density taken as 1026 kg m⁻³ in the upper ocean; D₂₆ is the depth in m of the 26°C isotherm; and T(z) is the temperature in °C.

Results

Recently Observed Bimodal Distribution in TC LMI

The PDF of WNP LMI for the period 1979–1997 exhibits a quasi-trimodal structure with three peaks at ∼50, 85, and 130 kt, corresponding to Saffir-Simpson Hurricane Wind Scale ratings of: tropical storm (TS), category 1–3 intensity, and category 4–5 intensity (Figure 1a). We find a similar distribution for the adjusted TC intensity in the JMA, CMA and HKO data sets (Figure S1 in Supporting Information S1). However, from 1998 to 2021, the distribution shifted to two peaks at 50 and 130 kt, with the peak representing category 1–3 disappearing. This indicates that the ratio of TCs in different intensity categories has changed over the past couple of decades. The increase in weak and strong TCs and the decrease in moderate TCs have jointly caused the increased bimodal structure of the LMI distribution (Figure S2 in Supporting Information S1). Annual TC frequency shows a slight downward trend (Figure 1b), while the annual average LMI has not changed significantly (Figure 1c). The standard deviation (STD), quantifying the amount of variation or dispersion in a set of values, shows a significant increasing trend (Figure 1d). Over the past four decades, weak TCs have become weaker, strong TCs have become stronger, and there are fewer TCs of moderate intensity (i.e., category 1–3).

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To explore the possible reasons for this shift to a bimodal LMI distribution, we next correlate decadal LMI STD and global SST (Figure 2a), with the aim of exploring the impact of global SST on WNP LMI STD at decadal and longer time scales. The correlation pattern between WNP LMI STD and global SST shows a global warming pattern, implying a significant impact of global warming on WNP.

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LMI STD. This is further supported by the high correlation between the timeseries of LMI STD and GMSST (Figure 2b). As mentioned above, the STD of LMI is determined by varying trends in TC frequency in different intensity categories. We find similar results when we correlate category 1–3 TCs and category 4–5 TCs with the global SST field (Figures S3a and S3c in Supporting Information S1). We do note that the correlation pattern that we observe somewhat resembles the Pacific decadal oscillation (PDO; Zhang et al., 1997). Prior studies have suggested that the PDO can influence WNP TC intensity (Wang et al., 2015; Zhao et al., 2016, 2019). We therefore further examine the potential relationship between the PDO and WNP LMI STD. The correlation between the frequency of category 1–3 TCs and both GMSST and the PDO index have a stronger relationship than for category 4–5 TCs. This implies that moderate TCs are more influenced by anthropogenic warming than strong TCs.

For TSs, there is no coherent correlation observed with global SST (Figure S4a in Supporting Information S1). The observed trend in TSs vanishes when removing TSs with lifespans of less than or equal to 2 days (Figure S4b in Supporting Information S1). This suggests that the increase in the number of TSs can be attributed primarily to those with short lifespans. Most of these short-lived storms form in the South China Sea (Figure S5 in Supporting Information S1) and are likely associated with improved observational capabilities, similar to what Landsea et al. (2010) found in the North Atlantic basin. Drawing from the results of Zheng and Wang (2023), one notable finding was the westward shift in tropical cyclone (TC) genesis, particularly during the low-intensity period (1994–2002), which led to the formation of less intense storms. This observation aligns with Figure S5 in Supporting Information S1, where we similarly find short-lived TSs forming in the western part of the South China Sea.

Potential Role of Changes in TC Tracks

Past studies have identified RITCs as a key factor driving the bimodal distribution (Lee et al., 2016; Soloviev et al., 2014; Song et al., 2018). We examined the relationship between decadal LMI STD and TC characteristics, including position, LMI and RI (Table S2 in Supporting Information S1). We find that LMI STD is highly correlated with RITC intensity, frequency and intensification rate. To understand the impact of increasing RITC proportion and intensity on the LMI distribution in the WNP, we quantified RITCs across different categories. The WNP TC intensification rate is increasing, with a northwestward shift in position, primarily due to increased SST and relative vorticity (Huang et al., 2024). While the frequency of RITCs in category 1–3 has decreased by 0.3 RITCs per decade, the frequency of RITCs in category 4–5 has increased by 0.6 RITCs per decade (Figure S6a in Supporting Information S1). This trend suggests an overall intensification of RITCs in the WNP (Figure S6b in Supporting Information S1), likely driven by higher SST and ocean heat content (Song et al., 2020). The intensification rate for all RITCs is rising, especially for category 4–5 RITCs, indicating that faster RI leads to stronger RITCs. With minimal changes in RITC frequency, increased RI events amplify RITC intensity, facilitating a quicker transition from category 1–3 to 4–5, reducing the middle peak of the LMI distribution and enhancing the right peak (e.g., the strongest peak) of the former quasi-trimodal structure.

As shown in Figure S6d in Supporting Information S1, there is an increasing trend in category 4–5 TCs off the eastern coast of Taiwan. This shift in RI positions correspond with changes in TC tracks, with more TCs taking a northward path in the WNP in recent decades. This TC track trend is expected to continue in the future climate (C. Wang & Wu, 2015). Our statistical analysis from 1979 to 2021 shows a more pronounced trend in RITCs (Figure 3). We observed a significant decrease in westward and northward tracks of weaker RITCs (Figure 3c) and an increase in northwestward tracks of stronger RITCs (Figure 3d). These trends seem to indicate that RITC intensity may be related to their tracks. Previous studies have indicated that TC intensity is influenced by the spatial distribution of environmental parameters (Wu et al., 2018). We find significant increases in upper ocean heat content (Figure S7 in Supporting Information S1), especially low latitude TCHP, which favors RI. Conducive atmospheric conditions also support TC intensification including reduced vertical wind shear and increased mid-level relative humidity (Figures S7c, S7d, S9a, and S9b in Supporting Information S1). However, we recognize that the locations with increasing RI do not perfectly align with all environmental factors, which is understandable since RI occurs after TCs pass through regions with favorable thermodynamic conditions, allowing them to accumulate energy. The combined effects of TC track transitions and favorable environmental factors have generally increased RITC intensity, reinforcing the right peak in the TC LMI distribution.

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Enhanced Contribution of Changes of TC Tracks to TC Rapid Intensification

To identify factors influencing TC path changes, we conducted singular value decomposition (SVD) analysis of RITC track density across different categories and global SST fields from 1979 to 2021 (Figure 4). The leading mode, accounting for 67% and 70% of the variance, respectively, highlights a declining trend in westward track of RITCs in category 1–3 and a northwestward trend in category 4–5 RITCs, similar to Figures 3c and 3d. The warming SST pattern closely correlates with the pattern in Figure 2a, with correlations of 0.96 for category 1–3 and 0.89 for category 4–5. This indicates a consistent variation pattern between RITC track density and SST.

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ENSO has been shown to have a significant impact on WNP TC intensity via its modulation of the large-scale dynamic and thermodynamic environment (Camargo & Sobel, 2005; Kang et al., 2019; B. Wang and Chan, 2002; Zhao et al., 2011), with stronger TC intensities during El Nino years relative to La Nina years. Since correlations between the first leading SVD mode and RITC track density for categories 4–5 are lower than for categories 1–3 on decadal or longer time scales, we hypothesize that the annual maximum intensity of TCs is more strongly modulated by ENSO on interannual time scales for TCs in categories 4–5 relative to TCs in categories 1–3. We find the opposite relationship on decadal or longer time scales. Weaker RITCs exhibit a more stable and stronger response, likely due to a weaker variation in SST but a higher correlation between the two variables. We find similar results when examining TC and RITC track changes (Figure S8 in Supporting Information S1). This confirms that TC track changes are related to the warming climate, but further research is needed to understand the underlying causes.

TC motion is primarily influenced by the large-scale steering flow (Wang et al., 2011; Wu et al., 2005). Long-term linear trends of the steering flow indicate an apparent northwest-oriented migration (Figure S9a in Supporting Information S1). TCs typically move toward areas with positive changes in vorticity (Chan, 2005). Consequently, TCs will be more likely to move in that direction, as shown in the track density changes in Figures 3b and 3d. These changes correspond well to the region northeast of Taiwan that shows positive vorticity changes. In this sense, the steering flow and low-level vorticity trends likely drive TC track changes. In Figure S9b in Supporting Information S1, weakening westerlies around 30°N and increased divergence offshore of eastern China contribute to positive low-level vorticity trends. Figure S9c in Supporting Information S1 highlights a trend toward anomalous descent around 10°N and ascent around 30°N, forming a circulation with anomalous low-level southerlies. This trend may be part of the weakening Hadley cell, which has been tied to anthropogenic warming (Chemke & Yuval, 2023). Both weakened westerlies and a weakened Hadley cell induce a northwestern trend in the steering flow along the prevailing TC path, causing the TC track trend shown in Figure 3. By integrating changes in global SST, large-scale steering flow, and TC tracks, we can effectively explain the increase in strong TCs during recent decades. These changes contribute to the growth in the right peak in Figure 1a, causing an amplified bimodal TC intensity distribution in recent years.

Summary and Discussion

This study explored the PDF of WNP TC LMI in WNP and found a transition from a quasi-trimodal to an amplified bimodal distribution in recent decades. This shift suggests that strong TCs are getting stronger and weak TCs are getting weaker. Consequently, TCs that surpass the TS threshold are increasingly likely to reach higher intensities, posing greater risks to coastal areas in the WNP region.

Our findings suggest that the amplified bimodal distribution is driven by different factors in recent decades. The increase in the left peak is attributed to an increase in short-lived, weaker TSs in the South China Sea. On the other hand, the right peak, predominantly RITCs, is influenced by shifts in the large-scale steering flow. The large-scale steering flow in the WNP has tended to shift northwestward, allowing more TCs to enter a favorable environment for RI. Similar to projected changes in the large-scale steering flow in climate simulations under a global warming scenario (Wang et al., 2011; Wu et al., 2005), we hypothesize that anthropogenic global warming has changed the steering flow. Environmental factors such as increased TCHP, decreased vertical wind shear and increased mid-level relative humidity, have allowed more RITCs to achieve higher intensities. This shift in TC track patterns, driven by the weakened Hadley cell, enhances the right peak of the TC LMI distribution.

When extending the JTWC TC intensity record from 1945 to 2022, we continue to find an increasing STD of annual LMI (Figure S10 in Supporting Information S1), arising from more intense TCs and fewer moderate TCs with time. Given changes in measurement and analysis practices at JTWC since the mid-1940s, there are systematic biases in reported TC wind speeds that must be accounted for when analyzing changes in the LMI distribution. Studies have also suggested that TC maximum wind speeds in the JTWC data set before 1973 were overestimated (Emanuel, 2005, 2007). Here, the TC intensity record in the JTWC data set before 1973 is adjusted following Emanuel (2005). Using a downscaling technique originally developed by Emanuel et al. (2006, 2008), Zhao et al. (2014) suggested that the adjusted TC records in the JTWC data set and their simulation results showed strong consistency, especially regarding the frequency of category 4–5 TCs since the 1940s. In this sense, the recently observed amplified bimodal probability density distribution of WNP TC LMI may be due to global warming.

Although we have identified trends in the PDF of LMI in WNP, its applicability on a global scale still needs to be examined. In the WNP, TC track changes are a significant factor influencing the LMI distribution. For other basins, we need to determine if TC track shifts are critical or if other TC changes, such as formation locations, might influence the LMI distribution. Additionally, further study is needed to understand the increase in short-lived TS in the South China Sea region—whether these changes are due to improved observational technology, interdecadal shifts in circulation patterns or a combination of both factors. This study proposes new avenues for understanding observed changes in the LMI distribution as well as examining its future implications under changing climate conditions.

Acknowledgments

HZ acknowledges support by the National Key R&D Program of China (2022YFF0801602) and the Natural Science Foundation of China (42192551). PK acknowledges support by the G. Unger Vetlesen Foundation. TS acknowledges Guidance Project for Industrial Technology Development and Application Plan of Fujian Province (2024Y0075).

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Data Availability Statement

The JTWC best track is available at . The IBTrACS data set is available at (Gahtan et al., 2024). The NOAA ERSST-v5 data are available at (Huang et al., 2017). The SODA 3.15.2 are available at . The ERA5 monthly averaged reanalysis data on pressure levels is available at (Hersbach et al., 2023). The PDO index is available at: .

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