Climatological Analysis of Tropical Cyclone Intensity Changes Under

1 Climatological Analysis of Tropical Cyclone Intensity Changes under

2 Moderate Vertical Wind Shear

∗ 3 Rosimar Rios-Berrios

4 Department of Atmospheric and Environmental Sciences,

5 University at Albany, State University of New York, Albany, New York

6 Ryan D. Torn

7 Department of Atmospheric and Environmental Sciences,

8 University at Albany, State University of New York, Albany, New York

∗ 9 Corresponding author address: University at Albany, State University of New York, DAES-ES

10 325, 1400 Washington Ave., Albany, NY 12222

11 E-mail: [email protected]

Generated using v4.3.2 of the AMS LATEX template 1 ABSTRACT

12 Although infrequent, tropical cyclones (TCs) can intensify under moder-

13 ate vertical wind shear (VWS). A potential hypothesis is that other fac-

14 tors—associated with both the TC and its environment—can help offset the

15 effects of VWS and aid intensiﬁcation. This hypothesis was tested with a large

16 dataset of six-hourly best tracks and environmental diagnostics for global

17 TCs between 1982–2014. Moderate VWS was objectively deﬁned as 4.5–

−1 th th 18 11.0 m s , which represents the 25 to 75 percentiles of the global dis-

19 tribution of 200–850 hPa VWS magnitude around TCs. Intensifying events

20 (i.e., unique six-hourly data points) were compared against steady state events

21 to determine which TC and environmental characteristics favored intensiﬁca-

22 tion under moderate VWS. This comparison showed that intensifying events

23 were signiﬁcantly stronger, closer to the equator, larger, and moving with a

24 more westward motion than steady state events. Furthermore, intensifying

25 events moved within environments characterized by warmer sea-surface tem-

26 peratures, greater midtropospheric water vapor, and more easterly VWS than

27 steady state events. Storm-relative, shear-relative composites suggested that

28 the coupling of water vapor, surface latent heat ﬂuxes, and storm-relative ﬂow

29 asymmetries was conducive for less dry air intrusions and more symmetric

30 rainfall in intensifying events. Lastly, the comparison showed no systematic

31 differences between environmental wind proﬁles possibly due to the large

32 temporal variability of VWS, which has been neglected in recent modeling

33 studies.

2 34 1. Introduction

35 Environmental vertical wind shear (VWS) is one of the most inhibiting factors for tropical cy-

36 clone (TC) intensiﬁcation. Climatological analyses for TCs in different basins consistently show

37 that the stronger the VWS, the smaller the chances of intensiﬁcation (Merrill 1988; DeMaria and

38 Kaplan 1994; Kaplan and DeMaria 2003; Paterson et al. 2005; Hendricks et al. 2010). The impor-

39 tance of VWS is also evident in statistical-dynamical intensity prediction models, which typically

40 rank VWS as one of the top environmental predictors of TC intensity changes (e.g., DeMaria and

41 Kaplan 1999; Emanuel et al. 2004). However, the timing and likelihood of intensiﬁcation becomes

42 highly uncertain under moderate VWS (Zhang and Tao 2013; Tao and Zhang 2015), which can be

43 deﬁned as the range of VWS magnitudes that are neither too weak to have little inﬂuence on in-

44 tensity changes nor too strong to completely halt intensiﬁcation. Intensity forecasts for TCs under

45 moderate VWS are characterized by large errors (Bhatia and Nolan 2013), presumably because

46 other factors—associated with both the TC and its environment—can help offset the negative ef-

47 fects of VWS and lead to intensiﬁcation. A comprehensive analysis of moderate VWS scenarios is

48 thus needed to identify TC and environmental characteristics that are conducive for intensiﬁcation

49 under moderate VWS.

50 Certain kinematic aspects of the TC vortex, such as size, intensity, and latitude, can impact in-

51 tensity changes in sheared environments (Jones 1995; DeMaria 1996; Reasor et al. 2004; Riemer

52 and Montgomery 2011; Tang and Emanuel 2012; Riemer et al. 2013; Reasor and Montgomery

53 2015). Given that VWS tilts the vortex from its upright position, vortex re-alignment is a process

54 by which a TC can overcome the effects of VWS and intensify in a sheared environment. Vari-

55 ous mechanisms have been proposed to explain vortex re-alignment, including vortex precession

56 (Jones 1995) and damping of vortex Rossby waves (Reasor et al. 2004). Regardless of the physical

3 57 mechanism, theoretical and idealized modeling studies agree that large, strong, and high-latitude

58 TCs have a faster vortex re-alignment than small, weak, low-latitude TCs (Jones 1995; DeMaria

59 1996; Reasor et al. 2004; Reasor and Montgomery 2015). The size and intensity of a TC circula-

60 tion can also modulate the resilience of a TC to another effect of VWS —dry air intrusion. Large

61 and strong TCs in numerical simulations are less prone to dry air intrusions from the environment

62 than small and weak TCs (Riemer and Montgomery 2011; Riemer et al. 2013). Furthermore, the

63 effects of dry air intrusion on intensity change depend on the initial intensity, such that the same

64 amount of dry intrusion could be more detrimental for a weak TC than for a strong TC (Tang and

65 Emanuel 2010, 2012). The applicability of these results to observed TCs under moderate VWS

66 remains unknown.

67 Kinematic aspects of the TC environment, such as the shape of the environmental wind proﬁle,

68 can also inﬂuence intensity changes in sheared environments (Zeng et al. 2010; Onderlinde and

69 Nolan 2014, 2016; Wang et al. 2015; Finocchio et al. 2016). Through idealized simulations and

70 a statistical analysis, Onderlinde and Nolan (2014) showed that development and intensiﬁcation

71 happens faster when the environmental winds rotate clockwise with height [i.e., positive tropical

72 cyclone-relative helicity (TCREH)] compared to when the environmental winds rotate counter-

73 clockwise with height (negative TCREH). Intensiﬁcation happens faster under positive TCREH

74 because surface latent heat ﬂuxes are stronger cyclonically upwind of the shear-organized convec-

75 tion, and this setup aids the azimuthal propagation of convection and vortex re-alignment (Onder-

76 linde and Nolan 2016). Using the same modeling framework as in Onderlinde and Nolan (2014),

77 Finocchio et al. (2016) tested the sensitivity of intensity changes to the height and depth of VWS.

78 Their simulations showed that intensiﬁcation happens faster in environments where the shear is

79 concentrated in the upper troposphere than in environments where the shear is concentrated in the

80 lower troposphere. Finocchio et al. (2016) attributed the different intensiﬁcation rates to the tim-

4 81 ing and likelihood of vortex re-alignment. A potential limitation of these studies is that VWS was

82 held constant throughout ﬁve days, which may not be a good representation of real environments.

83 Nevertheless, recent statistical studies have also found sensitivity of intensity change to the layer

84 of VWS (Zeng et al. 2010; Wang et al. 2015) .

85 Thermodynamic characteristics of the TC and the environment can also be inﬂuential for inten-

86 siﬁcation under moderate VWS (Molinari et al. 2004, 2006; Shelton and Molinari 2009; Nguyen

87 and Molinari 2012; Reasor and Eastin 2012; Chen and Gopalakrishnan 2015; Nguyen and Molinari

88 2015). Persistent convective outbreaks in the downshear quadrant and inside the radius of max-

89 imum winds were linked to the intensiﬁcation of Hurricane Danny (1997; Molinari et al. 2004),

90 Hurricane Irene (1999; Nguyen and Molinari 2012), Tropical Storm Gabrielle (2001; Molinari

91 et al. 2006; Nguyen and Molinari 2015), and Hurricane Claudette (2003; Shelton and Molinari

92 2009). These convective outbreaks happened when the TCs moved over warm sea-surface temper-

◦ 93 atures (SST) of at least 28 C and through near-saturated environments. In the case of Hurricane

94 Claudette, convective activity ceased and the TC weakened shortly after dry air from the upshear

95 quadrant was mixed into the eyewall. Similarly, Molinari et al. (2013) noted that Tropical Storm

96 Edouard (2002)—despite having intense downshear convection—did not intensify possibly be-

97 cause convection was far removed from the center and the upshear semicircle was remarkably dry.

98 These ﬁndings hint that environmental thermodynamic characteristics, such as SST and upshear

99 moisture, can modulate convective outbreaks and intensity changes under moderate VWS.

100 Recent modeling studies agree that intensity changes under moderate VWS are inﬂuenced by

101 SST and environmental moisture (Ge et al. 2013; Munsell et al. 2013; Tao and Zhang 2014; Rios-

◦ 102 Berrios et al. 2016a,b). Increasing the SST from 27 to 29 C increased the chances of intensiﬁcation

−1 103 under VWS as large as 12.5 m s in the idealized simulations of Tao and Zhang (2014). The in-

104 creased chances of intensiﬁcation were attributed to enhanced surface latent heat ﬂuxes leading

5 105 to more vigorous convection and faster vortex re-alignment. Rios-Berrios et al. (2016a,b) demon-

106 strated the inﬂuence of environmental moisture in ensemble intensity forecasts of Hurricanes Ka-

107 tia (2011) and Ophelia (2011). Signiﬁcant differences in lower tropospheric moisture right of the

108 shear vector led to differences amongst ensemble intensity forecasts of Katia (Rios-Berrios et al.

109 2016a), whereas signiﬁcant differences in middle tropospheric moisture left of the shear vector

110 led to differences amongst ensemble intensity forecasts of Ophelia (Rios-Berrios et al. 2016b). In

111 both cases, enhanced moisture led to intensiﬁcation through a combination of vortex stretching in

112 the lower troposphere and tilting of horizontal vorticity in the middle troposphere. Discrepancies

113 in the location of enhanced moisture between the two cases demand further investigation with a

114 larger sample size.

115 A climatological analysis could potentially provide more information about TC intensity

116 changes under moderate VWS by considering multiple cases. However, previous climatologi-

117 cal studies have been limited to either correlation analyses with a relatively small sample size

118 (DeMaria 1996), composites of airborne Doppler observations of predominantly major Atlantic

119 hurricanes (Rogers et al. 2013), or statistical analyses based on individual basins (Zeng et al.

120 2010; Wang et al. 2015). This study addresses those limitations by performing a comprehensive

121 climatological analysis based on multiple cases from multiple basins. A combined dataset of best

122 tracks and environmental diagnostics was created to consider all TCs around the world between

123 1982–2014. Data sources and restrictions applied to the database are discussed in Section 2. The

124 database was used to identify TCs with different intensity changes, objectively deﬁne moderate

125 VWS, and to perform a statistical comparison of TCs that either did or did not intensify under

126 moderate VWS. Results from this comparison are discussed in Section 3, followed by conclusions

127 and suggestions for future work in Section 4.

6 128 2. Approach

129 a. Datasets

130 1) BESTTRACKS

131 The International Best Tracks Archive for Climate Stewardship (IBTrACS; Knapp et al. 2010)

132 dataset was used to gather information about TCs around the world. IBTrACS combines best

133 tracks from all operational centers in individual ﬁles that are consistently formatted for all basins.

134 In this study, the IBTrACS-all dataset was used to obtain best tracks from the National Hurri-

135 cane Center (NHC) for North Atlantic and eastern North Paciﬁc TCs, and from the Joint Typhoon

136 Warning Center (JTWC) for western North Paciﬁc, Southern Paciﬁc, and Indian Ocean TCs. The

137 location of each TC was identiﬁed using the latitude, longitude, and distance from land variables

138 from IBTrACS. TC intensity was measured by the maximum sustained wind speed. Additional

139 quantities were derived from the aforementioned variables, including TC intensity change (tem-

140 poral change of maximum sustained wind speed) and TC translational speed (determined from a

141 12-h centered ﬁnite difference in TC position).

142 2) REANALYSIS DATASETS

143 The atmospheric state surrounding TCs was evaluated using reanalysis datasets. This choice

144 was made because direct observations over the tropical oceans are sparse; however, a reanalysis

145 should be considered as an approximation rather than a true representation of the atmosphere.

146 Two different reanalysis datasets were used to verify the robustness of the climatological analysis:

147 the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Interim (ERAI)

148 dataset (ECMWF 2009; Dee et al. 2011), and the National Centers for Environmental Prediction

7 149 (NCEP) Climate Forecast System Reanalysis (CFSR) version 2 dataset (Saha et al. 2010, 2014).

150 Results based on both datasets were consistent; therefore, only ERAI results are presented here.

151 The ERAI is produced with the ECMWF Integrated Forecast System, which consists of three

152 fully-coupled components for the atmosphere, land surface, and ocean waves (Dee et al. 2011).

153 Multiple observations are combined with short-term forecasts using a four-dimensional data as-

154 similation system. Six-hourly analyses are produced on a gridded domain with approximately

155 79-km horizontal grid spacing and 60 vertical levels extending up to 0.1 hPa. This study consid-

156 ered six-hourly analyses between 1982–2014 for consistency with the IBTrACS and SST datasets.

157 Due to the large number of cases considered and extensive calculations performed, interpolated

158 analyses were obtained from the Research Data Archive (RDA) via the National Center for Atmo-

159 spheric Research (NCAR) Yellowstone computing system (CISL 2012). Interpolated ﬁelds were

◦ ◦ 160 available on a 0.703 ×0.702 latitude-longitude Gaussian grid on isobaric surfaces.

161 3) SST ANALYSES

162 SST information near each TC center was obtained from the National Oceanic and Atmospheric

163 Administration (NOAA) Optimum Interpolation (OI) SST version 2 dataset (Reynolds et al. 2002).

164 This dataset—produced by NOAA’s National Climatic Data Center—provides different analysis

165 products that combine in-situ ocean observations (e.g., from buoys, ships, etc.) with infrared satel-

◦ ◦ 166 lite measurements. The speciﬁc product used here provided daily SST analyses on a 0.25 ×0.25

167 latitude-longitude grid (NCDC/NESDIS/NOAA 2007; Reynolds et al. 2007; Banzon and Reynolds

168 2013). This dataset was also accessed through the RDA.

8 169 4) PRECIPITATION RATES

170 Precipitation rates were evaluated as proxies for rainfall associated with the TCs. The Trop-

171 ical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) dataset

172 (TRMM 2011) was used here due to the high spatial coverage (approximately 80%) of the tropical

173 atmosphere and high temporal availability (Huffman et al. 2007). The TMPA dataset combines

174 microwave-infrared measurements from various low earth orbit satellites into a global three-hourly

◦ ◦ 175 gridded analysis at 0.25 × 0.25 latitude-longitude resolution. Due to data availability, precipita-

◦ 176 tion rates from TMPA were obtained only for TCs between 1998–2014 located within 50 of the

177 equator.

178 b. Methods

179 A database was created to combine information from the aforementioned datasets only at syn-

180 optic times (0000 UTC, 0600 UTC, 1200 UTC, and 1800 UTC). For consistency across basins,

−1 181 genesis was deﬁned as the ﬁrst instance of 17 m s (i.e., tropical storm intensity). Data points

182 corresponding to waves, disturbances, subtropical cyclones, extratropical cyclones, and TCs over

183 land or within 24 h of landfall were not included in the database. These conditions result in 47,287

184 six-hourly data points (“events” hereafter) associated with 2,631 individual TCs. Events were

185 considered separately because TCs can encounter different environments and experience different

186 intensity changes over time. For each event, TC and environmental characteristics were obtained

187 from IBTrACS, ERAI, CFSR, and the NOAA SST analyses. TC characteristics included vari-

188 ables derived or calculated from IBTrACS (e.g., intensity, latitude, translational speed) as well

1 189 as the radius of outermost closed isobar (ROCI) , which was calculated from reanalysis by tak-

190 ing the average radius of the closed isobar (at 1-hPa intervals) furthest away from the TC cen-

1This metric was used because it can be determined from reanalysis rather than subjectively estimated, such as the radius of 34-kt winds.

9 191 ter. In addition, various environmental characteristics that were investigated in previous studies

192 (c.f. Section 1) were computed, including VWS, SST, precipitable water (PW), and lower- and

193 middle-tropospheric relative humidity (RH).

194 Environmental characteristics were calculated from ERAI and CFSR using a similar approach

195 as in statistical prediction models (e.g., DeMaria and Kaplan 1999). Given the large errors in TC

196 location within both reanalysis datasets (Schenkel and Hart 2011), the TC center in ERAI and

2 197 CFSR was identiﬁed via the absolute maximum 850-hPa relative vorticity within a 500×500 km

198 box centered on the IBTrACS position. The vortex was removed from the wind ﬁeld following

199 the methods of Davis et al. (2008) and Galarneau and Davis (2013). In summary, the rotational

200 and divergent wind components within a 500-km radius from the TC center were calculated with

201 a Poisson equation and were subtracted from the total wind ﬁeld. This method was used to ensure

202 that VWS was representative of the environment rather than the TC itself. Figure 1 shows an

203 example of the wind ﬁeld before and after removing the vortex associated with Hurricane Earl on

204 0000 UTC 30 August 2010. After removing the kinematic component of the vortex, area-averaged

205 quantities were calculated within a 500-km radius from the TC center. The only exception was the

206 SST, which was obtained from the closest grid point of each TC location in the NOAA OI SST

207 analysis. The SST value was retrieved from the analysis three days prior to TC passage in order to

208 remove the effect of TC-induced ocean surface cooling. Maximum potential intensity (MPI) was

209 calculated from the retrieved SST and thermodynamic proﬁles at the TC center using the algorithm

210 of Bister and Emanuel (2002).

2This method provided the most robust and consistent identiﬁcation of the TC center. Other methods, such as vorticity centroid, were not

consistent across cases and basins.

10 211 1) INTENSITY CHANGE TIMESCALE AND CLASSIFICATION

212 Once all variables were combined into one database, the lagged correlation between VWS mag-

213 nitude and intensity was evaluated to pick a time period to identify and compare intensifying and

214 non-intensifying events. This calculation was based on all events over water for at least 120 h to

215 use a uniform sample size (14,778) at all lead times. Moreover, three different VWS deﬁnitions

216 were used to account for the sensitivity of the lagged correlation to the VWS deﬁnition (Velden

217 and Sears 2014). All deﬁnitions used the same vortex removal approach summarized in the previ-

218 ous subsection, but the difference was either the radii used for the area average calculation (either

219 0–500 km or 200–800 km) or the levels used to calculate VWS (either the winds at 200 and 850

220 hPa or the layer-averaged winds between 150–300 hPa and 700–850 hPa). Regardless of the deﬁ-

221 nition, the same relationship exists between VWS at 0 h and intensity up to 120 h later: a negative

222 correlation exists at all lead times, with a peak correlation of −0.3 around 36–42 h (Fig. 2a). Given

223 the similarities between VWS deﬁnitions, the rest of the calculations considered only the 500-km

224 area-averaged vector difference between 200 and 850 hPa.

225 The lagged correlation suggests that TC intensity is most negatively affected by VWS during

226 the 36–42 h period after a TC moves into a sheared environment; however, that analysis does not

227 account for temporal changes of VWS. Persistence of VWS was tested by comparing the standard

228 deviation of VWS magnitude to the root mean square error (RMSE) associated with assuming a

229 constant VWS magnitude over time. The RMSE was deﬁned as follows: s N 2 ∑ (Vt ,i −Vt,i) RMSE(t) = i=1 0 , N

230 where Vt0 is the VWS magnitude at the beginning of the ﬁve-day period, Vt is the VWS magnitude

231 at each lead time, and N is the sample size (14,778). Figure 2b shows that the standard devia-

232 tion of VWS magnitude increases at a slower rate than the RMSE; while the standard deviation

11 −1 −1 233 increases from 4.0 to 7.4 m s , the RMSE increases from 0 to 9.0 m s during the 120-h period.

234 Consequently, the RMSE becomes larger than the standard deviation after 36 h, which indicates

235 that a random drawing of VWS from climatology would be a better estimate than the initial VWS.

236 Because this result indicates that VWS varies substantially over time, intensity changes were eval-

237 uated at different time periods no longer than 36 h. The focus here will be 24-h intensity changes,

238 but other time periods were also considered to test the consistency and robustness of results.

239 Three groups of different intensity changes were identiﬁed for comparison: intensifying, steady

−1 240 state, and weakening events. An event with an intensity change greater than 5 m s was con-

−1 241 sidered an intensifying event, whereas an event with an intensity change smaller than −5 m s

−1 242 was considered a weakening event. Intensity changes between −5 and 5 m s were classiﬁed

−1 243 in the steady state group. A threshold of 5 m s was used for each group because it represents

244 the uncertainty of best track intensity (Torn and Snyder 2012; Landsea and Franklin 2013), thus

−1 245 an absolute intensity change greater than 5 m s should be detectable with current observational

246 systems. All variables were evaluated at the beginning (t0) of the intensity change period under

247 the assumption that TC and environmental characteristics at t0 inﬂuence the intensity at t0 + 24

248 h. Time-averaged variables over the intensity change period were also considered to account for

249 temporal evolutions; however, those variables must be interpreted with caution because different

250 intensity changes could alias onto time-averaged quantities.

251 2) MODERATE VWS DEFINITION

252 The database was further separated into three VWS categories: weak, moderate, and strong.

253 Categories were deﬁned based on percentiles of VWS experienced by all events in the database

254 because no consistent deﬁnitions appeared in previous literature. Figure 3 shows the global and

255 basin-based distributions of VWS magnitude constructed with all events over water for at least

12 256 24 h. All distributions are skewed towards small values, which is expected as weak VWS is a

257 necessary condition for TC development (e.g., Gray 1968). The mean and median of the global

−1 258 distributions (7 and 8 m s , respectively) fall within the range of VWS magnitudes associated

259 with large uncertainty in TC intensity forecasts (Bhatia and Nolan 2013; Zhang and Tao 2013; Tao

260 and Zhang 2015). To include those values into the moderate VWS category, the lower and upper

th th 261 bound of moderate VWS were deﬁned as the 25 and 75 percentiles of the global distribution

−1 th 262 (approximately 4.5 and 11.0 m s , respectively). Weak VWS was designated as the bottom 25

−1 263 percentile (shear magnitudes smaller than 4.5 m s ), whereas strong VWS was deﬁned as the top

th −1 264 25 percentile (shear magnitudes greater than 11.0 m s ). These deﬁnitions are representative

−1 265 for TCs in all basins because the medians and means of individual basins fall within 2.5 m s

266 from the global values despite the different ranges of VWS magnitudes across basins (Fig. 3).

267 The separation of VWS per percentiles provides a reasonable representation of moderate

268 VWS—a range of VWS magnitudes that are neither too weak to have little inﬂuence on TC

269 intensity nor too strong to halt intensiﬁcation. Figure 4 shows normalized distributions of 24-h

270 intensifying, steady state, and weakening events for any VWS as well as separated by moderate,

271 weak, and strong VWS. The distributions were normalized with respect to the number of events

272 in each VWS category to facilitate comparison between categories. Such comparison reveals that

273 the percentage of events that intensiﬁed under moderate VWS (27.4%) was nearly identical to

274 the percentage of events that intensiﬁed under any VWS magnitude (25.1%; Fig. 4). By com-

275 parison, this percentage substantially increased (from 27.4% to 36.2%) or decreased (from 27.4%

276 to 9.5%) when the VWS was weak or strong, respectively. Steady state and weakening events

277 also remained nearly the same under any or moderate VWS, but the percentages of those events

278 decreased or increased under weak or strong VWS, respectively. These differences in intensity

th th 279 rate distributions demonstrate that the 25 and 75 percentiles of the global VWS distribution are

13 280 reasonable boundaries to distinguish between weak, moderate, and strong VWS. Only moderate

281 VWS cases were considered in this study, reducing the sample size to 23,643 events.

282 3) ANALOGSSELECTION

283 To investigate which factors inﬂuence intensity changes under moderate VWS, a global statis-

3 284 tical comparison of intensifying and steady state events was performed. An initial comparison

285 of these groups revealed three characteristics of the dataset that could potentially bias the results.

286 First, intensifying events had signiﬁcantly weaker VWS than steady state events (Fig. 5a). Given

287 the large inﬂuence of VWS on intensity changes, this characteristic could mask the importance

288 of other factors. Second, intensifying events were further from their MPI than steady state events

289 (Fig. 5b). This difference could alias onto both TC and environmental characteristics because in-

290 tensiﬁcation often follows genesis, when TCs are weak and over favorable environments. Third,

291 intensifying events happened less often than steady state events (Fig. 4b): intensifying events rep-

292 resent 27.4% (6,489 events) of all cases that experienced moderate VWS, whereas steady state

293 events represent 53.9% (12,741 events) of those cases.

294 To address these issues, an analog approach was employed to compare two groups of equal size

295 containing intensifying or steady state events with comparable 200–850 hPa VWS magnitude and

296 deviation from MPI. For each intensifying event, an analog steady state event was randomly se-

297 lected under the requirement that the VWS magnitude and deviation from MPI were within 1.25 m

−1 −1 298 s and 5 m s , respectively, from the corresponding values of the intensifying event. The thresh-

299 olds for VWS magnitude and deviation from MPI were chosen after testing the algorithm for both

300 speed and accuracy in selecting analog events. This process was repeated for each intensifying

301 event that had an analog steady state event, resulting in two groups of equal size (6,420 events)

3Weakening events were not considered because most of those events were high-latitude TCs close to extratropical transition or landfall.

14 302 with no signiﬁcant differences in either VWS magnitude or deviation from MPI (Fig. 5c-d). The

303 random drawing of analog groups was repeated 1,000 times, such that each time two new groups

304 of 6,420 intensifying and 6,420 steady state events were randomly drawn from the database. This

305 repetition was needed to consider all cases in the database, but the results were insensitive to the

306 number of random drawings.

307 After the analog groups were selected, distributions and means of TC and environmental char-

308 acteristics associated with intensifying and steady state events were compared to identify factors

309 conducive for intensiﬁcation under moderate VWS. Statistical signiﬁcance of the mean differ-

310 ences between the groups was evaluated using a bootstrap resampling approach similar to what

311 was employed by Rios-Berrios et al. (2016a). In short, a distribution of mean differences between

312 two groups was created by randomly drawing two groups of equal size from the moderate VWS

313 database. The distribution was used to test the null hypothesis that the mean difference between

314 intensifying and steady state events was the same as the difference between two randomly selected

315 groups from the database. All statistical signiﬁcance tests were evaluated at the 99.9% conﬁdence

316 level.

317 3. Statistical comparison of intensifying and steady state events

318 a. Tropical cyclone characteristics

319 A comparison of TC characteristics reveals signiﬁcant differences between intensifying and

320 steady state events at the beginning of the 24-h intensity change period. Intensifying events are

321 slightly stronger than steady state events as shown by the distributions of maximum wind speed

−1 322 (Fig. 6a). The mean difference between the two subgroups is 4.02 m s and is signiﬁcant at the

323 99.9% conﬁdence level. Intensifying events are also closer to the equator than steady state events

15 324 as shown by the comparison of absolute latitude (Fig. 6b). The mean difference between the

◦ 325 groups is −1.36 and is statistically signiﬁcant at the 99.9% conﬁdence level. Intensifying events

326 are also larger than steady state events as revealed by the ROCI metric (Fig. 6c). Intensifying

4 327 events have an average ROCI that is 26.52 km signiﬁcantly larger than the ROCI of steady state

328 events. However, this difference could result from the different latitudinal location of intensifying

329 and steady state events because ROCI is negatively correlated with latitude (not shown).

330 Another TC characteristic that should be evaluated is the translational speed because this variable

331 could also inﬂuence intensity changes in sheared environments (Zeng et al. 2010; Rappin and

332 Nolan 2012; Onderlinde and Nolan 2016). Comparing the translational speed between intensifying

333 and steady state events reveals that both groups move at nearly the same speed (Fig. 6d). Both

−1 334 groups have mean translational speeds of approximately 4.4 m s , resulting in a small difference

−1 335 of 0.12 m s between intensifying and steady state events. Separating the translational speed into

336 zonal and meridional components shows signiﬁcant differences between the groups (Fig. 6e-f).

337 On average, intensifying events have a more westward motion as revealed by the average zonal

−1 338 velocity difference of −0.54 m s . Intensifying events also have a more northward motion, but

−1 339 the difference between the groups (0.24 m s ) is half the difference in the zonal component.

340 These results suggest that the speed of motion is not a distinguishing factor between intensifying

341 and steady state events, though the zonal component of motion is more important.

342 These results are generally consistent when considering time-averaged TC characteristics, dif-

343 ferent intensity change periods, and different latitudinal regions (Fig. 7). Intensifying events are

344 consistently stronger (Fig. 7a) and closer to the equator (Fig. 7b) than steady state events. The

345 relationship between TC size and intensity change is not consistent, but intensifying events are

346 larger than steady state events during 18-, 24-, and 30-h intensity changes (Fig. 7c). TC trans-

4Not all events had a closed isobar, which reduced the sample size for this variable.

16 347 lational speed differences are generally insigniﬁcant at all periods considered (Fig. 7d), but the

348 differences between the individual components consistently show more westward and northward

◦ 349 motion (Fig. 7e-f). These results are most representative of TCs located equatorward of 20 lat-

350 itude. Figure 8 shows the composite differences between the distributions of intensifying and

351 steady state events stratiﬁed by latitude. Intensifying events are initially stronger regardless of

◦ 352 latitudinal location (Fig. 8a). Poleward of 20 latitude, the relationship between initial size and

353 intensity change is unclear (Figs. 8b), but intensifying events are generally slower than steady state

354 events (Figure 8c).

355 Overall, this comparison shows both consistent and inconsistent results with previous studies.

356 On average, intensifying events are signiﬁcantly stronger, closer to the equator, and larger than

357 steady state events. Theoretical and idealized modeling studies indicate that strong and large TCs

358 are more likely to withstand the effects of VWS than weak and small TCs (Jones 1995; DeMaria

359 1996; Reasor et al. 2004; Tang and Emanuel 2010; Riemer and Montgomery 2011; Tang and

360 Emanuel 2012; Riemer et al. 2013; Reasor and Montgomery 2015). However, some of those stud-

361 ies also predict that TCs further away from the equator have greater chances of overcoming the

362 effects of VWS than TCs closer to the equator (Jones 1995; DeMaria 1996; Reasor et al. 2004;

363 Reasor and Montgomery 2015). Figure 6 stands in contrast with those studies because intensi-

364 fying events are closer to the equator than steady state events. A possible explanation for this

365 discrepancy is that other factors, such as thermodynamic conditions, can be more favorable for

366 intensiﬁcation closer to the equator. Recent modeling studies without VWS ﬁnd that TCs at low

367 latitudes intensify faster than TCs at high latitudes (Smith et al. 2014, and references therein);

368 however, a recent study that included VWS found the opposite relationship regardless of thermo-

369 dynamic environment (Zhou 2015). Thermodynamic environmental conditions will be compared

370 in the following subsection to clarify the discrepancies amongst the current study and other studies.

17 371 b. Thermodynamic environmental characteristics

372 A comparison of thermodynamic environmental characteristics at the beginning of the 24-h

373 intensity change period also shows statistically signiﬁcant differences between intensifying and

374 steady state events (Fig. 9). Intensifying events move over signiﬁcantly warmer SSTs than steady

◦ 375 state events as evidenced by the mean SST difference of 0.82 C (Fig. 9a). Environmental PW

376 is also more favorable for intensifying events as the mean PW difference between the groups is

377 3.48 mm and is signiﬁcant at the 99.9% conﬁdence level (Fig. 9b). Given that PW is a vertically-

378 integrated quantity, RH must be evaluated at different vertical levels to determine the layer with

379 greatest contribution to the PW discrepancies. Lower and middle tropospheric RH were evaluated

380 as the 900–600 hPa and 400–600 hPa layer averages, respectively (Figs. 9c-d). In the lower tro-

381 posphere, the mean RH of intensifying events is 4.73% signiﬁcantly larger than the mean RH of

382 steady state events (Figs. 9c). The mean difference between the middle tropospheric RH of both

383 groups is twice as large; intensifying events have 10.04% signiﬁcantly larger RH than steady state

384 events (Figs. 9d).

385 These results are consistent at different intensity change periods, but some important variations

386 appear for different latitudinal locations. At all time periods considered, intensifying events have

387 more favorable thermodynamic conditions characterized by warmer SSTs (Fig. 10a), greater PW

388 (Fig. 10b), and greater RH both in the lower and middle troposphere (Fig. 10c-d). The larger dif-

389 ferences in time-averaged than 0-h PW and RH could result from the fact that intensifying events

390 are becoming stronger and potentially moistening more their environments. SST and midtropo-

391 spheric RH are consistent distinguishing factors of intensifying and steady state events regardless

392 of latitudinal locations (Fig. 8d,g); however, PW and lower tropospheric RH appear to favor in-

◦ ◦ 393 tensiﬁcation primarily equatorward of 30 . Intensifying events poleward of 30 have less PW and

18 394 smaller lower RH than steady state events (Fig. 8e,f), suggesting that different processes modulate

395 the chances of intensiﬁcation at high latitudes.

396 These results indicate that thermodynamic aspects of the environment largely inﬂuence TC in-

397 tensity changes under moderate VWS. Overall, these results are consistent with previous obser-

398 vational (Molinari et al. 2004, 2006; Shelton and Molinari 2009; Nguyen and Molinari 2012) and

399 modeling studies (Rios-Berrios et al. 2016a,b) that investigated the intensiﬁcation of individual

400 TCs. These results are also consistent with previous theoretical and idealized modeling studies that

401 demonstrated the crucial role of near-TC moisture on intensity changes in sheared environments

402 (Tang and Emanuel 2010; Riemer et al. 2010; Tang and Emanuel 2012; Tao and Zhang 2014).

403 Nevertheless, Rios-Berrios et al. (2016a,b) emphasized the importance of the three-dimensional

404 moisture distribution during intensity changes of sheared TCs. Environmental RH differences

405 between intensifying and steady state events must be further evaluated to determine if there is a

406 preferred radius and azimuth of enhanced RH that favors intensiﬁcation.

407 A storm-centered, shear-relative framework was employed to assess the spatial distribution of

408 RH differences between intensifying and steady state events. In this framework, ﬁelds are cen-

409 tered on the 850-hPa TC center in the reanalysis and rotated into a natural coordinate framework

410 based on the 200–850 hPa VWS direction. Global composites were created by ﬂipping the lati-

411 tude of Southern Hemisphere ﬁelds to account for shear-relative asymmetries (Chen et al. 2006).

412 Figure 11 shows ﬁelds in this storm-centered, shear-relative framework, where the ordinate repre-

413 sents downshear (positive values) or upshear (negative values) distance and the abscissa represents

414 upwind (positive values) or downwind (negative values) distance with respect to the VWS vector.

415 By combining both hemispheres into one global framework, shear-relative quadrants are deﬁned

416 as follows (counterclockwise from top right): downshear upwind, downshear downwind, upshear

417 downwind, and upshear upwind.

19 418 An evaluation of storm-centered, shear-relative RH revealed the largest differences on the 500-

419 hPa level (not shown); therefore, the following discussion focuses on that level (Fig. 11a,d). The

420 mean ERAI ﬁelds (black contours) contain the well-documented wavenumber-one asymmetry of

421 sheared TCs (Corbosiero and Molinari 2002; Chen et al. 2006; Hence and Houze 2011; DeHart

422 et al. 2014). Large RH values (up to 80 %) are evident in the downshear half, with smaller values

423 (up to 72 %) in the upshear half. Composite differences between the groups (shading) reveal that

424 intensifying events have signiﬁcantly greater RH for all azimuths both at the beginning and during

425 the intensiﬁcation period. These differences suggest that intensifying events are less prone to

426 dry air intrusions, which agrees with previous literature (Riemer and Montgomery 2011; Riemer

427 et al. 2013; Tang and Emanuel 2012) because intensifying events are also stronger and larger than

428 steady state events. Furthermore, the largest differences between the groups appear within the

429 inner 200 km of the upshear half, where intensifying events have up to 12.5% greater RH than

430 steady state events (Fig. 11a). These differences amplify in both magnitude and spatial coverage

431 during the intensiﬁcation period (Fig. 11d). As the upshear half is typically characterized by dry

432 air and subsidence, this result suggests that intensifying events have a more symmetric RH ﬁeld

433 than steady state events. A comparison of the wavenumber-one structure conﬁrms that intensifying

434 events have a more symmetric midtropospheric RH (not shown); however, symmetry differences

435 could result from either different TC structure, different distribution of environmental moisture, or

436 a combination of both TC and environmental factors.

437 A more symmetric midtropospheric RH distribution likely favors more symmetric convection;

438 however, this hypothesis cannot be assessed with reanalysis datasets because of the coarse resolu-

439 tion and the use of a cumulus parameterization scheme in the underlying model. Instead, precipita-

440 tion rate estimates were obtained from the TMPA dataset to diagnose rainfall differences between

441 intensifying and steady state events. Figure 11b,e shows a comparison of precipitation rates for

20 442 intensifying and steady state events with available data (1998–2014). As in the composite analy-

443 sis of RH, the mean TMPA ﬁelds depict a wavenumber-one asymmetry with larger precipitation

444 rates in the downshear half than in the upshear half. Composite differences between intensifying

445 and steady state events conﬁrm that intensifying events have higher precipitation rates than steady

446 state events. Signiﬁcant differences are evident at all azimuths, but especially within a 200-km ra-

−1 447 dius, where intensifying events have up to 3 mm hr greater precipitation rates than steady state

448 events. Consistent with the RH differences, composite precipitation rate differences amplify dur-

449 ing the intensiﬁcation period (Fig. 11e). There is a slight preference for greater differences in the

450 downwind quadrants. That region is characterized by the largest amplitude of wavenumber-one

451 rainfall asymmetry (Chen et al. 2006), suggesting that intensifying events have stronger and more

452 symmetric convection.

453 Even though both the RH and precipitation rate comparison suggest more symmetric convective

454 activity in intensifying events, the azimuthal distribution of surface latent heat ﬂuxes (LHF) also

455 inﬂuences the symmetry of convection (Rappin and Nolan 2012; Onderlinde and Nolan 2016).

456 Surface LHF were computed from the reanalyses via the bulk aerodynamic formula:

∗ LHF = LvckρdU10 (qSST − q),

457 where Lv is the latent heat of vaporization, ck is the enthalpy exchange coefﬁcient, ρd is the dry air

∗ 458 density, U10 is the 10-m wind speed, qSST is the saturated speciﬁc humidity at SST, and q is the 2-

459 m specific humidity. An evaluation of the fluxes shows that intensifying events have significantly

460 greater surface LHF than steady state events at all azimuths both at the beginning and during the

461 intensity change period (Fig. 11c,f). The largest differences appear in the upshear upwind quad-

−2 462 rant, where intensifying events initially have up to 20 W m signiﬁcantly stronger surface LHF

463 than steady state events (Fig. 11c). Composite differences also amplify during the intensiﬁcation

21 464 period (Fig. 11f), indicating that surface LHF are consistently providing conducive conditions for

465 the azimuthal propagation of convection around intensifying events. These results are consistent

466 with previous modeling studies (Rappin and Nolan 2012; Onderlinde and Nolan 2016), which

467 showed that enhanced surface LHF in the upshear semicircle promote the axisymmetrization of

468 convection during intensiﬁcation in sheared environments.

469 Notwithstanding the signiﬁcant RH, precipitation rate, and surface LHF differences between

470 intensifying and steady state events, the presence of air masses with different moisture does not

471 solely explain intensity changes. As previous studies have indicated, a mixing mechanism is nec-

472 essary for dry air to dilute convection and hence inﬂuence intensity changes (Riemer and Mont-

473 gomery 2011; Tang and Emanuel 2012; Ge et al. 2013). Two potential hypotheses can be motivated

474 by the RH differences shown in Fig. 11: (1) storm-relative ﬂow differences between the groups

475 can modulate the import of dry air or export of moist air, and (2) a stronger primary circulation in

476 intensifying events can favor the re-circulation of moist parcels around the TCs.

477 c. Kinematic environmental characteristics

478 Storm-centered, shear-relative composites of storm-relative radial and tangential winds were

479 considered to assess asymmetries in the kinematic ﬁeld and to relate those asymmetries to the RH

480 differences (Fig. 12). The composite 500-hPa storm-relative radial wind depicts inﬂow downwind

481 of the shear vector and outﬂow upwind of the shear vector (black contours in Fig. 12a,c). This

482 inflow-outflow setup rotates with height such that at 850 hPa the inflow appears in the downshear

483 half and outﬂow appears in the upshear half (not shown). This pattern is consistent with airborne

484 Doppler radar composites of the kinematic ﬁeld of sheared TCs shown by Reasor et al. (2013)

485 even though that study only considered the inner-core region. The storm-relative tangential wind

486 at 500 hPa also shows asymmetries, with the strongest tangential wind located upwind of the shear

22 487 vector (black contours in Fig. 12b,d). This pattern also rotates with height, such that at 850 hPa

488 the maximum tangential wind is upwind of the shear vector (not shown).

489 Comparing intensifying and steady state events shows statistically signiﬁcant differences be-

490 tween the storm-relative ﬂow of the two groups. The storm-relative radial wind exhibits a storm-

491 centered dipole with negative differences in the downshear half and positive differences in the

492 upshear half (shading in Fig. 12a,c). This pattern, which also appears in the lower troposphere

493 (not shown), indicates that intensifying events have stronger inﬂow in the downshear half and

494 stronger outﬂow in the upshear half. A wavenumber-one asymmetry was evident in the RH com-

495 posite ﬁeld (Fig. 11a,d), thus the stronger inﬂow is located in a region of higher RH than the region

496 of stronger outﬂow. Intensifying events also have stronger storm-relative tangential winds at all

−1 497 azimuths, but the largest differences (3.2 m s ) appear downwind of the shear vector (shading in

498 Fig. 12b,d). Given that the composite mean shows maximum tangential wind upwind of the shear

499 vector, the location of largest differences suggests that intensifying events have a stronger, broader,

500 and more symmetric midlevel circulation than steady state events. These different storm-relative

501 ﬂow conﬁgurations, together with the RH composites, suggest that intensifying events are import-

502 ing more moist air towards the TC, exporting more dry air away from the TC, and re-circulating

503 air parcels with higher RH around the TC.

504 The existence of signiﬁcant differences in storm-relative winds hint at differences between the

505 wind proﬁles and/or the large-scale structure of the TC vortex. Intensifying and steady state events

506 considered here have the same 200–850 hPa VWS magnitude (c.f. Fig. 5c), but not necessarily the

507 same VWS direction. Previous studies suggest that easterly VWS can be more favorable to in-

508 tensiﬁcation than westerly VWS (Black et al. 2002; Ritchie and Frank 2007; Zeng et al. 2010).

509 However, subsequent studies suggest that the angle between VWS and TC motion is more impor-

510 tant, such that a TC moving in opposite direction to the VWS vector is more likely to intensify

23 511 than a TC moving in the same direction as the VWS (Rappin and Nolan 2012; Onderlinde and

512 Nolan 2016). Furthermore, the proﬁle of environmental winds between 200 and 850 hPa could

513 also cause different vortex-scale kinematic asymmetries (Finocchio et al. 2016). Another possibil-

514 ity is that the differences in storm-relative ﬂow result from the response of the TC vortex (e.g., tilt)

515 to the VWS (Jones 2000). While the datasets employed in this study may be sufﬁcient to test the

516 hypothesis related to the wind proﬁle, a higher resolution dataset is needed to evaluate differences

517 related to vortex structure.

518 Two basic metrics that can be used to assess variability in the wind proﬁle are the 200–850 hPa

519 VWS direction and the angle between the 200–850 hPa VWS and TC motion. The distribution

520 of 200–850 hPa VWS direction shows two relative maxima, one corresponding to easterly VWS

521 and another for westerly VWS (Fig. 9e). On average, intensifying events have more easterly

522 VWS and less westerly than steady state events as indicated by the mean, signiﬁcant difference

◦ 523 of −27.29 between the groups. This result is consistent for all basins even though the mean

524 VWS direction varies per basin (not shown), and is also consistent over different time periods

525 (Fig. 10e). A latitudinal dependency appears for this result as westerly VWS is more favorable for

◦ 526 intensiﬁcation poleward of 30 latitude (Fig. 8g). The distribution of the angle between VWS and

527 TC motion also shows signiﬁcant differences between the subgroups, albeit contrary to the result

528 from previous studies (Fig. 9f). On average, intensifying events have a signiﬁcantly smaller angle

529 between VWS and TC motion than steady state events, which indicates that intensifying events

530 are more likely to experience VWS in the same direction as motion. This relationship is consistent

531 for other intensity change periods (Fig. 10f) as well as for all latitudinal locations (Fig. 8i).

532 These results suggest that easterly VWS and westward TC motion are more favorable for inten-

533 siﬁcation. The result about VWS direction is consistent with previous studies, but VWS direction

534 is negatively correlated with environmental thermodynamics (not shown). This relationship indi-

24 535 cates that environments with easterly VWS are also characterized by warm SSTs and high PW. The

536 TC motion-VWS result contradicts idealized modeling studies that suggest TC motion opposite to

537 the VWS direction is more favorable for development and intensiﬁcation. A challenging aspect

538 of comparing results is that intensifying and steady state events have nearly similar translational

539 speed (c.f. Fig. 6d-f), whereas modeling studies prescribed two or more different TC motion sce-

540 narios in their simulations (e.g., easterly versus westerly mean ﬂow). Details of the wind proﬁles

541 between 200 and 850 hPa must be further evaluated to determine if other factors associated with

542 the environmental winds, such as shape or VWS layer, distinguish intensifying and steady state

543 events.

544 A K-means clustering analysis (MacQueen 1967) was employed to investigate variability

545 amongst environmental zonal and meridional wind proﬁles. This analysis was necessary because

546 this study focuses on global data; therefore, it is possible that variability amongst the background

547 wind proﬁles between basins could mask out differences between intensifying and steady state

548 events. The 200–850 hPa VWS direction was used as the metric to cluster wind proﬁles after

549 several tests with different variables, including the magnitude of storm-relative ﬂow at different

550 levels and the variance of zonal and meridional environmental winds. One of the caveats of the

551 K-means algorithm is that the number of clusters should be speciﬁed a priori. To determine the

552 optimal number of clusters, the cohesiveness of the clusters was measured via the “silhouette”

553 metric (Rousseeuw 1987): min(b) − a S = , max[a,min(b)]

554 where a is the average distance between a point and all other points in the same cluster, and

555 b is the average of the average distance between a point and all other points in other clusters.

556 Positive silhouette values close to one represent cohesive clusters. The K-means algorithm was

557 applied using different number of clusters, resulting in mean silhouette values between 0.55 for

25 558 six clusters and 0.72 for two clusters. Six clusters were speciﬁed because the mean silhouette was

559 relatively high and the clusters contained comparable number of events (Table 1).

560 Clusters identiﬁed with the K-means algorithm were used to compare the environmental storm-

561 relative winds of intensifying and steady state events. Six distinctive VWS directions were iden-

562 tiﬁed: (1) southeasterly (Fig. 13a), (2) northwesterly (Fig. 13b), (3) easterly (Fig. 13c), (4) north-

563 easterly (Fig. 13d), (5) southwesterly (Fig. 13e), and (6) westerly (Fig. 13f). The number of

564 intensifying events per each cluster is comparable to the number of steady state events (Table 1),

565 thus allowing a fair comparison between both groups. Composite hodographs of storm-relative en-

566 vironmental winds show no appreciable differences between intensifying and steady state events

567 in each cluster; the composite hodograph of intensifying events roughly matches the composite

568 hodograph of steady state events (Fig. 13). Furthermore, the mean TCREH from the environmen-

2 569 tal wind proﬁles is close to zero, which results in a small difference between the groups (0.5 m

−2 570 s , not shown). Wind variations with height are also remarkably similar between the groups,

571 with no clear indication of distinct layers of VWS in intensifying versus steady state events. Ad-

572 ditional analyses with different methodologies (e.g., Principal Component Analysis), with smaller

573 datasets (e.g., only one basin), and with time-averaged wind proﬁles yielded similar conclusions

574 (not shown).

575 These results indicate that the shape of the environmental wind proﬁle has little ability to dis-

576 tinguish intensifying and steady state events under moderate VWS. A potential implication of

577 this result is that the timescale needed for the environmental winds to have an effect of intensity

578 change is longer than the timescale of VWS itself. Onderlinde and Nolan (2014) found the largest

579 impact of TCREH on intensity change for time periods between 96–168 h, whereas Finocchio

580 et al. (2016) found the largest intensity variance due to VWS height between 36–72 h. Those

581 studies also considered TC development and intensity changes with constant VWS during 120 h,

26 582 which might not be reasonable for real-world TCs. As was shown in Fig. 2b, the RMSE associated

583 with the assumption of constant VWS becomes larger than the standard deviation of VWS after

584 36 h, thus indicating that environmental winds in the real atmosphere evolve at a faster rate than

585 the timescales employed by idealized modeling studies. Zeng et al. (2010) and Wang et al. (2015)

586 considered similar timescales as in this study; however, those studies only considered correlation

587 analyses for individual basins and for all VWS magnitudes. Future studies may want to consider

588 idealized simulations that allow temporal VWS variations to elucidate these discrepancies regard-

589 ing the sensitivity of TC intensity to the shape of the wind proﬁle.

590 4. Summary and Conclusions

591 The goal of this study was to identify TC and environmental characteristics that aid intensiﬁ-

592 cation under moderate VWS. A database was created to combine best tracks and environmental

593 diagnostics for all TCs that reached at least tropical storm intensity and remained tropical and over

594 water during a 24-h period between 1982–2014. Using this comprehensive database, moderate

−1 th th 595 VWS was deﬁned as 4.5–11.0 m s , which represents the 25 to 75 percentiles of the global

596 distribution of 200–850 hPa VWS magnitude. This deﬁnition captured the essence of moderate

597 VWS—a range of VWS magnitudes that are neither too weak to have little impact on intensity

598 changes nor too strong to halt intensiﬁcation. Two groups with different intensity changes under

−1 599 moderate VWS were compared: events with intensity changes greater than 5 m s (named inten-

−1 600 sifying events) and events with intensity changes between −5 and 5 m s (named steady state

601 events). The comparison focused on short-term (e.g., 24 h) intensity changes because an analysis

602 of VWS persistence showed that VWS varies substantially over time. A unique analog approach

603 was employed to statistically compare intensifying and steady state events with nearly identical

27 604 VWS magnitude and deviation from MPI. This approach was necessary to identify factors—other

605 than VWS and deviation from MPI—that favored intensiﬁcation under moderate VWS.

606 The global statistical comparison of TC and environmental characteristics showed signiﬁcant

607 differences between intensifying and steady state events. Intensifying events were signiﬁcantly

608 stronger, closer to the equator, larger, and moving with a more westward motion than steady state

609 events. Furthermore, intensifying events moved within environments with warmer SSTs, greater

610 PW, and more easterly VWS than steady state events. These variables are correlated with each

611 other, such that both thermodynamic and kinematic aspects of the environment work together to

612 provide more favorable conditions for intensiﬁcation under moderate VWS. Environmental char-

613 acteristics were further examined to ﬁnd the preferred horizontal and vertical location of ther-

614 modynamic differences between the groups. Storm-centered, shear-relative ﬁelds revealed that

615 intensifying events had greater midtropospheric RH and surface LHF at all azimuths, but espe-

616 cially in the upshear half. These results hinted at more favorable environments for heavy and

617 symmetric precipitation in intensifying events, which was conﬁrmed with composite differences

618 of satellite-estimated rainfall rates.

619 Additional analysis revealed that intensifying and steady state events also had different kine-

620 matic structures. Intensifying events had stronger midlevel storm-relative inﬂow within the down-

621 shear half and stronger midlevel storm-relative outﬂow within the upshear half. This pattern was

622 superposed with a wavenumber-one asymmetry of RH, thus indicating that intensifying events

623 were importing more moist air towards the TCs and exporting more dry air away from the TCs.

624 Intensifying events also had stronger midlevel storm-relative tangential winds, which could also

625 contribute to re-circulating moist air parcels around the TC. Altogether, these ﬁndings agree with

626 previous modeling work that showed that strong and large TCs could prevent more dry air intru-

28 627 sions from the environment than weak and small TCs (Riemer and Montgomery 2011; Tang and

628 Emanuel 2012; Riemer et al. 2013).

629 Kinematic differences between the groups were hypothesized to be related to the vertical proﬁle

630 of environmental winds, the structure of the TC vortex, or a combination of both the environmental

631 winds and the vortex structure. Results indicate that the latter is the most likely reason because the

632 analysis did not show clear systematic differences between the wind proﬁles of intensifying and

633 steady state events. Vortex structure differences could not be evaluated due to the coarse resolution

634 of the dataset. Based on the composite differences of storm-relative ﬂow, a plausible hypothesis is

635 that intensifying events are both stronger and less tilted than steady state event. Intensifying events

636 had stronger tangential winds and associated stronger circulation at all vertical levels (not shown).

637 Moreover, intensifying events had a stronger inﬂow in the downshear half, which would result in

638 ﬂow opposite to the VWS vector and potentially opposite to the vortex tilt. This hypothesis should

639 be tested in the future with high-resolution composites of vortex tilt. Regardless of the reasoning

640 for the different storm-relative winds, results show that the combined effects of RH asymmetries

641 and storm-relative winds can modulate intensity changes under moderate VWS.

642 The lack of systematic differences between the environmental wind proﬁles contradicts previous

643 modeling studies that considered the sensitivity of intensity changes to properties of the environ-

644 mental wind proﬁles (Onderlinde and Nolan 2014; Finocchio et al. 2016). The underlying conclu-

645 sions of those studies were similar; different environmental winds led to different tilt conﬁgura-

646 tions, which led to different convective distributions and different timings of vortex re-alignment.

647 Other studies reached similar conclusions by changing other properties of the environment such

648 as the location of dry air (Ge et al. 2013) or the boundary-layer moisture (Tao and Zhang 2014).

649 The similarity between conclusions in spite of the different methods suggests that the underlying

650 key to intensiﬁcation is the axisymmetrization of convection and vortex re-alignment. However,

29 651 all the aforementioned studies employed constant VWS through the entire simulation period. This

652 approach is not representative of the true atmosphere as the error associated with assuming a

653 constant VWS becomes larger than the climatological variability of VWS after 36 h. Additional

654 work, preferably within a modeling framework that allows temporal variations of VWS, is needed

655 to elucidate the mechanisms driving intensiﬁcation under moderate VWS.

656 Several caveats may limit the results presented in this study. First, the results presented here

657 relied on reanalysis datasets, which are not true representations of the atmosphere and only capture

658 the large-scale aspects of TCs. The consistency between two reanalysis datasets (i.e., ERAI and

659 CFSR) lends conﬁdence on the results, but additional efforts are needed to link the large-scale

660 results to vortex-scale and convective-scale processes. Second, some of the differences found here

661 were small and probably difﬁcult to measure with current observational systems. Third and last,

662 all basins were considered together even though TC and environmental characteristics may vary

663 from basin to basin. Ongoing work is repeating the analysis on a basin-by-basin basis.

664 Acknowledgments. The authors would like to thank Christopher A. Davis, Robert Fovell, and two

665 anonymous reviewers for their constructive comments about this research. Alan Brammer helped

666 with access to reanalysis and TMPA datasets, and Kerry Emanuel provided code to calculate max-

667 imum potential intensity. High-computing resources and access to the RDA datasets was provided

668 by NCAR’s Computational Information Systems Laboratory via the Yellowstone supercomput-

669 ing system (http://n2t.net/ark:/85065/d7wd3xhc). This work was funded by the National Science

670 Foundation Graduate Research Fellowship Grant No. DGE 1060277 and the National Oceanic and

671 Atmospheric Administration Grant No. NA14OAR4830172.

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41 897 LIST OF TABLES

898 Table 1. Summary of each cluster of environmental wind profiles. (left to right) The 899 first column indicates the cluster number, the second column indicates the mean 900 200–850 hPa vertical wind shear direction, the third column provides the num- 901 ber of events in each cluster (N), and the fourth and fifth columns provide the 902 number of intensifying (N1) and steady state (N2) events, respectively, in each 903 cluster...... 43

42 904 TABLE 1. Summary of each cluster of environmental wind proﬁles. (left to right) The ﬁrst column indicates

905 the cluster number, the second column indicates the mean 200–850 hPa vertical wind shear direction, the third

906 column provides the number of events in each cluster (N), and the fourth and ﬁfth columns provide the number

907 of intensifying (N1) and steady state (N2) events, respectively, in each cluster.

◦ Cluster Centroid VWS direction ( )N N1 N2

1 127.7 1,812 942 870

2 329.7 1,781 803 978

3 74.5 3,147 1,744 1,403

4 28.5 2,338 1,369 969

5 209.0 1,626 678 948

6 270.5 2,136 884 1,252

43 908 LIST OF FIGURES

909 Fig. 1. Illustrative example of vortex removal methodology applied to Hurricane Earl (2010). Pan- 910 els show (a,c) 200-hPa and (b,d) 850-hPa winds at 0000 UTC 30 August 2010 before (top) 911 and after (bottom) removing the vortex from the analysis. The red hurricane symbol and 912 circle depict the center and the 500-km radius from the center of Hurricane Earl...... 46

913 Fig. 2. (a) Time series of six-hourly correlation between vertical wind shear at 0 h and intensity 914 between 0 and 120 h. Lines represent different shear deﬁnitions: (solid) 0–500 km area- 915 averaged differences between 200 and 850 hPa, (long dashed) 200–800 km area-averaged 916 difference between 200 and 850 hPa, and (short dashed) 0–500 km area-averaged difference 917 between 150–300 hPa and 700–925 hPa layer-averaged winds. (b) Time series of six-hourly 918 root mean square error (solid) and standard deviation (dashed) of 200–850 hPa vertical wind 919 shear magnitude...... 47

920 Fig. 3. Distributions of 200–850 hPa shear magnitude averaged between 0–500 km radius from each 921 tropical cyclone center after vortex removal. Standard boxplots are used to summarize the th 922 distributions; whiskers extend from minimum to maximum, boxes extend from the 25 to th 923 the 75 percentile, horizontal lines within the boxes represent medians, and dots represent 924 means of the distributions. The first boxplot from left to right represents the distribution 925 of all events in the database, whereas the rest of the boxplots represent the North Atlantic 926 (AL), eastern North Pacific (EP), western North Pacific (WP), Northern Indian Ocean (NI), 927 Southern Indian Ocean (SI), and Southern Pacific (SP) basins...... 48

928 Fig. 4. Percentage of 24-h intensifying (red), steady state (blue), and weakening (gray) events that 929 encountered any, moderate, weak, or strong vertical wind shear. See text for a deﬁnition of 930 each category...... 49

931 Fig. 5. Normalized distributions of (a,c) 200–850 hPa shear magnitude and (b,d) deviation from 932 maximum potential intensity (MPI) of (red) intensifying and (blue) steady state events. Top 933 panels show the original distributions, whereas the bottom panels show the distributions of 934 analogs with similar 200–850 hPa shear magnitude and deviation from MPI. The mean of 935 each group is represented by a dashed line along with a star or circle, where a star indicates 936 that the difference between the means is signiﬁcant at the 99.9% conﬁdence level. . . . . 50

937 Fig. 6. Normalized histograms of (a) maximum wind speed, (b) absolute value of latitude, (c) radius 938 of outermost closed isobar, (d) tropical cyclone translational speed, and (e) zonal and (f) 939 meridional components of tropical cyclone motion of (red) intensifying and (blue) steady 940 state events. The mean of each group is represented by a dashed line along with a star or 941 circle, where a star indicates that the difference between the means is signiﬁcant at the 99.9% 942 conﬁdence level...... 51

943 Fig. 7. Average differences between tropical cyclone characteristics of intensifying and steady state 944 events for various intensity change periods. Panels correspond to the panels in Fig. 6. Blue 945 symbols depict differences at the beginning of each time period, whereas orange symbols 946 depict time-averaged differences. Stars are shown when the average differences are statisti- 947 cally signiﬁcant at the 99.9% conﬁdence level and circles are shown otherwise...... 52

948 Fig. 8. Normalized distribution differences between intensifying and steady state events stratiﬁed 949 by latitude. Panels show the percentage difference at each bin (shading, %) for (a) maximum 950 wind speed, (b) radius of outermost closed isobar, (c) translational speed, (d) sea-surface 951 temperature, (e) precipitable water, (f) lower tropospheric relative humidity, (g) middle tro-

44 952 pospheric relative humidity, (h) 200–850 hPa shear direction, and (i) angle between 200-850 953 hPa shear and tropical cyclone motion vectors...... 53

954 Fig. 9. Normalized distributions of (a) sea-surface temperature, (b) precipitable water, (c) lower 955 tropospheric relative humidity, (d) middle tropospheric relative humidity, (e) 200–850 hPa 956 shear direction, and (f) angle between 200–850 hPa shear and tropical cyclone motion vec- 957 tors of (red) intensifying and (blue) steady state events. The mean of each group is repre- 958 sented by a dashed line along with a star or circle, where a star indicates that the difference 959 between the means is signiﬁcant at the 99.9% conﬁdence level...... 54

960 Fig. 10. Average differences between environmental characteristics of intensifying and steady state 961 events for various intensity change periods. Colors and symbols are the same as in Fig. 7, 962 and panels correspond to the panels in Fig. 9...... 55

963 Fig. 11. Storm-centered, shear-relative analyses of (a,d) 500-hPa relative humidity (%), (b,e) precip- −1 −2 964 itation rate (mm hr ), and (c,f) surface latent heat flux (W m ) at 0 h (top) and averaged 965 between 0–24 h (bottom). Black contours represent the mean of all intensifying and steady 966 state events, shading represents the composite difference between intensifying and steady 967 state events, and the stippling pattern represents statistically significant differences at the 968 99.9% confidence level. All fields were rotated with respect to the 200–850 hPa shear vec- 969 tor such that the shear vector (black and white arrow) points along the positive ordinate. 970 56

−1 971 Fig. 12. As in Fig. 11, except for (a,c) 500-hPa storm-relative radial wind (m s ), and (b,d) 500-hPa −1 972 storm-relative tangential wind (m s )...... 57

973 Fig. 13. Composite hodographs of environmental storm-relative winds for two sets of analog inten- 974 sifying (red) and steady state (blue) events. Filled circles depict composite winds every 50 975 hPa from 850 to 200 hPa. Panels show clusters by 200–850 hPa shear direction: (a) south- 976 easterly, (b) northwesterly, (c) easterly, (d) northeasterly, (e) southwesterly, and (f) westerly. 977 58

45 978 FIG. 1. Illustrative example of vortex removal methodology applied to Hurricane Earl (2010). Panels show

979 (a,c) 200-hPa and (b,d) 850-hPa winds at 0000 UTC 30 August 2010 before (top) and after (bottom) removing

980 the vortex from the analysis. The red hurricane symbol and circle depict the center and the 500-km radius from

981 the center of Hurricane Earl.

46 982 FIG. 2. (a) Time series of six-hourly correlation between vertical wind shear at 0 h and intensity between 0

983 and 120 h. Lines represent different shear deﬁnitions: (solid) 0–500 km area-averaged differences between 200

984 and 850 hPa, (long dashed) 200–800 km area-averaged difference between 200 and 850 hPa, and (short dashed)

985 0–500 km area-averaged difference between 150–300 hPa and 700–925 hPa layer-averaged winds. (b) Time

986 series of six-hourly root mean square error (solid) and standard deviation (dashed) of 200–850 hPa vertical wind

987 shear magnitude.

47 988 FIG. 3. Distributions of 200–850 hPa shear magnitude averaged between 0–500 km radius from each tropical

989 cyclone center after vortex removal. Standard boxplots are used to summarize the distributions; whiskers extend th th 990 from minimum to maximum, boxes extend from the 25 to the 75 percentile, horizontal lines within the boxes

991 represent medians, and dots represent means of the distributions. The ﬁrst boxplot from left to right represents

992 the distribution of all events in the database, whereas the rest of the boxplots represent the North Atlantic (AL),

993 eastern North Paciﬁc (EP), western North Paciﬁc (WP), Northern Indian Ocean (NI), Southern Indian Ocean

994 (SI), and Southern Paciﬁc (SP) basins.

48 995 FIG. 4. Percentage of 24-h intensifying (red), steady state (blue), and weakening (gray) events that encoun-

996 tered any, moderate, weak, or strong vertical wind shear. See text for a deﬁnition of each category.

49 997 FIG. 5. Normalized distributions of (a,c) 200–850 hPa shear magnitude and (b,d) deviation from maximum

998 potential intensity (MPI) of (red) intensifying and (blue) steady state events. Top panels show the original

999 distributions, whereas the bottom panels show the distributions of analogs with similar 200–850 hPa shear

1000 magnitude and deviation from MPI. The mean of each group is represented by a dashed line along with a star or

1001 circle, where a star indicates that the difference between the means is signiﬁcant at the 99.9% conﬁdence level.

50 1002 FIG. 6. Normalized histograms of (a) maximum wind speed, (b) absolute value of latitude, (c) radius of

1003 outermost closed isobar, (d) tropical cyclone translational speed, and (e) zonal and (f) meridional components

1004 of tropical cyclone motion of (red) intensifying and (blue) steady state events. The mean of each group is

1005 represented by a dashed line along with a star or circle, where a star indicates that the difference between the

1006 means is signiﬁcant at the 99.9% conﬁdence level.

51 1007 FIG. 7. Average differences between tropical cyclone characteristics of intensifying and steady state events

1008 for various intensity change periods. Panels correspond to the panels in Fig. 6. Blue symbols depict differences

1009 at the beginning of each time period, whereas orange symbols depict time-averaged differences. Stars are shown

1010 when the average differences are statistically signiﬁcant at the 99.9% conﬁdence level and circles are shown

1011 otherwise.

52 1012 FIG. 8. Normalized distribution differences between intensifying and steady state events stratiﬁed by latitude.

1013 Panels show the percentage difference at each bin (shading, %) for (a) maximum wind speed, (b) radius of

1014 outermost closed isobar, (c) translational speed, (d) sea-surface temperature, (e) precipitable water, (f) lower

1015 tropospheric relative humidity, (g) middle tropospheric relative humidity, (h) 200–850 hPa shear direction, and

1016 (i) angle between 200-850 hPa shear and tropical cyclone motion vectors.

53 1017 FIG. 9. Normalized distributions of (a) sea-surface temperature, (b) precipitable water, (c) lower tropospheric

1018 relative humidity, (d) middle tropospheric relative humidity, (e) 200–850 hPa shear direction, and (f) angle

1019 between 200–850 hPa shear and tropical cyclone motion vectors of (red) intensifying and (blue) steady state

1020 events. The mean of each group is represented by a dashed line along with a star or circle, where a star indicates

1021 that the difference between the means is signiﬁcant at the 99.9% conﬁdence level.

54 1022 FIG. 10. Average differences between environmental characteristics of intensifying and steady state events

1023 for various intensity change periods. Colors and symbols are the same as in Fig. 7, and panels correspond to the

1024 panels in Fig. 9.

55 1025 FIG. 11. Storm-centered, shear-relative analyses of (a,d) 500-hPa relative humidity (%), (b,e) precipitation

−1 −2 1026 rate (mm hr ), and (c,f) surface latent heat ﬂux (W m ) at 0 h (top) and averaged between 0–24 h (bottom).

1027 Black contours represent the mean of all intensifying and steady state events, shading represents the composite

1028 difference between intensifying and steady state events, and the stippling pattern represents statistically signif-

1029 icant differences at the 99.9% conﬁdence level. All ﬁelds were rotated with respect to the 200–850 hPa shear

1030 vector such that the shear vector (black and white arrow) points along the positive ordinate.

56 −1 1031 FIG. 12. As in Fig. 11, except for (a,c) 500-hPa storm-relative radial wind (m s ), and (b,d) 500-hPa storm-

−1 1032 relative tangential wind (m s ).

57 1033 FIG. 13. Composite hodographs of environmental storm-relative winds for two sets of analog intensifying

1034 (red) and steady state (blue) events. Filled circles depict composite winds every 50 hPa from 850 to 200 hPa.

1035 Panels show clusters by 200–850 hPa shear direction: (a) southeasterly, (b) northwesterly, (c) easterly, (d)

1036 northeasterly, (e) southwesterly, and (f) westerly.