The East Asian Subtropical Jet Stream and Atlantic Tropical Cyclones

Home , Jet stream

1 The East Asian Subtropical Jet Stream and Atlantic Tropical 2 Cyclones 3 4

6 Wei Zhang1,*, Gabriele Villarini1, Gabriel A. Vecchi2,3

10 1IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, Iowa, USA

11 2Department of Geosciences, Princeton University, Princeton, NJ, USA

12 3Princeton Environmental Institute, Princeton University, Princeton, NJ, USA

13 Submitted to GRL

15 *Corresponding author: Wei Zhang, Ph.D., IIHR-Hydroscience & Engineering, The University

16 of Iowa, Iowa City, Iowa, USA. Email: [email protected]

17 18 19 20 21 22

23 Abstract

24 Atlantic tropical cyclones (TCs) can cause significant societal and economic impacts, as 2019’s 25 Dorian serves to remind us of these storms’ destructiveness. Decades of effort to understand and 26 predict Atlantic TC activity have improved seasonal forecast skill, but large uncertainties still 27 remain, in part due to an incomplete understanding of the drivers of TC variability. Here we 28 identify an association between the East Asian Subtropical Jet Stream (EASJ) during July-October 29 and the frequency of Atlantic TCs (wind speed ≥ 34 knot) and hurricanes (wind speed ≥ 64 knot) 30 during August-November based on observations for 1980-2018. This strong association is tied to 31 the impacts of EASJ on a stationary Rossby wave train emanating from East Asia and the tropical 32 Pacific to the North Atlantic, leading to changes in vertical wind shear in the Atlantic Main 33 Development Region (80°W-20°W, 10°N-20°N).

34 35 36 37 38 39 Plain Language Summary 40 Atlantic tropical cyclones (TCs) are responsible for significant societal and economic impacts in

41 terms of fatalities and property damage, as 2017's Harvey, Irma and Maria, 2018's Florence and

42 Matthew and 2019's Dorian serve to highlight. There are still large uncertainties in current seasonal

43 predictions for Atlantic TCs, which partly arise from our incomplete understanding of the drivers

44 of the variability and nature of such storms. Here we find an association between the East Asian

45 Subtropical Jet Stream (EASJ) during July-October and the frequency of Atlantic TCs during

46 August-November based on observations for 1980-2018, arising from the impacts of EASJ on the

47 propagation of Rossby wave trains from East Asia to the North Atlantic, leading to changes in

48 vertical wind shear over the Atlantic Main Development Region (MDR).

49 50 1. Introduction 51 Atlantic tropical cyclones (TCs) receive significant interest both from the scientific

52 community and the general public because of their large societal and economic impacts in terms

53 of fatalities and property damage [Czajkowski et al., 2011; 2017; Klotzbach et al., 2018; Pielke Jr

54 et al., 2008]. Understanding the association between climate drivers and Atlantic TCs represents

55 the fundamental basis for seasonal predictions of TC activity: improving this understanding is a

56 critical step towards prediction improvement [e.g., Elsner and Schmertmann, 1993; Emanuel,

57 2005; Gray, 1984; Gray et al., 1993; Keith and Xie, 2009; Klotzbach and Gray, 2009; Landsea et

58 al., 1998; Vecchi et al., 2010; 2014; Vecchi and Soden, 2007; Vecchi and Villarini, 2014; Villarini

59 et al., 2019].

60 Numerous studies have explored the controls on variations of seasonal Atlantic TC

61 frequency, finding a strong linkage with African easterly waves [Gray et al., 1993], West Sahel

62 rainfall [Landsea and Gray, 1992], relative sea surface temperature (SST) [Vecchi and Soden,

63 2007], tropical mean SST [Latif et al., 2007], Atlantic Main Development Region (MDR; 80°W-

64 20°W, 10°N-20°N) SST [Saunders and Harris, 1997], Atlantic Meridional Mode [Kossin and

65 Vimont, 2007], and the El Niño Southern Oscillation (ENSO) [Goldenberg et al., 2001; Gray,

66 1984]. Much of the existing literature has focused on the association between these storms and

67 climate “modes” arising in the tropical oceans, which can impact atmospheric and oceanic

68 conditions in the North Atlantic, because of their potential as sources for seasonal prediction. For

69 example, ENSO and the Atlantic Meridional Mode have been shown to modulate Atlantic TCs. In

70 addition, local vertical wind shear [Aiyyer and Thorncroft, 2011; Latif et al., 2007] is a good

71 indicator for Atlantic TCs. Moreover, Caribbean 200-mb zonal winds and sea level pressures have

72 been used for forecasting hurricanes [Gray et al., 1994], though one must exercise caution in

73 interpreting causality from strong statistical agreement between seasonal lower-troposphere

74 atmospheric wind anomalies and hurricane activity [Swanson, 2008]. Nonetheless, little attention

75 has been paid to remote variations in extratropical atmospheric systems in the Pacific and Eurasia.

76 Jet streams meander around our planet’s atmosphere, affecting high-impact weather events

77 such as atmospheric rivers, heat waves, cold waves, flooding and extreme precipitation [Cohen et

78 al., 2014]. As a prominent component of the weather and climate system in the Asia–Pacific sector

79 [Thompson et al., 2003], the East Asian Subtropical Jet stream (EASJ) is associated with

80 precipitation and temperature in North America. For example, EASJ is tied to winter climate in

81 North America [Yang et al., 2002], atmospheric rivers making landfall over the western United

82 States [Zhang and Villarini, 2018], and the precipitation pattern of the continental United States

83 [Zhu and Li, 2016]. Moreover, stationary Rossby trains can be modulated by the EASJ, as Rossby

84 waves disperse energy along strong westerly jets [Hoskins and Ambrizzi, 1993] that tend to refract

85 the waves toward the core of the jet stream, and act as efficient waveguides [Branstator, 2002;

86 Held et al., 2002; Hoskins and Ambrizzi, 1993]. Mounting observational evidence has supported

87 the importance of the tropospheric jets as waveguides for teleconnections [Branstator, 2002;

88 Branstator and Teng, 2017; Chen, 2002; Hsu and Lin, 1992]. During boreal summer, because the

89 EASJ is shifted poleward, it cannot directly interact with the Rossby waves forced from the deep

90 tropics [Graf and Zanchettin, 2012; Zhu and Li, 2016]. However, the Rossby wave energy can be

91 trapped by the EASJ in East Asia, triggering the downstream development of a Rossby wave train

92 along the EASJ towards North America and the North Atlantic [Graf and Zanchettin, 2012;

93 Swanson et al., 1997; Zhu and Li, 2016]. Here we examine whether and the extent to which the

94 EASJ is associated with the frequency of Atlantic TCs and hurricanes.

95 The remainder of this manuscript is organized as follows. Section 2 presents data and

96 methodology. Section 3 discusses the results, followed by Section 4 that summarizes concluding

97 remarks.

99 2. Data and Methodology

100 We use four monthly reanalysis datasets to obtain information about the three-dimensional

101 structure of the atmosphere, including: the National Aeronautics and Space Administration

102 (NASA)’s Modern-Era Retrospective Analysis for Research and Applications, version 2

103 (MERRA-2) at 0.5° × 0.625° spatial resolution for 1980-present [Gelaro et al., 2017]; the

104 European Centre for Medium-Range Weather Forecasts (ECMWF)’s ERA-5 at 0.25° × 0.25°

105 spatial resolution (1979-present) [Hersbach et al., 2020]; the National Centers for Environmental

106 Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data at 2.5°

107 × 2.5° spatial resolution (1948-present); and the Japanese 55-year ReAnalysis (JRA-55, 1958-

108 present) at 1.25° × 1.25° spatial resolution [Kobayashi et al., 2015]. Our focus is on the period

109 1980-2018 which is the common period for the four reanalysis data sets. In terms of observed TC

110 information, we use the Atlantic hurricane database (HURDAT2) for observed basin-wide TCs

111 every six hours during the lifetime of the recorded TCs [Landsea and Franklin, 2013]. We only

112 consider TCs that reach the intensity level of tropical storm or above during August-November.

113 No lifetime constraint is considered for defining tropical storm and hurricane.

114 The Niño3.4 index was calculated based on the monthly SST data obtained from the Met

115 Office Hadley Center (HadISST1.1) at 1° × 1° resolution [Rayner et al., 2003]; it was computed

116 as the SST anomaly averaged over the domain (5°S-5°N, 120°E-170°W) using the base period

117 1981-2010. Vertical wind shear is defined as the magnitude of the differences in winds between

118 200 hPa and 850 hPa levels. We use the definition for the intensity of EASJ as the 200-hPa zonal

119 wind averaged within 30°-35°N and 130°E-180°E. The results based on EASJ defined in 130°-

120 170°E or 130°-160°E [Yang et al., 2002] are similar to but slightly weaker than those using 130°E-

121 180°E.

122 To quantify the development of Rossby waves, the Rossby wave source (RWS) is defined

123 as [Sardeshmukh and Hoskins, 1988]:

124 �� = −(�∇ ∙ �� + �� ∙ ∇�) (1)

125 where � is the absolute vorticity and �� is the divergent (irrotational) wind component. The Rossby

126 wave source diagnostic has been widely used to analyze Rossby wave generation [Held et al.,

127 2002; Renwick and Revell, 1999].

128 The atmospheric general circulation model (AGCM) we use to gain a deeper insight into

129 the physical processes is constructed based on the dynamic core of the Geophysical Fluid

130 Dynamics Laboratory (GFDL) [Held and Suarez, 1994; Wang et al., 2003]. The AGCM is set up

131 with five sigma levels (an interval of 0.2, a top level at σ = 0, and a bottom level at σ = 1) and a

132 horizontal resolution of T42. The basic equations of this model contain momentum, temperature,

133 and logarithm of surface pressure equations together with the diagnostic equation for the vertical

134 velocity.

135

136 3. Results

137 There is a strong (inverse) correlation over recent decades between EASJ and TC activity,

138 as evident from visual inspection of the time series and scatterplots of the August-November

139 (ASON) frequency of Atlantic TCs and the strength of the July-October (JASO) EASJ (i.e., the

140 200-hPa zonal wind averaged within 30°N-35°N and 130°E-180°E) (Figures 1 and S1-2), with the

141 correlation coefficient that ranges between -0.70 and -0.68 for 1980-2018 using the four reanalysis

142 data sets (Table 1). Moreover, the correlation coefficients between JASO EASJ and the frequency

143 of ASON Atlantic hurricanes (wind speed ≥ 64 knot) is about -0.57 for 1980-2018 (Table 1 and

144 Figure S1). The partial correlation between the ASON frequency of Atlantic TCs and the strength

145 of the JASO EASJ by controlling the Niño3.4 index is still statistically significant (Table S1).

146 Overall, this relationship is quite promising compared with existing climate modes which modulate

147 Atlantic TCs [Klotzbach and Gray, 2009; Kossin and Vimont, 2007]. Moreover, the one-month

148 time lag indicates that EASJ could potentially be used to predict the frequency of Atlantic TCs and

149 hurricanes – particularly if seasonal EASJ variations could be predicted using climate models

150 archived, for instance, in North America Multi-Model Ensemble (NMME) or any other operational

151 seasonal forecasting system.

152 To correctly interpret the strong empirical relationship between EASJ and Atlantic TCs, it

153 is important to uncover the mechanisms behind this connection. Because vertical wind shear is a

154 strong modulator of TC activity, we performed a regression analysis of ASON vertical wind shear

155 onto the JASO EASJ over the period 1980-2018 (Figure 2). Strong EASJ in JASO is associated

156 with ASON positive vertical wind shear anomalies over the Atlantic MDR. These positive shear

157 anomalies based on 200 hPa and 850 hPa level winds would act to suppress TC genesis (Figure

158 2), consistent with the negative correlation between JASO EASJ and ASON Atlantic TCs and

159 hurricanes (Figure 1 and Table 1). The anomalously large vertical wind shear associated with the

160 strong EASJ is related to the anomalously westerly 200-hPa (Figure 2, top panel) and easterly 850-

161 hPa (Figure 2, bottom panel) winds. Moreover, the 200-hPa winds regressed onto EASJ exhibit a

162 wave train pattern propagating from East Asia (East China, Japan and Korea) to the North Atlantic.

163 The spatial pattern of the 200-hPa wind fields associated with the EASJ (Figure 2, top panel) bears

164 resemblance to the teleconnections excited by ENSO [Trenberth et al., 1998]. To linearly isolate

165 the possible modulation by ENSO, we calculated the regression of 200-hPa and 850 hPa winds

166 during ASON onto the EASJ index during JASO with its linear fit to the Niño3.4 index removed

167 (Figure S3a). Previous studies have found the nonlinear impacts and behaviors of ENSO [An and

168 Jin, 2004; Ohba and Ueda, 2009]. Although the connection between EASJ and Atlantic TCs still

169 exists after we linearly remove the impacts of ENSO, there may be some residual influence by

170 ENSO beyond its linear impact. After removing the linear impacts of ENSO, the atmospheric wave

171 patterns from the tropical Pacific associated with ENSO almost disappear while the significant

172 association between EASJ and vertical wind shear region still exists over a large portion of the

173 Atlantic MDR (Figure S3a). Similar results are found for the 850-hPa winds after we remove the

174 linear impacts of ENSO (Figure S3b). These results indicate that the large-scale circulation

175 patterns associated with EASJ during JASO can exist when removing the linear impacts of ENSO.

176 To overcome the limitation of selecting the Niño3.4 domain for ENSO variability, we use a

177 different method to evaluate the association between tropical SST and EASJ. We first regress the

178 SST in the equatorial band (0°E-360°E, 20°S-20°N) onto the EASJ index, leading to a loading

179 pattern that is similar to the El Niño-like pattern (Figure S4). The SST in the equatorial band is

180 then projected onto the regressed loading pattern, leading to a projected time series. The coefficient

181 of determination (i.e., R2) between the projected time series and the EASJ index is 0.32, suggesting

182 that the SST variability in the equatorial region may explain 32% of the total variance of the EASJ

183 index. In addition, the projected time series has a correlation of 0.94 with the Niño 3.4 index.

184 We hypothesize that the physical mechanisms underlying the time-lagged association arise

185 from the interactions between EASJ and a stationary Rossby wave train emanating from East Asia

186 and the Pacific. To diagnose the propagation of Rossby waves, we compute the Rossby wave

187 source (RWS; see Section 2) from the horizontal winds at 200hPa [Sardeshmukh and Hoskins,

188 1988]. An interpretation of the RWS is that it forces Rossby wave trains that are a key information

189 conduit behind extra-tropical atmospheric teleconnections [Sardeshmukh and Hoskins, 1988].

190 Climatologically, a large-magnitude negative Rossby wave source is located near China,

191 Japan, and Korea and is superimposed onto a strong 200-hPa westerly jet stream during JASO

192 (Figure 3). Overall, the EASJ during JASO is located more poleward and is weaker than during

193 boreal winter (Figure S5), consistent with previous studies [Graf and Zanchettin, 2012; Zhu and

194 Li, 2016]. A weaker subtropical jet stream in the subtropical Pacific during July-October may fail

195 to trap Rossby waves excited by the SST forcing (e.g., Pacific-North America teleconnections

196 (PNA)) in the tropical Pacific [Graf and Zanchettin, 2012; Zhu and Li, 2016]. In contrast, a

197 stronger EASJ could play an important role as a wave-guide for Rossby wave trains from East

198 Asia [Ding and Wang, 2005; Graf and Zanchettin, 2012; Zhu and Li, 2016]. The regression of the

199 ASON 200 hPa eddy streamfunction (zonal average removed) onto EASJ in JASO is characterized

200 by a pattern of Rossby wave trains propagating from East Asia to the North Atlantic (Figure 3).

201 This suggests that an anomalously strong EASJ tends to guide Rossby waves [Branstator, 2002;

202 Branstator and Teng, 2017; Graf and Zanchettin, 2012] which propagate from East Asia to the

203 North Atlantic (Figure 3). The propagation of Rossby waves modulated by EASJ is responsible

204 for significant vertical wind shear over the Atlantic MDR and leads to a suppression of Atlantic

205 TCs and hurricanes (Figure 1). Note that the EASJ index is based on the latitudes 30°N-35°N,

206 which is just equatorward of the core of the 200-hPa westerlies (Figure S5): this is because this

207 region represents the right entrance of the jet streak [Bluestein, 1993], where the strongest

208 ascending motion occurs. In addition, the latitudes 30°N-35°N are the region where strong

209 westerly appears when moving poleward from the equator during JASO (Figure S5).

210 The linear association between the EASJ defined by 35°N-40°N or 40°N-45°N or 45°N-

211 50°N and Atlantic TCs is much weaker than 30°N-35°N (Table S2). The meridional change of the

212 EASJ index using latitudes 30°N-35°N, 35°N-40°N, 40°N-45°N, and 45°N-50°N is associated

213 with different teleconnection patterns (Figures S6-8). The EASJ index based on 30°N-35°N is

214 associated with a teleconnection pattern with an upper-level cyclonic pattern over the Atlantic

215 MDR (Figure S6), while the EASJ index based on the other latitudinal ranges exhibits either weak

216 or no signal over the Atlantic MDR (Figure S7-S8). The above results suggest that the meridional

217 movements of the jet influencing the wind patterns over the Atlantic MDR related to vertical wind

218 shear and the EASJ index using latitudes 30°N -35°N is closely associated with circulation pattern

219 over the Atlantic MDR.

220 We hypothesize that the strong subtropical westerlies in 30°N-35°N can be perturbed by

221 not only the latent heating release associated with the East Asian Summer Monsoon (EASM), but

222 also by the teleconnections originated from the tropical western and central Pacific. In other words,

223 due to the northward shift of the EASJ during JASO, the Rossby wave associated with the heating

224 in the EASM region or the tropical/subtropical western Pacific cannot perturb the subtropical jet

225 stream until the Rossby wave trains propagate and reach ~30°N. To verify the mechanisms, we

226 perform several experiments using the GFDL AGCM by prescribing heating sources in the EASM

227 region, tropical western Pacific and tropical central Pacific. For the experiments by prescribing a

228 heating source in the tropical western Pacific or in the tropical central Pacific, the Rossby waves

229 propagate towards East Asia and interacts with the EASJ after they reach the latitude 30°N (Figures

230 S9-S10). In addition, the experiment by prescribing the heating in the EASM region exhibits very

231 strong responses to the heating and the jet stream guides the Rossby wave trains towards the North

232 Atlantic (Figure S11). Therefore, as summarized in a schematic diagram (Figure S12), the strong

233 subtropical westerlies (EASJ) can not only be perturbed by the Rossby waves excited by heating

234 in the tropical western and central Pacific and develop the Rossby waves downstream, but it can

235 also guide Rossby wave train excited by the heating related to EASM.

236 To further decipher the role of the EASJ in predicting Atlantic TCs, we fit the frequency

237 of these TCs against three groups of predictors: (1) JASO EASJ, ASON Atlantic SST and Niño3.4,

238 (2) ASON Atlantic SST and Niño3.4, and (3) ASON relative SST. The skill, as measured by the

239 correlation coefficient and root mean square error between observations and the median of the

240 fitted Poisson model for the Atlantic TCs in the model using JASO EASJ, ASON Atlantic SST

241 and Niño3.4 (Figure 4a) is slightly higher than the other two models. This is also the model with

242 the lowest Akaike Information Criterion (Figure 4, panels b and c), indicating that the information

243 content from the addition of the EASJ outweighs the cost associated with a more complex model.

244 However, the skill of the model with JASO EASJ, ASON Atlantic SST and Niño3.4 for Atlantic

245 hurricanes (Figure S13a) is similar to the one with ASON Atlantic SST and Niño3.4 and the one

246 with ASON relative SST (Figure S13, panels b and c).

247

248 4. Conclusion

249 We have identified a significant correlation between the EASJ in July-October and the

250 frequency of Atlantic TCs and hurricanes in August-November in observations, which is robust

251 across four different reanalysis data sets. A strong EASJ in JASO is associated with large vertical

252 wind shear for ASON over the Atlantic MDR region. The one-month lag in the peak correlation,

253 which indicates causality moving from the EASJ to Atlantic TCs, may arise from the EASJ and

254 the propagation of Rossby waves from East Asia to the North Atlantic (Figure 3). Our results have

255 shown that the EASJ may guide the Rossby wave trains to propagate from East Asia to the North

256 Atlantic, modulating the vertical wind shear over the North Atlantic MDR, which modulates

257 Atlantic TCs and hurricanes. During JASO, the EASJ makes teleconnections emanating from the

258 tropics more efficient, and at the same time it acts as a conduit for signals that originate in East

259 Asia (i.e., it plays a significant role after removing the ENSO effects), modulating TC activity in

260 the North Atlantic.

261 Our findings suggest a potential predictor for the frequency of Atlantic TCs and hurricanes

262 and a new aspect that could improve the simulation of these storms with climate models.

263 Furthermore, decadal changes in EASJ may be an influence into Atlantic TC activity in the future,

264 highlighting the need to assess the extent that the climate models can capture the mean state,

265 seasonal variations and forced changes of the EASJ, as well as the mechanisms behind and

266 character of the EASJ response to radiative forcing. Beyond the newly discovered physical insights

267 in terms of North Atlantic TC activity, this relationship could be leveraged towards seasonal

268 forecasting of these storms by forecasting the JASO EASJ using outputs from global climate

269 models. The skill in predicting the EASJ and the impacts in terms of improved TC forecasting

270 should be the topic of future studies.

271

272

273 Acknowledgements: 274 The authors thank the two anonymous reviewers for insightful comments. This work was partly 275 supported by the National Science Foundation under Grant EAR-1840742 and the U.S. Army 276 Corps of Engineers’ Institute for Water Resources (GV and WZ). GAV was supported by 277 NOAA/MAPP (NA18OAR4310273), and the Carbon Mitigation Initiative (CMI) as well as the 278 Cooperative Institute for Modeling the Earth System (CIMES) (NAOAR4320123) at Princeton 279 University. The hurricane data set is obtained from National Hurricane Center’s website 280 (https://www.nhc.noaa.gov/data). The atmospheric variables (e.g., winds) based on NCEP/NCAR 281 (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html), ERA-5 282 (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5), MERRA2 283 (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/), and JRA-55 (https://jra.kishou.go.jp/JRA-

284 55/index_en.html). SST is obtained from the Met Office Hadley Center 285 (https://www.metoffice.gov.uk/hadobs/hadisst/). 286 287 288 289

290 Figure Legends 291 Figure 1. Frequency of Atlantic basin-wide tropical cyclones (orange; right y-axis) for August- 292 November of 1980-2018 and the intensity of the EASJ indices (blue; left y-axis) during July- 293 October in four different reanalysis products. 294 Figure 2. Regression of (a) 200 hPa and (b) 850 hPa wind vectors in ASON onto EASJ in JASO. 295 The shading represents the regions with significant correlation (at the 5% level) between ASON 296 vertical wind shear and JASO EASJ over the period 1980-2018 based on ERA-5 reanalysis data. 297 The black rectangles represent the Atlantic MDR (80°W-20°W, 10°N-20°N). 298 Figure 3. Climatology of 200-hPa wind fields (vector, ms-1) and Rossby wave source (shading, 299 ×10-11 s-1) during JASO of 1980-2018. Contours represent the regression of ASON 200-hPa eddy 300 streamfunction (zonal mean removed; ×106 m2s-1) onto the EASJ index during JASO. The black 301 rectangle represents the Atlantic MDR (80°W-20°W, 10°N-20°N). 302 Figure 4. Fitted and observed frequencies of Atlantic TCs using predictors: (a) EASJ, tropical 303 Atlantic SST and Niño3.4, (b) tropical Atlantic SST and Niño3.4 and (c) relative SST. The white 304 circles represent the observations. The black line represents the median of the fitted Poisson 305 distribution, while the dark (light) grey regions the 25–75th (5–95th) percentiles. Tropical Atlantic 306 SST is defined as the SST averaged over the Main Development Region (80°W-20°W, 10°N- 307 20°N). 308 309

310 Table 1 Correlation between the frequency of Atlantic TCs, hurricanes during ASON and EASJ 311 during JASO for 1980-2018. The symbol “*” denotes values that are significant at the 0.05 level. Correlation Atlantic TCs Hurricanes NCEP/NCAR -0.68* -0.56* MERRA2 -0.70* -0.57* ERA5 -0.68* -0.56* JRA-55 -0.69* -0.57* 312 313 314

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-2 20

NCEP/NCAR MERRA2 18 -4 ERA5 JRA-55 Atlantic TCs 16 -6

14 -8

12 -10 TCs

EASJ(*-1) 10

-12 8

-14 6

-16 4

-18 2 1980 1990 2000 2010 70°N (a) 200 hPa (EASJ)

50°N

30°N

1ms-1 20°S 70°N (b) 850 hPa (EASJ)

50°N

30°N

1ms-1 20°S 100°E 180 100°W 20°W

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 70°N 0.5 0.5 -1

-1

0.5

1 50°N -0.5 -0.5 0.5 0.5 -0.5

-1 0.5 -1 -0.5 0.5

30°N 0.5 1

1 -0.5 1.5 0.5

-0.5

-0.5 0.5

EQ -1 0.5 0.5 -1 -0.5 -0.5 1 -1.5

-1.5 1 8m-1s-1 -2 1 20°S

-30 -20 -10 0 10 20 30 a) Atlantic TCs vs Tropical Atlantic SST & EASJ COR = 0.69 RMSE = 2.66 20

10 Frequency

b) Atlantic TCs vs Tropical Atlantic SST COR = 0.52 RMSE = 3.14 20

10 Frequency

1980 1990 2000 2010