manuscript submitted to Geophysical Research Letters

1 Forcing of the Preferred Positions of the North

2 Atlantic Eddy-driven Jet: from Above and Below

1,2 2 3 3 Rachel H. White , Casey Hilgenbrink , Aditi Sheshadri

1 4 Barcelona Supercomputing Center, 08034 Barcelona, Spain 2 5 University of Washington, Seattle, WA 98195 3 6 Stanford University, Stanford, CA 94305, USA

7 Key Points:

8 • Greenland orography creates the northern preferred latitude of the North Atlantic 9 jet latitude index by forcing Greenland tip jet events

10 • Forced meridional shifts in the North Atlantic jet typically manifest as changes 11 in the probability of preferred latitudes

12 • CMIP5 biases in simulating a northern preferred latitude are connected to clima- 13 tological jet position biases

Corresponding author: Rachel H. White, [email protected]

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14 Abstract 15 The atmospheric eddy-driven jet over the North Atlantic exhibits three ‘preferred po- 16 sitions’, latitudes where the jet maximum occurs more frequently than others. Using a 17 state-of-the-art dynamical atmosphere model (WACCM), we explore the forcing of these 18 preferred positions by upper-atmosphere circulation and northern hemisphere mountain 19 ranges. The latitude of the northern preferred position shifts only when the latitudinal 20 position of Greenland is changed, and this preferred position disappears when Green- 21 land orography is flattened. We propose that Greenland tip jet events create the appear- 22 ance of an eddy-driven jet northern ‘preferred position’. In ERA-interim data, years with 23 a higher frequency of tip jet events show a higher frequency of the northern preferred 24 position. Additionally, we show that CMIP5 models underestimate the frequency of the 25 northern preferred position when the climatological jet is too narrow or biased too far 26 south. This is a cautionary tale of the interpretation of results from reducing the dimen- 27 sionality of data.

28 1 Introduction

29 Amongst the intrinsic variability of our weather, there is evidence of patterns that 30 show anomalously high persistence or occurrence relative to other states (Rex, 1950; Namias, 31 1964; Reinhold & Pierrehumbert, 1982; Branstator, 1987; Vautard & Legras, 1988; Vau- 32 tard, 1990; Hannachi et al., 2017). These are known as weather regimes, and can be con- 33 sidered attractor states (Marshall & Molteni, 1993; Crommelin et al., 2004). Extreme 34 weather events have been related to weather regimes (Franzke, 2013; Coumou et al., 2014; 35 Hoskins & Woollings, 2015; Grotjahn et al., 2016; Messori et al., 2018); understanding 36 the underlying dynamics of such regimes may therefore improve our understanding of 37 the occurrence of extreme events. There is, however, a lack of consensus on even the op- 38 timum number of regimes required to characterize observed atmospheric non-Gaussian 39 behaviour (Hannachi et al., 2017), suggesting that much progress remains to be made 40 on understanding regime behaviour in our atmosphere.

41 Atmospheric blocking is a weather regime during which regions of high pressure 42 remain quasi-stationary (Rex, 1950; Woollings et al., 2018), associated with weather anoma- 43 lies both locally and downstream (Carrera et al., 2004; Bao & Wallace, 2015), includ- 44 ing extreme events (Carrera et al., 2004; Horton et al., 2016; Lau & Kim, 2011). A block- 45 ing event in Spring 2018, dubbed the Beast from the East in the media, was associated 46 with extreme cold temperatures over Northern Europe. Blocks in di↵erent locations have 47 been associated with di↵erent weather regimes. In the northern hemisphere atmospheric 48 blocks tend to occur over the north-east Atlantic/western Europe or over the North Pa- 49 cific (Barriopedro et al., 2006; Dunn-Sigouin & Son, 2013). North-east Atlantic/Europe 50 blocking is often subcategorized into Greenland blocking, and European (or Scandina- 51 vian) blocking (Barriopedro et al., 2006; Madonna et al., 2017).

52 Over the North Atlantic region, the North Atlantic Oscillation (NAO) is the strongest 53 mode of variability (Wallace & Gutzler, 1981), associated with meridional shifts of the 54 low-level, eddy-driven jet (Eichelberger & Hartmann, 2007). The probability density func- 55 tion of the NAO index is negatively skewed, suggesting the existence of two regimes: one 56 blocking regime (negative NAO), and a zonal (or no-blocking) flow regime (Woollings, 57 Charlton-Perez, et al., 2010). Rossby wave breaking has been proposed as a potential 58 mechanism for this NAO regime behaviour (Hannachi, 2007; Woollings et al., 2008).

59 Recently, Woollings, Hannachi, and Hoskins (2010) identified three preferred po- 60 sitions of the Atlantic eddy-driven jet by defining a relatively simple jet latitude index 61 (JLI); this index finds the latitude of the maximum value in daily Atlantic low-level zonal 62 mean zonal wind. The probability density function of this index shows three distinct lat- 63 itude peaks, or preferred positions of the jet. These preferred positions are unique to the 64 North Atlantic sector and are most distinct in winter (Woollings, Hannachi, & Hoskins,

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65 2010; Woollings et al., 2018). Many general circulation models (GCMs) are unable to 66 reproduce this observed behaviour of the jet (Anstey et al., 2013; Hannachi et al., 2013; 67 Iqbal et al., 2017), limiting our ability to understand future changes in this behavior in 68 a warming climate.

69 The southern preferred position of the JLI has been connected with a previously 70 identified weather regime, Greenland blocking (Madonna et al., 2017; Hannachi et al., 71 2012); however, a conclusive explanation for the separation of the central and northern 72 preferred positions has remained elusive. Correlations exist between the northern peak 73 and both the NAO, and the Eastern Atlantic pattern (Franzke, 2013; Woollings et al., 74 2018; Hannachi et al., 2012), as well as with European blocking (Davini, Cagnazzo, Fogli, 75 et al., 2014), and anti-cyclonic vs. cyclonic Rossby wave-breaking has been suggested as 76 a mechanism for producing these preferred positions (Franzke, 2013; Woollings et al., 2011).

77 In this study we use re-analysis data and atmospheric GCM simulations to study 78 the response of the preferred positions of the Atlantic eddy-driven jet to forcing from the 79 stratosphere and to the presence/absence of orography. We propose a mechanism for the 80 separation of the distinct northern preferred position from the central position, based 81 on Greenland orography. In the framework of this mechanism we present evidence that 82 biases in the simulation of the eddy-driven jet mean climatological position and width 83 can explain GCMs biases in the frequency of the northern preferred position.

84 2 Model and Methods

85 2.1 Data, model, and simulations

86 Analysis of the JLI requires daily troposphere zonal wind data across the Atlantic 87 sector at all latitudes for many years, which are unavailable in true observations; we thus 88 use ERA-Interim (ERA-I) re-analysis data (Dee et al., 2011). To extend beyond that which 89 can be gleaned from observations, we perform simulations using the Whole Atmosphere 90 Community Climate Model 4 with Specified Chemistry (WACCM-SC) (Marsh et al., 2013), 91 with a horizontal resolution of 2.5longitude by 1.9latitude. The observed climatology 92 and variability of atmospheric circulation, including the stratosphere, is reproduced well 93 by the WACCM (Marsh et al., 2013; de la Torre et al., 2012). In our simulations, sea 94 surface temperatures (SSTs) and sea ice fraction are fixed to observed values from the 95 merged Hadley NOAA Optimum Interpolation (NOAA/OI) data set (Hurrell et al., 2008). 96 For comparison to ERA-I, simulations are run with observed monthly SSTs from 1979- 97 2010 (WACCM CTL); while monthly climatological SSTs (1982-2001) are used for all 98 other experiments to allow for longer simulations of 60 years while avoiding any climate 99 change trends. The first year is discarded as spin-up. In all simulations atmospheric con- 100 stituents (greenhouse gases, aerosols) are fixed to year 2000 levels.

101 To explore the response of the Atlantic JLI to orography (large-scale mountain ranges 102 and ice sheets), four main experiments are performed: Flat - all global orography is flat- 103 tened, No H, - the Himalaya are flattened, including the Tibetan plateau and more north- 104 ern Mongolian mountains; No R - the North American Rockies are flattened; and No G 105 - Greenland orography is flattened. Supplemental figure S1 shows the lower boundary 106 condition surface height for each experiment. Where orography is flattened the surface 107 height is reduced to 50 m above sea level, and variables describing sub-grid scale oro- 108 graphic variability are reduced to typical values of low-lying land; see White et al. (2017) 109 for further details.

110 2.2 Jet Latitude Index and uncertainty estimates

111 To calculate the JLI, daily 850mb zonal winds are zonally averaged over the North 112 Atlantic sector (60W-0W; 15N - 75N) following Woollings, Hannachi, and Hoskins

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113 (2010). Cubic splines are used to interpolate the meridional profile of daily zonal-mean 114 zonal wind in a 3 latitude band centered on the maximum value. The jet latitude for a 115 given day is the latitude at which the maximum value of this interpolant occurs. The 116 probability density function (PDF) of this JLI is estimated using a kernel density esti- 117 mation method (Silverman, 1981).

118 We estimate uncertainty on our JLI PDFs through a bootstrap resampling approach 119 (Efron & Tibshirani, 1986): we construct a synthetic jet latitude timeseries by randomly 120 sampling (with replacement) n seasons from the jet latitude timeseries, where n is the 121 number of seasons in the original timeseries. We construct a JLI PDF from this synthetic 122 timeseries as described above. This process is repeated 1000 times, to produce an en- 123 semble of PDFs. Finally, we compute the standard deviation of these 1000 JLI PDFs 124 as a function of latitude in order to estimate confidence intervals.

125 In studying the JLI distribution following SSWs, we identify SSWs using a rever- 126 sal of the zonal mean zonal wind at 10 mb, following Charlton and Polvani (2007), and 127 sample the 10-40 days following each SSW start date. This gives a systematic shift to- 128 wards the end of winter, or even into Spring, and thus the climatological wintertime JLI 129 distribution is not the appropriate CTL. We construct a new CTLSSW by re-sampling 130 the jet latitude timeseries using the same 10-40 days following all identified SSWs date, 131 but randomizing the years. For example, the first SSW in ERA-I is 29-Feb-1980. Thus 132 the first 30 days in the post-SSW distribution are 10-Mar-1980 to 8-Apr-1980, while the 133 first 30 days in the CTLSSW distribution are 10-Mar-XXXX to 8-Apr-XXXX, where the 134 year is selected at random. Repeating this sampling 1000 times provides an estimate of 135 uncertainty of the CTLSSW . Using de-seasonalized data and selecting m 30 day subsets 136 at random from winter (December-February; DJF) produces similar results, where m is 137 the number of SSWs.

138 While previous work often applies a 10-day low pass filter to daily data prior to 139 calculating the jet latitude index, we find very similar results when using the raw daily 140 data; and thus use unfiltered data for our main analysis to avoid unnecessary process- 141 ing. Supplemental figure S2 provides a comparison of unfiltered vs low-pass-filtered re- 142 sults for ERA-I, the CTL simulation, and key results.

143 2.3 CMIP data analysis

144 For analysis of GCM data from the Coupled Model Intercomparison Project (CMIP5; 145 Taylor et al. (2012)), daily zonal windspeeds are downloaded from the Earth System Grid 146 Federation (Cinquini et al., 2014). We use only AMIP historical experiments from 1980- 147 2010, i.e. simulations forced with the observed SSTs and sea ice distribution. Models with 148 these data available were: ACCESS1.0, CanAM4, CMCC-CM, EC-EARTH, GFDL-CM3, 149 GFDL-HIRAM-C180, GFDL-HIRAM-C360, HadGEM2-A, IPSL-CM5A-MR, INM-CM4, 150 MIROC5, MIROC-ESM, NorESM1-M. We also include an in-house AMIP-style simu- 151 lation using CAM4.

152 To investigate the relationship between the time-mean jet and the existence (or fre- 153 quency of occurrence) of a northern preferred position, we calculate the ‘climatological 154 jet latitude’, the ‘climatological jet halfwidth’, and a ‘northern peak index’ (NPI) as fol- 155 lows. The ‘climatological jet latitude’ is the latitude of the maximum value in the DJF 156 climatological zonal wind, zonally averaged between 60W-0W. The ‘jet halfwidth’ 157 is the latitudinal distance between this peak, and the latitude to the north where the cli- 158 matological jet speed first reaches half the magnitude of its maximum value. The NPI 159 is calculated as NPI =(b c)/(a c), where: 160 a =maximum value of JLI between 40N and 50N (central peak)

161 b =maximum value of JLI between 56N and 66N (northern peak)

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162 c =minimum value of JLI between 50N and 56N(minimumbetweenthecen- 163 tral and northern peak)

164 If there is no northern peak, then b = c, and NPI = 0. Conversely, if there is a 165 distinct northern peak, then NPI > 0. If the northern peak has the same frequency 166 of occurrence as the central peak, then a = b, and NPI = 1.

167 3 Response of the Preferred Positions to Forcing

168 3.1 Climatology: WACCM model vs ERA-interim comparison

169 The DJF climatological zonal wind and JLI of the North Atlantic eddy-driven jet 170 are presented in figure 1 for both ERA-I and WACCM. The WACCM model simulates 171 the strength, position, and tilt of the wintertime Atlantic jet well (figure 1a). The ob- 172 served distribution of the wintertime JLI is also well simulated by the WACCM model 173 (figure 1b), although the southern peak is less pronounced than in ERA-I. This is sub- 174 stantially better than the CAM4 model (the low-top version of WACCM), as well as many 175 other CMIP5 models (Anstey et al., 2013). Low-pass filtered data give similar results 176 (supplemental figure S2a). The reduced magnitude of the southern peak in WACCM is 177 consistent with a known underestimation of Atlantic blocking frequency (Marsh et al., 178 2013).

Figure 1. (a) Climatological DJF zonal wind at 850 hPa (m/s) for the WACCM CTL (colors) and ERA-I (black contours). The contour interval is 2 m/s, starting from -11 m/s, with negative contours dashed. (b) Probability density function of the DJF JLI for the WACCM CTL (solid red) and ERA-I (dotted black). Shading represents an estimate of the 95% confidence interval calculated from bootstrap resampling. Vertical lines show the mean jet latitude as diagnosed from the maximum value of climatological DJF zonal mean zonal wind from 300-360E.

179 We define three separate jet positions (southern, central, northern) using latitude 180 thresholds of the JLI selected as the latitudes where the gradient of the JLI distribution 181 in the ERA-interim data becomes zero. Composite maps of the jet spatial distribution 182 for each preferred position are shown in figure 2 for both ERA-I (black contours) and 183 WACCM (colours). The WACCM reproduces the spatial distribution of each preferred 184 position well, although the jet in the southern position is more tilted than in ERA-I.

185 3.2 Forcing from above: Stratospheric Forcing

186 Following Sudden Stratospheric Warmings (SSWs), the Atlantic eddy-driven jet 187 is known to shift equatorward (Gerber et al., 2009; Kidston et al., 2015; Butler et al., 188 2017). Figure 3a and b show the JLI distribution for the 10-40 days following SSWs in 189 ERA-I and WACCM; the time-mean jet shift (shown by the vertical lines) manifests as 190 a reduction in the probability of the northern peak and an increase in the probability

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Figure 2. Composites of zonal wind at 850 hPa (m/s) for days in each of the three preferred position, defined by changes in the gradient of the jet latitude index in Figure 1b (see meth- ods). Colors show the WACCM control run, and black contours ERA-I (contour intervals 2m/s, starting from -15m/s, with dashed contours denoting negative values for ERA-I).

191 of the southern peak, with the latitude of these peaks remaining approximately constant. 192 The increase in the frequency of the southern peak is consistent with an increase in the 193 probability of Atlantic blocking following SSWs (Woollings, Charlton-Perez, et al., 2010; 194 Davini, Cagnazzo, & Anstey, 2014).

195 3.3 Forcing from below: Orographic Forcing

196 Figure 3c shows that, on a completely flat Earth, the PDF of the JLI would be dras- 197 tically altered, with no distinct northern peak. Flattening the Himalaya and surround- 198 ing orography increases the probability of the northern peak (figure 3d), whilst flatten- 199 ing the Rockies has no statistically significant response (figure 3e). In both of these lat- 200 ter experiments, the northern and central preferred positions are still apparent. Conversely, 201 when Greenland is flattened (figure 3f) the JLI becomes wide-peaked, but uni-modal. 202 These simulations provide evidence that Greenland orography plays a critical role in the 203 separation of the central and northern peaks of the JLI.

204 To further explore this connection between the JLI northern peak and Greenland 205 orography a simulation is run in which Greenland (land and orography) is shifted ap- 206 proximately 4 degrees northwards (2 grid-boxes): Shift G. The northern peak in JLI shifts 207 northward in step with the Greenland orography (figure 3g), confirming the connection. 208 We have presented results for winter; however, the e↵ect of Greenland on the northern 209 peak is also observed in spring, when distinct central and northern peaks are also present 210 in the WACCM.

211 4 Proposed mechanism for the Northern Preferred Position

212 A distinct northern peak in the JLI distribution exists in our WACCM simulations 213 only when Greenland orography is present. The presence of Greenland produces clima- 214 tological changes in sea level pressure and 500 mb geopotential height that do not project 215 onto the NAO pattern (see supplemental figure S3). Sea level pressure changes do, how- 216 ever, show a distinct regional low just o↵the tip of Greenland (supplemental figure S3a), 217 remarkably similar to that seen during Greenland tip jet events (Doyle & Shapiro, 1999; 218 V˚ageet al., 2008, 2009). These are narrow increases in zonal winds at 59N, just down- ⇠ 219 stream of the southern tip of Greenland; they are associated with the descent of north- 220 westerly winds on the eastern side of Greenland, and typically last around 3 days (Pickart 221 et al., 2003). We propose that, during tip jet events, these strong winds cause maximum 222 jet speeds over the Atlantic to be found more frequently in the vicinity of the tip jet, cre- 223 ating the distinct northern peak in the JLI.

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Figure 3. (a-b) JLI distribution for CTLSSW (black dashed) and 10-40 days following an SSW (blue solid), for (a) ERA-interim and (b) WACCM. (c-g) DJF JLI distribution for WACCM orography experiments, with CTL (black dashed) and experiment (orange). (c) Flat, (d) Flat Himalaya/Mongolia, (e) Flat Rockies, (f) Flat Greenland, and (g) Shifted Greenland. Grey shad- ing gives an estimate of the 95% confidence intervals based on bootstrap re-sampling (see text). Vertical lines show the latitude of the maximum value of the corresponding time-mean jet.

224 Substantial interannual variability in Greenland tip jet frequency exists (V˚ageet 225 al., 2009), and we study the correlation of the frequency of these events with the JLI in 226 the ERA-I data. Tip jet events are more likely during the positive phase of the NAO (V˚age 227 et al., 2009), consistent with correlations between the NAO and the northern peak in JLI 228 (Franzke, 2013; Woollings, Hannachi, Hoskins, & Turner, 2010; Hannachi et al., 2012). 229 Figure 4 shows that the 8 years with the highest frequency of tip jet events have a stronger 230 northern peak than the climatology, whilst the 8 years with the lowest frequency have 231 a substantially muted northern peak. We present results for n = 8 years; however, the 232 conclusions are robust for all n [1..12]. 2

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Figure 4. JLI distributions for ERA-I DJF climatology (black dashed), the 8 winters with the highest number of Greenland tip jet events (dark blue) and the 8 winters with the fewest tip jet events (light blue).

233 4.1 CMIP5 biases in the Jet Latitude Index

234 Given that all CMIP5 models include Greenland orography, an obvious question 235 remains as to why, therefore, do so many CMIP5 models fail to simulate a distinct north- 236 ern peak? Section 3.2 showed that, as the Atlantic eddy-driven jet shifts south follow- 237 ing an SSW, there is a reduction in the frequency of the northern peak; the northern peak 238 does not shift as the mean position of the jet changes. This suggests a correlation be- 239 tween the mean position of the jet, and the frequency of occurrence of the northern pre- 240 ferred position in JLI. We hypothesise that biases in CMIP5 simulated JLI distributions 241 (Iqbal et al., 2017) may be associated with biases in the time mean Atlantic eddy-driven 242 jet. If the eddy-driven jet typically does not extend far enough north in the Atlantic basin, 243 then interactions with Greenland, or with Greenland tip jet events, may be reduced, de- 244 creasing the probability of the jet maximum being detected at the tip jet latitude.

245 This hypothesis is examined by computing a Northern Peak Index (NPI) to quan- 246 tify the degree to which CMIP5 models exhibit a preferred northern position in the JLI, 247 as well as the DJF climatological jet latitude and jet half-width (see section 2.3 for de- 248 tails). Biases in each of these variables are calculated with respect to ERA-I. Figure 5a 249 shows the correlation between the NPI bias (y-axis) and the bias in the climatological 250 jet latitude (x-axis), for CMIP5 models (circles) as well as the WACCM CTL, No H and 2 251 No T experiments (diamonds). ERA-I is shown by the star for reference. With an r of 252 0.53 (p<0.001), there is a strong correlation between these variables. This correlation, 253 however, is dependent on the inclusion of the WACCM simulations (diamonds), which 254 contribute substantially to the total variability in NPI and jet latitude biases; exclud- 255 ing these simulations, there is no correlation between climatological jet latitude bias and 256 NPI bias.

257 To understand the variability in the CMIP5 models, we consider that the typical 258 northward extent of the eddy-driven jet is a↵ected by the meridional variability of the 2 259 jet, as well as its mean latitude. Figure 5b shows a strong correlation (r =0.40, p = 260 0.01) between the NPI bias (y-axis) and the bias in the climatological jet half-width (x- 261 axis) for the CMIP5 models only. There is no correlation between the climatological jet 262 latitude and its halfwidth. A multiple linear regression with both the climatological jet 2 263 latitude and halfwidth as predictors produces r =0.55 (p =0.003) for all experiments, 2 264 and r =0.45 (p =0.02) when the WACCM simulations are excluded. In figures 5a 265 and b the colours of the markers show the values of the second predictor.

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Figure 5. The relationship between biases in the Northern peak index (y-axis; see text for definition), the latitude of the climatological jet latitude (panel a. x-axis, panel b. colours) and the climatological jet halfwidth (panel a. colours, panel b. x-axis) for CMIP5 AMIP model exper- iments, our WACCM experiments (diamonds) and ERA-I (star).

266 5 Discussion and Conclusions

267 We propose that the distinct northern peak in Atlantic wintertime jet latitude in- 268 dex is a direct consequence of the presence of Greenland orography and associated Green- 269 land tip jet events. We have presented the following evidence: I. Simulations with the 270 WACCM show that the distinct northern peak vanishes when Greenland, or all orogra- 271 phy, is flattened. II. The latitude of the northern peak only shifts when Greenland it- 272 self is moved northward in the model. III. In ERA-interim re-analysis data the winter- 273 time JLI has a stronger northern peak in years with a high frequency of tip jet events, 274 and a weaker northern peak in years with a low frequency of tip jet events.

275 With this insight, revisiting previous work finds that maps of zonal wind for the 276 northern jet ‘preferred position’ show an acceleration of wind downstream of the tip of 277 Greenland, in contrast to the other preferred positions, which show maximum wind speeds 278 in the western half or centre of the Atlantic basin (see figure 2, and Madonna et al. (2017); 279 Woollings, Hannachi, and Hoskins (2010)). As Greenland tip jet events are a low-level 280 phenomenon (V˚ageet al., 2009), one way of looking at meridional variability of the eddy- 281 driven jet, without interference from surface phenomena, could be to use the JLI higher 282 in the troposphere: in ERA-I at 700mb the northern peak in the JLI is substantially re- 283 duced, and it is not present at 500mb (see supplemental figure S4).

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284 We show that the inability of many GCMs to simulate a distinct northern peak in 285 the JLI distribution may stem from their inability to correctly simulate the northward 286 extent of the Atlantic eddy-driven jet, either due to a bias in the mean position of the 287 jet, or in the jet halfwidth. Previous work has also shown a strong correlation between 288 the mean latitude of the eddy-driven jet and the skewness of the JLI (Barnes & Hart- 289 mann, 2010b). This is consistent with our result that, as the Atlantic eddy-driven jet 290 shifts south following Sudden Stratospheric Warmings, there is a reduction in the fre- 291 quency of the northern peak.

292 In ERA-interim reanalysis data we establish correlation, but not causality, between 293 Greenland tip jet events and the northern JLI preferred position; however, our WACCM 294 simulations show a casual link between Greenland orography and the northern JLI pre- 295 ferred position. Preferred transitions both to and from the northern position (Franzke 296 et al., 2011; Frame et al., 2011; Hannachi et al., 2012) suggest that interactions occur 297 between the Atlantic eddy-driven jet and the Greenland tip jet. We have not addressed 298 any such interactions in our analysis: do models with too southerly an eddy-driven jet 299 under-simulate the strength and/or frequency of Greenland tip jet events, which leads 300 to a lack of northern peak in the JLI? Or is an interaction between a Greenland tip jet 301 and a northerly eddy-driven jet required to simulate the northern peak, and thus too southerly 302 a jet does not interact suciently with the Greenland tip jet?

303 Our conclusions suggest that the northern ‘preferred position’ in the JLI is a regime 304 only in a recurrence sense, not in a persistence sense (Hannachi et al., 2017). This is con- 305 sistent with previous research showing that a northerly displaced jet exhibits less per- 306 sistence than a southerly jet (Woollings, Hannachi, & Hoskins, 2010; Barnes & Hartmann, 307 2010a; Barnes et al., 2010; Frame et al., 2011), although there is some disagreement in 308 the literature on this (Franzke et al., 2011). This conclusion is also consistent with re- 309 sults showing that only the negative NAO exhibits anomalous persistence (Fereday, 2017), 310 supporting the view that the North Atlantic atmospheric circulation can perhaps be bet- 311 ter characterized by two regimes: a ’blocked regime’ that exhibits anomalous persistence 312 (the southern preferred position in JLI), and a non-blocked, or zonal flow, regime (Woollings 313 et al., 2008; Woollings, Hannachi, Hoskins, & Turner, 2010), that includes Greenland tip 314 jet events as well as other recurrence regimes. Other research, however, shows a corre- 315 lation between the northern preferred position in JLI and blocking over Europe (Davini, 316 Cagnazzo, Fogli, et al., 2014); which may suggest a correlation between Greenland tip 317 jet events and European blocking.

318 This study provides a cautionary tale of the interpretation of results from reduc- 319 ing the dimensionality of data - previous attempts to explain the northern ‘preferred lat- 320 itude’ found by the jet latitude index invoked Rossby wave breaking or blocking regimes; 321 however, we show that this northern ‘preferred latitude’ can be explained by the exis- 322 tence of a rather simple, orographically forced jet.

323 Acknowledgments

324 The authors thank David S. Battisti for valuable discussions and ideas on this work, and 325 Erica Madonna, Isaac Held and Noah Di↵enbaugh for useful discussions. RHW received 326 funding from the European Unions Horizon 2020 research and innovation programme 327 under the Marie Skodowska-Curie Grant Agreement No. 797961, from the National Sci- 328 ence Foundation grant AGS-1665247, and from the Tamaki Foundation. Simulations were 329 performed on Stanford’s Research Computing Center and at the Department of Atmo- 330 spheric Sciences at the University of Washington.

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–13– Supporting Information for Forcing of the Preferred Positions of the North Atlantic Eddy-driven Jet: from Above and Below

Rachel H. White1,2, Casey Hilgenbrink2, Aditi Sheshadri3

1Barcelona Supercomputing Center, 08034 Barcelona, Spain

2University of Washington, Seattle, WA 98195

3Stanford University, Stanford, CA 94305, USA

Contents of this file

1. Figures S1 to S4

Introduction This supporting information provides additional figures to supplement the main text.

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Figure S1. Orographic height boundary conditions for the WACCM experiments shown in

Figure 3. a. CTL, b. Flat, c. No H, d. No R, e. No G, and f. Shift G.

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Figure S2. Lines show the PDF of the Jet Latitude Index for unfiltered daily data (black dashed) and 10 day Lanczos low-pass filtered data (red solid) for (a) ERA-I, (b) WACCM CTL,

(c) WACCM No G(d)WACCMShiftG.

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Figure S3. DJF climatological changes in sea level pressure (a,b) and 500 mb geopotential height (c,d) for the impact of Greenland (CTL - No G; left column), and the e↵ect of shifting

Greenland north by 4 degrees latitude (CTL - Shift G; right columns).

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Figure S4. JLI for ERA-interim data at 500mb (light blue), 700mb (dark blue) and standard

850mb (black dashed)

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