Contribution of variations in Northern Hemisphere annular mode to the near-surface wind speed changes over Eastern China for 1979-2017 in the Boundary Layer over the Low-Latitude

Studies have shown that large-scale ocean-atmosphere circulations (LOACs) played the major role to the near-surface wind speed (NWS) changes over China; however, the mechanisms whereby LOACs influences 17 NWS to have received little attention. In this study, the processes of the Northern Hemisphere annular mode 18 (NAM) influencing the NWS changes are revealed over eastern China for 1979-2017. The results showed a 19 slowdown in NWS, at a rate of −0.09±0.01 m s -1 decade -1 ; meanwhile, this decline could be partly driven by 20 the weakening of the zonal wind component. When the NAM exhibits positive phases, the zonal-mean 21 westerly weakens at the low-to-mid-latitudes (10°–40°N); meanwhile, in the troposphere descending flows 22 prevail near 40°N and ascending flows prevail near 65°N, and in the lower troposphere there are northerly 23 anomalies at the low-to-mid-latitudes and southerly anomalies at mid-to-high latitudes (40°–70°N). The 24 anomalous meridional flows transport heat from lower latitudes to higher latitudes and weaken the 25 north–south air temperature gradient. The decreased air temperature gradient over East Asia reduces the 26 pressure-gradient near the surface in eastern China, thereby decreasing the NWS. Furthermore, the effects of 27 NAM on NWS changes are more significant at interannual scale than decadal scale. 32.0±15.8 % of the 28 changes in the annual mean NWS are caused by the variations in NAM; meanwhile, the NAM contribution 29 to the interannual changes in the zonal component of NWS reach 45.0±12.9 %. 30

To assess the consistency of the phases between two data series, the probability of an anomaly appearing

159
To analyze whether the decrease in NWS is caused by the weakening of zonal or meridional circulations, 160 the observed NWS is decomposed into zonal and meridional components based on the wind direction of the 161 ERA5 reanalysis dataset, this being because the wind direction of the observed daily mean NWS is not 162 available. Cressman objective analysis method is employed to interpolate the stations' observational data to 163 grid at a resolution of 0.75° (Cressman 1959). A Gaussian low-pass filter with a 9-yr window is used to accounted for only 5.6% of the decreasing trend of NWS (Fig. 2c). The trends were calculated based on 185 LSM were consistent with that were computed based on TSA. Additionally, the correlation coefficients 186 between the total wind speed and u and v were 0.60 (p<0.01) and 0.15 (p>0.10), respectively, the PAST 187 between the NWS and u and v were 69.2% and 58.9%, respectively, and the values of the residual sum of 188 squares of the linear fitting between the NWS and u and v were 0.13 and 0.39 m 2 s -2 , respectively.

189
Consequently, the significant reduction in NWS was mainly caused by the reduction in u.   207 We discovered that changes in the near-surface and troposphere wind speeds over eastern China could be first analyzed how NAM modulates the large-scale wind fields over the Northern Hemisphere. During a 214 NAM+, a negative wind speed anomaly occurred over and around 30°N in the Northern Hemisphere, and a 215 positive wind speed anomaly occurred over and around 60°N (Fig. 5a). The spatial pattern of the composite 216 difference in the zonal-mean westerly between NAM+ and NAM− (Fig. 5b) is consistent with Fig. 5a. The 217 correlation coefficient between the NAMI and wind speed exhibited a zonal pattern. The negative and 218 positive correlation coefficients located at mid-latitudes and high latitudes, respectively, and that the 219 significant correlations above a significance t-test at 0.10 level located around 30°N and 60°N, respectively, 220 implying that accompanied by the variations of NAM, the wind speed decreased at mid-latitudes and 221 increased at high latitudes (Fig. 5c). The spatial pattern of the correlation coefficient between the NAMI and 222 zonal wind was consistent with that between the NAMI and wind speed, which also presented a zonal 223 annular belt (Fig. 5d). These results implied that the continuously positive anomaly of NAM could induce 224 the decrease of NWS at mid-latitudes of Northern Hemisphere (China lies in this region); moreover, the 225 influence of the NAM on the NWS changes can be due to its modulation of zonal-mean westerlies.

227
The abovementioned results show that the effects of the NAM on the large-scale zonal flows are 228 pronounced. Here, we investigate the processes behind the NAM that control the observed NWS changes.

229
Vertical characteristics of the composite differences between NAM+ and NAM− are shown in Fig. 6. A 230 negative zonal-mean zonal wind speed difference (denoted by ZWSD) between NAM+ and NAM− was 231 found at 10°-40°N, and a positive zonal-mean ZWSD was found from 40°N to polar. The strongest negative 232 ZWSD were found around 30°N and positive ZWSD were found around 55°N (contour). Accordingly, 233 accompanied by the variations of NAM, the zonal westerly decreased over mid-latitudes and increased over 234 high-latitudes. A negative zonal-mean meridional wind speed difference (denoted by MWSD) between found at 40°-65°N in the lower troposphere (shaded). These results indicate that the northerly anomalies at  Consequently, the Ferrell cell at high latitudes enhanced along with the continuous NAM warm phases.

241
Hence, the NAM had considerable effects on the vertical circulation field. Actually, these characteristics can 242 also be produced at four seasons (Fig. S1).

243
The NAM caused the anomalies of meridional winds in the lower troposphere over the mid-and high-244 latitudes in Northern Hemisphere; meanwhile, the descending flows of Ferrell cell further increased the 245 southerly in the low troposphere. The increased southerly in the lower troposphere transport heat from lower 246 latitudes to higher latitudes near the surface; thus, the surface air temperature (SAT) at mid-to-high latitudes 247 could rise. Consequently, the SAT difference between NAM+ and NAM− at the near-surface layer are 248 investigated (Fig. 7a). The SAT was higher at mid-to-high latitudes between 30°N and 70°N during a NAM+ 249 than it was during a NAM−; meanwhile, a significant SAT difference occurred at mid-to-high latitudes of 250 East Asia, which exceeded +0.8°C (p<0.10). The SAT at subtropical and low latitudes was lower during a 251 NAM+ than it was during a NAM−, although the SAT difference failed to exceed the significance t-test at 252 the 0.10 level. These results indicate that the SAT increased at mid-to-high latitudes accompanied by the 253 continuous NAM warm phases from 1979-2017, especially for East Asia. The north-south SAT difference 254 between mid-to-high latitudes and low-latitudes over East Asia could decrease due to the significant positive 255 SAT anomaly that occurred at mid-to-high latitudes in East Asia. Therefore, we investigated further the 256 north-south SAT difference between mid-to-high latitudes (35°-60°N, 60°-140°E) and low latitudes 257 (0°-20°N, 60°-140°E) over East Asia (denoted as SATD) (Fig. 7b). The temporal changes in SATD 258 exhibited a downward trend, at a rate of −0.21±0.066 °C decade -1 (p<0.01); meanwhile, the NAM and SATD 13 exhibited a negative correlation of −0.60 (p<0.01) (Fig. 7c). These results mean that NAM strengthening 260 considerably reduced the SATD between mid-to-high latitudes and low latitudes of East Asia. (the blue and red rectangles in Fig. 9a and 9b) were also pronounced at interannual scale (Tab. 1), although the percentage differences for the grid with the significant negative and positive correlations were not 305 significant. Compared to Fig. 5d, the annular belt pattern of the correlation coefficient between the NAMI 306 and zonal wind was reproduced well at interannual scale, and significant negative and positive correlations 307 exceeding a confidence level of 0.10 were also located around 30°N and 60°N, respectively (Fig. 9c). The 308 correlations between the NWS over eastern China and the SLP field over Northern Hemisphere were also 309 analyzed (Fig. 9d). Compared to Fig. 9a, Fig. 9d shows the reverse spatial pattern of the correlation China and the zonal wind was more evident at interannual scale (Fig. 9f). Accordingly, the annular belt 319 pattern of the correlation coefficient between the NWS and the zonal wind was more significant at 320 interannual scale, especially for the annular belts around 30°N and 60°N.

321
The vertical characteristics of the circulation pattern associated with the NAM at interannual scale are 322 also compared (Fig. 10). The NAM and zonal-mean meridional wind exhibited negative and positive 323 correlations at 10°-40°N and 45°-70°N in the lower troposphere, respectively (Fig. 10a). Significant 324 ascending flow was found around 65°N and descending flow was found around 40°N in the troposphere 325 (Fig. 10b). Quantitatively, the percentages of grids with negative (positive) correlation coefficient based on 326 raw and interannual sequences over the region with descending (ascending) flows at 30°-50°N (60°-72°N) were 92.0% (84.7%) and 93.6% (82.4%), respectively (Tab. 2). Compared to Fig.6, the Ferrell cell at high 328 latitudes was well reproduced in Fig. 10a; consequently, the effects of NAM on vertical circulations can be NWS and zonal-mean meridional wind as shown in Fig. 10e were consistent with those shown in Fig. 10c, 339 but the more significant correlations were found at the interannual scale (Fig. 10c). At interannual scale, the 340 significant positive correlation exceeded the 0.10 level between NWS and descending flow mainly located 341 around the latitude belt of 40°N and negative correlation between the NWS and ascending flow mainly 342 located around the latitude belts of 65°N, in particular for the region of the Ferrell cell (Fig. 10d). These 343 results cannot be well presented when the decadal signals of the NWS were not excluded (Fig. 10f).

344
The quantitative results show that the mean correlation coefficients computed based on raw and   and between near-surface wind speed (NWS) and SLP at different timescales. Top: correlation coefficients computed 673 based on raw sequences of NAM (NWS) and SLP; bottom: same as top but for interannual sequences. *, **, and *** 674 denote correlation coefficient (R) exceeding significance t-test at 0.10, 0.05, and 0.01 levels, respectively. Regions 1 675 and 2 are shown in Fig. 9 by blue and red rectangles, respectively.