Distinct linkage between winter Tibetan Plateau snow depth and early summer Philippine Sea anomalous anticyclone

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 7: (26) Published online 8 February 26 in Wiley Online Library (wileyonlinelibrary.com) DOI:.2/asl.646 Distinct linkage between winter Tibetan Plateau snow depth and early summer Philippine Sea anomalous anticyclone Hong-Chang Ren, Weijing Li,,2 * Hong-Li Ren 2,3 and Jinqing Zuo,2 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, China 2 Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China 3 Joint Center for Global Change Studies (JCGCS), Beijing, China *Correspondence to: W. Li, Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, No. 46 Zhongguancun Nandajie, Beijing 8, China. liwj@cma.gov.cn Received: 23 June 25 Revised: 23 October 25 Accepted: 3 December 25 Abstract This article demonstrates that the above-normal Tibetan Plateau snow depth (TPSD) in winter appears to be followed by an intensified Philippine Sea anticyclone in June (PSAC-J), and vice versa. This linkage is clearly independent of the relationship between the El Niño-Southern Oscillation (ENSO) and the PSAC-J. Moreover, winter TPSD anomalies are typically associated with a PSAC-J pattern that shifts northwards compared with that associated with ENSO. A better understanding of the combined effects of the winter TPSD and ENSO on the PSAC-J could improve our ability to predict both the East Asia summer monsoon and variations in the Meiyu Changma Baiu rainbelt. Keywords: Tibetan Plateau snow depth; ENSO; Philippine Sea anomalous anticyclone. Introduction The anomalous low-level Philippine Sea anticyclone (PSAC), which develops during the boreal winter (typically following the mature phase of El Niño events) and persists through the following spring and early summer, is one of the key factors impacting East Asian and western Pacific climate variability. Previous studies suggest that PSAC is an important bridge that links El Niño/Southern Oscillation (ENSO) events to East Asian climate variability (Chang et al., 2; Wang et al., 2; Chou et al., 23; Lau and Nath, 26). The PSAC (cyclone) is initiated and maintained by a positive feedback resulting from the thermodynamic coupling of atmospheric Rossby waves and the oceanic mixed layer in the presence of strong remote forcing from the equatorial eastern central Pacific associated with El Niño (La Niña) events (Wang et al., 2). Tropical Indian Ocean (TIO) sea-surface temperature (SST) anomalies, which usually follow the mature phase of ENSO, also make a contribution to the persistence of the PSAC (Watanabe and Jin, 22; Yoo et al., 26; Yang et al., 27; Schott et al., 29; Xie et al., 29, 2; Ding et al., 2; Chowdary et al., 2). The capacitor effect of the Indian Ocean allows the PSAC to survive into the early summer following the El Niño winter peak, and thus allows El Niño to affect the climate in East Asia. The PSAC is accompanied by surface southwesterly anomalies on its northwestern flank, which favour heavy precipitation over the area between the Yangtze River valley and southern Japan. However, the influences of ENSO events and TIO SST anomalies on the East Asian climate are not always apparent, and the aforementioned relationship cannot explain all of the variance of the PSAC. During the period 96 22, only half of the 2 strongest anomalous PSAC years correspond with a decaying El Niño (6 years: 983, 988, 995, 998, 23, 2) or TIO warming (6 years: 969, 983, 988, 998, 23, 2). This implies that other external forcing factors may also exert an important effect on the variability of PSAC. Among them, Tibetan Plateau (TP) snow depth anomalies are believed to play an important role in the formation and variability of the East Asian summer monsoon (EASM) and western Pacific atmospheric circulation (Tao and Ding, 98; Hsu and Liu, 23; Wu and Qian, 23; Zhang et al., 24; Zhao et al., 27). An increase in winter TP snow depth tends to precede a weakened EASM, which can generate more precipitation between the Yangtze River valley and southern Japan (Zhang and Tao, 2; Wu and Qian, 23; Ding et al., 29). Recent research has proposed that decadal changes in the TP snow depth have shifted the pattern of summer precipitation over East Asia (Si and Ding, 23). Additionally, TP snow cover is negatively correlated with typhoon formation and landfall numbers over the western North Pacific (Xie et al., 25). In this study, we focus on the distinct impact of winter TP snow depth anomalies on the early summer PSAC by comparing its impact with those of ENSO on the PSAC. 2. Data Monthly mean surface snow depth observations used in this study were provided by the National 26 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

2 224 H.-C. Ren et al. Table. Correlations (R) of the monthly PSAC index from May to August with the previous winter (DJF) TPSD index, DJF Niño-3.4 (5 S 5 N, 7 2 W) SST index, and spring (MAM) TIO (2 S 2 N, 4 E) SST index for the periods of (a) PSAC Index May June July August DJF TPSD.6.39 ***.2.7 DJF Niño ***.3 **.5 ***.29 ** MAM TIO.55 ***.27 **.34 **.4 *** Note: ** 95% confidence level; *** 99% confidence level. Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA). To ensure the representativeness of the data records and for quality control purposes, this study analysed data recorded at 25 stations since 96 (Figure S, Supporting Information). The TP snow depth index (the TPSD index) is the mean snow depth recorded at the 25 stations over the TP. In addition, we used monthly atmospheric reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF), including the 4-year ECMWF Reanalysis dataset (ERA4) (Uppala et al., 25) and the ERA-Interim dataset (Dee et al., 2). Both ERA4 and ERA-Interim data have a horizontal resolution of 2.5 latitude by 2.5 longitude. ERA4 data were analysed for the period , and ERA-Interim data were analysed for the period SST data were obtained from the Hadley Centre HadISST dataset with a horizontal resolution of (Rayner et al., 23). 3. Results To examine the relationship between the winter (December January February; DJF) TP snow depth and the PSAC, we calculated the correlations between the winter TPSD and monthly PSAC indices from the following May to August over the period Here, the PSAC index is defined after Wang and Zhang (22) as sea-level pressure anomalies over the Philippine Sea ( 2 N, 2 5 E). Table shows that the winter TPSD index is closely correlated with the PSAC index in June. The correlation coefficient was.39 for the entire 53-year period (96 22), and is significant at the 99% confidence level (Student s t-test). The time series of winter TPSD and June PSAC indices (Figure (a)) shows that the anomalous winter TPSD tends to be in phase with the June PSAC, and this is confirmed by the 2-year sliding correlation analysis between the two indices (Figure (b)). These positive correlations have increased markedly since the mid-98s. The above results indicate that the June PSAC is closely linked to TP snow depth anomalies from the previous winter. As demonstrated by Wang et al. (2) and Lau and Nath (26), summer PSAC (b) Figure. (a) Time series of the DJF TPSD and June PSAC indices. (b) Sliding correlations between the DJF TPSD and June PSAC indices with a 2-year moving window for the period Dashed line denotes the significance at the 9% confidence level based on Student s t-test. variability is also related to ENSO. However, we found that the June PSAC index has a moderate correlation coefficient with the preceding winter Niño3.4 SST index (.3; Table ), and is lower than that with the winter TPSD index (.39). We further examined the correlation of the June PSAC indices with the spring (March April May; MAM) TIO SST index, defined as regional-mean SST anomalies over 2 S 2 N and 4 E E(Xieet al., 29). The correlation coefficient for PSAC and preceding spring TIO SST index is.27 (Table ), which is much lower than that associated with the winter TP snow depth. ENSO may also contribute to changing the winter TP snow depth through a stationary wave teleconnection (Shaman and Tziperman, 25). Thus, a question that arises is: can the close relationship between winter TPSD and June PSAC be attributed to the effect of ENSO on the TP snow depth anomalies? To answer this question, we examined the correlation coefficients between the June PSAC and winter TPSD indices after linearly removing winter Niño3.4 and spring TIO SST signals (Figure 2). Interestingly, the correlation between winter-tpsd and June PSAC indices changed little after removing the winter Niño3.4 SST signal, or after removing the linear effect of spring TIO SST anomalies (Figure 2). These results indicate that TPSD results in the most significant correlation coefficient of the three PSAC-related factors, and the high correlation remains almost constant after removing the linear effects of the prior ENSO cycle and spring TIO SST anomalies. In addition, the results of the partial 26 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd Atmos. Sci. Let. 7: (26)

3 Distinct linkage between winter TP snow depth and june PSAC 225 Figure 2. Correlation coefficients between the original winter TPSD and June PSAC indices (red), and the indices being linearly removed the signals of winter Niño3.4 (green), spring TIO SST (blue) and both the winter Niño3.4 and spring TIO SST (yellow). Dashed lines indicate the 9, 95, and 99% confidence levels from the bottom, respectively. correlation analysis were inconsistent with the above results (Figure S2). Therefore, we conclude that winter TP snow depth anomalies have a significant relationship with the June PSAC, and this relationship appears to be independent of the effects of ENSO and TIO SST anomalies. Another question arises regarding what are the differences between the impacts of winter TP snow depth anomalies and ENSO events on the June PSAC? Figure 3(a) shows wind anomalies at 85 hpa in June regressed against the TPSD index from the previous winter. This demonstrates that above normal winter TP snow depth corresponds to an anomalous anticyclone over the Philippine Sea and adjacent regions in June, which is consistent with the results from the correlation analysis. In addition, we investigated the composites of the June wind anomalies at 85 hpa for deep (above.5 standard deviations) and shallow (under.5 standard deviations) snow depth (Figure 3(b) and (c)). Deeper winter TP snow depth tends to be followed by a stronger anticyclone in the northern Philippine Sea in June (Figure 3(b)). In contrast, shallower snow depth appears to correspond to an anomalous cyclonic wind pattern (Figure 3(c)), but this is much weaker compared with the deep snow case. The regressed pattern of 85-hPa wind anomalies in June onto the previous winter Niño3.4 index (Figure 3(d)) also shows an anomalous anticyclone over the western Pacific. A comparison between Figure 3(a) and (d) indicates that the central position of the anomalous PSAC associated with winter TPSD differs to that associated with the winter Niño3.4. The former is mainly centred over the northern Philippine Sea at about 2 N, and is accompanied by stronger southwesterly anomalies over southeast China, whereas the latter is confined to about 5 N and is concurrent with a weaker southerly anomaly over coastal southern China. Composites were also compiled for 85-hPa wind anomalies in June for El Niño (Figure 3(e)) and La Niña (Figure 3(f)) events. The comparison between Figure 3(b) and (e) is in excellent agreement with the above result, and its sign reverses in Figure 3(c) and (f). To further examine different features of the PSAC associated with previous winter TP snow depth anomalies and ENSO events, composite patterns of 85-hPa wind anomalies in June are shown with respect to the different phases of the winter TPSD index and ENSO (Figure 4). The years used for the composites in Figures 3 and 4 are shown in Table S. For neutral ENSO events, a significant anomalous cyclone appears when snow is shallow (Figure 4(b)), and an anticyclone appears when snow is deep (Figure 4(c)). This result confirms the crucial role of winter TP snow depth anomalies in inducing the June PSAC. When La Niña events are accompanied by a normal TP snow depth anomaly in winter, there is an anomalous cyclone over the Philippine Sea and adjacent regions in the following June (Figure 4(g)). Therefore, it appears that either TP snow depth or ENSO become the dominant factor while the other is in a neutral state. We note that there is no significant anticyclone over the Philippine Sea and adjacent regions in June during El Niño events accompanied by a normal winter TP snow depth (Figure 4(d)). This is mainly because these cases are mostly El Niño-Modoki events in the preceding winter, the anticyclone associated with El Niño-Modoki events is centred over southern Japan during the decaying summer period (Yuan et al., 22). It is interesting that when the winter TP snow depth anomaly and ENSO have an opposing influence on the June PSAC, the dominant control appears to be the one in its positive phase (El Niño or deep snow). When El Niño events accompanies shallow TP snow in winter, it tends to lead to a positive PSAC in the following June (Figure 4(e)), and La Niña events accompanied by deep winter TP snow also results in a positive PSAC in the following June (Figure 4(i)). In addition, comparison between Figure 4(e) and (i) indicates that the PSAC pattern is noticeably different in the two scenarios. The PSAC pattern in Figure 4(e) is similar to that associated with ENSO (Figure 3(d)), whereas the PSAC pattern in Figure 4(i) is almost the same as that associated with the winter TP snow depth anomaly (Figure 3(a)). This indicates that El Niño or deep TP snow depth dominates the June PSAC, and overcomes the contrary influence of a shallow TP snow depth or La Niña. When an El Niño (La Niña) event accompanies deep (shallow) TP snow depth in winter, there tends to be an anomalous anticyclone (cyclone) in the Philippine Sea (Figure 4(f) and (h)). Figure 3(a) and (d) demonstrates that the June PSAC pattern associated with the winter TPSD index differs to that associated with the winter Niño3.4 index in terms of the position of the PSAC. The former is located to the north and accompanied by stronger southwesterly surface wind anomalies on its northwestern flank, favouring more water vapour transport to the 26 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd Atmos. Sci. Let. 7: (26)

4 226 H.-C. Ren et al. 4 N (a) Regression onto DJF TPSD Index Regression onto DJF Niñ 3.4 index 4 N (d) 9 E 2 E 5 E 8 9 E 2 E 5 E 8 Composite of Deep Snow Composite of EI Niño 4 N 4 N (b) (e) 9 E 2 E 5 E 8 9 E 2 E 5 E 8 Composite of Shallow Snow Composite of La Niña 4 N 4 N (c) (f) 9 E 2 E 5 E 8 9 E 2 E 5 E / Figure 3. Regressions of June wind and stream function anomalies at 85 hpa onto the (a) DJF TPSD index and (b) Niño3.4 index. The composite anomalies of wind and stream function anomalies at 85 hpa with respect to (c) deep snow and (e) shallow snow conditions, and (d) El Niño, (f) La Niña events. Vectors (unit: m s ) are significant at the 9% confidence level. Yangtze River valley and southern Japan. The northward location of the PSAC is shown in Figure 4(c), (f), and (i) while the TP snow depth is above normal in the preceding winter. The pattern in Figure 3(c) more closely resembles that in Figure 4(h) and (g), rather than Figure 4(b). Therefore, the composite result of shallow snow depth alone may mainly reveal the signals accompanied by La Niña events. These asymmetric relationships are important for our understanding of East Asia climate variability through the early summer. From the above results, we conclude that winter TP snow depth anomalies have a significant relationship with PSAC in June. Such a relationship is independent of the ENSO impact, and the corresponding spatial patterns are distinct from those of ENSO in terms of the central position of the PSAC pattern. 4. Discussion and conclusions This study has shown that a significant relationship exists between winter TP snow depth and the subsequent early summer PSAC, as well as the mutual cooperation consequence of both TP snow depth and the ENSO. This relationship is independent of ENSO. When either winter TP snow depths are greater or El Niño occurs, an anomalous anticyclone develops over the Philippine Sea in the following June, regardless of whether other factors are in a neutral or negative phase (shallow TP snow depth or La Niña). The anomalous PSAC associated with the TP snow depth anomaly tends to be located to the north and is accompanied by stronger southwesterly wind anomalies on its 26 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd Atmos. Sci. Let. 7: (26)

5 Distinct linkage between winter TP snow depth and june PSAC N neutral ENSO normal Snow neutral ENSO shallow Snow neutral ENSO deep Snow 4 N 4 N (a) (b) (c) 9 E 2 E 5 E 8 9 E 2 E 5 E 8 9 E 2 E 5 E 8 4 N EI Niño normal Snow EI Niño shallow Snow EI Niño deep Snow 4 N 4 N 2 (d) (e) (f) E 2 E 5 E 8 9 E 2 E 5 E 8 9 E 2 E 5 E 8 4 N La Niña normal Snow La Niña shallow Snow La Niña deep Snow 4 N 4 N (g) (h) (i) 9 E 2 E 5 E 8 9 E 2 E 5 E 8 9 E 2 E 5 E / Figure 4. Composites anomalies of June 85-hPa wind (vectors, unit: m s ) and stream function (shading, levels 5 m 2 s ) with respect to the phase of winter ENSO and TP snow depth anomalies. Only the vectors with significant at 9% confidence level are shown. 26 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd Atmos. Sci. Let. 7: (26)

6 228 H.-C. Ren et al. northwestern flank. These wind anomalies play an important role in East Asian climate variability. Thermodynamic processes over the TP may explain the distinct relationship identified in this article. Variation in the snow depth can modulate surface albedo as well as hydrological processes on the TP (e.g. Yamazaki 989; Yasunari et al., 99; Chou et al., 23). Above-normal snow depth tends to cause a weak land sea thermal contrast between the TP heat source and the heat sink over the adjacent ocean, resulting in a weak EASM (Zhang and Tao, 2). It may also lead to a southerly shift in the position of the western Pacific subtropical high (WPSH), and a negative SST anomaly in the northwestern Pacific (Chen et al., 2). The above atmospheric circulation anomaly pattern favours high pressure and an anticyclonic wind field over the Philippine Sea and adjacent regions. The dominant influence of the winter TP snow depth anomaly on East Asian climate variability can be explained by the aforementioned mechanisms. The relationship between TPSD and PSAC can be reproduced by the recent version of the Community Atmospheric Model (CAM; version 5.3) coupled to the Community Land Model (CLM; version 4.), developed by the National Center for Atmospheric Research (NCAR; Figure S3). However, the physical processes associated with the influence of TP thermal forcing on the multiscale climate variability of East Asia remain to be identified. As more accurate observational data are obtained and model simulations improve, more in-depth studies will be required to advance our understanding of the influence of TP thermal forcing on East Asian western Pacific climate variability. Furthermore, it is interesting to note that the distinct relationship between winter TPSD and June PSAC is particularly significant in June. Many studies have reported that in spring and summer, a huge heat source exists over the TP, and the thermal forcing of the TP reaches maximum in June (Ye and Gao, 979; Weng, 986; Wu and Zhang, 998). In the mean time, snow on the TP generally melts in spring and early summer (Figure S4), and it should be noted that snow depth decreases significantly in June. Previous studies have demonstrated that the impact of snow on the atmospheric circulation is most significant during the snowmelt period and this is due to both hydrological and albedo effects (Xu and Dirmeyer, 2). Therefore, it may reach a conclusion that the impact of TP snow on the atmospheric circulation is strongest in June. In addition, the South Asian high moves abruptly northwestwards to the TP from May to June (Krishnamurti, 985; He et al., 987; Yanai et al., 992), which acts as a role of bridge that facilitates the significant influence of TP thermal anomalies on the atmospheric circulation at lower latitudes, such as WPSH (Mason and Anderson, 963; Krishnamurti et al., 973). Therefore, the correlation coefficient between TPSD and PSAC increases rapidly from May (.6) to June (.39), but becomes insignificant after June. Acknowledgements This research was jointly supported by the National Basic Research Program of China (grant no. 23CB4323), the China Meteorological Special Programs (grants nos. GYHY2563 and GYHY23633), the National Science Foundation of China (grant no ), and the Research Innovation Project for College Graduates of Jiangsu Province (KYLX_84). The authors declare no conflict of interest regarding this manuscript. Supporting information The following supporting information is available: Appendix S. Supporting information for the numerical experiment. Figure S. Distribution of surface observation stations (red dots) over the Tibetan Plateau. Figure S2. Correlation coefficients between the original winter TPSD and June PSAC indices (red), and those that partially exclude the effect of winter Niño3.4 (green), spring TIO SST (blue), and both the winter Niño3.4 and spring TIO SST (yellow). Dashed lines indicate the 9% (bottom), 95% (middle), and 99% (top) confidence levels. Figure S3. Difference in geopotential height (shading; gpm) and horizontal wind (vector; m s ) between the (a) deep and (b) shallow snow experiments and the control run in June. Dots indicate the difference of geopotential height is significant at the 9% confidence level (Student s t-test). Figure S4. 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