sign in sign up
<<
Home , Siberian high

1MARCH 2012 C H A N G A N D L U 1773

Intraseasonal Predictability of Siberian High and East Asian Monsoon and Its Interdecadal Variability

CHIH-PEI CHANG Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan, and Department of Meteorology, Naval Postgraduate School, Monterey, California

MONG-MING LU Central Weather Bureau, Taipei, Taiwan

(Manuscript received 6 September 2011, in final form 7 November 2011)

ABSTRACT

Current skill in the seasonal prediction of the Asian monsoon falls rapidly north of 408N, where the Siberian high (SH) is a prominent manifestation of the East Asian winter monsoon (EAWM). Variations in the SH are closely related to winter weather over a large latitudinal span from northern to the equator. Here it is shown that during the three recent decades the SH had an intraseasonal variation that tended to be seasonally synchronized, which produced an out-of-phase relationship between November and December/January. This implies a special intraseasonal predictability that did not exist in the two previous decades. If this relationship continues, the EAWM will be the only known major circulation system whose intensity can be predicted to reverse from the previous month. It is hypothesized that this predictability is related to the reduced frequency of blocking events during the positive phase of the (AO). While this suggests the predictability may diminish if the AO phase is reversed, it may become more prevalent in the future if the prediction of more frequent positive AO-like patterns in a warming world forced by greenhouse gases is borne out.

1. Introduction the EAWM strength (Gong and Ho 2004). The SH is not only linked to severe cold waves in northern Asia— The seasonal prediction of the Asian monsoon is one including , , northern , Japan, and of the most challenging topics in short-term climate pre- Korea (Ding and Krishnamurti 1987)—its movement is diction. With the advance of multimodel ensemble fore- also closely associated with cold surges into the tropics, casts, significant progress has been achieved (Krishnamurti which are one of the most important mechanisms gen- et al. 2006; Yang et al. 2008). However, the progress has erating stormy weather in southern China, Indochina been mostly limited to the tropical region and southern Peninsula, the Maritime Continent, and the Southern midlatitudes because the predictability is rooted in the Hemisphere tropics (Chang et al. 2003, 2005; Chan and El Nin˜o–Southern Oscillation (ENSO) (Wang et al. 2009), Li 2004). Thus, the prediction of SH is crucial for the which does little to contribute to the predictability of seasonal forecasts of the EAWM for all latitudes. With the northern midlatitudes. little evidence of a significant relationship with ENSO, A most prominent feature of the East Asian winter most research of SH variability has been concentrated monsoon (EAWM) is the Siberian high (SH), which has in its relationship with the North Atlantic Oscillation the highest sea level pressure in the world (Ding 1994; (NAO)/Arctic Oscillation (AO) (Gong et al. 2001; Wu Chang et al. 2006, 2011). The SH has a distinct annual and Wang 2002; Park et al. 2011). The results suggest cycle, appearing in fall and disappearing in spring. The that a relationship may exist at the interdecadal scale but intensity of the SH has been used as an index to represent not the interannual scale.

Corresponding author address: Chih-Pei Chang, Department of 2. Intraseasonal phase change Meteorology, Naval Postgraduate School, 1 University Way, Mon- terey, CA 93943-5000. Recent research in intraseasonal oscillations (ISO), E-mail: [email protected] particularly the Madden–Julian oscillation (Wheeler and

DOI: 10.1175/JCLI-D-11-00500.1

Ó 2012 American Meteorological Society Unauthenticated | Downloaded 09/28/21 05:25 AM UTC 1774 JOURNAL OF CLIMATE VOLUME 25

Hendon 2004), has suggested the possibility of improved TABLE 1. (top) Correlations between SH indexes of the in- forecast skill of the Asian monsoon due to the implied dividual months from October to March and each of the 3-month intraseasonal predictability. Since these ISOs are also periods for the 1979–2008 . (bottom) Correlations among SH indexes of individual months from October to February for the mainly tropical phenomena, they offer hope for increased 1979–2008 winters. DJ indicates the average of December and skill in forecasting tropical features, such as the onset of January time series. Boldface and italic numbers indicate signifi- the monsoon, the large-scale tropical convec- cance at the 95% and 99% levels, respectively. tion, and even tropical cyclogenesis (Goswami et al. 2011). 1979–2008 Oct Nov Dec Jan Feb Mar There is as yet no evidence that suggests this promise can be extended to the northern latitudes (Kang and OND 0.55 0.45 0.57 20.24 20.09 20.17 NDJ 0.13 0.12 0.65 0.44 0.20 20.13 Kim 2011). DJF 20.05 20.41 0.53 0.62 0.76 0.05 The seasonal forecasts made by all major operational JFM 20.06 20.26 20.05 0.64 0.83 0.53 centers are for rolling 3-month periods, which implies 1979–2008 Oct Nov Dec Jan Feb an expectation that the anomalies of a 3-month season are positively correlated with each of the three indi- Oct 1 Nov 0.12 1 vidual member months. To examine whether this is the Dec 20.04 20.30 1 case for the midlatitude EAWM, we used the 1979–2008 Jan 0.10 20.42 20.02 1 mean sea level pressure (MSLP) data from the NCEP– Feb 20.12 20.12 0.05 0.31 1 NCAR reanalysis (Kalnay et al. 1996) to examine running DJ 0.04 20.51 0.75 0.65 0.24 3-month periods from October to February. The results were essentially unchanged using the National Centers for Environmental Prediction (NCEP)–Department of TheSVD1modeexplains67.9%ofthecovarianceand Energy (DOE) reanalysis that starts in 1979. Corre- has a largely in-phase pattern covering the entire tropics lations of an SH index of daily MSLP anomalies aver- and most of the midlatitude domain that persists from aged over a 3-month period with those averaged over November to December/January. The maximum am- each of its three member months for October–December plitude is near the and the Maritime Conti- (OND), November–January (NDJ), December–February nent. The correlation between the two coupled patterns (DJF), and January–March (JFM) are shown in Table 1. is 0.68. This mode is clearly related to ENSO as the cor- Here the SH area is defined as from 408 to 608N, 708 to relations with the DJF Nin˜o-3.4 sea surface temperature 1208E (Chang et al. 2006), and the MSLP anomalies are (SST) is 20.60 for November and 20.75 for December/ the departures from the 30-yr mean for each calendar January. day. All correlations remain virtually unchanged when The SVD2 mode explains 19.3% of the covariance detrended (not shown). and has a clearly out-of-phase pair of patterns with The NDJ period stands out as November is the only maximum amplitudes in the midlatitudes. This mode member month that is not significantly correlated with describes the negative correlation between November the 3-month values. Table 1 shows that the intercorre- and December/January, with a correlation coefficient lation among individual months is generally very low, of 0.59 (exceeding 99% significance). The November except for the correlations between November and pattern has maximum amplitude in central Asia with the two following months. The November/December a meridional dipole whose northern center is between correlation is 20.30 (90% significance level), and the 458 and 508N and southern center around 208N. The November–January correlation is 20.42 (98% signifi- main pattern covers the entire northern half of the do- cance level). When the December and January data are main, with a secondary center near Lake Baikal. The combined (DJ), the correlation coefficient becomes pattern appears to extend from this center eastward to 20.51, which exceeds the 99% significance level. This northern Japan and southward to southern China. The negative correlation suggests that a positive SH anom- December/January pattern resembles the winter mean aly in November tends to be followed by a negative SH pattern of cold air outbreaks with the SH situated along anomaly in December/January and vice versa. 508N adjacent to Lake Baikal and migrating anticycloni- We next perform a singular value decomposition cally southward to bring cold surges to the southern (SVD) analysis between November and the December/ China coast. The low correlations with the DJF Nin˜ o- January MSLP to find the spatial structure of this 3.4 SST indicate that this mode is not related to ENSO. negative relationship. The analysis covers 08–608Nand The phase reversal between November and December/ 608–1508E or most of Asia. The first two modes—SVD1 January that is manifest by SVD2 is quite remarkable. It (Figs. 1a,b) and SVD2 (Figs. 1c,d)—are not apprecia- implies an intraseasonal predictability such that a useful bly changed when the domain is expanded to global. forecast of the December/January EAWM can be made

Unauthenticated | Downloaded 09/28/21 05:25 AM UTC 1MARCH 2012 C H A N G A N D L U 1775

FIG. 1. SVD of SH between November and December/January for 1979–2008. The fractional covariance for (a),(b) SVD1 is 67.9% and for (c),(d) SVD2 is 19.3%. The correlation between the two subperiods in (a) and (b) is 0.68 for SVD1 and for the two subperiods in (c) and (d) is 0.59 for SVD2, both with 99% significance. The correlations with NIN˜ O-3.4 SST are given in the lower corner of each panel; red numbers indicate significance at 99%. in late November based on the sign of the November decadal NAO/AO phase was mostly negative. None of anomalies. the resultant SVD modes exhibit a significant out-of- phase relationship (not shown). That the phase reversal mode was absent in the two earlier decades is also con- 3. Possible interdecadal changes firmed in the insignificant positive correlations between The cause of the phase reversal is unclear. The effect November, December, and January. During 1958–78 the of snow cover has been a frequently mentioned pa- correlation between November and DJ is 0.31 instead rameter for forecasting seasonal variations of the Asian of 20.51 during 1979–2008. This interdecadal change is monsoon (Ding 1994; Ding and Ma 2007), but snow unlikely due to the known MSLP errors in the NCEP– cover persists on a seasonal scale, so its role is a per- NCAR reanalysis (Yang et al. 2002; Wu et al. 2005), as sistence effect that tends to contribute to a positive the 40-yr European Centre for Medium-Range Weather correlation between adjacent months. In addition to the Forecasts (ECMWF) Re-Analysis data also show a posi- lack of a correlation with ENSO, the correlations be- tive November–DJ correlation (0.40) for 1958–78 and a tween SVD2 and NAO/AO are also weak. negative November–DJ correlation (20.49, 95% signifi- The dataset used in computing the SVD2 starts in cance level) for 1979–2002. 1979, which is the year when the increase of global sur- In the decades since the late 1970s, the AO phase was face temperature becomes more evident (Trenberth positive and the strength of EAWM, as measured by the et al. 2007). It also coincides with the beginning of the SH intensity, declined noticeably from the negative AO transition period of the interdecadal-scale phase change phase of the earlier decades. There was also a general of NAO/AO from negative (cold) to positive (warm). decrease of higher frequency oscillations, particularly To examine the EAWM evolution during the earlier at the synoptic scale (Gong and Ho 2004), with a cor- decades, the November versus December/January SVD responding decrease in the occurrence of cold surges analysis was repeated for the period 1958–78 when the (Wang and Ding 2006; Park et al. 2011). Nevertheless,

Unauthenticated | Downloaded 09/28/21 05:25 AM UTC 1776 JOURNAL OF CLIMATE VOLUME 25

FIG. 3. Composite time series of blocking events for 1979–2008. (a) 90-day time series of composite blocking indices (Lu and Chang 2009; Tibaldi and Molteni 1990) averaged over the Pacific blocking longitudes (1608E–172.58W). For the November positive polarity the data are plotted from 20 days prior to the maximum value of the blocking index GHGS (Lu and Chang 2009) in November until 69 days afterward. For the November negative polarity the data are plotted from 60 days prior to the maximum value in December FIG. 2. SH index for (a) November and (b) December/January from 1958 to 2008. Vertical dashed line indicates 1979—the be- until 29 days afterward. (b) Composite Pacific blocking days from 1 ginning of the more recent multidecadal period. The horizontal Oct to 24 Jan for the two polarities, and their fourth-order poly- lines indicate 10.5 and 20.5 standard deviation of the magnitude nomial fit. A blocking day is defined when the blocking index in all of the difference between November and December/January SH. five longitudinal points are nonzero. Years with opposite polarity for the two multidecadal periods are listed at the bottom. shows the composite of Pacific blocking during 1979– 2008. For the November positive polarity, a strong Pacific the intraseasonal variation of the SH remained signifi- blocking event tends to occur in November and followed cant. Takaya and Nakamura (2005a,b) pointed out that by a lull that lasts several weeks. For the December the SH is influenced by blocking patterns on both sides. positive polarity, a long period of suppressed blocking To the east, retrogression of the Pacific blocking re- condition tends to start 2 months earlier and lasts inforced by the strong feedback from the Pacific at least 7 weeks. This behavior is reconfirmed in the track forces the SH. To the west, sustained forcing is calendar-based composite of Pacific blocking days per associated with a quasi-stationary Rossby wave train year (Fig. 3b), where the November positive polarity has propagating across the Eurasian continent, which is fa- higher values for most of early winter and lower values cilitated by Atlantic and Ural blockings. Since blocking for most of late winter. Therefore, if a blocking event activities are more active during negative AO when the occurred in early winter, the tendency during the recent circumpolar flow is less zonal (Thompson and Wallace three decades is for the SH to be stronger in November 2001; Luo 2005), the SH may have experienced less and weaker in December/January. If not much block- frequent forcing during the recent decades than in the ing occurred in early winter, then the SH tended to be earlier decades. This leads us to propose a hypothesis weaker and was followed by increased blocking activi- to explain the intraseasonal phase reversal in the re- ties in December/January and a stronger SH. cent decades. To compare the Pacific blocking activity of the recent We will composite the blocking events based on the decades (1979–2008) with the earlier decades (1958–78), time series of the November and DJ SH index (Fig. 2). we constructed a calendar-based composite of the mod- During the 30 winters of 1979–2008, 18 phase reversals ified (Lu and Chang 2009) Pacific blocking indices are identified, in which 11 have a negative November (Tibaldi and Molteni 1990) of the two periods from followed by positive DJ, and 7 are opposite. This com- 1 October to 24 January based solely on the sign of the pares with the earlier decades of 21 winters (1958–78) November anomaly. (The blockings were active in the when only 7 phase reversals (5:2) are identified. Figure 3a last week of January for all cases.) During the earlier

Unauthenticated | Downloaded 09/28/21 05:25 AM UTC 1MARCH 2012 C H A N G A N D L U 1777

FIG. 4. Composite Pacific blocking index with respect to the sign of the November SH anomaly only, for (a) 1958–78 and (b) 1979– FIG. 5. As in Fig. 4 except for the Ural blocking index averaged 2008. over 308–908E (Wang et al. 2010). decades, the sign of the composite anomaly persisted the result of an eastward shift of the averaged blocking through late January (Fig. 4a). During the recent de- position from closer to to closer to Asia. cades, the sign changed within the same season (Fig. 4b), The above results suggest that the lower frequency of so a positive anomaly in November changed to negative the Pacific and Ural blockings during the recent positive after early December and vice versa. AO decades may contribute to an intraseasonal varia- Lu and Chang (2009) showed that the Atlantic tion of the SH. A stronger forcing of blocking events in blocking does not correlate well with the strength of SH November may be followed by a period of inactive except in extreme cases. The composites of the Atlantic forcing in December/January, which then results in the blocking in 1979–2008 indeed do not show the pattern phase reversal of the SH anomaly. of intraseasonal and interdecadal changes indicated by the Pacific blocking. The blocking tends to be stronger 4. Concluding remarks from November to mid-December for the November negative–DJ positive polarity. After mid-December the The phase reversal of SH is rather unique in the sea- blocking for the two polarities are similar (not shown). sonal march of the atmosphere. We computed the cor- Therefore, a prolonged Atlantic blocking in early winter relations between MSLP of all adjacent months over the may lead to a stronger SH intensity in midwinter, but global domain during the last three decades (not shown) cannot explain the phase reversal. and found the SH to be the only large area with a sig- Wang et al. (2010) reported that a pronounced re- nificant out-of-phase feature. Thus, the EAWM may be lationship between the Ural blocking (308–908E) and the the only known major circulation system whose in- SH emerged after the mid-1970s, when the frequency of tensity can be predicted to reverse from the previous blocking decreased. Figure 5 shows the composites of month. This relationship calls into question the prac- the Ural blocking. During 1979–2008 the blocking index tice of making rolling 3-month forecasts of the EAWM for the November positive polarity is stronger before for November–January, but raises the possibility that a mid-December and weaker afterward. During 1958–78 forecast of November may be extended into December the blocking remains stronger during most of NDJ for and January and thus extend the intraseasonal pre- the November positive composite. These tendencies are dictability. We present evidence that this intraseasonal similar to those of the Pacific blocking and suggest a phase reversal may be related to the less frequent Pacific similar effect to force the SH phase reversal in the more and Ural blockings during a positive AO period. There- recent decades. The Ural blocking region is close to the fore, this predictability may diminish if the phase of AO SH center, and the two features may be linked by the is reversed, but it may also become more prevalent if large westward vertical tilt in strong baroclinic sys- the prediction of more frequent positive AO-like patterns tems. Wang et al. (2010) showed that the stronger Ural in a warming world forced by greenhouse gases is borne blocking–SH relationship after the mid-1970s may be out (Hori et al. 2007; Wu et al. 2007; Choi et al. 2010).

Unauthenticated | Downloaded 09/28/21 05:25 AM UTC 1778 JOURNAL OF CLIMATE VOLUME 25

Acknowledgments. This research was supported in South Asian monsoon using multiple coupled models. Tellus, part by the National Science Council of Taiwan, R.O.C. 58A, 487–507, doi:10.1111/j.1600-0870.2006.00184.x. Ms. Yonghua Ji provided critical help in data processing. Lu, M.-M., and C.-P. Chang, 2009: Unusual late-season cold surges during the 2005 Asian winter monsoon: Roles of Atlantic blocking and central Asian . J. Climate, 22, 5205– REFERENCES 5217. Luo, D., 2005: Why is the North Atlantic block more frequent and Chan, J. C. L., and C. Li, 2004: The winter monsoon. East long-lived during the negative NAO phase? Geophys. Res. Asian Monsoon, C.-P. Chang, Ed., World Scientific Series on Lett., 32, L20804, doi:10.1029/2005GL022927. Meteorology of East Asia, Vol. 2, World Scientific, 54–106. Park, T.-W., T. Won, C.-H. Ho, and S. Yang, 2011: Relationship Chang, C.-P., C.-H. Liu, and H.-C. Kuo, 2003: Vamei: An between the Arctic Oscillation and cold surges over East Asia. equatorial tropical formation. Geophys. Res. Lett., 30, J. Climate, 24, 68–83. 1150, doi:10.1029/2002GL016365. Takaya, K., and H. Nakamura, 2005a: Geographical dependence of ——, P. A. Harr, and H.-J. Chen, 2005: Synoptic disturbances over upper-level blocking formation associated with intraseasonal the equatorial South China Sea and western Maritime Con- amplification of the Siberian high. J. Atmos. Sci., 62, 4441–4449. tinent during boreal winter. Mon. Wea. Rev., 133, 489–503. ——, and ——, 2005b: Mechanisms of intraseasonal amplification ——, Z. Wang, and H. Hendon, 2006: The Asian winter monsoon. of the cold Siberian high. J. Atmos. Sci., 62, 4423–4440. The Asian Monsoon, B. Wang, Ed., Springer, 89–127. Thompson, D. W. J., and J. M. Wallace, 2001: Regional climate ——, M.-M. Lu, and B. Wang, 2011: The East Asian winter mon- impacts of the Northern Hemisphere annular mode. Science, soon. The Global Monsoon System: Research and Forecast, 293, 85–89. C.-P. Chang et al., Eds., World Scientific Series on Asia-Pacific Tibaldi, S., and F. Molteni, 1990: On the operational predictability Weather and Climate, Vol. 5, World Scientific, 99–109. of blocking. Tellus, 42A, 343–365. Choi, D.-H., J.-S. Kug, W.-T. Kwon, F.-F. Jin, H.-J. Baek, and S.-K. Trenberth, K. E., and Coauthors, 2007: Observations: Surface and Min, 2010: Arctic Oscillation responses to greenhouse warm- atmospheric . Climate Change 2007: The Physical ing and role of synoptic feedback. J. Geophys. Res., 115, Science Basis, S. Solomon et al., Eds., Cambridge University D17103, doi:10.1029/2010JD014160. Press, 235–336. Ding, Y. H., 1994: Monsoons over China. Atmospheric Sciences Wang, B., and Coauthors, 2009: Advance and prospectus of sea- Library, Vol. 16, Kluwer Academic Publishers, 432 pp. sonal prediction: Assessment of the APCC/CliPAS 14-model ——, and T. N. Krishnamurti, 1987: Heat budget of the Siberian ensemble retrospective seasonal prediction (1980–2004). Cli- high and the winter monsoon. Mon. Wea. Rev., 115, 2428– mate Dyn., 33, 93–117, doi:10.1007/s00382-008-0460-0. 2449. Wang, L., W. Chen, W. Zhou, J. C. L. Chan, D. Barriopedro, and ——, and X. Ma, 2007: Analysis of isentropic potential vorticity for R. Huang, 2010: Effect of the climate shift around mid 1970s a strong in 2004/2005 winter. Acta Meteor. Sin., 65, on the relationship between wintertime Ural blocking circu- 695–707. lation and East Asian climate. Int. J. Climatol., 30, 153–158, Gong, D.-Y., and C.-H. Ho, 2004: Intra-seasonal variability of doi:10.1002/joc.1876. wintertime temperature over East Asia. Int. J. Climatol., 24, Wang, Z.-Y., and Y.-H. Ding, 2006: Climate change of the cold 131–144. wave frequency of China in the last 53 years and possible ——, S.-W. Wang, and J.-H. Zhu, 2001: East Asian winter monsoon reasons. Adv. Atmos. Sci., 30, 1068–1076. and Arctic Oscillation. Geophys. Res. Lett., 28, 2073–2076, Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time doi:10.1029/2000GL012311. multivariate MJO index: Development of an index for moni- Goswami, B. N., M. C. Wheeler, J. C. Gottschalck, and D. E. toring and prediction. Mon. Wea. Rev., 132, 1917–1932. Waliser, 2011: Intraseasonal variability and forecasting: A Wu, A., W. W. Hsieh, G. J. Boer, and F. W. Zwiers, 2007: Changes review of recent research. The Global Monsoon System: Re- in the Arctic Oscillation under increased atmospheric green- search and Forecast, C.-P. Chang et al., Eds., World Scientific house gases. Geophys. Res. Lett., 34, L12701, doi:10.1029/ Series on Asia-Pacific Weather and Climate, Vol. 5, World 2007GL029344. Scientific, 389–408. Wu, B., and J. Wang, 2002: Winter Arctic Oscillation, Siberian high Hori, M. E., D. Nohara, and H. L. Tanaka, 2007: Influence of Arctic and East Asian winter monsoon. Geophys. Res. Lett., 29, 1897, Oscillation towards the Northern Hemisphere surface temper- doi:10.1029/2002GL015373. ature variability under the global warming scenario. J. Meteor. Wu, R., J. L. Kinter III, and B. P. Kirtman, 2005: Discrepancy of Soc. Japan, 85, 847–859. interdecadal changes in the Asian region among the NCEP– Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re- NCAR reanalysis, objective analyses, and observations. J. Cli- analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471. mate, 18, 3048–3067. Kang, I. S., and H. M. Kim, 2011: Intraseasonal prediction and Yang, S., K.-M. Lau, and K.-M. Kim, 2002: Variations of the East predictability for boreal winter. The Global Monsoon System: Asian jet stream and Asian–Pacific–American winter climate Research and Forecast, C.-P. Chang et al., Eds., World Sci- anomalies. J. Climate, 15, 306–325. entific Series on Asia-Pacific Weather and Climate, Vol. 5, ——, Z. Zhang, V. Kousky, R. W. Higgins, S.-H. Yoo, J. Liang, and World Scientific, 409–419. Y. Fan, 2008: Simulations and seasonal prediction of the Asian Krishnamurti, T. N., A. K. Mitra, T. S. V. Vijaya Kumar, W. T. summermonsoonintheNCEPClimateForecastSystem. Yun, and W. K. Dewar, 2006: Seasonal climate forecasts of the J. Climate, 21, 3755–3775.

Unauthenticated | Downloaded 09/28/21 05:25 AM UTC