SHORT COMMUNICATION INTERANNUAL VARIATIONS OF STORM TRACKS IN THE SOUTHERN HEMISPHERE AND THEIR CONNECTIONS WITH THE ANTARCTIC OSCILLATION

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 23: (2003) Published online in Wiley InterScience ( DOI: /joc.948 SHORT COMMUNICATION INTERANNUAL VARIATIONS OF STORM TRACKS IN THE SOUTHERN HEMISPHERE AND THEIR CONNECTIONS WITH THE ANTARCTIC OSCILLATION V. BRAHMANANDA RAO,* ALEXANDRE M. C. DO CARMO and SERGIO H. FRANCHITO Centro de Previsão de Tempo e Estudos Climáticos, CPTEC, Instituto Nacional de Pesquisas Espaciais, INPE, CP 515, , São José dos Campos, SP, Brazil Received 6 January 2003 Revised 17 June 2003 Accepted 17 June 2003 ABSTRACT Interannual variations of storm tracks in the Southern Hemisphere (SH) are studied. Large interannual variations are found in the enveloping function of meridional wind v e and these suggest some dominant periodicities. Some of these periodicities are a quasi-biennial oscillation in the subtropical branch of the winter storm track and the midlatitude spring storm track, a 3 year period and a long period of 8 years. The Antarctic oscillation (AAO) seems to be connected to the interannual variation of storm tracks in the SH. A significant negative correlation between v e and the AAO index in the high latitudes, a positive correlation in the midlatitudes and again a negative correlation in the subtropics are found throughout the year. During the high index phase of the AAO, the low-level zonal wind shear increases in the region of midlatitude storm tracks and the static stability decreases, increasing the growth rate of baroclinic eddies, i.e. increase of v e (the opposite occurs during the negative phase). This explains the positive correlation in the midlatitudes between v e and the AAO index. In the region of the subtropical storm track during the high index phase the wind shear decreases and the static stability increases, decreasing the growth rates, thus explaining the negative correlation between v e and the AAO index. Copyright 2003 Royal Meteorological Society. KEY WORDS: storm tracks; Antarctic oscillation; interannual variations; Southern Hemisphere 1. INTRODUCTION There is a large body of literature regarding the formation and movement of cyclones in the Northern Hemisphere (NH) mid and high latitudes, and there are a few studies that discuss the extratropical cyclones in the Southern Hemisphere (SH; Sinclair, 1997; Sinclair and Revell, 2000; Simmonds and Keay, 2000a,b; Keable et al., 2002). Sinclair (1997) developed methods of objective identification of cyclones and discussed their climatology. Sinclair and Revell (2000) examined about 40 developing cyclones occurring between 1990 and 1994 in the southwest Pacific. A subjective classification based on synoptic-scale upper tropospheric flow characteristics before the cyclone intensification suggested four classes. Three categories involved direct coupling with the upper jet and the fourth one involved cyclones forming beneath the pre-existing intense upper-level trough. Simmonds and Keay (2000b) discussed the climatology of SH extratropical cyclones using 40 years ( ) of National Centers for Environmental Prediction National Center for Atmospheric Research (NCEP NCAR)reanalysis.When stratified by 20 -wide latitude bands, the greatest number of cyclone systems * Correspondence to: V. Brahmananda Rao, Centro de Previsão de Tempo e Estudos Climáticos, CPTEC, Instituto Nacional de Pesquisas Espaciais, INPE, CP 515, , São José dos Campos, SP, Brazil; vbrao@cptec.inpe.br Copyright 2003 Royal Meteorological Society

2 1538 V. B. RAO, A. M. C. DO CARMO AND S. H. FRANCHITO is found at S in all seasons. The axis of the greatest system density is found south of 60 S inall seasons, with high values in the Indian and western Pacific Oceans in autumn and winter. The structure is similar in summer, but the density is less. In the midlatitudes there is a split in the cyclone density in the western Pacific, which is most marked in winter. Keable et al. (2002) used a 40 year period of NCEP six-hourly global geopotential height analysis to study the seasonal climatologies of extratropical cyclones in the SH. They found that in all seasons a high-latitude core of maximum system density is found close to the latitude of the surface circumpolar trough. A broad band of enhanced cyclone system density is evident across the South Pacific from southeast Australia to South America, and this is most developed in winter. The 500 hpa systems move, on average, in an eastward direction, whereas systems at the surface move more generally in a more southeasterly or east-southeasterly direction. Also, the mean number of midlatitude cyclones has exhibited a significant downward trend over the record, whereas cyclone vigour has increased. The above-mentioned studies represent the Lagrangian analysis of cyclone behaviour. The other approach used in the literature is Eulerian analysis. In the latter approach the word storm-track is used. Blackmon et al. (1977) were among the first to study eddy activity in space in terms of the variability exhibited in various frequency bands. Studies of Eulerian analysis have shown that midlatitude synoptic disturbances in both hemispheres are organized mainly into storm tracks situated slightly poleward and downstream of the position of the midlatitude jet stream (Blackmon et al., 1977; Trenberth, 1991). Storm tracks are regions where the variance of variables like geopotential and meridional wind is at its highest. In the case of the NH, two storm tracks are found: one over the Atlantic and the other over the Pacific (Chang and Yu, 1999). Some recent studies have examined storm tracks in the SH (Berbery and Vera, 1996; Chang, 2000; Rao et al., 2002). In the SH, the principal storm track at 300 hpa lies between 45 and 55 S in all seasons, although in winter an additional branch of the storm track is seen at S between longitudes 150 and 90 W (Rao et al., 2002). It should be mentioned here that the two approaches of studying the extratropical disturbances commented on above are important, and both should be undertaken. It is also important to keep in mind that the two analyses give different aspects of storm tracks and that a direct comparison is not always possible (Jones and Simmonds, 1993). In his recent study using the Eulerian approach, Chang (1999) documented the seasonal variations of storm tracks in both hemispheres. However, he examined only summer and winter seasons. Rao et al. (2002) extended this study by analysing all four seasons. They found that the SH storm tracks, as given by the variance of unfiltered meridional wind, are strongest in austral autumn (March, April and May; MAM) and weakest in austral spring (September, October and November). Some recent papers have studied the interannual variations of storm tracks using the Lagrangian approach (Simmonds and Keay, 2000a; Keable et al., 2002). It is interesting to undertake a study of interannual variation using the Eulerian approach. This is the purpose of the present paper. Since the main activity of weather-producing synoptic disturbances in midlatitudes is concentrated in storm tracks, a knowledge of their interannual variation is of practical importance. Till recently, lack of coherent data, particularly in the SH, prevented meaningful interannual studies. Principal data sources, such as (European Centre for Medium- Range Weather Forecasts ECMWF) and (National Meteorological Center NMC), suffered spurious interannual variations arising due to changes in analysis procedures adopted with time to improve the model performance. However, the NCEP NCAR reanalysis (Kalnay et al., 1996) are devoid of this problem because of the usage of a frozen state of the art. However, in later years, as more data became available for assimilation, the impact of biases in the model became less. This introduces some spurious variability because of the changes in the database. Hines et al. (2000) examined 50 years of NCEP NCAR reanalysis from 1949 to 1998 to study the surface pressure trends over the Southern Ocean and Antarctica. They found a large trend in surface pressure of about 0.20 hpa year 1 near 65 S. Observations at Antarctic stations do not support such a large trend, although short-term interannual variations are reasonably well captured starting from about In the present study we propose to discuss the interannual variations from Also, the amount of data incorporated from surface and upper air sources has generally increased over time starting from the late 1960s. Kistler et al. (2001) mentioned that

3 SOUTHERN HEMISPHERE STORM TRACKS VARIATIONS 1539 reanalysis data can be used for daily to seasonal and interannual time scales, although they did not recommend estimation of long-term trends. They also noted a steadily increasing reanalysis skill for synoptic scales in the SH. Simmonds and Keay (2000a) mentioned that a significant number of automatic weather stations (AWSs) have been deployed over the Antarctic continent over the last two decades and improved the detection of synoptic systems. Also, they suggested that the decreasing trend in the number of cyclones gives confidence to the analysis, because if the number of cyclones increases then this may be due to the better data coverage. Here, we study the interannual variations of storm tracks in the SH using NCEP NCAR reanalysis data. We will investigate how the Antarctic oscillation (AAO; Gong and Wang, 1999), a dominant mode of lowfrequency variability in the SH, affects the storm tracks in the SH. 2. DATA SOURCES AND METHOD OF ANALYSIS In the present study we use the gridded data from NCEP NCAR reanalysis for the period January 1974 through December A detailed description of NCEP NCAR assimilation systems and output was given by Kalnay et al. (1996). Since time filtering can alter the temporal evolution characteristics of wave packets (Chang, 1993; Berbery and Vera, 1996) in this study we use unfiltered data. Data are given on a (longitude, latitude) grid and meridional wind v at 300 hpa is used in this study. Since we are interested in transient waves associated with storm tracks, we removed the stationary component by subtracting the long-period monthly mean from the individual values. This is represented by v. Assuming a single wave function for v, v = A sin ωt, it can be shown that v 2 = A 2 /2, where the bar indicates the time mean over one period. Thus, the instantaneous amplitude of v is represented as the square root of the envelope function, after multiplying the squared time series of v by a factor of 2. Since the (square-rooted) envelope function represents a local, instantaneous amplitude of the fluctuations in v, it should be a good measure of the local amplitude (or activity) of baroclinic waves. This we represent by v e. This method of obtaining v e is essentially the same as the one used by Nakamura and Wallace (1990). To verify how the storm tracks are related to the rainfall we use the (Global Precipitation Climatology Project GPCP) 1 daily combination for global precipitation for the years The data and additional information are available at daily comb html. The AAO data were kindly supplied by Dr D. Gong, and are defined as the difference in normalized mean sea-level pressure between 40 and 65 S (Gong and Wang, 1999). 3. RESULTS Figure 1 shows the mean and standard deviation of v e and rainfall. Figure 1(a) shows that the midlatitude storm track (as given by v e 18 m s 1 ) is most intense in MAM (Rao et al., 2002). In austral winter (June, July and August; JJA) there is an additional storm track in the subtropics. Figure 1(c) shows that corresponding to these two storm tracks there are two precipitation maxima. This shows, as expected, the importance of transient baroclinic disturbances associated with storm tracks in generating the precipitation. In Figure 1(c) one can note another region of maximum precipitation in summer (December, January and February) in low latitudes, probably associated with monsoonal circulations. In Figure 1(b) a region of high interannual variability (high standard deviation) co-located with a subtropical storm track can be noted in winter (JJA). In the higher latitudes, the region of high interannual variability is seen around S in autumn (MAM). Thus, the maximum interannual variability in midlatitudes is somewhat farther south of the maximum in mean v e, although both occur in autumn (MAM). Interannual variability in daily precipitation (Figure 1(d)) shows a maximum of 7 mm day 1 associated with the subtropical storm track in winter. Also, a broad region of high variability is seen in the region of the midlatitude storm track. However, as seen in Figure 1(b), the high variability in the interannual variation of v e is somewhat southward around S. A region of large interannual variability is seen in Figure 1(d) in the low latitudes in summer extending into autumn, probably associated with interannual variability of monsoons in this region.

4 1540 V. B. RAO, A. M. C. DO CARMO AND S. H. FRANCHITO Figure 1. Latitude time distribution of: (a) mean v e (contour interval: 1 m s 1 ); (b) standard deviation of v e (contour interval: 0.2 m s 1 ); (c) mean rainfall (contour interval: 1 mm day 1 ); (d) standard deviation of rainfall (contour interval: 1 mm day 1 ) Figure 2 shows the interannual variability of v e for four months (January, April, July and October) representing the four seasons. For all months, large interannual variations in v e are seen, particularly in the region of storm tracks, and in winter (July) an additional region of interannual variability is seen in the subtropics. All the figures suggest the existence of periodicities. To verify the possible occurrence of periodicities, v e values were subjected to the maximum entropy method (MEM) of spectral analysis (Burg, 1972; Ulrych and Bishop, 1975). In this method (MEM) the spectral values are computed using a maximum entropy condition for an autoregressive estimation. Autoregressive

5 SOUTHERN HEMISPHERE STORM TRACKS VARIATIONS 1541 Figure 2. Interannual variation of v e for: (a) January; (b) April; (c) July; (d) October. Contour interval: 2 m s 1 spectral estimation (MEM) has superior frequency resolution compared with conventional fast Fourier transformation (FFT) techniques. With the nonparametric periodogram approach we can determine confidence limits for the spectral peaks, whereas for the parametric method of MEM the significance of the peaks is unknown. In any case, further analysis with larger data sets is needed to confirm the results of the spectral analysis made here. However, the present study served the purpose of revealing the spectral peaks in the variability of v e. Figure 3 shows the spectral density of different periods for the four months for the midlatitudes (around S) and also for July for the subtropics (25 35 S), because of the existence of a storm track in this

6 1542 V. B. RAO, A. M. C. DO CARMO AND S. H. FRANCHITO Figure 3. Power spectral density of v e for different periodicities region. The dominant periods are given at the top right hand corner of Figure 3. In the subtropical storm track in winter and in the midlatitude storm track in October (when it is most intense) a quasi-biennial oscillation with a period of 2.5 years is seen. In October, another dominant periodicity of 8.33 years seems to occur. In each of the four months a periodicity of around 3 4 years is seen. In addition, in the subtropical storm track a periodicity of about 7 years is seen. In the SH the interannual variations of storm tracks might be related to the AAO. As pointed out by Gong and Wang (1999), the AAO might clarify low-frequency variations in the SH similar to the North Atlantic oscillation, North Pacific oscillation and Arctic oscillation in the NH. The Antarctic and Arctic oscillations are remarkably similar despite the contrasting land sea distributions. These are denominated as annular modes because of their high zonal symmetry. In the SH the AAO is associated with low-frequency zonal wind variability (Thompson and Wallace, 2000), and this results from the interaction between transient eddies and zonal mean flow (Robinson, 1991; Yu and Hartmann, 1993). Stationary waves are weak in the SH, and thus the eddy interaction with the zonal mean flow is entirely by transient eddies. These transient waves are generated by instability of zonal mean flow, and synoptic wave structures vary with zonal flow. Because of the changes in the structures of synoptic waves, associated eddy momentum fluxes interact with the zonal wind (Hartmann, 1995; Hartmann and Lo, 1998). Since storm tracks are regions of strong eddy activity and since synoptic eddies are generated by instability of zonal flow, the AAO associated with zonal wind variability might influence the storm track variability. Figure 4(a) shows the isolines of correlation coefficient (CC) between the AAO index and v e. A CC of 0.38 is significant at the 95% confidence level by a two-sided t-test. It can be seen from Figure 4 that in the high latitudes there is a negative correlation throughout the year, in the midlatitudes there is a positive correlation

7 SOUTHERN HEMISPHERE STORM TRACKS VARIATIONS 1543 Figure 4. Isolines of correlation coefficient between: (a) AAO index and v e ; (b) Eddy growth rates and AAO index; (c) Brunt Vaisala frequency and AAO index; (d) zonal wind shear and AAO index. Contour interval: 0.1. Negative values are shown by broken lines and in the subtropics there is negative correlation, except in winter. A positive (negative) correlation implies an increase (decrease) of v e (baroclinic activity of the transient disturbances). The AAO represents a largescale exchange of mass between midlatitude surface pressure and high-latitude surface pressure. Previous work, such as that by Chang and Orlanski (1993), has shown that storm track amplitude v e depends on local baroclinicity as well as upstream wave activity. To get an idea of the local baroclinicity we calculated the Eddy growth rate given by σ = 0.31f dv/dz N 1 following Lindzen and Farrel (1980). In this expression, f is the Coriolis parameter, N is the Brunt Vaisala frequency and dv/dz is the vertical shear. Baroclinic waves grow through the sensible heat transport to the poles and decay by transporting momentum (Randel and

8 1544 V. B. RAO, A. M. C. DO CARMO AND S. H. FRANCHITO Stanford, 1985). Since sensible heat transport is concentrated in the lower atmosphere, here we calculate the Eddy growth rate for the lower troposphere. However, the baroclinic waves grow first in the lower atmosphere and later propagate to the upper troposphere (Randel and Stanford, 1985). So, in this study, v e is taken for 300 hpa to represent the storm tracks. Figure 4(b) shows the CC between the estimated Eddy growth rate (day 1 ) for the hpa layer and the AAO index. In the midlatitudes there is a positive correlation with significant values at the beginning of the year, similar to what is noted in Figure 4(a). Since the principal storm track lies in the midlatitudes, this shows that the AAO modulates the storm track. From Figure 4(a) and (b) it can be seen that the highest (significant) correlations are at the beginning of the year and the storm track intensity is highest in MAM. But the general positive correlation in midlatitudes in Figure 4(a) and (b) shows that the local baroclinicity is important, although upstream baroclinic activity might also play a role. From Figure 4(a) and (b) it can be seen that, in the region of the subtropical storm track in winter, negative correlations occur, again showing the importance of local baroclinicity. In the high latitudes, negative correlations in Figure 4(a) and a positive but weak correlation in Figure 4(b) in this region show that local baroclinicity may not explain the connection. Since the Eddy growth rate is given by the vertical shear dv/dz and the Brunt Vaisala frequency (or static stability), we calculated the CC between the AAO index and these parameters. Figure 4(c) and (d) shows these CC. From these figures it can be seen that the positive correlation in the midlatitudes (Figure 4(b)) is due to a positive correlation between the AAO index and the vertical shear and to a negative correlation between the AAO index and the Brunt Vaisala frequency. That is, the increase (decrease) of the Eddy growth rate is due to an increase (or decrease) in vertical shear and a decrease (increase) in static stability. The negative correlation in the region of the winter subtropical storm track is due to the decrease in wind shear and increase in static stability. The positive correlations in midlatitudes and negative correlations in the subtropics and high latitudes as seen in Figure 4(d) are consistent with those noted by Limpasuvan and Hartmann (2000) (their figure 4(a)). Thus, the AAO affects the SH storm tracks by modulating the vertical wind shear and static stability. 4. CONCLUSIONS Midlatitude regions are critically affected by the passage of weather-bearing upper-level baroclinic waves and the associated surface cyclones and anticyclones. These baroclinic waves are concentrated into the storm tracks. In the SH the principal storm track lies between 45 and 55 S in all seasons, although in winter an additional branch of the storm track is seen at S between longitudes 150 and 90 W (Raoet al., 2002). In this article, interannual variations of storm tracks are studied for the period January 1974 through December Large interannual variation is seen in v e, suggesting the existence of dominant periodicities. Some of the dominant periodicities are the quasi-biennial, a 3 year and a long-term periodicity of 8 years. As one of the dominant oscillations in the SH, the AAO has the potential of relating its variability to the other climatic variations. Here, we examined how the AAO is related to the interannual variability of the storm tracks in the SH. Since storm tracks are regions of strong synoptic eddy activity, since synoptic eddies are generated by the instability of zonal flow and since the AAO is associated with zonal wind variability, a close association is expected between the AAO and storm tracks. Significant negative correlation between v e and the AAO index is found in the high latitudes, a positive correlation is found in the midlatitudes and a negative correlation is found in the subtropics. During the high index phase of the AAO, low-level zonal wind shear in the region of the storm tracks in midlatitudes increases and static stability decreases, increasing the growth rates of baroclinic eddies. The opposite occurs in the negative phase. However, in the region of the subtropical storm track in winter, the wind shear decreases and static stability increases, decreasing the growth rates, thus explaining the negative correlation between v e and the AAO index. ACKNOWLEDGEMENTS Thanks are due to the two official reviewers for useful suggestions.

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