Recei ed 24 December 1999 Re ised 30 August 2000 Accepted 31 August INTRODUCTION

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 21: (2001) DOI: /joc.606 THE SPATIAL AND TEMPORAL BEHAVIOUR OF THE LOWER STRATOSPHERIC TEMPERATURE OVER THE SOUTHERN HEMISPHERE: THE MSU VIEW. PART I: DATA, METHODOLOGY AND TEMPORAL BEHAVIOUR ROSA H. COMPAGNUCCI, M. ALEJANDRA SALLES and PABLO O. CANZIANI* 1 Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Uni ersidad de Buenos Aires/CONICET, Buenos Aires, Argentina Recei ed 24 December 1999 Re ised 30 August 2000 Accepted 31 August 2000 ABSTRACT The lower stratosphere monthly temperature anomalies over the Southern Hemisphere derived from soundings made by the Microwave Sounding Unit (MSU) between 1979 and 1997 are analysed. Specifically MSU channel 4 temperature retrievals are considered. Principal component (PC) analysis with the S-mode approach is used in order to isolate grid points that covary in a similar manner and to determine the main features of their temporal behaviour. The first six PCs explain 81.3% of the variance and represent the different time variability patterns observed over the Southern Hemisphere for the ten area clusters determined by the method. The most important feature is common to all the PC score pattern time series and corresponds to a negative linear trend present in almost all the Southern Hemisphere except over New Zealand and surrounding areas. The negative trend is largest over Antarctica. The remaining features of the temporal variability are different for each PC score and therefore for each cluster region over the Southern Hemisphere. The first PC score pattern shows the impact of the Chichón and Mt Pinatubo eruptions that each produced a 2-year warming over the tropical and sub-tropical lower stratosphere. This variability is orthogonal with the behaviour present over Antarctica. There are different anomalies between 1987 (El Niño) and 1988 (La Niña). This second PC does not show any evidence whatsoever of the volcanic eruptions. The semi-annual wave is present in the anomaly recurrence at mid to high latitudes. Over very low latitudes, close to the Equator, the Quasi-Biennial Oscillation (QBO) band of frequency is also present. Copyright 2001 Royal Meteorological Society. KEY WORDS: stratosphere; temperature; variability; MSU; principal component analysis 1. INTRODUCTION As pointed out in SPARC (1998), there is a significant need for a quantitative and critical analysis of stratospheric temperature trends and behaviour, as well as the determination of their causes. In particular, the temperature trends and variability in the lower stratosphere are influenced by multiple factors, such as ozone depletion (Newman and Randel, 1988; Randel and Wu, 1999). For example, the inter-seasonal and inter-annual variability and the negative trends observed in total ozone (Randel and Cobb, 1994) could be acting upon the behaviour of the lower stratospheric temperature (Ramaswamy et al., 1996). Negative ozone trends at high latitudes are dominant due to heterogeneous chemical losses on polar stratospheric and lower stratospheric aerosols because of the increase in chlorine loading resulting from anthropogenic emissions of chlorofluorocarbons (CFCs) (Hood and Zaff, 1995, and references therein). It * Correspondence to: Departamento de Ciencias de la Atmósfera y los Océanos, FCEN-UBA, Pabellón II-2do Piso, Ciudad Universitaria, 1428 Capital Federal, Argentina; canziani@rosario.at.fcen.uba.ar 1 Also at: Laboratorio de Investigaciones de Sistemas Ecológicos y Ambientales, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, Argentina. Copyright 2001 Royal Meteorological Society

2 420 R.H. COMPAGNUCCI ET AL. must be pointed out that there is evidence of enhanced stratospheric cooling in summer over Antarctica due to the slow recovery of the ozone in the region after the breakup of the polar vortex and the so-called ozone hole late in spring. Furthermore, another source of human influence upon the atmosphere and climate is the accumulation of greenhouse gases, in particular CO 2 and sulphate aerosols in the troposphere (IPCC, 1995). Warming of the troposphere can lead to cooling of the lower stratosphere, as seen in model runs and observations (Golitsyn et al., 1996; Santer et al., 1996; Tett et al., 1996). Hood and Zaff (1995) discussed the changes in the total ozone distribution of the Northern Hemisphere mid-latitudes, associated with changes in the hemispheric circulation. On the other hand, volcanic eruptions at low latitudes can inject significant amounts of SO 2 into the stratosphere. The SO 2 evolves into sulphuric acid aerosols, which are significant both to ozone depletion and to the warming of the lower tropical stratosphere (Angell and Koshover, 1983; Labitzke et al., 1983; Labitzke and McCormick, 1992; Angell, 1993). In order to reach a better understanding of climate and the role of the stratosphere in its behaviour, the spatial and temporal structure of the lower stratosphere temperature fields, and their variability in various scales must be analysed. SPARC (1998) has pointed out that there has been a lack of comprehensive investigation of the stratospheric temperature during the last decade. The lower stratosphere temperature soundings obtained by the Microwave Sounding Unit (MSU), flown onboard the TIROS-N NOAA satellites provide a very valuable source of data for such a study. The MSU family of sensors provide a time series that extends back to late 1978 and at least up to 1997, yielding a continuous 19-year long data series. The time series with global coverage are now long enough that a study of the spatial and temporal variability of the lower stratospheric temperature can be carried out. So as to work towards an understanding of the climate system, in this case focused on the Southern Hemisphere, a study of the temperature mean monthly anomalies in the vicinity of 70 hpa (MSU channel 4, from now referred to as T4) is carried out using the analysis tools of synoptic climatology (Barry and Perry, 1973; Yarnal, 1993). Principal component (PC) analysis is thus used to obtain the spatial anomaly patterns (applying the T-mode) and to define the temporal trend and variability of the grid points (applying the S-mode). The data and methodologies are further discussed in the second and third sections, respectively. The temporal trend and variability of the grid point time series obtained applying the S-mode approach are presented in the fourth section. These results are discussed in the fifth section. The complementary spatial anomaly patterns and their behaviour are discussed in the companion paper (Salles et al., 2001, this issue, from now on Paper II), where the spatial behaviour is presented and discussed as well as a final analysis combining the results presented here. 2. DATA A climatological study of the lower stratospheric temperature requires a reliable source of data with extended coverage in space and time. The data must have a confirmed reliability in time to ensure that the observed trends conform to real physical processes. A number of sources of data are available for such a study, in particular, radiosondes and the MSU series of instruments. Though lacking the vertical resolution of the radiosondes, the MSU data set provides an excellent geographic coverage, useful for the purposes of this work. The MSU instrument is a nadir viewing, cross-track scanning system, which measures the atmospheric temperature at different heights in the troposphere and lower stratosphere: surface (channel 1), lower troposphere (channel 2), upper troposphere (channel 3) and lower stratosphere (channel 4-T4) (Spencer, 1993). The instruments are flown in the TIROS-N/NOAA family of operational satellites, which follow a sun-synchronous orbit. Since late 1978, ten instruments have been launched one after the other, providing both continuous coverage and adequate overlapping, thus allowing for the appropriate intercalibration between successive instruments, and the capability to ensure the reliability of a long-term data set. The instrument provides pole to pole coverage and the data are available in a grid.

3 LOWER STRATOSPHERIC TEMPERATURE. PART I 421 The T4 temperature can be considered a pure air temperature because it is not influenced by other geophysical processes, as is the case for the tropospheric retrievals. It corresponds to a deep layer between 150 and 50 hpa, with a maximum contribution from the layer close to 70 hpa (Spencer and Christy, 1993). At mid and high latitudes this height range implies a pure stratospheric sampling while at low latitudes and equatorial regions there is an increasing input from the upper troposphere, given that the mean equatorial tropopause is found close to 100 hpa. The main concern about using a space-time data set that merges observations from a series of similar instruments is the stability of the time series. Christy et al. (1998) addressed this and other problems as well as the manner in which the difficulties were overcome, in order to generate a homogenous time series, using data from nine of the ten sensors. The data used in this study, which are the latest version available at the MSU website, correspond to the mean monthly temperature anomalies, for the period January 1979 December These anomaly fields are calculated by removing the long-term monthly means. These monthly means were calculated for the period METHODOLOGIES During the last two decades numerous studies were carried out, using eigentechniques in various forms, to interpret geophysical observations: empirical orthogonal functions (EOF), PC analysis and, less frequently, common factor analysis (CFA), among others. Due to this, a diverse nomenclature is being used in the literature. The results of the eigenfunction analysis are different and its interpretation depends on the particulars of the method applied and the definitions considered (e.g. use of correlation matrix, covariance matrix or cross products, among others). In consequence, a brief overview of the PC analysis used in this work is now provided. The basic definitions and concepts can be reviewed in Green (1978). According to Cattell (1952) two different analysis can be performed for a set of multivariate data, composed of a group of observations taken at different points in space, at different times. The S-mode is that in which the statistical variables are the points in space, e.g. grid points or meteorological stations for which the data are available, and the statistical observations are the times at which the variables are measured. In other words the behaviour of the time series at each grid point is studied. The aim of this approach is to isolate subsets of the space domain, which have similar time variations, either in phase (direct) or anti-phase (inverse), and regions of homogeneous or quasi-homogeneous behaviour in time. Thus the patterns of temporal variability are obtained for such regions. This is the most common use of the technique since Lorenz (1956), who was the first to apply this methodology in the field of meteorology. The other approach is the T-mode, for which the statistical variables are the snapshots or spatial fields at a given time of the physical variable under study. The application of this mode permits the isolation of subsets of fields that have similar spatial variability. It will be further discussed in Paper II, and applied to the same data set. It must be noted that the results obtained by these two methods are complementary and mutually exclusive, each describing different aspects of a real process. In some special cases, both groups of results appear to be similar for some of the PCs. A relationship will possibly exist between these similarities, if there is an intrinsic and large linear relation between time and space. In all cases this needs to be verified. Summing up, the S-mode yields temporal patterns, which cannot generally be reproduced by mathematical processing of the T-mode spatial products, and ice ersa. The analysis presented here refers to the study of the temporal behaviour of the lower stratospheric temperature anomalies for the period January 1979 December 1997 (228 months). Applying the S-mode, the variables are the grid points of the domain (n=5148) and the observations are the time steps (m=228), where n and m are the number of variables and observations, respectively. Using as the PC analysis input a correlation matrix, the resulting principal component scores are the non-dimensional time series, which describe the principal types or patterns of variability in time for the temperature anomaly.

4 422 R.H. COMPAGNUCCI ET AL. The principal loading patterns (spatial) show the correlation fields determined by the principal component scores time series and all the points in the space domain. These correlation fields show areas where the grid point time series present an in-phase or anti-phase temporal behaviour similar to the PC score time series, when the correlation values are significantly different from zero and high. This is due to the fact that the square of the PC loading at each grid point is the amount of variance explained for the time series at that point by the PC score. Richman and Gong (1999) show that the hyperplane corresponding to low PC loadings do not correspond to the behaviour of original observations for that particular PC, and values within the hyperplane could be either due to noise or due to the fact that another PC describes the behaviour there. In this paper the correlation values between 0.3 reside in the hyperplane. For a more explicit description of the hyperplane concept, refer to Richman and Gong (1999). 4. TEMPORAL BEHAVIOUR OBTAINED WITH THE S-MODE APPROACH Using the S-mode approach, the temporal behaviour of the MSU retrievals was characterized. For the data set under study, 81.3% of the total variance in the sample can be explained by the first six components. These six components are significantly above the noise tail, as can be seen in the Lev diagram (Figure 1) which displays the log 10 of the eigenvalues versus the order of the corresponding PCs (Craddock and Flood, 1969; Farmer, 1971). Inspection of the PC loadings shows that even for the sixth PC, the highest order considered here, there are areas over the Southern Hemisphere with absolute values larger than 0.3. That means that in those areas the component loadings reside within the hyperplane as a response to real physical processes. These six PCs are enough to explain the most significant variance of the temporal variability in the lower stratospheric temperature. Some of the remaining components could be significantly different from noise but their contribution towards explained variance is very small. Hence they could represent particular or extreme events which do not influence the climatological description. The test proposed by North et al. (1982) suggests that the eigenvalues for PC 1 and PC 2 may Figure 1. Lev diagram (log 10 of eigenvalue versus eigenvector order): the first six PC-eigenvalues are highlighted with crosses and the fitted linear trend for the noise tail is shown. The attached table displays the eigenvalues, the corresponding percentage of the explained variance and the cumulative variance of the first six PCs

5 LOWER STRATOSPHERIC TEMPERATURE. PART I 423 be degenerate. Nevertheless the good fit obtained between PC 1 and PC 2 temporal patterns and the respective, corresponding real time series, together with the physical conclusions that can be reached for each one, as discussed below, show that they are not degenerate. The results of the six PCs will now be shown in order of decreasing explained variance. Each corresponding figure includes the PC scores, which are the non-dimensional types of the temporal series, the PC loading map, which shows the region(s) where the time series is representative of the temporal behaviour of the temperature anomalies, and the spectrum of the temporal series. Areas surrounded by PC loading isopleths whose absolute values are equal or greater than 0.3 are shaded grey in the maps, to show where the PC scores are significant, i.e. where they belong inside the hyperplane and thus relate to the time series of the grid points. In other words the raw data time series for the grid points included in those shaded areas will have a behaviour described, either in direct or inverse mode, by the PC score time series and can be considered to form a cluster of grid points with a similar temporal evolution. The correlation maps obtained by correlating the best fitted real time series with all the rest are also shown. In the cases where there is more than one cluster of direct and inverse modes, both maps are shown. This is a further test of the quality of the PC loading map. The PC score time series (in red), the raw data time series for the grid points with the largest positive and/or negative loadings (in blue) and the decadal trend (in black) are shown in Plate 1. The linear trend for the PC score time series is non-dimensional, like the PC scores themselves. Hence the PC scores can, at best, provide the sign of the trend, if any. The actual trends were calculated using the linear trend for the time series corresponding to the grid points shown in Plate 1, i.e. for the most representative time series for each resulting PC score temporal pattern in a given cluster. These dimensional trends are the ones taken to be representative of the corresponding modes and included in the plots. The spectra for the PC score time series were calculated with the Blackman Tukey method and Parzen s window (Jenkins and Watts, 1968). In all cases the Markovian red spectrum was calculated for the 90% confidence level, as a reference PC 1 The temporal behaviour shown by the PC score pattern (Figure 2(a)) occurs in the tropical and low subtropical latitudes, but is somewhat less significant over the equatorial region, as can be seen in the PC loading map (Figure 2(b)). This is a quasi-annular region, weakened over the central South Pacific Ocean. This signal clearly vanishes towards mid and high latitudes, where other temporal anomaly patterns are present, explained by subsequent PC scores. It must be pointed out that the negative values over Antarctica are not sufficiently large in their absolute value, i.e. less than 0.3, as to expect an anti-phase behaviour there, associated with tropical latitudes. The high latitude area is clearly within the hyperplane, and corresponds to the physical behaviour described by the subsequent PC. The temporal behaviour shown by the PC score series has two main characteristics. The first of these are the two major warming events followed by persistent positive anomalies. These events are separated by approximately 9 years and last for almost 2 years. The beginning of each event is located during the first half of 1982 and 1991, respectively. Specifically, the first positive value of a large sequence of positive anomalies takes place, for the first case, in March 1982 and June 1991, for the latter. The real series best represented by this mode is located at S, 26 5 E. It can be seen in Plate 1(a), scaled to the PC 1. The warming anomaly is of the order of 2 C. The most likely mechanism, which can explain such a temporal behaviour, is the impact of major volcanic eruptions upon the stratosphere at low latitudes. During the period of this study, two major eruptions in the tropics injected large amounts of SO 2 into the stratosphere, resulting in the formation of sulphuric acid aerosols in the lower stratosphere. These large aerosol loadings developed shortly after the eruptions in an aerosol layer ring about the equator and the tropical latitudes contained within the tropical stratospheric reservoir (Hitchman et al., 1994). The enhanced aerosol layer results in a significant warming of the lower stratosphere. El Chichón first erupted in April 1982 and the Mt Pinatubo eruption began on 15 June The beginning of the periods with large positive anomalies coincide with the

6 424 R.H. COMPAGNUCCI ET AL. Figure 2. Main aspects of the first PC: (a) PC score time series. (b) PC loading pattern: contour values are every 0.05 (positive in full line and negative in dotted line). Shaded areas correspond to values higher than +0.3 or less than 0.3. (c) Normalized spectral analysis according to Tukey with a Parzen window given as R( f ) 100 versus cycles/month (full line) and the 90% confidence level considering theoretical Markov process. Significant peaks are highlighted. (d) Correlation map for the correlation between the best fitted real time series and the time series corresponding to all other grid points in the sample eruptions. Both positive anomaly sequences last for about 24 months: for El Chichón the anomaly ends in February 1984 and for Pinatubo in June 1993, approximately. Angell (1988) found a similar behaviour resulting from the Mt Agung eruption in Studying the spatial influence of the equatorial eruptions, Angell and Koshover (1983) found, in a radiosonde study of the impacts of Mt Agung and El Chichón eruptions, significant impacts in the subtropics of the Northern Hemisphere between 10 and 30 N. Kinnison et al. (1994) carried out an in-depth study of the impacts of the Mt Pinatubo eruption using aerosol data obtained by SAGE 11 and CLAES instruments. Their plots of the zonal mean aerosol extinction coefficients, as measured by SAGE 11 between June 1991 and December 1993, show that the maximum values in the tropical/equatorial region were not centred over the Equator but rather shifted

7 Plate 1. The PC score time series (in red) are compared with selected observed temperature anomalies (in blue). The linear trends are also shown (in black). (a) The first PC and the monthly lower stratospheric temperature anomaly time series at S, E (in C). (b) The second PC and the monthly anomaly lower stratospheric temperature time series at S, E (in C). (c) The third PC and the monthly lower stratospheric temperature anomaly time series at S, E (in C). (d) The fourth PC and the monthly lower stratospheric temperature anomaly time series at S, W (in C). (e) The fifth PC and the monthly lower stratospheric temperature anomaly time series at 1 15 S, W (in C). (f) The sixth PC and the monthly lower stratospheric temperature anomaly at S, W (in C). Copyright 2001 Royal Meteorological Society Int. J. Climatol. 21(4) (2001)

8 LOWER STRATOSPHERIC TEMPERATURE. PART I 425 towards the Southern Hemisphere in agreement with the observed warming. The model runs shown in Kinnison et al. (1994) have distinct warming signatures at stratospheric heights that are coincident with the latitude of largest correlation, i.e. between 10 and 20 S as can be seen in Figure 2(b). The second important characteristic is the cooling trend estimated at 0.36 C/decade, for the best correlated grid-point located at S, E (Plate 1(a), black line). This trend is representative of a zonal mean temperature trend at low to very low southern latitudes, between 5 and 25 S, given the annular structure of the cluster (Figure 2(b)). This result falls within the 95% confidence bar calculated by Angell (1988), for the equatorial/subtropical negative trends obtained from radiosonde data ( ) in the hpa layer. Removing the period , corresponding to the El Chichón eruption and the period , corresponding to the Mt Pinatubo eruption, the trend does not show significant changes. The trend obtained is probably somewhat smaller than that obtained by Angell (1988) because the sampling of the MSU in the tropics is not purely stratospheric. The uppermost tropical troposphere could be contributing a positive or null temperature trend (e.g. Santer et al., 1996; Tett et al., 1996) to the height-weighted MSU temperature. A similar result was also obtained by Graf et al. (1998) in their comparison of the global trend for the same data set considered here together with the Northern Hemisphere radiosonde analysis from the Free University of Berlin. The trend does not change despite the removal of these anomalies, implying that their impact on the stratospheric temperature were of the same magnitude and of limited duration. A similar cooling trend was also observed in model results (Santer et al., 1996; Tett et al. 1996). Ramaswamy et al. (1996) pointed out that high latitude ozone depletion could have a significant impact on the lower stratospheric temperatures even at very low latitudes. The effects upon the temperature of high latitude ozone depletion could be at least as large as the contribution from greenhouse gases if not larger. In order to consider whether the tropical lower stratospheric temperature is influenced by El Niño Southern Oscillation (ENSO), we consider the El Niño/La Niña dipole that took place between 1986 and Negative anomalies are predominant during most of During the second half of 1987, an extreme positive temperature anomaly occurs, i.e. when there is no volcanic influence. A negative extreme peak then occurred during the second half of Again we consider an ENSO event for the period This period also shows a mainly negative anomaly with the most negative value in the Austral summer. This period corresponds to the end of a sequence of weak warm sea-surface temperature (SST) anomalies in the Equatorial Pacific. From mid-1995 until December 1996 there is a weak cold SST anomaly in the Pacific. Negative tropical stratospheric temperature anomalies are observed for that period. These are rapidly replaced by a large, positive anomaly starting during the Austral fall of Little can be said for the and events due to the presence of the extreme positive anomalies associated with the volcanic eruptions. Stenchikov et al. (1998) pointed out that aerosol radiative forcing is insensitive to climate variations and hence any climatic signal would indeed vanish from the time series. With the currently limited data available it would only be possible to infer that a warm sea-surface temperature anomaly (SST anomaly) can always be associated with a warming in the lower stratosphere and ice ersa. The PC score and the real time series belonging to this cluster show considerable higher frequency variability. The spectral analysis (Figure 2(c)) yields two significant cycles (greater than the 90% confidence level). One corresponds to a 4-month period and the other to a 5-month period. The similarity between the PC loading map (Figure 2(b)) and the correlation map between this best-fitted time series at S, 26 5 E and the time series for all other grid points (Figure 2(d)) is astounding. If a similar correlation map was plotted for a grid point over the Antarctic, there would be no correlation between this region and the subtropics. Even more so, the correlation field thus obtained is very different from the first PC loading plot and actually resembles that obtained for the second PC as can be seen below PC 2 The pattern of the temporal behaviour yielded by the second PC corresponds to grid points at very high latitudes over Antarctica (Figure 3). These grid points are found in an almost circular area, which is very

9 426 R.H. COMPAGNUCCI ET AL. Figure 3. The same as Figure 2, for the main aspects of the second PC close to being centred over the South Pole, i.e. coincident with the core of the Antarctic polar vortex during the Austral spring. The inverse behaviour is not detected anywhere in the Southern Hemisphere. Hence, there is no associated anti-phase temperature behaviour at other latitudes with respect to the temperature variability over this polar region. The correlation between the PC 1 best fitting time series at tropical latitudes (13 35 S, 15 E) and that corresponding to PC 2 at S, E (Plate 1(b)), at high latitudes, is 0.04, i.e. not significantly different from zero. Hence a possible temperature oscillation between high and low latitudes in the Southern Hemisphere is discarded. Again the correlation map between the time series at S, E and those for all other grid points (Figure 3(d)) is very similar to the PC loadings map. The temporal evolution of the PC score time series (Figure 3(a)), as well as the best adjusted real time series (Plate 1(b)), shows a very interesting pattern. It has very weak anomalies throughout the

10 LOWER STRATOSPHERIC TEMPERATURE. PART I 427 observation period, except for the months of October and November (spring season), where very prominent peaks appear, representing both positive and negative anomalies. There is no clear evidence of a volcanic signature, as was the case for PC 1, at low latitudes. The seasonal behaviour is in agreement with Graf et al. (1998), which shows a significant zonal trend at high latitudes for the month of October. Indeed there are more frequent positive anomalies at this time of the year at the beginning of the sample, and more frequent negative ones at the end. It is interesting to note that the most important negative anomaly in the data time series occurs in November 1987, a period in which a significant deepening of the ozone hole took place with respect to previous years. On the other hand, a prominent positive anomaly appears for November 1988, towards the end of the weakest ozone hole observed since the discovery of this phenomenon in The behaviour of the Southern Hemisphere during 1987 and 1988 was indeed very different. Randel (1988) analysed the behaviour for 1987, while Kanzawa and Kawaguchi (1990) discussed the anomalous behaviour during In 1987 the final warming was delayed, while in 1988 there was a strong warming event in August. This warming event was not followed by a return to the pre-warming low temperatures. Furthermore planetary waves were particularly active, resulting in a more effective transport of heat and ozone into the high latitudes. The extreme oscillation between November 1987 and November 1988 could be associated with the El Niño La Niña dipole discussed for the first PC. While the response was positive and weaker near the Equator (Plate 1(a)) during 1987, the response at high latitudes is stronger and of opposite sign (Plate 1(b)). This same inverse behaviour between low and high latitudes holds for Inspection of TOMS total ozone retrievals shows that, after this period, positive and negative peak anomalies observed in the PC score and real time series do not appear to have any relationship with the strength of the ozone depletion taking place at a given time. The trend for this portion of the hemisphere is very important. The grid point whose time series, best represented by this PC, located at S, E (Plate 1(b)) has a linear trend of 1.24 C/decade, for the period under study. This trend too is of the same order as that described in Angell (1988). This trend is more than three times as large as that observed at the tropical/low subtropical latitudes with PC 1. It could be associated, given the geographical distribution of the PC loading map, with the cooling resulting from the high ozone depletion by heterogeneous processes within the polar vortex. The spectrum for the PC scores (Figure 3(c)) shows only two significant peaks at the 90% confidence level, which are very close together, having periods of 7.24 and 6 months, respectively. These two peaks have frequencies close to the semi-annual oscillation (SAO) which has been detected in surface atmospheric pressure over Antarctica (Van Loon, 1967; Hsu and Wallace, 1976; König-Langlo et al., 1998). Meehl (1991) and references therein point out that the SAO extends throughout the height of the troposphere, affecting temperature gradients, heights and winds at middle and high southern latitudes. The SAO is characterized by the expansion and contraction of cyclonic activity at mid and high southern latitudes. Van Loon (1972) pointed out that the amplitude of the SAO in pressure gradients and zonal mean geostrophic wind increases with height throughout the troposphere. The zonal mean zonal geostrophic wind shows a significant amplitude at 100 hpa between 60 and 70 S. There is a weaker peak between 35 and 45 S. Meehl et al. (1998), reviewing the SAO issue, concluded that the mechanism originally proposed by Van Loon (1967) is indeed applicable to describe this oscillation, i.e. the existence of a polar continent south of about 65 S surrounded by oceans at mid-latitudes and the role of the SST are crucial in the modulation of the SAO variability. Given that synoptic activity that extends throughout the troposphere does have an impact at least upon the tropopause, and the fact that there is a significant SAO signal in the geostrophic zonal wind, it is then not surprising then that oscillations with the same periodicity are observed here PC 3 This is the first of the PCs to yield a PC loading map with distinct dipole structure (Figure 4(b)) at mid to high latitudes. The dipole structure has areas of significant correlation located to the west of the Bellingshausen Sea (with the best fit at S, W) and midway between the eastern coast of South Africa and Antarctica (with the best fit at S, E). The latter extends more into the mid-latitude region than the other side of the dipole. The area of influence is larger in the Indian Ocean.

11 428 R.H. COMPAGNUCCI ET AL. Figure 4. The same as Figure 2, for the main aspects of the third PC The PC score time series shows higher frequency oscillations than for the first two PCs as can be seen in the spectrum (Figure 4(c)). The more prominent spectral peak has a 2.4-month period. The temperature series oscillate in anti-phase at these two locations: the correlation between the two series is This implies that this is a real oscillation pattern. It is interesting to note that, as was the case with PC 2, both the PC score time series and the anomaly time series show the most important peaks during the ozone hole season. In particular, PC 3 has this behaviour mainly during the months of August, September and October, i.e. early Austral spring. In particular, the time series corresponding to observations from the Pacific Ocean sector has maxima in September and October. The most prominent negative anomaly corresponds to November 1987 and the positive one to October In the case of an observed temperature series corresponding to the South Indian Ocean sector, the most important positive anomaly is observed in September 1988, and another in June 1992, while the most negative occurred in October Please note that again this PC yields an extreme in 1988.

12 LOWER STRATOSPHERIC TEMPERATURE. PART I 429 Figure 4(d) and (e) show the result of correlating the best-fitted grid points at each end of the dipole. As was previously the case these correlation maps are in excellent agreement with the PC loadings map. Given the negative correlation between the two clusters that form the dipole, this is a true dipole with an anti-phase behaviour between its two main centres of action. The time series for both clusters of the dipole have similar negative trends, of the order of 0.36 C/decade for the period (Plate 1(c), shown over the Southern Indian Ocean). This behaviour points to a possible link with the ozone depletion process, given the time of occurrence of the most prominent anomalies PC 4 The PC loading map (Figure 5(b)) corresponding to the fourth PC appears to show a dipole as well, rotated by about 90 with respect to PC 3. It is interesting to note that the latitude band is also the same as for the previous mode. The best fitting data time series for this PC score (Figure 5(a)) are located at S, W and S, E respectively. PC 3 and PC 4 determine four regions with distinct behaviour covering mid to high latitude bands in the hemisphere. This implies that the temporal behaviour of temperature anomaly at mid to high latitudes of the Southern Hemisphere is more complex than both the tropical/subtropical region and the high polar latitudes. In this case the regions of high correlation are located over the Weddell Sea and the southern part of South America, i.e. Patagonia, and southward from New Zealand. While the first two PCs have an explained variance of the same order (26% and 22%, respectively) and the third PC 16%, this PC represents only about 9% of the variance, i.e. there is a significant decrease in the amount it explains. The PC score time series (Figure 5(a)) shows greater variability than the previous cases. However, it is still possible to observe prominent peaks. The inspection of this time series shows that, as was the case for the two previous PCs, these significant anomalies tend to occur during the Austral spring, though there are a number of events in April and June. Furthermore there is an enhancement in the variability of the PC score after The most important negative anomalies take place between 1990 and 1996, either in September or November. In this case the data time series in each region of the dipole that have the best correlation with the PC score series show behaviour that are somewhat more different. The data series in the New Zealand Pacific Ocean sector (not shown here) does not show as many significant negative anomaly peaks, as does the PC score. It even shows a large number of positive anomalies after It does not show a well-defined overall trend. Bodeker et al. (1998) demonstrated that for the radiosonde temperatures over Lauder (45 3 S, E), New Zealand, close to the core of the dipole, there is no significant trend in the height region sampled by the MSU, during the period They also find a positive trend in the temperature close to the tropopause, and a warming trend above 50 hpa, associated with an enhancement of the ozone content there. This is in good agreement with the MSU results in that region, for the layer under study. On the other hand the data series corresponding to the Atlantic sector (Plate 1(d)) shows an increase in the occurrence and size of the negative anomalies starting during the late 1980s. The linear trend for this particular sample is significant at 0.88 C/decade. The spectral analysis of the PC score time series (Figure 5(c)) yields two peaks close to or above the 90% confidence level. The corresponding periods are, in order of decreasing importance: 6 months and 2.5 months. Furthermore, there is a significant spectral power at the lowest frequency range. While the third PC did not have the 6-month oscillation, it appears again in this case as a very prominent well-defined peak. If this 6-month oscillation is the stratospheric projection of the tropospheric SAO, this could mean that there is an important zonal asymmetry in the SAO in the stratosphere outside polar regions. The correlation maps (Figure 5(d) and (e)) show that while the general features of the spatial structure of the PC loadings map is reproduced for the best-fitted time series at each end of the dipole, the correlation between the two cores of the dipole is low. Furthermore, the correlation between the best-fitted time series from each of the two clusters is low and positive (0.271). This is good agreement with the different behaviour of the trends on each side of the South Pole, as discussed above. It is interesting to note that before 1990 the correlation between the series is practically zero. After 1990 there

13 430 R.H. COMPAGNUCCI ET AL. Figure 5. The same as Figure 2, for the main aspects of the fourth PC is a weak positive correlation. All these results imply that there is a change in the behaviour of the temperature anomalies before and after 1990 and that this is not a true dipole as was the case of PC PC 5 This fifth component (Figure 6) is again an equatorial mode, even more so than the tropical/subtropical PC 1. It also includes a subtropical/mid-latitude region of influence over Southern Australia and the Pacific Ocean almost all of the way to the coast of Chile. There are also two small regions affected by this mode over the central South Atlantic and to the south of Madagascar. The PC score pattern shows a distinct oscillation with a periodicity similar to that of the Quasi-Biennial Oscillation (QBO). This is confirmed by the spectral analysis (Figure 6(c)): the most significant peak for this time series has

14 LOWER STRATOSPHERIC TEMPERATURE. PART I 431 Figure 6. The same as Figure 2, for the main aspects of the fifth PC approximately a 30-month period. This is in excellent agreement with the average period for the equatorial QBO, which is about 28 months. The quality of this signal is surprising, considering that the MSU samples the lowermost stratosphere in the Equator and part of the upper troposphere, i.e. in the region where the main QBO signal is weakest and vanishes. The temperature anomaly time series with the highest correlations are located at 1 15 S, W (direct correlation see Plate 1(e)) and S, W (inverse correlation), for each of the regions respectively, show two distinct behaviours. Furthermore, the correlation between the two real time series is far too low (0.15) in order to mention that these regions behave in anti-phase. The comparison between the PC score time series and the best-correlated equatorial grid point is good, while the comparison for the Pacific mid-latitudes one is not. Hence, the behaviour of the PC score time series is a better representation of the equatorial mode. The most important signature is given by the QBO signal, modified by high frequency variability of smaller amplitude. The spectral analysis of this PC score time series

15 432 R.H. COMPAGNUCCI ET AL. (Figure 6(c)) displays a 25- to 30-month peak significant at approximately the 0.1 level and a few small peaks around 4.7- and 3.2-month periods. The comparison of the correlation maps for the best fitted real time series with the time series from all other grid points shows an excellent relationship between the PC loadings map near the equator and the equatorial time series (Figure 6(d)). There is no significant correlation at mid-latitudes. However, the correlation map for the mid-latitude grid point (Figure 6(e)) yields a correlation map that does not resemble the PC loading map at all. Hence, this PC is not a true dipole at all. Up to this point the only PC which had a distinctive volcanic signature was the first. However, careful inspection of the time series mentioned above shows that there is a weak signal of positive anomalies during the volcanic periods. Despite the important variability during these 2-year periods following the El Chichón and Mt Pinatubo eruptions, very few, if any, negative anomalies are observed. The QBO signature appears shifted towards the positive anomalies. It is interesting to compare the behaviour of the equatorial time series with the behaviour of the QBO during the period under study (available at sets/qbo). Overlaid on the PC score time series, the phase of the Singapore zonal wind at 70 hpa can be seen in Figure 6(a). According to Andrews et al. (1987), the secondary circulation resulting from the descending alternating westerly and easterly mean flows results in a cooling/warming in the region of westerly/easterly shears. This process is indeed observed in the temperature anomaly time series, with well-defined changes between positive and negative anomalies, particularly up until 1991, except during the El Chichón eruption, and during the Mt Pinatubo aerosol cloud. It becomes distinct again after the dilution of the high aerosol loading from the latter eruption. Furthermore, inspection of phase of the Singapore zonal wind time series (Figure 6(a)) shows that, for the period between the end of 1987 and the Mt Pinatubo eruption, the QBO signal vanished close to or near the level of maximum contribution to the MSU retrievals. This period was associated with rather weak vertical shears, particularly in the protracted westerlies in the lower stratosphere. Such a weakening leads to the predominance of higher frequency variance during this period as well as in a more vague response of the temperature anomaly field to the QBO forcing. A similar behaviour is also observed during the westerly phase that lasted till the middle of 1992 in a thin layer below 50 hpa. The behaviour after 1993 resembles more than that observed before 1986 both for the temperature anomaly signal and the Singapore zonal wind. The calculated trend shows a decrease of 0.35 C/decade in the equatorial region. This trend is very close to that observed in the Tropics with PC 1. This is also in good agreement with the models mentioned there PC 6 The behaviour of the PC 6 is no longer so clear cut as in the previous cases (Figure 7). While the Lev diagram shows that this mode is above the noisy tail of the diagram, the explained variance is small. A quick inspection of the PC loading map (Figure 7(b)) shows that the structure of the map is not simple as in previous cases. The main homogenous region is a horseshoe opening towards Africa at about 50 S, in anti-phase with the subtropical region over the central Pacific Ocean. The comparison of the PC score pattern with the temperature time series corresponding to the two significant regions is not particularly good (Plate 1(f)). Only the general anomaly features appear to coincide, but the higher frequency variability, both in amplitude and phase, has important differences. This is even more so for the higher latitude grid point series. Hence, it is only possible to consider the lower frequency variability of this mode. There appears to be a modulation of the time series variance. The period has the least variability in the sample. The amplitudes of both positive and negative anomalies are the smallest observed during this period. This time the correlation maps for the best fitted series bear far less resemblance with the PC loadings maps. Only the point over the central Pacific Ocean yields a region of high correlation (Figure 7(d)) which is similar to that part of the PC loadings map. The correlation map for the other grid point, at high latitudes actually is closer to that corresponding to PC 4.

16 LOWER STRATOSPHERIC TEMPERATURE. PART I 433 Figure 7. The same as Figure 2, for the main aspects of the sixth PC According to the PC score time series spectrum (Figure 7(c)), the peaks show significant variance at periods of 27.8 months (i.e. the QBO), 5 months and 2.4 months. The mid to high latitude area also yields a weak QBO signal, but not so the lower latitude time series where the spectrum shows a peak at the lowest frequencies and oscillations with periods close to 2 3 months. The major differences observed even in the high correlation areas are due to the fact that the temporal variability between 30 and 60 S is mostly explained by PC 3 and PC FINAL REMARKS Probably one of the most significant conclusions from the present analysis is that the behaviour of the lower stratosphere temperature field over the Southern Hemisphere is more complex that would have been

17 434 R.H. COMPAGNUCCI ET AL. previously expected. In particular, southern mid to high latitudes yield temporal evolutions that are not zonal. The existence of two crossed dipole structures for the temporal behaviour show how complex this can be. The above analysis demonstrates that the temporal variability of the Southern Hemisphere lower stratosphere can be described in terms of a limited number of PCs. Indeed, four are enough to explain the variability for most of the geographic regions of the hemisphere. The fifth one explains a process at equatorial latitudes distinct from that explained by the first PC. While some of these modes have a zonal or quasi-zonal area of occurrence, there are two modes, PC 3 and PC 4 that divide the southern mid-latitudes into four regions of temporal behaviour. In the case of PC 3 this behaviour is in anti-phase across the South Pole, whereas for PC 4 the similarities between the two ends of the dipole and with the PC score time series are no longer as significant. In particular, the fact that there is trend over Patagonia and the Weddell Sea, while there is no detectable trend over New Zealand and the region south of these islands, must be kept in mind. This latter behaviour is in agreement with radiosonde trend studies (Bodeker et al., 1998). The comparison of the PC loading maps with the corresponding correlation maps between the best-fitted time series and the remaining grid point series shows that the first three PC score time series are excellent representations of the real temporal behaviour of the temperature anomaly over specific areas of the hemisphere (shaded in grey in the PC loadings maps.). As the amount of explained variance decreases significantly, the quality of the representation deteriorates. However, these plots show that the PC analysis is capable, even in the lower variance modes, to pick up features of interest such as the QBO signature in PC 5. The study yields negative temperature anomaly trends in all regions of the hemisphere except over the New Zealand and the South Pacific sector. In all cases the trends obtained through the S-mode PC analysis are within the 95% confidence bar of the trends obtained by Angell (1988) using radiosonde data. The trend observed over Antarctica is weaker than that obtained by Graf et al. (1998) because they are considering the monthly trends for specific months, while our approach yields the mean annual or equivalent decadal trend. The results at mid and high latitudes appear to suggest that the source of this stratospheric cooling is ozone depletion. Indeed the lack of a trend over New Zealand is associated with an enhancement of ozone near the tropopause as well as in the middle and upper stratosphere over Lauder (Bodeker et al., 1998). This is good agreement with Ramaswamy et al. (1996). There also exists a decline in lower stratospheric temperature over the Equator and subtropical latitudes that is not masked by the volcanic eruptions, in a region where there is no detectable trend in ozone (WMO, 1994). The trend in this region could be due both to the greenhouse gas accumulation and to ozone depletion at higher latitudes (Ramaswamy et al., 1996). Graf et al. (1998) point out that there is a shift of about 1 month between the time of occurrence of the temperature minimum and ozone minimum at high latitudes. The peaks in the second PC score and the associated temperature anomalies do show similar behaviour at times. The rather spiky nature of the time series would appear to point towards ozone depletion at high latitudes. However the comparison with the minimum total ozone values reached each year in the Antarctic ozone hole shows that there is no direct relationship, at least for this PC. Nor does there appear to be a relationship with the size of the ozone hole. In the case of the next two PCs (third and fourth), this spiky nature is preserved despite the fact that they refer mostly to the behaviour for higher mid-latitudes (50 60 S). Such behaviour could point to the influence of polar ozone depletion. Again, there does not seem to be a direct relationship between the size of the ozone depletion in a given year with the amplitude of the temperature anomaly events. The time series for the fourth PC over Patagonia does show an almost continuous increase in the magnitude of the anomaly during the last five years of the sample. This anomaly, particularly in the PC score occurs in November. On the other end of the dipole of the temperature series does not show such a spiky seasonal behaviour, even though certain mirror behaviour is present with respect to the behaviour over Patagonia. Rather, it shows continuous fluctuations, whose frequency increases during the second half of the sample, with less prominent spikes. In this case the anomaly spikes, both positive and negative, do appear during other times of the year.