NOTES AND CORRESPONDENCE Nonstationary Impacts of the ...

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NOTES AND CORRESPONDENCE Nonstationary Impacts of the Southern Annular Mode on Southern Hemisphere Climate GABRIEL SILVESTRI AND CAROLINA VERA Centro de Investigaciones del Mar y la Atmo´sfera/CONICET-UBA, and DCAO/Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina (Manuscript received 15 January 2009, in final form 27 May 2009) ABSTRACT The temporal stability of the southern annular mode (SAM) impacts on Southern Hemisphere climate during austral spring is analyzed. Results show changes in the typical hemispheric circulation pattern associated with SAM, particularly over South America and Australia, between the 1960s–70s and 1980s–90s. In the first decades, the SAM positive phase is associated with an anomalous anticyclonic circulation developed in the southwestern subtropical Atlantic that enhances moisture advection and promotes precipitation increase over southeastern South America (SESA). On the other hand, during the last decades the anticyclonic anomaly induced by the SAM’s positive phase covers most of southern South America and the adjacent Atlantic, producing weakened moisture convergence and decreased precipitation over SESA as well as positive temperature anomaly advection over southern South America. Some stations in the Australia–New Zealand sector and Africa exhibit significant correlations between the SAM and precipitation anomalies in both or one of the subperiods, but they do not characterize a consistent area in which the SAM signal can be certainly determined. Significant changes of SAM influence on temperature anomalies on multidecadal time scales are observed elsewhere. Particularly over the Australia–New Zealand sector, significant positive correlations during the first decades become insignificant or even negative in the later period, whereas changes of opposite sign occur in the Antarctic Peninsula between both subperiods.

1. Introduction The leading mode of circulation variability in the Southern Hemisphere (SH) on low frequencies is the southern annular mode (SAM, also referred as the Antarctic Oscillation or high-latitude mode). The SAM is characterized by a strong zonally symmetric pattern at polar latitudes with a phase reversal at middle latitudes. Positive (negative) SAM phase is associated with negative (positive) pressure anomalies over Antarctica and positive (negative) anomalies at middle latitudes. The pattern has been identified and discussed in many previous studies (e.g., Rogers and van Loon 1982; Kidson 1988, 1999; Thompson and Wallace 2000) and plays an important role in the climate variability over different regions of the SH. Sen Gupta and England (2006), among

Corresponding author address: Gabriel Silvestri, CIMA/CONICETUBA, Intendente Guiraldes 2160, Ciudad Universitaria, Pabelloń II, 2do, Piso (C1428EGA), Buenos Aires, Argentina. E-mail: [email protected] DOI: 10.1175/2009JCLI3036.1 Ó 2009 American Meteorological Society

others, show that the SAM positive phase during the period 1979–2005 is mainly associated with negative annual temperature anomalies over most of Antarctica and Australia, with significant positive anomalies over the Antarctic Peninsula, southern South America, and southern New Zealand. During that particular period, the SAM positive phase is also associated with negative annual precipitation anomalies over southern South America, New Zealand, and Tasmania and with positive anomalies over much of Australia and South Africa. Moreover, South American summer rainfall variability associated with SAM is evident not only at interannual (Zhou and Lau 2001) but also at intraseasonal time scales (Carvalho et al. 2005). Recently, Ummenhofer and England (2007) showed that New Zealand rainfall variability is predominantly modulated by the ENSO and SAM, with a latitudinal gradation in the influence of the respective phenomena and a notable interaction with orographic features. Among others, Hendon et al. (2007) found that during austral winter (summer), SAM positive phase is associated with decreased (increased)

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daily rainfall over southeast and southwest Australia (southern east coast of Australia). In addition, Reason and Roualt (2005) found that six (six) of the seven (eight) wettest (driest) winters during 1948–2004 occur in South Africa during the negative (positive) SAM phase. Southeastern South America (SESA) seems to be the continental region in which SAM and precipitation anomalies exhibit the strongest relationship in the period beginning around 1979 (Sen Gupta and England 2006). This relationship was particularly examined by Silvestri and Vera (2003, hereafter SV03). Through the analysis of the period 1979–99, SV03 found that SAM influence is largest during austral spring, particularly over the region encompassing northeastern Argentina, southern Brazil, and Paraguay, approximately between 178 and 308S from 508 to 648W. The SAM positive (negative) phase is associated with the intensification of an upper-level anticyclonic (cyclonic) anomaly over the southeastern Pacific Ocean, which gives rise to weakened (enhanced) moisture convergence and thus decreased (increased) precipitation over SESA. SV03 also showed that during that particular season and period, both SAM and ENSO indexes are significantly correlated (20.41, statistically significant at the 95% level using a Student’s t test), which results in a strong modulation of the ENSO signal on SESA precipitation anomalies by SAM activity. Nevertheless, Gillett et al. (2006) computed the relationship between SAM and precipitation anomalies considering a longer period (1957–2005) and their results do not show the strong SAM influence on precipitation anomalies observed in SESA during more recent periods. From these previous works it seems then that the relationship between SAM and precipitation anomalies in SESA might not be stable. There are some evidences of low-frequency variability of precipitation over SESA. Rusticucci and Penalba (2000) analyzed the precipitation regime over South America south of 208S for the period 1901–90 and found significant variations in the percentage of variance explained by the annual cycle on multidecadal time scales. Variability with periods around 15–17 yr have also been identified in river discharge anomalies in SESA (e.g., Robertson and Mechoso 2000; Berbery and Barros 2002). Nevertheless, the nature of the low-frequency variability of precipitation anomalies over SESA is neither fully described nor extensively understood yet. Recently, Fogt and Bromwich (2006) examined the decadal variability of the ENSO teleconnection to the high-latitude South Pacific, also known as the Pacific– South America (PSA) pattern. They showed notable decadal changes in the SAM–ENSO correlation between 1979 and 2001. During the 1980s, the teleconnection was weak due to the interference between the PSA pattern

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and the SAM. On the other hand, during the 1990s an inphase relationship between circulation anomaly responses associated with these two modes amplified the height and pressure anomalies in the South Pacific, producing stronger teleconnections. Fogt and Bromwich conclude that the significantly positive correlation found between ENSO and SAM only during times of strong teleconnection suggests that both the tropics and high latitudes need to work together for ENSO to strongly influence the climate of the South Pacific sector. The aim of this note is to analyze how stationary both the SAM activity and its impact on Southern Hemisphere climate is during austral spring [October–December (OND)]. The note also focuses on better understanding the low-frequency variability of precipitation anomalies in SESA. The note is organized as follows: Data and methodology are described in section 2; changes in the SAM influence on precipitation, atmospheric circulation, and temperature anomalies during the last fifty years are analyzed in section 3; and conclusions are summarized in section 4.

2. Data and methodology Monthly mean values of precipitation at 102 stations and surface temperature at 103 stations located all around the continental regions in the SH—available at Servicio Meteorologico Nacional (SMN) of Argentina, the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR) and the British Antarctic Survey (BAS)—were used in the analysis. In addition, precipitation anomalies over SESA were particularly described through a precipitation index (PPindex), constructed averaging monthly mean rainfall anomalies at seven stations located in the region where the most significant signal of SAM in austral spring precipitation was found in SV03. The stations correspond to Asuncioń (25.258S, 57.518W), Concepcioń (23.418S, 57.308W), Villarrica (25.758S, 56.438W), Encarnacioń (27.318S, 55.838W), and Pilar (26.858S, 58.318W), from Direccioń de Meteorologıá e Hidrologıá (DINAC-Paraguay); Iguazu´ (25.738S, 54.468W) from SMN; and Saenz Penã (26.738S, 60.488W) from Instituto Nacional de Tecnologıá Agropecuaria (INTA-Argentina). Hemispheric circulation anomalies were described by monthly mean sea level pressure (SLP) at 95 stations located around the SH, available from SMN, NCAR, and BAS. Also, the analysis have been complemented using monthly mean fields of SLP, 500-hPa geopotential heights (Z500), and 850-hPa wind (WIND850) from the National Centers for Environmental Prediction (NCEP)– NCAR reanalysis (Kalnay et al. 1996).

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The SAMindex defined by Marshall (2003) (see http:// www.nerc-bas.ac.uk/icd/gjma/sam.html), based on sea level pressure in situ observations, was considered. Sea surface temperature anomalies in the El Ninõ 3.4 region (EN34), available at the Climate Prediction Center (http://www.cdc.noaa.gov/Correlation/nina34.data), were considered as the ENSO index. The period of analysis is from 1958 to 2004, which concentrates the largest amount of information available for the variables under analysis. Anomalies have been defined as departures from the corresponding OND seasonal climatological means computed over the 1958–2004 period. A linear trend was also removed from all anomaly series considered. In addition, correlation values were calculated, ensuring that the correlations were not dominated by few specific cases.

3. Results a. ENSO and SAM A significant relationship between SAM and ENSO oscillations during austral spring has been described in previous works (e.g., SV03; Fogt and Bromwich 2006; L’Heureux and Thompson 2006), particularly over the last 20 years of the twentieth century. The temporal stability of such a relationship is further explored here using correlation values between SAM and EN34, computed over different subperiods of at least 22 years, within the period 1958–2004 (Table 1a). It is evident that the correlation between both indices over the entire period is negligible. Significant correlation values of negative sign are only found over the last decades, being the maximum ones of those obtained for the last 22 years considered. On the other hand, both SAM and ENSO had independent activity during the first decades of the period under study. It is well known that the ENSO signal of precipitation anomalies over SESA is strong (e.g., Aceituno 1988). During austral spring, warm (cold) ENSO phases are associated with increased (decreased) precipitation over SESA. The temporal stability of such relationships is explored in Table 1b. The correlation between the PPindex and EN34index is significantly positive, not only over the entire period but also for most of the subperiods considered. This result confirms that, in general, the ENSO influence on precipitation anomalies over SESA is relatively stable. Accordingly, Garreaud and Battisti (1999) show that interannual and interdecadal variability of the circulation anomalies in the SH associated with ENSO exhibit similar spatial signatures in the SLP, low-level winds, and temperature. Furthermore, previous works have identified interdecadal variation associated with ENSO (e.g.,

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TABLE 1. Correlations of (a) the SAMindex–EN34, (b) PPindex– EN34, and (c) PPindex–SAMindex with the effect of EN34 linearly removed. Periods start (finish) in the years indicated in the first row (column) of each table. One (two) asterisk(s) indicate correlations statistically significant at the 90% (95%) for a Student’s t test. 1979

1984

1989

(a) 1958 0.04 0.05 0.15 1963 — 0.13 20.03 1968 — — 0.09 1973 — — — 1978 — — — 1983 — — — (b) 1958 0.41* 0.36* 0.36** 1963 — 0.15 0.27 1968 — — 0.25 1973 — — — 1978 — — — 1983 — — — (c) 1958 0.37* 0.19 0.18 1963 — 20.05 20.13 1968 — — 20.32 1973 — — — 1978 — — — 1983 — — —

1994 0.07 20.07 20.21 20.32 — — 0.41** 0.34* 0.16 0.01 — — 0.03 20.16 20.37* 20.08 — —

1999

2004

20.07 20.10 20.15 20.39* 20.41** —

20.14 20.30* 20.42** 20.42** 20.49** 20.50**

0.43** 0.52** 0.40** 0.30* 0.47** —

0.45** 0.52** 0.47** 0.43** 0.51** 0.51**

20.19 20.33** 20.50** 20.41** 20.55** —

20.16 20.30** 20.47** 20.57** 20.63** 20.64**

Setoh et al. 1999). Nevertheless, the analysis of the influence of such variations on the climate variability in South America is beyond the scope of this study. The temporal stability of the relationship between SAM and precipitation variability over SESA was also explored. The ENSO signal was linearly removed from PPindex and SAMindex (by a linear regression based on EN34index) before the computation of the correlations displayed in Table 1c. Results show that SAM and precipitation anomalies in SESA are independent when the entire period is considered, in agreement with Gillett et al. (2006). On the other hand, significant positive correlations are found in the first decades (0.37 for 1958– 79) whereas significant negative correlations characterize the last decades (20.64 for 1983–2004). The analysis of Table 1 shows a change in the relationship between SAM and both ENSO and precipitation anomalies over SESA from the first decades (1958–79) to the last decades (1983–2004) of the period considered. The issue whether such change is also noticeable in the climate variability over the entire SH is further explored in the following section.

b. SAM signal on SH climate In this section, correlation maps over the SH between SAMindex and different atmospheric variables are analyzed. As described in previous sections, the ENSO signal was linearly removed from all anomaly time series.

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FIG. 1. Correlations of the SAMindex with (a),(b) in situ precipitation, (c),(d) in situ SLP, (e),(f) reanalyzed SLP, (g),(h) reanalyzed Z500, and (i),(j) in situ surface temperature for the periods (top) 1958–79 and (bottom) 1983–2004. Correlations statistically significant at the 90% and 95% levels of a Student’s t test are shaded; gray dots in cases of in situ observations indicate stations with no significant correlation.

Figures 1a and 1b show the correlation maps between SAMindex and precipitation anomalies from ground stations over the periods 1958–79 and 1983–2004. The maps clearly depict the changes in sign and intensity of the SAM influence on precipitation anomalies in SESA between both subperiods described in the previous section. The correlation between SAM and precipitation anomalies is also significantly negative (positive) in 1958–79 (1983–2004) at the few stations located in the southern sectors of both Africa and Australia. However, correlation values are insignificant at most of the stations for both periods, limiting considerably the determination of a consistent SAM signal over those two regions. The analysis of the precipitation anomalies over the Antarctic region was not included owing to the poor quality of the data available, especially for the periods before 1979. In agreement, Bromwich et al. (2000), exploring the interdecadal changes of ENSO signal in Antarctic precipitation, found serious deficiencies in the representation of the Antarctic net precipitation from the reanalysis datasets. The SAM signals on the circulation anomalies in the SH for both subperiods were also analyzed in order to identify changes in the circulation anomaly spatial patterns that might explain the changes observed for the precipitation anomalies. In that sense, correlation maps between the SAMindex and SLP and Z500 are shown in Figs. 1c–h. In the case of SLP, correlation maps derived from the NCEP–NCAR reanalysis are shown together with those obtained from the 95 stations described in section 2. The double analysis of SLP anomalies was done

because of the relatively low quality of the reanalysis in depicting the climate variability, especially at the high latitudes of the SH before 1979, mainly associated with the lack of satellite information over the oceans. Figures 1c–f show, in general for both subperiods, the circulation anomaly pattern typically associated with SAM and characterized by negative correlations at polar latitudes and positive ones at middle latitudes. However, a poleward migration of the correlation centers located at middle latitudes, particularly over South America and Australia, is noticeable between both subperiods. Similar spatial behavior of the SLP anomalies has been associated with an observed trend of the SAM toward a more positive phase (see Marshall et al. 2006, and references therein). Nevertheless, considering that the anomalies used here have been previously detrended, the results confirm that changes in the SLP anomaly gradient between middle and high latitudes can also be associated with natural low-frequency variability of the climate system. A more detailed analysis at regional scales shows that most of stations located in the Australia and New Zealand sector are significantly correlated with SAM in 1958–79, but only few of them show significant correlation in 1983– 2004. On the other hand, no significant changes are evident in the SAM signature of the SLP anomalies in the vicinity of Africa. Changes in the spatial structure of the annular correlation center over the Antarctica are discernible between both subperiods from the reanalysis but cannot be confirmed from in situ observations. Such changes could be associated with the low quality of

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FIG. 2. Correlations of the SAMindex with SLP and regressions of the SAMindex– with WIND850. Areas where correlations are statistically significant at the 90% (95%) for a Student’s t test are shaded in light (dark) gray.

the reanalyzed fields over Antarctica. Marshall (2003), among others, shows that the quality of the NCEP– NCAR reanalysis in the presatellite data is principally poor at high southern latitudes. In the vicinity of South America, the development of a large positive correlation center over the southwestern Atlantic Ocean is particularly evident in 1983–2004 (Fig. 1f); it is weaker and located to the northeast in 1958–79 (Fig. 1e). The analysis of the corresponding low-level wind anomalies confirms that during 1958–79 (Fig. 2a) positive SAM phases were associated with anomalous southward wind anomalies over SESA induced by the anticyclonic circulation anomaly center located in the southwestern subtropical Atlantic. Such circulation enhances moisture advection and promotes increase of precipitation over SESA, resulting in the positive significant correlation between PPindex and SAM depicted in Table 1c for that subperiod. The correlation map between SAM and SLP anomalies for the 1958–79 period was also made with 40-yr ECMWF Re-Analysis, ERA-40, (not shown) to account for NCEP– NCAR reanalysis uncertainties. Correlation maps from both reanalysis datasets are, in general, very consistent. Over South America, both reanalyses agree well in describing positive correlation centers over the Atlantic and Pacific subtropical coasts. In particular, the positive correlation for the Atlantic coast (leading to the precipitation anomalies) is similar in both reanalyses. The

main differences between both reanalyses take place over Africa and eastern Antarctica. ERA-40 describes positive significant correlations over most of southern Africa that are not observed from NCEP–NCAR data. Moreover, negative significant correlations cover the entire Antarctic continent in ERA-40, while a wide portion in the eastern Antarctic sector is described by NCEP–NCAR reanalysis with no significant values. During 1983–2004, the anticyclonic anomaly associated with the SAM positive phase is located farther south, covering most of the southern tip of South America and the adjacent Atlantic. This circulation anomaly pattern is associated with weakened moisture convergence (Fig. 2b) and decreased precipitation over SESA (Fig. 1b), which justifies the negative significant correlation between PPindex and SAM displayed in Table 1c. In addition, Fig. 2b shows that the anticyclonic anomaly pattern is related to negative correlations between precipitation anomalies over the southernmost region of South America (Patagonia) and the SAMindex (Fig. 1b). Under normal conditions, the Andes Mountains extending along the western coast force the ascent of the westerly flow, causing abundant cloudiness and precipitation in the surroundings of southern Andes (e.g., Schwerdtfeger 1976). In that sense, the anomalous anticyclonic circulation depicted in Fig. 2b weakens the eastward flow, promotes subsidence conditions over the area, and thus inhibits precipitation (Fig. 1b).

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Correlation maps between SAM and Z500 are shown in Figs. 1g,h and, in general, agree with the features found for SLP. That confirms the equivalent barotropic structure of the SAM-related circulation anomaly pattern described in previous publications (e.g., Thompson and Wallace 2000). Correlation maps for Z500 during 1983–2004 (Fig. 1h) show a zonal wavenumber-4-like pattern in middle latitudes that is absent in the corresponding map for 1958–79 (Fig. 1g). That feature is partially discernible in the correlation map for SLP for the same subperiod (Fig. 1f). However, owing to the lack of enough upper-air stations along the SH over the entire period, it is not possible to verify whether the wavenumber-4-like pattern was present in the presatellite period or not. Previous works, such as Sen Gupta and England (2006) among others, have described the SAM influence on surface temperature anomalies in the SH, which seem to be characterized by a SAM positive phase related to negative temperature anomalies over the Antarctic continent and positive anomalies over the Antarctic Peninsula. Correlation maps between the SAMindex and surface temperature anomalies for the two subperiods under study are shown in Figs. 1i,j. Negative significant correlations are observed in New Zealand in 1958–79 but are not significant in 1983–2004. In addition, one of the most noticeable changes takes place in Australia. In fact, significant positive correlations cover most of the southern Australian territory between 1958 and 1979, but the pattern changes considerably between 1983 and 2004 when only four stations show significant correlation values of negative sign. Over the Antarctica continent, except the peninsula, negative correlations are observed in both subperiods, although they are more significant in 1958–79 than in 1983–2004. In the vicinity of the Antarctic Peninsula, the correlations change notably between both periods. Correlations are significantly negative during 1958–79, whereas they are significant and with positive sign at only one station during 1983–2004. In addition, significant positive correlation values covering most of the Patagonia region are clearly identifiable during 1983–2004. It is suggested that the anticyclonic circulation anomaly observed over the southern continent and the adjacent Atlantic during that period (Fig. 2b) might promote positive temperature anomaly advection into the region.

4. Conclusions The nonstationary nature of SAM impacts on Southern Hemisphere climate during austral spring was explored in this note. A significant change in the spatial circulation anomaly pattern typically associated with SAM was found between the 1960s–70s and 1980s–90s.

A poleward migration of the anomaly centers located in middle latitudes is noticeable between both periods, especially over South America and Australia. In the first decades, the development of an anticyclonic circulation anomaly center in the southwestern subtropical Atlantic associated with SAM positive phase is evident. That circulation anomaly enhances moisture advection and promotes precipitation increase over SESA. In contrast, during the last decades the anticyclonic anomaly covers most of the southern tip of South America and the adjacent Atlantic, producing a weakened moisture convergence and decreased precipitation over SESA as well as positive temperature anomaly advection into the Patagonia region. A few stations in the Australia–New Zealand sector and Africa exhibit significant correlations between SAM and precipitation anomalies in both or one of the subperiods, but they do not characterize a consistent area where the SAM signal can be explicitly determined. On the contrary, there are changes in the influence of the SAM on temperature anomalies elsewhere, such as the change from a significant positive relationship over southern Australia in the early period to a weaker negative relationship in the later period. Finally, changes in the SAM–temperature anomaly relationship were observed in the Antarctic Peninsula—from negative in the early periods to positive in the later period. This suggests that changes in the temperature anomalies identified over the Antarctic Peninsula could be also influenced by the natural climate variability. Acknowledgments. Comments and suggestions provided by two anonymous reviewers were very helpful in improving this paper. This research was supported by ANPCyT/PICT04-25269, CONICET/PIP-5400, and the European Community’s Seventh Framework Programme (FP7/2007-2013), under Grant Agreement 212492.

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