Mariano S. Alvarez1, C. S. Vera1, G. Kiladis2 and B. Liebmann2 Variabilidad intraestacional de invierno en sudamérica: su impacto en la precipitación Mariano S. Alvarez1, C. S. Vera1, G. Kiladis2 and B. Liebmann2 1 Centro de Investigaciones del Mar y la Atmósfera, CONICET-UBA, DCAO/FCEN, UMI IFAECI/CNRS 2 Earth System Research Laboratory/NOAA, CIRES/University of Colorado Good morning everyone, my name is Mariano Alvarez and I will be presenting the work I’ve done with Carolina Vera, George Kiladis and Brant Liebmann regarding the Winter Intraseasonal Variability in South America. alvarez@cima.fcen.uba.ar

OBJETIVOS Documentar las características principales que la variabilidad IS exhibe durante el invierno en Sudamérica. Explorar su relación con la variabilidad observada en la circulación en el Hemisferio Sur. Explorar la modulación IS que podría estar actuando en la ocurrencia de días consecutivos de precipitación. The main objectives in this work were to document the principal characteristics that IS variability exhibits during winter in South America, as well as to explore its relation to the variability observed in the circulation in the Southern Hemisphere. Furthermore, we intended to explore the IS modulation that might be acting in the occurrence of wet spells.

DATOS Se usaron 28 temporadas frías (151 días centrados en JJA) desde 1979 a 2006. Reanálisis I del NCEP/NCAR de alturas geopotenciales y viento medios diarios fueron utilizados (Kalnay y coautores, 1996). Datos de precipitación diaria de estaciones fueron obtenidos de la base de CLARIS-LPB (disponible online). Datos de precipitación diaria reticulados en Sudamérica (Liebmann y Allured, 2005). The period of study were 28 austral cold seasons, that we defined, following previous studies, as the period of 151 days centered in the JJA trimester, form 1979 to 2006. NCEP/NCAR Reanalysis were used to represent daily means of geopotential heights and winds, and daily precipitation data was obtained from two different datasets: STATION data from CLARIS-LPB database, and GRIDDED data in South America from Liebmann and Allured’s database. As to the methodology we used, all variable anomalies were calculated substracting the seasonal cycle. Moreover, daily anomalies were then bandpassed using a 10-90-day bandpass Lanczos filter with 101 weights. We remark the 10-90-day bandpass filtered OLR anomalies, hereafter referred as FOLR, that is one of the key variables to be used in this study. METODOLOGÍA Todas las anomalías de las variables fueron calculadas respecto a su ciclo estacional. Las anomalías de OLR (FOLR) fueron filtradas usando un filtro pasa banda de Lanczos de 10-90 días.

Desvío estándar de las anomalías de OLR filtradas en 10-90 días (FOLR) Temporada Fría Temporada cálida 5 N 5 N 15 S 15 S The standard deviation of FOLR was computed, in order to estimate the magnitude of the typical IS variability during the cold season. In this figure we show that the region of maximum standard deviation during the cold season is located over southeastern South America, with a NW-SE tilt. In comparison with the IS variability observed during the warm season, it is evident that the maximum of IS variability associated to the SACZ is not present during the cold season as the SACZ is a warm season feature. Furthermore, during the cold season the region of maximum values over SESA exhibits smaller values and is displaced southward. 35 S 35 S 65W 45W 65W 45W 5 8 11 14 17 20 23 26 29 32

Desvío estándar de anomalías filtradas en 10-90 días Alturas geopotenciales de 250 hPa Viento meridional en 250 hPa 90W 90W 180 In order to describe the IS variability of SH circulation, we computed the standard deviation of the 10-90-days filtered geopotential height anomalies at 250 hPa, which we show on our left. A latitudinal band of maximum values is observed along 65ºS, with peak values over the southeastern Pacific Ocean and southwestern Atlantic Ocean. Those values are larger than those observed in summer. It is noteworthy that the region of maximum mean variability on IS timescales does not match that associated to the storm-tracks that extends from southern Atlantic Ocean to southern Indian Ocean. The standard deviation of the 10-90 day filtered meridional wind anomalies at 200 hPa is shown on our right, as is more useful to describe circulation variability at lower latitudes. Two regions of maximum values are evident: one along the subtropics that locates along the subtropical jet region, and a band of maximum variability along the subpolar regions over the southeastern Pacific, that locates eastwards of the exit region of the subpolar jet core, located over the Indian Ocean. 90E 90E

EOF1 de FOLR: Patrón Cold Season IntraSeasonal Índice CSIS: componente principal estandarizada del EOF1. Valores positivos del índice CSIS están asociados a FOLR negativa en SA central. 15 S Here we show The first pattern of FOLR as resulted from an EOF analysis. The leading pattern or CSIS, which explains 20.7% of the variance, shows a large center of action extended over northern Paraguay, northeastern and southeastern Brazil, and a weaker center of opposite sign located equatorwards. The criterion developed by North et al. was used to discard whether these EOF was part of a degenerated pair. The associated first principal component was standardized to create the CSIS index, and positive values of the CSIS index are associated to negative FOLR in central SA. Using the CSIS index, we defined positive CSIS events as those periods of at least 5 consecutive days associated to CSIS index greater than 1. Using OLR to describe IS variability might be questionable as this particular variable can be affected during cold season by other processes besides those associated to convection. Eventos positivos (negativos) del CSIS: períodos de al menos 5 días consecutivos asociados al índice CSIS > (<) 1 (-1) 35 S 65W 45W

Day -8 Day -6 Day -4 Mapas de regresiones contra el índice CSIS de FOLR (contornos) y precipitación (sombreado). 5 N 15 S 35 S Day -2 Day 0 Day +2 Las anomalías negativas (positivas) de regresión de FOLR están asociadas a anomalías positivas (negativas) de regresión de precipitación. 5 N 15 S 35 S Therefore, in order to assess whether the IS oscillations described by OLR anomalies are also present in precipitation anomalies, lagged regression maps of daily anomalies of both OLR and precipitation over South America were computed against the CSIS index. In this figure we show the evolution of both OLR and precipitation regressed anomalies from 8 prior to 8 days following CSIS peak, which by design occurs at day 0. Along the whole evolution, positive (negative) values of precipitation anomalies are associated to negative (positive) values of OLR anomalies. The alternation of negative, positive and then negative precipitation anomalies over SESA along the full evolution, seems to indicate the relevance of an oscillation with dominant period of around 17 days in association to CSIS activity. The fact that evolution of the CSIS is clearly discernible in both OLR and precipitation anomalies, confirms the ability of the CSIS index, based on FOLR, in describing the main features of the IS variability in South America. Day +4 Day +6 Day +8 Las anomalías de FOLR pueden describir las principales características de la variabilidad IS de la precipitación. 5 N 15 S 35 S

Modulación de la precipitación diaria por la actividad del patrón CSIS CSIS Pattern This figure represents the evolution of daily precipitation amounts in SESA and the CSIS index, during the cold season of 1986. As can be seen, consecutive days of precipitation might be affected by the existence of an IS oscillation. Evolución de la precipitación diaria (barras celestes) en el sudeste de Sudamérica y el índice CSIS (línea sólida) durante la temporada fría de 1986.

Rachas húmedas en el sudeste de Sudamérica Racha húmeda (al menos 2 días consecutivos con pp>1mm) Racha húmeda P75 (al menos 2 días consecutivos con pp>18.5mm (percentil 75)) In this figure we show the climatological distribution of the wet spell frequencies identified from SESA’s precipitation time series in terms of spell duration. It can be seen that the occurrence of wet spells decays exponentially with duration. Considering those wet spells in which daily precipitation was greater than the 75th percentile of the precipitation distribution, the results are enhanced. Magenta: Distribución climatológica de la frecuencia de rachas húmedas en el sudeste de Sudamérica. Azul: Porcentaje de días de lluvia explicados por rachas húmedas.

Modulación de rachas húmedas P75 en el sudeste de Sudamérica por la actividad del patrón CSIS Relacionada con el signo del índice CSIS Relacionada con la etapa evolutiva del índice CSIS positivo. Relating the occurrence of the wet spells to the sign of the CSIS index, we present this figure. As can be seen in green bars, larger wet spell frequencies were observed during a positive phase of the CSIS index, and the negative phase of CSIS index partly inhibits the occurrence of wet spells. By taking only those wet spells that occurred during a positive phase of CSIS index, we further classified them according to the evolution stage of CSIS index, that is, if the index was growing, undergoing a positive event, or if it was decaying. The growing stage of positive CSIS phases is the most likely period for wet spell occurrence. Mayor frecuencia de rachas húmedas asociadas con valores positivos del índice CSIS. La etapa de crecimiento de las fases positivas del índice CSIS es el período más probable para la ocurrencia de rachas húmedas.

Modulación de rachas húmedas en el sudeste de Sudamérica por la actividad del patrón CSIS Relacionada con el signo del índice CSIS Relacionada con la etapa evolutiva del índice CSIS positivo. In a similar way, for P75 wet spells, larger wet spell frequencies are associated to positive CSIS index values. Moreover, the growing stage and positive events of the CSIS index are both the most likely periods for P75 wet spell occurrence. Mayor frecuencia de rachas húmedas asociadas con valores positivos del índice CSIS. La etapa de crecimiento de las fases positivas del índice CSIS y los eventos positivos son los períodos más probables para la ocurrencia de rachas húmedas.

Mapas de regresiones contra el índice CSIS de FOLR. Evolución similar a la MJO Day -14 Centro X que maximizará en el día 0 en el SESA. Day -12 Day -10 Valores positivos sobre el SESA Day -8 Centros alternados se desarrollan y se propagan lentamente hacia el NE Day -6 Day -4 Intensificación del centro X que se estaciona Day -2 This figure displays he evolution of OLR regressed anomalies from 14 prior and 4 days following the peak of CSIS index. *On day -14 a center of negative OLR anomalies is located at around 86ºW, 35ºS. This negative center, although weak, is particularly identified because at day 0 will be the one maximizing in magnitude over SESA, and will be referred to as the X center. *On day -14 negative anomalies are also evident over the equatorial Indian and Pacific Oceans towards Australia while positive anomalies are discernible over Africa. The evolution of these two features along the following days resembles that related to the Madden and Julian Oscillation (MJO) *On day -8 the X center locates over northern Patagonia; positive anomalies intensify over Paraguay and SE of Brazil, while negative anomalies still persist over northeastern South America. // Alternate centers are observed over the southeastern Pacific region. *On day -4, both, the magnitude intensification and stall of the X center over SESA and the weakening of the two centers located equatorwards over tropical South America, is clear. The alternate anomalies located upstream of center X, extend between Australia and the southeastern Pacific Ocean, being more intense those close to South America. *On day 0, the X center reaches its peak, when it extends with a NW-SE orientation over subtropical South America. // Also, over the Equatorial Indian and western Pacific Oceans the positive anomalies have weakened considerably. Anomalías debilitadas sobre el Océano Índico Day 0 Magnitud máxima sobre Sudamérica Day +2 Day +4 60E 180 60W

-20 -10 Mapas de regresión contra el índice CSIS de alturas geopotenciales de 250 hPa Fase de SAM positiva Se desarrolla fase negativa del SAM Maximización de la magnitud del centro Y -18 -8 +2 -16 -6 +4 El SAM se debilita El centro Y se debilita y se mueve hacia el este -14 -4 +6 Maximización de la anomalía anticiclónica corriente arriba del centro Y Se desarrolla un tren de ondas In this figure we show the evolution of 250 hPa geopotential heights regressed anomalies from 20 prior and 8 days following the peak of CSIS index. *On day -20, a cyclonic anomaly over Antarctica and three centers of anticyclonic anomalies in middle latitudes could be observed, similarly to the Southern Annular Mode (SAM) with an embedded wave three. *The positive SAM phase weakens along the following days. *On day -14, a wavetrain develops, and a cyclonic anomaly that will reach its minimum value on day 0 over SESA, can be discerned. Hereafter it will be referred to as the Y center. *On day -10, An anticyclonic anomaly was observed over Antarctica, similar to a negative SAM with a number 3 to 4 wave embedded in subpolar latitudes *The wavetrain continue to itensify, and on day -4 the anticyclonic anomaly upstream of the Y center maximizes. *On day 0, the magnitude of the Y center maximizes over SESA, and afterwards, it weakens as it moves eastwards. Centro Y que maximizará sobre Sudamérica en el día 0 -12 -2 +8

CONCLUSIONES La variabilidad IS descrita por las anomalías de OLR filtradas en 10-90 días (FOLR) explica un gran porcentaje de varianza en SA durante la temporada fría El patrón principal de FOLR, o patrón CSIS está caracterizado por un monopolo extendido con una orientación NO-SE sobre el sudeste de Sudamérica. La fase positiva del CSIS está asociada a valores positivos de anomalías de precipitación en la región. La actividad del CSIS induce una fuerte modulación de las anomalías de precipitación diarias y especialmente de las rachas húmedas y rachas húmedas P75. Las etapas de crecimiento y maduras de las fases positivas del CSIS son los períodos más probables para observar rachas húmedas. El análisis de las condiciones de gran escala asociadas a la actividad del CSIS muestra relaciones con actividad similar a la actividad de la MJO sobre los océanos Índico Tropical y Pacífico Oeste. Las anomalías de circulación de gran escala a lo largo del HS muestran evidencias tanto de actividad del SAM como del desarrollo de trenes de onda de Rossby asociados a la evolución del CSIS.