Relations between ENSO and the South Atlantic SST modes and their effects on the South American rainfall

Size: px
Start display at page:

Download "Relations between ENSO and the South Atlantic SST modes and their effects on the South American rainfall"

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 33: (2013) Published online 6 August 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3569 Relations between ENSO and the South Atlantic SST modes and their effects on the South American rainfall Mary T. Kayano, a * Rita V. Andreoli b and Rodrigo A. Ferreira de Souza b a Instituto Nacional de Pesquisas Espaciais, Centro de Previsão de tempo e Estudos Climáticos, São José dos Campos, SP, Brazil b Universidade do Estado do Amazonas, Escola Superior de Tecnologia, Manaus, AM, Brazil ABSTRACT: This paper analyses the relations between the El Niño/Southern Oscillation (ENSO) mode in the tropical Pacific and the sea surface temperature (SST) modes in the South Atlantic for the period. In the South Atlantic, two modes are analysed in more detail: the South Atlantic dipole (SAD) mode, with centres at (15 S, Greenwich longitude) and at (37.5 S, 25 W) and the southwestern South Atlantic (SWSA) mode, with centres at (30 S, 40 W) and at the southern midlatitudes. The ENSO and SAD modes are lagged or lead connected depending on the period of analysis. An El Niño (a La Niña) precedes by up to 6 months the establishment of a positive (negative) SAD mode during the and periods. Otherwise, a positive (negative) SAD mode precedes by up to 1 year the establishment of a La Niña (El Niño) during the period. On the other hand, the SWSA is strongly driven by the ENSO. The effects of both the SAD and SWSA modes on the South American rainfall are also discussed on a seasonal basis. In general, the SAD (ENSO) mode has a weak influence on the ENSO-related (SAD-related) rainfall anomalies over South America. On the other hand, in general, the SWSA and ENSO modes have a combined effect on rainfall of the southern and southeastern regions of South America. A particularly important result of the present analysis for climate monitoring and forecasting purposes seems to be the changing relations between the SAD and ENSO modes. Copyright 2012 Royal Meteorological Society KEY WORDS climatology; climate variability; El Niño/Southern Oscillation Received 27 October 2011; Revised 25 June 2012; Accepted 1 July Introduction It is well known that the El Niño/Southern Oscillation (ENSO) teleconnections established through Rossbywave trains or through east west type circulations associated with anomalous tropical heating (Nogués- Paegle et al., 2002) and, also through the ENSO-related changes in the Hadley circulation (Zhou and Lau, 2001) are responsible for interannual climate variations in large portions of the tropics. Over South America, the ENSOrelated climate variations have mostly been documented for the precipitation (Ropelewski and Halpert, 1987; Aceituno, 1988; Kayano et al., 1988; Kiladis and Diaz, 1989; Kousky and Ropelewski, 1989; Ropelewski and Halpert, 1989; Rao and Hada, 1990; Pisciottano et al., 1994; Grimm et al., 1998; Giannini et al., 2000; Grimm et al., 2000; Montecinos et al., 2000; Souza et al., 2000; Giannini et al., 2001; Zhou and Lau, 2001; Cazes-Boezio et al., 2003; Andreoli and Kayano, 2005; Kayano and Andreoli, 2006). In fact, drier (wetter) than normal conditions over the northern and northeastern sectors and the opposite conditions over the western equatorial coast and southern/southeastern sectors of this continent have Correspondence to: M. T. Kayano, Instituto Nacional de Pesquisas Espaciais, Centro de Previsão de Tempo e Estudos Climáticos, Avenida dos Astronautas, 1758, São José dos Campos, SP, Brazil. mary.kayano@cptec.inpe.br been attributed to the occurrence of El Niño (La Niña) episodes. However, the interannual sea surface temperature (SST) variability modes in the South Atlantic Ocean may also contribute to the South American climate (Andreoli and Kayano, 2006). Indeed, severe droughts in northeastern Brazil (NEB) have been associated with the presence of negative SST anomalies in the tropical South Atlantic (Markham and McLain, 1977). Diaz et al. (1998) obtained positive simultaneous correlations between the SST anomalies in the southwestern subtropical Atlantic and rainfall anomalies over Uruguay and southern Brazil during austral summer. They proposed that both the SST anomalous patterns in the southwestern Atlantic and the rainfall anomalies over southern South America might be local manifestations of the ENSO teleconnections. Differently, Barros and Silvestri (2002) showed that the rainfall over southeastern South America is modulated by the SST in the subtropical central South Pacific during El Niño years and, by the SST in the subtropical South Atlantic during La Niña years. Notwithstanding the important implications of the SST variability modes in the South Atlantic for the South American climate, they only received attention since the end of the 1990s. Venegas et al. (1997), using the SST and sea level pressure (SLP) data for the period, and the single value decomposition Copyright 2012 Royal Meteorological Society

2 SOUTH ATLANTIC SST MODES 2009 (SVD) technique determined three-coupled modes of the atmosphere ocean variability in the South Atlantic, all of them related to variations in the intensity or position of the South Atlantic Subtropical high (SASH). According to these authors, the first SVD mode describes the SASH intensity variations at a year time scale and features an SST dipole with one centre located in the equatorial South Atlantic [centre approximately at (17 S, 12 W)], and a secondary opposite sign centre in the extratropical South Atlantic [centre at (40 S, 25 W)]. Bombardi and Carvalho (2011) referred to this mode as the South Atlantic dipole (SAD). A positive (negative) SAD mode features positive (negative) SST anomalies in the northern centre and negative (positive) SST anomalies in the extratropical South Atlantic, and relates to weakened (intensified) SASH (Venegas et al., 1997). The second SVD mode obtained by Venegas et al. (1997) shows an SST monopole with its centre at (10 S, 0 W), and describes the east west shifts of the SASH at a 6 to 7 year time scale. Finally, their third SVD mode shows more structure in space with the strongest SST anomalies in a large latitudinal area bounded at 15 S, 35 S, 40 W and 10 E, and relates to north south shifts of the SASH at a 4 year time scale. They also showed that this mode is strongly linked to the ENSO, with the SST anomalies in the South Atlantic preceding the Pacific warming by 1 2 months. Sterl and Hazeleger (2003) studied the coupled atmosphere ocean variability in the South Atlantic for the period using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. They showed that the SST anomalies are mostly induced by the atmospheric circulation anomalies through latent heat flux and mixed layer deepening. They also found that the variability in the South Atlantic Ocean is largely independent of the variability in the other oceans. They found only a weak relation with the ENSO. Consistently, Bombardi and Carvalho (2011), using ten global coupled climate models for the 20th century climate scenario from 1971 to 2000, found that the SAD variability is independent of the ENSO. Using observational and reanalysis data sets from 1950 to 2008, Nnamchi et al. (2011) proved the existence of the SAD mode in the South Atlantic as a distinct mode from the Atlantic equatorial mode (AEM), and independent of the direct influence of the ENSO in the tropical Pacific. The AEM is an interannual SST mode with its main anomalous pattern in the eastern side of the equatorial Atlantic (Zebiak, 1993). Colberg et al. (2004), using simulations with an ocean general circulation model (ORCA2) forced with the NCEP/NCAR reanalysis data for the period, found significant influence of the ENSO, through the wind stress anomalies, on the upper ocean temperatures in the South Atlantic. They suggested that under El Niño onset, the negative SLP anomalous centre in the South Atlantic causes a weakening of the southerly trades and a strengthening of the midlatitude westerlies. In consequence, the reduction of the southward Ekman heat transport in the tropics and the enhancement of the northward Ekman heat transport in the midlatitudes cause a warming between the equator and 25 S and a cooling in the midlatitudes during the season from October to December (OND). They noted similar, but not exactly reversed patterns, during OND of the La Niña onset years. The composite patterns obtained by Colberg et al. (2004) and the third SVD mode presented by Venegas et al. (1997) show differences which are indicative that they might not be the same mode. In fact, the ENSO-related SST composites obtained by Colberg et al. (2004), for some stages, present close resemblances with the first SVD mode shown by Venegas et al. (1997). So, most papers searching for the connections between the ENSO and the South Atlantic SST variability modes considered them simultaneously or when the Pacific mode precedes the South Atlantic modes. From another point of view, several authors showed that the AEM is strongly linked to the ENSO, with the SST anomalies in the Atlantic preceding the opposite sign anomalies in the eastern equatorial Pacific. Indeed, Losada et al. (2010) showed that a warm AEM in May June, through an anomalous Atlantic Walker circulation, favours the establishment of La Niña conditions in the tropical Pacific during the July August September period. Under warm AEM conditions, the enhanced surface divergence in the central and eastern Pacific due to the descending branch of the anomalous Atlantic Walker circulation shallows the equatorial thermocline, and triggers the La Niña (Rodríguez-Fonseca et al., 2009). More recently, Kayano et al. (2011) noted that the Atlantic SST anomalous conditions associated with the AEM are persistent and might be noted 8 9 months before the establishment of the ENSO conditions in the tropical Pacific. They also documented the evolving patterns of the SST and SLP leading to the ENSO extremes forced by or independent of the AEM, as well as their relations to the rainfall over South America. To summarize, some authors found close relations between the ENSO and some of the South Atlantic SST modes, while others did not. In addition, the temporal precedence of the ENSO and South Atlantic modes, particularly the SAD mode, was not clearly established yet. Thus, in this paper we will further examine the connections of the ENSO with the dominant South Atlantic SST variability modes as well as their relations with the rainfall variations over South America. The following sections are organized as follows: a brief description of data and methods in Section 2; analyses of the South Atlantic and tropical Pacific SST modes, and their relations and influences on the South American rainfall, in Section 3. Conclusions are given in Section Data and methods The dataset used in this article is the version 3B of the reconstructed monthly SST series obtained by

3 2010 M. T. KAYANO et al. Smith et al. (2008) at 2 by 2 latitude longitude resolution grid. According to these authors, this version was obtained using in situ SST data and improved statistical tools that allowed stable reconstruction using sparse data. They also stated that in this version, the improved low-frequency tuning and historical merged analysis of SST eliminated most of the low-frequency damping error. They also used improved high-frequency tuning. According to these authors, as the local and short-term variations were smoothed in the version 3B SST data, this dataset is appropriate for long-term global and basin-wide studies. With this information in mind, the SST data is used here for large-scale and long-term analyses. The SST time series are selected in two areas, one in the South Atlantic bounded at equator, 60 S, 70 W and 15 E and in the tropical Pacific limited at 30 N, 30 S, 120 E and 70 W for the period. Monthly gridded precipitation time series at 1 resolution derived from the gauge-based reconstructions of the Global Precipitation Climatology Center (GPCC) (Beck et al., 2005; Rudolf and Rubel, 2005) are also used here. The GPCC full data reanalysis version 5 is used. Precipitation time series are selected for the period in the area bounded at 15 N, 50 S, 90 W and 30 W, which encompasses most of the South American region. The gridded precipitation data obtained from the GPCC were generated by an operational analysis system which includes integration of data from different sources, quality-control and calculation of area-averaged precipitation on the grid cells (Rudolf and Schneider, 2005). Although errors might exist in this dataset due to deficiencies in the measuring process or to regionally sparse data, it contains high-quality gridded precipitation data. Error analysis in this dataset is out of the scope of the present article. So, taking into account that one of the possible sources of errors is sparse observational data, we will focus our analysis in the South American regions with dense rain gauge network. The general aspects of the data processing are described here. Prior to any calculation, the linear trend is removed from the SST time series at each grid point. The SST and precipitation climatologies are calculated for each calendar month at each grid point by averaging the data over the , and periods, respectively. Monthly anomalies are determined as departures from the mean annual cycle of each variable. The monthly precipitation anomaly series at each grid point is standardized by the corresponding monthly standard deviation. The SST variability modes are obtained from the empirical orthogonal function (EOF) analyses of the SST anomalies in selected areas. In these analyses, the covariance matrix is used and the method proposed by North et al. (1982) is adopted to examine the separation of the modes. The eigenvectors are presented as correlation patterns. To examine the correlation statistical significance, the number of degrees of freedom is estimated as the time interval for two independent realizations, the lag needed to obtain autocorrelation coefficients of the time series close to zero. The principal component (PC) is standardized and gives the temporal variations of the corresponding mode. Spectral features of the PC time series are obtained through the Morlet wavelet transform whose calculation is given by Torrence and Compo (1998). The Morlet wavelet is a complex exponential modulated by a Gaussian, e iω η e η2 /2, with η = t/s, where t is the time, s is the wavelet scale and ω 0 is a non-dimensional frequency (Torrence and Compo, 1998). The partial correlation analysis is also used here. The partial correlation between two variables, X 1 and X 3, while excluding the effects of a third independent variable, X 2, is defined as (Panofsky and Brier, 1968): r 13,2 = (r 13 r 12 r 23 )/ (1 r 212 ) (1 r23 2 ) where, r 13, r 12 and r 23 are the linear correlations between X 1 and X 3, between X 1 and X 2 and between X 2 and X 3, respectively. The linear correlation between X 1 and X 2 is referred to as X 1 X 2, and the partial correlation between X 1 X 3 without the effects of X 2, X 1 X 3 X Results 3.1. EOF modes for monthly SST data The EOF analyses of the monthly SST anomalies in the South Atlantic and tropical Pacific are done separately for the two oceanic sectors. The mean number of degrees of freedom for the tropical Pacific is 50 and for the South Atlantic is 57. The Student s t-test for 50 and 57 degrees of freedom give respectively the thresholds of 0.35 and 0.33, for the correlations to be significant at the 99% confidence level. The leading mode in the tropical Pacific explains 40.0% of the total variance for the SST in this oceanic sector (Figure 1(a)), and is well separated from the higher modes (North et al., 1982). The largest significant positive loadings are centred in the central tropical Pacific, and extend meridionally in the eastern side of the basin. As one might expect, this mode reflects mainly the ENSO-related SST variations in the tropical Pacific. Indeed, the largest positive (negative) values of the corresponding PC time series (PPC1) occur mostly during El Niño (La Niña) events (Figure 1). The Morlet wavelet spectral analysis of PPC1 shows the largest variances dominantly at the interannual time scale which yield a significant peak approximately at 4 year (figure not shown). So, the PPC1 time series may be used to describe the ENSO cycles. The patterns of the first three EOF modes of the SST anomalies in the South Atlantic and the corresponding PC time series (APC1, APC2 and APC3) are displayed in Figure 2. These modes explain respectively 19.7, 13.3 and 9.3% of the total variance for the SST in the South Atlantic. According to the North et al. (1982) method, they are well separated from each other. The first mode features a negative SAD mode with centres at (15 S, Greenwich longitude) and at (37.5 S,

4 SOUTH ATLANTIC SST MODES 2011 Figure 1. First EOF mode pattern of the SST monthly anomalies in the tropical Pacific for the period. Contour interval is 0.20; the continuous (dashed) line is positive (negative), and the zero line, omitted. Shaded areas encompass significant correlations at the 99% confidence level. The lower panel displays the principal component of this mode (PPC1). Explained variance is on top of the panel in percentage. 25 W). These centres show significant negative and positive loadings with nearly balanced magnitudes. The SAD mode obtained here holds resemblances with the first SVD mode in the South Atlantic found by Venegas et al. (1997). The APC1 time series shows interannual to decadal time scale fluctuations. The Morlet wavelet analysis of this time series (figure not shown) shows significant interannual time scale variances scattered during the period of analysis, which lead to a significant peak approximately at 1.5 year. The equatorial sector of the SAD mode holds similarities with the AEM mode previously documented by Zebiak (1993) and retrieved by Kayano et al. (2011) through EOF analysis of the interannual SST anomalies in the tropical Atlantic. To examine this aspect the interannual component of the APC1 and the PC time series of the AEM obtained by Kayano et al. (2011) are compared. The APC1 time series is then filtered with a band-pass 1 6 year Morlet wavelet filter which is the same one used by Kayano et al. (2011). The linear correlation between these two interannual time scale series is 0.9 which, using the Student t-test, is highly significant at a confidence level above 99.9%. Since the analysis by Kayano et al. (2011) is limited to the tropics, some cases of the AEM identified in their study might be indeed the equatorial side of the SAD mode. Furthermore, Nnamchi et al. s (2011) criterion to identify an AEM is based on a weak or inexistent centre in the southwestern South Atlantic (SWSA). With this criterion, some weak SAD modes may be identified as a pure AEM. All these indicate that some cases of AEM identified in previous studies might be the equatorial side of an SAD mode. The second mode in the South Atlantic features only one main area with significant loadings located between 35 S and 50 S (Figure 2). The largest loadings of this mode extend over an area with sparse data measurements in the past, most of which was avoided in the analysis done by Venegas et al. (1997). Consistently, this mode has no correspondence to any of the South Atlantic SVD modes described by Venegas et al. (1997). The APC2 shows interannual to multidecadal variations before 1965, and considerably reduced amplitudes from 1965 to 1990 (Figure 2). The Morlet wavelet analysis of APC2 (figure not shown) shows significant variances from seasonal to interannual and from decadal to multidecadal time scales before 1960, and no significant variances afterwards. Since the high- and low-frequency signals are stronger prior 1960 when observational data were fewer than afterwards, these signals in the southeastern South Atlantic might not represent a physical mode. So, this mode will not be further analysed. The third SST mode in the South Atlantic shows a dipolar structure with a strong negative centre at (30 S, 40 W) in the SWSA that extends between 15 S and 45 S, and a less extensive positive centre in the southern midlatitudes (Figure 2). Since the negative centre is more extensive and stronger than the positive one, the negative centre will be used to refer to this mode. So, this mode will be referred to as the SWSA mode. It is dominated by interannual fluctuations, as shown in

5 2012 M. T. KAYANO et al. Figure 2. The EOF patterns and the corresponding APC time series of the first three modes of the SST monthly anomalies in the South Atlantic for the period. Display is the same as in Figure 1. its PC time series (APC3) (Figure 2) and confirmed by the Morlet wavelet analysis of APC3 that shows the largest variances at the interannual time scale, with a significant peak at 4 year (figure not shown). The SWSA mode holds some similarities with the third SVD mode obtained by Venegas et al. (1997), which they related to the ENSO, and with the SST anomaly composite for El Niño during January February March (JFM) season shown by Colberg et al. (2004). It is apparent in the analysis here that the APC3 are mostly negative (positive) during El Niño (La Niña) years. So, the surface waters off southern Brazil and Uruguay warm up (cool down) during El Niño (La Niña) years. This result confirms the El Niño relation to warmed surface waters off southern Brazil and Uruguay noted in previous papers (Diaz et al., 1998; Barreiro, 2010). Moreover, the results here evidence that the El Niño-related warmed (La Niña-related cooled) SST pattern in the SWSA is part of a dipole pattern of the SWSA mode that extends from tropical latitudes to midlatitudes Relations between the Atlantic modes and the ENSO The relationships of the SAD and SWSA modes in the South Atlantic to the ENSO, represented here by the first SST mode in the tropical Pacific, are searched for using correlation analysis. Since the PPC1 shows large interannual variability, and the APC1 and APC3 feature higher frequency variability superimposed to decadal time scale variability, their corresponding seasonal time series are obtained. Each seasonal time series contains 484 seasonally averaged values spanning from December January February to September- October-November This procedure eliminates the difference in the high-frequency variability between the PPC1 and the Atlantic PC time series. Lag and simultaneous 41-season running correlations between the ENSO and the Atlantic PC time series are calculated. The statistical significance of the correlations is examined using the random-phase test suggested by Ebisuzaki (1997). So, 1000 random-phase time series with the same power

6 SOUTH ATLANTIC SST MODES 2013 Figure 3. Time versus lag plot of the 41-season running correlations between PPC1 and APC1. Lag interval in the abscissa is one season. Shaded areas encompass significant correlations at the 95% confidence level. spectrum as the PPC1 time series are generated. The distribution of the correlations between the random-phase time series and the Atlantic PC time series is then used to define the threshold for significant correlations. The 95% percentile is used. The resulting running correlations are limited to the period and displayed in time versus lag plots. Negative (positive) lags indicate that PPC1 time series leads (lags) the Atlantic PC time series. Figure 3 shows the running correlations between PPC1 and APC1 displayed in time versus lag plot. It is interesting to note that the correlations between PPC1 and APC1 for negative (positive) lags are predominantly negative (positive). Furthermore, the PPC1 and APC1 show very weak simultaneous correlations for some periods, such as during and This result concords with those obtained by Bombardi and Carvalho (2011) who found no significant correlation for ENSO simultaneous to SAD or for ENSO leading SAD for the period. The negative correlations are noticeable from lags 2 seasons to +1 season particularly during the and periods. For the negative lag, the interpretationisthatanelniño (a La Niña) precedes by up to two seasons the establishment of a positive (negative) SAD in the South Atlantic. For the positive lag, the interpretation is that a positive (negative) SAD precedes by up to one season the establishment of an El Niño (La Niña). Simultaneous occurrences of an El Niño (a La Niña) and a positive (negative) SAD were previously documented by Kayano and Andreoli (2006). They showed that this relation occurs for an ENSO event with its main centre located in the central Pacific and without SST ENSO signature in the eastern equatorial Pacific off the South American coast. In agreement, Rodrigues et al. (2011), analysing the warm ENSO phase, found that a positive SAD develops in boreal winter only during weak and short El Niño events located in the central Pacific. The PPC1 and APC1 time series are significantly and positively correlated from lag +1 to lag +4 seasons during the period (Figure 3). So, a negative (positive) SAD in the South Atlantic precedes by one season to 1 year the establishment of an El Niño (a La Niña) event. Once the negative (positive) SAD persists, the persistent descending (ascending) motion in the equatorial Atlantic related to the cooling (warming) there gradually drives an anomalous east west circulation in the equatorial vertical plane with an ascending (descending) branch in the eastern equatorial Pacific. This east west circulation contributes to weaken (enhance) the surface divergence in the eastern equatorial Pacific, leading to the sinking (shallowing) the equatorial termocline. Under this persistent condition, an El Niño (a La Niña) is established one season to 1 year later. This mechanism is the same discussed previously concerning the inter- Pacific-Atlantic east west SST gradient which through a feedback process relates the ENSO and the AEM, with the AEM preceding by some months the establishment of the ENSO mode (Wang, 2006; Losada et al., 2010; Rodríguez-Fonseca et al., 2009; Kayano et al., 2011). The results show that the ENSO and SAD modes are closely lagged or lead connected depending on the period. Another interesting aspect is the modulation of the correlations by the decadal and multidecadal variability. Indeed, the significant positive correlations for positive lags during the period show maxima at a decadal time scale. Furthermore, the multidecadal modulation of the positive correlations for positive lags as well as the decadal modulation of the largest negative correlations for negative lags during the period are also apparent. These modulations result from the decadal component of the SAD mode. Figure 4 shows the running correlations between PPC1 and APC3 displayed in time versus lag plot. These time series are negatively correlated from lag 4 to lag +2 seasons during most of the period. However, significant negative correlations are conspicuous in three periods: , and for the lag interval from 4 seasons to 0 season. The interpretation is that an El Niño (a La Niña) event precedes by up to 1 year the establishment of positive (negative) SST anomalies in the SWSA between 15 S and 45 S. The mechanism relating the ENSO and the SST anomalies in

7 2014 M. T. KAYANO et al. Figure 4. Time versus lag plot of the 41 month running correlations between PPC1 and APC3. Display is the same as in Figure 3. the southwestern Atlantic Ocean might be that suggested by Colberg et al. (2004). This mechanism involves variations in the low-level winds which cause alteration in the Ekman heat transport. They suggested that under El Niño onset, the weakening of the southerly trades and the strengthening of the midlatitudes westerlies yield a reduction of the southward Ekman heat transport in the tropics and an enhancement of the northward Ekman heat transport in the midlatitudes. In consequence, a warming in the band from the equator to 25 S andacoolingin the midlatitudes are noted one season later. They noted similar, but not exactly reversed patterns, during the La Niña onset years. Significant negative correlations are also noted during the and periods for positive lags up to two seasons. In this case, the interpretation is that positive (negative) SST anomalies in the SWSA between 15 S and 45 S precedes by up to two seasons the establishment of an El Niño (a La Niña) event Atlantic seasonal SST modes To examine the seasonal differences of the dominant SST variability modes in the South Atlantic, separated EOF analyses are done for seasonally averaged SST anomalies in the South Atlantic. The seasons are referred to with the initials of the months such as DJF for summer, MAM for autumn, JJA for winter and SON for spring. The mean number of degrees of freedom in the South Atlantic for each season is 20. The Student s t-test for 20 degrees of freedom gives the threshold of 0.42, for the correlations to be significant at the 95% confidence level. The patterns of the first three EOF modes of the seasonal SST anomalies in the South Atlantic for DJF, MAM, JJA and, SON are displayed in Figures 5(a) (d), respectively. As in the monthly analysis, the first three modes of each season are well separated from each other (North et al., 1982). The seasonal analyses reproduce the major features of the first three EOF modes of the monthly analysis. For sake of comparison, the monthly APC time series are seasonally averaged. Table I lists the correlations between the APC time series of the seasonal analyses and the seasonally averaged APC time series of the monthly analysis. For the significance of these correlations, the estimated number of degrees of freedom is 20 for each season. These correlations are all highly significant at confidence levels above 99.9%, except the correlation of 0.45 between the APC3 of the seasonal analysis and the seasonal average APC2 for JJA which is significant at the 95% confidence level. These highly significant correlations indicate that the first three EOF modes of the monthly analysis are features of the low-frequency SST variability in the South Atlantic, as shown above. The SAD mode with a northeast southwest dipolar structure is reproduced in the first EOF seasonal modes (left column in Figure 5). Its northeastern and southwestern centres show nearly balanced magnitudes for SON and DJF seasons when the SAD mode is well defined. On the other hand, the weaker southwestern centre than the northeastern centre during MAM and JJA indicates a weak SAD mode during these seasons. The seasonal dependence noted here on the SAD mode intensity is in agreement with previous results. Indeed, Morioka et al. (2011) found that the SAD mode growth phase occurs in late spring, its mature phase, in summer, and its decay phase, in early autumn. Haarsma et al. (2003) found the strongest atmospheric response to the SAD mode during austral summer. Since Nnamchi et al. (2011) also used the EOF analyses to get seasonal SST variability modes in the South Atlantic, comparisons between the modes obtained here and their modes (their Figure 2) are done. Contrasting with the results here, Nnamchi et al. (2011) found that the SAD mode is stronger during winter. The possible sources of the discrepancies are the differences in the data processing procedures, period of analysis and data source. Here, the linear trends have been removed from the SST time series. This justifies why they obtained the SAD mode in the second EOF, while here it is the first EOF mode. Whereas they standardized the data in each grid point prior to the EOF analyses, here the data are not standardized and the covariance matrix is used in the EOF calculations. We chose to avoid standardization because this procedure gives equal importance to regions of low

8 SOUTH ATLANTIC SST MODES 2015 Figure 5. The EOF patterns of the first three modes of the seasonal SST anomalies in the South Atlantic for the period: (a) DJF, (b) MAM, (c) JJA, (d) SON. Contour interval is 0.20; the continuous (dashed) line is positive (negative), and the zero line, omitted. Shaded areas encompass significant correlations at the 95% confidence level. Explained variance of each mode is on top of the corresponding panel in percentage. Table I. Correlation between seasonally averaged APC (indicated by M ) and seasonal APC (indicated by S ) for each season. APC number season M1 S1 M2 S2 M3 S3 M2 S3 M3 S2 DJF MAM JJA SON and high temporal variance and a more uniform spatial structure in the EOF modes. We suspect that this is the main cause of discrepancies between our and Nnamchi et al. s (2011) results. The second SST variability mode for DJF, MAM, SON and the third SST variability mode for JJA (Figure 5) are similar to the second SST variability mode of the monthly analysis (Figure 2). Because this mode might not be a physical mode, as stated above, the corresponding seasonal modes are not further examined. The third SST variability mode for DJF, MAM, SON and the second variability mode for JJA (Figure 5) are similar to the SWSA mode of the monthly analysis (Figure 2). The SWSA mode is well reproduced for MAM, JJA and SON. The dipolar structure of this mode with a strong centre in the SWSA and an opposite sign centre in the southern midlatitudes is less defined in DJF. This explains the lowest correlation of 0.68 between the APC3 of the seasonal and monthly analyses for DJF compared to the relatively high correlations between the corresponding APC time series for the other seasons

9 2016 M. T. KAYANO et al. (see Table I). However, in general the SWSA mode is reproduced in the seasonal analyses Influences of seasonal SST modes on the South American rainfall Although the lagged correlations between the PPC1 and APC1 show changes during the period of analysis, the corresponding ENSO and SAD modes might separately or together affect the South American rainfall. In addition, the ENSO and SWSA modes may also separately or together affect the South American rainfall. So, the simultaneous effects of the ENSO and SAD modes as well as of the ENSO and SWSA modes on the South American rainfall are examined here for the period. These analyses are done on seasonal basis using correlation and partial correlation calculations. To carry out seasonal analyses, the monthly precipitation anomalies, PPC1, APC1 and APC3 from the monthly analysis are seasonally averaged. The use of seasonally averaged PPC1, APC1 and APC3 instead of the PC time series from the seasonal analyses simplifies the calculations and is justified because the ENSO, SAD and SWSA modes are well reproduced in the seasonal analyses (Figures for ENSO are not shown here). For 106 degrees of freedom (number of years), the Student t-test gives the threshold values of 0.2 for correlations to be significant at the 95% confidence level. The correlation maps between PPC1 and the precipitation (PPC1 Prp), as well as the partial correlation maps between PPC1 and precipitation while removing the effect of the APC1 (PPC1 Prp APC1) and while removing the effect of the APC3 (PPC1 Prp APC3) are presented in Figure 6. In general, the El Niño- (La Niña-) related dipolar structure of the correlations with significant negative (positive) values in the northern portion of South America and opposite sign significant values in the southeastern/southern portion of this continent is reproduced in all seasons. However, the centres of this dipole show seasonal differences in their locations and intensities. In fact, significant negative correlations Figure 6. Seasonal correlation maps between PPC1 and precipitation (Prp) for: (a) DJF, (b) MAM, (c) JJA and (d) SON. Seasonal partial correlation maps between PPC1 and Prp while removing the effect of APC1 for: (e) DJF, (f) MAM, (g) JJA and (h) SON. Seasonal partial correlation maps between PPC1 and precipitation while removing the effect of APC3 for: (i) DJF, (j) MAM, (k) JJA, and (l) SON. Contour interval is 0.20; the continuous (dashed) line is positive (negative), and the zero line, omitted. Shaded areas encompass significant correlations at the 95% confidence level. The period of analyses is

10 SOUTH ATLANTIC SST MODES 2017 Figure 7. Seasonal correlation maps between APC1 and precipitation for: (a) DJF, (b) MAM, (c) JJA, and (d) SON. Seasonal partial correlation maps between APC1 and precipitation while removing the effect of PPC1 for: (e) DJF, (f) MAM, (g) JJA, and (h) SON. Display is the same as in Figure 6. extend from NEB westward to the central Amazon during MAM and occupy a region in the northern and northwestern South America during the other seasons. Most months of MAM overlaps the northeastern and central Amazon rainy season. So, the main seasonal system modulating the rainy season in this area, the Atlantic intertropical convergence zone (ITCZ) (Ratisbona, 1976; Hastenrath and Heller, 1977), is affected by the ENSO. Another interesting seasonal aspect is the well-defined centre with significant positive correlations over Uruguay, southern and eastern Brazil during austral spring. This aspect reflects the strongest impacts of the El Niño and La Niña events noted over Uruguay (Pisciottano et al., 1994) and southern Brazil (Rao and Hada, 1990; Grimm et al., 1998) during SON. The correlation and partial correlation maps show quite similar patterns for all seasons. This is particularly true for PPC1 Prp and PPC1 Prp APC1. So, the SAD mode has a weak influence on the ENSO-related rainfall anomalies over South America. Otherwise, the differences between PPC1 Prp and PPC1 Prp APC3 are quite evident in the southern and southeastern sectors of the continent, where significant positive partial correlations are weakened and/or occupy relatively smaller areas than the significant positive correlations. The interpretation is that the wet (dry) conditions in the southeastern and southern South America associated with the El Niño (La Niña) are reinforced by the anomalously warm (cold) surface waters in the SWSA. Thus, the SWSA mode and ENSO have combined effects in increasing the precipitation anomalies in the southern and southeastern South America. The correlation maps between APC1 and the precipitation (APC1 Prp) show interesting seasonal differences (Figure 7). A dipolar structure with significant positive correlations over northern South America and the opposite sign correlations over northeastern South America is noted during DJF and MAM. A less organized structure with significant negative correlations over northern South America and the positive ones scattered over central and eastern South America is noted during JJA and SON. To interpret these results it is important to take into account that the main system modulating the seasonal rainfall over northern and northeastern South America, the Atlantic ITCZ, performs a seasonal meridional migration with its extreme southerly position in March April and its extreme northerly position in September October (Ratisbona, 1976). A negative (positive) SAD mode prevailing during the austral summer and autumn seasons impedes (facilitates) the seasonal southward migration of the ITCZ. In consequence, above (below) normal rainfall occurs in areas over northern South America and below (above) normal rainfall, over NEB. Otherwise, as the ITCZ migrates northward from the austral winter to spring, during these seasons the rainfall over most areas of South America might be under a more direct influence of the SAD mode. In fact, the negative (positive) SAD mode during JJA and SON might have a local influence causing below (above) normal rainfall over northern South America (eastern Venezuela, Guyana, French Guiana and surrounding northern Brazil areas for JJA and northern Brazil for SON), and above (below) normal rainfall in small areas scattered over central and eastern South America.

11 2018 M. T. KAYANO et al. Figure 8. Seasonal correlation maps between APC3 and precipitation for: (a) DJF, (b) MAM, (c) JJA, and (d) SON. Seasonal partial correlation maps between APC3 and precipitation while removing the effect of PPC1 for: (e) DJF, (f) MAM, (g) JJA, and (h) SON. Display is the same as in Figure 6. The ENSO influence on the relation between the SAD mode and the rainfall over South America is mostly weak, except during DJF. The partial correlation map (APC1 Prp PPC1) for this season shows that most of the significant positive correlations noted over northwestern South America for the correlation map are weakened or absent (Figure 7). The seasonal maps of correlations between APC3 and the precipitation (APC3 Prp) and of the partial correlations between APC3 and the precipitation while removing the effect of the PPC1 (APC3 Prp PPC1) are displayed in Figure 8. The correlation maps show remarkable seasonal differences. Significant negative correlations are noted over northern Peru only during DJF and over southeastern South America in all seasons. The significant negative correlations are found over southeastern South America in JJA, and over Uruguay, southern and southeastern Brazil during SON. Significant positive correlations are found over NEB in MAM, and over part of northern South America in JJA and SON. However, most of these significant correlations seem to be due to the ENSO action. Indeed, most of the significant correlations noted in the seasonal correlation maps are weakened or absent in the corresponding partial correlation maps. This is the case of the significant positive correlations noted over NEB, during MAM, and over northern South America during JJA and SON, and of the negative ones, over Uruguay, and southern and southeastern Brazil during SON. Thus, in general, the significant correlations between APC3 and precipitation represent mostly the ENSO effect on the South American rainfall through the SWSA mode. Therefore, the SWSA, third EOF mode for the SST in the South Atlantic, is closely related to the ENSO, as shown above with the running correlation analysis of the APC3 and PPC1. Nevertheless, the significant negative correlations noted over Peru during DJF, and over part of southeastern South America during DJF, MAM and JJA remain quite strong for the corresponding partial correlation maps (APC3 Prp PPC1). In these cases, the interpretation is that the dry (wet) conditions over these regions and seasons are ENSO independent and in part related to cooled (warmed) surface waters off southern Brazil and Uruguay Relations between the seasonal SST modes and the South American rainfall for three sub-periods As illustrated above, the relations between the ENSO and Atlantic SST modes show a temporal dependence (Figures 3 and 4). In the previous sub-section, the influences of these modes on the South American rainfall are analysed without considering the temporal dependence of the relations between the Pacific and Atlantic SST modes. This aspect is examined by repeating the correlation and partial correlation analyses for three non-overlapping subperiods: , , and To facilitate comparisons among the sub-periods, they are selected with a same length but their choice is based on Figure 3. For 30 degrees of freedom (number of years), the Student t-test gives the threshold values of 0.3 for correlations to be significant at the 90% confidence level. Differences among the sub-periods are noted in the correlation maps between the SST PC time series (PPC1, APC1 and APC3) and the rainfall anomalies. However, the correlation and partial correlation maps for each

12 SOUTH ATLANTIC SST MODES 2019 Figure 9. Seasonal correlation maps between PPC1 and precipitation for: (a) DJF, (b) MAM, (c) JJA, and (d) SON for the sub-period; (e) DJF, (f) MAM, (g) JJA, (h) SON for the sub-period; (i) DJF, (j) MAM, (k) JJA, (l) SON for the sub-period. Contour interval is 0.30; the continuous (dashed) line is positive (negative), and the zero line, omitted. Shaded areas encompass significant correlations at the 90% confidence level. sub-period hold similar relations as those for the whole period. These relations refer to increase, decrease or maintenance of the correlation values. Therefore, the partial correlation maps for the sub-periods are not shown here. The seasonal correlation maps between the PPC1 and the rainfall anomalies are shown in Figure 9. The correlation map differences among the sub-periods are mostly in the spatial extension of the significant values. For all seasons, the correlation maps of the sub-period show smooth patterns with significant negative values over northern South America; and the positive ones to the south extend over southeastern South America in DJF, central South America in MAM, southern South America and part of eastern central Brazil in JJA, and central and eastern South America in SON. The sub-period correlation maps show significant values in relatively large and well-defined areas in DJF and SON, and in small areas scattered in southern and southeastern South America during the other seasons. The correlation maps of the sub-period are very similar to those of the period. The seasonal correlation maps between the APC1 and rainfall anomalies are depicted in Figure 10. Again, the correlation map differences among the sub-periods are in the spatial extension of the significant values. The smoothest patterns are also noted in the subperiod, in particular during DJF, MAM and JJA. In this case, for each season the correlation map of the period seems to be a combination of the corresponding sub-period seasonal maps. The seasonal correlation maps between APC3 and the South American rainfall anomalies are illustrated in Figure 11. For each season, they show differences among the sub-periods in the extension of the significant correlation areas. Again, the smoothest correlation patterns are noted for the sub-period. Furthermore, most correlation maps for the and sub-periods show only very small areas with significant values. The exception is the JJA correlation map

13 2020 M. T. KAYANO et al. Figure 10. Seasonal correlation maps between APC1 and precipitation for: (a) DJF, (b) MAM, (c) JJA, and (d) SON for the sub-period; (e) DJF, (f) MAM, (g) JJA, (h) SON for the sub-period; (i) DJF, (j) MAM, (k) JJA, (l) SON for the sub-period. Display is the same as in Figure 9. for This map features significant positive correlations over northern South America and the negative ones in a large area extending from central southeastern South America northward to southern Amazon and then eastward to NEB. This map resembles the corresponding map for the sub-period. The correlation maps between SST PC time series and the South American rainfall anomalies show differences among the three 30 year sub-periods. These differences among sub-periods are mostly in the spatial extension of the significant correlations. However, as the correlation and partial correlation maps for each sub-period hold relations similar to those for the whole period, the discussion above for the can also be extended to the sub-periods. 4. Concluding remarks This work examines the relations between the ENSO in the tropical Pacific and the dominant SST modes in the South Atlantic as well as their influences on the South American rainfall variability. The SST modes are obtained from independent EOF analyses of the SST anomalies in these two oceanic areas. Of the first three SST modes in the South Atlantic, the SAD and the SWSA modes are analysed in more detail. The existence of the SAD mode as the dominant SST variability mode in the South Atlantic has been demonstrated since the end of the 1990s (Venegas et al., 1997; Bombardi and Carvalho, 2011; Nnamchi et al., 2011). It has been associated with the SASH intensity variations at a decadal time scale (Venegas et al., 1997). It is worth mentioning that the methods adopted here to get and to examine the SAD mode do not allow us to make a distinction between the SAD and the AEM, as done by Nnamchi et al. (2011). This distinction is out of the scope of this analysis. Rather, a time series giving the temporal variations of the SAD mode, necessary to perform correlation, partial correlation and wavelet analyses, is aimed here.

14 SOUTH ATLANTIC SST MODES 2021 Figure 11. Seasonal correlation maps between APC3 and precipitation for: (a) DJF, (b) MAM, (c) JJA, and (d) SON for the sub-period; (e) DJF, (f) MAM, (g) JJA, (h) SON for the sub-period; (i) DJF, (j) MAM, (k) JJA, (l) SON for the sub-period. Display is the same as in Figure 9. An important result here is that the ENSO and SAD modes are closely lagged or lead related depending on the period of analysis. This relation is modulated by the decadal component of the SAD mode. The negative correlations for negative lags between PPC1 and APC1 during the and periods indicate that an El Niño (a La Niña) precedes by up to two seasons the establishment of a positive (negative) SAD mode. The simultaneous occurrence of an El Niño (a La Niña) and a positive (negative) SAD mode during austral autumn was previously documented to occur in association with weak ENSO events located in the central Pacific (Kayano and Andreoli, 2006; Rodrigues et al., Although most El Niño and La Niña events classified as this type occurred during the and periods, other types of El Niño and La Niña events occurred in these periods (Kayano and Andreoli, 2006). On the other hand, the positive (negative) SAD mode during the period precedes by up to 1 year the establishment of a La Niña (El Niño). In this case, previous results indicate that the relations between the Pacific and Atlantic modes might occur through the east west circulation in the equatorial latitudes (Nogués- Paegle et al., 2002; Wang, 2006; Rodrigues-Fonseca et al., 2009; Kayano et al., 2011). The SWSA mode is indeed the South Atlantic SST mode driven by the ENSO as previously found in some papers (Diaz et al., 1998; Colberg et al., 2004). The mechanism relating the ENSO and the SST anomalies in the southwestern Atlantic Ocean might involve variations in the low-level winds which cause alteration in the Ekman heat transport as suggested by Colberg et al. (2004). They suggested that under El Niño onset, the weakening of the southerly trades and the strengthening of the midlatitude westerlies yield a reduction of the southward Ekman heat transport in the tropics and an enhancement of the northward Ekman heat transport in the midlatitudes. In consequence, a warming is noted between equator and 25 S and a cooling in the midlatitudes.

A Diagnosis of Rainfall over South America during the 1997/98 El Niño Event. Part I: Validation of NCEP NCAR Reanalysis Rainfall Data

A Diagnosis of Rainfall over South America during the 1997/98 El Niño Event. Part I: Validation of NCEP NCAR Reanalysis Rainfall Data 502 JOURNAL OF CLIMATE VOLUME 15 A Diagnosis of Rainfall over South America during the 1997/98 El Niño Event. Part I: Validation of NCEP NCAR Reanalysis Rainfall Data V. BRAHMANANDA RAO, CLÓVIS E. SANTO,

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 23 April 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index

More information

Interannual Variability of the South Atlantic High and rainfall in Southeastern South America during summer months

Interannual Variability of the South Atlantic High and rainfall in Southeastern South America during summer months Interannual Variability of the South Atlantic High and rainfall in Southeastern South America during summer months Inés Camilloni 1, 2, Moira Doyle 1 and Vicente Barros 1, 3 1 Dto. Ciencias de la Atmósfera

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 5 August 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 11 November 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 25 February 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 15 July 2013 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 24 September 2012

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 24 September 2012 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 24 September 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño

More information

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 9 November 2015

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 9 November 2015 ENSO: Recent Evolution, Current Status and Predictions Update prepared by: Climate Prediction Center / NCEP 9 November 2015 Outline Summary Recent Evolution and Current Conditions Oceanic Niño Index (ONI)

More information

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 30 October 2017

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 30 October 2017 ENSO: Recent Evolution, Current Status and Predictions Update prepared by: Climate Prediction Center / NCEP 30 October 2017 Outline Summary Recent Evolution and Current Conditions Oceanic Niño Index (ONI)

More information

SE Atlantic SST variability and southern African climate

SE Atlantic SST variability and southern African climate SE Atlantic SST variability and southern African climate Chris Reason Oceanography Dept, Univ. Cape Town Overview of southern African climate and tropical Atlantic SST South American monsoon, Benguela

More information

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: August 2009

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: August 2009 North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Nicholas.Bond@noaa.gov Last updated: August 2009 Summary. The North Pacific atmosphere-ocean system from fall 2008 through

More information

Seasonal Climate Watch January to May 2016

Seasonal Climate Watch January to May 2016 Seasonal Climate Watch January to May 2016 Date: Dec 17, 2015 1. Advisory Most models are showing the continuation of a strong El-Niño episode towards the latesummer season with the expectation to start

More information

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP July 26, 2004

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP July 26, 2004 ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP July 26, 2004 Outline Overview Recent Evolution and Current Conditions Oceanic NiZo Index

More information

TROPICAL PACIFIC AND SOUTH ATLANTIC EFFECTS ON RAINFALL VARIABILITY OVER NORTHEAST BRAZIL

TROPICAL PACIFIC AND SOUTH ATLANTIC EFFECTS ON RAINFALL VARIABILITY OVER NORTHEAST BRAZIL INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 26: 1895 1912 (2006) Published online 3 May 2006 in Wiley InterScience (www.interscience.wiley.com).1341 TROPICAL PACIFIC AND SOUTH ATLANTIC EFFECTS

More information

Malawi. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1

Malawi. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1 UNDP Climate Change Country Profiles Malawi C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO

The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 1, 25 30 The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO HU Kai-Ming and HUANG Gang State Key

More information

WATER VAPOR FLUXES OVER EQUATORIAL CENTRAL AFRICA

WATER VAPOR FLUXES OVER EQUATORIAL CENTRAL AFRICA WATER VAPOR FLUXES OVER EQUATORIAL CENTRAL AFRICA INTRODUCTION A good understanding of the causes of climate variability depend, to the large extend, on the precise knowledge of the functioning of the

More information

Will a warmer world change Queensland s rainfall?

Will a warmer world change Queensland s rainfall? Will a warmer world change Queensland s rainfall? Nicholas P. Klingaman National Centre for Atmospheric Science-Climate Walker Institute for Climate System Research University of Reading The Walker-QCCCE

More information

lecture 10 El Niño and the Southern Oscillation (ENSO) Part I sea surface height anomalies as measured by satellite altimetry

lecture 10 El Niño and the Southern Oscillation (ENSO) Part I sea surface height anomalies as measured by satellite altimetry lecture 10 El Niño and the Southern Oscillation (ENSO) Part I sea surface height anomalies as measured by satellite altimetry SPATIAL STRUCTURE OF ENSO In 1899, the Indian monsoon failed, leading to drought

More information

Winter Steve Todd Meteorologist In Charge National Weather Service Portland, OR

Winter Steve Todd Meteorologist In Charge National Weather Service Portland, OR Winter 07-08 Steve Todd Meteorologist In Charge National Weather Service Portland, OR Overview Winter Weather Outlook How to stay informed Winter Outlook LaNina conditions are present across the tropical

More information

Zambia. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G.

Zambia. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G. UNDP Climate Change Country Profiles Zambia C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

KUALA LUMPUR MONSOON ACTIVITY CENT

KUALA LUMPUR MONSOON ACTIVITY CENT T KUALA LUMPUR MONSOON ACTIVITY CENT 2 ALAYSIAN METEOROLOGICAL http://www.met.gov.my DEPARTMENT MINISTRY OF SCIENCE. TECHNOLOGY AND INNOVATIO Introduction Atmospheric and oceanic conditions over the tropical

More information

Inter ENSO variability and its influence over the South American monsoon system

Inter ENSO variability and its influence over the South American monsoon system Inter ENSO variability and its influence over the South American monsoon system A. R. M. Drumond, T. Ambrizzi To cite this version: A. R. M. Drumond, T. Ambrizzi. Inter ENSO variability and its influence

More information

CLIMATE SIMULATION AND ASSESSMENT OF PREDICTABILITY OF RAINFALL IN THE SOUTHEASTERN SOUTH AMERICA REGION USING THE CPTEC/COLA ATMOSPHERIC MODEL

CLIMATE SIMULATION AND ASSESSMENT OF PREDICTABILITY OF RAINFALL IN THE SOUTHEASTERN SOUTH AMERICA REGION USING THE CPTEC/COLA ATMOSPHERIC MODEL CLIMATE SIMULATION AND ASSESSMENT OF PREDICTABILITY OF RAINFALL IN THE SOUTHEASTERN SOUTH AMERICA REGION USING THE CPTEC/COLA ATMOSPHERIC MODEL JOSÉ A. MARENGO, IRACEMA F.A.CAVALCANTI, GILVAN SAMPAIO,

More information

The Relationships between Tropical Pacific and Atlantic SST and Northeast Brazil Monthly Precipitation

The Relationships between Tropical Pacific and Atlantic SST and Northeast Brazil Monthly Precipitation APRIL 1998 UVO ET AL. 551 The Relationships between Tropical Pacific and Atlantic SST and Northeast Brazil Monthly Precipitation CINTIA BERTACCHI UVO Lund University, Department of Water Resources Engineering,

More information

Forced and internal variability of tropical cyclone track density in the western North Pacific

Forced and internal variability of tropical cyclone track density in the western North Pacific Forced and internal variability of tropical cyclone track density in the western North Pacific Wei Mei 1 Shang-Ping Xie 1, Ming Zhao 2 & Yuqing Wang 3 Climate Variability and Change and Paleoclimate Working

More information

Observed ENSO teleconnections with the South American monsoon system

Observed ENSO teleconnections with the South American monsoon system ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 11: 7 12 (2010) Published online 8 January 2010 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/asl.245 Observed ENSO teleconnections with the

More information

the 2 past three decades

the 2 past three decades SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2840 Atlantic-induced 1 pan-tropical climate change over the 2 past three decades 3 4 5 6 7 8 9 10 POP simulation forced by the Atlantic-induced atmospheric

More information

Decadal variability of northern northeast Brazil rainfall and its relation to tropical sea surface temperature and global sea level pressure anomalies

Decadal variability of northern northeast Brazil rainfall and its relation to tropical sea surface temperature and global sea level pressure anomalies JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi:10.1029/2004jc002429, 2004 Decadal variability of northern northeast Brazil rainfall and its relation to tropical sea surface temperature and global sea

More information

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: September 2008

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: September 2008 North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Nicholas.Bond@noaa.gov Last updated: September 2008 Summary. The North Pacific atmosphere-ocean system from fall 2007

More information

Climate Forecast Applications Network (CFAN)

Climate Forecast Applications Network (CFAN) Forecast of 2018 Atlantic Hurricane Activity April 5, 2018 Summary CFAN s inaugural April seasonal forecast for Atlantic tropical cyclone activity is based on systematic interactions among ENSO, stratospheric

More information

How Patterns Far Away Can Influence Our Weather. Mark Shafer University of Oklahoma Norman, OK

How Patterns Far Away Can Influence Our Weather. Mark Shafer University of Oklahoma Norman, OK Teleconnections How Patterns Far Away Can Influence Our Weather Mark Shafer University of Oklahoma Norman, OK Teleconnections Connectedness of large-scale weather patterns across the world If you poke

More information

Benguela Niño/Niña events and their connection with southern Africa rainfall have been documented before. They involve a weakening of the trade winds

Benguela Niño/Niña events and their connection with southern Africa rainfall have been documented before. They involve a weakening of the trade winds Benguela Niño/Niña events and their connection with southern Africa rainfall have been documented before. They involve a weakening of the trade winds in the equatorial western Atlantic in the early monsoon,

More information

Impacts of modes of climate variability, monsoons, ENSO, annular modes

Impacts of modes of climate variability, monsoons, ENSO, annular modes Impacts of modes of climate variability, monsoons, ENSO, annular modes Iracema Fonseca de Albuquerque Cavalcanti National Institute for Space Research INPE Modes of variability- preferred patterns of variability.

More information

Mozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1

Mozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1 UNDP Climate Change Country Profiles Mozambique C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2.Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

The El Niño Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences

The El Niño Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences 15 JANUARY 2003 GRIMM 263 The El Niño Impact on the Summer Monsoon in Brazil: Regional Processes versus Remote Influences ALICE M. GRIMM Department of Physics, Federal University of Paraná, Curitiba, Brazil

More information

El Niño Seasonal Weather Impacts from the OLR Event Perspective

El Niño Seasonal Weather Impacts from the OLR Event Perspective Science and Technology Infusion Climate Bulletin NOAA s National Weather Service 41 st NOAA Annual Climate Diagnostics and Prediction Workshop Orono, ME, 3-6 October 2016 2015-16 El Niño Seasonal Weather

More information

Seasonal Climate Outlook for South Asia (June to September) Issued in May 2014

Seasonal Climate Outlook for South Asia (June to September) Issued in May 2014 Ministry of Earth Sciences Earth System Science Organization India Meteorological Department WMO Regional Climate Centre (Demonstration Phase) Pune, India Seasonal Climate Outlook for South Asia (June

More information

lecture 11 El Niño/Southern Oscillation (ENSO) Part II

lecture 11 El Niño/Southern Oscillation (ENSO) Part II lecture 11 El Niño/Southern Oscillation (ENSO) Part II SYSTEM MEMORY: OCEANIC WAVE PROPAGATION ASYMMETRY BETWEEN THE ATMOSPHERE AND OCEAN The atmosphere and ocean are not symmetrical in their responses

More information

MDA WEATHER SERVICES AG WEATHER OUTLOOK. Kyle Tapley-Senior Agricultural Meteorologist May 22, 2014 Chicago, IL

MDA WEATHER SERVICES AG WEATHER OUTLOOK. Kyle Tapley-Senior Agricultural Meteorologist May 22, 2014 Chicago, IL MDA WEATHER SERVICES AG WEATHER OUTLOOK Kyle Tapley-Senior Agricultural Meteorologist May 22, 2014 Chicago, IL GLOBAL GRAIN NORTH AMERICA 2014 Agenda Spring Recap North America Forecast El Niño Discussion

More information

Why Has the Land Memory Changed?

Why Has the Land Memory Changed? 3236 JOURNAL OF CLIMATE VOLUME 17 Why Has the Land Memory Changed? QI HU ANDSONG FENG Climate and Bio-Atmospheric Sciences Group, School of Natural Resource Sciences, University of Nebraska at Lincoln,

More information

SHORT COMMUNICATION EXPLORING THE RELATIONSHIP BETWEEN THE NORTH ATLANTIC OSCILLATION AND RAINFALL PATTERNS IN BARBADOS

SHORT COMMUNICATION EXPLORING THE RELATIONSHIP BETWEEN THE NORTH ATLANTIC OSCILLATION AND RAINFALL PATTERNS IN BARBADOS INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 6: 89 87 (6) Published online in Wiley InterScience (www.interscience.wiley.com). DOI:./joc. SHORT COMMUNICATION EXPLORING THE RELATIONSHIP BETWEEN

More information

ENSO-RELATED RAINFALL ANOMALIES IN SOUTH AMERICA AND ASSOCIATED CIRCULATION FEATURES DURING WARM AND COLD PACIFIC DECADAL OSCILLATION REGIMES

ENSO-RELATED RAINFALL ANOMALIES IN SOUTH AMERICA AND ASSOCIATED CIRCULATION FEATURES DURING WARM AND COLD PACIFIC DECADAL OSCILLATION REGIMES INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 25: 217 23 (25) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 1.12/joc.1222 ENSO-RELATED RAINFALL ANOMALIES IN SOUTH AMERICA

More information

NOTES AND CORRESPONDENCE. El Niño Southern Oscillation and North Atlantic Oscillation Control of Climate in Puerto Rico

NOTES AND CORRESPONDENCE. El Niño Southern Oscillation and North Atlantic Oscillation Control of Climate in Puerto Rico 2713 NOTES AND CORRESPONDENCE El Niño Southern Oscillation and North Atlantic Oscillation Control of Climate in Puerto Rico BJÖRN A. MALMGREN Department of Earth Sciences, University of Göteborg, Goteborg,

More information

Interannual Variability of the Rainy Season and Rainfall in the Brazilian Amazon Basin

Interannual Variability of the Rainy Season and Rainfall in the Brazilian Amazon Basin 4308 JOURNAL OF CLIMATE Interannual Variability of the Rainy Season and Rainfall in the Brazilian Amazon Basin BRANT LIEBMANN NOAA CIRES Climate Diagnostics Center, Boulder, Colorado JOSÉ A. MARENGO Centro

More information

Effect of anomalous warming in the central Pacific on the Australian monsoon

Effect of anomalous warming in the central Pacific on the Australian monsoon Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L12704, doi:10.1029/2009gl038416, 2009 Effect of anomalous warming in the central Pacific on the Australian monsoon A. S. Taschetto, 1

More information

The Planetary Circulation System

The Planetary Circulation System 12 The Planetary Circulation System Learning Goals After studying this chapter, students should be able to: 1. describe and account for the global patterns of pressure, wind patterns and ocean currents

More information

Antarctic Sea Ice: Mean state and variability in CCSM control run. Laura Landrum, Marika Holland, Dave Schneider, Elizabeth Hunke

Antarctic Sea Ice: Mean state and variability in CCSM control run. Laura Landrum, Marika Holland, Dave Schneider, Elizabeth Hunke Antarctic Sea Ice: Mean state and variability in CCSM4 1850 control run Laura Landrum, Marika Holland, Dave Schneider, Elizabeth Hunke Overview Model years and variables Mean state and some comparisons

More information

South & South East Asian Region:

South & South East Asian Region: Issued: 15 th December 2017 Valid Period: January June 2018 South & South East Asian Region: Indonesia Tobacco Regions 1 A] Current conditions: 1] El Niño-Southern Oscillation (ENSO) ENSO Alert System

More information

Wind: Global Systems Chapter 10

Wind: Global Systems Chapter 10 Wind: Global Systems Chapter 10 General Circulation of the Atmosphere General circulation of the atmosphere describes average wind patterns and is useful for understanding climate Over the earth, incoming

More information

East-west SST contrast over the tropical oceans and the post El Niño western North Pacific summer monsoon

East-west SST contrast over the tropical oceans and the post El Niño western North Pacific summer monsoon GEOPHYSICAL RESEARCH LETTERS, VOL. 32, L15706, doi:10.1029/2005gl023010, 2005 East-west SST contrast over the tropical oceans and the post El Niño western North Pacific summer monsoon Toru Terao Faculty

More information

TROPICAL-EXTRATROPICAL INTERACTIONS

TROPICAL-EXTRATROPICAL INTERACTIONS Notes of the tutorial lectures for the Natural Sciences part by Alice Grimm Fourth lecture TROPICAL-EXTRATROPICAL INTERACTIONS Anomalous tropical SST Anomalous convection Anomalous latent heat source Anomalous

More information

Rainfall variability over the Indochina peninsula during the Boreal Winter, Part I: Preliminary data analysis

Rainfall variability over the Indochina peninsula during the Boreal Winter, Part I: Preliminary data analysis Rainfall variability over the Indochina peninsula during the Boreal Winter, Part I: Preliminary data analysis Sirapong Sooktawee*, sirapong@deqp.go.th; Atsamon Limsakul, atsamon@deqp.go.th, Environmental

More information

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (February 2018)

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (February 2018) UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (February 2018) 1. Review of Regional Weather Conditions for January 2018 1.1 The prevailing Northeast monsoon conditions over Southeast Asia strengthened in January

More information

St Lucia. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. Precipitation

St Lucia. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. Precipitation UNDP Climate Change Country Profiles St Lucia C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

The ENSO s Effect on Eastern China Rainfall in the Following Early Summer

The ENSO s Effect on Eastern China Rainfall in the Following Early Summer ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 26, NO. 2, 2009, 333 342 The ENSO s Effect on Eastern China Rainfall in the Following Early Summer LIN Zhongda ( ) andluriyu( F ) Center for Monsoon System Research,

More information

Interannual Variability of the Coupled Tropical Pacific Ocean Atmosphere System Associated with the El Niño Southern Oscillation

Interannual Variability of the Coupled Tropical Pacific Ocean Atmosphere System Associated with the El Niño Southern Oscillation 1312 JOURNAL OF CLIMATE Interannual Variability of the Coupled Tropical Pacific Ocean Atmosphere System Associated with the El Niño Southern Oscillation RONG-HUA ZHANG AND SYDNEY LEVITUS Ocean Climate

More information

Cuba. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G.

Cuba. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G. UNDP Climate Change Country Profiles Cuba C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

MPACT OF EL-NINO ON SUMMER MONSOON RAINFALL OF PAKISTAN

MPACT OF EL-NINO ON SUMMER MONSOON RAINFALL OF PAKISTAN MPACT OF EL-NINO ON SUMMER MONSOON RAINFALL OF PAKISTAN Abdul Rashid 1 Abstract: El-Nino is the dominant mod of inter- annual climate variability on a planetary scale. Its impact is associated worldwide

More information

Delayed Response of the Extratropical Northern Atmosphere to ENSO: A Revisit *

Delayed Response of the Extratropical Northern Atmosphere to ENSO: A Revisit * Delayed Response of the Extratropical Northern Atmosphere to ENSO: A Revisit * Ruping Mo Pacific Storm Prediction Centre, Environment Canada, Vancouver, BC, Canada Corresponding author s address: Ruping

More information

Antigua and Barbuda. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature

Antigua and Barbuda. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature UNDP Climate Change Country Profiles Antigua and Barbuda C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research

More information

EL NIÑO MODOKI IMPACTS ON AUSTRALIAN RAINFALL

EL NIÑO MODOKI IMPACTS ON AUSTRALIAN RAINFALL EL NIÑO MODOKI IMPACTS ON AUSTRALIAN RAINFALL Andréa S. Taschetto*, Alexander Sen Gupta, Caroline C. Ummenhofer and Matthew H. England Climate Change Research Centre (CCRC), University of New South Wales,

More information

1. INTRODUCTION. Copyright 2004 Royal Meteorological Society

1. INTRODUCTION. Copyright 2004 Royal Meteorological Society INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 24: 415 435 (2004) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1000 THE INFLUENCE OF THE TROPICAL AND SUBTROPICAL

More information

New Zealand Climate Update No 223, January 2018 Current climate December 2017

New Zealand Climate Update No 223, January 2018 Current climate December 2017 New Zealand Climate Update No 223, January 2018 Current climate December 2017 December 2017 was characterised by higher than normal sea level pressure over New Zealand and the surrounding seas. This pressure

More information

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017)

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017) UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017) 1. Review of Regional Weather Conditions for November 2017 1.1 In November 2017, Southeast Asia experienced inter-monsoon conditions in the first

More information

El Niño, South American Monsoon, and Atlantic Niño links as detected by a. TOPEX/Jason Observations

El Niño, South American Monsoon, and Atlantic Niño links as detected by a. TOPEX/Jason Observations El Niño, South American Monsoon, and Atlantic Niño links as detected by a decade of QuikSCAT, TRMM and TOPEX/Jason Observations Rong Fu 1, Lei Huang 1, Hui Wang 2, Paola Arias 1 1 Jackson School of Geosciences,

More information

2013 ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Cat Response

2013 ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Cat Response 2013 ATLANTIC HURRICANE SEASON OUTLOOK June 2013 - RMS Cat Response Season Outlook At the start of the 2013 Atlantic hurricane season, which officially runs from June 1 to November 30, seasonal forecasts

More information

J5.3 Seasonal Variability over Southeast Brazil related to frontal systems behaviour in a climate simulation with the AGCM CPTEC/COLA.

J5.3 Seasonal Variability over Southeast Brazil related to frontal systems behaviour in a climate simulation with the AGCM CPTEC/COLA. J5.3 Seasonal Variability over Southeast Brazil related to frontal systems behaviour in a climate simulation with the AGCM CPTEC/COLA. Iracema F.A.Cavalcanti and Luiz Henrique R. Coura Silva Centro de

More information

THE INFLUENCE OF CLIMATE TELECONNECTIONS ON WINTER TEMPERATURES IN WESTERN NEW YORK INTRODUCTION

THE INFLUENCE OF CLIMATE TELECONNECTIONS ON WINTER TEMPERATURES IN WESTERN NEW YORK INTRODUCTION Middle States Geographer, 2014, 47: 60-67 THE INFLUENCE OF CLIMATE TELECONNECTIONS ON WINTER TEMPERATURES IN WESTERN NEW YORK Frederick J. Bloom and Stephen J. Vermette Department of Geography and Planning

More information

On the Relationship between Western Maritime Continent Monsoon Rainfall and ENSO during Northern Winter

On the Relationship between Western Maritime Continent Monsoon Rainfall and ENSO during Northern Winter 1FEBRUARY 2004 CHANG ET AL. 665 On the Relationship between Western Maritime Continent Monsoon Rainfall and ENSO during Northern Winter C.-P. CHANG Department of Meteorology, Naval Postgraduate School,

More information

CHAPTER 1: INTRODUCTION

CHAPTER 1: INTRODUCTION CHAPTER 1: INTRODUCTION There is now unequivocal evidence from direct observations of a warming of the climate system (IPCC, 2007). Despite remaining uncertainties, it is now clear that the upward trend

More information

Grenada. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. Precipitation

Grenada. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. Precipitation UNDP Climate Change Country Profiles C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk

More information

Investigate the influence of the Amazon rainfall on westerly wind anomalies and the 2002 Atlantic Nino using QuikScat, Altimeter and TRMM data

Investigate the influence of the Amazon rainfall on westerly wind anomalies and the 2002 Atlantic Nino using QuikScat, Altimeter and TRMM data Investigate the influence of the Amazon rainfall on westerly wind anomalies and the 2002 Atlantic Nino using QuikScat, Altimeter and TRMM data Rong Fu 1, Mike Young 1, Hui Wang 2, Weiqing Han 3 1 School

More information

The North Atlantic Oscillation: Climatic Significance and Environmental Impact

The North Atlantic Oscillation: Climatic Significance and Environmental Impact 1 The North Atlantic Oscillation: Climatic Significance and Environmental Impact James W. Hurrell National Center for Atmospheric Research Climate and Global Dynamics Division, Climate Analysis Section

More information

CPTEC and NCEP Model Forecast Drift and South America during the Southern Hemisphere Summer

CPTEC and NCEP Model Forecast Drift and South America during the Southern Hemisphere Summer CPTEC and NCEP Model Forecast Drift and South America during the Southern Hemisphere Summer José Antonio Aravéquia 1 Pedro L. Silva Dias 2 (1) Center for Weather Forecasting and Climate Research National

More information

The Changing Impact of El Niño on US Winter Temperatures

The Changing Impact of El Niño on US Winter Temperatures 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 The Changing Impact of El Niño on US Winter Temperatures Jin-Yi Yu *1, Yuhao Zou

More information

Charles Jones ICESS University of California, Santa Barbara CA Outline

Charles Jones ICESS University of California, Santa Barbara CA Outline The Influence of Tropical Variations on Wintertime Precipitation in California: Pineapple express, Extreme rainfall Events and Long-range Statistical Forecasts Charles Jones ICESS University of California,

More information

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (May 2017)

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (May 2017) UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (May 2017) 1. Review of Regional Weather Conditions in April 2017 1.1 Inter monsoon conditions, characterised by afternoon showers and winds that are generally

More information

ENSO and April SAT in MSA. This link is critical for our regression analysis where ENSO and

ENSO and April SAT in MSA. This link is critical for our regression analysis where ENSO and Supplementary Discussion The Link between El Niño and MSA April SATs: Our study finds a robust relationship between ENSO and April SAT in MSA. This link is critical for our regression analysis where ENSO

More information

South & South East Asian Region:

South & South East Asian Region: Issued: 10 th November 2017 Valid Period: December 2017 May 2018 South & South East Asian Region: Indonesia Tobacco Regions 1 A] Current conditions: 1] El Niño-Southern Oscillation (ENSO) ENSO Alert System

More information

The Influence of Intraseasonal Variations on Medium- to Extended-Range Weather Forecasts over South America

The Influence of Intraseasonal Variations on Medium- to Extended-Range Weather Forecasts over South America 486 MONTHLY WEATHER REVIEW The Influence of Intraseasonal Variations on Medium- to Extended-Range Weather Forecasts over South America CHARLES JONES Institute for Computational Earth System Science (ICESS),

More information

Why the Atlantic was surprisingly quiet in 2013

Why the Atlantic was surprisingly quiet in 2013 1 Why the Atlantic was surprisingly quiet in 2013 by William Gray and Phil Klotzbach Preliminary Draft - March 2014 (Final draft by early June) ABSTRACT This paper discusses the causes of the unusual dearth

More information

Analysis Links Pacific Decadal Variability to Drought and Streamflow in United States

Analysis Links Pacific Decadal Variability to Drought and Streamflow in United States Page 1 of 8 Vol. 80, No. 51, December 21, 1999 Analysis Links Pacific Decadal Variability to Drought and Streamflow in United States Sumant Nigam, Mathew Barlow, and Ernesto H. Berbery For more information,

More information

Different impacts of Northern, Tropical and Southern volcanic eruptions on the tropical Pacific SST in the last millennium

Different impacts of Northern, Tropical and Southern volcanic eruptions on the tropical Pacific SST in the last millennium Different impacts of Northern, Tropical and Southern volcanic eruptions on the tropical Pacific SST in the last millennium Meng Zuo, Wenmin Man, Tianjun Zhou Email: zuomeng@lasg.iap.ac.cn Sixth WMO International

More information

A study of the impacts of late spring Tibetan Plateau snow cover on Chinese early autumn precipitation

A study of the impacts of late spring Tibetan Plateau snow cover on Chinese early autumn precipitation N U I S T Nanjing University of Information Science & Technology A study of the impacts of late spring Tibetan Plateau snow cover on Chinese early autumn precipitation JIANG Zhihong,HUO Fei,LIU Zhengyu

More information

Thai Meteorological Department, Ministry of Digital Economy and Society

Thai Meteorological Department, Ministry of Digital Economy and Society Thai Meteorological Department, Ministry of Digital Economy and Society Three-month Climate Outlook For November 2017 January 2018 Issued on 31 October 2017 -----------------------------------------------------------------------------------------------------------------------------

More information

Name: Date: Hour: Comparing the Effects of El Nino & La Nina on the Midwest (E4.2c)

Name: Date: Hour: Comparing the Effects of El Nino & La Nina on the Midwest (E4.2c) Purpose: Comparing the Effects of El Nino & La Nina on the Midwest (E4.2c) To compare the effects of El Nino and La Nina on the Midwest United States. Background Knowledge: The El Nino-Southern Oscillation

More information

CHAPTER 9 ATMOSPHERE S PLANETARY CIRCULATION MULTIPLE CHOICE QUESTIONS

CHAPTER 9 ATMOSPHERE S PLANETARY CIRCULATION MULTIPLE CHOICE QUESTIONS CHAPTER 9 ATMOSPHERE S PLANETARY CIRCULATION MULTIPLE CHOICE QUESTIONS 1. Viewed from above in the Northern Hemisphere, surface winds about a subtropical high blow a. clockwise and inward. b. counterclockwise.

More information

Interannual variations of early summer monsoon rainfall over South China under different PDO backgrounds

Interannual variations of early summer monsoon rainfall over South China under different PDO backgrounds INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 31: 847 862 (2011) Published online 25 March 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.2129 Interannual variations of

More information

Components of precipitation and temperature anomalies and change associated with modes of the Southern Hemisphere

Components of precipitation and temperature anomalies and change associated with modes of the Southern Hemisphere INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 29: 809 826 (2009) Published online 4 December 2008 in Wiley InterScience (www.interscience.wiley.com).1772 Components of precipitation and temperature

More information

NOTES AND CORRESPONDENCE. Time and Space Variability of Rainfall and Surface Circulation in the Northeast Brazil-Tropical Atlantic Sector

NOTES AND CORRESPONDENCE. Time and Space Variability of Rainfall and Surface Circulation in the Northeast Brazil-Tropical Atlantic Sector April 1984 P.-S. Chu 363 NOTES AND CORRESPONDENCE Time and Space Variability of Rainfall and Surface Circulation in the Northeast Brazil-Tropical Atlantic Sector Pao-Shin Chu* Department of Meteorology,

More information

ENSO effects on mean temperature in Turkey

ENSO effects on mean temperature in Turkey Hydrology Days 007 ENSO effects on mean temperature in Turkey Ali hsan Martı Selcuk University, Civil Engineering Department, Hydraulic Division, 4035, Campus, Konya, Turkey Ercan Kahya 1 Istanbul Technical

More information

Chapter 1 Climate in 2016

Chapter 1 Climate in 2016 Chapter 1 Climate in 2016 1.1 Global climate summary Extremely high temperatures were frequently observed in many regions of the world, and in particular continued for most of the year in various places

More information

Climate Outlook for December 2015 May 2016

Climate Outlook for December 2015 May 2016 The APEC CLIMATE CENTER Climate Outlook for December 2015 May 2016 BUSAN, 25 November 2015 Synthesis of the latest model forecasts for December 2015 to May 2016 (DJFMAM) at the APEC Climate Center (APCC),

More information

An Introduction to Coupled Models of the Atmosphere Ocean System

An Introduction to Coupled Models of the Atmosphere Ocean System An Introduction to Coupled Models of the Atmosphere Ocean System Jonathon S. Wright jswright@tsinghua.edu.cn Atmosphere Ocean Coupling 1. Important to climate on a wide range of time scales Diurnal to

More information

Long-term changes in total and extreme precipitation over China and the United States and their links to oceanic atmospheric features

Long-term changes in total and extreme precipitation over China and the United States and their links to oceanic atmospheric features INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: 286 302 (2014) Published online 27 April 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3685 Long-term changes in total

More information

Eurasian Snow Cover Variability and Links with Stratosphere-Troposphere Coupling and Their Potential Use in Seasonal to Decadal Climate Predictions

Eurasian Snow Cover Variability and Links with Stratosphere-Troposphere Coupling and Their Potential Use in Seasonal to Decadal Climate Predictions US National Oceanic and Atmospheric Administration Climate Test Bed Joint Seminar Series NCEP, Camp Springs, Maryland, 22 June 2011 Eurasian Snow Cover Variability and Links with Stratosphere-Troposphere

More information

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017)

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017) UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017) 1. Review of Regional Weather Conditions in August 2017 1.1 Southwest Monsoon conditions continued to prevail in the region in August 2017. The

More information

1.4 USEFULNESS OF RECENT NOAA/CPC SEASONAL TEMPERATURE FORECASTS

1.4 USEFULNESS OF RECENT NOAA/CPC SEASONAL TEMPERATURE FORECASTS 1.4 USEFULNESS OF RECENT NOAA/CPC SEASONAL TEMPERATURE FORECASTS Jeanne M. Schneider* and Jurgen D. Garbrecht USDA/ARS Grazinglands Research Laboratory, El Reno, Oklahoma 1. INTRODUCTION Operational climate

More information