Exploring extreme-rainfall forcings over Tucumán (Argentina) in the last 108 years and an extension to Subtropical South America

doi:10.21203/rs.3.rs-4178594/v1

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Exploring extreme-rainfall forcings over Tucumán (Argentina) in the last 108 years and an extension to Subtropical South America

https://doi.org/10.21203/rs.3.rs-4178594/v1

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In this work the maximum daily rainfall (Rx1) is analyzed based on the longest and highest-quality daily precipitation record available in Northwestern Argentina (NWA). Rx1 is a proxy of the daily-rainfall intensity and, thus, the analysis is useful due to their relation to the flood events. The selected series has a length of 108 years and corresponds to San Miguel de Tucumán (TUC, 26.8°S, 65.2°W), a city located in Subtropical South America (SSA). The methodology proposed was detecting the period of minimum p-value (PmPV) in the linear correlation coefficients to determine closer relationships between Rx1 and large-scale climate forcings. Results show a transition from a stronger Rx1-El Niño Southern Oscillation (ENSO) association in 1945–1974 to a tighter Rx1-Southern Annular Mode (SAM) relationship in 1974–2007. The PmPV of ENSO indices aligns with the cold PDO phase, while SAM's PmPV coincides with a warm PDO phase, highlighting their significant impact on Rx1 relationships. On the other hand, using HadEX3 and ERA5 data, it was shown that the results are consistent over part of SSA respecting Rx1 and atmospheric variables behavior. Analysis reveals a shifting Rx1-ENSO relationship over NWA in contrast with the observed in eastern Argentina. Also, a positive association Rx1-SAM over NWA, western Paraguay, eastern Bolivia and central Brazil during the PDO positive phase was found. We show that changes in Rx1 arise in response to changes in ENSO and SAM teleconnections driven by PDO. Thus, this study underscores the role of global variability in driving regional extreme precipitation.

extreme rainfall

ENSO

SAM

AMO

PDO

Rainfall is a climatic variable widely studied due to its major socio-economics implications. It is closely related to one of the key risks highlighted by O'Neill et al. (2022) in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), namely the risk to water security, encompassing a range of water-related aspects including floods and droughts. These events driven by regional substantial reductions or enhancements in precipitation are associated with water scarcity and water-related disasters that have the potential to become severe.

The maximum-daily rainfall (Rx1) is analyzed in this work, which is usually used as an indicator of rainfall intensity (de la Casa et al., 2019). The occurrence of local flooding or flash flooding events happen when intense rainfall takes place over a small area in a short period of time (Avashia and Garg, 2020), for which Rx1 can be a good proxy. This study focuses on San Miguel de Tucumán (TUC) located at 26.8°S-65.2°W, which is the capital of the Tucumán Province, Argentina. This city is the most populated of northern Argentina (Ministerio del interior, Argentina, data available on: https://www.argentina.gob.ar/sites/default/files/poblacion_urbana_dnp.pptx_.pdf), located in subtropical South America (SSA), east of the Andes Mountain Range. The flood hazard in this city is significant due to the presence of lowlands and urban stream channels with poor maintenance (Fernández and Lutz, 2010). In fact, hundreds of people have been evacuated in the past decades due to flooding. As examples, in recent years some episodes of floods occurred: January 24, 2018, with 350 evacuated people and 1 person dead (https://www.diariopopular.com.ar/general/intenso-temporal-tucuman-un-electrocutado-y-cientos-evacuados-n339456) or March 5, 2022, with 120 evacuated people (https://www.clarin.com/sociedad/tucuman-sufrio-tormenta-feroz-calles-convertidas-rios-120-evacuados_0_LYvgbzdzGR.html). In all cases, significant material damages were reported, especially in the poor sectors of the population, which have precarious houses in banks of canals, rivers and other floodable sectors. The problematic is repeated in all Tucumán province. For example, the February 24, 2020, Latin America & The Caribbean Weekly Situation Update Report of the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) (available at https://www.unocha.org/publications/report/world/latinoam-rica-el-caribe-resumen-de-situaci-n-semanal-17-23-febrero-2020-al-24-de), reports that due to flooding in Tucumán after extreme precipitation events, around 100 families were evacuated. In particular, the town of Lamadrid, in the southeastern part of Tucumán province, has experienced recurring floods in recent decades due to extreme rainfalls, which cause the overflowing of the Marapa River. All of these episodes ended, aside of significant material damage, in mass evacuation of most of Lamadrid population which has ~ 4,000 residents as of 2022 (https://www.municipalidad-argentina.com.ar/municipalidad-lamadrid-t.html). Thus, it is essential to comprehend the variability of heavy rainfall events to improve their prediction accuracy and minimize their impact.

Our study focuses to improve the knowledge on interannual to multidecadal scales of rainfall variability and their relation to large-scale variability in the city of study. The possibility of non-stationary associations between rainfall and large-scale modes of variability is explored using a century-long daily-rainfall record from TUC. However, due to the implications of the rainfall variability and the advantage of having this long record, we take the opportunity to determine if the obtained results can be in part generalized to the Subtropical South America (SSA) region. As well exists woks that shows the existence of longer rainfall records than TUC (e.g., Saurral et al., 2017; Ferrero y Villalba, 2019; Hurtado et al., 2020), these works are based on monthly records whose daily records are not available (e.g., for La Quiaca exist monthly records beginning on 1903, but daily data is available since 1957), or the existent records have low quality (e.g., daily records for Salta city beginning on 1930, but have important inhomogeneities according to Hurtado et al., 2020). Thus, TUC data is the longest and with higher quality daily record available in NWA.

The study region is located in Northwestern Argentina (NWA), where the rainfall pattern is dominated by the South-American Monsoon (Marengo et al., 2012), which is substantially affected by the temperature anomalies in the Tropical Pacific Ocean (Garreaud and Aceituno, 2001; Mantua and Hare, 2002; Ferrero and Villalba, 2019). The Tropical Pacific Ocean has two dominant modes of variability: El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). ENSO is a coupled atmosphere-ocean phenomenon (Wang et al., 2017; Cai et al., 2020) with a periodicity of 2 to 7 years consisting of an alternation of cold (La Niña, ENSO–) and warm (El Niño, ENSO+) temperature anomalies on the central equatorial Pacific (Wang et al., 2017; Krepper and Garcia, 2004). Long-term variations in ENSO are also present (Hurtado et al., 2020), but they have received less attention in the literature. On the other hand, PDO presents multidecadal fluctuations (Newman et al., 2016; Wang et al., 2017), with relatively cold conditions of PDO observed in 1910–1925 and 1947–1976 periods (negative PDO phase, PDO–), and relatively warm conditions in 1926–1945 and 1977–1998 (positive PDO phase, PDO+) on the past century (Wang et al., 2017).

Previous studies on rainfall variability in Argentina have mainly focused on the association with ENSO. In a recent review, Cai et al. (2020) analyzed the impacts of ENSO over South America. During ENSO + years, a pattern of stationary Rossby wave-trains promotes heavy rainfall and flooding across southern Brazil, Uruguay, and northern Argentina. Particularly, in northern Argentina, the relationship between rainfall and ENSO events is strong from September to January (Hurtado and Agosta, 2021; Montecinos et al., 2000). On the other hand, few studies have examined the low-frequency relationship between ENSO and rainfall. In a recent study, Hurtado et al. (2020) identified jumps in the warm-season total rainfall of Argentina in the mid 1950s, 1976/77 and early 1980s by a multi-breakpoint detection analysis, although they do not analyze TUC series. They associated the mid-1950s and early 1980s breakpoints with changes in the ENSO flavors, while the 1976/77 case was linked to a PDO phase shift, from negative to positive. Most of the existing research on the ENSO-rainfall relationship in this region has focused on monthly/seasonal total rainfall over central and eastern Argentina, where the linkage is stronger and sustained over time. Thus, there is then a gap in our understanding of the relationship between ENSO and rainfall in the NWA region that this study aims to address.

In addition, TUC daily rainfall data is not publicly available and, in consequence, has been less studied than other series in the literature. Scardilli et al. (2017) analyzes total rainfall, wet days and wet spells for TUC from the daily record and identifies interdecadal variations resulting in a positive general trend, but without analyzing the causes. They also detect jumps or breakpoints between 1954 and 1956, in agreement with findings for annual total rainfall in northern Argentina (Minetti and Vargas, 1998; Hurtado et al., 2020). In this sense, for NWA Ferrero and Villalba (2019) find positive jumps in centennial monthly records between 1956–1960 and 1973–1979, while they find negative jumps between 2008–2013. Comparing these detected breakpoints with results of Hurtado et al. (2020), we can suggest that they are linked to phase shifts in ENSO and PDO.

Beyond this, the influence of ENSO on the interannual variability of rainfall in the NWA region is controversial. Rivera and Penalba (2015), using the Standardized Precipitation Index for the 1961–2008 period, concluded that NWA does not show any relationship with ENSO. In contrast, Marwan et al. (2003) showed for the 1979–1999 period that the precipitation anomalies for ENSO events have a dipolar structure in NWA. This dipolar structure implies positive rainfall anomalies over the east and negative anomalies on the west of NWA in ENSO + events, and vice versa in ENSO–. Observing the described pattern (Figs. 1 and 2 of Marwan et al., 2003), TUC location is in the transition zone between positive and negative rainfall anomalies and, thus, present a weak response to ENSO in the 1979–1999 period. However, Trauth et al. (2003) detected that during ENSO + events 1965–1966, 1969–1970 and 1982–1983 the TUC annual rainfall was significantly higher than its mean. Hence, there is evidence suggesting that ENSO may serve as a predictor of rainfall variability in TUC, although the outcomes vary depending on the time period and index used for the analysis. Therefore, the use of the TUC centennial rainfall record proves valuable in identifying distinct periods when ENSO indices exhibit a stronger association.

It should be noted that the onset of summer rainfall in TUC is influenced by the Subtropical Oscillation, which is a low-frequency (20–26 year) variability in land-ocean moisture transport. This oscillation can modulate, or mask, the ENSO effects in the region (Vargas et al., 2002). This signal is observed in total summer rainfall, but not in Rx1 (Medina et al., 2021), implying that different processes may drive the variability in each rainfall metric. It is plausible then, that the connection between ENSO and Rx1 in TUC could be more pronounced compared to the total seasonal or annual rainfall.

In contrast to seasonal and annual total rainfall, much less works exist about the association between ENSO and extreme-daily rainfall. Among them, Grimm and Tedeschi (2009) analyzed a large set of daily station rainfall data and found that ENSO-related changes in the frequency of extreme-daily rainfall events are generally coherent with total monthly rainfall changes. However, since significant changes in daily-extremes were much more extensive, they argue that the highest sensitivity to ENSO seems to be in the extreme-daily precipitation instead of monthly totals. Li et al. (2020) analyzing hourly rainfall in a global study detects that ENSO modulates rainfall mainly through the number of rainy hours. Thus, unlike the total rainfall, there may be significant associations between ENSO and Rx1 in TUC.

Less studied than ENSO, another rainfall forcing is the Southern Annular Mode (SAM), which is known to affect the climate of the Southern Hemisphere (Thompson and Wallace, 2000; Fogt and Marshall, 2020). SAM positive phase (SAM+) is associated with anomalously low-pressure over Antarctica and high-pressure over the mid-latitudes of the Southern Hemisphere, and vice versa during its negative phase (SAM–). It alters the strength and position of cold fronts and mid-latitude storm systems (González et al., 2017), with SAM+ (SAM–) inhibiting (favoring) the passage of cold-fronts over Argentina. The relationship between SAM and summer precipitation is different in the east and west of Argentina. Cavalcanti et al. (2021) analyzing composites of rainfall anomalies for the two SAM phases in the 1999–2010 period found that anomalies of precipitation in Northern Argentina have a dipolar structure. They found under SAM + positive anomalies of summer rainfall in NWA and negatives in Northeastern Argentina (NEA), and vice versa under SAM–. Similar results are found in Garbarini et al. (2021), who analyzed the relation between the intensity of South Pacific High (SPH) and rainfall in the 1979–2012 period. They found that SPH intensity is modulated by SAM (or Antarctic Oscillation, AAO), in a way that SAM– implies weaker SPH and negative (positive) rainfall anomalies in NWA (NEA). SAM effects in rainfall were also detected in surrounding regions as Uruguay, Brazil and Central Chile (Vasconcellos and Cavalcanti, 2010; Garreaud et al., 2020; Reboita et al., 2021).

In the multidecadal scale exists the Atlantic Multidecadal Oscillation (AMO), which is analogous to PDO but in the North Atlantic (Knight et al., 2006), and thus it has positive (AMO+) and negative (AMO–) phases. Extreme-rainfall changes in Argentina were significantly influenced by long-term changes associated with PDO and AMO (Robledo et al. 2020). The interaction between the PDO + and AMO– (PDO– and AMO+) increases (decreases) the moisture transport from tropics to Northern Argentina and drives positive (negative) rainfall anomalies in the región (Barreiro et al, 2014). In addition, there exists evidence that PDO and AMO modulate teleconnection patterns of ENSO and SAM altering their patterns of rainfall anomalies (Kayano et al., 2019; Wachter et al., 2020).

Taking into account the regional relevance of Rx1 due to their socio-economic impacts and the need of improve the knowledge about the causes of their temporal variability, the aims of this research are:

- analyze the relations Rx1-ENSO and Rx1-SAM in TUC by identifying periods with significant correlation and determine if the relations are limited to certain sub-periods
- determine the mentioned sub-periods when the relations Rx1-ENSO and Rx1-SAM are more significant applying a novel approach consisting in maximize the statistical significance of the correlation
- generalize the results analyzing the coherence with the Rx1 and atmospheric variables behavior over SSA
- determine if PDO and AMO phases affect (favoring or inhibiting) the detected associations Rx1-ENSO and Rx1-SAM.

This work is structured as follows: Section 2 provides a description of the study region, Sections 3 and 4 detail the data and methodology used for the analysis, Section 5 presents the results, and Section 6 summarizes and discusses the main findings and draws conclusions.

The location of TUC and NWA regional topography are shown in Fig. 1. TUC is located ~ 500 m above sea level, in the transition zone between the Chaco Plains and the Puna Plateau. The region has a quasi-monsoonal precipitation cycle with a maximum in summer months, accounting for more than 50% of the annual precipitation (Poblete et al., 1989; Medina et al., 2021). Rainfall in NWA is dominated by the South-American monsoon (Marengo et al., 2012), which consists of the Chaco-Low (CL), Bolivian-High (BH), Low Level Jet (LLJ) and South-American Convergence Zone (SACZ) (Zhou and Lau, 1998). BH, encompassed approximately between 15–22°S and 60–70°W, is an upper-level high-pressure cell driven as a response to the strong convective heating over the Amazon in summer. BH enhances moisture advection from the Amazon southward (Vuille, 1999). At low levels a deep continental thermal low denominated the CL is present over Northern Argentina. The interactions between circulation associated with the CL and the Andes reinforces the transport of moisture from tropical to SSA. The regional intensification of the air circulation east of the Andes is denominates LLJ, which transports considerable moisture between the Amazon and La Plata basin over the subtropical plains as far as 35° S (Nogues-Paegle and Mo, 2002; Saulo et al., 2004). The LLJ leads to enhanced precipitation with a southeastward extension toward the Atlantic Ocean, referred to as the SACZ (Marengo et al., 2012). Rainfall is maximum in TUC and nearby regions as a result of the interaction between moisture flux and the Andes Mountain Range slope (Bookhagen & Strecker, 2008). The annual rainfall of TUC serves as an indicator of the NWA’s precipitation variability (Vargas et al., 2002; Ferrero and Villalba, 2019), highlighting the importance of analyzing this extended record.

3.1 Station rainfall data for TUC

We analyze the series of Rx1 values for the December-January-February-March (DJFM) season, obtained from the TUC daily-rainfall record. We consider this season taking into account, as mentioned in Section 2, the quasi-monsoonal rainfall regime in the region with the occurrence of a dry and a wet well-defined period in the year. The summer rainfall accounts for more than 50% of the total annual precipitation (Poblete et al., 1989; Carvalho and Cavalcanti, 2016; Grimm et al., 2020; Medina et al., 2021; Reboita et al., 2023). Also, in Fig. 2a can be observed that most of the annual Rx1 values occur inside the DJFM season.

Data provided by Estación Experimental Agroindustrial Obispo Colombres (EEAOC), a government agency of Tucumán province, was used in this work. The daily record spans the 1911–2019 period and was obtained from a conventional station located in San Miguel de Tucumán (26.8°S, 65.2°W). As we analyze the DJFM season, and the years were named as the calendar-year of the January month, the period analyzed is 1912–2019.

In the case of TUC records, the meteorological station was operated by the Servicio Meteorologico Nacional (SMN) which collected the data until the 1970s. Then, the station was moved to Tucumán airport, but the EEAOC continued operating in the original location. Therefore, there has been no change in the station's location. In addition, this time series has been used and has successfully passed quality controls in previous studies (Penalba and Robledo, 2010; Scardilli et al., 2017; Saurral et al., 2017). However, to ensure the quality of our data, we checked some aspects of the daily rainfall record. We detect missing data only in the period of July-December 1943, which represents 0.5% of the entire record. As our analysis focuses solely on the DJFM, the missing data lowers to 0.2%. The time series was also checked for obvious erroneous data (e.g., negative rainfall values or values excessively high) and we did not find any. Further, for each month we compute monthly total rainfall, maximum daily rainfall and maximum consecutive days without rainfall. All values that deviated four standard deviations or more respect to the monthly mean were flagged as suspected of mistakes, a criterion used in other works based on daily rainfall series (Cavalcante et al., 2020; Medina et al., 2023). Flagged data were compared with neighboring stations, when available, to decide about its acceptance. No outliers were detected in the total rainfall for DJFM season. For maximum daily rainfall only one outlier was detected on date 3/3/1973 but we confirmed its value with a neighboring station (at ~ 40 km distance). In the case of consecutive dry days, outliers were detected in January of 1917 and March of 1933. In the first case, we were unable to compare with neighboring stations' values, while in the second case we compared them with one station at ~ 140 km distance. In both cases, data was rejected for caution. However, even with these considerations, the proportion of missing data increased to only 0.6%, and therefore should not have a substantial impact on our results. From this final daily rainfall record, Rx1 for the DJFM season was assessed.

3.2 Gridded Rx1 data for Subtropical South America

To evaluate the spatial coherence of the obtained results for TUC, monthly gridded Rx1 data from HadEX3 (Dunn et al., 2020) was considered over the area encompassed between 15–40°S and 75 − 30°W (approximately SSA). Data was provided by Met Office Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadex3/download.html). HadEX3 is based on daily data from 17000 rainfall gauges around the world. This dataset provides 12 monthly and annual rainfall indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), among which is Rx1. Indices are computed in each location of rainfall gauge, and later are interpolated in a common horizontal grid of 1.25° x 1.875° using the Inverse Distance Weighted scheme. HadEX3 only considers station data with at least 20 years of complete data and with coverage up to 2009 or later to avoid inhomogeneities that affect the long term analysis. The dataset spans the 1901–2019 period, however, due to scarcity of measurements exists grid points with missing data in the beginning of the period. Taking into account this, data of Rx1 DJFM for the 1962–2019 period was selected because this period is the longest where exists data for all grid points in SSA. The representation of the rainfall indices in HadEX3 agree with similar previous products (HadEX, HadEX2 and GHCNDEX), and in comparison this has more grid points with complete data in the cover period (Dunn et al., 2020).

3.3 Climate indices

Four large-scale modes of variability are considered in this study: ENSO, SAM, PDO, and AMO. Time series were averaged for the DJFM season in coincidence with the season selected for Rx1. A brief description of each mode and its availability is provided below.

ENSO indices: we select Niño 1.2 and Niño 3.4, because different flavors of ENSO exist: Eastern-Pacific and Central-Pacific (Hurtado and Agosta, 2021). Results of Hurtado el al. (2020) indicate that the Eastern-Pacific flavor is well represented by the Niño 1.2 index, while Hurtado and Agosta (2021) indicate that Central-Pacific flavor is well represented by the Niño 3.4 index. Thus, selecting these ENSO indices we take into account that there can exist differences in the impacts of ENSO in Rx1 by the flavor, affecting our results. Niño 1.2 and Niño 3.4 indices are temperature anomalies in regions Niño 1.2 and Niño 3.4, respectively (data available on the NOAA’s webpage: https://psl.noaa.gov/gcos_wgsp/Timeseries/). Anomalies are computed using the HadISST1 dataset (Rayner et al., 2003) referred to the 1981–2010 period.
SAM index: three time series were considered: (1) SAM-20 from the The Twentieth Century Reanalysis Project (20CRV2c) (Gong and Wang (1999) methodology) encompassing the 1912–2011 period (available on: https://psl.noaa.gov/data/20thC_Rean/timeseries/monthly/SAM/), (2) SAM-M from the British Antarctic Survey which is a station-based index (Marshall (2003) methodology) encompassing the 1957–2019 period (available on: https://legacy.bas.ac.uk/met/gjma/sam.html), and (3) a concatenated time series consisting in SAM-20 for the 1912–1956 period jointly with the SAM M for the 1957–2019 period. Both SAM-20 and SAM-M are computed as the difference of the zonal means at 40°S and 65°S, but in the first case gridded data are used, while in the other case six unevenly distributed stations are considered. We performed the analysis of this work with the 3 time series and results were similar. However, we chose the concatenated time series because it showed comparatively better results and had a longer temporal coverage;
PDO index (Mantua, 1999) is defined as the leading principal component of mean SST anomalies for the Pacific Ocean to the north of 20°N latitude, with the global mean SST anomaly first removed for each month in order to reduce the influence of the long-term trends (available on: http://research.jisao.washington.edu/data_sets/pdo/);
AMO index: area weighted average of the North Atlantic Ocean (0–70°N) temperature calculated from the Kaplan SST dataset (Kaplan et al., 1998) (available on: https://www.psl.noaa.gov/data/timeseries/AMO/).

3.4 Atmospheric fields

Monthly atmospheric fields of multiple variables related with the tropospheric circulation and rainfall (geopotential height, sea level pressure, relative humidity in low levels, precipitable water total and meridional and zonal wind in different pressure levels) from the ERA5 reanalysis (Hersbach et al., 2020) were used. ERA5 is a reanalysis of global coverage in a horizontal resolution of 0.25°x0.25° and 137 pressure levels from the surface to a height of 80 km. Data for the 1940–2019 period was provided for Copernicus Climate Data Store (https://cds.climate.copernicus.eu/).

As explained in the introduction, precipitation in NWA is affected by global circulation patterns induced by ENSO and SAM. Additionally, these circulation patterns can be influenced on a multi-decadal scale by PDO and AMO. To detect these associations, calculating the value of the linear correlation coefficient (RHO) by simply taking the complete period of two series is the simplest analysis to detect linear association. Particularly, for the Rx1 series analyzed in this work, the values of RHO are very low for Rx1-ENSO (0.08) and Rx1-SAM (0.19). There are several reasons that may explain the low value of RHO obtained. One could be a weak nature of the Rx1-ENSO and Rx1-SAM association. Another reason could be the absence of stationarity in the relationships due to interactions with other forcings, i.e., due to nonlinearity in the system's response. Therefore, more complex methodologies than a simple correlation analysis should be used to statistically detect associations. Based on this, with the methodology proposed below, we aim to determine whether the Rx1-ENSO and Rx1-SAM relationships are non-stationary, and furthermore, whether this non-stationarity is modulated by PDO and/or AMO phases.

First, the problem is addressed with a new approach to the method known as the triangle representation used by Fink et al. (2010) and Bahaga et al. (2019). The proposed new approach aims to detect subperiods within the series (Rx1, ENSO, and SAM) where a more significant linear correlation exists between them. Following this analysis, the spatial representativeness of the previous results was evaluated by constructing Rx1 anomaly composites using HadEX3 data in the detected subperiods and distinguishing ENSO and SAM phases. Similarly, composites of atmospheric variables anomalies from ERA5 were performed aiming to provide physical support to the detected Rx1-ENSO and Rx1-SAM relationships. Finally, the roles of PDO and AMO were evaluated with a moving correlation analysis, and on the other hand, by separating Rx1, ENSO, and SAM data according to PDO and AMO phases. The results of these proposed analyses were evaluated as a whole to conclude about the local and regional Rx1-ENSO and Rx1-SAM relationships, associated circulation anomalies, and the role of PDO and AMO. Below, we describe these proposed analyses in detail.

4.1 Identification of the periods with the most significant correlation

To detect significant associations within sub-periods, a common method is employing a temporal moving-window of a fixed length, shorter than the whole period, and compute RHO. This fixed window length (WL) is usually selected based on an identified periodicity or based on an a-priori knowledge of the system under study. Instead of using this conventional method, we analyzed all possible WL values within the length of our time series. This methodology is the triangle-representations used in Fink et al. (2010) and Bahaga et al. (2019). We do a different focus: searching for the lowest p-value, rather than only observing the RHO values. In this case, the Pearson correlation coefficient is assessed, together with the corresponding p-value from the Student t-test as its significance measure. For each WL, the correlated time series were first linearly detrended. The shorter WL considered is 20 years, due to us searching a signal temporally more sustained, while the longer is 108 years in coincidence with the length of the complete series.

As a first step, a matrix containing four columns is constructed based on the RHO values between Rx1 and either ENSO or SAM. The 1st column contains the start-year of the window, the 2nd WL, the 3rd the corresponding RHO value, and the 4th the corresponding p-value. Within the matrix, the lowest p-values correspond to a certain WL with a given start- and end-years, which determine the sub-period of minimum p-value (PmPV). For the PmPV, for definition, the RHO value has the greatest significance between all RHO values of all possible sub-periods. This means that PmPV is the longest period when an association between Rx1 and either ENSO or SAM is more likely to exist. The advantage of this criterion over detecting the highest RHO, is avoiding the identification of short periods which pass the significance test, but that do not correspond to the period with the higher significance for the linear association.

The period with maximum WL (PMWL), for which a statistically significant RHO value can be obtained, is also identified. In general, PMWL contains PmPV with identical RHO sign, implying that the correlation can be detected along a longer time than indicated by PmPV. However, in general a weakening outside the PmPV period can be observed in a way that low RHO can be found in PMWL. The difference in the length between PmPV and PMWL is an indicator of the speed of weakening in the relation between Rx1 and the climate index (ENSO or SAM), as the PmPV is enlarged up to the PMWL. On the other hand, it can occur that the PmPV is not contained in the PMWL and that RHO signs result opposite. In these cases, it can be concluded that the relationship between Rx1 and the climate index is chaotic and could even not exist. In addition to the PmPV and PMWL, the triangle-representations for Rx1-ENSO and Rx1-SAM are plotted to show all results of RHO, p-values and their temporal variations.

4.2 Composites of anomalies for Rx1 and atmospheric variables

Composites of Rx1 anomalies in SSA were constructed using HadEX3 for the PmPV of ENSO and SAM inside and outside it. Anomalies were calculated relative to the entire selected period of HadEX3 (1962–2019), with different phases of ENSO and SAM being distinguished. In all cases, differences were tested using the Student’s t-test for difference in the mean of samples with unequal variances (95% confidence level). This analysis conducted within and beyond the PmPV aimed to determine if changes in the Rx1-ENSO and Rx1-SAM relationships, identified for TUC, are also observed in SSA. For SAM and ENSO, we define a positive (negative) phase when the value of either ENSO or SAM is above (under) the 75th (25th) percentile of the series in the 1912–2019 period (Table 1).

Similarly, composites of anomalies of atmospheric variables were constructed to explore the circulation patterns of ENSO and SAM phases inside and outside the respectives PmPV. The atmospheric variables selected were geopotential height in 500 hPa (z500) and 850 hPa (z850), and wind vector and specific humidity in 850 hPa. These variables showing better results between the analyzed and they are closely related with the regional rainfall. In all cases were considered the mean values of the DJFM season and the climatological period was the whole period of ERA5 (1941–2019). This analysis conducted within and beyond the PmPV aimed to determine if changes in the relations Rx1-ENSO and Rx1-SAM are related to circulation patterns varying across the analyzed sub-periods.

Table 1

Years classified by ENSO (using Niño 3.4) and SAM phases. Phase was determined as positive (negative) when the mean of DJFM was above (under) 75th (25th) percentile of the whole series (1912–2019).
	Negative phase (-)	Positive phase (+)
ENSO	1917 1918 1923 1925 1934 1939 1943 1950 1951 1955 1956 1971 1974 1976 1984 1985 1989 1996 1999 2000 2001 2006 2008 2009 2011 2012 2018	1912 1914 1915 1919 1924 1926 1931 1940 1941 1942 1958 1966 1969 1973 1977 1978 1983 1987 1988 1992 1995 1998 2003 2010 2015 2016 2019
SAM	1912 1913 1914 1917 1919 1922 1923 1924 1925 1926 1929 1931 1932 1933 1937 1939 1943 1945 1949 1950 1958 1965 1966 1967 1969 1975 1977	1951 1955 1960 1962 1963 1974 1982 1989 1990 1994 1995 1996 1997 1998 1999 2000 2002 2007 2008 2009 2012 2013 2014 2015 2016 2018 2019

4.3 Two methods to determine PDO and AMO role on a non-stationary association

If it is observed that the correlation becomes evident only during certain sub-periods, the association between Rx1-ENSO or Rx1-SAM could be non-stationary. In this case, an additional forcing can drive the changing associations of Rx1. The easiest way to evaluate the possible role of an additional forcing would be to consider a multilinear correlation instead. However, if it has a non-linear interaction with Rx1, this method would fail in revealing its role. In our case, there are two possible additional forcings which could interact with Rx1 in a non-linear way, by favoring or inhibiting the effects of ENSO and SAM: PDO and AMO. As was mentioned in the introduction, PDO and AMO drive the multidecadal variability of the rainfall in northern Argentina. Thus, it could be thought that drives changes in the teleconnections patterns associated with ENSO and SAM.

One approach is to check if the running correlations between Rx1 and ENSO or SAM become significant and have the same sign along a certain PDO and/or AMO phase (positive or negative).. This is done qualitatively, by plotting the running correlation coefficient simultaneously with PDO and AMO smoothed series.

A second possibility is to assess the correlations between Rx1 and ENSO or SAM, by filtering first the time series according to the phases of PDO and AMO. Thus, we split the series by PDO phase (PDO + and PDO–), by AMO (AMO+: positive AMO phase, and AMO–: negative AMO phase), and by their combinations (PDO + and AMO+, PDO + and AMO–, PDO– and AMO–, PDO– and AMO+). After splitting the time series, RHO is assessed for each case. If the PDO and/or AMO phase favors or inhibits ENSO effect on Rx1, for example, then a significant RHO should be obtained for one of the phases, and a null or non-significant one in the other phase. This analysis is similar to that used by Labitzke (1987) and Labitzke and van Loon (1988) to detect the non-linear QBO role on the association between polar stratospheric temperature and solar activity.

For the analysis above proposed, a 10-year running mean of PDO and AMO averaged over DJMF was performed to focus on the multidecadal component of the series. The years which correspond to each PDO and AMO phases for the splitting process are listed in Table 2. The initial and final years are 1916 and 2014, instead of 1912 and 2019, due to the running mean process (with centered window) losing half of the window length at the beginning and the other half at the end of the time series.

Table 2

Periods of positive and negative phases of PDO and AMO independent of each other, and periods of combined phases.
PDO or AMO phase	Periods	PDO and AMO phases	Periods
+ PDO (56 years)	1922–1944 1978–2006 2011–2014	PDO+ & AMO+ (27 years)	1930–1944 1999–2006 2011–2014
- PDO (43 years)	1916–1921 1945–1977 2007–2010	PDO+ & AMO– (29 years)	1922–1929 1978–1998
+ AMO (51 years)	1930–1964 1999–2014	PDO– & AMO+ (24 years)	1945–1964 2007–2010
- AMO (48 years)	1916–1929 1965–1998	PDO– & AMO– (19 years)	1916–1921 1965–1977

5.1 Annual cycle and variability of Rx1

Figure 2a displays the yearly count of annual Rx1 occurrence in each month for the 1912–2019 period. January has the highest count, with 35 out of the 108 annual Rx1 occurring in this month. Other months with high counts include December, February, and March. Overall, the period from December to March (DJFM) encompasses 88% of the annual Rx1 events, which justifies its selection as the focus of our study.

Figure 2b shows Rx1 series. High variability is observed in the raw series with a mean value of 89 mm, and extreme values of 189 mm (in March of 1973) and 36.5 mm (in February of 1916). The relative variation of Rx1 is 31% (ratio between the standard deviation and the mean). On the other hand, the series of anomalies are shown in Fig. 2c, showing the alternancy of years with higher and lower Rx1 values. Exists a major predominance of higher values from the late 1950s. Anomalies with a 10-year running mean (Fig. 2d) indicates multidecadal variability consisting of higher Rx1 values since 1960, especially in the 1980s and 1990s decades; while from 2010’s series seems to change again to lower Rx1 values.

5.2 Correlation Rx1-ENSO and Rx1-SAM and obtention of PmPV and PMWL

The triangle-representation (see Fig. 3) gives RHO values in terms of the window's start-year and the length (WL). It can be interpreted as follows: moving from the start-year horizontally to the right in the WL-axis, colors represent the RHO values for a fixed start-year and for increasing WL values. Moving from the bottom vertically upward yields RHO for a fixed WL and increasing start-years (abscissa). Only correlations at the 95% of significance level (p < 0.05) are shown in the color scale. Figure 3 shows the triangle-representations for Rx1 vs Niño 1.2, Niño 3.4 and SAM. In each figure, PmPV and PMWL values are identified (black-filled and black-empty stars, respectively).

For Niño 1.2 and Niño 3.4 (Fig. 3) at WL = 20, significant positive RHO values (0.6–0.8) begin around the 1950s. As the WL is increasing, positive RHO values are observed for start-years before the 1950s, due to the fact these windows encompass 1950s when a noticeable correlation exists. For WL > 30 years approximately, RHO values tend to steadily decay under 0.6 up to loss of statistical significance. On the other hand, smaller periods with negative RHO values are observed for both Niño 1.2 and Niño 3.4. In both cases, a RHO of approximately − 0.4 is observed. For Niño 1.2, this occurs starting in 1987 for WL = 20 (covering the period from 1987 to 2006), while for Niño 3.4, it starts in 1923 for WL = 21 (spanning the period from 1923 to 1942). Thus, although ENSO indices mainly exhibit positive RHO values, negative RHO values are also observed, albeit for shorter time periods and with lower absolute values.

For the SAM index (Fig. 3), only positive RHO values are observed. At WL = 20, the start-years with significant RHO values ranging between 0.4 and 0.6 are mainly concentrated around the 1970s and 1980s. As WL increases to its maximum, RHO values remain above 0.4 only for the start-years corresponding to the 1970s. This indicates that from around the 1970s up to almost the end of the series, a significant and steady positive RHO exists between Rx1 and SAM.

In Fig. 4 are shown the PmPV and PMWL for comparing the periods with more noticeable influence of ENSO and SAM in Rx1. The PmPV results show a change in the association Rx1-climate indices. The PmPV for ENSO indices (Niño 1.2 and Niño 3.4) is 1945–1974, while for SAM is 1974–2007, in both cases with RHO ~ 0.6. Thus, around 1974 a change in the coupling of Rx1 occurred, from a noticeable correlation with ENSO to a noticeable correlation with SAM. On the other hand, in concordance with the triangles-representations, the PMWL of Fig. 4 shows that for WL greater than PmPV, the RHO values decrease abruptly. This is because, as WL increases, years with lower correlations, and even non-significant ones, are included in the window. This comparison between the PmPV and PMWL confirms that the main influences of ENSO and SAM are specially confined to their corresponding PmPV.

The described results demonstrate the existence of non-stationary associations Rx1-ENSO and Rx1-SAM. Also, the sub-periods with association between Rx1 and either ENSO or SAM are different. In the case of Niño 1.2, a little period with a negative RHO is observed inside the PmPV of SAM, however it shows less extent and lower RHO magnitude than SAM.

The association Rx1-ENSO and Rx1-SAM along the corresponding PmPVs occurs on interannual timescales and this co-variability decays when the whole period is considered. This can be seen in the individual series (Fig. 5a-b, and 6a-b) and in the scatter plots for Rx1 vs Niño 3.4 and Rx1 vs SAM (Fig. 5c-d, and 6c-d). In addition, we show boxplots of Rx1 separating ENSO phases (Fig. 5e-f) and SAM phases (Fig. 6e-f). For the PmPV of ENSO (Fig. 5e), under ENSO + the Rx1 values have a median of 125 mm, under ENSO– of 70 mm and for neutral ENSO (ENSO N) of 90 mm. In contrast for the whole period (Fig. 5f), differences are much less noticeable, with a median of ~ 80 mm under ENSO– and ENSO N and 90 mm under ENSO+. Also, in the case of SAM noticeable differences are observed in the PmPV (Fig. 6e), with a median of 90 mm under SAM+, 87 under neutral SAM (SAM N) and 50 mm under SAM–. For the whole period (Fig. 6f), the difference of medians between SAM phases is lower, being 90 mm under SAM + and SAM N, and 80 mm for SAM–.

5.3 Composites of Rx1 anomalies in SSA

In Fig. 7 are shown the Rx1 anomalies on SSA under different ENSO phases in a period inside the PmPV (1962–1973) and other outside PmPV (1975–2007). The 1974 year was not included because it is present in the PmPV of ENSO and SAM and we want to analyze them separately. Also, the first period does not coincide exactly with the PmPV of ENSO because, as was explained, we use data of HadEX3 from 1962. On the other hand, the second period outside the PmPV of ENSO was selected coincident with the PmPV of SAM to evaluate the Rx1 response to ENSO in this period.

In agreement with the results for TUC, in the PmPV under ENSO– there are lower Rx1 values (negative anomalies) over the center and north of Argentina, north of Bolivia and center of Brazil (Fig. 7a). On the other hand, under ENSO– predominates higher Rx1 values over the center and north of Argentina (Fig. 7b). In both cases, differences are statistically significant at 95% in regions neighboring to TUC (green points in the figures). Differences between ENSO + and ENSO– inside the PmPV (Fig. 7c) show positive values over center-west and north of Argentina and over the center of Brazil. In contrast, outside the PmPV the anomalies under ENSO– are weaker, losing significance over much of the region (Fig. 7d), except over center-west Argentina. Under ENSO+ (Fig. 7e) is seen a dipole with positive anomalies over eastern Argentina and southern Brazil, and negative anomalies over southern Bolivia, northern Chile and the northwestern extreme of Argentina, with TUC located in a transition zone without significant anomalies. Similarly, the differences between ENSO + and ENSO– outside the PmPV (Fig. 7f) show the dipole more markedly and with a greater extent of the positive anomalies over northern Uruguay, southern Brazil and northern Bolivia. In conclusion, the anomalies pattern of Rx1 associated with ENSO changed their spatial structure over the region through the considered sub-periods.

Analogously, in Fig. 8 are shown results for SAM. Outside the PmPV of SAM, under SAM– (Fig. 8a) significant positive anomalies are observed only over southeastern Brazil, while under SAM+ (Fig. 8b) in general significant anomalies are not observed. The difference between SAM + and SAM– outside the PmPV (Fig. 8c) shows that Rx1 is significantly higher under SAM + over the center-west of Argentina and lower over northern Paraguay and the center of Chile. On the other hand, in the PmPV under SAM– (Fig. 8d) is observed a great region of significant negative anomalies over the center-west of Brazil, western Paraguay and parts of NWA to the location of TUC. This behavior is coherent with the lower Rx1 values observed in SAM– in TUC. For SAM+ (Fig. 8e), the anomalies on the mentioned regions turn positive as is expected based on the analysis for TUC, but in this case anomalies are not significant. In addition, negative significant anomalies appear over the center-west of Argentina.The difference between SAM + and SAM– in the PmPV (Fig. 8f) shows higher Rx1 values in SAM + over center-west Brazil and western Paraguay, while lower values are observed over southern Brazil, northern Chile and western Bolivia. In agreement with that observed for ENSO, the patterns of anomalies of Rx1 promoted by SAM changed according to the analyzed sub-period.

Thus, the relations Rx1-ENSO and Rx1-SAM detected with TUC data are coherent with the anomaly patterns of Rx1 over SSA, which changed between sub-periods. Results of this analysis shows that the significant relation Rx1-ENSO detected for TUC is observed over the center and north of Argentina, northern Bolivia and the center of Brazil. This relation implies higher Rx1 values under ENSO+ (ENSO–) in the PmPV. On the other hand, the detected relation Rx1-SAM is coherent with the relation observed over NWA, western Paraguay and the center-west of Brazil. In this case, higher (lower) values of Rx1 under SAM+ (SAM) are observed in the PmPV.

5.4 Composites of anomalies of atmospheric variables for ENSO

Under ENSO– in the PmPV (Fig. 9a) is observed an anomalies pattern of z500 that resembles a Rossby wave-train emanating from tropical Pacific and another that seems emanating from the Indian Ocean. The anomalies pattern of z500 established consists of a dipole with negative values over southern South America and positive values over southeastern Brazil and the surrounding Atlantic. Also, this is observed in the anomalies pattern of z850 (Fig. 10a), which promotes a cyclonic circulation over Argentina and negative anomalies of q850 there (Fig. 11a). This last explains the existence of lower Rx1 values under ENSO– in the PmPV over the center and north of Argentina. At the same time, anticyclonic anomalies between 10–30°S over the continent promote negative q850 anomalies in the center of Brazil and northern Bolivia driving lower Rx1 values there. On the other hand, the described circulation anomalies promote a convergence of q850 over Paraguay driving higher Rx1 values.

Under ENSO + in the PmPV the z500 anomalies (Fig. 9b) also shows a pattern similar to two Rossby wave trains emanating one from the tropical Pacific and other from the east of Indian Ocean and northern Australia. This pattern has positive anomalies over southern South America, contrary to the observed under ENSO–. The z850 anomalies pattern (Fig. 10b) shows negative values over the Atlantic between 20–60°S, off the coast of Argentina, and over NWA. This pattern promotes an enhancement of q850 flux (Fig. 11b) from the north towards the center and north of Argentina.

Outside the PmPV, the z500 anomalies pattern under ENSO– (Fig. 9c) is modified in comparison with that observed inside the PmPV (Fig. 9a). Again a pattern similar to Rossby wave-trains emanating from tropical Pacific and Indian Oceans is observed, but the pattern changed in a way that z500 negative anomalies are displaced outside southern South America. Also, positive anomalies appear over the Atlantic off the coast of Argentina and negative anomalies appear over the center and south of Brazil and the surrounding Atlantic. This pattern is also observed in z850 (Fig. 10d) and promotes a weakening of the q850 negative anomalies (Fig. 11d) in comparison with that observed in the PmPV (Fig. 11a). This promotes that the significant anomalies of Rx1 are confined only over the center-west of Argentina (Fig. 7d).

Under ENSO + outside the PmPV, also the pattern of z500 is modified in comparison to that observed in the PmPV. The positive z500 anomalies over southern South America are moved northward in comparison with the PmPV and positive anomalies off the coast of Brazil appear. Similar configuration is observed in z850 (Fig. 10e), which promotes an anomalous flux of q850 towards northeastern Argentina, Uruguay and southern Brazil, where higher Rx1 values occur in these conditions (Fig. 7e). At the same time, a weakening of the q850 flux over the west of NWA, southern Bolivia and northern Chile is observed with lower Rx1 values there.

In coincidence with the described above, the difference between ENSO + and ENSO– patterns in the PmPV shows that in ENSO + positive anomalies of z850 (Fig. 10c) exist over southern South America and negative anomalies are observed over the Atlantic off the coast of South America. This promotes an enhancement of the q850 flux over the center and north of Argentina under ENSO+ (Fig. 11c) driving higher Rx1 values there. Outside the PmPV, the difference between ENSO + and ENSO– shows positive z850 anomalies (Fig. 10f) displaced southward in comparison with the PmPV and positive anomalies appear over Brazil. This promotes positive anomalies of q850 (Fig. 11f) over northeastern Argentina, Uruguay and Brazil with higher Rx1 values (Fig. 7f) and, contrary, negative anomalies of q850 over west of NWA, southern Bolivia and northern Chile with lower Rx1 values.

5.5 Composites of anomalies of atmospheric variables for SAM

The z500 anomalies under SAM– outside the PmPV (Fig. 12a) show a pattern with negative values between 30–50°S, standing out three anticyclonic centers over south of the Indian Ocean, southeastern Australia and the Atlantic off the coast of Argentina and southern Brazil. A similar pattern is observed in z850 (Fig. 13a) promoting negative anomalies of q850 over eastern Argentina due to the weakening of moisture flux from the north (Fig. 14a). This pattern also shows positive anomalies of q850 over southeastern Brazil with positive anomalies of Rx1 under these conditions.

In the case of SAM + outside the PmPV, the z500 anomalies pattern (Fig. 12b) consist of positive values between 30–60°S and negative values in the rest of latitudinal bands, among which stands out a anomalous cyclonic center over eastern Argentina and southern Brazil. In the z850 pattern (Fig. 13b) a center of negative anomalies is observed over southern Brazil and the Atlantic off the coast of Argentina. This promotes a cyclonic circulation in the region and negative anomalies of q850 (Fig. 14b), mainly over northern Patagonia and center of Chile, with lower Rx1 values (Fig. 8b).

In the PmPV, under SAM– is seen a pattern of anomalies in z500 (Fig. 12c) modified in comparison with that observed in the PmPV (Fig. 12a). Although in general negative anomalies are observed between 30–50°S, the center of negative values is displaced towards the northeastern in comparison and, as a counterpart, positive anomalies appear from Antartica up to southern South America. This pattern is also reflected in z850 (Fig. 13d) with the addition of positive anomalies over southern Brazil, which promotes anticyclonic anomalies between 10–40°S (Fig. 14d). This pattern produces negative anomalies of q850 (Fig. 14d) over NWA, western Paraguay, and central-west Brazil coinciding with significantly lower values of Rx1 (Fig. 8d), while the opposite is observed over southern Brazil. In SAM + during the PmPV, positive anomalies of z500 (Fig. 12d) predominate mainly between 30–60°S and they are more extensive zonally compared to what happened outside the PmPV. The appearance of positive anomalies of z500 over the Atlantic off the coast of Argentina is highlighted, which are not observed outside the PmPV. This pattern is replicated in z850 (Fig. 13e) promoting an anticyclonic flow over the Atlantic and a cyclonic flow between 15–40°S over the continent, generating negative anomalies of q850 over the central-west of Argentina (Fig. 14e), where lower values of Rx1 are also observed under these conditions (Fig. 8e).

The difference between SAM + and SAM − outside the PmPV shows that in SAM − negative anomalies of z850 (Fig. 13c) predominate over the Atlantic, off the coasts of Brazil and Argentina, promoting a decrease in q850 (Fig. 14c) over eastern Argentina and an increase towards southeastern Brazil where the highest values of Rx1 are favored under these conditions. Within the PmPV, the difference between SAM + and SAM − shows that z850 anomalies (Fig. 13f) over the Atlantic, off the coast of Argentina and Brazil, reverse their sign according to the phase of SAM, generating cyclonic anomalies in SAM + between 10–40°S over the continent (Fig. 14f). This pattern generates higher values of q850 in SAM + than in SAM− (Fig. 14f), mainly over central Brazil, western Paraguay, eastern Bolivia, and sectors of NWA, which explains the higher values of Rx1 observed in SAM + compared to SAM− (Fig. 8f), and the inverse situation in southern Brazil.

5.6 PDO and AMO roles in the non-stationary relationships of Rx1 with ENSO and SAM

In the assessment of the role of the PDO in the non-stationarity relationship between Rx1 and ENSO (SAM), we found that the relation is promoted under PDO– (PDO+). Figure 15 shows the RHO values of a running correlation (WL = 20) for Rx1 vs ENSO and Rx1 vs SAM indices. WL is selected in 20 years because it is closer to the approximate length of PDO phases (see periods of PDO + and PDO–). Also in Fig. 15, the phases of PDO index are shown, which were obtained by applying a 10-year running mean to PDO series. It can be noticed that, during the extended period 1945–1977 of PDO–, a significant RHO is only observed for Rx1 vs ENSO (blue and red points). Contrary, during PDO + along 1978–2007, RHO becomes notably significant for Rx1 vs SAM (green points), while a few points with negative RHO and just above the significance threshold appear for Rx1 vs Niño 1.2 (blue points). For the other time interval of PDO– (1922–1944) a few points with negative RHO for Rx1 vs Niño 3.4 are observed, while non-significant correlation with SAM is observed. No associations are noticed if AMO is considered instead of PDO (not shown).

To further analyze for a possible influence of the PDO and AMO phases over the correlation of Rx1 with ENSO and SAM, Table 3 shows the RHO values when the series are separated according to the phases of PDO and AMO. These results corroborate that PDO– favors the Rx1-ENSO positive correlation. On the other hand, the PDO– inhibits this positive correlation and seems to favor a negative correlation of lower magnitude in comparison with the other case. In addition, in the PDO + appears a positive correlation between Rx1 and SAM. Contrasting with results of Fig. 9, in this case the RHO values are lower, which is because all years with either PDO + or PDO– were joined and some of these years can not show a relation Rx1-ENSO or Rx1-SAM. When Rx1, ENSO and SAM are separated according to AMO phases and then correlated, the results do not support a consistent role of their different phases. On the other hand, when the combination of PDO and AMO phases is used to split the data series, only ENSO indices show significant RHO values for PDO–, but regardless of the AMO phase. Thus, AMO shows no influence in the associations Rx1-ENSO and Rx1-SAM.

The analysis of Fig. 15 and Table 3 shows that PDO is possibly the forcing that can explain the non-stationarity of the relations Rx1-ENSO and Rx1-SAM. PDO– favors the positive Rx1-ENSO association, while the PDO + favors a positive Rx1-SAM and a weaker negative Rx1-ENSO association. However, in the case of SAM, significant correlation was not found for the first PDO– (1922–1944), which can be due to deficiencies in the SAM index used or it can be due to the absence of a relationship in this period. In relation to the former hypothesis, the relationship is clearly observed in PDO+, when the station-based Marshall’s SAM is available (after 1957), while it is not observed for the period corresponding to the 20CRV2c SAM (prior to 1957). It is known that the sensitivity of the relationship between SAM and rainfall depends on the choice of SAM index and the data set used to calculate it (Ho et al., 2012).

Table 3

Correlation coefficients between Rx1 and Niño 1.2, Niño 3.4 and SAM considering the complete period (1912–2019), and the periods split according to: the PDO positive (PDO+) and negative (PDO–) phases, the AMO positive (AMO+) and negative (-AMO) phases, and the four possibilities of the PDO and AMO combined positive and negative phases. The number of years used in each correlation is indicated for each phase or phase combination. Only significant values (95% confidence level, p-value < 0.05) are shown.
	Rx1 vs. Niño 1.2	Rx1 vs. Niño 3.4	Rx1 vs. SAM
Complete series	-	-	-
+ PDO (56 years)	-0.29	-0.30	0.28
PDO– (43 years)	0.48	0.43	-
AMO+ (51 years)	-	-	-
-AMO (48 years)	-	-	-
PDO+ & -AMO (29 years)	-	-	-
PDO– & AMO+ (24 years)	0.65	0.45	-
PDO+ & AMO+ (27 years)	-	-	-
PDO– & -AMO (19 years)	0.44	0.46	-

In this work, we analyze the longest daily record available of NWA, which has been less analyzed in the literature. The results obtained at the local level with this series are consistent with the regional behavior of Rx1 according to HadEX3. The proposed method for determining PmPV, as a novel approach in correlation analysis and triangle representation, enabled the detection of non-stationary relationships between Rx1-ENSO and Rx1-SAM. Specifically, a transition from a closer relationship Rx1-ENSO (1945–1974) to a closer relationship Rx1-SAM (1974–2007) was detected. Primarily, this transition in 1974 could be attributed to the phase shift in PDO, the impact of which has been detected in several precipitation series in Argentina (Hurtado et al., 2020). To the best of our knowledge, Rx1 had not been studied with this approach in the NWA and SSA regions prior to this research. Therefore, the main contribution of this chapter lies in the discovery of potential PDO impacts on Rx1-ENSO and Rx1-SAM relationships.

Specifically, the PmPV of ENSO indices aligns with PDO–, while the PmPV of SAM coincides with PDO+. Outside of PmPV, the relationships between Rx1-ENSO and Rx1-SAM sharply decline. The non-stationary nature of these detected relationships is the reason why some studies analyzing precipitation variability in the NWA region do not find significant correlations with ENSO or SAM (Rivera and Penalba, 2015; Medina et al., 2021).

The decline in the association between precipitation and ENSO since the 1970s has also been observed in other regions of the world, for example, in India (Kucharski et al., 2007). On the other hand, according to Torralba et al. (2015), in Northeast Brazil, the relationship between precipitation and ENSO has strengthened since the 1970s. Therefore, a global shift in the 1970s has altered ENSO teleconnection patterns, affecting its relationship with precipitation variability in different regions of the world. In this regard, Barreiro et al. (2014) suggest that the influence of the Pacific and Atlantic Oceans may explain a significant portion of the transition from drier to wetter decades over the NWA during the mentioned decade. According to the results obtained here, the mentioned global shift is also observed in Rx1 over SSA, although the focus was on the Rx1-ENSO and Rx1-SAM relationships rather than on precipitation values themselves. It was demonstrated that PDO alone is sufficient to explain the changes in the aforementioned relationships; that is, AMO has no effect according to the results.

The regional anomaly patterns of Rx1 (Figs. 7 and 8) and atmospheric anomalies (Figs. 9 to 14) driven by ENSO and SAM changed between PDO phases. These results indicate that the non-stationarity of Rx1-ENSO and Rx1-SAM relationships is due to changes in circulation patterns induced by ENSO and SAM between PDO phases. It is known that sea surface temperature patterns and atmospheric circulation in the tropical Pacific related to PDO are similar to those of ENSO, albeit with a greater meridional extent on the eastern side of the Pacific (Mantua and Hare, 2002). Therefore, a modification of the global circulation response and rainfall patterns to ENSO is expected under different PDO phases. In this regard, Andreoli and Kayano (2005) demonstrate that differences in the relationship between precipitation and ENSO in South America under different PDO phases are explained by changes in the 200 hPa stream function. They show that during ENSO + events in cold PDO phases, strong cyclonic circulation is established over southern South America. Conversely, during ENSO + events in warm PDO phases, there is a cyclonic center over eastern Brazil and a weak anticyclonic center over southeastern South America. These differences in induced patterns during ENSO + events in different PDO phases imply a displacement of the region where anomalies of mean precipitation are favored according to PDO phases. The results presented in this chapter agree with these described patterns (Figs. 9 to 11). The novel contribution of the analysis conducted here is that, instead of analyzing mean precipitation, these patterns have been related to changes in the Rx1-ENSO relationship in SSA. Particularly, ERA5 reanalysis shows that during PDO– under ENSO + events, there are positive anomalies of z500 and z850 over the southern tip of South America and negative anomalies over the Atlantic and NWA. This strengthens low-level moisture flux over much of central and northern Argentina and promotes high Rx1 values. Conversely, during PDO + phases, there is a northward shift of positive anomalies of z500 and z850 over southern South America and the appearance of positive anomalies off the coast of Brazil, which together favor moisture convergence towards northeastern Argentina, Uruguay, and southern Brazil and high Rx1 values.

Regarding SAM, its relationship with Rx1 is enhanced during the warm phase of PDO. PDO phases modify circulation anomaly patterns in the Southern Hemisphere associated with SAM. For instance, according to Wachter et al. (2020), PDO modifies z500 anomalies in the region between Antarctica and southern South America in such a way that positive anomalies are strengthened in PDO + phases, consistent with what is observed in Fig. 12c. Additionally, they also demonstrated that PDO + promotes negative anomalies in the South Atlantic off the coast of southern South America, consistent with what is shown in Fig. 12c. In this work was shown that during PDO + under SAM+, z850 anomalies consist of cyclonic anomalies between 10–40°S over the continent and an increasing in low-level moisture and Rx1 values over central Brazil, western Paraguay, eastern Bolivia, and sectors of the NWA; the opposite is observed in southern Brazil. During PDO– under SAM-, negative z850 anomalies prevail over the Atlantic off the coasts of Brazil and Argentina, promoting a decrease in low-level moisture over eastern Argentina with lower Rx1 values, while there is an increase in moisture and Rx1 values towards southeastern Brazil.

In both phases of PDO, a Rx1-ENSO relationship is observed over eastern Argentina, which aligns with the known relationship for mean precipitation; that is, under ENSO+, there are higher precipitations, and vice versa during ENSO–. Towards western Argentina, the intensity of the Rx1-ENSO relationship varies between PDO phases, being narrower in PDO–, when moisture flux from the north is strengthened there. In the analysis for TUC, no differences were observed between the results using Niño 1.2 and Niño 3.4 indices during PDO–. Conversely, during PDO+, it was observed that in the first warm period, there is a weak negative correlation of Rx1 with Niño 3.4, and in the second with Niño 1.2. However, these correlations are low, and in shorter periods, compared to those obtained for SAM, which is why this study decided not to further analyze this result.

The results obtained for Rx1-SAM in PDO + are consistent with the positive association observed between mean precipitation and SAM over the NWA (Cavalcanti et al., 2021; Garbarini et al., 2021) and Brazil (Vasconcellos and Cavalcanti, 2010; Rebota et al., 2021). During PDO + phases, cyclonic (anticyclonic) anomalies are established in SAM+ (SAM–) over these regions, which favor (inhibit) the increase of moisture in the lower layers and affect Rx1. The findings of this work contribute to understanding the influence of SAM on precipitation intensity in SSA, a region where the primary focus of research works is on variability related to ENSO.

Rx1 exhibits significant interannual and interdecadal variability in TUC. The interannual variability is partially explained by the influence of ENSO and SAM in a non-stationary relationship conditioned by PDO phases. Regarding multidecadal variability, there is a clear shift from years with lower Rx1 values to higher values since the late 1950s, especially in the 1980s and 1990s, with an apparent return to lower values in the 2010s. These results are consistent with those of Ferrero and Villalba (2019) for total precipitation during the wettest months in the NWA. They detected a non-significant jump towards higher rainfall in the late 1950s, a notable increase in rainfall in the 1970s, and the onset of a drier period beginning between 2008 and 2013. Although this work did not extensively analyze this multidecadal behavior, it is observed that decades with heavier rainfall (1980s and 1990s) coincide with the warm phase of PDO. During these decades, Rx1 values show a positive correlation with SAM and a weak negative correlation with Niño 1.2. The subsequent return to weaker rainfall values coincides with a shift from a warm phase to a cold phase of PDO (DJFM average) in the late 2000s.

It is important to highlight the significance of analyzing the relationship between precipitation variability and climate forcings, considering the potential non-stationarity of these relationships. This is clearly demonstrated in the case of the precipitation series analyzed, where when correlating Rx1 without separating the series into sub-periods, statistically significant correlations are not obtained, or very low values are obtained.

Finally, in this work, data from a single meteorological station in the NWA were analyzed due to its length and quality. However, it was shown using HadEX3 and ERA5 that changes in Rx1-ENSO and Rx1-SAM relationships and the role of PDO are part of changes observed on a much larger scale than that of the analyzed locality. Therefore, the results indicate that the variability in precipitation intensity in the region is generated partly by modes of global variability.

Author Contribution

Conceptualization: all authors; Methodology: all authors; Formal analysis and investigation: Franco D. Medina; Writing - original draft preparation: Franco D. Medina; Writing - review and editing: all authors; Supervision: Ana G. Elias.

Acknowledgement

The authors acknowledge research project PIP 2957 (CONICET). We thank Estación Experimental Agroindustrial Obispo Colombres (EEAOC) for providing Tucuman rainfall data.

Data Availability

Data of climate indices are available on the followings webpages: ENSO (https://psl.noaa.gov/gcos_wgsp/Timeseries/), SAM (https://psl.noaa.gov/data/20thC_Rean/timeseries/monthly/SAM/ and https://legacy.bas.ac.uk/met/gjma/sam.html), PDO (http://research.jisao.washington.edu/data_sets/pdo/) and AMO (https://www.psl.noaa.gov/data/timeseries/AMO/). Rainfall data is not publicly available due to authors have not permission to share it, but can be request to Estación Experimental Agroindustrial Obispo Colombres (EEAOC, Tucumán, Argentina, website: https://www.eeaoc.gob.ar/). ERA5 data is available by Copernicus Climate Data Store (https://cds.climate.copernicus.eu/). HadEX3 data is available by Met Office Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadex3/download.html).

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Exploring extreme-rainfall forcings over Tucumán (Argentina) in the last 108 years and an extension to Subtropical South America

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Abstract

Figures

1. Introduction

2. Study region

3. Data

3.1 Station rainfall data for TUC

3.2 Gridded Rx1 data for Subtropical South America

3.3 Climate indices

3.4 Atmospheric fields

4. Methodology

4.1 Identification of the periods with the most significant correlation

4.2 Composites of anomalies for Rx1 and atmospheric variables

4.3 Two methods to determine PDO and AMO role on a non-stationary association

5. Results

5.1 Annual cycle and variability of Rx1

5.2 Correlation Rx1-ENSO and Rx1-SAM and obtention of PmPV and PMWL

5.3 Composites of Rx1 anomalies in SSA

5.4 Composites of anomalies of atmospheric variables for ENSO

5.5 Composites of anomalies of atmospheric variables for SAM

5.6 PDO and AMO roles in the non-stationary relationships of Rx1 with ENSO and SAM

6. Discussion and conclusions

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

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