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Influence of teleconnection patterns on global moisture transport during peak precipitation month

Influence of teleconnection patterns on global moisture transport during peak precipitation month INTRODUCTIONThe atmospheric branch of the hydrological cycle has been a topic of special interest in the past two decades given its important effect on the climate system. Several studies have recently investigated moisture transport in terrestrial areas at both local (Sodemann and Zubler, 2010; Drumond et al., 2014; Scoccimarro et al., 2018; Allan et al., 2020; Insua‐Costa et al., 2022) and global (Gimeno et al., 2010; 2013; van der Ent et al., 2010; Nieto et al., 2019) scales. Identifying and analysing sources with the strongest influence on continental precipitation is critical to understanding the water cycle and changes associated with global warming. Although from a global perspective specific sources are responsible for most of the moisture, at local scales moisture sources for continental precipitation differ between regions. Castillo et al. (2014) and Gimeno et al. (2010) defined 14 predominantly oceanic regions (listed in section 2.1) as the main sources at a global scale that change on monthly time scales (Nieto et al., 2019). However, some continental areas were identified over South America, the Sahel region, and South Africa. Although the influence of these sources on climatological precipitation was addressed by those studies, a detailed analysis of variability and the relationship with teleconnection patterns during the peak precipitation month has not been previously conducted in‐depth.Teleconnection patterns are generally associated with climate variability at different spatiotemporal scales and are frequently associated with anomalous tropical/subtropical heat sources (Grimm and Dias, 1995). Several studies have identified the influence of teleconnection patterns on precipitation over different regions (Wise et al., 2015; Zhong et al., 2017; Broman et al., 2020) and specifically during months of higher precipitation (Ríos‐Cornejo et al., 2014; Tan and Shao, 2017; Atif et al., 2020). At the global and hemispheric scales, four teleconnection patterns can be considered the main modes of climate variability. The El Niño–Southern Oscillation (ENSO) is a planetary‐scale climate phenomenon considered the most important pattern at interannual scales (Chang and Zebiak, 2015). The Southern Oscillation is an atmospheric phenomenon and consists of an interannual variation in the sea level atmospheric pressure over the tropical Indo‐Pacific. On the other hand, El Niño consists of changes in sea surface temperature in the equatorial Pacific. The effect of ENSO is critical in Pacific tropical latitudes involving large heat exchanges between the ocean and atmosphere, creating extreme situations in the hydrological cycle such as torrential precipitation or drought (e.g., Wang and Chan, 2002; Vicente‐Serrano et al., 2017; Muis et al., 2018; Stojanovic et al., 2021). The influence of ENSO on the precipitation patterns has been observed away from the Pacific, reaching the tropics over other ocean basins (e.g., Giannini et al., 2001; Kang et al., 2015; Sasaki et al., 2015). Its influence on precipitation also extends to farther regions in extratropics (Dai and Wigley, 2000), such as Europe (Shaman, 2014; Tabari and Willems, 2018), Africa (Gaughan et al., 2016), and South America (Grimm et al., 2000; Grimm, 2003; 2004; 2011; Tedeschi et al., 2015; Tedeschi et al., 2016; Cai et al., 2020). In particular, over South America, ENSO has been proved to influence the precipitation variability and extreme precipitation in all seasons (Grimm, 2011; Tedeschi et al., 2015; Tedeschi et al., 2016), even in monsoonal regimes (Grimm, 2003; 2004). Castillo et al. (2014) found that the main global moisture sources are generally affected by the occurrence of El Niño and La Niña events.The Arctic (AO) and Antarctic (AAO) Oscillation atmospheric patterns are also highly related to precipitation variability over the Northern and Southern Hemispheres, respectively (Silvestri and Vera, 2003; Hu and Feng, 2010; Kryzhov and Gorelits, 2015; Rosso et al., 2018). These patterns, also named hemispheric annular modes, are modulated by shifts in the position and intensity of wind belts around the Arctic and Antarctica (Thompson and Wallace, 2000). For the AO, winds rotating counterclockwise intensify in the positive phase, confining the cold air in polar areas, while in the negative phase, the weakening of this belt allows the entry of Arctic air masses towards lower latitudes (Thompson and Wallace, 1998). For the AAO, this mode describes the north–south movement of the westerly wind belts in the Southern Hemisphere. This movement occurs in such a way that it moves towards Antarctica during the positive phase, limiting the entry of polar air into lower latitudes, while its displacement towards the Equator in the negative phase increases the number and intensity of low‐pressure systems in the middle latitudes (Gillett et al., 2006). The effect of both patterns on moisture transport was previously investigated by Nieto et al. (2014), who suggested that these modify the moisture flux from the Atlantic and Pacific regions towards continents in both patterns.Another teleconnection pattern showing an important influence over the Northern Hemisphere is the Pacific‐North American Oscillation (PNA), which consists of an alternating pattern between pressure centres over the Central Pacific Ocean with centres of action located over western Canada and the southeastern United States (Oliver, 2005). The positive phase of the pattern is characterized by above‐average heights over the Rocky Mountains and below‐average heights south of the Aleutian Islands and over the southeastern United States, being the pattern reversed for the negative phase. Despite its area of higher influence over North America (Leathers et al., 1991; Mallakpour and Villarini, 2016; 2017), some previous studies have shown an effect on other regions such as Asia (Aizen et al., 2001) and Europe (Fuentes‐Franco and Koenigk, 2020). Over North America, several studies have found a positive correlation between precipitation over the western part of the continent, while the opposite occurs over the eastern part (Liu et al., 2013; Mallakpour and Villarini, 2017). Liu et al. (2013) suggested a link to shifts in the position of the polar jet and changes in synoptic air mass frequencies.This study conducted a detailed analysis of the variability of moisture transport associated with ENSO, AO, AAO, and PNA during the peak precipitation months, from major global oceanic and terrestrial sources associated with maxima in precipitation.METHODOLOGYMoisture transport computationTo investigate global moisture transport associated with teleconnection patterns in the months of maximum precipitation by grid, the outputs of the Lagrangian particle dispersion model FLEXPART v9.0 (Stohl et al., 2005) were used. This model generates the trajectories of a high number of atmospheric particles, which allow the investigation of changes in specific humidity along with them. To determine the moisture transport, a global experiment was performed in which the total global atmosphere was divided into ~2 million air parcels (hereinafter denoted as “particles”) of equal mass (m), following the atmospheric mass distribution (Stohl and James, 2004). The particles were then allowed to freely move with the wind data from the ECMWF reanalysis ERA‐Interim (Dee et al., 2011), for the complete period 1980–2018. With a 6 hr time step, the position (latitude, longitude, and altitude) and the specific humidity (q) were recorded for each particle along trajectories of 15 days, since it is the maximum time obtained by Nieto and Gimeno (2019) as the optimal integration time for Lagrangian studies. Along each trajectory, it is possible to analyse the moisture variation of the particles through changes in q, and then to compute over each grid cell the moisture contributions. The complete procedure is described in more detail hereunder.This study analysed the moisture contributions from the main oceanic and terrestrial sources over each gridded area in its month of higher precipitation. These sources were defined by month previously in Nieto et al. (2019): the North Pacific (NPAC), South Pacific (SPAC), Gulf of Mexico and Caribbean Sea (MEXCAR), North Atlantic (NATL), South Atlantic (SATL), Zanzibar Current and Arabian Sea (ZANAR), Agulhas Current (AGU), Indian Ocean (IND), Coral Sea (CORALS), Mediterranean Sea (MED), Red Sea (REDS), South America (SAM), Sahel region (SAHEL), and Southern Africa (SAFR). The methodology used here concerning the moisture computation was first established by Stohl and James (2004, 2005) and has been widely used (Zandonadi Moura and Lima, 2018; Drumond et al., 2019; Algarra et al., 2020). For the global experiment, to detect moisture sinks, particles located over each moisture source were selected for each day from 1980 to 2018, and the moisture variation experienced by each particle was computed, for up to 15 days forward in time, following the equation e−p=mdqdt, where e and p represent evaporation and precipitation processed in time (t = 6 hr) by each particle of mass m. Following this procedure, for each of the particles that left a specific source, on a specific day in the period 1979–2018, we obtained the (e − p) values for 60 time steps (15 days every 6 hr). For example, for a particle leaving an oceanic area on January 1, 1980, we obtained the (e − p) on their trajectory from January 1 at 0000 UTC until January 15 at 1800 UTC. The same for the particles that left the area on January 2, 1980 at 0000 UTC, that ended on January 16 at 1800 UTC, and for all the days in the period of the study. So, the 15 days of individual trajectories for all the particles followed from each source forward in time are individually available for each day every 6 hr from December 17, 1979 until December 31, 2018.This trajectories information can be used to compute the total atmospheric moisture budget (E − P) over each grid cell computed following the next equation:1E−P=∑k=1Ke−pkA,where K is the total number of particles within the atmospheric column over an area of base A (in this study, a gridded area with 0.5 horizontal resolution). E − P was individually computed for each day along the study period, for all the days from December 17, 1979 to December 31, 2018. For each day, all the particles over a moisture source were followed forward for 15 days (Figure 1) and, for each day of transport at each grid cell, E − P was calculated by adding the (e − p) values of all individual particles over each column of A base (our defined grid size) following Equation (1). Figure 1 shows an example of a pair of trajectories that leave a source of moisture. Figure 1a–c represents one trajectory during the first day (December 16, 1979) of our period of study, and Figure 1d–f another one during the last day (December 31, 2018). In both columns, the trajectories are represented during the 1st day forward in time (Figure 1a,d), the 2nd day (Figure 1b,e), and the 15th day (Figure 1c,f). The E − P computation is represented over the grid cell on which the particle is located on each day of the trajectory (shaped areas A). The E − P value is performed as in Equation (1), by considering (e − p) values for all the particles that reach the same atmospheric column; in this case, for the particle represented in the example and for the remaining particles that leave the source (blue dots in Figure 1). This procedure was individually performed for each of the days in the study period (considering not only the sample particle but all the particles on all grid points belonging to the sink) and along the 15 days of the trajectories tracking.1FIGURERepresentation of the (E − P)all‐tracks files computation from one moisture source. The black dots and the black line represent the trajectory followed by a specific particle from the source. The columns represent the days in the period December 16, 1979–December 31, 2018 and the rows represent the number of days the trajectories were followed. The grey boxes represent the total atmospheric column in which the moisture variation (e − p) for all the particles (blue and black dots) are added in order the compute the total atmospheric moisture budget following Equation (1) [Colour figure can be viewed at wileyonlinelibrary.com]So, finally for a moisture source a total of 208,275 (13,885 days in the period × 15 days of trajectories) E − P fields are available, and the corresponding files are stored to be used in the next step. As the interest is on moisture contributions for precipitation, only negative values of E − P were considered for the computation and retained in the E − P files. These fields are named hereafter as (E − P)all‐tracks. Further details and clarifications on the E − P computation can be found in Vázquez et al. (2020).For each continental grid point, Nieto and Gimeno (2019) defined the optimal integration time (topt, ranging from 1 to 15 days) for Lagrangian calculations that regionally achieve the best correspondence between the moisture for precipitation transported from the sources and the precipitation. This optimal integration times (topt) was used to compute the total moisture contributions over continental areas, by using the individual (E − P)all‐tracks files computed previously. The complete procedure is described below.The methodology for computing the daily moisture contributions over each grid point is represented in Figure 2. Three grid points with different topt are shown, being represented in green one with a topt = 5 days, in blue for topt = 7 days, and in red for topt = 15 days. The total daily moisture contributions were performed by adding the values of the (E − P)all‐tracks fields previously computed. It is important to notice that the (E − P)all‐tracks fields were computed taking into account the dates in which the particles leave the sources; however, it takes some time (between 1 to topt days) to reach the continental areas over which the final moisture contributions is computed For example and following the schematic Figure 2, to compute the moisture contributions on January 1, 1980, if the topt over a grid cell is 15 days (red square in Figure 2), its final moisture contributions was computed by adding the (E − P)all‐tracks fields from the particles that leaving the moisture source on December 31, 1979 take 1 day to reach the grid cell, the particles leaving the source on December 30, 1979 that take 2 days, and so on, until the particles that take 15 days on December 17, 1979. So, in this example, the final daily moisture contributions were composed adding 15 values of (E − P)all‐tracks from December 31, 1979 to December 17, 1979 for those tracks that take from 1 to 15 days to arrive at the sink cell.2FIGURERepresentation of the daily moisture contributions computation by grid point for different optimal integration times (topt). Example for the January 1, 1980. The squares represent the areas over which the moisture contributions are computed, representing the colours different topt (5 days for green, 7 days for blue and 15 days for red). Each of the rows represents a different time duration (1, 5, 7 and 15 days) in the transport from the source to the continental grid areas (on January 1, 1980), and the arrows represent the hypothetical trajectories of some particles (dots) considered in the E − P computation. The dates in purple represent the date in which the moisture contributions are computed, while the date in orange is the date in which the particles leave the source. The grey squares represent those grid areas in which the duration of the transport is higher than the topt and then are not considered in the moisture contributions computation. [Colour figure can be viewed at wileyonlinelibrary.com]The same procedure was performed for all the continental grid areas, considering in each case the topt. Therefore, over the green cell in Figure 2, which has topt = 5 days, the (E − P)all‐tracks values added were 5 and go from December 31, 1979 until December 27, 1979 for the trajectories taken from 1 to 5 days of transport until reaching the sink cell. The same applies for the blue cell with topt = 7 days, the daily final moisture contributions added the (E − P)all‐tracks values from December 31, 1979 until December 25, 1979, 7 days of transport before, accounting tracks that take from 1 to 7 days to arrive the sink cell.From these daily moisture contributions, a monthly climatological mean can be obtained. The procedure here described was performed individually for all the 14 oceanic and terrestrial sources listed before and defined in Nieto et al. (2019).The model and the methodology here presented highlight some limitations that should be considered in the interpretation of the results. First of all, the methodology does not consider the moisture phase change along trajectories, which may lead to overestimation of the contribution to precipitation, for example, when the positive (e − p) values are associated with cloud formation. Moreover, the number of days considered in the moisture trajectories may affect the results. For this reason, we individually consider the time of integration of the trajectories accordingly with the optimal time for Lagrangian studies defined by Nieto and Gimeno (2019). Despite these deficiencies, the methodology has proven to be valid for different latitudes across regions (from equatorial to polar regions). Finally, the results presented in this study are also susceptible to the limitations of the data feeding the model. In this sense, ERA‐Interim data shows some limitations in the characterization of the hydrological cycle, in some areas such as Africa, due to the lack of assimilated observational data (Dunning et al., 2016; Hill et al., 2016). Moreover, several authors have highlighted problems with the assimilation of rain‐affected radiances that produced a sub‐estimation of precipitation over oceans (Dee et al., 2011; Allan et al., 2014; Craig et al., 2017).Assignment of the preferred sources of moisture for precipitation by grid for the peak precipitation monthThe analysis presented here was based on a ranking of the contribution from each moisture source, computed using the Lagrangian outputs, over each continental grid point during its peak precipitation month (PPM) (Figure 3a). The PPM used in this study was previously defined by Nieto et al. (2019), using mean monthly precipitation data for the period 1980–2018 taken from the CPC Global Unified Gauge‐Based Analysis of Daily Precipitation (Chen et al., 2008), and represents (at every grid point) the month with higher mean precipitation.3FIGURE(a) Month of maximum precipitation (PPM) for every grid point computed from CPC daily data for 1980–2018. Climatological (b) preferred (PS) and (c) secondary (SS) sources calculated from daily moisture contributions from main global oceanic and continental sources. The small figure between (a) and (b) represents the maximum extension of the moisture sources (the higher area reached for the source in all months). Note that for this small figure and for (b) and (c) the colourbar is the same and represents moisture sources [Colour figure can be viewed at wileyonlinelibrary.com]The preferred source (PS; Figure 3b) was defined as the source showing the highest moisture contributions over a grid in the PPM, and the secondary source (SS; Figure 3c) was the secondmost important source. The order of the contributing moisture sources (PS, SS) was obtained following the same methodology as in Nieto et al. (2019). This methodology ranks the climatological moisture contributions from each source by month over each grid point, using as reference the mean value for the whole period 1980–2018.In contrast to Nieto et al. (2019), in which the moisture contributions for precipitation were directly computed in monthly terms, this work was done using daily moisture contributions, and the monthly values were obtained by computing the mean from them. Another difference between both methodologies was the step in which the positive E − P values are removed. In Nieto et al. (2019) it was not removed until the end of the monthly computation; however, in this work, it was removed daily. Although this change may cause some differences in the order of the preferred sources, we consider that this procedure is more suitable to adequately characterize the percentage contribution of moisture sources for the PPMs, as the possible moisture uptake on some specific days does not detract the real moisture contributions on daily precipitation.Despite the ranking for the PS and SS definition being done from a climatological point of view using the mean values for the period 1980–2018, the moisture contributions from each source were computed for each month over the 39 years of the study. The contribution from the remaining sources, those that are not the PS or SS, had been considered in this study as an ensemble and named REST (the sum of the contributions from the remaining sources).Teleconnection indices and composite analysisThe indices used to investigate AO, AAO, ENSO, and PNA were extracted from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) on a monthly time scale. For ENSO conditions, the Bivariate ENSO time series (BEST; Smith and Sardeshmukh, 2000; available for the period 1948–2020) was used. This index combines a standardized SOI and a standardized Niño3.4 sea surface temperature time series, in which El Niño/La Niña events correspond to high/low BEST index values. The PNA index (available for the period 1948–2021) was calculated based on rotated principal component analysis (RPCA) applied on monthly mean standardized 500 mb height anomalies in the region 20°–90°N (Barnston and Livezey, 1987). The AO (available for the period 1950–2021) and AAO (available for the period 1979–2020) patterns corresponded with the leading pattern in the empirical orthogonal function (EOF) analysis of monthly mean height anomalies at 1,000 hPa in the Northern Hemisphere and 700 hPa in the Southern Hemisphere, respectively. A detailed description of these calculations can be obtained from NOAA (https://psl.noaa.gov/data/climateindices/list/).A composite analysis was performed to analyse the positive and negative phases of the four indices. The positive (negative) phase was defined as the months showing values above (below) 1 (−1) standard deviation in the time series of the indices. The number of years in each phase and the specific years are presented in Tables 1 and S1–S4, respectively.1TABLEThe number of years considered for the composite analysis in every phase of the teleconnection patternsJanFebMarAprMayJunJulAugSepOctNovDecPNA+574656598985PNA−798867365688AO+810134420114512AO−910102422137711AAO+693310895105313AAO−343558857287BEST+596796688988BEST−1077521335687Note: Red colour marks months not considered in the study.Taking into account the years in every phase for every month, a composite analysis was performed by considering the mean moisture contributions for each phase. The composite computation was only performed if there were at least 3 years from 1980 to 2018 for each phase of the index. Those months not considered for each index, according to this criterion, are represented in red in Table 1. With this information, the moisture contributions to precipitation from the sources by each index and phase at each continental grid could be obtained. Once the composite is performed for each month, each moisture source, and the different phases of the indices; at each grid point, the values of the composites are retained for the PS and SS to the corresponding PPM. The same procedure was also applied to the total contribution from the remaining moisture sources altogether (REST).To investigate how the moisture contributions varied in the positive and negative phases for every index, the difference in the composites was calculated, and a significance bootstrap test (Efron, 1992) was applied to the results. The bootstrap test is based on the resampling of the initial data to address significant variations. For the PPM and PS (SS, REST) at every grid point, from the 1980–2018 annual time series of moisture contribution results, two subsamples with the size the number of years of the positive and negative phases according to Table 1 are randomly selected. Then, the difference in the random samples is performed analogously to the composite difference. The resample and difference is repeated until obtaining a total of 1,000 subsamples. By considering the subsamples, the composite difference is expected to be significant at a 90% significant level, if it is higher than the 90th percentile of the 1,000 subsamples obtained with the resampling. The results for the PS, SS, and REST were presented at every grid point for the corresponding PPM.RESULTSTo investigate the moisture contributions from the main oceanic and terrestrial sources during the PPM in association with the different teleconnection patterns, a composite analysis for the years corresponding to each phase of the indices was performed as described in section 2. The results presented here are based on the difference in moisture contributions between the composites of the positive and negative phases (positive minus negative). The positive (negative) values in the following figures represent the areas where the moisture contributions are higher in the positive (negative) phase. The figures present only the significant values at the 90% confidence level. Moreover, the results are only presented in the hemisphere where the pattern showed influence (PNA and AO for the Northern Hemisphere, AAO for the Southern Hemisphere, and ENSO for both).As previously described, the results presented here are based on the climatological PS and SS (Figure 3b,c). Despite following a slightly different methodology than that established by Nieto et al. (2019), the obtained results did not differ in general terms. Some differences in PS or SS may have occurred if only the years in which teleconnection pattern events occur (positive and negative phase events) were taken into consideration during calculation. However, as these differences were small, and to facilitate the interpretation of the results, the PS and SS used here were based on the climatological calculations as described in section 2.2 and Nieto et al. (2019) (Figure 3). To facilitate the interpretation of the results, the PPM over the region with positive and negative differences in each pattern is presented in Figures S1–S4.PNA teleconnection patternFigure 4 shows the difference in moisture contributions (|E − P < 0| values) at each grid point between the positive and negative phase of PNA index from the PS (Figure 4a), SS (Figure 4b), and REST (Figure 4c). It is important to note that in the plots here presented there is a mixture of sources and seasons, as they were represented for the PS/SS/REST in the PPM (see Figure 3). To facilitate the interpretation of the results, the sources associated with observed differences for PS and SS are presented in Figure 4d–g. In both figures, the sources and the PPM are shown separately for areas with positive and negative significant differences. Figure 4d,f represent the PS associated with the areas of positive (Figure 4d) and negative (Figure 4f) differences observed in Figure 4a. The same for SS is presented in Figure 4e,g. Figure S1 shows the climatological PPM (as in Figure 3a) separately for the regions where positive and negative significant differences occur in the pattern.4FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between the positive and negative phases of PNA for 1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]The difference in the composites for the contribution from PS in the PPM over each grid point (Figure 4a) showed an opposite sign over eastern and western North America. Over the former, the contribution from PS decreased, associated with the positive phase, but over the latter the opposite occurred, with the moisture contributions associated with PS higher during the negative phase of the PNA. It is important to note that there are differences in PS for eastern and western North America. While NPAC is the PS over most of the region, especially over the western part, over the eastern region there are also contributions from NATL, MEXCAR, and NPAC (Figure 4f).The increased contribution of the negative PNA phase over eastern North America continued to appear when SS was analysed (Figure 4b). In general, the three sources showed an increased contribution over this area, though this was higher when associated with the Atlantic sources (NATL and MEXCAR; Figure 4a,f).PNA also influenced the moisture contributions from the main sources over Europe, most evidently in the western area associated with PS (Figure 4a). Over the Iberian Peninsula, NATL (which acted as PS over most of the area) showed a lower contribution associated with the positive phase of the pattern over the southwestern part of the peninsula and the northern regions (Figure 4a,f). However, the opposite was observed over the northwestern corner of the peninsula (Figure 4a,d). A positive relationship between PNA and moisture contributions over northwestern Iberia was also observed when REST was analysed (Figure 4c). However, this signal did not appear for SS (Figure 4b), which corresponded with NPAC over this area. The increased moisture contributions from NATL, when acting as PS and associated with the positive phase of PNA, were also observed over the British Isles and northern France (Figure 4a,d). An area of important positive values in the difference between phases was observed expanding from the Iberian Peninsula to central France (Figure 4) associated with both NATL (PS; Figure 4d) and MED (SS; Figure 4e). This area should be distinguished from the rest of western Europe because of the season when PPM occurred. While the other areas showed PPM in late autumn and winter, over this area PPM occurred in spring (Figure S1). Finally, increased moisture contributions associated with the positive phase of the pattern from all sources were generally observed over some areas of southern Asia. This was especially evident for ZANAR (PS; Figure 4a,d) and IND (SS; Figure 4b,e).Arctic oscillationThe significant differences between the moisture contributions from the main global moisture sources (as PS, SS, and REST) during the PPM in positive and negative phases of AO, as well as the sources associated with the differences, are shown in Figure 5. It is important to note that a very small amount, or no cases, of positive and negative phases of AO appeared in summer for this teleconnection pattern (Table 1). Over large areas, PPM occurred in this season, so the results are only presented over specific areas of the Northern Hemisphere.5FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between the positive and negative phases of AO for 1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]Over Europe, the negative phase of the index produced an increase in moisture contributions from NATL (the PS; Figure 5a,f) over most of the Iberian Peninsula, the European Atlantic coast, and northern Africa (with only small areas showing the opposite signal) and particularly for PPM in late autumn and winter (Figure S2). However, the MED contribution to the eastern Iberian Peninsula increased during the positive phase of the AO (Figure 5b,e). In general, NATL and MED modulated moisture contributions over western Europe in AO conditions, and no important variations are observed related to the rest of the main global moisture sources (Figure 5c). Over southern Europe (Italy, Greece), in general, the contributions of moisture from both the PS (MED) and the SS (NATL), were stronger during the negative phase of the AO than during the positive phase (Figure 5a,b,f,g). The same behaviour was found for the rest of the sources (REST; Figure 5c).The AO pattern also showed some influence over North America. In the eastern region, increased contributions from MEXCAR (PS; Figure 5a,d) were observed associated with the positive phase. On the other hand, in the western region, NPAC (PS; Figure 5a,f) increased its moisture contributions during the negative phase of the pattern, also for PPM in late autumn and winter (Figure S2).Antarctic oscillationThe results for the AAO pattern are presented in Figure 6. During the positive phase, an enhanced contribution from CORALS occurred over eastern Australia when it acts as PS (Figure 6a,d) and over southeastern and northern areas when acting as SS (Figure 6b,e). Over northwestern Australia, a reduction in the moisture contributions from IND (Figure 6a,f) was observed in the positive phase. These results showed an east/west shift in circulation over this region associated with AAO, which promoted the input of moisture from the Pacific in the positive phase of the index and from the Indian Ocean in the negative phase for PPM in Southern Hemisphere summer (January and February; Figure S3).6FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between the positive and negative phases of AAO for 1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]In Africa, some important contributions from the sources appeared to be associated with this pattern. First, over eastern Africa, an important area with increased moisture contributions associated with the positive phase of the index was observed, mainly in coastal areas from Mozambique to southern Somalia, for Southern Hemisphere summer PPM (November–February; Figure S3). This increased contribution was generally associated with the main moisture sources (PS and SS). Over the western part of the continent, a dipole pattern was observed southward from the Gulf of Guinea. This area was mainly fed by moisture from SATL. This difference between positive values over southern areas and negative values over northern areas suggested a southward shift in SATL's influence during the positive phase of the index.In South America, the influence of the pattern also showed its signal. In general, an increase in the moisture contributions from NATL was observed in the positive phase, located over the Amazon region when this source acted as PS (Figure 6d) and over southern areas when it was SS (Figure 6e). However, the influence of SATL was unclear. Although some increases in the moisture contributions associated with the positive phase were observed from SATL over some coastal areas in eastern South America (Figure 6a,d), the signal reversed over large areas (Figure 6a,f). SPAC seemed to increase its moisture contributions in the positive phase over southern South America (Figure 6a,f).El Niño–Southern OscillationFigure 7 shows the variation in moisture contributions from the main global moisture sources associated with ENSO. In this figure, positive (negative) values represent regions with an increased contribution associated with El Niño (La Niña) events (Figure 7a–c). Moreover, it is also shown the PS and SS associated with positive (Figure 7d–e) and negative (Figure 7f–g) significant differences in the pattern. Figure S4 shows the PPM (as in Figure 3a) separately for the regions where positive and negative significant differences occur in the pattern.7FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between El Niño and La Niña events for1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]In El Niño events the moisture contributions from SPAC (when acting as PS; Figure 7a,d) increased over southern South America. However, over northern and central South America, the occurrence of La Niña events seemed to favour contributions from the Atlantic Ocean to continental areas, especially for NATL. The SATL influence seemed to be favoured during La Niña events (as PS; Figure 7a,f) and SS (Figure 7b,g) over the northwestern region. However, the contribution during La Niña appeared to be reduced over the southeastern area, suggesting a shift in circulation from SATL towards South America associated with this ENSO phase.In Africa, as for AAO, ENSO seemed to modulate the entrance of moisture from SATL into western Africa. La Niña events produced an increased contribution from SATL over southern regions, while El Niño events contributed to SATL moisture contributions over northern areas. Over the Horn of Africa, El Niño events produced an increase in moisture contributions from ZANAR, though a decreased contribution was observed from IND.In general, ZANAR's influence seemed to be affected by the ENSO phase, as its effect showed a shift not only over Africa but also over Asia. Decreased (increased) moisture contributions from the PS (ZANAR; Figure 7a,f) and SS (IND; Figure 7b,g) were observed over northern and eastern India associated with El Niño (La Niña) events. Over the Indochinese Peninsula, the opposite signal was observed by increasing (decreasing) the moisture contributions from PS (ZANAR; Figure 7a,d) associated with La Niña (El Niño) events. The ENSO pattern has a well‐known influence on the Indian Ocean precipitation pattern. In general terms, El Niño events are often associated with a suppression in the Indian monsoonal rainfall, being the opposite true for La Niña events in association with the changes in the walker circulation which accompany the ENSO modulation (Krishnamurthy and Goswami, 2000; Bracco et al., 2005).Over Europe, the ENSO pattern showed a clear influence over the western coast, where the contribution from PS increased with El Niño events (Figure 7a), caused by NATL. Over the eastern Mediterranean coast, the moisture contributions from PS (MED) decreased. In North America, El Niño events produced increased moisture over the western and northern regions from NPAC (acting as PS; Figure 7a,d).DISCUSSION AND CONCLUSIONSThis study explored the effect of teleconnection patterns on moisture transport from main global moisture sources during PPM periods. To focus the discussion of the results, and to link them with some previous studies, for each of the indices a specific area was selected and discussed (but as it was shown previously, the effects due to them were far‐reaching). For the PNA the discussion is focused on North America, for AO on Western Europe, for AAO is centred on Africa, and finally, ENSO results are mainly discussed over South America. Figure S5 presents the effect of teleconnection patterns on precipitation taken from the CPC Global Unified Gauge‐Based Analysis of Daily Precipitation (Chen et al., 2008) provided by the NOAA/OAR/ESRL Physical Science Division for the same period.The PNA pattern showed its maximum influence over North America, though some influence also occurred over Europe and southern Asia. Over North America, an east–west dipole of moisture contributions can be observed (Figure 4a) with the difference between phases being opposite and higher than 1.5 mm·day−1. This dipole was also described in previous research (Liu et al., 2013; Mallakpour and Villarini, 2017). Over the western part of the region, the positive phase was associated with an increase in moisture contributions from NPAC. In this phase of the index, an enhancement of the circulation occurred between 10°N and 30°N (where NPAC is located) and this affected the moisture contributions from this source. The dipole pattern associated with PNA causes, by the intensification of the Aleutian Low (Overland et al., 1999), higher pressures over tropical areas that produce an intensification of flow over the Pacific Ocean. This favoured the inflow of moisture over the western coast of North America. Liu et al. (2013) also suggested that increases in moisture contributions from lower latitudes are favoured in winter, associated with the positive phase of the index. Over eastern North America, the opposite sign was observed, with a decrease in moisture contributions (especially from NATL and MEXCAR) in the positive phase of the index (Figure 4a). Over this region and associated with this phase, the entrance of polar air mass was favoured over eastern North America due to the equatorward shift in the polar jet stream (Liu et al., 2013; Mallakpour and Villarini, 2017). This caused the southern and eastern moisture sources to reduce their contribution during this phase. In general terms, the contribution pattern is similar to the observed in total precipitation (Figure S5) however some differences are observed over the south part of the region.In the case of AO, the area of higher impact was observed over western Europe (Figure 5). The positive phase of the AO is associated with the shift in the Atlantic storm track towards northern latitudes (Dickson et al., 2000; Thompson and Wallace, 2000). This produces increased precipitation over northern Europe and a decrease over southern regions (Kryzhov and Gorelits, 2015). The results presented in this manuscript suggest NATL and MED modulation of moisture transport over western Europe. Over the northwestern areas, NATL moisture contributions were favoured in the negative phase of the index with increased contribution higher than 2 mm·day−1 in this phase, while MED moisture contributions increased over the southeastern region in the positive phase. This behaviour resembles the total precipitation pattern, in agreement with Boer et al. (2001), who described a meridional shift in mid‐latitudes associated with annular modes. The positive phase of AO was associated with anomalously strong easterlies over the southern Northern Hemisphere, matching the observed increased contribution from MED (Figure 5e) in this phase over the eastern Iberian Peninsula and the total increased precipitation over southern Europe (see Figure S5).During the AAO, as previously addressed, the positive phase of the index was characterized by a poleward displacement of the wind belt around the Antarctic, producing a shift in the synoptic weather system (Thompson and Wallace, 2000). The poleward displacement of the westerlies allowed easterlies from the Indian Ocean to penetrate Africa, affecting moisture transport eastwards. Over eastern Africa, the contribution increased from the western Indian basin (ZANAR and AGU; Figure 6d). In agreement with this result, Malherbe et al. (2014) found a positive relationship between the AAO and moisture contributions from tropical Indian cyclones over eastern Africa. AGU's contribution also affected precipitation over southwestern areas, though over this region, increased moisture contributions were observed from SATL (Figure 6d). Tropical SATL has been suggested to influence precipitation patterns over southern Africa (Vigaud et al., 2007; Ndarana et al., 2020) and a general increase in total precipitation is associated with the positive phase of the index (Figure S5). The results presented here suggest a shift in the area of influence of this source in AAO events, by decreasing (increasing) its moisture contributions over northwestern Africa in high (low) AAO events and increasing (decreasing) over southern and coastal areas. An important influence is also observed in South America, being the difference in the moisture contribution from the preferred source into both phases higher than 1.8 mm·day−1 over most of the area.Regarding ENSO results, in El Niño years, the strengthening of the subtropical jet intensifies synoptic systems in the southern region while weakening their propagation northward (Tedeschi et al., 2013), favouring moisture contributions from SPAC over this area. The opposite pattern is observed over northwestern South America for both precipitation and SPAC contributions. Not only the Pacific but also the Atlantic moisture transport was affected by ENSO. SATL showed increased (decreased) moisture contributions during El Niño (La Niña) events over southeastern Brazil, while the opposite occurred further west (Figure 7a,d,f). Moreover, a decreased (increased) contribution was observed over the Amazon River basin during these events associated with NATL (Figure 7a,f). The results here presented agree with the moisture flux anomalies observed by Grimm (2003, 2004) during El Niño (La Niña) events. Over western North America and Canada, El Niño events are associated with increased moisture contribution from the Pacific Ocean. During the El Niño events, the Aleutian low is intensified and the subtropical jet extends to the south and east with an anomalous cyclonic flow around it. This causes increased moisture transport towards North America (Kim et al., 2019). To a lesser degree, the influence of ENSO on the moisture contribution was also observed over other regions. For example, El Niño events are associated with increased contribution from the Atlantic Ocean over the western.In general terms, the results presented here show a clear influence of the main teleconnection pattern on the moisture contributions from major moisture sources in the peak precipitation month, especially in association with the preferred source. This influence is evident for some patterns and over some specific regions, such as the Mediterranean Sea and North Atlantic Ocean modulation of western European precipitation for Arctic Oscillation, or the altered moisture transport observed over Australia and eastern Africa for Antarctic Oscillation. Although this analysis was based on monthly precipitation for the month with peak precipitation, in a climate change framework, analyses of extreme precipitation on a daily scale would be of special interest. Several studies have highlighted the increased influence of global teleconnection patterns on this kind of event (Casanueva et al., 2014; Zecca et al., 2018; Do et al., 2020); this study establishes the basis of a methodology that could be extended in future research. The teleconnection pattern here analysed tries to establish a general overview at a global scale, however (as can be observed in Figure S6) they do not properly explain the variability in precipitation over many areas. At a regional scale, some other teleconnection patterns can play a role. For example, North Atlantic Oscillation has a strong impact on European precipitation patterns and the Indian Ocean Dipole highly influence precipitation over the western and eastern sides of the Indian Ocean. The study of the influence of these patterns' scale, as well as the link between different teleconnection patterns at a regional scale and the possible causes associated with the changes (e.g., changes in sea surface temperature), will be the scope of future analysis.ACKNOWLEDGEMENTSThis work forms part of the LAGRIMA project (RTI2018‐095772‐B‐I0O) funded by Ministerio de Ciencia, Innovación e Univesidade, co‐funded by the ERDF, in the framework of the Operational Program Galicia 2014–2020. Marta Vazquez is supported by the Xunta de Galicia under grants ED481B 2018/062 and ED481D‐2022‐020. Margarida L. R. Liberato acknowledges funding from Fundação para a Ciência e a Tecnologia, Portugal (FCT) and Portugal Horizon 2020 through project WEx‐Atlantic (PTDC/CTA‐MET/29233/2017, LISBOA‐01‐0145‐FEDER‐029233, NORTE‐01‐0145‐FEDER‐029233) and for the academic mobility to the Environmental Physics Laboratory (EPhysLab), Universidade de Vigo, Spain under Fundación Carolina (C.2019). This work was partially supported by Xunta de Galicia under the Project ED431C 2021/44 (Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva) and Consellería de Cultura, Educación e Universidade). Funding for open access charge: Universidade de Vigo/CISUG.REFERENCESAizen, E.M., Aizen, V.B., Melack, J.M., Nakamura, T. and Ohta, T. (2001) Precipitation and atmospheric circulation patterns at mid‐latitudes of Asia. 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Influence of teleconnection patterns on global moisture transport during peak precipitation month

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Abstract

INTRODUCTIONThe atmospheric branch of the hydrological cycle has been a topic of special interest in the past two decades given its important effect on the climate system. Several studies have recently investigated moisture transport in terrestrial areas at both local (Sodemann and Zubler, 2010; Drumond et al., 2014; Scoccimarro et al., 2018; Allan et al., 2020; Insua‐Costa et al., 2022) and global (Gimeno et al., 2010; 2013; van der Ent et al., 2010; Nieto et al., 2019) scales. Identifying and analysing sources with the strongest influence on continental precipitation is critical to understanding the water cycle and changes associated with global warming. Although from a global perspective specific sources are responsible for most of the moisture, at local scales moisture sources for continental precipitation differ between regions. Castillo et al. (2014) and Gimeno et al. (2010) defined 14 predominantly oceanic regions (listed in section 2.1) as the main sources at a global scale that change on monthly time scales (Nieto et al., 2019). However, some continental areas were identified over South America, the Sahel region, and South Africa. Although the influence of these sources on climatological precipitation was addressed by those studies, a detailed analysis of variability and the relationship with teleconnection patterns during the peak precipitation month has not been previously conducted in‐depth.Teleconnection patterns are generally associated with climate variability at different spatiotemporal scales and are frequently associated with anomalous tropical/subtropical heat sources (Grimm and Dias, 1995). Several studies have identified the influence of teleconnection patterns on precipitation over different regions (Wise et al., 2015; Zhong et al., 2017; Broman et al., 2020) and specifically during months of higher precipitation (Ríos‐Cornejo et al., 2014; Tan and Shao, 2017; Atif et al., 2020). At the global and hemispheric scales, four teleconnection patterns can be considered the main modes of climate variability. The El Niño–Southern Oscillation (ENSO) is a planetary‐scale climate phenomenon considered the most important pattern at interannual scales (Chang and Zebiak, 2015). The Southern Oscillation is an atmospheric phenomenon and consists of an interannual variation in the sea level atmospheric pressure over the tropical Indo‐Pacific. On the other hand, El Niño consists of changes in sea surface temperature in the equatorial Pacific. The effect of ENSO is critical in Pacific tropical latitudes involving large heat exchanges between the ocean and atmosphere, creating extreme situations in the hydrological cycle such as torrential precipitation or drought (e.g., Wang and Chan, 2002; Vicente‐Serrano et al., 2017; Muis et al., 2018; Stojanovic et al., 2021). The influence of ENSO on the precipitation patterns has been observed away from the Pacific, reaching the tropics over other ocean basins (e.g., Giannini et al., 2001; Kang et al., 2015; Sasaki et al., 2015). Its influence on precipitation also extends to farther regions in extratropics (Dai and Wigley, 2000), such as Europe (Shaman, 2014; Tabari and Willems, 2018), Africa (Gaughan et al., 2016), and South America (Grimm et al., 2000; Grimm, 2003; 2004; 2011; Tedeschi et al., 2015; Tedeschi et al., 2016; Cai et al., 2020). In particular, over South America, ENSO has been proved to influence the precipitation variability and extreme precipitation in all seasons (Grimm, 2011; Tedeschi et al., 2015; Tedeschi et al., 2016), even in monsoonal regimes (Grimm, 2003; 2004). Castillo et al. (2014) found that the main global moisture sources are generally affected by the occurrence of El Niño and La Niña events.The Arctic (AO) and Antarctic (AAO) Oscillation atmospheric patterns are also highly related to precipitation variability over the Northern and Southern Hemispheres, respectively (Silvestri and Vera, 2003; Hu and Feng, 2010; Kryzhov and Gorelits, 2015; Rosso et al., 2018). These patterns, also named hemispheric annular modes, are modulated by shifts in the position and intensity of wind belts around the Arctic and Antarctica (Thompson and Wallace, 2000). For the AO, winds rotating counterclockwise intensify in the positive phase, confining the cold air in polar areas, while in the negative phase, the weakening of this belt allows the entry of Arctic air masses towards lower latitudes (Thompson and Wallace, 1998). For the AAO, this mode describes the north–south movement of the westerly wind belts in the Southern Hemisphere. This movement occurs in such a way that it moves towards Antarctica during the positive phase, limiting the entry of polar air into lower latitudes, while its displacement towards the Equator in the negative phase increases the number and intensity of low‐pressure systems in the middle latitudes (Gillett et al., 2006). The effect of both patterns on moisture transport was previously investigated by Nieto et al. (2014), who suggested that these modify the moisture flux from the Atlantic and Pacific regions towards continents in both patterns.Another teleconnection pattern showing an important influence over the Northern Hemisphere is the Pacific‐North American Oscillation (PNA), which consists of an alternating pattern between pressure centres over the Central Pacific Ocean with centres of action located over western Canada and the southeastern United States (Oliver, 2005). The positive phase of the pattern is characterized by above‐average heights over the Rocky Mountains and below‐average heights south of the Aleutian Islands and over the southeastern United States, being the pattern reversed for the negative phase. Despite its area of higher influence over North America (Leathers et al., 1991; Mallakpour and Villarini, 2016; 2017), some previous studies have shown an effect on other regions such as Asia (Aizen et al., 2001) and Europe (Fuentes‐Franco and Koenigk, 2020). Over North America, several studies have found a positive correlation between precipitation over the western part of the continent, while the opposite occurs over the eastern part (Liu et al., 2013; Mallakpour and Villarini, 2017). Liu et al. (2013) suggested a link to shifts in the position of the polar jet and changes in synoptic air mass frequencies.This study conducted a detailed analysis of the variability of moisture transport associated with ENSO, AO, AAO, and PNA during the peak precipitation months, from major global oceanic and terrestrial sources associated with maxima in precipitation.METHODOLOGYMoisture transport computationTo investigate global moisture transport associated with teleconnection patterns in the months of maximum precipitation by grid, the outputs of the Lagrangian particle dispersion model FLEXPART v9.0 (Stohl et al., 2005) were used. This model generates the trajectories of a high number of atmospheric particles, which allow the investigation of changes in specific humidity along with them. To determine the moisture transport, a global experiment was performed in which the total global atmosphere was divided into ~2 million air parcels (hereinafter denoted as “particles”) of equal mass (m), following the atmospheric mass distribution (Stohl and James, 2004). The particles were then allowed to freely move with the wind data from the ECMWF reanalysis ERA‐Interim (Dee et al., 2011), for the complete period 1980–2018. With a 6 hr time step, the position (latitude, longitude, and altitude) and the specific humidity (q) were recorded for each particle along trajectories of 15 days, since it is the maximum time obtained by Nieto and Gimeno (2019) as the optimal integration time for Lagrangian studies. Along each trajectory, it is possible to analyse the moisture variation of the particles through changes in q, and then to compute over each grid cell the moisture contributions. The complete procedure is described in more detail hereunder.This study analysed the moisture contributions from the main oceanic and terrestrial sources over each gridded area in its month of higher precipitation. These sources were defined by month previously in Nieto et al. (2019): the North Pacific (NPAC), South Pacific (SPAC), Gulf of Mexico and Caribbean Sea (MEXCAR), North Atlantic (NATL), South Atlantic (SATL), Zanzibar Current and Arabian Sea (ZANAR), Agulhas Current (AGU), Indian Ocean (IND), Coral Sea (CORALS), Mediterranean Sea (MED), Red Sea (REDS), South America (SAM), Sahel region (SAHEL), and Southern Africa (SAFR). The methodology used here concerning the moisture computation was first established by Stohl and James (2004, 2005) and has been widely used (Zandonadi Moura and Lima, 2018; Drumond et al., 2019; Algarra et al., 2020). For the global experiment, to detect moisture sinks, particles located over each moisture source were selected for each day from 1980 to 2018, and the moisture variation experienced by each particle was computed, for up to 15 days forward in time, following the equation e−p=mdqdt, where e and p represent evaporation and precipitation processed in time (t = 6 hr) by each particle of mass m. Following this procedure, for each of the particles that left a specific source, on a specific day in the period 1979–2018, we obtained the (e − p) values for 60 time steps (15 days every 6 hr). For example, for a particle leaving an oceanic area on January 1, 1980, we obtained the (e − p) on their trajectory from January 1 at 0000 UTC until January 15 at 1800 UTC. The same for the particles that left the area on January 2, 1980 at 0000 UTC, that ended on January 16 at 1800 UTC, and for all the days in the period of the study. So, the 15 days of individual trajectories for all the particles followed from each source forward in time are individually available for each day every 6 hr from December 17, 1979 until December 31, 2018.This trajectories information can be used to compute the total atmospheric moisture budget (E − P) over each grid cell computed following the next equation:1E−P=∑k=1Ke−pkA,where K is the total number of particles within the atmospheric column over an area of base A (in this study, a gridded area with 0.5 horizontal resolution). E − P was individually computed for each day along the study period, for all the days from December 17, 1979 to December 31, 2018. For each day, all the particles over a moisture source were followed forward for 15 days (Figure 1) and, for each day of transport at each grid cell, E − P was calculated by adding the (e − p) values of all individual particles over each column of A base (our defined grid size) following Equation (1). Figure 1 shows an example of a pair of trajectories that leave a source of moisture. Figure 1a–c represents one trajectory during the first day (December 16, 1979) of our period of study, and Figure 1d–f another one during the last day (December 31, 2018). In both columns, the trajectories are represented during the 1st day forward in time (Figure 1a,d), the 2nd day (Figure 1b,e), and the 15th day (Figure 1c,f). The E − P computation is represented over the grid cell on which the particle is located on each day of the trajectory (shaped areas A). The E − P value is performed as in Equation (1), by considering (e − p) values for all the particles that reach the same atmospheric column; in this case, for the particle represented in the example and for the remaining particles that leave the source (blue dots in Figure 1). This procedure was individually performed for each of the days in the study period (considering not only the sample particle but all the particles on all grid points belonging to the sink) and along the 15 days of the trajectories tracking.1FIGURERepresentation of the (E − P)all‐tracks files computation from one moisture source. The black dots and the black line represent the trajectory followed by a specific particle from the source. The columns represent the days in the period December 16, 1979–December 31, 2018 and the rows represent the number of days the trajectories were followed. The grey boxes represent the total atmospheric column in which the moisture variation (e − p) for all the particles (blue and black dots) are added in order the compute the total atmospheric moisture budget following Equation (1) [Colour figure can be viewed at wileyonlinelibrary.com]So, finally for a moisture source a total of 208,275 (13,885 days in the period × 15 days of trajectories) E − P fields are available, and the corresponding files are stored to be used in the next step. As the interest is on moisture contributions for precipitation, only negative values of E − P were considered for the computation and retained in the E − P files. These fields are named hereafter as (E − P)all‐tracks. Further details and clarifications on the E − P computation can be found in Vázquez et al. (2020).For each continental grid point, Nieto and Gimeno (2019) defined the optimal integration time (topt, ranging from 1 to 15 days) for Lagrangian calculations that regionally achieve the best correspondence between the moisture for precipitation transported from the sources and the precipitation. This optimal integration times (topt) was used to compute the total moisture contributions over continental areas, by using the individual (E − P)all‐tracks files computed previously. The complete procedure is described below.The methodology for computing the daily moisture contributions over each grid point is represented in Figure 2. Three grid points with different topt are shown, being represented in green one with a topt = 5 days, in blue for topt = 7 days, and in red for topt = 15 days. The total daily moisture contributions were performed by adding the values of the (E − P)all‐tracks fields previously computed. It is important to notice that the (E − P)all‐tracks fields were computed taking into account the dates in which the particles leave the sources; however, it takes some time (between 1 to topt days) to reach the continental areas over which the final moisture contributions is computed For example and following the schematic Figure 2, to compute the moisture contributions on January 1, 1980, if the topt over a grid cell is 15 days (red square in Figure 2), its final moisture contributions was computed by adding the (E − P)all‐tracks fields from the particles that leaving the moisture source on December 31, 1979 take 1 day to reach the grid cell, the particles leaving the source on December 30, 1979 that take 2 days, and so on, until the particles that take 15 days on December 17, 1979. So, in this example, the final daily moisture contributions were composed adding 15 values of (E − P)all‐tracks from December 31, 1979 to December 17, 1979 for those tracks that take from 1 to 15 days to arrive at the sink cell.2FIGURERepresentation of the daily moisture contributions computation by grid point for different optimal integration times (topt). Example for the January 1, 1980. The squares represent the areas over which the moisture contributions are computed, representing the colours different topt (5 days for green, 7 days for blue and 15 days for red). Each of the rows represents a different time duration (1, 5, 7 and 15 days) in the transport from the source to the continental grid areas (on January 1, 1980), and the arrows represent the hypothetical trajectories of some particles (dots) considered in the E − P computation. The dates in purple represent the date in which the moisture contributions are computed, while the date in orange is the date in which the particles leave the source. The grey squares represent those grid areas in which the duration of the transport is higher than the topt and then are not considered in the moisture contributions computation. [Colour figure can be viewed at wileyonlinelibrary.com]The same procedure was performed for all the continental grid areas, considering in each case the topt. Therefore, over the green cell in Figure 2, which has topt = 5 days, the (E − P)all‐tracks values added were 5 and go from December 31, 1979 until December 27, 1979 for the trajectories taken from 1 to 5 days of transport until reaching the sink cell. The same applies for the blue cell with topt = 7 days, the daily final moisture contributions added the (E − P)all‐tracks values from December 31, 1979 until December 25, 1979, 7 days of transport before, accounting tracks that take from 1 to 7 days to arrive the sink cell.From these daily moisture contributions, a monthly climatological mean can be obtained. The procedure here described was performed individually for all the 14 oceanic and terrestrial sources listed before and defined in Nieto et al. (2019).The model and the methodology here presented highlight some limitations that should be considered in the interpretation of the results. First of all, the methodology does not consider the moisture phase change along trajectories, which may lead to overestimation of the contribution to precipitation, for example, when the positive (e − p) values are associated with cloud formation. Moreover, the number of days considered in the moisture trajectories may affect the results. For this reason, we individually consider the time of integration of the trajectories accordingly with the optimal time for Lagrangian studies defined by Nieto and Gimeno (2019). Despite these deficiencies, the methodology has proven to be valid for different latitudes across regions (from equatorial to polar regions). Finally, the results presented in this study are also susceptible to the limitations of the data feeding the model. In this sense, ERA‐Interim data shows some limitations in the characterization of the hydrological cycle, in some areas such as Africa, due to the lack of assimilated observational data (Dunning et al., 2016; Hill et al., 2016). Moreover, several authors have highlighted problems with the assimilation of rain‐affected radiances that produced a sub‐estimation of precipitation over oceans (Dee et al., 2011; Allan et al., 2014; Craig et al., 2017).Assignment of the preferred sources of moisture for precipitation by grid for the peak precipitation monthThe analysis presented here was based on a ranking of the contribution from each moisture source, computed using the Lagrangian outputs, over each continental grid point during its peak precipitation month (PPM) (Figure 3a). The PPM used in this study was previously defined by Nieto et al. (2019), using mean monthly precipitation data for the period 1980–2018 taken from the CPC Global Unified Gauge‐Based Analysis of Daily Precipitation (Chen et al., 2008), and represents (at every grid point) the month with higher mean precipitation.3FIGURE(a) Month of maximum precipitation (PPM) for every grid point computed from CPC daily data for 1980–2018. Climatological (b) preferred (PS) and (c) secondary (SS) sources calculated from daily moisture contributions from main global oceanic and continental sources. The small figure between (a) and (b) represents the maximum extension of the moisture sources (the higher area reached for the source in all months). Note that for this small figure and for (b) and (c) the colourbar is the same and represents moisture sources [Colour figure can be viewed at wileyonlinelibrary.com]The preferred source (PS; Figure 3b) was defined as the source showing the highest moisture contributions over a grid in the PPM, and the secondary source (SS; Figure 3c) was the secondmost important source. The order of the contributing moisture sources (PS, SS) was obtained following the same methodology as in Nieto et al. (2019). This methodology ranks the climatological moisture contributions from each source by month over each grid point, using as reference the mean value for the whole period 1980–2018.In contrast to Nieto et al. (2019), in which the moisture contributions for precipitation were directly computed in monthly terms, this work was done using daily moisture contributions, and the monthly values were obtained by computing the mean from them. Another difference between both methodologies was the step in which the positive E − P values are removed. In Nieto et al. (2019) it was not removed until the end of the monthly computation; however, in this work, it was removed daily. Although this change may cause some differences in the order of the preferred sources, we consider that this procedure is more suitable to adequately characterize the percentage contribution of moisture sources for the PPMs, as the possible moisture uptake on some specific days does not detract the real moisture contributions on daily precipitation.Despite the ranking for the PS and SS definition being done from a climatological point of view using the mean values for the period 1980–2018, the moisture contributions from each source were computed for each month over the 39 years of the study. The contribution from the remaining sources, those that are not the PS or SS, had been considered in this study as an ensemble and named REST (the sum of the contributions from the remaining sources).Teleconnection indices and composite analysisThe indices used to investigate AO, AAO, ENSO, and PNA were extracted from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) on a monthly time scale. For ENSO conditions, the Bivariate ENSO time series (BEST; Smith and Sardeshmukh, 2000; available for the period 1948–2020) was used. This index combines a standardized SOI and a standardized Niño3.4 sea surface temperature time series, in which El Niño/La Niña events correspond to high/low BEST index values. The PNA index (available for the period 1948–2021) was calculated based on rotated principal component analysis (RPCA) applied on monthly mean standardized 500 mb height anomalies in the region 20°–90°N (Barnston and Livezey, 1987). The AO (available for the period 1950–2021) and AAO (available for the period 1979–2020) patterns corresponded with the leading pattern in the empirical orthogonal function (EOF) analysis of monthly mean height anomalies at 1,000 hPa in the Northern Hemisphere and 700 hPa in the Southern Hemisphere, respectively. A detailed description of these calculations can be obtained from NOAA (https://psl.noaa.gov/data/climateindices/list/).A composite analysis was performed to analyse the positive and negative phases of the four indices. The positive (negative) phase was defined as the months showing values above (below) 1 (−1) standard deviation in the time series of the indices. The number of years in each phase and the specific years are presented in Tables 1 and S1–S4, respectively.1TABLEThe number of years considered for the composite analysis in every phase of the teleconnection patternsJanFebMarAprMayJunJulAugSepOctNovDecPNA+574656598985PNA−798867365688AO+810134420114512AO−910102422137711AAO+693310895105313AAO−343558857287BEST+596796688988BEST−1077521335687Note: Red colour marks months not considered in the study.Taking into account the years in every phase for every month, a composite analysis was performed by considering the mean moisture contributions for each phase. The composite computation was only performed if there were at least 3 years from 1980 to 2018 for each phase of the index. Those months not considered for each index, according to this criterion, are represented in red in Table 1. With this information, the moisture contributions to precipitation from the sources by each index and phase at each continental grid could be obtained. Once the composite is performed for each month, each moisture source, and the different phases of the indices; at each grid point, the values of the composites are retained for the PS and SS to the corresponding PPM. The same procedure was also applied to the total contribution from the remaining moisture sources altogether (REST).To investigate how the moisture contributions varied in the positive and negative phases for every index, the difference in the composites was calculated, and a significance bootstrap test (Efron, 1992) was applied to the results. The bootstrap test is based on the resampling of the initial data to address significant variations. For the PPM and PS (SS, REST) at every grid point, from the 1980–2018 annual time series of moisture contribution results, two subsamples with the size the number of years of the positive and negative phases according to Table 1 are randomly selected. Then, the difference in the random samples is performed analogously to the composite difference. The resample and difference is repeated until obtaining a total of 1,000 subsamples. By considering the subsamples, the composite difference is expected to be significant at a 90% significant level, if it is higher than the 90th percentile of the 1,000 subsamples obtained with the resampling. The results for the PS, SS, and REST were presented at every grid point for the corresponding PPM.RESULTSTo investigate the moisture contributions from the main oceanic and terrestrial sources during the PPM in association with the different teleconnection patterns, a composite analysis for the years corresponding to each phase of the indices was performed as described in section 2. The results presented here are based on the difference in moisture contributions between the composites of the positive and negative phases (positive minus negative). The positive (negative) values in the following figures represent the areas where the moisture contributions are higher in the positive (negative) phase. The figures present only the significant values at the 90% confidence level. Moreover, the results are only presented in the hemisphere where the pattern showed influence (PNA and AO for the Northern Hemisphere, AAO for the Southern Hemisphere, and ENSO for both).As previously described, the results presented here are based on the climatological PS and SS (Figure 3b,c). Despite following a slightly different methodology than that established by Nieto et al. (2019), the obtained results did not differ in general terms. Some differences in PS or SS may have occurred if only the years in which teleconnection pattern events occur (positive and negative phase events) were taken into consideration during calculation. However, as these differences were small, and to facilitate the interpretation of the results, the PS and SS used here were based on the climatological calculations as described in section 2.2 and Nieto et al. (2019) (Figure 3). To facilitate the interpretation of the results, the PPM over the region with positive and negative differences in each pattern is presented in Figures S1–S4.PNA teleconnection patternFigure 4 shows the difference in moisture contributions (|E − P < 0| values) at each grid point between the positive and negative phase of PNA index from the PS (Figure 4a), SS (Figure 4b), and REST (Figure 4c). It is important to note that in the plots here presented there is a mixture of sources and seasons, as they were represented for the PS/SS/REST in the PPM (see Figure 3). To facilitate the interpretation of the results, the sources associated with observed differences for PS and SS are presented in Figure 4d–g. In both figures, the sources and the PPM are shown separately for areas with positive and negative significant differences. Figure 4d,f represent the PS associated with the areas of positive (Figure 4d) and negative (Figure 4f) differences observed in Figure 4a. The same for SS is presented in Figure 4e,g. Figure S1 shows the climatological PPM (as in Figure 3a) separately for the regions where positive and negative significant differences occur in the pattern.4FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between the positive and negative phases of PNA for 1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]The difference in the composites for the contribution from PS in the PPM over each grid point (Figure 4a) showed an opposite sign over eastern and western North America. Over the former, the contribution from PS decreased, associated with the positive phase, but over the latter the opposite occurred, with the moisture contributions associated with PS higher during the negative phase of the PNA. It is important to note that there are differences in PS for eastern and western North America. While NPAC is the PS over most of the region, especially over the western part, over the eastern region there are also contributions from NATL, MEXCAR, and NPAC (Figure 4f).The increased contribution of the negative PNA phase over eastern North America continued to appear when SS was analysed (Figure 4b). In general, the three sources showed an increased contribution over this area, though this was higher when associated with the Atlantic sources (NATL and MEXCAR; Figure 4a,f).PNA also influenced the moisture contributions from the main sources over Europe, most evidently in the western area associated with PS (Figure 4a). Over the Iberian Peninsula, NATL (which acted as PS over most of the area) showed a lower contribution associated with the positive phase of the pattern over the southwestern part of the peninsula and the northern regions (Figure 4a,f). However, the opposite was observed over the northwestern corner of the peninsula (Figure 4a,d). A positive relationship between PNA and moisture contributions over northwestern Iberia was also observed when REST was analysed (Figure 4c). However, this signal did not appear for SS (Figure 4b), which corresponded with NPAC over this area. The increased moisture contributions from NATL, when acting as PS and associated with the positive phase of PNA, were also observed over the British Isles and northern France (Figure 4a,d). An area of important positive values in the difference between phases was observed expanding from the Iberian Peninsula to central France (Figure 4) associated with both NATL (PS; Figure 4d) and MED (SS; Figure 4e). This area should be distinguished from the rest of western Europe because of the season when PPM occurred. While the other areas showed PPM in late autumn and winter, over this area PPM occurred in spring (Figure S1). Finally, increased moisture contributions associated with the positive phase of the pattern from all sources were generally observed over some areas of southern Asia. This was especially evident for ZANAR (PS; Figure 4a,d) and IND (SS; Figure 4b,e).Arctic oscillationThe significant differences between the moisture contributions from the main global moisture sources (as PS, SS, and REST) during the PPM in positive and negative phases of AO, as well as the sources associated with the differences, are shown in Figure 5. It is important to note that a very small amount, or no cases, of positive and negative phases of AO appeared in summer for this teleconnection pattern (Table 1). Over large areas, PPM occurred in this season, so the results are only presented over specific areas of the Northern Hemisphere.5FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between the positive and negative phases of AO for 1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]Over Europe, the negative phase of the index produced an increase in moisture contributions from NATL (the PS; Figure 5a,f) over most of the Iberian Peninsula, the European Atlantic coast, and northern Africa (with only small areas showing the opposite signal) and particularly for PPM in late autumn and winter (Figure S2). However, the MED contribution to the eastern Iberian Peninsula increased during the positive phase of the AO (Figure 5b,e). In general, NATL and MED modulated moisture contributions over western Europe in AO conditions, and no important variations are observed related to the rest of the main global moisture sources (Figure 5c). Over southern Europe (Italy, Greece), in general, the contributions of moisture from both the PS (MED) and the SS (NATL), were stronger during the negative phase of the AO than during the positive phase (Figure 5a,b,f,g). The same behaviour was found for the rest of the sources (REST; Figure 5c).The AO pattern also showed some influence over North America. In the eastern region, increased contributions from MEXCAR (PS; Figure 5a,d) were observed associated with the positive phase. On the other hand, in the western region, NPAC (PS; Figure 5a,f) increased its moisture contributions during the negative phase of the pattern, also for PPM in late autumn and winter (Figure S2).Antarctic oscillationThe results for the AAO pattern are presented in Figure 6. During the positive phase, an enhanced contribution from CORALS occurred over eastern Australia when it acts as PS (Figure 6a,d) and over southeastern and northern areas when acting as SS (Figure 6b,e). Over northwestern Australia, a reduction in the moisture contributions from IND (Figure 6a,f) was observed in the positive phase. These results showed an east/west shift in circulation over this region associated with AAO, which promoted the input of moisture from the Pacific in the positive phase of the index and from the Indian Ocean in the negative phase for PPM in Southern Hemisphere summer (January and February; Figure S3).6FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between the positive and negative phases of AAO for 1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]In Africa, some important contributions from the sources appeared to be associated with this pattern. First, over eastern Africa, an important area with increased moisture contributions associated with the positive phase of the index was observed, mainly in coastal areas from Mozambique to southern Somalia, for Southern Hemisphere summer PPM (November–February; Figure S3). This increased contribution was generally associated with the main moisture sources (PS and SS). Over the western part of the continent, a dipole pattern was observed southward from the Gulf of Guinea. This area was mainly fed by moisture from SATL. This difference between positive values over southern areas and negative values over northern areas suggested a southward shift in SATL's influence during the positive phase of the index.In South America, the influence of the pattern also showed its signal. In general, an increase in the moisture contributions from NATL was observed in the positive phase, located over the Amazon region when this source acted as PS (Figure 6d) and over southern areas when it was SS (Figure 6e). However, the influence of SATL was unclear. Although some increases in the moisture contributions associated with the positive phase were observed from SATL over some coastal areas in eastern South America (Figure 6a,d), the signal reversed over large areas (Figure 6a,f). SPAC seemed to increase its moisture contributions in the positive phase over southern South America (Figure 6a,f).El Niño–Southern OscillationFigure 7 shows the variation in moisture contributions from the main global moisture sources associated with ENSO. In this figure, positive (negative) values represent regions with an increased contribution associated with El Niño (La Niña) events (Figure 7a–c). Moreover, it is also shown the PS and SS associated with positive (Figure 7d–e) and negative (Figure 7f–g) significant differences in the pattern. Figure S4 shows the PPM (as in Figure 3a) separately for the regions where positive and negative significant differences occur in the pattern.7FIGUREThe difference in moisture contributions, estimated as the difference in |E − P < 0 (mm·day−1), between El Niño and La Niña events for1980–2018 and (a) PS, (b) SS, and (c) the rest of the sources (REST). Only values at a 90% significant level are represented. The moisture source associated with the differences observed in (a, b) are represented individually for positive (d, e) and negative (f, g) difference values [Colour figure can be viewed at wileyonlinelibrary.com]In El Niño events the moisture contributions from SPAC (when acting as PS; Figure 7a,d) increased over southern South America. However, over northern and central South America, the occurrence of La Niña events seemed to favour contributions from the Atlantic Ocean to continental areas, especially for NATL. The SATL influence seemed to be favoured during La Niña events (as PS; Figure 7a,f) and SS (Figure 7b,g) over the northwestern region. However, the contribution during La Niña appeared to be reduced over the southeastern area, suggesting a shift in circulation from SATL towards South America associated with this ENSO phase.In Africa, as for AAO, ENSO seemed to modulate the entrance of moisture from SATL into western Africa. La Niña events produced an increased contribution from SATL over southern regions, while El Niño events contributed to SATL moisture contributions over northern areas. Over the Horn of Africa, El Niño events produced an increase in moisture contributions from ZANAR, though a decreased contribution was observed from IND.In general, ZANAR's influence seemed to be affected by the ENSO phase, as its effect showed a shift not only over Africa but also over Asia. Decreased (increased) moisture contributions from the PS (ZANAR; Figure 7a,f) and SS (IND; Figure 7b,g) were observed over northern and eastern India associated with El Niño (La Niña) events. Over the Indochinese Peninsula, the opposite signal was observed by increasing (decreasing) the moisture contributions from PS (ZANAR; Figure 7a,d) associated with La Niña (El Niño) events. The ENSO pattern has a well‐known influence on the Indian Ocean precipitation pattern. In general terms, El Niño events are often associated with a suppression in the Indian monsoonal rainfall, being the opposite true for La Niña events in association with the changes in the walker circulation which accompany the ENSO modulation (Krishnamurthy and Goswami, 2000; Bracco et al., 2005).Over Europe, the ENSO pattern showed a clear influence over the western coast, where the contribution from PS increased with El Niño events (Figure 7a), caused by NATL. Over the eastern Mediterranean coast, the moisture contributions from PS (MED) decreased. In North America, El Niño events produced increased moisture over the western and northern regions from NPAC (acting as PS; Figure 7a,d).DISCUSSION AND CONCLUSIONSThis study explored the effect of teleconnection patterns on moisture transport from main global moisture sources during PPM periods. To focus the discussion of the results, and to link them with some previous studies, for each of the indices a specific area was selected and discussed (but as it was shown previously, the effects due to them were far‐reaching). For the PNA the discussion is focused on North America, for AO on Western Europe, for AAO is centred on Africa, and finally, ENSO results are mainly discussed over South America. Figure S5 presents the effect of teleconnection patterns on precipitation taken from the CPC Global Unified Gauge‐Based Analysis of Daily Precipitation (Chen et al., 2008) provided by the NOAA/OAR/ESRL Physical Science Division for the same period.The PNA pattern showed its maximum influence over North America, though some influence also occurred over Europe and southern Asia. Over North America, an east–west dipole of moisture contributions can be observed (Figure 4a) with the difference between phases being opposite and higher than 1.5 mm·day−1. This dipole was also described in previous research (Liu et al., 2013; Mallakpour and Villarini, 2017). Over the western part of the region, the positive phase was associated with an increase in moisture contributions from NPAC. In this phase of the index, an enhancement of the circulation occurred between 10°N and 30°N (where NPAC is located) and this affected the moisture contributions from this source. The dipole pattern associated with PNA causes, by the intensification of the Aleutian Low (Overland et al., 1999), higher pressures over tropical areas that produce an intensification of flow over the Pacific Ocean. This favoured the inflow of moisture over the western coast of North America. Liu et al. (2013) also suggested that increases in moisture contributions from lower latitudes are favoured in winter, associated with the positive phase of the index. Over eastern North America, the opposite sign was observed, with a decrease in moisture contributions (especially from NATL and MEXCAR) in the positive phase of the index (Figure 4a). Over this region and associated with this phase, the entrance of polar air mass was favoured over eastern North America due to the equatorward shift in the polar jet stream (Liu et al., 2013; Mallakpour and Villarini, 2017). This caused the southern and eastern moisture sources to reduce their contribution during this phase. In general terms, the contribution pattern is similar to the observed in total precipitation (Figure S5) however some differences are observed over the south part of the region.In the case of AO, the area of higher impact was observed over western Europe (Figure 5). The positive phase of the AO is associated with the shift in the Atlantic storm track towards northern latitudes (Dickson et al., 2000; Thompson and Wallace, 2000). This produces increased precipitation over northern Europe and a decrease over southern regions (Kryzhov and Gorelits, 2015). The results presented in this manuscript suggest NATL and MED modulation of moisture transport over western Europe. Over the northwestern areas, NATL moisture contributions were favoured in the negative phase of the index with increased contribution higher than 2 mm·day−1 in this phase, while MED moisture contributions increased over the southeastern region in the positive phase. This behaviour resembles the total precipitation pattern, in agreement with Boer et al. (2001), who described a meridional shift in mid‐latitudes associated with annular modes. The positive phase of AO was associated with anomalously strong easterlies over the southern Northern Hemisphere, matching the observed increased contribution from MED (Figure 5e) in this phase over the eastern Iberian Peninsula and the total increased precipitation over southern Europe (see Figure S5).During the AAO, as previously addressed, the positive phase of the index was characterized by a poleward displacement of the wind belt around the Antarctic, producing a shift in the synoptic weather system (Thompson and Wallace, 2000). The poleward displacement of the westerlies allowed easterlies from the Indian Ocean to penetrate Africa, affecting moisture transport eastwards. Over eastern Africa, the contribution increased from the western Indian basin (ZANAR and AGU; Figure 6d). In agreement with this result, Malherbe et al. (2014) found a positive relationship between the AAO and moisture contributions from tropical Indian cyclones over eastern Africa. AGU's contribution also affected precipitation over southwestern areas, though over this region, increased moisture contributions were observed from SATL (Figure 6d). Tropical SATL has been suggested to influence precipitation patterns over southern Africa (Vigaud et al., 2007; Ndarana et al., 2020) and a general increase in total precipitation is associated with the positive phase of the index (Figure S5). The results presented here suggest a shift in the area of influence of this source in AAO events, by decreasing (increasing) its moisture contributions over northwestern Africa in high (low) AAO events and increasing (decreasing) over southern and coastal areas. An important influence is also observed in South America, being the difference in the moisture contribution from the preferred source into both phases higher than 1.8 mm·day−1 over most of the area.Regarding ENSO results, in El Niño years, the strengthening of the subtropical jet intensifies synoptic systems in the southern region while weakening their propagation northward (Tedeschi et al., 2013), favouring moisture contributions from SPAC over this area. The opposite pattern is observed over northwestern South America for both precipitation and SPAC contributions. Not only the Pacific but also the Atlantic moisture transport was affected by ENSO. SATL showed increased (decreased) moisture contributions during El Niño (La Niña) events over southeastern Brazil, while the opposite occurred further west (Figure 7a,d,f). Moreover, a decreased (increased) contribution was observed over the Amazon River basin during these events associated with NATL (Figure 7a,f). The results here presented agree with the moisture flux anomalies observed by Grimm (2003, 2004) during El Niño (La Niña) events. Over western North America and Canada, El Niño events are associated with increased moisture contribution from the Pacific Ocean. During the El Niño events, the Aleutian low is intensified and the subtropical jet extends to the south and east with an anomalous cyclonic flow around it. This causes increased moisture transport towards North America (Kim et al., 2019). To a lesser degree, the influence of ENSO on the moisture contribution was also observed over other regions. For example, El Niño events are associated with increased contribution from the Atlantic Ocean over the western.In general terms, the results presented here show a clear influence of the main teleconnection pattern on the moisture contributions from major moisture sources in the peak precipitation month, especially in association with the preferred source. This influence is evident for some patterns and over some specific regions, such as the Mediterranean Sea and North Atlantic Ocean modulation of western European precipitation for Arctic Oscillation, or the altered moisture transport observed over Australia and eastern Africa for Antarctic Oscillation. Although this analysis was based on monthly precipitation for the month with peak precipitation, in a climate change framework, analyses of extreme precipitation on a daily scale would be of special interest. Several studies have highlighted the increased influence of global teleconnection patterns on this kind of event (Casanueva et al., 2014; Zecca et al., 2018; Do et al., 2020); this study establishes the basis of a methodology that could be extended in future research. The teleconnection pattern here analysed tries to establish a general overview at a global scale, however (as can be observed in Figure S6) they do not properly explain the variability in precipitation over many areas. At a regional scale, some other teleconnection patterns can play a role. For example, North Atlantic Oscillation has a strong impact on European precipitation patterns and the Indian Ocean Dipole highly influence precipitation over the western and eastern sides of the Indian Ocean. The study of the influence of these patterns' scale, as well as the link between different teleconnection patterns at a regional scale and the possible causes associated with the changes (e.g., changes in sea surface temperature), will be the scope of future analysis.ACKNOWLEDGEMENTSThis work forms part of the LAGRIMA project (RTI2018‐095772‐B‐I0O) funded by Ministerio de Ciencia, Innovación e Univesidade, co‐funded by the ERDF, in the framework of the Operational Program Galicia 2014–2020. Marta Vazquez is supported by the Xunta de Galicia under grants ED481B 2018/062 and ED481D‐2022‐020. Margarida L. R. Liberato acknowledges funding from Fundação para a Ciência e a Tecnologia, Portugal (FCT) and Portugal Horizon 2020 through project WEx‐Atlantic (PTDC/CTA‐MET/29233/2017, LISBOA‐01‐0145‐FEDER‐029233, NORTE‐01‐0145‐FEDER‐029233) and for the academic mobility to the Environmental Physics Laboratory (EPhysLab), Universidade de Vigo, Spain under Fundación Carolina (C.2019). This work was partially supported by Xunta de Galicia under the Project ED431C 2021/44 (Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva) and Consellería de Cultura, Educación e Universidade). Funding for open access charge: Universidade de Vigo/CISUG.REFERENCESAizen, E.M., Aizen, V.B., Melack, J.M., Nakamura, T. and Ohta, T. (2001) Precipitation and atmospheric circulation patterns at mid‐latitudes of Asia. 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Journal

International Journal of Climatology – Wiley

Published: Feb 1, 2023

Keywords: global moisture sources; Lagrangian analysis; peak precipitation; teleconnection patterns

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