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The influence of the Western Mediterranean Oscillation upon the spatio-temporal variability of precipitation over Catalonia (northeastern of the Iberian Peninsula)
Journal Pre-proof The influence of the Western Mediterranean Oscillation upon the spatio-temporal variability of precipitation over Catalonia (northeastern of the Iberian Peninsula)
Joan A. Lopez-Bustins, Marc Lemus-Canovas PII:
S0169-8095(19)31288-8
DOI:
https://doi.org/10.1016/j.atmosres.2019.104819
Reference:
ATMOS 104819
To appear in:
Atmospheric Research
Received date:
4 October 2019
Revised date:
13 December 2019
Accepted date:
19 December 2019
Please cite this article as: J.A. Lopez-Bustins and M. Lemus-Canovas, The influence of the Western Mediterranean Oscillation upon the spatio-temporal variability of precipitation over Catalonia (northeastern of the Iberian Peninsula), Atmospheric Research(2019), https://doi.org/10.1016/j.atmosres.2019.104819
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© 2019 Published by Elsevier.
Journal Pre-proof The influence of the Western Mediterranean Oscillation upon the spatio-temporal variability of precipitation over Catalonia (northeastern of the Iberian Peninsula) Joan A. Lopez-Bustins and Marc Lemus-Canovas Corresponding author: Joan A. Lopez-Bustins ([email protected]) Climatology Group, Department of Geography, University of Barcelona, c/ Montalegre, 6, Barcelona, PO: 08001, Catalonia, Spain Abstract
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Previous studies have demonstrated the existence of a statistically significant influence of the Western Mediterranean Oscillation index (WeMOi) upon the precipitation of Catalonia (northeast Spain). In the present study, we analyse the statistical relationship between the WeMOi-precipitation correlation coefficients and two statistical indices at the seasonal and annual timescales. The rainfall database used in the analyses comprises 70 pluviometric series covering the 1950-2015 (66 years) study period, and they are spatially distributed throughout Catalonia. The two statistical indices considered are the coefficient of variation (CV) and the disparity consecutive index (S). The results of the spatial variability of precipitation showed the strongest influence of the WeMO over locations in which precipitation irregularity was highest (high CV and S values), and vice versa. The results for temporal rainfall variability showed that in the subperiods in which the correlation coefficients between the WeMOi and precipitation were weak, rainfall variability showed a decrease (low CV and S values), and vice versa. The best results were found to occur in autumn and winter, and in annual rainfall, under the influence of the negative phase of the WeMO. In summer, the positive phase of the pattern shows a predominance due to convective rainfall. The CV and S indices provided high and very high values for rainfall variability on the coast, especially in southernmost Catalonia. The S values are more accurate with regard to identifying the precipitation areas typically influenced by Mediterranean flows. The main conclusion is that the WeMO pattern strongly determines precipitation variability in its areas of influence. Keywords: Catalonia, statistical indices, precipitation variability, WeMOi. 1. Introduction
The Mediterranean basin is under the transition latitude between the winter westerlies and the subtropical anticyclone belt in summer. The physical geography of the Mediterranean basin is highly heterogeneous: it presents several peninsulas and mountai n ranges and the Mediterranean Sea is practically cut off from any other water bodies. All these factors endow the basin with a highly variable temporal and spatial distribution of precipitation (Martin-Vide, 2004; Cortesi et al., 2012; Mathbout et al., 2018). Furthermore, the western Mediterranean basin will be directly affected by global warming: a decrease in total annual precipitation, an increase in extremes of precipitation and more frequent and long-lasting droughts are expected for the coming decades (Christensen et al., 2013; Lopez-Bustins et al., 2013; Cramer et al., 2018; Greve et al., 2018). Eastern façade of the Iberian Peninsula (IP) is located in the northwestern Mediterranean basin and displays a seasonal precipitation maximum in autumn (De Luis et al., 2010) that differs from the typical precipitation regime (rainy winters and dry summers). In Spain, water management is more dependent upon rainfall variability than on the rainfall annual mean (Lopez-Bustins, 2018). A supplementary water source involving the
Journal Pre-proof use of fog-water collectors has recently been tested throughout eastern Spain (Estrela et al., 2019); this alternative is vital with regard to compensating for the low-to-moderate rainfall in the area combined with the high temporal irregularity of precipitation. Furthermore, intense rainfall over east Spain lead frequently to flood cases along the coast (Gil -Guirado et al., 2019).
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Many studies statistically correlate several teleconnection indices with the rainfall series for the Iberian Peninsula at different timescales (Trigo et al., 2004; Gonzalez-Hidalgo et al., 2009; Ríos-Cornejo et al., 2015; Merino et al., 2016; Redolat et al., 2019). Most of the rainfall series referring to the Iberian Peninsula are closely related to the NAO (Rodó e t al., 1997; EstebanParra et al., 1998). However, rainfall in many regions of eastern Spain poorly correlates with the NAO index (NAOi) (Muñoz-Díaz and Rodrigo, 2004; Lopez-Bustins et al., 2008; Mathbout et al., 2019 ). These regions are leeward of the Atlantic circulation, and rainfall therein depends directly on the wet flows of the eastern component from the Mediterranean Sea, which are often torrential. The Western Mediterranean Oscillation (WeMO) was found to constitute the teleconnection pattern most statistically and significantly correlated with annual, monthly and daily precipitation on the littoral fringe of eastern Spain (Martin-Vide and Lopez-Bustins, 2006; González-Hidalgo et al., 2009; Lemus-Canovas and Lopez-Bustins, 2016). The daily timescale of the WeMO index (WeMOi) could prove to constitute a potential tool for analytical study of the frequency of torrential events in some regions of the western Mediterranean; these torrential episodes cause an increase in the values of the variability indices of precipitation at all timescales. The WeMO is defined as the atmospheric connection between the southwest of the IP (San Fernando) and the Po valley (Padua). The synoptic window 30º-60ºN - 15ºW-20ºE is found to best represent the WeMO phases (Arbiol-Roca et al., 2018). The positive phase of the WeMO corresponds to the Azores anticyclone and the Genoa low (Fig. 1, left); its negative phase coincides with an anticyclone located over inland Europe and a low over the Gulf of Cadiz (Fig. 1, right).
Fig. 1. (Left) Most extreme positive phase of the Western Mediterranean Oscillation (WeMO) in a daily synoptic situation during the 1950-2015 period (2nd December 1976). (Right) Most extreme negative WeMO phase in a daily synoptic situation during the 1951-2016 period (28th November 2014).
Journal Pre-proof The present study aims to analyse the statistical relationship between the coefficients of the WeMOi-rainfall correlation and two statistical indices at annual and seasonal timescales, i.e. to establish a spatial and temporal WeMO effect upon rainfall variability. In section 2, we describe the main orography and pluviometric features of the study area of Catalonia. The methods employed to calculate the statistical indices are also explained in section 2. In section 3, the results of the spatio-temporal relationship between the WeMO influence and rainfall irregularity are analysed and discussed. Finally, in section 4 we derive conclusions. This study will serve as a roadmap to study the relationship between the presence and intensity of the teleconnection indices influence and the rainfall variability in other regions. 2. Study area, data and methods
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Catalonia is a territory with a high orographic complexity, considering its relatively small surface area. The geography is conditioned by the Mediterranean coastline, to the east, with 580 kilometers of coastline, and the large relief units of the Pyrenees to the north. The rainfall database used in the analyses comprises 70 homogeneous and reconstructed pluviometric series, at monthly scale, from the Meteorological Service of Catalonia, covering the 1950-2015 study period (66 years), and spatially distributed throughout Catalonia (Fig. 2, left).With regard to average annual rainfall (Fig. 2, right), wet Catalonia (≥700 mm; Pyrenees and northeast, Girona province) can be distinguished from the dry part (<700 mm; inland and south, Tarragona province). the 700 mm threshold was used from the expert knowledge of the area of study. The wettest season is autumn, particularly on the littoral. The dry seasons are summer (except for the Pyrenees, due to convective rainfall) and winter.
Fig. 2. (Left) Location and orography of Catalonia, and spatial distribution of the rain gauges (white dots) used in the study. Red dots show the stations used in Fig. 11. (Right) Average annual rainfall for the study period (1950-2015). The two statistical indices considered are the coefficient of variation (CV) and the consecutive disparity index (S). The CV is calculated as follows (eq. 1) :
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CV
x 100 s
(eq.1)
N
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Where s is the standard deviation of the precipitation series and x the precipitation series mean. Annual CV values ≥20% and seasonal ones ≥35% can be considered to constitute moderate variability of precipitation influenced by a typical Mediterranean climate (Lemus Canovas & Lopez-Bustins, 2016). Although statistical analysis of precipitation with the CV provides information on the variability of the phenomenon, it does not provide relevant information on the consecutive disparity of precipitation records, and therefore does not enable the chronological order and temporal structure of the series to be evaluated. Consequently, for the study of rainfall variability, apart from employing the CV we included two dimensions of analysis: the statistical dispersion and the chronological order of the series values, evaluated by means of the S index (eq. 2) proposed by Martin-Vide (1986):
i 1
N 1
(eq. 2)
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Where x i is annual rainfall and N is the number of years of the series. This index ranges from zero to infinity, where values close to zero account for a temporal regul arity of the precipitation, whereas very high values of this index denote greater irregularity. An annual value of 0.25 and a seasonal value of 0.55 S can be considered to indicate moderate variability of precipitation influenced by a typical Mediterranean climate.
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The differences between the CV index and the S index should be highlighted, as the results of both indices do not always coincide. We consider three hypothetical annual rainfall series for analysis (Fig. 3). In the first case (above), the series has an average precipitation value of approximately 600 mm, but without extreme values, i.e. with a very low degree of variability, and we therefore obtained a low CV and a low S. In the second case (middle), the precipitation series exhibits an average close to 450 mm, so that most of the years can be observed to be clearly distant from this average value and as a result we obtained a high CV value. On the contrary, with regard to the S index, only one jump appears in the whole series, which implies a moderate S index. Finally, the third example (bottom) shows a situation presenting high CV and S index values, since all the values of the series are found to be far from the average and in addition, a clear temporal disorder is identified in which dry years are interspersed with rainy years. The latter example represents the worst scenario and would have the greatest economic impact upon the region’s water management (Olcina et al., 2016), giving rise to social water scarcity (Iglesias et al., 2009).
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Fig. 3. Comparison between the execution of the coefficient of variation (CV) and the consecutive disparity index (S) for three different theoretical rainfall series.
To evaluate the spatial interaction between the WeMO and rainfall variability, we used Pearson’s correlation (R). We mapped coefficients (using the 95% and 99% confidence level for statistical significance on maps and in scatterplots, respectively) and statistical indices using GIS techniques. The most suitable interpolation method for this purpose was ordinary Kriging. We assessed the WeMOi-rainfall variability relationship using 21-year temporal windows. These windows overlap along the whole study period (1950-2015), following 3-years shifts; hence, 16 subperiods are considered for temporal analysis (1950-1970, 1953-1973...). In each temporal window we computed the CV and S index, and the R between the precipitation and the WeMOi. Finally, we mapped the R by means of the relationship of the all temporal windows of CV/S and the R between precipitation and WeMOi (Fig. 4)
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Fig. 4. Scheme to assess the relationship between the WeMOi and the rainfall variability using temporal windows. Note that we only considered the coefficient of variation (CV) in this example.
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3. Results and discussion
3.1. The relationship between the WeMOi and rainfall
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The correlation between the WeMOi and annual precipitation in Catalonia (Fig. 5) does not show a strong correlation, except for the extreme northwest region, where the statistically significant coefficient R is between 0.25 and 0.5. This means that in years when the WeMOi is positive, precipitation in this area is usually higher than when a negative WeMOi was recorded (Lemus-Canovas and Lopez-Bustins, 2016). However, there is hardly any statistically and significant correlation between the WeMOi and annual precipitation, and this correlation should therefore be addressed on a seasonal scale. The wintertime correlation between the WeMOi and precipitation is statistically significant, with the R coefficient betw een -0.5 and 0.25 in most of Catalonia, except for the area of the western Pyrenees, where this R decreases until it becomes inverted in the extreme northwest, with a positive and statistically significant R. These results show that negative WeMO phases tend to be associated with rainy episodes in most of the territory due to Mediterranean humid easterly flows, as many of the abovementioned studies have already established (e.g. Lopez-Bustins et al., 2008). Conversely, in the extreme northwest of Catalonia, rainy episodes tend to occur with a positive WeMO linked to northwesterly circulation. A situation similar to that of winter occurs in autumn, when the R-value is negative and statistically significant along the coast and extends into inland Catalonia along the river valleys. Spring only shows a negative and statistically significant correlation in the southernmost part of Catalonia. During the summer season, the correlation becomes positive but only statistically significant in the extreme northeast and along a strip mainly inland of the provinces of Lleida and Tarragona; this is probably related to summer storms favoured by cold air at the mid-tropospheric level during days with a northwesterly atmospheric circulation (Azorin-Molina and Lopez-Bustins, 2004, 2008).
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Fig. 5. Spatial interpolation of the correlation coefficients between the WeMOi and precipitation at the annual and seasonal scales.
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3.2. Rainfall variability and its relationship with the WeMOi
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Apart from being closely associated with seasonal precipitation, the WeMOi also has a strong relationship with rainfall variability. In this case, the areas with a more negative correlation between precipitation and the WeMOi (shown in Fig. 5) reveal a notable coincidence with areas presenting a higher CV (Fig. 6). Similarly, the areas with the most positive correlation between the WeMOi and precipitation are those exhibiting the lowest variability within the study area. In the set of maps, the north-westernmost area, which is the one presenting the most positive correlation between the teleconnection index and precipitation at different timescales, can be observed to reflect an annual CV lower than 20% and lower than 35-40% in spring, summer and autumn. During winter, the CV of this north-westernmost area presents a moderate-to-high CV (45-55%), the lowest in the study area. Northwestern Catalonia is somehow influenced by the Atlantic climate; indeed, in Val d’Aran county (north-westernmost Catalonia) streams flow towards the Atlantic Ocean. Similarly, the entire Catalan coast, especially its southernmost part, presents a high degree of variability, reaching an annual CV between 30-35 %, and exceeding 65 % in the 4 seasons. The easternmost part of Catalonia (Cape Creus) also displays very high variability values. This is caused by mountain ranges that run in a N-S direction in both areas (Fig. 2, left), which constitute an orographic barrier to the humid easterly flows (Lopez-Bustins, 2007) and which cause torrential events to occur with greater frequency (Martin-Vide and Lopez-Bustins, 2006; Lopez-Bustins et al., 2016).
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Fig. 6. Spatial interpolation of the CV index at the annual and seasonal scales.
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The spatial distribution of the S index (Fig. 7) follows a distribution similar to that of the CV. The hypothesis of a subtropical gradient is reinforced with the S Index in relation to the CV as one moves from north to south — the easternmost area is weakened with the S index. As in the case of the CV, the greatest temporal precipitation disorder occurs in southern Catalonia and close to the coastline. Therefore, the areas displaying the most negative correlation between the WeMOi and precipitation coincide with the highest values of the S index. On the other hand, in most of the Pyrenees, especially in the westernmost area of this mountain range, the temporal disorder is very low, which means that precipitation does not differ much from one year to another or from one season to another. In general terms, the S ind ex denotes a major influence of the NW Atlantic flows because the typically Mediterranean S values (annual 0.15 and seasonal 0.55) spatially diminish towards the coast.
Fig. 7. Idem as Fig. 6, for the S index.
Journal Pre-proof As for the relationship between rainfall variability, provided by the CV and S indices, and the correlation between the WeMOi and precipitation, Fig. 8 shows a linear regression that relates both parameters at different timescales (annual and seasonal) for the 70 rain gauges distributed throughout the study area. The results of these scatterplots show that in all cases, the linear regression between both parameters is statistically significant at the 99% confidence level, obtaining a maximum R-adjustment in spring (-0.74) and a minimum in autumn (-0.46). This demonstrates a clear relationship between the CV and the influence of the WeMOi on annual and seasonal precipitation. It should be noted, however, that apart from summer, all other seasons follow the same pattern in which the CV decreases as the correlation between the WeMOi and precipitation becomes positive. In summer, this relationship is opposite to that of the other seasons due to the convective rainfall (summer thunderstorms) generated in moderate positive phases of the WeMO (NW and N advections).
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The linear regressions between the S index and the WeMOi correlation coefficient with precipitation are similar to CV. In this case, the R-adjustment is even higher in the spring (0.77) and autumn (-0.58) seasons than in the CV analysis, which corroborates the fact that the higher the negative correlation between precipitation and the WeMOi, the rainfall tends to present a greater temporal disorder. On the other hand, in the summer (R = 0.35) and winter (R = -0.36) seasons the correlation is not as strong, although it remains statistically significant at the 99 % confidence level.
Fig. 8. Scatterplots of the linear regression of the correlation between the WeMOi and precipitation at different timescales, and the CV and S indices.
Journal Pre-proof 3.3. The temporal factor in rainfall variability and its relationship with the WeMOi
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We demonstrated the existence of a strong relationship between the rainfall variability quantified by the CV and S indices, on one hand, and the correlation between the WeMOi and precipitation, on the other. One step further would involve investigating whether this relationship between both parameters responds to a temporal factor. We analysed the 19502015 rainfall series in 21 subperiods or time windows to ascertain whether periods in which the correlation between WeMOi and precipitation are more negative, the variability is also seen to increase with respect to the periods presenting a positive correlation. These results are shown in Fig. 9.
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Fig. 9. Spatial interpolation of the correlation between the WeMOi and precipitation in different subperiods and the CV at annual and seasonal scales.
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The temporal relationship between the CV, on one hand, and the correlation between the WeMOi and annual precipitation, on the other, demonstrated that in coastal areas, periods with a high negative correlation imply higher values of CV. Indeed, in the extreme south of Catalonia, this correlation is very close to -1. This coastal-inland dichotomy is reflected in a similar way in autumn. In spring, precipitation variability in southern Catalonia is also dependent on the WeMO; and in winter, the influence of the WeMO upon CV values is detected in inland areas and in the western Pyrenees. In summer, the influence of the WeMO has the opposite effect, so that the periods exhibiting a more positive correlation between the WeMOi and precipitation are those associated with greater variability. A similar spatial pattern is reproduced in the case of the relationship with the S index (Fig. 10); the littoral area is the one in which variability shows the most evident increase in periods presenting a more negative correlation between the WeMOi and precipitation. In winter, positive correlation values provided by the S index are enhanced in southern Catalonia, and in spring, negative correlation values show a decrease in the southern part; in summer, negative correlation values stronger than those provided by the CV are observed in northeastern Catalonia.
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Fig. 10. Spatial interpolation of the correlation between the WeMOi and precipitation in different periods and the S Index at the annual and seasonal scales.
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Finally, we analysed two specific observatories to evaluate the temporal relationship according to subperiods between correlation values between the WeMOi and annual precipitation, on the one hand, and the CV and S indices (Fig. 11), on the other. We observed that in the case of Tivissa (Tarragona province), the periods displaying the most negative correlation between the WeMOi and annual precipitation (the 1959-79, 1962-82 and 1965-85 subperiods) tend to present a high or very high CV, between 30 and 35 % or between 40 and 45 %, respectively. On the other hand, 3 out of 4 periods containing a positive correlation between the WeMOi and annual precipitation (the 1986-06, 1992-12 and 1995-15 subperiods) present a moderate CV, i.e. the minimum of the 1950-2015 series. Interestingly, the most recent subperiods are those with a lower CV, as opposed to the older ones, which provide a higher CV value. Furthermore, the relationship between the WeMOi and annual precipitation and the S index in Llinars del Vallès (province of Barcelona) shows a pattern very similar to that of Tivissa. Periods with a very high or high value of the S index only correspond to those presenting a negative correlation between the WeMOi and annual precipitation. Likewise, towards the range of values with a low S index, this correlation becomes positive; we therefore did not detect any years negatively correlated with a low S index value. As with Tivissa, the most recent periods reveal moderate or low S index values, whereas the older periods exhibit high or very high ones.
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Fig. 11. Temporal relationship between the correlation coefficients of the WeMOi/precipitation and annual CV, according to subperiods, for Tivissa (province of Tarragona), and annual S for Llinars del Vallès (province of Barcelona) . See figure 1 to locate the two observatories presented. Our results confirm the Mediterranean origin of the heavie st rainfall in many regions of the Iberian Peninsula (Millán et al., 2005). The negative phase of the WeMO favours a humid easterly flow that gives rise to intense precipitation over the eastern façade of the Iberian Peninsula; in turn this torrential event causes an increase in precipitation concentration indices such as the CV and the S. The Mediterranean origin of the intense episodes is associated with the negative phase of the WeMO (Lopez-Bustins and Azorin-Molina, 2004). González-Hidalgo et al. (2009) identified a fringe along the eastern littoral of the Iberian Peninsula as the zone most strongly influenced by the WeMO. This fringe registered the highest torrential episodes in Spain, e.g. >800 mm in 24 h in south Valencia province (Peñarrocha et al., 2002). The highest accumulation of torrential episodes is in the littoral area; particularly in southernmost Catalonia (Lopez-Bustins et al., 2016), where the strongest relationship is detected between the influence of the WeMO and precipitation variability with the use of both the CV and the S indices (Lemus-Canovas and Lopez-Bustins, 2016). The WeMO’s influence can vary over the decades, and years in which rainfall is most strongly correlated with the WeMOi could be
Journal Pre-proof associated with a rise in CV and S values, thus indicating greater precipitation concentration and irregularity (De Luis et al., 2011). 4. Conclusions
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The present research highlights the predictive capacity of the WeMOi with regard to distinguishing periods of high and low rainfall variability at different timescales. Autumn and winter rainfall over the littoral region is statistically and significantly influenced by the negative phase of the WeMO. In summer, the positive phase of the pattern shows a predominance due to convective rainfall caused by the presence of cool air in polar advections. The CV and S indices provided high and very high values of rainfall variability on the coast, especially in southernmost Catalonia. Moderate values are detected in continental areas and the lowest values are observed in the Pyrenees. The season exhibiting the highest value for rainfall variability is winter. Generally speaking, the S values depicts better than CV those areas of Catalonia with a Mediterranean origin precipitation.
Acknowledgements
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There is a statistically significant correlation in relation to spatial distribution between the WeMOi coefficient values and the CV and S values. The stronger the influence of the WeMOi, the higher the pluviometric irregularity, and vice versa; the exception is summer, when the WeMO has the opposite effect. Results for temporal rainfall variability show that, in the subperiods in which the coefficients of correlation between the WeMOi and precipitation are very negative, rainfall variability shows an increase (high CV and S values) in the littoral areas, and vice versa in the continental areas (Lleida province). Our principal conclusion is that this Mediterranean teleconnection pattern strongly determines precipitation variability within its area of influence, and this effect on precipitation should be taken into account in water planning in dry regions.
References
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The present research was conducted within the framework of the Climatology Group of the University of Barcelona (2017 SGR 1362, Catalan Government), and the CLICES project (CGL2017-83866-C3-2-R) of the Spanish Ministry of Economy, Industry, and Competitiveness. M.L-C is granted with a pre-doctoral FPU Grant (FPU2017/02166) from the Spanish Ministry of Science, Innovation and Universities.
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Journal Pre-proof Lopez-Bustins, J.A., 2018. Lluvias fuertes, pero mal repartidas. El caso del clima mediterráneo. Biblio3W vol. 23, nº 1243. Lopez-Bustins, J.A., Martin-Vide, J., Sanchez-Lorenzo, A., 2008. Iberia winter rainfall trends based upon changes in teleconnection and circulation patterns. Global and Planetary Change 63, 171-176. Lopez-Bustins, J.A., Pascual, D., Pla, E., Retana, J., 2013. Future variability of droughts in three Mediterranean catchments. Natural Hazards 69, 1405–1421. Lopez-Bustins, J.A., Martin-Vide, J., Prohom, M., Cordobilla, M.J., 2016. Variabilidad intraanual de la Oscilación del Mediterráneo Occidental (WeMO) y ocurrencia de episodios torrenciales en Cataluña. In: Olcina, J., Rico, A.M., Moltó, E. (eds.), Clima, sociedad, riesgos y ordenación del territorio, pp. 171-182. Alicante (Spain): Asociación Española de Climatología (AEC).
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Martin-Vide, J., 2004. Spatial distribution of a daily precipitation concentration index in peninsular Spain. International Journal of Climatology 24, 959-971.
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Martin-Vide, J., Lopez-Bustins, J.A., 2006. The Western Mediterranean Oscillation and rainfall in the Iberian Peninsula. International Journal of Climatology 26, 1455-1475.
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Mathbout, S., Lopez-Bustins, J.A., Royé, D., Martin-Vide, J., Bech, J., Rodrigo; F.S., 2018. Observed changes in daily precipitation extremes at annual timescale over the Eastern Mediterranean during 1961-2012. Pure and Applied Geophysics 175, 3875-3890.
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Merino, M., Fernández-Vaquero, M., López, L., Fernández-González, S., Hermida, L., Sánchez, J.L., García-Ortega, E., Gascón, E., 2016. Large-scale patterns of daily precipitation extremes on the Iberian Peninsula. International Journal of Climatology 36, 3873-3891. Millán, M.M., Estrela, M.J., Miró, J., 2005. Rainfall Components: Variability and Spatial Distribution in a Mediterranean Area (Valencia Region). Journal of Climate 18, 2682-2705. Muñoz-Díaz, D., Rodrigo, F.S., 2004. Impacts of the North Atlantic Oscillation on the probability of dry and wet winters in Spain. Climate Research 27, 33-43. Olcina, J., Sauri, D., Hernández, M., Ribas, A., 2016. Flood policy in Spain: a review for the period 1983-2013. Disaster Prevention and Management 25, 41-58. Peñarrocha, D., Estrela, M.J., Millán, M., 2002. Classification of daily rainfall patterns in a Mediterranean area with extreme intensity levels: the Valencia region. International Journal of Climatology 22, 677-695. Redolat, D., Monjo, R., Lopez-Bustins, J.A. and Martin-Vide, J., 2019. Upper-level Mediterranean oscillation index and seasonal variability of rainfall and temperature. Theoretical and Applied Climatology 135, 1059–1077.
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Journal Pre-proof Highlights
WeMOi has a predictive capacity with regard to distinguishing periods of high and low rainfall variability at different timescales.
The spatial distribution of the coefficient values between the WeMOi precipitation is in relation to the pluviomet ric indices values of CV and S.
The stronger the effect of the WeMOi on precipitation, the higher the pluviometric irregularity, and vice versa.
WeMOi strongly determines precipitation variability within its area of influence, and this is important for water planning in dry regions.
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Joan A. Lopez-Bustins, Marc Lemus-Canovas PII:
S0169-8095(19)31288-8
DOI:
https://doi.org/10.1016/j.atmosres.2019.104819
Reference:
ATMOS 104819
To appear in:
Atmospheric Research
Received date:
4 October 2019
Revised date:
13 December 2019
Accepted date:
19 December 2019
Please cite this article as: J.A. Lopez-Bustins and M. Lemus-Canovas, The influence of the Western Mediterranean Oscillation upon the spatio-temporal variability of precipitation over Catalonia (northeastern of the Iberian Peninsula), Atmospheric Research(2019), https://doi.org/10.1016/j.atmosres.2019.104819
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© 2019 Published by Elsevier.
Journal Pre-proof The influence of the Western Mediterranean Oscillation upon the spatio-temporal variability of precipitation over Catalonia (northeastern of the Iberian Peninsula) Joan A. Lopez-Bustins and Marc Lemus-Canovas Corresponding author: Joan A. Lopez-Bustins ([email protected]) Climatology Group, Department of Geography, University of Barcelona, c/ Montalegre, 6, Barcelona, PO: 08001, Catalonia, Spain Abstract
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Previous studies have demonstrated the existence of a statistically significant influence of the Western Mediterranean Oscillation index (WeMOi) upon the precipitation of Catalonia (northeast Spain). In the present study, we analyse the statistical relationship between the WeMOi-precipitation correlation coefficients and two statistical indices at the seasonal and annual timescales. The rainfall database used in the analyses comprises 70 pluviometric series covering the 1950-2015 (66 years) study period, and they are spatially distributed throughout Catalonia. The two statistical indices considered are the coefficient of variation (CV) and the disparity consecutive index (S). The results of the spatial variability of precipitation showed the strongest influence of the WeMO over locations in which precipitation irregularity was highest (high CV and S values), and vice versa. The results for temporal rainfall variability showed that in the subperiods in which the correlation coefficients between the WeMOi and precipitation were weak, rainfall variability showed a decrease (low CV and S values), and vice versa. The best results were found to occur in autumn and winter, and in annual rainfall, under the influence of the negative phase of the WeMO. In summer, the positive phase of the pattern shows a predominance due to convective rainfall. The CV and S indices provided high and very high values for rainfall variability on the coast, especially in southernmost Catalonia. The S values are more accurate with regard to identifying the precipitation areas typically influenced by Mediterranean flows. The main conclusion is that the WeMO pattern strongly determines precipitation variability in its areas of influence. Keywords: Catalonia, statistical indices, precipitation variability, WeMOi. 1. Introduction
The Mediterranean basin is under the transition latitude between the winter westerlies and the subtropical anticyclone belt in summer. The physical geography of the Mediterranean basin is highly heterogeneous: it presents several peninsulas and mountai n ranges and the Mediterranean Sea is practically cut off from any other water bodies. All these factors endow the basin with a highly variable temporal and spatial distribution of precipitation (Martin-Vide, 2004; Cortesi et al., 2012; Mathbout et al., 2018). Furthermore, the western Mediterranean basin will be directly affected by global warming: a decrease in total annual precipitation, an increase in extremes of precipitation and more frequent and long-lasting droughts are expected for the coming decades (Christensen et al., 2013; Lopez-Bustins et al., 2013; Cramer et al., 2018; Greve et al., 2018). Eastern façade of the Iberian Peninsula (IP) is located in the northwestern Mediterranean basin and displays a seasonal precipitation maximum in autumn (De Luis et al., 2010) that differs from the typical precipitation regime (rainy winters and dry summers). In Spain, water management is more dependent upon rainfall variability than on the rainfall annual mean (Lopez-Bustins, 2018). A supplementary water source involving the
Journal Pre-proof use of fog-water collectors has recently been tested throughout eastern Spain (Estrela et al., 2019); this alternative is vital with regard to compensating for the low-to-moderate rainfall in the area combined with the high temporal irregularity of precipitation. Furthermore, intense rainfall over east Spain lead frequently to flood cases along the coast (Gil -Guirado et al., 2019).
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Many studies statistically correlate several teleconnection indices with the rainfall series for the Iberian Peninsula at different timescales (Trigo et al., 2004; Gonzalez-Hidalgo et al., 2009; Ríos-Cornejo et al., 2015; Merino et al., 2016; Redolat et al., 2019). Most of the rainfall series referring to the Iberian Peninsula are closely related to the NAO (Rodó e t al., 1997; EstebanParra et al., 1998). However, rainfall in many regions of eastern Spain poorly correlates with the NAO index (NAOi) (Muñoz-Díaz and Rodrigo, 2004; Lopez-Bustins et al., 2008; Mathbout et al., 2019 ). These regions are leeward of the Atlantic circulation, and rainfall therein depends directly on the wet flows of the eastern component from the Mediterranean Sea, which are often torrential. The Western Mediterranean Oscillation (WeMO) was found to constitute the teleconnection pattern most statistically and significantly correlated with annual, monthly and daily precipitation on the littoral fringe of eastern Spain (Martin-Vide and Lopez-Bustins, 2006; González-Hidalgo et al., 2009; Lemus-Canovas and Lopez-Bustins, 2016). The daily timescale of the WeMO index (WeMOi) could prove to constitute a potential tool for analytical study of the frequency of torrential events in some regions of the western Mediterranean; these torrential episodes cause an increase in the values of the variability indices of precipitation at all timescales. The WeMO is defined as the atmospheric connection between the southwest of the IP (San Fernando) and the Po valley (Padua). The synoptic window 30º-60ºN - 15ºW-20ºE is found to best represent the WeMO phases (Arbiol-Roca et al., 2018). The positive phase of the WeMO corresponds to the Azores anticyclone and the Genoa low (Fig. 1, left); its negative phase coincides with an anticyclone located over inland Europe and a low over the Gulf of Cadiz (Fig. 1, right).
Fig. 1. (Left) Most extreme positive phase of the Western Mediterranean Oscillation (WeMO) in a daily synoptic situation during the 1950-2015 period (2nd December 1976). (Right) Most extreme negative WeMO phase in a daily synoptic situation during the 1951-2016 period (28th November 2014).
Journal Pre-proof The present study aims to analyse the statistical relationship between the coefficients of the WeMOi-rainfall correlation and two statistical indices at annual and seasonal timescales, i.e. to establish a spatial and temporal WeMO effect upon rainfall variability. In section 2, we describe the main orography and pluviometric features of the study area of Catalonia. The methods employed to calculate the statistical indices are also explained in section 2. In section 3, the results of the spatio-temporal relationship between the WeMO influence and rainfall irregularity are analysed and discussed. Finally, in section 4 we derive conclusions. This study will serve as a roadmap to study the relationship between the presence and intensity of the teleconnection indices influence and the rainfall variability in other regions. 2. Study area, data and methods
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Catalonia is a territory with a high orographic complexity, considering its relatively small surface area. The geography is conditioned by the Mediterranean coastline, to the east, with 580 kilometers of coastline, and the large relief units of the Pyrenees to the north. The rainfall database used in the analyses comprises 70 homogeneous and reconstructed pluviometric series, at monthly scale, from the Meteorological Service of Catalonia, covering the 1950-2015 study period (66 years), and spatially distributed throughout Catalonia (Fig. 2, left).With regard to average annual rainfall (Fig. 2, right), wet Catalonia (≥700 mm; Pyrenees and northeast, Girona province) can be distinguished from the dry part (<700 mm; inland and south, Tarragona province). the 700 mm threshold was used from the expert knowledge of the area of study. The wettest season is autumn, particularly on the littoral. The dry seasons are summer (except for the Pyrenees, due to convective rainfall) and winter.
Fig. 2. (Left) Location and orography of Catalonia, and spatial distribution of the rain gauges (white dots) used in the study. Red dots show the stations used in Fig. 11. (Right) Average annual rainfall for the study period (1950-2015). The two statistical indices considered are the coefficient of variation (CV) and the consecutive disparity index (S). The CV is calculated as follows (eq. 1) :
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CV
x 100 s
(eq.1)
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Where s is the standard deviation of the precipitation series and x the precipitation series mean. Annual CV values ≥20% and seasonal ones ≥35% can be considered to constitute moderate variability of precipitation influenced by a typical Mediterranean climate (Lemus Canovas & Lopez-Bustins, 2016). Although statistical analysis of precipitation with the CV provides information on the variability of the phenomenon, it does not provide relevant information on the consecutive disparity of precipitation records, and therefore does not enable the chronological order and temporal structure of the series to be evaluated. Consequently, for the study of rainfall variability, apart from employing the CV we included two dimensions of analysis: the statistical dispersion and the chronological order of the series values, evaluated by means of the S index (eq. 2) proposed by Martin-Vide (1986):
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N 1
(eq. 2)
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Where x i is annual rainfall and N is the number of years of the series. This index ranges from zero to infinity, where values close to zero account for a temporal regul arity of the precipitation, whereas very high values of this index denote greater irregularity. An annual value of 0.25 and a seasonal value of 0.55 S can be considered to indicate moderate variability of precipitation influenced by a typical Mediterranean climate.
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The differences between the CV index and the S index should be highlighted, as the results of both indices do not always coincide. We consider three hypothetical annual rainfall series for analysis (Fig. 3). In the first case (above), the series has an average precipitation value of approximately 600 mm, but without extreme values, i.e. with a very low degree of variability, and we therefore obtained a low CV and a low S. In the second case (middle), the precipitation series exhibits an average close to 450 mm, so that most of the years can be observed to be clearly distant from this average value and as a result we obtained a high CV value. On the contrary, with regard to the S index, only one jump appears in the whole series, which implies a moderate S index. Finally, the third example (bottom) shows a situation presenting high CV and S index values, since all the values of the series are found to be far from the average and in addition, a clear temporal disorder is identified in which dry years are interspersed with rainy years. The latter example represents the worst scenario and would have the greatest economic impact upon the region’s water management (Olcina et al., 2016), giving rise to social water scarcity (Iglesias et al., 2009).
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Fig. 3. Comparison between the execution of the coefficient of variation (CV) and the consecutive disparity index (S) for three different theoretical rainfall series.
To evaluate the spatial interaction between the WeMO and rainfall variability, we used Pearson’s correlation (R). We mapped coefficients (using the 95% and 99% confidence level for statistical significance on maps and in scatterplots, respectively) and statistical indices using GIS techniques. The most suitable interpolation method for this purpose was ordinary Kriging. We assessed the WeMOi-rainfall variability relationship using 21-year temporal windows. These windows overlap along the whole study period (1950-2015), following 3-years shifts; hence, 16 subperiods are considered for temporal analysis (1950-1970, 1953-1973...). In each temporal window we computed the CV and S index, and the R between the precipitation and the WeMOi. Finally, we mapped the R by means of the relationship of the all temporal windows of CV/S and the R between precipitation and WeMOi (Fig. 4)
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Fig. 4. Scheme to assess the relationship between the WeMOi and the rainfall variability using temporal windows. Note that we only considered the coefficient of variation (CV) in this example.
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3. Results and discussion
3.1. The relationship between the WeMOi and rainfall
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The correlation between the WeMOi and annual precipitation in Catalonia (Fig. 5) does not show a strong correlation, except for the extreme northwest region, where the statistically significant coefficient R is between 0.25 and 0.5. This means that in years when the WeMOi is positive, precipitation in this area is usually higher than when a negative WeMOi was recorded (Lemus-Canovas and Lopez-Bustins, 2016). However, there is hardly any statistically and significant correlation between the WeMOi and annual precipitation, and this correlation should therefore be addressed on a seasonal scale. The wintertime correlation between the WeMOi and precipitation is statistically significant, with the R coefficient betw een -0.5 and 0.25 in most of Catalonia, except for the area of the western Pyrenees, where this R decreases until it becomes inverted in the extreme northwest, with a positive and statistically significant R. These results show that negative WeMO phases tend to be associated with rainy episodes in most of the territory due to Mediterranean humid easterly flows, as many of the abovementioned studies have already established (e.g. Lopez-Bustins et al., 2008). Conversely, in the extreme northwest of Catalonia, rainy episodes tend to occur with a positive WeMO linked to northwesterly circulation. A situation similar to that of winter occurs in autumn, when the R-value is negative and statistically significant along the coast and extends into inland Catalonia along the river valleys. Spring only shows a negative and statistically significant correlation in the southernmost part of Catalonia. During the summer season, the correlation becomes positive but only statistically significant in the extreme northeast and along a strip mainly inland of the provinces of Lleida and Tarragona; this is probably related to summer storms favoured by cold air at the mid-tropospheric level during days with a northwesterly atmospheric circulation (Azorin-Molina and Lopez-Bustins, 2004, 2008).
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Fig. 5. Spatial interpolation of the correlation coefficients between the WeMOi and precipitation at the annual and seasonal scales.
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3.2. Rainfall variability and its relationship with the WeMOi
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Apart from being closely associated with seasonal precipitation, the WeMOi also has a strong relationship with rainfall variability. In this case, the areas with a more negative correlation between precipitation and the WeMOi (shown in Fig. 5) reveal a notable coincidence with areas presenting a higher CV (Fig. 6). Similarly, the areas with the most positive correlation between the WeMOi and precipitation are those exhibiting the lowest variability within the study area. In the set of maps, the north-westernmost area, which is the one presenting the most positive correlation between the teleconnection index and precipitation at different timescales, can be observed to reflect an annual CV lower than 20% and lower than 35-40% in spring, summer and autumn. During winter, the CV of this north-westernmost area presents a moderate-to-high CV (45-55%), the lowest in the study area. Northwestern Catalonia is somehow influenced by the Atlantic climate; indeed, in Val d’Aran county (north-westernmost Catalonia) streams flow towards the Atlantic Ocean. Similarly, the entire Catalan coast, especially its southernmost part, presents a high degree of variability, reaching an annual CV between 30-35 %, and exceeding 65 % in the 4 seasons. The easternmost part of Catalonia (Cape Creus) also displays very high variability values. This is caused by mountain ranges that run in a N-S direction in both areas (Fig. 2, left), which constitute an orographic barrier to the humid easterly flows (Lopez-Bustins, 2007) and which cause torrential events to occur with greater frequency (Martin-Vide and Lopez-Bustins, 2006; Lopez-Bustins et al., 2016).
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Fig. 6. Spatial interpolation of the CV index at the annual and seasonal scales.
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The spatial distribution of the S index (Fig. 7) follows a distribution similar to that of the CV. The hypothesis of a subtropical gradient is reinforced with the S Index in relation to the CV as one moves from north to south — the easternmost area is weakened with the S index. As in the case of the CV, the greatest temporal precipitation disorder occurs in southern Catalonia and close to the coastline. Therefore, the areas displaying the most negative correlation between the WeMOi and precipitation coincide with the highest values of the S index. On the other hand, in most of the Pyrenees, especially in the westernmost area of this mountain range, the temporal disorder is very low, which means that precipitation does not differ much from one year to another or from one season to another. In general terms, the S ind ex denotes a major influence of the NW Atlantic flows because the typically Mediterranean S values (annual 0.15 and seasonal 0.55) spatially diminish towards the coast.
Fig. 7. Idem as Fig. 6, for the S index.
Journal Pre-proof As for the relationship between rainfall variability, provided by the CV and S indices, and the correlation between the WeMOi and precipitation, Fig. 8 shows a linear regression that relates both parameters at different timescales (annual and seasonal) for the 70 rain gauges distributed throughout the study area. The results of these scatterplots show that in all cases, the linear regression between both parameters is statistically significant at the 99% confidence level, obtaining a maximum R-adjustment in spring (-0.74) and a minimum in autumn (-0.46). This demonstrates a clear relationship between the CV and the influence of the WeMOi on annual and seasonal precipitation. It should be noted, however, that apart from summer, all other seasons follow the same pattern in which the CV decreases as the correlation between the WeMOi and precipitation becomes positive. In summer, this relationship is opposite to that of the other seasons due to the convective rainfall (summer thunderstorms) generated in moderate positive phases of the WeMO (NW and N advections).
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The linear regressions between the S index and the WeMOi correlation coefficient with precipitation are similar to CV. In this case, the R-adjustment is even higher in the spring (0.77) and autumn (-0.58) seasons than in the CV analysis, which corroborates the fact that the higher the negative correlation between precipitation and the WeMOi, the rainfall tends to present a greater temporal disorder. On the other hand, in the summer (R = 0.35) and winter (R = -0.36) seasons the correlation is not as strong, although it remains statistically significant at the 99 % confidence level.
Fig. 8. Scatterplots of the linear regression of the correlation between the WeMOi and precipitation at different timescales, and the CV and S indices.
Journal Pre-proof 3.3. The temporal factor in rainfall variability and its relationship with the WeMOi
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We demonstrated the existence of a strong relationship between the rainfall variability quantified by the CV and S indices, on one hand, and the correlation between the WeMOi and precipitation, on the other. One step further would involve investigating whether this relationship between both parameters responds to a temporal factor. We analysed the 19502015 rainfall series in 21 subperiods or time windows to ascertain whether periods in which the correlation between WeMOi and precipitation are more negative, the variability is also seen to increase with respect to the periods presenting a positive correlation. These results are shown in Fig. 9.
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Fig. 9. Spatial interpolation of the correlation between the WeMOi and precipitation in different subperiods and the CV at annual and seasonal scales.
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The temporal relationship between the CV, on one hand, and the correlation between the WeMOi and annual precipitation, on the other, demonstrated that in coastal areas, periods with a high negative correlation imply higher values of CV. Indeed, in the extreme south of Catalonia, this correlation is very close to -1. This coastal-inland dichotomy is reflected in a similar way in autumn. In spring, precipitation variability in southern Catalonia is also dependent on the WeMO; and in winter, the influence of the WeMO upon CV values is detected in inland areas and in the western Pyrenees. In summer, the influence of the WeMO has the opposite effect, so that the periods exhibiting a more positive correlation between the WeMOi and precipitation are those associated with greater variability. A similar spatial pattern is reproduced in the case of the relationship with the S index (Fig. 10); the littoral area is the one in which variability shows the most evident increase in periods presenting a more negative correlation between the WeMOi and precipitation. In winter, positive correlation values provided by the S index are enhanced in southern Catalonia, and in spring, negative correlation values show a decrease in the southern part; in summer, negative correlation values stronger than those provided by the CV are observed in northeastern Catalonia.
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Fig. 10. Spatial interpolation of the correlation between the WeMOi and precipitation in different periods and the S Index at the annual and seasonal scales.
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Finally, we analysed two specific observatories to evaluate the temporal relationship according to subperiods between correlation values between the WeMOi and annual precipitation, on the one hand, and the CV and S indices (Fig. 11), on the other. We observed that in the case of Tivissa (Tarragona province), the periods displaying the most negative correlation between the WeMOi and annual precipitation (the 1959-79, 1962-82 and 1965-85 subperiods) tend to present a high or very high CV, between 30 and 35 % or between 40 and 45 %, respectively. On the other hand, 3 out of 4 periods containing a positive correlation between the WeMOi and annual precipitation (the 1986-06, 1992-12 and 1995-15 subperiods) present a moderate CV, i.e. the minimum of the 1950-2015 series. Interestingly, the most recent subperiods are those with a lower CV, as opposed to the older ones, which provide a higher CV value. Furthermore, the relationship between the WeMOi and annual precipitation and the S index in Llinars del Vallès (province of Barcelona) shows a pattern very similar to that of Tivissa. Periods with a very high or high value of the S index only correspond to those presenting a negative correlation between the WeMOi and annual precipitation. Likewise, towards the range of values with a low S index, this correlation becomes positive; we therefore did not detect any years negatively correlated with a low S index value. As with Tivissa, the most recent periods reveal moderate or low S index values, whereas the older periods exhibit high or very high ones.
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Fig. 11. Temporal relationship between the correlation coefficients of the WeMOi/precipitation and annual CV, according to subperiods, for Tivissa (province of Tarragona), and annual S for Llinars del Vallès (province of Barcelona) . See figure 1 to locate the two observatories presented. Our results confirm the Mediterranean origin of the heavie st rainfall in many regions of the Iberian Peninsula (Millán et al., 2005). The negative phase of the WeMO favours a humid easterly flow that gives rise to intense precipitation over the eastern façade of the Iberian Peninsula; in turn this torrential event causes an increase in precipitation concentration indices such as the CV and the S. The Mediterranean origin of the intense episodes is associated with the negative phase of the WeMO (Lopez-Bustins and Azorin-Molina, 2004). González-Hidalgo et al. (2009) identified a fringe along the eastern littoral of the Iberian Peninsula as the zone most strongly influenced by the WeMO. This fringe registered the highest torrential episodes in Spain, e.g. >800 mm in 24 h in south Valencia province (Peñarrocha et al., 2002). The highest accumulation of torrential episodes is in the littoral area; particularly in southernmost Catalonia (Lopez-Bustins et al., 2016), where the strongest relationship is detected between the influence of the WeMO and precipitation variability with the use of both the CV and the S indices (Lemus-Canovas and Lopez-Bustins, 2016). The WeMO’s influence can vary over the decades, and years in which rainfall is most strongly correlated with the WeMOi could be
Journal Pre-proof associated with a rise in CV and S values, thus indicating greater precipitation concentration and irregularity (De Luis et al., 2011). 4. Conclusions
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The present research highlights the predictive capacity of the WeMOi with regard to distinguishing periods of high and low rainfall variability at different timescales. Autumn and winter rainfall over the littoral region is statistically and significantly influenced by the negative phase of the WeMO. In summer, the positive phase of the pattern shows a predominance due to convective rainfall caused by the presence of cool air in polar advections. The CV and S indices provided high and very high values of rainfall variability on the coast, especially in southernmost Catalonia. Moderate values are detected in continental areas and the lowest values are observed in the Pyrenees. The season exhibiting the highest value for rainfall variability is winter. Generally speaking, the S values depicts better than CV those areas of Catalonia with a Mediterranean origin precipitation.
Acknowledgements
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There is a statistically significant correlation in relation to spatial distribution between the WeMOi coefficient values and the CV and S values. The stronger the influence of the WeMOi, the higher the pluviometric irregularity, and vice versa; the exception is summer, when the WeMO has the opposite effect. Results for temporal rainfall variability show that, in the subperiods in which the coefficients of correlation between the WeMOi and precipitation are very negative, rainfall variability shows an increase (high CV and S values) in the littoral areas, and vice versa in the continental areas (Lleida province). Our principal conclusion is that this Mediterranean teleconnection pattern strongly determines precipitation variability within its area of influence, and this effect on precipitation should be taken into account in water planning in dry regions.
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The present research was conducted within the framework of the Climatology Group of the University of Barcelona (2017 SGR 1362, Catalan Government), and the CLICES project (CGL2017-83866-C3-2-R) of the Spanish Ministry of Economy, Industry, and Competitiveness. M.L-C is granted with a pre-doctoral FPU Grant (FPU2017/02166) from the Spanish Ministry of Science, Innovation and Universities.
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Journal Pre-proof Highlights
WeMOi has a predictive capacity with regard to distinguishing periods of high and low rainfall variability at different timescales.
The spatial distribution of the coefficient values between the WeMOi precipitation is in relation to the pluviomet ric indices values of CV and S.
The stronger the effect of the WeMOi on precipitation, the higher the pluviometric irregularity, and vice versa.
WeMOi strongly determines precipitation variability within its area of influence, and this is important for water planning in dry regions.
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