trends and correlations in annual extreme precipitation...

ORIGINAL PAPER

Trends and correlations in annual extreme precipitationindices for mainland Portugal, 1941–2007

M. Isabel P. de Lima & Fátima Espírito Santo &Alexandre M. Ramos & Ricardo M. Trigo

Received: 7 March 2013 /Accepted: 19 December 2013# Springer-Verlag Wien 2014

Abstract Precipitation extremes in mainland Portugal (south-western Europe) using daily precipitation data recorded in theperiod 1941–2007 (67 years) at 57 meteorological stationsscattered across the area are studied at an annual scale. Trendsin selected precipitation annual indices that are calculatedfrom these data are investigated, in particular trends in theintensity, frequency and duration of extreme precipitationevents. Special attention is dedicated to local and regionalvariability. The spatial correlations between the annual trendsin mean precipitation and in the extremes are analysed. More-over, the relationships between the variability of the NorthAtlantic Oscillation (NAO) index and several indices relatedto the frequency and intensity of the precipitation at the 57stations were also investigated. Results show that severalstations have predominantly negative tendencies in the pre-cipitation indices, although the majority of stations did notshow statistically significant change over time in the 1941–2007 period. At the regional level, the decreasing trend in the

simple daily precipitation intensity index is the only onestatistically significant at the 5 % level and appears to berelated to the predominance of the positive phase of theNAO. For the period 1976–2007, the proportion of the totalprecipitation attributed to heavy and very heavy precipitationevents increased and, consequently, daily precipitation eventsshow a tendency to become more intense. Moreover, correla-tion analysis show that the most extreme events could bechanging at a faster absolute rate in relation to the mean thanmore moderate events.

1 Introduction

Over the twentieth century, changes in global and land pre-cipitation have been observed across different time scales,which are expected to result from the variability and changein the climate. Particularly, changes in extreme precipitationare of general concern because of the expected impact onsociety and ecosystems; an extreme (weather or climate) eventis generally defined as the occurrence of a weather or climatevariable above (or below) a given threshold near the upper (orlower) endpoints of the range of observed values of thevariable (e.g. IPCC 2012). Moreover, global climate variabil-ity and change are expected to be accompanied also by ad-justments in other climate variables, which increase the com-plexity of the weather and climate systems. Water, soil andenergy are at the core of the discussions on this topic; they arekey factors for environmental and societal sustainability,which depends much on the local conditions including resil-ience and adaptation capacities.

Insight into the properties of local and regional land pre-cipitation in the recent past can be obtained by analysingavailable ground-based point data; other alternative methods(e.g. climate models) have often limited usefulness at smalltime and space scales. However, precipitation measurement

M. I. P. de Lima (*)Department of Civil Engineering, University of Coimbra,Coimbra, Portugale-mail: [email protected]

M. I. P. de LimaInstitute of Marine Research, Marine and Environmental ResearchCentre, Department of Civil Engineering, University of Coimbra,Coimbra, Portugal

F. E. SantoThe Portuguese Sea and Atmosphere Institute—IPMA, I. P.,Lisbon, Portugal

A. M. Ramos :R. M. TrigoInstituto Dom Luiz, Universidade de Lisboa, Lisbon, Portugal

R. M. TrigoDepartamento de Engenharias, Universidade Lusófona,1749 Lisbon, Portugal

Theor Appl ClimatolDOI 10.1007/s00704-013-1079-6

limitations (e.g. data resolution, limited length of records) andthe sparse network of in situ ground-based precipitation mon-itoring contribute to the difficulties in detecting precipitationlow frequency fluctuations and change (both in space andtime), which is associated with large uncertainty. Findingsmay also differ due to the different spatial and temporal scalesinvestigated and even the methodology used for each study.Thus, the exploration of different scales and precipitationparameters give opportunity to many studies to complementprevious analyses, towards a better understanding of thosesystems and relevant processes.

Studies conducted at the global scale have usually beenconsistently reporting increasing trends in extreme precipita-tion indices, which has also been found in some studies forEurope, but at the regional level the findings may differ.Despite general trends, the local precipitation structure, rele-vant generating mechanisms and other specificities can dictatesometimes unexpected differences in precipitation character-istics and change over short distances that cannot bedisregarded. Examples of some of these studies are the fol-lowing: for global scale, Frich et al. (2002), Alexander et al.(2006) and IPCC (2007); for the countries of the westernIndian Ocean, Vincent et al. (2011); for China, Wang et al.(2013) and Jiang et al. (2013); for Australia, Alexander et al.(2007); for Central America and northern South America,Aguilar et al. (2005); for southern and western Africa, Newet al. (2006); for central and southern Asia, Klein Tank et al.(2006); at the European level, Klein Tank et al. (2002), KleinTank and Können (2003), Moberg and Jones (2005), Moberget al. (2006) and Karagiannidis et al. (2012); for the Mediter-ranean region and Iberian Peninsula, Xoplaki et al. (2004),Norrant and Douguédroit (2006), Gallego et al. (2006),Rodrigo and Trigo (2007), Rodrigo (2010), Gallego et al.(2011), Acero et al. (2012), van den Besselaar et al. (2012)and García-Barrón (2013); for mainland Portugal, Mirandaet al. (2002, 2006), de Lima et al. (2007, 2010a, b), Costa et al.(2012), de Lima et al. (2013) and Espírito Santo et al. (2013b);and particularly for the south of mainland Portugal, Costa andSoares (2009), Durão et al. (2009) and Mourato et al. (2010)and for the north, Santos and Fragoso (2013).

In general, no significant annual precipitation trends havebeen reported for mainland Portugal, although some studiesfound a decreasing tendency in extreme precipitation indices;similar findings have been reported for the Mediterraneanregion and Iberian Peninsula (see references, above). Howev-er, the majority of the studies that support these results haverelied on very few stations over western Iberia, where main-land Portugal is located; exceptions are the high densitystation studies by Costa and Soares (2009) and Mouratoet al. (2010), but they are restricted to the southernmost regionof mainland Portugal. The strong precipitation spatial variabil-ity and gradients that are observed in the whole territoryrequires that a higher number of locations is explored (e.g.

Trigo and DaCamara 2000; Miranda et al. 2002, 2006; Belo-Pereira et al. 2011). We note that some studies have examineda restricted record period of only a few decades, which caneasily bias the estimation of trends (e.g. de Lima et al. 2010a, b).

Our study aims mainly to contribute to the increased un-derstanding of precipitation in mainland Portugal by investi-gating daily precipitation data recorded at 57 meteorologicalstations scattered across mainland Portugal. Trends in precip-itation extremes in the period 1941–2007 (67 years) werestudied by testing time series of selected precipitation annualindices over different multi-decadal periods: the indices werecalculated from daily data both at the local and regional levels.These indices provide a view into the frequency of extremeprecipitation events and their intensity (see e.g. Frich et al.2002; Moberg et al. 2006). Additionally, the correlationsbetween annual mean conditions and annual extreme precip-itation indices, as well as correlations between trends in themeans and in the extremes were investigated. Moreover, thecorrelation between precipitation indices and the most impor-tant pattern of large scale circulation in the northern hemi-sphere, i.e. the North Atlantic Oscillation (NAO), will beanalysed. With these analyses, we extended for Portugal thework carried out by Klein Tank and Können (2003) forEurope, thus providing a stronger basis for comparison oftrends in precipitation obtained for different locations andEuropean regions. Moreover, the analysis involves now moreindices, and longer and denser data sets than in previousstudies. These are the main contributions of our work, whichfocuses on identifying trends in precipitation indices andexploring whether such trends can be explained, in whole orin part, by atmospheric circulation changes.

2 Study area and precipitation data

2.1 Brief description of the study area

Located in south-western Europe, mainland Portugal is con-fined between parallels 37°N and 42°N and within the rela-tively narrow meridional band that develops between 6.5 and9.5°W. It lays in the transitional region between the sub-tropical anticyclone and the sub-polar depression zones. Inthis territory, the latitude, orography and effect of the AtlanticOcean are the factors that dominate more the climate. Figure 1shows a relief map of mainland Portugal; the highest altitudeis roughly 2,000 m.

Regional precipitation exhibits large inter-annual variabil-ity and very marked north–south and east–west gradients.Mean annual precipitation varies from about 3,000 mm inthe north to roughly 500 mm in the southern part of thecountry. The precipitation climate is also characterized bystrong seasonality: on average, about 40 % of the annualprecipitation falls in winter (December to February); summer

M.I.P. de Lima et al.

(June to August) contributes to the total amounts with only 6%,approximately; in autumn and spring the amount of precipita-tion is highly variable (e.g. Miranda et al. 2002, 2006). Thesmall-scale precipitation variability has been less explored sofar and thus this process is still not fully characterized at smallspatial and temporal scales, in particular the extremes.

2.2 Records of precipitation extremes in the recent past

In mainland Portugal, the large temporal variability in precip-itation leads to maximum precipitation extreme events (poten-tially leading to floods) and long drought spells, which arerelatively frequent. These types of events both affect differentregions, although the incidence and severity of the episodesvary with location.

Major floods that occurred since the beginning of the1980s, roughly in the last 30 years, were in December 1981(Zêzere et al. 2005), November 1983 (catastrophic flooding inthe neighbouring Lisbon and Setúbal regions; e.g. Godinho1984; Liberato et al. 2013), December 1983 (WMO 1984),November and December 1989 (Zêzere et al. 2005), winter

1995/1996 (Santos et al. 2009), October–November 1997(Carvalho 1997; Espírito Santo et al. 1998), autumn–winter2000/2001 (Espírito Santo et al. 2001; Ventura et al. 2001;WMO 2001) and winter 2009/2010 (Vicente-Serrano et al.2011). In addition, on 18 February 2008, an active low-pressure system produced very intense precipitation that af-fected particularly Lisbon and the Beira Interior and Alentejoregions (IM 2008). The station Lisbon/Geofísico recorded thatday a total of 118.4 mm, which surpassed the previous max-ima of daily rainfall recorded at this station since 1864: 110.7mm, on 5 December 1876, and 101.2 mm, on 30 January 2004(Espírito Santo et al. 2004; IM 2008).

Long drought spells, which are more common in southernPortugal, bring usually damages to agriculture and affectwater resources and availability for different uses. The de-cades of the 1940s, 1980s and 1990s are identified as beingparticularly dry throughout the Iberian Peninsula territory (e.g.Vicente-Serrano 2006a, b). Up to the present, the 2004/2005episode is the worst drought event in the last 140 years and hasproduced major socioeconomic impact not only in Portugalbut also in Spain (e.g. Espírito Santo et al. 2004; Garcia-Herrera et al. 2007). Attention is drawn to the drought situa-tion that was recorded in the last hydrological year 2011/2012,which affected the whole territory and with greater severity inthe months of February and March.

2.3 Precipitation data set

The precipitation data cover the period 1941–2007 (67 years)and were recorded at 57 climatological weather stations andrain gauges from the networks of the Portuguese Sea andAtmosphere Institute (Instituto Português do Mar e daAtmosfera, IPMA, I.P.) and Portuguese Environment Agency(Agência Portuguesa do Ambiente, APA). Data from 11 sta-tions were provided by IPMA, while data from the other 46stations were provided by the National Water Resources In-formation System (Sistema Nacional de Informação deRecursos Hídricos), managed by APA.

The location of the stations selected in mainland Portugal isshown in Fig. 1. These stations are representative, on the whole,of the distribution of altitude of the territory; i.e. the percentage ofstations by classes of altitude is comparable to the area–altitudedistribution for mainland Portugal. Yet, there are differences inthe classes of lower altitude (i.e. the higher frequencies): morestations in classes 0–100 m and 200–300 m and less in the class100–200 m. We also note the lack of stations placed above1,000 m: only one station is included in our data set.

2.4 Data processing and selection

The data were chosen from 200 precipitation series, whichwere initially available; the selection was based on an exigentcombination of criteria related to the spatial distribution of the

Fig. 1 Relief map of mainland Portugal and location of the 57 climato-logical weather stations used in this study

Annual extreme precipitation indices for mainland Portugal

series over mainland Portugal and data length, completeness,quality and homogeneity:

(a) Completeness: Only stations with less than 2 % of miss-ing values were used; any given month was consideredcomplete if no more than 3 days were missing from therecords; a year was considered complete if no more than15 days were missing;

(b) Quality: Basic quality controls have been applied to allthe series with the purpose to identify errors in the data;the daily data were searched for anomalous values (“out-liers”), which were defined as having values that deviat-ed more than 3 standard deviations from the climatologicdaily average, but no such extremes were identified;

(c) Homogeneity: Standard homogeneity tests (e.g. Wanget al. 2007; Wang 2008a; Wang and Feng 2010) basedon the penalized t test (Wang et al. 2007), the penalizedmaximal F test (Wang 2008b) and a Quantile Matchingalgorithm (Wang 2009) were applied to the precipitationdata. The procedure used detected possible change pointsin the monthly series that were checked against stationmetadata records (when available). Series exhibiting ev-idence of discontinuities of non-climatic origin in thestudy period were not used.

After the application of these tests, the number of sta-tions’ data that were considered adequate to our study hasdropped to 57. The selection of the stations aimed also tomaximize the study period. The major limitations are notonly the data scarcity before 1941 (many series have startedbeing recorded at this time) but also and more importantlythe effective reduction of climate observations after 2007, asa result of the reduction of stations in the monitoring net-work and the problems faced in the maintenance of theexisting ones; this resulted in gaps and a high percentageof missing values. That justifies the difficulty of extendingthis study beyond that date, while maintaining such a densenetwork. More details about the data selection, quality con-trol and homogenization can be found in de Lima et al.(2013) and Espírito Santo et al. (2013b); these studies havealso used this data set.

3 Methods

3.1 Climate indices

A total of 13 indices of precipitation extremes calculated fromdaily data were selected in this work for exploring changes inthe intensity, frequency and proportion of extremes in totalprecipitation, in mainland Portugal. The description of theselected indices is given in Table 1; these indices were firstlydefined by the joint CCl/WCRP-CLIVAR/JCOMM Expert

Team Climate Change Detection and Indices (Peterson et al.2001) and reviewed by Zhang et al. (2011). Here, the indiceswere calculated at the annual scale for each individual stationand also for the study region (i.e. mainland Portugal) as awhole. The regional indices were assessed as simple averagesover all 57 stations’ individual indices (i.e. giving all thestations equal weight) and were used to obtain insight intothe inter-annual variability of precipitation in mainland Portu-gal. Anomalies are for the 1961–1990 reference period. Awetday is a day with an accumulated precipitation of at least1.0 mm.

The more relevant selected indices are the following:

– Spell indices, consecutive wet days (CWD) and consec-utive dry days (CDD), defined as the maximum numberof consecutive days with precipitation above/below1 mm, respectively; thus, the CWD is the maximumlength of wet spells, which could intensity flooding,whereas the CDD index can assess the region’s vulnera-bility to drought;

– Absolute (fixed) thresholds, defined as the number ofdays on which a precipitation value falls above a fixedthreshold: number of heavy precipitation days ≥10 mm(R10), number of very heavy precipitation days ≥20 mm(R20) and number of extremely heavy precipitation days≥25 mm (R25);

– The maxima of multi-day rainfall events’ indices, such asthe maximum precipitation on 1 and 5 days, RX1D andRX5D; the RX5D index can be used as an indicator offlood-producing events because, on the space scales con-sidered here, severe floods are generally not caused by asingle heavy thunderstorm event but, more likely, bylong-lasting heavy precipitation events that extend overa region;

– Percentile (non-fixed) thresholds: the 90th, 95th and99th percentiles of precipitation on wet, very wet andextremely wet days, respectively, R90p, R95p andR99p indices;

– The R95pTot index, which is the ratio R95p/PrecTot(precipitation fraction due to very wet days), representsthe percentage of annual total wet-day precipitation dueto events with precipitation above the 95th percentile; itcan be used to analyse the possibility of having a rela-tively larger variation in extreme precipitation events thanin total amount (Groisman et al. 1999).

The indices selected here have also been used in other studiesfor different regions around the world: e.g. Frich et al. (2002),Klein Tank and Können (2003), Aguilar et al. (2005), Alexanderet al. (2006), Herrera et al. (2010) and Jiang et al. (2013); forregions in mainland Portugal, see e.g. Costa et al. (2008), Costaand Soares (2009), Durão et al. (2009), Costa et al. (2012), deLima et al. (2013) and Espírito Santo et al. (2013b).


3.2 North Atlantic Oscillation index

The NAO is the most important mode of atmospheric vari-ability over the North Atlantic Ocean that affects significantlyAtlantic weather patterns, particularly in Europe and the Med-iterranean Basin (e.g. Trigo et al. 2002, 2004; Vicente-Serranoand López-Moreno 2008). The NAO index measures thestrength of the zonal flow across the North Atlantic andconsists in the pressure difference between Iceland and theAzores (e.g. Hurrell 1995; Serreze et al. 1997; Hurrell andDeser 2010) or Lisbon or Gibraltar (Jones et al. 1997).

The NAO index can be interpreted in terms of a large-scalemeridional exchange of atmospheric mass (van Loon andRogers 1978) or as the oscillation of a large-scale anomalouspressure (or geopotential) pattern (Wallace and Gutzler 1981);it has been found to correlate with surface climate in most ofthe European region (e.g. Hurrell 1995; Hurrell and van Loon1997; Trigo et al. 2002; Vicente-Serrano and López-Moreno2008), in particular Portugal (e.g. Ulbrich et al. 1999; Trigoet al. 2004; Santos et al. 2009).

In this work, we analyse the relationship between the NAOand precipitation in mainland Portugal, using the precipitationindices (Table 1) calculated from the daily data described inSection 2.3. The annual NAO index used here was computedas the difference between the normalized sea level pressure inPonta Delgada (Azores archipelago) and Stykkisholmur/Reykjavik (Iceland) and the data were provided by the Cli-mate Analysis Section, NCAR, Boulder, USA (Hurrell 1995).

The relationship between precipitation and the NAO wasalready reported for the Iberian Peninsula in other studies: formonthly or seasonal precipitation series, e.g. Trigo et al.(2004) and Lopez-Bustins et al. (2008); for daily precipitationseries, e.g. Goodess and Jones (2002), Gallego et al. (2005)

and Rodrigo and Trigo (2007). Albeit in a very limited numberof studies, this relationship was also analysed for Portugal: atthe monthly or seasonal scales, by e.g. Zhang et al. (1997),Ulbrich et al. (1999), Trigo and DaCamara (2000) and Miran-da et al. (2002, 2006); and using daily data, by Santos et al.(2009).

3.3 Trends and correlations

Linear trends in the time series of indices of precipitationextremes were estimated by ordinary least squares fitting(OLS). The t test was used to assess the statistical significanceof the trends with different levels of probability (see e.g.Peterson et al. 2001; Klein Tank and Können 2003; KleinTank et al. 2009; van den Besselaar et al. 2012). In using thismethodology, we anchor on other studies that have reportedthat the magnitude of the trends estimated by using the OLSmethod and a more robust non-parametric method are verysimilar (see e.g. Önöz and Bayazit 2003; Moberg and Jones2005). Önöz and Bayazit (2003) showed that the parametric ttest is slightly more powerful than the non-parametric Mann–Kendall test when the probability distribution is normal; theyalso showed that such relative power decreases with theincrease in skewness but that for moderately skewed distribu-tions the t test is almost as powerful as the Mann–Kendall test.This implies that the two tests can be used interchangeably inpractical applications, with identical results in most cases.Nevertheless, Nicholls (2001) recommended that in additionto the test results at the 5 % significance level, also the resultsat the 25 % level should be presented, which we took intoconsideration here. The definition of the precipitation indiceslisted in Table 1 shows that we are not examining extremelyrare precipitation events, for which the computation of

Table 1 Definition of the precipitation indices used in this study

Index Description Definition

PrecTot (mm) Annual total wet-day precipitation Annual total precipitation from days ≥1 mmSDII (mm) Simple daily intensity index Annual total precipitation divided by number of wet days (≥1 mm)CDD (days) Consecutive dry days Maximum length of dry spell (RR 95th percentile


significant trends could be a priori hampered by the smallsample sizes (e.g. Tebaldi et al. 2006).

In this work, the analyses were carried out for the full 67-year record period (1941–2007) and for two consecutive sub-periods of approximately the same length: 1945–1975 and1976–2007. In addition to the precipitation trends calculatedfor individual stations, trends were also computed for theregion as a whole using the regional indices (see Section 3.1),which were obtained by averaging over all stations. It isexpected that this averaging procedure will not decrease thetrend but will reduce the effect of natural variability (e.g. KleinTank et al. 2009), which anticipates more robust results.

Analysis of correlations between precipitation indices wasalso used here as an additional tool to inspect the data closely;the analysis was undertaken for mean conditions’ indices(wet-day total precipitation (PrecTot) and simple daily precip-itation intensity (SDII)) and all the other indices listed inTable 1, which were calculated site-by-site, for all 57 precip-itation stations, and at the regional level. The correlations werecalculated using the linear Pearson (product moment) correla-tion coefficient r and their statistical significance was assessedat the 5 % level. This type of analysis might help to identifystations’ data with anomalous behaviour (e.g. Moberg et al.2006), which could result, for example, from a few individualhighly erroneous daily values in the data that could corrupt thestatistics for the entire series. The linear trends in the PrecTotindex and the precipitation extremes’ indices, for all the 57stations, were also analysed: we aimed at investigating howthe trends in the mean and extremes vary at each station andhow well they are correlated. Because not all the precipitationindices have the same units (see Table 1), the linear trends foreach index were calculated as a percentage of the average.

The Pearson’s correlation coefficient was also used toexplore the relationship between the precipitation indicesand the NAO index. The statistical significance of the corre-lation coefficients was calculated using the Student’s t test(two-tailed test of the Student t distribution).

4 Observed trends and correlations in precipitationindices

The results of trend analyses of 13 precipitation indices’ timeseries are summarized in Tables 2 and 3, for the 1941–2007,1945–1975 and 1976–2007 periods. Table 2 gives the per-centage of precipitation stations that had positive/negativetrends in annual precipitation indices calculated individuallyfor the 57 stations’ data and the corresponding percentage ofstatistically significant results at the 5 % level. And Table 3shows the trends in annual regional precipitation indices andthe corresponding 95 % confidence intervals.

Figure 2 provides an overview of the trends in the regionalindices in the 1941–2007 period; for each index, the

corresponding 95 % confidence interval is highlighted bythe width of the shaded area at each dot.

In the next sections, we dedicate special attention to someof the results obtained. If not stated otherwise, the statisticalsignificance of the results given below is at the 5 % level.

4.1 Indices of wet-day total precipitation (PrecTot) and simpledaily precipitation intensity (SDII)

The mapping of the trends in the SDII and PrecTot indices,which are indicators of mean conditions, is shown in Fig. 3 forthe three periods investigated.

The regional average anomalies for the SDII and PrecTotindices are plotted in Fig. 4 (top), which shows the consider-able inter-annual variability in these indices. The lowest SDIIregional indices (1,200 mm)happened in 1963 and 1960.

Overall, these two regional indices show decreasing trendsbut there is only one result statistically significant in the 1941–2007 period: SDII, which decreases on average−0.13 mm day−1 decade−1 (Table 3); more than 70 % of thestations’ data show small negative trends in this index that arestatistically significant in 40 % of the cases (see also Table 2).The regional average series of PrecTot indicates a decreasingtrend of −13.21 mm decade−1 in the full 67-year period and amore marked decrease of −44.60 mm decade−1 in the 1976–2007 sub-period (Table 3). In this sub-period, more than 90 %of the stations show decreasing trends, which range from −54to −136 mm decade−1; however, only data from 11 % of thestations show a statistically significant decrease. An increas-ing tendency is found for the 1945–1975 sub-period, but noneof the results is significant.

4.2 Indices of precipitation extremes

Themarked inter-annual variability, and overall change, foundin the regional extreme precipitation indices’s time series isshown in Figs. 4 and 5. The highest values of the regionalindices were in 1997 for RX1D, RX5D, R90p, R95p andR99p, and in 1963 for R10 and R20. In general, the lowestvalues of the regional extreme precipitation indices were in thedrought years of 1980, 2004, 2005 and 2007, and in 1971 forRX1D and R99p.

Overall, the results show statistically non-significant de-creasing trends in regional extreme precipitation indices forthe 1941–2007 and 1976–2007 periods; the exceptions are theRX1D and R99p regional indices, which show a non-significant increase in this last 32-year period.


4.2.1 Indices of maximum length of dry and wet periods

The inter-annual variability of the regional CDD and CWDindices is also shown in Fig. 4 (bottom) and trends are given inTable 3.

The regional CDD index shows a decreasing trend of −1.3day decade−1 in the 1941–2007 period. Negative trends werefound in 80 % of the stations; yet only roughly 18 % of themexhibited a statistically significant decrease. In the 1976–2007period, the decreasing trend is −1.2 day decade−1, but the trendis not statistically significant.

The regional CWD annual indices take the lowest values(

daily events) in 1941–2007 and 1976–2007, and an increasingtrend in these indices in 1945–1975; however, trends are notstatistically significant (Table 3). Overall, this trend pattern issimilar to the one in the regional PrecTot index. But for the1941–2007 period, the decrease in R10 and R20 is significantonly at the 25 % level.

The frequency of heavy, very heavy and extremely heavyprecipitation daily events in the station’s data generallyfollowed the tendency of their respective regional average.More than 65 % of the stations show a decreasing trend in theR10, R20 and R25 indices, but significant decreasing trendsare found in only 10 to 16 % of them, in the 67-year recordperiod. This behaviour is more pronounced in the 1976–2007period: between roughly 80 and 90 % of the stations showdecreasing trends in these threshold indices (up to5 days decade−1), but only between 7 and 12 % of the stationshave statistically significant results.

The percentage change in precipitation amount and numberof wet days have opposite signs at some stations (i.e. negative/positive; when one decreases the other increases).

4.2.3 Indices of extreme precipitation events of 1- and 5-daydurations

The regional RX1D and RX5D indices exhibit decreasingtrends in the 1941–2007 and 1945–1975 periods but the trendsare not statistically significant (Table 3). Contrastingly, thetrend in the RX1D regional index is positive in 1976–2007. Inthis period, the trends in the RX1D index range between −5and +7 mm decade−1 across mainland Portugal; only 3.5 % ofthe stations show significant positive trends and none of thestations show significant negative trends.

Figure 6 maps the stations’ trends in the RX5D index in the1941–2007, 1945–1975 and 1976–2007 periods: between 50and 65 % of the stations show decreasing trends for the threeperiods; 14 % of the stations report a significant decreasingtrend for the 1941–2007 and 1945–1975 periods; for the period1976–2007, the trends range from −16 to +10 mm decade−1 andonly 5 % of the stations show significant negative trends.

4.2.4 Daily precipitation percentile threshold indices

The stations’ percentile-based R90p, R95p and R99p indicesdo not show consistent trends. Only a few stations’ trends(about 10 %) are statistically significant.

In general, the regional average series of these indices shownegative trends in the 1941–2007 period that accompanies thePrecToc trend. But the trends in the regional R99p and PrecTotindices have different sign (i.e. positive, negative) in the1945–1975 and 1976–2007 sub-periods. The trend in theR99p index is positive (1.2 mm decade−1) in the 1976–2007period; the contribution of the extremely wet days to the totalprecipitation amount is between 11 and 127 mm. The R95pindex shows a negative trend (−3.0 mm decade−1) in the full67-year record period, while the contribution of the precipita-tion due to very wet days (above the 95th percentile) to thetotal precipitation varies between 73 and 350 mm (approxi-mately 10 and 30 %); this index exhibits a slight increase(0.33 mm decade−1) in the 1945–1975 period and a decrease(−5.9 mm decade−1) in the 1976–2007 period. The R90pindex has a trend pattern similar to R95p, although morepronounced with an increase (2.1 mm decade−1) in 1945–1975 and a large decrease (−13.5 mm decade−1) in the subse-quent period (1976–2007); on average, the contribution of wetdays (above the 90th percentile) to the total annual precipita-tion varies between 120 and 500 mm in the 1941–2007period.

On average, the trend in the annual precipitation fractiondue to very wet days (R95pTot) is −0.14 % decade−1 in 1941–2007, which is not statistically significant (see Table 3). How-ever, in the 1976–2007 period, the R95pTot trend is increasing(0.30 % decade−1), which is accompanied by a decreasingtrend in the annual precipitation; these opposite trends mayindicate that the very wet days are less affected than the otherwet days.

Figure 7 shows the station’s trends in the R95p andR95pTot indices in 1941–2007, which are similar. Positivetrends in the R95pTot index are found for 20 % of the stationsand are accompanied by increases in the total annual precip-itation amount, but the result is not statistically significant.

Fig. 2 Overview of the trends in annual regional precipitation indices inthe 1941–2007 period: averages over the 57 stations (black dots); for eachindex, the 95 % confidence intervals are highlighted by the width of the

shaded area at each dot. The indices on the horizontal axes are defined inTable 1; those that belong to the same class (e.g. R10, R20, R25) areconnected with a line


Only two stations that exhibit a statistically significant de-crease in the annual precipitation show a significant change inR95pTot. This may indicate a disproportionate large change inthe extremes relative to the total amount, but this is only appar-ent in areas associated with wetting trends, not drying trends.

For the 1976–2007 period, around 40 % of the stations (themajority of them located in the Alentejo region, south of Portu-gal) show statistically non-significant positive trends in theR95pTot index, and this coincides with the observation of adecrease in the PrecTot index.

4.3 Relationship between annual mean precipitationand extreme precipitation indices

4.3.1 Correlations between extreme and mean precipitationindices

The correlation between eight extreme precipitation indicesand indices of wet-day precipitation (PrecTot) and intensity(SDII) were investigated for the 1941–2007 period; the eightextreme precipitation indices include the three threshold

Fig. 3 Trends in 1941–2007 (left), 1945–1975 (centre) and 1976–2007 (right) for SDII (top) and PrecTot (bottom). The dots are scaled according to themagnitude of the trend: blue for increasing (wetting) trends and yellow for decreasing (drying) trends


indices (R10, R20 and R25), the two absolute indices (RX1Dand RX5D) and the three percentile-based indices (R90p,R95p and R99p) listed in Table 1. For all the stations’ data,the indices are positively and statistically significantly corre-lated; the average correlations for all the stations are equal toor above 0.5.

On average, the correlations with PrecTot are stronger(above 0.8) for R10, R20 and R90p; only four stations havecorrelations between PrecTot and R90p below 0.8. For theR25 and R95p indices, the correlations are also, on average,above 0.76, so fairly strong. But for the R99p index, about halfof the stations have correlation coefficients below 0.5, and the

Fig. 4 Regional annual anomalies for the SDII, PrecTot, CDD and CWD indices (Table 1) for 1941–2007. Superimposed are the piecewise trendscalculated for the sub-periods 1945–1975 and 1976–2007

Fig. 5 Regional annual anomalies for the extreme precipitation R10, RX5D, R90p and R99p indices in 1941–2007. Superimposed to the time series arethe piecewise trends calculated for the sub-periods 1945–1975 and 1976–2007


spread in correlation coefficients among all the stations islarger than for the other two percentiles indices, particularlyR90p.

The correlations between SDII and the R25, RX1D, RX5Dand R99p indices are on average stronger than the correlationsbetween PrecTot and those same indices. The average corre-lations between SDII and R10, R20 and R25 are strong (r>0.7).For some stations located in the Alentejo region (low altitude),the correlations are very close to 1 (r>0.95), and increase whengoing from R10 to R25; it is for these stations that these corre-lations are stronger. For SDII andRX1D, only eight stations havecorrelations coefficients above 0.8, and these stations are alsolocated in the southern region of mainland Portugal. The corre-lations between SDII and the percentile indices decrease thenearer we get to the tail of the precipitation distribution, partic-ularly for the precipitation on extremely wet days (R99p); thispattern is similarly followed by the correlations between thePrecTot index and the percentile indices.

4.3.2 Correlations between trends in precipitation extremesand trends in mean precipitation

Here, we investigate the linear relationship between percenttrends (i.e. percentage of change over time) in the PrecTotindex and extreme precipitation indices, for all stationsindividually. Table 4 and Fig. 8 show that percent trendsin precipitation extremes are, in general, highly spatiallycorrelated with the percent trend in PrecTot; moreover, allthe correlations are statistically significant. This is particu-larly true for the number of heavy to extremely heavyprecipitation days (R10 to R25) and precipitation on wetand very wet days (R90p and R95p). Except for CDD,CWD and R10, the slope of the line of best fit for all otherindices is above 1, indicating enhanced variability in thetrends of precipitation extremes compared to the meantrends. The largest change (i.e. slope) is found for RX1Dand this result is statistically significant.

Fig. 6 Trends in 1941–2007 (left), 1945–1975 (centre) and 1976–2007 (right) for the RX5D index, in millilitre per decade. The dotsare scaled accordingto the magnitude of the trend: blue for increasing (wetting) trends and yellow for decreasing (drying) trends

Table 4 Spatial correlations between annual percent trends in extreme precipitation indices and percent trends in annual total wet-day precipitation forthe 57 stations across mainland Portugal in the 1941–2007 period

Index CDD CWD R10 R20 R25 RX1D RX5D R90P R95P R99P

s −0.3 0.5 0.7 1.0 1.4 4.9 1.1 1.4 2.0 2.8r −0.31 0.48 0.85 0.87 0.87 0.57 0.78 0.90 0.88 0.58R2 0.10 0.23 0.72 0.75 0.81 0.32 0.60 0.81 0.77 0.34

Slopes of the line of best fit (s) and correlations (r) that are statistically significant at the 5 % level are in bold; the coefficients of determination (R2 ) arealso given


We should highlight here the statistically significant resultsobtained for R95p and R99p (Fig. 8): even though the corre-lations between PrecTot and R99p are lower than betweenPrecTot and R95p (see also Table 4), the largest slope of theline of best fit estimated for R99p (Fig. 8f) compared to R95p(Fig. 8e) might suggest that the most extreme events could bechanging at a faster absolute rate in relation to the meanconditions than more moderate events. A multiple OLS re-gression was carried out to quantify the relationship betweentrends in annual precipitation and extreme precipitation indi-ces, and geographical coordinates across mainland Portugal.For elevation, latitude and longitude, the correlations are

weak; so the OLS results are unsatisfactory, indicating thatthese attributes represent less than 6 % of the variation in thetrends. Only for R95 and R90, they represent 10 and 13 % ofthe variation in their respective trends.

4.4 Correlation between the NAO index and precipitationindices

Most research for the Northern Hemisphere on the relation-ship between the NAO and precipitation has been focused onwinter season (e.g. Hurrel 1995; Trigo et al. 2002, 2004);recent works have stressed the need to look into summer

Fig. 7 Trends (top) and regional annual anomalies (bottom) for the R95p(left) and R95pTot (right) indices, in 1941–2007. Top: the dots are scaledaccording to the magnitude of the trend—blue for increasing (wetting)

trends and yellow for decreasing (drying) trends. Bottom: superimposed tothe time series are the piecewise trends calculated for the sub-periods1945–1975 and 1976–2007


NAO impacts but they are not significant for Portugueseprecipitation (e.g. Bladé et al. 2012). Starting from the early1940s to the mid-1970s, the winter NAO exhibited a slightdownward trend followed by a significant trend towards pos-itively values during the following decades until the mid-1990s (e.g. Hurrell and van Loon 1997); since 1995, thewinter NAO has been declining but with large inter-annualvariance. The summer NAO has been characterized by lessdecadal variability, recording a continuous increasing tenden-cy between the 1950s and late 1970s followed by a decreaseafterwards. The correlations between the NAO and the region-al average of the precipitation indices were analysed over the

full period of the records and the two sub-periods 1945–1975and 1976–2006; the temporal correlations for these two sub-periods are shown in Fig. 9.

Only the CDD index shows positive correlations with theNAO for the three periods, although the correlations are notstatistically significant. For the 1941–2007 period, there is astatistically significant anti-correlation between the precipita-tion indices and the NAO, with correlation coefficients vary-ing between −0.5 and −0.3 (not shown). However, overall thecorrelation coefficients are stronger (except for the regionalCDD index) in 1945–1975, ranging between −0.6 and −0.36.It was in this period that the NAO stayed in a negative phase

(a) (b)

(c) (d)

(e) (f)

Fig. 8 Trends (in percent per decade) in extreme precipitation indicesa R20; b R25; c RX1D; d RX5D; e R95p and f R99p plotted againsttrends (in percent per decade) in annual total wet-day precipitation(PrecTot) for the 57 stations across mainland Portugal. Each dot

represents a station. The line of best fit was calculated using totalleast-squares regression, with s being the slope of this line and R2 thecorresponding coefficient of determination (see also Table 4)


and this condition yielded also the strongest anti-correlationwith the RX5D index (an indicator of flood prone conditions).These results are consistent with the occurrence of consecu-tive low pressure systems and associated fronts, which arecharacteristics of an intensified Atlantic storm track (e.g. Trigoet al. 2004). But in the following sub-period (1976–2007),which is associated with a positive phase of the NAO (after themid-1970s), the respective anti-correlation is weaker (andnon-significant) in comparison with the other period. In addi-tion, the other correlations are overall weaker (−0.4 to −0.2) inthis period and only five indices (PrecTot, R90p and R10,R20, R25) show significant correlations.

Overall, the major differences between the trends in theannual regional indices of precipitation (Table 3) in the twosub-periods are the observation of a wetter period until themid-1970s, which reflects the tendency triggered by the neg-ative phase of the winter NAO in that time, and a drier periodsince then that might reflect the dominance of a NAO positivephase in the last decades. For the same periods, the percent-ages of the 57 precipitation stations with positive and negativecorrelations between the NAO and the precipitation indicesare given in Table 5. With the exception of the CDD index, allother indices are dominated by negative correlations for themajority of stations.

In the three periods, the anti-correlation with the NAO isobserved for all the stations for the PrecTot, R10 and R20indices. However, it is clearly during the 1941–2007 periodthat the anti-correlation ismore frequently observed, includingthose correlations that are statistically significant; in this peri-od, all the stations show negative correlations between theNAO and the other precipitation indices except for the RX1D,R99p and R95pTOT indices. When positive correlations be-tween the NAO and the precipitation indices are observed, anyof the results is statistically significant, except for the CDDindex. In the entire 1941–2007 period, for all the other indices,their correlation with the NAO is negative for 84 to 100 % ofthe stations, depending on the index; the results are statistical-ly significant in 12 to 95 % of the stations. The strongestresults are observed for the PrecTot and R10 indices in 1941–2007: negative correlations are found for all the stations,which are significant in 95 % of them. The positive correla-tions between the NAO and the CDD index are more abun-dant in the 1976–2007 period; they are found in 68 % of thestations and are significant in 7 % of them.

The correlations between the NAO index and the indicesthat describe the number of heavy (R10) to extremely heavy(R25) rainy days are negative for all the stations in the entire1941–2007 period and also during 1945–1975, but the

Table 5 Percentages of the 57 precipitation stations with positive (+) and negative (−) correlations between the NAO and the precipitation indices, andthe respective statistically significant correlations (Sig+/Sig−) at the 5 % level. The data are for the 1941–2007, 1945–1975 and 1976–2007 periods

Period PrecTot SDII CDD CWD R10 R20 R25 RX1D RX5D R90p R95p R99p R95pTot

1941–2007 Sig+ 0 0 5 0 0 0 0 0 0 0 0 0 0

Sig− 95 61 0 58 95 89 72 12 33 74 54 9 14+ 0 0 53 0 0 0 0 16 0 0 0 12 14

− 100 100 47 100 100 100 100 84 100 100 100 88 861945–1975 Sig+ 0 0 0 0 0 0 0 0 0 0 0 0 0

Sig− 84 47 0 44 81 65 54 9 42 60 46 12 12+ 0 4 54 0 0 0 0 25 7 0 5 16 12

− 100 96 46 100 100 100 100 75 93 100 95 84 881976–2007 Sig+ 0 0 7 0 0 0 0 0 0 0 0 0 0

Sig– 32 18 0 5 39 37 30 5 4 23 9 4 7

+ 0 7 68 21 0 0 4 25 19 4 5 25 19

− 100 93 32 79 100 100 96 75 81 96 95 75 81

Fig. 9 Correlations between theNAO and 12 precipitationregional indices for the 1945–1975 and 1976–2007 sub-periods. The horizontal dashedlines indicate the 5 % significancelevel. The labels on the horizontalaxes are defined in the text and inTable 1


number of stations having statistically significant correlationsdecrease as the precipitation threshold increases from 10 to25mm. Likewise, for all periods, the number of stations with astatistically significant negative correlation for the percentileindices decreases as the percentile increases from R90p toR99p. The strongest result found for the RX5D index is in1945–1975, a period when the NAO stayed in a negativephase (already discussed above): 93 % of the stations shownegative correlations with NAO, which are statistically signif-icant in 42 % of them. Overall, the results for the RX1D indexare less significant; the strongest results are observed for theentire period 1941–2007: 84 % of the stations show negativecorrelations with NAO, which are statistically significant inonly 12 % of them. So, overall, there is anti-correlationbetween NAO and the indicators of more intense rainy events,but this is less significant for the extreme events. In fact, themovement of the Atlantic storm track expressed by the NAOis clearly more related with phenomena of frontal origin (seee.g. Miranda et al. 2002), whereas the more intense events areusually associated with local convective phenomena.

Figure 10 shows maps of the spatial distribution of thePearson’s correlation between the NAO and selected dailyprecipitation indices (R10, R90p, SDII, PrecTot) in the1941–2007 period, which confirm the existence of coherentregions that are revealed by correlations of the same order ofmagnitudes; the maps were constructed by interpolating thecorrelations obtained for all the 57 stations over mainlandPortugal. They illustrate that the northeast and southeast haveprecipitation regimes and patterns that are less influenced bythe NAO mode, when compared to the majority of areas inmainland Portugal. It is likely that in these regions precipita-tion is less modulated by mid-latitude low pressure systems.

5 Discussion

The annual regional indices of precipitation extremes thatwere used to characterize extreme wet and intense precipita-tion events in mainland Portugal do not suggest any markedpattern of change over the 1941–2007 period, but the trends inall the indices are negative, although the majority of trends arenot statistically significant and only the SDII index is statisti-cally significant at the 5 % level.

The site-by-site assessment of the data from the 57 clima-tological stations shows that, in general, there is a mix ofincreasing and decreasing trends in precipitation extremesacross mainland Portugal, but there are more stationsexhibiting decreasing trends than increasing trends in the1941–2007 period, and this decreasing tendency is even morenotorious in the 1976–2007 sub-period.

We found a somewhat noticeable reduction in the regionalannual total wet-day precipitation index in the 1941–2007period, which indicates a drying trend overmainland Portugal;

however, this result is not statistically significant. Neverthe-less, at the regional scale, average annual precipitation (about900 mm) presents a reduction over the full 67-year period ofabout 10 %. The site-by-site analysis shows that the total wet-day precipitation reduction is statistically significant for 16 %of the stations. Klein Tank and Können (2003) analysed sixprecipitation series across mainland Portugal (1946–1999)and did not found significant trends in the annual total wet-day precipitation (PrecTot) and the precipitation fraction dueto very wet days (R95pTot); only the data from one station,Porto, showed significant decreasing trends in these indices.However, the different smaller period and the limited dataexamined by the authors in that study hamper the comparisonof results.

The regional CDD index showed also a decreasing trend(statistically significant at the 25 % level) in this period; thisreduction is observed in 81 % of the stations, which is statis-tically significant in 17 % of them. Similar tendency towardsless extended dry periods has been reported by, e.g. Kiktevet al. (2003), Alexander et al. (2006) and Tebaldi et al. (2006).Conversely, the CWD regional index shows a decreasingtrend of −0.18 day decade−1 (statistically significant at the25 % level) in the 1941–2007 period, which is more negative(−0.50 day decade−1) in the 1976–2007 period (over 90 % ofthe stations exhibit decreasing trends, but only 5% of them arestatistically significant).

Decreasing trends in the 1941–2007 period were further-more observed in RX1D and RX5D, and R10, R20 and R25regional indices; however, only the trend in R10 (number ofdays above 10mm) is statistically significant at the 25% level.In addition, the results obtained for all the selected annualextreme precipitation indices based on absolute precipitationand relative (i.e. percentile) thresholds show that data fromonly less than 16% of the stations have statistically significantdecreasing trends; for RX1D, R95p and R99p less than 7 % ofthe stations show significant increasing trends.

Despite all this, at the regional scale, the consistency of theresults for the different stations suggests less annual wet-dayprecipitation along with fewer days with heavy precipitation.Overall, these results hint a drying trend over mainland Por-tugal for the period 1941–2007. However, in this period, thenumber of heavy and very heavy precipitation events (R10and R20) decreased more significantly than the mean totalwet-day precipitation (PrecTot); so the proportion of the totalprecipitation attributed to these events increased and, conse-quently, daily precipitation events tend to become moreintense.

The results also show that, on average, there is no statisti-cally significant change in annual precipitation indices forboth sub-periods 1945–1975 and 1976–2007. But in general,the precipitation indices detect a weak tendency towards moreextreme precipitation events in 1976–2007, particularly in thesouthern region; this was shown by indicators of intensifying


Fig. 10 Correlations between the NAO and aR10, bR90p, cSDII anddPrecTot indices across mainland Portugal in 1941–2007. Isolines arefor the Pearson correlation coefficients found for the 57 precipitation

stations; triangles indicate negative and statistically significant (5 %level) correlations and crosses indicate non-significant correlations


precipitation, such as RX1D and R99p. The percentage con-tribution of extremely wet days (upper 1 %) to the annual wet-day precipitation is higher in 1976–2007 than in 1941–1975.

Correlation analysis suggests that precipitation extremesare highly correlated with the mean precipitation and trendsin the precipitation extremes are highly correlated withtrends in mean precipitation, particularly for the R10, R20,R25, R90 and R95 indices. We found evidence that the mostextreme events could be changing at a faster absolute rate inrelation to the mean conditions than more moderate events.This result is consistent with Groisman et al. (1999) whofound a disproportionate change in precipitation intensitieswhenever the mean precipitation changed; this was also shownby Katz (1999). Furthermore, Tebaldi et al. (2006) point out thatthere is a trend for intensified precipitation, with a greater fre-quency of heavy-precipitation and high-quantile events.

It is worth noting that, for the study area, other studiesidentified a cooling tendency in the 1945–1975 sub-periodand a warming tendency in the 1976–2007 sub-period (seee.g. Miranda et al. 2002, 2006; Ramos et al. 2011; de Limaet al. 2013; Espírito Santo et al. 2013a). A similar overallpattern was also found for Europe’s temperature (with thewarming period starting in the mid-1970s, see e.g. Klein Tanket al. 2002; Klein Tank and Können 2003) and the globalaverage temperature (e.g. Rozelot and Lefebvre 2006). ForPortugal, the link between trends in air temperature and precip-itation at the local and regional scales is outside the scope of thiswork but should be investigated in future research. Also, thecorrelation between the precipitation indices and geographicattributes should be explored; Costa et al. (2010) and Durãoet al. (2010) have already analysed these relationships, but justfor southern Portugal and a limited number of indices andrecord length. Nevertheless, we noticed that a larger percentageof stations’ data from the central and southern regions ofPortugal showed positive trends in the extreme intensity, dura-tion and percentile precipitation indices compared to the north-ern stations, for the entire 1941–2007 period and the last sub-period 1976–2007; but these trends are not statistically signif-icant. This behaviour is consistent with the results reported forthe south by Costa and Soares (2009) and for the north bySantos and Fragoso (2013), despite the different data and thelimited range of indices that were examined in those studies.

The correlation between the NAO index and precipitationin western Iberian Peninsula highlights that precipitation inthis area, particularly mainland Portugal, is dominantly asso-ciated to mid-latitude low pressure systems and their fronts,depending strongly on the exact location of the Atlantic stormtrack (see e.g. Miranda et al. 2002). Strong positive phases ofthe NAO tend to be associated with below-average precipita-tion over southern and central Europe whereas oppositeabove-average precipitation anomalies are typically observedduring strong negative phases of the NAO (e.g. Trigo et al.2002, 2004).

While, in general, spatially coherent regions of bothincreasing and decreasing extreme precipitation in main-land Portugal did not emerge from the analysis of theselected annual extreme indices, the result is different whenthe precipitation is examined at sub-annual scales. We donot focus on that behaviour here but other studies addressedthis issue: trends in monthly precipitation are discussed by,e.g. de Lima et al. (2007, 2010a, b) based on the study oflong time series, some of them dating back to the nineteenthcentury; Espírito Santo et al. (2013b) report a detailedanalysis of seasonal trends using a large number of indica-tors of daily precipitation extremes. According to thesestudies, there are indications of the (re)distribution of pre-cipitation during the year, with positive (increasing) trendsin some sub-annual periods (partially) offsetting negative(decreasing) trends in other periods, leading in general tonon-significant trends at the annual scale.

6 Concluding remarks

The main purpose of the present study was to provide a morecomprehensive discussion of the precipitation structure andchanges in mainland Portugal, at the annual scale, using abroader set of precipitation indices than in some previous studies.Additionally, we aimed to use a higher number of stations, longerrecords and to investigate relationships between the most impor-tant atmospheric circulation pattern and precipitation trends.

Analysis of a set of 13 annual precipitation indices, whichincludes 11 extreme precipitation indices, derived from daily datarecorded at 57 stations distributed across mainland Portugalidentified several noticeable characteristics of precipitation inthe area in the 1941–2007 period. In general, the results foundin this study are in agreement with other studies that inspectedchange in precipitation in western Iberia, where mainland Portu-gal lays (several references are given in Section 1); but we shouldnote that some of those studies were supported by monthly orseasonal data whereas here we focussed on daily precipitationdata, which have been less explored.

The results of the site-by-site analysis highlight thefollowing:

– Despite the presence of several stations with predomi-nantly negative tendencies in the precipitation indices, themajority of stations did not show statistically significantchange over time in the 1941–2007 period;

– In the 1976–2007 period, there was a tendency towardsmore extreme precipitation events, which was confirmedby the highest daily precipitation amount (RX1D) and theprecipitation on extremely wet days (R99p), particularlyin the southern region of mainland Portugal;

– Precipitation extremes are highly correlated with meanprecipitation and trends in the precipitation extremes are


highly correlated with trends in mean precipitation, par-ticularly for the R10, R20, R25, R90 and R95 indices;

– The most extreme precipitation events seem to be chang-ing at a faster rate than the more moderate extreme eventsand their intensity is increasing.

The results of the analysis of the regionally averaged indi-ces show that:

– There is an important but not statistically significantdecrease in regional average total precipitation;

– The majority of precipitation regional indices, exceptSDII, show small (non-significant) negative trends;

– The regional averaged total wet-day precipitation hasdeclined, although this result is not statistically significantat the 5 % level; however, since 1976 there is a largereduction (−44 mm decade−1) in total wet-day precipita-tion, but this result is only statistically significant at the25 % level; these drier conditions might reflect the pre-dominance of warmer conditions in the last decades (e.g.Klein Tank et al. 2005) but the relationship betweenchanges in temperature and precipitation needs to bebetter understood;

– Except for the CDD index, there is an overall significantanti-correlation between the precipitation indices and theNAO;

– The decreasing trend found for SDII index may be relatedto the predominance of the positive phase of the NAOsince the mid-1970s;

Although the average monthly and seasonal precipitation inthe Iberian Peninsula is highly influenced by the NAO (e.g.Goodess and Jones 2002; Trigo et al. 2004), it depends also onother modes of variability such as the Scandinavian and EasternAtlantic patterns; this was shown previously by, e.g. Trigo et al.(2008) and Espírito Santo et al. (2013b). Moreover, at the dailyscale, smaller atmospheric circulation patterns play an impor-tant role favouring or damping the advection of moisture (Trigoand DaCamara 2000) and may be better related (than simpleNAO indices) to the precipitation regime across the entireIberian Peninsula (e.g. Cortesi et al. 2013).

Acknowledgments The authors wish to thank Álvaro Silva and SofiaCunha (The Portuguese Sea and Atmosphere Institute, I. P.), for their helpin processing the maps in Figs. 1, 4, 6, 7 (top) and 10. Alexandre M.Ramos was supported by the Portuguese Foundation for Science andTechnology (FCT) through grant FCT/DFRH/SFRH/BPD/84328/2012.

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Trends and correlations in annual extreme precipitation indices for mainland Portugal, 1941–2007AbstractIntroductionStudy area and precipitation dataBrief description of the study areaRecords of precipitation extremes in the recent pastPrecipitation data setData processing and selection

MethodsClimate indicesNorth Atlantic Oscillation indexTrends and correlations

Observed trends and correlations in precipitation indicesIndices of wet-day total precipitation (PrecTot) and simple daily precipitation intensity (SDII)Indices of precipitation extremesIndices of maximum length of dry and wet periodsDaily precipitation absolute threshold indicesIndices of extreme precipitation events of 1- and 5-day durationsDaily precipitation percentile threshold indices

Relationship between annual mean precipitation and extreme precipitation indicesCorrelations between extreme and mean precipitation indicesCorrelations between trends in precipitation extremes and trends in mean precipitation

Correlation between the NAO index and precipitation indices

DiscussionConcluding remarksReferences

trends and correlations in annual extreme precipitation...

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