Theor Appl Climatol
DOI 10.1007/s00704-013-1079-6
ORIGINAL PAPER
Trends and correlations in annual extreme precipitation
indices 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 (southwestern Europe) using daily precipitation data recorded in the
period 1941–2007 (67 years) at 57 meteorological stations
scattered across the area are studied at an annual scale. Trends
in selected precipitation annual indices that are calculated
from these data are investigated, in particular trends in the
intensity, frequency and duration of extreme precipitation
events. Special attention is dedicated to local and regional
variability. The spatial correlations between the annual trends
in mean precipitation and in the extremes are analysed. Moreover, the relationships between the variability of the North
Atlantic Oscillation (NAO) index and several indices related
to the frequency and intensity of the precipitation at the 57
stations were also investigated. Results show that several
stations have predominantly negative tendencies in the precipitation indices, although the majority of stations did not
show statistically significant change over time in the 1941–
2007 period. At the regional level, the decreasing trend in the
M. I. P. de Lima (*)
Department of Civil Engineering, University of Coimbra,
Coimbra, Portugal
e-mail: [email protected]
M. I. P. de Lima
Institute of Marine Research, Marine and Environmental Research
Centre, Department of Civil Engineering, University of Coimbra,
Coimbra, Portugal
F. E. Santo
The Portuguese Sea and Atmosphere Institute—IPMA, I. P.,
Lisbon, Portugal
A. M. Ramos : R. M. Trigo
Instituto Dom Luiz, Universidade de Lisboa, Lisbon, Portugal
R. M. Trigo
Departamento de Engenharias, Universidade Lusófona,
1749 Lisbon, Portugal
simple daily precipitation intensity index is the only one
statistically significant at the 5 % level and appears to be
related to the predominance of the positive phase of the
NAO. For the period 1976–2007, the proportion of the total
precipitation attributed to heavy and very heavy precipitation
events increased and, consequently, daily precipitation events
show a tendency to become more intense. Moreover, correlation analysis show that the most extreme events could be
changing at a faster absolute rate in relation to the mean than
more moderate events.
1 Introduction
Over the twentieth century, changes in global and land precipitation have been observed across different time scales,
which are expected to result from the variability and change
in the climate. Particularly, changes in extreme precipitation
are of general concern because of the expected impact on
society and ecosystems; an extreme (weather or climate) event
is generally defined as the occurrence of a weather or climate
variable above (or below) a given threshold near the upper (or
lower) endpoints of the range of observed values of the
variable (e.g. IPCC 2012). Moreover, global climate variability and change are expected to be accompanied also by adjustments in other climate variables, which increase the complexity of the weather and climate systems. Water, soil and
energy are at the core of the discussions on this topic; they are
key factors for environmental and societal sustainability,
which depends much on the local conditions including resilience and adaptation capacities.
Insight into the properties of local and regional land precipitation in the recent past can be obtained by analysing
available ground-based point data; other alternative methods
(e.g. climate models) have often limited usefulness at small
time and space scales. However, precipitation measurement
M.I.P. de Lima et al.
limitations (e.g. data resolution, limited length of records) and
the sparse network of in situ ground-based precipitation monitoring contribute to the difficulties in detecting precipitation
low frequency fluctuations and change (both in space and
time), which is associated with large uncertainty. Findings
may also differ due to the different spatial and temporal scales
investigated and even the methodology used for each study.
Thus, the exploration of different scales and precipitation
parameters give opportunity to many studies to complement
previous analyses, towards a better understanding of those
systems and relevant processes.
Studies conducted at the global scale have usually been
consistently reporting increasing trends in extreme precipitation indices, which has also been found in some studies for
Europe, but at the regional level the findings may differ.
Despite general trends, the local precipitation structure, relevant generating mechanisms and other specificities can dictate
sometimes unexpected differences in precipitation characteristics and change over short distances that cannot be
disregarded. Examples of some of these studies are the following: for global scale, Frich et al. (2002), Alexander et al.
(2006) and IPCC (2007); for the countries of the western
Indian 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, New
et al. (2006); for central and southern Asia, Klein Tank et al.
(2006); at the European level, Klein Tank et al. (2002), Klein
Tank and Können (2003), Moberg and Jones (2005), Moberg
et al. (2006) and Karagiannidis et al. (2012); for the Mediterranean 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, Miranda
et 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 and
Soares (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 have
been reported for mainland Portugal, although some studies
found a decreasing tendency in extreme precipitation indices;
similar findings have been reported for the Mediterranean
region and Iberian Peninsula (see references, above). However, the majority of the studies that support these results have
relied on very few stations over western Iberia, where mainland Portugal is located; exceptions are the high density
station studies by Costa and Soares (2009) and Mourato
et al. (2010), but they are restricted to the southernmost region
of mainland Portugal. The strong precipitation spatial variability and gradients that are observed in the whole territory
requires that a higher number of locations is explored (e.g.
Trigo and DaCamara 2000; Miranda et al. 2002, 2006; BeloPereira et al. 2011). We note that some studies have examined
a restricted record period of only a few decades, which can
easily bias the estimation of trends (e.g. de Lima et al. 2010a, b).
Our study aims mainly to contribute to the increased understanding of precipitation in mainland Portugal by investigating daily precipitation data recorded at 57 meteorological
stations scattered across mainland Portugal. Trends in precipitation extremes in the period 1941–2007 (67 years) were
studied by testing time series of selected precipitation annual
indices over different multi-decadal periods: the indices were
calculated from daily data both at the local and regional levels.
These indices provide a view into the frequency of extreme
precipitation events and their intensity (see e.g. Frich et al.
2002; Moberg et al. 2006). Additionally, the correlations
between annual mean conditions and annual extreme precipitation indices, as well as correlations between trends in the
means and in the extremes were investigated. Moreover, the
correlation between precipitation indices and the most important pattern of large scale circulation in the northern hemisphere, i.e. the North Atlantic Oscillation (NAO), will be
analysed. With these analyses, we extended for Portugal the
work carried out by Klein Tank and Können (2003) for
Europe, thus providing a stronger basis for comparison of
trends in precipitation obtained for different locations and
European regions. Moreover, the analysis involves now more
indices, and longer and denser data sets than in previous
studies. These are the main contributions of our work, which
focuses on identifying trends in precipitation indices and
exploring whether such trends can be explained, in whole or
in 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 confined between parallels 37°N and 42°N and within the relatively narrow meridional band that develops between 6.5 and
9.5°W. It lays in the transitional region between the subtropical anticyclone and the sub-polar depression zones. In
this territory, the latitude, orography and effect of the Atlantic
Ocean are the factors that dominate more the climate. Figure 1
shows a relief map of mainland Portugal; the highest altitude
is roughly 2,000 m.
Regional precipitation exhibits large inter-annual variability and very marked north–south and east–west gradients.
Mean annual precipitation varies from about 3,000 mm in
the north to roughly 500 mm in the southern part of the
country. The precipitation climate is also characterized by
strong seasonality: on average, about 40 % of the annual
precipitation falls in winter (December to February); summer
Annual extreme precipitation indices for mainland Portugal
1995/1996 (Santos et al. 2009), October–November 1997
(Carvalho 1997; Espírito Santo et al. 1998), autumn–winter
2000/2001 (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 lowpressure system produced very intense precipitation that affected particularly Lisbon and the Beira Interior and Alentejo
regions (IM 2008). The station Lisbon/Geofísico recorded that
day a total of 118.4 mm, which surpassed the previous maxima of daily rainfall recorded at this station since 1864: 110.7
mm, 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 southern
Portugal, bring usually damages to agriculture and affect
water resources and availability for different uses. The decades of the 1940s, 1980s and 1990s are identified as being
particularly dry throughout the Iberian Peninsula territory (e.g.
Vicente-Serrano 2006a, b). Up to the present, the 2004/2005
episode is the worst drought event in the last 140 years and has
produced major socioeconomic impact not only in Portugal
but also in Spain (e.g. Espírito Santo et al. 2004; GarciaHerrera et al. 2007). Attention is drawn to the drought situation that was recorded in the last hydrological year 2011/2012,
which affected the whole territory and with greater severity in
the months of February and March.
2.3 Precipitation data set
Fig. 1 Relief map of mainland Portugal and location of the 57 climatological weather stations used in this study
(June to August) contributes to the total amounts with only 6 %,
approximately; in autumn and spring the amount of precipitation is highly variable (e.g. Miranda et al. 2002, 2006). The
small-scale precipitation variability has been less explored so
far and thus this process is still not fully characterized at small
spatial 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 precipitation leads to maximum precipitation extreme events (potentially leading to floods) and long drought spells, which are
relatively frequent. These types of events both affect different
regions, although the incidence and severity of the episodes
vary with location.
Major floods that occurred since the beginning of the
1980s, roughly in the last 30 years, were in December 1981
(Zêzere et al. 2005), November 1983 (catastrophic flooding in
the neighbouring Lisbon and Setúbal regions; e.g. Godinho
1984; Liberato et al. 2013), December 1983 (WMO 1984),
November and December 1989 (Zêzere et al. 2005), winter
The precipitation data cover the period 1941–2007 (67 years)
and were recorded at 57 climatological weather stations and
rain gauges from the networks of the Portuguese Sea and
Atmosphere Institute (Instituto Português do Mar e da
Atmosfera, IPMA, I.P.) and Portuguese Environment Agency
(Agência Portuguesa do Ambiente, APA). Data from 11 stations were provided by IPMA, while data from the other 46
stations were provided by the National Water Resources Information System (Sistema Nacional de Informação de
Recursos Hídricos), managed by APA.
The location of the stations selected in mainland Portugal is
shown in Fig. 1. These stations are representative, on the whole,
of the distribution of altitude of the territory; i.e. the percentage of
stations by classes of altitude is comparable to the area–altitude
distribution for mainland Portugal. Yet, there are differences in
the classes of lower altitude (i.e. the higher frequencies): more
stations in classes 0–100 m and 200–300 m and less in the class
100–200 m. We also note the lack of stations placed above
1,000 m: only one station is included in our data set.
2.4 Data processing and selection
The data were chosen from 200 precipitation series, which
were initially available; the selection was based on an exigent
combination of criteria related to the spatial distribution of the
M.I.P. de Lima et al.
series over mainland Portugal and data length, completeness,
quality and homogeneity:
(a) Completeness: Only stations with less than 2 % of missing values were used; any given month was considered
complete if no more than 3 days were missing from the
records; a year was considered complete if no more than
15 days were missing;
(b) Quality: Basic quality controls have been applied to all
the series with the purpose to identify errors in the data;
the daily data were searched for anomalous values (“outliers”), which were defined as having values that deviated more than 3 standard deviations from the climatologic
daily average, but no such extremes were identified;
(c) Homogeneity: Standard homogeneity tests (e.g. Wang
et al. 2007; Wang 2008a; Wang and Feng 2010) based
on the penalized t test (Wang et al. 2007), the penalized
maximal F test (Wang 2008b) and a Quantile Matching
algorithm (Wang 2009) were applied to the precipitation
data. The procedure used detected possible change points
in the monthly series that were checked against station
metadata records (when available). Series exhibiting evidence of discontinuities of non-climatic origin in the
study period were not used.
After the application of these tests, the number of stations’ data that were considered adequate to our study has
dropped to 57. The selection of the stations aimed also to
maximize the study period. The major limitations are not
only the data scarcity before 1941 (many series have started
being recorded at this time) but also and more importantly
the effective reduction of climate observations after 2007, as
a result of the reduction of stations in the monitoring network and the problems faced in the maintenance of the
existing ones; this resulted in gaps and a high percentage
of missing values. That justifies the difficulty of extending
this study beyond that date, while maintaining such a dense
network. More details about the data selection, quality control and homogenization can be found in de Lima et al.
(2013) and Espírito Santo et al. (2013b); these studies have
also used this data set.
3 Methods
Team Climate Change Detection and Indices (Peterson et al.
2001) and reviewed by Zhang et al. (2011). Here, the indices
were calculated at the annual scale for each individual station
and also for the study region (i.e. mainland Portugal) as a
whole. The regional indices were assessed as simple averages
over all 57 stations’ individual indices (i.e. giving all the
stations equal weight) and were used to obtain insight into
the inter-annual variability of precipitation in mainland Portugal. Anomalies are for the 1961–1990 reference period. A wet
day is a day with an accumulated precipitation of at least
1.0 mm.
The more relevant selected indices are the following:
–
–
–
–
–
Spell indices, consecutive wet days (CWD) and consecutive dry days (CDD), defined as the maximum number
of consecutive days with precipitation above/below
1 mm, respectively; thus, the CWD is the maximum
length of wet spells, which could intensity flooding,
whereas the CDD index can assess the region’s vulnerability to drought;
Absolute (fixed) thresholds, defined as the number of
days on which a precipitation value falls above a fixed
threshold: 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 as
the maximum precipitation on 1 and 5 days, RX1D and
RX5D; the RX5D index can be used as an indicator of
flood-producing events because, on the space scales considered here, severe floods are generally not caused by a
single heavy thunderstorm event but, more likely, by
long-lasting heavy precipitation events that extend over
a region;
Percentile (non-fixed) thresholds: the 90th, 95th and
99th percentiles of precipitation on wet, very wet and
extremely wet days, respectively, R90p, R95p and
R99p indices;
The R95pTot index, which is the ratio R95p/PrecTot
(precipitation fraction due to very wet days), represents
the percentage of annual total wet-day precipitation due
to events with precipitation above the 95th percentile; it
can be used to analyse the possibility of having a relatively larger variation in extreme precipitation events than
in total amount (Groisman et al. 1999).
3.1 Climate indices
A total of 13 indices of precipitation extremes calculated from
daily data were selected in this work for exploring changes in
the intensity, frequency and proportion of extremes in total
precipitation, in mainland Portugal. The description of the
selected indices is given in Table 1; these indices were firstly
defined by the joint CCl/WCRP-CLIVAR/JCOMM Expert
The indices selected here have also been used in other studies
for different regions around the world: e.g. Frich et al. (2002),
Klein Tank and Können (2003), Aguilar et al. (2005), Alexander
et al. (2006), Herrera et al. (2010) and Jiang et al. (2013); for
regions in mainland Portugal, see e.g. Costa et al. (2008), Costa
and Soares (2009), Durão et al. (2009), Costa et al. (2012), de
Lima et al. (2013) and Espírito Santo et al. (2013b).
Annual extreme precipitation indices for mainland Portugal
Table 1 Definition of the precipitation indices used in this study
Index
Description
Definition
PrecTot (mm)
SDII (mm)
CDD (days)
CWD (days)
R10 (days)
R20 (days)
R25 (days)
RX1D (mm)
RX5D (mm)
R90p (mm)
R95p (mm)
R99p (mm)
Annual total wet-day precipitation
Simple daily intensity index
Consecutive dry days
Consecutive wet days
Heavy precipitation days
Very heavy precipitation days
Extremely heavy precipitation days
Highest precipitation amount in one-day period
Highest precipitation amount in 5 consecutive days
Precipitation on wet days
Precipitation on very wet days
Precipitation on extremely wet days
R95pTot (%)
Precipitation fraction due to R95p
Annual total precipitation from days ≥1 mm
Annual total precipitation divided by number of wet days (≥1 mm)
Maximum length of dry spell (RR <1 mm)
Maximum length of wet spell (RR ≥1 mm)
Number of days per year with RR ≥10 mm
Number of days per year with RR ≥20 mm
Number of days per year with RR ≥25 mm
Annual maximum precipitation on 1-day intervals
Annual maximum precipitation sums on 5-day intervals
Precipitation amount per year above a site-specific threshold value for wet,
very wet and extremely wet days, calculated as the 90th, 95th and 99th
percentile of the distribution of daily precipitation amounts on days with
1 mm or more precipitation in the 1961–1990 reference period
Percentage of annual total wet-day precipitation due to events with
precipitation >95th percentile
3.2 North Atlantic Oscillation index
The NAO is the most important mode of atmospheric variability over the North Atlantic Ocean that affects significantly
Atlantic weather patterns, particularly in Europe and the Mediterranean Basin (e.g. Trigo et al. 2002, 2004; Vicente-Serrano
and López-Moreno 2008). The NAO index measures the
strength of the zonal flow across the North Atlantic and
consists in the pressure difference between Iceland and the
Azores (e.g. Hurrell 1995; Serreze et al. 1997; Hurrell and
Deser 2010) or Lisbon or Gibraltar (Jones et al. 1997).
The NAO index can be interpreted in terms of a large-scale
meridional exchange of atmospheric mass (van Loon and
Rogers 1978) or as the oscillation of a large-scale anomalous
pressure (or geopotential) pattern (Wallace and Gutzler 1981);
it has been found to correlate with surface climate in most of
the European region (e.g. Hurrell 1995; Hurrell and van Loon
1997; Trigo et al. 2002; Vicente-Serrano and López-Moreno
2008), in particular Portugal (e.g. Ulbrich et al. 1999; Trigo
et al. 2004; Santos et al. 2009).
In this work, we analyse the relationship between the NAO
and precipitation in mainland Portugal, using the precipitation
indices (Table 1) calculated from the daily data described in
Section 2.3. The annual NAO index used here was computed
as the difference between the normalized sea level pressure in
Ponta Delgada (Azores archipelago) and Stykkisholmur/
Reykjavik (Iceland) and the data were provided by the Climate Analysis Section, NCAR, Boulder, USA (Hurrell 1995).
The relationship between precipitation and the NAO was
already reported for the Iberian Peninsula in other studies: for
monthly or seasonal precipitation series, e.g. Trigo et al.
(2004) and Lopez-Bustins et al. (2008); for daily precipitation
series, e.g. Goodess and Jones (2002), Gallego et al. (2005)
and Rodrigo and Trigo (2007). Albeit in a very limited number
of studies, this relationship was also analysed for Portugal: at
the monthly or seasonal scales, by e.g. Zhang et al. (1997),
Ulbrich et al. (1999), Trigo and DaCamara (2000) and Miranda 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 precipitation
extremes were estimated by ordinary least squares fitting
(OLS). The t test was used to assess the statistical significance
of the trends with different levels of probability (see e.g.
Peterson et al. 2001; Klein Tank and Können 2003; Klein
Tank et al. 2009; van den Besselaar et al. 2012). In using this
methodology, we anchor on other studies that have reported
that the magnitude of the trends estimated by using the OLS
method and a more robust non-parametric method are very
similar (see e.g. Önöz and Bayazit 2003; Moberg and Jones
2005). Önöz and Bayazit (2003) showed that the parametric t
test is slightly more powerful than the non-parametric Mann–
Kendall test when the probability distribution is normal; they
also showed that such relative power decreases with the
increase in skewness but that for moderately skewed distributions the t test is almost as powerful as the Mann–Kendall test.
This implies that the two tests can be used interchangeably in
practical applications, with identical results in most cases.
Nevertheless, Nicholls (2001) recommended that in addition
to the test results at the 5 % significance level, also the results
at the 25 % level should be presented, which we took into
consideration here. The definition of the precipitation indices
listed in Table 1 shows that we are not examining extremely
rare precipitation events, for which the computation of
M.I.P. de Lima et al.
significant trends could be a priori hampered by the small
sample sizes (e.g. Tebaldi et al. 2006).
In this work, the analyses were carried out for the full 67year record period (1941–2007) and for two consecutive subperiods of approximately the same length: 1945–1975 and
1976–2007. In addition to the precipitation trends calculated
for individual stations, trends were also computed for the
region as a whole using the regional indices (see Section 3.1),
which were obtained by averaging over all stations. It is
expected that this averaging procedure will not decrease the
trend but will reduce the effect of natural variability (e.g. Klein
Tank et al. 2009), which anticipates more robust results.
Analysis of correlations between precipitation indices was
also 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 precipitation intensity (SDII)) and all the other indices listed in
Table 1, which were calculated site-by-site, for all 57 precipitation stations, and at the regional level. The correlations were
calculated using the linear Pearson (product moment) correlation coefficient r and their statistical significance was assessed
at the 5 % level. This type of analysis might help to identify
stations’ data with anomalous behaviour (e.g. Moberg et al.
2006), which could result, for example, from a few individual
highly erroneous daily values in the data that could corrupt the
statistics for the entire series. The linear trends in the PrecTot
index and the precipitation extremes’ indices, for all the 57
stations, were also analysed: we aimed at investigating how
the trends in the mean and extremes vary at each station and
how well they are correlated. Because not all the precipitation
indices have the same units (see Table 1), the linear trends for
each index were calculated as a percentage of the average.
The Pearson’s correlation coefficient was also used to
explore the relationship between the precipitation indices
and the NAO index. The statistical significance of the correlation coefficients was calculated using the Student’s t test
(two-tailed test of the Student t distribution).
corresponding 95 % confidence interval is highlighted by
the width of the shaded area at each dot.
In the next sections, we dedicate special attention to some
of the results obtained. If not stated otherwise, the statistical
significance of the results given below is at the 5 % level.
4.1 Indices of wet-day total precipitation (PrecTot) and simple
daily 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 for
the three periods investigated.
The regional average anomalies for the SDII and PrecTot
indices are plotted in Fig. 4 (top), which shows the considerable inter-annual variability in these indices. The lowest SDII
regional indices (<8.0 mm) occurred in the years 2004, 2005
and 2007, which were the driest on record, and these occurrences coincide temporally with the lowest PrecTot regional
indices (<500 mm). The highest values of SDII (>11 mm)
were in 1989, 1997 and 1947, and of PrecTot (>1,200 mm)
happened in 1963 and 1960.
Overall, these two regional indices show decreasing trends
but 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 the
stations’ data show small negative trends in this index that are
statistically significant in 40 % of the cases (see also Table 2).
The regional average series of PrecTot indicates a decreasing
trend of −13.21 mm decade−1 in the full 67-year period and a
more 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 −54
to −136 mm decade−1; however, only data from 11 % of the
stations show a statistically significant decrease. An increasing tendency is found for the 1945–1975 sub-period, but none
of the results is significant.
4.2 Indices of precipitation extremes
4 Observed trends and correlations in precipitation
indices
The results of trend analyses of 13 precipitation indices’ time
series are summarized in Tables 2 and 3, for the 1941–2007,
1945–1975 and 1976–2007 periods. Table 2 gives the percentage of precipitation stations that had positive/negative
trends in annual precipitation indices calculated individually
for the 57 stations’ data and the corresponding percentage of
statistically significant results at the 5 % level. And Table 3
shows the trends in annual regional precipitation indices and
the corresponding 95 % confidence intervals.
Figure 2 provides an overview of the trends in the regional
indices in the 1941–2007 period; for each index, the
The marked inter-annual variability, and overall change, found
in the regional extreme precipitation indices’s time series is
shown in Figs. 4 and 5. The highest values of the regional
indices were in 1997 for RX1D, RX5D, R90p, R95p and
R99p, and in 1963 for R10 and R20. In general, the lowest
values of the regional extreme precipitation indices were in the
drought years of 1980, 2004, 2005 and 2007, and in 1971 for
RX1D and R99p.
Overall, the results show statistically non-significant decreasing trends in regional extreme precipitation indices for
the 1941–2007 and 1976–2007 periods; the exceptions are the
RX1D and R99p regional indices, which show a nonsignificant increase in this last 32-year period.
Annual extreme precipitation indices for mainland Portugal
Table 2 Percentages of the 57 precipitation stations with linear positive (+)
and negative (−) trends in annual precipitation indices, and the respective
number of statistically significant trends (Sig+/Sig−) at the 5 % level.
Period
1941–2007
1945–1975
1976–2007
Sig+
Sig−
+
−
Sig+
Sig−
+
−
Sig+
Sig−
+
−
Positive (+) indicates significant wetting trends and negative (−) indicates
significant drying trends; it is the opposite for the CDD index. The data are
for the 1941–2007, 1945–1975 and 1976–2007 periods
PrecTot
SDII
CDD
CWD
R10
R20
R25
RX1D
RX5D
R90p
R95p
R99p
R95pTot
0
16
21
79
0
2
77
23
0
11
7
93
2
40
26
74
2
21
42
58
0
16
25
75
0
18
19
81
4
4
28
72
2
4
44
56
2
11
23
77
4
0
60
40
0
5
9
91
0
16
16
84
0
4
72
28
0
11
7
93
0
16
28
72
0
7
63
37
2
12
18
82
2
11
33
67
0
7
53
47
2
7
18
82
5
5
53
47
0
12
46
54
4
0
47
53
0
14
33
67
0
14
49
51
0
5
42
58
2
11
37
63
0
7
51
49
0
7
21
79
7
7
37
63
0
11
54
46
2
5
32
68
4
0
51
49
0
9
44
56
4
2
53
47
5
7
40
60
0
14
39
61
0
4
49
51
4.2.1 Indices of maximum length of dry and wet periods
The inter-annual variability of the regional CDD and CWD
indices is also shown in Fig. 4 (bottom) and trends are given in
Table 3.
The regional CDD index shows a decreasing trend of −1.3
day decade−1 in the 1941–2007 period. Negative trends were
found in 80 % of the stations; yet only roughly 18 % of them
exhibited a statistically significant decrease. In the 1976–2007
period, the decreasing trend is −1.2 day decade−1, but the trend
is not statistically significant.
The regional CWD annual indices take the lowest values
(<6 days) in 1982, 1944 and 2005, which also correspond to
the largest absolute values of the negative anomalies (Fig. 4).
In the 1941–2007 and 1976–2007 periods, CWD shows a
decreasing tendency for the majority of stations (77 and
91 %, respectively), but the statistical significance is low; only
a very small percentage of stations have statistically significant
trends, respectively, 10 and 5 %. This percentage of statistically
significant results is about the limit (assuming a 5 % rule of
thumb) of what could be considered random exceedance of the
significance level (see e.g. Livezey and Chen 1983).
4.2.2 Daily precipitation absolute threshold indices
Regional indices’ time series indicate fewer days with precipitation above 10, 20 and 25 mm (R10, R20 and R25, respectively, heavy, very heavy and extremely heavy precipitation
Table 3 Trends per decade (with the 95 % confidence intervals in brackets) in the annual regional indices of precipitation for the 1941–2007, 1945–1975
and 1976–2007 periods
Index
Unit
1941–2007
1945–1975
1976–2007
PrecTot
SDII
CDD
CWD
mm
mm/day
days
days
−13.21 (−37.42 to 11.01)
−0.13 (−0.25 to −0.01)
−1.32 (−2.92 to 0.28)
−0.18 (−0.46 to 0.10)
26.19 (−53.83 to 106.21)
−0.10 (−0.45 to 0.25)
−2.34 (−7.78 to 3.11)
0.20 (−0.71 to 1.12)
−44.60 (−118.83 to 29.64)
−0.19 (−0.63 to 0.26)
−1.16 (−5.86 to 3.54)
−0.50 (−1.37 to 0.36)
R10
R20
R25
RX1D
RX5D
R90p
R95p
R99p
R95pTot
days
days
days
mm
mm
mm
mm
mm
%
−0.60 (−1.48 to 0.27)
−0.30 (−0.72 to 0.12)
−0.17 (−0.47 to 0.13)
−0.14 (−1.04 to 0.76)
−1.56 (−4.24 to 1.13)
−6.44 (−18.84 to 5.96)
−3.00 (−11.72 to 5.71)
−0.86 (−2.61 to −4.33)
−0.14 (−0.73 to 0.46)
0.70 (−2.22 to 3.63)
0.18 (−1.21 to 1.56)
0.14 (−0.83 to 1.12)
−0.69 (−3.35 to 1.97)
−0.75 (−8.72 to 7.22)
2.14 (−37.02 to 41.30)
0.33 (−26.38 to 27.05)
−2.96 (−12.75 to 6.82)
−0.71 (−2.54 to 1.13)
−1.69 (−4.36 to 0.98)
−0.71 (−2.04 to 0.62)
−0.43 (−1.40 to 0.53)
0.25 (−2.90 to 3.40)
−2.29 (−11.23 to 6.66)
−13.54 (−54.77 to 27.69)
−5.90 (−35.65 to 23.84)
1.17 (−11.09 to 13.44)
0.30 (−1.67 to 2.28)
Significance level at 5 % is presented in bold, at 25 % in italics
M.I.P. de Lima et al.
Fig. 2 Overview of the trends in annual regional precipitation indices in
the 1941–2007 period: averages over the 57 stations (black dots); for each
index, the 95 % confidence intervals are highlighted by the width of the
shaded area at each dot. The indices on the horizontal axes are defined in
Table 1; those that belong to the same class (e.g. R10, R20, R25) are
connected with a line
daily events) in 1941–2007 and 1976–2007, and an increasing
trend in these indices in 1945–1975; however, trends are not
statistically significant (Table 3). Overall, this trend pattern is
similar to the one in the regional PrecTot index. But for the
1941–2007 period, the decrease in R10 and R20 is significant
only at the 25 % level.
The frequency of heavy, very heavy and extremely heavy
precipitation daily events in the station’s data generally
followed the tendency of their respective regional average.
More than 65 % of the stations show a decreasing trend in the
R10, R20 and R25 indices, but significant decreasing trends
are found in only 10 to 16 % of them, in the 67-year record
period. This behaviour is more pronounced in the 1976–2007
period: between roughly 80 and 90 % of the stations show
decreasing trends in these threshold indices (up to
5 days decade−1), but only between 7 and 12 % of the stations
have statistically significant results.
The percentage change in precipitation amount and number
of wet days have opposite signs at some stations (i.e. negative/
positive; when one decreases the other increases).
4.2.4 Daily precipitation percentile threshold indices
4.2.3 Indices of extreme precipitation events of 1- and 5-day
durations
The regional RX1D and RX5D indices exhibit decreasing
trends in the 1941–2007 and 1945–1975 periods but the trends
are not statistically significant (Table 3). Contrastingly, the
trend in the RX1D regional index is positive in 1976–2007. In
this period, the trends in the RX1D index range between −5
and +7 mm decade−1 across mainland Portugal; only 3.5 % of
the stations show significant positive trends and none of the
stations show significant negative trends.
Figure 6 maps the stations’ trends in the RX5D index in the
1941–2007, 1945–1975 and 1976–2007 periods: between 50
and 65 % of the stations show decreasing trends for the three
periods; 14 % of the stations report a significant decreasing
trend for the 1941–2007 and 1945–1975 periods; for the period
1976–2007, the trends range from −16 to +10 mm decade−1 and
only 5 % of the stations show significant negative trends.
The stations’ percentile-based R90p, R95p and R99p indices
do not show consistent trends. Only a few stations’ trends
(about 10 %) are statistically significant.
In general, the regional average series of these indices show
negative trends in the 1941–2007 period that accompanies the
PrecToc trend. But the trends in the regional R99p and PrecTot
indices have different sign (i.e. positive, negative) in the
1945–1975 and 1976–2007 sub-periods. The trend in the
R99p index is positive (1.2 mm decade−1) in the 1976–2007
period; the contribution of the extremely wet days to the total
precipitation amount is between 11 and 127 mm. The R95p
index shows a negative trend (−3.0 mm decade−1) in the full
67-year record period, while the contribution of the precipitation due to very wet days (above the 95th percentile) to the
total precipitation varies between 73 and 350 mm (approximately 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 R90p
index has a trend pattern similar to R95p, although more
pronounced with an increase (2.1 mm decade−1) in 1945–
1975 and a large decrease (−13.5 mm decade−1) in the subsequent period (1976–2007); on average, the contribution of wet
days (above the 90th percentile) to the total annual precipitation varies between 120 and 500 mm in the 1941–2007
period.
On average, the trend in the annual precipitation fraction
due to very wet days (R95pTot) is −0.14 % decade−1 in 1941–
2007, which is not statistically significant (see Table 3). However, in the 1976–2007 period, the R95pTot trend is increasing
(0.30 % decade−1), which is accompanied by a decreasing
trend in the annual precipitation; these opposite trends may
indicate that the very wet days are less affected than the other
wet days.
Figure 7 shows the station’s trends in the R95p and
R95pTot indices in 1941–2007, which are similar. Positive
trends in the R95pTot index are found for 20 % of the stations
and are accompanied by increases in the total annual precipitation amount, but the result is not statistically significant.
Annual extreme precipitation indices for mainland Portugal
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 the
magnitude of the trend: blue for increasing (wetting) trends and yellow for decreasing (drying) trends
Only two stations that exhibit a statistically significant decrease in the annual precipitation show a significant change in
R95pTot. This may indicate a disproportionate large change in
the extremes relative to the total amount, but this is only apparent in areas associated with wetting trends, not drying trends.
For the 1976–2007 period, around 40 % of the stations (the
majority of them located in the Alentejo region, south of Portugal) show statistically non-significant positive trends in the
R95pTot index, and this coincides with the observation of a
decrease in the PrecTot index.
4.3 Relationship between annual mean precipitation
and extreme precipitation indices
4.3.1 Correlations between extreme and mean precipitation
indices
The correlation between eight extreme precipitation indices
and indices of wet-day precipitation (PrecTot) and intensity
(SDII) were investigated for the 1941–2007 period; the eight
extreme precipitation indices include the three threshold
M.I.P. de Lima et al.
Fig. 4 Regional annual anomalies for the SDII, PrecTot, CDD and CWD indices (Table 1) for 1941–2007. Superimposed are the piecewise trends
calculated for the sub-periods 1945–1975 and 1976–2007
indices (R10, R20 and R25), the two absolute indices (RX1D
and 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 correlated; the average correlations for all the stations are equal to
or above 0.5.
On average, the correlations with PrecTot are stronger
(above 0.8) for R10, R20 and R90p; only four stations have
correlations between PrecTot and R90p below 0.8. For the
R25 and R95p indices, the correlations are also, on average,
above 0.76, so fairly strong. But for the R99p index, about half
of the stations have correlation coefficients below 0.5, and the
Fig. 5 Regional annual anomalies for the extreme precipitation R10, RX5D, R90p and R99p indices in 1941–2007. Superimposed to the time series are
the piecewise trends calculated for the sub-periods 1945–1975 and 1976–2007
Annual extreme precipitation indices for mainland Portugal
Fig. 6 Trends in 1941–2007 (left), 1945–1975 (centre) and 1976–2007 (right) for the RX5D index, in millilitre per decade. The dots are scaled according
to the magnitude of the trend: blue for increasing (wetting) trends and yellow for decreasing (drying) trends
spread in correlation coefficients among all the stations is
larger than for the other two percentiles indices, particularly
R90p.
The correlations between SDII and the R25, RX1D, RX5D
and R99p indices are on average stronger than the correlations
between PrecTot and those same indices. The average correlations 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 when
going from R10 to R25; it is for these stations that these correlations are stronger. For SDII and RX1D, only eight stations have
correlations coefficients above 0.8, and these stations are also
located in the southern region of mainland Portugal. The correlations between SDII and the percentile indices decrease the
nearer we get to the tail of the precipitation distribution, particularly for the precipitation on extremely wet days (R99p); this
pattern is similarly followed by the correlations between the
PrecTot index and the percentile indices.
4.3.2 Correlations between trends in precipitation extremes
and trends in mean precipitation
Here, we investigate the linear relationship between percent
trends (i.e. percentage of change over time) in the PrecTot
index and extreme precipitation indices, for all stations
individually. Table 4 and Fig. 8 show that percent trends
in precipitation extremes are, in general, highly spatially
correlated with the percent trend in PrecTot; moreover, all
the correlations are statistically significant. This is particularly true for the number of heavy to extremely heavy
precipitation days (R10 to R25) and precipitation on wet
and very wet days (R90p and R95p). Except for CDD,
CWD and R10, the slope of the line of best fit for all other
indices is above 1, indicating enhanced variability in the
trends of precipitation extremes compared to the mean
trends. The largest change (i.e. slope) is found for RX1D
and this result is statistically significant.
Table 4 Spatial correlations between annual percent trends in extreme precipitation indices and percent trends in annual total wet-day precipitation for
the 57 stations across mainland Portugal in the 1941–2007 period
Index
CDD
CWD
R10
R20
R25
RX1D
RX5D
R90P
R95P
R99P
s
r
R2
−0.3
−0.31
0.10
0.5
0.48
0.23
0.7
0.85
0.72
1.0
0.87
0.75
1.4
0.87
0.81
4.9
0.57
0.32
1.1
0.78
0.60
1.4
0.90
0.81
2.0
0.88
0.77
2.8
0.58
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 ) are
also given
M.I.P. de Lima et al.
Fig. 7 Trends (top) and regional annual anomalies (bottom) for the R95p
(left) and R95pTot (right) indices, in 1941–2007. Top: the dots are scaled
according to the magnitude of the trend—blue for increasing (wetting)
trends and yellow for decreasing (drying) trends. Bottom: superimposed to
the time series are the piecewise trends calculated for the sub-periods
1945–1975 and 1976–2007
We should highlight here the statistically significant results
obtained for R95p and R99p (Fig. 8): even though the correlations between PrecTot and R99p are lower than between
PrecTot and R95p (see also Table 4), the largest slope of the
line of best fit estimated for R99p (Fig. 8f) compared to R95p
(Fig. 8e) might suggest that the most extreme events could be
changing at a faster absolute rate in relation to the mean
conditions than more moderate events. A multiple OLS regression was carried out to quantify the relationship between
trends in annual precipitation and extreme precipitation indices, and geographical coordinates across mainland Portugal.
For elevation, latitude and longitude, the correlations are
weak; so the OLS results are unsatisfactory, indicating that
these attributes represent less than 6 % of the variation in the
trends. Only for R95 and R90, they represent 10 and 13 % of
the variation in their respective trends.
4.4 Correlation between the NAO index and precipitation
indices
Most research for the Northern Hemisphere on the relationship between the NAO and precipitation has been focused on
winter season (e.g. Hurrel 1995; Trigo et al. 2002, 2004);
recent works have stressed the need to look into summer
Annual extreme precipitation indices for mainland Portugal
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 8 Trends (in percent per decade) in extreme precipitation indices
a R20; b R25; c RX1D; d RX5D; e R95p and f R99p plotted against
trends (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 total
least-squares regression, with s being the slope of this line and R2 the
corresponding coefficient of determination (see also Table 4)
NAO impacts but they are not significant for Portuguese
precipitation (e.g. Bladé et al. 2012). Starting from the early
1940s to the mid-1970s, the winter NAO exhibited a slight
downward trend followed by a significant trend towards positively values during the following decades until the mid1990s (e.g. Hurrell and van Loon 1997); since 1995, the
winter NAO has been declining but with large inter-annual
variance. The summer NAO has been characterized by less
decadal variability, recording a continuous increasing tendency between the 1950s and late 1970s followed by a decrease
afterwards. The correlations between the NAO and the regional average of the precipitation indices were analysed over the
full period of the records and the two sub-periods 1945–1975
and 1976–2006; the temporal correlations for these two subperiods are shown in Fig. 9.
Only the CDD index shows positive correlations with the
NAO for the three periods, although the correlations are not
statistically significant. For the 1941–2007 period, there is a
statistically significant anti-correlation between the precipitation indices and the NAO, with correlation coefficients varying between −0.5 and −0.3 (not shown). However, overall the
correlation coefficients are stronger (except for the regional
CDD index) in 1945–1975, ranging between −0.6 and −0.36.
It was in this period that the NAO stayed in a negative phase
M.I.P. de Lima et al.
Fig. 9 Correlations between the
NAO and 12 precipitation
regional indices for the 1945–
1975 and 1976–2007 subperiods. The horizontal dashed
lines indicate the 5 % significance
level. The labels on the horizontal
axes are defined in the text and in
Table 1
and this condition yielded also the strongest anti-correlation
with the RX5D index (an indicator of flood prone conditions).
These results are consistent with the occurrence of consecutive low pressure systems and associated fronts, which are
characteristics of an intensified Atlantic storm track (e.g. Trigo
et al. 2004). But in the following sub-period (1976–2007),
which is associated with a positive phase of the NAO (after the
mid-1970s), the respective anti-correlation is weaker (and
non-significant) in comparison with the other period. In addition, the other correlations are overall weaker (−0.4 to −0.2) in
this period and only five indices (PrecTot, R90p and R10,
R20, R25) show significant correlations.
Overall, the major differences between the trends in the
annual regional indices of precipitation (Table 3) in the two
sub-periods are the observation of a wetter period until the
mid-1970s, which reflects the tendency triggered by the negative phase of the winter NAO in that time, and a drier period
since then that might reflect the dominance of a NAO positive
phase in the last decades. For the same periods, the percentages of the 57 precipitation stations with positive and negative
correlations between the NAO and the precipitation indices
are given in Table 5. With the exception of the CDD index, all
other indices are dominated by negative correlations for the
majority of stations.
In the three periods, the anti-correlation with the NAO is
observed for all the stations for the PrecTot, R10 and R20
indices. However, it is clearly during the 1941–2007 period
that the anti-correlation is more frequently observed, including
those correlations that are statistically significant; in this period, all the stations show negative correlations between the
NAO and the other precipitation indices except for the RX1D,
R99p and R95pTOT indices. When positive correlations between the NAO and the precipitation indices are observed, any
of the results is statistically significant, except for the CDD
index. In the entire 1941–2007 period, for all the other indices,
their correlation with the NAO is negative for 84 to 100 % of
the stations, depending on the index; the results are statistically significant in 12 to 95 % of the stations. The strongest
results 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 correlations between the NAO and the CDD index are more abundant in the 1976–2007 period; they are found in 68 % of the
stations and are significant in 7 % of them.
The correlations between the NAO index and the indices
that describe the number of heavy (R10) to extremely heavy
(R25) rainy days are negative for all the stations in the entire
1941–2007 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, and
the respective statistically significant correlations (Sig+/Sig−) at the 5 % level. The data are for the 1941–2007, 1945–1975 and 1976–2007 periods
Period
1941–2007
1945–1975
1976–2007
PrecTot
SDII
CDD
CWD
R10
R20
R25
RX1D
RX5D
R90p
R95p
R99p
R95pTot
Sig+
Sig−
+
−
Sig+
0
95
0
100
0
0
61
0
100
0
5
0
53
47
0
0
58
0
100
0
0
95
0
100
0
0
89
0
100
0
0
72
0
100
0
0
12
16
84
0
0
33
0
100
0
0
74
0
100
0
0
54
0
100
0
0
9
12
88
0
0
14
14
86
0
Sig−
+
−
Sig+
Sig–
+
−
84
0
100
0
32
0
100
47
4
96
0
18
7
93
0
54
46
7
0
68
32
44
0
100
0
5
21
79
81
0
100
0
39
0
100
65
0
100
0
37
0
100
54
0
100
0
30
4
96
9
25
75
0
5
25
75
42
7
93
0
4
19
81
60
0
100
0
23
4
96
46
5
95
0
9
5
95
12
16
84
0
4
25
75
12
12
88
0
7
19
81
Annual extreme precipitation indices for mainland Portugal
number of stations having statistically significant correlations
decrease as the precipitation threshold increases from 10 to
25 mm. Likewise, for all periods, the number of stations with a
statistically significant negative correlation for the percentile
indices decreases as the percentile increases from R90p to
R99p. The strongest result found for the RX5D index is in
1945–1975, a period when the NAO stayed in a negative
phase (already discussed above): 93 % of the stations show
negative correlations with NAO, which are statistically significant in 42 % of them. Overall, the results for the RX1D index
are less significant; the strongest results are observed for the
entire period 1941–2007: 84 % of the stations show negative
correlations with NAO, which are statistically significant in
only 12 % of them. So, overall, there is anti-correlation
between NAO and the indicators of more intense rainy events,
but this is less significant for the extreme events. In fact, the
movement of the Atlantic storm track expressed by the NAO
is clearly more related with phenomena of frontal origin (see
e.g. Miranda et al. 2002), whereas the more intense events are
usually associated with local convective phenomena.
Figure 10 shows maps of the spatial distribution of the
Pearson’s correlation between the NAO and selected daily
precipitation indices (R10, R90p, SDII, PrecTot) in the
1941–2007 period, which confirm the existence of coherent
regions that are revealed by correlations of the same order of
magnitudes; the maps were constructed by interpolating the
correlations obtained for all the 57 stations over mainland
Portugal. They illustrate that the northeast and southeast have
precipitation regimes and patterns that are less influenced by
the NAO mode, when compared to the majority of areas in
mainland Portugal. It is likely that in these regions precipitation is less modulated by mid-latitude low pressure systems.
5 Discussion
The annual regional indices of precipitation extremes that
were used to characterize extreme wet and intense precipitation events in mainland Portugal do not suggest any marked
pattern of change over the 1941–2007 period, but the trends in
all the indices are negative, although the majority of trends are
not statistically significant and only the SDII index is statistically significant at the 5 % level.
The site-by-site assessment of the data from the 57 climatological stations shows that, in general, there is a mix of
increasing and decreasing trends in precipitation extremes
across mainland Portugal, but there are more stations
exhibiting decreasing trends than increasing trends in the
1941–2007 period, and this decreasing tendency is even more
notorious in the 1976–2007 sub-period.
We found a somewhat noticeable reduction in the regional
annual total wet-day precipitation index in the 1941–2007
period, which indicates a drying trend over mainland Portugal;
however, this result is not statistically significant. Nevertheless, at the regional scale, average annual precipitation (about
900 mm) presents a reduction over the full 67-year period of
about 10 %. The site-by-site analysis shows that the total wetday precipitation reduction is statistically significant for 16 %
of the stations. Klein Tank and Können (2003) analysed six
precipitation series across mainland Portugal (1946–1999)
and did not found significant trends in the annual total wetday precipitation (PrecTot) and the precipitation fraction due
to 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 data
examined by the authors in that study hamper the comparison
of results.
The regional CDD index showed also a decreasing trend
(statistically significant at the 25 % level) in this period; this
reduction is observed in 81 % of the stations, which is statistically significant in 17 % of them. Similar tendency towards
less extended dry periods has been reported by, e.g. Kiktev
et al. (2003), Alexander et al. (2006) and Tebaldi et al. (2006).
Conversely, the CWD regional index shows a decreasing
trend of −0.18 day decade−1 (statistically significant at the
25 % level) in the 1941–2007 period, which is more negative
(−0.50 day decade−1) in the 1976–2007 period (over 90 % of
the stations exhibit decreasing trends, but only 5 % of them are
statistically significant).
Decreasing trends in the 1941–2007 period were furthermore observed in RX1D and RX5D, and R10, R20 and R25
regional indices; however, only the trend in R10 (number of
days above 10 mm) is statistically significant at the 25 % level.
In addition, the results obtained for all the selected annual
extreme precipitation indices based on absolute precipitation
and relative (i.e. percentile) thresholds show that data from
only less than 16 % of the stations have statistically significant
decreasing trends; for RX1D, R95p and R99p less than 7 % of
the stations show significant increasing trends.
Despite all this, at the regional scale, the consistency of the
results for the different stations suggests less annual wet-day
precipitation along with fewer days with heavy precipitation.
Overall, these results hint a drying trend over mainland Portugal for the period 1941–2007. However, in this period, the
number of heavy and very heavy precipitation events (R10
and R20) decreased more significantly than the mean total
wet-day precipitation (PrecTot); so the proportion of the total
precipitation attributed to these events increased and, consequently, daily precipitation events tend to become more
intense.
The results also show that, on average, there is no statistically significant change in annual precipitation indices for
both sub-periods 1945–1975 and 1976–2007. But in general,
the precipitation indices detect a weak tendency towards more
extreme precipitation events in 1976–2007, particularly in the
southern region; this was shown by indicators of intensifying
M.I.P. de Lima et al.
Fig. 10 Correlations between the NAO and a R10, b R90p, c SDII and
d PrecTot indices across mainland Portugal in 1941–2007. Isolines are
for 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
Annual extreme precipitation indices for mainland Portugal
precipitation, such as RX1D and R99p. The percentage contribution of extremely wet days (upper 1 %) to the annual wetday precipitation is higher in 1976–2007 than in 1941–1975.
Correlation analysis suggests that precipitation extremes
are highly correlated with the mean precipitation and trends
in the precipitation extremes are highly correlated with
trends in mean precipitation, particularly for the R10, R20,
R25, R90 and R95 indices. We found evidence that the most
extreme events could be changing at a faster absolute rate in
relation to the mean conditions than more moderate events.
This result is consistent with Groisman et al. (1999) who
found a disproportionate change in precipitation intensities
whenever the mean precipitation changed; this was also shown
by Katz (1999). Furthermore, Tebaldi et al. (2006) point out that
there is a trend for intensified precipitation, with a greater frequency of heavy-precipitation and high-quantile events.
It is worth noting that, for the study area, other studies
identified a cooling tendency in the 1945–1975 sub-period
and a warming tendency in the 1976–2007 sub-period (see
e.g. Miranda et al. 2002, 2006; Ramos et al. 2011; de Lima
et al. 2013; Espírito Santo et al. 2013a). A similar overall
pattern was also found for Europe’s temperature (with the
warming period starting in the mid-1970s, see e.g. Klein Tank
et al. 2002; Klein Tank and Können 2003) and the global
average temperature (e.g. Rozelot and Lefebvre 2006). For
Portugal, the link between trends in air temperature and precipitation at the local and regional scales is outside the scope of this
work but should be investigated in future research. Also, the
correlation between the precipitation indices and geographic
attributes should be explored; Costa et al. (2010) and Durão
et al. (2010) have already analysed these relationships, but just
for southern Portugal and a limited number of indices and
record length. Nevertheless, we noticed that a larger percentage
of stations’ data from the central and southern regions of
Portugal showed positive trends in the extreme intensity, duration and percentile precipitation indices compared to the northern stations, for the entire 1941–2007 period and the last subperiod 1976–2007; but these trends are not statistically significant. This behaviour is consistent with the results reported for
the south by Costa and Soares (2009) and for the north by
Santos and Fragoso (2013), despite the different data and the
limited range of indices that were examined in those studies.
The correlation between the NAO index and precipitation
in western Iberian Peninsula highlights that precipitation in
this area, particularly mainland Portugal, is dominantly associated to mid-latitude low pressure systems and their fronts,
depending strongly on the exact location of the Atlantic storm
track (see e.g. Miranda et al. 2002). Strong positive phases of
the NAO tend to be associated with below-average precipitation over southern and central Europe whereas opposite
above-average precipitation anomalies are typically observed
during strong negative phases of the NAO (e.g. Trigo et al.
2002, 2004).
While, in general, spatially coherent regions of both
increasing and decreasing extreme precipitation in mainland Portugal did not emerge from the analysis of the
selected annual extreme indices, the result is different when
the precipitation is examined at sub-annual scales. We do
not focus on that behaviour here but other studies addressed
this issue: trends in monthly precipitation are discussed by,
e.g. de Lima et al. (2007, 2010a, b) based on the study of
long time series, some of them dating back to the nineteenth
century; Espírito Santo et al. (2013b) report a detailed
analysis of seasonal trends using a large number of indicators of daily precipitation extremes. According to these
studies, there are indications of the (re)distribution of precipitation during the year, with positive (increasing) trends
in some sub-annual periods (partially) offsetting negative
(decreasing) trends in other periods, leading in general to
non-significant trends at the annual scale.
6 Concluding remarks
The main purpose of the present study was to provide a more
comprehensive discussion of the precipitation structure and
changes in mainland Portugal, at the annual scale, using a
broader set of precipitation indices than in some previous studies.
Additionally, we aimed to use a higher number of stations, longer
records and to investigate relationships between the most important atmospheric circulation pattern and precipitation trends.
Analysis of a set of 13 annual precipitation indices, which
includes 11 extreme precipitation indices, derived from daily data
recorded at 57 stations distributed across mainland Portugal
identified several noticeable characteristics of precipitation in
the area in the 1941–2007 period. In general, the results found
in this study are in agreement with other studies that inspected
change in precipitation in western Iberia, where mainland Portugal lays (several references are given in Section 1); but we should
note that some of those studies were supported by monthly or
seasonal data whereas here we focussed on daily precipitation
data, which have been less explored.
The results of the site-by-site analysis highlight the
following:
–
–
–
Despite the presence of several stations with predominantly negative tendencies in the precipitation indices, the
majority of stations did not show statistically significant
change over time in the 1941–2007 period;
In the 1976–2007 period, there was a tendency towards
more extreme precipitation events, which was confirmed
by the highest daily precipitation amount (RX1D) and the
precipitation on extremely wet days (R99p), particularly
in the southern region of mainland Portugal;
Precipitation extremes are highly correlated with mean
precipitation and trends in the precipitation extremes are
M.I.P. de Lima et al.
–
highly correlated with trends in mean precipitation, particularly for the R10, R20, R25, R90 and R95 indices;
The most extreme precipitation events seem to be changing at a faster rate than the more moderate extreme events
and their intensity is increasing.
The results of the analysis of the regionally averaged indices show that:
–
–
–
–
–
There is an important but not statistically significant
decrease in regional average total precipitation;
The majority of precipitation regional indices, except
SDII, show small (non-significant) negative trends;
The regional averaged total wet-day precipitation has
declined, although this result is not statistically significant
at the 5 % level; however, since 1976 there is a large
reduction (−44 mm decade−1) in total wet-day precipitation, but this result is only statistically significant at the
25 % level; these drier conditions might reflect the predominance of warmer conditions in the last decades (e.g.
Klein Tank et al. 2005) but the relationship between
changes in temperature and precipitation needs to be
better understood;
Except for the CDD index, there is an overall significant
anti-correlation between the precipitation indices and the
NAO;
The decreasing trend found for SDII index may be related
to the predominance of the positive phase of the NAO
since the mid-1970s;
Although the average monthly and seasonal precipitation in
the Iberian Peninsula is highly influenced by the NAO (e.g.
Goodess and Jones 2002; Trigo et al. 2004), it depends also on
other modes of variability such as the Scandinavian and Eastern
Atlantic patterns; this was shown previously by, e.g. Trigo et al.
(2008) and Espírito Santo et al. (2013b). Moreover, at the daily
scale, smaller atmospheric circulation patterns play an important role favouring or damping the advection of moisture (Trigo
and DaCamara 2000) and may be better related (than simple
NAO indices) to the precipitation regime across the entire
Iberian Peninsula (e.g. Cortesi et al. 2013).
Acknowledgments The authors wish to thank Álvaro Silva and Sofia
Cunha (The Portuguese Sea and Atmosphere Institute, I. P.), for their help
in processing the maps in Figs. 1, 4, 6, 7 (top) and 10. Alexandre M.
Ramos was supported by the Portuguese Foundation for Science and
Technology (FCT) through grant FCT/DFRH/SFRH/BPD/84328/2012.
References
Acero FJ, Gallego MC, García JA (2012) Multi-day rainfall trends over
the Iberian Peninsula. Theor Appl Climatol 108:411–423. doi:10.
1007/s00704-011-0534-5
Aguilar E, Peterson TC, Ramírez Obando P, Frutos R, Retana JA, Solera
M, Soley J, González García I, Araujo RM, Rosa Santos A, Valle
VE, Brunet M, Aguilar L, Álvarez L, Bautista M, Castañón C,
Herrera L, Ruano E, Sinay JJ, Sánchez E, Hernández Oviedo GI,
Obed F, Salgado JE, Vázquez JL, Baca M, Gutiérrez M, Centella C,
Espinosa J, Martínez D, Olmedo B, Ojeda Espinoza CE, Núñez R,
Haylock M, Benavides H, Mayorga R (2005) Changes in precipitation and temperature extremes in Central America and northern
South America, 1961–2003. J Geophys Res 110, D23107. doi:10.
1029/2005JD006119
Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank
AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour
A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson
DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P,
Rusticucci M, Vazquez-Aguirre JL (2006) Global observed changes
in daily climate extremes of temperature and precipitation. J
Geophys Res 111. doi:10.1029/2005JD006290
Alexander LV, Hope P, Collins D, Trewin B, Lynch A, Nicholls N (2007)
Trends in Australia’s climate means and extremes: a global context.
Aust Meteorol Mag 56:1–18
Belo-Pereira M, Dutra E, Viterbo P (2011) Evaluation of global precipitation data sets over the Iberian Peninsula. J Geophys Res 116,
D20101. doi:10.1029/2010JD015481
Bladé I, Liebmann B, Fortuny D, van Oldenborgh GJ (2012) Observed
and simulated impacts of the summer NAO in Europe: implications
for projected drying in the Mediterranean region. Clim Dyn 39:709–
727. doi:10.1007/s00382-011-1195-x
Carvalho RC (1997) Nota sobre o temporal de 5/6 de Novembro de 1997.
IM - Instituto de Meteorologia, internal report. Lisboa, Portugal
Cortesi N, Trigo RM, Gonzalez-Hidalgo JC, Ramos AM (2013) High
resolution reconstruction of monthly precipitation of Iberian
Peninsula using circulation weather types. Hydrol Earth Syst Sci
17:665–678. doi:10.5194/hess-17-665-2013
Costa AC, Soares A (2009) Trends in extreme precipitation indices
derived from a daily rainfall database for the South of Portugal. Int
J Climatol 29(13):1956–1975. doi:10.1002/joc.1834
Costa AC, Durão R, Pereira MJ, Soares A (2008) Using stochastic
space-time models to map extreme precipitation in southern
Portugal. Nat Hazards Earth Syst Sci 8:763–773. doi:10.5194/
nhess-8-763-2008
Costa AC, Soares A, Pereira MJ, Durão R (2010) Evaluation of spacetime dynamics in extreme precipitation frequency using
geostatistical cosimulation with elevation. WSEAS Transactions
on Environment and Development 6:44–53
Costa AC, Santos JA, Pinto JG (2012) Climate change scenarios for
precipitation extremes in Portugal. Theor Appl Climatol 108:217–
234. doi:10.1007/s00704-011-0528-3
de Lima MIP, Marques ACP, de Lima JLMP, Coelho MFES (2007)
Precipitation trends in Mainland Portugal in the period 1941–
2000. IAHS publ no 310:94–102
de Lima MIP, Carvalho SCP, de Lima JLMP (2010a) Investigating annual
and monthly trends in precipitation structure: an overview across
Portugal. Nat Hazards Earth Syst Sci 10:2429–2440. doi:10.5194/
nhess-10-2429-2010
de Lima MIP, Carvalho SCP, de Lima JLMP, Coelho MFES (2010b)
Trends in precipitation: analysis of long annual and monthly time
series from mainland Portugal. Adv Geosci 25:155–160. doi:10.
5194/adgeo-25-155-2010
de Lima MIP, Espírito Santo F, Ramos AM, de Lima JLMP (2013)
Recent changes in daily precipitation and surface air temperature
extremes in mainland Portugal, in the period 1941–2007. Atmos Res
27:195–209. doi:10.1016/j.atmosres.2012.10.001
Durão R, Pereira MJ, Costa AC, Corte-Real JM, Soares A (2009) Indices
of precipitation extremes in Southern Portugal—a geostatistical
approach. Nat Hazards Earth Syst Sci 9:241–250. doi:10.5194/
nhess-9-241-2009
Annual extreme precipitation indices for mainland Portugal
Durão RM, Pereira MJ, Costa AC, Delgado J, del Barrio G, Soares A
(2010) Spatial–temporal dynamics of precipitation extremes in
southern Portugal: a geostatistical assessment study. Int J Climatol
30:1526–1537. doi:10.1002/joc.1999
Espírito Santo F et al. (1998) Caracterização climática do ano de 1997. IM
- Instituto de Meteorologia, internal report. Lisboa, Portugal
Espírito Santo F et al. (2001) Outono/Inverno de 2000/2001. Análise das
condições meteorológicas. IM - Instituto de Meteorologia, internal
report. Lisboa, Portugal
Espírito Santo F et al. (2004) Caracterização climática 2004. IM Instituto de Meteorologia, internal report. Ministério da Ciência,
Tecnologia e Ensino Superior, Lisboa, Portugal
Espírito Santo F, de Lima MIP, Ramos AM, Trigo RM (2013a) Trends in
seasonal surface air temperature in mainland Portugal, since 1941.
Int J Climatol. doi:10.1002/joc.3803
Espírito Santo F, Ramos AM, de Lima MIP, Trigo RM (2013b)
Seasonal changes in daily precipitation extremes in mainland
Portugal from 1941 to 2007. Reg Environ Chang. doi:10.
1007/s10113-013-0515-6
Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank
AMG, Peterson T (2002) Observed coherent changes in climatic
extremes during the second half of the twentieth century. Clim Res
19:193–212
Gallego MC, García JA, Vaquero JM (2005) The NAO signal in daily
rainfall series over the Iberian Peninsula. Clim Res 29:103–109. doi:
10.3354/cr029103
Gallego MC, García JA, Vaquero JM, Mateos VL (2006) Changes in
frequency and intensity of daily precipitation over the Iberian
Peninsula. J Geophys Res 111:D24105. doi:10.1029/
2006JD007280
Gallego MC, Trigo RM, Vaquero JM, Brunet M, García JA, Sigró J,
Valente MA (2011) Trends in frequency indices of daily precipitation over the Iberian Peninsula during the last century. J Geophys
Res 116:D02109. doi:10.1029/2010JD014255
García-Barrón L, Morales J, Sousa A (2013) Characterisation of the intraannual rainfall and its evolution (1837–2010) in the southwest of the
Iberian Peninsula. Theor Appl Climatol. doi:10.1007/s00704-0130855-7
Garcia-Herrera R, Paredes D, Trigo RM, Trigo IF, Hernández H,
Barriopedro D, Mendes MT (2007) The outstanding 2004–
2005 drought in the Iberian Peninsula: associated atmospheric
circulation. J Hydrometeorol 8:483–498
Godinho SF (1984) Aspectos meteorológicos das inundações de 1983.
INMG – Instituto Nacional de Meteorologia e Geofísica, internal
report. Lisboa, Portugal
Goodess CM, Jones PD (2002) Links between circulation and changes in
the characteristics of Iberian rainfall. Int J Climatol 22(13):1593–
1615
Groisman PY, Karl TR, Easterling DR, Knight RW, Jamason PF,
Hennessy KJ, Suppiah R, Page CM, Wibig J, Fortuniak K,
Razuvaev VN, Douglas A, Forland E, Zhai P-M (1999) Changes
in the probability of heavy precipitation: important indicators of
climatic change. Clim Chang 43:243–283
Herrera S, Fita L, Fernández J, Gutiérrez JM (2010) Evaluation of the
mean and extreme precipitation regimes from the ENSEMBLES
regional climate multimodel simulations over Spain. J Geophys Res
115, D21117. doi:10.1029/2010JD013936
Hurrell JW (1995) Decadal trends in the North Atlantic Oscillation:
regional temperatures and precipitation. Science 269:676–679
Hurrell JW, Deser C (2010) North Atlantic climate variability: the role of
the North Atlantic Oscillation. J Mar Syst 79:231–244. doi:10.1016/
j.jmarsys.2009.11.002
Hurrell JW, van Loon H (1997) Decadal variations associated with the
North Atlantic Oscillation. Clim Chang 36:301–326
IM (Instituto de Meteorologia) (2008) Informação climática-relatório
mensal: Fevereiro 2008. IM, internal report, Março de 2008.
Ministério da Ciência, Tecnologia e Ensino Superior, Lisboa,
Portugal
IPCC (2007) Climate change 2007: the physical science basis.
Contribution of Working Group I to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change, Solomon S, Qin
D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller
HL (eds), Cambridge University Press, Cambridge, UK and New
York, NY, USA, 996 pp
IPCC (2012) Managing the risks of extreme events and disasters to
advance climate change adaptation. A Special Report of Working
Groups I and II of the Intergovernmental Panel on Climate Change
[Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL,
Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M,
Midgley PM (eds.)]. Cambridge University Press, Cambridge, UK,
and New York, NY, USA, 582 pp
Jiang F-Q, Hu R-J, Wang S-P, Zhang Y-W, Tong L (2013) Trends of
precipitation extremes during 1960–2008 in Xinjiang, the
Northwest China. Theor Appl Climatol 111(1–2):133–148. doi:10.
1007/s00704-012-0657-3
Jones PD, Jonsson T, Wheeler D (1997) Extension to the North Atlantic
Oscillation using early instrumental pressure observations from
Gibraltar and south-west Iceland. Int J Climatol 17:1433–1450
Karagiannidis AF, Karacostas T, Maheras P, Makrogiannis T (2012)
Climatological aspects of extreme precipitation in Europe, related
to mid-latitude cyclonic systems. Theor Appl Climatol 107:165–
174. doi:10.1007/s00704-011-0474-0
Katz RW (1999) Extreme value theory for precipitation: sensitivity analysis for climate change. Adv Water Resour 23:133–139
Kiktev D, Sexton DMH, Alexander L, Folland CK (2003) Comparison of
modeled and observed trends in indices of daily climate extremes. J
Clim 16:3560–3571
Klein Tank AMG, Können, GP (2003) Trends in indices of daily temperature and precipitation extremes in Europe, 1946–99. J Climate
16:3665–3680. doi:http://dx.doi.org/10.1175/1520-0442(2003)
016<3665:TIIODT>2.0.CO;2
Klein Tank AMG, Wijngaard JB, Können GP, Böhm R, Demarée G,
Gocheva A, Mileta M, Pashiardis S, Hejkrlik L, Kern-Hansen C,
Heino R, Bessemoulin P, Müller-Westermeier G, Tzanakou M, Szalai
S, Pálsdóttir T, Fitzgerald D, Rubin S, Capaldo M, Maugeri M, Leitass
A, Bukantis A, Aberfeld R, van Engelen AFV, Forland E, Mietus M,
Coelho F, Mares C, Razuvaev V, Nieplova E, Cegnar T, López JA,
Dahlström B, Moberg A, Kirchhofer W, Ceylan A, Pachaliuk O,
Alexander LV, Petrovic P (2002) Daily dataset of 20th-century surface
air temperature and precipitation series for the European Climate
Assessment. Int J Climatol 22:1441–1453. doi:10.1002/joc.773
Klein Tank AMG, Peterson TC, Quadir DA, Dorji S, Zou X, Tang H,
Santhosh K, Joshi UR, Jaswal AK, Kolli RK, Sikder AB,
Deshpande NR, Revadekar JV, Yeleuova K, Vandasheva S,
Faleyeva M, Gomboluudev P, Budhathoki P, Hussain A, Afzaal
M, Chandrapala L, Anvar H, Amanmurad D, Asanova VS, Jones
PD, New MG, Specktorman T (2006) Changes in daily temperature
and precipitation extremes in central and south Asia. J Geophys Res
111(D16105). doi:10.1029/2005JD006316
Klein Tank AMG, Zwiers FW, Zhang X (2009) Guideline on analysis of
extremes in a changing climate in support of informed decisions for
adaptation. Climate Data and Monitoring. WCDMP-No. 72, WMOTD No. 1500, Geneve, Switzerland
Liberato MRL, Ramos AM, Trigo RM, Trigo IF, Durán-Quesada AM,
Nieto R, Gimeno L (2013) Moisture sources and large-scale dynamics associated with a flash flood event. In: Lin J, Brunner D, Gerbig
C, Stohl A, Luhar A, Webley P (eds) Lagrangian modeling of the
atmosphere. American Geophysical Union, Washington, D. C. doi:
10.1029/2012GM001244
Livezey RE, Chen WY (1983) Statistical field significance and it’s
determination by Monte Carlo Techniques. Mon Weather Rev 111:
46–59
M.I.P. de Lima et al.
López-Bustins J-A, Martin-Vide J, Sanchez-Lorenzo A (2008) Iberia
winter rainfall trends based upon changes in teleconnection and
circulation patterns. Global Planet Chang 63:171–176
Miranda PMA, Coelho F, Tomé AR, Valente MA, Carvalho A, Pires
C, Pires HO, Cabrinha VP, Ramalho C (2002) 20th Century
Portuguese climate and climate scenarios. In: Santos FD,
Forbes K, Moita R (eds) Climate change in Portugal: scenarios,
impacts and adaptation measures. Gradiva, Lisbon, Portugal, pp
27–83
Miranda PMA, Valente MA, Tomé AR, Trigo R, Coelho MFES,
Aguiar A, Azevedo EB (2006) O Clima de Portugal nos
séculos XX e XXI. In: Santos FD, Miranda P (eds)
Alterações climáticas em Portugal. Cenários Impactos e
Medidas de Adaptação. Projecto SIAM II. Gradiva, Lisbon,
Portugal, pp 47–113
Moberg A, Jones PD (2005) Trends in indices for extremes in daily
temperature and precipitation in central and western Europe 1901–
1999. Int J Climatol 25:1149–1171
Moberg A, Jones PD, Lister D, Walther A et al. (2006) Indices for daily
temperature and precipitation extremes in Europe analyzed for the
period 1901–2000. J Geophys Res 111(D22106). doi:10.1029/
2006JD007103
Mourato S, Moreira M, Corte-Real J (2010) Interannual variability of
precipitation distribution patterns in Southern Portugal. Int J
Climatol 30:1784–1794. doi:10.1002/joc.2021
New M, Hewitson B, Stephenson DB, Tsiga A, Kruger A, Manhique A,
Gomez B, Coelho B, Caio AS, Masisi DN, Kululanga E,
Mbambalala E, Adesina F, Saleh H, Kanyanga J, Adosi J, Bulane
L, Fortunata L, Mdoka ML, Lajoie R (2006) Evidence of trends in
daily climate extremes over Southern and West Africa. J Geophys
Res 111:D14102. doi:10.1029/2005JD006289
Nicholls N (2001) The insignificance of significance testing. Bull Am
Meteorol Soc 82:981–986
Norrant C, Douguédroit A (2006) Monthly and daily precipitation trends
in the Mediterranean (1950–2000). Theor Appl Climatol 83(1–4):
89–106. doi:10.1007/s00704-005-0163-y
Önöz B, Bayazit M (2003) The power of statistical tests for trend
detection. Turk J Eng Environ Sci 27:247–251
Peterson TC, Folland C, Gruza G, Hogg W, Mokssit A, Plummer N
(2001) Report on the Activities of the Working Group on Climate
Change Detection and Related Rapporteurs 1998–2001. World
Meteorological Organization, WCDMP–No. 47/WMO–TD
No.1071: Geneva, Switzerland
Ramos A, Trigo RM, Santo FE (2011) Evolution of extreme temperatures
in Portugal: reporting on recent changes and future scenarios. Clim
Res 48:177–192. doi:10.3354/cr00934
Rodrigo FS (2010) Changes in the probability of extreme daily precipitation observed from 1951 to 2002 in the Iberian Peninsula. Int J
Climatol 30:1512–1525. doi:10.1002/joc.1987
Rodrigo FS, Trigo RM (2007) Trends in daily rainfall in the Iberian
Peninsula from 1951 to 2002. Int J Climatol 27:513–529. doi:10.
1002/joc.1409
Rozelot JP, Lefebvre S (2006) Is it possible to find a solar signature in the
current climatic warming? Phys Chem Earth 31(1–3):41–45. doi:10.
1016/j.pce.2005.01.005
Santos M, Fragoso M (2013) Precipitation variability in Northern
Portugal: data homogeneity assessment and trends in extreme precipitation indices. Atmos Res 131:34–45
Santos JA, Andrade C, Corte-Real J, Leite S (2009) The role of
large-scale eddies in the occurrence of winter precipitation deficits in Portugal. Int J Climatol 29(10):1493–1507. doi:10.1002/
joc.1818
Serreze MC, Carse F, Barry RG, Rogers JC (1997) Icelandic low cyclone
activity: climatological features, linkages with the NAO, and relationships with recent changes in the Northern Hemisphere circulation. J Clim 10:453–464
Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the
extremes: an intercomparison of model-simulated historical and
future changes in extreme events. Clim Chang 79:185–211
Trigo RM, DaCamara CC (2000) Circulation weather types and their
influence on the precipitation regime in Portugal. Int J Climatol 20:
1559–1581
Trigo RM, Osborn TJ, Corte-Real J (2002) The North Atlantic Oscillation
influence on Europe: climate impacts and associated physical mechanisms. Clim Res 20:9–17
Trigo RM, Pozo-Vazquez D, Osborn TJ, Castro-Diez Y, Gámis-Fortis S,
Esteban-Parra MJ (2004) North Atlantic Oscillation influence on
precipitation, river flow and water resources in the Iberian
Peninsula. Int J Climatol 24:925–944. doi:10.1002/joc.1048
Trigo RM, Valente MA, Trigo IF, Miranda PM, Ramos AM, Paredes D,
García-Herrera R (2008) North Atlantic wind and cyclone trends
and their impact in the European precipitation and Atlantic significant wave height. Ann N Y Acad Sci 1146:212–234. doi:10.1196/
annals.1446.014
Ulbrich U, Christoph M, Pinto JG, Corte-Real J (1999) Dependance of
winter precipitation over Portugal on NAO and Baroclinic Wave
Activity. Int J Climatol 19:379–390
van den Besselaar EJM, Klein Tank AMG, Buishand TA (2012) Trends
in European precipitation extremes over 1951–2010. Int J Climatol.
doi:10.1002/joc.3619
van Loon H, Rogers JC (1978) The seesaw in winter temperatures
between Greenland and Northern Europe. Part I: general description.
Mon Weather Rev 106:296–310
Ventura JE, Espírito Santo F, Moita S, Silva A (2001) Le caractère
exceptionnel des précipitations d’hiver de 2000/2001 au Portugal
Continental: Caractérisation, Causes et Conséquences. XIV
Colloque International de Climatologie, Sevilla, Spain
Vicente-Serrano SM (2006a) Differences in spatial patterns of drought on
different time scales: an analysis of the Iberian Peninsula. Water
Resour Manag 20:37–60
Vicente-Serrano SM (2006b) Spatial and temporal analysis of
droughts in the Iberian Peninsula (1910–2000). Hydrol Sci J
51:83–97
Vicente-Serrano SM, López-Moreno JI (2008) Nonstationary influence
of the North Atlantic Oscillation on European precipitation. J
Geophys Res 113, D20120. doi:10.1029/2008JD010382
Vicente-Serrano SM, Trigo RM, López-Moreno JI, Liberato MLR,
Lorenzo-Lacruz J, Beguería S, Morán-Tejeda E, El Kenawy A
(2011) Extreme winter precipitation in the Iberian Peninsula in
2010: anomalies, driving mechanisms and future projections. Clim
Res 46:51–65
Vincent LA, Aguilar E, Saindou M, Hassane AF, Jumaux G, Roy D,
Booneeady P, Virasami R, Randriamarolaza LYA, Faniriantsoa FR,
Amelie V, Seeward H, Montfraix B (2011) Observed trends in
indices of daily and extreme temperature and precipitation for the
countries of the western Indian Ocean, 1961–2008. J Geophys Res
116, D10108. doi:10.1029/2010JD015303
Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential
height field during the Northern Hemisphere winter. Mon Weather
R e v 1 0 9 : 7 8 4 – 8 1 2 . d o i :1 0 . 11 7 5 / 1 5 2 0 - 0 4 9 3 ( 1 9 8 1 )
109<0784:TITGHF>2.0.CO;2
Wang XL (2008a) Accounting for autocorrelation in detecting meanshifts in climate data series using the penalized maximal t or F test.
J Appl Meteorol Climatol 47:2423–2444. doi:10.1175/
2008JAMC1741.1
Wang XL (2008b) Penalized maximal F-test for detecting undocumented
mean-shifts without trend-change. J Atmos Ocean Technol 25(3):
368–384. doi:10.1175/2007/JTECHA982.1
Wang, XL (2009) A quantile matching adjustment algorithm for Gaussian
data series. Climate Research Division, Science and Technology
Branch, Environment Canada. 5 pp. http://cccma.seos.uvic.ca/
ETCCDMI/software.shttm
Annual extreme precipitation indices for mainland Portugal
Wang XL, Feng Y (2010) RHtestsV3 User Manual. Climate Research
Division, Science and Technology Branch, Environment Canada. 27
pp. http://cccma.seos.uvic.ca/ETCCDMI/RHtest/RHtestsV3_
UserManual.doc
Wang XL, Wen QH, Wu Y (2007) Penalized maximal t-test for detecting
undocumented mean change in climate data series. J Appl Meteorol
Climatol 46(6):916–931. doi:10.1175/JAM2504.1
Wang H, Chen Y, Chen Z (2013) Spatial distribution and temporal trends
of mean precipitation and extremes in the arid region, northwest of
China, during 1960-2010. Hydrol Process 27:1807–1818. doi:10.
1002/hyp.9339
WMO (World Meteorological Organization) Bulletin (1984) Unusual
weather in 1983—Part I. July 1984, 33(3):216–220
WMO RA–VI Bulletin (2001) Annual Bulletin on the Climate in WMO
Region VI–2001–Europe and Middle East, 58 pp
Xoplaki E, González-Rouco JF, Luterbacher J, Wanner H (2004) Wet
season Mediterranean precipitation variability: influence of largescale dynamics and trends. Clim Dyn 23:63–78. doi:10.1007/
s00382-004-0422-0
Zêzere JL, Trigo RM, Trigo IF (2005) Shallow and deep landslides
induced by rainfall in the Lisbon region (Portugal): assessment of
relationships with the North Atlantic Oscillation. Nat Hazards Earth
Syst Sci 5:331–344
Zhang X, Wang XL, Corte-Real J (1997) On the relationships between
daily circulation patterns and precipitation in Portugal. J Geophys
Res 12:2474–2489
Zhang X, Alexander L, Hegerl C, Jones P, Klein Tank A, Peterson C,
Trewin B, Zwiers Francis W (2011) Indices for monitoring changes
in extremes based on daily temperature and precipitation data.
WIREs Clim Change 2011. doi:10.1002/wcc.147