Reg Environ Change (2014) 14:1765–1788 DOI 10.1007/s10113-013-0515-6
ORIGINAL ARTICLE
Seasonal changes in daily precipitation extremes in mainland Portugal from 1941 to 2007 Fa´tima Espı´rito Santo • Alexandre M. Ramos M. Isabel P. de Lima • Ricardo M. Trigo
•
Received: 9 March 2012 / Accepted: 8 July 2013 / Published online: 8 August 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract This study aims mostly at understanding seasonal variations in the intensity, frequency and duration of extreme precipitation events in mainland Portugal. For this purpose, selected precipitation indices that mainly focus on extremes were calculated at the seasonal scale for daily data recorded in the period 1941–2007 at 57 meteorological stations scattered across the area. These indices were explored for trends at the local and regional levels. The results show that there are marked changes in precipitation indices at the seasonal scale. Trends in spring and autumn precipitation have opposite signals. In spring, statistically significant drying trends are found together with a reduction in extremes. In autumn, wetting trends are detected for all indices, although overall they are not significant at the 5 % level. In addition, the relationship between seasonal extreme precipitation indices and atmospheric large-scale
F. Espı´rito Santo The Portuguese Sea and Atmosphere Institute, I. P. (IPMA, IP), Lisbon, Portugal A. M. Ramos R. M. Trigo Instituto Dom Luiz, Universidade de Lisboa, Lisbon, Portugal M. I. P. de Lima Marine and Environmental Research Centre, Institute of Marine Research, Civil Engineering Department, University of Coimbra, Coimbra, Portugal M. I. P. de Lima (&) Coimbra College of Agriculture, Polytechnic Institute of Coimbra, Coimbra, Portugal e-mail:
[email protected] R. M. Trigo Departamento de Engenharias, Universidade Luso´fona, Lisbon, Portugal
modes of low-frequency variability is analysed by means of a seasonal correlation analysis. Four modes of low-frequency variability are explored. Results confirm that, over mainland Portugal, the North Atlantic Oscillation is one of the most important teleconnection patterns in any season and the mode of variability that has the greatest influence on precipitation extremes in the area, particularly in the winter and autumn. Keywords Precipitation extremes Trend analysis Climate variability Modes of low-frequency variability Mainland Portugal
Introduction In the twentieth century, precipitation generally increased over land in high northern latitudes, while decreases were observed from 10°S to 30°N from the 1970s (e.g. Bates et al. 2008). This was also found for Europe, with the Mediterranean region experiencing a precipitation decline particularly in its western and central sectors (e.g. IPCC 2007). Thus, precipitation trends calculated from empirical data often suggest different behaviour in the precipitation regimes at different locations. A common explanation for these differences might be that precipitation trends depend crucially, for example, on the length of the time series analysed and the months considered (e.g. de Lima et al. 2010); moreover, in many regions, precipitation trends are not statistically significant owing to the large temporal variability (e.g. Norrant and Dougue´droit 2005; Sousa et al. 2011). Measurement problems and the sparse network of in situ-based precipitation measurements are also responsible for high uncertainty in the detection of both spatial and temporal precipitation changes.
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Changes in precipitation extremes in the Mediterranean region and the Iberian Peninsula (in south-western Europe) are of particular concern since they can be responsible, for example, for flash floods (e.g. Ferraris et al. 2002), hydrological droughts (e.g. Garcia-Herrera et al. 2007; Sousa et al. 2011) and landslides (e.g. Zeˆzere et al. 2008). Extreme events are generally defined as values of meteorological variables above or below a certain threshold, which have low probability of occurrence and major impact on society and ecosystems (e.g. Heino et al. 1999; IPCC 2012). Some studies have focused on changes in precipitation extremes in the Iberian Peninsula, e.g. Rodrigo and Trigo (2007), Gallego et al. (2011). However, they have generally relied on a very few stations in western Iberia (fewer than 7), where mainland Portugal is located. Some studies of daily precipitation trends over mainland Portugal have been mostly restricted to the southern dry area of Portugal (e.g. Costa et al. 2008; Costa and Soares 2009; Dura˜o et al. 2009). Other studies have also reported high variability in monthly and annual precipitation in mainland Portugal (e.g. de Lima et al. 2007, 2010). The strong spatial variability in precipitation that characterizes this region (e.g. Trigo and DaCamara 2000; de Lima et al. 2007; BeloPereira et al. 2011; de Lima et al. 2013) makes it necessary to examine changes in precipitation with the support of a large number of stations, and this has not yet been fully achieved. Mainland Portugal is located in the transitional region between the sub-tropical anti-cyclone and the sub-polar depression zones confined by parallels 37° and 42°N and within the relatively narrow meridional band that spans 6.5° and 9.5°W. The other climate factors that most influence mainland Portugal are the orography (Fig. 1) and the effect of the Atlantic Ocean. The variability of precipitation in mainland Portugal, where mean annual precipitation is around 900 mm (1961–1990), is characterized by a strong annual cycle. On average, about 40 % of the annual precipitation falls in the winter. Summer months contribute very little to the total amounts (*6 %), but in the transition months of spring and autumn the amount of precipitation is highly variable (e.g. Trigo and DaCamara 2000; Miranda et al. 2002, 2006; Gallego et al. 2011). Frontal storms dominate the occurrence of precipitation in mainland Portugal, especially in winter and late autumn, while convective storms are frequent in summer and spring (e.g. de Lima et al. 2002; Ramos et al. 2011). The western Iberia sector is affected by a relatively small number of large-scale modes of atmospheric circulation variability (e.g. Trigo et al. 2008). These patterns include the North Atlantic Oscillation (NAO), which is the main mode of low-frequency variability in the North Atlantic European sector and is correlated with the surface
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Fig. 1 Location of the 57 climatological weather stations used in this study, which are listed in Table 1. Topography of mainland Portugal is also shown
climate in most of the European region (e.g. Hurrell and van Loon 1997; Trigo et al. 2002; Jones et al. 2003; Vicente-Serrano and Trigo 2011), affecting Portugal in particular (e.g. Ulbrich et al. 1999; Trigo et al. 2004). The NAO affects not only temperature (e.g. Castro-Diez et al. 2002) and precipitation (e.g. Trigo et al. 2004), but also river flows (e.g. Lorenzo-Lacruz et al. 2011), landslides (e.g. Zeˆzere et al. 2005) and even vegetation activity (e.g. Gouveia et al. 2008). In addition to the NAO index, other modes that generally affect the region (e.g. Rodrı´guezPuebla et al. 1998; Lorenzo and Taboada 2005; Nieto et al. 2007; Ramos et al. 2010) are the East Atlantic (EA), East Atlantic/Western Russia (EA/WR) and Scandinavia (SCA) indices. This work aims mainly to assess seasonal changes in the frequency and intensity of precipitation extremes in mainland Portugal (using a high density station network) and to identify the modes of low-frequency variability that are responsible for the variability in precipitation extremes in the region. It investigates trends in precipitation indices calculated from daily precipitation data from 57
Seasonal changes in daily precipitation extremes in mainland Portugal
meteorological stations, recorded in the period 1941–2007. The study is conducted for different multi-decadal periods, at both the station and regional scales. Additional analyses include the study of the correlations between seasonal precipitation indices and the large-scale influence of the most important four modes of low-frequency variability on these indices.
Precipitation data set Station data The precipitation data cover the period 1941–2007. They include daily data from 57 climatological weather stations, which were chosen based on a combination of tests for data length, completeness, quality and homogeneity, and their scattered spatial distribution over mainland Portugal (see ‘‘Precipitation time series’’ section). The data from 11 stations were provided by the Portuguese Sea and Atmosphere Institute (Instituto Portugueˆs do Mar e da Atmosfera—IPMA), while the data for the other 46 stations were provided by the National Water Resources Information System (Sistema Nacional de Informac¸a˜o de Recursos Hı´dricos—SNIRH) managed by the Portuguese Environment Agency (Ageˆncia Portuguesa do Ambiente— APA). The stations’ locations in mainland Portugal are shown in Fig. 1, which also illustrates the region’s main topographic features. Their names are given in Table 1 together with the corresponding mean seasonal precipitation for the period 1961–1990. On average, for these data, the contributions of seasonal precipitation to annual precipitation are: spring, 24 %; summer, 6 %; autumn, 28 % and winter, 42 %. Precipitation time series The precipitation stations included in this study were selected according to quite a strict set of rules. The first step in this selection procedure was to screen the data for missing figures; when long-term trends in empirical data are analysed it is crucial that only a very few years have missing figures, and also that such gaps are not clustered together (e.g. Klein Tank et al. 2009). The following general criteria were therefore used to assess completeness of data: 1. 2. 3.
Any given month is considered complete if no more than 3 days are missing from the records. A year is considered complete if no more than 15 days are missing. A 3-month season is considered complete if no more than 4 days are missing.
1767 Table 1 Names of the climatological weather stations and mean seasonal precipitation for the period 1961–1990 Station
Mean seasonal precipitation (mm)
Nr
Name
Spring
Summer
Autumn
Winter
1
Gestosa
218
81
254
369
2
Travancas
241
88
266
367
3 4
Braganc¸aa Ponte Lima
170 392
68 107
194 444
297 689
5
Tinhela
229
72
245
403
6
Braga
372
112
390
627
7
Sta Marta
433
126
424
701
8
Chacim
222
68
233
364
9
293
79
278
538
162
67
178
228
11
Torre Pinha˜o Alfaˆndega da Fe´ Re´gua
207
61
233
401
12
Porto
309
88
344
514
13
Aguiar da Beira
300
95
318
539
14
Pinhel
152
59
176
224
15
Sta Comba Da˜o
259
73
290
474
16
P. Douradas
398
119
481
707
10
17
Oliveira Hospital
238
82
248
411
18 19
233 159
69 51
256 164
376 229
20
Coimbra/Bencanta Ladoeiro Ferreira Zeˆzere
251
53
298
486
21
Cela
193
42
228
328
22
Gavia˜o Abra˜
190
51
217
357
23
227
49
254
442
24
Castelo de Vide
204
50
239
333
25
C. Carvoeiro
137
26
177
255
26
Portalegre
216
54
250
362
27
Chouto
183
46
217
315
28
Almeirim
162
28
192
279
29
Praganc¸a
234
44
260
407
30
Magos/Barragem
167
40
195
271
31
Pavia
160
35
177
250
32
S. Julia˜o Tojal
168
25
224
322
33
Vila Vic¸osa
178
34
210
330
34 35
Canha Lisboa
164 167
39 30
211 216
294 329
36
Moinhola ´ guas de Moura A
165
32
194
302
37
163
28
184
305
38
E´vora
148
39
173
255
39
138
34
148
218
40
Reguengos Alca´c¸ovas
170
32
196
324
41
Comporta
127
22
172
247
42
Viana Alentejo
165
36
187
294
43
140
35
155
214
44
Amareleja Graˆndola
149
21
184
296
45
Cuba
144
27
164
244
46
Beja
146
27
161
243
123
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Table 1 continued Station
Mean seasonal precipitation (mm)
Nr
Name
Spring
Summer
Autumn
Winter
47
Serpa Relı´quias
129
26
149
204
48
163
22
199
298
49
Castro Verde
138
19
154
231
50
152
21
177
271
51
Aldeia Palheiros Almodoˆvar
140
20
170
263
52
Santana da Serra
161
22
203
307
53
Martim Longoa
120
23
160
210
54
Aljezur
147
16
197
247
55 56
S. Bart. Messines S. Bra´s Alportelb Loule´
160 182
18 24
208 248
331 440
145
19
199
341
57
are caused by changes in instrumentation, station relocation, changes in the general surroundings of a station (e.g. local environment, urbanization) or in the method of data collection and observation. Relative homogeneity tests, i.e., using reference time series, are considered to be more powerful than absolute tests since the inhomogeneities are more easily distinguished from real climate variations (Wijngaard et al. 2003; Toreti et al. 2011). A two-step approach was therefore employed to guarantee the homogeneity of the time series from each of the 70 stations selected previously (‘‘Precipitation time series’’ section): 1.
The location of these stations in mainland Portugal is shown in Fig. 1. Stations retrieved from the IM network are highlighted in bold a
Stations with data starting in 1942
b
Station with data starting in 1943
4.
The total amount of missing data (i.e. daily data) cannot exceed 2 % of the corresponding number of days in the entire station records.
Originally, data series from more than 200 meteorological stations from both IPMA and APA were available for this study, but after applying the criteria described above we were left with 70. The application of the supplementary selection criteria described in ‘‘Data quality control and homogeneity’’ section, which included quality control and homogeneity criteria, further reduced the number of stations that were finally adopted in this study to 57. Data quality control and homogeneity Quality control and homogeneity of climate series are required for reliable climate analysis (e.g. Alexandersson 1986; Klein Tank et al. 2009; Toreti et al. 2011). The purpose of data quality control is to identify errors in daily series (e.g. negative precipitation, non-existent dates and erroneous outliers), which could interfere with the correct assessment of the extremes. These errors could have been introduced during data processing, such as errors in manual keying. The daily precipitation data were screened for anomalous values that fell more than three standard deviations outside the climatological daily average, but no such extreme values were identified for the daily precipitation data. Homogeneity assessment consisted of detecting jumps and/or gradual shifts in long-term climate time series which
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2.
The series of monthly means of the daily precipitation were examined for temporal homogeneity using the RHTestsV3 software package (Wang and Feng 2010) to detect change points (i.e. shifts in the mean) and check against station metadata records (when available), and adjust for single or multiple change points in a series. Significant change points were found in 16 stations and, using the RHtests_dlyPrcp package, 3 daily precipitation series (Braganc¸a, Coimbra and Cela, see Fig. 1; Table 1) were adjusted based on the penalized maximal t test (Wang et al. 2007), the penalized maximal F test (Wang 2008a, b) and a Quantile Matching algorithm (Wang 2009). The other 13 time series identified as being inhomogeneous (with more than two change points), and with no metadata available, were excluded and not used further in the analysis. The 57 stations series that remained were also tested using the Standard Normal Homogeneity Test (SNHT) for a single break (Alexandersson 1986). In the majority of cases, the relative SNHT test was applied using the good quality Lisbon station data as the reference series (e.g. Wijngaard et al. 2003). We have performed an annual and seasonal correlation analysis between the Lisbon precipitation and all the other precipitation time series. Results obtained (not shown) confirm that the precipitation series from Lisbon is well correlated with the majority of the other precipitation series. Therefore, the Lisbon station was selected as the overall reference station. A few exceptions had to be considered for the NE region of Portugal where we have used data from a neighbouring station (Travancas) as the reference series. The homogeneity tests were applied to the annual wet-day precipitation and number of wet days’ time series (wetday precipitation C 1 mm), to confirm that the data did not contain discontinuities of non-climatic origin. The SNHT test takes as null hypothesis that the variable tested is independent and identically distributed, whereas the alternative hypothesis is associated
Seasonal changes in daily precipitation extremes in mainland Portugal
with the existence of deviations in the mean. For the two variables, no evidence of the presence of statistically significant (at 1 % level) breaks in the data was found.
1769 Table 2 Definition of indices selected for analysis of extreme precipitation in mainland Portugal Indices
Definition
Unit
CDD
Consecutive dry days—maximum length of dry spell (RR \ 1 mm)
days
CWD
Consecutive wet days—maximum length of wet spell (RR C 1 mm)
days
R10
Heavy precipitation days—number of days with RR C10 mm
days
R20
Very heavy precipitation days—number of days with RR C20 mm
days
R25
Extremely heavy precipitation days—number of days with RR C25 mm
days
RX1D
Highest 1-day precipitation amount—maximum 1 day precipitation
mm
Methodology
RX5D
Highest 5-day precipitation amount—maximum 5 consecutive days precipitation
mm
Precipitation indices
R90p
Precipitation due to wet days ([90th percentile of wet days, based on 1961–1990 period)
mm
A set of descriptive indices of climate extremes have been defined by the joint CCl/WCRP-CLIVAR/JCOMM Expert Team Climate Change Detection and Indices (ETCCDI) (http://cccma.seos.uvic.ca/ETCCDI, Peterson et al. 2001) and these indices were used by several authors in the last decade in works dealing with changes in precipitation climate extremes (e.g. Frich et al. 2002; Klein Tank et al. 2002; Klein Tank and Ko¨nnen 2003; Klein Tank et al. 2006; Alexander et al. 2006; Moberg et al. 2006). In this work, a total of 12 selected precipitation-related indices, derived from daily point precipitation data, were computed at the seasonal scale and explored for changes in the intensity, frequency and duration of precipitation extremes in mainland Portugal. These indices are described in Table 2. The reference period considered in this work is the climatological normal period 1961–1990. The seasonal analysis is justified by the marked intra-annual variability of precipitation in mainland Portugal that was referred to in ‘‘Introduction’’ section. The seasons investigated are defined as follows: spring (March through May, MAM), summer (June through August, JJA), autumn (September through November, SON) and winter (December through February, DJF).
R95p
Precipitation due to very wet days ([95th percentile)
mm
R99p
Precipitation due to extremely wet days ([99th percentile)
mm
SDII
Simple daily intensity index—mean precipitation amount on a wet day (RR [ 1 mm)
mm day-1
PrecTot
Total wet-day precipitation ([1 mm)
mm
The combination of the test results and metadata (when available) was used to select the best subset of station series for trend analysis. In the end, given the data scarcity before 1941, a high percentage of missing values after 2007 and the selection procedure, only during the period 1941–2007 was station density considered adequate for performing trend analysis of precipitation over mainland Portugal.
Trend estimation Trend analyses of precipitation indices were carried out for the entire 67-year record period of 1941–2007. Linear trends were calculated by ordinary least squares (OLS) while trend significance was tested using Student’s t test. There are various robust nonparametric trend estimators that would be equally suitable, but we decided to use this methodology. It has been used in many studies (e.g. Klein
Tank et al. 2002; Klein Tank and Ko¨nnen 2003; Klein Tank et al. 2006; Goodess and Jones 2002; Moberg et al. 2006; Rodrigo and Trigo 2007) and several other authors have reported that the magnitude of the estimated trends obtained by using the OLS method and a more robust nonparametric method are very similar (e.g. Moberg and Jones 2005). In addition to the precipitation trends calculated for individual stations, trends were also computed for the region as a whole. The regional seasonal precipitation trends were obtained by testing the arithmetic mean of the indices calculated for all individual 57 stations. Correlations To investigate similarities, or differences, between precipitation indices for mean conditions (PrecTot and SDII) and other indices for extremes, we used the linear Pearson correlation coefficient. Separate correlations between the PrecTot and SDII indices and all other indices in Table 2 have been calculated for each station. According to Moberg et al. (2006), this type of analysis might help to identify any station data exhibiting anomalous behaviour, which could result, for example, from a few individual highly erroneous daily values in the data that could undermine the statistics for the entire series. So we have used the analysis of
123
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correlations between indices as an additional tool to examine the data closely. The significance of the Pearson’s r was assessed using a standard one-tailed Student’s t test. The statistical significance of the correlations was considered at the 5 % level. Modes of low-frequency variability The North Atlantic Oscillation (NAO) is one of the most important teleconnection patterns in all seasons (e.g. Wallace and Gutzler 1981; Barnston and Livezey 1987). Strong positive phases of the NAO are associated with below-average precipitation over southern and central Europe (e.g. Trigo et al. 2002; Vicente-Serrano and Lo´pezMoreno 2008; Hurrell et al. 2003). Additionally, there is a clear relationship between mainland Portugal precipitation variability and the NAO, and this has already been reported in several previous studies (e.g. van Loon and Rogers 1978; Zorita et al. 1992; Corte-Real et al. 1995; Hurrell 1995; Jones et al. 1997; Rodrı´guez-Puebla et al. 1998; Trigo et al. 2002). According to those results, positive (negative) values of NAO indices are related to below (above) average total precipitation over Portugal. From the early 1940s to the early 1970s, the NAO index exhibited a downward trend, and since 1980 the NAO has been in a highly positive phase (e.g. Hurrell and van Loon 1997).
The second important mode of low-frequency variability over the North Atlantic is the East Atlantic (EA). This mode pattern is structurally similar to the NAO although shifted meridionally, and consists of a north–south dipole of anomaly centres spanning the North Atlantic from east to west (e.g. Barnston and Livezey 1987). The positive phase of the EA pattern is associated with below-average precipitation across southern Europe (e.g. Wibig 1999; Trigo et al. 2008). The third prominent teleconnection pattern is the East Atlantic/West Russia that consists of four main anomaly centres. The positive phase of the EA/WR pattern reflects below-average precipitation across central Europe (e.g. Krichak et al. 2002; Krichak and Alpert 2005). The Scandinavia pattern (SCA) is the fourth mode of low variability considered here and consists of a primary circulation centre over Scandinavia. The positive phase of the Scandinavia pattern is associated with above-average precipitation across central and southern Europe (e.g. Wibig 1999; Trigo et al. 2008). For this work, the indices relative to the East Atlantic (EA), East Atlantic/Western Russia (EA/WR) and Scandinavia (SCA) patterns were obtained from the Climate Prediction Center (CPC) of the National Oceanic and Atmospheric Administration (NOAA), for the years 1951–2007 (http://www.cpc.noaa.gov/data/teledoc/telecon tents.shtml). These modes of low-frequency variability
Table 3 Number of precipitation stations with positive/wetting (?) and negative/drying (-) trends and the corresponding number of statistically significant (p B 0.05) trends for seasonal precipitation indices over mainland Portugal (57 stations) in the period 1941–2007 CDD
CWD
R10
R20
R25
RX1D
RX5D
R90p
R95p
R99p
SDII
PrecTot
Spring Sig?
9
0
0
0
0
0
0
0
0
0
0
0
Sig-
0
13
39
34
26
20
22
34
23
9
23
43
51 6
7 50
0 57
0 57
1 56
3 54
1 56
3 54
3 54
13 44
3 54
0 57
Sig?
0
0
1
0
0
0
0
0
0
0
0
0
Sig-
10
4
0
1
1
2
1
1
2
0
4
0
?
6
23
19
22
23
17
13
30
27
22
13
22
-
51
34
38
35
34
40
44
27
30
35
44
35
Sig?
0
8
7
10
9
8
5
13
10
5
7
17
Sig-
4
0
0
0
0
0
0
0
0
0
2
0
?
5
53
56
54
51
40
48
52
48
35
35
57
-
52
4
1
3
6
17
9
5
9
22
22
0
Sig?
0
0
0
0
0
0
0
0
0
0
0
0
Sig-
1
7
0
3
4
8
12
3
3
1
16
1
27 30
9 48
6 51
13 44
14 43
13 44
3 54
16 41
17 40
19 38
9 48
9 48
? Summer
Autumn
Winter
? -
The exception is the CDD index: positive/negative trends indicate drying/wetting
123
Seasonal changes in daily precipitation extremes in mainland Portugal
indices were computed from the 500-hPa geopotential height field for the entire Northern Hemisphere (20–90°N), using rotated principal component analysis (PCA) (e.g. Barnston and Livezey 1987). More details are given in ‘‘Regional average of the modes of low-frequency variability correlations’’ section. The NAO index for the period 1941–2007 was retrieved from http://www.cru.uea.ac.uk/cru/data/vinther/nao1821. txt, with Tim Osborn’s NAO Update (after 1999), http:// www.cru.uea.ac.uk/*timo/datapages/naoi.htm. To evaluate the impact of the large-scale modes of atmospheric circulation on seasonal precipitation extremes, we computed the Pearson correlation coefficient between the seasonal indices for each atmospheric circulation mode and the corresponding seasonal precipitation indices (see ‘‘Large-scale influence on seasonal precipitation extremes’’ section).
Results Observed changes in seasonal precipitation indices Overview The results of the seasonal trend analyses for the period 1941–2007 are summarized in Tables 3, 4, 5. Table 3 presents the number of stations that showed positive/negative seasonal trends in the selected daily precipitation indices (defined in Table 2) and the corresponding numbers
1771
of statistically significant trends (p B 0.05). The percentage of positive and negative significant trends and nonsignificant trends are given in Table 4. Table 5 gives the seasonal trends for the regional precipitation indices, and their corresponding 95 % confidence intervals and statistical significance. The most significant results are obtained for spring and autumn. In particular, total wet-day precipitation (PrecTot index) has decreased significantly in spring (Tables 3, 4, 5). The overview of trends in the 12 precipitation indices for spring and autumn is given in Fig. 2 for the period 1941–2007; the plots show the fractions (in percentage) of the 57 individual stations that have positive and negative significant trends at the 5 % level, and non-significant trends. A simultaneous analysis of Table 4 and Fig. 2 shows that between 16 and 75 % of the stations have statistically significant (p B 0.05) drying trends in spring, expressed by all the indices, and that spring indices do not reveal significant wetting trends at any station. Overall, in summer, autumn and winter, more than 75 % of the stations have statistically non-significant trends. In summer, between 2 and 18 % of the stations reveal statistically significant wetting trends that are found, respectively, in indices R10 and CDD; in autumn, between 7 and 30 % of the stations have significant wetting trends and only a few stations (3.5 %) reveal significant drying trends (found in index SDII); in winter, between 12 and 28 % of the stations have significant drying trends in the four
Table 4 Percentages of the 57 precipitation stations that have statistically significant (p B 0.05) and non-significant trends for seasonal precipitation indices over mainland Portugal (period 1941–2007) CDD
CWD
R10
R20
R25
RX1D
RX5D
R90p
R95p
R99p
SDII
PrecTot
-
15.8
22.8
68.4
59.6
47.4
36.8
38.6
59.6
40.4
15.8
40.4
75.4
?
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
n.s
84.2
77.2
31.6
40.4
52.6
63.2
61.4
40.4
59.6
84.2
59.6
24.6
-
0.0
7.0
0.0
1.8
1.8
3.5
1.8
1.8
3.5
0.0
7.0
0.0
?
17.5
0.0
1.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
n.s
82.5
93.0
98.2
98.2
98.2
96.5
98.2
98.2
96.5
100.0
93.0
100.0
Spring
Summer
Autumn -
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.5
0.0
?
7.0
14.0
12.3
17.5
17.5
14.0
8.8
22.8
17.5
8.8
12.3
24.6
n.s
93.0
86.0
87.7
82.5
82.5
86.0
91.2
77.2
82.5
91.2
84.2
75.4
Winter -
0.0
2.3
1.8
5.3
7.0
14.0
21.1
5.3
5.3
1.8
28.1
1.8
?
1.8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
n.s
98.2
87.7
98.2
94.7
93.0
86.0
78.9
94.7
94.7
98.2
71.9
98.2
Positive (?) indicates significant wetting trends and negative (-) indicates significant drying trends; it is the opposite for the CDD index
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Table 5 Trends per decade (with 95 % confidence intervals in brackets) for seasonal regional indices of precipitation (significance levels 5 % bold; 25 % italics), in the period 1941–2007 Indices
Unit
CDD
Days
Spring 0.62 (-0.23 to -1.48)
Summer
Autumn
-1.04 (-2.42 to -0.33)
-0.58 (-1.64 to -0.47)
Winter 0.01 (-1.1 to -1.14)
CWD
Days
-0.19 (-0.44 to -0.06)
-0.01 (-0.15 to -0.12)
0.22 (-0.06 to -0.50)
-0.25 (-0.62 to -0.13)
R10
Days
-0.54 (-0.87 to -0.22)
-0.04 (-0.16 to -0.09)
0.34 (-0.09 to -0.77)
-0.25 (-0.98 to -0.48)
R20
Days
-0.26 (-0.41 to -0.12)
-0.01 (-0.06 to -0.04)
0.18 (-0.03 to -0.39)
-0.15 (-0.51 to -0.22)
R25
Days
-0.17 (-0.27 to -0.07)
-0.01 (-0.04 - 0.02)
0.13 (-0.02 - 0.29)
-0.08 (-0.34 - 0.17)
RX1D
mm
-1.23 (-2.05 to -0.40)
-0.35 (-1.12 to -0.43)
0.83 (-0.43 to -2.08)
-0.71 (-1.93 to -0.51)
RX5D R90p
mm mm
-3.13 (-5.77 to -0.49) -7.96 (-12.68 to -3.23)
-0.67 (-2.45 to -1.11) -0.42 (-2.09 to -1.25)
1.80 (-1.39 to -4.98) 5.13 (-0.95 to -11.22)
-2.92 (-6.56 to -0.72) -3.68 (-13.59 to -6.23)
R95p
mm
-5.16 (-8.71 to -1.62)
-0.30 (-1.48 to -0.89)
3.66 (-0.73 to -8.06)
-2.59 (-9.22 to -4.05)
R99p
mm
-1.60 (-3.36 to -0.17)
-0.12 (-0.6 to -0.36)
0.91 (-0.85 to -2.68)
-0.87 (-3.58 to -1.84)
SDII
mm/day
-0.31 (-0.47 to -0.14)
-0.19 (-0.43 to -0.05)
0.05 (-0.17 to -0.26)
-0.24 (-0.49 to -0.01)
PrecTot
mm
-13.51 (-22.44 to -4.59)
-0.94 (-4.35 to -2.47)
10.14 (-1.99 to -22.28)
-6.65 (-26.72 to -13.43)
together with the 95 % confidence intervals. The average trends in the spring indices R10, R20, R25, RX1D, RX5D, R90p, R95p, SDII and PrecTot have confidence intervals that do not cross the zero level, which strengthens the significance of the results; the largest average decreasing trends are found for indices R95p (-5.2 mm decade-1), R90p (-8.0 mm decade-1) and PrecTot (-13.5 mm decade-1). The wetting trends for autumn, however, are not statistically significant. Some of the results obtained are discussed in more detail in the next sections. Total precipitation and intensity
Fig. 2 Overview of trends in the 12 precipitation indices for the period 1941–2007. The percentages of the 57 stations correspond to stations that have statistically significant (at the 5 % level) and non-significant trends, for (a) spring and (b) autumn. Red indicates significant drying trends; blue significant wetting trends and black non-significant trends. The red/blue code for the CDD index is reversed. The indices on the horizontal axes are defined in the main text and Table 2; those that belong to the same class and are computed similarly (e.g. R10, R20, R25) are connected with a line (color figure online)
indices RX1D, RX5D, CWD and SDII (extreme, duration and intensity indices). An overview of the spring and autumn average trends in regional indices of precipitation is illustrated in Fig. 3,
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An overview of the seasonal trends for the period 1941–2007 in mainland Portugal is given in Figs. 4, 5 and 6, respectively, for the PrecTot, SDII and CDD indices. In general, drying trends can be seen in spring and wetting trends in autumn. Moreover, quite a number of stations reveal a significant reduction in the number of consecutive dry days in summer, while other stations reveal a decrease in the daily precipitation intensity in winter. Table 3 and Fig. 4 show that all the seasons except autumn exhibit a reduction in the PrecTot index in the period 1941–2007. In spring, this result is statistically significant: all individual stations show drying (i.e. negative) trends, which are significant for more than 75 % of the stations; the decreasing trends range from -25 to -3 mm decade-1. In autumn, all individual stations show positive trends, but there are significant trends in only 30 % of the stations; the increasing trends vary between 1 mm decade-1 and 24 mm decade-1. The simple daily intensity index (SDII) in Fig. 5 also shows a predominance of negative trends in the majority of the seasons for the 1941–2007 period. The exception is autumn, with this index revealing a very weak positive
Seasonal changes in daily precipitation extremes in mainland Portugal
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Fig. 3 Overview of trends in precipitation indices over the 1941–2007 period: averages over the 57 stations (grey dots), with the respective 95 % confidence intervals (grey bars), for spring (left) and autumn (right). The indices on the horizontal axes are defined in the main text and Table 2
trend. In spring, the negative trend in index SDII is observed for almost all the stations and is statistically significant in 40 % of them; in winter, about 30 % of the stations reveal significant trends. The maximum number of consecutive wet and dry days (CWD and CDD indices) has opposite trend signals in summer and winter. The decrease in CDD shown in Fig. 6, in summer, is particularly notable. We highlight that in Fig. 6, the colour code (i.e. blue and yellow) is reversed in relation to Figs. 4 and 5 for wetting and drying trends. The results show that in the spring there are more stations (23 %) with significant decreasing trends in CWD and fewer (16 %) with significant increasing trends in CDD. The different signal trends that are observed for indices CDD and CWD in autumn means that these duration indices reveal wetting trends: more than 90 % of the stations reveal non-significant decreasing trends for CDD and non-significant increasing trends for CWD. With respect to the change observed across mainland Portugal (Fig. 6), the CDD index shows an increasing trend in spring (fewer than 2 days decade-1) in the centre and southern regions and, in summer, a decreasing trend in the north and centre regions of up to -3 days decade-1. In general, the decrease in the CDD index is greater than the increase, describing a tendency towards shorter dry periods. The inter-annual variability of the spring and autumn regional averages of the total wet-day precipitation and
intensity indices is presented in Fig. 7 for the period 1941–2007. This figure shows that the highest values of the SDII index are not always associated with the highest values of the PrecTot index. Notably, in 2000 and 2001, the total wet-day precipitation in spring reached high values that had not been recorded since the late 1960s. In the recorded period, the driest spring years were 1995 and 1982, while the summers of 1988 and 1997 were the rainiest. The autumn of 1997 was the wettest on record (associated with the highest value of SDII) and five of the ten wettest autumns have occurred after 1990. Precipitation extremes Table 3 shows that in 1941–2007 the RX1D and RX5D indices reveal a decreasing trend in spring, significant for more than 35 % of the stations; in autumn, more than 70 % of the stations reveal positive trends in these extreme precipitation indices. These drying/wetting trends are accompanied by a corresponding fall/rise in the number of heavy, very heavy and extremely heavy rainfall days (respectively, indices R10, R20 and R25), for more than 90 % of the stations. Figure 8 illustrates the spatial pattern trend in indices R10, RX1D and R90p; the data are for spring and autumn, in the period 1941–2007. The seasonal indices R10, R20 and R25 for the individual stations show the same trend behaviour in each
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Fig. 4 Trends per decade in PrecTot seasonal index, for the 1941–2007 period. The dots are scaled according to the magnitude of the trend: blue for increasing (wetting) trends and yellow for decreasing (drying) trends (color figure online)
season (Tables 3, 4). A decreasing trend in these indices is observed in spring and is significant in 68 % (R10) to 46 % (R25) of the stations. Positive trends are observed for the majority of the stations in autumn, but are statistically significant in only 12 % (R10) to 8 % (R25) of them. Many of the observed significant increases in indices R10, R20 and R25 are linked with stations located in areas of low altitude, mainly in the south (Fig. 8). In spring, summer and winter, the R90p, R95p and R99p indices, which are related to the contribution of wet to extremely wet days (i.e. exceeding the long-term 90, 95 and
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99th percentiles of daily precipitation) to seasonal precipitation, show a decreasing trend for the majority of the stations. However, the most noteworthy decrease, statistically significant at the 5 % level, occurs in spring; for example, the decreasing trend in the spring R90p index is significant in 60 % of the stations. The increasing trend found in the autumn R90p index is significant in 23 % of the stations, and the stations with a statistically significant positive trend are mainly in the southern region of Portugal. The inter-annual variability (and associated trends) in the regional averages of seasonal indices R10, RX1D and
Seasonal changes in daily precipitation extremes in mainland Portugal
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Fig. 5 Trends per decade in SDII seasonal index, for the 1941–2007 period. The dots are scaled according to the magnitude of the trend: blue for increasing (wetting) trends and yellow for decreasing (drying) trends (color figure online)
R90p are shown in Fig. 9, for spring and autumn (1941–2007); this variability is large and these indices span a considerable range of values. Regional averages of the spring indices show a negative statistically significant trend in the three selected indices in Fig. 9. The lowest spring values were in the drought year of 2005 for all indices except the RX1D index: its lowest value is for 1977. Results for autumn indices, however, have positive trends that are only statistically significant at the 25 % level. The highest values of autumn indices are found in
1997 for RX1D and R90p. But there are several other occurrences of the highest values of the extreme autumn precipitation indices in the last three decades of the 67-year period covered by the dataset. Correlations between seasonal precipitation indices At the seasonal scale, the linear correlation (‘‘Correlations’’ section) between indices associated with the average precipitation behaviour (PrecTot and SDII) and eight extreme precipitation indices derived from daily data were
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Fig. 6 Trends per decade in CDD seasonal index, for the 1941–2007 period. The dots are scaled according to the magnitude of the trend: blue for decreasing (wetting) trends and yellow for increasing (drying) trends (color figure online)
investigated. These eight indices comprise three threshold indices (R10, R20 and R25), two absolute indices (RX1D and RX5D) and three percentile-based indices (R90p, R95p and R99p). For each of the 57 precipitation stations, the Pearson correlation coefficients were calculated and the statistical significance was assessed at the 5 % level. The results for all the seasons (1941–2007) are shown in Fig. 10; PrecTot on the left and SDII on the right. The PrecTot and SDII indices and the extreme precipitation indices are positively correlated for all the seasons’ and
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stations’ data. The spread in the results occurs in all the cases and is also represented in Fig. 10. The correlation values between PrecTot and SDII and the other indices are all statistically significant at the 5 % level, except for the summer R99p index. This index does not show significant correlations with PrecTot for 4 stations or with SDII for 8 stations. These stations are mainly located in southern Portugal’s Alentejo region, where precipitation is scarce in summer anyway. On average, it is also notable for all the seasons (Fig. 10) that the PrecTot’s correlations are stronger for the
Seasonal changes in daily precipitation extremes in mainland Portugal
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Fig. 7 Inter-annual variability of regional averages of PrecTot and SDII seasonal precipitation indices, for spring (left) and autumn (right). Grey lines represent the linear trends for the period 1941–2007 (significance levels: 5 % bold; 25 % italics)
number of heavy and very heavy precipitation days (R10 and R20) and weaker when we focus on the tail of the precipitation distribution (in particular, for the precipitation amount on extremely wet days, R99p), while the spread tends to increase. The average correlation coefficients between the PrecTot and the other indices (except percentile-based indices) are between 0.95 (for R10, in winter) and 0.65 (for R25, in summer). For index R10, the results fall within the interval 0.89–0.95. For summer, the spread for the R25 index is larger than for the other seasons and threshold indices (Fig. 10). The correlation coefficients obtained between PrecTot and the RX1D and RX5D indices range, on average, between 0.65 and 0.80, with the highest values being observed in summer, for the two latter indices. The spread of correlation values among stations is larger for RX1D and this index shows weak correlations (less than 0.5) with PrecTot in autumn and winter, for a few stations. The correlations between PrecTot and the percentilebased indices R90p and R95p are, on average, quite strong: correlation coefficients are between 0.7 and 0.9, for all the seasons. For R99p, the correlation tends to drop and the spread of correlation among the 57 stations grows more than for the other two percentile indices. The weak correlations are certainly related to the character of mainland Portugal’s climate, where the seasonal lowest precipitation occurs during summer, corresponding to only 6 % of the annual precipitation and with a small number of wet days.
As a consequence, the R99p index tends to be zero in many years while the PrecTot index varies from year to year, with large inter-annual variability. On average, for all the seasons, the correlations between SDII and the extreme precipitation indices are in general weaker and exhibit a larger spread among stations than do the results discussed for the PrecTot index. The average correlation coefficients between index SDII and the number of heavy, very heavy and extremely heavy precipitation days (R10, R20 and R25, respectively) range between 0.59 (R10, in summer) and 0.78 (R20, in winter). On average, for autumn and winter, the SDII correlations increase with the change from heavy to extremely heavy precipitation days, which contrasts with the results obtained for the PrecTot index. The correlation coefficients between the SDII index and the RX1D and RX5D indices range, on average, between 0.68 and 0.81, with the highest values being found in summer for RX1D, and in winter for RX5D. The spread is larger for RX1D (except in summer). Strong correlations (r [ 0.8) are found between the SDII and RX1D indices for 60 % of the stations in summer, and between SDII and RX5D for 42 % of the stations in winter. Also for the SDII index, the correlation coefficients between this index and the percentile indices R90p and R95p vary, on average, between 0.64 and 0.81 for all the seasons. For all 57 stations, the correlation coefficients between the SDII index and the R90p and R95p indices
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1778 Fig. 8 Trends per decade in spring (left) and autumn (right) for R10, RX1D and R90p indices, for the 1941–2007 period. The dots are scaled according to the magnitude of the trend: blue for increasing trends and yellow for decreasing trends (color figure online)
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Seasonal changes in daily precipitation extremes in mainland Portugal
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Fig. 9 Inter-annual variability of regional averages of seasonal precipitation indices R10, RX1D and R90p, for spring (left) and autumn (right). Grey lines represent the linear trends for the 1941–2007 period (significance levels: 5 % bold; 25 % italics)
fall in the interval 0.45–0.89, in all seasons. Only in summer and for a few stations does the R95p index show weak correlations (r \ 0.5) with the SDII index. The R90p index shows strong correlations (r [ 0.8) with the SDII index for 40 % of the stations in autumn and 65 % in winter. Like the correlations between PrecTot and the indices for precipitation extremes, the correlations between SDII and those indices decrease the nearer we get to the tail of the precipitation distribution, in particular for precipitation on extremely wet days (R99p). Among the stations, the spread of correlation coefficients between the SDII and R99p indices is notably larger than for the other two percentile indices (R90p and R95p). All correlation coefficients between indices SDII and R99p are smaller than 0.8 for all the seasons, and between 23 % (for spring) and 53 % (for summer) of the stations show correlation coefficients smaller than 0.5. Finally, it should
be stressed that all the eight stations that are associated with the weakest correlations (r \ 0.2) between these indices in summer (Fig. 10) are located in the Alentejo region (southern Portugal). Large-scale influence on seasonal precipitation extremes Regional average of the modes of low-frequency variability correlations The association between large-scale modes of low-frequency variability (Modes of low frequency variability) and seasonal precipitation extremes was investigated by means of a correlation analysis between the regional seasonal average of the precipitation indices and four modes of low-frequency variability (NAO, EA, EA/WR and SCA)
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F. E. Santo et al. SDII
WINTER
AUTUMN
SUMMER
SPRING
PrecTot
Fig. 10 Correlations between seasonal extreme precipitation indices and two indices for average conditions: correlations with PrecTot (left) and SDII (right). Black dots show the average correlation for all
stations and the grey dots indicate correlations for the individual stations in Fig. 1. The labels on the horizontal axes are defined in the text and in Table 2
for the period 1951–2007 (57 years). Although the EA, EA/WR and SCA indices are only available from 1951, for NAO it was possible to conduct this analysis for the entire 1941–2007 period using the index from http://www.cru. uea.ac.uk/cru/data/vinther/nao1821.txt (see Sect. Modes of low frequency variability). This NAO index is used from this point onwards. Because the correlation between the two NAO indices (i.e. from different sources) is higher
than 0.9 for the period 1951–2007 (significant at the 1 % level), we proceeded by comparing the role played by the four modes of low-frequency variability: NAO, EA, EA/ WR and SCA. Thus, the 67-year correlations between the NAO index and the regional seasonal average of the precipitation indices, and the 57-year correlations between the three other modes EA, EA/WR and SCA and the same regional indices were studied (Fig. 11).
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Seasonal changes in daily precipitation extremes in mainland Portugal
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NAO
EA
EA/WR
SCA
Fig. 11 Mean correlations between the teleconnections patterns (NAO, EA, EA/WR and SCA) and 12 precipitation indices for all seasons. For NAO the period is 1941–2007 and for EA, EA/WR and
SCA is 1951–2007. The horizontal dashed lines indicate the 5 % significance level. The precipitation indices on the horizontal axes are defined in Table 2
There is a significantly high anti-correlation in winter between precipitation indices and NAO, with coefficient correlations varying between -0.4 and -0.6. In autumn, the correlations are lower than in winter, but they are still significant, except for SDII and R99p. For these seasons, the highest correlation values can be found between the PrecTot and R10 indices. Correlations are also negative in spring and summer, but they are weaker and usually not statistically significant, except for the PrecTot and R10 indices. The CDD index is strongly related to the PrecTot index, with very similar correlation patterns, but reversed. Regarding the other modes of low-frequency variability, EA, EA/WR and SCA, their impact on the precipitation indices is considerably weaker than that of the NAO (Fig. 11). The highest correlation values (statistically significant at the 5 % level) were found for the NAO index. Thus, as the NAO is the mode of low-frequency variability that has, on average, the greatest influence on precipitation extremes over mainland Portugal, particularly in winter and autumn, the analysis in the next section will focus only on the relationship between the NAO and the extreme precipitation indices for all individual stations, at a seasonal scale.
Correlation between the NAO and seasonal precipitation indices The spatial distribution of the NAO’s impact on the PrecTot, CDD and R95 precipitation indices calculated for individual stations’ data can be found in Fig. 12 (PrecTot), 13 (CDD) and 14 (R95). There is a highly significant anti-correlation in winter between the PrecTot index and the NAO index (Fig. 12): the correlation for the individual stations varies between -0.38 and -0.71, with correlation coefficients below -0.50 in 82 % of the stations. It is worth pointing out that winter precipitation contributes a great deal to the annual total precipitation. The results for autumn are very similar to those found for winter but the correlations are less strong. In summer, most of the negative correlations are not statistically significant, yet in the southern part of Portugal some results are statistically significant. Meanwhile, the fairly large number of negative (but statistically significant) correlations in spring is found especially in the northern regions of Portugal. Positive correlations between the CDD index and the NAO index are found in all the stations, for all the seasons (Fig. 13). In this case, the effect of the NAO is most prominent in winter and spring, when more than 90 % of
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Fig. 12 Correlation between the NAO index and PrecTot index for spring (MAM), summer (JJA), autumn (SON) and winter (DJF), in the 1941–2007 period. Red/blue dots indicate that positive/ negative correlations are significant at the 5 % level; all dots are blue (color figure online)
the locations show statistically significant correlations. In summer and autumn, the behaviour is very similar, with statistically significant correlations being found in the southern part of Portugal and few in the north. Finally, the R95p index is analysed (Fig. 14). In general, no statistically significant correlations between this index and the NAO are found in spring and summer, while in autumn some scattered weather stations show significant correlations. Negative statistically significant correlations are found in all locations in winter.
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Discussion Seasonal changes in precipitation extremes In general, trends observed for seasonal precipitation (especially for summer; autumn and winter) are statistically non-significant and only spring precipitation exhibits significant trends. For example, the daily precipitation intensity shows decreasing tendencies in winter, spring and summer, and increasing tendencies in autumn. On a
Seasonal changes in daily precipitation extremes in mainland Portugal
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Fig. 13 Correlation between the NAO index and CDD index for spring (MAM), summer (JJA), autumn (SON) and winter (DJF), in the 1941–2007 period. Red/blue dots indicate that positive/negative correlations are significant at the 5 % level; all dots are red (color figure online)
regional scale, we also find a reduction in the number of consecutive dry days in summer and autumn. For CDD, this is observed for a large number of stations that reveal significant downward trend, particularly in summer; in autumn, the downward trend is accompanied by an increasing tendency in the number of consecutive wet days. In general, we also found a tendency towards higher precipitation extremes in autumn, revealed by indicators of intensifying precipitation, such as RX5D, R90p and R95p; this is particularly valid in the last three decades, in the
centre and southern regions of mainland Portugal, which are the most vulnerable ones and therefore the most affected by these changes (e.g. Miranda et al. 2006). Thus, overall, these results are consistent with previous studies that suggest the absence of long-term statistically significant precipitation trends in mainland Portugal (e.g. de Lima et al. 2007, 2010), while simultaneously reporting a statistically significant decline in spring precipitation. The significant decreasing trend in spring total wet-day precipitation was first described for mainland Portugal in the early
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Fig. 14 Correlation between the NAO index and R95p index for spring (MAM), summer (JJA), autumn (SON) and winter (DJF), in the 1941–2007 period. Red/blue dots indicate that positive/negative correlations are significant at the 5 % level (color figure online)
1990s by Mendes and Coelho (1993). It was then confirmed by several authors using different methodological approaches (e.g. Corte-Real et al. 1998; Trigo and DaCamara 2000; Miranda et al. 2002; Paredes et al. 2006; de Lima et al. 2007, 2010). This significant negative trend was found to be associated with a decrease in westerly circulation weather types after 1960, particularly during March (e.g. Zhang et al. 1997; Trigo and DaCamara 2000). Interestingly, the increasing trend in precipitation observed in
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autumn has been much less noticed, although already reported for monthly precipitation in mainland Portugal (e.g. de Lima et al. 2007, 2010). At the same time, multiple episodes of very heavy rain that have caused severe episodes of flooding and landslides have occurred during the autumn months, roughly in the last 40 years (e.g. Zeˆzere et al. 2008). It should be noted that the comparison between different studies is sometimes limited by the different time scales and periods investigated.
Seasonal changes in daily precipitation extremes in mainland Portugal
Regional influence of modes of low-frequency variability As mentioned before, frontal storms dominate the occurrence of precipitation in mainland Portugal, especially in winter and late autumn, while convective storms are more frequent in summer and spring (e.g. de Lima et al. 2002; Ramos et al. 2011). Frontal systems and extra-tropical cyclones’ storm tracks are also affected by the NAO phase. For instance, positive values of the NAO index during winter are often associated with a northward shift in the Atlantic storm activity, with enhanced activity from southern Greenland across Iceland into northern Europe and a modest decrease in activity to the south, which cause drier conditions over southern Europe (e.g. Trigo et al. 2008). For these reasons, the effect of the NAO on the PrectTot index is more prominent in the winter, but it is still discernible in autumn and early spring. Contrasting with the observations for the CDD and PrecTot indices, the effects of the NAO on extreme precipitation are only conspicuous in the autumn and winter months, when the frontal storms and extra-tropical cyclones’ related precipitation dominates (which has been explained above). In the other seasons (especially spring and summer), the total precipitation in the region has a strong component of convective precipitation. In spring (April and May), the combination of a heat source in inland Portugal (as the result of the amplified daily heating cycle) and the presence of relatively frequent cold air masses at high altitude is very important to the formation of afternoon spring thunderstorms. In summer (June through September), the development of afternoon thunderstorms in inland areas is the result of daily heating in association with a thermal low over Iberia that induces a cyclonic circulation over Portugal (e.g. Ramos et al. 2011). These local features are less influenced by the NAO pattern, and therefore, the impact of the NAO on extreme precipitation in these months is less, as shown by the R95p index. Thus, it was shown that the North Atlantic Oscillation has a strong influence on the precipitation regime in the area, particularly on the extremes, confirming some preliminary results already obtained for the Iberian Peninsula with precipitation time series (e.g. Rodrigo and Trigo 2007). However, it should be noted that the relationship between the NAO and precipitation in Europe cannot be considered fully stable over time, in winter. In a recent study, Vicente-Serrano and Lo´pez-Moreno (2008) emphasized that the non-stationary relationship between the NAO and precipitation is linked to inter-decadal variability in the position of the NAO pressure centre. Moreover, the spatial configuration of the NAO changes substantially prior to the occurrence of clear shifts in the magnitude and spatial distribution of its influence on
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precipitation patterns in Europe. But for mainland Portugal, after 1940, it is not possible to see (Fig. 2 in VicenteSerrano and Lo´pez-Moreno 2008) any particular change in the relationship between the NAO index and precipitation, the correlation being always above -0.5 and significant at the 5 % level.
Conclusions The main purpose of this study was to understand seasonal variations in the intensity, frequency and duration of extreme precipitation events in mainland Portugal. A thorough trend analysis was conducted on seasonal precipitation extremes recorded in mainland Portugal during the period 1941–2007, based on a set of 12 selected precipitation indices calculated from 57 stations’ daily data. These indices were explored for trends at the local and regional levels. The most important findings are: a.
The results for the different precipitation indices that are associated with drying trends in spring, summer and winter, and wetting trends in autumn seem to indicate a tendency for a reduction in the duration of the rainy season. b. At the intra-annual scale, in general, there are spatially coherent regions of both increasing and decreasing seasonal extreme precipitation. For all the seasons’ and stations’ data, total wet-day precipitation and intensity are strongly and positively correlated with the extreme precipitation indices. c. On average, for all the seasons, the correlations between the mean precipitation amount on wet days index (SDII) and the extreme precipitation indices are in general weaker and exhibit a larger spread among stations than do the results discussed for the total wetday precipitation index (PrecTot). Moreover, the correlations obtained between these two indices (SDII and PrecTot) and indices of precipitation extremes decrease when the analysis converges towards the tail of the precipitation distribution, particularly for the precipitation on extremely wet days (R99p). d. The importance of the NAO index for the variability of precipitation in mainland Portugal is confirmed. This index is particularly well anti-correlated in winter and autumn with the precipitation indices (precipitation regime). In spring, statistically significant correlations are found, especially in northern Portugal, while in summer the few cases that show statistically significant correlations are located in the south. In conclusion, the results presented in this study are in agreement with several other findings of changes in
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precipitation in western Iberia (e.g. Jacobeit 2000; Trigo and DaCamara 2000; Miranda et al. 2002, 2006; Paredes et al. 2006; de Lima et al. 2007, 2010), where mainland Portugal is located. But some of those findings were mostly supported by monthly data and a sparse density of stations whereas here we have focused on daily and extreme precipitation, which have been less well explored. Thus, the important precipitation data set used here (i.e. density of stations, length of the records and quality of the data) and the range of precipitation indices examined provided a key contribution to characterizing the precipitation structure and changes in mainland Portugal and in the Iberian Peninsula. Moreover, this study also clarifies the effect on the region’s precipitation regime (particularly the extremes) of different modes of low-frequency variability, where the influence of the North Atlantic Oscillation deserves a special mention. ´ lvaro Silva and Acknowledgments The authors wish to thank A Sofia Cunha (Institute of Meteorology, Portugal) for their help in processing the maps in Figs. 1, 4, 5, 6, 8 and 12, 13, 14. Alexandre M. Ramos was supported by the Portuguese Foundation for Science and Technology (FCT) through grant FCT/DFRH/SFRH/BPD/84328/ 2012. Ricardo Trigo was supported by Project DISASTER—GIS database on hydro-geomorphologic disasters in Portugal: a tool for environmental management and emergency planning (PTDC/CS/ GEO/103231/2008) also funded by FCT. Comments by two anonymous referees are acknowledged.
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