Variabilidad espacio-temporal de los efectos de la NAO en Europa: impactos a distintas escalas temporales, variabilidad interdecadal y escenarios futuros Instituto Pirenaico de Ecología, CSIC, Campus de Aula Dei, P.O. Box 202, Zaragoza 50080, Spain; Vicente-Serrano SM, López-Moreno JI

Homogeneous regions according to the precipitation variability We used a monthly gridded precipitation database at a latitude/longitude resolution of 10’ for the entire European continent, as compiled by the Climate Research Unit of the University of East Anglia ( [Mitchell and Jones, 2005]).

A) B) Drought index calculation in each 10’ pixel at different time scales (1-12 months) A) Region 5: British Islands B) Region 6: Iberian Peninsula

SPI ANOMALIES IN THE BRITISH ISLANDS A) Positive phases B) Negative phases Positive phases Negative phases SPI anomalies according to the positive and negative phases of the NAO SPI ANOMALIES IN THE DIFFERENT REGIONS A) Positive phases B) Negative phases

12-months SPI anomalies in August

Non-stationary relationships. Noticeable differences between the and periods Difference [( )-( )] in the average values of SPI at time scales from 1 to 12 months between December (1) and November (12) during the positive (A) and negative (B) extreme phases of the NAO in the 17 homogeneous regions. Black lines indicate significant differences (  < 0.05) between the average SPI during the positive and negative years between the first and the second half of the twentieth century.

Non-stationary relationships. Noticeable differences between the and periods

Differences in the average anomalies of SLP during the positive (A) and negative (B) extreme phases of the NAO between the and periods. During positive phases changes reinforced flows from the NW, and these flows affected most of Europe. This reinforcement was most pronounced in March. The results indicate the attenuation of the pressure fields that are typically associated with negative phases of the NAO during winter. The decrease in SPI values recorded over large areas of western and central Europe is possibly related to the generally positive SLP differences recorded in this region during negative phases.

CONCLUSIONS: The response of droughts to positive and negative phases of the NAO vary spatially and also depend on the month and decade and time scale of the SPI. In general, during positive phases, negative SPI averages are recorded in Southern Europe and positive averages in Northern Europe (Scandinavia and the British Islands), whereas the opposite trends occur during negative phases. It is necessary to highlight the outstanding influence of positive and negative phases of the wintertime NAO on drought conditions during the succeeding months. In the areas of the Iberian Peninsula, South Italy, and the Balkans, strong negative SPI averages, indicative of severe drought conditions, are recorded in summer and autumn during positive phases when medium and long time scales of SPI (6–12 months) are considered. The same trends are recognized for Sweden, Finland, Lithuania, Latvia, and West Norway as a response to the negative phases of the NAO. The fact that this behavior is found in several regions is indicative of the important impact of positive and negative phases of the wintertime NAO on usable water sources throughout the year. Another important result observed for multiple regions is the fact that the magnitude of anomalies in average SPI values differs between positive and negative phases of the NAO. This indicates the asymmetric response of droughts to the NAO. The effects of positive and negative phases of the NAO on droughts within the European continent at different time scales were not stable during the 20th century. In general, the influence of positive phases of the NAO on droughts is strengthened in the second half of the 20th century compared to the influence in the period 1901– In contrast, the negative phases of the wintertime NAO show a weaker influence on the SPI during the second half of the 20th century. This behavior is related to changes in the SLP patterns associated with positive and negative phases of the NAO during the first and second halves of the 20th century.

Right: spatial distribution of correlations between winter precipitation and the NAO index obtained from observations between 1902 and Left: leading principal component of winter SLPs. T-mode PCA We used a monthly gridded precipitation database at a latitude/longitude resolution of 10’ for the entire European continent, as compiled by the Climate Research Unit of the University of East Anglia ( [Mitchell and Jones, 2005]). The pressure data set was provided by NCEP-NCAR ( with a spatial latitude/ longitude resolution of 5º. The 5º latitude/longitude grids begin in 1899, and cover the North Atlantic region (20ºN–80ºN, 90ºW–40ºE). The few data gaps in the data set were filled by interpolation (splines with tension) using the data of neighboring grid points. 20th century pattern of the NAO and NAO/precipitation relationship

Example of 31-year moving-window correlations between the winter NAO and precipitation for points throughout the European continent. Red lines represent the temporal evolution of NAO-precipitation correlations for selected points in the gridded database (grey in the map). Blue lines represent moving-window correlations for instrumental observatories with complete records for the period (black in the map). Horizontal dotted lines represent the significance threshold at 95%. Observatory data were obtained from the Global Historical Climatology Network ( monthly/). Evolution of moving correlations inform about the markedly unstable response with time of European precipitation to NAO index.

Right: temporal evolution of three different NAO indices. GIBRALTAR shows the NAO index calculated as the difference between the monthly standardized surface pressures at Gibraltar (south-west Iberian Peninsula) and Reykjavik (south-west Iceland). AZORES was obtained from the Ponta Delgada station (the Azores), and the third NAO index corresponds to the principal component (PRINC. COMPONENT) time series of the leading PC of SLP anomalies over the Atlantic sector (20º–80ºN, 90ºW– 40ºE). A–C) Maps of the correlation between the precipitation series and the GIBRALTAR, AZORES, and PRINC. COMPONENT NAO indices, respectively. Units are Pearson coefficients of correlation (r). The correlations were calculated for 31-year periods, of which the central years are indicated in each plot. Dotted lines enclose areas with significant correlations (p < 0.05). Non-stationarity does not depend on the method used to obtain the NAO index. The evolution of NAO indices is rather similar whether they are obtained from pattern-based methods or station/grid point-based approaches.

Right: spatial distribution of correlations between winter precipitation and the NAO index during the 20th century. The year indicated on each map represents the midpoint of each 31-year period. Units are Pearson coefficients of correlation (r). Dotted lines enclose areas with significant correlations (p < 0.05). Left: leading principal components of winter SLPs obtained from T-mode PCA. The leading principal components were obtained using moving-window periods of 31 years, centered on the year indicated in each plot. The percentage of variance explained by the leading mode in each period is also shown. Since the sign of the PC pattern is arbitrary and to make easier the comparison, a common sign of the NAO was selecting for plotted (negative in the North and positive in the South). Although the general pattern of negative (positive) correlations in the south (north) of Europe between winter precipitation and NAO index appears generally well defined for all subperiods, the spatial pattern and magnitude of the correlations varied noticeably over time. The spatial patterns of changes in correlations are in agreement with the shifts in position of the main sea level pressure (SLP) centers that characterized the NAO dipole throughout the twentieth century

Right: spatial distribution of correlations between winter precipitation and the NAO index during the 19th century. The year indicated on each map represents the midpoint of each 31-year period. Units are Pearson coefficients of correlation (r). Dotted lines enclose areas with significant correlations (p < 0.05). Left: leading principal components of winter SLPs obtained from T-mode PCA. The leading principal components were obtained using moving-window periods of 31 years, centered on the year indicated in each plot. The percentage of variance explained by the leading mode in each period is also shown. Since the sign of the PC pattern is arbitrary and to make easier the comparison, a common sign of the NAO was selecting for plotted (negative in the North and positive in the South). Results are based on reconstructions performed by the Climatology and Meteorology Research Group of the University of Bern (Switzerland). Interdecadal shifts in the locations of NAO pressure centers were not only unique for the twentieth century, and they also contribute to most of the changes recorded in the NAO-precipitation relationship since at least the late eighteenth century, analyzed by means of climate reconstructions

1. Right: spatial distribution of correlations between winter precipitation and the NAO index obtained from observations between 1902 and Left: leading principal component of winter SLPs form observations. 2. Right: spatial distribution of correlations between winter precipitation and the NAO index obtained from CGCM3.1(T63) model, Canadian Climate Modeling and Analysis Centre, simulations between 1870 and Left: leading principal component of winter SLPs simulated from the CGCM3.1(T63) model under the 20th century emissions scenario. The percentage of variance explained by the leading modes is also shown. Right: spatial distribution of correlations between winter precipitation and the NAO index from the outputs of the CGCM3.1(T63) model under the 20th century emissions scenario. The year indicated on each map represents the midpoint of each 31-year period. Units are Pearson coefficients of correlation (r). Dotted lines enclose areas with significant correlations (p < 0.05). Left: leading principal components of winter SLPs obtained from T-mode PCA. The leading principal components were obtained using moving-window periods of 31 years, centered on the year indicated in each plot. The percentage of variance explained by the leading mode in each period is also shown. Since the sign of the PC pattern is arbitrary and to make easier the comparison, a common sign of the NAO was selecting for plotted (negative in the North and positive in the South). Similar results were obtained from climate data under a twentieth century emissions scenario (20C3M) modeled using the CGCM3.1(T63) model. Although some differences between the NAO spatial pattern obtained from observations and the model can be observed: mainly the split of the southern center and the southern shift of the eastern part of the southern center; the model records the main features, although more simplified than observations.

A) B) A) Spatial distribution of rotated principal component scores obtained from leading PCs in the SLP moving- window T-mode PCA procedure for 20th century observations. Time series of loadings are also shown for each component to identify the periods for which the NAO pattern is representative. B) As for A), but using climate simulations of the CGCM3.1(T63) model for the IPCC 20th century emissions experiment. The general patterns show that the dominant NAO configuration changes noticeably over decadal timescales. Each of the four patterns appears as the dominant NAO configuration during several time slides. In the four patterns the spatial structure, based on a pressure dipole, is clearly evident and is always associated with the NAO pattern. Similar spatial structure has been found using observations and simulations.

CONCLUSIONS: The nonstationary relationship of the NAO is linked to interdecadal variability in the positions of the NAO pressure centers. The spatial configuration of the NAO changes substantially prior to marked shifts in the magnitude and spatial influences of the NAO on precipitation patterns in Europe. It points to the possible existence of different NAO types related to the location and surface extent of main pressure centers, which lead to very different climatic impacts over the European continent. The reliability of the results obtained is supported by evidence from the consistency of analyses conducted with observed data during the twentieth century, reconstructed precipitation and sea level pressure since 1785, and physically modeled climate by Atmosphere-Ocean Global Climate Models (AOGCMs). Our findings suggest that climate prediction and climate change scenarios based on the NAO must take interdecadal shifts in the position of NAO pressure centers into account.

The simulations of sea level pressure (SLP) for the North Atlantic region (20º–80ºN, 90ºW–40ºE) and precipitation for Europe from the AOGCM CGCM 3.1 (T63) model (single run) provided by the Canadian Centre for Climate Modelling and Analysis does not show changes in the NAO index obtained for the 20th century under the 20C emission scenario, and for the 21st century for the B1 and A2 scenarios. No important changes between the different emission scenarios in the spatial pattern of the NAO dipole and the NAO-precipitation correlations

Analysis of moving window correlations showed marked differences among the four emission scenarios. In the PI and 20C scenarios the spatial pattern of NAO/precipitation correlations changed noticeably among different periods. The 31-year slices for the B1 and A2 emission scenarios also show some changes in the NAO/precipitation correlations but they are more stable than observed for the PI and 20C scenario. This is related to the few differences in the position and magnitude of the NAO pressure centers among different periods. It seems to have some relationship between the percentage of variance explained by the NAO and the magnitude and surface extent of correlations. In general, a smaller variance explained by the leading PC (NAO) transfers to some extent into generally weaker relationship with precipitation and vice versa.

Higher variance values indicate a higher variability of the NAO-precipitation correlations throughout the emission scenario simulation. There were large differences among the four scenarios. Higher variance values were found for the PI and 20C emission scenarios. There was a decrease in variability in the NAO/precipitation relationship in line with increased greenhouse gas concentrations. CONCLUSIONS: The scenarios showed a general decrease in the surface affected with significant positive correlations in northern Europe and an increase in the magnitude of negative correlations in southern Europe, in parallel to increased greenhouse gas concentrations. The differences are in agreement with shifts in the location of the NAO pressure centers. Even more important than the changes in the spatial patterns are the changes expected in the temporal stability of the NAO/precipitation relationship among the four emission scenarios. The PI and 20C scenarios showed large differences in the magnitude and spatial extent of the NAO/precipitation correlations among different periods, as a consequence of shifts in the position of the NAO pressure centers. In addition, the NAO pressure centers appear to be more stable under increased greenhouse gas concentrations.