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Oct 28, 2009 - ABSTRACT: The trends of precipitation over the Iberian Peninsula (IP) and the North Atlantic Oscillation (NAO) index from December to March ...
INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 30: 1807–1815 (2010) Published online 28 October 2009 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.2035

Trends of precipitation over the Iberian Peninsula and the North Atlantic Oscillation under climate change conditions Concepci´on Rodr´ıguez-Pueblaa * and Susana Nietob a b

Department of Atmospheric Physics, University of Salamanca, Spain Department of Applied Mathematics, University of Salamanca, Spain

ABSTRACT: The trends of precipitation over the Iberian Peninsula (IP) and the North Atlantic Oscillation (NAO) index from December to March are compared using observations and model simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3). The evaluation is performed in two multi-models as derived by averaging simulations corresponding to two experiments: one represents climate in the 20th century (20C3M) and the other the scenario with medium forcing IPCC SRES A1B in the 21st century. The NAO index shows a trend to more positive phases and precipitation tends to decrease in the case of observations, with greater significance in the NAO than in precipitation. The simulations in the 20th century underestimate the trend both in the NAO and in precipitation compared to observations. The multi-model in the 21st century indicates a change in the NAO regimes to being more positive; this leads to a reduction of precipitation over the IP. To explain the precipitation trend changes we consider the response of precipitation to the NAO, the regression between sea level pressure (SLP) over the Euro-Atlantic area onto precipitation averaged over the IP and the composite of precipitation for years with greater and lower NAO. Copyright  2009 Royal Meteorological Society KEY WORDS

Iberian Peninsula precipitation; North Atlantic Oscillation; climate models; precipitation trend

Received 12 January 2009; Revised 23 July 2009; Accepted 5 September 2009

1.

Introduction

Regional precipitation change under global warming is the subject of this study that concerns different socioeconomic aspects of our country. The Intergovernmental Panel on Climate Change Four Assessment Report (IPCC AR-4) (Solomon and coauthors, 2007) indicated a precipitation increase in high latitudes and a decrease in the Mediterranean area for a future warmer climate. However, precipitation is highly variable spatially and temporally and there is rather uncertainty in precipitation changes. Therefore, it is of great interest to quantify precipitation trends on the regional scale. Phenomena such as circulation patterns have to be considered in order to understand some of the changes in precipitation, as they are likely to be affected by global warming (Randall and coauthors, 2007). The sea level pressure (SLP) is projected to increase over the subtropics and mid-latitudes and to decrease over high latitudes (Miller et al., 2006). Thus, there is a positive trend in the North Atlantic Oscillation (NAO) under climate change. The NAO is a large-scale circulation pattern, statistically and physically robust, that characterizes northern hemisphere climate variability (Branstator, 2002; Hurrell et al., 2001, * Correspondence to: Concepci´on Rodr´ıguez-Puebla, Departamento de F´ısica de la Atm´osfera, Facultad de Ciencias, Plaza de la Merced s/n, 37008 Salamanca, Spain. E-mail: [email protected] Copyright  2009 Royal Meteorological Society

2003; Wallace, 2000). The NAO is a measure of the strength of the Icelandic Low and the Azores High and it accounts for much of the precipitation variability over the Euro-Atlantic area. The links between precipitation and NAO over the Iberian Peninsula (IP) have been shown in many papers (Hurrell, 1995; Lamb and Peppler, 1987; Rodr´ıguez-Puebla et al., 1998; Qian et al., 2000; Goodess and Jones, 2002; Trigo et al., 2004; Lopez-Moreno and Vicente-Serrano, 2008; L´opez-Bustins et al., 2008). The NAO is related to westerly winds, storm track and jet stream (Hurrell et al., 2003; Nieto and Rodr´ıguez-Puebla, 2006). Therefore, the NAO becomes an important teleconnection pattern to explain the precipitation decreases in the IP, because the NAO is related to shifts in the storm tracks and consequent precipitation. Trends in the observed NAO and Arctic Oscillation or the Northern Annual Mode (NAM) indices have been reported by different authors (Hurrell, 1995; Thompson et al., 2000; Ostermeier and Wallace, 2003). Both teleconnection patterns are two paradigms of the same phenomenon (Wallace, 2000), however concerning this study we focused on the NAO index, which is best associated with surface processes in the Atlantic sector (Huth, 2006). Other studies have investigated the ability of models to simulate the NAO and NAM (Zorita and Gonzalez-Rouco, 2000; Gillett et al., 2003; Osborn, 2004), finding that they are able to reproduce the main

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spatial characteristics, however there is no agreement concerning the representation of the trend and other interdecadal variations. In general, the models reproduce the sign of the NAO trend but the time series are smaller than observed (Solomon et al., 2007). The reasons for the discrepancies have been the objective of many studies based on forcing factors such as greenhouse gases (Schneider et al., 2003; Kuzmina et al., 2005; Stephenson et al., 2006), sea surface temperature (Hurrell et al., 2004), variability in the stratosphere (Scaife et al., 2005; Miller et al., 2006) and the contribution of the Madden Julian Oscillation (MJO) to the NAO variability (Cassou, 2008). Stephenson et al. (2006) found that the precipitation and temperature changes cannot be explained only by corresponding changes in the NAO by using simulations of the coupled models that participated in phase 2. Vicente-Serrano and Lopez-Moreno (2008) found large differences in the temporal stability of the NAO, and also in the NAO-precipitation relationship among the scenarios using one model simulation. The coupled models of phase 3 have improved in resolution and physical parameterizations compared with previous versions and we can provide newer results about the response of precipitation over the IP to the NAO regimes under climate change conditions. Some of the uncertainties in climate projections are the simulation of precipitation and the attribution the long term changes in precipitation. The ability of models to simulate precipitation over the IP was analysed in previous studies (Nieto et al., 2004). Methods to downscale precipitation (Frias et al., 2006) indicated that SLP is a good predictor for the IP precipitation in contrast with Scandinavian precipitation, which needs the contribution of relative humidity. The present study was carried out with the following aims: to compare the precipitation trend using observations, 20th Century Climate in Coupled Models (20C3M) simulations for the period 1950–2000 and the Special Report on Emission Scenarios (SRES) A1B for the period 2001–2099 (Meehl and coauthors, 2007); to explore modifications in the relationships between the NAO and precipitation over the IP under warming conditions. This study is applied to the December–March (DJFM) season because the links between precipitation and atmospheric circulation tend to be the strongest in winter. The idea of this study is based on the results of Stephenson et al. (2006) and Zhang et al. (2007) concerning the anthropogenic forcing effects on observed changes in average precipitation. The paper is organized as follows: Section 2 indicates the data used and the approaches for deriving the NAO using models, as well as the methods applied to identify the trend and to determine the precipitation response to the NAO index; Section 3 compares the trends of precipitation and the NAO using observations and coupled models of 20C3M and the SRES A1B emission scenario, and quantifies the response of precipitation to NAO changes; Section 4 summarizes the major findings. Copyright  2009 Royal Meteorological Society

2.

Data and methods

Precipitation and SLP from the data set of 15 global models of CMIP3 for the 20th century experiment (20CM3) and the IPCC SRES A1B scenario for the 21st century as shown in the IPCC AR-4 were used. The model names, the centres that have drawn up the models and the horizontal and vertical resolutions are listed in Table I; other additional information is available in Randall and coauthors (2007) and on the web page of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) referred to in the acknowledgements section. A description concerning the utility of these models is presented in Meehl et al. (2007). The simulations included external forcing of natural and anthropogenic sources. We selected the A1B scenario because it contains medium forcing of CO2 . Owing to the different spatial resolutions of models and to facilitate the comparison, the model outputs were re-gridded to the same spatial resolution of the data corresponding to the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) re-analysis project (Kalnay et al., 1996). In the case of SLP we re-gridded to 2.5° × 2.5° (Latitude × Longitude), the area of coverage is 20° N to 80° N, 60 ° W to 30 ° E. We re-gridded to the Gaussian grid of approximately 1.9° in the case of precipitation. For comparison, we used precipitation observations corresponding to 55 stations irregularly distributed over the IP for the period 1950–2007 provided by the ‘Agencia estatal de Meteorolog´ıa’ (AEMet) of Spain and by the ‘Instituto de Meteorolog´ıa’ of Portugal; these observed data were re-gridded to approximately 0.5° × 0.5° for the area 36° N to 44° N, 10 ° W to 4 ° E. We used the NAO index provided in the Climate Prediction Center (CPC) of the National Weather Service of the United States following the method by Barnston and Livezey (1987) and the SLP from NCEP-NCAR re-analysis to derive a reference pattern for the NAO. Different procedures can be applied to derive the NAO index, for instance: identification of correlated SLP time series, empirical orthogonal function (EOF) (Wallace and Gutzler, 1981; Bretherton et al., 1992), selection of stations that represent the meridional SLP gradient (Jones et al., 1997), the difference in SLP averaged over two large areas of the Subtropical mid-Atlantic and Southern Europe region and the North Atlantic Northern European region (Kuzmina et al., 2005; Stephenson et al., 2006). We compared the NAO derived by using two approaches, the first one was based on an EOF analysis of the SLP (Hannachi et al., 2007) and the second one consisted of regressing the SLP of model outputs onto a reference NAO pattern. Previously, SLP anomalies were weighted as a function of the cosine of latitude (Wu and Straus, 2004). The reference NAO pattern (Figure 1(c)) was derived by projecting the SLP data from re-analysis onto the NAO index provided by the CPC. The resulting NAO indices of models were normalized to have unit standard deviation. For each model we found that the leading EOF of the SLP was well correlated with the Int. J. Climatol. 30: 1807–1815 (2010)

Copyright  2009 Royal Meteorological Society

UKMO HadGEM1

MRI-CGCM2.3.2 PCM UKMO-HadCM3

GFDL-CM2.1 INGV-SXG INM-CM3.0 IPSL-CM4 MIROC3.2 (medres)

FGOALS-g1.0

ECHAM5/MPI-OM ECHO-G

CSIRO-Mk3.5

BCCR-BCM2.0 CGCM3.1 (T63)

Model names

Bjerknes Centre for Climate Research, Norway Canadian Centre for Climate Modelling and Analysis, Canada Commonwealth Scientific and Industrial Research Organisation, Australia Max Planck Institute for Meteorology, Germany Meteorological Institute of the University of Bonn, Meteorological Research Institute of the Korea, Germany, Korea National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, China NOAA/Geophysical Fluid Dynamics Laboratory, USA Instituto Nazionale di Geofisica e Vulcanologia, Italy Institute for Numerical Mathematics, Russia Institut Pierre Simon Laplace, France Center for Climate System Research (University of Tokyo, Japan) Meteorological Research Institute, Japan National Center for Atmospheric Research, USA Hadley Centre for Climate Prediction and Research/Met. Office, UK Hadley Centre for Climate Prediction and Research/Met. Office, UK

Centre, Country

−0.60 ± 0.03 −0.70 ± 0.04 −0.70 ± 0.04 −0.60 ± 0.02 −0.76 ± 0.03 −0.70 ± 0.03 −0.58 ± 0.04 −0.78 ± 0.04 −0.74 ± 0.03 −0.85 ± 0.03 −0.75 ± 0.04 −0.67 ± 0.04

−0.52 ± 0.06 −0.62 ± 0.07 −0.64 ± 0.05 −0.73 ± 0.03 −0.80 ± 0.04 −0.72 ± 0.04 −0.68 ± 0.06 −0.76 ± 0.04 −0.57 ± 0.05 −0.79 ± 0.05 −0.65 ± 0.05 −0.68 ± 0.06 −0.57 ± 0.06

T42 L26 2.0° × 2.5° L24 (Delworth et al., 2006) T106 L19 4° × 5° L21 2.5° × 3.75° L19 T42 L20 T42 L30 T42 L26 (Kiehl and Gent 2004) 2.5° × 3.75° L19 (Pope et al., 2007) 1.3° × 1.9° (Martin et al., 2006)

T63 L31 (Roeckner and Coauthors, 2003) T30 L19

T63 L18 (Gordon and Coauthors, 2002)

−0.62 ± 0.05

−0.60 ± 0.04 −0.66 ± 0.03

−0.67 ± 0.05 −0.62 ± 0.04

T63 L31 (Deque et al., 1994) T63 L31 (Flato et al., 2000)

SRES A1B correlation NAO/precipitation (IP)

20C3M correlation NAO/precipitation (IP)

Resolution and references

Table I. Correlation between the NAO and precipitation for each individual model in the 20C3M and SRES A1B scenarios.

TRENDS OF PRECIPITATION AND NAO

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Observed precipitation and NAO index

NAO index

Precipitation (mm/day)

(a)

Correlation: Precipitation and NAO

(c)

Correlation: SLP and Precipitation

Latitude

Latitude

(b)

Longitude

Longitude

Figure 1. (a) Time series (in bars) and linear trend (gray line) of precipitation averaged over the Iberian Peninsula and NAO index (black line) for DJFM season. (b) Correlation of precipitation and NAO index (multiplied by 100 in contour lines), leading EOF of precipitation (multiplied by 100 in shaded for DJFM. (c) Correlation between SLP and precipitation averaged over the IP (multiplied by 100 in contour lines) and NAO pattern (multiplied by 100, shaded). This figure is available in colour online at wileyonlinelibrary.com/journal/joc

NAO indices derived by using the latter approach, the correlation coefficients vary between 0.86 and 0.99 for the 20C3M and between 0.91 and 0.99 for the SRES A1B models. The NAO indices based on the reference pattern were used because it considers the two centres of the NAO fixed for all the models, which facilitates the interpretation of the trend results. To obtain the trend we applied Sen’s method (Sen, 1968), which estimates the median slope as a change in time; the significance of the trend is measured with Kendall’s Z test (Press et al., 1996). In this study linearity is assumed between precipitation and the NAO index, therefore a least squares regression model has been applied to estimate the response of precipitation because of the NAO changes. Some interpretations of their relationships are given by obtaining composite maps of SLP and precipitation for greater and lower NAO values. 3.

Results

Figure 1(a) shows the time series of observed precipitation averaged over the IP in mm/day and the NAO index from the CPC for the DJFM season. Besides the year to year fluctuations, the figure indicates a decrease in the linear trends in the case of precipitation and an increase in the case of the NAO index. The correlation Copyright  2009 Royal Meteorological Society

coefficient between the two time series is −0.67 ± 0.06. The linear trend is subtracted from the NAO and precipitation time series to minimize the impact of external forcing, and the correlation coefficient decreases to the value −0.64 ± 0.06, therefore the significance of the association is maintained. For the period 1950–2007, precipitation decreases at a rate of 4.3 mm/month per decade and Kendall’s Z test is −2.2, which is significant at a 95% significance level (s.l.). The NAO index increases at a rate of 0.17 per decade and Kendall’s Z test is 3.5 (>95% s.l.). The links between the NAO index and precipitation over the IP were previously reported (Rodr´ıguez-Puebla et al., 2001; L´opez-Bustins et al., 2008), however to ascertain the performance of the models, here we show the results of the relationships for DJFM corresponding to the 1950–2007 period. Figure 1(b) shows the correlation pattern between precipitation over the IP onto the NAO index multiplied by 100 in contour lines. The leading EOF of precipitation is represented in the same figure, multiplied by 100 and shaded, measured in terms of the correlation coefficients between precipitation and the first principal component that accounts for 74% of the total precipitation variance. This Figure 1(b) indicates that precipitation in the western part of the IP is well linked to the NAO Int. J. Climatol. 30: 1807–1815 (2010)

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with the opposite sign. Figure 1(c) shows the NAO pattern (shaded), characterized by the correlation between the SLP and the NAO index. It also represents the correlation pattern between the SLP and averaged precipitation over the IP (contour lines). The good correspondence between the NAO and winter precipitation is clear from Figure 1(b) and (c). However, in the case of summer, Bo´e et al. (2009) found that the role of the NAO for the precipitation changes over Southern Europe is generally weak. The links between positive (negative) NAO with drier (wetter) conditions over the IP are shown in Figure 2(a)–(d), representing the composite maps of SLP, wind and precipitation anomalies corresponding to the 5 years with greater positive and negative NAO values. NAO+ leads to dry airflow from the NW (Figure 2(a)) causing negative precipitation anomalies (Figure 2(b)) while NAO− leads to warm and humid airflow from tropical regions (Figure 2(c)) causing positive precipitation anomalies (Figure 2(d)). Changes to more positive NAO phases are associated with weakening south westerlies and changes in the transport of water vapour, which will lead to a reduction in precipitation over the IP. The NAO simulated from 20C3M data did not show significant trends, however some models in the SRES Composite of SLP anomalies for NAO+

(b)

Longitude

Composite of SLP anomalies for NAO

Longitude

(d)

Composite of precipitation anomalies for NAO

Latitude

Latitude

(c)

Composite of precipitation anomalies for NAO+

Latitude

Latitude

(a)

A1B experiment such as MIROC3.2 (medres), ECHOG, GFDL-CM21 and BCCR-BCM2.0 gave a statistically significant increasing trend for the NAO. The discrepancies in the NAO trends are probably because of model internal variability. As we are investigating the trend and effects of external forcings onto precipitation, we derived a multi-model by averaging the models corresponding to the same experiment. As a result, individual model biases tend to cancel out, and better agreement with observations is expected. First, we determine the contribution of the multi-model common variance to the total simulations’ variance following the Rowell (1998) method. There is no contribution of multi-model common variance to the total for the 20C3M experiment, while 9.8% is the common variance related to the total for the SRES A1B experiment in the case of NAO simulation. Therefore, the signal of SRES A1B multi-model might give better climate change information about the NAO than the separate models. From now on, we will focus on the results of the multi-model. Figure 3(a) shows the time series of the precipitation averaged over the IP for the 20C3M and SRES A1B models, the box represents the standard deviation or spread of the simulations for each year, and the whiskers the most extreme precipitation simulated. The time series of the multi-model and the linear trend are shown with the thick line. We have not found a significant trend in precipitation for the 20C3M multi-model,

Longitude

Longitude

Figure 2. Composite of SLP (hPa) and wind anomalies (m/s) for the five years with greater NAO+ values (a) and lower NAO− (c). Composite precipitation (mm/day) for the five years with greater NAO+ values (b) and lower NAO− (d). This figure is available in colour online at wileyonlinelibrary.com/journal/joc Copyright  2009 Royal Meteorological Society

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Precipitation from 20C3M and SRES A1B models

(b)

NAO from 20C3M and SRES A1B models

Percentage of precipitation change

Latitude

(c)

Longitude

Figure 3. (a) Time series of precipitation for 20C3M (light gray) and SRES A1B (dark gray) models for the DJFM season. The thick lines represent the multimodel and linear trend, the boxes represent the spread or standard deviation, and the whiskers represent the simulated extreme precipitation. (b) As in figure 3a but for the NAO. (c) Percentage of precipitation change for the 2080 to 2099 period using SRES A1B contrasted with the 1980 to 1999 period using 20C3M. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

but a significant decreasing trend is found for the SRES A1B multi-model corresponding to the 21st century, with a decrease rate of about −1.0 mm/month per decade and a Kendall’s Z test of −4.7 (>95% s.l.). Figure 3(b) shows the NAO time series for the 20C3M and SRES A1B models. The box represents the standard deviation or the spread of the NAO for each year, and the whiskers the most extreme NAO simulated. The time series of the multi-model and the linear trend are shown with the thick line as in Figure 3(a) for precipitation. The NAO of the multi-model corresponding to the SRES A1B simulations in the 21st century tends to increase at a rate of 0.15 per decade and Kendall’s Z test is 4.2 (>95% s.l.). The result for the NAO trend under climate change Copyright  2009 Royal Meteorological Society

conditions agrees with previous studies (Osborn, 2004; Gillett et al., 2005; Kuzmina et al., 2005; Stephenson et al., 2006). As expected, the changes in precipitation are associated with the NAO changes with the opposite sign. Although the precipitation (NAO) decrease (increase) rate is greater for the observed than for the SRES A1B multimodel data, the significance of the trend is greater for SRES A1B as a result of the fact that a longer period was used in these simulations than in observations. Figure 3(c) shows the relative precipitation changes in percentages for the 2080–2099 period using SRES A1B, with respect to the 1980–1999 period using 20C3M. A precipitation decrease over the IP is obtained, especially pronounced towards the south, where it is approximately Int. J. Climatol. 30: 1807–1815 (2010)

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30% less. The mechanism behind such an effect must be associated with a weakening south westerly airflow under warmer climate. Regarding the question of precipitation modifications as a response to the NAO under a warmer climate, we applied the linear regression method. The precipitation averaged over the IP and the NAO indices are well correlated for all the models, as can be seen in Table I. The maximum correlation coefficient corresponds to GFDLCM21 (−0.80 ± 0.04) in the 20C3M experiment and to MRI-CGCM2.3.2 (−0.85 ± 0.03) in the SRES A1B experiment. The correlation coefficient between precipitation and the NAO index for the 20C3M multi-model is −0.64 ± 0.01 and for the SRES A1B multi-model it is−0.77 ± 0.01. The bootstrap technique was applied to determine the correlation coefficient errors (Wilks, 2006). The slopes of the linear regression models between precipitation and the NAO are −0.07 for the 20C3M multimodel and −0.12 for the multi-model SRES A1B experiment. These values provide the expected change in precipitation (mm/day) accounted for by a change in the NAO index. Therefore, the contribution of the NAO to changes in precipitation is greater for the SRES A1B than for the 20C3M multi-model, indicating an intensification of the precipitation response to changes in the NAO under a warmer climate. The different responses are consistent with the structures of the correlation maps between SLP and precipitation averaged over the IP, which are shown in Figure 4(a) and (b) for 20C3M and the SRES A1B multi-models, respectively. The shaded areas of these figures represent the NAO pattern and the contour lines represent the correlation map between SLP and precipitation averaged over the IP. The comparison of these figures indicates that a larger region of the Atlantic SLP is significantly linked with precipitation in the case of SRES A1B compared with 20C3M. As a result, an increase in the greenhouse gases will probably cause drier conditions over the IP as a result of more NAO positive phases as well as a strengthening of the links between NAO and precipitation.

Conclusions

We analysed possible changing links between the NAO and precipitation over the IP from the 20th to the 21st centuries. In the case of observations we obtained a trend to more positive phases of the NAO associated with a trend to less precipitation. This result was reported in previous studies, however we found that the significance of the trend is greater for the NAO than for precipitation. In the case of the 20th century simulations, a significant trend was not found either in the NAO or in precipitation. The 21st century multi-model showed changes in the NAO regimes to be more positive and, therefore precipitation tends to decrease. The linear regression between precipitation and the NAO predicts that drier conditions under the NAO increases for all the models (Table I). On the basis of these results, the response of precipitation to the NAO would be likely to be stronger in SRES A1B than in the 20C3M multi-models. We compared the correlation maps between SLP and the averaged precipitation over the IP, and the differences of the correlation configurations indicated that the relationships between precipitation and SLP would amplify in the SRES A1B scenario when compared with 20C3M. Therefore, not only the changes in atmospheric circulation, but also the strengthening of the relationships between precipitation and the NAO will be responsible for the expected precipitation decrease over the IP under a warmer climate. If the NAO trend continued to increase because of greenhouse gases, the consequences for winter precipitation would be dramatic toward the southern part of the IP. However, there are uncertainties in the precipitation represented by models and the effects of a warmer climate require considering the contribution of other factors that are independent from the NAO as well as analysing the precipitation trend in other seasons. Acknowledgements We thank the international modelling groups for providing their data, the PCMDI for collecting and archiving the (b)

SRESA1B

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20C3M

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(a)

4.

Longitude

Longitude

Figure 4. Correlation of the SLP and precipitation averaged over the IP (multiplied by 100, in contour lines) and the NAO pattern (multiplied by 100, shaded) for 20C3M (a) and for SRES A1B (b). This figure is available in colour online at wileyonlinelibrary.com/journal/joc Copyright  2009 Royal Meteorological Society

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