Potential seasonal predictability of the observed Euro-Atlantic atmospheric

Transcription

1 Q. J. R. Meteorol. Soc. (2003), 129, pp doi: /qj Potential seasonal predictability of the observed Euro-Atlantic atmospheric variability using SST forced ECHAM4-T42 simulations By P. FRIEDERICHS 1 and C. FRANKIGNOUL 2 1 Meteorologisches Institut der Universität Bonn, Germany 2 Laboratoire d Océanographie Dynamique et de Climatologie, France (Received 21 June 2002; revised 10 March 2003) SUMMARY To estimate the potential seasonal predictability of the Euro-Atlantic atmospheric variability, canonical correlation analysis is used, and a comparison is made between the NCEP reanalysis with ensemble simulations of ECHAM4-T42 forced with observed sea surface temperature (SST) and sea-ice boundaries for and changing CO 2 concentration. The method identi es those atmospheric modes of variability that have similar temporal evolution in the observations and the ensemble mean of the simulations. Signals due to long-term changes in the forcing were rst reduced by removing a third-order polynomial from all data. Signi cant covariability in the 500 hpa geopotential height over the Euro-Atlantic region is found from autumn to spring. The best agreement between the patterns is seen in late winter, where a mixed Paci c North America (PNA) and tropical/northern-hemisphere teleconnection pattern is the dominant signal. Although it tends to modulate the North Atlantic Oscillation (NAO), the direct in uence over Europe is limited. In all cases, the covariability seems to be due to remote forcing by tropical Paci c SST. However, the model response to the El Niño Southern Oscillation (ENSO) always shows a PNA pattern, while the related observed signal undergoes large seasonal changes. The sea level pressure (SLP) over the Euro-Atlantic region seems to be much less sensitive to remote ENSO forcing, although traces of its in uence can be detected in late winter. On the other hand, a highly signi cant covariability is found between modelled and observed SLP anomalies in autumn, re ecting the in uence of the North Atlantic SST on the NAO. However, the model does not reproduce the observed SLP structure. It is also shown that the reproducibility of the NAO in the undetrended ECHAM4 ensemble simulations in winter that was found by Latif et al. in the same simulations is related to long-term trends in tropical Paci c and Indian Ocean SST variability. However, its origin cannot be determined as the CO 2 concentration was speci ed from the observations in the simulations. KEYWORDS: Canonical correlation analysis ECHAM4 1. INTRODUCTION The atmospheric variability over Europe and, in particular, the North Atlantic Oscillation (NAO) exhibit substantial variability on the interannual to decadal timescales, and recent studies suggest that it may be in part due to sea surface temperature (SST) anomaly in uence. Czaja and Frankignoul (1999) found observational evidence for a direct forcing of the NAO in early winter by North Atlantic SST anomalies, and Czaja and Frankignoul (2002) suggested that the signal would yield a forecast skill of up to 15% of its monthly variance several months in advance. The in uence of North Atlantic SST over Europe was also investigated by Rodwell and Folland (2002), who found ocean-to-atmosphere forcing in all seasons. Except in summer, the forced geopotential height pattern projected well onto the NAO. There is also growing evidence that some predictability over Europe might result from teleconnections from the tropical Paci c SST. For instance, van Oldenborgh et al. (2000) observed signi cant correlations between strong El Niño events and spring precipitation over northern Europe. The in uence of SST anomalies has been widely studied using atmospheric general circulation models (hereafter AGCMs) forced by the observed SST and sea ice boundary variability (e.g. Venzke et al. 1999; Rodwell et al. 1999; Latif et al. 2000; Cassou and Terray 2001). Since the forcing is realistic, the AGCM simulations can be directly Corresponding author: Meteorologisches Institut der Universität Bonn, Auf dem Hügel 20, Bonn, Germany. pfried@uni-bonn.de c Royal Meteorological Society,

2 2880 P. FRIEDERICHS and C. FRANKIGNOUL compared with observations. For seasonal means, potential predictability over the Euro-Atlantic sector appears to be limited to 10 15% of the total variability, but some of the AGCMs reproduce parts of the observed low-frequency uctuations of the NAO in winter. However, the AGCM studies do not provide a consistent picture of the mechanisms that affect Euro-Atlantic climate (Robinson 2000; Kushnir et al. 2002). For instance, Rodwell et al. (1999) identi ed with the Hadley Centre Atmosphere model HadAM2b a tripole SST anomaly pattern in the North Atlantic that forced parts of the decadal variability of the NAO, and they suggested a positive feedback between NAO and SST anomalies. On the other hand, the reproducibility of the multi-annual variations of the winter NAO index in the ECHAM4 -T42 simulations seems to be linked to tropical Paci c SST (Latif et al. 2000), and Hoerling et al. (2001) attributed the simulated shift towards a positive NAO over the last 50 years to tropical SST in the Indian and Paci c Ocean. Venzke et al. (1999) with HadAM1, and Cassou and Terray (2001) with ARPEGE pointed out the dominant role of the El Niño/Southern Oscillation (ENSO) in forcing atmospheric variability over Europe in winter and spring, but they were also able to identify a tropical and subtropical Atlantic in uence. To assess predictive skill, previous studies have mostly focused on determining the most reproducible patterns in an ensemble of AGCM simulations, or comparing selected atmospheric patterns like the NAO. In this study we attempt to identify the patterns that best correlate in the Euro-Atlantic sector in the observations and an ensemble of AGCM simulations. Bretherton and Battisti (2000) suggested that, even if the long-term behaviour of the NAO was reproduced, predictability from midlatitude SSTs would be limited to the inherent time-scale of the ocean mixed layer (see Czaja and Marshall 2000). In any case, our main focus here is on the potential predictability on the seasonal time-scale, where SST changes are largely predictable. The main tool is canonical correlation analysis (CCA), and we use the mean of several simulations to increase the signal-to-noise ratio in the model. A CCA between observed and mean modelled variability thus combines the detection of the most reproducible and skillful patterns, without a priori prescribing them, nor the cause of the predictability. A similar approach, but with a slightly different statistical method, has been used to assess predictability in precipitation and storm-track activity in ensemble simulations (Ward and Navarra 1997; Moron et al. 1998; Carillo et al. 2000), and to reduce systematic model errors in seasonal predictions (Smith and Livezey 1999; Feddersen et al. 1999). The paper is organized as follows. The data and the statistical approach are discussed in section 2. Section 3 rst uses the geopotential height at 500 hpa (Z500), then the sea level pressure (SLP) to detect potentially predictable signals over the Euro-Atlantic region. The results are summarized and discussed in section DATA AND STATISTICAL TOOLS Four simulations with the Hamburg global atmospheric GCM ECHAM4 with a spectral resolution of T42 (Röckner et al. 1992) have been forced with the global SST and sea ice boundary variability, using the GISST2.2 data (Rayner et al. 1996) from 1903 to Two additional simulations were available for To increase the signal-to-noise ratio, we only consider the ensemble mean of the six simulations during In all the simulations, the CO 2 concentration varies with time, as estimated from observations (Latif, personal communication). The simulations are the same as those described by Latif et al. (2000). European Centre for Medium-Range Weather Forecasts model HAMburg version. Action de Recherche Petite Echelle et Grande Echelle.

3 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2881 The observational database for is the monthly SLP and Z500 from the NCEP NCAR reanalysis (Kalnay et al. 1996). We consider running seasonal anomalies (December to February (DJF), JFM, FMA, MAM and so on) and, unless speci ed, removed a third-order polynomial at each grid point in order to minimize the in uence of the greenhouse gas forcing and other long-term trends, which are not subject in this study. Such a high-pass ltering only removes periods of the order of, or larger than, the length of the time series (44 years). The covariability between the different elds is investigated using the CCA, which calculates the linear transformation of two multidimensional variates that maximizes the correlation between them (von Storch and Zwiers 1999). We used the parametric test statistic after Wilks (Anderson 1984) to estimate the statistical error level of rejecting the null hypothesis that there exists no linear relationship between the two elds. For details see Friederichs and Hense (2003). A CCA without a priori reduction of spatial degrees of freedom is subject to large sample errors (Bretherton et al. 1992). The CCA was therefore performed in a spatial subspace where the degrees of freedom were reduced by projecting the data onto a truncated empirical orthogonal function (EOF) basis (Barnett and Preisendorfer 1987). The choice of the truncation is subjective. Hence, the calculation was made by systematically varying the EOF truncation for each variable, thereby documenting the robustness of the statistical signi cance and the spatial structure (Friederichs and Hense 2003). To identify predictable atmospheric structures over the Euro-Atlantic region, de- ned as 90 B W 45 B E, 10 B N 80 B N, we performed a CCA between the modelled and observed elds, thus assessing the potential predictability of the observed variability using as predictor the ensemble mean of the ECHAM4 simulations. As detection variable or predictor we used Z500 and SLP. There is no constraint on the spatial structure of the most predictable variability, nor any assumption of the role of SST and sea ice, even if the potential predictability in the model must be due to boundary forcing. A multivariate estimate of the hindcast skill can be derived from the canonical correlations, but it is highly overestimated in small samples as the CCA includes an undesired t to random variability. To avoid over tting, we calculate cross-validated expansion coef cients (ECs) (Michaelsen 1987). In this procedure the canonical patterns are calculated from a reduced sample obtained by successively removing each season in the time sequence, and corrected ECs for the withheld data are obtained by projecting them onto the canonical patterns. Although the year-to-year autocorrelation of the seasonal means of the extratropical SLP and Z500 elds is generally weak, the preceding and following year were also withheld when calculating the cross-validated ECs. Note that even if a mode turns out to be highly signi cant, the cross-validated correlation might be small (Moron et al. 1998). This occurs when the canonical modes are weakly separated and therefore sensitive to statistical sampling, which makes interpretation dif cult. Since sensitivity to sampling increases with the number of EOFs, patterns will be illustrated by cases where the cross-validated correlation is reasonably high and the number of EOFs small. Only the rst CCA mode will be shown, since the higher modes were seldom signi cant. 3. POTENTIAL PREDICTABILITY IN THE PERIOD To investigate whether the simulated atmospheric variability leads to a potential predictability of the observed one, we consider separately Z500 and SLP, even though National Centers for Environmental Prediction National Center for Atmospheric Research.

4 2882 P. FRIEDERICHS and C. FRANKIGNOUL Figure 1. Wilks s test statistic of canonical correlation analysis between three-monthly observed and modelled Euro-Atlantic 500 hpa geopotential height ( ) with an empirical orthogonal function (EOF) truncation between four to eight EOFs (x-axis for ECHAM4, y-axis for NCEP). The numbers represent the cross-validated canonical correlations (times 100). Light grey indicates a 10% level of signi cance, medium grey 5%, and dark grey 1%. The circles indicate the canonical modes that are shown in Figs. 2 to 5. SLP and Z500 are generally considered as equivalent detection variables, in view of the predominantly barotropic structure of the low-frequency uctuations of the extratropical atmosphere. It will be shown indeed that the CCA identi es different predictable modes in the two cases. (a) Using Z500 as the detection variable The CCA was performed between the Euro-Atlantic Z500 of the ensemble mean of the six simulations and the NCEP reanalysis by retaining between four and eight EOFs for each eld. The rst four EOFs explain 50% to 70% of the total Z500 variability in this region, depending on the season, and the rst eight EOFs 75% to 90%. In each case, the null hypothesis that there is no linear relationship between modelled and observed variability was tested as described in section 2, yielding the statistical signi cance (grey scale) and the cross-validated canonical correlation in Fig. 1. A signi cant covariability is primarily found from mid-autumn to spring, while the results show little robustness during summer and early autumn. The correlation of the cross-validated ECs is strongest in JFM, FMA, and AMJ, often exceeding 0.5, while it is weaker in OND, NDJ and MAM. In OND the second canonical mode also turned out to be signi cant. To illustrate the canonical patterns, we chose an EOF truncation

5 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2883 Figure 2. First canonical correlation analysis mode between observed and modelled Euro-Atlantic 500 hpa geopotential height in January to March: (a) expansion coef cients (ECs) of NCEP (full line) and ECHAM4 (dashed line), and homogeneous regression patterns for (b) NCEP, and (c) ECHAM4. Shading indicates 10% (light), 5% (medium), and 1% (dark) error level. The ECs are normalized and cross-validated, contour intervals are at 5 m with negative values shown dashed. that yields a highly signi cant signal and a reasonably strong cross-validated correlation coef cient (circles in Fig. 1). (i) Winter signal. The mode selected for JFM has a cross-validated correlation of 0.75, which suggests that more than half of its seasonal variability is potentially predictable. However, the canonical mode only explains 12% of the observed Z500 variance in the Euro-Atlantic sector. The cross-validated ECs (Fig. 2(a)) are highly correlated on all time-scales. The canonical patterns are shown in Fig. 2 in terms of homogeneousregression patterns for NCEP (Fig. 2(b)) and ECHAM4 (Fig. 2(c)), which are obtained by projecting the elds on their respective ECs. The patterns resemble each other strikingly well, showing signi cant covarying anomalies predominantly over the western part of the domain and over northern Africa. As the anomalies seem to be part of a larger scale structure, we have regressed in Fig. 3 Z500 (top) and SLP (middle) globally onto the ECs for NCEP (left) and ECHAM4 (right). The similarity is large not only in the CCA domain, but also over the whole globe. The negative height anomaly throughout the whole tropical band re ects a global cooling of the lower tropical atmosphere, while the tropical SLP resembles the southern oscillation with a negative anomaly over Australia and a positive one over the eastern tropical Paci c. The extratropical regression patterns re ect a mixture between the Paci c North American (PNA) and the tropical/northern-hemisphere (TNH) teleconnection pattern (Wallace and Gutzler 1981; Mo and Livezey 1986). The latter tends to be a preferred mode of response in late winter (Livezey and Mo 1987), and it was also identi ed in the response of HadAM1 to the warm phase of ENSO (Davies et al. 1997). In the extratropics, the vertical structure is approximately equivalent barotropic. However, it is shown in section 3(b) that the PNA/TNH-like response in SLP is obscured

6 2884 P. FRIEDERICHS and C. FRANKIGNOUL Figure 3. Linear regression for leading canonical correlation analysis mode for Euro-Atlantic 500 hpa geopotential height (Z500) in January to March (JFM) (areas indicated by rectangles): (a) observed Z500 on expansion coef cients (ECs) of NCEP, (b) modelled Z500 on EC of ECHAM4, (c) observed sea level pressure (SLP) on EC of NCEP, (d) modelled SLP on EC of ECHAM4, (e) Global sea-ice and Sea Surface Temperature (GISST) in October to December on EC of NCEP, and (f) GISST in JFM on EC of ECHAM4. Shaded areas indicate 10% (light), 5% (medium) and 1% (dark) error level. Contour intervals are 10 m for Z500, 0.5 Pa for SLP, and 0.2 K for sea surface temperature (SST). Negative values are shown dashed. by noise and therefore dif cult to detect by CCA. As in ARPEGE (Cassou and Terray 2001) and HadAM1 (Venzke et al. 1999), the low over the northern part of North America and the North Atlantic extends further eastward in the model than in the observations, but to a much lesser extent in ECHAM4. In the Euro-Atlantic sector, the signal thus projects weakly onto the NAO, which suggests that it modulates the NAO variability. To link the (potentially) predictable signal to the boundary forcing, the SST anomalies that are associated with the ECs were obtained by regression analysis (Figs. 3(e) and (f)). However, in order to separate cause and effects, we used in the observations the seasonal means that precede the canonical mode by one season, as the SST anomaly pattern that varies in phase with the atmosphere primarily re ects the SST response to atmospheric forcing (Frankignoul and Hasselmann 1977). Both SST patterns are dominated by a strong negative anomaly in the tropical Paci c that resembles La Niña conditions. Negative, but weaker, anomalies are also seen in the Indian Ocean and the tropical Atlantic. The positive SST anomaly found in the central North and South Paci c during La Niña conditions is also seen in the two elds, while there is little signal in the

7 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2885 Figure 4. As Fig. 3 but for April to June, but January to March SSTs are used in (e). Contour intervals are 5 m for Z500, 0.3 Pa for SLP, and 0.1 K for SST. Negative values are shown dashed. North Atlantic in NCEP (OND) (Fig. 3(e)), and none in ECHAM4 (JFM) (Fig. 3(f)). Note that differences between the SST patterns are essentially due to the different seasonal means, and not to the fact that they were projected onto different ECs, which are highly correlated. As the signal in the model is very likely to be due to the boundary forcing, we interpret the relation between SST and atmosphere in Fig. 3 as primarily re ecting the atmospheric response to the SST anomalies. Since the atmospheric response to tropical Paci c SST anomalies rapidly generates SST anomalies in the extratropical Paci c and in the tropical Atlantic via an atmospheric bridge (Lau and Nath 1994, 2001), it is likely that the predictable signal re ects a teleconnection from the tropical Paci c SST anomalies. Very similar features are found for DJF and FMA, although they are less signi cant in DJF, so throughout winter there is a signi cant potential predictability in the Atlantic sector from the tropical Paci c SST. (ii) Spring signal. In MAM, the signal becomes weaker and does not reach the 1% signi cance level, although the cross-validated correlation reaches 0.55 (Fig. 1). The anomaly patterns (not shown) show essentially the same structures as in JFM, except that the anomalies in the tropical and subtropical Atlantic are more pronounced. In AMJ, statistical signi cance increases again, with a cross-validated correlation exceeding 0.6. As shown in Fig. 4, the bulk of predictability for Z500 is located in the tropical and subtropical band between 40 B S and 30 B N. Signi cance is small at

8 2886 P. FRIEDERICHS and C. FRANKIGNOUL Figure 5. As Fig. 3 but for October to December, but July to September SSTs are used in (e). Contour intervals are 5 m for Z500, 0.3 Pa for SLP, and 0.1 K for SST. Negative values are shown dashed. higher latitudes, and the patterns are different: while the signal in ECHAM4 remains PNA/TNH-like as in JFM, albeit with a smaller amplitude, the NCEP anomalies show a smaller scale wave train with the opposite phase over Canada. The related SLP anomalies (Figs. 4(c) and (d)) are weak and suggest very little predictability, except perhaps over the tropical North Atlantic. In NCEP, the preceding JFM SST anomalies (Fig. 4(e)) strongly resemble those obtained in JFM with ECHAM4 (Fig. 3(f)), while the AMJ anomalies in the model (Fig. 4(f)) recall the classical evolution of the ENSO anomalies, with a teleconnection to the tropical Atlantic SST in spring, 3 4 months after the maximum signal in the tropical Paci c (Klein et al. 1999). The AMJ signal in Fig. 4 persists until JJA, although the statistical signi cance is somewhat weaker. Thus in spring the analysis suggests that the whole tropical and subtropical band primarily responds to tropical SST anomalies. Outside the tropical band, no coherent anomalies are found in the observations and the model. (iii) Autumn signal. While there is no potential predictability in late summer, a highly signi cant signal is obtained in autumn, peaking in OND (Fig. 1). However, the cross-validated correlation is smaller and the potential predictability limited to a few per cent of the Z500 variance in the Euro-Atlantic sector. Figure 5 shows that there is practically no resemblance between the modelled and the observed Z500 and SLP patterns. While the observations suggest a barotropic wave train over the Euro-Atlantic

9 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2887 Figure 6. As Fig. 1 but for sea level pressure. The circles indicate the canonical modes that are shown in Figs. 9 and 12. region, the model again shows the PNA/TNH pattern which in OND projects well onto the NAO. Thus, even if the CCA suggests some potential predictability, the model fails to reproduce the observed signal in the Euro-Atlantic sector. The associated SST anomalies (Figs. 5(e) and (f)) correspond again to La Niña conditions, with practically no signi cant signal in the Atlantic. The second canonical mode suggests the presence of wave trains propagating away from Indian ocean SST anomalies (not shown), but the patterns are very noisy and the resemblance between model and observations is small. (b) Using SLP as the detection variable When SLP is used in the CCA instead of Z500, the results are different. As shown in Fig. 6, a highly signi cant and robust covariability in the Euro-Atlantic sector is found in late autumn, peaking in OND. To a lesser extent, there is also signi cant covariability from JJA to SON, although the results depend on the EOF truncation. Nearly no signi cant canonical modes are found in winter and spring. (i) Winter signal. Our analysis shows that there exists no signi cant linear relationship in winter between Euro-Atlantic SLP in the observations and the simulations (Fig. 6). However, Latif et al. (2000) investigated the same ensemble of ECHAM4-T42 simulations and showed that the observed long-term variability of the NAO was partly reproduced. Using a CCA between modelled and observed SLP anomalies over the North Atlantic, they obtained a signi cant correlation between NAO-like anomalies in

10 2888 P. FRIEDERICHS and C. FRANKIGNOUL Figure 7. Linear regression for leading canonical correlation analysis mode for Euro-Atlantic sea level pressure (SLP) in December to February (DJF) (areas indicated by rectangles): (a) observed SLP on expansion coef cients (ECs) of NCEP, (b) modelled SLP on EC of ECHAM4, (c) Global sea-ice and Sea Surface Temperature (GISST) in September to November on EC of NCEP, and (d) GISST in DJF on EC of ECHAM4. Shaded areas indicate 10% (light), 5% (medium) and 1% (dark) error level. Contour intervals are 0.5 Pa for SLP and 0.1 K for SST. Negative values are shown dashed. DJF and suggested that the reproducibility of the NAO change was related to tropical Paci c SST. The discrepancy arises from our removal of the low-frequency trend in the data. When no cubic trend is removed, the CCA in DJF indeed shows a weakly signi cant (10% level) covariability for one EOF truncation, namely ve EOFs for each eld (slightly more robust results are found when starting in 1975). The cross-validated correlation is 0.32, so that the predictive skill is very limited on the seasonal scale. Since signi cance is already lost when a linear trend is subtracted, the NAO predictability discussed by Latif et al. (2000) must be mainly due to the trend in the data. The SLP patterns obtained by the CCA when no trend is removed are shown in Fig. 7. In NCEP (Fig. 7(a)) as well as in ECHAM4 (Fig. 7(b)) the regression patterns strongly project on the NAO, although with a much smaller amplitude in the model. Note that the observed pattern over the Euro-Atlantic section strongly resembles the observed linear trend since 1950 discussed by Hoerling et al. (2001), with a SLP increase over Europe and decrease over Greenland and Siberia, going along with an increase in the NAO. The SST regression patterns in Fig. 7, preceding (SON) for NCEP (Fig. 7(c)) in order to separate cause and effect as discussed before, and in-phase (DJF) for ECHAM4 (Fig. 7(d)), are very similar and con rm the suggestion of Latif et al. (2000) and Hoerling et al. (2001) that North Atlantic SST anomalies play a minor role. Signi cant SST anomalies are found in all oceans, with stronger anomalies in the Indian Ocean and the tropical Paci c, as well as south of Greenland. However, since long-term trends and the observed increase in CO 2 are included in the simulations, no conclusions about cause and effect can be drawn. The lack of SLP signal in winter when the data are detrended (Fig. 6) contrasts with the results obtained with Z500 (Fig. 1). However, for two EOF truncations in JFM the CCA reaches the 10% signi cance level with weak cross-validated correlations.

11 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2889 Figure 8. Univariate analysis of variance (ANOVA) of (a) 500 hpa geopotential height and (b) sea level pressure in ECHAM4 for January to March seasonal means. The percentage of variance explained by forcing external to the ensemble is represented by contours and shading (%). The 10% signi cance level corresponds to the 5% explained variance level, and the 1% signi cance level to the 10% explained variance level. The CCA patterns (not shown) resemble those in Fig. 3, but despite the equivalent barotropic structure of the signal, predictability is much smaller using SLP. To explain the differences between Z500 and SLP in JFM, we use the univariate analysis of variance (ANOVA) after Rowell et al. (1995) which estimates the ratio between the variance that is externally forced and the total variance in the simulations. We use detrended data in the Euro-Atlantic region, thus the external forcing is primarily due to SST and sea-ice boundary changes. Figure 8 shows that the fraction of variance explained by the external forcing is larger for Z500 than SLP. In the tropics, it reaches 90% for Z500 against 60% for SLP, and at midlatitudes it is also substantially larger over North America. The signal-to-noise ratio is thus smaller for SLP, and tropical teleconnections more dif cult to detect. This was already pointed out by Wallace and Gutzler (1981). Despite the approximately equivalent barotropic structure of the teleconnection patterns, the more regional ones like the PNA were more clearly de ned at mid-tropospheric levels. This is also in agreement with Hoskins and Karoly (1981), who showed that the linear response to an isolated heat source in the subtropics was mostly con ned to the source region at low levels, while the upper levels showed an equivalent barotropic propagation over large distances. (ii) Autumn signal. In OND the canonical mode is highly signi cant and has a crossvalidated correlation of 0.49 for the selected EOF truncation, explaining 18% of the NCEP SLP variability in the Euro-Atlantic sector. Similar, but less signi cant, results are obtained in NDJ. However, the regression patterns (Fig. 9) differ from those that were found in the CCA for Z500. The domain shown in Fig. 9 is only slightly larger than the Euro-Atlantic region considered in the CCA, since no signi cant anomalies are found elsewhere. In contrast to the ENSO-related signal found with Z500, the signal detected in the SLP mostly represents a local phenomenon of equivalent barotropic structure. Despite the good predictive skill, the resemblance between observed and modelled SLP in Figs. 9(a) and (b) is limited, both patterns show a signi cant low east of Newfoundland. Although, the observed pattern somewhat resembles the NAO, while the modelled structure is close to a propagating wave train with half the amplitude. The modelled signal has some similarity to the response to an idealized positive SST anomaly east of Newfoundland simulated by Palmer and Sun (1985).

12 2890 P. FRIEDERICHS and C. FRANKIGNOUL Figure 9. Linear regression for leading canonical correlation analysis mode for Euro-Atlantic sea level pressure (SLP) in October to December (OND) (areas indicated by rectangles): (a) observed SLP on expansion coef cient (EC) of NCEP, (b) modelled SLP on EC of ECHAM4, (c) observed 500 hpa geopotential height (Z500) on EC of NCEP, (d) modelled Z500 on EC of ECHAM4, (e) Global sea-ice and Sea Surface Temperature (GISST) in July to September on EC of NCEP, and (f) GISST in OND on EC of ECHAM4. Shaded areas indicate 10% (light), 5% (medium) and 1% (dark) error level. Contour intervals are 0.5 Pa for SLP, 3 m for Z500, and 0.1 K for SST. Negative values are shown dashed. The associated SST anomalies were obtained by regression as before. For the NCEP reanalysis (Fig. 9(e)), we again projected the EC on the SST anomalies of the preceding season (JAS) in order to separate those anomalies that are generated by the atmospheric anomalies from those that are forcing it. The SST pattern in JAS strongly resembles the North Atlantic horseshoe pattern described by Czaja and Frankignoul (1999, 2002), which was shown to generate a NAO-like response in late autumn early winter. Consistent with their results, the tropical Paci c SST anomalies appear to play no signi cant role. Although many features are similar in ECHAM4, the inphase regression pattern (Fig. 9(f)) differs, showing a stronger SST anomaly north of the equator, and less east west contrast. This SST anomaly pattern is much more complex

13 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2891 Figure 10. Homogeneous regression pattern of the dominant canonical correlation analysis mode between (a) June to August North Atlantic sea surface temperature (SST) and (b) October to December Euro-Atlantic sea level pressure (SLP) anomalies in the NCEP reanalysis (area indicated by rectangles). Shaded areas indicate 10% (light), 5% (medium) and 1% (dark) error level. Contour intervals are 0.5 Pa for SLP and 0.1 K for SST. Negative values are shown dashed. than the monopole SST anomaly east of Newfoundland considered by Palmer and Sun (1985). Note that most of the differences between the two SST patterns in Figs. 9(e) and (f) are due to the different season used. With a shorter time lag the NCEP SST regression pattern shifts towards the in-phase pattern for ECHAM4. Czaja and Frankignoul (2002) have shown by lagged regression analysis that the observed JAS pattern was slowly evolving during the autumn until it suddenly transformed into a classical tripole-like pattern in NDJ. As such a rapid change could not be explained by ocean dynamics, they argued that it was due to the response of the NAO in late autumn to the SST horseshoe pattern, that in turn generated the classical SST anomaly tripole. As the SST anomaly pattern that forces the NAO strongly projects onto the SST tripole that is generated by it, they suggested that this air sea interaction resulted in a positive feedback. Note that the covariability in the NCEP data was as clearly seen in Z500 (Czaja and Frankignoul 2002), while in ECHAM4 the signal was only detected with the SLP. To verify if similar dynamics are at play, we performed a lagged CCA between the North Atlantic SST and the OND Euro-Atlantic SLP separately for the observations and the model. In observations, the highest correlation with preceding North Atlantic SST anomalies is found for SST anomalies in JJA, with a cross-validated correlation of about 0.5. As shown in Fig. 10, the patterns broadly resemble those in Figs. 9(a) and (e), and mimic those discussed by Czaja and Frankignoul (2002). In ECHAM4, the dominant covariability between the North Atlantic SST and Euro-Atlantic SLP in OND re ects the ENSO in uence (Frankignoul et al. 2002). To focus on the Atlantic impact, we rst removed the SST and SLP variability that is linearly related to ENSO. This was done by seasonal regression analysis. ENSO was de ned by the rst two principal components of the SST anomalies in the tropical Paci c, and the regression coef cient for each month of the year was calculated using successive sets of 3 months, as by Frankignoul and Kestenare (2002). After ENSO removal, the highest correlation is obtained when the North Atlantic SST anomalies precede the OND Euro-Atlantic SLP by one month, which suggests that the atmospheric response may not be as fast as commonly assumed. The correlation is signi cant at the 1% level and the cross-validated EC correlation is 0.5. The homogeneous regression pattern for SLP in OND (Fig. 11(b)) shows some resemblance with the SLP pattern in Fig. 9(b), although the signal is stronger and the low is now centred over the

14 2892 P. FRIEDERICHS and C. FRANKIGNOUL Figure 11. Homogeneous regression pattern of the dominant canonical correlation analysis mode between (a) September to November North Atlantic sea surface temperature (SST) and (b) October to December Euro-Atlantic sea level pressure (SLP) anomalies in the ECHAM4 ensemble mean simulation (area indicated by rectangles). The ENSO related variability was removed. Shaded areas indicate 10% (light), 5% (medium) and 1% (dark) error level. Contour intervals are 0.2 Pa for SLP and 0.1 K for SST. Negative values are shown dashed. Labrador Sea; while there is another low in the tropical North Atlantic. As shown in Fig. 11(a), the SST pattern in SON resembles the North Atlantic horseshoe SST anomaly. It also has some similarities with the OND signal in Fig. 9(f), except that the anomaly off western Europe is much larger in SON. One interpretation is that in ECHAM4 the atmospheric response time is of several weeks in the extratropics, hence larger than commonly assumed, but faster in the tropics. A CCA between the OND Euro-Atlantic SLP and the tropical Atlantic SST (20 B S 20 B N) indeed shows that the correlation is strongest when SST and SLP are in phase. Consequently, it is the tropical part of the SST anomaly that stood out in the in-phase correlation of Fig. 9(d). To con rm that the modes in Figs. 9, 10, and 11 correspond to the same OND signal in the model and the observation, we veri ed that the correlation between the cross-validated SLP ECs in the various CCAs is indeed signi cant. The predictable Euro-Atlantic SLP signal in Fig. 9 is thus related to Atlantic SST anomalies. However, the resemblance between the SST patterns is limited. (iii) Late summer signal. Robust and signi cant results are also found in ASO (Fig. 6), with a cross-validated correlation reaching It resembles the JJA signal, which is less robust and therefore not shown. The regression patterns are displayed in Fig. 12. In this case, the SLP patterns show some similarity over the Euro-Atlantic sector, with a north south dipole over Europe, even though the southern part is much stronger in NCEP. On the other hand, the patterns differ over the American continent. In NCEP, the SST regression pattern in the preceding MJJ shows again the North Atlantic horseshoe pattern, while in ECHAM4 the main (in-phase) SST signal is a broad anomaly with a positive sign in most of the Atlantic. As in autumn, SST anomalies outside the Atlantic seem to be negligible. A lead lag CCA between the North Atlantic SST and the Euro-Atlantic SLP anomalies in NCEP showed no signi cant correlation with the leading SST anomalies, consistent with Czaja and Frankignoul (2002). Thus, the NCEP SLP signal in Fig. 12 may not be directly linked to the MJJ SST. In ECHAM4, the strongest correlation between the Euro-Atlantic SLP and SST is found when Atlantic SST leads by one month. The patterns have been discussed by Frankignoul et al. (2002), but they have very little resemblance with those in Fig. 12(d). The late summer results must therefore be viewed with caution.

15 POTENTIAL PREDICTABILITY OF EURO-ATLANTIC CLIMATE 2893 Figure 12. As Figs. 9(a), (b), (e) and (f) but for August to October, but May to July sea surface temperatures (SSTs) are used in (c). Contour intervals are 0.2 Pa for sea level pressure and 0.1 K for SST. 4. CONCLUSIONS To estimate the potential predictability of Z500 and SLP at the seasonal scale in the Euro-Atlantic sector, we have used a CCA between the observed variability and that in the mean of six simulations with ECHAM4-T42 forced with observed SST and sea ice boundaries during The simulations were additionally forced with a changing CO 2 concentration, but the removal of a third-order polynomial reduces the effect of long-term changes in the forcing. The method identi es those patterns that have similar temporal evolution, without an a priori constraint on the spatial patterns nor on the sources of the predictability. The detection analysis for Z500 shows a signi cant potential predictability from autumn to spring, with a cross-validated correlation of 0.55 to 0.75 in winter and spring, and about 0.35 in autumn. In all seasons the signal is closely related to the tropical Paci c SST, indicating a remote ENSO in uence. In winter and early spring the modelled Z500 anomalies strongly resemble the observed ones, suggesting a response that consists of a mixture of the PNA, with centres over the northern central Paci c and North America, and the TNH teleconnection pattern that extends over the North Atlantic. Thus the modelled PNA/TNH signal also projects on the NAO, as commonly found in AGCMs of similar resolution. However, in autumn and in late spring, the observed Z500 anomalies largely differ from those predicted by the model, that systematically shows a PNA/TNH structure, while the ENSO related signal in the observations undergoes large changes from autumn to spring. The causes of this unrealistic behaviour of the model needs to be understood. Regression analysis with SLP showed that the extratropical signal is equivalent barotropic. However, the signal-to-noise ratio in SLP is small, since the signal could

16 2894 P. FRIEDERICHS and C. FRANKIGNOUL not be detected in the CCA based on SLP. This is in agreement with Wallace and Gutzler (1981) where the PNA teleconnection pattern was better identi ed in the midtroposphere. Thus for predicting a remote ENSO in uence in the Euro-Atlantic sector using ECHAM4, the Z500 seems the more appropriate variable. Using detrended data, we found no signi cant covariability in the Euro-Atlantic SLP in winter and spring. On the other hand, there was signi cant potential predictability in autumn and summer. In contrast to Z500, the SLP anomalies are related to North Atlantic SST anomalies. In OND the observed SLP signal re ects the relation between the NAO and a previous horseshoe-like SST anomaly in the North Atlantic that was discussed by Czaja and Frankignoul (1999, 2002). A corresponding effect is also found in ECHAM4, but only shows limited resemblance with the NAO. Our analysis also suggests that the horseshoe anomaly has the strongest in uence on the model OND SLP when it precedes by one month, and that the atmospheric response time is of several weeks for midlatitude SST forcing, but faster for tropical forcing. Unlike Czaja and Frankignoul (2002), the signature in Z500 is masked by the dominant remote response to ENSO, so that ECHAM4 SLP is a more appropriate detection variable for the local response to Atlantic SST anomalies. Finally it was shown that the low-frequency covariability between the winter NAO in the model and the observations found by Latif et al. (2000) was primarily due to the trend in the data. Since the simulations have increasing CO 2 concentration, the apparent link between changes in the NAO and SST in the tropical Paci c and the Indian Ocean may not re ect a cause to effect relationship. ACKNOWLEDGEMENTS The NCAR/NCEP reanalysis data were provided through the National Oceanic and Atmospheric Administration Cooperative Institute for Research in the Environmental Sciences, Climate Diagnostics Center, Boulder, Colorado ( The ECHAM4-T42 simulations were kindly provided by Mojib Latif (Max Planck Institute Hamburg, now) and Heiko Päth (Meteorologisches Institut der Universität Bonn). We would like to thank the anonymous reviewers for helpful comments. The research was funded in part by the Museum National d Histoire Naturelle, the Programme National d Etude de la Dynamique du Climat (France), and the European Commission in the Mechanisms and Predictability of Decadal Fluctuations in Atlantic European Climate project (EVK2-CT ). REFERENCES Anderson, T. W 1984 An introduction to multivariate statistical analysis. John Wiley and Sons Barnett, T. P. and Preisendorfer, R Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon. Weather Rev., 115, Bretherton, C. and Battisti, D. S An interpretation of the results from atmospheric general circulation models forced by the time history of the observed sea surface temperature distribution. Geophys. Res. Lett., 27, Bretherton, C., Smith, C. and Wallace, J. M An intercomparison of methods for nding coupled patterns in climate data. J. Climate, 5, Carillo, A., Ruti, P. M. and Navarra, A Storm track and zonal mean ow variability: a comparison between observed and simulated data. Clim. Dyn., 16, Cassou, C. and Terray, L Oceanic forcing of the wintertime low frequency atmospheric variability in the North Atlantic European sector: a study with the ARPEGE model. J. Climate, 14,

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