Evolution of seasonal temperature disturbances and solar forcing in the US North Pacific

ARTICLE IN PRESS Journal of Atmospheric and Solar-Terrestrial Physics 72 (2010) 83–89

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Evolution of seasonal temperature disturbances and solar forcing in the US North Pacific ¨ a, E. Blanter a,b, M. Shnirman a,b V. Courtillot a,n, J.L. Le Mouel a b

Geomagnetism and Paleomagnetism, Institut de Physique du Globe de Paris, Place Jussieu, Paris, France International Institute of Earthquake Prediction Theory and Mathematical Geophysics, Moscow, Russia

a r t i c l e in fo

abstract

Article history: Received 15 July 2009 Received in revised form 25 September 2009 Accepted 18 October 2009 Available online 5 November 2009

We analyze the long-term evolution of seasonal temperature disturbances in a 2.5  2.51 area of the US North Pacific. Late Fall and early Winter display significant correlation of temperature disturbances and are investigated in detail. The long-term evolution of the Fall temperature disturbances from 1945 to 2008 closely follows that of solar activity. The robustness of these results is successfully controlled in a 2.5  2.51 area immediately north of the studied region. The modulation of temperature disturbances is very large (  30%) compared to the corresponding changes in solar irradiance, and has significant variability, even at small geographical scale. The physical mechanism of solar forcing of temperature disturbances remains to be understood, but a relation with cloudiness and influence of the Madden– Julian oscillation in the North Pacific is suggested. & 2009 Elsevier Ltd. All rights reserved.

Keywords: Temperature disturbances Solar forcing North-West Pacific Solar irradiance

1. Introduction Although the Sun played a dominant role in pre-industrial climate change (e.g. Lean and Rind, 1999; Solanki et al., 2004; Usoskin et al., 2005; Scafetta and West, 2007; see also reviews by de Jager, 2005; Kane, 2005), causes of climate variability in the 20th century and identification of natural vs. anthropogenic contributions are a subject of ongoing debate. The current majority view holds that anthropic addition of greenhouse gases is principally responsible for ‘‘anomalous’’ global warming in the 20th century (IPCC, 2007). Other contributions to this warming, in particular solar influence, are generally considered negligible. Various authors, however, have argued for some significant amount of solar influence (e.g. Marcus et al., 1999; ¨ et al., 2004; Foukal et al., (2006); Solanki et al., 2004; Le Mouel Kamp and Tung, 2007; Scafetta and West, 2007). De Jager (2005) points out that, due to the extremely complex nature of the Earth’s climate system, evidence for a solar signature may be highly heterogeneous in both time and space. The same author recently argued for strong links between solar activity and climate (de Jager, 2008). ¨ et al., 2008), we analyzed In earlier work (Le Mouel temperature data from 153 meteorological stations in 6 climatic regions of the USA and 44 stations from Europe. We selected stations with long, homogeneous series of daily temperature,

n

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covering most of the 20th century with few or no gaps. We monitored the long-term behavior of temperature disturbances by calculating the mean-squared inter-annual variations or a related parameter, the ‘‘lifetime’’ (akin to the mean duration of temperature disturbances) of the data series. We found that the resulting curves correlated remarkably well at the longer periods, within and between the regions we analyzed. The multi-decadal trend of all of these curves is similar, with a rise from 1900 to 1950, a decrease from 1950 to 1975 and a small subsequent increase. This trend is the same as that found for a number of solar indices, such as sunspot number. We concluded that significant solar forcing is present in temperature disturbances in the areas ¨ et al., 2009a) of we analyzed. In a subsequent study (Le Mouel daily temperature and pressure series from 55 European stations, we provided further evidence of significant solar forcing of shortterm variations in European temperature lasting up to the Present. Evidence was particularly strong when the winter period January–February, corresponding to the highest disturbances, was considered. The relationship between solar forcing and European climate is not stationary over a year, but strongly depends on the season. The solar signature was found to be present all over the 20th century in the wintertime European temperature disturbances, linked to the persistent winds blowing from the Atlantic Ocean to Europe. Squared temperature disturbances were shown to vary by a factor reaching 1.5 over the 20th century. The ¨ et al., 2009a) regional and seasonal approaches used by (Le Mouel were essential in extracting the signatures of solar forcing. Recently, Bond and Vecchi (2003) have shown that atmospheric circulation anomalies over the North Pacific Ocean and

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precipitations in California, Oregon and Washington in the period from 1979 to 2000 were systematically affected by the Madden– Julian oscillation (MJO) in both early Winter (OND) and late Winter (JFM). The phases of the MJO that promote enhanced precipitation are substantially different between these two seasons. Temperature disturbances in the NW US region are strongly influenced by the Pacific Ocean. In Europe, they are strongly influenced by proximity of the Atlantic Ocean. Both regions are influenced in a similar way by Arctic blocking (Diao et al., 2006), on one hand, and by tropical circulation realized via the NAO in Europe (Hurrell et al., 2003) and the MJO in the North-East Pacific (Zhang, 2005), on the other hand. These similar boundary positions make temperature disturbances in both regions very sensitive to any change in the equilibrium of the global climate system. The above considerations have led us to try and extend our ¨ et al., 2008, previous analyses from, mainly, Europe (Le Mouel 2009a) to the NW US region analyzed in detail by Bond and Vecchi (2003). In the present paper, we therefore study the evolution of surface temperature disturbances in Oregon and Washington and test the significance of correlations between the evolutions of climate disturbances and solar activity. With this goal in mind, we analyze in detail a set of 24 meteorological stations in a 2.5  2.51 area of Oregon and southern Washington states in the USA. Another set of 29 stations in the 2.5  2.51 area immediately to the north, in Washington state and Canada, is used as a supplementary control. More precisely, we analyze minimum daily temperature data from essentially the same regions, and over the same seasons as Bond and Vecchi (2003). We first describe the data and sources we use, then the methods: we use the methods described in our two previous papers, i.e. we monitor temperature disturbances using the meansquared inter-annual variations and a related parameter, the ‘‘lifetime’’, which is discussed further below and is defined in ¨ et al. (2009a). Both squared detail in the appendix of Le Mouel annual variation and lifetime give similar results most of the time, but lifetime is often more stable. Lifetime is a measure of the memory of a random process, and in the present case of the residence time of strong disturbances. Similar methods have been successfully used by Blanter et al. (2005, 2006). We describe the seasonal variability of temperature lifetimes, and discuss their possible significance in terms of sources of forcing of the multidecadal behavior which we evidence.

2. Temperature data and associated lifetimes We use all minimum daily temperature series Tmin which have at least 70 yr of data from the global historical climatological network (GHCN-DAILY, ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/dai ly). They are readily obtained through the KNMI Climate Explorer gateway (available via http://climexp.knmi.nl/). The first set of data contains 24 Tmin series with latitudes ranging from 44 to 46.51N, and longitude between –124.0 and –121.51E. The time span covered by more than one record goes from 1894 to 2008. The control set up north has the same longitude range and latitudes from 46.5 to 491N, with 29 Tmin series. Three Tmin series were excluded due to significant recording problems. Stations are shown in Fig. 1 (coordinates listed in Table S1; supplementary material). We have checked in previous work that daily minimum, ¨ et al., 2008, 2009a) maximum and mean temperatures (Le Mouel ¨ et al., 2009b) yield similar results as far and their range (Le Mouel as the present study is concerned. We therefore present here only results for daily minimum temperatures. The evolution of disturbances of temperature series Ti ðtÞ can be characterized by either the mean-squared inter-annual

°

°

°

°

°

°

°

°

°

°

Fig. 1. Map of 24 climatological stations in Oregon (in white) and 29 stations in Washington state (in yellow) with daily minimum temperature data analyzed in this paper. Station numbers in Table S1.

Pt þ X=2 2 variation t ¼ tX=2 ðTi ðt þdDtÞ  Ti ðtÞÞ or by the more elaborate lifetime, both estimated through an 11-yr boxcar filter O (i.e. 4017 days; all times are measured in days): Pt þ X=2 2 t ¼ tX=2 ðTi ðt þdDtÞ  Ti ðtÞÞ Li ðtÞ ¼ Pt þ X=2 Dt ð1Þ 2 t ¼ tX=2 ðTi ðt þ DtÞ  Ti ðtÞÞ where Dt, the sampling interval, is equal to 1 day, and dDt to 1 yr or 365 days. Li(t), which has the dimension of time and is also measured in days, is mainly governed by its numerator, the meansquared inter-annual variation. Normalizing by the mean-squared daily variation in the denominator renders it more stable against jumps in the data record. If Ti(t) is an auto-regressive process of first order Zðt þ DtÞ ¼ aZðtÞ þ xðt þ DtÞ, then Li(t) provides an ¨ estimate of its lifetime l ¼ Dt=ð1  aÞ, as shown in Le Mouel ¨ et al. (2009a) et al. (2008, 2009a). An Appendix in Le Mouel provides further mathematical background and proofs.

3. Seasonal variability of temperatures and lifetimes Analysis of variability of temperatures using the classical method of superimposed epochs (all values of the data series for the first day of the year are averaged and assigned to day 1 and so on for all days of the year) shows that most of the variance in temperature disturbances occurs in the late Fall and early Winter (Fig. 2a): lifetimes are significantly larger from mid-October to mid-March. This corresponds to the OND and JFM seasons

ARTICLE IN PRESS V. Courtillot et al. / Journal of Atmospheric and Solar-Terrestrial Physics 72 (2010) 83–89

selected in their study of the same area by Bond and Vecchi (2003). Larger lifetimes express that these are seasons when there is larger autocorrelation of strong temperature disturbances. Would some form of seasonal forcing be present, we suggest that it should trigger a synchronization of temperature disturbances at different stations in a given season. As a result, the correlations between the individual time-series of temperature disturbances should be significantly greater than the correlations between the temperature time-series themselves (‘‘inner’’ correlations): we can test this and check whether we find a correlation between series of temperature disturbances larger than, and therefore unexplainable in terms of the inner correlations of the original temperature data set. To ensure that we focus on the relevant season, we require stability of the mean temperature disturbance curves with respect to small shifts in the season.

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In order to study correlations between temperature series in a given season, we consider the seasonal temperature running means /Ti(t, y)SO averaged for all values within a given season y over the 11-yr window O centered on t. We consider 31 twomonth long ‘‘seasons’’ ymin r t r ymax from September 5 to April 5, with 5-day offsets (indexed by their middle point y ). We then estimate the mean seasonal lifetime as an average over the set of P 24 given stations Lðt; yÞ ¼ ð1=24Þ 24 i ¼ 1 Li ðt; yÞ. In the computation of Li(t, y) using Eq. (1) all temperature values TðtÞ for t outside the given season [ymin;ymax] are ignored. Two seasons are considered as being ‘‘far’’ from one another if they have no overlap. The cross-correlation of ‘‘far’’ seasons is shown in Fig. 2b in the form of the mean value C(yi) of all coefficients of correlation between a pair of ‘‘far’’ seasons, one of which is yi (with one standard deviation about the mean). Correlation coefficients

6 5.5

Lifetime (days)

5 4.5 4 3.5 3 2.5 2 1.5 15.1 15.2

15.3 15.4 15.5 15.6 15.7 15.8 15.9 15.10 15.11 15.12 Date (day.month)

1.0 0.8

Correlation coefficient

0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1.0 5.10

20.10

5.11

20.11

5.12

20.12

5.1

20.1

5.2

20.2

5.3

Central date (day.month) of 2-month long "season" Fig. 2. (a, top) Mean seasonal variability of lifetimes (in days) of daily minimum temperatures for the period (1945–2008) as a function of time in the year for 24 meteorological stations in Oregon, evaluated using the method of superimposed epochs. Thin lines show uncertainties in these estimates. Two 2-month long ‘‘seasons’’ where the signal has most energy are singled out (05.10–05-12 in green lines and 15.12–15.02 in purple lines, corresponding to late Fall and early Winter). The period from 10.09–10.04 separated by black vertical dashed lines is enlarged in the bottom part of the figure. (b, bottom) Means and standard deviations of correlation coefficients between lifetimes for non-overlapping pairs of 2-month long ‘‘seasons’’ estimated for the period (1900–1950 in blue) and (1950–2000 in red). The vertical dashed lines correspond to the central dates of the two ‘‘seasons’’, late Fall and early Winter, outlined above. The lack of correlation for the red curve centered on 20.11 is due to the change in season from Fall to Winter.

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Cor(L(t, yi), L(t, yj)) are evaluated based on mean seasonal lifetimes L(t, y) as defined above. The mean is calculated for all couples of seasons which are ‘‘far’’ from each other. If f(yi) is the number of seasons yj ‘‘far’’ from season yI, then the mean correlation  P Cor ðLðt; yi Þ; coefficient for a given season yi is Cðyi Þ ¼ 1=f ðyi Þ Lðt; yj ÞÞ. We see (Fig. 2b) that for data after 1950 the mean correlation coefficient is generally rather high, with an overall average of about 0.5 and maxima at or above 0.6 for the 2-month seasons centered on 05.11 (late Fall) and 15.01 (early Winter), whereas for data prior to 1950 it is always low, averaging near 0. We discuss these differences further in Section 5.

4. Results We now focus on the 1945–2008 period, in which we select the two seasons which demonstrate the most significant overall mean correlation with all others, i.e. late Fall (October 5– December 5) and early Winter (December 15–February 15; vertical dashed lines in Fig. 2). This is quite close to the 3-month

long cold seasons OND and JFM of Bond and Vecchi (2003), and Fig. 2b provides physical support for selecting these particular seasons as having the largest correlated lifetimes. Fig. 3 shows (for each one of the 24 stations) the correlation between that station and all (23) other ones of individual seasonal temperature mean series (blue) and their lifetime series (red) for both early Winter (Fig. 3a) and late Fall (Fig. 3b). In Winter, correlations between temperatures at different stations are quite high (0.68). The series are therefore not independent in that season and significant correlation of their disturbances may be expected. But in the Fall, the correlation coefficient for temperatures at different stations drops to a low value of 0.30. Now, the correlation coefficients between temperature lifetimes at different stations are high both in Winter and Fall (0.69 and 0.54, respectively). The correlation of lifetimes in Winter can be accounted for by the correlation of temperatures, but such is not the case for the Fall, when we infer the presence of some form of seasonal forcing. Fig. 4a shows the evolution over the entire 20th century of the mean seasonal temperature lifetime separately for late Fall (red)

1 0.8

Correlation coefficient

0.6 0.4 0.2 0 -0.2 -0.4 -0.6 early Winter (15.12-15.02) -0.8 -1 1

6

12 Station number

18

24

1 0.8

Correlation coefficient

0.6 0.4 0.2 0 -0.2 -0.4 -0.6 late Fall (05.10-05.12)

-0.8 -1

1

6

12 Station number

18

24

Fig. 3. Cross-correlation of temperatures (blue) and lifetimes (red) of individual stations in Oregon for the early Winter season (a, top) and late Fall (b, bottom). The mean values are shown as asterisks with vertical bars denoting standard deviations of the correlation coefficients of a given station with the other stations.

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Mean lifetime (days)

Winter

Fall

Sunspot number

Sq. daily var. of Z

Mean lifetime (days)

Mean lifetime (days)

Squared daily variation of Z (Eskdalemuir)

Year

Year

Year

Fig. 4. (a, top) Evolution of mean lifetime (in days) for the early Winter (blue) and late Fall (red) compared with solar proxies: sunspot numbers (orange) and the squared ¨ et al., 2004). The vertical line in 1950 emphasizes the change daily variation of the Z-component of the geomagnetic field at Eskdalemuir observatory (see text and Le Mouel of correlation of time-series. (b, bottom): comparison between mean Fall season lifetime for 24 Oregon stations (red and right scale in days) and solar proxy (squared daily variation of Z at Eskdalemuir; green and left scale) between 1950 and 2003. (c, bottom): comparison between mean Fall season lifetime for 24 Oregon stations (red and left scale in days) and 29 Washington stations (purple and right scale in days).

and early Winter (blue). The evolution over the same period of solar activity is given by two of several available proxies: one is the 11-yr running means of sunspot numbers, another is provided by the squared daily variation of almost any component of the geomagnetic field at almost any geomagnetic observatory as ¨ et al. (2004). The figure illustrates the case shown by Le Mouel with the vertical component Z at the Eskdalemuir observatory in Scotland. Table 1 is a complement to Fig. 4a and provides the numerical values of the relevant correlation coefficients. We note that the large (positive and negative) values of correlation coefficients obtained for the period prior to 1950 are mainly due to the long-term solar trend and are therefore not significant. There is no correlation between either data set prior to 1945 (Fig. 4a; see Section 5). From 1950 onwards, the evolution of temperature lifetime for the Fall season follows rather closely solar activity (Fig. 4a and b), and all correlation coefficients are positive (Table 1). On the other hand, Winter season

Table 1 Correlation coefficients between seasonal temperature disturbances and solar activity. Temperature disturbances are represented by the 11-yr running means of temperature lifetimes L(t, y) averaged over the 24 Oregon stations and compared for the late Fall (05.10–05.12) and early Winter (15.12–15.02) seasons y. Solar activity is represented by 11-yr running means of the squared daily variation of the Z-component of the geomagnetic field at Eskdalemuir and sunspot number WN. Correlation coefficients are calculated for four time spans: the whole time span of this study, data prior to 1950, data after 1950 and the joint interval of time when all 24 stations have data. Boundaries of these four time intervals are given with respect to the central point of the 11-yr window. Note that 5.5 yr of data are lost at each end.

Fall Winter

1906–2002

1906–1950

1950–2002

1946–1996

ESK-Z

WN

ESK-Z

WN

ESK-Z

WN

ESK-Z

WN

0.38 –0.16

0.66 –0.15

0.38 –0.71

0.72 –0.26

0.56 0.23

0.56 0.26

0.61 0.12

0.65 0.13

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disturbances do not follow solar activity and correlation coefficients are small (Table 1). All these observations for the Oregon stations are vindicated by the stations in the control region to the North in Washington, which further supports the non-random nature of our observations. The shape of the post-1950 Fall lifetime curve is stable when we move to the nearby control region to the North (Fig. 4c). We do not expect the response of this complex system to be linear, and indeed there is significant variability in amplitudes, but it is important to underline that phases remain the same. We note that separate indices definitely related to solar activity (Fig. 4a) deviate from one another as much as the Winter and Fall curves deviate from the solar ones. After 1945, all of the Fall lifetime, Winter lifetime, sunspot number and Eskdalemuir squared magnetic daily variation time series display the same long term trends. This is also the ‘‘overall magnetic tendency’’ ¨ et al. (2004), and the long-term trend of outlined by Le Mouel sunspots in the second half of the 20th century outlined by a number of authors.

5. Discussion The long-term evolution of seasonal temperature disturbances shows a significant increase in the coherence of different stations and seasons after 1950. There are at least two possible and nonexclusive explanations of these observations: insufficient data quality or lower and non-stationary solar activity prior to 1950. The first hypothesis finds support in the amount of gaps and increasing number of stations providing temperature data, which reaches 75% of the total number of stations only around 1945. The second hypothesis would be supported by the increased crosscorrelation of the two solar proxies we consider, which also occurred in the second part of 20th century: the correlation coefficients of 11-yr running means of sunspot numbers and Eskdalemuir squared magnetic daily variation are 0.89 after 1950 but 0.38 before. Also, solar cycles prior to 1940 were significantly smaller in amplitude and therefore it is expected that any solar forcing would have been subdued. Therefore, the period before 1945 is difficult to analyze for a combined set of reasons: the change in trend of solar activity, the relative scarcity of data, possible problems in the recording of the magnetic Z component and a lower level of solar activity. A full study would be necessary to unravel these and this is not the main goal of the present paper. We can in any case conclude that the period after 1950 recovers a solar signature, with a robust correlation. There remains the need to investigate its seasonal characteristics and to try and find the forcing agent. The minimal temperature in the cold season is the ‘‘night’’ temperature, which is sensitive to changes in cloudiness and precipitation. Longer-life disturbances, which mainly contribute to the lifetimes we have calculated, are affected by longer-life precipitation events as well as by the longer-life periods with ‘‘clear’’ weather. We cannot discriminate which of these two mechanisms mainly controls the increase in temperature lifetime. But we note that cool/cold season precipitations in the Oregon– Washington region are influenced by longer-lived MJO events (e.g. in Bond and Vecchi, 2003). Therefore, it seems possible that the increase in the lifetimes of temperature disturbances is related to longer-life precipitation and cloudiness conditions. The consistent behavior of different stations may then be explained by the persistent pressure and wind conditions affecting the whole region. Our choice of specific two-month long seasons might seem arbitrary. One month is not long enough to define a season or to identify a consistent pattern. Some longer-lived atmospheric circulation patterns have durations in excess of one month. The

MJO has characteristic times in the range of 30–60 days, with a main spectral peak at 45–50 days. More recent descriptions of the MJO indicate periods up to 90 days. The two seasonal intervals which demonstrate the highest coherence with all others belong to two seasons with somewhat different MJO conditions, called early Winter and late Winter by Bond and Vecchi (2003) (resp. close to our late Fall and early Winter). We stress again that Fig. 3 shows significant differences in the correlation behavior of temperatures and temperature lifetimes in the two seasons; correlation values are rather high and similar for temperatures and their lifetimes in early Winter, but much smaller for temperatures than lifetimes in late Fall: this vindicates differences in climatic patterns previously established for this region (Bond and Vecchi, 2003). Whereas the correlation of lifetimes in Winter can be accounted for by the correlation of temperatures, such is not the case for the Fall: the Fall temperature lifetimes imply some forcing with a solar signature. We recall that the region under analysis was chosen because it is under the well established influence of the tropical MJO. We therefore suggest that the MJO may be responsible for the solar signature found in the Fall temperature disturbances. The MJO was first reported in 1971 (Madden and Julian, 1971) and no-one knows whether it operated in the North Pacific domain prior to 1950 in the same way as it now does. Therefore, the main signal in temperature variability common to meteorological stations in the 250 by 550 km studied area is sensitive to solar forcing. But sources of geographical heterogeneity in the original temperature data largely mask this signal. The corresponding modulation is not small. Fig. 4 shows that the amplitude range of change in temperature lifetimes is 30–40% of its absolute mean value, similar to the modulation of the filtered sunspot numbers. This is a considerable amplification compared to the relative change in solar irradiance over the same period, ¨ which is on the order of one per mil (e.g. Frohlich, 2006; Scafetta and Willson, 2009). This small value is often argued to be a reason to neglect the influence of solar irradiance changes. Although the physical mechanism of this large amplification still needs to be found, the observational evidence we provide here argues in favor of a modulation of climate disturbances (and the MJO) by the Sun, which we observe in Oregon and Washington, over the second half of the 20th century. We note that it is not presently possible to better measure the degree of solar forcing without an adequate model of that forcing. This is particularly difficult given the regional and seasonal variability of the effect and the complex nature of the climate system. Incidentally, Soon et al. (2000) find that variable fluxes of solar charged particles or cosmic rays modulated by the solar wind may influence the terrestrial tropospheric temperature on timescales of months to years. Now, we have ensured that the mean Fall temperature lifetime curve we have obtained (1) is stable with respect to a shift in season or region, (2) correlates well with individual lifetime curves, and (3) cannot be explained by some dependence between the original temperature series and therefore requires some common forcing. One could argue that the similarities we observe between proxies of solar activity and lifetimes of temperature (as illustrated in Fig. 4b, c) might happen by chance. The probability of such an occurrence would seem not to be small, since the effective number of observations (or degrees of freedom) would be the ratio of the length of the time series (5–10 decades) to the length of the averaging window (11 yr). However, this reasoning would be wrong, since it would suppose implicitly that the daily values of the different series are independent at each moment. Such a stochastic argument would indeed be irrelevant: in fact, we have two non-random curves which obviously present similarity. We underline once again that the same behavior is ¨ et al., 2008, obtained for European temperatures (Le Mouel

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2009a, b): this is a strong argument in favor of the reality of a solar forcing of the observed evolution. We wish to emphasize an interesting finding of this paper (and several of our previous papers on this subject), which is well illustrated by Fig. 4c, namely the fact that there is a remarkable correlation of curve shapes in terms of phases, but not of the ratio of amplitudes which can even change from being larger than one to smaller than one from one cycle to the next. This we believe is a possible tell-tale sign of non-linear behavior of a complex system. Phase locking by a driving mechanism requires far less energy than amplitude locking. This is also why many more traditional time-series analysis tools would fail to properly reveal the correlations we have shown here and elsewhere. We have recalled the fact that temperature disturbances are strongly influenced by the Pacific Ocean in the NW US and by proximity of the Atlantic Ocean in Europe, both regions being influenced in a similar way by Arctic blocking and tropical circulation. Similar boundary positions and relative continent/ ocean/atmosphere geographical arrangements make temperature disturbances in both regions extremely sensitive to any change in the equilibrium of the global climate system. We hypothesize that there could be other areas in the world, in addition to Europe and ¨ et al., 2008, 2009a), that display a solar North America (Le Mouel signature in climate data, but there should also be areas where it ¨ et al., 2008). This signature is subdued or even absent (Le Mouel and its strength might also vary with time at a given station (see Fig. 4c). In other words, a correlation will exist at some time, and fade at some other time. This may for instance be a reason why in ¨ et al., our previous study of six climate regions of the US (Le Mouel 2008), we found that, in some of them, the strong solar signal after 1940 was not present prior to that date (see also Figs. 2b, 4a). These complexities, linked to spatial and temporal variabilities, and seasonal aspects, need to be taken into account and make the analysis of temperature data delicate. This may be a reason why a clear correlation between temperature disturbances and solar activity as displayed in Fig. 4 has apparently not been reported so far. Solar forcing is clearly a worldwide phenomenon. But due to its complexity, the atmospheric system does not respond to it everywhere and all the time in the same global way. The response displays regional and temporal inhomogeneities at different scales. If one wishes to try and reveal this solar signature in temperature series, it is preferable not to look for it without a plan, but to select places where and times when the response shows up with particular strength, as we have found to be the ¨ et al., 2009a, b), and in the US North case in Europe (Le Mouel Pacific, as shown in this paper.

Acknowledgements We thank Jean-Paul Poirier, Claude Jaupart and Fre´de´ric Fluteau for reading and commenting on the first version of this paper. This is IPGP contribution NS 2558.

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Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jastp.2009.10.011.

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