central england temperatures: long‐term variability and teleconnections

INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 19: 391–403 (1999)

CENTRAL ENGLAND TEMPERATURES: LONG-TERM VARIABILITYAND TELECONNECTIONS

TIMOTHY C. BENNER*Program in Atmospheric and Oceanic Sciences, Uni6ersity of Colorado, Campus Box 311, Boulder, CO 80309-0311, USA

Recei6ed 25 March 1998Re6ised 8 August 1998

Accepted 1 September 1998

ABSTRACT

Past long-term climate variability is important for the prediction of both future climate and human impacts onclimate. Teleconnections help to reveal the interactions between the components of the climate system. This researchexamines the central England temperature record from 1659 to 1997, both for its own variability and for itsrelationship to other climatic records. Results support a possible warming trend, especially in recent years. Fourindependent spectral analyses show several common, prominent periods of oscillation in the record, from a few yearsto nearly two centuries. Wavelet analysis emphasizes the non-stationary nature of this variability. Temperatures maybe related to solar irradiance and sunspot numbers over long periods. They show a connection to the North AtlanticOscillation, especially over periods of 7–8 years. However, they show no apparent relationship to the El Nino–South-ern Oscillation. Copyright © 1999 Royal Meteorological Society.

KEY WORDS: Central England temperature; spectral analyses; wavelet analyses; solar irradiance; sunspots; North Atlantic Oscilla-tion; spectra; ENSO

1. INTRODUCTION

Understanding long-term climate variability is of great importance, both for predicting the future climateand for estimating the possible impacts of human activities on it. Long climatic records provide a meansfor detecting anthropogenic changes and a background against which to search for these changes.Consequently, many studies have examined such long records originating from all over the globe (Stockerand Mysak, 1992; Mahasenan et al., 1997; Overpeck et al., 1997). These records may involve, for example,tree rings, ice cores, isotope ratios, pollen counts, instrumental records, or documentary evidence.Relationships between different long climatic records are also of great importance, as they provide themeans for understanding the interactions of different components of the climate system.

The central England temperature (CET) record is the longest instrumental temperature time series,making it a valuable resource for investigating long-term climate variability. Manley (1953, 1974)compiled it from a variety of sources, and others have carried on this work. The record now stretchesfrom January 1659 to 1998, with values recorded every month, and since 1772, every day (Parker et al.,1992). Jones and Hulme (1997) provided a thorough overview of the record. This study considers 339years of the time series from January 1659 to December 1997, using either monthly values or annualaverages where appropriate. It analyzes the CET record for trends and prominent periods of oscillationand looks for connections between the CET record and other long time series. While many studies haveexamined specific aspects of the CET record, sometimes with a single analysis tool, this research attemptsto provide a more comprehensive, yet concise, look at the record and its relationship to other elementsof the climate system.

* Correspondence to: Program in Atmospheric and Oceanic Sciences, University of Colorado, Campus Box 311, Boulder, CO80309-0311, USA. Tel.: +1 303 4928653; fax: +1 303 4923524; e-mail: [email protected]

CCC 0899–8418/99/040391–13$17.50Copyright © 1999 Royal Meteorological Society

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2. ANALYSIS

2.1. Trends

Potential trends in the temperature data are of particular interest, as they may represent long-termclimatic change. Figure 1 shows the full time series, with the mean and annual cycle removed and a10-year Gaussian filter applied. Visual inspection of these data reveals several major features. First, theearliest part of the temperature record is the coldest, with a consistent cold anomaly and a large departurefrom the mean. It has been suggested that this may represent the major cold spell of the ‘Little Ice Age,’a climatic phenomenon that was geographically variable and probably lasted into the 19th century (Jonesand Bradley, 1992b). Second, the middle part of the record shows several fluctuations of up to ahalf-degree around the mean. Third, the latter part of the record exhibits an apparent warming trend,especially after ca. 1900. Of course, such observations require quantification. Probert-Jones (1984)concluded that the temperature from 1659 to 1973 was statistically homogeneous, but noted that the latterpart of the 17th century was particularly cold. Extending the time series to 1997 yields some similarresults. The coldest long-term (10-, 30-, and 50-year) means all fall during the cold, early part of therecord, while the warmest long-term means occur during the 20th century. However, Matyasovszky (1989)found a general warming trend underlying the many fluctuations in the 1659–1973 temperature record.Jones and Bradley (1992a) and Jones and Hulme (1997) also found long-term warming trends for moreup-to-date CET records. Simple quadratic fits applied here, whether to the full time series, the period after1800, or particularly the period after 1900, all support an overall warming trend, although these fits arenot remarkably good; these are shown in Figure 1. The magnitude of the overall warming correspondswell to that found by Jones and Hulme (1997), who used linear fits. One might initially suspect that atleast some of the recent temperature increase might stem from the urban heat island effect. However, theCET record is a combination of multiple station records (Manley, 1953, 1974), some of them less urban,and there have been small corrections for urbanization in recent decades (Parker et al., 1992). In any case,any potential trends must be separated from long-period oscillations in the data.

2.2. Oscillations

Also of interest are potential oscillations in the temperature record. There are many ways to determinesuch oscillations, and many of these methods have already been applied to the CET record. These includestandard power spectra (Dyer, 1976; Stocker and Mysak, 1992), the maximum entropy method (MEM;Mason, 1976; Folland, 1983), singular spectrum analysis (SSA; Plaut et al., 1995; Mahasenan et al., 1997),and the global wavelet spectrum (Baliunas et al., 1997). However, when used independently, each of these

Figure 1. Monthly CETs from 1659 to 1997, with a 10-year digital filter applied to emphasize interannual variability. Horizontaldashed line shows average monthly temperature for the entire record. Curved lines are quadratic fits to all the data (solid), data after

1800 (dashed) and data after 1900 (dash–dot)

Copyright © 1999 Royal Meteorological Society Int. J. Climatol. 19: 391–403 (1999)

CENTRAL ENGLAND TEMPERATURES 393

Table I. Periods of oscillation (in years) in the CET record determined from fourspectral techniques

FFT LSP SSA Wavelet

Significance (%) Significance (%)PeriodPeriod

99 3.09 99.9 — —3.08—3.39/3.40983.46983.46

5.16 99 5.15/5.165.06 97 —— — 5.90/5.95— — —

7.68/7.74997.40 7.59997.3799 15.23 99.9 14.61/15.54 14.6715.41

23.6226.17/32.7799.923.7899.924.2167.78 99.9 —67.80 99.9 69.77

112.97 99.9 —113.00 99.9 108.53— 184.1199.9193.67——

methods has its weaknesses. Fourier spectra have poor frequency resolution at lower frequencies. TheMEM is subject to roundoff error and spurious peaks, and it lacks error analysis capabilities, while SSAis controversial due to trend handling and climatic quasi-periodicities (Baliunas et al., 1997). The globalwavelet spectrum is extremely smooth and may miss some spectral features, and it omits the greateststrengths of wavelet analysis, i.e. simultaneous time and frequency localization. Consequently, previousstudies have all obtained somewhat different results. The use of different end-points for the time seriesfurther exacerbates this disparity. All of these analyses, however, have shown evidence of decadal andbidecadal oscillations in the CET record, and an oscillation of 7–8 years has also been common.

This study used four methods to search for significant oscillations in the monthly 1659–1997 CETrecord; the results appear together for comparison in Table I and Figure 2. The mean and annual cycleswere removed prior to these analyses. Where applicable, red noise significance levels were calculated usinga background spectrum based on the lag-1 autocorrelation (Gilman et al., 1963) and a x2/n distribution.The first method used was the standard Fourier transform (FFT). Figure 2a shows the resulting spectrumand significance levels. Several significant oscillations are readily apparent.

The second method used was the Lomb–Scargle periodogram (LSP; Lomb, 1976; Scargle, 1982). Thismethod attempts to fit sinusoids of different periods to the data using least squares. In this case, theprincipal advantage of the LSP—handling unevenly spaced data—was not a factor, since the data wereevenly spaced. Rather, the LSP was chosen for its oversampling capability, which allows the use of anarbitrarily large number of periods, far more than a standard FFT. This produces a smoother spectrumand locates spectral peaks more accurately. Figure 2b shows the LSP spectrum, in which the periods havebeen oversampled by a factor of 20. This spectrum was normalized to the FFT power level, and red noisesignificance levels were computed as for the FFT. The major peaks agree with those of the FFT, althoughthe oversampling has localized them more accurately. One interesting feature is the split of the FFT peakat 113 years into two peaks at about 113 and 194 years.

The third method was SSA, for which the monthly data were annually averaged. This produced a setof empirical orthogonal functions (EOFs), pairs of which represented specific oscillations in the data.Results varied for different parameters, such as the maximum lag and the starting month of the averagingperiod. For example, the periods of given EOFs were shifted, and the fractional variance contained ineach pair was altered, sometimes considerably. The exact period of each EOF was computed using agreatly oversampled LSP, resulting in a set of sharply-peaked spectra. Using the starting parameters ofPlaut et al. (1995), for example, yielded results mainly consistent with theirs. However, the choice of a40-year lag meant that the longer-period oscillations in the FFT and LSP spectra did not appear in theSSA results; these results appear in Figure 2c. Note that the periods for a given pair of EOFs may or maynot be identical; this results from the use of a finite lag. When the window length is much larger than theperiods of a given EOF pair, then those periods will tend to merge. When the window length and the


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periods are comparable, the periods will be less similar. The rather small number of points in the annualtime series is also a confounding factor (Vautard and Ghil, 1989). Plaut et al. (1995) used their SSA resultsto predict future temperatures. This demonstrates the inherent complexity and difficulty of climateprediction, as recently observed CET values have been more than half a degree warmer than theirestimates.

The fourth method used the global wavelet spectrum, computed in accordance with Torrence andCompo (1998) and oversampled in period by a factor of ten. This oversampling is analogous to that ofthe LSP. As shown superimposed on the FFT spectrum in Figure 2a, this spectrum is very smooth.Consequently, the shorter-period oscillations seen in the other analyses are not discernible here. There isa good match for the longer-period oscillations, however, including the two century-scale oscillationsresolved by the oversampled LSP.

Taken together, these results are quite consistent. It may be hoped that combining these four differentanalysis methods compensates for the inherent weakness of any individual method, thus increasing thereliability of the results. The temperature record exhibits a wide variety of significant oscillations, rangingin period from a few years to nearly two centuries, and these are summarized in Table I with their

Figure 2. Spectra of CET data from 1659 to 1997, based on four methods: (a) Fourier transform and global wavelet spectrum; (b)Lomb–Scargle periodogram; and (c) singular spectrum analysis. Data for (a) and (b) are monthly; data for (c) are annual averages.

Solid lines in (a) and (b) are 95% red noise significance levels. Dashed lines in (c) show peak periods



Figure 3. Normalized local wavelet power spectrum of monthly CET data. Dash–dot line indicates the cone of influence (COI).Solid isopleths are 95% red noise significance level. Dashed horizontal lines show major periods of oscillation found by other

spectral techniques

appropriate red noise significance levels. One might naturally suspect the validity of the very long-periodoscillations, as they can undergo only a small number of realizations in the span of the time series, buttheir appearance in two separate analyses lends them some credence. Furthermore, Stocker and Mysak(1992) have suggested thermohaline circulation forcing of atmospheric temperature on century time scales,which could help explain these long-period oscillations.

These four methods, and others like them, still share one common weakness. It is difficult to determinewhether or not a given oscillation persists through the entire time series and, if not, when it is strong andwhen it is weak. One possible method is to split the time series and examine the spectrum of each subseriesfor common oscillations. However, depending on the number of subseries, the time resolution is coarse,and splitting reduces the spectral resolution and the maximum resolvable period. Fortunately, waveletanalysis is admirably suited to provide this information. Therefore, the CET record was subjected towavelet analysis using the techniques of Torrence and Compo (1998), with a Morlet wavelet base, periodsoversampled by a factor of ten, and 95% red noise significance levels based on the lag-1 autocorrelation.The resulting normalized local wavelet power spectrum appears in Figure 3. It is immediately apparentthat none of the oscillations found with the previous analysis methods persist through the entire CETrecord. Rather, they appear for part of the record, or only sporadically. As one might expect, theoscillation appearing most consistently throughout the time series is the longest, at about 180 years. Ofall the significant oscillations in the spectrum, it most closely resembles the spectrum of a pure oscillation,and it exceeds the 95% significance level throughout the entire record. It must be viewed with caution,however, as it lies entirely within the cone of influence (COI) of the wavelet analysis. (The COI is theregion of the wavelet spectrum where edge effects from the Fourier transform become important, thusreducing the reliability of the results.) Similarly, the 110-year oscillation also exceeds 95% significancethroughout the record, although it is much stronger in the early part of the record. It too lies mainlywithin the COI and therefore must also be viewed with some caution. Conversely, the significantshorter-period oscillations lie mainly outside the COI, where they are not influenced by edge effects. The68-year oscillation is not readily discernible, but the strong 24-year oscillation extends throughout theentire first half of the record, especially in the first quarter. Power corresponding to the 15-year oscillation


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appears most strongly in the first half and sporadically in the second half. Shorter-period oscillationsappear sporadically throughout the record. This reaffirms the known fact that the CET record, like anyreal climate record, is much more complex than a series of discrete oscillations. Although spectral analysistechniques may discover evidence of such oscillations, it must be remembered that they may not existthroughout the entire record, which suggests that the physical mechanisms driving them may not either.

3. CONNECTIONS

It is worthwhile to look for connections between the temperature record and other parameters, climaticor otherwise. Such connections may serve to explain the physical mechanisms underlying features in therecord, such as the prominent oscillations; they may prove useful when applied to agriculture ormeteorology, for example; or they may simply demonstrate that geophysical records do not exist in avacuum. Burroughs (1985) found a relationship between the CET record and French wine harvest dates,and Duncan (1991) compared the CET record to a similar time series of temperature data fromEdinburgh, Scotland.

3.1. Solar irradiance and sunspots

The sun provides the energy that drives the earth’s climate system, and many researchers havepostulated solar causes for terrestrial climate change (Crowley and Kim, 1996; Haigh, 1996; Stevens andNorth, 1996). Sunspot numbers are often used as a convenient proxy for solar activity, as they have beenrecorded for centuries and are therefore readily available. Annual average numbers begin in 1650, whilemonthly numbers start in 1749. Eddy (1976) discussed the reliability of these numbers in various historicalepochs and described evidence to support the reality of the Maunder Minimum from 1645 to 1715. Morerecently, Lean et al. (1995) presented a series of reconstructed solar total irradiance values since 1610,combining the 11-year Schwabe cycle with a longer-term variability component. These irradiance valueshave been related to Northern Hemisphere temperatures (Lean et al., 1995) and global-average upperocean temperatures (White et al., 1997).

This study compared the annual means of central England temperature, reconstructed solar totalirradiance, and sunspot number from 1659 to 1997. These data appear in Figure 4. LSP spectra for thedata, oversampled in period by a factor of 100, appear in Figure 5 with the appropriate 95% red noisesignificance levels. For periods less than a few decades, the spectrum of the irradiance data shows onlyweak oscillations, while the spectrum of the sunspot data shows two clusters of weaker peaks at 5.5 and8.5 years and many other much weaker oscillations. (For this figure, the overwhelming 11-year Schwabecycle was removed from the sunspot data by harmonic fitting to better show the other peaks.) There areno convincing common short-period peaks between the temperature spectrum (described above) and theirradiance and sunspot spectra. The Schwabe cycle is conspicuously absent from the CET data. Thismakes sense, as it is also relatively weak in the irradiance data, and temperature should respond toirradiance. Over long periods, however, the irradiance and sunspot spectra show the same two large peaksat about 110 and 190 years, as does the temperature spectrum, albeit at lower significance levels. Theseoscillations appear in the wavelet power spectra of all three time series and are prominent in thecross-wavelet spectra (not shown). They probably correspond to the Gleissberg cycle, which modulates theSchwabe cycle, and the Seuss cycle, which is commonly seen in carbon-14 records (Hoyt and Schaten,1997). Over shorter periods, the wavelet spectrum of the CET data has little in common with those of theirradiance and sunspot data. (These same wavelet spectra also clearly show that the shorter-periodsunspot oscillations, excluding the Schwabe cycle, are sporadic rather than cyclic (Schonwiese, 1978).)Finally, Figure 6 shows the coherence of the CET data with the irradiance and sunspot data. Thiscoherence is a measure of the degree to which the series vary together or to which they are jointlyinfluenced by oscillations of a particular period. The CET data exhibits relatively strong coherence withthe irradiance and sunspot data at periods longer then a few decades, well above the 99% significance



level, but only weak coherence at shorter periods. Taken together, this all suggests that variations intemperature are thus connected to solar activity over long time scales. Some caution is warranted,however, since these long-period oscillations can undergo only a small number of realizations in the spanof the time series, providing few samples for the spectral calculations. In addition, they lie almost entirelywithin the COI of the wavelet analysis, meaning their significance is not assured. At the same time, itappears that variations in temperature are not connected to solar activity at short time scales. Mason(1976) suggested that the peak in the CET spectrum near two decades may be associated with the doubleSchwabe sunspot cycle. However, comparison of the wavelet spectrum of the CET data and the timeseries of sunspot data clearly shows that the two-decade oscillation is strongest in the early part of theCET record, at times when the amplitude of the sunspot cycle is relatively low.

Linear correlations between the CET data and the solar irradiance and sunspot data appear in TableII. The annual data are correlated to the 99% level. The correlations improve substantially after decadalaveraging or 30-year low-pass filtering (as in Schonwiese, 1978), remaining 99% significant and explainingmore of the variance at longer time scales. Interestingly, temperature is more strongly correlated to solarirradiance after the start of the industrial period (nominally 1800) than before. This is directly oppositeto the findings of Lean et al. (1995) regarding solar irradiance and Northern Hemisphere temperature,

Figure 4. Time series of annually averaged (a) CET data, (b) reconstructed solar total irradiance and (c) sunspot number from 1659to 1997. Thick solid lines show 30-year low-pass filtered data. Vertical dash-dot lines show limits of subseries correlations described

in text


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Figure 5. Lomb–Scargle periodograms of annually averaged (a) central England temperature data, (b) reconstructed solar totalirradiance and (c) sunspot number from 1659 to 1997. Solid lines are 95% red noise significance levels. The 11-year Schwabe sunspot

cycle has been removed to facilitate comparison in this figure

which were more strongly correlated before the industrial period, when carbon dioxide was less likely tobe a confounding influence. The correlation between temperature and sunspots does not appear to beparticularly sensitive to this. Of course, one might expect a more sensible correlation with large-scalehemispheric temperature than with a smaller-scale regional temperature like the CET, where other localinfluences might be more likely to swamp the solar signal; therefore, some caution is also warranted here.A simple numerical simulation shows that the correlation between two red noise time series may beexpected to increase as the series is low-pass filtered, although the correlations found in the data aresubstantially larger than expected from random chance. Furthermore, the magnitude of the correlationdepends strongly on the subset of the series being considered. As seen in Figure 4, the series are mostsimilar at their beginning and end, and this heavily weights the correlation coefficient. When only themiddle 194 years of the 30-year low-pass filtered data are used, as indicated by the dotted lines, thecorrelation is far smaller and B75% significant. Conversely, when only the outer remainders are used, thecoefficient is even higher, being 99.9% significant. Scatter plots, which for such smoothed data areanything but scattered or symmetric, show this quite vividly. At the beginning of the time series, up toabout 1720, the central England temperatures show one of the cold spells of the Little Ice Age, while the



Figure 6. Plots of the coherence of the annually averaged central England temperature data with (a) reconstructed solar totalirradiance and (b) sunspot number from 1659 to 1997. Horizontal dashed lines indicate 99% significance levels

irradiance and sunspot numbers clearly show the Maunder Minimum. At the end, after about 1900, allthree series show an increase, although it is unclear whether the temperature rise is a natural variation,a true result of solar activity, or an anthropogenic increase.

Therefore, there may be a connection between long-term solar activity and the temperature of centralEngland, even given the caveats discussed above. The correlations are large and significant at long timescales, and long solar oscillations appear in the temperature data. This supports the idea that thelarge-scale variations in solar activity may help to drive significant climatic events such as the Little IceAge or the Medieval Climatic Optimum (Eddy, 1976); however, a causal relationship is difficult to prove.There is still no clearly defined mechanism for the longer-period solar cycles, and the presence of a cyclein climate data does not necessarily imply the existence of a physical mechanism directly driving it.

3.2. North Atlantic Oscillation

Another parameter of interest is the North Atlantic Oscillation Index (NAOI). This is a measure of thesea level pressure (SLP) difference between the Azores and Iceland, and it reflects the strength andposition of the Azores High and the Icelandic Low. The magnitude of this pressure gradient hasdiscernible effects on the European climate. Hurrell (1995) found that surface westerly winds over Europe

Table II. Linear correlations between CET and solar irradiance and sunspots, fordifferent subseries of the data

Irradiance Sunspots

Yearly0.35 0.25All

B1800 0.23 0.29\1800 0.32 0.15

Decadal0.63 0.62All

B1800 0.33 0.890.580.75\1800

30-year0.700.67All0.68B1800 0.48

0.75 0.66\1800Inner −0.15 0.17Outer 0.89 0.89


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Figure 7. Lomb–Scargle periodograms of annually averaged (a) central England temperature data and (b) NAOI from 1865 to1995. Solid lines are 95% red noise significance levels

are 8 m s−1 stronger during high NAOI winters than during low NAOI winters, and that a high NAOIcorresponds to increased moisture transport and warmer winters over northern Europe. Wilby et al.(1997) found significant positive correlations between the NAOI and the CET record in all seasons exceptsummer. They also found significant correlations, both positive and negative, with rainfall in differentregions of the British Isles.

This study compared the seasonal and annual means of CET and NAOI from 1865 to 1995. These tworecords are correlated to at least 95% significance in all seasons except summer (Wilby et al., 1997) andare also correlated (0.35) to 99.5% significance in the annual average. The relationship is strongest inwinter (0.62). The annual average NAOI is also negatively correlated (−0.27) to the maximumtemperature difference in a given year of the CET record, i.e. the temperature of the warmest monthminus that of the coldest month, to 97% significance. This agrees with the findings of Wilby et al. (1997),who found a significant correlation between the NAOI and the Lamb (1972) westerly weather type, whichis associated with cooler summers and milder winters. FFT spectra and LSP spectra, the latteroversampled by a factor of 100, were computed for both time series. The LSP spectra for the wintermeans are shown in Figure 7. Since the beginning of the CET record was cut off to match the time rangeof the NAOI, several prominent peaks described above are absent. While the two spectra containnumerous peaks, they have only one significant peak in common. This lies at 7.65 years in the FFTspectra and 7.57 years in the LSP spectra. In both cases, it exceeds the 99% significance level and is themost significant peak in the spectrum. It appears in the spectrum of the annual data, but it is strongestin the winter data, and less apparent in other seasons. This oscillation appears in the wavelet spectra ofboth time series and in the cross-wavelet spectrum (not shown). It is strong only in certain time ranges,but these ranges are quite similar in both wavelet spectra, leading to pronounced power in thecross-wavelet spectrum. This power lies mainly outside the COI, confirming its reliability. Within the 7–8year band, the correlation between the scale-averaged wavelet power of the two series is 0.57, meaningthat times of strong oscillation in one series correspond to times of strong oscillation in the other. This



implies that this oscillation of the NAO may be one factor driving the corresponding CET oscillation.(The scale-averaged wavelet power is the weighted sum of the wavelet power spectrum over all thescales in a particular band; it produces a time series of the power in that band.) This oscillation maybe related to the 9–12 year mode that Deser and Blackmon (1993) found in Atlantic sea surfacetemperature (SST) and SLP, and it may be caused in part by sea ice fluctuations in the Labrador Sea.

The NAO may be related to the North Atlantic branch of the thermohaline circulation (THC; e.g.Wilby et al., 1997, and references therein), particularly on interdecadal time scales. A stronger circula-tion transports more warm water northward, raising the SST in the vicinity of the sinking branch ofthe THC and reducing the SST difference between the Azores and Iceland. This in turn may decreasethe SLP difference and thus may be associated with the negative phase of the NAO. Conversely, aweaker thermohaline circulation transports less warm water northward, which leads to a greater SSTdifference between the Azores and Iceland; this may correspond to the positive phase of the NAO. Oninterannual time scales, Atlantic SST and SLP are more strongly affected by changing surface windpatterns and surface fluxes of latent and sensible heat. Deser and Blackmon (1993) and Kushnir(1994) discuss the relevant components of Atlantic surface climate in greater detail. Changing atmo-spheric circulations shift the position of the North Atlantic storm tracks. A higher NAOI leads tostronger westerly winds over the British Isles, which increases the frequency of synoptic disturbancesoriginating in the deeper Icelandic Low and drives them further into Europe. This results in higheraverage temperatures (e.g. in central England), especially in winter, as well as increased moisturetransport and greater precipitation. Conversely, a lower NAOI leads to weaker westerlies or eveneasterlies. This can lead to strong blocking over the North Atlantic, with fewer synoptic disturbancescarried over the British Isles, causing cooler temperatures and decreased rainfall or, in some instances,hot dry summers (Davies et al., 1997).

3.3. NIN0 O SST

Interest in El Nino has increased considerably in recent years, and there is some evidence connect-ing El Nino events to the weather over the North Atlantic and Europe (Fraedrich and Muller, 1992;Fraedrich, 1994) and to rainfall over the British Isles (Wilby, 1993). Thus, a final parameter of interesthere is the NIN0 O 3 SST, which is often used as a measure of the amplitude of the El Nino–SouthernOscillation (ENSO). For completeness, the nearby NIN0 O 1+2, NIN0 O 4, AND NIN0 O 3.4 SSTs arealso considered. These are average SSTs over four particular regions of the equatorial Pacific Ocean(NIN0 O 1+2, 0–10oS, 80–90oW; NIN0 O 3, 5oS–5oN, 90–150oW; NIN0 O 4, 5oS–5oN, 160oE–150oW;Nino 3.4, 5oS–5oN, 120–170oW). Annual and seasonal average values were used here from 1871 to1997, and the beginning of the CET record was cut off to match this range.

Comparison of the CET and NIN0 O 3 SST records does not suggest any convincing relationship.The linear correlation between the two annual records is only 0.02, and their maximum cross-correla-tion is only 0.15 after several years lag. Seasonal correlations range from −0.05 (autumn) to 0.13(summer). The annual spectra, whether FFT or oversampled LSP, do not show any significant oscilla-tions in common. Nor do the individual wavelet spectra or the cross-wavelet power spectrum. Muchof the variance in the NIN0 O 3 SST is in the 2–8 year band, so one might expect to see itslong-distance effects in this band. Therefore, a 2–8 year Gaussian band-pass filter was applied to theCET and SST data. The linear correlation between the filtered time series is −0.04, rising to amaximum cross-correlation of only 0.18 at a lag of 1.4 years. Results for the other NIN0 O SST regionsare similarly negative for annual, seasonal and filtered data. Correlations range from −0.24 forautumn in the NIN0 O 1+2 region, to 0.14 for spring in the same region. Cross-correlations rangefrom −0.35 for autumn in the NIN0 O 1+2 region to 0.26 for winter in the NIN0 O 4 region. None ofthese correlations are especially significant, nor do they have the potential to explain more than asmall fraction of the variance. While this certainly does not eliminate the possibility of ENSO eventsaffecting the weather of the British Isles, it does show that the signature of such events is not presentin the CET record.


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4. SUMMARY AND CONCLUSIONS

Analysis of the central England temperature record has revealed some notable features, both in the recorditself and in relation to other relevant time series. There is evidence of an overall warming trend, and thereis a definite warming in recent years. Whether or not this is related to human activity, in particularincreasing emissions of greenhouse gases, is still uncertain. The temperature anomalies at the beginning ofthe time series, associated with the Little Ice Age, are just as large. Multiple spectral techniques showseveral prominent periods of oscillation in the data, such as those at 3.1, 3.5, 5.1, 7.4, 15, 24, 68, 110 and190 years. Wavelet analysis, however, shows that most of these oscillations are non-stationary and do notpersist for the entire span of the time series. This may reflect intermittent or singular forcing mechanisms.Temperatures appear to be related to solar activity on century time scales, corresponding to thelongest-period oscillations seen in the data. This supports the idea of solar influences on terrestrialclimate. Temperatures are also related to the North Atlantic Oscillation, especially at periods of about7–8 years. This depicts the coupling of large-scale Atlantic Ocean circulations to atmospheric temperatureover the British Isles. However, these temperatures do not appear to be related to the El Nino/SouthernOscillation, which suggests limits to the global influence of these important phenomena. Together, theseresults place the CETs in a larger climatic context, and they provide some insight into the variability ofthe British climate over the last three centuries.

ACKNOWLEDGEMENTS

Thanks to G. Compo, R. Tomas and C. Torrence for their advice on spectral and wavelet analysis.Thanks to J. Curry for constructive criticism and the freedom to pursue this project. Data were acquiredfrom the following sources: (1) CET from the UK Meteorological Office; (2) solar irradiance from J. Leanat the Naval Research Laboratory; (3) sunspots from the National Geophysical Data Center; (4) NAOIfrom the National Center for Atmospheric Research; and (5) NIN0 O SST from the UK MeteorologicalOffice and the National Meteorological Center. Computing was done at the PAOS Computer Facility.Wavelet software was provided by C. Torrence and G. Compo and is available at http://paos.colorado.edu/research/wavelets/.

REFERENCES

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central england temperatures: long‐term variability and teleconnections

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