1076 VOLUME 15J O U R N A L O F C L I M A T E

q 2002 American Meteorological Society

One Hundred Plus Years of Wintertime Climate Variability in the EasternUnited States*

TERRENCE M. JOYCE

Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

(Manuscript received 24 April 2001, in final form 16 October 2001)

ABSTRACT

Interannual anomalies of climate variability in the eastern United States for the past 1001 yr have beenstudied for their spatial EOF structure, long-term changes, and the covariability with several climate indices:the Southern Oscillation index (SOI), North Pacific index (NPI), and North Atlantic Oscillation (NAO) index.Especially for air temperature, wintertime (December–February) variability is much more pronounced thansummertime (June–August). The leading principal component (PC) of wintertime air temperature, which explains70% of the interannual variance, is significantly correlated with the NAO, while the leading PC of wintertimeprecipitation correlates with the SOI. The spatial structure of the leading EOFs have a similar spatial characterwhen compared to the correlation between the data and the climate indices, suggesting that the EOFs can bethought of as proxies for mapping the effects of climate indices upon the eastern United States. The effects ofthe SOI and NPI are generally the same; however, these two climate indices are not independent. The long-term sensitivity of the eastern U.S. climate to the Pacific indices seems only weakly dependent with time, whereasthe NAO has grown considerably in importance with time since the beginning of the twentieth century. Surrogatetemperature data from New Haven, Connecticut, has been used to extend this 1001 yr analysis back into theprevious century, and the apparent long-term trend in the sensitivity to the NAO completely disappeared in thelatter part of the nineteenth century. If a measure of potential predictability is the degree to which interannualclimate covaries with these climate indices, the recent period (post 1960) may overestimate this predictabilitybased on the long-term changes observed in sensitivity.

1. Introduction

The goal of this endeavor is to understand the spatialpatterns and temporal variability of interannual ‘‘cli-mate’’ variability in the eastern half of the United Statesduring the twentieth century. Studies examining climatevariability have usually started with the relationship ofclimate to a given phenomenon, such as the El Nino–Southern Oscillation (ENSO) or the North Atlantic Os-cillation (NAO). In particular, Higgins et al. (2000) usetropical Pacific precipitation to define a high-passed,ENSO-like index and a low-passed, North Pacific in-dex–like (NPI) index during the period 1964–93. A thirdindex employed is the Artic Oscillation (AO) index fromThompson and Wallace (1998). The Higgins et al. re-sults are quite promising for potential predictability ofU.S. wintertime climate as a significant amount of the

* Woods Hole Oceanographic Institution Contribution Number10451.

Corresponding author address: Dr. Terrence M. Joyce, WHOI, 360Woods Hole Rd., Mail Stop 21, Woods Hole, MA 02543.E-mail: tjoyce@whoi.edu

interannual signal in temperature and precipitation canbe related to these three indices. However, as we shallsee, this recent period is one in which climate variabilityis highly correlated with these climate indices comparedto an extended record for the twentieth century as awhole. In this study, we shall start with the dominantspatial patterns and temporal variability in a 1001 yrdataset and then relate different spatial patterns to cli-mate signals. The primary dataset used in this study isbased on the National Climatic Data Center (NCDC)divisional temperature and precipitation data (Karl andKnight 1985; NCDC 1994), a summary of monthly min-imum and maximum air temperature and precipitationdata going back to 1895 and organized by regions withinthe United States. Various climate indices, their corre-lation with one another, and their relationships to theair temperature–precipitation data will be examined. Inparticular, the wintertime period is selected because itexhibits the greatest variability of any season in air tem-perature, and is most easily tied to various indices,which often serve as metrics of wintertime climate var-iability. It will be shown that wintertime data in theeastern United States are sensitive to important indices

1 MAY 2002 1077J O Y C E

FIG. 1. Plot of location of climate time series used. Station detailsare given in Table 1. The box outlined in the ocean is one in whichthe SST will be used for long-term sensitivity to the NAO.

TABLE 1. Data locations for surface meteorology in the easternUnited States.

Site no. State Division Location

123456789

1011121314151617181920

FloridaConnecticutIllinoisLouisianaMaineMichiganGeorgiaMassachusettsMissouriNorth CarolinaNew YorkNew YorkPennsylvaniaVirginiaOhioTennesseeTexasVermontKansasAlabama

33293193274934543235

DaytonaNew HavenChicagoNew OrleansCamdenL’AnseSavannahPlymouthSt. LouisRaleighLong IslandBuffaloPhiladelphiaShenandoahColumbusMemphisDallasBurlingtonKansas CityBirmingham

of climate, and that this sensitivity changes over mul-tidecadal time periods.

2. Seasonal climatologies

We begin with an examination of the seasonally vary-ing surface air temperature and amount of precipitationfrom a series of selected stations in the eastern UnitedStates. Because the spatial scales of variability that wewill uncover are large, we have selected 20 represen-tative locations (Fig. 1, Table 1) all having continuousdata going back to 1895 and analyzed up through 1999,a total time span of 105 years. The temperature data areavailable as monthly means of averaged daily minimumand maximum air temperatures. Our analysis focuses onseasonal and interannual variability. Monthly recordsfor the entire period are pooled to first produce a yearlyclimatology of monthly temperature and precipitation.These climatologies are used to form monthly and thenseasonal anomalies. The annual cycle of variability isillustrated (Figs. 2a,b) for three regions: coastal Georgia,coastal Massachusetts, and Missouri. The range of var-iation of Georgia air temperature is less than Massa-chusetts, with the former having a yearly minimum(maximum) temperature of 118C (278C) and the latter228C (228C). Missouri has the greatest range, havinga more continental climate approaching the Georgiamaximum in summer and the Massachusetts minimumin winter. For precipitation, Missouri has a wintertimeminimum and a spring maximum, Massachusetts haslittle variation, and Georgia has a large July maximumof nearly 18 cm of rainfall. The monthly anomalies forall 20 sites have been aggregated into 4 seasonal groups:winter (December–February), spring (March–May),summer (June–August), and fall (September–Novem-

ber). The ‘‘winter’’ record for 1900, for example, in-cludes the December data from 1899.

3. Interannual wintertime variability

As interannual variations in the wintertime climate isthe largest of the seasons and most tied to large-scaleatmospheric and oceanic climate signals, we will con-centrate on this season (December–February) for ouranalyses. We will, however, contrast some of the wintervariability with the summer period. We have used em-pirical orthogonal function (EOF) analysis (Davis 1976)based on the covariance matrices to characterize thedominant modes of interannual variability. For 20 sta-tions, each having equal variance and each being sta-tistically independent, a Monte Carlo simulation re-vealed that the leading 3 EOFs would account for onlyabout 10%, 9%, and 8% of the total variance, respec-tively. However, this is clearly not the case (Fig. 3) foreither air temperature or precipitation. For the former,the first EOF explains nearly 70% of the total variance,with the second and third EOFs containing 14% and10%, respectively. For precipitation, the three leadingEOFs explain 32%, 21%, and 14% of the variance, re-spectively. We will therefore concentrate our attentionon these leading EOFs, whose spatial structure is givenin Fig. 4. In this display a smoother has been used witha search radius of approximately 700 km, before inter-polation (a similar approach was used for Fig. 7, below).

The leading temperature EOF T1 is of one sign ev-erywhere, with a broad maximum extending from coast-al New England to Georgia and westward to the Mis-sissippi River. The second EOF T2 is positive in thesouth and negative in the north, while T3 varies fromwest to east, with largest positive values in the west.For precipitation, the leading EOF P1 is of one sign and

1078 VOLUME 15J O U R N A L O F C L I M A T E

FIG. 2. Monthly climatology of temperature, (8C, top) and precip-itation (cm month21, bottom) for coastal Massachusetts (dashed line),coastal Georgia (solid), and Missouri (dotted).

FIG. 3. EOF structure for (left) winter temperature and (right) pre-cipitation in terms of the total variance explained by each EOF fortheir respective interannual time series.

largest in the east with a maximum from ChesapeakeBay to southeast Massachusetts, possibly due to vari-ations of the extratropical storm track and its affect onthe coastal region. Second precipitation EOF P2 is pos-itive in the southern United States and negative in thenorth. Third EOF P3 is large and negative in the south,most prominently over Louisiana, Mississippi, and Al-abama and positive in the northeast. We have plottedthe leading temperature and precipitation principal com-ponents scaled to physical units (Figs. 5, 6) showing aleast squares estimate of a trend for each. For contrast,we have also shown the corresponding principal com-ponents (PCs) and trends for the summertime period.The leading summertime EOFs of temperature (55% ofvariance) and precipitation (24% of variance) explainless of their total variance than their wintertime coun-terparts and less overall variance (especially for tem-perature). Long-term systematic trends indicate increas-ing amounts of wintertime precipitation (0.67 cm cen-tury21) and increasing air temperatures (0.758C centu-ry21) over the 105-yr record, with smaller or nolong-term trends in either summertime precipitation orair temperature. It was noted (Houghton et al. 1996) thatin recent decades the increase in air temperature wasfaster in the daily minimum than the daily maximum

temperature. We see here that this same pattern mayapply over the eastern United States on a seasonal basis,with larger trends in winter months than in summer.However, given the amount of variance in the two win-tertime series, the trends are significantly nonzero onlyat the 90% confidence level.

On each of the plots in these figures we have alsoshown the time series of the mean temperature and pre-cipitation anomaly of the 20 stations; in most cases, itis indistinguishable from the leading PC. Since the lead-ing wintertime EOFs represent more of the total varianceand that variance is higher in winter than summer, cli-mate variability is predominantly a wintertime phenom-enon, especially for air temperature. This is not sur-prising since the meridional temperature gradient is sig-nificantly larger in winter than summer. This can be seenby comparing the temperature difference between Mas-sachusetts and Georgia (Fig. 2) in summer (68C) versuswinter (138C), the latter having more than a factor of 2increase in the baroclinicity of the lower atmosphereover the former. The interannual variation in air tem-perature is substantial. For example, one can see a periodin the late 1970s (encompassing the blizzard of 1978)with temperature anomalies of about 238C. The authorrecalls this period in which one could last walk out ontothe waters of Buzzards Bay in Massachusetts, whichwas covered with sea ice.

a. Relationship to and among climate indices

Since it is of some practical interest to understand thepossible sources (and possibly the potential predict-ability) of climate variability, we consider several dif-ferent climatic indices. The Southern Oscillation index(SOI) is defined by the normalized pressure differencebetween Tahiti and Darwin, Australia. The time series

1 MAY 2002 1079J O Y C E

FIG. 4. EOF structure for the leading three modes of (left) winter temperature (T1, T2, and T3)and (right) precipitation (P1, P2, and P3). A nearest neighbor method was used with a searchradius of approximately 700 km prior to gridding.

was made available by P. Jones (2000, personal com-munication; available online at http://www.cru.uea.ac.uk/cru/data/) up through 1997 and afterward from theNational Centers for Environmental Prediction (NCEP)Climate Prediction Center; wintertime (December–March) averages were calculated. The Pacific DecadalOscillation (PDO) index is defined as the leading prin-cipal component of North Pacific monthly sea surfacetemperature variability (poleward of 208N for the 1900–93 period). Digital values of the PDO index were madeavailable by Mantua et al. (1997; and are available on-line at http://tao.atmos.washington.edu/pdo/) and weused winter averages (December–March). The NPI

(Trenberth and Hurrell 1994) characterizing the strengthof the Aleutian low in the North Pacific was averagedover winter months (December–March) and is alsoavailable online at (http://www.cgd.ucar.edu:80/cas/cli-mind/np.html). Finally, the NAO index from Hurrell(1995) is the normalized sea level pressure (SLP) dif-ference between Lisbon, Portugal, and Iceland. All ofthese indices were selected because they had an ex-tended time series throughout the twentieth century.With the exception of the PDO, all of the above indicesare also based upon SLP data. Each of these indices hasbeen correlated independently against the wintertimetemperature and precipitation data (Table 2). Since each

1080 VOLUME 15J O U R N A L O F C L I M A T E

FIG. 5. Time variation of the leading PC for EOF1 (dark line) intemperature (top, winter; bottom, summer). The dashed line (all butinvisible in this figure but visible in Fig. 6) represents the time seriesof the numerical mean air temperature anomaly for the 20 sites. Thestraight line is a least squares fit to the 105-yr trend.

FIG. 6. As in Fig. 5, but for precipitation, units are cm month21

(the seasonal total anomaly shown by the dashed line is 3 times).

TABLE 2. The correlation between the leading EOFs and the climate indices are given for the periods 1900–99 and 1950–99. We haveshown in bold the correlation coefficients that exceed z0.24z in magnitude for 1900–99. The 99% (95%) confidence limits are 60.26 (0.20)for 1900–99 and 60.36 (0.28) for 1950–99. The same elements shown in bold for 1900–99 are also shown in bold for 1950–99.

T1 T2 T3 P1 P2 P3 NAO SOI NPI PDO

1900–99T1T2T3P1P2P3NAOSOINPIPDO

1 20.031

20.0420.01

1

0.0320.3620.31

1

20.2120.38

0.250.011

20.4320.16

0.300.000.011

0.250.180.000.01

20.1220.26

1

20.050.44

20.2520.2720.49

0.090.031

0.110.37

20.3220.0520.4520.19

0.160.461

20.1920.25

0.300.020.350.19

20.0620.4220.57

1

1950–99T1T2T3P1P2P3NAOSOINPIPDO

1 0.081

0.0120.25

1

20.0220.3020.21

1

20.1820.39

0.380.091

20.4520.23

0.220.08

20.061

0.430.160.05

20.0320.0320.34

1

20.020.45

20.4020.2720.60

0.1520.15

1

0.150.42

20.4120.1320.4620.2420.10

0.561

20.2820.44

0.310.130.450.250.10

20.5620.73

1

of the indices are available over a different time span,we chose a common period, beginning in 1900 for thisanalysis. Therefore, the time span for the display ofcorrelation (100 yr.) for the twentieth century is some-what different than that of the temperature and precip-itation data (105 yr.), that define the various EOFs andPCs. Because the PDO and NPI indices are significantlyanticorrelated (r 5 20.57) and the results that followare similar (except with a change in sign) for the NPI

and PDO, we will not focus on the PDO in our sub-sequent discussion.

The NAO is positively correlated with air tempera-tures over the entire region and this spatial structure(Fig. 7, upper left) roughly follows that of EOF1 (Fig.4, upper left), though with a more southeast U. S.weighting, perhaps due to the influence of the strongNAO signal present in the Sargasso Sea, to the southof the Gulf Stream. The NPI and SOI correlation patternin Fig. 7 is positive in the south and negative in thenorth, similar to that of the second air temperature EOFwith the SOI correlation better aligned spatially with

1 MAY 2002 1081J O Y C E

FIG. 7. Correlation coefficient of the (upper) NAO, (middle) NPI, and (lower) SOI with thewinter (left) air temperature and (right) precipitation. For a stationary, white noise process the95% significance level is 60.2. The percent variance explained by each process (if independent)is the square of the correlation coefficient. However, the various indices may not be independentand this must be taken into account before an assessment can be made of the potential predictabilitybased on these indices.

the second temperature EOF than the NPI, but with bothexplaining comparable variance. These correlation mapsare similar to the regression plots for hemispheric win-tertime temperature study by Hurrell (1996) for a 60-yr period from 1935 to 1994, but we shall soon see thatthe amount of variance explained can vary substantiallyover time. For precipitation, the correlation pattern withthe NAO is most similar to EOF3 (Fig. 4) with majoraffects over east Texas and the lower Mississippi Riverbasin. Both the NPI and SOI are similar to precipitationEOF2, with a change of sign, with the SOI influence

extending farther to the north into the plains states andalong the Carolina coast. This latter probably influencesthe relatively weak correlation between the SOI and theleading precipitation EOF (EOF1, containing 32% ofthe variance), but the spatial pattern of this EOF doesnot match up with any of the correlation patterns asclearly as does EOF2.

The nature of the correlation with the NAO is thatmost of the region will have warm winter temperaturesduring high NAO years with wetter than normal wintersover the Mississippi River extending into Texas. Posi-

1082 VOLUME 15J O U R N A L O F C L I M A T E

FIG. 8. Moving average correlation between the NAO and PC1 forair temperature (dark lines) and PC3 for precipitation (light lines).Solid lines represent raw correlation (with time-varying means sub-tracted) and dashed lines represent detrended variables. The thin hor-izontal lines are the 95% confidence limits for 24 degrees of freedom.Note that PC3 for precipitation has been multiplied by 21 to condensethe display.

tive phases of the SOI and NPI will produce warmer,dryer winters in the South. Both the NPI and SOI arerelatively more important for precipitation overall thanthe NAO and for temperature their effect is comparableto the NAO in the southeast, but otherwise smaller thanthe NAO in the eastern United States. This is contraryto many media reports, which often attribute any neg-ative temperature anomalies in New England to El Nino(or La Nina) and all positive anomalies to global warm-ing. Substantial climate variations can arise when twoor more indices are in phase or antiphase. For example,a regression against the three indices independently (notshown) would suggest that in the southeastern UnitedStates, for example, a wintertime temperature increaseof 18C would result from a positive, two-sigma increasein the NAO index, with a similar temperature increasefrom a two-sigma increase in the NPI. When these twoare in phase and positive (or negative), the effects canbe doubled, when antiphase, the effects nearly cancel.

Table 2 summarizes the correlation between the lead-ing EOFs and the climate indices for the periods 1900–99 and 1950–99. We have shown in bold the correlationcoefficients that exceed | 0.24 | in magnitude (the 99%confidence limit is 60.26) for 1900–99. Those sameelements that are bold for 1900–99 are also shown tobe bold for 1950–99. In every case in which there wasa significant correlation for 1900–99 between the tem-perature or precipitation ‘‘signal’’ and the climate indexor a climate index with another index, the magnitudeof the correlation coefficient is either the same (1 case)or increased (14 cases) for 1950–99, suggesting a realincrease in importance of eastern U.S. climate associ-ated with these climate indices during the second halfof the century. The interrelationships of the climate in-dices with themselves will be discussed later. Here wenote that the NPI and SOI indices are significantly cor-related in both periods, in agreement with findings byTrenberth and Hurrell (1994) and Hurrell (1996).

b. Nonstationarity of 100-yr records

The pattern of spatial correlation (as shown in Fig. 7for the 100-yr period) found for the 1960–99 period(not shown) was examined and found to be similar tothat found over the whole period, except for increasesin magnitude, especially for the NAO. This suggeststhat the stability of the EOF pattern is not the issue butrather the changing importance of the different climateindices over time. In order to further examine the changein importance of climate indices over time, 24-yr blocksof data have been selected and their correlation withwintertime modes of temperature and precipitation cal-culated in a moving average sense, beginning in 1900and extending until the present. The PCs for temperatureand precipitation used have remained constant through-out the period with no recalculation in the 24-yr blocks.Our hypothesis is that the long record best defines theimportant spatial modes of the data and we seek only

to understand how these modes relate to individual cli-mate indices over time. Since the 24-yr records maycontain trends (due to longer-term, low-frequency var-iability), we have calculated a raw correlation and onewith both signals first detrended over the 24-yr timeblock. In Figure 8, temperature PC1 (T1) and precipi-tation PC3 (P3) are correlated with the NAO index. Overtime, the relative importance of the NAO increasessteadily for temperature, from levels that are not sig-nificant in the first half of the century to significantlevels in recent years, with the period from 1960 to thepresent being the most robust. The precipitation peaksearly and late in the record with a distinct minimum inthe middle between 1920 and 1940. There is no sub-stantial difference between the raw and detrended cor-relation in either case. For the NPI and SOI, and PC2for temperature and precipitation (Figs. 9 and 10), thecorrelation is much more stable over time with a sug-gestion of lower values in the middle of the record forthe NPI and elevated values for the SOI in recent de-cades. An interesting feature was found in the SOI anal-ysis (Fig. 10) for precipitation: around 1920 and againin the early 1960s, the correlation of P2 with the SOIwas below the 95% significance line. During only thesetwo periods, the correlation (not shown) between theSOI and the leading precipitation mode, P1, becamesignificant. There is general agreement between thesefigures and Table 2. These long-term variations indicatethat the relative sensitivity to different climate indicesmay vary over time in importance to the eastern U.S.climate as represented by the simple EOF decomposi-tion used here. Correlations using the summertime cli-mate indices for either that year or the previous year

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FIG. 9. As in Fig. 8, but for the NPI and PC2 for air temperatureand precipitation, the latter again multiplied by 21 for the presen-tation.

FIG. 10. As in Fig. 9, but for the SOI and PC2 for temperatureand precipitation.

FIG. 11. Moving average correlation among the three climateindices (raw are solid lines and detrended are dashed as in Fig. 8).

were examined but found to be less significant than thewinter indices for the NAO, SOI, and NPI; periods oflow correlation are not due to shifts in sensitivity to adifferent season of the climate index.

Using a similar approach to that above, the cross cor-relation of the different climate indices with one anotherwas examined (Fig. 11). The NPI and SOI are signifi-cantly correlated over most of the record (again in agree-ment with Table 2). The period from 1930 to 1945 isone in which all three climate indices are apparently inphase with one another. If one takes the approach ofregressing the temperature and precipitation data ontothese three different climate indices, one cannot avoidthe fact that over some periods, they are correlated;further, they may be correlated over the whole periodtaken as a basis. This is particularly important for theSOI and NPI as they are significantly correlated overmuch of the record. Thus, the percent variance explainedby the sum of the three regressions on three climateindices is not the same as that expected by summingthe R2 values for each separately, because of this non-independence of the indices. A similar result for thesetwo SOI and NPI indices was also found by Hurrell(1996). An alternate approach might be to use the threeindices to define an orthogonal index set and use thisfor the decomposition. It is remarkable that the threeindices used by Higgins et al. are only weakly corre-lated. Unfortunately, they do not extend far enough intothe past to be of use for longer-term studies over thetwentieth century.

c. An extended look at the NAO influence from 1865

The Hurrell (1995) NAO record of sea level pressuredifference between Lisbon and Iceland extends backlonger (to 1865) than the atmospheric records used so

far. Long time series observations are available at se-lected locations in the eastern United States all the wayback into the eighteenth century. One site, New Haven,in coastal Connecticut, will be used as a surrogate toillustrate some interesting features about the NAO–airtemperature correlation, extending the record back until1865. Here we have combined the New Haven recordwith the NCDC (Table 1) record from coastal Con-necticut, recalculated a seasonal climatology, selectedwinter months, and correlated this record (Tnh) with theNAO signal used above with the same 24-yr window,but beginning in 1865 (Fig. 12).

As in Fig. 8, the correlation between the New Havenair temperature (Tnh) and the NAO is low in 1920 andincreases in time, reaching significant levels after 1960.The longer record shows that the initial points on the

1084 VOLUME 15J O U R N A L O F C L I M A T E

FIG. 12. (top) Moving average correlation of the NAO and winterair temperature in New Haven, CT, (Tnh) over the extended recordbeginning in 1865 and the moving average of the NAO index. Asearlier, the detrended correlation is shown as a thick, dashed line.(bottom) The NAO and Tnh autocorrelation values at a 1-yr lag areshown, which should be zero, or below the significance level for zerocorrelation, for white noise signals. Also plotted is the unlagged cor-relation coefficient (thin black line) between the NAO and the de-trended SST time series near the eastern U.S. coast, south of the GulfStream, from Kaplan et al. (1997).

earlier curve using PC1 for air temperature were actuallythe low point in the extended record, with correlationincreasing as one moves backward in time to years ear-lier than 1920. In the late nineteenth century, the cor-relation is again significant, suggesting that any ‘‘trend’’in Fig. 8 is really part of a longer period cyclic change.Rather than a long-term increase in sensitivity of airtemperature at New Haven to the NAO, we see a sub-stantial decrease in sensitivity in the years 1905–45,when the correlation between the two records is for themost part ,0.2. An interesting pattern in the lag-1 au-

tocorrelation of the NAO (especially) and the Tnh recordas well is a change in character from short, quasi-bi-ennial scales to longer timescales in recent years (Fig.12, lower panel). The autocorrelation function of theNAO from 1964 to 1999 (not shown) suggests a sig-nificant negative lobe at 4-yr lags (corresponding to 8-yr periods). This is in contrast to that in the early partof the record, where the 1-yr lag is negative and sig-nificantly greater than zero. These findings are in agree-ment with Hurrell and van Loon (1997.) who did piece-wise spectral analysis of the NAO record over a longperiod of time and found changes in spectral characterof the NAO. Hurrell and Van Loon also found, usingan extended NAO record and the wintertime air tem-perature in Copenhagen, Denmark, that the highest co-herence was found in narrow bands that change fromquasi-biennial in the early part of the record to having6–10-yr energy in recent years. These changes do notapparently depend on the value of the NAO since thelow-passed NAO record indicates very long timescalesof variability with a pair of minima and maxima in therecord, although one must acknowledge that the presentregime of high NAO may not be over yet. In the presentcase for Connecticut, it remains a puzzle that a more‘‘tuned,’’ narrowband NAO signal might be linked to abetter correlation with the winter temperature record.

The sea surface temperature (SST) data product pre-sented by Kaplan et al. (1997) and available online upto the present has been used as an additional variablein the analysis. These data are from the winter months(January–March) and represent the average SST in a108 3 108 square centered on 308N, 708W, off the eastcoast of the United States, south of the location of theGulf Stream (the data location is presented in Fig. 1).This region is one in which the SST and the NAO arepositively correlated, the SST signal being one of thethree lobes in the SST tripole associated with the NAO(Cayan 1992). The correlation of this signal with theNAO, tracks that of Tnh (the two temperature recordsare significantly correlated) and both reach their lowestlevels of sensitivity to the NAO in the same periodaround 1920. Since both the air and sea temperaturecorrelation with the NAO are decreasing in recent yearsas the NAO peaks, it is of some interest to speculateon whether the cycle leading to the 1920 minimum willbe repeated in the coming decade(s).

4. Discussion

Our initial motivation for starting on this analysis wasto understand how wintertime temperatures in the east-ern United States varied over time using a 1001 yrdataset prepared by NCDC. The spatial patterns of cov-ariability between local measurements and these climateindices closely follow the spatial patterns of the win-tertime temperature and precipitation EOFs based onthe 1001 years of climate measurements alone, sug-gesting that the EOFs themselves may independently

1 MAY 2002 1085J O Y C E

reflect the long-term relationships with various climateindices. Sensitivity to changes in the SOI and NPI werefound to be relatively weak during the century. How-ever, the twentieth-century data alone suggested a grow-ing importance over time between the observed easternU.S. climate and the NAO. An extended analysis usingNew Haven as a proxy, suggested that this was merelya manifestation of an even longer-term cyclic behavior,with the period of the 1920s one of low sensitivity tothe NAO.

As was noted, the Higgins et al. (2000) work waslimited to a 30-yr period from 1964 to 1993 and wasone in which significant relationships of wintertime U.S.climate were uncovered with variability in the tropicalPacific tied to ENSO, the PDO/NPI, and with the AO,which is essentially the same as the NAO according toDeser (2000). We used a different set of climate indicesfor our study, in large part because of the extended timeseries offered by mainly SLP ‘‘indices’’ obtained fromlocal averages (NPI) or point differences in SLP (SOI,NAO). While these point measurements offer an ex-tended time series, they differ from more robust EOFdefinitions based on a more complete dataset (Deser2000). Subtle shifts of the centers of action or spatialstructure of different climate phenomena may not bereflected in these point indices. Furthermore, our resultsindicate that restricting one’s attention to recent datawill inflate the degree to which different climate indicesare estimated to affect eastern U.S. climate. This is mostnotable for the NAO.

Rogers (1985) has shown that the correlation of theNAO with Oslo, Norway, wintertime temperatures hasundergone similar century-long changes with the NAO.In particular, there was a period in the beginning of thetwentieth century when the correlation was zero. Heattributes this to changes in the zonal location of theIcelandic low for the same values of the zonal flow index(NAO). Thus the pattern of interannual variability inwintertime climate is not completely captured by theNAO index alone. We have also examined long-termtemperature records in Oslo and west Greenland, usingthe same 24-yr running analyses as for New Haven. Oslois correlated with New Haven and west Greenland isanticorrelated, as we would expect from the thermalanomaly pattern of the NAO as first pointed out byWalker (1924). Oslo has a low point in its sensitivityto the NAO in 1924 (and again in 1960), while westGreenland has its minimum in 1908. This is well withinthe low-correlation period for New Haven, which ex-hibits the greatest change in sensitivity of the three sites.Thus, subtle changes in the pattern represented by theNAO index may have more impact on the eastern U.S.winter climate than in northern Europe.

Another potential cause of long-term sensitivitychanges may be the changing spectral character of theNAO variability: a whiter spectrum may produce lessof a climate signal in the eastern United States than onewith energy concentrated in narrow bands, whether they

are quasi-biennial or decadal. Thus, an approach thatexamines the spectral evolution of the coupling, as inMann and Park (1996), might provide further insight.In that study, the authors noted the degree to which thequasi-biennial temperature variability (mainly in winter)varied significantly over time and had a spatial structuresimilar the NAO pattern. Thus, changes in both the spa-tial pattern and the spectral characteristics of the forcingmay affect the robustness of the climate response to theNAO.

We have not examined the long-term sensitivity ofthe SOI or NPI to the eastern U.S. climate extendingback more than a century in time as we have above forthe NAO. This was in part because the NAO variationsover time seem to be larger than the SOI during thetwentieth century, although some shift in SOI sensitivitybetween precipitation EOFs occurred during two timeperiods in the record. It is also in part because therehave been previous studies of long-term sensitivity oftropical SST to ENSO. Elliot and Angell (1988) sug-gested that long-term changes in the centers of actionof the SOI might be at the root of long-term changesin sensitivity of tropical SST to various indices ofENSO. And as noted by Gu and Philander (1995), thevarying spectral characteristics of ENSO forcing mayalso play a significant role in the SST response of thetropical Pacific. The period of low correlation betweenthe NAO and New Haven, centered around 1925, is onein which there appears to be ‘‘unusual behavior’’ in thetropical Pacific, when the Southern Oscillation ‘‘fadedaway’’ according to Gu and Philander. As noted above,there was nothing remarkable about this period in thesensitivity of eastern U.S. wintertime climate to the SOI,except for the switch in precipition EOF pattern in theSOI sensitivity already mentioned.

Although the wintertime signals have been the focusof this work, the summertime results shown have in-dicated that the amplitude of interannual variability insummer temperatures is much smaller than in the winterbased on the leading principal component. Furthermore,long-term trends in summertime air temperatures are notseen whereas there is an upward trend in wintertime airtemperature and precipitation of about 0.758C century21

and 0.67 cm century21, respectively, although these areonly significantly positive at the 90% level. Finally, wehave estimated from the correlation between PC1 of thewinter and summer season that there is no significantcorrelation between the wintertime temperature and thatof the preceding summer (0.14), whereas the correlationbetween the winter and the following summer (0.25) issignificant at the 95% level, suggesting that there issome seasonal persistence and therefore some weak pre-dictability of the summer season based on the previouswinter.

Acknowledgments. We acknowledge the support ofNSF Grant OCE98-18465; the efforts of Jane Dun-worth-Baker in collecting the data and performing many

1086 VOLUME 15J O U R N A L O F C L I M A T E

of the calculations; Chris Weidman, who first pointedout the long-term cycles in the New Haven record andthe NAO as layed down in clam shells; Cecilie Maur-itzen for comments on the long-term variability and theNAO in European data; Jeffrey Park and an anonymousreviewer.

REFERENCES

Cayan, D. R., 1992: Latent and sensible heat flux anomalies over thenorthern oceans: Driving sea surface temperature. J. Phys.Oceanogr., 22, 859–881.

Davis, R. E., 1976: Predictability of sea surface temperature and sealevel pressure anomalies over the northern Pacific Ocean. J.Phys. Oceanogr., 6, 249–266.

Deser, C., 2000: On the teleconnectivity of the ‘‘Arctic Oscillation.’’Geophys. Res. Lett., 27, 779–782.

Elliot, W. P., and J. K. Angell, 1988: Evidence for changes in SouthernOscillation relationships during the last 100 years. J. Climate,1, 729–737.

Gu, D., and S. G. H. Philander, 1995: Secular changes of annual andinterannual variability in the Tropics during the past century. J.Climate, 8, 864–876.

Higgins, R. W., A. Leetmaa, Y. Xue, and A. Barnston, 2000: Dominantfactors influencing the seasonal predictability of U.S. precipi-tation and surface air temperature. J. Climate, 13, 3994–4017.

Houghton, J. T., L. G. Meira Filho, B. A. Callander, N. Harris, A.Kattenberg, and K. Maskell, Eds.,1996: Climate Change 1995:The Science of Climate Change. Cambridge University Press,572 pp.

Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillationregional temperatures and precipitation. Science, 269, 676–679.

——, 1996: Influence of variations in extratropical wintertime tele-connections on Northern Hemisphere temperature. Geophys.Res. Lett., 23, 665–668.

——, and H. van Loon, 1997: Decadal variations in climate associatedwith the North Atlantic Oscillation. Climate Change, 36, 301–326.

Kaplan, A., Y. Kushnir, M. Cane, and B. Blumenthal, 1997: Reducedspace optimal analysis for historical datasets: 136 years of At-lantic sea surface temperatures. J. Geophys. Res., 102, 27 835–27 860.

Karl, T. R., and R. W. Knight, 1985: Atlas of Monthly and SeasonalPrecipitation Departures from Normal (1895–1985) for the Con-tiguous United States. Historical Climatological Series, Vol.3.12, National Climate Data Center, 219 pp.

Mann, M. E., and J. Park, 1996: Joint spatiotemporal modes of surfacetemperature and sea level pressure variability in the NorthernHemisphere during the last century. J. Climate, 9, 2137–2162.

Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis,1997: A Pacific inderdecadal climate oscillation with impacts onsalmon production. Bull. Amer. Meteor. Soc., 78, 1069–1079.

NCDC, 1994: Time bias corrected divisional temperature–precipi-tation–drought index. NOAA NCDC CIRS Tech. Doc. TD-9640,12 pp. [Available from DBMB, NCDC, NOAA, Federal Build-ing, 37 Battery Park Ave., Asheville, NC 28801-2733.]

Rogers, J. C., 1985: Atmospheric circulation changes associated withthe warming over the northern North Atlantic in the 1920s. J.Climate Appl. Meteor., 24, 1303–1310.

Thompson, D. J. W., and J. M. Wallace, 1998: The Arctic Oscillationsignature in wintertime geopotential height and temperaturefields. Geophys. Res. Lett., 25, 1297–1300.

Trenberth, K. E., and J. W. Hurrell, 1994: Decadal atmosphere–oceanvariations in the Pacific. Climate Dyn. 9, 303–319.

Walker, G. T., 1924: Correlations in seasonal variations of weather,IX. Mem. Indian Meteor. Dept., 24, 275–332.