Possible effects of atmospheric teleconnections and solar variabilityon tropospheric and stratospheric temperaturesin the Northern Hemisphere

L. Sfîcă a, M. Voiculescu b,n

a Department of Geography, Faculty of Geography and Geology, Al. I. Cuza University of Iaşi, Bd. Carol I, no. 20A, 700505 Iaşi, Romaniab Department of Chemistry, Physics, and Environment, “Dunărea de Jos” University of Galati, St. Domnească, 111, 800201 Galati, Romania

a r t i c l e i n f o

Article history:Received 19 February 2013Received in revised form2 September 2013Accepted 28 December 2013Available online 8 January 2014

Keywords:ClimateTeleconnectionsTropospheric temperatureStratospheric temperatureSolar effects

a b s t r a c t

Possible relationships between tropospheric and stratospheric temperatures in the Northern Hemisphereand atmospheric oscillations, solar and geomagnetic activity are described, using correlation analysis.The dependence of correlations on season, solar activity level and phase of the Quasi Biennial Oscillation(QBO) is also investigated. An important finding is that the variability of the hemispheric tropospherictemperature is well connected to the Scandinavian Pattern, to the Pacific North American teleconnectionand less with the North Atlantic Oscillation. There is also a possible link with the Southern Oscillation(SO) for winter. Solar UV and cosmic ray flux might influence tropospheric temperature during warmseasons, solar maximum or QBO West. Significant correlations between the Northern stratospherictemperature and the SO is observed especially during the Eastern phase of QBO and solar minimum.Signatures of geomagnetic variability are seen in the winter stratospheric temperature. The stratospherictemperature correlates with the cosmic ray flux and solar UV at annual level at solar maximum and QBOWest. The UV effect at the stratospheric level is less clear than expected. The existence of somecorrelations between tropospheric/stratospheric temperatures and internal and external parametersunder certain climatic circumstances and during different solar cycle phases might help in identifyingprocesses that transfer energy from the Sun to different atmospheric layers and in assessing their role inclimate variability.

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1. Introduction

The effect of solar activity variations on climate is far frombeing correctly quantified, which leads to difficulties in distin-guishing between various causes of climatic variations (Solomonet al., 2007; Gray et al., 2010). Solar effects on climate are usuallyinvestigated in terms of correlations between climatic parameterssuch as temperatures (global or regional averages), cloud cover,teleconnections or circulation patterns and solar proxies, such ascosmic ray (CR) flux, UV and total solar irradiance (TSI), geomag-netic indices or sunspot number. Existing relationships betweensolar variability and climate have been reviewed by Gray et al.(2010) and we will mention only some of these in the following.The variation in the TSI over the 11 year solar cycle is about0.24 W/m2 at the top of Earth0s atmosphere, which is too small tohave significant effects on the climate (Marsh and Svensmark,

2003). However, mechanisms exist, that might amplify the effectsof solar irradiance on climate (e.g. Gray et al., 2010; Lockwoodet al., 2010a). Correlations between 11-year running means of thesolar activity and the global and Northern Hemisphere (NH)surface temperatures, respectively, were found by Mendoza(2005) for a period of 90 years ending in 1970. They claim thatless than half of the observed temperature changes during the20th century could be attributed to TSI variations. Other authorsare more reserved towards the possible effect of solar irradiancevariations on global temperatures, pointing out that solar activityforcing might be related to less than a third to half of the observedglobal heating (Lean et al., 1995; Cliver et al., 1998; Alley et al.,2007). A quantitative examination of the association between solaractivity, described by the sunspot number, and terrestrial climateindicated by global temperatures revealed that a linear relationexists between the two parameters through the recent 135 years(Stauning, 2011). Some studies claim that the correlation oftemperature anomaly with sunspot numbers is higher aroundthe 22 year solar cycle band, which apparently has a higher impactover temperature than the 11 year cycle for both hemispheres(Souza Echer et al., 2009, 2011). Gray et al. (2010) show that proofs

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Journal of Atmospheric and Solar-Terrestrial Physics

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n Corresponding author.E-mail addresses: sfical@yahoo.com (L. Sfîcă), Mirela.Voiculescu@ugal.ro,

mirelavoiculescu@yahoo.com (M. Voiculescu).

Journal of Atmospheric and Solar-Terrestrial Physics 109 (2014) 7–14

for solar impact on climate do exist but their quantification is stillunder debate.

Correlations between temperature anomalies and solar proxieshave also been found at regional level. In a study of severaltemperature time series at various European locations, Le Mouëlet al. (2009, 2010) found that correlation coefficients betweenlocal temperatures in Europe and solar variations exist. Theirresults have been criticized by Legras et al. (2010); however,correlations between temperature and solar proxies have beenfound in various other regions. Lockwood et al. (2010b) suggestedthat some colder winters might be associated with low solaractivity and their effects on blocking events. Kilcik et al. (2008)present correlations between solar irradiance and surface tem-peratures for Turkey. Dobrica et al. (2009) have found positivecorrelation between mean air temperature in Romania and geo-magnetic activity for the last century. Temperature disturbances inUS from 1945 to 2008 are closely linked to solar variability(Courtillot et al., 2010). Marsh and Svensmark (2003) andCrowley (2000) suggested that the warming trend during the20-th century may be attributed to the extremely high solaractivity during the last solar cycles and that solar activity has animportant effect upon our climate before the industrial era.However, other studies suggest that starting with 1985 a differentforcing mechanism, greenhouse gases0 growth, contributes toclimate variability (Stauning, 2011; Lockwood and Froehlich,2007; Foukal et al., 2006; Scafetta and West, 2005).

The relationship between Sun and climate might depend on thephase of Quasi Biennial Oscillation (QBO) and on the level of solaractivity (van Loon and Labitzke, 1994; Labitzke, 2003). Many sun-climate mechanisms rely on the role played by QBO (Moore et al.,2006). When QBO is in the West phase (QBO-W), the correlationbetween the solar activity and polar stratospheric temperatureabove the North Pole is strongly positive, whereas during the Eastphase of QBO (QBO-E), the correlation is weakly negative (vanLoon and Labitzke, 1994). A strong correlation between the10.7 cm solar radio flux (F10.7) and the stratospheric zonal meantemperatures in the Northern Hemisphere exists in July for QBO-Ewhereas the correlation is low for the QBO-W (Labitzke, 2003).Solar UV radiation is absorbed by the stratospheric ozone, whichwarms the stratosphere and the stratopause. Stratospheric andocean responses during solar maximum might have an importanteffect on tropospheric weather (Meehl et al., 2009). A possiblesun-climate mechanism could be the downward propagation ofcirculation perturbations due to UV heating of the stratosphere(Hameed and Lee, 2005). Changes in jet-streams in the uppertroposphere following stratospheric heating caused by UV(Simpson et al., 2009). On the other hand Moore et al. (2006)suggest that tropospheric processes might affect the stratospheremore than the other way around. However, the effect of solaractivity on climate is far from being known and internal climaticfactors might have significant direct or indirect contribution totemperature variations via the so-called top-down or bottom-upmechanisms (Haigh, 2003; Lockwood et al., 2010a). The work ofGray et al. (2010) offers a comprehensive view on possible linksbetween solar activity and terrestrial climate.

Teleconnections are links between atmospheric anomalies atplanetary scale which often manifest as persistent relationshipsbetween pressure fields of various geopotential heights at far-apart locations (Glantz, 1990). They affect the weather at regionaland global scale but their effect on temperature anomalies remainspartly unknown. Possible effects of solar activity have beensuggested e.g. for the Scandinavian Pattern or North AtlanticOscillation (Barriopedro et al., 2008; Woollings et al., 2010; Dimaet al., 2004), Pacific North American (Trouet and Taylor, 2010),Southern Oscillation (Kirov and Georgieva, 2002), and ArcticOscillation (Huth et al., 2007).

In this paper we investigate the relationship between solar andinternal climatic variabilities and temperature anomalies in theNorthern Hemisphere at tropospheric and stratospheric levels.The study is based on correlation analysis of relationships betweentwo climatic proxies, i.e. the tropospheric and stratospherictemperature anomalies for the Northern Hemisphere, on the onehand, and five teleconnection indices and three solar proxies, onthe other hand. Using datasets spanning over 45 years (1959–2005) we will analyze whether correlations between temperatureanomalies and internal and external parameters are seasonallydependent. By ‘internal’ parameters we mean teleconnections,measured by their indices, while ‘external’ refer to solar andgeomagnetic activity. Effects of solar activity level and of QBOphase on correlations are also investigated.

2. Data

We have used global hemispheric averages of temperatureanomaly data based on radiosonde measurements in the NorthernHemisphere at tropospheric level (850–300 hPa) and stratosphericlevel (100–50 hPa), which can be found at http://cdiac.ornl.gov/trends/temp/sterin/sterin.html (Sterin, 2007). Data from the Com-prehensive Aerological Reference Data Set (CARDS) (Eskridge et al.,1995) were taken as primary input for obtaining the series. Theradiosonde data are considered adequate for studying varioussignals present in troposphere, such as ENSO, volcanic or QBO(Siedel et al., 2004). This is valid especially for the NorthernHemisphere (Oort, 1978). The primary CARDS data, from whichSterin set is developed, have some discontinuities, but these arerelatively small and thus datasets which were used here remainrelevant for climate studies (Lanzante et al., 2003). The presentstudy covers the time interval between January 1959 and Decem-ber 2004. In the following, ‘temperature’ refers to temperatureanomaly, i.e. the difference between the value of each annual orseasonal mean temperature and the corresponding average for the1960–1975 period. Besides global hemispheric temperatures,extra-tropical Northern Hemisphere temperatures were also con-sidered (30–901N). Possible internal climatic parameters thatmight be related to temperatures are described by teleconnectionindices. Teleconnections were considered, which have hemi-spheric extent, as the Arctic Oscillation, or regional extent, as theNorthern Atlantic Oscillation, the Scandinavian Pattern, the PacificNorth American Oscillation. We have also included the SouthernOscillation Index, taking into account its global impact. Datasources for all teleconnection indices are taken from the NOAAsite (http://www.cpc.ncep.noaa.gov).

The Northern Atlantic Oscillation (NAO) refers to changes in thedifference between sea level pressures in the Arctic and thesubtropical Atlantic and is one of the most prominent climaticoscillations, with important effects at a global scale (Hurrel, 1995).The associated index is defined as the difference of normalized sealevel pressure (SLP) between Lisbon, Portugal and Stykkisholmur/Reykjavik, Iceland and is calculated since 1864. Positive values ofthe index, i.e. higher pressure over Southern Atlantic ocean, aretypically associated with stronger-than-average westerlies overmiddle latitudes, more intense weather systems over the NorthAtlantic and wetter/milder weather over western Europe (Hurrel,1995). In CPC data, the NAO index is obtained from rotated PCA at500 hPA height.

The Arctic Oscillation (AO) is represented by the leading mode(the first empirical orthogonal function) of low-frequency varia-bility of wintertime geopotential between 1000 and 10 hPa, AOanomalies typically appearing first in the stratosphere and propa-gate downward (Baldwin and Dunkerton, 1999). The principalmode of climate variability in the Northern Hemisphere is the

L. Sfîcă, M. Voiculescu / Journal of Atmospheric and Solar-Terrestrial Physics 109 (2014) 7–148

Arctic Oscillation (AO) (Thompson et al., 2000), which is a measureof the intensity of the polar vortex. The loading pattern of AO isdefined by the Climate Prediction Centre of NOAA as a first loadingmode from the EOF analysis of monthly mean height anomalies at1000 hPA.

The Scandinavian Pattern (SP) consists of a primary circulationcenter over Scandinavia, with weaker centers of opposite sign overwestern Europe and eastern Russia/western Mongolia, which hasbeen previously referred to as the Eurasia-1 pattern (Barnston andLivezey, 1987). The SP index is obtained from rotated PCA from500 hPa height.

The Pacific North American (PNA) teleconnection patternwas identified by Wallace and Gutzler (1981) and describes thevariability of pressure centers over Hawaii and central NorthAmerica, on the one hand, and SW Canada and southeasternUnited States, on the other hand. The PNA index (PNAI) isconstructed on the basis of a modified point wise method takinginto account the monthly mean 500 hPa height anomaly for theseregions (Wallace and Gutzler, 1981).

The Southern Oscillation (SO) describes the alternating highand low pressures in the southeastern Pacific and the central/eastern Indian Ocean (Fairbridge, 2005) and it is described by theSO Index (SOI) calculated using the pressure differences (hPa)between Tahiti and Darwin. The Australian Government Bureau ofMeteorology identifies El Niño episodes by SOI values lowerthan �8, while La Nina episodes are indicated by values higherthan þ8.

Three solar proxies were selected to check possible solar effectson NH temperatures: the UV irradiance, the geomagnetic activitymeasured by the Ap index and the cosmic rays flux (CR). Thevariation of the UV irradiance at the top of the atmosphere is welldescribed by the Mg-to-core ratio in satellite data, which on timescales up to the solar cycle length has been found to correlate withsolar UV at 150–400 nm (Lockwood and Froehlich, 2007). Thisparameter represents a clear proxy for solar irradiance (Viereckand Puga, 1999; Viereck et al., 2001; Dudok de Wit et al., 2009). UVdaily data from the Space Environment Technologies (http://www.spacewx.com) are available starting with 1979, thus the dataset isconsiderably shorter than temperature, teleconnection or geomag-netic data-set.

The Ap index measures the geomagnetic activity which isgoverned by the solar wind-magnetosphere- Earth interaction;daily data that were used to obtain annual and seasonal averagescome from the data base of the National Geophysical Data Centerat NOAA (http://www.ngdc.noaa.gov/stp/geomag/kp-ap.html).

The cosmic ray (CR) flux is measured at various sites (Usoskinet al., 2011). Since galactic cosmic rays imping nearly isotropicallyon Earth, any station would be representative for cosmic rayvariability. However, because of the geomagnetic shielding whichis absent in (sub)polar regions, high-latitude cosmic ray stationsdepict the greater magnitude of the CR variability (signal/noiseratio). We have chosen the Oulu neutron monitor which is one ofthe most stable and continuously operating ones, providing49 years of homogeneous data, according to Ahluwalia andYgbuhay (2013). Data recording started in 1965 and are availableat http://cosmicrays.oulu.fi.

Annual and seasonal averages were calculated for each of thepreviously described parameters. Seasons are defined in theclassical manner, i.e. Spring is March, April, May, Summer is June,July, August, Fall is September, October, November and Winter isDecember, January, February. All time-series were then separatedaccording to the phase of the QBO i.e. East and West, using thecriterion of Holton and Tan (1980) based on the wind direction inthe lower stratosphere at about 45 hPa, also used by Labitzke andKunze (2009). Solar maximum and solar minimum years werethen selected, similarly to Labitzke and van Loon (1995) who set

the limit between maximum and minimum years at 155 solar fluxunit (sfu). Both tropospheric and stratospheric temperaturesexhibit significant trends. In order to avoid the effect of trend oncorrelations, which most likely would be a strong one (see Fig. 1),all datasets were detrended using a linear square-fit and allcalculations were done using detrended data.

3. Results

Correlation coefficients between temperature anomalies, onthe one hand, and internal climatic and solar proxies, on the otherhand, were calculated for annual averages. The same analysis was

1960 1970 1980 1990 2000−1

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Fig. 1. Annual averages for all data included in the analysis.

L. Sfîcă, M. Voiculescu / Journal of Atmospheric and Solar-Terrestrial Physics 109 (2014) 7–14 9

applied for years when QBO was East, years with QBO West, yearswith solar maximum (sol max) and solar minimum (sol min).Seasonal dependence of correlation was then checked by consid-ering winter, spring, summer and fall data. When all years wereconsidered, i.e. no selection was made according to QBO phase orsolar cycle, significance tests were performed using the non-parametric random-phase test described by Ebisuzaki (1997),which has the advantage of preserving the serial correlation ofthe series (Usoskin et al., 2006). Basically, the FFT of one series wascomputed, its phase was randomized (all but the phase of the firstpoint in the dataseries) and an artificial series was obtained,applying the inverse FFT transform to the so-called 0shuffled’series. The correlation coefficient between the modified seriesand the other series was then computed and compared to theinitial bivariate correlation coefficient. This was then repeated forthe second timeseries. A number of 1000 runs were applied foreach case and the significance was calculated as the relativenumber of runs when the correlation coefficient is higher thanthe original one. However, this method slightly underestimates theconfidence interval (i.e. gives a higher p) in cases when the powerspectrum of the original series is dominated by one periodicity(Ebisuzaki, 1997; Usoskin et al., 2006). For the rest of the cases,when years were selected according to the QBO or solar cyclephase, Spearman correlation was applied, which is more appro-priate for unevenly distributed data (Wilks, 2011). No autocorrela-tion is expected for these series after separation and t-test wasapplied for assessing the significance. In both cases only correla-tion coefficients with a confidence level higher than 90% wereconsidered ðpr0:1Þ. The number of data for each selectioncriterion is given in Table 1.

The resulting correlations are presented in a comprehensivemanner in Fig. 2, which is organized as a matrix whose elements,cjk, are the significant correlation coefficients between each of thefour temperature anomalies (rows) and all internal (NAO, AO, SP,PNA, SOI) and external (UV, AP, CR) parameters (columns).Coefficients are shown in terms of color coding, with dark bluemeaning relatively strong anticorrelations (absolute values higherthan 0.5) and deep red showing strong positive correlations. Whitecells show lack of significant correlation. ‘Tropo’ and ‘strato’ standfor average Northern Hemispheric temperatures anomalies, while‘tropoextra’ and ‘stratoextra’ are extra-tropical averages of tem-perature anomalies in the Northern Hemisphere (i.e. between 301and 901). The big matrix is split in 32 small 5�5 matrices. Theelements of the small matrices are correlation coefficientsbetween temperature and each internal/external parameter, atannual and seasonal scale (columns named ‘A’, ‘W’, ‘Sp’, ‘Su’, ‘F’) foreach of the five selection criteria previously described (rowsnamed ‘All’, ‘QE’, ‘QW’, ‘sm’, ‘SM’). The use of color code slightlyreduces the precision but allows a comprehensive view of correla-tions between temperature and various parameters and alsoshows whether the correlation still exists when various selectioncriteria are used. Values of correlation coefficients and theirp-values for consistent correlations (i.e. which are seen also whensome selection criteria are applied) are shown in Tables 2 and 3.

Fig. 2 shows that some relationships seem to be consistent. TheNH tropospheric temperature is correlated with PNA, SO and SP.Surprisingly, the correlation with NAO is weaker than expectedand exists during QBO-West and solar minimum. No correlationwith AO can be seen. The anticorrelation with SP and thecorrelation with the PNA are seen at annual level and in winter,and is better during solar minimum and the West phase of theQBO. The anticorrelation with SO is seen only in winter, regardlessof solar activity, but only during QBO-W. Solar effects at thetropospheric level are less clear. The relationship between the NHtemperature and UV appears at solar maximum. There is a clear linkbetween geomagnetic activity and temperature. The positive corre-lation with UV is not consistent, since it is seen at extratropical

Table 1Number of years of temperature dataset and for each selection.

Criterion All years QBO-E QBO-W Sol max Sol min

Number of years 47 21 26 14 33

Fig. 2. Correlation coefficients between average hemispheric temperature anomalies at tropospheric and stratospheric level, for the entire Northern Hemisphere and forextratropical latitudes. Correlations are calculated for the entire series (Ann), for years with QBO West (QBOW), for years with QBO East (QBOE), solar minimum years (sm)and solar maximum years (SM). Correlations for the entire year (A) and all seasons: winter (W), spring (Sp), summer (Su), fall (F) are also shown. (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version of this paper.)

L. Sfîcă, M. Voiculescu / Journal of Atmospheric and Solar-Terrestrial Physics 109 (2014) 7–1410

latitudes during QBO-W at spring but during QBO-E in summer.Also, there is no clear dependence on solar activity, thus no clearconclusion can be drawn. Surprisingly, the relationship with CRseems stronger than with UV and is seen during QBO-E and solarmaximum at annual level and during summer.

A surprising result is the anticorrelation between stratospherictemperature and SO, which is the strongest and most consistentrelationship seen in our results. High stratospheric temperaturesare seen in the Northern Hemisphere during the negative phase ofthe SO, i.e. during El Niño episodes, while La Niña coincides withlower stratospheric temperatures. The solar activity level makesno clear difference, since the anticorrelation is observed duringboth solar minimum and solar maximum for various seasons.However, a clear effect of the QBO phase exists, since the correla-tion vanishes for QBO-W. There is no latitudinal effect on the SO-stratospheric temperature.

Stratospheric temperatures correlate negatively with CR. Themost surprising result is the poor correlation between the NHstratospheric temperature and UV, which is seen only for QBO-Wand solar maxima at annual level. Positive correlation betweenstratospheric temperature and Ap is seen during winter, regardlessof the phase of QBO and solar activity.

4. Discussion

NAO is widely recognized as the most significant pattern ofclimate variability in the North Atlantic sector and a strongcompetitor of ENSO in terms of global significance (Marshallet al., 2001; Visbeck et al., 2001). The NAO variability duringwinter months could explain about one-third of the NorthernHemisphere inter-annual surface temperature variance (Hurrel,1995). There are studies showing that a positive correlation existsbetween NAO and surface temperature (Bojariu, 1992; Gimenoet al., 2003). Our study indicates that the link is weak betweenNAO and tropospheric temperature and is seen only during

QBO-W and solar minima. The link between NAO and solar activityis well discussed in the literature. Positive NAO index occurs whenthe solar activity is high (Gimeno et al., 2003; Kirov and Georgieva,2002; Lukianova and Alekseev, 2004; Bochniî¡ek and Hejda, 2004).This might indicate that positive NAO and high tropospherictemperature could be partially driven by the solar activity. TheNAO influence on tropospheric temperature might be importantwhen the NAO index is high, which happens more during highsolar activity. Our results do not show any link between AO andtropospheric or stratospheric temperatures. Thus NAO and AOeffects on temperature might exist at surface level and/or atregional level, but are less clear at upper troposphere heights.

Correlations between tropospheric temperature anomaly andseveral teleconnections, SP, SOI and PNA, and are seen at annuallevel, winter and, partly, at summer. Negative tropospheric tem-perature anomaly coincides with anticyclones over the Scandina-vian Peninsula, which is the positive phase of the SP. The positivephase of this pattern is associated with major blocking antic-yclones over Scandinavia and western Russia and below-averagetemperatures across central Russia and also over western Europe(Barnston and Livezey, 1987). Recent studies prove that blockingepisodes (i.e. positive SP index) last longer during years of lowsolar activity (Barriopedro et al., 2008; Woollings et al., 2010),which implies that negative temperature excursions are largerduring solar minima than during solar maxima. This is in accor-dance with our results which show that the tropospheric tem-perature is negatively correlated with the SP in solar minimumyears, especially in winter. Lockwood et al. (2010a) observed thatblocking phenomena, associated to positive SP, seem to be animportant link in the top-down mechanism by which the solarsignal is transmitted to the temperature over certain regions. Thismight also explain the dependence of correlation on theQBO phase.

Another important observation is that all correlations at thetropospheric level depend on the solar activity and phase of QBOin a similar manner: they are significant especially during winter,

Table 2Correlation coefficients between stratospheric temperature anomaly and some internal (SO) and external (CR) parameters. Corresponding p-values are shown in smallerfonts, within parentheses.

Param Season All years QBO East QBO West Solar max Solar min

Str-SO Ann �0.47(0.002) �0.51(0.017) – �0.65(0.012) �0.30(0.085)Win �0.41(0.008) �0.57(0.006) – – �0.36(0.039)Spr �0.28(0.056) �0.42(0.057) – – –

Sum �0.29(0.048) – – – �0.34(0.052)Fal �0.48(0.001) �0.37(0.098) �0.37(0.068) �0.61(0.022) �0.30(0.093)

Str-CR Ann �0.47(0.034) – �0.44(0.032) �0.78(0.002) –

Win �0.43(0.076) – �0.41(0.047) – –

Sum �0.52(0.010) – �0.53(0.009) �0.64(0.024) –

Fal – �0.46(0.049) – �0.75(0.004) –

Table 3Correlation coefficients between tropospheric temperature anomaly and some internal (SP, PNA) and external (CR) parameters. Corresponding p-values are shown in smallerfonts, within parentheses.

Param Season All years QBO East QBO West Solar max Solar min

Tr-SP Ann �0.32(0.092) – �0.50(0.010) – �0.39(0.027)Win �0.33(0.036) – �0.44(0.022) – �0.33(0.061)Sum – �0.45(0.041) – – –

Fal – – �0.35(0.084) – –

Tr-PNA Ann 0.38(0.038) – 0.47(0.015) – 0.38(0.028)Win 0.50(0.002) – 0.67(0.001) – 0.63(0.001)Sum – – 0.36(0.071) – 0.35(0.046)

Tr-CR Ann – – �0.40(0.063) �0.52(0.080) –

Spr �0.36(0.064) – �0.46(0.026) – –

Sum – – �0.36(0.084) �0.52(0.080) –

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when solar activity is low and QBO is West. This might be theresult of a common driving mechanism linked to the atmosphericcirculation which develops mostly during winter (Coleman andRogers, 2007) and is influenced by stratospheric anomalies alsoduring winter (Garfinkel and Hartmann, 2010). This mechanismmight also be favored during certain directions of the stratosphericwind. The lack of correlation during solar maxima might be theresult of other mechanisms taking over internal processes. ThePNA pattern refers to the relative amplitudes of the ridge overwestern North America and troughs over central North Pacific andsoutheastern United States. Such a pattern tends to be mostpronounced in winter months, when the variability of PNA ishigher, thus is a major feature of hemispheric-scale circulation(Coleman and Rogers, 2007; Simmons et al., 1983). The PNA tendsto have little impact on surface temperature variability over NorthAmerica during summer (Oliver, 2005). This might explain the factthat our results show that the correlation is significant only duringcold seasons. A high PNA index corresponds to atmosphericconditions favoring powerful zonal jet-streams which transportslarge amounts of warm oceanic air masses over the continents.Most of the radiosonde observations used on our analysis comefrom continental areas, thus a high PNA index should be linked topositive temperature anomaly, which could explain the observedcorrelation between PNA and upper troposphere anomaly.

The positive phase of the PNA pattern tends to be associatedwith Pacific warm episodes (El Niño), thus with negative SO index,while negative PNA indices tend to be associated with Pacific coldepisodes (La Niña), meaning positive SO index (Hunt and Elliott,2003). This might explain the link between SO and NorthernHemispheric temperatures. Major episodes of El Niño, i.e. negativeSO index, are more probable during low solar activity (Kirov andGeorgieva, 2002). An intensification of the SO teleconnectionduring QBO-W was observed by Garfinkel and Hartmann. Theyalso show that the PNA pattern is apparent mainly under QBOWand much less under QBOE. Expectedly, the effect of teleconnec-tions on temperature increases with their strength, which couldexplain the dependence of correlations between tropospherictemperature and SO, respectively PNA, pattern, on QBO phase.

The QBO West phase seems to favor both internal and externallinks to the tropospheric temperatures, which might indicate thatenergy transport mechanisms either at tropospheric level orbetween atmospheric layers is reduced during the East phase ofthe QBO.

The absence of a good correlation between UV and tropo-spheric temperature seems to contradict the conclusions of Foukalet al. (2006), who showed that a good correlation exists betweenUV and global surface temperature. However, this is not necessa-rily true, since our study refers to troposphere and not to surfacetemperatures. This demonstrates that some results regardingglobal temperatures are valid only for surface temperatures andnot for the entire upper troposphere.

An interesting feature which might help in identifying mechan-isms that link troposphere and stratosphere is the strong andpersistent anti-correlation between SO and NH stratospheric tem-perature. El Niño years (negative SO index) coincide with warmerNorthern stratosphere while during La Niña years the NH strato-sphere is colder. This is in accordance with results obtained by Calvoand Marsh (2011) using simulations based on the WACCM model.The correlation between stratospheric temperature and SO tele-connection cannot pinpoint to a certain mechanism connecting thetwo parameters, but could help in identifying possible bottom-upmechanisms by which tropospheric variations are transmitted atthe stratospheric level. The correlation between SOI and NHtemperature is stronger and more consistent at the stratosphericlevel than at the tropospheric level. Moreover, there is no correla-tion with PNA index, which removes the possibility that this

correlation is spuriously induced. This suggests that the effect ofthe SO on stratospheric temperature might be real and could be theresult of bottom-up processes favoring the propagation of SO intothe stratosphere, at global scale. The correlation between SOI andNH stratospheric temperature disappears during QBO-W, i.e. whenthe correlation with tropospheric temperature is stronger, whichsuggests that the bottom-up process might be favored by certainstratospheric wind directions.

The solar effect on the NH upper troposphere is weak andseems to manifest mostly via CR flux. Regional observations haveshown that the occurrence of cold winters in central England ismore probable during years with high flux of CR (Lockwood et al.,2010b). Temperature variations are induced, partially, by cloudcover variations. Marsh and Svensmark (2000, 2003) have sug-gested that cosmic ray flux is the main contributor to climatechange via cloud cover modification, opening a hot debate aboutpossible effects of solar variability on cloud cover. Positive correla-tions between CR and low clouds at annual scale have been found,for instance, by Marsh and Svensmark (2000), Pallé et al. (2004),and Usoskin et al. (2006). Voiculescu et al. (2006, 2007) andVoiculescu and Usoskin (2012) found that both CR inducedionization and UV irradiance might affect cloud cover dependingon the geographical zone and on the cloud altitude. Harrison andStephenson (2006) have shown that days with increased cloudcover (thus lower temperature) are more numerous during highCR flux than during low CR flux. This might explain why hightropospheric temperatures coincide with low CR flux. On the otherhand the effect is no longer seen during solar minima (high CRflux). Interestingly, tropospheric temperature correlates to exter-nal (solar) effects during solar maxima, while teleconnectionsseem to take over during solar minima.

The CR flux correlates also with stratospheric temperature, thusanother explanation could rely on stratospheric effects propagat-ing downwards to the surface and indirectly affecting the tropo-spheric temperature (Hameed and Lee, 2005). It is difficult to finda reason for a direct effect of CR on stratospheric temperatureanomaly. One possibility is that correlations with CR are spuriouslyinduced by correlations with solar irradiance (since these areknown to be anticorrelated). The UV dataseries is shorter(27 years), compared to CR (42 years) and Ap (47 years) data,which affects the significance of the correlation coefficients. Thecorrelation coefficients between annual, respectively winter, tem-peratures and UV is 0.37, respectively 0.33, but fails the signifi-cance test. Another possibility is that, if existing, CR modulation ofcloud cover influences atmospheric circulation, which propagatesupwards to the stratosphere in a bottom-up effect.

The correlation between hemispheric stratospheric temperatureand UV is weak. Labitzke (2003) observed a positive correlationbetween UV and stratospheric temperature, which was confirmed bymodel simulations of Egorova et al. (2004). This is not clear in ourresults, which show that UV and stratospheric temperature arerelated only during QBO-W and solar maximum. Our results supportFoukal et al. (2009), who concluded that UV effects on climate mightexist, they are not seen in global stratospheric temperature.

According to Valev (2006), the geomagnetic forcing predominatesover the solar activity forcing on the global and hemispheric surfaceair temperatures. Bucha and Bucha (1998) found that surface airpressure in the Northern Atlantic decreases, the Iceland low deepensand a zonalization of the 500 hPa circulation occurs as a consequenceof geomagnetic storms. Our results show no dependence of the NHtropospheric temperature. Positive correlation is seen between Apand temperature at the stratospheric level, in winter, for QBO Westand solar maxima, similar to the CR-temperature link.

The response of stratospheric temperature anomaly to internaland, respectively, external triggers depend on both solar activity andQBO phase. Correlations with SO exist mostly during QBO-E while

L. Sfîcă, M. Voiculescu / Journal of Atmospheric and Solar-Terrestrial Physics 109 (2014) 7–1412

possible solar effects might take over during QBO-W. Solar minimumseems to favor correlation with teleconnection while during solarmaxima solar effects might be more important. This means that, ifexisting, the response of temperature to solar forcing maximizesduring solar maxima. During solar minima the effect might still existbut is too small relatively to other atmospheric processes.

5. Conclusions

The aim of the present paper was to evaluate the relativeinternal (teleconnections) and external (solar, CR and UV, andgeomagnetic proxies) forcing on the average tropospheric andstratospheric temperature anomalies in the Northern Hemisphere,using correlation analysis. The seasonal dependence of correla-tions as well as the effects of solar activity level and phase of thestratospheric QBO on relationships between temperatures andvarious parameters were investigated.

Our results show that the tropospheric temperature variabilitymight be influenced by the Pacific North America pattern (positivecorrelation) and by the Scandinavian Pattern (negative correlation),especially during cold seasons, West phase of QBO and minimumsolar activity level. The Southern Oscillation has an important effecton tropospheric temperature only during winter regardless of solaractivity. Some known relationships, such as NAO–temperature orUV–temperature, are seemingly not valid for the hemispheric tropo-sphere. The solar effect at the tropospheric level is rather small andseems to be mostly due to CR flux during solar maximum years andQBO West. Thus, if existing, the solar modulation of electric atmo-spheric properties or/and cloud cover might play an important role inclimate and weather, at least at hemispheric scale.

Another interesting result is the strong correlation between theNorthern stratospheric temperature and the Southern Oscillation,with no important dependence on season or solar activity level.The correlation vanishes only during the West phase of QBO. Thiscorrelation is strong and holds for the entire hemisphere. Thismight be explained by a transport energy process involving abottom-up mechanism by which processes triggered by theSouthern Oscillation propagate at higher altitudes.

Interestingly, the troposphere response to internal and externalparameters depends on solar activity, while the stratosphericresponse depends on the QBO phase. Tropospheric temperaturecorrelates to external (solar) effects during solar maxima, whileteleconnections seem to take over during solar minima. Theresponse of stratospheric temperature to both internal and exter-nal triggers varies with both solar activity and QBO phase.Correlations with internal parameters maximize during solarminima and QBO-E, while correlations with external parametersare stronger during solar maxima and QBO-W. This alternateresponse might show that mechanisms of energy transfer betweendifferent atmospheric layers, as well as atmospheric circulation,are different for particular conditions.

The lack of some expected correlations at hemispheric scalemight be due to the fact that regional effects might cancel eachother so that no effect is clear at global or hemispheric scale. Thisimplies that regional analyses are required for a better assessmentof the response of temperatures at mid-high latitudes to solar andinternal triggers.

Acknowledgments

This work was supported by project PN-II-ID-PCE-2011-3-0709,SOLACE of the Romanian NPRDI-II, UEFISCDI. Dave Bouwer andSpace Environment Technologies are acknowledged for MG IIwing-to-core ratio used for our UV data. Cosmic ray data of Oulu

NM are available at http://cosmicrays.oulu.fi. Data sources forNAOI, AOI, SPI, PNAI, SOI (http://www.cpc.ncep.noaa.gov) andAp (http://www.ngdc.noaa.gov/stp/geomag/kpap.html) are alsoacknowledged.

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