ORIGINAL PAPER

Impact of solar activity on the growth of pine trees: case study

Ivan Dorotovic • Jose Luis Louzada •

Jose Carlos Rodrigues • Vladimır Karlovsky

Received: 18 October 2013 / Revised: 24 January 2014 / Accepted: 27 January 2014 / Published online: 6 February 2014

� Springer-Verlag Berlin Heidelberg 2014

Abstract In this work, we focus on the analysis of data

for the ring width of two pine tree species: samples of six

maritime pines (Pinus pinaster), grown in the north of

Portugal, and one sample of Scots pine (Pinus sylvestris),

grown in the south of Slovakia. A negative impact of solar

activity (SA) was observed on the growth of these pine

trees with particularly strong impact in the case of the

maritime pines. The width of the annual rings was gener-

ally smaller in the years of maximum SA; furthermore, it

was found that it is latewood width that is affected whereas

earlywood width is not affected; as a corollary, the per-

centage of latewood also shows a significative negative

correlation with SA.

Keywords Dendrochronology � Growth rings � Wood

density

Introduction

The Sun produces radiation at all wavelengths through a

variety of mechanisms and in a wide variety of physical

contexts. The energy of solar radiation, together with

plasma and charged particles in solar wind, governs a

variety of processes in the earth’s atmosphere and on its

surface. Many earlier and recent studies, based on sampling

from various localities all over the world, indicate that

solar activity (SA) can affect tree growth induced by

changes in climatic conditions on the earth’s surface

because of SA variations (LaMarche and Fritts 1972; Eddy

1980; Ogurtsov et al. 2002; Feng and Han 2009; Prestes

et al. 2011; Shumilov et al. 2011). Prestes et al. (2011)

applied iterative regression and the wavelet method to tree

growth ring data of araucarias from Brazil and found

periodicities and trends associated with solar activity, the

lunar (Saros) cycle, and the Bruckner cycle, respectively.

The SA variations were significantly lower during

2007–2009 (prolonged SA minimum) than in the earlier

period covering 1999–2001 (SA maximum). Satellite

measurements have revealed the variability of the solar

‘constant’ (an average of *0.1 % during a sunspot cycle)

in past decades (Frolich and Lean 2004). He et al. (2007)

revealed the strong response of tree rings to solar activity

and global environmental change by studying tree ring

width chronology from 58 samples taken from Chinese

pine trees. Shumilov et al. (2011) assessed regional cli-

matic response to solar and volcanic activity impacts by

analysing a 561-year (A.D. 1445–2005) pine tree-ring

chronology (based on living trees) from a northern tree line

location in the Kola Peninsula in Russia.

In this paper, we investigate the relationship between

11-year solar cycles and pine tree growth using data sets of

ring width (RW), earlywood width (EWW), and latewood

Communicated by Aaron R Weiskittel.

I. Dorotovic (&) � J. C. Rodrigues

Forestry and Forest Products Centre, Tropical Research Institute

(IICT), Lisbon, Portugal

e-mail: id@ca3-uninova.org

I. Dorotovic

UNINOVA-CA3, Campus da FCT/UNL, 2829-516 Caparica,

Portugal

J. L. Louzada

Dpto Florestal, Centro de Investigacao e de Tecnologias Agro-

Ambientais e Biologicas (CITAB), Universidade Tras-os-

Montes e Alto Douro (UTAD), 5001-801 Vila Real, Portugal

V. Karlovsky

Astronomical Observatory and Planetarium of M.R. Stefanik,

Sladkovicova 41, 92001 Hlohovec, Slovak Republic

123

Eur J Forest Res (2014) 133:639–648

DOI 10.1007/s10342-014-0792-8

width (LWW), respectively, of the maritime pine (Pinus

pinaster) and the Scots pine (Pinus sylvestris).

Input data and methods

We used the following input data in our analysis:

1. Ring width, earlywood width, latewood width, and

wood density, respectively, of maritime pine (Pinus

pinaster) derived from samples of six individual pine

trees aged 79, 79, 80, 78, 81, and 85 years, respec-

tively (Geres, Portugal);

2. Ring width, earlywood width, latewood width, and

wood density, respectively, of Scots pine (Pinus

sylvestris) aged 180 years (Hurbanovo, Slovakia);

3. Sunspot data (Wolf sunspot number—SSN) from the

SIDC in Brussels, Belgium (http://www.sidc.be/sun

spot-data/), and the dates of minima and maxima of

sunspot cycles from the NGDC in Boulder, CO, USA

(http://www.ngdc.noaa.gov/stp/space-weather/solar-data/

solar-indices/sunspot-numbers/miscellaneous_in-pro

cess/docs/maxmin.new).

The Geres experimental site is located in the north of

Portugal (N 41�43030.3400, W 8�9056.9800) at an elevation of

500–600 m. The region has a maritime climate with some

influence of altitude (Csb Koppen–Geiger climate classi-

fication). Average annual temperature is 14.3 �C, annual

precipitation is 2,994 mm, with a potential evapotranspi-

ration of 747 mm (Thornthwaite method) and water deficit

of 95 mm (Mendes and Bettencourt 1980; Miranda et al.

2006). The soils are coarse-textured, rich in organic matter

Fig. 1 Evolution of the ring width—RW, earlywood width—EWW,

and the latewood width—LWW, respectively, in comparison with

evolution of the annual Wolf sunspot number—SSN (upper panel).

Bivariate fit of the RW (second panel from the top), the EWW (third

panel from the top), and the LWW (bottom panel), respectively, of the

maritime pine for solar (sunspot) activity (annual SSN)

Fig. 1 continued

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and acids, with a low concentration of bases and a high

concentration of acidity. They show low levels of assimi-

lable phosphorus and medium to high assimilable potas-

sium (Agroconsultores and Coba 1991).

Fig. 2 Wavelet power spectrum (two upper panels) for red noise

background, cross-wavelet transform (two middle panels), and

wavelet coherence (two bottom panels) of the Wolf sunspot number

and the RW time series of both Pinus pinaster (upper of each pair of

panels) and Pinus sylvestris (lower of each pair of panels). The cone

of influence is marked as a shaded area, and the contour lines show

the 95 % confidence level. (Color figure online)

Fig. 2 continued

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123

The Hurbanovo sampling site is located in the south-west

of Slovakia (N 47�52033.2800, E 18�11037.9300). The region

(differently from the site of samples of the maritime pine) has

a continental, lowland climate. Average annual temperature

Fig. 3 Wavelet power spectrum (two upper panels) for red noise

background, cross-wavelet transform (two middle panels), and

wavelet coherence (two bottom panels) of the Wolf sunspot number,

EWW, and LWW, respectively, for Pinus pinaster. The cone of

influence is marked as a shaded area and the contour lines show the

95 % confidence level. (Color figure online)

Fig. 3 continued

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123

is 10 �C. Average summer temperatures in July reach

20.2 �C, and winter ones -1.5 �C in January. The climate is

dry, and average annual rainfall is 550 mm (Miklos and

Hrnciarova 2002). The most common vegetation type in the

surveyed region is agricultural monoculture. In natural types

of vegetation, one can find lowland floodplain forests and

willow-poplar floodplain forests (Michalko et al. 1986). The

Scots pine tree was planted inside the town of Hurbanovo, in

the park of the Astronomical Observatory, and grew between

1830 and 2010.

Measurements of the annual growth of all tree samples

were performed by a microdensitometric method using a

Joyce-Loebl MK3 apparatus as described in Louzada (2000).

We investigated the relationship between the 11-year SA

cycles and pine tree growth by studying data distributions in

scatter plots of tree growth and SA relationships and applied

bivariate fit as a reference marker. The data series of sun-

spot activity and tree ring width were then analysed with a

wavelet tool in order to extract any periods of tree growth

evolution evidenced within the data. The algorithm used

was an adapted version of Torrence and Compo’s (1998)

wavelet routine as provided by Grinsted et al. (2004). Fur-

thermore, both white and red noise backgrounds were

employed in order to verify the consistency of the results.

According to our knowledge, this is the first time that

the eventual impact of solar activity on tree growth has

been studied, not only by means of RW data but also

through separate consideration of the EWW and the LWW

time series (proportion of the rings formed in the spring

and summer seasons, respectively).

Results

Pinus pinaster

In the case of the maritime pine, we present only the results for

one particular sample aged 79 years, since the results for the

other five samples are very similar. Figure 1 shows the

bivariate fit of the RW, EWW, and LWW, respectively, of the

maritime pine for solar (sunspot) activity. As can be observed,

there is a highly significant (p value \ 0.0001) negative cor-

relation between sunspot activity and ring widths (R = 0.49)

for the period 1925–1980 and an even more significant neg-

ative correlation between sunspot activity and latewood

growth rings (R = 0.656). On the other hand, we found

insignificant correlation close to zero between sunspot activity

and earlywood growth rings (R = 0.033). Note that the initial

period from 1910 to 1925 corresponding to the development

of juvenile wood was not considered in the analysis.

To be able to investigate periods of tree growth evolution

present within the data and their temporal variation, we used a

wavelet tool (Torrence and Compo 1998; Grinsted et al.

2004), and we performed wavelet transform (CWT), cross-

wavelet transform (XWT) and wavelet transform coherence

(WTC), respectively, on the data of the maritime pine tree

growth (RW, EWW, and LWW, respectively). Figure 2

shows the results of the wavelet analysis for the RW time

series of both Pinus pinaster and Pinus sylvestris. Figure 3

shows the results of the wavelet analysis of the EWW and

LWW time series, respectively, of Pinus pinaster. Table 1

summarises the results of wavelet analysis (for the red noise),

i.e., significant periods found in the power spectrum of the

CWT and XWT during high power intervals of years. No

significant wavelet power was found in the EWW evolution in

the CWT analysis. The table also contains significant phase

angle orientations identified by means of WTC where phase

angle 0� means a strong correlation between two input

parameters and phase angle 180� indicates strong anticorre-

lations between parameters. A confidence level of 95 % was

assumed in almost all cases as a threshold of significance.

Pinus sylvestris

Similarly, Fig. 4 shows the bivariate fit of the RW, EWW,

and LWW, respectively, of the Scots pine for solar

Table 1 Significant periods found in the power spectrum of the wavelet transform (CWT) in the case of the RW and LWW analysis,

respectively (no significant wavelet power was found in the EWW evolution)

Wavelet analysis CWT CWT CWT XWT XWT XWT WTC WTC WTC

Ring width type RW EWW LWW SSN/RW SSN/EWW SSN/

LWW

RW EWW LWW

Intervals of years 1950–1989 – 1952–1987 1924–1988 1926–1960/

1969–1983

1923–1989 1920–1989 1935–1950 1920–1989

Significant periods

(years)

8–12 – 8–12 8–14 8–14/10–14 8–14 x x x

Phase angle in

WTC (�)

x x x x x x 180 0 180

Significant periods found in the power spectrum of the cross-wavelet transform (XWT) of the Wolf sunspot number (SSN) and the tree RW,

EWW, and LWW, respectively. Significant phase angle orientations found in the power spectrum of the wavelet coherence. Confidence level is

in most cases higher than 95 % for Pinus pinaster

Eur J Forest Res (2014) 133:639–648 643

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(sunspot) activity. These results depict a behaviour similar

to that observed for the maritime pine, although with

slightly lower values of correlation (Fig. 4).

Results of the wavelet analysis for the RW time series of

both Pinus pinaster and Pinus sylvestris have already been

shown in Fig. 2. Figure 5 shows the CWT, XWT, and WTC of

the time series of the Wolf sunspot number (SSN) and the

EWW and LWW of Pinus sylvestris for the years 1845–2010.

In contrast to Pinus pinaster’s wavelet transform results, the

wavelet power spectrum for the Pinus sylvestris data shows

high wavelet power only in the case of LWW analysis for

1930–1960. Arrows in the figures of cross-wavelet transform

and wavelet coherence indicate that for the range of

8–12 years (which coincides with the 11-year sunspot cycle),

the growth of the juvenile pine tree is in phase with SSN

evolution (phase angle is near 0�), whereas in the years

1915–2002, the phase angle turns to *180� (significant an-

ticorrelation of both time series). Table 2 summarises the

results of wavelet analysis (for the red noise), i.e., significant

periods found in the power spectrum of the CWT and XWT

during high power intervals of years and also significant phase

angle orientations identified by means of the WTC.

Solar activity versus tree density

Figure 6 shows the bivariate fit of the medium density of

the maritime pine. Figure 7 shows the wavelet power

spectrum, cross-wavelet transformation, and the wavelet

coherence of the time series of the Wolf sunspot number

(SSN) and the tree density of Pinus pinaster and Pinus

sylvestris. Evolution of the wavelet power is very similar as

in the case of the wavelet analysis of the Wolf sunspot

number and the tree ring width (RW).

Fig. 4 Evolution of the ring width—RW, earlywood width—EWW,

and the latewood width—LWW, respectively, in comparison with

evolution of the annual Wolf sunspot number—SSN (upper panel).

Bivariate fit of the ring width—RW (second panel from the top),

earlywood width—EWW (third panel from the top), and the latewood

width—LWW (bottom panel), respectively, of the Scots pine for solar

(sunspot) activity (annual Wolf sunspot number—SSN)

Fig. 4 continued

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Discussion and conclusions

The results depicted similar behaviours to the ones iden-

tified by Surovy et al. (2008), i.e, the tree growth seems to

Fig. 5 Wavelet power spectrum (two upper panels) for red noise

background, cross-wavelet transform (two middle panels), and

wavelet coherence (two bottom panels) of the Wolf sunspot number,

EWW, and LWW, respectively, for Pinus sylvestris. The cone of

influence is marked as a shaded area and the contour lines show the

95 % confidence level. (Color figure online)

Fig. 5 continued

Eur J Forest Res (2014) 133:639–648 645

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be higher in the periods of SA minima than in the periods

of SA maxima. This means that a negative impact of the

SA is observed in the growth of pine trees. It was found

that the effect of solar activity is more pronounced in the

characteristics of density and growth of the wood formed

during the summer/autumn (latewood) than the wood

formed during the spring (earlywood) (see the bivariate fits

in Figs. 1 and 4, respectively). It was also found that the

greater the solar activity, the lower the maximum density,

the density of latewood, and therefore also the lower the

average ring density (RD) of the formed tree rings. With

regard to growth characteristics, these rings also showed a

reduction in the width of the latewood (LWW), latewood

percentage, and ring width. The result suggests that it is the

latewood width (LWW) that is affected by SA, whereas the

earlywood width (EWW) is not affected by SA and is a

very important finding of this paper. Statistical significance

is low, however. The authors are also aware that processing

of further tree growth and density data is needed to achieve

higher statistical significance. In spite of that fact, we

decided to publish our results as a case study on the

eventual different impacts of SA on EWW and LWW in

relation to the growth data of pine trees. A possible

explanation of the coupling between SA and tree growth

could be photograph assimilation in trees: high SA can

indirectly (through changes evoked in the earth’s global

climate) affect (slow down) the assimilation process in tree

growth. In summer, the characteristics of the atmosphere

are more permeable to sunlight, taking less distance in the

atmosphere to reach the surface and reaching the treetops

with higher energy. Thus, in the years of maximum SA,

these rays destroy the existing chlorophyll in the leaves,

reducing the amount of sugars produced by photosynthesis.

Hence, it is expected that during the summer/autumn of a

year with higher SA, trees show a reduction of their pho-

tosynthetic activity, and they form fewer cells with thinner

walls, resulting in a reduction of ring width and the for-

mation of less dense wood. Of course, there are many other

factors (e.g., local surface temperature, precipitation, soil

quality, etc.) that can have even stronger influence on tree

growth than solar activity.

Wavelet transform allowed us to study temporal changes

in the evolution of RW, EWW, LWW, and RD both for

Pinus pinaster and for Pinus sylvestris. In general, wavelet

power seems to be higher in the case of the comparison of

the evolution of solar activity and the evolution of RW,

LWW, and RD of Pinus pinaster than in the case of the

comparison of the evolution of solar activity and the evo-

lution of RW, LWW, and RD of Pinus sylvestris. Changes

found in the phase angles of wavelet coherence, in the

range of 8–12 years, are probably caused by the non-solar

factors mentioned above. Therefore, it would be useful in

Table 2 Significant periods found in the power spectrum of the wavelet transform (CWT) in the case of the LWW analysis, respectively (no

significant wavelet power was found in the RW- and the EWW evolution, respectively)

Wavelet

analysis

CWT CWT CWT XWT XWT XWT WTC WTC WTC

Ring width

type

RW EWW LWW SSN/RW SSN/EWW SSN/LWW RW EWW LWW

Intervals of

years

– – 1930–1960 1845–1860/

1915–2002

1842–1895/

1907–1929/

1970–1990

1845–1862/

1925–2003

1842–1850/

1917–1974/

1842–1908

1840–1880 1930–2000

Significant

periods

(years)

– – 8–12 9–13/6–10 9–13/9–10/6–11 11–13/7–12 x x x

Phase angle

in WTC (�)

x x x x x x 0/170–200/10–50 0 180

Significant periods found in the power spectrum of the cross-wavelet transform (XWT) of the Wolf sunspot number (SSN) and the tree RW,

EWW, and LWW, respectively. Significant phase angle orientations found in the power spectrum of the wavelet coherence. Confidence level is

in most cases higher than 95 % for Pinus sylvestris

Fig. 6 Bivariate fit of the medium density of the maritime pine

646 Eur J Forest Res (2014) 133:639–648

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future research to compare our results with an analysis of

temperature and precipitation data evolution in the corre-

sponding years.

Acknowledgments This work was supported by grant number

SFRH/BPD/44018/2008 from the Fundacao para a Ciencia e

Fig. 7 Wavelet power spectrum (two upper panels) for red noise

background, cross-wavelet transform (two middle panels), and

wavelet coherence (two bottom panels) of the Wolf sunspot number

and the wood density time series of both Pinus pinaster (upper of

each pair of panels) and Pinus sylvestris (lower of each pair of

panels). The cone of influence is marked as a shaded area and the

contour lines show the 95 % confidence level. (Color figure online)

Fig. 7 continued

Eur J Forest Res (2014) 133:639–648 647

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Tecnologia, MCTES, Lisbon, Portugal (I.D.). The authors are grateful

to the SIDC Team for providing the sunspot numbers. The authors

would also like to acknowledge the technical assistance of V. Calfa

and S. Macansky (SCO Hurbanovo) in obtaining the samples from

Pinus sylvestris. Wavelet and wavelet coherence were calculated with

a modified computing algorithm of wavelet transform, the original of

which was developed and provided by A. Grinsted and is available

at URL: http://www.pol.ac.uk/home/research/waveletcoherence/down

load.html.

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