The height-increment record of summer temperature extended over the last millennium in Fennoscandia

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1 Research paper The height-increment record of summer temperature extended over the last millennium in Fennoscandia The Holocene 21(2) The Author(s) 21 Reprints and permission: sagepub.co.uk/journalspermissions.nav DOI: / Markus Lindholm, 1 Risto Jalkanen, 1 Hannu Salminen, 1 Tarmo Aalto 1 and Maxim Ogurtsov 2 Abstract New data have allowed us to extend a previous height-increment chronology of Scots pine (Pinus sylvestris L.) at the northern Fennoscandian timberline 817 years backwards in time, from 1561 to 745. Our final transfer model accounts for 31% of the dependent instrumental (mean June August) temperature variance between 198 and 27. According to the 1263 yr long summer temperature proxy, the most severe summers were experienced in 161, 179 and 782. Correspondingly, the summers of 1689, 885 and 1123 were the most favourable for growth. Two drastic shifts in temperature variability were also found. The twentieth century experienced a multidecadal change as the cold period was immediately followed by a warm period from 1915 to An even more prominent shift occurred in the Middle Ages, as the most severe cold spell during was preceded by the warmest period only a decade earlier, during The Fourier spectrum of the reconstruction shows significant concentrations of variance around 33.3, 23.3 and 11 years, and between 2.6 and 3. years. The wavelet spectrum was able to date several centres of fluctuating periodicities between 745 and 27. Furthermore, daily temperature records allowed us to define the major growth forcing climatic factor in more detail than in previous response analyses. The mean temperature during a 53 day season from 14 June to 6 August produced the strongest positive growth response (r 2 =.36). Keywords Fennoscandia, height increment, proxy, Scots pine, temperaure, timberline Introduction The temperature of the previous summer is the dominant height-growth limiting factor of Scots pine at the northern timberline (e.g. Jalkanen and Tuovinen, 21; Lindholm et al., 29a, b; Pensa et al., 25; Salminen and Jalkanen, 25; Salminen et al., 29). In addition to the mean July temperature of the previous year, the previous June and August temperatures also produce a significant height-growth response (Jalkanen and Tuovinen, 21; Lindholm et al., 29a, b). Our main goal was to extend a previous reconstruction of June August temperature variability from the height increment of Scots pine by Lindholm et al. (29b, hereafter REC45) backwards in time as new increment data have recently been compiled. Moreover, we aimed at delimiting the response window in the temperature/height-growth relationship more precisely than with monthly means. Exploring daily temperature records, we sought a climate variable that would define the actual duration of the height-growth forcing conditions during the previous summer. Monthly climate variables are convenient but rather rough and arbitrary units with respect to the start and end of tree growth. This is especially true in marginal regions such as the northern timberline, where the vegetation growing season is very short and intensive, more likely lasting weeks rather than months (Salminen and Jalkanen, 27; Schmitt et al., 24; Seo et al., 21). Since the height-growth chronology has now been extended to more than twice its previous length, the rank and hence date of extreme years and periods discussed in REC45 will potentially also change and thus need to be reanalyzed. We have applied both the Fourier and wavelet methods in order to date the concentrations of fluctuating or oscillating periodicities along the timeline. Knowledge of the various timescales of natural past temperature variability is important in attempts to extract the trends of anthropogenic warming signal predicted over the high northern latitudes by the end of this century by the Intergovernmental Panel on Climate Change (IPCC, 27). Data and methods Annual height increments of 92 living and dead Scots pine (Pinus sylvestris L.) trees from the northern timberline were measured 1 Metla, Rovaniemi Research Unit, Finland 2 A.F. Ioffe Physico-Technical Institute of Russian Academy of Sciences, Russia Received 15 October 29; revised manuscript accepted 18 May 21 Corresponding author: Markus Lindholm, Metla, Rovaniemi Research Unit, PO Box 16, FI 9631 Rovaniemi, Finland markus.lindholm@metla.fi

2 32 The Holocene 21(2) Figure 1. The time spans of all the 152 samples included in the chronology. Each sample is represented by a horizontal bar, with length equal to the number of annual increments, and position corresponding to the dendrochronological cross-dating Year and combined with the previously analyzed data from 6 trees (REC45). Data collection, preparation and measurement were carried out according to the guidelines of Aalto and Jalkanen (1998). The Laanila site in northern Finland is located at N; E; m a.s.l., about 8 km south of the pine treeline. Monthly and daily mean temperature records from the Sodankylä meteorological station, 14 km from the sampling site, are available since 198. The chronology was constructed using basic dendrochronological tools such as cross-dating, standardization, averaging, calibration and verification (e.g. Briffa and Jones, 199; Briffa et al., 1988; Cook, 1985; Cook and Peters 1981; Cook et al., 199; Fritts, 1976; Fritts et al., 199; Gordon, 1982; Holmes et al., 1986; Lindholm, 1996; Pilcher, 199; Wigley et al., 1984). The same 33% n splines were applied in standardization as in REC45. A non-linear optimization algorithm (Hooke and Jeeves, 1961) was used to select the calendar period that would produce the highest correlation between various combinations of daily temperatures from the 1 June to 31 August and the height-growth chronology during The task was to maximize the correlation and the optimization algorithm simply varied the start and end dates of the temperature period until the highest correlation was achieved. Furthermore, we applied both the Fourier and wavelet methods to the upgraded chronology. Fourier analysis provides a frequency domain representation of the original function (chronology) in the time domain. It is a spectral description in terms of cycles of varying length, i.e. the actual frequencies that generate the original series. The wavelet approach has advantages over the more traditional methods for analyzing potentially non-stationary signals, which have discontinuities and non-periodic characteristics (Daubechis, 1998; Mallat, 1999; Torrence and Compo, 1998). Results The chronology and the duration of temperature forcing Most of the new samples came from submerged trees (lakewood). Samples from lakewood and landwood overlapped by 15 years. The oldest increment of the landwood dated to the 154s. The temporal distribution of all the 152 cross-dated samples from individual trees is relatively even up to the twentieth century (Figure 1). Sample replication is highest during the late twentieth century, although only five series cover the most recent 4 years. The mean lifespan in terms of available annual shoots of all the trees is 126 years (SD 4.2), ranging from 35 years to 37 years. Combinations of daily temperature averages were screened for more precise time limits of the effective growth forcing temperature factor. We found that during the last 1 years, height-growth responded most favourably to the mean temperature of a 53-day period from the 14 June to 6 August (hereafter GF53d). Although GF53d yields the highest correlation (r =.613), the coefficient value of the various daily combinations decreases only very gradually when their correlations with height-growth are arranged in descending order. The second highest value is produced by the mean temperature of a 52 day period also starting on 14 June, but ending on 5 August. This factor has only marginally lower correlation values (r =.68). Calibration and verification of the two reconstruction models The new height-growth chronology, extended to 28 with five samples, was used as a predictor of both the mean June August temperature (JJA) and GF53d. Thus, two sets of reconstruction models were developed and the coefficients of determination,

3 Lindholm et al. 321 Table 1. Calibration and verification statistics of transfer models using the height-growth chronology as a predictor of mean June August temperature (JJA) and GF53d, which is mean daily temperature between 14 June and 6 August. The temperature of the previous year corresponds to current growth (A) Calibration: Chronology vs. JJA and GF53d a a a JJA R Intercept Weight GF53d R Intercept Weight (B) Verification: modelled versus measured values Calibration Verification Calibration Verification JJA RE CE r GF53d RE CE.18. r Measured vs. predicted temperatures in centigrade A Early calibration Early verification Late calibration Late verification B 2 Residuals Year Figure 2. The measured mean June August temperatures (thick line) plotted together with the reconstructed values (thin line) (A). The difference between measured and reconstructed values (B) intercepts as well as weights of the transfer models during the whole calibration period and two split periods were compared (Table 1A). The late calibrated models generally produce higher R 2 values (.351 and.391 during ) than the early calibrated models (.28 and.36 during ). The GF53d produces a higher overall R 2 than JJA (.36 and.31, respectively). In addition, the GF53d and JJA models are both quite time-stable, as indicated by the similar values of coefficients of determination (Table 1A) and the explained variance over the two subperiods (Table 1B). A comparison of the verification statistics (Table 1B) shows that the RE values for JJA and GF53d are positive for both subperiods. However, despite an improved R 2, the CE during the late calibration and early verification of GF53d falls markedly below the RE. The actual CE value (.3) was rounded to zero (Table 1B). As any negative value is generally considered as model failure, we will present further details only for the JJA reconstruction. The final reconstruction at interannual to decadal scales Our final transfer model accounts for 31% of the dependent instrumental mean June August temperature variance between 198 and 27 (Table 1A, Figure 2A, y = 3.14x , where y is the mean June August temperature in one year and x is the

4 322 The Holocene 21(2) Reconstruction in centigrade A EPS RBar 1, B,8,6,4,2 Sample depth 4 C Year Figure 3. Reconstructed mean June August temperature anomalies during superimposed by 2-year moving averages to emphasize medium-frequency variability. Centuries separated by thin vertical lines (A). EPS and RBar are calculated between all samples over a moving 7-year window. The.85 threshold value indicated by dashed line (B). Sample depth (replication) is the number of samples present in each year (C) height-growth index value in the next year). The residuals of observed and reconstructed temperatures (Figure 2B) indicate that the most recent divergent period (starting in 1998 and deepening in 22 and 25) may be coming to an end and that the earlier good synchrony between temperature and growth returns after year 27 (Figure 2A, B). The mean June August temperature during the calibration period at Sodankylä station is 12.5 C, with a maximum of 15.5 C and a minimum of 1. C. These are the limits for interpolation (Figure 2A). The MSE calculated between the predicted and estimated temperatures is.84. The calibration error and thus 95% confidence intervals are RBar, which was calculated using 7-year moving averages, varies between.14 and.69, and it has a mean of.39 between 78 and 1972 (Figure 3B). EPS ranges from.55 to.96 and has a mean of.89 (Figure 3B). The chronology confidence occasionally falls below a theoretical threshold value (.85) during , , , and These periods are marked off with parentheses (Table 2) and omitted from any further analyses and discussion in this paper because of somewhat reduced reliability. Sample replication permanently rises above five in 745, except for the last four years (Figure 3C). The extended height-increment proxy clearly expresses interannual variability as well as crests and troughs on decadal and multidecadal scales from 745 to 27 (Figure 3A). The warmest summer in the reconstruction was experienced in 1689 and the coldest in 161 (Table 2A). None of the twentieth-century summers were among the warmest, but the summers of 193 and 192 were among the eight coldest. The warmest 1-year period in the record lasted from 1115 to 1124, while the coldest 1-year mean occurred only a decade later, between 1135 and 1144 (Table 2B). The only twentieth-century decade among the most extreme 1-year means was the cold period (Table 2B). Conversely, the next three decades between 1915 and 1944 were the fifth warmest among the 3-year means (Table 2C). Among the 42 non-overlapping 3-year periods between 76 and 1992, the period was the warmest and the coldest (Table 2C). The Fourier spectrum of the reconstruction shows six distinct and significant peaks around , 11., 23.3, and 33.3 years (Figure 4B). These peaks exceed the.95 confidence level. Concentrations of periodicities with a roughly 33-year cycle length are found around 8 as well as in the early twelfth century in the wavelet spectrum (Figure 4A). Approximately 23-year periodicities are dated most notably to the early nineteenth century. Periodicities with peaks centred on 11 years generally appear less conspicuous, but are present during several centuries and also evident in the early twentieth century. Discussion Confident 1263-year proxy and past natural fluctuations Our temperature proxy is mostly reliable with an average EPS of.89, i.e. above the.85 threshold value (Briffa and Jones, 199; Wigley et al., 1984), except for a few periods in the Middle Ages and between 1669 and The longest and most conspicuous spell of inconsistency in the whole calibration period was experienced during (Figure 2B, for potential causes of this discrepancy see REC45). The most recent years indicate that the strong and confident temperature signal of the height growth may be returning to the average level from a prolonged divergent period (Figure 2A). Two drastic shifts in the mean value of decadal temperature variability were found in our reconstruction. The twentieth century experienced such a multidecadal change, as the cold period from 195 to 1914 was immediately followed by a warm period from 1915 to An even greater shift occurred in the Middle Ages as the most severe cold spell (during , Table 2C) was preceded by the warmest period only a decade earlier ( , Table 2B). Multidecadal (c. 33-year, Figure 3A, B) fluctuations seem to particularly dominate the early part of the reconstruction, i.e. the pre-thirteenth century era, while the bulk of the bidecadal (c. 23-year) fluctuations do not appear until after 18. Several focal points of roughly 11-year fluctuations are scattered more evenly throughout the centuries. Based solely on the arising periodicities,

5 Lindholm et al. 323 Table 2. Largest temperature anomalies extracted from the reconstruction (JJA, ). (A) Extreme summers, (B) 1-year non-overlapping periods, and (C) 3-year non-overlapping periods. The scale is deviation from the mean in degrees Celsius (DEP). Years and periods, when EPS <.85 are in parentheses Positive anomalies DEP Negative anomalies DEP (A) Individual years (782) (781) (81) (798) (B) 1-year means (795 84).75 ( ) ( ) (15 114) ( ) (C) 3-year means ( ) such variability may be connected with the North Atlantic Oscillation (NAO), which has been reported to have time variations of 6 1 years as well as 2 25 years (Cook et al., 1998; Hurrel and van Loon, 1997; Rogers, 1984). In addition, the Arctic circulation regime has a potential 1 15 years fluctuation (Proshutinsky and Johnson, 21). Other well-known periodicities include the Brückner 35-year cycle, the Schwabe 11-year cycle, and the Hale quasi-22-year cycle. Previous ring-width chronologies of Fennoscandian pine have shown positive correlation to the seasonal variability of the NAO (Lindholm et al., 21). In addition to northern geographical position, Fennoscandian weather and climate conditions depend on the strength of the low-pressure system usually found near Iceland and the high-pressure systems in the Azores and Siberia. Since the position and magnitude of these systems vary, one of them can dominate the weather for a considerable time (Aguado and Burt, 1999). Although airflows from the Atlantic are generally warm and moist, the clouds accompanying them also reduce the amount of sunshine received during the summer. By contrast, the continental high pressure system at times extends to Fennoscandia and counteracts the maritime influences, manifesting itself as severe cold and dryness in winter and extreme heat in summer. Thus, the Fennoscandian climate is a variable combination of maritime and continental characteristics. When westerly winds prevail, they bring air currents warmed by the Gulf Stream and the North Atlantic Drift Current. Cyclones are formed and developed along the polar front from waves caused by the juxtaposition of cold air moving toward the equator and hot air moving toward the poles (Bjerknes and Solberg, 1922). These cyclone tracks are displaced farther northward in July reflecting the more northward position of the polar front in summer. Comparison with other proxies Using pentads Briffa et al. (28) observed a significant temperature impact (r =.63) on the radial growth of Scots pine in the Fennoscandian region encompassing late June, July and early August. The result corresponds with the magnitude (r =.6) as well as the response time-window of GF53d in relation to our height-growth chronology. Further comparisons were made in this work only between the new JJA proxy and the same tree-ring-based reconstructions (Briffa et al., 199, 1992, 1995; Helama et al., 22; Lindholm and Eronen, 2) as in REC45, in which the compatibility of these records was discussed. The year 161 (Table 2A) remains the most severe in the whole 1263 year long height-increment-based reconstruction. This has been recorded from a wide network of tree-ring chronologies in North America and Europe (Briffa et al., 1992, 1995, 1998; Helama et al., 22; Jones et al., 1995; Lindholm and

6 324 The Holocene 21(2) Period of variation, years 223,9 133,1 79,2 47, ,6 9,9 5,9 3,5 SPD(w) A Years 33.3 yr B 23.3 yr 11. yr.95 c.l yr,1,2,3,4,5 Frequency w, year 1 Figure 4. The Morlet wavelet spectrum of the reconstructed June August temperatures (A). The wavelet spectrum was normalized by variance and its confidence level was calculated for red noise (α =.8) (Torrence and Compo, 1988). The Fourier spectrum of the reconstruction (B). SPD is spectral density and dotted line is.95 confidence level 6, 5,5 5, 4,5 4, 3,5 3, 2,5 2, 1,5 1,,5 Eronen, 2). The potential origin of this severe and large-scale decline in growing conditions has been discussed by Briffa et al. (1998) and de Silva and Zielinski (1998). The year 161 belongs to the ninth coldest 3-year mean during (Table 2C). This pointer year also begins the third coldest 2-year period ( ) in the proxy by Briffa et al. (199) and overlaps very closely with the second coldest 2-year period ( ) in the record by Lindholm and Eronen (2). Briffa et al. (1998) have found potential volcanic pointer years in a network of tree-ringdensity chronologies. Several of these event years (1836, 1695 and 1666) coincide relatively closely with our extreme years (1837, 1695 and 1667). The last two are the 14th and 22nd coldest years in our record (not shown in Table 2A). The second coldest year in our record (179) is not listed as an extreme one in the other proxies used here for comparison. However, the fourth coldest summer in our record (1138) is close to the most severe event (1139) in the proxy by Briffa et al. (199). In our reconstruction (Table 2A), the early twentieth century also experienced some very cold summers (Table 2A). The time between 191 and 191 was the most severe 1-year period in REC45. However, as the height-increment chronology has now been extended back in time by 817 years, six earlier episodes appear to have been harsher than the early nineteenthcentury cold spell ( in Table 2B), viz , , , , and In the extended record, the coldest 1-year mean is dated between 1135 and 1144, and furthermore the third coldest 3-year mean occurred between 1135 and Corresponding evidence of unusually severe mid-twelfth century conditions was also reported by Lindholm and Eronen (2) and Briffa et al. (199). The former authors found the third coldest 2-year mean during , while the latter authors note the most negative 2-year anomaly during and most negative 5-year anomaly during In the frequency domain, the main features of the Fourier spectrum appear similar to those presented in REC45, although the higher order peaks (previously at 2.6 and 3.7 years) are now somewhat less significant. Lamb (1972) also found prominent features around 11.3, 18, 23 and 3 years in the power spectrum of Lapland tree-ring data for the period 1463 to 196. In addition, Sirén and Hari (1971) analyzed tree-ring data from the same region and reported major cycles at 3.3, 22.8, and 32 years. Moreover, a 33.3-year peak is also evident in the reconstruction by Briffa et al. (199; for the variance spectrum, see Briffa et al., 1992). Conclusions The height-growth chronology of Scots pine and hence temperature reconstruction at the timberline in northern Fennoscandia now extends over the last millennium, 1263 years back from the present. Height growth and the summer temperature are known to co-vary predominantly at high-to-medium frequencies (calibration period, REC45). Thus, the reconstruction should be considered relative to any longer-term trends or fluctuations potentially present in the real climate. Such trends may not be extracted from these data or they may simply be unattainable with the indexing methods applied here. If compatible, longer (e.g. centennial) trends of the temperature signal were extracted and became available from other annually resolved, confident proxies from the same region, it would be reasonable to combine them with our record to complement the climate history of the region. We have now empirically defined the height-growth response of pine to climatic factors at a daily resolution. The start and end dates of GF53d (14 June and 6 August) are plausible with respect to the local ecological conditions, e.g. the general absence of both snow cover and ground frost. Nevertheless, the GF53d may present too narrow a response window for the whole 1-year calibration period, since the reconstruction model failed, although only barely so. However, it is noteworthy that the GF53d reconstruction is a linear combination of the same height-growth chronology (Table 1A). Thus, it would have the same shape as the JJA reconstruction (Figure 3A) and would potentially exhibit the same extreme years and periods as JJA (Table 2), although with a different scale. The GF53d model may be improved in the future by further refining the optimal period of growth, e.g. allowing some leverage in the start and end dates of different vegetation/ growing season parameters by using pentads, or using still other parameters in response functions such as the sum of growing degree days (threshold value of 5 C), the number of frost days during the growing season, and the date with the maximum temperature during the growing season. The detection of recent warming (IPCC, 27) is challenged by the presence of natural climate pulses, as they may co-evolve with the anthropogenic warming trend at decadal to secular scales in the subarctic region. Such multidecadal natural phenomena frequently appear in the height-increment record of the preindustrial climate history. The strength of the observed and predicted anthropogenic warming signal may thus be masked or correspondingly overstated during the calibration period, the last and present centuries.

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