A critical evaluation of multi-proxy dendroclimatology in northern Finland

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1 JOURNAL OF QUATERNARY SCIENCE (2010) 25(9999) 1 8 ISSN DOI: /jqs.1408 A critical evaluation of multi-proxy dendroclimatology in northern Finland DANNY MCCARROLL, 1 * MERVI TUOVINEN, 2 ROCHELLE CAMPBELL, 1 MARY GAGEN, 1 HÅKAN GRUDD, 3,4 RISTO JALKANEN, 5 NEIL J. LOADER 1 and IAIN ROBERTSON 1 1 Department of Geography, Swansea University, Swansea, UK 2 Department of Geography, University of Oulu, Finland 3 Abisko Scientific Research Station, Royal Swedish Academy of Sciences, Abisko, Sweden 4 Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, Sweden 5 Finnish Forest Research Institute, Rovaniemi Research Unit, Rovaniemi, Finland Received 22 July 2008; Revised 3 March 2010; Accepted 20 March 2010; Published online xx xx xx ABSTRACT: Twentieth-century summer (July August) temperatures in northern Finland are reconstructed using ring widths, maximum density and stable carbon isotope ratios (d 13 C) of Scots pine tree rings, and using combinations of these proxies. Verification is based on the coefficient of determination (r 2 ), reduction of error (RE) and coefficient of efficiency (CE) statistics. Of the individual proxies, d 13 C performs best, followed by maximum density. Combining d 13 C and maximum density strengthens the climate signal but adding ring widths leads to little improvement. Blue intensity, an inexpensive alternative to X-ray densitometry, is shown to perform similarly. Multi-proxy reconstruction of summer temperatures from a single site produces strong correlations with gridded climate data over most of northern Fennoscandia. Since relatively few trees are required (<15) the approach could be applied to long sub-fossil chronologies where replication may be episodically low. Copyright # 2010 John Wiley & Sons, Ltd. KEYWORDS: dendrochronology; climate reconstruction; stable carbon isotopes; densitometry; tree rings; treeline. Introduction Tree ring width measurements are one of the most powerful proxy measures of past climate, providing well-replicated and absolutely dated chronologies from many parts of the globe (Hughes, 2002). They dominate the data used in the various attempts to reconstruct the climate of the last one to two thousand years and have been critical in informing the debate about the degree to which recent warming is unusual in a longer-term context (Mann et al., 1999; Esper et al., 2004; IPCC, 2007). However, the rings of trees, like ice cores and lake sediment sequences, provide a physical and chemical as well as a numerical archive, and many other potential palaeoclimate proxies can be extracted. It should be possible, therefore, to apply a multi-proxy (or multi-parameter) approach to dendroclimatology to extract the maximum amount of Holocene palaeoclimate information from tree rings, in the same way that a multi-proxy approach has proven so successful in studying longer-term changes using peat profiles, lake sediments and ice cores (Lorius et al., 1992; Charman and Chambers, 2004; Birks and Birks, 2006). A promising place to explore this approach is the Boreal treeline area of northern Europe, where trees are growing under stress and are therefore sensitive to environmental changes, and where tree stems are well preserved both as standing and fallen dead wood and as sub-fossil material in lakes, mires and river gravels (Kirchhefer, 2001; Eronen et al., 2002; Grudd et al., 2002; Gunnarson and Linderholm, 2002; Helama et al., 2002; Grudd, 2008; Lindholm et al., 2009). The range of potential palaeoclimate proxies that can be extracted from trees is large and continues to expand, but many are difficult and expensive to measure. In a pilot study, McCarroll et al. (2003) compared a range of physical and chemical proxies from pine trees in northern Finland using short datasets. The main aim was to examine the nature and strength of correlations with climate parameters, for individual proxies and combinations of proxies, and verification was based only * Correspondence to: D. McCarroll, Department of Geography, Swansea University, Singleton Park, Swansea SA2 8PP, UK. d.mccarroll@swan.ac.uk on bootstrap methods. One of the conclusions was that combining proxies that have the same dominant control, but where secondary controls differ, is likely to lead to a stronger climate signal. This is because averaging such records tends to accentuate the common signal while attenuating the different sources of noise. In that example, ring widths, maximum densities and stable carbon isotope ratios all provided strong proxies for summer temperature. The aim of this paper is to critically test the hypothesis that combining these proxies, with levels of replication that could realistically be applied to long Fennoscandian pine chronologies, will provide a strong and reliable summer temperature reconstruction. Meteorological records covering the last century allow split calibration and verification periods and the skill of the reconstructions to be tested using the range of criteria recommended by the National Research Council (2007). Methodology For ring width and density measurements, 15 dominant >200- year-old Scots pine (Pinus sylvestris L.) trees were sampled at the Laanila research area in northern Finland ( to N, to E; m above sea level (a.s.l.)). Cores or sections of wood were taken from two radial directions. Thin laths (1.25 mm) were cut and treated by Soxhlet extraction in ethanol to remove resin and other mobile substances using standard procedures (Schweingruber et al., 1978), then X-rayed using an Itrax WoodScanner system (Bergsten et al., 1991). Grey-level intensity in the resulting radiographs, with a resolution of 0.01 mm (2540 d.p.i.), was calibrated to density using a standard wedge (Schweingruber, 1988) and the ring widths and maximum densities were determined using WinDendro. Non-climatic trends in the ringwidth series were removed using a 10-year spline, and the chronology was constructed using the robust mean. This is a harsh detrending procedure, and will remove longer-term climate information. However, a range of more conservative approaches yielded lower correlations with climate at this site. The stable carbon isotope chronology used here is that produced by Gagen et al. (2007). The full chronology includes 12 trees: 10 taken from the Laanila research area and two from Copyright ß 2010 John Wiley & Sons, Ltd.

2 2 JOURNAL OF QUATERNARY SCIENCE further north, near Utsjoki ( N, E; 110 m a.s.l.). Over the last 180 years replication varies between seven and nine trees. Isotopic analyses were performed on the latewood cellulose of each individual tree ring using standard methods (Loader et al., 1997, 2003; Rinne et al., 2005). The juvenile portion of each individual tree series was removed and corrections made for changes in the isotopic ratio of atmospheric carbon dioxide and for changes in CO 2 concentration (McCarroll and Loader, 2004, 2005; McCarroll et al., 2009). The mean d 13 C values are calculated after standardising the individual series using the difference from the mean value over periods of common overlap. Full details of sample preparation and data treatment are described in Gagen et al. (2007). Results The closest meteorological station to Laanila is at Ivalo ( N, E), where records extend back only to All three proxies are significantly correlated with the mean temperatures of July and August but not with June, reflecting the short growing season (Table 1). For maximum density and d 13 C the correlation with the mean temperature of July and August is stronger than the correlation with either month individually, but for ring widths July is dominant. Carbon isotopes perform best, explaining more than 52% of the variance in summer (July August) temperature. A simple way to combine proxies is to take a weighted mean of the indexed values (McCarroll et al., 2003; Gagen et al., 2004, 2006; Loader et al., 2007, 2008), where the weight is determined by the percentage of variance explained (r 2 ). When this procedure is used to combine maximum density and stable isotopes, the correlation with summer temperature increases from 0.72 to Adding ring width does not improve the correlation any further. The Ivalo climate data are too short to be split into separate calibration and verification periods, but a much longer temperature series (from 1908) is available from Sodankylä, 150 km to the south ( N, E). Over the common period the two July August mean temperature records are very similar, with a correlation of r ¼ 0.98 (Table 2). The mean temperature at Sodankylä is slightly higher, and the range of temperatures is slightly less than at Ivalo (Table 2). If the Sodankylä mean July August temperature over the period is used for calibration, then a direct comparison can be made with the results obtained using the local (Ivalo) station. However, since the Sodankylä record is much longer, the remaining period ( ) can be used for verification (Fig. 1 and Table 3). Over the common calibration period ( ) the three individual proxies produce very similar results at the two climate stations (Tables 2 and 3), confirming that meteorological data from Sodankylä provide a reasonable approximation to conditions at the sampling site. Table 2. Comparison of temperature (8C) datasets over the common period at Ivalo and Sodankylä. Mean SD Maximum Minimum Range Ivalo Sodankylä Although the strength of the correlation between a proxy time series and instrumental climate measurements is the most commonly used indicator of likely skill in reconstructing palaeoclimate, the mean squared error (MSE) and reduction of error statistics (RE) provide additional information on the fit between measured and estimated time series and have been recommended by the National Research Council (2007) as appropriate for verification. The reduction of error statistic compares the skill of the estimated values with that obtained by simply using the mean value of the calibration period for every year. It is particularly useful because it checks whether a proxy is able to follow the lower frequency changes in climate between the calibration and verification periods (Wahl and Ammann, 2007). The coefficient of efficiency compares the skill of the estimated values with that obtained by using the mean value of the verification period for every year. Although the correlation between ring width and summer temperature is statistically significant over the calibration and verification periods, producing positive RE and coefficient of efficiency (CE) statistics, the CE statistic is very close to zero (0.09). These results suggest that ring width at this site is not a strong indicator of the mean temperature of July and August. Maximum density performs better, explaining almost half of the variance in summer temperature during the calibration period (Table 3). Over the verification period 30% of the variance is Table 1. Correlation (r) between the three proxies, and their weighted combinations, and the mean monthly temperature measured at the local climate station at Ivalo. Ivalo temperature RW MXD d 13 C All three MXD/d 13 C June July August July August RW, ring width; MXD, maximum density. Figure 1. Mean July August temperature reconstructed using regression (bold lines) compared with the meteorological data from Sodankylä (grey lines). For calibration and verification statistics, see. Table 4.

3 MULTI-PROXY DENDROCLIMATOLOGY IN NORTHERN FINLAND 3 Table 3. Calibration and verification statistics for the three proxies and their weighted combinations using the longer records from Sodankylä meteorological station. Calibration Verification Verification Sodankylä r 2 MSE r 2 MSE RE CE r 2 MSE RE CE Ring width MXD d 13 C All three MXD/d 13 C MSE, mean square error; r 2, coefficient of determination; RE, reduction of error; CE, coefficient of efficiency; MXD, maximum density. explained and both RE and CE statistics are positive (0.33 and 0.26). Stable carbon isotope ratios provide the strongest single proxy (Table 3). They perform better than maximum density over the calibration period, but the difference is much greater in the verification period where 41% of the variance is explained. However, although the r 2 value for d 13 C in the verification period is much higher than that obtained for maximum density, the RE and CE statistics are only slightly better. Combining the proxies improves the estimates of summer temperature. In the calibration period, combining density and isotopes, without ring width, gives the best results (Table 3). In the verification period, however, combining all three proxies gives the best results, with 47% of the variance explained and 0.46 and 0.40 for the RE and CE, respectively. Although stable carbon isotope ratios provide the best individual proxy results for the calibration and verification periods, the errors are not equally distributed through time. The fit between the measured July August temperatures and those estimated on the basis of carbon isotopes is extremely good from the present back to 1917, but in the earliest years of the Sodankylä record the fit is poor (Fig. 1). The problem lies in the period , when there is an abrupt drop in the isotope values and a smaller but nevertheless clear decline in wood density (Fig. 1). This period represents the years immediately following the eruption of Novarupta at Katmai in Alaska, which was the largest eruption (globally) of the 20th century. It was a Plinian type eruption, with an estimated total ejecta volume of km 3 (Wood and Kienle, 1990; Fierstein and Hildreth, 1992; Brantley, 1995). The volcano is located near the Arctic Circle, so the atmospheric effects would have been concentrated at high northern latitudes (Clark, 1913; Oman et al., 2005). Since stable carbon isotopes, and to a lesser extent wood density, are more sensitive to the strength of sunlight at the surface than to air temperature (McCarroll and Pawellek, 2001; McCarroll et al., 2003; Young et al., 2010), we should expect to see a strong direct effect on these proxies, irrespective of the influence on air temperature. Both proxies show a marked underestimate of summer temperature in the 3 years following the eruption, which is not seen anywhere else in the record. The normal lifetime of volcanic aerosols is 1 3 years (Oman et al., 2005). If the verification tests are repeated without the early years (before 1917) the fit between measured temperatures and those estimated from carbon isotopes is extremely good (Table 3). More than 60% of the variance is explained and RE and CE rise to 0.58 and 0.52, respectively. Ring width does not perform any better over this truncated verification period, but there is a modest improvement for maximum density. Reversing the calibration and verification periods produces similar results (Table 4), with carbon isotopes proving the strongest of the individual proxies over the (early) calibration period and the weighted combinations improving the results further. The combination of all three proxies explains 64% of the variance in mean July August temperature over the calibration period. Over the (recent) verification period, ring width performs poorly, with RE and CE values very close to and just below zero. Although d 13 C gives a higher r 2 than maximum density, the latter results in slightly higher RE and CE values. The best results are obtained when these two proxies are combined. Over the whole period of (Fig. 2), d 13 C gives the highest correlation with Sodankylä mean July August temperature (0.75), followed by density (0.64) and ring width (0.45). The weighted combination of maximum density and d 13 C raises the correlation between measured and predicted temperatures to r ¼ 0.78 (Fig. 2), and adding ring width hardly changes the coefficient (0.79). By combining the proxies, therefore, it is possible to explain more than 60% of the variance in summer temperatures, even though the meteorological station is more than 100 km south of the field site. Substituting the density results used here with the inexpensive alternative series based on blue intensity (McCarroll et al., 2002), generated for this site by Campbell et al. (2007), provides comparable results. Over the period the correlation with mean July August temperature is 0.66 and the variance explained by the combination of blue intensity and d 13 C is 62% (Fig. 2). The blue intensity method needs to be tested at other sites and on other species, but at this site it appears to provide palaeoclimate information equivalent to that obtained using X-ray density. One of the effects of using the standard palaeoclimate regression technique (inverse calibration) to reconstruct climate is an inevitable bias towards the mean and therefore an underestimation of the variability of climate in the past. The magnitude of this reduction in the amplitude of the reconstructed climate is a function of the strength of the correlation Table 4. Sodankylä Calibration and verification statistics. Calibration Verification r 2 MSE r 2 MSE RE CE Ring width MXD d 13 C All MXD/d 13 C MSE, mean square error; r 2, coefficient of determination; RE, reduction of error; CE, coefficient of efficiency; MXD, maximum density.

4 4 JOURNAL OF QUATERNARY SCIENCE Figure 2. Comparison of mean July August temperature measured at Sodankylä for the period with reconstructed temperature based on individual proxies and combinations of proxies. with climate during the calibration period (Birks, 1995; Robertson et al., 1999; Esper et al., 2005). To avoid this problem some reconstructions, particularly covering large areas, have employed a scaling approach, where the mean and variance of the proxy data are adjusted so that they have the same mean and variance as the target climate variable over a calibration period (Esper et al., 2005). The effect of this approach is demonstrated in individual and combined proxies (Fig. 3). Scaling generally results in lower verification statistics, since regression minimises the mean squared error (MSE) over the calibration period. It is notable, however, that even after the temperature reconstructions have been scaled to match the mean and variance over the calibration period, the multi-proxy approach yields very high RE and CE statistics. Using the calibration period , and the verification period , the RE and CE statistics for the weighted average reconstruction based on maximum density and d 13 C are 0.59 and 0.53, and when these periods are reversed the values are 0.57 and The area represented by the summer temperature reconstructions can be demonstrated using the pattern of correlation with gridded climate data (Fig. 4). The pattern produced using the measured July August temperature at Sodankylä (Fig. 4(A)) can be regarded as the target that would be obtained with a perfect proxy. Ring width produces significant correlations over a much smaller area (Fig. 4(B)), but the area covered for maximum density (Fig. 4(C)) is very close to the target. Using stable carbon isotope ratios reduces the extent of significant correlations to the east and southeast of the field site, but raises the strength of the correlations in northern Fennoscandia (Fig. 4(D)). The multi-proxy estimated July August temperatures (Fig. 4(E), (F)) produce significant correlations that cover all of Fennoscandia, with r-values >0.5 extending over most of central and northern Norway, Sweden and Finland. Over much of Swedish and Finnish Lapland and coastal north Norway, the r-values are >0.7. The veracity of the multi-proxy approach over timescales longer than a century is difficult to test directly here because of the paucity of meteorological data from this region that extends back beyond the 20th century. It is clear, however, that the potential to retain long-term, low-frequency climate information is strongly determined by the detrending techniques that are used. Sophisticated techniques are now available to minimise the signal loss in both ring width and relative density data, but they require very well replicated chronologies with a specific age structure (Briffa et al., 1992, 1995; Cook et al., 2000; Esper et al., 2002). There are few sites where samples are sufficiently abundant to apply these techniques beyond the last few centuries. With the replication available for most long chronologies, more traditional detrending methods are necessary, and they inevitably result in some loss of low-frequency, long-term climate information (Cook et al., 1995). The proxy most likely to retain climate information over the very long term is d 13 C, since for northern conifers it seems that no detrending is

5 MULTI-PROXY DENDROCLIMATOLOGY IN NORTHERN FINLAND 5 It is possible that tree ring stable carbon isotopes respond more strongly to sunshine than to temperature. The marked response to the Novarupta volcanic eruption, proposed here, supports that hypothesis since there was no synchronous change in summer temperature. If there have been periods in the past when temperature and sunshine do not change in parallel, as suggested by Young et al. (2010), then stable carbon isotope chronologies are likely to diverge from those based on ring widths and to a lesser extent densities. In such circumstances combining the records would not strengthen the climate signal. Figure 3. Mean July August temperatures reconstructed using scaling (black lines) compared with the meteorological data from Sodankylä (grey lines). necessary in the pre-industrial period (Gagen et al., 2007, 2008). Tree ring density is likely to retain climate information over longer timescales than ring widths because the detrending required is generally more conservative. To produce reliable climate reconstructions that extend back for many centuries or millennia, using trees that rarely live for more than 250 years (such as Scots pine), it may be necessary to filter the proxies to extract the reliable signal prior to combining them to reconstruct climate. If a multi-proxy approach to dendroclimatology is to be useful then it must first be demonstrated that the long-term variability in tree ring d 13 C series faithfully records lowfrequency variations in climate. There are no very long meteorological records available from northern Finland with which to test this, the nearest being in northern Sweden (Tornedalen). The Tornedalen composite record (Klingbjer and Moberg, 2003) stretches back to 1802 and July August temperatures of the period fall about 0.758C below the mean value for the period The stable carbon isotope ratios from Laanila, however, predict mean summer temperatures for these two periods that are almost identical (Gagen et al., 2007). It seems likely that the isotopic record is overestimating the true temperature at least for some time intervals. Summer temperature during the period , for example, is reconstructed as slightly warmer (0.38C) than most of the 20th century ( ) by the d 13 C results (Gagen et al., 2007), but was cooler in both the Tornedalen ( 0.98C) and Uppsala ( 0.58C) records, and also at Vardø on the northeastern tip of Norway ( 0.38C). Despite the excellent calibration and verification statistics obtained for the last century, therefore, some caution will be necessary in extending stable carbon isotope-based temperature reconstructions far back into the pre-instrumental period. Conclusions Of the three proxies used to reconstruct July August temperature, d 13 C proved stronger than maximum density and both were much stronger than ring width. Ring width in northern Fennoscandia records July temperature but is relatively insensitive to the temperature of August (Lindholm et al., 1996; Lindholm and Eronen, 2000; Kirchhefer, 2001; Grudd et al., 2002; Helama et al., 2002; Linderholm, 2002; Tuovinen et al., 2009). Blue intensity, used as a simple and inexpensive alternative to measuring X-ray density (McCarroll et al., 2002; Campbell et al., 2007), produced almost identical results. The best and most consistent results in terms of all three measures of skill (r 2, RE and CE) were obtained by combining d 13 C and maximum density (or blue intensity) and taking a weighted average of the predicted temperatures. Weighting was based on the percentage of variance explained (r 2 ). At this site adding ring width yielded little if any improvement. The combination of maximum density and d 13 C seems to provide a very promising way to extract a strong climate signal from a relatively small number of trees, allowing the approach to be used on long chronologies where strong replication is often difficult to obtain. Apart from a small divergence in the years following the largest volcanic eruption of the 20th century, the fit between measured and reconstructed temperatures is strong and consistent, suggesting that the instrumental period provides a strong and reliable calibration for reconstructing annual to multi-decadal scale variations in summer temperature in northern Fennoscandia. It must be stressed, however, that the techniques used here have only tested the behaviour of the multi-proxy approach over the 20th century. The simple procedure of combining the temperature estimates using a weighted average may not be appropriate over much longer timescales because detrending of the density (and ring width) removes low-frequency information. To apply multi-proxy dendroclimatology over centuries and millennia it may be necessary first to quantify exactly the temporal properties of each dataset and the influence of each data treatment, and then to filter the individual proxies in order to retain and combine only those frequencies that carry a common climate signal. Characterisation of the temporal properties of palaeoclimate reconstructions remains a significant challenge, but it is essential to producing robust multiproxy ensembles without loss or disruption of the lower frequency bands. There are many potential proxies that carry a strong highfrequency signal of summer temperature. Ring width measurements are often adequate at this scale and some more innovative methods, such as annual height increment and needle dynamics (Jalkanen and Tuovinen, 2001; McCarroll et al., 2003; Pensa et al., 2005; Lindholm et al., 2009) can produce extremely high correlations with instrumental temperature. The low-frequency, long-term signal in tree ring proxies will be most difficult to define and is most likely to be preserved in isotopic records. Gagen et al. (2007, 2008) have

6 6 JOURNAL OF QUATERNARY SCIENCE Figure 4. Spatial field correlations with the CRU-TS2.1 gridded mean July August temperature for the period performed using KNMI Climate Explorer (Oldenborgh et al., 2004). (A) Measured July August temperature at Sodankylä, 100 km south of the field site. (B) Ring width. (C) Maximum density. (D) Stable carbon isotope ratios of latewood cellulose. (E) Weighted mean of all three proxies. (F) Weighted mean of maximum density and d 13 C. demonstrated that, for pre-industrial Scots pine in northern Fennoscandia, after a short and definable juvenile phase there is no long-term change in stable carbon isotope ratios as trees age. Since it is not necessary to statistically detrend tree ring d 13 C series, they may have the potential to retain climate information at all temporal frequencies. The fit between d 13 C and summer temperature over the last century in northern Finland is very strong, and the lower frequency trends seem to be traced as well as the inter-annual variability. When the records are extrapolated back into the 19th century, however, comparison with the few available long meteorological series identifies periods where d 13 C may be overestimating summer temperature. These may be periods when the relationship between sunshine and temperature was different (Young et al., 2010). Whether tree ring d 13 C series carry a low-frequency temperature signal that is sufficiently strong and consistent to be useful therefore remains uncertain. Long records with good replication will be required to test this. Acknowledgements. This work was conducted as part of the EUfunded projects FOREST (ENV4-CT ), PINE (EVK2-CT ) and Millennium (017008). We would like to thank Paula Santillo and Jonathan Woodman-Ralph at Swansea for their tireless precision in the laboratory and our many friends in the PINE and Millennium projects for useful discussion. NJL thanks the UK NERC (NE/B501504/1 & NE/C511805/1) for research support. References Bergsten U, Lindeberg J, Rindby A, Evans R Batch measurements of wood density on intact or prepared drill cores using X-ray microdensitometry. Wood Sciences and Technology 35: Birks HJB Quantitative palaeoenvironmental reconstructions. In Statistical Analysis of Quaternary Science Data: Technical Guide 5, Maddy D, Brew JS (eds). Quaternary Research Association: Cambridge, UK; Birks HH, Birks HJB Multi-proxy studies in palaeolimnology. Vegetation History and Archaeobotany 15:

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