DENDROCLIMATIC SIGNALS IN LONG TREE-RING CHRONOLOGIES FROM THE HIMALAYAS OF NEPAL

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 23: (23) Published online in Wiley InterScience ( DOI: 1.12/joc.911 DENDROCLIMATIC SIGNALS IN LONG TREE-RING CHRONOLOGIES FROM THE HIMALAYAS OF NEPAL EDWARD R. COOK, a, * PAUL J. KRUSIC a and PHILIP D. JONES b a Lamont Doherty Earth Observatory, Palisades, NY 1964, USA b Climatic Research Unit, University of East Anglia, Norwich NR4 7TJ, UK Received 9 January 22 Revised 1 March 23 Accepted 1 March 23 ABSTRACT We describe the development of a tree-ring chronology network in Nepal that is suitable for reconstructing temperaturerelated climate forcing over the past few hundred years. The network is composed of 32 tree-ring chronologies and is represented by five indigenous tree species. An empirical orthogonal function analysis of the chronologies over the common interval indicates the existence of coherent large-scale signals among the tree-ring chronologies that are hypothesized to reflect, in part, broad-scale climate forcing related to temperatures. A long monthly temperature record for Kathmandu is developed and used to test this hypothesis. In so doing, significant monthly and seasonal temperature responses are identified that provide guidance for the formal reconstruction of two temperature seasons: February June ( ) and October February (165 91). Each reconstruction indicates the occurrence of unusually cold temperatures in , which coincides with the eruption of Tambora in Indonesia. A novel method is also used to add probable missing multi-centennial temperature variance to each reconstruction. The resulting adjusted reconstructions strongly reflect patterns of temperature variability associated with Little Ice Age cooling and warming into the 2th century, with the October February season exhibiting the strongest increase in temperature over the past 4 years. Only the October February season shows any evidence for late- 2th century warming, whereas February June temperatures have actually cooled since 196 (as with the observational series). Copyright 23 Royal Meteorological Society. KEY WORDS: Himalayas; Nepal; tree rings; dendroclimatology; temperature reconstruction; monsoon; Little Ice Age; climate change 1. INTRODUCTION The highlands of south-central Asia possess a diversity of natural archives from which long detailed palaeoclimatic records might be developed (e.g. lake sediments, loess, tree rings, ice cores, glacier fluctuations, geomorphologic features, and palaeobotanical fossils). Despite this potential, relatively little is known concerning climatic changes in this region over the past millennium. Nearly all of the empirical research undertaken on the Asian monsoon complex has been derived mainly from analyses of instrumental meteorological data from India and south Asia (e.g. Hingane et al., 1985; Pant and Hingane, 1988; Kulkarni et al., 1992; Parthasarathy et al., 1994; Kripalani et al., 1995, 1996). Other studies of the dynamics between the Indian/Southwest, Plateau, and East Asian monsoons cite the influence of the highlands of central Asia (Chang, 1981; Hahn and Shukla, 1976; Yanai et al., 1992; Kripalani and Kulkarni, 1999) and the El Niño southern oscillation (Parthasarathy and Pant, 1985; Joseph et al., 1994; Kumar et al., 1999). Yet, they too provide little insight into climate variability and forcing of Asian monsoon variability over the past millennium. The annual ring-width series of old-age trees are the most widely available source of high-resolution palaeoclimate information from the highlands of south-central Asia (e.g. Hughes, 1992; Bräuning, 1994; Wu and Shao, 1995; Borgaonkar et al., 1996; Yadav et al., 1997; Chaudhary et al., 1999; Esper, 2). Here, we * Correspondence to: Edward R. Cook, Lamont Doherty Earth Observatory, Palisades, NY 1964, USA; drdendro@ldeo.columbia.edu Copyright 23 Royal Meteorological Society

2 78 E. R. COOK, P. J. KRUSIC AND P. D. JONES describe a major advance in the use of this resource through the development of an important new tree-ring network in the Himalayas of Nepal. With this network, we demonstrate that the large-scale properties of tree growth are related to temperature variability over Nepal. This finding enables us to develop two long seasonal temperature reconstructions covering the past few centuries, which greatly expands our knowledge of climatic variability and change in this region of High Asia. 2. THE NEPAL TREE-RING NETWORK The Nepal tree-ring network is composed of 46 annual tree-ring chronologies, with most covering the past 3 5 years. The tree-ring sampling was largely completed over a 6 year period that required weeks of strenuous trekking with Nepali guides and porters into extremely remote and difficult terrain. This was necessitated by the general lack of reasonably undisturbed forests to sample within 1 2 days trek of any moderately sized village. In many cases, 4 5 days of trekking were required to reach the first suitable treering site, with the extreme case being 13 days. Earlier tree-ring collections in Nepal made in the late-197s by Rudolf Zuber, as described in Bhattacharyya et al. (1992), also augmented our new collections. The original Zuber tree-ring samples were kindly loaned to us by Dr Fritz Schweingruber and processed at our laboratory. Dr Burghardt Schmidt also kindly contributed several important pine ring-width data sets collected from living trees and archaeological wood (Schmidt, 1993) in the dry inner valleys of north-central Nepal. A map showing the locations of the 32 longest tree-ring chronologies used here is shown in Figure 1, and Table I provides a list of these chronologies. The species represented include fir (A. spectabilus), spruce (Picea smithiana), hemlock (T. dumosa), juniper (J. recurva), pine (Pinus wallichiana), and elm (U. wallichiana). The median elevation of the sites is 3 m, but none is a true upper-timberline site. The tree-ring samples were processed following standard dendrochronological practices (Stokes and Smiley, 1968; Fritts, 1976; Cook and Kairiukstis, 199). These included careful laboratory preparation of the samples, followed by rigorous cross-dating and precise measurement of the annual ring-width series. The quality of the 31 N NEPAL TREE-RING NETWORK 3 N TIBET 29 N NEPAL TREE-RING SITES 28 N KTM 27 N INDIA 26 N 8 E 82 E 84 E 86 E 88 E Figure 1. Map of the tree-ring site locations in Nepal. Kathmandu (KTM) is located towards the centre of the tree-ring network. A total of 32 tree-ring chronologies are represented by these sites. See Table I for a listing of the chronologies. This figure is available in colour online at

3 NEPAL DENDROCLIMATIC SIGNALS 79 Table I. The 32 Nepal tree-ring chronologies analysed in this paper. The species (SPEC) are Abies spectabilus (ABSP), Tsuga dumosa (TSDU), Pinus wallichiana (PIWA), Juniperus recurva (JURE), Picea smithiana (PISM), and Ulmus wallichiana (ULWA). The common period of the chronologies is a Site name SPEC LAT LON ELEV IFYR ILYR NYR NC MSL GurchieLehk ABSP KatyaKhola ABSP AboveGheri ABSP Deorali ABSP Banal ABSP ChardungDanda ABSP Chardung ABSP DobiniDanda ABSP BhulePokari ABSP NeheKarka ABSP KaumaKarka ABSP RachelsDeath ABSP Mumbuk ABSP Budorouk ABSP DobiniDanda JURE BhulePokari JURE KatyaKhola PISM KonaSouth-BS PIWA Mustang-BS PIWA ManagKone-BS PIWA Ngawal-BS PIWA Pisang-BS PIWA Mod/Archeo-BS PIWA Bhratang PIWA Deorali TSDU Sanu TSDU Langtang TSDU Tragdobuk TSDU LukuchiKhola TSDU Budorouk TSDU AboveHatiya TSDU KatyaKhola ULWA a LAT = site latitude; LON = site longitude; ELEV = site elevation in metres; IFYR = first year; ILYR = last year; NYR = chronology length; NC = number of cores in the chronology; MSL = median segment length. cross-dating was also checked using the COFECHA program (Holmes, 1983), which is the de facto standard quality assurance/quality control program in dendrochronology. As is standard to most dendroclimatic studies, the raw ring-width measurements of each site and species were detrended and standardized (sensu Fritts, 1976) to remove long-term growth trends thought to be induced by non-climatic influences, like aging and stand dynamics effects (Cook, 1987). Most of the treering sites were in closed-canopy forests with various levels of natural and anthropogenic disturbance. The standardized tree-ring indices were than averaged together to form a site tree-ring chronology that is useful for dendroclimatic modelling and climatic reconstruction. The particular detrending method chosen here was the hybrid double-detrending method, in which the ring-width series are detrended first with a deterministic linear or negative exponential growth curve (Fritts et al., 1969) fitted by least squares. The detrended tree-ring series are than filtered with a cubic smoothing spline (Cook and Peters, 1981) with a 5% frequency response function cutoff equal to two-thirds the length of the series. The spline is used in this case to remove the effects

4 71 E. R. COOK, P. J. KRUSIC AND P. D. JONES of common systematic bias in the fit of the deterministic growth curve to the ring-width series. Neither of these growth curves removes variance that is easily differentiated from the overall long-term trend in the data (Cook, 1985), which is assumed here to be mostly non-climatic in origin. The detrending procedure just described does not come without cost. Cook et al. (1995) showed that the amount of theoretically resolvable low-frequency variance due to climate in a tree-ring chronology is related to the segment lengths of the individual ring-width series used in forming the chronology and the way in which the series were detrended. The former is controlled directly by the tree-ring samples themselves, and the latter is determined by the detrending method chosen. Unfortunately, Cook et al. (1995) demonstrated that even the most conservative detrending methods will lose potential climatic variance at time scales longer than the segment lengths being detrended. Consequently, the median segment length of a tree-ring chronology is a useful diagnostic for determining the maximum resolvable low-frequency variance in a tree-ring chronology due to climate after its individual ring-width series have been detrended. The median segment lengths of the 32 tree-ring chronologies are provided in Table I for this purpose. About half are over 2 years long, and the grand median of these medians is 195 years. This result implies that climatic variability at time scales up to 2 years might be usefully resolved from the collective use of these tree-ring chronologies in climate reconstructions. Unfortunately, this is probably an overestimate due to the mix of segment lengths and the transition bandwidth of the frequency response function of the smoothing spline filter (Cook and Peters, 1981), which will always remove some higher frequency variance as well. Consequently, the maximum realizable low-frequency variance due to climate in these chronologies is probably more like 1 15 years overall. This could be a serious problem if past temperatures are being reconstructed because of the multi-centennial character of past temperature variability in the Northern Hemisphere (e.g. Briffa et al., 1992a; Jones et al., 1998; Mann et al., 1999; Briffa, 2; Esper et al., 22). Unfortunately, the individual site tree-ring data are not well suited for applying detrending methods that might preserve multi-centennial trends due to climate, like the regional curve standardization method (Briffa et al., 1992a) or the age-band method (Briffa et al., 21). The number of radial ring-width series available for each site (see Table I) is generally insufficient to use such methods because they tend to produce tree-ring chronologies with greater error variance than the more classical tree-ring standardization method used here. However, there may be a way of recovering missing multi-centennial variance through the collective use of all tree-ring data that contribute to a given climate reconstruction. This possibility will be investigated later. 3. THE LONG KATHMANDU TEMPERATURE RECORD One of the major difficulties in undertaking dendroclimatic research in Nepal relates to the paucity of long meteorological records for statistically calibrating the tree rings (Bhattacharyya et al., 1992). Presently, Nepal has over 2 weather stations distributed throughout the country, but most were only established after 196 for precipitation and 197 for temperature (Shrestha et al., 1999, 2). The longest meteorological records in all of Nepal are from the Indian Embassy in Kathmandu, located at an elevation of 1372 m in the Middle Mountain physiographic province of east-central Nepal (Shrestha et al., 2). These records begin in 1851 for precipitation and 1879 for temperature, and both end in 1977 when meteorological data collections at the embassy ceased. The published observations contain a gap spanning the period (Kripalani et al., 1996; Pant and Rupa Kumar, 1997), with the missing data thought to have been lost. Although the many short station records may be of some use in our dendroclimatic studies, they do not provide the necessary length for the rigorous calibration/verification procedures (sensu Fritts, 1976) that are necessary to produce valid dendroclimatic reconstructions. Therefore, we decided to use the long Kathmandu climate records for our dendroclimatic studies. To do this, we located the original published data sources to obtain all of the monthly climate data. These sources included the Climatological Records of Nepal (DIHM, 1977) for data, the India Meteorological Memoirs (Eliot, 192) for data, and the Annual Summaries of the Monthly Weather Reviews for The first two sources provided the identical monthly data that was previously described by Kripalani et al. (1996) and Pant and Rupa Kumar (1997), still with the data gap. The

5 NEPAL DENDROCLIMATIC SIGNALS 711 last data source, found in the British Meteorological Office Hadley Centre Library, contained the missing 2 years of data. The recovered data included precipitation, maximum temperature, minimum temperature, and atmospheric pressure. Save for a few missing monthly observations, this enabled us to produce nearly complete monthly precipitation and maximum/minimum temperature records for the Kathmandu Indian Embassy from 1851 to 1977 and 1879 to 1977 respectively. The precipitation and temperature data were than updated from 1978 to 1992 (the last year of published meteorological data for Nepal) using records from nearby meteorological stations at comparable elevations to the Indian Embassy in the Kathmandu Valley. Because we will only be using monthly mean Kathmandu temperature data in the dendroclimatic analyses described here, we only describe some properties of these data here. A full description of the recovered Kathmandu climate records will be published elsewhere. The availability of both maximum and minimum temperaturesenabled usto examine the diurnaltemperature range for indications of abrupt change potentially related to the way in which temperatures were measured and recorded. In so doing, we found an abrupt increase in the diurnal range before 1893, which may be due to a change in the type of shelter used to house the thermometer. It is presently impossible to correct the pre-1893 data because of the lack of suitable nearby temperature data. For this reason, the data were not used in our dendroclimatic analyses. In 1896, a few months of data were also missing. This resulted in the decision to use the Kathmandu monthly temperature data only from 1897 to We then averaged the monthly maximum and minimum temperature data to produce the monthly mean data used in our analyses here. Next, we describe a seasonal decomposition of these data to characterize 2th century temperature variability over Nepal. The Southwest monsoon dominates the climatology of Nepal, as it does India to the south. Consequently, descriptions of seasonal climate are usually associated with the timing of the monsoon. Thus, in their analyses of 49 Nepal temperature records since 1971, Shrestha et al. (2) partitioned the monthly data into four seasons: winter (December February), pre-monsoon (March May), monsoon (June September), and postmonsoon (October November). Here, we have chosen a different method of seasonalization based on the correlation structure between months. Specifically, we used rotated empirical orthogonal function (REOF) analysis (Richman, 1986) on the monthly mean temperature data to partition the data objectively into natural, albeit statistical, seasons. The normalized varimax method of rotation was used to preserve orthogonality among the resultant factors and their scores. For comparative purposes, we applied the same procedure to the closest grid point (to Kathmandu) of interpolated temperature data from the.5 latitude longitude gridded data set of New et al. (2), covering the period The interpolated data over Nepal are principally based on temperature records from northern India and do not use any Nepal data at all. Therefore, the REOF comparisons of the Kathmandu and New et al. (2) temperature data are based on independent data. The REOF analyses were applied to each monthly data set using a red noise version of the Monte Carlo rule-n test (Preisendorfer et al., 1981) to determine the number of significant EOFs (p <.5) to rotate. In each case, only three significant EOFs were identified, which were than rotated using the varimax method. The rotated monthly factors are shown in Figure 2. There is little ambiguity in the partitioning of each monthly temperature record into the same three seasons: October January, February May, and June September. Even the variances accounted for within each season are similar between data sets. The similarity of the seasonal factors is also well expressed in the factor scores (Figure 2). The correlations between the pairs of seasonal factor scores are all statistically significant (p <.1) and as high as.83 for the February May season. Therefore, the Kathmandu monthly temperature data appear to be homogeneous with respect to the independent New et al. (2) gridded data. The seasonal factor scores reveal that 2th century temperatures over Kathmandu have been cooling in general during the pre-monsoon (February May) and monsoon (June September) seasons. Cooling of the monsoon season is consistent with a general increase in monsoon rainfall at Kathmandu, particularly after 195 (not shown), which would tend to suppress temperatures. Only during the post-monsoon (October January) season is there some suggestion of warming, mainly since 196. That these patterns are well replicated in the New et al. (2) factor scores indicates that they are large-scale expressions of temperature variability over Nepal and northern India. These results run counter to some of the conclusions of Shrestha et al. (2),

6 712 E. R. COOK, P. J. KRUSIC AND P. D. JONES 1.8 A1. KAT#1-17.1% CRU#1-21.1% 3 2 A2. R =.68 KAT CRU LOADING.6 SCORE O N D J F M A M J J A S MONTH YEAR LOADING B1. KAT#2-14.9% B2. CRU#2-13.2% 3 R = 4 O N D J F M A M J J A S MONTH SCORE YEAR LOADING C1. KAT#3-15.8% C2. CRU#3-15.6% 3 R =.83 O N D J F M A M J J A S MONTH SCORE YEAR Figure 2. REOF analysis of monthly Kathmandu (KAT) temperatures and CRU interpolated temperatures near Kathmandu. The EOF loadings are shown in A1, B1, and C1 and the corresponding factor scores are shown in A2, B2, and C2. The number of EOFs to rotate was determined by Monte Carlo methods and three seasons have been objectively identified. Note the similarity in the seasonal structure of the loadings and the high correlations between the factor scores. See the text for details which suggested a more general warming trend in Nepal since the mid-197s. Liu and Chen (2) also identified a general warming trend over the Tibetan Plateau since the mid-195s, especially for winter, and reported evidence that the rate of warming increased with elevation. Since some of the stations used by Shrestha et al. (2) are higher than Kathmandu, it is possible that the 2th century temperature changes shown in Figure 2 do not reflect the extent of recent warming in Nepal. However, most of the stations in Nepal used by Shrestha et al. (2) are below 2 m elevation, so the putative elevation effect is unlikely to be large in those data either. 4. EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS OF THE TREE-RING DATA Prior to attempting a climate reconstruction from the Nepal tree-ring network, we performed an exploratory analysis of the 32 tree-ring chronologies using unrotated EOF analysis. The point here is to demonstrate the existence of spatially coherent modes of tree-ring variation that plausibly reflect large-scale modes of climate forcing on tree growth. This analysis is challenging because of the mix of tree species, their unknown responses to climate, and the enormous orographic barriers that separate many of the sites. Indeed, if no

7 NEPAL DENDROCLIMATIC SIGNALS 713 significant spatially coherent modes of tree growth can be identified, then it is unlikely that the Kathmandu climate data would be useful for calibrating much of the tree-ring data at all. As before, a red noise version of the Monte Carlo rule-n test (Preisendorfer et al., 1981) was used to determine the number of significant EOFs (p <.5). The average first-order autocorrelation that defines the red noise in the rule-n test was.52, with a range of This procedure selected the first four EOFs, which accounted for 5% of the total variance. The first EOF alone accounts for 23.2% of the total variance, a level that easily exceeds the expected value by chance alone (7.2%) estimated by the Monte Carlo procedure. This level of common variance found among a diverse set of tree species and sites separated by many of the highest mountains on Earth is quite remarkable. It is difficult to see how such connectedness could be related to precipitation patterns across Nepal, which are likely to be highly localized due to orographic effects. Rather, it is likely to be related to large, spatially coherent climate features such as air temperature fields. The EOFs are displayed in Figure 3 as a function of longitude because the distribution of sites follows a nearly linear northwest southeast transect in Nepal (see Figure 1). EOF#1 (23.2% of the variance) represents the signal most common to all series. Most of the loadings, coded by species, are comparable in magnitude, with the most anomalous one being the sole hardwood species in the lower left-hand corner (ULWA). EOF#2 (13.3%) basically highlights the pines (PIWA) in the collection that are located in the dry inner valleys of north-central Nepal. EOF#3 (7.1%) shows a conspicuous east west gradient in the loadings, with the end members being of comparable and opposite sign. Note again the anomalous nature of the hardwood chronology LOADING.3.1 A. EOF# % ABSP TSDU PIWA JURE PISM ULWA B. EOF# % C. EOF# % D. EOF# % LOADING LONGITUDE ( E) LONGITUDE ( E) Figure 3. Plots of the EOF loadings as a function of longitude. The symbols are coded by species. Lowess robust smoothing has been applied to highlight longitudinal trends in the loadings. EOFs #1 and 3 express geographic patterns that are largely independent of species, and EOFs #2 and 4 tend to emphasize species differences. This figure is available in colour online at

8 714 E. R. COOK, P. J. KRUSIC AND P. D. JONES (ULWA). Finally, EOF#4 (6.6%) highlights differences between chronologies located in the eastern end of the network, with an overall tendency for fir (ABSP) and juniper (JURE) to load opposite to hemlock (TSDU). In general, the EOF analysis indicates that species differences are most pronounced in EOF#2 and #4, whereas EOF#1 and #3 are dominated by geographic patterns that are not strongly linked to species similarities and differences. The principal components (PCs) of the EOFs are shown in Figure 4. The PCs have been extended back from 1796 to 1445 using all available tree-ring data in the 32-chronology ensemble. As shorter chronologies dropped out, the variance in each PC was adjusted upwards to account for the lost variance. However, the 5 A. PC# % PRINCIPAL COMPONENT SCORES B. PC# % C. PC# % -4 4 D. PC# % -4 NUMBER E. NUMBER PER YEAR YEAR Figure 4. PCs of the first four EOFs of 32 Nepal tree-ring chronologies, with 2-year low-pass smoothing applied (smooth black curve). The bottom plot shows the number of chronologies available per year

9 NEPAL DENDROCLIMATIC SIGNALS 715 reliability of the PCs will, nonetheless, decline back in time as the number of available chronologies declines, along with the sample size within each chronology. To provide some guidance here, the sample size per year is shown in Figure 4(E). Prior to 1559 the sample size declines to below one-third of the original total, which suggests that special care needs to be taken in interpreting the pre-156 data. After 1996, the sample size likewise loses some reliability. The PCs indicate a considerable degree of multi-decadal variability likely due in part to climate, but little in the way of long-term trends. This may be a consequence of the detrending applied to the tree-ring measurements, as pointed out earlier. Unfortunately, it is impossible to determine how much multi-centennial variability may have been lost based solely on the 2th century temperature data for Nepal. The Kathmandu temperature data (Figure 2) do not indicate that much temperature change has occurred in Nepal. However, this does not mean that significant trends in temperature did not occur over the past 5 years in Nepal prior to the 2th century, which could now be missing from the detrended tree-ring series. This likely limitation of the tree-ring chronologies must be kept in mind when making interpretations of low-frequency patterns possibly related to climate. PC#1 is striking for the extremely sharp negative growth period indicated in the period, with a minimum in 1819 that is almost five standard deviations below the mean. The timing of this anomalous growth period is consistent with that of the Tambora eruption in Indonesia and may be related to this climatically influential event (Harington, 1992). It also coincides with the coldest decade of summer temperatures estimated for the Northern Hemisphere extra-tropics from tree-ring-density chronologies covering the past 6 years (Briffa et al., 1998a and b). That epoch of unusually cold summers was also linked by Briffa et al. (1998a,b) to the Tambora eruption and, perhaps, to an earlier unrecorded eruption in (Dai et al., 1991). PC#3 and PC#4 also indicate generally below-average growth during this interval. So, the inferred impact of the Tambora eruption on climate and tree growth in Nepal appears to have been severe. In contrast, PC#2, which is dominated by PIWA chronologies from the dry inner valleys of north-central Nepal, does not show much growth reduction in that period. This difference may reflect the more xeric nature of the PIWA tree-ring sites, which might cause those trees to benefit from cooler temperatures by reducing evapotranspiration demand. 5. CLIMATE CALIBRATION TESTS The interpretations of the PCs have been based on the assumption that the dominant climate signals in them are probably related to temperature. It is important now to demonstrate that this is so. Although not optimal for this task, the long Kathmandu temperature record ( ) provides the best means we have to test for temperature signals in the PCs. Kathmandu is centrally located among most of the chronologies (see Figure 1) and its length allows for statistical validation tests to be applied. It would be useful to model the PCs for precipitation signals as well. However, the spatial coherence of precipitation in highly mountainous environments is poor, and the local records are typically less than 4 years in length (Shrestha et al., 2) and often serially incomplete. After several failed attempts to identify verifiable precipitation signals in the Nepal tree-ring data using the long Kathmandu rainfall record, we abandoned this aspect of the dendroclimatic modelling and report on the temperature modelling only. The Kathmandu monthly temperature data were first organized into a 21 month dendroclimatic year (sensu Fritts, 1976) beginning in January of the calendar year preceding radial growth and ending in September of the radial growth year. This year includes two complete radial growth seasons, plus the months leading up to the monsoon, which allows for the identification of prior-year climatic preconditioning effects on radial growth. We also tested the stability of the identified relationships by calibrating the PCs with the temperature data over the period and verifying their estimates over the period. The calibration period correlations of monthly temperature with each PC are shown in Figure 5, along with their two-tailed 9% confidence limits. Given the remoteness of the tree-ring sites from Kathmandu and the obvious uncertainty concerning the true response of the Nepal tree-ring chronologies to climate, it is not surprising that few of the correlations are significant (p <.1). Yet, there are distinct seasonal patterns of correlation that may

10 716 E. R. COOK, P. J. KRUSIC AND P. D. JONES A. PC#1 B. PC#2 CORRELATION % C.L. 9% C.L. C. PC#3 D. PC#4.3 CORRELATION PREVIOUS GROWTH YEAR CURRENT GROWTH YEAR PREVIOUS GROWTH YEAR CURRENT GROWTH YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL AUG SEP MONTHS MONTHS Figure 5. The simple correlations of the PCs with Kathmandu monthly temperatures organized into a 21 month dendroclimatic year that includes the current and previous growth years be collectively significant. For example, PC#1 correlates positively with autumn winter temperatures, PC#2 correlates negatively with current growth year spring summer temperatures, PC#3 correlates positively with winter spring summer temperatures during the previous growth year, and PC#4 correlates positively with previous-year monsoon temperatures. PC#2, which is dominated by the PIWA chronologies from dry inner valleys, stands out as most different. The predominantly negative correlations with temperature in the year concurrent with growth may reflect a moisture stress signal, wherein below-average temperatures promote growth by reducing evapotranspiration demand. In contrast, the other PCs are mostly correlated positively with temperature, which probably reflects the cool moist, upper-elevation sites where the other tree species are found. Of course, some of these patterns may be induced by the monthly persistence in the temperature data and the chance covariance between the chronologies that determines the eigenloadings in Figure 3. Therefore, some means of model validation is needed. Principal components regression (PCR) was used for this purpose. The mathematical and procedural details of this method as implemented here are described by Cook et al. (1999). Specifically, each PC was calibrated as a linear function of those monthly temperatures that correlated best (p <.1) with it over the calibration period (see Figure 5). PCR was also applied to the best average seasonal block of temperatures also indicated in Figure 5, irrespective of the significance of the individual monthly correlations within the block. For PC#1 4 the selected seasons were October February, current February September, previous

11 NEPAL DENDROCLIMATIC SIGNALS 717 February September, and previous June October respectively. The temperature estimates of each PC were than validated against the withheld PC data over the verification period. These results are shown in Table II. The statistics provided for the calibration period are the Pearson correlation coefficient r and the coefficient of determination R 2. The verification period statistics are r, r 2, reduction of error (RE), and coefficient of efficiency (CE), with CE being the most difficult to pass. R 2, r 2, RE, and CE are all measures of shared variance between climate and tree rings, which are identical in the calibration period. In the verification period, r 2, RE, and CE will usually be lower than the calibration period R 2 and can be significantly different from each other. RE and CE can also assume negative values when the regression estimates are especially bad in the verification period. In general, a positive RE or CE is evidence for a valid regression model. See Cook et al. (1994, 1999) for details. Interestingly, only PC#3 yielded consistently verifiable monthly and seasonal temperature models. PC#4 was a close second, but failed on the RE and CE tests (i.e. RE and CE < ). These results were also replicated in all essential details using completely independent temperature data from the Northwest, North-central, and Interior Peninsula regions of India (Hingane et al., 1985). The first two regions border Nepal to the south and the three Indian regions make up a large fraction of the Indian subcontinent. So, there is little doubt that PC#3 reflects a robust temperature signal over Nepal that extends southward into India as well. Given the remoteness of the tree-ring chronologies and the massive orographic barriers that separate them, it is remarkable that Kathmandu and regional Indian temperatures have produced such positive results. For this reason, it is likely that the temperature signal in PC#3 is stronger than that indicated by the calibration/verification statistics in Table II. 6. RECONSTRUCTION OF PAST NEPAL TEMPERATURES Building upon the results just described, we returned to the original 32 tree-ring chronologies and used them directly in PCR to reconstruct Nepal temperatures. The PCR procedure used here closely follows that outlined by Cook et al. (1999). In this case, the calibration and verification periods were and respectively, and years t and t + 1 of each chronology were considered as candidate predictors. Based upon the temperature correlations shown in Figure 5, we tried a number of seasonal temperature combinations and report here on the two most successful ones: February June and October February. Other temperature seasons, including those related to the monsoon itself, could not be successfully calibrated and verified. Table II. Monthly and seasonal temperature models for the Nepal tree-ring PCs. See Figure 5 for the selected months (p <.1). a The calibration period is and the verification period is Only PC#3 consistently verifies PC Model NPRED Calibration Verification r R 2 r r 2 RE CE PC#1 Months b Season (Oct Feb) b PC#2 Months b Season (current Feb Sep) b PC#3 Months Season (previous Feb Sep) PC#4 Months Season (previous Jun Oct) a NPRED is number of predictors used in the model (either months or season). Significant (p <.5) or RE/CE >. b No r 2 when verification r is negative.

12 718 E. R. COOK, P. J. KRUSIC AND P. D. JONES 6.1. The February June season The temperature correlations with PC#3 (Figure 5(C)) were used as a guide to determine the optimal combination of monthly temperatures to reconstruct. Specifically, we tested six different temperature seasons: February September, February August, February July, February June, February May, and April May. Only those candidate year-t and t + 1 predictors that first correlated with temperature at the.1 significance level or better (two-tailed test) in the calibration period were retained for PCR. This procedure resulted in the selection of nine chronologies and 15 tree-ring predictors. The calibration/verification results are given in Table III. In terms of calibrated variance, the February September and February June temperature seasons were superior to the others and nearly identical. However, the February June model was clearly superior to all others in terms of model verification, with very little degradation in its fidelity from the calibration to verification periods. The February June regression model based on 15 explicitly screened tree-ring predictors explained 38.6% of the temperature variance, a substantial improvement over the calibrated variance for PC#3 shown in Table II. More importantly, the verification statistics improved even more. When the same verification tests were applied to the actual and estimated temperatures after first-differencing to emphasize high-frequency relationships only, the results (not shown) were somewhat weaker, but still statistically significant (p <.5). This indicates that the calibration procedure was not seriously degraded by autocorrelation in the tree-ring and temperature data. However, it does suggest that the strength of the calibration lies more so in the lower frequency agreement between temperature and tree growth. This issue will be investigated later through cross-spectral analysis. Given the success of this effort, the temperature reconstruction was extended back in time from 1796 using eight longer subsets of the tree-ring chronologies selected for PCR, with each new model tested for verification as before. This process produced a total of nine verified reconstructions, which were linked together to form the complete Kathmandu February June temperature reconstruction back to This record is shown in Figure 6, along with information on the number of chronologies used in estimating each subset reconstruction prior to Given that even the longest five-chronology model successfully verified, the reconstruction should be useful over its entire period. The simple correlations and regression model β weights (standardized regression coefficients) of the original tree-ring variables are plotted as a function of longitude in Figure 7 for the full model using all 15 predictors. The respective β weights of the eight subset models (not shown) were extremely similar to those of the full model. The geographic spread of the β weights shows that the temperature reconstruction is based on a common broad-scale signal among the selected predictors that is not dominated by any particular chronology or region in Nepal. Table III. Seasonal temperature models tested for reconstruction based on the PC#3 results. See Figure 5(C) for the rationale for the selected months. a The calibration period is and the verification period is Season NTR NPC Calibration Verification r R 2 r r 2 RE CE Apr May Feb May Feb Jun Feb Jul Feb Aug Feb Sep Season is the block of months tested; NTR is the number of tree-ring predictors selected after screening for PCR; NPC is number of tree-ring PC predictors used in model. Significant (p <.5) or RE/CE >.

13 NEPAL DENDROCLIMATIC SIGNALS 719 A. FEBRUARY-JUNE TEMPERATURE RECONSTRUCTION TEMPERATURE ( C) B. NUMBER OF CHRONOLOGIES NUMBER FRACTIONAL VARIANCE.5 C. MODEL CALIBRATION AND VERIFICATION RESULTS.3 R 2 r 2.1 R E C E YEAR Figure 6. The February June temperature reconstruction (solid line) with actual data (dash line) superimposed (A), the number of tree-ring chronologies available back in time (B), and the calibration/verification results for the models based on the available subsets of chronologies (C). See the text for details The temperature variance calibrated by the tree-ring chronologies is undoubtedly significant statistically. However, it still remains somewhat modest and is probably influenced by low-frequency (i.e. interdecadal) agreement between the tree-ring and temperature data. The fidelity of tree-ring reconstructions of past climate can be somewhat band-limited due to the way in which the available climate data relate to the tree-ring chronologies (Guiot, 1985; Osborn and Briffa, 2), with certain frequency bands being better related to climate than other bands. Consequently, it is useful to perform cross-spectral analysis on the actual and estimated temperature data in order to determine the fidelity of the tree-ring reconstruction in the frequency domain. Figure 8 shows the two power spectra and their squared coherency spectrum (a measure of fractional common variance as a function of frequency) based on the combined calibration/verification period data. As suspected, the tree-ring estimates do a much better job at estimating temperature at lower frequencies, with a clear breakpoint in coherency occurring at a period of 5 years. Indeed, the average squared coherency at periods greater than 5 years is.62, whereas that at periods less then 5 years is 4. So, there is little doubt that the February June temperature reconstruction is most reliable at multi-annual time scales, especially those >5 years. The greater reliability on multi-annual time scales is despite the calibration being on a year-to-year basis. The lower fidelity at periods less than 5 years is, in part, a consequence of the distance correlation decay length

14 72 E. R. COOK, P. J. KRUSIC AND P. D. JONES CORRELATION - A. CORRELATIONS YEAR t YEAR t +1 KATHMANDU LONGITUDE - B. BETA WEIGHTS KATHMANDU LONGITUDE BETA WEIGHT LONGITUDE ( E) Figure 7. The simple correlations (A) and regression β weights (B) of the predictors used in estimating February June temperatures. These are shown for year-t (circles) and t + 1 (triangles). This figure is available in colour online at wiley.com/ijoc between Kathmandu temperatures and those at more distant tree-ring sites. The correlation decay length for a given region tends to be shorter at higher frequencies and longer at lower frequencies (Jones et al., 1997). Consequently, low-frequency temperature variability should be more similar between Kathmandu and the tree-ring sites and, therefore, easier to reconstruct. In addition, the massive orographic barriers that separate the tree-ring sites from the Kathmandu Valley, coupled with differences in elevation (1372 m at Kathmandu and an average of 3 m at the tree-ring sites), should collectively act as a low-pass filter that emphasizes coherent, large-scale multi-year temperature variability in the reconstruction The October February season The basic procedures used successfully to reconstruct February June temperatures were applied to the reconstruction of the October February season as well. As before, a number of monthly combinations were tried, in this case October November, October January, and October February. The rationale for trying these combinations can be seen in the monthly temperature correlations with PC#1 in Figure 5(A). The biggest change in how we calibrated the tree rings for these seasons was in terms of how we selected the tree-ring variables to use as candidates in PCR. Recall that, for the February June season, tree-ring variables

15 NEPAL DENDROCLIMATIC SIGNALS A. POWER SPECTRA LOG POWER ACT REC 1 B. COHERENCY SPECTRUM COHERENCY % 95% 9% NULL FREQUENCY (CYCLES/YEAR) Figure 8. The power spectra and coherency spectrum of actual (ACT) and reconstructed (REC) February June temperatures for the period The highest fidelity of the reconstruction is largely restricted to periods greater than 5 years for years t and t + 1 were tested for correlation with year-t temperature, and those significant above the.1 significance level were used in PCR. This level of screening worked well, because a subset of chronologies could be found that produced highly significant calibration and verification statistics. Such was not the case for the cold seasons tested here. Rather, the best models in terms of both calibration and verification utilized all of the available tree-ring chronologies for years t and t + 1inPCR. The calibration/verification results for these three seasons are shown in Table IV, and one season stands out: October February. As with the February June regression model, this one explains 38.6% of the temperature variance, which in this case is over twice the calibrated variance as that for PC#1 in Table II. In addition, the Table IV. Seasonal temperature models tested for reconstruction based on the PC#1 results. See Figure 5(A) for the rationale for the selected months. a The calibration period is and the verification period is Season NTR NPC Calibration Verification r R 2 r r 2 RE CE Oct Nov Oct Jan Oct Feb a Season is the block of months tested; NTR is the number of tree-ring predictors used in PCR; NPC is number of tree-ring PC predictors used in model. Significant (p <.5) or RE/CE>.

16 722 E. R. COOK, P. J. KRUSIC AND P. D. JONES model verified strongly. However, the first-difference verification statistics (not shown) weakened more than before. As will be shown, most of the successfully calibrated and verified variance is at interdecadal time scales. As before, the temperature reconstruction was extended back in time from 1796 using increasingly longer and smaller subsets of the tree-ring chronologies until the model failed to verify. This process produced a total of 13 well-verified reconstructions back to 165, which were linked together to form the complete Kathmandu October February temperature reconstruction. This record is shown in Figure 9, along with information on the number of chronologies used in estimating each subset reconstruction prior to 1796 and the associated calibration/verification statistics. The quality of the reconstruction diminishes somewhat prior to 1796, but remains acceptably stable and significant back to 165. The simple correlations and regression model β weights of the original tree-ring variables are plotted as a function of longitude in Figure 1 for the full model using all 64 predictors. Unlike the comparable plots for the February June season (Figure 7), the number of positive and negative β weights differs considerably from those of the correlations. This difference reflects the more complex interrelationships within the much larger suite of predictors used here, although the total number of PCs entered into the model was the same. The geographic spread of the β weights again shows that this temperature reconstruction is not dominated by any particular chronology or region in Nepal. TEMPERATURE ( C) A. OCTOBER-FEBRUARY TEMPERATURE RECONSTRUCTION 4 B. NUMBER OF CHRONOLOGIES NUMBER FRACTIONAL VARIANCE.5 C. MODEL CALIBRATION AND VERIFICATION RESULTS.3 R 2 r 2 RE.1 CE YEAR Figure 9. The October February temperature reconstruction (solid line) with actual data (dash line) superimposed (A), the number of tree-ring chronologies available back in time (B), and the calibration/verification results for the models based on the available subsets of chronologies (C). See the text for details

17 NEPAL DENDROCLIMATIC SIGNALS 723 A. CORRELATIONS KATHMANDU LONGITUDE CORRELATION - YEAR t YEAR t+1 B. BETA WEIGHTS BETA WEIGHT.1 KATHMANDU LONGITUDE LONGITUDE ( E) Figure 1. The simple correlations (A) and regression β weights (B) of the predictors used in estimating October February temperatures. These are shown for year-t (circles) and t + 1 (triangles). This figure is available in colour online at wiley.com/ijoc The fidelity of the tree-ring reconstruction in the frequency domain is shown in Figure 11. As suspected, the tree-ring estimates do a much better job at estimating temperature at lower frequencies, with a very sharp break in coherency occurring at a period of 11 years. The average squared coherency at periods greater then 11 years is.73, whereas that at periods less then 11 years is.19. So, it is clear that the October February temperature reconstruction is most reliable at interdecadal to centennial time scales (Figure 11). 7. RECOVERING LOST MULTI-CENTENNIAL TEMPERATURE VARIANCE At time scales that exceed 1 15 years, it is likely that some longer term temperature variation in the treering reconstructions has been lost. As described earlier, the way in which the Nepal raw tree-ring measurement series were originally detrended virtually guarantees the loss of some multi-centennial variability or trend due to climate. Unfortunately, the degree to which this is so is impossible to estimate in the present reconstructions, because the time history of such variability has been lost. In an attempt to recover probable lost multicentennial temperature variance, we revisited the raw ring-width measurement series of those chronologies that contributed most strongly to the temperature reconstructions. For the February June season, all nine tree-ring chronologies that contributed to the reconstruction, regardless of sign, were re-analysed (see Figure 7). For the October February season, we re-analysed the 15 chronologies that correlated best with temperature in a positive sense only. The rationale for constraining