Significance This paper describes the production and climatic interpretation of a tree-ring width chronology that is currently the longest, absolutely dated series produced for the northeastern Tibetan Plateau and one of the longest in the world. The method of chronology construction enables comparison of variations in precipitation totals over long timescales as well as shorter periods. Precipitation in this region during the last 50 years has been historically high—likely higher than for any equivalent length period in at least 3,500 years, even when considering the chronology and interpretational uncertainty. Notable dry periods occurred in the 4th century BCE and in the second half of the 15th century CE.

Abstract An annually resolved and absolutely dated ring-width chronology spanning 4,500 y has been constructed using subfossil, archaeological, and living-tree juniper samples from the northeastern Tibetan Plateau. The chronology represents changing mean annual precipitation and is most reliable after 1500 B.C. Reconstructed precipitation for this period displays a trend toward more moist conditions: the last 10-, 25-, and 50-y periods all appear to be the wettest in at least three and a half millennia. Notable historical dry periods occurred in the 4th century BCE and in the second half of the 15th century CE. The driest individual year reconstructed (since 1500 B.C.) is 1048 B.C., whereas the wettest is 2010. Precipitation variability in this region appears not to be associated with inferred changes in Asian monsoon intensity during recent millennia. The chronology displays a statistical association with the multidecadal and longer-term variability of reconstructed mean Northern Hemisphere temperatures over the last two millennia. This suggests that any further large-scale warming might be associated with even greater moisture supply in this region.

The value of high-resolution proxy climate data for reconstructing climate variability beyond the limited time span of direct instrumental observations is borne out by their use in contemporary studies of the causes of late-Holocene climate changes, in particular research that seeks to distinguish the fingerprint of anthropogenic change from the background of natural climate variability (1). Long paleoclimate records, with annual resolution and good dating control, represent an ideal basis for rigorous statistical comparison of hypothetical climate forcing and observed climate responses, whether based on statistical climate change detection methodology (2) or through the “validation” of climate model simulations of past climate (3). Climate proxy data are more valuable where it can be demonstrated that they represent a wide spectrum of climate variability, encompassing year-to-year, decadal, and multicentennial timescale evidence of past changes. Such records are rare, and tree-ring–based reconstructions are among the most prominent (4). Many of these records provide evidence of past warm-season temperature variability and are located in near–tree-line regions at high-elevation or high-latitude sites (5, 6). Long precipitation-sensitive tree-ring chronologies are generally from lower latitudes and elevations (7⇓⇓⇓⇓–12).

In the last decade, dendroclimatic research on the Tibetan Plateau (TP) has expanded rapidly, with numerous localized studies of past tree growth and its climate controls and temporal ranges extending from hundreds to thousands of years. Here we build on previous studies (13⇓⇓⇓–17) by describing a new multimillennial-length chronology constructed from a combination of moisture-sensitive ring-width measurements from archaeological, subfossil and living trees from multiple sites on the northeastern TP. Processing these data in a manner designed to identify and preserve common low-frequency variability provides a history of tree growth stretching over 4,000 y and one that is shown to be a likely reliable indicator of yearly moisture changes encompassing interannual up to millennial scales of variability back at least to –1500 (Methods).

Chronology Development In the cold and arid conditions of the northeastern TP, Qilian juniper (Juniperus przewalskii Kom.) grows widely on high, south-facing slopes. Exceptionally, these trees can reach ages above 3,000 y, and living trees over 2,000 y old are not rare. Numerous core samples from living trees and from standing or fallen dead trees have been collected from a total of 17 sites in the Qilian Mountains, all located between ∼37–38.7°N and 97–100°E, with elevations ranging from 2,863 to 4,175 m above sea level. These sites are clustered in two areas, centered near Yeniugou and Delingha (Fig. 1 and SI Appendix, Table SA1). Ring-width measurements from these samples are combined here with similar data from around Dulan: previously published measurements from archaeological wood from a series of tombs just south of Dulan (∼35.8–36.3°N; 98–99°E), living trees from Shenge (37.0°N; 98.3°E) and Dulan (36.0°N; 98.0°E) (14), and more recently collected tree-ring data from Dulan (∼35.5–36.3°N; 98.1–98.7°E). By combining the measurement series from a total of 1,203 trees, it has been possible to establish an absolutely dated ring-width chronology that represents a continuous record of year-to-year growth variability for this species and general location spanning 4,649 y from –2637–2011. Fig. 1. Map of the study region. Locations of tree-ring and weather station sites (refer to the key; metadata provided in SI Appendix). (Inset) Study location (A) within China in relation to the locations of other paleoclimate records considered here and to the approximate limit of the direct summer monsoon (dashed line in the main map and Inset). Having established the firm dating of the measurement series using high-frequency cross-matching (SI Appendix), they must then be processed for interpreting past climate influences on tree growth at longer as well as short timescales. The tendency for annual growth rings to become thinner in old trees must be removed from the measurement data, but information about past long-term changes in climate must be retained to produce a chronology that represents climate changes over a wide spectrum of timescales. Here, we use a recent form of processing known as Signal-Free Regional Curve Standardization (SF-RCS). This establishes an empirical model of “expected” ring width as a function of tree age, but with the influence of changing climate on tree growth having been removed (18). Dividing measured ring widths by the appropriate expected values for the correct ring age converts the measurements into relative indices, effectively removing a tree-age–related trend from the measurement data for each tree: a process known in dendroclimatology as standardization. The resulting standardized indices are averaged in the correct calendar years to produce an annually resolved chronology representing the changing signal of common tree-growth forcing over past millennia. In practice, trees show a range of growth rates even when growing in parallel under the same climate conditions, and so multiple standardization curves are used, each curve applied as described above but to standardize a specific subset of the total data, representing a particular growth-rate class of trees (Methods). The growth habit of this species is characteristically complex with older trees growing slowly and forming convoluted multistem shapes. In very old age it is not unusual for the trees to grow around a contorted stem (SI Appendix, Figs. SA3–SA6). The ring-width series can exhibit large interannual variability compared with the amplitude of longer-term growth trends, with the latter displaying notable variability within and between trees. These complex growth forms present a particular problem for extracting a reliable chronology of low-frequency variability. The multiple-curve RCS method can account for part of this issue—namely, different mean growth rates between different trees—but cannot account for inhomogeneous changes in growth rate during the lifespan of a tree that might arise from changes in growth form or from sampling along different paths through the tree cross-section. Like inhomogeneities that can occur in trees with simpler growth forms (e.g., with a change from suppression to release), they introduce additional noise and uncertainty into a chronology. Combining data from a large sample of trees will reduce the influence of this noise, to an extent that depends on how systematic its occurrence is. Here, due to the complex growth forms in some of the sampled trees, data from a large number of trees are required to achieve an acceptable signal-to-noise ratio in the expression of long-timescale variability. Parallel variability displayed in the chronologies produced from independent subdivisions of the data (into growth-rate classes) corroborates the interpretation that the common external influence on the growth of these trees over time is being expressed reliably. Fig. 2A shows a group of four independent SF-RCS chronologies, each produced using its own RCS curve. Each RCS curve is produced for one of four growth-rate classes (i.e., from faster- to slower-growing trees) by aligning the measurement data from inner (pith) year to outer rings and averaging for each ring age. Where there are progressively fewer series reaching older ages, the mean curves become noisy, but each is smoothed to provide the reference SF-RCS curve for standardizing the measurements from trees within that class (Methods and SI Appendix). The resulting four subchronologies are shown superimposed, after smoothing with a 50-y filter, to emphasize the longer-timescale variance. Fig. 2. Consistency between subsets of tree-ring data. Chronologies formed by averaging tree indices (i.e., standardized tree-ring width) across different subsets of trees and using different SF-RCS standardization approaches. (A) Four growth-rate chronologies (an equal number of trees are assigned to each set, grouped according to their mean growth rate), each standardized with a separate SF-RCS curve. (B) Seven site chronologies, each the average of indices obtained using the four overall growth-rate SF-RCS curves. (C) Seven site chronologies, where each site dataset was standardized using two growth-rate SF-RCS curves. (D) Overall QLS chronologies formed from either the four growth-rate chronologies (blue, with ±2 chronology SEs in light blue) or the seven site chronologies (red). All chronologies are smoothed with a 50-y spline (end values are more uncertain), and sections comprising less than six trees are shown as thinner lines. Chronology values after 1850 are repeated with (Right) and without (Left) smoothing for the (E) four growth-rate chronologies and (F) two overall QLS chronologies. Similar multicentennial and even millennial timescale variability is apparent in these independent series back to about –1500. Before this, where the number of samples in each series becomes very small, the series diverge although they still display clear common variability on annual and multidecadal timescales (SI Appendix, Figs. SB9 and SB10). Fig. 2B shows the same index data but averaged by site location to provide local-area chronologies. Fig. 2C shows similar local-site chronologies but with the original measurement data for each site having been separately standardized using only two RCS curves, one for faster and one for slower growth trees at that site. Similar variability is again apparent in these local chronologies where replication is high (Fig. 2 B and C), but less agreement when it is low. The average chronologies produced from the indices shown in Fig. 2 A and C are shown as red and blue curves, respectively, in Fig. 2D. The correspondence in low-frequency variability between these alternatively processed chronologies is excellent. Using only a single RCS curve to standardize all data series overestimates the expected growth of slower-growing trees over their lifetime and underestimates that of faster-growing trees, producing overly small indices for the slower (earlier) growing archaeological data and overly large indices for the faster (predominately later) growing trees, respectively (SI Appendix). We note that using different numbers of multiple RCS curves to standardize different growth-rate classes of trees produces largely the same pattern of millennial variability (whether two, three, four, five, or six RCS curves are used), but all remove the likely bias seen in the single-RCS curve implementation (SI Appendix, Fig. SB1). We adopt the four-curve SF-RCS chronology (blue curve in Fig. 2D) to interpret as a climate proxy for this region. Henceforth this is referred to as the Qilian Mts. Chronology (QLS). The recent data from the alternative chronologies (shown smoothed in Fig. 2 A and D) are shown as annual values in Fig. 2 E and F. This demonstrates that the unusually high chronology values apparent in the final decades of the record are real and not an artifact of the smoothing filter used in Fig. 2 A–D.

Materials and Methods Calendar. Unless otherwise explicitly stated (such as where the BCE, CE calendar convention is used), the Astronomical Calendar is used throughout; i.e., the year before 1 CE is designated as year zero, and progressively earlier years are indicated by increasingly larger negative values. Chronology Construction. The ring-width data from newly collected samples were cross-dated, and the dating of additional (published) measurements, some accessed from the International Tree-Ring Data Bank, was similarly assessed, using multiple comparisons of high-pass–filtered series. The dated measurements and the evidence of dating fidelity in the form of cross-correlation tables are available at www.cru.uea.ac.uk/cru/papers/yang2013pnas/. Where there are multiple cores from the same tree, the measurements were averaged to form mean tree series. These data series were then “standardized” to remove nonclimate-related variance associated with changes in tree geometry that affect the distribution of radial wood production through the life of a tree. The method used is Signal-Free Regional Curve Standardization (SF-RCS) as discussed in the main text. Indices are measured ring-width values divided by expected values, defined by the appropriate SF-RCS curve values for the appropriate ring age. Simple arithmetic means of raw (measured) ring width by ring age (including pith offset estimates), or of indices by calendar year, were used to produce the RCS curve(s) or standardized chronology values, respectively. The RCS curves were smoothed using an age-dependent low-pass spline, applied only where sample replication was greater than three trees; where replication falls to three trees or less, a horizontal linear extension of the RCS curve was applied. The RCS curve was allowed to rise when representing the expected ring width for trees older than 1,500 y to allow for increasing ring width sometimes apparent in old-age trees that develop a strip-bark–like form. Chronologies created from subdivisions of the full measurement data set were used to explore potential source bias (SI Appendix). All SF-RCS curves and subchronologies are shown in SI Appendix. Where multiple RCS curves were used for different growth-rate classes of tree, the data were sorted into subgroups (of nearly equal numbers) according to the mean relative growth rate, i.e., the radial increment for each tree relative to the radial increment of a single RCS curve generated using all trees, where the comparison is over the (common) period of growth of the specific tree. An empirical method was used to transform the values of all tree indices so that their probability distribution function follows a normal distribution (SI Appendix). Chronology confidence is represented by the SEM (SE), usually shown as ±2 SE, calculated for the timescale of chronology variance being represented. An “adjusted” Expressed Population Signal (EPS) is also used to measure chronology confidence, as shown in SI Appendix. Regional Precipitation Time Series. Monthly mean observations from six weather stations were combined to represent precipitation over the study region (Fig. 1) for 1957–2011. The three southern stations are drier than the northern stations (SI Appendix, Figs. SA1 and SA3) and, because some monthly values are missing, a simple average of the station series could result in a biased regional mean series (e.g., the data from the drier Chaka station are not available after 2000, and a simple average would increase after 2000). Instead, each station series is first scaled so that its long-term mean is equal to the average long-term mean across all stations. The scaling is calculated separately for each month of the year, using long-term mean values calculated only from those years when all six stations have an observed value for the given month. The SD of a station series is also affected by the scaling, such that the coefficient of variation is constant. A simple average of the scaled station series is then used as the regional precipitation series. Note that the scaling increases the contribution of the drier stations relative to the wetter stations. Reconstruction Calibration. Multiple methods of scaling the QLS tree-ring chronology to represent annual (prior July to current June) regional precipitation were compared (SI Appendix, Section D), in all cases using the calibration data for the period 1957–2011. The results for the different calibrations (shown as scatter diagrams; SI Appendix, Fig. SD2) are all similar, with the explained variance (r2) ranging from 61% (high-pass–filtered case) to 79% (detrended, low-pass–filtered case). When expressed as the deviation in mm of annual rainfall represented by one SD of the chronology, the scaling coefficients (β) range from 33 to 65 mm/SD. The reconstruction shown in the main paper (Fig. 3) is based on the detrended case with β = 45 mm/SD; r = 0.79 [i.e., SI Appendix, Section D, case (iii)(b)]. Additional cross-validation tests were used to demonstrate the validity of the calibration process. These and the calculation of chronology and rainfall reconstruction uncertainty are also described in detail in SI Appendix. Composite of Northern Hemisphere Temperature Reconstructions. A compilation of 11 NH temperature reconstructions (SI Appendix) was assembled from previously published estimates, selecting only those reconstructions that began on or before 1000 and that were not considered to have been closely related to, or superseded by, later versions. Each reconstruction was filtered with a low- or band-pass filter to represent the timescale considered, with truncation to remove values near each end of the filtered time series that are more uncertain due to the end effects of the filter. Each filtered series was then “normalized” to have zero mean and unit SD over a common overlap period, and the composite mean and SD were calculated in each year. Data and Software. Tree-ring measurement and chronology data, precipitation observations and the precipitation reconstruction data, and the CRUST software (18) used to standardize and process the tree-ring data are all available at www.cru.uea.ac.uk/cru/papers/yang2013pnas/. Data are also available from the World Data Center for Paleoclimatology (www.ncdc.noaa.gov/paleo/).

Acknowledgments We are very grateful to two anonymous referees whose comments helped improve the final manuscript. We thank L. Xia for help with fieldwork. Spatial correlation maps were calculated via the Koninklijk Nederlands Meteorologisch Instituut Climate Explorer (http://climexp.knmi.nl/). The study was jointly funded by the National Science Foundation of China (NSFC) (Grant 41325008), the National Basic Research Program of China (973 Program) (2010CB950104), and NSFC (Grant 41272189). T.M.M., T.J.O., and K.R.B. acknowledge support from Natural Environment Research Council (NE/G018863/1). We acknowledge the International Tree-Ring Data Bank from where we obtained some of the QLS data.

Footnotes Author contributions: B.Y., T.M.M., T.J.O., and K.R.B. designed research; B.Y., C.Q., J.W., M.H., T.M.M., T.J.O., and K.R.B. performed research; B.Y., C.Q., J.W., M.H., T.M.M., T.J.O., and K.R.B. contributed new reagents/analytic tools; B.Y., C.Q., J.W., M.H., T.M.M., T.J.O., and K.R.B. analyzed data; and B.Y., C.Q., J.W., M.H., T.M.M., T.J.O., and K.R.B. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: Tree-ring measurement and chronology data, and the precipitation reconstruction data have been deposited in the World Data Center for Palaeoclimatology (www.ncdc.noaa.gov/paleo/).

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1319238111/-/DCSupplemental.