Climatic signals in TRW and MXD

TRW and MXD data were obtained from 153 trees (175 cores) at five sites near the upper treeline in >4,000 m asl on the southeastern TP (Fig. 1). The longest series reaches back to 1509 and the shortest to 1688 (Supplementary Table 1; Supplementary Fig. 3). Significant correlations of the TRW and MXD site chronologies (Supplementary Tables 2 and 3) indicate that regional TRW and MXD composite records can be produced. Assessments of climate–growth relationships using Pearson correlation coefficients show that both the site and composite MXD chronologies contain strong summer temperature signals, while the TRW chronologies respond significantly to winter temperature (Supplementary Figs 4 and 5). The climatic signals are strongest in the regional composite chronologies, including a maximum response to November–February temperature in TRW and to July–September temperature in MXD. These seasonal patterns are consistent with previous studies18,19,20,22,23,24,25.

Figure 1: Map showing the locations of tree-ring sampling sites and meteorological stations on the Tibetan Plateau. Specific information of latitude, longitude and elevation for each tree-ring site and meteorological station is listed in Supplementary Tables 1 and 4. This map was created using the software ArcMap 9.2. Full size image

To examine the influence of potential co-linearities among climate variables on the climate–growth relationships, responses patterns are additionally assessed using the Seascorr program30 (see Methods), which revealed identical results (Supplementary Figs 6 and 7). Partial correlation coefficients show that MXD has a nonsignificant response to July–September precipitation and that TRW has a nonsignificant response to previous November to February precipitation. However, previous autumn and winter precipitation show significant partial correlations with the regional MXD and TRW, respectively. While there is not an obvious physiological explanation for the previous autumn precipitation signal in MXD, the significant partial correlation between TRW and previous winter precipitation might indicate an influence of the snow cover on the subsequent cambial activity21,26. Partial correlation analysis (see Methods) also shows that the correlations between MXD and winter temperature and between TRW and summer temperature are nonsignificant (grey bars in Supplementary Fig. 5). This indicates that there is no covariation influence of winter and summer temperature on the climate–growth relationships. For further analyses, we define summer temperature as the average of July to September and winter temperature as the average of the previous November to February.

Tree-ring residual time series

To assess long-term changes in the annual temperature cycle, we establish three tree-ring residual time series (TMres) derived from the difference between MXD and TRW (that is, MXD minus TRW; see Methods). TMres1 is a time series derived from the detrended residual (MXD minus TRW) of a single series (for each core) and subsequent biweight averaging. TMres2 is a residual series (MXD minus TRW) obtained from the detrended MXD and TRW chronologies. TMres3 is a time series established by averaging all the single residual series (MXD minus TRW) and subsequent detrending. Based on the consideration of the expressed population signal (EPS)31>0.85 and the obviously increased s.d. before 1700 resulting from decreased sample size (Supplementary Fig. 8), three residual series between 1700 and 2011 are used for further analyses. Summer minus winter temperature residuals are calculated as the difference of July–September mean temperature minus the previous November–February mean temperature using instrumental station data (Supplementary Table 4). The tree-ring residual time series (TMres1–3, Supplementary Fig. 9) correlate significantly with observational temperature residuals, but TMres1 shows the strongest correlation (Supplementary Table 4). This outcome indicates that the method used to establish TMres1 eliminated more non-climatic noise, although the other approaches (TMres2 and TMres3) also revealed significant correlations (see Methods for detail). TMres1 is used for further temperature seasonality analysis.

Response of TMres1 to seasonal temperature difference

When comparing TMres1 with the instrumental summer minus winter temperature difference, the highest correlations are recorded at the Qamdo and Xinglong stations (r=0.65; Supplementary Table 4). Since the Qamdo station covers a longer period from 1955 to 2011 (Xinglong from 1961 to 2011), this station, as well as a regional average, is used for further analysis (Fig. 2a,b). The results indicate that TMres1 is a good representative of interannual variability of the annual temperature cycle for the local and regional scales. Comparisons of TMres1 with the summer minus winter temperature difference recorded in northeastern India and the large-scale Eurasia mean (2.5–150° E, 25–70° N; Fig. 2c–f) reveal good agreements at the decadal scale and in the long-term trend. This outcome indicates that long-term changes in the annual temperature cycle as reconstructed for the TP are potentially representative for a larger region.

Figure 2: Comparison between TMres1 and instrumental temperature residual time series. (a) Comparison between the TMres1 and summer minus winter temperature difference in the Qamdo station over the 1955–2011 common period. (b) Comparison between the TMres1 and regional instrumental difference series (mean of 13 stations) over the 1952–2011 common period. (c) Comparison between the TMres1 and summer minus winter temperature difference in northeast India from 1902–2007. (e) Comparison between the TMres1 and the large-scale average (2.5–150° E, 25°–70° N) of gridded summer minus winter temperature time series from 1851 to 2011. (d,f) Eleven-year moving averages of the series shown in c,e. Lines in c,e indicate the linear trend. Full size image

Reconstruction of summer minus winter temperature difference

To capture the strongest signal of temperature seasonality on the TP, the summer–winter residual recorded at the Qamdo station was chosen as the reconstruction target. The highly significant correlation (r=0.81, n=59, P<0.0001) between the Qamdo and regional residual series indicates the representativeness of the station record for larger spatial scales. The skill of the transfer regression model is verified using leave-one-out cross-validation methods (Supplementary Table 5)32. The reconstructed seasonal temperature difference back to 1700 shows a change in trend behaviour over time (Fig. 3a), and regime shift detection33 reveals three turning points in 1863, 1874 and 1955 (Fig. 3b,c). The year 1863 indicates the starting point of an increasing trend, while 1874 and 1955 mark the start of trend decreases. Overall, a slightly increasing trend is recorded before the 1870s (0.21 °C per 100 years from 1700 to 1873) and is replaced by an almost threefold negative trend thereafter (−0.59 °C per 100 years). The same trend changes are revealed in the model-simulated temperature seasonality estimates of the TP (Fig. 3d).

Figure 3: Temperature seasonality changes reconstructed from TP tree rings and its comparison with the multi-model ensemble mean. (a) Linear regressions fitted to the reconstructed temperature seasonality changes (black) over the 1700–1873 (olive) and 1874–2011 (magenta) periods. (b) Regime shift index for the regime shift detection in the TMres1 (arrows indicate the turning points and theirs shift direction, up means increase and down means decrease). (c) Mean changes of the regime shift detection in the TMres1 series. (d) Comparisons between normalized TMres1 and summer-versus-winter temperature residuals derived from an ensemble mean of 11 models over the 1700–2000 period (Supplementary Table 6). Note that curves in d are the 11-year moving average. The grey shaded area in a indicates the 95% confidence interval of the reconstruction series (black). Full size image

To further assess seasonal temperature change, we performed independent summer and winter temperature reconstructions using MXD and TRW, respectively (Fig. 4; Supplementary Table 5). Reconstructed regional winter and summer temperatures explain 32.5% and 54.8% of the variance in the instrumental records, respectively (Fig. 4d,e). Although the MXD and TRW chronologies show higher correlations with regionally averaged temperature than with the Qamdo station record (Fig. 4a,b), the summer minus winter temperature differences derived from the summer and winter temperature reconstructions have a better match with summer minus winter temperature differences recorded at the Qamdo station (r=0.62) compared with regional averages (r=0.54; Fig. 4c). This finding is similar to the result obtained from TMres1 (Fig. 2a,b), but TMres1 has a higher correlation with the record at the Qamdo station (r=0.65; Fig. 2a,b). Additional analyses show that the variance of summer minus winter temperature difference explained by independent summer and winter temperature reconstructions has more similarity to that explained by the Qamdo station records than by the regional average (Methods). This similarity is the likely reason for why the reconstructed summer minus winter temperature series correlates much higher with records from the Qamdo station than the regional average.

Figure 4: TRW- and MXD-based winter and summer temperature reconstructions. (a) Comparisons of the regional TRW chronology (magenta) with winter temperature (previous November to February) records in regional average (black) and the Qamdo station (olive). (b) Comparisons of the regional MXD chronology (magenta) with summer temperature (July–September) records in regional average (black) and the Qamdo station (olive). (c) Comparisons of the reconstructed summer temperature minus reconstructed winter temperature (magenta) with instrumental summer temperature minus instrumental winter temperature in regional average (black) and the Qamdo station (olive). (d) Winter temperature reconstruction derived from the regional TRW chronology (anomalies wrt 1981–2010). (e) Summer temperature reconstruction derived from the regional MXD chronology (anomalies wrt 1981–2010). (f) The difference between summer temperature reconstruction and winter temperature reconstruction over the period 1700–2011. Grey shaded areas in d–f indicate the 95% confidence interval of the reconstruction series (black). Full size image

Both the winter and summer temperature reconstructions show a smaller trend before the 1870s than thereafter. Since the 1870s, a larger increase rate can be found in the winter temperature reconstruction compared with the summer temperature reconstruction (0.74 °C per 100 years for winter and 0.29 °C per 100 years for summer; Fig. 4d,e). In contrast to the summer minus winter temperature differences derived from TMres1 (Fig. 3a), the result obtained from the winter and summer temperature reconstructions show a smaller downward trend since the 1870s (Fig. 4f). This trend likely occurred because the latter reconstructions have a larger uncertainty and less variance explained of instrumental data (Fig. 4). Therefore, both the summer minus winter temperature differences derived from the two methods demonstrate the persistent weakening of the annual temperature cycle since the 1870s, but TMres1 as the proxy is a better choice.

Spatial characteristics of the annual temperature cycle

To assess the spatial characteristics of annual temperature cycle and to verify our proxy-based reconstruction against observational data, both extreme years and trends identified in the reconstruction are analysed using instrumental and model-simulated temperatures (Supplementary Table 6). Two extremely positive years (1983 and 1992) and two extremely negative years (1987 and 1999) show consistent spatial variations across the TP (Supplementary Fig. 10). The distance-related correlation decay of TMres1 with observed summer minus winter temperature differences (Fig. 2a,c,e) indicates that interannual variability of the annual temperature cycle differs between the TP and the remote regions. However, the consistent weakening trend (Figs 2d,f and 5) demonstrates that a long-term weakening of temperature seasonality since the 1870s occurred at larger spatial scales. The trend of the annual cycle shown in Fig. 5 in regions with short instrumental data (that is, 1950s–2011; Supplementary Fig. 11) is identical with the observed change on the TP (about −2 °C per 100 years; Supplementary Fig. 1). Regions with a longer observational data (that is, 1874–2011; Supplementary Fig. 11) reveal trends close to our reconstructed change rate (near −1 °C per 100 years; Fig. 3a).

Figure 5: Large-scale trend of summer minus winter temperature differences from 1874 to 2011. The trend (°C per 100 years) was calculated using the CRU grid temperature data set (CRUTEM.4.3). The pink rectangle denotes the Tibetan Plateau. For data availability of each grid, please see Supplementary Fig. 11. Full size image

The comparison of TMres1 with seasonality estimates derived from an ensemble of 11 global climate models (Supplementary Table 6) provides additional evidence of reduced temperature seasonality. Although the interannual consistence is weak and there is also partial disagreement in decadal variability (for example, 1740s–1750s and 1700s–1710s), the obvious turning point in temperature seasonality in the 1870s can be seen in both the proxy- and model-based evidence (Fig. 3c,d).

Possible drivers of the weakened temperature seasonality

Studies based on observational data from the TP showed that cold season temperatures experienced a greater warming rate than warm season temperatures over the last decades34. Similar results were obtained in other regions of the Northern Hemisphere land mass3. For the different seasonal warming rates in high northern latitudes, East Asia and Europe, model-simulated studies indicate that anthropogenic forcing is the most important driver3,35. On longer timescales, ice-core records reveal that atmospheric sulfate concentrations on the TP were relatively low and constant in the period 1000–1870, but thereafter, concentrations increased significantly36. The main sources of the increased sulfate concentrations since 1870 on the TP are almost certainly anthropogenic in origin36. Sulfate aerosols force climate by reflecting sunlight into space and also by acting as condensation nuclei, which tend to make clouds more reflective and change their lifetimes, causing a net cooling37,38. It has been suggested that atmospheric sulfate has a greater insolation in summer than winter and that the shortwave radiative influence of anthropogenic sulfate aerosol is greater in summer leading to a decreasing warming rate as compared with winter39,40. These results imply that the human-induced increase of atmospheric sulfate concentrations might be responsible for the persistent weakening of temperature seasonality over the TP since the 1870s.

In this study, a novel approach of using MXD minus TRW residuals shows a better ability in capturing seasonal temperature signals compared with the traditional method (that is, using TRW and MXD chronologies; see Methods). This new approach can be used to extract information on climate seasonality in regions where TRW and MXD are distinctively sensitive to climatic signals in different seasons. Importantly, our study indicates an early shift of changing temperature seasonality that already started in the late 19th century on the TP, and it was likely associated to changes in atmospheric sulfate concentrations. These results imply that future alterations of atmospheric composition driven by human activity might further influence the temperature seasonality, triggering phenology changes (for example, earlier phase shift1) and ecological effects (for example, plant developmental genetics5 and animal distributions6,7).