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Climatic drivers of growth interannual variability. We assessed the climatic drivers at each site during two 30-year periods between 1930–1960 and 1960–1990 CE. Prior to 1930, the quality of the used CRU-TS3.21 climate dataset (41) was insufficient at 301 remote sites in boreal North America and Asia and in South America. After 1990, the decrease in available tree-ring data toward the present (8) critically reduced the covered space. We obtained four gridded monthly climate parameters from CRU-TS3.21: T, P, VPD, and SPEI. VPD was calculated as saturated minus actual vapor pressure, and SPEI was calculated using the R package “SPEI” (42). We calculated regular and partial (data S1) Pearson correlation coefficients between the detrended tree-ring chronologies and monthly to seasonal climate from the corresponding CRU-TS3.21 grid cell over a 16-month window between previous June (January) and current September (April) for the Northern (Southern) Hemisphere. This window was chosen to account for potential 1-year lag effects in the climate response (11, 18, 20).

In a first step of generalization, the relevant climatic seasons for interannual growth variability were identified using affinity propagation clustering (22) that included the site-level correlations over all months and consecutive seasons. This “message-passing” algorithm independently selects exemplary sites, without a predefined number of clusters. We set the “input preference” (the tendency of sites to become exemplars) to the lowest quantile to achieve maximum generalization. This analysis yielded 18 clusters that group into four distinct climate response types (Fig. 1). Correlations from all sites within each group were averaged, and their significance levels were assessed using Fisher’s method (23).

Projection of climate response into space. In a second step of generalization, we projected the site-level climate response into growing season T and annual P space to achieve continuous coverage of the climatic domains occupied by extratropical forests. Problematically, the 0.5° spatial resolution of CRU-TS3.21 resulted in coarse representation of site climate, particularly in steep terrain. To correctly place sites in climate space, we obtained the long-term mean (1950–2000 CE) monthly T and P data for each site from the WorldClim database (43) at 1-km resolution. The monthly T and P data from CRU-TS3.21 were scaled to WorldClim over 1950–2000 CE before calculating growing season T and annual P for both 30-year periods.

Our cluster analysis (see above) showed no single season that explained interannual growth variability at global scales. Hence, to further generalize the diverse site-level climatic drivers for upscaling, we adapted an integrative approach (11) that targets the overall climate response and averages out seasonal responses that can have opposite signs (observed at 38% of sites). The monthly correlations were summarized asEmbedded Imagewhere climint is the growth response to the respective climate parameter over n significant (P < 0.1) months [integrated using Fisher’s method (23)], i is the focal month, TRW is detrended tree-ring width, and clim is the focal climate parameter.

Site-level climint were interpolated using a third-order polynomial trend surface that included site elevation as an additional predictor (see custom code in data S5). We chose this relatively rigid interpolation over locally driven (e.g., spline) approaches to reduce potential biases originating from the fact that the ITRDB is not a systematic sample of forest biomes (8, 35). Extracting the interpolated climint for each grid cell based on its growing season T and annual P produced continuous climate response maps.

Uncertainty of the spatial interpolation. We evaluated the spatial interpolation in several steps: (i) We examined the sample experimental variogram for directional biases related to growing season T or annual P in the spatial autocorrelation. No such biases were observed. (ii) We tested the residuals of linear models between observed and interpolated climint values for dependency on climate or geographic parameters (latitude, longitude, and elevation). No significant relationships (P < 0.1) were found. (iii) We adopted a Monte Carlo approach and selected random subsets of the tree-ring network, thereby iteratively decreasing the number of sites by 5%. For each step, we sampled the tree-ring network and performed the spatial interpolation 1000 times (with replacement), reporting their bootstrapped means and SDs as uncertainty (Fig. 3C and fig. S3). In addition, we compared the climint distribution at each step with the distribution of the interpolation based on the full network (Kolmogorov-Smirnov test). Significant differences (P < 0.1) were not found before the network replication dropped below 60%. (iv) We performed the spatial interpolation on the basis of geographic subsets and compared the results to those obtained from the full network (table S3). The number of sites (n = 874) and the represented climate space (3.8° to 21.4°C growing season T; 226- to 2265-mm annual P) were the smallest for Europe. Hence, we restricted the analysis to this climate space, where 1182 sites from North America and 2509 sites from the full network fell. We selected random subsets (n = 874) from North America and the full network 1000 times with replacement. The means of these 1000 runs were used in the linear models presented in the upper half of table S3. (v) We performed the spatial interpolation based on climate space subsets and compared the results to those obtained from the full network (table S3, lower half). For this purpose, the climate space of the full network was divided into four subsets with above- and below-median growing season T and annual P: warm-dry (WD; 511 sites), warm-wet (WW; 807 sites), cold-dry (CD; 826 sites), and cold-wet (CW; 510 sites).

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