Supplemental Information (SI): Differential radial growth response of three coexisting dominant tree species to local and large-scale climate variability in a subtropical evergreen broad-leaved forest of China
Hongxin Sua, Jan C. Axmacherb, Bo Yanga,c, Weiguo Sanga*
a State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
b UCL Department of Geography, University College London, Pearson Building, Gower Street, London WC1E 6BT, UK
cKey Laboratory of Specialty Plant Resources of Jiangxi Province, Jingdezhen University, Jingdezhen 333000, China
* Corresponding author: email: , Tel: +86-10-82599519
The projected monthly local climate data
For the projected monthly local climate data from 2009 to 2099, including the average daily temperature, daily maximum and minimum temperatures, total precipitation and percentage of sunshine, we used simulations from nine Global Climate Models (GCM) selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models (Table S1, see http://cmip-pcmdi.llnl.gov/cmip5/availability.html for detailed information).
For the CMIP5, which represents a series of modeling studies conducted for the IPCC AR5 assessment (Taylor et al. 2012), four new emission scenarios were developed based on a range of projections of future population growth, technological development and societal responses; these are referred to as Representative Concentration Pathways, RCP2.6, RCP4.5, RCP6.0, and RCP8.5, based on Moss et al. (2010). Here, we selected two scenarios, RCP4.5 and RCP8.5, for our modeling of future changes in tree growth. RCP8.5, which represents a “high emission scenario,” has a radiative forcing that increases throughout the twenty-first century before reaching a level of approximately 8.5 Wm−2 at the end of the century. In contrast, the intermediate scenario RCP4.5 reaches a level of only about 4.5 Wm−2 by the end of the 21st century.
The selected models include both 20th century climate simulations (referred to as historical experiments) and 21st century climate projections under the RCP4.5 and RCP8.5 scenarios. All of the data are interpolated onto a common 0.5° × 0.5° grid. For such projections, a multi-model ensemble provides more reliable results than a single model (Xu and Xu 2012). Therefore, the projections of temperature, precipitation and sunshine are derived from the arithmetic ensemble mean of the nine GCMs’ outputs. Finally, comparisons were drawn between historical simulations and observations from the Kaihua weather station to estimate the bias and correct the future climate scenario data using the delta change method (temperature), relative anomalies and multiplication (precipitation and sunshine).
Climate-growth analyses
Monthly analysis showed the detailed relationship between tree growth and climate, which indicates that the three tree species responded distinctly differently to climatic change (Fig. S1). A significant positive response in Pinus massonianaP. massoniana to October temperatures was observed, and it also had a significant negative response to January temperatures. A positive relationship with the monthly temperature was the main characteristic of the tree growth model for Castinopsis eyreiC. eyrei, but only the response to April temperatures became significant (P 0.05). The correlations between Schima superbaS. superba and the temperature were all significantly negative in the previous December, January, February and March.
The relationships between precipitation, percentage sunshine and the tree growth were generally opposite. A significant negative correlation was observed between growth of P. massoniana and precipitation in March, and a parallel positive relationship between its growth and the percentage of sunshine in March. Similarly, the significantly positive response of S. superba to the percentage of sunshine occurred in August; correspondingly, a significantly negative correlation existed between growth of S. superba and August precipitation amounts. The monthly solar and moisture signals were not as clear for C. eyrei as was observed for the other two species.
Annual analysis showed that radial growth in S. superba was strongly linked to climatic factors, with increased sunshine hours supporting its growth, while hot and wet conditions adversely affected its radial growth (Table S2). Contrarily, C. eyrei growth showed a positive link to high temperatures. The radial growth pattern of P. massoniana was not significantly (P = 0.05) linked to any annual climatic signals.
Model verification
The validity of the statistical models was established by testing their temporal stability using the traditional data-splitting method (Fritts 1976). The entire period examined for growth-climate relationships was divided into early (1958–1983) and late (1984–2008) sections. Predictive models were developed using the tree-ring growth and climatic conditions for the early period (calibration period). Climatic data of the late period (verification period) were then substituted into the model to predict tree-ring widths, and observed and predicted tree-ring widths during the late period statistically compared here. Calibration and verification were repeated alternating the calibration and verification periods. Verification was done using a Pearson correlation test, sign test and reduction of error (Fritts 1976; Cook et al. 1987, 1999; Sokal and Rohlf 1995; Takahashi and Okuhara 2013). The sign test was used to compare the direction of changes (+ or –) in each year between the observed and predicted tree-ring widths. Then, the statistical difference between the two changed numbers was tested with a binomial distribution (Takahashi and Okuhara 2013). The computation of reduction of error (RE) was:
RE = 1 − SSR/SSM
where SSR is the sum of squares of the differences (residuals) between observed and predicted tree-ring widths, and SSM is the sum of the squares of the differences of the observed data from the mean of the dependent data set used for the calibration.
Table S1 Nine Global Climate Models selected from the Coupled Model Intercomparison Project Phase 5 used in this study
Beijing Climate Center Climate System Model 1.1 (BCC-CSM1-1) / Beijing Climate Center, China Meteorological Administration / 64, 128 / Wu et al., 2013
Centre National de Recherches Météorologiques Climate Model 5 (CNRM-CM5) / Centre National de Recherches Météorologiques / Centre Européen de Recherche et Formation Avancée en Calcul Scientifique / 128, 256 / Voldoire et al., 2012
Goddard Institute for Space Studies Model E2-Russell ocean (GISS-E2-R) / NASA Goddard Institute for Space Studies / 90, 144 / Schmidt et al., 2006
Hadley Global Environment Model 2-Carbon Cycle (HadGEM2-CC) / Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by the Instituto Nacional de Pesquisas Espaciais) / 145, 192 / Collins et al., 2011
HadGEM2-Earth System (HadGEM2-ES) / Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by the Instituto Nacional de Pesquisas Espaciais) / 145, 192 / Collins et al., 2011
Institut Pierre-Simon Laplace-global general Circulation Model 5A-Low Resolution (IPSL-CM5A-LR) / Institut Pierre-Simon Laplace / 96, 96 / Dufresne et al., 2013
Model for Interdisciplinary Research on Climate-Earth System Models (MIROC-ESM) / Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and the National Institute for Environmental Studies / 64, 128 / Watanabe et al., 2011
Max Planck Institute for Meteorology Earth System Model-Low Resolution (MPI-ESM-LR) / Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) / 96, 192 / Raddatz et al., 2007
Meteorological Research Institute Coupled Global Climate Model 3 (MRI-CGCM3) / Meteorological Research Institute / 160, 320 / Yukimoto et al., 2011
Table S2 Correlation between tree growth and annual meteorology factors of the three species analyzed here
Pinus massoniana / 0.04 / 0.06 / 0.10 / –0.13 / –0.01
Castinopsis eyrei / 0.34* / 0.22 / 0.36** / –0.15 / –0.08
Schima superba / –0.43** / –0.08 / –0.50** / –0.32* / 0.32*
*, P < 0.05 (2-tailed); **, P < 0.01 (2-tailed).
Figure S1 Correlation coefficients between tree growth and three monthly climate variables of monthly mean temperatures, total precipitation, and mean sunshine percentage
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