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: swg@ibcas.ac.cn, 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 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 eyrei, but only the response to
April temperatures became significant (P < 0.05). The correlations between Schima
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
Model name
Institution
Number of
grid cells
(latitude,
longitude)
Reference
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
Species
Temperature
Maximum
Minimum
temperature
temperature
Precipitation
Percentage of
sunshine
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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