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Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with
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RCP8.5 climate projections from 19 GCMs
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Supplementary Materials
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Miao Yu 1,2, Guiling Wang1*, Dana Parr1, Kazi Farzan Ahmed1
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1. Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06269
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2. Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science
and Technology, Nanjing 210044, China
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*Corresponding contact:
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gwang@engr.uconn.edu
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860 486-5648
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1. Impact of new modifications on model results:
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To illustrate the impact of the modifications on water-controlled phenology scheme, two 20-year sensitivity
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experiments were conducted: one using the default version of CLM4-CN model and the other including the
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refinement of the water-controlled phenology scheme for tropical broadleaf drought-deciduous trees. In both
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simulations, vegetation distribution is prescribed, so 20 years of simulation is sufficient. Using one grid point as
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an example where vegetation is dominated by tropical broadleaf drought-deciduous trees, Fig.
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simulated monthly ELAI from these two experiments and the monthly precipitation from the meteorological
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forcing. The default version of CLM4-CN model produces a spurious increase of ELAI in the middle of the dry
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season. This error is corrected by our refinement of the water-controlled phenology scheme. The drought-incurred
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senescence is captured reasonable well.
shows the
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To illustrate the impact of all the modifications on the simulated vegetation distribution, four sensitivity
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experiments were conducted, each using a different version of the CLM4-CN-DV model. Results from these
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experiments are presented in Figs. S2 and S3 using tropical broadleaf evergreen and drought-deciduous trees as
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examples. The default version of the model produces too much coverage of broadleaf evergreen trees in the
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southeastern part of Amazon and in Central Africa (Figs. S2a and S3a), due to the well-documented
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overestimation of GPP (reference – should be Bonan’s paper on the new GPP parameterization); The GPP
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parameterization from CLM4.5 substantially reduces the model-simulated tree coverage in these regions (Figs.
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S2b and S3b). The refinement of the water-controlled phenology scheme caused slight expansion of the tree
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coverage, especially for drought deciduous trees (Figs. S2c and S3c); adding a threshold of soil drought season
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length above which evergreen trees cannot survive increased areas where the simulated vegetation is dominated
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by drought deciduous trees, especially along the northern and southern borders of the forest in Africa (Figs. S2d
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and S3d).
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2. Simulated Present Day Vegetation Distributions:
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Needleleaf evergreen trees are mainly located in the Northern Hemisphere with the maximum coverage
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between 40ºN and 70ºN (Fig. S4a). This is captured very well by the CLM-CN-DV control run (Fig. S4b).
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However, the model overestimates the density of needleleaf evergreen trees in Southeast Asia. The model also
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simulates some coverage of needleleaf evergreen trees in the Southern Hemisphere (e.g., in western and
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southeastern South America and in Central Africa) that are not observed from the MODIS data. Gotangco Castillo
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et al. (2012) suggested that the simulated distribution of needleleaf evergreen trees is more reasonable than
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MODIS because MODIS dataset confines this type of vegetation in Northern Hemisphere. Most of the
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GCM-driven present-day simulations produce similar results with the control run except for IPSL-CM5A-MR and
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MIROC5. A spurious presence of needleleaf evergreen trees are produced in Central Africa by the simulations
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driven with output from BNU-ESM, CCSM, FGOALS-g2, the three GFDL models (GFDL-CM3, GFDL-ESM2G
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and GFDL-ESM2M) and INM-CM4, and in the Amazon by the simulation driven with the INM-CM4 climate
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(Fig. S4c-u), probably as a result of cold bias in these GCMs for the regions involved.
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Broadleaf evergreen trees are mainly distributed throughout the Tropics, including the Amazon, Central Africa
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and the maritime subcontinents (between South Asia and Australia) (Fig. S5a). This pattern can be reproduced by
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the control run, but the simulated fractional coverage is smaller, with the values less than 50% (Fig. S5b). This is
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also the case for simulations forced by GCMs output, which results from a deficiency of CLM-CN-DV.
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Specifically, the model simulates a half-half split of vegetation between evergreen and drought-deciduous trees in
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the wet Tropics, where MODIS indicates dominance by evergreen trees. While all the GCM-driven runs show
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coverage of broadleaf evergreen trees in the maritime subcontinents, not all the experiments produce coverage in
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both the Amazon and Central Africa. There is much less coverage of broadleaf evergreen trees in Africa under the
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climate of CMCC-CM, HadGEM2-ES and MRI-CGCM3, and much less in the Amazon under the GFDL-family
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models’ climates, the BCC-CSM1.1m, IPSL-CM5A-LR and MIROC5 climates. Simulations driven by
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CNRM-CM5 and IPSL-CM5A-MR climates produce little or no coverage of broadleaf evergreen trees both in
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Amazon and in Central Africa.
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MODIS shows broadleaf deciduous trees mainly in eastern North America, Europe, Southeast Asia,
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mid-latitude Eurasia, eastern Amazon, Sahel and areas south of the Congo Basin (Fig. S6a). The model control run
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captures the spatial distribution well in the extratropics although with some overestimation, but strong biases exist
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in South America and Central Africa (Fig. S6b). This is partly related to the deficiency of CLM-CN-DV in
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distinguishing evergreen trees and deciduous trees in the Tropics. This deficiency is also reflected by simulations
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driven by the GCM climates. Broadleaf deciduous trees in Southeast Asia can be simulated under most of the
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GCM climates, but those in Europe and eastern North America are mostly underestimated. Compared with the
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control run, the HadGEM2-ES-driven present-day simulation produces the most similar deciduous tree coverage
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in the extratropics.
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MODIS data indicates that shrubs dominate the northern high latitudes and regions south of 30ºS (Fig. S7a).
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CLM-CN-DV simulations including both the control run and those driven by GCM climates can produce shrubs
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in the Southern Hemisphere but fail to capture the existence of shrubs in the northern high latitudes (Fig. S7b-u).
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The model control run underestimates grass coverage in central U.S., and central and eastern Asia. The spatial
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pattern of grass coverage is simulated well by the control run in eastern South America and Africa, although the
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coverage fraction is higher than in MODIS (Fig. S8a, b). Most of the GCM-driven simulations produce a similar
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grass distribution to the control run. Some of them even outperform the control run in capturing the grass
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coverage in central U.S., Asia and Africa. However, the present-day simulations corresponding to climate of
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several GCMs (e.g., BCC-CSM1.1m, CNRM-CM5, the three GFDL models, IPSL-CM5A-LR and
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IPSL-CM5A-MR) significantly overestimate grass coverage in the Amazon region, which is related to their
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underestimation of tree coverage in that region.
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Table Captions
Table S1 Models specification
Figure Captions
Fig. S1 Last five years of monthly precipitation (grey bars, in mm/day) and the simulated monthly ELAI from
experiments using the original version of CLM4-CN model (dash line) and the modified version including the
refinement of the water-controlled phenology scheme (solid line) for one grid point which is occupied primarily
by tropical broadleaf deciduous trees
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Fig. S2 Last 20-year average of fractional coverage (%) of tropical broadleaf evergreen trees simulated by the
CLM4-CN-DV model (a) in its default version, (b) with the incorporation of GPP parameterization from CLM4.5,
(c) with both the incorporation of CLM4.5 GPP parameterization and the refinement of water-controlled
phenology scheme, and (d) with all modifications in (c) and the addition of a threshold for the length of soil
drought above which tropical broadleaf evergreen trees cannot survive (d). The model version used in producing
results shown in (d) is used in this study
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Fig. S3 Same as Fig. S2. But for broadleaf deciduous trees-tropical
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Fig. S4 Fractional coverage (%) of needleleaf evergreen trees derived from MODIS data (a), and from the last 20
years average in present-day simulations driven with Qian et al.’s forcing (b) and with the 19 GCMs output (c-u)
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Fig. S5 Same as Fig. S4, but for broadleaf evergreen trees
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Fig. S6 Same as Fig. S4, but for broadleaf deciduous trees
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Fig. S7 Same as Fig. S4, but for shrubs
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Fig. S8 Same as Fig. S4, but for grasses
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Table S1 Models specification
Model name
Modeling center
Original
References
resolution
(lonlat)
ACCESS1.0
CSIRO (Commonwealth Scientific and Industrial
145192
(Bi et al. 2013)
64128
(Wu et al. 2013)
160320
(Wu et al. 2014)
64128
(Ji et al. 2014)
Research Organisation, Australia), and BOM
(Bureau of Meteorology, Australia)
BCC-CSM1.1
Beijing Climate Center, China Meteorological
Administration
BCC-CSM1.1(m)
Beijing Climate Center, China Meteorological
Administration
BNU-ESM
College of Global Change and Earth System
Science, Beijing Normal University
CCSM4
National Center for Atmospheric Research
192288
(Gent et al. 2011)
CMCC-CM
Centro Euro-Mediterraneo per I Cambiamenti
240480
(Bellucci et al. 2013)
128256
(Voldoire et al. 2012)
60128
(Li et al. 2013)
Climatici
CNRM-CM5
Centre National de Recherches
Meteorologiques/Centre Europeen de Recherche
et Formation Avancees en Calcul Scientifique
FGOALS-g2
LASG, Institute of Atmospheric Physics, Chinese
Academy of Sciences
GFDL-CM3
Geophysical Fluid Dynamics Laboratory
90144
(Griffies et al. 2011)
GFDL-ESM2G
Geophysical Fluid Dynamics Laboratory
90144
(Dunne et al. 2012)
GFDL-ESM2M
Geophysical Fluid Dynamics Laboratory
90144
(Dunne et al. 2012)
HadGEM2-ES
Met Office Hadley Centre, contributed by
144192
(Collins et al. 2011)
Instituto Nacional de Pesquisas Espaciais
INM-CM4
Institute for Numerical Mathematics
120180
(Volodin et al. 2010)
IPSL-CM5A-LR
Institut Pierre-Simon Laplace
9696
(Dufresne et al. 2013)
IPSL-CM5A-MR
Institut Pierre-Simon Laplace
143144
(Dufresne et al. 2013)
MIROC5
Atmosphere and Ocean Research Institute (The
128256
(Watanabe et al. 2010)
64128
(Watanabe et al. 2011)
64128
(Watanabe et al. 2011)
160320
(Yukimoto et al. 2012)
University of Tokyo), National Institute for
Environmental Studies, and Japan Agency for
Marine-Earth Science and Technology
MIROC-ESM
Japan Agency for Marine-Earth Science and
Technology, Atmosphere and Ocean Research
Institute (The University of Tokyo), and National
Institute for Environmental Studies
MIROC-ESM-CHEM
Japan Agency for Marine-Earth Science and
Technology, Atmosphere and Ocean Research
Institute (The University of Tokyo), and National
Institute for Environmental Studies
MRI-CGCM3
Meteorological Research Institute
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Fig. S1 Last five years of monthly precipitation (grey bars, in mm/day) and the simulated monthly ELAI from
experiments using the original version of CLM4-CN model (dash line) and the modified version including the
refinement of the water-controlled phenology scheme (solid line) for one grid point which is occupied primarily
by tropical broadleaf deciduous trees
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Fig. S2 Last 20-year average of fractional coverage (%) of tropical broadleaf evergreen trees simulated by the
CLM4-CN-DV model (a) in its default version, (b) with the incorporation of GPP parameterization from CLM4.5,
(c) with both the incorporation of CLM4.5 GPP parameterization and the refinement of water-controlled
phenology scheme, and (d) with all modifications in (c) and the addition of a threshold for the length of soil
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2
3
4
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drought above which tropical broadleaf evergreen trees cannot survive (d). The model version used in producing
results shown in (d) is used in this study
Fig. S3 Same as Fig. S2, but for tropical broadleaf deciduous trees.
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3
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Fig. S4 Fractional coverage (%) of needleleaf evergreen trees derived from MODIS data (a), and from the last 20
years average in present-day simulations driven with Qian et al.’s forcing (b) and with the 19 GCMs output (c-u)
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1
2
3
Fig. S5 Same as Fig. S4, but for broadleaf evergreen trees
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Fig. S6 Same as Fig. S4, but for broadleaf deciduous trees
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1
2
3
Fig. S7 Same as Fig. S4, but for shrubs
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1
2
3
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Fig. S8 Same as Fig. S4, but for grasses
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3
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