Download Effects of vegetation feedback on future climate change over West

Clim Dyn
DOI 10.1007/s00382-015-2795-7
Effects of vegetation feedback on future climate change
over West Africa
Miao Yu1 · Guiling Wang1 · Jeremy S. Pal2 Received: 10 September 2014 / Accepted: 6 August 2015
© Springer-Verlag Berlin Heidelberg 2015
Abstract This study investigates the impact of climatevegetation interaction on future climate changes over
West Africa using a regional climate model with synchronous coupling between climate and natural vegetation, the
RegCM4.3.4-CLM-CN-DV. Based on the lateral boundary
conditions supplied by MIROC-ESM and CESM under the
greenhouse gas Representative Concentration Pathway 8.5,
significant increase of vegetation density is projected over
the southern part of Sahel, with an increase of leaf area
index and a conversion from grass to woody plants around
7–10°N of Sahel. Regardless of whether the model treats
vegetation as static or dynamic, it projects an increase of
precipitation in eastern Sahel and decrease in the west. The
feedback due to projected vegetation change tends to cause
a wet signal, enhancing the projected increase or alleviate
the decrease of precipitation in JJA in the areas of projected
vegetation increase. Its impact is negligible in DJF. Vegetation feedback slightly enhances projected warming in most
of West Africa during JJA, but has a significant cooling
effect during DJF in regions of strong vegetation changes.
Future changes of surface runoff are projected to follow the
direction of precipitation changes. While dynamic vegetation feedback enhances the projected increase of soil water
content in JJA, it has a drying effect in DJF. The magnitude
of projected ET changes is reduced in JJA and increased
in DJF due to vegetation dynamics. A high sensitivity of
* Guiling Wang
[email protected]
1
Department of Civil and Environmental Engineering,
and Center for Environmental Sciences and Engineering,
University of Connecticut, Storrs, CT 06269, USA
2
Department of Civil Engineering and Environmental Science,
Loyola Marymount University, Los Angeles, CA, USA
climate projection to dynamic vegetation feedback was
found mainly in semiarid areas of West Africa, with little
signal in the wet tropics.
Keywords Dynamic vegetation feedback · Future climate
change · West Africa
1 Introduction
West Africa is considered one of the most severely affected
areas by drought. It experienced a decrease of precipitation during the second half of twentieth century and
endured severe drought in 1970s–80s, which caused devastating famines (Mortimore and Adams 2001). Conditions in the region have partially recovered, with increased
rainfall since the end of twentieth century (Prince et al.
1998; Hulme et al. 2001; Lebel and Ali 2009). Given the
primarily rain-fed agriculture in the region, it is important
that we understand the variation and future changes of
regional climate in order to assess the potential impact of
future changes and to develop adaptation and mitigation
strategies.
While sea surface temperature has been shown to have
a major impact on rainfall variability in Sahel especially
at the inter-annual to decadal time scales (Giannini et al.
2003), a large body of literature has also documented
the importance of land surface properties and processes
(Nicholson 2013) including vegetation conditions and
dynamics. On one hand, climate is the primary natural element controlling vegetation changes (Woodward 1987;
Stenseth et al. 2002). On the other hand, vegetation plays
an important role in regulating global and regional climates
through biogeophysical and biogeochemical pathways by
modifying surface albedo, Bowen ratio, roughness length,
13
M. Yu et al.
and carbon and nitrogen fluxes (Pielke et al. 1998; McPherson 2007; Bonan 2008; Swann et al. 2012).
The pioneering work by Charney (1975) argued that
reduction of vegetation in Sahel could lead to an increase
of albedo and decrease of precipitation, which could then
trigger a positive feedback with the drought in this region.
The reduction of precipitation caused by vegetation degradation was supported by desertification sensitivity experiments of Xue and Shukla (1993), and this impact extended
beyond the specified desertification area and period (Xue
1997). The self-organized drought with the absence of vegetation was corroborated by results from both conceptual
models (Brovkin et al. 1998; Wang 2004) and physically
based numerical models (e.g., Wang and Eltahir 2000c).
The abrupt collapse of vegetation accompanied by the
abrupt severe drought in the mid-Holocene (6000–4000 years
ago) over northern Africa is a widely studied paleo-climate
phenomenon (deMenocal et al. 2000). This sudden desertification from a “green Sahara” was captured by the asynchronously coupled global atmosphere-biome model (Claussen 1997) and the synchronously coupled general circulation
atmosphere–ocean-dynamic vegetation model (Liu et al.
2007). Vegetation-atmosphere interactions are shown to
amplify the impact of the mid-Holocene earth orbital forcing
(Claussen et al. 1999; Irizarry-Ortiz et al. 2003), although the
vegetation feedback is not strong enough to trigger the abrupt
vegetation collapse (Kröpelin et al. 2008).
In modern climate, land–atmosphere interaction was
found to play an important role in amplifying the inter-decadal variability of oceanic-forced rainfall in Sahel during
the twentieth century (Zeng et al. 1999; Wang and Eltahir 2000a; Giannini et al. 2003; Wang et al. 2004; Giannini et al. 2008). The severe and long-lasting drought in the
last four decades of the twentieth century was suggested to
be maintained by a positive feedback between vegetation
and the atmosphere (Wang and Eltahir 2000c; Wang et al.
2004). Using a zonally symmetric, synchronously coupled
biosphere–atmosphere model (Wang and Eltahir 2000b),
Wang and Eltahir (2000a) indicated that vegetation dynamics may enhance the low-frequency rainfall variability in
West Africa. A similar conclusion was drawn from a study
using a simplified coupled atmosphere-land-vegetation
model (Zeng et al. 1999), and was supported by several
studies using comprehensive general circulation models
(GCMs) coupled with dynamic vegetation models (Kucharski et al. 2013; Delire et al. 2004, 2011).
At the seasonal to inter-annual time scales, significant impact of vegetation feedback on local precipitation
throughout the Tropics was found in a sensitivity experiment using Meteorological Office Hadley Centre Unified
Model (HadAM3) (Osborne et al. 2004). Liu et al. (2006)
found positive although limited vegetation feedback on
local precipitation over some areas in the Tropics through
13
proxy observational data analysis. Similar impact was
found in an asynchronously coupled global atmospherebiosphere model for the future climate (Jiang et al. 2011).
In other studies, conversion from savanna to grassland in
the Sahel was suggested to cause a warmer climate with an
insignificant decrease of precipitation (Hoffmann and Jackson 2000), and a projected increase of cropland coverage
at the expense of forest from 1961 to 2015 was found to
cause a decrease of rainfall related with a delayed onset of
the wet season core in July (Taylor et al. 2002). However,
Abiodun et al. (2008)’s study suggested that both deforestation and desertification are likely to enhance the monsoon
flow over West Africa. Zheng and Eltahir (1998) indicated
that deforestation along the coastal areas of West Africa
may induce a collapse of the monsoon circulation while
desertification between Sahara and West Africa has a minor
impact on regional precipitation.
Using a regional climate model (RCM) asynchronously
coupled with a dynamic vegetation model, Alo and Wang
(2010) found that the vegetation response to climate and
CO2 changes in West Africa can lead to comparable or even
larger magnitude of future changes in hydrological processes relative to the contribution from radiative and physiological effects of CO2 changes. Opposite trends of annual
precipitation in Sahel were predicted in their simulations
with and without vegetation dynamics, and the impact of
dynamic vegetation feedback differs between the earlymonsoon season and the peak monsoon (Wang and Alo
2012). However, to reduce uncertainties in our understanding of the role that biosphere–atmosphere feedback plays
in the climate system’s response to anthropogenic changes,
it is necessary that we use a synchronously coupled model
(Wang 2004; Dekker et al. 2010; Sun and Wang 2011;
Martin and Levine 2012). For example, Martin and Levine
(2012) demonstrated significant impact including interactive vegetation component in the HadGEM2 family model
on the simulated present-day and future changes of Asian
summer monsoons. Compared with the asynchronous coupling approach that simulates climate and vegetation iteratively (e.g., every year or several years) using two different
models that often contain different parameterizations for
the same processes simulated by both models, the synchronous coupling approach simulates the states of the vegetation and climate and their simultaneous variation with time
using one integrated model thus eliminating the physical
inconsistency related to the use of two separate models.
Despite its clear importance in climate changes, vegetation dynamics is represented in only a few of the GCMs
that participated in CMIP5 ensemble simulations/projections conducted for IPCC AR5. It is even less common
among the RCMs participating in CORDEX for various
regions. In this study, a synchronously coupled regional
climate-vegetation model is used to investigate the role
Effects of vegetation feedback on future climate change over West Africa
of vegetation feedback in future climate changes in West
Africa. The synchronously coupled climate-ecosystem
model provides an efficient tool for investigating the vegetation feedback on climate and climate changes, and helps
address the uncertainty in model results related to the use
of the asynchronously coupled approach in previous studies. The model and experimental design are described in
Sect. 2. Simulation results are analyzed in Sect. 3. Section 4
presents the summary and conclusions.
2 Model and experimental design
2.1 RegCM4.3.4 coupled with CLM4.0‑CN‑DV
The model used in this study is the International Center for
Theoretical Physics (ICTP) Regional Climate Model Version 4.3.4 (RegCM4.3.4) modified to include the National
Center for Atmospheric Research (NCAR) Community
Land Model version 4.0 (CLM4.0) and CLM4.5 (Wang
et al. 2015). The ICTP RegCM4.3.4 (Giorgi et al. 2012) is
a limited area model that uses hydrostatic and compressible dynamics equations and runs on sigma-p vertical coordinate and an Arakawa B-grid. The dynamics are similar
to that of the hydrostatic version of the Pennsylvania State
University Mesoscale Model version 5 (MM5, Grell et al.
1994). It contains a modified radiative transfer scheme
from the Community Climate Model version3 (CCM3,
Kiehl et al. 1996), a relatively new planetary boundary layer (PBL) scheme (Grenier and Bretherton 2001;
Bretherton et al. 2004), a cumulus convection scheme
(Emanuel and Živkovic-Rothman 1999), a resolved scale
precipitation scheme (Pal et al. 2000), relative new oceanair flux scheme (Zeng and Beljaars 2005), and two options
of land surface scheme. The two options of land surface
scheme are the Biosphere–Atmosphere Transfer Scheme
(BATS) (Dickinson et al. 1993) and the CLM3.5 (Oleson
et al. 2008). Better performance was shown especially in
the simulation of the observed seasonal timing and magnitude of mean monsoon precipitation when CLM3.5 was
coupled with the RegCM3 (Steiner et al. 2009). Recently,
CLM4 (the latest version of CLM when this study was initiated) with additional modifications (Yu et al. 2014; Wang
et al. 2015) was coupled to RegCM4.3.4, and the resulting
coupled model (Wang et al. 2015) is employed here.
CLM4.0 (Oleson et al. 2010) depicts the physical, chemical and biological interactive processes between terrestrial
ecosystem and climate. Nested subgrid hierarchy is used to
represent spatial land surface heterogeneity, including five
land units, soil/snow columns, and 16 plant functional types
(PFTs). Fifteen layers of soil and up to five layers of snow
are defined. For considering the biogeophysical and biogeochemical mechanisms in terrestrial ecosystem, CLM4.0
contains a prognostic carbon–nitrogen dynamics of biogeochemistry model (CN) (Thornton et al. 2002; Thornton and
Rosenbloom 2005) and a dynamic vegetation model (DV)
(Levis et al. 2004). CN is used to simulate terrestrial carbon and nitrogen cycling and plant phenology; DV is used
to handle plant competition, establishment and survival at
an annual time step based on the carbon budgets produced
by CN. Compared to the MODIS-derived vegetation data,
CLM4.0-CN-DV has been shown to simulate present-day
vegetation distribution reasonably well (Gotangco Castillo
et al. 2012).
In addition to coupling the public release version of
CLM4.0-CN-DV into RegCM4.3.4, we have incorporated
into the model an improved gross primary production
(GPP) parameterization (Bonan et al. 2011), which is a
new feature of the more recently released CLM4.5. Other
modifications to the model include improvement of the
stress-deciduous phenology scheme (which includes tropical drought deciduous trees and grass in our model domain)
and an added climate filter for tropical broadleaf evergreen
trees. The results of these improvements on the offline
CLM-CN-DV model is documented in Yu et al. (2014), and
on the coupled RegCM4.3.4-CLM-CN-DV model in Wang
et al. (2015). The RegCM4.3.4-CLM-CN-DV can be run
with and without dynamic vegetation. When the CN-DV
component is active, vegetation parameters such as leaf
area index (LAI) and vegetation coverage are predicted by
the model; otherwise, the model uses Moderate Resolution
Imaging Spectroradiometer (MODIS)-derived LAI data
with the vegetation structure and distribution prescribed
according to the climatological values. The coupled model
of RegCM4.3.4-CLM-CN-DV provides an efficient tool to
study the impact of vegetation-atmosphere interactions on
regional scale that is urgently needed in providing high-resolution climate projections.
2.2 Methodology and experimental design
To investigate the impact of vegetation feedback on future
climate changes, a total of eight simulations are conducted.
These include two groups of simulations, each containing
two pairs. One group is driven with the initial and boundary conditions derived from the fifth phase of the Coupled
Model Inter-comparison Project (CMIP5, Taylor et al.
2012) runs of MIROC-ESM (Watanabe et al. 2011), and
the other group driven with output from the CMIP5 runs
of the Community Earth System Model (CESM). In each
group of simulations, one pair is simulated with CN-DV
(using RCM-CLM-CN-DV) and one without (using RCMCLM). Each pair consists of a “present-day” simulation for
the period 1980–2000 and a future simulation for the period
2080–2100. The greenhouse gas concentrations (GHGs)
are based on historical observations for the present-day and
13
M. Yu et al.
the Representative Concentration Pathway 8.5 (RCP8.5)
for the future. The RCP8.5 pathway corresponds to a high
greenhouse gas (GHG) emission level (Moss et al. 2008,
2010) and is chosen to highlight the effect of CO2 concentration change and the resulting climate change on future
vegetation change. The two GCMs are chosen with special considerations. Among the GCMs that participated in
CMIP5 (Taylor et al. 2012), present-day climate forcing
from MIROC-ESM produced the most reasonable vegetation distribution when used to drive the offline CLM-CNDV model, based on comparison with observational data
from MODIS (Yu et al. 2014). While the necessary data
from CESM was not available when the Yu et al. (2014)
study commenced, CLM-CN-DV driven with the climate
of CCSM4 (an earlier version of CESM) also captured the
observed vegetation distribution well.
The experiments are performed on a domain that spans
the approximate of 20°S–35°N, 32°W–53°E at a 50-km
horizontal grid spacing. There are 18 vertical levels from
surface to 50 hPa. To provide the initial vegetation conditions for the two RCM-CLM-CN-DV simulations in each
group of simulations, we first conduct two 200-year CLMCN-DV offline simulations over West Africa domain to
derive the natural vegetation states in quasi-equilibrium
with climates from two 20-year RCM-CLM simulations
(Present and Future) in which vegetation is prescribed
based on its present-day conditions. The NCAR-provided
CN initial file described in Kluzek (2012)’s is interpolated
to our simulation domain and is used to initialize the two
200-year CLM-CN-DV offline simulations. CO2 concentrations are set at 353.8 and 850.0 ppm for Present and Future
offline simulations, respectively. The resulting soil and vegetation states at the end of the two 200-year offline simulations are then used to initialize the 20-year Present and
Future RCM-CLM-CN-DV simulations, respectively. To
allow the coupled vegetation-climate system to approach
its corresponding equilibrium state, the Present and Future
RCM-CLM-CN-DV experiments are cycled through their
respective 20-year periods twice. Only the last 20 years of
results from each simulation are analyzed. This process is
carried out for the MIROC-ESM driven simulations and
CESM driven simulations respectively.
To assess the model performance in simulating presentday climate, monthly precipitation and 2-m surface temperature are compared to the University of Delaware (UDEL)
dataset (Legates and Willmott 1990a, b). The MODIS-derived
spatial coverage for different Plant Functional Types (PFTs)
(Lawrence and Chase 2007) and the long-term monthly
LAI from Global Inventory Monitoring and Modeling Studies (GIMMS, Tucker et al. 2005; Zhou et al. 2001) during
December 1981 to December 2000 are used to compare the
present-day vegetation distribution from observations.
13
3 Results
3.1 Simulated present‑day climate and vegetation
To evaluate the performance of the model driven with
ICBCs from MIROC-ESM and CESM, the general features
of precipitation and near-surface temperature simulated by
both GCMs and by RCM-CLM driven with each GCM for
the present-day climate are compared to the UDEL dataset (Figs. 1, 2). The comparison is performed to the annual,
winter (December, January, and February; DJF), and summer (June, July, and August; JJA) means of each variable.
To evaluate the performance of RCM-CLM-CN-DV in
predicting vegetation, simulated vegetation is compared to
estimates from the MODIS-derived dataset (Fig. 4). The
means over the area of 10°S–20°N, 18°W–40°N from the
observations and the simulations are given in Table 1, and
so are the root mean square errors (RMSEs) and spatial
correlation coefficients (R).
Both the RCM-CLM driven with MIROC-ESM and that
driven with CESM reproduce the general features of precipitation, with the observed JJA rainfall maxima around
2–10°N and DJF maxima in the area southeast of the
Congo Basin well captured (Fig. 1). The migration of the
precipitation maximum from southeastern Africa in winter to the areas between 5 and 15°N in summer is captured
reasonably in both RCM-CLM simulations. In West Africa,
however, JJA precipitation is overestimated along the Guinean Coast and underestimated in the Sahel region by both
RCM-CLM simulations. The CESM-driven simulation
shows smaller bias of JJA precipitation than the MIROCESM-driven simulation.
Similarly, both the RCM-CLM driven with MIROCESM and that driven with CESM reproduce the general
spatial pattern and seasonal variation of the near-surface
temperature (Fig. 2). The temperature maxima along 10°N
in winter and over the Sahara desert in summer are well
simulated. JJA and DJF near-surface temperatures, however, are underestimated throughout much of the domain
particularly in the Sahara desert where biases are as large
as 5 °C. In West Africa, the model bias of JJA near-surface
temperature in the CESM-driven simulation is larger than
the MIROC-ESM-driven simulation while in DJF it is
smaller.
The biases of precipitation and near-surface temperature are caused not only by the RCM model itself, but also
by the lateral boundary conditions supplied by the GCMs.
In addition, the UDEL dataset is station based and gages
in most of Africa are sparse. To some degree, the RCMCLM rectifies the MIROC-ESM’s performance in simulating precipitation patterns (Fig. 1) and the CESM’s performance in simulating near-surface temperature patterns
Effects of vegetation feedback on future climate change over West Africa
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
Fig. 1 JJA (1st and 3rd column) and DJF (2nd and 4th column) precipitation (in mm/day) over 1981–2000 from the University of Delaware datasets (a, e), MIROC-ESM (b, f), CESM (i, l), RCM-CLM
driven with MIROC-ESM (c, g), RCM-CLM driven with CESM
(j, m), and the biases for RCM-CLM driven with MIROC-ESM (d,
h) and RCM-CLM driven with CESM (k, n). The magenta box in
Fig. 1a shows the area over which the statistical matrix for models
performances in Table 1 is computed
(Fig. 2). For the domain we focus on, especially the region
south to the Sahara desert, the model biases are relatively
small.
When the dynamic vegetation feedback is included (in
RCM-CLM-CN-DV), the spatial pattern of precipitation
biases remains similar, but the magnitude of the biases
becomes larger (Fig. 3a, b, e, f). The impact on nearsurface temperature is small. A slight cooling effect is
introduced due to the dynamic vegetation feedback in the
Guinea Coast and Congo Basin (Fig. 3c, d, g, h). In the
area of 10°S–20°N, 18°W–40°E, the RMSEs of precipitation and near-surface temperature are increased after the
introduction of dynamic vegetation feedback in the model,
except for the DJF precipitation from the CESM-driven
RCM-CLM-CN-DV simulation. However, changes in the
correlation coefficients between simulations and observations are small. For the annual and DJF near-surface temperature from the MIROC-ESM-driven simulations, and
for the DJF near-surface temperature from the CESMdriven simulations, including dynamic vegetation feedback
in the model increases the correlation coefficients with
observational data (Table 1). The increase of precipitation
bias indicates that the RCM-CLM-CN-DV model simulates a positive feedback between vegetation and precipitation in this region. This feedback leads to a climate drift
in the model, causing the climate biases to be larger than
those in the RCM-CLM model with prescribed vegetation and vegetation biases larger than those in the offline
13
M. Yu et al.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
Fig. 2 Same as Fig. 1, but for 2 meter temperature (in °C)
CLM-CN-DV model driven with observed climate forcing
(not shown).
The RCM-CLM-CN-DV driven with MIROC-ESM
tends to overestimate the presence of woody plants and
bare ground and underestimate the presence of grasses
when compared to the MODIS-derived dataset (Fig. 4d,
e, f). More specifically, the simulated wooded areas in the
southern part of West Africa and Central Africa extend into
the observed grassland regions. Similarly, the grassland in
the Sahel and surrounding the Congo Basin are simulated
to be considerably less extensive than observed and lower
in vegetation density. The RCM-CLM-CN-DV model
driven with CESM simulates the woody plants in West
Africa and northwestern Central Africa well, but underestimates the presence of woody plants in the northern Congo
Basin. The CESM-driven simulation overestimates the
13
presence of grass in the central and southern Congo Basin.
The location of grassland in the CESM-driven simulation
is further south in West Africa than that in the MIROCESM-driven simulation. In both simulations, the transition
from woody plants to grasses and the vegetation coverage
reduction with latitude are more abrupt than in the MODISderived dataset, and the grass coverage over approximate
6–15°N and around Central Africa is shown to be smaller
than that from MODIS data.
The underestimation of vegetation (and precipitation)
over the Sahel region (Figs. 3, 4) results partly from biases
of vegetation simulation in the CLM-CN-DV model and
partly from the underestimation of precipitation in the
GCM-driven RCM-CLM model (a bias that propagates
from the GCM to the RCM), and these biases get amplified through the climate-vegetation feedback. For example,
Effects of vegetation feedback on future climate change over West Africa
Table 1 Annual, JJA and DJF means for precipitation, 2 m temperature, and LAI over the area shown by the magenta box in Fig. 1a from
observations, and from the four simulations of RCM-CLM and RCMPrecipitation
T2m
Mean (mm/ RMSE (mm/day)
day)
Observations
Annual
2.55
/
JJA
3.40
/
DJF
1.44
/
RCM-CLM driven with MIROC-ESM
Annual
3.15
1.51
JJA
3.85
2.62
DJF
1.62
1.12
RCM-CLM-CN-DV driven with MIROC-ESM
Annual
3.20
1.75
JJA
3.84
2.90
DJF
1.76
1.41
RCM-CLM driven with CESM
Annual
2.45
0.88
JJA
2.82
1.59
DJF
0.92
1.12
RCM-CLM-CN-DV driven with CESM
Annual
2.35
1.01
JJA
2.60
1.75
DJF
0.99
CLM-CN-DV driven with MIROC-ESM and CESM respectively, and
the RMSEs and spatial correlation coefficients between observations
and simulations
1.11
LAI
R
Mean (°C)
RMSE (°C)
R
Mean
RMSE
R
/
/
/
25.84
26.27
23.80
/
/
/
/
/
/
1.34
1.38
1.20
/
/
/
/
/
/
0.85
0.81
0.91
24.08
25.73
21.50
2.56
1.79
3.16
0.78
0.92
0.76
/
/
/
/
/
/
/
/
/
0.83
0.78
0.91
23.82
25.43
21.10
2.76
2.05
3.44
0.80
0.91
0.78
2.17
2.20
2.15
1.81
1.94
1.94
0.82
0.76
0.83
0.88
0.88
0.90
25.55
27.20
22.58
2.07
1.92
3.02
0.73
0.92
0.72
/
/
/
/
/
/
/
/
/
0.86
0.86
25.56
27.33
2.14
2.14
0.73
0.91
1.16
1.16
1.56
1.74
0.69
0.63
0.88
22.37
3.06
0.74
1.25
1.50
0.70
the model when prescribing vegetation according to observations underestimates precipitation in the Sahel region,
a region where vegetation growth is limited by water
(Fig. 1). This dry bias propagates to influence the vegetation simulation in the coupled RCM-CLM-CN-DV model,
leading to underestimated vegetation cover which then further enhances the dry bias in the model (Fig. 3). Similarly,
even when driven with the observed meteorological forcing, the CLM-DN-DV model underestimates the spatial
extent of vegetation cover (primarily grass) in the Sahel
region (Yu et al. 2014). This underestimation of vegetation cover propagates in the coupled RCM-CLM-CN-DV
model, causing precipitation to be biased low which then
further limits vegetation in the Sahel. This climate drift
in the coupled model was documented and discussed in
Wang et al. (2015).
In addition to potential model deficiencies, the overestimation of forest coverage in the model may also have to
do with the lack of land use representation in the model.
“Slash-and-burn” to make land for agriculture and pasture
is a common practice in the Tropics (Fujisaka et al. 1996;
Tinker et al. 1996). The natural or potential vegetation over
at least part of the current grassland and crop land regions
is likely forest. Since the model simulates the natural
potential vegetation only, the overestimation of forest coverage by the model is therefore expected.
LAI derived from satellite dataset shows maximum values mainly in the Congo Basin, which is covered primarily by dense forest throughout the year (Fig. 5). Seasonal
variation is small between DJF and JJA (results not shown)
despite the large differences in precipitation between the
seasons. Areas with LAI greater than 2 extend north to
12°N in JJA over the central and eastern Sahel. The dominant LAI spatial patterns are simulated well, with the
model capturing the northward gradual reduction in West
Africa. Consistent with the bias in vegetation distribution
and precipitation, LAI over Sahel is underestimated in both
the MIROC-ESM- and CESM-driven simulations, and
underestimation is also found in the Congo Basin by the
CESM-driven simulation. The model driven with MIROCESM overestimates LAI in Central Africa, but this overestimation is likely exaggerated as the satellite-derived LAI
tends to be biased low compared to in situ data over dense
tropical forests (Roberts et al. 1996). The simulation driven
with CESM produces negative biases in the Congo Basin.
The correlation coefficients between model simulations and
13
M. Yu et al.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)
(n)
(o)
(p)
Fig. 3 The model biases of RCM-CLM-CN-DV driven with
MIROC-ESM (a–d) and CESM (i–l), and the differences between
dynamic vegetation and static vegetation (RCM-CLM-CN-DV minus
RCM-CLM) based on the MIROC-ESM-driven simulations (e–h) and
CESM-driven simulations (m–p), for precipitation (left two columns,
in mm/day) and 2 meter temperature (right two columns, in °C) in
JJA (1st and 3rd column) and DJF (2nd and 4th column) seasons over
1981–2000. Only areas with values exceeding the two-tailed 99 %
confidence are plotted in e–h and m–p
observational data are higher for the MIROC-ESM driven
simulation than that from the CESM driven simulation, but
the RMSEs are larger (Table 1).
Overall, while significant biases exist in each of the simulations, both RCM-CLM and RCM-CLM-CN-DV capture
the general features of climate in West and Central Africa,
and RCM-CLM-CN-DV captures the general distribution
of vegetation.
3.2 Simulated future precipitation and vegetation
changes
13
In this section, we consider the simulations of future climate with both static vegetation (RCM-CLM) and dynamic
vegetation (RCM-CLM-CN-DV). The simulated changes
between the future and present-day conditions provide a
measure of the impact of increasing greenhouse gases,
Effects of vegetation feedback on future climate change over West Africa
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
Fig. 4 Averaged fractional coverage (%) of woody plants (left column), grasses (middle column) and bare ground (right column) from the
MODIS-derived dataset (a–c), simulated by RCM-CLM-CN-DV driven with MIROC-ESM (d–f) and CESM (g–i) over 1981–2000
and differences in future changes between RCM-CLM and
RCM-CLM-CN-DV provide a measure for the impact of
vegetation feedback.
Some common characteristics of future changes emerge
from the simulations driven with MIROC-ESM and CESM.
Compared to 1981–2000, vegetation coverage is projected to
increase significantly (and bare soil is to decrease) in West
Africa and in east and/or southwest of the Congo Basin by
the end of the century (Fig. 6c, h). The increase is mainly
in the form of forest expansion into areas partially covered
by grass and partially barren in the present-day simulations
(Fig. 6a, f). This results in the recession of grass in most areas
and a northward expansion of grasses in others (primarily in
areas north of the Congo forest; Fig. 6b, g). Related to this
conversion from grass to woody plants, annual LAI is projected to increase (Fig. 6d, i). The main difference between
the two groups of simulations is with the location where vegetation coverage is projected to increase, in the Sahel along
the 8-10°N band from the MIROC-ESM-driven simulations and close to the coast from the CESM-driven simulations. Apart from the CO2 fertilization, precipitation increase
(which is more conspicuous in the MIROC-ESM-driven
simulations) (Fig. 6e, j) is an important factor causing the
increase of vegetation density (Fig. 6d, i).
During JJA, both the simulations with and without
dynamic vegetation driven by MIROC-ESM produce
13
M. Yu et al.
Fig. 5 JJA LAI over 1982–2000 from GIMMS dataset (a), simulated by RCM-CLM-CN-DV driven by MIROC-ESM (b) and CESM (d) and
their model biases (c, e)
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
Fig. 6 Future changes of fractional coverage (%) of woody plants
(a), grasses (b) and bare ground (c), annual LAI (d) and the corresponding change of annual precipitation (e, in mm/day) simulated
by RCM-CLM-CN-DV driven with MIROC-ESM (a–e) and CESM
13
(g–j) as of 2081–2100 compared to 1981–2000. Areas with values
exceeding the two-tailed 99 % confidence level with a t distribution
are plotted in a–d and f–i, and stippled in e and j
Effects of vegetation feedback on future climate change over West Africa
increased precipitation in the southern Cote D’Ivoire (Ivory
Coast) and Ghana, eastern Sahel, and southeastern Sahara
and decreased precipitation in the western Sahel and the
Congo Basin (Fig. 7). Compared with the precipitation
changes projected by the model with static vegetation,
dynamic vegetation feedback reverses the dry signal in the
southern edge of Nigeria (Fig. 7a, b) and enhances the wet
signals along the Guinea Coast and near 8°N in Central
Africa (Fig. 7c). During DJF season, the MIROC-ESMdriven simulations produce a slight dry signal in West Africa
(Fig. 7d, e), and no significant difference is found between
the dynamic and static vegetation simulations (Fig. 7f).
The CESM-driven simulations with or without dynamic
vegetation produce JJA precipitation changes with a spatial
pattern that is similar to the MIROC-ESM-driven simulations in West Africa, but different elsewhere. A significant
dry signal is projected for western Sahel and Nigeria, and
very little change is projected for Central Africa (Fig. 7g, h).
The projected DJF precipitation changes are generally small
and not significant. Despite the rather different spatial pattern of future JJA precipitation changes between the CESMdriven and MIROC-ESM-driven projection, the impact
of including dynamic vegetation feedback is very similar
between the two, with a significant wet signal along about
8°N in Central Africa in JJA. The effect of dynamic vegetation feedback leads to an increase of DJF precipitation
in the central and northern Congo Basin from the CESM
driven simulations, although not significant (Fig. 7l). In both
JJA and DJF, significant precipitation differences introduced
by vegetation dynamics in RCM-CLM-CN-DV driven with
either of the two GCMs are primarily limited to the areas
where vegetation is projected to become denser. This suggests that the dynamic vegetation supports a wetter climate
where denser vegetation is projected, reflecting a positive
feedback between climate and vegetation.
The seasonal migration of monsoon can be examined
based on daily precipitation zonally averaged between
15°W and 15°E (Fig. 8). In the MIROC-ESM present climate with static vegetation, the 2 mm/day isoline extends
as far north as 14°N during the first half of June, and
remains there until early July. It then extends to 16°N in the
first half of August, and retreats south in the end of September. In the MIROC-ESM future climate, it extends further
north by about 1° during both the pre- and post-monsoon
seasons indicting earlier pre-monsoon expansion and later
post-monsoon retreat. Despite the longer monsoon duration
projected in the future, a slight decrease of precipitation is
projected during the peak monsoon (Fig. 8a).
Simulations with dynamic vegetation driven with
MIROC-ESM project a seasonal pattern of precipitation
changes that is similar to those with static vegetation, including increased precipitation during the pre- and post-monsoon seasons and associated longer duration, and reduced
precipitation during the peak monsoon season (Fig. 8b).
However, the presence of the dynamic vegetation feedback
enhances the projected increase of pre- and post-monsoon
precipitation and weakens the projected reduction of precipitation during the peak monsoon (Fig. 8c). The dynamic
vegetation feedback plays a similar role in modifying the
projected changes of the monsoon precipitation in the
CESM-driven simulations (Fig. 8d–f). In both the MIROCESM-driven and CESM-driven simulations, precipitation
year round between 5°N and 10°N is enhanced by vegetation dynamics, and some weakening of precipitation north
of 10°N during the pre-monsoon and post-monsoon seasons
(Fig. 8c, f). Overall, the dominant signal is an enhancement
of monsoon precipitation by vegetation dynamics, which is
consistent with the finding of Wang and Alo (2012).
Changes of daily precipitation extremes are assessed
using the 90th and 99th percentiles (P90 and P99, respectively). The MIROC-ESM- and CESM-driven simulations
produce similar patterns for the future changes of precipitation extremes. The spatial pattern of P90 changes somewhat resembles that of the mean precipitation changes in
that the largest P90 increases (decreases) occur in areas
where the mean precipitation increases (decreases) (Fig. 9
using MIROC-ESM-driven experiments as an example).
Changes of the more extreme events P99 tend to be more
dominated by an increase signal. When dynamic vegetation
is considered, the increased heavy rainfall event intensity is
enhanced along the coastline, around 8°N in West Africa,
and in the areas north and southwest of the Congo Basin
(Fig. 9). In general, consideration of dynamic vegetation
enhances the projected increase of extreme precipitation
intensity, with its strongest effects over areas with the largest projected increases of vegetation coverage.
3.3 Simulated future changes of other climate variables
The precipitation and vegetation changes are accompanied
by significant changes in other surface state and flux variables. Not surprisingly, a strong warming signal is projected
by both the MIROC-ESM- and CESM-driven simulations
in the entire model domain (Fig. 10). The warming is
stronger in the MIROC-ESM-driven simulations. Regardless of whether vegetation is static or dynamic, simulations driven with the two different GCMs produce a very
similar spatial pattern of warming, especially in JJA. For
example, near-surface temperature over West Africa in JJA
is projected to increase less than over the North and Central
Africa. In DJF, the maximum warming area is located in the
western Sahel. During DJF, dynamic vegetation feedback
causes a fairly strong cooling impact in the well-defined
region where forest is projected to expand (Fig. 10f, l); during JJA, the impact on temperature is smaller and does not
follow the spatial pattern of vegetation changes (Fig. 10c,
13
M. Yu et al.
Fig. 7 Future changes of JJA (1st and 3rd row) and DJF (2nd and 4th
row) precipitation (in mm/day) simulated by the MIROC-ESM driven
RCM-CLM (a, d) and RCM-CLM-CN-DV (b, e), CESM driven
RCM-CLM (g, j) and RCM-CLM-CN-DV (h, k). The effects due to
13
the dynamic vegetation feedback are show in the right column. Areas
with values passing the two-tailed 99 % confidence level are stippled
(black or purple dots)
Effects of vegetation feedback on future climate change over West Africa
Fig. 8 Spatiotemporal variation
of zonal averaged daily precipitation in mm/day averaged
from 15°W–15°E over land in
present (black lines) and future
(red lines) from experiments
driven by MIROC-ESM (a–c)
and CESM (d–f) driven experiments, and the future changes
(shaded in the top and middle
rows). The effect of dynamic
vegetation from each group of
simulations are in the 3rd row.
Variabilities more frequent than
11 days were filtered out
Fig. 9 Future change of 90th percentile of daily precipitation (in mm/day) simulated by the MIROC-ESM driven experiments without dynamic
vegetation (a), with dynamic vegetation (b) and their differences (c, estimated as b–a)
13
M. Yu et al.
Fig. 10 Future changes of 2 m temperature (in °C) in JJA (1st and
3rd row) and DJF (2nd and 4th row) simulated by the MIROC-ESM
(a–f) and CESM (g–l) driven experiments without dynamic vegetation (left column), with dynamic vegetation (middle column) and their
13
differences (right column, estimated as middle column minus left
column). Areas with value exceeding the two-tailed 99 % confidence
level are stippled (black or purple dots)
Effects of vegetation feedback on future climate change over West Africa
i). This differs from the impact on precipitation that follows
the spatial pattern of vegetation changes in JJA but not in
DJF.
In the JJA season, from both the MIROC-ESM- and
CESM-driven simulations with static vegetation (Figs. 11,
12), the spatial pattern of the projected future changes of
evapotranspiration (ET), soil moisture and surface runoff
is similar and follows that of precipitation, with increases
in the southwestern West Africa and the eastern Sahel and
decreases in the western Sahel and the Congo Basin. The
absorbed solar radiation is simulated to increase significantly in most part of the West Africa primarily due to the
increase of incident solar radiation caused by the decrease
of cloudiness related to precipitation decrease there. The
surface wind velocity is suggested to increase significantly
over the Sahel.
The increases in JJA precipitation attributed to dynamic
vegetation feedback (Fig. 7c, i) leads to significant
increases in soil moisture along about 8°N based on the
MORIC-ESM-driven simulations (Fig. 11) and in areas
slightly south of the 8°N belt based on CESM-driven simulations (Fig. 12). However, due to the significant weakening of surface wind velocity and the enhanced decrease
of absorbed solar radiation, the ET increase is weakened
by the dynamic vegetation feedback in these areas. The
increase of near-surface temperature is therefore enhanced
due to the weakened ET increase (Fig. 10c, i). Over most
of the Congo Basin in both GCM-driven simulations, ET in
JJA is enhanced by vegetation dynamics.
During the DJF season, future change of surface runoff in West Africa is small which follows the precipitation
change in both groups of simulations with static vegetation.
But ET and soil water content are projected to increase in
the central and eastern part of Sahel and decrease in western Sahel from the MIROC-ESM-driven simulations, and
show little changes in the CESM-driven simulations.
The effect of vegetation dynamics on future changes
of surface runoff is small in West Africa due to the small
effect on future changes of precipitation during DJF in
both groups of simulations. For near-surface soil moisture, vegetation dynamics causes a dry signal in eastern
Sahel in the MIROC-ESM-driven simulations, and has little impact based on the CESM-driven simulations. However, in the areas around 8°N in the MIROC-ESM-driven
simulations and slightly south of 8°N in the CESM-driven
simulations, vegetation dynamics leads to a large magnitude of increase in DJF ET. This may be related to the significant increase of warming and absorbed solar radiation
due to the albedo decrease that results from the expansion
of woody vegetation cover and increase of LAI, despite
a decrease of wind velocity in those areas. ET dampens
the warming in those areas. Despite the differences in
the future change projected by the simulations with static
vegetation driven by the two different GCMs, the impact
of dynamic vegetation on ET, solar radiation, and wind is
remarkably similar between the simulations driven by the
two GCMs.
Overall, although the simulations driven by different GCMs produce different future changes in precipitation, near-surface temperature and other variables, a common feature of dynamic vegetation effect on the future
climate changes emerges under GHG RCP8.5 scenario.
Briefly, the projected increase of vegetation density causes
a decrease of surface wind velocity, which competes with
the changes of absorbed solar radiation in their impact on
ET and therefore temperature. In JJA, the decrease of surface wind velocity is dominant, which leads to the decrease
of ET and therefore a warming effect and increase of soil
water content. The increase of soil water content is also
partially related to the increase of precipitation in JJA
due to dynamic vegetation feedback. However in DJF, the
increase of absorbed solar radiation is dominant, causing
an increase of ET. The enhanced ET causes a cooling effect
and a decrease of soil water content (because precipitation
changes resulting from dynamic vegetation feedback are
small and insignificant in DJF).
4 Summary and discussion
In this study, the effects of vegetation dynamics on future
climate changes over West Africa are investigated using
the synchronously coupled regional climate-ecosystem
model RegCM4.3.4-CLM-CN-DV. Simulations driven by
MIROC-ESM and CESM present-day and RCP8.5 future
climates are performed, using the regional model with prescribe static vegetation and with prognostic dynamic vegetation respectively. Comparisons of the four present-day
simulations with observations are first performed to assess
model performance. Differences between the present-day
and future simulations are then used to evaluate potential impacts of climate change, and differences between
the static and dynamic vegetation simulations are used to
assess the effects of vegetation feedback on future climate
changes. Although the MIROC-ESM- and CESM-driven
simulations produce different patterns for future climate
changes, they produce very similar results on the effects
of dynamic vegetation on regional climate, suggesting a
robust relationship between vegetation feedback and climate changes in West Africa.
In response to the future increase of CO2 concentration
and the resulting climate changes, vegetation density is projected to significantly increase around the approximate belt
of 7–10°N in West and Central Africa and in the eastern and
southwestern portions of the Congo Basin. This increase
of density is mostly related to the increase of woody plant
13
M. Yu et al.
Fig. 11 Future changes (1st and 3rd colomns) of ET (mm/day), soil
water in top 10 cm (kg/m2), surface runoff (mm/day), absorbed solar
radiation (W/m2) and near-surface wind velocity (mm/s) and the
13
effects due to dynamic vegetation feedback (2nd and 4th columns) in
JJA (left two columns) and DJF (right two columns) seasons derived
from the MIROC-ESM driven experiments
Effects of vegetation feedback on future climate change over West Africa
Fig. 12 Similar to Fig. 11, but for the CESM driven experiments
coverage at the expense of grassland with partial vegetation cover. Through its feedback to climate, vegetation
dynamics leads to a significant increase of precipitation
around the belt of projected vegetation increase. Vegetation feedback influences seasonal variation of precipitation
changes, increasing the pre- and post-monsoon rainfall and
13
M. Yu et al.
decreasing it during the peak monsoon. The impact of vegetation dynamics on warming depends on the hydrological
regime, with a cooling effect being dominant during DJF
in regions of strong projected changes in vegetation and
warming during JJA in West Africa.
Vegetation feedback takes different roles between wet
and dry seasons in regulating surface water balances. The
future changes in surface runoff are primarily determined by
the future changes of precipitation and are enhanced by vegetation feedback during JJA season. The future change of
soil water content in JJA follows that of precipitation change
and this effect can be retained in DJF. Due to dynamic vegetation feedback, more water is retained in the soil during wet
seasons. During dry seasons however, vegetation dynamics has a drying effect. Future changes of ET follow the
changes of soil water content. Dynamic vegetation tends to
increase the solar radiation absorption due to the decreased
surface albedo. This effect is more pronounced in DJF and
is rather limited in JJA, as the increase of JJA cloudiness
reduces the incident solar radiation reaching the surface. In
JJA, the decrease of ET enhances the projected warming in
a major fraction of West Africa. However in DJF, the effect
of ET increase due to increased solar radiation absorption
becomes dominant, leading to a cooling effect and therefore
dampened increase in surface temperature.
The underestimation of precipitation and vegetation density over Sahel is a potential source of uncertainty for results
in this study. Therefore investigating the feedback between
dynamic vegetation and climate in this area is still a challenge
task along with the development and further improvement of
coupled climate-vegetation models. Another source of uncertainty is the use of only two GCMs to drive the regional climate model, despite the similarity of results between the two
on the role of vegetation dynamics in regional climate. Investigation on uncertainties of model results using a larger number of host GCMs is a subject for future research.
Acknowledgments Funding support for this study was provided by
the NSF (AGS-1063986, and AGS-1064008). The authors thank the
two anonymous reviewers for their constructive comments.
References
Abiodun B, Pal JS, Afiesimama E, Gutowski W, Adedoyin A (2008)
Simulation of West African monsoon using RegCM3 Part II:
impacts of deforestation and desertification. Theoret Appl Climatol 93:245–261
Alo C, Wang G (2010) Role of dynamic vegetation in regional climate predictions over western Africa. Clim Dyn 35:907–922.
doi:10.1007/s00382-010-0744-z
Bonan GB (2008) Forests and climate change: forcings, feedbacks,
and the climate benefits of forests. Science 320:1444–1449
Bonan GB et al (2011) Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields
13
empirically inferred from FLUXNET data. J Geophys Res Biogeosci 2005–2012:116
Bretherton CS, McCaa JR, Grenier H (2004) A new parameterization
for shallow cumulus convection and its application to marine
subtropical cloud-topped boundary layers. Part I: Description
and 1D results. Monthly Weather Review 132
Brovkin V, Claussen M, Petoukhov V, Ganopolski A (1998) On the
stability of the atmosphere-vegetation system in the Sahara/Sahel
region. J Geophys Res Atmos (1984–2012) 103:31613–31624
Charney JG (1975) Dynamics of deserts and droughts in Sahel. Q J
Roy Meteor Soc 101:193–202
Claussen M (1997) Modeling bio-geophysical feedback in the African
and Indian monsoon region. Clim Dyn 13:247–257
Claussen M, Kubatzki C, Brovkin V, Ganopolski A, Hoelzmann P,
Pachur HJ (1999) Simulation of an abrupt change in Saharan
vegetation in the mi-Holocene. Geophys Res Lett 26:2037–2040
Dekker S, Boer H, Brovkin V, Fraedrich K, Wassen M, Rietkerk
M (2010) Biogeophysical feedbacks trigger shifts in the modelled vegetation-atmosphere system at multiple scales. Biogeosciences 7:1237–1245
Delire C, Foley JA, Thompson S (2004) Long–term variability in a
coupled atmosphere–biosphere model. J Clim 17:3947–3959.
doi:10.1175/1520-0442
Delire C, De Noblet–Ducoudre N, Sima A, Gouriand I (2011) Vegetation dynamics enhancing long–term climate variability confirmed by two models. J Clim 24:2238–2257. doi:10.1175/201
0JCLI3664.1
deMenocal P, Ortiz J, Guilderson T, Adkins J, Sarnthein M, Baker L,
Yarusinsky M (2000) Abrupt onset and termination of the African Humid Period: rapid climate responses to gradual insolation
forcing. Quat Sci Rev 19:347–361
Dickinson RE, Henderson-Sellers A, Kenedy PJ (1993) BiosphereAtmosphere Transfer Scheme (BATS) version 1e as coupled to the
NCAR Community Climate ModelNCAR/TN-387+STR, 72 pp
Emanuel KA, Živkovic-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos
Sci 56:1766–1782
Fujisaka S, Bell W, Thomas N, Hurtado L, Crawford E (1996) Slashand-burn agriculture, conversion to pasture, and deforestation in two Brazilian Amazon colonies. Agric Ecosyst Environ
59:115–130
Giannini A, Saravanan R, Chang P (2003) Oceanic forcing of Sahel
rainfall on interannual to interdecadal time scales. Science
302:1027–1030. doi:10.1126/science.1089357
Giannini A, Biasutti M, Verstraete MM (2008) A climate model-based
review of drought in the Sahel: desertification, the re-greening
and climate change. Glob Planet Change 64:119–128
Giorgi F et al (2012) RegCM4: model description and preliminary
tests over multiple CORDEX domains. Clim Res 52:7–29.
doi:10.3354/cr01018
Gotangco Castillo CK, Levis S, Thornton P (2012) Evaluation of the
new CNDV option of the Community Land Model: effects of
dynamic vegetation and interactive nitrogen on CLM4 means
and variability. J Clim 25:3702–3714
Grell GA, Dudhia J, Stauffer DR (1994) Description ofthe fifth generation Penn State/NCAR Mesoscale Model (MM5), 121 pp
Grenier H, Bretherton CS (2001) A moist PBL parameterization
for large-scale models and its application to subtropical cloudtopped marine boundary layers. Mon Weather Rev 129:357–377
Hoffmann WA, Jackson RB (2000) Vegetation–climate feedbacks
in the conversion of tropical savanna to grassland. J Clim
13:1593–1602
Hulme M, Doherty R, Ngara T, New M, Lister D (2001) African climate change: 1900–2100. Clim Res 17:145–168
Effects of vegetation feedback on future climate change over West Africa
Irizarry-Ortiz MM, Wang GL, Eltahir EAB (2003) Role of the biosphere in the mid-Holocene climate of West Africa. J Geophys
Res Atmos 108. doi:10.1029/2001JD000989
Jiang D, Zhang Y, Lang X (2011) Vegetation feedback under future
global warming. Theoret Appl Climatol 106:211–227
Kiehl JT, Hack JJ, Bonan GB, Boville BA, Briegleb BP, Williamson
DL, Rasch PJ (1996) Description of the NCAR Community Climate Model (CCM3)
Kluzek E (2012) CESM research tools: CLM4 in CESM1.0.4 user’s
guide documentation. NCAR
Kröpelin S et al (2008) Climate-driven ecosystem succession in the
Sahara: the past 6000 years. Science 320:765–768
Kucharski F, Zeng N, Kalnay E (2013) A further assessment of vegetation feedback on decadal Sahel rainfall variability. Clim Dyn
40:1453–1466
Lawrence PJ, Chase TN (2007) Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). J
Geophys Res 112:G01023. doi:10.1029/2006jg000168
Lebel T, Ali A (2009) Recent trends in the Central and Western Sahel
rainfall regime (1990–2007). J Hydrol 375:52–64
Legates DR, Willmott CJ (1990a) Mean seasonal and spatial variability in gauge—corrected, global precipitation. Int J Climatol
10:111–127
Legates DR, Willmott CJ (1990b) Mean seasonal and spatial variability in global surface air temperature. Theoret Appl Climatol
41:11–21
Levis S, Bonan GB, Vertenstein M et al (2004) The community land
model’s dynamic global vegetation model (CLM-DGVM): technical description and user’s guide NCAR/TN-459+IA
Liu Z, Notaro M, Kutzbach J, Liu N (2006) Assessing global vegetation-climate feedbacks from observations. J Clim 19:787–814
Liu Z et al (2007) Simulating the transient evolution and abrupt
change of Northern Africa atmosphere–ocean–terrestrial ecosystem in the Holocene. Q Sci Rev 26:1818–1837
Martin G, Levine R (2012) The influence of dynamic vegetation on
the present-day simulation and future projections of the South
Asian summer monsoon in the HadGEM2 family. Earth Syst
Dyn Discuss 3:759–799
McPherson RA (2007) A review of vegetation-atmosphere interactions and their influences on mesoscale phenomena. Prog Phys
Geogr 31:261–285
Mortimore MJ, Adams WM (2001) Farmer adaptation, change and
‘crisis’ in the Sahel. Glob Environ Change 11:49–57
Moss RH et al (2010) The next generation of scenarios for climate
change research and assessment. Nature 463:747–756
Moss R, Babiker M, Brinkman S et al (2008) Towards new scenarios
for analysis of emissions, climate change, impacts, and response
strategies: technical summary. Intergovernmental Panel on Climate Change, Geneva, pp 15–25
Nicholson SE (2013) The West African Sahel: a review of recent studies on the rainfall regime and its interannual variability. ISRN
Meteorol 2013:32. doi:10.1105/2013/453521
Oleson KW et al (2008) Improvements to the Community Land
Model and their impact on the hydrological cycle. J Geophys
Res 113:G01021. doi:10.1029/2007jg000563
Oleson et al KW (2010) Technical description of version 4.0 of the
Community Land Model (CLM)NCAR/TN-478+STR, 257 pp
Osborne T, Lawrence D, Slingo J, Challinor A, Wheeler T (2004)
Influence of vegetation on the local climate and hydrology in the
tropics: sensitivity to soil parameters. Clim Dyn 23:45–61
Pal JS, Small EE, Eltahir EA (2000) Simulation of regional‐scale
water and energy budgets: representation of subgrid cloud and
precipitation processes within RegCM. J Geophys Res Atmos
(1984–2012) 105:29579–29594
Pielke RA, Avissar R, Raupach M, Dolman AJ, Zeng X, Denning
AS (1998) Interactions between the atmosphere and terrestrial
ecosystems: influence on weather and climate. Glob Change Biol
4:461–475
Prince SD, Colstoun D, Brown E, Kravitz L (1998) Evidence from
rain-use efficiencies does not indicate extensive Sahelian desertification. Glob Change Biol 4:359–374
Roberts JM, Cabral OMR, Costa JP, McWilliam ALC, Sa TDA (1996)
An overview of the leaf area index and physiological measurement during ABRACOS
Steiner A et al (2009) Land surface coupling in regional climate simulations of the West African monsoon. Clim Dyn 33:869–892.
doi:10.1007/s00382-009-0543-6
Stenseth NC, Mysterud A, Ottersen G, Hurrell JW, Chan K-S, Lima
M (2002) Ecological effects of climate fluctuations. Science
297:1292–1296
Sun S, Wang G (2011) Diagnosing the equilibrium state of a coupled global biosphere-atmosphere model. J Geophys Res Atmos
1984–2012:116
Swann AL, Fung IY, Chiang JC (2012) Mid-latitude afforestation
shifts general circulation and tropical precipitation. Proc Natl
Acad Sci 109:712–716
Taylor CM, Lambin EF, Stephenne N, Harding RJ, Essery RL (2002)
The influence of land use change on climate in the Sahel. J Clim
15:3615–3629
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5
and the experiment design. Bull Am Meteorol Soc 93:485–498
Thornton PE, Rosenbloom NA (2005) Ecosystem model spin-up: estimating steady state conditions in a coupled terrestrial carbon and
nitrogen cycle model. Ecol Model 189:25–48
Thornton P et al (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric For Meteorol 113:185–222
Tinker PB, Ingram JSI, Struwe S (1996) Effects of slash-and-burn
agriculture and deforestation on climate change. Agrocult Ecosyst Environ 58:13–22
Tucker CJ et al (2005) An extended AVHRR 8-km NDVI dataset
compatible with MODIS and SPOT vegetation NDVI data. Int J
Remote Sens 26:4485–4498
Wang G (2004) A conceptual modeling study on biosphere-atmosphere interactions and its implications for physically based climate modeling. J Clim 17:2572–2583
Wang G, Alo C (2012) Changes in precipitation seasonality in West
Africa predicted by RegCM3 and the impact of dynamic vegetation feedback. Int J Geophys. doi:10.1155/2012/597205
Wang G, Eltahir EA (2000a) Role of vegetation dynamics in enhancing the low-frequency variability of the Sahel rainfall. Water
Resour Res 36:1013–1021. doi:10.1029/1999wr900361
Wang G, Eltahir EA (2000b) Biosphere-atmosphere interactions over
West Africa. I: development and validation of a coupled dynamic
model. Q J R Meteorol Soc 126:1239–1260
Wang G, Eltahir EA (2000c) Ecosystem dynamics and the Sahel
drought. Geophys Res Lett 27:795–798
Wang G, Eltahir E, Foley J, Pollard D, Levis S (2004) Decadal variability of rainfall in the Sahel: results from the coupled GENESIS-IBIS atmosphere-biosphere model. Clim Dyn 22:625–637
Wang G, Yu M, Pal J S, Mei R, Bonan G B, Levis S, Thornton P
(2015) On the development of a coupled regional climate-vegetation model RCM-CLM-CN-DV and its validation in Tropical
Africa. Clim Dyn 54. doi:10.1007/s00382-015-2596-z
Watanabe S et al (2011) MIROC-ESM 2010: model description and
basic results of CMIP5-20c3m experiments. Geosci Model Dev
4:845–872. doi:10.5194/gmd-4-845-2011
Woodward FI (1987) Climate and plant distribution. Cambridge University Press, Cambridge
Xue Y (1997) Biosphere feedback on regional climate in tropical
north Africa. Q J R Meteorol Soc 123:1483–1515
13
M. Yu et al.
Xue Y, Shukla J (1993) The influence of land surface properties on
Sahel climate. Part I: desertification. J Clim 6:2232–2245
Yu M, Wang G, Parr D, Ahmed KF (2014) Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven
with RCP8.5 climate projections from 19 GCMs. Clim Change
127:257–271
Zeng X, Beljaars A (2005) A prognostic scheme of sea surface skin
temperature for modeling and data assimilation. Geophys Res
Lett 32. doi:10.1029/2005GL023030
13
Zeng N, Neelin JD, Lau K-M, Tucker CJ (1999) Enhancement of
interdecadal climate variability in the Sahel by vegetation interaction. Science 286:1537–1540
Zheng X, Eltahir EA (1998) The role of vegetation in the dynamics of
West African monsoons. J Clim 11:2078–2096
Zhou L, Tucker CJ, Kaufmann RK, Al E (2001) Variations in Northern vegetation activity inferred from satellite data of vegetation
index during 1981 to 1999. J Geophys Res 106:20069–20083