* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Effects of vegetation feedback on future climate change over West
Climatic Research Unit documents wikipedia , lookup
Politics of global warming wikipedia , lookup
Numerical weather prediction wikipedia , lookup
Economics of global warming wikipedia , lookup
Climate engineering wikipedia , lookup
Climate change adaptation wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate governance wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Global warming wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climate sensitivity wikipedia , lookup
Physical impacts of climate change wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change and poverty wikipedia , lookup
Climate change in the United States wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Effects of global warming wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Atmospheric model wikipedia , lookup
Solar radiation management wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Climate change, industry and society wikipedia , lookup
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