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CO2-Induced Sahel Greening in Three CMIP5 Earth System Models [Journal of Climate](Journal of Climate Via Acquire Media NewsEdge) ABSTRACT The existence and productivity of vegetation is the basis for food and energy supply in the Sahel. Past changes in climate and vegetation abundance have raised the question whether the region could become greener in the future as a result of higher CO2 levels. By analyzing three Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with dynamic vegetation, the authors demonstrate why an answer to this question remains elusive in contrast to more robust projections of vegetation cover in the extratropics. First, it depends on the location and the time scale whether vegetation expands or retreats. Until the end of the twenty-first century, the three models agree on a substantial greening in the central and eastern Sahel due to increased CO2 levels. This trend is reversed thereafter, and vegetation retreats in particular in the western Sahel because the beneficial effect of CO2 fertilization is short lived compared to climate change. Second, the vegetation cover changes are driven by different processes in different models (most importantly, precipitation change and CO2 fertilization). As these processes tend to oppose each other, the greening and browning trends are not a reliable result despite the apparent model agreement. The authors also find that the effect of vegetation dynamics on the surface energy balance crucially depends on the location. In contrast to the results of many previous studies, the Sahel appears as a hotspot where the physiological effects of CO2 can exert a cooling because vegetation structure and distribution overcompensate for the decreased stomatal conductance. 1. Introduction The Sahel is a climatic transition region where the supply of water, food, and even energy crucially depends on climate. Poverty, low development, and political conflicts tend to enforce this dependency and may even aggravate it in a future climate (Scheffran et al. 2012). The large population of the Sahel is therefore very vulnerable to climate variability (as the drought of the 1970s-80s demonstrated) and to anthropogenic climate change. Reconstructions of vegetation abundance and climate show that northern Africa was much wetter and greener at the beginning of the mid-Holocene (Jolly et al. 1998a,b; Prentice et al. 2000; Lézine et al. 2011). In this period, the different Earth orbit enhanced the summer in- solation in the Northern Hemisphere and thus the West African summer monsoon (Kutzbach 1981). Petit-Maire (1990) therefore posed the question ''Will greenhouse green the Sahara?'' and outlined a future between the two historical cases of an expanded desert during the last ice age and the green Sahara during the early Holocene. However, such analogies are of limited use for pre- dictions, as the spatial pattern and the rate of change are very different for natural and anthropogenic forcing (Petit-Maire 1990; Claussen et al. 2003). It is therefore unclear how natural vegetation in northern Africa will respond to the anthropogenic forcing scenarios of the next centuries. Trends in recent satellite observations are spatially inhomogeneous (de Jong et al. 2012), depend on the chosen time period (Anyamba and Tucker 2005; Fensholt and Rasmussen 2011), and are difficult to at- tribute (Fensholt and Rasmussen 2011). Presumably, the most important drivers of future veg- etation distribution in the Sahel are changes in land use, CO2 concentration (because of its effect on vegetation physiology), and precipitation (caused by the radiative effect of CO2). In this study, we only investigate the po- tential natural vegetation dynamics resulting from CO2 concentration changes. These resulting changes in the vegetation composition and distribution are therefore not affected by any land-use change scenario but rather provide the precondition for future land use. Changes in precipitation can occur for different rea- sons, which are difficult to separate in observations. First, a larger land-sea temperature contrast is expe- cted to intensify the West African monsoon (WAM) (Monerie et al. 2012; Skinner et al. 2012). However, the effect of the radiative forcing of CO2 over land on sur- face evaporation (Giannini 2010), atmospheric stability, and the distribution of moisture (Chou and Neelin 2004; Chou et al. 2009) must also be considered. Second, aerosols have been found to affect the monsoon system in model simulations (Ackerley et al. 2011; Kim et al. 2008). Third, there is evidence for an influence of SSTs in all major tropical and subtropical oceans (Giannini et al. 2003; Lu and Delworth 2005; Herceg et al. 2007; Cook 2008; Mohino et al. 2011; Patricola and Cook 2010). For example, in the Atlantic, an enhanced in- terhemispheric SST gradient with warmer water in the North Atlantic is believed to induce a northward shift of the intertropical convergence zone (ITCZ), which then affects Sahelian rainfall (Hoerling et al. 2006; Cook 2008), presumably as a result of an intensified West African westerly jet (WAWJ) (Pu and Dickinson 2012). There is evidence that, in the twentieth century, changes in SSTs have been the dominating driver of the observed decadal variability in Sahelian rainfall (Joly et al. 2007; Biasutti et al. 2008). Vegetation dynamics have probably enhanced this decadal component (Zeng et al. 1999). However, the quantitative attribution to SSTs in different ocean basins and the superimposed global warming trend is unclear (Rodríguez-Fonseca et al. 2011). In the future, it is probable that changes in SSTs will be less important than in the twentieth cen- tury, because the radiative forcing over land increases (Patricola and Cook 2010, 2011; Monerie et al. 2012)and mayevenbecomethedominantdriverofSahelianrain- fall changes (Haarsma et al. 2005; Biasutti et al. 2008; Giannini 2010). For the productivity of vegetation, other aspects of the terrestrial moisture balance must also be considered. In particular, changes in evapotranspiration may offset precipitation trends. Furthermore, other environmental conditions will affect productivity and vegetation com- position. In this regard, physiological effects of CO2 become important, and these effects differ between the two relevant photosynthetic pathways: C3 and C4. First, higher levels of CO2 tend to enhance the carboxylation efficiency of Rubisco in C3 plants. Second, elevated at- mospheric CO2 levels have been observed to decrease stomatal conductance (Field et al. 1995; Long et al. 2004; Ainsworth and Long 2005; Ainsworth and Rogers 2007; Leakey et al. 2009; Norby and Zak 2011). Therefore, they can result in decreased transpiration and increased soil moisture. This increased water-use efficiency can further enhance productivity in C3 as well as C4 plants (Long et al. 2004; Ainsworth and Rogers 2007; Leakey et al. 2009). The sum of both mechanisms is known as CO2 fertilization. Consequently, the potential increase in net primary productivity (NPP) may allow an expansion of vegetation in arid regions (Mahowald 2007; Donohue et al. 2013). As we focus on land cover changes, we only refer to CO2 fertilization because of its impact on vege- tation distribution. In contrast, we more generally refer to ''physiological effects'' to address any climatic changes arising from sto- matal closure, not restricted to that caused by CO2 fertil- ization. Model results show that the reduced transpiration and reduced low-level cloud cover due to CO2-induced stomatal closure tend to warm the surface, especially over tropical forests (Doutriaux-Boucher et al. 2009; Andrews et al. 2011). This ''physiological forcing'' has been found to contribute more than 10% to the CO2-induced warming over land (Sellers et al. 1996; Cox et al. 1999; Boucher et al. 2009; O'ishi et al. 2009; Cao et al. 2010). The physiological forcing generally refers to changes in stomatal closure as- suming a fixed leaf area. However, increases in leaf area index (LAI) may occur as a result of the increased pro- ductivity and an altered vegetation distribution and com- position. These changes can enhance precipitation because of the low albedo of vegetation (Otterman 1974; Charney 1975; Charney et al. 1975; Claussen 1997) and organic soils (Claussen 2009; Vamborg et al. 2011). Also, the expansion of vegetation into desert areas tends to increase evapo- transpiration (Charney et al. 1977; Claussen 1997). It has been argued that such structural changes (LAI and vege- tation distribution) could overcompensate for the effect of stomatal conductance change (Betts et al. 1997; Leipprand and Gerten 2006). Among other important environmental conditions besides CO2 are temperature and nutrient availability, although changes in the latter are not consid- ered in the simulations we analyze. With regard to these interactions, it seems most ap- propriate to incorporate all substantial drivers of vege- tation changes as well as the important interactions between vegetation and climate in order to assess the possible future vegetation distribution. In this study, we follow such an integrated view by analyzing results from three Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that include a dynamic vegetation model. However, it is not our intention to aim at a robust projection of Sahelian vegetation cover. In contrast, we address the main ca- veats that prevent a reliable projection, even if more models and simulations were available. These caveats involve the dependency of the different processes on location and the time horizon, as well as the large pro- cess and modeling uncertainty. As we will show, the three models provide a useful set in order to demon- strate these caveats. 2. Models and experiments a. Models In this study we analyze simulations performed by the ESMs MPI-ESM-LR (Giorgetta et al. 2013), HadGEM2-ES (Collins et al. 2011; Martin et al. 2011), and MIROC-ESM (Watanabe et al. 2011). These models (expansions and summaries for the CMIP5 models used in this study are provided in Table 1) in- clude comprehensive descriptions of atmosphere and ocean circulation as well as terrestrial vegetation pro- cesses on time scales from minutes (stomatal conduc- tance and photosynthesis) to centuries (changes in vegetation distribution). We do not analyze output from other CMIP5 models with dynamic vegetation because either the experiments we analyze have not been performed with them or they were applied with prescribed vegetation cover fractions. The Max Planck Institute for Meteorology (MPI) ESM in low resolution (MPI-ESM-LR) includes the general circulation models ECHAM6 (Stevens et al. 2013; Roeckner et al. 2003) for the atmosphere with a horizontal resolution of T63 (approximately 1.88 ); the MPI Ocean Model (MPI-OM) (Jungclaus et al. 2013)for the ocean and sea ice (approximately 1.58); and the Hamburg Model of the Ocean Carbon Cycle, version 5 (HAMOCC5) ocean biogeochemistry model (Ilyina et al. 2013; Maier-Reimer et al. 2005). Land-atmosphere ex- change is represented by the Jena Scheme for Biosphere- Atmosphere Coupling in Hamburg (JSBACH) terrestrial vegetation model (Raddatz et al. 2007). Its photosynthesis schemes are based on Farquhar et al. (1980) for C3 plants and on Collatz et al. (1992) for C4 plants. In the experi- ments analyzed in this study, eight natural and four anthropogenic plant functional types (PFTs) are distin- guished by JSBACH. A dynamic vegetation scheme (Brovkin et al. 2009) determines their cover fractions as well as the area fraction of total vegetation cover. The distribution of woody versus grass cover is determined by light competition and disturbances caused by fire and windthrow. Competition between different tree or grass types is based on the productivity of the individual types. Similarly, vegetation can establish wherever the environ- mental conditions allow a positive productivity (Reick et al. 2013). The preindustrial global vegetation distri- bution compares reasonably to the Vegetation Continu- ous Fields dataset (Hansen et al. 2007) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) (Fig. 1; see also Figs. 1 and 2 in Brovkin et al. 2013a). North Africa is covered by too much vegetation, especially in its western part. However, the LAI of this vegetation is small (less than 1) as a result of the dry climate. MPI-ESM-LR shows the strongest land carbon- concentration feedback (impact of CO2 concentration on land uptake) of all CMIP5 ESMs compared in Arora et al. (2013). The direct effect of CO2 on vegetation dy- namics is therefore also expected to be large. HadGEM2-ES comprises atmosphere, ocean and sea ice dynamics, terrestrial hydrology, and a terrestrial and marine carbon cycle. The horizontal resolution is approx- imately 1.258 latitude 3 1.8758 longitude. The interaction between land surface and atmosphere is calculated by the Met Office Surface Exchange Scheme, version 2 (MOSES2) (Essery et al. 2003). Leaf-level photosynthesis is based on Collatz's models for C3 (Collatz et al. 1991)and C4 plants (Collatz et al. 1992). Vegetation distribution and composition are calculated by the Top-Down Represen- tation of Interactive Foliage and Flora Including Dynam- ics (TRIFFID) dynamic vegetation model (Cox 2001), which distinguishes five different plant types. Competition is based on NPP and height. The produced biomass is first used to increase the local carbon pools; in case of suffi- cient productivity, it is invested in the expansion of cover fractions. The deficiencies in the tropical vegetation dis- tribution are too much forest cover as well as a too- southern Sahara-Sahel boundary (Fig. 1; see also Figs. 11 and 15 in Collins et al. 2011). HadGEM2-ES shows an above-average land carbon-concentration feedback when compared to other ESMs (Arora et al. 2013). Like the other two models, MIROC-ESM includes general circulation models of atmosphere (resolution: T42; approximately 2.88) and ocean. It also includes an ocean ecosystem model and the spatially explicit individual-based Dynamic Global Vegetation Model (SEIB-DGVM) terrestrial ecosystem model (Sato et al. 2007). SEIB calculates interactions between individual trees of a sample plot at each grid point. Its parameters have been tuned to match observations of forest struc- ture and dynamics. A uniform grass layer is assumed to exist under the tree canopy. Vegetation is classified into 13 PFTs (11 trees and 2 grasses). Photosynthesis is cal- culated daily from temperature and the availability of light, CO2, humidity, and soil moisture based on em- pirical relationships. Stomatal conductance is calculated following Ball et al. (1987) and Leuning (1995). Like MPI-ESM-LR and HadGEM2-ES, MIROC-ESM dis- tinguishes different plant functional types, but, in con- trast to the other two models, MIROC-ESM does not apply a tiling approach to describe the effect of vegetation dynamics. Instead, a representative patch with individual trees and an underlying grass cover is calculated and ex- trapolated to the gridbox size (Sato et al. 2007). The global distribution of trees partly depends on empirical relationswithclimate(e.g.,becauseofestablishment processes). A desert fraction is not directly defined within the model but classified as the area of natural vegetation with an annual maximum LAI less than 0.2 (Sato et al. 2007) or with an annual NPP of 0 (T. Hajima 2013, personal communication). Although the CMIP5 vegeta- tion cover fraction is therefore not exactly the same property as in the other two models, it is well comparable at the desert margins, where changes in the quantities of LAI, vegetation cover, and productivity are closely re- lated. This is due to the common concept in all three models that an increased productivity drives the expan- sion of vegetation into a desert. Because of wet biases in MIROC-ESM, the global preindustrial distribution of deserts differs substantially from observations in the Northern Hemisphere, where no deserts other than the Sahara exist (Fig. 1). However, the position of the Sahara's southern margin is captured reasonably. MIROC-ESM has a comparatively small land carbon-concentration feedback (Hajima et al. 2012; Arora et al. 2013). Since the models differ in the location and movement of the transition region between the Sahara and the tropical African forest, we do not show multimodel averages. In- stead, we will follow a model-based definition of the Sahel as this transition region and define the desert boundary as the 80% vegetation cover line in each model. b. Experiments All simulations we analyze in this study (summa- ries and expansions of the CMIP5 experiments ana- lyzedinthisstudyareprovidedinTable 2) have been conducted within the latest Coupled Model Inter- comparison Project and follow the setup described in Taylor et al. (2012). The models were forced by the radiative effects, biogeochemical effects, or the com- bination of both effects of increasing atmospheric CO2 concentration. To this end, CO2 concentrations are prescribed in all experiments and mostly follow an idealized trajectory. In RCP8.5, the CO2 concentration shows an acce- lerating increase until the year 2100 (when a radiative forcing of approximately 8.5 W m22 is reached), fol- lowed by a stabilization period with a decelerating in- crease. In the year 2250, the CO2 concentration reaches its final level of almost 2000 ppm. CO2 concentration and radiative forcing both remain constant after the year 2250 (see Meinshausen et al. 2011, Figs. 4 and 5). In RCP8.5 we analyze the period 2100-2300 (extended RCP8.5) where the fractions of managed land were kept constant at year 2100 values. Before 2100, we analyze simulations with the identical CO2 forcing where land use was fixed to the year 2006. These simulations are called L2A8.5 and were conducted within the Land- Use and Climate, Identification of Robust Impacts (LUCID) project (Pitman et al. 2009; Brovkin et al. 2013b). For MIROC-ESM, the extended RCP8.5 sce- nario was not available. Our combination of L2A8.5 and RCP8.5 and the fact that we do not analyze the differences between them allows us to exclude any anthropogenic land-use changes (which occur in RCP8.5 before 2100). In the idealized CMIP5 scenarios called 1pctCO2 (in the following: RADPHYS, to be consistent with similar studies before CMIP5), esmFdbk1 (RAD), and FixClim1 (PHYS), CO2 is increased by 1% each year until CO2 concentration has quadrupled after 140 years. In RAD, the CO2 change only affects radiation, while the terrestrial vegetation sees preindustrial CO2. In PHYS, the physiological effects of CO2 are consid- ered, but radiation is calculated from preindustrial CO2. In RADPHYS, both effects of CO2 are active. These experiments have been used to analyze feed- backs in the carbon cycle and their nonlinearity (Arora et al. 2013) but less so for the analysis of biogeophysical effects. Finally, we analyze a preindustrial control simulation with climatological SSTs and sea ice (sstClim), and a similar simulation with quadrupled CO2. In these ex- periments, both effects of CO2, radiative and physio- logical, are active. However, because of the fixed ocean state, the forcings do not affect SSTs. 3. Results a. Comparison of vegetation cover changes Figure 2 shows the changes of vegetation cover fraction for the three ESMs in response to the L2A8.5 and RCP8.5 concentration scenarios. In general, HadGEM2- ES and MPI-ESM-LR agree that there is a greening in the northern extratropics. In these areas, the increases in precipitation, warming, and CO2 fertilization all tend to enhance the establishment of vegetation. Therefore, this extratropical greening agrees with expectations. MIROC- ESM shows no further greening in the extratropics (apart from outer Greenland) because the vegetation cover for present day is already 100% in these areas. As in the other two models, the LAI increases on most parts of the ex- tratropical land. Compared to the greening in the extratropics, vegeta- tion changes are less robust and inhomogeneous in the tropics. However, MIROC-ESM and MPI-ESM-LR agree that there is a greening of the Sahel in response to increased atmospheric CO2 levels until 2100. A greening also occurs in parts of the central Sahel in HadGEM2-ES but not in the west. Interestingly, this zonal contrast also occurs in MPI-ESM-LR, but after 2100: In case of the very high CO2 concentration in RCP8.5 toward the year 2300, the initial greening continues in the east but reverses in the western Sahel and Sahara. In contrast, HadGEM2- ES shows a southward expansion of the desert at all longitudes after 2100. In MIROC and HadGEM2-ES the vegetation changes can mostly be attributed to C4 grass, which grows near the desert margin; in MPI-ESM-LR, C3 trees and C4 grass types are both affected because of the more heterogeneous vegetation composition (Fig. 3). To attribute the obtained changes to the influences of CO2 fertilization and precipitation, we investigate how the evolution of precipitation compares to the evolution of vegetation cover in different areas. We thereby distinguish the western and central to eastern Sahel as indicated by the black rectangles in Fig. 2. As preindustrial vegeta- tion cover in MPI-ESM-LR already extends more to the north, the largest changes also occur in more northern areas, as in HadGEM2-ES, where they are confined to a comparatively thin latitudinal band. We therefore consider the two northern boxes for MPI-ESM-LR and MIROC-ESM and the two southern boxes for HadGEM2- ES to create time series of the spatial and annual means (Fig. 4). In addition, we analyze idealized experiments with separated forcings in section 3b. MPI-ESM-LR and HadGEM2-ES show a similar pattern of precipitation change, most importantly a progressive drying in the west and a wettening in the southeast of northern Africa. In the long term, vege- tation cover roughly follows the trend in precipitation (Fig. 4). However, in MPI-ESM-LR, vegetation cover in the western Sahel increases until somewhat after 2100 in RCP8.5, whereas precipitation decreases. In this initial phase, CO2 fertilization appears to be the driver of the Sahel greening (also apparent in the northeastern box until 2230). Another exception occurs in HadGEM2-ES in the southeastern box where vegetation cover decreases despite the increase in precipitation (and CO2) after 2100 as a result of the high temperature at certain grid points (an effect to be discussed in section 3b). MPI-ESM-LR and HadGEM2-ES agree on the trends and their geographical pattern but disagree on the timing because CO2 fertilization is stronger in MPI-ESM-LR. In contrast, the greening in MIROC-ESM very closely fol- lows annual-mean precipitation, which increases sub- stantially over northern Africa. As explained in section 2a, the dynamics of vegetation cover directly result from LAI changes in MIROC-ESM, while a much longer time scale is involved in the other two models. Therefore, the in- terannual variability is much larger in MIROC-ESM. As changes in precipitation are such an important driver of vegetation dynamics in the Sahel, it is necessary to expand the view to results from other models at this point. Compared to the ensemble of CMIP5 and also previous CMIP3 models, the tendency of a drying in the western and a wettening of the more eastern Sahel like in HadGEM2-ES and MPI-ESM-LR can also be seen in other models. The reason for these tendencies is a shift in the seasonal distribution of rain: The majority of the coupled climate models show a delayed rainy season, which leads to a drying in spring (Biasutti and Sobel 2009). In the western Sahel, the spring drying dominates the change in annual rainfall, while in the center and east it is overcompensated for by a wetter autumn (see Figs. 2b and 5inBiasutti 2013). Consequently, Fontaine et al. (2011) identified a northern shift of the area of moisture flux convergence in the east and center of the African mon- soon region in most CMIP3 models under the A1B sce- nario and no shift or a southern shift in the west. Monerie et al. (2012) attributed the changes in the eastern parts to an intensification of the low-level monsoon flow and the western drying to increased subsidence and moisture flux divergence in higher atmospheric levels. However, as the relevant regional physical processes are not well un- derstood and very crudely represented in global models (Cook 2008; Patricola and Cook 2010; Druyan 2011; Ruti et al. 2011; Fasullo 2012; Knutti and Sedlacek 2013; Roehrig et al. 2013), the overall response of Sahelian rainfall to greenhouse forcing nonetheless remains highly uncertain. Although the large increase in North African precipitation in MIROC is exceptional, it should there- fore not automatically be regarded as less realistic (Cook 2008). Starting with the next section, we will also examine another source of uncertainties: the effect of CO2 on physiological processes. b. Separation of radiative and physiological effects of CO2 To isolate the impacts of physiological effects and radiative forcing on vegetation distribution, we analyze the idealized scenarios RADPHYS, RAD, and PHYS (Table 2). The analysis follows the logic of a factor separation (Stein and Alpert 1993) with physiological effects and radiative forcing as two (conceptually) in- dependent factors. Figure 5 shows the differences in African vegetation cover compared to the preindustrial simulation for each of the three experiments as well as the synergy of the effects. The synergy represents the nonlinearity of the system. In case of vegetation cover, it can be interpreted as the effect of CO2 fertilization on the climate change impact or, alternatively, the effect of climate change on CO2 fertilization (Claussen et al. 2013). It becomes obvious that radiative and physio- logical effects tend to oppose each other in their impact on vegetation cover in the dry subtropical areas. This contrast is most apparent in MPI-ESM-LR (Fig. 5, top). While a decrease in precipitation in all subtropical areas of the region leads to a decreased vegetation cover in RAD, the CO2 fertilization acts to green the Sahel and the northwestern Sahara as well as the Arabian Penin- sula and the Middle East. In the Sahel, the CO2 fertil- ization is the dominating effect, as can be seen in RADPHYS. The vegetation expansion in PHYS con- sists of C3 as well as C4 plants, as their photosynthesis is modeled in a very similar way and because climate change in PHYS is small. A similar contrast between RAD and PHYS is obtained in HadGEM2-ES (Fig. 5, middle), although the changes in vegetation cover are not as uniform and the net effect depends on the region. In the western Sahel, the climate change dominates the vegetation response and leads to a retreat of vegetation. In the rest of the Sahel, the CO2 fertilization dominates and causes a greening. The distribution of C3 plants thereby expands, and the belt of C4 plants (Fig. 3) shifts farther north while soil moisture increases south of 138N. Although pre- cipitation increases in most parts of the central Sahel, climate change acts to decrease vegetation cover, with the exception of a small region to the east of Lake Chad. The reason for this vegetation retreat is probably the very high temperatures (around 338C), which tend to decrease pro- ductivity. Figure 6d illustrates that the vegetation retreat in areas with increased precipitation is largest where the temperature increase is large and where temperature was already high in the preindustrial climate. Although climate and vegetation are not in equilibrium with each other and with CO2 at the end of RAD, it is suggestive that the changes cluster in different quadrants in Fig. 6.Thiseffect also explains the vegetation retreat in the RCP8.5 simula- tion (Fig. 4; HadGEM2-ES, SE). Changes in the seasonal distribution of precipitation could play an additional role. Hence, MPI-ESM-LR and HadGEM2-ES agree that radiative forcing and CO2 fertilization are similarly im- portant on the time scale of 100 yr and that a fast increase in CO2 would green large parts of the Sahel because of an enhanced productivity. However, the longer the climate system can respond to the radiative forcing, the more likely it is that the climate change will counteract this greening, as we showed in section 3a.Itisalsostriking that the synergy of the effects is of a similar magnitude to the individual effects. In MPI-ESM-LR and HadGEM2- ES, a north-south pattern appears with negative values in the north and positive values in the south. As vegetation cover is limited to values between 0% and 100%, the dependence of vegetation cover on environmental con- ditions is inevitably nonlinear. Therefore, the synergy as a measure of nonlinearity is potentially large in all areas where vegetation cover comes close to these limits. In particular, the desert regions under preindustrial forcings cannot lose any vegetation when exposed to drying. However, if they become greener as a result of fertiliza- tion, the negative climate impact can take effect. At the other end of the scale, where vegetation cover is close to 100%, fertilization can only take little effect, whereas a concurrent drying will allow the fertilization mechanism to become active by keeping vegetation cover high. Therefore, the synergy pattern only reflects the initial vegetation distribution at the desert boundaries. The results presented so far are not confirmed by the third model under analysis: Fig. 5 (bottom) demonstrates that the greening in MIROC-ESM is almost completely due to climate change: namely, the large increase in precipitation by up to 250 mm over northern Africa. In contrast, the impact of CO2 fertilization is hardly distin- guishable from the pattern of natural variability. There- fore, it becomes obvious that the three models yield similar results because of very different reasons. Figure 6 illustrates these model differences: In MPI-ESM-LR and MIROC-ESM, vegetation cover changes in RAD are both driven by precipitation changes but of different signs. In HadGEM2-ES, the vegetation decrease in RAD cannot be explained with mean precipitation changes alone (Figs. 6c,d). A hypothetical climate model with a climate response to radiative forcing similar to MPI- ESM-LR or HadGEM2-ES but a response to CO2 fer- tilization as weak as MIROC-ESM would show a reduced vegetation growth rather than a greening. In this regard, the multimodel result can hide potential uncertainties. As the processes in the three models are represented so differently, the future development of natural vegetation in the Sahel remains highly uncertain. c. Impacts of physiological effects on climate So far, we have discussed climatic and physiological effects separately. This view neglects that there is also a climatic change in the experiment without radiative forcing (PHYS) because of decreased stomatal con- ductance and increases in LAI and vegetation cover. PHYS therefore allows us to investigate the potential effects of desert greening on climate in MPI-ESM-LR and HadGEM2-ES. We do not analyze results from MIROC-ESM further because the physiological effect on climate in this model is indiscernible from the natural variability. Compared to the preindustrial simulation, the last 30 yr of PHYS show a global warming of 0.25 K in MPI-ESM-LR and 0.65 K in HadGEM2-ES and re- duced evapotranspiration, relative humidity, and cloud cover over land. Globally, this hydrological response is in line with some previous studies (Cox et al. 1999; Betts et al. 2007; Boucher et al. 2009; Cao et al. 2010; Pu and Dickinson 2012). However, there are regional exceptions to these global changes. First, despite the decrease in stomatal conductance, the vegetation enhances precipitation in the African subtropics, especially in western Africa. Applying the factor separation method to precipitation changes reveals that this effect is of discernible magnitude when compared to the effect of radiative forcing (Fig. 7): While there are locations with significantly positive, sig- nificantly negative, and insignificant precipitation changes in the experiment with combined effects, the contribu- tions of the pure radiative and physiological effects alone are much clearer. While radiative forcing tends to dry the complete Sahel in RAD, physiological effects significantly increase precipitation. Second, there is a very pronounced tendency of the Sahel not to warm like the rest of the continent (Fig. 8, bottom). To our knowledge, no previous experiment on physiological impacts of CO2 on climate resulted in a temperature pattern as pronounced as in MPI-ESM-LR and HadGEM2-ES (information and model expansions for previous studies are provided in Table 3): Betts et al. (1997) and Bounoua et al. (2010) obtained substantial effects only in the extratropics, while in Kergoat et al. (2002), Bala et al. (2006), Notaro et al. (2007),andO'ishi et al. (2009) the evapotranspiration change did not dominate the temperature response in the Sahel. In contrast, by forcing the Lund-Potsdam-Jena (LPJ) vegetation model with observed atmospheric data, Leipprand and Gerten (2006) obtain an increased evapotranspiration (although no maps are shown in their study). When comparing the CMIP5 temperature anomalies to the changes of vegetation cover in Figs. 2 and 5,itis apparent that the cool stripe of the Sahel coincides with the preindustrial border of vegetation cover. South of this transition region, the decrease of stomatal conduc- tance leads to a warming. In previously bare regions north of the cool belt, the lower albedo of the surface increases temperature because of more absorbed shortwave radi- ation. As the latent heat flux (LH) and net shortwave radiation (SWnet) are the terms with the largest changes, their superposition coincides well with the pattern of temperature anomalies (Fig. 8). In the case of MPI-ESM-LR, the physiological effects of CO2 on the surface energy balance in different re- gions becomes obvious in scatter diagrams because the transition region is wider than in HadGEM2-ES. Con- sidering all grid points in the four boxes of Fig. 2 where the preindustrial vegetation cover is above 80%, the anomalies of temperature and latent heat flux are closely related (Fig. 9a). No such relation exists between temperature and absorbed shortwave radiation. When only considering grid points where preindustrial vege- tation cover is below 80%, the opposite is true. Figures 9e-h reveal why this occurs by showing changes in LAI on the horizontal axis. In the vegetated areas with small changes in LAI, the reduced stomatal conductance dominates and decreases LH. However, the larger the increase in LAI, the more positive the anomaly be- comes. As the bare ground fraction in these initially vegetated areas is low, an increased LAI has compara- tively little influence on the surface albedo and thus SWnet. In the areas with an initially substantial fraction of desert, the expansion of vegetation into these bare regions causes an albedo decrease. Although LH also tends to increase with increasing LAI in these areas, the absorbed shortwave radiation dominates the surface energy balance. However, the latent heat-induced cooling in the Sahel can be caused not only by the local vegetation cover change, but also by remote effects. For example, the climate change in the extratropics can lead to a shift in circulation and thus a precipitation increase in the Sahel. Even without any change in local LAI, the increase in precipitation may lead to latent heat cooling. This effect may explain the results by Cao et al. (2010) and Pu and Dickinson (2012), who applied different versions of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) where no LAI change was permitted (Table 3). Consequently, an offline experiment with prescribed atmospheric conditions with the NCAR land surface model CLM3.5 does not show any increase in evapotranspiration (Gopalakrishnan et al. 2011). The mechanism of precipitation-induced cooling also becomes apparent in HadGEM2 (called HadGEM2-A in this setup) in two sstClim experiments where both effects of CO2, radiative and physiological, are active (Table 2). As the temperature contrast between land and ocean is ar- tificially increased in sstClim4xCO2 because of the fixed SSTs, there is a massive increase in precipitation over sub-Saharan Africa (Fig. 10). This precipitation increase causes regionally very different responses in latent heat flux: Where forest coverage is high, the stomatal response causes a decrease in LH. In the subtropics, where LH is most limited by precipitation, an increase occurs that is large enough to decrease surface air temperature in some locations despite the very large longwave heating. A similar effect on Sahel hydrology is obtained by Andrews et al. (2012), who show that a 2 3 CO2 experiment with fixed SSTs leads to an exceptional increase in relative humidity in the Sahel (see their Fig. 4e). In HadGEM2, the evaporative cooling occurs despite negligible changes in the vegetation's condition: All plant functional types are fixed to the same distribution in sstClim4xCO2 and sstClim, and LAI shows only very small differences. In MPI-ESM-LR, a large LAI increase occurs between the equator and approximately 178N, as the phenology of raingreen plant types in JSBACH is sensitive to soil moisture and NPP. Therefore, the remote and local causes of the evaporative cooling in sstClim4xCO2 can- not be strictly separated in MPI-ESM-LR. Although an attribution of the cool Sahel to the local vegetation change in the PHYS experiment is also not strictly possible, we argue that this local effect is probably more important than remotely induced changes. In the case of HadGEM2-ES, Boucher et al. (2009) obtained a warming that was largest over the forest areas but not a particularly cool Sahel when they prescribed LAI in a previous model version. A similar response was obtained by Doutriaux-Boucher et al. (2009) and Andrews et al. (2011), who only analyze the first 5 yr after an instan- taneous CO2 increase. Assuming that the differences to the CMIP5 results presented in Fig. 8 and the latter three studies are not due to model differences other than the effect of changes in LAI or vegetation cover, the CMIP5 results suggest that vegetation dynamics are of particular importance in the Sahel. However, the northward shift of the ITCZ over the Atlantic in PHYS implies that vegeta- tion dynamics in other regions may also have an effect. Whether an increase in precipitation at a particular grid point is induced by the local vegetation or by the vegeta- tion cover changes elsewhere is therefore hard to separate in HadGEM2-ES. In case of MPI-ESM-LR, it is obvious from Figs. 5, 8,and9 that latent cooling is strongest in regions where vegetation cover change is largest (under the precondition that initial vegetation cover is already large). Finally, as we know that CO2 fertilization is large in MPI-ESM-LR and, as precipitation changes are stronger over land than over the ocean, we expect the vegetation expansion to be caused by the local effect rather than by teleconnections. We therefore conclude that vegetation dynamics and structural changes are of particular impor- tance in the Sahel in MPI-ESM-LR. 4. Conclusions We have shown that three CMIP5 ESMs with dy- namic vegetation indicate a Sahel greening due to in- creased CO2 until 2100. However, the reason for this change differs among the models. In MIROC-ESM, the greening is the result of an increased precipitation in North Africa. In MPI-ESM-LR, it is the result of CO2 fertilization. In HadGEM2-ES, there is only little veg- etation cover change in the first decades, while a sub- sequent decrease in rainfall and the large temperatures initiate a retreat of vegetation in case of the very high CO2 levels of the RCP8.5 scenario. In MPI-ESM-LR and HadGEM2-ES, there is a tendency of a vegetation retreat in the west of North Africa. In MPI-ESM-LR this retreat does not start until 2100, when climate change proceeds while the fertilization effect levels off because of the stabilized CO2 concentration. The dependency of vegetation anomalies on the time horizon would prob- ably be even more problematic in a scenario with a CO2 concentration that is reduced after a large peak (im- plying very low emissions or even anthropogenic carbon removal activities). Under such conditions, the fertil- ization effect would decrease with the concentrations, while the committed climate change from the high- emission period would also act to reduce the vegetation's productivity. The impact of CO2 would then tend to resemble the RAD scenario. As there are several important drivers of vegetation changes in the Sahel that can oppose each other and are insufficiently understood and modeled, the three models only represent three trajectories that do not represent the full spectrum of possibilities. In particular, improved parameterizations of surface and boundary layer pro- cesses, tropical convection, and mesoscale systems are crucial to better represent Sahelian rainfall in climate models (Pohl and Douville 2011; Ruti et al. 2011; Fasullo 2012; Rowell 2012; Roehrig et al. 2013). Considering the large spatial gradients and the importance of small-scale features and land-atmosphere coupling of the Sahel region, in combination with the scarcity of observations, progress may remain slow, and the application of re- gional models may be of particular benefit (Cook 2008; Patricola and Cook 2010). As many global models project rather small changes in annual rainfall (Biasutti 2013), it seems likely that physiological effects of CO2 will be of particular im- portance and a further source of uncertainty considering the low level of understanding of these effects on long time scales (Field et al. 1995; Long et al. 2004; Norby and Zak 2011; Reich and Hobbie 2013). The idealized factor separation experiments of CMIP5 indicate that CO2 fertilization is one of the dominating drivers of vegeta- tion cover changes in MPI-ESM-LR and HadGEM2- ES, which is similar to earlier results performed with FOAM-LPJ by Notaro et al. (2007). It also seems clear that such structural vegetation changes affect the Sahelian climate, although the relative importance of changes in albedo and evapotranspiration is inconsistent among models. As several studies showed similar effects on both aspects of the energy balance, we consider it likely that previous experiments could not reveal the spatial separation of mechanisms because of their lower resolution. However, the balance of effects depends on the physical properties of plants, represented as constant parameters of plant functional types (PFTs) in current models. Also, the translation of changes of productivity on a leaf level and short time scales into the dynamics of vegetation distribution and composition over centuries remains a major uncertainty. To isolate the climatic effect of local vegetation changes from tele- connections, it may be of value in the future to perform additional simulations with an offline land model where precipitation is prescribed (similar to Gopalakrishnan et al. 2011), to compare simulations with and without structural vegetation changes (as, e.g., in Betts et al. 1997), or to allow dynamic vegetation only in some regions of the world but not in others. The CMIP5 results also have implications for pre- viously used concepts of analysis: First, it is a common practice to analyze the difference between RADPHYS and RAD experiments to determine the physiological effects (Andrews et al. 2011; Betts et al. 1997; Bounoua et al. 2010; Levis et al. 1999, 2000; Doutriaux-Boucher et al. 2009; Joshi and Gregory 2008; O'ishi et al. 2009). The substantial synergy effect implies that this differ- ence is not identical to the pure PHYS effect. While the chosen approach may make no difference for fast effects such as stomata closure, it leads to differences whenever large changes in vegetation cover occur. Differences will appear mainly in those quantities that are much affected by vegetation cover. Of course, the question of which approach is more appropriate depends on the aim of the analysis. To determine the additional contribution of physiology on climate change, analyzing RADPHYS 2 RAD appears to be an appropriate approach. Second, the concept of climate forcings (in terms of Wm22)andfeedbacks(intermsofWm22 K21)isques- tioned when physiological effects are important. The fast processes of nonradiative forcing (stomatal closure and subsequent changes in cloud cover) are usually added to radiatively induced adjustments: for example, adjust- ments of stratospheric temperature, lapse rate, and cloud cover (Andrews and Forster 2008; Andrews et al. 2012). However, the slow structural responses of vegetation to enhanced CO2 are not related to temperature-driven feedbacks (Bony et al. 2006) and must conceptually also be regarded as forcing adjustments that are so slow that they cannot be separated from climate feedbacks. As CO2 contributes most to the anthropogenic radiative forcing and, as climate change and CO2-driven vegetation dynamics evolve on similar time scales, it can be argued that CO2-induced vegetation cover changes can concep- tually be formulated as a climate feedback (Cao et al. 2010). However, the CMIP5 results indicate the limits of this assumption, as changes in LAI and vegetation dis- tribution are faster than the warming of the deep ocean. Third, it has been speculated whether land-atmosphere feedbacks in North Africa can cause abrupt transitions (Claussen et al. 1999; deMenocal et al. 2000) or even multiple equilibria (Brovkin et al. 1998). Although the feedback appears to be too small to allow for such non- linear effects in current ESMs, CO2 fertilization may make the region more susceptible to precipitation changes than in the past. Acknowledgments. We acknowledge financial support by the Cluster of Excellence Integrated Climate System Analysis and Prediction (CliSAP) DFG EXC 177/2, as well as by the European Commission's 7th Framework Pro- gramme under Grant Agreement 282672 (EMBRACE project). We also acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison pro- vides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. 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SEBASTIAN BATHIANY Max Planck Institute for Meteorology, Hamburg, Germany MARTIN CLAUSSEN Max Planck Institute for Meteorology, and Centrum fueuror Erdsystemforschung und Nachhaltigkeit, Universitaeurot Hamburg, Hamburg, Germany VICTOR BROVKIN Max Planck Institute for Meteorology, Hamburg, Germany (Manuscript received 29 August 2013, in final form 25 June 2014) Corresponding author address: Sebastian Bathiany, Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany. E-mail: [email protected] (c) 2014 American Meteorological Society |