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Old-growth rainforests in the Amazon basin store c. 93 ± 23 Pg of carbon (Pg C) in their biomass (Malhi et al., 2006). Annually, tropical forests process c. 18 Pg C through respiration and photosynthesis (Malhi & Grace, 2000). This is more than twice the present amount of fossil fuel emissions (Dirzo & Raven, 2003). Currently the Amazon rainforest appears to be a net sink for atmospheric CO2, but drought events, such as the 2005 drought, potentially affect tropical forest by changing forest structure and dynamics, which lead to loss of biomass carbon (Phillips et al., 2009). The quantitative assessment of this risk is a prerequisite for the stabilization of Amazonian rainforests and is therefore of global importance for climate protection measures.
Climate projections from the current generation of general circulation models (GCMs) suggest an average increase in global temperature of c. 3.3°C by the end of the 21st century (IPCC, 2007), but they differ widely in their projections of rainfall because of different assumptions about the underlying mechanisms of rainfall formation (Li et al., 2006). The uncertainty for changes in rainfall regimes is therefore high. Shifts in the rainfall regime may significantly alter vegetation structure and composition in the Amazon basin (Lapola et al., 2009; Malhi et al., 2009). Field observations indicate that prolonged drought events may lead to increasing plant physiological stress and reduced productivity of trees (Brando et al., 2008; Phillips et al., 2009). Decreases in evapotranspiration and therefore convective precipitation could further accelerate drought conditions and destabilize the tropical ecosystem as a whole (Betts et al., 2004). The so-called ‘Amazon forest dieback’ by the middle of the 21st century was first simulated by the Hadley Centre coupled ocean–atmosphere–vegetation model and was caused by a positive feedback from increasing atmospheric CO2 concentrations, which increased warming and decreased precipitation (Cox et al., 2000). This led to vegetation biomass loss in the Amazon basin, which further accelerated CO2 emissions and, in turn, increased temperature and reduced precipitation (Cox et al., 2004). Owing to the massive local effects of this feedback along with its system-wide repercussions, the Amazon rainforest has been identified as one potential ‘tipping element’ of the Earth system (Lenton et al., 2008).
The probability for large-scale Amazon forest dieback is currently discussed in the literature. Field observations (Malhi et al., 2004, 2006; Phillips et al., 2009), drought manipulation experiments (Nepstad et al., 2007; Brando et al., 2008), remote sensing (Saleska et al., 2007) and modelling studies (Cox et al., 2004; Cramer et al., 2004; Sitch et al., 2008) have given contrasting results: field observations and experiments indicate high sensitivity of tropical forest biomass and structure to the degree of drought conceivable under the climate change projections. For example, the observed responses of rainforests to drought events such as the 1997/1998 El Niño event range from high tree mortality (c. 26%) in a forest with seasonal rainfall in East Kalimantan (Van Nieuwstadt & Sheil, 2005) to no mortality in Panama (Condit et al., 2004) and several intermediate responses (Condit et al., 1995; Kinnaird & O’Brien, 1998; Williamson et al., 2000). During the 2005 drought in Amazonia, Phillips et al. (2009) measured greatly increased tree mortality alongside rather small declines in growth in the surviving trees. By contrast, some remote sensing studies suggest a vegetation green-up during dry periods as a result of enhanced plant productivity from increased solar radiation (Huete et al., 2006; Saleska et al., 2007). Modelling studies display a great variety of projections for future changes in neotropical vegetation, ranging from a potential reduction in forest cover (Cox et al., 2000, 2004; Cramer et al., 2004; Schaphoff et al., 2006; Scholze et al., 2006; Salazar et al., 2007; Sitch et al., 2008) to no dangerous reduction in forest cover (Walker et al., 2009).
One explanation for the differences between these studies appears to be linked to the different representation of physiological processes in these models – in particular, there is considerable disagreement concerning the amount of CO2-related buffering against drought stress. Cowling & Shin (2006) have analysed the extent to which these processes are sensitive to temperature, precipitation and CO2, but were unable to identify a single key factor or threshold. Another, highly significant, part of the variability between published assessments appears to be the result of the selection of climate forcings from different projections. Since rainfall shows the highest variation between models, here we study the uncertainty in rainfall projections and how it propagates to projections of future biomass change in the Amazon region.
Implicitly, all earlier climate projections have been treated as if they were equally plausible, independent of the quality of the underlying climate model (IPCC, 2007; Malhi et al., 2008, 2009). Assuming that greater agreement between model simulations and current climate implies higher model quality, Jupp et al. (2010) instead weighted the climate projections based on the ability of each climate model to produce key aspects of the observed climate. In their study, they derived regional probability density functions (PDFs) as weightings for the 24 IPCC-AR4 rainfall projections for Amazonia (IPCC-AR4, Inter-governmental Panel on Climate Change, Assessment Report 4). Here, we apply these weights to forest biomass simulations obtained by the Lund–Potsdam–Jena Dynamic Global Vegetation Model for managed land (LPJmL, Sitch et al., 2003; Gerten et al., 2004; Bondeau et al., 2007) for five regions throughout South America. By emphasizing the results of simulated biomass from climate projections with higher weightings (i.e. better ability to reproduce current rainfall patterns), and de-emphasizing the results from climate projections with lower weightings, the range of estimated biomass change can be seen as being more robust. We use a probabilistic approach to separately quantify the uncertainty in ecosystem response to changes in rainfall from different climate models, as well as the uncertainty arising from different assumptions about the effects of rising atmospheric CO2 concentrations on vegetation growth and water-use efficiency (CO2 fertilization, e.g. Hickler et al., 2008; Lapola et al., 2009). Our main goals are to estimate the relative probability of dangerous biomass loss in these regions with a particular focus on Amazonia; to discuss the ecophysiological bases for responses to CO2 and climate; and to evaluate the upper and lower limits of potential biomass change.
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Rainfall is a direct driver of vegetation dynamics in the Amazon region and throughout Brazil. Thus, assessing future rainfall conditions is a crucial step for estimating the risks of future Amazon forest dieback. Evaluating the quality of regional projections as simulated in GCMs is difficult because of the complex nature of these models and the underlying processes and circulations. Therefore, evaluating the ability of these models to reproduce currently observed precipitation serves as a proxy that can be used to weight different GCM projections of future rainfall (Jupp et al., 2010). Future biomass projections vary strongly with the projected climate as shown in the present study (Table S1). The Bayesian biomass weightings give a more differentiated picture of likely biomass changes in comparison with studies that treat potential changes with the same probability (Malhi et al., 2008, 2009; Lapola et al., 2009). Our results show that under the assumption of strong CO2 effects, biomass increases are more likely in all five regions of Brazil. However, if CO2 effects are weak, biomass reductions become much more likely. In the following, we discuss the limitations of our study approach, the ecophysiological bases of biomass responses to CO2 and climate, and the range of biomass change in the five regions of northern South America.
Limitations of the study approach
The presented probabilities for biomass change are based on rainfall weightings described by Jupp et al. (2010). Higher weightings imply the assumption that climate models that are better able to reproduce the mean and variability of the current rainfall also produce more reliable projections of future rainfall. Future dynamics, however, may be related to processes not important for present climate. As discussed by Jupp et al. (2010), the highest ranked models differ between the five regions, which shows that it is currently not possible to determine one climate model that describes the underlying processes of rainfall patterns in South America.
In our study, we applied the weightings from Jupp et al. (2010) to estimate the probability of dangerous amounts of biomass loss. A further step would be to create such biomass weightings directly by evaluating the ability of different vegetation–climate model combinations to reproduce current vegetation patterns. In this way the likelihoods of different representations of direct CO2 effects could also be estimated. In the present study, we approached the uncertainty of CO2 effects via two scenarios testing the range of potential responses of vegetation to CO2 fertilization (i.e. strong effects vs no effects). Our study concentrates on climate change scenarios from the A1B-SRES emission scenario. Thus it is very likely that the corresponding weightings could differ under other SRES emission scenarios, but this was not the focus of the current study. Effects of climate change under different CO2 emission scenarios on Amazon rainforest are published in Poulter et al. (2009).
Our biomass PDFs suggest that future climate in the Amazonian rainforest region is less suitable for biomass production, but that strong CO2 effects could nevertheless lead to biomass increases. However, the magnitude of these effects remains highly uncertain. Further uncertainties arise from a lack of knowledge of how the effects of increasing CO2 concentrations on plants may change forest community structure as a result of the differential responses of different plant types (Granados & Körner, 2002; Phillips et al., 2002; Körner, 2003), whether the positive response of CO2 on plants may level off (Bazzaz, 1990) or even lead to reduction in forest carbon storage (Körner, 2004); the effects of nutrient availability (Hungate et al., 2003; Chambers & Silver, 2004; Powers et al., 2005); and likewise future changes in climate and vegetation associated with non-CO2 emissions (Ramanathan & Feng, 2008). Accounting for these additional factors will most probably lead to a higher estimated risk of biomass loss in forest ecosystems in Amazonia.
Ecophysiological bases for biomass responses to CO2 and climate
Our results show that key uncertainties for estimating the consequences arise from the uncertain role of direct CO2 fertilization in terms of enhanced photosynthetic capacity and water-use efficiency. The role of increased atmospheric CO2 concentrations for photosynthesis (Norby et al., 1999, 2005; Körner, 2003; Long et al., 2004) and for stomatal conductance (Collatz et al., 1991; Körner, 2004; Ainsworth & Long, 2005; Körner et al., 2007) has been widely discussed in the literature. CO2 plays a major role as a limiting resource for carbon assimilation by plants (Farquhar et al., 1980). Several small-scale and open-top chamber experiments have shown an enhancement of photosynthesis in C3 plants under elevated CO2 concentrations, leading to increased NPP (Curtis & Wang, 1998; Norby et al., 1999). The long-term effects on real ecosystems, however, are unclear (Norby et al., 1999). Dynamic vegetation models such as LPJ generally suggest a substantial impact of CO2 on NPP (Cramer et al., 2001). Measurements from large-scale free-air CO2 enrichment (FACE) experiments in temperate forests (Norby et al., 2005) have been compared with LPJ model simulations, and showed that the model reproduced the overall response of forest productivity to elevated CO2 (Hickler et al., 2008). However, these experiments may not be representative of tropical forests. The simulated productivity enhancement in tropical forests was 10% higher than in boreal forests under elevated CO2, but supporting data for this response are not available (Hickler et al., 2008).
If the direct impacts of CO2 on plant productivity and water-use efficiency are great, as assumed in LPJmL and most other global vegetation models (Friedlingstein et al., 2006; Sitch et al., 2008), the dieback risk is almost eliminated. Bayesian weighting of the climate projections shifts the projected biomass changes towards increasing biomass. The consequences for tropical forest ecosystems may be manifold, including faster growth, faster closing canopy gaps, faster reached steady-state leaf area index, and changed successional patterns (Körner, 1998). Different tree species respond differently so that increasing atmospheric CO2 concentrations in combination with a changing climate may lead to shifts in species composition ( Körner, 1998; Raizada et al., 2009).
If direct CO2 effects are small, the risk of biomass loss remains significant in the Amazon region across most scenarios (c.f. Figs 2 and 3). We investigate the probability of biomass loss and dieback, which we define as an arbitrary limit of biomass reduction of 25% or higher. In a study in southern Amazonia, Alencar et al. (2006) shows that this reduction corresponds to the difference in biomass between a tropical forest to a different forest type (ranging from dense to transitional and open forests). A loss of 25% corresponds in our study to a range of 1.5–3.8 kg C m−2 biomass loss across the five regions. Phillips et al. (2009) estimates biomass losses of 0.17–0.80 kg C m−2 during the 2005 drought in the Amazon region. Equivalent biomass losses of 0.8 kg C m−2 or more have a 62% probability in eastern Amazonia, a 72% probability in northwestern Amazonia and a 100% probability in southern Amazonia under future conditions assuming no CO2 fertilization effects. Phillips et al. (2009) found that the biomass losses during the 2005 drought were driven by occasional large mortality increases and by widespread but small declines in growth. Mainly light-wooded trees were affected by cavitation or carbon starvation. Brando et al. (2008) measured tree mortality and reduction in wood production in a drought experiment in Tapajos, which is located in our eastern Amazonia study region. They observed losses of c. 3.1 kg C m−2 persisting through their first post-treatment year. Losses were caused mainly by mortality of large trees, and the dead biomass of trees with stem diameter > 30 cm reached 2.3 kg C m−2 in the drought treatment plots (Nepstad et al., 2007). According to our results, events similar to this have a low probability in eastern and northwestern Amazonia.
Range of potential biomass change in five regions of northern South America
The responses of vegetation to climate change in the five large regions of South America investigated can be categorized into three groups: small biomass loss or increasing biomass in eastern and northwest Amazonia, which hold the highest amounts of biomass in dense tropical moist broadleaved forests (as described by Olson et al. (2001); Fig. 1) and most of the still intact rainforests. The region has been least affected by land use and hosts the highest amount of biodiversity as a result of stable historical climate conditions (Malhi et al., 2008). Rainforests in northwest Amazonia are highly vulnerable and thus strong efforts should be undertaken to protect this region from deforestation. The uncertainty of biomass change is highest in southern Amazonia. If the actual effects of CO2 fertilization turn out to be weak, there is a 60% probability for losses of > 25% of the total biomass. The region is covered by highly diverse ecosystems such as the Brazilian savannahs (cerrado) and dry as well as moist tropical forest (Olson et al., 2001). Also in the region of northeastern and southern Brazil the uncertainty is high, but weighting shifts biomass projections towards either no change or an increase in biomass. In contrast to tropical forests in the Amazon basin, the vegetation in this region is adapted to seasonal dry conditions (cf. Fig. 1). These ecosystems are – to a certain extent – less vulnerable to further drying and, in consequence, the projected probability of rainfall reduction (Jupp et al., 2010) does not necessarily lead to biomass loss.
Conclusions and outlook
We conclude that the risk of Amazon forest dieback is almost eliminated if the direct impacts of CO2 on plant productivity and water-use efficiency are great. The range of potential biomass change arising from climate model uncertainty remains significant but is, however, smaller than the uncertainty arising from CO2-fertilization effects. If direct CO2 effects are small, the risk of biomass loss is significant in the Amazon region. Thus, CO2 effects are one of the key unknowns in assessing the risk of Amazonian forest dieback in response to 21st-century climate change. Further research is needed on the differences between the total impacts of climate change caused by CO2 and other climate-forcing agents on vegetation (e.g. increases in methane or reductions in sulphate aerosols). This has implications for international climate policy, which currently treats all radiative forcings as equally damaging. The risk of Amazon forest dieback may be many times larger if accelerated climate change arises from agents other than CO2.