Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change

Transcription

1 Research Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change David Galbraith 1,2, Peter E. Levy 1, Stephen Sitch 3,4, Chris Huntingford 5, Peter Cox 6, Mathew Williams 1 and Patrick Meir 1 1 Centre for Ecology and Hydrology, Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB, UK; 2 School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, UK; 3 School of Geography, University of Leeds, Leeds LS2 9JT, UK; 4 Met Office Hadley Centre, Fitzroy Road, Exeter, Devon EX1 3PB, UK; 5 Centre for Ecology and Hydrology, Wallingford, Maclean Building, Wallingford OX10 8BB, UK; 6 School of Engineering, Computing and Mathematics, University of Exeter, Exeter EX4 4QF, UK Summary Author for correspondence: David Galbraith Tel: +44 (0) darga@ceh.ac.uk Received: 10 March 2010 Accepted: 10 May 2010 doi: /j x Key words: Amazon die-back, Amazon drought, CO 2 fertilization, dynamic global vegetation models (DGVMs), elevated temperatures, photosynthesis, plant respiration. The large-scale loss of Amazonian rainforest under some future climate scenarios has generally been considered to be driven by increased drying over Amazonia predicted by some general circulation models (GCMs). However, the importance of rainfall relative to other drivers has never been formally examined. Here, we conducted factorial simulations to ascertain the contributions of four environmental drivers (precipitation, temperature, humidity and CO 2 ) to simulated changes in Amazonian vegetation carbon (C veg ), in three dynamic global vegetation models (DGVMs) forced with climate data based on HadCM3 for four SRES scenarios. Increased temperature was found to be more important than precipitation reduction in causing losses of Amazonian C veg in two DGVMs (Hyland and TRIFFID), and as important as precipitation reduction in a third DGVM (LPJ). Increases in plant respiration, direct declines in photosynthesis and increases in vapour pressure deficit (VPD) all contributed to reduce C veg under high temperature, but the contribution of each mechanism varied greatly across models. Rising CO 2 mitigated much of the climate-driven biomass losses in the models. Additional work is required to constrain model behaviour with experimental data under conditions of high temperature and drought. Current models may be overly sensitive to long-term elevated temperatures as they do not account for physiological acclimation. Abbreviations: CPTEC, Centro de Previsão de Tempo e Estudos Climáticos; CRU, Climate Research Unit; DC veg, Amazonian vegetation carbon; DC veg, The change ( ) in Amazonian vegetation carbon; DGVM, Dynamic Global Vegetation Model; FACE, Free-air CO 2 Enrichment; GCM, General Circulation Model; IPCC, Intergovernmental Panel for Climate Change; HadCM3, Hadley Centre Coupled Model, version 3; LPJ, Lund Potsdam Jena; MOSES, Met Office Surface Exchange Scheme; Q 10, factor by which respiration increases following a 10-degree increase in temperature; SRES, Special Report on Emissions Scenarios; TFE, throughfall exclusion; TRIFFID, Top-down Representation of Interactive Foliage and Flora Including Dynamics. 647

2 648 Research New Introduction The possibility of substantial loss of Amazonian rainforest cover and carbon as a consequence of climate change ( Amazon die-back ) was first reported by White et al. (1999) following offline simulations performed with the Hybrid dynamic global vegetation model (DGVM). The loss of the Amazonian rainforest was also a key part of the amplifying climate carbon cycle feedback simulated by Cox et al. (2000). Given the importance of the Amazon Basin in carbon storage (Melillo et al., 1993) and in moisture and heat exchange with the atmosphere (Gash et al., 2004), extensive loss of Amazonian rainforest could have pronounced effects on global climate, and may constitute a tipping point in the Earth System (Lenton et al., 2008). Changes to the Amazonian climate which might reduce rainforest cover in favour of savanna are predicted by a number of global climate models (Salazar et al., 2007; Malhi et al., 2009a). The most severe losses of Amazonian rainforest have been simulated using the HadCM3 (Hadley Centre Coupled Model, version 3) model (Cox et al., 2000, 2004; Betts et al., 2004), which predicts particularly strong reductions in rainfall and increases in temperature over Amazonia. Although it predicts extreme changes in Amazonian climate in the 21st century, the HadCM3 model simulates key aspects of tropical climate variability more realistically than most other global climate models (Cox et al., 2004, 2008; Li et al., 2006). The reduction in rainfall predicted by HadCM3 has generally been considered to be the major cause of simulated rainforest loss (Betts et al., 2004; Huntingford et al., 2008). However, the role of rainfall relative to other climatic factors has not been analysed explicitly. Several other environmental factors will influence modelled Amazonian carbon stocks, including temperature, humidity and atmospheric CO 2 concentration. The relative importance of each of these (and their interactions) is unclear. The means by which these environmental drivers affect plant functioning in models may also be complex. For example, rising temperature affects net primary productivity (NPP) directly through effects on photosynthesis and respiration, but also through increases in leaf-to-air vapour pressure deficit (VPD) (Lloyd & Farquhar, 2008) (Fig. 1). Increased VPD can reduce photosynthesis in the short-term through reduced stomatal conductance or in the longer term through changes in plant water balance as a result of increased evapotranspiration. The relative importance of each of these temperature mechanisms in causing simulated changes in Amazonian vegetation carbon stocks has not been investigated. This study sought to analyse the mechanisms underlying the predicted change in rainforest vegetation structure and function across the Amazon in three DGVMs: Hyland (Levy et al., 2004), Lund Potsdam Jena (LPJ) (Sitch et al., 2003) and MOSES-TRIFFID, a combination of the Met Office Surface Exchange Scheme (MOSES) and the TRIFFID (Top-down Representation of Interactive Foliage and Flora Including Dynamics) vegetation model. We designed a factorial experiment to quantify the relative contributions of CO 2, humidity, precipitation and temperature (and their interactions) to changes in Amazonian vegetation carbon. We also conducted further simulations to investigate the importance of direct and indirect effects of temperature. We conclude by discussing whether the representation of the response of Amazonian rainforest to climatic change in these DGVMs is likely to be accurate. Materials and Methods Terminology We define the Amazon region as being a rectangle, extending from 3.0 N to 12.5 S and from 70 W to48 W; hereafter, all values refer to this domain unless stated otherwise. We subdivide this domain into eastern and western regions along the 60 W longitude. We denote the carbon present in the biomass of Amazonian vegetation as C veg in Pg C, and the change in this quantity between 2003 and 2100 as DC veg. Fig. 1 Schematic illustration of the mechanistic pathways, both direct and indirect, through which increasing temperatures can affect net primary productivity (NPP). The exact mechanisms differ from model to model, but the major processes are shown here.

3 Research 649 Vegetation models Three dynamic vegetation models, developed primarily for global and regional applications, were used in this study. The models were selected as they showed varying decreases in Amazonian C veg in a recent model intercomparison study by Sitch et al. (2008). Here we provide only a brief description of key processes in each model for understanding changes in vegetation carbon, the key variable of interest in this study. For more comprehensive mathematical descriptions of the models, the reader is referred to the original papers (Friend et al., 1997; Cox, 2001; Sitch et al., 2003). Hyland is a DGVM largely based on the Hybrid model (Friend et al., 1997; Friend & White, 2000) and modified according to Levy et al. (2004). The model simulates three plant functional types (PFTs; evergreen tree, deciduous tree and C 3 grass), which compete with each other for light. Soil moisture availability is simulated using a one-layer bucket model, where the soil water holding capacity is a dynamic function of soil organic matter (as a surrogate for rooting depth). Soil moisture status, temperature, CO 2 and VPD have a direct effect on stomatal conductance, which is based on the Jarvis Stewart multiplicative model (Jarvis, 1976; Stewart, 1988). The canopy energy balance is solved (as a function of air temperature, isothermal net radiation, humidity, and resistances to heat and water transfer assuming a constant wind speed) to give surface temperature and evapotranspiration rate (Friend, 1995). Photosynthesis is based on Farquhar et al. (1980), with modifications made by Friend (1995). Plant respiration is taken to be a constant fraction (0.5) of gross primary production (GPP) (Levy et al., 2004). The input variables to Hyland are air temperature, precipitation, humidity, downward shortwave radiation and the atmospheric CO 2 concentration. The LPJ DGVM (Sitch et al., 2003) simulates competition for water and light between nine plant functional types. Soil moisture availability is simulated using a two-layer soil model with a total depth of 2 m. Photosynthesis is based on the scheme of Collatz et al. (1991, 1992), itself a modification of Farquhar et al. (1980), as implemented by Haxeltine & Prentice (1996). The impact of drought on photosynthesis is via a canopy conductance feedback which takes into account both the atmospheric demand and the supply of water from the roots. Stomatal conductance is reduced through increases in atmospheric CO 2, but does not affect the surface temperature which is assumed to be equal to the air temperature. The temperature response of plant respiration is independent of that of photosynthesis and follows an Arrhenius function, based on Lloyd & Taylor (1994). In this version, the inputs to LPJ are rainfall, temperature, the monthly averaged daily percentage of sunshine hours and the atmospheric CO 2 concentration. The TRIFFID model (Cox, 2001) was run coupled to the MOSES land surface scheme (Cox et al., 1998) and the MOSES-TRIFFID combination mimics that used by Cox et al. (2000), where widespread loss of the Amazon rainforest was first reported with the model. TRIFFID simulates five plant functional types (broadleaf tree, evergreen tree, shrub, C 3 grass and C 4 grass) which compete with each other following Lotka Volterra dynamics (Cox, 2001). A four-layer soil model is simulated with a total depth of 3.0 m, although individual plant functional types differ in their rooting depths. The soil moisture status directly scales net leaf photosynthesis, which is calculated based on Collatz et al. (1991, 1992). The surface energy balance is calculated separately for each plant functional type (PFT) using a variant of the Penman Monteith equation (Essery et al., 2003), which diagnoses the evapotranspiration and surface temperature given the aerodynamic resistance and stomatal conductance for each PFT. Stomatal conductance is sensitive to temperature, vapour pressure deficit, soil moisture status and photosynthetically active radiation, and reduces with increasing CO 2 concentration (Cox et al., 1998). As a result this model is known to produce significant reductions in evapotranspiration and therefore increases in runoff (Gedney et al., 2006) and surface temperature (Betts et al., 2004) under increasing CO 2. Plant respiration increases exponentially with temperature in this version of the model, according to a simple "Q10" dependency where Q10 = 2.0 (see Cox, 2001). In this offline study, the inputs to MOSES- TRIFFID are: downward shortwave and longwave radiation at the surface; rainfall; humidity and temperature at screen level (1.5 m); windspeed at 10 m; and the atmospheric CO 2 concentration. Climate data For the historical period ( ), we used the observed climatology from the Climate Research Unit (CRU) data set (New et al., 2002), aggregated to HadCM3 resolution (3.75 longitude 2.5 latitude as described by Sitch et al., (2008). A near-present day control climatology was defined as the period within these data. This was used for control simulations with no mean change in future climate, but with interannual variability as observed during this period. Future climate ( ) was based on a pattern-scaling approach to reproduce HadCM3 simulations (Huntingford & Cox, 2000) to enable comparison with previous studies (Cox et al., 2004; Huntingford et al., 2004; Huntingford et al., 2008; Sitch et al., 2008). Simulations were conducted for four IPCC SRES scenarios (A1FI, A2, B1 and B2). Anomalies from the HadCM3 simulation were added to the control climatology, thereby maintaining the same interannual variability as the control but with a change in the mean. Changes in the mean value of each of the climate variables across the Amazon region, and for each scenario, are shown in Fig. 2. The four scenarios are associated with prescribed CO 2

4 650 Research New Fig. 2 Predictions of change in key environmental factors over the Amazon region, derived by adding anomalies from the HadCM3 climate model to the mean climatology. concentrations based on Nakicenovic et al. (2000). CO 2 concentrations in 2100 ranged from 531 ppm (B1 scenario) to 925 ppm (A1FI scenario). Relative to the final year of the historical simulation (2002), mean annual temperature increased by 2.76 C in the most conservative scenario, and by 7.16 C in the most severe scenario. Precipitation decreased from an annual mean value of 2138 mm in 2002 to mm yr )1 in 2100, while mean annual relative humidity decreased from 77.8% in 2002 to % in Factorial simulations Each model was run to its preindustrial equilibrium state using data from the first decade of the CRU data set ( ), as described in Sitch et al. (2008). Models were then run from their preindustrial equilibrium in 1901 through to To investigate the contribution of environmental input variables to variation in DC veg, a four-factor two-level full factorial experimental design was used for each SRES scenario over the future climate period ( ). The four factors were precipitation, temperature, relative humidity and CO 2. The two levels corresponded to either the control climatology or the HadCM3 simulation of climate change. All combinations of the environmental variables of interest were included, giving a total of 16 simulations for each SRES scenario for Hyland and TRIFFID (Table 1) (LPJ does not take humidity as an input and thus only eight simulations were conducted for the LPJ model for each SRES scenario). Evaluation of simulated current vegetation carbon Simulated vegetation carbon at the end of the historical period ( ) was compared against a data set derived by remote sensing produced by Saatchi et al. (2007), which estimates aboveground living biomass for Amazonia at 1-km resolution. The Saatchi et al. (2007) data were aggregated to the resolution used in the study (3.75 longitude 2.5 latitude) for comparison with the model output. We also compare the regional biomass values simulated by each model with a range of literature estimates. The vegetation models used in this study do not distinguish between aboveground and belowground woody biomass, grouping both pools into a single metric of vegetation carbon (C veg ). To enable comparison with model output, we assumed that belowground biomass in Amazonia is equal to 21% of aboveground living biomass (Malhi et al., 2006; Saatchi et al., 2007), and thus multiplied the Saatchi et al. (2007) estimates by Analysis of variance (ANOVA) To quantify the effects of individual environmental drivers and their interactions on DC veg, we applied factorial analysis of variance (ANOVA) to the model output. Comprehensive descriptions of the application of ANOVA to sensitivity analysis of model experiments have been provided by previous authors (e.g. Campolongo & Saltelli, 2000; Chan et al., 2000) and here we provide only a brief overview of its application in this study. ANOVA has traditionally been applied

5 Research 651 Table 1 Summary of all simulations conducted as part of this study Factorial simulations (all models) Simulation Description SRES scenario Control No future climate change, but variability maintained NA P Precipitation varies according to SRES; other variables according to control climatology A1FI, A2, B1, B2 C CO 2 varies according to SRES; other variables according to control climatology A1FI, A2, B1, B2 H Humidity varies according to SRES; other variables according to control climatology A1FI, A2, B1, B2 T Temperature varies according to SRES; other variables according to control climatology A1FI, A2, B1, B2 PC Precipitation and CO 2 vary according to SRES; other variables according to control climatology A1FI, A2, B1, B2 PH Precipitation and humidity vary according to SRES; other variables according to control climatology A1FI, A2, B1, B2 PT Precipitation and temperature vary according to SRES; other variables according to control A1FI, A2, B1, B2 climatology CH CO 2 and humidity vary according to SRES; other variables according to control climatology A1FI, A2, B1, B2 CT CO 2 and temperature vary according to SRES; other variables according to control climatology A1FI, A2, B1, B2 HT Humidity and temperature vary according to SRES; other variables according to control climatology A1FI, A2, B1, B2 PCH Precipitation, CO 2 and humidity vary according to SRES; other variables according to control A1FI, A2, B1, B2 climatology PCT Precipitation, CO 2 and temperature vary according to SRES; other variables according to control A1FI, A2, B1, B2 climatology PHT Precipitation, humidity and temperature vary according to SRES; other variables according to A1FI, A2, B1, B2 control climatology CHT CO 2, humidity and temperature vary according to SRES; other variables according to control A1FI, A2, B1, B2 climatology PCHT Precipitation, CO 2, humidity and temperature vary according to SRES; other variables according to control climatology A1FI, A2, B1, B2 Simulations with additional input variables (TRIFFID only) complement to factorial analysis TRI-ALL Full simulation with all variables according to SRES: includes additional input variables not considered in the factorial analysis wind, longwave radiation and surface pressure A1FI, A2, B1, B2 Simulations to evaluate the importance of individual mechanisms to temperature-induced Amazonian carbon losses (all models) T-IND Temperature associated with VPD varies according to SRES; temperature associated with direct photosynthesis and respiration from control run A2 (only temperature effect considered) T-PHOT Temperature associated with photosynthesis varies; temperature associated with VPD and respiration from control run A2 (only temperature effect considered) Simulations with alternate prescriptions of plant respiration (all models) RESP-FIX Plant respiration taken to be a fixed fraction (0.5) of GPP; this is already the default prescription in Hyland RESP-EXP Plant respiration taken to be an exponential function of temperature in TRIFFID (Cox et al., 2000) and Hyland (based on Ryan et al., 1991) and Arrhenius function in LPJ (Lloyd & Taylor, 1994). These correspond to the default prescriptions in TRIFFID and LPJ and to the prescription used in an earlier version of Hyland Simulation of Tapajos and Caxiuanã drought experiment conditions TFE Stepped (50%) reduction in incident rainfall applied to CRU climatology in 2002 and continued for 10 yr. Only the grid cells containing the coordinates of the drought experiment sites in Tapajós (3 04 S, W) and Caxiuanã (1 43 S, W) were considered. Models were run with their default, global parameter settings A2 (only temperature effect considered) A2 (only temperature effect considered) NA NA, not applicable; CRU, Climate Research Unit; GPP, gross primary productivity; VPD, vapour pressure deficit. to experimental field studies, where natural variability in measurements may be high and replication is necessary to provide confidence in the statistical significance of a particular result (e.g. the difference between two treatment groups). However, ANOVA is increasingly being used to analyse output from deterministic models (e.g. Stevens et al., 1996; Cameron et al., 2005; Hodson & Sutton, 2008; Shuman & Shugart, 2009) where there is no stochastic element and thus replication is not relevant. In such models, such as the DGVMs used in this study, a common use of ANOVA is to assess the average contribution of each input factor (main effect) and their interactions to the

6 652 Research New overall outcome variance, defined as the sum of the squared differences between each individual simulation and the overall mean (Campolongo & Saltelli, 2000). In the context of this experiment, for factor x, the main effect is equivalent to DC veg in the simulation where only factor x is varied, minus DC veg from the control simulation. Interactions are present where the effect of one factor depends on the level of another factor, and interaction effects quantify the additional combined effects of factors on the response variable. The main effects and interactions of all factors were estimated for each DGVM and scenario, and these are presented both in terms of DC veg and in terms of the fraction of overall variance accounted for. Effect of other variables not included in factorial analysis In addition to the factorial simulations, one further simulation (TRI-ALL in Table 1) for each scenario was run for the TRIFFID model to incorporate the additional effect of other variables predicted to change by HadCM3, but not used as inputs in the other two models. These variables included the windspeed and downward longwave radiation which are used to calculate the surface energy balances and surface temperatures in MOSES-TRIFFID. To simplify the analysis, these additional variables were treated together. Their combined effect was calculated as the difference relative to the simulation with only the four main factors included. Partitioning of direct and indirect effects of temperature Additional simulations were conducted to separate the effect of temperature into direct and indirect effects on plant physiology (Fig. 1). In Hyland and TRIFFID, the indirect effect of temperature via increased VPD was explicitly represented, while in LPJ, the indirect effect was via the effect of temperature on equilibrium evapotranspiration rates. To separate these effects, we performed an additional simulation (T-IND in Table 1) for each model, whereby we supplied the plant physiology components of the model with temperatures as calculated in the control simulation, while calculating VPD (Hyland and TRIFFID) equilibrium evapotranspiration rates (LPJ) with temperature from HadCM3 simulations for the A2 scenario. All other environmental variables were held at their control values. We also conducted another simulation (T-PHOT in Table 1) for each model which sought to separate the contributions of plant respiration and of photosynthesis. In this case, we calculated respiration components using temperatures from the control simulation, while photosynthesis was calculated using temperatures from HadCM3 simulations for the A2 scenario. Sensitivity of DC veg to temperature dependence of plant respiration To examine the sensitivity of vegetation carbon change to the prescription of plant respiration dependence on temperature, we conducted new simulations (RESP-FIX and RESP-EXP in Table 1), including spin-up and historical runs ( ) with two alternate representations of plant respiration, either an explicit temperature dependence independent of that of photosynthesis or a constant fraction (0.5) of GPP. Both these simulations used temperatures from the HadCM3 simulation for the A2 scenario, with all other environmental variables held at control climatology values. Table 1 provides further details of these simulations. Simulation of Amazonian throughfall exclusion (TFE) experiments Runs were conducted with each DGVM where we attempted to simulate the throughfall exclusion (TFE) experiments at Caxiuanã National Forest and at Tapajós National Forest. In these runs, we applied the same spin-up procedure and ran the models as described above but with CRU climate data ( ) for the two grid cells containing these sites. We then applied a 50% stepped reduction in incident rainfall for a period of 10 yr. The default configurations of the models were used for these runs (i.e. no site-specific parameterizations were applied). We compared the simulated changes in biomass with observed values at each site (Brando et al., 2008; da Costa et al., 2010). Results Evaluation of simulated current biomass Hyland and LPJ both simulated a total present-day vegetation carbon of c. 115 Pg C over the Amazonian region used in this study (c. 6 million km 2 ), while TRIFFID simulated a total current vegetation carbon of c. 77 Pg C. These values are within the published range of Amazonian biomass estimates (Table 2), from c. 60 to 140 Pg C for a similar area. Fig. 3 shows a comparison between the Saatchi et al. (2007) estimates and our modelled values. The models generally overestimated the Saatchi et al. (2007) values, particularly in areas with low biomass. This may partly be a result of the models relative insensitivity to low rainfall, as discussed in the section on the simulation of the Amazonian drought experiments. However, some of the discrepancy will be attributable to the fact that the models represent potential vegetation, while Saatchi et al. (2007) depicts actual vegetation. The medians of the observed and modelled data sets are rather close. Median simulated vegetation carbon ranged from 14.9 kg C m )2 in TRIFFID to

7 Research 653 Table 2 Comparison of modelled estimated of total Amazonian biomass with a number of literature estimates Source Vegetation carbon (Pg C) Area (km 2 ) Methodology Hyland DGVM (this study) Model potential vegetation only LPJ DGVM (this study) Model potential vegetation only TRIFFID DGVM (this study) Model potential vegetation only Saatchi et al. (2007) Remote sensing and field observations includes forest and nonforest biomes and managed land Malhi et al. (2006) Interpolation of field estimates Houghton et al. (2001) Seven approaches including field estimates, environmental gradients and remote sensing Vegetation carbon estimates include belowground vegetation carbon stocks but exclude dead biomass components. DGVM, dynamic global vegetation model. Fig. 3 Comparison of simulated biomass with remotely sensed estimates. The data on the x-axis are the mode of the 1-km biomass values from Saatchi et al. (2007) data for each of the 52 grid cells comprising the Amazonian region. The Saatchi et al. (2007) data were increased by 21% to account for belowground carbon stocks, to enable direct comparison with dynamic global vegetation model (DGVM) biomass estimates kg C m )2 in Hyland. Median observed vegetation carbon ranged from 15.1 kg C m )2 (Saatchi et al., 2007) to 18.0 kg C m )2 across 227 Amazonian plots compiled by Malhi et al. (2006). Factorial simulations with HadCM3 climate data With all factors included, all models simulated net losses in C veg between 2003 and 2100, except for Hyland in the A2, B1 and B2 scenarios (the net effect in Fig. 4). CO 2 had the largest effect of any single variable, and consistently increased C veg (Table 3, Fig. 4). Gains in C veg under increased CO 2 were highest in the Hyland model ( Pg C; %) and lowest in the LPJ model (16.5 s 33.9 Pg C; %). C veg decreased in all DGVM SRES scenario combinations where CO 2 was held constant. Temperature change acted to decrease C veg across all models, though in a rather more variable way, and had the greatest effect in TRIFFID, where increased temperature led to a decrease in C veg of Pg C ( % relative to 2002). In Hyland, the contribution of temperature to DC veg varied widely between emission scenarios, ranging from )2.9 to )34.1 Pg C ( %). In LPJ, rising temperatures accounted for a loss of Pg C ( %). In LPJ, the magnitude of the effects of precipitation and temperature increase were similar, causing decreases in C veg in all four scenarios (loss of Pg C; %). Of particular note is the finding that precipitation change contributed little to overall DC veg in Hyland and TRIFFID, being directly responsible for mean losses of 5.4 (4.6%) and 2.9 Pg C (3.8%), respectively, across all scenarios. In the case of TRIFFID, this is a magnitude less than the effect of temperature. In Hyland, decreases in humidity further reduced C veg in all four scenarios (losses of Pg C; %). In TRIFFID, other variables not included in the factorial analysis and which were not inputs to other models (see the section on Effect of other variables not included in factorial analysis ) also contributed significantly to losses of DC veg (loss of Pg C; %) and thus were important in determining the net DC veg (Fig. 4). In terms of variance accounted for, interaction terms among variables were relatively unimportant, explaining < 2% of the total variance in DC veg (Table 3). Interaction terms had the largest combined effect in the TRIFFID

8 654 Research New Table 3 Percentage of variance in the change in carbon present in the biomass of Amazonian vegetation (DC veg ) explained by each factor and their interactions Factor Hyland LPJ TRIFFID P C H T P C P H P T C H C T H T P C H P C T P H T C H T P C H T % variance explained by main effects % variance explained by interaction effects Results are the average from ANOVA analyses of all four IPCC scenarios. The factors are: P, precipitation; C, carbon dioxide; H, humidity; T, temperature. Fig. 4 Contribution of environmental factors to simulated changes in Amazonian vegetation carbon (C veg ) for four SRES scenarios in three dynamic global vegetation models (DGVMs). Main effects and the sum of all interaction terms, as quantified in the factorial ANOVA, are shown. The overall net effect of including all factors is shown as the inner grey bar. model (Fig. 4), where a considerable positive interaction term between CO 2 and temperature was observed (i.e. the net effect when both varying temperature and varying CO 2 were included in the simulations was more positive than the added individual effects of CO 2 and temperature for the simulations where only one of the variables was changed). In Hyland, the combined effect of the interaction terms was negative in the more severe (A1FI and A2), but positive in the less extreme (B1 and B2) scenarios. This is because the positive interaction between CO 2 and temperature is more important in the more conservative scenarios, whereas the negative interaction term between humidity and temperature becomes more important in the more severe scenarios. Interaction terms had virtually no effect in the LPJ model. The geographical distribution of DC veg is shown in Fig. 5 for a subset of the A1FI (most extreme) factorial simulations. Increased CO 2 on its own led to increased C veg across all Amazonian grid cells in all models (Fig. 5a). In Hyland, decreased relative humidity reduced C veg in all Amazonian grid cells, although this effect was slightly more pronounced in southeastern Amazonia (Fig. 5b). In TRIFFID, decreasing relative humidity had a small but variable effect. With decreased precipitation (Fig. 5c), Hyland and TRIFFID showed only small losses (generally < 10%) in C veg across most Amazonian grid cells. In fact, substantial losses of vegetation carbon as a result of rainfall reduction alone were largely restricted to grid cells in southeast Amazonia. These are located close to the semi-arid caatinga region of northeast Brazil, where all models simulate large losses of C veg in response to decreasing precipitation. In

9 Research 655 Fig. 5 Geographical distribution of changes in Amazonian vegetation carbon as simulated in a subset of factorial runs (HadCM3, A1FI scenario). The top four rows show results from simulations where all inputs were held constant at baseline values except CO 2 (a), relative humidity (b), precipitation (c) and temperature (d). The final row shows the results when all of these were varied (PCHT simulation; Table 1). Red indicates loss of carbon (% of original vegetation carbon), green indicates gain, and white indicates little or no change. The rectangle shows the Amazonian domain as defined in this study. both Hyland and TRIFFID, West Amazonia was found to be very insensitive to reduced rainfall, even under the most extreme SRES scenario. Although some grid cells in West Amazonia received up to 50% less rainfall in 2100 than in the historical period (Fig. 6a,c), C veg in West Amazonia was only reduced by an average of 6% in Hyland and 3% in TRIFFID, compared to average reductions of 10 and 13%, respectively, in East Amazonia. LPJ simulated DC veg of )21 and )26% in West and East Amazonia, respectively (Figs 5c, 6b, A1FI scenario). The geographical pattern of the response to temperature increase was generally consistent in all three models, with slightly greater loss of vegetation carbon in West Amazonia than in the East, reflecting HadCM3 predictions of higher temperatures in West Amazonia compared with East Amazonia. TRIFFID in particular simulated very high temperature-driven losses of C veg across all Amazonian grid cells, with over 85% of grid cells losing > 50% of original C veg in the A1FI scenario (Fig. 5d). Across all Amazonian grid cells, a much closer correspondence was observed between DC veg and change in temperature than change in precipitation (Fig. 6). This demonstrates that DGVM forests respond to precipitation reductions once a threshold (or tipping point) has been exceeded. In LPJ and TRIFFID, any rise in temperature, without changes in other environmental factors, was sufficient to induce losses in C veg, even in the most conservative (B1) scenario where some grid cells experienced an increase in temperature of < 2 C. In Hyland, the average DC veg caused by increased temperature was very small ()3.7% compared to )10.1% for LPJ and )26.6% for TRIFFID) in the B1 scenario and a few grid cells did not show reductions in C veg. With all factors included, losses in C veg were larger in West Amazonia than in East Amazonia in all three models (Fig. 5e).

10 656 Research New (a) (d) (b) (e) (c) (f) Fig. 6 Relationship between simulated change in vegetation carbon ( ) (kg m )2 ) and change in precipitation (left-hand panels) and temperature (right-hand panels) for all Amazonian grid cells. Data from all four SRES scenarios are shown. All other variables, including atmospheric CO 2 concentrations, are held at baseline values (simulations P and T in Table 1). Indirect vs direct temperature effects The indirect effect of temperature (via increased VPD or transpiration) was found to be a minor contributor to temperature-induced DC veg in the TRIFFID and LPJ models (Fig. 7), where it was responsible for 2% (1.3 out of 54 Pg C) and 6% (1.4 out of 22 Pg C) of the total temperatureinduced DC veg in the A2 scenario. In the Hyland model, the indirect effect of temperature was found to be more important than the direct effect of temperature on plant physiology, being responsible for approximately two-thirds (12 out of 19 Pg C) of temperature-induced DC veg in the A2 scenario. Analysis of stomatal conductance, evapotranspiration and soil moisture output for this model showed that the indirect effect of temperature on reduced GPP is a result of the effect of VPD on stomatal conductance, rather than changes in evapotranspiration and soil moisture status (data not shown). Separation of the direct effect of temperature on DC veg into the direct physiological effects on photosynthesis and respiration revealed that the two processes contributed approximately equally to temperature-driven DC veg in the TRIFFID model. In the LPJ model, reduced photosynthesis was the primary cause of temperature-driven DC veg, being responsible for approximately two-thirds of the total reduction, while increasing respiration was responsible for onethird. Fig. 7 Contributions of a direct effect of temperature on plant physiology (black bars) and an indirect effect via increased vapour pressure deficit (VPD; grey bars) and or transpiration to simulated changes in Amazonian vegetation carbon (C veg ). Other environmental factors (e.g. rainfall and CO 2 ) are held constant (simulation T-IND in Table 1).

11 Research 657 Effect of assumptions regarding temperature dependence of plant respiration The chosen parameterization of plant respiration dependence on temperature had significant consequences for predicted DC veg in all three models (Fig. 8). In the simulation where temperature was changed according to the A2 scenario and all other variables held at control values, C veg decreased by 96% by 2100 in the model configuration where plant respiration increases exponentially with temperature. This compares to only a 14% loss in the default configuration where plant respiration is a fixed fraction of GPP. With the same forcing data, LPJ simulated a 19% loss of C veg in the model configuration based on Lloyd & Taylor (1994), compared to a 10% loss in the fixed fraction simulation. TRIFFID simulated a loss of 51% in C veg in the configuration where respiration varies exponentially with temperature while only resulting in a loss of 9.5% when plant respiration was considered to be a fixed fraction of GPP. Simulation of biomass changes at Amazonian TFE experiments All three models were found to be very insensitive to the simulated reductions in rainfall analogous to the conditions of the Amazonian TFEs (i.e. a stepped 50% reduction in rainfall, Fig. 9). The simulated TFE treatment had little effect in the Hyland simulations, while causing small reductions in biomass in LPJ (c. 5% loss of biomass after 10 yr of drought) and in TRIFFID (c. 2 3% loss of biomass). These simulated reductions are much lower than the reported losses of biomass of c. 20% following 7 yr of TFE treatment at Caxiuanã (da Costa et al., 2010) and c. 25% reduction following 4 yr of TFE treatment at Tapajós (Brando et al., 2008). Discussion Modelled mechanisms of Amazon rainforest DC veg This study found that, in the DGVMs considered, rainfall was not the most important driver of modelled Amazonian biomass reduction. Indeed, two of the DGVMs (Hyland and TRIFFID) were very insensitive to future reductions in rainfall. In these DGVMs, even in some grid cells receiving c. 50% less rainfall in 2100, precipitation-driven losses of C veg were minimal (< 10%), with substantial losses only occurring in grid cells receiving an annual rainfall in 2100 below a threshold value of c. 700 mm. This threshold was frequently exceeded in previous work using the fully coupled Hadley Centre GCM (Betts et al., 2004), because of much lower simulated present-day precipitation over the Amazon and the effects of land atmosphere feedbacks. Therefore, the coupled model may be more sensitive to future precipitation reduction than our DGVM simulations. LPJ showed the greatest sensitivity to reduced precipitation. Analysis of modelled soil moisture output of the three models revealed that soil moisture was depleted more for a given reduction in precipitation than in Hyland and TRIFFID. Thus, differences in the parameterization of soil hydraulic properties across models may partially explain the differences in sensitivity to reduced precipitation. Harris et al. (2004) found that calibrating soil parameters in the TRIFFID model with local data greatly reduced the amount Fig. 8 Impact of representing plant respiration as an exponential function of temperature (broken lines) or fixed fraction of productivity (solid lines) on simulated changes in Amazonian vegetation carbon (C veg ). In these simulations, all factors are held at present-day values (simulations RESP-FIX and RESP-EXP in Table 1).

12 658 Research New (a) (b) Fig. 9 Simulated effect of a precipitation reduction similar to that of experimental drought studies in Amazonia (i.e. a 50% reduction in incident rainfall from year 0 onwards) (simulation TFE in Table 1). The graphs show the response of aboveground biomass to imposed drought at (a) Caxiuanã National Forest and (b) Tapajós National Forest. of plant available water in the model and increased modelled soil moisture stress at two Amazonian sites. It is therefore feasible that use of more appropriate soil parameters (Harris et al., 2004; Fisher et al., 2008) could lead to greater drought sensitivity and increased losses of C veg in TRIFFID. Soil hydraulic parameters such as soil water holding capacity and soil moisture content at wilting point (the point where stomata close completely under water stress) are generally derived from studies in temperate ecosystems and may be unsuitable for tropical ecosystems. For instance, field studies have shown that tropical clay soils generally have lower bulk density, higher permeability and lower available water capacity than temperate soils (Tomasella & Hodnett, 2004). Temperature was found to be a major factor determining DC veg in all three models. The effect of temperature was especially pronounced in the TRIFFID model, where it was responsible for up to 12 times more loss of C veg than precipitation, but was important across all DGVMs. Interestingly, the mechanisms determining temperature-induced losses of C veg varied across models (Fig. 7). In Hyland, the indirect effect of temperature via increased VPD was the dominant mechanism, while in LPJ and TRIFFID the direct effects of temperature on plant physiology predominated. Moreover, in LPJ, temperature-induced decline in photosynthetic rate was more important in causing loss of C veg than temperature-driven increases in respiration, while in TRIFFID the two processes contributed approximately equally to biomass losses. Differences between models can be understood in terms of the underlying representations of the temperature dependences of photosynthesis and respiration in the models. In the current version, Hyland does not explicitly model plant respiration but assumes that it is a fixed fraction (0.5) of GPP. Application of this approach to the other two DGVMs greatly reduced the amount of modelled biomass loss. In turn, representing plant respiration as an exponential function of temperature in Hyland led to much increased losses of biomass in that model (Fig. 8). A common feature of the DGVMs in this study is that the response of photosynthesis to temperature is modelled with a fixed temperature optimum, and losses in C veg would be expected to occur once the optimal temperature is surpassed. The exact location of this fixed optimum varies between models (Fig. 10), but is particularly low in LPJ, where present-day temperatures over Amazonia already exceed the photosynthetic optimum. In LPJ, the optimum leaf temperature for photosynthesis in the dominant tree PFTs over Amazonia is 21 C, compared to 28 C in TRIFFID and 32 C in Hyland. Humidity was only found to be an important contributor to DC veg in the Hyland model, where it is a more important driver of DC veg than precipitation. In the other models used in this study, the effect of humidity on stomatal conductance was either nonexistent (LPJ) or very weak (TRIFFID). Stomatal conductance in Hyland is modelled according to the Jarvis Stewart multiplicative model (Jarvis, 1976; Stewart, 1988), where VPD has a strong effect on stomatal conductance, independent of the effects of soil moisture and temperature per se. Rising temperatures also contribute to VPD-induced stomatal closure through the Jarvis Stewart scheme, possibly explaining why the indirect effect of temperature is most important in Hyland.

13 Research 659 Fig. 10 Response of gross photosynthetic rate to leaf temperature for the dominant Amazonian tree plant functional type in three dynamic global vegetation models (DGVMs). CO 2 concentrations are set at 380 ppm, irradiance is fixed at 1000 W m )2, and no water stress is assumed. All rates are expressed as a fraction of the maximum rate. Dashed lines indicate mean Amazonian air temperature from 1983 to 2002, and mean Amazonian air temperature in 2100 under the most conservative (B1) and most severe (A1FI) scenarios considered in this study. All models showed high CO 2 fertilization responses, resulting in gains in forest biomass of up to 40% in 2100 relative to present-day biomass. This CO 2 effect mitigated most of the biomass loss attributable to climate change in our simulations. This is similar to the result of Lapola et al. (2009), who forced the CPTEC potential vegetation model (version 2) with climate data from an ensemble of IPCC AR4 models and reported that simulations without an included CO 2 fertilization response invariably led to shifts to drier and less productive biomes. Are modelled mechanisms of DC veg realistic? Our results raise the question of whether the modelled mechanisms driving losses of forest biomass are realistic. Here we review the evidence from observational studies in the region to gain insights into the plausibility of model predictions and reach four broad conclusions. Conclusion 1: the DGVMs used in this study may be underestimating the effect of soil moisture stress The two throughfall exclusion experiments in Amazonia, where throughfall reaching the soil was reduced by c. 50%, indicate that the Amazon is more sensitive to decreases in precipitation than the DGVMs in this study suggest. In fact, the models used in this study were unable to capture the considerable (20 30%) reductions in standing biomass observed at both Amazonian TFE experiments following 4 7 yr of drought treatment (Fig. 9). Further evidence of the vulnerability of Amazonian rainforest to reduced precipitation comes from studies of natural drought events. Phillips et al. (2009) reported that the 2005 drought that affected large areas of Amazonia was in fact sufficient to reverse the regional long-term carbon sink. Interestingly, the HadCM3LC coupled climate carbon cycle model appears to be able to reproduce 2005-like droughts (Cox et al., 2008), although our results suggest that its land model (MOSES-TRIFFID) may in fact be undersensitive to rainfall reductions. Other studies examining the effect of El Niño events in Amazonia also show almost immediate effects on tree growth and mortality, although these are often short-lived (Williamson et al., 2000). We note that this apparent insensitivity to drought occurs despite the absence of a number of phenomena which may confer additional resilience to seasonal and multi-year drought, such as hydraulic redistribution (Lee et al., 2005; Oliveira et al., 2005) and deep rooting depths (Williams et al., 1998; Baker et al., 2008; Poulter et al., 2009). Conclusion 2: the DGVMs used in this study may be overestimating the effect of temperature on Amazonian carbon balance Little experimental work has been carried out in Amazonia to assess the mid- to long-term impact of elevated temperatures on the Amazonian rainforest carbon balance. No large-scale warming experiments, analogous to the TFE experiments, exist in the Amazon. However, increasing temperature has been associated with reductions in tree growth in Central American and Malaysian rainforests (Clark et al., 2003; Feeley et al., 2007) and has also been implicated as an important causal factor of increasing tree mortality in western North America (van Mantgem et al., 2009). The photosynthetic temperature responses in the DGVMs closely approximate the limited field measurements existing for the Amazon and other tropical regions. For example, Tribuzy (2005) and Doughty & Goulden (2008) report a decline in leaf photosynthetic rates at c. 30 C, which is in agreement with the temperature optima in Hyland and TRIFFID and with other tropical studies (Koch et al., 1994; Keller & Lerdau, 1999; Graham et al., 2003; Leakey et al., 2003). Most of these studies, however, have generally focused on short-term fluctuations in photosynthetic rate at the individual leaf level. None of the models used in this study accounts for the possibility of acclimation of both photosynthesis and respiration to rising

14 660 Research New Table 4 Nonexhaustive list of potentially important processes for modelling climate change impacts on Amazonian biomass that are not generally included in the current generation of dynamic global vegetation models (DGVMs) Process Scientific consensus Model development status Increased decreased sensitivity of Cveg to changes in climate and atmospheric composition (relative to non-inclusion in DGVMs) Key references Physiological physical processes Thermal acclimation of photosynthesis Acclimation of photosynthetic capacity to high CO2 Thermal acclimation of respiration Response of plant respiration components to drought Nutrient limitation on CO2 fertilization Supported by a number of studies, although greater process-based understanding is required Limited data available for tropical ecosystems FACE studies support acclimation of photosynthetic capacity to CO2, but this has a very limited impact on carbon gains under high CO 2. No FACE experiments exist for tropical ecosystems Supported by a number of studies, although greater process-based understanding is required (Atkin et al., 2005) Respiration generally inhibited less than photosynthesis under drought; responses of different tissues may vary; root respiration decreases more than leaf respiration, which may even increase under drought Growth of Amazon rainforests is thought to be limited by phosphorus (Quesada et al., 2009). However, there is little experimental evidence linking P availability and CO2 fertilization. Nitrogen believed to be nonlimiting in Amazonia apart from regrowing forests Hydraulic redistribution Observed across range of ecosystems including Amazonia Ecological processes Drought-induced tree mortality (especially of large canopy trees) through xylem cavitation or carbon starvation Strong evidence that mortality of large trees is the major driver of biomass reductions during drought in Amazonia but the physiological mechanism for this still requires investigation Not incorporated Decreased: GPP maintained at higher temperatures Not incorporated Increased: any acclimation should reduce carbon gain but the magnitude of the effect is uncertain Included in a small number of ecosystem models (Wythers et al., 2005 and King et al., 2006) and in one DGVM (Atkin et al., 2008) Often no explicit effect of drought on plant respiration components. However, there are indirect effects. First, via reduced respiratory substrate as a result of reduced photosynthesis and second via changes in leaf temperature due to changes in stomatal conductance affecting surface energy balance. Fully interactive N cycle included in small number of models (e.g. Xu-Ri & Prentice, 2008) but P not incorporated Included in a small number of ecosystem models (Lee et al., 2006; Baker et al., 2008 Drought mortality processes generally not mechanistically incorporated Decreased: NPP would be maintained at higher temperatures Possibly increased: respiration may increase more under moisture stress than currently simulated, resulting in lower NPP Possibly increased: nutrient limitation (especially P) may decrease uptake rate of CO 2 in Amazonia. However, some mechanisms exist that might enhance P uptake under high CO2 (Lloyd et al., 2001) Decreased (short-term): might lead to short-term increase in water nutrient availability Possibly increased: may lead to faster declines in biomass than current model approaches Medlyn et al. (2002); Hikosaka et al. (2006); Kattge & Knorr (2007) Leakey et al. (2009) Atkin & Tjoelker (2003); Atkin et al. (2005); Atkin et al. (2008), King et al. (2006), Wythers et al. (2005) Flexas et al. (2006); Meir et al. (2008); Atkin & Macherel (2009); Metcalfe et al. (2010) McKane et al. (1995); Lloyd et al. (2001); Xu-ri & Prentice (2008); Quesada et al. (2009); Wang & Houlton (2009) Oliveira et al. (2005); Li et al. (2005), Baker et al. (2009) Nepstad et al. (2007); McDowell et al. (2008); da Costa et al. (2010)