When forced by a ‘business-as-usual’ (IS92a) emissions scenario, a version of the Hadley Centre general circulation model (GCM) (Gordon et al. 2000) extended to model the global carbon cycle (including a dynamic global vegetation model (DGVM)) predicts that climate change could cause a major loss of the Amazon rainforest (Cox et al.
2000). Besides acting as a positive feedback on climate, whereby additional carbon dioxide is released back into the atmosphere, the loss of the rainforest in itself would clearly be a significant environmental matter. Cox et al. (2004) suggest that the main driver of such ‘dieback’ could, qualitatively, be related to GCM projections of persistent ‘El Nin˜o-like’ oceanic conditions, triggering major rainfall reductions over the Amazon Basin.Further analysis by Harris et al. ( 2008) demonstrates that the changes in rainfall may be more complex and that the modelled GCM drying is also forced by predicted changes in the gradient of Atlantic sea surface temperatures between the Northern and Southern Hemispheres. Cox et al.
(in preparation) analyse the Amazonian drought of 2005, and provide evidence that, for that particular year, the north–south gradient of Atlantic sea surface temperatures was anomalously high. The particular format and timing of the drought are consistent with modelled emerging signals by HadCM3.It is important to understand the uncertainties related to the Cox et al. (2000) prediction of dieback. These can arise through uncertainty in both simulated regional climate change and the modelled land surface response.
We use perturbed physics ensembles of HadCM3 (part of the ‘quantifying uncertainty in model predictions’ (QUMPs) initiative) to explore uncertainties in the predicted climate drivers affecting future Amazon rainforest stability. The ensembles extend the Murphy et al. (2004) work to fully transient simulations of historical and future climate (using the SRES A1B scenario; Nakic´enovic´ & Swart 2000; Collins et al.
2006; Murphy et al. 2007). Two uncertainties in the structure of the land surface model are considered, where enhanced ecological realism addresses potential weaknesses in the original modelling system.
First, we introduce a more advanced representation of canopy light interception containing an explicit description of interception for different canopy levels (Sellers 1985), giving a multilayer approach to scaling from leaf- to canopy-level photosynthesis. Second, we consider the contribution of the representation of vegetation dynamics to the dieback response, by replacing the TRIFFID DGVM with the ecosystem demography (ED) model (Moorcroft et al. 2001). ED is a size- and age-structured approximation of an individual-based gap model (Friend et al. 1997), modified to allow the gap model vegetation dynamics to be employed at large spatial scales.The ED individual-based model with its enhanced biophysical representation of vegetation is a logical step on from the TRIFFID model; the latter has a more empirical representation of vegetation competition and interaction. As both models are driven with the same canopy photosynthesis and surface exchange scheme, the contribution of modelled vegetation dynamics to dieback is isolated from that of plant physiology.
GCM simulations of century-scale climate change typically take three months to complete, even with supercomputing facilities. They are highly sophisticated numerical models of the climate, but these two aspects make it difficult to explore new numerical depictions of Earth system processes, such as the land surface response. Hence, a spectrum of modelling tools is required where complexity is retained in the processes of interest, but other components of the Earth system are approximated. The Integrated Model Of Global Effects of climatic aNomalies model (IMOGEN) strives to achieve this, combining the ‘GCM analogue model’ to emulate surface climate (Huntingford & Cox 2000) but with the full GCM land surface model. IMOGEN is described in Huntingford et al. (2004), where it was applied to the early analysis of potential Amazonian dieback.
In the standard IMOGEN system, CO2 emissions are prescribed and the model simulates terrestrial carbon fluxes from the land surface scheme and oceanic fluxes using the impulse response function of Joos et al. (1996), generating atmospheric CO2 concentrations. Here, we analyse ED model projections for the Amazon Basin, noting that ED was originally developed with plant functional types (PFTs) specific to Amazonia (Moorcroft et al.
2001). ED is not yet fully tested for temperate and boreal regions, preventing predictions of the global land– atmosphere net carbon exchange and hence atmospheric CO2 content for prescribed emissions of CO2. Instead, IMOGEN is run with prescribed CO2 concentrations identical to those derived by the Hadley Centre GCM contribution to the Coupled Carbon Cycle Climate Model Intercomparison Project (C4MIP; Friedlingstein et al. 2006). Although the ability of the land surface to affect atmospheric CO2 concentrations through large-scale biogeochemical feedbacks is lost, the vegetation change in Amazonia was responsible for only 10% of the total biosphere– atmosphere positive feedback predicted by Cox et al.
(2000, 2004).Huntingford & Cox (2000) demonstrate that, to a reasonable level of accuracy, surface climate (by both geographical position and season) as depicted by HadCM3 transient simulations exhibits linearity in global mean temperature over land. We recalculate such propagating patterns based on each member of the QUMP perturbed physics ensemble, and hence the IMOGEN system explores how Amazon dieback is sensitive to different predictions of surface climate. The IMOGEN system is also used to consider how altered representations of light interception and vegetation dynamics influence the rainforest response to simulated drying and raised temperatures.For the IMOGEN simulations we perform, the trajectory of climatic forcing is similar to that of the QUMP simulations themselves (so the GCM ‘analogue model’ component of IMOGEN could have been overridden with direct climatological predictions from the QUMP ensemble).
However, the existence of propagating patterns of climatological change allows extrapolation of existing GCM simulations to a range of different emission profiles. Hence, the system presented below is now available for future simulations corresponding to a diverse range of future pathways in atmospheric greenhouse gas concentrations, including uncertainty bounds based on the QUMP simulations.
(a) Perturbed physics simulations
Climatological driving data required by IMOGEN are created based on 16 perturbed physics transient HadCM3 simulations. These simulations translate uncertainties first explored in Murphy et al. (2004) and extended by Webb et al.
(2006) into transient climate responses over the historical period and future (to the year 2100, using the SRES A1B scenario) by incorporating a dynamical ocean component. The 16-member ensemble samples uncertainties in cloud and atmospheric processes, land surface and sea ice parametrizations. The methodology for these simulations is described in Collins et al. (2006), although our analysis uses a subsequently refined set of 16 perturbed physics simulations with reduced(b) Impact of a multilayer canopy light interception model
The MOSES land surface scheme used by Cox et al. (2000) assumes the functioning of the plant canopy scales as a ‘big-leaf’ and follows Beer’s law. We introduce a more realistic depiction of light levels within a canopy (Jogireddy et al. 2006; Mercado et al.
2007), calculate its effect on stomatal conductance and thus control on photosynthesis and evaporation, and determine the impact on modelled vegetation and soil carbon for the Amazon rainforest during the twentyfirst century. We include an explicit scaling-up from leaf-to-canopy, using a multilayer canopy radiation interception algorithm based on an analytical twostream model (Sellers 1985). For such a multilayer approach, absorption and scattering losses of incident radiation, for both direct and diffuse radiation, are calculated at different levels in the canopy.These include contributions from the visible and nearinfrared wavebands, from which the absorbed photosynthetically active radiation (PAR) is derived.
Using the calculated absorbed PAR at each layer of the canopy, leaf photosynthesis, leaf respiration and stomatal conductance are calculated and summed to provide canopy values. The parametrization of the vertical profile of leaf nitrogen through the canopy has also been modified to follow observations from a site in central Amazonia (Mercado et al. 2007). The observed vertical profile of nitrogen is less steep than that predicted under the original Beer’s law (this implies higher total canopy nitrogen when observed profiles are used).The improved light interception model has been successfully tested against eddy correlation measurements for a rainforest site in Manaus (Mercado et al.
2007); there the authors found the main improvement of introducing the ‘two-stream/multilayer’ description was to allow a more realistic modelling of the response of photosynthesis to light, and the associated impact on the diurnal cycle of modelled carbon and water fluxes. The MOSES-modelled photosynthesis tends to saturate quickly for increasing solar radiation, generating a ‘flat’ response in the middle of the day, whereas measurements indicate photosynthetic response to varying light levels for the entire diurnal period.The introduced scheme simulates higher gross primary production, but lower net primary production (and thus lower plant and soil carbon pools relative to the original big-leaf simulation; figure 2). This is as a consequence of significantly higher plant respiration costs associated with the higher canopy nitrogen contents. Overall, this improved treatment of radiation absorption yields little alteration (when using prescribed patterns of climate change based on the HadCM3 simulation of Cox et al.
(2000)) to the original dieback result obtained with the standard MOSES model (figure 2). The dominant cause of dieback remains to be the prescribed reduced rainfall causing severe soil moisture stress, affecting both simulations (figure 2) independent of the improved description of photosynthetic behaviour.
(c) Introduction of the ED model
Cox et al. (2000) used the TRIFFID DGVM, based on a large-scale competition between trees, shrubs and grasses. The dominant cover is determined by the balance between ability to ‘fix’ carbon by photosynthesis and loss of carbon by litterfall. The combination of both modelled warming and simultaneous rainfall decreases by HadCM3 means that trees are projected to become unsustainable, and the dominant vegetation type then becomes shrubs. Towards the end of the twenty-first century, these are superseded by first grasses and finally desert. The TRIFFID model is described in Cox (2001) and the behaviour of the dominant vegetation class and route to dieback is given in Huntingford et al.
(2000).We replaced the TRIFFID DGVM with the ED model in IMOGEN. The ED model (Moorcroft et al. 2001) is unique among DGVMs using a size- and agestructured approximation of a gap model, to allow both operation at a large spatial scale and representation of vegetation dynamics, turnover, competition and mortality in an ecologically realistic fashion. ED is controlled by parameters that are more amenable to ground measurements (a criticism of existing DGVMs is that their parametrization of vegetation dynamics, competition and species replacement is often difficult to constrain with ecological data). The parameters of the vegetation dynamics component (including specific leaf area, wood density, leaf lifespan, mortality rates, allocation patterns and PFTs) were all derived from ground measurements3. Discussion and Conclusion
The impact of uncertainties in modelled climate response on Amazon rainforest sustainability for increasing concentrations of atmospheric greenhouse gases has been investigated using a ‘perturbed physics ensemble’. In the context of predictions by other modelling centres, the HadCM3 ensemble spans the range of global mean temperature responses in the AR4 multi-model ensemble (Collins et al. 2006), but samples a smaller range of the precipitation uncertainty in the Amazon region.
Cox et al. (2004) suggest a relationship between wet season precipitation and trends in the future El Nin˜o-Southern Oscillation (ENSO) state. The ensemble members share to a greater or lesser extent the tendency in the original HadCM3 response towards an enhanced El Nin˜o-like state in the future (and hence wet season reduction of rainfall).However, Collins et al. (2005) conclude that across different GCMs, there is a roughly equal likelihood between El Nin˜o or La Nin˜a trends among the multi-model ensemble. Good et al. ( 2008) illustrate further a linkage between shifts in the Intertropical Convergence zone and the dry season rainfall in this region. That the perturbed physics ensemble does not capture the full range in future rainfall responses is an important caveat that should be addressed in future work.
Nevertheless, the response of climate drivers in the HadCM3 family of models remains credible for the Amazon region and the robustness of the dieback result to the uncertainty in climate drivers for that GCM represents a substantial step forward in predictions.IMOGEN, now calibrated against the QUMP ensemble, is available to assess the likelihood of dieback for a range of emissions trajectories. These could include pathways to atmospheric stabilization (e.g.
those of Wigley et al. 1996) or the emerging concept of climate ‘overshoot’ (e.g.
Huntingford & Lowe 2007), whereby a potentially dangerous level of climate change is found to have beenFigure 4. Spatial representation of vegetation carbon for the identical simulation with the ED model as given in figure 3. (a) The pre-industrial period and (b) centred on the last decade of the twenty-first centurypassed, followed by massive reductions in emissions in an attempt to fall back below that level. For the Amazon rainforest, this raises issues regarding hysteresis and recovery from any dieback. The sensitivity of modelled Amazon dieback to the description of the land surface model has been explored. In parallel calculations (see Sitch et al.
in press), five DGVMs are coupled to IMOGEN (again with patterns of climate change based on HadCM3) and forced with four different SRES CO2 emission scenarios. The quantitative response of the DGVMs to drought differs among models, with TRIFFID and Hyland DGVMs most sensitive to reduced rainfall and elevated temperatures across Amazonia, whereas LPJ and Orchidee simulate moderate forest dieback. Salazar et al. (2007) run the CPTEC-PVM (Oyama & Nobre 2004) potential vegetation model with future climatologies from 15 climate models and for two different SRES emission scenarios (A2 and B1). Their results project a reduction in forest coverage for all simulations despite large uncertainties in both magnitude and sign of climate model projections of future rainfall across Amazonia.Salazar et al. (2007) highlight that for GCMs predicting higher future rainfall amounts across the Amazon Basin, elevated temperatures alone are sufficient to cause conversion of forest to savannah ecosystems.
Hence, Amazon rainforest dieback may be less sensitive to the choice of GCM pattern of changing rainfall than hitherto expected. We have targeted two particular aspects of Amazon’s response: first, the introduction of a multilayer two-stream canopy light module, thus improving the light response of photosynthesis and diurnal cycles of carbon and water fluxes, and, second, the adoption of ED, a DGVM that represents a first step towards incorporating a greater process-based understanding of vegetation dynamics, turnover, competition and mortality. In all circumstances, we find that dieback is still probable by the end of the twenty-first century for the business-as-usual emission profile selected.Deforestation has a large impact on tropical forests (Achard et al. 2002). The effects of both deforestation and global warming are predicted to negatively impact Amazonian forest extent (Cramer et al. 2004; Salazar et al.
2007) and change both regional and global climate (Sitch et al. 2005; Costa et al. 2007). To further improve our ability to project the fate of Amazonian forests, ecosystem models need to incorporate land use and cover changes.Amazonian ecosystem models need further verification against carbon and water flux data. The majority of flux tower and experimental studies in the region do not detect any hydraulic limitation of evapotranspiration or gross primary productivity in the dry season, with many attributing this behaviour to the existence of deep roots (Hodnett et al. 1995; Grace et al.
1996; Arau´jo et al. 2002; Carswell et al. 2002; Saleska et al. 2003; da Rocha et al. 2004; Goulden et al. 2004; Fisher et al. 2007; Nepstad et al.
2007). Two examples where hydraulic limitation was measured are Malhi et al. (1998; which was tested against the IMOGEN surface model by Harris et al.
(2004)) and a more recent manipulation study (Fisher et al. 2007) finding that when a 50% reduction in through-fall was imposed on the forest, a large (up to 80%) reduction in forest transpiration (by implication, photosynthesis) resulted within a single year.These results suggest that the deep roots do not entirely buffer the forest from the imposed dry conditions and comparison of model predictions against all these observations remains a high research priority. It is probable that alterations of modelling rooting depths and the responses of vegetation to high temperatures (Salazar et al. 2007) are necessary to correctly simulate contemporary and future patterns of gas exchange. If the total rainfall falls below a threshold defined by the total evaporative demand, the effect of rainfall storage in the dry season becomes unimportant, so the impact of deep roots will probably delay the impact of any drying and dieback, but not be able to prevent it entirely if this threshold is breached.We have shown that the dieback result of Cox et al. (2000) is robust within the structural constraints of HadCM3 climatology across the existing atmosphere parameter uncertainty.
Large-scale forest dieback across Amazonia is a robust projection with enhanced representations of canopy light interception and with a more process-based DGVM, ED. The ED model was parametrized independently of any GCM, hence eliminating the risk of compensating biases between the climate and land surface models. These results, taken together with findings from other recent studies using multiple DGVMs, climate models and projections of land use and cover change, suggest that the Amazon rainforest must be considered to be highly vulnerable to future global change induced by raised concentrations of atmospheric greenhouse gases.
- Achard, F., Eva, H. D., Stibig, H.-J.
, Mayaux, P., Gallego, J., Richards, T. & Malingreau, J. P. 2002 Determination of deforestation rates of the world’s humid tropical forests.
Science 297, 999–1002. (doi:10.1126/science.1070656)
- Arau´jo, A. C. et al.
2002 Comparative measurements of carbon dioxide fluxes from two nearby towers in a central Amazonian rainforest: the Manaus LBA site. J. Geophys. Res. Atmos. 107, 8090.
- Betts, R. A., Cox, P. M., Collins, M., Harris, P. P.
, Huntingford, C. & Jones, C. D.
2004 The role of ecosystem–atmosphere interactions in simulated Amazonian precipitation decrease and forest die-back under global climate warming. Theor. Appl. Climatol.
78, 157–175. (doi:10.1007/s00704-004-0050-y)
- Betts, R., Sanderson, M. & Woodward, S. 2008 Effects of large-scale Amazon forest degradation on climate and air quality through fluxes of carbon dioxide, water, energy, mineral dust and isoprene. Phil. Trans.
R. Soc. B 363, 1873–1880. (doi:10.1098/rstb.
- Carswell, F. E. et al. 2002 Seasonality in CO2 and H2O flux at an eastern Amazonian rain forest.
J. Geophys. Res. Atmos. 107, 8076. (doi:10.1029/2000JD000284)
- Collins, M.
and the CMIP Modelling Groups. 2005 El Nin˜oor La Nin˜a-like climate change? Clim. Dynam. 24, 89–104.
- Collins, M., Booth, B. B. B., Harris, G. R., Murphy, J.
M., Sexton, D. M. H. & Webb, M. J.
2006 Towards quantifying uncertainty in transient climate change. Clim. Dynam. 27, 127–147. (doi:10.1007/s00382-006-0121-0)
- Costa, M.
H., Yanagi, S. N. M.
, Souza, P. J. O.
P., Ribeiro, A. & Rocha, E. J. P. 2007 Climate change in Amazonia caused by soybean cropland expansion, as compared to caused by pastureland expansion. Geophys. Res.
Lett. 34, L07706. (doi:10.1029/2007GL029271)
- Cox, P.
M. 2001 Description of the TRIFFID dynamic global vegetation model. Technical note 24, Hadley Centre, Met Office, Exeter, UK. Cox, P. M., Huntingford, C. & Harding, R. J.
1998 A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol. 213, 79–94. (doi:10.1016/ S0022-1694(98)00203-0)
- Cox, P.
M., Betts, R. A., Bunton, C.
, Essery, R. L. H.
, Rowntree, P. R. & Smith, J. 1999 The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim.
Dynam. 15, 183–203. (doi:10. 1007/s003820050276)
- Cox, P. M., Betts, R. A.
, Jones, C. D., Spall, S. A. & Totterdell, I. J. 2000 Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187.
- Cox, P. M., Betts, R. A.
, Collins, M., Harris, P. P., Huntingford, C. & Jones, C.
D. 2004 Amazonian forest die-back under climate-carbon cycle projections for the 21st century. Theor. Appl.
Climatol. 78, 137–156. (doi:10. 1007/s00704-004-0049-4)
- Cox, P. M., Harris, P. P.
, Huntingford, C., Betts, R. A., Collins, M.
, Jones, C. D., Marengo, J.
& Nobre, C. In preparation. The 2005 Amazonian drought in the context of climate change.
- Cramer, W., Bondeau, A., Schaphoff, S., Lucht, W.
, Smith, B. & Sitch, S. 2004 Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation. Phil. Trans. R. Soc. B 359, 331–343.
- daRocha,H.R., Goulden,M. L., Miller, S.
D.,Menton, M. C., Pinto, L.
D. V. O.
, de Freitas, H. C. & Figueira, A. M.
E. S. 2004 Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia. Ecol. Appl.
- Essery, R. L. H.
, Best, M. J., Betts, R. A., Cox, P. M.
& Taylor, C. M. 2003 Explicit representation of subgrid heterogeneity in a GCM land surface scheme. J.
Hydrometeorol. 4, 530–543. (doi:10.1175/1525-7541 (2003)004!0530:EROSHIO2.0.CO;2)
- Fisher, R. A.
, Williams, M., Lola da Costa, A., Malhi, Y., da Costa, R. F., Almeida, S. & Meir, P.
2007 The response of an Eastern Amazonian rain forest to drought stress: results and modelling analyses from a throughfall exclusion experiment. Glob. Change Biol. 13, 1–18. (doi:10.1111/ j.1365-2486.2006.
- Friedlingstein, P. et al. 2006 Climate-carbon cycle feedback analysis: results from the (CMIP)-M-4 model intercomparison.
J. Clim. 19, 3337–3353. (doi:10.1175/ JCLI3800.1)
- Friend, A. D., Stevens, A.
K., Knox, R. G. & Cannell, M. G. R. 1997 A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.
0). Ecol. Model. 95, 249–287.
- Gash, J. H. C. & Nobre, C. A. 1997 Climatic effects of Amazonian deforestation: some results from ABRACOS.
Bull. Am. Meteorol. Soc. 78, 823–830. (doi:10.
- Good, P., Lowe, J. A.
, Collins, M. & Moufouma-Okia, W. 2008 An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dryseason climate variability and change. Phil. Trans. R. Soc. B 363, 1761–1766. (doi:10.1098/rstb.2007.0024)
- Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B. & Wood, R. A. 2000 The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim. Dynam. 16, 147–168. (doi:10.1007/s003820050010)
- Goulden, M. L., Miller, S. D., da Rocha, H. R., Menton, M. C., de Freitas, H. C., Figueira, A. M. E. S. & de Sousa, C. A. D. 2004 Diel and seasonal patterns of tropical forest CO2 exchange. Ecol. Appl. 14, S42–S54. (doi:10. 1890/02-6008)
- Grace, J., Malhi, Y., Lloyd, J., McIntyre, J., Miranda, A. C., Meir, P. & Miranda, H. S. 1996 The use of eddy covariance to infer the net carbon dioxide uptake of Brazilian rain forest. Glob. Change Biol. 2, 209–217. (doi:10.1111/j.1365-2486.1996.tb00073.x)
- Harris, P. P., Huntingford, C., Gash, J. H. C., Hodnett, M. G., Cox, P. M., Malhi, Y. & Araujo, A. C. 2004 Calibration of a land-surface model using data from primary forest sites in Amazonia. Theoret. Appl. Climatol. 78, 27–45. (doi:10.1007/s00704-004-0042-y)
- Harris, P. P., Huntingford, C. & Cox, P. M. 2008 Amazon basin climate under global warming: the role of the seasurface temperature. Phil. Trans. R. Soc. B 363, 1753–1759. (doi:10.1098/rstb.2007.0037)
- Hodnett, M. G., Dasilva, L. P., Darocha, H. R. & Senna, R. C. 1995 Seasonal soil water storage changes beneath central Amazonian rainforest and pasture. J. Hydrol. 170, 233–254. (doi:10.1016/0022-1694(94)02672-X)
- Huntingford, C. & Cox, P. M. 2000 An analogue model to derive additional climate change scenarios from existing GCM simulations. Clim. Dynam. 16, 575–586. (doi:10. 1007/s003820000067)
- Huntingford, C. & Lowe, J. 2007 Overshoot scenarios and climate change. Science 316, 829. (doi:10.1126/science. 316.5826.829b)
- Huntingford, C., Cox, P. M. & Lenton, T. M. 2000 Contrasting responses of a simple terrestrial ecosystem model to global change. Ecol. Model. 134, 41–58. (doi:10. 1016/S0304-3800(00)00330-6)
- Huntingford, C., Harris, P. P., Gedney, N., Cox, P. M., Betts, R. A., Marengo, J. A. & Gash, J. H. C. 2004 Using a GCM analogue model to investigate the potential for Amazonian forest die-back. Theor. Appl. Climatol. 78, 177–186. (doi:10.1007/s00704-004-0051-x)
- Jogireddy, V., Cox, P. M., Huntingford, C., Harding, R. J. & Mercado, L. M. 2006 An improved description of canopy light interception for use in a GCM land-surface scheme: calibration and testing against carbon fluxes at a coniferous forest. Hadley Centre Technical note 63, The Hadley Centre, Exeter, UK.
- Joos, F., Bruno, M., Fink, R., Siegenthaler, U. & Stocker, T. F. 1996 An efficient and accurate representation of complex oceanic and biospheric models of anthropogenic carbon uptake. Tellus B 48, 397–417. (doi:10.1034/j.1600- 0889.1996.t01-2-00006.x)
- Malhi, Y., Nobre, A. D., Grace, J., Kruijt, B., Pereira, M. G. P., Culf, A. & Scott, S. 1998 Carbon dioxide transfer over a Central Amazonian rain forest. J. Geophys. Res. 103, 31 593–31 612. (doi:10.1029/98JD02647)
- Meehl, G. A. et al. 2007 Global climate projections. In Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change (eds S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor & H. L. Miller), pp. 747–845. Cambridge, UK; New York, NY: Cambridge University Press.
- Mercado, L. M., Huntingford, C., Gash, J. H. C., Cox, P. M. & Jogireddy, V. 2007 Improving the representation of radiation interception and photosynthesis for climate model applications. Tellus B 59, 553–565. (doi:10.1111/ j.1600-0889.2007.00256.x)
- Moorcroft, P. R., Hurtt, G. C. & Pacala, S. W. 2001 A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol. Monogr. 71, 557–586.
- Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M. & Stainforth, D. A. 2004 Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768–772. (doi:10.1038/nature02771)
- Murphy, J. M., Booth, B. B. B., Collins, M., Harris, G., Sexton, D. & Webb, M. 2007 A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Phil. Trans. R. Soc. A 365, 1993–2028. (doi:10.1098/rsta.2007.2077)
- Nakic´enovic´, N. & Swart, R. (eds) 2000 IPCC special report on emissions scenarios, p. 570. Cambridge, UK: Cambridge University Press.
- Nepstad, D. C. et al. 1999 Large-scale impoverishment of Amazonian forests by logging and fire. Nature 398, 505–508. (doi:10.1038/19066)
- Nepstad, D. C., Tohver, I. M., Ray, D., Moutinho, P. & Cardinot, G. 2007 Mortality of large trees and lianas following experimental drought in an amazon forest. Ecology 88, 2259–2269. (doi:10.1890/06-1046.1)
- New, M., Hulme, M. & Jones, P. 2000 Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238. (doi:10.1175/ 1520-0442(2000)013!2217:RTCSTCO2.0.CO;2)
- Oyama, M. D. & Nobre, C. A. 2004 A simple potential vegetation model for coupling with the simple biosphere model (SIB). Rev. Bras. Meteorol. 1, 203–216.
- Salazar, L. F., Nobre, C. A. & Oyama, M. D. 2007 Climate change consequences on the biome distribution in tropical South America. Geophys. Res. Lett. 34, L09708. (doi:10. 1029/2007GL029695)
- Saleska, S. R. et al. 2003 Carbon in Amazon forests: unexpected seasonal fluxes and disturbance-induced losses. Science 302, 1554–1557. (doi:10.1126/science.1091165)
- Sanderson, M. G., Jones, C. D., Collins, W. J., Johnson, C. E. & Derwent, R. G. 2003 Effect of climate change on isoprene emissions and surface ozone levels. Geophys. Res. Lett. 30, 1936. (doi:10.1029/2003GL017642)
- Sellers, P. J. 1985 Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 6, 1335–1372. (doi:10. 1080/01431168508948283)
- Sitch, S., Brovkin, V., von Bloh, W., van Vuuren, D. & Ganopolski, A. 2005 Impacts of future land cover changes on atmospheric CO2 and climate. Glob. Biogeochem. Cycles 19, GB2013. (doi:10.1029/2004GB002311)
- Sitch, S. et al. In press. Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon feedbacks using 5 Dynamic Global Vegetation Models (DGVMs). Glob. Change. Biol.
- Webb, M. J. et al. 2006 On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim. Dynam. 27, 17–38. (doi:10.1007/ s00382-006-0111-2)
- Wigley, T. M. L., Richels, L. R. & Edmonds, J. A. 1996 Economic and environmental choices in the stabilisation of atmospheric CO2 concentrations. Nature 379, 242–245. (doi:10.1038/379240a0)
- Woodward, S., Roberts, D. L. & Betts, R. A. 2005 A simulation of the effect of climate-change induced desertification on mineral dust aerosol. Geophys. Res. Lett. 32, L18810. (doi:10.1029/2005GL023482)