amazonian forest dieback under climate-carbon cycle...

Theor. Appl. Climatol. (2004)DOI 10.1007/s00704-004-0049-4

1 Hadley Centre for Climate Prediction, Research, Met Office, Exeter, UK2 Department of Meteorology, University of Reading, Reading, Berks, UK3 Centre for Ecology and Hydrology, Wallingford, Oxon, UK

Amazonian forest dieback under climate-carboncycle projections for the 21st century

P. M. Cox1, R. A. Betts1, M. Collins2, P. P. Harris3,C. Huntingford3, and C. D. Jones1

With 10 Figures

Received March 28, 2003; revised August 16, 2003; accepted October 9, 2003Published online April 27, 2004 # Springer-Verlag 2004

Summary

The first GCM climate change projections to includedynamic vegetation and an interactive carbon cycleproduced a very significant amplification of global warmingover the 21st century. Under the IS92a ‘‘business as usual’’emissions scenario CO2 concentrations reached about980 ppmv by 2100, which is about 280 ppmv higher thanwhen these feedbacks were ignored. The major contributionto the increased CO2 arose from reductions in soil carbonbecause global warming is assumed to accelerate respira-tion. However, there was also a lesser contribution from analarming loss of the Amazonian rainforest. This paperdescribes the phenomenon of Amazonian forest diebackunder elevated CO2 in the Hadley Centre climate-carboncycle model.

1. Introduction

About half of the current anthropogenic emis-sions of carbon dioxide are being absorbed bythe ocean and by land ecosystems (Schimelet al., 1996). The processes involved are knownto be sensitive to climate. Temperature affects thesolubility of carbon dioxide in sea-water and therate of terrestrial and oceanic biological pro-cesses. Vegetation also responds directly to ele-vated CO2 through increased photosynthesis andreduced transpiration (Sellers et al., 1996; Field

et al., 1995), and may also change its structureand distribution in response to any associatedclimate change (Betts et al., 1997). The biospheretherefore has great potential to produce a feed-back on the climate change due to anthropogenicCO2 emissions. However, simulations carried outwith General Circulation Models (GCMs) havegenerally neglected the coupling between the cli-mate and the biosphere. Instead, vegetationdistributions have been static and atmosphericconcentrations of CO2 have been prescribed basedon results from simple carbon cycle models,which neglect the effects of climate change.Recently some groups have begun to include

representations of the carbon cycle within GCMs(Friedlingstein et al., 2001; Cox et al., 2001). Thefirst climate change projection to include both aninteractive carbon cycle and dynamic vegetationwas carried out at the Hadley Centre, and thisshowed a significant acceleration of CO2

increase and climate change arising from theadditional feedback loops (Cox et al., 2000).Under the IS92a ‘‘business as usual’’ emissionscenario the Hadley Centre coupled-climate car-bon cycle model produced a CO2 concentrationof about 980 ppmv by 2100, compared to about

700 ppmv when climate effects on the carboncycle were excluded. This resulted in an ampli-fication of global warming from about 4K toabout 5.5K.

Table 1 summarises the change in carbonstores from 1860–2100 with and without carboncycle feedbacks (‘‘online’’ and ‘‘offline’’ experi-ments respectively). In the offline case 1000GtCof the integrated emissions are absorbed by theland (633GtC) and oceans (367GtC). However,once climate effects on the carbon cycle areincluded land storage decreases by about98GtC over the 1860–2100 period. The netchange in land carbon storage of 731GtC isonly slightly offset by increased oceanic uptakeof 128GtC because of higher CO2. As a result,the fully coupled ‘‘online’’ run has 731–128¼603GtC more atmospheric carbon by 2100,which is equivalent to about 280 ppmv (seeFig. 1a).

Figure 1 shows the reason for this accelerationof climate change. In the absence of climatechange (dashed lines) the land takes up carbonas a result of CO2-fertilisation of growth, satu-rating at a global land carbon sink of about5.5GtC yr�1. The additional terrestrial carbon isstored in both vegetation and soils (respectiveincreases of about 223GtC and 410GtC from1860 to 2100). However, once climate-carboncycle feedbacks are included (continuous linesin Fig. 1) the historical land carbon sink isstrongly suppressed, and ultimately flips to be aglobal land carbon source from the middle of the21st century (after the atmospheric CO2 con-centration passes about twice its pre-industrialvalue). The negative impacts of climate changeare most evident in the soil carbon store whichreleases about 150GtC by 2100, rather thanstoring 410GtC.

Table 1. Change in carbon stores from 1860 to 2100 from the fully interactive climate-carbon cycle projection (‘‘Online’’ run),and a run in which there were no climate effects on the carbon cycle (‘‘Offline’’ run). Changes in South American soil andvegetation carbon are shown for comparison

‘‘Offline’’ ‘‘Online’’

CO2 emissions (GtC) 1883 1883Atmospheric Change (GtC) 883 1486Ocean Storage (GtC) þ367 þ495Global Land Storage (GtC) þ633 �98S. American Soil Carbon Storage (GtC) þ76 �55S. American Vegetation Carbon Storage (GtC) þ64 �73

Fig. 1. Impact of climate-carboncycle feedbacks on projectionsof (a) atmospheric CO2 concen-tration, (b) global land carbon up-take, (c) global vegetation carbonand (d) global soil carbon. Thecontinuous line represents the fullycoupled climate-carbon cycle runand the dashed line is from the runwithout climate effects on the car-bon cycle

P. M. Cox et al.

This response of soil carbon to climate changeis consistent with the standard assumption thatdecomposition occurs more rapidly in warmerconditions (Jenkinson et al., 1991). The HadleyCentre Dynamic Global Vegetation Model,‘‘TRIFFID’’, updates a single soil carbon storebased on litterfall inputs from vegetation, andsoil respiration (i.e. decomposition) losses whichare dependent on soil temperature and soil mois-ture, as well as the size of the soil carbon pool(Cox, 2001). Specific soil respiration (i.e. the rateper unit of soil carbon) is assumed to double withevery 10K increase in temperature (i.e. q10 ¼ 2),which represents a central estimate of the tem-perature sensitivity based on laboratory and fieldmeasurements (Raicha and Schlesinger, 1992).Doubts have been raised about the longer-termresponse of soil decomposition to warming(Giardina and Ryan, 2000), but interannual CO2

variability suggests that q10 ¼ 2 is a goodassumption at least out to the decadal timescale(Jones and Cox, 2001).

In soil models of this type, warming enhancesthe respiration loss term, reducing the size of therespiring soil carbon pool until the absoluterespiration flux (i.e. specific respiration rate�respiring soil carbon) once again balances thelitter inputs. Thus even such simple models ofsoil carbon dynamics produce ‘‘acclimation’’ ofthe absolute soil respiration flux to higher tem-peratures, but they do this at the cost of dynamicreductions in the size of the respiring carbonpool. As a result the land becomes an overallcarbon source at high CO2, provided a few sim-ple conditions are met (Cox et al., 2001).

Figure 1c shows that climate change also has anegative impact on carbon storage in vegetation,resulting in a reduction of global biomass fromthe middle of the 21st century onwards. As aconsequence the fully coupled run accumulatesonly about 55GtC of vegetation carbon from1860 to 2100, rather than about 223GtC whenclimate effects on the carbon cycle are excluded.The reduction in vegetation carbon is dominatedby South America which loses about 73GtC ofbiomass over the 1860–2100 period (see lowestrow of Table 1). Cox et al. (2000) describe this asdue to climate-driven ‘‘dieback’’ of the rainfor-est, resulting from regional drying in Amazonia.Such a loss of rainforest would have catastrophicimpacts on the biodiversity and ‘‘ecosystem

services’’ of Amazonia, similar to those antici-pated under the most extreme scenarios of directhuman deforestation (Nepstad et al., 1999). It istherefore vital that we estimate the risk ofclimate-driven Amazonian forest dieback, andidentify any relevant thresholds in the climate-carbon cycle system.This paper serves as an overview of collabora-

tive work which has been carried out to analyseaspects of the Hadley Centre’s climate-carboncycle model response in Amazonia. Subsequentpapers in this LBA special issue will deal spe-cifically with biophysical vegetation feedbacks(Betts et al., 2004), improvements to the parame-trization of land-atmosphere CO2 fluxes (Harriset al., 2004), and the impacts of these improve-ments plus errors in the GCM control climate onthe dieback phenomenon (Huntingford et al.,2004).The next section describes the Hadley Centre

climate-carbon cycle GCM, and Section 3 com-pares its simulation of Amazonian climate andvegetation to observations. The projections ofclimate change are described in Sections 4 and5, the vegetation response is discussed in Section6 and the related biophysical feedbacks are sum-marised in Section 7 (see Betts et al. (2004) forfurther details). Section 8 discusses outstandingquestions, and suggests the additional researchrequired to address these.

2. Model description

The climate-carbon cycle GCM used in thisstudy (‘‘HadCM3LC’’) is identical to that usedby Cox et al. (2000) and described in detailelsewhere (Cox et al., 2001). The GCM is basedon the third Hadley Centre coupled ocean-atmosphere model, HadCM3 (Gordon et al.,2000), coupled to an ocean carbon cycle model(‘‘HadOCC’’) and a dynamic global vegetationmodel (‘‘TRIFFID’’). The atmospheric physicsand dynamics are identical to those used inHadCM3, with a longitude–latitude grid of3.75� � 2.5� and 19 levels in the vertical. As inHadCM3, runoff generated on the land is routedto specified river outflow points using a discre-tised map of the largest river basins. The addi-tional computational expense of including aninteractive carbon cycle made it necessary toreduce the ocean horizontal resolution from the

Amazonian forest dieback under climate-carbon cycle projections for the 21st century

standard 1.25� � 1.25� in HadCM3, to a2.5� � 3.75� grid congruent with the atmosphericmodel. Heat and freshwater flux-adjustmentswere used in the ocean component to preventthe development of climate errors which mightcompromise the simulation of the land and oceancarbon cycles. Comparisons between HadCM3and HadCM3LC have shown very similar ratesand patterns of warming under the same scenario(and with fixed vegetation), despite the use offlux-adjustments in the latter.

2.1 Ocean carbon cycle

HadOCC simulates the movements of carbonwithin the ocean system, including exchange ofcarbon dioxide gas with the atmosphere, the cir-culation of dissolved inorganic carbon (known asDIC or tCO2) within the ocean, and the cyclingof carbon by the marine biota. The principlecomponents of the model are handled as tracerswithin the physical ocean model. They are:(nitrogenous) nutrient, phytoplankton, zooplank-ton, detritus, tCO2 and alkalinity.

The air-to-sea flux of carbon dioxide is calcu-lated using standard parametrizations:

FAS ¼ Kðca � coÞ ð1Þwhere ca and co are respectively the partial pres-sures of CO2 in the atmosphere and ocean at agiven location. K parametrizes the effect of thewindspeed on the gas transfer velocity, using theformulation of Wanninkhof (1992). Winds areobtained from the atmospheric model. The par-tial pressure of CO2 in the surface waters isdetermined by solving equations representingthe seawater acid-base system. The expressionsfor the dissociation constants of carbonic acid,hydrogen carbonate, boric acid and water andfor the solubility of CO2 in seawater are takenfrom DOE (1994). Using the salinity dependentboron concentration of Peng (1987), the acidbase system is solved using the method ofBacastow and Keeling (1981) to yield the con-centration of carbonic acid and hence the partialpressure of CO2. The temperature and salinityvalues used in these calculations are the localvalues from the ocean model.

The biological component of HadOCC is anexplicit ecosystem model consisting of the fourcomponents; nutrient (assumed to be nitrate),

phytoplankton, zooplankton and (sinking) detri-tus (Palmer and Totterdell, 2001). The complex-ity of the model was restricted to just fourcompartments in order for it to be economicalenough for use in long integrations. This meansthat the behaviours of many different species andsize-fractions are aggregated into a single com-ponent for each of phytoplankton and zooplank-ton. The model calculates the flow of nitrogenbetween the four components of the ecosystemat each grid box, and also computes the asso-ciated transfers of carbon and alkalinity. The car-bon flows have no direct effect on the behaviourof the ecosystem as growth of phytoplankton isnot limited by availability of carbon.The phytoplankton population changes as a

result of the balance between growth, which iscontrolled by light level and the local concentra-tion of nutrient, and mortality, which is mostly asa result of grazing by zooplankton. Detritus,which is formed by zooplankton excretion andby phyto- and zooplankton mortality, sinks at afixed rate and slowly remineralises to reformnutrient and dissolved inorganic carbon. Thusboth nutrient and carbon are absorbed by phyto-plankton near the ocean surface, pass up the foodchain to zooplankton, and are eventually remin-eralised from detritus in the deeper ocean.

2.2 Land carbon cycle

TRIFFID defines the state of the terrestrial bio-sphere in terms of the soil carbon, and the struc-ture and coverage of five plant functional types(Broadleaf tree, Needleleaf tree, C3 grass, C4

grass and shrub) within each model gridbox.The areal coverage, leaf area index and canopyheight of each type are updated based on acarbon balance approach, in which vegetationchange is driven by net carbon fluxes calculatedwithin the ‘‘MOSES 2’’ land surface scheme(Essery et al., 2003). MOSES 2 is a tiled versionof the land surface scheme described by Cox et al.(1999), in which a separate surface flux and tem-perature is calculated for each of the land-covertypes present in a GCM gridbox. In its standardconfiguration, MOSES 2 recognises the fiveTRIFFID vegetation types plus four non-vegeta-tion land-cover types (bare soil, inland water,urban areas and land ice). Carbon fluxes for eachof the vegetation types are derived using the

P. M. Cox et al.

coupled photosynthesis-stomatal conductancemodel developed by Cox et al. (1998), whichutilises existing models of leaf-level photosynthe-sis in C3 and C4 plants (Collatz et al., 1991,1992). Plant respiration is broken-down into agrowth component, which is proportional to thephotosynthetic rate, and a maintenance compo-nent which is assumed to increase exponentiallywith temperature (q10 ¼ 2). The resulting rates ofphotosynthesis and plant respiration are depen-dent on both climate and atmospheric CO2 con-centration. Therefore, with this carbon-balanceapproach, the response of vegetation to climateoccurs via climate-induced changes in thevegetation to atmosphere fluxes of carbon.

The land-atmosphere fluxes are calculated byMOSES 2 on every 30 minute GCM timestep andtime-averaged before being passed to TRIFFIDevery 10 days. TRIFFID allocates the average netprimary productivity over this coupling periodinto the growth of the existing vegetation (leaf,root and wood biomass), and to the expansion ofthe vegetated area in each gridbox. Leaf phenol-ogy (bud-burst and leaf drop) is updated on anintermediate timescale of 1 day, using accumu-lated temperature-dependent leaf turnover rates.After each call to TRIFFID the land surfaceparameters required by MOSES 2 (e.g. albedo,roughness length) are updated based on the newvegetation state, so that changes in the biophys-ical properties of the land surface, as well aschanges in terrestrial carbon, feedback onto theatmosphere. The land surface parameters are cal-culated as a function of the type, height and leafarea index of the vegetation. Full details onTRIFFID are given by Cox (2001).

3. Simulation of the pre-industrialclimate and vegetation of Amazonia

Before any climate projections could be carriedout HadCM3LC needed to be brought to a ‘‘pre-industrial’’ equilibrium state. A good approxima-tion to equilibrium is particularly important forthe carbon cycle because land and ocean carbonsinks are only a small fraction of the grosssurface-atmosphere CO2 exchanges, so even arelatively small model imbalance can swamp thesignal of net carbon uptake. The model spinupwas carried out as a multistage process involvinga long ocean-only run (2000 model years), and

coupled runs (150 model years in total) withfixed CO2 of 290 ppmv to derive the equilibriumvegetation state consistent with the model’s pre-industrial climate (Cox et al., 2001). Once thelong-term net land-atmosphere and ocean-atmo-sphere carbon fluxes were close to zero the atmo-spheric CO2 was let free to respond to variabilityin the model’s global carbon cycle (assumingzero pre-industrial anthropogenic CO2 emis-sions). The coupled climate-carbon cycle modelwas then integrated for 100 years so that its meanstate and variability could be diagnosed.Figure 2 and Fig. 3 compare the spatial and

seasonal variations from this pre-industrial runto climate and vegetation observations for cur-rent day Amazonia (New et al., 1999; Lovelandet al., 2000). A completely clean comparison isnot possible because of the absence of accessiblepre-industrial observations. However, it is still auseful validation exercise since pre-industrial topresent day changes are generally much smallerthan projected changes over the 21st century (seeSection 4).The Hadley Centre atmospheric models have

typically been amongst the more accurate GCMsover Amazonia (see for example Gedney et al.,2000), largely because modelling this region hasbeen a long-standing priority at the Met Office(Lean and Rowntree, 1993, 1997). However, defi-ciencies in the simulation are still very apparent.Figure 2 shows maps of the annual mean rainfall,temperature and vegetation cover over SouthAmerica. The model correctly produces a rainfallmaximum in the west, and minima in the east andover the Andes. However, rainfall is generallyunderestimated especially along the north-eastcoastline (a common problem in GCMs). Theblack boxes on the maps of Fig. 2 represent thedefinition of Amazonia for the purposes of cal-culating area mean values (70�W–50�W, 15� S–0�N). This region has been selected to maintainconsistency with previous studies (Gedney et al.,2000), but its definition is unlikely to affectthe qualitative conclusions drawn in this paper.The area mean rainfall over this box is under-estimated in the model by about 20%(4.63mmday�1 as opposed to 5.78mmday�1,New et al., 1999), which has implications forthe timing of Amazonian dieback (Huntingfordet al., 2004). Annual mean temperature is muchmore accurately captured with patterns and


magnitudes well reproduced (compare panels (b)and (e) of Fig. 2). The modelled mean air tem-perature over the Amazon box (25.90 �C) isremarkably close to the observational estimateof 25.94 �C (New et al., 1999).

Panels (c) and (f) of Fig. 2 compare modelledand observed biome distributions over SouthAmerica. TRIFFID models the fractional areacovered by each of its 5 plant functional types,so these biomes are derived by post-processingusing the rules summarised in Table 2. Similar

processing of the IGBP land-cover map (pre-viously converted to TRIFFID PFTs), yieldspanel (c). The model does a reasonable job ofreproducing the locations of the grasslands,deserts and semi-deserts of South America, andalso correctly simulates tropical forests in theAmazon box. However, trees are regularly over-predicted in the savanna regions. Only a fractionof this error can be attributed to the neglect ofdirect human deforestation in the model. Theremaining error is most likely to be due to the

Fig. 2. Rainfall, screen-level air temperature and biome distributions over South America from observational datasets (a, b, c)and the model simulation of the pre-industrial state (d, e, f). Climate observations are from New et al. (1999) and the biomedistribution is derived from Loveland et al. (2000)

P. M. Cox et al.

absence of fire-disturbance processes in this ver-sion of TRIFFID. A forest fire model is under-development to correct this deficiency, but for thetime-being it is worth noting that TRIFFID has atendency to overestimate rather than underesti-mate the robustness of tropical forests to climatevariation.

Figure 3 compares the seasonal cycles in rain-fall, and mean, maximum and minimum tem-perature, to an observational dataset (New et al.,

1999). The error in the annual mean rainfall isseen to be largely due to an underestimate of wetseason rainfall in northern hemisphere winter(Dec–Feb) and spring (March–May). The mini-mum rainfall is well captured, although the dryseason appears a month early. However, the recov-ery from the dry season appears to be well repro-duced by the model.Although the observed annual mean daily

maximum and minimum temperatures (31.3 �Cand 20.7 �C respectively) are closely matchedby the model (32.3 �C and 20.4 �C respectively),this is inspite of the tendency of the modelto overestimate seasonal temperature variation.Mean, maximum and minimum temperaturesall seem to differ too much between wet anddry seasons, perhaps suggesting a deficiency incloud cover. However, more definite conclusionson this will need to await further analysis ofboth the model and the observational dataset(for which significant interpolation of sparse

Fig. 3. Seasonal cycle in (a) rainfall, (b) air temperature, (c) daily maximum air temperature and (d) daily minimum airtemperature, over the Amazon box (see previous figure). The pre-industrial simulation is shown by the continuous line and thesymbols represent the observational climatology (New et al., 1999)

Table 2. Rules used to convert TRIFFID vegetation frac-tions into Biome types as used in Fig. 2

Biome type TRIFFID area fractions

Total vegetation Total tree Bare soil

Forest � 0.5Savanna � 0.5 � 0.2, <0.5Grassland � 0.5 <0.2Semi-desert � 0.2, <0.5Desert >0.8


available observations is likely to have beenrequired).

Overall, this validation exercise has served toreinforce the view that HadCM3LC has at leastcomparable performance in South America tomost other coupled ocean-atmosphere GCMs,even though these typically exclude the addi-tional complexities of dynamic vegetation.Nevertheless, the deficiencies outlined here willneed to be borne in mind when assessing theclimate sensitivity of the model in subsequentsections.

4. Projection of 21st century climatechange in Amazonia

Figure 4 shows maps of the change in climateand land carbon (between 2000 and 2100) from

the GCM experiment reported by Cox et al.(2000), which used the fully interactive carboncycle with IS92a CO2 emissions. Other green-house gas concentrations were prescribed as afunction of year based on offline atmosphericchemistry calculations. Sulphate aerosols werenot included in this experiment. Subsequent runshave shown that the cooling effects of anthropo-genic and volcanic aerosols act to improve thesimulation of the historical CO2 rise, and alsoslightly delay the sink-to-source transition inthe terrestrial carbon cycle (Jones et al., 2003).However, these important climatic forcings donot qualitatively change the projected impactson the Amazonian forest, so here we choose toanalyse the original Cox et al. (2000) avoidingthe additional complexities of the atmosphericsulphur cycle.

Fig. 4. Maps of changes in climate and land carbon storage over the 21st century from the fully coupled climate-carbon cycleprojections. (a) Screen temperature, (b) precipitation, (c) vegetation carbon and (d) soil carbon. These maps were calcu-lated as the differences between the means for the 2090s and the 1990s. Only areas for which the projected change is greaterthan 95% significant (according to a paired student t-test) are shown. In each map the box over South America represents thedefinition of Amazonia for the purposes of this study (70� W–50� W, 15� S–0� N), while the boxes over the Pacific show theNINO3 region (150� W–90� W, 5� S–5� N), and the western Equatorial Pacific region as used in Fig. 5 (120� E–180� E,5� S–5� N)

P. M. Cox et al.

The global mean warming of about 5.5K by2100 is consistent with an equilibrium climatesensitivity to doubling CO2 of about 3.2K, whichis near the centre of the range quoted by theIPCC (1.9–5.2K, IPCC (2001)). Figure 4a showsa significant land-sea temperature contrast imply-ing a greater potential for impacts on terrestrialecosystems. The extra warming over land ispartly due to CO2-induced stomatal closurewhich suppresses evaporative cooling at highCO2 (Cox et al., 1999), but there are also im-portant contributions from cloud feedbacks(Williams et al., 2001). As is usual in GCMs,there is a tendency for the warming to be greatestin the high northern latitudes due to snow andsea-ice albedo feedbacks. However, the mostsevere warming is actually seen in westernAmazonia where temperatures are projected torise by more than 10K by 2100. This area ofextreme temperatures is concident with theregion of maximum rainfall reduction (Fig. 4b),suffering a decrease of more than 3mmday�1

between 2000 and 2100. There is a very signifi-cant tendency to warm and dry over most of thenorthern half of South America.

Vegetation carbon increases in the forestedregions of the northern hemisphere mostly as aresult of CO2-fertilisation of photosynthesis(Fig. 4c). Warming also leads to a longer snow-free period and therefore an extended growingseason in the boreal regions. Tropical ecosystemsfair less well from the projected changes as thewarming generally leads to temperatures whichare above the optimum for photosynthesis. Onceagain the most marked change in vegetation car-bon is in Amazonia, where biomass drops bymore than 8 kgCm�2. The red regions ofFig. 4c roughly denote the area of ‘‘Amazonian

forest dieback’’. Figure 4 shows that the loss ofsoil carbon is much more widespread, with onlythe far northern tundra regions accumulating soilcarbon between 2000 and 2100. The tendency tolose soil carbon elsewhere is an inevitable con-sequence of the assumption that decompositionrate continues to increases with temperature,while photosynthesis saturates at high CO2

(Jenkinson et al., 1991; Cox et al., 2001).Table 3 summarises the area mean changes

over the Amazon box (as marked on Fig. 4)and constrasts these to global mean changes.Amazonian temperature rises by more than9K over the 21st century, and rainfall dropsby an alarming 64%. Together these changes leadto a 78% loss in vegetation carbon and a 72%loss in soil carbon. By contrast, over the sameperiod the global mean temperature rises byabout 4K and rainfall increases by 3%. Globalvegetation carbon increases by about 10GtCdespite the large losses in South America, butglobal soil carbon decreases by about 155GtC.The carbon loss in the Amazon basin of about50GtC accounts for 23 ppmv of the total CO2

increase of about 600 ppmv over the 21st century,or about a third of the total terrestrial carbonreduction of 145GtC.Table 4 serves to separate the causes of these

changes in land carbon, by comparing the fullycoupled ‘‘online’’ run to the ‘‘offline’’ run inwhich there were no climate effects on the car-bon cycle. The online and offline runs were pre-viously shown in Fig. 1 as the continuous anddashed lines respectively. In the absence ofclimate-carbon cycle feedbacks direct CO2

effects lead the Amazon box to accumulate about10GtC each in vegetation and soil, which is asmall contribution to the global terrestrial carbon

Table 3. Mean climate and land carbon storage from the fully interactive climate-carbon cycle projection for the decades of the1990s (left two columns) and the 2090s (right two columns). Values for Amazonia are contrasted with global mean values (landplus ocean). For the purpose of this study Amazonia is defined as the longitude–latitude box 70� W–50� W, 15� S–0� N

1990s 2090s

Globe Amazonia Globe Amazonia

Screen Temperature (K) 288.0 301.4 292.1 310.6Precipitation (mmday�1) 2.96 4.56 3.05 1.64Evaporation (mmday�1) 2.96 3.14 3.05 1.20Vegetation Carbon (GtC) 539.5 45.6 549.7 10.1Soil Carbon (GtC) 1204.5 19.8 1049.2 5.5


accumulation of about 464GtC. The coupledonline run turns both these accumulations intothe previously mentioned losses over the globeand the Amazon box of 145GtC and 50GtC,due to the negative impacts of climate changeon land carbon storage. Climate-carbon cyclefeedbacks therefore result in a net loss of about610GtC over the 21st century from global vege-tation and soils, of which the Amazon boxcontributes about 70GtC or 11% (this regionaccounts for just over 2% of the global landarea). This is to be compared to a difference inatmospheric CO2 at 2100 of about 600GtC be-tween the online and offline runs (see Table 1).

We conclude this section by recognising thatAmazonian dieback provides a small but signifi-cant contribution to the climate-carbon cyclefeedback reported by Cox et al. (2000). Carbonlosses in the Amazon box account for about atenth of the amplification of CO2 by 2100 (com-paring online and offline runs), and about onethird of the total terrestrial carbon losses pro-jected for the 21st century in the coupled (online)run. Vegetation and carbon losses in Amazoniaare associated with the areas of most extremewarming and drying within the model. The nextsection discusses this pattern of climate changeand its possible relationship to the El Ni~nnoSouthern Oscillation (ENSO).

5. Climate change and ENSO

Model projections of 21st century climate differmost markedly at small scales, implying signifi-cant uncertainty in regional climate change andits impacts. Nevertheless, some common featuresdo emerge from GCM patterns of climate change(IPCC, 2001). The amplification of high latitudewarming by snow and sea-ice albedo feedbacks

is almost universally seen in GCM simulations.However, there are other regional details whichare agreed upon by significant subsets of GCMs,such as the tendency for mid-latitude continentsto dry in summer under global warming. TheThird Assessment report of the IPCC also statesthat ‘‘many models show a mean El Ni~nno-likeresponse in the tropical Pacific, with the centraland eastern equatorial Pacific sea surface tem-peratures projected to warm more than thewestern Pacific and with a corresponding meaneastward shift of precipitation’’ (IPCC (2001),Section F.7, p. 73). Returning to Fig. 4 note thatthis statement is broadly consistent with theHadCM3LC climate-carbon cycle projection.HadCM3LC produces a larger warming in the

eastern equatorial Pacific than in the west (seeFig. 4a). This pattern is common to all previousand current Hadley Centre models, but appearsstrongest in 3rd generation models likeHadCM3LC (Williams et al., 2001). Table 5 liststhe terms contributing to the warming of the eastand west Pacific regions. In the west there is asmall reduction in cloud fraction (�0.04), butnegligible change in the shortwave radiationfluxes. A much larger change can be seen inthe downward longwave (29.2Wm�2), whichincreases primarily because of CO2-inducedwarming of the atmospheric column. The result-ing increase in available energy is balanced byincreases in the upward longwave (19Wm�2)and latent heat fluxes (7.2Wm�2), which bothtend to increase in response to warming of theocean surface.There is a larger warming in the east

(4.1K versus 3.2K in the west) but this is actu-ally associated with a significant increase incloud cover (0.15), and a significant decrease indownward shortwave radiation at the surface

Table 4. Change in mean climate and land carbon storage (2090s–1990s) from the fully interactive climate-carbon cycleprojection (‘‘Online’’ run), and a run in which there were no climate effects on the carbon cycle (‘‘Offline’’ run). Values forAmazonia are contrasted with land mean values

‘‘Offline’’ ‘‘Online’’

Globe Amazonia Globe Amazonia

� Screen Temperature (K) 0.3 0.9 4.1 9.2� Precipitation (mmday�1) 0.0 �0.1 0.1 �2.9� Evaporation (mmday�1) 0.0 �0.25 0.1 �1.9� Vegetation Carbon (GtC) 159.0 10.3 10.2 �35.6� Soil Carbon (GtC) 304.9 10.3 �155.3 �14.3

P. M. Cox et al.

(�16.7Wm�2). The downward longwave radia-tion increases by even more than it does in thewestern Pacific (41.1Wm�2 versus 29.2Wm�2

in the west), most likely because of the ‘‘blanketeffect’’ of increasing high clouds in the east.Dynamical processes in the ocean and atmo-sphere export heat from both regions, but thisexport changes more in the east Pacific whereheat divergence (i.e. heat export) decreases by10.2Wm�2 as opposed to an increase of2.4Wm�2 in the west. As a result more energyis available to heat up the surface in the east,leading to larger warming and a greater enhance-ment of the upward longwave and latent heatfluxes.

This mechanism for a differential E–W warm-ing seems quite different from that diagnosed inthe NCAR GCM (Meehl and Washington, 1996),in which differential SW cloud feedbacks wereimplicated. It therefore seems the reasons for arelative warming of the east Pacific differ evenamongst the subset of climate models that pro-duce such a pattern. Whatever its cause, such aneast–west variation in Equatorial Pacific SSTs isreminiscent of an El Ni~nno state, in which similarSST patterns give rise to rainfall reductions innorthern Brazil. So a key question is; could along-term El Ni~nno-like SST state give rise torainfall reductions in Amazonia which couldthreaten the existence of the Amazonian rain-forest?

Previous work has shown that HadCM3LCdoes a reasonable job in reproducing spatialand temporal patterns of ENSO variability andtheir impacts on the global carbon cycle (Joneset al., 2001). The parent HadCM3 model hasbeen judged to be one of few GCMs in the

Coupled Model Intercomparison project (CMIP)which is able to simulate recognisable NINO3frequency spectra (AchutaRao and Sperber,2002; Collins, 2001). Comparison to HadCM3suggests that HadCM3LC, despite the loweroceanic horizontal resolution, has at least com-parable ENSO performance. NINO3 anomalieshave a standard deviation of around 0.8K andhave a broad peak in the spectra at periods of3–6 years, both features which are quantitativelysimilar to that observed. In addition, and a likelyconsequence of the inclusion of the flux-correc-tion term in HadCM3LC which corrects some ofthe errors in the mean climate in the region, thepattern of ENSO variability is more similar to theobserved pattern than in the case of HadCM3.Deficiencies remain (for example an over corre-lation of land temperatures with NINO3 overtropical land), but HadCM3LC seems to beone of the more appropriate GCMs for the studyof these links between El Ni~nno and climatechange.This contention is broadly supported by Fig. 5

which compares the modelled and observedrelationships between Amazonian rainfall andENSO. Observed climate comes from the CRUdataset (New et al., 1999) and observed SSTs arefrom HadISST (Rayner et al., 2003). For bothmodel and data SST indices are calculated overeast Pacific NINO3 region: 150�W–90� W, 5� S–5�N, and over the west Pacific: 120� E–180� E,5� S–5�N as December–February (DJF) meanSST and the difference (east–west) is taken asa measure of both interannual and longer termENSO-like variability. The east–west SST gradi-ent removes the effect of any mean global warm-ing, which would be evident for example in the

Table 5. Change in surface energy balance (2090s–1990s) of the West Pacific (120� E–180� E, 5� S–5� N) and East Pacificregions (150� W– 90� W, 5� S–5� N), from the fully interactive climate-carbon cycle projection

West Pacific East Pacific

� Screen Temperature (K) 3.2 4.1� Cloud Fraction �0.04 0.15� Downward Surface SW (Wm�2) �0.7 �16.7� Downward Surface LW (Wm�2) 29.2 41.2� Upward Surface SW (Wm�2) 0.0 �0.8� Upward Surface LW (Wm�2) 19.0 25.7� Latent Heat Flux (Wm�2) 7.2 10.5� Sensible Heat Flux (Wm�2) �0.1 �0.7� Residual Heat Divergence (Wm�2) 2.4 �10.2


NINO3 index. The use of a SST gradient is morephysically justifiable as it is directly related tothe position of mean atmospheric convectionwhich drives the teleconnection from the ENSOregion to South America. Amazon rainfall is cal-culated as DJF (wet season) means over theregion marked in Fig. 4 (70�W–50�W, 15� S–0� N). Comparing panels (a) and (b) of Fig. 5note that the observations and model have similarinterannual variability in both Amazonian rain-fall and NINO3 index.

The model correctly reproduces the negativecorrelation between the ENSO SST differenceand Amazonian rainfall on interannual timescales, matching the tendency for rainfall to bereduced during El Ni~nno events. Indeed, themodel seems to have a slightly higher correlationbetween the two variables although the observedestimate may be artificially low because of pos-sible errors in the observations. The key featureof Fig. 5b is that this correlation is maintained forlonger timescale trends so that as the pattern of

Fig. 5. Comparison of modelled andobserved relationship between ENSOand Amazon rainfall. (a) Amazon rain-fall (dashed line, right hand axis) andan east Pacific minus west Pacific SSTindex (thick line, left hand axis) againstyear from observational datasets, (b)Amazon rainfall and the same SSTgradient index against year from theclimate-carboncyclemodel, (c)Amazonrainfall versus SST gradient index,Observed (asterixes) and Modelled(diamonds). Linear relationships areindicated for the model 1860–2000and 2000–2100 periods, and for theobservations

P. M. Cox et al.

SSTs changes to a more ‘‘El Ni~nno-like’’ meanpattern (still with the interannual variabilitypresent) the Amazonian rainfall corre-spondingly reduces. The amplitude of the rela-tion on interannual time scales is slightly largerin the model for the period 1860–2000(�0.74mmday�1 K�1) in comparison with theobservations (�0.63mmday�1 K�1).

For the period 2000–2100 the model relation-ship strengthens to �2.03mmday�1 K�1, onlypartly because of local rainfall-vegetation feed-back in Amazonia. The detailed mechanism forthis stronger sensitivity is under investigation(using the atmospheric component of the modelforced with different SST patterns). For the timebeing Fig. 5 can be considered as providing cir-cumstantial evidence for a link between El Ni~nno-like climate change and Amazonian climatechange in HadCM3LC. As such, it suggests thatstudy of the climate-carbon cycle system overnatural ENSO cycles can provide valuableinsights into the potential for Amazonian forestdieback under global warming.

6. Vegetation response to the projectedclimate change

Figure 6 shows the evolution of Amazonian land-cover from the fully coupled online simulation. Itshould be noted here that this run (and all othersreported in this paper) ignore both direct anthro-pogenic deforestation and also natural fire distur-bance. Nevertheless, as the Amazonian rainfalldrops the broadleaf tree fraction is initially main-

tained at about 80% by direct CO2 effects, butultimately starts to reduce once the annual meanrainfall drops below a threshold of about3mmday�1 (see Fig. 7). The precise locationof this rainfall threshold is dependent on thecomplexities of the soil-vegetation systemunder drying conditions, and it is therefore likelyto be both uncertain and model-specific. How-ever, the monotonic decrease in rainfall producedby this GCM would inevitably lead to forestdieback at some critical CO2 level, regardlessof the precise way in which these processesmight be represented (i.e. no rainfall means norainforest for all conceivable land-surface repre-sentations!).The mechanism of broadleaf tree dieback is

the reduction of net primary productivity (NPP)below the levels necessary to maintain litterlosses, which arise from both local processes(e.g. leaf-fall) and Lotka-type intraspecies com-petition (Cox, 2001). NPP reduces due to bothsoil moisture limitations on photosynthesis, andalso increases in maintanence respiration costs(Cox et al., 1999). This version of TRIFFIDassumes that leaf dark respiration and plantmaintenance respiration increase exponentiallywith surface temperature, with a q10 of 2 (Cox,2001). The alternative assumption that respira-tion remains approximately proportional tophotosynthesis even at high temperatures, actsto prevent NPP becoming negative and can delayforest dieback (Huntingford et al., 2000). How-ever, once again such a modification can onlyinfluence the critical point at which diebackbegins, since reducing NPP (as a result of mois-ture limitations) must ultimately make rainforestunsustainable.When the forest fraction begins to drop (from

about 2040 onwards) C4 grasses initially expandto occupy some of the vacant lands. However, therelentless warming and drying make conditionsunfavourable even for this plant functional type,and the Amazon box ends as predominantly bare-soil (area fraction >0.5) by 2100. As with thetiming of dieback, the details of this simulationof the land-cover change should be treated withcaution since they depend on known limitationsand uncertainties in the TRIFFID vegetationmodel. For example, even at mean annual tem-peratures of approaching 40 �C a sparse cover ofsemi-desert CAM plants might be possible, but

Fig. 6. Evolution of the vegetation cover in the Amazonbox from the coupled climate-carbon cycle simulation


this is not possible in TRIFFID since such heatand drought tolerant species are not recognisedby the model. Despite these model deficiencies, itseems clear that the HadCM3LC climate changein Amazonia would lead to rainforest loss (per-haps via increased fire frequency), and thereforedrastic land-cover change.

This assertion is backed-up by results from acomparison of dynamic global vegetation models(DGVMs), in offline experiments driven by out-put from the HadCM2 GCM (Cramer et al.,2001). An early offline version of TRIFFID (withsimplfied surface energy balance and hydrologycalculations) was amongst the 6 DGVMs used inthis study. HadCM2 also produced a dryingunder high CO2 in NE Brazil but this was lesswidespread than in HadCM3 or HadCM3LC.Nevertheless the DGVM intercomparison studydoes provide some insight into the dependenceof the Amazonian dieback on the vegetationmodel details, since it subjected all DGVMs tothe same climate change scenario. Figure 8shows broadleaf tree biomass calculated overthe Amazon box for simulations which excludedthe direct effects of CO2 on plant physiology.Each model was therefore subject to climatechange impacts alone.

Although the detailed trajectories differ, thereis a general tendency for Amazonian forest bio-mass to decrease through the 21st century as tem-peratures increase and rainfall drops (althoughless rapidly than in HadCM3LC). The mostmarked change was seen in the individual-based

Fig. 7. Evolution of climate and biomass over the Amazon box, from three separate HadCM3LC simulations with dynamicvegetation. (a) Screen temperature, (b) precipitation, (c) evaporation, (d) vegetation carbon. The continuous line representsthe fully coupled climate-carbon cycle run, the dashed line is from the run without climate effects on the carbon cycle, and thestars are from a run with prescribed IS92a CO2 concentrations. The related CO2 scenarios are shown in Fig. 1

Fig. 8. Impact of climate change alone on Amazonian for-est biomass in 6 different Dynamic Global Vegetation Mod-els. In this particular experiment each DGVM was drivenby the same climate change scenario from the HadCM2GCM, but the direct effects of CO2 on plant growth wereexcluded. Results from an early offline version of TRIFFIDare shown as the continuous line

P. M. Cox et al.

gap model ‘‘Hybrid’’ (dotted line), which pro-duced significant Amazonian dieback under bothHadCM2 and HadCM3 scenarios even whendirect CO2 effects were included (Friend et al.,1997; White et al., 1999). The offline version ofTRIFFID (continuous line in Fig. 8) projects asimilar rate of carbon loss under climate changealone, but produced a stronger direct CO2-effectwhich prevented such large-scale dieback underthe HadCM2 scenario (Cramer et al., 2001). Ittherefore seems that DGVMs show a qualita-tively similar tendency to reduce rainforest coverunder a common scenario of warming and dryingin Amazonia, but that the extent and rate of thisreduction is model dependent. Reducing theuncertainty associated with vegetation modelresponse to climate change will rely on makingbetter use of field measurements to constrain pro-cess representations and internal model param-eters (Harris et al., 2004).

7. Vegetation feedbackson the climate change

Changes in land-cover can influence climatethrough a number of feedback loops. Thepotential for changes in land-carbon storage tofeedback on climate by modifying the rate ofatmospheric CO2 increase was discussed inSection 4. However, land-cover also plays alarge part in determining the surface-to-atmo-sphere heat, moisture and momentum fluxes,which in turn affect local energy balance (e.g.through changes in evaporative cooling andcloud cover) and the atmospheric circulation.Increased CO2 tends to cause stomatal closurewhich acts to suppress transpiration and amplifysurface warming (Cox et al., 1999), butincreased leaf area index may counteract thereduced moisture flux per leaf area (Bettset al., 1997). Models of the climatic impactsof Amazonian deforestation yield a range ofresults, but generally indicate the potential forremoval of the rainforest to produce less rain-fall through reductions in moisture recyclingand atmospheric moisture convergence (see forexample Zeng et al. (1996) for a summary).Therefore, the climate change-driven deforesta-tion in HadCM3LC might be expected to pro-duce significant biophysical and biogeographicalfeedbacks.

A full analysis of vegetation feedbacks is pro-vided by Betts et al. (2004). The impacts of forestdieback on the simulated Amazonian climate arebriefly summarised here. In order to diagnosethis two further GCM experiments were carriedout, both using prescribed IS92a CO2 concentra-tions to eliminate carbon cycle feedbacks. Thisscenario provides a trajectory of CO2 which isvery similar to that produced in the offlineHadCM3LC experiment (see Fig. 1a), rising toa marginally higher concentration of 713 ppmvby 2100. One of the GCM experiments usedfixed vegetation (equivalent to most existingGCM projections), while the other allowed forvegetation changes via TRIFFID. Figure 9 com-pares the evolution of the mean temperature,rainfall, evaporation and moisture convergence(P–E), from these two runs. In both cases thedecadal mean screen-level temperature rises toabout 309K by the 2090s. By comparison theonline run (which has much higher CO2 by2100) produced a 2090s temperature about 2Khigher (see Fig. 7a). The impacts of the diebackare more obvious in the Amazonian rainfall(Fig. 9b), with land-cover change producing anamplification of the drying in the last two de-cades of the 21st century. Decadal variabilitymakes it difficult to estimate the magnitude ofthis effect with any certainty, but it appears tobe up to 0.5mmday�1 by the 2090s, which issimilar to the additional rainfall reduction arisingfrom the extra CO2 in the online run (see Fig. 7b).Surprisingly, the runs with and without vege-

tation change produce remarkably similar evap-oration rates (see Fig. 7c), possibly becausesoil moisture is so strongly limiting in the latterstages of both runs (such that vegetation changehas a relatively small impact). In contrast thereducing forest cover has a clear impact on therate at which moisture convergence reduces(Fig. 7d), with convergence reducingmore quicklywhen vegetaion dynamics are included. The lat-ter result is broadly consistent with the thermo-dynamic analysis of Amazonian deforestation byZeng et al. (1996), which suggests that precip-itation and moisture convergence will bothdecrease approximately linearly with reducingevaporation.In HadCM3LC forest dieback is therefore act-

ing to amplify the Amazon drying which causesit, and is therefore involved in the propagation of


the drying pattern from east to west (Betts et al.,2004). However, vegetation feedbacks are not theprimary drivers for the drying, since this occurseven in the HadCM3LC experiment which hasprescribed CO2 and fixed vegetation.

8. Discussion

The analysis presented in this paper provides aqualitative explanation of the Amazonian forestdieback phenomenon in the Hadley Centreclimate carbon cycle model. Further work isrequired to relate the model to aspects of the realEarth system which would allow us to quantifythe probability of climate-driven forest diebackin Amazonia. In this section some of the keyscientific questions are listed, along with sometentative answers and suggestions based on theresults presented in this paper.

(i) What is the mechanism of Amazonian dry-ing in the Hadley Centre’s climate-carboncycle model?Our analysis suggests that the primary

cause of Amazonian climate change is theEl Ni~nno-like SST warming pattern whichemerges in HadCM3LC as CO2 increases.CO2-induced stomatal closure also acts towarm and dry the Amazon basin, addingslightly to the regional climate change(Betts et al., 2004; Cox et al., 1999). The

Amazonian drying leads ultimately toforest dieback which releases CO2 to theatmosphere, contributing about one tenth tothe CO2 amplification by 2100, and furtherreducing rainfall over Amazonia (by about0.5mmday�1) through changes in the prop-erties of the land-surface. Further analysis isrequired to quantify the contributions of thevarious physical and biological feedbacks tothe overall model response.

(ii) How realistic is the El Ni~nno-like drying pat-tern in Amazonia as CO2 increases?

All of the third generation Hadley Centremodels tend to produce greater warming inthe tropical eastern Pacific than the west (i.e.an El Ni~nno-like mean SST pattern), leadingto reductions in rainfall along the northeastcoast of South America. A number of otherGCMs produce a similar SST warming pat-tern (IPCC, 2001), but this is by no meanscommon to all models. Figure 10 shows asimple estimate of current uncertainty ineast–west SST gradient change and Amazonrainfall change in all models submitted toPhase 2 of the Coupled Model Intercompar-ison project (CMIP2 Covey et al. (2003) –http:==www-pcmdi.llnl.gov=cmip). There isa tendency for the models to fall around adiagonal with drying conditions in Amazo-nia associated with a warming of the tropical

Fig. 9. Evolution of climate and hy-drology over the Amazon box, from2 separate HadCM3LC simulationswith prescribed IS92a CO2 concen-trations. (a) Screen temperature,(b) precipitation, (c) evaporation,(d) moisture convergence (P–E). Thestars are from a run with dynamicvegetation and the diamonds arefrom a run with fixed vegetation

P. M. Cox et al.

east Pacific relative to the west, andincreased rainfall associated with a relativewarming of the west, but the scatter is sig-nificant. The models have been numberedaccording to the realism with which eachsimulates the interannual relationship be-tween SST gradient and Amazonian rain-fall. HadCM3LC has the strongest positivetrend in the E–W SST contrast associatedwith the strongest negative trend in Amazo-nian rainfall, but it is also the model whichmost closely matches the observed interan-nual relationship between these variables(the slope of the relationship is slightly dif-ferent from that shown in Fig. 5 as this iscomputed from the HadCM3LC control, notyears 1860–2000 of the transient run).The mechanism for the El Ni~nno-like SST

pattern in HadCM3LC appears to be relatedto differential cloud feedbacks in the easternand western Pacific, enhanced by coupledocean-atmosphere dynamics, as similar pat-terns are seen when the atmospheric compo-nent is coupled to a thermodynamic slabocean (Williams et al., 2001). Note also thatthe use of flux-adjustments in HadCM3LCappears not to have played a critical role in

the development of such a ‘‘permanent ElNi~nno’’, since a similar pattern of warmingis seen in the unflux-adjusted HadCM3model (Model 4 in Fig. 10).The similarity of the climate change pat-

tern to an El Ni~nno state offers some hope ofvalidating some of the key model sensitiv-ities using data on interannual variability. Aprevious study has shown that HadCM3LCcan reproduce ENSO variability and therelated global carbon cycle response withreasonable accuracy (Jones et al., 2001), thusproviding a constraint on a key internalmodel parameter (Jones and Cox, 2001). Akey area for future research should be inrelating interannual data and results fromother models to allow some estimate of theuncertainty bounds on the response of Ama-zon rainfall to an El Nino-type SSTanomaly.

(iii) How realistic is the vegetation modelresponse to the simulated Amazon drought?The extent of the projected warming anddrying of Amazonia (10K warming plus a60þ% rainfall reduction by 2100) inevi-tably makes the rainforest unsustainable.However, the threshold for forest diebackis undoubtedly vegetation-model dependent

Fig. 10. Century time scale trends in DJF mean east–west SST gradient versus DJF mean Amazon rainfall (regions defined inFig. 5) from 20 model simulations submitted to the Coupled Model Intercomparison Project (CMIP2) in which CO2 isincreased at the rate of 1% per year (compounded) plus HadCM3LC. To make a more fair comparison, the HadCM3LCresponse is taken from years 2000–2080 of a simulation with IS92a CO2 and fixed vegetation (in the simulation withinteractive vegetation the rainfall reduction is approximately twice the value with fixed vegetation). The models are numberedaccording to how well they reproduce the interrannual relationship between SST gradient and Amazon rainfall which isindicated in the table to the right of the figure. The Hadley Centre models are marked in bold, with HadCM3LC ranked asnumber 1


and therefore uncertain. The TRIFFIDassumption that plant maintenance respira-tion increases with temperature makes therainforest less robust to warming than itmight be (Huntingford et al., 2000), butthe neglect of forest fire probably makesthe modelled vegetation more robust to dry-ing than it is in reality. The HadCM3LCmodel also underestimates the rainfall inpresent-day Amazonia by about 20%, suchthat any thresholds in rainfall are likely tobe passed earlier (and at lower CO2 concen-trations) than they might be in the real Earthsystem (Huntingford et al., 2004). Neverthe-less, the magnitude of the Amazonian cli-mate change in HadCM3LC seems sure tolead to eventual loss of rainforest in allvegetation models, as suggested by an off-line intercomparison of DGVMs (Crameret al., 2001).The increasing availability of CO2 flux

data offers the possibility of refining and vali-dating vegetation models to a new level,thereby providing more robust projectionsof the response of the Amazonian forest toan imposed climate change (Harris et al.,2004). In addition, artificial droughtingexperiments and observational campaignsduring El Ni~nno events, can provide valuableinformation on the response of real forestecosystems to moisture stress.

9. Conclusions

This paper has summarised the phenomenon ofAmazonian forest dieback within climate changeprojections carried out with the Hadley Centreclimate-carbon cycle GCM. In this model, highCO2 leads to an El-Nino-like SST warming pat-tern which suppresses rainfall across northernAmazonia. Although CO2-fertilisation of photo-synthesis is able to maintain the rainforest coverfor the first half of the 21st century, the extremewarming and drying eventually lead to abruptreductions in the forest fraction. The loss of rain-forest exacerbates the Amazonian climate changeby releasing CO2 to the atmosphere, and bychanging the properties of the land-surface.

The modelled Amazonian dieback phenom-enon is therefore qualitatively understood, butwe are still a long way from being able to esti-

mate the probability of such an ecologicalcatastrophy occurring in the real Earth system.Further progress on this issue will rely onanalysing results from a range of GCMs andvegetation models (e.g. as part of the CoupledClimate-Carbon Cycle Model IntercomparisonProject, ‘‘C4MIP’’), each validated more thor-oughly with the latest observational data. Thisresearch is urgently required if we are to provideuseful guidance on potentially dangerous levelsof CO2 for the rainforest of Amazonia.

Acknowledgements

The authors of this work were supported by the UK DETRClimate Prediction Programme under contract PECD7=12=37 (Peter Cox, Richard Betts and Chris Jones), andthe Natural Environment Research Council (MatthewCollins, Phil Harris, and Chris Huntingford).

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Authors’ addresses: Peter Cox (e-mail: peter.cox@metoffice. com), R. A. Betts and C. D. Jones, Hadley Centrefor Climate Prediction and Research, Met Office, ExeterEX1 3PB, U.K.; M. Collins, Department of Meteorology,University of Reading, Reading, Berks, U.K.; P. P. Harrisand C. Huntingford, Centre for Ecology and Hydrology,Wallingford, Oxon OX10 8BB, U.K.

P. M. Cox et al.: Amazonian forest dieback under climate-carbon cycle projections for the 21st century

amazonian forest dieback under climate-carbon cycle...

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