Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters

7/28/2019 Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters

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T. M. Osborne D. M. Lawrence J. M. SlingoA. J. Challinor T. R. Wheeler

Influence of vegetation on the local climate and hydrologyin the tropics: sensitivity to soil parameters

Received: 15 July 2003 / Accepted: 23 February 2004 / Published online: 19 May 2004 Springer-Verlag 2004

Abstract Land use change with accompanying majormodifications to the vegetation cover is widespread inthe tropics, due to increasing demands for agricultural

land, and may have significant impacts on the climate.This study investigates (1) the influence of vegetation onthe local climate in the tropics; (2) how that influencevaries from region to region; and (3) how the sensitivityof the local climate to vegetation, and hence land usechange, depends on the hydraulic characteristics of thesoil. A series of idealised experiments with the HadleyCentre atmospheric model, HadAM3, are described inwhich the influence of vegetation in the tropics is as-sessed by comparing the results of integrations with andwithout tropical vegetation. The sensitivity of the resultsto the soil characteristics is then explored by repeatingthe experiments with a differing, but equally valid,

description of soil hydraulic parameters. The resultshave shown that vegetation has a significant moderatingeffect on the climate throughout the tropics by coolingthe surface through enhanced latent heat fluxes. Theinfluence of vegetation is, however, seasonally depen-dent, with much greater impacts during the dry seasonwhen the availability of surface moisture is limited.Furthermore, there are significant regional variationsboth in terms of the magnitude of the cooling and in theresponse of the precipitation. Not all regions show afeedback of vegetation on the local precipitation; thisresult has been related both to vegetation type and to theprevailing meteorological conditions. An important

finding has been the sensitivity of the results to thespecification of the soil hydraulic parameters. The

introduction of more freely draining soils has changedthe soil-moisture contents of the control, vegetatedsystem and has reduced, significantly, the climate sensi-

tivity to vegetation and by implication, land use change.Changes to the soil parameters have also had an impacton the soil hydrology and its interaction with vegetation,by altering the partitioning between fast and slow runoffprocesses. These results raise important questions aboutthe representation of highly heterogeneous soil charac-teristics in climate models, as well as the potentialinfluence of land use change on the soil characteristicsthemselves.

1 Introduction

The role of the land surface in climate has receivedconsiderable attention in recent decades, both throughobservational and modelling studies. It has been shownthat the land and the atmosphere interact over a broadrange of temporal and spatial scales and that the vege-tation plays a key role in this interaction (see Pielke et al.1998 for review). The development of our understandingof landatmosphere interaction processes has beenaccelerated by development of sophisticated land surfaceschemes for use in general circulation models (GCMs;e.g. SiB Sellers et al. 1986 and BATS Dickinson 1984).These schemes are designed to represent a range of

vegetation types and their biophysical relationships withthe overlying atmosphere.

Among the many advantages of coupled landatmosphere models, is that they enable investigations ofthe impact of land-cover change on climate and climateprocesses. Land-cover change studies have focused on anumber of susceptible regions where past or currentland-cover change has been observed. For example,Charney (1975), in a pioneering study, developed thehypothesis that an increase in surface albedo, related tothe reduction in Sahelian vegetation cover, altered the

T. M. Osborne (&) D. M. Lawrence J. M. SlingoA. J. ChallinorCentre for Global Atmospheric Modelling,Department of Meteorology, Earley Gate, University of Reading,PO Box 243, Reading, Berkshire RG6 6BB, UKE-mail: [email protected]

T. R. WheelerDepartment of Agriculture, University of Reading, Reading, UK

Climate Dynamics (2004) 23: 4561DOI 10.1007/s00382-004-0421-1


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atmospheric circulation so that there was enhancedsubsidence, thereby reducing rainfall and exacerbatingdrought conditions. Since then, Sahelian drought hasbeen extensively studied (Xue and Shukla 1993; Nich-olson et al. 1998; Zheng and Eltahir 1998), and whileland-cover change does appear to be involved in theinterdecadal shift in Sahelian rainfall, the ocean alsoappears to play a role (Zeng et al. 1999). Following therealisation that such drastic changes in land cover cansignificantly impact the local climate, the effects of moresubtle changes have begun to be studied with GCMs(Taylor et al. 2002; Hoffman and Jackson 2000) Suchstudies are possible because of the increased effort thathas been made to represent vegetation realistically andto develop realistic scenarios of past and future land usechanges.

Another area of regional focus is Amazonia, wherethe tropical rainforest is rapidly diminishing as forestsare cleared for forestry and agricultural purposes.Numerous GCM studies have attempted to understandand predict the impact of large-scale Amazonian defor-estation on the regional climate (e.g. Lean and Rowntree

1993; Sud et al. 1996). Such studies generally concludethat a primary consequence of deforestation is a reduc-tion in surface evaporation and therefore latent heat flux.The reduced latent heat flux contributes to warmer sur-face temperatures and, in some models, to a decrease inprecipitation. A major uncertainty associated with thesetypes of experiments is whether or not the large-scaleatmospheric circulation will adjust and perhaps com-pensate, through changes in moisture convergence, forthe reduction in local recycling of water by the trees. Forexample, Hahmann and Dickinson (1997) summarisedthe results of 15 tropical deforestation studies andfound that the majority of models simulated a decrease

in moisture convergence following deforestation.The vast majority of GCM tropical land use change

studies have focused on single regions, such as Amazo-nia or the Sahel, or single types of land use change. Inthe last decade there has been an effort to explore thesensitivity of climate to deforestation in different parts ofthe Tropics. Zhang et al. (1996) and McGuffie et al.(1995) both found that the regional climate of Amazoniawas the most sensitive to deforestation when comparedto deforestation over tropical Africa and Southeast Asia.Polcher and Laval (1994) also examined the climatesensitivity to deforestation over Amazonia and tropicalAfrica and found that the changes in the surface climate

variables such as temperature and evaporation weregreater over Amazonia. Interestingly, they also foundthat deforestation increased precipitation in both re-gions due to increased moisture convergence andattributed this to an enhancement of the convectiveactivity of the ITCZ.

There has been little effort to explore the generality ofthese results to other regions in the tropics. It is possiblethat some regions are more sensitive to changes in veg-etation than others, often called hot spots in the climatesystem. In this study, a highly idealised forced

experimental design is used to investigate how and wherevegetation has an important influence on tropical cli-mate. Two GCM simulations of climate are producedwith the Hadley Centre Unified Model: one with thepresent-day vegetation distribution and the other wherethe vegetation cover in tropical regions has been re-placed with bare soil. A comparison of the simulatedclimates should not necessarily be directly compared toprevious land-cover change experiments results but willillustrate areas within the tropics where a change invegetation cover could significantly affect the local cli-mate.

Although it is recognised that vegetation plays animportant role in soil hydrology by altering the parti-tioning of runoff between fast and slow processes andenabling efficient extraction of soil water from sub-sur-face layer via the root structure, there has been limitedresearch on the degree to which soil characteristicsinfluence the projected effects of devegetation and landuse change. Pitman et al. (1993) assessed a climatemodels sensitivity to prescribed deforested landscapesand concluded that the relative magnitudes of the

changes in albedo and vegetation roughness length wereimportant determinants of the size of the climate change.The authors ranked them equal with the choice of GCMand the length of the simulation, and more importantthan changes in soil characteristics. Observationalstudies have demonstrated that changes in land use canalter the characteristics of the underlying soil such asporosity and density (Wright et al. 1996) However, suchchanges are rarely included in deforestation scenarioGCM experiments, an exception being the study of Leanand Rowntree (1997) who found that the modelled re-gional changes in rainfall and evaporation were verysensitive to a change in the infiltration rate following

deforestation.More fundamentally, the impact of soil parametri-

sation on GCM climate simulations is relatively unex-plored. This is at least partly due to the lack of ordifficulty in acquiring quality global information on soilcharacteristics, as well as the large variation of soilproperties on subgrid scales (Ek and Cuenca 1994).Because of this, the parametrisation of soils in GCMsremains relatively uncertain. Since soil parametrisationcurrently is not adequately bounded it is reasonable toconsider how important this uncertainty may be whenconsidering sensitivity experiments such as the tropicalvegetation experiments conducted for this study. A

secondary aspect, then, is a parallel investigation intohow the specification of soil parameters affects the sen-sitivity of climate to tropical vegetation. In an attempt torepresent the uncertainty in the specification of soilparameters at the GCM gridbox scale, two different soilparametrisations derived from independent global soildatasets are used to investigate how the sensitivity of themodel to vegetation changes depends on soil character-istics.

Section 2 highlights some of the key features of theland surface model. The process of removing the tropical

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vegetation is described in Sect. 3.1. To investigate howthe regional sensitivity to vegetation depends on themodels soil characteristics, the numerical experimentswere repeated with an alternative soils dataset which isdescribed in Sect. 3.2. Results are presented in Sect. 4with concluding remarks in Sect. 5.

2 Description of model and land surface scheme

The experiments were conducted with the Meteorologi-cal Office Hadley Centre Unified Model (HadAM3) asdescribed by Pope et al. (2000) which has a horizontalgrid of 2.5 latitude by 3.75 longitude with 19 verticallevels. The model is hydrostatic and uses the Arakawa Bgrid and Eulerian advection. Moist and dry convectionare parametrised with a mass flux scheme closed on nearsurface buoyancy and clouds only form in grid boxeswith relative humidity greater than a critical value of70%. The land surface scheme is the MeteorologicalOffice Surface Exchange Scheme (MOSES2) which hasfour soil layers of depths 0.1, 0.25, 0.65, and 2.0 m. It

utilises a tiling scheme to represent subgrid scale heter-ogeneity of land surface characteristics, including vege-tation (Essery et al. 2003). Nine surface tiles are definedincluding five vegetation or plant-functional types(PFTs: broadleaf tree, needleleaf tree, temperate C3grass, tropical C4 grass, and shrubs) and four non-veg-etated surface types (urban, inland water, bare soil, andice). Surface fluxes and temperatures are calculatedseparately for each surface type and are aggregatedaccording to each tiles fractional coverage before beingpassed to the atmospheric model.

MOSES2 represents many aspects of the surfacehydrological cycle. Precipitation is intercepted by the

vegetated canopy and re-evaporated into the boundarylayer if the canopy is not already saturated. Precipitationnot intercepted falls through to the soil surface where iteither enters the top soil layer or, if the top soil layer issaturated, runs off the surface. Water in the soil canmove between layers, the speed and direction of themovement depending on soil water suction and gravi-tational potentials. If the soil water amount in the bot-tom layer exceeds the soil saturated hydraulicconductivity the excess is drained out of the bottom assub-surface runoff. Soil water can exit the soil via eitherplant functional type transpiration (from top three soillayers) or soil evaporation (from the top soil layer).

Bare-soil evaporation occurs from the bare-soil tile andfrom a fraction of vegetated tiles, dependent on the leafarea. Transpiration rate is determined by the availabilityof soil water, the fraction of roots in each soil layer andthe canopy conductance, calculated by the photosyn-thesis module and dependent on solar irradiance,humidity deficit and soil moisture availability.

The standard vegetation distribution used in MO-SES2 is derived from the International Geosphere-Bio-sphere Programme (IGBP) land-cover dataset. Thisdataset uses information from the Advanced Very High

Resolution Radiometer (AVHRR) data to define 14land-cover classes at 1 km resolution (Hansen et al.2000). The mapping between these classes and assumedfractions of the MOSES 2 surface types are given inEssery et al. (2003). An annual cycle of vegetation foreach PFT, based on International Satellite Land SurfaceClimatology Project (ISLSCP) II Leaf Area Index (LAI)data, has been incorporated into MOSES 2 to representvegetation phenology (Lawrence and Slingo 2004). Thecalculation of surface albedo has also been revised toinclude a dependency on surface soil moisture.

The surface tiling approach, however, is not extendedto the gridbox representation of soil. The 1 resolutionsand, silt and clay fractions from Wilson and Hender-son-Sellers (1985, WHS) are aggregated for each grid-box, and from these fractions the soil parameters arecalculated using the regression technique of Cosby et al.(1984). This results in the soil in each gridbox beingdefined by nine parameters. Those relevant to this studyare three volumetric soil moisture concentrations;hs,which defines the amount of water that the soil holdswhen saturated, and hc, hW which define the amount of

water in the soil at the critical and wilting points and,therefore, determine the range of soil moistures overwhich transpiration by vegetation is reduced due tostomatal closure. To calculate the movement of waterinto the top soil layer, between soil layers and out of thebottom layer, values of soil water suction (w) andhydraulic conductivity (K) are required for each soil le-vel. These are derived using the Clapp and Hornberger(1978) empirical formulations which require three moresoil dependent parameters; the saturated soil watersuction, ws, saturated hydraulic conductivity, Ks, and aClappHornberger exponent, b.

3 Experimental design

3.1 Sensitivity of regional tropical climate to vegetation

To investigate how and where vegetation is a significantdriver of local climate in the tropics, a modified vege-tation distribution was created where vegetation wasremoved from all tropical regions. This is done by set-ting the fractional coverage for each of the five PFTs tozero in the appropriate grid boxes and increasing thebare soil fractional coverage accordingly. Therefore, ingridboxes from which the vegetation has been removed,

leaf area index, canopy height and rooting depth will bezero, while the infiltration enhancement factor and thecanopy capacity will be set to the equivalent bare-soilvalues. The tile albedo in MOSES2 is dependent on thesurface soil moisture state, the bare-soil albedo and theamount and type of vegetation coverage. Therefore, it ispossible for the albedo of a gridbox to decrease whenvegetation is removed and the surface soil becomes wet.For a more detailed description of MOSES2 and thealbedo-soil moisture dependency the authors recom-mend Lawrence and Slingo (2004). Our approach,

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therefore, ensures that the sensitivity of climate to allsurface characteristics dependent on vegetation cover(e.g. roughness length, albedo, rooting depth) as well asvegetation cover itself (i.e. leaf area index) is investi-gated.

In order to avoid sharp spatial changes in vegetationcover the delineation between vegetation and tropicaldevegetation is chosen to lie along natural vegetationboundaries wherever possible. Figure 1 shows annualmaxima of two important vegetation parameters, leafarea index (LAI) and canopy height for the fully vege-tated state. Ideally, the transition from fully vegetated todevegetated zones should lie along areas where theseparameters are local minima. For consistency, the de-vegetated area is also selected to encompass all areasaffected by tropical monsoonal systems even where thatmeans extending the devegetated region outside the deeptropics. For example, it extends north of the Tropic ofCancer in Asia so that the South Asian and SoutheastAsian monsoon regions are fully included. The resultantdevegetation (NOVEGT) distribution and control(VEG) distribution are depicted in Fig. 2, which shows

the fractional vegetation coverage percentage for eachexperiment. It is important to note that, due to the use ofa prescribed vegetation phenology, the magnitude of thechange in surface characteristics between VEG andNOVEGT not only depends on the PFT present in VEGbut also the time of year.

3.2 Sensitivity to soil parameters

Recent efforts to improve GCM land surface schemeshave focused on the representation of vegetation

(Dickinson et al. 2002) and the heterogeneity of the landsurface (Essery et al. 2003). Part of the developmentalprocedure of land surface schemes is simple sensitivitystudies of the models performance to the choice ofvegetation parameters such as roughness length andstomatal resistance (Beringer et al. 2002). However,these studies often neglect to investigate the sensitivity to

the chosen soil parameters, probably because of theuncertainties in their values and a lack of accurateobservational data. Sophisticated parameter-estimationtechniques have been used recently to find optimalparameter sets for particular land surface scheme vege-tation types in areas where there is sufficient observa-tional data. The main focus of these studies is on thevegetation specific parameters but Sen et al. (2001)showed recently that including soil properties in thecalibration in an Amazonian region can improve thesimulation of observed fluxes.

Most land surface schemes are similar to MOSES2 inthe employment of the formulations of Clapp and

Hornberger (1978) to derive values of hydraulic con-ductivity and soil water suction. It has been shown,however, that the parameters used in the empirical for-mulations, derived for homogeneous soils, have largeuncertainties when applied to heterogeneous soils. Ekand Cuenca (1994) demonstrated that simulated surfacefluxes and boundary layer development were sensitive tovariations in the exponent, b, while Xue et al. (1996)found that the specification of three ClappHornbergerparameters in a simplified version of the Simple Bio-sphere Model (SSiB) (Sellers et al. 1986) profoundly

Fig. 1 Annual maximum: a LAI (dimensionless), b canopy height(m)

Fig. 2 Percent vegetation cover for a VEG and bNOVEGT.Vegetation cover is sum of fractional coverage of the five PFTs.Boxes on bindicate areas over which averages were made forAmazonia and South Asian Monsoon regions



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impacted the model simulation. Ducharne and Laval(2000) identified a strong sensitivity in the LMD GCM,which differs from MOSES2 in its determination of soilwater concentrations, to the prescription of the soilwater holding capacity. Such sensitivities have impor-tant implications in the context of land use change,especially where the soil characteristics are likely tochange. However, as identified by Ducharne and Laval(2000), there is no consensus amongst the GCM mod-elling community as to how to represent present-day soilmoisture accurately.

In an effort to begin to understand how the specifi-cation of soil parameters impacts both GCM climatesimulations as well as GCM sensitivity experiments, theVEG and NOVEGT experiments were both repeatedwith a second soil dataset. This dataset is derived fromthe high-resolution IGBP-DIS Soil Data Task and hasbeen used in a recent study of land use change in theSahel (Taylor et al. 2002). The IGBP soil dataset, inprinciple, represents tropical soils better than the WHSsoil dataset. This new soil dataset makes use of highresolution source data to determine gridbox mean soil

parameters that nominally take into account some of thesoil heterogeneity. The soil saturated hydrological con-ductivities have been increased to account for sub-griddistribution of soil types and, in general, the IGBP soilsdrain more than the WHS soils, an improvement insandy soil regions, e.g. the Sahel, but unvalidated else-where. Although a number of potential improvementshave been mentioned, the quality of the IGBP soildataset has not been verified and the effects on climatehave not been fully investigated. The IGBP soil datasetis selected as an alternative soil specification not neces-sarily because it is perceived as an improvement on thestandard WHS soils, but because it is another attempt to

represent soils, and one which will therefore permit aninvestigation into how soil specification affects the sen-sitivity to changes in vegetation. The fact that recentresearch has revealed that HadAM3/MOSES is rarelymoisture-limited (Gedney et al. 2000), potentiallyaffecting the degree of landatmosphere coupling, addsweight to the choice of a freer draining soil dataset.

3.3 Description of integrations

The use of two vegetation distributions (VEG for globalvegetation distribution and NOVEGT for devegetated

tropics vegetation distribution) and two soil datasets(WHS and IGBP) results in a total of four HadAM3climate simulations: VEGWHS, NOVEGTWHS, VE-GIGBP, and VEGTIGBP. In cases where the soil dataset isnot specified, it should be assumed that the experiment isrun with the standard WHS soil dataset. Each simula-tion is run for 25 years using climatological SSTs. Thefirst five years are discarded to allow for spinup of thedeep soil moisture and soil temperature fields. The last20 years of the integrations are used to determine cli-matological averages and are sufficient to conduct

Student t-tests to identify the statistical significance ofdifferences in climatological sample means.

Annual mean precipitation distributions for thecontrol simulations (VEGWHSand VEGIGBP) are shownin Fig. 3. With either soil dataset, HadAM3 simulates afairly realistic climate by comparison to observed annualmean precipitation estimates. The main discrepancy isan underestimation of precipitation over the MaritimeContinent as documented by Neale and Slingo (2003).The impact of changing the soil parameters on themodels precipitation field is very slight and barely sig-nificant. The major difference between the VEGWHSandVEGIGBP climate simulations is in the soil-moisturecontent (not shown). The hydraulic parameters for theIGBP soils permit a much faster flow of water throughthe soil layers, resulting in lower mean VEGIGBP soil

Fig. 3 Annual mean precipitation for a observations (CMAP, Xieand Arkin 1996), b VEGWHS, cVEGIGBP and d VEGIGBP VEGWHS. White dots indicate significant differences at 95%confidence level



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moisture contents by approximately 37% in all soillevels averaged over the devegetated region.

4 Results

4.1 Influence of vegetation on tropical climateand hydrology

4.1.1 Regional climate sensitivity

For the sake of discussion, the results are presented anddescribed from the perspective of how vegetation con-trols the local climate in the tropics rather than howdevegetation (or deforestation) may perturb the localclimate; therefore, area mean differences are listed andmaps are shown for VEGNOVEGT differences. Beforediscussing some important regional differences in howthe local climate responds to vegetation, it is interestingto consider the tropics-wide impact. Table 1 lists theannual mean values averaged over all tropical devege-tated grid points for selected key surface variables

including surface temperature, precipitation, humidity,and the surface energy balance components for VEG,NOVEGT, and VEGNOVEGT. The main influenceof vegetation on the climate is to cool the surfacetemperature. This is mainly due to an increased latentheat flux which also increases the atmospheric humidity.Precipitation is slightly increased in the presence ofvegetation which is most likely due to the increase inlocal recycling of water because the moisture conver-gence is decreased.

With the inclusion of vegetation the downwellingsurface radiation is increased. This is predominantly dueto a decrease in the loss of longwave radiation from the

surface following the surface cooling effect of thechanges in the latent heat flux noted already. The netsurface shortwave radiation is also decreased due to anincrease in cloud amount despite a decrease in surfacealbedo when the brighter soil is covered with vegetation.The impact on albedo in this experiment is not as largeas in previous sensitivity studies (e.g. Charney et al.1977). Vegetation generally decreases the albedo by lessthan 10% although in some specific regions (e.g. China)

the albedo is actually increased. This is due to the addeddependency of albedo in MOSES2 to the surface soilmoisture, i.e. the vegetation albedo is greater than thesoil albedo in this case.

The influence of vegetation on the latent heat flux,and therefore the surface temperature, was found to bedependent on the seasonality of the availability of water,i.e. precipitation. Therefore, VEGNOVEGT differencemaps will be shown averaged over the months with thethree lowest and three highest precipitation rates at eachgridpoint, producing approximate dry and wet seasoncomposite plots, respectively. It was found that therewas no change in the seasonality of precipitation be-tween VEG and NOVEGT. Such an analysis allows abetter analysis of the seasonality of the influence ofvegetation on climate than the usual DJF and JJA sea-sonal means commonly used in devegetation studies.

Figure 4 shows that surface temperature is consis-tently decreased in the presence of vegetation in both wetand dry seasons and throughout the tropics. The largestcooling, however, occurs in the dry season when theresponse exhibits more regional variation than in the wet

season. The largest cooling occurs in the Amazon andCongo basins and over South China. This regionality inthe response is due to the close relationship betweensurface temperature and rates of surface evaporation.

In general, throughout the tropics and across allmonths of the year, the most consistent impact of vege-tation is to substantially increase the surface evaporationrates. Figure 5 shows wet and dry season maps of VEGNOVEGT latent heat flux, which is directly proportionalto evaporation. The largest differences, up to andexceeding +70 Wm2, are found in the dry season, and inthe tropical rainforest regions which are dominated inVEG by large leaf area, tall canopy, broadleaf trees.

Theseasonalityof theresponse is due to theseasonalityof precipitation and evaporation efficiency which is afunction of soil water availability. During the wet seasonthere is enough moisture in the surface soil layer in NO-VEGT to meet evaporative demand. During the dry sea-son, the surface soil layer is relatively dry and in VEG theaccess to sub-surface moisturestores is greater than that inNOVEGT due to plant transpiration. Figure 6 shows theeffect of vegetation on the rate of evapotranspiration (soil

Table 1 Annual mean of climate and surface energy balance variables, averaged over devegetated gridpoints, from WHS soil simulations.All differences are significant at the 95% significance level

Variable Units VEG NOVEGT VEG NOVEGT

Temperature (K) 293.7 295.0 1.3Precipitation (mm day1) 3.10 2.98 0.12Specific humidity (g kg1) 10.55 9.99 0.57Moisture convergence (mm day1) 1.17 1.60 0.42Latent heat flux (Wm2) 58.1 42.9 15.2Sensible heat flux (Wm2) 40.0 37.4 2.6Net surface radiation at surface (Wm2) 98.6 81.1 17.5Net downward shortwave radiation at surface (Wm2) 187.3 193.0 5.7Net upward longwave radiation from surface (Wm2) 88.7 111.9 23.2Cloud amount (%) 46.4 44.0 2.4Albedo 0.20 0.21 0.01



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evaporation + plant transpiration). The ability of plantsto access moisture deep in the soil is evident in the positive

anomaly in the dry season. The negative anomaly in thewet season implies that bare-soil evaporation alone inNOVEGT exceeds evapotranspiration in VEG in the wetseason. This may be related to the addition of an extra(stomatal) resistance in the evaporative pathway as well asthe interception and re-evaporation of precipitationreducing the amount of water reaching the soil. On theother hand, in the wet season the positive latent heat fluxanomalies in Fig. 5 are due to increases in evaporation ofwater from saturated parts of the vegetation canopy inVEG.

While all tropical regions with significant vegetationcover exhibit an increase in latent heat flux, there are

regional differences in the dry season response comparedto the wet season response. These can be related to theseasonality of vegetation and to the seasonality of pre-cipitation and evaporation efficiency, which in turn is afunction of soil water availability. The equatorial trop-ical rainforest regions (characterised by two rainy sea-sons, centred around the vernal and autumnalequinoxes) exhibit the largest seasonal differences inlatent heat flux between the VEG and NOVEGT inte-grations. The VEG annual latent heat flux cycle is rel-atively flat since the trees can access deep soil moisture

Fig. 4 Mean differences(VEGWHSNOVEGTWHS) intemperature (K) during threewettest and three driest monthsat each gridpoint. White dotsindicate significant differencesat 95% confidence level

Fig. 5 Mean differences(VEGWHSNOVEGTWHS) of

latent heat flux (Wm2) duringthree driest and three wettestmonths at each gridpoint.White dots indicate significantdifferences at 95% confidencelevel



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to meet evaporative demand throughout the year. InNOVEGT, the latent heat flux drops off considerablyduring both dry seasons as the surface soil layer driesout.

The VEGNOVEGT latent heat flux difference isrelatively small, however, in the dry season in India,Sahel, and Southeast Asia. In these regions the domi-nant vegetation type is either annual grasses or cropswhich exhibit a strong annual cycle in vegetation cover.The largest increase in the latent heat flux, therefore,coincides with the peak of the local vegetation annualcycle which tends to follow the end of the rainy season in

most regions (and therefore does not appear in the wet/

dry season analysis). At this time, transpiration is at itsannual maximum as plants at the peak of their devel-opment can access, through their well-developed rootstructure, the vast sub-surface moisture stores that arebuilt up during the monsoon season.

The VEGNOVEGT sensible heat flux differencemaps are shown in Fig. 7. The sensible heat flux differ-ences are not spatially correlated with the correspondinglatent heat flux differences. In areas where a large latentheat flux increase is seen in Fig. 5 in the dry season, acorresponding but weaker decrease in sensible heat fluxis also seen. However, the sensible heat flux is not uni-

formly lower in VEG. For example, in the Sahel and

Fig. 6 Mean differences(VEGWHSNOVEGTWHS) ofevapotranspiration (mm day1)during three wettest and threedriest months at each gridpoint.White dots indicate significantdifferences at 95% confidencelevel

Fig. 7 Mean differences(VEGWHSNOVEGTWHS) ofsensible heat flux (Wm2)during three wettest and threedriest months at each gridpoint.White dots indicate significantdifferences at 95% confidencelevel



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India during the dry season, when vegetation has a rel-atively weak but still positive impact on latent heat flux,the sensible heat flux is also larger in VEG implying thatthe available energy at the surface must also have in-creased in the presence of vegetation. Averaged over theyear, the sensible heat flux VEGNOVEGT difference isconsiderably lower than the latent heat flux difference,averaging +3 Wm2 and +15 Wm2, respectively.

The regionality of the response to tropical vegetationis particularly varied for precipitation (Fig. 8). Overall,vegetation results more frequently in a wetter ratherthan a drier climate. In regions characterised by a dryseason, precipitation is very low in both VEG andNOVEGT and therefore is unaffected by vegetation.Over the equatorial rainforest regions, however, thedriest months still have substantial rainfall which is in-creased by the presence of vegetation. Figure 9 showsthat during these months moisture convergence intothese regions is actually decreased due to a reduced land-sea temperature contrast driven by the cooling effect ofthe increased latent heat flux. Therefore, the increase inprecipitation in VEG is due to an increase in the local

recycling of moisture. This is consistent with estimates ofAmazonian basin-wide precipitation recycling between25 and 50% (Hahmann and Dickinson 1997). Duringthe wet season, however, vegetation has a smaller effecton surface temperature, allowing increased convergenceinto parts of East Africa and South America where in-creased rates of precipitation are observed. The differentresponse of moisture convergence in the wet and dryseasons emphasises the role of surface temperature indetermining changes in the general circulation. Differ-ences in the temperature sensitivity to deforestationcould explain the disagreement between models in theirsimulated changes in moisture convergence mentioned

in Sect. 1.

Another contributing factor which can lower mois-ture convergence is the reduction in wind speed over theland areas due to the increased drag of a vegetatedsurface compared to smoother bare-soil. Such changesacross the Amazon basin reduce the moisture flow to theAndes in the west, contributing to the negative anomalyin precipitation in this area.

An interesting difference in precipitation responses tovegetation is also apparent between the Indian andSoutheast Asian monsoons. The vigorous South Asianmonsoon is unaffected by the inclusion of vegetation,whereas Southeast Asian monsoon precipitation is sig-nificantly increased. It is possible that this difference inresponse is related to the orientation of the low-levelmonsoonal circulation. The low-level flow into the In-dian subcontinent is loaded with moisture as it crossesthe Arabian Sea. In contrast, low-level flow into south-ern China, where the largest increase in VEG precipi-tation is found, passes over a large stretch of tropicalrain forest in Malaysia and Thailand. When that rainforest is absent, the low-level air may not be steadilyreplenished with moisture as it passes from the Bay of

Bengal to southern China.An important feature of the precipitation sensitivity

to vegetation is the decreased precipitation in both wetand dry seasons over the Maritime Continent. Becausemoisture is not in short supply, this is believed to be aresult of the cooling effect of vegetation reducing thefrequency of convective events over the islands.

4.1.2 Hydrology

Over India, the precipitation annual cycle, and thereforewater input into the soil, is virtually identical for both

the VEG and NOVEGT experiments, which makes it a

Fig. 8 Mean differences(VEGWHSNOVEGTWHS) inprecipitation (mm/day) duringthree wettest and three driestmonths at each gridpoint.White dots indicate significantdifferences at 95% confidencelevel



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convenient region to use to analyse the influence ofvegetation on surface hydrology. Figure 10 shows VEGand NOVEGT annual cycle time series of selected keyhydrology variables including precipitation, surfaceevaporation, surface runoff, sub-surface runoff, surfacesoil moisture content, and total soil moisture content.Clearly, vegetation has a considerable impact on theannual hydrological cycle. Evaporation is strongly en-hanced throughout the dry season (November toMarch) which results in a substantially drier VEG soilmoisture profile. Since all evaporation in NOVEGTmust, by definition, come directly from the top soil layer,

while VEG evaporation can be drawn from deeper soillayers as well as the vegetation canopy, the VEG surfacesoils end up slightly wetter for most of the year. Thetotal soil moisture contents are roughly equivalent dur-ing the wet season; consequently, sub-surface runoff isalso similar in both experiments. Surface runoff, how-ever is considerably stronger during the wet season inNOVEGT due to two supporting factors. First, sincethere is no vegetation canopy to intercept any of theincoming precipitation, for the same magnitude pre-ciptitation event a much larger amount of water reachesthe surface, not all of which can be absorbed by the soils.Second, the roots that are associated with vegetation

influence how readily water striking the surface caninfiltrate into the soil profile. In MOSES the base surfaceinfiltration rate, which is one of the soil parameters, isscaled by an enhancement factor, larger for trees thanfor grasses, that represents the increase in infiltrationcaused by the roots.

Averaged over the whole of the tropics the rate ofevaporation is increased, as mentioned previously. Ta-ble 2 shows that the increase is mainly due to the addi-tion of canopy evaporation, since the inclusion oftranspiration does not vastly exceed bare-soil

evaporation in the absence of vegetation. As for India,vegetation dramatically decreases surface runoff whileincreasing sub-surface runoff slightly. The decrease insurface runoff and reduction in the area of bare-soilincreases the amount of water in the surface soil layer,while the impact of transpiring plants is to reduce thetotal amount of water in the soil profile.

4.2 Sensitivity of vegetation influence to soil parameters

Overall, introducing tropical vegetation into HadAM3/

MOSES2 acts to cool and moisten the tropical surfaceclimate and the consistency, especially for surface tem-perature, across all times of the year and virtually allvegetated regions suggests that this is a robust result. Itis interesting, however, to compare the models sensi-tivity to tropical vegetation using the standard WHSsoils to its sensitivity using the IGBP soils. As noted inSect. 3, precipitation is not substantially different whe-ther the WHS or the IGBP soil dataset is used. Soilmoisture content, however, is substantially lower in theIGBP soil control run due, primarily, to differences inthe hydraulic parameters. Table 4 when compared toTable 2 shows that, averaged over all tropical land grid

points, VEGIGBPsoil moisture content is 37% lower thanin VEGWHS.

The difference in soil-moisture content contributes toa difference in the senstivity of surface temperature tothe inclusion of vegetation. Figure 11 shows VEGIGBPNOVEGTIGBP surface temperature difference using thesame contours levels as those used for the analagousmaps of VEGWHSNOVEGTWHS shown in Fig. 4.There is a large degree of similarity in the regionality ofthe surface air temperature response to vegetation withthe largest amplitude differences apparent in the tropical

Fig. 9 Mean differences(VEGWHSNOVEGTWHS) in850 hPa divergence (s1) duringthree wettest and three driestmonths at each gridpoint.White dots indicate significantdifferences at 95% confidencelevel



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rainforest regions for both sets of experiments. The mostsignificant difference, though, is that the surface airtemperature sensitivity to the inclusion of vegetation isconsiderably smaller with the IGBP soils. Averagedacross the tropics the VEGWHSNOVEGTWHS temper-ature difference is 1.3 K whereas the VEGIGBPNO-VEGTIGBP temperature difference is only 0.8 K.Figure 12 shows the magnitude and significance of the

effect of soil parametrisation on the modelled tempera-ture sensitivity to vegetation. Large areas of SouthAmerica and Central Africa demonstrate a sensitivity tothe soil specification of the same magnitude as the ori-ginal sensitivity to vegetation with the WHS soils. Thelarge, coherent sensitivity over China in the dry season ison the borderline of significance at the 95% level. Thereduced sensitivity to tropical vegetation is also seen for

Fig. 10af Seasonal variationof hydrology variables forVEGWHS (dotted) andNOVEGTWHS (solid)simulations averaged overSouth Asian monsoon region(17.5 to 25N, 80 to 85E).Vertical bars represent 95%confidence interval



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precipitation and latent heat flux (not shown). In fact,averaged over the tropical land surface,the impact ofvegetation is smaller for virtually all the surface energybalance and hydrology variables when IGBP soils areused (compare VEGIGBPNOVEGTIGBP column in Ta-

ble 3 to VEGWHSNOVEGTWHS column in Table 1).The question, then, is why the IGBP soil runs are less

sensitive to the inclusion of tropical vegetation than theWHS soil runs. The smaller surface air temperaturedifferences appear linked to smaller latent heat fluxdifference, which, in the annual mean, is about 50%smaller for VEGIGBPNOVEGTIGBP(see Table 4).VEGWHS latent heat flux averages 58 Wm

2 whereasVEGIGBP latent heat flux is slightly lower at 54 Wm

2.The small reduction in the control experiment latentheat flux is due to drier mean soils contributing to more

frequent soil moisture stress on plants and therefore lesstranspiration. NOVEGTWHS latent heat flux averages43 Wm2 which is 15 Wm2 lower than VEGWHS .NOVEGTIGBP latent heat flux averages 46 Wm

2, whichis actually stronger than then NOVEGTWHS latent heat

flux. At first, this result appears counterintuitive sinceNOVEGTIGBP has much drier surface soils and onemight expect that to result in a lower latent heat flux.The positive shift in soil evaporation is related to dif-ferences in the critical soil moisture concentration, hc,one of the nine soil parameters used by MOSES. Thisparameter is involved in the calculation of soil conduc-tance which itself is an important variable in the calcu-lation of evaporation efficiency, E/Ep, where Ep is thepotential evaporation determined by surface environ-mental conditions, particularly the atmospheric humid-

Table 2 Annual mean of hydrological cycle variables, averaged over devegetated gridpoints, from WHS soil simulations. All differencesare significant at the 95% significance level


Precipitation (mm day1) 3.10 2.98 0.12Total evaporation (mm day1) 1.92 1.39 0.53Evapotranspiration (mm day1) 1.56 1.39 0.17Canopy evaporation (mm day1) 0.37 0 0.37Surface runoff (mm day1) 0.23 0.80 )0.57Sub-surface runoff (mm day1) 0.92 0.77 0.15Total runoff (mm day1) 1.15 1.57 0.42Total soil moisture (mm) 676.4 733.9 57.5Surface soil moisture (mm) 17.7 13.1 4.6

Table 4 Annual mean of soil hydrology variables, averaged over devegetated gridpoints, from IGBP soil simulations. Differences marked* are not significant at the 95% level


Precipitation (mm day1) 3.07 3.04 0.03*Total evaporation (mm day1) 1.78 1.50 0.28

Evapotranspiration (mm day1) 1.42 1.50 0.08Canopy evaporation (mm day1) 0.36 0 0.36Surface runoff (mm day1) 0.12 0.22 0.10Sub-surface runoff (mm day1) 1.15 1.30 0.15Total runoff (mm day1) 1.27 1.51 0.25Total soil moisture (mm) 423.6 449.5 25.9Surface soil moisture (mm) 11.6 10.2 1.4

Table 3 Annual mean of climate and surface energy balance variables, averaged over devegetated gridpoints, from IGBP soil simulations.Differences marked *are not significant at the 95% level


Temperature (K) 294.0 294.7 0.76Precipitation (mm day1) 3.07 3.04 0.03*

Specific humidity (g kg1) 10.28 10.03 0.25Moisture convergence (mm day1) 1.30 1.54 0.24Latent heat flux (Wm2) 53.9 46.1 7.8Sensible heat flux (Wm2) 43.9 36.0 7.9Net surface radiation at surface (Wm2) 98.3 82.9 15.4Net downward shortwave radiation at surface (Wm2) 188.4 192.6 4.2Net upward longwave radiation from surface (Wm2) 90.1 109.7 19.6Cloud amount (%) 46.3 45.1 1.2Albedo 0.20 0.21 0.01



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ity deficit. A higher soil conductance means a higherevaporation efficiency. Soil conductance is determined

according to the following formula:

gsoil 1

100

h1

h c

21

where h1 is the top layer soil moisture concentration.The critical soil moisture concentration is uniformlylower for the IGBP soils, resulting in a more rapid andmore realistic drying of the surface soil layer subsequentto a rainfall event, at least in the Sahel (Taylor personalcommunication 2003).

The effect of this difference in parameter values onthe latent heat flux response and consequently on the

surface air temperature response is clearest in regionswhere there is a large seasonality in precipitation andlittle seasonality of vegetation. Figure 13 shows the an-nual cycle of key variables averaged over Amazoniangrid points for all four simulations. The amplitude of thecooling appears closely related to the amplitude of thelatent heat flux difference. The largest latent heat fluxdifference occurs for both sets of experiments during thetwo Amazonian dry seasons (JJAS and JFM), as it doesfor the South Asian monsoon shown in Fig. 10. This isdue to the ability of the soil to evaporate at near

Fig. 11 Mean differences(VEGIGBPNOVEGTIGBP) oftempreature (K) during threedriest and three wettest monthsat each gridpoint. White dotsindicate significant differencesat 95% confidence level

Fig. 12 Mean difference oftemperature sensitivity tovegetation cover between thesoil parameterisations(VEGWHSNOVEGTWHS)(VEGIGBPNOVEGTIGBP)during three driest and threewettest months at eachgridpoint. (Fig. 4 minusFig. 11) White dotsindicatesignificant differences at 95%onfidence level



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potential rates during the wet seasons when surface soilmoisture is constantly replenished. The sensitivity of soilevaporation to the soil specification can be seen clearlyby comparing the rates of evapotranspiration with andwithout vegetation. With the WHS soils, bare-soilevaporation has an annual cycle that is precisely inphase with the precipitation annual cycle. In the

presence of vegetation, which is essentially constantthroughout the year in the tropical rainforest regions,canopy evaporation and evapotranspiration combine togenerate a reasonably constant evaporation ratethroughout the annual cycle. For the IGBP soils, twodifferences are worth noting. First, the lower soil mois-ture at depth reduces transpiration and hence total

Fig. 13 Seasonal variation ofclimate variables and landsurface characteristics averagedover the Amazonia region (10Sto 5N, 70 to 55W) for VEG(dotted line) and NOVEGT(solid) simulations with bothsoil parametrisations: WHS(left panels) and IGBP (right).Vertical bars represent 95%confidence interval. Bottom

panels also show soil moistureavailability factor (dashed line)for VEG simulation



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evaporation in the VEGIGBPsimulation. Second, in theabsence of vegetation, the annual cycle of soil evapora-tion is smaller in NOVEGTIGBP than in NOVEGTWHS,resulting in stronger annual mean soil evaporation and asmaller dry season VEGNOVEGT difference. There-fore, the effect of vegetation on the total rate of evapo-ration in this region is less with the IGBP soils resultingin a much smaller cooling effect on the surface temper-ature. The annual cycle of the key variables affectingeach process (soil conductivity, gsoil, and soil moistureavailability, b) are shown in the bottom panels ofFig. 13.

It is interesting to compare the hydrological annualcycle for the IGBP soil experiments and for the WHSsoil experiments. Figure 14 shows mean annual cycles ofprecipitation, evaporation, surface runoff, sub-surfacerunoff, surface soil moisture, and total soil moisture forVEGIGBP and NOVEGTIGBP averaged over the SouthAsian monsoon region. These plots can be directlycompared to similar plots for VEGWHS and NO-VEGTWHSshown in Fig. 10. In general, the character ofthe influence of vegetation on the hydrological annual

cycle is the same for both sets of experiments. There aredifferences, though. For example, while peak evapora-tion is roughly the same for both soils, the amplitude ofthe difference in dry season VEGNOVEGT evapora-tion is consistently lower for the IGBP soil runs byabout 0.5 mm day1. This difference leads to a smallerdifference in end-of-dry-season soil moisture content. Inaddition, mean surface runoff is weaker and mean sub-surface runoff is stronger for the IGBP soil runs due to alarger parametrised base infiltration rate.

5 Summary and discussion

This work describes a series of idealised experiments toexplore the sensitivity of the local climate in the tropicsto the presence of vegetation, and how that sensitivitydepends on the characteristics of the underlying soils.We chose to investigate tropical vegetation alone be-cause most of the significant current and future projec-tions of land use change are likely to occur in theseregions. However, the authors do not consider that theresults are equivalent to those of deforestation or landuse change GCM scenario experiments but may help intheir interpretation.

The comparisons of simulated climate with and

without tropical vegetation suggest that the importanceof vegetation on climate varies from region to regionand throughout the year. Although the impact of vege-tation on near surface temperature is consistentthroughout the tropics and throughout the year, itsinfluence on precipitation is less consistent. It is appar-ent that the role of forests is self-sustaining by increasingthe local precipitation. Whether this is due to thechanges in roughness length, rooting depth or evapora-tion rates is hard to distinguish. Betts (1998) was able toseparate the effects of each by introducing the vegetation

characteristic one at a time in GCM simulations andconcluded that the change in roughness length had thegreatest influence on precipitation. A comparison of theevaporation rates in Figs. 10 and 13 suggests thatthe influence of shorter vegetation such as C3 grass onthe evaporation rate was of the same order as that of theforest vegetation type. Therefore, the fact that the pre-cipitation over grassland regions was unchanged goessome way to confirming his conclusion. However, as wasdemonstrated by the complicated response of the Asianmonsoon, the location of each region with respect togeneral circulation patterns is also crucial.

The parallel experiments with the IGBP soils dem-onstrated the sensitivity of evaporation, and hencetemperature, to the soil specification. The smaller tem-perature increase following devegetation was due to acombination of a lower rate of transpiration by theplants because of the lower soil moisture content andhigher rates of bare-soil evaporation. That such a sen-sitivity is found with such a drastic change to the landsurface may not be that surprising, but is anotherwarning with regard to the interpretation of GCM land

use change scenarios. That the results of such studies aremodel dependent has never been doubted, but the pos-sibility that the results are dependent on the soil speci-fication is rarely considered. The likelihood that realland use changes may themselves alter the soil propertiesadds another complication for the modelling communityto consider.

The specification of soil type, texture and hydraulicproperties in GCMs is inherently difficult (Ek and Cu-enca 1994). Both soil datasets used in this study attemptto represent the same properties that vary widely atscales finer than the resolution of the model and no at-tempt has been made to say which dataset is better. The

results of this study do suggest, however, the need forinvestigation into the role of soil specification on theland-atmosphere coupling. Koster et al. (2002) showedthat the degree of coupling between the land surface andatmosphere varies significantly between models. In theirstudy the Hadley Centre model was characterised by aweaker coupling compared to the other models. Com-bined with the results of Gedney et al. (2000) whodemonstrated that the Hadley Centre model is rarelymoisture limited, the impact of soil parameters on soilmoisture concentrations, and subsequently, on climatesensitivity in this study suggests that the apparentinsensitivity found by Koster et al. (2002), could be

dependent on the specification of certain soil hydraulicparameters.

This study has succeeded in its aim to identify hotspots in the climate system where the vegetation playsan important role in determining the local climate. Thefast rate of Amazonian deforestation has motivatednumerous modelling and observational studies into theinfluence of tropical rainforests on climate. Our resultsagree with the general conclusion that forests play a self-sustaining role in their local climate system. This studyhas also highlighted differences between non-forested



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regions and also gives an indication of the sensitivity ofclimate to the land surface in other tropical vegetatedregions. It appears that the state of the land surface ofIndia has a relatively small influence on the monsoonalclimate, whereas the climate of China is sensitive to thepresence of vegetation cover. This is an important con-clusion when one considers current and future land use

changes in this region and increasing concerns aboutdesertification in China. The final conclusion of thisstudy regarding the sensitivity to soil specification of theinfluence of vegetation on climate is a new and inter-esting result which should be given considerable atten-tion by the land use change modelling community, notonly because of the uncertainty in the specification of

Fig. 14af Monthly meanhydrology variables forVEGIGBP (dotted) andNOVEGTIGBP (solid)simulations averaged overSouth Asian monsoon region(17.5 to 25N, 80 to 85E).Vertical bars represent 95%confidence interval



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current soil characteristics in climate models but thelikelihood of the soil characteristics themselves alteringfollowing land use change.

Acknowledgements Tom Osborne acknowledges the support of hisPhD Studentship from NERC. David Lawrence was funded underthe EU Framework 5 PROMISE project (EVK2-CT-1999-00022).Julia Slingo acknowledges the support of the NERC Centres forAtmospheric Science. We thank Peter Cox and Richard Betts of theHadley Centre for their valuable discussions, and Christopher

Taylor of the NERC Centre for Ecology and Hydrology for theprovision and advice on the IGBP soils dataset.

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Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters