Global Hydroclimatological Teleconnections Resulting from Tropical Deforestation

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Global Hydroclimatological Teleconnections Resulting from Tropical Deforestation

RONI AVISSAR AND DAVID WERTH

Department of Civil and Environmental Engineering, Edmund T. Pratt Jr. School of Engineering, Duke University,Durham, North Carolina

(Manuscript received 8 February 2004, in final form 8 August 2004)

ABSTRACT

Past studies have indicated that deforestation of the Amazon basin would result in an important rainfalldecrease in that region but that this process had no significant impact on the global temperature orprecipitation and had only local implications. Here it is shown that deforestation of tropical regions sig-nificantly affects precipitation at mid- and high latitudes through hydrometeorological teleconnections. Inparticular, it is found that the deforestation of Amazonia and Central Africa severely reduces rainfall in thelower U.S. Midwest during the spring and summer seasons and in the upper U.S. Midwest during the winter

and spring, respectively, when water is crucial for agricultural productivity in these regions. Deforestationof Southeast Asia affects China and the Balkan Peninsula most significantly. On the other hand, theelimination of any of these tropical forests considerably enhances summer rainfall in the southern tip of theArabian Peninsula. The combined effect of deforestation of these three tropical regions causes a significantdecrease in winter precipitation in California and seems to generate a cumulative enhancement of precipi-tation during the summer in the southern tip of the Arabian Peninsula.

1. Introduction

The effects of deforesting the Amazon basin on theglobal climate have been studied with general circula-tion models (GCMs; Henderson-Sellers and Gornitz1984; Dickinson and Henderson-Sellers 1988; Lean and

Warrilow 1989; Shukla et al. 1990). While these variousmodeling studies provide somewhat different results, ingeneral, they agree that deforestation causes a reduc-tion in precipitation and evaporation and an increase insurface temperature in the Amazon basin. They alsoindicate that the basin deforestation has no detectable,significant impact on the global hydroclimate. How-ever, it is well known that El Nio has a major impacton the hydroclimate of many regions very far awayfrom the eastern Pacific Ocean (e.g., Shabbar et al.1997). With a relatively warm ocean surface, atmo-spheric moisture and instability above it are relativelyhigh, providing appropriate conditions for the enhance-ment of thunderstorm activity. Thunderstorms are the

conduit to transfer heat, moisture, and wave energy tohigher latitudes (Riehl and Malkus 1958; Riehl andSimpson 1979; Ting 1996), which alter the ridge andtrough patterns associated with the polar jet stream

(Hou 1998), a mechanism known as a teleconnection(Glantz et al. 1991; Namias 1978; Wallace and Gutzler1981). Since thunderstorms only occur in a relativelysmall part of the Tropics, not surprisingly, a change intheir spatial patterns and frequency has the potentialfor global hydroclimate consequences.

The fishbone land-cover pattern created by defor-estation in the Amazon Basin also modifies the fre-quency and location of thunderstorms in that region(Baidya Roy and Avissar 2002; Avissar et al. 2002).Therefore, Avissar et al. (2002) speculated that tropicaldeforestation, through teleconnections, should also beable to modify the hydroclimate of remote locationsoutside of the Tropics. In their numerical simulation ofAmazon deforestation, Gedney and Valdes (2000) in-deed noted a geopotential wave train, with its attendantprecipitation changes, that extended into the wintermidlatitudes. And using the National Aeronautics andSpace Administration (NASA) Goddard Institute forSpace Studies (GISS) general circulation model (GCM;

Hansen et al. 1983), Werth and Avissar (2002) identi-fied a clear relation between deforestation of the Ama-zon basin and a reduction of precipitation in NorthAmerica.

As deforestation of tropical regions continues at analarming pace [15 000 km2 annually from 1978 to 1988in the Brazilian Amazon alone according to Skole andTucker (1993)], it is important to reevaluate conclu-sions from earlier modeling studies and quantify theeffects of such a reduction in rainforest on the local,

Corresponding author address: Dr. Roni Avissar, Departmentof Civil and Environmental Engineering, Edmund T. Pratt Jr.School of Engineering, Duke University, 123 Hudson Hall,Durham, NC 27708-0287.E-mail: [email protected]

134 J O U R N A L O F H Y D R O M E T E O R O L O G Y VOLUME 6

2005 American Meteorological Society


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regional, and global hydroclimate. The main objectiveof this paper, which is a continuation of the study ofWerth and Avissar (2002), is to assess the existence andimportance of teleconnections resulting from the defor-estation of all tropical regions (hereafter referred to asland-cover teleconnections).

2. Numerical experiments

In simulating the climate system with a GCM, it iscustomary to produce several simulations with per-turbed initial conditions to differentiate between thevariability due to a real signal from the natural vari-ability of the climate system. Each simulation repre-sents an independent realization and the set of simu-lations is referred to as an ensemble. Five ensemblesof six 12-yr realizations (Werth and Avissar 2004a,manuscript submitted to Geophys. Res. Lett.) were pro-duced with the NASA GISS GCM II (Hansen et al.1983), with each ensemble representing a different de-

forestation scenario. The first 4 yr of each realizationwere discarded as spinup, thus resulting in each monthof the year being simulated 48 times (six realizations of8 yr each) for each ensemble.

In the version used for this study, the NASA GISSGCM II has 12 vertical layers and a horizontal grid sizeof 4 by 5. Heat and humidity are advected with aquadratic upstream scheme, and momentum is ad-vected with a second-order scheme. The model hasboth shallow and deep convection, and a second-orderclosure planetary boundary layer scheme for moistureand heat transfer is applied at the surface. The modeluses six soil layers and a hydrology scheme that ac-counts for soil moisture transfer and root extraction

(Rosenzweig and Abramopoulos 1997), the latter ofwhich depends on the vegetation specified within a gridelement.

Observed monthly mean sea surface temperatures(SSTs) derived from multiyear climatological recordsfrom the Hadley Centre were used for all simulations.

With this type of forcing, the interannual variabilitythat would be introduced in the climate system as aresult of year-to-year oceanic variability is intentionallyeliminated. While this suppresses important interac-tions between deforestation and hydroclimatic processessuch as the El NioLa Nia cycle, it considerably sim-plifies the detection of land-cover teleconnections.Also, with the long-term memory in the simulated cli-mate system limited to only a few months after theocean feedback has been concealed (Liu and Avissar1999a, b), any month from a given year is assumed to bestatistically independent from that same month in theother years.

In the control ensemble, a vegetation map devel-

oped by Matthews (1983) for the period starting in 1960and ending in 1979 (before heavy deforestation started)was adopted for the simulations. In three other sepa-rate ensembles, Amazonia, Central Africa, and South-east Asia were individually deforested and replacedwith a mixture of shrubs and grassland. In the fifthensemble, all three regions were deforested simulta-neously. This is referred to as the total deforestationcase. A global land-cover map illustrating the three de-forested regions is presented in Fig. 1.

This land-cover change is expressed in the model bya change of albedo (from a value of 0.06 for tropicalforest to 0.1 for the deforested land), vegetation heightthat affects surface roughness (from 25 to 5 m), leaf

FIG. 1. Global land-cover map (1-km resolution), produced by NASA, emphasizing with red rectangles the threeregions in which all tropical forests (green color) are replaced with a mixture of shrubs and grassland in ourdeforestation experiments.

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area index (from 6 to 1), cumulative root distribution,and stomatal conductance. The cumulative root distri-bution is defined by root depth and two empirical pa-rameters, which are vegetation specific. For tropicalforest, these three parameters are 0.8, 1.1, and 0.4 m,respectively. Corresponding values for the deforested

land are 1.5, 0.8, and 0.4 m. Stomatal conductance iscontrolled by three empirical constants, which are alsovegetation specific. It significantly affects the redistri-bution of energy received at the ground surface intosensible and latent heat. Werth and Avissar (2004b),Costa et al. (2004), and Avissar and Werth (2004) dis-cuss the impact of this parameter on the Amazonianhydroclimatology simulated with the NASA GISSGCM II and other models.

3. Model performance

The NASA GISS GCM II has been used extensivelyto produce climate scenarios under various forcing con-ditions, and its capability to reproduce the current cli-mate has been described in various publications (e.g.,

Hansen et al. 1983; Lau et al. 1996). While an additionaldetailed evaluation of this models performance is be-yond the scope of this study, its capability to simulateregional and global precipitation, which is the key pa-rameter for the present study, is briefly discussed be-low.

Figure 2 provides a comparison between the globalmonthly merged precipitation analyses of the NASAGlobal Precipitation Climatology Project (GPCP; see

FIG. 2. Monthly mean precipitation (mm day1) obtained from (a) the NASA GPCP for the 25-yr period from Jan 1979 until Jan 2004;and (b) an ensemble of six control realizations performed with the NASA GISS GCM II. Note that the scales in (a) and (b) are notexactly similar.



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http://precip.gsfc.nasa.gov/ for a description of the dataproduct) and the ensemble-mean control simulation.The GPCP images are based on data collected duringthe 25-yr period, from January 1979 until January 2004.Figures 35 provide comparisons of the annual mean,zonal average, and global precipitation, respectively,

between the control simulations and the GPCP data.While the general spatial and temporal pattern of

precipitation is quite well represented by the model(Figs. 24), in general, it overestimates precipitation byabout 10%20%. This is well illustrated by the globalprecipitation comparison presented in Fig. 5. Thisseems to be due mostly to the too high precipitationsimulated by the model near the highest mountains(i.e., the Andes and the Himalayas) and, more gener-

ally, near significant topographical features in tropicalregions (e.g., Indonesia). This overestimate is particu-larly noticeable during the wet season in these areas(Fig. 2). The zonal means depicted in Fig. 4 confirm thistropical/topographical bias. It should be mentioned thatthis deficiency is not specific to the NASA GISS GCM

II, as it appears that other GCMs suffer from the sameproblem (e.g., McCabe and Dettinger 1995).

In evaluating the significance of this bias between themodel and the GPCP data, one needs to keep in mindthat the model is forced by multiyear, monthly meanSSTs and fixed land-cover types dated from the 1960s.These conditions eliminate some of the interannualvariability, which might have an impact on the monthly,annual, and global mean, through nonlinear interac-

FIG. 2. (Continued)



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tions in the climate system. Furthermore, at the reso-lution of the NASA GISS GCM II, topography is onlyschematically represented by the model, and the nu-merical techniques used to approximate atmosphericflow near topography tend to create erroneous motionin the mountain lee.

However, it is also noticeable that the three regionswhere the deforestation experiments are being per-

formed, namely Amazonia, Central Africa, and South-east Asia, are generally well simulated by the model.

4. Impacts of deforestation

Figure 6 shows those locations worldwide where pre-cipitation has either significantly decreased or in-creased for at least 3 months of the year as a result of

FIG. 3. Same as in Fig. 2, but for the annual-mean precipitation.



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deforestation of Amazonia (see next section for statis-tical analysis). The annual cycle and change of monthlymean rainfall at a few continental locations mostly af-fected in this scenario are also provided. The selectionof these locations was subjectively made based on ourperception of where the most impressive impacts occur,both in terms of absolute magnitude and time duration.

For brevity, it is not possible (and justified) to presentsuch an annual cycle for all locations (which are indi-cated by the color-coded points on the world map), andthe purpose of the few examples presented here is justto illustrate various intricate ways by which the annualcycle of precipitation is perturbed by the deforestationin Amazonia.

Figures 79 present the corresponding results for thedeforestation of Central Africa, Southeast Asia, andthe three tropical regions together, respectively. Lo-cally, deforestation causes a significant reduction ofprecipitation in all three tropical regions during the en-tire year. However, the season mostly affected by thedeforestation is not the same in the three cases. In

Amazonia, the wet season (summer) registers a de-crease of precipitation as high as 50%60% in Decem-ber and January, but the dry season (winter), whichexperiences only a small amount of rainfall, is barelyaffected by the deforestation. In Central Africa andSoutheast Asia, the impact of deforestation on rainfallis spread over the entire year. However, it is the dryseason (JuneJuly in Central Africa and MayJune inSoutheast Asia) that mostly suffers from deforestation,with a decrease of precipitation on the order of 30%

FIG . 4. Zonally averaged, monthly-mean precipitation(mm day1) obtained from the NASA GPCP for the 25-yr periodfrom Jan 1979 to Jan 2004 (black line), and an ensemble of sixcontrol realizations performed with the NASA GISS GCM II (grayline) for (a) Jan, (b) Apr, (c) Jul, and (d) Oct. (e) The correspond-ing zonally averaged, annual-mean precipitation (mm day1).

FIG. 5. Annual variation of global, monthly mean precipitation(mm day1) obtained from the NASA GPCP for the 25-yr periodfrom Jan 1979 to Jan 2004 (dark gray line); an ensemble of sixcontrol realizations performed with the NASA GISS GCM II(black line); and an ensemble of six total tropical deforestationrealizations performed with the NASA GISS GCM II (light graydashed line).



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during that period, versus a decrease of 10%20% dur-ing the wet season. When all three regions are defor-ested together, the local impact of deforestation in eachone of the tropical regions remains dominant as dem-onstrated by the lack of significant change in the annualcycle of precipitation of the three regions depicted in

Fig. 9, as compared to those obtained in Figs. 68.While the major impact of deforestation on precipi-

tation is found in and near the deforested regions them-selves, a strong impact is propagated by teleconnectionsalong the equatorial regions and, to a lesser yet stillstatistically significant extent, to midlatitudes and evenhigh latitudes. One can notice that, as a result of thedeforestation of Amazonia, the largest decrease of pre-cipitation in continental regions outside of the Tropicsis seen in North America, where this deforestation

causes a decrease of rainfall in the Gulf of Mexico re-gion, with a particularly severe impact in Texas (about25%) and northern Mexico, during the spring and sum-mer seasons. Asia, mostly in a region spreading fromTurkistan in the west to the Gobi Desert in the east, isalso affected by the deforestation of Amazonia. How-

ever, the extent of the impact in that region is lesssignificant from a water resource perspective given thesmall, absolute amount of rainfall received there.

Deforestation of Central Africa causes a decrease ofprecipitation of about 5%15% in the Great Lakes re-gion, mostly centered in Illinois, with a peak decreaseof about 35% in February. It also affects Ukraine andRussia (north of the Black Sea), where precipitationthere is reduced by as much as 25% in May. The impactof the deforestation of Southeast Asia is mostly felt in

FIG. 6. Worldwide locations where precipitation has either significantly (top) decreased or (bottom) increased during a period of atleast 3 months of the year, as a result of deforestation of Amazonia simulated with the NASA GISS GCM II. The ensemble-meanannual cycle of precipitation (mm day1) for the control (green line) and deforested (red line) cases at continental locations mostseverely affected by the deforestation is also represented. The color scale indicates the number of months registering a statisticallysignificant change (95% level of confidence) during the annual cycle.



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China and the Balkan Peninsula, with a decrease of20%25% in western Turkey during a large part of theyear.

As illustrated in Fig. 5, the global amount of precipi-tation is unaffected by the deforestation in any of thesenumerical experiments (only the control and total de-forestation scenarios are presented in Fig. 5, but thethree individual deforestation cases did not produceany different curves than the two superposed ones in

this figure). This implies that the regional decreasesdescribed above are counterbalanced by an enhance-ment of precipitation at other locations, as is indeeddepicted in Figs. 69. Most remarkable is the impactthat each one of the three deforested regions has on therainfall in the Arabian Peninsula and East Africa (nearthe Red Sea). Deforestation of Amazonia results in anincrease of rainfall as high as 45% in August and Sep-tember, and the deforestation of Central Africa andSoutheast Asia enhances the rainfall in that region by

15%30% during this period (note, however, the de-crease of precipitation during the JuneJuly period).Other regions, such as northern Europe and North Af-rica, also experience a precipitation enhancement mostof the time as a result of these deforestations, yet it isfar less significant than in the Arabian Peninsula (Figs.6 and 8).

The total deforestation case reveals that the globalimpact of deforestation is not equivalent to the cumu-

lative impacts of the three individual regions, empha-sizing some synergy (i.e., nonlinear effects) between thethree deforested regions. For example, the strongestimpact in the United States has shifted to Californiaduring the winter as a result of total tropical defores-tation. The Midwest now experiences a less severe de-crease of precipitation during a shorter period of theyear, which nevertheless remains quite significant.There is a noticeable decrease of precipitation in south-east Africa that was not identified in the individual de-

FIG. 7. Same as in Fig. 6, but for the deforestation of Central Africa.



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forestations, and the enhancement of precipitation seenin northern Europe and Siberia as a result of the de-forestation of Amazonia alone has disappeared. But afew regions do experience a cumulative impact. Mostnotably, the southeastern part of the Arabian Peninsulaand northeastern India, which register enhancements of70% and 35% of their summer (i.e., AugustSeptem-ber) precipitation, respectively.

We explain these findings as follows: As a result oftropical deforestation, the sensible and latent heat re-

leased into the atmosphere is considerably altered(Shukla and Mintz 1982). The associated change ofpressure distribution modifies the zones of atmosphericconvergence and divergence, which shift the typicalpattern of the Polar Jet Stream and the precipitationthat it engenders as far away from the Tropics as mid-and high latitudes. Such a mechanism is not unique todeforestation, as it is also the probable reason for theimpacts of El Nio on the global weather and climate(e.g., Trenberth et al. 1998).

5. Statistical analysis

For each scenario described in the previous sections,we assessed the impact of deforestation by calculatingthe change of monthly mean precipitation (averagedover the 48 simulated months) relative to the controlcase at each grid point of the GCM. We formulated thehypothesis that the possible change detected for eachmonth of the year at a particular location (i.e., gridpoint) was caused by the deforestation. To test that

hypothesis, Students t value was calculated using themeans and standard deviations of the control and de-forested ensembles. With 94 degrees of freedom [(2 48)2], a one-tailed t value (increases and decreasesare considered separately) of 1.66 indicates significanceat 95%. This is similar to the method outlined byChervin and Schneider (1976), but the formula used toget the combined variance is from Edwards (1971). Tostrengthen our confidence that the observed impact wasindeed meaningful, we subjectively ignored any de-

FIG. 8. Same as in Fig. 6, but for the deforestation of Southeast Asia.



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crease or increase of precipitation that lasted less than3 months a year. Thus, all points colored in Figs. 6 9indicate a statistically significant impact of deforesta-tion that lasted at least 3 months. It should be notedthat a statistically significant change of precipitationdoes not necessarily mean that its absolute magnitude isimportant enough to have any practical implicationfrom a hydrometeorological point of view.

We also adopted the Randomized InterventionAnalysis proposed by Carpenter et al. (1989) to check

the impact of deforestation on precipitation away fromthe deforested regions. Accordingly, the root-mean-square difference (rmsd) between the ensemble,monthly mean global precipitation of the control anddeforested scenarios (hereafter referred to as trueensembles) was calculated and compared to the rmsd offalse ensembles of control and deforestation sce-narios. These false ensembles were obtained by ran-domly combining three control and three deforestedrealizations performed for each scenario. A total of 200

FIG. 9. Same as in Fig. 6, but for the deforestation of Amazonia, Central Africa, and Southeast Asia.



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such ensembles was obtained for each scenario. Figure10 summarizes the results of this comparison. It clearlyshows that in all months for the total deforestation sce-nario and most months for the individual deforestationcases, the rmsd of the true ensembles are more signifi-cant than those of the false ensemble.

Figure 10 also presents histograms of the rmsd ofannual-mean global precipitation calculated for each of

these 200 false ensembles and the position on thesehistograms of the true ensembles. For the Amazoniandeforestation and the total deforestation cases, the an-nual-mean rmsd of the true ensemble was higher thanany of the 200 false ensembles, clearly emphasizing thatthe precipitation difference obtained between the con-trol and deforested cases is undoubtedly due to theland-cover change. While this measure is less significantfor the deforestation of Central Africa and SoutheastAsia (above 85% and 75% of the false ensembles, re-spectively), it remains nevertheless a strong signalbased on this analysis. Reducing the period consideredfor this analysis from the full year to the season mostlyaffected by deforestation (i.e., SeptemberDecember

for Central Africa and MarchJune for SoutheastAsia), positioned the true ensemble in the upper 93%and 89% of the histogram for Central Africa and south-east Asia, respectively.

Finally, we performed a correlation analysis betweenmeteorological variables in the deforested regions andat the main remote locations identified to have experi-enced a statistically significant change of rainfall to em-phasize the teleconnections between the regions. Ingeneral, strong correlations are found in all cases.

6. Conclusions

While our numerical experiments were carefully de-signed to provide a high level of statistical confidence inour results, we caution that our discovery is based onnumerical simulations performed with a single GCM,with no interannual SST variability. Thus, our resultsemphasize the potential importance of land-cover tele-

connections in hydroclimate studies, but they were notdesigned to simulate a specific, realistic hydroclimate.In spite of this cautioning remark, we trust that some ofthe strongest events identified in our study will befound as well with other GCMs under a more naturalinteracting climate system. To gain more confidence onthe realism of our findings, we suggest that reproducingour experiments with other GCMs is desirable. Usingglobal coupled oceanatmosphere models could pro-vide additional insights on the spatial and temporalvariability of the teleconnections identified here. Weare currently in the process of conducting such addi-tional numerical experiments with the GISS Atmo-sphere Ocean Model (AOM2; Lucarini and Russell

2002), the Community Atmosphere Model (CAM) de-veloped and maintained by the National Center for At-mospheric Research (Kiehl et al. 1998), and with theOceanLandAtmosphere Model (OLAM), a new gen-eration of earth system model developed at Duke Uni-versity (Walko and Avissar 2004, manuscript submittedto Environ. Fluid Mech.).

While this study is still preliminary, if additional ex-periments with other GCMs and more realistic SSTconfirm its findings, land-cover change in tropical re-

FIG. 10. (top) Global, monthly mean rmsd of precipitation (mm month1) between the true control ensemble and the true defor-estation ensemble (light gray bars), and false control ensemble and false deforestation ensemble (dark gray bars) for (a) Amazonia, (b)Central Africa, (c) Southeast Asia, and (d) the total tropical deforestation. (bottom) Histogram of global, annual-mean precipitationrmsd (mm yr1) for 200 false ensembles. The arrows show the position of the true ensemble.



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gions may have quite devastating consequences on ag-riculture, water resources, and related activities at vari-ous remote locations. This emphasizes that not onlyregional emissions of pollutants have global hydro-climatological impacts, but that regional land-coverchange is another parameter that needs to be consid-

ered in climate change policies.Explaining the dynamics of the landatmosphere in-

teractions involved in these land-cover teleconnectionsis not trivial. While our preliminary analysis indicatesinteresting correlations between the hydrometeorologyof the deforested regions and that of remote areas, acareful analysis of geopotential heights, radiative forc-ing, and moisture convergence and divergence is stillrequired to elucidate the main mechanisms involved inthese global-scale interactions. But the activation ofRossby waves is likely involved in such teleconnections.We are in the process of performing such an analysisand plan to publish its results in a future paper.

Acknowledgments. This research was supported bythe National Aeronautics and Space Administration(NASA) under Grants NAG 5-8213 and NAG 5-9359,by the National Science Foundation (NSF) under GrantATM-0346554, and by the Gordon and Betty MooreFoundation. The views expressed herein are those ofthe authors and do not necessarily reflect the views ofNASA, NSF, or the Moore Foundation.

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Global Hydroclimatological Teleconnections Resulting from Tropical Deforestation