Policy Paper Government

Simulated Changes in Northwest U.S. Climate in Response to Amazon Deforestation*

DAVID MEDVIGY

Department of Geosciences and Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey

ROBERT L. WALKO

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

MARTIN J. OTTE

Atmospheric Modeling and Analysis Division, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

RONI AVISSAR

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

(Manuscript received 27 October 2012, in final form 16 April 2013)

ABSTRACT

Numerical models have long predicted that the deforestation of the Amazon would lead to large regional

changes in precipitation and temperature, but the extratropical effects of deforestation have been a matter of

controversy. This paper investigates the simulated impacts of deforestation on the northwest United States

December–February climate. Integrations are carried out using the Ocean–Land–Atmosphere Model

(OLAM), here run as a variable-resolution atmospheric GCM, configured with three alternative horizontal

grid meshes: 1) 25-km characteristic length scale (CLS) over the United States, 50-km CLS over the Andes

and Amazon, and 200-km CLS in the far-field; 2) 50-km CLS over the United States, 50-km CLS over the

Andes and Amazon, and 200-km CLS in the far-field; and 3) 200-km CLS globally. In the high-resolution

simulations, deforestation causes a redistribution of precipitation within the Amazon, accompanied by vor-

ticity and thermal anomalies. These anomalies set up Rossby waves that propagate into the extratropics and

impact western North America. Ultimately, Amazon deforestation results in 10%–20% precipitation re-

ductions for the coastal northwest United States and the Sierra Nevada. Snowpack in the Sierra Nevada

experiences declines of up to 50%. However, in the coarse-resolution simulations, this mechanism is not

resolved and precipitation is not reduced in the northwest United States. These results highlight the need for

adequate model resolution in modeling the impacts of Amazon deforestation. It is concluded that the de-

forestation of the Amazon can act as a driver of regional climate change in the extratropics, including areas of

the western United States that are agriculturally important.

1. Introduction

Many numerical models have predicted that Amazon

deforestation would lead to local increases in surface

temperature and decreases in precipitation (Henderson-

Sellers et al. 1993; Lean and Rowntree 1993; Gash and

Nobre 1997; Hahmann and Dickinson 1997; Costa

and Foley 2000; Gedney and Valdes 2000; Werth and

Avissar 2002; Avissar and Werth 2005; Findell et al.

2006; Sampaio et al. 2007; Hasler et al. 2009; Medvigy et al.

2011). Using numerical models, some studies have con-

cluded that Amazon deforestation can impact extra-

tropical climate (Gedney and Valdes 2000; Werth and

Avissar 2002; Medvigy et al. 2012); however, other

modeling studies have not found a statistically significant

response (Findell et al. 2006). Using observations to di-

rectly assess extratropical impacts of deforestation is

* Supplemental information related to this paper is available at

the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-

00775.s1.

Corresponding author address: David Medvigy, Department of

Geosciences, Princeton University, 418B-Guyot Hall, Princeton,

NJ 08544.

E-mail: [email protected]

15 NOVEMBER 2013 M E D V I G Y E T A L . 9115

DOI: 10.1175/JCLI-D-12-00775.1

� 2013 American Meteorological Society

extremely difficult because large-scale deforestation has

only been occurring for a few decades and thus any

signal would be obscured by natural climate variability.

Furthermore, the total deforested area in the Amazon

may increase by a factor of 2–3 in the next few decades

to 40%–60% (Soares-Filho et al. 2006; Walker et al.

2009), and this may lead to a very different climatic re-

sponse than that arising from the pattern of defor-

estation that exists today (Ramos da Silva et al. 2008).

It is possible that other climate anomalies can be used

to gain insights on the ultimate impacts of Amazon de-

forestation. El Ni~no events, for example, arise from the

natural variability of tropical climate and bring increased

near-surface temperatures and increased convective ac-

tivity to the eastern tropical Pacific. This situation results

in the strengthening and contraction of the Hadley cell

and an equatorward shift of the tropospheric zonal jets

(Seager et al. 2003, 2005). Midlatitudes are affected by

changes in transient eddy momentum fluxes and in the

eddy-driven mean meridional circulation that results

from changes in the jet (Seager et al. 2005). The north-

west United States, including northern California and

western Oregon and Washington, is particularly strongly

affected. Composite maps show an intensified Aleutian

low and ridging high pressure in the Pacific Northwest,

which results inwarmdry weatherinthis sector (Ropelewski

and Halpert 1987, 1989; Redmond and Koch 1991;

Wallace et al. 1992; Cayan 1996). This warm and dry

anomaly has important societal and ecological implica-

tions, affecting drought, snowpack, and fires (Dai et al.

1998; Enfield et al. 2001; McCabe et al. 2004; Seager

et al. 2010).

Previous studies have considered the potential simi-

larities and differences between El Ni~no and Amazon

deforestation. Eltahir and Bras (1993) pointed out that,

for both El Ni~no and Amazon deforestation, near-

surface temperature increases might be expected to lead

to convergent circulations. In the case of El Ni~no, in-

creased precipitation leads to increased latent heat re-

lease aloft, and this heating reinforces the convergent

circulation. In the case of Amazon deforestation, many

GCMs have simulated large decreases in precipitation,

suggesting reduced latent heating aloft and a competing

divergent circulation. This putative difference in latent

heat release would distinguish El Ni~no from Amazon

deforestation. However, recent high-resolution model-

ing studies (Ramos da Silva et al. 2008; Walker et al.

2009; Medvigy et al. 2011) have simulated much smaller

precipitation reductions in the Amazon resulting from

deforestation. In particular, Medvigy et al. (2011) sim-

ulated the Amazon with a 25-km characteristic length

scale (CLS) grid mesh and found that reductions in

evapotranspiration were nearly balanced by increases in

moisture convergence. If this balancing holds, precipitation

changes may be small and Amazon deforestation may

be more similar to El Ni~no than previously expected.

Amazon deforestation and El Ni~no differ in other

ways, including the obvious fact that the Amazon is

situated to the east and somewhat to the south of the

eastern tropical Pacific. However, different historical El

Ni~no events having differences in equatorial sea surface

temperature (SST) anomalies have consistently been

associated with drying in the northwest United States

(Hoerling and Ting 1994; Yu and Zou 2013). This mo-

tivates the idea that there will be important similarities

between the extratropical responses to El Ni~no and to

Amazon deforestation, although we do not necessarily

expect them to be exactly the same. This study will focus

on the northwest United States because this region is

known to be highly sensitive to El Ni~no.

Analysis of this problem in the context of numerical

models is difficult. Many climate models give large un-

derestimates of the climatological precipitation in the

Amazon (Randall et al. 2007), and it is uncertain if this

would compromise their ability to simulate the impacts

of Amazon deforestation. In one recent study, it was

shown that simulation of the Amazon hydroclimate

markedly improved when the model resolution of the

Andes became finer, and that the model captured in-

terannual variability of precipitation in the Amazon only

when the Andes were simulated at ,100-km resolution (Medvigy et al. 2008). Furthermore, in the United States,

the impacts of El Ni~no are highly regional and may be

challenging to resolve with current GCMs. Previous

work has shown that adequate resolution of topography

is critical for correctly simulating precipitation in the

northwest United States (Leung and Qian 2003; Leung

et al. 2003a,b; Zhang et al. 2012). For example, Leung

et al. (2003a,b) carried out sensitivity analyses and found

that a 40-km resolution was adequate for simulating

seasonal and interannual precipitation variability in the

region. Leung and Qian (2003) compared simulations at

40 and 13 km and found that the simulation of snowpack

was greatly improved at the higher resolution, but dif-

ferences in precipitation biases were small.

The computational problem of carrying out high-

resolution simulations becomes more tractable by using

a variable-resolution GCM. Variable-resolution GCMs

allow for fine resolution in the region of interest with

a coarser, more computationally efficient resolution in

the far-field. This enables the simulation of regional-

scale circulations without the need for lateral boundary

conditions, while maintaining a reasonable computational

cost (Medvigy et al. 2008, 2010, 2011). In this study, we use

the Ocean–Land–Atmosphere Model (OLAM; Walko

and Avissar 2008a,b, 2011) variable-resolution GCM to

9116 J O U R N A L O F C L I M A T E VOLUME 26

investigate the impacts of Amazon deforestation on the

United States. Unlike past studies, we use locally fine-

resolution grid spacing over both North America and

South America. Because El Ni~no has particularly large

effects over the United States during winter (e.g.,

Harrison and Larkin 1998), by analogy our focus is on

the December–February (DJF) season. The objectives

of this work are to identify the impacts of Amazon de-

forestation on the northwest United States during DJF,

identify relevant mechanisms, and assess the sensitivity

of the mechanisms to model resolution.

2. Model simulations

We used the OLAM model (Walko and Avissar

2008a,b, 2011) run as an atmospheric GCM with pre-

scribed SSTs. The model’s ability to simulate Amazon

precipitation has already been evaluated by us for dif-

ferent grid configurations (Medvigy et al. 2008, 2010, 2011),

interannual variability (Medvigy et al. 2008), decadal

averages (Medvigy et al. 2010), and the frequency and

intensity of daily rainfall (Medvigy et al. 2011). Amazon

deforestation has also previously been simulated with

OLAM (Medvigy et al. 2011, 2012). In this study, we

carried out multiple pairs of simulations. The pairs of

simulations differed only in their grid mesh, whereas the

pair members differed in their land cover specification.

In our first pair (FINE), the grid mesh had a 50-km CLS

over the Andes, most of the Amazon, and also over the

contiguous United States (Fig. 1a). This 50-km CLS was

previously shown to be adequate for OLAM to simulate

South American hydroclimate (Medvigy et al. 2008).

The grid mesh gradually expanded to 200 km away from

North and South America. Second, we carried out a pair

of simulations with the same horizontal grid mesh as

FINE but with enhanced vertical resolution (FINEV).

Third, we carried out a pair of simulations with the same

vertical resolution as FINE but with a more refined

horizontal mesh. This pair (XFINE) had a 50-km CLS

over the Andes and most of the Amazon and a 25-km

CLS over most of the contiguous United States (Fig. 1b).

The purpose of the FINEV and XFINE pairs was to

evaluate and challenge the conclusions stemming from

the FINE pair. Finally, we carried out a coarse pair of

simulations (COARSE), in which the entire globe was

simulated with a uniform 200-km CLS, which is a typi-

cal GCM resolution (Fig. 1c). This pair would be most

comparable to previous investigations of the extra-

tropical impacts of Amazon deforestation.

The FINE, XFINE, and COARSE simulations used

a Cartesian vertical grid consisting of 53 levels, with the

grid spacing stretching from 200 m near the surface to

2 km near the model top at 45 km. The FINEV simula-

tions also used a Cartesian vertical grid but in this case

there were 74 levels, with the grid spacing stretching

from 100 m near the surface to 2 km near the model top

at 45 km. As a postprocessing step, upper-air variables

were interpolated to pressure levels. For the convective

parameterization, we used the Eta version of the Kain–

Fritsch scheme (Kain 2004). All other parameterizations

are the same as those used in Medvigy et al. (2011). (The

‘‘namelist’’ file that contains all the necessary in-

formation to configure our simulations is available as

supplemental material at the Journals Online website:

http://dx.doi.org/10.1175/JCLI-D-12-00775.s1.)

In each pair, the two pair members correspond to two

land cover scenarios, tagged as forested (FOR) and

deforested (DEF). These land cover scenarios are de-

scribed in detail in Medvigy et al. (2011) and are only

briefly described here. In our FOR runs, each land grid

FIG. 1. OLAM grid mesh and topography (m) for the different simulations. All simulations were global, but the panels show only the

Americas for clarity. (a) FINE and FINEV pairs. (b) XFINE pair. (c) COARSE pair. The boxes over South America denote the de-

forested region.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9117

cell is assigned a single land cover classification ac-

cording to the Olson Global Ecosystem framework

(Olson 1994a,b), which was based on satellite imagery

from 1992/93. About 10% of the Amazon sector is clas-

sified as agriculture or short grass in FOR. Our corre-

sponding DEF runs are identical to the corresponding

FOR runs in every way except land cover classification.

The DEF runs, meant to represent the total de-

forestation of the Amazon, classes all land grid cells

between 158S–08 and 758–498W (boxed areas in Fig. 1) as deforested land cover. The land surface and vegetation

properties of these deforested grid cells are prescribed

according to in situ measurements at pasture sites (Gash

and Nobre 1997) and have been tested in previous

studies (Gandu et al. 2004; Avissar and Werth 2005;

Ramos da Silva et al. 2008; Hasler et al. 2009; Medvigy

et al. 2011). A more sophisticated treatment might dis-

tinguish between pasture, soy, and cultivation of other

crops, but we expect differences between these types to

be much smaller than the differences between tropical

forest and pasture (Sampaio et al. 2007). The naming

convention that we use for our simulations combines the

grid mesh identifier (FINE, FINEV, XFINE, or

COARSE) with the land cover identifier (FOR or DEF),

for example, FINE-FOR. Our simulations are summa-

rized in Table 1.

Atmospheric and soil initial conditions were pre-

scribed from the National Centers for Environmental

Prediction (NCEP) reanalysis from 0000 UTC 1 October

1996 (Kalnay et al. 1996). All simulations were forced

with weekly 18 SSTs (Reynolds et al. 2002) and sea ice extent from the NCEP reanalysis (Kalnay et al. 1996).

The CO2 and other greenhouse gas concentrations were

held fixed throughout all the simulations at current-day

levels to enable us to isolate the effects of deforestation.

We simulated the period from 1 October 1996 to 1 April

2012. To estimate the amount of time required to spin up

the soil moisture, we carried out preliminary simulations

similar to FINE-FOR and FINE-DEF but driven with

climatological SSTs. We computed the average soil

moisture in the top 50 cm of soil in the deforested sector

(158S–08, 758–498W). The time series of this quantity is shown in Fig. 2a. Soil water in this layer increases for the

first ;180 days of the simulation, but then it oscillates about 20–21 cm. The maxima occur near the end of the

wet season, and the minima occur near the end of the dry

season. The amplitude of the oscillation is about 2 cm. A

similar pattern is seen in the deforested simulation, ex-

cept that the amplitude of the seasonal oscillation is

greater. In addition, deforestation induces an anomalous

spatial pattern (Fig. 2b). The soil in the northwest and

central Amazon is somewhat drier in the deforested

simulation than in the forested simulation, and the soil in

the northeast and southeast Amazon is somewhat wetter.

Based on these spinup results, we discarded the first

26 months of each simulation (October 1996–November

1998), leaving 14 years for analysis. The 1998–2012 pe-

riod includes a wide variety of sea surface temperature

configurations (though by no means all possible config-

urations). This period includes moderate El Ni~nos in

2002 and 2009 based on the Oceanic Ni~no index (ONI;

obtained at http://www.cpc.ncep.noaa.gov/products/

analysis_monitoring/ensostuff/ensoyears.shtml), moderate-

to-strong La Ni~nas in 1999, 2007, and 2010 based on the

ONI, positive and negative anomalies of the Pacific

decadal oscillation (data obtained from http://jisao.

washington.edu/pdo/), and sea surface temperature

variability in the Atlantic (Fig. 8 of Zeng et al. 2008). In

this paper, we limited our analysis to DJF, though out-

put variables from the other months were saved to disk

and are available for future analyses.

We performed a series of statistical tests to evaluate

the significance of differences between the DEF and

FOR simulations. The 95% confidence level is taken as

the threshold for the statistical significance throughout

this paper. Tests were performed for each pair (FINE,

FINEV, XFINE, or COARSE) by itself, as well as for

the ensemble consisting of the FINE, FINEV, and the

XFINE pairs (note that the COARSE pair was excluded

for reasons that will become evident later). All statistical

TABLE 1. Description of the numerical simulations used in this study. CLS is the characteristic length scale of the grid mesh.

Simulation

CLS (Andes

and Amazon)

(km)

CLS

(United States)

(km)

Maximum

vertical

resolution (m) Amazon land cover

Grid cells

per vertical

level

Number of

vertical levels

Time

step (s)

FINE-FOR 50 50 200 From the 1990s 22 065 60 60

FINE-DEF 50 50 200 Complete deforestation 22 065 60 60

FINEV-FOR 50 50 100 From the 1990s 22 065 74 60

FINEV-DEF 50 50 100 Complete deforestation 22 065 74 60

XFINE-FOR 50 25 200 From the 1990s 25 887 60 40

XFINE-DEF 50 25 200 Complete deforestation 25 887 60 40

COARSE-FOR 200 200 200 From the 1990s 12 963 60 90

COARSE-DEF 200 200 200 Complete deforestation 12 963 60 90

9118 J O U R N A L O F C L I M A T E VOLUME 26

tests are conducted in R (R Development Core Team

2008). A t test can be used to test the null hypothesis that

the means from the DEF and FOR simulations are

equal, provided that the DEF and FOR samples are

independent and normally distributed. If the normality

assumption does not hold, a nonparametric test such as the

Wilcoxon signed-rank test (wilcox.test in R; Hollander and

Wolfe 1999) may be used instead of the t test. The null

hypothesis of the Wilcoxon signed-rank test is that the

median difference between the DEF and FOR samples

is zero. We used the Shapiro–Wilk test (shapiro.test in R;

Royston 1982) to test for normality. We found that the

normalityassumption was violatedforasmany as 15%–20%

of the grid cells, and so we conservatively adopted the

Wilcoxon signed-rank test as our test of choice in this

paper. We used the Ljung–Box test (Box.test in R; Ljung

and Box 1978) to test for independence. In no case did we

find that the assumption of independence was violated for

more than 5% of the grid cells, and so we adopted

independence as a generally reasonable assumption.

3. Results

a. Model evaluation for North America

We limited our model evaluation to North America

because model evaluation for South America has al-

ready been carried out (Medvigy et al. 2008, 2010, 2011,

2012). Precipitation and near-surface temperature are

evaluated using the Princeton Global Forcings (PGF)

dataset at 0.58 resolution (Sheffield et al. 2006). This da- taset blends surface and satellite observations with re-

analysis and is available for 1948–2008. Because our study

focuses on DJF quantities and our post-spinup begins in

December 1998, we defined a climatological winter daily

precipitation rate and daily temperature by averaging the

daily values of these quantities over all days in December,

January, and February within the period December 1998

through December 2008. We constructed climatological

averages for our FINE-FOR, FINEV-FOR, XFINE-

FOR, and COARSE-FOR simulations for this same

time period. For consistency with the PGF dataset, we

excluded our simulations of 2009–12 from these com-

parisons to the PGF dataset. However, our subsequent

analyses do correspond to the full post-spinup period

(1 December 1998 through 1 April 2012).

The PGF precipitation dataset (Fig. 3a) is generally

well represented by the FINE-FOR (Fig. 3b) simulation.

The model captures such features as the rainfall maxima

along coastal British Columbia, Washington, northwest

California, and the Sierra Nevada. Precipitation is gen-

erally overestimated in the rain shadow on the eastern

side of the Rockies and is underestimated along the east

coast of North America. The FINEV-FOR simulation is

very similar to FINE-FOR in the western United States

but gives slightly improved precipitation estimates in

the Midwest (Fig. 3c). The XFINE-FOR simulation

(Fig. 3d) is a slightly better match to the PGF data than

FINE-FOR in the Appalachian Mountain region. The

COARSE-FOR simulation generally gives lower rain-

fall amounts than the other simulations, leading to un-

derprediction of the rainfall maximum on the west coast

and exacerbating the underprediction on the east coast

(Fig. 3e). However, it gives a better simulation of the

low precipitation values in the rain shadow of the

Rockies.

FIG. 2. Spinup of soil moisture in the top 50 cm of soil for sim-

ulations similar to FINE-FOR and FINE-DEF but driven with

climatological SSTs. (a) Time series of average soil moisture in the

Amazon (158S–08, 758–498W). (b) Average soil moisture difference (mm; deforested minus forested) for the final year of spinup.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9119

The PGF temperature dataset (Fig. 4a) was also well

simulated by FINE-FOR (Fig. 4b) for most of the

United States. However, one notable bias occurred for

parts of the Great Basin, where the model was too cool.

This is potentially related to biases in the 18 SST data, especially near relatively small-scale features like the

Gulf of California. Most of the nearby offshore tem-

peratures were also lower in the simulations than in the

PGF, and advection of this relatively cool air may be

biasing the model over land. A second bias was that the

model was too warm in eastern Canada. Temperature

biases in FINEV-FOR (Fig. 4c), XFINE-FOR (Fig. 4d),

and COARSE-FOR (Fig. 4e) were similar to those in

FINE-FOR.

The Pacific Northwest relies heavily on winter snowfall

to provide water for the summer months because there is

relatively little summer precipitation. To measure snow

water equivalent (SWE), the U.S. Department of Agri-

culture’s Natural Resources Conservation Service main-

tains a network of snow courses throughout Oregon,

Washington, Idaho, and other western states. The earliest

records go back to 1915. The California Department of

Water Resources has an independent network of snow

courses. Details on the measurements have been pre-

viously published (Clark et al. 2001; McCabe and

Dettinger 2002). Mote (2003) reported that SWE on 1

April has typically ranged from 40 to 50 cm for the past

;25 years for a region that includes mountainous areas of Oregon, Washington, and Idaho. In California, Howat

and Tulaczyk (2005) found a peak SWE of about 120 cm

along much of the central spine of the Sierra Nevada.

We compared these observation-based numbers to

values simulated by OLAM. Our intentions here are to

explore the effects of model resolution and compare

typical simulated values to the observations. A detailed

spatiotemporal analysis may require simulations at finer

characteristic length scales than those simulated here, as

previous studies with other models have shown that

snowpack was underestimated at 40- (Leung and Qian

2003) and 27-km (Pavelsky et al. 2011) resolution.

However, at equivalent resolution, we expected the

‘‘shaved cell’’ method in OLAM (Walko and Avissar

2008b) would represent terrain features better than the

terrain-following coordinates that have typically been

used in previous studies, and it is not known exactly how

this will affect simulations of snowpack.

FIG. 3. DJF mean daily precipitation (mm day 21

) as represented in the (a) PGF dataset and as simulated by the OLAM model.

(b) Results from FINE-FOR, which has 50-km CLS over the United States. (c) Results from FINEV-FOR, which has enhanced vertical

resolution. (d) Results from XFINE-FOR, which has enhanced horizontal CLS. (e) Results from COARSE-FOR, which has coarse

horizontal CLS.

9120 J O U R N A L O F C L I M A T E VOLUME 26

We consider the higher-resolution simulations first.

The corresponding 1 April SWE simulated in XFINE-

FOR averaged over 1999–2012 is shown in Fig. 5a. Values

in the northwest United States are generally consistent

with the observed values and ranged from about 65 cm

in the southern Cascades to about 20 cm in northwest

Oregon to about 50 cm in western Idaho. Peak values

along the Sierra Nevada reached 100–120 cm and are

also consistent with observations. The simulation with

enhanced vertical resolution, FINEV-FOR, also gave

reasonable results for central California (Fig. 5b) but

gave less SWE than XFINE-FOR in other sectors. In

contrast, simulated values from FINE-FOR were

much lower (Fig. 5c), which is unsurprising given its

coarser representation of topography (Leung and Qian

2003; Pavelsky et al. 2011). Peak SWE in southern

Oregon and the Sierra Nevada reached only 50 cm in

FINE-FOR. Finally, in COARSE-FOR simulated

SWE was less than 35 cm throughout the western

United States and was negligible in California (Fig. 5d).

These results show that the quality of the simulation

of snowpack degrades sharply with coarsening model

resolution.

To summarize, we find that the simulated SWE was

dramatically larger (and closer to observations) for

XFINE-FOR and FINEV-FOR than for FINE-FOR. In

contrast, precipitation differences between these three

simulations were modest. We therefore focus our anal-

ysis on the FINEV and XFINE simulations of SWE but

consider the FINE, FINEV, and XFINE simulations for

precipitation and other hydroclimatic variables.

b. Impacts of deforestation on surface climate

We found that FINE-DEF had a large, statistically

significant precipitation deficit relative to FINE-FOR

throughout the northwest United States (Fig. 6a). Pre-

cipitation differences were typically 10%–20% (or

1–2 mm day 21

) and reached up to 30%. There was also a

comparable precipitation deficit along the western

slopes of the Sierra Nevada range, but this difference

was not statistically significant at the 95% confidence

level. Statistically significant precipitation reductions

occurred in other locations (e.g., off the east coast of the

United States), but the magnitudes of these reductions

are much smaller than the reductions in the northwest

United States.

With any significance test, it is expected that some

percentage of the field will pass the test merely by

chance (in our case 5%). It is therefore critical to check

for consistency by comparing the results of the FINE

simulation pair to the other simulation pairs. In the

XFINE pair, XFINE-DEF had large precipitation

FIG. 4. As in Fig. 3, but for daily mean

near-surface temperature (8C).

15 NOVEMBER 2013 M E D V I G Y E T A L . 9121

deficits relative to XFINE-FOR in the northwest United

States, and these precipitation deficits were statistically

significant near the Sierra Nevada as well as in Oregon

and Washington (Fig. 6b). Differences between FINEV-

DEF and FINEV-FOR (not shown) were very similar to

the differences between FINE-DEF and FINE-FOR.

Finally, in the combined ensemble consisting of FINE,

FINEV, and XFINE, there is a region with statistically

significant precipitation deficits encompassing Wash-

ington, Oregon, Idaho, northern California, and Ne-

vada (Fig. 6c). That the northwest United States signal

is present in all simulation pairs as well as in the com-

bined ensemble provides evidence that Amazon

deforestation impacts precipitation in the northwest

United States. The combined ensemble also has sta-

tistically significant precipitation changes in other lo-

cations, including just north of Minnesota. However,

this signal is much smaller in magnitude than the signal

in the northwest United States, and we will not con-

sider it further.

Our analysis of the COARSE simulation pair led to

very different results (Fig. 6d). In this case, COARSE-

DEF actually had more precipitation than COARSE-

FOR in the northwest United States, although this

difference was not statistically significant. The effects of

model resolution will be discussed in more detail below.

FIG. 5. Average 1 Apr SWE (cm) as simulated in the (a) XFINE-FOR, (b) FINEV-FOR, (c) FINE-FOR,

(d) COARSE-FOR, (e) XFINE-DEF, and (f) FINEV-DEF simulations.

9122 J O U R N A L O F C L I M A T E VOLUME 26

We also computed changes in other important hydro-

climatic variables, including evapotranspiration, moisture

convergence, and temperature. The precipitation deficits

in the western United States occurred almost entirely

because of changes in moisture convergence in the FINE

simulation pair (Fig. 7a), and changes in evapotranspi-

ration were small for all grid cells (Fig. 7b). Similar results

held for the FINEV and XFINE simulation pairs (not

shown). In the northwest United States, FINE-DEF was

generally about 0.58C cooler than FINE-FOR (Fig. 7c).

However, these changes were generally not statistically

significant in the locations where the largest precipitation

changes occurred, including western Washington, western

Oregon, and California. Temperature differences be-

tween XFINE-DEF and XFINE-FOR were small,

generally having a magnitude of less than 0.28C in the

western United States (Fig. 7d). Temperature differ-

ences from this simulation pair were statistically signif-

icant only in the southeast United States. Temperature

differences between FINEV-DEF and FINEV-FOR

FIG. 6. Simulated changes in daily mean precipitation (DEF minus FOR; mm day 21

) from the (a) FINE;

(b) XFINE; (c) combined FINE, FINEV, and XFINE ensemble; and (d) COARSE simulation pairs. Areas with

statistically significant changes are hatched.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9123

and between COARSE-DEF and COARSE-FOR were

also small and generally not statistically significant (not

shown).

Given the large decreases in precipitation and rela-

tively small changes in temperature in the XFINE sim-

ulation pair, we expected that SWE would decrease in

the mountains of the northwest United States and the

Sierra Nevada. The 1 April SWE averaged over 1999–

2012 from XFINE-FOR and XFINE-DEF are shown in

Figs. 5a and 5e, respectively. SWE from XFINE-DEF

was much lower than in XFINE-FOR. Values over the

central Sierra Nevada were reduced by about half, with

values in XFINE-DEF generally ranging from 30 to

90cm. In XFINE-DEF, the snowpack was eliminated

from parts of northern California and was reduced by

over 50% in southern Oregon, but areas farther north and

east were not strongly affected. Similar results were ob-

tained for the FINEV simulation pair, with FINEV-FOR

(Fig. 5b) having much more snowpack than FINEV-DEF

(Fig. 5f).

FIG. 7. Impacts of deforestation on hydroclimatic variables. All panels show DEF minus FOR. (a) Change in

moisture convergence (mm day 21 ) in the FINE simulation pair. (b) Change in evapotranspiration (mm day

21 ) in the

FINE simulation pair. (c) Change in near-surface temperature (8C) in the FINE simulation pair. (d) Change in near- surface temperature (8C) in the XFINE simulation pair. Areas with statistically significant changes are hatched.

9124 J O U R N A L O F C L I M A T E VOLUME 26

c. Comparison to El Ni~no

The simulated reductions in precipitation in the

northwest United States resulted from Amazon defor-

estation, but similar precipitation anomalies are com-

monly observed during El Ni~no events (Redmond and

Koch 1991). We now pursue this analogy a bit further.

During DJF, precipitation in the northwest United

States is strongly controlled by the jet stream position. In

El Ni~no years, the jet has a tendency to split into two

branches, with one over the Queen Charlotte Islands

and the other over the southern tier of the United States

(Yu and Zou 2013). This directs storms away from the

northwest United States and toward British Columbia

and the southwest United States. In addition, a deepened

Aleutian low and warm SSTs off the coast of California

facilitate low-level advection of warm moist air to

southern California and the southwest United States

(Seager et al. 2005). Recent work has distinguished be-

tween different types of El Ni~nos depending on the

longitude of the tropical heating anomaly (e.g., Ashok

et al. 2007), but the northwest United States is relatively

dry regardless of whether the warm anomaly is over the

central tropical Pacific or eastern tropical Pacific.

In our FINE simulations, deforestation resulted in pos-

itive anomalies of 1–3ms21 in the 250-hPa zonal winds for

northern British Columbia and northern Mexico, while

negative anomalies of 2–4 m s21 were evident over the

northwest United States (Fig. 8a). These changes were

statistically significant, but hatching was omitted from

the figure to reduce visual clutter. Thus, as with El Ni~no,

deforestation modifies the jet stream so as to divert

storms away from the northwest United States. In the

XFINE simulation pair, deforestation also causes a re-

duction in 250-hPa zonal wind speed over the western

United States (Fig. 8b). However, the magnitudes of the

changes are about 1 m s21 smaller and are shifted to the

south relative to the FINE simulation pair. The FINEV

simulation pair (not shown) was very similar to the

FINE simulation pair.

Unlike typical El Ni~no events, deforestation does not

result in a wet anomaly over the southwest United States

(Figs. 6a–c). Our simulations show a decrease in con-

vergence of the 800-hPa horizontal wind over the north-

west United States resulting from deforestation and very

little change in low-level convergence over the southwest

United States (Fig. 9a). In addition, the northwest United

States and California both have negative anomalies in

low-level humidity (Fig. 9b). In contrast, during a typical

El Ni~no, there are often positive humidity anomalies over

the southwest United States due to anomalously warm

SSTs off of the coast (Zhang et al. 2012). Because our

simulations used the same SSTs for forested and defor-

ested runs, we were unable to determine how SSTs off the

FIG. 8. The 250-hPa winds and wind differences. (a) The arrows show the mean DJF winds from FINE-FOR and the colors show the zonal

wind anomalies (DEF minus FOR; m s 21 ). (b) As in (a), but for the XFINE simulations.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9125

North American coast would be affected by Amazon

deforestation. The negative humidity anomalies that we

simulated in response to deforestation were associated

with strong midlevel subsidence over the western United

States (Fig. 9c). However, in the eastern subtropical Pa-

cific at about 108N, positive vertical velocity and low-level humidity anomalies are simulated. Mo and Higgins

(1998) pointed out that enhanced convection in this sec-

tor can lead to subsidence and suppressed precipitation

over California, even during El Ni~no years.

It has also been suggested that the southeast United

States experiences relatively cool and wet conditions

during El Ni~no winters (Ropelewski and Halpert 1986,

1987). If this is the case, then we might expect similar

anomalies in our deforestation simulations. We found

that deforestation did result in slightly reduced tem-

peratures in the southeast United States, but this sig-

nal was not significant in all simulations (Figs. 7c,d).

Furthermore, in part of Florida only, we also found

a statistically significant precipitation decrease in our

combined (FINE, FINEV, and XFINE) ensemble

(Fig. 6c).

These small anomalies are consistent with the idea

that there are factors besides El Ni~no that exert strong

controls on southeast United States winter climate.

Katz et al. (2003) investigated the correlations between

southeast United States climate and the El Ni~no–

Southern Oscillation, the Pacific–North America pat-

tern, the North Atlantic Oscillation, and the Bermuda

high, and concluded that the Bermuda high (Davis et al.

1997) had the strongest correlation with winter tem-

perature and precipitation in the southeast United

States. As an index for the Bermuda high, Katz et al.

(2003) used the seasonally averaged Bermuda minus

New Orleans pressure difference, which indicates the

approximate position of the western edge of the At-

lantic subtropical high. Deforestation produced very

small changes in this index (less than 0.4 hPa in all

simulation pairs).

d. Planetary-scale impacts

Previous modeling experiments have demonstrated

how a tropical heating anomaly can act as a source of

Rossby waves that can propagate from the tropics to the

extratropics. Hoskins and Karoly (1981) used linearized

vorticity and potential temperature equations to argue

that a thermal forcing in the tropics would generate

strong poleward velocity anomalies. This was confirmed

in their model simulations, which showed height per-

turbations that arced across great circle paths from the

tropics into higher latitudes. Because the mean absolute

vorticity and the associated stretching increase as the

heating and divergence anomalies are moved poleward,

the amplitude of the wave train can also increase as it

moves poleward (Held et al. 2002). Subsequent studies

have obtained similar results; see Held et al. (2002) for

a review. Furthermore, GCM studies have found that

extratropical responses to tropical heating tend to be

enhanced in the North Pacific and over North America

(Ting and Yu 1998; Hoerling and Kumar 2002).

We now illustrate how this plays out in our simula-

tions. In our FINE simulation pair, deforestation resul-

ted in reduced precipitation of about 2mmday21 toward

the north of the deforested region (Fig. 10a). This pre-

cipitation reduction was partially balanced by precipitation

increases in the southwestern part of the deforested re-

gion and just to the northeast of the deforested region.

There was little impact on the easternmost portion of

the deforested region. Medvigy et al. (2011) also found

spatial shifts in the Amazon precipitation in response to

FIG. 9. Changes in winds and humidity resulting from deforestation. All panels show FINE-DEF minus FINE-FOR. (a) Change in

800-hPa horizontal wind divergence (day21). (b) Change in 800-hPa specific humidity (g kg21). (c) Change in 500-hPa vertical

velocity (cm s 21

).

9126 J O U R N A L O F C L I M A T E VOLUME 26

deforestation. The corresponding spatial pattern in the

XFINE simulations was similar, although the strength of

the signal was somewhat weaker (Fig. 10b). The pre-

cipitation changes in FINEV were also similar to those

of FINE, except that there were larger reductions in

precipitation near 58S, 608W and smaller reductions near 08S, 708W (Fig. 10c). However, the differences between FINE, FINEV, and XFINE were much smaller than the

differences between any of these pairs and the COARSE

simulation pair (Fig. 10d). In the COARSE simulations,

there were more widespread precipitation reductions in

the north and northwest of the deforested area. There

were also stronger and more widespread precipitation

increases, but these fell more toward the southeast of the

deforested area than in the other simulation pairs.

These precipitation changes were accompanied by

changes in the dynamics. In all simulation pairs, we

found negative 500-hPa vertical velocity anomalies in

the northern part of the Amazon that experienced pre-

cipitation reductions and positive 500-hPa vertical ve-

locity anomalies in the southern part of the Amazon that

experienced precipitation increases (Fig. 11). However,

this circulation was modulated by the prevailing east-

erlies. The easterlies have relatively strong vertical shear,

especially in the western part of the basin, because of

topography. This is illustrated in Fig. 12 for FINE-FOR

FIG. 10. Simulated changes in daily mean precipitation (DEF minus FOR; mm day21) from the (a) FINE, (b) XFINE,

(c) FINEV, and (d) COARSE simulation pairs.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9127

(for simplicity, we will illustrate the remainder of our

results for the FINE simulation pair only, except where

prominent differences between simulation pairs exist).

Combined with the latitudinal gradients in vertical ve-

locity, the wind shear generates relative vorticity anom-

alies that can then propagate poleward according to the

mechanism of Hoskins and Karoly (1981). In our FINE

simulation pair, examination of the deforestation-

induced changes in the 250- (Fig. 13a) and 850-hPa

(Fig. 13b) wind fields indeed reveal that wave trains were

excited in both hemispheres, with the higher-amplitude

wave train in the Northern (winter) Hemisphere.

We also found changes in the thermodynamics. As

reported in many previous studies, we found that

deforestation acted to increase the sensible heat flux and

the near-surface temperature in the deforested region

(Figs. 14a,b). Tropospheric diabatic heating rates are

also of interest, but they are not written to disk in the

default version of OLAM. To investigate these, we re-

peated our simulation of December 2000 through Feb-

ruary 2001 for the FINE-DEF and FINE-FOR. This

period was chosen because the Amazon precipitation

anomaly during this interval was more highly correlated

(r 5 0.52) with the 14-yr DJF average anomaly than that of any other single DJF period. During this period,

changes in column-integrated diabatic heating (Fig. 14c)

were strongly collocated with changes in precipitation

and were most influenced by contributions from the

FIG. 11. As in Fig. 10, but for 500-hPa vertical velocities (cm s 21 ).

9128 J O U R N A L O F C L I M A T E VOLUME 26

cumulus parameterization and from sensible heating

at the surface. Changes in the vertical distribution of dia-

batic heating were generally positive near the surface over

the deforested region (due to changes in sensible heating)

and negative between 500 and 1500m. At higher levels,

the spatial pattern of diabatic heating was very similar to

the spatial pattern of the column average (Fig. 14c).

These changes bear marked similarities to the GCM

experiments of Jin and Hoskins (1995), who analyzed the

quasi-steady extratropical response to a thermal source

over the Amazon. In particular, they found an upper-level

negative vorticity anomaly in the northern vicinity of their

heating source and a positive vorticity anomaly in the

southern vicinity of their heating source. The reverse

configuration was realized at low levels. These anomalies

generated Rossby wave trains that propagated into both

hemispheres, and North America in particular was af-

fected. We obtained very similar results in our simulations.

Our corresponding southern upper-level anomaly is lo-

cated south of the deforested region and spans approxi-

mately from 908 to 408W (Fig. 13a). Our corresponding northern upper-level anomaly is located in the northern

part of the deforested area but is limited in spatial extent,

perhaps due to constraints imposed by the Andes (Fig.

13a). Our corresponding lower-level northern anomaly is

evident in the northern part of the deforested region,

though our corresponding lower-level southern anomaly,

just south of the deforested area, is weak (Fig. 13b). Re-

sults from the XFINE simulation pair (Figs. 13c,d) are

broadly similar, with the main differences being slightly

stronger lower-level anomalies and slightly weaker upper-

level anomalies in the deforested sector.

In the COARSE simulation pair, deforestation also

generated wave trains (Fig. 15). However, over South

America the 250-hPa velocity anomalies (Fig. 15a) were

different from the other simulation pairs (Figs. 13a,c).

And in the extratropics, the wave trains from COARSE

were nearly 1808 out of phase with those from FINE, XFINE, and FINEV. To facilitate quantitative com-

parison of simulations on different grid meshes, we in-

terpolated the DEF minus FOR changes in the 250-hPa

meridional winds from FINE, XFINE, FINEV, and

COARSE simulation pairs onto a common 38 longitude by 38 latitude grid. We then computed the Spearman’s r correlation coefficients between FINE and XFINE and

between FINE and COARSE for all grid cells between

308 and 488N. We found a positive correlation (r 5 0.27; p , 1 3 1025) between FINE and XFINE and a negative correlation (r 5 20.28; p , 1 3 1025) between FINE and COARSE. Unsurprisingly then, the COARSE

simulation pair gave a (not statistically significant) in-

crease in precipitation for northwestern North America,

rather than a decrease (Fig. 6d). These differences be-

tween COARSE and FINE are consistent with previous

work that underlined the importance of a high-resolution

representation of topography for the simulation of

Amazon precipitation (Medvigy et al. 2008) and local

impacts of deforestation (Ramos da Silva et al. 2008;

Medvigy et al. 2011).

Figure 13 also suggests interesting differences between

the FINE and XFINE configuration in the Southern

Hemisphere extratropics. To assess whether there were

robust changes in the 250-hPa meridional wind in both

hemispheres, we combined the FINE, XFINE, and FINEV

FIG. 12. Simulated zonal winds (m s21) in FINE-FOR at (a) 850 and (b) 500 hPa.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9129

simulation pairs into a single ensemble. Each ensemble

member consisted of the 250-hPa meridional wind dif-

ference (DEF minus FOR) for a particular year and grid

configuration. Because each simulation had 14 years of

post-spinup output, this led to a total sample size of 42

configuration years for each grid cell. Next, we used the

Wilcoxon signed-rank test for each grid cell to deter-

mine the statistical significance that its median differ-

ence was different from zero. The resulting p values

(Fig. 16) suggest two wave trains emanating out of the

Amazon, one in the Northern Hemisphere and one in

the Southern Hemisphere. As expected, there is a clear

FIG. 13. Deforestation-induced wind changes (DEF minus FOR). The arrows show the changes in the wind vector and the colors

show the changes in the meridional component of the wind (m s 21

). (a) The 250-hPa FINE simulations, (b) 850-hPa FINE simulations,

(c) 250-hPa, XFINE simulations, and (d) 850-hPa XFINE simulations.

9130 J O U R N A L O F C L I M A T E VOLUME 26

feature over North America. Based on these results, we

conclude that there are robust wave train signals in both

the Northern and Southern Hemispheres in our FINE,

XFINE, and FINEV sets of simulations.

4. Discussion and conclusions

a. Comparison to previous deforestation and El Ni~no studies

This work has focused on some potential extratropical

responses to the complete deforestation of the Amazon.

We found that precipitation in the northwest United

States and in parts of California was strongly reduced

during DJF because of deforestation. Such an effect has

not been seen in previous analyses (Gedney and Valdes

2000; Werth and Avissar 2002; Avissar and Werth 2005;

Findell et al. 2006; Hasler et al. 2009). Model resolution

is a critical difference between our simulations and

previous simulations. Whereas previous work was car-

ried out at resolutions of about 200 km, we studied

simulations that used a mesh with a characteristic length

scale (CLS) of 25–50 km for much of North and South

FIG. 14. Changes in heating (FINE-DEF minus FINE-FOR) resulting from deforestation. (a) Change in sensible heat flux (W m22),

averaged over 14 DJF periods. (b) Change in near-surface temperature (8C), averaged over 14 DJF periods. (c) Change in column- integrated diabatic heating rate (W m

22 ), December 1999–March 2000 only. The region of Amazon deforestation is boxed.

FIG. 15. Deforestation-induced wind changes (DEF minus FOR) in the COARSE simulation pair. The arrows show the changes in the

wind vector and the colors show the changes in the meridional component of the wind (m s 21 ): (a) 250 and (b) 850 hPa.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9131

America. This permitted the simulation of regional-

scale circulations in the Amazon that are important for

the propagation of waves from the tropics to the extra-

tropics. When we ran simulations at a CLS typical of

previous studies, the wave trains resulting from de-

forestation had a different phase and their impacts on

the northwest United States were strongly reduced.

This study supports the suggestion made by Avissar

and Werth (2005) that substantial similarities may exist

between the extratropical effects of Amazon de-

forestation and of El Ni~no. A conceptual diagram illus-

trating our mechanism is given in Fig. 17. Like El Ni~no,

we find that Amazon deforestation generates vorticity

and diabatic heating anomalies, and these factors gener-

ate Rossby wave trains in both hemispheres. The impli-

cation of this for western North America is that the jet

shifts south and negative vertical velocity anomalies de-

velop. Western Washington and Oregon receive much

less precipitation. However, the extratropical signature of

deforestation extends farther south than that of El Ni~no,

and consequently the Sierra Nevada are also strongly

impacted by deforestation. Furthermore, because tem-

peratures do not drastically change, the snowpack on the

Sierra Nevada is markedly reduced.

b. Benefits and costs associated with different resolutions

Although there were some differences between the

FINE, XFINE, and FINEV simulation results, we em-

phasize that the basic concepts in Fig. 17, including the

precipitation reduction in the northwest United States in

response to Amazon deforestation, held for all 3 simu-

lations pairs. When compared to precipitation and tem-

perature observations, the distinctions between FINE,

FINEV, and XFINE were relatively small, though

FINEV did a slightly better job of simulating precipita-

tion in the Midwest (Figs. 3, 4). However, for snow water

equivalent, FINEV and XFINE were both more realistic

than FINE. In response to Amazon deforestation, large

reductions in snow water equivalent were simulated in

both the XFINE and FINEV pairs (Fig. 5).

Our results also highlight some advantages of a

variable-resolution model. The FINE and FINEV con-

figurations include only 21% of the number of grid cells

that would be required in a simulation that used 50-km

CLS globally (Table 1), representing substantial com-

putational savings while still achieving fine resolution in

regions of interest. In terms of the total number of grid

FIG. 16. Regions where there are robust deforestation-induced changes in 250-hPa meridi-

onal winds based on the combined FINE, XFINE, and FINEV ensemble. The two contours

indicate p , 0.10 and , 0.05.

9132 J O U R N A L O F C L I M A T E VOLUME 26

points, XFINE is 17% more computationally expensive

than FINE (Table 1). However, it also requires a shorter

time step, so that the total cost of XFINE is 1.76 times

that of FINE. In contrast, the total cost of FINEV is only

1.23 times that of FINE. This suggests that FINEV may

be the best computational bargain of the three, especially

if one is interested in snow water equivalent. However,

these results include only a small number of grid config-

urations, and further analysis should be done at different

combinations of finer horizontal and vertical resolution.

c. Broader implications of this study

To the extent that our simulations are consistent with

reality, the deforestation of the Amazon will have

enormous consequences for the irrigation-fed agricul-

ture in California. In the United States, agriculture and

food sectors contribute 4.8% of the gross domestic

product and are the source of 15.8 million jobs nation-

wide. California has been the nation’s number one state

for food and dairy production during the past 50 years.

The ability of California to maintain its large output is

directly related to the availability of irrigation water

(Draper et al. 2003). Our work complements the many

previous studies that have investigated the impact of cli-

mate warming on California hydrology (Lettenmaier and

Gan 1990; Kim et al. 2002; Maurer 2007). In response to

increases in greenhouse gases, climate models have con-

sistently simulated a warmer, slightly wetter California,

with overall reduced end-of-winter snowpack. Our sim-

ulations indicate that Amazon deforestation would likely

exacerbate this snowpack reduction.

Natural ecosystems would also be strongly affected by

the rainfall reductions simulated here. In the relatively

wet forests of western Oregon and Washington, fires are

relatively rare. However, fuel accumulations are high,

and when fires do occur, they can lead to complete stand

replacement (Mote et al. 2003). In California, the

California Floristic Province has been designated as 1 of

33 global biodiversity hotspots as a result of its large

number of native and endemic species (Myers et al.

2000). Drawdown of freshwater resources is one of the

principal threats facing the area.

d. Future research needs

This study represents an initial effort at using a locally

high-resolution GCM to investigate intercontinental

effects of Amazon deforestation. Limits on computa-

tional resources required us to balance many factors in

determining our simulation design; including grid reso-

lution, simulation length, and land cover scenarios.

Previous modeling studies of Amazon deforestation can

generally be characterized as long term (more than

a decade) at coarse resolution (.150 km) or short term (less than a year) at fine resolution (,50km). Our study fills an important niche because critical regions of North

and South America were simulated at ,50-km resolution and the simulation length also exceeded a decade. How-

ever, the SST configurations realized in 1998–2012 are still

limited, and future work should include longer-term sim-

ulations that include a wider range of SST configurations.

The extratropical response to tropical forcing depends

on the magnitude, location, and vertical distribution of

the forcing, and so it is important to consider how errors

in simulating the forcing might affect the response. In

terms of magnitudes, it is expected that the response

would simply scale in proportion to the source (Held et al.

2002). Previous studies of responses to El Ni~nos can help

inform the sensitivity to location of the source. Notably,

the northwest United States is anomalously dry both for

El Ni~no events corresponding to heating in the central

Pacific and for El Ni~no events corresponding to heating in

the eastern Pacific (Yu and Zou 2013). In this study, we

find that the Northwest also becomes anomalously dry

when a deforested Amazon acts as a heat source.

FIG. 17. Conceptual diagram illustrating impacts of deforestation. A redistribution of precipitation results in vorticity anomalies and

tropospheric heating. This generates Rossby waves that then propagate to midlatitudes. Over the western United States, the jet shifts

south and negative vertical velocity anomalies develop that suppress precipitation. Meanwhile, in the subtropical eastern Pacific, positive

vertical velocities develop that lead to increases in precipitation.

15 NOVEMBER 2013 M E D V I G Y E T A L . 9133

The vertical distribution of the source also affects the

response; for example, a source confined near the sur-

face is not expected to generate Rossby waves capable

of propagating out of the tropics (Hoskins and Karoly

1981). In our simulations, we find broadly similar im-

pacts in the tropics and extratropics for our FINE,

XFINE, and FINEV simulations, suggesting that our

results are robust within the context of our model and

not particularly sensitive to vertical resolution. How-

ever, like most GCMs, our model uses parameteriza-

tions for cumulus convection, turbulence, radiation, and

microphysics. Errors in these parameterizations can po-

tentially affect the magnitude of the upper-level source

and hence the Rossby wave generation. One can envision

several ways to further test the robustness of our results.

First, it would be relatively straightforward (but com-

putationally expensive) to rerun the simulations with

perturbed values for key parameters in the physics pa-

rameterizations. Second, it would be possible—but

again, computationally expensive—to run OLAM at

much finer resolutions. Ideally, we would like to simu-

late the deforested sector with resolution sufficient to

adequately resolve convection in the Amazon (;1 km; Ramos da Silva and Avissar 2006). Although it is not

obvious that it would be feasible to run decadal-scale

simulations at this fine of a resolution in the near future,

seasonal simulations at this resolution may be possible.

An alternative way forward would be to run OLAM with

a multiscale modeling system or superparameterization

(Tao et al. 2009). We have run some preliminary tests of

this (Walko et al. 2011) and work is ongoing.

Future work should also investigate other mechanisms

whereby deforestation can affect climate. For example,

the fires that frequently accompany deforestation affect

aerosol concentrations. Aerosols can intensify updrafts

(Williams et al. 2002; Andreae et al. 2004) and poten-

tially increase the vigor of individual convective events

even if annual average rainfall decreases. In an analysis

of satellite-based data over partially deforested areas of

the Amazon, Ten Hoeve et al. (2011) found that cloud

fraction more strongly increased aerosol optical depth

over deforested land than over deforested land. Aerosol

absorption of radiation may also be important, and this

effect is expected to reduce cloud fraction and cloud height,

especially for small shallow clouds (Koren et al. 2008).

Our experimental design was simplified in that it con-

sidered the complete deforestation of the Amazon. Pre-

sumably, this would lead to the strongest extratropical

signal and would be the easiest to detect given the con-

straints of limited computational resources. However,

now that this work has demonstrated a reason to suspect

a signal in the northwest United States, it will be impor-

tant to carry out new simulations including less aggressive

deforestation scenarios. About 40% of the Brazilian

Amazon is in some form of a protected area (Walker

et al. 2009), and deforestation may be less severe in these

areas. Furthermore, actual future spatial patterns of

deforestation may be complex (Soares-Filho et al. 2006)

and induce local- or regional-scale circulations. Thus,

future analyses should consider more realistic spatial

patterns of deforestation.

Our study raises several other questions that should

be explored in future research. First, our simulations

used a relatively coarse resolution over most of the

world outside of the Americas. Pursuing the analogy of

Amazon deforestation with El Ni~no, additional Amazon

deforestation experiments should be carried out using

model grid meshes with fine resolution over other areas

known to be sensitive to El Ni~no. Second, our model

simulations were designed to isolate the impacts of

Amazon deforestation, and so they did not consider the

effects of changes in greenhouse gases. Assessing the

combined effect of Amazon deforestation and green-

house gas increases on the northwest United States and

California should be a priority. Third, there are a num-

ber of physical processes that were either parameter-

ized, like cumulus convection, or not represented at all,

like fires, aerosol effects, and the dynamic responses of

terrestrial ecosystems. These are all processes that

merit future attention. Fourth, our simulations were

driven by historical SSTs and so they did not account

for ocean feedbacks. Coupled atmosphere–ocean

GCMs should be used to assess the extent to which the

ocean can buffer (or exacerbate) the changes simulated

here. Fifth, although our precipitation changes were

statistically significant, additional runs, especially those

carried out with independent models, would bolster

this assessment.

Acknowledgments. The authors gratefully acknowl-

edge support from National Science Foundation Awards

1151102 (to D.M.) and 0902197 (to R.A. and R.L.W.).

NCEP reanalysis 2 data were provided by the NOAA/

OAR/ESRL PSD, Boulder, Colorado, United States

(from their website at http://www.esrl.noaa.gov/psd/).

The simulations presented in this article were performed

on computational resources supported by the PICSciE-

OIT High Performance Computing Center and Visuali-

zation Laboratory at Princeton University. We are also

grateful to the editor and to four anonymous reviewers

whose criticisms have greatly improved the quality of this

manuscript. Disclaimer: Although this work was re-

viewed by EPA and approved for publication, it may not

necessarily reflect official agency policy. Mention of

commercial products does not constitute endorsement

by the agency.

9134 J O U R N A L O F C L I M A T E VOLUME 26

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