Direct climate effects of perennial bioenergycrops in the United StatesMatei Georgescua,1, David B. Lobellb, and Christopher B. Fieldc

aSchool of Mathematical and Statistical Sciences and Center for Environmental Fluid Dynamics, Arizona State University, Tempe, AZ 85287; bDepartmentof Environmental Earth System Science and Program on Food Security and the Environment, Stanford University, Stanford, CA 94305; and cDepartmentof Global Ecology, Carnegie Institution for Science, Stanford, CA 94305

Edited by Robert E. Dickinson, University of Texas, Austin, TX 78712, and approved January 28, 2011 (received for review June 20, 2010)

Biomass-derived energy offers the potential to increase energysecurity while mitigating anthropogenic climate change, but a suc-cessful path toward increased production requires a thoroughaccounting of costs and benefits. Until recently, the efficacy of bio-mass-derived energy has focused primarily on biogeochemical con-sequences. Here we show that the biogeophysical effects thatresult from hypothetical conversion of annual to perennial bioe-nergy crops across the central United States impart a significantlocal to regional cooling with considerable implications for thereservoir of stored soil water. This cooling effect is related mainlyto local increases in transpiration, but also to higher albedo. Thereduction in radiative forcing from albedo alone is equivalent toa carbon emissions reduction of 78 t C ha−1, which is six timeslarger than the annual biogeochemical effects that arise from off-setting fossil fuel use. Thus, in the near-term, the biogeophysicaleffects are an important aspect of climate impacts of biofuels, evenat the global scale. Locally, the simulated cooling is sufficientlylarge to partially offset projectedwarming due to increasing green-house gases over the next few decades. These results demonstratethat a thorough evaluation of costs and benefits of bioenergy-related land-use change must include potential impacts on thesurface energy and water balance to comprehensively addressimportant concerns for local, regional, and global climate change.

regional climate modeling ∣ agriculture ∣ landscape modification ∣ CO2

Securing energy independence and lessening the human fin-gerprint on climate are two principal motivations behind

increased production of bioenergy. Recognition of the full arrayof costs and benefits of increased production, such as effects onenergy and food security, anthropogenic climate change mitiga-tion, and maintenance of biodiversity, will assist in realization ofprincipal objectives (1–8). Prior research gauging the effective-ness of bioenergy has estimated potential impacts based ongreenhouse gas (GHG) emission changes through direct or indir-ect land-use change (LUC) and by means of life cycle analysis(LCA). In addition to impacts on GHGs, LUC also modifiesthe surface energy and water balance (9), with implications fornear-surface temperature and precipitation, and serves as anadditional first-order climate forcing on global (10, 11) and regio-nal (9, 12) spatial scales.

One of the main proposed strategies for bioenergy productionis widespread planting and harvesting of perennial grasses, suchas switchgrass (Panicum virgatum L.) or miscanthus (MiscanthusX giganteus). One LCA study suggested that net GHG savingsrelative to fossil fuels of greater than 200 g CO2e-C m−2 yr−1may be expected for biomass (switchgrass) conversion to ethanol(13) (roughly double for hybrid poplar). Potential mitigation,however, is complicated by variability in inventory componentsand system boundaries (i.e., LCA methodology) that leadsto GHG displacement estimates that differ in sign, even for thesame species (13–15). Whereas a consensus accounting of carbonsavings remains elusive, such analyses should be complementedby consideration of the direct climate effects associated with bioe-nergy-related LUC, a currently omitted and potentially important

environmental consequence. Although such effects may not beglobal in scale, they could have significant influence on localand regional climates important to millions of people and foodproduction.

Here we use the Weather Research and Forecasting Model(WRF) (16) (see Materials and Methods) to evaluate the climateeffects of converting agricultural areas in the central UnitedStates to perennial crops. Based on recent side-by-side growingseason observations of miscanthus and maize (Zea mays; anannual bioenergy and food crop) (17) we represented the mod-ification of surface vegetation properties by shifting WRF’sdefault vegetation characteristics (albedo, leaf area index, andvegetation fraction), largely representative of annual croppingsystems, over agricultural areas within the central United States(covering 839;000 km2; see Figs. S1 and S2) by 1 mo in eachdirection (hereafter “Perennials”). That is, vegetation character-istics are advanced by 1 mo, relative to the default, in the springseason (representing earlier green-up) and delayed by 1 mo in thefall season (depicting lagged senescence) (Fig. S2). Vegetationproperties were held constant for 2 mo at roughly midwaythrough the growing season (July 31st) to allow for delayed per-ennial bioenergy crop senescence in the fall. We also performed apair of additional sensitivity experiments to assess the simulatedcontribution of albedo (hereafter “Perennials-NoAlb”) and thetendency of perennials to exhibit a deeper rooting depth (here-after “Perennials-2m”) (18, 19) (Table S1).

Our approach accounts for biogeophysical impacts on climateby considering properties that directly influence the manner inwhich energy is absorbed at the surface and redistributed to theoverlying atmosphere. Modification of the albedo, or the reflec-tivity of the surface, establishes the surface’s energy availability.In conjunction with the fraction of bare ground covered byvegetation these parameters modulate the partitioning betweensensible, latent, and ground heat fluxes. Changes in the green-up and eventual die-down of vegetation, or phenology, will neces-sarily modify the transport of heat and water vapor to the atmo-sphere. In addition to the direct impact of the altered landscape,atmospheric feedbacks to modified surface energy inputs mayimpact cloud formation, with additional consequences for near-surface temperature and precipitation.

The maximum mean growing season near-surface coolingassociated with the conversion of annual to perennial cropswas nearly 0.9 °C (Fig. 1A). Maintaining albedo at default valuesdecreases the simulated cooling. However, this effect appearsto be of secondary importance, on near-surface temperature,

Author contributions: M.G., D.B.L., and C.B.F. designed research; M.G. and D.B.L.performed research; M.G. and D.B.L. analyzed data; and M.G., D.B.L., and C.B.F. wrotethe paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Freely available online through the PNAS open access option.1To whom correspondence should be addressed. E-mail: Matei.Georgescu@asu.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1008779108/-/DCSupplemental.

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compared to impacts of evapotranspiration (ET) (Fig. 1 A and Band Table 1), as more of the incoming energy is used to evaporatewater rather than heat the ground in the model experiments withperennial crops. Local growing-season-averaged cooling effectsreach 1.5 °C for LUC to a perennial bioenergy crop with a2 m rooting depth (Fig. 1C and Fig. 2A). Averaged over all landpixels our experiments indicate cooling imparted by perennialsbetween 0.08 °C and 0.16 °C, with mean regional cooling overthe growing season exceeding 0.45 °C for all perennial experi-ments (Table 1).

Based on net fossil fuel savings of 140 g CO2e-C m−2 yr−1 (13),we estimate that complete conversion from annual to perennialbioenergy crops over the region would save an additional13 t C ha−1 yr−1. Accounting for albedo changes resulting fromthis conversion (i.e., biogeophysical impact) translates to anemissions-equivalent reduction of 78 t C ha−1 (see Materials andMethods for detailed calculation). Conversion from annual toperennial bioenergy crops has a significantly larger impact onglobal radiative forcing (RF) when including biogeophysicaland biogeochemical impacts, together. We estimate that it wouldtake 7 yr for the biogeochemical impacts to surpass biogeophy-sical effects, for the area of land undergoing conversion in ourexperiments. Understanding the full spectrum of effects asso-ciated with biomass energy expansion will therefore require morethan the biogeochemical impact considered to date (20), andmust include a careful assessment of potential consequences forlocal and regional climate (21) and hydrology, as presented here.

Simulated near-surface thermal effects are greatest duringspring and fall, with impacts lessened during the summer season

(Fig. 2A), when differences in vegetation characteristics betweencropping systems approach zero. Given the lack of biogeophysicalobservational data associated with bioenergy crops, our approachmost probably omits additional features associated with summerseason variability (e.g., albedo, canopy resistance) whose influ-ence may be substantial (21). Differences in vegetation coveragebetween annual and perennial bioenergy crops at the start andend of the growing season account for the simulated enhance-ment in perennial crop ET. Mean ET for perennial crops exceedsthat of annuals by 0.1 mmday−1, with shoulder-season effects ofconsiderably greater magnitude (Fig. 2B).

The impact of a deeper rooting depth more than doublessimulated ETrelative to the effects of using a 1 m rooting depth.The addition of transpired water moistens the near-surface airand serves as the chief cooling mechanism in our simulations.This transport of water has implications for the reservoir of storedsoil water, necessarily decreasing shallow and deep water avail-ability with time (Fig. 2 C and D). While deep water availabilitydisplays a progressive decrease with time for Perennials-2m(by the end of the simulation, this difference, relative to Annuals,exceeds 5%) the shallow (i.e., within the top 40 cm of the soilsurface) reservoir of soil water increases. The substantial increasein ET (0.22 mmday−1 averaged throughout the growing season;Fig. 2B and Table 1) is sufficient to enhance local recycling ofrainfall (Fig. S3), further moistening the near-surface soil. In thissense, our experiments suggest a positive feedback, wherebydirect initial cooling resulting from the enhancement of ET (alsosupported by the increase in 2 m dew-point temperature pre-sented in Table 1) leads to an increase in the vertically integratedcloud and water vapor mixing ratio and rainfall, subsequentlyreducing net surface shortwave radiation (Table 1), cooling thesurface further. The vertical extent of simulated surface coolingextends above the planetary boundary layer and outweighs theimpact of enhanced longwave emission due to greater watervapor and cloud mixing ratio, resulting in a net decrease in long-wave radiation at the surface for the perennial bioenergy crops(Table 1).

To test the possibility that the ET effect of the perennials istransient, we conducted a three-yr offline simulation using repre-sentation of Perennials-2m and Annual crops, with the High-Resolution Land Data Assimilation System (Version 3.1), whichruns the Noah land surface model (LSM) in uncoupled mode.The sustained ET difference for each growing season indicatesthat our simulated ET effect is not transient (Fig. S4).

Whereas the impact of perennials may offset a significant frac-tion of future greenhouse warming at local scales, it remains smallwhen compared to projected warming from global GHG emis-sions at large scales (Fig. 3). Locally, and through 2040, the mod-eled conversion of annual to perennial bioenergy crops balancesGHG warming (see Materials and Methods) though this effectoccurs largely during spring and fall with reduced impacts duringmidsummer. Through this time frame the two forcing mechan-isms nearly balance each other at about 4 million ha, with GHG-induced warming playing a relatively greater role at larger spatialscales. Through 2100, local LUC effects are greatly diminished,compensating for about one-fourth of projected warming (4 °C)(Fig. 3). Consideration of a lower (i.e., one more consistent withwidespread use of biomass-derived energy) emissions trajectoryreveals LUC effects that offset nearly one half of projected warm-ing (B1 scenario) at local scales whereas near-surface coolingimpacts play a reduced role for higher emissions trajectories(A2 scenario) (Fig. S5).

Our experiments suggest that phenological contrasts asso-ciated with conversion of annual to perennial bioenergy cropsin the central United States may impart significant local andregional influence and through nearly the midcentury are ofsimilar order of magnitude as projected impacts due to risingGHGs. In addition to direct cooling effects, feedbacks related

Fig. 1. Simulated timemean (APR–OCT)difference in (A) 2-mtemperature [°C](Perennials minus Annuals); (B) as (A) but perennial crop representationdoes not include albedomodification; (C) as (A) but perennial crop representa-tion includes rooting depth of 2 m.

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to deep-soil moisture depletion illustrate additional unintendedconsequences requiring added attention in light of increasingreliance on biomass-derived energy (22–24). By the end of thetwenty-first century local cooling effects associated with LUC aremuch smaller than large-scale warming associated with increasedGHGs. Additional biophysical differences between croppingsystems (e.g., canopy resistance) during green-up and senescencemay lead to further impacts, highlighting the necessity of improv-ing location- and vegetation-specific representation of bioenergycropping systems. Further work assessing the long-term (i.e.,decadal-scale or longer) evolving nature of soil water depletionand associated equilibrium ET resulting from soil moisture/temperature and atmospheric changes are necessary. To more

comprehensively gauge the full spectrum of anthropogenicimpacts, such forcing that directly influences the scale of humaninteraction is a necessary component to bridge the large-scalebiogeochemical bioenergy impacts studied to date with the local-to-regional-to-global scale biogeophysical effects associated withagricultural practices.

Materials and MethodsWRF Modeling System. WRF (version 3.1) is a state-of-the-art, fully compres-sible, nonhydrostatic mesoscale code that has been widely used in a numberof applications ranging from urban canopy level modeling, to air qualityresearch, and has been extended to a variety of longer climate scale applica-tions. The vertical coordinate is terrain-following and the horizontal coordi-nate is staggered on an Arakawa C-grid. The modeling system has a detailed

Fig. 2. (A) Simulated evolution of daily mean temperature [°C] difference (Perennials minus Annuals) over grid cells where land surface was perturbed.Blue line: Perennials-2m minus Annuals; red line: Perennials minus Annuals; green line: Perennials-NoAlb minus Annuals. (B) As (A) but for ET [mmday−1].(C) Simulated evolution of near-surface (top two soil layers: Surface-40 cm) volumetric soil moisture [m3 m−3] averaged over grid cells where land surfacewas perturbed. Blue line: Perennials-2m; purple line: Annuals; red line: Perennials; green line: Perennials-NoAlb. (D) As (C) but for deep soil (bottom twosoil layers: 40–200 cm).

Table 1. Mean difference (APR–OCT) response of climate variables between perennials and annuals

2 m temp.[°C]:

all land

2 m temp.[°C]:

perturbed pixels

2 m dew-pointtemp:

[°C]: all land

2 m dew-pointtemp [°C]:

perturbed pixelsET

[mmday−1]Net surfaceSW [Wm−2]

Net surfaceLW [Wm−2]

Perennials—Annuals −0.08 −0.51 0.02 0.16 0.1 −2.36 −0.65Perennials-NoAlb—Annuals −0.07 −0.45 0.02 0.18 0.1 −1.42 −0.23Perennials-2m—Annuals −0.16 −0.84 0.09 0.54 0.22 −3.63 −1.06

Where not specified, calculations are for perturbed pixels only. ET, evapotranspiration; SW, shortwave; LW, longwave

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level of complexity, includes multiple parameterization options for convec-tion, radiation, boundary layer, and cloud microphysics, including multipleland-surface model options. We use the 4-layer Noah land surface scheme(25), used operationally at the National Center for Environmental Prediction,to update soil temperature and moisture following model initialization. TheNoah land surface model has been widely used in the regional climate mod-eling community. Important instances include its utility in the developmentof the 25-yr North American Regional Reanalysis (NARR) atmospheric andland surface hydrology dataset (26), and operation as part of the NorthAmerican Regional Climate Change Assessment Program (http://narccap.ucar.edu/data/rcm-characteristics.html), which utilizes WRF and its predeces-sor (MM5) as two of the six atmospheric models used. Of the six atmosphericmodels, three made use of the Noah LSM to treat interactions between theland surface and overlying atmosphere. For subgrid-scale convective pro-cesses, we use a recently modified version of the Kain–Fritsch convectiveparameterization scheme (27).

We initialize and force all experiments with data extracted from the NARRdataset (26). Initial soil moisture and temperature were also retrieved fromNARR, but following the initial model timestep the Noah LSM was used toupdate these fields and calculate the necessary fluxes required by the YonseiUniversity Planetary Boundary Layer scheme (28).

Our representation of albedo is based on a monthly, five-yr, 0.144° clima-tological dataset assumed to be valid at the 15th of each month, with globalcoverage (16, 29). This albedo dataset implicitly includes variable vegetationdensity effects, thereby accounting for both soil and vegetated contribu-tions. Total ET, however, is dependant on the vegetation fraction, accountingfor both direct (i.e., contribution from bare soil) evaporation as well as con-tribution from intercepted canopy water and transpired water from canopyand roots. Our representation of vegetation fraction is based on a monthly,five-yr, 0.144° climatological dataset assumed to be valid at the 15th of eachmonth, with global coverage (30).

A detailed inventory of setup options is presented in Table S2.

WRF Simulations.We conducted four 8 mo simulations initialized on March 1,1995, and completed on October 31, 1995, characterizing a full growingseason (Tables S1 and S2). Our simulation domain consisted of the entireUnited States, northern Mexico, and southern Canada, and portions ofthe Atlantic and Pacific oceans (Fig. S1). To represent earlier green-up of per-ennial bioenergy crops we used the default May 1 biogeophysical character-istics (i.e., those pertaining to “Annuals”) for April 1 Perennials. As with thedefault vegetation characteristics, which are updated daily, April 2 Perennialsbiogeophysical characteristics are updated accordingly based upon theMay 2Annuals representation. Updating of vegetation properties for Perennials is

maintained such that it lags that of Annuals by 1 mo, until July 1 (correspond-ing to July 31 for Annuals). Shifting this date back (i.e., corresponding to July24 for Annuals) and forth (i.e., corresponding to August 7 for Annuals) by oneweek did not alter the conclusions of our sensitivity experiments. Landsurface Perennials representation are maintained at constant values for theensuing 2 mo with updating resumed once more on September 1 (corre-sponding to August 1 for Annuals; Perennial(s) senescence is now lagging).Updating of vegetation properties for Perennials is continued, daily (basedon the Annuals representation), until October 31 (corresponding to October1 for Annuals). Monthly mean differences at the start and end of thegrowing season illustrate phenological contrasts between Annuals andPerennials (Fig. S2).

For most areas of the world, including our study region, albedo is higherfor a vegetation canopy compared to soil. For example, it has been shown,using in situ observations, that a maize field experiences an albedo increaserelative to the background soil albedo during the growing season (31). Over-all, the albedo contrast between biofuel crops and current land cover, whichdrives the main result related to global climate forcing, is a robust featurethat is likely not specific to our particular model or implementation.

The initial month was used as spin-up (for all experiments) with theremaining months of the default simulation (Annuals) evaluated againstsuitable observations of temperature and precipitation (Fig. S6; see below).

To evaluate the mechanisms behind climate responses, a third scenariothat was identical to Perennials but did not include the albedo modification(Perennials-NoAlb) was considered (Table S1). Evaluation of potential conse-quences of deep root systems associated with perennial crops served asthe source of one additional experiment that included a 2-m root depth(Perennials-2m; all other experiments utilized a rooting depth of 1 m).

Evaluation of WRF Simulations. We evaluate Annuals (which makes use ofthe 24-class US Geological Survey land use and land cover dataset as thedefault WRF landscape representation) against suitable observations ofgridded temperature and precipitation (Fig. S6). We use the University ofDelaware Global Air Temperature data, courtesy of the Earth SystemsResearch Laboratory (http://www.cdc.noaa.gov) for model comparison totemperature and the Climate Prediction Center US Unified Precipitation dataprovided by the National Oceanic and Atmospheric Administration/Office ofOceanic and Atmospheric Research/Earth System Research Laboratory (http://www.cdc.noaa.gov) for model comparison to precipitation. We emphasizethat the purpose of these sensitivity experiments was not to replicate theclimate of the growing season precisely, and consequently no tuning of themodel was carried out to improve correspondence to observations. Ourprimary goal of understanding and quantifying the sensitivity to prescribedbiogeophysical landscape change associated with bioenergy crops wasenhanced due to usage of spectral nudging (32, 33), a technique recentlyimplemented in the version ofWRF utilized for this study. Usage of this meth-od constrains the model solution of the longest atmospheric wavelengths(above the PBL only) to that of the driving boundary fields and leads to morereliable sensitivity results than would otherwise be expected (32, 33).

Differences betweenmodeled and observed temperature for the growingseason are least over the eastern one-half of the United States (includingthe Corn Belt states), southwestern United States, and western United States,with temperature differences generally within 1 °C. WRF does have a positivewarm bias over the central and southern plains ranging between 1–3 °C(this warm bias is greatest over parts of Oklahoma and Texas and reachesto nearly 5 °C). Overall, WRF does an excellent job in simulating the broad,continental temperature distribution.

WRF-simulated precipitation over the course of the growing season showsgood general agreement, with greater precipitation in the east and less inthe west and southwest. WRF properly simulates increased rainfall totalsalong the Gulf Coast states but misses the observed peak precipitation alongOklahoma, Kansas, Missouri, which instead falls further downstream (alongthe Corn Belt states). Nevertheless, the broad distribution of precipitation,rather than point-specific totals, is reasonably simulated, and providesconfidence in the model’s ability to ably reproduce the United States climateduring this time period.

Finally, it is important to highlight that we selected 1995 as a simulationyear because external large-scale dynamical forcing agents (e.g., El Nino)were not a primary driving factor in the season’s climatic evolution. Ourintent was to quantify bioenergy-related LUC effects separate from otherforcing factors.

Carbon Savings Calculations.Our imposed conversion of annuals to perennialsover the central United States encompassed a planted area of 83.9 million ha[this is roughly equivalent to 2016 agricultural area projections within the

Fig. 3. Comparison of near-surface temperature change (April throughOctober) associated with simulated conversion from annual to perennialbioenergy crops (i.e., biogeophysical effect) against projected WCRP CMIP3warming (i.e., global GHG emissions), with increasing spatial scale (centeredon lat: 40.0 °N, longitude: 87.75 °W).

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“Corn Belt,” “Lake States,” and “Northern Plains” states (as defined by theUS Department of Agriculture)]. Using a carbon saving estimate (near-termcarbon savings of switchgrass [approximately 210 gCO2e -Cm−2 yr−1] minuscarbon savings of corn-soy [approximately 70 gCO2e-Cm−2 yr−1], relative tofossil fuels) of 140 g CO2e-Cm−2 yr−1 (13), yields a savings of:

140 gCO2e-Cm−2 yr−1 × 1 kg × 1000 g−1

¼ 0.140 kgCO2e-Cm−2 yr−1

0.140 kgCO2e-Cm−2 yr−1 × 10;000m2 ha−1 × 83.9E7 ha

¼ 1.175E11 kgCO2e-C yr−1

1.175E11 kgCO2e-C∕2.13E12 kgC

¼ approximately 0.06 ppmv yr−1

Conversion of annual to perennial bioenergy crops over this area would yielda carbon savings of 0.06 ppmv yr−1.

We calculated the contribution of our imposed land-use change to globalRF by dividing the local (i.e., over perturbed pixels only) top of the atmo-sphere (TOA) net shortwave (SW) by the earth’s surface area (34, 35):

FðorcingÞ ¼ ½Local effect×Area of perturbation�∕Area of Earth

F ¼ ð−3.21 Wm−2Þ × ð83.9 mill:haÞ∕ð5.1 × 108 km2Þ

F ¼ ð−3.21 Wm−2Þ × ð83.9 × 104 km2Þ∕ð5.1 × 108 km2Þ

F ¼ approximately − 0.0053 Wm−2:

Our TOA SW RF value of −3.21 Wm−2 represents an entire growingseason time average (APR–OCT) difference between Perennials-2m andAnnuals. We assume that for the remaining months (i.e., NOV–MAR) snowentirely covers the landscape and reduces RF to zero. This conservativeapproach yields a global RF of approximately −0.0053 Wm−2.

Using a radiative forcing efficiency (α) of 5.35 Wm−2 (36) and the previousestimate of CO2 radiative forcing: RF ¼ approximately − 0.0053 Wm−2, weobtain:

RF ¼ −0.0053 Wm−2 ¼ 5.35 × ln½1þ ΔC∕C0�; [1]

where C0 is the total (i.e., reference value) CO2 concentration as of 2005 (379parts per million by volume; 37) and ΔC is the globally averaged atmosphericCO2 concentration change required to give an equivalent global RF tothat obtained from our imposed conversion to perennial bioenergy crops.Solving [1] for ΔC yields:

ΔC ¼ C0 × ½expðRF∕5.35Þ − 1�

ΔC ¼ ð379 ppmvÞ × ½expð−0.0053 Wm−2∕5.35Þ − 1Þ

ΔC ¼ −0.3739 ppmv ∼ −0.37 ppmv:

Given our assumption of zero RF for the remaining months:

ΔC ¼ approximately − 0.37 ppmv:

We estimate that it would take about 6 years for the RF associated withthe carbon savings effect (approximately −0.06 ppmv yr−1) to exceed that as-sociated with biogeophysical impacts resulting from a transition to perennialbioenergy crops (approximately −0.37 ppmv). Our estimate of carbon savedis based on the highest GHG displacement values reported in the literature(for switchgrass) and a more conservative value would yield less savings (14)and a longer time period until biogeochemical impacts exceed biogeophysi-cal effects on global RF.

The terrestrial carbon stock change (34) owing to atmospheric CO2

concentration change (i.e., ΔC) may be obtained from:

ΔCT ¼ 2 × ðMc∕MaÞ ×ma × ðΔC∕C0Þ:Where ΔCT is the terrestrial carbon stock change, Mc and Ma are the

molecular weights of carbon and dry air, ma is the mass of the atmosphere,the factor of 2 accounts for an airborne emissions fraction of 0.5 (38), andΔC and C0 are as before.

We calculate ΔCT and estimate the emissions-equivalent of shortwaveforcing (34) for both the carbon savings impacts (relative to fossil fuels)and the biogeophysical impacts resulting from conversion to perennial bioe-nergy crops.

ΔCTðbiogeophys:Þ ¼ 2 × ð12.01 gmol−1∕28.96 gmol−1Þ× 5E18 kg × ð0.37 ppmv∕379 ppmvÞ

ΔCTðbiogeophys:Þ ∼ 4.0E15 kg∕5.1E10 ha ∼ 78 t C ha−1

ΔCTðbiogeochem:Þ ¼ 2 × ð12.01 gmol−1∕28.96 gmol−1Þ× 5E18 kg × ð0.06 ppmv∕379 ppmvÞ

ΔCTðbiogeochem:Þ ¼ 6.6E14 kg∕5.1E10ha ∼ 13 t C ha−1

Two of the experiments (Perennials-2m and Annuals) were repeated,identical to previously discussed simulations except for the use of an alter-nate SW radiation scheme [Rapid Radiation Transfer Model for Global appli-cations(RRTMG)-SW scheme; 16, 39]. Usage of this scheme permitted TOA SWRF as an output parameter (the initial scheme did not) and was used in thecarbon savings calculations. Differences between Perennials-2m and Annualsutilizing this radiation scheme were similar to those outlined previously(Fig. S7). For example, near-surface growing season temperature differences(Perennials-2m minus Annuals) over perturbed pixels using the Dudhia SWscheme (Table 1) of −0.84 °C were comparable to those using the RRTMG-SWscheme (difference of −0.72 °C). The similarity in differences despite variabil-ity in choice of radiation scheme on climate variables (e.g., soil moisture,near-surface temperature), and associated physical mechanisms, increasesconfidence in the robustness of our conclusions.

Simulated Rainfall Recycling. Differences (Perennials-2m minus Annuals) inmonthly precipitation are illustrative of enhanced local recycling of rainfall(Fig. S3). Whereas simulated summer (or near-summer) precipitation shouldbe expected to display a greater sensitivity to the choice of convectivescheme, the tail end of the growing season (i.e., October) should not, as totalprecipitation is less reliant on subgrid convective processes and less sensitiveto the choice of scheme, as propagating storm systems are innately largerscale.

The majority of the region where we alter land surface properties israinfed. We do not artificially add irrigation for some experiments whileremoving it from others and therefore none of our conclusions are basedon projected changes in irrigation.

Biogeochemical and Biogeophysical Forcing Comparison. To project the degreeof warming due to increased GHGs that may be offset by the conversion ofannual to perennial bioenergy crops we obtained Lawrence LivermoreNational Laboratory (LLNL)-Reclamation-Santa Clara University downscaledclimate projections data derived from the World Climate Research Pro-gramme’s (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3)multimodel dataset, stored and served at the LLNL Green Data Oasis. Multi-model projections corresponding to high (A2), medium (A1b), and low (B1)emission trajectories from 36, 39, and 37 General Circulation Models (for A2,A1b, and B1 scenarios, respectively) of mean April through October tempera-ture change for 2020–2039, 2040–2059, 2060–2079, and 2080–2099 wereobtained, and the degree of warming relative to 1980–2000 was calculatedfor each 2-yr subset. Special Report on Emission Scenarios WCRP CMIP3 dataand WRF output data were regridded to identical resolutions to enabledirect comparison of forcing agents for the simulation period (April throughOctober). We compared LUC cooling to projected CMIP3 warming across in-creasing spatial scales starting with the locally greatest effect (centered onlat: 40.0 °N, longitude: 87.75 °W) and proceeded to increasingly larger scales.

ACKNOWLEDGMENTS. We thank two anonymous reviewers for their thought-ful critique of the manuscript. This work was made possible through supportfrom the Stanford University Global Climate and Energy Project and NationalScience Foundation Grant 0934592 at Arizona State University.

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