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Biophysical forcings of land-use changes from potential forestry activities in North America

October 2014
Ecological Monographs 84(2):329-353

DOI: 10.1890/12-1705.1

Authors:

The Ohio State University

Land-use changes through forestry and other activities alter not just carbon storage, but biophysical properties, including albedo, surface roughness, and canopy conductance, all of which affect temperature. This study assessed the biophysical forcings and climatic impact of vegetation replacement across North America by comparing satellite-derived albedo, land surface temperature (LST), and evapotranspiration (ET) between adjacent vegetation types. We calculated radiative forcings (RF) for potential local conversions from croplands (CRO) or grasslands (GRA) to evergreen needleleaf (ENF) or deciduous broadleaf (DBF) forests. Forests generally had lower albedo than adjacent grasslands or croplands, particularly in locations with snow. They also had warmer nighttime LST, cooler daily and daytime LST in warm seasons, and smaller daily LST ranges. Darker forest surfaces induced positive RFs, dampening the cooling effect of carbon sequestration. The mean (6SD) albedo-induced RFs for each land conversion were equivalent to carbon emissions of 2.2 6 0.7 kg C/m 2 (GRA-ENF), 2.0 6 0.6 kg C/m 2 (CRO-ENF), 0.90 6 0.50 kg C/m 2 (CRO-DBF), and 0.73 6 0.22 kg C/m 2 (GRA-DBF), suggesting that, given the same carbon sequestration potential, a larger net cooling (integrated globally) is expected for planting DBF than ENF. Both changes in LST and ET induce longwave RFs that sometimes had values comparable to or even larger than albedo-induced shortwave RFs. Sensible heat flux, on average, increased when replacing CRO with ENF, but decreased for conversions to DBF, suggesting that DBF tends to cool near-surface air locally while ENF tends to warm it. This local temperature effect showed some seasonal variation and spatial dependence, but did not differ strongly by latitude. Overall, our results show that a carbon-centric accounting is, in many cases, insufficient for climate mitigation policies. Where afforestation or reforestation occurs, however, deciduous broadleaf trees are likely to produce stronger cooling benefits than evergreen needleleaf trees provide.

Besides carbon, biophysics matters in assessing climate benefits of forestry projects: Forests and non-forest vegetation have contrasting biophysical properties, resulting in differing land–air interactions. Compared to non-forest lands, forests typically (1) have lower albedo and absorb more solar energy; (2) often have higher surface roughness, facilitating the exchange of water and heat between surfaces and the air; (3) are often cooler, emitting less thermal radiation; and (4) have higher leaf areas and deeper roots, likely increasing evapotranspiration. Larger latent heat fluxes and smaller sensible heat fluxes over forests can decrease the lifting condensation level (cloud base height), thus lowering cloud height and increasing the chance for cloud formation. The relative magnitudes of surface energy fluxes for the four vegetation types studied here (grasslands [GRA], deciduous forests [DBF], croplands [CRO], and evergreen forests [ENF], as depicted clockwise from the top in the graph) are indicated by the sizes of arrows. These biophysical differences highlight that reforestation and afforestation impact climate via biophysical pathways in addition to carbon sequestration.

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Ecological Monographs, 84(2), 2014, pp. 329–353

Ó2014 by the Ecological Society of America

Biophysical forcings of land-use changes from potential forestry

activities in North America

KAIGUANG ZHAO

1,2,4

AND ROBERT B. JACKSON

1,3

Division of Earth and Ocean Sciences and Center on Global Change, Nicholas School of the Environment, Duke University, Durham,

North Carolina 27708 USA

School of Environment and Natural Resources, Ohio Agricultural Research and Development Center, The Ohio State University,

Wooster, Ohio 44691 USA

School of Earth Sciences, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford,

California 94305 USA

Abstract. Land-use changes through forestry and other activities alter not just carbon

storage, but biophysical properties, including albedo, surface roughness, and canopy

conductance, all of which affect temperature. This study assessed the biophysical forcings

and climatic impact of vegetation replacement across North America by comparing satellite-

derived albedo, land surface temperature (LST), and evapotranspiration (ET) between

adjacent vegetation types. We calculated radiative forcings (RF) for potential local

conversions from croplands (CRO) or grasslands (GRA) to evergreen needleleaf (ENF) or

deciduous broadleaf (DBF) forests. Forests generally had lower albedo than adjacent

grasslands or croplands, particularly in locations with snow. They also had warmer nighttime

LST, cooler daily and daytime LST in warm seasons, and smaller daily LST ranges. Darker

forest surfaces induced positive RFs, dampening the cooling effect of carbon sequestration.

The mean (6SD) albedo-induced RFs for each land conversion were equivalent to carbon

emissions of 2.2 60.7 kg C/m

(GRA–ENF), 2.0 60.6 kg C/m

(CRO–ENF), 0.90 60.50 kg

C/m

(CRO–DBF), and 0.73 60.22 kg C/m

(GRA–DBF), suggesting that, given the same

carbon sequestration potential, a larger net cooling (integrated globally) is expected for

planting DBF than ENF. Both changes in LST and ET induce longwave RFs that sometimes

had values comparable to or even larger than albedo-induced shortwave RFs. Sensible heat

ﬂux, on average, increased when replacing CRO with ENF, but decreased for conversions to

DBF, suggesting that DBF tends to cool near-surface air locally while ENF tends to warm it.

This local temperature effect showed some seasonal variation and spatial dependence, but did

not differ strongly by latitude. Overall, our results show that a carbon-centric accounting is, in

many cases, insufﬁcient for climate mitigation policies. Where afforestation or reforestation

occurs, however, deciduous broadleaf trees are likely to produce stronger cooling beneﬁts than

evergreen needleleaf trees provide.

Key words: albedo effect; biophysical forcing; carbon accounting; carbon sequestration; climate

regulation; ecosystem services; forestry; land-use change; radiative forcing.

INTRODUCTION

Accompanying the need to combat global warming is

an increasing interest in how ecosystems regulate climate

(e.g., Heimann and Reichstein 2008). Along with

traditional goods and services, such as biodiversity

conservation and watershed protection, the climatic

beneﬁts of ecosystems are generally assessed from a

carbon-centric perspective (McAlpine et al. 2010).

Alterations to ecosystems can indeed change carbon

sinks or sources that dampen or accelerate global

warming. Since 1850, for instance, land-use change has

released ;150 billion metric tons of carbon, accounting

for 35%of anthropogenic CO

emissions (Houghton

2003). Safeguarding and enlarging terrestrial carbon

pools are thus key strategies to mitigate climate change,

typically through forestry practices such as reforesta-

tion, afforestation, avoided deforestation, and forest

management (e.g., Jackson and Baker 2010, McKinley

et al. 2011).

Land alterations by forestry and other activities

modify not only carbon stocks, but also energy

partitioning, water cycling, and atmospheric composi-

tion (Fig. 1). These changes occur through altered

biophysical characteristics, including albedo, surface

roughness, sensible and latent heat ﬂuxes, canopy

conductance, soil moisture, surface temperature, emis-

sivity, leaf area, and rooting depth (Kueppers et al.

2007, Anderson et al. 2011, Jayawickreme et al. 2011).

For instance, forested surfaces often have lower albedo

and more uneven canopies compared to other vegeta-

tion, absorbing more sunlight and facilitating the mixing

Manuscript received 2 October 2012; revised 6 August 2013;

accepted 11 September 2013; ﬁnal version received 19 October

2013. Corresponding Editor: A. O. Finley.

E-mail: zhao.1423@osu.edu

329

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of air (Betts 2000). Locally, these climate forcings affect

temperature more than the CO

reduction does. At

regional and global scales, biophysical forcings can

either amplify or diminish the cooling beneﬁt of carbon

uptake. Because of such interactions, researchers have

recently recommended that climate policies for crediting

forestry projects should go beyond a carbon-centric

accounting to include biophysical effects (Jackson et al.

2008, Montenegro et al. 2009, McAlpine et al. 2010).

Although considerations of both biophysical and

biogeochemical mechanisms are undoubtedly important

in formulating policies to optimize climate beneﬁts of

forestry or land management activities, the science for

such integration is still evolving (e.g., West et al. 2011).

One unresolved issue is how best to capture the

spectrum of climate forcings for biophysical and

biogeochemical processes that tend to occur at vastly

different spatial and temporal scales (Bonan 2008). For

instance, the climate effects of carbon sequestration are

global and long lasting and are quantiﬁed primarily in

terms of radiative inﬂuences on global mean tempera-

tures. In contrast, biophysical impacts are dominantly

local or regional; they occur with altered lands and

diminish if the lands revert. Biophysical changes also

exert both radiative and non-radiative inﬂuences,

modifying air temperatures and hydrologic cycles. These

disparities in mechanisms raise issues as to how to

combine biophysical and biogeochemical regulations

into policy measures for climate change mitigation.

Comparisons of carbon sequestration and biophysics

for climate regulation by ecosystems are typically

assessed in terms of radiative forcing (RF), deﬁned as

the perturbation to the radiation balance of the climate

system (Betts 2000, Rotenberg and Yakir 2010). With

tree planting, reduced albedo and carbon uptake

typically cause a positive shortwave (warming) and a

negative longwave (cooling) RF, respectively. The

opposite is often true when clearing forests for other

land uses. Agricultural land use during the past 300

years is estimated to have led to a global RF of 0.15 W/

and a cooling of 0.098C due to biophysical effects

(Matthews et al. 2003). Additionally, climatic conse-

quences of natural disturbances to forests, such as ﬁre,

insect infestation, windfall, and drought, have been

examined with RF or its equivalent carbon metrics,

incorporating the effects of both carbon release and

albedo change (O’Halloran et al. 2011).

Vegetation replacement also alters the exchange of

energy and matter between ecosystems and the atmo-

sphere, particularly through the re-partitioning of

sensible and latent heat (Juang et al. 2007). These non-

radiative forcings modify the boundary layer and

inﬂuence local climate (West et al. 2011). Increased

sensible heat ﬂux warms the near-surface air and the

mixed layer directly. Increased evapotranspiration (ET)

of trees not only moistens the air, but can also offset the

extra solar absorption incurred by lower albedo (No-

setto et al. 2011), thus tending to cool the surface locally

and sometimes the near-surface air. This evaporative

cooling varies with season and place, being most

pronounced in tropical forests (Anderson et al. 2011).

Moreover, alterations in ET mediate land-air interac-

tions through potential changes in lapse rate, longwave

RF, and cloudiness.

Observations and earth-system models are both

powerful tools for examining the climatic footprint of

land-use change (Bonan 2008). For instance, climate

simulations indicated a global cooling effect from

replacing short vegetation with forest, attributable

mainly to the enhanced ET that fostered low-level

cloudiness and attenuated sunlight (Ban-Weiss et al.

2011). Paired model simulations have also suggested

that deforestation should be avoided in the tropics and

reforestation discouraged at high latitudes to harness

climatic beneﬁts of trees, although the latter result is

controversial (Randerson et al. 2006, Bala et al. 2007 ).

Meanwhile, differences in climate model structure and

parameterization sometimes generate conﬂicting results

(Jackson et al. 2005, Diffenbaugh 2009). In particular,

uncertainties exist as to whether the biophysical effects

of reforestation in temperate zones will strengthen or

weaken the cooling from carbon sequestration (Betts

2000, Jackson et al. 2008, Montenegro et al. 2009).

Despite growing recognition of the biophysical

regulation of climate by ecosystems, quantifying their

effects is challenging for academic researchers, let alone

for resource managers and policy makers (McAlpine et

al. 2010, West et al. 2011). Existing efforts to quantify

biophysical regulations have typically considered albedo

but neglected other important biophysical forcings. For

instance, altered ET and land surface temperature (LST)

induce longwave RFs that can sometimes be comparable

to the albedo-induced shortwave RF (Swann et al.

2010). Improved assessments are needed for biophysical

forcings of land-use changes and their policy implica-

tions.

We combined remotely sensed observations and

climate model outputs to examine the biophysical

forcings and climatic impacts of potential land-use/

land-cover changes across North America. We empha-

sized transitions from non-forest to forest vegetation

relevant to climate mitigation policies, speciﬁcally

cropland (CRO) and grassland (GRA) conversions to

evergreen needleleaf forest (ENF) and deciduous broad-

leaf forest (DBF). We examined surface variables

important for temperature and energy balance, includ-

ing albedo, LST, and ET (Table 1). We evaluated the

magnitudes and directions of differences in these

biophysical variables between adjacent sites of contrast-

ing vegetation across North America between 208–608

N, using paired comparisons to assess the changes in

surface biophysics associated with land conversions.

Observed differences were then used to (1) calculate

shortwave and longwave RFs or equivalent carbon

emissions, (2) infer the redistribution of surface energy

for conversions from GRA or CRO to ENL or DBF,

KAIGUANG ZHAO AND ROBERT B. JACKSON330 Ecological Monographs

Vol. 84, No. 2

and (3) assess potential impacts on near-surface

temperature. A primary goal of our work is to foster a

more complete accounting of climate regulation for

ecosystem and land-use management and policy mitiga-

tion.

METHODS

Data

This study focused on the vegetated lands of North

America between 208–608N including the conterminous

USA (Appendix: Fig. A1). A range of surface and

atmospheric variables derived from remote-sensing obser-

vationswas compiled from threegeoportals: the Moderate-

Resolution Imaging Spectroradiometer (MODIS; data

available online)

land surface products, the Clouds and the

Earth’s Radiant Energy System (CERES; data available

online)

data archive, and the Advanced Microwave

Scanning Radiometer-Earth Observing System (AMSE-

E; data available online).

The MODIS data we used

comprised yearly land cover (MOD12Q1; Friedl et al.

2002), eight-day 500-m bidirectional reﬂectance distribu-

tion function (BRDF)/Albedo (MCD43A1 and A2

Collection 5; Schaaf et al. 2002), eight-day 1-km daytime

and nighttime LSTs (MOD11A2 Collection 5; Wan et al.

2004), and eight-day 1-km ET (MOD16A2; Mu et al.

2011). The MODIS land-cover data included both 500-m

Collection-5 maps for years 2000–2008 and a 1-km

Collection-4 map for the year 2001 (Friedl et al. 2002),

with the latter being used as a baseline vegetation map for

FIG. 1. Besides carbon, biophysics matters in assessing climate beneﬁts of forestry projects: Forests and non-forest vegetation

have contrasting biophysical properties, resulting in differing land–air interactions. Compared to non-forest lands, forests typically

(1) have lower albedo and absorb more solar energy; (2) often have higher surface roughness, facilitating the exchange of water and

heat between surfaces and the air; (3) are often cooler, emitting less thermal radiation; and (4) have higher leaf areas and deeper

roots, likely increasing evapotranspiration. Larger latent heat ﬂuxes and smaller sensible heat ﬂuxes over forests can decrease the

lifting condensation level (cloud base height), thus lowering cloud height and increasing the chance for cloud formation. The

relative magnitudes of surface energy ﬂuxes for the four vegetation types studied here (grasslands [GRA], deciduous forests [DBF],

croplands [CRO], and evergreen forests [ENF], as depicted clockwise from the top in the graph) are indicated by the sizes of arrows.

These biophysical differences highlight that reforestation and afforestation impact climate via biophysical pathways in addition to

carbon sequestration.

https://lpdaac.usgs.gov/data_access

https://eosweb.larc.nasa.gov

http://nsidc.org/data/ae_dysno

May 2014 331LAND USE, BIOPHYSICS, AND FORESTRY

TABLE 1. Major concepts and terms pertinent to the quantiﬁcation of biophysical forcings of land-use change from forestry

activities, with common units supplied whenever applicable.

Concepts and terms Common units Explanation

Climate regulation Ecosystems offer regulating services by inﬂuencing climate via both

biogeochemical and biophysical pathways.

Climate forcing W/m

An energy imbalance imposed on the climate system either naturally or by

human activities, such as C emissions arising from altered ecosystem

structure.

Radiative forcing W/m

The change in radiative energy ﬂux resulting from climate forcing agents

such as a CO

increase or albedo change. Positive radiative forcings,

either longwave or shortwave, increase global mean temperature.

Climate sensitivity K/[W/m

] A measure of how responsive the climate system is to the radiative forcing

of a forcing agent. It is often quantiﬁed as the increase in global mean

air temperature given a unit of radiative forcing

Climate efﬁcacy unitless The global temperature response per unit radiative forcing of an agent

relative to that of CO

. It is deﬁned as the ratio of climate sensitivity

between a forcing agent and CO

change.

Non-radiative forcing W/m

An energy imbalance that does not directly involve radiation, such as the

increase in evapotranspiration due to irrigation.

Biophysical forcing W/m

The imbalance of energy ﬂuxes resulting directly or indirectly from changes

in biophysics, including albedo, emissivity, sensible and latent heat, and

surface roughness. These biophysical changes can be caused by both

natural and anthropogenic processes such as land conversion, ecosystem

disturbances, and ecosystem management.

Albedo unitless Reﬂectivity of sunlight by land surfaces, as contributed from both soils and

vegetation. Conversions of croplands or grasslands to forests often

reduce surface albedo and induce positive shortwave radiative forcings,

diminishing the cooling beneﬁt of forest carbon sequestration.

Emissivity unitless The relative ability of land surfaces to emit thermal radiation. Forests often

have slightly higher emissivity than do croplands or grasslands. The

biophysical forcing of altered emissivity from land-use change is typically

much smaller than that of altered albedo.

Sensible heat ﬂux W/m

The ﬂux of heat between land and the air via conduction and convection.

Sensible heat directly warms the air. Altered sensible heat ﬂux due to

vegetation shift is a direct warming or cooling effect on local climate.

Latent heat ﬂux W/m

The ﬂux of heat between land and the air via evapotranspiration or

condensation. Latent heat doesn’t directly warm the air. Altered latent

heat ﬂux due to vegetation shift is a nonlocal biophysical forcing, which

modiﬁes surface energy balance, the hydrological cycle, atmospheric

water vapor, and cloud formation.

Land surface temperature K or 8C The temperature of the composites of vegetation and soils over vegetated

surfaces, which can be deﬁned either radiometrically, thermodynamically,

or aerodynamically. Changes in vegetation structure affect surface energy

partitioning and thus strongly affect land surface temperature.

Near-surface air temperature K or 8C The temperature of the air two meters above a vegetation-speciﬁc

displacement height for a vegetated surface. This is the temperature

metric used here to directly evaluate the temperature effect of land-use

change.

C sequestration potential kg C/m

kg Cm

2

yr

1

The amount of carbon potentially drawn from the air for a given forestry

project due to biological carbon sequestration. Its exact value is difﬁcult

to estimate and in this study is considered simply as the difference in

steady-state total carbon stock between the forest and the replaced

vegetation.

Carbon-emission equivalent kg C/m

kg Cm

2

yr

1

The amount of hypothetical carbon emission that can cause the same

change in global mean temperature as the temperature change due to

biophysical forcings. It helps to quantify the temperature effects of

biophysical forcings in terms of carbon. Negative carbon emission

equivalent represents a carbon sink, suggesting a global cooling effect

from the biophysical forcings.

Net carbon drawdown kg C/m

kg Cm

2

yr

1

The difference between C sequestration potential and C-emission equivalent

as a C metric to assess the combined effect of biological carbon

sequestration and biophysical forcings on temperature for forestry

projects. It can serve as an index to quantify the climate regulation value

of ecosystems.

Greenhouse gas value kg C/m

kg Cm

2

yr

1

An integrated quantiﬁcation of climate regulation services in terms of C

equivalents. The integration typically encompasses diverse factors,

including biophysical forcings, ﬂuxes of greenhouse gases, the carbon

footprints of operations and energy costs, and carbon leakage from

disturbances. Conversions of individual factors to carbon emissions often

occur through the concept of global warming potential for non-CO

greenhouse gases, as used in life-cycle analysis and other comparative

frameworks.

KAIGUANG ZHAO AND ROBERT B. JACKSON332 Ecological Monographs

Vol. 84, No. 2

generating the MODIS ET time series products (Mu et al.

2011). The CERES data included monthly averaged

products of top of atmosphere (TOA)/surface longwave

and shortwave ﬂuxes and monthly gridded cloud products,

with a spatialresolution of 18318(Wielicki et al. 1998). T he

advanced microwave scanning radiometer-EOS (AMSR-

E) data included the ﬁve-day Level 3 global snow water

equivalent (SWE) at 25-km resolution (Kelly et al. 2003).

For each product, we analyzed all available data for

the most recent version as of December 2011. All

products except MODIS ET were retrieved directly from

satellite radiometric signals using dedicated algorithms;

the MODIS ET product was derived using an empirical

Penman-Monteith model by Mu et al. (2007) from

meteorological and remote-sensing observed inputs such

as air temperature and leaf area index. The theoretical

basis, retrieval algorithms, and data validation for each

product are available from the citations in the preceding

paragraph. In particular, MODIS albedo and LST

products have proven useful for characterizing biophys-

ical variables of contrasting land surfaces at pixel scales

(e.g., Montenegro et al. 2009). We also used two

ancillary datasets: the 90-m digital evaluation model

(DEM) from the Shuttle Radar Topography Mission

(SRTM; data available online)

and the ratio of diffuse/

direct downward surface shortwave ﬂuxes calculated

from a one-year model simulation using the coupled

Weather Research Forecast-Community land model

(WRF-CLM; Lu and Kueppers 2012).

Comparisons of surface biophysics between contrasting

vegetation

MODIS products were used primarily to evaluate

differences in surface albedo, LST, and ET between

contrasting vegetation types (Table 1). Our evaluations

emphasized paired adjacent sites (i.e., pixels) for two

contrasting vegetation types. These adjacent sites were

most likely to be found in ecotones and disturbed lands

where the potential of future land-cover changes due to

either natural processes or human activities is high. By

using adjacent sites, we attempted to isolate vegetation

controls on surface biophysics to the greatest extent

possible and to minimize the inﬂuences of confounding

factors such as topography, solar angle, and rainfall.

Such comparisons are particularly relevant for land-use/

land-cover change because forestry conversions occur

mostly at small scales. Differences between adjacent

pixels/sites should also mimic future changes in surface

biophysical characteristics with vegetation replacement.

Because we were interested mainly in comparing

typical differences in biophysical variables for vegetation

types, MODIS products such as albedo, ET, and LST

were averaged across years from 2001 to 2011 for each

of the 46 eight-day observation periods to smooth out

interannual variability. In this averaging, the 11 MODIS

quality-control ﬂags associated with years 2001–2011 for

each pixel and eight-day observation period were

checked to select the years that had the best data

quality; if the number of years with the best quality was

less than ﬁve out of 11, we gradually incorporated the

years with the next best quality; however, these years

were assigned a weighting factor only half that of the

higher quality years. This weighted-averaging procedure

helped to reduce random errors and any quality-control

issues in the MODIS products.

To determine the spatial distributions of vegetation,

we derived a land-cover map at 500-m resolution by

synthesizing the nine yearly 500-m MODIS land-cover

layers for 2001–2008. A pixel was assigned a particular

vegetation class only if the pixel was classiﬁed as this

class with at least a probability of 0.50 for more than ﬁve

out of nine years; otherwise, the pixel was discarded

from our analysis. This ﬁltering helped to suppress the

confounding effects of classiﬁcation errors and potential

land-cover changes that occurred between 2001 and

2008. Additionally, the resultant 500-m base map was

aggregated to 1-km resolution, with a 1-km pixel labeled

as a vegetation class only if its four 500-m component

pixels all belonged to the same class; otherwise, the pixel

was discarded from our analysis. The 500-m and 1-km

land-cover maps each contain a total of 17 land-cover

types, but we considered only four vegetation types:

CRO, GRA, ENF, and DBF. The two synthesized maps

helped to derive vegetation-speciﬁc albedo and LST at

500-m and 1-km resolutions, respectively. However,

vegetation-speciﬁc ET was derived based on the third

map, the 2001 1-km MODIS Collection-4 land cover,

because it is the reference map for generating MODIS

ET (Mu et al. 2011).

The adjacent sites chosen to compare biophysical

variables between contrasting vegetation types were

determined based on the three land-cover maps using a

customized local searching-window procedure. To sup-

press topographic inﬂuences, we considered only the

sites with slopes of ,158. Using comparisons of albedo

between DBF and CRO to illustrate this procedure, for

each DBF pixel, all the CRO pixels within a 15-km

radius of it were identiﬁed and a DEM ﬁlter was applied

to select only those CRO pixels that had elevation

differences ,10 m from the reference DBF pixel. The

average albedo over all the ﬁnal CRO pixels was then

computed and compared against that for the reference

DBF pixel. This local-scale comparison could also be

performed using CRO pixels as the reference; our results

showed that both potential choices of reference class

gave essentially identical results. Additionally, this

circular window of 30 km in diameter was moved across

the study area to identify all the possible pairs of

adjacent sites of contrasting vegetation.

Of the MODIS surface biophysical variables studied

here, albedo depends not only on vegetation and soil

properties, but also on solar angle and atmospheric

conditions. To isolate the dependence of albedo on

surface characteristics, we considered MODIS broad-

http:srtm.usgs.gov/index.php

May 2014 333LAND USE, BIOPHYSICS, AND FORESTRY

band white-sky albedo in our comparisons of paired

sites. White-sky albedo is also called diffuse albedo,

representing bi-hemispherical reﬂectance under isotropic

skylight illumination; therefore, it is independent of sky

conditions (Schaaf et al. 2002). ET is often constrained

by water availability. Currently, the MODIS ET

algorithm did not explicitly differentiate between irri-

gated and nonirrigated lands (Mu et al. 2011). However,

the algorithm has synthesized information from some

input variables responsive to soil water content;

therefore, it indirectly captured the effect of irrigation

such that irrigated lands in the MODIS ET often have

high ET. To examine the spatial patterns of differences

in albedo or ET, we applied the k-means algorithm to

group the paired sites associated with each pair of

vegetation types into three spatial clusters based on the

similarity of seasonal variations in albedo or ET. More

importantly, differences in biophysical variables be-

tween contrasting vegetation at the paired sites were

used to evaluate biophysical forcings for potential

vegetation shifts. These included shortwave and long-

wave radiative forcings and changes in surface sensible

and latent heat ﬂuxes, as detailed in the next four

subsections.

Shortwave radiative forcing (SF) induced

by albedo change

Altered surface albedo from land-use change induces

shortwave RF, often evaluated at three levels: surface,

atmosphere, and top of atmosphere (TOA). Speciﬁcally,

RF at the surface (SF

sfc

) affects the surface energy

balance and partitioning. RF at the TOA (SF

toa

) is the

quantity related to the change in global mean temper-

ature through climate sensitivity parameters. Atmo-

spheric RF (SF

atm

) is the difference between TOA and

surface RFs (i.e., SF

atm

¼SF

toa

SF

sfc

); it represents the

radiative imbalance of the atmospheric column and

provides information on expected changes in precipita-

tion and vertical mixing. Considerations of the vertical

structure of RF have been recently requested for future

climate assessments by the U.S. National Research

Council (2005), although TOA RF still remains the most

commonly used metric for quantifying and ranking the

climatic impacts of different forcing agents. Calculations

of TOA RF (SF

toa

) require translating surface albedo

sfc

), as measured by MODIS, to planetary albedo at

the TOA (a

toa

), generally with a

sfc

contributing to only a

small fraction of a

toa

. This translation requires vertical

proﬁles of atmospheric optical properties as determined

by atmospheric compositions such as aerosol and cloud

cover.

The single-layer radiative transfer model of Liou

(2002) offers a simple yet effective scheme relating

surface a

sfc

to TOA albedo, a

toa

. This model uses two

column-integrated optical parameters, namely, single-

pass atmospheric reﬂectance Rand transmittance T.We

extended this model to further discriminate clear and

cloudy skies within a grid as follows:

atoaðasfc Þ¼F"

toa

¼cRcld þð1cÞRclr þcasfc

cld

1asfcRcld

þð1cÞasf c

clr

1asfcRclr

ð1Þ

where Sand F"

toa are the incident and reflected solar

fluxes at the TOA, respectively; cis the fraction of cloud

cover; R

cld

and T

cld

are the single-pass atmospheric

reflectance and transmittance for the cloudy sky,

respectively; and R

clr

and T

clr

are for the clear sky.

The sum of the first two terms cR

cld

þ(1 c)R

clr

treated as the atmospheric contribution to TOA albedo

toa

, whereas the sum of the last two terms is the surface

contribution to a

toa

. Accordingly, the relative fraction of

surface albedo contributed to TOA albedo is calculated

casfc

cld

1asfcRcld

þð1cÞasfc

clr

1asfcRclr



.asfc:

Further, downward and upward shortwave fluxes

(sunlight) at the surface are given by

sfcðasfc Þ¼S3cTcld

1asfcRcld

þð1cÞTclr

1asfcRclr



sfcðasfc Þ¼asfcF#

sfcðasfc Þ:ð2Þ

The dependences of TOA and surface fluxes on albedo

sfc

have been made explicit on the left-hand side of Eqs.

1 and 2.

Following a method similar to Donohoe and Battisti

(2011), we estimated the atmospheric reﬂectance Rand

transmittance Tof Eqs. 1 and 2 for both the clear and

cloudy sky portions monthly for each 18318grid, using

the CERES daytime cloud cover data and the CERES

cloudy-sky and clear-sky TOA/surface shortwave ﬂuxes.

Our estimated atmospheric reﬂectance Rand transmit-

tance Tcharacterize the actual atmospheric optical

properties and allow us to compute surface, TOA, and

atmospheric shortwave RFs as follows:

SFtoa ¼S3hatoaðasfc;2Þatoaðasfc;1Þi

SFsfc ¼F#

sfcðasfc;2Þ3ð1asfc;2ÞF#

sfcðasfc;1Þ3ð1asfc;1Þ

SFatm ¼SFtoa SFsfc:ð3Þ

Here, the RFs are driven by a change in surface albedo

from a

sfc,1

to a

sfc,2

while assuming that the atmospheric

optical properties, including c,R,andT,remain

unaffected. A positive shortwave RF in Eq. 3 indicates

that the system absorbs extra solar radiation after land

conversion. Eqs. 1–3 are applicable for computing

instantaneous or short-time RFs that can then be

integrated to estimate long-term RFs such as annual

RFs.

KAIGUANG ZHAO AND ROBERT B. JACKSON334 Ecological Monographs

Vol. 84, No. 2

Using Eqs. 1–3, we calculated monthly RFs for four

scenarios of non-forest to forest conversions (GRA or

CRO to ENF or DBF) at a 18318resolution,

compatible with the spatial and temporal resolutions

of Rand Tderived from the CERES data. In the

calculation, we considered actual surface albedo for a

sfc

which was estimated as the average of MODIS black-

sky (direct) and white-sky (diffuse) albedos weighted by

direct and diffuse downward ﬂuxes from the regional

climate simulation of WRF-CLM. Also importantly,

albedos of adjacent sites as obtained from local

comparisons of paired vegetation types were used in

Eq. 3 for a

sfc,1

and a

sfc,2

to mimic realistic local

vegetation shifts. The albedo values of all the paired

sites within each 18318grid were averaged, and the

resulting mean albedos were then applied to Eq. 3 for

estimating the grid-level RF. Only those grids containing

at least three pairs of adjacent sites were considered.

Carbon emission equivalent to albedo change

Albedo-induced shortwave RF at the TOA (SF

toa

;in

W/m

) is often converted to carbon emission equivalent

(dC

alb

; in kg/m

), a C density that can be compared

against the C sequestration potential dC

seq

of land

management to contrast biogeochemical and biophysi-

cal effects (Fig. 2). To date, the standard conversion

method has generally overlooked the fact that the RFs

from altered albedo and CO

manifest different vertical

structures, so that the same amount of RFs from these

two forcing agents leads to different changes in global

mean temperature (Betts 2000). Typically, these differ-

ing responses are characterized by climate sensitivity (k),

deﬁned as the change in global mean temperature per

unit RF for a forcing agent and taken here as k

alb

¼0.52

K/(W/m

) for albedo and k

co2

¼1.0 K/(W/m

) for CO

(Davin et al. 2007). Accounting for this disparity may

alter the conclusions of some earlier studies that

assumed the same climate sensitivity for the two types

of RFs in evaluating temperature beneﬁts of reforesta-

tion.

We revised the standard method of converting RF

toa

to carbon-emission equivalent dC

alb

by differen-

tiating the two climate sensitivity parameters k

alb

and

co2

. For a RF of SF

toa

resulting from albedo change

FIG. 2. Schematic of the major biophysical forcings we examined: Land-use change by forestry alters the surface biophysics to

induce both radiative and non-radiative forcings (left) that modify the cooling effect of forest carbon uptake (right). Radiative

forcings, either shortwave (SF) or longwave (LF), perturb the radiation balance at the surface (sfc) and the top of the atmosphere

(toa), or within the atmosphere column (atm). Non-radiative biophysical forcings exert strong controls on the redistribution of

surface energy. In particular, enhanced evapotranspiration (ET) from forests lowers land surface temperature (LST) while a

reduced input of sensible heat to the air tends to cool the near-surface air locally. The relative magnitudes of the competing effects

of reduced albedo and carbon storage associated with reforestation and afforestation is often gauged by a metric called net carbon

drawdown. In the schematic, gray-ﬁlled boxes denote components whose inﬂuences are not locally conﬁned to the converted land.

The equations we used are also labeled; k

lcc

and k

CO2

denote climate sensitivities for land use and CO

changes, respectively. As an

observation-based study, our analyses do not capture all the feedbacks and interactions between land and the atmosphere.

May 2014 335LAND USE, BIOPHYSICS, AND FORESTRY

over a local area s

lcc

), the total radiative perturba-

tion is SF

toa

s

lcc

, which becomes SF

toa

(s

lcc

) if spread

evenly across the globe with S

¼5.1 310

being the

total surface area of the Earth. Further, this global

shortwave RF is multiplied by the ratio of climate

sensitivity k

alb

co2

to obtain an effective CO

-induced

longwave RF SF

toa

(s

lcc

)k

alb

co2

. The ratio k

alb

co2

is often termed the climate efﬁcacy (Hansen et al. 2005)

and is applied here to ensure that the two types of RFs

yield the same amount of global temperature response

according to their respective climate sensitivities. Then,

the effective global longwave RF, SF

toa

(s

lcc

)k

alb

co2

, is converted to a change in atmospheric CO

concentration dC

co2

(parts per million per volume

[ppmv]) via the efﬁciency parameter of 5.35 W/m

follows:

dCco2 ¼exp

SFtoa 3

kalb

kco2

3slcc

5:35 1

3Cco2 ð4Þ

where C

co2

¼391 ppmv is the reference CO

concentra-

tion. Finally, the CO

change dC

co2

is translated to the

land C emission dC

alb

(kg/ m

) over the area s

lcc

follows:

dCalb ¼j3dCco2

0:50

slcc

’Cco2 3j3SFtoa

0:50 35:35 3SE

¼0:611 3kalb

kco2

3SFtoa ð5Þ

where the constant of 0.50 in the denominator is the

airborne fraction, representing the portion of C emission

that remains in the air after being absorbed by the ocean

and other terrestrial sinks (Betts 2000, Montenegro et al.

2009), and j(2.13 310

kg/ppmv) is the coefﬁcient

converting C from ppmv to kg. The term dC

alb

refers to

the emission of carbon and can be converted to a CO

equivalent by multiplying by 3.67.

The C emission equivalent dC

alb

converted from SF

toa

can be used to adjust the actual C sequestration

potential of forests dC

seq

, which results in a net carbon

drawdown metric (i.e., dC

seqalb

¼dC

seq

dC

alb

) for

quantifying the temperature beneﬁt of forestry projects.

The value of dC

alb

is typically positive for conversions to

forests because of the reduced albedo and increased

shortwave absorption in consequence; it represents the

minimum C uptake that the trees need to sequester

compared to the replaced vegetation for offsetting the

warming of reduced albedo. Therefore, positive net

carbon drawdown (i.e., dC

seqalb

.0) indicates a global

cooling in terms of the integrated effects of albedo

reduction and CO

uptake. Calculating net carbon

drawdown dC

seqalb

requires the actual C sequestration

potential dC

seq

, a quantity often estimated as the

difference in steady-state C stocks before and after land

conversion. However, to our knowledge no reliable data

sets are available for spatially explicit mapping of C

stocks of different vegetated lands at a scale commen-

surate with the satellite data we used, especially for

belowground carbon. Moreover, the deﬁnitions and

calculations of carbon sequestration, dC

seq

, for forestry

projects varied considerably among prior studies,

particularly concerning how the studies treated land-

use history and forest management practices. Therefore,

we did not estimate exact values of dC

seq

or dC

seqalb

but just inferred the potential signs of net carbon

drawdown dC

seqalb

by referring to previous estimates of

approximate C sequestration potential, dC

seq

, of forestry

projects (Betts 2000, Claussen et al. 2001, Gibbard et al.

2005, Montenegro et al. 2009).

Longwave radiative forcing induced by changes in surface

temperature/emissivity and ET

Altered surface biophysical properties modify the

longwave radiative regime through at least two mech-

anisms, one pertinent to LST and emissivity and another

to ET (Fig. 2). Speciﬁcally, surface longwave RF from

the altered LST and emissivity is deﬁned as the change in

net downward longwave radiation at the surface and is

calculated by

LFLST

sfc ¼ðe1r1T4

1e2r2T4

2ÞþL#ðe2e1Þð6Þ

where T

and T

denote LSTs before and after land

conversion, with the corresponding emissivity being e

and e

is the downward sky longwave ﬂux and is

assumed to be unchanged; and r(5.67 310

8

Wm

K

4

) is the Stefan-Boltzmann constant. Of the

two surface terms in Eq. 6, the ﬁrst, attributable mainly

to the temperature change, dominates, whereas the

second, attributable to the emissivity change, is small

and often negligible. A positive value of LFLST

sfc indicates

the suppression of thermal emission after the land

conversion or, expressed differently, less longwave

radiation dissipated from the converted surface attrib-

utable to its lowered LST. This suppression also

decreases both the longwave radiation absorbed by the

atmosphere and that escaping at the TOA. Globally,

only an average of 22 W/m

surface emission out of 390

W/m

(;5.6%) escapes into space (Costa and Shine

2012), a value lower than the previous estimate of 40 W/

(;10%) by Trenberth et al. (2009). This global-scale

partitioning provides ratios to roughly apportion

surface longwave forcing LF

sfc

into longwave RFs at

the TOA and for the atmosphere as follows:

LFLST

toa ¼22=390 3LFLST

sfc

LFLST

atm ¼368=390 3LFLST

sfc ð7Þ

where the ‘‘minus’’ sign in the apportioning for LF

atm

ensures that a positive LF value means that the system

gains more longwave radiation or loses less radiation

compared to the original vegetation. In our analyses of

the four classes of vegetation replacement, LST and

downward longwave ﬂux as used in Eq. 6 are obtained

from the MODIS daytime and nighttime LSTs and the

KAIGUANG ZHAO AND ROBERT B. JACKSON336 Ecological Monographs

Vol. 84, No. 2

CERES longwave ﬂux product, respectively. Surface

emissivity is estimated from albedo using an empirical

relationship developed by Juang et al. (2007), e¼0.99 –

0.16a

sfc

The second type of longwave RF is caused by the

change in atmospheric water vapor concentration due to

altered ET. Different from the albedo-induced RF of

Eq. 3 or the LST-induced longwave RF of Eq. 6, this

type of RF is not necessarily conﬁned to locations where

ET is altered, attributable to the dynamic nature of

atmospheric water vapor transport, which makes its

computation difﬁcult. To provide rough estimates for

ET-induced longwave RF only, we considered an

extreme case assuming that the water vapor is well

mixed at the global scale. This assumption allows us to

calculate this RF, LFET

toa, for a given time tin a way

similar to that of CO

, but with a different forcing

parameter of 20.7 W/m

, as follows:

LFET

toaðtÞ¼20:7log 1 þslcc 3DmH2OðtÞ

MH2O



3SE

slcc

¼0:83 3DmH2OðtÞð8Þ

where MH2O is the total mass of water vapor in the

troposphere, taken simply as 1.27 310

kg; s

lcc

and S

again are the respective areas of the converted land and

the Earth; the value of 20.7 for the forcing parameter is

derived from the simulation results of Collins et al.

(2006); and DmH2O (kg/m

) is the cumulative change in

the atmospheric water vapor at time tcontributed by the

altered ET input per unit area. This water vapor change

is estimated by assuming a mean residence time of 10

days for water vapor:

DmH2O ¼Zt

DETðsÞ3exp ðtsÞ



dsð9Þ

where the change in ET at a given time s(day) for a land

conversion, DET(s) (kg H

Om

2

d

1

; hereafter ex-

pressed as kgm

2

d

1

), is obtained from the MODIS

ET comparison based on adjacent sites. In Eq. 8, S

lcc

is applied to transform the global mean ET-induced

longwave forcing (i.e., 20.7 log(1 þs

lcc

DmH2OðtÞ/MH2O ))

to its effective local value LFET

toa to be comparable to the

local shortwave RF SF

toa

. Our estimate of LFET

toa is

approximate; we have not attempted to translate this

ET-induced longwave forcing into an equivalent C

emission.

Non-radiative forcings and re-partitioning of surface

energy associated with land conversion

Non-radiative forcings associated with changes in

biophysical properties, such as surface roughness,

canopy conductance, canopy structure, and rooting

depth, also affect temperature. We examined the local

impact of these forcings at the surface through the

redistribution of sensible (H) and latent (kET) heat

(Fig. 2). In our monthly or annual analyses, the

downward energy ﬂux into the ground is typically small;

thus, the surface energy balance equation becomes R

HþkET or DR

¼DHþkDET. Here, DR

represents

the change in surface net radiation after land conver-

sion, and it is dominated by LST- and albedo-induced

RFs, DR

¼SF

sfc

þLFLST

sfc , because the contributions of

ET- and CO

-induced longwave RFs to the local energy

balance are close to zero. The change in latent heat ﬂux

kDET was obtained from the adjacent comparisons of

MODIS ET using the heat of vaporization kas the

conversion factor. Then, the change in sensible heat ﬂux

DHwas estimated as

DH¼SFsfc þLFLST

sfc kDET:ð10Þ

Because the warming of near-surface air is fueled

directly by sensible heat, we expect that a land

conversion with increased sensible heat (DH.0),

regardless of the signs of DR

and DET, would tend to

warm the planetary boundary layer locally and that

conversely, a conversion with a negative DHwould tend

to cool the near-surface air locally. For example, an

increase of 1 W/m

in the sensible ﬂux for a heating cycle

of 12 h raises the temperature of a 250-m mixed layer by

as much as 0.14 K, which is estimated approximately

according to the simple formula of West et al. (2011). In

contrast, enhanced latent heat (i.e., kDET .0) does not

immediately warm the near-surface air, even though this

extra energy will be turned into sensible heat somewhere

in the upper air, when condensing, and thus, will modify

the energy balance of the atmosphere overall. This extra

latent heat impacts the local or regional surface energy

balances through indirect pathways, such as the

greenhouse effect of the associated water vapor and

the attenuation of sunlight if the water vapor condenses

into cloud droplets. Such interactions are difﬁcult to

track directly from MODIS data. Rather, we referred

mainly to ET-induced longwave RFs as a metric for

assessing the potential impacts of altered ET on regional

and global temperature.

The direct heating or cooling of the local atmospheric

column above a disturbed land area is determined by

both RF RF

atm

¼SF

atm

þLF

atm

and non-radiative

forcing DH. Unlike the change in sensible ﬂux DH, the

atmospheric radiative forcing RF

atm

affects temperature

throughout the air column, with the maximum inﬂuence

expected to occur in the middle layer, although the exact

altitude depends on the atmospheric opacity at the

respective spectral bands. Therefore, the direct effects of

the atmospheric RFs SF

atm

and LF

atm

on the near-

surface air temperature are negligible compared to the

non-radiative forcing DH.

RESULTS

Albedo

Latitudinal and seasonal variations in albedo resem-

bled changes in snow-water equivalent (Appendix: Figs.

A2 and A3), implying the critical role of snow in

determining surface albedo (Figs. 3 and 4). Lands

May 2014 337LAND USE, BIOPHYSICS, AND FORESTRY

covered with herbaceous plants or short vegetation

normally had brighter surfaces than lands with woody

vegetation throughout the year, especially when snow is

present (Fig. 5). The zonally averaged MODIS white-

sky albedo over 458–608N around day of year 17 in

January, for example, was 0.57 60.05, 0.50 60.07, 0.26

60.04, and 0.20 60.02 (mean 6SE) for CRO, GRA,

DBF, and ENF, respectively. Unlike multilayered forest

canopies, croplands and grasslands, which are covered

with little or no low-lying live or dead biomass in winter,

are more likely to be buried under snow. Foliage losses

in deciduous forests exposed more snow-covered ground

than in evergreen forests, enhancing the observed

wintertime albedo of DBF somewhat compared to

ENF (Figs. 3 and 4).

Paired local comparisons of adjacent vegetation types

show consistent differences in albedo (Fig. 5). Albedos

of CRO and GRA were, on average, greater than those

of ENF and DBF. For instance, albedos of GRA and

ENF differed by 0.21 in January and 0.054 in July when

averaged over paired sites (P,0.001, n¼317 911 paired

sites), representinganincreaseof97%and 51%,

respectively. Croplands generally had albedo values

similar to those of adjacent grasslands, although

croplands on average were slightly brighter (i.e., an

annual mean albedo of 0.216 vs. 0.211 averaged over all

the CRO–GRA sites, P,0.0001, n¼2 037 020). At

most of the paired sites, ENF had lower albedo than

DBF throughout the year (i.e., annual mean albedo of

0.138 vs. 0.166, P,0.0001, n¼103 766 ); thus, of the

two forest types, DBF tended to have albedo closer to

that of CRO or GRA. Another observed pattern was

that the four types of vegetated surfaces in temperate

regions, especially DBF, all showed a gradual increase in

albedo at the beginning of snow-free seasons, and then a

gradual decline before snowfall in autumn (Fig. 4). This

pattern is driven primarily by seasonal foliage dynamics.

The magnitude of albedo differences between adjacent

vegetation types varied with location, as indicated in the

results from the k-means clustering (Fig. 6 ). The

resultant clusters correspond to distinct geographic

regions and were determined mainly by wintertime

albedo, reﬂecting the differing regimes of snow and

vegetation interactions across regions. For example, the

three clusters of the DBF–CRO sites occupied distinct

latitudinal bands in the Eastern USA (Fig. 6 ); thus,

latitude can serve as a proxy to explain the observed

pattern in albedo difference between DBF and CRO. As

another example, the difference in annual albedo

between CRO and GRA was 0.005 (P,0.001) when

averaged over all the paired CRO–GRA sites, compared

to those of 0.0007 (P¼0.012), 0.02 (P,0.001), and

0.036 (P,0.001) when averaged separately over the

three clusters (Fig. 6): This spatial pattern suggests some

differences in wintertime standing biomass of grasses

and crops across regions, which affect albedo dynamics

of snow-covered surfaces and are caused in part by

differences in crop types and management practice. The

pattern may also be inﬂuenced by the spatial variation in

amount and length of snow cover.

Land surface temperature (LST)

The seasonal and latitudinal variations of land surface

temperature (LST) were determined by both the

incoming TOA solar radiation and land surface

characteristics. The percentages of spatiotemporal var-

iations in LST explained by the TOA solar radiation

were 79.4%, 82.8%, 67.2%, and 82.6%for ENF, DBF,

GRA, and CRO, respectively (Appendix: Fig. A4). The

unexplained variations partially underscore the effects of

surface characteristics on LST. In terms of zonally

averaged summertime LST, GRA often appeared to be

the warmest, followed by CRO, ENF, and DBF. For

example, at 358N, GRA surfaces were ;5.0 K warmer

than ENF in July. Our paired local comparisons further

reveal the apparent controls of vegetation on LST (see

Fig. 7). In terms of daily LST, forested surfaces were

FIG. 3. Comparisons of zonally averaged MODIS white-

sky albedo among four land-cover types, including grassland

(GRA), cropland (CRO), evergreen needleleaf forest (ENF),

and deciduous broadleaf forest (DBF), along the latitude range

of 208–608N for a winter (top) and a summer (bottom) around

day of the year 17 and 233, respectively. The zonal averaging

was performed using a 0.18-latitude bin for zones with more

than ﬁve MODIS pixels of a vegetation class. For comparison,

the associated advanced microwave scanning radiometer-EOS

(AMSR-E) snow water equivalent (SWE) is also depicted. Note

the different y-axis scales in January vs. August.

KAIGUANG ZHAO AND ROBERT B. JACKSON338 Ecological Monographs

Vol. 84, No. 2

cooler in warm seasons, but warmer in cold seasons than

adjacent GRA and CRO lands (Fig. 7). Surfaces of

GRA were, on average, slightly warmer than adjacent

CRO, especially in summer (i.e., 301.2 K vs. 299.1 K in

the June–August mean daily LST, P,0.0001, n¼

151 644); therefore, during warm seasons, the warming

of GRA surface relative to adjacent forests was more

pronounced than that of CRO to forests.

The examination of daytime and nighttime LST

suggests a diurnal asymmetry in temperature differences

(Fig. 7). Daytime LST of CRO or GRA compared to

adjacent forests was markedly higher during warm

seasons, but was similar or slightly lower during cold

seasons. For instance, the difference in daytime LST

between CRO and ENF was 6.4 K in July, but 0.046 K

in January averaged across all paired sites (n¼10 105).

In contrast, nighttime LST of CRO or GRA was

consistently lower than adjacent forests throughout the

year (i.e., a mean annual nighttime LST difference of

0.28 K between CRO and ENF; Fig. 7). This diurnal

LST asymmetry indicates that the patterns of differences

in daily LST between non-forest and forest types were

dominated by differences in daytime LST during the

warm seasons and by nighttime LST during the cold

seasons.

Additionally, our paired comparisons between adja-

cent vegetation illustrate clear patterns in daily temper-

ature range (DTR), deﬁned as the difference between

MODIS-observed daytime and nighttime LSTs. For a

given vegetation type, DTR during the warm seasons

was larger than during the cold seasons (Fig. 8). In most

cases, ENF and DBF had smaller DTRs than adjacent

GRA and CRO sites, with an annual DTR of 3.98 K

and 5.23 K for DBF and CRO (n¼23 544 ), respectively

(Fig. 8). The DTR values, however, likely underestimate

the true values because satellites rarely measure extreme

temperatures due to insufﬁcient temporal resolutions.

Evapotranspiration

Our large-scale analyses of zonally averaged ET

clearly revealed the controls of vegetation on surface

water ﬂuxes (Appendix: Fig. A5). Among the four

vegetation types, deciduous forests annually evaporated

the most water, and grasslands generally evaporated the

least water. When spatially averaged over our study

area, the mean annual ET for DBF, ENF, CRO, and

GRA was 1.61, 1.36, 1.42, and 0.98 kgm

2

d

1

respectively, with DBF evaporating an average of 18%

more water than ENF; the mean summertime ET for

DBF, ENF, CRO, and GRA was 2.86, 2.01, 2.19, and

1.32 kgm

2

d

1

, respectively. Although the spatially

averaged annual ET of CRO was larger that of ENF,

their relative magnitudes depended on latitude and

season. For example, over 208–308N, the annual ET of

ENF and CRO averaged 1.84 and 1.78 kgm

2

d

1

, with

ENF evaporating slightly more water than CRO.

Seasonal ET variations are synchronous with growth

seasons (Appendix: Fig. A5), and the overall latitudinal

FIG. 4. Seasonal variations in zonally averaged MODIS albedo for four vegetation types, including grassland (GRA), cropland

(CRO), evergreen needleleaf (ENF), and deciduous broadleaf forest (DBF), at selected latitudes: 258N, 358N, 458N, and 558N.

The zonal averaging was performed over the 0.18-latitude bin centered at each selected latitude. The associated AMSR-E snow

water equivalent (SWE) is also presented. Note that the scales for y-axes vary for better visualization.

May 2014 339LAND USE, BIOPHYSICS, AND FORESTRY

dependence of ET is linked to a shortening of growth

period at high latitude.

Paired comparisons based on adjacent sites further

revealed the effects of vegetation on surface water ﬂuxes

(Table 2, Fig. 9). Overall, the results were consistent

with those from the large-scale comparison, though not

identical. In particular, DBF evaporated more water

than adjacent ENF. For example, averaged over the

paired DBF–ENF sites, ET of DBF and ENF in July

was 3.30 and 2.69 kgm

2

d

1

, representing a 22%

increase for DBF relative to ENF (P,0.001, n¼

29 390). Of the two non-forest types, CRO, on average,

FIG. 5. Comparisons of MODIS albedo between spatially adjacent vegetation types. For each pair of vegetation types (e.g.,

DBF vs. CRO on the lower right), the upper part depicts the mean albedo averaged over all the paired sites; the lower part shows

distributions of albedo difference between the two vegetation types on each observation date throughout a year. The distribution

for each date represents the relative number of sites that have a given value of albedo difference on that date and, for ease of

display, is depicted in a grayscale scheme: Darker color suggests a larger number of sites. The solid curve enveloped in gray denotes

the medians of albedo difference as a function of date; and the two dashed lines conﬁning the envelope indicate the upper and lower

25%percentiles. The sites chosen in each comparison were identiﬁed across North America as locations where pixels of the two

contrasting vegetation types co-occur within a 30-km window. Note that the scales for y-axes vary for better visualization.

KAIGUANG ZHAO AND ROBERT B. JACKSON340 Ecological Monographs

Vol. 84, No. 2

evaporated more water than adjacent GRA during the

growing season (e.g., 2.36 vs. 2.26 kgm

2

d

1

in July, P

,0.001, n¼225 904), but annually, the average ET of

CRO was slightly smaller than that of GRA (e.g., 1.28

vs. 1.33 kgm

2

d

1

,P,0.001). In winter or dormant

seasons, ET differences were marginal between DBF

and ENF or between CRO and GRA, partly because ET

remained low in all cases.

Forests were found to generally evaporate more water

than adjacent non-forest vegetation (Fig. 9). When

averaged over all paired sites, ENF annually evaporated

slightly more water than did CRO (i.e., 1.32 vs. 1.25

FIG. 6. A further analysis, as in Fig. 5, comparing albedo between matched adjacent sites of contrasting land covers. The sites,

where a pair of land covers is co-located nearby, were subdivided by the k-means clustering algorithm into three distinct clusters

according to seasonal patterns of albedo difference. For each pair of vegetation classes, the geographic distributions of the three

resultant clusters are mapped in color (the leftmost of each row), and the mean albedos averaged over all the sites of each cluster are

also displayed (the three plots on the right in the same color respectively).

May 2014 341LAND USE, BIOPHYSICS, AND FORESTRY

kgm

2

d

1

for ENF and CRO, P,0.001, n¼28 547);

DBF evaporated 0.25 kg/m

more water per day than

CRO over the year (i.e., 1.79 vs. 1.54 kgm

2

d

1

for

DBF and CRO, P,0.001, n¼18 223). On average,

forests also showed higher annual ET than did adjacent

grasslands (Fig. 9), although the differences were small;

for example, the annual ET averaged over the paired

DBF–GRA sites were 1.71 and 1.67 kgm

2

d

1

for DBF

and GRA (P,0.001, n¼19 216 ), respectively. In

addition, the ET differences between adjacent non-forest

and forest sites also showed some spatial and temporal

variability (Fig. 9; Appendix: Fig. A6), reﬂecting

multiple controls on ET such as climate, variations in

crop/grass phenology, and land management practices.

The partitioning of surface energy for CRO was greatly

inﬂuenced by crop type and the timing of planting and

harvesting. Many croplands with high annual ET, as

delineated by the k-means clustering (Appendix: Fig.

A6), coincided roughly with the irrigated areas derived

by Pervez and Brown (2010), again emphasizing the

inﬂuences of crop management. Moreover, these mixed

results may be affected by uncertainties in the MODIS

ET products.

Atmospheric regulation of albedo effect

The analysis of the CERES data highlights that RF

from land-use change is determined not just by the

magnitude of surface albedo change, but also by

atmospheric conditions. This observation arises in part

from atmospheric attenuation of upwelling surface

reﬂection, thereby diminishing the surface contribution

to the TOA albedo. The annual TOA albedo over our

study area averaged 0.32, with a contribution of 0.28

from the atmosphere and only 0.04 from the land

surface. The surface contribution represented only

;29.5%of the actual surface albedo. Moreover, the

fraction of surface contribution to TOA albedo was

negatively correlated to the amount of clouds, with an

estimated coefﬁcient of 0.82 (P,0.001) over North

America (Appendix: Fig. A7). This correlation indi-

cates that the atmospheric opacity, as determined

largely by cloudiness, exerts additional controls on

the observed change in TOA albedo and, correspond-

ingly, on the magnitude of RF associated with land

conversion. All else being equal, the TOA RF from

land albedo reduction is larger in less cloudy areas.

Thus, the same reduction in surface albedo will lead to

a stronger warming for reforestation and afforestation

in the USA west of 958W, where the cloud fraction and

the fraction of surface contribution to TOA albedo

averaged 47.7%and 20.3%, respectively, compared to

the eastern half of USA, where the two fractions

averaged 62.0%and 10.9%.

Shortwave RF and carbon emission equivalent

The conversions from CRO or GRA to DBF

generally yielded smaller RFs than did the conversions

to ENF (Table 2), as expected from the observed albedo

differences between adjacent vegetation types (Fig. 5).

When averaged over the common locations where the

conversions of CRO to ENF and DBF are both possible

(i.e., ‘‘triplets’’), primarily in the eastern USA, the TOA

shortwave RF was estimated to be 7.55 W/m

for the

CRO–ENF conversion, almost twice the value of 3.87

W/m

for CRO–DBF. Moreover, a strong spatial

dependence was evident in the TOA RF induced by

the non-forest to forest transitions, as determined by

spatial patterns in albedo difference and atmospheric

opacity (Fig. 10; Appendix: Fig. A5). Large RFs were

more frequently observed at more northern latitudes or

in the western USA, but the overall latitudinal

dependence was weak. The control of the atmosphere

on RF was revealed such that many regions yielding

large RFs coincided with areas that have large fractions

of surface contributions to TOA albedo or small

atmospheric attenuation, especially over the Rocky

Mountains (Fig. 10). Correlations between RFs and

the fractions of surface contribution to TOA albedo

were substantial, for example being 0.48 (P,0.001) and

0.52 (P,0.001) for the CRO–ENF and the CRO–DBF

conversions, respectively.

In addition to its distinct spatial pattern, albedo-

induced shortwave RF exhibited some characteristics

that differ from those of CO

-induced RF. In particular,

the magnitude of albedo-induced RF was always larger

at the surface than at the TOA (i.e., SF

sfc

.SF

toa

). This

result is also revealed from the relationship SF

sfc

¼SF

toa

SF

atm

, where SF

atm

,0 for reduced surface albedo,

because less solar energy will be reﬂected upward to

radiatively heat the atmosphere. For example, the

estimated annual mean shortwave RFs for the CRO–

ENF conversion were 8.45 W/m

, 6.11 W/m

, and 2.33

W/m

at the surface, TOA, and for the atmosphere,

respectively. The surface RF SF

sfc

does not necessarily

increase the surface temperature because this energy

(e.g., 8.45 W/m

) is further re-partitioned; in contrast,

the atmospheric RF forcing SF

atm

(e.g., 2.33 W/m

)

directly cools the atmospheric column. On average

(6SD), about 27.6%63.0%of the surface RF was

derived from the loss of radiation absorbed by the

atmosphere, but the exact value for this ratio varied with

scale (;29.2%at the monthly scale). Both the values of

27.6%and 29.2%appeared close to but slightly higher

than 23%as used by Montenegro et al. (2009). In

addition, the geographic patterns of surface RF were

observed to be similar to those of TOA RF; therefore,

the conversion to DBF generally had smaller surface RF

than that to ENF.

Conversions of non-forest vegetation, GRA or CRO,

to DBF usually had considerably smaller carbon

emission equivalents than conversions to ENF had.

Carbon emission equivalents, dC

alb

, were obtained by

multiplying TOA shortwave RFs by a factor of 0.312

(kg/W). By doing so, the global longwave RF induced

by this carbon emission is equivalent to the local albedo-

induced shortwave RF when spread over the globe in

KAIGUANG ZHAO AND ROBERT B. JACKSON342 Ecological Monographs

Vol. 84, No. 2

terms of temperature response (see Eq. 4 or Fig. 2). As a

result, the spatial patterns of carbon-emission equiva-

lents are the same as those of TOA RFs (Fig. 10). The

mean carbon-emission equivalents over the lands

bounded by (958–708W) 3(208–458N) were estimated

to be 2.1 60.4 kg/m

(CRO–ENF), 0.87 60.47 kg/m

(CRO–DBF), 2.1 60.5 kg/m

(GRA–ENF), and 0.82 6

0.34 kg/m

(GRA–DBF), for the four land conversions,

respectively. These values are smaller than the previous-

ly reported estimation (Betts 2000), due partly to our

correction for the difference in climate sensitivity

between CO

- and albedo-induced RFs (see Eq. 4 or

Fig. 2).

The net carbon drawdown, dC

seqalb

, for land

conversions from CRO or GRA to forests in our study

area was in most cases positive, suggesting that the

combined effect of reduced albedo and CO

uptake on

global temperature is cooling. Previous estimates of

carbon sequestration potential of forestry projects in our

study area fall within the range of 5.5–18 kg C/m

(Betts

2000, Claussen et al. 2001, Gibbard et al. 2005,

Montenegro et al. 2009). These estimates of dC

seq

are

larger than our estimated carbon emission equivalent to

albedo-induced RFs dC

alb

, resulting in positive values of

net carbon drawdown dC

seqalb

. Even if forests drawn

down additional carbon by only 5.5 kg C/m

in their

below- and aboveground pools, the carbon emission

FIG. 7. Comparisons of MODIS land surface temperature (LST) between adjacent sites of contrasting vegetation for six pairs

of vegetation types (e.g., ENF vs. CRO and ENF vs. GRA). For each pair of vegetation types, the upper, middle, and bottom

panels of each subﬁgure refer to differences in daily, daytime, and nighttime temperature, respectively. The gray color scheme

indicates the distribution of LST differences for each date, which is the relative number of sites that have a given value of LST

difference on that date; darker color suggests a larger number of sites. The red solid curves denote the medians of LST difference as

a function of date; the dashed lines indicate the upper and lower 25%percentiles of LST differences between the paired vegetation

types. The units for LST are in degrees Kelvin (K).

May 2014 343LAND USE, BIOPHYSICS, AND FORESTRY

TABLE 2. Changes in surface biophysical properties for potential land conversions from non-forest (croplands [CRO] or

grasslands [GRA]) to forest (deciduous forests [DBF] or evergreen forests [ENF]) in North America and the associated

biophysical forcings as derived from the combination of multiple satellite observations and products.

Altered biophysics CRO!DBF CRO!ENF GRA!DBF GRA!ENF

DAlbedo 0.0540 60.035 0.0841 60.037 0.0535 60.040 0.0996 60.050

DTOA albedo 0.0148 60.012 0.0247 60.012 0.0148 60.013 0.0288 60.015

DLST 0.046 60.42 0.55 61.3 0.11 60.81 1.27 61.6

sfc

SF 4.35 62.1 8.45 62.4 4.18 62.8 9.63 62.9

LF (LST) 0.266 62.3 2.50 66.1 0.67 64.6 6.36 68.3

toa

SF 3.13 61.6 6.11 61.9 3.01 62.1 7.01 62.2

LST 0.015 60.13 0.14 60.34 0.038 60.26 0.36 60.47

ET 3.24 62.4 0.016 65.4 1.26 62.1 0.44 62.3

atm

SF 1.23 60.53 2.33 60.58 1.17 60.73 2.61 60.97

LF(LST) 0.25 62.1 2.36 65.7 0.63 64.3 6.00 67.8

DLatent heat 7.36 65.1 0.012 65.2 2.48 65.6 0.029 65.5

DSensible heat 4.82 65.3 10.54 67.0 3.64 67.9 13.59 67.7

Notes: Radiative forcings are reported for both longwave (LF) and shortwave (SF) at the surface (sfc), the top of the atmosphere

(toa), or for the atmosphere column (atm). Longwave radiative forcings (LF) can be induced by altered land surface temperature

(LST) or evapotranspiration (ET). The values reported here represent the spatial averages and standard deviations (SD) of annual

means of each variable across all valid 18318grids where potential land conversions occur. Note that different sets of grids were

used for evaluating different variables. Also note that LST here refers to the average of daytime and nighttime temperatures. Units

are K for LST and W/m

for other variables except albedo (unitless).

FIG. 8. Comparisons of daily surface temperature range (DTR) between matched adjacent sites of contrasting vegetation for six

pairs of vegetation classes. In each subﬁgure, the upper panel refers to mean DTR and the bottom refers to the distributions of

difference in DTR; that is, the relative number of sites that have a given value of DTR difference. The interpretation of dashed lines

and grayscale gradients is the same as in Fig. 5. The units for DTR are in degrees Kelvin (K).

KAIGUANG ZHAO AND ROBERT B. JACKSON344 Ecological Monographs

Vol. 84, No. 2

equivalent to albedo-induced RF (e.g., an average of 2.2

kg C/m

for the conversion of GRA to ENF), is still

likely to be counterbalanced. Given the same carbon

sequestration potential, the net carbon drawn for DBF

is on average larger than that for ENF because the

conversions to DBF yielded smaller albedo-induced RFs

than did the conversions to ENF. In this case, DBF are

more likely to cool globally.

Longwave RF

Annual mean longwave RFs induced by changes in

LST (i.e., LFLST

sfc and LFLST

toa ) were found to be smaller in

general than the corresponding shortwave RFs SF

induced by albedo reduction. This comparison was

valid when examined at both the surface and the TOA,

although the longwave RF for the atmospheric column

LFLST

atm sometimes had a larger magnitude than its

shortwave counterpart SF

atm

(Table 2). For example,

in the CRO–ENF conversion, the surface forcing LFLST

sfc

averaged over all paired sites was 2.50 W/m

, associated

with a local atmospheric and TOA longwave RF of

2.36 and 0.14 W/m

, respectively. This surface long-

wave RF was more than three times smaller than its

shortwave counterpart of 8.45 W/m

. For the GRA–

ENF conversion, the surface longwave RF LFLST

sfc

became comparable to the shortwave RF SF

sfc

(i.e.,

6.36 W/m

for LF vs. 9.63 W/m

for SF), leading to a

total surface RF of 16.0 W/m

. Even in this case, the

FIG. 9. Comparisons of MODIS evapotranspiration (ET) between contrasting vegetation at adjacent sites. For each of the six

pairs of vegetation classes, depicted are the mean ET (upper panel) and the distributions of ET difference between the paired

vegetation classes. The distribution for each date is the relative number of sites that have a given value of ET difference (lower

panel) and is depicted in a grayscale scheme: A darker color means a larger number of sites. The gray solid curve represents the

median value of ET difference; and the dashed lines indicate the upper and lower 25%percentiles. The sites considered for this

analysis are locations where both vegetation classes of the pair co-occur within a 30-km window.

May 2014 345LAND USE, BIOPHYSICS, AND FORESTRY

TOA longwave RF LFLST

toa was still marginal compared

to the TOA shortwave RF SF

toa

(i.e., 0.36 vs. 7.01 W/

), but the atmosphere column showed a negative

longwave forcing LFLST

atm of 6.00 W/m

, larger in

magnitude than its shortwave counterpart SF

atm

2.61 W/m

Unlike the LST-induced TOA longwave RF LFLST

toa ,

the TOA longwave forcing induced by altered ET LFET

toa

was sometimes comparable to the albedo-induced

shortwave TOA RF SF

toa

(Table 2). For instance, the

CRO–DBF conversion produced a mean TOA forcing

of 3.24 W/m

for LFET

toa and 3.13 W/m

for SF

toa

.Asa

result, the ET-induced longwave RF might not be

negligible, as assumed in many studies. In other words,

considering only albedo-induced RF potentially exag-

gerates the global cooling effect of reforestation and

afforestation due to the omission of positive ET-induced

longwave RF. Rigorous assessments of biophysical

effects of land-use change should therefore carefully

examine longwave RFs other than just the albedo-

induced shortwave RF.

The relative magnitudes of shortwave and longwave

RFs depend not only on location, but also on the time of

year examined. For example, the surface longwave RF

from altered LST exceeded the associated shortwave RF

in certain months, as depicted for the conversion from

CRO to DBF in June and July (Fig. 11). Rotenberg and

Yakir (2010) also observed that, in a dryland ecosystem,

the suppression of thermal emission from forests relative

to adjacent open lands yielded an annual mean surface

longwave RF slightly larger than the surface shortwave

RF induced by albedo reduction.

Re-partitioning of sensible and latent heat

The combination of the surface longwave and

shortwave RFs yielded changes in surface net radiation

that were found to be mostly positive for conversions to

forests (Table 2). The proportion of this positive energy

that is dissipated via sensible heat (or more precisely, the

ratios of the change in sensible heat to the total RF)

varied by land conversion, with medians of 99%(CRO–

ENF), 93%(GRA–ENF), 141%(GRA–DBF), and

143%(CRO–DBF) for the four conversion scenarios,

respectively. The negative ratio of 143%means that the

sensible heat ﬂux after conversion was reduced, with a

magnitude larger than the gain in surface net radiation.

For example, after converting CRO to DBF, the surface

on average gained 2.54 W/m

net radiation, but released

4.82 W/m

less sensible heat, with the difference

attributable to an increase in latent heat by 7.36 W/m

Conversions of CRO or GRA to DBF in many cases

led to a negative change in sensible heat ﬂux whereas

conversions to ENF led to a positive change (Table 2,

Fig. 10). This result is particularly important because it

suggests that conversions to DBF are more likely to

cause a local cooling to the near-surface air, in contrast

to conversions to ENF, which tend to warm the air

locally (Fig. 10). The patterns of local cooling or

warming manifested spatial and seasonal dependence

(Figs. 10 and 11). For example, although the CRO–DBF

conversion led, on average, to a local cooling, with a

mean reduction of sensible heat by 4.8 W/m

, there were

several spatial clusters (e.g., western North Carolina,

Northern Minnesota, North Dakota, and Vermont

[USA]) that showed positive changes in sensible heat,

though small in magnitude, indicating a possible local

warming over these regions after conversion (Fig. 10).

Similar to the surface shortwave and longwave RFs, the

changes in sensible and latent heat ﬂuxes also varied

across timescales and exhibited some pronounced

seasonal patterns (Fig. 11).

DISCUSSION

Land-use change affects climate through multiple

factors beyond net greenhouse gas concentrations. As

shown here, one pathway through which land conver-

sions to forests can warm local and regional climate is to

reduce surface albedo and induce positive shortwave

RFs. This albedo effect is particularly important at mid

or high latitudes through snow-masking because of

differences in canopy structure and height (Jackson et al.

2008). The strength of this albedo-induced RF is also

determined by abiotic factors such as atmospheric

opacity and soil color. Reduced albedo from growing

trees can counteract at least some of the cooling beneﬁt

of carbon uptake, an observation with policy implica-

tions for land and ecosystem management (McAlpine et

al. 2010). One example concerns woody-plant invasion

that transforms the herbaceous landscapes of grasslands

or savannas. The biophysical changes accompanying

these transformations, such as albedo and ET, should be

realistically evaluated to determine how the biophysical

effect may revise the carbon balance effects of woody-

plant invasion. As another example, considering the

biophysical effects of removing dryland forests resulted

in debates about the role of desertiﬁcation in affecting

climate (e.g., Rotenberg and Yakir 2010).

RFs from altered surface albedo and atmospheric

have different vertical structures and spatial

patterns and, thus, different climate sensitivities (Hansen

et al. 2005). Previous studies that have assumed the same

temperature responses for CO

- and albedo-induced

RFs may have overestimated the albedo-induced global

warming of forests by a factor of up to two. In contrast,

we believe that our differentiation of climate sensitivity

for CO

- and albedo-induced RFs provides an improve-

ment for quantifying the climate regulation value of land

management. However, unlike well-mixed greenhouse

gases, land-use change does not have a single, global

value of climate sensitivity, which instead depends on

the type, location, and extent of land conversion

(Gibbard et al. 2005, Brovkin et al. 2006, Pongratz et

al. 2009). The value of 0.53 K/[W/m

] that we used

corresponds to the global replacement of forest with

grassland as inferred by Davin et al. (2007). This value is

only approximate for local-scale vegetation replacement

KAIGUANG ZHAO AND ROBERT B. JACKSON346 Ecological Monographs

Vol. 84, No. 2

FIG. 10. Spatial patterns in biophysical forcings for land conversions from CRO to ENF (left column) and DBF (right column).

Displayed from top to bottom are shortwave radiative forcing (RF) at the top of the atmosphere (TOA) induced by changes in albedo

SFalbedo

toa , with its equivalent carbon emission (values in parentheses),TOA longwave RF induced byaltered ET (LFET

toa), surface longwave

RF induced by altered LST, changes in latent heat ﬂux (DLE), and changes in sensible heat ﬂux (DH). The mapped values hererepresent

the annual mean of each forcing at each grid. This analysis considers only those 18318grids containing at least three MODIS pixels of

both vegetation types.Albedo-inducedTOA RF was calculated basedon MODIS 500-m products and the otherforcings were basedon 1-

km MODISproducts, explaining why morevalid grids werefound for albedo-induced TOARF than the maps of otherforcings. Unitsare

W/m

for RF and energy ﬂuxes, and kg C/m

for equivalent carbon (values in parentheses).

May 2014 347LAND USE, BIOPHYSICS, AND FORESTRY

in our analyses. A more strict treatment of the disparity

in climate sensitivity between the two forcing agents has

not, to our knowledge, been adequately explored.

The use of RF for quantifying the climate-regulation

services of different land-use practices should also

distinguish between different types of forcings to better

represent their effects. RFs from different forcing agents

often manifest different temporal, spectral, vertical, and

spatial characteristics (Bonan 2002, Rotenberg and

Yakir 2010). For example, RF for well-mixed green-

house gases typically refers to the global longwave

radiative imbalance once the stratosphere reaches a new

equilibrium, but this deﬁnition is not applicable to local

shortwave RF from small-scale land-use change (Na-

tional Research Council 2005). Reduced albedo and

increased CO

both induce positive RFs at the surface

and TOA; however, for reduced albedo, the surface RF

is larger than the TOA RF, by a factor of ;1.37,

whereas for a doubling of CO

, the surface RF is only

;30%of the TOA RF (Andrews et al. 2009). This

contrast in vertical structure further implies that the

direct radiative effects in the troposphere will be a

cooling for reduced surface albedo but a heating for

increased CO

. Another key distinction between the two

forcing agents is on their operating timescales. Biophys-

ical forcings occur with land alterations and will

disappear if the lands revert to preconditions; in

contrast, RFs from CO

emissions are long-lasting

because the CO

remains in the air for many years.

Overall, these differing characteristics make it difﬁcult to

implement a universal RF-based metric for all climate

change assessments.

As emphasized here, in addition to shortwave RF,

longwave RF from vegetation shifts is another impor-

tant type of biophysical forcing that has been largely

ignored in many previous studies. In particular, sup-

pressed surface thermal emissions for cooler forested

surfaces lead to a radiative imbalance and could be

treated as longwave RF, although others have argued

that such suppression is better treated as a feedback (Lee

et al. 2011). Nonetheless, this LST-induced surface

longwave RF was sometimes comparable in magnitude

to the albedo-induced shortwave RF and cannot always

be ignored for evaluating net surface radiation. Howev-

er, this LST-induced longwave RF is always small at the

TOA because only an average of ;5.6%of surface

thermal emission escapes into space, with the rest

absorbed by the atmosphere. Therefore, the inclusion

of suppressed surface longwave radiation rather than its

TOA value as a positive RF into the calculation of

carbon emission equivalents (e.g., Rotenberg and Yakir

2010) may be inappropriate in some cases and can

exaggerate the warming effect of the forests on global

mean temperature (sometimes by about an order of

magnitude in our results).

Longwave RF attributable to altered ET is another

important forcing that should be evaluated when

inferring the climatic consequences of land-use change.

Unlike altered surface albedo or LST, the radiative

effect of altered ET is not conﬁned locally to the altered

landscape. This nonlocal radiative effect also contrasts

FIG. 11. Seasonal variations in surface (sfc) biophysical forcings for land conversions from CRO to ENF (left) and DBF

(right). The radiative forcings (RF) include albedo-induced shortwave RF, longwave RF induced by changed land surface

temperature (LST), and changes in sensible and latent heat ﬂuxes, which combine to determine the redistribution of energy at the

surface. Values plotted here represent the spatially averaged mean of each forcing at the monthly scale.

KAIGUANG ZHAO AND ROBERT B. JACKSON348 Ecological Monographs

Vol. 84, No. 2

with evaporative cooling, which is pronounced locally.

Accurate estimates of such longwave RFs require

realistic depictions of the distribution and fate of water

vapor, which can be better inferred using integrated

climate models than satellite observations (Claussen et

al. 2001). For example, climate simulations of global

deforestation suggested that the altered ET led to a

longwave RF with a magnitude of ;31%of the albedo-

induced shortwave RF (Davin et al. 2007). Swann et al.

(2010) also found that when planting deciduous trees in

the Arctic, the enhanced ET generated a longwave RF

up to 1.5 times larger than the shortwave RF from the

lowered albedo. Along with these prior ﬁndings, our

results add further evidence that ET-induced longwave

RF at the TOA, unlike that due to altered LST, is not

always negligible compared to albedo-induced short-

wave RF. Because ET has often been considered as a

cooling trend in many studies quantifying the climate

value of forests, its warming effect due to the positive

longwave RF is often overlooked, potentially overesti-

mating the regional or global cooling of forests.

Uncertainties in estimating RF, net carbon draw-

down, and ecosystem greenhouse values can be large,

contributing to conﬂicts in assessing the climate impacts

of land-use change. Estimates of carbon sequestration

potential sometimes differ for the same forests (e.g., 6 vs.

17 kg/m

in Betts [2000] and Montenegro et al. [2009],

respectively, for Canadian forests); their potential

uncertainty is far larger than our estimated carbon

emission equivalent to albedo-induced RF (e.g., 0–5 kg/

). Further, conversions from albedo-induced RF to

carbon emission equivalent or from carbon sequestra-

tion to equivalent RF depend on the time frame over

which the impacts of land-use change are examined

(Anderson-Teixeira and DeLucia 2011). For example,

precise estimates of CO

-induced RF require character-

izing the dynamics of carbon storage after vegetation

replacement (O’Halloran et al. 2011). Also, the use of

observational data to untangle biophysical processes

underlying altered ET remains challenging. Our

MODIS-based estimation of ET-induced RF has not

captured the full spectrum of interactions and feedbacks

associated with altered distributions of water vapor,

especially those related to changes in cloud patterns.

Our analyses suggest that the combined effect of

albedo reduction and CO

uptake on global tempera-

tures was, on average, a cooling for reforestation and

afforestation in North America, a ﬁnding consistent

with that of Montenegro et al. (2009). However, two

caveats accompany this estimated cooling effect. First,

the local temperature effect of land-use change is

determined almost exclusively by biophysical effects,

not carbon uptake, regardless of the combined global

effect of the two. Second, this global cooling refers only

to the net effect of albedo reduction and carbon uptake;

therefore, the actual overall effect on global temperature

after considering all the biophysical effects will likely be

different. Our analysis emphasizes that albedo reduction

from planting forests is not the only biophysical forcing

affecting temperature (Pielke et al. 2002). Concurrent

surface changes in ecophysiological and aerodynamic

characteristics alter energy balance and partitioning,

sometimes counteracting the local or regional warming

of reduced albedo, because of the rougher surface,

higher canopy conductance, and deeper roots of forests

compared to the replaced vegetation (Jackson et al.

2008, Anderson et al. 2011). The combined effects of

these radiative and non-radiative forcings are also

moderated by other environmental variables (Juang et

al. 2007, Lee et al. 2011). Therefore, whether a given

type of land conversion cools or warms the climate

depends on its location and extent, as well as the

relevant spatial and temporal scales examined (Bonan

2008, Kueppers et al. 2008, Arora and Montenegro

2011).

The use of albedo-induced RF or its carbon emission

equivalent metric neglects other biophysical forcings,

both radiative and non-radiative (Feddema et al. 2005,

Davin et al. 2007). The biophysical forcings of land-use

change, including altered sensible and latent heat ﬂuxes,

strongly modify the vertical distribution of atmospheric

heating, especially at local or regional scales. The

redistribution of net surface radiation associated with

non-radiative forcings modiﬁes the radiative forcing

effect of altered albedo, thus reducing the importance of

albedo-induced RF alone on the local/regional temper-

ature response. In fact, Lee et al. (2011) suggested that

the main contributor to the warmer air over forests

compared to adjacent, more open lands in North

America is not their darker surface, because the higher

air temperatures over forests were observed most at

night when the albedo effect was absent.

The lower daytime surface temperatures that we

observed for forests compared to non-forest lands are

consistent with other studies that examined effects of

vegetation on micrometeorological conditions (Holbo

and Luvall 1989, Chen et al. 1993, Jackson et al. 2008,

Nosetto et al. 2011). At a site in the southeastern USA at

;368N, for instance, the grassland had a LST 1.2 K and

0.9 K warmer than did the nearby pine and hardwood

forests, respectively (Juang et al. 2007). A U.S.

northwestern deciduous forest around 458N was 4.5 K

cooler on a summer day and 2 K warmer at night than a

nearby clear-cut (Chen et al. 1993). On the other hand,

our data for LST appear to differ from some studies

suggesting a local cooling effect of deforestation (i.e., a

warming effect of forests). Deforestation in the U.S.

Midwest was found to lower both daily maximum and

minimum air temperatures with a reduced DTR (Bonan

2001). Direct comparisons are difﬁcult between our

study that emphasized changes in LST and those

deforestation studies that emphasized near-surface air

temperatures, as discussed next.

Assessments of the climate impacts of reforestation

and deforestation are confounded by the use of differing

temperature metrics (Pielke et al. 2007, Mildrexler et al.

May 2014 349LAND USE, BIOPHYSICS, AND FORESTRY

2011). Some studies inferred a cooling effect of forests

on air temperature by extrapolating LST data (Juang et

al. 2007, Mildrexler et al. 2011). However, Andre et al.

(1989) provided a case wherein the LST of a forest

relative to adjacent croplands was 2 K cooler, but the air

above the forest was 1 K warmer. Rigorous climate

assessments of land conversions should explicitly differ-

entiate LST from near-surface air temperatures (i.e., air

temperature at 2 m above the displacement height; see

Table 1). The two are more likely decoupled at higher

values and over smoother surfaces and have been

observed to differ by as much as 20 K (Mildrexler et

al. 2011). Another complexity is the disparity in physical

deﬁnitions between three types of temperature for

climate research: radiometric, thermodynamic, and

aerodynamic temperatures; these metrics are </

Biophysical forcings of land-use changes from potential forestry activities in North America

Abstract and Figures