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Quantifying surface albedo and other direct biogeophysical climate forcings of
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forestry activities: A review
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Running Head: Biogeophysical climate forcings and forestry
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Ryan M. Bright*12, Kaiguang Zhao3, Robert B. Jackson4, Francesco Cherubini2
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Norwegian Forest and Landscape Institute, Ås, Norway
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Industrial Ecology Program, Energy and Process Engineering, Norwegian University of
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Science and Technology, Trondheim, Norway
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School of Environment and Natural Resources, Ohio Agricultural Research and
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Development Center, The Ohio State University, Wooster, Ohio, USA
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Energy, Stanford University, Palo Alto, California, USA
School of Earth Sciences, Woods Institute for the Environment and Precourt Institute for
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*
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[email protected]
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Keywords:
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management, climate metric
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Paper type: Invited Review
Corresponding author contact:
Ryan M. Bright, phone:
+47 649 49003; email:
forest management, land use change, biophysical, climate forcing, land
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Abstract
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The regulation by vegetation of heat, momentum, and moisture exchanges between the land
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surface and atmosphere is a major component in Earth’s climate system. By altering surface
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biogeophysics, forestry and other land use activities often perturb these exchanges and affect
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climate. Although long recognized scientifically as being important, surface biogeophysical
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climate forcings continue to evade inclusion in climate policies enveloping forestry and other
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land management projects.
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atmospheric science and terrestrial ecology in light of three main objectives: i) to elucidate
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the challenges associated with quantifying biogeophysical climate forcings connected to land
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use and land management, with a particular focus on the forestry sector; ii) to identify and
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describe current scientific approaches and/or metrics that facilitate their quantification; and
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iii) to identify and recommend research priorities that can help overcome challenges in the
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quantification and interpretation of biogeophysical climate impacts, serving to bridge the
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knowledge gap between the climate modeling, forest ecology, and resource management
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communities.
Here, we review the scientific literature in the field of
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Table 1. Nomenclature. Variable “type” denotes how they are used in surface energy and
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moisture budget equations.
Variable
notation
LAI
LAI e
SAI
hc
Unit
Definition
Type of Variable
Function of:
m 2 m 2
m 2 m 2
Leaf Area Index
Effective LAI
Structural
Structural
LAI
m 2 m 2
Stem Area Index
Canopy height
Structural
Structural
m
f cs
%
Fraction of canopy
intercepted snow
Structural &
Environmental
fg
%
unitless
Ground fraction
Snow-free ground
albedo
Structural
Physiological &
Environmental
Fraction of ground
covered snow
Snow albedo
Structural &
Environmental
Environmental
LAI; Ta ; P
Physiological &
Environmental
Moisture budget
term
Moisture budget
term
Moisture budget
term
Environmental
 leaf ; branch
g0
f gs
%
 sn
c0
unitless
P
mm
Snow-free canopy
albedo
Precipitation
RO
mm
Run-off
I
mm
Infiltration
Ta
˚C
Air temperature
Ts
˚C
Surface temperature

˚C(Wm-2)-1

RLW
Wm 2

Wm-2˚C -4
s
kPa ˚C -1
VPD
kPa
Temperature
sensitivity
Outgoing longwave
radiation
Stefan-Boltzmann
constant
Slope of saturation
vapor pressuretemperature curve
Vapor pressure deficit
ew
kPa
e* w
kPa
AP
RH

unitless
SAI; LAI; Ta ;
P
LAI
 leaf ;  soil ;
 leaf
P; Ta
H; L(E&T)
Surface energy
budget term
Environmental
 ; RN
Surface energy
budget term
Constant
Ts ;  s ; 
Environmental
Ta ; RH
Environmental
Ta ; RH
Environmental
Ta ; AP
Environmental
Ta ; AP
kPa
%
Partial pressure of
water vapor
Equilibrium vapor
pressure
Air pressure
Relative humidity
Environmental
Environmental
ew ; e* w
kgm-3
Air density
Environmental
3

kPa ˚C -1
s
unitless
Psychrometric
constant
Surface emissivity
s
unitless
Surface albedo
Cs
Jm ˚C
Cp
Jm-3˚C -1
ks
Wm-1˚C -1

RLW
Wm 2
RG
Wm 2
L( E  T )
Wm 2
-3
L
MJkg-1
E
mm
-1
Environmental
Surface energy
budget term
Surface energy
budget term
Surface energy
budget term
RH;


RSW
/ RSW
,clear
Ground heat storage
from conduction
Latent heat flux from
evaporation &
transpiration
Surface energy
budget term
Surface energy
budget term
LAI; Cs ; ks
Latent heat of
vaporization
Evaporation
Environmental
Ta ;
Environmental
ra ; C p ;  ;
Environmental
Environmental
Transpiration
Physiological;
Environmental
H
Wm 2
Sensible heat flux
Surface energy
budget term
z0
m

RSW
Wm-2
RN
Wm-2

unitless
 sn ;  c 0
Environmental
mm
ms 1
m
f g ;  g 0 ; f gs ;
Soil volumetric heat
capacity (thermal
inertia)
Air volumetric heat
capacity (thermal
inertia)
Soil thermal
conductivity
Incoming longwave
radiation
T
u
d
C p ; AP
Wind speed
Zero plane
displacement height
Momentum roughness
length
Incoming shortwave
radiation incident at
surface (insolation)
Net radiation
Bowen Ratio
VPD ; s;  ;
( RN  RG )
VPD ; s;  ;
( RN  RG )
ra ; rc ; C p ;  ;
VPD ; s;  ;
( RN  RG )
ra ; C p ;  ; Ts
- Ta
Environmental
Structural
LAI; hc
Structural
LAI; hc
Surface energy
budget term
Surface energy
budget term
Surface energy
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ra ; rc ; C p ;  ;


;  s ; RLW
RSW
;  ;  s ; Ts
H ; L( E  T )
2 1
2
ga (ra )
ms
gc (rc )
m2 s 1 ( sm2 )
gs (rs)
m2 s 1 ( sm2 )
zr
m
Wm-2
TOA
SW
RF

TSW
( sm )
Unitless
VCC
VMC
FCC
FMC
Bulk aerodynamic
conductance
(resistance)
Bulk canopy or
surface conductance
(resistance)
Leaf stomatal
conductance
(resistance)
Rooting depth
Shortwave radiative
forcing at top-ofatmosphere (TOA)
Share of reflected SW
radiation at surface
arriving at TOA
Vegetation Cover
Change
Vegetation
Management Change
Forest Cover Change
Forest Management
Change
budget term
Structural &
Environmental
LAI; hc; u
Structural &
Physiological
LAI e ; gl
Physiological
Physiological
FCC/FMC climate
metric
 s
Environmental
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1. Introduction
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The terrestrial biosphere and Earth’s climate are closely entwined. Climate strongly
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influences terrestrial productivity and biome distributions. In turn, the vegetation, soils, and
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other components of the terrestrial biosphere influence climate through the amount of energy,
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water, carbon, and other chemical species that they store and exchange with the atmosphere.
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Human interventions directly alter vegetation cover and structure through the conversion of
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one land use type to another (i.e., forest to cropland), or through a change in management for
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an existing land use type (i.e., conversion of hardwood to softwood forest; addition of
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irrigation or fertilization; extension of rotation length, etc.). In turn, such changes alter not
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just the carbon balance of a system but perturb surface solar and thermal infrared radiation
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budgets and atmospheric turbulence, leading to alterations in the fluxes of heat, water vapor,
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momentum, CO2/other trace gases, and organic and inorganic aerosols between the land
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surface and the atmosphere (e.g., Pielke Sr. et al., (1998, 2011). A deeper quantitative
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understanding of how human intervention on land will affect climate regulation -- and over
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which temporal and spatial scales -- is essential for successful climate change mitigation
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(Feddema et al., 2005, Jackson et al., 2008, Mahmood et al., 2010).
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Historically, extensive vegetation cover or management changes (henceforth referred to as
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VCC or VMC) have entailed forest conversions to crops or grasslands for agriculture
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(Goldewijk, 2001, Pongratz et al., 2008). Although the pace of global deforestation has
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slowed in recent decades (FAO &
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assessment modeling (IAM) community (van Vuuren et al., 2011) and the 5th Assessment
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Reports of the IPCC suggest that forests will play an increasingly large role in climate change
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mitigation and adaptation (Scholes & Settele, 2014, Smith & Bustamante, 2013) – whether
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land areas are re-/afforested or whether existing forests are managed more intensively.
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Relative to our understanding of forests’ role in the global carbon cycle, our understanding of
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their non-CO2 influences on atmospheric chemistry and climate is in its infancy. Apart from
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providing carbon sequestration services, forest ecosystems emit biogenic volatile organic
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compounds (BVOCs) that can rapidly oxidize in the atmosphere, generating O3 and secondary
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organic aerosols (SOAs) (Arneth et al., 2010). This biogeochemical mechanism impacts
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climate both directly and indirectly (Scott et al., 2014, Spracklen et al., 2008) and its global
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magnitude has only recently been examined (Unger, 2014).
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However, more established scientifically are forests’ direct biogeophysical contributions to
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the climate system: that is, their regulation of the exchanges of energy, water, and momentum
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between the earth’s surface and lower atmosphere. Within the climate science and global
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change research communities, forest cover and management changes and the corresponding
JRC, 2012, Meyfroidt et al., 2010), the integrated
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changes in surface biophysics are increasingly recognized as important forcings of local,
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regional, and global climate (Abiodun et al., 2008, Avissar & Werth, 2005, Betts et al., 2007,
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Bright et al., 2014a, Bright et al., 2014b, Chapin III et al., 2012, Chen et al., 2012, Durieux et
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al., 2003, Juang et al., 2007, Klingaman et al., 2008, Lawrence et al., 2012, Lee et al., 2011,
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Mohr et al., 2003, Peng et al., 2014, Ray et al., 2006, Rotenberg & Yakir, 2010, Swann et al.,
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2011, Wang et al., 2014, Zhang et al., 2014, Zhao & Jackson, 2014, Zheng et al., 2002).
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Outside these communities, however, biogeophysical climate impacts from VCC and VMC
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are rarely quantified or even acknowledged, with forest sector policies based strictly on
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carbon cycle dimensions. Most climate assessments overlook forest biogeophysical effects
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altogether due to the many complexities and challenges involved in quantifying them (Pielke
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Sr. et al., 2002).
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Our aim here is therefore to shed light on some of the challenges involved in measuring and
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quantifying biogeophysical climate change effects connected to VCC and VMC, with a focus
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on forestry (henceforth FCC and FMC). To that end, we review the scientific literature,
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relying as much as possible on observation-based studies while recognizing modeling studies
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that have made important contributions. We strive to limit our review to literature not
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covered in previous reviews; for example, those enveloping general land use-atmosphere
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climate dynamics (Foley et al., 2003, Pielke, 2001, Pielke Sr. et al., 2007, Pielke Sr. et al.,
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1998), biogeophysical impacts of land use/cover changes on climate (Mahmood et al., 2013,
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Pielke Sr. et al., 2011), and those with a special emphasis on forested ecosystem (Anderson et
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al., 2010, Bonan, 2008, Jackson et al., 2008).
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Our review is structured as follows: In Section 1, we present surface energy and moisture
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budgets and describe how they are modulated by both biological and environmental controls
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(i.e., biogeophysical). We illustrate how the surface energy balance equation can be
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manipulated for purpose of estimating the relative contribution by radiative vs. non-radiative
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processes that shape the response by local surface temperature following FCC/FMC. In
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Section 2, we elaborate in greater detail the mechanisms responsible for the non-radiative
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biogeophysical climate forcings linked to FCC/FMC, while in Section 3, we elaborate on
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shortwave radiative forcings connected to FMC/FMC through changes in surface albedo.
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Section 4 reviews recently proposed metrics/indicators for biogeophysical climate forcings,
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and in Section 5 we present two case studies and apply the concepts presented in Sections 1-3,
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demonstrating the importance of biological vs. environmental and of radiative vs. non-
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radiative factors responsible for shaping the local climate services provided by forests. We
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conclude in Section 6 with a discussion on the relevancy of FMC/FMC metrics and identify
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critical research needs.
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1. Mechanisms
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Surface Energy and Moisture Budgets
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FCC/FMC affects climate by altering surface moisture and energy budgets, which can be
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written as:
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


RSW
(1   s )  RLW
 RLW
 RN  RG  H  L( E  T )
(1)
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P  RO  I  E  T
(2)
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


where RN is the sum of the net short- ( RSW
) radiative
(1   s ) ) and longwave ( RLW
 RLW
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

fluxes; RSW
is insolation;  s is surface albedo; RLW
is the downwelling longwave radiation
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

flux; RLW
is the upwelling longwave radiation flux (equal to: RLW
(1   s )   s Ts4 , where  s
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is the surface emissivity,  is the Stefan-Boltzmann constant, and Ts is the surface
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radiometric temperature); E is evaporation (from soils and the physical environment); T is
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transpiration (biologically-controlled); RG is heat stored in the ground and vegetation; H is
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the turbulent sensible heat flux; L( E  T ) is the turbulent latent heat flux, with L as the latent
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heat of vaporization; P is precipitation; RO is runoff; and I is infiltration.
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Eq. (1) represents the surface energy budget, while Eq. (2) represents the surface moisture
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budget. They are presented together because they are intimately linked (Pielke, 2001). For
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instance, the latent heat flux L( E  T ) is directly related to the amount of moisture exchanged
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from the surface to the atmosphere (E + T) which is governed largely by moisture availability
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(P – RO - I) (Wang & Dickinson, 2012). For example, owed to their deeper rooting depths
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and enhanced ability to access water stored in soils, L( E  T ) fluxes in temperate forests can
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remain relatively high compared to grasslands during times of drought, when L( E  T ) fluxes
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would otherwise be similar to forests under wet conditions (Stoy et al., 2006). Thus a change
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to any term in Eq.’s (1) or (2) will affect the heat and moisture fluxes within the planetary
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boundary layer and potentially act on atmospheric water vapor, cloud formation, precipitation,
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and atmospheric circulation patterns. These boundary layer processes are dynamic, variable,
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and difficult to predict, which generally limits the ability to predict the impact of land-use
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change and landscape dynamics on climate patterns (Cotton & Pielke, 1995, Pielke, 2001).
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Quantifying the full climate change effect of forest cover or management changes at
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(inter)annual time scales is thus unrealistic for site-level observations. Such an approach
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would require coupled surface-atmosphere models to account for boundary layer dynamics,
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atmospheric albedo from clouds, and frontal and convective precipitation.
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Figure 1. Differences in the annual surface energy and moisture budgets between a temperate
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forest and an open grassland during dry (A) and wet (B) conditions. Horizontal fluxes of heat
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and moisture are excluded, and “RG” includes heat stored by both the ground and vegetation.
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Although annual RN is partitioned differently under arid and wet conditions, annual sensible
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heat fluxes in temperate forests are typically higher relative to the grassland.
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temperature; “ra” = aerodynamic resistance.
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“Ta” = air
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However, an analysis of changes in the major components of the surface energy and moisture
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budgets due to FCC/FMC can contribute to an understanding of first order biogeophysical
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effects. Changes in Eq. (1) will result in a change in the land surface temperature, since the
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radiation that impinges on the surface must be balanced by the reflected and emitted radiation
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and by energy lost or gained through sensible heat, latent heat, and conduction.
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temperatures can also be affected by a changes in H and L(E+T), with a magnitude that
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depends on the depth of the atmospheric boundary layer (Baldocchi & Ma, 2013). FCC/FMC
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that results in a long-term perturbation in air temperature can potentially affect ecosystem
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structure and functioning (Chapin III et al., 2012). For example, a switch in dominant tree
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species (FMC) could create warmer surface conditions and enhance rates of soil respiration,
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thus decreasing ecosystem productivity in the short-term (Rustad et al., 2001). In the longer-
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term, the warmer surface could shift the distribution of understory plant species in ways that
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enhance primary productivity (Chapin III et al., 1995, McGuire et al., 2006). It is therefore
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important to understand feedbacks from FCC/FMC on local climate, and in particular,
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attribute them quantitatively to biogeophysical factors. This necessitates a deeper
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understanding of the relative roles of structural, physiological, and environmental controls on
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surface energy and moisture budgets.
Air
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Vegetation Structure
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Structural parameters like leaf area index (LAI) and vegetation height play an important role
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in determining resistances (or conductivities) to heat, moisture, and momentum transfer.
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When a parcel of turbulent air meets a vegetated stand, wind speed is reduced, transferring
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momentum from the atmosphere to the surface, creating turbulence that mixes the air and
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transports heat and water from the surface into the lower atmosphere (Bonan, 2002, Monteith
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& Unsworth, 2008, Oke, 2002). The transport of momentum, heat, and moisture is more
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efficient with height above the surface and with densely vegetated canopies. LAI and forest
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canopy heights thus play an essential role in determining roughness lengths and aerodynamic
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resistances to heat, moisture, and momentum between the canopy and the atmospheric surface
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layer. Relative to shorter-statured vegetation like crop- and grasslands, forested surfaces have
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higher roughness lengths and lower aerodynamic resistances that facilitate more sensible heat
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and water vapor dissipation away from the surface during the daytime (Hoffmann & Jackson,
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2000).
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Together with stem area index (SAI), LAI is also an important structural variable determining
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the surface albedo and hence net radiation R N . SAI and LAI control the amount of solar
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radiation incident at the ground level which is often covered in snow during winter in many
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temperate and boreal regions. The albedo of snow is much higher than the albedo of foliage
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or branches, thus SAI/LAI play a central role in regulating radiation budgets in regions with
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long snow cover seasons due to the “masking” effects of forested canopies on the underlying
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snow surface and hence the total albedo. Across North America between 45-60°N, the
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zonally averaged white-sky albedo in January was at least twice as high for croplands and
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grasslands (0.57 and 0.50, respectively) compared to locations with deciduous broadleaf or
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evergreen needleleaf forests (0.26 and 0.20, respectively (Zhao & Jackson, 2014).
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LAI is also an important variable determining bulk canopy conductance to heat and moisture
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transfer, thus acting as controls on T as well as intercepted precipitation and canopy E. In
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non-arid regions in summer, E & T is often highly correlated with LAI (see Wang &
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Dickinson (2012) and cited studies therein).
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Vegetation Physiology
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Tree physiology plays an important role in governing T and I.
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conductance (inverse of resistance) directly control rates of T at the individual leaf level,
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while root structure and depth affect T through access to soil water. Root structure and depth
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also affect I and the water storage capacity of soils (and thereby indirectly that which is
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ultimately available for T and soil E). Forest management decisions that lead to a change in
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tree species directly affects physiological controls of surface energy and moisture budgets.
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For example, under non-drought conditions and given equal LAIs, a shift towards more
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loblolly pine plantation area (Pinus taeda L.) at the expense of oak-hickory hardwood area
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(Quercus – Carya) would increase regional T (and L(T)) in the SE USA due the loblolly
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pine’s higher leaf stomatal conductance (Stoy et al., 2006).
For example, stomatal
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Environmental Controls and Feedbacks with the Energy Balance
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Local meteorological conditions can play an equally large role in determining surface energy
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
and moisture budgets. For instance, R N is determined by RSW
which is affected by cloud
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cover and by surface albedo  s , which can be affected by temperature and P (snow has a
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higher albedo than dry soil which has a higher albedo than wet soil). The partitioning of the
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turbulent heat fluxes ( R N  RG ) into H and L( E  T ) is also partially controlled by differences
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between the air and surface temperatures and by differences in saturated vs. actual vapor
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pressures (i.e., the vapor pressure deficit), with vapor pressure having an exponential
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relationship with air temperature (Bonan, 2002, Monteith & Unsworth, 2008). Wind speed
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also determines the aerodynamic resistance of the surface-atmospheric boundary layer, with
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resistance decreasing as wind speeds increase.
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Table 2 describes the individual variables in Eq. (1) in terms of their important controls. Refer
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to Table 1 for variable descriptions.
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Table 2. Surface energy budget variables (Eq. (1)) and their controls.
Variable Unit
notation
Definition
s
unitless

RSW
Wm 2
Surface albedo,

 1
RSW
RSW
Insolation at
surface

LW
2
R
Wm
RG
Wm 2
H
Wm 2
L( E  T )
Wm 2
Longwave
irradiance at
surface
Heat storage flux
from conduction
Turbulent sensible
heat flux
Turbulent latent
heat flux
s
unitless
Surface emissivity

RLW
Wm 2
Longwave
emittance at
surface
Controls
Environmental
 sn ; P; Ta
Structural
LAI; SAI
Physiological
 leaf ;  leaf
 atm ;  atm ;
latitude
 atm ;  atm ;  atm ;
Ta ; RH
ks ; Cs
LAI; SAI
C p ;  ; u;
hc ; LAI (in
Ts  Ta
VPD, C p ;  ;
z0 , in ra )
LAI e (in rc )
rs , zr
Ta ; u ; s; 
Ts ;  s
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Energy Budget Decomposition Analysis
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A useful approach allowing for an assessment of the contribution by radiative, aerodynamic,
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and physiological (i.e., the biogeophysical) factors to local climate forcings connected to
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FCC/FMC is to rearrange the various terms of the surface energy balance equation (Eq. (1)).
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The local climate forcing may be measured in terms of a surface (radiative) temperature
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change, ∆Ts, or by an air temperature change, ∆Ta. Some have formalized approaches to
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estimate a discrete change in Ts by rearranging terms of the surface energy balance and taking
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first order derivatives to allow for an assessment of both radiative and non-radiative terms in
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isolation (Juang et al., 2007, Lee et al., 2011, Luyssaert et al., 2014). Based on the findings
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of Juang et al. (2007) that heat storage ( RG ) and emissivity (  s ) terms of Eq. (1) are
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negligible on annual time scales, Lee et al. (2011) formulated an alternative model that
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recognizes that a radiative forcing at the surface must be compensated by atmospheric
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feedbacks governing the energy re-distribution at the surface, which is brought about by the
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concomitant changes to important aerodynamic and physiological attributes:
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Ts 
0
1 f
RFSFC

s
0
(1  f )2
RN f
(3)
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where RFSFC
is the radiative forcing at the surface1 due to changes in albedo, and f is an
s
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energy “redistribution efficiency” parameter that is determined by the intrinsic aerodynamic
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and physiological attributes of the vegetation:
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f 
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 C p  L( E  T ) 
1 

4ra Ts3 
H

(4)
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where ra is the bulk aerodynamic resistance (defined in Section 2, Eq. (8)),  is air density
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(kg m-3), and C p is the thermal inertia of air (J m-3 ˚C-1).
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temperature sensitivity resulting from the longwave radiation feedback ( 1 / (4 s Ts3 ) ; a weak
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function of Ts with units in ˚C(W m-2)-1) (Lee et al., 2011, National Research Council, 2003).
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The 0 term essentially sets an upper limit on surface radiative forcing contribution to Ts ; in
1
In Eq. (3), 0 is the local
We use the term ”radiative forcing” to denote an energy balance perturbation that is ”radiative” in nature as
opposed to non-radiative, regardless of atmospheric level. Note the IPCC definition of “radiative forcing” refers
strictly to planetary energy balance perturbations (i.e., at TOA).
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other words, the first right-hand term in Eq. (3) would be the change in surface skin
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temperature if radiative processes were the only energy transfer mechanisms at play.
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However, the actual surface temperature response following a radiative forcing depends on
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internal energy re-distribution through convection and evapotranspiration, which in turn
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depends on the structural and physiological properties of the vegetation and on ambient
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environmental conditions (i.e., air temperature, humidity, wind speed). These non-radiative
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factors are largely responsible for f and can be equally important in determining the overall
270
local Ts connected to FCC/FMC.
271
272
2. Non-radiative Forcing
273
Convective heat transfer of sensible heat H is directly proportional to the difference in air
274
temperature at some reference height and at the surface level, and is inversely related to an
275
overall resistance:
276
H   C p (Ta  Ts ) / ra
277
where  is the air density (kg m-3), C p is the heat capacity of air (J kg-1 ˚C-1), and ra is the
278
overall aerodynamic resistance to heat transfer from the surface to the atmospheric boundary
279
layer.
280
Transfer of latent heat ( L( E  T ) ) is directly proportional to the difference in the vapor
281
pressure of air at some reference height and at the surface level, and is also inversely related
282
to an overall resistance. Latent heat exchanges can be parameterized in a variety of ways,
283
although the Penman-Monteith equation (Monteith, 1965) is widely considered an accurate
284
expression to estimate E+T (Allen et al., 1989, Allen et al., 1998), developed to use surface
285
radiation, temperature, and humidity data (Wang & Dickinson, 2012). For forests, the
(5)
16
286
surface can be regarded as a “big leaf” (Deardorf 1978) in which a separate resistance to
287
water vapor transfer from the canopy is introduced:
288
L( E  T ) 
289
where s is the slop of the saturation vapor pressure-temperature curve (kPa ˚C-1) (Murry,
290
1967, Tetens, 1930), VPD is the vapor pressure deficit (kPa; a function of Ta and relative
291
humidity),  is the psychrometric constant (kPa ˚C-1), and rc is the canopy resistance to water
292
vapor transfer. Eq.’s (5) and (6) demonstrate that both the canopy and aerodynamic resistance
293
terms are critical parameters controlling turbulent heat (and moisture) exchanges with the
294
atmosphere, with the former being aerodynamically controlled and the latter being both
295
aerodynamically and physiologically controlled.
296
Canopy resistance
297
At the scale of an individual leaf, stomatal control of transpiration is known as the leaf
298
stomatal resistance rl . At the scale of a canopy of leaves, canopy resistance rc is used to
299
describe the aggregate resistance. It is often calculated by scaling up the leaf stomatal
300
resistance ( rs ) of the leaves acting in parallel while treating the canopy as one “big leaf”:
301
rc 
302
where LAI e is the effective LAI which is empirically equal to the actual LAI for LAI ≤ 2,
303
LAI/2 for LAI ≥ 4, and 2 for others (Ding et al., 2014). “Dual-leaf” canopy resistance models
304
that take into account the share of sunlit vs. shaded leaves in the canopy often give more
305
accurate results than the “big leaf” model but require additional computations of the sunlit
s( RN  RG )   C pVPD / ra
(6)
s  (1  rc / ra )
rl
LAI e
(7)
17
306
fraction and separate values of mean leaf stomatal resistances for shaded and unshaded leaves
307
(Ding et al., 2014, Irmak et al., 2008, Zhang et al., 2011).
308
Table 3. Typical minimum canopy ( rc ) and leaf stomatal resistances ( rs ) for various
309
vegetation types (adapted from Kelliher et al., (1995)). Effective LAI (“ LAI e ”) is deduced
310
with Eq. (7).
Vegetation Type
rs (s m-1)
Temperate grassland
125
Coniferous forest
175
Temperature deciduous 215
forest
Tropical rainforest
165
Cereal crops
90
Broadleaved herbaceous 80
crops
rc (s m-1)
60
50
50
LAI e
2.1
3.5
4.3
80
30
35
2.1
3
2.3
311
312
Canopy resistance is sometimes referred to as surface resistance when it describes the
313
aggregate resistance of all transpiration and evaporation processes occurring on the ground
314
and in the canopy including the evaporation of water intercepted by the canopy. The most
315
advanced models for predicting E & T typically estimate separate surface resistances for T, E
316
occurring at the ground level, and E occurring in the vegetation canopy (Wang & Dickinson,
317
2012).
18
318
319
Figure 2. Differences in the partitioning of daily E & T between adjacent young and mature
320
spruce-dominant stands (Picea abies (L.) H. Karst.) in eastern Norway during 2006. Adapted
321
from Bright et al. (2014a).
322
Figure 2 illustrates the contribution to the daily latent heat flux from T and E occurring at the
323
surface and in the canopy (from intercepted moisture) at neighboring sites in eastern Norway
324
estimated with the Penman-Monteith scheme of Mu et al. (2011). L( E  T ) between the
325
two sites (sharing identical ambient environmental conditions) stems mostly from the
326
additional contribution by T and canopy E at the mature forest site. The region is not
327
moisture-limited, thus the contribution from soil E is large at both sites and dominates total
328
E+T throughout most of the year (Fig. 2, green).
329
Aerodynamic resistance
19
330
Sources of heat and water vapor will generally be found lower in the canopy than the apparent
331
sink of momentum, thus the overall aerodynamic resistances to heat and mass transfer may
332
therefore be described in terms of ram -- the aerodynamic resistance to momentum transfer,
333
and rb -- an additional resistance term assumed to be identical for heat and water vapor
334
(Monteith & Unsworth, 2008):
335
ra  ram  rb   log(( z  d ) / z0 )  / k 2u ( z )  2(ku* ) 1
336
where k is von Karman’s constant (0.41), z is the reference height (m), d is the zero plane
337
displacement height (m), u(z) the wind speed at reference height (m s-1), z0 the roughness
338
length of momentum (m), u* is the friction velocity (around 0.05-0.1u) (m s-1), Sc is the
339
Schmidt, and Pr is the Prandtl number (the ratio of the two being the Lewis number – or the
340
ratio of thermal to mass diffusivity). The second right-hand expression in Eq. (8) is the
341
additional resistance term ( rb ) for rough or fibrous vegetation surfaces and is based on the
342
empirical works of Thom (1972) and Wesely & Hicks (1977).
343
Key terms in Eq. (8) influencing the value of ra are momentum roughness length z0 and the
344
zero plane displacement height d – both of which are often parameterized as a function of
345
vegetation structure (Pereira et al., 1999, Perrier, 1982):
346
2


d  hc 1 
1  e  LAI /2  

 LAI

(9)
347
z0  hc e LAI / 2 (1  e  LAI / 2 )
(10)
348
where hc is canopy height (m). Eq.’s (9) & (10) are valid for LAI ≥ 0.5 (Colaizzi et al., 2004).
349
Other empirical formulations can involve additional forest structural attributes like stand
2
20
Sc
Pr
0.67
(8)
350
density (number of trees per hectare) (Nakai et al., 2008), but many modelers simply scale d
351
with canopy height hc (2/3hc for forests and 1/8hc for uniform crops (Allen et al., 1998)).
352
What should be apparent when looking at Eq.’s (5) - (10) is that both canopy (or surface) and
353
aerodynamic resistances are key terms controlling L(E+T) and H, and these terms are in turn
354
both largely determined by vegetation structure (i.e., LAI and hc on d, z0 , and rc ) and
355
physiology (i.e., rl ). While it is often assumed that decreases in L( E  T ) result in an
356
increase in surface temperature, this is not always necessarily the case. Ts responds to
357
changes in both aerodynamic roughness and in the Bowen ratio – or the ratio of sensible to
358
latent heat -- which can be either positive or negative in response to FCC (Lee et al., 2011).
359
360
Air Temperature
361
362
Thus far we have dealt with the principles by which the energy absorbed when vegetation
363
exposed to radiative fluxes is transferred to the atmosphere by convection and evaporation of
364
water (latent heat transfer) and the resulting effects on the surface radiative temperature, Ts .
365
These energy transfers take place through a boundary layer with properties dependent on the
366
viscosity of air and the transport of momentum from moving air to the vegetation surface
367
(Monteith & Unsworth, 2008). As such, the amount of warming in the air ( Ta ) depends on
368
the extent of turbulent mixing in the atmosphere, which is described by the depth of the
369
atmospheric boundary layer (Oke, 2002).
370
properties, forests are more efficient at dissipating sensible heat away from the surface and
371
into the boundary layer relative to open areas with shorter vegetation, particularly during the
372
daytime (Hoffmann & Jackson, 2000, Lee et al., 2011, Zhang et al., 2014). At nighttime,
373
however, their higher roughness properties can also serve to bring more heat from the stably
Due to their larger aerodynamic roughness
21
374
stratified nocturnal boundary layer down towards the surface layer relative to open areas. The
375
net result for forests in many extra-tropical regions is a net warming effect over the diel (24-
376
hr.) cycle (see, for example, Fig. 2 in Lee et al. (2011) and Fig. 4 in Zhang et al. (2014)).
377
378
Due to atmospheric turbulence, changes in the radiometric temperature of the surface ( Ts )
379
following FCC/FMC may not always provide the best indication of the actual air temperature
380
change, Ta . When comparing seasonal Ts and Ta observations (24-hr.) between a mature
381
coniferous and a recently clear-cut stand in boreal Canada (Figure 3), for example, we find
382
that the sign of the Ta does not equal that of Ts for all seasons except winter (DJF, Figure
383
3). This is more noticeable in summer (JJA), where Ta is negative and Ts is positive at
384
the Clear-cut site relative to the Old Jack Pine site.
385
22
386
387
388
Figure 3. Observed 2004-2010 mean seasonal differences in diel (24-hr.) surface ( Ts ) and air
389
( Ta ) temperatures (shown with 1 SD of inter-annual variations) in a Canadian boreal forest
390
cluster of different species and age compositions. The “Clear-cut” site was established in
391

2002. The sites share approximately identical global radiation ( RSW
) and other background
392
meteorological forcings (i.e, P). “DJF” = Dec.-Jan.-Feb.; “MAM” = Mar.-Apr.-May; “JJA” =
393
Jun.-Jul.-Aug.; “SON” = Sep.-Oct.-Nov. Ta data are from Fluxnet Canada (Barr & Black,
394
2013a, Barr & Black, 2013b, Barr et al., 2013), while Ts data are from MODIS (ORNL
395
DAAC, 2014).
396
23
397
Figure 3 demonstrates that considering either Ts or Ta in isolation can sometimes lead to
398
different conclusions about the relative local climate benefits of different vegetation cover
399
types. Although simple approximations can be applied to estimate Ta directly from H by
400
assuming a fixed mixing layer height in the convective boundary layer (West et al., 2011),
401
more accurate predictions require dynamic mixing layer models that take into account its
402
growth over the diurnal cycle (McNaughton & Spriggs, 1986). Without the application of a
403
dynamic boundary layer model, for example, Baldocchi and Ma (2013) could not have
404
explained the observed differences in air temperatures ( Ta ) between an oak savannah and
405
adjacent grassland site despite having equal sensible heat fluxes ( H = 0) during some
406
periods of the year. In other words, the convective boundary layer can serve as a buffer to the
407
turbulent sensible heat being dissipated away from the surface, complicating predictions of
408
Ta .
409
410
3. Radiative Forcing
411
Surface Albedo
412
Surface albedo is one of the most important biogeophysical mechanisms acting on radiation
413
budgets at both surface and top-of-atmosphere levels hence it affects both local and global
414
climate (Cess, 1978, Otterman, 1977). Forests and taller vegetation are often darker than
415
those with sparse or shorter vegetation (Henderson-Sellers & Wilson, 1983), particularly
416
when the underlying surface is covered in snow or light-colored soil. In temperate and boreal
417
regions, the interactions between forested vegetation and snow significantly complicate the
418
relationship between FCC/FMC and surface albedo changes (Boisier et al., 2012, Bright et
419
al., 2014b, de Noblet-Ducoudré et al., 2012, Pitman et al., 2009).
24
420
Parameterizations of surface albedo  s for forested areas are diverse with respect to treatment
421
of ground masking by vegetation, which can be classified according to three prevailing
422
methods introduced in Qu & Hall (2007) (and later described in Essery (2013)). Briefly, the
423
first method estimates radiative transfer between the vegetation canopy and the ground
424
surface; the second method combines the vegetation and ground albedos with weights
425
determined by vegetation cover; and the third method combines the snow-free and snow
426
albedo with weights determined by snow cover.
427
necessarily superior to another (Bright et al., 2014b, Essery, 2013), we find it helpful to
428
describe the albedo dynamics using the second approach, where the surface albedo is a
429
weighted share of the albedo of the ground and of the forest canopy, including the share of
430
ground and canopy covered in snow (Roesch & Roeckner, 2006, Verseghy et al., 1993):
431
 s  f g (1  f gs ) g 0  f gs sn   (1  f g ) (1  f cs ) c 0  f cs sn 
432
where f g is the fraction of exposed ground (sometimes referred to as the “canopy gap
433
fraction”), 1- f g the fraction of the exposed canopy (sometimes referred to as the “canopy
434
radiative fraction”), f cs the fraction of canopy covered with snow, f gs the fraction of ground
435
covered in snow,  sn the albedo of snow,  g 0 the snow-free ground albedo, and  c 0 the
436
snow-free canopy albedo. f g (or 1- f g ) is determined by vegetation structure and is often a
437
function of LAI and SAI.
438
determined by both vegetation structure and local meteorology (Bartlett et al., 2006, Essery et
439
al., 2009, Hedstrom & Pomeroy, 1998, Niu & Yang, 2004).  c 0 is an intrinsic property of
440
the vegetation largely determined by leaf level albedo and canopy structure (Hollinger et al.,
441
2010, Ollinger et al., 2008, Sellers, 1985).  g 0 is largely determined by soil geology but
442
varies with environmental factors influencing soil moisture (Idso et al., 1975), whereas  sn is
Although no one particular approach is
(11)
f cs is often analogous to canopy intercepted snow which is
25
443
purely controlled by environmental factors like precipitation rates, wind, temperature, and
444
other factors influencing snow grain size and impurities (like soot deposition) (Pirazzini,
445
2009, Wiscombe & Warren, 1980).
446
FCC/FMC primarily affects  s through alterations in vegetation structure and to some extent
447
physiology. In addition to the surface energy budget, changes to  s (henceforth  s ) can
448
directly alter the top-of-the-atmosphere (TOA) radiation balance and hence global mean
449
temperature (Arora & Montenegro, 2011, Bala et al., 2007, Davin & de Noblet-Ducoudré,
450
2010). Shortwave  s RFs at the TOA can be approximated with information on local
451
insolation and atmospheric conditions:
452


RFTOA
 s  RSW  sTSW
(12)
453


Where RSW
is the local insolation,  s is the local albedo change, and TSW
is the fraction of
454

reflected RSW
arriving back at TOA. During multiple reflection and on the final trajectory of
455
the reflected shortwave radiation towards TOA, there are opportunities for additional
456
atmospheric absorption which reduces the impact of  s upon the TOA flux change relative
457
to its impact at the surface (Winton, 2005). This is accounted for in Eq. (12) through the use
458

of the upward atmospheric transmittance parameter TSW
.
459
measure/obtain, particularly if it is estimated using vertical profiles of optical properties,
460
leading some to apply a value corresponding to the global annual mean (Bright et al., 2014a,
461
Caiazzo et al., 2014, Cherubini et al., 2012, Muñoz et al., 2010). Bright and Kvalevåg (2013)
462
showed that this may be a reasonable assumption.
463
As an alternative to Eq. (12) one could apply “radiative kernels” (Shell et al., 2008, Soden et
464
al., 2008) that relate  s directly to TOA RFs (for example, as in Flanner et al., (2011) and
26

Locally, TSW
is difficult to
465
Ghimire et al., (2014)). A radiative kernel describes the change in TOA fluxes for a standard
466
change in a surface property like  s and depends on the radiative properties and base state of
467
the climate model from which they are derived (Shell et al., 2008, Soden et al., 2008). Global
468
annual mean radiative kernels for  s have been estimated to range between 1.29 and 1.61
469
(W m-2 (0.01  s )-1) depending on the radiative transfer scheme and climate model (Shell et
470
al., 2008, Soden et al., 2008).
471
Global Mean Temperature Change
472
The extent to which RFTOA
 s impacts global mean surface temperatures can be estimated with
473
application of a global climate sensitivity parameter  that may describe either the transient,
474
effective, or equilibrium response by global mean temperature to a unit RFTOA
(in
s
475
C (Wm2 )1 ). The equilibrium climate sensitivity refers to the net change in the annual mean
476
global near-surface Ta when the climate system (or model) attains a new equilibrium with the
477
forcing change (typically corresponding to a doubling of atmospheric CO2-eq. concentration)
478
(Planton, 2013).
479
The effective climate sensitivity is a related measure that circumvents the requirement of
480
equilibrium. It is evaluated from model output for evolving non-equilibrium conditions and is
481
a measure of the strengths of the climate feedbacks at a particular time which may vary with
482
forcing history and climate state.
483
The transient climate response defined by the IPCC is the change in the global surface
484
temperature, averaged over a 20-year period, centered at the time of atmospheric carbon
485
dioxide doubling (year 70 in a 1% yr–1 compound carbon dioxide increase experiment with a
486
global coupled climate model) (Planton, 2013). However, the term is often loosely applied in
487
the literature to denote the transient response to a forcing regardless of the type of forcing
27
488
(CO2) or perturbation rate (1%/yr). It is essentially a measure of the strength and speed of the
489
response by Ta to a radiative forcing, and is typically lower than the equilibrium sensitivity
490
due to the long timescales of heat uptake by oceans.
491
The global climate sensitivity to RFTOA
 s is highly uncertain and is discussed in greater detail in
492
Section 6.
493
4. Climate Metrics for Land Use-Climate Forcings
494
Climate Regulation Index
495
West et al. (2011) developed “climate regulation indices” that combined two dominant
496
process influencing regional variations in climate: i) the biogeophysical regulation of heat
497
and moisture fluxes from local land surface processes; ii) the advection (transport) of heat and
498
moisture from large-scale atmospheric circulation. With such metrics, the local surface
499
energy and moisture balance impacts of V/FCC are scaled relative to the influence of
500
advection, thus providing an indication of the relative importance of the intrinsic
501
biogeophysical properties of the vegetated land surface: as advection increases, the relative
502
importance of the intrinsic biophysical mechanisms on local climate ( Ta in ˚C and moisture in
503
the atmospheric water column in mm) decreases.
504
To the best of our knowledge, these so-called “heat and moisture regulation indices” represent
505
the only spatially-explicit (0.5˚ x 0.5˚) V/FCC-climate metrics with global coverage
506
developed without the use of coupled (land surface-atmosphere) models and that characterize
507
the V/FCC biogeophysical impact the furthest along the local cause-effect chain from changes
508
in
509
V / FCC  RN  Ts  Ta ). Further work is required, however, to improve the temporal
510
resolution of the prescribed state variables used in the climate forcing characterizations, which
net
radiation
to
changes
in
near-surface
28
air
temperatures
(i.e.,
511
the authors themselves acknowledge. For example, West et al. (2011) prescribed a mixing
512
layer height of the near-surface atmosphere that is fixed annually, although in reality it varies
513
with season in response to V/FCC (Baldocchi & Ma, 2013, Oke, 2002). Further, the metrics
514
are developed using the theoretical potential vegetation cover for each grid cell compared to
515
bare ground/no vegetation; efforts would be required to make similar metrics more amenable
516
to forest management contexts.
517
Climate Regulation Value
518
Anderson-Teixeira et al. (2012) quantified both biogeochemical and biogeophysical
519
ecosystem climate services of 18 eco-regions across the Americas and combined them into a
520
single indicator referred to as the “Climate Regulation Value” (CRV) that indexes the relative
521
importance of biogeophysical to biogeochemical eco-physiological processes on land. CRV
522
is essentially the time-integrated net change in global LW GHG forcing at TOA less the local
523
surface energy balance change ( RFGHG  RN  L( E  T ) ; in Watts per global m2) relative to
524
a pulse emission of CO2 occurring in the year of LULCC.
525
The metric essentially combines the local direct biogeophysical climate effect (normalized to
526
the area of the earth) with the global biogeochemical effect which is normalized to CO2 as the
527
common currency (to obtain units in “CO2-eq.”). “Climate effect” here is simply the energy
528
gained or removed from climate system at multiple levels of the atmosphere (surface and
529
TOA). The metric does not account for the change in surface skin ( Ts ) or near-surface air
530
temperature ( Ta ) due to, for example, changes in surface aerodynamic properties (governed
531
by roughness) influencing convective heat transfer (Lee et al., 2011, Mahmood et al., 2013,
532
Pielke Sr. et al., 2011) -- which is of greater relevance to humans and to the functioning of
533
local ecosystems (Betts, 2007, Pielke Sr. et al., 2002). Additionally, local non-radiative (i.e.,
534
from ∆H) and global radiative (i.e., from CO2 RF) effects are summed, making meaningful
29
535
interpretation difficult. Like those derived by West et al. (2011), the baseline land cover in
536
Anderson-Teixeira et al. (2012) used to derive the CRV metric is bare soil depleted of organic
537
matter, which is not a realistic V/FCC scenario.
538
Entropy Production
539
Entropy production is a thermodynamic measure of the strength of dissipative processes
540
which perform physical work. Kleidon (2006), and later Stoy et al. (2014), argued that
541
entropy production is meaningful because it characterizes climate sensitivity (local Ta ) not
542
by how much warming or cooling occurs but by how much the suitability to perform physical
543
work differs by physical dissipative processes such as the turbulent exchange of the surface
544
and the atmosphere.
545
changes reduced the ability of natural ecosystems to perform work and that entropy
546
production rates may be a more meaningful measure of climate sensitivity than the global
547
mean Ta . Entropy reduction scales positively with increasing land use intensity (i.e., human
548
appropriation of NPP). In northern latitudes, this reduction stems from reductions in RN due
549
to reductions in SWNet (increased in albedo); in southern latitudes, entropy reductions stem
550
from a reduction in L(E+T) fluxes.
551
5. Case Studies
552
Environmental vs. Biological Controls
553
To what extent can the environmental controls (i.e., global radiation, precipitation, wind, etc.)
554
relative to the intrinsic biological properties of the vegetation itself (i.e., stomatal
555
conductance, LAI, vegetation height, etc.) influence the vegetation feedbacks on climate? For
556
any two forests having similar structural and physiological properties (i.e., LAI, hc, gs, zr),
557
 s will mostly be driven by differences in air temperature and precipitation as it affect snow
Kleidon showed that climate impacts from large-scale land cover
30
558

cover (when f g and  g 0 are assumed negligible), by differences in RSW
due to differences
559
in latitude and atmospheric conditions (i.e., cloud cover, aerosol optical depth), and by
560
differences in 
561
Understanding the role of environmental controls is relevant for regionally-optimized climate
562
motivated forest management strategies and policies. Peng et al. (2014) showed that the
563
benefits of afforestation in China (in terms of local Ts relative to grassland or cropland) are
564
likely to be enhanced in wetter regions (P >1200 mm yr-1) due to higher L(E+T) and can
565
even be counter-productive in dryer regions. For instance, the annual mean Ts between
566
plantation forests and open lands was found to be ~ -2ºC for regions experiencing P >1600
567
mm yr-1 while ~ +2.5ºC for regions experiencing P = 400-600 mm yr-1.
568
Another example is in Norway, where coastal regions in the west are being considered for
569
large-scale afforestation of fallow pasture and cropland with spruce plantations. The climate
570
of the region is characterized as having much higher P and relatively milder annual mean air
571
temperatures (P = >3000 vs. 500-100 mm yr-1 and T = 6.3 C vs. 2.7 C, respectively)
572
(Norwegian Meteorological Institute, 2013).
573
contribute to a lower annual mean  s and higher L(E+T) relative to forests of similar
574
structure (basal area, LAI, dominant species) located in the cooler and dryer eastern regions.
575
Thus despite experiencing slightly higher mean annual RN loads, afforestation in the coaster
576
regions would warm Ts and cool Ta locally relative to similar forests in eastern regions of
577
Norway, as illustrated in Table 4.
578
Table 4. Site pair comparison demonstrating the relative importance of environmental versus
579
structural and physiological (“Biological”) controls on annual mean air and surface
580
temperatures. Flux data for the USA are for 2005 and are adapted from Katul & Oren (2011a,
H
due to differences in humidity, precipitation, and wind speed.
L( E  T )
31
Differences in these environmental factors
581
2011b), and data from 2004-2009 for “eastern Norway” are adapted from Bright et al.
582
(2014a). L(E+T),  s , and Ts for “western Norway” are means over the same time period
583
acquired from MODIS retrievals (ORNL DAAC, 2014). Radiation budget variables for
584


“western Norway” ( RSW
; RLW
) are from NASA (2014).
Environmental Control Examples

H
RN
 s
RSW

∆ Definition
2
Evergreen Needleaf,
western Norway (59.4 N;
6.1 E) – Evergreen
Needleleaf, eastern
Norway (61.2 N, 12.4 E)
Evergreen Needleleaf, SE
USA – Broadleaf
Deciduous, SE USA
Evergreen Needleleaf,
easternNorway –
Broadleaf Deciduous,
eastern Norway
2
L( E  T )
,
[Wm ]
[Wm ]
-0.02
2 (= 113 –
4 (= 50 -
(Bowen)
-0.8 (= 0.4
(= 0.10
- 0.12)
111)
46 )
– 1.2)
Biological Control Example
-0.04 0 (= 194 – 12 (= 108 -0.1 (= 0.39
-0.09
Ts [ C ]
Ta [ C ] ,
,
Local
1.4
Local
-1.4b [H =
14 - H = 24 ]
0.4a
0.3b [H = 30
194)
– 96)
– 0.44)
- H = 28]
0 (=111 –
7 (=46 –
0.5 (= 1.2 – 0.1
0.4b [H = 24
111)
39)
0.8)
- H = 21]

and an emissivity of 0.95
RLW
585
a
Calculated with
586
b
Calculated with a 12-hr. heating cycle and boundary layer mixing height of 250 m (West et al. 2011)
587
588
The importance of “Biological Controls” – or vegetation structure and physiology – becomes
589
apparent when one compares sites sharing identical environmental forcings, as in Table 4.
590
The lower  s of the coniferous relative to deciduous sites in both geographic regions is
591
attributed to a higher LAI during fall-spring months (Bright et al., 2014a, Juang et al., 2007).
592
The lower  s translates to higher radiation loads ( RN ) and higher H, resulting in mean air and
593
surface temperatures that were higher at the coniferous site relative to the deciduous site in the
594
SE USA, despite having higher L(E+T) (lower Bowen ratio) during 2005 (Juang et al.,
595
2007). As for the Norwegian site comparison, despite having larger RN loads due to the
32
596
higher fall-spring LAI and lower  s at the coniferous site, Ts was much less pronounced
597
owed to a larger surface roughness and lower aerodynamic resistance.
598
Radiative vs. Non-Radiative Contributions
599
The contribution by aerodynamic and physiological (non-radiative) relative to radiative
600
effects on Ts can be quantified in terms of the energy redistribution efficiency parameter
601
(i.e., f in Eq. (4)) formalized by Lee et al. (2011). This term can either be estimated using
602
aerodynamic and surface resistances (the latter is needed to estimate L(E+T)) as in Eq. (4) –
603
or with Eq. (13) below:
604
f 
0
Ts  Ta
( RN  RG )  1
(13)
605
where RN  RG is the net turbulent heat flux, 0 is the longwave radiation feedback, and
606
Ts  Ta is the temperature gradient between the surface and the air. With this information, f
607
can be approximated for different forest biomes or management regimes (Table 5), and the
608
model of Lee et al. (2011) (Eq. 3) can be used to approximate Ts between any two sites
609
provided they share the same background climate conditions (i.e., the environmental controls
610
are identical). Table 5 shows estimates of f (unitless) for a site cluster in boreal Canada
611
together with important micrometeorological and biophysical observations. Knowing f , one
612
can approximate the contribution to Ts by non-radiative mechanisms with Eq. (3). A high f
613
denotes a greater role played by the non-radiative mechanisms at keeping Ts low.
614
Table 5 reveals some interesting insights about the relative contributions of f on the annual
615
mean Ts across the Canadian boreal site cluster. Although  s is lower and RN is higher at the
616
Old Jack Pine site relative to the Old Aspen site, Ts is lower owed to a higher f , which we
33
617
suspect is due to a larger aerodynamic roughness serving to dissipate heat (H) more efficiently
618
away from the surface during the daytime, despite its higher Bowen ratio relative to the Old
619
Aspen stand over the observed time period.
620
Table 5. Site cluster comparison of the impacts of surface intrinsic biogeophysical properties
621
on annual mean local air ( Ta ) and surface ( Ts ) temperatures (24-hr.). Ts and L( E  T ) data
622
are from MODIS (ORNL DAAC, 2014), f is calculated with Eq. (13), and all other variables
623
are from Fluxnet (Barr, 2013, Barr & Black, 2013a, Barr & Black, 2013b, Barr et al., 2013).
624
Ta is measured 1-2 m above the surface.
s
Old
Aspen
(SKOA19)
Old Jack
Pine (SKOJP14)
Clear-cut
Pine (SKHJP02)
0

RSW
(1   s )
RN
[Wm2 ]
[Wm 2 ]
0.15
110.0
60.7
1.55
0.68
0.220
0.62
14.3
0.12
114.8
62.8
1.06
0.51
0.220
1.22
24.2
0.29
94.2
44.4
0.61
0.25
0.221
0.91
26.3
Clear-cut Pine – Old
Pine
Ts [ C ]
Ta [ C ]
[ C (Wm 2 ) 1 ]
H
L( E  T )
(Bowen)
Clear-cut Pine –
Old Aspen
Old Aspen – Old Pine
Ts
[ C ] -0.47
-0.49
0.79
Ts ,
[ C ] -0.20
-0.29
0.49
f
Modeled
625
626
Relative to the Old Jack Pine and Old Aspen sites, the value of f and the non-radiative
627
contribution to the observed and modeled Ts at the Clear-cut Pine site is surprisingly high,
628
which we suspect could stem from high rates of soil evaporation. The higher Bowen ratio2 at
2
Although L(E+T) is estimated over a spatial footprint much larger than the actual site area (~1 km), the Bowen
ratios trends are consistent with those reported in Eugster et al. (Eugster W, Rouse WR, Pielke Sr. RA et al.
(2000) Land-atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to
34
629
the Old Jack Pine site could be attributed to a number of factors like lower transpiration
630
(relative to Old Aspen) and lower soil evaporation (relative to Clear-cut Pine) due to a ground
631
surface that is masked by the vegetated canopy year round (i.e., more shaded ground area).
632
Inserting the values for f into Eq. (1), the non-radiative contribution to Ts from 2004-2010
633
at the Old Aspen site relative to the Old Jack Pine site is 0.56 ˚C/yr while that from RFSFC
 s is -
634
0.07 ˚C/yr. Relative to the Old Jack Pine and Old Aspen sites, non- RFSFC
 s contributions to
635
Ts at the Clear-cut site are 0.03 ˚C/yr and -0.16 ˚C/yr, respectively. Although the modeled
636
Ts is not entirely accurate since we do not subtract the storage term ( RG ) from RN (as shown
637
in Eq. (13), it gives us the right sign of the forcing and, perhaps more importantly, a way to
638
estimate the approximate relative contribution of radiative vs. non-radiative processes to Ts
639
that one may attribute to FCC/FMC.
640
6. Critical Research Needs and Future Directions
641
Climate Sensitivity and RF Efficacy
642
In recent years, radiative forcing contributions from  s have been increasingly included in
643
climate impact assessment studies. Yet the extent to which a RFTOA
 s from local  s affects
644
global surface temperature is complicated, particularly when compared to the global forcing
645
from well-mixed CO2.
646
temperature change in response to RF (in C(Wm2 )1 ) – depends on the spatial distribution of
647
the RF (Hansen & Nazarenko, 2004, Hansen et al., 2005, Joshi et al., 2003). RFs at high
648
latitudes can be over twice as effective as RFs at low latitudes (Forster et al., 2000, Hansen &
649
Nazarenko, 2004, Hansen et al., 1997, Hansen et al., 2005, Joshi et al., 2003). This result is
The equilibrium climate sensitivity  – or the equilibrium
climate. Global Change Biology, 6, 84-115.) for open vs. closed canopy and coniferous vs. deciduous forests
types in boreal regions.
35
650
due to the stimulation of positive snow/ice albedo feedbacks and to the relative stability of the
651
atmospheric temperature profile at high latitudes (Hansen & Nazarenko, 2004, Hansen et al.,
652
2005). This has given rise to RF adjustments with a factor sometimes referred to as climate
653
“efficacy” (Hansen et al., 2005), which is defined as the ratio of  for some forcing agent
654
relative to that for CO2. For instance, Zhao & Jackson (2014) applied an efficacy of 0.5 to
655
adjust albedo change RFs connected to mid-latitude FCC/FMC in the southeast USA, and
656
Cherubini et al. (2012) applied a higher efficacy of 1.94 for a change in snow albedo because
657
the forest management question mostly pertained to seasonal (winter-spring) albedo changes
658
connected to changes in snow masking by forest canopies.
659
Recall from Sections 1 & 2, however, that changes to the aerodynamic and physiological
660
properties of the surface also act on near-surface temperatures by governing the efficiency by
661
which RN is dissipated from (or accumulated by) the surface following a RF. These non-
662
radiative internal feedbacks dampen the externally-driven radiative temperature change at the
663
surface (i.e., Eq. (3)) (Davin & de Noblet-Ducoudré, 2010, Lee et al., 2011). For these
664
reasons, Davin et al. (2007) report a  connected to historical global land use changes (VCC)
665
of 0.52 C(Wm2 )1 which gives a climate efficacy of ~0.5 for the particular climate model.
666
However, Hansen et al. (2005) report an efficacy of 1.02 +/- 0.6 for their global historical
667
VCC simulations using the same vegetation maps (i.e., (Ramankutty & Foley, 1999)), which
668
demonstrates the dependency of  (and efficacy terms) on the particular climate model from
669
which they are derived.
670
Table 6. Reported global climate sensitivities (  ; C(Wm2 )1 ) and/or efficacies (  / CO
671
; unitless) for RFTOA
s .
s
Model
(VCC)
 , RFTOA

 Type
s
Efficacy
s
36
Reference
2
IPSL-CM4
0.52
Effective
0.5
(Davin et al., 2007)
IPSL
0.93a
Effective
0.78
(Davin & de NobletDucoudré, 2010)
GISS E vIII
0.45
Effective
1.02
(Hansen et al., 2005)
IAP RAS
0.49
Effective
N/A
(Eliseev, 2011)
CCSM4 v8
0.62
Equilibrium
0.79
(Jones et al., 2013)
CCSM4 v4
0.36b
Transient
N/A
(Lawrence et al., 2012)
CM
FCC only; b Ta
/ RFSFC

672
a
673
However, in a more recent modeling study limited to global-scale deforestation (FCC), Davin
674
and de Noblet-Ducoudré (2010) report a  of 0.93 C(Wm2 )1 – a value resembling that of
675
CO2 and thus giving an efficacy of ~0.8 for the particular model and set-up. Thus to some
676
extent uncertainty also stems from the type of vegetation changes that are modeled.
677
Jones et al. (2013) showed that global radiative forcings from well-mixed GHGs like CO2 and
678
those from VCC (  s ) – even if adjusted with efficacies – do not produce the same global
679
mean temperature response when added together, in part because of spatial heterogeneity,
680
non-radiative effects, and other factors.
681
responses should be added directly.
682
This argument should not detract from efforts to move down the cause-effect chain from  s
683
TOA
to RFTOA
 s . The metric RF s provides information about the net energy gained or lost from
684
the climate system and can be quantified for stand level perturbations, whereas a global
685
temperature response cannot unless quantified using a fully coupled global climate model and
686
a relatively large perturbation signal. If climate modelers worked towards quantifying and
s
They argue instead that the individual climate
37
687
building consensus on regional responses from VCC/VMC-induced RFTOA
 s , then non-climate
688
modelers and resource managers could better characterize FCC/FMC impacts. Davin & de
689
Noblet-Decoudré (2010) present latitudinal averages of the climate response from their
690
deforestation modeling simulations; if the corresponding RFTOA
 s values were also known,
691
researchers could derive regional climate sensitivities (or RF efficacies) for site-level
692
application in climate impact assessment studies.
693
Metrics of Forest Cover and Management Changes and Their Policy Implications
694
Figure 4 illustrates the principle biogeophysical forcing mechanisms at play following
695
TOA
vegetation perturbation on land.  s can be causally and linearly linked to RFSFC
 s , RF s ,
696
Ta , global , and any C-normalized metric through C-eq.’s. These metrics can be converted from
697
each other, although the conversion factors and/or procedures differ from one analyst to
698
another. Differences stem from the particular radiative transfer code used to convert  s to
699
TOA
RFTOA
 s and in the choice of the climate sensitivity term (  ) required to convert from RF s to
700
Ta , global .
701
38
702
Figure 4. Conceptual illustration of the biogeophysical mechanisms modulating local and
703
global Ta and corresponding metrics. “E” = L(E+T).
704
Using  s as the only biogeophysical forcer in land use studies should be done with caution.
705
While the  s metrics shown in Fig. 4 can be derived and converted from one to another with
706
ease, local Ta and Ts cannot. This is owed to the non-linear role of heat dissipation by
707
surface roughness and evapotranspiration. Relative to open areas, for example, forests in
708
many extra-tropical regions have generally been shown to cool locally (Ta and Ts) during the
709
daytime despite have lower surface albedos (Lee et al., 2011, Peng et al., 2014, Zhang et al.,
710
2014, Zhao & Jackson, 2014). Although H and L( E  T ) are intimately linked to Ts (In
711
Eq. (3) and Fig. (4)), Ta is also affected by turbulent mixing and the dynamics of the
712
boundary layer (Baldocchi & Ma, 2013, Oke, 2002) and has no direct relationship with Ts
713
(recall Fig. 3, where Ts and Ta displayed different signs between the coniferous example
714
(“Old Jack Pine”) and the open area example (“Clear-cut”) in most seasons).
715
Summary and Recommendations
716
Biogeophysical factors associated with forestry activities -- including albedo and turbulent
717
heat exchange -- are rarely considered by policy makers, despite the fact that such factors can
718
affect local climate in ways counter to carbon sequestration. Researchers need to assist policy
719
makers if they are to move beyond a strictly carbon-centric accounting framework for forest
720
mitigation activities. Recent suggestions to include global mean impacts from albedo change
721
RFs are an improvement but fall short of a full biophysical accounting. Forestry impacts on
722
local Ta are often more important than effects on the mean global Ta , despite the challenges
723
associated with quantifying them or comparing direct observations between sites, since air as
724
a fluid is very dynamic and unpredictable. For analyses at site level, it is often easier to
39
725
justify taking the difference between surface biophysical variables as they impact vertical heat
726
exchanges (i.e., L(E+T), H) and Ts because they are directly determined by the canopy-
727
ground composites.
728
Metrics based on RF will have greater policy relevance if appropriate adjustments are made to
729
account for differences in the local/regional response by temperature due to the internal
730
feedbacks from the non-radiative forcings.
731
coupled
732
micrometeorologists to quantify the energy redistribution efficiency parameter f (see Eq. 13)
733
for a variety of forest types and other terrestrial ecosystems.
734
The differences between local and global effects are relevant for mitigation activities
735
involving forestry. The net radiative forcing to date from CO2 emissions accompanying
736
global deforestation is ~0.4 W m-2; the accompanying global effect of increased surface
737
albedo is about -0.2 W m-2, but the local albedo effect can be two orders or magnitude greater,
738
as much as ~20 W m-2 in boreal and arid temperate forests, for instance (Betts 2007,
739
Houspanossian et al. 2013)(Chapin III et al., 2012, Rotenberg & Yakir, 2010). Thus some
740
forestry activities will cool globally while warming the land surface locally. Similarly, the
741
increased evapotranspiration of forests compared to grasslands or croplands often cools the
742
land surface locally. Globally the direct effect of increased L(E+T) is less clear as the net
743
global energy balance will effectively be zero when the water condenses elsewhere.
744
However, if the extra water vapor increases cloud cover, then a cooling factor may be
745
introduced due to enhancements in atmospheric albedo (Ban-Weiss et al., 2011). A small
746
warming factor is also introduced because water is a potent greenhouse gas. Determining the
747
net effect of these interactions remains difficult and requires both meso- and global-scale
748
models. Understanding these indirect interactions should be a future research priority.
climate
modelers
to
This approach requires sustained efforts by
quantify
40
regional
climate
sensitivities
and
by
749
As for the direct biogeophysical climate forcings connected to land use and land management,
750
we have reviewed and identified different approaches and metrics to quantify them. We have
751
also recommended research priorities to help overcome some of the challenges associated
752
with measuring radiative and non-radiative forcings. Such knowledge should help build
753
bridges among the climate modeling, forest ecology, and resource management communities
754
and, ultimately, allow us to include all biogeophysical forcings in our estimates of the climate
755
benefits of different land use activities.
756
Acknowledgements
757
This work was performed under the project ‘Approaches for integrated assessment of forest
758
ecosystem services under large scale bioenergy utilization’ funded by the Norwegian
759
Research Council (grant number: 233641/E50). Additional funding has been provided by
760
USDA-AFRI (grant number: 2012-00857).
761
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