Metrics for Biogeophysical Climate Forcings from Land Use and

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Metrics for Biogeophysical Climate Forcings from Land Use and Land Cover Changes and Their Inclusion in Life Cycle Assessment: A Critical Review Ryan M. Bright* Norwegian Forest and Landscape Institute, N-1431 Ås, Norway Industrial Ecology Program, Department of Energy and Process Engineering, The Norwegian University of Science and Technology (NTNU), N-7491 Trondheim, Norway ABSTRACT: The regulation by vegetation of heat, momentum, and moisture exchanges between the land surface and the atmosphere is a major component in Earth’s climate system. By altering surface biogeophysics, anthropogenic land use activities often perturb these exchanges and thereby directly affect climate. Although long recognized scientifically as being important, biogeophysical climate forcings from land use and land cover changes (LULCC) are rarely included in life cycle assessment (LCA). Here, I review climate metrics for characterizing biogeophysical climate forcings from LULCC, focusing mostly on those that do not require coupled land− atmosphere climate models to compute. I discuss their merits, highlight their pros and cons in terms of their compatibility with the LCA framework, outline near-term practical guidelines and solutions for their integration, and point to areas of longer term research needs in both the climate science and LCA research communities.

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INTRODUCTION Climate change mitigation scenarios of the Intergovernmental Panel on Climate Change (IPCC) envisage a world increasingly dependent on the terrestrial biosphere as a source of food, material, and energy.1 Human interventions in terrestrial ecosystems directly alter vegetation cover, structure, and other surface biogeophysical properties when land is converted from one use type to another (land cover change, LCC; i.e., from forest to cropland)or from a change in management regime within a single land use type (land management change, LMC; i.e., from hardwood to softwood forest, from nonirrigated to irrigated cropland, and so on). LCC and LMC henceforth referred to as land use and land cover change (LULCC)can perturb surface solar and thermal infrared radiation budgets and atmospheric turbulence, leading to alterations in the fluxes of heat, water vapor, momentum, CO2/other trace gases, and aerosols (both organic and inorganic) exchanged between the land surface and the atmosphere.2−5 The mechanisms by which the vegetated land surface directly influences climate are often characterized by their biogeophysical or biogeochemical effects, as illustrated in Figure 1. Biogeochemical mechanisms include those which act on the chemical composition of the atmosphere with effects on shortwave and longwave radiation balances. The most important and widely recognized biogeochemical mechanism © XXXX American Chemical Society

by which terrestrial ecosystems influence climate is through the sequestration and emission of CO2, acting directly on Earth’s longwave radiation balance.6 Terrestrial ecosystems, predominately forests, also emit biogenic volatile organic compounds (BVOCs) such as terpenes and isoprenes that can rapidly oxidize in the atmosphere, generating O3 and secondary organic aerosols (SOAs).7 These impact climate both directly and indirectly via atmospheric scattering and absorption of shortwave radiation and by facilitating cloud formation.8,9 The contribution by BVOCs to Earth’s energy balance and the global climate system, however, has only recently been examined.10,11 Terrestrial ecosystems also influence climate via a number of biogeophysical mechanisms (Figure 1). Vegetation regulates the fluxes of heat, momentum, and moisture exchanged within the planetary boundary layer which can, in turn, moderate cloud formation, convective and frontal precipitation, and atmospheric circulation patterns.4,5,12 Relative to a grassland or cropland, for example, a forest would typically absorb more solar shortwave radiation due to its lower albedo,13,14 eject more moisture into the atmosphere via transpiration due to its Received: November 10, 2014 Revised: February 13, 2015 Accepted: February 26, 2015

A

DOI: 10.1021/es505465t Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

Figure 1. Conceptual illustration of the principle physical mechanisms by which vegetated surfaces directly affect climate. “SOA” = secondary organic aerosols; “CCN” = cloud condensation nuclei.

Table 1. Qualitative Illustration of the Relative Importance of Key Biogeophysical and Biogeochemical Climate Regulation Mechanisms (or Attributes) in Different Global Forest Biomes biogeophysical relevant scale

a

local

mechanism

surface roughness

boreal temperate tropical

moderate high high

biogeochemical local

a

local and global

evaporation and transpiration low moderate high

a

surface albedo high moderate low

a

global carbon density high moderate high

local and global b

biogenic aerosolsc low moderate high

Based on refs29,30 bBased on units of kgC/m2 and ref23 cDirect radiative effect only (W/m2), based on ref9

ability to access moisture stored deeper in soils,15 and create more turbulence in the atmospheric boundary layer due to its higher aerodynamic roughness.16 Direct biogeophysical climate effects connected to LULCC are often felt locally, although they can also have global implications.17,18 For example, the amount of solar radiation that is reflected by the land surface (albedo) can strongly influence local temperature by affecting how much energy the land absorbs. At the same time, a change in albedo at the surface also changes the planetary albedo such that less solar radiation is absorbed by the Earth in total, also affecting the global mean temperature. The relative influence of biogeophysical and biogeochemical climate forcings from LULCC depends on local environmental conditions as much as the physical attributes of the vegetation itself.19,20 For example, the biogeophysical climate forcing from the conversion of an evergreen forest to a cropland would differ whether the conversion took place in boreal Canada or temperate USA. Since the evergreen forest canopy masks the

ground surface, the longer snow cover season in Canada results in a larger albedo change upon conversion to cropland relative to the same conversion type in a temperate region of the USA because the albedo of previously unexposed snow is higher than vegetation.13,21,22 However, because temperate forests often store less carbon23 than boreal forests, the magnitude of the resulting biogeochemical climate forcing from LULCC (from changes in the atmospheric CO2 concentration) would also be lower. Local environmental factors also dictate whether other biogeophysical factors important to local climate, such as evaporation and transpiration (E + T), are relevant to consider. For example, temperature and precipitation are strong predictors of E + T,24 and higher E + T often corresponds to lower surface temperatures.13,14 Assuming the temperate forest in the preceding example is not water limited, local biogeophysical impacts from the change in E + T would be larger relative to the boreal example due to the longer growing season (warmer mean annual temperatures). B


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Figure 2. Illustration of the different pathways by which LULCC affects climate. “AGWP” = absolute global warming potential (W m−2); “AGTP” = absolute global temperature change potential (°C).

promising step forward, other important nonradiative biogeophysical climate forcings with predominantly local effects have yet to be included. These can be equally important. For example, regional climate modeling simulations showed that replacement of annual crops with perennials in the U.S. Midwest led to significant regional cooling benefits from both enhanced albedo and enhanced evapotranspiration, with the latter being the more dominant biogeophysical mechanism at the local scale.25 Similar conclusions regarding large regional cooling benefits from enhanced evapotranspiration and albedo were reached in a recent observation study of historical impacts connected to the conversion of soy to sugar cane crops in the Brazilian cerrado, a large agricultural region.26 Attributing such biogeophysical climate impacts to the land use sectors and to the specific product or technological systems connected to them is imperative to avoid implementation of counterproductive climate change mitigation (or adaptation) measures. In this review, I focus on important direct biogeophysical impacts from LULCC, that is, those arising directly from changes in the surface or planetary energy balances. Although indirect mechanisms such as cloud formation, convective and frontal precipitation, and atmospheric circulation have been linked to LULCC,5,37−39 they are more uncertain and require sophisticated models to quantify and attribute to LULCC. I structure my review around four main objectives: (1) to elucidate the fundamental biogeophysical mechanisms by which the land surface directly modulates climate, both locally and globally; (2) to review existing quantitative methods or metrics for quantifying direct biogeophysical land use climate forcings; (3) to discuss these in the context of their compatibility with the LCA framework; and to (4) recommend near-term best practice guidelines and practical solutions for the LCA community while outlining

Table 1 illustrates the relative importance of biogeophysical and biogeochemical climate regulation mechanisms in different geographic regions and at the different spatial scales. The biogeophysical climate response is mostly felt locally, such as, for instance, through a change in evaporative cooling13,14,25,26 or absorbed solar radiation at the surface.27,28 With the exception of albedo, global direct climate impacts from LULCC are mostly due to changes in the biogeochemical mechanisms. However, relative to biogenic aerosols, the direct biogeophysical climate forcings following LULCC are better understood scientifically, yet the development of meaningful biogeophysical climate metrics for LULCC remains problematic31,32evidenced not only by their limited inclusion in LCA but also by the lack of attention given to them in the latest assessment reports of the IPCC. For example, climate impacts from LULCC in the representative concentration pathways (RCPs) employed by the IPCC are limited to perturbations in greenhouse gas concentrations.1 Including biogeophysical impacts would alter conclusions about the mitigation potential of some land use strategies. For instance, in RCP4.5, a scenario in which large-scale afforestation is employed as a mitigation strategy of the land use sector, global mean biogeophysical warming impacts due to the lower surface albedo of forests has been recently shown to outweigh their biogeochemical cooling benefits serving as enhanced C-sinks.33 Only recently have efforts to include biogeophysical impacts in LCA been carried out. When adding the albedo change impacts to the biogeochemical LULCC impacts (GHGs) of various global biofuel pathways, Caiazzo et al.34 showed, for example, that for some pathways the sign of the net life cycle climate impact was dominated by the albedo effect. Other recent LCA studies of bioenergy systems have reached similar conclusions.35,36 Although these efforts may be seen as a C


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Figure 3. Conceptual illustration of the differences in surface energy budgets between a wet temperate forest and a grassland. Differences in the partitioning of net radition (RN) result in different local climate effects (Ts and Ta). Ts = surface skin temperature; Ta = near-surface air temperature; ra = aerodynamic resistance to heat, mass, and momentum transfer.

vegetative attributes of the land surfacesuch as the ability to evaporate and transpire moisture (“ΔSurface conductance”) or enhance atmospheric turbulence (vertical mixing of air; “ΔSurface roughness”)affects how the available energy at the surface (“Net radiation”) is partitioned into fluxes of sensible and latent heat exchanged with the lower atmosphere. These exchanges affect both surface skin (Tsurface) and nearsurface air temperatures (Tair). Changes in the way energy is partitioned at the surface stem from changes in biological attributes such as leaf area and vegetation height, with magnitudes tightly coupled to local environmental factors such as wind, solar radiation, and soil moisture, etc. Unlike the biogeochemical RF pathway, relationships between the biogeophysical perturbations and their climate effects are mostly nonlinear. This nonlinearity between cause and effect, dictated by site-specific environmental and biological factors, does not currently align with the linearized climate impact assessment framework in LCA. This important distinction is revisited later in the review, but first it is essential to examine the biogeophysical mechanisms in greater detail, starting with a more detailed description of the mechanisms affecting the surface energy balance.

longer term research directions for both the climate science and LCA research communities alike.

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CLIMATE IMPACT ASSESSMENT IN LCA The radiative forcing (RF) cause−effect chain represents the physical basis underlying familiar IPCC emission metrics such as the global warming potential (GWP) or the global temperature change potential (GTP).40 Emissions of substances such as CO2 and other GHGs alter the chemical composition of the atmosphere, hence changing its radiative properties (i.e., the scattering, absorption, and emission of solar and thermal radiation). The resulting RFor change in Earth’s energy balancemust then be compensated for by a subsequent temperature change in order to reach a new equilibrium state, typically occurring over decadal-to-centennial time scales (which is largely owed to the large thermal inertia of Earth’s ocean heat sinks). LULCC climate impact assessment in LCA is currently restricted to geochemical impacts occurring along the RF cause−effect chain (Figure 2) measured (or “characterized”) with factors such as the GWP and GTP. This is a convenient fit for LCA given its methodological structure which does not (typically) concern itself over when and where emissions occur during the life cycle inventory modeling phase.41 Climate impact assessment in LCA is a separate phase altogether, typically carried out after inventories of airborne emission substances from all processes connected to the product system of interest have been compiled. Substances emitted to air are traditionally limited to those which are globally well-mixed in the troposphere (so-called long-lived greenhouse gases such as CO2, N2O, CH4, and HFCs, etc.),42 thus making the location of emission irrelevant. The geographic location of a biogeophysical LULCC perturbation is, however, highly relevant for two main reasons: (1) mechanisms are dictated by site-specific environmental and biological factors, and (2) effects are predominately local. Figure 2 illustrates these two distinctions. A change in the

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SURFACE ENERGY BALANCE The amount of energy available at the land surface, or net radiation RN, is governed by surface albedo and emissivity:

where R↓SW is the externally supplied incoming solar radiation flux incident at the surface, R↓LW is the downwelling longwave ↑ is the upwelling longwave (thermal) radiation flux, RLW ↓ radiation flux (equal to RLW(1 − εs) + εsσTs4); αs is the surface albedo (R↑SWR↓SW−1), εs the surface emissivity, Ts the surface temperature, and σ the Stephan−Boltzmann constant (W m−2 K−4). Depending on the intrinsic aerodynamic and physiological attributes of the vegetated surface as well as on the local environmental conditions (i.e., ambient air temperD


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Environmental Science & Technology ature, wind speed, and humidity, etc.), RN (W m−2) is partitioned differently into that which is stored and that which is removed from the surface as turbulent sensible or latent heat: R N = R G + H + L (E + T )

modeled with radiative transfer models of varying complexity.55−57 Following LULCC, a change in surface albedo (Δαs) and the ensuing climate forcing is often permanent over analytical time scales, both regionally25,26 and globally,18,58 although it can also be temporary (i.e., from a decision to harvest a forest).35,59,60 A permanent Δαs climate forcing connected to a permanent LULCC (i.e., from forest to crop or from rural to urban) can significantly alter conclusions about the climate impacts of some product systems (see refs 61, 62, and 63, respectively). A temporary (or transient) Δαs linked to LULCC can in some contexts be just as important. For example, following the application of biochar as a soil amendment to annual crop production in Germany (an example of LMC), Meyer et al.64 estimated that 78% of the Δαs impact occurred in just the first 3 years following application. Transient albedo change impacts can also be important in forestry contexts and to the attribution of LULCC climate forcings to forest-based bioenergy35,36,65,66 or other forest product67 systems. The distinction between temporary and permanent Δαs is important, as it influences the amount of information required to conduct the climate impact analysis and/or compute the metric value. For a permanent Δαs, one simply needs to know the old and new αs values; for a transient change, one needs to know how the albedo change evolves over time, which often requires additional empirical information, modeling, and/or assumptions. This is discussed further in subsequent subsections. Shortwave Instantaneous Radiative Forcings from Surface Albedo Changes. The shortwave radiative forcing at the surface level (SFC) is driven solely by changes in surface albedo:

(2)

where H is the sensible heat flux, L(E + T) is the latent heat flux from evaporation E (environmentally controlled) and transpiration T (biologically controlled) with L being the latent heat of vaporization, and RG is the heat stored in the ground and vegetation which is usually negligible on annual time scales.43 Thus, in addition to the perturbation in the amount of available energy at the surfaceor RN + ΔRNLULCC affects how it is partitioned into RG, H, and L(E + T) (Figure 3). Conversion from forest to grassland, for example, would likely lead to ΔRN (via Δαs and Δεs), ΔL(E + T), and ΔH, whereas the addition of irrigation to cropland (an example of LMC) might not alter RN (ΔRN = 0) yet still result in a repartitioning of the turbulent heat fluxes (i.e., higher L(E + T) relative to H). Both cases would result in a change in both the surface skin temperature ΔTs and air temperature ΔTa (°C); thus ΔRN alone (W m−2) is not the most descriptive metric. Air and surface temperatures are governed not only by the amount of available energy at the surface but by how that energy leaves the surface through the intrinsic biogeophysical mechanisms governing evaporation and transpiration, convection, and the emission of thermal infrared energy.30,43 Energy partitioning is controlled by structural attributes of the vegetated surface (such as leaf area index and height), physiological attributes (such as leaf stomata conductance and rooting depth), and environmental conditions (ambient wind speeds, air temperatures, humidity, and soil moisture, etc.). Figure 2 illustrates that biogeophysical effects are the combination of both radiative and nonradiative mechanisms acting on the surface and global energy balances. Nonradiative forcings from changes in surface aerodynamic (“Surface roughness”) or physiological (“Surface conductance”) properties affect local energy balance partitioning with subsequent impacts on air and surface temperatures, which are important for ecosystem functioning.44 The number of structural, environmental, and physiological parameters required to accurately compute the nonradiative biogeophysical climate forcings are many; I refer the interested reader to refs 2, 45, and 46 for additional detail here. Top-of-the-atmosphere radiative forcings from changes in surface albedo are, however, less computationally demanding to compute and can be characterized with the familiar IPCC metrics currently employed in life cycle impact assessment.

↓ RFSFC Δαs (t , i) = R SW (t , i) Δαs(t , i)

(3)

where R↓SW is the local incoming solar radiation incident at the surface level (W m−2) and Δαs is the local albedo change in region i at time step t. A change in the amount of solar energy absorbed by the surface can result in a local surface temperature change ΔTshenceforth ΔTSFCthat is governed by the local climate sensitivity resulting from the longwave radiation feedback:30,68 SFC ΔTΔSFC αs (t , i) = RFΔαs (t , i)λ 0

(4)

where λ0 is the local climate sensitivity (1/(4εsσTs3), with units in K (W m−2)−1), which ranges from 0.3 to 0.15 K (W m−2)−1 when εs is 1 and for typical Ts ranges of 245 and 310 K, respectively.69 However, without including the internal energy SFC re-distribution (the repartitioning of RN), ΔTSFC Δαs from RFΔαs should only be seen as the theoretical upper limit to the net ΔTSFC. The actual surface temperature change will also depend on the efficiency by which the intrinsic biophysical mechanisms re-partition the new RN. Analytical expressions for quantifying these so-called nonradiative forcing contributions to the net ΔTSFC have been formulated elsewhere30,43,70 and are not discussed further here. These are typically more important in temperate and tropical regions. The shortwave RF at the top-of-the-atmosphere (TOA) can be approximated with information on upwelling atmospheric transmittance of shortwave radiation, or the quantity of R↓SWΔαs arriving back at the TOA:

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SURFACE ALBEDO CHANGE AND RF-BASED METRICS Surface albedo (α s ) is one of the most important biogeophysical mechanisms acting on radiation budgets at surface and top-of-atmosphere levels and hence governs both local and global climates.47,48 It is influenced by local geology (soil albedo), vegetation structure (leaf area index and height), vegetation physiology (leaf albedo), and the environment (snow, soil moisture, and so on). It is therefore both geographically and temporally dependent. It can be measured directly in situ,49,50 via satellite remote sensing algorithms,22,51,52 or via aerial53,54 flight campaignsor it can be E


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Environmental Science & Technology a Table 2. Influences of Time Increment Resolution on Estimates of the Annual Mean RFTOA Δαs (Instantaneous)

Δαs R↓SW (W m−2)

Jan.

Feb.

Mar.

Apr.

May

Jun.

Jul.

Aug.

Sep.

Oct.

0.40 70

0.36 110

0.2 151

0.025 189

0.013 224

0.004 239

0.004 242

0.055 207

0.009 158

0.025 107

−2 ↓ RFTOA Δαs (W m ), with annual mean Δαs and RSW

Albedo data are from Jørgensen et al.,

62

and Δαs is “CropForest”.

−2 ↓ ↑ RFΔTOA αs (t , i)/(W m ) = R SW (t , i) Δαs(t , i) TSW (t , i)

annual mean

0.175 0.425 68 57 annual mean

0.142 151

−2.27 × 10−14

R↓SW

are from NASA,

88

and

T↑SW

is a constant with value 0.854.74.

the LULCC question mostly pertained to seasonal (winter− spring) Δαs connected to the snow masking effect by vegetative canopies. Recall from the previous section, however, that changes in land surface aerodynamic and physiological properties from LULCC also act on near-surface temperatures by governing the efficiency by which turbulent heat fluxes are dissipated away from the surface following an external RF. These nonradiative feedbacks are internal and often dampen the externally driven radiative temperature change at the surface.29,30 For these reasons, Davin et al.86 report an equilibrium climate sensitivity connected to RFTOA Δαs from historical global LULCC of 0.52 K −2 −1 (W m ) , giving a climate efficacy of ∼0.5. However, Hansen et al.80 report an efficacy of 1.02 ± 0.6 for their global historical LULCC simulations using the same vegetation maps (i.e., Ramankutty and Foley87), which demonstrates the strong dependency of λ and efficacies on the specific climate model employed in the research. In a more recent climate modeling study limited to globalscale deforestation, Davin and de Noblet-Ducoudré29 report a global climate sensitivity of 0.93 K (W m−2)−1a value resembling that of CO2 and thus giving an efficacy of 0.78 for the particular model and experimental setup. Thus, to some extent, uncertainty also stems from the type of vegetation changes that are modeled (due to differences in their intrinsic biophysical properties and their role in governing the internal energy re-distribution). Nevertheless, in life cycle impact assessment or in “offline” climate modeling studies, going from RFTOA Δαs to a global mean ΔTa requires use of the global climate sensitivity term (λ), a term which can only be quantified with fully coupled global climate models. A recent review of six LULCC climate TOA modeling studies suggests that λ from RFΔα following s LULCC is likely to be anywhere between 50 and 100% to that of CO2.46 Time Considerations in RFΔαs-Based Metrics. The TOA treatment of time is important in deriving RFΔα and s normalized metrics such as GWP. For example, taking monthly mean values for Δαs and R↓SW as opposed to the annual means as in eq 5 gives a more accurate estimate of RFTOA Δαs :

A(t , i) AEarth

Dec.

−3.68 × 10−14

−2 ↓ RFTOA Δαs (W m ), with monthly mean Δαs and RSW

a

Nov.

(5)

where R↓SW is the local insolation in region i at time step t, Δαs is the local albedo change, T↑SW is an upward atmospheric transmittance term, A is the local perturbed surface area, and AEarth is the area of Earth’s surface. During multiple reflection and on the final trajectory of the reflected shortwave radiation toward TOA, there are opportunities for additional atmospheric absorption which reduces the impact of Δαs upon the TOA flux change relative to its impact at the surface.71 This is accounted for through the use of the upward atmospheric transmittance term T↑SW. Locally and seasonally T↑SW is difficult to measure/ obtain, leading some to apply a value corresponding to the global annual mean exiting a clear sky34,35,60,63,72 that is based on earlier radiative transfer modeling experiments of Lacis and Hansen.73 Bright and Kvalevåg74 showed that this value (0.854) may be a reasonable assumption for site-specific applications. As an alternative to eq 5 one could apply “radiative kernels”75,76 to relate Δαs directly to TOA RFs (i.e., as in Flanner et al.,77 and Ghimire et al.,78). A radiative kernel describes the change in the TOA flux for a change in αs and depends on the radiative properties and base state of the climate model from which they are derived.75,76 Global annual mean radiative kernels for Δαs have been estimated to range between 1.29 and 1.61 W m−2 (0.01Δαs)−1, depending on the radiative transfer scheme and climate model.75,76 Once an instantaneous time profile of RFTOA Δαs has been established, normalized and scalar-based characterizations (such as GWP) can be formalized along the radiative forcing cause− effect chain40 as any other forcing agent, although some caveats are discussed later. Climate Sensitivity and Global Mean Temperature Change. Moving down the RF cause−effect chain (Figure 2), the response by global mean near-surface air temperature to an instantaneous RF can be approximated with a term known as the global climate sensitivity, λ (K (W m−2)−1). The global climate sensitivity to an albedo change RF (RFTOA Δαs ) is highly uncertain. Some have argued that the global mean equilibrium temperature change in response to a RF at TOA depends on the spatial distribution of the RF.79−82 RFs at high latitudes can be twice as effective as RFs at low latitudes.79−81,83,84 This is due to the stimulation of positive snow/ice albedo feedbacks and to the relative stability of the atmospheric temperature profile at high latitudes.80,81 Relative to more homogeneously distributed RFs from CO2, the global mean temperature response to RFs from, for instancechanges in snow or ice albedo at high latitudescan be up to four times larger in magnitude.80,81,85 This has given rise to RF adjustments with factors sometimes referred to as climate “efficacies”.80 Cherubini et al.35 and Bright et al.60 applied an efficacy of 1.94 for high-latitude RFs linked to snow albedo changes, since

−2 RFΔTOA αs (t , i)/(W m ) m = 12

=

↓ ↑ ∑m = 1 R SW (t , m , i) Δαs(t , m , i) TSW (t , m , i )

A(t , m , i) AEarth

12 (6)

where m is the monthly and t the annual time step. This is because Δαs and R↓SW vary throughout the year, particularly at higher latitudes where snow may be present from late autumn to early spring, yet solar radiation is more intense from late F


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↓ Figure 4. Two alternate GWP characterizations of the annual mean RFTOA Δαs presented in Table 2 (using the monthly means of Δαs and RSW; in kg of −2 CO2-equiv m ) with different treatments of CO2 atmospheric residence time in the denominator of the GWP equation (eq 7).

RF-based IPCC emission metrics40 such as, for instance, in the denominator of the GWP equation:

spring to early autumn. Table 2 illustrates quantitatively differences in the annual mean RFTOA Δαs given the choice of ↓ time step for Δαs and RSW, where the two annual mean RFTOA Δαs estimates deviate by ∼62%. The case study of Jørgensen et al.62 and the example presented here correspond to an LULCC scenario (forest to cropland) whereby Δαs is assumed permanent over both the ecological and analytical time horizons (for example, 100 years). As aforementioned, however, Δαs can evolve on interannual time scales in some land management cases, such as the clear-cut harvesting of a slow growing forest59,65 or the amending of agricultural soils with biochar.64 In such cases additional knowledge and/or modeling assumptions about the transient changes in future albedo Δαs(t=1:TH) are required. Some have based Δαs(t) on other ecosystem variables known to be correlated with albedo over time (various forest structural parameters such as leaf area index, canopy cover fraction, and vegetation heights, etc.),35,59,65,89,90 while others have applied empirically based predictive models.60,91 A third way in which the treatment of time is important is in the derivation of normalized metricsor metrics that benchTOA mark RFTOA Δαs (or time-integrated RFΔαs ) to that of CO2 (such as GWP; expressed in kg of CO2-equiv m−2). In such comparisons, some have chosen to bypass the explicit treatment of CO2’s lifetime and behavior in the atmosphere13,21,61,63,90 by applying the CO2 airborne fraction (AF) as a scaling factoror the ratio of the annual increase in atmospheric CO2 to the CO2 emissions from anthropogenic sources (i.e., the proportion of human-emitted CO2 that remains in the atmosphere) typically between 0.45−0.50 (kg kg−1).6 Others have chosen to explicitly account for the lifetime and dynamic behavior of CO2 in the atmosphere35,36,60,64 as is done in the derivation of standard

TH

GWPΔαs(TH)/[kg of CO2 ‐equiv (m 2)−1] =

∫t = 0 RFΔTOA α (t ) k CO2 ∫

TH

t=0

yCO (t ) 2

(7)

where yCO2(t) is the impulse-response function (IRF) for CO2, ranging from 1 at the time of the emission pulse (t = 0) to 0.41 after 100 and 0.24 after 1,000 years.92 In eq 7 kCO2 is the radiative efficiency of CO2 in the atmosphere (W m−2 kg−1) at a constant background concentration (389 ppm in Joos et al.92) and TH is the analytical time horizon. Over 100 years following a CO2 emission pulse at t = 0, the mean fraction of CO2 in the atmosphere is around 0.52 kg, so adopting the AF approach appears fortuitously reasonable in this LULCC example if the analytical or metric TH is ∼100 years. However, when TH is notably shorter or longer, explicit treatment of atmospheric residence time (i.e., yCO2(t)) gives widely deviating results. This can be seen in Figure 4, which plots GWP calculated both with and without explicit treatment of time in the denominator of eq 7. For the reader’s convenience, the values of yCO2 for THs of 20, and 500 years are 0.60, and 0.28, respectively.

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RELEVANCE AND APPLICATION IN LCA LCA consists of two primary analytical stages: (i) the life cycle inventory (LCI) phase, where all inputs and outputs of material substances (including land) and emissions connected to individual processes are quantified and compiled; and (ii) life cycle impact assessment (LCIA), where the impacts to human health and the environment from emissions generated in each process are characterized (i.e., with GWP).41 Climate impact G


H

Δ(E + T); qadv

°C m−2

mm m−2 MRI

a

kg of CO2-equiv m GTPΔαs

Nomenclature not previously defined: hML = height of the mixing layer; Cp = heat capacity of air; ρ = air density; ΔC = change in stored terrestrial carbon; Δra = change in aerodynamic resistance.

unique to one specific LULCC scenario

97

94 unique to one specific LULCC scenario ΔH; Hadv

heat regulation index; air temperature change from ΔH (LULCC) and advected H moisture regulation index; change in water vapor supply from Δ(E + T) and advected moisture (qadv)

global temperature change potential from Δαs

RFTOA Δα ;

HRI

eq 7; global warming potential characterization of Δαs kg of CO2-equiv m−2 GWPΔαs

−2

local air temperature change from ΔH °C ΔTa, local

RFTOA CO2 ;

λΔαs; λCO2

λΔαs is dependent on coupled climate models

35 and 36 assumes RFTOA Δα = LW RF from CO2

2 and 13 more relevant than ΔTs to ecosystem functioning

96 λΔαs; RFTOA Δα °C ΔTa, global

ΔH; hML; Cp; ρ Normalized Metrics TOA RFTOA Δα ; RFCO2

30 and 43

quantification depends on knowledge of SEB partitioning; can be obtained directly from satellite products λΔαsquantification depends on coupled climate models λ0; ΔH(L(E+T))−1; Δra °C ΔTs, local

actual surface temperature change from ΔRN and energy repartitioning (sum of radiative + nonradiative forcings) global mean air temperature change from RFTOA

74−76 30, 43, and 70 simple to quantify forms the basis of many IPCC climate metrics simple to quantify, but ΔTSFC Δαs ≠ ΔTs Metrics Δαs; R↓SW Δαs; R↓SW; T↑SW λ0; RFSFC Δα Absolute eq 3; change in local SW radiation at surface from Δαs eq 5; change in global SW radiation at TOA from Δαs eq 4; max theoretical surface temperature change from RFSFC Δα W m−2 W m−2 °C RFSFC Δα RFTOA Δα ΔTSFC Δαs , local

dependent variables description unit metric

Table 3. List of Alternative Climate Metrics for LULCC-Induced Biogeophysical Climate Forcingsa

remarks

supporting references

assessment of LULCC in LCA involves summing the LCI emissionsusually constrained to GHGsand multiplying them with their GWP characterization factors as is done for all other GHGs emitted in the product system (across the life cycle).42 Recall from previous sections that LULCC also perturbs surface biogeophysical properties that lead directly to both radiative and nonradiative climate forcings. Since changes in surface albedo directly affects Earth’s energy balance at TOA, impacts can be quantified rather easily in LCIA using familiar IPCC metrics such as GWP (eq 7), although the requisite sitelevel information not typically collected during the LCI phase would still be required, such as, for example, the monthly albedo and solar radiation profiles of the two land cover types in question, illustrated in Table 3. Through nonradiative mechanisms, LULCC will also directly impact local Ts and Ta; in general, local climate impacts are not included in LCIA, although temperature can be vital for maintaining healthy ecosystem functioning (i.e., respiration and primary productivity)44 in addition to human health (i.e., heat stress).93 Recall that the magnitude of the net local temperature change following LULCC is determined by the repartitioning of net radiation (RN) into fluxes of latent (L(E+T)) and sensible heat (H). Air temperature will generally increase with increasing H.13,94,95 As important as they may be, these fluxes are challenging to quantify since they require large amounts of additional site-specific information regarding local vegetation attributes and environmental conditions. A way to overcome this challenge would be to precharacterize the local biogeophysical climate impacts for a variety of typical LULCC situations and map them globally at high resolution, serving as look-up “maps” for use in LCIA. A good example of such maps are the “climate regulation indices” developed by West et al.94 that combine two dominant processes influencing regional variations in climate: (i) the biogeophysical regulation of heat and moisture fluxes from local land surface processes; (ii) the advection (transport) of heat and moisture from large-scale atmospheric circulation. Essentially, the local surface energy and moisture balance impacts of the vegetated surface are scaled relative to the influence of advection, thus providing an indication of the importance of the intrinsic biogeophysical properties of the land surface: as advection increases, the relative importance of the intrinsic biophysical mechanisms in providing local climate regulation services (near-surface air temperatures and water vapor supply) decreases. West et al.’s94 so-called “heat and moisture regulation indices” (HRI and MRI, Table 3) were presented in the form of high-resolution (0.17°) maps with units in local °C and mm of water vapor per m−2 and geolocated grid cell. Although the heat and moisture indices were developed using the theoretical potential vegetation cover relative to a bare ground/no vegetation baseline for each grid cell, they represent, conceptually, an attractive framework for possible integration in LCA. High spatial resolution biogeophysical climate metrics (i.e., Ts or Ta) in the form of look-up maps could be developed for a broad range of realistic LULCC situations for application in LCIA. Such look-up maps would only function, however, if geographic information connected to the LULCC is recorded in the LCI phase. Figure 5 illustrates conceptually how mapbased metrics of local biogeophysical LULCC impacts might be integrated in LCA.

94

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Figure 5. Example of how existing biogeophysical climate metrics such as the “heat regulation index” (HRI) developed by ref 94 could be integrated in the LCA framework. Maps shown are adapted with permission from ref 94. Copyright 2011 Ecological Society of America.

Table 4. Direct LULCC Climate Impacts from the Conversion of 1 ha of Annual Crops (Mean of Corn−Soybean and Corn− Soybean−Alfalfa Rotation, Conventional Tillage) to Perennial Grasses (Switchgrass) in the Midwestern USAa biogeophysical, local

a

stressor

Δ(E + T)

Δαs (RFSFC Δα )

metric

ΔTa (°C ha−1)

ΔTa (°C ha−1)

perennial grassesannual crops

−0.45

−0.06

biogeophysical, global

biogeochemical, global

Δαs (RFTOA Δα )

biogenic CO2

ΔTa, net (°C ha−1)

GWP100 (t of CO2-equiv ha−1)

GWP100 (t of CO2-equiv ha−1)

−0.51

−69

−3.7

Data are from refs 25 and 98.

Case Study Example. The importance of accounting for biogeophysical climate effects of LULCC is illustrated in Table 4. In the Midwestern USA, the conversion of annual cropping systems to perennial systems such as switchgrass could increase yields, on average, by about 1 t of C ha−1 year−1.98 At the same time, the shift to switchgrass also increases both the annual surface albedo and evapotranspiration.25 The local radiative forcing from the albedo increase is −3.21 W ha−2 year−1.25 This results in a GWP100 (eq 7) of −69 t of CO2-equiv ha−1, an effect that is approximately 19 times larger in magnitude than the biogeochemical benefit (additional C-sequestration). Further, although the increase in albedo reduces the net radiation load at the surface, surface conductance is enhanced (increasing evaporation and transpiration) resulting in relatively higher latent heat and lower sensible heat fluxes compared to annual crops. Locally, this corresponds to near-surface air temperature differences of −0.51 °C ha−1 (April to October), which mitigates approximately half of the projected regional

temperature increase (April to October) for the period 2020− 2040.25 This example illustrates the importance of including biogeophysical climate impacts from LULCCat both local and global scales, and from both radiative and nonradiative mechanismsfor attribution to product systems in LCA (such as bioenergy, for example). LCA Compatibility and Integration. Traditionally, LCAs are executed with the assistance of large databases containing thousands of individual processes with previously quantified material inputs and outputs (i.e., their “inventories”). The LCA analyst will usually compile a detailed LCI of their own system under focusor “foreground” systemusing original data whenever possible. This foreground system is connected to a broader “background” system of auxiliary processes supporting the foreground system, and an LCA analyst will typically rely on an LCI database to complete the background LCI. The foreground system can be analogous to, for instance, the production of bioenergy from our switchgrass example, which I


Critical Review


models can be more easily attributable to specific model processes or parametrizations. This will ultimately allow for improvements in model predictions at regional scales and hence a reduction in the uncertainty connected to the climate sensitivity values relied on in the computation of the linearized metrics used in LCA. Until then, one should consider abstaining from any adjustment procedure (i.e., λΔαs = λCO2) in the derivation of normalized metrics such as GWP or GTP until there is clearer scientific evidence and consensus for doing so.104 The local nonradiative climate forcings from LULCC can also be important to consider in LCA and can be quantified with metrics such as Ts or Ta. Ts can be acquired for a number of locations and time periods thanks to advancements in satellite remote sensing algorithms.105 Ts may also be quantified directly but requires knowledge of the surface energy balance terms and hence local environmental and biological variables.30,43,46,70 Ta is more relevant for ecosystem functioning and human health and may be quantified if the sensible heat flux is known (H),13,94 although more accurate quantifications require coupling with an atmospheric model.95 Sustained and coordinated research and collaboration between the climate science and LCA (and integrated assessment modeling) communities is ultimately necessary to achieve the longer, broader goal of attributing the full scope of all local and global biogeophysical climate impacts of LULCC to specific technological systems. Pursuing the development of map-based metrics such as the “climate regulation indices”94 elaborated for a variety of common LULCC situations make them more amenable to LCA and, in particular, to LCA software and databases. LCA would, however, need to evolve to accommodate geospatial information connected to land-based process inventories. Development of the metrics themselves needs to be a priority for the climate science and global change research communities, while the practicalities surrounding their integration into the LCA framework (i..e, Figure 5) ought to be led by the LCA research community.

may include the following processes: switchgrass cultivation, switchgrass transport, and bioenergy production, among others. The LULCC incurred when switchgrass supplants more traditional annual crops will be recorded as a “land transformation” event in LCA jargon which currently is not associated with any climate impact characterization method in Recipe,42 a leading LCIA methodological framework widely employed in LCA. Although challenging, it is not inconceivable to imagine that an LCA analyst would be capable of computing some biogeophysical metricssuch as those based on the RF cause−effect pathway (i.e., for the global albedo change impact). Indeed, this is already occurring more frequently today.34,35,62,64,99,100 However, for the processes occurring in the background connected to the foreground system also requiring land inputssuch as, for example, the production of potash used to produce the fertilizer used in the switchgrass productionany LULCC-induced biogeophysical climate impact linked to potash production would go uncharacterized since the requisite site-specific information to do so is not contained in any LCI database. As previously mentioned, flows of inputs and outputs in LCA do not contain information about their occurrence in time and space, and since biogeophysical climate impacts connected to LULCC are highly dependent on time and space, accounting for the full extent of the biogeophysical impact occurring across the life cycle is not feasible given the current structure of commercial LCI databases to which most LCA analysts rely. Land use processes in LCI databases would either need to be adapted to accommodate all of the relevant surface energy balance terms (i.e., αs, L(E+T), R↓SW, etc.) needed to compute biogeophysical climate impacts (with methods or metrics that would need to be standardized in LCIA)or, alternativelyamend or augment land-based process inventories with geographic information (coordinates) so that precomputed metrics in the form of look-up maps can be applied (Figure 5) in LCIA. Recommendations and Future Research Directions. In the near term, efforts to include albedo change impacts connected in LCA should be sustained (at least for LULCC connected to foreground processes). Not including them can alter conclusions about the life cycle climate impacts/benefits of some product systems.34,35,64,99,100 This review has outlined some of the necessary steps and tools available for doing so. Equations 3 and 6 implemented with monthly rather than annual means of albedo and solar radiation lead to the more accurate albedo change RF characterizations, as was demonstrated in Table 2. Given the recent emergence and availability of high-quality albedo51,101 and radiation budget (i.e, R↓SW) data products88,102 (or look-up maps103) based on satellite remote sensing having high temporal and spatial resolution and extentit is hard to justify the exclusion of Δαs impacts in LCA or any other type of climate impact assessment of LULCC. However, some uncertainties remain surrounding the TOA which has implications for climate sensitivity of RFΔα normalization procedures that relate the spatially heterogeTOA neous SW RFTOA Δα to the spatially homogeneous LW RFCO2 . Global climate sensitivities (λ) and thus climate efficacies −1 connected to RFTOA Δα (λΔαs(λCO2) ) from LULCC range from 86 80,81 0.5 to 1.02 and depend on the specific model and on the intensity and location of the simulated LULCC. Climate modelers should continue efforts to harmonize LULCC data sets and experiments in model intercomparison studies so that large spreads in regional and global climate sensitivities across

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: +47 64 94 90 03. Fax: +47 64 94 80 01. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was performed under the project “Approaches for integrated assessment of forest ecosystem services under large scale bioenergy utilization” funded by the Norwegian Research Council, Grant No. 233641.

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REFERENCES

(1) Clarke, L.; Jiang, K. Assessing transformation pathways. Climate Change 2014: Mitigation of Climate Change, Contribution by Working Group III to the Fifth IPCC Assessment Report; Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2014; Chapter 6, p 141. (2) Bonan, G. B. Ecological Climatology: Concepts and Applications; Cambridge University Press: Cambridge, U.K., 2002; p 682. (3) Scholes, R.; Settele, J., Terrestrial and inland water systems−Final draft. In Climate Change 2014: Impacts, Adaptation, and Vulnerability, Contribution by Working Group II to the Fifth IPCC Assessment Report; Fischlin, A., Moreno, J. M., Root, T., Eds.; Intergovernmental J


Critical Review

Environmental Science & Technology Panel on Climate Change (IPCC): Geneva, Switzerland, 2014; Chapter 4. (4) Cotton, W. R.; Pielke, R. A. Human Impacts on Weather and Climate; Cambridge University Press: Cambridge, U.K.; New York, NY, USA; Melbourne, Australia, 1995. (5) Kabat, P.; Claussen, M.; Dirmeyer, P. A.; Gash, J. H. C.; de Guenni, L. B.; Meybeck, M.; Pielke, R. A., Sr.; Vörösmarty, C. J.; Hutjes, R. W. A.; Lütkemeier, S. Vegetation, Water, Humans, And the Climate: A New Perspective on an Interactive System; Springer-Verlag: Berlin, Heidelberg, New York, 2004. (6) Ciais, P.; Sabine, C.; Bala, G.; Bopp, L.; Brovkin, V.; Canadell, J. G.; Chhabra, A.; DeFries, R.; Galloway, J.; Heimann, M.; Jones, C.; Le Quéré, C.; Myneni, R. B.; Piao, S.; Thornton, P. E. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P. M., Eds.; Cambridge University Press: Cambridge, U.K., and New York, NY, USA, 2013. (7) Arneth, A.; Sitch, S.; Bondeau, A.; Butterbach-Bahl, K.; Foster, P.; Gedney, N.; de Noblet-Ducoudré, N.; Prentice, I. C.; Sanderson, M.; Thonicke, K.; Wania, R.; Zaehle, S. From biota to chemistry and climate: Towards a comprehensive description of trace gas exchange between the biosphere and atmosphere. Biogeosciences 2010, 7 (1), 121−149. (8) Spracklen, D. V.; Bonn, B.; Carslaw, K. S. Boreal forests, aerosols and the impacts on clouds and climate. Philos. Trans. R. Soc., A 2008, 366 (1885), 4613−4626. (9) Scott, C. E.; Rap, A.; Spracklen, D. V.; Forster, P. M.; Carslaw, K. S.; Mann, G. W.; Pringle, K. J.; Kivekäs, N.; Kulmala, M.; Lihavainen, H.; Tunved, P. The direct and indirect radiative effects of biogenic secondary organic aerosol. Atmos. Chem. Phys. 2014, 14 (1), 447−470. (10) Unger, N. Human land-use-driven reduction of forest volatiles cools global climate. Nat. Clim. Change 2014, 4, 907−910. (11) Unger, N. On the role of plant volatiles in anthropogenic global climate change. Geophys. Res. Lett. 2014, No. 2014GL061616. (12) Pielke, R. A., Sr.; Avissar, R.; Raupach, M. R.; Dolman, A. J.; Zeng, X.; Denning, A. S. Interactions between the atmosphere and terrestrial ecosystems: Influence on weather and climate. Global Change Biol. 1998, 4, 461−475. (13) Zhao, K.; Jackson, R. B. Biophysical forcings of land-use changes from potential forestry activities in North America. Ecol. Monogr. 2014, 84 (2), 329−353. (14) Peng, S.-S.; Piao, S.; Zeng, Z.; Ciais, P.; Zhou, L.; Li, L. Z. X.; Myneni, R. B.; Yin, Y.; Zeng, H. Afforestation in China cools local land surface temperature. Proc. Natl. Acad. Sci. U. S. A. 2014, 111 (8), 2915−2919. (15) Stoy, P. C.; Katul, G. G.; Siqueira, M. B. S.; Juang, J.-Y.; Novick, K. A.; McCarthy, H. R.; Christopher Oishi, A.; Uebelherr, J. M.; Kim, H.-S.; Oren, R. A. M. Separating the effects of climate and vegetation on evapotranspiration along a successional chronosequence in the southeastern US. Global Change Biol. 2006, 12 (11), 2115−2135. (16) Oke, T. R. Boundary Layer Climates, 2nd ed.; Taylor & Francis e-Library: Oxford, U.K., 2002. (17) Bala, G.; Caldeira, K.; Wickett, M.; Phillips, T. J.; Lobell, D. B.; Delire, C.; Mirin, A. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. U. S. A. 2007, 104 (16), 6550−6555. (18) Pongratz, J.; Reick, C. H.; Raddatz, T.; Claussen, M. Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett. 2010, 37 (8), No. L08702. (19) Anderson-Teixeira, K.; Snyder, P.; Twine, T.; Cuadra, S.; Costa, M.; DeLucia, E. Climate-regulation services of natural and agricultural ecoregions of the Americas. Nat. Clim. Change 2012, 2177181. (20) Betts, R. Implications of land ecosystem-atmosphere interactions for strategies for climate change adaptation and mitigation. Tellus, Ser. B 2007, 59 (3), 602−615.

(21) Betts, R. A. Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 2000, 408, 187−190. (22) Henderson-Sellers, A.; Wilson, M. F. Albedo observations of the earth’s surface for climate research. Philos. Trans. R. Soc., B 1983, 309 (1508), 285−294. (23) Carvalhais, N.; Forkel, M.; Khomik, M.; Bellarby, J.; Jung, M.; Migliavacca, M.; Mu, M.; Saatchi, S.; Santoro, M.; Thurner, M.; Weber, U.; Ahrens, B.; Beer, C.; Cescatti, A.; Randerson, J. T.; Reichstein, M. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 2014, 514 (7521), 213−217. (24) Wang, K.; Dickinson, R. E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys. 2012, 50, No. RG2005. (25) Georgescu, M.; Lobell, D. B.; Field, C. B. Direct climate effects of perennial bioenergy crops in the United States. Proc. Natl. Acad. Sci. U. S. A. 2011, 108 (11), 4307−4312. (26) Loarie, S. R.; Lobell, D. B.; Asner, G. P.; Mu, Q.; Field, C. B. Direct impacts on local climate of sugar-cane expansion in Brazil. Nat. Clim. Change 2011, 1, 105−109 DOI: 10.1038/nclimate1067. (27) Rotenberg, E.; Yakir, D. Contribution of semi-arid forests to the climate system. Science 2010, 327 (5964), 451−454. (28) Houspanossian, J.; Nosetto, M.; Jobbágy, E. G. Radiation budget changes with dry forest clearing in temperate Argentina. Global Change Biol. 2013, 19 (4), 1211−1222. (29) Davin, E. L.; de Noblet-Ducoudré, N. Climatic impact of globalscale deforestation: Radiative versus nonradiative processes. J. Clim. 2010, 23 (1), 97−112. (30) Lee, X.; Goulden, M. L.; Hollinger, D. Y.; Barr, A.; Black, T. A.; Bohrer, G.; Bracho, R.; Drake, B.; Goldstein, A.; Gu, L.; Katul, G.; Kolb, T.; Law, B. E.; Margolis, H.; Meyers, T.; Monson, R.; Munger, W.; Oren, R.; Tha Paw U, K.; Richardson, A. D.; Schmid, H. P.; Staebler, R.; Wofsy, S.; Zhao, L. Observed increase in local cooling effect of deforestation at higher latitudes. Nature 2011, 479, 384−387. (31) Marland, G.; Pielke, R. A., Sr.; Apps, M.; Avissar, R.; Betts, R. A.; Davis, K. J.; Frumhoff, P. C.; Jackson, S. T.; Joyce, L. A.; Kauppi, P.; Katzenberger, J.; MacDicken, K. G.; Neilson, R. P.; Niles, J. O.; Niyogi, D. S.; Norby, R. J.; Pena, N.; Sampson, N.; Xue, Y. The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy. Clim. Policy 2003, 3, 149−157. (32) Pielke, R. A., Sr.; Marland, G.; Betts, R. A.; Chase, T. N.; Eastman, J. L.; Niles, J. O.; Niyogi, D. S.; Running, S. W. The influence of land-use change and landscape dynamics on the climate system: Relevance to climate-change policy beyond the radiative effect of greenhouse gases. Philos. Trans. R. Soc. London, Ser. A 2002, 360, 1705−1719. (33) Davies-Barnard, T.; Valdes, P. J.; Singarayer, J. S.; Pacifico, F. M.; Jones, C. D. Full effects of land use change in the representative concentration pathways. Environ. Res. Lett. 2014, 9 (11), No. 114014. (34) Caiazzo, F.; Malina, R.; Staples, M. D.; Wolfe, P. J.; Yim, S. H. L.; Barrett, S. R. H. Quantifying the climate impacts of albedo changes due to biofuel production: A comparison with biogeochemical effects. Environ. Res. Lett. 2014, 9 (2), No. 024015. (35) Cherubini, F.; Bright, R. M.; Strømman, A. H. Site-specific global warming potentials of biogenic CO2 for bioenergy: Contributions from carbon fluxes and albedo dynamics. Environ. Res. Lett. 2012, 7, No. 045902. (36) Bright, R. M.; Cherubini, F.; Strømman, A. H. Climate impacts of bioenergy: inclusion of temporary carbon cycle and albedo change in life cycle impact assessment. Environ. Impact Assess. Rev. 2012, 37, 2−11. (37) Mahmood, R.; Pielke, R. A., Sr.; Hubbard, K. G.; Niyogi, D.; Dirmeyer, P. A.; McAlpine, C.; Carleton, A. M.; Hale, R.; Gameda, S.; Beltrán-Przekurat, A.; Baker, B.; McNider, R.; Legates, D. R.; Shepherd, M.; Du, J.; Blanken, P. D.; Frauenfeld, O. W.; Nair, U. S.; Fall, S. Land cover changes and their biogeophysical effects on climate. Int. J. Climatol. 2013, 34 (4), 921−953 DOI: 10.1002/joc.3736. (38) Pielke, R. A., Sr.; Pitman, A.; Niyogi, D.; Mahmood, R.; McAlpine, C.; Hossain, F.; Goldewijk, K. K.; Nair, U.; Betts, R. A.; Fall, K


Critical Review

Environmental Science & Technology S.; Reichstein, M.; Kabat, P.; de Noblet, N. Land use/land cover changes and climate: Modeling analysis and observational evidence. WIREs Clim. Change 2011, 2, 828−850. (39) Pielke, R. A., Sr.; Adegoke, J.; Beltrán-Przekurat, A.; Hiemstra, C. A.; Lin, J.; Nair, U. S.; Niyogi, D.; Nobis, T. E. An overview of regional land-use and land-cover impacts on rainfall. Tellus, Ser. B 2007, 59 (3), 587−601. (40) Myhre, G.; Shindell, D. Anthropogenic and natural radiative forcingFinal draft underlying scientific-technical assessment. In Climate Change 2013: The Physical Science Basis, Working Group I Contribution to the IPCC Fifth Assessment Report (Ar5); Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2013; Chapter 8, accessed at http://www.climatechange2013.org/ images/uploads/WGIAR5_WGI-12Doc2b_FinalDraft_Chapter08. pdf. (41) Rebitzer, G.; Ekvall, T.; Frischknecht, R.; Hunkeler, D.; Norris, G.; Rydberg, T.; Schmidt, W. P.; Suh, S.; Weidema, B. P.; Pennington, D. W. Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications. Environ. Int. 2004, 30 (5), 701−720. (42) Goedkoop, M. J.; Heijungs, R.; Huijbregts, M.; De Schryver, A.; Struijs, J.; Van Zelm, R. Report 1: Characterisation. ReCiPe: A life cycle impact assessment method which comprises harmonised category indicator at the midpoint and the endpoint level, 1st ed.; Ruimte en Millieu: The Hague, Netherlands, 2008; accessed online Nov. 2, 2014 at http:// www.lcia-recipe.net; 2008. (43) Juang, J.-Y.; Katul, G.; Siqueira, M.; Stoy, P.; Novick, K. Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett. 2007, 34, No. L21408. (44) Chapin, F. S., III; Matson, P. A.; Vitousek, P. Principles of Terrestrial Ecosystem Ecology, 2nd ed.; Springer: New York, 2012. (45) Monteith, J. L.; Unsworth, M. H. Principles of Environmental Physics; 3rd ed.; Elsevier Academic Press: London, 2008. (46) Bright, R. M.; Zhao, K.; Jackson, R. B.; Cherubini, F. Quantifying surface albedo changes and direct biogeophysical climate forcings of forestry activities (invited review). Global Change Biol. 2015, submitted for publication. (47) Cess, R. D. Biosphere−albedo feedback and climate modeling. J. Atmos. Sci. 1978, 35 (9), 1765−1768. (48) Otterman, J. Anthropogenic impact on the albedo of the earth. Clim. Change 1977, 1 (2), 137−155. (49) Eugster, W.; Rouse, W. R.; Pielke, R. A., Sr.; McFadden, J. P.; Baldocchi, D. D.; Kittel, T. G. F.; Chapin, F. S., III; Liston, G. E.; Luigi Vidale, P.; Vaganov, E. Land-atmosphere energy exchange in Arctic tundra and boreal forest: Available data and feedbacks to climate. Global Change Biol. 2000, 6, 84−115. (50) Baldocchi, D. D.; Kelliher, F. M.; Black, A.; Jarvis, P. Climate and vegetation controls on boreal zone energy exchange. Global Change Biol. 2000, 6, 69−83. (51) Schaaf, C. B.; Gao, F.; Strahler, A. H.; Lucht, W.; Li, X.; Tsang, T.; Strugnell, N. C.; Zhang, X.; Jin, Y.; Muller, J.-P.; Lewis, P.; Barnsley, M.; Hobson, P.; Disney, M.; Roberts, G.; Dunderdale, M.; Doll, C.; d’Entremont, R. P.; Hu, B.; Liang, S.; Privette, J. L.; Roy, D. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 2002, 83 (1−2), 135−148. (52) Los, S. O.; Pollack, N. H.; Parris, M. T.; Collatz, G. J.; Tucker, C. J.; Sellers, P. J.; Malmström, C. M.; DeFries, R. S.; Bounoua, L.; Dazlich, D. A. A global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data. J. Hydrometeorol. 2000, 1, 183−199. (53) Allen, S. J.; Wallace, J. S.; Gash, J. H. C.; Sivakumar, M. V. K. Measurements of albedo variation over natural vegetation in the Sahel. Int. J. Climatol. 1994, 14 (6), 625−636. (54) Perovich, D. K.; Tucker, W. B.; Ligett, K. A. Aerial observations of the evolution of ice surface conditions during summer. J. Geophys. Res.: Oceans 2002, 107 (C10), No. 8048. (55) Sellers, P. J. Canopy reflectance, photosynthesis, and transpiration. Int. J. Remote Sens. 1985, 6 (8), 1335−1372.

(56) Nilson, T.; Kuusk, A.; Lang, M.; Lükk, T. Forest reflectance modeling: theoretical aspects and applications. Ambio 2003, 32 (8), 535−541. (57) Dickinson, R. E. Land surface processes and climate-surface albedos and energy balance. Adv. Geophys. 1983, 25, 305−353. (58) Pongratz, J.; Reick, C. H.; Raddatz, T.; Caldeira, K.; Claussen, M. Past land use decisions have increased mitigation potential of reforestation. Geophys. Res. Lett. 2011, 38, No. L15701. (59) Lutz, D.; Howarth, R. Valuing albedo as an ecosystem service: Implications for forest management. Clim. Change 2014, 1−11. (60) Bright, R. M.; Antón-Fernández, C.; Astrup, R.; Cherubini, F.; Kvalevåg, M. M.; Strømman, A. H. Climate change implications of shifting forest management strategy in a boreal forest ecosystem of Norway. Global Change Biol. 2014, 20, 607−621. (61) Caiazzo, F.; Malina, R.; Staples, M. D.; Wolfe, P. J.; Yim, S. H. L.; Barrett, S. R. H. Quantifying the climate impacts of albedo changes due to biofuel production: A comparison with biogeochemical effects. Environ. Res. Lett. 2014, 9 (2), No. 024015. (62) Jørgensen, S. V.; Cherubini, F.; Michelsen, O. Biogenic CO2 fluxes, changes in surface albedo and biodiversity impacts from establishment of a miscanthus plantation. J. Environ. Manage. 2014, 146, 346−354. (63) Muñoz, I.; Campra, P.; Fernández-Alba, A. R. Including CO2emission equivalence of changes in land surface albedo in life cycle assessment. Methodology and case study on greenhouse agriculture. Int. J. Life Cycle Assess. 2010, 15, 672−681. (64) Meyer, S.; Bright, R. M.; Fischer, D.; Schulz, H.; Glaser, B. Albedo impact on the suitability of biochar systems to mitigate global warming. Environ. Sci. Technol. 2012, 46 (22), 12726−12734. (65) Bright, R. M.; Strømman, A. H.; Peters, G. P. Radiative forcing impacts of boreal forest biofuels: A scenario study for Norway in light of albedo. Environ. Sci. Technol. 2011, 45 (17), 7570−7580. (66) Singh, B.; Guest, G.; Bright, R. M.; Strømman, A. H. Life cycle assessment of electric and fuel cell vehicle transport based on forest biomass. J. Ind. Ecol. 2014, 18 (2), 176−186. (67) Guest, G.; Bright, R. M.; Cherubini, F.; Strømman, A. H. Consistent quantification of climate impacts due to biogenic carbon storage across a range of bio-product systems. Environ. Impact Assess. Rev. 2013, 43, 21−30. (68) Hansen, J. E.; Lacis, A.; Rind, D.; Russell, G. Climate Processes and Climate Sensitivity. AGU: Washington, DC, 1984; Vol. 29, p 368. (69) Panel on Climate Change Feedbacks, Climate Research Committee, National Research Council. Understanding Climate Change Feedbacks; National Academies Press: Washington, DC, USA, 2003; p 166. (70) Luyssaert, S.; Jammet, M.; Stoy, P.; Estel, S.; Pongratz, J.; Ceschia, E.; Churkina, G.; Don, A.; Erb, K.-H.; Ferlicoq, M.; Gielen, B.; Grünwald, T.; Houghton, R.; Klumpp, K.; Knohl, A.; Kolb, T.; et al. Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Change 2014, 4 (5), 389−393. (71) Winton, M. Simple optical models for diagnosing surfaceatmosphere shortwave interactions. J. Clim. 2005, 18, 3796−3806. (72) Lenton, T. M.; Vaughan, N. E. The radiative forcing potential of different climate geoengineering options. Atmos. Chem. Phys. 2009, 9, 5539−5561. (73) Lacis, A. A.; Hansen, J. E. A parameterization for the absorption of solar radiation in the earth’s atmosphere. J. Atmos. Sci. 1974, 31, 118−133. (74) Bright, R. M.; Kvalevåg, M. M. Technical note: Evaluating a simple parameterization of radiative shortwave forcing from surface albedo change. Atmos. Chem. Phys. 2013, 13, 11169−11174. (75) Soden, B. J.; Held, I. M.; Colman, R.; Shell, K. M.; Kiehl, J. T.; Shields, C. A. Quantifying climate feedbacks using radiative kernels. J. Clim. 2008, 21 (14), 3504−3520. (76) Shell, K. M.; Kiehl, J. T.; Shields, C. A. Using the radiative kernel technique to calculate climate feedbacks in NCAR’s community atmospheric model. J. Clim. 2008, 21 (10), 2269−2282. L


Critical Review


estimations of future risks in Central Italy. Sci. Total Environ. 2012, 441 (0), 28−40. (94) West, P. C.; Narisma, G. T.; Barford, C. C.; Kucharik, C. J.; Foley, J. A. An alternative approach for quantifying climate regulations by ecosystems. Front. Ecol. Environ. 2011, 9 (2), 126−133. (95) Baldocchi, D.; Ma, S. How will land use affect air temperature in the surface boundary layer? Lessons learned from a comparative study on the energy balance of an oak savanna and annual grassland in California, USA. Tellus, Ser. B 2013, 65, No. 19994. (96) Bindoff, N. L.; Stott, P. A. Detection and attribution of climate change: From global to regional. Climate Change 2013: The Physical Science Basis, Contribution from Working Group I to the Fifth Assessment Report of the IPCC; Intergovernmental Panel on Climate Change (IPCC): Cambridge, U.K., 2013; Chapter 10. (97) Cherubini, F.; Bright, R. M.; Strømman, A. H. Climate impacts of forest bioenergy: What, when, and how to measure? Environ. Res. Lett. 2013, 8, No. 014049, DOI: 10.1088/1748-9326/8/1/014049. (98) Adler, P. R.; Grosso, S. J. D.; Parton, W. J. Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems. Ecol. Appl. 2007, 17 (3), 675−691. (99) Smith, P.; Bustamante, M. Final draft: Agriculture, forestry, and other land use (AFOLU). Climate Change Mitigation, Contribution by Working Group III to the Fifth IPCC Assessment Report; Intergovernmental Panel on Climate Change (IPCC): Geneva, Switzerland, 2013; Chapter 11. (100) Bruckner, T.; Bashmakov, A. B.; Mulugetta, Y. Final draft: Energy systems.. Climate Change Mitigation, Contribution by Working Group III to the Fifth IPCC Assessment Report; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2013; Chapter 7. (101) Lucht, W.; Schaaf, C.; Strahler, A. H.; d’Entremont, R. Remote sensing of albedo using the BRDF in relation to land surface properties. In Observing Land from Space: Science, Customers and Technology; Verstraete, M., Menenti, M., Peltoniemi, J., Eds.; Kluwer: Dordrecht, The Netherlands, 2000; Vol. 4, pp 175−186. (102) Loeb, N. G.; Lyman, J. M.; Johnson, G. C.; Allan, R. P.; Doelling, D. R.; Wong, T.; Soden, B. J.; Stephens, G. L. Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat. Geosci 2012, 5 (2), 110−113. (103) Gao, F.; He, T.; Wang, Z.; Ghimire, B.; Shuai, Y.; Masek, J.; Schaaf, C.; Williams, C. Multi-scale climatological albedo look-up maps derived from MODIS BRDF/albedo products. J. Appl. Remote Sens. 2014, 8 (1), No. 083532. (104) Jones, A. D.; Collins, W. D.; Torn, M. S. On the additivity of radiative forcing between land use change and greenhouse gases. Geophys. Res. Lett. 2013, 40 (15), 4036−4041. (105) Wan, Z. MODIS land-surface temperature algorithm theoretical basic document (LST ATBD), version 3.3; Institute for Computational Earth System Science, University of California: Santa Barbara, CA, USA, 1999.

(77) Flanner, M. G.; Shell, K. M.; Barlage, M.; Perovich, D. K.; Tschudi, M. A. Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nat. Geosci. 2011, 4 (3), 151−155. (78) Ghimire, B.; Williams, C. A.; Masek, J.; Gao, F.; Wang, Z.; Schaaf, C.; He, T. Global albedo change and radiative cooling from anthropogenic land-cover change, 1700 to 2005 based on MODIS, land-use harmonization, radiative kernels and reanalysis. Geophys. Res. Lett. 2014, No. 2014GL061671. (79) Joshi, M.; Shine, K.; Ponater, M.; Stuber, N.; Sausen, R.; Li, L. A comparison of climate response to different radiative forcings in three general circulation models: Towards an improved metric of climate change. Clim. Dyn. 2003, 20 (7−8), 843−854. (80) Hansen, J.; Sato, M.; Ruedy, R.; Nazarenko, L.; Lacis, A.; Schmidt, G. A.; Russell, G.; Aleinov, I.; Bauer, M.; Bauer, S.; Bell, N.; Cairns, B.; Canuto, V.; Chandler, M.; Cheng, Y.; Del Genio, A.; Faluvegi, G.; Fleming, E.; Friend, A.; Hall, T.; Jackman, C.; Kelley, M.; Kiang, N.; Koch, D.; Lean, J.; Lerner, J.; Lo, K.; Menon, S.; Miller, R.; Minnis, P.; Novakov, T.; Oinas, V.; Perlwitz, J.; Perlwitz, J.; Rind, D.; Romanou, A.; Shindell, D.; Stone, P.; Sun, S.; Tausnev, N.; Thresher, D.; Wielicki, B.; Wong, T.; Yao, M.; Zhang, S. Efficacy of climate forcings. J. Geophys. Res.: Atmos. 2005, 110 (D18), No. D18104. (81) Hansen, J.; Nazarenko, L. Soot climate forcing via snow and ice albedos. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (2), 423−428. (82) Shindell, D. T. Inhomogeneous forcing and transient climate sensitivity. Nat. Clim. Change 2014, 4 (4), 274−277. (83) Hansen, J.; Sato, M.; Ruedy, R. Radiative forcing and climate response. J. Geophys. Res.: Atmos. 1997, 102 (D6), 6831−6864. (84) Forster, P. M.; Blackburn, M.; Glover, R.; Shine, K. P. An examination of climate sensitivity for idealised climate change experiments in an intermediate general circulation model. Clim. Dyn. 2000, 16 (10−11), 833−849. (85) Bellouin, N.; Boucher, O. Climate Response and Efficacy of Snow Albedo Forcings in the HadGEM2-AML Climate Model, Hadley Centre Technical Note, HCTN82; U.K. Met Office: Exeter, U.K., 2010; p 8. (86) Davin, E. L.; de Noblet-Ducoudré, N.; Friedlingstein, P. Impact of land cover change on surface climate: Relevance of the radiative forcing concept. Geophys. Res. Lett. 2007, 34, No. L13702. (87) Ramankutty, N.; Foley, J. A. Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles 1999, 13 (4), 997−1027. (88) NASA. Prediction of Worldwide Energy Resource (POWER); NASA Langley Research Center: Hampton, VA, USA; accessed May 3, 2013 at http://power.larc.nasa.gov/common/php/POWER_ AboutPOWER.php: 2013. (89) Lutz, D. A.; Howarth, R. B. The price of snow: The use of climate metrics for forest albedo valuation. Environ. Res. Lett. 2014, submitted for publication. (90) Lohila, A.; Minkkinen, K.; Laine, J.; Savolainen, I.; Tuovinen, J.P.; Korhonen, L.; Laurila, T.; Tietäväinen, H.; Laaksonen, A. Forestation of boreal peatlands: Impacts of changing albedo and greenhouse gas fluxes on radiative forcing. J. Geophys. Res.: Biogeosci. 2010, 115 (G4011), 1−15. (91) Kirschbaum, M. U. F.; Whitehead, D.; Dean, S. M.; Beets, P. N.; Shepherd, J. D.; Ausseil, A.-G. E. Implications of albedo changes following afforestation on the benefits of forests as carbon sinks. Biogeosciences 2011, 8, 3687−3696. (92) Joos, F.; Roth, R.; Fuglestvedt, J. S.; Peters, G. P.; Enting, I. G.; von Bloh, W.; Brovkin, V.; Burke, E. J.; Eby, M.; Edwards, N. R.; Friedrich, T.; Frölicher, T. L.; Halloran, P. R.; Holden, P. B.; Jones, C.; Kleinen, T.; Mackenzie, F.; Matsumoto, K.; Meinshausen, M.; Plattner, G.-K.; Reisinger, A.; Segschneider, J.; Schaffer, G.; Steinacher, M.; Strassmann, K.; Tanaka, K.; Zimmermann, A.; Weaver, A. J. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: A multi-model analysis. Atmos. Chem. Phys. 2013, 13, 2793−2825. (93) Morabito, M.; Crisci, A.; Moriondo, M.; Profili, F.; Francesconi, P.; Trombi, G.; Bindi, M.; Gensini, G. F.; Orlandini, S. Air temperature-related human health outcomes: Current impact and M


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