Atmospheric Environment 40 (2006) 1464–1479 Development of species-based, regional emission capacities for simulation of biogenic volatile organic compound emissions in land-surface models: An example from Texas, USA Lindsey E. Gulden, Zong-Liang Yang Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, 1 University Station #C1100, Austin, TX 78712-0254, USA Received 13 July 2005; received in revised form 20 October 2005; accepted 22 October 2005 Abstract This paper introduces a method to incorporate species-based variation of the emission of biogenic volatile organic compounds (BVOCs) into regional climate and weather models. We convert a species-based land-cover database for Texas into a database compatible with the community land model (CLM) and a database compatible with the Noah land-surface model (LSM). We link the LSM-compatible land-cover databases to the original species-based data set as a means to derive region-specific BVOC emission capacities for each plant functional type (in the CLM database) and for each land- cover type (in the Noah database). The spatial distribution of inherent BVOC flux (defined as the product of the BVOC emission capacity and the leaf biomass density) derived using the Texas-specific BVOC emission capacities is well correlated with the spatial distribution of inherent BVOC flux calculated using the original species data (r ¼ 0:89). The mean absolute error for the emission- capacity-derived inherent flux distribution is an order of magnitude lower than the statewide range of inherent fluxes. The ground-referenced land-cover databases derived here are likely more accurate than their satellite-derived counterparts; they can be used for a variety of regional model simulations in Texas. The inherent BVOC flux distributions derived using region-specific BVOC emission capacities are more consistent with observations than the BVOC flux distribution derived using the CLM3 standard BVOC emission capacities, which are top-down estimates based on the literature. When used in conjunction with detailed land-cover data sets, region-specific BVOC emission capacities produce reasonably accurate inherent BVOC fluxes. r 2005 Elsevier Ltd. All rights reserved. Keywords: BVOC; Biogenic emissions; Regional climate models; Regional weather models; Emission capacity 1. Introduction Comprehensive, stand-alone models of the cli- mate system require the accurate simulation of processes that involve biogenic emissions. Biogenic volatile organic compounds (BVOCs) are messen- gers between the land surface and atmosphere. Atmospheric conditions influence the rate at which BVOCs are emitted to the atmosphere, and BVOCs, in turn, influence atmospheric chemistry, cloud formation, Earth’s radiative balance, and the global carbon cycle. ARTICLE IN PRESS www.elsevier.com/locate/atmosenv 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.10.046 Corresponding author. Tel.: +1 512 471 3824; fax: +1 512 471 9425. E-mail address: [email protected] (Z.-L. Yang).
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Atmospheric Environment 40 (2006) 1464–1479
www.elsevier.com/locate/atmosenv
Development of species-based, regional emission capacities forsimulation of biogenic volatile organic compound emissions in
land-surface models: An example from Texas, USA
Lindsey E. Gulden, Zong-Liang Yang�
Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin,
1 University Station #C1100, Austin, TX 78712-0254, USA
Received 13 July 2005; received in revised form 20 October 2005; accepted 22 October 2005
Abstract
This paper introduces a method to incorporate species-based variation of the emission of biogenic volatile organic
compounds (BVOCs) into regional climate and weather models. We convert a species-based land-cover database for Texas
into a database compatible with the community land model (CLM) and a database compatible with the Noah land-surface
model (LSM). We link the LSM-compatible land-cover databases to the original species-based data set as a means to
derive region-specific BVOC emission capacities for each plant functional type (in the CLM database) and for each land-
cover type (in the Noah database).
The spatial distribution of inherent BVOC flux (defined as the product of the BVOC emission capacity and the leaf
biomass density) derived using the Texas-specific BVOC emission capacities is well correlated with the spatial distribution
of inherent BVOC flux calculated using the original species data (r ¼ 0:89). The mean absolute error for the emission-
capacity-derived inherent flux distribution is an order of magnitude lower than the statewide range of inherent fluxes.
The ground-referenced land-cover databases derived here are likely more accurate than their satellite-derived
counterparts; they can be used for a variety of regional model simulations in Texas. The inherent BVOC flux distributions
derived using region-specific BVOC emission capacities are more consistent with observations than the BVOC flux
distribution derived using the CLM3 standard BVOC emission capacities, which are top-down estimates based on the
literature. When used in conjunction with detailed land-cover data sets, region-specific BVOC emission capacities produce
processes that involve biogenic emissions. Biogenicvolatile organic compounds (BVOCs) are messen-gers between the land surface and atmosphere.Atmospheric conditions influence the rate at whichBVOCs are emitted to the atmosphere, and BVOCs,in turn, influence atmospheric chemistry, cloudformation, Earth’s radiative balance, and the globalcarbon cycle.
ARTICLE IN PRESSL.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–1479 1465
The long-term pattern of atmospheric variationdetermines the composition of plant species thatcovers the land surface; vegetation species exertsprimary control on the magnitude of BVOC flux.On a shorter time scale, variation in atmosphericconditions determines the amount of photosyntheticactive radiation (PAR) reaching the leaf surface,leaf-surface temperature, and soil moisture, all ofwhich influence variation in BVOC emissions (e.g.,Guenther et al., 1991; Plaza et al., 2005). Onceemitted to the atmosphere, BVOCs react in thepresence of nitrogen oxides to increase the concen-tration of tropospheric ozone (e.g., Wiedinmyeret al., 2001a, b), which is a respiratory irritant andmajor component of smog. The oxidation productsof both isoprene and non-isoprene BVOCs con-dense to form secondary organic aerosols (Claeyset al., 2004; Kavouras et al., 1998), which directlyalter Earth’s radiative balance and can serve ascloud-condensation nuclei (Andreaevc and Crutzen,1997). Most BVOCs are precursors to atmosphericcarbon dioxide and are thus a mechanism forcarbon exchange between the land surface and theatmosphere (e.g., Guenther, 2002). The magnitudeof the BVOC flux between the land surface andatmosphere is considerable: Guenther et al. (1995)estimate that the combined global BVOC emissionscontribute 1150Tg of carbon to the atmosphereeach year.
Large-scale study of land-surface–atmospherefeedbacks that involve biogenic emissions requiresthat BVOC fluxes be accurately represented withinland-surface models (LSMs), which serve as thelower boundary for weather and climate models.The ability of LSMs to correctly represent BVOCflux limits the value of future improvements toweather and climate models’ parameterizations ofBVOC-related atmospheric processes.
The dependence of BVOC emission on plantspecies poses a significant obstacle to the accuraterepresentation of biogenic emissions within LSMs.Although changes in environmental conditions causethe greatest variation in the quantity of BVOCsemitted from a single plant, interspecies variation inBVOC emission capacity exerts primary control onthe differences in the rates at which disparatelandscapes emit BVOCs. Current-generation LSMsrepresent vegetation either as static land-cover classes(e.g., as in the Noah LSM; Chen and Dudhia, 2001)or as mosaics of plant functional types (PFTs), suchas ‘‘temperate needleleaf evergreen tree’’ and ‘‘tem-perate broadleaf deciduous shrub’’ (e.g., as in the
community land model (CLM) version 3; Olesonet al., 2004; Bonan et al., 2002). The vast number ofspecies represented by a single LSM land-cover typemakes accurate representation of BVOC fluxes usingLSM land-cover types a challenge.
In their BVOC emissions module, which isstandard within CLM3, Levis et al. (2003) (here-after, ‘‘Levis et al.’’) represent broad-scale inter-species variability in BVOC emissions by assigningunique emission capacities to each PFT. They basetheir emissions module on the work of Guentheret al. (1995), who modeled BVOC flux as a functionof foliar density, PAR, leaf-surface temperature,and an ecosystem-specific emission capacity. For agiven PFT, Levis et al. represent the emission ofBVOC type i (isoprene, monoterpene, other volatileorganic compounds [OVOCs], other reactive vola-tile organic compounds [ORVOCs], or CO) as
Fi ¼ eiDðLsun þ LshadeÞiT i, (1)
where Fi is the flux to the atmosphere of BVOC typei (units: mgCm�2 h�1), D the foliar density (units: gdry leaf matter (gdlm)m�2 of ground covered by thePFT), ei a PFT-specific emission capacity (units:mgCgdlm�1 h�1), Lsun and Lshade are dimensionlessfactors that modulate BVOC emissions from sunlitand shaded leaves in response to changes in PAR,and Ti is a dimensionless factor that adjusts BVOCflux in response to changes in canopy temperature.
Global observational estimates of total BVOCemissions do not yet exist; however, the magnitudeand spatial variability of the global BVOC fluxsimulated offline by Levis et al.’s module areconsistent with other model estimates (e.g., Guentheret al., 1995). When Levis et al. simulated BVOCemissions at 3.751� 3.751 resolution for 10 modelyears using the fully coupled community climatesystem model with dynamic vegetation, interannualvariability in total BVOC flux reached 29%. This isconsistent with the work of Abbot et al. (2003), whoused space-based atmospheric column measurementsof formaldehyde as a proxy for isoprene emissionsover North America. The scientists found thatbetween 1995 and 2001, interannual variability inestimated isoprene emissions was �30% (Abbotet al., 2003); the spatial distribution and magnitudeof their estimated isoprene emissions generallymatched the predictions of models with emissionsparameterizations similar to that of Levis et al. Plot-scale observational studies also report appreciableinterannual variation in BVOC emission (e.g.,Hakola et al., 2003).
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Levis et al. used globally constant PFT-specificemission capacities. The ‘‘top-down’’ assignment ofPFT-specific emissions capacities is a defensiblesimplification for qualitative global simulations butis likely insufficient for regional simulations. Leviset al. suggest using region-specific emission capa-cities as a way to increase the accuracy with whichCLM3 simulates BVOC fluxes on a regional scale.Here, we present a ‘‘bottom-up’’ method fordetermining Texas-specific BVOC emission capaci-ties for PFTs using a species-based, ground-refer-enced land-cover database (Wiedinmyer et al.,2001b). We also apply this method to developregional BVOC emission capacities for the staticland-cover types used in the Noah LSM, which isused as the lower boundary for the fifth-generationPenn State/NCAR Mesoscale Model (MM5) (Grellet al., 1995) and for the weather research andforecasting (WRF) model (Skamarock et al., 2005).CLM is an LSM representative of those used inclimate model simulations; the Noah LSM is widelyused in both the weather- and climate-modelingcommunities. Methods described here can beapplied to other LSMs as research needs dictate.
In this paper, we address only the BVOC emissioncapacity of the land surface. We do not reportsimulation results. Unique contributions of thisstudy are (1) the development of two ground-referenced, species-based, 1-km land-cover data-bases for Texas that can be used as input to LSMs(CLM and Noah) and (2) the demonstration andevaluation of a method for deriving region-specificBVOC emission capacities for use in regionalclimate and weather models.
2. Data and methods
2.1. The Wiedinmyer database
The Wiedinmyer et al. (2001b) database describesthe Texas landscape using 600+ species-based land-cover classes. Wiedinmyer and colleagues developedthe database as input to an MS Access-based BVOCemissions module, GLOBEIS (http://www.globeis.com; Yarwood et al., 1999). Where BVOC emis-sions are typically high (in eastern and centralTexas), the researchers conducted field surveys todetermine species composition and density informa-tion. The researchers then mapped that informationto the land-cover classes contained in 10 existing,often overlapping land-cover databases for variousregions in Texas. A detailed description of the
methods used to develop the data set can be foundin Wiedinmyer et al. (2000, 2001b).
The resolution of the Wiedinmyer database is�1 km; some urban areas have a finer resolution.We employed ESRI’s ArcGIS software to convertthe original Wiedinmyer database to a uniform1� 1-km2 gridded data set.
Each Wiedinmyer land-cover class contains spe-cies composition information. One class containsbetween 1 and 115 of 289 possible componentspecies. For every species that makes up a land-cover class, the database contains the percent of theland-cover class ground area that is covered by thespecies and the foliar density of the species, Di,which is the leaf biomass of the given species that iscontained in each unit area of the land-cover class.Di is defined as
Di ¼Aidi
Atotal, (2)
where di is the leaf biomass of species i per unit areacovered by species i, Ai the land-cover–class groundarea covered by species i, and Atotal the total groundarea covered by the land-cover class. (Ai Atotal
�1 is thepercent of the area of the given land-cover class thatis covered by species i.) The sum of the area fractionof all species in a land-cover class is the total percentvegetated area; the remaining percentage is assumedunvegetated. When linked to tables contained inGLOBEIS, the Wiedinmyer database also containsBVOC emission capacities (units: mgC gdlm�1 h�1)for each of the contained 289 vegetation species.
2.2. Conversion of Wiedinmyer database to PFTs
We used theWiedinmyer database to develop a dataset that describes the Texas landscape in terms ofCLM-compatible PFTs (Fig. 1). Every species con-tained in theWiedinmyer database was assigned to onePFT. We assumed all PFTs in Texas are temperate(i.e., because the state is entirely located in the mid-latitudes, there are neither tropical PFTs nor borealPFTs in Texas). A list of definitions of PFTs present inTexas can be found in the footnotes of Table 1.
Species in phylogenetic divisions Cycadophyta,Ginkgophyta, and Magnoliophyta were consideredbroadleaf; species in division Coniferophyta, nee-dleleaf. To determine whether a species is evergreenor deciduous, we used the USDA Plants NationalDatabase (http://plants.usda.gov) as our primarysource of information. Secondary sources includedthe Texas A&M Trees and Shrubs of Texas
databases (http://aggie-horticulture.tamu.edu/ornamentals/natives/tamuhort.html) and the workof Stahl and McElvaney (2003). If informationregarding the leaf life of a species was contradictory,priority was given to the information in the PlantsNational Database. If information was unavailable,the species was considered deciduous or evergreenbased on the characteristics of plants sharing thesame genus and based on local knowledge.
The Wiedinmyer database does not containinformation that allows for objective descriptionof species as trees or shrubs. In the initial conver-sion, all shrubs were considered trees. For example,Atriplex canescens, Four-Wing Saltbush, althoughmorphologically a shrub, was considered a broad-
leaf deciduous tree (BDT) in the first analysis. Afterthe initial conversion, all ‘‘trees’’ in Wiedinmyerland-cover classes whose descriptive names con-tained the words ‘‘Shrub’’, ‘‘Brush’’, or ‘‘Shrub andBrush’’ were reclassified as shrubs. (It is worthwhileto note that this method prevents the coexistence oftrees and shrubs in a given 1� 1-km2 grid cell,which likely misrepresents reality in transitionalregions such as the savannas of central Texas.) Thestandard initialization of the CLM does not containa temperate needleleaf evergreen shrub (NES), butthe prevalence of temperate evergreen coniferousshrub species (e.g., Alligator Juniper [Juniperus
deppeana]) in some regions of western Texasjustified the inclusion of NES as a component
broadleaf evergreen shrub; BDT, broadleaf deciduous tree; BDS, broadleaf deciduous shrub.bFor the calculations of statewide inherent fluxes using Levis et al.’s emission capacities, the NES emission capacity was assumed to
have the same value as the Levis et al. emission capacity for NET. NDT (a temperate PFT) was assumed to have the same value as the
Levis et al. emission capacity for boreal NDT.cWater is not a PFT in CLM; however, in the Wiedinmyer database, water bodies can have BVOC-emitting capacity. Consequently, an
emission capacity for water was calculated.
L.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–14791468
PFT. The standard initialization of CLM also doesnot contain a temperate needleleaf deciduous tree(NDT), but the presence of bald cypress (Taxodium
distichum) in eastern Texas justified the addition oftemperate NDT to the PFTs existing in Texas.
All crop species, regardless of photosyntheticpathway, were considered part of the same PFT(crop). Because the Wiedinmyer database does notprovide species information for vegetation identifiedas ‘‘grass’’, we were unable to use the Wiedinmyerdatabase to distinguish between C3 and C4 grasses.To define grass as C3, C4, or a C3–C4 mix, wefollowed the methods of Bonan et al. (2002). Grasspopulations in locations where the mean monthlytemperature never reaches or exceeds 22 1C weredefined as pure C3. No location in Texas has meetsthe criteria to contain pure C4 grass, which requiresthat mean monthly temperatures exceed 22 1Cthroughout the year. Remaining grasslands wereconsidered to be 50% C3 and 50% C4. To
approximate monthly mean temperature, we aver-aged PRISM 1971–2001 mean monthly maximumand minimum temperature data (see http://www.ocs.oregonstate.edu/prism). The native resolution ofthe PRISM grids was 4 km; we used nearest-neighborhood interpolation to obtain 1-km grids.
For each Wiedinmyer land-cover class, wecalculated (1) the percent of ground area coveredby every PFT and (2) the foliar density of eachcomponent PFT. The former was calculated bysumming the area fraction (units: m2m�2) of allspecies in the class identified as a PFT. The later wasdefined as the sum of the foliar densities of all plantspecies contained within the land-cover class identi-fied as a PFT. Fig. 1 shows a schematic diagram ofthe process, and Fig. 2 displays the resulting PFTdistribution. Employing information containedwithin the Wiedinmyer database, we createdgridded landunit-level maps of urban and water-covered areas in Texas.
L.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–14791470
2.3. Conversion of Wiedinmyer database to Noah-
compatible land-cover types
Like many LSMs, Noah uses static land-covertypes (e.g., ‘‘Mixed Forest’’, ‘‘Cropland/GrasslandMosaic’’) to represent surface vegetation. Because aNoah land-cover type can contain more than onetype of plant (e.g., ‘‘Savanna’’ contains both grassand tree species), conversion from the species-basedWiedinmyer data set directly to Noah land-covertypes could be accomplished only by using sub-jective methods. We chose instead to determine thedistribution of Noah land-cover types in Texas byusing the PFT percent composition information asinput to a decision-tree algorithm (Fig. 3). Theresulting database can be used as input toWRF–Noah version 2.1, the default version ofwhich uses land-cover categories identical to thoseused in the USGS land-use/land-cover system(Anderson et al., 1976). Fig. 4 compares theWiedinmyer-derived Noah land cover with thedefault USGS-based land cover.
Breakpoint values within the decision tree (e.g.,‘‘MinValTrees’’, the minimum percentage of treecover required for a land-cover class to beconsidered a type of forest, savanna, woodedwetland, or a cropland–woodland mosaic; seelegend of Fig. 3) were chosen so that the definitionsof the land-cover classes were broadly consistentwith the work of other researchers (e.g., Hansenet al., 2000) (Fig. 5).
2.4. Calculation of Texas-specific BVOC emission
capacities
The CLM emissions module calculates BVOCfluxes for isoprene, monoterpene, and OVOCs, aswell as for CO and ORVOCs. In the standardversion of CLM3, emission capacities for CO andORVOCs do not differ between PFTs. The GLO-BEIS-linked Wiedinmyer database contains plant-species-specific emissions rates for only isoprene,monoterpene, and OVOCs. We calculated region-specific emission capacities for only these three
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Fig. 4. Results of conversion of Wiedinmyer database to Noah land-cover (Panel A); default USGS-based Noah land-cover distribution
BVOC species. The emission capacity of PFTx withrespect to a single BVOC type, ePFTx (units:mgCgdlm�1 h�1), was calculated as
ePFTx¼
FPFTx
WPFTx
, (3)
where FPFTxis the total BVOC emissions per hour
emitted by PFTx across Texas (units: mgCh�1) and
WPFTx the total leaf biomass (gdlm) of PFTi inTexas. We defined
FPFTx¼Xn
j¼1
Aj
XmPFTx
i¼1
eiDi
!j
, (4)
where n is the number of land-cover classesin Texas, Aj the total area in Texas covered by
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land-cover class j, mPFTxthe number of species in
land-cover class j identified as PFTx, ei the emissionsrate per unit biomass (units: mgC gdlm�1 h�1) of theith species identified as PFTx in land-use code j,andDi the foliar density (as defined in Eq. (2)) of theith species in land-use code j. Fig. 5 shows thedistribution of leaf biomass density, according tothe Wiedinmyer database. We calculated
WPFTx ¼Xn
j¼1
Aj
XmPFTx
i¼1
Di
!j
, (5)
where Aj, Di, and mPFTxare as described above.
There exists significant variation in the emissioncapacity of the Texas-native plant species thatcompose each PFT. It is possible that plant-species’emission capacities vary regularly as a function of
Table 2
BVOC emission capacities (ei, units: mgCgdlm�1 h�1) for Noah land-c
Land covera Urban Crop Crop/Grass Crop/Wood Grass Shrub
mosaic; Shrub, shrubland; Shrub/Grass, shrubland/grassland mosaic; D
Mixed, mixed forest; Herb. Wet., herbaceous wetland. Deciduous need
included in the above table because, in this analysis, nowhere in Texas is
mixture of DBF, EBF, ENF, and/or DNF, in which no tree type cover
Fig. 3).bEmission capacities shown are rounded to the nearest tenth, but ra
inherent emission rates.
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0 - 50>50 - 100>100 - 150>150 - 200
>200 - 250>250 - 300>300 - 400>400 - 600
Leaf biomass density ( )gdlm
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Fig. 5. Leaf biomass density (gdlmm�2) according to the
Wiedinmyer database.
location within Texas. (For instance, species classi-fied as BDT that are native to western Texas mayhave emission characteristics that differ significantlyfrom those of species classified as BDT that arenative to eastern Texas.) To explore this possibility,we derived two sets of region-specific PFT emissioncapacities for two sub-regions in Texas (eastern andwestern Texas). The 99th meridian formed theapproximate boundary between the two sub-regions.
Table 1 compares the Levis et al. emissioncapacities with the region-specific emission capa-cities derived here. Following methods similar tothose outlined above, we also derived region-specificemission capacities for use with the Noah land-cover database (Table 2).
2.5. Calculation of specific leaf area for use in Noah
LSM and CLM model runs
The Levis et al. emissions module computesbiogenic emissions as a function of leaf biomassdensity, but CLM tracks variations in vegetationbiomass using leaf area index (units: m2 leafm�2 -ground). Levis et al. assign each PFT a time-invariant specific leaf area (units: m2 leaf gdlm�1),which allows for conversion between leaf area indexand leaf biomass density (Foley et al., 1996; Levis etal., 2003). Using static leaf area index and leafbiomass density values provided in the Wiedinmyerdatabase, we derived specific leaf area for each PFTand each Noah land-cover type using a biomass-weighted averaging method analogous to that usedto derive region-specific BVOC emission capacities(Table 3).
Specific leaf area values are not necessary forevaluation of the region-specific emission capacities;
over types
Shrub/Grass Savanna DBF ENF Mixed Water Herb. Wet.
0.1 0.0 36.7 12.2 15.0 2.2 1.4
0.2 0.0 0.5 2.6 0.6 1.4 0.9
1.3 0.0 1.8 1.8 1.8 1.6 1.1
ass, cropland/grassland mosaic; Crop/Wood, cropland/woodland
aSee first footnote of Table 1 for full names corresponding to the PFT acronyms; see first footnote of Table 2 for full names
corresponding to land-cover type abbreviated names.bTemperate NDT and NES do not exist in the standard version of CLM3; specific leaf areas for NDT and NES are assumed to be equal
to the Levis et al. specific leaf area for NET.cThe specific leaf area calculated for grass species using the data provided in the Wiedinmyer database (SLAE0.00001) was
unreasonably small; consequently, we recommend the use of the standard-CLM specific leaf area for grass.dBecause the specific leaf area calculated for grass is unreasonably small (see previous footnote), the specific leaf area calculated for
grasslands (which contain a large percentage of grass species) may also be a significant underestimate.
however, we present them here because they arenecessary components of model simulations that usethe Texas-specific emission capacities derived in thisstudy. It should be noted that the Noah LSM usesgreenness fraction, not leaf area index, to trackvariations in biomass. The specific leaf area valuesfor each Noah land-cover code shown in Table 3must, therefore, be used in conjunction with amechanism to convert greenness fraction to leafarea index.
2.6. Calculation of inherent BVOC flux
We used inherent BVOC flux as a measure bywhich to evaluate the accuracy of the emissioncapacities derived here. Inherent flux, F, for eachtype of BVOC was defined as
F ¼Xn
i¼1
eiDi, (6)
where n is the number of species or land-cover typescovering the grid cell, ei the emission capacity ofspecies, land-cover type, or PFT i, and Di the foliardensity of i (as defined in Eq. (2)). In all cases, weused the foliar density information provided in theWiedinmyer database to compute inherent BVOCflux. For computations using PFTs, we considered
Di to be the sum of the foliar densities of all speciesin a grid cell identified as PFT i. Because Noah land-cover classes are a static composition of plant types(n ¼ 1 in Eq. (6)), the inherent flux for a given 1� 1-km2 grid cell is F ¼ eD, where e is the emissioncapacity for the land-cover class and D the grid-cell–mean foliar density (as defined in Eq. (2)).Evaluation of results using inherent BVOC fluxcalculated using a standard leaf biomass densitymap allowed us to focus entirely on variation inBVOC emission resulting from differences in emis-sion capacities, neglecting differences resulting fromenvironmental variation.
3. Results and discussion
We defined the total inherent BVOC flux as thesum of the inherent fluxes for isoprene, mono-terpene, and OVOCs. Fig. 6 shows the statewidetotal inherent BVOC fluxes calculated using theLevis et al. emission capacities and the Wiedinmyer-derived CLM-compatible land-cover data set(‘‘Levis’’); calculated using the one-region, Texas-specific PFT emission capacities and the CLM-compatible data set (‘‘One-region’’); calculatedusing the two-region, Texas-specific PFT emissioncapacities and the CLM-compatible data set (‘‘Two-region’’); and calculated using the Texas-specific
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Maximum emissions capacity ( )
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Fig. 6. Total inherent BVOC emission capacity (sum of the inherent emission capacity for isoprene, monoterpene, and OVOC). The panel
labels (e.g., ‘‘Levis’’) describe the method used to calculate the total inherent BVOC emission capacity.
L.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–14791474
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Table 5
Correlation and error of calculated inherent BVOC fluxes
Method used to calculate BVOC emissions Pearson’s product-
moment correlation
coefficient (r)
Mean absolute
erroraRoot mean square
errora
Calculated directly from species-specific emission capacities. 1.0000 0 0
CLM: Levis et al. emission capacities (globally constant) 0.8502 1668 2577
Noah land-cover emission capacities and the Noah-compatible land-cover data set (‘‘Noah’’). Whenevaluating the ability of the region-specific emissioncapacities to represent BVOC emissions in Texas,we considered the inherent BVOC flux deriveddirectly from the species-specific emission capacitiesand the species-based land-cover map to be the‘‘observed’’ data (‘‘Wiedinmyer’’ in Fig. 6).Although the spatial distribution of the ‘‘Levis’’inherent fluxes is qualitatively similar to that of theobserved data, the inherent fluxes calculated withregion-specific emission capacities greatly improvethe LSMs’ ability to simulate BVOC emissions.
Table 4 shows univariate statistics for the resultsshown in Fig. 6; Table 5 displays the correlationcoefficient, the mean absolute error (MAE), and theroot mean square error for the results shown inFig. 6. The inherent BVOC fluxes calculated usingthe region-specific emission capacities are bettercorrelated with the observed fluxes than the inherentfluxes derived using Levis et al. emission capacities.
A comparison of the MAE values for the inherentflux data sets shows that regional emission capa-cities greatly improve the ability of LSMs tosimulate inherent BVOC fluxes, and consequently,BVOC emissions.
All MAE values for the region-specific inherentfluxes are 41% or less of the mean inherent flux, andall are an order of magnitude lower than therange of values in the observed data sets. Thiscompares quite favorably to the MAE of the‘‘Levis’’ inherent emission rates, which is 73% ofthe data set’s mean value and 19% of the range.When the Texas-specific emission capacities derivedin this study are used in climate and weathersimulations, the spatial distribution of BVOCemissions will better reflect observed emissions,which will consequently improve the spatial accu-racy of the modeled climate and weather processesthat depend on biogenic emissions (e.g., secondaryorganic aerosol formation and tropospheric ozoneproduction).
ARTICLE IN PRESS
San Antonio Austin Houston Dallas-Fort Worth
Inhe
rent
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issi
onra
te (
)
�g C
m2 ·
h6000
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Wiedinmyer Two-regionLevis Noah
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Fig. 7. Comparison of the average inherent BVOC flux in four Texas cities calculated using the ‘‘observed’’ species data (‘‘Wiedinmyer’’),
the globally constant PFT-specific emission capacities used by Levis et al. (‘‘Levis’’), the Wiedinmyer-derived land-cover-type emission
capacities for use in the Noah LSM (‘‘Noah’’), and the Wiedinmyer-derived PFT-specific emission capacities calculated using statewide
plant-species data (‘‘One-region’’) and species data from east and west Texas (‘‘Two-region’’).
L.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–14791476
Use of the globally constant emission capacitiesfrom Levis et al. substantially underestimates thetotal BVOC emissions in Texas: the cumulativestatewide inherent BVOC flux calculated using theLevis et al. emission capacities was only 85% of theobserved (Wiedinmyer) cumulative statewide inher-ent flux. Because the method presented herepreserves the total mass of BVOCs emitted fromthe landscape under normalized temperature andPAR conditions, each of the three Wiedinmyer-derived inherent flux distributions has cumulativestatewide inherent BVOC fluxes that are identical tothat computed using the original Wiedinmyer dataset (Table 4). However, the different spatialdistribution of inherent fluxes obtained using theWiedinmyer-derived emission capacities results indifferent average BVOC fluxes at regional scales.Fig. 7 shows the average inherent BVOC fluxfor four major metropolitan areas in Texas. In eachof the four urban areas, use of any of the threeWiedinmyer-derived emission capacities (‘‘One-re-gion’’, ‘‘Two-region’’, and ‘‘Noah’’ in Fig. 7)substantially improves upon the average inherentflux calculated using Levis et al.’s values (‘‘Levis’’ inFig. 7).
Use of emission capacities derived from two sub-regions improves the spatial distribution of inherentemission capacities, especially in central Texas;however, the improvement in accuracy is not asgreat as one might expect. The lower foliar densityof the west-Texas landscape (Fig. 4) likely accountsfor a greater portion of the difference in the inherentfluxes than do the slight variations in the regional
emission capacities between the species native toeast and west Texas (Table 1). Different choice ofsub-regions (e.g., north and south Texas) might alsoprovide a greater increase in accuracy.
Further improvement of BVOC-emission simula-tion could likely be gleaned by the addition of‘‘high-emitting’’ land-cover types to CLM andNoah. For example, a BDT that shares all physicalparameters except its isoprene emission capacitywith the CLM-standard BDT would represent BDTspecies with anomalously high-isoprene emissioncapacities (e.g., oak species). Emission capacitiescalculated for the ‘‘high-isoprene’’ BDT and thestandard BDT would consequently be more repre-sentative of the emission capacities of their con-stituent species. Similar high-emitting PFTs forplant species with anomalous monoterpene orOVOC emission capacities would improve modelsimulation results in regions where such species areprevalent. Future work will include the addition ofsuch land-cover types to both CLM and Noah.
The analysis presented here focuses on therelative improvement gained by using region-spe-cific, species-based emission capacities. Evaluationof the absolute improvement in accuracy gained byusing the ecologically based emission capacities ischallenging because of a dearth of large-scale, high-resolution empirical data. However, the ecologicallybased, ‘‘bottom-up’’ method presented here ispresumably more plausible than the ‘‘top-down’’assignation of emission capacities to land-covertypes used in similar modeling studies (e.g., Leviset al.; Naik et al., 2004). Means for evaluation of the
ARTICLE IN PRESSL.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–1479 1477
absolute accuracy of the magnitude and spatialdistribution of BVOC emissions and the weatherand climatic processes that depend upon them is anarea of ongoing research.
Application of this method to other regions maybe hindered by the lack of availability of detailed,species-based land-cover databases such as theWiedinmyer et al. database. Less-detailed species-based vegetation data sets, such as the 1-kmBiogenic Emissions Landcover Database (Kinneeet al., 1997), can be used in similar bottom-upfashion to derive region-specific emission capacitiesfor LSMs. In the US, myriad less extensive vegeta-tion species databases sponsored by state and federalagencies (e.g., the Calflora vegetation species data-base [http://www.calflora.org/]) augment the species-distribution data available to researchers.
The BVOC emission capacities derived for Texasmay be representative of those in humid-to-aridtransition zones and may thus be transferable toregions with similar climate. Sen et al. (2001) usedobservational data from five sites, each representa-tive of a different biome, to calibrate the vegetationparameters for a general circulation model; theirmodel simulations using the field-calibrated vegeta-tion data showed more skill than did those usingstandard parameters. A similar approach usingBVOC emission capacities derived from representa-tive regions might improve modeled BVOC emis-sions in continental- and global-scale simulationswithout requiring detailed, species-based vegetationmaps for all regions. However, because BVOCemissions vary at the species level, extensive furtherstudy is necessary to determine whether suchgeneralization is defensible.
As a derivative product, this study inherits alluncertainty associated with the input Wiedinmyerland-cover data set. As Wiedinmyer et al. (2000,2001b) point out, the uncertainty associated withthe original data set is difficult to quantify. Theextensive field surveys undertaken by Wiedinmyerand colleagues and the field-surveys underpinningthe 10 land-cover data sets that formed the basis forthe Wiedinmyer data set serve to lessen theassociated uncertainty; however, field-survey verifi-cation of the Wiedinmyer data set was limited tocentral and eastern Texas, where BVOC emissionsrates are high. Although ground surveys for thedata sets underpinning the Wiedinmyer data setwere generally not as localized, the uncertainty ofthe Wiedinmyer data set increases from east to west(Wiedinmyer et al., 2001b).
The accuracy of the inherent BVOC fluxescalculated here depends on the accuracy of theemissions data contained within GLOBEIS. Intras-pecies variation in BVOC emissions and the depen-dence of emissions on environmental variableslessens the transferability of site-specific, BVOC-emissions measurements to regional modeling appli-cations. Intraspecies variation is likely considerable:e.g., Funk et al. (2005) found that under normalclimate conditions, isoprene emissions from differentred oak trees within a single stand differed by afactor of two. Interspecies variation also introducesuncertainty. The number of vegetation species inTexas is considerably greater than the 289 speciescontained within the Wiedinmyer database; for mostspecies in Texas, observed BVOC emission capacitiesdo not exist. New field and laboratory data (e.g.,Geron et al., 2001) combined with methods forassigning BVOC emission capacities to species forwhich no measurements exist (e.g., Karlik et al.,2002) will lessen but not eliminate this uncertainty.
Another source of uncertainty inherent to themethod presented here results from the use of atime-varying property (leaf biomass density) toderive a time-invariant property (PFT-specificemission capacity). The emission capacities for usewith the Noah-compatible database also depend onthe selection of the breakpoint values in thedecision-tree algorithm used to derive the Noah-compatible database from the PFT database.Detailed factorial analysis could help determinethe most accurate breakpoint values (e.g., Hender-son-Sellers, 1993).
4. Summary and conclusions
The CLM-compatible and Noah-compatible datasets developed for this study can be used for a widevariety of regional weather and climate simulations.They are the first high-resolution, ground-referencedvegetation data sets for Texas for use in LSMs.
Use of the PFT emission capacities that arestandard in CLM3 likely underestimates the cumu-lative BVOC emissions from vegetation in Texasand fails to capture the range of inherent BVOCfluxes. Region-specific emission capacities, calcu-lated using either one region or two smaller sub-regions, improve the ability of LSMs to simulate theinherent BVOC flux. The correlation between theobserved inherent fluxes and the inherent fluxescalculated using region-specific emission capacities(r ¼ 0:89) is better than that between observations
ARTICLE IN PRESSL.E. Gulden, Z.-L. Yang / Atmospheric Environment 40 (2006) 1464–14791478
and the fluxes calculated using the CLM standardemission capacities (r ¼ 0:85). Use of region-specificemission capacities lessens the MAE of the calcu-lated inherent flux. (MAE is 1668 mgCm�2 h�1
when using standard CLM emission capacities;1111 and 1085 mgCm�2 h�1 when using region-specific CLM emission capacities derived from oneand two regions, respectively; and 945 mgCm�2 h�1
when using region-specific Noah emission capaci-ties.) Sub-division of Texas into two smaller sub-regions improved the accuracy of the spatialdistribution of inherent emission capacities. How-ever, for regions the size of Texas, a single set ofregional PFT BVOC emission capacities may beused without a large compromise in the overallaccuracy of the spatial distribution of emissions.
Region-specific, species-derived BVOC emissioncapacities, when used in conjunction with detailedland-cover data sets, allow for reasonably accuratesimulation of BVOC emissions on a regional scale.Climate and weather models that employ region-specific BVOC emission capacities will be betterequipped to study the impact of biogenic emissionson atmospheric processes and the influence ofatmospheric variation on biogenic emissions.
Acknowledgements
The Environmental Protection Agency providedfunding for this research (Grant No. RD83145201).We thank Christine Wiedinmyer, Victoria Jun-quera, and William Vizuete for providing us withthe original data set and for their insight andassistance over the course of the research. We wouldalso like to thank Robert Dickinson for valuablecomments regarding methods for improving simula-tion of BVOC emissions in LSMs. This manuscriptwas substantially improved by the insightful com-ments of two anonymous reviewers and of LaurenGreene.
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