Introduction
Volatile organic compounds (VOCs) in the atmosphere play an important role in
atmospheric chemistry, and therefore can significantly affect ozone and
secondary organic aerosol (SOA) formation and ultimately air quality and
climate (e.g., Chameides et al., 1992; Fehsenfeld et al., 1992; Andreae and
Crutzen, 1997; Pierce et al., 1998; Poisson et al., 2000; Sanderson et al.,
2003; Claeys et al., 2004; Arneth et al., 2010). Significant effort has been
made on obtaining accurate predictions of atmospheric VOC concentrations;
however, there remain large differences between observed and simulated
values. These uncertainties result from many factors, including biogenic
emission rates that are influenced by near-surface meteorological processes,
sub-surface processes, representation of vegetation distributions and plant
biology (Guenther, 2013).
Biogenic emissions are a major source of VOCs (e.g., Zimmerman et al., 1978;
Müller, 1992) in the atmosphere. In particular, isoprenoids (consisting
mainly of isoprene and monoterpenes) that dominate biogenic VOCs (BVOCs) have
been extensively investigated during the last 5 decades (e.g., Went, 1960;
Rasmussen, 1972; Zimmerman, 1979; Lamb et al., 1987; Pierce et al., 1998;
Niinemets et al., 1999, 2002; Arneth et al., 2007; Schurgers et al., 2009;
Guenther et al., 1995, 2012). BVOC emissions were originally computed
offline, producing prescribed emission inventories used by regional and
global models (e.g., Huang et al., 2011). However, emissions of BVOCs depend
on diurnal, multi-day and seasonal variations in light intensity,
temperature, soil moisture, vegetation type and leaf area index (LAI) (e.g.,
Pierce et al., 1998; Niinemets et al., 1999, 2002; Arneth et al., 2007;
Schurgers et al., 2009; Guenther et al., 2012). Therefore, various BVOC
emission algorithms have been proposed that extrapolate limited laboratory
and field measurements to prescribed regional and global ecosystems (e.g.,
Pierce et al., 1998; Niinemets et al., 1999, 2002; Arneth et al., 2007;
Schurgers et al., 2009; Guenther et al., 1995, 2012). The uncertainties in
biogenic emission schemes are mainly due to the scarcity of observations of
BVOC fluxes and vegetation distributions over regional scales. Inappropriate
coupling strategies between biogenic emission and land surface schemes may
also introduce errors in estimating atmospheric BVOCs. For example, some
models specify different vegetation distributions for biogenic emissions and
land–atmosphere interaction processes as applied in different parts of
models.
BVOCs play a significant role in affecting the air quality and regional
climate over California, where there have been many studies, such as the
Carbonaceous Aerosol and Radiative Effects Study (CARES) (Zaveri et al.,
2012) and the California Nexus of Air Quality and Climate Experiment
(CalNex) (Ryerson et al., 2013), investigating the impacts of BVOCs and
their interaction with anthropogenic pollutants. In the past 20 years,
California's economy has grown rapidly and the population has increased by
33 % (Cox et al., 2009). Although California has reduced the emissions of
most primary pollutants, poor air quality still affects the well-being of
millions of people. Nearly all Californians live in areas that are
designated as non-attainment for the state (about 99 %) and national (about
93 %) health-based O3 and/or PM standards. Accurate predictions of
O3 and PM concentrations are needed to develop effective attainment
strategies, but this is complicated, in part, due to uncertainties
associated with long-range transport of pollutants and local natural
emission sources such as BVOCs.
In California, the complex topography and distribution of vegetation makes
it difficult for models to capture the variability of BVOCs at regional and
local scales. For example, Fast et al. (2014) showed that simulated biogenic
emissions varied by as much as a factor of 2 within 8 km of an observation
site in Cool, California. They also found that daytime mixing ratios of
isoprene and monoterpenes from a regional simulation using the Weather
Research and Forecasting model with chemistry (WRF-Chem) (Grell et al.,
2005; Fast et al., 2006) are usually a factor of 2 smaller than the
observations collected both at the rural Cool site and an urban Sacramento
site. Conversely, simulated monoterpene mixing ratios were similar to
observations during the day but by a factor of 3 too high at night at
the observation site in Cool. They suggested that the biogenic emission
rates calculated based on the Model of Emissions of Gases and Aerosols from
Nature version 2.0 (MEGAN v2.0) might contribute to major biases in their
simulations. Knote et al. (2014) also found that their simulations using
WRF-Chem with MEGAN v2.0 produced BVOC concentrations that were too small
over Los Angeles, and suggested that there might be deficiencies in the
description of vegetation in urban areas. Thus, it is evident that
uncertainties in simulated atmospheric BVOCs can arise from how well
vegetation is represented in models. Furthermore, to our knowledge, none of
the numerous chemical transport modeling studies for California have
investigated the sensitivity of BVOC simulations to land surface schemes and
vegetation distributions.
To better understand the uncertainties in simulating BVOCs associated with
land surface schemes and vegetation distributions in California, the latest
version of MEGAN (MEGAN v2.1) is coupled into the CLM4 (Community Land
Model version 4.0) land surface
scheme of WRF-Chem in this study. Multiple sensitivity experiments are
conducted using this improved modeling framework at a relatively high spatial
resolution to capture the region's complex topography and vegetation
distribution. Simulations are conducted using WRF-Chem with a fully coupled
version of CLM4 and MEGAN v2.1 (i.e., CLM4 and MEGAN share a consistent
vegetation data set) and compared with the measurements collected during
CARES and CalNex conducted in June 2010. This new coupling also adds the
capability of quantifying the impact of different vegetation distributions on
simulating BVOCs. Simulations are also performed using two land surface
schemes (Noah and CLM4) coupled with MEGAN v2.0. As with previous studies
using WRF-Chem, MEGAN v2.0 uses a different vegetation data set from the land
surface schemes. The WRF-Chem experiments with MEGAN v2.0 and MEGAN v2.1 are
included together here as a reference for future studies in the community and
for users interested in migrating from the widely used v2.0 to v2.1.
The rest of manuscript is organized as follows. Sections 2 and 3 describe
the WRF-Chem model and the observations used in this study, respectively.
The sensitivity of modeling BVOCs to the land surface schemes and the
vegetation distributions are analyzed in Sect. 4. The findings are then
summarized and discussed in Sect. 5.
Model description and experimental design
WRF-Chem
The WRF-Chem (v3.5.1) configuration is similar to that used by Fast et
al. (2014) for studying aerosol evolution over California, except that this
study excludes aerosols and focuses on simulated BVOCs. The model includes
numerous options for the treatment of physics and chemistry processes. In
this study, the SAPRC-99 (Statewide Air Pollution Research Center
1999) photochemical mechanism
(Carter, 2000a, b) is selected to simulate gas-phase chemistry, and the
Fast-J parameterization (Wild et al., 2000) for photolysis rates. For all the
simulations in this study, we use the Yonsei University (YSU)
parameterization (Hong et al., 2006) for the planetary boundary layer (PBL),
the Monin–Obukhov similarity theory (Paulson, 1970) to represent the surface
layer, the Morrison two-moment parameterization (Morrison et al., 2009) for
cloud microphysics, the Kain–Fritsch parameterization (Kain, 2004) for
sub-grid scale clouds and precipitation and the rapid radiative transfer
parameterization (RRTMG) for longwave and shortwave radiation (Iacono et al.,
2008). Since Fast et al. (2014) has already evaluated the simulated
meteorological fields and gases and aerosols with a similar model
configuration, this study will focus primarily on the BVOC simulation.
Land surface schemes
Two land surface schemes, Noah and CLM4.0, are used to quantify how
differences in the treatment of land surface processes, including latent and
sensible heat fluxes, soil moisture and surface albedo, affect near-surface
meteorological conditions and consequently simulated BVOC emissions and
concentrations. The Noah land surface scheme, described by Barlage et al. (2010) and LeMone et al. (2010a, b), has been used in numerous studies
with WRF-Chem. Noah has four soil layers, with a total depth of 2 m
and a single slab snow layer that is lumped with the top-soil layer, which
is set to a combined depth of 10 cm. It uses the 24 United States Geological
Survey (USGS) land use types, and does not treat sub-grid scale variability
within a model grid cell.
The CLM4 (Community Land Model version 4.0) (Lawrence et al., 2011; Jin and
Wen, 2012) was recently coupled and released with WRF (since v3.5) as one of
the land surface scheme options. CLM4 in global and region applications has
been shown to be accurate in describing snow, soil and vegetation processes
(Zeng et al., 2002; Jin and Miller, 2007; Zhao et al., 2014). CLM4 includes 5
layers for snow, 10 layers for soil and 1 layer for vegetation. The soil is
divided into 19 categories defined according to percentages of sand and clay.
The two-stream approximation (Dickinson, 1983) is applied to vegetation when
calculating solar radiation reflected and absorbed by the canopy as well as
radiation transfer within the canopy. Each model grid cell can be divided
into a maximum of 10 smaller cells to account for sub-grid scale
heterogeneity and its impact on the land surface processes. The 24 USGS land
use types are mapped to the 16 plant functional types (PFTs) in CLM4 based on
a lookup table derived from Bonan (1996). Additional technical details of
CLM4 are provided in Oleson et al. (2010).
MEGAN and coupling with CLM4
MEGAN is a modeling framework for estimating fluxes of biogenic compounds
between terrestrial ecosystems and the atmosphere using simple mechanistic
algorithms to account for the major known processes controlling biogenic
emissions (Guenther et al., 2006, 2012). Two versions (v2.0 and v2.1) of
MEGAN are used in this study. MEGAN v2.1 is an update from MEGAN v2.0
(Guenther et al., 2006; Sakulyanontvittaya et al., 2008) that includes
additional compounds, emission types, and controlling processes. MEGAN v2.1
estimates emissions (Fi) for 19 compound classes (i) from terrestrial
landscapes based on emission factors (εi,j) at standard
conditions for vegetation type j with fractional grid box areal coverage
χj, i.e., Fi=γiΣεi,jχj, where γi is emission activity factor
from the processes controlling emission responses to environmental and
phenological conditions (Guenther et al., 2006, 2012).
For emission factors, MEGAN v2.0 enabled users to customize vegetation
emission type schemes ranging from detailed (e.g. individual plant species
or sub species) to generic (e.g. a few broad vegetation categories).
MEGAN2.1 emission factors can be specified from gridded maps based on
species composition and species-specific emission factors or by using PFT
distributions and the PFT specific emission factors. MEGAN2.0 defines
emission factors as the net flux of a compound into the atmosphere, while
MEGAN2.1 emission factor represents the net primary emission that escapes
into the atmosphere but is not the net flux because it does not include the
downward flux of chemicals from above canopy. The difference in the
definition (net flux vs. primary emission) of emission factors affects
the emission factors of compounds with bi-directional exchange but does not
impact MEGAN isoprene and monoterpene emission factors because they have
small deposition rates relative to emission rates. In this study, both MEGAN v2.0 and v2.1 estimate biogenic species emissions based on the PFT
distributions and the PFT specific emission factors. MEGAN v2.0 and v2.1 use
4 and 16 PFTs, respectively, as described below in Sect. 2.4.
The publicly available version of WRF-Chem includes the MEGAN v2.0 scheme for
calculating BVOC emission fluxes (WRF-Chem user guide:
http://ruc.noaa.gov/wrf/WG11/Users_guide.pdf). It has been widely used
for gas and aerosol simulations (e.g., Shrivastava et al., 2011, 2013; Gao et
al., 2011, 2014; Knote et al., 2014; Fast et al., 2014). In the released
version, MEGAN v2.0 can be used with any land surface scheme available in
WRF-Chem including Noah and CLM4. However, MEGAN v2.0 was originally not
coupled into the land surface scheme in WRF-Chem (since v3.1). The biogenic
emission calculation in MEGAN uses both instantaneous and the past-days'
surface air temperature and solar radiation. MEGAN v2.0 obtains the
instantaneous value from the land surface scheme and the past-days' value
from the climatological monthly mean data set. In contrast, MEGAN v2.1
obtains both values directly from CLM. Figure 1 shows the example of the
comparison between the input climatological and model simulated monthly mean
surface air temperature in June. It is apparent that the monthly averaged
simulated surface air temperature is much different from the climatology
value. In addition, the vegetation data set (referred to as VEG-M; will be
discussed in Sect. 2.4) used in MEGAN v2.0 for calculating BVOC emission
fluxes is also different from the one used by the land surface scheme, which
allows MEGAN v2.0 to be used with any of the available land surface schemes
(e.g., Noah and CLM4) in WRF-Chem. This inconsistency in vegetation
distributions may introduce errors in simulating emissions and concentrations
of BVOC. To avoid this inconsistency, we have coupled MEGAN v2.1 with
WRF-Chem embedded in the CLM4 land surface scheme. Therefore, the coupling of
MEGAN v2.1 and CLM4 in WRF-Chem now has the same functionality as CLM4 in the
Community Earth System Model (CESM) (Lawrence et al., 2011). With this
coupling strategy, MEGAN v2.1 also uses the same vegetation data set (i.e.,
16 PFTs converted from the USGS data set as discussed in Sect. 2.2) that CLM4
uses for all other land surface processes; this means, however, that
MEGAN v2.1 can only be used with CLM4 in WRF-Chem. In addition, MEGAN v2.1
can compute BVOC emissions that account for the sub-grid variability of
vegetation distributions within CLM4.
Vegetation data sets
As mentioned previously, the first 16-PFT data set (referred to as USGS
hereafter) used by CLM4 is converted from the default 24 USGS land cover data
set used by WRF-Chem based on a lookup table derived from Bonan (1996). This
method is also applied to three other 16-PFT data sets (referred to as VEG1,
VEG2 and VEG3) used by CLM4 in WRF-Chem. The sensitivity of simulating BVOC
emissions by CLM4 to these four 16-PFT data sets is quantified. The VEG1,
VEG2 and VEG3 data sets are derived from different sources as described next.
Spatial distributions of monthly mean surface air temperature in
June 2010 from the MEGAN v2.0 climatology data set (MEANv20, prescribed) and
the WRF-Chem simulations with the Noah (Noah, simulated) and CLM4 (CLM,
simulated) land surface schemes.
![]()
The VEG1 data set is from the PFT fractional cover product by Ke et al. (2012), which was developed from the Moderate Resolution Imaging
Spectroradiometer (MODIS) PFT classifications for the year 2005 for
determining seven PFTs including needleleaf evergreen trees, needleleaf
deciduous trees, broadleaf evergreen trees, broadleaf deciduous trees,
shrubs, grass and crops for each 500 m pixel. The WorldClim 5 arcmin
(0.0833∘) (Hijmans et al., 2005) climatological global monthly
surface air temperature and precipitation data were interpolated to a 500 m
grid and used to further reclassify the PFTs into 15 PFTs, and fractions of
crop grasses were mapped based on the method presented in Still et al. (2003). Pixels with barren land and urban areas were reassigned to the bare
soil class. The bare soil and the 15 PFTs from the 500 m grid were then
aggregated to a 0.05∘ grid.
The VEG2 data set is obtained from the NCAR (National Center for Atmospheric
Research) CESM data repository
(Oleson et al., 2010), available on a 0.05∘ grid and derived using a
combination of the 2001 MODIS Vegetation Continuous Field (VCF), the MODIS
land cover product for year 2000 (Lawrence and Chase, 2006, 2007) and
1992–1993 AVHRR (Advanced Very High Resolution
Radiometer) Continuous Field Tree
Cover Project data (Lawrence and Chase, 2007; Lawrence et al., 2011). The
monthly surface air temperature and precipitation data from Willmott and
Matsuura (2001) was used to further reclassify the 7 PFTs into bare soil and
15 PFTs in the tropical, temperate and boreal climate groups based on climate
rules described by Bonan et al. (2002). Fractions of crop grasses were mapped
based on the method presented in Still et al. (2003).
Spatial distribution of dominant PFTs over the simulation domain
from the four data sets: USGS, VEG1, VEG2, and VEG3. The PFT number
refers to the list in Table 1.
![]()
The VEG3 data set is derived from a high-resolution (30 arcsec) data set
over the USA with 16 PFT classifications for the year 2008. The data set was
created by combining the National Land Cover Dataset (NLCD; Homer et al.,
2004) and the Cropland Data Layer (see
http://nassgeodata.gmu.edu/CropScape/), both of which were based on the
30 m LANDSAT-TM (Land Satellite Thematic Mapper) satellite data. Vegetation species composition information was
obtained from the Forest Inventory and Analysis (see
http://www.fia.fs.fed.us) and the soil data from the Natural Resources
Conservation Services (see http://sdmdataaccess.nrcs.usda.gov/). The
processing included adjusting the NLCD tree cover estimates in urban areas to
account for the substantial underestimation of trees in the LANDSAT-TM data
(Duhl et al., 2012). This was accomplished using the regionally specific
adjustment factors for urban NLCD developed by Greenfield et al. (2009),
using high-resolution imagery.
Figure 2 shows the spatial distributions of the dominant PFT in each
4 km × 4 km grid cell of the simulation domain from each of the
four data sets. Not only are the grid-dominant PFTs very different among the
four data sets, but also the sub-grid distributions of PFTs are different (not shown). The domain-averaged fractions of 16 PFTs from the four data sets
listed in Table 1 also illustrate the differences in PFT distributions. For
example, the fraction of temperate broadleaf deciduous trees ranges from
0.4 % in VEG1 to 1.8 % in VEG2 and the fraction of temperate
broadleaf deciduous shrubs ranges from 10.8 % in VEG3 to 37.5 % in
VEG1. In MEGAN v2.0 of WRF-Chem, only four PFTs (refer to VEG-M), i.e.,
broadleaf tree, needleleaf tree, shrub and herbaceous vegetation categories,
are considered for the biogenic emission calculation because they are the
only ones included in the MEGAN v2.0 PFT scheme. As discussed previously,
these are different from the USGS vegetation distribution used by Noah and
CLM4 and may cause additional biases. The distributions of the four PFTs used
by MEGAN v2.0 are shown in Fig. 3. This difference in PFT distributions can
affect the BVOC emission calculations primarily through determining
distributions of PFT specific emission factors and LAI
that are prescribed with PFTs in this study. For example, Fig. 4 shows the
biogenic isoprene emission factor for each PFT prescribed in MEGAN v2.0 and
MEGAN v2.1 in CLM4. In MEGAN v2.1, it shows that the temperate broadleaf
deciduous tree (PFT 7 listed in Table 1) has a large isoprene emission
factor, while the temperate needleleaf evergreen tree (PFT 1 listed in Table 1)
has a small isoprene emission factor. A similar difference between broadleaf
trees and needleleaf trees is indicated for MEGAN v2.0. Figure 5 shows the
spatial distributions of averaged biogenic isoprene emission factor used in
MEGAN v2.0 and v2.1 with different PFTs. It is evident that the difference in
the distributions of PFTs results in a significant difference in spatial
distributions of the isoprene emission factor. Figure 6 shows the spatial
distributions of LAI used for MEGAN v2.0 and v2.1. The differences in the
spatial distributions of LAI can significantly affect the biogenic emission
calculation in MEGAN. It should be noted that in MEGAN v2.0 used in WRF-Chem,
the LAI used for the calculation of the biogenic emissions is prescribed
using the four PFTs, which is different than the land scheme that uses the LAI
derived from the 24 USGS land categories.
Spatial distribution of percentage of the four PFTs from the VEG-M
used by MEGAN v2.0 over the simulation domain.
![]()
Average percentage of PFTs over the simulation domain.
PFT no. and description
USGSa
VEG1b
VEG2c
VEG3d
0
Bare soil
26.0
7.6
38.1
41.6
1
Needleleaf evergreen tree – temperate
13.0
12.5
9.1
10.7
2
Needleleaf evergreen tree – boreal
0.0
0.1
0.0
4.9
3
Needleleaf deciduous tree – boreal
0.1
0.0
0.0
0.0
4
Broadleaf evergreen tree – tropical
0.0
0.0
0.0
0.0
5
Broadleaf evergreen tree – temperate
0.0
0.4
1.9
0.0
6
Broadleaf deciduous tree – tropical
2.9
0.0
0.0
0.0
7
Broadleaf deciduous tree – temperate
1.5
0.4
1.8
1.5
8
Broadleaf deciduous tree – boreal
0.0
0.0
0.0
0.3
9
Broadleaf evergreen shrub – temperate
21.1
5.3
0.0
0.3
10
Broadleaf deciduous shrub – temperate
20.0
37.5
27.4
10.8
11
Broadleaf deciduous shrub – boreal
0.9
0.2
0.0
1.0
12
C3 arctic grass
0.0
0.0
1.2
2.2
13
C3 grass
1.0
28.0
14.9
18.9
14
C4 grass
10.4
0.0
0.0
0.0
15
Crop
3.2
6.5
4.1
6.3
a USGS is the 16-PFT data set converted from the
default 24 USGS land cover data set based on a lookup table derived from
Bonan (1996); b VEG1 is from the PFT fractional cover product by
Ke et al. (2012); c VEG2 is obtained from the NCAR CESM data
repository (Oleson et al., 2010); d VEG3 is derived from a data
set over the USA with 16-PFT classifications by combining the National Land
Cover Dataset (NLCD; Homer et al., 2004) and the Cropland Data Layer (see
http://nassgeodata.gmu.edu/CropScape/).
Biogenic isoprene emission factor for each PFT in
(a) MEGAN v2.0, the PFT number 1–4 refers to broadleaf, needleleaf,
shrub, and herbs, respectively; (b) MEGAN v2.1, the PFT number 0–15
refers to the list in Table 1.
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Numerical experiments
The simulations are performed using a domain encompassing California (Fig. 1)
with a horizontal grid spacing of 4 km and 279 × 279 grid cells
(113–128∘ W, 32–43∘ N) and 51 vertical layers up to
100 hPa with about 35 layers below 2 km. The simulation period is from 25 May to 30 June 2010, but only the results in June are used for analysis to
allow for the model to spin-up realistic distributions of trace gases.
The initial and boundary conditions are prescribed by large-scale
meteorological fields obtained from the North American Regional Reanalysis
(NARR) data with updates provided at 6 h intervals, which also provide the
prescribed sea surface temperature (SST) for the simulations. The modeled u
and v wind components and temperature in the free atmosphere above the
planetary boundary layer are nudged towards the NARR reanalysis data with a
timescale of 6 h (Stauffer and Seaman, 1990). Chemical lateral boundary
conditions are from the default profiles in WRF-Chem, which are based on the
averages of mid-latitude aircraft profiles from several field studies over
the eastern Pacific Ocean (McKeen et al., 2002).
Spatial distribution of PFT-weighted mean biogenic isoprene emission
factor derived with the VEG-M in MEGAN v2.0 and the USGS, VEG1, VEG2 and
VEG3 in MEGAN v2.1.
![]()
Spatial distribution of leaf area index (LAI) from the VEG-M in
MEGAN v2.0 and from the USGS, VEG1, VEG2 and VEG3 in MEGAN v2.1.
![]()
Anthropogenic emissions were obtained from the CARB 2008 Arctic Research of
the Composition of the Troposphere from Aircraft and Satellite (ARCTAS)
emission inventory developed for the NASA ARCTAS mission over California
(Pfister et al., 2011). The CARB inventory contains hourly emissions for a
13-day period using a 4 km grid spacing over California. We created
diurnally averaged emissions from 5 of the weekdays and 2 of the weekend days
and used those averages for all weekdays and weekends and applied these over
the entire simulation period. Anthropogenic emissions from the 2005 National
Emissions Inventory (NEI) (WRF-Chem user guide from
http://ruc.noaa.gov/wrf/WG11/Users_guide.pdf) were used for regions
outside of California. Biomass burning is not considered in the present
study, because satellite detection methods indicated that there were very few
fires in California during the simulation period. Biogenic emissions were
computed online using the MEGAN model and lumped into isoprene, terpenes and
sesquiterpenes for the SAPRC-99 photochemical mechanism.
As discussed previously, multiple numerical experiments summarized in Table 2 are conducted with different combinations of land surface schemes and
vegetation data sets to investigate the sensitivity of BVOC simulation to
land surface schemes and vegetation distributions. First, we conduct two
experiments using MEGAN v2.0 coupled with the Noah (Mv20Noah) and CLM4
(Mv20CLM) land surface schemes. The Noah land surface scheme
is only coupled with MEGAN v2.0 in WRF-Chem. In these two experiments, the
two land surface schemes use the USGS vegetation distributions while MEGAN v2.0 uses a separate vegetation map (VEG-M) to estimate BVOC emissions. By
comparing these two experiments, the impact of land surface schemes on
simulated BVOC concentrations are examined. Second, we conduct four
experiments using MEGAN v2.1 embedded in the CLM4 land surface scheme with
four different vegetation data sets, i.e., USGS (Mv21USGS), VEG1 (Mv21V1),
VEG2 (Mv21V2) and VEG3 (Mv21V3). The differences among these four
experiments show the impact of vegetation distributions on simulated BVOC
concentrations.
We note that MEGAN v2.0 and v2.1 use different vegetation data sets and are
implemented in WRF-Chem in different ways, but the objective of this study
is not to explore how the formulations of these two versions of MEGAN affect
BVOC concentrations. The better way for exploring the version difference of
MEGAN is to implement both versions in the same way and use the same
vegetation data set. The simulated BVOC emissions and concentrations by
WRF-Chem with MEGAN v2.0 and MEGAN v2.1 are included together here as a
reference for future studies in the community and for users interested in
migrating from the widely used v2.0 to v2.1.
Experiments of WRF-Chem.
Surface
BVOC
Plant function type data set
scheme
scheme
USGS/VEG-M
USGS
VEG1
VEG2
VEG3
WRF-Chem
CLM4.0
MEGANv2.0
Mv20CLM
–
–
–
–
MEGANv2.1
–
Mv21USGS
Mv21V1
Mv21V2
Mv21V3
Noah
MEGANv2.0
Mv20Noah
–
–
–
–
Observations
Measurements of VOCs collected by proton transfer reaction mass spectrometer (PTR-MS) instruments (Lindinger et al., 1998) and a gas chromatography
instrument (Gentner et al., 2012) over California during June of 2010 as part
of the CARES and CalNex campaigns are used to evaluate the simulated isoprene
and monoterpene concentrations. CARES was designed to address science issues
associated with the interactions of biogenic and anthropogenic precursors on
SOA, black carbon mixing state, and the effects of organic species and
aerosol mixing state on optical properties and the activation of cloud
condensation nuclei (Zaveri et al., 2012). As shown in Fig. 7, ground-based
instruments were deployed at two sites (T0 and T1) in northern California: T0
in Sacramento (38.649∘ N, -121.349∘ W; ∼ 30 m m.s.l.; denoted by red upward triangle) and T1 in Cool
(38.889∘ N, -120.974∘ W; ∼ 450 m m.s.l.; denoted
by red downward triangle), a small town located about 40 km northeast of
Sacramento. The U.S. Department of Energy (DOE) Gulfstream 1 (G-1) research
aircraft sampled meteorological, trace gas, and aerosol quantities aloft in
the vicinity of the T0 and T1 sites, denoted by black lines in Fig. 8. Zaveri
et al. (2012) described the instrumentation for each of the surface sites and
Shilling et al. (2013) described VOC measurements on the G-1. Most of the
sampling during CARES occurred between 2 and 28 June, and only the aircraft
sampling within 1 km of the surface is used to evaluate model simulations
because G-1 sampled below 1 km for the majority of time.
CalNex was designed to address science issues relevant to emission
inventories, dispersion of trace gases and aerosols, atmospheric chemistry
and the interactions of aerosols, clouds and radiation (Ryerson et al.,
2013). Ground-based instruments were deployed at two sites in southern
California as shown in Fig. 7: one in Pasadena (34.141∘ N,
-118.112∘ W; ∼ 240 m m.s.l.; denoted by the red circle)
and one in Bakersfield (35.346∘ N, -118.965∘ W; ∼ 123 m m.s.l.; denoted by the red square). The NOAA (National Oceanic and
Atmospheric Administration) WP-3D
research aircraft sampled meteorological, trace gas and aerosol quantities
aloft along flight paths shown in Fig. 7 (denoted by blue lines). While most
of the CalNex aircraft tracks below an altitude of 1 km were conducted in
southern California in the vicinity of the Los Angeles basin, the WP-3D also
flew within the Central Valley and in the vicinity of Sacramento on some
days. A detailed description of the instrumentation for each of the CalNex
surface sites and mobile platforms is given by Ryerson et al. (2013). Most of
the sampling during CalNex was conducted before 16 June and only the aircraft
sampling below 1 km is used to evaluate the model simulations.
Summary and discussion
In this study, the latest version of MEGAN (v2.1) is coupled within the CLM4
land scheme as part of WRF-Chem. Specifically, MEGAN v2.1 is implemented into
the CLM4 scheme so that a consistent vegetation map can be used for
estimating biogenic VOC emissions as well as surface fluxes. This is unlike
the older version of MEGAN (v2.0) in the public-released WRF-Chem that uses a
stand-alone vegetation map that differs from what is used in land surface
schemes. With this improved WRF-Chem modeling framework coupled with
CLM4-MEGAN v2.1, the sensitivity of biogenic VOC emissions and hence of
atmospheric VOC mixing ratios to vegetation distributions is investigated.
The WRF-Chem simulations are also conducted with the two land surface
schemes, Noah and CLM4, with the MEGAN v2.0 scheme for biogenic emissions in
each case. The comparison between the Noah- and CLM4-driven MEGAN v2.0
biogenic emissions not only serves for investigating the impact of different
land surface schemes on the emissions but also provides a reference for all
previous studies that used the Noah land surface scheme. Experiments are
conducted for June 2010 over California, compared with the measurements from
the CARES and CalNex campaigns. The main findings about the modeling
sensitivity to the land surface schemes and vegetation distributions include
The WRF-Chem simulation with the CLM4 land surface scheme and the MEGAN v2.0
module (Mv20CLM) produces similar biogenic isoprene and monoterpene
emissions in terms of spatial patterns, magnitudes and diurnal variations
as the one with the Noah land surface scheme (Mv20Noah) in June over
California. The similarity in the biogenic emissions between the experiments
using two different land schemes is primarily because of using MEGAN v2.0
and the same vegetation map in the two experiments. The spatial patterns and
magnitudes of surface isoprene + MVK + MACR and monoterpene mixing ratios
are generally similar between the two experiments with the Noah and CLM4
land surface schemes, although there are significant differences at some
specific locations due to their differences in the near-surface meteorology
such as surface net radiation and temperature. Compared with surface and
aircraft measurements, both experiments generally underestimate the daytime
mixing ratios of isoprene + MVK + MACR but overestimate the nighttime mixing
ratios of monoterpenes.
The experiments with the four vegetation data sets result in much larger
differences in biogenic isoprene and monoterpene emissions than the ones
with the two land surface schemes. The simulated total biogenic isoprene and
monoterpene emissions over California can differ by a factor of 3 among the
experiments and the difference can be even larger over specific locations.
The comparison of mixing ratios of isoprene + MVK + MACR and monoterpenes
with the observations indicates the simulation biases can be largely reduced
with accurate vegetation distributions over some regions of California. For
example, at an observation site at the forested foothills of Sierra Nevada,
two experiments with the vegetation distributions from the USGS and VEG3
data sets capture the observed daytime surface mixing ratios of
isoprene + MVK + MACR well, with values that are much larger than the
experiments with the other two vegetation data sets.
Although vegetation distributions from some data sets do significantly
improve the model performance in simulating BVOC mixing ratios more than
others, the optimal vegetation data set cannot be determined, because the
improvement by vegetation data sets depends on both the region and
BVOC species of interest. For example, over the Central Valley, the
experiments with the VEG1 and VEG3 vegetation data sets simulate
isoprene + MVK + MACR mixing ratios that are much closer to observations
than the USGS and VEG2 data sets, while the VEG3 data set significantly
underestimates the observed monoterpene mixing ratios. Large biases over
some regions of California in all the experiments with current vegetation
data sets imply that more effort is needed to improve land cover data sets
and/or biogenic emission factors.
There are still some large biases existing over some regions of California
regardless of the vegetation distributions. For example, all the experiments
significantly underestimate the observed isoprene + MVK + MACR mixing ratios
below an altitude of 1 km over the forest-covered Sierra Nevada. Over the
Pasadena area, all the experiments simulate significantly smaller
monoterpene mixing ratios than observed. The biases in BVOCs identified in
this study may be partly due to inaccurate vegetation distributions in all
the vegetation distribution data sets. The biases can also result from the
uncertainties in BVOC emission factors for the individual types of
vegetation commonly found in California. The constraints on BVOC emission
factors applied in models are limited due to sparse measurements of BVOC
emission fluxes. The MEGAN scheme in WRF-Chem uses the global-averaged
emission factors for BVOC emissions for each PFT. Over California, the
broadleaf temperate trees are primarily oaks that have relatively higher
BVOC emission factors compared to the global mean values for temperate
broadleaf trees. In addition, the needleleaf trees are pines that have
relatively larger monoterpene emission factors compared to global mean
values. These biases in emission factors may partly explain why all the
experiments generally underestimate mixing ratios of isoprene + MVK + MACR
and monoterpenes over the regions with large amounts of trees. The MEGAN
scheme using the location-specified emission factor maps that accounts for
species composition of trees may provide a better estimate on regional
scales.
This study demonstrates large difference between the experiments with the
two versions of MEGAN (v2.0 vs. v2.1), and that MEGAN v2.1 results in a
better comparison with the observations over some parts of the study domain.
However, this difference should not be fully attributed to the improvement
of MEGAN between the two versions, because the two versions also use
different vegetation distributions. The results highlight the importance of
sub-grid vegetation distributions in simulating biogenic emissions even at a
relatively high horizontal grid spacing (e.g., 4 km in this study). The
biogenic emissions can be significantly different even though the dominant
vegetation within a model grid box is similar. The comparison of the
simulations and the observations at the surface sites and along the aircraft
tracks reflects the large spatial variability of biogenic emissions and BVOC
mixing ratios over California. It is challenging for model to capture such a
spatial heterogeneity of BVOCs if the vegetation distributions are not
appropriately represented in the simulation. The relatively large LOD of
instruments on the aircrafts for monoterpenes compared to the true
concentrations also make the evaluation of simulated monoterpenes difficult.
Over a region with relatively low monoterpene concentrations, an instrument
with lower LOD is needed. It is also noteworthy that this study is in a
relatively dry and warm season; therefore, the impact of biogenic emission
treatments may change for other seasons and during periods with higher
cloudiness. A multiple-season investigation may be needed in the future.
Finally, it is also noteworthy that factors other than biogenic emissions
can influence the simulated BVOC mixing ratios over California, such as
anthropogenic emissions and the oxidation mechanism of BVOCs used in
simulations. Therefore, additional direct measurements of biogenic emission
fluxes are needed for a better evaluation of simulated BVOC fluxes.
Code availability
The WRF-Chem version 3.5.1 release can be obtained at
http://www2.mmm.ucar.edu/wrf/users/download/get_source.html.
Code modifications for implementing MEGANv2.1 into CLM are available upon
request by contacting the corresponding author and will be released to
public WRF-Chem version.