Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.
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 O
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.
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.
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 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 (
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:
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
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
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
Figure 2 shows the spatial distributions of the dominant PFT in each
4 km
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.
Biogenic isoprene emission factor for each PFT in
The simulations are performed using a domain encompassing California (Fig. 1)
with a horizontal grid spacing of 4 km and 279
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
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.
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
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
Figure 7 shows the spatial distributions of biogenic isoprene emissions
averaged over June for the six simulations listed in Table 2. Biogenic
isoprene emissions occur in vegetated regions of California with the highest
emission rates along the foothills of the Sierra Nevada where oak trees are
the dominant plant species. To show the difference in biogenic isoprene
emissions among the cases more clearly, Fig. 8a and b zoom in on the CARES
(northern California) and CalNex (southern California) sampling regions,
respectively. In both regions the differences in land surface schemes had a
relatively small impact on the biogenic isoprene emissions over California in
terms of both spatial distribution and magnitude, although the emissions from
Mv20CLM were a little larger than those from Mv20Noah. The domain summed
biogenic isoprene emissions for the entire month of June from Mv20Noah and
Mv20CLM are 1.4
Spatial distributions of biogenic isoprene emissions averaged in June estimated in the six simulations as listed in Table 2. The four observation sites are shown as T0 (white upward triangle), T1 (white downward triangle), Bakersfield (white square) and Pasadena (white circle). The CalNex WP-3D flight tracks below 1 km (blue line) during June 2010 are also shown. The black and red boxes denote the predominant CARES and CalNex regions, respectively.
The similarity in estimating biogenic emissions between the experiments with two land surface schemes is also summarized in Figs. 10 and 11, which show the average diurnal biogenic isoprene and monoterpene emission rates at the four observation sites. The similarity between Mv20Noah and Mv20CLM (red and orange lines) is likely due to the same vegetation map in MEGAN v2.0 to estimate biogenic emissions. Although the two land surface schemes produce slightly different values of surface temperature (Fig. 1), soil moisture (not shown) and net solar radiation near the surface (not shown), their impact on the biogenic emissions was small. Both BVOC species have peak emission rates in the early afternoon. One noteworthy difference in diurnal variation of the two biogenic species emission rates is that there is no isoprene emitted during the night while the amount of monoterpenes emitted during the night is small but not negligible. This can contribute to differences in the diurnal variation of the mixing ratios of two biogenic species, as will be discussed next.
Same as Fig. 8, except for biogenic monoterpene emissions.
Figures 12a and b and 13a and b show the spatial distributions of monthly averaged
surface mixing ratios of
isoprene
Average diurnal variation of biogenic isoprene emissions at the four observation sites from the six simulations listed in Table 1.
Same as Fig. 10, except for biogenic monoterpene emissions.
Same as Fig. 12, except for monoterpene.
Monthly averaged diurnal variation of surface
isoprene
Monthly averaged diurnal variation of surface monoterpene mixing ratios at the four observation sites from the observations and six simulations as listed in Table 2. There are no observations for the Bakersfield and Pasadena sites in June.
There is a difference between the two experiments at specific locations, which
is partly reflected in the comparison of average diurnal variations of
surface mixing ratios of isoprene
Comparison of isoprene
Same as Fig. 16 except for monoterpene mixing ratios.
Figures 16 and 17 show the comparison of the observed and simulated mixing
ratios of isoprene
In Fig. 16, it is interesting to note that both experiments Mv20Noah and
Mv20CLM reasonably capture the variability seen in the
Figure 17 shows that both experiments Mv20Noah and Mv20CLM significantly
underestimate the monoterpene mixing ratios over all the regions sampled by
the
Figures 8a and b and 9a and b show that the differences in biogenic isoprene and
monoterpene emission distributions due to using the various vegetation
data sets are larger than the differences resulting from the two land surface
schemes. The domain summed biogenic isoprene emissions for the entire month
of June are 2.3, 0.76, 1.7 and 0.92 (
The differences in the spatial distributions of biogenic isoprene and monoterpene emissions due to various vegetation distributions is also illustrated by the average diurnal biogenic isoprene emission rates at the four observation sites shown in Figs. 10 and 11. For example, the Mv21V3 simulation produces the largest biogenic isoprene and monoterpene emissions at three of the sites. At the T1 site over the forested foothills of the Sierra Nevada, the Mv21USGS and Mv21V3 simulations produce much larger biogenic isoprene emissions than Mv21V1 and Mv21V2. Even though forest is the dominant vegetation type along the foothills of the Sierra Nevada in all four vegetation data sets (Fig. 2), their different forest classifications and coverage result in biogenic isoprene emission rates that differ by as much as a factor of 8 at the T1 site. Similar to isoprene emissions, the Mv21USGS simulation produces the largest monoterpene emissions at the T1 site. However, the differences in monoterpene emissions among the four vegetation data set experiments are smaller overall than that for biogenic isoprene emissions. Different vegetation distributions for a typical urban area can also lead to differences in biogenic isoprene and monoterpene emissions. For example at the urban T0 and Pasadena sites, biogenic isoprene and monoterpene emission rates are almost 0 in the Mv21USGS and Mv21V1 experiments, while the rates were significant larger in the Mv21V3 experiment. This could have profound implications on local oxidant chemistry influencing urban air quality.
As expected, the differences in biogenic isoprene and monoterpene emissions
among the four different vegetation distribution experiments lead to large
differences in the simulated surface isoprene
At the T1 site located in the forested foothills of Sierra Nevada, the
Mv21V1 simulation produces the lowest isoprene
At the T0 site, an urban site, the vegetation coverage in both the Mv21USGS
and Mv21V1 experiments is small so that the isoprene
At the Bakersfield site, the experiments often simulate significantly larger isoprene mixing ratios than the observations, except for the Mv21V1 simulation that was always too small. The Mv21V3 simulation produced the highest isoprene mixing ratios among the experiments. This is consistent with its biogenic isoprene emission rates (Fig. 10). In addition, the observed surface isoprene mixing ratios show negligible diurnal variation in contrast to the experiments that produced larger diurnal variations. The Mv21V3 simulation produced peak isoprene mixing ratios during the daytime that were likely controlled by its large daytime local biogenic isoprene emission rates (Fig. 10). The Mv21USGS and Mv21V2 simulations produced peak isoprene mixing ratios during the early evening, possibly the result of chemistry and transport from regions with higher biogenic emissions. All four experiments produce small diurnal variation of surface monoterpene mixing ratios. The Mv21USGS and Mv21V3 simulations produce larger monoterpene mixing ratios than the other two, consistent with their local emission rates (Fig. 11).
At the Pasadena site, the Mv21V3 simulation reproduces the observed diurnal
variation of isoprene
As discussed previously, all four experiments simulate significantly
different isoprene
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 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 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
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
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.
The WRF-Chem version 3.5.1 release can be obtained at
This work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research's Atmospheric Systems Research (ASR) Program and Atmospheric Radiation Measurement (ARM) Climate Research Facility. A portion of this research was supported by the US NOAA's Atmospheric Composition and Climate Program (NA11OAR4310160). The simulations required for this work were performed on the National Energy Research Scientific Computing Center, supported by the Office of Science of the U.S. Department of Energy. We acknowledge Tom Jobson and Bentram Knighton for their measurements during the CARES campaign. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. NCAR is operated by the University Corporation of Atmospheric Research under sponsorship of the National Science Foundation. Edited by: G. A. Folberth