Biosphere–atmosphere interactions strongly influence the
chemical composition of the atmosphere. Simulating these interactions at a
detailed process-based level has traditionally been computationally
intensive and resource prohibitive, commonly due to complexities in
calculating radiation and light at the leaf level within plant canopies.
Here we describe a surrogate canopy physics model based on the MEGAN3
detailed canopy model parameterized using a statistical learning technique.
This surrogate canopy model is specifically designed to rapidly calculate
leaf-level temperature and photosynthetically active radiative (PAR) for use
in large-scale chemical transport models (CTMs). Our surrogate model can
reproduce the dominant spatiotemporal variability of the more detailed
MEGAN3 canopy model to within 10 % across the globe. Implementation of
this surrogate model into the GEOS-Chem CTM leads to small local changes in
ozone dry deposition velocities of less than 5 % and larger local changes
in isoprene emissions of up to ∼40 %, though annual global
isoprene emissions remain largely consistent (within 5 %). These changes
to surface–atmosphere exchange lead to small changes in surface ozone
concentrations of ±1 ppbv, modestly reducing the northern hemispheric
ozone bias, which is common to many CTMs, here from 8 to 7 ppbv. The use
of this computationally efficient surrogate canopy model drives emissions of
isoprene and concentrations of surface ozone closer to observationally
constrained values. Additionally, this surrogate model allows for the
further development and implementation of leaf-level emission factors in the
calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus
of this work, this ultimately enables a complete implementation of the
MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr-1
of isoprene for 2012.
Introduction
The biosphere plays an important role in modulating the abundance and
variability of trace gases and aerosol in the atmosphere. Direct emissions
of gas-phase species are drivers of the majority of the natural reactivity
in the atmosphere and are important precursor sources to pollutants and
climate-relevant species like ozone and particulate matter
(Guenther
et al., 2012; IPCC, 2013; Safieddine et al., 2017). On the other hand,
vegetation serves as a direct sink for these same species through a process
known as dry deposition
(Lelieveld and Dentener,
2000; Silva and Heald, 2018). The physical structure of the vegetation can
also influence the production and loss of atmospheric constituents through
changes to atmospheric turbulent transport and reductions in the actinic
flux below the canopy (e.g., Makar et al.,
2017). Additionally, chemical reactions occurring within the plant canopy
act as a source and sink for reactive species in the above-canopy atmosphere
(Goldstein et al.,
2004; Makar et al., 1999). Ultimately, the balance between the role
vegetation plays as a chemical source and sink is a controlling factor for
the abundance and variability of trace gases and aerosol across many regions
of the globe (e.g.,
Geddes
et al., 2016; Silva et al., 2016; Unger, 2014). It is thus important to
properly account for these processes when simulating the composition and
chemistry of the atmosphere.
Explicitly simulating biosphere–atmosphere interactions necessitates a
detailed representation of physical, chemical, and biological processes that
occur at the scale of an individual plant. This is typically achieved by
integrating a set of energy and radiative balance equations vertically
throughout a canopy (e.g.,
Ashworth
et al., 2015, 2016; Goudriaan and Laar, 1994). This sort of physical model
of the canopy calculates the environmental parameters that drive the
biological and chemical processes, which ultimately impact the atmospheric
fluxes of trace gases and aerosol
(Guenther
et al., 2012; Lamb et al., 1996). These canopy models tend to be
computationally quite expensive and are based on measurements taken at very
fine resolution (e.g., meter or less), while most atmospheric chemical
transport models operate on the 10–200 km scale. Reconciling these
differences in scale and addressing the steep computational requirements
inherent in both canopy models and atmospheric chemical transport models are
critical challenges in simulating chemically relevant interactions between
the biosphere and the atmosphere.
Given the computational costs, atmospheric chemical transport models
approximate canopy physics and the resulting effects on biosphere–atmosphere
interactions through various parameterizations. (e.g.,
Guenther
et al., 2006; Wesely, 1989; Zhang et al., 2003). Most of these
parameterizations are based on observed relationships and are intended to
reduce the computational load around the calculation of the temperature of
leaves and the amount of light (specifically photosynthetically active
radiation, PAR) reaching leaves throughout the canopy. These model
parameterizations commonly assume that the temperature of leaves is equal to
the air temperature just above the plant canopy (e.g.,
Guenther
et al., 2006; Millet et al., 2010) or are based on parameterizations that
ignore leaf temperature entirely (e.g.,
Wesely, 1989). The parameterizations
for leaf-level PAR vary widely, from assuming that the PAR reaching leaves
in the canopy is equal to the flux of PAR incident on the top of the canopy,
to having some sort of reduced-complexity multiplicative factor that
represents the bulk canopy effects (e.g., shading of leaves,
Guenther
et al., 2006; Wang et al., 1998). To our knowledge, the overall impact of
these parameterized assumptions on the fidelity of modern chemical transport
models has not been comprehensively characterized. However, for biogenic
isoprene emissions, these canopy approximations can lead to regional
differences of greater than 20 % relative to a fully detailed canopy model
(Guenther et al., 2006).
Direct representation of these processes is a necessary step to improve
model reliability and validity, particularly in a rapidly changing Earth
system (Committee on the Future of Atmospheric Chemistry
Research et al., 2016). Currently, many processes related to canopy energy
and radiative balance are not represented in models of atmospheric chemistry
due to computational constraints. In this work, we present a reduced-complexity canopy model to calculate leaf temperature and PAR for use in
large-scale chemical transport models. This reduced-complexity model removes
the need for approximating the bulk effects of plant canopies on leaf-level PAR
and leaf temperature, and it allows for a more explicit process-based
representation of these effects on biosphere–atmosphere interactions at the
leaf level. Our reduced model reproduces the output of the more detailed
vegetation model well, without the large computational overhead.
MEGAN3 canopy model
We develop and implement a computationally efficient surrogate of the
MEGAN3.0 canopy model (https://bai.ess.uci.edu/megan, last
access:
4 September 2019),
an update from previous versions of MEGAN (Guenther et al., 2006, 2012).
This canopy model calculates leaf temperature and leaf-level PAR for a
five-layer plant canopy for both sunlit and shaded leaves, whereby each canopy
layer represents a fraction of the total plant canopy. The model is
originally based largely on Goudriaan and Laar (1994) and a brief
description follows; for more information see
Guenther
et al. (1999, 2006, 2012).
In the MEGAN3 canopy model the fraction of sunlit leaves in the canopy
decreases exponentially as a function of the local solar elevation angle,
canopy leaf area index (LAI), a clustering coefficient that accounts for
leaf geometries, and a canopy transparency coefficient representing the
fraction of the canopy that does not intercept incident radiation. The leaf
temperature is calculated from a system of energy balance equations based on
Goudriaan and Laar (1994) and
Leuning et al. (1995), with a maximum absolute difference between the air
temperature and leaf temperature of 10 ∘C. Leaf-level PAR is
computed as a function of incoming radiation incident to the canopy top, the
sunlit fraction of leaves, LAI, and a suite of geometric and radiative lookup table characteristics, predominantly based on
Goudriaan
and Laar (1994), Leuning et al. (1995), and Spitters (1986). The full MEGAN3
canopy model takes as input time (day and hour), geographical location
(latitude and longitude), air temperature, incident radiation on the top of
the canopy, wind speed, humidity, air pressure, LAI, and a set of canopy
characteristics (canopy biomass distribution, clustering coefficients, etc.)
in the form of a lookup table that varies by six vegetation types. The six
vegetation types are needleleaf trees, tropical forest trees, temperate
broadleaf trees, shrubs, herbaceous plants, and crops. It is important to
note that differing canopy model choice and parameter selection can result
in substantial changes to the ultimate estimates of biosphere–atmosphere
exchange (Keenan et al., 2011).
The MEGAN3 canopy model has been specifically developed for use in
simulating biogenic emissions and has been extensively applied in related
studies (e.g.,
Chen et al., 2018; Geron et al., 2016; Guenther et al., 2006).
Additionally, the MEGAN framework has been widely adopted across a variety
of regional and global models (e.g., GEOS-Chem, WRF-CHEM, and CESM). Thus, the
MEGAN3 canopy model is a good candidate for surrogate model development
because it enables a direct implementation of improved process-based canopy
physics into a variety of 3D models without the need for substantial model
architecture development.
Surrogate model development
To begin the surrogate model development, we first use a variable selection
approach to evaluate and rank which of the suite of model input variables
are most important for the simulation of both leaf-level PAR and
temperature. To do this, we use a machine-learning regression method for
model simplification and parameterization, specifically LASSO (least
absolute shrinkage and selection operator; Hastie et al., 2001).
As applied here, LASSO is a regression method that calculates linear
coefficients through a modified least squares cost function, with the
addition of a penalized L1 norm (the sum of the absolute value of the
coefficients). While LASSO was originally developed as a complete regression
method, we follow the recommendations of Hastie et al. (2001) and
use LASSO only for variable importance ranking and dimensionality reduction
of the input variable space to the model.
We apply the linear LASSO method for rankings across a full year of 3-hourly
simulated canopy physics from the MEGAN3 canopy model at the global scale
for the year 2012. Input meteorology is from MERRA-2 assimilated
meteorological fields at 2∘×2.5∘ horizontal resolution
(Gelaro et al., 2017) and the
vegetation distribution from the Olson et al. (2001) land maps. LAI is derived from
the MODIS TERRA MOD15A2 product
(Myneni
et al., 2002, 2007) regridded to 2∘×2.5∘ horizontal
resolution and a monthly timescale. These input data are identical to those
used in the GEOS-Chem chemical transport model (CTM), described below for
direct comparison with prior work and ease of implementation into that CTM.
The spatial distribution of vegetation and LAI is summarized in Figs. 1
and 2, respectively. In general, forested land classes have the highest LAI
values and are spread throughout the tropics and the northern latitudes.
Crops, grasses, and shrubs are located predominantly in transitionary
landscapes and near regions of larger population (e.g., India, central North
America, etc.), and they tend to have lower LAI values.
The percent of each 2∘×2.5∘ grid box
occupied by each vegetation class used in this work. Panel (a) is forested
vegetation, and panel (b) is crops, grasses, and shrubland.
Annual average LAI from MODIS for the year 2012.
The LASSO importance rankings are remarkably consistent for both sunlit and
shaded leaves and for all vertical levels of the canopy. For each quantity,
the two highest ranked variables are consistent at each layer throughout the
canopy and have substantially larger importance to the final result than
any additional variable. For brevity we discuss only those first two
variables here. The two most important variables for the calculation of leaf
temperature are air temperature and wind speed. Air temperature dominates in
importance for the calculation of leaf temperature, with a larger
coefficient emerging at a higher L1 norm penalty weighting. Other variables
that are physically important in nature (e.g., solar radiation) do not appear
important in the LASSO rankings due in part to how they covary with air
temperature and how the rankings are derived separately for sunlit and
shaded leaves. For the calculation of leaf PAR we find that the two most
important variables are PAR out of the lowermost atmospheric grid box
(incident on the canopy) and the local vegetation LAI. We use these
selected variables to develop a simplified parameterization for leaf
temperature and PAR.
We model leaf temperature for a given canopy level, i (Ti,leaf, K), as
linear with 2 m air temperature (Tair, K):
Ti,leaf=Ai+Bi×Tair,
where Ai and Bi are fitted parameters per canopy level (i). This
ignores the addition of the second most important variable, wind speed.
However, the addition of wind speed to the regression only improves the
performance of the model by less than 1 % in total bias and R2; thus,
for simplicity, we neglect this variable.
For the calculation of leaf-level PAR at a given canopy level
(PARi,leaf, µmolm-2s-1), we use an exponential
Beer's law analog, including the influence of leaf area index (LAI) and the
PAR incident to the top of the canopy (PARtoc, W m-2):
PARi,leaf=PARtoc×exp(Ci+Di×LAI),
where Ci and Di are fitted parameters per canopy level (i). This
exponential functional form is chosen due to the observed and simulated
relationships between LAI and canopy light interception
(Engel
et al., 1987; Goudriaan and Monteith, 1990) following a similar functional
form.
We fit Eqs. (1) and (2) for all layers of the canopy and for sunlit and
shaded leaves, resulting in 20 total free parameters necessary to model the
entire plant canopy across the globe. In this regression method, we ignore
the role of differing vegetation classes and apply the regression agnostic
to vegetation type. This is done to keep the total necessary number of free
parameters low (20 versus 120) and because this more parsimonious model
performs quite well (see Sect. 3.1) without the need for additional
vegetation-type-specific coefficients. The resulting surrogate model
coefficients are summarized in Table 1.
Regression coefficients for the canopy surrogate model. Canopy
level 1 represents the top of the canopy.
The final quantity necessary for the canopy model is the fraction of sunlit
and shaded leaves. Here, that fraction in each layer of the plant canopy is
calculated directly following the MEGAN3 code (see
Guenther
et al., 2006, 2012), without any model simplification. The sunlit fraction
is calculated as follows:
3Kb=0.5×C1sinβα1sinβ,4sunlit fraction=expKb×LAI1-α2×f,
where β is the solar angle above the horizon, Kb is the
extinction coefficient for black leaves, C1 is the canopy-clustering
coefficient, C2 is the canopy transparency, and f is the fraction of
biomass in the canopy light travels through to reach a given leaf (the
vector [0.05, 0.23, 0.5, 0.77, 0.95]). Consistent with the MEGAN3 parent
canopy model, we assume a Gaussian distribution of biomass in the canopy,
centered in the middle canopy layer, a canopy transparency of 0.2, and a
leaf-clustering coefficient of 0.9.
From this relatively simple three-function parameterization (leaf
temperature, leaf PAR, and sunlit leaf fraction), we are able to implement
more physically realistic parameterizations for biosphere–atmosphere
interactions in atmospheric chemical transport models.
Surrogate model performance
Here, we evaluate the surrogate model performance for all vegetation
globally.
Temperature
The surrogate-model-simulated annual canopy average leaf temperature
distribution and performance for 2012 are summarized in Figs. 3 and 4. In
Fig. 3, the average for each canopy layer is calculated as a sum of the
sunlit and shaded leaves, weighted by the sunlit fraction of that layer. In
turn, the canopy average is calculated as the weighted sum of the layer
averages, weighted by the fraction of the canopy biomass in each layer. The
annual average temperature is shown in Fig. 3a, where it largely follows a
latitudinal gradient. The warmest temperatures are ∼310 K in
the tropics, and the coldest average leaf temperatures are ∼280 K in the northern high-latitude boreal regions. The surrogate model is
linear with the 2 m “near-surface” air temperature and therefore
follows that spatial distribution directly.
The surrogate model for leaf temperature performs well, with the annual
average spatial R2 and mean bias relative to the full model shown in
Fig. 3b and c, respectively. Across all regions, the R2 is very
high, indicating that a linear relationship between 2 m air temperature
and canopy average temperature is a good approximation for capturing the
variability of the full MEGAN3 canopy model. The temperature R2 drops
below 0.90 only in coastal regions and grid boxes that contain very little
vegetation, representing less than 5 % of all vegetated areas. The
temperature surrogate bias is also generally quite low, as shown in Fig. 3c.
The majority of regions have an absolute mean bias of less than 1 K, and
more than 90 % of the annual average mean biases are less than 2 K. The
surrogate model generally computes temperatures that are biased cool over
highly vegetated tropical and subtropical regions, and it slightly
overestimates temperature over northern boreal forests (by ∼0.1 K).
The most substantial overestimations occur in or near the hot and
arid regions of the globe and always in regions where there is little
vegetative cover at all. On a relative scale these biases are quite small;
all are less than 1 % of the total magnitude. The model development
process described in Sect. 3 removed the vegetation type discrimination in
the parent MEGAN3 canopy model. This leads to some spatial coherence in the
global bias patterns, whereby regions dominated by coniferous forests,
grasses, and shrubs (e.g., boreal Northern Hemisphere and the western United
States) tend to be slightly biased warm, and the other vegetation types are
biased slightly cool.
Surrogate model performance for the annual canopy average leaf
temperature in 2012. Panels are as follows: (a) annual average surrogate
model leaf-level temperature (Kelvin), (b)R2 between the surrogate and
the full model leaf-level temperature, and (c) annual average leaf-level
temperature bias (surrogate–full model, K).
The vertical profile of annual average leaf temperature is shown in Fig. 4a.
The broad shape of the canopy profile is consistent across vegetation
types and the globe. The upper canopy layers are cooler than the lower
canopy layers, as an insulating effect from air temperature occurs within
the canopy. The higher-order variability (e.g., small differences between
adjacent layers at the top and bottom of the canopy) stems from the more
detailed representation of canopy energy balance in the full MEGAN3 model,
which includes the influence of terms like PAR, relative humidity, LAI, and
wind speed. However, this higher-order variability is quite consistent,
allowing it to be reproduced in the simplified surrogate model.
Similar to the spatial performance, the overall surrogate model performs
well throughout the canopy. The surrogate model temperature R2 is shown
in Fig. 4b. The values are all near 1.0, with the lowest value of 0.97 in
the middle of the canopy, where the transition from cooler to warmer leaves
is slightly more difficult for the surrogate model to capture. As
demonstrated in Fig. 4c, the bias throughout the canopy is low as well. On
a global annual average, the surrogate model is biased cool but only
slightly (on both a relative and absolute scale). The highest-magnitude bias
is at the top canopy layer at -0.04 K.
Surrogate model performance for the annual average vertical canopy
temperature profile in 2012. Panel (a) shows the vertical average surrogate
model leaf-level temperature (Kelvin). Panel (b) shows the surrogate model
R2 against the full model. Panel (c) shows the leaf-level temperature
bias (K) of the surrogate model compared to the full model.
Photosynthetically active radiation
The annual canopy average leaf-level PAR for 2012 is shown spatially in
Fig. 5a. In Fig. 5, canopy temperature averages are calculated using the
same method as for canopy temperature. Annual average PAR varies from
∼200µmolm-2s-1
to greater than 600 µmolm-2s-1.
This spatial variability is a function of both PAR
incident on the top of the canopy (largely related to cloud cover and solar
angle) and the canopy LAI. Leaf-level PAR in the surrogate model varies
linearly with incident PAR and decreases exponentially with LAI.
Additionally, the reduction under high LAI is exacerbated due to a higher
fraction of shaded leaves in high-LAI canopies, which have substantially
lower average PAR. The highest leaf-level PAR values are generally located
in arid regions, where LAI and the number of cloudy days are quite low. The
lowest values are located in the equatorial tropical rainforests and the
northern boreal forests. The low leaf-level PAR values in the rainforest are
coincident with the highest LAI values globally, leading to very strong
shading effects below the first canopy layer. The northern boreal forests
have low leaf-level PAR, in part due to relatively high LAI but also due to
reduced incoming PAR in the winter months when the solar angle is low.
The annual average performance of the surrogate leaf-level PAR relative to
the full model is shown in Fig. 5b and c. The temporal R2 over a
full year in Fig. 5b is generally quite high, indicating that the
surrogate formulation captures the majority of the temporal variability
inherent in the full model. The R2 values range from 0.92 to 1.0. The
highest R2 values are in regions with low LAI, where the effects of
shading and other canopy physical processes are greatly reduced. The worst
model R2 performance is over two characteristic regions: higher
elevations with low vegetation densities (i.e., global deserts in Central
Asia and South America) and those with the highest LAI. Both regions
represent extreme scenarios for canopy radiative physics. The higher-elevation regions with low vegetation densities have very little leaf
shading at all throughout the year, and thus the canopy physics represented
with a simple exponential decay are no longer as relevant. In the high-LAI
regions, the elevated importance of shading and resulting complexity in the
PAR calculation are more challenging for the simplified representation of the
surrogate model. However, this poor performance still has a quite high
R2, with the lowest value of 0.92. The annual average model biases are
generally within ±40µmolm-2s-1, with a few more
extreme values reaching ±200µmolm-2s-1. The
surrogate model is broadly biased high over regions with lower LAI and
slightly low over regions with high LAI. In a relative sense, these changes
are nearly all within 10 %–15 %, with a maximum normalized mean bias of 0.4.
Surrogate model performance for the annual canopy average leaf PAR
in 2012. Panels are as follows: (a) annual average surrogate model leaf-level
PAR (µmolm-2s-1), (b)R2 between the leaf-level PAR
simulated using the surrogate and the full model, and (c) annual average leaf-level PAR bias (surrogate–full model, µmolm-2s-1).
The average vertical distribution of leaf-level PAR throughout the canopy
and the associated surrogate model performance are shown in Fig. 6. To
explore the additional dependence on LAI, the quantities shown are separated
into three LAI ranges. These are as follows: a low range with LAI less than
0.5, a midrange with LAI between 0.5 and 5, and a high-LAI range containing
canopies with a total LAI greater than 5. The low LAI range represents
∼40 % of all vegetation throughout the year, the middle range
represents nearly 60 %, and the high-LAI range contains only a small
fraction of all vegetation (∼1 %).
The average distribution of PAR across canopy levels is shown in Fig. 6a.
As LAI increases, there is a substantial reduction in leaf-level PAR deeper
into the vegetated canopy. This is particularly obvious with the high-LAI
range, consistent with substantial shading and light interception above the
bottom of densely vegetated canopies. On the other hand, the variability
throughout the low-LAI canopies is quite small. This LAI dependence explains
in part the relatively low canopy average leaf-level PAR throughout the
tropical forests in Fig. 5a. The variability in the PAR at the top canopy
layer (canopy level 1 in Fig. 6a) stems from two major sources. The first
is simply the spatial distribution of these LAI ranges in relationship to
the annual average incident PAR to the canopy top. Very high LAI values
occur primarily over the tropics, where sunlight is consistently high
throughout the year and the seasonal effects of changing solar angles is
small. The opposite is true for many of the regions with smaller LAI values,
which are distributed more evenly across the globe. A second-order effect in
the MEGAN3 canopy model is that of in-layer attenuation of light and shading
throughout the canopy, whereby leaves in a given layer may intercept light and
shade leaves lower within that same layer. This has the effect of reducing
the layer average leaf-level PAR as a function of leaf geometries and LAI,
and it explains why the highest canopy layer average leaf-level PAR is not the
same as the average PAR incident on the top of the canopy.
Figure 6b and c summarize the statistical performance of the surrogate
model vertically through the canopy in terms of the R2 and the mean
bias, respectively. Overall, the surrogate model reproduces the PAR
variability compared to the full parent model well. For both the low and
middle LAI ranges (LAI less than 5), all R2 values are greater than
∼0.9. The only substantially lower R2 values are from the lower
canopy in high-LAI regions, where PAR is generally quite small (see Fig. 6a).
The surrogate model struggles somewhat to capture this lower canopy
variability, due in large part to the increased complexity of resolving
canopy shading and radiative physics in high-LAI canopies. However, the
ultimate influence on the total canopy-scale bias is generally low.
The vertical distribution of that bias is shown in Fig. 6c. Broadly, the
absolute PAR bias is low (less than 5 % on a relative scale) and decreases
throughout the canopy. All biases are positive except for the top canopy
layer for high-LAI-range canopies; this poor fit is likely related to
the limited representation of high-LAI regions in the full dataset (only
∼1 % of all vegetated area) and is not present if a lower
cutoff for high LAI ranges is used (e.g., LAI >3). The decreasing
magnitude throughout the canopy is largely related to the decreasing overall
leaf-level PAR (see Fig. 6a). It important to note that the bias terms are
all sensitive to the choice of LAI bin ranges, and the variability in bias
at each level can be quite large (e.g., above 50 µmolm-2s-1
in the top canopy layer). For both the high and middle LAI ranges, the
absolute magnitude of the PAR bias decreases throughout the canopy, and the
bias remains relatively constant for low-LAI-range vegetation. On a relative
scale, these biases are all quite small, with a normalized mean bias usually
less than 5 %. The exception to this is the lowest layer of the high-LAI-range canopies. In this low canopy layer the magnitude of the bias is quite
low, as is the total magnitude of leaf-level PAR; the resulting difference
between small numbers leads to a relatively large normalized mean bias of
∼0.3.
Surrogate model performance for the annual average vertical canopy
PAR profile in 2012 as a function of LAI. Panel (a) shows the vertical
average surrogate model leaf-level PAR (µmolm-2s-1) for low-LAI (red), midrange LAI (blue), and high-LAI canopies. Panel (b) shows the
surrogate model R2 against the full model. Panel (c) shows the leaf-level
PAR bias (µmolm-2s-1) of the surrogate model compared to
the full model. Level 1 is the top of the canopy.
An essential function of canopy models used in CTMs is to calculate the
amount of light that falls on already light-saturated leaf surfaces. This
regulates the effect of a change in PAR incident on the canopy on various
biological and physical processes (e.g., biogenic emissions). We estimate the
fraction of leaves that are light-saturated using the γPAR
formulation from the MEGAN algorithm (Guenther et al., 2006, 2012). This
variable aims to capture the amount of light saturation on a given leaf and
ranges from 0 to 1, with higher values corresponding to more saturated
leaves. To explore light saturation, we examine cases in which the γPAR value is greater than 0.9. A scatterplot of the annual average
fraction of leaves that are light-saturated (γPAR≥0.9)
per model grid box for both the full model and the surrogate model throughout
the canopy is shown in Fig. 7. The surrogate model reproduces the full
model fraction of light-saturated leaves well, generally to within
∼5 %, with a median bias of -2 %.
The annual grid box average fraction of light-saturated leaves as
simulated by the full and surrogate models throughout the canopy for the
year 2012. The color bar represents the number of observations in a given
hex. The 1:1 line is shown in black.
Ultimately, this assessment demonstrates that the surrogate model reproduces
the parent MEGAN3 canopy model well for both leaf temperature and leaf-level
PAR. The exponential relationship between leaf-level PAR and canopy incident
PAR and the linear relationship between leaf temperature and near-surface
air temperature capture the majority of the information inherent in the
parent model. Some higher-order variability in the absolute magnitude of the
variables is missing from this surrogate model; however, the biases are
generally all within ∼10 %.
Chemical transport model description
We evaluate the impact of the canopy model parameterization on atmospheric
composition using the GEOS-Chem v12.3.0 chemical transport model
(http://www.geos-chem.org, last access: 30 May 2020, 10.5281/zenodo.2620535, The International GEOS-Chem User Community, 2019). GEOS-Chem is a computational model for simulating
atmospheric chemistry, including a detailed HOx–NOx–BrOx
tropospheric chemical mechanism
(Bey
et al., 2001; Mao et al., 2013; Travis et al., 2016). We drive GEOS-Chem
with MERRA-2 meteorology at 2∘×2.5∘ spatial resolution
with 47 vertical layers (Gelaro
et al., 2017). The time steps for convection and chemistry are 10 and 20 min, respectively. Identically to the canopy model input data, we use
LAI values from the MODIS Terra MOD15A2 product
(Myneni
et al., 2002, 2007) and plant functional types (PFTs) from the Olson et al. (2001) dataset. Fire emissions are
from the Global Fire Emissions Database v4 (GFED4;
Giglio et al.,
2013), and global anthropogenic emissions are from the Community Emissions
Data System inventory
(CEDS;
Hoesly et al., 2018). Regional emissions over the United States, Africa, and
Asia are from the NEI 2011
(Travis
et al., 2016), DICE-Africa (Marais and
Wiedinmyer, 2016), and MIX
(Li
et al., 2017) emissions inventories, respectively. Soil NOx emissions
are calculated following
Hudman
et al. (2012). Simulations are shown for the years 2012 and 2013, with the
first year discarded for spin-up when considering gas-phase chemical
impacts.
MEGAN emissions
The biogenic emissions scheme in GEOS-Chem v12.3.0, MEGAN2.1, is based on
Guenther et al. (2006, 2012) and
Millet
et al. (2010). The emissions of a given compound are calculated from base
canopy-level emission factors multiplied by “activity factors”
representing standard processes that govern biogenic emissions (temperature,
PAR, light dependence, etc.) and “stress factors” modeling the effect of
vegetative stress (heat, drought, etc.) on biogenic emissions. Each of these
activity and stress factors vary with the environmental state. The base
emission factor itself varies with vegetation type, and these activity
factors respond to leaf temperature, leaf-level PAR, leaf age, leaf area
index, soil moisture, and atmospheric CO2 concentrations. The base
emission factors used in this work are consistent with those used in
GEOS-Chem v12.3.0; an example for isoprene is shown in Fig. 8. The
emission factors are highest in forested regions and lowest over areas with
little vegetation (e.g., deserts). These emission factors are regridded from
the original resolution of 0.25∘×0.3125∘ to match the
GEOS-Chem resolution of 2∘×2.5∘.
Base isoprene emission factors used in this work.
As GEOS-Chem v12.3.0 has no representation of plant canopy physics, the LAI,
temperature, and PAR activity factors are all reparameterized following
Guenther et al. (2006) in the standard model. In this parameterization, leaf
temperature is assumed to equal air temperature in the calculation of the
temperature activity factor. The LAI and PAR activity factors are calculated
in an approach known as the parameterized canopy environment emission
activity (PCEEA) approach that does not include any description of the
vertical distribution of vegetation and only includes responses to the LAI,
PAR incident to the top of the canopy, and the solar zenith angle.
We modify the MEGAN implementation in GEOS-Chem to allow for the
representation of canopy physics described in Sect. 3. In order to properly
scale all emission factors to the plant canopy using a canopy model, a
normalization factor must be applied at a set of standard environmental and
ecological conditions
(Guenther
et al., 2006, 2012). This normalization factor varies depending on the
choice of those standard conditions and the canopy model used. In MEGAN2.1
these standard conditions are LAI of 5, current air temperature of 303 K,
current incident PAR at the canopy top of 1500 µmolm-2s-1,
and a 10 %/80 %/10 % split of growing, mature, and senescent leaves
(Guenther
et al., 2012; Kaiser et al., 2018). We calculate other necessary standard
conditions, specifically the 24 h average air temperature and PAR, from
the meteorological fields conditional on locations that meet the previously
described standard conditions. In situations in which all of the previous
instantaneous standard conditions (e.g., current temperature 303 K,
current PAR 1500µmolm-2s-1, and LAI 5) are jointly
met to within ±10 %, we calculate the 24 h average prior
meteorological conditions from the global reanalysis fields and use the
mean of those calculations as the standard 24 h average conditions. The
resulting standard conditions for 24 h average temperature and 24 h
average PAR are 298.5 K and 740 µmolm-2s-1, respectively.
These standard conditions result in a normalization factor of 0.21 using the
surrogate canopy model developed in this work. The value of 0.21
is lower than those used in implementations of previous MEGAN model versions
in other models such as CLM (0.3) and WRF-Chem (0.57)
(Guenther
et al., 2012). It is important to note that the normalization factor
approximately scales with the square of the current temperature conditions
and linearly with the current PAR conditions. For the 24 h average
conditions, the scaling is reduced to approximately linear for temperature
and as a square root for PAR. Given this, small deviations from these
standard conditions (e.g., those that could arise from different 24 h
averaging methodology) can lead to substantial changes in the normalization
factor. Additionally, consistent with previous work these standard
conditional calculations are likely variable across model meteorological
configurations and should be recalculated on a model-specific basis
(Guenther et al., 2012). Since this normalization factor is applied
consistently to all emissions globally at all times, it linearly modulates
all biogenic emissions. As such, the total emissions calculated by the
MEGAN2.1 emissions framework are highly sensitive to the parameter choices
made in this normalization processes.
In GEOS-Chem v12.3.0 we update the activity factors associated with PAR,
LAI, and temperature as well as the normalization to take advantage of our
new canopy surrogate model. This enables a full implementation of the most
recent MEGAN3 emission activity algorithm in the GEOS-Chem model. In the
PCEEA implementation of MEGAN in the base version of GEOS-Chem, activity
factors are calculated separately for PAR (γP), LAI (γLAI), and temperature (γT) and then multiplied together
following Guenther et al. (2006):
γPCEEA=γLAIγTγP.
Following MEGAN3, we implement
PAR and temperature activity factors that are calculated jointly per canopy
level and summed together weighted by the vertical canopy biomass
distribution. In this work, as in previous non-PCEEA versions of the MEGAN
framework
(Guenther
et al., 2006, 2012), the effect of LAI is calculated through direct
multiplication of the emission factor by LAI as opposed to an activity
factor formulation, along with a canopy normalization factor (CCE).
6γCanopy=CCELAIγTP7γTP=∑l=15wlγPγT
These activity factors for PAR, LAI, and temperature are the same as those
in Guenther et al. (2012) as averages throughout the canopy weighted by the
biomass fraction within a given canopy layer (wl). There is an
additional canopy depth emission activity response applied to the light-dependent activity factors, which is intended to model the variability of
emissions throughout the canopy (e.g., Harley et al., 1996). This
canopy depth activity factor is a multiplicative factor that varies linearly
as a function of LAI and canopy depth, with a value between 0 and 1.3. For
clarity, we refer to the MEGAN emissions implementation in GEOS-Chem using
the γPCEEA activity factors as “MEGANPCEEA” and those
using the γCanopy activity factors as “MEGANCanopy”. While
γT in the MEGANPCEEA approach follows a similar functional
form to that in MEGANCanopy, the lack of vertical canopy structure in
the MEGANPCEEA configuration leads to a very different treatment of the
joint effects of temperature, PAR, and LAI on emissions. Specifically, the
MEGANPCEEA approach aims to approximate the joint effects of shading
and temperature change within a canopy, whereas MEGANCanopy aims to
directly simulate those processes. We use the canopy physics surrogate model
described in Sect. 3 to calculate the leaf temperature and PAR in the
MEGANCanopy implementation.
Though stress factors in the MEGAN framework allow for the additional
capability to evaluate the impact of vegetative stress processes on
emissions (e.g. Geron et al.,
2016), we do not enable those processes in this study. The other activity
factors (leaf age, soil moisture, and CO2 inhibition) are the same in
both MEGANCanopy and MEGANPCEEA.
Dry deposition
Dry deposition in GEOS-Chem v12.3.0 is calculated through a
resistor-in-series approach based on the Wesely (1989) parameterization,
originally described and implemented in Wang et
al. (1998). In this approach, the dry depositional flux of gas-phase species
is calculated as the surface concentration of that gas multiplied by a
transfer velocity known as the “dry deposition velocity”. A recent
assessment of the dry deposition velocity parameterization in GEOS-Chem
found that biases in simulated dry deposition velocities are in general
quite low, though there is evidence that missing key processes may be
responsible for missing variability in the simulation
(Silva and Heald, 2018).
Prior to this work, canopy effects were not directly considered in GEOS-Chem
dry deposition and only approximated in bulk using a polynomial
decomposition scheme (Wang et al., 1998) that
calculated a single constant jointly representing both a multiplicative
factor (1+b/PARleaf, b=50µmolm-2s-1) to the
stomatal resistance from Baldocchi et al. (1987)
based on leaf-level PAR and a normalization of the stomatal resistance by
LAI. Here, we replace the polynomial decomposition scheme and use the
leaf-level PAR calculations from the canopy surrogate to directly calculate
the multiplicative factor and then explicitly normalize by LAI. The LAI
normalization in the original polynomial decomposition calculates values
that are a factor of ∼1.7 higher than those calculated
through direct normalization when using the surrogate model. To maintain the
same magnitude of the simulated dry deposition velocities as in the standard
model, which are generally unbiased (Silva and
Heald, 2018), we scale the stomatal resistance by a factor of 0.6.
Surrogate model integration into GEOS-Chem
Implementing the updated canopy surrogate in a global model directly impacts
the surface–atmosphere exchange processes of biogenic emissions and dry
deposition, which together influence the chemical composition of the
atmosphere. In this section we outline the changes to both surface
processes, focusing on isoprene emissions and ozone dry deposition, followed
by the changes to surface-level ozone concentrations in the GEOS-Chem model.
The impact of the canopy model on isoprene emissions in 2012 is summarized
in Fig. 9. The annual average isoprene emissions using the
MEGANCanopy emissions implementation are shown in Fig. 9a, with the
highest emissions in the tropics and subtropics, as well as the southeastern
United States. Though not distinct in Fig. 9, the boreal forests are a
substantial emitter of biogenic species during the summer months. The
relatively small emissions from this region during the winter months reduce
the prominence of these emissions on the annual average. The global annual
total of isoprene emitted in 2012 from the MEGANCanopy configuration is
∼350 Tg C yr-1.
The annual average differences in the simulated isoprene emissions following
implementation of the surrogate canopy model (MEGANCanopy –
MEGANPCEEA) are shown in Fig. 9b. In general, emissions decrease over
forested regions and increase over non-forested (grasses, crops, and
shrubland) areas. The highest absolute changes are the decrease in the
equatorial Amazon and the increase in northern Australia. On a relative
scale, the forested and non-forested differences are more apparent. This
relative change (MEGANCanopy/MEGANPCEEA) is shown in Fig. 9c.
While there are relatively modest decreases in tropical and boreal forests,
the emissions increase in the heavily cropped Indian subcontinent and
sub-Saharan Africa shows the largest relative change. Though the spatial
variability in the relative difference is substantial, the annual global
isoprene emissions from the canopy model are within 5 % of the original
model version (∼340 Tg C yr-1). These results are
consistent with those from Guenther et al. (2006), who found that the global
total biases in isoprene emissions were low, but spatial variability was
large, when using a parameterized approach (MEGANPCEEA) over a direct
canopy model implementation in the MEGAN framework (as in the surrogate
model application, MEGANCanopy). On aggregate, these changes are all
well within the stated uncertainty of the MEGAN isoprene emissions of
approximately a factor of 2 (Guenther et al., 2012).
Annual average (2012) isoprene emissions simulated in GEOS-Chem
driven by the surrogate model canopy physics (MEGANCanopy). Panel (a)
shows the annual average emissions. Panel (b) shows the difference between the
surrogate model and the base version of simulated emissions. Panel (c) shows the
relative difference between the surrogate model and the base version of
simulated emissions (surrogate–base model).
It is not possible to directly parse the individual process contributions to
the total emissions changes due to the fundamentally different coupled
treatments of the influence of temperature, PAR, and canopy structure on
biogenic emissions through the activity factors in both the MEGANCanopy
and the MEGANPCEEA configurations. However, a comparison of the
isoprene differences between the two simulations against LAI, leaf-level
PAR, and leaf temperature (Fig. 10) indicates that the changes are most
strongly driven by the leaf-level PAR and LAI effects. The isoprene
emissions changes are directly proportional to leaf-level PAR, inversely
proportional to LAI, and show no substantial relationship to leaf
temperature. The forested and non-forested differences in Fig. 9 can be
explained further by the correlations shown in Fig. 10. The forested
areas with the largest decreases in isoprene emissions tend to have high LAI
values and lower canopy average leaf PAR, whereas the opposite is true for
the non-forest locations. The relationships in Fig. 10 support the
interpretation that the leaf-level PAR and LAI effects are the largest
drivers of change in biogenic isoprene emissions between the two model
versions. Overall, these results indicate that the representation of canopy
radiative physics is more important than thermodynamically resolving the
difference between air and leaf temperature for simulating biogenic
emissions in the MEGAN framework.
Difference in annual average isoprene emissions between the
surrogate canopy model (MEGANCanopy) and the base simulation
(MEGANPCEEA) (atoms C cm-2 s-1; see Fig. 9b) as a function
of LAI, leaf-level PAR (µmolm-2s-1), and leaf temperature
(K). Grid boxes dominated by water were filtered and removed from these figures.
The color bar represents the number of observations in a given hex.
There are few spatial constraints on isoprene emissions that can act as
independent validation data for the new model framework. However, recent
work over the southeastern United States
(Kaiser
et al., 2018; Travis et al., 2016; Yu et al., 2016) indicates that the base
version of GEOS-Chem used here (v12.3.0), which uses MEGANPCEEA,
overestimates isoprene emissions by 15 %–40 %. The MEGANCanopy
configuration reduces isoprene emissions in most locations in the southeastern
United States by ∼10 % and locally leads to reductions as
large as ∼20 %, bringing the model into better agreement
with these observational constraints.
The MEGAN emissions framework calculates the emissions of other non-isoprene
biogenic species as well, including monoterpenes. The influence of the
canopy surrogate model on monoterpene emissions is shown in Fig. 11. The
annual total monoterpene emissions in 2012 from MEGANCanopy are
∼95 Tg C yr-1. These emissions are shown in Fig. 11a
and are highest over the densely vegetated regions of the world, in
particular the tropics. Similar to isoprene emissions, monoterpene emissions
in the northern-latitude forests peak during summer months. The
implementation of the canopy surrogate model reduces global annual total
monoterpene emissions by approximately 20 %. The annual average absolute
and relative changes to monoterpene emissions due to the canopy surrogate
model (MEGANCanopy – MEGANPCEEA) are shown in Fig. 11b and
c, respectively. Simulated monoterpene emissions differ from isoprene
emissions in that monoterpene emissions are more sensitive to temperature,
with an additional influence of a light-independent emission factor that
varies with leaf temperature
(Guenther
et al., 2012). There is a fairly constant 20 %–30 % decrease across regions
with lower LAI values, including the African savannahs and the Indian
subcontinent. The highest absolute changes are in transitional areas near
high-LAI forests with warmer temperatures (the tropics and subtropics). The
high-LAI areas of the tropical and northern forests show smaller decreases
of ∼5 %. These changes, while substantial, are well within
the stated uncertainty ranges in monoterpene emissions of the MEGAN model
(300 %–400 %;
Guenther
et al., 2012).
Annual average (2012) monoterpene emissions simulated in
GEOS-Chem driven by the surrogate model canopy physics. Panel (a) shows the
annual average emissions. Panel (b) shows the difference between the surrogate
model and the base version of simulated emissions. Panel (c) shows the relative
difference between the surrogate model and the base version of simulated
emissions (surrogate–base model).
Changes in simulated ozone dry deposition velocities in 2012 are summarized
in Fig. 12. Figure 12a shows the annual average spatial distribution of
ozone dry deposition velocities. The values vary from less than
0.1 cm s-1 over the global oceans to above 0.5 cm s-1 in densely
vegetated regions like the tropical rainforests.
The impact of the updated canopy model on ozone dry deposition velocities is
in general quite small, with an average change of near zero (∼0.004 cm s-1).
The annual average relative change is shown in Fig. 12b and
the absolute difference in Fig. 12c, both in relation to the base
version of GEOS-Chem v12.3.0. These changes are nearly all within ±5 %, or ±0.01 cm s-1, with a maximum change of 15 % (0.04 cm s-1). Relative changes track most strongly with broadleaf and
coniferous forested areas. This is consistent with those regions being most
sensitive to stomatal deposition (Silva and
Heald, 2018), as the canopy scheme implemented here changes dry deposition
only through the calculation of the stomatal resistance term.
Annual average (2012) ozone dry deposition velocities simulated
in GEOS-Chem when driven by the surrogate model canopy physics. Panel (a)
shows the annual average dry deposition velocities (cm s-1). Panel (b)
shows the difference between the surrogate model and the base version of
simulated dry deposition velocities (cm s-1). Panel (c) shows the
relative difference between the surrogate model and the base version of
simulated dry deposition velocities (surrogate–base model).
The small overall changes to surface–atmosphere exchange processes
associated with the updated canopy scheme produce only a modest impact on
simulated atmospheric composition. We describe the changes to surface ozone
here as an illustrative example.
The annual average spatial difference in surface ozone between a simulation
using the canopy physics described here and the base version of GEOS-Chem is
shown in Fig. 13. These changes are all generally quite small; all are
within 10 % of the base simulated annual averages. The changes are
generally within ±1 ppbv, with the largest absolute changes over
regions with the largest changes in isoprene emissions. The distribution of
differences largely follows well-known NOx–VOC ozone formation
patterns. The NOx-limited regions of the world, in particular the
remote tropics, show an inverse relationship with isoprene emissions. This
is consistent with ozone titration by isoprene in the presence of low
NOx. The largest changes in ozone over the VOC-limited regimes of India
and China directly correspond to the changes in isoprene emissions,
with enhanced isoprene emissions over the Indian subcontinent increasing
ozone concentrations and the decrease in isoprene emissions over China
leading to a decline in ozone. The overall influence of the changes in ozone
dry deposition velocity is fairly negligible. Even regions where the dry
deposition velocity change is the largest (e.g., the Amazon) are dominated by
the shift in isoprene emissions.
In total, the changes in surface ozone concentrations slightly ameliorate
known biases. There is a persistent high bias (∼10 ppbv)
across chemical transport models in simulating surface ozone concentrations
over the continental midlatitudes
(Travis
et al., 2016). The addition of the new canopy physics parameterization very
modestly reduces this bias in GEOS-Chem (approximately 8 ppbv) by about 1 ppbv,
driving simulated ozone closer to observations.
Annual spatial average surface ozone difference (ppbv) between
the updated model version with surrogate canopy physics and the base version
of GEOS-Chem (surrogate–base).
Implementation of MEGAN3 emission factors
In addition to improved process representation, the canopy surrogate model
presented here allows for the direct application of new emission factors
generated using the MEGAN3 Emission Factor Processor (https://bai.ess.uci.edu/meganTS6, last access: 4 September 2019), which allows users to generate emission
factors from various input datasets. While the focus of this work is on the
impact of representing canopy physics, we include a description of this
full implementation of MEGAN3 in the GEOS-Chem model for completeness. We
calculate landscape-average emission factor distributions using the global
growth form and ecotype distributions, the emission type speciation, and the
leaf-level emission factor database available from the MEGAN3 Emission
Factor Preprocessor. All MEGAN3 Emissions Factor Preprocessor options are
kept at their default values (i.e., confidence rating J=0 and 20 total
species classes). The land cover and emissions data are the same as those used
for MEGAN2.1 except that the land cover updates described by Yu et al. (2017)
were used for the contiguous US. The updated land cover is based on high-resolution (30 mm) PFT and detailed vegetation types and is expected to more
accurately represent the land cover distributions in this region. The spatial
distribution of the MEGAN3 activity factors is shown in Fig. 14. It is
important to note that these new emission factors are input at the leaf
level with units on a per LAI basis as opposed to the canopy-scale factors
used in previous versions of MEGAN (applied earlier in this paper),
which makes direct comparisons of emission factor magnitudes infeasible.
This canopy to leaf-level change ultimately has the consequence of removing
the need for the normalization factor in the activity factor calculation
(see Sect. 4.1). When these emission factors are scaled to the same units
as in MEGAN2.1 (i.e., the per LAI basis in the MEGAN3 emission factors is
accounted for) using the MODIS LAI product applied in this work, the
resulting emission factors are relatively similar (within ±75 %),
though the MEGAN3 emission factors are lower than those used with MEGAN2.1
in GEOS-Chem. Generally, more than half of all changes are within 1000 µgm-2h-1, and 90 % of all emission factors are within 3000 µgm-2h-1. This comparison is not exact due to the fact
that the MODIS LAI product used here is different from the input vegetation
files used to create the original MEGAN2.1 emission factors. Despite the
differences in absolute magnitude, the spatial patterns in emission factors
in Fig. 14 are very similar to those used earlier in this work (Fig. 8),
with a spatial R2 of 0.91.
We implement the MEGAN3 emission factors in GEOS-Chem v12.3.0 using the
canopy surrogate model activity factor formulation. For clarity, we refer to
“MEGAN3Full” as the emissions implementation in GEOS-Chem v12.3.0
using the MEGAN3 leaf-level emission factors and MEGAN3 activity factors
with canopy physics calculated following the canopy surrogate model
described in Sect. 3. The annual isoprene emissions simulated using
MEGAN3Full are higher than using the MEGAN2.1 canopy-scale factors in
GEOS-Chem (as in both MEGANCanopy and MEGANPCEEA) but more in
line with previous work (Guenther et al. 2012). Specifically, annual total
isoprene emissions for 2012 are ∼570 Tg C yr-1 in
MEGAN3Full, which is a factor of 1.6 larger than those configurations
discussed earlier in this paper. The largest contribution to these
differences is not the differences in emission factor maps but is instead
the removal of the normalization factor of 0.21, which additionally removes
the need for the somewhat arbitrary choice of “standard conditions” for
emissions (see Sect. 4.1). This 570 Tg yr-1 emissions total is much
more similar to the magnitude of global emissions from versions of MEGAN2.1
implemented outside the GEOS-Chem model (535–578 Tg yr-1) given by
Guenther et al. (2012) and within the stated uncertainty range for MEGAN
isoprene emissions (Guenther et al., 2012). These annual average isoprene
emissions using MEGAN3Full are shown in Fig. 15 below. In general, the
spatial pattern in the emissions in Fig. 15 matches those from the
MEGANCanopy configuration (Fig. 9), with an R2 of
∼0.8.
Annual average (2012) isoprene emissions simulated in GEOS-Chem
driven by the surrogate model canopy physics and the MEGAN3 emission and
activity factors.
Since the isoprene emissions calculated using the MEGAN3Full algorithm
are so much larger than those used in previous versions of GEOS-Chem, they
alter the composition of the atmosphere significantly. For example, annual
average surface ozone concentrations in the southeastern US increase by
nearly 5 ppbv relative to the base version of GEOS-Chem v12.3.0 (which uses
MEGANPCEEA), exacerbating the existing model bias further (Travis et
al., 2016). However, MEGAN3Full represents a more up-to-date and
physical characterization of biogenic emissions. Future work reconciling the
differences between these bottom-up isoprene emissions estimates and top-down constraints from measurements of composition (e.g., Kaiser et al., 2018)
is needed.
Conclusions
We describe a novel method for simulating canopy physics relevant to
atmospheric chemistry at very low computational cost. Our surrogate canopy
model is based on the detailed canopy model in the MEGAN3 code base and
simplified using a statistical learning technique for the determination of
variable importance. This updated scheme allows for an improved physical
process representation of biosphere–atmosphere interactions, including a
full implementation of the MEGAN3 emissions scheme activity factors and a
more direct implementation of the light and LAI dependence of dry
deposition.
When implemented into a chemical transport model, this canopy scheme impacts
the spatial distribution of isoprene emissions but maintains the global
total to within 5 %. Consistent with prior work
(Kaiser et al., 2018),
isoprene emissions are reduced over the southeastern United States, with local
absolute changes that can exceed 30 %. This difference in
surface–atmosphere exchange ultimately has a modest impact on surface ozone,
with absolute annual average changes generally less than 1 ppbv, though it
does drive ozone concentrations closer to observed values. The surrogate
model additionally allows for integrating new leaf-level emission factor
maps into GEOS-Chem, which we show leads to substantial changes in biogenic
emissions.
In a rapidly changing Earth system, it is critical to represent chemical,
biological, and physical processes with as high fidelity as possible.
Surrogate models that allow for the rapid implementation of computationally
expensive processes can play a key role in representing these processes. The
work presented in this paper represents a step toward further explicit
descriptions of biosphere–atmosphere interactions in models of atmospheric
chemistry. Future work should include more detailed observational
constraints and characterization of in-canopy chemical reactions, turbulent
exchange, and biological processes, as well as their resulting impacts on the
abundances of trace gases in the atmosphere.
Code availability
The MEGAN3 and GEOS-Chem model codes are available at https://bai.ess.uci.edu/megan/data-and-code (last access: 4 September 2019), and
10.5281/zenodo.2620535 (The International GEOS-Chem User Community, 2019), respectively. The updated GEOS-Chem
code containing the canopy model changes is available at
10.5281/zenodo.3614062 (Silva, 2020).
Author contributions
CLH and SJS designed the study. SJS developed and implemented the surrogate
model and performed the simulations and analysis. ABG developed the MEGAN3 model
code. All authors contributed to the paper preparation.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This research has been supported by the National Science Foundation, Division of Atmospheric and Geospace Sciences (grant no. 1564495), and the National Aeronautics and Space Administration (grant no. NNX16AN92H).
Review statement
This paper was edited by Christoph Knote and reviewed by two anonymous referees.
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