Introduction
Pollen grains are released from plants to transmit the male genetic material
for reproduction. When lofted into the atmosphere, they represent a natural
source of coarse atmospheric aerosols, ranging typically from 15 to
60 µm in diameter, while sometimes exceeding 100 µm
(Cecchi, 2014; Sofiev et al., 2014). In the midlatitudes, much of the
vegetation relies dominantly on anemophilous, or wind-driven, pollination (Lewis et al., 1983), representing a closely coupled
relationship of pollen emissions to weather and climate. Anemophilous
pollinators include woody plants such as trees and shrubs, as well as other
non-woody vascular plants such as grasses and herbs. Pollen emissions are
directly affected by meteorological (e.g., temperature, wind, relative
humidity) and climatological (e.g., temperature, soil moisture) factors (Weber, 2003). Aerobiology studies indicate that after
release, pollen can be transported on the order of 10–1000 km (Sofiev et al., 2006; Schueler and Schlünzen, 2006; Kuparinen et al., 2007) but
there are still large uncertainties regarding emissions and transport of
pollen.
Prognostic pollen emissions are useful for the scientific community and
public, specifically for forecasting allergenic conditions or predicting the
flow of genetic material. The interest and growing wealth of knowledge of
allergenic pollen have been recently reviewed by Beggs et al. (2017). To date, most pollen emissions models focus on
relatively short, seasonal timescales and smaller locales for a limited
selection of taxa (Sofiev et al., 2013; Liu et al., 2016; R. Zhang et al., 2014). Climatic changes in
large-scale pollen distributions are mostly absent from scientific
literature, though multiple studies on phenological changes in the pollen
season have been published (Ziska, 2016; Yue et al., 2015; Y. Zhang et al., 2015a). Only recently have regional-scale
modeling studies of pollen dispersion been conducted for Europe, and they
have been used to assess the impacts of climate change on airborne pollen
distributions (Sofiev and Prank, 2016; Lake et al., 2017). In contrast to most meteorological pollen models, climate models
require long-term (e.g., decadal- to century-scale) emissions at a range of
resolutions covering continental regions up to the global scale. This
distinction in both time and space requires a flexible model that can
account for emissions without taxon-specific emission data (i.e.,
differentiation between genera or species) and can be used within aggregated
vegetation descriptions, such as plant functional types (PFTs). Given recent
interest in airborne biological particles and their role in climate (Després et al., 2012; Myriokefalitakis et al., 2017), an emissions model that captures
longer temporal scales and broader spatial scales is key to developing
global inventories and understanding pollen's role in the climate system.
Here, we develop a model for use in the climate modeling community that can
be used specifically to simulate pollen emissions on the decadal or
centurial timescale for large regions using conventional climate or Earth
system models.
Existing pollen forecasting models are often classified as either
process-based phenological models or observation-based models (Scheifinger
et al., 2013). Process-based phenological models employ a parameterization of
plant physiology and climatic conditions (e.g., relating to the timing of
flowering to a chilling period, photoperiod, or water availability). Pollen
season phenology in an anemophilous species is inherently connected to its
environment via relationships in the growing season dynamics (e.g., bud burst
and temperature; Fu et al., 2012), and many models apply the same techniques
to flowering as for bud burst (Chuine et al., 1999). This approach to
phenology could be suited to climate models, given its flexibility for
adaptive phenological events and regional-scale studies. Typically, these
types of phenological models are taxa specific as well as regionally
dependent, e.g., Betula in Europe or ragweed in California (Sofiev
et al., 2013; Siljamo et al., 2013; R. Zhang et al., 2014). These models are
usually calibrated to local data only even though distinct geographic
differences exist for pollen phenology. Thus, such models may not perform
equally well in other locations. Though process-based models draw a
connection between an atmospheric state variable, i.e., temperature, and
pollen emissions, at least three parameters are required for optimization and
they are susceptible to overfitting (Linkosalo et al., 2008). While some
process-based models may be scaled up to larger regions while maintaining
appreciable accuracy (García-Mozo et al., 2009), such models are
generally not practical for implementation in larger-scale climate modeling
with regional climate models (RCMs) and global climate models (GCMs) because
sufficient land cover data are not available at the appropriate taxonomic
level.
In contrast to process-based models, observation-based methods determine the
phenology of vegetation with statistical–empirical approaches (e.g.,
relating the start of the pollen season with mean temperatures preceding the
pollen event) and often rely on regression models or time series modeling (Scheifinger et al., 2013). Time series modeling utilizes
observations to define the deterministic and stochastic variability of
pollen count observations and is frequently used in aerobiological studies (Moseholm et al., 1987; Box et al., 1994). Regression models, either using a single or multiple explanatory
variable(s), exploit past relationships to define the magnitude of emissions
as well as timing variables such as the start date and duration of the
pollen season (Emberlin et al., 1999; Smith and Emberlin, 2005; Galán et al., 2008). Using local
pollen count data, Y. Zhang et al. (2015b) completed a regional phenological analysis using
multiple linear regressions for pollen in southern California for six taxa.
Olsson and Jönsson (2014) show that empirical models based solely on spring temperature perform
just as well as process-based models using the temperature forcing concept,
and better than those including a chilling or dormancy-breaking requirement.
Observation-based methods assume stationarity, or the likelihood that the
statistics of pollen counts or climate variables are not changing over time.
For these models to apply outside of calibration period, they require that
the driving pattern or relationship is maintained in the future (or past).
For example, as the Earth's climate changes, these models do not represent
the complex connections between pollen emissions and a warming world aside
from the relationships determined empirically. However, these models provide
clear and often simple formulations that have predictable behaviors and
forgo the nuance of fitting ambiguous and uncertain parameters. We therefore
choose to employ elements of the observational methods for this pollen
emissions model formulation, as described in Sect. 4.
Locations of American Academy of Allergy, Asthma
and Immunology (AAAAI) station and geographic regions used in
this study: Northeast (NE; 38–48∘ N, 70–100∘ W) in blue,
Southeast (SE; 25–38∘ N, 70–100∘ W) in green, Mountain
(MT; 25–48∘ N, 100–116∘ W) in red, California (CA;
25–40∘ N, 116–125∘ W) in orange, and Pacific Northwest
(PNW; 40–48∘ N, 116–125∘ W) in dark grey.
![]()
In addition to understanding the release of pollen grains, a second
consideration is the large-scale transport of pollen. Once emitted to the
atmosphere, pollen is mixed within the atmospheric boundary layer by
turbulence and, depending on large-scale conditions, can be transported far
from the emission source. Prior studies have used both Lagrangian (Hunt et al., 2002; Hidalgo et al., 2002)
and Eulerian techniques to simulate the transport of pollen, with the former
typically used for studies of crop germination and the latter primarily for
allergen forecasting. For example, Helbig et al. (2004) used
the meteorological model KAMM (Karlsruher Meteorologisches Modell) with the
DRAIS (Dreidimensionales Ausbreitungs- und Immissions-Simulationsmodell)
turbulence component to simulate daily pollen counts for regions over Europe.
Schueler and Schlünzen (2006) use a mesoscale atmospheric model (METRAS) to
quantify the release, transport, and deposition of oak pollen for a 2-day
period over Europe. Sofiev et al. (2013) includes the
long-range transport of birch pollen over western Europe by developing a
birch pollen map and a flowering model to trigger release in the Finnish
System for Integrated modeling of Atmospheric coMposition (SILAM).
Efstathiou et al. (2011) developed a pollen emissions
model for use within the regional air quality model (the Community
Multi-scale Air Quality model; CMAQ) and tested their model with birch and
ragweed taxa. R. Zhang et al. (2014) implemented a similar pollen emissions scheme with a
regional numerical weather prediction model (the Weather Research and
Forecasting (WRF) modeling system). Zink et al. (2013)
developed a generic pollen modeling parameterization for use with a
numerical weather prediction model (COSMO-ART) that is flexible to include
differing pollen taxa. Collectively, these relatively new developments
suggest a growing interest in the prognostic estimation of pollen in the
short term for seasonal allergen forecasting on the weather (e.g., 1- to 2-week) timescale.
In this paper, we build on these coupled emissions–transport models and
develop a comprehensive emissions model (Pollen Emissions for Climate
Models; PECM) for use at climate model timescales that covers the majority
of pollen sources in subtropical to temperate climes, including woody
plants, grasses, and ragweed. First, we summarize the spatial distribution
and seasonality of pollen counts for various taxa in the United States based
on current observations (Sect. 2). Then, we develop new pollen emissions
parameterization for climate studies (Sect. 4), transport these emissions
over the continental United States (CONUS) using the Regional Climate Model
version 4 (RegCM4) (Giorgi et al., 2012), and evaluate the results using 8 years of observed pollen count data
(Sect. 5). We implement two different land cover classification schemes to
illustrate the uncertainties associated with vegetation representation for
trees including (1) detailed family- or genus-level tree distributions
over CONUS, and (2) the use of PFT level
distributions, which group vegetation types by physiological
characteristics (Sect. 3). As the latter provides a greater opportunity
for expansion into regional- and global-scale climate models over multiple
domains, we discuss the effects that the PFT-based categorization has on the
total estimated source strength of pollen. Finally, the limitations of this
emissions framework and suggestions for future developments are included
(Sect. 6).
Observed pollen phenology
Data description
The National Allergy Bureau (NAB) of the American Academy of Allergy, Asthma
and Immunology (AAAAI) conducts daily pollen counts at 96 sites in cities
across the United States (US), its territories, and several locations in
southern Canada. All NAB sites implement a volumetric air sampler and
certified pollen count experts to conduct daily pollen counts (grains m-3) for up to 42 plant taxa at either the family level (e.g.,
Cupressaceae, Poaceae), genus level (e.g., Acer, Quercus), or for four generic
categories termed “other grass pollen”, “other tree pollen”, “other
weed pollen”, or “unidentified”. We use NAB pollen count data ranging from
2003 to 2010 at all stations in the continental United States (Fig. 1) for
selected taxa to develop and evaluate PECM, and to determine the phenology
of wind-driven pollen. Individual station locations and descriptions are
included in Table S1 in the Supplement.
Daily observed average time series of daily pollen count data
(2003–2010) for the four representative plant functional types (deciduous
broadleaf forest, DBF; evergreen needleleaf
forest, ENF; grasses, GRA; and ragweed, RAG) averaged over the five regions in Fig. 1: (a)
Northeast, (b) Southeast, (c) Mountain, (d)
California, and (e) Pacific Northwest.
![]()
We evaluate the observed pollen counts to determine the vegetation types
that emit the largest magnitude of pollen over the continental United
States. Since many of the taxa reported at the 96 NAB sites frequently have
very low pollen counts (e.g., less than 10 grains m-3), a threshold for
the grain count is set to select the taxa with the highest pollen counts. We
calculate the average of the annual maximum pollen count across all years
(2003–2010), Pavgmax, at each site for each counted taxon. We then
select taxa to include in PECM using two criteria: (1) the maximum of
Pavgmax among all stations exceeds 100 grains m-3, and (2) the
average Pavgmax among all stations exceeds 70 grains m-3 (Table S2). Using these two criteria, 13 taxa are selected for inclusion in the
model, including Acer, Alnus, Ambrosia, Betula, Cupressaceae, Fraxinus, Poaceae, Morus, Pinaceae, Platanus, Populus, Quercus, and Ulmus. These 13 taxa
account for about 77 % of the total pollen counted across the United
States during 2003–2010.
The 13 dominant pollen types are grouped into four main categories by plant
functional type: deciduous broadleaf forest (DBF), evergreen needleleaf
forest (ENF), grasses (GRA), and ragweed (RAG). Plant functional type is a
land cover classification commonly used in the land surface component of
climate models, and this categorization will allow flexibility to apply the
emissions model to other climate models. The DBF category includes nine
genus-level taxa (Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, and Ulmus) and the ENF category includes two family-level taxa
(Cupressaceae and Pinaceae). The grass PFT utilizes pollen count data from
the Poaceae family, although we note that the grass PFT classification may
include herbs and other non-woody species that may emit pollen as well.
Ambrosia (ragweed) is segregated as its own category (RAG), due to its high pollen
counts in the early autumn and unique land cover features. Daily pollen
counts were summed for each PFT prior to calculating an 8-year average
pollen time series.
Observed seasonality of pollen emissions
Pollen counts are analyzed over five subregions based on their climatic
differences (Fig. 1; Table S1) to identify emissions patterns over the
continental United States. These five subregions are the “Northeast”
(temperate; 38–48∘ N and 70–100∘ W; 34 stations), “Southeast” (temperate, subtropical;
25–38∘ N and 70–100∘ W; 29 stations),
“Mountain” (varied climate; 25–48∘ N and
100–116∘ W; 9 stations), “California”
(Mediterranean, varied climate; 25–40∘ N
and 116–125∘ W; 13 stations), and “Pacific
Northwest” (temperate rainforest; west of 116∘ W and north of
40∘ N; 4 stations). Figure 2 shows the observed average
PFT daily pollen counts averaged over all stations within the defined
subregions.
For deciduous broadleaf forest (DBF) taxa, the Southeast has the highest
average pollen maximum, reaching up to about 700–1200 grains m-3
around day 100. In the Northeast, DBF is the dominant PFT, reaching up to an
average of 400 grains m-3 and peaking slightly later (around day 120)
than in the Southeast. California sites show an average peak around
150 grains m-3 occurring slightly earlier around day 80. A sharp
maximum of 775 grains m-3 appears in the Mountain subregion at about
day 80, with a secondary emission reaching around 150 grains m-3 on
day 125. In the Northwest, DBF pollen has the earliest maximum (day 70) at
about the same magnitude as California (∼ 200 grains m-3). In some locations, there is a secondary DBF peak in
the late summer and early fall due to the late flowering of Ulmus crassifolia and Ulmus parvifolia, located predominantly in the
Southeast and California (Lewis, et al.1983). In the
Southeast, this occurs between days 225 and 300, while in California this
occurs twice around day 245 and day 265.
The two ENF families exhibit pollen release at two distinct but overlapping
times, with Cupressaceae peaking before Pinaceae. Cupressaceae in the
Southeast emits pollen earlier than in other subregions, with a maxima at
just over 400 grains m-3 around day 10 and counts above 200 grains m-3 in December of the prior year. Cupressaceae dominates the total
emissions for the Southeast, with a smaller maximum from Pinaceae of about
180 grains m-3 near day 110. In the Northeast, the bimodality of ENF is
evident with the Cupressaceae family reaching a maximum of 100 grains m-3 near day 85 with a secondary Pinaceae maximum approximately 65 days
later at about half the magnitude (∼ 50 grains m-3). In
the Mountain and Pacific Northwest subregions, the maximum occurs around days
50–80 and can reach up to 350 grains m-3 in the Mountain subregion, but
in both subregions it is generally much lower than in the eastern United States
(approximately 50 grains m-3). In the California subregion, ENF
emissions are comparatively low (< 50 grains m-3) which is
likely due to the bias in sampling locations.
The grasses (Poaceae) have comparatively low average pollen counts
(< 25 grains m-3) throughout the season in all subregions except
the Northwest, where the maximum reaches 75 grains m-3. However, the
average maximum Poaceae pollen count at individual stations is close to
100 grains m-3, with the individual annual maxima reaching several
hundreds of pollen grains m-3. In the AAAAI data, there are two
distinct maxima in the Northeast Poaceae count, and we attribute the first
seasonal maximum to C3 grasses (peak around day 155) and the second
grass maximum mainly to C4 grasses (peak around day 250). Observations
by Craine et al. (2012) of Poaceae in an American prairie have
indicated that C3 and C4 grass flowering occurs at distinctly
different times, with C3 in the late spring and C4 in mid-to-late
summer. Similarly, Medek et al. (2016) observed two grass pollen peaks in
Australia, with a stronger, late-summer peak at lower southern latitudes
where there is higher incidence of C4 grass. However, the authors note
that sometimes this may be due to a second flowering of some C3 grass
species. Although the C3–C4 separation cannot be confirmed in the
AAAAI pollen count data because they are not distinguished during pollen
identification, this distinction is included in the model as discussed in
Sects. 3.1 and 4.2 below. In the Southeast, this separation of the Poaceae
pollen counts is less apparent because both of the emission maxima are
broader and intersect one another. In the Southeast, the first observed
pollen maximum (assessed as C3 grass pollen) peaks earlier around day
140, while the second maximum (assessed as C4 grasses) has a similar,
yet smaller, value around day 250. In the Mountain subregion, the first grass
maximum occurs later in the year (day 175), and the second grass maximum
occurs around day 250 in the late summer. Pollen counts in California are
only substantial during the earlier flowering time (C3 grasses) and have a
similar duration to those in the Northeast, peaking at around day 135. For the Pacific
Northwest, there is one strong early peak of grass pollen in the middle of
the summer (day 170) and a secondary maximum is negligible, although counts
below 10 grains m-3 register around days 250–270.
Ragweed (Ambrosia) pollen is segregated from other grasses and herbs
because of the strong allergic response in humans to this specific species
and the unique timing of emissions. Because it is a short-day plant (i.e.,
its phenology driven by a shortening photoperiod and cold temperatures; Deen
et al., 1998), ragweed pollen seasons are generally constrained to the late
summer with the exception of the Mountain region where some counts occur in
the spring. Emissions in the Northeast reach a maximum around day 240 at
60 grains m-3, while they occur slightly later in the Southeast,
peaking around day 270 with twice the magnitude (120 grains m-3). In
the Mountain subregion, ragweed pollen has an expected peak at around day
245,
but also an earlier peak at around day 130 with no confirmed cause.
Ambrosia is not detected in the station averages for California and
the Pacific Northwest, although some individual sites in these regions record
relatively low counts on the order of 10 grains m-3.
Land cover fraction (% coverage) for 11 tree taxa from the
Biogenic Emissions Landuse Database version 3 (BELD) regridded to a 25 km resolution
grid, including (a) Acer (maple),
(b) Alnus (alder), (c) Betula (birch),
(d) Cupressaceae (cedar/juniper), (e) Fraxinus (ash),
(f) Morus (mulberry), (g) Pinaceae (pine),
(h) Platanus (sycamore), (i) Populus
(poplar/aspen), (j) Quercus (oak), and (k)
Ulmus (elm).
![]()
Model input data
Land cover data
With a goal of developing regional to global pollen emissions, one of the
greatest limitations is the description of vegetation at the appropriate
taxonomic level and spatial resolution. While land cover databases specific
to species level are available for some regions, they are not available
globally. Alternatively, vegetation land cover in regional to global models
can be represented by classifications based on biophysical characteristics.
For climate models, a common approach to represent land cover is with PFTs, and global PFT data are readily available and used by
many regional and global climate models to describe a variety of terrestrial
emissions (Guenther et al., 2006) and biophysical processes in land–atmosphere exchange models. The
creation of a pollen emissions model with PFT categorization would be of use
at a broad range of spatial scales and domains while integrating more
readily with climate models. In the pollen emissions model development and
evaluation (Sects. 4 and 5), we compare two different vegetation
descriptions of broadleaf deciduous and evergreen needleleaf trees including
(1) family- or genus-specific land cover and (2) land cover categorized by
PFT.
Production factors (P) and phenological regression coefficients for
the start day of year (sDOY) and end day of year (eDOY) as a function of
temperature for the 13 individual pollen-producing taxa. Individual taxa and
families are organized into the four PFTs, with the two aggregated tree PFTs
denoted as DBF and ENF. Regression slope (days ∘C-1) and coefficient of
determination are provided for both sDOY and eDOY (slope/R2).
Taxon or PFT
pannual
Reference for pannual
sDOY
eDOY
107 grains
(slope/R2)
(slope/R2)
m-2year-1
days ∘C-1
days ∘C-1
Deciduous broadleaf forest (DBF)
Acer
89.1
Tormo Molina et al. (1996)
-1.78/0.15
-1.56/0.06
Alnus
210
Helbig et al. (2004)
-8.82/0.46
-4.88/0.26
Betula
140
Jato et al. (2007)
-3.46/0.54
-3.45/0.35
Fraxinus
45.1
Tormo Molina et al. (1996)
-4.69/0.50
-2.92/0.32
Morus
10
N/A
-4.00/0.53
-2.97/0.29
Platanus
121
Tormo Molina et al. (1996)
-4.47/0.40
-2.65/0.20
Populus
24.2
Tormo Molina et al. (1996)
-2.23/0.24
-0.31/< 0.01
Quercus
78
Tormo Molina et al. (1996)
-4.09/0.53
-2.03/0.19
Ulmus (early, late)
3.55
Tormo Molina et al. (1996)
-4.61/0.59,
-2.37/0.16,
3.06/0.12
5.12/0.29
DBF
80.1
This paper
-4.55/0.46
-1.94/0.13
Evergreen needleleaf forest (ENF)
Cupressaceae
363
Hidalgo et al. (1999)
-5.67/0.48
-2.67/0.17
Pinaceae
22.2
Tormo Molina et al. (1996)
-5.72/0.45
-5.03/0.41
ENF
193
This paper
-5.95/0.40
-4.96/0.33
Grasses (GRA)
Poaceae (C3, C4)
8.5, 0.85
Prieto-Baena et al. (2003)
-4.76/0.48,
-1.08/0.04,
0.05/< 0.01
2.96/0.32
Ragweed (RAG)
Ambrosia
119*
Fumanal et al. (2007)
1.08/0.08
3.42/0.37
* Ambrosia production factor in 107 grains plant-1.
The Biogenic Emissions Landuse Database version 3 (BELD) provides vegetation
species distributions at 1 km resolution over the continental United States
based on satellite imagery, aerial photography, and ground surveys, as well
as other land cover classification data such as geographical boundaries (Kinnee et al., 1997;
https://www.epa.gov/air-emissions-modeling/biogenic-emissions-landuse-database-version-3-beld3). The BELD database includes 230 different tree, shrub, and crop taxa across
the United States as a fraction of the grid cell area at either the genus or
species level. For family- and genus-level pollen emissions, the BELD land
cover fraction for the 11 dominant pollen-emitting tree taxa identified in
Sect. 2.1 is utilized (Table 1; Fig. 3). For species-level land cover
data, land cover fraction is calculated as the aggregate of all species
within a family or genus.
BELD (a, c) and CLM4 (b, d, e, f) land cover for
the four PFT categories that produce pollen emissions, including (1)
deciduous broadleaf forest for (a) BELD and (b) CLM4, (2)
evergreen needleleaf forest for (c) BELD and (d) CLM4, (3)
grasses, including (e) C3 grasses and (f) C4 grasses,
and (g) ragweed, represented by crop and urban CLM4 categories.
![]()
For the PFT land cover, we use the Community Land Model 4 (CLM4)
(Oleson et al., 2010) surface dataset
that employs a 0.05∘ resolution satellite-derived land cover
fraction from the International Geosphere-Biosphere Programme (IGBP)
classification (Lawrence and Chase 2007). We sum all three biome PFT categories (temperate, tropical, and
boreal) for deciduous broadleaf forests (DBF) and two biome PFT categories
(boreal and temperate) for evergreen needleleaf forests (ENF) to produce the
model PFT land cover.
Total spatial coverage (km2) of tree taxa and PFTs from BELD and CLM4 land cover datasets in the five US subregions
(Northeast, NE; Southeast, SE; Mountain, MT; California, CA; Pacific Northwest,
PNW). All individual tree taxa are from the BELD database. BELD DBF and ENF
land cover are the sums of the land cover of the taxa belonging to each
PFT.
Land cover class
NE
SE
MT
CA
PNW
Acer
6.79E+04
2.88E+04
1.89E+03
1.97E+02
3.09E+03
Alnus
3.37E+00
1.23E-01
6.49E+01
1.71E+02
9.56E+03
Betula
2.99E+04
2.68E+03
2.78E+02
2.64E+00
4.82E+02
Fraxinus
3.96E+04
1.10E+04
3.14E+03
3.94E+01
2.76E+02
Morus
3.99E+03
2.25E+03
3.89E+01
0.00E+00
0.00E+00
Platanus
3.18E+03
3.38E+03
1.33E+01
1.44E+02
0.00E+00
Populus
5.48E+04
1.23E+03
4.37E+04
1.96E+02
1.55E+03
Quercus
1.30E+05
2.25E+05
2.51E+04
2.82E+04
1.40E+04
Ulmus
4.96E+04
2.81E+04
1.37E+03
0.00E+00
0.00E+00
BELD DBF
3.79E+05
3.03E+05
7.56E+04
2.90E+04
2.90E+04
CLM4 DBF
6.67E+05
4.03E+05
1.72E+05
7.93E+03
4.18E+04
Cupressaceae
1.85E+04
2.11E+04
7.84E+04
9.64E+03
2.35E+04
Pinaceae
8.34E+04
1.58E+05
1.79E+05
2.95E+04
1.10E+05
BELD ENF
1.02E+05
1.79E+05
2.58E+05
3.91E+04
1.34E+05
CLM4 ENF
1.44E+06
4.26E+05
4.66E+05
4.57E+04
5.34E+05
Figure 4a–d compares the BELD land cover (summed by PFT) and CLM4 land cover
for the two tree PFTs. Region-by-region comparison of land cover for all
BELD taxa and each tree PFT (from both BELD and CLM4) is given in Table 2.
An important distinction is that CLM4 land cover extends beyond US borders
because it is derived from a global dataset, whereas BELD is constrained to
the continental United States. BELD and CLM4 land cover show general
agreement on the regional distribution of both tree PFTs. DBF is
predominantly in the eastern portion of the United States with a gap in the
midwestern corn belt. ENF is present in the Southeast, the Northeast along
the US–Canadian border, along the Cascade and Coastal mountain ranges, and
throughout the northern Rockies. A notable difference is the CLM4
representation of ENF, which shows a strong, dense band extending from the
Sierra Nevada through the Canadian Rockies. The BELD ENF broadly covers the
Rocky Mountain range, yet more diffusely (land cover percentage up to
76 %), whereas the CLM4 dataset shows sparser and dense ENF land cover
(e.g., up to 100 %) in the same range. For the DBF category, another
notable difference is that the strong band of oaks around the Central Valley
of California, which is evident in BELD but missing from the CLM4 dataset.
Additionally, the CLM4 has far greater densities of DBF along the
Appalachian range than BELD. Overall, the CLM4 land cover fractions for
forest PFTs are higher on average than the summed BELD taxa, about 2 to 10
times as much in each region, with the exception of California subregion DBF
where CLM4 land cover is about half of that in the BELD dataset (Table 2).
Grass spatial distributions are given by C3 (non-arctic) and C4
grass PFT land cover classes from CLM4 (Fig. 4e, f), which correspond to
the observed family-level Poaceae pollen subdivided into C3 and
C4 categories (described in Sect. 2.2). C3 coverage is evident
across the United States, with broad coverage throughout the Southeast,
Midwest, and Northern Great Plains (Fig. 4e). C4 coverage is
concentrated in the Southeast and Southern Great Plains at lower densities
(Fig. 4f).
Ragweed requires a different land cover treatment, as land cover
distributions are not available for ragweed across the entire continental
United States. Ragweed is known to arise in areas of human disturbances
(Forman and Alexander 1998; Larson 2003) and is found mainly in disturbed or developed areas such
as cities and farms (Katz et al., 2014; Clay
et al., 2006). Ambrosia land cover (Fig. 4g) is derived from the urban and crop
categories of the CLM4 land cover, which are sourced from LandScan 2004 (Jackson et al., 2010) and the CLM4
datasets, respectively. The urban data are subdivided by urban intensity,
which is determined by population density. We assume that ragweed is
unlikely to grow in the densest of urban areas (such as city centers) and
utilize the lowest urban density category that is also the most widespread.
Ragweed land cover (plants m-2) in urban areas is determined by
multiplying the average urban ragweed stem density given by Katz et al. (2014) by the
urban land cover fraction. For crops, the CLM4 subdivides land cover
fraction into categories including corn and soybean crops, and Clay et al. (2006) provide
ragweed stem densities in soybean and corn cropland. Thus, we calculate the
ragweed land cover in stems m-2 (frag):
frag=αdsoyfsoy+dcornfcorn+βdurbfurb,
where dsoy, dcorn, and durb represent the stem density
(stems m-2) of ragweed in soybean, corn, and urban areas, respectively, and the
fsoy, fcorn, and furb represent the fractional land cover for
soybean, corn, and urban, respectively. α and β are tuning
parameters to that are determined by a preliminary evaluation between
modeled and observed ragweed pollen counts, where α=0.01 for crop
and β=0.1. Zink et al. (2017) show that a
ragweed land cover representation developed by combining land use and local
pollen count information evaluates better against observed pollen counts
than even ragweed ecological models, giving confidence to this choice of
land cover representation.
All land cover data are regridded to a 25 km resolution across the United
States to provide emissions at the same spatial resolution as the regional
climate model (see Sect. 5).
Meteorological data for phenology
To develop the emissions model, we use two sources of meteorological data.
The first is a high-resolution meteorological dataset to develop the
phenological relationships for the timing of pollen release. Because reliable
measurements are not available at all pollen count stations and there is
uncertainty in the siting of these stations (e.g., they may be in urban areas
with highly heterogeneous temperature), we use a gridded observational
meteorological product for consistency across all sites (Maurer et al.,
2002). The gridded Maurer dataset interpolates station data to a 1/8∘
grid across the continental United States on a daily basis, representing a
high spatial resolution gridded data product where data from each
meteorological station have been subject to consistent quality control. Higher-resolution
DayMet temperatures (daily 1 km) (Thornton et al., 2014) were
used in lieu of Maurer data at NAB sites where the Maurer dataset did not
provide information at the collocated grid cell (Table S1). For offline
emission calculations input into the regional climate model, we use
annual-average temperatures computed from monthly Climate Research Unit (CRU)
temperature data (Harris et al., 2014). These data were interpolated from a
0.5∘ × 0.5∘ grid to the 25 km regional climate
model grid used for pollen transport.
PECM model description
Emission potential
The pollen emissions model is a prognostic description of the potential
emissions flux of pollen (Epot; grains m-2 d-1) for an
individual taxon i:
Epot,ix,y,t=fix,ypannual,i∫0365γphen,ix,y,tdtγphen,ix,y,t,
for a model grid cell of location x and y at time t. In this
expression, f(x,y) is the vegetation land cover fraction (Sect. 2.1;
m2 vegetated m-2 total area), pannual is the daily
production factor (grains m-2 year-1), and γphen is the
phenological evolution of pollen emissions that controls the release of
pollen (description below). Equation (2) can apply to either a single taxa or
PFT, depending on the prescription of land cover through f(x,y). In the
simulations described here, emissions are calculated offline based on this
equation and provided as input to a RCM. This
emission potential is later adjusted based on meteorological factors in the
RCM where the pollen grains are transported as aerosol tracers (Sect. 5.1.1).
In the future, Eq. (2) can be coupled directly within the climate model
for online calculation of emissions. The phenological and production factors
are described in greater detail below.
Phenological factor (<inline-formula><mml:math id="M240" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">γ</mml:mi><mml:mi mathvariant="normal">phen</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)
Based on the observed pollen counts, a Gaussian distribution is used to
model the phenological timing of pollen release (γphen):
γphen,ix,y,t=e-(t-μx,y)22σ(x,y)2,
where μ(x,y) and σ(x,y) are the mean and half width of the Gaussian,
respectively, and can be determined based on the start day of year (sDOY)
and end day of year (eDOY) calculated by an empirical phenological model:
μx,y=sDOYx,y+eDOY(x,y)2σx,y=eDOYx,y-sDOY(x,y)a.
The fit parameter, a, accounts for the conversion between the empirical
phenological dates based on a pollen count threshold and the equivalent
width of the emissions curve. Based on evaluation versus observations, a=3 was selected for initial offline simulations.
Linear regressions of observed sDOY and eDOY from individual pollen count
stations versus temperature are used to empirically determine sDOY and eDOY
that drive γphen. An important criterion is the grain count used
to determine the sDOY and eDOY, and we utilize a count threshold adaptable
to bimodal emission patterns such as those noted for Ulmus and Poaceae. Sofiev et al. (2013)
selected dates on which the 5th and 95th percentiles of the annual index
(annual sum of pollen counts) were reached, while Liu et al. (2016)
combined a 5 grains m-3 threshold with the additional condition that
2.5 % (97.5 %) of the annual sum of pollen was reached before the start
(end) date. Here, we implement a pollen count threshold of 5 grains m-3
and found this was sufficient to reproduce the observed seasonal cycle. To
account for smaller signals that may be due to count errors (e.g., an
exceedance of the 5 grains m-3 threshold but not followed by an
increase in emissions), we used a moving window with a threshold of 25 grains m-3 for the sum of pollen counts in the nearest 10 neighboring
days; when the sum of the neighbors failed to meet this threshold, the data
point was omitted. In this manner, we calculated the sDOY and eDOY for the
full 8-year time series for each taxon at each station. If more than one
start or end date was found in a single year at a single station for a taxon
that was not clearly bimodal, only the first set of dates was retained for
the linear regression. For taxa with an observed bimodal peak, the second
peak was treated as a separate taxon (e.g., early and late Ulmus, C3 and
C4 Poaceae) with a separate phenology. Once the sDOY and eDOY were
determined, outliers in these dates were determined by bounding the data for
each taxon at 4 times the mean absolute deviation of sDOY and eDOY.
Near-surface atmospheric temperature (e.g., 2 m height) is an important
factor of vegetation phenology. In the interest of having a regional model
of emissions that prognostically calculates the start dates, the previous-year
annual average temperature (PYAAT) based on near-surface atmospheric
temperature from Maurer et al. (2002) and Thornton et al. (2014)
(Sect. 2.2) is the explanatory variable in the linear regressions. For
example, for a start date of 2 February 2007, the PYAAT would be the mean
temperature for the year 2006. For Pinus and Cupressaceae, PYAAT is calculated
differently from 1 July 2005 to 30 June 2006 because emissions of these
families begin in the early winter (December). Prior studies have shown that
the meteorology of the year previous to the pollen season influences pollen
production, especially temperature, suggesting that PYAAT may be a good
predictor variable (Menzel and Jochner, 2016). While emissions in
this study are calculated using offline meteorological data, this also could
be coupled to a dynamic land surface model to predict reasonably accurate
pollen phenological dates.
Phenological regressions for Betula (birch) pollen for
(a) sDOY and (b) eDOY
versus previous-year annual average temperature (PYAAT; ∘C). Each
point signifies one station per year for pollen count data from 2003 to 2010
(total denoted as N).
![]()
Monthly average emissions potential (E; Eq. 1) for BELD model
DBF (2003–2010) in grains m-2 day-1. (a) January,
(b) February, (c) March, (d) April, (e)
May, (f) June, (g) July, (h) August, (i)
September, (j) October, (k) November, and (l)
December.
![]()
To exemplify this method, Fig. 5 shows the phenological dates and
regression lines for the Betula (birch) genus, with all 13 modeled taxon shown in
Figs. S1 and S2 in the Supplement. The sDOY and eDOY of the pollen season show a moderate
and considerable trend with temperature for most taxa and PFTs (Table 1;
Figs. S1, S2). The linear regression models for sDOY explain 41 % of
the variance on average for DBF taxa, 47 % on average for ENF taxa, 48 %
for C3 Poaceae, and 8 % for Ambrosia, while having a negligible R2 for
C4 Poaceae. For eDOY, the linear regression models explain 21 % of
the variance on average for DBF taxa, 29 % for ENF, 4 % for C3
Poaceae, 32 % for C4 Poaceae, and 37 % for Ambrosia. All trends except
C4 Poaceae, late elm, and Ambrosia are negative, indicating that warmer
previous-year temperatures result in earlier start and end dates. For most
tree taxa, the trend of both sDOY and eDOY are negatively correlated with
PYAAT, with a steeper negative slope for sDOY. The correlation for the
duration of the pollen season (eDOY–sDOY) is then positive for all taxa
except Cupressaceae. This suggests that warmer climates have earlier pollen
season start and end dates but longer season lengths.
Trends for grass in Australasia show that the correlation of the end date of
the pollen season with average spring temperature is positive, while the
same relationship for the start date is negative, suggesting also that
season start dates are earlier and season duration increases with warmer
climates (Medek et al., 2016). The apparent trend in the season end date for Ambrosia with PYAAT could be due to the
increased number of frost-free days, consistent with global warming, and a
strong relationship between frost-free days and changes of ragweed season
length (Easterling 2002; Ziska et al., 2011).
This agrees with earlier findings that suggest the pollen season will, on
average, start earlier with a warmer global climate and have a longer
duration (Confalonieri et al., 2007). The
spatiotemporal heterogeneity of climate change may affect which regions and
seasons will be most influenced by climate change (Ziska 2016). In fact, there is imperfect agreement that earlier start dates and
longer seasons will occur unanimously throughout the United States region,
at least for trees (Yue et al., 2015). It is understood that photoperiod and the dormancy-breaking process controlled
by chilling temperatures play a significant role in the phenology of trees (Myking and Heide 1995; Ziska 2016), and it is
generally accepted that a plethora of other factors, such as plant age,
mortality, and nutrient availability, also affect observed phenological dates (Jochner et al., 2013). However, even without
these factors, the current phenological model is applicable to large regions
and provides a clear response of plants to interannual climate variability
as well as long-term climate changes. For this first assessment of PECM, we
assume that the pollen production factor (pannual) does not change with
time and that the phenological model described above captures the main
features of pollen emissions.
Same as Fig. 6 but for PFT model DBF.
![]()
Annual pollen production (<inline-formula><mml:math id="M265" display="inline"><mml:mrow><mml:msub><mml:mi>p</mml:mi><mml:mi mathvariant="normal">annual</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)
Annual production factors (grains m-2 year-1, where m-2 refers
to vegetated area, or grains stem-1 year-1 for ragweed) for each
modeled taxon are provided in Table 1. The annual pollen production factor
(pannual) defines the amount of pollen produced per vegetation
biomass per year based on literature values. Tormo Molina et al. (1996)
report the annual pollen productivity in grains tree-1 year-1
measured from three representative trees from several taxa. Morus
has no known reference for production factor and was assumed to be
10×107 grains m-2 year-1, conservatively at the low
end of the range for other deciduous broadleaf taxa. Other tree taxa and
grasses are reported in grains m-2 year-1, while ragweed is
reported in grains stem-1 year-1 (Helbig et al., 2004; Jato et al., 2007;
Hidalgo et al., 1999; Prieto-Baena et al., 2003; Fumanal et al., 2007). To
convert the production factors from Tormo Molina et al. (1996) (grains tree-1 year-1),
the production factors for each representative tree are multiplied by the
tree crown area, calculated as the circular area of the tree crown diameter
given in Table II of Tormo Molina et al. (1996). The resulting individual production
factors (grains m-2 year-1) are then averaged for each taxa.
After sensitivity experiments of running pollen emissions in RegCM4, we find
that the literature value of pannual for Poaceae provides better
agreement with observations for C4 grass when reduced by a factor of
10; thus, we use this value. To obtain the coefficient of daily pollen
production over the duration of the phenological curve, γphen, the
integral of the daily pollen production is normalized to pannual as
demonstrated by Eq. (2).
Offline emission simulations
We calculate emissions offline for two versions of PECM that differ in the
land cover input data for woody plants. The first uses the detailed BELD
tree database (Fig. 3) for tree pollen emissions (hereinafter, the “BELD”
simulation), and the second uses globally based PFT data for tree pollen
emissions (Fig. 4b, d) (hereinafter, the “PFT” simulation). For the
grass and ragweed taxa, the emissions calculations are identical between the
two simulations as the input land cover is the same for these two
categories. While the family and genus levels are useful for the allergen
community, the respective taxon land cover databases needed to develop a
global, adaptable model are not always available. While many plant traits
are found to vary quite strongly within individual PFTs (Reichstein et al., 2014), the PFT convention
is accepted and remains in use in climate models, particularly because of
the lack of species-level land cover data at large scales. For the PFT
version, pollen counts from individual taxa were summed within each PFT
prior to calculation of the phenological regression (Table 1). We
exclude the bimodality in Ulmus for the PFT version because it is the only tree
taxon that exhibits this behavior, and late Ulmus pollen emissions are relatively
small compared to the major DBF season. The production factors for each PFT
are calculated as the unweighted average of the production factors for all
the taxa within the PFT (Table 1).
Same as Fig. 6 but for BELD model ENF.
![]()
Same as Fig. 6 but for PFT model ENF.
![]()
Same as Fig. 6 but for C3 and C4 grass.
![]()
Same as Fig. 6 but for ragweed.
![]()
Figures 6–11 show the monthly averages of the 2003–2010 emissions potential
calculated by the offline models described in Sect. 4.1 (Epot;
Eq. 2). The seasonal cycle can be clearly identified in the emissions'
potential, with the onset of pollen emissions beginning in the warmer south
and moving northward along the gradient of annual average temperature. Colder
locales such as those at high elevations can interrupt this general trend.
Though pollen seasons generally end later in the colder parts of the domain
just as they start later, modeled pollen emission seasons tend to be shorter
at colder locations for most taxa (about 1 day per 1 ∘C, on
average). The highest maximum emissions for DBF occur over the Appalachian
range between April and May for both the BELD and PFT versions (Figs. 6, 7).
For ENF, the maximum occurs in April in the American west for the BELD
version where Cupressaceae land cover is dominant, while it is consistent in
magnitude between the Southeast and west coast for the PFT-based version
(Figs. 8, 9). The grass PFT maximum emissions occur in June in the northern
Rockies for C3 and in September in the south-central Great Plains for
C4 (Fig. 10). Ragweed pollen emissions reach their maximum during
September throughout the Corn Belt where soybean and corn crops dominate the
land surface, with local maxima apparent in urban centers (Fig. 11).
Emissions' implementation and evaluation
Emissions' implementation in a regional climate model
To evaluate PECM, emissions calculated offline are included within a
regional climate model to compare simulated atmospheric pollen
concentrations with ground-based observations from the NAB pollen network.
The two phenological pollen emissions estimates (BELD and PFT) described
above are prescribed as daily emissions, after which they are scaled by
meteorological factors and undergo atmospheric transport. We use the
Regional Climate Model version 4 (Giorgi et al., 2012),
which is a limited-area climate model that includes a coupled aerosol tracer
module (Solmon et al., 2006) that readily accommodates pollen tracers (Liu et al., 2016). The pollen tracer transport scheme is extended from one to four bins
in this study to simulate the four PFTs (DBF, ENF, GRA, and RAG), with
tracer bin particle effective diameters of 28, 40, 35 and 20 µm, respectively. Additionally, the temporal
emissions input is updated to accommodate daily pollen emissions (grains m-2 day-1).
RegCM4 is based on the hydrostatic version of the Penn State/NCAR mesoscale
model MM5 (Grell et al., 1994) and configured for long-term climate
simulations. In our RegCM4 configuration, we use the Community Land Model
version 4.5 (CLM4.5; Oleson et al., 2010), the Emanuel cumulus precipitation
scheme over land and ocean (Emanuel, 1991), and the SUBEX resolvable scale
precipitation (Pal et al., 2000). The horizontal resolution is 25 km with
144 × 243 grid cells on a Lambert conformal projection, centered on
39∘ N, 100∘ W, with parallels at 30 and 60∘ N (Fig. 1). The vertical resolution includes 18 vertical sigma
levels. Boundary conditions are driven by ERA-Interim reanalysis while sea
surface temperatures are prescribed from NOAA optimum interpolation SSTs (Dee
et al., 2011; Smith et al., 2008). Two 8-year simulations of pollen emissions
and transport in RegCM4 were conducted from 2003 to 2010 with the BELD and PFT
versions of the offline emissions model. A total of 6 months of spin-up (July–December
2002) are run for both simulations that we exclude from the following
analysis.
In the model, we calculate the fate of four pollen tracers corresponding to
the four PFTs (DBF, ENF, GRA, and RAG) from the PECM offline emissions.
Because individual tracers add to the computational cost of the simulations,
BELD-based tree emissions are summed into DBF and ENF PFTs before they are
emitted into the model atmosphere. To calculate the emissions, the emission
potential calculated offline for each PFT (Epot) is scaled
according to surface meteorology following the methods of Sofiev et
al. (2013):
Epollen,ix,y,t=Epot,ix,y,tfwfrfhfw=1.5-e-u10+uconv5fr=1,pr<prlowprhigh-prprhigh-prlow,prlow<pr<prhigh0,pr>prhighfh=1,rh<rhlowrhhigh-rhrhhigh-rhlow,rhlow<rh<rhhigh0,rh>rhhigh,
where fw, fr, and fh are the wind,
precipitation, and humidity factors, respectively. The meteorological
parameters in these equations are from online RegCM4 variables, including
u10 and uconv as the 10 m horizontal wind speed and
vertical wind speed, and pr and rh are precipitation and relative
humidity with low and high thresholds. These scaling factors account for the
effects of wind, precipitation, and humidity on the emission of pollen from
flowers and cones. The humidity and precipitation factors are piecewise
linear functions of the near-surface (10 m) RH and total precipitation and
range from 0 (high precipitation or humidity) to 1 (no precipitation or low
humidity). The wind factor ranges from 0.5 to 1.5, as even in calm conditions
turbulent motions can trigger pollen release with high winds releasing more
pollen. These scaled emissions are then transported according to the tracer
transport equation (Eq. 10) of Solmon et al. (2006) that includes advection,
horizontal and vertical diffusion (FH and FV),
and convective transport (Tc), as well as wet (RWls
and RWc, representing large-scale and convective precipitation
removal) and dry deposition (Dd) of an individual tracer (χ), represented by i=1 to 4 for each PFT pollen emission:
∂χi∂t=V¯⋅∇χi+FHi+FVi+TCi+Si-RWlsi-RWci-Ddi.
Average (2003–2010) time series of daily pollen counts comparing
model and observations for four PFTs (a–e, deciduous broadleaf,
DBF; f–j, evergreen needleleaf, ENF; k–o, grasses, GRA;
p–t, ragweed, RAG) across five US subregions (columns from left to
right: Northeast, NE; Southeast, SE; Mountain, MT; California, CA; Pacific
Northwest, PNW). Shading for the observations and model represents the mean
absolute deviation from the average for each day of the time series. Note
that the scale of the y axes varies by region and PFT.
![]()
Model evaluation against observations
We evaluate the efficacy of PECM in simulating the timing and magnitude of
pollen emissions across the continental United States by evaluating RegCM4
tracer concentrations versus observations. We compare the average daily
simulated near-surface pollen counts and observed, ground-based pollen
counts for each of the four modeled PFTs (Fig. 12). The observed pollen
time series in Fig. 12 are the spatial averages of the average daily pollen
counts at all pollen counting stations comprising each of the five major
US subregions (Sect. 2.2) and are compared with the modeled average
daily pollen counts, which averages the individual grid cells that contain
the pollen counting stations. Interannual variability is assessed using the
relative mean absolute deviation for each day of the average time series.
The interannual variability in observed daily pollen counts throughout the
year is, on average, 81, 78, 78, and 77 % of the mean (DBF, ENF, GRA, and
RAG, respectively), while this variability from the simulations is
53 % for the BELD version of the DBF model and 61 % for the PFT version,
55 and 92 % for the BELD and PFT versions of the ENF model, 43 % for
grasses, and 49 % for ragweed (Fig. 12). This indicates that the model
is capturing the relative interannual variability of the pollen counts
between PFTs but not all of the variability in pollen counts from season to
season. The unexplained variability in pollen concentrations could be due to
the lack of sensitivity of annual pollen production factor to the
environment, as this may be closely tied with precipitation (Duhl et al., 2013)
or temperature (Jochner et al., 2013). Additionally, the average
observed and simulated pollen counts are analyzed using box-and-whisker
plots to assess the models' representativeness of pollen count magnitude in
spite of phenology (Fig. 13). These metrics are discussed in detail by PFT
and US subregion below.
Box-and-whisker plots showing the statistical spread of the pollen
count magnitudes from the regional averages presented in Fig. 12. Columns
from left to right: Northeast, NE (a, f); Southeast,
SE (b, g); Mountain, MT (c, h); California, CA (d, i);
Pacific Northwest, PNW (e, j). DBF and ENF PFTs are shown in the top
row (a–e) and grass and ragweed PFTs are shown in the bottom row
(f–j). Boxes and whiskers from bottom to top represent the minimum,
lower quartile, median, upper quartile, and maximum. Maxima that are not
visible in panels (b, c, and e) are
1177, 1233, and 766 gains m-3, respectively. All y axes are the same scale for each row.
![]()
DBF
In the Northeast, the BELD model captures both the observed seasonal timing
and the magnitude of DBF pollen counts (Fig. 12a). Observed DBF phenology
is also simulated by the PFT-based emissions with even greater statistical
accuracy in reproducing the observed pollen counts, though the BELD model
more accurately reproduces the annual maximum (Fig. 13a). The accuracy in
this subregion is not surprising, as northeastern pollen counting stations
contributed the greatest number of data points to the phenological
regression analyses. Observed DBF pollen counts in the Southeast have a
large maximum that is greater than the average seasonal maximum of all four
other subregions and all three other PFTs (Fig. 12b), which is
predominantly from Quercus. Neither the BELD nor PFT version of the simulation
recreates this sharp peak, but they do simulate a large majority of the
pollen count distribution (Fig. 13b), especially the PFT-based model for
which the lower 75 % of simulated average pollen counts agrees well with
the lower 75 % of observed average pollen counts. The PFT model does not
specifically resolve Quercus, and while the BELD model does resolve Quercus, it fails to
model this maximum. This may be because the linear regression producing the
phenological dates is an average, where a longer season may result from
earlier start dates and/or later end dates that will reduce the maximum of
the Gaussian distribution of pollen counts in the time series. In the
Mountain region, there is an observed maximum early in the spring that is
not simulated by either model because the DBF phenology at several cold
Mountain sites is exceptionally early and falls well below the regression
lines (Figs. S1, S2). However, both the BELD and PFT model simulate the
second Mountain subregion peak with the correct magnitude. The BELD-simulated
maximum DBF in California is about 40 days later than the observed
peak, also due to the regionally anomalous phenology in California as
compared with the rest of the US, and though the PFT model peaks much
closer to the observations, it underestimates DBF pollen counts. In the
Pacific Northwest, the observed pattern is quite similar to the DBF pollen
phenology in the Mountain subregion, with only a slightly weaker early spring
peak due to low-elevation pollen. The observed phenological pattern (Fig. 12e) and pollen count magnitudes (Fig. 12e) are both more accurately
simulated by the BELD model, likely due to the earlier spring maximum that
does not appear in the PFT simulation.
ENF
Like DBF, the BELD ENF in the Northeast is well represented by simulating
two distinct Cupressaceae and Pinaceae maxima, although the model slightly
underestimates observed Pinaceae pollen counts (Fig. 12f). The PFT model
ENF phenology emits from the start of the earlier Cupressaceae season to the
end of the later Pinaceae season, while overestimating the maximum pollen
count by about a factor of 2. In the Southeast, the winter peak is not
captured by the model phenology (Fig. 12g). However, the spring Pinaceae
maximum is accurately captured by the BELD simulation. The PFT model follows
the observed Pinaceae phenology more closely, though overestimating pollen
counts by a factor of 2 to 3 and estimating a later ending date by about 40
days. In the Mountain subregion, ENF start and end dates are simulated by
the BELD model with improved accuracy than the DBF phenology in this
subregion, though the predicted spring maximum is later than observed
(Fig. 12h). As with DBF, there is good agreement between the BELD model
with the later part of the season in this subregion. The PFT model, again,
simulates the peak ENF emissions in the later part of the season and
overpredicts the pollen counts by a factor of 2 to 3. In the California
subregion, the tails of the pollen distributions by both models closely
resemble the pollen count magnitudes, yet the majority of these pollen
counts (the top 75 %, Fig. 13i) lie above the observed maximum (Fig. 12i). Finally, in the Pacific Northwest, the BELD model phenology shows some
agreement with the model mean (Fig. 13j), with the simulated pollen count
showing a stronger Gaussian distribution than observed (Fig. 12j). In
contrast, the PFT model grossly overpredicts the observed pollen counts by
up to a factor of 10 at its maximum, likely due to the greater
representation of the ENF PFT than the BELD model in this region. The
simulated average start date of the PFT model is within a few days of the
observed average start date, while the end date is about 20 days later than
observed.
Grasses
Grass phenology across all subregions for both C3 and C4 types is
captured by the emissions estimates (Fig. 12k–o). However, the pollen
count magnitude in northeastern C3 grass peak is overestimated by about
a factor of 7, even when using the minimum value of the annual
production factor in the range estimated by Prieto-Baena (2003) (Fig. 12k). The
secondary peak, which we attribute to C4 grasses and is only about half
as large, is well represented. In the Southeast, the simulated pollen count
magnitudes are much closer to observations, while the C3 peak is
overestimated here by only a factor of 2 and the C4 peak is within 5 grains m-3 (Fig. 12l). In this region, the observed duration of the
pollen emissions is not fully captured by the simulated grass phenology in
the Southeast, and this is probably due to the non-Gaussian shape of the
observed time series. In the Mountain subregion, the C3 pollen count is
overestimated by the model, but the phenology is represented by a gradual
rise in low emissions beginning in March to match the maximum burst of
emissions in June (Fig. 12m). C4 grass pollen counts are not
simulated in the Mountain region due to the relatively low C4 land
cover in the CLM4 dataset (Fig. 4f). In California, there is a single
observed grass peak, which the model attributes to C3 pollen, and the
peak count in the simulation is about 5 days late and about 2 to 10 times
too large (Fig. 12n). In the Pacific Northwest, the average C3 season
is accurately simulated with the exception that the phenology is shifted 20
days earlier than observed (Fig. 12o). A small C4 peak in the
observations at around day 260 is not simulated in this region due to
negligible land cover for C4 grasses in the CLM4 land cover data
(Fig. 4f).
Ragweed
Simulated ragweed phenology in the Northeast, Southeast, and Mountain
subregions follows the observed phenology of late-summer ragweed very
closely, where the peaks of both the simulated and the observed time series
averages occur within a day of each other (Fig. 12p–t). Close evaluation
of each regional phenological time series reveals that many of the observed
features, like those determined by the rate of increase or decrease of the
pollen count, are reproduced by the model. The magnitude of the modeled
ragweed maxima in the Northeast and Mountain subregions is slightly greater
than observed (Fig. 12p and r), while there is a clear underestimation
by a factor of 4 or 5 in the Southeast (Fig. 12q). There is a yet
unidentified observed spring peak of ragweed pollen at about day 125 in the
Mountain subregion, possibly due to an identification error. The observed
average ragweed pollen counts in California and the Pacific Northwest are
negligible, though the simulation predicts them to be similar in magnitude
and timing to the other three subregions (Fig. 12s and t). These
discrepancies may be due to the land use description developed for ragweed
(Sect. 3.1), which may overestimate the ragweed potential in the western
United States, or potentially the relatively spare observational stations in
these regions may be poorly placed relative to emissions sources.
Conclusions
We have developed a climate-flexible pollen emissions model (PECM) for the
13 most prevalent wind-pollinating taxa in the United States based on
observed pollen counts. PECM was adapted to the PFT categorization common to
climate and Earth system models with four major temperate-zone PFTs (DBF,
ENF, grasses and ragweed); thus, it is possible to apply this model to larger
geographic regions where specific taxon-level data are unavailable. We
evaluated PECM using a regional climate model (RegCM4) to transport
emissions and evaluated resulting pollen counts versus observations. PECM
generally captures the observed phenology, and observed surface pollen
concentrations can be simulated within an order of magnitude. While many
emissions models to date have focused on smaller geographical regions with
more detailed land cover information and pollen information, this model
represents the first of its kind to simulate multiple taxa over broad
spatial areas. This transition to a larger scale does have its
disadvantages, and we define several major sources of uncertainty to
consider when scaling up pollen emissions to the regional or global scale:
(1) pollen production factors, (2) climatic sensitivities in phenological
timing, (3) land cover data, and (4) taxa specificity. We discuss each of
these uncertainties in greater detail.
A large source of uncertainty is the use of a constant annual production
factor for pollen (Sect. 4.3). It has been reported that wind-driven
pollen production has increased historically and is expected, potentially,
to increase in the near future
(R. Zhang et al., 2014; Lake et al., 2017; Confalonieri et al., 2007; Ziello et al., 2012).
Some of more effective improvements to the emissions model would be to
create a pollen production model that is sensitive to multiple environmental
factors such as soil moisture, temperature, and nutrient status (Jochner et al., 2013). The interannual
variability in observed daily pollen counts is, on average, substantially
greater than that of the modeled pollen counts, which is likely due to this
lack of production sensitivity. The current production factors for woody
plants could be enhanced by studies that extend the number of representative
units (i.e., individual trees) of vegetation used to determine the average
pollen production. In a PFT representation, there is an inevitable
limitation to the accuracy of any single PFT's ability to account for taxa
differences within the PFT. Furthermore, the current model also assumes that
there are no interspecies differences that affect the performance of the
BELD model as well as the PFT model, whereas in reality it may vary by an
order of magnitude within a genus (Duhl et al., 2013). However, despite
the assumption of a constant production factor, observed surface pollen
counts for all PFTs are typically reproduced within a single order of
magnitude, as apparent in emissions model evaluation.
Second, the use of observed relationships between pollen count and
temperature to determine the phenological pollen start and end dates also
adds uncertainty to our modeling framework. Firstly, we assume stationarity
in the phenological relationships, and this assumption may be violated.
Secondly, based on the subregions defined for the analysis, there appears to
be a bias in the linear regressions toward subregions with more available
pollen counting stations, therefore affecting performance differences in
these regions. Lastly, even though generally the Gaussian time series model
of the pollen phenology performs well in our analysis, in the PFT
representation, the Gaussian absorbs or misses some of the phenological
details in the observed pollen seasonality, and in some cases taxa (e.g.,
grasses in the Southeast subregion) may not be captured by the existing
phenology.
Third, the specificity of land cover data provides an important constraint
in the overall simulation of emissions. The representation of land cover is
a key factor to accurately capturing regional features, especially in areas
with a high degree of topographical variation and therefore greater variance
in the land cover. For example, we notice large differences in the two model
simulations when considering tree-specific taxa, such as in the western
United States for ENF (Sect. 5.2.2). Also, our definition of the land
cover available for ragweed used assumptions based on crop cover and urban
area, which overestimated emissions in the western United States (Sect. 5.2.4). Interestingly, even though ragweed lacks an exact spatial
distribution, distinct observed features of the ragweed phenology in three
of the five subregions emerged using the current ragweed land cover
parameterization.
Fourth, the aggregation of emissions to the PFT level affects the
representativeness of the production factors, phenology, and land cover. When
comparing the two models of the tree pollen (BELD versus PFT), the
individual phenology of each of the 11 tree taxa is resolved by the BELD
simulation, whereas they are either folded into or excluded from the single
phenology modeled by the PFT simulation. This results from either treating
the taxa in the phenological regressions individually, as in the BELD model,
or as a sum, as in the PFT model. With a few exceptions (e.g., the ENF
family distinctions), the PFT model does generally reproduce the regional
phenology throughout the United States domain, which is a priority of this
study.
Despite these limitations, the empirical formulation presented here is the
first of its kind to predict a broad range of different pollen emissions
across a large geographic region. Even with univariate phenology and
invariable pollen production factors, the model includes seasonal dynamics
sensitive to climate change consistent with observations and is also able to
simulate observed pollen magnitudes. As a result, the model can be useful
for estimation of how allergenic risk or plant reproductive potential will
be redistributed by climate change, as well as studying pollen as an aerosol
in the climate system. While the empirical phenological models can be
reproduced for any set of regional pollen counting stations, PECM as a whole
can be easily adapted to various community climate and Earth system models,
global and regional, to extend research on the relationships and
interactions between pollen and climate.