ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-4213-2016Potential sensitivity of photosynthesis and isoprene emission to direct radiative effects of atmospheric aerosol pollutionStradaSusannasusanna.strada@lsce.ipsl.frUngerNadinehttps://orcid.org/0000-0001-7739-2290School of Forestry and Environmental Studies, Yale University, New Haven, CT, USAnow at: Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, FranceSusanna Strada (susanna.strada@lsce.ipsl.fr)4April20161674213423416July201517September201513February201615March2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/4213/2016/acp-16-4213-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/4213/2016/acp-16-4213-2016.pdf
A global Earth system model is applied to quantify the impacts of direct
anthropogenic aerosol effective radiative forcing on gross primary
productivity (GPP) and isoprene emission. The impacts of different pollution
aerosol sources (anthropogenic, biomass burning, and non-biomass burning) are
investigated by performing sensitivity experiments. The model framework
includes all known light and meteorological responses of photosynthesis, but
uses fixed canopy structures and phenology. On a global scale, our results
show that global land carbon fluxes (GPP and isoprene emission) are not
sensitive to pollution aerosols, even under a global decline in surface solar
radiation (direct + diffuse) by ∼9%. At a regional scale,
GPP and isoprene emission show a robust but opposite sensitivity to pollution
aerosols in regions where forested canopies dominate. In eastern North
America and Eurasia, anthropogenic pollution aerosols (mainly from
non-biomass burning sources) enhance GPP by +5–8 % on an annual
average. In the northwestern Amazon Basin and central Africa, biomass burning
aerosols increase GPP by +2–5 % on an annual average, with a peak
in the northwestern Amazon Basin during the dry-fire season
(+5–8 %). The prevailing mechanism varies across regions: light
scattering dominates in eastern North America, while a reduction in direct
radiation dominates in Europe and China. Aerosol-induced GPP productivity
increases in the Amazon and central Africa include an additional positive
feedback from reduced canopy temperatures in response to increases in canopy
conductance. In Eurasia and northeastern China, anthropogenic pollution
aerosols drive a decrease in isoprene emission of -2 to -12% on
an annual average. Future research needs to incorporate the indirect effects
of aerosols and possible feedbacks from dynamic carbon allocation and
phenology.
Introduction
Terrestrial gross primary productivity (GPP), the amount of carbon dioxide
(CO2) taken up every year from the atmosphere by plant
photosynthesis, is the largest single flux in the carbon cycle and therefore
plays a major role in global climate change. GPP is closely connected with
climatic variables (e.g., temperature, water, light) . In
turn, terrestrial vegetation provides the main source of isoprene to the
atmosphere, which controls the loading of multiple short-lived climate
pollutants and greenhouse gases (ozone, methane, secondary aerosols).
Isoprene production is closely linked to plant photosynthesis
. Hence, both GPP and isoprene emission may
be influenced by a change in surface solar radiation (SSR, the sum of the
direct and diffuse radiation incident on the surface) and surface atmospheric
temperature (SAT). Anthropogenic aerosols affect directly the Earth's
radiation flux via (a) scattering, which alters the partitioning between
direct and diffuse radiation, increases the diffuse fraction of SSR, and
affects SAT ; and (b) absorption, which reduces SSR and SAT
. Furthermore, aerosols may attenuate indirectly SSR
by acting as cloud condensation nuclei, thus perturbing cloud cover and cloud
properties .
In 1991, Mount Pinatubo (Philippines) injected 20 megatons of sulfur dioxide
(SO2) into the stratosphere, causing a massive production of sulfate
aerosols, with substantial impacts on climate, and on the water and carbon
cycles . In the aftermath of
the eruption, a loss in net global radiation at the TOA (top of the
atmosphere) and a concomitant cooling were observed, and ultimately led to
drying . By efficiently scattering light, the
volcanic sulfate aerosol production caused a significant increase in diffuse
solar radiation. In 1991 and 1992, at two northern mid-latitude sites,
recorded an increase in clear-sky diffuse
radiation by +50%, compensated for by a concomitant decrease in
direct radiation of -30%. Over the same period, in a deciduous
forest in North America, ascribed to increased diffuse
radiation an enhancement in plant productivity of +23 and +8%
in the 2 years following the Pinatubo eruption. On the global scale,
enhancement in the terrestrial carbon sink was proposed as one of the main
drivers of the sharp and rapid decline in the rate of atmospheric CO2
rise observed in the post-Pinatubo period, which resulted in a decrease of
3.5 ppmv by 1995 in atmospheric CO2. The “Mount Pinatubo
experiment” suggested a possible global response of terrestrial vegetation
to the “diffuse fertilization effect” (DFE). Observational and theoretical
studies show that plant productivity is more efficient under
multi-directional diffuse rather than direct light because shaded
non-light-saturated leaves increase their photosynthetic rate
.
The DFE on plant photosynthesis has been extensively observed at ecosystem
scale under cloudy skies e.g.,
and a chronic aerosol loading
e.g., in diverse ecosystems
(rainforest, deciduous and needleleaf forest, croplands and grasslands). The
main conclusions of these studies are the following: (1) DFE prevails in
complex and closed canopies, such as forests
; and (2) intermediate aerosol optical
depth (AOD) enhances plant productivity, while high AOD (>2–3) reduces the
carbon uptake rate because of a large reduction in direct radiation
. An ecosystem-scale
measurement study in a European mixed needleleaf and deciduous forest
reported increased isoprene emissions under conditions of higher diffuse
light .
A few modeling studies have investigated aerosol-induced effects on plant
productivity. Regional- and daily-scale assessments have been performed over
the Yellow River region (China), selecting a period of 5 days
, and over the eastern United States, selecting
two growing seasons . Results in both studies are
consistent with the main conclusions of the local observational studies.
demonstrated the importance of both
aerosol-induced radiative (i.e., change in light amount and its partitioning)
and thermal (i.e., change in surface temperature) effects on plant
transpiration and productivity. However, these studies focus on short time
periods and a limited number of ecosystems using offline models with
single-layer canopy schemes.
By applying a multi-layer canopy scheme in an offline modeling framework
(i.e., aerosol, radiative transfer, and land-surface models are coupled
offline), performed a regional- and decadal-scale
assessment of aerosol-induced effects on plant productivity in the Amazon
Basin from 1998 to 2007. The authors specifically focused on biomass burning
aerosols (BBAs) and assessed the fact that BBAs increase the annual mean
diffuse light and net primary production (NPP) by, respectively, ∼5 and
∼2.5%. Deforestation fires play a key role and drive ∼40% of the estimated changes in light and photosynthesis.
Moreover, assessed that in the Amazon Basin during
1998–2007 the DFE (a) was larger than the CO2 fertilization effect,
and (b) it could counteract the negative effect of droughts on land carbon
fluxes.
A global-scale assessment of the aerosol-induced effects on the carbon cycle
was performed by using an offline land-surface model
with a multi-layer canopy scheme. The authors concluded that DFE enhanced the
global land carbon sink by +23.7% over the 20th century, under an
overall radiation (direct + diffuse) change of +9.3%.
reconstructed historical SSR using radiative transfer
calculations and a global climate data set for the “global dimming” (period
1950–1980) and the “global brightening” period (after the 1990s)
. Recently,
applied an atmospheric radiative transfer module coupled with a terrestrial
ecosystem model to quantify aerosol direct radiative effects on global
terrestrial carbon dynamics during 2003–2010. Using transient atmospheric
CO2 and the prognostic leaf area index (LAI, one-sided green leaf
area per unit ground area), the authors evaluated aerosol impacts on plant
phenology, thermal and hydrological conditions, as well as solar radiation.
estimated that, on a global scale, aerosols enhance
GPP by 4.9 PgCyr-1 and slightly affect respiration.
accounted for all atmospheric aerosols and they did
not target anthropogenic pollution aerosols.
Understanding all anthropogenic factors that influence the land carbon cycle
is crucial to better manage terrestrial vegetation and to any effort to
mitigate climate change by stabilizing atmospheric CO2
concentrations. In the present study, we quantify the sensitivity of GPP and
isoprene emission to the direct radiative effects of a realistic present-day
pollution aerosol loading. Using a global Earth system model that represents
vegetation–oxidant–aerosol–climate coupling, we perform sensitivity
simulations to isolate the impact of the present-day pollution aerosols on
GPP and isoprene emission. We tackle the direct aerosol effect only
(absorption + scattering) and its impact on SSR and SAT that affects land
carbon fluxes. Aerosol indirect effects on cloud properties are not addressed
in this study due to the large uncertainties .
This study focuses on GPP because it is the first step in the
long-term storage of atmospheric CO2 in the living tissues of plants
and is directly affected by solar radiation. We do not address aerosol
effects on other land carbon cycle fluxes (e.g., respiration, net ecosystem
exchange). We employ the effective radiative forcing (ERF) concept metric
introduced in the IPCC AR5 in which all physical variables are allowed to
respond to the direct aerosol–radiation perturbations except for those
concerning the ocean and sea ice . The inclusion of these
rapid adjustments in the ERF metric allows us to investigate the multiple
aerosol-induced concomitant meteorological impacts on the biosphere.
Section describes the global Earth system model tool (NASA
ModelE2-YIBs) and the experimental design. In Sect. , we
evaluate simulated present-day atmospheric aerosols and GPP against global
observational data sets including AOD from the Moderate Resolution Imaging
Spectroradiometer (MODIS) and global gridded GPP that was generated using
data-orientated diagnostic upscaling of site-derived GPP from FLUXNET
. In addition, we present the
analysis of results from the sensitivity simulations. In
Sect. , we discuss the results and summarize conclusions.
MethodologyGlobal Earth system model: NASA ModelE2-YIBs
We apply the NASA GISS ModelE2 global chemistry–climate model at
2∘×2.5∘ latitude by longitude horizontal resolution
with 40 vertical layers extending to 0.1hPa.
The Yale Interactive Terrestrial Biosphere Model (YIBs) is embedded inside
NASA ModelE2 in a framework known as NASA ModelE2-YIBs .
The global climate model provides the meteorological drivers for the
vegetation physiology. The land-surface hydrology submodel provides the
grid-cell-level soil characteristics to the vegetation physiology. The model
framework fully integrates the land biosphere–oxidant–aerosol system such
that these components interact with each other and with the physics of the
climate model. Online oxidants affect aerosol production and online aerosols
provide surfaces for chemical reactions and influence photolysis rates. The
chemistry and aerosol schemes and their coupling have been well documented
and extensively compared with observations and other global models
e.g.,.
The aerosol package includes mass-based simulation of sulfate, nitrate and
sea salt e.g.,, carbonaceous aerosols (black carbon,
BC, and primary organic matter, OC) , mineral dust
, and biogenic secondary organic aerosol (BSOA)
. The model assumes log-normal size
distributions with effective radii: 0.2µm (sulfate);
0.3µm (nitrate); 0.1µm (BC); 0.3µm
(OC). Sea salt aerosols are represented by two size bins with effective radii
of 0.44 and 5µm. Mineral dust aerosols are tracked in four size
bins, ranging from 0.1 to 10µm, and can be coated by sulfate
and nitrate aerosols. Hygroscopic aerosols (sulfates, nitrates, sea salt, and
organic carbon) increase in size with increasing relative humidity, which
increases the aerosol scattering efficiency and radiative forcing
.
The direct effect interaction between aerosols and radiation is reproduced by
the online (two-way coupled) mode: aerosol fields are simulated at each model
time step (30min) and influence the simulated shortwave and
longwave radiation through scattering and absorption in the radiation
submodel, which in turn influences the climate dynamics. Thus, aerosols
induce (a) changes in simulated diffuse and direct photosynthetically active
radiation (PAR, spectral range of surface visible solar radiation,
400–700 nm, used by plants to photosynthesize) that are passed from
the radiation submodel to the vegetation model; and (b) rapid adjustment
changes in meteorology (temperature, precipitation, circulation) that are
passed from the model's atmosphere and land surface to the vegetation model.
The Yale Interactive Terrestrial Biosphere model (YIBs)
The vegetation structure describes eight plant functional types (PFTs):
tundra, grassland, shrubland, deciduous broadleaf forest, savannah, tropical
rainforest, evergreen needleleaf forest, and cropland. The PFT-specific
vegetation cover fraction and LAI are the standard atlas-based distribution
in NASA GISS ModelE2 . The LAI for each PFT is
prescribed according to regular seasonal sinusoidal variation between
PFT-specific minimum and maximum seasonal LAI values that is insensitive to
climate drivers or carbon balances
. Each model PFT fraction
in the vegetated part of each grid cell represents a single canopy. The
canopy radiative transfer scheme assumes a closed canopy and divides vertical
canopy layers into sunlit and shaded leaves, as well as the different
contributions from direct and diffuse PAR (from the climate model's radiation
scheme) at the leaf level . The leaf-level carbon and
water fluxes are scaled up to the canopy level by integrating over each
canopy layer, using an adaptive number of layers (typically 2–16)
. After upscaling from leaf to canopy, the carbon and
water fluxes are exchanged with the atmosphere on the 30 min physical
integration time step of the global climate model.
The vegetation biophysical fluxes are calculated using the well-established
leaf model of photosynthesis
and the stomatal conductance model of Ball and Berry . In
the leaf model, the rate of net CO2 uptake in the leaves of C3
plants is the result of three competing processes: Jc, the
carboxylation-limited rate; Je, the electron transport-limited
photosynthesis rate; and Js, the export-limited rate to use
photosynthesis products. The coupled photosynthesis, stomatal conductance,
and diffusive CO2 flux transport equations are solved analytically at
the leaf level using a cubic function in the net carbon assimilation rate.
Isoprene emission is calculated as a function of Je,
intercellular and atmospheric CO2, and canopy temperature
.
As theoretical and observational studies have demonstrated, the aerosol
effect on plant photosynthesis strongly depends on the canopy separation into
sunlit and shaded leaves. These two parts of the canopy have different
responses to the change in light partitioning driven by aerosols
. Under low PAR, both shaded and sunlit leaves are
in a light-limited environment (Je controls the photosynthesis
rate). Under high PAR, sunlit leaves are light-saturated and in a
Rubisco-limited environment (Jc controls the photosynthesis
rate), while shaded leaves are in a light-limited environment
(Je). Hence, sunlit canopy photosynthesis depends on both
direct and diffuse light, and on both Jc and Je
photosynthesis rates, while shaded canopy photosynthesis is directly
influenced by diffuse light and mainly depends on the Je
photosynthesis rate. The aerosol light-scattering directly influences
Je; hence, it mainly affects shaded leaves
.
Linkages between vegetation and atmospheric aerosols are extremely complex.
This version of the land carbon cycle model captures the meteorological
(light, temperature, relative humidity, precipitation) responses of
photosynthesis. The use of fixed canopy structures and phenology means that
leaf mass is not driven by photosynthetic uptake of CO2 and a closed
carbon cycle is not simulated. Thus, the simulated GPP and isoprene emission
responses may be underestimated because the LAI is insensitive to CO2
uptake and climate. The objectives here are to examine the meteorological
responses in detail and to offer a benchmark for future research that will
incorporate additional feedbacks from dynamic LAI and phenology. For example,
aerosol-induced effects on light and surface temperature may alter (i) the
onset and shutdown dates of photosynthesis and growing season length
and (ii) the carbon allocation, LAI, and tree height that
provide a feedback to GPP .
Simulations
The atmosphere-only configuration of NASA ModelE2-YIBs is used to perform a
control simulation (SimCTRL) representative of the present-day
(∼ 2000 s). Prescribed decadal average monthly varying sea surface
temperature (SST) and sea ice observations for 1996–2005 from the HadSST
data set provide the lower boundary conditions for the
global climate model. The present-day trace gas and aerosol emissions are
prescribed to year 2000 values from the historical inventory developed for
IPCC AR5 . Atmospheric levels of long-lived greenhouse
gases are prescribed to CO2=370ppmv, CH4=1733ppbv in the Southern Hemisphere, and 1814ppbv in the
Northern Hemisphere; N2O=316ppbv. A set of three
sensitivity perturbation simulations is performed that selectively removes
anthropogenic short-lived gas-phase precursor and primary aerosol emissions:
all anthropogenic emissions, including biomass burning, are removed in SimNOant;
only biomass burning emissions are removed in SimNObb;
all industrial emissions, which means all anthropogenic emissions except
biomass burning emissions (e.g., industry, power generation, road vehicles; hereafter,
we refer to these emissions as “non-biomass burning emissions”), are removed in SimNOind.
Global annual average of aerosol column burden (ACB,
mgm-2) as simulated by NASA ModelE2-YIBs in the control and
sensitivity present-day simulations for, in order, sulfates, nitrates,
organic (OC) and black carbon (BC) from industrial (ind) and biomass
burning (bb), and secondary organic aerosols (SOA). Cases filled with
“–” refer to negligible values of ACB (i.e., order of magnitude
pgm-2). For sensitivity simulations, percentage values in
parentheses indicate the contribution of target emissions (i.e.,
anthropogenic, biomass burning, and non-biomass burning) to each aerosol
component.
Global annual average of effective radiative forcing (ERF) for
aerosol–radiation interactions (Wm-2) as simulated by NASA
ModelE2-YIBs in present-day simulations for, in order, sulfates, nitrates,
organic (OC) and black carbon (BC) from industrial (ind) and biomass
burning (bb), and secondary organic aerosols (SOA). The global annual
average ERF is calculated as the difference between the control experiment
(SimCTRL) and sensitivity experiments: SimNOant, without all anthropogenic
emissions; SimNObb, without biomass burning emissions; and SimNOind, without
anthropogenic emissions except biomass burning. Percentage values in
parentheses specify the contribution of target emissions (i.e.,
anthropogenic, biomass burning, and non-biomass burning) to the ERF of the
selected aerosol component. The abbreviation “ns” indicates differences
that are not statistically significant at the 95 % confidence level
(based on a Student's t test).
The control and sensitivity simulations are run for 32 model years recycling
the year 2000 boundary conditions every year but allowing the changes in
atmospheric aerosol composition to influence meteorology and the land
biosphere. By prescribing SSTs and sea ice cover at climatological values,
while letting all other physical components of the Earth system respond until
they reach steady state, we capture the short-term response of the
land-surface climate to the aerosol radiation perturbation. This fixed-SST
technique allows us to compute ERF, the forcing metric that accounts for
rapid tropospheric adjustments and better characterizes drivers in the
troposphere (e.g., aerosols) . Hence, the fixed-SST
technique enables us to analyze multiple meteorological effects of the direct
aerosol–radiation interactions. The long run time is necessary to allow the
fast land and atmosphere climatic feedbacks to respond to the aerosol
perturbations and the TOA radiation fluxes to equilibrate. Integrations of 32
model years are completed for all simulations (control and sensitivity runs).
The global atmospheric oxidant–aerosol composition usually takes about 2
years to spin up, while the atmospheric dynamics and land-surface climate
takes about 10 years to reach steady state due to an imposed aerosol
radiative forcing. Therefore, we discard the first 12 model run years as
spin-up. The remaining 20 model run years are averaged for analysis. Twenty
model years of data are necessary such that any aerosol-driven variable
differences between the control and sensitivity simulations are statistically
significant relative to internal climate model variability. Our goal is to
isolate the effects of aerosol pollution on the land biospheric fluxes.
Therefore, we compute the absolute differences in X variable as ΔX=Xctrl-Xsens. Percentage changes in X are calculated
relative to the control experiment (i.e., Δ%X=ΔX/Xctrl×100) and, for selected variables, are gathered in
the Supplement. Applying the same methodology, we compute absolute and
percentage differences in annual and seasonal averages over selected regions.
Hereafter, we define as “significant” all absolute/percentage changes that
are statistically significant at the 95 % confidence level.
ResultsEvaluation of present-day control simulation
Present-day values of the global mean aerosol column burden (ACB) and ERF for
aerosol–radiation interactions (i.e., aerosol direct effect) are presented
by component in Tables and . The IPCC AR5
provides RF (not ERF) by single aerosol species
. NASA ModelE2-YIBs ERF values for single
aerosol species are consistent with the AR5 RF ranges. Nitrate ERF is on the
lower bound of the AR5 RF range (-0.30 to -0.03Wm-2). ERFs
of sulfate, BC from industrial sources, and SOAs fall into the AR5 RF ranges
(respectively, -0.60 to -0.20, +0.05 to +0.80, and -0.27 to
-0.20Wm-2). OC from industrial sources and BBAs show ERFs
consistent with the AR5 RF values (OCind:
-0.09Wm-2; BBAs: 0.00Wm-2). Based on a
combination of methods (i.e., global aerosol models and observation-based
methods), the AR5 report estimates the total ERF due to aerosol–radiation
interactions: -0.45 (-0.95 to +0.05) Wm-2; in AR5, the
best total RF estimate of the aerosol–radiation interaction is -0.35
(-0.85 to +0.15) Wm-2. The total ERF
is computed in NASA ModelE2-YIBs as the arithmetic mean of all anthropogenic
aerosol components (i.e., sulfate, nitrate, OC, and BC from both industrial
and biomass burning sources, SOA, and dust). The NASA ModelE2-YIBs estimates
a total ERF due to aerosol–radiation interactions of -0.34 (-0.76 to
+0.18) Wm-2, at the low end of the IPCC AR5 range.
Global annual average gross primary productivity (GPP), isoprene
emission, and shortwave visible (SW VIS) total, direct, and diffuse solar
radiation as simulated by NASA ModelE2-YIBs in the control and sensitivity
present-day simulations. For sensitivity simulations, percentage changes
compared to the control simulation are indicated in parentheses and reported
only if changes are statistically significant at the 95 % confidence
level.
Similarly to the aerosols, the present-day land carbon fluxes are in good
agreement with previous estimates (Table ).
Simulated global annual GPP (116.0PgCyr-1) is in reasonable
agreement with current understanding of the present-day carbon cycle budget
(based on FLUXNET: 123± 8PgCyr-1,
; based on MODIS: 109.29PgCyr-1,
; based on the Eddy Covariance-Light Use Efficiency
model: 110.5± 21.3PgCyr-1, ). The
global isoprene source is 402.8TgCyr-1, which is at the low
end of the range of previous global estimates (e.g.,
400–700 TgCyr-1, ). However, a
recent study suggests a larger range of 250–600TgCyr-1. The photosynthesis-based isoprene emission models tend
to estimate a lower global isoprene source than empirical models because the
scheme intrinsically accounts for the effects of plant water availability
that reduce isoprene emission rates .
Linear correlation Pearson's coefficient (Pearson's R), Pearson's
R squared (R2), and root-mean-squared error (RMSE) as computed for model
evaluation for annual and seasonal average coarse aerosol optical depth (AOD)
and gross primary productivity (GPP). Performances of the NASA ModelE2-YIBs
in the control present-day simulation (∼ 2000s) are compared to
(1) MODIS AOD (at 550 nm; averaged over 2000–2007) for NASA
ModelE2-YIBs PM10 optical depth and the (2) global FLUXNET-derived
GPP product (averaged over 2000–2011). Only boreal summer (JJA) and winter
(DJF) seasonal averages are reported.
We use the quality assured Terra MODIS Collection 5 (C5.1) monthly mean
product (Level 3), a globally gridded data set at 1∘×1∘ resolution re-gridded to 2∘×2.5∘
resolution for comparison with the global model. To infer clear-sky
(non-cloudy) aerosol properties in part of the visible and shortwave infrared
spectrum, MODIS C5.1 relies on two algorithms depending on surface
reflectance: (1) the Dark Target (DT) algorithm, under conditions of low
surface reflectance (e.g., over ocean, vegetation) ; and
(2) the Deep Blue (DB) algorithm, designed to work under high surface
reflectance, such as over desert regions . To
cover both dark and bright surfaces, we merge the DT and DB AOD products
(i.e., DT missing data are filled in with DB values). We use MODIS TERRA C5.1
AOD data from 2000 to 2007 because DB AOD data are only available for this
period due to calibration issues . The MODIS instrument also
measures the fine model weighting (ETA) at 550nm; consequently,
the fine-mode AOD can be computed as fine AOD = AOD × ETA, where
fine AOD is the fraction of the AOD contributed by fine-mode sized particles
(i.e., effective radius ≪1.0µm)
. Quantitative use of MODIS fine AOD is not
appropriate because fine-mode aerosols play a main role in the scattering
process .
NASA ModelE2-YIBs provides separately all-sky and clear-sky AOD diagnostics;
we focus on clear-sky output since that is more comparable to the spaceborne
observations. The model coarse-mode (PM10, atmospheric particulate
matter with diameter < 10µm) AOD includes all simulated
aerosol species (sulfate, nitrate, organic and black carbon, SOA, sea salt,
and mineral dust); the model fine-mode (PM2.5, atmospheric PM with
diameter < 2.5µm) AOD accounts for all simulated aerosol
species except sea salt and dust.
Annual and seasonal average coarse aerosol optical depth (AOD) seen
by (a, c, e) the MODIS instrument (at 550 nm; averaged over
2000–2007) and (b, d, f) NASA ModelE2-YIBs in the control
present-day simulation (∼ 2000 s). Global mean values are
given in the upper left corner of each map. Only boreal summer (JJA) and
winter (DJF) seasonal averages are shown. For NASA ModelE2-YIBs, only
clear-sky (CS) values in the visible (Vis) range are used to define
PM10 optical thickness (OT).
As Fig. for fine-mode aerosol optical
depth: (a, c, e) MODIS fine AOD and (b, d, f) model
PM2.5 OT.
Annual and seasonal average gross primary productivity (GPP, in
gm-2day-1) as seen by (a, c, e) a global
FLUXNET-derived GPP product (averaged over 2000–2011), and
(b, d, f) NASA ModelE2-YIBS in the control present-day simulation
(∼ 2000s). Global mean values are given in the upper left
corner of each map. Only boreal summer (JJA) and winter (DJF) seasonal
averages are shown.
Figure compares the spatial distribution of annual
and seasonal (boreal summer and winter) mean coarse-mode AOD in NASA
ModelE2-YIBs (control present-day simulation) with observations from the
MODIS satellite instrument (averaged over 2000–2007). Model global mean
coarse-mode AODs are consistent with MODIS AOD global means. NASA
ModelE2-YIBs reproduces strong biomass burning and dust episodes over Africa.
In contrast, on both annual and seasonal averages the model underestimates
the optical thickness of the aerosol layer over China and India, which is
likely related to dust. The model's underestimate of Asian dust should not
influence the focus of this study, to assess the impacts of anthropogenic
pollution aerosols on the land carbon fluxes. The spatial and temporal
distribution of fine-mode aerosols in NASA ModelE2-YIBs is consistent with
MODIS observations (Fig. ). In general, the model
shows a slightly higher fine-aerosol layer compared to MODIS (e.g., over
Europe, India, and South America). Over China, model fine-AOD distribution is
consistent with MODIS on the annual average; however, the model does not show
the seasonal variability that MODIS observes. To quantify the model
evaluation, on an annual average the NASA ModelE2-YIBs coarse-mode AOD global
means present an acceptable correlation with the MODIS AOD global means
(R= 0.7, R2= 0.5, and RMSE = 0.05;
Table ). Between boreal summer and winter, boreal summer
shows the best model performance (R= 0.8, R2= 0.6, and
RMSE = 0.06; Table ). During boreal winter, outside the
growing season, the NASA ModelE2-YIBs overestimates coarse-mode AODs. Since
quantitative use of MODIS fine-AOD is not recommended, we do not quantify
model performance for fine-mode AODs.
Gross primary productivity (GPP)
In Fig. , we compare the spatial distribution of
annual and seasonal (boreal summer and winter) mean GPP in the NASA
ModelE2-YIBs model (control present-day simulation) with a global
FLUXNET-derived GPP product (averaged over 2000–2011). The model is
consistent with the broad spatio-temporal variability in FLUXNET-derived GPP.
We find a weaker annual and seasonal signal in the model GPP over the cerrado
area in central South America. However, since the FLUXNET-derived GPP product
mainly relies on the availability of FLUXNET sites, which are densely
distributed in temperate zones not in the tropics, FLUXNET-derived GPP may be
biased over central South America. On an annual average, the NASA
ModelE2-YIBs GPP highly correlates with the FLUXNET-derived GPP (R= 0.9,
R2= 0.7, RMSE = 1.0, Table ). Between boreal
summer and winter, boreal winter presents the best model performance
(R= 0.9, R2= 0.9 and RMSE = 1.1, Table ).
Recently, performed a site-level evaluation of the YIBs
model over 145 sites for different PFTs. Depending on PFT, GPP simulation
biases range from -19 to +7%. For monthly average GPP, among
the 145 sites, 121 have correlations higher than 0.8. High correlations (>0.8) are mainly achieved at deciduous broadleaf and evergreen needle leaf
sites; crop sites show medium correlation (∼0.7).
Aerosol pollution changes in sensitivity simulationsGlobal scale
Table shows the aerosol column burden (ACB) by component in
the control and the three sensitivity simulations. Anthropogenic pollution
emissions (SimCTRL-SimNOant) contribute 0.85mgm-2 to
sulfate ACB (36% of the total sulfate burden due to both
anthropogenic and natural emissions), 4.47mgm-2 to nitrate
ACB (87 %), and 0.99mgm-2 to SOA ACB
(72%). Biomass burning emissions (SimCTRL-SimNObb) contribute
1.62mgm-2 to nitrate ACB (31 %) and
0.23mgm-2 to SOA ACB (17 %), while they do not
significantly contribute to sulfate ACB. Non-biomass burning emissions
(SimCTRL-SimNOind) contribute 0.89mgm-2 to sulfate ACB
(37 %), 3.69mgm-2 to nitrate ACB (72 %),
and 0.47mgm-2 to SOA ACB (34 %). For carbonaceous
aerosols, anthropogenic pollution emissions contribute
1.45mgm-2 to the total OC ACB (0.48mgm-2 from
non-biomass burning, OCind, and 0.97mgm-2 from
biomass burning, OCbb) and 0.26mgm-2 to the total
BC ACB (0.17mgm-2 from non-biomass burning,
BCind, and 0.09mgm-2 from biomass burning,
BCbb). Non-biomass burning emissions contribute
0.15mgm-2 to OCbb ACB (15 %) and
0.01mgm-2 to BCbb ACB (15 %).
Table presents, by aerosol component, the ERF for
aerosol–radiation interactions due to anthropogenic pollution and biomass
burning and non-biomass burning emissions. Anthropogenic pollution emissions
contribute -0.31Wm-2 to sulfate ERF (40% of the
total sulfate ERF due to both anthropogenic and natural emissions),
-0.38Wm-2 to nitrate ERF (85 %), and
+0.10Wm-2 to SOA ERF (63 %). Biomass burning
emissions contribute -0.14Wm-2 to nitrate ERF (30 %)
and -0.03Wm-2 to SOA ERF (16 %), while they do not
significantly contribute to sulfate ERF. Non-biomass burning emissions
contribute -0.30Wm-2 to sulfate ERF (40 %),
-0.31Wm-2 to nitrate ERF (70 %), and
-0.05Wm-2 to SOA ERF (29 %). For carbonaceous
aerosols, anthropogenic pollution emissions contribute
-0.17Wm-2 to the total OC ERF (-0.06Wm-2 from
non-biomass burning, OCind, and -0.11Wm-2 from
biomass burning, OCbb) and +0.30Wm-2 to the total
BC ERF (+0.18Wm-2 from non-biomass burning,
BCind, and +0.12Wm-2 from biomass burning,
BCbb). Non-biomass burning emissions contribute
-0.01Wm-2 to OCbb ERF (9 %) and
+0.02Wm-2 to BCbb ERF (11 %).
Five key regions
Beyond the global results, our simulations reveal five strongly sensitive
regions that correspond to important sources of aerosol pollution: eastern
North America, Eurasia, northeastern China, the northwestern Amazon Basin,
and central Africa (green boxes in Fig. ). Besides a
substantial contribution to primary aerosol (PA) sources (i.e., BC and OC),
all selected regions considerably contribute to secondary aerosol (SA)
sources such as sulfate, nitrate, and SOA (Table S1 for ACB and Table S2 for
ERF in the Supplement). We focus on SAs since, being finer than PAs, they
play a key role in scattering and may trigger DFE.
In terms of aerosol burden, in the five key regions, nitrate is the dominant
aerosol source, with a larger contribution from non-biomass burning compared
to biomass burning emissions. Sulfate source is mainly governed by
non-biomass burning emissions, except in central Africa, where biomass
burning emissions importantly contribute to sulfate ACB. For SOA sources,
both biomass and non-biomass burning emissions feed SOA ACB, with a larger
contribution from biomass burning in central Africa.
Eastern North America and Eurasia share a similar contribution to nitrate ACB
(∼14–15mgm-2; ∼93%) and ERF
(-1.2–1.3mgm-2; ∼94%) due to anthropogenic
emissions, with the largest input from non-biomass burning emissions (ACB:
12.7mgm-2; ERF: -1.1mgm-2, ∼80%) compared to biomass burning emissions (ACB:
3.4mgm-2; ERF: -0.3mgm-2, ∼20%). Eastern North America and Eurasia also show a similar
contribution to SOA sources due to anthropogenic emissions (ACB:
2.1mgm-2, ∼78%; ERF:
-0.2mgm-2, ∼72%). In both regions,
non-biomass burning emissions provide a larger input to SOA sources compared
to biomass burning emissions, with a larger contribution in Eurasia compared
to eastern North America (ACB: 1.4 vs. 0.9mgm-2, 52 vs.
32%) and even a different sign in ERF (-0.2 vs.
+0.08mgm-2, 45 vs. 25%). Compared to eastern
North America and Eurasia, northeastern China presents nearly a half nitrate
source, while contributions to sulfate ACB due to anthropogenic emissions are
about 0.5–1Wm-2 (5–10 %) larger, and lead to
more intense negative ERF (by 0.4–0.6Wm-2,
5–10%). In northeastern China, anthropogenic emissions largely
contribute as well to SOA sources, with a share between biomass and
non-biomass burning similar to Eurasia. The northwestern Amazon Basin shows
the smallest contributions to SA sources. However, compared to the other key
regions, biomass burning and non-biomass burning emissions contribute the
same amount to SOA sources (ACB: 0.5Wm-2,
24–29%; ERF: -0.06Wm-2, 24–29%). As
previously commented, central Africa substantially contributes to sulfate
source via both biomass (ACB: 0.6Wm-2, 30%; ERF:
-0.2Wm-2, 30%) and non-biomass burning emissions
(ACB: 0.7Wm-2, 40%; ERF:
-0.3Wm-2, 45%). In this region, biomass burning
emissions substantially feed SOA sources, with contributions that nearly
double those from non-biomass burning emissions (ACB: 2.1 vs.
1.1Wm-2, 44 vs. 22%; ERF: -0.16 vs.
-0.08Wm-2, 48 vs. 23%).
Spatial distribution of annual absolute change in shortwave visible
(SW VIS) (a, d, g) total, (b, e, h) direct, and
(c, f, i) diffuse solar radiation (in Wm-2). Changes are
computed between the control experiment (SimCTRL) and sensitivity
experiments: (a–c) without all anthropogenic emissions (SimNOant);
(d–f) without biomass burning emissions (SimNObb); and
(g–i) without anthropogenic emissions except biomass burning
(SimNOind). All experiments are set in a present-day
climatic state. Shaded regions indicate areas where changes in solar radiation
are significant at the 95 % confidence level. Green boxes on
plot (a) highlight key regions selected for discussion.
Aerosol pollution changes to surface solar radiationGlobal scale
The global annual average shortwave visible solar radiation (total, direct,
and diffuse) for each simulation (control and sensitivity) is gathered in
Table . Hereafter, we shorten “shortwave visible
solar radiation” to “radiation”. Relative to the control simulation
(SimCTRL), changes in global total and diffuse radiation are slightly
affected by the pollution aerosol burden (absolute change for total
radiation: from +1.6 to +5.1Wm-2; absolute change for
diffuse radiation: from -1.3 to -3.8Wm-2; relative change:
1.7–2.5%). By contrast, changes in direct radiation show a
larger sensitivity range to the aerosol burden (absolute change:
2.9–8.9Wm-2; relative change: 3.6–11.2%). In
the present-day world, anthropogenic pollution aerosols drive a decrease in
global total and direct radiation by -2.3%
(-5.2Wm-2) and -11.2% (-9.0Wm-2),
respectively, while global diffuse radiation increases by +2.5%
(+3.7Wm-2). Biomass burning aerosols have almost zero effect
on global total and diffuse radiation, while they reduce direct radiation by
-3.6% (-2.9Wm-2). Non-biomass burning aerosols
(non-BBAs) decrease global total radiation by -1.7%
(-3.8Wm-2) and increase global diffuse radiation by the same
percentage (absolute change: +2.6Wm-2), while global direct
radiation decreases by -8.0% (-6.4Wm-2). In
summary, anthropogenic pollution aerosols drive an overall SSR
(direct + diffuse) global decline of ∼5Wm-2. In the
literature, estimates for the overall SSR decline during the “global
dimming” (period 1950–1980) range from 3 to 9Wm-2. In percentage, anthropogenic pollution aerosols drive an
overall SSR global decline of 8.7 %.
Absolute and percent changes in annual average shortwave visible (SW
VIS) solar radiation, surface atmospheric temperature (SAT), transpiration
efficiency, canopy temperature, gross primary productivity (GPP), and
isoprene emission in eastern North America, Eurasia, northeastern China, the
northwestern Amazon Basin, and central Africa (green boxes in
Fig. a). Changes are computed between the control
experiment (SimCTRL) and sensitivity experiments: SimNOant, without all
anthropogenic emissions; SimNObb, without biomass burning emissions; and
SimNOind, without anthropogenic emissions except biomass burning. The
abbreviation “ns” indicates differences that are not statistically
significant at the 95 % confidence level (based on a Student's
t test).
Figure shows the spatial distribution of aerosol-driven
annual absolute changes in surface radiation (for annual percentage and
seasonal absolute changes: Figs. S1 and S2). Regionally, on both annual and
seasonal average, eastern North America, Eurasia, northeastern China, the
northwestern Amazon Basin and central Africa are highly affected by
aerosol-induced changes in surface solar radiation. For these five key
regions, Table presents absolute and percent
changes in annual average radiation (total, direct, and diffuse) between the
control and sensitivity simulations. Eastern North America shows the largest
increase in annual diffuse radiation due to all anthropogenic aerosols
(+8.6Wm-2; +6.2%), followed by northeastern China
and central Africa, which experience similar changes (∼+7.4–7.9Wm-2; ∼+5.7%). Over eastern North
America, the increase in diffuse radiation maximizes during boreal summer
(+13.6Wm-2; +8.9%), with changes that are
1.6–5.7Wm-2 (1.9–3.3%) higher that those
observed over northeastern China and Eurasia (Table S3). Driven by non-BBAs,
Eurasia and northeastern China undergo the largest reduction in total and
direct radiation, with a larger increase over northeastern China (total:
-12.3Wm-2, -6%; direct:
-19.4Wm-2, -26.1%) than Eurasia (total:
-9.5Wm-2, -4.8%; direct: -14Wm-2,
-23.8%). Over Eurasia and northeastern China, decreases in total
and direct radiation maximize during boreal summer, with changes that double
those observed over eastern North America (Table S3). In central Africa and
the northeastern Amazon, on an annual average basis, BBAs drive changes in
surface radiation that are similar in magnitude to those driven by non-BBAs.
However, in these tropical ecosystems, the BBA effects on surface radiation
exhibit a strong seasonal cycle, with the maximum signal in the dry-fire
season (boreal summer–boreal autumn, JJA–SON).
Spatial distribution of annual absolute change in transpiration
efficiency (beta, in %; left column panels), surface relative
humidity (RH, in %; middle column panels), and canopy temperature
(in K; right column panels) between the control experiment (SimCTRL) and
sensitivity experiments: (a, d, g) without all anthropogenic emissions
(SimNOant); (b, e, h) without biomass burning emissions (SimNObb); and
(c, f, i) without anthropogenic emissions except biomass burning (SimNOind). All experiments are set in a present-day climatic state. Shaded
regions indicate areas where changes are significant at the
95 % confidence level.
For these five key regions, our results are broadly consistent with
and , with one exception. Over
the Amazon Basin, simulated an aerosol-driven
decrease in diffuse radiation; the authors ascribed this behavior to the
combination of an aerosol-driven decrease in total radiation (less solar
radiation to be scattered above, and subsequently under, clouds) with the
high cloud fractions over the Amazon Basin (cloud scattering effectively
limits aerosol light scattering).
Aerosol pollution changes to surface meteorology
Accounting for only the direct aerosol effect and using the fixed-SST
technique, we limit the influence of pollution aerosols on the Earth system
to direct changes in surface radiation that affect the atmosphere and land
surface only. For this reason, in the following we mainly relate changes in
land carbon fluxes to changes in surface radiation, surface meteorology
(e.g., SAT and relative humidity), and plant conditions (e.g., transpiration,
canopy temperature).
The radiation changes caused by anthropogenic aerosol pollution do not exert
a statistically significant change in global and annual average SAT because
our experiments use fixed SSTs and do not consider aerosol indirect effects
on clouds. The rapid adjustments are allowed for the atmosphere and land
surface only. For the same reasons, we do not find statistically significant
changes in precipitation or in cloud water content due to anthropogenic
aerosol pollution (not shown). The model does simulate statistically robust
changes in annual average SAT in the two tropical key regions: the
northwestern Amazon Basin and central Africa (Fig. S3 in the Supplement).
From the sensitivity experiments, we ascertain that the SAT changes are
associated with the BBAs in the tropical regions (Fig. S3 and Table S4). The
mechanism occurs through a bio-meteorological feedback described below.
Figure shows changes in annual transpiration
efficiency (i.e., a proxy of canopy conductance), relative humidity, and
canopy temperature driven by anthropogenic pollution aerosols in the three
sensitivity cases (Fig. S4 for the corresponding annual percentage changes
and Fig. S5 for the seasonal absolute changes). In the model, photosynthesis
and stomatal conductance are coupled through the Farquhar–Ball–Berry
approach. Direct radiative forcing-driven (DRF-driven) increases in
photosynthesis and GPP are associated with increases in canopy conductance
and relative humidity (RH) via increased transpiration. Under anthropogenic
aerosol pollution, transpiration efficiency shows significant modifications
in all five key regions (Fig. and
Table ). The northwestern Amazon Basin records
the largest absolute increase in transpiration efficiency due to BBAs (∼0.51; percentage change: ∼5%). Among industrialized regions,
the largest absolute increases in transpiration efficiency are observed in
Eurasia due to all anthropogenic aerosols (0.16; percentage change: ∼5%), one-third of the increases in transpiration efficiency
observed in the northwestern Amazon Basin. Among the five key regions,
changes in canopy temperature are statistically robust only in the
northwestern Amazon Basin, central Africa, and northeastern China. The
northwestern Amazon Basin experiences the largest decrease in canopy
temperature driven by BBAs (-0.31K; -0.10%), which is
∼0.1K larger than the decrease in canopy temperature over
central Africa and northeastern China. Due to anthropogenic pollution
aerosols, central Africa and northeastern China experience a similar decrease
in canopy temperature (-0.23K; -0.08%), and, compared
to the northwestern Amazon Basin, they undergo substantial decreases in
direct radiation (-35% in central Africa and -29% in
northeastern China vs. -8% in the northwestern Amazon Basin).
To summarize, in the model, reductions in the canopy temperature observed in
the northwestern Amazon Basin represent a positive feedback on plant
productivity (further increases) in response to the DRF-driven increases. In
industrial key regions such as eastern North America and Eurasia, changes in
the quantity and quality of surface solar radiation play the main role in
affecting plant photosynthesis. In northeastern China and central Africa,
multiple aerosol-driven effects may combine to affect plant photosynthesis:
changes in the quantity and quality of surface solar radiation (as in eastern
North America and Eurasia) and reductions in the canopy temperature (as in
the northwestern Amazon Basin).
Sensitivity of GPP to aerosol pollutionGlobal scale
Changes in the global annual average GPP flux between the control and the
sensitivity simulations are reported in Table .
Global GPP shows a weak sensitivity to pollution aerosols (∼1–2 %). Global GPP is increased by up to 2.0%
(2.4PgCyr-1) at most due to all anthropogenic aerosol
pollution. Biomass burning and non-biomass burning aerosols have a comparable
effect on global GPP. In contrast to , our model results
do not suggest a substantial change in global GPP due to pollution
aerosols.
Five key regions
Anthropogenic aerosol pollution drives regional increases in annual average
plant productivity (GPP) that affect the five key regions
(Fig. and, for percentage changes, Fig. S6). The
strongest increases in GPP occur in eastern North America and Eurasia
(+0.2–0.3gCm-2day-1; +5–8%)
(Figs. a and S7a). In the northwestern Amazon Basin, BBAs
drive similar absolute increases in GPP
(+0.2–0.3gCm-2day-1; +2–5%)
(Figs. b and S7b).
Spatial distribution of annual absolute change in gross primary
productivity (GPP, in gCm-2day-1) between the control
experiment (SimCTRL) and sensitivity experiments: (a) without all
anthropogenic emissions (SimNOant); (b) without biomass burning
emissions (SimNObb); and (c) without anthropogenic emissions except
biomass burning (SimNOind). All experiments are set in
a present-day climatic state. Shaded regions indicate areas where changes
in GPP are significant at the 95 % confidence level. Green boxes
on plot (a) highlight key regions selected for discussion.
Anthropogenic aerosols drive the strongest absolute enhancement in GPP in
Eurasia (+0.62PgCyr-1; ∼5%), followed by
eastern North America, which experiences a third of the absolute increase in
GPP but similar relative increases (+0.21PgCyr-1; ∼5%) (Table ). In northeastern China,
anthropogenic aerosols drive the lowest enhancement in GPP, which is
one-tenth of the absolute increases in GPP observed in Eurasia
(+0.06PgCyr-1; 1.2%;
Table ). The northwestern Amazon Basin and
central Africa record increases in GPP that are slightly stronger than those
observed in northeastern China (+0.07–0.10PgCyr-1;
1.6–2.4%; Table ).
In each key region, increases in GPP are governed by different aerosol types.
In the industrial key regions, non-BBAs play a key role in GPP enhancement,
while, in biomass burning regions (i.e., the northwestern Amazon Basin and
central Africa), BBAs govern GPP enhancement. In northeastern China, BBAs do
not drive any robust change in GPP; in Eurasia, BBAs drive increases in GPP,
that is, two-thirds of the increases due to non-BBAs (+0.2 vs.
+0.3PgCyr-1; 1.5 vs. 2.4%)
(Table ). In eastern North America, BBAs and
non-BBAs contribute a similar amount to GPP enhancement
(+0.1PgCyr-1, ∼2%;
Table ). In central Africa, BBAs entirely control
increases in GPP, whereas, in the northwestern Amazon Basin, BBAs drive
increases in GPP larger than the increase due to all anthropogenic aerosols
and non-BBAs (+0.1PgCyr-1, 3.4%;
Table ).
During boreal summer, anthropogenic aerosol pollution increases GPP in
eastern North America and Eurasia by up to +5–8%,
0.6–0.8gCm-2day-1 (Figs. a
and S7c); particularly, in Eurasia aerosol pollution from non-BBAs drives the
increase in GPP (Figs. c and S7f). Driven by BBAs in the
dry-fire season, GPP increases by
+0.05–0.4gCm-2day-1 (+2–5%) in
eastern Europe (boreal evergreen and mixed forests), and by
+0.4–0.6gCm-2day-1 (+5–8%) in the
northwestern Amazon Basin (Figs. b and S7e).
As Fig. for seasonal (boreal summer, JJA) absolute
change in gross primary productivity (GPP, in gCm-2day-1)
between the control experiment (SimCTRL) and sensitivity experiments:
(a) SimNOant, (b) SimNObb, and (c) SimNOind.
During boreal summer, Eurasia shows the largest absolute enhancement in GPP
(+1.8PgCyr-1; +6%), mainly driven by non-BBAs
(+1.1PgCyr-1; +3.4%) compared to BBAs
(+0.5PgCyr-1; +1.5%). The absolute GPP increase
in eastern North America is one-third of that observed in Eurasia
(+0.5PgCyr-1; +6%) (Table S3). In the
northwestern Amazon Basin, the largest enhancement in GPP occurs during
boreal autumn driven by BBAs (+0.2PgCyr-1;
+6%), when the largest decrease in canopy temperature is observed
as well; by contrast, changes in surface radiations maximize during boreal
summer (Table S3). Likewise, in central Africa, changes in surface radiations
peak during boreal summer, while the largest enhancements in GPP (and
decreases in canopy temperature) occur during boreal winter (Table S4). The
area selected to represent central Africa mostly stretches toward south of
the equator, where boreal winter corresponds to the growing season. The
seasonal behavior of GPP in central Africa suggests that the
bio-meteorological feedback to canopy temperature has a larger influence on
plant productivity than reduction in direct radiation.
The GPP sensitivities to aerosol pollution in the five key regions presented
in this work agree well with values from previous measurement-based and
modeling studies
e.g.,.
Consistent with previous measurement-based studies, pollution aerosols have
the largest impacts on GPP for these PFTs with complex canopy architectures
e.g.,. For instance, the
five key regions are all populated by PFTs with multi-layer canopies, large
canopy heights and LAIs, such as deciduous broadleaf forests, evergreen
needleleaf forests, mixed forests, and tropical rainforests, which happen to
be co-located with high sources of anthropogenic aerosol pollution. In the
Amazon Basin, previous studies measured enhancement in CO2 uptake at
ecosystem scale during the biomass burning season; these observationally
based studies attributed the rise in CO2 uptake to the increase in
diffuse light, although substantial changes in surface temperature and
humidity were also measured
e.g.,. Using a modeling
framework, estimated that BBAs enhance GPP by
0.7–1.6%, for an increase in diffuse radiation of
3.4–6.8%. Their estimated GPP sensitivity for this region is
lower than values presented here because did not account
for aerosol-induced reductions in leaf temperature.
Anthropogenic aerosol pollution substantially enhances plant productivity at
a regional scale. This analysis suggests that aerosol-driven enhancements in
GPP result from different mechanisms that depend on region. In the model,
light scattering and DRF dominate in eastern North America, reduction in
direct radiation dominates in Eurasia and northeastern China, and tropical
ecosystems (i.e., the northwestern Amazon Basin and central Africa) benefit
from a bio-meteorological feedback to canopy temperature.
Sensitivity of isoprene emission to aerosol pollutionGlobal scale
Changes in the global annual average isoprene emission between the control
and the sensitivity simulations are reported in
Table . Similar to GPP, global isoprene emission
shows a weak sensitivity to pollution aerosols (∼1–2%).
Global isoprene emission decreases by up to 1.7%
(6.9PgCyr-1) for SimNOant. Global isoprene emissions are
sensitive to industrial emissions but not to biomass burning emissions.
Spatial distribution of annual absolute change in isoprene emission
(in mgCm-2day-1) between the control experiment (SimCTRL)
and sensitivity experiments: (a) without all anthropogenic emissions
(SimNOant); (b) without biomass burning emissions (SimNObb); and
(c) without anthropogenic emissions except biomass burning
(SimNOind). All experiments are set in a present-day
climatic state. Shaded regions indicate areas where changes in isoprene emission
are significant at the 95 % confidence level. Green boxes on plot (a)
highlight key regions selected for discussion.
Five key regions
Anthropogenic aerosol pollution drives a decrease in annual average isoprene
emission of -0.5 to -1mgCm-2day-1 (-2 to
-12%) over Europe and China (Figs. and S7).
Non-biomass burning sources are mainly responsible for the observed regional
decrease in annual average isoprene emission. In peak growing season in the
temperate and tropical zone, pollution aerosols do not affect isoprene
emission (Fig. S8). On an annual average basis, anthropogenic aerosols mainly
from non-biomass burning sources (i.e., BBAs have no robust effect) drive the
largest decreases in isoprene source over northeastern China
(-1.04TgCyr-1; -5.6%) and Eurasia
(-0.86TgCyr-1; -2.7%)
(Table ).
In response to aerosol pollution from non-biomass burning sources, Europe and
China show a large decrease in annual average direct radiation
(-24–26%) but a similar increase in diffuse radiation
(+3–5%) to eastern North America
(Table ). Hence, over Europe and China,
aerosol-driven reduction in direct light is not adequately sustained by an
increase in diffuse radiation, which limits isoprene emission, due to the
reduced light supply (reduced Je). Thus, in Europe and China,
we find that aerosol-induced reduction in direct radiation drives isoprene
decreases and concomitant GPP increases. Even when photosynthesis is
light-saturated (in a Rubisco-limited environment), isoprene emission
continues to rise under increasing PAR . This
divergent response has been observed at the ecosystem scale
. At 20 ∘C and at any photon flux,
the authors recorded nearly no isoprene emission; at 30 ∘C,
isoprene emission increased with photon flux up to 1600µmolm-2s-1, while photosynthesis was already saturated; at
40 ∘C, isoprene emission maximized at 1000µmolm-2s-1; afterwards, it decreased when the photon flux was
raised to 1600µmolm-2s-1.
In the northwestern Amazon Basin, annual average isoprene emission increases
are simulated in response to BBAs (+0.4TgCyr-1;
+2.4%) (Table ), although the area of
statistical significance is small. In this region, the influence of increases
in GPP on isoprene emission overrides the influence of the cooler canopy
temperatures (Table ).
Discussion and conclusions
Aerosol-induced effects on land carbon fluxes (GPP and isoprene emission)
were investigated using a coupled global vegetation–chemistry–climate
model. By performing sensitivity experiments, we isolated the role of
pollution aerosol sources (anthropogenic, biomass burning, and non-biomass
burning). Our results suggest that global-scale land carbon fluxes (GPP and
isoprene emission) are not sensitive to pollution aerosols, even under a
robust overall SSR (direct + diffuse) global change (∼9%). We found substantial but divergent sensitivities of GPP and
isoprene emission to pollution aerosols at a regional scale. In eastern North
America and Eurasia, anthropogenic pollution aerosols (mainly from
non-biomass burning sources) enhance GPP by +5–8% on an annual
average. In the northwestern Amazon Basin and central Africa, biomass burning
aerosols increase GPP by +2–5% on an annual average
(+5–8% at the peak of the dry-fire season in the northwestern
Amazon Basin). In Eurasia and northeastern China, anthropogenic pollution
aerosols (mainly from non-biomass burning sources) drive a decrease in
isoprene emission of -2 to -12% on annual average. Our model
results imply that reductions of anthropogenic pollution aerosols over Europe
and China below the present-day loadings may trigger an enhancement in
isoprene emission, with consequences for ozone and aerosol air quality.
We acknowledge three main limitations. Firstly, we tackled the direct aerosol
effects only and did not consider first and second indirect effects of
aerosols such that we are partly missing the impact of aerosol–cloud
interactions on land carbon fluxes. Secondly, we used the fixed SST
technique; hence, we accounted only for rapid adjustments of land-surface
climate to aerosol radiation perturbation. Thirdly, we did not include
feedbacks from dynamic LAI and phenology that may lead to an underestimation
of the effects of aerosol-induced effects on plant productivity. Future
research will address these three limitations. Future changes in regional
atmospheric aerosol loadings will have substantial implications for the
regional land carbon cycle.
The Supplement related to this article is available online at doi:10.5194/acp-16-4213-2016-supplement.
S. Strada and N. Unger designed the experiments. S. Strada
performed the simulations. S. Strada and N. Unger prepared the manuscript.
Acknowledgements
This project was supported in part by the facilities and staff of the Yale
University Faculty of Arts and Sciences High Performance Computing Center.
Edited by: D. Spracklen
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