ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-1301-2019Studying the impact of biomass burning aerosol radiative and climate effects
on the Amazon rainforest productivity with an Earth system modelAerosol radiative and climatic effects on the Amazon rainforestMalavelleFlorent F.f.malavelle@exeter.ac.ukhttps://orcid.org/0000-0002-2754-9226HaywoodJim M.MercadoLina M.https://orcid.org/0000-0003-4069-0838FolberthGerd A.https://orcid.org/0000-0002-1075-440XBellouinNicolashttps://orcid.org/0000-0003-2109-9559SitchStephenArtaxoPaulohttps://orcid.org/0000-0001-7754-3036CEMPS, University of Exeter, Exeter, EX4 4QE, UKUK Met Office Hadley Centre, Exeter, EX1 3PB, UKCLES, University of Exeter, Exeter, EX4 4RJ, UKCentre for Ecology and Hydrology, Wallingford, OX10 8BB, UKDepartment of Meteorology, University of Reading, Reading, RG6 6BB, UKDepartment of Applied Physics, Institute of Physics, University of São
Paulo, São Paulo, BrazilFlorent F. Malavelle (f.malavelle@exeter.ac.uk)31January2019192130113263September201818September201819December20187January2019This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/19/1301/2019/acp-19-1301-2019.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/19/1301/2019/acp-19-1301-2019.pdf
Diffuse light conditions can increase the efficiency of photosynthesis and
carbon uptake by vegetation canopies. The diffuse fraction of
photosynthetically active radiation (PAR) can be affected by either a change
in the atmospheric aerosol burden and/or a change in cloudiness. During the
dry season, a hotspot of biomass burning on the edges of the Amazon
rainforest emits a complex mixture of aerosols and their precursors and
climate-active trace gases (e.g. CO2, CH4, NOx). This
creates potential for significant interactions between chemistry, aerosol,
cloud, radiation and the biosphere across the Amazon region. The combined
effects of biomass burning on the terrestrial carbon cycle for the
present day are potentially large, yet poorly quantified. Here, we quantify
such effects using the Met Office Hadley Centre Earth system model
HadGEM2-ES, which provides a fully coupled framework with interactive aerosol, radiative
transfer, dynamic vegetation, atmospheric chemistry and biogenic volatile
organic compound emission components. Results show that for present day,
defined as year 2000 climate, the overall net impact of biomass burning
aerosols is to increase net primary productivity (NPP) by +80 to +105 TgC yr-1,
or 1.9 % to 2.7 %, over the central Amazon Basin on annual mean. For
the first time we show that this enhancement is the net result of multiple
competing effects: an increase in diffuse light which stimulates
photosynthetic activity in the shaded part of the canopy (+65 to +110 TgC yr-1), a reduction in the total amount of radiation (-52 to -105 TgC yr-1)
which reduces photosynthesis and feedback from climate adjustments in
response to the aerosol forcing which increases the efficiency of biochemical
processes (+67 to +100 TgC yr-1). These results illustrate that despite a
modest direct aerosol effect (the sum of the first two counteracting
mechanisms), the overall net impact of biomass burning aerosols on
vegetation is sizeable when indirect climate feedbacks are considered. We
demonstrate that capturing the net impact of aerosols on vegetation should be
assessed considering the system-wide behaviour.
Introduction
The Amazon rainforest is the largest expanse of tropical forest on Earth. It
provides invaluable ecological services and plays a major role in the Earth
system and climate (Malhi et al., 2008). The Amazon rainforest is a net sink of
atmospheric CO2, although drought frequency and intensity, which are
expected to increase in the future, could have severe consequences for future
forest resilience and potentially shift the Amazon rainforest from a sink
to a net source of atmospheric CO2 (Cox et al., 2000, 2004; Phillips et al., 2009;
Doughty et al., 2015; Duffy et al., 2015; Sakschewski et al., 2016; Zemp et al., 2017). This
possibility motivated intense research to develop a better understanding of
the rainforest response to environmental stresses via integrated explicit
representations of the carbon cycle in Earth system models (ESMs) (Cox et al.,
2000). Response to many of these environmental stresses is now well
documented and represented in ESMs, including the effects of surface
temperature, atmospheric composition, water availability, or the amount and
quality of accessible light (direct versus diffuse) for plant photosynthesis
(e.g. Nemani et al., 2003; Sitch et al., 2007; Cox et al., 2008; Mercado et al., 2009a; Beer et al., 2010;
Ciais et al., 2013; Pacifico et al., 2015; Unger et al., 2017).
In parallel to the above-mentioned environmental stresses, forest fires are
also an intrinsic component of some forest lifecycles, providing an
additional mechanism for depleting land carbon reservoirs. Intense biomass
burning events present a notorious pressure on tropical regions and typically occur during the dry season – i.e. between around August and
September in the Amazon region (Artaxo et al., 2013; Brito et al., 2014). Fires in
general occur naturally; however, a significant fraction results from the
anthropogenic pressure that continually erodes the fragmented forest edges
(Cochrane, 2003). Despite a decreasing trend in the rate of deforestation
over the last decade as a result of stricter environmental policies
(Kalamandeen et al., 2018), it is estimated that 293 Tg of carbon per year
(TgC yr-1) is directly released back into the atmosphere from fires in the
Amazon (van der Werf et al., 2006). Fires can also have an indirect impact on the
rainforest carbon budget that is harder to quantify; for instance, fires
alter surface properties (e.g. albedo) in the burnt area, which can modify
surface fluxes and the water cycle (e.g. Zemp et al., 2017). Additionally, fires
emit a complex mixture of gases (CO2, CO, CH4, NOx and volatile organic compounds – VOCs),
aerosols and aerosol precursors which can affect remote regions of the
rainforest after being dispersed by the wind. Pacifico et al. (2015) illustrated
such a mechanism by analysing the potentially harmful effect of near-surface
ozone (O3) associated with biomass burning and estimated that the
rainforest gross primary productivity (GPP) was reduced by up to
approximately -230 TgC yr-1, a number of similar magnitude to the direct carbon loss from fires.
Assessing the overall impact of Amazonian forest fires on ecosystems is
challenging as it encompasses a combination of direct losses and indirect
impacts from the fire by-products which can depend on intricate interactions
among several Earth system components, including the biosphere, atmospheric
composition, radiation and energy budget, clouds, and the water cycle (Bonan,
2008). Here, we aim to specifically elucidate the impact of biomass burning
aerosols (BBAs) that are associated with forest fires and quantify their
potential effect on the Amazon forest productivity.
Significant amounts of BBAs are emitted in South America, which strongly
modify the radiative budget by scattering and absorbing solar radiation.
This reduces the level of photosynthetically active radiation (PAR),
traditionally defined as the radiation between wavelengths of 300 and 700 nm, reaching the surface and used by plants to photosynthesise (i.e. to
assimilate carbon from the atmosphere). Contrary to intuition, an increase
in the diffuse light fraction can be beneficial to plants as the shaded,
non-light-saturated leaves, typically found in the understory or lower
canopy layers, receive more radiation under diffuse light conditions than
they would normally experience under direct light conditions owing to the
shading by leaves fully exposed to sunlight. As a result, this trade-off
between experiencing less PAR overall and receiving more evenly distributed
light across the canopy favours higher rates of canopy photosynthesis. The
first comprehensive estimation of this diffuse PAR fertilisation effect (DFE) at the global scale was documented by Mercado et al. (2009a), who used a
combination of offline aerosol distributions, radiative transfer and a land
surface model to estimate that DFE may have increased the global land carbon
uptake by up to 25 % during the global dimming period (1950–1980; Stanhill
and Cohen, 2001). More recently, Rap et al. (2015) used a similar framework of
offline models to assess the role of BBA over the Amazon region. They showed
that BBA increases the annual mean diffuse light and net primary productivity (NPP) by 3.4 %–6.8 %
and 1.4 %–2.8 %, respectively. Strada and Unger (2016) took a step further using a coupled modelling framework to estimate
biomass burning aerosol impacts on Amazon forest GPP, obtaining an increase
of 2 %–5 % on annual means. Recently, Moreira et al. (2017) also applied a
coupled framework using a regional model (BRAMS) to conclude that BBA could
increase the GPP of the Amazon forest by up to 27 % during the peak of the
biomass burning season. The study of Moreira et al. (2017) assumed high BBA
emissions and did not account for the effect of cloudiness on the diffuse
fraction of radiation, so it provides an upper estimate of the potential
impact of the effects of the attenuation of total solar radiation and the
enhancement of the diffuse solar radiation flux inside the vegetation
canopy.
Despite a growing body of evidence supporting the DFE mechanism, both from
observational and modelling perspectives (e.g. Cohan et al., 2002; Gu et al., 2003;
Robock et al., 2005; Yamasoe et al., 2006; Mercado et al., 2009a; Kanniah et al., 2012; Cirino et al.,
2014; Cheng et al., 2015), a full quantification of the BBA impact on ecosystems
remains poor because aerosol–radiation interactions (ARI), and to some
extent aerosol–cloud interactions (ACI), not only create the conditions for
a DFE but also modify the climate locally. For example, a regional haze of
aerosols can perturb regional hydroclimates (Nigam and Bollasina, 2010),
force clouds to adjust to aerosol semi-direct and indirect effects which
modify the way clouds interact with radiation (Hansen et al., 1997; Haywood and
Boucher, 2000; Koren et al., 2004), or create a positive cooling effect on
productivity by reducing surface heat stress in hot environments, allowing
for a more efficient uptake of atmospheric CO2 through leaf stomata
(Robock et al., 2005; Xia et al., 2016; Strada and Unger, 2016). Neglecting such
essential coupling pathways may overemphasise the relative contribution of
the DFE due to loss of internal consistency that does not allow variability
within non-linear relationships. Only a limited number of studies have
considered the DFE within a fully coupled Earth system framework (e.g.
Strada and Unger, 2016; Unger et al., 2017; Yue et al., 2017, using the NASA GISS
ModelE2–YIBs) to investigate the role of aerosols and haze on vegetation.
Although these studies have investigated the role of diffuse radiation on
GPP and isoprene emissions (Strada and Unger, 2016; Unger et al., 2017),
understanding of the indirect impact of climate effects from aerosols on
vegetation productivity remains very uncertain. This was addressed over
China by Yue et al. (2017), who demonstrated that aerosol-induced
hydroclimatic feedbacks can promote ecosystem NPP. In the present study, we
apply an ESM modelling framework to quantify the impact of present-day BBA
via the quantification of individual and net effects of changes in diffuse
radiation, direct radiation and climate upon the vegetation productivity in
the Amazon rainforest specifically. For this endeavour, we have implemented
an updated representation of plant photosynthesis and carbon uptake that is
sensitive to diffuse light radiation in the UK Met Office Hadley Centre HadGEM2-ES Earth
system model (Mercado et al., 2007, 2009a). In addition, a framework that
disentangles the vegetation response has been developed to provide a deeper
understanding of the contributions of different plant environmental
variables affected by aerosols. The role of O3 precursor emissions and
in situ formation of O3 associated with biomass burning (Pacifico et al.,
2015) is not considered here.
The methodology and the experimental set-up are described in Sect. 2. Results
are discussed in Sect. 3, including first a model evaluation in Sect. 3.1,
then the net effect of BBA in Sect. 3.2, and individual contributions from
the diffuse light fraction, the reduction in total PAR and the climate feedbacks associated
with the BBA perturbation in Sect. 3.3. These findings are contextualised in
Sect. 3.4 by analysing the results from four additional sensitivity
experiments designed to elucidate the role of aerosol optical properties,
aerosol–cloud interactions, the atmospheric CO2 concentration and
vertical distribution of nitrogen through the canopy. Concluding remarks and
a summary of this study's main results are provided in Sects. 4 and 5,
respectively.
Method
We evaluate the effects of biomass burning aerosol–radiation interactions
upon the Amazon rainforest primary productivity for present-day conditions
using the Met Office Hadley Centre Global Environment Model HadGEM2-ES (The
HadGEM2 Development Team, 2011), which provides a fully coupled framework.
The model is briefly described in Sect. 2.1.
We present the results of a sensitivity experiment (Sect. 3) which consists
of varying the biomass burning aerosol emissions only over South America.
“Real world” fires also emit greenhouse gases (e.g. CO2, CO, CH4)
and ozone precursors (NOx, VOCs) which can potentially affect the
biosphere. Ozone is particularly critical as it is a pollutant which
harms plants and reduces their productivity, and thus their ability to draw
CO2 from the atmosphere (Sitch et al., 2007). Whereas the damaging effect of
ozone is not accounted for in this study, we will briefly discuss the
potential fertilisation effect from the increased CO2 background that can
result from biomass burning in Sect. 4. The ozone damage effect was
documented by Pacifico et al. (2015) using a similar modelling framework as in the
present study, and we refer readers to that study for further details.
Atmospheric particles such as aerosols and cloud droplets scatter radiation,
which increases the fraction of radiation that is diffuse. Diffuse
conditions result in higher light use efficiency of plant canopies, which can
enhance carbon uptake (Roderick et al., 2001; Gu et al., 2002). An increase in diffuse
radiation is concomitant with a decrease in the overall amount of radiation
(Fig. S1 in the Supplement). These two opposing effects will be referred to in
the rest of the paper as “change in diffuse fraction” and “reduction in total PAR”, respectively, and will be quantified
separately in Sect. 3.3. Finally, BBA effects impact the coupled system,
which controls the rate of biochemical processes of vegetated land
surfaces itself. We will simply refer to these adjustments to the BBA effects as
“climate feedback” in the remainder of the paper. The sum of climate feedback, change in diffuse fraction
and reduction in total PAR is referred as the
“net impact” of BBA on plant productivity. The framework we developed to disentangle
these three terms is described in Sect. 2.4.
Model description
HadGEM2-ES is an Earth system model built around the HadGEM2
atmosphere–ocean general circulation model and includes a number of Earth
system components such as
the ocean biosphere Diat-HadOCC (Diatom-Hadley Centre Ocean Carbon
Cycle) model, developed from the HadOCC model of Palmer and Totterdell (2001);
the sea ice component (The HadGEM2 Development Team, 2011);
the Top-down Representation of Interactive Foliage and Flora Including
Dynamics (TRIFFID) dynamic global vegetation model (Cox, 2001), and the
land surface and carbon cycle model MOSES2 (Met
Office Surface Exchange Scheme), collectively known as JULES (Cox et al., 1998, 1999; Essery et al., 2003);
the interactive Biogenic Volatile Organic Compounds (iBVOC) emission model
(Pacifico et al., 2012);
the UKCA tropospheric chemistry scheme (O'Connor et al., 2014).
The atmospheric model resolution is N96 (1.875∘ by 1.25∘) with 38 vertical levels with the model top at ∼39 km. Our modelling
framework is similar to the configuration used by Pacifico et al. (2015), who
provided a detailed analysis of the successful model performance against
observations.
For clarity, we provide some additional details on the treatment of aerosols
and their coupling with radiation and clouds as well as on the updated
representation of the canopy interaction with radiation. The radiative
transfer code in the atmospheric part of HadGEM2-ES is SOCRATES (Edwards and
Slingo, 1996), which parameterises radiative fluxes using a “two-stream”
approximation (Meador and Weaver, 1980). The radiative transfer is solved
for six wavebands in the shortwave and nine in the longwave. This scheme accounts
for the interaction of radiation with aerosol particles by defining three single
scattering properties on a layer: optical depth, single scattering albedo
(the ratio of scattering efficiency to total extinction) and an asymmetry
parameter. Together, these properties determine the overall transmission and
reflection coefficients of each atmospheric layer. At the interface between
the lowest atmospheric level and the land surface, the total and the direct
radiances for the shortwave band 320–690 nm, which approximates the PAR,
calculated by the SOCRATES radiation scheme are transferred to the land
surface routines to calculate plant photosynthesis.
In the JULES land surface model, the total and direct irradiance components
of PAR calculated by the atmospheric model provide the boundary conditions
at the top of the canopy. The diffuse PAR fraction is calculated as the
difference between total and direct radiation, divided by the total
radiation. The canopy is discretized into 10 vertical layers, and the
radiative transfer in the canopy is also parameterised with a two-stream
approximation but uses more detailed assumptions to represent light
interception by foliage (Sellers, 1985). The photosynthesis model is based
upon the observed processes of gas and energy exchange at the leaf scale,
which are then scaled up to represent the canopy. It takes into account
variations in direct and diffuse radiation on sunlit and shaded canopy
photosynthesis at each canopy layer. In this way, photosynthesis of sunlit
and shaded leaves is calculated separately under the assumption that shaded
leaves receive only diffuse light and sunlit leaves receive both diffuse and
direct radiation (Dai et al., 2004; Clark et al., 2011). Leaf-level photosynthesis is
calculated using the biochemistry of C3 and C4 photosynthesis from Collatz
et al. (1991, 1992).
This canopy radiation scheme was first developed to quantify the impact of
anthropogenic aerosol emissions on the global carbon cycle (Mercado et al., 2007,
2009a) and was consequently implemented in JULES (Clark et al., 2011). It is a novel
addition to HadGEM2-ES as it was not available during the HadGEM2-ES
contribution to CMIP5. HadGEM2-ES with the previous canopy radiation scheme
had a tendency to overestimate GPP (Shao et al., 2013), which has to be balanced
by high plant respiration (RESP) to get satisfactory estimates of global NPP
(i.e. NPP = GPP-RESP). The new representation of light interception that we
have implemented is able to reproduce higher light use efficiency (LUE)
under diffuse light conditions (Sect. 3.1 and Fig. S2 in the Supplement).
However, the ratio of GPP to plant respiration in HadGEM2-ES with the new
canopy radiation model remains too high when compared to
observationally based estimates (e.g. Luyssaert et al., 2007). To correct this
deficiency, we decreased the ratio of nitrogen allocated in the roots
relative to the nitrogen in the leaves from 100 % to 50 % (Clark et al., 2011,
Table 2 therein). Additionally, we reduced the leaf dark respiration
coefficient that relates leaf dark respiration and Vcmax from 15 % to
10 % (Clark et al., 2011, Eq. 13 therein). These changes are based on a
sensitivity analysis that we performed with the stand-alone version of
JULES. We used the meteorological observations from the tropical French
Guiana site (assumed to be fully covered by broadleaf trees) to drive JULES
and investigate the sensitivity to parameters such as the leaf nitrogen
content at canopy top (NL0), the dark respiration coefficient and the
nitrogen allocation throughout the canopy via the value of the nitrogen
profile extinction coefficient (Clark et al., 2011, Eq. 33 therein and Sect. 2.3.4
of the present study). Fast carbon fluxes (GPP, RESP and NPP) were
calculated at a 3 h temporal resolution by varying one of these three
parameters individually (Fig. S3a–c) and then averaged to
annual mean values (Fig. S3d–f). The annual means were then
used to construct contour surfaces for the fast carbon fluxes by varying
combinations of the selected parameters (Fig. S4). This method
enables us to ultimately pre-calibrate the fast carbon fluxes in the
HadGEM2-ES model offline.
Aerosols are represented by the CLASSIC aerosol scheme (Bellouin et al., 2011)
which is a one-moment mass prognostic scheme. This aerosol module contains
numerical representation of up to eight tropospheric aerosol species. Here,
ammonium sulfate, mineral dust, sea salt, fossil fuel black carbon (FFBC),
fossil fuel organic carbon (FFOC), biomass burning aerosols and secondary
organic (also called biogenic) aerosols are considered. Dust and sea salt are from
diagnostic schemes based on the near-surface wind speed, while other
emissions including biogenic aerosols are represented by a relatively simple
climatology (Bellouin et al., 2011). Transported species experience boundary
layer and convective mixing and are removed by dry and wet deposition. Wet
deposition by large-scale precipitation is corrected for re-evaporation of
precipitation: tracer mass is transferred from a dissolved mode to an
accumulation mode in proportion to re-evaporated precipitation. For
convective precipitation, accumulation mode aerosols are removed in
proportion to the simulated convective mass flux. Emissions of biomass
burning aerosols are the sum of the biomass burning emissions of black and
organic carbon. Grass fire emissions are assumed to be located at the
surface, while forest fire emissions are injected homogeneously across the
boundary layer (0.8–2.9 km).
The direct radiative effect due to scattering and absorption of radiation by
all eight aerosol species represented in the model is included. The
semi-direct effect, whereby aerosol absorption tends to change cloud
formation by warming the aerosol layer, is thereby included implicitly.
Wavelength-dependent specific scattering and absorption coefficients are
obtained using Mie calculations from prescribed size distributions and
refractive indices. All aerosol species except mineral dust and fossil fuel
black carbon are considered to be hydrophilic, act as cloud condensation
nuclei, and contribute to both the first and second indirect effects on
clouds, treating the aerosols as an external mixture. Jones et al. (2001)
detailed the parameterization of the indirect effects used in HadGEM2-ES. The cloud
droplet number concentration (CDNC) is calculated from the number
concentration of the accumulation and dissolved modes of hygroscopic
aerosols. For the first indirect effect, the radiation scheme uses the CDNC
to obtain the cloud droplet effective radius. For the second indirect
effects, the large-scale precipitation scheme uses the CDNC to compute the
auto-conversion rate of cloud water to rainwater (Jones et al., 2001).
Experimental design: main experiment
The HadGEM2-ES model was initiated on 1 December 2000 from a previous
historical simulation. We consider the year 2000 to be a good surrogate for
present-day climate, which will enable us to assess the impact of present-day
BBA emissions on vegetation. As historical simulations are transient climate
simulations, we constrain the carbon cycle to present-day values as well (to
be described in the next paragraph). The model is then integrated for a
period of 40 years using periodic forcing for the year 2000 to construct an
ensemble that captures the model internal variability. Results reported here
are the multi-annual means over the final 30 years of the model integration.
The domain of analysis is defined by the coordinates 0–15∘ S, 70–53∘ W and is primarily covered by broadleaf
trees for this configuration of HadGEM2-ES (Fig. S5).
The HadGEM2-ES model is set-up in an Atmospheric Model Intercomparison
Project (AMIP; Jones et al., 2011) type configuration using prescribed
climatologies of monthly mean sea surface temperatures (SSTs) and sea ice
cover (SIC), which enables us to analyse the rapid adjustments of land surface
climate to aerosol radiation perturbations. The introduction of a new canopy
radiation interaction model introduces a significant departure in the carbon
cycle balance. To prevent the need of a complex spin-up exercise, we
prescribe the vegetation cover and carbon reservoirs to present-day level.
This is achieved by reducing the call frequency of the TRIFFID dynamic
vegetation model to 30 years in order to maintain the vegetation in a steady
state. A similar approach is discussed in Strada and Unger (2016). Overall,
this enables us to focus our analysis on the fast carbon flux responses
(i.e. NPP, GPP) and their sensitivity to the perturbation induced by the
biomass burning aerosols.
Aerosols and their precursor emissions are the dataset used during CMIP5
(Lamarque et al., 2010). We use the decadal mean emissions centred around the
year 2000 to represent present-day emission rates. Biogenic volatile organic
compound (BVOC) emissions from vegetation (Pacifico et al., 2012) are sensitive
to changes in plant productivity and hence sensitive to DFE. These emissions
are calculated online but are not taken into account in the CLASSIC aerosol
scheme. Instead, the climatology of BVOCs (also called secondary organics)
from CMIP5 is used. The biomass burning emissions are based on the GFEDv2
inventory (van der Werf et al., 2006; Lamarque et al., 2010). Given the substantial
inter-annual variability of biomass burning on a global and regional scale, a
present-day climatology (i.e. average year) is calculated as the GFEDv2
1997–2006 average (Lamarque et al., 2010). These are the standard emission
scenarios for the simulation labelled as BBAx1 for the main experiment. A total of
five simulations are conducted in the main experiment where the standard
biomass burning aerosols emissions are varied by -100 %, -50 %, 0 %,
+100 % and +300 %, respectively (simulation BBAx0, BBAx0.5, BBAx1,
BBAx2 and BBAx4, respectively). A multiplication factor is applied to the
emission only for the BB sources over South America (40∘ S, 85∘ W; 15∘ N, 30∘ W). We
define the control simulation as the simulation without BBA being emitted
over South America (i.e. BBAx0). The changes in fast carbon fluxes are
calculated as the departure from this reference simulation (e.g. Δ
NPPnetimpactBBAx1=NPPBBAx1-NPPBBAx0 and represents the
net change in NPP due to standard emissions of BBA).
Sensitivity experiments
In parallel to the five simulations for the main experiment, we have conducted
the following four additional sensitivity experiments to further appreciate the
role of (i) aerosol optical properties, (ii) aerosol–cloud interactions, (iii) the
canopy nitrogen profile and (iv) atmospheric carbon dioxide concentration. A
listing of the simulations done for the main experiment and the sensitivity
experiments is provided in Table 1.
List of model simulations done for the five experiments.
The representation of BBA in HadGEM2-ES is based on the measurements
collected during the SAFARI 2000 campaign near South Africa (Abel et al., 2003;
Bellouin et al., 2011). It describes the size distribution of BBA as an external
mixture of two mono-modal smoke species. For the fresh smoke, a log-normal
distribution with a median geometrical radius (r), r=0.1µm, and a
geometric standard deviation (σ), σ=1.30, are assumed.
For aged smoke, r=0.12µm and σ=1.30. Fresh biomass
smoke is converted to aged smoke at an exponential rate assuming an
e-folding time of 6 h, which typically accounts for the ageing of the
smoke plume due to condensation of chemical species (e.g. sulfate or
organic compounds; Abel et al., 2003). Optical properties for the two modes are
calculated a priori (i.e. offline) using Mie theory for various levels of
relative humidity (RH) to account for hygroscopic growth. These optical
properties – specific extinction, absorption coefficients and asymmetry
parameter – are then prescribed in the HadGEM2-ES radiative transfer look-up table of optical properties.
BBA optical properties may vary significantly depending on the type of
vegetation burnt, combustion regime and the meteorological conditions (Reid
et al., 2005). Many observational campaigns since SAFARI 2000 have reported
more absorbing BBA in other regions of the world (e.g. Johnson et al., 2008,
2016). Even at the regional scale, variation in BBA optical properties may
occur. For example, aircraft observations in Brazil during SAMBBA show that
flaming combustion associated with Cerrado burning in the eastern regions
produces more BC and less organic aerosol, and therefore a more absorbing BBA,
while smouldering forest burning in the west produces a less absorbing BBA
(Johnson et al., 2016). The degree of aerosol absorption is characterised by
the single scattering albedo (SSA), which is the ratio of aerosol scattering
over aerosol extinction. BBAs with low SSA (e.g. ∼0.80) absorb more
solar radiation than BBAs with higher SSA (e.g. ∼0.90). This can have
implications from the vegetation perspective as a layer made of absorbing BBA
would transmit less radiation to the surface than a layer made of a more
scattering BBA, limiting the amount of energy available for photosynthesis.
In this experiment, we investigate this aspect by varying BBA SSA by
±10 % by scaling the specific scattering (Ksca in
m2 kg-1) and absorption (Kabs in m2 kg-1)
coefficients (Ksca in m2 kg-1) directly in the look-up tables, ensuring that specific
extinction remains constant. The asymmetry parameter is assumed to be
unaffected. Dry BBA optical properties at 550 nm for the aged smoke are
reported in Table 2.
Dry (relative humidity is 0 %) optical properties at 550 nm for
the aged smoke biomass burning aerosols.
For this sensitivity experiment, the BBAx0, BBAx1 and BBAx2 simulations are
rerun twice, once assuming a more absorbing BBA and once assuming a more
scattering BBA (simulations labelled BBAx0DIFF_OP,
BBAx1DIFF_OP and BBAx2DIFF_OP for
the diffuse case and BBAx0ABS_OP,
BBAx1ABS_OP and BBAx2ABS_OP for the
absorbing case, respectively). Figure S6 in the Supplement shows how
HadGEM2-ES simulates the ambient SSA of BBA (Fig. S6a) and of all
aerosols (Fig. S6b) after modifying the BBA optical properties. Figure S6c shows that the amount of direct PAR is unaffected as expected because of
the constraint imposed on Kext. In the higher SSA case (i.e. more
diffusing BBA), the amount of diffuse PAR reaching the surface is increased,
resulting in a higher amount of total PAR which contrasts with the lower SSA
case.
Aerosol–cloud interactions
Clouds critically affect the amount of radiation reaching the surface (e.g.
Roderick et al., 2001; Cohan et al., 2002; Pedruzo-Bagazgoitia et al., 2017). Aerosols have
the potential to alter cloud properties (i.e. how they interact with
radiation; Haywood and Boucher, 2000) and hence alter surface radiation.
This experiment aims to address whether aerosols can affect vegetation
productivity indirectly by interacting with clouds. Although aerosol–cloud
interactions remain very challenging to represent in ESMs (Ghan et al.,
2016; Malavelle et al., 2017), we will investigate whether the representation of
these processes in the ESM used here can have a detectable impact over the
region considered in this study. The BBAx0, BBAx1 and BBAx2 simulations are
done twice. In the first set of simulations (labelled BBAx01stAIE, BBAx11stAIE and BBAx21stAIE),
aerosols impact on precipitation efficiency is switched off
(i.e. no second aerosol indirect effect, 2ndAIE, through alteration of
liquid water path via auto-conversion) but can still modify cloud albedo by
altering the cloud droplet effective radius (i.e. the first aerosol indirect
effect, 1stAIE). In the second set of simulations (labelled
BBAx0noAIE, BBAx1noAIE and
BBAx2noAIE), all aerosol indirect effects are switched off.
As turning off AIE reverts back CDNC to prescribed values, the BBA effect on
vegetation will be calculated as a difference between simulations with the
same indirect effect configuration (e.g. BBAx11stAIE–BBAx01stAIE).
Canopy nitrogen profile
Photosynthesis not only requires light, CO2 and water but also
nutrients that are essential in the chemistry cycles of photosynthesis.
Nitrogen can be considered the most critical of those nutrients and could
act as a bottleneck for plant photosynthesis (e.g. Bonan, et al., 2011; Ciais et al.,
2014; Fernández-Martínez et al., 2014; Wieder et al., 2015; Houlton et al., 2015; Zaehle et al., 2015).
Optimisation arguments suggest that, in order to maximise
the rates of carboxylation and the rate of transport of photosynthetic
products, nitrogen resources should be allocated at the top of the canopy
(i.e. a steep decrease in the nitrogen profile) where light absorption is
maximum (Alton and North, 2007). However, observations support a more even allocation
of the nitrogen resources (i.e. a shallow decrease in the nitrogen profile
throughout the canopy; Mercado et al., 2009b; Lloyd et al., 2010; Dewar et al., 2012).
Nitrogen limitation and the nitrogen cycle are not yet represented
explicitly in HadGEM2-ES but will be in future versions of this Earth system
model (i.e. UKESM1). Presently, nitrogen allocation at the leaf level
(NLeaf) within the canopy is represented via an exponential profile in
the land surface code of HadGEM2-ES, that is
NLeaf(L)=NL0e-KNL,
where L is the leaf-level leaf area index, NL0 is the nitrogen
concentration at canopy top (in kgN kgC-1) and KN is a dimensionless
constant representing the steepness of the nitrogen profile. A shallow
nitrogen profile (KN=0.128) is the JULES default (Mercado et al., 2007)
and is assumed in HadGEM2-ES for the main experiment. For this sensitivity
experiment, we investigate the consequence of assuming a steeper nitrogen
profile (KN=0.5). Under these conditions, one might expect lesser
light use efficiency under diffuse light conditions as shaded leaves become
nitrogen limited (Hikosaka, 2014). We rerun the BBAx0, BBAx1 and BBAx2
simulations using the steeper nitrogen profile (labelled
BBAx0STEEP_N, BBAx1STEEP_N and
BBAx2STEEP_N, respectively).
To derive a new parameter value of KN which still provides consistent
global NPP fluxes, we repeated the offline analysis described in Sect. 2.1. We used JULES to perform 1-D simulations of a tropical site with varying
combinations of the KN and NL0 parameters to derive biochemical
fluxes (Fig. S4b–c). The parameter combinations were
chosen such that the mean canopy carboxylation rate (Vcmax,25,C) is
conservative and remained at the same level as in the main experiment (i.e.
about 27 µmolCO2 m-2 s-1 for broadleaf trees). With nitrogen
allocation being represented by an exponential decay, the mean canopy
Vcmax,25,C can be calculated as follows:
Vcmax,25,C=neNL01-e-KNLAIKNLAI,
where LAI is the leaf area index at canopy level, ne is a constant that
has values of 0.0008 and 0.0004 mol CO2 m-2 s-1 kgC (kgN)-1 for C3 and C4 plants, respectively (Mercado et
al., 2007).
Atmospheric CO2 concentration
It is hypothesised that in a richer CO2 world, rates of photosynthesis
would increase and in addition plants could afford a reduced stomatal opening
to fix the same amount of CO2, resulting in a higher water use
efficiency which should further enhance plant productivity – the so-called
CO2 fertilisation effect (e.g. Keenan et al., 2013). As stated earlier,
fires do not only release aerosol particles but also CO2, amongst other
gases, which locally increases background CO2 levels (e.g. Wittenberg
et al., 1998). Additionally, it is expected that the rise in atmospheric CO2
will continue given current projections of anthropogenic emissions
(O'Neill et al., 2016). The details of the CO2 fertilisation effect are complex
because environmental changes occur simultaneously (e.g. van der Sleen et al.,
2015; Zhu et al., 2016). It would be far beyond the scope of this study to fully
characterise the CO2 fertilisation effect strength in HadGEM2-ES, but it
is certainly of interest to evaluate if the effect of aerosols on vegetation
through alteration of the surface PAR differs when the atmospheric
background CO2 is varied. For this experiment, the BBAx0, BBAx1 and
BBAx2 simulations are done twice: once with the level of background CO2
increased by +25 ppm globally and once with an increase of +50 ppm
globally. Increments of +25 and +50 ppm should be representative of the
CO2 level expected in 12.5 and 25 years, respectively, if one assumes a
2 ppm yr-1 increase (as supported by the mean rate of CO2 increase measured
at Mauna Loa for the period 2000–2010).
A framework to analyse the changes in fast carbon fluxes
As stated previously, aerosols can affect photosynthetic rates through
different pathways (e.g. Bonan, 2008 and Fig. S7). Firstly, by
altering the amount of light (the reduction in total PAR) and light quality (the change in diffuse fraction of PAR).
Secondly, aerosols interact with radiation and clouds impacting the climate
directly and indirectly which affects the radiative balance therefore the
energy budget, forcing the coupled system to adjust to the aerosol
perturbations. These adjustments (the climate feedback) can feedback into the calculations
of the rate of vegetation biochemical processes – e.g. by altering the
surface temperature. A simple theoretical framework can be used to
discriminate a fast carbon flux, e.g. NPP, as a function of the diffuse fraction,
fd, the total PAR, TotPAR, and the climate feedback, clim, such as NPP(fd, TotPAR, clim). Neglecting the
interdependency between the three terms enables the following decomposition:
δNPP≅∂NPP∂fdδfd+∂NPP∂TotPARδTotPAR+∂NPP∂ClimδClim.
To evaluate how these three terms contribute individually to the total
change in NPP (the net impact), we have developed three new model diagnostics in
HadGEM2-ES. For each model time step, we diagnose four surface fluxes of PAR
which are the total and direct PAR, considering or excluding the aerosol
radiative effects. This is achieved by calling the radiative transfer
routines twice (i.e. a double call) within the same model time step, i.e.
first call with the aerosol radiative effects considered and second call
assuming “clean-sky” conditions where the radiative effects of aerosols are
not considered (Ghan, 2013). Note that the effect of clouds on the radiative
fluxes are always considered during the two calls. The next model iteration
(i.e. the prognostic call) always includes the aerosol radiative effects in
order to account for their impact on the atmospheric state. That means that
the calculation of vegetation processes which occurs after the radiative
transfer will always “see” the climate that has been modified by the
aerosols. After the radiative transfer calculations, the four fluxes of PAR
that were calculated are passed to the physiology routines of JULES to
calculate plant productivity. Prior to calculating the biochemical fluxes,
we define two values of fd and TotPAR using the four PAR fluxes previously
introduced; one that considers the effect of aerosols (fd.aer and TotPAR.aer) and one
that considers clean-sky conditions (fd.clean and TotPAR.clean).
Model quantities calculated during the triple call of the
physiology routines (see text).
Aerosol effect on model variables during the triple call: with (.aer) and without (.clean) aerosol effect fdTotPARclimBiochemical flux diagnostic (e.g. NPP)CommentsCall order of thephysiology routinesNo. 1fd.cleanTotPAR.cleanclim.aerNPPclim.aer,TotPAR.clean,fd.cleanNPP of vegetationonly experiencing thechange in climateNo. 2fd.cleanTotPAR.aerclim.aerNPPclim.aer,TotPAR.aer,fd.cleanNo. 2 minus no. 1is the impact of change intotal amount of PARNo. 3fd.aerTotPAR.aerclim.aerNPPclim.aer,TotPAR.clean,fd.aerNo. 3 minus no. 2is the impact of change indiffuse fraction of PAR
The physiology routines are then called three times (i.e. a triple call, see
Table 3) within the same model time step. On the
first call, both the reduction in total PAR and the change in diffuse fraction are ignored (i.e. the vegetation only sees
the climate feedback). The biochemical fluxes calculated during this first call are saved
in a specific model diagnostic
(NPPclim.aer,TotPAR.clean,fd.cleanBBAxx). On the second call,
the reduction in total PAR due to aerosols is then considered, but the change in diffuse fraction of PAR is not accounted
for, and a new set of biochemical fluxes are saved in a specific model
diagnostic
(NPPclim.aer,TotPAR.aer,fd.cleanBBAxx).
For the last prognostic call, both aerosol effects on reduction in total PAR and the change in diffuse fraction are taken
into account in the calculation of the biochemical fluxes and saved in a
specific model diagnostic (NPPclim.aer,TotPAR.aer,fd.aerBBAxx).
With these new diagnostics available, we are able to isolate the impacts of
change in diffuse fraction, reduction in total PAR and climate feedback by comparing model simulations which include or exclude the BBA
emissions. For instance, the effect of BBA in the BBAx1 simulation (i.e. the
standard emissions scenario) can be expressed as follows:
ΔNPP‾netimpactBBAx1=NPP‾BBAx1-NPP‾BBAx0≅ΔNPP‾fdBBAx1+ΔNPP‾TotPARBBAx1+ΔNPP‾climBBAx1
with
ΔNPP‾fdBBAx1=NPP‾clim.aer,TotPAR.aer,fd.aerBBAx1-NPP‾clim.aer,TotPAR.aer,fd.cleanBBAx1-NPP‾clim.aer,TotPAR.aer,fd.aerBBAx0-NPP‾clim.aer,TotPAR.aer,fd.cleanBBAx0,ΔNPP‾TotPARBBAx1=NPP‾clim.aer,TotPAR.aer,fd.cleanBBAx1-NPP‾clim.aer,TotPAR.clean,fd.cleanBBAx1-NPP‾clim.aer,TotPAR.aer,fd.cleanBBAx0-NPP‾clim.aer,TotPAR.clean,fd.cleanBBAx0,ΔNPP‾climBBAx1=NPP‾Clim.aer,TotPAR.clean,fd.cleanBBAx1-NPP‾Clim.aer,TotPAR.clean,fd.cleanBBAx0,
where overbars denote quantities averaged over a time period long enough for
vegetation fast responses to adjust to the aerosol effects.
Global annual estimates of gross primary productivity (GPP, a, c, e)
and net primary productivity (NPP, b, d, f). Observationally based estimates
from FLUXCOM MTE analysis (a), MODIS MOD17A2 (b) and HadGEM2-ES (c, d).
Zonal means are shown in panels (e) and (f). The circles on the NPP maps (b, d)
represent in situ estimates from the EMDI project.
Observations used in model evaluation
We evaluate global fields of simulated GPP and NPP using GPP fields derived
by the FLUXCOM project (Tramontana et al., 2016; Jung et al., 2017a) and the global
annual mean NPP retrievals based on the MODIS MOD17A2 product (Running et al.,
1994) (Fig. 1a, b). The GPP from FLUXCOM is derived from a model that
has been trained on observational data, so we will refer to this estimate as
a “reconstructed” GPP. In addition, in situ estimates of NPP from the EMDI
project (http://gaim.unh.edu/Structure/Intercomparison/EMDI/, last access: 25 January 2019)
are also presented in the form of overlaid circles depicted in Fig. 1b.
Note, simulated values of HadGEM2-ES GPP and NPP used in the comparison with
observational data are sampled where the corresponding observationally based
dataset contains non-missing data.
The simulated aerosol loading is evaluated against the record of aerosol
optical thicknesses (AOTs) retrieved from the MODIS instrument measurements
on board of the Terra satellite. The dataset used corresponds to the Level-3
MODIS Atmosphere Monthly Global Product collection 6.1 (at 1-degree resolution) that was derived
from the MYD06_L2 products for the period extending between
2001 and 2016.
Additional evaluation of the model skill against observations is provided in
the Supplement (Fig. S8). This includes comparisons
of the modelled solar fluxes at the surface against the SSF1deg Terra
Edition 2.8 product based on the CERES radiation data, and comparisons of the
modelled surface precipitation against the GPCP version 2.3 product.
Multi-annual mean for the June–July–August season (JAS) of the
aerosol optical thickness (AOT) at 550 nm (a, b) and the seasonal cycle (c, d) of the AOT calculated over the domain highlighted in red for the MODIS
Terra retrieval (a, c) and the HadGEM2-ES model (b, d). The MODIS seasonal
cycle (c) shows the multi-year (2001–2016) mean with the black line, and the
individual years are overlaid with red dashed lines. The seasonal cycle for
HadGEM2-ES (d) shows the 30-year mean for the five experiments with varying
biomass burning emissions (see text, Sect. 2.2).
ResultsEvaluationCarbon exchange
Global annual mean GPP and NPP as simulated by HadGEM2-ES with the new
representation of canopy light interception are shown in Fig. 1c, d. The
global GPP modelled by HadGEM2-ES is +115 PgC yr-1 in the updated version of
HadGEM2-ES and smaller than the estimate of +129 PgC yr-1 from the FLUXCOM
dataset (Fig. 1a) but closer to the reference of +118 PgC yr-1 cited by Shao
et al. (2013). The standard configuration of HadGEM2-ES that participated in CMIP5
had a global GPP of the order of +140 PgC yr-1 for present-day conditions
(Shao et al., 2013). The underestimation of the GPP in the updated HadGEM2-ES
configuration is comparable in magnitude to the overestimation of the GPP in
the HadGEM2-ES configuration. However, the ratio of NPP over GPP (not shown)
in the updated version of HadGEM2 is more consistent with
observationally based ratio estimates (e.g. Luyssaert et al., 2007). Despite
the inherent uncertainties in the two reference estimates of the global GPP
(i.e. between +118 and 129 TgC yr-1), it suggests that the updated version
of HadGEM2-ES is able to provide a more consistent global GPP estimate. Over
the central Amazon domain, which is represented by the region encapsulated in
the red box in Fig. 2a., the HadGEM2-ES average GPP in August (respectively
September) is 2750±250 gC m-2 yr-1 (respectively 2600±200 gC m-2 yr-1 for September) compared to 2250±125 gC m-2 yr-1
(respectively 2500±180 gC m-2 s-1 for September) for FLUXCOM.
The global NPP modelled by HadGEM2-ES is +54 PgC yr-1 (Fig. 1d) and in good
agreement with the satellite-based estimate of +50 PgC yr-1 (Fig. 1b) and
the “best guess” value of +56 PgC yr-1 reported by Shao et al. (2013). The
updated configuration of HadGEM2-ES performs well in mid and high latitudes,
particularly against EMDI data (Fig. 1d), but biases still remain in the
tropics (Fig. 1f), particularly over South America in areas dominated by C3
grass (Fig. S5). Despite obvious overestimation by HadGEM2-ES
of the NPP on annual mean over South America when compared to MODIS MOD17A2
(Fig. 1b, d), the fluxes are well captured during the peak of the fire
season over the central Amazon. The average GPP from HadGEM2-ES in August
(respectively September) is 1080±140 gC m-2 yr-1 (respectively
975±100 gC m-2 yr-1 for September) compared to 990±550 gC m-2 yr-1 (respectively 1025±590 gC m-2 s-1 for September) for
MODIS MOD17A2.
Biomass burning aerosols
Biomass burning is highly variable from year to year. This can be readily
observed by monitoring the AOT, a proxy for the
amount of aerosol particles present in the atmosphere. Figure 2a shows the
average AOT retrieved at 550 nm for the months July–August–September (JAS)
between 2001 and 2016 by the MODIS instrument on board of the Terra
satellite. Although most of man-made fires occur in the so-called arc of
deforestation on the edge of the rainforest (Cochrane, 2003), the hot spot of
high AOT (> 0.6) is actually observed over the Rondônia state
(Brazil) near the Bolivian border. This hotspot can be explained by (i) the
action of the large-scale atmospheric circulation that recirculates aerosols
over South America and (ii) the contribution of natural fires that occur
concomitantly with fires of anthropogenic origin. Figure 2c provides more
detail on the AOT variability by showing the seasonal cycle calculated over
the central Amazon (i.e. the region encapsulated in the red box shown in Fig. 2a
using the multi-year data record from MODIS). Despite year-to-year
variability, AOT is found to peak in September over this region that is, at
the expected peak of the fire season, supporting that BBAs are the dominant
component of the total aerosol loading during that period.
The AOT modelled by HadGEM2-ES in the simulation that assumes standard BBA
emission (i.e. the BBAx1 simulation) is in overall good agreement with the
MODIS observations for the JAS period (Fig. 2a, b; Johnson et al., 2016).
However, the AOT at the peak of the fire season (i.e. in September) is
underestimated (Fig. 2d). In contrast, the modelled AOT for September in the
BBAx2 simulation is in better agreement with the satellite retrievals. We
will therefore consider in the remainder of this paper that the combination
of BBAx1 and BBAx2 scenarios are representative of present-day levels of BBA
and will use them to discuss the effects of BBA on the rainforest
productivity. There is huge variation in the inter-annual variation in the
magnitude of the AOT (Fig. 2c), which justifies the upper bound for our
simulation scenarios; the simulations BBAx0.5 and BBAx4 will be considered representative of emissions for years with low and high fire activity,
respectively (Fig. 2c). These simulations will provide a lower and upper estimate, respectively, of the BBA impact on vegetation.
Modelled seasonal cycle from HadGEM2-ES for the total PAR (a, b),
the diffuse PAR (c, d) and fraction of radiation that is diffuse (e, f) for
the five BBA emission experiments. Absolute values (a, c, e) and relative
anomalies (b, d, f) w/r to experiment BBAx0 (i.e. no biomass burning
aerosols) are shown. Transparent coloured areas in panels (a, c, e) correspond to
±1 standard deviation. Dashed lines are the multi-year annual means.
Surface radiation
Figure 3 illustrates the impact of BBA on the radiative fluxes in the
HadGEM2-ES simulations. The seasonal cycle of the total PAR (TotPAR) shows a
strong decrease during the whole dry season with the strongest reduction
occurring in August and September. The reduction in TotPAR is in the range of
-18.0 to -7.5 W m-2 (i.e. -14.0 to -5.5 %) in the BBAx1 and BBAx2
experiments, respectively (Fig. 3a, b). For the most extreme emission
scenarios (BBAx4), the reduction in TotPAR is as high as -30 W m-2, or
-25 %, in August. Conversely, the diffuse component of PAR (DiffPAR) increases with
aerosols as expected from the theory of light scattering (Fig. 3c, d).
The diffuse PAR reaching the top of the canopy is increased by approximately
+6.0 to +12.0 W m-2 (i.e. approximately +14.0 to +31.0 %)
during August and September in the BBAx1 and BBAx2 simulations (Fig. 3c, d).
Overall this leads to an increase in the diffuse fraction of PAR (i.e.
fd) of +20.0 to +55.0 % (Fig. 3e, f).
Showing the total PAR (TOTPAR, a), the diffuse PAR (DIFFPAR, b)
and the fraction of PAR that is diffused (DIFF_FRAC, c)
reaching the surface versus the total aerosol optical thickness (AOT) at
550 nm and the net primary productivity (NPP, d) against the fraction of PAR
that is diffused. Circles represent the binned data from the HadGEM2-ES
simulations, while plain lines are the corresponding second-order
polynomial fits. Prior to binning, data were first collected at all grid
cells in the Amazon region (i.e. the red box region in Fig. 2) for all five
BBA emission experiments. We then aggregate all grid cells into 30 AOT bins
ranging from 0 to 3 at an interval of 0.1. In each bin, we calculate average
AOT and corresponding TOTPAR, DIFFPAR and DIFF_FRAC (Fig. 4a, b and c, respectively; we calculate average DIFF_FRAC and
corresponding NPP in Fig. 4d).
An alternative representation of the impact of BBA on the radiative fluxes
is depicted in Fig. 4 for August and September. Here, the composite plot is
constructed using the four simulations that include BBA emissions to
calculate the TotPAR (Fig. 4a), DiffPAR (Fig. 4b) and fd (Fig. 4c) at the surface as
a function of the total AOT (i.e. BBA + background aerosols). The
composite was constructed by first averaging each simulation over time to
create climatologies for the specific months, and then all pixels contained in
the domain of analysis were sampled to construct the scatterplots of the
desired quantities. It is important to note that radiative quantities were
sampled for the full sky grid box and that no conditional sampling was
applied a priori; therefore cloud effects are implicitly accounted for in these
statistics. Subsequently, further averaging of the data into 30 bins of AOT
(respectively fd for Fig. 4d) was applied to smooth the signal. Figure 4a shows the expected monotonic decrease in TotPAR with AOT. Concomitantly, the
DiffPAR (Fig. 4b) increases with AOT up to values of around 1.75 and decreases for
higher AOTs. This illustrates that increasing AOT could only increase the
amount of diffuse light reaching the surface up to a point; above this
point, the effect of the attenuation of TotPAR dominates. This AOT threshold
around 1.75 maximises the amount of diffuse radiation reaching the canopy
top. However, as it will be detailed in following sections, this threshold
does not correspond to the maximum effect of aerosols on vegetation
productivity.
The net impact of BBA on forest productivity
Figure 4d represents NPP as a function of fd for the months of August and
September in the same way as the surface radiative fluxes against AOT are
depicted (Fig. 4a–c). This shows that NPP is likely to reach an optimum
when fd approximately equals 52 %–56 %. The existence of an optimum
fd that would maximise carbon sequestration is consistent with findings
reported in past modelling studies (e.g. Knohl and Baldocchi, 2008; Mercado
et al., 2009a; Pedruzo-Bagazgoitia et al., 2017; Yue and Unger, 2017). Such an optimum, however,
depends strongly on factors such as the vegetation canopy architecture
environmental conditions, solar zenith angle or the optical properties of
the scattering particles. The fact that an optimum diffuse fraction emerges
is consistent with our understanding of the DFE mechanism. When fd is
lower than the optimum, an increase in the amount of diffuse radiation
increases carbon assimilation because a larger area of shaded leaves become
photosynthetically active. For fd beyond the optimum, the effect of the
attenuation of TotPAR dominates, and sunlit leaves are no longer light saturated,
resulting in an overall decrease in biochemical fluxes at the canopy level
with further increase in fd.
Figure 4c could be used to infer an AOT for which fd is getting close
to the optimum value of 0.55 (Fig. 4d). This would approximately occur at an
AOT of ∼0.9–1 (Fig. 4c). However, we do not observe that the
highest NPP enhancement occurs around these values of AOT in our simulations
(see Sect. 3.3). This can be understood as a consequence of equifinality
because both the effects of clouds and the effects of aerosols on radiation
occur concomitantly. There are then many possible combinations of cloud and
aerosol scenarios that could create optimum conditions maximising the DFE.
It would be possible to disentangle the effect of BBA from the effect of
clouds on carbon sequestration by either screening out cloudy scenes or
diagnosing the biochemical fluxes in the clear-sky portion of the model
grid boxes, providing a mean to quantify the maximum potential impact of BBA
on carbon sequestration. This approach was used by Moreira et al. (2017) to
conclude that BBA could increase the GPP of the Amazon forest by up to
27 %. While this study is insightful, our aims here are different as we
seek to understand the impact of BBA while considering the system-wide
behaviour that is including the effects of both aerosols and clouds. This
alternative approach was used by Yue and Unger (2017) to analyse aerosol impacts
on vegetation over China and show that clouds are a dominant feature,
controlling the diffuse fraction of radiation which modulates the diffuse
fertilisation effect from aerosols (Yue and Unger, 2017, Fig. 5 therein). In Sect. 3.3, we will show that similar conclusions could be drawn over South
America.
NPP anomalies (relative to the experiment BBAx0) for the 30-year
mean for the four varying BBA emissions (see text, Sect. 2.2) during the
August (a, c, e, g) and September (b, d, f, h) months. Mean fluxes (labelled AVG) and
accumulation (labelled TOT) are calculated over the domain delimited by the
pink borders. Hatched areas represent the regions where changes are
significant at the 95 % confidence level. Green contours show the 550 nm
AOT anomalies.
Despite cloudiness affecting how much aerosols can interact with radiation,
we notice that NPP is enhanced in the central part of the Amazon when BBA
emissions are increased (Fig. 5). The most statistically significant
enhancement of the NPP, which is depicted by the stippling in Fig. 5, occurs
during August, in phase with the period when the radiative impacts of BBA
are the most pronounced in the model simulations (Fig. 3, Sect. 3.1.3).
Although the simulated AOTs are of similar magnitude during September, NPP
enhancement is not as robust as in August (i.e. there is a less
statistically significant signal in the NPP anomalies). This can be partially explained by the fact that plant productivity simulated by HadGEM2-ES
reaches a minimum in September (Fig. S8a, b). As a
result, the vegetation is less active in September and the potential impact
of the BBA perturbation is reduced.
Mean seasonal cycle of NPP (a), relative changes (b) and
absolute changes (c) for the five BBA emission scenarios (see text,
Sect. 2.2) averaged over the Amazon Basin. Differences are calculated with regard
to experiment BBAx0. The short-dash curves in panel (c) correspond to the
accumulated anomalies (right y axis).
Overall, based on the BBAx1 and BBAx2 simulations, we estimate that BBAs
increase NPP by about +80 to +105 TgC yr-1, or 1.9 % to
2.7 %, (Fig. 6b, c) over the domain of analysis. This estimate of the enhancement in carbon
uptake is remarkably similar to the estimate provided by Rap et al. (2015), who
found that Amazonian fires increase NPP by 1.4 %–2.8 % corresponding to an
increase of +78 to +156 TgC yr-1. This is encouraging as the authors used
the stand-alone version of JULES (i.e. the land surface component in the
HadGEM family of models). However, as it will be discussed in Sect. 3.3 and
Sect. 4.2, we attribute the enhancement in carbon sequestration to different
mechanisms. The Rap et al. (2015) study used a combination of offline models which
do not account for climatic adjustment to the aerosol radiative
perturbation. This supports the fact that the increase in modelled NPP results from
DFE in their simulations. Conversely, we will show (Sect. 3.3) that DFE is
negligible over the region considered in our model simulations, but the
overall aerosol impacts on vegetation remains significant thanks to the
contribution of climate feedbacks that are experienced by the vegetation.
Similar to Fig. 6c, showing the variation in NPP due solely to
(a) change in diffuse fraction, (b) reduction in total PAR and (c) the climate feedback.
Disentangling the impact of radiation changes from those of climate
adjustments
We have quantified the net impact of BBA on NPP in the previous section. Following
the framework described in Sect. 2.4, we now separately address the
individual contribution from the change in diffuse fraction, fd, the
reduction in total PAR, TotPAR, and the climate feedbacks to the BBA net impact on
vegetation productivity. Figure 7 shows the seasonal cycle of NPP anomalies
averaged over the domain of analysis (left axis) and the corresponding
accumulated anomalies (right axis) for the four simulations with varying BBA
emissions. The increase in NPP due to the change in diffuse fraction is unambiguous (Fig. 7a),
corresponding to an enhancement in plant net carbon uptake of +65 to
+110 TgC yr-1 in the BBAx1 and BBAx2 simulations, respectively. As expected,
the reduction in total PAR has the opposite effect and systematically decreases NPP (Fig. 7b)
with increasing negative NPP anomalies. This corresponds to a reduction in
plant net carbon uptake of -52 to -105 TgC yr-1 in the BBAx1 and BBAx2
simulations, respectively. The combination of the change in diffuse fraction and the reduction in total PAR effects
represents the DFE. We estimate that the DFE from BBA increases the
vegetation NPP by +13 and +5 TgC yr-1 in the BBAx1 and BBAx2 simulations,
respectively.
The impact of BBA on NPP via the DFE is in stark contrast with the increase
in forest productivity, which we have discussed in the previous Sect. 3.2
describing the net impact of BBA (+80 to +105 TgC yr-1 for the BBAx1 and BBAx2
simulations respectively). This would indicate that in our simulations the
net impact of BBA on forest productivity is not mostly due to the DFE.
Figure 7c shows that the climate feedback term is actually the dominant contribution and
systematically increases NPP, with an enhancement of +67 to +100 TgC yr-1
in the BBAx1 and BBAx2 simulations, respectively.
It is worth mentioning that the maximum impact of the change in diffuse fraction occurs during
August in the BBAx4 simulation, which increases the NPP by +41 TgC m-1. The
corresponding impact of the reduction in total PAR decreases NPP by -66 TgC m-1. This illustrates
that for a year with intense burning, the system actually seems to shift
past the point where the balance between the total and the diffuse PAR does
not increase the efficiency of photosynthesis anymore (i.e. BBA DFE leads to
reduction of -42 TgC yr-1 on an annual basis for the BBAx4 scenario).
Interestingly, in this simulation, despite the negative impact on NPP from
DFE, we note that the impact of climate feedback is much larger (+194 TgC yr-1), resulting
in the net impact of BBA on the vegetation to be overall positive (+151 TgC yr-1).
Shown on panel (a) the relative changes in NPP (ΔNPPNet, in grey), the relative changes in NPP due to the change in diffuse fraction (ΔNPPFracDiff, in blue), reduction in total PAR (ΔNPPTOTPAR, in red), the sum of change in diffuse fraction and reduction in total PAR (ΔNPPfd+TOTPAR, in green, i.e. the DFE), and the climate feedback
(ΔNPPAdjust, in yellow) against the anomalies in the
AOT at 550 nm for the month of August. Shown on panel (b) the relative changes in
NPP due to change in diffuse fraction and reduction in total PAR
(ΔNPPfd+TOTPAR=DFE) – i.e. the changes in NPP only due to
change in surface radiation, the DFE, for August (green) and September
(blue) as a function of the total AOT at 550 nm. Note this is shown against
the total AOT. The dashed lines on panel (b) highlight the AOT thresholds
where the DFE switches from a positive to a negative impact.
To compare the relative contribution of the DFE (i.e. change in diffuse fraction plus reduction in total PAR) and the
climate feedbacks on vegetation NPP as the atmospheric aerosol content ramps up, Fig. 8a
depicts the relative change in NPP (%) as a function of AOT for the month
of August. This NPP change is further decomposed into individual
contributions from the change in diffuse fraction (blue solid line), the reduction in total PAR (red solid line), the
DFE (green solid line), the climate feedback (yellow solid line) and the net impact (black solid
line). The resulting attribution plot shown in Fig. 8a was constructed in
the same way as Fig. 4 (see Sect. 3.1), i.e. by first averaging each
simulation over time, then sampling the NPP changes associated with each of
the three terms in all the model grid boxes from the domain of analysis and
finally aggregating the sampled quantities into 30 bins of AOT.
Overall, it is clear from Fig. 8a that BBAs enhance NPP across the entire
range of AOT considered here (i.e. the net impact of BBA is strictly positive) which
is consistent with the geographic distribution of anomalies displayed in
Fig. 5. The impact of the change in diffuse fraction and the reduction in total PAR, respectively, consistently
increases and decreases vegetation NPP, respectively, as discussed in the
previous paragraph. However, the impact of DFE from the BBA (represented by
the green solid line in Fig. 8a), changes its sign around AOT of
∼0.75. At lower AOTs DFE from BBA contributes to an increase
in NPP, whereas at higher AOTs it has the opposite effect. To help visualise
the transition in the DFE regime, we have replotted the NPP changes due to
the DFE contribution only in Fig. 8b. Here, the changes are
represented for August and September and are shown against the total AOT
(BBA + background aerosols). It is interesting to note that the AOT
threshold occurs at a smaller value in September (0.62) than in August (0.89).
This suggests that the state of the climate have implication for the
strength of the DFE from aerosols (e.g. via the amount of cloudiness).
As discussed in Sect. 3.1, changes in NPP due to DFE from BBA alone are
calculated under all sky conditions which also account for cloud radiative
effects. A plausible explanation for the observed reduction in the range of
AOT creating a positive DFE would be that cloudiness increases over the
analysed model domain between August and September (see Fig. S10) as the regional climate progresses towards the wet season. This is
supported by the increase in fd between August and September in the
simulation that excludes BBA (i.e. black solid line in Fig. 3c). These
results are consistent with those of Yue and Unger (2017) who discussed how the
impact of anthropogenic aerosol DFE over China vary depending on the cloud
cover which allows for smaller or larger perturbations in the radiative
balance for the same atmospheric aerosol loading.
Shown in panel (a) is a box-and-whisker plot of the net primary
productivity monthly means for August averaged over the central Amazon.
Results are shown for the main experiment (see text, Sect. 2.2) and the four
additional sensitivity experiments (see text, Sect. 2.3). Individual
members of the 30-year run are represented by the green dashes. Black dots
correspond to the ensemble mean. Dashed white lines are the 25th,
50th and 75th percentiles. Shown in panel (b) are the changes in NPP in
each sensitivity experiments, calculated relative to their respective
baseline simulation (e.g. X1+25ppm – X0+25ppm is the differences
between the BBAx1 and BBAx0 simulations with +25 ppm increase in CO2
concentration).
Sensitivity experiments
Here, we present the results from the four additional sensitivity
experiments described in Sect. 2.3. These experiments were designed to
further elucidate the role BBA play in vegetation productivity while
changing some of the underlying assumptions in the previous experiments
which relate to (i) aerosol optical properties, (ii) aerosol–cloud interactions,
(iii) the canopy nitrogen profile and (iv) atmospheric carbon dioxide concentration.
Figure 9a shows a box-and-whisker plot of NPP averaged over the central Amazonia
during August for all BBAx0, BBAx1 and BBAx2 simulations from the main
experiment (those analysed in the Sect. 3) and from the four additional
sensitivity experiments. The mean changes in NPP due to biomass burning
aerosols are shown in Fig. 9b.
The results can be summarised as follows.
Aerosol optical properties (experiments DIFF_OP and ABS_OP). The optical properties of BBA were altered in order to make the
biomass burning aerosols more (DIFF_OP) or less (ABS_OP) scattering by modifying the BBA SSA
(Fig. S6a, b). The mass specific extinction is invariant
(see Sect. 2.3.3), which implies that for the same AOT, the direct radiation
reaching the surface is also independent of the aerosol scattering and absorbing
efficiency assumptions (Fig. S6c). More scattering or
absorbing BBAs, respectively, increase or decrease the diffuse fraction of solar
radiation reaching the surface (Fig. S6c). As a result,
scattering BBAs should produce a stronger DFE and absorbing BBAs should
analogously produce a weaker DFE. However, we do not observe a significant
change in the modelled BBA impact on vegetation productivity for the varying
BBA scattering and absorbing assumptions (Fig. 9b). In the standard simulations,
the net change in NPP due to BBA is +28.4 to 38.6 TgC month-1 in August. For
the DIFF_OP simulation (respectively ABS_OP) the net change in NPP is +32.1 to 36.2 TgC month-1 (respectively +17.9 to 18.2 TgC month-1). For September (not
shown), we actually found that the ABS_OP simulation had the largest increase in
NPP, which is not consistent with our assumption. In summary, the effect of
BBA optical properties on NPP changes is within the noise and considered
negligible. This can be explained in the light of the results discussed in
Sect. 3.3, where we showed that the DFE from present-day BBA is small
(∼+5 TgC month-1 in August in BBAX1) for this model in this
region of the world. Therefore, altering the ratio of diffuse fractions
reaching the ground via the aerosol optical properties, that is modulating
the magnitude of the DFE, does not have a measurable effect on vegetation
productivity.
Aerosol–cloud interactions (experiments 1stAIE and NoAIE). We have emphasised the potential role of clouds in Sect. 3.3. One
could expect that increasing aerosol emissions which provide the necessary
CCNs will increase cloud droplet numbers and reduce their sizes. The
reduction in droplet size leads to cloud brightening (1stAIE) and
possibly cloud amount (2ndAIE), which could eventually alter the
surface radiation balance. We note that the impact of BBA on NPP is of
similar magnitude in the main experiment and in the experiments without
aerosol–cloud interactions (Fig. 9b) – i.e. neglecting ACI does not change
the impact of BBA on vegetation productivity over the region considered. A
possible explanation can be found in the type of the clouds that
predominates in this region. We note that most of the precipitation in
HadGEM2-ES stems from convective clouds. Aerosols are only coupled to the
large-scale precipitation scheme in HadGEM2-ES (i.e. aerosols can only alter
the properties of stratiform clouds). The absence of any impact from ACI
over this region is then to be expected. Whether or not ACI can affect
vegetation productivity remains a research topic for future studies, and
these should focus on regions where aerosols and clouds are likely to
interact as a consequence of the cloud representation in the models (e.g.
Chameides et al., 1999). Alternatively, the ACI effects in the cloud
representation should be revisited and improved in the models (Malavelle et al.,
2017).
Canopy nitrogen profile (experiment STEEP_N). We modified the shape of the nitrogen
profile for the modelled canopy to represent a steeper decrease in leaf
nitrogen content (Sect. 2.3.4). The available nitrogen to leaves decreases
from the canopy top downwards. This change in leaf nitrogen allocation means
that sunlit leaves have access to more resources, whereas shaded leaves tend
to be more nitrogen limited (Hikosaka et al., 2014). Despite this modification in
nitrogen availability, we do not observe a significant change in the
modelled BBA impact on vegetation productivity. The reasons for this absence
of sensitivity to nitrogen availability are similar as in the experiments
testing the role of aerosol optical properties; i.e. the DFE from BBA is
already too small to have a discernible impact, and reducing the allocated
nitrogen in the shaded portion of the canopy only reduces its impact more.
AtmosphericCO2concentration (experiments 25 and +50 ppm). While increasing atmospheric
CO2 concentration leads to an unambiguous increase in NPP (Fig. 9a),
the BBA impact is of similar magnitude as in the main experiments (Fig. 9b).
It may appear that the impact of BBA is somehow reduced in the +25 ppm case
compared to the main experiment and the +50 ppm experiment. However, the
level of model internal variability in NPP is too pronounced (Fig. 9a) to
draw robust conclusions on the impact of a variation in CO2 on the
BBA-induced DFE. Note that the atmospheric CO2 concentration increased
globally. It was also allowed to affect the radiative balance, resulting in a
warming climate in these two experiments. Potentially, this could increase
the model's internal variability further. If one were to repeat these
experiments, only the leaf-internal CO2 concentration should be
increased to avoid additional statistical noise produced in the warming
climate.
Concluding remarks
From our model experiments we concluded that the diffuse PAR fertilisation
effect from biomass burning aerosols in HadGEM2-ES (Sect. 3.3) is
comparatively modest, amounting to between +13 and +5 TgC yr-1 based on the
result from the simulations BBAx1 and BBAx2. This may seem at odds with the
+78 to +156 TgC yr-1 estimate (assuming standard BBA
emissions and 3 times the standard BBA emissions, respectively) reported by Rap et al. (2015),
who used the JULES land surface model in an offline framework specifically
designed to assess the DFE of biomass burning aerosols. Some differences
between the two studies that could explain the apparent differences are
obvious, such as for instance the fact that we are not reporting estimates
for the BBA impact over the same area (i.e. our domain is smaller) or that
we did not use the same aerosol properties or emission inventories. We
recalculated the impact from biomass burning aerosols in our simulations
over a larger domain that approximately matches the area considered by Rap
et al. (2015). In this situation, we found that the net increase in NPP is about
+145 to +148 TgC yr-1 for the BBAx1 and BBAx2, respectively, of which only
+15 to +5 TgC yr-1 are attributable to the DFE. This confirms that the
magnitude of the DFE from BBA effect is small, increasing plant productivity
in our simulations over the Amazon forest.
Biases in the cloud amount, which are inherent of coarse model
parameterisations, may affect the surface radiation and impact the magnitude
of the DFE from biomass burning aerosols (and indeed all aerosols). Those
uncertainties can be partially contained using an offline framework where
the state of the model can be forced closer to the distribution of input
observations. However, in this approach, internal consistency is lost by not
allowing variability within non-linear relationships (e.g. how cloudiness is
changed due to aerosol–radiation interactions, how plant dark respiration is
changed due to the surface cooling). This then poses a problem and a risk of
overestimating the response of a component (e.g. vegetation productivity) to
a perturbation such as those introduced by aerosols. By including more
complexity in a coupled framework as in the present study, we believe that
our estimate of the DFE is more consistent, albeit low due to possible
uncertainties or biases, and we would argue that earlier estimates of the DFE
from BBA in this region (Rap et al., 2015) are probably on the high end.
Nonetheless, despite showing that the DFE from BBA is not an efficient
mechanism in our simulations over this region, we have demonstrated a
pathway where BBA can significantly influence vegetation productivity. We
assessed this pathway by calculating the term representing the biomass burning
aerosol climate impact on vegetation, which represents the rapid adjustments of
land surface climate to aerosol radiation perturbation. We estimated this
term to be about +67 to +100 TgC yr-1 over the domain analysed in this
study in the BBAx1 and BBAx2 simulations, respectively. This is a novel
contribution which could not be accounted for in an offline modelling
framework and has therefore not been properly assessed in past studies. This
term is non-negligible and potentially in line with the impact from other
biomass burning by-products.
We can now proceed to compare the impact of BBA over Amazonia with the
effect of O3 on the vegetation that is produced from O3 precursors
emitted by forest and grassland fires. Although Pacifico et al. (2015) reported
the changes in GPP, their results can be directly compared to the changes in
NPP derived from our simulations because the effects of BBA in HadGEM2-ES
are predominantly affecting the GPP, whereas the impact on plant respiration
is of second order over this region of the world under the present-day climate
(Fig. S9). Using the same modelling set-up as in the present
study, Pacifico et al. (2015) estimated that present-day O3 produced from
precursors emitted by forest and grassland fires in the Amazon region
reduces the vegetation GPP by approximately -230 TgC yr-1 over the same region
that was analysed in this study. This is about 2 times, but of the
opposite sign, the magnitude of the net impact of BBA estimated in this study (i.e.
+80 to +105 TgC yr-1 for the BBAx1 and BBAx2 scenarios), which includes the
climate feedbacks. However, it is important to emphasise that the result from Pacifico et al. (2015) is based on an approach of modelling the O3 effects on
photosynthesis that includes a “high” and “low” parameterization for
each plant functional type to represent species more sensitive and less
sensitive to O3 effects. The -230 TgC yr-1 decrease in GPP reported there
is based on the high sensitivity mode to establish the maximum response.
It is also worth noting that due to a lack of knowledge and data on the
impacts of O3 on tropical vegetation, the O3 damage
parameterization in the work by Pacifico et al. (2015) was derived from data from
the temperate and boreal regions. As discussed in the previous paragraph,
the BBA-induced DFE is small in our simulations, and if an upper estimate of
the BBA were to be considered, it is then possible to argue that BBAs have
the potential to virtually counteract the O3 leaf damage resulting from
biomass burning in the area. However, while the biomass burning and O3
impacts are potentially of the same magnitude but of the opposite sign, they are
not geographically collocated. This means that BBA and O3 do not
necessarily affect the same regions of the Amazon rainforest. As reported in
Pacifico et al. (2015), O3 tends to show its highest concentrations upwind of
the fires which are located over dense areas of broadleaf trees in the model.
In contrast to this, the highest AOT from BBA is found downwind of the fires
and located over predominantly grassland areas. Future research aimed at
assessing the overall net impact of forest and grassland fires on ecosystems
through the O3 and DFE effects should therefore consider modelling the
two effects simultaneously in a fully coupled framework.
We showed in Sect. 3.3 that the impact of BBA on vegetation over the Amazon
rainforest is dominated by the contribution of the term we have referred as
climate feedbacks. The (bio)physical mechanisms involved behind this term are numerous, and
it is beyond the scope of this paper to completely untangle and quantify
them. Future work should seek to understand how aerosol can benefit
vegetation productivity when the DFE does not suffice to explain the
increase in vegetation NPP. Two working hypotheses for making progress are
proposed. First we have noted that BBAs are capable of cooling surface
temperatures significantly, which potentially reduces evapotranspiration (ET)
and consequently water stress due to a low soil moisture content
(Fig. S11a, b). Remarkably, the canopy-level water use
efficiency (WUE = GPP/ET) is significantly enhanced under higher BBA
conditions (Fig. S11d). Given the modest increase in GPP
reported earlier, it probably implies that the decline in ET was steeper
than the increase in GPP, and this would suggest that vegetation is able to
balance water loss and carbon uptake with increasing aerosol concentrations.
Secondly, we suggest that future studies put an emphasis on how BBA can
modify the biotic (e.g. rate of carboxylation of the rubisco enzyme,
Vcmax, leaf temperature) and abiotic factors (air temperature, vapour
pressure deficit, PAR, leaf surface temperature, CO2 concentration and
air pressure) which control the vegetation response (Lloyd and Farquhar, 2008; Wang
et al., 2018). We found that the cooling effect of BBA (Fig. S12a)
actually reduces the leaf temperature beyond the Vc,max temperature
optimum, which works to reduce plant productivity (Fig. S12c). But the
aerosol cooling also lowers the VPD (vapour pressure deficit) which can
stimulate stomatal conductance and thus enhance canopy photosynthesis
(Fig. S12b). The antagonistic effects from VPD and
Vcmax changes are particularly relevant to the sunlit leaves as this
population of leaves is mostly rubisco-limited in our modelling framework
(not shown). Assessing the role of these ecophysiological mechanisms is
critical for developing a better understanding of the ecosystem–climate
feedbacks which control the carbon flux from the atmosphere to the
land surface, and more attention should be paid to this issue. Further
research on the ecosystem–climate feedback will also contribute
significantly to understanding the complex relationships between aerosols and
ecosystems (e.g. Schiferl and Heald, 2018).
Summary
Intense biomass burning events happen regularly in the vicinity of the
Amazon rainforest during the dry season (∼ August–September),
releasing huge amounts of trace gases, aerosols, and ozone and aerosol
precursors. This potentially leads to very large interactions between
chemistry, aerosol, clouds, radiation and the ecosystems.
In this study, we have investigated the impact of biomass burning aerosol (BBA) emissions under present-day conditions on the photosynthesis rate and
net primary productivity (NPP) of the Amazon rainforest. Aerosol impacts
have many impacts that could influence the ecosystems on a regional scale.
Amongst these, light scattering from aerosols is often expected to promote
more efficient use of radiation by vegetation through the so-called diffuse
PAR fertilisation effect (DFE). To understand the potential impact of BBA in
this region, we have implemented an updated representation of plant
photosynthesis and carbon uptake that is sensitive to diffuse light
radiation in the UK Met Office Hadley Centre HadGEM2-ES Earth system model.
Overall, our simulations indicate that the net impact of BBA increases vegetation
NPP by +80 to +105 TgC yr-1 over the central Amazon Basin (Sect. 3.2). For
the first time we have separated the contribution from the individual
radiative and climatic processes that contribute to our estimate of the BBA
net impact on the vegetation. We found that the increase in diffuse PAR (i) stimulates photosynthesis in the shaded part of the canopy and increases NPP
by +65 to +110 TgC yr-1 in our simulations, (ii) reduces leaf temperature
and together with other climatic feedbacks increase NPP by +67 to +100 TgC yr-1, and (iii) reduces the total amount of radiation, therefore decreasing
NPP by -52 to -105 TgC yr-1, with an overall impact of BBA beneficial for the
vegetation.
In our simulations, the DFE from BBA aerosols is small over the analysis
region. Our results do not imply, however, that diffuse light is not
effective at stimulating vegetation productivity, rather that it is only one of
a number of responses to a perturbation in the flux of BBA to the
atmosphere. We have discussed some possible reasons why the DFE from BBA
appears to be weak in our modelling study (Sects. 3.3 and 4.2). Aerosols are
not the only light scatterers present in the atmosphere; clouds too,
strongly modify the amount and quality of the radiation reaching the
surface. Aerosol-induced DFE impacts may then also depend on cloud cover,
which allows for smaller or larger radiative perturbations for the same
level of aerosols (e.g. Cohan et al., 2002; Yue and Unger, 2017). Future studies seeking
to investigate the DFE of aerosols should therefore critically asses the
role played by clouds in providing the baseline diffuse light conditions at
the surface before assessing the perturbation associated with aerosol
emissions.
The novel result from this study shows that aerosol impacts on
vegetation can be significant thanks to the contribution of the climate feedbacks, which are
the result from the system adjustment to the aerosol perturbations which
ultimately affect vegetation productivity. Those impacts can only be
captured when considering the BBA effects in a fully coupled modelling
framework. Because the aerosol cooling at the surface has a strong effect on
biotic and abiotic processes which control the vegetation response (Wang et al.,
2018), future work should invest effort into understanding how the effects
of BBA, and other aerosols more generally, can affect the surface energy
budget which preconditions photosynthetic activity. This step will certainly
become even more relevant as advances in the representation of vegetation
physiology and phenology in ESMs are made (e.g. increasing plant functional
types or improving vegetation traits), which would likely lead to different
vegetation sensitivities to aerosol effects.
Our modelling study specifically aimed at quantifying the changes in the
fast ecosystem responses (e.g. NPP or GPP) in response to the effects of BBA.
Because the design of our simulations prevents the slow carbon pools to
adjust, we cannot investigate how BBA affects carbon allocation, and the
potential impact it could have on vegetation structure and dynamics. More
research is required to investigate how the impacts of BBA, and indeed all
aerosols, on light and on the surface energy budget may alter the onset and
shutdown dates of photosynthesis, growing season length, and the canopy
structure that provide a feedback to vegetation productivity (Yue et al., 2015).
Such feedbacks could become even more relevant under a future warmer climate
as anthropogenic aerosol emissions are expected to decrease, while vegetation
will continue to experience more and more stressful climatic conditions
(e.g. Schiferl and Heald, 2018).
HadGEM2-ES, JULES and SOCRATES codes are available from
https://code.metoffice.gov.uk/ (last access: 25 January 2019) for registered users. To register for an
account, users should contact their local institutional sponsor. If in
doubt, please contact Scientific_Partnerships@metoffice.gov.uk for advice stating your affiliate institution
and your reason for wanting access.
The MODIS cloud and aerosol products (10.5067/MODIS/MYD06_L2.006, last access: 25 January 2019) (Platnick et al., 2015) are available from
https://ladsweb.modaps.eosdis.nasa.gov/ (last access: 25 January 2019). The CERES radiation data are from
SSF 1-degree Terra edition 2.8, available from
https://ceres.larc.nasa.gov/order_data.php (last access: 25 January 2019) (Smith et al., 2019). GPCP version 2.3
combined precipitation datasets are available from
https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html (last access: 25 January 2019) (Adler et al., 2018). The FLUXCOM data
are available from the data portal of the Max Planck Institute for
Biogeochemistry https://www.bgc-jena.mpg.de/geodb/projects/Home.php (last access: 25 January 2019) (Jung et al., 2017b). The CRU
datasets are available from http://www.cru.uea.ac.uk/data (last access: 25 January 2019) (Jones et al., 2014). MODIS MOD17A2 NPP
product was accessed from
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD17A2_M_PSN (last access: 25 January 2019) (Running et al., 2015).
The EMDI data are accessible from
http://gaim.unh.edu/Structure/Intercomparison/EMDI/ (last access: 25 January 2019) (GAIM, 2019). Figures were prepared
using the NCAR Command Language (NCL, version 6.4.0) 10.5065/D6WD3XH5 (UCAR/NCAR/CISL/TDD, 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-1301-2019-supplement.
FFM contributed to the text, implemented the parameterisation in the HadGEM2-ES model,
set up the HadGEM2-ES, JULES and SOCRATES simulations, and performed the processing and
analysis of the model outputs. GAF, LMM, SS and NB contributed to the text, developed the model
and implemented the parameterisation in JULES and HadGEM2-ES.
PA and JMH contributed to the text, coordinated the SAMBBA project and performed the aircraft
deployment.
The authors declare that they have no conflict of interest.
This article is part of the special issue “South AMerican Biomass Burning Analysis (SAMBBA)”. It is not
associated with a conference.
Acknowledgements
This work was funded by the Natural Environment Research Council (NERC)
South AMerican Biomass Burning Analysis (SAMBBA) project under grant code
NE/J010057/1. Gerd A. Folberth was supported by the joint UK BEIS–Defra Met Office
Hadley Centre Climate Programme (GA01101) and the European Union's Horizon
2020 Programme for Research and Innovation under grant agreement no. 641816
(CRESCENDO). Florent F. Malavelle and Jim M. Haywood were partly funded by the NERC SWAAMI grant
NE/L013886/1. Lina M. Mercado and Nicolas Bellouin were partly supported by the UK Natural
Environment Research Council through The UK Earth system modelling project
(UKESM, grant no. NE/N017951/1). Paulo Artaxo acknowledges FAPESP (Fundacao de
Amaparo a Pesquisa do Estado de São Paulo) projects 2017-17047-0,
2013/05014-0 and 2012/14437-9 and acknowledges the support from LBA Program
that is managed by INPA (The Brazilian National Institute for Amazonian
Research).
Edited by: Hugh Coe
Reviewed by: two anonymous referees
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